The use of GIS technology has had a big impact in improving the ways in which health related information is acquired, processed, and presented in public health. GIS is used in spatial epidemiology as an important tool to address queries related to distributions and determinants of health and illness in space and time. When multiple data sets are superimposed on each other, GIS helps the public health workers to see the spatial variation, recognize the hotspots and develop appropriate prevention strategies. In its modern form, GIS was applied to public health beginning with cholera map drawn by John Snow in 1854. Today there are numerous GIS applications ranging from disease mapping, resource allocation, to the assessment of environmental health. Directions in GIS include remote sensing, mobile data collection, and the use of artificial intelligent to provide real time analysis and predictive analysis. The above developments help in response to diseases outbreaks that include COVID-19 where GIS was useful in mapping the virus spread and response measures. However, there are challenges to the extensive use of GIS in public health such as data quality, ethical issues in the use of privacy and the problem of the digital divide in restricted access to GIS technologies in low resource settings. Nonetheless, GIS remains evidence of its applicability to the current emerging public health issues such as monitoring and spread of communicable diseases, as well as the effect of hazardous environmental factors. Clearly, with steady improvements in technological growth and interprofessional cooperation, GIS will continue to be a valuable resource in the fight toward establishing health equity as well as in the improvement of decisions made within the realm of public health.

Lipids contain fatty acids as fundamental components and their boiling and melting temperatures matter significantly for industrial uses. The researchers employed the Quantitative Structure-Property Relationship (QSPR) method for predicting melting and boiling points in fatty acids along with their derived substances. Measuring properties of chemical structures with molecular descriptors enabled the development of QSPR models that received validation by experimental results. During the training phase the predictive accuracy reached high levels as the coefficients of determination (R²) values turned out to be 0.948 for melting points and 0.938 for boiling points. The cross-validation validation produced R² values at 0.925 for estimating melting points and also 0.925 for estimating boiling points which shows robust predictive capability. Due to their reliable nature QSPR models exhibit strong performance in predicting thermal characteristics of fatty acids for uses in biodiesel production as well as food processing and cosmetics industries.

This research investigates the use of n-octadecane as a phase change material (PCM) for the thermal management of lithium-ion batteries. During simulation runs in ANSYS CFD the research team evaluates heat distribution together with the n-octadecane phase change processes during its entire solidification and melting period. N-octadecane serves as a heat conduction agent during battery charging and behaves similarly as a heating source during discharging to achieve proper battery temperature control. The temperature interval from 28 °C to 30 °C serves as the total melting point range for n-octadecane and possesses a latent heat of fusion value equal to 200 kJ/kg. The localization of battery-PCM interface after melting happens primarily due to the thermal conductivities of solid-phase n-octadecane at 0.222 W/mK and liquid-phase n-octadecane at 0.198 W/mK. Multiple research investigations confirm the temperature regulating characteristics of n-octadecane to safeguard batteries from thermal failure together with preserved operational functioning. Research findings indicate that PCM phase change efficiency improves when cylindrical holes in the PCM casing receive optimized placement for better heat distribution. To achieve long-term battery safety and stability of battery performance active cooling systems need to integrate with PCM technology. Data shows that n-octadecane thermal systems possess potential as electric vehicle battery storage management solutions because they ensure stable performance and safety levels.

The digital transformation era has significantly reshaped the landscape of human resource management (HRM). This paper explores adaptive HR strategies essential for fostering sustainable workforce development in organizations undergoing digital change. By integrating modern HR technologies, agile practices, and data-driven approaches, organizations can enhance talent acquisition, retention, and engagement. The study proposes a comprehensive framework that aligns adaptive HR strategies with organizational goals, ensuring resilience and sustainable growth.

The aim of this research was the optimal management of overvoltage in the photovoltaic system with the aim of maintaining voltage stability and reducing network losses. In the simulation process, we considered the number of buses and 10 scattered production sources as the path and simulation process. We considered the number of 10 scattered production sources with a capacity between 18 and 25 MW. Examining the preliminary results of the system has shown that the range is low Medium and high stability is considered based on the distance between each bus with scattered production sources. So that basses 1 to 3 have the lowest range of oscillation because they are located in the closest distance to the production sources. Similarly, basses 7 to 17 have more distances than the production source and have more fluctuation. In order to meet the needs of the network for optimumThe distribution of the production of scattered production sources, which usually have non-constant conditions, especially from bus 7 onwards, it is possible to observe the amount of waste in the network due to the lack of coordination of the scattered production with the network demand, we used the optimal management of overvoltage . The amount of overvoltage of each distributed generation source varies from 25 MW to 18 MW and in each bus this requirement is investigated in the network. and the measurement has been placed. This optimal distribution rate was matched with the amount of consumption and demand of the network in overvoltage and we showed that, for example, in the first bus, the amount of demand of the network is 800 megawatts and the amount of production of the main network in overvoltage is equal to 770 megawatts and a deficit of 30 megawatts has been observed. Using the optimal management of overvoltage of 25 megawatts of the entire networkFrom scattered productions, the power is transferred with this bus to compensate the deficit to a large extent. In the following, in order to formulate an optimal management model for overvoltage distribution, the establishment of a balance point for the activity of network buses along with the ten sources of distributed generation has been investigated. For this purpose, by categorizing all network buses into four modes that include all buses, each mode (average We compared multiple bus sets) in each bus and showed that in the first bus and the first mode, the optimization rate of overvoltage control management was equal to 68.73% and in one turn, not considering the first mode for the bus network and only in Considering the fourth mode, which includes buses 7 to 17, the amount of network optimization has increased by 11.71%. In other words, in the fourth mode Without having six buses, we were able to optimize the network by 11.71% with the help of overvoltage control management.

Considering the study of optimal location of distributed units to improve network reliability under network operation conditions and the focus on using the genetic algorithm method for locating distributed generation units in the distribution system, there is no comprehensive and relevant research in this field. In this research, to improve network reliability, location with variable numbers and sizes of distributed generation resources is performed. In the proposed method, multi-objective optimization using weight coefficients to combine two objective functions as the overall objective function is used.

Health and Safety at Work (K3) is a major concern in the work environment and a program designed to protect workers and business owners (entrepreneurs). This research aims to identify the challenges faced and the implementation of health and safety at work at Hotel Eastin Astha Resort Canggu. This research employs qualitative descriptive analysis techniques, with data collection conducted in the field through observation, interviews, and documentation studies. The results show that Hotel Eastin Ashta Resort Canggu has implemented K3 policies well, including compliance with K3 regulations (100%), BPJS Employment participation (100%), provision of personal protective equipment and work protective equipment (90%), as well as the preparation and implementation of K3 SOPs (85%). However, there have been several work accidents that indicate the need for improved training and employee understanding of K3. Factors influencing the effectiveness of training include employee knowledge and skills (75%), work environment conditions (70%), and work accident incidents (5 incidents). The main obstacles to implementing K3 in this hotel include a lack of in-depth understanding of K3 risks, inadequate orientation and training for new employees, and insufficient routine training related to emergencies. Several strategies that can be applied to improve K3 effectiveness include updating or revising K3 SOPs, collaborating with external parties for K3 training, and installing K3 signs. Implementing these strategies is expected to enhance health and safety at work at Hotel Eastin Ashta Resort Canggu.

Renewable energy systems coupled with the domestic battery storage are becoming more and more necessary for sustainable energy solutions. Despite this, the thermal management of lithium ion (Li ion) battery packs is a major issue due to temperature sensitivity. Thermal regulation is critical, and by doing so they can effectively optimize performance, safety, and longevity, which includes passion for burning holes through in the search of an innovative cooling strategy. This thesis attempts to examine the role phase change materials (PCMs) play in improving thermal stability of domestic battery packs. The passive cooling method offers by PCMs is based on the fact that they are able to absorb and lose heat by means of their phase transitions, and thus decrease risks of temperature fluctuations. This research focuses on thermal challenges of Li ion batteries at extreme temperatures in the off nominal temperature range resulting in increased internal resistance and reduced power output, potentially resulting in thermal run away. Active cooling systems like liquid cooling are compared with the passive PCM based solutions. Recently developed PCM integration methods, including composite materials that enhance thermal conductivity and thermal conductivity cooling, are reviewed in the study as well. Experimental studies together with computational simulations show that PCMs effectively reduces peak temperature of the battery and maintains uniform heat distribution across cells of the battery. Traditional PCMs have limitations in thermal conductivity but by incorporating such additives as expanded graphite or a metal foam the performance is considerably improved. It is also demonstrated that hybrid systems that combine PCMs with active cooling methods will prove useful for thermal regulation. It presents the opportunity for PCMs to be a viable, and energy efficient, thermal management solution for domestic battery storage. The future work could aim to optimize the PCM material properties, include the hybrid cooling strategies, and progress with the real time monitoring technologies in order to improve the battery performance and reliability in renewable energy applications.

Embedded systems1 are increasingly deployed in critical applications, where reliability and availability are paramount to ensuring operational continuity and safety. Predicting these parameters accurately requires a comprehensive understanding of how environmental factors impact system performance. This paper explores an innovative approach to reliability and availability prediction by integrating environment modeling2 and simulation techniques. The proposed methodology captures the dynamic interplay between embedded systems and their operating environments, enabling precise estimation of failure probabilities and system downtime under varying conditions. The core of this study lies in developing a multi-faceted simulation framework that incorporates environmental stressors such as temperature3, humidity, vibration, and electromagnetic interference4. The framework models these stressors in real time5, using historical and simulated data to evaluate their cumulative effects on the embedded system's components. By coupling these models with system-level fault injection and degradation analysis, the proposed method facilitates the identification of vulnerabilities and critical failure modes. These insights are crucial for implementing targeted mitigation strategies, such as hardware redundancy6 or software fault-tolerance7 mechanisms, to improve system robustness. The findings demonstrate that environment-aware modeling significantly enhances the accuracy of reliability and availability predictions compared to traditional approaches that overlook external influences. The paper highlights case studies involving automotive and aerospace applications, showcasing how the framework reduces design-cycle time and supports decision-making in system design and maintenance planning. By bridging the gap between environmental simulation and system reliability assessment, this work provides a scalable and practical solution to predict and optimize the long-term performance of embedded systems in diverse operating conditions.

Traffic estimation is a crucial aspect of transportation planning to anticipate increasing mobility and provide transportation infrastructure. Traditional methods, such as household travel surveys and traffic counting, often require significant time and financial resources. Therefore, this study explores the use of geolocation data from Twitter as an alternative for projecting trip generation in East Bandung, a rapidly developing area with new facilities such as the Gelora Bandung Lautan Api Stadium and the Summarecon Bandung commercial area. Data were collected using the Twitter API over one year (November 2023–November 2024) within a 700-meter radius and analyzed through spatial mapping and the development of an origin-destination (OD) matrix. Validation was conducted using the Mean Absolute Percentage Error (MAPE) and the Spatially Weighted Structural Similarity Index (SW-SSIM). The results identified Gedebage, Arcamanik, and Rancasari as the areas with the highest trip generation concentrations. A MAPE of 18.5% and an SW-SSIM of 0.72 indicate that Twitter data is sufficiently representative for modeling mobility patterns. Despite limitations such as population bias, Twitter data offers cost and time efficiency, making it a potential alternative to support more adaptive and real-time data-driven transportation planning in urban areas like East Bandung.

Modern enterprises operate under an expanding set of regulatory, security, and internal governance obligations while simultaneously adopting cloud-native architectures, continuous delivery pipelines, and decentralized engineering ownership models. This convergence has transformed compliance from a periodic audit function into a persistent systems reliability problem. Traditional compliance mechanisms characterized by manual policy interpretation, static control checklists, and retrospective audits are fundamentally misaligned with the velocity, scale, and dynamism of contemporary infrastructure and software systems. As a result, organizations experience delayed violation detection, excessive operational toil, fragmented accountability, and elevated systemic risk. This paper introduces a Predictive Compliance Automation Framework (PCAF) that reframes compliance as a continuous, anticipatory control plane embedded within enterprise platforms. The framework integrates Natural Language Processing (NLP) techniques for structured interpretation of regulatory and policy text with Policy-as-Code (PaC) for executable enforcement, and augments these capabilities with predictive risk modeling to identify likely future compliance drift before violations occur. Unlike existing approaches that rely on reactive rule evaluation or static guardrails, PCAF enables proactive compliance posture management by correlating policy semantics, infrastructure change events, telemetry signals, and historical drift patterns. The primary contribution of this work is a systemic architecture that unifies regulatory cognition, automated enforcement, and predictive governance within a single operational framework. We describe the design principles, layered architecture, lifecycle flows, and governance mechanisms required for safe enterprise deployment, emphasizing human-in-the-loop oversight, auditability, and failure containment. Operational evaluation demonstrates meaningful reductions in mean time to detection (MTTD), compliance drift duration, and manual audit effort, while preserving accountability and regulatory interpretability. This work positions predictive compliance as a first-class reliability function, analogous to availability and security, and outlines a path toward scalable, resilient governance in complex distributed systems.