Search Results (347)

Forest trees have emerged as a promising passive solution for mitigating low-frequency ground vibrations generated by railway operations, offering ecological and cost-effective advantages. This study proposes a three-dimensional semi-analytical method developed for evaluating the dynamic responses of the coupled track–ground–tree system. The thin-layer method is employed to derive an explicit Green’s function corresponding to a har-monic point load acting on a layered half-space, which is subsequently applied to couple the foundation with the track system. The forest trees are modeled as surface oscillators coupled on the ground surface to evaluate the characteristics of multiple scattered wavefields. The vibration attenuation capacity of forest trees in mitigating railway-induced ground vibrations is systematically investigated using the proposed method. In the direction perpendicular to the track on the ground surface, a graded array of forest trees with varying heights is capable of forming a broad mitigation frequency band below 80 Hz. Due to the interaction of wave fields excited by harmonic point loads at multiple locations, the attenuation performance of the tree system varies significantly across different positions on the surface. The influence of variability in tree height, radius, and density on system performance is subsequently examined using a Monte Carlo simulation. Despite the inherent randomness in tree characteristics, the forest still demonstrates notable attenuation effectiveness at frequencies below 80 Hz. Among the considered parameters, variations in tree height exert the most pronounced effect on the uncertainty of attenuation performance, followed sequentially by variations in density and radius. Full article

In this research project a large linear electromagnetic actuator (LLEA) was designed and manufactured. The electromagnetic performance was published in previous works, but in this paper we focus on the technological challenges related to the manufacturing in particular. This LLEA was based on the magnet-free switched-reluctance principle, having six effective energised stator “teeth” and four passive mover parts (4:6 ratio). Various aspects and challenges encountered during the manufacturing, transport, and assembly are discussed. Thermal expansion of steel contributed to the decision of the modular design, with each module having 1.3 m in length, with a 2 mm longitudinal dilatation gap. The initial prototype was tested with a 10.6 m length, with plans to extend the test track to 60 m, which was fully achievable due to the modular design and required 29 tons of electrical steel to be built. The stator laminations were cut by a bespoke progressive tool with stamping, and other parts by a CO₂ laser. Mounting was based on welding (back of the stator) and clamping plates (through insulated bolts). The linear longitudinal force was on the order of 8 kN, with the main air gap of 7.5–10 mm on either side of the mover. The lateral forces could exceed 40 kN and were supported by appropriate construction steel members bolted to the concrete floor. The overall mechanical tolerances after installation remained below 0.5 mm. The technology used for constructing this prototype demonstrated the cost-effective way for a semi-industrial manufacturing scale. Full article

This paper comprehensively reviews structural vibration control systems for earthquake mitigation in civil engineering structures. Structural vibration control is vital for enhancing the resilience and safety of infrastructure subjected to seismic activity. This study examines various control strategies, including passive, active, and hybrid methods, with a focus on the advantages of semi-active systems, which offer a balance of energy efficiency and adaptive capabilities. Semi-active devices, such as magnetorheological dampers, are highlighted for their ability to offer adaptive control without the high energy demands of fully active systems. The review discusses challenges like time delays, sensor placement, and model uncertainties that can impact the practical implementation of these systems. Experimental studies and real-world applications demonstrate the effectiveness of semi-active systems in reducing seismic responses. This paper emphasizes the need for further research into optimizing control algorithms and addressing practical challenges to enhance the reliability and robustness of these systems. It concludes that semi-active control systems are a promising solution for enhancing structural resilience in earthquake-prone areas, offering a practical alternative that strikes a balance between performance and energy requirements. Full article

In today’s fast-paced world of software development, it is essential to ensure that programs run smoothly without any issues. When dealing with complex applications, the objective is to predict and resolve problems before they escalate. The prediction of software defects is a crucial element in maintaining the stability and reliability of software systems. This research addresses this need by combining advanced techniques (ensemble techniques) with seventeen machine learning algorithms for predicting software defects, categorised into three types: semi-supervised, self-supervised, and supervised. In supervised learning, we mainly experimented with several algorithms, including random forest, k-nearest neighbors, support vector machines, logistic regression, gradient boosting, AdaBoost classifier, quadratic discriminant analysis, Gaussian training, decision tree, passive aggressive, and ridge classifier. In semi-supervised learning, we tested are autoencoders, semi-supervised support vector machines, and generative adversarial networks. For self-supervised learning, we utilized are autoencoder, simple framework for contrastive learning of representations, and bootstrap your own latent. After comparing the performance of each machine learning algorithm, we identified the most effective one. Among these, the gradient boosting AdaBoost classifier demonstrated superior performance based on an accuracy of 90%, closely followed by the AdaBoost classifier at 89%. Finally, we applied ensemble methods to predict software defects, leveraging the collective strengths of these diverse approaches. This enables software developers to significantly enhance defect prediction accuracy, thereby improving overall system robustness and reliability. Full article

Global data reveal ongoing inequalities faced by minorities, often reinforced by media portrayals that depict them as threats, victims, or passive individuals without agency. While media framing has been extensively studied, especially in terms of media content and representation, few studies have examined how journalists perceive and navigate the coverage of minorities. This study addresses that gap by examining how Greek journalists perceive mainstream media coverage of refugees and migrants, LGBTQ+ individuals, and people with mental health challenges, with particular attention to their sourcing practices and sense of ethical responsibility. Fourteen journalists participated in semi-structured interviews, and thematic analysis was applied to identify key patterns. Journalists described dominant media narratives as fragmented, stereotypical, and dehumanizing, noting the frequent use of linguistic inaccuracies, misinformation, and the absence of personal stories. At the same time, they reported opportunities within their own sourcing practices to promote more inclusive and accurate coverage. Ethical concerns were expressed on three levels—union; corporate; and personal—with calls for clearer editorial guidelines and dedicated training. Many participants emphasized the role of personal ethics as a guiding compass in navigating complex newsroom pressures. Full article

Soil moisture content (SMC) is a critical parameter for agricultural productivity, particularly in semi-arid regions, where irrigation practices are extensively used to offset water deficits and ensure decent yields. Yet, the socio-economic and remote context of these regions prevents sufficiently dense SMC monitoring in space and time to support farmers in their work to avoid unsustainable irrigation practices and preserve water resource availability. In this context, our study addresses the challenge of high spatial resolution (i.e., 20 m) SMC estimation by integrating remote sensing datasets in machine learning models. For this purpose, a dataset made of 166 soil samples’ SMC along with corresponding SMC, precipitation, and radar signal derived from Soil Moisture Active Passive (SMAP), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Sentinel-1 (S1), respectively, was used to assess four machine learning models’ (Decision Tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB) reliability for SMC mapping. First, each model was trained/validated using only the coarse spatial resolution (i.e., 10 km) SMAP SMC and IMERG precipitation estimates as independent features, and, second, S1 information (i.e., 20 m) derived from single scenes and/or composite images was added as independent features to highlight the benefit of information (i.e., S1 information) for SMC mapping at high spatial resolution (i.e., 20 m). Results show that integrating S1 information from both single scenes and composite images to SMAP SMC and IMERG precipitation data significantly improves model reliability, as R² increased by 12% to 16%, while RMSE decreased by 10% to 18%, depending on the considered model (i.e., RF, XGB, DT, GB). Overall, all models provided reliable SMC estimates at 20 m spatial resolution, with the GB model performing the best (R² = 0.86, RMSE = 2.55%). Full article

This paper explores the optimal configuration strategies for building-integrated photovoltaic (BIPV) systems in response to the low-carbon transformation needs of semi-outdoor substations, aiming to reconcile the contradiction between photovoltaic (PV) power generation efficiency and indoor environmental control in industrial buildings. Taking a 220 kV semi-outdoor substation of the China Southern Power Grid as a case study, a building energy consumption–PV power generation coupling model was established using EnergyPlus software. The impacts of three PV wall constructions and different building orientations on a transformer room and an air-conditioned living space were analyzed. The results show the EPS-filled PV structure offers superior passive thermal performance and cooling energy savings, making it more suitable for substation applications with high thermal loads. Building orientation plays a decisive role in the net energy performance, with an east–west alignment significantly enhancing the PV module’s output and energy efficiency due to better solar exposure. Based on current component costs, electricity prices, and subsidies, the BIPV system demonstrates a moderate annual return, though the relatively long payback period presents a challenge for widespread adoption. East–west orientations offer better returns due to their higher solar exposure. It is recommended to adopt east–west layouts in EPS-filled PV construction to optimize both energy performance and economic performance, while further shortening the payback period through technical and policy support. This study provides an optimized design path for industrial BIPV module integration and aids power infrastructure’s low-carbon shift. Full article

This paper presents the results of investigating the application of magnetorheological fluids in controlling the lateral and angular vibrations of a high-speed elevator. Numerical simulations are performed for a mathematical model with two degrees of freedom. The lateral and rotational accelerations are analyzed for different travel speeds to determine passenger comfort levels. To attenuate the elevator vibrations, the introduction of a magnetorheological damper in parallel with the passive damper of the elevator rollers is considered. To semi-actively control the dissipative forces of the magnetorheological fluids, a State-Dependent Riccati Equation (SDRE control) is proposed. The numerical results demonstrate that using an MR damper makes it possible to reduce the acceleration levels of the elevator cabin, thus improving passenger comfort and reducing the elevator’s vibration levels and wear on the mechanical and electronic components of the elevator. In addition to the results, a detailed sensitivity analysis is presented. Full article

This study develops a calibrated multiscale simulation of three lightweight industrial warehouses located in Tecámac, Mexico, to evaluate the dual role of rooftop photovoltaic (PV) arrays as renewable energy generators and passive thermal modifiers. Dynamic energy models were developed using EnergyPlus via Ladybug Tools v. 1.8.0 and calibrated against 2021 real-world electricity billing data, following ASHRAE Guideline 14. Statistical analyses conducted in RStudio v2024.12.1 Build 563 confirmed significant passive cooling effects induced by PV integration, achieving up to 15.3 °C reductions in peak indoor operative temperatures and improving thermal comfort rates by approximately 10 percentage points. While operational energy savings were evident, the primary focus of this research was on the multiscale modeling of thermal performance enhancement in composite metallic-PV roofing systems under semi-arid climatic conditions. These results provide new insights into computational approaches for optimizing passive thermal performance in lightweight industrial envelopes. Full article

Objectives: Dietary conditions are closely related to maternal health. This study aims to investigate the causal relationship between the first–second-trimester Dietary Inflammatory Index (DII) and developing anemia in the third trimester. Methods: This prospective cohort study comprised 545 pregnant women, with dietary data assessed via a semi-quantitative food frequency questionnaire (FFQ). Hemoglobin levels were obtained by hospital laboratory tests and used to diagnose anemia. Multivariable logistic regression models—adjusted for baseline serum iron, age, pre-pregnancy body mass index (BMI), occupation, education, history of adverse pregnancy outcomes, parity, serum iron, passive smoking exposure, and iron supplementation use during pregnancy—were employed to evaluate the relationships between the first-trimester DII, second-trimester DII, first–second-trimester average DII, and third-trimester anemia. Results: After multivariable adjustment, the first–second-trimester average DII in the pro-inflammatory diet group demonstrated a 3.73-fold elevated risk of third-trimester anemia compared to the anti-inflammatory diet group (Odds Ratio [OR] = 3.73, 95% Confidence Interval [CI]: 1.50–9.25). Conclusions: Pro-inflammatory dietary patterns during pregnancy exhibit a significant correlation with developing third-trimester anemia. This study demonstrates that reducing dietary pro-inflammatory components through prenatal nutrition programs may lower third-trimester anemia risk. Notably, this study carries potential risks of bias, including self-reporting bias in dietary data and incompletely controlled confounding factors (such as unmeasured biomarkers). Full article

To explore the impact of different agroforestry systems on carbon sequestration, the carbon management index, and carbon fractions, a long-term (37 years) field trial was conducted using three tree-based agroforestry systems consisting of tree species, namely Acacia tortilis, Hardwickia binata, and Tecomella undulata, along with fallow land in a semi-arid region of India. The soil samples were taken at four distinct depths (0–15, 15–30, 30–60, and 60–90 cm) with eight replications and analyzed for soil total organic carbon (TOC), soil organic carbon fractions, soil carbon stocks, and the carbon management index (CMI). In the topsoil layer (0–30 cm), the Acacia tortilis-based agroforestry system recorded a total organic carbon (TOC) content of 4.09%, which was 42.5% higher than that of fallow land. In this layer, the active carbon pool (ACP) was more prominent than the passive carbon pool (PCP). Compared to fallow land, the ACP increased by 68.3%, 59%, and 53.6% for the Acacia tortilis-, Hardwickia binata-, and Tecomella undulata-based systems, respectively. Similarly, the PCP increased by 18.4%, 11.8%, and 8.2% for the same respective systems in the topsoil layer. For the 0–90 cm soil layer, the Acacia tortilis-based agroforestry system sequestered the highest amount of total organic carbon (39.34 Mg C ha⁻¹), followed by agroforestry systems based on Hardwickia binata (37.86 Mg C ha⁻¹), Tecomella undulata (36.99 Mg C ha⁻¹), and fallow land (30.65 Mg C ha⁻¹). Carbon sequestration is higher in the subsurface soil layers (30–90 cm) than in the surface layers. This trend is observed across all agroforestry systems. The carbon management index registered higher for the Acacia tortilis-based agroforestry system (166.58) at the top soil layer than others. Hence, long-term agroforestry systems could improve soil carbon storage and the carbon management index as compared to fallow land. A 37-year field study in a semi-arid region of India revealed that Acacia tortilis-based agroforestry significantly enhances soil carbon sequestration, active carbon pools, and the carbon management index, especially in deeper soil layers, compared to fallow land. Full article

In view of the deterioration of vehicle dynamic performance caused by the increased radial stiffness of a non-pneumatic wheel and its nonlinearity, a semi-active suspension error tracking backstepping control strategy based on the model reference method is proposed. Firstly, the segmental linearization of the nonlinear stiffness of the non-pneumatic wheels is carried out, and a quarter-vehicle system model integrating the non-pneumatic wheel and the semi-active suspension is established. Subsequently, a model reference system based on the $H_{\infty }$ control theory is designed. On this basis, a semi-active suspension error tracking backstepping controller based on the reference model is developed. Finally, comparative dynamics simulations are carried out to verify the effectiveness of the controller. The results indicate that the designed controller exhibits a superior control effect compared with the existing controllers. Specifically, the proposed control method reduces the root-mean-square (RMS) value of sprung mass acceleration by 20.0% under random road excitation compared to passive suspension while ensuring system constraints, and reduces the peak-to-peak (P-P) values of sprung mass acceleration and dynamic wheel load by 60.6% and 42.5% under bumpy road excitation, respectively. Full article

In Saudi Arabia’s hot and arid climate, residential buildings account for over half of national electricity consumption, with cooling demands alone responsible for more than 70% of this use. This paper explores the hypothesis that contemporary villa designs are inherently inefficient and that current building regulations fall short of enabling adequate thermal performance. This issue is expected to become increasingly significant in the near future as external temperatures continue to rise. The study aims to assess whether passive design strategies rooted in both engineering and architectural principles can offer substantial reductions in cooling energy demand under current and future climatic conditions. A typical detached villa was simulated using IES-VE to test a range of passive measures, including optimized window-to-wall ratios, enhanced glazing configurations, varied envelope constructions, solar shading devices, and wind-tower-based natural ventilation. Parametric simulations were conducted under current climate data and extended to future weather scenarios. Unlike many prior studies, this work integrates these strategies holistically and evaluates their combined impact, rather than in isolation while assessing the impact of future weather in the region. The findings revealed that individual measures such as insulated ceilings and reduced window-to-wall ratios significantly lowered cooling loads. When applied in combination, these strategies achieved a 68% reduction in cooling energy use compared to the base-case villa. While full passive performance year-round remains unfeasible in such extreme conditions, the study demonstrates a clear pathway toward energy-efficient housing in the Gulf region. Full article

Vibration control is a critical aspect of engineering, particularly in structures and mechanical systems where excessive oscillations can lead to fatigue, noise, or failure. Vibration suppression is essential in aerospace, automotive, civil, and industrial applications to enhance performance and longevity of systems. Piezoelectric shunt circuits (PSCs) offer a passive or semi-active approach to damping vibrations by leveraging the electromechanical properties of piezoelectric materials. Traditional passive damping methods, such as viscoelastic materials, are effective but lack adaptability. Active control systems, while tunable, require external power and complex electronics, increasing cost and weight. Piezoelectric shunt circuits provide a middle ground, utilizing piezoelectric transducers bonded to a structure and connected to an electrical circuit to dissipate vibrational energy as heat or store it electrically. This review synthesizes the fundamental mechanisms, circuit designs, and practical applications of this technology. It also presents the modeling of lumped and distributed parameter systems coupled with PSCs. It complements the recent reviews and primarily focuses on the period from 2019 to date in addition to the earlier seminal works on the subject. It explores the principles, configurations, advantages, and limitations of piezoelectric shunt circuits for vibration control, alongside recent advancements and potential future developments. It sheds light on the research gaps in the literature that future work may tackle. Full article

This review provides a comprehensive and multidisciplinary overview of recent advancements in solar tracking systems (STSs) aimed at improving the efficiency and adaptability of photovoltaic (PV) technologies. The study systematically classifies solar trackers based on tracking axes (fixed, single-axis, and dual-axis), drive mechanisms (active, passive, semi-passive, manual, and chronological), and control strategies (open-loop, closed-loop, hybrid, and AI-based). Fixed-tilt PV systems serve as a baseline, with single-axis trackers achieving 20–35% higher energy yield, and dual-axis trackers offering energy gains ranging from 30% to 45% depending on geographic and climatic conditions. In particular, dual-axis systems outperform others in high-latitude and equatorial regions due to their ability to follow both azimuth and elevation angles throughout the year. Sensor technologies such as LDRs, UV sensors, and fiber-optic sensors are compared in terms of precision and environmental adaptability, while microcontroller platforms—including Arduino, ATmega, and PLC-based controllers—are evaluated for their scalability and application scope. Intelligent tracking systems, especially those leveraging machine learning and predictive analytics, demonstrate additional energy gains up to 7.83% under cloudy conditions compared to conventional algorithms. The review also emphasizes adaptive tracking strategies for backtracking, high-latitude conditions, and cloudy weather, alongside emerging applications in agrivoltaics, where solar tracking not only enhances energy capture but also improves shading control, crop productivity, and rainwater distribution. The findings underscore the importance of selecting appropriate tracking strategies based on site-specific factors, economic constraints, and climatic conditions, while highlighting the central role of solar tracking technologies in achieving greater solar penetration and supporting global sustainability goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). Full article