Universities such as higher education institutions and science and technology developers also have a responsibility in developing a sustainable campus environment. The implementation and provision of Eco-Friendly Transportation (EFT) is one way to achieve environmental sustainability in the campus environment. This study aims to decide student perceptions of climate change mitigation awareness on the use of EFT, decide the implementation of innovative strategies for providing EFT, and analyze the barriers and opportunities for EFT implementation on several campuses in Indonesia. This research is a type of mixed methods research with survey, direct systematic observation, walk-in audits, and descriptive qualitative. Data analysis was conducted using descriptive statistics with the help of the SPSS version 22 application. The results show that student perceptions of climate change mitigation awareness at mean score 78.82, the indicator with the highest score is environmental attitudes at mean score 33.4. In addition, statistical analysis showed a good correlation between students' perceptions and field observations, which showed that many students use EFT on campus for their mobility. This study provides recommendations for practical steps that can be taken to overcome existing barriers, while creating a greener and more sustainable campus environment.
This study investigates the factors influencing environmental sustainability by examining the roles of environmental knowledge, attitudes, behaviors, and awareness. Although these variables have been widely studied in global contexts, limited research addresses how they manifest among Indonesian students. This study fills that gap by focusing on 409 ninth-grade students from middle schools in Pekanbaru, Riau Province, and Solok City, West Sumatra, Indonesia. A quantitative approach using survey questionnaires was employed to measure students’ environmental knowledge, attitudes, behaviors, and sustainability awareness. Results showed that environmental knowledge, attitudes, and behaviors significantly influenced sustainability awareness, with standardized coefficients of 0.35, 0.42, and 0.28, respectively ($p < $ 0.001 for all). Among these, environmental attitude had the most substantial impact. These findings highlight the need for a multidimensional approach to environmental education that integrates cognitive, emotional, and behavioral components. By focusing on a regional context often underrepresented in sustainability research, this study contributes to a deeper, culturally grounded understanding of how young learners in Indonesia engage with environmental issues. It offers valuable insights for educators and policymakers in designing curricula and interventions that not only build knowledge but also nurture positive attitudes and sustainable behaviors among students.
Chemical dyes are routinely discharged into ecosystems via textile industry effluents and landfill leachates. Adsorption using engineered adsorbents presents a viable strategy for pollutant removal in water treatment. Activated carbon (AC) and carbon nanoparticles are composite materials that integrate nanomaterials, rendering them less susceptible to these processes. This study involved preparing and characterizing AC using UV-Vis, fourier-transform infrared (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) techniques. We subsequently assessed its capacity to remove methylene blue (MB) under varying conditions of pH, initial dye concentration, adsorbent dosage, and contact time. The dye is often utilized in the textile and chemical industries. The adsorbent achieved a removal efficacy of 99.9% under optimal conditions: a temperature of 25 ℃, a pH range of 7–9, and a contact time of 60–120 minutes. The removal process was characterized by pseudo-second-order kinetics and the Freundlich isotherm model. The results indicated that the adsorbent’s surface was heterogeneous, consisting of many layers. The calculated thermodynamic parameters were $\Delta G^{\circ}$ = -4.24 kJ/mol, $\Delta H^{\circ}$ = -0.0975 kJ/mol, and $\Delta S^{\circ}$ = -0.3125 kJ/kg/K.
The Industrial Era 4.0 has seen industries start shifting towards implementing Decision Support System (DSS) in the manufacturing sector. Technological advancements have made it possible for the development of DSS to be based on Artificial Intelligence (AI) using past data generated by industry, especially in the furniture manufacturing industry. The furniture manufacturing industry is now faced with the challenge of Extreme Programming (XP) model complexity that hinders production and inventory management. The manufacturing industry finds it difficult to comprehend which industries to produce based on the current market trends. This research, therefore, seeks to comprehend how an AI-based DSS system can learn furniture model production trends. Based on such problems, this research can potentially assist in designing an AI-based DSS employing the Autoregressive Integrated Moving Average (ARIMA) model from the XP system development paradigm. This research is segmented into five phases, i.e., problem identification, decision model design, data collection and processing, system development and integration, and implementation. The delivery of this research is a list of best-selling furniture fads from market analysis generated through DSS. These findings are useful in the development of DSS, especially in AI to make predictions of furniture model trends.
The functional value of a watershed is often degraded by anthropogenic activities. Land cover changes, urban expansion, and industrial development can significantly affect river water quality. Consequently, rapid and comprehensive monitoring is required to represent conditions across the entire river system. Advances in Earth observation satellite technology provide efficient tools for monitoring natural resources and environmental quality. This study aims to estimate concentrations of Total Suspended Solids (TSS) and Dissolved Oxygen (DO) in the Krueng Pase River Basin, North Aceh, Indonesia, using satellite imagery. The analysis employed Sentinel-2A data acquired during both dry and rainy seasons from 2020 to 2022, with a spatial resolution of 60 m. Concurrent field measurements collected by the Aceh Environmental Service were used for accuracy assessment. The results reveal seasonal variations in sediment levels within the Krueng Pase Watershed. Validation against in situ observations produced Nash–Sutcliffe Efficiency (NSE) values of 0.949 (very good) for Period I and 0.645 (satisfactory) for Period II. Percent Bias (PBIAS) values were 15.668 (very good) and 21.0307 (very good), respectively. These findings are supported by the estimated DO concentrations, which consistently $>$5 mg/L. Such levels indicate good oxygen conditions, sufficient to sustain productive aquatic biota and showing no evidence of severe pollution. This study demonstrates that satellite imagery-based estimation of TSS and DO concentrations is a reliable approach for land and water management, particularly in evaluating water pollution.
This study examined climate-related risks to public health, settlements and human security in Thailand, with a particular focus on vulnerable groups such as children and the elderly. Distinguishing itself from traditional assessments, this research innovatively integrated future climate projections from 2016–2035 under a high-emission scenario of RCP8.5 with data about current structural vulnerability, based on the Multidimensional Poverty Index (MPI) in 2024. This approach proactively identified “at-risk” areas where future environmental hazards might exacerbate existing social inequalities. The analysis on 76 provinces except Bangkok, utilized Bivariate Polygon Render to visualize risk-poverty intersections and Local Spatial Autocorrelation (Local Moran’s I) to rigorously detect statistically significant spatial clusters. Results indicated that the Northeastern and Western regions consistently faced elevated risks. Quantitative analysis confirmed critical “High-High” hotspots in the Northeast, specifically in Khon Kaen (LMI = 1.103, p = 0.004) and Buriram (LMI = 1.724, p = 0.008), where high climate exposure significantly overlapped with child multidimensional poverty. Conversely, Mae Hong Son emerged as a significantly “Low-High” spatial outlier (LMI = -0.634, p = 0.008), highlighting a region with concentrated elderly vulnerability despite lower relative climate risks. These findings underscored the utility of MPI over simple population counts for policy targeting. Ultimately, the study supports climate justice principles by providing spatially explicit evidence to guide interventions that address both local needs and structural inequalities.
Limited studies have assessed the specific health risks associated with ozone exposure among informal waste workers in landfill environments, particularly in developing countries. This study addresses this gap by evaluating the Risk Quotient (RQ) of ozone pollutants among scavengers at the Sarimukti Landfill, West Bandung Regency, Indonesia. Applying the Environmental Health Risk Assessment (EHRA) approach, ozone concentrations were measured over three periods across two sampling points. Data were collected from 101 scavengers, including variables such as exposure time, frequency, body weight, and inhalation rate. Intake values, RQ, safe concentration thresholds (Cnk), safe exposure duration (t$_\text{Enk(safe)}$), and safe exposure frequency (f$_\text{Enk(safe)}$) were calculated under both real-time and 30-year projection scenarios. The results showed that real-time RQ values substantially exceeded the safe threshold (mean RQ = 27.183), indicating substantial short-term health risks. Although the projected 30-year values were lower (mean RQ = 7.630), they remained above the acceptablelimit (RQ $>$ 1), reflecting potential chronic health risks. The average safe exposure time at maximum concentration was only 0.147 hours/day, while the safe frequency was limited to 5 days/year. These findings highlight the urgent need for integrating occupational health protections, air quality monitoring, and regulatory enforcement into landfill waste management systems.
Accurate diagnosis of lung cancer brain metastasis is often hindered by incomplete magnetic resonance imaging (MRI) modalities, resulting in suboptimal utilization of complementary radiological information. To address the challenge of ineffective feature integration in missing-modality scenarios, a Transformer-based multi-modal feature fusion framework, referred to as Missing Modality Transformer (MMT), was introduced. In this study, multi-modal MRI data from 279 individuals diagnosed with lung cancer brain metastasis, including both small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), were acquired and processed through a standardized radiomics pipeline encompassing feature extraction, feature selection, and controlled data augmentation. The proposed MMT framework was trained and evaluated under various single-modality and combined-modality configurations to assess its robustness to modality absence. A maximum diagnostic accuracy of 0.905 was achieved under single-modality missing conditions, exceeding the performance of the full-modality baseline by 0.017. Interpretability was further strengthened through systematic analysis of loss-function hyperparameters and quantitative assessments of modality-specific importance. The experimental findings collectively indicate that the MMT framework provides a reliable and clinically meaningful solution for diagnostic environments in which imaging acquisition is limited by patient conditions, equipment availability, or time constraints. These results highlight the potential of Transformer-based radiomics fusion to advance computational neuro-oncology by improving diagnostic performance, enhancing robustness to real-world imaging variability, and offering transparent interpretability that aligns with clinical decision-support requirements.
The study explores the link between digital leadership and cloud intelligence in the context of ethical artificial intelligence (EAI) in relation to three telecom operators in Jordan: Orange, Zain, and Umniah. A total of 424 e-questionnaires were also sent to managers (senior and junior) and staff. The results were processed using SmartPLS4 in PLS-SEM. These results demonstrate that we can develop improved cloud technology solutions to enhance our ethical AI capabilities. This ethical AI facilitates the mediating process in the link between digital leadership and business innovation. Findings lead telecom companies to be much more responsible and ethical in their responsiveness and trust-building with the help of AI-induced cloud intelligence. Finally, the results will summarize theoretical and empirical evidence about responsibility dimensions in AI innovation and data-intensive telecom companies. Along with other important variables such as innovation and integrity, the study emphasizes that telecom managers in today’s digital leadership era are expected to think ahead to ensure that both technological advancement and corporate social responsibility not only develop but genuinely prosper.
The Kara University Hospital generates an average of 17133.1 m$^3$ of wastewater per year. These hospital effluents contain specific substances likely to disseminate pathogenic germs. The objective of the study is to assess the risks associated with the poor management of hospital effluents from the Kara University Hospital. The study involved the characterization of the effluents. The results obtained show that in addition to the temperature and the hydrogen potential (pH), the values of the other physico-chemical parameters in particular, suspended solids (SS) (58.07 mg/L) and nitrates (84.79 mg/L) exceed the discharge standards of World Health Organization (WHO). The values of the microbiological parameters sought, in particular total coliforms (1.106 CFU/100 mL), thermotolerant coliforms (4.105 CFU/100 mL), sulphite-reducing anaerobes (3.6103 CFU/100 mL) and faecal streptococci (5.103 CFU/100 mL) exceed the discharge standards accepted by the WHO. Exposed individuals were identified through an exposure level assessment matrix. The analysis shows that at the production stage, healthcare personnel are the most exposed with a critical rate of 64% (16/25); at the evacuation stage, the workers in charge of evacuation show a moderate exposure level of 48% (12/25). After disposal in nature, populations living near landfill areas are the most exposed with a rate of 48% (12/25). After disposal in nature, populations living near landfill areas are the most exposed with a rate of 48% (12/25). The wearing of personal protective equipment by staff and the establishment of a treatment plant will reduce the risks and ensure sustainable management of effluents from the Kara University Hospital.
Rapid expansion of biodiesel production has generated large streams of low-value crude glycerol, whose role in industrial systems is partially explored. Since this stream is a by-product of policy-driven renewable energy and simultaneously a burden of waste management, its use as a metalworking fluids (MWFs) base stock provides a direct test of whether the transition of energy could be translated into cleaner manufacturing rather than impact shifting. This paper examined whether deploying glycerol-based MWFs in machining could reconfigure waste flows and occupational exposures, to be in line with circular economy and industrial-ecology principles, and under what conditions this could support sustainability transitions. Using a critical narrative review of technical, environmental, and policy literature, we synthesized evidence on the performance of glycerol as a base fluid and the system-level constraints that governed its adoption. The synthesis suggested that, in suitable machining regimes and under enforceable governance conditions, prospective gains included the reclassification of metallic residues from hazardous to non-hazardous streams and improved occupational safety by reducing reliance on biocides and volatile organic compounds. These prospective gains were conditional: adoption was constrained by thermal instability, possible acrolein formation at elevated temperatures, and inconsistent feedstock quality. The paper therefore offered a transdisciplinary synthesis connecting technical performance, waste-classification regimes, and governance instruments. The derived policy needs covered the minimum impurity specifications for industrial glycerol, clearer waste-coding guidance for swarf and spent fluids, and incentives for monitoring and process adaptation to secure net sustainability benefits. In this connection, Hephaestus serves as a metaphor for glycerol-based MWFs: a marginal by-product that could rework glycerol and metallic residues into useful resources, when technical optimization and institutional coordination (including standards and partnerships aligned with SDG 17) are in place.
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