bluuebunny/crossref_metadata_2025 huggingface dataset:
Protective effect of CGRP on acute pancreatitis: Based on metabolomics
Authors: Hu Jian-Xiong , Chen Ying-Kai , Chen Shi-Jun , Lin Yan-Ya , Chen Jun-Nian , Xie Ying , Zhao Cheng-Fei , Chen Cun-Rong
Published: May 2024

To study the mechanism of calcitonin gene-related peptide(CGRP) protecting acute pancreatitis based on metabolomics. 24 adult male rats were randomly divided into control group (Con), acute pancreatitis model group (AP), CGRP treatment group (CGRP + AP, abbreviated as CGRP) and CGRP antagonist(CGRP(8–37)) pretreatment group (preCGRP(8–37) + AP, abbreviated as CGRP37), with 6 rats in each group. After different interventions, pancreases of rats in each group were collected for pathological analysis, and serum was collected for metabolomics analysis. Pathological examination of the pancreas suggested that the inflammation of pancreatitis in AP group was significant, the inflammation of pancreatitis in CGRP group was significantly reduced, and the pancreatitis in CGRP37 group was aggravated. Metabolomics of rat serum suggested that the differences in metabolites in each group were mainly related to amino acid metabolism, coenzyme/vitamin metabolism, carbohydrate metabolism, lipid metabolism, digestive system and other metabolic pathways. According to the trend of metabolite changes, we found 6 differential metabolites that were significantly correlated with CGRP intervention, including L-Valine, 5-Aminopentanoic acid, 4-oxo-L-proline, L-glutamine, L-proline, and Ornithine, all of which were related to amino acid metabolism. CGRP can effectively protect acute pancreatitis, possibly by regulating amino acid metabolism to alleviate acute pancreatitis.
GIS-based Spatial Autocorrelation Queen Contiguity Order-One Analysis of the Renewable Energy Consumption and Production of African Countries with Economic and Environmental Indices
Authors: Gwani Alhaji Abdullahi , Kun Sek Siok
Published: May 2024

The worldwide undertaking to achieve sustainable energy solutions has emphasized the need to comprehend the patterns of renewable energy consumption (REP), and production (REC), and socioeconomic and environmental variables. This work utilizes geographic information system (GIS) methodologies and spatial autocorrelation analysis, specifically Queen contiguity order 1 (Q1), to examine the spatial pattern of renewable energy consumption across African nations. This research seeks to reveal regional patterns, detect clusters of comparable renewable energy consumption behaviors, and assess the economic and environmental consequences linked to these patterns, REP, REC, economic indicators, and environmental indices such as African countries' gross domestic product (GDP) and carbon dioxide emissions (CO2) using data from 57 countries from 1990 to 2020. This study measured the similarities of these variables between neighbouring nations using the Q1 spatial autocorrelation methodology. The findings show concentrated areas with high and low REP, REC, and GDP, revealing regional patterns and inequities. This study also analysed trends to determine the eco-nomic and environmental evolution as well as the REP and REC patterns. Additional related variables, including the ecological footprint (EF), economic complexity index (ECI), and global index (GI), were used in Moran’s I statistical analysis. These findings can help policymakers, researchers, and stakeholders establish strategies for sustainable energy, economic growth, and environmental protection across the continent. This multidisciplinary method uses GIS, spatial analysis, economics, and environmental elements to fully comprehend the complex relationships between the variables under investigation. Furthermore, the study recommends an econometric regression model approach to evaluate the link between these variables, with the hope that this technique will facilitate a more thorough understanding of the influence of renewable energy use on the economic and environmental welfare of African nations.
Knowledge-driven design of fluorinated ether electrolytes via a multi-model approach
Authors: Rajput Nav Nidhi , Atwi Rasha , Gribble Daniel , Nguyen Dan Thien , Song Minyung , Babbar Agrim , murugesan vijayakumar , Pol Vilas
Published: May 2024

Fluorinated ether solvents (FLS) can enhance the cycle life of Li-S batteries by mitigating the polysulfide shuttle effect. However, developing fluorinated electrolytes with reduced polysulfide solubility and uncompromised transport properties is underexplored. We integrate high-throughput density functional theory, molecular simulations, machine learning, and experimental analyses to explore ~1,000 FLS to be used as co-solvent with 1,3-dioxolane. Only 14 FLS in our library have been previously reported in Li-S literature. Through a rigorous screening process, we identify and test two new solvents which demonstrate reduced polysulfide solubility. One solvent exhibits electrochemical performance on par with the widely used 1,1,2,2-tetrafluoro-3-(1,1,2,2-tetrafluoroethoxy)propane (TTE) solvent, yet with superior electrolyte viscosity and ionic conductivity. Interpretable machine learning models indicate fluorination degree, steric effects on ether oxygen, and fluorine proximity to ether oxygen are crucial in dictating oxidation reactions and polysulfide solubility. This work not only introduces new promising co-solvents for Li-S batteries but also provides a framework for knowledge-driven electrolyte design.
Dendrometry, production and nutritional value of Mimosa caesalpiniifolia Benth. under monocrop and silvopastoral system
Authors: Izidro José Lypson Pinto Simões Izidro , de Mello Alexandre Carneiro Leão , Cunha Márcio Vieira da , Silva Valdson José da , Costa Suellen Brandão Miranda , Santos José Ronaldo dos , Carvalho Chrislanne Barreira de Macêdo , Santos Mércia Virgínia Ferreira dos , Costa Nivaldo Azevedo , Dubeux José Carlos Batista
Published: May 2024

Legumes have the potential to provide diverse ecosystem services, therefore, it is important to understand the quantitative and qualitative aspects of their development in different cropping systems. The objective of this study was to evaluate the dendrometric, productive characteristics and nutritional value of Mimosa caesalpiniifolia Benth. in monocrop and a silvopastoral system with signal grass, in Brazil. The treatments were distributed under a randomized block design and consisted of M. caesalpiniifolia monocrop system and silvopasture (signal grass + M. caesalpiniifolia). Evaluations were carried out every 56 days for two years. The data were analyzed using repeated measures over time using SAS on demand (2021) and the treatment means were compared using PDIFF with Tukey's test (P < 0.05). There was no effect of cropping systems (P > 0.05) on plant height (~ 5.2 m). The greatest values ​​for diameter at breast height (16.32 cm), basal diameter (20.54 cm), and dry matter production per plant (36 g DM plant− 1) were observed in the silvopasture system (P < 0.05). Forage mass was higher (P < 0.05) in the monocrop system compared to silvopasture (69 and 22 kg DM ha− 1, respectively) in the first year of evaluation. Total forage accumulation was greater in the monocrop system compared to silvopasture (383 vs. 116 kg DM ha− 1 year− 1) in the first year of evaluation. The nutritional value was not influenced (p < 0.05) by the cultivation systems in the first year of evaluation, however, higher content of CP (221 g kg− 1), ADF (449 g kg− 1), and IVDMD (383 g kg− 1) were observed in the rainy season, while the highest DM content (426 g kg− 1) occurred in the dry season. M. caesalpiniifolia showed good dendrometric characteristics in the silvopasture system. The silvopasture provides greater forage production per plant of M. caesalpiniifolia and better nutritional value of the forage than the legume monocrop.
Memristor: An Innovative Approach towards
Modern Electronics and its SPICE Model for Nonlinear Dopant Drift
Authors: Talukder Safwan , Rafid Md. Tahsin , Bhuiyan A.B.M. Noushad
Published: May 2024

Memristor is the fourth fundamental passive circuit element and a novel tool for modern electronics. While MOS transistors are the most important components in electronics, memristors will pave a new path towards more efficient electronic circuits with better performance. This paper studies the various models of memristors and proposes a model with new window function to overcome the latencies of previous models. We propose a Non Linear Ion Drift Model with a new window function that serves as a SPICE model to be simulated in computers to study and analyze the behavior of memristors. The proposed model surpasses the output values in terms of Current, Resistance and Maximum Power compared to other memristor models. We focused on these parameters with a view to ensure better efficiency from the proposed model. Our model achieved improvements of up to a double value in terms of current, compared to other models. The results demonstrate an efficient model that can be implemented as SPICE simulations of memristors for precise analysis of its behavior. The proposed method ensures the proper functioning of memristors that can be implemented in electronic circuits.
Enhancing Anxiety Diagnosis through ADABOOST-Assisted Decision-Level Fusion
Authors: Hosseini Seyedeh sara , Maghooli Keivan
Published: May 2024

Background: Humans naturally respond with anxiety to mental stress caused by a variety of circumstances. Anxiety impairs memory function and makes it difficult to learn and retain information. Additionally, sustaining high productivity while balancing life's stresses can be achieved through good anxiety and stress management. New
Method: This paper presents an effective technique for automatically classifying two anxiety levels: normal and anxious, using an analysis of EEG data. The EEG signals found in the DASPS database were utilized. This database includes 14-channel EEG recordings taken under normal and anxious settings from 23 individuals (10 male and 13 female, average age 30 years). Brain subbands were extracted from EEG signals using wavelet transform. Different features such as Hjorth coefficients, entropy, autoregressive, and energy were extracted. The feature vector was reduced by the PCA method, and the classification was carried out by the Adaboost classification method.
Results: The results demonstrate the effectiveness and efficiency of the proposed model in diagnosing anxiety, with an accuracy of 80.58%. Comparison with Existing Methods: Our study highlights the superior performance of the Adaboost method compared to other methods, showcasing its potential for accurate anxiety classification using EEG data.
Conclusions: In conclusion, the proposed method shows promise for automatic classification of anxiety levels using EEG data. By leveraging machine learning techniques and EEG analysis, our approach could contribute to improved anxiety diagnosis and stress management strategies.
Mathematical Modelling and Optimizing Design of Heliostat Field
Authors: Cheng Zijie , Zheng Ang
Published: May 2024

The heliostat field is a form of solar thermal power plant, and establishing a heliostat field is of great significance for reducing carbon emissions. This paper focuses on the mathematical modelling and optimization design problem of the heliostat field. For three different design scenarios, firstly, based on the data provided by the China Undergraduate Mathematical Contest in Modeling 2023, a geometric optical efficiency model is proposed to determine the annual average optical efficiency and output thermal power under specific heliostat field design parameters. Secondly, aiming at the optimal design parameters of the solar furnace field under the condition of rated annual average output thermal power is 60\(\text{M}\text{W}\), Particle Swarm Optimization (PSO) combing with efficiency model are utilized to determine the optimal design parameters of the heliostat field. Finally, this paper uses Quasi -Monte Carlo ray tracing (QMCRT) model to perform simulation, exploring how to obtain the optimal design parameters of the heliostat field with the goals of maximizing the output thermal power and ensuring high efficiency. On this basis, the influence of normal disturbances on the annual average output thermal power is further explored.
Optimization and development of an airfoil by Bezier curve through computational analysis
Authors: Vala Jignesh , Umathe Manisha , Patel D. K. , Panchal Hitesh
Published: May 2024

Airfoils play a pivotal role in the turbomachinery field, influencing energy efficiency in various applications, including aircraft design and fluid product blending. This research focuses on the optimization of airfoil shapes, aiming to design a new generalized airfoil with adjustable parameters through the application of Bezier curve theory. The investigation employs a combination of experimental techniques in a Low-Speed Open-Type Wind Tunnel and computational simulations using ANSYS Fluent as the flow solver. The study considers the existing C4 Profile and introduces a novel airfoil with varying angles of attack(A.O.A), ranging from 0 degrees to + 30 degrees in 5-degree increments. Steady-state simulations are conducted to solve Reynolds Averaged Navier-Stokes (RANS) Equations, utilizing the shear stress transport (SST) k-ω turbulence model and the Standard k-epsilon turbulence model as closure models, with a moderate turbulence intensity of 5%. The newly designed airfoil, referred to as the Circular arc cambered airfoil, is created using the Bezier curve theory, resulting in a generalized form with adjustable shape parameters. The research findings indicate that the Circular arc cambered airfoil exhibits superior aerodynamic efficiency, generating increased lift and reduced drag when compared to the conventional C4 Profile airfoil. This study contributes to the ongoing exploration of airfoil design optimization, offering insights into the potential enhancements achievable through Bezier curve-based adjustments in shape parameters.
Discovering Drug Slang on Social Media: A Word2Vec Approach with Reddit Data
Authors: Holbrook Eric , Wiskur Brandt , Nagykaldi Zsolt
Published: May 2024

Background
The ongoing opioid crisis in the United States, which resulted in more than 80,000 deaths in 2021, underscores the critical need for innovative approaches to monitoring and intervention. Social media platforms like Reddit provide timely and dynamic community conversations on drug use patterns, offering enhanced perspectives that can circumvent time constraints associated with traditional data collection methods.
Methods
Utilizing the Gensim Python library and its Word2Vec neural network model, this study developed an autoencoder neural network, enabling the innovative analysis of drug-related discussions downloaded from the Reddit website. This innovative approach enabled the discovery of nuanced, context-specific opioid slang that is difficult for traditional methods to identify, providing a more robust and nuanced picture of substance use dynamics.
Results
The incorporation of slang terms into the Reddit analysis was instrumental in expanding the dataset by 200,000 specific mentions of opioid drugs, effectively doubling the dataset and revealing significant overlaps between opioid and other drug category discussions. This analysis unveiled a broader trend of polysubstance abuse, a crucial insight for developing targeted public health interventions. These findings underscore the improved detection capabilities that the Word2Vec approach brings, significantly enhancing traditional methods and enabling near real-time surveillance of drug abuse trends.
Conclusions
This research is a significant step in public health surveillance, expanding the known lexicon of drug-related terms and demonstrating a novel application of neural networks in this field. By automating the detection of slang, this method offers substantial improvements in the speed and accuracy of drug trend analysis and monitoring, marking a substantial stride in combating the opioid crisis through technology-driven solutions. The approach enhances understanding of current trends and sets a precedent for rapid, adaptable public health responses in the face of evolving challenges.
Influence of binding agent on the cutting performance and wear characteristics of catalytic composite abrasive cluster
Authors: Ding Yangyang , Wang Zhankui , Huang Shangci , Liu Kuncheng , Feng Wei , Pang Minghua , Chen Jiapeng , Su Jianxiu
Published: May 2024

To improve the polishing efficiency and process stability of catalytic composite abrasive clusters (CCAC), the CCAC containing different binding agent ratios were investigated in this study. Five groups of CCAC with different binding agent ratios were prepared for friction chemical polishing (FCP) of silicon carbide (SiC) respectively. Based on material removal rate (MRR) and surface roughness (Ra) as evaluation metrics, the polishing performance of five groups of CCAC were compared. Subsequently, wear experiments were carried out by the block-shaped CCAC (the diameter is 1.6mm) and the single crystal diamond (SCD) abrasives with the same particle size. The friction coefficient and the cross-sectional area of the worn area were measured to evaluate the effect of binding agent content on the performance of continuous cutting and the tribological properties of the abrasive. The experimental results showed that the proportion of binding agent in CCAC directly affects the processing performance of abrasives. When the proportion of binding agent in the abrasive is 25%, the CCAC has highest machining efficiency (MRR reaches 330.535 nm/min) and better surface roughness (Ra reaches 22.741 nm). This study provides certain theoretical guidance for achieving efficient polishing of SiC wafers.
Using net-zero carbon debt to track climate overshoot responsibility
Authors: Pelz Setu , Ganti Gaurav , Lamboll Robin , Grant Luke , Pachauri Shonali , Rogelj Joeri , Riahi Keywan , Thiery Wim , Gidden Matthew J.
Published: May 2024

Current emissions trends are likely to deplete a 1.5°C consistent carbon budget soon after the year 2030, resulting in a period of overshoot. To navigate responsibilities for and during this period we contrast ‘fair’ allocations of a remaining carbon budget with projected carbon emissions trends until net-zero. We term this measure the ‘net-zero carbon debt’, or the expected overconsumption of a ‘fair’ allocation in the year a party achieves net-zero carbon emissions. The ‘net-zero carbon debt’ measure thus provides an estimate today of a party’s expected responsibility for climate overshoot under certain considerations of fairness. We apply this measure to assess all vetted mitigation scenarios evaluated in the IPCC’s sixth assessment report and two bespoke scenarios that assess current policies and net-zero targets, considering the equal per capita allocation of a remaining 1.5°C carbon budget from the year 1990. The resulting regional net-zero carbon debt quantifications are used to determine regional: i) responsibility for overshoot, ii) exceedance drawdown obligations, and iii) increase in extreme climate exposure if drawdown does not occur. Our work generates a new domestic signal from a typically international climate equity discourse and can inform the operationalization of intergenerational and interregional effort sharing mechanisms that persist beyond exhaustion of a remaining carbon budget.
Bring Retrieval Augmented Generation to Google Gemini via External API: An Evaluation with BIG-Bench Dataset
Authors: Lee Ha-rin , Kim Seo-hyun
Published: May 2024

The integration of Retrieval Augmented Generation (RAG) into existing large language models represents a significant shift towards more dynamic and context-aware AI systems. In this work, Google Gemini, a state-of-the-art language model, has been enhanced with RAG capabilities to leverage external, real-time data sources during the response generation process. This augmentation aims to address traditional limitations of language models, particularly in generating responses that require up-to-date information and adaptability to complex user queries. The performance of the RAG-enhanced Google Gemini was rigorously evaluated using the BIG-Bench dataset, which includes tasks designed to test the bounds of language models in terms of reasoning, contextuality, and factual accuracy. Quantitative results from this evaluation demonstrate marked improvements in accuracy and contextual relevance across various tasks, indicating the effectiveness of RAG in enhancing model performance. Qualitative assessments further support these findings, highlighting the model’s improved ability to generate precise and relevant responses. However, the integration of RAG also introduces challenges related to computational efficiency and scalability, emphasizing the need for further optimization. This paper discusses potential future research directions, including the application of RAG to other datasets, exploration of different RAG configurations, and the development of more sophisticated data handling techniques to enhance the model’s performance and applicability. The ongoing advancement of RAG technologies promises to significantly broaden the utility of AI-driven systems in real-world applications, making them more adaptable and useful across diverse and dynamic scenarios.
CONCEPT OF AGNI IN ATHEROSCLEROSIS AND ITS MANAGEMENT THROUGH YOGA& AYURVEDA
Authors:   M.D. (Ayu), Assistant Professor, Department of SarirRachana, IAAMC, USTM, Meghalaya., Sultana Rozlin
Published: May 2024

In Ayurveda, the concept of Agni is inevitable in determining the overall health of an individual, where Agni is said to be the base (moola) of life. The whole catabolic and metabolic processes are carried out in human body, regulated by Agni .But if due to Pragyaparadh or faulty lifestyle, the Agni is deranged then the metabolic activity becomes disturbed at cellular tissue and organ level and it fascilitates to the formation of Ama. Hence, Ama is a triggering factor of many diseases that in turn destroys the Agni. Atherosclerosis occurs mainly due to lifestyle abnormality which is a direct result of Ama accumulation in the body. It is observed that 17.7 million people die each year from Cardiovascular Disorders, in which Atherosclerosis is the precipitating factor and an estimated 31% of all deaths worldwide (acc to WHO). Atherosclerosis is a condition where the arteries become narrowed and hardened due to a build up of Plaque around the artery wall. This disease disrupts the flow of blood around the body, posing the risk of serious complications. The term Â‘AtherosclerosisÂ’ is derived from the Greek word Â‘atheroÂ’ meaning gruel or wax corresponding to the necrotic core area at the base of atherosclerotic plaque and Â‘sclerosisÂ’ for hardening the plaques luminal edge. Apart from drug, Yoga therapy may play a pivotal role for alleviation of Atherosclerosis. Yoga therapy improves the blood flow to the organs by neuromuscular action. Practice of Pranayama stimulates and changes the lumen size of the arterioles, which helps to regulate the circulation.
THE PROPHETS TURK - ITS CONCEPT AND CONTROLS
Authors:   Senior Lecturer, Kulliyyah of Shariah & Law, Universiti Islam Antarabangsa Sultan Abdul Halim Muadzam Shah (UniSHAMS), 09300, Kuala Ketil, Kedah, Malaysia., Ramadan Mohmed Ahmed Harec Ahmed , Gomaa Gomaa Zaghloul Saad ,   Senior Lecturer, Kulliyyah of Shariah & Law, Universiti Islam Antarabangsa Sultan Abdul Halim Muadzam Shah (UniSHAMS), 09300, Kuala Ketil, Kedah, Malaysia., Aboumelih Mohamed Soliman Ragab ,   Associate Prof. Dr., Kulliyyah of Shariah & Law, Universiti Islam Antarabangsa Sultan Abdul Halim Muadzam Shah (UniSHAMS), 09300, Kuala Ketil, Kedah, Malaysia., Abous Maryam ,   Lecturer, Kulliyyah of Shariah & Law, Universiti Islam Antarabangsa Sultan Abdul Halim Muadzam Shah (UniSHAMS), 09300, Kuala Ketil, Kedah, Malaysia.
Published: May 2024

This paper deals with thesubject of the Prophets abandonment , and this issue is one of the topics that are controversial among the contemporary scholars between a militant in understanding it and a strait of its meaning until it became for them corresponding to the Sunnah, and it became in the rule of bad heresy or forbidden.The fact of the abandonment that we are talking about is that the Prophet (peace and blessings of Allah be upon him) left something that he did not do or left the righteous predecessors without having a hadith or effect by forbidding that thing that is left behind requires that it be forbidden or hated.Has more than inference Baltrk many of the latecomers to prohibit things or vilified, and overused some of the puritanical Almttain in the innovation and infidelity of Muslims through the understanding of the sick of the meaning of Turk, hence the importance of this research to show the truth of the Prophets Turks, and does it indicate abstract for the presumption of prohibition or hatred as claimed by the Almttain Saqimi understanding, either that it has special connotations, and does not indicate the prohibition or hatred only presumption associated with it.
The Impact of Artificial Intelligence on Legal Systems: Challenges and Opportunities
Authors: Madaoui Nadjia
Published: May 2024

The integration of artificial intelligence into legal systems has engendered a paradigm shift in the legal landscape, presenting a complex interplay of challenges and opportunities for the legal profession and the justice system. This Comprehensive research delves into the multifaceted impact of artificial intelligence on legal systems, focusing on its transformative potential and implications. Through an extensive analysis of the integration of artificial intelligence technologies, including natural language processing, machine learning, and predictive analytics, the study illuminates the profound improvements in legal research, decision-making processes, and case management, emphasizing the unprecedented efficiency and accessibility that artificial intelligence offers within the legal domain.
Furthermore, the research critically examines the ethical and societal challenges stemming from artificial intelligence integration, including concerns related to data privacy, algorithmic bias, and the accountability of artificial intelligence-driven legal solutions. By scrutinizing the existing regulatory frameworks governing artificial intelligence implementation, the study underscores the necessity of responsible and ethical artificial intelligence integration, advocating for transparency, fairness, and equitable practices in the legal profession. The findings contribute to the ongoing discourse on the ethical implications and effective management of artificial intelligence integration in legal systems, providing valuable insights and recommendations for stakeholders and policymakers to navigate the complexities and ensure the responsible adoption of artificial intelligence technologies within the legal sphere
Dynamics of legal regulation of exemptions in the payment and calculation of land fees in the conditions of martial law on the basis of judicial practice
Authors: Harkusha Vladyslav
Published: May 2024

The relevance of the research topic is stipulated by the new challenges faced by our country in connection with the armed aggression of the Russian Federation against Ukraine and the need to adapt the legal regulation of land payment in terms of its calculation and payment during the period of martial law. The purpose of the article is to trace the dynamics of legal regulation of land payment during martial law, and to review the current case law of both national courts regarding the resolution of disputes on the cancellation of a tax assessment notice and the case law of the European Court of Human Rights in terms of determining the conceptual foundations for the construction of legal provisions.  The study of the issue of payment and accrual of land payment is of both applied and theoretical nature, since violation of the means and rules of rule-making technique leads to tax disputes.  The analysis of legal constructions of the Tax Code of Ukraine and other regulatory legal acts made it possible to identify problems in the regulation of land payment, which consist in violation of the rule of law principle provided for by the Constitution of Ukraine. The Constitutional Court of Ukraine has defined the principles of legal certainty and legal predictability as components of the rule of law.  The inconsistency of the provisions of the Tax Code of Ukraine and bylaws has led to a dual interpretation of their rights and obligations by taxpayers and controlling authorities. The author analyzes the positions of the controlling authorities in terms of amendments to the regulation of the calculation and payment of tax liabilities for taxation of land plots located in the temporarily occupied territories or in the territories where military operations are (were) conducted.
Effluent quality improvement in sequencing batch reactor-based wastewater treatment processes using advanced control strategies
Authors: Dey Indranil , Ambati Seshagiri Rao , Bhos Prashant Navnath , Sonawane Shirish , Pilli Sridhar
Published: May 2024

The treatment of wastewater is highly challenging due to large fluctuations in flowrates, pollutants, and variable influent water compositions. A sequencing batch reactor (SBR) and modified SBR cycle-step-feed process (SSBR) configuration are studied in this work to effectively treat municipal wastewater while simultaneously removing nitrogen and phosphorus. To control the amount of dissolved oxygen in an SBR, three axiomatic control strategies (proportional integral (PI), fractional proportional integral (FPI), and fuzzy logic controllers) are presented. Relevant control algorithms have been designed using plant data with the models of SBR and SSBR based on ASM2d framework. On comparison, FPI showed a significant reduction in nutrient levels and added an improvement in effluent quality. The overall effluent quality is improved by 0.86% in FPI in comparison with PI controller. The SSBR, which was improved by precisely optimizing nutrient supply and aeration, establishes a delicate equilibrium. This refined method reduces oxygen requirements while reliably sustaining important biological functions. Focusing solely on the FPI controller's performance in terms of total air volume consumption, the step-feed SBR mechanism achieves an excellent 11.04% reduction in consumption.
Emerging contaminants in water environments: progress, evolution, and prospects
Authors: Wang Ruiqi , Tang Huanchen , Yang Ruitao , Zhang Jingduo
Published: May 2024

This article employs bibliometric tools like VOSviewer, Bibliometrix, and CiteSpace for a comprehensive visual analysis of 1,612 documents on Emerging Contaminants in Waters from the Web of Science database. The objective is to elucidate the historical development, research hotspots, and trends in international studies of this field, offering valuable insights and guidance for future research directions. The analysis reveals a consistent increase in publications from 2003 to 2023, with the United States, China, and Spain being the most prolific contributors. A detailed examination of keyword co-occurrence and cluster analysis shows a predominant focus on themes such as pollutant detection, risk assessment, and biogeochemical cycling. Furthermore, the study underscores the significance of forming interdisciplinary networks among authors and institutions, highlighting its critical role in enhancing the quality and innovation of scientific research. The findings of this study not only chart the progression and focal points of research in this domain but also underscore the pivotal role of international collaboration, serving as an indispensable reference for shaping future research trajectories and fostering global cooperation.
LncRNA LINC00466 Promotes the Progression of Breast Cancer via miR-4731-5p/EPHA2 Pathway
Authors: Han Xue , Shi Fan , Guo Shujun , Li Yao , Wang Hongtao , Song Chuanwang , Wu Shiwu
Published: May 2024

Background:

Breast Cancer (BC) is a female malignancy with a high mortality rate.
Novel diagnostic and prognostic biomarkers are valuable for reducing BC mortality. Our study
is designed to undrape the precise role of the LINC00466/miR-4731-5p/EPHA2 axis in BC.

Methods:

The Cancer Genome Atlas (TCGA) sequencing dataset was utilized to compare the
levels of LINC00466. The levels of LINC00466, miR-4731-5p, and EPHA2 were tested by qRTPCR.
Cell proliferation and cycle were detected by CCK-8 assay and flow cytometer. In vivo
role of LINC00466 was tested by Xenograft nude models. Binding sites were predicted by TargetScan
and Starbase. The binding relationship was employed by Dual-luciferase reporter gene
assay and RNA pull-down assay.

Results:

LINC00466 was increased in human breast cancer tissues. LINC00466 was negatively
associated with miR-4731-5p and positively correlated with EPHA2 in human breast cancer
tissues. Down-regulation of LINC00466 suppressed the proliferation and arrested the cell cycle
of breast cancer cells, and inhibited tumor growth in vivo.

Conclusion:

LINC00466 promoted BC development via mediating the miR-4731-5p/EPHA2
axis, which has the potential value as a promising therapeutic target in BC.
Global and Local Ancestry and its Importance: A Review
Authors: Goli Rangasai Chandra , Chishi Kiyevi G. , Ganguly Indrajit , Singh Sanjeev , Dixit S.P. , Rathi Pallavi , Diwakar Vikas , C Chandana Sree , Limbalkar Omkar Maharudra , Sukhija Nidhi , Kanaka K.K
Published: May 2024

The fastest way to significantly change the composition of a population is through admixture,
an evolutionary mechanism. In animal breeding history, genetic admixture has provided
both short-term and long-term advantages by utilizing the phenomenon of complementarity and
heterosis in several traits and genetic diversity, respectively. The traditional method of admixture
analysis by pedigree records has now been replaced greatly by genome-wide marker data that enables
more precise estimations. Among these markers, SNPs have been the popular choice since
they are cost-effective, not so laborious, and automation of genotyping is easy. Certain markers
can suggest the possibility of a population's origin from a sample of DNA where the source individual
is unknown or unwilling to disclose their lineage, which are called Ancestry-Informative
Markers (AIMs). Revealing admixture level at the locus-specific level is termed as local ancestry
and can be exploited to identify signs of recent selective response and can account for genetic
drift. Considering the importance of genetic admixture and local ancestry, in this mini-review,
both concepts are illustrated, encompassing basics, their estimation/identification methods, tools/-
software used and their applications.
Iron(III) Monoglycerolate: A Potential Hemostatic Agent for Topical Application
Authors: Khonina Tat’yana G. , Safronov Alexander P. , Bulatova Maria A. , Larionov Leonid P. , Antropova Irina P. , Dobrinskaya Maria N. , Izmozherova Nadezhda V. , Tishin Denis S. , Alekseenko Semyon G. , Bogdanova Ekaterina A. , Karabanalov Maxim S. , Chupakhin Oleg N.
Published: May 2024

Background:

Glycerolates of biogenic elements are of interest because of their pharmacological
activity. Some of them are used as active substances in agents for topical application
and as biocompatible precursors in sol-gel synthesis of bioactive materials.

Objective:

In this work, morphostructural feature, ability to hydrolyze, and the pharmacological
activity of previously synthesized iron(III) monoglycerate were studied.

Methods:

Analytical techniques, including SEM, TEM, XRD, TGA, IR spectroscopy, DLS and
ELS, were used. Hemostatic activity was studied in vivo, and primary toxicological studies were
carried out on experimental animals. Antimicrobial activity was studied using the agar diffusion
method.

Results:

When dispersed in glycerol, solid crystalline iron(III) monoglycerolate transforms into
an amorphous state, forming aggregates with an average particle size of 250 nm (according to
DLS data). It slowly hydrolyzes in water at room temperature, while hydrolysis does not take
place in an aqueous glycerol media. Iron(III) monoglycerolate is nontoxic and exhibits pronounced
hemostatic activity and low antibacterial activity (relative to the strain S. аureus).

Conclusion:

Iron(III) monoglycerolate can be considered a potential hemostatic agent, showing
promise for topical application in medical and veterinary practice, as well as a novel biocompatible
precursor in the sol-gel synthesis of practically useful substances.
An Improved Multi-point Chord Reference Measurement Device and
Error Correction Method for Corrugation Measuring in Sharp Curves
Authors: Lv Wenqing , Yao Huiming
Published: May 2024

Background:

The traditional chord reference method and its device have faced challenges
matching the sharp curve track. Thus, it is of utmost importance to research and develop a measurement
device and method that can obtain accurate and comprehensive information regarding rail
running band corrugation.

Aims:

It provides a solution for the measurement of the multi-point chord reference method on
sharply curved rails and, at the same time, meets the demand for data richness and accuracy in theoretical
studies on the causes of rail corrugation and wheel-rail contact relationships.

Objective:

This paper aims to research and design a measurement device based on the multi-point
chord reference method for the rail running band corrugation. Meanwhile, the error in measuring
the sharp curve rail is corrected and verified by simulation.

Methods:

The lateral information of rail running band is obtained by using a line laser profile sensor;
the rail corrugation measurement model and error correction model of the rail running band area
are established based on multi-point reference system; the simulation of the system model is carried
out using Matlab; and the mechanical structure is built for static testing.

Results:

The measurement model based on the profile sensor and multi-point chord reference system
can accurately obtain the rail corrugation waveform of any longitudinal measurement line in
the running band area. The measurement results can be accurately corrected for a radius of 200m-
400m, the error parameters can be effectively improved, and the mechanical mechanism of the
measurement system runs stably.

Conclusion:

The results show that the measurement model can accurately measure the rail corrugation
of the sharp curve section, and the multi-waveform in the rail running band area has good
measurement performance, but the mechanical structure can be optimized and improved in the light
weight.
Long-Term Implicit Epigenetic Stress Information in the Enteric Nervous
System and its Contribution to Developing and Perpetuating IBS
Authors: Noemi Császár-Nagy , Bob Petr , Bókkon István
Published: May 2024

:

Psychiatric and mood disorders may play an important role in the development and persistence
of irritable bowel syndrome (IBS). Previously, we hypothesized that stress-induced implicit
memories may persist throughout life via epigenetic processes in the enteric nervous system (ENS),
independent of the central nervous system (CNS). These epigenetic memories in the ENS may contribute
to developing and perpetuating IBS. Here, we further elaborate on our earlier hypothesis. That
is, during pregnancy, maternal prenatal stresses perturb the HPA axis and increase circulating cortisol
levels, which can affect the maternal gut microbiota. Maternal cortisol can cross the placental barrier
and increase cortisol-circulating levels in the fetus. This leads to dysregulation of the HPA axis, affecting
the gut microbiota, microbial metabolites, and intestinal permeability in the fetus. Microbial metabolites,
such as short-chain fatty acids (which also regulate the development of fetal ENS), can modulate
a range of diseases by inducing epigenetic changes. These mentioned processes suggest that
stress-related, implicit, long-term epigenetic memories may be programmed into the fetal ENS during
pregnancy. Subsequently, this implicit epigenetic stress information from the fetal ENS could be conveyed
to the CNS through the bidirectional microbiota-gut-brain axis (MGBA), leading to perturbed
functional connectivity among various brain networks and the dysregulation of affective and pain processes.
A Review on Categorization of the Waste Using Transfer Learning
Authors: Jamdaade Krantee M. , Biswal Mrutunjay , Pitre Yash Niranjan
Published: May 2024

In this paper, we have aimed to develop a system that will help waste
collectors segregate different types of waste without needing much human intervention.
We have experimented with various deep learning and transfer learning techniques to
determine which model is more suited for this purpose. The dataset we used contained
8369 images that are classified into 9 classes: batteries, clothes, e-waste, glass, light
bulbs, metal, organic, paper, and plastic. We used models like VGG16, Inceptionv3,
ResNet50, MobileNET, NASNetMobile and Xception. We have also conducted a
survey to know about the waste management habits of the respondents. Our
experiments showed that models like MobileNET gave us the best accuracy of 93.17%
and identified all the waste categories correctly and the Xception model predicted
images correctly with the use of both Adam and Adadelta.
Exploring the Applications of Complex Adaptive Systems in the Real World: A Review
Authors: Kunjir Ajinkya
Published: May 2024

Complex Adaptive Systems (CAS) are gradually becoming the primary
modelling framework in the industry where autonomously evolving and self-adaptive
systems exist. There has been a quick escalation in the research results in the past few
years as the concept of CAS is emerging in the working sectors due to its capabilities
and vital properties to shape an organizational workflow. CAS exhibits selforganization, adaptability, modularity, and others beyond complex systems. Designing
CAS models is a tedious task as the intra-system components are composed of subcomponents that interact with running operations across the system. Researchers in
engineering, healthcare, defense and military automation are extensively progressing in
adapting the CAS framework and conceptualizing the systems for increasing
performance efficiency. This paper primarily argues for the relevance and value of the
CAS approach and then presents a detailed discussion on the core concepts of CAS and
Agent-based modelling, highlighting the difference between them. Furthermore, the
paper provides a detailed review of the applications of CAS, such as Manufacturing
(Assembly systems), Defense and Analysis, Internet of Things (IoT), Distributed
Networks, Healthcare organizations and a few social-ecological systems (SES). Many
pieces of software agent-based modelling, tools for CAS development and data
visualizations are surveyed and discussed in the second half of the paper.
Enhancing Efficiency in Content-based Image Retrieval System Using Pre-trained Convolutional Neural Network Models
Authors: Mahalle Vishwanath S. , Kandoi Narendra M. , Patil Santosh B. , Banubakode Abhijit , Bagal Vandana C.
Published: May 2024

Traditionally, image retrieval is done using a text-based approach. In the
text-based approach, the user must query metadata or textual information, such as
keywords, tags, or descriptions. The effectiveness and utility of this approach in the
digital realm for solving image retrieval problems are limited. We introduce an
innovative method that relies on visual content for image retrieval. Various visual
aspects of the image, including color, texture, shape, and more, are employed to
identify relevant images. The choice of the most suitable feature significantly
influences the system's performance. Convolutional Neural Network (CNN) is an
important machine learning model. Creating an efficient new CNN model requires
considerable time and computational resources. There are many pre-trained CNN
models that are already trained on large image datasets, such as ImageNet containing
millions of images. We can use these pre-train CNN models by transferring the learned
knowledge to solve our specific content-based image retrieval talk. In this chapter, we propose an efficient pre-trained CNN model for content-based
image retrieval (CBIR) named as ResNet model. The experiment was conducted by
applying a pre-trained ResNet model on the Paris 6K and Oxford 5K datasets. The
performance of similar image retrieval has been measured and compared with the stateof-the-art AlexNet model. It is found that the AlexNet architecture takes a longer time
to get more accurate results. The ResNet architecture does not need to fire all neurons
at every epoch. This significantly reduces training time and improves accuracy. In the
ResNet architecture, once the feature is extracted, it will not extract the feature again. It
will try to learn a new feature. To measure its performance, we used the average mean
precision. We obtained the result for Paris6K 92.12% and Oxford5K 84.81%. The
Mean Precision at different ranks, for example, at the first rank in Paris6k, we get
100% result, and for Oxford5k, we get 97.06%.
Feature Selection and Classification Models of Intrusion Detection Systems -A Review on Industrial Critical Infrastructure Perspective
Authors: Karthigha M. , Latha L. , Madhumathi R.
Published: May 2024

It is self-evident that recently, humanity has entered the fourth industrial
revolution. With the advent of the Internet of Things, cloud computing, and Artificial
Intelligence, industrial critical infrastructures such as power plants, oil and gas plants,
heavy industries, nuclear plants, and water treatment facilities are experiencing
disruptive growth. This era of industrialization, nevertheless, has carried with it its new
myriad of issues, notably regarding cyber security threats. Nowadays, industrial
processes are openly connected to the internet, and internet-connected machines are
almost always highly susceptible to security breaches by adversaries despite sufficient
cyber security safeguards. Intrusion detection systems (IDS) are designed to employ
classification models to detect malicious attacks such as service attacks, probing
attacks, etc. In intrusion detection, the phase that reduces the number of similar traffic
attributes while sustaining the accuracy of classification is a requirement that
considerably improves an intrusion detection system's overall efficacy. This chapter
focuses on (i) various feature selection methods in IDS, (ii) ML&DL classification
models in IDS of industrial systems, (iii) Various ensemble feature selection models
are analyzed, and a novel ensemble feature selection model for IDS is proposed.