Journal of Mechanical, Civil and Industrial Engineering
Journal of Mechanical, Civil and Industrial Engineering (ISSN: 2710-1436) is an international peer-reviewed monthly journal published by Al-Kindi Center for Research and Development. The journal welcomes publications of high-quality papers on theoretical developments and practical applications in mechanical, civil and industrial engineering. Original research papers, state-of-the-art reviews, and high-quality technical notes are invited for publication. The scopes of the journal include, but are not limited to Mechanism Design, Manufacturing Engineering, Engineering Design, Electrotechnology, Fluid Dynamics, Thermodynamics, Automotive Engines, Engineering Mathematics, Mechatronics, Control Theory [...] Read More
Al-Kindi Center for Research and Development
Additive Manufacturing of Lightweight, Fire-Resistant Alloys for Automotive and Aerospace Applications
The current review has critically discussed the industrial preparedness of additive manufacturing (AM) in lightweight, fire-resistant alloys, and it is found that its so-called transformative potential is oversold in recent literature. Empirical research has pointed out continuous mechanical unreliability, anisotropy, and defect fragility, which have compromised operational reliability in aerospace and automotive systems. Long-term performance has also been limited by thermal instability, phase heterogeneity, and narrow process windows, and scalability is limited by the fact that post-processing is extensively dependent. Successes in methods like selective laser melting (SLM), electron beam melting (EBM), and directed energy deposition at the laboratory scale have scarcely been translated into reproducible industrial results and residual stresses, irregular microstructures, and quality assurance gaps remain. Alloy progression techniques such as alloy modification and coating on surfaces have not been fully tested to be accepted by regulations as fire resistant, indicating how difficult it has been to balance the mechanical, thermal and safety needs. Regulatory, certification and standardisation have also been found to hinder adoption of the industry, as well as high energy consumption and lifecycle inefficiencies. To mitigate these shortcomings, studies have laid more and more stress on scalable process optimisation, enhanced in-situ monitoring, predictive modelling and stringent life-cycle testing in real conditions. The cooperation of the manufacturers and regulators with the certification bodies has been confirmed to be critical towards creating standard qualification routes. Altogether, AM has shown conditional capability to form lightweight, fire-resistant alloy, but has not yet been able to become industrialised and thus is limited to niche or experimental use instead of providing the radicalising effect that is often advertised.
Additive manufacturing, lightweight alloys, fire-resistant alloys, aerospace, automotive, mechanical reliability, thermal stability, process reproducibility, certification, industrial adoption
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Blockchain-Integrated AI for Transparent and Adaptive Supply Networks
Global supply networks are now more prone to volatility, fragmentation, and trust issues, driven by geopolitical risks, sustainability pressures, regulatory demands, and supplier fragmentation. While Artificial Intelligence (AI) has made significant strides in predictive analytics and optimization, its capabilities are still restricted by data silos, lack of transparency, and data integrity issues in global supply ecosystems. On the contrary, blockchain technology offers decentralized trust, immutability, and transparent transaction tracking, but it does not provide adaptive intelligence and predictive capabilities for decision-making. The current research proposes the concept of Blockchain-Integrated Artificial Intelligence (BI-AI), which promises the capability for transparent and adaptive global supply networks. The proposed BI-AI architecture integrates the blockchain technology for building a transparent supply network with Artificial Intelligence for analytics, demand forecasting, risk prediction, and autonomous response capabilities. The current research proposes a multi-layer governance structure that integrates IoT technology for data acquisition, blockchain for building trust, AI for analytics and response, and compliance for regulatory demands. The strategic implications, implementation challenges, and future research directions are also discussed in the context of the proposed BI-AI architecture. The current research contributes to the emerging discussion on the concept of intelligent decentralized global supply ecosystems and offers opportunities for future research and empirical validation for the proposed architecture.
Blockchain-enabled supply chains, Artificial intelligence integration, Transparent supply networks, Adaptive decision-making systems, Digital trust and governance
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Utilizing LLM models for advanced automation, manufacturing operations
With the high rate of digitalization in manufacturing operations, extensive data gathering and local optimization have been made possible; however, the fragmented nature of the decision-making process in manufacturing operations, in the domains of production, quality, and maintenance, still persists. The state-of-the-art automation and artificial intelligence solutions that are currently available in the market are largely specific to the task and do not possess the ability to understand unstructured knowledge, reason across diverse contexts, and provide transparent support for the decision-making process to engineers and operators. The recent advances in large language models (LLMs) open new opportunities to provide cognitive and semantic reasoning capabilities in manufacturing operations. In this paper, a conceptual and design-oriented framework is proposed to utilize large language models (LLMs) to facilitate sophisticated automation in manufacturing operations. The proposed architecture for utilizing LLMs in manufacturing operations considers LLMs as a cognitive orchestration and decision support layer, where enterprise and shop-floor systems, operational context knowledge, and analytical and optimization tools are integrated. The proposed framework has clearly defined functional operational roles for LLMs in manufacturing operations, such as production, quality, and maintenance, and a formalized human-in-the-loop approach to ensure accountability, safety, and regulatory compliance in manufacturing operations, along with governance and validation mechanisms to address the risk of unreliable results from LLMs. The research provides a manufacturing-related architectural perspective for the automation enabled by large language model (LLM) technology, highlighting the role of LLM technology in the facilitation of semantic interoperability, cross-functional coordination, and explicable decision-making. Although the research does not include any validation, the suggested framework provides a basis for the upcoming industrial pilot research and supports the move towards robust and human-centric manufacturing systems, as suggested by the industry 5.0 concept.
Large language models; Smart manufacturing automation; Human-AI collaboration; Manufacturing operations management; Industry 5.0
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Gendered Patterns of Public Space Use: An Architectural Analysis of U.S. Cities
Public spaces in contemporary U.S. cities are often described as neutral and universally accessible; however, patterns of uneven use and exclusion persist across gender and social groups. While existing research has examined these disparities through social, cultural, and behavioral lenses, the role of architectural design itself in producing such outcomes remains insufficiently articulated. This paper addresses that gap by examining how specific spatial design decisions influence gendered patterns of occupation, comfort, visibility, and participation in public urban environments. Through an architectural analysis of public spaces in multiple urban contexts within the United States, the study evaluates how design variables—including spatial configuration, seating typologies, circulation paths, scale, and degrees of enclosure—shape user behavior and perception. Drawing on direct observation and comparative spatial assessment, the research demonstrates that these architectural elements systematically affect how different groups engage with public space. Rather than treating inequitable use as an external social condition, the findings show that architectural form actively structures inclusion and exclusion within the built environment. The paper contributes an architectural framework for evaluating public space performance through the lens of inclusivity. By translating observed patterns of use into design-based criteria, the research establishes a method for assessing how spatial decisions influence equity and accessibility within U.S. urban public spaces. This framework provides architects and urban designers with a structured approach for identifying design conditions that discourage participation and for understanding how relatively modest spatial adjustments can meaningfully improve public space performance. By reframing gendered experience as an architectural issue, this work advances design-based discourse on equity in public space and positions architecture as an active tool for shaping inclusive urban environments. The framework developed through this study is applicable beyond individual sites and offers a transferable method for evaluating public spaces across diverse American urban contexts.
Copyright (c) 2026 Journal of Mechanical, Civil and Industrial Engineering
Industrial Power Load Forecasting for Grid Operation Using a CNN-Transformer-BiLSTM Model
In the time of AI era, Industrial power load gradually rising due to the rapid expansion of the chip manufacturing facilities. So that accurate forecasting of industrial power load is important to achieve efficient grid planning and overall energy management. But, due to the nonlinear, volatile and multi scale nature of industrial power load data, the conventional statistical model face challenges in forecasting efficiently. To address these challenges, a novel hybrid deep learning model, CNN-Transformer-BiLSTM has been proposed that integrates the feature extraction capacity of convolutional neural networks (CNN), the long-range dependency modeling of the transformer architecture and the sequential learning strength of bidirectional long, short-term memory (BiLSTM) networks. The CNN layers efficiently capture the local temporal patterns and feature correlations within the load data sets, Transformer layers employs self-attention mechanisms to model complex long-term dependencies and contextual relationships. The BiLSTM layer further enhances temporal representation by learning bidirectional dependencies, thus improving the overall prediction accuracy. Historical monthly industrial electricity load data from the U.S. Energy Information Administration (EIA) spanning over two decades are used to train and evaluate the model. The proposed model output has been compared with other standalone and hybrid deep learning models. The proposed CNN-Transformer-BiLSTM achieves superior forecasting accuracy with Mean Absolute Percentage Error (MAPE) of 1.23%, Root Mean Square Error (RMSE) of 1,276 MWh and Mean Absolute Error (MAE) of 1,040 MWh.
Human-Robot Collaboration in Mechatronic Systems: Towards Ergonomic and Safe Mechanical Design
Collaboration between human and robot is rapidly increasing. The safety trend is moving from ‘keep humans out’ to ‘design for safe and intentional interaction.’ In mechatronic systems, how you design your hardware, where components are connected, where the end-effector is, how flexible or how tight everything is, how far apart components are, and where everything goes, is often what determines whether a human-robot collaboration can occur. The paper proposes a design-oriented framework for HRC mechatronic systems. It combines risk thinking and safety-by-design concepts of safety function planning. In this framework, rather than relying on real-world information or experiments, known safety-by-design, robot safety, and body design thinking can be combined into a process. The process can be applied to the requirements phase of design. The proposed design-oriented framework produces verifiable outputs, which can be a combined list of hazards and ergonomics, a relationship between hazards and design changes, and a plan to verify both simulation and documentation-based verification.
Sarodagh, Chromite, Ophiolite, Goshta, Peridotite, Nangarhar, Jalalabad, Afghanistan
Study Experimental and Numerical of Cross-Laminated Bamboo with Epoxy Adhesive
One of the most promising processed wood products today is cross-laminated timber (CLT). Another alternative to cross-laminated timber (CLT) is cross-laminated bamboo (CLB), which can address the challenges of using wood. Cross-laminated bamboo (CLB) is a floor and wall slab made from bamboo slats arranged at 90° angles. Variations in bamboo type and size were analyzed using ANSYS numerical modeling. ANSYS is a FEM software used for the simulation and analysis of engineering products and systems. The purpose of this study was to analyze the flexural strength (MOR), modulus of elasticity (MOE), and ductility of cross-laminated bamboo (CLB) as a floor and wall slab structure. This study investigated whether it could produce satisfactory results using numerical modeling. This study used a two-point method to obtain the modulus of elasticity (MOE), flexural strength (MOR), and ductility. The experimental cross-laminated bamboo (CLB) used Petung bamboo as the test specimen, measuring 1360 mm in length, 660 mm in width, and 44 mm in thickness. Epoxy/Melamine Formaldehyde (MF) glue was used as the adhesive. Numerical modeling of the CLB, with dimensions similar to those used in the experiment, involved modeling two bamboo variations: Petung bamboo and Apus bamboo. Epoxy/Melamine Formaldehyde (MF) glue was used for the numerical modeling. The test results for the cross-laminated bamboo (CLB) showed an average modulus of elasticity (MOE) of 14.56 GPa, an average flexural strength (MOR) of 17.15 MPa, and an average ductility of 5.01. The damage behavior of the CLB was characterized by cracks in the bamboo slats and peeling of the glue. Comparison of the difference between experimental and numerical values ​​of CLB Petung bamboo at the maximum load of 19.96% and the final loading of the deflection difference of 16.29%. In numerical terms, with different types of material variations, namely Petung bamboo and Apus bamboo, the deflection difference at maximum load and final load.
Cross-laminated bamboo, MOE, MOR, Ductility, Ansys Software
Prediction of Fatigue Life of Asphalt Layers in Desert Environments Using Neural Networks and SHAP Analysis: Al-Kufrh Case Study
This study develops a predictive framework using Artificial Neural Networks (ANN) combined with SHAP analysis to estimate the fatigue life of asphalt pavements in Al-Kufra, Libya, a desert environment characterized by extreme temperatures and daily thermal fluctuations. Field data from five roads were analyzed to evaluate the influence of asphalt content and air voids. The ANN model achieved high accuracy (R² = 0.91, MSE = 0.015), showing that asphalt content between 5.5–6.0% significantly improves fatigue resistance, while air voids above 4% reduce service life. SHAP analysis provided transparent interpretation of variable importance, confirming asphalt content as the most supportive factor and air voids as the most detrimental. This research represents the first integrated application in Al-Kufra, bridging local and global scholarship, and offers practical recommendations for sustainable pavement design in hot desert climates.
Fatigue life, ANN, SHAP, asphalt content, air voids, desert pavements, Libya
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Predictive Maintenance in Smart Manufacturing: An IoT-Integrated Framework
The very swift evolution of Industry 4.0 technologies has transformed conventional manufacturing into data-centric, networked manufacturing systems where efficiency, reliability, as well as sustainability are prime considerations. Against this setting, predictive maintenance (PdM) has gained considerable momentum as a significant method for reduction in equipment breakdown as well as resource utilization optimization. The current paper discusses the application of Internet of Things (IoT)-empowered predictive maintenance in smart manufacturing systems, while indicating its possibilities for migration from reactive as well as preventive strategies to data-centric, proactive strategies. The research posits a conceptual strategy that utilizes IoT-enabled sensing, real-time communications, data analytics, as well as statistical models to assemble forecasts of equipment breakdowns prior to their commencement. Strategic benefits that encompass cost optimization, increased product output, as well as sustainability enhancement are discussed in concurrence with challenges in the form of interoperability, cybersecurity risks, organizational reluctance, as well as the lack in standardized frameworks. The discussion also involves predictive maintenance evolution towards prescriptive ones, integration in digital twins, as well as the human-centric Industry 5.0 approach. Considering that empirical adoption remains in budding stages, this research identifies the necessity for scalable architectures as well as future studies that converge technical innovations as well as commercial viability. Overall, this study places IoT-enabled predictive maintenance as the foundation upon which resilience, sustainability, as well as competitiveness in next-generation smart manufacturing systems can obtain.
IoT-enabled Predictive Maintenance, Smart Manufacturing Systems, Industry 4.0, Industry 5.0
A Spatial Sensitivity Analysis of Flood Control Capability in the Mandalika Special Economic Zone, Central Lombok Regency, using a GIS-Based Multi-Attribute Decision Making Approach
The Mandalika Special Economic Zone (SEZ) on Lombok Island continues to develop as a national strategic area, particularly driven by rapid tourism expansion. Intensive land-cover changes, vegetation degradation, and increasing rainfall in recent years have contributed to the rise of recurrent flooding. These conditions highlight the need for a deeper assessment of flood sensitivity to support mitigation efforts and adaptive spatial planning. This study aims to evaluate the MADM scoring–weighting process, identify dominant parameters and the effects of weight variation on flood sensitivity, estimate the extent of vulnerability classes, and formulate mitigation recommendations for the area. Six parameters were used: rainfall, slope, elevation, land cover, river density, and population density. The Multi-Attribute Decision Making (MADM) method was applied using two weighting scenarios (35%–13% and 25%–15%), supported by visual verification based on morphology, color distribution, and administrative boundaries. The results indicate that medium-vulnerability zones dominate the study area, especially in transitional regions between lowlands and hilly terrain. Land cover and slope emerge as the most influential parameters, producing more stable and logical spatial patterns with weights of 0.30 and 0.25. Elevation and river density fall into the moderate-influence category, with weights of 0.15 and 0.12. Rainfall and population density show lower discrimination due to data limitations, with respective weights of 0.15 and 0.08. Land cover and slope also demonstrate the highest sensitivity, while elevation, rainfall, and river density show moderate sensitivity, and population density represents the lowest level. Medium vulnerability dominates the study area at 50.66%, followed by low vulnerability at 26.52% and high vulnerability at 22.83%. To reduce flood risk, government authorities and area managers should strengthen land-cover rehabilitation, improve spatial-planning regulations, reorganize river networks to enhance flood-control capacity, and manage population density through zoning and development monitoring.
Copyright (c) 2025 Journal of Mechanical, Civil and Industrial Engineering
Analysis of Hydrological Station Rationalization in Dompu Regency
This study aims to rationalize the hydrological station network in Dompu Regency in order to establish an efficient, effective, and regionally representative hydrological monitoring system for the watershed. The analysis was carried out using the Kagan–Rodda Method, the Stepwise Method, and the World Meteorological Organization (WMO) Density Standard. The dataset consists of rainfall observations, river discharge data, and climatological variables sourced from the Nusa Tenggara I River Basin Authority I, the Indonesian Agency for Meteorology, Climatology, and Geophysics, and the Agency for Food and Horticultural Crop Protection. The results show that the existing hydrological station network does not meet the minimum World Meteorological Organization standards for station density and spatial distribution. Based on the Kagan Method, the ideal number of rainfall stations in Dompu Regency is 19, with an interpolation error of 1.01%. Meanwhile, the Stepwise Method indicates that the correlation between rainfall stations and water level gauging station remains low. According to WMO guidelines, Dompu Regency requires at least 93 rainfall stations, 8 water level gauging stations, and 8 climate stations to meet the representativeness standards for small mountainous island regions. Therefore, additional stations are needed along with the revitalization of hydrological equipment to improve data accuracy and the operational efficiency of the network.
Rationalization, Hydrological Stations, Network Efficiency
Optimization of Irrigation Water Allocation at The Meninting Dam to Substitute The Water Discharge to The Jangkok - Babak HLD
Water resources are vital components for sustaining life and supporting economic development; therefore, their management must be carried out in an integrated and sustainable manner. The Lombok River Basin (WS Lombok), as one of Indonesia’s National Strategic River Basins, comprises 197 watersheds (DAS), 52 of which have been utilized for water supply. The imbalance of water distribution between the western region (which tends to have a surplus) and the southern region (which faces a deficit) necessitates an efficient and equitable water management system. One of the key solutions developed to address this issue is the construction of the Meninting Dam, designed to balance water availability through the High-Level Diversion (HLD) Jangkok–Babak Channel. This study aims to simulate and optimize water allocation from the Meninting Dam to several major irrigation areas, namely D.I. Sesaot, D.I. Jurang Sate Hulu, D.I. Jurang Sate Hilir, and D.I. Jurang Batu. The methodology includes hydrological simulation and water allocation optimization using a trial-and-error approach to achieve flow balance between the supplying rivers (Meninting, Jangkok, and Sesaot) and the HLD as the receiving system. The results indicate that water availability in each irrigation area reaches 2,480 L/s for D.I. Sesaot, 10,049 L/s for D.I. Jurang Sate Hulu, 8,596 L/s for D.I. Jurang Sate Hilir, and 4,536 L/s for D.I. Jurang Batu, with a total inflow to the HLD system of 13,970 L/s. Following the operation of the Meninting Dam, irrigation efficiency (K Factor) increased from 85.6% to 98.3% in D.I. Sesaot, and from 86.3% to 87.7% in the Jurang Sate–Jurang Batu system. Water distribution also shifted, with 61.4% of the flow directed to the HLD system and 38.6% to D.I. Sesaot, as the dam fully meets the irrigation demand in D.I. Sesaot. These findings demonstrate that integrated inter-watershed water management through the operation of the Meninting Dam and the HLD system significantly improves water distribution efficiency and supports the enhancement of cropping intensity in southern Lombok.
Mechanical Properties of Lightweight Concrete Incorporating Perlite, LECA, and Supplementary Materials
Lightweight structural materials are increasingly essential in modern construction, particularly in seismic-prone regions, as they reduce self-weight while maintaining mechanical reliability and improving earthquake resilience. This study investigates the mechanical behavior of lightweight concrete mixtures produced using expanded perlite and Light Expanded Clay Aggregate (LECA), combined with stone powder and microsilica to enhance matrix densification, with Ordinary Portland Cement (Type I) and potable tap water used across all mixes. Four lightweight mixtures—C350L40P15, C300L60P5, C400L40P15, and C400L50P10—were designed and compared with a reference concrete. Results showed substantial reductions in compressive strength (35.4%–54.2%), with C400L40P15 achieving the highest value of 26 MPa. Indirect tensile strength decreased by 16.3%–32.5%, yet mixtures with higher perlite content demonstrated notable improvements, achieving up to 19.4% greater strength compared with lower-perlite. Flexural tensile strength declined by 28.6%–41.3% relative to the reference, with C400L40P15 again showing the best performance (4.5 MPa). The elastic modulus exhibited the largest reductions (54.3%–65.7%) due to lower density, though it increased with higher cement content and reduced water-to-cement ratio. Overall, the findings indicate that optimized perlite content, controlled density, and the incorporation of stone powder collectively enhance the tensile, flexural, and stiffness properties of lightweight concrete, highlighting its potential for structural applications in earthquake-prone areas where reduced density is crucial for improving seismic performance without severely compromising mechanical strength.
Lightweight Concrete, LECA, Perlite, Superplasticizer
Sensitivity Analysis of Mock and NRECA Method Parameters on Water Availability in the Jangkok Hulu Watershed
The Jangkok watershed, as one of the watersheds with high utility, plays an important role as a supplier of supplementary water to the communities living in the surrounding area. If there is a decrease in base flow in the Jangkok Watershed, it will have a significant impact on the irrigation water supply in Jangkok-Babak-Jurang Sate, raw water supply, and industrial water in Mataram City and West Lombok Regency. Because the Jangkok watershed is widely used for low flow management in the form of river and spring water, water flow availability can be obtained using the F.J Mock and NRECA methods. Each parameter has its own sensitivity that can directly affect changes in water availability. The parameter with the greatest sensitivity is determined based on the average deviation percentage and discharge change. The initial stage involved a calibration process with Qdefault results for the Mock and NRECA methods of 1.261 m³/dt and 0.968 m³/dt, respectively. Next, sensitivity analysis was performed using calibrated default parameters with overestimate and underestimate conditions of 5%, 10%, 15%, and 20%. Based on the results of the sensitivity analysis of the F.J. Mock Method, the parameter with the highest sensitivity was the Groundwater Recession Constant (k) under an underestimate condition of 20%, which affected the increase in water availability discharge in January 2024 to 6.457 m³/dt with an average deviation of 245.90%. In the NRECA Method, the parameter with the highest sensitivity was Percent Sub Surface (PSUB) under the 20% underestimate condition, which influenced the increase in water availability discharge in January 2024 to 0.993 m³/dt with an average deviation of 20.08%.
An Analysis of Erosion Hazard Levels Using the USLE Method in the Mandalika Special Economic Zone
The Mandalika Special Economic Zone (SEZ) is located in Pujut District, Central Lombok Regency, West Nusa Tenggara Province, which has been designated as a tourism area. Land use changes due to tourism development have caused increased erosion, sedimentation, and flood discharge. This study aims to estimate the extent of erosion and sedimentation in the watersheds located in the Mandalika SEZ, namely the Tebelo, Ngolang, and Balak watersheds. Erosion calculations were performed using the Universal Soil Loss Equation (USLE) method, and sedimentation was calculated using the Sediment Delivery Ratio (SDR) following the Menhut (2005) method. The results show erosion rates of 101,189.01 tons/year (Tebelo watershed), 79,158.05 tons/year (Ngolang I watershed), 16,387.94 ton/year (Ngolang II watershed), 123,557.66 tons/year (Balak I watershed), and 2,701.15 ton/year (Balak II watershed). The level of erosion hazard in all watersheds varies from very light to very severe. The sedimentation values produced reach 8,666.24 m³/year (Tebelo watershed), 7,019.14 m³/year (Ngolang I watershed), 1,986.82 m³/year (Ngolang II watershed), 9,990.7 m³/year (Balak I watershed), and 447.25 m³/year (Balak II watershed).
Automated Pick & Stow Robot System for Sorting in an e-Commerce Warehouse
Technology is an important factor influencing the development of a company. The Industrial Revolution 4.0 is a new era with an independent field of science in the form of data digitization. Reliable technology has the ability of a flexible, customer-oriented control system with the Just in Time (JIT) concept, and can function as a continuous control system process. Problems that often occur in e-commerce warehouses are improper management, lack of inventory control carried out by machines, and lack of integration with operators, so it takes a long time to analyze errors in the logistics process. This study proposes a sorting robot to reduce the workload in a logistics warehouse. The system consists of three main modules, including a six-axis arm and its controller, software, and flexible end-effectors. The robot automates the picking and storing of objects from and to shelves in an e-commerce warehouse. The prototype system can be demonstrated through several simulations and real-world experiments with real robots.
Integration of nature-based solutions in urban planning: policy, governance, and institutional frameworks
The concept of the Nature-Based Solutions (NBS) inclusion in urban planning has become one of the strategic avenues towards the attainment of sustainable, climate-resilient, and habitable urban areas. Nevertheless, even with the increased understanding of their environmental and social importance, NBS are not uniformly reflected in urban policies and institutions. This paper focuses on the convergence of policy framework, governance systems and institutional structures that influence mainstreaming of NBS in urban settings. On the basis of an extensive analysis of the current state of empirical and conceptual research, the paper reveals the main problems of governance, namely the fragmentation of regulatory tools, lack of cross-sectoral coordination, and institutional capacity. The review indicates that a successful NBS integration relies on adaptive governance schemes, coherent multilevel alignment of policy, and participatory institutional schemes that are conducive to innovation and accountability. In addition, European and world case studies have shown that policy planning (collaboration), long-term funding approach and inter-agency collaboration are important to maintain NBS outcomes. The paper suggests that to entrench NBS into more inclusive urban governance and planning systems, regulatory reform is necessary, as well as, the development of inclusive and knowledge-based institutional ecosystems with the potential of bridging science, policy, and community practice.
Nature-Based Solutions (NBS), Urban Planning, Policy Integration, Governance, Institutional Frameworks, Sustainability
Safety Analysis of Construction Projects in Kabul Using the FMEA Method with a Worker Behavioral–Psychological Approach
Construction safety in developing countries such as Afghanistan is a major concern due to weak regulations and cultural influences that significantly increase risks. This field-based study employed the Failure Modes and Effects Analysis (FMEA) method to identify hazards, with particular emphasis on both technical and behavioral aspects.An analytical investigation was conducted across ten construction projects, including governmental, residential, and commercial buildings, to identify, assess, and prioritize hazards. Initially, the primary risk factors within the projects were identified. For each hazard, three parameters—severity, occurrence probability, and detectability—were determined. Subsequently, prioritization criteria were applied, and hazards were ranked based on the Risk Priority Number (RPN).The findings revealed that the highest RPN value (560) was associated with work at heights. Contributing factors included discomfort caused by personal protective equipment (PPE), lack of safety harnesses, rushing, and a culture of indifference, along with managerial negligence driven by cost reduction and underestimation of worker health. Conversely, the lowest RPN value (180) was linked to disregard for PPE, stemming from weak work culture, lack of incentive and penalty systems, and insufficient training. The results also highlighted significant risks posed by crane operations, temporary electrical work, and the absence of a reporting culture. The RPN findings suggest that Afghan authorities should implement supervisory controls, enforce regulations, provide crane operation training, ensure the use of PPE, and strengthen the reporting culture to mitigate critical hazards and enhance the culture of construction safety in Kabul.
Construction Safety, FMEA, RPN, PPE, Kabul
Case Accuracy for Offshore Assets Using the Nkansah Reconciliation Model: Technical Report & Case Study
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Re-Imagining the Spaces In-Between: Design Interventions to Transform Urban Voids in Downtown Dayton into Active, Breathing Areas
In today's rapidly expanding urban landscapes, space paradoxically grows scarce, yet underutilized voids remain overlooked. Downtown Dayton, like many cities, faces the challenge of meaningfully integrating these neglected spaces into its urban fabric. With its dense concentration of offices, downtown Dayton needs places of tranquility-areas where employees and residents alike can engage in psychogeographical exploration, experiencing moments of personal solitude and quiet reflection. Such subtle, personal interactions with the city can emerge in liminal spaces, akin to what Roland Barthes calls the punctum - moments that "prick" individual consciousness, creating personal significance. To address this lack of tranquil spaces, I propose transforming Dayton's neglected urban voids into areas where solitude and fresh air can be deeply experienced. Rather than focusing solely on high-activity spaces, a balanced emphasis on areas for solitary exploration and contemplation, alongside active public spaces, is essential. My design interventions aim to reclaim Dayton's overlooked voids, forming a network of "breathing spaces" that invite exploration of the downtown's architecture and social fabric. As Michel de Certeau describes, pedestrians' unpredictable movements through these spaces will carve "rhetorical" paths that challenge Dayton's urban order, reimagining it as a dynamic landscape enriched by personal stories and experiences.
Rethinking Sprawl: How High-Density, Low-Rise Housing Can Reshape Small Cities like Baton Rouge
This paper examines the potential of low-rise, high-density housing as a solution to urban sprawl in Baton Rouge, Louisiana. Driven by zoning regulations that favor single-family homes, Baton Rouge's outward expansion contributes to environmental harm and car dependency. Arguing for a shift towards higher-density living, this paper proposes low-rise, mixed-use developments as a more sustainable and community-focused approach. By analyzing case studies and examining the benefits of mixed-use integration, this paper demonstrates how such developments can create a more vibrant, walkable, and environmentally conscious urban environment for Baton Rouge. Furthermore, it addresses concerns related to traffic and infrastructure, highlighting how thoughtful design and planning can mitigate potential challenges. Finally, the paper proposes policy recommendations, including zoning reform, incentivizing affordable housing, investing in public transportation, and community engagement, to facilitate this transition towards denser, more resilient urban growth.
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Sustainable Building Materials in the Traditional Architecture of Nuristan Province, Afghanistan: A Case Study of Wood, Stone, and Indigenous Materials in Parun and Wama Districts
In many developing countries, construction methods differ significantly from those in developed regions, often relying on readily available but low-quality materials. Afghanistan, as a developing country, displays a wide range of traditional construction practices, particularly in remote provinces. This study focuses on the traditional residential construction techniques of Nuristan Province, with an emphasis on the use of wood, stone, and other indigenous materials in the Parun and Wama districts. The research investigates how these materials are applied in local building practices and evaluates their performance in terms of sustainability, structural resilience, and climatic suitability. Data were collected through field observations, interviews, questionnaires, and physical measurements conducted during site visits in 2025. The findings reveal that the indigenous materials used in Nuristan are not only practical and cost-effective but also environmentally sustainable and culturally significant.
Nuristan Province, Traditional Architecture, Indigenous Building Materials, Wood and Stone Construction, Sustainability, Seismic Adaptation, Vernacular Techniques
As is clear, the human body is mainly composed of water and electrolyte fluids. Accordingly, we can say that the human body generally acts as a conductor. The body also has resistance, due to which part of the electrical energy inside the body is converted into heat. The main factors affecting electric shock are voltage, current, resistance and other factors. Voltage, current and electrical safety are considered vital aspects in the design and operation of electrical systems. This article examines the effects of current-voltage and related matters on electrical safety and analyzes voltage drops and power fluctuations in cables and transmission lines, as well as the effects of different currents on the body. The main objective of this research is to evaluate the effects of high voltage and different currents on the health and safety of the body, maintain equipment and investigate methods for reducing voltage drops in electrical circuits. To achieve this goal, analytical and computational methods have been used to examine the effects of current and voltage drops and compare them with existing safety and security standards. The results of the study show that voltage drops in many of the systems studied are beyond the permissible limits of the standards, which can lead to reduced efficiency and increased electrical safety risks. Based on these findings, suggestions are made to improve the design and appropriate selection of cables and protective systems. This research can help improve the understanding and management of electrical safety and protection in various systems and help designers and engineers reduce risks and increase the efficiency of electrical systems.
Current, Electrical safety, Human body, Hazards, Voltage
Particle Swarm Optimized Robust Backstepping Control of a Quadrotor Unmanned Aerial Vehicle under Pink Noise
Technological developments in sensors, actuators, and energy storage devices have allowed the development of quadrotor unmanned aerial vehicles (UAVs). Quadrotor UAVs are used in sensitive tasks such as surveillance, search and rescue, mapping, mining, cargo carriage, agricultural spraying, firefighting, and photography. Quadrotor UAVs are exposed to effects such as noise and vibration while performing these sensitive tasks. Therefore, robust controller design that is resistant to noise and vibration gains great importance. Noise and vibration can be caused by the sensors, actuator, and propellers of the quadrotor. Background noise in electronic devices is called pink noise. The primary sources of pink noise in electronic devices are generally slow fluctuations of the properties of the condensed matter materials of the devices. These contain fluctuating defect configurations in metals, fluctuating trap occupancy in semiconductors, and fluctuating field structures in magnetic materials. In this study, a particle swarm optimized (PSO) robust backstepping controller is designed for a quadrotor that can follow altitude and attitude references under pink noise. The rise time, overshoot, and settling time of the PSO-optimized proposed backstepping controller and classical PID controller were compared. It has been proven by simulations that the designed PSO-optimized backstepping controller performs more successfully than the classical PID controller.
Study and Evaluation of the Extraction of Northern Oil Fields on Income Generation in the Afghan Economy
The study and investigation of oil extraction processes play a key role in improving economic productivity and increasing national income. As the main sources of income in many countries, these activities play an important role in economic development, creating employment, and providing financial resources for development projects. Economic analyses show that proper management and advanced technologies in oil and gas extraction can have a significant impact on improving productivity and reducing costs, and as a result, increasing the income generation of the economy. In addition, policies based on sustainable financial principles and proper utilization of resources can prevent price fluctuations and the decline of fossil fuels and ensure economic stability. Afghanistan, as one of the countries rich in natural resources, has significant potential in the oil sector, although major extraction and exploitation has not been carried out in this sector so far. Lack of infrastructure, lack of modern technologies, and internal insecurity are major obstacles to the development of this sector. The release and identification of oil reserves in parts such as the northern and western regions of the country provide great economic opportunities. If these resources are extracted in sustainable and effective ways, they can play an important role in increasing government revenues and improving the livelihoods of the people. If properly and fairly exploited, oil revenues can be an important source of finance for development projects, education, health, and infrastructure. Job creation and poverty reduction as a result of extraction activities are among the direct benefits of these resources. Despite the high potential, Afghanistan faces challenges such as insecurity, weak technology, lack of appropriate investment, and inability to manage resources. These factors prevent full and profitable exploitation and limit economic opportunities. By improving security, developing modern technologies, and attracting international investment, Afghanistan can exploit its oil resources, which can be effective in increasing national income, reducing poverty, and improving public welfare. Attention to proper management, transparency, and compliance with environmental standards are crucial in this path.
Extraction, Oil, Job Creation, Economy, Social Welfare, Northern Afghanistan
A look at the oil and gas industry of Afghanistan
Afghanistan is one of the oil and gas-rich countries in the world, especially Asian countries. Also, one of the reasons for the periodic developments and instability of Afghanistan's political currents is the presence of hidden and even untouched mines, especially oil and gas. Oil exploration in Afghanistan dates back to the 1950s, when in 1958, Afghan and Soviet experts conducted a topographic and geological survey in the northern provinces of Afghanistan, then two years later announced the discovery of oil and gas in the cities of Sheberghan, Sar-e-Pul and Faryab. Oil and gas play a vital role in the sustainable development of a country, because they provide the main energy sources and have a wide impact on the economy, technology and environment. Proper exploitation of these resources can lead to economic growth, job creation and improvement of people's living standards. On the other hand, the importance of oil and gas in sustainable development leads to a balance between economic exploitation and environmental protection. The main objective of this article is the importance of oil and gas in defining Afghanistan’s position in the world, especially in Asia. The research method in this study is considered by separating the research and statistical perspectives, which was selected from the perspective of the development goal, from the perspective of the qualitative method, and from the perspective of the nature of the data, descriptive-analytical. In this study, a questionnaire was distributed to experts, specialists in the oil and gas field, graduates of this field, and university professors and analysed with SPSS software. The results of this study showed that Afghanistan is in the category of rich and mineral-rich countries; but a suitable percentage has not yet been used to provide community facilities and government self-sufficiency. The reason for this is political instability, lack of relative security, lack of technical and skilled personnel, and also the lack of technical facilities for mining. Fortunately, recently, necessary investments have been made in the extraction of oil and gas mines in the northern provinces of the country (Jawzjan and Sar-e-Pul).
Innovative Simulation Model for Analyzing the Effects of Supplier Disruptions on Supply Chain Distributors
In the current business landscape, supply chain disruptions, have become increasingly prevalent, impacting operational efficiency and profitability. These disruptions have resulted in significant ripple effects throughout the supply chain, resulting in increased delivery times and lost sales that, in extreme cases, can reach Statistics up to 76,169 units. Despite the growing recognition of the importance of managing these disruptions, comprehensive models that quantitatively assess their propagation and impact on downstream distributors are lacking. This study aims to address this gap by developing a discrete-time Markov chain model integrated with a Baye.sian network to analyze the ripple effects of supplier disruptions on distributors. The research focuses on key metrics such as lead times and lost sales, revealing that disruptions can increase lost sales by up to 44% in definite scenarios. By employing this dynamic modeling approach, the study illustrates the pathways through which disruptions propagate, identifies critical risk factors, and evaluates supplier vulnerabilities. The findings underscore the necessity for robust supply chain strategies to mitigate the effects of disruptions, providing valuable insights for managers to make informed decisions regarding supplier selection and inventory management. The scientific implications of this research contribute to the evolution of more comprehensive frameworks for disruption management in multi-tier supply chains, paving the way for future studies to expand on these insights and enhance supply chain resilience.
Supplier Disruptions, Supply Chain, Simulation Model, Ripple Effect, Distributors
Optimization of complex dynamic DC Microgrid using non-linear Bang Bang control
DC microgrids are gaining importance in various power electronics applications because of their efficiency in integrating diverse distributed energy sources. However, designing such complex systems is a great challenge, especially when it comes to high efficiency with minimum power losses and signal distortions that would affect the quality of the output. These systems are inherently dynamic and nonlinear, requiring robust control strategies to accomplish stability and quick dynamics under changing load and operating conditions. Linear and nonlinear control techniques can adopt solutions to the present challenges. For a proposed system, a nonlinear bang- bang control strategy is adopted for DC microgrid performance enhancement. It provides fast operation and enhances the system's overall stability. The performance of the system has been rigorously evaluated and analyzed under different scenarios to validate its effectiveness and reliability. Results are stated in favor of the feasibility and efficiency of the proposed control in the maintenance of the desired output characteristics.
DC Microgrid, Power Electronics, Non-linear Control, Bang-Bang Control, System Optimization, Signal Distortion, Dynamic Systems, Control Techniques, System Stability, Efficiency Enhancement
Copyright (c) 2020 https://creativecommons.org/licenses/by/4.0/
Predictive Maintenance in Telecom: Artificial Intelligence for predicting and preventing network failures, reducing downtime and maintenance costs, and maximizing efficiency
Predictive maintenance (PdM), leveraging Artificial Intelligence (AI), is transforming the telecommunications industry by enabling the prediction and prevention of network failures. This proactive strategy reduces network outages and maintenance costs while enhancing overall system performance. By employing AI technologies such as machine learning algorithms, big data analytics, and sensor data analysis, telecom operators can identify patterns and anomalies indicative of potential component failures. AI-driven models continuously monitor network health, facilitating highly accurate failure predictions and enabling timely interventions. This article examines the application of AI for PdM within the telecom sector, focusing on its impact on operational efficiency, resource optimization, and service stability. The findings highlight significant cost reductions and operational improvements achievable with PdM systems. Furthermore, the paper discusses implementation challenges and key considerations for transitioning to these systems. The future outlook for telecom PdM suggests a continued evolution towards more automated, seamless network management and an improved customer experience.
Predictive Maintenance, Telecom, Artificial Intelligence, network failures, maintenance costs
Investigating the Drawbacks of Urban Trash in Taloqan City
According to a WHO report in 2012, five million people have died because of air pollution. It is estimated that air pollution reduces the quality of people's lives and the rate of efficiency; for this reason, the world pays 5 trillion dollars every year, and trash is the most influential reason for air pollution. In this research, which was done in Taloqan city, Takhar province, Afghanistan, we used a quantitative method consisting of a questionnaire paper among Taloqan city people from both male and female genders. We were looking for rules for trash in Taloqan city air pollution and strategies for getting people aware of trash drawbacks and strategies for trash prevention. We attached to the issue was accepted by the most percentage of Taloqan citizens, which trash is the most important factor of air pollution and destroyer the beauty of the city. for notifying people about religious erudite and educational organization teachers is the best option and about strategies for preventing from trash spreading, and it's disadvantage collecting the wastes separately is the most useful way also putting wastebasket among the city, processional work for trash removing and working individually for waste deleting are other ways that we found it in our research according to our research aims.
Environment pollution, Urban Trash, Air Quality, Solid Waste Management, Recycling Strategies, Health Hazards, Clean City Initiatives, Municipal Responsibility