Environmental challenges are increasingly addressed through movements that promote environmental care and awareness. Consumers with a high degree of environmental consciousness are more inclined to purchase environmentally friendly or “green" products, perceiving these purchases as a contribution to environmental sustainability. This research aims to analyze the relationship between purchasing decisions for green products and the factors that influence them, further ensuring the stability of findings in environmentally friendly marketing in the context of company development to assess long-term stability. The research utilizes two methodologies: Structural Equation Modeling (SEM) with Partial Least Squares (PLS) is employed to identify and quantify the relationships between green product purchasing decisions and key influencing factors, including green product knowledge, consumer perception, and perceived price. Meanwhile, dynamic system simulation is used to measure the stability and evolution of green product purchasing decisions over time. The findings reveal that the relationships between these influencing variables are statistically significant and demonstrate a stable trend. The dynamic simulation indicates that the expected values for green product purchasing decisions are consistently achieved annually, reaching a stable equilibrium within a decade. These outcomes provide valuable insights for designing marketing strategies that enhance consumer awareness of green products and assist in decision-making processes, thereby promoting sustainable consumer behavior. The practical implications of this research are twofold: it offers strategic guidance for companies aiming to market green products effectively and provides consumers with a framework to make informed purchasing decisions that align with environmental sustainability goals.
This study evaluates the usability and effectiveness of a newly developed special education (SPED) campus navigation system designed for students with visual impairment (SVI) at the Federal College of Education (Special), Oyo, Oyo State, Nigeria. The primary objective was to assess the system's capability to facilitate self-navigation for SVI and identify challenges encountered in a campus environment. A mixed-methods approach, combining quantitative data from questionnaires and qualitative insights from interviews, was employed. Twenty SVI, selected through purposive sampling, participated in the study, using the system over a five-week period. The findings indicate significant improvements in the orientation and mobility of SVI, resulting in increased confidence in navigating the campus. Participants reported that the navigation system effectively aided in locating key areas, detecting obstacles, and ensuring safety. However, several critical challenges were identified, such as the system's voice being drowned out in noisy environments and the frequent need for battery recharging every five days. Participants suggested enhancements, including the incorporation of volume control to accommodate various environmental conditions and regular device charging to prevent battery depletion. These improvements are deemed essential for enhancing the system's reliability and usability for SVI.
Advancements in 3D printing technology have enabled the creation of highly efficient and cost-effective suppressors, offering significant safety benefits for firearm users. Exposure to firearm noise, even in controlled environments such as shooting ranges, poses serious health risks, necessitating improved noise reduction measures. This study explores the potential of 3D printing to produce novel suppressor designs that effectively reduce sound pressure levels in firearms, specifically focusing on their application with a .22 LR caliber rifle. Suppressors capable of reducing sound levels to below 135 dB, making them safe for adult use without hearing protection, were the primary focus. The research was conducted in two phases: initially, optimal suppressor designs were modeled using SolidWorks computational fluid dynamics (CFD), featuring innovations such as perforated baffles, additional expansion chambers, deep and curved expansion chambers, and perforated tubes extending along the suppressor's length. Following the simulation of these designs, live fire testing was conducted in a controlled shooting range environment. The results demonstrated that all tested designs effectively reduced sound pressure to safe levels. However, the suppressor with a conventional baffle layout supplemented by partitioned expansion chambers proved to be the most efficient, particularly when paired with subsonic ammunition. This study highlights the potential of 3D printing technology to revolutionize suppressor design, offering customizable solutions that enhance both user safety and environmental protection.
Food waste is a social problem as it reduces safe, nutritious food, increases contamination risks through improper disposal, attracts pests, promotes harmful bacteria growth, and heightens environmental issues that threaten agricultural productivity. The study’s goals are to look into how hospital food waste can be turned into nutrient-rich animal feed and fertiliser by setting standards based on the amount of food waste from inpatients, seeing how different waste treatment methods affect the growth and uptake of nutrients by larvae, and and assess the optimization of bioconversion processes through Structural Equation Modeling (SEM). The data analysis employed Analysis of Variance (ANOVA) and WarpPLS (Warp Partial Least Squares) Path Analysis, using Black Soldier Fly (BSF) as the bioreactor medium. Although the C/N ratio does not meet the standards set by the Indonesian Minister of Agriculture, results show that BSF larvae consuming rice and non-rice waste can serve as alternative raw materials for animal feed and fertilizer. Implementing Hermetia illucens or BSF for organic waste management is a creative solution that can reduce methane emissions and contribute to sustainable waste management. Using SEM to convert hospital waste into high-value products and minimize disposal supports sustainable waste management and a circular economy.
This study explores the impact of Standard Operating Procedures (SOPs) on the optimization of hotel management performance through the application of a path model. Relationships among key factors, including service quality (X1), operational efficiency (X2), SOP implementation (M), customer satisfaction (Y1), and employee behaviour (Y2), were examined. Pre- and post-implementation scenarios were simulated using an empirical dataset, offering insights into the role of SOPs in improving managerial outcomes. The analysis reveals significant contributions from service quality and operational efficiency to the implementation of SOPs, which in turn drive enhancements in customer satisfaction and employee behaviour. Furthermore, a strategic monitoring framework was introduced to ensure the ongoing adherence to SOPs and the continuous improvement of operational efficiency. The findings underscore the importance of a structured approach to SOP implementation and provide actionable strategies for hotel managers seeking to elevate service standards and performance outcomes.
In an increasingly competitive market landscape, companies must innovate by allocating a significant portion of product sales revenue, specifically at least 22%, towards research and development (R&D). Collaboration between companies and universities, which actively engage in R&D, is crucial in this context. At Andalas University, the Research and Community Service Institute (LPPM) oversees R&D initiatives and community services, including the management of the Science Techno Park. To achieve commercialization objectives, it is imperative to identify and address the factors that inhibit the commercialization of research products at Andalas University. The Fuzzy Analytical Hierarchy Process (FAHP) method has been employed to ascertain the primary factors impeding commercialization. The research findings indicate that the foremost factor inhibiting commercialization is resource availability, assigned a weight of 0.221. This is followed by intellectual property considerations, with a weight of 0.215, and marketing challenges, with a weight of 0.160. These insights provide a foundational basis for the development of strategies aimed at enhancing the commercialization of research products at Andalas University.
The research and development of energy optimization methodologies in parallel pumping systems in recent years have aimed to impact operational costs, energy savings, and system reliability. Operational costs are correlated with the number of units operating simultaneously, considering power demand, operating point, and system reliability. Additionally, the optimization strategy must manage the operation of pumping units by regulating the output flow according to process dynamics and the energy tariff structure. In this document, an energy optimization model is presented for parallel pumping systems operating under variable demand conditions. The optimization problem is addressed through an iterative constraint-based analysis model, capable of predicting the number of units that should operate simultaneously and their corresponding speeds during future time intervals. The methodology suggests analyzing system operation indicators as inputs for the prediction model. The effectiveness of the methodological strategy for optimal dispatching of parallel pumping units is verified in a utility sector pumping system. The results obtained demonstrate savings between 20% and 25% in energy costs for system operation, which represents a contribution in the search for a significant use of energy and energy sustainability.
Rapid growth in the global electric vehicle (EV) market has sparked extensive research, with charging time remaining a critical concern. This study presents a descriptive analysis of research publications on EV charging time from 2017 to April 2024, highlighting trends, characteristics, and global perspectives and identifying research gaps. Scopus, an extensively utilized and frequently cited bibliographic database among the global research community, served as the primary data source for this investigation. Based on the Scopus database, the analysis reveals a growing interest in optimizing charging times, with notable peaks and troughs in publication trends. Interdisciplinary collaboration is evident, with engineering, computer science, and energy research leading the field. Key thematic clusters focus on charging infrastructure, battery optimization, and integration with renewable energy sources. Research gaps and emerging areas include fast-charging technology, battery management systems, and grid integration. A future research roadmap suggests investigating fundamental charging mechanisms, developing intelligent charging systems, exploring socio-economic implications, and fostering international collaborations. While progress has been made, further research is needed to address challenges and drive innovation in EV charging technology for sustainable transportation solutions.
The Ponan tradition, deeply rooted in Poto Village, Moyo Hilir District, Sumbawa Regency, Indonesia, exemplifies a unique form of local wisdom with significant social, cultural, and economic potential. Despite its cultural prominence, the integration of this tradition into tourism development has been hindered by inadequate infrastructure, insufficient promotional strategies, and limited community engagement. This study addresses these challenges by exploring the role of the cc tradition as a form of social capital in fostering sustainable tourism development. A qualitative case study approach was employed, incorporating in-depth interviews, participatory observations, and focus group discussions (FGDs). The findings highlight that the sustainable development of tourism in Poto Village is contingent upon three critical factors: the enhancement of infrastructure, the implementation of targeted promotional campaigns, and the active participation of the local community in preserving and promoting their cultural heritage. Furthermore, it was observed that the Ponan tradition serves as a vital mechanism for community empowerment, fostering a sense of ownership and pride among villagers while simultaneously attracting cultural tourism. The study underscores the importance of adopting a holistic approach to tourism management that harmonizes economic objectives with cultural preservation and community well-being. By offering actionable insights, this research contributes to the broader discourse on sustainable tourism and cultural studies, providing a framework for policymakers and practitioners to develop inclusive and culturally sensitive tourism strategies. However, the study is limited by its regional focus and qualitative methodology, suggesting the need for future research to explore broader applications of local wisdom in tourism development across diverse cultural contexts.
The increasing global awareness of environmental issues has driven consumers toward eco-friendly products, making green marketing an essential aspect of contemporary business strategy. Originating in the 1980s, the concept of "going green" has significantly evolved, positioning sustainability at the forefront of corporate agendas. Indian companies, in particular, are increasingly adopting green marketing strategies to appeal to environmentally conscious consumers and maintain competitiveness in an eco-aware marketplace. However, significant challenges persist, primarily concerning perceived costs and the effectiveness of these strategies. This study aims to critically examine the landscape of green marketing in India, exploring the concept’s evolution, its strategic importance, and the challenges faced by businesses in this domain. By analyzing secondary data from academic literature and credible sources, the study provides a comprehensive overview of the current state of green marketing in India. The findings highlight that the integration of green marketing practices not only enhances corporate competitiveness but also contributes to broader environmental goals. Nevertheless, the successful adoption of these strategies requires overcoming substantial barriers, including misconceptions about financial implications and the need for greater governmental support. The paper concludes that every incremental effort towards environmental sustainability can significantly impact the resolution of contemporary ecological challenges. As such, the incorporation of green marketing strategies represents a logical and necessary progression for companies aiming to achieve long-term sustainability and societal benefits. The promotion of green marketing, supported by governmental incentives, is essential for fostering a greener future for current and future generations.
The utilization of remote sensing (RS) techniques plays a crucial role in the efficient planning and monitoring of afforestation projects within constrained timeframes. This study evaluates the progress of the Billion Trees Afforestation Project (BTAP) in Dera Ismail Khan (DIK), Khyber Pakhtunkhwa, Pakistan, using RS technology. Geographical positioning systems were employed to delineate the boundaries of the plantation areas, and two temporal Sentinel-2 images from 2016 (the commencement of the plantation) and 2018 were analyzed to calculate the normalized difference vegetation index (NDVI). The results revealed that the survival rate of plantations varied between 37.39% and 85.15%, while the area of unstocked regions ranged from 14.84% to 62.60%. Overall, in 2016, the survival rate was determined to be 61.28%, with 38.72% of the area remaining unstocked. The NDVI values in 2016 ranged from -1 to -0.43, whereas in 2018, they spanned from -0.43 to 0.80, indicating significant progress in plantation growth and a substantial reduction in unstocked areas. The RS-based assessment proved to be highly effective, suggesting its adoption for the rapid detection and evaluation of plantation efforts. It is recommended to use high-resolution satellite images and drone technology to enhance accuracy further. Additionally, measures such as the establishment of closures, pit sowing, appropriate site and species selection, and effective soil and water conservation techniques are essential to maximizing the survival rate of plantations.
This study aims to identify the concentration levels of physical-chemical, inorganic and organic parameters through hydrochemistry, determining their origin, abundance, distribution and migration in the Pachapaqui mining unit. Methodologically, a detailed study was carried out that included ground and surface water sampling, chemical analysis to determine water quality, measurements of levels and flow rates, using different hydrogeological modelling diagrams to understand the dynamics of water flow in the study area. The main results revealed, through Piper's diagram, that the waters of the mine mouths contain predominantly Ca and SO4 ions, classifying them as sulphate (CaSO4). This composition, consistent with the local geology, was confirmed by the Stiff diagram. In addition, significant variability in SO4 levels was observed, suggesting the influence of factors such as lithology and acid mine drainage. The final conclusion indicates that the hydrochemical studies of the mine mouth waters are predominantly sulphate (CaSO4), influenced by geological factors and acid mine drainage. These findings, crucial for groundwater resource management, do not imply significant risks for the construction of pit plugs.
This study aims to assess the probability of unsafe operations on horizontal curves resulting from speed variation, employing both statistical analysis and machine learning (ML) techniques. The statistical analysis was conducted using Minitab software to assess the probability of non-compliance through the Monte-Carlo simulation method. Additionally, the research applied three ML classification models—a novel optimized version of the Random Forest (RF) classifier, Naive Bayes (NB), and Extreme Gradient Boosting (XGBoost). Nine curves with radii ranging from 700m to 2000m were selected from two rural roads in Egypt for the study. The evaluation of non-compliance probability on each curve involved contrasting the supply (design speed, a fixed value) with the demand (actual speed, characterized by actual speed distributions). Findings revealed that using the 85th percentile speed in the analysis, the probability of non-compliance during off-peak hours exceeded 50% for all curves except two, where it reached 100%. This indicates that approximately 100% of vehicles engage in unsafe operations during off-peak hours on these specific curves. Accuracy results of the ML classifiers showed that the proposed RF classifier performed exceptionally well with a perfect score of 1.0, followed by XGB and NB classifiers for all curves. A comparative analysis between the results of statistical analysis and ML in estimating curve safety suggests that ML outperforms statistical analysis, demonstrating its potential as a more reliable tool for assessing road safety on horizontal curves.
A torque-based optical fiber flow sensor has been designed and experimentally tested to assess its potential for fluid flow measurement. The sensor utilizes an optical fiber strength modulation principle to achieve flow detection. Detailed attention is given to the design of the sensor structure, including the sensor probe and fiber bundle probe, and the working principle of the torque-based flow sensor is systematically described. A theoretical model of the sensor is established, considering key parameters such as torque (m), radius (r), sensor joint stiffness (SJ), refractive index (n), and radius of curvature (R), which significantly affect its detection performance. Simulations are conducted to obtain Q-M curves under varying parameter conditions, revealing the relationship between sensor output and fluid flow rate. A gas flow detection experiment is subsequently performed on a custom-built experimental platform to evaluate the sensor’s practical performance. The results indicate that the sensor output decreases monotonically with increasing fluid flow for different parameter settings, demonstrating a good linear response within a specific detection range. It is found that the sensitivity of the sensor is influenced by the selection of critical performance parameters and the characteristics of the fluid being measured. For gas flow detection, the sensor output voltage shows an approximately linear decrease with the increase in gas flow. The comparison between simulation and experimental data confirms that both exhibit similar trends, thereby validating the sensor’s applicability in fluid flow detection. This study highlights the potential of torque-based optical fiber flow sensors for accurate and reliable fluid flow measurements.
The COVID-19 pandemic has prompted extensive modeling efforts worldwide, aimed at understanding its progression and the myriad factors influencing its spread across diverse communities. The necessity for tailored control measures, varying significantly by region, became apparent early in the pandemic, leading to the implementation of diverse strategies to manage the virus both in the short and long term. The World Health Organization (WHO) has faced considerable challenges in mitigating the impact of COVID-19, necessitating adaptable and localized public health responses. Traditional mathematical models, often employing classical integer-order derivatives with real numbers, have been instrumental in analyzing the virus's spread; however, these models inadequately address the fading memory effects inherent in such complex scenarios. To overcome these limitations, fuzzy sets (FSs) were introduced, offering a robust framework for managing the uncertainty that characterizes the pandemic’s dynamics. This research introduces innovative methods based on complex Fermatean FSs (CFFSs), alongside their corresponding geometric aggregation operators, including the complex Fermatean fuzzy weighted geometric aggregation (CFFWGA) operator, the complex Fermatean fuzzy ordered weighted geometric aggregation (CFFOWGA) operator, and the complex Fermatean fuzzy hybrid geometric aggregation (CFFHGA) operator. These advanced techniques are proposed as effective tools in the strategic decision-making process for reducing the spread of COVID-19. A compelling case study on COVID-19 vaccine selection was presented, demonstrating the practical applicability and superiority of these methods, effectively bridging theoretical models with real-world applications.
Open Access
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