The interest in healthy cognitive aging (HCA) has increased substantially over the past decade. Researchers are interested in exploring how health can be promoted and cognitive decline mitigated when pathology is not present. Identifying the necessary strategies is crucial as the gradual accumulation of small declines can lead to negative effects on quality of life over time. However, the conceptualization of HCA is not agreed upon. In fact, authors often turn to the use of traditional pathology screeners in the context of HCA because of their clear threshold results and their wide use in the different fields. This leads to the assumption that individuals are either cognitively unhealthy and therefore may have some form of dementia or are dementia-free and cognitively healthy. We believe that this view is an overly simplistic approach to the understanding of the aging process. In this work, we explore how HCA has been defined and conceptualized within the different fields. We further discuss how time and variability are key concepts that are often missing when studying HCA and propose a definition that aims to unify the findings from the multidisciplinary research that studies HCA and simplify the translation of knowledge. Incorporating these two novel dimensions to the study of HCA has already been proposed methodologically but has yet been discussed at the conceptual level. We believe that the proposed new approach will allow the identification of individual factors that cause changes in cognitive health and will help build new cognitive health strategies and mitigate further declines. Research on Healthy Cognitive Aging (HCA) has increased 7-fold in the past decade . ReasonsIn fact, authors often turn to the use of traditional pathology screeners in the context of HCA because of their clear threshold results and their wide use in the field . HoweverWe believe that at the core of this problem lies an unclear definition of HCA. Given the multidisciplinary nature of the field of HCA , it is iThis commentary paper aims to discuss why HCA cannot be examined through the lens of pathological aging alone and how an unclear definition and new findings, such as the importance of subtyping, are at the core of the need to redefine HCA. Different authors have proposed definitions surrounding HCA, but to date, no consensus has been achieved. In this work we will explore some of the different proposed definitions, highlight their limitations and how they may overlap.To address this goal, we explored the literature surrounding HCA. To the best of our knowledge, this is the first paper to compare and analyze a range of proposed definitions of HCA in different scientific fields. Most of the available literature that has proposed conceptual definitions, does so within the framework of their scientific field, limiting is translation across disciplines. In exploring the literature, we found that the term “healthy cognitive aging” is typically assumed to be the opposite of neuropathological aging , and thaThe need for a definition on HCA often appears to be motivated by the increasing demand of better understanding available data. This data is commonly census data, behavioral data or direct measures of the brain . As a reIn Section 1.1, we observe that several of the proposed definitions discuss that a key determinant of HCA is the maintenance of cognitive health in older ages. We also observe the overlap between definitions of Normal, Healthy and Successful Cognitive Aging. An example of this is present in the definitions of HCA proposed by Section 1.2 highlights that included in some definitions of HCA are indicators of cognitive function such as memory  or otherSection 1.3 presents definitions identified within the field of Gerontology. Similarly, to the previous sections, here authors also suggest that HCA is defined in terms of a preservation or maintenance of cognitive health. Further, cognitive and functional preservation were also included in some of the definitions, similarly to what was presented in previous sections . In defiOverall, In investigating the different proposed definitions, we created 5 categories to facilitate their comparison within and across fields. As observed in Let us consider the binary view as true for the following argument: if health is the opposite of pathology, pathology screeners can be used to assess whether an individual is healthy (as they would not score below the pathology threshold to be diagnosed). However, pathology screeners have been designed to have high specificity and sensitivity to the pathology they were created to detect. Therefore, an individual with some degree of pathology may be deemed healthy only because the disease has not sufficiently progressed. Given the healthy prognosis, no further investigation is typically carried out.Overall, if a comparison to a pathological state is not included in the definition of HCA, pathology-specific testing would no longer be an appropriate approach. Instead, researchers would have to identify alternative methods to measure health in cognitive aging that is sensitive to the subtle changes associated with the aging process. An example of the importance of this tool is made clear when SMC are discussed. SMC are not detected through the use of pathology tests but are a key indicator of accelerated cognitive decline and are linked to increased risk of dementia . As descThe tables above also highlight that in some cases, the applicability of the definitions proposed are only suitable for a specific field. As an example, the definition proposed by may not be suitable for researchers interested in studying brain and cognitive reserve in HCA. The National Research Committee on Assessing Behavioral and Social Science on Aging discussed that when projects are highly conservative in their research disciplines, they fail to convert findings into useful and usable applications .Overall, when definitions are discipline specific, they limit the transferability of the different concepts across fields. Discipline specific definitions also carry increased assumptions associated to that given field. This is a crucial point of consideration given that a consensus would allow researchers to work towards a common goal. Given the interdisciplinarity of HCA and the many lenses that have been used to study it, a global definition would facilitate the transfer of knowledge across fields, which is currently challenging.One dimension absent from the multiple proposed definitions of HCA is the “non-stationary” aspect of health. Indeed, the definitions above highlight that few authors consider differences in individual variability between persons or over time. A reason for this may be linked to (once again) the pathological context that HCA is often framed around. The changes in HCA will be much smaller than those observed in pathological aging which can lead to them being neglected. However, the gradual changes that accumulate over time can result in negative effects on quality of life, and should therefore be considered .Within the context of HCA, irrespective of absolute scores in cognitive function at a given point in time, longitudinal measures of cognitive change can capture meaningful variation in declines that are relevant for adults’ everyday lives . As thesFurthermore, this limitation is peculiar, as there is a big body of literature supported by both cross-sectional and longitudinal studies, that indicates that older adults show a great range of variability in cognitive functioning . In specGiven the relevance of the heterogeneity present in cognitive aging, apparent through extensive reviews such as the one discussed above by Given the previously presented work and the gaps highlighted by Variability: To detect the smaller changes in cognitive health linked to the highly variable population of healthy aging, different methodological techniques have been proposed. These approaches range from a characterization of a normative trajectory of HCA as proposed by Despite the methodological advances and the valuable distinction between healthy and pathological cognitive health, a limitation of the former approach lies with averaging of different scores which may fail to capture important idiosyncrasies relevant in alternative trajectories. On the other hand, the latter approach can lead to challenges in obtaining a reliable and noise-free assessment of intraindividual variability. As an alternative approach that addresses the limitations of the normalization or individualization strategies previously described, Time: Regarding the time component associated with HCA, researchers suggest that not all individuals age the same, and that while some individuals experience little change, others experience complex non-linear dynamics . ExplanaThe implementation of these concepts has been facilitated in the past years due to the increasing availability of health data. In fact, the number of publications on big data and health has grown exponentially in the last decade . CurrentIn this work we reviewed proposed definitions of HCA found in several fields of the literature. Through this process, we identified that cognitive health in healthy aging varies throughout the individual’s lifespan and is related to factors such as general health and social engagement . We idenThrough this paper, we have discussed that the core concepts of Variability and Time give rise to the notion of trajectory, which through repeated appropriate assessments allow for an increased likelihood of capturing change and a better understanding of HCA. From a methodological perspective, researchers have started exploring heterogeneity in the context of population subtyping. This approach requires a nonlinear understanding of cognitive aging and credits part of the heterogeneity to previously viewed as “noise,” as “subtype-specific.” We believe that it is crucial to integrate these principles in the conceptualization of HCA in a way that is meaningful across disciplines. Overall, given the current literature, the advantages and limitations of the different proposed definitions, and the key highlighted gaps, we propose that HCA is defined in the following way:Healthy Cognitive Aging is the cognitive trajectory characterized by the individual profile that mitigates the effects of age-related social, behavioral and neural decline.This definition provides a clear conceptualization of HCA while allowing for individual differences to be considered. It defines HCA in a longitudinal manner, not restricting its assessment to a specific discipline. It further considers age-related changes within the older adult population, recognizing that these may not be the same across the population. Finally, it is also flexible in how these age-related changes may be interpreted .Incorporating these two novel dimensions to the study of HCA has already started  and it iThe original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.ER and SM provided substantial contributions to the conception of the work and contributed to drafting and revising the work critically for important intellectual content. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The traditional LDPC encoding and decoding system is characterized by low throughput and high resource consumption, making it unsuitable for use in cost-efficient, energy-saving sensor networks. Aiming to optimize coding complexity and throughput, this paper proposes a combined design of a novel LDPC code structure and the corresponding overlapping decoding strategies. With regard to structure of LDPC code, a CCSDS-like quasi-cyclic parity check matrix (PCM) with uniform distribution of submatrices is constructed to maximize overlap depth and adapt the parallel decoding. In terms of reception decoding strategies, we use a modified 2-bit Min-Sum algorithm (MSA) that achieves a coding gain of 5 dB at a bit error rate of  Low-density parity-check (LDPC) codes are linear block codes that correct errors by adding parity bits, first proposed by R. Gallager in 1962 . These cQuasi-Cyclic LDPC (QC-LDPC) codes are a type of LDPC that can offer several advantages over other error-correcting codes, including low complexity implementation, high error correction performance, flexibility, and wide usage in practical applications. They can be efficiently encoded using simple operations such as shifting and XOR . Their rThe data processing speed of many specialized sensors, such as optical sensors, intelligent sensors, and biomaterial-based sensors, has been a pressing need for improvement ,13,14. WThe conventional decoder designs of LDPC codes, which employ the variable node processing unit (VNU) and check node processing unit (CNU) sequentially in each iteration cycle, often result in low HUE and low throughput. Although data interleaving has been proposed to enhance HUE by allowing variable node processing (VNP) and check node processing (CNP) to work on separate block data simultaneously, it doubles the memory requirements . FoldingThe choice of decoding algorithm is a crucial factor affecting the performance of LDPC decoders. The Min-Sum algorithm (MSA) simplifies the multiplication operation in the decoding process to an addition operation, and approximates the summation process to finding the minimum value, thereby significantly reducing the algorithm’s complexity . BuildinIn this paper, in response to the inability to perform deep overlap decoding for CCSDS  LDPC codes, we propose a method for designing a QC-LDPC that is suitable for overlap decoding, while maintaining an equivalent submatrix structure and code length. We design a specialized partially parallel decoder based on the Modified 2-bit MSA for our proposed LDPC code, which exhibits a substantial coding gain of 5 dB at a BER of This paper is organized as follows. In l, n and k represent code length before and after coding, respectively, and the PCM can be constructed using the base graph and shift factors QC-LDPC codes are a type of LDPC codes whose PCM can be decomposed into cyclic submatrices of equal size . For an matrices . The oriSpecifically The PCM of CCSDS  LDPC is presented in In traditional partially parallel decoder architectures, CNU and VNU are performed sequentially in each iterative cycle, as shown in The indices of the first-row elements within each submatrix of the original CCSDS  PCM are randomized. When arranging all the elements within submatrices into a For instance, considering the original CCSDS  PCM, the decoder might employ 16 VNU and 2 CNU for partial parallel iteration . This approach maximizes the utilization of its cyclic shift properties to save resources and increase throughput. Each VNU initiates computations in the rightwards direction from the position indexed as 1 in each column submatrix, until the current submatrix computation concludes. Therefore, while calculating VNU, it is essential that the column’s corresponding elements have been updated with the latest information from CNU iteration.In Therefore, we propose a LDPC code based on CCSDS  LDPC with a greater overlap depth, which is composed of 32 submatrices in 2 rows and 16 columns, and has submatrices of m stands for the number of shift factors, and Algorithm 1: Permutation Vector.Input: [ Output:  We employ a permutation vector shown in Algorithm 1 and defined in  to deterUpon obtaining the permutation vectors, we proceed to uniformly arrange the data in the submatrices with a size of Utilizing the matrix we have proposed, it becomes remarkably convenient to design decoders with higher rates and lower resource consumption using the overlap scheme. When the submatrix parallelism level is set to 57, this approach can achieve convenient and regular deep overlap scheduling. The specific scheduling methodology will be discussed in subsequent sections.The process of LDPC decoding has been enhanced through the utilization of various algorithms, such as the Belief Propagation (BP) algorithm, Likelihood Ratios BP Algorithm (LLR-BP), and the MSA ,28,29. TAlgorithm 2: Min-Sum Algorithm.Input: rx and PCM Initialization: Output: Decoded Data x The complete MSA is shown as Algorithm 2, where In order to implement high-speed hardware for LDPC decoding, it is necessary to quantize the message values. Quantization with fewer bits can save memory and simplify the hardware structure, but it may also lead to a reduction in BER performance . Based oBsBm . Unlike decision . When Bmquations , where rAfter receiving the message Equation , where IWhen implementing on FPGA, direct use of adders or comparators consumes more LUT resources compared to combinational logic circuits. To enhance the HUE and to adapt to the dual-port characteristics of the gate circuit, we have devised an improved algorithm that builds on these observations.To optimize the memory structure, in CNU, only 3 bits are used to store each c2v message, representing the product of sign bits, min1 and min2, instead of storing all the fore use . Due to To optimize the hardware implementation of the CNUs, a pipeline operation can be performed to divide the 32 inputs into four groups. This enables the calculation of four sets of min1 and min2, which can then be cascaded to a second stage by inputting these sets. By doing so, the min1 and min2 of all 32 w for W for There are four steps in VNU. First, four 3-bit c2v messages are inputted based on the column weight. Next, four 2-bit Equation , which wEquation  and g(·)Equation . The magAnd the origin computation in VNU can formulated as Equation  with f is shifted by the previous row (or column). This means that the bits on the left of the bits processed in this clock are the bits needed for the next clock. In this way, once we decide the read port of the vector, with the cyclic shift of the register, the bits of this position are always the pending ones. This reduces the routing complexity.Furthermore, the storage method with a fixed read/write position can eliminate the read/write latency and further accelerate the decoding process.To achieve high throughput in LDPC decoding, minimizing both intra- and inter-iteration waiting times is crucial . To ensuw, W, and The method to determine the parameters is finding the value with best BER performance at the same SNR. After simulations in MATLAB, we determined the values of The six solid lines show the results of the MATLAB simulation. Three distinct decoding algorithms were employed, each executed over eight iterations, for both the CCSDS  LDPC and the proposed LDPC code, yielding comprehensive simulation results. Initially, employing identical decoding algorithms, a minor coding gain loss of 0.5 dB was observed when applying the proposed LDPC in comparison to the original CCSDS  LDPC code. This outcome underscores the effectiveness of our devised PCM, which yields a favorable performance comparable to the original CCSDS  LDPC, and this 0.5 dB coding gain loss is acceptable considering that the throughput can be significantly improved by using the proposed LDPC with the same resource consumption. And, it can be found that no matter which PCM is used, there is a 1 dB coding gain loss using 2-bit MSA compared to MSA when the BER reaches Meanwhile, we have selected four decoders with high throughput or low implementation complexity as the design principle and similar code rate, and compared the BER performance; the results are shown by four dashed lines in coder in  uses thecoder in  uses thecoder of  uses CCSAs an additional facet of our analysis, r is the code’s rate, l is the code’s length, f is the frequency of the decoder, n is the number of iterations, and c is the number of clocks per iteration [For our design, we used Xilinx Virtex UltraScale+ FPGA VCU118 Evaluation Board (XCVU 9P) as the implementation platform. The throughput of a decoder is often a crucial performance metric, and it can be calculated using the following formula:teration .For our optimized decoder designed for overlap, we can finish a single iteration in 18 clocks. Suppose we use a clock frequency of 156.25 MHz and perform eight iterations, which is a common choice for high-performance LDPC decoders. The code we use has a rate of 0.875, a length of 8176 and, thus, the throughput of the decoder can be calculated as follows:Therefore, our optimized decoder can achieve a throughput of 7.76 Gbps, which is quite high and suitable for high-speed systems. If overlap control is not used under the same decoder, one iteration takes 114 clocks, resulting in a throughput of 4.08 Gbps. In other words, overlap decoding scheme doubles throughput while maintaining unchanged resources and performance.The utilization of the FPGA is presented in Furthermore, a comparison of the implementation and performance of the proposed low-complexity high-throughput decoder with various other LDPC decoders with high HUE or similar PCM is presented in The findings detailed in This paper presents a novel overlap-optimized decoder for high-speed communications, aimed at fulfilling the high data rate requirements of this domain. A QC-LDPC code is constructed, which allows for decoding time overlap of up to one third, thereby enhancing the decoder’s throughput. To strike a balance between decoding performance and implementation complexity, a Modified 2-bit MSA is used. In addition, a Boolean operation-based CNU and VNU is proposed to facilitate c2v/v2c message calculation by logic circuits, thereby reducing resource overhead. To address the quasi-cyclic characteristic and reduce read/write latency, a shift-register-based memory strategy is employed. The proposed decoder achieves a coding gain of 5 dB at a BER of


Metal–organic frameworks (MOFs) are a class of porous materials that have shown exciting potential as adsorbents for CO2 capture due to their high surface area and tunable properties. Among several implemented technologies, direct air capture (DAC) using MOFs is a promising strategy for achieving climate targets as it has the potential to actively reduce the atmospheric CO2 concentration to a safer levels. In this study, we investigate the stability and regeneration conditions of N,N′-dimethylethylenediamine (mmen) appended Mg2(dobpdc), a MOF with exceptional CO2 adsorption capacity from atmospheric air. We employed a series of systematic experiments including thermogravimetric analysis (TGA) coupled with Fourier transformed infrared (FTIR) and gas chromatography mass spectrometer (GCMS) (known as TGA-FTIR-GCMS), regeneration cycles at different conditions, control and accelerated aging experiments. We also quantified CO2 and H2O adsorption under humid CO2 using a combination of data from TGA-GCMS and coulometric Karl-Fischer titration techniques. The quantification of CO2 and H2O adsorption under humid conditions provides vital information for the design of real-world DAC systems. Our results demonstrate the stability and regeneration conditions of mmen appended Mg2(dobpdc). It is stable up to 50% relative humidity when the adsorption temperature varies from 25–40 °C and the best regeneration condition can be achieved at 120 °C under dynamic vacuum and at 150 °C under N2.The increase in the atmospheric carbon dioxide level is a significant threat to our planet, and therefore the selective removal of CO 2(dobpdc) as a direct air capture adsorbent to determine its practical feasibility and performance for potential industrialization in mitigating climate change by reducing atmospheric CO2 levels.A comprehensive examination of mmen-Mg Reducing emissions will no longer be sufficient to mitigate this crisis, and we must establish facilities and governmental policies that can remove billions of tons of CO2 from the air annually.1,2 Among different methods of atmospheric CO2 reduction such as flue gas capture and other point source capture strategies, direct air capture (DAC) has shown promise in several literature and environmental reports for climate change moderation.3,4 DAC aims to extract CO2 selectively from ambient air where concentrations are as low as 400 ppm. Despite having the advantage that the capture point can be anywhere and recent favorable reports and successes of DAC technologies, finding an appropriate, reversible, cyclable, sorbent material for DAC of CO2 from dilute air (400 ppm of CO2) is still a major challenge.5,6 Selective adsorption of CO2 by liquid amine solution is one of the industrially viable options, but the high cost of regeneration, decomposition of amines with time, and its severe corrosive nature are major drawbacks for practical application in a DAC setup.7,8As the carbon dioxide  have received growing attention due to their high degree of tunability, selectivity, and porosity  of MOFs was first introduced by Férey and Chang et al. where the unsaturated Cr(iii) sites of MIL-101 are functionalized with ethylenediamine.14 Subsequently, in 2012 Jones et al. reported the first ethylenediamine (en) grafted Mg-MOF-74 (Mg2(dobdc), dobdc4− = 2,5-dioxidoterephthalate) and explore the possibility of CO2 capture. The CO2 uptake capacity of en-Mg-MOF-74 is comparable to that of polyethyleneimide (PEI)-impregnated silica and amine grafted silica which is a well-known benchmark material.15,16 Mg-MOF-74 has unsaturated magnesium (Mg) centers that can act as Lewis acid and interact with guest molecules. Upon post-synthetic functionalization various diamines can be appended to an open metal site (OMS) in κ1 fashion leaving the other amine side free to interact with carbon dioxide. Long et al. have developed diamine appended pore expanded derivative of Mg-MOF-74 labeled as Mg2(dobpdc)  which has an increased pore diameter (Mg-MOF-74: 11 Å vs. Mg2(dobpdc): 18.4 Å) and aids to tether more amine molecules within the channel.17 Ethylenediamine appended Mg2(dobpdc), named as en-Mg2(dobpdc) has capacity of capturing 2.83 mmol g−1 CO2 at 25 °C and 0.39 mbar or atmospheric CO2 levels, however a 6% loss of capacity was observed after first five cycles in simulated air. Long and others have tested various homo and heterodiamines to address the challenge of amine loss during regeneration to no avail.18,19 Among different homo and hetero diamines, N,N′-dimethylethylenediamine (mmen) appended Mg2(dobpdc) has been known to have high CO2 adsorption capacity at very low-pressure ranges with enhanced stability.20 A mechanistic study for CO2 adsorption in mmen-M2(dobpdc)  has proposed that the adsorption of CO2 at ambient temperatures is possible via cooperative insertion into the mmen-M2(dobpdc) to form one dimensional carbamate chain and the uncoordinated amine of a mmen molecule acts as a strong base to remove the acidic proton from the metal-bound amine of a neighboring mmen molecule.20 Although the volatilization of coordinated diamine during the regeneration process upon dry and humid conditions remains unclear, some reports claim that diamine tethered Mg2(dobpdc) loses CO2 capture capacity due to a loss of diamine from the grafted sites.18,21Amine-functionalized solid porous adsorbents are an emerging class of materials which can selectively bind COporosity .11 Amine2 have critical impact on the efficiency of the adsorbents, wherein the fastest kinetics allow for the highest cost effectiveness. mmen-Mg2(dobpdc)  coupled with Fourier transformed infrared (FTIR) and gas chromatography mass spectrometer (GCMS) (known as TGA-FTIR-GCMS) allow us for deeper understanding of the mechanistic pathways of thermal regeneration. Since humidity is variable globally and temporally, it is important to evaluate the capture of CO2 in presence of water. Different experimental conditions were utilized to further understand the regeneration performance and suggest the desorption mechanism in dry and wet conditions. Moreover, we have performed 50 regeneration cycles under dry and humid conditions with different adsorption–desorption conditions along with long cycles test to assess their sorption performance. Finally, the capture of CO2 was quantified in presence of water by TGA-GCMS coupled instruments for determination of practical applicability of mmen-Mg2(dobpdc) globally.Therefore, it is essential to determine if the capacity fade is due to the degradation of the amine during the regeneration process or if the MOF has simply not been fully regenerated under standard conditions. Adsorption and desorption kinetics of CO(dobpdc)  has been2(dobpdc) and diamine grafted MOF are aligned with simulated powder X-ray diffraction (PXRD) patterns. Moreover, crystallinity of the MOF is not affected after amine appendant as confirmed by PXRD measurement  and mmen-Mg2(dobpdc), was examined by exposing the samples to ambient conditions for several days while monitoring the CO2 uptake efficiency at 25 °C (2(dobpdc) loses CO2 uptake capacity after three days, while the diamine appended MOF shows comparable CO2 uptake even after 10 days. The color of the pristine Mg2(dobpdc), which is linked to the phase, changes from off white to blue within 3 days of exposure to atmosphere whereas the off white color of mmen-Mg2(dobpdc) remains same even after 1 month. This additional stability from the diamine functionalization has been reported in literature.17,23 In addition to the increased stability the diamine appended MOF also shows stronger affinity towards CO2 at low pressure indicating involvement of chemisorption  at 400 ppm and 25 °C is 1.5 mmol g−1  was functionalized with propylamine (pa) which is a monoamine following the same process used for diamine functionalization. PXRD patterns show the crystallinity is unchanged  (2(dobpdc) (2 adsorption isotherm at 25 °C illustrates that the pa-Mg2(dobpdc) is not able to capture comparable amounts of CO2 as adsorbed by mmen-Mg2(dobpdc) and chemisorption part at lower pressure is also absent (2 from atmosphere through the cooperative mechanism.20The N(dobpdc) . Pore si(dobpdc) . CO2 adso absent . These r2(dobpdc) was then aged through accelerated conditions at 80% relative humidity and 70 °C for 12 hours, 1H NMR indicates that upon exposure to such harsh conditions, amine loading was reduced from 60% to trace amount ) due to a reduction in the coordinated amines which is responsible for low pressure chemisorption (2 uptake observed (2 and re-amination of the parent Mg2(dobpdc) is possible.Finally, the mmen-Mgt Fig. S5. Consequsorption . Interesobserved . This ex2(dobpdc) fully regenerates when heated at 100 °C for four hours under dynamic vacuum as confirmed by PXRD and N2 isotherms. Determination of the stability of the amines in the MOFs, was evaluated with thermogravimetric analysis (TGA) coupled with FTIR and GCMS. To verify the diamine stability in DAC conditions, it is also important to evaluate its thermal stability under humid conditions when it is fully adsorbed with CO2. The thermogram under humid He does not show any significant difference than that under dry He condition (2 (m/z: 44) is detected in GCMS at regeneration temperature 90–100 °C  are detected after 200 °C  and fragmentations of diamine (m/z: 58 and 88) molecules are released only after 200 °C. The first one indicates the stability of adsorbent without any CO2 adsorption , was first regenerated and cycled at 120 °C for 30 minutes under N2 after exposing it to the ambient air. The desorption behavior under two humidity levels (25–30% RH and 40–50% RH) of the laboratory were assessed. After running 25–30% RH for 30 minutes (2 and/or H2O uptake from air was not decreased. But when the same cycle was performed after exposing to 45–50% RH (2 + H2O) uptake was noticed.The mmen-Mg minutes  for 10 c5–50% RH , with 15−1 at 90% RH , after exposing it to open environment  for several days, show that no amine leaching occurred at the regeneration temperature of 120 °C. However, CO2 and H2O are detected from the GCMS , but after 20 cycles it reduces to 7.5% from 9%, which means 80% of the materials capacity is retained  and 88% of total capacity is retained  capacity is maintained throughout the cycles  captures both CO2 and H2O from air, as detected by TGA-GCMS during the regeneration of the MOF. Herein we report a technique to quantify the amount of CO2 and H2O using the coupled TGA-GCMS technique. Integrated intensities of chromatogram for CO2 (m/z: 44) and H2O (m/z: 18) were plotted against retention time of GCMS (2 (50% RH), the mmen-Mg2(dobpdc) adsorbed 1.7 mmol g−1 of CO2 (7.5% w/w) and 3 mmol g−1 of H2O (5.7% w/w) at 120 °C. Karl-Fischer coulometric titration on the sample (after re-exposure) corroborated that the sample uptakes around 3 mmol g−1 of water. Similar CO2 uptakes with other amine appended Mg2(dobpdc) and Mg2(dotpdc)  have been reported by Long et al.18,24 The overall amount of adsorbed CO2+H2O is also consistent with the weight loss measured by TGA  is a well-known MOF which can capture CO2 at pressures as low as 400 ppm, making it a superior screening material for direct air capture. However, performance of these MOFs is dependent on the stability and recyclability under humid conditions. We have carried out a systematic study to assess the applicability of N,N′-dimethylethylenediamine (mmen) appended Mg2(dobpdc) MOF for direct air capture. With the help of coupled TGA-GCMS-FTIR, we have screened the performance of mmen-appended Mg2(dobpdc) under real DAC conditions. The quantification of exhaust gas revealed that this amine appended-Mg2(dobpdc) can uptake significant amount of CO2 (∼1.7 mmol g−1) in presence of 50% relative humidity. Regeneration is possible under appropriate conditions and can be enhanced through introduction of N2 gas under the regeneration conditions. These results have far-reaching implications that will afford the opportunity to engineer and design the process parameters like temperature, humidity, adsorption/regeneration conditions in similar diamine-appended MOF as future DAC sorbent.Amine appended-MgInformation supporting this article has been uploaded as part of the ESI.O. K. F. supervised the project. S. B., T. I., and O. K. F. conceived the project and led the investigation. S. B., D. S., T. I., and O. K. F. designed the experiments and interpreted the results, with help from K. I., H. X., and X. W. TGA-GCMS-FTIR characterization and controlled experiments were performed by S. B., C. D. M., and N. M. B. Manuscript was written by S. B., D. S., M. L. B., T. I., V. P. D., and O. F. K, and all other authors commented on and revised the manuscript.The authors declare the following competing financial interest(s): O. K. F. has a financial interest in the start-up company NuMat Technologies, which is seeking to commercialize metal–organic frameworks.SC-014-D3SC02554C-s001


In addition to an in-depth analysis of existing approaches, this review also addresses unresolved research challenges within this domain. These unexplored areas offer exciting opportunities for advancements and innovations, aiming to enhance the accuracy and effectiveness of personalized video recommendations. Overall, this article serves as a valuable resource for researchers, practitioners, and stakeholders in the video domain. It offers insights into cutting-edge algorithms, successful applications, and areas that merit further exploration to advance the field of video recommendation.Video platforms have become indispensable components within a diverse range of applications, serving various purposes in entertainment, e-learning, corporate training, online documentation, and news provision. As the volume and complexity of video content continue to grow, the need for personalized access features becomes an inevitable requirement to ensure efficient content consumption. To address this need, recommender systems have emerged as helpful tools providing personalized video access. By leveraging past user-specific video consumption data and the preferences of similar users, these systems excel in recommending videos that are highly relevant to individual users. This article presents a comprehensive overview of the current state of  Recommender systems (RS) support various decision-making scenarios ranging from the recommendation of simple items, such as books or movies, to more complex ones, like financial services and digital equipment , which are gaining significant attention in entertainment, as well as industrial contexts, due to the rapidly increasing number of available video items.Popular video platforms, for example, YOUTUBESeveral reviews related to video recommendations have been published in the past years. In Véras et al.  recommenWhile those related reviews specialize in specific video-related recommender aspects, our overview provides a concise summary of video item recommendations, serving as a comprehensible summary of the state-of-the-art for practitioners and researchers in this area. This overview should enhance understanding of the various technical approaches within this field and their applications. Additionally, it identifies open issues that should be addressed in future research to further develop the field.The article is structured as follows: In Section 2, we outline the analysis method employed in our literature review. In Section 3, we conduct an in-depth analysis of the existing literature on VRS, categorizing it based on different fundamental approaches of recommender systems and the technologies utilized. Following that, in Section 4, we discuss the findings and offer insights to comprehend which approaches excel in various recommendation scenarios. Additionally, we address future research considerations and discuss unresolved issues. Finally, the article concludes in Section 5.Firstly, we present an extensive overview of the current state-of-the-art in VRS, covering research developments from the past decades. Secondly, we provide valuable guidance for selecting suitable recommendation approaches based on individual scenarios. Thirdly, we engage in a comprehensive discussion of open research issues, highlighting the potential for future work in this evolving field of research.The major contributions of this article can be summarized as follows: videos, independent of the domain. This includes entertainment, e.g., movies or videos on social networks, as well as video advertisements, learning videos, news videos, and others.The main objective of this article is to provide an overview of state-of-the-art video recommender systems to increase understanding of this topic, derive guidance for choosing appropriate approaches, and identify issues for future research. In this context, we include recommender systems where the recommended items are video recommender systems”, “video recommender”, “video recommendation”, “movie recommender systems”, “movie recommender”, and “movie recommendation”. With these, queries were triggered in the digital libraries of ACM,Our analysis of related work is based on a bibliographic review method. As an initial step, we collected and reviewed existing publications on VRS over the last 20 years. The search for related papers was performed on the basis of different keywords, including, “Following the review, publications were categorized by their recommendation approach , and further divided into subcategories of different applied algorithms. The results are outlined below. The topic of video content representation, which is relevant for content-based and many hybrid recommender approaches, is summarized in a separate section. From these findings, guidance in selecting appropriate technologies is derived and open topics for future research are identified.Video recommender systems suggest videos to users based on their individual preferences. An overview of a typical pipeline used for video recommendation is illustrated in retrieval and ranking phase  algorithm to order its video catalog based on user profiles, video popularity, and temporal viewing trends. Different algorithms are applied on top for various purposes: (1) Identifying the most relevant items from the catalog for each user. (2) Ordering videos users have started watching. (3) Unpersonalized prediction of short-term temporal trends for events like Halloween or Christmas, or unplanned incidents, such as a hurricane or other natural catastrophes currently populated by the news. (4) Recommending videos with similar content. (5) Enhancing content presentation by selecting thumbnails and presented metadata. Furthermore, NETFLIX employs a page generation algorithm to define the selection and ordering of rows presented in the UI. It considers that one account is mostly used by multiple users, e.g., family members, aiming for a diverse content presentation that is relevant to each user in front of the screen.The variability of VRS applications can be illustrated by taking NETFLIX as an example , hybrid recommendation, and group recommendation. In In the following, the literature on VRS is discussed. Foremost, the methods used to represent the content of videos are discussed. Subsequently, publications are categorized by the applied recommendation approach, including multimodality. Videos consist of three modalities: (1) Aural (audio information), (2) Visual , and (3) Textual , which can be expressed in varying degrees of semantic detail. This characteristic makes videos multi-modal, as they include all three modalities, whereas a music piece without lyrics is uni-modal, as it only features aural elements  Mid-level features require interpretation knowledge and are derived from low-level features, representing syntactic features. (3) High-level features resemble human interpretation of the content, providing a semantic description. Based on the classification in Deldjoo , video fInternet Movie Database (IMDb)Open Movie Database (OMDB)semantic web data, illustrated in Hopfgartner and Jose . Initiatives like the Netflix Prize have contributed to this emphasis by providing real-life data to improve movie recommendation accuracy  and high-level semantic concepts  contribute to accurate recommendations. Combining these features through a multi-modal approach showed potential for improving accuracy.Generating appropriate suitable video representations is crucial in video recommendation and has been extensively studied. The study in Elahi et al.  focused Block-Level-Features) and low-dimensional representations of acoustic signals (I-Vector Features). Visual features included Aesthetic Visual Features (AVF), categorized by color, texture, and objects, as well as high-level features extracted with Deep Neural Networks (DNN). Utilizing multi-modal representations with weighted aggregation again demonstrated the potential for improving accuracy.A related study found similar results with automatically extracted aural and visual features   to learn word relationships. This representation incorporating semantic features allows for human comprehension of recommendations and offers the potential to explain why a video is suggested.High-level visual features such as faces, objects, and recognized celebrities were automatically extracted in Elahi et al. , to creaRestricted Boltzmann Machines (RBM) are a type of neural network (NN) used in Hazrati and Elahi , commonly found on social network platforms, like TIKTOK.A special task of VRS is the recommendation of Multi-Modal Graph Contrastive Learning (MMGCL) is introduced in Yi et al.  that captures user interactions like selecting, pausing, or seeking videos. This feedback helps adapt modalities' relevance and results in improved video representations.The Content-based recommenders, also known as Content-based Filtering (CBF), utilize item characteristics or features that users are interested in to find unseen items with similar attributes and present those as recommendations  as input to predict whether an item is relevant with respect to individual preferences. It comprises classification, i.e., the assignment of items to predefined categories like relevant/irrelevant, and regression analysis, i.e., the prediction of a numerical value like a user rating. Thereby, different features, feature representations, encoding of user preferences, and classification techniques are applied, depending on the context.Supervised learning algorithms for content-based recommendation take the feature descriptions of items and user preferences (often defined as k-Nearest-Neighbors (kNN)  as search queries to obtain a video selection and the available video keywords. Those are transformed to a TF-IDF representation, used for the kNN recommendation.A predominantly used algorithm in content-based video recommendation is Besides comparing the similarity between video items, often a user profile reflecting the individual user preferences is used to identify similar videos. Mostly, this profile is based on past video consumption and represented in the same embedding space as the videos, which enables computation of the distance between them. An elaborated example is presented in Zhu et al. , using aRandom Forest  are more similar compared to others. For content-based recommendation, this approach is used to identify similar items to a seed or user preferences represented in the same embedding space. Any kind of content representation can be taken into account (see Section 3.1), and the approach is applicable to a variety of domains, e.g., for clustering sports videos based on recognized human actions  and the item is minimized, given a distance metric .Hidden Markov Model (HMM) , user specificity (compatibility with the user watch history), and diversification .Also, Self-supervised learning algorithms for content-based recommenders use automatically generated item embeddings  network uses a combination of temporal windows to capture short-term dynamics of user interest, and multi-grained attention mechanisms to describe the diverse user interest. While category-level attention describes the diverse interest of users, fine-grained user interests are described with item-level attention. Using a hierarchical attention mechanism, short-term and long-term properties of user behavior are modeled.In Chen et al. , a deep Micro video recommendation faces the challenge of dynamic and diverse user interests, leading to the development of various solutions. One baseline strategy uses time decay to reduce the significance of videos watched further back in the past. An advanced version employs a temporal graph-guided network, as described in Li et al. , to predMulti-scale Time-aware user Interest modeling Network (MTIN) is proposed to address this issue. MTIN incorporates a parallel temporal mask network to capture varying importance over time. Additionally, the model utilizes a grouping approach for videos and assigns users to multiple interest groups, allowing for a more accurate representation of their diverse preferences.Using a static time decay heuristic fails to consider personalized and individual preferences, where older videos might be more important for some users. In Jiang et al. , a MultiTo handle the dynamically changing user interests in micro-video applications, a real-time re-ranking solution was proposed in Gong et al. . RecogniEmbedding and Multilayer Perceptron (MLP) paradigm are commonly used for this task. These models map input features to low-dimensional embedding vectors, which are then transformed and concatenated in MLP layers to capture non-linear relationships among the features  was introduced in Zhou et al. (Preference Matching Network (PMN) model was presented in Liu et al.  is based on the concept that users with similar preferences in the past will continue to have similar preferences. Hence, CF exploits past ratings to suggest unseen items by considering items liked by users with similar preferences  is applied for improved cluster centroid assignment. The Artificial Bee Colony (ABC) algorithm optimizes user-cluster assignments  condenses data dimensions by removing less significant data. Secondly, this dense data is clustered to identify similar users.Many methods use the k-Means algorithm for clustering similar users and enhancing the accuracy with varied optimization techniques. For instance, in Katarya and Verma  ParticleKatarya, . In WangKatarya, , k-MeansFuzzy C-means (FCM) algorithm, permits users to belong to multiple clusters with varying degrees of membership  quickly extracts user features, e.g., preferred genres and movie ages. User ratings are encoded as a binary matrix, where each movie corresponds to a column, and each rating value option is represented by a row . This matrix is then used as input for the DBN to generate a user feature vector. The feature vectors for all users are used to find nearest neighbors using the Euclidean distance.In Hongliang and Xiaona , a Deep Neural network-based Collaborative Filtering (NCF) presented in He et al.  model, which splits user embeddings into common and site-specific parts, adapting feature weights via an attention mechanism. This approach captures both shared and unique user preferences across services.The discrepancy of user interests in different services, stating that user interest features include cross-site commonalities and site peculiarities, is observed in Yan et al. . The stuIn the domain of e-learning, cross-correlation of videos can be applied to leverage the use of videos across different courses, emphasizing the correlation of knowledge between courses , forms a powerful method for collaborative filtering that improves traditional matrix factorization  for CF is introduced. AIS mimics biological immune systems, comprising antigens (unclassified training data) and antibodies (generated in response to antigens). These antibodies construct specialized immune networks signifying their similarity to antigens, representing specific training data. After training, the final immune network predicts user ratings for a target user (antigen). This involves identifying nearest neighbors via similarity assessment of user groups (immune networks) and users within those groups (antibodies). By leveraging this immune system-inspired approach, accurate predictions can be made for the target user's ratings.In Chen et al. , an ArtiInterest Preferences of Categories (IPoC) can be deduced as implicit ratings from user logs .One option is to represent social media users and videos in a common attribute space  unites deep representation learning for content and collaborative filtering for ratings , which is a deep learning model that learns a hierarchical representation of data by removing noise and reconstructing clean input. Using this model, CDL generates accurate rating predictions for user-video pairs.Hybrid video recommenders using deep neural networks often aim to enhance recommendation accuracy by incorporating content features and user ratings. timeSVD++. This model considers user preferences, item features, and temporal rating dynamics. The process starts by extracting and processing movie plots for relevant words. A bag-of-words vector captures item similarity. These vectors train the SDAE to extract item content features. The trained features are the input for the CF model that predicts the ratings of items with few or no ratings based on similar items which are already sufficiently rated.In Wei et al. , the colDynamic Recurrent Neural Networks (DRNN)  is a machine learning approach that trains one model for multiple related tasks, boosting performance through shared representations  architecture for MTL. MMoE has a shared bottom layer and separate expert layers per objective. The expert layers learn task-specific data from inputs. Gating layers for each task incorporate expert and shared input. The expert layer output is fed into a task layer predicting binary objectives  or regression tasks . In Song et al. , presented in Tang et al. (negative transfer and the seesaw phenomenon. Negative transfer in RS occurs when unrelated objectives lower performance compared to single-task systems. The seesaw phenomenon is the trade-off between improved performance for one task and a decline in others in MTL. PLE is built on the Customized Gate Control (CGC) model, segregating shared and task-specific experts to avoid parameter interference. Task-specific experts focus on learning distinct knowledge, receiving input from their expert network and the shared expert network through a gating network for dynamic fusion. PLE extends CGC to a generalized model with multi-level gating networks and progressive separation routing, stacking CGC expert networks and creating extraction networks. Each extraction network receives fused outputs from lower-level networks, gradually learning deeper semantic representations and extracting higher-level shared information. By separating task parameters in upper layers, PLE enables the extraction of deeper semantic representations for each task, fostering generalization.g et al. , forms aAdsorption with content-based similarity to improve the quality of recommendation was presented in Öztürk and Kesim Cicekli  (user profile creation from demographic characteristics) offers one possibility to address the cold start problem  algorithm uses video and user characteristics to create prediction models for different co-clusters, aiding users with minimal ratings by assigning them to the closest cluster. For users without any ratings, the cluster description and demographics determine the best prediction models. The first approach estimates the probability distribution for each co-cluster and calculates the predicted rating as a weighted sum, while the second, more resource-intensive method, constructs a video-by-video classifier involving only users who have rated the video.Combining CF with The problem of sparse user ratings is addressed in Liu et al.  by compuGroup recommendation involves recommending items to a collective group rather than individual users, assuming the preferences of group members are known or can be obtained through recommender systems  nearby. By collecting cinema activities like selected movies, time, and location, individual cinema profiles are created, integrating POIs, movie details, and ratings. Using this data, the system employs CF to model temporal and spatial dynamics. Temporal dynamics cover the Periodic Effect , Recency Effect (preference for new movies), and Audience Crowd Drifting Effect . The spatial context is modeled through the Spatial Neighboring Effect (similar audiences in cinemas with similar POI patterns) and the Spatial Popularity Effect . This enables the prediction of movie ratings for specific cinemas at given times.The recommendation of movies for on-demand cinemas presents a unique application of context-aware group recommendation systems  classifier is used. SVM is a supervised machine learning method that determines an optimal decision boundary to classify data into classes, maximizing the margin between the closest data points of each class.Several VRS incorporate affective data for recommendations. In Roy and Guntuku , the emoA related idea is applied in Niu et al.  to recomElectroencephalograms (EEG), which measure brain neural activity, to capture user emotions and attention while watching videos is explored in Bandara et al.  and temporal context (current time) and their impact on the recommendation process. Context awareness can be introduced to an existing video recommender by filtering or re-ranking its suggestions based on user context  are video parts that users highlight while watching because they are interesting to them. The intention is that users like specific parts of videos more than others. In Dias et al.  as metric. The goal was to predict the number of stars a user would rate a movie on a 1 to 5 scale. This competition resulted in significant advancements in RS, with matrix factorization methods becoming key technologies for collaborative filtering, surpassing classical nearest-neighbor techniques. The winning solution is detailed in Koren  Over-specialization occurs as these approaches mainly focus on suggesting similar items to those previously liked, potentially missing diverse content relevant to the user. (3) The cold start problem describes a ramp-up phase of new users to a system, requiring new users to provide enough ratings for the system to generate useful recommendations, which may take time.In general, content-based approaches have some common weaknesses  the new item problem, where new items require enough ratings to be recommended. Furthermore, the sparsity of ratings challenge those systems, as a sufficient number is crucial for accurate recommendations , limited attention has been given to live content analysis. For instance, in Dai et al. , an apprKnowledge-based recommender systems leverage information about items and users to make reasoned decisions about which items align with user requirements in an interactive manner Burke, . Users sVideo items are characterized by multi-modality, incorporating various dimensions that describe their content (see Section 3.2). yielding rich information potential yet posing efficiency challenges in representation. While existing studies . This leaves a gap in publicly available datasets from other domains like e-learning, where the content is substantially different. As a result, evaluation outcomes derived from entertainment datasets might not accurately reflect system performance in other scenarios. Given the increasing significance of videos across diverse domains, particularly in knowledge transfer, there is a need for advancing research and introducing new datasets to aid the development of specialized systems.JointRec framework, presented in Duan et al. . For instance, in knowledge transfer, suggesting relevant segments based on a user's existing knowledge can enhance efficiency by avoiding the repetition of known topics. Future research could explore methods to recognize feedback on specific video parts and interpret this feedback to identify segment borders. Additionally, incorporating user knowledge into their profile preferences is crucial for providing valuable recommendations in such scenarios.This article offers a comprehensive overview of recommendation approaches in the video domain. The methodology used in this study analyzed recent publications, categorizing them based on their underlying recommendation approaches. By examining the various systems, we highlighted their respective strengths and weaknesses, providing valuable insights for selecting the most suitable approach for specific application contexts. In this overview, we identified the challenges and opportunities faced by video recommender systems. By improving the understanding of limitations and potential areas of improvement, we aim to inspire further research and development in the field.SL: Writing—original draft, Writing—review & editing. AF: Writing—review & editing. MT: Writing—review & editing.


The European Society for Clinical Nutrition and Metabolism (ESPEN) recognized three different types of malnutrition (or undernutrition), which includes disease-related malnutrition (DRM) with and without inflammation, and malnutrition/undernutrition without disease . DRM is The Global Leader Initiative on Malnutrition (GLIM) proposed criteria for the diagnosis of malnutrition, including unintentional weight loss, low BMI, and decreased muscle mass as phenotypic criteria, and impaired food intake or assimilation and burden of disease/inflammation as etiologic criteria. To diagnose malnutrition, at least one phenotypic, and one etiological criterion are required; however, criteria for determining inflammation as etiology are not provided, authors only mention that C-reactive protein (CRP), albumin, or pre-albumin could be proxy measures . DRM witThis Research Topic is a second edition of the Nutritional Assessment Tools for Identification and Monitoring of Malnutrition in Patients with Chronic Disease, which addresses the current and novel nutritional assessment tools for the identification and monitoring of malnutrition in patients with chronic disease. In volume 1, a total of 12 articles were included, covering different aspects of malnutrition, such as sarcopenia, its prognosis value, predictor factors, and potential therapeutic strategies, among other relevant topics. Due to the high interest expressed in this Research Topic and the number of meaningful contributions received, volume 2 was released.Kang et al. showed that the mini nutritional assessment (MNA) screening tool has a better performance to predict various negative outcomes, including 3-month all-cause mortality and geriatric syndrome, compared to serum albumin, one of the biomarkers most commonly used for nutritional assessment, in hospitalized older patients. Another study on the geriatric population conducted by Peng et al. demonstrated that a high risk of malnutrition status identified by the geriatric nutrition risk index (GNRI) was able to predict poor prognosis in elderly patients in the intensive care unit (ICU) setting. The GNRI was also tested along with other nutritional and inflammatory markers for predicting overall survival in early-onset colorectal cancer in a study carried out by Xiang et al.. Authors found that among all nutritional and inflammatory indicators studied, the systemic immune inflammation index (SII) and the GNRI had higher prognostic values, and both were correlated with tumor stage. The prognostic nutritional index (PNI), an indicator of nutritional status and systemic inflammation, was tested for the first time in decompensated liver cirrhosis by Xie et al.. They showed an association between decreased PNI and increased risk of death, suggesting that PNI may be a prognostic marker in this patient population. Also, PNI was identified to be associated with the decision-making on the choice of renal replacement therapy (RRT) modality in adults with advanced chronic kidney disease (ACKD) in a study by Álvarez-García et al.. PNI score was significantly lower in patients who chose home-based RRT (18.8%) compared with in-center RRT (81.2%). However, in the multivariate binary regression analysis, it was not independently associated with the free decision-making to choose in-center and home-based RRT modality but were age, Charlson comorbidity index, follow-up at ACKD unit >6 months, and serum albumin. Chen D. et al. studied the prognostic performance of the Nutritional Risk Screening 2002 (NRS 2002), the Nutrition Risk in Critically Ill (NUTRIC) score, and modified Nutrition Risk in Critically Ill (mNUTRIC) nutritional screening tools that for the first time in patients with severe acute pancreatitis. The three studied scores were predictors for mortality at 28- and 90-days; however, the NUTRIC and mNUTRIC showed better predictive ability in this patient population. In China, the GLIM criteria were modified by removing the muscle-related indicators since these are not based on Chinese population standards. Guo et al. examined the effects of the GLIM-China on the diagnosis of malnutrition in patients with hematopoietic stem cell transplants. Authors concluded that a large proportion of patients with reduced muscle mass indicators will be missed for the diagnosis of malnutrition by using the GLIM-China, highlighting the relevance of muscle mass indicators for the diagnosis of malnutrition.In this second edition, 13 articles were published. Eight out of 13 studies provided evidence of the clinical relevance and prognosis value of diverse indexes to screen for nutritional risk and assess malnutrition in different patient populations. Wu et al. evaluated the role of bioelectrical impedance analysis-derived phase angle as a predictive marker for sarcopenia in patients with cancer and non-cancer diseases, suggesting that phase angle is an emerging and reliable predictor of sarcopenia in patients with different types of cancer; however, its association with non-cancer conditions is less clear. Also, further investigation is needed to determine cutoff values to screen for pre-sarcopenia and sarcopenia. Additionally, Kuang et al. conducted a study to assess the contribution of body composition fat mass (FM) and lean body mass (LBM) to non-alcoholic fatty liver disease (NAFLD), demonstrating higher LBM is associated with a lower risk of NAFLD and higher FM increasing the risk of this condition, particularly in women. Undoubtedly, body composition analysis represents a key aspect of nutritional status assessment; however, any nutritional assessment needs to guide a decision-making process to manage malnutrition when identified. In this sense, in a retrospective study of patients with Crohn's disease, Jiang et al. confirmed that pre-operative nutritional status correlates with post-operative outcomes and that enteral nutrition was associated with an improvement in nutritional parameters and a reduced rate of postoperative complications in patients with Crohn's disease undergoing surgery.Two studies addressed the importance of body composition parameters in nutritional status assessment and its role in predicting the risk of clinical conditions. A mini-review by Chen M. et al. reported that the facial temporal region and periorbital depression indicators extracted by 3D image recognition technology were associated with the phenotype of malnutrition-related muscle and fat loss in patients with cancer, providing an alternative for a clinical auxiliary tool for malnutrition screening and assessing phenotypic indicators of malnutrition.Evidence on the usefulness of other technologies such as 3D facial image recognition was also provided in this Research Topic. Domenech-Briz et al. contributed with a systematic review of 14 the importance of nutritional assessment tools in critically ill patients pointed out the benefits of screening or assessing malnutrition for predicting mortality risk and early initiation of nutritional therapy, reducing the number of complications and length of stay related to malnutrition or adjusting energy requirements. Authors concluded that among the studied tools, the most widely used and effective were the modified Nutrition Risk in the Critically Ill (mNUTRIC) score, the Nutrition Risk Screening 2002, and the Subjective Global Assessment, either independently or in combination with each other.Finally, In conclusion, further studies are needed to demonstrate that in addition to the identification of DRM, with above mentioned nutritional assessment tools, nutritional and exercise interventions are justified, and changes in nutrition outcomes could be detected after these interventions. It is crucial to include a decision-making process to guide the management when malnutrition is present.EC-R wrote the introduction and central part with comments on the cited papers and references. LC-M wrote the conclusion and reviewed/edited the introduction and central part. All authors contributed to the article and approved the submitted version.


Frailty, sarcopenia, and malnutrition are highly prevalent among the older population. A previous study reported that 17% of older adults living in a community were frail, 14% suffered from sarcopenia, and 13% were afflicted by malnutrition . With anFrailty, sarcopenia, and malnutrition have been associated with poor prognoses. In a study on patients admitted to an intensive care unit, frail patients had a higher risk of mortality and were less likely to be discharged than non-frail patients. Further, among hospitalized patients, frailty is a predictor of falls . SarcopeScreening and assessment are essential for providing nutritional care and rehabilitation to patients with or at high risk of frailty, sarcopenia, or malnutrition. The screening tools for frailty include the FRAIL scale and the Edmonton frail scale ,18. In aDespite the existence of various tools for screening and assessing frailty, sarcopenia, and malnutrition, uniform criteria and more convenient and reliable screening and assessment tools are needed. Uniform assessment methods would allow for the comparison of the prevalence, interventions, and outcomes of frailty, sarcopenia, and malnutrition worldwide. Further research is needed to validate the reliability of the screening and assessment methods employed. Nutritional care and rehabilitation have been reported to be effective for treating frailty, sarcopenia, and malnutrition. However, there is insufficient evidence on optimal intervention methods, improvements in clinical outcomes, cost-effectiveness, and the sustainability of improvements. Moreover, not all the causes of sarcopenia, frailty, and malnutrition are known. Thus, the underlying factors need to be investigated. Additional research is needed to determine the optimal interventions and their effects on preventing and ameliorating frailty, sarcopenia, and malnutrition.


Individuals spend time on online video-sharing platforms searching for videos. Video summarization helps search through many videos efficiently and quickly. In this paper, we propose an unsupervised video summarization method based on deep reinforcement learning with an interpolation method. To train the video summarization network efficiently, we used the graph-level features and designed a reinforcement learning-based video summarization framework with a temporal consistency reward function and other reward functions. Our temporal consistency reward function helped to select keyframes uniformly. We present a lightweight video summarization network with transformer and CNN networks to capture the global and local contexts to efficiently predict the keyframe-level importance score of the video in a short length. The output importance score of the network was interpolated to fit the video length. Using the predicted importance score, we calculated the reward based on the reward functions, which helped select interesting keyframes efficiently and uniformly. We evaluated the proposed method on two datasets, SumMe and TVSum. The experimental results illustrate that the proposed method showed a state-of-the-art performance compared to the latest unsupervised video summarization methods, which we demonstrate and analyze experimentally. Individuals spend time on online video-sharing platforms such as YouTube to search for videos. To reduce the search time, thumbnails or summary videos are used to efficiently and quickly grasp the video content . Over thThe reinforcement learning (RL) based unsupervised video summarization method proposed in  demonstrIn this paper, we propose a new reinforcement learning-based video summarization framework with the interpolation method, which is composed of a new network and a new reward function such as a temporal consistency reward. To increase the performance, we used graph-level features. We also present the transformer and convolutional neural network (CNN)-based video summarization network to accurately predict the importance scores, as shown in Video summarization methods are divided into supervised and unsupervised learning-based methods. Both methods use the video summarization dataset, which includes the frame-level or shot-level importance scores of the video annotated by several users ,7. The sIn , the memHowever, the issue with supervised learning-based methods is that it is very difficult to make a human-labeled video summarization dataset including videos of various categories. However, the unsupervised learning-based method does not need a human-labeled dataset. Many unsupervised learning-based methods have been proposed.In , the attDeep reinforcement learning combines a deep neural network with a reinforcement learning method . The polThe video summarization problem is formulated as a keyframe selection problem using the importance score predicted by the video summarization network. As illustrated in First, suppose the sequence length is oogleNet , which iIn recent years, graph neural networks (GNNs) have been extensively studied and it has shown state-of-the-art performance in various deep learning applications . In thisAs illustrated in terp-SUM , since tInterpolation is an estimation method that finds new data points based on the range of certain known data points. Specifically, piecewise linear interpolation connects the data points with the linear line and calculates intermediate data points on the line . We firsTo select the keyframes as a summary, the Bernoulli distribution (2) was used, which is a discrete probability distribution to convert the importance score To train the video summarization network efficiently, we adopted the diversity reward function and the representativeness reward function , and we The diversity reward function Let the indices of the selected keyframes be The representativeness reward function To create a superior summary of the video and increase performance, we applied the importance score The temporal consistency reward function To explain the temporal consistency reward in detail, as described in The quality of the generated summary was evaluated with the sum of the rewards. With the reward, the proposed video summarization network was trained as a parameterized policy was used . If the To calculate the objective function of UREX The baseline, an essential technique for the policy gradient to reduce the variance of the gradient estimate and improve computational efficiency, was applied. The baseline is calculated as the moving average of rewards experienced thus far. To reduce the variance efficiently, we calculated the baselines per input video. As illustrated in the equation below, baseline The regularization term posed in  was usedAfter all of the loss functions were computed, the final loss for video summarization Algorithm 1. Training Video Summarization Network2: Output: Proposed network’s parameters  method, which detects change points such as shot boundaries, is used . The shoOur proposed video summarization method was evaluated on two datasets: SumMe  and TVSuFor a fair comparison with other methods on two datasets, the evaluation method used in  to compuThe 5-fold cross-validation was used to find the performance of our method for a fair comparison. Our method was tested for five different random splits and the result of the average performance was taken. To create random splits, the videos were split into training and validation datasets. Moreover, the F-measure was computed using the validation dataset.The proposed video summarization network was developed using Pytorch 1.7.1 and consisted of a transformer encoder network and a Pointwise 1D Conv network. The transformer encoder network consisted of six transformer encoder layers with eight heads and 512 hidden units. Moreover, the Pointwise 1D Conv network was based on 15 kernel sizes and three stride sizes. The Adam optimizer was used to train the network with a learning rate of 0.00001 for 1000 epochs.As illustrated in We noted that the performance was decreased because of the lack of context information caused by the shorter importance score candidates as the kernel size and stride size increased. In addition, the performance decreased rapidly as the stride size increased. We observed that the performance degradation was caused by the missed context information between scenes due to the short length of the importance score candidates because the length of the importance score candidates is mainly influenced by the stride size. The result of the TVSum dataset showed better performance than the result of the SumMe dataset at a smaller kernel size. This is because the training efficiency of the video summarization network was low since the length of the importance score candidates rapidly shortened when the kernel size was large, and the video length was long like the videos in the TVSum dataset. In other words, the training efficiency of the video summarization network was decreased by the shortened importance score candidates. We chose the proposed method as the best-performing method showing the best F-measure (51.66) on the SumMe dataset and the second best F-measure (59.86) on the TVSum dataset. We chose the best-performing method because increasing the performance of a small number of videos such as the SumMe dataset is more complicated than the TVSum dataset.As illustrated in In this paper, we proposed an unsupervised video summarization method based on deep reinforcement learning with an interpolation method. We designed a lightweight video summarization network to predict the accurate importance score candidates of keyframes and we interpolated the importance score candidates with a piecewise linear interpolation method to generate a natural sequence of summary and to mitigate the high variance problem. To train the video summarization network by the reinforcement learning method efficiently, we used graph-level features and proposed a temporal consistency reward function to select keyframes uniformly and adopted the representativeness and diversity reward functions. The experimental results illustrate that the proposed method showed state-of-the-art performance compared to the latest unsupervised video summarization methods, which we demonstrated and analyzed experimentally. Moreover, the proposed method robustly summarized various types of videos in the two datasets. However, based on the experimental results, the proposed method showed a lower performance on long videos due to the high variance problem. Conversely, the proposed method showed the best performance on the videos that were shorter than about 300 keyframes. Therefore, the proposed method is very useful for the video summarization of short-form videos.