bluuebunny/crossref_metadata_2025 huggingface dataset:
Sorting Online Reviews of Restaurant Merchants Based on Narrativity
Authors: Tianqi Dai
Published: January 2025

With the rapid development of e-commerce, online reviews have become the core reference information for consumers to make purchase decisions. Traditional review sorting methods mostly rely on the number of likes, timeliness and other factors, ignoring the textual characteristics of the reviews themselves. In this paper, we innovatively propose to take the "narrativity" of reviews as an important dimension of review sorting. We present a measurement method for "narrativity" based on NLP, and combine it with other review metrics (e.g., timeliness, text length, rating deviation, user identity, etc.) to build a comprehensive review sorting model. We crawled 52,500 reviews from 500 stores on the Dianping platform and trained a classification model based on the transformer architecture to categorize these reviews into high-value and low-value groups. Subsequently, we developed a narrative-based sorting model to reorder the reviews of these stores, and observing whether the ranks of high-value reviews have improved relative to the original order. The experimental results reveal that more high-value reviews have been prioritized to the forefront, leading to an overall improvement of 39.4% compared to the original sorting. Our experimental demonstrated that the narrative-based sorting model can optimize the review sorting, so that high-value reviews can get more exposure and improve consumers' shopping experience.
Application of Information Fusion in Product Packaging Art Design
Authors: Mengnan Ding
Published: January 2025

At present, the contents and forms of environmental protection cultural and creative products in the market are mostly the same, generally lack of cultural connotation, and it is difficult to meet the diversified needs of consumers. Therefore, combined with practice, this paper studies the innovative design method of environmental protection cultural and creative products. Based on the design concept of cross-border integration and people-oriented, this paper discusses new ideas for the design and development of environmental protection cultural and creative products from the four dimensions of "excavating demand, getting close to life", "stimulating emotion and shaping experience", "inheriting wisdom and carrying forward culture" and "combining current politics and seeking development". This paper puts forward nine key points of innovative design of cultural and creative products, such as cultural depth, ancient and modern integration, exclusive characteristics, artistic aesthetics, practical functions, interesting experience, educational influence, green environmental protection and brand concept, and creatively analyzes them in combination with design practice. This paper provides a certain reference value for promoting the dissemination of China's environmental protection culture.
Implementation of Strategic Surveillance Systems Based on Machine Learning for Human Talent Management and Organizational Knowledge
Authors: Luis Miguel Mejía Paucar
Published: January 2025

The implementation of strategic surveillance systems based on machine learning has revolutionized the management of human talent and organizational knowledge. This article explores how these emerging technologies contribute to identifying trends, predicting needs, and optimizing human resources in dynamic environments. Through a descriptive and analytical methodology, recent case studies are examined and the impacts on productivity and organizational development are evaluated. The results show a significant improvement in decision-making and strategic alignment of organizational capabilities. Finally, the ethical implications and challenges of adoption in this context are discussed.
Gamification as a Pedagogical Strategy for the Strengthening of Reading Comprehension in Basic Primary School
Authors: Hernán Javier Guzmán Murillo
Published: January 2025

This study explores the impact of a pedagogical proposal based on gamification to improve reading comprehension in fifth-grade primary school students in Montería, Colombia. Through a qualitative approach with an action-research design, four phases were developed: diagnosis, proposal design, implementation, and evaluation. The gamified platform Ta-tum was implemented, allowing students to engage with reading activities organized as literary cases for detectives, earning points and medals. Results show significant improvement in literal and inferential levels, while critical reading skills demonstrated slight progress. The findings highlight that gamification not only increases student motivation and participation but also contributes to the development of reading competencies. This approach reinforces the need to integrate innovative technological tools into the teaching-learning process.
Educaplay as a Pedagogical Strategy for the Strengthening of Reading and Writing Skills in Basic Primary
Authors: Hernán Javier Guzmán Murillo
Published: January 2025

This study analyzes the implementation of the Educaplay platform as a pedagogical strategy to strengthen the teaching-learning processes of reading and writing in third-grade primary school students in Montería, Colombia. Using a quantitative approach and the Pedagogical Action Research model, interactive activities incorporating multimedia elements were designed and evaluated. The findings show significant improvements in literal and inferential levels of reading comprehension, as well as slight progress in critical reading skills. Moreover, students demonstrated increased motivation toward reading, enhanced reading habits, and expanded their ability to tackle narrative and non-continuous texts. This study highlights the importance of integrating innovative technological tools to foster reading and writing skills from an early age.
Fluoride and Caries Prevention: A Scoping Review of Public Health Policies in Saudi Arabia
Authors: Omnya Ali Abdulrahman Alghamdi
Published: January 2025

This scoping review explores the role of fluoride in preventing dental caries and evaluates public health policies in Saudi Arabia. Dental caries remains a significant public health concern, especially among children, with approximately 70% of preschoolers affected. Despite the proven effectiveness of fluoride in reducing caries prevalence through remineralization and preventive measures, challenges persist in the implementation of fluoride-related initiatives. These challenges include insufficient funding, limited community engagement, and socio-economic disparities that hinder equitable access to treatments. The review highlights the successes of past policies, such as fluoride varnishing and drinking water fluoridation, while identifying gaps in public awareness and policy monitoring. Recommendations include improving resource allocation, developing culturally tailored education programs, and ensuring equitable access to fluoride interventions. This review aims to inform future research and policy development to enhance dental health outcomes in Saudi Arabia.
Effects of Antidepressants on Neurotransmitter Systems and Their Mechanisms of Action in the Treatment of Depression
Authors: Jiarui Du
Published: January 2025

Antidepressants are widely used for treating anxiety and depression, conditions affecting nearly 20% of the global population. The objective of this study is to investigate how antidepressants affect neurotransmitter systems understanding both their therapeutic and side effects. The main objective of this study is to identify the adaptive changes that are induced in brain structures by these drugs as well as describe their function in increasing synaptic levels of neurotransmitters like catecholamines and indoleamines. These functions are essentially exerted either by inhibiting the reuptake of neurotransmitters by neurons or by the metabolic deamination exerted by monoamine oxidase. However, the study concerns the nonappearance of a straightforward relationship between an action of antidepressants upon specific postsynaptic receptor sites and their therapeutic strength as well as adverse effects associated with adrenergic, muscarinic, and histaminergic receptors. The study focuses deeper into comprehension of long-term changes in neurotransmitter transmission as it could open up avenues that shed light upon issues related to the efficacy and optimization of antidepressant treatments for millions of people worldwide.
Research on On-Line Monitoring System of Flexible DC Converter Valve Submodule Based on Machine Vision
Authors: Guohua Zhang
Published: January 2025

In this paper, a flexible DC converter valve sub-module online monitoring system based on machine vision is designed and implemented. The system uses machine vision technology to acquire and analyze the surface state and operating parameters of the flexible DC converter valve submodule in real time. By introducing Lesit life model and linear damage accumulation algorithm, the system can accurately evaluate the fatigue damage of submodules and predict their remaining service life. This paper discusses the algorithm design of the monitoring system in detail, including image processing, feature extraction, damage recognition and life prediction module, and verifies the effectiveness of the algorithm through system simulation. The simulation results show that the designed system can accurately identify the minor damage of the submodule and predict its development trend under various working conditions, and the prediction error is controlled within a reasonable range. Through accurate data analysis, the advantages of the monitoring system in real-time, accuracy and reliability are verified. The application of this system not only improves the safety and stability of the flexible HVDC system, but also provides a new idea and method for the on-line monitoring technology of power equipment.
Optimal Allocation of Energy Storage Allocation based on Sceneario Method
Authors: Bo Yang
Published: January 2025

The time characteristic of pv is one of the important factors that restrict the accommodation of pv. In this paper, the main purpose is to improve the utilization rate of pv. Battery energy storage system is used to absorb the pv curtailment, and many benefits of energy storage in the operation process are analyzed, such as pv connected benefits, environmental protection benefits and so on. According to the time series -sequence characteristics, a large number of scenarios are generated and the scenario reduction technology based on KD distance is used to synthetically consider the benefit items and energy storage costs, so as to obtain comprehensive benefits with universality. Based on this goal, a model is constructed to optimize the capacity allocation of energy storage. In this paper, a mutually exclusive technology is proposed to optimize the operation strategy of energy storage. By introducing 0-1 auxiliary variables, considering the interrelated characteristics of charging and discharging power and over-limit power of energy storage during operation, the piecewise function representing the operation strategy of energy storage is linearized, thus significantly reducing the calculation time of the system. The effectiveness of the proposed method is verified by an example.
A Novel Axiomatic Design-based Method for Membrane Material Supplier Selection Considering Group Consensus
Authors: Aihua Liu
Published: January 2025

This paper proposes an axiomatic design-based mathematical programming method for membrane material supplier selection with real number and linguistic term, in which the weights of experts and attributes are unknown. Firstly, this paper proposes a new information measure based on axiomatic design and defines the information content of linguistic term set. Then, based on the assumption that the attribute weights with respect to each expert are different, this paper constructs a mathematical programming model through maximizing the deviation to determine the attribute weights. Subsequently, considering the differences among different experts, this paper constructs a mathematical programming model to derive experts’ weights by maximizing group consensus. The group information content of each alternative is calculated to determine the ranking order of alternatives. Finally, the proposed method is applied to solve the membrane material supplier selection problem for verifying its practicability and effectiveness. Besides, the proposed method is compared with other methods to show its advantages.
An Analytical Method for Recognizing Cat Meowing States Utilizing Short-Time Fourier Transform and Vision Transformers
Authors: Jian Huang
Published: January 2025

Sound Event Detection (SED) technology is currently one of the research hotspots in the field of audio signal processing. Its goal is to identify the event categories present in an audio segment and label the start and end times of each event. Using sound detection technology to analyze and identify animal sound signals is important for understanding animal behavior patterns and detecting animal status. In view of the complex noise environment and low detection accuracy in practical application scenarios, this paper takes complex audio as the analysis object and explores the animal sound event detection method combining short-time Fourier transform technology and deep learning, which is an exploratory work for further developing practical animal sound recognition systems. The main work and innovations are as follows: (1) extracting the characteristics of animal sound events by analyzing the spectrogram imaging parameters, and (2) proposing a method for detecting animal state sound events based on short-time Fourier transform and deep learning.
AI-Assisted Art Critique Systems for Enhancing Fine Arts Learning
Authors: Gewu Zhang
Published: January 2025

The use of artificial intelligence in education has evolved several learning fields, including the fine arts. Traditional critiques are valuable but subjective often time-consuming and sometimes inconsistent. This study develops an AI-assisted system to provide objective, data-driven feedback to enhance the learning of fine arts. The system integrates three advanced AI techniques: CNN for feature extraction, YOLO for real-time object detection, and K-means clustering for color analysis. These technologies enable the system to evaluate key elements such as composition, focal points, style accuracy, and color harmony. The CNN model obtained 93.2% accuracy in categorizing art styles, YOLO identified compositional elements with a mean Average Precision (mAP) of 91.8%, and detection speed at 35 milliseconds per image. K-means clustering succeeded in color palette categorization with a silhouette index of 0.87. A user study conducted by students and instructors demonstrated excellent satisfaction (4.6/5) and achieved an 18% enhancement in student performance following the AI-generated feedback. This research demonstrates how AI-assisted critique systems can change art education. This is based on the idea of continuous, scalable, and insightful feedback which complements traditional approaches to art. It thus supports learners by giving objective analysis of their work; thus, deepening their artistic knowledge and skill in being creative. The results showed that, indeed, AI can contribute to the success of a fine arts education program, help make art criticism more feasible and efficient, and open opportunities for further advances in implementing AI with creative disciplines.
Learning Classical Music Through AI-Powered Practice Tools
Authors: Jiang Qi
Published: January 2025

This study explores the utilization of AI-powered practice tools that combine machine learning algorithms, real-time feedback systems, and speech and audio recognition technologies to enhance the acquisition of classical music. Classical music learning often lacks real-time, personalized feedback that impedes student progress and engagement. This paper aims to measure how AI tools improve key musical performance metrics - pitch accuracy, rhythm consistency, and tempo regulation - relative to traditional learning approaches. Machine learning algorithms process real-time learner performances to generate customized feedback and correction; speech and audio recognition ensure accurate assessment of voice and instrumental practice. The study also explores the impact of these tools on learner engagement, motivation, and skill retention. Results indicate significant improvements in pitch accuracy, rhythm consistency, and tempo regulation, with participants using AI tools reporting higher motivation, engagement, and long-term retention of learned skills. This research highlights the potential of AI-powered practice tools to revolutionize classical music education, offering more personalized, interactive, and efficient learning experiences for students.
Research on Trajectory and Pose Planning of Robotic Arms for 3D Template Sewing
Authors: Yuanpeng Wei,
Published: January 2025

The technology of template sewing is one of the key techniques in the field of garment stitching. However, traditional flat template sewing often fails to meet the demands of multi-layered stitching structures. Therefore, three-dimensional template sewing is required to address these shortcomings. This paper focuses on three-dimensional template sewing technology, using hat manufacturing as a case study. A simple three-dimensional template model has been designed, employing NURBS curves to fit the sewing path. The adaptive arc-length interpolation method and spherical linear interpolation method are utilized to tackle issues related to speed smoothing and posture adjustment during the sewing process, while also planning the actual stitching pose for the robotic arm. Simulation experiments demonstrate that the proposed approach can accurately fit irregular nonlinear curved paths. The interpolation algorithms exhibit good performance in terms of speed smoothness and posture continuity, ensuring stability and precision throughout the robotic arm's sewing process. This research provides a novel perspective for advancing three-dimensional template sewing techniques.
Bridging Information Technology Capability and Firm Performance: Unveiling the Mediating Role of Supply Chain Collaboration
Authors: Ibaa Mahdi Saleh
Published: January 2025

This research investigates the mediating role of supply chain collaboration in the relationship between information technology capability and firm performance. The study adopts a quantitative, applied approach, utilizing a descriptive-survey methodology. Data were collected using both library research and field methods, with a standard questionnaire administered to managers and senior experts from operational manufacturing companies in Baghdad. The content validity of the questionnaire was confirmed by experts, while its reliability, assessed using Cronbach's alpha, reached 0.875, indicating high internal consistency. Divergent and convergent validity tests were also conducted. Structural equation modeling (SEM) and Smart PLS software were employed for data analysis. The results reveal that supply chain collaboration significantly mediates the impact of IT capability on firm performance. The findings suggest that organizations with robust IT infrastructures can further improve their performance outcomes by fostering stronger collaborative efforts within their supply chains, thereby optimizing operational efficiencies and enhancing competitive advantage.
Depth Detection Based on Multi-Scale Residual Multi-Layer Perceptron Iterative Network
Authors: Minghao Sun
Published: January 2025

Three-dimensional (3D) reconstruction can be used to satisfy the requirement of people on 3D cinema, 3D games, 3D medical imaging and 3D map. The traditional method on 3D reconstruction needs large computation and possesses low accuracy. With the development of AI, more attention to convolutional neural network for three-dimensional reconstruction is put. Current 3D reconstruction methods limit the robustness and integrity and reduce the accuracy of reconstructed models when dealing with occluded, texture-free, or low-textured local backgrounds. Aiming at the problems of low accuracy and cost volume construction in the current 3D reconstruction process, a 3D reconstruction method based on iterative refinement of multi-scale residuals was proposed. The basic idea is to obtain the initial depth map by feature extraction to generate point clouds, and then use multi-scale feature fusion pyramid multi-layer perceptron (MLP) network to obtain point features at different levels. This new multi-scale residual MLP iterative network is used to predict the depth value, and the residual between the depth prediction and the real value is used to estimate the loss. This method attains more precise depth information that enhances the ability of 3D reconstruction of objects, simplifies the network model, and reduces the computational burden. Investigational outcomes show that the suggested method can be used in processing the binocular vision with occluded, texture-free or low-textured local backgrounds and generating higher quality 3D reconstructed objects than the previous methods.
Laboratory Tests and Simulations on Loading Performance of Cold-Formed Steel Purlin-to-Sheet Roofing Structures
Authors: Ming Gao
Published: January 2025

Cold-formed steel (CFS) purlins, which possess a high load-carrying capacity compares to their weight, are extensively utilized in roof systems. Within purlin-to-sheet systems, roofing sheets provide additional lateral restraint to purlins, thereby enhancing the load-bearing capacity of the system. This research conducts both laboratory tests and simulations to evaluate the behavior of purlin-sheet systems under downward loading, focusing on ultimate bending moment and failure modes. To enhance predictive precision, a meticulous exploration of influential parameters is undertaken through comprehensive parametric analysis, identifying purlin thickness, steel material strength, roofing sheet thickness, and the number of lateral struts as critical factors influencing load-bearing capacity. It indicates that thickness, yield strength and lateral sturts of the purlins significantly impact structural load-bearing performance, whereas the contribution of roof panel thickness to load capacity is comparatively minor. For the purlin with small shell thickness (i.e., less than 1.5 mm), the main failure modes are distortional buckling at the upper flange and local buckling at the web. As the thickness of the purlin increases, the main buckling mode gradually shifts towards distortional buckling.
Research on the Regional Flood Disaster Prevention Path Based on the Multi-Source Flow Membrane Model
Authors: Zhong Sun
Published: January 2025

It had occurred frequently to the flood disasters in recent years, and the scope of influence has been expanding, causing serious casualties and economic losses to the global climate change. It introduces the multi-source flow model in order to analyze the factors of the flood and the waterlogging prevention and control and the degree of synergy. According to the coupling relationship between the flood and the waterlogging prevention and the multi-source flow model, it is divided into the factors of the flood and waterlogging disaster to the water flow, the information flow, the facility flow, etc. According to the synergistic relationship of the influencing factors, it is constructed to the multi-source flow model of flood and waterlogging prevention and control. At the same time, it is established an evaluation index system of three categories, 10 categories and 21 categories of flood and waterlogging prevention and control synergy. Through the calculation of the degree of synergy, Clarify the problems in flood disaster prevention and control, and propose the path of "the water flow, the information flow, and the facility flow" for the flood disaster prevention and control. The proposed path of the regional flood disaster prevention and control based on the multi-source flow model can't only control the flood disaster from multiple aspects, but also ensure the synchronization of flood disaster prevention and control elements, and achieve the combination of disaster prevention social construction and flood prevention and control during disasters, It will provide new methods and technical support for the flood disaster prevention and control in China and the world, and ensure the sustainable development of cities and society.
Construction and Application of Classification Model of Art Design Patterns based on Random Matrix
Authors: Ning Ma
Published: January 2025

With the continuous enhancement of computer technology, digital technology brings people more convenience with its powerful functions, and the classification accuracy of art design patterns is getting higher and higher, which greatly expands the creative space of art design. This paper improves the traditional model based on a random matrix, mainly to improve the pattern recognition efficiency and pattern classification accuracy, focusing on the construction and application of the art design pattern classification model, using the random matrix model proposed in this paper can better classify the art design pattern classification, art design pattern classification accuracy is higher, can better promote the combination of art and technology to promote the development of the industry This paper presents a random matrix model to classify art design patterns with higher accuracy, which can better promote the development of art and technology combination. Traditional machine learning methods are used to classify art and design patterns by changing the weights and focusing on the desired target. However, updating the model weights is on the architecture of the original model, which only involves the iterative update of the weights, and the accuracy of the art design pattern classification cannot be significantly improved, still, there is a problem of poor classification accuracy for art pattern classification, and this problem exists, the recognition model based on the random matrix is improved. Based on 73388 art design samples, this paper extracts art design patterns, and improves the accuracy by using the improvement of the random matrix algorithm model.
Research on the Construction of Japanese Interactive Learning Platform based on Internet of Things Technology
Authors: Lingling Zheng
Published: January 2025

In order to improve the effect of Japanese teaching, this paper analyzes the interactive process of Japanese teaching by combining the Internet of Things technology and the identification of student characteristics in online classrooms. Starting from the kinematics model of the human body standing up in the interactive teaching process, this paper derives the inverse kinematics parameters of the aided standing mechanism of the student body model, and analyzes the motion speed function of each mechanism. Moreover, this paper adopts the Lagrangian method to study the dynamics of standing up with the assistance of the assisting mechanism, and calculates the provided assisting force and torque under the assumed initial conditions. In addition, this paper builds a Japanese interactive learning platform based on Internet of Things technology. It can be seen from the experimental results that the interactive learning platform for moral education based on the Internet of Things technology proposed in this paper has a good effect on language teaching.
Impact of Energy Conservation and Emission Reduction on E-Commerce Development
Authors: Hui Wang
Published: January 2025

Introduction: Enterprise performance evaluation can guide the behavior of enterprises, but the existing enterprise performance evaluation methods generally have defects such as focusing on economic benefits, ignoring environmental benefits, focusing on internal benefits of enterprises, and ignoring social benefits, resulting in enterprise operators simply pursuing internal economic benefits of enterprises while ignoring social benefits and environmental protection and energy conservation.
Objectives: This paper expounds the impact of e-commerce on low-carbon economy in the operation process through the theoretical analysis of low-carbon economy, circular economy, network economy and e-commerce.
Methods: Through the analysis of e-commerce operation process, including many empirical studies on energy conservation and consumption saving and low-carbon emission reduction of e-commerce, this paper analyzes the promotion effect of e-commerce on low-carbon economy. In addition, this paper also analyzes the negative impact of e-commerce on low-carbon economy. This paper analyzes the waste of resources in the packaging process of e-commerce in the field of logistics, and puts forward the method of recycling.
Results: The research results show that good ecological development can promote the development of e-commerce and make it more in line with the needs of low-carbon economy.
Conclusions: In view of the problems in the development of low-carbon packaging model for e-commerce websites, and in view of the huge waste of its packaging links, this paper analyzes the low-carbon management model of foreign e-commerce packaging, and puts forward the reference significance of foreign models for our e-commerce enterprises to develop low-carbon packaging model
Grid Connected Oscillation Mode of Virtual Synchronous Generator Based on Clean Energy
Authors: Xingyu Dou
Published: January 2025

Introduction: With the large-scale application of distributed generation in power grid, power grid is developing towards low inertia and low damping. As the virtual synchronous generator (VSG) has the characteristics of synchronous generator and inverter, it may face the risk of oscillation during grid connected operation.
Objectives: To solve this problem, this paper proposes current balance control and power oscillation suppression strategies without changing the characteristics of the virtual synchronous generator.
Methods: At varius vel pharetra vel turpis nunc eget lorem. Feugiat scelerisque varius morbi enim nunc. Cras semper auctor neque vitae tempus quam pellentesque nec. Faucibus purus in massa tempor nec feugiat nisl. Congue nisi vitae suscipit tellus mauris a. Est sit amet facilisis magna etiam tempor. Dictum varius duis at consectetur. Purus semper eget duis at tellus at urna. Ipsum consequat nisl vel pretium. Viverra maecenas accumsan lacus vel facilisis volutpat est. Bibendum arcu vitae elementum curabitur vitae nunc sed. Nisl tincidunt eget nullam non nisi est. Ac turpis egestas integer eget aliquet nibh praesent.
Results: On this basis, the relationship between VSG current inner loop, power outer loop, VSG parallel operation and different oscillation frequencies is analysed. In this paper, a small signal model is established under the condition of different influencing factors. At the same time, this paper systematically analyses the high frequency, power frequency and low frequency oscillation modes existing in VSG grid connected operation.  
Conclusions: Finally, through MATLAB/Simulink simulation, this paper verifies the correlation between different oscillation modes and corresponding dominant influencing factors when VSG is connected to the grid.
Research on Application of Multi-Physical Coupling Model in Prediction and Control of Surface Subsidence in Coal Mining
Authors: Meng Huang
Published: January 2025

Addressing the issue of terrain depression stemming from coal extraction, this investigation delves into the utility of a multi-physics integration model for forecasting and managing subsidence phenomena. The impact of coal excavation operations on the surface configuration is a sophisticated amalgamation of multi-physics field interactions, encompassing the interplay of pressure, fluid dynamics, thermal conditions, and additional physical domains. Through the strategic design of algorithms, the system achieves a high level of precision in terrain depression prediction. Simulation outcomes reveal a pronounced non-linear correlation between terrain depression and pivotal factors like excavation depth, rate of extraction, and the expanse of the mining zone, under specified mining circumstances. Statistical evaluation indicates that the accuracy in predicting terrain depression surpasses 97%, with the margin of error maintained under 3%. Moreover, this treatise scrutinizes the repercussions of varying mining tactics on terrain depression, uncovering that through the refinement of mining variables, terrain depression can be efficiently curtailed, thus mitigating the adverse impacts on the natural habitat. This inquiry not only furnishes a theoretical foundation for the prediction of terrain depression in the context of coal mining, but also offers technical directives and a pragmatic pathway towards implementing eco-friendly mining practices and safeguarding the environment, bearing considerable practical importance and applicability for the enduring progress of the coal sector.
Experimental Study on Water - Flooding Development of Horizontal Wells in Tight Oil Reservoirs
Authors: Zuocai Liao
Published: January 2025

Based on the similarity of pore permeability as well as pore throat structure, nonlinear seepage characteristics and clay mineral content, the outcrop cores are selected to make large-scale physical models. These experiments of staged fracturing horizontal wells are carried out about Madong 2 well area with natural energy exploitation and supplementary energy exploitation. The experimental results show that: (1) the parts of the horizontal well that are easy to water out lie in the wellhead end and the fracture communication area of the horizontal well, the heel end of the horizontal well is easy to water out, and the toe end is difficult to use; (2) The spindle fracture network formed by staged fracturing is easier to control water outflow; (3) For the seven point injection production well pattern staggered by vertical wells and staged fracturing horizontal wells, the heel should be injected early, the middle should be controlled, and the toe should be injected more.
Investigating the Atomic-Scale Liquid Diffusion Coefficient of Nb-Zr Alloy for Enhanced Public Security Technology
Authors: Hui Sun
Published: January 2025

In the realm of public security technology, precise comprehension of material behavior is crucial, particularly in forensic science and explosive detection. This study aimed to simulate the diffusion coefficient of Nb90Zr10 alloy using the angle-dependent potential method, examining the influence of cooling rates and atomic morphologies on diffusion coefficients. Our innovative approach revealed a direct correlation between cooling rate and diffusion coefficient, with the latter increasing as the former accelerates-a finding that provides critical insights into material behavior under rapid cooling conditions. Furthermore, we determined the critical cooling rate of 1.0×1011 K/s for the phase transition from crystalline to amorphous state in Nb90Zr10 alloy, offering significant implications for the development of advanced protective materials. By manipulating cooling rates, we can optimize material structures to enhance their resistance to high-velocity impacts, thus advancing the design of sensitive and reliable equipment for public security applications.
Calculation of Bearing Capacity of Fiberglass Grid Reinforced Asphalt Mixture Based on Stress-Strain Relationship
Authors: Yangjun Meng
Published: January 2025

It can effectively reduce the damage of the asphalt mixture for bridge deck pavement to lay fiberglass grid in the asphalt mixture bridge deck pavement. However, there is little research on the bearing capacity of reinforced grid asphalt mixtures. Therefore, based on the material constitutive relationships and the plane section assumption, some relevant researches is conducted. Firstly, it is clear that the compression process of asphalt mixture can be divided into four stages, which are reverse bending stage, linear stage, hyperbolic stage, and failure stage. The stress-strain fitting formulas for each stage are different, and the stress on asphalt mixture bridge deck pavement is generally in the hyperbolic stage. Secondly, based on the assumption of plain section, the bearing capacity calculation formula for a double reinforced rectangular section with fiberglass grid reinforcement is derived by considering the compression and tension of the asphalt mixture separately. Finally, with specific examples, the correctness of the formula is verified. Meanwhile, in order to uniquely determine the area of fiberglass grid and steel reinforcement, we need to add an additional condition, usually with the lowest cost as the supplementary condition. Moreover, considering the stress effect of asphalt mixture and the effect of fiberglass grid reinforcement, a height of the compression zone of section is slightly reduced, and the structural bearing capacity is slightly improved.
Androgenic Alopecia: Recent Understanding of the Androgenetic Receptor-Mediated Molecular Mechanisms and Current Pharmacotherapy
Authors: K. Suresh Preeti , Ramkar Shweta
Published: January 2025

Androgenic alopecia (AGA), also called male-pattern hair loss, is a hair loss condition facilitated by dihydrotestosterone (DHT), the potent form of testosterone. The hallmark of this disorder is a patterned decrease in scalp hair density. In androgenic alopecia, 5α-reductase iso-enzyme, which is responsible for conversion of testosterone to DHT, is increased in the scalp follicles. In the presence of DHT, hair cycle is shortened and progressively miniaturizes scalp follicles. Like most tissues, hair undergoes continuous turnover throughout life. Hair follicles are replaced periodically; the time interval is dependent on location and by the process of hair cycling. AGA occurs in a sequential step. In genetically predisposed men the initial development is bitemporal recession, followed by diffuse frontal loss and after there a bald patch over the vertex of the scalp accurse. Ultimately, all the hair over the crown is lost. With each new cycle, hair follicles are regenerated and follicular stem cells are responsible for this and recapitulate many of the signals of embryologic development. Various factors are influence and alter the regenerating hair follicle with advancing age. In this review, the recent advances in the understanding of molecular mechanisms of androgenic alopecia are discussed. The recent developments in the pharmacotherapy of this condition is also reviewed and presented.
Obesity: Homeostatic Regulation and Hedonic Aspect of Food Intake
Authors: Chahirou Yassine , Mesfioui Abdelhalem , Lamtai Mouloud , El Midaoui Adil , Bikri Samir , El Hessni Aboubaker
Published: January 2025

Compelling evidence supports the primary role of the diet and metabolic regulation of food intake in the establishment of metabolic diseases. This review describes the hypothalamic and brainstem structures involved in the control of food intake. The arcuate nucleus (AN) is a metabolic relay receiving the metabolic signals from the periphery and projecting to the paraventricular nucleus (PVN) and lateral hypothalamus (LH), which are involved in the sensation of satiety and hunger, respectively. Also, the role of the solitary tract nucleus (STN) in the regulation of food intake is discussed herein. Food intake is not controlled only by the structures associated with the vegetative life but also by structures of hedonism that often incites individuals to consume palatable foods. The emotion towards the food created at the level of the central amygdala (CeA) arrives at the nucleus accumbens (Nacc), which projects to the ventral tegmental area (VTA) in order to increase the production of dopamine that is suppressed by the prefrontal cortex (PFC). This review addresses the intervention of these structures in food intake by evoking the state of addiction to the diet observed in people with obesity.
A Systematic Review of Bioinformatics' Influence on Drug Developments
Authors: Afrin Sadia , Pillai HP Jai Shanker , Emmanuel E S Challaraj , Viktor Patrik , Al Hafiz Md. , Aboqade Al-Aouadi Raed Fanoukh , Ahmed Mahedi Rezwan , mudhafar Mustafa , Batra H.S. , Babu Kondaveeti Suresh , Syrmos Nikolaos , Chand Jamali Mohammad , J. Al-Azzawi Abdul Kareem , R. Wei Calvin , Enam Elahi Mohammad Mahfuz
Published: January 2025

Drug development is both a task and a promise for fighting illnesses and improving health. Innovative treatments for cancer, infectious diseases, chronic illnesses, and uncommon genetic abnormalities have long been difficult and resource-intensive to develop. In the 21st century, bioinformatics has become a pioneering and essential technique for drug development. Bioinformatics uses computational algorithms, statistical analysis, and data-driven insights to understand life's core biological processes. Bioinformatics helps drug discovery researchers understand illness biological mechanisms, identify therapeutic targets, screen candidate molecules, and improve drug development pipelines. Drug development has been defined by fortuitous discoveries and tedious laboratory work, requiring years or decades to get a possible therapeutic agent to the patient bedside. Bioinformatics has transformed drug development in biomedicine today. This comprehensive study examines how bioinformatics has changed medication development. We want to study and analyse bioinformatics' many uses. We discover the complex network of genomes, proteomics, structural biology, and computational modelling that underscores bioinformatics' vital significance as we investigate this dynamic subject. This review illuminates the difficult balance between biological knowledge and computational capabilities to produce innovative treatments. The systematic review uses a thorough literature search, study selection criteria, and data extraction. Genomic target identification, virtual screening, pharmacophore modelling, pathway analysis, and toxicity prediction are among the bioinformatics applications in drug development we find and classify. These examples demonstrate bioinformatics' ability to accelerate medication development. The systematic review covers bioinformatics' drug discovery applications and shows how this diverse discipline is changing pharmaceutical research. It shows how to improve medication development, reduce side effects, and achieve precision medicine. The combination of bioinformatics and drug development offers optimism for easing human suffering, extending lifespans, and fighting a broad range of illnesses.
In Silico Study of CNS Depressant Drug
Authors: P. Kulkarni Chaitrali , H. Rohane Sachin
Published: January 2025

Molecular Docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. In this research Molecular Docking we are perform on Phenothiazine and Barbiturate by using Auto dock and Discovery Studio Software. QSAR study revealed that substitution of different electron donating or withdrawing group at different position on phenothiazine and barbiturate lead nucleus elaborate change in pharmacological activity. Molecular docking done by substituting or replacing different group at different position affected the potency of drug on addition of nitro, Scandium trihydride, Nitrogen dioxide, Benzene ring at different position where reduced the potency of phenothiazine while addition of trifluoromethyl enhance the potency phenothiazine. In barbiturate, replacement or substitution of alkyl group enhances the potency while amine, aromatic ring and carbonyl group reduces potency.
Optimization of Load Balancing in Cloud Computing through Nature-Inspired Metaheuristic Algorithms
Authors: Roopali Gupta
Published: January 2025

Cloud computing has introduced completely new ways to administer resources and distribute services, thereby enhancing load balancing for better service provision. Based on this assumption, this paper presents an approach to optimize load balancing in cloud environments through Nature-Inspirited Metaheuristic Algorithms. These algorithms tend to imitate natural processes such as evolution, swarm behavior, and biological adaptation. Therefore, NIMA can be strong solutions in addressing the complexities of multi-criteria service selection inside dynamic cloud environments by finding workloads efficiently, enhancing system efficiency, minimizing latency, and ensuring better resource utilization. The approach presented in this paper evaluates these nature-inspired algorithms towards possible comparison of their efficiency in handling the diverse and changing demands for cloud services, considering both Particle Swarm Optimization and Genetic Algorithms. The outcome justifies the effectiveness of load balancing, offering a scalable and flexible solution to service providers of cloud services. The outcome presented in the paper further suggest to speed up the cloud computing process by optimizing the distribution of loads and improving the quality of services delivered.
Exploring the Determinants Behind Real Estate Buying Decisions
Authors: Sujata Kumari
Published: January 2025

Real estate purchase decisions for personal use involve a complex interplay of rational, emotional, and cultural factors. This study examines the determinants shaping residential property choices, drawing on data from 134 respondents through a structured questionnaire. Key factors include location, property features, financial considerations, lifestyle needs, accessibility, environmental aspects, and emotional influences. The findings highlight location and construction needs as primary considerations, with proximity to essential amenities and outdoor spaces being highly valued. Financial affordability, hidden costs, and favorable mortgage options significantly impact decision-making, alongside lifestyle preferences such as walkability and access to public transportation. Environmental sustainability, including green spaces and favorable climates, has gained prominence, while technological infrastructure and smart home features enhance desirability. Emotional and cultural dimensions, such as community belongingness and aesthetic appeal, further underscore the multifaceted nature of real estate decisions. This study provides actionable insights for developers and policymakers to design sustainable, consumer-focused housing solutions.
DIGITAL LITERACY AND ITS INFLUENCE ON INSURANCE TECHNOLOGY ADOPTION IN RURAL REGIONS
Authors: Shafique Ahmad
Published: January 2025

Digital literacy plays a critical role in the adoption and effective use of insurance technology in rural regions. The increasing reliance on digital platforms for insurance services presents both opportunities and challenges for rural populations, many of whom may face barriers to digital engagement due to factors such as limited internet access, low digital skills, and cultural or socioeconomic constraints. This paper examines how digital literacy influences the adoption of insurance technology, focusing on rural areas where technological infrastructure and digital education may be underdeveloped. By analyzing case studies and surveying rural populations, we explore the correlation between digital literacy levels and the willingness to adopt insurance technologies such as mobile apps, online claims processing, and digital policy management. The findings suggest that enhancing digital literacy in rural regions can significantly boost the adoption of insurance technology, improve financial inclusion, and foster greater economic stability in these communities. We also discuss strategies for overcoming digital divides and fostering an inclusive insurance ecosystem.