bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrated Forecasting Method of College Financial Data Based on Deep Learning
Authors: Yue Fanqi
Published: September 2024

In today’s data-driven era, the accuracy and forward-looking prediction of university financial data are of vital significance for the rational allocation of educational resources and strategic planning. However, the financial data of colleges and universities come from various sources, have complex structure, and are scattered in different database systems, which brings great challenges to data integration and prediction. In order to predict university financial data more accurately, a research on university financial data integration forecast based on deep learning is put forward. Firstly, the Ontology method is used to integrate university financial data. Through data preprocessing, a shared vocabulary is constructed, and the semantic information of college finance is expressed by means of formal ontology, and the ontology attributes of key concepts are extracted. In addition, MC algorithm is used for security processing of integrated data to ensure the security of data in distributed database, and redundant processing of integrated data to generate unified XML format integrated data. Next, with the help of deep belief network in deep learning, feature extraction and dimensionality reduction are carried out on the integrated university financial data. Then, the university financial data prediction model based on these characteristics is constructed, and the university financial data integrated prediction based on deep learning is realized. The experimental results show that the proposed method not only achieves remarkable results in data integration, but also performs well in prediction. This research not only provides a new solution for university financial data forecasting, but also has important significance in theory and practice. In theory, it enriches the theoretical framework of deep learning and data integration. In practice, by improving the accuracy of financial data forecast, the proposed method can help universities to better allocate resources and make strategic planning, and promote the sustainable development of education.
A Lot-Based VMD-CNN-BIGRU Indoor Mariculture Water Quality Prediction Method Including Attention Mechanism
Authors: Chen Lin , Zhang Yonghui , Xu Bo , Shao Kaixuan , Yan Jialin , Bhatti Uzair Asalm
Published: September 2024

The water quality in indoor mariculture systems directly impacts the health of aquaculture species. Dissolved oxygen (DO) is a crucial indicator in these systems, and accurate, rapid prediction of DO levels is essential for improving the yield and quality of aquaculture products. This study addresses the challenge of complex DO variations and low prediction accuracy in indoor aquaculture environments. We propose a hybrid prediction model combining Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Bidirectional Gated Recurrent Unit (BiGRU) with an attention mechanism. The model utilizes water quality data from grouper aquaculture facilities. The VMD algorithm enhances data stability by decomposing non-smooth signals into multiple non-frequency-scaled subsequences. These subsequences are then trained and predicted using the combined model. We conducted comparative experiments with four models: BiGRU, CNN, CNN-BiGRU, and VMD-CNN-BiGRU with an attention mechanism. The VMD-CNN-BiGRU-Attention model demonstrated superior performance, with a root mean square error (RMSE) of 0.0306, a mean absolute error (MAE) of 0.0252, and a coefficient of determination ([Formula: see text]) of 0.9817. Our findings indicate that the VMD-CNN-BiGRU-Attention model meets the predictive needs for DO in indoor mariculture, providing a reliable reference for monitoring and early warning control of water quality in aquaculture systems.
Research on Engineering Cost Construction System Based on Computer Cloud Computing Algorithm
Authors: Changjiang Li , Changfang Li
Published: September 2024

This study is devoted to exploring the method of optimizing engineering cost construction system by using computer cloud computing algorithm. By synthesizing relevant studies at home and abroad, it is found that the current engineering cost construction system generally suffers from slow data processing speed, high computational complexity and insufficient accuracy. In terms of system design, specific technologies, methods and algorithms are adopted. First, a stable cloud computing platform was established to ensure the reliability and stability of the system. Then, techniques such as data mining and machine learning are used to process and analyze a large amount of engineering data to achieve accurate prediction of engineering costs. At the same time, a series of algorithms are designed to optimize the computational efficiency and accuracy of the system. The effectiveness and feasibility of the proposed system are verified through experimental design and analysis. The research results show that the engineering cost construction system based on cloud computing algorithms can significantly improve the efficiency and accuracy of the system, which has high application value and provides a new solution for the engineering construction industry.
Monochromatization of e+e− colliders with a large crossing angle
Authors: Telnov V. I.
Published: September 2024

The relative center-of-mass energy spread [Formula: see text] at [Formula: see text] colliders is [Formula: see text], which is much larger than the widths of narrow resonances [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] mesons, tauonium and some others. Its reduction would significantly increase the resonance production rates and open up great opportunities in the search for new physics. In this paper, we propose a new monochromatization method for colliders with a large crossing angle (which can provide a high luminosity). The contribution of the beam energy spread to [Formula: see text] is canceled by introducing an appropriate energy–angle correlation at the interaction point; [Formula: see text] appears possible.
Optical bandgap engineering and urbach energy in Ni-doped CdSe nanostructured thin films
Authors: Siahkali Zahra , Ghobadi Nader , Mehrparvar Dariush
Published: September 2024

The Ni-doped CdSe nanostructured thin films during the deposition procedure that govern the altering of the optical transitions have been investigated. The concentration of Ni as doping inside CdSe from 0.01, 0.02 to 0.03[Formula: see text]mol in the form of nanostructured thin layers have been fabricated through a simple chemical solution deposition approach. Doping of the Ni to CdSe adds the new transitions related to NiSe, which means the tiny concentration of Ni ions creates the new transition to CdSe that sample has multiple optical bandgaps. There is an order in band gap changing with doping concentration raising so that increasing the Ni[Formula: see text] ions decreases the optical band gap to 1.59[Formula: see text]eV although the optical band gap for CdSe in bulk state is 1.74[Formula: see text]eV, so doping procedure decreases optical bandgap of CdSe that is mean doping procedure creates the new structure. It has shown that it’s possible to determine the Urbach energy of the samples from the absorption spectrum without the need to determine the adsorption coefficient from the determination of thickness. Degradation efficiency for pure CdSe is 86% while a sample of Ni-doped CdSe enhanced degradation efficiency until 93%.
Bayesian Learning of Optimal Utility Functions for Message Routing in Vehicular Ad Hoc Networks
Authors: Tao Tao , Fujita Satoshi
Published: September 2024

This paper explores the application of machine learning techniques for acquiring utility functions in message routing within Vehicular Ad Hoc Networks (VANETs), aiming to enhance the performance of message routing. Message routing in VANETs employs a store-carry-and-forward protocol, where vehicles (nodes) holding messages traverse suitable trajectories and opportunistically forward messages upon entering the communication range of other nodes. The core of the protocols involves context-dependent decision-making on whether to forward messages to an encountered node and, if so, determining which message to transmit. To enhance the message arrival to the destination with short delay, we introduce a method building upon Wu et al.’s prior work on utility function acquisition through the structure learning of Bayesian networks. Our proposed method incorporates two key innovations: (1) integrating destination information of forwarded messages as an attribute within the Bayesian network and (2) extending the crossover operation from genetic algorithms to optimize the attribute order in the structure learning. Through extensive experiments conducted on the ONE simulator, our method demonstrates superior utility compared to the existing baselines, showcasing its effectiveness in VANET message routing improvement.
A Review on Big Data Applications and their Challenges
Authors: Prabhugouda Amruta , Asra Syeda
Published: September 2024

Understanding huge amounts of data with a wide variety of data kinds is referred to as big data analytics. “Human, Machine and Material” development strategy will result in an enormous amount of data. The management department may enhance its potential to process big data by assessing and analysing current network big data issues. As a consequence, it considerably plays a role in minimising resource costs and consumption in every sector. Every sector can effortlessly transition into the following information and digitalisation phase of development. Big data will aid in tackling challenges and enhancing knowledge across various sectors. Although, the efficiency of big data analytics is still questioned by some challenges. The challenges that arise in big data analytics are storage, data quality, lack of data science professionals, data accumulation and data validation. Therefore, this discusses the term “Big data analytics” by configuring its applications, tools, Machine Learning (ML) models and challenges in existing approaches. A comprehensive analysis of over 58 research papers, covering various aspects of big data analytics across multiple domains including healthcare, education, agriculture, multimedia and travel is presented in this study. The main objective of this survey is to contribute to advancing knowledge, facilitating informed decision-making and guiding future research efforts in the dynamic and rapidly evolving landscape of big data analytics. Through meticulous paper selection, a diverse representation of the latest advancements in big data analytics techniques was curated. Each domain underwent a thorough review, elucidating methodologies, tools, datasets and performance measures. Further, the general steps involved in big data analytics techniques are outlined by providing a foundational understanding. Key areas of analysis include chronological review, algorithms utilised, tools and datasets employed and performance evaluation measures. By addressing these aspects, the study offers valuable insights into the evolution, methodologies and performance of big data analytics techniques across diverse domains. Additionally, it identifies research gaps and challenges, paving the way for future research to address critical issues such as data interoperability, privacy concerns and scalability. This study serves as a comprehensive resource for researchers, practitioners and policymakers, contributing to advancing knowledge and facilitating informed decision-making in the rapidly evolving landscape of big data analytics.
Estimating the Effects of China’s Output and Monetary Policy Shocks on African Economies
Authors: Rasaki Mutiu Gbade
Published: September 2024

Given the increasing economic ties between African countries and China, this study investigates the effects of China’s output and monetary policy shocks on African countries’ macroeconomic performance for the period 1995q1 to 2021q4. The study formulates and estimates a dynamic stochastic general equilibrium (DSGE) model with the Bayesian technique. The results show that China’s output and monetary policy shocks impact African countries’ macroeconomic performance. The findings indicate that China’s output and monetary policy shocks affect output, inflation and exchange rates in African economies through the trade and financial channels. However, the results show that China’s output shocks have greater impacts on output and inflation while China’s monetary policy shocks have stronger impacts on exchange rate in African countries. The study recommends that African countries should deepen their intra-regional trade, promote economic diversification and monitor China’s business cycles and policies so as to reduce their vulnerabilities to China’s macroeconomic shocks.
Impacts of Climate Change on the Distribution of African Wild Olive Tree in the Arid Environments of Northern Ethiopia
Authors: Gufi Yirga , Tesfamariam Berihu , Manaye Ashenafi , Desalegn Ambago , Gidey Tesfay , Abrha Haftu , Hintsa Sibhatleab
Published: September 2024

Climate change is impacting the sustainability of indigenous tree species. However, the impacts of climate change on African olive tree (Olea europaea subspecies Cuspidata) are less‐explored. This study, conducted in Tigray, northern Ethiopia, aims to determine the impacts of climate change on the current and future distributions of the species. A total of 225 presence points and abundance of O. europaea within 20 m × 20 m plots at each location were collected. Additional input variables, such as the 19 bioclimatic variables, 3 topographic variables, and Pedologic data, were used. Maxent software was employed to predict the distribution of the species under current and future climate scenarios. The correlation between the tree’s abundance and environmental factors was ascertained using Spearman’s correlation. The findings indicated that the most crucial factors affecting the distribution of the tree were temperature seasonality, altitude, and precipitations during the driest month. The current range of the species covered 1979 km2 (3.01%) of Tigray. This coverage is expected to increase by 13.64% under 2070_RCP4.5 scenario. However, under 2070_RCP8.5, a total loss of suitable habitats is anticipated. Rainfall, slope, altitude, soil organic carbon, and silt contents had a positive correlation with the species abundance, whereas sand, clay, bulk density, and soil pH had a negative correlation (p < 0.05). In conclusion, the species may locally disappear due to the adverse effects of future climate change under the RCP8.5. Hence, the study recommends immediate in situ and ex situ conservation efforts to sustain the populations and important values of the tree.
Fatty Acid Analysis to Classify Iberian Hams Quality Categories from Different Brands as Affected by the Sampling
Authors: Sánchez Soledad , Antequera Teresa , Pajuelo Abraham , Perez-Palacios Trinidad
Published: September 2024

Iberian meat products are classified into four quality categories based on the crossbreeding and feeding of pigs. Meat industries also commonly apply chromatographic analysis to major fatty acids found in the subcutaneous fat taken from the carcass to classify Iberian meat products. Despite the influence of the dry‐cured processing, it has not been considered to stablish quality categories of this product, and neither utilizing different lipid sampling methods for analysis. This study evaluated fat from different areas and hams from various brands and quality categories to assess the correspondence between the fatty acid composition from different fat samples, the quality categories of the current classification of Iberian meat products, and the sensory profile of dry‐cured hams. The analysed samples included fat from the coccyx in the carcass (CAR), subcutaneous fat from fresh and dry‐cured Iberian hams (SCF and SCD, respectively), and intramuscular fat (IMF) from sliced and vacuum‐packed dry‐cured hams (IMF). These samples were sourced from different brands within each quality category of Iberian products and were evaluated based on their fatty acid composition. Additionally, a quantitative descriptive sensory analysis was conducted on the dry‐cured hams in this study. Significant differences in the fatty acid composition and results from the principal component analysis were observed in all four types of analysed samples, indicating similarities among brands from the White and Green quality categories on the one hand and among brands from the Red and Black quality categories on the other hand. Sensory attributes of the dry‐cured hams showed minimal significant differences among quality categories, likely due to the high variability among brands. The four types of sampling analysed in this work may be used for quality control in the industries, especially subcutaneous and intramuscular fat from dry‐cured Iberian hams, which are easily obtained and handled, and are available until the end of processing.
Electrochemical Analysis of Methanol with Nafion‐Coated Copper Oxide Nanoparticles
Authors: Sawatmuenwai Pantipa , Kaewket Keerakit , Ngamchuea Kamonwad
Published: September 2024

This work introduces a Nafion‐coated copper(I) oxide nanoparticle electrode (Nafion/Cu2O/GC) designed for the electrochemical detection of methanol (CH3OH). The responses of the composite material toward CH3OH were enhanced by the selective permeation of CH3OH through the hydrophilic channels of the Nafion membrane in combination with the electroactivity of Cu2O nanoparticles. The sensor displayed a linear detection range of 0.33–100 mM CH3OH with a sensitivity of 0.17 μA·mM−1 and a detection limit (3 s/m) of 0.10 mM. It exhibited excellent reproducibility with a relative standard deviation of <5%. The sensor’s practical applicability was demonstrated through recovery studies on hand sanitizer samples, achieving ca. 100% recovery. The sensor was further used to elucidate CH3OH adsorption on activated carbon, revealing that the process conforms to the Langmuir isotherm model.
Shh Signaling from the Injured Lung Microenvironment Drives BMSCs Differentiation into Alveolar Type II Cells for Acute Lung Injury Treatment in Mice
Authors: Wu Mengyu , Liu Jing , Zhang Shu , Jian Yi , Guo Ling , Zhang Huacai , Mi Junwei , Qu Guoxin , Liu Yaojun , Gao Chu , Cai Qingli , Wen Dalin , Liu Di , Sun Jianhui , Jiang Jianxin , Huang Hong
Published: September 2024

Alveolar type II (AT2) cells are key effector cells for repairing damaged lungs. Direct differentiation into AT2 cells from bone marrow mesenchymal stem cells (BMSCs) is a promising approach to treating acute lung injury (ALI). The mechanisms of BMSC differentiation into AT2 cells have not been determined. The Sonic Hedgehog (Shh) pathway is involved in regulating multiple differentiation of MSCs. However, the role of the Shh pathway in mediating the differentiation of BMSCs into AT2 cells remains to be explored. The results showed that BMSCs significantly ameliorated lung injury and improved pulmonary function in mice with ALI. These improvements were accompanied by a relatively high proportion of BMSCs differentiate into AT2 cells and an increase in the total number of AT2 cells in the lungs. Lung tissue extracts from mice with ALI (ALITEs) were used to mimic the injured lung microenvironment. The addition of ALITEs significantly improved the differentiation efficiency of BMSCs into AT2 cells along with activation of the Shh pathway. The inhibition of the Shh pathway not only reduced the differentiation rate of BMSCs but also failed to mitigate lung injury and regenerate AT2 cells. The results confirmed that promoting AT2 cell regeneration through the differentiation of BMSCs into AT2 cells is one of the important therapeutic mechanisms for the treatment of ALI with BMSCs. This differentiation process is highly dependent on Shh pathway activation in BMSCs in the injured lung microenvironment.
Damage Identification Using Nonlinear Manifold Learning Method under Changing Environments
Authors: Guo Peng , Li Dong-sheng , Huang Jie-zhong , Qiao Hou , Li Hong-nan
Published: September 2024

Damage identification is a key aspect of structural health monitoring (SHM). However, any measurement of the structural response can be impacted by environmental and operational variations (EOVs), which can affect the system and hinder damage detection. It is therefore important to distinguish between damage‐induced changes in structural dynamic properties and changes caused by EOVs. To address this issue, this paper proposes a damage identification method based on nonlinear manifold learning, specifically Laplacian eigenmaps (LEs). The method eliminates the impact of EOVs on the damage index by treating them as embedded variables and does not require the direct measurement of environmental parameters. The Gaussian process regression (GPR) prediction model results in small residuals when the structure is healthy and significant increases when the structure is damaged, demonstrating the effectiveness of the method in removing environmental influences. The proposed method is demonstrated using computer‐simulated data, where the environmental conditions have a nonlinear effect on the vibration features. The proposed LE‐GPR algorithm is then applied to the Z24 and KW51 bridges and successfully identifies structural damage. The advantage of the proposed approach is its ability to eliminate the effects of ambient temperature and accurately identify structural damage.
Effect of Dietary Protein and Lipid Ratios on Zootechnical Performance and Body Composition of African Native Freshwater Prawn Macrobrachium macrobrachion (Herklots, 1851) Juveniles
Authors: Atchamou Jean-Baptiste , Kpogue Diane , Lokonon Fabrice , Dagoudo Missinhoun , Koussovi Guillaume , Benhaïm David , Montchowui Elie
Published: September 2024

Determination of protein and lipid requirements is crucial in prawn species aquaculture. The present study investigated the impact of six diets with different protein and lipid levels (T1, 30/08; T2, 30/12; T3, 35/08; T4, 35/12; T5, 40/08; and T6, 40/12), in triplicate, on the zootechnical performance and body composition of juvenile Macrobrachium macrobrachion over 56 days. These protein and lipid levels were chosen based on the requirements of other freshwater prawn species. The stocking density was one individual per liter. Results show that prawn survival was lower in T6 (with 40% protein and 8% lipid) compared to all others. The treatment T3 (35% protein and 8% lipid) shows higher survival (with 40.0 ± 24.0%) but not significantly different from other diets. The specific growth rate obtained in the present study was not different between diets. Negative values were recorded for treatments T2, T3, and T4 (30%, 35%, and 35% proteins and 12%, 8%, and 12% lipids, respectively) and positive values for treatments T1, T5, and T6 (30%, 40%, and 40% proteins and 8%, 8%, and 12% lipids, respectively). The protein efficiency coefficient (PEC) and the lipid efficiency coefficient (LEC) did not differ between treatments; however, there was a trend for these coefficients to be lower in T4, T5, and T6 (35%, 40%, and 40% proteins and 12%, 8%, and 12% lipids, respectively) compared with the other diets. The highest protein efficiency rates were recorded with the T3 and T4 treatments (35% proteins and 8% and 12% lipids, respectively), while the best lipid level was obtained with T4 (35% proteins and 12% lipids). According to dose–response models, the optimal protein/lipid ratio requirements for M. macrobrachion juveniles can be estimated at 35/8. This study is the first to provide relevant information on the nutritional needs of juveniles of African native freshwater prawn M. Macrobrachion. Further research is needed to optimize nutritional requirements at early stages of development.
Community Paramedicine Supporting Community Needs: A Scoping Review
Authors: Lunn Tyne M. , Bolster Jennifer L. , Batt Alan M.
Published: September 2024

Health and social needs exist along a dynamic continuum. Recognising that health status is inextricably impacted by social determinants of health, community paramedicine has opportunities and a responsibility to reduce inequities. The objective of this scoping review was to investigate the peer‐reviewed and grey literature to explore how community paramedicine supports community needs along a health and social continuum. We conducted a scoping review of the English language literature using the JBI Scoping Review methodology. We searched CINAHL, EMBASE, MEDLINE, Google Scholar, and organisational websites. 30 peer‐reviewed and 13 grey literature articles met the inclusion criteria. The findings describe the ways community paramedicine models evolved from minimising system pressures on emergency health services to addressing health and social needs. A key recommendation across the literature was the need to meaningfully engage communities early in the program development to understand how best to implement and codesign integrated service models addressing specific community needs although there was a lack of evidence to guide this approach. There is a notable lack of evidence pertaining to optimising technologies in the program design and implementation. The results highlight opportunities to determine the best practices for conducting holistic community needs assessments that include equitable stakeholder engagement and enhancing education to prepare paramedics for expanded roles. Community paramedicine provides opportunities to better meet the needs of structurally marginalised communities. There is a social responsibility and opportunity to engage communities to codesign service delivery, advance paramedic education, and enhance interprofessional collaboration to better support community needs and generate upstream solutions for individuals and communities.
People Skills in the 21st Century: A Perspective on the Smart City in an Emerging Economy
Authors: Tanantong Tanatorn , Moolngearn Papon , Kraiwanit Tanpat , Limna Pongsakorn , Rafiyya Aishath
Published: September 2024

Smart cities are hubs of innovation and rapid development, where technology plays a significant role in shaping the urban environment. In such settings, the ability to adapt and think flexibly is crucial for individuals to thrive. The study’s goal is to understand which factors influence how well individuals are equipped with 21st‐century skills and how they apply them in the dynamic context of modern urban living. Employing a quantitative research approach, data were initially collected from 600 Thai respondents through convenience sampling. A subsequent data‐cleansing process refined the focus to 568 respondents, selected based on their high‐score attainment. The study utilized percentages, means, and binary logistic regression in a comprehensive data analysis. Its key findings illuminate the influence of various factors, including score, age, career trajectory, and engagement with social media platforms like Instagram, LINE, and X, on skill development within the smart city context. This research offers a nuanced perspective on the myriad elements that foster individual and collective success in technologically advanced urban environments. It moves beyond mere acknowledgment of innate abilities or age‐related wisdom, highlighting the critical roles of career advancement and digital engagement in skill enhancement. The implications of these findings are far reaching, especially for policymakers, educators, and industry leaders. They underscore the need for a comprehensive approach to shaping the future of urban living and working spaces, ensuring that individuals are not just equipped with the necessary skills but are also adept at applying them effectively in the smart cities of tomorrow. This study serves as a guide for these stakeholders, emphasizing the importance of fostering environments that support continuous learning, innovation, and adaptability in the face of technological advancements.
High Incidence of Refeeding Syndrome during the Transition from F75 to Ready‐to‐Use Therapeutic Feeds among Children 6 to 59 Months with Severe Acute Malnutrition at the Pediatric Nutritional Unit of Mulago Hospital
Authors: Muzeyi Wani , Ochieng Andra Teddy , Oriokot Lorraine , Musiime Victor
Published: September 2024

Background. Refeeding syndrome is a complication developed by children being managed for severe acute malnutrition (SAM). It is caused by changes in electrolyte balance once high‐caloric feeding is reinitiated. Phosphorus, potassium, and magnesium are the main electrolytes affected when it occurs. However, hypophosphatemia is the hallmark of the diagnosis of refeeding syndrome. WHO recommends inpatient management of patients with complicated SAM with initially F75 which is low in calories and later transitioned to RUTF which is high in calories but also has a higher phosphorus content. Objective. This study aims to determine the incidence and factors associated with refeeding syndrome in the transition phase when treating children aged 6 to 59 months with severe acute malnutrition at the Mwanamugimu Nutritional Unit, Mulago. Methods. We conducted a prospective cohort study at the Mwanamugimu Nutritional Unit of Mulago National Referral Hospital. A total of 150 children between 6 and 59 months with SAM were enrolled into the study. We measured serum electrolytes (phosphorus, sodium, and potassium) at admission, initiation of RUTF, and 48 hours after transition. The refeeding syndrome was diagnosed by a drop in serum phosphorus of more than 0.3 mmol from baseline. The data were analyzed using STATA 17.0. Incidence of refeeding syndrome was determined as the proportion of participants whose serum phosphorus declined by more than 0.3 mmol from baseline. For factors associated, a multivariate‐modified Poisson regression analysis reporting relative risk was performed with a 0.2 level of significance at bivariate and 0.05 at multivariate analyses. Results. Of the 150 children recruited, 35 were lost to follow‐up and 115 children had their data analyzed. Of the 115 participants in the study, 40 developed refeeding syndrome indicating a cumulative incidence of 34.8% with a 95% CI of 26.5–44%. A low baseline serum sodium (RR: 0.89, 95% CI: 0.80–0.99, and P value: 0.038) and having edematous malnutrition (RR: 0.90, 95% CI: 0.81–0.99, and P value; 0.042) at admission were found to be significant (P < 0.05) risk factors of refeeding syndrome. Conclusion. The cumulative incidence of RFS of 34.8% is very high. RFS is found to be associated with low baseline sodium and pedal edema. Children with a low baseline sodium and edema should undergo a cautious transition of feeds.
Assessing Peak Load Reduction Potential on Distribution Network: A Case Study of Residential Households in Freetown, Sierra Leone
Authors: Rogers Joseph Kugbea , Diawuo Felix Amankwah , Domfeh Martin Kyereh
Published: September 2024

Sierra Leone struggles with electricity with a 38% electrification rate. The expanding distribution network with limited generation capacity leads to frequent power outages during peak hours. Demand response (DR), a widely recognized demand‐side management strategy, has effectively balanced the electricity supply and generation worldwide. The primary objective of this study is to assess the potential for DR in the residential electricity sector, focusing on drivers such as security, environmental concerns, and incentives. The case study was conducted in Freetown, Sierra Leone. This study employs diversified demand modeling with household energy audit data to estimate the potential reduction in peak load achievable through voluntary demand response (VDR). This study generated load profiles for 12 household appliances and an aggregated load profile for the Freetown distribution network. Notably, the study achieved a substantial peak load reduction of 4.19 and 3.54 MW during the morning and evening peak hours, respectively. Additionally, this research highlights the significant roles of power security, price incentives, and environmental factors in encouraging residential consumers in Sierra Leone to participate in DR programs. This study underscores the response’s role in Sierra Leone’s electricity challenges, providing insights into peak load reduction and the motivation for residential consumer participation in DR programs.
Intelligent Route Planning Recommendation for Electric Bus Transport
Authors: Zhou Chunjie , Dai Pengfei , Huang Xin , Wang Fusheng
Published: September 2024

Electric bus transport, a popular mode of public transportation, offers punctual, safe, and comfortable services to passengers through the efficient and effective use of designated road space. The performance of electric bus transport systems depends largely on the design of proper locations of bus stops, with the consideration of passenger demands, waiting time, and traveling time. Optimal electric bus route planning can attract an increasing number of passengers and increase public transit services. Aiming to provide guidance for the electric bus route planning of developing cities, this study proposed an intelligent route planning method to minimize the waiting time and traveling time of passengers, in order to achieve the best comfortable level. In addition, a self‐learning anomaly detection method based on reinforcement learning (RL) was proposed to eliminate abnormal data caused by traffic accidents or emergencies. With a large spatiotemporal dataset collected over 3 years from a real electric bus project in Yantai, China, we developed a prototype system and conducted extensive experiments to evaluate the proposed intelligent route planning method. The results showed that the proposed method can reduce the passengers’ waiting time and attract more passengers traveling by electric bus. In addition, the proposed method has achieved optimal route planning recommendation (RPR) subject to 1,872,391 passenger demands on electric bus services; more than 86% of them were accurately predicted, and more than 97% were satisfied with recommendation results.
THz Scattering and Tunable Plasmonic Resonances from Graphene‐Coated Gold Nanowires
Authors: Razzaz Faroq , Akram M. N. , Yaqoob M. Z.
Published: September 2024

The radiative response of antenna structures highly depends upon the extinction efficiency of the structures and the major parameter for the optimization of nanoantenna designs. To optimize the radiative response of the plasmonic antenna design, i.e., the oblique scattering of the THz radiation form, graphene‐coated gold (Au) nanowires have been studied. The present work provides a universal solution for the graphene‐coated cylindrical geometries. The optical conductivity of graphene has been modeled by the use of Kubo formalism, while the analytical modeling of the gold (Au) is done by employing Drude’s model. The Jacobi–Anger expansion method has been adopted for the transformation of electromagnetic fields in terms of series of cylindrical waves and has been modeled in terms of cylindrical vector wave functions (CVWFs). The analytical formulation has been presented for both polarizations, i.e., parallel polarization (TM) and perpendicular polarization (TE). The unknown scattering coefficients have been calculated by applying impedance boundary conditions at graphene‐gold nanowire interface. The analytical and numerical results have been computed for extinction efficiency (Qext) under parallel and perpendicular polarization states. The Qext under different parameters of graphene, diameter of metallic nanowire (R) and angle of incidence θi, has been computed. It is reported that the graphene‐coated Au nanorod supports the localized surface plasmon resonance (LSPR) under perpendicular polarization at a high THz frequency range. The surface plasmon resonance can be actively controlled under suitable parameters, i.e., graphene parameters, core size, and angle of incidence. The numerical results reveal that the radiative response of the plasmonic structures can be tuned actively from THz to Far‐IR frequency range under appropriate parameters. The provided results have potential applications in subwavelength devices and plasmonic antenna designs.
The Mediating Role of Perceived Body Weight, Fat, and Muscularity in the Relationship Between Weight Status and Psychological Health
Authors: Lai Catie Chun Wan , Brooks Kevin R. , Boag Simon
Published: September 2024

Background: Past studies have primarily focused on the impact of body weight perception on psychological health. However, other components (e.g., perceived body fat and muscularity) may also play a role. This study aimed to examine the association between weight status (i.e., normal weight and obesity/overweight) and psychological health, and how different components of body perception mediate the association.

Methods: From February to June 2022, 621 undergraduates completed an online cross sectional survey, which included questions about height, weight, psychological health (i.e., well‐being, quality of life, depression, anxiety, and stress), and body perception. Participants with a body mass index (BMI) between 18.5 kg/m2 and 25 kg/m2 were classified as normal weight, while those over 25 kg/m2 were classified as individuals with obesity/overweight. Path analysis tested the association between weight status and psychological health and the mediating role of perceived body weight, fat, and muscularity.

Results: Obesity/overweight was associated with lower well‐being (β = 0.128, p = 0.002) and physical health‐related quality of life (β = 0.089, p = 0.024), and higher depression (β = −0.133, p = 0.002) and stress (β = −0.104, p = 0.013). These associations were fully mediated by perceived body fat, with estimates ranging from −0.122 to 0.112. Perceived body weight and perceived muscularity did not significantly mediate these associations.

Conclusion: Perceived body fat was the only component of body perception that explained the association between weight status and psychological health. This finding suggests that research on psychological health in individuals with obesity or overweight needs to extend beyond mere perceived body weight and incorporate different components of body perception. Furthermore, interventions aimed at improving psychological health in individuals with obesity or overweight need to consider the potential effect of perceived body fat.
The Role of Environmental and Social Innovations in the Cost Leadership Strategy, Value Creation, and Business Performance: A Comparative Analysis of SMEs and Large Manufacturing Companies
Authors: Abubakar Ahmed , Al. Jahwari Noura Said , Bakheet Adil Hassan
Published: September 2024

The study examines how social and environmental innovations affect cost leadership strategies, value creation, and overall firm/business performance. It has compared how SMEs handle the ever‐changing landscape of sustainability‐driven business practices with large manufacturing companies. With a cross‐sectional design, the study examined 179 manufacturing businesses to test the research framework. A structured questionnaire based on a five‐point Likert scale with organization as a unit of analysis was used to collect data. The research’s measurement and structural models were derived from data analysis conducted using Smart PLS. Overall, the research revealed that environmental and social innovations have significant impacts on business performance through cost leadership strategies and value creation in both SMEs and large manufacturing companies. However, a direct relationship was not established between social innovation and value creation in the case of SMEs, but it was significant for large companies. Environmental innovation was found to have direct relationships with cost leadership and value creation in both SMEs and large manufacturing companies. This research shows managers and policymakers how companies of any size can balance cost leadership with environmental and social concerns to maximize resources and efficiency. It encourages sustainable practices that benefit society and the environment while boosting performance and guiding strategic decision‐making. The novelty of this study is the comparative analysis of SMEs and large companies that provide empirical evidence of the intricate connection between environmental and social innovations, cost leadership, value creation, and business performance.
Classification of Gastrointestinal Diseases Using Hybrid Recurrent Vision Transformers With Wavelet Transform
Authors: Mulugeta Abuhayi Biniyam , Agegnehu Bezabh Yohannes , Melese Ayalew Aleka , Alemayehu Lakew Miraf
Published: September 2024

Gastrointestinal (GI) diseases are a significant global health issue, causing millions of deaths annually. This study presents a novel method for classifying GI diseases using endoscopy videos. The proposed method involves three major phases: image processing, feature extraction, and classification. The image processing phase uses wavelet transform for segmentation and an adaptive median filter for denoising. Feature extraction is conducted using a concatenated recurrent vision transformer (RVT) with two inputs. The classification phase employs an ensemble of four classifiers: support vector machines, Bayesian network, random forest, and logistic regression. The system was trained and tested on the Hyper–Kvasir dataset, the largest publicly available GI tract image dataset, achieving an accuracy of 99.13% and an area under the curve of 0.9954. These results demonstrate a significant improvement in the accuracy and performance of GI disease classification compared to traditional methods. This study highlights the potential of combining RVTs with standard machine learning techniques and wavelet transform to enhance the automated diagnosis of GI diseases. Further validation on larger datasets and different medical environments is recommended to confirm these findings.
Environmental Impact Assessment of Heat and Power Cogeneration in Tehran Wastewater Treatment Plant Based on the Life Cycle Assessment
Authors: Rahmati Mohammad , Rasouli Majid , Haji Agha Alizadeh Hossein , Ataeiyan Behnam
Published: September 2024

One of the well‐known methods for correctly managing sludge disposal is using produced sludges as fuel in biogas‐burning power plants. This study employed the ReCiPe2016 life cycle assessment methodology in conjunction with SimaPro software to assess the environmental impacts of a biogas‐fired power plant in Tehran with a total capacity of 2.4 MW. The results were analyzed, and the 13 necessary pieces of information for the biogas burning system per 1 MWh produced heat and power by the combined heat and power unit were considered. In anaerobic digestion, heat and power in all classes had reducing effects on the environment. The heat and power classes, reinforced steel, chrome steel, low alloy steel, copper, and cast iron, exhibited the highest environmental impacts within the integrated heat and power system. Among all impact classes, power (for anaerobic digestion) in the class of carcinogenic toxicity in humans with a value of −6.01976 kg 1,4‐DCB and copper (for combined heat and power) in the marine environmental toxicity class with a value of 0.731434 kg 1,4‐DCB had the most reduction effect and the most impact of pollutant on the environment among the considered classes, respectively. Conclusively, in the anaerobic digestion process, heat and power had a positive effect on the environment in all impact classes. So, sludge can be an adequate candidate and anaerobic digestion can be an eco‐friendly method for power generation. Other effect classes are presented in detail as results. This study could guide the development of similar facilities globally, considering factors like wastewater volume and combined heat and power technology.
A Rare Case of Cladophialophora bantiana Intracranial Infection: Highlighting the Utility of Next‐Generation Sequencing in Diagnosis
Authors: Whitman Melissa , Vissichelli Nicole
Published: September 2024

Cladophialophora bantiana is a dematiaceous fungi and the most common cause of cerebral phaeohyphomycosis. Here, we report a rare case of cerebral ventriculitis with development of a cerebral abscess due to C. bantiana diagnosed by cell‐free DNA next‐generation sequencing. Noninvasive diagnostics led to earlier disease identification and initiation of antifungal therapy, which has the potential to reduce mortality in this highly fatal disease.
Aerodynamic Performance Comparisons of Two Kinds of Variable Nozzle Vanes for a Variable Geometry Turbine
Authors: Jiang Zhuyu , Qiu Ming , Chen Huanhuan , You Yunxia
Published: September 2024

Although cantilevered variable nozzle vane (VNV) is already used in engineering practice, few researches about cantilevered variable geometry turbine (VGT) aerodynamic performance can be found in public references. In order to find the difference between conventional and cantilevered VGT performances and provide references for VNV design practice, the aerodynamic performances of three single‐stage VGTs with different VNVs were compared by the three‐dimensional numerical simulation method. The VNVs of VGTs here included conventional, cantilevered, and no clearance VNVs. All three VGTs had spherical endwalls in the nozzle. The influences of nozzle spherical endwalls, vane end clearances, and vane rotation angles on VGT performances were investigated. The computation results show that the nozzle spherical endwalls enhance the radial mass flow immigration in the nozzle front part and increase the turbine mass flow rate by 0.79%. Some nozzle vane end clearance leakage flows near the vane trailing edge with higher axial speed can improve the mass flow rate further. Compared with the no clearance VGT, the conventional VGT efficiency is lower by 0.0082, and the cantilevered VGT efficiency is lower by 0.0293. When the VNV rotation angle increases by 1°, the VGT mass flow rate decreases by approximately 0.36 kg/s, and the expansion ratio improves by around 0.05. The cantilevered VGT had enormous VNV hub leakage loss, and its efficiency was lower than the conventional VGT by at least 0.01 within the range of VNV rotation angles. The cantilevered VNV hub creates more loss when VGT works at lower expansion ratio conditions.
A Novel Method for Dynamic Distribution Network Reconfiguration Utilizing Capacitor and DG Allocation
Authors: Sadeghi Sana , Jahangiri Alireza , Ghaderi Shamim Ahmad
Published: September 2024

Because of the low voltage level and high current in the distribution system, as well as the high ohmic resistance of the conductors, the majority of power system losses are attributed to the distribution system. As a result, it is necessary to address the issue of reducing distribution system losses. To reduce losses in distribution systems, a wide variety of methods and algorithms have been proposed and are still being developed. The allocation of capacitors in a distribution system is one of the earliest and most effective methods for reducing system losses. With the growth of distributed generations and the diversity of their productive power, the use of capacitors in distribution systems has decreased slightly. Distributed generation resource allocation has replaced this method. Conversely, the reconfiguration of distribution systems has proven to be one of the easiest and cheapest methods for reducing distribution system losses in the past half‐century. Distribution system reconfiguration has been the subject of many studies, each with different objectives. Many studies have also reconfigured the distribution system on a daily and hourly basis. A novel method for dynamic distribution network reconfiguration is presented in this study, which utilizes capacitors and distributed generation resources simultaneously. This method divides 24 h into several periods instead of considering real‐time and hourly data. By reconfiguring and allocating capacitors and distributed generation resources during these periods, the voltage profile is improved and losses are reduced. An IEEE 33 bus network is used to test the simulations. A genetic algorithm was used to reduce losses and improve the voltage profile using MATLAB software. Based on the simulation results, this model provides better results than previous approaches.