Labor market efficiency represents a multidimensional public policy problem that requires the simultaneous consideration of institutional, structural, as well as technological and dynamic factors. The aim of this paper is to identify and rank the strategies that, according to expert evaluation, contribute most to the long-term efficiency of the labor market of the Republic of Croatia. For this purpose, a multi-criteria evaluation model was developed, encompassing eight criteria and eight strategies identified on the basis of the relevant theoretical and empirical literature. The evaluation was conducted based on the assessments of nine experts from the academic community who deal with labor market issues. The fuzzy Modified Simple Weight Calculation (fuzzy M-SiWeC) method was applied to determine the relative importance of the criteria, while the ranking of the strategies was carried out using the fuzzy COmpromise Ranking from Alternative Solutions (fuzzy CORASO) method. The results show that technological adaptability and innovation incentives represent the most important evaluation criterion, whereas the reform of education and lifelong learning is the highest-ranked strategy for increasing the efficiency of the Croatian labor market. This is followed by active employment policies based on data analytics. The obtained results point to the need to direct Croatian labor market policies towards the systematic alignment of education and lifelong learning with labor market needs, as well as the development of active employment policies based on data analytics.
Human resource management today, in an era of rapid technological achievements and a changing work environment, represents a great challenge. Particular attention is being directed toward the digitalization of business operations and technically modern management, while human resources still remain in the background. Teamwork, adaptability to new circumstances and quick response to problem-solving are the central part of this paper. The aim of the paper is to conduct an integrated performance evaluation of utility vehicle crews that collect and transport waste in the city of Doboj (Bosnia and Herzegovina) on a daily basis, using multi-criteria decision-making (MCDM) methods. Five crews (each crew consists of a driver and two support workers) work daily to keep the city clean while properly disposing of municipal waste. The selection of the best-ranked utility vehicle crew for the month of March 2026 was carried out using the FUCOM (Full Consistency Method) and MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution) methods. The FUCOM method was used to determine the weights of nine influential criteria, while the MARCOS method was applied to rank five alternatives of utility vehicle crews. The research results showed that alternative A3 was ranked as the best-rated crew in the observed period, achieving the highest value of the utility function f(K3) = 0.878. The value f(K4) = 0.433 belongs to the worst alternative (A4) and represents the lower reference value within the MARCOS model.
This study aims to develop a novel Multi Criteria Decision Making (MCDM) methodological model for use as a decision support tool in industrial machine selection problems. To this end, a new integrated MCDM model consisting of Logarithmic normalization and Standard Deviation (LOGSTA), Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), Logarithmic Decomposition of Criteria Importance (LODECI), and Evaluation by Distance from Ideal Solution of Alternatives (EDISA) methods has been designed for a real-world lathe selection problem faced by a manufacturing company in Turkiye. The criterion weights obtained according to the three methods (LOGSTA, LOPCOW, and LODECI) were combined, and the combined criterion weights were transferred to the EDISA method to create an alternative lathe ranking. According to the combined criterion weights, the criterion with the highest importance level was failure frequency (C1), while the criterion with the lowest importance level was active working time (C5). According to the ranking obtained as a result of transferring the combined criterion weights to the EDISA method, the lathe with the highest performance was determined to be Doosan PUMA VT 900 (A2), while the lathe with the lowest performance was determined to be Doosan Puma 300 LM (A1). The comparison analysis shows that the EDISA method produces the same rankings as the ARAS, COPRAS, and RAWEC methods. It is believed that this framework enables manufacturing managers to compare acquisition cost, reliability, operating characteristics, and resale value within a transparent decision process.
Marine clay is characterized by high plasticity, low strength, and poor durability, which limit its suitability for geotechnical engineering applications. Although previous studies have reported improvements in the strength of soft soils using lime and industrial by-products, limited attention has been given to the comparative evaluation of the durability of lime-GGBS and lime-RHA-stabilized mixes under lime leaching conditions. Therefore, the present study investigates the engineering performance and durability of marine clay stabilized using lime as the primary stabilizing agent and ground granulated blast furnace slag (GGBS) and rice husk ash (RHA) as partial replacement materials. Lime was added at 0%, 3%, 6%, 9%, and 12%, while GGBS or RHA was incorporated at replacement at 5%, 10%, 15%, and 20%. The effects of stabilization on the plasticity, compaction, strength, and durability characteristics of marine clay were evaluated through Atterberg limits, compaction, unconfined compressive strength (UCS), California bearing ratio (CBR), and lime leaching tests. The influence of curing duration and wetting-drying cycles on lime retention in the stabilized soil mixes was also investigated. The results demonstrated a notable improvement in the soil characteristics. Among the investigated mixtures, MC+6% Lime+20% GGBS and MC+9% Lime+15% RHA exhibited the best engineering performance, achieving maximum UCS values of 225 kPa and 207 kPa, respectively. The lime leaching results indicated that the incorporation of GGBS and RHA, reduced calcium loss with increasing curing duration, thereby enhancing the durability of the stabilized soil. Overall, GGBS-stabilized mixes exhibited superior engineering and durability performance compared with RHA-stabilized mixes.
The flexibility of airspace use is at odds with its predictability. The extent of unplanned military activity, although a second-order factor relative to traffic variability, directly influences civil traffic and its pollution footprint. In this research, the assessment of the benefits of proactive planning of high-performance aircraft training operations in flexible airspace structures is performed to examine the possibility of reducing the pollution footprint and the improvement of environmental protection issues by shifting the operational time-frame. Assessment has been performed through a series of simulations on the actual traffic samples using the EUROCONTROL NEST - Network Strategic Tool. The initial impact scenario with flexible airspace structures available without restrictions proved the traffic sample validity. On top of the initial scenario, the reference scenario and the modified activation scenario were simulated, with flexible airspace structures not available in the periods reserved for military high-performance aircraft. Several civil-military ATM performance indicators were developed to compare the reference results and the modified activation scenarios. Specific findings of the research indicate the possible quantitative benefits of proactive planning of high-performance aircraft training operations in flexible airspace structures as well as the constraints of the process.
Water quality degradation in river catchments remains one of the most critical environmental and infrastructural challenges facing South Africa, which mainly involves urbanisation, industrial activities, agricultural runoff, and climate variability. This research presents a scientometric analysis of water quality modelling and catchment management research carried out in South Africa from 2005 to 2025, aiming to map knowledge trends, key contributors, and thematic developments. Using Scopus-indexed data and VOSviewer for network analysis, 50 relevant publications were identified and analysed. The results indicate a steady increase in research output, particularly after the year 2020, with dominant contributions from institutions such as Rhodes University, the University of KwaZulu-Natal, and the University of Cape Town and from funding organisations such as the Water Research Commission and the National Research Foundation. Thematic analysis reveals strong research focus areas in hydrological modelling, water pollution, nutrient dynamics, and decision-support systems, with emerging emphasis on climate change and sustainable water management. These trends strongly align with global sustainability frameworks, particularly Sustainable Development Goals 6 (SDG 6: Clean Water and Sanitation) and 13 (SDG 13: Climate Action), as well as integrated water resource management principles. Despite this progress, gaps remain in interdisciplinary integration and the translation of modelling outputs into policy and practice. This study provides a structured knowledge base to inform research, policy development, and sustainable catchment management strategies in South Africa, with broader applicability to water-stressed regions globally.
In pharmaceutical logistics, supply chain replenishment policies must balance financial efficiency with systemic resilience. Traditional policy selection relies on cost-centric multi-criteria decision-making (MCDM) frameworks that are susceptible to qualitative biases and hierarchical mathematical distortion. This study proposes a Human-in-the-Loop (HITL) Z-Number SWOT-VIKOR framework, synergizing human strategic governance with AI computational scalability. Human experts establish macro-strategic SWOT weights via the F-CIMAS method, while Large Language Models (LLMs) determine micro-operational weights anchored in CRITIC data variance. Evaluating nine Vendor-Managed Inventory (VMI) policies under stochastic demand, the extended VIKOR algorithm proposed a compromise set of four policies, governed by the hybrid SMT-SSP policy ( ) which strictly minimized individual regret ( ). Methodologically, this study contributes the novel "Strategic Modulation" mechanism, successfully resolving the internal "erasure effect" of classical hierarchical MCDM. By dynamically scaling baseline weights rather than applying strict categorical normalization, this mechanism preserved intrinsic data variance, preventing a statistically significant rank distortion ( ) in mid-tier policies. Furthermore, external mathematical benchmarking against TOPSIS and SAW algorithms proved that VIKOR's non-compensatory regret-minimization is strictly necessary for pharmaceutical logistics. The framework provides supply chain managers with a mathematically protected, bias-resistant blueprint for strategic decision-making in high-stakes healthcare environments.
Diesel engines remain widely used in transportation and power generation due to their high efficiency and durability. However, their combustion behaviour, performance, and emissions are strongly influenced by operating conditions such as exhaust backpressure (EBP). This study experimentally investigates the combined effects of compression ratio (CR), engine load, and EBP on combustion characteristics, thermal performance, heat transfer, and emissions of a diesel engine operating at 1 500 rpm. Results show that moderate CR values (14–16) and engine loads of 50–75% enhance in-cylinder pressure, heat release rate, and combustion efficiency. In contrast, excessive CR (18) and high EBP (60 kPa) increase in-cylinder temperature and residual gas fraction (RGF), leading to reduced heat release rates and longer ignition delay. The highest brake thermal efficiency (approximately 33%) is achieved at moderate load and low EBP, whereas high CR and load under elevated EBP reduce efficiency and increase brake-specific fuel consumption. Heat transfer analysis indicates that excessive CR and load significantly raise thermal loads, emphasizing the need for accurate predictive correlations in engine design. Emissions analysis reveals minimal CO and HC at moderate conditions, while NOx and smoke increase under extreme operating regimes. Overall, the findings identify an optimal operating window (CR 14–16, 50–75% load, EBP ≤45 kPa) and provide practical guidance for diesel engine under backpressure constraints, contributing to improved efficiency and emissions control.
In this paper, the dynamic analysis of a vibro-impact system with ideal excitation and various friction models is performed. A physical model of an oscillating mechanical system with possible impact occurrence is presented and the corresponding mathematical model is derived by using Lagrange’s equations of motion. To describe interaction between impact element and environment, three different friction models are considered: Coulomb, viscous and Coulomb-Stribeck model. Newton’s impact law with a coefficient of restitution is employed to describe relationship between pre-impact and post-impact velocities. The dynamic behavior of the vibro-impact system under the ideal excitation, where the system does not influence the excitation source, is investigated for each friction model by numerically solving the governing equations. The results of numerical analysis are presented through amplitude-frequency diagrams, displacement-time responses and phase portraits. The main objective is to determine the influence of different friction models on amplitude-frequency diagrams, particularly on the regions exhibiting impact and non-impact behavior. For parameter regions with multiple coexisting solutions, basins of attraction are constructed to illustrate the dependence of the system regime on initial conditions.
Technical rescue in traffic accidents represents one of the most dynamic and high-risk interventions for emergency services. This study aimed to apply the WHAT–IF analysis to systematic risk assessment during these operations, with particular focus on hydraulic equipment. Through a structured expert assessment methodology that included the Brainstorming and Delphi methods, 10 key adverse event scenarios were identified. The identified scenarios covered risks related to vehicle instability, fire, electric shock in electric/hybrid electric vehicles, equipment failure, and coordination errors. These were evaluated using three-level scales for probability and consequences. The assessments were integrated into a 3×3 risk matrix, allowing categorization into low, medium, high, and critical risk levels. The results showed that by implementing targeted treatment measures — including the standardization of stabilization procedures, Lockout/Tagout procedures for electric vehicles, the appointment of a safety officer, and the use of thermal imaging equipment — initially high and critical risks can be significantly reduced. Statistical analysis using the Wilcoxon signed-rank test confirmed that this reduction was statistically significant (p < 0.01), with a large effect size (r = 0.59), demonstrating a shift of risks into an acceptable zone in accordance with the ALARP (As Low as Reasonably Practicable) principle. It is concluded that the WHAT–IF analysis provides a clear, operational, and adaptable framework for improving the safety of rescuers and casualties, offering directly applicable guidelines for procedural and technological interventions, as detailed in the attached tabular manuals.
Effective cultural heritage preservation requires a holistic approach, guided by management that integrates governance, planning (including disaster prevention, risk management, promotion, education, community involvement, awareness-raising, and sustainable tourism), financing, and implementation. Despite existing frameworks, many historical sites face conservation challenges, often stemming from gaps in planning and management. This study aims to define the cultural heritage management components for Huta Siallagan, a historical village in Indonesia, located on an island within the UNESCO Global Geopark of Lake Toba. The village has preserved its unique Batak cultural heritage retaining its natural setting, site layout, building structures, while intangible practices continue to be actively maintained. The study examined current legal status of the site through national and international legislative frameworks as well as its overall condition. Finding indicate that gaps remain in planning and management frameworks. The study concludes with recommendations for multi-level strategies and heritage management components to strengthen existing conditions and enhance sustainable development.
Low-carbon AR400 abrasion-resistant steels are utilised as wear packages prolonging the production life of ground-engaging tools on earth-moving machines in South Africa's mining sector. While AR400 steel is renowned for its tribological properties and wear resistance, components frequently fail prematurely due to inadequate impact absorption, necessitating premature removal from the heavy-duty production environment. This study investigates a two-phase intercritical annealing process to enhance the ductility and impact energy absorption of AR400 steel. Phase 1 involves heat treating the specimens at 1000°C for 25 minutes, then oil quenching. Phase 2 reheats the specimens to 750°C for 20 minutes, followed by cool water quenching. The efficacy of this treatment was evaluated by comparing the mechanical properties, phase dispersions, and general grain morphology of the heat-treated specimens with those of the untreated control specimens. Microstructural analysis using optical and scanning electron microscopy revealed a transformation from the martensitic phase dispersion in the control specimens, typically associated with AR400, to a primarily bainitic microstructure in the intercritically annealed samples. This change improved the impact energy absorption and ductility of the 20 mm specimen by 200%. However, the results were inconsistent across material thickness. The 12 mm and 16 mm specimens showed a decrease in impact resistivity and approximately 40% reduction in material hardness and strength, irrespective of material thickness, compromising wear resistance. The results demonstrate that while the proposed two-phase intercritical annealing heat treatment can enhance impact properties, the concurrent reduction in hardness limits its practical application in industries requiring wear and impact resistance.
The cement industry accounts for approximately 7–8% of global carbon dioxide (CO2) emissions, primarily due to the energy-intensive clinker production process and reliance on fossil fuels. The environmental impact of this industry is particularly evident in the release of greenhouse gas (GHG) emissions. Therefore, the industry is exploring ways to reduce its energy costs and reliance on traditional fuels and mitigate environmental concerns by using waste-derived materials as a fuel substitute for cement production. In response to increasing environmental pressures, the substitution of fossil fuels with alternative fuels (AF) such as refuse-derived fuel (RDF), biomass, sewage sludge (SS), and used tires has emerged as a viable decarbonization strategy. This paper aims to provide a comprehensive analysis of AFs within the cement industry by reviewing previous studies, focusing on their GHG emissions and the technical, environmental, and economic implications of AFs adoption in cement kilns. A structured literature analysis was employed to evaluate fuel types, heating values, thermal substitution rates, combustion stability, and their effects on clinker quality. Data trends indicate that thermal substitution rates exceeding 80% are achievable with RDF and tire-derived fuels under optimized conditions, while biomass and SS require pretreatment for stable combustion. Environmental assessments report up to 30% reduction in CO₂ emissions and significant decreases in SOx and NOx with proper blending. The review also highlights key gaps in regional adoption and long-term performance evaluations. It concludes by recommending targeted policy support, plant-specific feasibility assessments, and integrated LCA-MCDM frameworks to scale the sustainable use of Afs.
Although large language models (LLMs) have recently become effective tools for language-conditioned control in embodied systems, instability, slow convergence, and hallucinated actions continue to limit their direct application to continuous control. A modular neuro-symbolic control framework that distinguishes between low-level motion execution and high-level semantic reasoning is proposed in this work. While a lightweight neural delta controller performs bounded, incremental actions in continuous space, a locally deployed LLM interprets symbolic tasks. We assess the suggested method in a planar manipulation setting with spatial relations between objects specified by language. Numerous tasks and local language models, such as Mistral, Phi, and LLaMA-3.2, are used in extensive experiments to compare LLM-only control, neural-only control, and the suggested LLM+Deep Learning (LLM+DL) framework. In comparison to LLM-only baselines, the results show that the neuro-symbolic integration consistently increases both success rate and efficiency, achieving average step reductions exceeding 70% and speedups of up to 8.83x while remaining robust to language model quality. The suggested framework enhances interpretability, stability, and generalization without any need of reinforcement learning or costly rollouts by controlling the LLM to symbolic outputs and allocating uninterpreted execution to a neural controller trained on artificial geometric data. These outputs show empirically that neuro-symbolic decomposition offers a scalable and principled way to integrate language understanding with ongoing control, this approach promotes the creation of dependable and effective language-guided embodied systems.
Water scarcity is a growing global issue, necessitating innovative and sustainable solutions for freshwater generation. Among the available technologies, reverse osmosis (RO) has become the primary method for seawater desalination due to its effective salt rejection and high energy efficiency. This study presents an integrated experimental and analytical investigation of a full-scale seawater reverse osmosis (SWRO) plant with a 2 MLD capacity at the Victoria and Alfred (V&A) Waterfront in Cape Town, South Africa. Operational data were collected over six months, including feedwater temperature (13.66 –16.78 °C), pressure (50–60 bar), total dissolved solids (32,883–38,387 mg/L), and pH (6.19–7.89). The plant consistently produced high-quality permeate with TDS around 500 mg/L, achieving a 31% recovery rate at an average energy consumption of 3 kWh/m³. Machine learning models, specifically multiple linear regression and decision trees, were used to predict RO performance and to explore the relationships between operational parameters. Results show that higher feed pressure improves permeate flux but raises energy use, increased feedwater temperature boosts flux and slightly reduces energy consumption, while deviations from near-neutral pH negatively impact product quality and efficiency. The novelty of this work lies in combining real plant operational data with predictive analytics to establish parameter-based performance relationships and identify optimal operating ranges (e.g., feed pressure ~52–55 bar, pH ~7). These insights provide a strong foundation for optimizing desalination processes, improving membrane efficiency, and guiding the design and operation of future RO desalination projects.
Maintenance and management of spare parts for electric vehicles requires a specific approach due to high-voltage components, technological complexity, and limited availability of specialized parts. Key challenges include optimizing inventory, monitoring the life of battery systems, ensuring compatibility of parts across different vehicle models, and organizing efficient procurement, storage, and distribution. Of particular importance are safety protocols when working with high voltages, the use of specialized technical and protective materials, and environmentally friendly waste management and battery recycling. Digital solutions, including ERP and CMMS systems, IoT sensors, remote diagnostics, and predictive analytics based on artificial intelligence, enable better maintenance planning, cost reduction, and increased vehicle reliability. The paper provides an analysis of the technical, safety, environmental, and economic aspects of electric vehicle maintenance, with an emphasis on the digitalization and optimization of logistics processes. Based on the analysis, recommendations are proposed for improving the maintenance system, including the integration of advanced technologies, process standardization, and strengthening the education of service personnel. In addition, the paper identifies key research questions and outlines directions for future research—particularly in areas such as the digital integration of spare parts logistics, the environmental impact of material uses and disposal, and the role of artificial intelligence in predictive maintenance strategies. The findings suggest that the greatest improvement potential lies in combining predictive maintenance, sustainable practices, and operational cost reduction, thereby contributing to the long-term reliability and competitiveness of electric mobility.
This study presents a comparative case study of the evolution of a software development course at Kindai University. We analyze two distinct pedagogical ecosystems: a traditional course based on Java Servlet/JSP with a local integrated development environment (IDE), and its subsequent iteration, a modern course employing the Ruby on Rails framework (a Web Application Framework, or WAF), Git for version control, a cloud-based IDE, and Platform as a Service (PaaS) for deployment. This study was not a controlled experiment isolating the effects of a WAF but rather an exploratory analysis of how a shift in the entire toolchain impacted student outcomes and perceptions. Quantitative analyses of student projects over three years for each course revealed that the modern Ruby-based ecosystem resulted in applications with approximately 50% more screens and screen transitions, despite requiring approximately 40% less source code. Furthermore, student surveys indicated significantly higher comprehension and interest in the modern courses. However, the number of data models and user stories remained consistent, suggesting that upstream design thinking was less affected by the technology stack. These findings suggest that adopting a modern, integrated development ecosystem can foster a more productive and engaging learning experience. We conclude by discussing the implications of these findings for curriculum design, emphasizing the value of incorporating contemporary, industry-aligned toolchains into software engineering education, while acknowledging that the observed benefits stem from the synergistic effect of multiple technologies rather than from a single component.
The quest to improve the properties of soil and other construction materials by incorporating industrial and agricultural wastes is a growing concern for the construction industry. Many researchers have recently focused on employing waste materials as stabilizers due to their good pozzolanic interactions with soil particles. Its significance in civil engineering projects such as foundations in buildings and pavement construction cannot be overemphasized. The construction of pavements usually involves using large quantities of natural earth/aggregate materials, often mixed with conventional stabilizers (cement, lime, and bitumen). There is already a shortage of natural aggregate materials in many developing nations. However, a significant quantity of waste, such as mining tailings, is generated by mining industries yearly. In contrast, the disposal of these wastes is not only expensive but has also resulted in various ecological and environmental problems. The literature has already explored methods of stabilization and solidification of mining tailings employing conventional agents. However, the use of traditional stabilizers indicates an important source of contamination for the environment. Therefore, alternative stabilizing materials are needed. The approach to this literature review included a systematic procedure for locating, choosing, and evaluating sources. The logic for the sources’ selection prioritized contemporary, peer-reviewed studies that particularly address the geotechnical properties of industrial waste products in road construction. This study reviews the geotechnical properties of industrial waste products, such as goldmine tailings, quarry dust, and coal ash and the technical benefits of using them for pavement construction.
Refractories are ceramic materials that are used in high-temperature applications, often above 1100oC. These materials find applications in reactors, kilns, ovens, and furnace linings. The need for refractory materials is consistently increasing to accommodate the ongoing development of different industries and factories. Local clays are currently under investigation to address the insufficient supply of refractory materials. Although the geo-morphology of the various locations where deposits of clay are found has been examined, comparatively little work has been done to fully evaluate these deposits to ascertain their suitability for use. This paper includes a review of the refractories produced from locally sourced clay deposits in Nigeria, followed by an evaluation of their usability for furnace lining. Generally, most of the findings reiterate the fact that the local clay has excellent properties for refractories; however, the local manufacturing of refractories in Nigeria has been very low, hence the nation still depends largely on imported refractories. Adequate funding for the local manufacturing of refractories would be needed to access and explore about 8 billion tonnes of clay deposits across Nigeria, thereby reducing imports and maximizing local production.
Seawater desalination is a highly successful and effective method of obtaining fresh water from saline water sources. Reverse osmosis (RO) is a key and pivotal technology in seawater desalination as it produces high-quality freshwater from seawater with low energy consumption, in comparison to alternative technologies. However, the practical modelling of a comprehensive full-scale RO system is challenging due to fluctuating operating conditions stemming from seasonal variations and progressive fouling of the membrane during prolonged filtration operation. This study presents a comprehensive modeling framework for a seawater reverse osmosis (SWRO) desalination plant using DuPont’s Water Application Value Engine (WAVE) software. The modeled system integrates ultrafiltration (UF) for pretreatment and ion exchange (IX) polishing for post-treatment, which reflects the actual operational structure of the Victoria & Alfred Waterfront desalination plant in Cape Town, South Africa. The model simulates the hydraulic and separation performance under steady-state conditions, using plant-specific data for feed salinity, pressure, flow rates, and membrane configuration. Results demonstrate the WAVE model’s capability to accurately predict key performance parameters, including permeate flow, energy consumption, recovery rate, and total dissolved solids (TDS) removal. Simulated results indicate improved recovery (45.7% vs. 31%) and reduced specific energy consumption (5.91 kWh/m³ vs. 6.58 kWh/m³) compared to actual plant data. The study validates the model's predictive accuracy and highlights its application in optimizing system design, minimizing operational costs, and guiding future desalination infrastructure development under varying operational conditions.
