bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Quantified Analysis of Lean First Then Automate for a Synergetic Effect
Authors: Urmee Sharmin Akter , Hossain Mosharraf 
Published: November 2024

ABSTRACTLean manufacturing has become a buzzword for the past few years due to its capability to reduce waste and make the system more efficient. To meet the numerous demands of consumers, companies are moving toward Industry 4.0, which can be accomplished through automation. Prior research demonstrated a conceptual integration of lean tools' effects into automation, but it lacked a quantitative analysis. The purpose of this study is to ascertain the effects of implementing lean tools prior to automation. To get real‐time data for the analysis of applying lean and automation, an empirical study was conducted on a manufacturing line. Using ARENA simulation software, four distinct simulation models were created using the data: the current assembly line model, the model with a lean tool (Inline Quality Check), the model with only automation, and the model with lean applied first and automation applied later. The productivity of the assembly line is used to compare the results. The comparative analysis demonstrated that applying lean tools prior to automation increases productivity in automated lines. This study could assist businesses understand that, before implementing automation, they should use lean technologies to improve system efficiency. An effective system can become considerably more efficient by being automated.
Multicentric study of adherence to antiseizure medications among adults with epilepsy attended in neurology departments in Burkina Faso
Authors: Dabilgou Alfred Anselme , Dravé Alassane , Kyelem Julie Marie Adeline Wendlamita , Kaboré Adama , Napon Christian , Millogo Athanase , Karfo Kapouné , Kaboré Jean 
Published: November 2024

AbstractObjectiveTo study the adherence of antiseizures medication in neurology in the city of Ouagadougou.Patients and MethodsWe conducted a multicentric cross‐sectional study on adherence to antiseizure medications among adult patients with epilepsy followed by outpatient neurology consultations. The patients were recruited from November 22, 2021 to February 22, 2022 in four departments of neurology. Adherence to antiseizure medications (ASM) was measured using the Morisky Medication Adhesion Scale (MMAS). Logistic regression analysis was used to investigate factors associated with adherence.ResultsOne hundred and seven patients with a mean age of 38.92 ± 16.06 years were included in the study. Most of the patients were men (52.34%). Twenty‐eight patients complied well with ASM (26.17%). The main causes of nonadherence to treatment were forgetfulness and lack of financial means. Factors associated with nonadherence were rural residence (p = 0.023), celibacy or divorce (p = 0.002), low level of education (p = 0.028), perception of stigma (p = 0.026), duration of epilepsy <5 years (p = 0.009).ConclusionsAdherence to ASM is low in Burkina Faso. The main causes of nonadherence were forgetting and insufficiency of financial resources. Rural residence, celibacy, or divorce, low level of education, perception of stigmatization, and short duration of epilepsy were associated with non‐adherence.Plain Language SummaryStudies on adherence to antiseizure medications are rare in Africa while patients do not have access to adequate treatment. The aim of our study was to evaluate the adherence to antiseizure medications among patients with epilepsy followed by the neurology departments in the city of Ouagadougou. Forgetfulness and financial insufficiency were the main causes of treatment interruption. Our study showed that most of the patients were non adherent. Several factors such as place of residence, level of education, and duration of epilepsy influence the level of compliance.
Integrated Method of Future Capacity and RUL Prediction for Lithium‐Ion Batteries Based on CEEMD‐Transformer‐LSTM Model
Authors: Hu Wangyang , Zhang Chaolong , Luo Laijin , Jiang Shanhe 
Published: November 2024

ABSTRACTAccurately predict the remaining useful life (RUL) of lithium‐ion batteries for energy storage is of critical significance to ensure the safety and reliability of electric vehicles, which can offer efficient early warning signals in a timely manner. Considering nonlinear changes in the aging trajectory of lithium‐ion batteries, a method for predicting the RUL of lithium‐ion batteries was proposed in this study based on a complementary ensemble empirical mode decomposition (CEEMD) as well as transformer and long short‐term memory (LSTM) neural network dual‐drive machine learning model. First, the CEEMD algorithm was adopted to decompose the raw aging data of lithium‐ion batteries into intrinsic mode function (IMF) sequences and residual sequence, where the number of modal layers was produced by the proposed posterior feedback entropy and relevance (PFER) method. Second, prediction models of LSTM and transformer neural networks were established to predict IMF and residual sequences. Simultaneously, the sparrow search algorithm (SSA) was used to obtain the optimal value of the hyperparameter learning rate for the RUL prediction model. Finally, the predicted IMF and residual sequences were combined to comprehensively calculate the future lifespan aging trajectory of lithium‐ion batteries. The aging data of two groups of lithium‐ion batteries were obtained from the CALCE at the University of Maryland as well as the laboratory at AQNU University to verify the proposed method. Experimental results demonstrated that the proposed method can effectively predict the RUL of lithium‐ion batteries; moreover, it exhibited better robustness and generalization ability.
Study on Damage Characteristics and Physical Field Characteristics of Roadway Surrounding Rock Under Multiple Disturbances
Authors: Zhang Jiuxin , Qin Hongyan , Ouyang Zhenhua , Zhang Ningbo , Zhang Yiyan , Liu Yang , Li Wenshuai , Zhou Ranran 
Published: November 2024

ABSTRACTDuring the mining process, repetitive stress disturbances induced by mining activities can lead to alterations in the physical properties of coal, potentially resulting in rockburst occurrences within tunnels. To investigate the propagation rule of physical field characteristics and characteristics of failure in roadway surrounding rock under multiple disturbance damage caused by dynamic load, a combined experimental and theoretical analysis is conducted to study the weakening effect of rock mass under various disturbance circumstances. A model of roadway surrounding rock loosening and failure under multiple disturbances was proposed. The degree of damage is quantified by defining the weakening coefficient Di, A “weakening variable method” is proposed to confirm the main parameters of the Holmquist‐John‐son‐Cook (HJC) model under different disturbance conditions. The reliability of these findings was validated through a microseismic event at the Tangshan coal mine's 0250 working face in 2020, followed by numerical simulation studies. The results indicate that damaged coal weakens the intensity of stress waves at the same source velocity, with the strongest effect observed at interfaces between different damage zones. Furthermore, damaged coal exhibits a stronger weakening effect on stress wave propagation speed compared to undamaged coal in non‐interface areas. The study on roadway stability reveals that severely damaged coal‐rock samples significantly weaken stress waves; however, they also exhibit lower minimum energy for dynamic failure in roadway surrounding rock, indicating that low‐stress waves cause greater damage under severe damage conditions. The study investigates the impact of coal rock mass degradation on the stability of surrounding roadways under various disturbance conditions, which holds significant implications for the timely identification of potential instability risks in damaged coal bodies, optimization of support strategies, and ensuring mining safety.
Sensitivity Analysis of a Nuclear Hybrid Energy System With Thermal Energy Storage in Deregulated Electricity Markets Considering Time Series Uncertainty in Electricity Price
Authors: Bryan Jacob A. , Wang Hailei , Talbot Paul W. 
Published: November 2024

ABSTRACTAdding thermal energy storage to nuclear power plants has been proposed as a way to allow nuclear plants to operate more flexibly and potentially be more competitive in deregulated electricity markets. The economics of these systems in deregulated markets are subject to uncertainties in capital costs, operating costs, and revenue. This study quantifies the uncertainty in the net present value of a nuclear power plant with integrated thermal energy storage in three U.S. deregulated electricity markets considering these sources of uncertainty and quantifies, for the first time, the relative contributions each source makes to the overall uncertainty. To accomplish this, a computationally efficient block bootstrap method is introduced to quantify uncertainty contributions from the stochastic time series of electricity prices, achieving a two order of magnitude decrease computational time compared to the model‐based methods used in previous works while also relaxing several strict assumptions made by the model‐based approach. Up to 18.5% of the overall variance in net present value is attributable to variance in the electricity price stochastic process, with this sensitivity varying significantly across markets.
Dendrite‐Free Zinc Anode for Zinc‐Based Batteries by Pre‐Deposition of a Cu–Zn Alloy Layer on Copper Surface via an In Situ Electrochemical Oxidation–Reduction Reaction
Authors: Davis Daiphi , Balakrishnan Jeena C. , Vadakkan Thomas Joy 
Published: November 2024

ABSTRACTThe prevention of uncontrollable growth of zinc dendrites on the zinc electrodes is one of the key challenges, hindering the widespread commercialization of zinc‐based energy storage technologies. Herein, we report a facile method to mitigate dendrite growth on a copper surface by an initial in situ coating of a Cu–Zn alloy layer, before zinc deposition. During further zinc deposition, the initially formed Cu–Zn alloy layer provides uniform nucleating sites and promotes homogeneous zinc deposition. A symmetrical cell, assembled using a Cu–Zn/Cu electrode could be stably cycled for over 1000 cycles in ZnSO4 solution, at a current density of 30 mA/cm2 and a coulombic efficiency of 99.9%. A zinc–air cell, assembled using Zn@Cu–Zn/Cu as the anode and rGO/Co3O4 composite as the cathode, exhibited a very stable performance at a high current density of 50 mA/cm2 and coulombic efficiency of ~93%, for over 400 cycles. After cycling experiments, the X‐ray photoelectron spectroscopy and the X‐ray diffraction analysis confirmed the formation of the Cu–Zn alloy layer. Hence, the present method provides an easy route for fabricating a dendrite‐free zinc electrode, for a wide range of zinc anode‐based batteries.
Integrating Phase Change Materials Into Cotton Ring Spun Yarn Structure for Thermoregulating Function
Authors: Yılmaz Demet , Aksoy Sennur Alay 
Published: November 2024

ABSTRACTPhase change materials (PCMs) have been incorporated into textiles to provide thermoregulation and temperature buffering effects on the human body. From this point of view, the aim of this study was to develop the phase change material (PCM) incorporated into the yarns for the production of textiles with a thermo‐regulating function. In the study, two types of capsules poly(methyl methacrylate‐co‐methacrylic acid) (P(MMA‐co‐MAA)) walled and 1‐tetradecanol core, and gelatin‐gum Arabic walled and n‐octadecane core were synthesized and applied to cotton textile fibers using an alternative application method developed by the authors. PCM dispersion with 6% concentration was incorporated into cotton ring spun yarns at 62.5 and 80 mL/h feeding rates. Morphological and thermal properties of the capsules and spun yarns were investigated. Thermoregulation properties of fabricated yarns were detailed evaluated by segmenting thermal history (T‐history) curves into four phases and logarithmic and linear trendlines were applied to the temperature change data for unloaded and PCM incorporated yarns. Data including temperature range (°C), R2 (coefficient of determination or regression factor), rate coefficient (a) and duration of phase (s) were analyzed for both capsule types and feeding rate values. The results indicated that PCM capsules with ideal spherical morphology and enough energy storage capacity were successfully applied into the cotton fibers. All cotton yarns with PCM additives exhibited lower surface temperature values greater than 2°C which is considered sufficient for the thermoregulation effect although with some distinct variations in their temperature profiles and rate coefficients. Compared to untreated cotton ring spun yarn, the temperature difference for 1‐tetradecanol core@P(MMA‐co‐MAA) walled capsules was found to be around 4.29°C–4.56°C, whereas it was around 8.2°C–9°C for n‐octadecane core@gelatin‐gum Arabic walled capsules. With respect to all the results, obtained novel heat storage cotton yarn is a promising material for thermal energy storage and desirable thermal comfort applications.
Electrochemical and Thermal Analysis of Lithium‐Ion Battery Pack With Different Cell Configurations
Authors: Kömürcü Büşra Namaldı , Elden Gülşah , Çelik Muhammet , Genç Mustafa Serdar 
Published: November 2024

ABSTRACTThe primary purpose of this research is to analyze and evaluate the effects of various discharge rates and cell configurations on the electrochemical and thermal behavior of a Li‐ion battery pack that is exposed to ambient air throughout the discharge process. The three‐dimensional numerical model is designed to accomplish this purpose and discusses two different cases. While the discharge rate is changed from 0.5 C to 2 C (stepping by 0.5 C) for each cell configuration considered in the first case, the numerical solutions are obtained for the various cell configurations (6S4P and 8S3P) by keeping the discharge rate constant at 1 C. The results obtained from these solutions show that the discharge rate affects a considerable amount of the battery performances and discharge times of the battery packs, activation, and ohmic losses occurring inside each battery cell. Moreover, 6S4P discharges over a longer period (about 25%) than 8S3P. While both activation and ohmic losses decrease with the increase of discharge rate, these losses remain almost constant at 0.5 C discharge rate in all analyzed conditions. As a result, having a battery pack with a long discharge time while maintaining low temperatures is useful and desired. With this in mind, while evaluating battery packs, the 6S4P battery pack looks to have the best arrangement.
Room‐Temperature Reversible Hydrogen Storage in Scandium‐Decorated [6]Cycloparaphenylene: Computational Insights
Authors: Parida Smruti Ranjan , Sahoo Rakesh Kumar , Jaiswal Ankita , Kour Paramjit , Chakraborty Brahmananda , Sahu Sridhar 
Published: November 2024

ABSTRACTThis study discusses the hydrogen storage and delivery capacity of Sc‐decorated [6]cycloparaphenylene ([6]CPP) using dispersion‐corrected density functional theory calculations (DFT + D3). The scandium atoms are decorated over [6]CPP via Dewar coordination with an average binding energy of 1.33 eV. Each Sc atom stores up to 5H2 molecules in quasi‐molecular form at an average adsorption energy ranging from 0.23 to 0.36 eV/H2. The system's stability before and after H2 adsorption is checked using reactivity parameters. The maximum hydrogen gravimetric capacity of the system is found to be 7.68 wt% at low temperatures at 1–60 bar pressure. With an increase in temperature (300–420 K), the gravimetric density is more than 5.5 wt% (US‐DOE target) below 60 bar. Atom‐Centered Density Matrix Propagation (ADMP)‐molecular dynamics (MD) simulations reveal that the desorption of H2 molecules from [6]CPP starts at around 300 K/1 bar, and complete desorption occurs above 480 K. The minimum Van't Hoff desorption temperature for [6]CPP‐Sc is 296.9 K at 1 atm pressure. Insignificant change in the structure of [6]CPP‐Sc during adsorption and desorption processes promises stability and reversibility of the system. Hence, we believe that Sc‐decorated [6]CPP can be a promising candidate for hydrogen storage applications.
Assessment of Sustainability in the Supply Chain of Sweet Red Pepper Paste Production With Exergy and Life Cycle Analyses
Authors: Adal Samiye , Erbay Zafer 
Published: November 2024

ABSTRACTThis study examines cumulative energy (CEnC), exergy (CExC), CO₂ emissions (CCO₂C), and life cycle assessment of sweet red pepper (SRP) paste production. A whole system approach in five improvement scenarios including different packaging materials and precision farming encompasses the supply chain from farm to fork and cradle to gate. The largest impact on SRP farming is caused by the use of diesel oil, the excessive use of chemical fertilizers, and the use of electricity. In SRP farming step, the CEnC is mainly caused by 86.5% fertilizer and 11% diesel usage. Hotspot impact categories were determined as abiotic (fossil) depletion potential, global warming potential, and human toxicity potential. The base case scenario has the greatest values for CEnC, CExC, and CCO₂C and impact assessment results. A CEnC value reduction of 48.6%, 50%, and 30% in the factory processing, packaging‐transportation step and whole process, respectively, is observed when the biodiesel scenario is performed. With a 40% reduction in global warming potential value, the combination of polyethylene terephthalate packaging, biodiesel, and precision farming scenario yielded the best results for each impact category analyzed in this study.
Forecasting Chinese Stock Market Volatility With Volatilities in Bond Markets
Authors: Lei Likun , He Mengxi , Zhang Yi , Zhang Yaojie 
Published: November 2024

ABSTRACTIn this paper, we investigate whether the bond markets contain important information that can improve the accuracy of stock market volatility forecasts in China. We use realized volatility (RV) implemented by different maturity treasury bond futures contracts to predict the Chinese stock market volatility. Our work is based on the heterogeneous autoregressive (HAR) framework. Empirical results show that the volatility of treasury bond contracts with longer maturities (especially 10 years) has the best effect on predicting the Chinese stock market volatility, both in sample and out of sample. Two machine learning methods, the scaled principal component analysis (SPCA) and the least absolute shrinkage and selection operator (lasso), are also more effective than the HAR benchmark model's prediction. Finally, mean–variance investors can achieve substantial economic gains by allocating their investment portfolios based on volatility forecasts after introducing treasury bond futures volatility.
Microbiological Characteristics of Beef Sausage Enriched With Roselle (Hibiscus sabdariffa L.) Sepal Extract
Authors: Ghorbani Sharif , Jafarian Sara , Soltani Mahdi Sharifi , Nasiraie Leila Roozbeh 
Published: November 2024

ABSTRACTAdding plant extracts to sausage and other meat products is very important to improve their quality, safety, and durability. The aim of this study was to evaluate the microbiological properties of beef sausage enriched with roselle (Hibiscus sabdariffa L.) sepal extract. The total content of phenolic and flavonoid compounds in hydroalcoholic extract of Roselle sepals was measured by Folin‐Ciocalteu test and aluminum chloride colorimetric method, and antioxidant activity was measured by DPPH method. Pectin was used to encapsulate the extract. The characteristics of capsules, including particle size, zeta potential, and capsule efficiency were measured. Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) of extracts were performed by tube dilution method. Microbial tests were performed on sausage treatment samples. Analysis of the obtained data was done using SPSS and Excel software. The study revealed that the total phenolic and flavonoid compounds in the extract were 174.6 mg of gallic acid per gram and 16.8368 mg of quercetin, respectively. The extract displayed the highest antioxidant activity at elevated concentrations. Particle size ranged from 16.833 to 640.534 μm. The zeta potential and capsule efficiency were found to be satisfactory. Compared to the encapsulated extract, the free roselle extract better inhibited Staphylococcus aureus (s. aureus), Salmonella typhimurium (S. typhimurium), and Escherichia coli (E.coli). During storage until day 20, these bacteria did not grow in the free or encapsulated extract groups. However, S. aureus appeared in the free extract group from day 25 and the encapsulated extract group from day 20. Roselle extract has antimicrobial properties that can improve the quality and safety of beef sausage during storage.
A Site‐Wise Reliability Analysis of the ABCD Diffusion Fractional Anisotropy and Cortical Thickness: Impact of Scanner Platforms
Authors: Pan Yezhi , Hong L. Elliot , Acheson Ashley , Thompson Paul M. , Jahanshad Neda , Zhu Alyssa H. , Yu Jiaao , Chen Chixiang , Ma Tianzhou , Liu Ho‐Ling , Veraart Jelle , Fieremans Els , Karcher Nicole R. , Kochunov Peter , Chen Shuo 
Published: November 2024

ABSTRACTThe Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9–10 years from 21 sites using standardized protocols for multi‐site MRI data collection and analysis. While the multi‐site and multi‐scanner study design enhances the robustness and generalizability of analysis results, it may also introduce nonbiological variances including scanner‐related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test–retest methodologies, such as intraclass correlation coefficients (ICC). We developed a site‐wise adaptive ICC (AICC) to evaluate the reliability of imaging‐derived phenotypes while accounting for ongoing brain development. AICC iteratively estimates the population‐level age‐related brain development trajectory using a weighted mixed model and updates age‐corrected site‐wise reliability until convergence. We evaluated the test–retest reliability of regional fractional anisotropy (FA) measures from diffusion tensor imaging and cortical thickness (CT) from structural MRI data for each site. The mean AICC for 20 FA tracts across sites was 0.61 ± 0.19, lower than the mean AICC for CT in 34 regions across sites, 0.76 ± 0.12. Remarkably, sites using Siemens scanners consistently showed significantly higher AICC values compared with those using GE/Philips scanners for both FA (AICC = 0.71 ± 0.12 vs. 0.46 ± 0.17, p < 0.001) and CT (AICC = 0.80 ± 0.10 vs. 0.69 ± 0.11, p < 0.001). These findings demonstrate site‐and‐scanner related variations in data quality and underscore the necessity for meticulous data curation in subsequent association analyses.