The sustainable development of peatland ecosystems is imperative due to their susceptibility to climate change. This study evaluates the sustainability of regency development in the peatland areas of Riau Province, Indonesia, utilizing the rapid appraisal technique combined with the Rapfish multidimensional scaling (MDS) algorithm. Critical attributes influencing sustainability were identified, including the Gross Regional Domestic Product (GRDP) in the agriculture, forestry, and fisheries sectors, unemployment rates, GRDP growth rates, investment levels, poverty rates, population growth, deforestation rates, waste management practices, environmental conservation efforts, community involvement, local wisdom, occurrence of peat fires, and groundwater level stations. The findings indicate that the sustainability status of regencies in peatland areas predominantly falls between less and moderately sustainable. Consequently, an urgent need exists to accelerate the implementation of new development paradigms, such as green and low-carbon development strategies, to achieve sustainable development goals in peatland regions effectively. Enhanced policies and practices are required to address the identified sustainability dimensions, fostering resilience and promoting long-term ecological balance.
This bibliometric analysis offers an in-depth examination of the research trajectory concerning carbon capture and storage (CCS), as documented in Scopus-indexed publications from 1998 to 2024. A marked increase in scholarly output has been observed, reflecting the growing academic and practical interest in CCS technologies as critical tools for mitigating climate change. The analysis identifies significant growth periods following key global climate agreements and technological advancements, underscoring the academic community’s engagement in developing and implementing solutions to reduce emissions. Additionally, periodic fluctuations in publication trends have been detected, which may indicate shifts in funding priorities, research focus, and the advent of competing technologies. The notable peak in 2024 suggests that CCS research has potentially reached a pivotal stage of maturity or has been revitalized in response to recent environmental policies or global events. This analysis emphasizes the need for future research to delve deeper into the evolution of CCS technologies, their integration with renewable energy strategies, and the role of policy and economic factors in shaping the CCS research landscape. Such inquiries are deemed essential for guiding global CCS research and policymaking toward effective and sustainable climate action.
This paper assesses green energy technology with respect to its profound impacts, particularly photovoltaic (PV) installed capacity, wind installed capacity and hydrogen fuel cells installed capacity on sustainable development as well as mitigating greenhouse gas emissions. Additionally, the study examines recent technological improvements and empirical facts that indicate how renewable sources of energy facilitates decrease in carbon emission and further supports global sustainability goals. As a result, major findings show significant declines in CO₂ releases after extensive PV, wind and hydrogen fuel cell technologies have been deployed. The examples from China, EU countries, USA, India and Japan demonstrate these accomplishments. Cumulative CO₂ emissions from 2015 to 2023 for China were 102.0 Gt; while the United States had 43.0 Gt; EU - 25.4 Gt; India – 21.7 Gt; Japan –10.0 Gt, respectively.
Drilling and blasting are essential operations within the mining industry, playing a critical role in material fragmentation. Despite advancements in various blasting technologies, the process remains a dominant contributor to overall mining costs. Achieving cost efficiency requires the precise configuration of blast design parameters, including explosive charge quantity, to attain desired outcomes in fragmentation, ground vibrations, fly rock, and air over-pressure. This study introduces a novel artificial intelligence (AI)-driven model, XGBoost-PSO-T, which combines eXtreme Gradient Boosting (XGBoost) with Particle Swarm Optimization (PSO) through the integration of the Tri-Weight technique. The PSO-Tri-Weight method optimizes the hyperparameters of the XGBoost model, enhancing its predictive capabilities. The model's performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²), with the results demonstrating that the XGBoost-PSO-T system outperforms the standard XGBoost approach, achieving an RMSE of 0.657 and an R² of 0.922. These findings suggest that the XGBoost-PSO-T model is a valuable tool for predicting fragmentation outcomes and optimizing blast designs in surface mining operations. The implementation of this system is recommended to improve blasting efficiency and reduce operational costs.
An extensive assessment of ambient air quality near a medical waste incineration (MWI) facility in Johannesburg, South Africa, was conducted, focusing on the gas-particle phase partitioning and the concentrations of polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), and dioxin-like polychlorinated biphenyls (dl-PCBs). It was found that highly chlorinated congeners, specifically hexa- to octa-chlorinated, predominate in the particulate phase, while tetra- and penta-chlorinated isomers were predominantly observed in the gas phase. The concentrations of ΣPCDD/Fs in ambient air ranged from 8.3 to 108.36 fg WHO2005-TEQ/m³, ΣPCBs from 4.43 to 6.06 fg WHO2005-TEQ/m³, and ΣPCDD/Fs in soil from 59.17 to 106.05 pg WHO-TEQ/g. Seasonal variations were marked, with peak concentrations typically occurring in winter and the lowest in summer. Globally, despite a decreasing trend, the concentrations of PCDD/Fs in South Africa remain higher than those reported in other regions. The study further revealed that the daily inhalation intakes of PCDD/F emissions by the local population exceeded the recommended tolerable daily intake levels, underscoring the need for a comprehensive risk assessment that considers all exposure pathways to fully evaluate potential health risks for residents living near the incineration facility.
The increasing urgency of climate change mitigation necessitates the adoption of renewable energy sources to meet the growing demand for clean energy. Solar energy, in particular, presents a viable solution, contingent on the availability of sufficient land to optimize power generation. River land offers an alternative location for solar power plants, potentially conserving valuable land resources while providing a natural cooling medium for solar panels to enhance efficiency. This study evaluates the techno-economic feasibility of establishing a solar power plant system (PLTS) on river land in Surakarta City, Indonesia, using simulations conducted with the Hybrid Optimization Model for Electric Renewables (HOMER) software. The simulation considers both on-grid and off-grid systems, with a daily energy demand of 2,947.236 kWh projected over a 25-year period at the Tirtonadi Dam site. The On-Grid system demonstrated a total annual energy production of 885,358 kWh, significantly outperforming the off-grid system, which produced 34,400 kWh annually. The Net Present Cost (NPC) for the on-grid system was calculated to be USD 1,805,634.01, while the off-grid system's NPC was substantially lower at USD 1,970.18. The Levelized Cost of Energy (COE) for the on-grid system was found to be USD 0.09 per kWh, compared to USD 0.10 per kWh for the off-grid system, indicating favourable investment potential. The breakeven point (BEP) for the On-Grid system was achieved in the 0.54th year. The initial capital expenditure required to implement the on-grid PLTS at Tirtonadi Dam was estimated at approximately USD 47,782.10, while the off-grid system's initial cost was around USD 1,923.77. These findings suggest that the deployment of solar power plants on river land, particularly with an on-grid configuration, is a technically viable and economically advantageous approach to enhancing renewable energy capacity in Indonesia.
Sentiment analysis, a crucial component of natural language processing (NLP), involves the classification of subjective information by extracting emotional content from textual data. This technique plays a significant role in the movie industry by analyzing public opinions about films. The present research addresses a gap in the literature by conducting a comparative analysis of various machine learning algorithms for sentiment analysis in film reviews, utilizing a dataset from Kaggle comprising 50,000 reviews. Classifiers such as Logistic Regression, Multinomial Naive Bayes, Linear Support Vector Classification (LinearSVC), and Gradient Boosting were employed to categorize the reviews into positive and negative sentiments. The emphasis was placed on specifying and comparing these classifiers in the context of film review sentiment analysis, highlighting their respective advantages and disadvantages. The dataset underwent thorough preprocessing, including data cleaning and the application of stemming techniques to enhance processing efficiency. The performance of the classifiers was rigorously evaluated using metrics such as accuracy, precision, recall, and F1-score. Among the classifiers, LinearSVC demonstrated the highest accuracy at 90.98%. This comprehensive evaluation not only identified the most effective classifier but also elucidated the contextual efficiencies of various algorithms. The findings indicate that LinearSVC excels at accurately classifying sentiments in film reviews, thereby offering new insights into public opinions on films. Furthermore, the extended comparison provides a step-by-step guide for selecting the most suitable classifier based on dataset characteristics and context, contributing valuable knowledge to the existing literature on the impact of different machine learning approaches on sentiment analysis outcomes in the movie industry.
The fatigue life of H-type rigid hangers, crucial components in bridge engineering, is investigated in this study, particularly under the influence of torsional vibrations induced by wind loads. These hangers, integral to the integrity and longevity of bridge structures, are characterized by their high aspect ratio and low torsional stiffness, which predispose them to fatigue under such conditions. The focus of the research is the hangers of Dongping Bridge, located in Foshan, Guangdong. Through the application of theoretical analysis and finite element simulation using ABAQUS, the effects of bolting actions were simulated using connector elements, which enhanced computational efficiency and facilitated the stress analysis at the bolt holes in node plates. Furthermore, fe-safe fatigue analysis software was utilized to evaluate the fatigue life, adhering to established guidelines. The findings reveal that selecting an appropriate stiffness for the connector elements is critical in accurately simulating the bolting action. It was determined that the torsional amplitude at mid-span is a viable indicator for assessing fatigue damage. A torsional vibration control threshold of 6.25° is recommended for hangers measuring 40.212 meters in length.
The spatial configuration of the pantograph-catenary system (PCS) is significantly altered by the superelevation present in curved railway tracks, leading to deviations in the system’s dynamic behaviour and imposing constraints on operational speeds. In this study, a detailed model of the PCS in curved sections has been developed to evaluate the dynamic performance of a dual PCS under these conditions. It was observed that the contact loss rate of the trailing pantograph increases markedly as train speed rises, with this effect being more pronounced in curved sections compared to straight tracks. This degradation in performance necessitates optimisation strategies to ensure operational efficiency at higher speeds. To address the issue, it is proposed that the static uplift force of the trailing pantograph be increased when trains traverse curved sections. Additionally, optimisation of the catenary system is recommended, involving both a reduction in the span length and an increase in the tension of the contact wire. By implementing these strategies, the dual PCS can sustain the necessary contact and satisfy dynamic performance criteria at speeds of up to 300 km/h in curved sections. These findings provide valuable insights for improving the reliability and safety of high-speed railway operations on complex track geometries.
Open Access
