The optimization of hybrid energy systems for sustainable development in remote Arctic communities is crucial to addressing the unique challenges posed by harsh climates, high energy costs, and the environmental impact of traditional energy sources. This study focuses on Arviat, a community in Nunavut, Canada, and utilizes HOMER Pro software to conduct a comprehensive analysis of combined energy solutions. The economic feasibility, environmental impact, and social benefits of integrating renewable energy sources (RES) with existing diesel systems are evaluated. The findings indicate that, while the initial capital investment required for implementing a hybrid microgrid is significantly higher than that for conventional diesel-only systems, the long-term advantages are substantial. These advantages include increased energy resilience, reduced greenhouse gas (GHG) emissions, lower operational and maintenance costs, and the potential for local job creation. Furthermore, the study highlights that hybrid systems, by reducing dependency on fossil fuels, can provide a more reliable energy supply in regions where logistics for fuel delivery are often hindered by extreme weather conditions. The optimization of hybrid energy systems not only aligns with Canada's net-zero emissions targets but also contributes to the economic and social sustainability of remote communities (RCs) by fostering local energy independence and resilience. This research underscores the transformative potential of adopting hybrid microgrids in isolated Arctic regions, advocating for policies and investments that prioritize sustainable and socially responsible energy strategies in line with national and international environmental goals.
The accurate estimation of the age of orange trees is a critical task in orchard management, providing valuable insights into tree growth, yield prediction, and the implementation of optimal agricultural practices. Traditional methods, such as counting growth rings, while precise, are often labor-intensive and invasive, requiring tree cutting or core sampling. These techniques are impractical for large-scale application, as they are time-consuming and may cause damage to the trees. A novel non-invasive system based on fuzzy logic, combined with linear regression analysis, has been developed to estimate the age of orange trees using easily measurable parameters, including trunk diameter and height. The fuzzy inference system (FIS) offers an adaptive, intuitive, and accurate model for age estimation by incorporating these key variables. Furthermore, a multiple linear regression analysis was performed, revealing a statistically significant correlation between the predictor variables (trunk diameter and height) and tree age. The regression coefficients for diameter (p = 0.0134) and height (p = 0.0444) demonstrated strong relationships with tree age, and an R-squared value of 0.9800 indicated a high degree of model fit. These results validate the effectiveness of the proposed system, highlighting the potential of combining fuzzy logic and regression techniques to achieve precise and scalable age estimation. The model provides a valuable tool for orchard managers, agronomists, and environmental scientists, offering an efficient method for monitoring tree health, optimizing fruit production, and promoting sustainable agricultural practices.
The Jaintiapur-Jaflong region, strategically positioned between the subsiding Surma Basin to the south and the uplifting Shillong Massif to the north, presents a unique geological setting. This study employed geological clinometers and other field methods to ascertain the geological characteristics of the area. The regional strike was determined to be N66˚W, with a dip direction of S24˚W and a dip angle of 42.25˚. Through extensive field investigations, including geological mapping, stratigraphic logging, rock sampling, fossil analysis, and structural analysis, complemented by Global Positioning System (GPS), photography, remote sensing, and Geographic Information System (GIS) technologies, seven lithostratigraphic units were identified. These include the variegated color sandstone, mottled clay, yellowish to reddish-grey sandstone, sandy shale with intercalated silty shale, pinkish sandstone, bluish to blackish-grey shale, and limestone units, corresponding sequentially to Dupi Tila, Girujan Clay, Tipam Sandstone, Surma Group, Jenum Shale Fm, Kopili Shale, and Sylhet Limestone Fm, respectively. Five critical geological contact boundaries were delineated, with notable boundaries identified at the Dupigaon-Sari River Section, the Lalakhal-Tetulghat Section, the Nayagang-Gourishankar Section, and between the Barail and Jaintia groups at the Tamabil-Jaflong Highway Road Cut Section. These findings elucidate the geological contacts and stratigraphic units, providing significant implications for paleoenvironmental reconstruction, resource potential assessment, and stratigraphic correlation, thus enhancing the understanding of regional geological history and laying a foundation for future research endeavors.
The efficiency of solar panels is highly affected by the ambient temperature, which limits electrical energy production. There are several cooling techniques to minimize the PV panels’ temperature. Using a Phase Change Material (PCM) is one of the critical techniques to choose. The desirable thermal, kinetic, and chemical properties of PCM are critical conditions for the steady-state thermal Latent Heat Storage Unit efficiency (LHSU). The Phase change material must be thermally stable and dependable. Due to the hot season temperatures in Jordan; RT35 has been chosen as a PCM. The poor thermal conductivity of this material is one of its drawbacks, thus, aluminum fins as a high conductivity material have been added to the proposed design in order to manage the temperature of the phase change material as a Thermal Conductivity Enhancer (TCE). This study intends to enhance the PV cells’ performance by proposing a new design of PV panels with RT35 and fins as a cooling technique. As a result, adding RT35 has a significant impact on reducing the PV panel’s temperature by 16℃, while adding the rectangular fins contributes to decreasing the temperature by another 2℃. Thus, adding RT35 in conjunction with fins will enhance the efficiency and the output power of the PV panel resulting in an 11% increase in electrical production. The results have been validated through ANSYS simulation as well as experimental work which shows a good correlation with the theoretical analysis.
The phenomenon of multiple directorships (MDs) within Boards of Directors of listed entities has garnered increasing attention due to its implications on corporate governance (CG) effectiveness. This study examines the prevalence and major implications of MDs on the governance of Maltese listed entities (MLEs), identifying key determinants and evaluating potential management strategies. A mixed-methods approach is utilized, comprising semi-structured interviews with fourteen directors and company secretaries of MLEs. The findings reveal a significant occurrence of MDs among MLE directors, with impacts that vary based on the number of directorships held, individual circumstances of the directors, and the specific corporate environments of the entities involved. Critical factors contributing to the prevalence of MDs include a limited pool of qualified candidates, directors’ aspirations to serve on multiple boards, and the corporate emphasis on the perceived reputation and quality associated with MD holders. The study highlights that director overcommitment, resulting from MDs, poses potential risks to CG effectiveness. Strategies proposed to mitigate these risks include enhanced nomination committee (NC) reviews, self-assessment mechanisms for board members, and the establishment of more comprehensive guidelines within the CG code specific to directors with MDs. The originality of this research lies in its focus on the unique context of MDs within smaller states like Malta, providing valuable insights into CG enhancement in similar environments. This study offers significant contributions to the literature on MDs and CG, particularly relevant for listed companies in smaller jurisdictions and their stakeholders, by proposing actionable strategies to improve governance practices amidst the challenges posed by MDs.
This study examines how Somalia’s economic growth is impacted by climate change. We employ time series data from 1991 to 2022 and the ARDL (Autoregressive Distributed Lag) model to investigate the impact of temperature, CO2 emissions, and rainfall on real GDP both in the short and long terms. The findings reveal a significant negative impact of climate change on Somalia’s long-term economic growth. However, the short-term effects seem less pronounced. Based on these results, the study recommends focusing on emission reduction through sustainable practices and renewable energy, alongside developing adaptation strategies for managing rainfall variability and promoting drought-resistant agriculture. Additionally, long-term planning that incorporates climate change considerations and fosters international collaboration is crucial. While limitations like data accuracy and the exclusion of other influencing factors exist, the study offers valuable insights for policymakers and future research directions to further explore this complex relationship.
The dynamics of consumer behavior within the rapidly evolving e-commerce landscape necessitate a nuanced understanding, particularly in digital marketplaces. This study investigates the complex relationships among Electronic Word of Mouth (e-WoM), customer reviews, trust, and purchase decisions, with a specific focus on Generation Z (Gen Z) consumers utilizing the Shopee platform in Indonesia. Given Shopee's prominence in Indonesia's flourishing e-commerce sector, insights derived from this analysis hold significant implications for both practitioners and scholars. Data were collected from 127 Gen Z respondents in Tangerang, Indonesia, and analyzed using Structural Equation Modeling (SEM). The findings reveal that e-WoM and customer reviews play a crucial role in shaping consumer trust, which in turn, exerts a significant influence on purchase decisions. Trust is identified as a key mediating factor that links e-WoM and customer reviews to purchase intentions, thereby underscoring its importance in the consumer decision-making process. The study further highlights the direct impact of both e-WoM and customer reviews on trust, which subsequently drives purchase behaviors. These results contribute to the broader understanding of consumer behavior in digital environments, emphasizing the strategic importance of fostering trust through e-WoM and customer reviews to enhance brand perception and increase sales. This research offers empirical evidence supporting the critical role of trust as a mediator in the relationship between e-WoM, customer reviews, and purchase decisions, particularly within the context of Gen Z consumers on the Shopee platform. The implications of these findings suggest that businesses should develop targeted marketing strategies that leverage e-WoM and customer reviews to build consumer trust and drive e-commerce growth.
Environmental issues have become a pressing global concern due to the increasingly consumptive human lifestyle. High levels of environmental literacy and responsibility are essential for reducing the rate of environmental damage, particularly among students studying Geography Education at Universitas Negeri Semarang (UNNES) who are future educators. This study analyzes students’ environmental literacy and responsibility in the UNNES Geography Education program. Employing a quantitative approach, the study assessed environmental literacy variables, including indicators of knowledge, competence, affective, behavior, and environmental responsibility variables. A sample of 168 UNNES Geography Education students from semesters 4, 6, and 8 participated in the study, selected using the proportional stratified random sampling method. Data collection methods included tests, questionnaires, observations, and documentation. Data analysis was conducted using descriptive percentage analysis techniques. The study findings indicate that the environmental literacy level of UNNES Geography Education students falls within the moderate category, with an average score of 50.86. Similarly, the level of environmental responsibility is categorized as moderate, with an average value of 82.04.
This paper aims to analyze the success of applying the Carbon Exchange policy in supporting the green banking concept in Indonesia. The Indonesian Carbon Exchange is a commitment of the government to fight against climate change by ratified the Paris Agreement and legalized Financial Service Authority Regulation (FSAR) No. 14 of 2023 on Carbon Trade through Carbon Exchange, where the results of carbon trade will be reinvested for projects to decrease emissions. This research employed in-depth socio-legal method. Research results showed that based on these six banks’ sustainability report of 2022, they have allocated funding to the Sustainable Business Activity Category sector which significantly increased from 2020 to 2022 as the banks’ contribution to support the green banking concept. Thus, it is certain that the Carbon Exchange policy has an effective role in increasing the portfolio of banks’ sustainable funding in the year 2023 as banks buy carbon units from companies that work in the Sustainable Business Activity Category sector. Based on the six green banking indicators of six banks, it was shown that carbon emission and paperwork or paperless aspects experienced an increase in 2022. Therefore, banks made efforts by buying carbon units from Carbon Exchange. Unfortunately, the green banking concept in Indonesia is yet to be measured precisely because it is still in the early stages of implementation. Therefore, support and collaboration from government, banking industry and society is an essential effort to accelerate Carbon Exchange policy.
Depression, a prevalent and severe medical condition, significantly impairs emotional well-being, cognitive functions, and behavior, often leading to substantial challenges in daily functioning and, in severe cases, an increased risk of suicide. Affecting approximately 264 million individuals worldwide across diverse age groups, depression necessitates effective and timely detection for intervention. In primary healthcare, the Patient Health Questionnaire-9 (PHQ-9) serves as a crucial tool for screening depression. This study leverages the PHQ-9 dataset, comprising 12 features and 534 samples, to evaluate depression levels using advanced machine learning (ML) techniques. A comparative analysis of the Support Vector Classifier (SVC) and AdaBoost Classifier (ABC) was conducted to determine their efficacy in classifying depression severity on a scale from 0 to 4. The SVC emerged as the superior model, achieving an accuracy of 94%. This research contributes to the early detection and prevention of depression by proposing an interactive interface designed to enhance user engagement. Future work will focus on expanding the dataset to improve model generalization and robustness, thereby facilitating more accurate and widespread applications in clinical settings.
In Canada, Indigenous peoples have been managing fires for generations. Challenges and alternatives related to power, jurisdiction, legislation, accreditation, liabilities, and resources exist in identifying and protecting forests from wildfires. Cultural burning can benefit community welfare, biodiversity, and wildfire risk reduction. This study compares Indigenous fire stewardship (IFS) in Canada with cultural burning practices in Indonesia, using literature and comparative research methodologies. Both countries face challenges to this issue. Canada allows cultural burning on reserves with supervision, while Indonesia permits local communities to burn up to two hectares without supervision. Community empowerment, Indigenous Ecological Knowledge (IEK), and fire management are crucial in both nations. Comparative analysis informs future cultural burning policies, emphasizing local expertise in risk reduction.
This study employs a combination of geological investigation, numerical simulation, and theoretical analysis to evaluate the applicability of the load-unload response ratio (LURR) theory in urban tunnels. The results indicate that using the sudden increase in the LURR at critical points or the equivalent plastic strain penetration between the tunnel and the ground surface as failure criteria for subway tunnels is feasible. Under critical instability loads, the equivalent plastic strain zones in the surrounding rock penetrate to the surface during the construction phase, leading to severe deformation of the tunnel chamber group and loss of load-bearing capacity in the surrounding rock. During the operation phase, the tunnel lining plays a primary load-bearing role. Under instability loads, a butterfly-shaped failure zone appears in the surrounding rock. These findings can be utilized for the quantitative evaluation of the overall safety margin of urban subway tunnels.
Open Access
