Abstract. Forest fires constitute a constant hazard for natural protected areas in Mexico, especially during dry seasons and near urban areas. Unplanned fires can escalate quickly, necessitating prompt human intervention through the dispatch of highly trained fire brigades, the allocation of available transportation resources, and seamless coordination, communication, and asset tracking. Given that forests span extensive areas with complex topography, human resources become a high-value asset for fire management. Ensuring precise control and safety is paramount during emergency responses, where location and communication are critical for effective coordination and management of the limited resources. This paper presents a tracking and monitoring system designed with off-the-shelf technologies, focusing on personnel and based on the Message Queue Telemetry Transport (MQTT) protocol for efficient message exchange. The system leverages existing mobile technologies with cellular connectivity, with a custom platform providing location information about fire brigades, individuals, and their current status to fire managers. Gathered data is integrated cartographically through a WEB mapping platform with content suited for fire management, bringing up-to-date fire brigade movements, providing per-unit status, and enhancing situational awareness. The results are compared with traditional two-way radio methodologies.

Floods remain one of the most devastating natural disasters, causing significant human, economic, and ecological losses worldwide. Accurate prediction of flood peaks is therefore essential for effective water resource management, urban planning, and disaster mitigation. However, modeling flood peak data is challenging because such observations are nonnegative, highly skewed, and often exhibit heavy-tailed characteristics. Traditional symmetric probability models, such as the normal distribution, fail to capture this behavior. In contrast, the skewed chi-square distribution, particularly its scaled and noncentral variants, offers a mathematically flexible and practically interpretable framework for modeling positively skewed hydrological extremes. This paper develops a statistical formulation for flood peak prediction based on skewed chi-square modeling, including parameter estimation, return level analysis, and diagnostic validation. To complement simulation-based evaluation, an empirical study using annual flood-peak records from a Midwestern U.S. catchment (1950–2020) was conducted with data obtained from USGS and NOAA archives. The real data analysis confirms that the scaled noncentral chi-square distribution accurately captures the strong right skewness and heavy tails observed in hydrological extremes, outperforming traditional gamma and lognormal models. The proposed approach aims to bridge the gap between theoretical distributional modeling and real-world flood risk assessment.

The food and beverage (FnB) industry is one of the main supporting sectors of tourism in Indonesia and has experienced rapid growth along with the increasing number of tourist activities and consumer demand. However, previous studies indicate that Service Quality in the FnB industry remains suboptimal, particularly in aspects related to human resources (HR). Issues such as inconsistent service performance, low responsiveness, and limited employee competence and work attitude are commonly identified. This study aims to systematically examine the role of human resources in Service Quality within the FnB industry and to identify key factors, management strategies, and existing research gaps. This research employed a Systematic Literature Review (SLR) method using the PICOC framework, analyzing 20 national and international journal articles published between 2015 - 2025 and retrieved from Google Scholar. The findings reveal that the most influential HR factors affecting Service Quality include competence, communication skills, work attitude, experience, and employee training. Furthermore, effective human resource management practices demonstrate a positive relationship with improved Service Quality. Nevertheless, the review identifies a lack of comprehensive studies integrating HR management and Service Quality within the specific Context of the Indonesian FnB industry, indicating opportunities for future research.

Water quality is a crucial aspect of environmental health, directly impacting human well-being and ecosystem sustainability. Dams, while providing benefits like irrigation and power generation, can significantly alter the physico-chemical characteristics of water bodies downstream. This study focuses on the physico-chemical analysis of water from Beni Sagar Dam, located in Chhatarpur district, Madhya Pradesh, India. Water samples were collected from various locations within the reservoir and analyzed for parameters such as pH, temperature, electrical conductivity, total dissolved solids (TDS), dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), turbidity, alkalinity, hardness, chloride, nitrate, and phosphate. The results were compared with the permissible limits prescribed by national and international standards, revealing the overall water quality status of the dam. The findings provide valuable insights into the potential impacts of the dam on water quality and contribute to the development of effective water resource management strategies for the region.

ABSTRACT The scalability, adaptability, and pay‐per‐use nature of cloud computing have contributed to its meteoric rise to prominence, enabling customers to access services regardless of their physical location. A major obstacle to effective resource management is the wide variety of services provided and the wide variety of user needs. Due to inadequate resource use and suboptimal scheduling tactics, cloud data centers, which consist of physical machines (PMs) hosting many virtual machines (VMs), frequently experience significant energy consumption. A task scheduling technique is introduced in this study to tackle energy efficiency in cloud environments. It integrates two meta‐heuristic algorithms. A Slack‐based classification algorithm is used first to cluster tasks and then rank them according to their criticality. In order to schedule vital work, we use the Remora Optimization Algorithm (ROA). For noncritical jobs, we use Particle Swarm Optimization (PSO). Researchers tested several configurations ofVMs and job counts in an experimental setting and then compared the outcomes to those of more conventional approaches like Genetic Algorithm (GA) and baseline PSO. The proposed approach shows promise as an efficient scheduling approach for environmentally conscious cloud computing, thanks to its substantial reductions in execution time and energy consumption. Evaluations were carried out in a simulated cloud environment, incorporating different task counts and VM configurations. The proposed mechanism underwent a comparative analysis with eight benchmark methods. The findings indicate that the proposed method shows a marked superiority over current techniques, realizing a 33.5% decrease in execution time (168.57 s compared to 253.47 s) and an 11%–52% enhancement in energy efficiency (0.653 kWh vs. a maximum of 0.852 kWh). The results validate the efficacy of the scheduling strategy in improving energy efficiency and performance within cloud computing environments.

Background and Aims: This research aims 1) to examine the personnel management practices of school administrators in Group 8 of the educational institutions under the Office of Special Education Administration, as perceived by teachers. 2) To compare the personnel management practices of school administrators in Group 8 of the educational institutions under the Office of Special Education Administration, based on teachers’ perceptions, categorized by gender, age, administrative experience, and type of school. Methodology: The sample group consisted of 285 teachers from educational institutions under the Office of Special Education Administration, Group 8, in the academic year 2024. The sample size was determined using Krejcie and Morgan's table, and simple random sampling was employed through a non-replacement lottery method. The statistics used were frequency, percentage, mean, standard deviation, t-test, and F-test. Results: The research results found that 1) The personnel management of school administrators in Group 8 of the educational institutions under the Office of Special Education Administration was found to be at a high level in all aspects. 2) When classified by gender, the overall personnel management showed statistically significant differences at the .01 level. However, when considering each aspect individually, it was found that male and female teachers had no significant difference in their opinions regarding the personnel development aspect. When classified by age, the overall personnel management practices of administrators in Group 8 showed statistically significant differences at the .01 level. When classified by administrative experience, overall personnel management practices also differed. In particular, the recruitment and appointment aspect showed a statistically significant difference at the .01 level. When classified by the type of institution, the overall personnel management of school administrators in Group 8 showed statistically significant differences at the .01 level. Conclusion: The study found that the personnel management of school administrators in Group 8 of the institutions under the Office of Special Education Administration was rated at a high level across all areas, reflecting effective human resource management. Nevertheless, the findings indicated statistically significant differences in personnel management practices when analyzed by personal factors in several dimensions.

Evaluating the spatial dependence of urban water supply and demand in the yellow river basin: A comprehensive analysis of Urban, land, population, and industry driving factors.

This study aims to explore the role of budget participation in enhancing managerial performance through a qualitative approach based on a scoping review. The background of this research is rooted in the importance of involving managers in the budgeting process as a key instrument for effective resource management in public sector organizations. The data were obtained from journal articles published between 2020 and 2024, accessed via Google Scholar using the Publish or Perish (POP) software. A purposive sampling technique was employed, with inclusion criteria focusing on articles that addressed the variables of budget participation and managerial performance. The findings indicate that budget participation positively influences managerial performance, particularly when supported by organizational commitment, effective leadership, and robust internal control systems. Budget participation facilitates dialogue between managers and superiors, enhances ownership of organizational goals, and strengthens accountability. However, the effectiveness of participation is not universal and depends on the organizational context and structural support. This study contributes theoretically to the field of public sector accounting and offers practical insights for improving budget management quality in local governments.

The disconnect between national development agendas and specific sectoral strategic initiatives hampers effective resource management in developing nations. This research investigates how a lack of strategic thinking skills among planners impacts the performance of Zambia's Ministry of Mines and Mineral Development (MMMD). The study utilizes Liedtka's five dimensions of strategic thinking, including systems perspective, focused intent, temporal reasoning, hypothesis-driven approaches, and intelligent opportunism, as its theoretical framework. An existential-phenomenological qualitative methodology, complemented by document analysis, guided the research. Data obtained from interviews with key informants, focus group discussions, and policy analyses were processed using interpretive phenomenological methods and thematic analysis. The primary result reveals a significantly lower usage of Liedtka's dimensions by strategic planners within the ministry. The research concludes that the lack of strategic thinking abilities among planners has limited the Ministry's capacity to effectively interpret the requirements of the 8th National Development Plan, which hinders reliable forecasting relating to the mining sector. Without improved strategic thinking skills, future organizational planning in the ministry is unlikely to bring about enhancements for the mining industry. Strategic planning stakeholders should focus on improving both the level and quality of strategic thinking within their divisions and allocate resources towards developing these strategic thinking capabilities.

Abstract Reference evapotranspiration is a key element in agricultural management, particularly in a changing global environment, and represents an important requirement for the effective planning, monitoring, and management of water resources. However, accurate evapotranspiration estimation requires spatially well-distributed continuous meteorological data to capture regional variations, and reanalysis datasets are valuable tools for this purpose. In this context, this study aimed to assess the performance of the Copernicus European Regional ReAnalysis (CERRA) dataset in the western Iberian Peninsula, focusing on Portugal and Galicia (Spain). Meteorological data (air temperature, relative humidity, solar radiation, and wind speed) from several surface stations were used to analyze the differences between the observations and CERRA hindcasts. The reference evapotranspiration ( ET o ) was then computed for both datasets to estimate CERRA’s consistency and accuracy. The results revealed that CERRA data strongly correlated with the observational data, accurately capturing the spatial and temporal atmospheric patterns. Daily air temperature was the most accurately represented variable, followed by relative humidity, solar radiation, and wind speed. ET o estimates from the CERRA dataset were closely aligned with observations. The high spatial resolution of CERRA enabled an accurate representation of the regional climatic variations, addressing the weaknesses found in other reanalysis datasets, particularly in coastal areas influenced by land‒sea interactions. The findings of this study indicate that CERRA is a highly valuable database for climate studies to validate the results of regional climate models with high resolution. These models are essential for developing effective adaptation and mitigation strategies to address agricultural planning and management in response to climate-related challenges. Graphical Abstract In this study, the performance of the Copernicus European Regional ReAnalysis (CERRA) dataset in replicating atmospheric variables and reference evapotranspiration (ET o ) for agrometeorological applications in the western Iberian Peninsula was assessed. CERRA hindcasts were compared with meteorological observations (minimum and maximum air temperature, relative humidity, wind speed, and solar radiation) from surface stations using Taylor diagrams, box plots, scatter plots, and the Kling–Gupta efficiency ( KGE ) metric for validation. The ET o was subsequently computed for both datasets. The results indicate strong correlations between CERRA and observational data, with CERRA effectively reproducing spatial and temporal patterns. ET o estimates from the CERRA dataset closely align with observations. This study emphasizes the ability of CERRA to accurately represent regional climatic variations because of its high spatial resolution, overcoming the limitations of other reanalysis datasets, particularly in coastal zones. The results suggest that CERRA is a valuable asset for climate studies, validation of high-resolution regional climate models, water resource management, and agricultural planning.

Effective water resource management in agriculture is a pivotal challenge for environmental sustainability and the economic viability of crop production. The present study, conducted at the CREA research station (Monterotondo, Italy), analyzed a precision irrigation strategy based on an automated drip irrigation system with soil moisture sensors, applied to a 15-year-old high-density poplar plantation for energy production. Five treatments were compared: a non-irrigated control (T0) and four irrigation levels based on soil moisture thresholds (T1 ≤ 20%, T2 ≤ 30%, T3 ≤ 40%, T4 ≤ 50%). The aim of this study was to assess the economic feasibility of irrigated poplar plantations, considering expected increases in biomass production and related environmental impacts. The economic evaluation used the Life Cycle Costing (LCC) method, while the environmental assessment applied Life Cycle Assessment (LCA) with the AWARE indicator to quantify the water scarcity footprint. Finally, an integrated assessment using the TOPSIS multi-criteria method was performed to identify the most sustainable treatment. Over the 15-year period, T0 (no irrigation) was the preferred option (Preferred Index Pi = 1.000), followed by T3 (Pi = 0.637) and T4 (Pi = 0.586), considering equal weighting of economic and environmental impacts. Conversely, the low irrigation treatment (T1) was the least sustainable (Pi = 0.379), followed by T2 (Pi = 0.486). While irrigation appears unviable if environmental impacts are prioritized, higher biomass value can improve the economic sustainability of treatments with greater water use (T3 and T4) when economic factors dominate.

The article conducts a comprehensive analysis of the dynamics of budget revenues of territorial communities of Ukraine for the period of 2018–2024, taking into account the impact of fiscal decentralization reforms, amalgamation of communities and the full-scale invasion of the russian federation in 2022. Structural changes in local budget revenues, including tax and non-tax revenues, capital transactions, trust funds and official transfers from the central budget, have been studied. It has been established that the reform of the budget system and the amalgamation of territorial communities in 2020–2021 ensured record revenue growth in most regions of Ukraine, in particular Kharkiv, Kyiv, Lviv, Dnipro and Odesa, due to budget consolidation and more effective management of financial resources. At the same time, the impact of the COVID-19 pandemic in 2020 and the full-scale war in 2022–2024 caused a significant drop in revenues in the regions of hostilities and occupation, a decrease in the share of local budgets in the consolidated state budget, and an increased dependence on state transfers. To assess the dynamics of income, methods of comparative analysis, calculation of growth rates and deviations were used, as well as graphical and tabular models reflecting regional differences and time trends were built. Particular attention is paid to the impact of reforms on increasing the financial capacity of communities, the opportunities to increase revenues through tax revenues and capital transactions, as well as risks associated with external factors, including hostilities. The results of the study can be used to plan budget policy, develop strategies for financial stabilization of territorial communities and increase their autonomy in the post-war period. The article also emphasizes the importance of integrating local budget data with national financial strategic plans to achieve sustainable community development and effective management of public resources.

Abstract Subseasonal predictions from 2 weeks to 2 months have made significant advancements over the past decade, driven by progress in physical understanding, climate modeling, computational capabilities, and artificial intelligence (AI). These predictions are increasingly in demand due to their potential to provide stakeholders with adequate lead time for effective disaster adaptation, mitigation, and resource management. However, there remain critical gaps in the engagement between prediction providers and service users. Providers often lack insight into the specific needs of users and do not have transferrable strategies to build trust through tailored evaluations and clear confidence levels, which often results in repeatedly devising approaches for each provider–user interaction. Further, users frequently struggle to interpret predictions and are hesitant to make decisions based on these uncertain outcomes. This paper attempts to make “last-mile efforts” by reviewing relevant literature, operational systems, and the most informative communications and engagement strategies with key sectors. It proposes a preliminary framework to standardize the approach for provider–user interaction in the context of subseasonal prediction and services, with potential applicability and extension to seamless prediction systems in the future. Lastly, we underscore future directions for subseasonal predictions, emphasizing the integration of dynamic climate modeling and AI-driven enhancements with large ensemble techniques to improve both reliability and confidence. This review is part of the United Nations Educational Scientific and Cultural Organization (UNESCO) International Decade of Sciences for Sustainable Development (2024–33) and contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment (SEPRESS) Program (2025–32), an initiative endorsed under this global framework.

Effective community-based water resource management requires integrated, accurate, and accessible information to support evidence-based decision making. However, in many local contexts, water-related data remain fragmented across multiple agencies and are rarely designed to support community learning or participatory planning. This study aimed to (1) conduct a systematic review and bibliometric analysis of research related to holistic water information systems for community decision making, (2) design an educational technology–based holistic water information repository, and (3) evaluate the performance and effectiveness of the developed system in a real-world community context. The study followed the PRISMA 2020 framework and employed bibliometric analysis using data retrieved from the Scopus database, resulting in 175 eligible studies for synthesis. Insights from the review informed the design of a holistic system architecture integrating data management, knowledge management, learning management, and decision-support functions. The system was developed following the System Development Life Cycle (SDLC) framework and implemented in Chachoengsao Province, Thailand. System evaluation involved expert assessment, a quasi-experimental design using pre-test and post-test measures, and user satisfaction surveys. The results indicated that the system architecture achieved the highest level of suitability (Mean = 4.61, S.D. = 0.48). Participants’ learning achievement significantly improved after system use (t = 7.21, p < .01), and user satisfaction was high across all evaluated dimensions. These findings demonstrate that integrating educational technology into a holistic water information repository enhances digital competencies, learning outcomes, and participatory community decision making in water resource management. This study contributes empirical evidence on how educationally grounded information systems can strengthen community capacity and support sustainable water governance.

This study examines logistics ecosystems and platforms in the sharing economy and seeks to achieve multiple goals that can help improve the performance and efficiency of these systems. The first goal is to design and implement operational and software solutions that lead to increased supply chain efficiency by improving coordination between stakeholders and effective resource management. Second, the establishment of service quality standards in logistics platforms and the examination of methods and tools for evaluating and improving performance have been considered in order to ensure service quality and increase user satisfaction. Third, promoting a culture of cooperation and trust between users and service providers, through training and information, is considered as one of the key factors for the success of sharing platforms. Fourth, the establishment of decision-making and data analysis centers is essential for the optimal use of collected information in order to improve management strategies and decisions. Ultimately, developing and proposing environmental and economic sustainability strategies that help improve performance and reduce negative impacts in the long term is a strategic goal. This thesis addresses the performance and efficiency of logistics platforms in the sharing economy by providing innovative and practical solutions and offers solutions to existing challenges.

High-throughput phytoplankton monitoring and screening of harmful and bloom-forming algae in coastal waters with updated functional screening database.

Effective management of limited and fragile groundwater resources is essential to ensure a sustainable, safe, and high-quality water supply. However, intensive anthropogenic activities are placing increasing pressure on groundwater systems worldwide. Groundwater contamination and changes in land use and land cover in water-scarce cities represent serious environmental challenges, particularly in the Agreste/Caatinga transition zone of northeastern Brazil. This study therefore assesses groundwater contamination risks and land use changes in the city of Campina Grande, located in northeastern Brazil. The following procedures were employed: (a) identification of wells through data collection from public agencies; (b) evaluation of the natural vulnerability to groundwater contamination using the Groundwater Overall Depth (GOD) method; (c) spatiotemporal analysis of land use and land cover changes using the Google Earth Engine platform; (d) identification of point sources of contamination and assessment of potential contamination risk using the Pollutant Origin and its Surcharge Hydraulically (POSH) method; and (e) mapping of groundwater vulnerability and contamination risk. The study area was classified into three distinct levels of natural vulnerability—low, medium, and high. The results indicate an increase in groundwater extraction beginning in the 1990s, particularly between 1995 and 2000, strongly correlated with drought periods. The findings also reveal rapid urban expansion and an increase in the number of pollution sources. Approximately 33% of the study area exhibits high vulnerability on the aquifer’s natural vulnerability map, as determined using the GOD index. It is concluded that land use changes and the associated risks of groundwater contamination in Campina Grande represent serious threats to environmental quality and water availability for the city.Graphical

Abstract Accurate hydrological modeling in high-mountainous snow-dominated basins is essential for effective water resource management, particularly in climate change-sensitive regions. To better understand the processes that govern hydrological responses, model calibration against multiple variables offers a valuable approach for reducing parameter uncertainty and model equifinality. In data-scarce environments, simple lumped-parameter hydrological models that account for snow accumulation and melting processes are particularly useful. In this study, we used the Témez lumped hydrological model enhanced by the integration of a new semi-distributed snow module to simulate key snow-related processes. We performed a novel sensitivity analysis of the efficiency of the models depending on the adopted multi-objective functions within an automatic procedure to calibrate and validate the models. We evaluated three calibration approaches by varying the weight of the snow cover objective $$\:{w}_{S}$$ . The first procedure consisted of a single-objective calibration against streamflow alone. The other procedures applied multi-objective calibration against streamflow and snow cover, which differed in the performance metric used for the snow component: Nash-Sutcliffe efficiency and Kling-Gupta efficiency. The results demonstrated that incorporating snow cover data into the calibration process improved snow cover simulation without significantly compromising streamflow efficiency, except when the streamflow weight $$\:{w}_{Q}$$ was reduced to zero. Notably, the KGE-based approach yielded a better-defined Pareto front with a more robust snow cover efficiency and reduced bias. Our findings also revealed that snow-related parameters were highly sensitive to the inclusion of snow cover data. Key parameters exhibited substantial changes, with a reduction in variability of approximately 30%.

Abstract Accurate inflow forecasting is critical for water resource management in semi-arid basins like the Shetrunji, which are characterized by extreme hydrological variability and complex spatiotemporal dynamics. While deep learning offers powerful tools, standard models often fail to capture the critical interplay between temporal sequences and spatial river network structures. To address this gap, this study provides a comparative evaluation of three distinct deep learning archetypes: a Multi-Layer Perceptron (MLP) as a non-temporal baseline, a hybrid LSTM-GRU to specifically capture complex temporal dependencies, and a Graph Convolutional Network (GCN) to explicitly model spatial relationships within the river basin. The significance of this comparison lies in systematically diagnosing the importance of spatial versus temporal modeling capabilities. The GCN achieved the highest accuracy (R 2 = 0.872, RMSE = 26.859), significantly outperforming the moderately accurate MLP (R 2 = 0.770) and the purely temporal LSTM-GRU model (R 2 = 0.681). The superior performance of the GCN, contrasted with the LSTM-GRU’s limitations, demonstrates that explicitly representing the river network’s spatial structure is paramount for reliable forecasting in this semi-arid context. These findings highlight the distinct advantage of graph-based methodologies and underscore their practical potential for advancing hydrological modeling and supporting more resilient water management strategies.

<p>Climate warming is intensifying water cycle, including the seasonal variations in land water availability (precipitation minus actual evapotranspiration (P–ET)). These changes in P–ET, particularly the fluctuations between wet and dry seasons, have profound implications for both human societies and ecosystems. Understanding the dynamics and drivers of these changes is crucial for effective water resource management and climate adaptation strategies. Here, we examined seasonal P–ET changes based on multiple datasets across the extratropical Northern Hemisphere over the past two decades. Our findings reveal a significant increasing trend in annual range between wet and dry season P–ET. Specifically, we observed that this trend is driven by both an increase in wet season P–ET and a decrease in dry season P–ET. Further analysis indicated that the changes in wet season P–ET were primarily due to increases in P, whereas the reductions in dry season P–ET were largely attributed to decreases in ET. Evaluation of 32 state-of-the-art Earth System Models shows that most models (22 of 32) capture these seasonal trends during recent decades, although some fail to do so. Our results highlight an enhanced seasonality in hemispheric water availability, driven by distinct changes in wet and dry season dynamics.</p>