bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical Simulation Analysis of Existing Structural Deformation Under Unloading Conditions of Soft Soil Layer
Authors: Li Heyong , Chen Yiliang , Li Kaicheng , Wang Biao , Tao Shuai
Published: December 2024

In order to study the influence law and mechanism of pipe jacking construction on the deformation disturbance of existing subway tunnels in soft soil area, the numerical simulation method was used to simulate and analyze the deformation disturbance law of existing subway tunnels during pipe jacking construction. A three-dimensional geological model of pipe jacking crossing existing subway tunnel is established by using structural and unstructured hybrid grids, and the simulation of pipe jacking construction process is realized. Through the servo mode, the soil stiffness, stress and density are controlled to decay with the calculation time step to simulate the excavation unloading process of the soil. The calculation results show that the HSS model can well simulate the influence of excavation disturbance on the vertical displacement of the tunnel measuring point. The maximum vertical settlement of the downward tunnel measuring point is about 11.3 mm, and the minimum is about 6.81 mm. The maximum vertical displacement of the measuring point of the ascending tunnel is about 17.4 mm, and the minimum is about 12.7 mm.
Analysis of Damaged Mountain Treatment Scheme Based on UAV Tilt Photogrammetry
Authors: Shen Cheng , Hao Zhe , Ning Fangzhou , Sun Jie , Jia Qichao
Published: December 2024

UAV tilt photography technology has the characteristics of high precision and high sense of reality, and has unique applicability to the damaged mountain management project. In this paper, we try to use UAV tilt photogrammetry to establish a scheme for the treatment of damaged mountain, and comprehensively analyze the technical characteristics of this method for the treatment of damaged mountain. Based on the UAV oblique photography, the 3D model of the real scene of the survey area is generated, and then the earthwork calculation and analysis of the survey area are carried out, so as to provide a solution for the treatment of the damaged mountain. The analysis results show that: The site can be divided into three engineering treatment areas, namely, treatment area No. 1, No. 2 and No. 3. The repair of the damaged mountain in the treatment area will produce a large amount of rubble, and some areas will need to be filled. The side slope ratio at the junction of No. 2 administrative area, No. 1 Administrative area and No. 3 Administrative area is no more than 1:0.75; The slope cutting project should be carried out in sections from top to bottom. The process will remove a lot of earth and stone. According to the analysis of filling and excavating, the balance of filling and excavating can be achieved on the whole. The proposed slope cutting and backfill treatment scheme provides a reliable basis for engineering design, has strong applicability, reduces field workload and personnel input, and saves production cost.
Assessment of Land Requirements for the Physical Expansion of Qorveh City
Authors: Salehi Usef , Azizi Zahra
Published: December 2024

The migration of rural populations to urban areas due to a lack of facilities and employment opportunities has led to marginalization, pollution, traffic congestion, deforestation, and increased population density in urban areas. Qorveh City, with a population center that includes 133 villages, has experienced a significant increase in its population. According to census statistics, the city’s population increased from 66,390 in 2006 to 71,232 in 2011. Urban planners have encountered challenges due to this rapid growth. Geographic Information Systems (GIS) have become increasingly popular in all fields, particularly in urban management and planning. This technology provides quick and accurate results. In this study, we used the fuzzy integration and classification method in GIS. We extracted layers’ elevation, slope, distance from roads, rivers, faults, city center, and land-use site selection for the required lands for the physical development of Qorveh City from the base map. We calculated the fuzzy Gamma coefficient for each map which was equal to 0.85. Then we classified each map into five classes: very high, high, medium, low, and very low value for physical development. By matching the output classified maps at the end of our research process we were able to extract a map of physical development possibility with an accuracy rate of 93.2%. Our results show that this method is both accurate and cost-effective. In conclusion, our study demonstrates that GIS technology can be effectively used for urban planning purposes such as assessing land requirements for physical expansion. This method can help planners make informed decisions about land use while minimizing costs and time spent on data collection and analysis.
Experimental Study on Wind Resistance of the Main Bridge of Chishi River Special Bridge
Authors: Li Jichao , Qiu Yunjun , Liu Chengzhe , Han Mingyu , Fan Qiang , Shao Xinlin , Zhang Pengjv , Guo Xiaoyang
Published: December 2024

For the wind resistance performance of the main bridge of the Chishi River Special Bridge, three-dimensional finite element numerical simulation and segmental physical model test are used to study the stability of vibration, chirp vibration, and vortex resonance in the bridge formation state and construction state. Based on the three-dimensional finite element numerical model, the critical vibration pattern and frequency of this bridge in the bridge formation state and construction state are calculated, and based on the results of numerical calculations and the “Norms,” the segmental test of the main bridge was carried out. The results show that the critical vibration wind speed is greater than the vibration test wind speed of the cable-stayed bridge (47.9 m/s); the results of the wind tunnel test of the segmental model show that the critical vibration wind speed of the main girder of the bridge is much greater than the vibration test wind speed in the construction stage and the bridge-forming state, and there is no apparent vortex-excited resonance; the drag coefficient, lift coefficient and torque coefficient of the main girder section in the bridge-forming state under the angle of attack of 0° are 0.958, –0.5, –0.5, and –0.0, respectively. Under 0° wind angle of attack, the drag, lift, and torque coefficients of the main girder section in the bridge state are 0.958, –0.250, and –0.017, respectively. The coefficient of chirp force is positive, and the chirp vibration stability meets the code’s design requirements.
On Seismic Response of Bridge with New Design of Seismic Isolating Support Part
Authors: Abirov Rustam , Miralimov Mirzakhid
Published: December 2024

Technology and approaches for reducing the negative effect of seismic impact on transport infrastructure by using innovative technical solutions such as bridge supports and seismic isolation support devices for overpasses are offered in this issue. The new utility model proposed as isolating support part for bridges and overpasses with load capacity up to 50 tons and seismic intensity up to 8 points by MSK scale. On the base of developed utility model was tested bridge (overpass) response on external impact. It is the first stage before experimental testing and determination of the response on isolators at different seismic magnitudes and local soil properties, traffic loading, etc. It is necessary for creating technical regulations and determining the trial period for isolators. In this research was provided calculation and comparing analysis of bridge response by using new utility model, standard and movable tangential supporting parts. This is a theoretical assessment that requires a wide range tests on different types of overpasses and bridges.
Treatment of Heavy Metals from Petroleum Wastewater Using Date Pits and Seashell
Authors: Mortula Md Maruf , Atabay Serter , Al Janahi Mariam , Elhelw Mariam , Almulla Rodha , Alnaqbi Reem
Published: December 2024

Heavy metals and other contaminants are present in significant amounts in the wastewater produced by the petroleum sector. However, cost effective treatment technologies are essential for sustainable waste management. This study evaluates the use of date pits and seashells as eco-friendly adsorbents to remove heavy metal contaminants from petroleum effluent, particularly Copper, Zinc, and Chromium. Locally available oil refinery wastewater was used for this study. Batch adsorption experiments were conducted to evaluate the efficacy of common local waste materials (i.e. date pits and seashells) as adsorbents of heave metals from real petroleum refinery wastewater. The results highlight how effective these organic wastewater materials are at adsorbing pollutants at different pH levels. Seashells removed all Zinc and Chromium completely at pH level of 9 and 5.03, according to batch experiments, while date pits remove all Zinc at pH 6.12. These findings demonstrate the crucial role pH plays in the selection of adsorbents, with Zinc’s chemical characteristics having a major impact on efficacy.
Impact of Indoor Thermal Comfort with Perforated Ceiling Air Supply Based on “Double Carbon”
Authors: Guo Lanxia , Zhang Huijuan
Published: December 2024

To study the thermal comfort with perforated ceiling air supply, the Airpak is used to modeling and numerical calculation four conditions of perforated ceiling air supply in winter. By comprehensive analysis and comparison indoor temperature profile, velocity profile and PMV-PPD distribution of various working conditions. The results showed that: The flow velocity in the jet area is larger, and the flow field distribution in the other areas is uniform and the flow velocity is small, and the floating of the lower heat flow affects the airflow diffusion of the upper air supply outlet. The temperature field and PMV/PPD distribution of the four conditions have a stratification phenomenon in the vertical direction, the amount of air supply has obvious influence on the distribution of indoor temperature field, with the same air supply volume, increasing the lower air supply volume can increase working area temperature, that in favor of effective use energy and achieve the goal of emission reduction.
Effect of Different High Pressure Edge Shape in Runner on Hump Characteristics of Pump Turbine
Authors: Chen Zhenyu , Li Chenhao , Hu Yang , Liu Jiahua , Zhu Guojun
Published: December 2024

To investigate the impact of different high-pressure edge shapes on the external and hump flow characteristics of a pump-turbine during pump operation, we selected the SST k – ω turbulence model to analyze the internal flow field of three designs (concave, oblique, and convex) under varying operating conditions. The results indicate that, under conditions of low flow rate (0.63QBEP–0.69QBEP), the convex scheme has better head and efficiency, thereby effectively enhancing the safety margin of the hump. In the low-flow hump condition, flow separation occurs at the runner inlet near the shroud, backflow appears at the outlet near the hub, and vortex occurs in the guide vanes across different schemes. These flow patterns deteriorate significantly across different schemes and working conditions, resulting in different hump characteristics. This study provides an important reference for the prediction of hump characteristics and the optimal design of runners.
Evaluation of the Pavement Condition Performance Using Artificial Neural Network
Authors: Shtayat Amir , Moridpour Sara
Published: December 2024

Roads are considered the artery of countries that feed cities and regions and have great importance in achieving high levels of services for its users in facilitating their transportation and their goods from one place to another. Governments attach increasing importance to road conditions to achieve sustainability in the services provided to roadway users and thus increase users’ satisfaction with government services. Perhaps one of the things that governments aim to monitor the road surface conditions. Where, Keeping the pavement in high-quality conditions can achieve the highest levels of comfort while driving, reducing vehicle maintenance costs, reducing traffic accidents, and thus increasing traffic safety. Monitoring the pavement condition is considered one of the significant applications to evaluate the pavement condition by determining the type and severity of defects on the pavement surfaces. In this study, a vibration-based method is used to measure the level of comfort riding a passenger car at a speed of 40 km/hr on local roads in Melbourne. The vibration signals are measured using an accelerometer sensor fixed on the front dashboard of a sedan car. The vibration signals then are smoothed and filtered to be used for the detection of the pavement defect locations according to the fluctuations in signals. Besides, the filtered vibration data was then divided into training and testing databases to develop an artificial neural network model named Long-Short-term memory LSTM. The LSTM is used to automatically detect and classify the quantity and quality of pavement defects according to the conducted vibration data.
Cascading Failure and Recovery Model for Subway Network Systems Considering Resource Allocation and Passenger Evacuation
Authors: Gui Wangzhe , Chen Qihang , Han Dingding
Published: December 2024

Subway systems serve as vital components of urban transportation infrastructure. Due to the critical concern for transportation safety, research on the resilience and recovery capabilities of subway systems is therefore of great significance. This paper proposes a recovery model for cascading failures in subway systems and the recovery measures and cascading failures are addressed simultaneously. The recovery measures in the model include both station repairs with resource allocation and passenger evacuation. The recovery model incorporates five key factors: the maximum number of stations in repair, the sequence of station repairs, the total amount of repair resources, the allocation of repair resources, and the speed of passenger evacuation. The model is applied to different subway networks, simulating the deliberate attack on the station with the highest load. Simulation results demonstrate the superiority of the proposed capacity-based resource allocation method. Moreover, the effectiveness and importance of passenger evacuation in halting the propagation of cascading failures and improving network recovery are examined. Finally, the impact of station capacity on the recovery outcome is explored, We reveal an optimal station capacity setting that could minimize network resilience loss during attacks. This study holds significant implications for the development of resilient and sustainable subway systems.
Application of Physics-Informed Deep Learning in Solving Safety-Related Road Transportation Problems - A Review
Authors: Parvez Md. Shohel , Moridpour Sara
Published: December 2024

The goal is to bring together an in-depth analysis of physics-based deep learning approaches in transportation domains and classify them according to their applicability. To carry out the systematic literature search, a Preferred Reporting Items for Systematic Reviews and Meta-Analysis flowchart is used with certain inclusion and exclusion criteria. Different keyword searches are carried out in the Scopus and Web of Science databases, followed by relevant references and citation analyses to find eligible papers subject to a full-text peer review. Finally, the classification and analysis of these papers take place based on their applicability. 141 and 39 records were found by the initial database search and referencing and citation analysis respectively. A total of 65 documents were selected to carry out full-text reviews, and finally, 35 documents were included in the study. Based on the applications of physics-informed deep learning in transportation engineering, the authors classified the literature into three major categories: 1) safety assessment and safety analysis, 2) model preparation, and 3) prediction and estimation. Finally, this research also provides the challenges and future directions in this emerging field.
Public Awareness and Perceptions of Autonomous Vehicles: Environmental Benefits and Sustainability Impact in Urban Mobility
Authors: Alkaabi Khaula , Senghore Salieu
Published: December 2024

This study examines public awareness and perceptions of autonomous vehicles (AVs) in the UAE, focusing on their environmental benefits and sustainability impact. An online survey of 458 respondents revealed high awareness of AV technology and its environmental benefits, particularly among women and those with higher education. A significant portion (88.8%) demonstrated above-average awareness, with differences based on gender, education, and AV usage. Respondents generally viewed AVs positively in terms of environmental protection and sustainability, agreeing they can reduce emissions and are more sustainable than human-driven cars. However, there was no consensus on banning human-driven cars in favor of AVs, and preferences were split between ride-sharing and privately-owned AVs. Overall, the study suggests that increasing awareness and positive perceptions of AVs’ environmental benefits can enhance their adoption.
Low Fidelity Digital Twin for Automated Driving Systems: Use Cases and Automatic Generation
Authors: Vlasak Jiri , Klapálek Jaroslav , Kollarčík Adam , Sojka Michal , Hanzálek Zdeněk
Published: December 2024

Automated driving systems are an integral part of the automotive industry. Tools such as Robot Operating System and simulators support their development. However, in the end, the developers must test their algorithms on a real vehicle. To better observe the difference between reality and simulation–the reality gap–digital twin technology offers real-time communication between the real vehicle and its model. We present a low fidelity digital twin generator and describe situations where automatic generation is preferable to high fidelity simulation. We validate our approach of generating a virtual environment with a vehicle model by replaying the data recorded from the real vehicle.
The Fuzzy Analysis of Western Windfarm Transportation Safety
Authors: Jhu Hong-Yi , Kao Sheng-Long , Moszyński Marek , Lee Sin-Der , Lo Tzu-Chieh
Published: December 2024

The previously completed route planning research of Navigational Transportation Safety in the Taiwan Western wind farm analyzed delves into the expansion of sustainable wind energy and navigation safety together along the Taiwan Western for confronting the challenge of balancing resource coexistence and displacement. Previous studies primarily focused on route planning and vessel traffic density which developed the necessary comprehensive analysis. Therefore, this study examines the area of Taiwan’s western Offshore Wind Farm (OWF) environmental issues to develop a Precautionary Area (PA) and RADAR handoff site location analysis. This model analysis introduces a new PA concept that considers navigational and ecological safety, and also wind and hydrological resources, and offshore RADAR handoff mechanisms. Automatic Identification System (AIS) and wind farm RADAR surveillance data are used for converting and analyzing information through the Marine Geographic Information System (MGIS) and Fuzzy Method (FM)that proposed an alternative PA and RADAR site location selection that could facilitate Taiwan’s Green Energy and Navigational Safety.
Full Traceability and Provenance for Knowledge Graphs
Authors: Dibowski Henrik
Published: December 2024

Knowledge graphs (KGs) continue spreading into industrial use cases due to their advantages and superiority over classical data representations. A problem that has not yet adequately been solved for KGs is the traceability and provenance of changes, which can be required in an enterprise or by regulations. KGs typically contain the current snapshot of data valid at a certain moment in time only. Changes over time are usually not recorded and no change history exists. The lack of suitable and scalable traceability solutions hinders the wider application of KGs. This paper presents a traceability and provenance solution for KGs, which can track all changes of a KG on triple level. It comprises a provenance engine that intercepts SPARQL/Update queries; PROV-STAR, an RDF-star enabled light-weight extension of the Provenance Ontology (PROV-O) for representing changes and their provenance; and a SPARQL query transformation approach for tracking the changes on a separate provenance KG with SPARQL-star queries. The solution supports full traceability of all changes, on the lowest possible level of triples, with each change being comprehended with detailed provenance information. From the provenance KG a detailed change history can be retrieved, and any past version of the KG can be restored with a single query. The implementation and validation have shown that changes can be tracked at runtime during the normal operation of a KG. Furthermore, the solution is scalable to large KGs and frequent updates, as only the delta of each change is stored.
A Short Term Electricity Load Forecasting Method Based on Residents in Region
Authors: Kong Juan , Shi Wen-Juan , Xiong Gen-Xin , Li Yun , Guo Qing-Ji
Published: December 2024

To improve the accuracy of power load forecasting, a short-term load forecasting method based on Principal Component Analysis (PCA), XGBoost (eXtreme boosting system), and Long Short Term Memory (LSTM) using neural networks is proposed. By studying the variation patterns of residential electricity load, a feature set is constructed using date factors, climate factors, and daily load factors as inputs. Firstly, the sample dataset is divided into a load dataset and an influencing factor dataset composed of date and climate factors for data preprocessing; Then, the PCA principal component analysis method is used to extract features with significant impact, and a strong correlation feature vector is formed with the load data. The results are input into XGBoost and LSTM networks for mathematical fusion; Finally, using the electricity load data of residents in a certain area of Nanjing, the parameters of each group of network models were trained and the load prediction values were output. The results were compared and verified with the Convolutional Neural Networks (CNN) model, LSTM network model, the Gated Recurrent Neural Network (GRU) model and XGBoost LSTM network model, respectively. The results showed that the method can effectively reduce the error between the predicted values and the true values.
A High Gain X-Band Horn Antenna Array with Wide-Angle Beam Steering in E-Plane
Authors: Ngo Thi Huong , Nguyen Dinh Son , Nguyen Van Phu , Hoang Minh Duc , Nguyen Van Loi
Published: December 2024

This proposal presents a high gain array antenna of double-ridged horn for X-band radar applications. The antenna array consists of 5184 radiating elements divided into 1296 subarrays, each subarray has 4 horn elements fed by a 4-port waveguide power divider in horizontal plane. The subarray is integrated with calibration signal to improve the system accuracy. The measurement results of 72×72 horn array in a bandwidth of 7% matching with the simulation results indicate an effective design approach: the design antenna has achieved a good active return loss (S11) less than –10 dB when beamforming upto ±550, a peak gain of 42.7 dBi, a pencil beam with half-power beamwidth (HPBW) less than 1.40 in both E and H plane in the operating bandwidth.
Reflections on the Environmental Test and Identification of Plateau UAV Equipment Under the New System
Authors: Wang Heng , Yuan Yuan , Liu Tao , Pan Jihui
Published: December 2024

This paper analyzes the gap between the current UAV equipment environmental engineering work and the test and appraisal requirements under the new system, summarizes and puts forward the overall solution idea of the equipment environmental adaptability test and appraisal under the new system, clarifies the tailoring method of the equipment environmental adaptability work project in the whole life cycle, and the complete set of equipment test and appraisal work items, and gives the determination principle of the equipment environmental adaptability requirements, as well as the set of product environmental adaptability requirements at each stage and at each level. The identification of environmental adaptability tests for all indicators provides guidance.
Monitoring Technology for Intermittent Partial Discharge Signals
Authors: Wang Shaojing , Lu Zhihao , Wu Xinye , Ding Min , Xu Peng , Shen Daoyi
Published: December 2024

The partial discharge signals induced by insulation defects are often intermittent and sporadic. It is challenging to detect these signals using conventional charge detection techniques, which can only be identified through uninterrupted continuous monitoring and the accumulation of long-term PRPD (Partial Discharge Phase Resolved Patterns) diagrams. This paper addresses the difficulty of conducting synchronized long-term continuous monitoring of all GIS (Gas Insulated Switchgear) equipment with mobile partial discharge detection devices by researching a sensor network architecture based on a daisy-chain series technology and real-time synchronization techniques for digital grid frequency. By developing a novel digital partial discharge ultra-high-frequency sensor with continuous signal transmission capabilities, we have achieved long-term monitoring and diagnosis of intermittent partial discharge signals.
Mobile Learning for the Enhancement of Social Inclusiveness for Students in Open Distance e-Learning Institutions in Developing Countries
Authors: Le Roux Petra , van Staden Corne
Published: December 2024

In developing countries there is a growing need for higher education institutions (HEI) to facilitate the delivery of online learning experiences for learners due to factors that include resources, technological advancements, demographic changes, and a demand for accessibility and equity. Advances in mobile, social, and physical computing technologies have moved Human Computer Interaction (HCI) into virtually all areas of human activity. This has led researchers within the HCI communities to propose that the development and evaluation of digital technology should not only include usability, but the broader range of user experiences, where the feelings, motivations, and values of a user are given as much, if not more, attention than efficiency, effectiveness, and basic subjective satisfaction experiences which it offers. As the learner community is growing increasingly diverse due to factors such as socioeconomics, cultural, ethnic, experience level, and motivation, the use of social media as a platform for mobile learning applications have shown a capacity for inclusiveness in the lives of the learning community since they allow for the sharing of knowledge, dialogue and conversations addressing diversity and inclusion. It also shown advantages in terms of relevance, appropriateness, flexibility, and participation highlighting its educational potential. In terms of the quality management, it is found to be responsive to the needs of the learning community due to participatory design followed before and during the learning process.
Study on Integration Between Industry and Education: A Perspective from Industrial College
Authors: Dong Yazhao
Published: December 2024

With deep integration of digital technology and real economy, the acceleration of digitalization process has led to explosive growth in digital economy and industrial chain, as a result, the demand for digital talents has increased greatly and the talent gap in the digital economy of the industrial supply chain continues to expand, which poses great challenges to the high-quality development of the digital economy. Aiming at solving the problem of digital talent shortage faced by the development of emerging industries in the digital era, this paper introduces industrial colleges as an effective means in deepening the integration of industry and education. This study makes an overview of industrial college including its concept, connotation, characteristics, construction paths, etc. This paper introduces current development situation and existing problems of industrial college construction, this paper then proposes countermeasures and suggestions on how to effectively constructing industrial colleges and how to further deepen the integration of industry and education, with the purpose of cultivating more applied talents and providing high quality intellectual support for the long-term and sustainable development of China’s educational, economic and social development.
Critical Thinking on the Promotion of 1+X Certificate for Applied English Majors in Vocational College from the Perspective of SWOT Analysis in AI Era
Authors: Sun Hui , Li Congli
Published: December 2024

VETS serves as an efficacious endorsement for the 1+X model in foreign language fields, emblematic of the pivotal role of new era vocational education reform and development. Adopting the SWOT(Strengths, Weaknesses, Opportunities and Threats) methodology, we distill the meritorious strengths of the Applied English program within higher vocational education’s implementation of the 1+X certification system: implementation of 1+X certificate–VETS(Vocational English Test System) is strongly supported in Vocational colleges; students’ high enthusiasm for obtaining VETS certificates; however, there are also weaknesses: poor alignment between course contents and the assessment criteria for the certificates; and weakness in practical teaching capabilities among English teachers. Opportunities include the sequential introduction of national policies providing substantial impetus and the establishment of training and evaluation organizations providing robust security; threats abound with the low acceptance rate of enterprises and an insufficient incentives mechanism. Proposing the advancement strategy for the 1+X credential framework within the Applied English majors: refining the implementing policies of the Applied English Major’s 1+X credential system, augmenting the professional practical abilities of its faculty in applied English, enhancing the integration of curriculum components for talent cultivation through course documentation.
XGBoost-Based Prediction Model for Identification of Gullible Populations
Authors: Liang Shuangjie , Zhang Yufeiyue , Ding Zhibang , Bai Xiaohui
Published: December 2024

In recent years, the surge in telecommunications fraud has become a global problem, and scholars and experts have conducted in-depth studies involving psychology, sociology and machine learning fraud detection. Such fraud seriously threatens people’s property and destabilizes society, and needs to be addressed urgently. Such events as telecommunication fraud not only bring serious threats to the property safety of the general public, but also invisibly undermine the harmony and stability of society [1]. Fraudsters rely on the convenience of modern communication technology, through the phone, SMS, the network and other channels, cleverly designed a variety of fraudulent words, they pose as public prosecutors and law enforcement agencies, bank employees or friends and relatives, luring the victim to make transfers and remittances, so as to easily cheat money. This type of fraud has a very high degree of covertness and speed of dissemination, fraudulent means are endless, so that the victim can not be defended. Once deceived, the victim’s personal property will suffer significant losses, and may even be in psychological distress, further triggering social instability.As the fight against constantly updated and iterative fraudulent techniques and the improvement of relevant laws and regulations always occur after the completion of the fraud, the people’s property safety has been violated. Taking the initiative is the right choice, identifying and predicting vulnerable groups before the fraud occurs, building a machine learning-based telecom fraud warning model, and launching intensive and targeted anti-fraud publicity and education for different vulnerable groups.
Educación, género e informalidad laboral en las áreas urbanas de Colombia
Authors: Galvis-Aponte Luis Armando
Published: December 2024

Este documento analiza la informalidad laboral urbana en Colombia, destacando su relación con el nivel educativo, las desigualdades de género y las brechas regionales. Utilizando datos de la Gran Encuesta Integrada de Hogares (GEIH) para las veintitrés principales áreas metropolitanas entre 2010 y 2023, se estiman las probabilidades de informalidad mediante un modelo probit con variable endógena, que permite abordar la relación entre la educación y la informalidad laboral empleando variables instrumentales. Los resultados muestran que cada año adicional de educación está asociado con una disminución de más de dos puntos porcentuales en la probabilidad de ser informal. El análisis también revela diferencias significativas entre hombres y mujeres, con una mayor incidencia de informalidad en las mujeres, especialmente en las regiones Caribe y Pacífica, que enfrentan condiciones económicas más precarias. A pesar de las mejoras en la formalización observadas en los últimos años, las brechas de género y región persisten, destacando la desigualdad estructural en el acceso a empleos formales. Estos hallazgos subrayan la importancia de implementar políticas públicas que promuevan la formalización del empleo, reduzcan las desigualdades de género y aborden las disparidades regionales, fomentando la inclusión laboral en las áreas más afectadas del país.
Revolutionizing Penetration Testing: AI-Powered Automation for Enterprise Security
Authors: Siva Krishna Jampani
Published: December 2024

This article explores the transformative impact of artificial intelligence and machine learning technologies on enterprise security through automated penetration testing frameworks. The article presents a comprehensive article analysis of an AI-powered penetration testing system, examining its architecture, implementation methodology, and performance metrics in real-world enterprise environments. The article findings demonstrate significant improvements in testing efficiency, with automated systems achieving superior vulnerability detection rates while substantially reducing testing time and resource requirements compared to traditional manual approaches. The article highlights how machine learning models, particularly deep neural networks and ensemble approaches, enable continuous, adaptive security assessment capabilities that effectively identify and respond to emerging threats. Through empirical analysis, we document substantial reductions in false positive rates and marked improvements in scalability across diverse enterprise architectures. The article also addresses critical considerations regarding ethical implications, compliance requirements, and integration challenges with existing security infrastructure. The article results indicate that AI-powered penetration testing represents a significant advancement in enterprise security, offering organizations more robust, efficient, and cost-effective means of protecting their digital assets against evolving cyber threats. This article research contributes to the growing body of knowledge in automated security assessment and provides valuable insights for organizations seeking to enhance their security posture through advanced technologies.
AI and Blockchain in Financial Data Transfer: Enhancing Efficiency, Security, and Compliance
Authors: Srianusha Kuchipudi
Published: December 2024

The financial services industry is experiencing a transformative revolution in data transfer systems by integrating artificial intelligence and blockchain technologies. This comprehensive analysis explores the challenges faced by traditional financial institutions in managing high-speed data transfers while optimizing efficiency, and maintaining security and regulatory compliance. The study examines the evolution from legacy systems to modern infrastructure, highlighting how AI-powered solutions optimize data routing and transfer efficiency while blockchain technology ensures security and integrity. Through an investigation of real-world implementations, the research demonstrates how combining AI and blockchain creates robust, efficient, and secure data transfer mechanisms that reshape the financial sector's operational capabilities. The analysis provides detailed insights into performance improvements, security enhancements, and operational efficiencies achieved through these technological integrations.
Challenges and Best Practices in Security Configuration for Containerized Environments
Authors: Venkat Marella
Published: December 2024

Through an analysis of theoretical frameworks, best practices, innovations, and problems, this research study delves into the complex field of Configuration Management (CM) in the current day. The fundamental knowledge of CM is formed by theoretical models, such as ITIL and the Three-Component Model, which direct the efficient identification, control, and status accounting of configuration items. Traditional CM approaches are being reshaped by innovations like DevOps integration, Infrastructure as Code (IaC), and containerization technologies, which provide solutions for scalability, automation, and flexibility. In a multi-cloud setting, containerization enables resource optimization and workload mobility. In recent years, containerization in multi-cloud systems has drawn a lot of interest from both academic and industry development viewpoints. In order to improve cyber safety in containerized systems, the research examines the comparative analysis of security solutions, difficulties, and best practices. In order to investigate safety flaws in containerization platforms, investigate methods for enhancing container isolation, and evaluate the critical role encryption techniques play in delivering secure applications, this review aims to shed light on the improved security posture of containerized environments. Additionally, the report offers helpful advice for companies looking to bolster their cyber security defences on containerization systems.
Creation of a model of automated traction control of shunting locomotives by using artificial intelligence methods
Authors: Gorobchenko Oleksandr , Zaika Denys
Published: December 2024

In the paper, a mathematical model of the automated traction transmission control system of the shunting locomotive was developed, using methods of fuzzy logic and the method of expert evaluations. The Mamdani algorithm was used for the proposed model. The algorithm includes the knowledge base of an intelligent system, which uses a production model to formalize and represent knowledge in memory, combining elements of logical and network management approaches. The resulting automated traction transmission control model of the shunting locomotive offers its optimal driving mode for a specific train and section. The model uses the generated fuzzy knowledge base. The result of the model calculation is a control signal for the movement of the shunting locomotive on 4 motors, using partially the 3rd and fully the 4th and 5th positions of the driver's controller. This mode of movement allows to reduce fuel consumption for shunting of the locomotive with partial loads on the traction electric transmission.
NANDA e NIC Aplicada ao Paciente Portador de Diabetes Mellitus
Authors: Guimarães Mateus Henrique Dias
Published: December 2024

O Diabetes representa um fator de risco para as doenças cardiovasculares e cardiopatias e outros tipos de iatrogenicidades. O presente estudo tem como objetivo implantar os Diagnósticos de Enfermagem da NANDA [2] e as Intervenções de Enfermagem do NIC [3] no paciente em estudo para o bem-estar e melhoria da qualidade de vida do paciente, realizando anamnese e exame físico, como fonte de coletas de dados, conhecendo o tratamento farmacológico por ele usado. Para a realização do presente estudo realizou-se acompanhamento do paciente durante o período de 02 a 04 de Junho de 2016, por meio de entrevista, exame físico e análise do prontuário do mesmo. Foi implantado a NANDA e NIC obtendo êxito e aprendizado com o tema, ressaltando também a importância do enfermeiro que representa um papel importante na sociedade, pois como abordado, a paciente em estudo faz o seu acompanhamento na rede de atenção básica.
Popular Cyberbullying Detection Methods on Social Media
Authors: Lamghari Zineb
Published: December 2024

In contemporary times, the utilisation of social networks entails inherent risks, particularly on platforms such as Instagram, Twitter, and TikTok, which facilitate anonymous usage and the creation of fictitious profiles. The cloak of anonymity has fostered an ideal environment for cyberbullying, a form of harassment that unfolds in the virtual realm accessed by children and youth through information and communication technologies like the Internet and cell phones. Cyberbullying is pervasive across numerous countries globally. Consequently, a multitude of studies have delved into diverse solutions aimed at preventing cyberbullying, including various approaches and techniques for identifying and detecting it through the analysis of text language. This systematic review seeks to encompass these scientific studies that primarily focus on the classification of cyberbullying based on the language employed in online communication.
Технології розвитку державно-приватного партнерства у сфері професійної (професійно-технічної) освіти у повоєнний час
Authors:   Інститут професійної освіти ЕАПН України, Кравець Світлана , Мордоус Ірина , Попова Валентина , Радкевич Валентина , Рябова Зоя , Царьова Євеліна , Чепуренко Янна
Published: December 2024

У посібнику висвітлено тенденції розвитку державно-приватного партнерства у сфері професійної (професійно-технічної) освіти; розкрито зміст, сучасні форми і засоби державно-приватного партнерства у сфері професійної (професійно-технічної) освіти; запропоновано методичні підходи до визначення очікуваного соціального ефекту державно-приватного партнерства; представлено технології впровадження моделі державно-приватного партнерства у вітчизняному освітньому просторі та управління закладами професійної (професійно-технічної) освіти у повоєнний час. Видання адресоване розробникам державної освітньої політики, представникам органів місцевого самоврядування, керівникам навчально (науково)-методичних центрів (кабінетів) професійно-технічної освіти, закладів професійної (професійно-технічної) освіти, педагогічним і науково-педагогічним працівникам, науковцям, аспірантам, докторантам, стейкхолдерам.
Система розвитку професійної компетентності педагогічних працівників фахових коледжів в умовах пандемії, воєнного та повоєнного часу
Authors:   Інститут професійної освіти НАПН України, Тітова Олена , Лузан Петро , Пащенко Тетяна , Мося Ірина , Остапенко Анна , Ямковий Олександр
Published: December 2024

У колективній монографії розкрито сучасну теорію розвитку професійної компетентності педагогічних працівників фахових коледжів в умовах пандемії, воєнного та повоєнного часу. Проаналізовано виклики і тенденції розвитку фахової передвищої освіти в умовах пандемії, воєнного та повоєнного часу, виявлено особливості розвитку професійної компетентності педагогічних працівників фахових коледжів у сучасній освітній практиці. Обґрунтовано методологічні підходи, визначено основні принципи, педагогічні умови розвитку професійної компетентності педагогічних працівників фахових коледжів. Представлено концепцію та методичну систему, висвітлено технологічні аспекти розвитку професійної компетентності педагогічних працівників фахових коледжів. Видання адресоване науковцям, розробникам державної політики в галузі освіти, педагогічним працівникам закладів фахової передвищої освіти, спеціалістам системи післядипломної педагогічної освіти, управлінцям і роботодавцям для: проведення занять на курсах підвищення кваліфікації, та перепідготовки педагогічних працівників.
Методичні рекомендації щодо розроблення валідних тестів у закладах фахової передвищої освіти
Authors:   Інститут професійної освіти НАПН України, Лузан Петро , Лапа Оксана , Пащенко Тетяна , Мося Ірина , Ваніна Наталія , Остапенко Анна , Ямковий Олександр
Published: December 2024

У методичних рекомендаціях представлена сучасна теорія тестів та особливості її застосування для оцінювання результатів навчання студентів. Обґрунтовано технології оцінювання компетентнісних досягнень здобувачів фахової передвищої освіти. Охарактеризовано організаційно-технологічні аспекти педагогічного тестування. Подано приклади проєктування валідних тестів оцінювання результатів навчання студентів. Для педагогічних працівників закладів професійної та фахової передвищої освіти, науковців, розробників державної політики в галузі освіти, спеціалістів системи післядипломної педагогічної освіти, управлінців, роботодавців, здобувачів освіти
Проєктний менеджмент у закладах професійної (професійно-технічної) освіти
Authors:   Інститут професійної освіти ЕАПН України, Бородієнко Олександра , Рябова Зоя , Радкевич Олександр
Published: December 2024

У посібнику розкрито зміст проєктного менеджменту та сучасні підходи до планування проєктної діяльності закладів професійної (професійно-технічної освіти); представлено технології стратегічного управління та прикладні інструменти проєктного менеджменту у закладах професійної (професійно-технічної) освіти. Для розробників державної політики у сфері професійної освіти, керівників і педагогічних працівників закладів освіти, працівників науково (навчально-методичних) центрів (кабінетів) професійно-технічної освіти, Державної служби зайнятості України, викладачів і студентів закладів професійної, передвищої та вищої освіти, науковців, аспірантів, докторантів.
The Best Machine Learning Model for Fraud Detection In Banking Sector: A Systematic Literature Review
Authors: Yanto Yanto , Lisah Lisah , Tandra Re’gina
Published: December 2024

In today's financial landscape, banks face an increasing number of sophisticated fraud attempts, driven by advancements in technology and the growing volume of digital transactions, especially accelerated by the COVID-19 pandemic. This systematic literature review (SLR) aims to identify the most effective machine learning models for fraud detection in the banking sector, based on open access articles published between 2014 and 2024. Data was collected using Dimensions.ai, focusing on keywords such as "machine learning," "fraud detection," and "banking." Various models, including Naive Bayes, Logistic Regression, XGBoost, Autoencoder, K-Nearest Neighbors (KNN), Light GBM, Random Forest, Autoencoder, Support Vector Machine (SVM), Bidirectional LSTM, and Generative Adversarial Network (GAN), were reviewed for their effectiveness in detecting banking fraud. The review highlights that models like XGBoost, Light GBM, and Random Forest consistently demonstrate high accuracy in detecting various types of fraud within the banking sector, with Autoencoder-XGB-SMOTE-CGAN achieving the highest accuracy of up to 98%. Hybrid models such as Autoencoder-XGB-SMOTE-CGAN show superior performance, achieving very high accuracy and top scores in metrics like MCC, TNR, and ACC. The most common types of fraud detected include credit card fraud, identity theft, and account takeover. Techniques for data balancing and preprocessing, such as SMOTE and BMR, significantly enhance the accuracy of fraud detection models. The findings suggest that while traditional models like Naive Bayes and Logistic Regression remain effective, advanced models like XGBoost and hybrid approaches provide the highest accuracy in fraud detection. Future research should explore the development of hybrid models and the application of real-time data to further improve fraud detection systems in the banking sector, ensuring they can adapt to the rapidly evolving threat landscape.