Search Results (31,280)

Although cannabinoids have proven therapeutic benefits, they are increasingly known for their capacity to disturb cardiac electrophysiology, particularly in individuals with hidden genetic issues such as channelopathies. This review consolidates molecular, clinical, epidemiological, and forensic findings linking cannabinoid exposure to arrhythmias and sudden cardiac death. It examines how phytocannabinoids, synthetic analogs, and endocannabinoids influence calcium and potassium currents through cannabinoid receptor-dependent and -independent pathways, affect autonomic regulation, and contribute to adverse conditions such as oxidative stress and inflammation in heart tissue. Genetic variants in key genes linked to SCD (SCN5A, KCNH2, KCNQ1, RYR2, and NOS1AP) can reduce repolarization reserve, transforming otherwise subclinical mutations into lethal substrates when combined with cannabinoid-induced electrical disruptions. Forensic research highlights the importance of comprehensive toxicological testing and postmortem genetic analysis in distinguishing between actual causes and incidental findings. There is an urgent need to re-evaluate the cardiovascular safety of cannabinoids, and this is underscored by the findings presented. The merging of molecular, clinical, and forensic evidence reveals that cannabinoid exposure—especially from high-potency synthetic analogs—can reveal latent channelopathies and precipitate fatal arrhythmias. Accordingly, this review advocates for a paradigm shift toward personalized risk stratification. If genetic screening is integrated with ECG surveillance and controlled cannabinoid dosing, risk assessment can be personalized. Ultimately, forensic and epidemiological data highlight the heart’s vulnerability, emphasizing its role as a target of cannabinoid toxicity and as a crucial aspect of public health monitoring. Full article

This work presents a frequency-domain and modal-domain model to analyze how the length of a three-phase power cable influences conducted emission (CE) voltages measured through a line impedance stabilization network (LISN). The measurement setup considered consists of an equipment under test (EUT) connected to the LISN via a power cable whose cross-section is defined in this study as quadrilateral, namely, four conductors arranged at the corners of a quadrilateral: typically the three phases and the protective earth or neutral conductor. The cable is modeled as a multiconductor transmission line (MTL). To evaluate the system performance both with and without the cable, the concept of voltage insertion ratio (IR) is introduced, defined as the reciprocal of the typical insertion loss. Closed-form expressions are derived for both common mode (CM) and differential mode (DM) emissions. The objective is twofold: to understand under which conditions the LISN measurements overestimate or underestimate the actual emissions at the EUT terminals, and to provide a predictive tool to assess the impact of electrically long cables on CE measurements. The model is validated through numerical simulations of quadrilateral cable configurations considering both a homogeneous and inhomogeneous cross-section, highlighting the need to account for cable length in system design and EMC test interpretation. Full article

Workstations in deep underground mines are among the most dangerous in the world. Workers are exposed to various hazards such as water hazards, climate hazards, and gas hazards. In this article, the authors proposed the most suitable method for measuring nitrogen oxides, such as nitric oxide(NO) and nitrogen dioxide (NO₂), under actual underground mine conditions. The selection of the method was based on a literature review, in which the authors presented a brief characterization of available measurement methods and proposed their classification into four categories: chemical methods, electrochemical methods, chemiluminescence methods, and analytical methods. A SWOT analysis was used to select the appropriate method for NOx determination. The authors focused on identifying the most universal method that can handle measurements in the harsh conditions of underground mines, with an emphasis on ease of use in the field. Due to the mine atmosphere being rich in harmful substances, the selectivity of the method was also taken into account. The method chosen by the authors is intended for measuring both low concentrations of NOx (in the atmosphere) and high concentrations (diesel exhaust emissions). Because of the versatility of the method and its potential application in both small and large laboratories, the cost criterion was also considered. Full article

Compared with conventional RC frame buildings, staggered-story frame buildings are prone to the formation of short columns due to the vertical staggering of beam members, which exerts an adverse impact on the seismic performance of the building. Therefore, steel tube-reinforced concrete (ST-RC) columns are usually adopted to address the issue of the insufficient ductility of short columns. For this purpose, to investigate the seismic performance of partial staggered-story RC frame buildings, an elastic–plastic model is established based on a specific practical building, with ST-RC columns installed in the staggered-story area. By varying the confinement coefficients of the ST-RC columns (1.087, 1.152, 1.224, and 1.307) and classifying the member-level performance states, the seismic performance of ST-RC columns in staggered-story buildings under different confinement coefficients is evaluated. The research results indicate the following: in the statistical analysis of the performance states of the positive sections of the ST-RC columns, the degree of damage of the ST-RC columns first decreases and then increases sharply with an increase in the confinement coefficient, and the member damage is minimized when the confinement coefficient is 1.224. In the statistical analysis of the performance states of the inclined sections of the ST-RC columns, the damage state of the ST-RC columns shows a decreasing trend as the confinement coefficient increases; when the confinement coefficients are 1.224 and 1.307, the ST-RC columns are completely in the elastic state. With an increase in the confinement coefficient, the shear force borne by the ST-RC columns first increases and then decreases, while the tensile strain and compressive strain generally show a decreasing trend. When the confinement coefficient is 1.224, the tensile strain and compressive strain of the ST-RC columns are the smallest. Therefore, when arranging ST-RC columns in staggered-story buildings, it is necessary to select an appropriate confinement coefficient according to the actual project conditions to maximize the ductility of the short columns. Full article

Autonomous underwater vehicles (AUVs) play a crucial role in marine environments, such as in inspecting marine structures and monitoring the condition of subsea pipelines. After completing their mission, AUVs dock with recovery systems at designated locations. However, underwater docking carries a significant risk of failure due to unpredictable maritime conditions. Considering the limitations in communication during the mission, docking failure can lead to the loss of collected data and failure of the entire AUV mission. In this study, a hypothetical AUV docking scenario was defined based on expert knowledge and without actual operational data. A Markov chain-based probabilistic model was employed to quantitatively assess the risk of the system during the mission. Environmental factors were excluded from the evaluation, and the simulation results were classified into five categories: success, timeout, internal component failure, exceeding a predefined sequence repetition limit, and spending the electrical energy under the battery SOC threshold. By analyzing the failure points of each category, strategies to improve the scenario success rate were discussed. This study quantitatively identified the interactions between constraints and risk factors that should be considered when establishing AUV docking plans through a virtual scenario-based failure analysis, thereby providing an evaluation framework that can be utilized in actual design. Full article

Rapid urbanization in developing countries has widened the gap between urban and rural development, due to inefficient land markets and weak institutional systems in rural areas. China’s innovative “Urban–rural Construction Land Linkage” policy was designed to address this imbalance by encouraging rural land consolidation and creating a transferable development rights mechanism. While this approach has shown potential in improving the utilization efficiency of existing construction land and continuously supplying urban development space, concerns remain about its actual benefits to villagers and rural development, with some arguing it disrupts traditional livelihoods and favors government interests over rural needs. To respond to this debate, this study investigates two core questions: first, does China’s transferable land development rights (TDR) program genuinely improve rural welfare as intended; second, why does the theoretically preferred self-organized governance model sometimes fail in practice? To address these research questions, this paper develops a new analytical framework combining the IAD framework of Ostrom with the hierarchical institutional framework of Williamson to examine three implementation approaches in China’s TDR implementation: government-dominated, market-invested, and self-organized models. Based on case studies, surveys, and interviews across multiple regions, this study reveals distinct strengths and weaknesses in each approach in improving villagers’ lives. Government-dominated projects demonstrate strong resource mobilization but limited community participation. Market-based models show efficiency gains but often compromise equity. While self-organized initiatives promise greater local empowerment, they frequently face practical challenges including limited management capacity and institutional barriers. Furthermore, this study identifies the preconditional institutional environment necessary for successful self-organized implementation, including clear land property rights, financial support, and technical assistance. These findings advance global understanding of how to combine efficiency with fair outcomes for all stakeholders in land governance, which is particularly relevant for developing countries seeking to manage urban expansion while protecting rural interests. Full article

Nowadays, there is an emphasis on reducing emissions due to industrial processes. In recent decades, filtration systems have become an integral part of the broadly understood heavy industry systems to reduce the emission of dust and other substances harmful to the environment and humans. Filters can also be found in heating, ventilation and air conditioning (HVAC) systems, in the transport industry, and their use in households is also increasing. The effective separation of micro- or nanometer contaminants is closely related to the development of new, sophisticated filter materials. Thanks to the use of modern tools for multiphase flow modeling, it becomes possible to model the flow inside the filter material. In this study, we propose a methodology to simulate the internal flow through porous structures with a fiber size of 5–30 µm. The geometry used to build the mathematical model is the actual geometry of the filter obtained using micro-Computed Tomography (CT) imaging method. The mathematical model has been validated against experimental data. In this article, we show the methodology to adapt a geometry scan for use in commercial Computational Fluid Dynamics (CFD) software (Ansys Fluent 2021 R1). Then we present the analysis of the influence of essential parameters of numerical model, namely the size of representative elementary volume (REV) of porous material, representation quality of porous matrix and numerical mesh density on the pressure drop in the filter. Based on the conducted research, the minimum size of the REV and the numerical mesh density were determined, allowing us to obtain a representative solution of the flow structure through the filtering material. The strong agreement between the model results and experimental data highlights the potential of using a multi-fluid mathematical model to understand filtration dynamics. Full article

Many companies engage in earnings manipulation that obscures their actual financial condition and sustainability efforts, undermining the credibility of financial reports and eroding stakeholder trust. To address these concerns, the United Kingdom has strictly adhered to International Financial Reporting Standards (IFRS), enhancing financial transparency and reducing the risk of manipulation. This study applies agency theory to examine the effects of earnings management and IFRS adoption on corporate sustainability performance, while also assessing the moderating roles of institutional ownership and board independence. Data were drawn from 248 companies listed on the London Stock Exchange between 2002 and 2024, using purposive sampling and sourced from Thomson Reuters Eikon DataStream. Advanced estimation techniques, specifically the Augmented Mean Group (AMG) and fixed effects models with Driscoll-Kraay standard errors, were employed to address cross-sectional dependence and slope heterogeneity. The results indicate that earnings management, as measured by discretionary accruals, has a significant negative impact on sustainability performance. In contrast, the adoption of IFRS has a positive and significant influence on sustainability outcomes. Additionally, institutional ownership and board independence significantly moderate the adverse effects of earnings management, leading to improved sustainability performance. The findings suggest that managers should enhance the clarity and accountability of financial reporting by implementing robust internal systems aligned with IFRS, conducting regular compliance audits, and training finance staff on current disclosure standards. Full article

Accurate prediction of a robot’s dynamic parameters, including mass and moment of inertia, is essential for adequate motion planning and control in autonomous systems. Traditional methods often depend on manual computation or physics-based modelling, which can be time-consuming and approximate for intricate, real-world environments. Recent advances in machine learning, primarily through artificial neural networks (ANNs), offer profitable alternatives. However, the potential of quantum-inspired models in this context remains largely uncharted. The current research assesses the predictive performance of a classical artificial neural network (CANN) and a quantum-inspired artificial neural network (QANN) in estimating a car-like mobile robot’s mass and moment of inertia. The predictive accurateness of the models was considered by minimizing a cost function, which was characterized as the RMSE between the predicted and actual values. The outcomes indicate that while both models demonstrated commendable performance, QANN consistently surpassed CANN. On average, QANN achieved a 9.7% reduction in training RMSE, decreasing from 0.0031 to 0.0028, and an 84.4% reduction in validation RMSE, dropping from 0.125 to 0.0195 compared to CANN. These enhancements highlight QANN’s singular predictive accuracy and greater capacity for generalization to unseen data. In contrast, CANN displayed overfitting tendencies, especially during the training phase. These findings emphasize the significance of quantum-inspired neural networks in enhancing prediction precision for involved regression tasks. The QANN framework has the potential for wider applications in robotics, including autonomous vehicles, uncrewed aerial vehicles, and intelligent automation systems, where accurate dynamic modelling is necessary. Full article

Copper is the core conductive material of power equipment, which has excellent conductivity and ductility. However, in actual operation, a copper conductor is often subjected to both atmospheric corrosion and a high-current field, and its stability is very important for equipment safety. At present, there are fewer systematic studies on the corrosion behavior of copper conductors under the coupling of high current field and atmospheric environment. In this paper, the corrosion behavior of copper conductor materials in the current field environment was studied through immersion and electrochemical experiments. The immersion tests showed that copper undergoes primarily pitting corrosion in 3.5 wt% NaCl solution, with the corrosion products identified as Cu₂O, CuO, and Cu₂Cl(OH)₃. As the applied current density increases, the pits deepen, and the corrosion rate increases significantly with an increasing applied current, rising from 3.88 mm·y⁻¹ at 0 A to 832.82 mm·y⁻¹ at 40 A. This is because the current causes the electrode potential to deviate from its equilibrium state and accelerates ion migration, promoting corrosion. The electrochemical tests indicated that at the same current, charge transfer resistance (Rct) first increases, and then decreases with the immersion time, while the corrosion current density first decreases, and then increases. This reflects that the corrosion product film provides protective effects in the initial stage, but is gradually damaged over time. Full article

The growing challenge of disinformation in the digital age poses significant hurdles for NGO communication practitioners, particularly in engaging Generation Z. Low digital literacy among young individuals also offers an explanation for lower levels of civic engagement. This study explores young people’s perceptions of civic engagement and the effects of disinformation through qualitative focus group discussions. Eight focus groups comprising young adults were conducted to gather insights into their motivations, experiences, and perspectives regarding social and political issues as well as fake news combating strategies. Findings from this research will contribute to the existing literature on the relationship between youth, fake news, and civic engagement. The results indicate that youth primarily rely on social media for information, with Instagram emerging as a key platform for real-time updates. While participants recognise the importance of credible sources, many demonstrated superficial strategies for assessing reliability, such as evaluating the number of followers or brand reputation, which may leave them vulnerable to fake news. This tendency highlights a gap between formal digital literacy education and actual media consumption practices. Ultimately, the findings underscore the critical need for education and awareness to equip young individuals with the tools necessary to discern credible information and engage meaningfully in civic discourse, offering valuable insights for civic NGO communication strategies. Full article

The cooling medium of the guide bearing heat exchanger in hydro generator sets comes from the upstream dam area, which contains numerous impurities even though it has undergone preliminary treatment. These impurities settle, accumulate, and adhere and form scaling layers in the heat exchanger, seriously affecting its heat transfer performance. This paper presents an innovative investigation of heat exchanger performance under impurity-laden cooling water conditions and proposes an optimization by replacing the conventional round tube structure with a spiral flat tube structure. Numerical simulations are conducted to analyze the flow velocity, pressure, impurity deposition, and temperature distribution of the cooler under actual operating conditions. The results show that the optimized cooler achieves improved velocity uniformity with a lower standard deviation, effectively reducing sediment accumulation. Compared to the prototype, the maximum pressure increases by 55.2% (from 0.562 MPa to 0.872 MPa), which enhances turbulence and improves heat transfer. The sediment volume fraction is significantly reduced by 49% in low-flow operating conditions and 73.7% in high-flow operating conditions. Furthermore, the maximum temperature drops by 5.43 °C, indicating improved thermal performance. These findings confirm the effectiveness of the spiral flat tube design in impurity-rich environments. Full article

To cope with the increasing global demand of energy and significant energy wastage caused by the use of different home appliances, smart load monitoring is considered a promising solution to promote proper activation and scheduling of devices and reduce electricity bills. Instead of installing a sensing device on each electric appliance, non-intrusive load monitoring (NILM) enables the monitoring of each individual device using the total power reading of the home smart meter. However, for a high-accuracy load monitoring, efficient artificial intelligence (AI) and deep learning (DL) approaches are needed. To that end, this paper thoroughly reviews traditional AI and DL approaches, as well as emerging AI models proposed for NILM. Unlike existing surveys that are usually limited to a specific approach or a subset of approaches, this review paper presents a comprehensive survey of an ensemble of topics and models, including deep learning, generative AI (GAI), emerging attention-enhanced GAI, and hybrid AI approaches. Another distinctive feature of this work compared to existing surveys is that it also reviews actual cases of NILM system design and implementation, covering a wide range of technical enablers including hardware, software, and AI models. Furthermore, a range of new future research and challenges are discussed, such as the heterogeneity of energy sources, data uncertainty, privacy and safety, cost and complexity reduction, and the need for a standardized comparison. Full article

Due to the local similarities between different facial expressions and the subjective influences of annotators, large-scale facial expression datasets contain significant label noise. Recognition-based noisy labels are a key challenge in the field of deep facial expression recognition (FER). Based on this, this paper proposes a simple and effective attention-based fusion consistency network (AFCN), which suppresses the impact of uncertainty and prevents deep networks from overemphasising local features. Specifically, the AFCN comprises four modules: a sample certainty analysis module, a label correction module, an attention fusion module, and a fusion consistency learning module. Among these, the sample certainty analysis module is designed to calculate the certainty of each input facial expression image; the label correction module re-labels samples with low certainty based on the model’s prediction results; the attention fusion module identifies all possible key regions of facial expressions and fuses them; the fusion consistency learning module constrains the model to maintain consistency between the regions of interest for the actual labels of facial expressions and the fusion of all possible key regions of facial expressions. This guides the model to perceive and learn global facial expression features and prevents it from incorrectly classifying expressions based solely on local features associated with noisy labels. Experiments are conducted on multiple noisy datasets to validate the effectiveness of the proposed method. The experimental results illustrate that the proposed method outperforms current state-of-the-art methods, achieving a 3.03% accuracy improvement on the 30% noisy RAF-DB dataset in particular. Full article

In flank milling of thin-walled workpieces, machining deformation is a key issue affecting workpiece accuracy and process stability. Although the traditional finite element method (FEM) offers high accuracy, its low computational efficiency makes it difficult to meet the requirements for rapid prediction in engineering practice. For this purpose, this paper proposes an efficient method for predicting workpiece deformation based on the voxel octree model. First, based on the analysis of the contact position between the cutting tool and the workpiece, the thin-walled workpiece is divided into six levels of voxel units, using a voxel octree model. Then, the stiffness matrix and update model of the voxel units are established. Finally, the deformation prediction is completed by calculating the micro-milling force and the voxel stiffness matrix. The experimental results show that the workpiece deformation predicted by the proposed method is highly consistent with the actual machining measurement. At the same time, compared with traditional FEM and voxel model methods, the calculation time is reduced by 90% and 13.2%, respectively. This method can provide rapid decision support for the optimization of thin-walled workpiece machining processes and effectively improve the efficiency of preliminary research in actual machining. Full article