The concept of life skills is related to the way of life that emphasises the mutual exchange of knowledge, attitudes, and interpersonal skills in education. Its objective is to develop diverse skills among students and prepare them to face life’s challenges with determination. The World Health Organization has defined life skills as “the positive behaviours and tendencies that enable a person to adapt in day-to-day life.” Life skills are the abilities that enable a person to adapt and exhibit positive behaviour, allowing them to deal effectively with the problems and challenges of daily life. Life is a unique gift. Therefore, by equipping life with various skills, happiness, peace, and prosperity are created. In this research, with the objectives of the study in mind, an analytical examination of life skills among secondary-level students has been conducted. This research study examines the effects of living conditions, gender, and social class on students’ life skills and presents the findings. Future researchers can build upon this, and other factors affecting the research can also be explored.
This study investigates the performance of alkali-activated mortar incorporating slag, fly ash, and desert sand, with a focus on flowability, mechanical properties, sulfate resistance, and microstructural characteristics. A four-factor, three-level orthogonal experimental design was used to analyze the effects of the fly ash substitution rate, alkali content (Na<sub>2</sub>O/b), activator modulus, and desert sand replacement rate for natural sand. The results indicate that increased slag and desert sand contents reduce mortar flowability. Despite this, the mortar exhibits excellent mechanical strength, with compressive strength reaching 77.7 MPa at 28 days and increasing to 89.34 MPa under sulfate exposure. However, after 120 days of sulfate erosion, a decline in strength is observed due to the formation of expansive products such as gypsum and caliche, leading to cracking. Microstructural analyses (XRD, SEM/EDS, MIP) reveal partial dissolution of desert sand under alkali activation, enhancing gel formation and reducing cumulative porosity. The pore structure predominantly consists of harmless pores. These findings demonstrate the potential of slag–fly ash–desert sand alkali-activated mortar as a durable and sustainable material for structural and construction engineering applications, especially in sulfate-rich environments or arid regions where desert sand is abundant.
This study investigates the seismic performance of the V3.0 220 kV standard-designed substation of the Southern Power Grid, located in a high-intensity seismic zone, with a focus on the application of seismic isolation technology. Seismic isolation and structural analysis were conducted and shaking table tests were performed on both isolated and non-isolated structural models. A total of 40 tests were carried out using three levels of ground motion intensity (i.e., 140 gal, 400 gal, and 800 gal) and in three directions (unidirectional, bidirectional, and triaxial). The dynamic characteristics, seismic response, and isolation effectiveness were evaluated. Results indicate that the test models exhibit strong agreement with theoretical and numerical predictions, with an average frequency deviation of 10.98%. The fundamental period of the isolated structure was extended by a factor of 2.33 compared to the non-isolated configuration. As the peak ground acceleration increased, structural frequency decreased, and the period increased. The isolated structure showed a lower first-period growth rate (4.82%) than the non-isolated structure (15.38%). Even under 800 gal excitations, the isolated structure remained within the elastic range. Seismic isolation significantly reduced structural response, with a control effectiveness exceeding 50%, enabling a one-degree reduction in seismic design intensity. A vulnerability analysis based on 200 simulated earthquake cases revealed that the isolated structure exhibited lower failure probabilities across four performance states. At 600 gal PGA, the failure probability in the LS3 state was reduced by 27.8%. These findings confirm the effectiveness and reliability of seismic isolation design for substations in high seismic intensity regions.
Torkel Bjørnskau, Ingeborg Storesund Hesjevoll, Rikke Ingebrigtsen
et al.
This study explores the characteristics of bicycle, e-bike, and e-scooter accidents that are not included in official Norwegian accident statistics, focusing on findings from the ReCyCLIST project in Agder County. Traditional accident reporting systems overlook most incidents involving vulnerable road users (VRUs), particularly single accidents, which represent the majority of such cases. ReCyCLIST introduced a digital self-reporting tool deployed in hospitals and clinics, collecting 671 accident cases between June 2022 and April 2024. The study analyses 487 incidents that occurred in traffic environments, revealing that 73% were single accidents, predominantly caused by infrastructure issues or loss of balance, rather than collisions. The data also highlight demographic differences in accident patterns by age, gender, and vehicle type. Notably, women were more frequently involved in e-scooter accidents, and men were overrepresented in racing bike collisions. Multivariate analysis shows that vehicle type, especially racing bikes, is a strong predictor of collisions. The findings emphasize the critical role of underreported single accidents and provide actionable insights for urban planning and policy development aimed at improving micromobility safety.
Transportation engineering, Transportation and communications
In this paper, we develop a profit-sharing-based optimal routing mechanism to incentivize horizontal collaboration among urban goods distributors. The core of this mechanism is based on exchanging goods at meet points, which is optimally planned en route. We propose a Collaborative Electric Vehicle Routing Problem with Meet Points (CoEVRPMP) considering constraints such as time windows, opportunity charging, and meet-point synchronization. The proposed CoEVRPMP is formulated as a mixed-integer nonlinear programming model. We present an exact method via branching and a matheuristic that combines adaptive large neighborhood search with linear programming. The viability and scalability of the collaborative method are demonstrated through numerical case studies, including a real-world case and a large-scale experiment with up to 500 customers. The findings underscore the significance of horizontal collaboration among delivery companies in attaining both higher individual profits and lower total costs. Moreover, collaboration helps to reduce the environmental footprint by decreasing travel distance.
Purpose – In an increasingly interconnected world, transportation infrastructure has emerged as a critical determinant of economic growth and global competitiveness. High-speed rail (HSR), characterized by its exceptional speed and efficiency, has garnered widespread attention as a transformative mode of transportation that transcends borders and fosters economic development. The Kuala Lumpur – Singapore (KL-SG) HSR project stands as a prominent exemplar of this paradigm, symbolizing the potential of HSR to serve as a catalyst for national economic advancement. Design/methodology/approach – This paper is prepared to provide an insight into the benefits and advantages of HSR based on proven case studies and references from global HSRs, including China, Spain, France and Japan. Findings – The findings that have been obtained focus on enhanced connectivity and accessibility, attracting foreign direct investment, revitalizing regional economies, urban development and city regeneration, boosting tourism and cultural exchange, human capital development, regional integration and environmental and sustainability benefits. Originality/value – The KL-SG HSR, linking Kuala Lumpur and Singapore, epitomizes the potential for HSR to be a transformative agent in the realm of economic development. This project encapsulates the aspirations of two dynamic Southeast Asian economies, united in their pursuit of sustainable growth, enhanced connectivity and global competitiveness. By scrutinizing the KL-SG High-Speed Rail through the lens of economic benchmarking, a deeper understanding emerges of how such projects can drive progress in areas such as cross-border trade, tourism, urban development and technological innovation.
Transportation engineering, Railroad engineering and operation
3D object-detection based on LiDAR point clouds can help driverless vehicles detect obstacles. However, the existing point-cloud-based object-detection methods are generally ineffective in detecting small objects such as pedestrians and cyclists. Therefore, a small-object-detection algorithm based on clustering is proposed. Firstly, a new segmented ground-point clouds segmentation algorithm is proposed, which filters out the object point clouds according to the heuristic rules and realizes the ground segmentation by multi-region plane-fitting. Then, the small-object point cloud is clustered using an improved DBSCAN clustering algorithm. The K-means++ algorithm for pre-clustering is used, the neighborhood radius is adaptively adjusted according to the distance, and the core point search method of the original algorithm is improved. Finally, the detection of small objects is completed using the directional wraparound box model. After extensive experiments, it was shown that the precision and recall of our proposed ground-segmentation algorithm reached 91.86% and 92.70%, respectively, and the improved DBSCAN clustering algorithm improved the recall of pedestrians and cyclists by 15.89% and 9.50%, respectively. In addition, visualization experiments confirmed that our proposed small-object-detection algorithm based on the point-cloud clustering method can realize the accurate detection of small objects.
Simegnew Yihunie Alaba, Ali C. Gurbuz, John E. Ball
The pursuit of autonomous driving relies on developing perception systems capable of making accurate, robust, and rapid decisions to interpret the driving environment effectively. Object detection is crucial for understanding the environment at these systems’ core. While 2D object detection and classification have advanced significantly with the advent of deep learning (DL) in computer vision (CV) applications, they fall short in providing essential depth information, a key element in comprehending driving environments. Consequently, 3D object detection becomes a cornerstone for autonomous driving and robotics, offering precise estimations of object locations and enhancing environmental comprehension. The CV community’s growing interest in 3D object detection is fueled by the evolution of DL models, including Convolutional Neural Networks (CNNs) and Transformer networks. Despite these advancements, challenges such as varying object scales, limited 3D sensor data, and occlusions persist in 3D object detection. To address these challenges, researchers are exploring multimodal techniques that combine information from multiple sensors, such as cameras, radar, and LiDAR, to enhance the performance of perception systems. This survey provides an exhaustive review of multimodal fusion-based 3D object detection methods, focusing on CNN and Transformer-based models. It underscores the necessity of equipping fully autonomous vehicles with diverse sensors to ensure robust and reliable operation. The survey explores the advantages and drawbacks of cameras, LiDAR, and radar sensors. Additionally, it summarizes autonomy datasets and examines the latest advancements in multimodal fusion-based methods. The survey concludes by highlighting the ongoing challenges, open issues, and potential directions for future research.
Massive multiple-input-multiple-output (MIMO) is a key enabler for obtaining higher data rates in the next generation wireless technology. While it has the power to transform cellular communication, with potential for spatial diversity and multiplexing, a bottleneck that often gets overlooked is the fronthaul capacity. The fronthaul link that connects a massive MIMO Remote Radio Head (RRH) and carries in-phase and quadrature (IQ) samples to the Baseband Unit (BBU) of the base station can throttle the network capacity/speed if appropriate data compression techniques are not applied, particularly in the uplink. This paper proposes an iterative technique for fronthaul load reduction in the uplink for massive MIMO systems utilizing the convolution structure of the received signals. The proposed algorithm provides compression ratios of about 30-<inline-formula> <tex-math notation="LaTeX">$50\times $ </tex-math></inline-formula>. This work provides extensive analysis of the performance of the proposed method for a plethora of practical scenarios and constraints, such as different channel parameters and models, receive antenna correlation, and under imperfect channel information. It also discusses the numerical convergence and complexity of the proposed algorithm and compares the performance against other existing compression techniques.
Telecommunication, Transportation and communications
The current High-Speed Railway (HSR) communications increasingly fail to satisfy the massive access services of numerous user equipment brought by the increasing number of people traveling by HSRs. To this end, this paper investigates millimeter-Wave (mmWave) extra-large scale (XL)-MIMO-based massive Internet-of-Things (IoT) access in near-field HSR communications, and proposes a block simultaneous orthogonal matching pursuit (B-SOMP)-based Active User Detection (AUD) and Channel Estimation (CE) scheme by exploiting the spatial block sparsity of the XL-MIMO-based massive access channels. Specifically, we first model the uplink mmWave XL-MIMO channels, which exhibit the near-field propagation characteristics of electromagnetic signals and the spatial non-stationarity of mmWave XL-MIMO arrays. By exploiting the spatial block sparsity and common frequency-domain sparsity pattern of massive access channels, the joint AUD and CE problem can be then formulated as a Multiple Measurement Vectors Compressive Sensing (MMV-CS) problem. Based on the designed sensing matrix, a B-SOMP algorithm is proposed to achieve joint AUD and CE. Finally, simulation results show that the proposed solution can obtain a better AUD and CE performance than the conventional CS-based scheme for massive IoT access in near-field HSR communications.
This paper describes an optimization model for an irregular train schedule. The aim is to optimize both the maximum train loading rate and the average deviation of departure intervals under time-varying passenger transport demand for an urban rail transit line in consideration of practical train operation constraints, i.e., headway, running time between stations, dwell time, and capacity. A heuristic simulated-annealing algorithm is designed to solve the optimization model, and a case study of an urban rail transit line is performed to assess its efficacy. The results show that, compared with the current regular train schedule, the total train dwell time under the optimized irregular schedule is reduced from 900 s to 848 s, and the reduction ratio for the maximum train loading rate is from 1.2% to 3.6% for different stations. When the average train departure interval is allowed to vary from 120 to 170 s, the optimized irregular schedule decreases the maximum train loading rate of the collinear and noncollinear sections by 3.21%–4.82% and 2.52%–3.64%, respectively. Sensitivity analysis is performed for a nonnegative weight coefficient, average train departure interval, and proportion of full-length and short-turn routings. The proposed approach can be used to support capacity improvement and schedule optimization for urban rail transit lines.
Transportation engineering, Transportation and communications
За результатами аналізу існуючих досліджень останнього періоду в напрямку досліджень міцності довгобазних вагонів-платформ встановлено необхідність експериментального обґрунтування конструктивних рішень рами довгобазних вагонів-платформ та оцінки точності теоретичних розрахунків шляхом проведення вібраційних випробувань на втому. Представлено методологію проведення вібраційних випробувань на втому довгобазного вагона-платформи. Наведено результати теоретичних досліджень з визначення міцності довгобазного вагона-платформи. Наведено результати експериментальних досліджень вібраційних випробувань на втому рами довгобазного вагона-платформи до та після конструктивного удосконалення. Приведено конструктивні зміни та удосконалення рами довгобазного вагона-платформи на етапі проведення експериментальних вібраційних випробувань на втому. Показано місце зародження та розвитку тріщини в конструкції рами довгобазного вагона-платформи. За результатами досліджень визначено фактичну міцність елементів конструкції вагона та обрано конструкцію довгобазного вагона-платформи з метою недопущення втомного руйнування та забезпечення необхідної експлуатаційної безпеки руху. Доведено необхідність удосконалення методів оцінки опору втоми зварних з'єднань рам довгобазних вагонів-платформ на стадії проектування.
Матеріали статті сприятимуть створенню нових моделей довгобазних вагонів-платформ для магістрального залізничного транспорту та модернізації існуючих моделей, а також підвищенню міцності та ресурсу під час їх експлуатації.