The accurate segmentation of visual data into semantically meaningful regions remains a critical task across diverse domains, including medical diagnostics, satellite imagery interpretation, and automated inspection systems, where precise object delineation is essential for subsequent analysis and decision-making. Conventional segmentation techniques often suffer from limitations such as sensitivity to noise, intensity inhomogeneity, and weak boundary definition, resulting in reduced performance under complex imaging conditions. Although fuzzy set-based approaches have been proposed to improve adaptability under uncertainty, they frequently fail to maintain a balance between segmentation precision and robustness. To address these challenges, a novel segmentation framework was developed based on Pythagorean Fuzzy Sets (PyFSs) and local averaging, offering enhanced performance in uncertain and heterogeneous visual environments. By incorporating both membership and non-membership degrees, PyFSs allow a more flexible representation of uncertainty compared to classical fuzzy models. A local average intensity function was introduced, wherein the contribution of each pixel was adaptively weighted according to its PyFS membership degree, improving resistance to local intensity variations. An energy functional was formulated by integrating PyFS-driven intensity constraints, local statistical deviation measures, and regularization terms, ensuring precise boundary localization through level set evolution. Convexity of the energy formulation was analytically demonstrated to guarantee the stability of the optimization process. Experimental evaluations revealed that the proposed method consistently outperforms existing fuzzy and non-fuzzy segmentation algorithms, achieving superior accuracy in applications such as medical image analysis and natural scene segmentation. These results underscore the potential of PyFS-based models as a powerful and generalizable solution for uncertainty-resilient image segmentation in real-world applications.
This research emphasizes analyzing existing transport logistics systems of the state, detecting problems within every mode of transport, and proposing solutions for them to advance towards the sustainable development of multimodal logistics. It also looks into how the nation's logistic infrastructure can be optimized, and challenges associated with shifting from one mode of transport to another within the Indian transport system are considered as such changes are deemed necessary to remedy the structural imbalance. Ex-ante and ex-post evaluations of the funding strategies were carried out as life cycle assessments using OpenLCA. The software and eco-invent database concluded that the new modal infrastructure would be less damaging when utilized than the available one. Building rail shipments' share of the total to 45% would significantly mitigate the adverse effects on the environment that the current structure of the modalities of freight transport. In addition, it was found that, hence why the changes were made, the displacement of transportation brought down global warming impacts by a commendable 9%, as well as the effects of emissions in ecotoxicity in the land, ocean and freshwater by 20% on average. These results highlight the need to boost rail traffic and build railway infrastructure as the most efficient strategy towards positive outcomes. The research admits some data-sourced weaknesses, but it contributes to appreciating the need to put in place an appropriate transport system that is environmentally sound for the country's anticipated development.
Heating, ventilation, and air-conditioning (HVAC) systems have been identified as major contributors to global energy consumption, underscoring the urgency of optimizing their performance for economic and environmental sustainability. This review presents a comprehensive examination of the thermofluid behavior, mathematical modeling techniques, and optimization strategies employed in HVAC systems. Particular emphasis is placed on the development and implementation of dynamic and steady-state models that enable predictive analysis and performance forecasting. The inherently nonlinear and time-varying nature of HVAC systems has necessitated the adoption of advanced computational approaches, including artificial intelligence (AI), machine learning (ML), genetic algorithm (GA), and simulated annealing (SA), to enhance system responsiveness and occupant comfort. AI- and ML- based control strategies have been shown to improve adaptability to real-time environmental and occupancy changes, thereby increasing operational efficiency. However, these approaches are often constrained by high data requirements and computational complexity. Multi-objective optimization frameworks have been proposed to balance energy efficiency with environmental impact, yet challenges remain regarding precision, scalability, and the seamless integration of emerging technologies. The application of digital twin technology has recently gained traction as a viable solution for real-time simulation and virtual testing, offering a non-intrusive means of performance evaluation and system tuning. It is suggested that the future of HVAC optimization lies in the convergence of classical thermodynamic and fluid dynamic modeling with intelligent control architectures, enabling the development of adaptive systems capable of autonomous decision-making. This integrated modeling paradigm is anticipated to support advancements in energy-aware design, occupant-centric climate control, and sustainable building operation. Through this synthesis of traditional and data-driven methodologies, new pathways were proposed for achieving robust, scalable, and intelligent HVAC systems that respond efficiently to evolving environmental and user-specific demands.
This study explores the application of Value Management (VM) and Value Engineering (VE) within Malta’s public service sector, focusing on environmental projects. Traditional cost-centric procurement approaches are proving insufficient in an era of heightened economic volatility, technological disruption, and increasing demands for public accountability. This research advocates for a structured, value-based decision-making framework that balances cost with quality, efficiency, and long-term sustainability considerations. A mixed-method, quasi-experimental design was employed, using Ambjent Malta as a case study. The methodology involved pre-intervention data collection, stakeholder information sessions, and post-intervention evaluation. Three environmental projects were analysed through stakeholder engagement workshops, incorporating VM tools such as function analysis and value criteria evaluation. The results demonstrate that VM can effectively address cost overruns, promote stakeholder collaboration, and improve project alignment with client and environmental objectives. Significant increases in stakeholder awareness and understanding of VM principles were observed, alongside a reported shift from cost minimisation to value optimisation in project planning. This research contributes to the limited knowledge of VM in the Maltese context. It underscores the potential of value-based methodologies to enhance public sector project outcomes and long-term efficiency.
This study's primary goals are to: Identify any damage that may have happened to the structural elements of steel I-girder-concrete composite spans; determine the static responses according to the influence of the vehicle and service loads (loads combination case) using numerical static analysis FEM using CSI-Bridge Ver. 25; measure the natural frequency of the bridge structure according to the influence of self-weight of the structure using modal analysis; determine the dynamic responses due to vehicle live load using numerical dynamic time history analysis by using Finite Element Method (FEM); assess the constructional effectiveness of bridge structures and identify methods for reinforcing and repairing damaged structural elements. Damage inspection results of steel I-girder span showed that the damage is not severe in the structural parts of span. Steel I-girders span shows no signs of rust or corrosion, but the main problem is in the expansion joints and they need to be repaired or replaced. Under the effect of vehicles live load and load combinations, maximum tensile stress appeared at the bottom of steel I-girder span, which was 13.56MPa and 86MPa respectively, lowering than the allowable value of tensile stresses from AASHTO LRFD BRIDGE, which is equal to 207MPa. The maximum deflection in the downward direction due to vehicles load and load combination was 10.9 mm and 91 mm, respectively. Meeting the allowable deflection values of 70 mm (live load) and 112 mm (loads combination). The Finite Element dynamic analysis described that the average value of vibration frequency is 6.42Hz. Compared with natural frequency, it is higher than 2.95Hz, indicating that the span of bridge will face vibration issues because this span has a long length. Therefore, this study recommended that to add more steel girders with more diaphragms (cross beams) to reduce the vibration of bridge span.
Poverty remains a pervasive and multifaceted challenge in developing countries, posing critical impediments to sustainable economic and social development. In alignment with the core objectives of the United Nations Sustainable Development Goals (SDGs), the present study aims to identify, evaluate, and prioritize the most effective poverty alleviation strategies within the context of developing economies. Through an extensive review of existing literature and expert consultation, seven primary strategies were identified, encompassing economic growth stimulation, economic and institutional reforms, prioritization of the basic needs of impoverished populations in national development policies, promotion of microfinance institutions and programs, development and improvement of marketing systems, provision of incentives to the private sector, and implementation of affirmative actions such as targeted cash transfers. To systematically assess the relative importance of these strategies, the Stepwise Weight Assessment Ratio Analysis (SWARA) technique was employed within a Fermatean fuzzy (FF) environment. The application of this hybrid method facilitated the extraction of nuanced expert judgments, thereby enhancing the robustness and credibility of the prioritization process. The findings indicate that fostering economic growth, implementing structural economic and institutional reforms, and promoting microfinance institutions and programs represent the most impactful and actionable strategies for poverty reduction. These results offer valuable insights for policymakers, development agencies, and stakeholders engaged in formulating targeted interventions to accelerate poverty eradication. The integration of the FF-SWARA approach further demonstrates its applicability in complex multi-criteria decision-making (MCDM) scenarios characterized by uncertainty and imprecise information, particularly in the domain of sustainable development planning.
Automated detection of vehicle dents remains a challenging task due to variability in lighting conditions, surface textures, and the presence of minor deformations that may mimic actual dents. This paper presents a novel hybrid framework that integrates color deviation analysis, fuzzy classification, and the Structural Similarity Index (SSI) to enhance detection robustness and accuracy. The proposed model employs an adaptive bounding box generation technique, optimized via morphological operations, for precise dent localization. A newly introduced Color Difference Metric (CDM), computed in the Hue, Saturation, and Value (HSV) color space, quantifies subtle color deviations induced by dents, improving the system’s sensitivity to minor deformations. Furthermore, a hybrid classification mechanism—merging step-function classification with fuzzy membership functions—ensures smoother transitions between dent severity levels, mitigating the risks of hard thresholding. SSI serves as a structural integrity validator, helping to differentiate true dents from surface irregularities and lighting artifacts. A Dent Confidence Score is computed as a weighted aggregation of the step-function output, fuzzy confidence levels, and SSI response, effectively balancing sensitivity and specificity. Dents are categorized into three interpretable classes: No Dent, Possible Dent, and Confirmed Dent. Evaluation on real-world datasets—encompassing diverse lighting conditions, vehicle colors, and camera angles—demonstrates the model’s superior performance. Compared to traditional approaches, our method significantly improves key metrics such as Intersection over Union (IoU), Dice Coefficient, Precision, Recall, and F1-Score, underscoring its applicability in real-world automated dent detection systems.
This paper reviews the seasonal impacts on driver behaviour, focusing on car-following dynamics in adverse weather conditions, including snow, icy roads, glaring sunlight, and fog. Existing literature underscores the significant effects of these weather conditions on traffic flow, driving behaviour, and accident rates. In colder climates, snow and ice disrupt traffic, slow vehicle speeds, and increase accidents, particularly affecting passenger cars more than trucks, which typically operate on strict schedules. In warmer climates, sun glare impairs visibility, contributing to congestion and accidents. The paper synthesises findings from various studies, revealing key research gaps, including the differing behaviours of heavy trucks and passenger cars under extreme weather, the combined effects of multiple adverse weather conditions, and the role of road geometry and maintenance in shaping driver behaviour. This review highlights the need for further investigation to better understand these factors and their impact on road safety. Future research should focus on integrating real-world driving data and exploring advanced technologies such as AI and IoT to mitigate the negative effects of seasonal weather. Ultimately, this research aims to inform more effective traffic management strategies and improve road safety across diverse climates.
This research examines the influence of anode surface area on the efficacy of aluminium-air batteries. Three varieties of aluminium anodes were produced: non-mesh, one-step nanomesh, and two-step nanomesh. The nanomesh structures were fabricated via a multi-step anodization procedure employing phosphoric acid, leading to enhanced surface area and pore density. Scanning electron microscopy demonstrated that the 2-step nanomesh anode possessed the highest average pore diameter of 180 nm, resulting in a substantial enhancement of active surface area. Electrochemical characterization methods, such as galvanostatic discharge testing, electrochemical impedance spectroscopy, and cyclic voltammetry, were utilized to assess battery performance. The findings indicated that the 2-step nanomesh anode had superior electron discharge rate, ionic conductivity, and oxidation stability relative to the 1-step nanomesh and non-mesh anodes. The 2-step nanomesh anode attained a specific capacity of 1.92 mAh and a power output of 59.71 mW, exceeding the performance of alternative anode topologies. The improved battery performance is due to the enlarged active surface area of the anode, which promotes more efficient ion transport and electrochemical processes. The findings underscore the importance of anode surface modification in enhancing the performance of aluminium-air batteries and offer insights for the design of high-capacity, high-power energy storage systems for diverse applications.
The stagnation point flow behavior of a micropolar nanofluid over an inclined stretching surface was numerically investigated. The formulation accounts for the combined effects of Brownian motion, thermophoresis, thermal radiation, velocity slip, and the presence of internal heat generation or absorption. The governing system of non-linear partial differential equations was transformed into a set of coupled ordinary differential equations through the application of appropriate similarity transformations. These transformed equations were solved numerically to analyze the behavior of the fluid near the stagnation region, where both the stretching velocity of the surface and the external free stream velocity are assumed to vary linearly with distance from the stagnation point. Special attention was paid to the influence of dimensionless parameters on key physical quantities, including skin friction coefficient, energy transfer, and Sherwood number. It was observed that increasing the stagnation point parameter leads to a reduction in skin friction, while the inclination angle demonstrates an opposing effect on heat and mass transfer rates. Data extracted from graphical results was tabulated to provide quantitative insights into the impact of varying parameters. The findings offer significant implications for microscale heat and mass transfer systems, particularly in processes involving inclined geometries and nanoparticle-enhanced fluids under magnetohydrodynamic (MHD) effects.
Global supply chains face increasing disruption from security-related risks, including cargo theft, illicit trade, document forgery, and cyberattacks—challenges that pose serious threats to sustainable development, especially in vulnerable and emerging economies. This study proposes a comprehensive decision-support framework designed to identify, assess, and rank logistics-related criminal threats, with the goal of strengthening the resilience and sustainability of international logistics systems. The model integrates Failure Mode and Effects Analysis (FMEA) for initial risk detection and prioritization, fuzzy Analytic Hierarchy Process (fuzzy AHP) to determine the relative importance of sustainability-relevant criteria (such as legal, environmental, financial, and reputational impacts), and the Additive Ratio Assessment (ARAS) method to perform final ranking. A real-world case study in international logistics demonstrates the framework’s applicability and robustness. Results highlight how this integrated approach can support informed decision-making by governments, port authorities, and global logistics firms to mitigate risk and enhance supply chain continuity. By aligning technical methods with sustainable risk governance principles, this study contributes practical insights into building more adaptive, secure, and sustainable logistics infrastructures across borders.
Efficient water management in agriculture increasingly depends on the ability to deliver uniform irrigation while minimizing energy consumption. Low-pressure irrigation systems have emerged as a sustainable alternative to traditional high-pressure networks, offering significant potential for small-scale and greenhouse applications. This study investigates the hydraulic and energy performance of low-pressure irrigation manifolds through a combined Computational Fluid Dynamics (CFD) analysis and performance assessment framework. The computational model simulates steady-state, incompressible flow within manifolds of two diameters (12 mm and 25 mm) and two emitter configurations (6 and 12 outlets), under inlet pressures of 50 kPa and 100 kPa. Detailed flow fields were analyzed in terms of pressure distribution, velocity contours, helicity, and wall shear stress, while outlet pressures and mass flow rates were used to evaluate distribution uniformity (DU). Mesh independence tests ensured numerical reliability, and hydraulic performance was quantified using standard indices such as the Coefficient of Variation (CV) and Christiansen’s uniformity coefficient (CU). The results demonstrate a consistent pressure and discharge decline from the inlet to the downstream outlets, with localized hotspots of velocity, shear, and rotational flow near emitter junctions. The manifolds with smaller diameters and higher inlet pressures led to greater non-uniformity (CV up to 14.8%, CU $\approx$ 87%), while the manifolds with larger diameters significantly improved uniformity (CV < 6%, CU > 95%) at lower inlet pressures. Energy analysis showed a strong link between hydraulic performance and pumping demand: designs with better uniformity required significantly less energy, with total pumping energy dropping from 4470 kWh in the least efficient case to just 1072 kWh in the optimal one. These findings highlight that manifold diameter, emitter spacing, and operating pressure are critical determinants of system efficiency. Optimized designs featuring larger diameters and moderate pressures offer a dual benefit of enhanced water-use efficiency and reduced energy consumption. The results provide actionable guidelines for the design of sustainable low-pressure irrigation systems, particularly in small-scale and greenhouse applications, where uniform distribution and energy savings are essential.
This study focused on the effects of welding speed and power intensity on distortion and corrosion resistance for alloy steel T-joint weldments. The research was theoretical but also included practical experiments to identify thermodynamic characteristics. The study focuses on the alterations in microstructure and the ability of the weld metal to resist corrosion. Three interconnected modeling operations included structural and thermal evaluations in calculating the microstructure and the deformation of the weld joint. The heat effect zone (HAZ) width in welding speed is 5mm/sec, about 47 mm in the Y direction and 60 mm in the X direction. For 6 mm/sec welding speed, the HAZ was 25 mm in the Y direction and 29 mm in the X direction, and finally, the HAZ width for weldment with 7.5 mm/sec welding speed was 21 mm in the Y direction and 26 mm in the X direction. The highest deformation with 1.08 mm was calculated when welding with lower welding speed and the highest source power. While 0.57 mm deformation was recorded when welding with the highest welding speed and lowest source power intensity. Samples of the weld metal were tested to monitor their weightlessness and corrosion level. The results showed that the size of the HAZ increased with increasing intensities of power. Results reveal that the distortion of weld joint varies inversely with welding velocity and directly relates to power intensity. A microstructural analysis shows that the weld metal has acicular interlocking, polygonal ferrite, and side plates. Acicular ferrite amount influenced weld metal corrosion resistance decreased as power intensities increased. The microstructure of the HAZ is significantly influenced by the intensity of the welding power, which in turn affects the microhardness of the HAZ.
The use of private vehicles can have many negative impacts on various aspects of life. Therefore, the government, as a public service provider, has tried to provide bus-based public transportation services. However, the growth in the number of private vehicles each year continues to increase, while the number of passengers using bus transportation is far below the targeted load factor. This study aims to: 1) assess the perceptions of private vehicle users on the performance of existing bus transportation services; 2) analyze bus service attributes that influence public's desire to switch from using private vehicles, and 3) examine user preferences for using bus as the main mode of transport for their daily mobility. Data collection was carried out using questionnaires distributed to 270 samples of people who live in the districts served by the bus route. The analysis techniques used were descriptive statistics, descriptive qualitative, content analysis, and quantitative analysis. The results showed: 1) the perception of private vehicle users on the performance of existing bus transportation services is dominated by positive comments and responses; 2) bus service attributes that influence public's desire to switch from using private vehicles, namely fare affordability, maintenance of bus stops and buses, and operating hours while the attribute with the smallest influence is the payment system; 3) User preferences for bus transportation services are in the form of security types, media types, and operating hours show compatibility with existing services. For walking and cycling distances, respondents chose <100 meters for walking and <500 meters for cycling; besides that, the majority of respondents prefer fares below current prices and feel comfortable with cash payment methods.
Open Access
