Functionally graded porous beams are increasingly used in lightweight engineering structures, where thermal effects and material inhomogeneity significantly influence vibration behavior. In this study, the thermoelastic free vibration of functionally graded porous Euler–Bernoulli beams with temperature-dependent material properties is investigated by considering uniform and symmetric porosity distributions, together with uniform, linear, and nonlinear temperature fields. The governing equations are derived based on classical Euler–Bernoulli beam theory and solved using the Differential Transformation Method, while the accuracy of the semi-analytical formulation is verified through a Hermite-based finite element model. The results show that increasing temperature reduces the bending stiffness due to thermal axial forces and leads to a rapid decrease in natural frequency as the critical buckling temperature is approached. Increasing porosity generally decreases the natural frequency, although a slight increase may occur in symmetric distributions because of the accompanying reduction in mass density. The present study provides a computational framework for the thermo-vibration analysis of functionally graded porous beams in lightweight structural applications. Full article

(This article belongs to the Section Acoustics and Vibrations)

37 pages, 3540 KB

Open AccessArticle

A Multimodal Time-Frequency Fusion Architecture for FaultDiagnosis in Rotating Machinery

by Hui Wang, Congming Wu, Yong Jiang, Yanqing Ouyang, Chongguang Ren, Xianqiong Tang and Wei Zhou

Appl. Sci. 2026, 16(7), 3269; https://doi.org/10.3390/app16073269 - 27 Mar 2026

Abstract

Accurate fault diagnosis of rotating machinery in complex industrial environments demands an optimal trade-off between feature representation capability and computational efficiency. Existing single-modality models relying solely on 1D time-series signals or heavy 2D time-frequency images often fail to simultaneously capture high-frequency transient impacts and long-range degradation trends. CLiST (Complementary Lightweight Spatiotemporal Network), a novel lightweight multimodal framework driven by time-frequency fusion, was proposed to overcome this limitation. The architecture of CLiST employs a synergistic dual-stream design: a LightTS module efficiently extracts global operational trends from 1D vibration signals with linear complexity, while a structurally pruned LiteSwin integrated with Triplet Attention captures local high-frequency textures from 2D continuous wavelet transform (CWT) images. This mechanism establishes explicit cross-dimensional dependencies, effectively eliminating feature blind spots without excessive computational overhead. The experimental results show that CLiST not only achieves perfect accuracy on the fundamental CWRU benchmark but also exhibits exceptional spatial generalization when independently evaluated on non-dominant sensor axes of the XJTUGearbox dataset. Furthermore, validation on the real-world dataset (Guangzhou port) proves that the framework has excellent robustness to the attenuation of the signal transmission path and reduces the performance fluctuation between remote measurement points. Ultimately, CLiST delivers highly reliable AI-driven image and signal-processing solutions for vibration monitoring in industrial equipment. Full article

22 pages, 2177 KB

Open AccessArticle

A Stackelberg Game-Based Model of the Distribution Network Planning in Local Energy Communities

by Javid Maleki Delarestaghi, Ali Arefi, Gerard Ledwich, Alberto Borghetti and Christopher Lund

Energies 2026, 19(7), 1662; https://doi.org/10.3390/en19071662 - 27 Mar 2026

Abstract

The electrical characteristics of distribution networks (DNs) are drastically changing, which is mainly due to widespread adoption of small-scale distributed energy resources (DERs) by end-users. In these cases, conventional planning models may lead to overinvestment choices. This paper presents a planning model for utility companies that explicitly incorporates a model of end-users’ energy-related decisions, considering a neighborhood energy trading scheme (NETS). The model is formulated based on the Stackelberg game (SG) approach, which guarantees the optimality of the final solution for each user and the utility. The proposed mixed-integer second-order cone programming (MISOCP) problem finds the optimal investment plan for transformers, lines, distributed generators (DGs), and energy storage systems (ESSs) for the utility, considering the scenarios of end-users’ investments in rooftop photovoltaic (PV) and battery systems that maximize their benefits. Additionally, a dynamic network charge (NC) scheme is designed to rationalize the network use. Also, Benders decomposition (BD) is used to improve the convergence of the solution algorithm. The numerical studies on a real 23-bus low voltage (LV) network in Perth, Australia, using real-world data reveals that the proposed planning model offers the lowest total cost and the highest penetration of DERs in comparison with conventional models. Full article

(This article belongs to the Special Issue Digital Modeling, Operation and Control of Sustainable Energy Systems)

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27 pages, 667 KB

Open AccessArticle

Greening Human Rights in Africa: The African Court and the Environmental Accountability of States and Corporations

by Adeline Auffret O’Neil, Indira Boutier and Emmanuel Maganaris

Laws 2026, 15(2), 22; https://doi.org/10.3390/laws15020022 - 27 Mar 2026

Abstract

The recognition of a clean, healthy, and sustainable environment as a human right has reshaped global human rights discourse, yet its operationalisation remains uneven. This article examines how the African human rights system which is uniquely grounded in collective rights, has reframed environmental protection as a constitutive element of development, sovereignty, and justice. Through doctrinal and case-law analysis, it traces the evolution from the African Commission’s foundational jurisprudence in SERAC, which extended state duties to the regulation of private and transnational corporate actors, to the African Court’s landmark judgment in LIDHO v. Côte d’Ivoire. The study demonstrates how the Court transforms the aspirational ‘greening’ of human rights into binding obligations by articulating a robust duty of vigilance and linking environmental harm to violations of the rights to life, health, and development. It further shows that LIDHO inaugurates a post-sovereign model of shared and polycentric responsibility, in which state accountability encompasses corporate conduct within their jurisdiction and, potentially, beyond it. The article concludes that the African Charter’s collective framework offers an implicit regional model of ecological justice, one capable of addressing extractive asymmetries and informing emerging climate-related obligations across the continent. Full article

(This article belongs to the Section Environmental Law Issues)

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33 pages, 3562 KB

Open AccessReview

Ethics in Artificial Intelligence: A Cross-Sectoral Review of 2019–2025

by Charalampos M. Liapis, Nikos Fazakis, Sotiris Kotsiantis and Yannis Dimakopoulos

Informatics 2026, 13(4), 51; https://doi.org/10.3390/informatics13040051 - 27 Mar 2026

Abstract

Artificial Intelligence (AI) has transitioned from a specialized research area to a ubiquitous socio-technical infrastructure influencing sectors from healthcare and law to manufacturing and defense. In tandem with its transformative promise, AI has created an exponentially expanding ethics literature questioning, fairness, transparency, accountability, and justice. This review synthesizes publications and key policy developments between 2019 and 2025, bringing sectoral discourses together with cross-cutting frameworks. Grounded in a systematic scoping review methodology, we frame the field along four meta-dimensions: trust and transparency, bias and fairness, governance & regulation, and justice, while we investigate their expression across diverse sectors. Special attention is dedicated to healthcare (patient trust and algorithmic bias), education (integrity and authorship), media (misinformation), law (accountability), and the industrial sector (data integrity, intellectual property protection, and environmental safety). We ground abstract principles in concrete case studies to illustrate real-world harms and mitigation strategies. Furthermore, we incorporate pluralistic ethics (e.g., Ubuntu, Islamic perspectives), environmental ethics, and emerging challenges posed by Generative AI and neuro-AI interfaces. To bridge theory and practice, we propose an operational governance framework for organizations. We contend that success involves transitioning from principles toward ethics-by-design, pluralistic governance, sustainability, and adaptive oversight. This review is intended for scholars, practitioners, and policymakers who need a comprehensive and actionable framework for navigating the complex landscape of AI ethics. Full article

17 pages, 1019 KB

Open AccessArticle

Indole-3-Acetic Acid-Assisted Microalgal Biofilm for High-Efficiency Wastewater Purification: Biomass Densification and Pollutant Removal Kinetics

by Qun Wei, Fu Pang, Dan Zhao, Wenxi Chu, Ziming Pan and Xiangmeng Ma

Water 2026, 18(7), 805; https://doi.org/10.3390/w18070805 - 27 Mar 2026

Abstract

The enhancement of startup and performance in a Tetradesmus obliquus-polyurethane sponge biofilm system was investigated via the regulation of the phytohormone Indole-3-acetic acid (IAA). IAA supplementation at 1 and 5 mg/L increased biofilm biomass and chlorophyll a content, with the maximum biofilm biomass reaching 48.2 mg/g, and improved nutrient removal performance under shock-loading conditions, particularly for total nitrogen (TN) and total phosphorus (TP). IAA treatment was associated with EPS remodeling, including an increase in the protein/polysaccharide ratio to 0.68 and a 16% enrichment in tryptophan-like protein components. These EPS-related changes coincided with a decrease in the absolute zeta potential to −2.49 mV, which may be relevant to enhanced initial biofilm development. The corresponding EPS-related changes were characterized by three-dimensional excitation–emission matrix (3D-EEM) and Fourier transform infrared (FTIR) analyses using representative concentrations. Furthermore, the IAA-treated biofilm showed improved resilience under low, medium, and high loading conditions, with the most favorable TN removal reaching 87% at 1 mg/L IAA. These results suggest that IAA supplementation at 1 and 5 mg/L can promote microalgal biofilm start-up and improve nutrient-removal resilience under the tested conditions, with 5 mg/L showing the strongest response in biofilm growth and structural characterization. Full article

(This article belongs to the Section Wastewater Treatment and Reuse)

21 pages, 29754 KB

Open AccessArticle

Land Use Structure Evolution in Resource-Based Cities: Drivers and Multi-Scenario Forecasting—Evidence from China’s Huaihai Economic Zone

by Yan Lin, Binjie Wang and Liyuan Zhao

Land 2026, 15(4), 555; https://doi.org/10.3390/land15040555 - 27 Mar 2026

Abstract

Resource-based cities face unique land use challenges due to resource dependence and path lock-in, yet the driving mechanisms and future trajectories of their land use transitions remain underexplored. This study examines the Huaihai Economic Zone (HEZ), a representative coal-rich region in eastern China, to analyze land use changes from 2000 to 2023 and simulate 2036 scenarios under different development pathways. Using land use transfer matrices, dynamic degree metrics, and the Patch-generating Land Use Simulation (PLUS) model, we systematically identified spatiotemporal evolution patterns, quantified the contributions of driving factors, and projected multi-scenario future land use patterns. Results reveal that land use change in the study area was dominated by the conversion of cultivated land to construction land, alongside spatial restructuring from a monocentric to a polycentric network pattern. Notably, construction land expansion was least evident in the central Mining-Affected Zone, where land use changes remained relatively sluggish compared to other sub-regions. Driving factor analysis indicates that socio-economic factors primarily influenced changes in construction and cultivated land, while natural factors strongly affected ecological land and unused land. Multi-scenario simulations for 2036 demonstrate diverging trajectories: an urban development scenario would accelerate cultivated land loss and unused land expansion; a natural development scenario would maintain current pressures; and an ecological protection scenario would effectively curb urban sprawl while actively promoting ecological land recovery. This study concludes that transcending simple land use control to actively orchestrate “mining-urban-rural-ecological” spatial synergy is critical for achieving a sustainable transition in resource-based regions facing similar transformation pressures. Full article

(This article belongs to the Special Issue Spatial Optimization of Land Use: A Perspective on Carbon Emission and Energy Landscape Planning)

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30 pages, 412 KB

Open AccessArticle

Sustaining What? From Corporate Sustainability to Agri-Food Transformation Through Commonist Value Theory

by S. A. Hamed Hosseini

Sustainability 2026, 18(7), 3290; https://doi.org/10.3390/su18073290 - 27 Mar 2026

Abstract

Corporate sustainability programs in agri-food systems have expanded dramatically, yet emissions, deforestation, hunger, and land concentration intensify. Why does corporate sustainability systematically fail to deliver transformation? This paper applies Commonist Value Theory (CVT) to show that this failure is structural, not contingent. CVT distinguishes between True Value, the life-supporting qualities that sustain human and more-than-human flourishing, and Fetish Value, abstracted forms oriented toward capital accumulation. CVT traces how corporate sustainability programs convert the former into the latter through ‘decommonization’: the perversion and enclosure of shared life-supporting relations. Drawing on investor analyses, carbon market assessments, and critical scholarship, this paper demonstrates that corporate sustainability programs function as civilizing meta-mechanisms. Rather than transforming food systems, they stabilize existing arrangements by absorbing critique and redirecting transformative energies into regime-compatible forms. Farmers’ knowledge is captured as proprietary data, living ecosystems are reduced to tradeable metrics, collaborative relationships are fragmented by corporate platforms, and movements for genuine alternatives are channeled into supply chain optimization. The analysis concludes that corporate sustainability cannot deliver genuine transformation because its structural function is to stabilize rather than supersede the current value regime. Genuine transformation requires commons-based alternatives from below and political–legislative shifts from above that structurally constrain decommonization. Full article

(This article belongs to the Section Sustainable Food)

23 pages, 7472 KB

Open AccessArticle

FPGA-Based Real-Time Simulation of Externally Excited Synchronous Machines

by Yannick Bergheim, Fabian Jonczyk, René Scheer and Jakob Andert

Energies 2026, 19(7), 1661; https://doi.org/10.3390/en19071661 - 27 Mar 2026

Abstract

Externally excited synchronous machines (EESMs) are a rare-earth-free solution for traction applications. However, variable field excitation and magnetic coupling increase control complexity. Efficient validation of the resulting control functionalities can be carried out using hardware-in-the-loop (HIL) testing, which requires high-fidelity real-time simulation models. This paper presents a semi-analytical, discrete-time EESM model tailored for HIL applications. Nonlinear magnetic saturation and magnetic coupling are captured using an inverted flux–current characteristic combined with a rotating coordinate transformation, which improves resource utilization. Spatial harmonics are included through a Fourier decomposition of the angle-dependent inverse characteristics. Additionally, different loss mechanisms are considered to accurately represent the physical behavior of the machine. The model is parameterized using finite element analysis (FEA) results from a 100

k

W

salient-pole EESM. It is implemented on a field-programmable gate array to achieve real-time capability at a simulation frequency of

2.5

M

Hz

. Validation results for the typical operating range show deviations below

0.1

compared to detailed FEA results, demonstrating accurate real-time simulation of the electromagnetic behavior. Full article

(This article belongs to the Topic Design and Control of Electrical Machines for Electric Vehicles)

27 pages, 3220 KB

Open AccessArticle

A Novel Load-Dependent Multimodal Vibration Signal Enhancement and Fusion Framework (LD-MVSEFF) for Load-Specific Condition Monitoring

by Shahd Ziad Hejazi and Michael Packianather

Machines 2026, 14(4), 372; https://doi.org/10.3390/machines14040372 - 27 Mar 2026

Abstract

This paper presents a Load-Dependent Multimodal Vibration Signal Enhancement and Fusion Framework (LD-MVSEFF) for load-specific condition monitoring, building on the Customised Load Adaptive Framework (CLAF). The proposed approach enhances the classification of CLAF load-dependent subclasses, namely, Healthy, Mild, Moderate, and Severe, by integrating complementary information from raw vibration signals and encoded signal representations. Three input channels are employed, combining time–frequency domain features with Continuous Wavelet Transform (CWT) and Gramian Angular Difference Field (GADF) image encodings, with each channel independently trained and evaluated to identify its most effective classifiers. To address the reduced separability of the Mild and Moderate fault subclasses under varying load conditions, a weighted decision-fusion strategy is introduced, assigning classifier contributions according to their class-specific strengths. Experimental evaluation over five runs demonstrates high and stable performance, with the best configuration achieving an overall accuracy of 99.04% ± 0.22% and an average training time of 18 min and 30 s. The results confirm the effectiveness of LD-MVSEFF as a robust multimodal methodology for load-specific condition monitoring. Full article

(This article belongs to the Special Issue Towards Electric Motors and Drives: Condition Monitoring, Performance Prediction and Fault Diagnosis, 2nd Edition)

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15 pages, 769 KB

Open AccessArticle

A Searchable Encryption Scheme Based on CRYSTALS-Dilithium

by Minghui Zheng, Anqi Xiao, Shicheng Huang and Deju Kong

Cryptography 2026, 10(2), 22; https://doi.org/10.3390/cryptography10020022 - 27 Mar 2026

Abstract

With the advancement in quantum computing technology, the number theory-based hard problems underlying traditional searchable encryption algorithms are now vulnerable to efficient quantum attacks. To address this challenge, this paper proposes Dilithium-PAEKS (Dilithium-Public Authenticated Encryption with Keyword Search), a searchable encryption scheme based on the post-quantum cryptographic algorithm CRYSTALS-Dilithium. By transforming the verification relationship of digital signatures into a matching relationship between trapdoors and ciphertexts, the scheme not only meets the functional requirements of searchable encryption but also demonstrates quantum resistance. The implementation enhances algorithm efficiency through keyword-based signatures and dynamic matching testing mechanisms. The security of the scheme is defined by the MLWE and MSIS hard problems, with proofs of keyword ciphertext indistinguishability and trapdoor indistinguishability under the random oracle model. Additionally, the scheme provides strong resistance against both outside and insider keyword guessing attacks through sender–receiver binding mechanisms and trapdoor indistinguishability properties. Experimental results show that, compared to the post-quantum schemes CP-Absel and LB-FSSE, the proposed scheme demonstrates superior overall computational efficiency while maintaining stronger quantum resistance than the traditional scheme SM9-PAEKS. Full article

16 pages, 10364 KB

Open AccessArticle

A Method for Filling Blank Stripes in Electrical Imaging Based on the Fusion of Arbitrary Kernel Convolution and Generative Adversarial Networks

by Ruhan A, Die Liu, Ge Cao, Kun Meng, Taiping Zhao, Lili Tian, Bin Zhao, Guilan Lin and Sinan Fang

Appl. Sci. 2026, 16(7), 3267; https://doi.org/10.3390/app16073267 - 27 Mar 2026

Abstract

Electrical imaging logging images play a crucial role in petroleum exploration; however, in practical applications, blank strips frequently appear due to instrument malfunctions or data transmission failures, severely compromising geological interpretation and hydrocarbon evaluation. Existing image inpainting methods have limited adaptability to blank strips at different depth scales and exhibit blurred high-resolution geological textures. To address these issues, this paper proposes a blank strip filling method that integrates Arbitrary Kernel Convolution (AKConv) with the Aggregated Contextual-Transformations Generative Adversarial Network (AOT-GAN). Specifically, the adaptive sampling mechanism of AKConv is incorporated into the generator network of AOT-GAN, enabling the model—to effectively capture long-range contextual information and adaptively handle blank strips of varying scales and shapes through multi-scale feature fusion. Experimental results on real oilfield datasets demonstrate that the proposed method achieves significant improvements in PSNR, SSIM, and MAE, exhibiting superior structural preservation and texture sharpness—especially in restoring deep and large-scale blank strips. Furthermore, visual comparisons confirm the method’s superior performance in recovering key geological features, such as bedding continuity and fracture structures, thus providing an effective approach for electrical imaging logging image restoration. Full article

(This article belongs to the Special Issue Applied Geophysical Imaging and Data Processing, 2nd Edition)

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18 pages, 1265 KB

Open AccessArticle

Robust Trajectory Tracking Control of Underactuated Overhead Cranes via Time Delay Estimation and the Sliding Mode Technique

by Ziyuan Lin and Xianqing Wu

Electronics 2026, 15(7), 1407; https://doi.org/10.3390/electronics15071407 - 27 Mar 2026

Abstract

As typical underactuated systems, overhead cranes are widely utilized in heavy-load transportation. However, their strong nonlinear coupling and underactuated characteristics complicate precise positioning and payload swing suppression. Furthermore, model uncertainties and external disturbances in practical environments increase control complexity and degrade system performance. To address these issues, this paper develops a trajectory tracking control scheme based on time delay estimation (TDE). Specifically, some transformations are made for the dynamic model and the TDE mechanism is used to estimate unknown nonlinear dynamics and external disturbances. Then, a sliding mode trajectory tracking controller, along with the TDE mechanism, is proposed for the trajectory tracking control and uncertainties estimation of the overhead crane system. Rigorous mathematical analysis is provided to demonstrate the asymptotic stability of the closed-loop system. Finally, simulation results verify the effectiveness of the proposed method in comparison with the existing control methods. Full article

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46 pages, 2125 KB

Open AccessReview

Big Data and Graph Deep Learning for Financial Decision Support from Social Networks: A Critical Review

by Leonidas Theodorakopoulos and Alexandra Theodoropoulou

Electronics 2026, 15(7), 1405; https://doi.org/10.3390/electronics15071405 - 27 Mar 2026

Abstract

Social network content is increasingly used as an auxiliary evidence stream for financial monitoring, risk assessment, and short-horizon decision support, yet many reported gains are hard to interpret because observability, timing, and attribution are handled inconsistently across studies. This review critically synthesizes the end-to-end pipeline that transforms social posts, interaction traces, linked artifacts, and related signals into decision-facing indicators, emphasizing evidence provenance, sampling bias, conditioning (bot/spam filtering, entity linking, timestamp alignment), and the modeling blocks typically used (text, temporal, relational, and fusion components) under deployment constraints. Across sentiment, relational, and multimodal or cross-platform signals, the analysis finds that apparent improvements often depend more on alignment discipline and conservative attribution than on architectural novelty, and that performance can be inflated by attention confounds, temporal leakage, and visibility effects. Relational indicators are most defensible for monitoring coordination and propagation patterns, while multimodal gains require clear ablations and realistic missing-modality tests. To support decision readiness, the paper consolidates assurance requirements covering manipulation, degraded observability, calibration and traceability, and provides compact reporting checklists and failure-mode mitigations. Overall, the review supports bounded claims and argues for time-aware evaluation and auditable pipelines as prerequisites for operational use. Full article

(This article belongs to the Special Issue Deep Learning and Data Analytics Applications in Social Networks)

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41 pages, 3552 KB

Open AccessReview

Towards Reliable Power Grid Modeling from Drawings: A Review of Intelligent Understanding, Topology Inference, and Model Generation

by Congying Wu, Haozheng Yu, Yu Liu and Chao Gong

Machines 2026, 14(4), 371; https://doi.org/10.3390/machines14040371 - 27 Mar 2026

Abstract

This paper presents a comprehensive review of the intelligent understanding of power grid drawings, with the aim of enabling reliable and executable grid modeling. First, a unified pipeline is established to describe the transformation from drawings to grid models, covering visual understanding, topology inference, and consistency validation. Second, existing methods are systematically analyzed within this framework, where visual understanding extracts components and textual information and topology inference reconstructs electrical connectivity and network structure. Third, model generation methods are investigated as a critical yet underexplored component, focusing on topology correctness and physical constraint verification. Compared with existing review studies that primarily focus on perception-level tasks such as detection and recognition, this paper explicitly emphasizes the reliability of the resulting models. It highlights that errors in connectivity inference and the lack of validation mechanisms significantly limit practical deployment. Key challenges, including connectivity ambiguity, error propagation, and the absence of standardized validation frameworks, are analyzed. Furthermore, emerging directions such as topology-aware learning and physics-constrained validation are discussed. This review provides a structured perspective on transforming power grid drawings into reliable models and offers insights for future research into power system digitalization. Full article

(This article belongs to the Special Issue Smart Electrified Energy–Motion Systems: Design, Control, and Intelligence from Machines and Vehicles to the Grid)

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