This study investigates the industry-wide and regional spillover effects of penalties for noncompliance with information disclosure regulations, focusing on publicly listed firms in China. The analysis is based on panel data from Chinese listed companies, revealing that penalties imposed by the China Securities Regulatory Commission (CSRC) on noncompliant firms lead to significant improvements in the quality of information disclosure by other firms in the same industry or geographical region that were not subject to penalties. These spillover effects are found to be contingent on factors such as the competitive dynamics within the industry and the level of regional economic development. Furthermore, the results indicate that the impact of penalties on neighbouring firms is amplified when the publication cycle for penalty announcements is shorter, though the effect diminishes over time as the information becomes less salient. These findings contribute to the understanding of regulatory enforcement mechanisms and their broader influence on corporate transparency, highlighting the role of both industry and regional contexts in shaping compliance behaviour.
Corporate governance remains a fundamental issue for stakeholders in the oversight of organisations, particularly within the context of public sector auditing. Effective governance, coupled with robust auditing practices, is essential for ensuring transparency and accountability in governmental operations. However, in many African nations, corporate governance frameworks have been either inadequately implemented or have failed to achieve their intended outcomes. This study explores the challenges faced by auditees in relation to corporate governance and their subsequent impact on the efficacy of public sector auditing across Africa. Employing a phenomenological research approach, the study utilised an exploratory sequential qualitative design to gather insights from focus group discussions. A total of 33 key affinities and 153 sub-affinities, encompassing critical corporate governance issues, were identified by three focus groups from selected Supreme Audit Institutions (SAIs) in Africa. These identified affinities included audit execution and recommendations, audit acceptance, political interference, ineffective audit committees, inadequate collaboration and communication, and weaknesses in legislative oversight. Among the key themes emerging from the analysis, the auditee corporate governance policy framework was highlighted as a significant factor influencing auditing outcomes. The findings provide a detailed examination of the unique factors affecting the effectiveness of public sector audits in promoting accountability and transparency. The study proposes a comprehensive policy framework based on a resource-based theoretical perspective, designed to enhance the impact of public sector auditing in African nations. This framework is intended to guide executive governments, legislative bodies, SAIs, citizens, and other stakeholders towards improving governance and securing better public sector outcomes. The empirical evidence provided herein offers valuable insights into the complex interplay between corporate governance and auditing effectiveness, contributing to the ongoing discourse on accountability and transparency in the African public sector.
This study introduces an advanced approach to the automated detection of skin tanning, leveraging image processing and computer vision techniques to accurately assess tanning levels. A method was proposed in which skin tone variations were analyzed by comparing a reference image with a current image of the same subject. This approach establishes a reliable framework for estimating tanning levels through a sequence of image preprocessing, skin segmentation, dominant color extraction, and tanning assessment. The hue-saturation-value (HSV) color space was employed to quantify these variations, with particular emphasis placed on the saturation component, which is identified as a critical factor for tanning detection. This novel focus on the saturation component offers a robust and objective alternative to traditional visual assessment methods. Additionally, the potential integration of machine learning techniques to enhance skin segmentation and improve image analysis accuracy was explored. The proposed framework was positioned within an Internet of Things (IoT) ecosystem for real-time monitoring of sun safety, providing a practical application for both individual and public health contexts. Experimental results demonstrate the efficacy of the proposed method in distinguishing various tanning levels, thereby offering significant advancements in the fields of cosmetic dermatology, public health, and preventive medicine. These findings suggest that the integration of image processing, computer vision, and machine learning can provide a powerful tool for the automated assessment of skin tanning, with broad implications for real-time health monitoring and the prevention of overexposure to ultraviolet (UV) radiation.
Urban ponds play a critical role in sustaining ecological balance, enhancing urban resilience, and promoting community well-being. However, the rapid expansion of urban settlements has resulted in the gradual degradation and conversion of these water bodies, leading to significant environmental impacts, including biodiversity loss. This study investigates the transformation of urban pondscapes within Serampore Municipality, located in the Kolkata Metropolitan Area (KMA). A total of 191 ponds were identified and classified using Google Earth satellite imagery, field surveys, and statistical analysis. The ponds were categorized based on their size, condition, and usage, with field observations used to assess their health. Descriptive statistical methods were employed to analyze the distribution and size variations of these ponds. Additionally, secondary data on water quality parameters, such as turbidity and chlorophyll levels, were analyzed to evaluate the overall ecological health of the ponds. The results indicate a marked decline in the number of ponds, with nine ponds having been converted into built-up areas between 2011 and 2024. These findings underscore the adverse effects of urbanization on blue infrastructure and highlight the inadequacies of current policies in safeguarding urban water bodies. The evidence calls for stronger policy interventions and the adoption of sustainable urban planning practices to protect and conserve these vital aquatic resources. Without the proper management of urban ponds, the environmental and social functions they provide will continue to deteriorate, posing further risks to urban ecosystems and human health. Enhanced governance, alongside the integration of blue infrastructure into urban planning frameworks, is crucial for mitigating these challenges and ensuring the resilience of urban landscapes.
The global energy crisis presents a significant challenge that impacts not only human populations but also ecosystems and biodiversity. In India, the demand for energy has escalated rapidly, driven by industrialisation, urbanisation, and population growth, resulting in increased pressure on both conventional energy sources and environmental systems. This study aims to evaluate the Energy Efficiency (EE) and renewable energy policies in India, examining the balance between economic growth, environmental sustainability, and government action. The “E-score” methodology is employed to assess the EE performance across selected Indian states, highlighting critical gaps in policy implementation and providing insights into opportunities for improvement. Furthermore, the feasibility of implementing District Cooling Systems (DCS) in Pune is explored, with the city selected as a representative case study due to its growing urban landscape and climate challenges. The adoption of DCS, a highly efficient cooling technology, is considered a promising solution to address urban heat islands and reduce the energy consumption associated with conventional cooling methods. Through a comprehensive analysis, this research underscores the necessity of an integrated approach that incorporates economic, environmental, and social dimensions in the formulation of sustainable energy strategies. The study further advocates for proactive measures at local, state, and national levels to facilitate a seamless transition to renewable energy sources and achieve long-term energy sustainability. The findings emphasise the importance of developing adaptive policies that are aligned with the broader objectives of climate change mitigation, highlighting the potential of DCS as a key component in India's energy transition. By contributing to the understanding of effective energy management and policy frameworks, the study provides valuable insights for policymakers, urban planners, and energy practitioners in the pursuit of a sustainable and resilient energy future for India's cities.
The effective allocation of emergency supplies is crucial in the aftermath of flood disasters, as it directly impacts response times and mitigates casualties and property losses. Traditional methods of material distribution predominantly rely on ground-based transportation, which often proves inefficient and inflexible under the dynamic conditions of a disaster. This study explores the potential of unmanned aerial vehicles (UAVs) as a transformative solution to the challenges associated with emergency material dispatch. Factors influencing UAV scheduling, including environmental constraints, payload capacity, and flight dynamics, are analyzed in depth. Optimization measures for improving UAV collaborative operations are proposed, with a focus on enhancing the efficiency and adaptability of disaster response systems. The integration of reinforcement learning (RL) is examined as a theoretical framework for optimizing UAV collaborative scheduling, facilitating autonomous decision-making in real-time scenarios. An empirical analysis is presented based on the “7-20” rainstorm and flooding disaster in Zhengzhou, illustrating the practical application of collaborative UAVs in disaster relief. The results demonstrate the significant optimization potential of UAV technology, with a notable reduction in response times and improved logistical coordination. Furthermore, the role of UAVs in future disaster relief operations is discussed, with emphasis on the integration of blockchain and smart dispatch systems to enable decentralized, autonomous coordination. These advancements are expected to enhance the overall efficiency of emergency material distribution and better address the complex challenges posed by post-disaster environments. The findings underscore the potential for UAV systems to revolutionize disaster management and contribute to more resilient, responsive strategies in future flood events.
In the ship hull plate welding process, different welding sequences directly affect the deformation of the current welding procedure, which in turn impacts the overall shipbuilding accuracy. This study takes a typical double T-shaped thin plate structure as an example. Based on welding numerical simulation and experimental validation, a corresponding dataset is obtained. To address the issue of BP neural networks being prone to local optima, which can lead to inaccurate results, a Simulated Annealing-Back Propagation (SA-BP) neural network model is used to analyze the dataset. The research aims to determine the optimal welding sequence that minimizes deformation. The training results show that the Mean Squared Error (MSE) of the SA-BP model decreased from 1.0144 in the BP model to 0.67388. Additionally, the SA-BP model's fitting performance is far superior to that of the BP model. Therefore, the SA-BP neural network model provides more stable and accurate results compared to the traditional BP neural network model. The comparison of the optimal welding sequence results derived from both models shows that welding with the optimized SA-BP neural network results in a 21.07% reduction in welding deformation compared to the traditional BP neural network.
In urban environments, the scarcity of available land often necessitates the construction of closely spaced, high-rise buildings, which rely heavily on pile foundations to support substantial loads. However, the pile-driving process, essential for such foundations, generates vibrations that can propagate through the ground and affect surrounding structures, potentially leading to adverse consequences. These vibrations can disrupt the comfort of residents and cause structural damage to adjacent buildings, including residential properties, hotels, and hospitals, where both the comfort and safety of occupants are of paramount importance. Furthermore, pile-driving-induced vibrations can result in the development of cracks in the architecture, settlement of foundations, or even severe structural failure in sensitive installations. To assess the effects of pile-driving on nearby buildings, a series of 77 finite element models were developed using PLAXIS 3D, which simulated varying pile-to-building distances and driving depths. The analyses focused on both the comfort of residents and the structural integrity of adjacent buildings, with comparisons drawn against international standards for vibration levels. The results revealed that the optimal driving depth could effectively minimize peak vibration levels, thereby reducing the risk of disruption to nearby structures. Additionally, the influence of parameters such as pile-driving load, pile penetration depth, and soil characteristics on vibration propagation was systematically explored. The findings provide critical insights into the mitigation of pile-driving-induced vibrations in urban settings and offer guidance for optimizing pile-driving operations to safeguard both resident comfort and structural safety.
Cutoff walls are an essential method for seepage prevention in dams. During the construction and operation of reservoirs, factors such as construction techniques, variations in groundwater conditions within the dam body, geological movements, and climatic factors may lead to potential seepage risks, necessitating inspection. Traditional methods like borehole coring and water pressure tests have limited monitoring ranges, while non-destructive methods like high-density electrical surveys and shallow seismic exploration have low deep-resolution capabilities, making them unsuitable for detecting deep-seated seepage in concrete walls. In recent years, Cross-borehole Tomography (CT) geophysical techniques, based on boreholes on both sides, have been widely applied in various engineering geophysical projects. Seepage in cutoff walls can lead to an increase in local moisture content, resulting in low-resistivity anomalies, providing a physical basis for the exploration using cross-borehole resistivity CT. This study investigates the resistivity response characteristics of cross-borehole resistivity CT through numerical simulation based on the resistivity characteristics of seepage in cutoff walls. The numerical simulation results indicate that this method effectively identifies seepage conditions in cutoff walls, and the resolution of cross-borehole resistivity CT is significantly related to the cross-hole spacing and the distance to the seepage points. This study provides a preliminary verification of the feasibility of applying cross-borehole resistivity CT for detecting seepage in cutoff walls and offers insights for seepage detection strategies.
Crowdsourced delivery, a pivotal component of crowd logistics, represents a transformative model for optimizing logistics resources through the efficient allocation of available capacities, thus responding to the flexibility demands of contemporary businesses. At the heart of this model are digital platforms that facilitate the coordination of activities between couriers, users, and service providers. In Serbia, several prominent platforms stand out due to their advanced functionalities, extensive product offerings, and rapid delivery capabilities. Simultaneously, smaller platforms face significant challenges in maintaining competitiveness within an increasingly saturated market. Despite the numerous advantages offered by the crowdsourcing model, couriers engaged in this sector encounter a variety of obstacles that undermine its full potential. These challenges encompass issues related to working conditions, contractual arrangements, and the stability and security of courier incomes, all of which are essential to the sustainability of the system. A survey was conducted to gain an in-depth understanding of the couriers' perspectives on the operational dynamics of crowdsourced delivery. The study aimed to gather empirical data on the daily challenges faced by couriers, their working conditions, job satisfaction, and relationships with platform companies. Additionally, insights were sought into the overall functioning of crowd logistics systems from the perspective of the couriers, with a particular focus on identifying areas where improvements could be made to enhance the working conditions and status of couriers. The findings are expected to inform strategies that could mitigate the current challenges, thereby contributing to a more equitable and efficient model of crowdsourced delivery. This research highlights the importance of addressing the couriers' concerns as a critical step toward the optimization of crowdsourcing logistics systems and the enhancement of their long-term viability.
Open Access
patricia aline bressiani 
, geiciane locatelli alves , inara giacobbo de marco , mariana tonello biffi , sabrina ishikawa , vilmar steffen , fernando césar manosso , eduardo michel vieira gomes , ticiane sauer pokrywiecki , ana paula de oliveira schmitz , elisângela düsman |
Available online: 12-30-2024
The excessive application of agrochemicals has resulted in significant workplace exposure for agriculturists and environmental interaction for the general public, particularly in communities adjacent to agricultural zones. Such exposure is associated with detrimental health effects, including mutagenic and cytotoxic impacts. Agrochemical contamination frequently occurs through water, especially in rural villages where conventional water treatment systems are not designed to address these specific contaminants. The efficacy of activated carbon was investigated in this study as an adsorbent for the removal of 2,4-dichlorophenoxyacetic acid (2,4-D) from contaminated water. The concentration of 2,4-D in water samples was quantified using ultraviolet-visible (UV-Vis) spectroscopy at a wavelength of 283 nm. Preliminary adsorption experiments identified pH 2 as the optimal condition for 2,4-D uptake. The adsorption kinetics were best described by the Elovich model, with an equilibrium time of 480 minutes. Equilibrium studies revealed that three isotherm models—Redlich-Peterson, Temkin, and Toth—effectively represented the experimental data, with a maximum adsorption capacity of 252.8 mg/g. The findings underscore the potential of activated carbon as a cost-effective and straightforward treatment method for the removal of 2,4-D from drinking water, particularly in rural areas lacking access to advanced water treatment infrastructure.
Nanofluids, which are suspensions of nanoparticles in base fluids, have demonstrated considerable potential in enhancing thermal conductivity, energy storage, and lubrication properties, as well as improving the cooling efficiency of electronic devices. Despite their promising applications, the industrial utilization of nanofluids remains in the early stages, with further research needed to fully explore their capabilities. This study investigates a generalized nanofluid model, incorporating fractal-fractional derivative (FFD), to better understand the thermophysical behaviors in vertical channel flow. The nanofluid consists of polystyrene nanoparticles uniformly dispersed in kerosene oil. An exact solution to the model is obtained by employing the Laplace transform technique (LTT) in combination with the numerical Zakian’s algorithm. The FFD operator with an exponential kernel is applied to extend the classical nanofluid model. Discretization of the generalized model is achieved using the Crank-Nicolson method, and numerical simulations are performed to solve the resulting equations. The study reveals that, at a nanoparticle volume fraction of 4% (0.04), the heat transfer rate of the nanofluid is significantly higher than that of the base fluid. Furthermore, the enhanced heat transfer leads to improvements in various thermophysical properties, such as viscosity, thermal expansion, and heat capacity, which are crucial for industrial applications. The numerical results are presented graphically to highlight the dependence of the flow and thermal dispersion characteristics on key physical factors. These findings suggest that the use of fractal-fractional models can provide a more accurate representation of nanofluid behavior, particularly for high-precision applications in heat transfer and energy systems.
