This study aims to examine the influence of employee and entrepreneur competencies on work efficiency and performance of export companies at the Nong Khai border checkpoint. The research conducted is a quantitative survey. The population for this study includes employees and entrepreneurs from the cross-border export service industry, exporters, and freight forwarder agents operating at the Nong Khai border checkpoint. A non-probability sampling method was employed to select participants. The sample size was Cochran estimated using Cochran’s formula. A structured questionnaire was used to collect data from 385 logistics employees and entrepreneurs selected through purposive sampling. The questionnaires were distributed to employees and entrepreneurs from the export entrepreneurial industry, cross-border export service providers, exporters, and freight forwarder agents at the Nong Khai border checkpoint. The findings revealed that employee and entrepreneur competencies have a direct influence on the work efficiency and performance of export companies. The study concludes that enhancing the competencies of employees and entrepreneurs positively impacts work efficiency and the overall export performance of the company. The research suggests that entrepreneurs should prioritize training and competency development for employees to further improve work efficiency.

Adequate sanitation is crucial for human health and well-being, yet billions worldwide lack access to basic facilities. This comprehensive review examines the emerging field of intelligent sanitation systems, which leverage Internet of Things (IoT) and advanced Artificial Intelligence (AI) technologies to address global sanitation challenges. The existing intelligent sanitation systems and applications is still in their early stages, marked by inconsistencies and gaps. The paper consolidates fragmented research from both academic and industrial perspectives based on PRISMA protocol, exploring the historical development, current state, and future potential of intelligent sanitation solutions. The assessment of existing intelligent sanitation systems focuses on system detection, health monitoring, and AI enhancement. The paper examines how IoT-enabled data collection and AI-driven analytics can optimize sanitation facility performance, predict system failures, detect health risks, and inform decision-making for sanitation improvements. By synthesizing existing research, identifying knowledge gaps, and discussing opportunities and challenges, this review provides valuable insights for practitioners, academics, engineers, policymakers, and other stakeholders. It offers a foundation for understanding how advanced IoT and AI techniques can enhance the efficiency, sustainability, and safety of the sanitation industry.

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.

Freshwater problems in coastal areas include the process of salt intrusion which occurs due to decreasing groundwater levels below sea level which can cause an increase in salt levels in groundwater so that the water cannot be used for water purposes, human consumption and agricultural needs. The main objective of this research is to implementation of RWH to fulfill clean water needs in tropical coastal area in Tanah Merah Village, Indragiri Hilir Regency, with the aim of providing clean water to coastal communities. The approach method used based on fuzzy logic (FL). The model input data includes the effective area of the house’s roof, annual rainfall, roof runoff coefficient, and water consumption based on the number of families. The BWS III Sumatera provided the rainfall data for this research, which was collected from the Keritang rainfall monitoring station during 2015 and 2021. The research findings show that FL based on household scale RWH technology is used to supply clean water in tropical coastal areas that the largest rainwater contribution for the 144 m² house type for the number of residents in a house of four people with a tank capacity of 29 m² is 99.45%.

Since 2007, Peru has implemented results-based budgeting in order to ensure the quality of public spending in State entities and that the population receives goods and services in a timely manner; However, the demands of the current legal and regulatory context require a progressive application to budget processes such as that of the National Penitentiary Institute, which is basically focused on the allocation of resources by the central government, the collections it receives for penitentiary work. and the TUPA; Likewise, it requires strategic programming based on results, refining the procedures for programming, formulation, execution and evaluation of the budget. The objective of this research work is to describe the relationship between results-based budget management and the quality of spending in the Altiplano-Puno Regional Directorate of the National Penitentiary Institute in the period 2019. To achieve the objective, the descriptive explanatory method was used; in addition, the questionnaire and documentary analysis were used as a data collection instrument to determine the relationship between the study variables. Finally, it is concluded that the results-based budget is significantly related to the quality of spending, which means that the entity managed to apply the results-based budgeting methodology efficiently, obtaining an improvement in the quality of spending, consequently focusing on the optimization of the use of financial resources to achieve the strategic objectives of the penitentiary administration in this region. This approach seeks not only to guarantee the correct execution of spending, but also to maximize its positive impact on the management and conditions of penitentiary centers. In this way, a results-based budget approach must be implemented and ensuring the quality of public spending will allow the Office Regional Altiplano Puno of the INPE use its resources more effectively, achieving the objectives of prison security and rehabilitation and improving conditions in penitentiary centers. The adoption of efficient and transparent management practices will contribute significantly to a more responsible and results-oriented public administration.