bluuebunny/crossref_metadata_2025_split huggingface dataset:
E3H2O-LE-TDR Algorithm for Optimizing Solar PV Cell Models and Solving Real-World Engineering Problems
Authors: Akl Dalia T. , Saafan Mahmoud M. , Haikal Amira Y. , El-Gendy Eman M. 
Published: August 2023

Abstract
Engineering and science have increasingly used metaheuristic algorithms to solve actual optimization problems. One of the challenging problems is the proper selection of the parameters of photovoltaic cells since these cells are a great source of clean energy. For such difficult situations, the Harris Hawks Optimization method can be a useful tool. However, HHO is susceptible to the local minimum. This study suggests a novel optimizer called Enhanced Exploration and Exploitation Harris Hawks Optimization using Logarithms, Exponentials, and Travelled Distance Rate (E3H2O-LE-TDR) algorithm, which is a modified version of HHO. The algorithm proposed in this study emphasizes the utilization of random location-based habitats during the exploration phase and the implementation of strategies 1, 3, and 4 during the exploitation phase. In the proposed algorithm, Harris hawks in the wild will change their perch strategy and chasing pattern according to updates in both the exploration and exploitation phases. Therefore, the cons of the original HHO have been solved. Furthermore, E3H2O-LE-TDR was also tested across multiple benchmarks to prove its credibility and efficacy. The approach was tested on the CEC2017, CEC2019, CEC2020, and 27 other benchmark functions with different modalities. The suggested approach is also evaluated on six traditional real-world engineering situations. E3H2O-LE-TDR is compared to state-of-the-art algorithms, as well as other modifications of HHO. The numerical results show that the proposed algorithm outperforms all its competitors, which is visually proven using different convergence curves. Furthermore, the results of the mean Friedman rank statistical test proved the superiority of the proposed algorithm. The results for the single and double diode pv cell model, E3H2O-LE-TDR presented the best performance as indicated by the absolute error in both the current and power values for different operating conditions.
New Analysis of the Laffer Tax Curve and Determination of the Optimal Performance Tax Rate in Triangular Distribution Conditions (Game Theory Approach)
Authors: Salimian Salah 
Published: August 2023

Abstract
Today, financing the government's expenses through taxes is the most important and at the same time the most stable and with the least negative consequences on the economy and by adopting optimal policies in recession and inflation conditions, it can stabilize the economic conditions. Due to the extent of the government's obligations in the economic and social fields, the costs for the government have also increased and obtaining resources to cover these costs requires the implementation of ways to increase tax revenues. This paper deals with the modeling of the game between the taxpayers and the tax administration in order to achieve the factors affecting the optimal performance tax rate and also to determine the optimal performance tax rate. It is assumed that the income of the tax affairs organization (government) is distributed triangularly in the interval [0, 1] and the quality of the investigating group is not known to the taxpayers, then by solving the proposed game, the theorems have been presented. The results showed that the income of the tax affairs organization has an inverse relationship with the costs of the investigating and a direct relationship with the fines received from the taxpayers. Also, under certain conditions, the income of the tax affairs organization increases with the increase in the quality of the assessment groups and finally, the relationship between the optimal tax rate and the variables of investigating quality and diagnostic tax is inverse.
JEL Classification: C70, O11, C63, H21.
Neurological Complications and Outcomes in Critically Ill Patients with COVID-19: Results from International Neurological Study Group from the COVID-19 Critical Care Consortium
Authors: Ahmad Syed Ameen , Mayasi Yunis , Kelly Lan , Suen Jacky , Battaglini Denise , Bassi Gianluigi Li , Fraser John F. , Premraj Lavien , Arora Rakesh C. , Bastos Diego , Whitman Glenn , Griffee Matthew , Fanning Jonathon P. , Robba Chiara , Cho Sung-Min 
Published: August 2023

Abstract
Abstract (300/300)
Background: In this COVID-19 Critical Care Consortium (CCCC) sub-study, we qualified neurological complications associated with COVID-19 infection, including incidence and outcomes across many countries.
Methods: The CCCC is an international, multicenter study. Eligible patients were COVID-19 patients admitted to intensive care units (ICU) in hospitals across 23 centers between January 7th, 2020, to June 23rd, 2022. Incidence of neurological complications was estimated as the number of events per hospital days and per admission using Poisson regression. Associations between neurological complications and clinical risk factors were assessed using multivariable Poisson regression.
Results: 713 patients were included, with a median age of 56 years (interquartile range (IQR)=45-65), of which 272 (38.1%) were female. Median ICU days was 14 (IQR=7-25). Neurological complications were reported in 61/480 patients (12.7%) with the most common being ischemic stroke (2.9%), intracranial hemorrhage (ICH) (2.8%), and seizures (2.6%). After adjusting for sex, age, pandemic era, country income status, comorbid neurological conditions, and mechanical ventilation or extracorporeal membrane oxygenation (ECMO), multivariable analysis for neurological complications per admitted days showed comorbid neurological conditions (incidence rate ratio (IRR)=6.35, 2.57-15.7) were an independent risk factor for ischemic stroke. ECMO (IRR=5.32, 1.52-18.6), low-middle income countries (LMIC) vs high income countries (HIC) (IRR=4.70, 1.62-13.7), and age >55 (IRR=3.66, 1.23-10.9) were independent risk factors for ICH. Co-morbid neurological conditions (IRR=3.43, 1.11-10.6), LMIC vs HIC (IRR=8.69, 2.15-35.2), July-December 2020 vs January-June 2020 (IRR=0.17, 0.04-0.69) and age >55 (IRR=4.05, 1.15-14.3) were independent risk factors for seizure. 6.8% of patients with neurological complications had favorable outcomes (modified Rankin Score (mRS) < 3) at discharge compared to 13.2% of patients without complications.
Conclusions: Neurological complications were not uncommon in patients with COVID-19 infection. Being from an LMIC was an independent risk factor for neurological complications. Decision-making should incorporate salient risk factors to inform the management of severe COVID-19 infection.
New solitary wave solutions of generalized fractional Tzitzéica-type evolution equations using Sardar sub-equation Method
Authors: Rehman Hamood , Amer Aamna , Amer Aatika 
Published: August 2023

Abstract
In this study, Sardar sub-equation method (SSM) is employed to obtain the solitary wave solutions for generalized fractional Tzitzéica type equations. By utilizing this method, novel solutions are derived for Tzitzéica, Tzitzéica Dodd-Bullough-Mikhailov (TDBM) and Tzitzéica-Dodd-Bullough (TDB) equations in terms of fractional derivatives. The benefit of SSM is that it offers a wide variety of soliton solutions, consisting of dark, bright, singular, periodic singular as well as combined dark-singular and combined dark-bright solitons. These solutions provide valuable insights into the intricate dynamics of generalized fractional Tzitzéica type evolution equations. Our findings reveal that the proposed method presents a comprehensive and efficient approach to explore exact solitary wave solutions for generalized fractional Tzitzeica type evolution equations.
Adolescents’ Social well-being modeling based on solving social problem and cognitive flexibility: The mediating role of emotional self-awareness
Authors: Izadi Fatemeh , Farrokhzad Farnaz 
Published: August 2023

Abstract
This research was conducted with the aim of modeling the social well-being of adolescents based on social problem solving and cognitive flexibility, considering the mediating role of emotional self-awareness. The research method of this study is descriptive-correlation; Statistical population of the research consisted of adolescents aged 15 to 18 years studying in high schools in Isfahan in the academic year 2022–2023. In order to determine the sample size, according to Klein (2010) in structural models, a sample size of at least 200 people is defensible. A total of 228 adolescent boys and girls were selected by available sampling method. They responded to Keyes and Magyar-Mae (2003) social well-being questionnaires, D’Zurilla et al. (2002) social problem solving, Riefee et al. (2007) emotional awareness, Dennis and Vander wall (2010) cognitive flexibility questionnaires. The direction of the data obtained from the questionnaire was analyzed using path analysis technique in structural equations in SPSS-26 and Amos-23 software. The results of direct paths indicated that the regression coefficient of social problem solving to the social well-being of adolescents is significant (P < 0.01), while the direct regression path of cognitive flexibility to social well-being is not significant (P > 0.05). Also, the results demonstrated that social problem solving (P < 0.05) and cognitive flexibility (P < 0.05) can indirectly influence the social well-being of adolescents through emotional self-awareness. According to the obtained results, it can be concluded that emotional self-awareness can play a mediating role in the relationship between solving social problems and the social well-being of adolescents.
Macrophage reprogramming—rather than depletion—is efficacious in a specific subset of colorectal tumor models
Authors: Mohamed Noha-Ehssan , Amirkhah Raheleh , Lavaud Xabier-Cortes , Gilroy Kathryn , Bartolini Robin , Mulholland Eoghan J. , Garg Abhishek D. , Pennel Kathryn , Jackstadt Rene , Ridgway Rachel A. , Nixon Colin , Hatthakarnku Phimmada , Campbell Andrew D. , Leedham Simon J. , Edwards Joanne , Dunne Philip D. , Barry Simon T. , Graham Gerard J. , Sansom Owen J. 
Published: August 2023

Abstract
Despite the abundance of macrophages in colorectal cancer (CRC), macrophage-targeted therapy has not demonstrated significant clinical benefit. Here, we show that macrophage populations differ across the consensus molecular subtypes (CMS) of CRC and report the first preclinical study of macrophage targeting using mouse models stratified by CMS class. Whereas pan-macrophage ablation, using a CSF1R-inhibitor, lacked efficacy across CMS classes, genetic deletion of inflammatory chemokine receptors (iCCRs) reprogrammed macrophages towards an anti-tumorigenic phenotype, curtailing tumorigenesis in models of CMS1 CRC. We identify an iCCR-independent anti-tumorigenic antigen-presenting macrophage population necessary for therapeutic efficacy. We further show that individual targeting of the CCR1, CCR2, and CCR5 receptors on CRC macrophages lacks benefit, whereas their combined targeting holds promise. We propose that selective targeting of immunosuppressive macrophage populations, whilst sparing antigen-presenting subsets, should be considered when trialling macrophage-targeted therapies.
Fetal Health Prediction Using Bio-Signal Cardiotocography Empowered with Blockchain Technology and Federated Machine Learning
Authors: Lee Sang-Woong , Nasir Muhammad Umar , Shahzad Tariq , Khan Muhammad Adnan , Ouahada Khmaies 
Published: August 2023

Abstract
Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic. Various cardiotocography measures infer wrongly and give wrong predictions because of human error. The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well. Fetal condition is very important to measure at numerous stages and gives proper medications to fetuses for their well-being. In the current period Machine learning (ML) is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results which are better than traditional results. This research article Federated machine learning (FML) and ML techniques to classify the condition of fetal. The proposed model for the detection of bio-signal cardiotocography uses FML and ML techniques to train and test the data. So, the proposed model of FML achieves 99.06% and 0.94% of prediction accuracy and misprediction rate respectively and the proposed model of ML uses K-nearest neighbor (KNN) and achieved 82.93% and 17.07% of prediction accuracy and misprediction accuracy respectively. So, by comparing both models FML outperformed the KNN technique and achieve the best and most appropriate prediction results.
Hybrid Solar PV–Hydro Controller for an Autonomous DC Microgrid
Authors: Nkundukize Prime , Adonis Marco 
Published: August 2023

Abstract
A control method for an off-grid hybrid DC microgrid that consists of solar PV and pumped hydro storage was developed to provide sustainable, reliable, flexible and accessible energy. The developed off-grid DC microgrid system (DCMGS) consists of a solar PV system of 100 kWp as a distributed energy resource and a pumped hydro storage system as energy storage system (ESS). An energy management system (EMS) was developed using a fuzzy logic controller (FLC) in the MATLAB/Simulink environment. The pumped hydro storage system (PHSS) is used as an ESS to improve the DCMGS stability and also operates as a backup power system. A load shedding scheme is part of the system to bring support to the supply by automatically disconnecting or re-connecting the secondary load to ensure reliability and continuous supply of power to the primary load and secondary load based on the available power and the water level in the reservoir. A DC-DC boost converter connects the solar PV to the rest of the DCMGS thereby improving the quality of power generated and allow the flow of power in the system. The performance analysis of the research design considered the intermittent power supply of the solar PV and the flexibility of the load demand under different circumstances and time of the day. The results showed that the adopted EMS ensured DC microgrid stability, improved reliability, increased system flexibility and effective power distribution. Again, the developed algorithm provided adequate power distribution between the solar PV and the PHSS and maintained the charging and discharging of the reservoir within acceptable level. The PHSS was able to augment power shortage in the system based on the different scenarios that were implemented. The developed EMS also provides an opportunity to remodel and change any parameter within the DCMGS.
MNASreID: Grasshopper Optimization based Neural Architecture Search for Motorcycle re-IDentification
Authors: Rampavan Medipelly , Ijjina Earnest Paul 
Published: August 2023

Abstract
Object re-IDentification (reID) is the process of matching the same object across multiple cameras. An approach for object reID is crucial for identifying objects like people, cars, motorcycles etc., in video surveillance systems. This is an active area of research in both industry and academia due to the ever-growing population and need for smart surveillance, public safety and traffic management. This work proposes an approach for automatically designing a deep convolutional neural network, named MNASreID, for motorcycle re-identification task. Most current reID methods use deep convolutional neural networks as the backbone that are manually designed, which does not have the optimum settings as the network complexity increases. MNASreID uses Neural Architecture Search (NAS) to search for the backbones of the Deep Neural Network (DNN) model. In addition, the designed search space includes parameters of the architecture and its hyperparameters to find the optimum architecture for the reID task. This work uses Grasshopper Optimization Algorithm (GOA) based NAS to find the optimal DNN model. The outcomes of experiments on two motorcycle datasets demonstrate that our method can automatically find the efficient DNN model for motorcycle reID.
Identification of ferroptosis related genes and pathways in prostate cancer cells under erastin exposure
Authors: Wu Fan , Huang Fei , Jiang Nili , Su Jinfeng , Yao Siyi , Liang Boying , Li Wen , Yan Tengyue , Zhou Sufang , Zhou Qingniao 
Published: August 2023

Abstract
Few studies are focusing on the mechanism of erastin acts on prostate cancer(PCa) cells, and essential ferroptosis-related genes (FRGs) that can be PCa therapeutic targets are rarely known. In the current study, in vitro assays were performed to evaluate the ferroptotic levels of PCa cells under erastin treatment. RNA-sequecing was used to measure the expression of differentially expressed genes (DEGs) in erastin-induced PCa cells. A series of bioinformatic analyses were applied to analyze the pathways, modules, transcription factors, and expression levels of DEGs. Erastin inhibited the expression of SLC7A11 and cell survivability in LNCaP and PC3 cells. After treatment with erastin, the concentration of malondialdehyde (MDA) and Fe2+ significantly increased, whereas the glutathione (GSH) and the oxidized glutathione (GSSG) significantly decreased in both cells. A total of 295 overlapping DEGs were screened and identified in two cells under erastin exposure and significantly enriched for association with several pathways, including DNA replication, steroid hormone biosynthesis, and cell cycle, et al. For four hub FRGs, TMEFF2 in PCa tissue is higher than in normal tissue and the expression levels of CLU, NRXN3, and UNC5B were lower in PCa tissue. The expression levels of SLC7A11 and cell survivability were inhibited after the knockdown of TMEFF2 in LNCaP cells but not in PC3 cells. The concentration of Fe2+ only significantly increased in TMEFF2 downregulated LNCaP cells. This study extends our understanding of the molecular mechanism in erastin-affected PCa cells, and provides potential treatment ideas for PCa therapy.
Advanced Skin Cancer Classification Using Yet Another Hill Climbing
Authors: Takefuji Yoshiyasu 
Published: August 2023

Abstract
Hill-climbing is a heuristic search or informed search technique having different weights or variants used for mathematical optimization problems in the field of artificial intelligence. This paper proposes a new hill-climbing technique to achieve the highest prediction accuracy in the skin cancer benchmark. Changing the termination condition forces the system state to escape from the local minima and reach a better solution. The effect of the termination condition changes is similar to the hill-climbing function. The termination conditions using baseline and epochs significantly play a key role in improving prediction accuracy. This paper demonstrated the effectiveness of the proposed hill-climbing method with data augmentation. Data augmentation can convert a severe imbalanced dataset to a balanced dataset. The proposed method achieves the highest prediction accuracy, more than 0.99 prediction accuracy in classification of seven skin cancers using an imbalanced HAM10000 dataset. This paper shows the effectiveness of epoch-baseline termination management and data augmentation in machine learning. With HAM10000 dataset, the proposed advanced skin classification achieved the highest prediction accuracy in the world. The robustness of the proposed algorithm was justified by repetitive random validation and the confusion matrix. The proposed hill climbing can be used for deep learning in general.
Engineering microbial fermentation-based delivery carriers capable of controlling drug release for inflammatory bowel disease therapy
Authors: Qiu Lei , Shen Renbin , Wei Lei , Xu Shujuan , Xia Wei , Hou Yan , Cui Jinxin , Qu Rong , Luo Jiale , Cao Jian , Yang Jie , Sun Jing , Ma Ronglin , Yu Qiang 
Published: August 2023

Abstract
Patients with inflammatory bowel disease (IBD) always suffer from severe abdominal pain and appear to be at high risk for colorectal cancer. Recently, the co-delivery of targeted drugs and gut microbiota has developed into an attractive strategy. A new strategy using gut microbiota fermentation to overcome the interspace diffuse resistance from the mucus layer to control drug release in inflammatory bowel sites (IBS sites) has not yet been available. Here, we designed an alginate hydrogel microsphere encapsulating bifidobacterium (Bac) and drug-modified nanoscale dietary fibers (NDFs). The hydrogel microsphere is responsible for protecting drugs from acidic and multi-enzymatic environments and delivering drugs to the colorectum. Subsequently, the fermentation of Bac by digesting NDFs and proteins as carbon and nitrogen sources can promote drug release and play a probiotic role in the gut microbiota. The in vitro evidence indicated that small-sized NDF (NDF-1) could significantly promote short-chain fatty acid (SCFA) expression. Notably, NDF-1 hydrogel microspheres showed a boost release of 5-ASA in the IBS sites, resulting in the amelioration of gut inflammation and remodeling of gut microbiota in chronic colitis mice. This study developed a novel controlled release system based on microbial fermentation for the treatment of IBD.
Identification of a Lipid Homeostasis Related Genes Signature in Predicting the Prognosis, Immunity, and Chemotherapeutic Effect for Patients With Gastric Cancer
Authors: Li Chao , Xiong Zhen , Han JinXin , Nian WeiQi , Wang Zheng , Cai KaiLin , Gao JinBo , Wang GuoBin , Tao KaiXiong , Cai Ming 
Published: August 2023

Abstract
Purpose Gastric cancer (GC) is one of the most common cancers which causes millions of deaths annually. Lipids homeostasis is essential for the development of tumor because lipids metabolism is one of the most important metabolic reprogramming process within tumor. Elucidating the mechanism of lipids homeostasis in GC might significantly improve the treatment strategies and prognosis.Methods GSE62254 was applied to construct lipids homeostasis related genes signature (HGS) by multiple bioinformatic algorithms. HGS was successfully validated in TCGA-STAD and GSE84437. Besides, the correlations between HGS, immune/stromal cells infiltration and chemotherapeutic effect were analyzed as well.Result HGS was constructed based on eighteen lipids homeostasis related genes by WGCNA. Additionally, HGS was strongly associated with advanced TNM stages and it showed satisfactory value in predicting GC prognosis in three independent cohorts. Furthermore, we found HGS was associated with tumor mutation burden (TMB) and immune/stromal cells infiltration which were related with GC prognosis, indicating that lipids homeostasis had impact on the formation of tumor microenvironment (TME). In GSCAlite platform, PLOD2 and TGFB2 was proved to be positively related with chemotherapeutic resistance while SLC10A7 was a favorable factor for chemotherapy. Finally, CCK8 assay showed that disrupted lipids homeostasis could attenuate GC cell viability and specific genes expression levels were evaluated by RT-qPCR.Conclusion We identified a lipids homeostasis related genes signature which was correlated with the survival, clinical characteristics, TME, and chemotherapeutic of GC. This research provided a new perspective in improving prognosis and guiding individual chemotherapy for patients with GC.