bluuebunny/crossref_metadata_2025_split huggingface dataset:
Global mathematical correlations for heat pipe and nanofluid based thermal energy storage system: Dimensional analysis approach
Authors: Kalbande Vednath P. , Vinchurkar Shilpa , Nandanwar Yogesh N. , Verma Shashi Kant , Walke Pramod 
Published: October 2023

AbstractThis research article presents the development of a mathematical model for a nanofluid‐based thermal energy storage (TES) system. The main objective of the study is to establish global correlations among various performance parameters of the TES system. The TES system considered in this research employs Al2O3/Soybean oil as the heat transfer fluid (HTF), and heat pipes are integrated to enhance heat transfer between the HTF and the phase change material. The mathematical model developed in this study is based on the Buckingham‐pi theorem. The derived correlations encompass the Nusselt number, stored energy, and thermal efficiency. Validation of the model is conducted for the Nusselt number, and a regression analysis is performed to assess the accuracy level of the mathematical model. Using the derived mathematical models, several key parameters of the TES unit are calculated. The convective heat transmission coefficient is determined to be 364.07 W/m2K, indicating the efficiency of heat transfer within the system. The thermal efficiency is found to be 89.10%, which provides insights into the overall effectiveness of the TES unit. Additionally, the thermal storage capacity of the TES unit is calculated as 3425.68 kJ, representing the amount of energy that can be stored and retrieved by the system. By equating the dimensions of variables within an equation, it becomes possible to deduce the relationship between the unknown parameters and the known quantities. This enables estimation of the unknown parameters even when direct measurements are unavailable, thereby providing a useful method for obtaining crucial information in data‐limited scenarios.
Design and fast uptake of fluoride using hybrid biopolymers encapsulated magnetic hydroxyapatite beads
Authors: Jeyaseelan Antonysamy , Viswanathan Natrayasamy , Kumar Ilango Aswin , Karami Abdulnasser M. 
Published: October 2023

AbstractHydroxyapatite is the one of the promising defluoridating material. For easy separation and to enhance defluoridation capacity (DC), magnetic iron oxide cum β‐cyclodextrin functionalized hydroxyapatite was prepared and because of its powder form it cannot be used in column studies. To make functionalized magnetic hydroxyapatite in usable bead form it was encapsulated using hybrid biopolymers chitosan and gelatin named as magnetic iron oxide/gelatin‐chitosan/hydroxyapatite/β‐cyclodextrin (MGCHC) composite beads were fabricated and utilized for rapid fluoride removal. The obtained MGCHC hybrid beads exhibited DC of 4985 mgF− kg−1 within 30 min. In co‐anions studies, Cl−, SO42− and NO3− had little influence on fluoride adsorption, whereas HCO3− had major influence on fluoride adsorption. The isotherm and kinetic data of MGCHC beads toward fluoride removal was effectively portrayed with Langmuir and pseudo‐second order model respectively. Defluoridation mechanism of MGCHC beads were mainly followed by electrostatic attraction, ion‐exchange and complexation. This investigation using MGCHC beads affords cost‐effective and eco‐friendly for fluoride removal which gives a practicable way for the fluoride treatment from field water.
A three‐dimensional analytical model based on Gaussian distribution for wind‐turbine wakes
Authors: Ling Ziyan , Zhao Zhenzhou , Liu Yige , Liu Huiwen , Ma Yuanzhuo , Wang Tongguang , Wang Dingding 
Published: October 2023

AbstractA Gaussian‐shaped, three‐dimensional (3DG‐k) wake model is proposed considering the shear wind effect. The model defines the wake radius rw to be equal to twice the Gaussian standard deviation σ and establishes a link with the expansion coefficient k of the physical wake boundary to describe the wake boundary. Garrad Hassan (GH) wind tunnel test data, Shiren wind farm measurement data, Nibe wind farm measurement data, and Vestas V80‐2MW wind turbine computational data by large eddy simulation method are used to validate the accuracy of the 3DG‐k, Jensen, 3D Gaussian (3DG), and 3D cosine (3D‐C) models. Results show that the 3DG‐k model exhibits higher accuracy for the four cases compared to other models. Due to its simplicity and universality, the model can be used for layout optimization and wake control of wind farms.
How ideas shape the EU's climate policy: Moving toward the sufficiency paradigm for the EU ETS
Authors: Wennick Daniel 
Published: October 2023

AbstractThis article examines the paradigm shifts in the European Union's (EU's) climate policy from 2009 to 2022 looking closer at the main policy instrument of EU's climate policy, the EU Emissions Trading System (EU ETS). The paradigm shifts of the EU ETS are linked to the climate policy paradigms prometheanism/growth unlimited (PGU), ecological modernization (EM), and sufficiency. The question of how ideas have shaped EU climate policy and the EU ETS in the past decade is answered, with the establishment of the EM paradigm and moving toward the sufficiency paradigm. There is a need for more research looking closer at how this paradigm shift impacts legislation for climate policy going forward.
Low‐carbon economic optimization method for integrated energy systems based on life cycle assessment and carbon capture utilization technologies
Authors: Li Mingwei , Qin Jingrao , Han Ziyuan , Niu Qunfeng 
Published: October 2023

AbstractIntegrated energy system (IES) is one of the ways to realize energy saving and emission reduction. Facing the crisis of fossil energy depletion and the challenge of global warming, low‐carbon technology and market economic guidance mechanism have an important impact on the low‐carbon operation of integrated energy system (IES). In this study, IES with carbon capture and utilization (CCU) technology is established, the carbon emission model based on life cycle theory is built for the input energy and energy system of IES, and the ladder carbon trading mechanism is considered in the market economy to establish the low‐carbon economic optimization model of IESs. A system combining carbon capture technology and power to gas can effectively consume new energy sources and provide energy dispatch flexibility. The ability to capture greenhouse gases generated by energy units reduces the system's carbon emissions. Based on the IES low carbon economy model, the model is analyzed with examples to analyze the correlation between different carbon trading prices and IES low carbon economy transport. We analyze the impact of CCU technology inputs on the low‐carbon nature of IES. The study proposes an IES dispatching model based on life cycle assessment and CCU technology to provide a new strategy for the operation of IESs and a new method for other energy systems to reduce low carbon emissions.
Design and performance analysis of hot side heat sink of thermoelectric cooler device based on simulation and experiment
Authors: Xu Zhinan , Li Cheng , Zhao Huadong , Zheng Yanping , Zhang Jingshuang 
Published: October 2023

AbstractAs for improving the heat diversion capacity of the hot side heat sink of the thermoelectric cooler (TEC), thereby improving the operational performance of the TEC, this study designed a type of double‐embedded flat heat pipe (EFHP) finned heat sink and compared with a standard aluminum (Al) heat sink. In experimental studies, TEC under two heat sinks were placed under different operating currents for heat transfer analysis and performance comparison. The experimental results demonstrated that the EFHP heat sink had better heat diversion ability, effectively improving the operational performance of TEC. When the TEC was input with a current of 6A, the thermal resistance of the double EFHP finned heat sink decreased by 19% compared to the Al heat sink; cooling capacity and coefficient of performance increased by 8.3% and 9.46%, respectively. Further, TEC's cold and hot surface temperatures decreased by 10.83% and 10.2%, respectively, and the heat uniformity of the heat sink improved. In addition, the CFD software Icepak was used to analyze the effects of double flat heat pipe spacing and length on the heat sink and TEC and the performance of two heat sinks and TEC under different heat dissipation conditions and obtained good agreement. This study can provide further guidance and assistance in designing the TEC cooling system.
Optimizing UAV computation offloading via MEC with deep deterministic policy gradient
Authors: Abbasi Ahmed Bashir , Hadi Muhammad Usman 
Published: October 2023

AbstractMobile edge computing (MEC) seems to be highly efficient to process the generated data from IoT devices by providing computational resources locating in close range to network edge. MEC can be promising in reduction of latency and consumption of energy from data transmissions from offloading computational tasks from IoT devices to nearby edge servers. In the context of the growing IoT ecosystem, there is an increasing need for efficient data processing and communication strategies. There is a demand of bridging the gap in current research with novel optimization algorithms tailored for UAV‐assisted MEC systems, shedding light on the necessity of efficient computation offloading in meeting the demands of the IoT era. In this article, a computation offloading optimization algorithm is proposed which is based on deep deterministic policy gradient for realistic Aurelia X6 Pro unmanned aerial vehicle (UAV)‐assisted MEC systems. The proposed algorithm optimizes the offloading decision for UAVs by taking task characteristics and the communication environment into consideration. To demonstrate the effectiveness of the proposed algorithm, comprehensive simulations were conducted, and the results indicate substantial improvements in MEC systems' competency. Our research not only showcases the feasibility of deep deterministic policy gradient in UAV‐assisted MEC systems but also highlights the importance of developing efficient computation offloading strategies for the evolving landscape of IoT and edge computing.
Spectral composition of LED light differentially affects biomass, photosynthesis, nutrient profile, and foliar nitrate accumulation of lettuce grown under various replacement methods of nutrient solution
Authors: Soufi Hamid Reza , Roosta Hamid Reza , Fatehi Foad , Ghorbanpour Mansour 
Published: October 2023

AbstractTo enhance crop yield and quality, plant cultivation in controlled‐growing systems is an alternative to traditional open‐field farming. The use of light‐emitting diode (LED) as an adjustable light source represents a promising approach to improve plant growth, metabolism, and function. The objective of this study was to assess the impact of different light spectra (red, red/blue (3:1), blue, and white) with an emission peak of around 656, 656, 450, and 449 nm, respectively, under various replacement methods of nutrient solution (complete replacement (CR), EC‐based replacement (ECBR), and replacing based on plant needs (RBPN)), on biomass, physiological traits, and macro‐ and micronutrient contents of two best‐known lettuce varieties, Lollo Rossa (LR) and Lollo Bionda (LB), in the nutrient film technique (NFT) hydroponic system. The results indicated that mix of red and blue LED spectra under RBPN method is the most effective treatment to enhance fresh and dry weights of lettuce plants. In addition, red LED spectrum under RBPN, and red and blue light under ECBR nutrient solution significantly increased leaf stomatal conductance, net photosynthesis and transpiration rate, and intercellular CO2 concentration of LR variety. Phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mn) content in LR variety, and iron (Fe), zinc (Zn), copper (Cu), and manganese (Mn) content in both varieties increased upon exposure to blue and red LED light spectrum with RBPN method. Our results suggest that exposure to combination of red and blue light along with feeding plants using RBPN and ECBR methods can increase absorption of macro‐ and micronutrient elements and improve photosynthetic properties, and eventually increase lettuce yield with lower nitrate accumulation.
Application of Manuka honey in treatment patients with GERD
Authors: Gośliński Michał , Nowak Dariusz , Mindykowski Roman , Kulewski Wojciech , Popławski Cezary 
Published: October 2023

AbstractGastro‐esophageal reflux disease has systematically increase in Western countries over recent years. Health benefits of Manuka honey allows to use it for medical purposes, for example reduction of inflammation of gastrointestinal mucosa. Thus, the aim of research was the application of Manuka honey in treatment patients with Gastro‐esophageal reflux disease (GERD). The study was conducted on a group of 30 patients, which consumed Manuka honey or placebo for a period of 4 weeks. The gastroscopy and histology has been made twice, that is before and after experiment. Furthermore, the symptoms reports and nutritional interviews have been collected. The endoscopical examination showed that in Manuka group the improvement was 73.3%. In the sub‐groups of esophagitis‐A and esophagitis‐B the improvement rate was 81.8%, and 50%, respectively. However, in the placebo group, the general improvement rate was much lower. The results have been confirmed in the histopathological examination. Moreover, it is worth noticing, that in sub‐group declaring non‐medication, the improvement was only for patients consuming Manuka honey. Changes of symptoms in subjective assessment of patients were evaluated after 2 and 4 weeks. The improvement rate in Manuka group was 86.7% and 100%, while in Placebo group it was only 26.7% and 40%, respectively. Manuka honey seems to be effective in GERD, which have been confirmed by subjective feelings of patients and by endoscopic and histopathologic examination. Our research is a pilot study before administration of Manuka honey to larger population. The results are promising and may facilitate the quality of life of patients with GERD.
Integrated Taguchi and response surface methods in geometric and parameter optimization of PEM fuel cells
Authors: Ngetich Christabel C. , Mutua James , Kareru Patrick , Karanja Kabini , Wanjiru Evan 
Published: October 2023

AbstractProton exchange membrane (PEM) fuel cell has emerged as a promising alternative to fossil fuels as it is employed to generate electricity for portable applications with low carbon emissions. However, the high cost of the technology hinders its widespread commercialization. Optimizing PEM fuel cell model parameters is crucial in performance improvement and production cost reduction. This study presents an optimization approach for a PEM fuel cell operating and design parameters by integrating Taguchi and Response Surface Methodology. A 3D‐CFD model of a PEM fuel cell is developed and used as a base model for the optimization study. Thirteen input parameters with notable effects on the fuel cell's performance are selected for this study. By the Taguchi method, these parameters decreased from thirteen to five. A response surface methodology (RSM) Box‐Behnken experimental design is employed to study the interactions of the parameters on the power density. The optimum working parameters that result in optimum power density of 0.8476 W cm−2include an operating pressure (2.5 bar), temperature (80°C), flow channel width (1.30 mm), membrane thickness (0.036 mm), and catalyst layer thickness of 0.01 mm. This study provides a quick and efficient way of optimizing PEM fuel cell model for maximum power density.
Impacts of unconditional cash transfers on health care utilisation in informal sector households in Togo
Authors: Atake Esso‐Hanam , Dandonougbo Yevessé , Gbeasor‐Komlanvi Fifonsi Adjidossi , Yagninim Malb Ama N’Danida , Kouevi Didier Koumavi 
Published: October 2023

AbstractBackgroundMost Togolese population earns their income from informal sector, and they are very often exposed to health outcomes. Cash transfers impact healthcare utilization by improving household's social capital, socio‐economic status, lifestyle choice, and physical health. The aim of this paper was to analyse the impact of unconditional cash transfers on health care utilisation in informal sector households.MethodsWe used the propensity‐score method to compare health care utilisation by households that received cash transfers from nonbeneficiary households and simulated a potential confounder to assess the robustness of the impacts of the estimated treatment (i.e., cash transfer). Data were obtained from a national survey that covered 1405 households.ResultsThe results show that women benefited the most from cash transfers (73.1%). Our estimates indicate that health care utilisation increased by 28.3% among workers in the informal sectors who benefited from unconditional cash transfers compared to nonbeneficiaries. The greatest impact was found on agriculture households with an increase by 31.3% in the health care utilisation. In general, cash transfer beneficiaries are more likely to use public health centres; there was an increase in public health facility attendance of 21.3%.ConclusionsCash transfers are a valuable social protection instrument that improve health care utilisation of populations in the informal sector. Policymakers could use cash transfer as the infusion of income and/or assets that may impact health outcomes. Cash transfers are an opportunity to alleviate barriers of access to health care by older people. Future research must examine impact of cash transfer on health of vulnerable groups such as older people, children, and people with disabilities.
Hand preference trajectories as predictors of language outcomes above and beyond SES: Infant patterns explain more variance than toddler patterns at 5 years of age
Authors: Contino Kaityn , Campbell Julie M. , Marcinowski Emily C. , Michel George F. , Ramos Michelle L. , Coxe Stefany , Hayes Timothy , Nelson Eliza L. 
Published: October 2023

AbstractPrior studies found that hand preference trajectories predict preschool language outcomes. However, this approach has been limited to examining bimanual manipulation in toddlers. It is not known whether hand preference during infancy for acquiring objects (i.e., reach‐to‐grasp) similarly predicts childhood language ability. The current study explored this motor‐language developmental cascade in 90 children. Hand preference for acquiring objects was assessed monthly from 6 to 14 months, and language skill was assessed at 5 years. Latent class growth analysis identified three infant hand preference classes: left, early right and late right. Infant hand preference classes predicted 5‐year language skills. Children in the left and early right classes, who were categorized as having a consistent hand preference, had higher expressive and receptive language scores relative to children in the inconsistent late right class. Consistent classes did not differ from each other on language outcomes. Infant hand preference patterns explained more variance for expressive and receptive language relative to previously reported toddler hand preference patterns, above and beyond socio‐economic status. Results suggest that hand preference, measured at different time points across development using a trajectory approach, is reliably linked to later language.Highlights
Hand preference trajectories reliably predict preschool language above and beyond SES.
Infants with a consistent hand preference for reaching had greater language skills at 5 years.
Infant hand preference explained more variance in language than toddler hand preference.
COVID‐associated arthritis after severe and non‐severe COVID‐19: A systematic review
Authors: Zarpoosh Mahsa , Amirian Parsa 
Published: October 2023

AbstractAimSince the coronavirus outbreak became a global health emergency in 2020, various immune‐based effects, such as inflammatory arthritis (IA), have been recorded. This study aimed to determine the role of COVID‐19 severity on post‐COVID arthritis.MethodsWe systematically reviewed 95 patients who developed arthritis after severe and non‐severe COVID‐19 infection by searching the databases, including PubMed, SCOPUS, and EMBASE. We used the term “COVID‐associated arthritis” because there was no definite diagnostic method for classifying arthritides after COVID‐19 infection, and the diagnosed arthritis types were based on the authors' viewpoints.ResultsAfter evaluating the data between the two severe and non‐severe COVID‐19‐infected groups of patients, the results showed that the COVID‐19 severity may affect the pattern of joint involvement in IA. In both groups, combination therapy, including oral nonsteroidal anti‐inflammatory drugs with different types of corticosteroids, was the most common treatment. In addition, the mean age and comorbidities rate was higher in the severe COVID‐19 group. Even though the patients in the severe COVID‐19 group developed more serious COVID‐19 symptoms, they experienced milder arthritis with better outcomes and more delayed onsets that required less aggressive therapy.ConclusionWe conclude that there may be an inverse relationship between COVID‐19 severity and arthritis severity, possibly due to weaker immunity conditions following immunosuppressant treatments in patients with severe COVID‐19.
Machine learning identification and immune infiltration of disulfidptosis‐related Alzheimer's disease molecular subtypes
Authors: Zhu Yidong , Kong Lingyue , Han Tianxiong , Yan Qiongzhi , Liu Jun 
Published: October 2023

AbstractBackgroundAlzheimer's disease (AD) is a common neurodegenerative disorder. Disulfidptosis is a newly discovered form of programmed cell death that holds promise as a therapeutic strategy for various disorders. However, the functional roles of disulfidptosis‐related genes (DRGs) in AD remain unknown.MethodsMicroarray data and clinical information from patients with AD and healthy controls were downloaded from the Gene Expression Omnibus database. A thorough examination of DRG expression and immune characteristics in both groups was performed. Based on the identified DRGs, we performed an unsupervised clustering analysis to categorize the AD samples into various disulfidptosis‐related molecular clusters. Weighted gene co‐expression network analysis was performed to select hub genes specific to disulfidptosis‐related AD clusters. The performances of various machine learning models were compared to determine the optimal predictive model. The predictive ability of the optimal model was assessed using nomogram analysis and five external datasets.ResultsEight DRGs showed differential expression between the AD and control samples. Two different molecular clusters were identified. The immune cell infiltration analysis revealed distinct differences in the immune microenvironment of the two clusters. The support vector machine model showed the highest performance, and a panel of five signature genes was identified, which showed excellent performance on the external validation datasets. The nomogram analysis also showed high accuracy in predicting AD.ConclusionWe identified disulfidptosis‐related molecular clusters in AD and established a novel risk model to assess the likelihood of developing AD. These findings revealed a complex association between disulfidptosis and AD, which may aid in identifying potential therapeutic targets for this debilitating disorder.