Reasonable Capacity Research Articles

Metaethical constructivism and the capacity of practical reason

Metaethical constructivism and the capacity of practical reason

Liquid Organic Hydrogen Carrier Concepts and Catalysts for Hydrogenation and Dehydrogenation Reactions.

The issue of renewable energy (RE) source intermittency, such as wind and solar, along with the geographically uneven distribution of the global RE potential, makes it imperative to establish an energy transport medium to balance the energy demand and supply areas. A promising energy vector to address this situation is hydrogen, which is considered a clean energy carrier for various mobile and portable applications. Unfortunately, at standard pressure and temperature, its energy content per volume is very low (0.01 kJ/L). This necessitates alternative storage technologies to achieve reasonable capacities and enable economically viable long-distance transportation. Among the hydrogen storage technologies using chemical methods, liquid organic hydrogen carrier (LOHC) systems are considered a promising solution. They can be easily managed under ambient conditions, the H2 storage/release processes are carbon-free, and the carrier liquid is reusable. However, the evolution of the proposals from the carrier liquid type and catalyst elemental composition point of view is scarcely studied, considering that both are critical in the performance of the system (operational parameters, kinetic of the reactions, gravimetric hydrogen content, and others) and impact in the final cost of the technology deployed. The latter is due to the use of the Pt group elements (PGEs) in the catalyst that, for example, have a high demand in the hydrogen production sector, particularly for polymer electrolyte membrane (PEM) water electrolysis. With that in mind, our objective was to examine the evolution and the focus of the research in recent years related to proposals of LOHCs and catalysts for hydrogenation and dehydrogenation reactions in LOHC systems which can be useful in defining routes/strategies for new participants interested in becoming involved in the development of this technology. For this systematic review, we searched the SCOPUS database and forward and backward citations for studies published in the database between January 2011 and December 2022. The criteria include articles which assessed or studied the effect of the type of catalyst, type of organic liquid, reactor design(s)/configuration(s), and modification of the reactor operational parameters, among others, over the performance of the LOHC system (de/hydrogenation reaction(s)). The relevant data from each reviewed study were collected and organized into a pre-designed table on an Excel spreadsheet, categorized by reference, year, carrier organic liquid, reaction (hydrogenation and/or dehydrogenation), investigated catalyst, and primary catalyst element. For processing the data obtained from the selected scientific publications, the data analysis software Orbit Intellixir was employed. For the study, 233 studies were included. For the liquid carrier side, benzyltoluene and carbazole dominate the research strategies. Meanwhile, platinum (Pt) and palladium (Pd) are the most employed catalysts for dehydrogenation reactions, while ruthenium (Ru) is preferred for hydrogenation reactions. From the investigated liquid carrier, those based on benzyltoluene and carbazole together account for over 50% of the total scientific publications. Proposals based on indole, biphenyl, cyclohexane, and cyclohexyl could be considered to be emerging within the time considered in this review, and, therefore, should be monitored for their evolution. A great activity was detected in the development of catalysts oriented toward the dehydrogenation reaction, because this reaction requires high temperatures and presents slow H2 release kinetics, conditioning the success of the implementation of the technology. Finally, from the perspective of the catalyst composition (monometallic and/or bimetallic), it was identified that, for the dehydrogenation reaction, the most used elements are platinum (Pt) and palladium (Pd), while, for the hydrogenation reaction, ruthenium (Ru) widely leads its use in the different catalyst designs. Therefore, the near-term initiatives driving progress in this field are expected to focus on the development of new or improved catalysts for the dehydrogenation reaction of organic liquids based on benzyltoluene and carbazole.

A Bayesian network approach to assess the ecosystem integrity of mangroves in Tampamachoco, Veracruz, Mexico

Given the alarming rates of mangrove forest loss, resource managers must count on information regarding the condition of the mangrove forests. We propose a Bayesian network (BN) to assess mangrove forests' ecosystem integrity (EI) to support a mangrove monitoring system in Mexico. This approach allowed us to infer the system's condition based on variables on forest structure and function. We defined the BN structure based on informal interviews with specialists on vegetation and coastal geomorphology. We applied the expectation-maximization learning algorithm to train the model. Data from plots in two mangrove areas of an Avicennia germinans forest, defined based on their undisturbed and disturbed conditions, were used as training datasets. We applied sensitivity analysis to determine the degree of influence of each model variable. We evaluated the prediction capacity of the BN with a k-fold cross-validation (the process is repeated five times, starting from the database in 2 parts). The variables selected for the model were the Holdridge complexity index (HCI, Holdridge et al., 1971), Leaf area index (LAI), litter production (g/month/m2), leaf C, N and P concentration (%), and leaf N:P ratio. The most critical variable to infer mangrove condition was leaf N:P (Variance reduction = 11%), followed by forest structure variables HCI and LAI (Variance reduction > 5%). The cross-validation to test the model resulted in a minimum square error of 0.3, which indicates a reasonable capacity to predict the condition of mangrove integrity. The BN constructed can diagnose the integrity of a monospecific mangrove forest with acceptable precision, considering the environmental factors that define the forest structure and functioning locally. We then asked the experts to review and modify the model to apply to multispecies mangrove ecosystems and environmental contexts.

Analysis of Midlife Approaches to Reduce the Risk Factors of Dementia Among Adults in the United Kingdom

Dementia is an illness of the brain in which there is deterioration in a person's cognitive abilities. This interferes with one's ability to carry out everyday tasks and maintain social autonomy by adversely affecting memory, behavior, reasoning, and social capacities. The study reviewed the epidemiological literature on dementia and its risk factors as well as the several midlife strategies for lowering the risk of dementia. This is with a view to explore the midlife strategies in lowering the risk factors of dementia among adults in the United Kingdom. The study adopted reviews of literature carried out systematically. The specifications described in the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)” were followed in reviewing the literature to ensure a comprehensive and transparent methodology. Literature search was conducted systematically in scientific databases to identify empirical studies that are pertinent. The comprehensive search strategy aimed to retrieve as many relevant studies as possible within the scope of the review. It employed a combination of keywords related to dementia risk factors, behavioral and psychological impacts, older people, and the UK context. The search terms were adapted to fit the specific requirements of each database, ensuring optimal retrieval of relevant literature. In total, 16 publications were included in the study after the eligibility of these articles was verified. By leveraging the potential advantages of internet-based, individualised health prevention measures, both the general public and primary care providers become more aware of dementia as well as help to solve the issue. Dementia and various cognitive impairments have been repeatedly associated to high blood pressure. Recent findings support the idea that decreasing blood pressure in middle age is an important strategy for preventing dementia in old age. Education has a protective impact and that the majority of previously investigated risk factors including physical inactivity, diabetes, air pollution, hearing loss, hypertension, obesity, social isolation, depression, and smoking increase the incidence of dementia. People who adhered to the MedDiet the best had a 23% reduced risk of dementia than those who adhered the least. The study concluded that significant modifiable risk variables, particularly socioeconomic and lifestyle factors, were more strongly associated with dementia and contributed to higher attributable fractions of dementia cases.

Predictor of willingness to pay for early warning climate system and agronomy advisories services in Albania: A case study of medicinal plant in “Malesi e Madhe”

Abstract Access to useful climate information is critical for adaptation needs of Albanian smallholder farmers, yet empirical studies documenting the socioeconomic, environmental and household predictors of access to and willingness to pay for climate information services have been limited to date. This paper addresses the need by identifying the predictors of access to and willingness to pay for climate information by smallholder farmers in the northern area of Albania, a “dangerous hotspot” where slight changes in rainfall and temperature often result in considerable yield losses. The study uses data collected from 201 household surveys in 5 communities across 29 districts in the “Malesia e Madhe” region of Albania using a simple random sampling method. Sampling was conducted in end 2023 and start of 2024 and participants were interviewed face to face by questionnaires following a contingent valuation method for collecting data for willingness to pay for early warning climate system. The EFA analysis was performed on farmer’s awareness to climate changes and their impact on quality and productivity. Our findings suggest that the majority of smallholders are aware of duration of drought (95%), increase of number of hot days (88.1%) and presence of weeds (59.7%). From the findings, farmers perceive that climate changes have affected the quality of medicinal plants in the last 5 years, by increasing the number of hot days (96%), number of rainy days (93.5%) and duration of drought (86.6%), also farmers perceive that climate change has impacted the yield level by duration of drought (87.1%), increase of hot nights (78.1%) and number of rainy days (76.1%). The findings suggest that the majority of smallholder farmers were not willing to pay for the cost of receiving climate information delivered via SMS. The results from the marginal analysis suggest that access to climate information is influenced by farm topology factors. The marginal effects logistic regression shows statistically significant effect in household predictors such as farmer’s size and income. Results suggest that the provision of climate information should be defined and adopted to meet the needs of smallholder farmers with different socioeconomic backgrounds to enable farmers manage climate risks and build reasonable adaptive capacity.

Large language models for automatic equation discovery of nonlinear dynamics

Equation discovery aims to directly extract physical laws from data and has emerged as a pivotal research domain in nonlinear systems. Previous methods based on symbolic mathematics have achieved substantial advancements, but often require handcrafted representation rules and complex optimization algorithms. In this paper, we introduce a novel framework that utilizes natural language-based prompts to guide large language models (LLMs) in automatically extracting governing equations from data. Specifically, we first utilize the generation capability of LLMs to generate diverse candidate equations in string form and then evaluate the generated equations based on observations. The best equations are preserved and further refined iteratively using the reasoning capacity of LLMs. We propose two alternately iterated strategies to collaboratively optimize the generated equations. The first strategy uses LLMs as a black-box optimizer to achieve equation self-improvement based on historical samples and their performance. The second strategy instructs LLMs to perform evolutionary operations for a global search. Experiments are conducted on various nonlinear systems described by partial differential equations, including the Burgers equation, the Chafee–Infante equation, and the Navier–Stokes equation. The results demonstrate that our framework can discover correct equations that reveal the underlying physical laws. Further comparisons with state-of-the-art models on extensive ordinary differential equations showcase that the equations discovered by our framework possess physical meaning and better generalization capability on unseen data.

The aspects of behavioral finance with new insights: an analysis of individual investors at Pakistan Stock Exchange (PSX)

PurposeThe cognitive biases exhibited by investors could hinder their capacity for logical reasoning and impact their perception and reaction to information when making financial choices. So, this study was done to identify the behavioral biases that hinder investors' sound decision-making at the Pakistan Stock Exchange (PSX).Design/methodology/approachA cross-sectional study was undertaken employing a causal research design approach. Questionnaires were administered to individual investors of the PSX as the data collection methodology. The data were subsequently analyzed through the utilization of the Smart PLS Structural Equation Modeling (SEM) technique.FindingsThe results suggest that information factors and cognitive biases, namely home bias, geographical bias, investor sentiment, salience, and over/under reaction have a positive association with the investors' choices at PSX.Research limitations/implicationsThe study’s emphasis is on the impact of behavioral biases on individual investors only, even though such biases also influence the investment decisions of institutional investors.Practical implicationsThe study holds implications for scholars engaged in the field of behavioral finance as well as professionals involved in the stock market, particularly those interacting with individual investors and personal finance. Additionally, the current study will take into account investors, financial advisors, practitioners, policymakers, investment experts, stakeholders or target groups, etc. to support various groups in their professional activity and to help them overcome such biases that influence their sound decision-making power.Originality/valueThe innovative aspect of this research is its ability to advance the understanding of the conceptual underpinnings and social structure of behavioral biases by critically analyzing the body of prior research and adding value to the existing body of literature on behavioral finance in Pakistan by investigating the combined impact of never-studied variables, i.e. geographical bias and information variables, understudied behavioral variables, i.e. home bias and salience and studied variables, i.e. investor sentiment and over/under reaction on individual investor investment decisions at PSX.

Crossing the Trust Gap in Medical AI: Building an Abductive Bridge for xAI

In this paper, we argue that one way to approach what is known in the literature as the “Trust Gap” in Medical AI is to focus on explanations from an Explainable AI (xAI) perspective. Against the current framework on xAI – which does not offer a real solution – we argue for a pragmatist turn, one that focuses on understanding how we provide explanations in Traditional Medicine (TM), composed by human agents only. Following this, explanations have two specific relevant components: they are usually (i) social and (ii) abductive. Explanations, in this sense, ought to provide understanding by answering contrastive why-questions: “Why had P happened instead of Q?” (Miller in AI 267:1–38, 2019) (Sect. 1). In order to test the relevancy of this concept of explanation in medical xAI, we offer several reasons to argue that abductions are crucial for medical reasoning and provide a crucial tool to deal with trust gaps between human agents (Sect. 2). If abductions are relevant in TM, we can test the capability of Artificial Intelligence systems on this merit. Therefore, we provide an analysis of the capacity for social and abductive reasoning of different AI technologies. Accordingly, we posit that Large Language Models (LLMs) and transformer architectures exhibit a noteworthy potential for effective engagement in abductive reasoning. By leveraging the potential abductive capabilities of LLMs and transformers, we anticipate a paradigm shift in the integration of explanations within AI systems. This, in turn, has the potential to enhance the trustworthiness of AI-driven medical decisions, bridging the Trust Gap that has been a prominent challenge in the field of Medical AI (Sect. 3). This development holds the potential to not only improve the interpretability of AI-generated medical insights but also to guarantee that trust among practitioners, patients, and stakeholders in the healthcare domain is still present.

Rechargeable Batteries with Earth Abundant Elements – Organic Redox Polymers for Li-Organic Batteries

The energy storage needs of the 21st century demand solutions that are both 'efficient' and 'sustainable.' The research explores alternatives to transition metal-based electrode materials traditionally used in LIBs, with a particular focus on redox-active organic materials, such as ample availability of precursors, structural diversity, non-toxicity, and low cost.[1] Conductive polymers, organosulfur compounds, organic radicals, small carbonyl compounds, carbonyl polymers, imine and azo compounds are the few classes of organics explored. This study focuses on the development of rechargeable batteries using earth-abundant elements, with a specific emphasis on organic redox polymers for Li-ion batteries (LIBs).[2] The investigation focuses on poly(3-vinyl-N-methylphenothiazine) (PVMPT), a p-type material, showcasing its excellent rate capability, cyclic stability, and reasonable theoretical capacity (112 mAh g−1). Despite the promising electrochemical performance of PVMPT, the electrode processing methods involve the use of toxic and flammable solvents, such as N-methyl-2-pyrrolidone (NMP), and expensive binders like polyvinylidene fluoride (PVDF).[3] To address these challenges, this study proposes the use of green solvents (Cyrene, Ƴ-valerolactone, and Dimethyl sulfoxide) for electrode processing, eliminating the need for toxic solvents and promoting the sustainability and safety of organic materials.[4] A shift to sustainable aqueous binders, such as carboxymethyl cellulose (CMC), sodium alginate (SA), and styrene-butadiene rubber (SBR),[5] is explored (Figure 1a-c), with the CMC system showing superior capacity retention.Additionally, the study delves into the Phenoxazine class of redoxomers, particularly polyvinylphenoxazine (PVMPO), a fast-charging cathode with a theoretical capacity of 120mAh g−1.[6] Challenges related to the dissolution of the oxidized state in the carbonate electrolyte are addressed by employing highly porous conductive additives and exploring different electrolyte compositions. A shift from toxic PVDF/NMP binder systems to aqueous binders (CMC, SBR, SA) is implemented, with the CMC-based electrodes demonstrating superior adhesion even after 10,000 cycles at 1C rate (Figure 1d-f).To conclude, the study believes that the strategic selection of conductive additives and electrolytes, coupled with the use of green solvents and aqueous binders, will contribute to the development of eco-friendly and sustainable energy storage systems. The comprehensive performance evaluation of the proposed systems is presented in detail, utilizing various characterization tools such as XRD, FT-IR, UV-VIS, and SEM.References Lee et al., Adv Mater., 2018, 42,1704682.Dühnen et al., Small Methods, 2020, 4, 2000039.Kolek et al., Energy Environ. Sci., 2017, 10, 2334.Ravikumar et al., ACS Appl. Energy Mater., 2021, 4, 696−703Bresser et al., Energy Environ. Sci., 2018,11, 3096-3127.Otteny et al., ACS Sustainable Chem. Eng., 2020, 8, 238−247 Figure 1

Synthesizing Si/SiOC composites through different sol-gel reaction routes for lithium-ion battery anode materials

Silicon oxycarbide (SiOC) exhibits good retention and a reasonable specific capacity and is an alternative to silicon used as an anode material for high-performance lithium-ion batteries. However, SiOC generally shows a low Initial Coulombic Efficiency (ICE), wasting the lithium from the cathode. This work explores different sol-gel routes to synthesize SiOC from silanes and compares their performance. We found that the crushed bulk acid-catalyzed SiOC is simple and cost-effective but with excellent performance. Adding dimethyldimethoxysilane (DMDMS) by decreasing the oxygen content further enhances the battery performance. Building upon the excellent performance of SiOC, we further embed nano-silicon into SiOC. The Si/SiOC composites achieved a significantly higher specific capacity of 2185 mAhg−1 and an impressive ICE of 77 % with acceptable battery retention.

GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding

Integrating large language models with knowledge graphs derived from domain-specific data represents an important advancement towards more powerful and factual reasoning. As these models grow more capable, it is crucial to enable them to perform multi-step inferences over real-world knowledge graphs while minimizing hallucination. While large language models excel at conversation and text generation, their ability to reason over domain-specialized graphs of interconnected entities remains limited. For example, can we query a model to identify the optimal contact in a professional network for a specific goal, based on relationships and attributes in a private database? The answer is no – such capabilities lie beyond current methods. However, this question underscores a critical technical gap that must be addressed. Many high-value applications in areas such as science, security, and e-commerce rely on proprietary knowledge graphs encoding unique structures, relationships, and logical constraints. We introduce a fine-tuning framework for developing Graph-aligned Language Models (GaLM) that transforms a knowledge graph into an alternate text representation with labeled question-answer pairs. We demonstrate that grounding the models in specific graph-based knowledge expands the models’ capacity for structure-based reasoning. Our methodology leverages the large-language model's generative capabilities to create the dataset and proposes an efficient alternate to retrieval-augmented generation styled methods.

A Comparative Study on Electrochemical Performance of Single versus Dual Networks in Lithium Metal/Polysulfide-Polyoxide Co-Network/Lithium Titanium Oxide Cathode

The present article introduces a strategy for controlling oxidation and reduction reactions within polymer electrolyte membrane (PEM) networks as a means of enhancing storage capacity through the complexation of dissociated lithium cations with multifunctional groups of the polymer network. Specifically, co-polymer networks based on polysulfide (PS) and polyoxide (PO) precursors, photo-cured in the presence of succinonitrile (SCN) and lithium bis(trifluoro methane sulfonyl imide) (LiTFSI) salt, exhibited ionic conductivity on the order of mid 10−4 S/cm at ambient temperature in the 30/35/35 (weight %) composition. Lithium titanate (LTO, Li4Ti5O12) electrode was chosen as an anode (i.e., a potential source of Li ions) against lithium iron phosphate (LFP, LiFePO4) cathode in conjunction with polysulfide-co-polyoxide dual polyelectrolyte networks to control viscosity for 3D printability on conformal surfaces of drone and aeronautic vehicles. It was found that the PS-co-PO dual network-based polymer electrolyte containing SCN plasticizer and LiTFSI salt exhibited extra storage capacity (i.e., specific capacity of 44 mAh/g) with the overall specific capacity of 170 mAh/g (i.e., for the combined LTO electrode and PEM) initially that stabilized at 153 mAh/g after 50th cycles with a reasonable capacity retention of over 90% and Coulombic efficiency of over 99%. Of particular interest is the observation of the improved electrochemical performance of the polysulfide-co-polyoxide electrolyte dual-network relative to that of the polyoxide electrolyte single-network.

Heat capacity of liquid octamethyltrisiloxane (MDM) and hexamethyldisiloxane (MM): Measurement and molecular dynamics study

Accurate heat capacity data and models of fluids are necessary to reduce the design margins of engineering equipment. Also, heat capacities are indispensable in the development of high-accuracy equation of state. In this work, liquid heat capacities of Octamethyltrisiloxane (MDM, C8H24O2Si3) and Hexamethyldisiloxane (MM, C6H18OSi2) were obtained with a modified steady-flow calorimeter. Experiments were performed over the temperature range of 313 to 453 K and pressures up to 10 MPa. The expanded relative uncertainty in measurement of heat capacity is less than 0.015. Then, the present experimental heat capacity data were correlated with an empirical model, yielding an average absolute deviation (AAD) of 0.26 % for MDM and 0.38 % for MM. In addition, non-equilibrium molecular dynamics was used to investigate the heat capacity of MDM and MM. The simulation results show not only a reasonable heat capacity’s dependence on temperature but also a high accuracy.

Abstract PO2-17-07: Risk analysis of the differences in eligibility criteria between monarchE and POTENT clinical trials

Abstract Background Luminal subtype recurrence risk classifications for breast cancer have been updated and put into practice with the development of treatment. Despite this, it is noteworthy that from 2008 to 2016, early breast cancer (EBC) of luminal subtype has been reported to have little improvement in survival probability. For early-stage luminal breast cancer, several clinical trials, such as monarchE, NATALEE (which is not available in Japan) or POTENT, were being conducted. In order to avoid overtreating individuals who might not benefit from adjuvant therapy, those at high risk of recurrence ought to be accurately picked up. In monarchE and POTENT trials, abemaciclib and S-1 have shown to be effective in adjuvant therapies for luminal breast cancer although it is unrevealed if the eligible patients for each criterion are at higher risk of recurrence than ineligible patients. Besides, some of the eligibility for POTENT overlaps with those for monarchE, the patients who met POTENT but did not meet monarchE criteria at how much risk of recurrence still remains unknown. Here, we investigated recurrence risk according to the criteria of each trial in Japanese patients in real world. Methods We reviewed the records of 1209 patients who received surgery for stage I–III breast cancer from January 2016 to May 2022 and selected 637 analytic cohort patients and retrospectively analyzed the recurrence-free survival (RFS) of the patients using the Kaplan–Meier method. High-recurrence-risk was defined according to monarchE trial and POTENT trial. Patients’ RFS was the primary endpoint. Results The 5-year RFS for all luminal breast cancer patients was 94.87% at Chiba University Hospital. Among monarchE eligible patients, the 5-year RFS was 82.49% and cohort 1 and cohort 2 eligible patients was 78.62% and 92.18% respectively, which were statistically lower than monarchE non-eligible patients (98.58%) (p < 0.0001, p = 0.0216, respectively). Even though the 5-year RFS rate for POTENT eligible patients (91.16%) was lower than POTENT non-eligible patients (99.13%) (p < 0.0001), while excluding those who met the monarchE criteria, the prognosis of POTENT eligible patients (5-year RFS rate 97.76%) had no significant differences from the patients with POTENT non-eligible breast cancer (p =0.0660). Our results suggested that the eligible patients of both monarchE and POTENT were associated with poor prognoses so the criteria set are considered to be appropriate. However, although POTENT criteria suggested a reasonable capacity for recurrence prediction, there was no dramatic difference in recurrence between POTENT non-eligible patients and the patients who are POTENT eligible but are not monarchE eligible. This might offer justification for reconsidering the use of S-1 in monarchE non-eligible patients. With the use of clinicopathological factors, it may assist in identifying individuals with high recurrence risk who would benefit from lengthier adjuvant chemotherapy regimens. Conclusion MonarchE criteria accurately identifies patients at high risk of relapse but the relapse in the patients who only qualified for POTENT is statistically almost the same as that in POTENT non-eligible patients. Citation Format: Muhan Yu, Mamoru Takada, Hideyuki Yamada, Hiroshi Fujimoto, Junta Sakakibara, Hiroto Yamamoto, Takeshi Nagashima, Masayuki Otsuka. Risk analysis of the differences in eligibility criteria between monarchE and POTENT clinical trials [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-17-07.

An Adaptive Steganography Insertion Technique Based on Cosine Transform

In the last couple decades, several successful steganography approaches have been proposed. Least Significant Bit (LSB) Insertion technique has been deployed due to its simplicity in implementation and reasonable payload capacity. The most important design parameter in LSB techniques is the embedding location selection criterion. In this work, LSB insertion technique is proposed which is based on selecting the embedding locations depending on the weights of coefficients in Cosine domain (2D DCT). The cover image is transformed to the Cosine domain (by 2D DCT) and predefined number of coefficients are selected to embed the secret message (which is in the binary form). Those weights are the outputs of an adaptive algorithm that analyses the cover image in two domains (Haar and Cosine). Coefficients, in the Cosine transform domain, with small weights are selected. The proposed approach is tested with samples from the BOSSbase, and a custom-built databases. Two metrics are utilized to show the effectiveness of the technique, namely, Root Mean Squared Error (RMSE), and Peak Signal-to-Noise Ratio (PSNR). In addition, human visual inspection of the result image is also considered. As shown in the results, the proposed approach performs better, in terms of (RMSE, and PSNR) than commonly employed truncation and energy based methods.

CK12: A Rounded K12 Knowledge Graph Based Benchmark for Chinese Holistic Cognition Evaluation

New NLP benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present a meticulously designed evaluation benchmark that leverages the knowledge graph. This evaluation comprises 584 level-1 knowledge points and 1,989 level-2 knowledge points, thereby encompassing a comprehensive spectrum of the K12 education domain knowledge. The primary objective is to comprehensively assess the high-level comprehension aptitude and reasoning capabilities of LLMs operating within the Chinese context. Our evaluation incorporates five distinct question types with 39,452 questions. We test the current mainstream LLMs by three distinct modes. Firstly, four prompt evaluation modes were employed to assess the fundamental capacity. Additionally, for choice questions, a result-oriented evaluation approach was designed through data augmentation to assess the model's proficiency in advanced knowledge and reasoning. Moreover, a subset with reasoning process is derived, and the process-oriented testing method is used to test the model's interpretability and higher-order reasoning capacity. We further show models' capability in our knowledge points, and anticipate the evaluation can assist in the assessment of the strengths and deficiencies of LLMs on knowledge points, thus fostering their development within the Chinese context. Our Dataset will be publicly available in https://github.com/tal-tech/chinese-k12-evaluation.

Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic Reasoning

Large Language Models (LLM) exhibit zero-shot mathematical reasoning capacity as a behavior emergent with scale, commonly manifesting as chain-of-thoughts (CoT) reasoning. However, multiple empirical findings suggest that this prowess is exclusive to LLMs that have exorbitant sizes (beyond 50 billion parameters). Meanwhile, educational neuroscientists suggest that symbolic algebraic manipulation be introduced around the same time as arithmetic word problems so as to modularize language-to-formulation, symbolic manipulation of the formulation, and endgame arithmetic. In this paper, we start with the hypothesis that much smaller LMs, which are weak at multi-step reasoning, can achieve reasonable arithmetic reasoning if arithmetic word problems are posed as a formalize-then-solve task. In our architecture, which we call SyReLM, the LM serves the role of a translator to map natural language arithmetic questions into a formal language (FL) description. A symbolic solver then evaluates the FL expression to obtain the answer. A small frozen LM, equipped with an efficient low-rank adapter, is capable of generating FL expressions that incorporate natural language descriptions of the arithmetic problem (e.g., variable names and their purposes, formal expressions combining variables, etc.). We adopt policy-gradient reinforcement learning to train the adapted LM, informed by the non-differentiable symbolic solver. This marks a sharp departure from the recent development in tool-augmented LLMs, in which the external tools (e.g., calculator, Web search, etc.) are essentially detached from the learning phase of the LM. SyReLM shows massive improvements (e.g., +30.65 absolute point improvement in accuracy on the SVAMP dataset using GPT-J 6B model) over base LMs, while keeping our testbed easy to diagnose and interpret, and within the reach of most researchers.

Polyacrylonitrile and polyacrylonitrile/cellulose‐based activated carbon nanofibers obtained from acrylic textile wastes for carbon dioxide capture

AbstractThe aim of this study is to develop nanofibrous CO2 adsorbents by using of textile wastes as precursor materials. Therefore, polyacrylonitrile (PAN) and PAN/cell nanofibers were produced from acrylic textile wastes. Nanofibrous membranes were subjected to stabilization, carbonization, and chemical activation processes, respectively, to produce activated carbon nanofiber (ACnF) structures. Elemental analysis, scanning electron microscope, FTIR, and Braunauer, Emmett, and Teller analyses were carried out and CO2 adsorption performance was tested. It was observed that all samples, except PAN/cell nanofibers carbonized at 900°C, preserved their nanofibrous structure. PAN ACnFs had a lower surface area; on the other hand, they were shown to have higher carbon dioxide adsorption values compared with PAN/cell ACnFs. Reasonable adsorption capacities as high as 3.53 and 2.99 mmol/g (at 298 K at a relative pressure of 0.989) were achieved by PAN and PAN/cell ACnFs, respectively. As a result, it was investigated that sustainable ACnF‐based carbon dioxide adsorbents could be produced by the thermochemical processing of electrospun nanofibers obtained from textile wastes.

A Review of Capacity Allocation and Control Strategies for Electric Vehicle Charging Stations with Integrated Photovoltaic and Energy Storage Systems

Electric vehicles (EVs) play a major role in the energy system because they are clean and environmentally friendly and can use excess electricity from renewable sources. In order to meet the growing charging demand for EVs and overcome its negative impact on the power grid, new EV charging stations integrating photovoltaic (PV) and energy storage systems (ESSs) have emerged. However, the output of solar PV systems and the charging demand of EVs are both characterized by uncertainty and dynamics. These may lead to large power fluctuations in the grid and frequent alternation of peak and valley loads, which are not conducive to the stability of the distribution network. The study of reasonable capacity configuration and control strategy issues is conducive to the efficient use of solar energy, fast charging of EVs, stability of the distribution network, and maximization of the economic benefits of the system. In this paper, the concept, advantages, capacity allocation methods and algorithms, and control strategies of the integrated EV charging station with PV and ESSs are reviewed. On the basis of the above research, the current problems and challenges are analyzed, and corresponding solutions and ideas are proposed.

Intrinsic dignity

Dignity has often been used to signify a ground for a particularly serious kind of moral consideration, the scope of which has been debated. Therefore, I argue that an inclusive conception of dignity is central in determining the normative aims of education, and the moral responsibilities that adult teachers should have in learning relationships with children. The aim of the paper is to argue for a view of dignity as a regulative ideal beyond the dichotomy of moral agents and patients that can be (imperfectly) realized through moral perfectionism. In this article I show how commonly held neo-Aristotelean and neo-Kantian interpretations that view dignity as an intrinsic value grounded in certain capacities, mainly the capacity for reason, can have limiting implications for the view of children as moral subjects. By presenting alternative interpretations of Aristotle and Kant, I will seek to challenge these limited views on dignity and expand the conception of dignity to not only being grounded in certain capacities of moral agents and patients, but in morality itself. The argument leads to problematizing moral perfectionism in education that is not counter-balanced by moments of transcending our own intentions to be good.