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This study proposes a nonlinear adaptive optimal control method, the adaptive H2 control method, applied to the trajectory tracking problem of the wheeled mobile robot (WMR) with four-wheel mecanum wheels. From the perspective of solving mathematical problems, finding an analytical adaptive control solution that satisfies the adaptive H2 performance criterion for the trajectory tracking problem of the WMR with four-wheel mecanum wheels is an extremely challenging task due to the high complexity of the dynamic system. To analytically derive the control law and adaptive control law for this trajectory tracking problem, a proportional-derivative (PD) type transformation is employed to formalize the trajectory tracking error dynamics between the WMR and the desired trajectory (DT). Based on an in-depth analysis of the trajectory tracking error dynamics, a closed-form adaptive control law is analytically derived from the highly complex nonlinear dynamic system equations. This control law provides a solution to the trajectory tracking problem of the WMR while satisfying the adaptive H2 performance criterion. The proposed adaptive nonlinear control method offers a simple control structure and advantages such as improved energy efficiency. Finally, simulations and experimental implementations were conducted to verify the performance of the proposed adaptive H2 control method and the H2 control method in tracking the DT. The results demonstrate that, compared to the H2 control method, the adaptive H2 control method exhibits superior trajectory tracking performance, particularly in the presence of significant model uncertainties.

This systematic review examines the capabilities, challenges, and practical implementations of the most widely utilized and emerging sensing technologies in structural health monitoring (SHM) for infrastructures, addressing a critical research gap. While many existing reviews focus on individual methods, comprehensive cross-method comparisons have been limited due to the highly tailored nature of each technology. We address this by proposing a novel framework comprising five specific evaluation criteria—deployment suitability in SHM, hardware prerequisites, characteristics of the acquired signals, sensitivity metrics, and integration with Digital Twin environments—refined with subcriteria to ensure transparent and meaningful performance assessments. Applying this framework, we analyze both the advantages and constraints of established sensing technologies, including infrared thermography, electrochemical sensing, strain measurement, ultrasonic testing, visual inspection, vibration analysis, and acoustic emission. Our findings highlight critical trade-offs in scalability, environmental sensitivity, and diagnostic accuracy. Recognizing these challenges, we explore next-generation advancements such as self-sensing structures, unmanned aerial vehicle deployment, IoT-enabled data fusion, and enhanced Digital Twin simulations. These innovations aim to overcome existing limitations by enhancing real-time monitoring, data management, and remote accessibility. This review provides actionable insights for researchers and practitioners while identifying future research opportunities to advance scalable and adaptive SHM solutions for large-scale infrastructure.

The interest in electric traction has reached a very high level in recent decades; there is no doubt that electric vehicles have become among the main means of transport and will be the first choice in the future, but to dominate the market, a lot of research efforts are still devoted to this purpose. Electric machines are crucial components of electric vehicle powertrains. The bulk of traction drive systems have converged in recent years toward having some sort of permanent magnet machines because there is a growing trend toward enhancing the power density and efficiency of traction machines, resulting in unique designs and refinements to fundamental machine topologies, as well as the introduction of new machine classes. This paper presents the technological aspect of the different components of the electric powertrain and highlights the important information on the electric vehicle’s architecture. It focuses on a multi-criteria comparison of different electric motors utilized in the electric traction system to give a clear vision to allow choosing the adequate electrical motor for the desired application. The proposed comparative analysis shows that the induction motor better meets the major necessities of the electric powertrain, whereas the permanent magnet synchronous motor is nonetheless the most used by electric vehicle manufacturers.

The spread of performance-based and variable pay systems has affected expectations on employee contributions and remuneration, which have become increasingly personalized and individualized. Based on a theoretical valuation studies approach, this study of performance-based pay systems in Sweden shows that performance appraisals are (e)valuations of employees’ yearly performance in which they are prized and (ap)praised at the same time. Through a document analysis of performance criteria from four organizations, the study analyzes how values expressed refer to Boltanski and Thévenot’s six orders of worth. The analysis resulted in a theoretical construction of a joint ideal of Employees of Greatness, against which employees are measured and remunerated. The existence of the ideal of employee greatness is explained by the increasing congruence of organizational ideals in private and public sectors, as principles from emotional and cognitive forms of capitalist organization are superimposed on traditional industrial capitalist organizational ideals.

Supply chain management (SCM) has gradually evolved beyond the straightforward logic of benefits and economic viewpoints. Supplier selection and performance evaluation are the crucial strategic components of any SCM system with a substantial economic impact and risk reduction. Several conflicting factors make supplier selection a challenging multi-criteria decision-making problem. This paper introduces a method called alternative ranking with the elected nominee (ARWEN) to select suppliers in Iran’s dairy product chain store. The primary principle of ARWEN is to choose the best alternative based on the lowest change rate rather than the elected nominee. Four extensions of the ARWEN method are proposed depending upon the nature and level of information available to the decision-makers. A fifth extended version termed E-ARWEN is also recommended to consider the negative form of the elected nominee. Two novel statistical tools, the ranking performance index and the Zakeri-Konstantas distance product correlation coefficient, are also put forth to validate the ARWEN extensions’ outcomes. The results and verification of this new method are carried out through two supplier selection case examples. Comprehensive comparisons were carried out to explore the new methods’ behaviors, indicating ARWEN III and E-ARWEN have similar behavior to VIKOR, SAW, and EDAS in generating rankings.

Although many modelling and prediction frameworks for corporate bankruptcy and distress have been proposed, the relative performance evaluation of prediction models is criticised due to the assessment exercise using a single measure of one criterion at a time, which leads to reporting conflicting results. Mousavi et al. (Int Rev Financ Anal 42:64–75, 2015) proposed an orientation-free super-efficiency DEA-based framework to overcome this methodological issue. However, within a super-efficiency DEA framework, the reference benchmark changes from one prediction model evaluation to another, which in some contexts might be viewed as “unfair” benchmarking. In this paper, we overcome this issue by proposing a slacks-based context-dependent DEA (SBM-CDEA) framework to evaluate competing distress prediction models. In addition, we propose a hybrid cross-benchmarking-cross-efficiency framework as an alternative methodology for ranking DMUs that are heterogeneous. Furthermore, using data on UK firms listed on London Stock Exchange, we perform a comprehensive comparative analysis of the most popular corporate distress prediction models; namely, statistical models, under both mono criterion and multiple criteria frameworks considering several performance measures. Also, we propose new statistical models using macroeconomic indicators as drivers of distress.

Mitigating CO2 emissions from long-haul commercial trucking is a major challenge that must be addressed to achieve substantial reductions in greenhouse gas (GHG) emissions from the transportation sector. Extensive recent research and development programs have shown how significant near-term reductions in GHGs from commercial vehicles can be achieved by combining technological advances. This paper reviews progress in technology for engine efficiency improvements, vehicle resistance and drag reductions, and the introduction of hybrid electric powertrains in long-haul trucks. The results of vehicle demonstration projects by major vehicle manufacturers have shown peak brake thermal efficiency of 55% in heavy-duty diesel engines and have demonstrated freight efficiency improvements of 150% relative to a 2009 baseline in North America. These improvements have been achieved by combining multiple incremental improvements in both engine and vehicle technologies. Powertrain electrification through hybridization has been shown to offer some potential reductions in fuel consumption. These potential benefits depend on the vehicle use, the details of the powertrain design, and the duty cycle. To date, most papers have focused on standard drive cycles, leaving a research gap in how hybrid electric powertrains would be designed to minimize fuel consumption over real-world drive cycles, which are essential for a reliable powertrain design. The results of this paper suggest that there is no “one-size-fits-all” solution to reduce the GHGs in long-haul trucking, and a combination of technologies is required to provide an optimum solution for each application.

Type of the article: Research Article AbstractPublic infrastructure projects can generate complex and potentially irreversible environmental and social effects; hence, the adequacy of Environmental Impact Assessment (EIA) frameworks is central to safeguarding people and ecosystems. This study provides a structured comparative analysis of Morocco’s EIA framework (Law 12-03 and its reform Law 49-17) and the World Bank’s Environmental and Social Framework (ESF), using a 17-criterion performance model to identify key alignments, gaps, and priorities for regulatory reform. A documentary analysis of binding laws, decrees, and official guidance was conducted, and each criterion was rated as met, partially met, not met, or not assessed. The ESF fully meets 15 criteria, partially meets one (climate change), and one criterion (costs and benefits) could not be assessed. Morocco’s framework fully meets 11 criteria, partially meets three, does not meet two, and the costs–benefits criterion could not be assessed. Convergence is observed in core project-level requirements, including the legal basis, scope, standardized reporting, review, mitigation, impact monitoring, and consultation. Remaining gaps in Morocco are concentrated in operational and system-level instruments, notably screening, implementation of strategic assessments, system monitoring, and explicit treatment of ecosystem services; climate change adaptation is also not operationalized in either system. The findings highlight practical implications for both frameworks, while identifying prioritized implementation directions for Morocco, particularly regulatory operationalization and institutional strengthening, to improve alignment with contemporary assessment standards.

Heat exchangers are necessary in most engineering systems that move thermal energy from a hot source to a colder location. The development of additive manufacturing technology facilitates the design and optimization of heat exchangers by introducing triply periodic minimal surface (TPMS) structures. TPMSs have shown excellent mechanical and thermal performance, which can improve heat energy transfer efficiency in heat exchangers. This current study intends to design and develop efficient, lightweight heat exchangers for aerospace and space applications. Using the TPMS structure, a porous construction encloses a horizontal tube that circulates heated fluid. Low-temperature water circulates inside a rectangular box that houses the complete system to remove heat from the horizontal pipe. Three porous structures, the gyroid, diamond, and FKS structures, were employed and examined. Porous models with various porosities and surface areas (15 cm2 and 24 cm2) were investigated. The results revealed that the gyroid structure exhibits the highest Nusselt number for heat removal (Nu max = 2250), confirming the highest heat transfer and lowest pressure drop among the three structures under investigation. The maximum Nusselt number obtained for the FKS structure is less than 1000, whereas, for the diamond structure, it is near 1250. A linear variation in the average Nusselt number as a function of the structure surface area was found for the FKS and diamond structures. In contrast, nonlinearity was observed in the gyroid structures.

The process of optimal operation of multipurpose reservoirs is accompanied by large dimensions of decision variables and the uncertainty of hydrological parameters and water demands. Therefore, in determining the optimal operation policies (OOPs), the decision making for water allocation is faced with problems and complexities. One of the effective approaches for sustainable management and optimal allocation from water resources is the multi-objective structural development based on the uncertainty of input parameters. The purpose of this study is to provide OOPs from Karaj AmirKabir multi-purpose reservoir with applying uncertainty in the inflow to reservoir and downstream water demand. The proposed approach has been investigated in two certain and uncertain models, and three objective functions of the system including maximizing hydropower generation, water supply demands, and flood control have been considered to formulate OOPs. Non-dominated sorting genetic algorithm-II (NSGA-II) was performed to optimize the three proposed objective functions and by applying multi-criteria decision-making (MCDM) methods, the best operation scenario was selected. In the uncertainty model, using the interval method and repeated implementation of the deterministic model for completely random scenarios that generated based on the variation interval of the uncertain parameters, the non-deterministic optimal allocation values were produced. Based on these optimal allocation values and the fitting of the standard probability distribution on it, the probability of occurrence of the deterministic allocation values was determined. Production of optimal probabilistic allocation policies can be very useful and efficient in providing real vision to managers to select appropriate policies in different conditions and rare hydrological events. The results obtained from the certain model shows that as a result of optimal allocation to demands, the fuzzy reliability, resiliency, and system stability indexes were improved to 67.81, 21.99, and 24.98 percentage, respectively. Also, in an uncertain model, applying changes of 48% and 22%, respectively, for the inflow and downstream demand has led to changes of 23%, 55%, and 18%, respectively, in the first, second, and third objective functions. The highest impact from uncertain conditions, has been related to the water supply demands with 55% of the range of variations So, the water supply demands, has a higher sensitivity and priority than other reservoir objective functions under uncertain conditions. Another important result extracted from this study is to determine the monthly probability of optimal allocations achievement. Accordingly, in the warm seasons and years in which the reservoir is facing drought, the occurrence probability of the optimal allocations decreases. Given the comprehensiveness of the proposed methodology, this approach is a very suitable tool for determining the optimal water allocations as probabilistic based on the scenarios desired by managers and reservoir operators.