A gradually detailed geophysical investigation took place on Ancient Marina territory. In that area was extended Ancient Tritaea, according to responsible Archaeological Services. The first approach had been attempted since 1988 by applied electric mapping based on a twin-probe array. Later, the survey extended to the peripheral zone under the relative request from the 6th Archaeological Antiquity. A new approach was implemented by combining three different geophysical techniques, like electrical mapping, total intensity, and vertical gradient. These were applied on discrete geophysical grids. Electric mapping tried to separate the area into low and high-interest subareas according to soil resistance allocation. That technique detected enough geometrical characteristics, which worked as the main lever for the application of two other geophysical techniques. The other two techniques would be to certify the existence of geometrical characteristics, which divorced them from geological findings. Magnetic methods were characterized as a rapid technique with greater sensitivity in relation to electric mapping. Also, vertical gradient focuses on the horizontal extension of buried remains. Processing of magnetic measurements (total and vertical) certified the results from electric mapping. Also, both of the techniques confirmed the existence of human activity results, which were presented as a cross-section of two perpendicular parts. The new survey results showed that the new findings related to results from the previous approach. Geophysical research in that area is continuing.

Renewable energy is gaining momentum in developing countries as an alternative to non-renewable sources, with rooftop solar power systems emerging as a noteworthy option. These systems have been implemented across various provinces and cities in Vietnam, accompanied by government policies aimed at fostering their adoption. This study, conducted in Ho Chi Minh City, Vietnam investigates the factors influencing the utilization of rooftop solar power systems by 309 individuals. The research findings, analyzed through the Partial least squares structural equation modeling (PLS-SEM) model, reveal that policies encouragement and support, strategic investment costs, product knowledge and experience, perceived benefits assessment, and environmental attitudes collectively serve as predictors for the decision to use rooftop solar power systems. Furthermore, the study delves into mediating and moderating effects between variables within the model. This research not only addresses a knowledge gap but also furnishes policymakers with evidence to chart new directions for encouraging the widespread adoption of solar power systems.

The pressing need to redefine the tourism industry's relationship with nature and local communities has never been more critical. Ecotourism, as a paradigm of sustainable travel, holds transformative potential—not only for preserving our planet's fragile ecosystems but also for fostering local cultural and economic development. In this context, the integration of circular economy principles offers innovative pathways to enhance sustainability across the tourism sector. The application of circular economy frameworks in tourism not only reduces environmental impact but also enhances economic viability by creating closed-loop systems. My interest in this topic stems from a personal conviction: Tourism should leave a positive mark, one that enriches rather than diminishes the destinations we visit. This study delves into how the hotel industry can align itself with ecotourism principles by embracing innovative, sustainable practices that minimize environmental impact while delivering authentic, high-quality experiences for travelers. Through the lens of green energy, resource optimization, and cultural integration, the research demonstrates that sustainability is both an ethical responsibility and a pathway to long-term competitiveness in tourism. By supporting local economies and protecting natural heritage, the industry can shift from being a passive observer of environmental degradation to a proactive steward of change. This work serves as a call to action for stakeholders: Our choices today will define the landscapes and cultural legacies available to future generations.

Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.

This research investigates the relationship between the quality of airline services, customer satisfaction, and brand loyalty with low-cost airlines in Bangkok’s aviation business. It uses structural equation modeling (SEM) to examine the replies of 521 passengers. The study demonstrates a robust and favorable correlation between the quality of service and customer satisfaction, with a direct impact coefficient of 0.961. Furthermore, service quality directly (0.708) and indirectly (0.284) impact brand loyalty. These impacts are mediated by customer satisfaction, which directly affects brand loyalty with a correlation of 0.296. The model explains 92.3% and 99.0% of the variation in customer satisfaction and brand loyalty, respectively, suggesting a robust and reliable match. The demographic study reveals that the predominant group of participants consists of well-educated, middle-income women who regularly use airline services. These results highlight the importance of service quality in improving customer satisfaction and promoting brand loyalty among travelers. Airlines should emphasize the ongoing enhancement of service quality and customer satisfaction to sustain their competitive edge. This research enhances the existing body of knowledge by emphasizing the intermediate function of customer satisfaction and presenting detailed observations relevant to Bangkok’s aviation industry, providing guidance for infrastructural development and investment. It also offers practical suggestions for managing service quality and implementing customer retention strategies.

Optimizing Storage Location Assignment (SLA) is essential for improving warehouse operations, reducing operational costs, travel distances and picking times. The effectiveness of the optimization process should be evaluated. This study introduces a novel, generalized objective function tailored to optimize SLA through integration with a Genetic Algorithm. The method incorporates key parameters such as item order frequency, storage grouping, and proximity of items frequently ordered together. Using simulation tools, this research models a picker-to-part system in a warehouse environment characterized by complex storage constraints, varying item demands and family-grouping criteria. The study explores four scenarios with distinct parameter weightings to analyze their impact on SLA. Contrary to other research that focuses on frequency-based assignment, this article presents a novel framework for designing SLA using key parameters. The study proves that it is advantageous to deviate from a frequency-based assignment, as considering other key parameters to determine the layout can lead to more favorable operations. The findings reveal that adjusting the parameter weightings enables effective SLA customization based on warehouse operational characteristics. Scenario-based analyses demonstrated significant reductions in travel distances during order picking tasks, particularly in scenarios prioritizing ordered-together proximity and group storage. Visual layouts and picking route evaluations highlighted the benefits of balancing frequency-based arrangements with grouping strategies. The study validates the utility of a tailored generalized objective function for SLA optimization. Scenario-based evaluations underscore the importance of fine-tuning SLA strategies to align with specific operational demands, paving the way for more efficient order picking and overall warehouse management.