bluuebunny/crossref_metadata_2025_split huggingface dataset:
Green synthesis of CdS:Sn NPs by Starch as a Covering Agent and Studying its Physical Properties
Authors: Yaaqoub Zainab , Abbas Nada K. 
Published: February 2023

The aim of this research is to employ starch as a stabilizing and reducing agent in the production of CdS nanoparticles with less environmental risk, easy scaling, stability, economical feasibility, and suitability for large-scale production. Nanoparticles of CdS have been successfully produced by employing starch as a reducing agent in a simple green synthesis technique and then doped with Sn in certain proportions (1%, 2%, 3%, 4%, and 5%).According to the XRD data, the samples were crystallized in a hexagonal pattern, because the average crystal size of pure CdS is 5.6nm and fluctuates in response to the changes in doping concentration 1, 2, 3, 4, 5 %wt Sn, to become 4.8, 3.9, 11.5, 13.1, 9.3 nm respectively. An increase in crystalline size has been noticed in the doped CdS than in the pure CdS. The particle size is within the range of 24-103 nm, according to SEM data from pure CdS and of the doped with Sn particles. The band gap's energy values, according to UV-Vis reflection spectroscopy were 3.06,2.61 ,2.63, 2.63, 2.66,2.69 eV for pure and doped with Sn 1%, 2%, 3%, 4%, 5% respectively. The grain size and roughness rate of pure CdS materials and doped with Sn are shown in AFM results 2.16,2.39,10.07,11.33, 12.47,18.56 nm and average diameter is 30.15, 11.71, 66.06, 48.27,82.011, 80.35 nm for pure and doped with tin 1%, 2%, 3%, 4%, 5% respectively.
Using VGG Models with Intermediate Layer Feature Maps for Static Hand Gesture Recognition
Authors: Fadhil Osamah Y. , S. Mahdi Bashar , R. Abbas Ayad 
Published: February 2023

A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different methods. First, the models were initialized with random weights and trained from scratch. Afterward, the pre-trained models were examined as feature extractors. Finally, the pre-trained models were fine-tuned with intermediate layers. Fine-tuning was conducted on three levels: the fifth, fourth, and third blocks, respectively. The models were evaluated through recognition experiments using hand gesture images in the Arabic sign language acquired under different conditions. This study also provides a new hand gesture image dataset used in these experiments, plus two other datasets. The experimental results indicated that the proposed models can be used with intermediate layers to recognize hand gesture images. Furthermore, the analysis of the results showed that fine-tuning the fifth and fourth blocks of these two models achieved the best accuracy results. In particular, the testing accuracies on the three datasets were 96.51%, 72.65%, and 55.62% when fine-tuning the fourth block and 96.50%, 67.03%, and 61.09% when fine-tuning the fifth block for the first model. The testing accuracy for the second model showed approximately similar results.
The Necessary and Sufficient Optimality Conditions for a System of FOCPs with Caputo–Katugampola Derivatives
Authors: Holel Moataz Abbas , Hasan Sameer Qasim 
Published: February 2023

The necessary optimality conditions with Lagrange multipliers are studied and derived for a new class that includes the system of Caputo–Katugampola fractional derivatives to the optimal control problems with considering the end time free. The formula for the integral by parts has been proven for the left Caputo–Katugampola fractional derivative that contributes to the finding and deriving the necessary optimality conditions. Also, three special cases are obtained, including the study of the necessary optimality conditions when both the final time and the final state are fixed. According to convexity assumptions prove that necessary optimality conditions are sufficient optimality conditions.
Neural and behavioral effects of parent training on emotion recognition in mothers rearing children with ADHD
Authors: Makita Kai , Yao Akiko , Shimada Koji , Kasaba Ryoko , Fujisawa Takashi X , Mizuno Yoshifumi , Tomoda Akemi 
Published: February 2023

Abstract
This randomized controlled study examined neurological changes in socioemotional processing skills through parent training in caregivers of children with attention-deficit/hyperactivity disorder. Thirty mothers of children with attention-deficit/hyperactivity disorder were stratified into parent training and non-parent training groups. Functional magnetic resonance imaging was performed during the “Reading the Mind in the Eyes” test, and parenting difficulties were evaluated using the Parenting Stress Index and the Parenting Scale, twice (before and after parent training). Only mothers in the parent training group showed a significant decrease in Parenting Stress Index and Parenting Scale scores. They also demonstrated increased activity in the left occipital fusiform gyrus during the task of estimating emotions from facial pictures. We presumed that these changes might reflect the potential impact of enrollment in parent training in reducing stress, which might have increased activation of the fusiform gyrus.
Ultralow overpotential nitrate reduction to ammonia via a three step relay mechanism
Authors: Han Shuhe , Li Hongjiao , Li Tieliang , Chen Fanpeng , Yang Rong , Yu Yifu , Zhang Bin 
Published: February 2023

AbstractAmmonia plays a significant role in agriculture and the next-generation carbon-free energy supply. Electrocatalytic nitrate reduction to NH3is attractive for nitrate removal and NH3production under ambient conditions. However, the energy efficiency is limited by the high reaction overpotential. Here, we propose a three-step relay mechanism composed of a spontaneous redox reaction, electrochemical reduction, and electrocatalytic reduction to overcome this issue. RuxCoyalloys are designed and adopted as model catalysts. Ru15Co85exhibits an onset potential of +0.4 V versus a reversible hydrogen electrode and an energy efficiency of 41.54 ± 1.72 %, which are both the best records. The high performance results in a low production cost of $0.49 ± 0.02 /kgammonia($0.58~1.02/kgammoniasold in the USA). Electrochemicalin situspectroscopy and theoretical simulations indicate that the three-step relay mechanism leads to excellent catalytic performance on Ru15Co85and can be extended to RuxFeyand RuxNiyalloys.
An Efficient Task Allocation Using Resource Shortest Scheduling and Fairness Firefly Algorithm In Cloud Computing
Authors: s dharma , P KAVITHA 
Published: February 2023

Abstract
The cloud computing has become significant part of day to day life. It is assimilated the several resource for the user’s requirement. However, the scale and highly dynamic environment of cloud application executes significant new challenges to resource management and efficient resource scheduling are highly demanded. The problem of Resource scheduling and Task allocation is an important aspect on the cloud performance. The propose a systematic approach to denote the reliability, running time, and failure processing of resource scheduling in cloud computing. The Resource Shortest Scheduling and Fairness Fire Algorithm (RSSFFA) is enhance the resource scheduling and task allocation during the time of loading the server in polynomial time and consume an execution time.
Strong HONO Emissions from Fertilized Soil in the North China Plain Driven by Nitrification and Surface Water Exchange
Authors: al. Xue et 
Published: February 2023

Abstract

Soil was proposed as an important nitrous acid (HONO) source but has not so far been demonstrated by field flux measurements and its atmospheric impacts are highly uncertain. Through field flux measurements we found strong soil HONO emission flux (FHONO) enhanced by nitrogen fertilizer use for agricultural fields. FHONO showed distinct diurnal variations with noontime peaks up to hundreds of ng-N m-2 s-1. With soil samples where field measurements took place, our laboratory experiments subsequently found that the release of soil nitrification-originated nitrite as HONO was largely accelerated by surface water exchange. This process was parameterized by a modified Arrhenius equation that reproduced field flux observations well. With the consideration of FHONO, the box model could well reproduce the observed ambient HONO and found that FHONO could significantly accelerate ozone production, highlighting the amplified role of nitrogen fertilizer in exacerbating regional ozone pollution. Moreover, we found that nitrification inhibitors could reduce HONO emissions by ~90%, indicating their great potential to mitigate air pollution. Furthermore, we estimated yearly HONO emission of 0.25 Tg-N from global fertilizer use for agriculture, representing a considerable reactive nitrogen source and significantly affecting atmospheric oxidizing capacity and air quality on a global scale.
A Comparative Study of the Pharmacovigilance System in Nigeria and Ghana using WHO-Indicator Based Pharmacovigilance Assessment Tool. (IPAT)
Authors: Onasanya Seun Sunday , Yamoah Peter , Oosthuizen Frasia 
Published: February 2023

Abstract
Background: Nigeria and Ghana are both members of the World Health Organization (WHO) Program for International Drug Monitoring (PIDM) operated by the Uppsala Monitoring Centre (UMC) in Sweden. The PIDM currently includes more than 150 countries and has an increasing influence on public health decisions and the development and implementation of Pharmacovigilance (PV) systems. The requirements to join the WHO PIDM include submission of 20 reports in the internationally accepted E2B format to UMC and a formal letter from the ministry of health requesting to join the program. Even though the two countries are members of the WHO PIDM, they have different capacities.
Purpose of the study: To assess and determine the functionality, scope of operation, strengths and limitations of the national pharmacovigilance systems in Nigeria and Ghana using WHO PV indicators as standard.
Method: National PV documents backed by law and other statutory documents governing the PV systems of both Nigeria and Ghana were examined using the WHO indicators. Indicator–based Pharmacovigilance Assessment (IPAT) was developed by WHO to assess PV systems, PV in the two countries were compared by reviewing the government policies on PV. The staff of the pharmacovigilance unit of the National Medicines Regulatory Authorities (NMRAs) were interviewed using the WHO Pharmacovigilance Indicators tool. The responses were recorded, and the data collected were analyzed.
Results: The PV systems in Nigeria and Ghana were supported by law and regulations in line with international standards. The standard operating procedures for reporting, processing, and communicating suspected adverse event reports from stakeholders of PV were in place in both countries. The Physician density per 1000 population in Nigeria and Ghana is 0.38 and 0.10 respectively. The two countries did not meet the 200 reports of suspected ADRs reported by 1 million of the population in 2020. There is no product quality report form, medication error reporting form or treatment error reporting form in either country. Nigeria and Ghana both have a designated budget for pharmacovigilance activities and an electronic ICSR reporting system. The national pharmacovigilance systems in the two countries do not have access to data on drug utilization.
Conclusion: The national pharmacovigilance systems in the two countries have a policy and legal frameworks defined by law and regulation to conduct pharmacovigilance activities. However, the two national PV systems are at different levels of dimension and performance concerning conducting PV activities. Varied targeted and directed interventions are needed to strengthen the PV systems in two countries to enable evidence-based decision making for patient safety.
Neurophenotypes of COVID-19: risk factors and recovery outcomes
Authors: Prabhakaran Divya , Day Gregory S. , Munipalli Bala , Rush Beth K. , Pudalov Lauren , Niazi Shehzad K. , Brennan Emily , Powers Harry R. , Durvasula Ravi , Athreya Arjun , Blackmon Karen 
Published: February 2023

Abstract
Coronavirus disease 2019 (COVID-19) infection is associated with risk of persistent neurocognitive and neuropsychiatric complications, termed “long COVID”. It is unclear whether the neuropsychological manifestations of COVID-19 present as a uniform syndrome or as distinct neurophenotypes with differing risk factors and recovery outcomes. We examined post-acute neuropsychological profiles following SARS-CoV-2 infection in 205 patients recruited from inpatient and outpatient populations, using an unsupervised machine learning cluster analysis, with objective and subjective measures as input features. This resulted in three distinct post-COVID clusters. In the largest cluster (69%), cognitive functions were within normal limits, although mild subjective attention and memory complaints were reported. Vaccination was associated with membership in this “normal cognition” phenotype. Cognitive impairment was present in the remaining 31% of the sample but clustered into two differentially impaired groups. In 16% of participants, memory deficits, slowed processing speed, and fatigue were predominant. Risk factors for membership in the “memory-speed impaired” neurophenotype included anosmia and more severe COVID-19 infection. In the remaining 15% of participants, executive dysfunction was predominant. Risk factors for membership in this milder “dysexecutive” neurophenotype included disease-nonspecific factors such as neighborhood deprivation and obesity. Recovery outcomes at 6-month follow-up differed across neurophenotypes, with the normal cognition group showing improvement in verbal memory and psychomotor speed, the dysexecutive group showing improvement in cognitive flexibility, and the memory-speed impaired group showing no objective improvement and relatively worse functional outcomes compared to the other two clusters. These results indicate that there are multiple post-acute neurophenotypes of long COVID, with different etiological pathways and recovery outcomes. This information may inform phenotype-specific approaches to treatment.
Graph Convolutional Neural Network for Predicting the Associations among Human Microbes and Diseases via Multi-layer Attention
Authors: Zhang Hanxue , Jin Min , Gong Houwu , Wang Peng , Meng Yajie , Xu Junlin , Tian Geng , Yang Jialiang 
Published: February 2023

Abstract
Various microorganisms are affinitive with the pathogenesis of Human-related diseases and have become new targets for disease therapy. Since the limitations of long development period and high development funds for the wet-lab validation of a Microbe-Disease association, it is important to Conduct insilico approaches to prioritize high-confidence disease-associated microbes for further validation. The insilico approach to identify Microbe-Disease associations can not only provide an opportunity to overview the etiology of human diseases at microbiome and diseasome levels, but also indicate novel microbe-derived disease treatment methods. Recently, the increasing availability of biological data on microorganisms and human diseases has provided an excellent opportunity for Machine Learning to predict Microbe-Disease Correlation. Though there are many computational tools developed for this challenging problem, their accuracies are yet to be improved. In this work, a new model was proposed, called MAGMDA, which can predict Microbe-Disease associations by a graph convolutional neural network based on a multi-layer attention mechanism. First, A heterogeneous network is integrated by known Microbe-Disease associations, and graph convolution operations were performed on this network to learn the embedding of microbes and diseases. The attention mechanism is used to integrate multiple graph convolutional layer embeddings the experimental results were analyzed by cross-validation, and our proposed method had the best prediction accuracy compared to other methods. Finally, the effectiveness of our proposed method was further demonstrated by case studies.
The efficacy and safety of an intranasal spray COVID-19 vaccine in a randomized double-blind placebo-controlled phase Ⅲ trial during Omicron period
Authors: Xia Ningshao , Zhu Fengcai , Huang Shou-Jie , Liu Xiaohui , Chen Qi , Zhuang Chunlan , Han Jinle , Jaen Anjuli , Do Thai Hung , Peter Jonathan , Dorado Alexander , Tirador Louie , Zabat Gelza , Villalobos Ralph , Gueco Gemalyn , Botha Lauren , Pertuz Shirley , Tan Jiaxiang , Zhu Kongxin , Quan Jiali , Lin Hongyan , Huang Yue , Jia Jizong , Chu Xiafei , Chen Junyu , Chen Yixin , Zhang Tianying , Su Ying-Ying , Li Changgui , Ye Xiangzhong , Wu Ting , Zhang Jun 
Published: February 2023

Abstract
The live-attenuated influenza virus vector-based intranasal vaccine (dNS1-RBD, Pneucolin®) is the world’s first COVID-19 mucosal vaccine to enter human trials, and has been issued for emergency use authorization in China. Here, we report safety and efficacy data from a multi-centre randomized, double-blind, placebo-controlled phase 3 clinical trial of Pneucolin® (two doses, 14 days apart) in adults (18 years and older) during the Omicron circulation. The result showed a favorable safety profile, with no difference in the incidence of adverse reactions between the vaccine and placebo groups. In the per-protocol set, for symptomatic COVID-19, the efficacies among total population, naïve participants, particiants with inactivated vacciantion history were 32.6% (95%CI 8.2 to 50.5), 55.2% (95%CI 13.8 to 76.7), 38.2% (95%CI -49.2 to 74.4) , and for symptomatic COVID-19 with three or more of the suspected symptoms, the efficacies were 42.3% (95%CI 15.7 to 60.5), 66.7% (95%CI 8.3 to 87.9) and 63.1% (95%CI -15.8 to 88.3) for the above mentioned 3 cohorts. An efficacy of 100.0% (95%CI -9.2 to 100.0) against hospitalized COVID-19 was observed in the whole enrolled population. We conclude that Pneucolin® is well-tolerated and can be an antibody-independent broad-spectrum effective tool to combat SARS-CoV-2 variants, as primary immunization or heterologous booster.
Changes in body mass index and its association with socio-demographic characteristics between 2010 and 2016 in Singapore
Authors: Zhang Yunjue , Abdin Edimansyah , Sambasivam Rajeswari , Shafie Saleha , Roystonn P. Kumarasan , Ajit Vaingankar Janhavi , Chong Siow Ann , Subramaniam Mythily 
Published: February 2023

Abstract
Background
Epidemiological studies have observed an increase in the prevalence of obesity in both western and Asian countries. This study aims to compare the distribution of body mass index (BMI) in the general population of Singapore between 2010 and 2016, and to explore the socio-demographic risk factors associated with it.
Methods
Data for this study were extracted from two national-wise studies in 2010 and 2016, two population-based, cross-sectional epidemiological studies. BMI cut-off scores were used as an indicator to assess obesity in this study, and the data included in the analysis was self-reported by the respondents.
Results
Overall, the study observed decreasing prevalence in underweight and normal weight categories; and an increasing prevalence in overweight and obesity categories in the Singapore adult population between 2010 and 2016. Age, gender, ethnicity, marital status, and educational level were found to be significantly associated with BMI categories.
Conclusion
The observed increase in the population’s BMI between 2010 and 2016 may lead to an increase in the incidence of chronic diseases in Singapore which would have an impact on the quality of life and the health care system. Our study findings add to the existing local literature BMI provides data for evidence-based policymaking on health-related interventions and programme planning.
Developing a novel optimisation approach for keeping heterogeneous diets healthy and within planetary boundaries for climate change
Authors: Colombo Patricia Eustachio , Elinder Liselotte , Nykänen Esa-Pekka , Patterson Emma , Lindroos Anna Karin , Parlesak Alexandr 
Published: February 2023

Abstract
Background and objectives: Current dietary habits have substantial negative impacts on the health of people and the planet. This study aimed to develop a novel approach for achieving health promoting and climate friendly dietary recommendations for a broad range of consumers.
Subjects and methods: Hierarchical clustering analysis was combined with linear programming to design nutritionally adequate, health-promoting, climate-friendly and culturally acceptable diets using Swedish national dietary data (n= 1,797). Diets were optimised for the average consumption of the total population as well as for three identified dietary clusters.
Results: All optimised diets had lower shares of animal-sourced foods and contained higher amounts of plant-based foods. These dietary shifts reduced climate impacts by up to 53% while leaving much of the diet unchanged. The optimised diets of the three clusters differed from the optimised diet of the total population. All optimised diets differed considerably from the food-group pattern of the EAT-Lancet diet.
Conclusions: The novel cluster-based optimisation approach was able to generate more acceptable and realistic alternatives for a sustainable diet.
A Novel Frequency-Dependent Lattice Boltzmann Model with Single Extended Force Term for Electromagnetic Wave Propagating in Dispersive Media
Authors: Ma Huifang , Tang Mingming , Ren Hao , Guo Wenyue , Zhang Kexin , Chen Feiye , Jiang Wanshun , Wang Ying 
Published: February 2023

Abstract
Electromagnetic wave simulation is of pivotal importance in the design and implementation of photonic nano-structures. In this study, we developed a lattice Boltzmann model with a single extended force term (LBM-SEF) to simulate the propagation of electromagnetic waves in dispersive media. By reconstructing the solution of the macroscopic Maxwell equations using the lattice Boltzmann equation, the final form only involves an equilibrium term and a non-equilibrium force term. The two terms are evaluated using the macroscopic electromagnetic variables and the dispersive effect, respectively. The LBM-SEF scheme is capable of directly tracking the evolution of macroscopic electromagnetic variables, leading to lower virtual memory requirement and facilitating the implementation of physical boundary conditions. The mathematical consistency of the LBM-SEF to the Maxwell equations was validated by using the Champman-Enskog expansion; while five practical models were used to benchmark the numerical accuracy, stability, and flexibility of the proposed method.