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Substandard housing remains a multidimensional urban problem because it intersects with structural safety, indoor environmental quality, sanitation, clean water, poverty, and the effectiveness of policy implementation. This study evaluates the implementation of the substandard housing improvement program (RTLH) in Lubuk Linggau City by integrating a technical audit of housing quality with an implementation analysis based on the variables of communication, resources, implementer disposition, and bureaucratic structure. A descriptive-evaluative mixed approach was applied through field observation, technical scoring of ten rehabilitated houses, document review, and structured interviews with regulators, field facilitators, and beneficiary households. The results show that the cumulative technical conformity index reached 3.325 out of 4.00, equivalent to 83%, and was classified as adequate housing. Structural resilience achieved the highest score at 88%, followed by space adequacy at 85%, natural lighting and ventilation at 80%, and sanitation and clean water at 75%. Five houses were classified as adequate housing, while five were classified as fairly adequate/light substandard housing. The implementation analysis indicates that clear practical communication and facilitator assistance strengthened structural quality, but limited financial resources, weak environmental-health prioritization, and administratively oriented supervision reduced sanitation and ventilation performance. The study recommends standardized septic systems, stronger post-construction technical verification, healthy-housing education, realistic unit budgets, and quality-oriented monitoring.

Background: Congestive Heart Failure (CHF) is a condition in which the heart is unable to pump blood effectively due to structural or functional disorders, commonly manifesting as dyspnea, fluid retention, and peripheral edema. Globally, cardiovascular disease is the leading cause of death, with CHF affecting more than 55-65 million people worldwide. Objective: To perform comprehensive nursing care on patient Ny. A with cardiovascular system disorder: Congestive Heart Failure (CHF) in the Mawar I Ward of RSUD dr. Soeselo, Tegal Regency. Methods: This study used a descriptive method through a case study approach. Data were collected via interview, observation, and physical examination. Results: Four nursing diagnoses were identified: ineffective breathing pattern, hypervolemia, activity intolerance, and knowledge deficit. Interventions included respiratory monitoring, hypervolemia management, energy management, and health education on disease process. After two days of nursing implementation, three diagnoses were resolved and one (hypervolemia) was partially resolved. Conclusion: Nursing care for Ny. A with CHF was carried out through assessment, nursing diagnosis, intervention, implementation, and evaluation. Continued interventions are recommended, including fluid balance monitoring, diuretic collaboration, and edema and ascites monitoring.

Agentic artificial intelligence (AI) systems capable of autonomous goal-directed behavior, multi-step planning, tool use, multi-agent coordination, and iterative self-correction represent a transition from passive clinical AI tools toward systems that can participate in complex healthcare workflows. However, empirical evidence remains fragmented across clinical decision support, patient monitoring, and administrative applications, and no systematic synthesis has evaluated which agentic principles have been technically demonstrated and which have accumulated sufficient evidence to support responsible clinical deployment. We conducted a PRISMA-informed systematic review of peer-reviewed empirical studies published between January 2025 and April 2026. Searches across five bibliographic databases and Google Scholar, supplemented by citation tracking, identified 443 unique records for screening, of which 25 met the predefined PICOS and quality appraisal criteria. Evidence was synthesized using an evidence-informed seven-principle framework derived from the integration of agentic AI, clinical AI, and healthcare governance literature. This framework provides a structured lens for examining how agentic principles are evaluated individually and in combination, enabling a deployment-readiness perspective that extends beyond capability-focused assessments alone. The evidence base was concentrated on technical capability principles, whereas human oversight, safety, compliance, and equity-related evaluation received comparatively limited attention. Most studies remained at the laboratory, benchmark, or proof-of-concept stage, and none reported demographic-stratified performance outcomes. Overall, the findings suggest a structural asymmetry in agentic healthcare AI: empirical research is advancing agentic capabilities more rapidly than it is generating evidence for the oversight, safety, equity, and governance mechanisms required for responsible clinical translation.

Abstract. This study aims to examine the causes, forms, and strategies for combating violence against female Indonesian migrant workers (PMI) from a legal, sociological, and gender perspective. The background of this research is based on the vulnerability of female PMI to various forms of physical, psychological, sexual, and economic violence, which occurs from the pre-placement stage to the post-placement stage, as well as the long-term impact on mental health, human rights, and socio-economic reintegration. The method used is empirical legal with primary data collection through in-depth interviews with female PMI victims of violence and accompanying institutions, as well as secondary data in the form of literature reviews, laws and regulations, and scientific publications. The analysis was conducted using descriptive qualitative methods, integrating human rights theory, Feminist Legal Theory, and international migration policy. The results of the study show that the vulnerability of female migrant workers is caused by economic factors, education, patriarchal culture, non-transparent recruitment processes, the characteristics of work in the informal sector, power imbalances with employers, and weak legal protection in the destination country. The forms of violence experienced include beatings, sexual harassment, threats, social isolation, wage withholding, and structural exploitation practices. Protection efforts implemented include preventive approaches through training, legal education, legal document guarantees, and assistance from law enforcement agencies, as well as repressive approaches through action against perpetrators of violence and bilateral cooperation with destination countries. This study emphasizes the importance of comprehensive and sustainable protection for female Indonesian migrant workers, covering all stages of labor migration as well as strengthening digital-based monitoring systems, officer capacity, and cross-sector collaboration to ensure the safety, dignity, and fulfillment of the human rights of female Indonesian migrant workers.  

A literature review on the implementation and evaluation of Convergence Action to Reduce Stunting in Indonesia was conducted as a comprehensive study to understand national patterns, challenges, and factors determining the success of the program. This study aims to examine the effectiveness of cross-sectoral interventions, identify structural barriers, and formulate strategic recommendations for strengthening stunting reduction governance. The method used was a Systematic Literature Review (SLR) with the PRISMA 2020 model, utilizing Crossref and Google Scholar sources through the Publish or Perish application. Of the articles found, only nine studies met the inclusion criteria and were analyzed using a thematic approach based on the CIPP framework. The SLR results showed that in terms of context, government regulations and commitment were strong, but there were geographical disparities, poor sanitation, and low nutrition literacy that affected program outcomes. In terms of inputs, limitations in nutrition human resources, facilities, funding, and the quality of e-PPGBM data were the main obstacles. In terms of process, the implementation of the 8 Convergence Actions has been carried out but remains partial, with suboptimal integration between OPDs and monitoring that has not yet produced adequate feedback. In terms of output, service coverage has increased, but the decline in stunting rates has been inconsistent across regions. The research results emphasize the importance of strengthening human resource capacity, data integration, cross-sectoral coordination, and adapting programs based on local contexts to ensure the accelerated and sustainable reduction of stunting.

A steel truss bridge is a structure constructed from a series of interconnected steel bars. The loads received by this structure are analyzed and transferred to the steel bars that make up the structure. Factors that need to be analyzed in bridge construction include the location and surrounding environmental conditions. After identifying the type of soil and bedrock beneath the surface, the materials used must meet strength or durability test standards before the bridge can be operated. The materials used must comply with the Indonesian National Standard (SNI). In addition to the factors mentioned above, the bridge must undergo a feasibility evaluation. Among the various tests used to evaluate bridge strength, one is vibration testing. The vibration test method, when compared to the load test method, shows higher cost efficiency and does not cause damage to the structure.

This study aims to design and implement a damage analysis system for concrete surfaces by utilizing digital image processing based on the Canny edge detection method. The developed system allows users to upload images of concrete surfaces, which are then processed through several stages: conversion to grayscale, transformation to binary images, and crack edge detection using the Canny operator. This process aims to automatically detect crack patterns on the concrete surface. The detection results, represented as edge lines, are used to calculate the percentage of the damaged area. Based on this percentage value, the system automatically classifies the damage level into light, moderate, or severe categories. System testing shows that the Canny method can accurately identify crack patterns, with sufficient detection levels to be used in monitoring the condition of concrete surfaces. The analysis results are then presented in both visual and numerical forms, providing valuable information for assessing the structural condition of concrete. Thus, this system can serve as an efficient and effective tool for early detection of structural damage in concrete infrastructure, ultimately supporting better maintenance and repair efforts.

The evaluation of information systems becomes important to ensure the effectiveness of the Hospital Information System (SIMRS) application and its positive impact in producing information that meets the standards of data quality with the HOT fit theory, which includes the core elements of the information system: human, organization, Technology and net benefits. The research was conducted using a cross-sectional design and involved 78 SIMRS users in X Hospital as the sample. Data were analysed using the partial least squares (PLS) structural equation modelling (SEM) method. The results of the study showed that user satisfaction, information quality, service quality, system quality, organizational environment, system utilization level, and organizational structure had significant relationships with SIMRS based on hypothesis testing. The significant findings included user satisfaction, net benefits, system utilization, service quality, system quality, organizational environment, and information quality. This study emphasizes the importance of routine and periodic maintenance, monitoring of SIMRS by relevant units, attention to factors influencing SIMRS adoption by users, and regular training related to the operation of the SIMRS application. Efforts to improve operational skills in the aspects of system quality, information quality, service quality, organizational structure, and organizational environment of SIMRS are crucial to optimize the net benefits generated by SIMRS.

Background: Structural Health Monitoring plays a critical role in ensuring the safety, reliability, and sustainability of high performance composite structures used in aerospace, civil infrastructure, and mechanical systems. Conventional externally mounted sensors often face challenges related to environmental interference, maintenance complexity, and long term stability. Objective: This study aims to develop and validate an integrated smart composite monitoring system with embedded sensing capabilities that enhances damage detection accuracy and operational durability under varying mechanical stress conditions. Method: Smart composite specimens were fabricated by embedding fiber optic and piezoelectric sensors within fiber reinforced polymer laminates, followed by tensile, fatigue, and vibration testing. Signal processing techniques including time frequency analysis were applied to extract damage sensitive features, which were then classified using machine learning algorithms to distinguish healthy and damaged structural states. Results: The experimental findings demonstrate high damage detection capability, stable sensor performance under cyclic loading, improved reliability compared to conventional monitoring approaches, and consistent monitoring accuracy throughout the fatigue life of the specimens. The integration of embedded sensing and data driven analytics significantly enhances structural response interpretation and supports predictive maintenance strategies.

Bridge infrastructure is a vital component of transportation systems that is vulnerable to structural damage caused by dynamic loads, environmental factors, and aging. Early crack detection is crucial to prevent structural failures that may lead to catastrophic consequences. This study aims to develop a non-destructive detection method based on acoustic sensors to identify cracks in bridge structures with higher sensitivity and accuracy compared to conventional visual inspections. The research was conducted through laboratory experiments and field tests using acoustic sensors, data acquisition devices, and signal analysis software. The procedure included sensor installation on a bridge model, simulation of artificial cracks with varying sizes and positions, recording of acoustic wave signals, and data analysis using frequency spectrum, amplitude, and waveform pattern approaches. The results show significant differences between normal and cracked conditions in the frequency spectrum, where cracks produced amplitude anomalies at specific frequencies. Amplitude analysis revealed a positive correlation between crack size and acoustic signal intensity, while waveform pattern analysis demonstrated the influence of crack position on distortion levels. Cracks located at the center generated the highest distortion, followed by joints and edges. These findings confirm that acoustic sensors, particularly fiber-optic-based ones, offer advantages such as high sensitivity, reliability under complex environmental conditions, and the ability to detect subsurface cracks. The implications of this research highlight the potential development of an acoustic sensor-based structural health monitoring system integrated with real-time analysis software, thereby supporting preventive maintenance, extending infrastructure lifespan, and enhancing transportation safety.

This study presents an advanced structural health monitoring (SHM) system for steel bridges based on wireless sensor networks (WSN) integrated with machine learning algorithms. The proposed system monitors and predicts structural integrity under various load conditions. The research focuses on developing a machine learning model capable of real-time anomaly detection, allowing for early warnings of potential failures. Experimental results from both simulation and field tests demonstrate the system’s effectiveness in prolonging bridge lifespan while reducing maintenance costs.