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.
In this paper, we derive a new conjugate gradient (CG) direction with random parameters which are obtained by minimizing the deviation between search direction matrix and self-scaled memoryless Broyden-Fletcher-Goldfard-Shanno (BFGS) update. We propose a new spectral three-term CG algorithm and establish the global convergence of new method for uniformly convex functions and general nonlinear functions, respectively. Numerical experiments show that our method has nice numerical performance on nonconvex functions and supply chain problems.
It can be described that high solar radiation intensity is the basis for the performance of solar photovoltaic modules. Therefore, it causes a decrease in the efficiency of the panel due to the increase in its surface temperature and thus affects its lifespan due to periodic thermal effects. This paper presents an analysis of the PV panel performance and thermal problems and attempts to solve them by cooling it during the day using water circulation in a heat exchanger embedded in the ground. The present work aims to analyze the thermal exchange process of geothermal heat exchangers by computational simulation approach. The research parameters included changing the depth of the copper pipe loop in the soil at 0.5, 1.0, and 1.5 m, and water flow rate of 0.0278 kg/s, copper pipe length, and thermal conductivity of soil in steady conditions employing the yearly weather data of southern desert in Iraq. The computational simulation results manifested that during the solar day, the fluctuations of outlet water temperature are diminished when the burial depth of the heat exchanger is around 2.0 m due to the soil's elevated thermal inertia. In addition, the temperature of the ground is comparatively stable and these values are higher than the inlet water temperature in winter with low values in summer.
Click fraud is a deceptive malicious strategy that relies on repetitive mimicking of human clicking on online advertisements, without actual intention to complete a purchase. This fraud can result in significant financial loses for both advertising companies and marketers, and at the same time destroying their public images. Nevertheless, detection of these illegitimate clicks is very challenging as they closely resemble to authentic human engagement. This study examines the utilization of artificial intelligence approaches to detect deceptive clicks, by identifying subtle correlations between the timing of the clicks, taking into account their geographical or network sources and linked application sources as indicators to separate legitimate from malicious activity. This study highlights the application of recurrent neural networks (RNNs) for this task, keeping in mind that the process of selection and tuning of the model's hyperparameters plays a vital role in the performance. An adapted implementation of crayfish optimization algorithm (COA) was consequently proposed in this paper, and used to optimize RNN models to enhance their general performance. The developed framework was evaluated utilizing actual operational datasets and yielded encouraging outcomes.
The growing global emphasis on climate change mitigation has intensified efforts to transition from carbon-intensive energy sources to sustainable, low-carbon alternatives. In this context, district heating facilities, particularly in regions like Russia, represent a key opportunity for reducing greenhouse gas emissions through innovative energy solutions. The paper reports on the results of studies exploring various avenues for a transition to a carbon-neutral economy, particularly in the Russian Federation. The research aims to develop a hydro-steam turbine installation for geothermal power plants and heating boilers to substantiate and create new energy production infrastructure. For this purpose, the authors identify the volume of the market for hydro-steam turbines for boiler houses required to predict the reduction of CO2 emissions as a result of the application of the installations in Russia. Proceeding from the performed calculations, the paper offers an estimate of the decrease in CO2 emissions due to the implementation of this innovation in Russia. The use of hydro-steam turbine installations in cogeneration schemes at heating plants will increase the reliability of power supply to district heating sources and reduce specific fuel consumption in the production of electricity. The total theoretical potential of greenhouse gas emission reduction due to the implementation of hydro-steam turbines in Russian boiler houses exceeds 500,000 tons CO2 annually.
A wide range of safety hazards exist in underground coal mines, characterized by unpredictability, randomness, and coupling effects. The increasing structural complexity and diversity of underground equipment present new challenges for fault state monitoring and diagnosis. To address the unique characteristics of underground equipment fault diagnosis, a characterization model of vibration hazards was proposed, integrating a time-frequency mask-based non-stationary filtering technique and sparse representation. Experimental analysis demonstrates that the time-frequency mask algorithm effectively filters out sharp non-stationary noise, restoring the original stationary healthy signal. Compared to Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Principal Component Analysis (PCA), the sparse representation algorithm exhibits superior performance in characterizing vibration hazards, achieving the highest accuracy.
Risk management in public-sector project portfolios within developing economies remains an understudied yet critical area, particularly in the context of resource-constrained administrative environments. This study examines the management of risk and uncertainty within the Directorate of Local Administration (DLA) of Ain-Temouchent, Algeria, employing a qualitative case study methodology. Data were collected through semi-structured interviews (n=8) and document analysis to explore the systemic barriers and inefficiencies that hinder effective portfolio-level risk management. The findings reveal that fragmented governance structures, a predominantly reactive approach to risk mitigation, and the limited integration of analytical tools contribute to project delays and subjective risk assessments. While these challenges align with broader critiques of public-sector risk management, significant divergences from Enterprise Risk Management (ERM) and adaptive governance frameworks are identified, primarily due to constraints in institutional capacity and resource availability. The necessity of addressing uncertainty at the portfolio level is emphasized, with a call for the adoption of reflective risk practices, proactive decision-making mechanisms, and the implementation of early-stage adaptive strategies to enhance resilience in multi-project public-sector settings. By contextualizing ERM and adaptive governance theories within a resource-limited administrative framework, this study provides a bridge between theoretical advancements and practical applications, offering actionable insights for policymakers and public administrators seeking to improve strategic alignment and project portfolio success in developing economies.
The detection of image defects under low-illumination conditions presents significant challenges due to unstable and uneven lighting, which introduces substantial noise and shadow artifacts. These artifacts can obscure actual defect points while simultaneously increasing the likelihood of false positives, thereby complicating accurate defect identification. To address these limitations, a novel defect detection method based on machine vision was proposed in this study. Low-illumination images were captured and decomposed using a noise assessment-based framework to enhance defect visibility. A spatial transformation technique was then employed to distinguish between target regions and background components based on localized variations. To maximize the contrast between these components, the Hue-Saturation-Intensity (HSI) color space was leveraged, enabling precise segmentation of low-illumination images. Subsequently, an energy local binary pattern (LBP) operator was applied to the segmented images for defect detection, ensuring improved robustness against noise and illumination inconsistencies. Experimental results demonstrate that the proposed method significantly enhances detection accuracy, as confirmed by both subjective visual assessments and objective performance evaluations. The findings indicate that the proposed approach effectively mitigates the adverse effects of low illumination, thereby improving the accuracy and reliability of defect detection in challenging imaging environments.
One of the main problems with a solar photovoltaic (PV) system is the partial shading condition (PSC). This results in a significant reduction of the output power of a solar PV system. This paper mainly aims at proposing and validating a novel optimization technique namely the Genetic Algorithm (GA) for Maximum Power Point Tracking (MPPT) in case of PSC. In this study, an experimental examination utilizing a PV emulator highlights the effect of PSC on PV system performance. PSC was found to result in a 37% decrease in maximum power, a 38% decrease in fill factor, and a 60% decrease in efficiency. Meta-heuristic techniques for P-V curves with several peaks can be used to track the maximum power point (MPP). GA is based on a metaheuristic methodology that has been applied to solve optimization problems in a variety of systems, such as PV systems with MPPT. With a convergence time of less than 2 ms, the suggested system can track the global MPP with 99% tracking efficiency. This demonstrates the improvement in tracking time and accuracy over traditional MPPT techniques. Additionally, the suggested system can also accomplish steady operation in dynamically changing environmental conditions and reduce the oscillations around MPP.
Green skincare products have become a significant global phenomenon, but the dominance of conventional skincare products in Indonesia faces immense challenges in adopting environmentally friendly products. Previous research explored consumer intentions to switch to green skincare products. However, there must be a critical gap in understanding the factors influencing the behavior of transitioning from conventional skincare products to green skincare products, primarily related to the barriers to adoption consumers face. This study aims to analyze the influence of motivator, pull, and inhibit factors on the intention and behavior of Indonesian consumers in switching to green skincare products. This study uses the Push-Pull-Mooring (PPM) framework as its conceptual framework. The survey was conducted online in various significant regions in Indonesia. The Partial Least Squares Structural Equation Modeling (PLS-SEM) method showed the validity value of 219 respondents. The results showed no significant social influences in encouraging Indonesian consumers to switch to green skincare products, while health and environmental benefits had a considerable influence. High price barriers to use, limited availability, and lack of information substantially inhibit consumer intent and weaken the positive influence of health and environmental benefits. Barriers to use do not moderate social influence on switching intentions, and consumer intentions to switch did not prove to be strong predictors of actual behavior. These findings highlight the importance of education strategies emphasizing health and environmental benefits and the need to address barriers to using green skincare products and encourage their use more effectively in Indonesia.
The current property tax on the forestry sector in Indonesia imposes a heavy burden on taxpayers which reduces the competitiveness of forest products and encourages deforestation amidst Indonesia's efforts to achieve net-zero emission targets. Forestry taxes should be able to balance business profitability and natural resource conservation through the strengthening of ecosystem services provided by forests. This study aims to provide alternative policy and improvements of the administration of property tax policy to support the carbon sequestration function of Indonesia's tropical rainforests and forestry industry. This research is a qualitative study that employs focus group discussions, in-depth interviews, and content analysis to collect data. The data analysis technique employed were successive approximation, illustrative method, and ideal types. Writers identified several policy and administration problems such as uncertain and complex land valuation, high tax rates, insignificant tax deductions, open interpretation of land classification, and numerous user charges. Alternative policies proposed are property tax incentives which consist of tax rate reduction and adjustment of deductions. The provision of tax incentives is expected to encourage reforestation efforts and reduce deforestation, therefore supporting the carbon sequestration of Indonesia's tropical rainforests in the context of climate change mitigation.
The surface hardness and estimated compressive strength of Lumle Rock (Pahara), situated in the Annapurna Rural Municipality of Kaski District, Nepal, were investigated using the Schmidt hammer (rebound hammer) test, a standardized non-destructive testing (NDT) method. This technique was employed to evaluate the geomechanical properties of the rock formation with specific attention to its potential for recreational rock climbing and site-specific geotechnical applications. Rebound values were collected in situ and statistically analyzed to determine characteristic strength at confidence intervals of 80%, 90%, and 95%, following the guidelines of IS 13311-Part 2. Critical factors influencing rebound measurements, including aggregate mineralogy, surface texture, moisture content, carbonation effects, and weathering conditions, were systematically considered and controlled where applicable. The results indicated that Lumle Rock (Pahara) exhibits sufficient surface hardness and mechanical integrity to support rock climbing activities. However, to ensure climber safety and to inform potential engineering uses, it is recommended that further subsurface investigations, including calibration, be conducted. The application of Schmidt Hammer testing in this context demonstrates the value of rapid, cost-effective assessment methods for evaluating the mechanical suitability of natural rock formations for recreational and civil engineering purposes.
The high number of students and the various activities that involve them can contribute to the increase in plastic waste on campus. Student environmental education in plastic waste management plays an important role in the sustainability of a green campus. The objective of this study was: 1) to evaluate the implementation of environmental education in plastic waste management on the green campus in Malang City; and 2) to determine the students’ achievements in environmental education about plastic waste management, focusing on the knowledge, awareness, behavior, skills, and participation. This study utilized a survey method with a quantitative descriptive approach, involving a total of 1,038 respondents. Data was acquired using a closed questionnaire that had been modified and distributed using a QR code. The Weighted Mean approach was utilized in data analysis. The study revealed the following findings: 1) The green campus in Malang City has implemented a curriculum that is integrated with environmental education but has different implementation specifications; 2) Students’ environmental education in plastic waste management at the three green campuses fits in the high category; 3) The results indicated that student achievement was very high in awareness, high in knowledge and behavior, but low in skills and participation. Integrating environmental education into the learning process, along with stimulating activities, is crucial to encourage student participation in plastic waste management.
Identifying and mapping plant types in the household environment that contribute to carbon uptake is the goal of this research. The research method used exploratory descriptive by exploring and collecting information from respondents and field observation about plant types that play a role in carbon absorption and sources of carbon emissions produced in households (LPG, electricity, and transportation emissions). Primary data was collected from four sub-districts (Suralaga, Labuhan Haji, Sakra, Sukamulia) with 75 respondents per sub-district. Data was analyzed quantitatively descriptively, which describes the amount of carbon emissions produced by households, plant types, and the amount of carbon uptake by various plant types. The results show that plants with the highest carbon uptake and also mostly found at research locations are Mango (Mangifera indica) at 445.3, followed by Matoa (Pometia pinnata) at 39.76, Jackfruit (Artocarpus heterophyllus) at 26.51 and the rest is a combination of several types of fruit plant, such as Srikaya (Annona squamosa), Soursop (Annona muricata), Coconut (Cocos nucifera), Banana (Musa acuminata), Guava (Syzygium), Sapodilla (Manilkara zapota), Papaya (Carica papaya), Longan (Dimocarpus longan), Rambutan (Nephelium lappaceum), Oranges (Citrus), and Avocados (Persea americana). The results of the analysis show that these trees cannot fulfill the carbon absorption resulting from LPG emissions, electricity emissions and transportation emissions from households, even though all calculation shows that it still unbalanced, and needs more plants to be planted. These findings can be used as a basis for making policies to regulate CO2 emissions originating from households.
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
