It is crucial to ensure system reliability in changing situations where systems are required to operate in uncertainty or against disturbances. Human-in-the-Loop (HITL) simulations have in recent times emerged as an important key in ensuring the robustness and resilience of the system via evaluation and testing. The method introduces human decision-making and adaptability as well as providing insight into zones of possible weak points of the system and failure modes which are not even captured by computer-based systems. By incorporating HITL simulations into the system, designers and engineers could simulate operational challenges in real life, identify unforeseen defects, and implement mitigative strategies to enhance both robustness-ensuring consistent performance in the nominal operation and resilience-maintaining functionality in and after disruptions. This article looks at the effectiveness of HITL simulations in various domains, and particularly at the role that these contribute to system robustness and resilience. Among the most significant issues will be the nature of building the simulation environments, the test for the range of scenarios, and the roles for humans to be simulated within the loop. We investigate the behavior of humans during stress and uncertainty, then provide valuable feedback to the system to help it learn. By revealing the vulnerabilities of the system design and acknowledging human effects on recovery and decision-making operations, HITL simulations finalize the development of more adaptable, stable systems that could recover rapidly from interruptions. To conclude, HITL simulations are a critical tool for improving the reliability of systems, hence providing a comprehensive framework to address either expected or unexpected challenges in complex operating environments.
This study presents a comprehensive comparison of modern cementitious composites, including UHPC, ECC, and GFRC, with traditional Ordinary Portland Cement (OPC) and ancient Opus Caementicium (Roman). Emphasis is placed on mechanical, physical, and rheological properties, as well as environmental and durability aspects. Advanced composites demonstrate superior short-termmechanical performance and improved impermeability, while Roman binders exhibit unparalleled long-term resilience in marine environments. Furthermore, the integration of pozzolanic materials and industrial by-products in contemporary mixes highlights ongoing efforts toward sustainable construction. Recent developments in China, including metakaolin–slag blends and nano-silica additives, as well as bio-inspired self-healing approaches, illustrate promising pathways for reducing carbon footprint and enhancing durability.
Substantial scientific consensus has confirmed that global warming, driven by climate change, poses significant risks to both environmental and occupational systems. In response, the Malaysian government has taken notable steps, including the enactment of the National Policy on Climate Change in 2009 and subsequent commitments to develop comprehensive legislation aimed at strengthening national climate strategies. Despite these institutional efforts, the dissemination and uptake of climate-related information remain hindered by misinformation campaigns and varying levels of public literacy. Among those most vulnerable are unskilled construction workers, who are increasingly exposed to occupational hazards, productivity disruptions, and worksite risks linked to extreme weather events. To evaluate how climate literacy and awareness influence the utilisation of climate-related information within this group, a cross-sectional study was conducted involving 144 randomly selected unskilled construction workers registered with the Construction Industry Development Board (CIDB) across the Malaysian states of Terengganu and Selangor. Data were collected using structured, self-administered questionnaires and analysed through structural equation modelling using analysis of moment Structures (SEM-AMOS). The results revealed that higher levels of climate literacy significantly enhanced the effective use of climate-related information, whereas general awareness of climate change did not demonstrate a statistically significant effect. This divergence indicates that while awareness may foster recognition of climate issues, it is the depth of literacy—defined as the ability to critically interpret, evaluate, and act upon climate information—that drives meaningful behavioural engagement. These findings underscore the necessity for policy frameworks and educational interventions to prioritise literacy-building rather than awareness campaigns alone. It is proposed that targeted capacity-building programmes, particularly within labour-intensive industries, be developed to equip vulnerable populations with the necessary tools for informed decision-making in climate-sensitive contexts. This study advances the academic discourse on climate communication and policy implementation by identifying literacy as a pivotal factor in climate information engagement among marginalised labour segments.
This study presented a novel mathematical functional-based algorithm designed to predict the risks of vehicular crashes by leveraging real-time traffic data collected from urban road networks. The proposed model integrated multiple critical variables, including traffic speed, vehicle density, visibility conditions, spatial coordinates, and time-of-day factors, to generate a comprehensive and dynamic assessment for foreseeing the likelihood of traffic crashes. The flexible functional framework enabled the incorporation of diverse traffic and environmental variables, thereby improving the accuracy and contextual sensitivity of risk predictions for road traffic. The model was calibrated and validated using real-world traffic data from five key locations in Islamabad, Pakistan, known for their varying traffic patterns. The results demonstrated that the model could effectively identify high-risk zones and specific time intervals during the day when the probability of crashes was elevated. For example, areas such as Inter-junction Principal (IJP) Road exhibited significantly higher risks of crashes during peak congestion hours, correlating strongly with increased vehicle density and reduced visibility. The study highlighted the potential of combining mathematical modeling with real-time data analytics to address the growing challenges of traffic safety in rapidly urbanizing cities. By providing spatially and temporally resolved estimations of risks, the proposed method enables urban planners and traffic authorities to implement proactive and targeted safety interventions, such as dynamic traffic signaling, speed regulation, and public awareness campaigns. This approach not only enhances urban traffic management but also contributes to reducing accident rates and improving overall road safety.
In modern foundry operations, the reliability and operational continuity of sand molding systems are pivotal to maintaining productivity, safety, and competitive advantage. In this study, Failure Mode, Effects, and Criticality Analysis (FMECA) has been employed to systematically evaluate and optimize the performance of a pneumatic molding cell utilized in the production of sand molds. Particular focus has been directed toward the pusher subsystem, which is frequently subjected to high mechanical loads and cyclic stress, rendering it susceptible to recurrent failures that compromise both uptime and process efficiency. Potential failure modes were exhaustively identified, categorized, and prioritized based on their severity, occurrence, and detectability. Critical components, including servo motors, pneumatic actuators, and gearbox assemblies, were found to pose substantial risk to system reliability due to wear-induced degradation, misalignment, and lubrication failure. For each high-priority failure mode, targeted mitigation strategies were proposed, encompassing enhanced condition monitoring, retrofitting of wear-resistant materials, and redesign of high-stress interfaces. Furthermore, failure detection mechanisms were improved through the integration of predictive maintenance protocols and sensor-based diagnostics. Implementation of these recommendations has resulted in measurable reductions in unplanned downtime, repair frequency, and maintenance overhead. This investigation demonstrates that FMECA, though underutilized in conventional foundry environments, offers a structured, data-driven methodology for uncovering latent failure risks and implementing preventive measures in complex industrial systems. By embedding FMECA within routine maintenance frameworks, a substantial improvement in operational resilience and equipment lifespan can be achieved. The findings support the strategic integration of reliability engineering methodologies into sand molding operations, contributing not only to cost efficiency but also to the broader adoption of systematic risk management practices in process-driven manufacturing sectors.
Reliable detection of road surface objects under foggy conditions remains a critical challenge for autonomous vehicle perception systems due to the severe degradation of visual information. To address this limitation, a novel framework was developed that integrates entropy-guided visibility enhancement, Pythagorean fuzzy logic, and structure-preserving saliency modeling to improve object detection performance in low-visibility environments. Visibility restoration was achieved through an entropy-guided weighting mechanism that selectively enhances salient image regions while preserving essential structural features critical for downstream detection tasks. Uncertainty and imprecision inherent to fog-degraded scenes were systematically modeled using Pythagorean fuzzy logic, enabling improved confidence estimation and robustness in object localization. A saliency mechanism that preserves structural characteristics further contributes to the accurate delineation of road-relevant elements. Extensive evaluations on multiple publicly available foggy road datasets were conducted, demonstrating substantial gains in detection performance, with notable improvements in accuracy, precision, recall, and F1-score metrics. Furthermore, enhancements in visual quality were verified using structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), natural image quality evaluator (NIQE), and blind/referenceless image spatial quality evaluator (BRISQUE) metrics. The computational efficiency of the proposed method was confirmed, supporting its applicability to near real-time deployment scenarios. Consistent performance was observed across varying fog densities, highlighting the framework’s scalability and generalizability. The integration of entropy-based visibility enhancement with fuzzy reasoning and saliency preservation offers a comprehensive and practical solution to the challenges of perception in visually degraded environments, contributing to the advancement of safe and intelligent transportation systems.
The rural Andean community of Yacubiana, Ecuador, currently lacks an adequate sanitation infrastructure, with domestic wastewater managed through individual septic tanks. These decentralized systems have exhibited significant infiltration issues, resulting in groundwater contamination, degradation of sensitive páramo ecosystems, and adverse public health outcomes. Furthermore, this environmental degradation has impeded the community’s potential for ecotourism-based development. To address these challenges, an integrated wastewater management strategy was developed, grounded in sanitary engineering principles and aligned with conservation priorities. The proposed framework encompassed four sequential phases: (i) a comprehensive analysis of existing data on water and wastewater practices within the community; (ii) a systematic evaluation of sanitation alternatives tailored to the community’s socio-environmental context and the ecological fragility of Andean paramos; (iii) the design of a selected sanitation solution in accordance with national and international technical standards; and (iv) a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis conducted with both technical experts in water resource management and local community representatives. This participatory evaluation aimed to identify strategic pathways for enhancing environmental stewardship, promoting circular water economies, and enabling sustainable tourism. The recommended intervention consists of a simplified, decentralized sewage collection system linked to a trickling filter-based treatment plant, designed for a hydraulic load of 2.79 L/s. The SWOT analysis revealed substantial institutional and infrastructural constraints, primarily due to limited governmental support; however, it also identified considerable ecotourism potential grounded in the area’s geological, ecological, and cultural assets. When implemented within a conservation-based framework, the proposed system is expected to support compliance with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 6 (Clean Water and Sanitation), and 11 (Sustainable Cities and Communities). The methodological approach developed herein offers a replicable model for integrated wastewater management in rural, environmentally sensitive regions, providing a viable foundation for community-led, sustainable socio-economic development.
To facilitate a rigorous evaluation of damage progression in in-service steel frame structures subjected to seismic loading, a seismic damage model that integrates the effects of atmospheric corrosion has been developed. Corrosion-induced deterioration significantly influences the structural integrity of bolted steel frames, yet its impact on seismic performance remains inadequately quantified. In this study, a performance-based seismic damage assessment framework has been established, wherein corrosion-related degradation is incorporated into the structural damage evolution process. Drawing on an extensive review of domestic and international research, a refined damage index classification system has been formulated to characterize varying levels of structural impairment. To validate the proposed model, a seismic collapse simulation was conducted on a 1:4 scaled-down steel frame specimen, enabling a comprehensive analysis of damage accumulation over different service durations. The results confirm that the developed model accurately captures the progressive deterioration and collapse behavior of corroded steel frames under seismic excitation. This study provides a quantitative basis for assessing the post-earthquake residual load-bearing capacity of in-service bolted steel frame structures, offering critical insights for structural resilience evaluation and maintenance planning.
A comprehensive statistical analysis was conducted to investigate the causes and prioritization of failure modes within a production line manufacturing leather covers for automotive interiors. The study was grounded in a Process Failure Mode and Effects Analysis (PFMEA), with a dual emphasis on evaluating the traditional Risk Priority Number (RPN) approach and the more contemporary Action Priority (AP) methodology, which has been increasingly adopted to enhance risk assessment sensitivity. Failure modes were classified and prioritized using both approaches, revealing notable differences in the ranking outcomes. To further elucidate the underlying contributors to these failure modes, causal factors were systematically categorized in accordance with the 5M+1E framework—Man, Machine, Method, Material, Measurement, and Environment—commonly employed in quality and reliability engineering. A cause-and-effect diagram was constructed to visualize the distribution of root causes across these categories. Descriptive statistics and correlation analyses were employed to quantify the relationship between each category and the prioritized failure modes. Particular attention was paid to examining the interdependencies among the core PFMEA parameters—Severity, Occurrence, and Detection—in order to determine their respective contributions to the variability in failure mode rankings. It was found that Severity exerted the most substantial influence on the prioritization outcomes under the AP model, while Occurrence was more dominant when the RPN method was applied. These findings suggest that the choice of prioritization method significantly alters the interpretation of risk and resource allocation for corrective actions. The integration of 5M+1E categorization with PFMEA metrics offers a structured pathway to enhance the diagnostic capability of reliability assessments and improve decision-making in failure prevention strategies. This approach is proposed as a more robust alternative to traditional analysis, enabling more precise targeting of corrective and preventive measures in high-precision manufacturing environments.
An artificial intelligence (AI)-powered agricultural advisory system, termed NDEMRI (Nurturing Digital Extension via Mobile and Responsive Intelligence), has been developed to provide evidence-based farming guidance to rural communities across sub-Saharan Africa through short message service (SMS). Designed for compatibility with basic GSM-enabled mobile phones and independent of internet access for end-users, the system integrates large language models (LLMs) via the ChatGPT API to generate contextually relevant, linguistically localized responses to a wide array of agricultural queries. A quasi-experimental evaluation was conducted in the northern regions of Cameroon over a four-month period, employing a matched control group methodology involving 831 treatment farmers and 400 controls. Statistically significant improvements were observed among participants using NDEMRI, with mean crop yields increasing by 16.6% and agricultural incomes rising by 23%, relative to the control group. Adoption of improved agronomic practices was notably higher among users of the system. A total of 2,487 unique messages were exchanged, covering themes such as pest management, planting schedules, soil health, and post-harvest storage, with 78% of users reporting that system responses were context-sensitive and adapted to local climatic and cultural conditions. The technical architecture is characterized by modular natural language understanding pipelines, embedded guardrails to minimize model hallucinations, and a reproducible framework for contextualization based on regional agricultural datasets. A detailed economic analysis demonstrated the financial sustainability of the intervention, with favorable cost-benefit ratios and scalability potential. These findings offer robust empirical evidence that the integration of accessible communication technologies with state-of-the-art AI can overcome infrastructural limitations, enhance decision-making in low-resource farming environments, and serve as a viable model for transforming agricultural extension services across the African continent.
Accurate forecasting of Gross Domestic Product (GDP) growth remains essential for supporting strategic economic policy development, particularly in emerging economies such as Indonesia. In this study, a hybrid predictive framework was constructed by integrating fuzzy logic representations with machine learning algorithms to improve the accuracy and interpretability of GDP growth estimation. Annual macroeconomic data from 1970 to 2023 were utilised, and 19 input features were engineered by combining numerical economic indicators with fuzzy-based linguistic variables, along with a forecast label generated via the Non-Stationary Fuzzy Time Series (NSFTS) method. Six supervised learning models were comparatively assessed, including Random Forest (RF), Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), Huber Regressor, Decision Tree (DT), and Multilayer Perceptron (MLP). Model performance was evaluated using Mean Absolute Error (MAE) and accuracy metrics. Among the tested models, the RF algorithm demonstrated superior performance, achieving the lowest MAE and an accuracy of 99.45% in forecasting GDP growth for 2023. Its robustness in capturing non-linear patterns and short-term economic fluctuations was particularly evident when compared to other models. These findings underscore the RF model's capability to serve as a reliable tool for economic forecasting in data-limited and volatile macroeconomic environments. By enabling more precise GDP growth predictions, the proposed hybrid framework offers a valuable decision-support mechanism for policymakers in Indonesia, contributing to more informed resource allocation, proactive economic intervention, and long-term development planning. The methodological innovation of integrating NSFTS with machine learning extends the frontier of data-driven macroeconomic modelling and provides a replicable template for forecasting applications in other emerging markets.
The Bandar tradition observed in Negeri Rutah represents a culturally embedded mechanism of informal economic exchange, whereby financial contributions are voluntarily extended by community members to support families with sons entering marriage. This study has revealed that such a system operates not only as a means of reducing the financial burden associated with wedding ceremonies but also as an instrument for reinforcing communal bonds, intergenerational solidarity, and the continuity of intangible cultural heritage. Despite the absence of formal financial records or institutional oversight, contributions are managed through a trust-based system underpinned by mutual reciprocity and collective memory. The persistence of the Bandar tradition in contemporary society has been examined through the lens of social accounting, with a particular focus on its potential alignment with modern principles of accountability, transparency, and cultural resilience. Through qualitative field research, it has been demonstrated that the practice continues to function effectively within the community, sustained by deep-rooted social norms and communal expectations. However, challenges such as urban migration, generational shifts in value systems, and external economic pressures have been identified as potential threats to its long-term sustainability. The integration of culturally sensitive social accounting frameworks has therefore been proposed as a viable strategy for safeguarding this tradition against socio-economic disruption while preserving its core values. The study contributes to a broader discourse on the intersection of indigenous cultural practices, informal economies, and contemporary accountability systems, offering a model through which traditional mechanisms can be adapted without compromising their cultural integrity.
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