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.
The enduring resilience of Roman infrastructure, exemplified by the Tiberius Bridge in Rimini—completed in the 1st century CE and remaining structurally sound after over two millennia—has long drawn scholarly attention. This study re-evaluates Roman construction methodologies with a particular focus on opus caementicium (Roman concrete) encased within durable permanent facings such as opus quadratum, opus incertum, and opus latericium. Central to this longevity was the use of pozzolanic binders, which underwent prolonged hydration reactions, enabling continued strength development over extended timescales—markedly contrasting with contemporary hydraulic cements engineered for rapid early-age strength gain. A comparative analysis is conducted between ancient Roman materials and modern high-performance cementitious composites, including High-Performance Concrete (HPC), Ultra-High Performance Concrete (UHPC), and Engineered Cementitious Composites (ECC). Contemporary practices are frequently guided by design codes such as Eurocode, which, while structurally robust, tend to prioritize short-term performance metrics. To bridge this gap, a hybrid construction strategy is proposed wherein additive manufacturing is employed to produce permanent structural formworks that mimic the load-bearing and protective functions of Roman facings. This approach enables the use of modern slow-maturing binders within digitally fabricated enclosures, thus integrating ancient durability principles into automated, scalable workflows. By reconciling historical construction insights with advances in modern materials science and digital fabrication, a new paradigm is introduced for designing infrastructure with service lives far exceeding the conventional 50–100 year design horizon. The implications of such an approach extend to both sustainability and resilience, offering a technically viable and historically informed route toward ultra-durable infrastructure in the face of evolving environmental and operational challenges.
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 increasing demand for pulp as a raw material for tissue towel manufacturing has significant environmental consequences, particularly due to its reliance on wood. Waste from the pulping process contains hazardous compounds, such as lignin and chlorine, which cause riverbed sedimentation, odor, and pollution. These issues align with Sustainable Development Goals (SDGs), particularly Goal 12, which emphasizes responsible consumption and production to minimize waste and its impacts. This research explores the use of coconut coir (Cocos nucifera L.), a sustainable alternative to wood, in tissue towel production. The organosolv process, employing organic solvents like ethanol (50%, 60%,70%) and cooking time (90 min, 120 min), offers an eco-friendly pulping method by eliminating sulfur and enabling black liquor recyclability. Experimental results revealed that increasing ethanol concentration and cooking duration reduced lignin and moisture content while enhancing cellulose yield. The optimal treatment involved a 60% ethanol solution and a 90-minute cooking time, producing tissue with 81.09% cellulose, 24.98% lignin, and desirable physical properties. This study supports SDGs by advancing green technology, promoting a circular economy, and fostering sustainable, environmentally friendly tissue towels.
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.
To investigate the dynamic response and potential structural degradation of carbon fiber sucker rod strings during operation, a torsional vibration model incorporating helical buckling-induced torque excitation has been developed. In this model, the upper suspension boundary condition is idealized as a torsional spring, whose stiffness is determined as a function of both axial displacement and applied load at the suspension point. The torsional stiffness is categorized into time-dependent and mean (average) components, both of which are examined through numerical simulation using the finite difference method. The results reveal pronounced torsional oscillations at the upper section of the rod string, indicating significant torsional deformation of the suspension assembly. A non-monotonic relationship is observed between stroke length and vibration amplitude, wherein torsional vibration initially intensifies with increasing stroke before attenuating, suggesting the presence of resonance phenomena within specific operational ranges. The simulations further demonstrate that time-varying and average torsional stiffnesses yield comparable influences on the overall torsional response. Helical buckling deformation is shown to play a critical role in amplifying torsional stress, with the induced torque predominantly localized in the mid-to-lower segments of the wellbore. The presented model provides an essential theoretical framework for understanding the complex interaction between axial deformation and torsional instability, offering new insights into the mechanisms that may precipitate longitudinal splitting or fatigue failure in carbon fiber sucker rod strings. These findings are expected to support the optimization of rod string design and operational strategies in advanced artificial lift systems.
Urban transportation systems in developing cities like Yogyakarta face challenges such as congestion, limited infrastructure, and fragmented policies. This study aims to develop a context-specific framework for Intelligent Transportation System (ITS) adoption by integrating the Technology Acceptance Model (TAM) with external readiness factors, including infrastructure quality, technology access, socioeconomic status, and policy support. A survey of 300 transportation users was conducted, and data were analyzed using Structural Equation Modeling with Partial Least Squares (SEM-PLS). Instrument validity was confirmed through expert review and Content Validity Index (CVI). The study introduced two new constructs Smart Readiness and Social Affordability to capture individual and systemic influences on technology adoption in ITS. Results show that perceived usefulness and ease of use mediate the relationship between external readiness and behavioral intention. Government policy and infrastructure were the strongest predictors of ITS adoption. The model explained 70% of the variance in behavioral intention, indicating strong explanatory power and model fit. In conclusion, contextual factors such as infrastructure, governance, and digital access play a pivotal role in enabling ITS adoption in mid-sized developing cities. The proposed framework extends TAM by incorporating systemic urban readiness, offering both theoretical advancement and practical guidance for policy makers and urban planners.
Renewable energy installations are rising at a fast pace because societies r./uire both emission reduction and alternative clean energy sources. Policymakers, together with industry stakeholders, find it troublesome to use traditional energy prediction models because these systems operate without clarity and fail to handle intricate market systems properly. This research solves these issues through a machine learning (ML) model prediction of renewable energy use. Then, it enhances predictions through explainable artificial intelligence (XAI) methods to achieve better accuracy and trustworthiness. Our analysis includes multiple ML algorithms from the ensemble category consisting of Random Forests (RF) and Gradient Boosting in addition to advanced boosting algorithms XGBoost and Light Gradient Boosting Machines (GBM). Local Interpretable Model-Agnostic Explanations (LIME) reveal the decision-making procedures during predictions while delivering understandable explanations about the model's conduct to users. The methodology adopts a thorough model testing methodology using extensive datasets, which include multiple variables related to renewable energy consumption, including economic metrics and environmental aspects. Researchers obtained predictive performance excellence with interpretability benefits from their models in predicting renewable energy usage. The Light GBM model delivered 97.40% accuracy when analyzing data, while the LIME process showed GDP growth and electricity access as key determining variables. XAI integration in renewable energy forecasting presents important progress that livers enhanced, transparent yet actionable energy predictions that build trusted reliability for use in the industry. The study demonstrates the power of uniting ML with XAI techniques for better comprehension of renewable energy patterns, which enables better decisions for sustainable energy development.
The influence of prestrain on the microstructural evolution and corrosion behaviour of copper-based alloys has been systematically investigated to elucidate the mechanisms by which mechanical preconditioning enhances structural integrity and electrochemical stability. Prestrain, applied prior to subsequent thermomechanical treatments, has been found to significantly alter dislocation density, grain size distribution, phase transformation pathways, and precipitate morphology and distribution. These changes collectively promote grain refinement and the formation of nanocrystalline domains, thereby improving both strength and ductility. Enhanced effects have been observed in Cu–Cr–Zr and Cu–Al–Ni alloys, particularly when prestrain is introduced via cold rolling or friction stir processing (FSP). In these systems, microstructural stability during post-deformation ageing is markedly improved due to the suppression of grain coarsening and the controlled precipitation of strengthening phases. Moreover, prestrain modifies the local chemical and crystallographic environment in a manner that critically impacts electrochemical behavior. Intermediate levels of mechanical stress have been shown to improve corrosion resistance by facilitating the formation of uniform, adherent passive films, while excessive strain introduces microstructural heterogeneities that serve as initiation sites for intergranular and stress corrosion cracking. These phenomena were characterized using X-ray diffraction, scanning and transmission electron microscopy (TEM), and electrochemical techniques including potentiodynamic polarization and electrochemical impedance spectroscopy. The interplay between mechanical preconditioning, microstructural refinement, and corrosion mechanisms has been clarified, offering insights into process–structure–property relationships. The findings hold particular relevance for the design and optimization of copper alloys in high-performance applications such as electronic interconnects, biomedical implants, and aerospace components, where dimensional stability, chemical resilience, and machinability are of paramount importance. The study underscores the critical role of prestrain not only as a structural refinement tool but also as a means of tailoring corrosion resistance through controlled microstructural engineering.
Transportation logistics for fuel delivery face persistent challenges in routing under uncertain demand and complex operational constraints. This study addresses the gap between theoretical models and practical fuel distribution by introducing a hybrid framework that integrates Deep Reinforcement Learning (DRL), graph-based spatial reasoning, and deterministic constraint validation. The method combines Proximal Policy Optimization (PPO) with a graph neural architecture to capture spatial dependencies in vehicle routing while ensuring operational feasibility via constraint-checking mechanisms. The approach was evaluated on 300 synthetic problem instances across three network scales (10, 50, and 100 stations) and a real-world case study involving 38 gas stations and 6 vehicles in a regional fuel distribution system. Compared to a standard deep learning baseline and a Clarke-Wright heuristic, our method reduced operational costs by 7.2% and 9.9%, respectively. Constraint violations dropped from 6% with classical reinforcement learning to 1%, demonstrating improved feasibility. While we report averaged results over large instance sets, formal statistical significance testing remains a direction for future work. The proposed approach maintained robust performance under varying levels of demand uncertainty and produced feasible daily routing plans within 45 seconds, confirming their practical applicability. By integrating learning, spatial reasoning, and operational compliance, this research advances scalable and adaptive optimization for fuel delivery in uncertain and dynamic environments.
Bioethanol can only continue as a viable cleaner alternative to fossil fuels by utilizing abundant, renewable, and eco-friendly feedstocks with high conversion efficiencies or by developing technologies that enhance efficiency and reduce inhibition. This study aims to compare the potential of producing bioethanol from Artocarpus heterophyllus Lam. (AHL) pulp starch with cassava (CAS) starch. Response surface methodology (RSM) was used to optimize the process conditions in acid and enzymatic hydrolysis for optimum reducing sugar and ethanol yield. The study demonstrated that AHL performed better than CAS in the enzymatic process with an optimum reducing sugar yield of 80.22 g/L compared to 70.61 g/L obtained for CAS. The conversion efficiencies for AHL and CAS at an optimum condition of 120 amylase and 310 amyloglucosidase unitg-1 starch were 91.2% and 80.24%, respectively. Consequently, in the acidic process, an optimum sugar yield was achieved at 0.5 M H2SO4, 45 mins hydrolysis time and 121℃. Under these conditions, AHL sugar yield was 19.05 g/L with 34.64% conversion efficiency while CAS produced 22.48 g/L with 40.87% conversion. The results of the ethanol yield obtained in both hydrolytic processes showed that AHL compared very favorably with CAS. Though AHL is characterized by higher amylose content (28.90) than CAS (20.43) which would easily hinder enzyme accessibility during hydrolysis, its type-A crystal structure paved the way for its starch to be easily assessed by the α-enzymes. Hence, this study provided a suitable, efficient and sustainable substitute to cassava or other first-generation feedstock for bioethanol production.
First-mile and last-mile connectivity remains a significant challenge in developing cities as inadequate feeder systems often hinder public transport efficiency. While prior studies have examined access and egress mode choices, few have explored how income levels and travel distance shape commuters’ travel mode behavior in Indonesia. This study addresses this gap by analyzing the influence of income level and travel distance on mode selection for first-mile and last-mile trips in Jakarta’s commuter rail system. This study used a multinomial logit model (MNL) to examine the hypotheses across 24 Jakarta Kota–Bogor stations. The findings show that lower-income commuters prefer to walk and use microtransit and Bus Rapid Transit (BRT), while higher-income groups prefer private vehicles and ride-hailing services. In addition, travel distance strongly influences mode choice, with walking decreasing significantly as the distance increases. The results also highlight a high private vehicle dependency for first-mile access and a tendency for ride-hailing in last-mile travel, reflecting a wide gap in Jakarta’s feeder system. This study recommends expanding and integrating feeder transport, improving pedestrian infrastructure, unifying fares across modes, and regulating ride-hailing services to enhance connectivity. These measures can promote sustainable urban mobility and reduce dependency on private vehicle.
This paper proposes a Hybrid AC/DC Microgrid (HMG) system comprising a wind energy control system (WECS), a photovoltaic (PV) system, and a battery system. Because microgrids emit fewer carbon gases and may be connected to the utility grid, researchers are finding them more and more appealing. The HMG increases system efficiency and power quality by reducing multiple reverse conversions. The VSC system functions as a DC/AC bus control system and employs used bacteria foraging optimization (BFO) algorithm to adjust the proportional integral (PI) controller settings to reduce AC and DC switching. Furthermore, for the battery energy storage system (BESS) and wind turbine speed regulation utilize two PI controllers. Lastly, the maximum power point (MPP) of the PV system was investigated using perturbation and observation (P&O), incremental conductance (IC), fuzzy logic controller (FLC), and maximum power point tracking (MPPT). The FLC technique showed the benefit of attaining the finest outcomes, specifically power (99.68 kW) and efficiency (99.84%). The results show that the suggested approach works well for accomplishing the primary goals of the hybrid microgrid. The simulations in this study were carried out using MATLAB/Simulink.
Open Access
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