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This study systematically investigates the effects of rope length, lifting position, and load on the dynamic behavior and wind-induced fatigue life of flat-jib tower cranes. Firstly, natural frequencies of a representative crane (40 m tower, 66 m jib) were accurately identified via on-site stress measurements. Subsequently, a finite element model was developed to analyze free vibration characteristics under various operational conditions. Fluctuating along-wind loads were simulated using harmonic synthesis, and transient dynamic analysis provided the wind-induced response. Finally, fatigue life of critical components was assessed through rain-flow counting, S–N curves per Chinese and European standards, and Miner’s linear damage rule. The results indicate that the first-order natural frequency in the along-wind direction decreases by approximately 12.5% and 14.2% with increasing lifted load and rope length, respectively, while the frequency at the jib tip is reduced by up to 37% compared to that at the jib root. Structural responses are more pronounced in the along-wind direction, predominantly exciting the first-order mode. Under operational conditions, stress in the jib’s main chord increases by approximately 30% to 50%, whereas stress fluctuations in other jib sections remain minimal. The fatigue life of tower crane components decreases by 13% to 21% relative to the unloaded state, with rope length exerting a greater influence than lifting load magnitude. Full article

(This article belongs to the Section Building Structures)

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40 pages, 31156 KB

Open AccessArticle

Prediction of Post-Impact Load-Bearing Capacity in Non-Crimp Fabric Composite Members

by Milad Kazemian and Aleksandr Cherniaev

Appl. Mech. 2026, 7(1), 17; https://doi.org/10.3390/applmech7010017 - 11 Feb 2026

Abstract

Non-crimp fabric (NCF) composites are increasingly adopted for structural components due to their high mechanical performance and processability. Like other fibre-reinforced plastics, NCFs remain vulnerable to in-service damage from tool drops or unintended collisions, which can substantially reduce load-bearing capacity. This study aimed to develop a validated numerical model capable of simulating damage initiation and post-impact behaviour through an integrated experimental–numerical approach. The mechanical properties of a representative unidirectional NCF composite were first experimentally established. Then, tubular NCF subcomponents were fabricated and tested under a two-phase loading protocol. In the first phase, damage was introduced using quasi-static indentation or controlled low-velocity impact. In the second phase, the residual load-bearing capacity of the damaged subcomponents was assessed under four-point bending. To support the research objective, a finite element model was developed in LS-DYNA to simulate both phases, using the MAT_ENHANCED_COMPOSITE_DAMAGE (MAT54) material formulation. Non-measurable input parameters, including stress limit factors and erosion strain thresholds, were calibrated via parameter estimation, sensitivity analysis, and iterative refinement. The final model showed close agreement with experiments in predicted damage location, deformation mode, and residual strength. X-ray computed tomography was used to validate delamination predictions. The findings support the development of reliable and cost-effective numerical tools for damage assessment in advanced composite structures. Full article

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22 pages, 2532 KB

Open AccessReview

Mapping Career Paths: A Systematic Review of Research Dynamics, Approaches and Perspectives

by Oumaima Lamhour, Larbi Safaa, Dalia Perkumienė, Marius Mažeika, Giedrė Adomavičienė and Judita Štreimikienė

Soc. Sci. 2026, 15(2), 111; https://doi.org/10.3390/socsci15020111 - 11 Feb 2026

Abstract

In a rapidly changing professional landscape marked by digitization, socio-economic transformations, and post-pandemic upheavals, understanding career trajectories has become an interdisciplinary concern. This study presents a systematic bibliometric review of 135 peer-reviewed articles published between 1990 and 2025 and extracted from the Scopus database. Using VOSviewer and Bibliometrix, the analysis maps the intellectual structure, thematic evolution, and methodological trends in career path research. The results reveal a high concentration of studies in Anglo-Saxon contexts, with a predominance of the education, health, and hospitality sectors. Key populations include students, women, and recent graduates, while seniors, informal workers, and non-Western contexts remain underrepresented. This field is conceptually diverse, structured around protean and borderless career models, and increasingly interested in themes such as sustainability, digital transformation, and gender inequality. Cross-sectional quantitative approaches dominate methodologies, while longitudinal and mixed designs are rare. Thematic mapping reveals four key clusters: sociodemographic factors, professional development, labor market dynamics and identity formation. Citation analysis reveals key contributions to career theory, social capital and organizational support. This review reveals gaps in geographic coverage, theoretical integration and methodological pluralism. It calls for a more inclusive, contextualized and interdisciplinary approach to better understand the complexity of contemporary careers. Full article

(This article belongs to the Section Work, Employment and the Labor Market)

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15 pages, 1112 KB

Open AccessArticle

Reliability and Sensitivity Analysis of Liquid Storage Tank Using Active Learning Kriging

by Qingqing Xu, Xue Li and Feng Zhang

Appl. Sci. 2026, 16(4), 1806; https://doi.org/10.3390/app16041806 - 11 Feb 2026

Abstract

This study proposed a Kriging surrogate model incorporating active learning to overcome the high computational costs associated with conducting reliability and sensitivity analyses of industrial liquid storage tank structures. In the proposed method, the Kriging surrogate model efficiently captures the functional relationships between basic variables and structural responses. Two learning functions, i.e., the U learning function and the EFF learning function, are adopted to screen the training sample pool to identify and iteratively update the optimal next training sample point in the model. This strategy significantly reduces the number of limit state functions and finite element analysis calculations required, considerably decreasing the computational cost of analysis. Results from the liquid storage tank case study demonstrate that the adaptive learning Kriging method can achieve failure probability estimation at the order of 10⁻⁵ with only approximately 100 limit state function (LSF) evaluations. Additionally, it is found that the pressure exerted by the tank contents has the most significant impact on the tank’s structural reliabilityreliability, followed by tank thickness and then tank radius. Full article

28 pages, 1257 KB

Open AccessArticle

Low-Carbon Policy in a Duopoly with Differentiated Products, Green R&D, and Knowledge Spillovers: A Cournot–Bertrand Comparison

by Chenyu Wang and Zhenqiang Li

Mathematics 2026, 14(4), 638; https://doi.org/10.3390/math14040638 (registering DOI) - 11 Feb 2026

Abstract

This study examines the optimal design of low-carbon policies for governments, firms, and consumers within a unified analytical framework. We develop a three-stage game-theoretic duopoly model with differentiated products, green R&D, and knowledge spillovers to analyze the effects and implications of low-carbon policies in a polluting industry. The analysis encompasses both Cournot and Bertrand competition under commitment and non-commitment regimes, as well as non-cooperative and cooperative R&D structures. Specifically, we (i) quantify the impacts of low-carbon policies on R&D, emissions, profits, and welfare across alternative competition modes, policy-timing regimes, and R&D organizations; (ii) examine the roles of key policy parameters across all scenarios; and (iii) provide an integrated and intuitive interpretation of the underlying economic mechanisms. Full article

(This article belongs to the Special Issue Game Theory in Economics and Operations Research)

35 pages, 3384 KB

Open AccessArticle

Research on Path Planning and Trajectory Tracking for Inspection Robots in Orchard Environments

by Junlin Zhang, Longbo Su, Zhenhao Bai, Simon X. Yang, Ping Li, Shuangniu Hong, Weihong Ma and Lepeng Song

Agriculture 2026, 16(4), 415; https://doi.org/10.3390/agriculture16040415 - 11 Feb 2026

Abstract

In complex, semi-structured orchard environments, mobile inspection robots often suffer from excessive turning points, low search efficiency, limited trajectory-tracking accuracy, and poor adaptability to dynamic obstacles. To address these issues, this study proposes an integrated autonomous navigation method that employs an improved A* algorithm for global path planning, a Fuzzy-Weighted Dynamic Window Approach (FW-DWA) for local path optimization, and a model predictive control (MPC)-based trajectory-tracking controller. First, a dynamic heuristic-weight adjustment strategy is introduced into the conventional A* algorithm, in which a correction factor adaptively tunes the heuristic weight; a two-stage node optimization procedure then removes hazardous and redundant nodes to improve path smoothness and safety. Second, the FW-DWA, grounded in fuzzy control theory, uses goal distance and obstacle distance to update the weights of the heading, clearance, and velocity evaluation functions in real time, thereby enhancing obstacle avoidance in dynamic environments. Finally, a discrete kinematic model is established to design the MPC Controller, which achieves high-precision tracking through receding-horizon optimization and feedback correction. Experiments conducted in real orchards demonstrate that the proposed method reduces path length by 5.79%, shortens planning time by 3.64%, and increases the minimum safety distance by 50%. Comparative results further show that the MPC Controller attains a mean position error of 0.032 m and a mean heading error of 3.14°, clearly outperforming a conventional Proportional–Integral–Derivative (PID) controller. These findings provide an effective solution for reliable autonomous navigation of orchard inspection robots and offer a valuable reference for smart agricultural robotics applications. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

18 pages, 1420 KB

Open AccessArticle

Development of a Compass Framework to Achieve an Agile and Sustainable Supply Network

by Lucila Palandella, Lourdes Perea Muñoz and Angel Ruiz

Sustainability 2026, 18(4), 1865; https://doi.org/10.3390/su18041865 - 11 Feb 2026

Abstract

Digital transformation offers significant potential to reshape supply chains; however, implementation efforts remain fragmented, technology-centric, and insufficiently aligned with strategic, organizational, and sustainability goals. Existing frameworks and maturity models tend to emphasize the technological dimension, offering limited guidance on how digital transformation should be integrated with people, processes, culture, and sustainability at the supply network level. Building on evidence synthesized through an umbrella review of the state of the art, this paper proposes the Agile and Sustainable Supply Network Compass, a holistic and actionable framework designed to support organizations in advancing toward agile and sustainable supply networks. The Compass incorporates three structural dimensions—Strategy, Processes, and Capabilities (related to digitalization and sustainability)—as foundational pillars for transformation. We hypothesize that an effective transformation requires the joint alignment of strategy, cross-functional processes, and capabilities, as well as the explicit identification of a reduced supply network, a focal firm, and its critical linkages. The results show that positioning agility and sustainability as shared strategic objectives at the supply network level enables coherent decision-making, targeted capability development and improved coordination across interconnected actors. Rather than prescribing specific technologies, the proposed framework provides a guiding methodological logic that explains how digitalization and sustainability can co-evolve within supply networks. This work contributes to both theory and practice by bridging conceptual gaps in the literature and establishing the groundwork for future maturity models and empirical applications. Full article

(This article belongs to the Special Issue Sustainable Manufacturing Systems in the Context of Industry 4.0)

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18 pages, 1136 KB

Open AccessArticle

Novel Cytotoxic Pt(IV) Compounds with Improved Safety Profiles

by Anastasia A. Antonets, Ksenia M. Voroshilkina, Ilya A. Shutkov, Dmitrii M. Mazur, Tatiana P. Serkova, Elena F. Shevtsova, Dmitrii S. Yakovlev, Mariya S. Pshenichnikova, Umida M. Ibragimova, Roman A. Litvinov, Alexander A. Spasov, Elena R. Milaeva and Alexey A. Nazarov

Int. J. Mol. Sci. 2026, 27(4), 1750; https://doi.org/10.3390/ijms27041750 - 11 Feb 2026

Abstract

Platinum(II)-based drugs, such as cisplatin, are commonly used to treat various types of cancer. However, their clinical use is limited due to a number of side effects and the development of resistance. To overcome these limitations, researchers have explored the development of platinum(IV) complexes as potential prodrugs that can be selectively activated under physiological conditions. In this study, we have incorporated synthetic analogs of vitamin E into the structure of platinum(IV) complexes to further improve their safety profile. The antioxidant properties of the compounds were evaluated using DPPH and CUPRAC assays, as well as lipid peroxidation inhibition models, revealing that incorporation of phenolic ligands confers pronounced antioxidant activity. Cytotoxicity was assessed towards cancer cell lines using the MTT assay, where the novel complexes showed significantly increased cytotoxic activity compared to cisplatin, while also demonstrating less toxicity toward normal fibroblast cells under the same in vitro conditions. These results suggest that the conjugation of antioxidant ligands to platinum(IV) scaffolds can modulate both redox processes and the biological activity of the resulting complexes. This proposed design strategy has the potential to create more effective platinum-based cancer treatments with enhanced biological characteristics. Full article

(This article belongs to the Collection 30th Anniversary of IJMS: Updates and Advances in Biochemistry)

46 pages, 4025 KB

Open AccessReview

Integrating Large Language Models into Traffic Systems: Integration Levels, Capability Boundaries, and an Information-Theoretic Perspective

by Wenwen Tu, Junfan Li, Feng Xiao, Xiaosa Wang and Yong Lu

Entropy 2026, 28(2), 211; https://doi.org/10.3390/e28020211 - 11 Feb 2026

Abstract

Large language models (LLMs) are fundamentally transforming intelligent traffic systems by enabling semantic abstraction, probabilistic reasoning, and multimodal information fusion across heterogeneous data. This review examines existing research on LLM integration, ranging from data representation to autonomous agents, through an information-theoretic lens, conceptualizing LLMs as entropy-minimizing probabilistic systems that shape their capabilities in uncertainty modeling and semantic compression. We identify core integration patterns and analyze fundamental limitations arising from the inherent mismatch between discrete, entropy-driven LLM reasoning and the continuous, causal, and safety-critical nature of physical traffic environments. This reflects a deep structural tension rather than mere technical gaps. We delineate clear boundaries: LLMs are indispensable for managing high semantic entropy in tasks like contextual understanding and knowledge integration, whereas classical physics-based and optimization models remain essential in domains requiring ultra-low physical, temporal, and causal/normative entropy, such as real-time control and safety verification. Finally, we propose a forward-looking research agenda centered on hybrid intelligence architectures that bridge semantic information processing with physical system modeling for next-generation traffic systems. Full article

(This article belongs to the Topic AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity, 2nd Edition)

24 pages, 3425 KB

Open AccessArticle

Robust μ-Synthesis Grid-Side Control for Inverter-Based Resources in Weak Grids

by Woo-Jung Kim, Yu-Seok Lee and Yeong-Han Chun

Energies 2026, 19(4), 946; https://doi.org/10.3390/en19040946 (registering DOI) - 11 Feb 2026

Abstract

With the increasing penetration of inverter-based resources (IBRs), modern power systems are experiencing undesirable dynamics, such as sub-synchronous oscillations in weak grids. Conventional PI control schemes, however, exhibit limited robustness against nonlinearities arising from varying operating points in weak grids, leading to instability. To address this challenge, we propose a robust controller for the outer loop of grid-side converters in IBRs based on robust μ-synthesis control theory. Specifically, this paper utilizes μ-synthesis to handle linearized model parameters associated with operating-point variations. The proposed controller replaces the PI controllers in the outer loop while retaining the established dq-frame control philosophy. Furthermore, during controller synthesis, the controller is optimized with a 2-by-2 multi-input multi-output structure to explicitly account for cross-coupling effects between the d- and q-axes. Finally, the proposed controller was validated using electromagnetic transient simulations of a detailed type-IV wind farm model implemented in MATLAB/Simulink R2025a, and its performance was compared with that of a conventional PI-based outer control loop. The wind farm was tested under very weak grid conditions, and the proposed controller demonstrated robust stability against varying operating points by providing superior damping performance. Full article

(This article belongs to the Section F1: Electrical Power System)

31 pages, 11297 KB

Open AccessArticle

Application of Viscoelastic Dampers for Seismic Retrofitting of Existing Reinforced Concrete Buildings

by Sennan Lee and Chun Jiang

Buildings 2026, 16(4), 738; https://doi.org/10.3390/buildings16040738 (registering DOI) - 11 Feb 2026

Abstract

Passive control systems that provide both stiffness and energy dissipation, such as viscoelastic dampers (VEs), offer a promising strategy for seismic retrofit of existing reinforced concrete (RC) buildings, especially critical facilities that must remain operational during construction. Unlike conventional retrofit methods that require concrete casting and occupant evacuation, VE-based systems can be installed with shorter construction periods and reduced environmental disturbance. This study experimentally investigates the dynamic behavior of VE material subjected to large shear strain amplitudes of 300% and 400%, the latter exceeding typical design limits, to clarify its performance under severe seismic demands. The test results are used to calibrate a numerical model that represents the stiffness and energy-dissipation characteristics of the VE over this strain range. The calibrated model is then implemented in the seismic retrofit design of a six-story RC hospital building and evaluated through nonlinear structural analyses. The results indicate that the proposed VE retrofit scheme can achieve the targeted performance objectives and demonstrate the feasibility of applying high-strain VE dampers in practical seismic retrofit projects. Full article

(This article belongs to the Special Issue Structural Vibration Analysis and Control in Civil Engineering)

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28 pages, 4217 KB

Open AccessArticle

A Shape–Memory–Programmable Tuning Fork Metamaterial with Adjustable Vibration Isolation Bands

by Rui Yang, Wenyou Zha, Ruixiang Zhang, Yongtao Yao and Yanju Liu

Vibration 2026, 9(1), 12; https://doi.org/10.3390/vibration9010012 - 11 Feb 2026

Abstract

Honeycomb structures are widely utilized in engineering due to their light weight, high strength, high stiffness, excellent energy absorption, and outstanding vibration isolation performance. In this study, we propose a novel tuning fork–honeycomb megastructure, which demonstrates excellent tunable vibration isolation capabilities. The geometric configuration of the structure before and after shape memory–induced deformation is described, and a theoretical model for the natural frequency of the initial configuration is established. The vibration isolation performance of the structure is validated through simulations and experiments, and three strategies for tuning its vibrational behavior are proposed. First, by exploiting variable stiffness, shape memory materials are used to achieve a linear shift in the bandgap position. At 75 °C, the starting frequency of the bandgap decreases to 95% of its value at room temperature. Second, based on shape memory programming, the deformed structure exhibits a 20% reduction in the center frequency of the first bandgap and a 47% reduction in the center frequency of the second bandgap compared to the undeformed configuration. Then, by altering the geometry of the tuning fork structure, in–plane deformation is shown to provide superior low–frequency vibration isolation performance compared to out–of–plane deformation. Finally, the design method of programmable mechanical pixel metamaterials is introduced. This method achieves tunable full–band vibration isolation through shape–memory–induced deformation and temperature–induced stiffness variation. It enhances the structural diversity, modularity, and reconfigurability. Moreover, a shape memory tuning fork structure could be combined with any type of cellular structure with excellent vibration isolation performance. It offers a new paradigm for designing structures with adjustable wide–frequency vibration isolation performance. Full article

(This article belongs to the Special Issue Vibration in 2025)

26 pages, 8916 KB

Open AccessArticle

Finite Element Modeling and Experimental Study of Foam Concrete and Polystyrene Concrete

by Alexey N. Beskopylny, Sergey A. Stel’makh, Evgenii M. Shcherban’, Diana M. Shakhalieva, Andrei Chernil’nik, Ivan Panfilov, Nikita Beskopylny and Yasin Onuralp Özkılıç

Buildings 2026, 16(4), 737; https://doi.org/10.3390/buildings16040737 (registering DOI) - 11 Feb 2026

Abstract

Predicting the physical and mechanical properties of polystyrene concrete is an important tool for determining its performance under various conditions. This article presents an experimental study and numerical modeling of polystyrene concrete under various types of loads: thermal and mechanical. The numerical model was developed in ANSYS in several stages. First, a foam concrete model was constructed in Materials Designer, and strength and thermal calculations were performed. The obtained data were entered into the polystyrene concrete model as input, polystyrene granules were added, and strength and thermal calculations were repeated. Using the Menetrey–Willam structural model, the numerical modeling sufficiently captured key mechanical properties of concrete. The parameters of the Menetrey–Willam model were adjusted based on experimental results from compression tests of foam concrete and polystyrene concrete. The results of numerical modeling, represented by stress and strain fields, allowed us to identify the dependence of thermal conductivity and compressive strength of polystyrene concrete on varying polystyrene granule contents. A comparison of the numerical analysis and experimental results showed good agreement. Errors in the obtained results were 6% for thermal conductivity and 7% for compressive strength. The resulting models revealed the characteristics of fracture sites, the relationship between structural changes, and the thermal and physical properties of polystyrene concrete, which can be used in the design of engineering structures. Full article

(This article belongs to the Section Building Materials, and Repair & Renovation)

34 pages, 779 KB

Open AccessArticle

Rethinking Ransomware Protection Targets for AI Systems

by Cheon-Ho Min and Jin Kwak

Electronics 2026, 15(4), 770; https://doi.org/10.3390/electronics15040770 (registering DOI) - 11 Feb 2026

Abstract

Artificial intelligence (AI) systems have become operational infrastructure whose value is increasingly dominated by trained models, behavioral configurations, and decision-making logic rather than by software binaries alone. As a result, ransomware threats against AI systems cannot be adequately addressed by conventional recovery strategies that assume service availability can be restored through file and code recovery. In AI environments, assets such as model parameters, training data, inference pipelines, and safety policies constitute primary attack targets, and their compromise can invalidate system behavior even when files are successfully restored. This study re-examines ransomware threats against AI systems from an asset-based protection perspective and demonstrates why traditional recovery assumptions structurally fail in AI-centric environments. Based on this analysis, we show that protection mechanisms limited to file integrity are insufficient and must be extended to include behavioral consistency and decision-making reliability. To address this gap, we propose a behavior-aware ransomware protection methodology, implemented as the Behavior-Aware Integrity Protection System (BIPS). BIPS augments existing ransomware response processes by redefining protection targets, establishing behavioral baselines, verifying post-recovery behavioral integrity, and supporting risk-based operational decisions. This work contributes by reframing ransomware threats against AI systems as an issue rooted in protection scope and recovery assumptions rather than isolated attack techniques, thereby extending ransomware response for AI systems toward a reliability- and risk-oriented protection framework. Full article

(This article belongs to the Special Issue AI-Driven Cybersecurity, Resilience, and Trust Frameworks for Future Urban IoT Systems)

24 pages, 1173 KB

Open AccessArticle

Application of the TPE-XGBoost Model in Predicting Breakdown Pressure for Horizontal Drilling Based on Physical Constraints

by Haibiao Wang, Mingyue Pang, Zheng Yuan, Changyin Dong, Fengxiang Xu and Yicheng Xin

Processes 2026, 14(4), 630; https://doi.org/10.3390/pr14040630 (registering DOI) - 11 Feb 2026

Abstract

Horizontal well fracturing serves as a critical technology for enhancing production from tight sandstone gas reservoirs, where accurate prediction of formation breakdown pressure is essential for optimizing fracture design and improving stimulation effectiveness. This study proposes a novel fusion-driven workflow for predicting breakdown pressure in horizontal wells by synergistically integrating physics-based mechanistic modeling with data-driven machine learning. The approach overcomes the computational limitations of conventional analytical models and mitigates the data scarcity constraints inherent in purely empirical methods by using high-fidelity mechanistic simulations to generate physically consistent training samples. Results demonstrate that the hybrid dataset, with an optimal fusion ratio of 1:1.5 between field data and mechanistic-derived samples, yields the highest predictive accuracy. The proposed model, built on an XGBoost algorithm whose hyperparameters are efficiently optimized via a tree-structured Parzen estimator (TPE), exhibits superior generalization capability and robustness, achieving an average prediction error of 7.45% on unseen well data. This work confirms that the fusion framework provides a reliable and practical tool for breakdown pressure prediction in cased horizontal wells, which can directly support the design and implementation of efficient and sustainable fracturing operations in tight gas reservoirs. Full article

(This article belongs to the Topic Petroleum and Gas Engineering, 2nd edition)

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