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Capacitor voltage imbalance among submodules in energy storage modular multilevel converters (MMCs) can lead to current distortion, power oscillations, and even system instability. Traditional voltage control strategies, inherited from non-storage MMCs, offer limited regulation capabilities and are insufficient to address the complex balancing requirements across phases, arms, and submodules in distributed Energy-Storage MMCs (ES-MMC). This paper proposes a self-synchronized common-mode current strategy to achieve capacitor voltage rebalancing in Carrier Phase-Shifted PWM (CPS-PWM) modulated ES-MMCs. The proposed method establishes both phase-level and arm-level power rebalancing pathways by utilizing the common-mode current in the upper and lower arms. Specifically, the DC component of the common-mode current is employed to regulate common-mode power between the arms, while the fundamental-frequency component, through its interaction with the fundamental modulation voltage, is used to adjust differential-mode power. By coordinating these two power components within each phase, the method enables effective capacitor voltage rebalancing among submodules in the presence of power imbalance caused by a nonuniform distributed energy storage converter. A comprehensive analysis of differential- and common-mode voltage regulation under CPS-PWM is presented. The corresponding control algorithm is developed to inject adaptive common-mode voltage based on capacitor voltage deviations, thereby inducing self-synchronized balancing currents. Simulation and experimental results verify that the proposed strategy significantly improves power distribution uniformity and reduces capacitor voltage deviations under various load and disturbance conditions. Full article

(This article belongs to the Special Issue Enhancing Power System Resilience: Advanced Algorithm, Control Strategies, and Topologies for a Sustainable Energy Future)

5 pages, 250 KB

Open AccessEditorial

Oleogels, Bigels, and Emulgels: Fabrication, Application and Research Trends

by Cristina Ghinea and Ana Leahu

Gels 2025, 11(10), 816; https://doi.org/10.3390/gels11100816 (registering DOI) - 12 Oct 2025

Abstract

Gels are created by entrapping liquid oil (oleogels) or water (hydrogels) into the well-organized three-dimensional network of a gelling agent [...] Full article

(This article belongs to the Special Issue Oleogels, Bigels, and Emulgels: Fabrication, Application and Research Trends)

21 pages, 3835 KB

Open AccessArticle

Quadratic Programming Vision-Based Control of a Scale-Model Autonomous Vehicle Navigating in Intersections

by Esmeralda Enriqueta Mascota Muñoz, Oscar González Miranda, Xchel Ramos Soto, Juan Manuel Ibarra Zannatha and Santos Miguel Orozco Soto

Actuators 2025, 14(10), 494; https://doi.org/10.3390/act14100494 (registering DOI) - 12 Oct 2025

Abstract

This paper presents an optimal control for autonomous vehicles navigating in intersection scenarios. The proposed controller is based on solving a Quadratic Programming optimization technique to provide a feasible control signal respecting actuator constraints. The proposed controller was implemented in a scale-sized vehicle and is executed using only on-board perception and computing systems to retrieve the state dynamics, i.e., an inertial measurement unit and a monocular camera, to compute the estimated states through intelligent computer vision algorithms. The stability of the error signals of the closed-loop system was proved both mathematically and experimentally, using standard performance indices for ten trials. The proposed technique was compared against LQR and MPC strategies, showing 67% greater accuracy than the LQR approach and 53.9% greater accuracy than the MPC technique, while turning during the intersection. Moreover, the proposed QP controller showed significantly greater efficiency by reducing the control effort by 63.3% compared to the LQR, and by a substantial 78.4% compared to the MPC. These successful results proved that the proposed controller is an effective alternative for autonomously navigating within intersection scenarios. Full article

(This article belongs to the Special Issue Nonlinear Control of Mechanical and Robotic Systems)

16 pages, 407 KB

Open AccessArticle

Environmental Efficiency of Agricultural Enterprises in Serbia: A Panel Regression Approach

by Slavica Stevanović, Jelena Minović, Aida Hanić and Petar Mitić

Agriculture 2025, 15(20), 2119; https://doi.org/10.3390/agriculture15202119 (registering DOI) - 12 Oct 2025

Abstract

The agricultural sector is a cornerstone of Serbia’s economy, ensuring national food security and contributing significantly to GDP, but it also generates notable environmental pressures, particularly through air and water pollution. This paper investigates the impact of agricultural enterprises’ environmental pressures on their financial performance between 2011 and 2021. The sample comprises 52 of the 63 agricultural enterprises listed in the national PRTR register as major air polluters in Serbia. Using enterprise-level data, environmental performance is measured through air emissions relative to revenues, while profitability is captured by return on assets (ROA). Panel regression analysis is conducted with Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS) estimators to assess the long-run relationship between eco-efficiency and financial outcomes. The results show that reductions in environmental pressure are associated with improved profitability, highlighting the trade-offs and synergies between ecological responsibility and economic performance. These findings underscore the importance of promoting eco-efficiency as both a managerial strategy and a public policy priority, offering evidence to support Serbia’s alignment with EU environmental and agricultural sustainability goals. Full article

(This article belongs to the Special Issue Innovation and Sustainability in Agribusiness: Policies and Market Dynamics)

30 pages, 2150 KB

Open AccessArticle

A Multi-Objective Artificial Bee Colony Algorithm Incorporating Q-Learning Search for the Flexible Job Shop Scheduling Problems with Multi-Type Automated Guided Vehicles

by Shihong Ge, Hao Zhang, Zhigang Xu and Zhiqi Yang

Appl. Sci. 2025, 15(20), 10948; https://doi.org/10.3390/app152010948 (registering DOI) - 12 Oct 2025

Abstract

The flexible job shop scheduling problem (FJSP) with transportation resources such as automated guided vehicles (AGVs) is prevalent in manufacturing enterprises. Multi-type AGVs are widely adopted to transfer jobs and realize the collaboration of different machines, but are often ignored in current research. Therefore, this paper addresses the FJSP with multi-type AGVs (FJSP-MTA). Considering the difficulties caused by the introduction of transportation and the NP-hard nature, the artificial bee colony (ABC) algorithm is adopted as a fundamental solution approach. Accordingly, a Q-learning hybrid multi-objective ABC (Q-HMOABC) algorithm is proposed to deal with the FJSP-MTA. First, to minimize both the makespan and total energy consumption (TEC), this paper proposes a novel mixed-integer linear programming (MILP) model. In Q-HMOABC, a three-layer encoding strategy based on operation sequence, machine assignment, and AGV dispatching with type selection is used. Moreover, during the employed bee phase, Q-learning is employed to update all individuals; during the onlooker bee phase, variable neighborhood search (VNS) is used to update nondominated solutions; and during the scout bee phase, a restart strategy is adopted. Experimental results demonstrate the effectiveness and superiority of Q-HMOABC. Full article

13 pages, 240 KB

Open AccessArticle

Factors Associated with Radiological Examination of Patients with Non-Specific Low Back Pain

by Asma S. Alrushud, Muteb J. Alqarni, Salman Albeshan, Areej S. Aloufi, Mawaddah H. Aljohani, Mohammed A. Alqarni, Somyah A. Alhazmi, Yazeed I. Alashban and Dalia M. Alimam

J. Clin. Med. 2025, 14(20), 7187; https://doi.org/10.3390/jcm14207187 (registering DOI) - 12 Oct 2025

Abstract

Background/Objectives: Non-specific low back pain (LBP), a highly prevalent musculoskeletal condition, may be associated with overuse of radiological imaging, despite clinical guidelines restricting its use to cases with suspected serious pathology. This study investigated demographic, clinical, and physiotherapy-related factors influencing radiological imaging use in patients with non-specific LBP. Methods: A retrospective cross-sectional study included 179 non-specific LBP patients from an outpatient physiotherapy clinic in Saudi Arabia. Patient data were anonymized and retrieved from electronic health records, including demographic, clinical, physiotherapy and imaging information. Independent variables included patient demographics, non-specific LBP characteristics, physiotherapy engagement, and pain-related outcomes. Descriptive, inferential, and multiple linear regression analyses were conducted to identify predictors of radiological imaging. Results: Among the total study sample (n = 179), 159 (88.8%) patients underwent radiological imaging, primarily X-ray (32.4%) and Magnetic Resonance Imaging (8.4%); 48.0% received multiple imaging modalities. Significant predictors of imaging use included gender (p < 0.001), higher body mass index (BMI) (p = 0.012), greater physiotherapist experience (p = 0.019), and presence of comorbidities (p = 0.023). Non-specific LBP medication use was negatively associated with imaging (p = 0.032). Physiotherapy engagement and pain-related outcomes showed no significant impact on imaging use. Conclusions: Gender, BMI, physiotherapist experience, and comorbidities could influence radiological imaging use in non-specific LBP patients. These findings highlight potential biases in imaging referral patterns and reinforce the need for adherence to evidence-based guidelines to prevent unnecessary imaging, reduce healthcare costs, and enhance patient care. Full article

(This article belongs to the Section Nuclear Medicine & Radiology)

15 pages, 576 KB

Open AccessArticle

Building Resilient and Sustainable Supply Chains: A Distributed Ledger-Based Learning Feedback Loop

by Tan Gürpinar and Mehmet Akif Gulum

Sustainability 2025, 17(20), 9023; https://doi.org/10.3390/su17209023 (registering DOI) - 12 Oct 2025

Abstract

Global supply chains face increasing disruptions from cyber threats, geopolitical instability, extreme weather events, and a range of economic, social, and environmental sustainability challenges. As these disruptions intensify, enhancing Supply Chain Resilience (SCR) has become a strategic priority. This study investigates how Distributed Ledger Technology (DLT) can contribute to SCR by mitigating vulnerabilities and strengthening key capabilities within global supply chains. A qualitative research approach is employed, utilizing expert evaluations to examine DLT’s impact on supply chain vulnerabilities and capabilities. Five workshops were conducted with 25 industry professionals from logistics, IT, procurement, and risk management. Experts examined how DLT could address disruptions stemming from supplier instability, poor traceability, and regulatory and environmental pressures, while highlighting its potential to drive ethical sourcing and environmentally responsible practices. The structured discussions were guided by theoretical frameworks and expert evaluations were synthesized into two analytical matrices illustrating DLT’s influence on SCR. The findings reveal that the contribution of DLT to SCR and sustainability is highly context-dependent, with its effectiveness hinging on how it is embedded within governance structures and aligned with the interplay of complementary technologies. Building on these insights, the study presents the DLT-LFL (Distributed Ledger Technology–Learning Feedback Loop) framework, which integrates sensing, decision-making, adaptation, and predictive learning from distributed operational data, allowing supply chains to better anticipate disruptions, adjust processes dynamically, and continuously strengthen resilience and sustainable practices. The study also develops a practical checklist to assess how effective DLT applications and their integration with predictive and AI-driven analytics reduce vulnerabilities, strengthen capabilities, mitigate risks, and support adaptive decision-making. Full article

(This article belongs to the Section Economic and Business Aspects of Sustainability)

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25 pages, 1442 KB

Open AccessArticle

Promoting Sustainable Life Through Global Citizenship-Oriented Educational Approaches: Comparison of Learn–Think–Act Approach-Based and Lecture-Based SDG Instructions on the Development of Students’ Sustainability Consciousness

by Aslı Koçulu

Sustainability 2025, 17(20), 9026; https://doi.org/10.3390/su17209026 (registering DOI) - 12 Oct 2025

Abstract

Promoting individuals’ sustainability consciousness (SC) is one of the important way of ensuring a sustainable world and finding ways toward a better life. Therefore, the purpose of the present study was to compare the effects of learn–think–act approach-based instruction and lecture-based instruction on the development of sustainability consciousness in students, with the Sustainable Development Goals (SDGs) acting as the subject of the instructions. The research was conducted with 80 seventh-grade students from a state school in Istanbul, Türkiye. While 40 of them were in a class where learn–think–act approach-based SDG instruction was implemented, the other 40 participants were trained with lecture-based SDG instruction for eight weeks. A quasi-experimental research design was followed in the research. The data was collected with the Sustainability Consciousness Questionnaire and obtained before and after SDG instruction. In the data analysis, paired and independent samples t-tests were used. The findings revealed that learn–think–act approach-based SDG instruction has a significantly larger effect (d = 1.62, 95% CI) on the development of sustainability consciousness in middle school students compared to lecture-based SDG instruction. Full article

(This article belongs to the Collection Sustainable Citizenship and Education)

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26 pages, 4124 KB

Open AccessArticle

Assessment of City-Scale Rooftop Photovoltaic Integration and Urban Energy Autonomy Across Europe

by Georgios Mitsopoulos, Vasileios Kapsalis and Athanasios Tolis

Appl. Sci. 2025, 15(20), 10950; https://doi.org/10.3390/app152010950 (registering DOI) - 12 Oct 2025

Abstract

This study suggests a newly developed model for estimating city-scale photovoltaic rooftop energy potential. This model aims to provide reasonable universal calculations regarding a city’s available space for mounting rooftop photovoltaic systems and their corresponding annual electricity production capacity. For the development of the model, a thorough literature review has been conducted, which compiles and presents mathematical expressions and performance coefficients. Necessary geographic and meteorological data have been obtained from European statistical repositories and the PVGIS tool, respectively. The main inputs refer to a city’s basic geographical data, population, total actual area, geographical coordinates, and, by extension, the optimum PV unit installation angle. This analysis presents a simple and accurate model applicable to European cities for assessing rooftop photovoltaic energy potential and suitable rooftop space for PV units. The findings can aid in advancing PV development in urban areas and contribute to creating environmentally neutral cities in the future. The methodology is verified with data retrieved from the Google Environmental Insights Explorer tool, which shows a deviation of 9.72%. According to the computational analysis for 40 European countries, the photovoltaic energy potential is between 12.31 GWh and 8200 GWh. These values correspond to a net available PV space between 0.03 km² and 31.86 km². The greatest photovoltaic coverage potential is equal to 117.4% for Patras, Greece, while the lowest is 7.27% for Oslo, Norway. Regarding the avoided greenhouse gas emissions, they are found to vary from 5.8 ktons of CO_2-equivalent for Valletta, Malta, and 8109.8 ktons for the city of London, United Kingdom. Finally, the final results of 86 additional cities located on the European continent are given. Full article

(This article belongs to the Special Issue Applications of Sustainable Energy Technologies and Energy Saving Technologies in Buildings)

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33 pages, 1449 KB

Open AccessReview

Rare Earth Elements: A Review of Primary Sources, Applications, Business Investment, and Characterization Techniques

by Fabiano Ferreira de Medeiros, Alexandre Pereira Wentz, Beatriz Almeida Santos Castro, Fabricio Dias Rodrigues, Sara Silva Alves, Maria das Graças Andrade Korn, Jefferson Bettini, Jeancarlo Pereira dos Anjos and Lílian Lefol Nani Guarieiro

Appl. Sci. 2025, 15(20), 10949; https://doi.org/10.3390/app152010949 (registering DOI) - 12 Oct 2025

Abstract

Minerals bearing rare earth elements (REEs) are formed through long geological processes, among which monazite, bastnasite, xenotime, and ionic adsorption clays are the most economically exploited. Although Brazil has one of the largest reserves of REEs on the planet, its production is still not significant on the world stage. China remains dominant, with the largest reserves of REEs and controlling more than half of world production. Due to their important application in advanced clean and low-carbon energy technologies, REEs have become fundamental to the energy transition process. Technological applications related to catalyst synthesis, ceramics production, and metallurgy have been explored. Furthermore, the use of REEs in devices of great demand today, such as computer memory, rechargeable batteries, and mobile phones, has been cited. With the growing demand for these critical minerals, large mining companies are seeking to implement cleaner production policies in their processes and save natural resources to minimize the environmental impacts of the exploration. Robust analytical techniques have made it possible to characterize these elements in multi-element geological matrices, with the increasing exploration and identification of new REE mineral reserves. Full article

(This article belongs to the Special Issue Recent Advances in Prospecting Geology)

21 pages, 5915 KB

Open AccessArticle

A Machine Learning Approach to Predicting the Turbidity from Filters in a Water Treatment Plant

by Joseph Kwarko-Kyei, Hoese Michel Tornyeviadzi and Razak Seidu

Water 2025, 17(20), 2938; https://doi.org/10.3390/w17202938 (registering DOI) - 12 Oct 2025

Abstract

Rapid sand filtration is a critical step in the water treatment process, as its effectiveness directly impacts the supply of safe drinking water. However, optimising filtration processes in water treatment plants (WTPs) presents a significant challenge due to the varying operational parameters and conditions. This study applies explainable machine learning to enhance insights into predicting direct filtration operations at the Ålesund WTP in Norway. Three baseline models (Multiple Linear Regression, Support Vector Regression, and K-Nearest Neighbour (KNN)) and three ensemble models (Random Forest (RF), Extra Trees (ET), and XGBoost) were optimised using the GridSearchCV algorithm and implemented on seven filter units to predict their filtered water turbidity. The results indicate that ML models can reliably predict filtered water turbidity in WTPs, with Extra Trees models achieving the highest predictive performance (R² = 0.92). ET, RF, and KNN ranked as the three top-performing models using Alternative Technique for Order of Preference by Similarity to Ideal Solution (A-TOPSIS) ranking for the suite of algorithms used. The feature importance analysis ranked the filter runtime, flow rate, and bed level. SHAP interpretation of the best model provided actionable insights, revealing how operational adjustments during the ripening stage can help mitigate filter breakthroughs. These findings offer valuable guidance for plant operators and highlight the benefits of explainable machine learning in water quality management. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Water Quality Monitoring)

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17 pages, 41056 KB

Open AccessArticle

The Effect of Y Content on the Strength and Toughness of Mg-Y-Zn Alloys

by Dong Zhao, Jie Hu, Ruanyu Wang, Guoqing Yan, Wenkai Song, Liang Liang and Jian Peng

Metals 2025, 15(10), 1134; https://doi.org/10.3390/met15101134 (registering DOI) - 12 Oct 2025

Abstract

The microstructure and properties of Mg-Y-Zn as-cast alloys with the Y/Zn ratio of approximately 1.3 (wt%) and different Y contents were studied, including an analysis of the main factors and mechanisms affecting their toughness and ductility, such as dendritic morphology, mass fraction of different compound phases, and degree of solid solution. The results showed that the alloys with Y contents ranging from 3.5% to 5% exhibit high toughness. The 4.4% alloy has the best comprehensive mechanical properties with the UTS, YS, EL, and tensile deformation work of 188.9 MPa, 124.4 MPa, 5.9%, and 0.50 MJ/m², respectively. It is beneficial for achieving higher strength and toughness by maintaining the content of the W phase and the combined content of the W phase and LPSO phase at approximately 5.8 wt% and 28.8 wt%, respectively. Full article

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15 pages, 1374 KB

Open AccessArticle

Determination of Microplastic Pollution in Commercial Fish in the Middle Black Sea (Samsun), Türkiye

by Arife Şimşek

Toxics 2025, 13(10), 865; https://doi.org/10.3390/toxics13100865 (registering DOI) - 12 Oct 2025

Abstract

This study aimed to determine the presence and characteristics of microplastics (MPs) in six commercially important fish species in Samsun city of, the Middle Black Sea Region: rainbow trout–Turkish salmon (Oncorhynchus mykiss), European seabass (Dicentrarchus labrax), gilthead seabream (Sparus aurata), red mullet (Mullus barbatus), horse mackerel (Trachurus mediterraneus), and whiting (Merlangius merlangus). The digestive systems of each species were examined, and MPs were classified according to their morphology, size, color, and polymer type. The analysis revealed that the number of MPs per individual ranged from 4.73 ± 1.13 to 9.26 ± 2.18, with the highest value found in rainbow trout and the lowest in whiting. MPs smaller than 100 µm were dominant (48.9%), and fiber (45.7%) and fragment (36.5%) types were the most common morphologies observed. Black and white/transparent colors were prominent in terms of color distribution, and ATR-FTIR analysis showed a dominance of widely used consumer plastics, such as polypropylene (PP, 31.3%) and polyethylene (PE, 23.9%). Scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM/EDS) results confirmed the presence of irregular, fibrous, and fragmented structures at microscopic scale, consistent with microplastic morphology. These findings indicate a potential risk of microplastic pollution in the region for both marine biota and human consumption. The study fills a significant data gap regarding the Middle Black Sea ecosystem and provides a foundation for future monitoring and risk assessment research. Full article

(This article belongs to the Special Issue Microplastics in Atmospheric, Aquatic, and Soil Ecosystems: Occurrence, Impacts, and Innovations in Detection and Mitigation)

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25 pages, 14721 KB

Open AccessReview

Biomass-Derived Hard Carbon Anodes for Sodium-Ion Batteries: Recent Advances in Synthesis Strategies

by Narasimharao Kitchamsetti, Kyoung-ho Kim, HyukSu Han and Sungwook Mhin

Nanomaterials 2025, 15(20), 1554; https://doi.org/10.3390/nano15201554 (registering DOI) - 12 Oct 2025

Abstract

Biomass-derived hard carbon (BHC) has attracted considerable attention as a sustainable and cost-effective anode material for sodium-ion batteries (SIBs), owing to its natural abundance, environmental friendliness, and promising electrochemical performance. This review provides a detailed overview of recent progress in the synthesis, structural design, and performance optimization of BHC materials. It encompasses key fabrication routes, such as high-temperature pyrolysis, hydrothermal pretreatment, chemical and physical activation, heteroatom doping, and templating techniques, that have been employed to control pore architecture, defect density, and interlayer spacing. Among these strategies, activation-assisted pyrolysis and heteroatom doping have shown the most significant improvements in sodium (Na) storage capacity and long-term cycling stability. The review further explores the correlations between microstructure and electrochemical behavior, outlines the main challenges limiting large-scale application, and proposes future research directions toward scalable production and integration of BHC anodes in practical SIB systems. Overall, these advancements highlight the strong potential of BHC as a next-generation anode for grid-level and renewable energy storage technologies. Full article

(This article belongs to the Section Energy and Catalysis)

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26 pages, 3377 KB

Open AccessArticle

Charge Neutralization During Peptide Transport in the Bacterial SecYEG Translocon

by Laura Nübl, Ekaterina Sobakinskaya and Frank Müh

Biomolecules 2025, 15(10), 1442; https://doi.org/10.3390/biom15101442 (registering DOI) - 12 Oct 2025

Abstract

The driving force behind protein translocation across the cell membrane is not yet fully understood. In bacteria, there is an electrochemical potential across the cell membrane, which can interact with charged residues in the translocation substrate. In this study, the protonation states of lysine and glutamate, serving as test residues in a peptide translocating across the bacterial channel SecYEG, are investigated by applying Poisson–Boltzmann continuum electrostatic free energy calculations and Monte Carlo titrations to snapshots of molecular dynamics (MD) simulations. A clear shift in protonation probability towards the uncharged state is found for both test residues as they move deeper into the channel. Thus, charge neutralization occurs irrespective of whether the original charge of the test residue is positive (lysine) or negative (glutamate). Electrostatic interactions of acidic and basic residues of SecYEG with the peptide cancel out. The main determinants of the test residue’s protonation state are the dielectric properties of its surroundings and interactions with non-titrating charges in the channel. Crucially, the membrane protein—including its water-filled pore—is assigned a low dielectric constant. The results are discussed in the context of the limitations inherent to continuum electrostatics and MD simulations with fixed protonation states. Full article

(This article belongs to the Special Issue Advances in Membrane Proteins: From Structure to Function and Molecular Engineering)

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