Qualitative Study of Household Contacts’ Perceptions of Barriers to Pulmonary Tuberculosis Transmission in the Kedaton Community Health Center Work Area, Bandar Lampung, Indonesia

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Pulmonary Tuberculosis (TB) remains a public health problem due to the high potential for transmission to family members living in the same house as the sufferer. Although various health education has been provided, transmission prevention behavior has not been carried out optimally and continuously. This study aims to examine the perception of barriers in household contact families regarding the transmission of pulmonary TB. The study was conducted using a qualitative method through a case study design in the working area of ​​the UPT Kedaton Health Center, Bandar Lampung, Lampung province Indonesia. Research informants consisted of 15 household contact family members, 1 health cadre, and 1 health worker selected using a purposive sampling technique. Data collection was carried out through in-depth interviews, observation, and documentation, then analyzed thematically based on the HBM construct. The results of the study indicate that the perception of barriers (perceived barriers) in the prevention and treatment of Pulmonary TB in household contact families still varies. Some informants admitted that they did not experience significant obstacles in carrying out preventive behaviors or accessing health services. Factors of cost, time, and access to health services were considered quite affordable due to the support of health facilities, BPJS, the environment, and family. This study found that perceived barriers were the most dominant factor influencing TB prevention behavior. These barriers included daily habits, environmental influences, social stigma, economic constraints, and low social support, all of which contributed to suboptimal TB prevention behavior. Furthermore, there are still beliefs that TB is caused by hereditary factors or mystical elements such as witchcraft. These findings emphasize the need for communicative, ongoing, and culturally appropriate health education to improve TB prevention behavior among household contacts.

Artificial Intelligence and Human Capital in The Context of Economic Security and Sustainable Development of Tourism

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In the context of increasing global economic instability, accelerated digitalization and increased security requirements, the tourism industry is faced with the need for a radical transformation of management models and the focus of this study is the interaction between intelligent technologies and human capital in the context of risk management, security and organizational resilience. For this purpose, the role of artificial intelligence and human capital as key factors for economic security and sustainable development of tourism is analyzed. Through a theoretical and conceptual approach, the study explores good practices for the integration of artificial intelligence with human capital, contributing to stability, competitiveness and long-term sustainability in the tourism industry. The article argues that sustainable tourism development requires a balanced approach, in which artificial intelligence serves as a supporting tool, while human capital retains its central role in strategic decision-making, ethical management and security management in tourism development. Through a theoretical and conceptual approach and a study of good practices.

Community Structure and Seasonal Dynamics of Wetland Birds in a Tropical Inland Wetland, Kilpathi Lake, Vriddhachalam, Tamil Nadu, India

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Wetland ecosystems play a critical role in supporting avian biodiversity, particularly in tropical regions. The present study evaluates the diversity, habitat selection and seasonal dynamics of wetland bird communities in Kilpathi Lake, Tamil Nadu, India, over a six-month period from September 2025 to February 2026. Standard ornithological survey techniques, including point count and line transect methods were employed to document species composition and abundance. A total of 18 species belonging to 6 families and 5 orders were recorded. Simpson’s Diversity Index (1–D) indicated higher diversity during winter (0.71 ± 0.02) compared to monsoon (0.68 ± 0.02). Seasonal analysis revealed variations in abundance, density and relative abundance of species with Egretta garzetta, Ardeola grayii and Microcarbo niger emerging as dominant taxa. The overall bird density was higher in winter (3984.19 individuals/km²) than in monsoon (2765.46 individuals/km²). Habitat selection was strongly influenced by water depth, vegetation structure and resource availability. The findings highlight the ecological importance of small inland wetlands as critical habitats for sustaining avian diversity and emphasize the need for targeted conservation strategies in human-modified landscapes.

Swearing Beyond Insult: A Semiotic Analysis of Swear Words as Emotional Communication

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Swearing has been associated to offensive words, hostility, and insults. Yet, today’s, instead of being just offensive, swear words regularly use as emotional and relational resources in daily conversations. This article looks at swearing words that use as signs for emotional communication in everyday communications of people who has similar and intense situation. This study observes how swear words serve as indicators of annoyance, empathy, humor, and solidarity by analyzing naturally occurring conversational data using a semiotic approach. The results show, the changing of taboo language in daily interaction in intense situations, functions as a communication technique that strengthens interpersonal ties and shared emotional experiences.

Health Worker Performance Factors in the Implementation of Infection Prevention and Control: A Literature Review

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Infection Prevention and Control (IPC) is a crucial component in improving the quality of care and patient safety in healthcare facilities. The success of IPC implementation is significantly influenced by the performance of healthcare workers in consistently implementing infection prevention standards and procedures. Poor adherence to IPC can increase the incidence of nosocomial infections, length of stay in hospital, mortality, and healthcare costs. This literature review aims to analyze factors influencing the performance of healthcare workers in implementing IPC. This study employed a literature review method, collecting literature through PubMed, ScienceDirect, Google Scholar, and Semantic Scholar using the Boolean operators AND and OR. The literature was limited to articles from 2021 to 2025 that discussed individual, psychological, and organizational factors in IPC implementation. The study results indicate that individual factors include knowledge and tenure. Psychological factors include motivation and attitude. Organizational factors include the availability of resources and facilities, supervision, compensation systems, and workload. IPC implementation is influenced by interrelated individual, psychological, and organizational factors, requiring a comprehensive approach to improving IPC compliance and patient safety.

Qualitative Study of Effectiveness of Early Warning System and Dengue Fever Case Response in West Tulang Bawang Regency, Lampung, Indonesia

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The Early Warning and Response System (EWARS) plays a role in detecting potential outbreaks of infectious diseases through weekly reports. West Tulang Bawang Regency experienced an increase in dengue fever cases in 2024–2025, but the early detection function of the EWARS has not been running optimally as indicated by the still low number of alerts compared to the number of reported cases. This study aims to explore the effectiveness of the EWARS for dengue fever cases in West Tulang Bawang Regency, Lampung, Indonesia. The study used a qualitative approach with a case study method. Data collection was carried out from October to December 2025 in three community health centers with 14 research informants consisting of village cadres, village midwives, community health center surveillance officers, Health Office surveillance officers, community health center heads, and village heads selected using a purposive sampling technique. Data collection was carried out through in-depth interviews, observation, and documentation with thematic data analysis. Input components: training is not evenly distributed, competencies are not in accordance with education, limited funds that are not specifically for EWARS reporting, limited facilities and infrastructure (computers, internet network constraints and disruptions to the EWARS web application system). Process components: tiered data collection from networks, health centers via WA to health offices, health centers do not yet have access to the EWARS web application, manual data entry, data validation for signal verification (alert), data presentation in graphical form, feedback via weekly bulletins in the WA group, monitoring, evaluation and follow-up have been carried out according to procedures. The effectiveness of EWARS for DHF cases in West Tulang Bawang Regency is influenced by limited funding, limited facilities and infrastructure, staff competencies that do not match education, uneven training causes different understanding of staff, implementers do not understand what to report, so that cases that are often reported are cases that are already positive or cases that have been treated in hospitals, this is not in accordance with the principles of EWARS. There is a need to increase human resource capacity, provide adequate facilities and infrastructure, integrate web-based reporting systems at the community health center level and mobile SDKR at the network level, encourage symptom-based reporting and provide funding support.

Quantum-Safe Artificial Intelligence: A Systematic Review of Post-Quantum Cryptography Applications

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Objective: The rapid development of quantum computing poses an existential threat to classical cryptographic systems that currently secure global digital infrastructure. In direct response to this quantum threat, post-quantum cryptography (PQC) has emerged as a critical field dedicated to designing algorithms resistant to quantum attacks. Simultaneously, artificial intelligence (AI) — particularly machine learning (ML) and deep learning (DL) — has demonstrated promising and emerging capabilities across cybersecurity domains, including cryptography.

Methods: This systematic review was conducted by searching IEEE Xplore, ACM Digital Library, Springer Link, Google Scholar, Scopus, and Web of Science using targeted keywords related to AI and PQC, covering literature published between 2015 and 2025. A total of 62 peer-reviewed studies meeting predefined inclusion criteria were analysed.

Results: A total of 38 key studies were identified and analysed across four principal application domains: algorithm design and parameter optimization (31.6%), cryptanalysis and security assessment (26.3%), side-channel attack detection and defense (23.7%), and secure deployment on resource-constrained devices (18.4%). Practical case studies demonstrate measurable performance gains, including a 27% reduction in key exchange time reported in a specific study [60] and 98.3% accuracy in side-channel attack detection reported in a specific study.

Conclusions: The synergy between AI and PQC represents a pivotal frontier in cybersecurity. This review provides a structured foundation for future interdisciplinary research in quantum-safe intelligent systems and identifies explainable AI (XAI) integration as the most critical open research direction.

Omnichannel Retail Strategy and Customer Loyalty: Evidence from The Emerging Market Consumers

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The rapid pace of digital transformation has brought significant changes to the retail industry. The advancement of information technology, the widespread use of the internet, and the increasing growth of online shopping activities have encouraged retail companies to adapt their business strategies. One approach that has been widely adopted is the omnichannel strategy, which integrates multiple sales channels, both online and offline, to create a more seamless shopping experience for customers. Through this approach, consumers can easily move between digital platforms and physical stores during the processes of information search, purchasing, and after-sales service.This study aims to examine how the implementation of omnichannel strategies in retail businesses influences customer loyalty, particularly among consumers in emerging markets. The research focuses on three main dimensions representing omnichannel implementation: channel integration, customer experience, and perceived service quality. These dimensions are considered crucial in building long-term relationships between companies and their customers.The study employs a quantitative approach, with data collected through a survey of 250 respondents who are retail consumers with experience shopping through both online channels and physical stores. The collected data were analyzed using the Structural Equation Modeling (SEM) method to examine the relationships among the research variables.The results indicate that channel integration within an omnichannel strategy has a significant effect on enhancing customer experience and perceived service quality. Both factors are found to contribute positively to the formation of customer loyalty. Furthermore, customer experience acts as a mediating variable that strengthens the relationship between the implementation of omnichannel strategies and customer loyalty. These findings suggest that effective integration between digital platforms and physical stores is a key factor in creating customer satisfaction and loyalty. By implementing an optimal omnichannel strategy, retail companies can strengthen long-term relationships with consumers while enhancing their competitiveness in an increasingly dynamic industry.

AI-Powered Token Prediction and Automated Trading in Web3 Using On-chain Data and Decentralized Exchanges

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This article investigates the efficacy of implementing an AI-powered automated trading system on the blockchain using advanced machine learning algorithms and smart contract technology. The work addresses the challenges of cryptocurrency market volatility, the need for real-time decision making and the limitations of traditional trading approaches that often result in suboptimal returns and exposure to increased risk. This work develops a comprehensive trading platform that combines Long Short-Term Memory (LSTM) neural networks, Q-Learning reinforcement learning algorithms and blockchain-based smart contracts to create an autonomous, intelligent trading system. The methodology follows a multi-layered approach that integrates real-time market data collection from CoinGecko and Snowtrace APIs, advanced AI model training using TensorFlow.js, and smart contract deployment on the Avalanche C-Chain using Hardhat and OpenZeppelin libraries.  LSTM model is used for price prediction and Q-Learning agent is used for trading strategy optimization, while comprehensive risk management is implemented using Value at Risk (VaR) calculations, portfolio rebalancing algorithms and automated stop-loss mechanisms. The trading execution is facilitated through direct integration with Pangolin DEX smart contracts to ensure decentralized and trustless trade execution. The performance of the system is evaluated using a sophisticated backtesting engine with Monte Carlo simulations, comparing the AI-driven strategy against traditional buy-and-hold approaches. The performance metrics used were Sharpe ratio, maximum drawdown, win rate, and total return. The AI-powered token prediction system demonstrates a superior performance due to its ability to process complex, non-linear market patterns and adapt to changing market conditions through reinforcement learning, and execute trades with minimal latency through blockchain integration. The findings are expected to provide cryptocurrency traders and institutional investors with a robust and automated trading solution that leverages the benefits of both artificial intelligence and blockchain technology for improved investment outcomes and risk management.

Job Satisfaction and its Effects on The Work Performance of Elementary Teachers

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Understanding job satisfaction is key to gaining insight into how teachers engage with their roles and contribute to the learning environment. This study assessed the level of job satisfaction and its effect on the work performance of elementary teachers in the Lanuza District. It also examined the relationship between teacher demographics and job satisfaction, identified which work performance domains had the most influence, and explored whether job satisfaction correlated with performance. Using a descriptive research design, data were collected through a validated researcher-made questionnaire from 93 elementary teachers (11 males and 82 females).

Results revealed that teachers were generally “Highly Satisfied” in areas such as responsibility, interpersonal relations, and the nature of the work itself. However, satisfaction was lower in domains related to salary and advancement opportunities. Work performance was consistently rated from “Very Satisfactory” to “Outstanding” across competency domains. Despite high satisfaction levels, the study found no significant relationship between job satisfaction and performance. Additionally, none of the five work performance domains significantly predicted performance outcomes. Most demographic factors were unrelated to satisfaction, although age showed a moderate association with satisfaction in areas like interpersonal relations, working conditions, and the work itself. Similarly, the number of trainings attended was moderately linked to satisfaction in professional growth and recognition.

The study concludes that while job satisfaction is generally high among teachers, it does not directly influence work performance. Further research is recommended to explore other factors that may mediate or moderate this relationship.