The study examined how sanitary landfill waste and its leachate affected groundwater quality in the Kirkuk Governorate, Iraq. Seven sites were selected to monitor groundwater contamination from the landfill cell to the nearest residential area to calculate their samples' leachate pollution and water quality indices. Physical, chemical, and biological parameters were measured for such environmental indicators. Groundwater maps have been predicted using GIS techniques. The nine-month test period ran from February to November 2024. The results demonstrated that leachate concentrations affect groundwater properties. The amounts of Chemical Oxygen Demand (COD), Biochemical oxygen demand (BOD), $\mathrm{SO}_4^{-2}, \mathrm{PO}_4^{-3}, \mathrm{NH}_3^{+}$, and phenol) were greater than permitted by WHO recommendations. Only the vicinity of the landfill cell showed the effects of heavy metals like Cr and Ni, while the residential areas remained unaffected. The LPI results for leachate samples ranged from 25.43 to 40.52. Also, the WQI of the test sites (GW1, GW2, GW3, and GW4) revealed that they were unsuitable for human use without treatment, whereas the groundwater at the other sites (GW5, GW6, and GW7) was adequate for limited irrigation. The findings of the correlation study indicated that the majority of the parameters had a substantial association with one another. The strong negative correlation between distance and parameters indicates that pollutant concentrations decrease when the distance from the landfill increases. The research recommends adopting scientific and technological means to mitigate pollution by using special pipe networks to prevent leachate leakage from the landfill cells and using modern techniques to treat leachate before it reaches the groundwater.

Textile manufacturing processes can produce harmful environmental impacts, particularly the generation of significant textile waste. Thus, in this study, researcher examined the potential of the needle felting technique as a method for reusing textile wastes. Needle felting is a unique textile technique that involves the interlocking of fibres using a barbed needle. This process can be used to transform fabric wastes into new fabrics without the need for spinning or weaving. Furthermore, natural dyes derived from plants such as Parkia speciosa pods can provide an eco-friendly alternative to synthetic dyes, thus reducing the environmental impacts of textile production. Accordingly, exploring the use of cotton wastes from a cotton-weaving factory in Thailand to develop a sustainable material for clothes through the application of needle felting and natural dyeing using Parkia speciosa pods. Moreover, the colour, weight, thickness, texture and colourfastness of needle-felted bitter-bean-pod–dyed cotton wastes were evaluated before and after the needle-felting process. The fabrics’ colour strength (K/S values) and weight (grams per square meter) were also assessed to determine the correlation between fabric weight and colour strength. Additionally, colourfastness to dry cleaning, washing, rubbing, light and perspiration was evaluated. Results showed that needle felting is a feasible technique for transforming textile waste into new, durable fabrics. The research further revealed that cotton wastes can be successfully dyed using natural colours, offering a sustainable alternative for prospective textile applications in the future.

Various physical or mathematical models have analyzed the relationship between Radiance and distance. Using data from VIIRS satellite imagery, this paper proposes a simplified method to analyze the relationship between the average Radiance áRñ and the distance r measured from the center point (highest Radiance). We reviewed three major cities in Indonesia: Jakarta, Bandung, and Yogyakarta. Analyzing the VIIRS raw data from 2012-2021, Jakarta follows the power-law relation: $\langle R\rangle \sim r^{-\alpha}$. Bandung and Yogyakarta follow the exponential relation: $\langle R\rangle \sim \exp (-\alpha r)$, where the values of $\alpha$ vary yearly. In addition, we also find that the average Radiance follows the power-law relation with the area A that is $\langle R\rangle \sim A^{-\beta}$,, where A is the region area at a distance r. The $\beta$ exponent varies every year and from all three cities. By comparing the numerical results to real-time VIIRS data, the study validates the reliability of this simplified approach. The findings underscore the impact of urban development on light pollution, offering a practical and extendable model to other regions. This research contributes to understanding urban lighting dynamics, providing implications for sustainable city planning and environmental protection efforts.

The increased burned land area from 1,219 hectares in 2022 to 2,632 hectares in 2023 indicates a significant escalation in environmental losses due to forest fires in Riau Province. The need for a quick and effective response from the government and the implementation of adaptive policies is crucial. This research has the potential to provide better policy guidance in dealing with forest fires in Riau Province to maintain environmental sustainability and the welfare of local communities. This research uses a qualitative approach with interviews, documentation, and field observation methods. The collected data was analyzed using Nvivo 12 Plus software. The main findings of this research show that forest fires in Riau Province are influenced by five dominant factors: land clearing with illegal burning, extreme weather, weak monitoring and law enforcement, land and ecosystem degradation, and lack of facilities and resources. Each of these factors contributes to increased fire frequency and intensity and negative impacts on the environment, public health, and regional economies. Stricter law enforcement and ecosystem restoration are considered crucial to overcome this problem. Increasing firefighting capacity, public education, early detection systems, and licensing arrangements are also needed to reduce the fire risk. Synergistic implementation of policies requires cooperation between government, society, and the private sector.

Water produced from power plants is one of the most important sources of water pollution, especially for areas like Baghdad, Contaminated industrial wastewater is a major environmental challenge due to the rapid growth of industries, leading to increased accumulation of harmful pollutants in water resources, the work is intended to study the impact of water generated from a power plant in the south on the level of heavy metals before and after the treatment process and after its discharge to the Tigris River. Objective is to determine the extent of heavy metals such as iron, copper, chromium, and zinc concentration in water extracted from various points and subsequently study the monthly variations of these elements with a view to assessment of water quality and efficiency of the treatment systems. Description: Water samples were collected from pre-treatment, post-treatment, and post-discharge points to the Tigris River. Measurements were carried out on a monthly basis for six months. The preparation of samples was done by filtration and preservation techniques by adding nitric acid. Results are showed that iron concentration reached its peak value of 1.70 mg/L in November 2021, while the minimum value of 0.10 mg/L was recorded in the month of October. Temporal variation: there is variation in metals on a monthly basis; for instance, zinc ranged from 0.40 mg/L during January to 2.70 mg/L during November. Standard comparison: the result was also checked against allowable values given by the World Health Organization and the Environmental Protection Agency to determine the level at which water meets the environmental standards. Heavy metal concentrations varied significantly before and after treatment, indicating unit efficiency. Iron, copper, chromium, and zinc showed reductions, though some exceeded limits, posing environmental risks. Future monitoring and improved treatment are essential to safeguard public health and the Tigris River's ecosystem.

Process of municipal solid waste into the electrical energy of Putri Cempo Landfill with gasification technology as an alternative to significantly reduce the environmental impact related to macro components as chemical constituents of waste. The Life Cycle Assessment (LCA) approach is used to holistically evaluate the environmental impact of the gasification process at the waste power plant (WPP) of Putri Cempo Landfill. This study aims to identify the potential environmental impact and determine the hotspot of the gasification process at the Putri Cempo Landfill WPP. The method used in this study is quantitative descriptive with the LCA method, which includes goal and scope definition, life cycle inventory, life cycle impact assessment, and interpretation, where the limitation in this study is gate to gate. The results showed that the largest potential environmental impacts are human carcinogenic toxicity of 41.64738 kg 1.4-DCB, freshwater ecotoxicity of 15.364229 kg 1.4-DCB, and marine ecotoxicity of 11.390976 kg 1,4-DCB. The lowest potential environmental impacts are mineral resource scarcity of 0.000182585 kg Cu eq, stratospheric ozone depletion of 0.041251 kg CFC11 eq, and land use of 0.046136128 m2a crop eq. The hotspot that contributes the greatest environmental impact is the gasifier with a direct gasification system.

Currently, the world is facing a triple planetary crisis consisting of climate change, pollution, and biodiversity loss, which have significant impacts on various sectors of life. West Nusa Tenggara (NTB) Province is one of the areas that feels the impact, as shown by data from the NTB BPBD which records the frequency of floods and droughts. Nevertheless, NTB has rich natural resources with various ecosystems that can drive economic growth and environmental conservation. The NTB government has launched a development vision of "NTB Asri and Sustainable," with priority programs such as NTB Hijau and NTB Zero Waste to achieve sustainable development goals (SDGs). To support these programs, synergy between governments at all levels and active participation of stakeholders are needed. This study aims to analyze the role of stakeholders in the implementation of ecological-based fiscal incentives in NTB, in order to ensure sustainability and stakeholder involvement in overcoming this environmental crisis. Data for this study were collected through several data collection instruments such as field observations, interviews with questionnaires, in-depth interviews, Focus Group Discussions (FGDs) and literature studies. The results of the analysis carried out show that each actor has the same view that this policy is important to continue to be implemented because it has positive implications for environmental improvement synergistically at various levels of government. This is also shown by the high level of participation and support from stakeholders in implementing the policy.

This research investigates the technical performance of finishing materials used in the facades of school buildings in hot and arid regions, addressing the lack of thorough evaluation in material selection. Current practices often result in rapid material degradation, necessitating frequent maintenance. The study seeks to establish technical standards and indicators for evaluating material durability and condition over time. By focusing on two selected school buildings, the research aims to provide insights into material performance and user behavior impacts. It includes a literature review, field surveys, and laboratory testing to evaluate material resistance to local environmental and human factors. The study's findings will contribute to developing guidelines for improving the durability of finishing materials in school buildings, thereby reducing maintenance costs and enhancing building longevity. One key conclusion is the inadequacy of current materials in withstanding local conditions, highlighting the need for specialized studies to establish local standards for material evaluation. The research encountered several obstacles, including technical challenges related to limited capabilities for sample testing. The second set of challenges were administrative in nature, which hindered the research due to the regulations and requirements for accessing school buildings. Additionally, there were difficulties in extracting samples of finishing materials and subsequently replacing them within the building.

Green buildings mitigate the adverse effects of construction on the environment. Water conservation in housing is a criterion significantly affected by residents' behavior. The inhabitants' behavior influences the extent of water conservation and energy efficiency implemented in the building. This study conducts a comprehensive bibliometric analysis of the scholarly literature on water usage and energy efficiency behaviors in sustainable buildings. The study utilizes data from Scopus, spanning the years 1996 to 2024. The study identified collaborations across many institutions and countries, emphasizing significant research accomplishments. The network visualization study was performed utilizing R Studio Biblioshiny software version 4.4.1. The findings of this study offer substantial insights for academics, professionals, policymakers, and funding entities pursuing a thorough understanding of contemporary trends and goals in this domain. The findings of this study establish a significant framework for future research initiatives and highlight the necessity of ongoing investment in energy efficiency and water conservation efforts moving forward.

Forest plantation, either on ground or on wetland as mangrove forest plantation, is commonly and continually practiced to maintain or increase the forest area, but mostly it is done by environmentalists from public, state enterprises, community, and/or private sectors. Others may receive direct and/or indirect benefits from the forest with more or less participation. This practical resarch article presents the mangrove forest plantation project at Chumphon Islands National Park, Chumphon province, in southern peninsular Thailand, of about 1,057 Rai (169.12 hectares) supported by the state enterprise Electricity Generating Authority of Thailand (EGAT). To investigate the results from this investment activity, this study examined the local people’s perceptions of the benefits from mangrove plantation project at Chumphon Islands National Park, Chumphon province, in southern peninsular Thailand. The face-to-face questionnaires developed for secondary data were reviewed, then responses were stratified collected from 339 local respondents of 21 villages in six sub-districts within a five-kilometer radius around the project. The results indicate that more than half of the respondents were uncertain about some direct benefits, while two-thirds of them received indirect benefits. Consequently, if the mangrove plantation project is organized in the prohibited area of the national park, the local people’s perceptions of the direct benefits are minimal.

Curved multi-layer beams, such as leaf springs, are widely used in vehicle suspension systems for both road and rail vehicles in automotive industry due to their capacity for high loads and their vibrational damping properties. To design suspension systems that experience a large number of load types and complexities of friction, we must first understand the nonlinear dynamic behavior of curved beams. In this paper, the governing equations for the nonlinear vibrations of curved two-layer beams in the presence of interlayer slip are first derived. Then, the characteristic equation, the longitudinal and transverse mode shapes of the beam, are determined independently using eigenvalue problem solutions. Subsequently, using the calculated mode shapes, different phases of the dynamics of these structures are investigated, taking into account interlayer friction. The results of numerical simulations are compared and validated with finite element analysis using ANSYS software. The results show that the dynamic behavior of curved two-layer beams experiences chaotic regimes after initial slip. Different regimes of periodic, quasi-periodic and chaotic motions are found in the dynamics of the system.

As market saturation and competitive pressure intensify within the banking sector, the mitigation of customer churn has emerged as a critical concern. Given that the cost of acquiring new clients substantially exceeds that of retaining existing ones, the development of highly accurate churn prediction models has become imperative. In this study, a hybrid customer churn prediction model was developed by integrating Sentence Transformers with a stacking ensemble learning architecture. Customer behavioral data containing textual content was transformed into dense vector representations through the use of Sentence Transformers, thereby capturing contextual and semantic nuances. These embeddings were combined with normalized structured features. To enhance predictive performance, a stacking ensemble method was employed to integrate the outputs of multiple base models, including random forest, Gradient Boosting Tree (GBT), and Support Vector Machine (SVM). Experimental evaluation was conducted on real-world banking data, and the proposed model demonstrated superior performance relative to conventional baseline approaches, achieving notable improvements in both accuracy and the area under the curve (AUC). Furthermore, the analysis of model outputs revealed several salient predictors of customer attrition, such as anomalous transaction behavior, prolonged inactivity, and indicators of dissatisfaction with customer service. These insights are expected to inform the development of targeted intervention strategies aimed at strengthening customer retention, improving satisfaction, and fostering long-term institutional growth and stability.

Significant advancements in artificial intelligence (AI) have transformed clinical decision-making, particularly in disease detection and management. Endometriosis, a chronic and often debilitating gynecological disorder, affects a substantial proportion of reproductive-age women and is associated with pelvic pain, infertility, and a reduced quality of life. Despite its high prevalence, non-invasive and accurate diagnostic methods remain limited, frequently resulting in delayed or missed diagnoses. In this study, a novel diagnostic framework was developed by integrating deep learning (DL) with explainable artificial intelligence (XAI) to address existing limitations in the early and non-invasive detection of endometriosis. Abdominopelvic magnetic resonance imaging (MRI) data were obtained from the Crestview Radiology Center in Victoria Island, Lagos State. Preprocessing procedures, including Digital Imaging and Communications in Medicine (DICOM)-to-PNG conversion, image resizing, and intensity normalization, were applied to standardize the imaging data. A U-Net architecture enhanced with a dual attention mechanism was employed for lesion segmentation, while Gradient-weighted Class Activation Mapping (Grad-CAM) was incorporated to visualize and interpret the model’s decision-making process. Ethical considerations, including informed patient consent, fairness in algorithmic decision-making, and mitigation of data bias, were rigorously addressed throughout the model development pipeline. The proposed system demonstrated the potential to improve diagnostic accuracy, reduce diagnostic latency, and enhance clinician trust by offering transparent and interpretable predictions. Furthermore, the integration of XAI is anticipated to promote greater clinical adoption and reliability of AI-assisted diagnostic systems in gynecology. This work contributes to the advancement of non-invasive diagnostic tools and reinforces the role of interpretable DL in the broader context of precision medicine and women's health.