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16 pages, 3160 KB

Open AccessArticle

Mechanical Behavior of a Reinforced Hourglass Lattice Structure

by Chong Liu, Wen Yang, Baifeng Sha, Henghao Zhang, Yongzhao Hou, Cheng Zhong, Meixian Jiang and Yongqiang Ma

Materials 2026, 19(4), 777; https://doi.org/10.3390/ma19040777 (registering DOI) - 16 Feb 2026

Abstract

Inspired by the networked venation structures, a reinforced hourglass lattice structure is proposed to overcome the insufficient face–core interaction and premature face-sheet buckling that limit the compressive performance of conventional lattice sandwich structures. The reinforced hourglass lattice structure is fabricated using a cutting–interlocking [...] Read more.

Inspired by the networked venation structures, a reinforced hourglass lattice structure is proposed to overcome the insufficient face–core interaction and premature face-sheet buckling that limit the compressive performance of conventional lattice sandwich structures. The reinforced hourglass lattice structure is fabricated using a cutting–interlocking assembly followed by vacuum brazing, enabling enhanced connectivity and increased effective contact area between the lattice core and the face sheets. Quasi-static in-plane and out-of-plane compression experiments, together with finite element simulations and theoretical analysis, are conducted to systematically investigate the compressive behavior of the reinforced hourglass lattice structure. The results demonstrate that the out-of-plane compressive strength of the reinforced hourglass lattice structure exhibits a pronounced dependence on relative density, increasing monotonically with increasing density. Under in-plane compression, comparative studies with conventional hourglass and pyramidal lattice structures reveal that the proposed reinforcement strategy significantly improves face–core load transfer and effectively suppresses local buckling of thin face sheets. As a consequence, the reinforced hourglass lattice structure exhibits higher initial stiffness, enhanced compressive strength, and superior structural stability. These findings indicate that reinforcing the reinforced hourglass core provides an effective design strategy for improving the compressive performance of lattice sandwich structures by strengthening face–core interaction and mitigating face-sheet buckling. Full article

(This article belongs to the Section Materials Simulation and Design)

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5 pages, 152 KB

Open AccessEditorial

Achievements in the Agri-Food Supply Chain Leading to Sustainable Foods IV

by Dimitris Skalkos

Sustainability 2026, 18(4), 2030; https://doi.org/10.3390/su18042030 (registering DOI) - 16 Feb 2026

Abstract

In the previous three Special Issues, we researched the unprecedented rate and unforeseen consequences of global change over the last five years [...] Full article

(This article belongs to the Special Issue Achievements in the Agri-Food Supply Chain Leading to Sustainable Foods IV)

15 pages, 2137 KB

Open AccessArticle

Influence of Skin Factor on Oil Recovery and Economic Performance in Synthetic Layered Carbonate Models Based on Pre-Salt Well Profiles

by Edson de Andrade Araújo, Mateus Palharini Schwalbert, Rafael Japiassú Leitão, Lorena Cardoso Batista Aum and Pedro Tupã Pandava Aum

Energies 2026, 19(4), 1039; https://doi.org/10.3390/en19041039 (registering DOI) - 16 Feb 2026

Abstract

Formation damage near the wellbore reduces permeability and limits well productivity, with its effect commonly quantified by the skin factor. This parameter can strongly influence both the technical performance and the economic feasibility of oil recovery projects. In Brazilian pre-salt carbonate reservoirs, acidizing [...] Read more.

Formation damage near the wellbore reduces permeability and limits well productivity, with its effect commonly quantified by the skin factor. This parameter can strongly influence both the technical performance and the economic feasibility of oil recovery projects. In Brazilian pre-salt carbonate reservoirs, acidizing is widely applied, often conducted immediately after well completion. However, the long-term production and economic implications of these treatments remain insufficiently quantified. In this study, synthetic carbonate reservoir models were constructed using porosity and permeability profiles derived from well data representative of pre-salt conditions. Ten models with flow capacities ranging from 3000 to 130,000 mD·m were simulated over 30 years of water injection, considering skin factors from −3 to +20. The results show that wells with flow capacities below 10,000 mD·m exhibited the strongest response to stimulation, achieving up to 35% higher cumulative oil recovery and more than a 100% increase in net present value (NPV) compared with unstimulated cases. For flow capacity values between 10,000 and 40,000 mD·m, production and economic improvements were marginal, with NPV differences typically within 10%. At higher flow capacity (>60,000 mD·m), the stimulation response became negligible, with NPV variations below 0.1%. These findings demonstrate that stimulation effectiveness is primarily governed by reservoir flow capacity. The integrated reservoir–economic evaluation framework developed in this study provides quantitative guidance for optimizing acidizing strategies in carbonate systems representative of deepwater pre-salt environments. Full article

(This article belongs to the Section H1: Petroleum Engineering)

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30 pages, 2117 KB

Open AccessArticle

Automated Structuring and Analysis of Unstructured Equipment Maintenance Text Data in Manufacturing Using Generative AI Models: A Comparative Study of Pre-Trained Language Models

by Yongju Cho

Appl. Sci. 2026, 16(4), 1969; https://doi.org/10.3390/app16041969 (registering DOI) - 16 Feb 2026

Abstract

Manufacturing companies face significant challenges in leveraging artificial intelligence for equipment management due to high infrastructure costs and limited availability of labeled data for failures. While most manufacturing AI applications focus on structured sensor data, vast amounts of unstructured textual information containing valuable [...] Read more.

Manufacturing companies face significant challenges in leveraging artificial intelligence for equipment management due to high infrastructure costs and limited availability of labeled data for failures. While most manufacturing AI applications focus on structured sensor data, vast amounts of unstructured textual information containing valuable maintenance knowledge remain underutilized. This study presents a practical generative AI-based framework for structured information extraction that automatically converts unstructured equipment maintenance texts into predefined semantic fields to support predictive maintenance in manufacturing environments. We adopted and evaluated three representative generative models—Bidirectional and Auto-Regressive Transformers (BART) with KoBART, Text-to-Text Transfer Transformer (T5) with pko-t5-base, and the large language model Qwen—to generate structured outputs by extracting three predefined fields: failed components, failure types, and corrective actions. The framework enables the structuring of equipment management text data from Manufacturing Execution Systems (MES) to build predictive maintenance support systems. We validated the approach using a large-scale MES dataset consisting of 29,736 equipment maintenance records from a major automotive parts manufacturer, from which curated subsets were used for model training and evaluation. Our methodology employs Generative Pre-trained Transformer 4 (GPT-4) for initial dataset construction, followed by domain expert validation to ensure data quality. The trained models achieved promising performance when evaluated using extraction-aligned metrics, including exact match (EM) and token-level precision, recall, and F1-score, which directly assess field-level extraction correctness. ROUGE scores are additionally reported as a supplementary indicator of lexical overlap. Among the evaluated models, Qwen consistently outperformed BART and T5 across all extracted fields. The structured outputs are further processed through domain-specific dictionaries and regular expressions to create a comprehensive analytical database supporting predictive maintenance strategies. We implemented a web-based analytics platform enabling time-series analysis, correlation analysis, frequency analysis, and anomaly detection for equipment maintenance optimization. The proposed system converts tacit knowledge embedded in maintenance texts into explicit, actionable insights without requiring additional sensor installations or infrastructure investments. This research contributes to the manufacturing AI field by demonstrating a comprehensive application of generative language models to equipment maintenance text analysis, providing a cost-effective approach for digital transformation in manufacturing environments. The framework’s scalability and cloud-based deployment model present significant opportunities for widespread adoption in the manufacturing sector, supporting the transition from reactive to predictive maintenance strategies. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies and Applications for Industry 4.0 and Smart Manufacturing)

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18 pages, 4591 KB

Open AccessData Descriptor

Individual-Level Behavioral Dataset Linking Trace Eyeblink Conditioning, Contextual Fear Memory, and Home-Cage Activities in rTg4510 and Wild-Type Mice with Doxycycline Treatment

by Ryo Kachi, Takuma Nishijo and Yasushi Kishimoto

Data 2026, 11(2), 42; https://doi.org/10.3390/data11020042 (registering DOI) - 16 Feb 2026

Abstract

This dataset provides synchronized multimodal behavioral measurements from 36 mice across four experimental groups: wild-type and rTg4510 tauopathy mice, each tested with or without doxycycline-mediated suppression of mutant tau expression. Of these, 34 mice had complete measurements across all three behavioral paradigms and [...] Read more.

This dataset provides synchronized multimodal behavioral measurements from 36 mice across four experimental groups: wild-type and rTg4510 tauopathy mice, each tested with or without doxycycline-mediated suppression of mutant tau expression. Of these, 34 mice had complete measurements across all three behavioral paradigms and were used for analyses requiring full cross-task linkage. At six months of age, all animals underwent three standardized behavioral paradigms: home cage monitoring, ten-day trace eyeblink conditioning, and contextual fear conditioning. The individual-level data included locomotor activity, rearing duration, conditioned response metrics, eyelid closure latencies, and contextual freezing percentages. All measurements were linked using unique mouse identifiers, enabling cross-task analysis without preprocessing or imputation. The dataset was accompanied by a complete data dictionary, processing workflow diagram, and validation analyses demonstrating cross-paradigm correlations. The cross-task associations are illustrated in the main figures, with additional early phase acquisition and temporal processing correlations provided in the main figures. Provided in an open CSV format with detailed metadata, this resource supports behavioral phenotyping, machine learning applications, and the investigation of learning mechanisms in tauopathy models. Full article

(This article belongs to the Section Computational Biology, Bioinformatics, and Biomedical Data Science)

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19 pages, 8239 KB

Open AccessArticle

Delayed Panicle Nitrogen Application Enhances Stem Nonstructural Carbohydrate Accumulation in Large-Panicle Rice Through the Sucrose–Starch Metabolic Network

by Yonggan Shi, Tiezhong Zhu, Feilong Shen, Chuan Tu, Congshan Xu, Qiangqiang Zhang, Haibing He, Cuicui You, Liquan Wu and Jian Ke

Agronomy 2026, 16(4), 464; https://doi.org/10.3390/agronomy16040464 (registering DOI) - 16 Feb 2026

Abstract

Accumulation of stem non-structural carbohydrates (NSC) at heading is crucial for mitigating grain-setting defects in large-panicle rice. While traditional panicle nitrogen fertilizer application at the emergence of the fourth leaf from the flag leaf stage (TL4) may weaken stem sink strength, delaying application [...] Read more.

Accumulation of stem non-structural carbohydrates (NSC) at heading is crucial for mitigating grain-setting defects in large-panicle rice. While traditional panicle nitrogen fertilizer application at the emergence of the fourth leaf from the flag leaf stage (TL4) may weaken stem sink strength, delaying application to the emergence of the third leaf from the flag leaf stage (TL3) significantly enhances NSC accumulation. This study aimed to elucidate the molecular mechanisms through which TL3 remodels stem sink strength to promote NSC storage. Using two large-panicle rice varieties (Huiliangyou 280 and Yangliangyou 228), we compared stem NSC dynamics under TL4 and TL3 treatments and integrated sugar-related metabolite profiling with transcriptome analysis during the critical NSC accumulation phase. The results showed that TL3 treatment significantly increased stem NSC content and NSC per spikelet at heading, leading to a higher percentage of filled grains. The period from 5 days before heading (DBH) to heading showed the highest NSC accumulation rate. At the molecular level, TL3 treatment specifically up-regulated eight key genes in the sucrose–starch metabolism pathway, increasing the activities of sucrose phosphate synthase, sucrose synthase, and ADP–glucose pyrophosphorylase, and thereby promoting the accumulation of sucrose, trehalose, and D-fructose. In summary, delaying panicle nitrogen application to TL3 enhances stem NSC storage by remodeling sink strength via coordinated regulation of the sucrose–starch metabolic network. Full article

(This article belongs to the Special Issue Sustainable Management and Tillage Practice in Agriculture—2nd Edition)

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24 pages, 2962 KB

Open AccessReview

Image-Guided Autonomous Robotic Surgery in the Context of Therapies Managed by Intelligent Digital Technologies: A Narrative Review

by Adel Razek

Surgeries 2026, 7(1), 26; https://doi.org/10.3390/surgeries7010026 (registering DOI) - 16 Feb 2026

Abstract

This narrative review aims to highlight and analyze the supervision of precision robotic surgical interventions. These are autonomous, closed-loop procedures, assisted by images and managed by intelligent digital tools. These administered procedures are designed to be safe and reliable, adhering to the principles [...] Read more.

This narrative review aims to highlight and analyze the supervision of precision robotic surgical interventions. These are autonomous, closed-loop procedures, assisted by images and managed by intelligent digital tools. These administered procedures are designed to be safe and reliable, adhering to the principles of minimal invasiveness, precise positioning, and non-toxicity. Thus, a precision intervention uses non-ionizing imaging-assisted robotics, controlled by a precise positioning device, forming an autonomous procedure augmented by artificial intelligence tools and supervised by digital twins. This intelligent digital management procedure allows staff to plan, train, predict, and execute interventions under human supervision. Patient safety and staff efficiency are linked to non-ionizing imaging, minimal invasiveness through image guidance, and strict delimitation of the intervention zone through precise positioning. This study includes, successively, sections covering an introduction, therapeutic and surgical interventions, imaging strategies integrating diagnostic and assistance functions, intelligent digital tools including digital twins and artificial intelligence, image-guided procedures including autonomous and precision robotic surgical interventions increased by machine learning, as well as augmented healthcare monitoring, and a discussion and conclusions of the review. All topics addressed in this analysis are supported by examples from the literature. Full article

(This article belongs to the Special Issue The Application of Artificial Intelligence in Surgical Procedures)

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19 pages, 6091 KB

Open AccessArticle

Systematic Evaluation of Zn²⁺, Ca²⁺, and Co²⁺ Doping for Tailoring the Thermal, Structural, Morphological and Magnetic Performance of CdBi_0.1Fe_1.9O₄@SiO₂ Nanocomposites

by Thomas Dippong, Ioan Petean and Oana Cadar

Nanomaterials 2026, 16(4), 259; https://doi.org/10.3390/nano16040259 (registering DOI) - 16 Feb 2026

Abstract

The influence of Zn²⁺, Ca²⁺ and Co²⁺ doping on the thermal, structural, morphological, and magnetic characteristics of CdBi_0.1Fe_1.9O₄ nanoparticles synthetized via the sol–gel technique and calcined at 300, 600, 900 and 1200 °C was [...] Read more.

The influence of Zn²⁺, Ca²⁺ and Co²⁺ doping on the thermal, structural, morphological, and magnetic characteristics of CdBi_0.1Fe_1.9O₄ nanoparticles synthetized via the sol–gel technique and calcined at 300, 600, 900 and 1200 °C was investigated. Thermal analysis revealed the initial formation of metallic glyoxylates up to 300 °C, followed by their decomposition into metal oxides and subsequent ferrite formation. X-ray diffraction revealed that the ferrites were poorly crystallized at lower temperatures, whereas at higher calcination temperatures all nanocomposites exhibited well-crystalized ferrites coexisting with the SiO₂ matrix, except for the Co_0.1Cd_0.9Bi_0.1Fe_1.9O₄@SiO₂ nanocomposite, which formed a single, well-defined crystalline phase. Atomic force microscopy images revealed spherical ferrite particles encapsulated within an amorphous layer, with particle size, surface area, and coating thickness influenced by both the type of dopant ion and the calcination temperature. The structural parameters estimated by X-ray diffraction, as well as the magnetic characteristics, were strongly influenced by the dopant type and thermal treatment. These results demonstrate that the structural and magnetic characteristics of CdBi₀.₁Fe₁.₉O₄ ferrites can be effectively tuned through controlled doping and calcination, providing insights for the design of tailored functional applications. Full article

(This article belongs to the Special Issue Preparation, Characterization, Properties, Simulation, and Applications of Nanostructured Materials)

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15 pages, 640 KB

Open AccessArticle

HLA DRB1*01 and *04 Predisposition to Rheumatoid Arthritis and Polymorphisms of the SLCO1B1, MTHFR and PNPLA3 Genes Are Not Associated with Fatty Liver and Hepatotoxicity

by Tatjana Zekić, Nataša Katalinić, Nada Starčević Čizmarević and Aleksandar Čubranić

J. Clin. Med. 2026, 15(4), 1568; https://doi.org/10.3390/jcm15041568 (registering DOI) - 16 Feb 2026

Abstract

Background: Nonalcoholic fatty liver disease (NAFLD) is common in rheumatoid arthritis (RA), and methotrexate (MTX) use raises concern about hepatotoxicity. We evaluated whether HLA-DRB1, PNPLA3, SLCO1B1, and MTHFR variants are associated with NAFLD, liver fibrosis, or MTX toxicity/pharmacokinetics in [...] Read more.

Background: Nonalcoholic fatty liver disease (NAFLD) is common in rheumatoid arthritis (RA), and methotrexate (MTX) use raises concern about hepatotoxicity. We evaluated whether HLA-DRB1, PNPLA3, SLCO1B1, and MTHFR variants are associated with NAFLD, liver fibrosis, or MTX toxicity/pharmacokinetics in RA, after accounting for clinical covariates. Methods: In a cross-sectional cohort of 159 patients with RA, NAFLD, and fibrosis were assessed by FibroScan (CAP ≥ 275 dB/m; LSM > 8 kPa). We compared baseline characteristics by NAFLD status and fitted multivariable models for NAFLD, fibrosis, ALT elevation, and MTX toxicity; MTX pharmacokinetics were analyzed in 111 MTX-treated patients. Multiple testing was controlled using the Benjamini–Hochberg method. Results: The prevalence of NAFLD was 36%, and that of fibrosis was 11%. NAFLD patients had higher CAP and LSM, and markedly greater adiposity indices (body weight, BMI, waist and hip circumference, WC). BMI and WC were independently associated with NAFLD (BMI OR 1.27 per kg/m², 95% CI 1.16–1.40; WC OR 1.06 per cm, 95% CI 1.01–1.12). No HLA-DRB1, PNPLA3, SLCO1B1, or MTHFR variant showed an association that survived multiple-comparison correction. Among MTX users, 21/111 (19%) experienced toxicity. SLCO1B1 and MTHFR variants did not influence MTX pharmacokinetics; age was associated with lower dose-normalized MTX exposure, and cumulative dose was positively associated with exposure. Conclusions: In RA, adiposity—not the tested candidate pharmacogenes—drives NAFLD risk, and SLCO1B1/MTHFR variants do not support MTX dose adjustment. The findings emphasize routine clinical risk factors over single-gene testing for NAFLD and MTX hepatotoxicity in this setting. Full article

(This article belongs to the Special Issue Pharmacotherapy and Patient Care in Rheumatoid Arthritis)

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20 pages, 1650 KB

Open AccessArticle

Assessment of Cu and As in Wheat (Triticum aestivum L.) and Arable Land in the Vicinity of Bor (Serbia): Implications for Food Safety and Human Health

by Danijela Simonović, Daniel Kržanović, Renata Kovačević, Mirjana Šteharnik, Sunčica Stanković, Danijela Urošević and Vesna Krstić

Plants 2026, 15(4), 631; https://doi.org/10.3390/plants15040631 (registering DOI) - 16 Feb 2026

Abstract

Mining exploitation and copper smelting in Bor (Serbia) have led to long-term environmental pollution with toxic metals, primarily copper (Cu) and arsenic (As). The aim of this research was to assess the contamination of arable land and the bioaccumulation of metals in wheat [...] Read more.

Mining exploitation and copper smelting in Bor (Serbia) have led to long-term environmental pollution with toxic metals, primarily copper (Cu) and arsenic (As). The aim of this research was to assess the contamination of arable land and the bioaccumulation of metals in wheat (Triticum aestivum L.), to determine significant differences in copper and arsenic concentrations between the soil and specific wheat tissues across six locations, and to evaluate environmental and health risks in agricultural areas around the Zijin Copper Mine, Serbia. Sampling was carried out at six locations (Brezonik, Veliki Krivelj, Oštrelj, Slatina, Zlot, and Gornjane; L1–L6, respectively). Analyses of soil and wheat to determine toxic elements were performed using the ICP-MS method, while contamination was assessed using descriptive statistics and a combination of several indices (CV, Igeo, EF, CF, Er, RI, PLI, BAF, TF, and HRA). In addition to Cu and As, accompanying elements (Fe and Al) were also included in the analysis, due to their importance as indicators of geogenic and anthropogenic origin. The analysis of the distribution within the root, stem, leaf, and grain of wheat enabled the assessment of bioaccumulation (BAF and TR) and implications for food safety (HRA). The results showed that concentrations of Cu and As at several locations significantly exceed the regulatory limit values, with Slatina-L4 and Oštrelj-L3 identified as the most polluted areas, while Gornjane-L6 can be considered a reference location with minimal risk. Background values were taken from location L6, considered a reference site due to the absence of direct mining and industrial influence (B_Cu—20 mg/kg, B_As—10 mg/kg, and B_ref._Al—33,300 mg/kg). The MANOVA analysis revealed statistically significant differences in copper and arsenic concentrations between the soil and various wheat tissues, with the effect being more pronounced for arsenic. The integrated analysis of indices (RI and PLI) confirmed the pronounced anthropogenic impact and location-specific risks, emphasizing the need for continuous monitoring, locally adapted remediation strategies, and sustainable land management. Full article

(This article belongs to the Section Plant–Soil Interactions)

23 pages, 1629 KB

Open AccessReview

Transcatheter Paravalvular Leak Closure: A Step-by-Step Guide

by Georgios E. Papadopoulos, Ilias Ninios, Sotirios Evangelou, Andreas Ioannides and Vlasis Ninios

J. Cardiovasc. Dev. Dis. 2026, 13(2), 96; https://doi.org/10.3390/jcdd13020096 (registering DOI) - 16 Feb 2026

Abstract

Paravalvular leak (PVL) remains a clinically important complication after surgical or transcatheter valve implantation, presenting predominantly with heart failure (HF) and/or high-shear hemolysis. While redo surgery can be definitive, contemporary candidates frequently carry prohibitive operative risk, positioning transcatheter PVL closure as a key [...] Read more.

Paravalvular leak (PVL) remains a clinically important complication after surgical or transcatheter valve implantation, presenting predominantly with heart failure (HF) and/or high-shear hemolysis. While redo surgery can be definitive, contemporary candidates frequently carry prohibitive operative risk, positioning transcatheter PVL closure as a key therapeutic alternative. However, available outcome data are largely derived from observational series and registries with heterogeneity in PVL mechanisms, prosthesis types, imaging protocols, and endpoint definitions. Standardized frameworks—such as those proposed by the PVL Academic Research Consortium—support harmonized PVL grading and clinically meaningful composite endpoints that integrate imaging/hemodynamic results with patient-centered outcomes. Across datasets, the most consistent determinant of benefit is residual PVL severity: procedural efficacy is most commonly defined as achieving ≤ mild residual regurgitation without prosthetic leaflet interference, device embolization, or major complications. This review provides a step-by-step, phenotype-driven approach to transcatheter PVL closure, emphasizing multimodality imaging (TEE and cardiac CT, with adjunct CMR and PET when appropriate), access and support planning tailored to valve position, and morphology-matched device selection—often requiring modular multi-device strategies for elongated crescentic channels, particularly in hemolysis-predominant presentations. We also synthesize evidence on complications and bailout management, with a focus on preventable high-severity events (leaflet impingement, embolization, stroke/air, vascular injury, tamponade) and standardized pre-release safety checks. Collectively, contemporary practice supports high implant success in experienced programs, with clinical improvement tightly coupled to procedural endpoint quality and careful Heart Team selection. Full article

(This article belongs to the Special Issue Emerging Trends and Advances in Interventional Cardiology)

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18 pages, 5183 KB

Open AccessArticle

Quantifying the Advantage of Vector over Scalar Magnetic Sensor Networks for Undersea Surveillance

by Wenchao Li, Xuezhi Wang, Qiang Sun, Allison N. Kealy and Andrew D. Greentree

Sensors 2026, 26(4), 1290; https://doi.org/10.3390/s26041290 (registering DOI) - 16 Feb 2026

Abstract

Magnetic monitoring of maritime environments is an important problem for monitoring and optimising shipping, as well as national security. New developments in compact, fibre-coupled quantum magnetometers have led to the opportunity to critically evaluate how best to create such a sensor network. Here [...] Read more.

Magnetic monitoring of maritime environments is an important problem for monitoring and optimising shipping, as well as national security. New developments in compact, fibre-coupled quantum magnetometers have led to the opportunity to critically evaluate how best to create such a sensor network. Here we explore various magnetic sensor network architectures for target identification. Our modelling compares networks of scalar vs. vector magnetometers. We implement an unscented Kalman filter approach to perform target tracking, and we find that vector networks provide a significant improvement in target tracking, specifically tracking accuracy and resilience compared with scalar networks. Full article

(This article belongs to the Section Sensor Networks)

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21 pages, 3027 KB

Open AccessArticle

Post-Expansion Carbon Price Forecasting in China’s Emissions Trading Scheme Based on VMD–SVR Model

by Yuehan Fang, Yan Li, Lei Chang, Jianhe Wang and Chuanyu Zhou

Sustainability 2026, 18(4), 2028; https://doi.org/10.3390/su18042028 (registering DOI) - 16 Feb 2026

Abstract

The planned inclusion of the steel and electrolytic aluminum sectors into China’s Carbon Emission Allowance (CEA) market—initially limited to thermal power since 2021—will expand its coverage to approximately 70% of national carbon emissions, significantly influencing carbon pricing. This study employs a Variational Mode [...] Read more.

The planned inclusion of the steel and electrolytic aluminum sectors into China’s Carbon Emission Allowance (CEA) market—initially limited to thermal power since 2021—will expand its coverage to approximately 70% of national carbon emissions, significantly influencing carbon pricing. This study employs a Variational Mode Decomposition–Support Vector Regression (VMD-SVR) model to forecast carbon price fluctuations under three post-expansion scenarios. The results indicate that, in addition to quota allocations, factors such as sectoral emission scales, the CSI 300 Power Index, and the Shanghai Energy Price Index substantially affect price trends. While market expansion induces a short-term price increase, it also stabilizes prices by reducing volatility. Furthermore, different quota allocation methods yield distinct outcomes: equal allocation facilitates a smoother market transition, whereas benchmarking provides stronger incentives for emissions reductions. Full article

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21 pages, 833 KB

Open AccessArticle

Reflexive Governance for UN SDG Implementation: Assessing Capacities in Bulgaria and Romania

by Aneliya Paneva

Sustainability 2026, 18(4), 2026; https://doi.org/10.3390/su18042026 (registering DOI) - 16 Feb 2026

Abstract

Progress toward achieving the 17 Sustainable Development Goals (SDGs) has largely failed to meet initial ambitions and is often associated with increased ecological footprints and spillovers, pointing to inherent tensions within the SDG framework and governance gaps. Applying the 2030 Agenda’s principles places [...] Read more.

Progress toward achieving the 17 Sustainable Development Goals (SDGs) has largely failed to meet initial ambitions and is often associated with increased ecological footprints and spillovers, pointing to inherent tensions within the SDG framework and governance gaps. Applying the 2030 Agenda’s principles places new demands on policy and scientific systems, underscoring the need for enhanced domestic capacities. Drawing on the understanding that addressing the SDGs’ problem characteristics requires moving beyond rational decision-making toward reflexive governance, the paper outlines implications for key cross-cutting capacities. The empirical analysis uses qualitative data from expert interviews and document analysis (2015–2025) to examine the responses of two EU Eastern enlargement countries to the global agenda, complemented by performance assessments. The comparison reveals uneven progress, with some advances in socioeconomic goals contrasted by slower, stagnant, or declining trends in environmental goal achievement. This underscores the need to prioritize environmental sustainability while addressing interdependencies and trade-offs with other goals to realize the transformative purpose of the 2030 Agenda and beyond. However, capacity shortcomings for pursuing such an integrated approach highlight the importance of continued capacity-building within public administration. Bulgaria shows limited SDG steering effects amid ongoing political instability, whereas Romania has emerged as a regional frontrunner through its innovative governance framework and capacity-building program, demonstrating a transformative political impact. Full article

18 pages, 2786 KB

Open AccessArticle

Integrating Bidirectional Mendelian Randomization with Multi-Omics Reveals Causal Serum Metabolites and Novel Metabolic Drivers of Multiple Myeloma

by Yuanheng Liu, Daoyuan Qin, Haohan Ye, Lujun Tang and Xiaoli Li

Int. J. Mol. Sci. 2026, 27(4), 1904; https://doi.org/10.3390/ijms27041904 (registering DOI) - 16 Feb 2026

Abstract

Multiple myeloma (MM) is a clonal plasma cell neoplasm characterized by autonomous immunoglobulin overproduction. Despite associations between serum metabolites and MM, causal mechanisms remain unclear. Here, we employed bidirectional Mendelian randomization (MR) using 452 serum metabolites to elucidate causal associations with MM risk. [...] Read more.

Multiple myeloma (MM) is a clonal plasma cell neoplasm characterized by autonomous immunoglobulin overproduction. Despite associations between serum metabolites and MM, causal mechanisms remain unclear. Here, we employed bidirectional Mendelian randomization (MR) using 452 serum metabolites to elucidate causal associations with MM risk. The inverse variance-weighted (IVW) method was prioritized, complemented by MR-Egger and weighted median (WM) analyses to address horizontal pleiotropy. Sensitivity analyses—including Cochran’s Q test, MR-Egger intercept evaluation, and leave-one-out (LOO) robustness checks—confirmed result stability. Pathway enrichment was performed using MetaboAnalyst 6.0. RNA-seq data were integrated to identify transcriptional regulators and signaling pathways mediating serum metabolite-driven MM. Among 21 metabolites significantly associated with MM, 8 exhibited protective inverse correlations, while 13 showed risk-enhancing effects. Sensitivity analyses further confirmed the validity of the observed relationships, while bidirectional MR confirmed no reverse causality. Pathway enrichment highlighted valine/leucine/isoleucine biosynthesis and biotin metabolism as pivotal pathways. Integrating transcriptomic data revealed 11 overlapping genes enriched in metal ion transmembrane transporter activity and glycosaminoglycan biosynthesis—chondroitin sulfate/dermatan sulfate. This study established a causal relationship between specific serum metabolites and MM and revealed that key genes may affect the development of MM through metabolic-epigenetic crosstalk, providing preliminary insights into potential therapeutic targets. Full article

(This article belongs to the Special Issue 25th Anniversary of IJMS: Updates and Advances in Molecular Oncology)

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9 pages, 622 KB

Open AccessArticle

Adolescents’ Experience with a Conversational Agent for Depression

by Alanna Testerman, Arjun Roshik Bharat, Tyrique Patterson and Eduardo Bunge

Information 2026, 17(2), 204; https://doi.org/10.3390/info17020204 (registering DOI) - 16 Feb 2026

Abstract

Conversational Agents have been showing promise for depression in adults in the short-term. Although, there has been little research done for conversational agents (CAs) with depression in adolescents. This study aimed to determine adolescents’ user experience with Athenabot, a behavioral activation CA for [...] Read more.

Conversational Agents have been showing promise for depression in adults in the short-term. Although, there has been little research done for conversational agents (CAs) with depression in adolescents. This study aimed to determine adolescents’ user experience with Athenabot, a behavioral activation CA for depression. The study included 66 participants who interacted with Athenabot. Participants were aged 13 to 18 (mean = 14.12) and predominantly identified as female (56.1%). Participants’ confidence in the CA’s utility to improve mood significantly increased from baseline to post-intervention (p < 0.001). Adolescents provided an acceptable Net Promoter Score of 6.73. Positive themes from feedback included the CA being helpful and favorably viewed, while negative themes included its perceived audience-dependency and impersonal nature. Recommendations for improvement included reducing repetitive questions and enhancing personalization. Adolescents significantly preferred multiple-choice questions over typed response questions (p < 0.05). However, there were no significant differences in preference for emojis, memes, or GIFs. Adolescents reported an increased confidence that the CA could improve their mood. While the CAs received acceptable support, feedback highlighted a need for improved engagement and personalization. Adolescents favored multiple-choice button questions over typed responses and preferred GIFs over memes and emojis, with no significant demographic differences. Full article

(This article belongs to the Special Issue Information Technology for Smart Healthcare)

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16 pages, 3335 KB

Open AccessArticle

A Robust mmWave Radar Framework for Accurate People Counting and Motion Classification

by Nuobei Zhang, Haoxuan Li, Adnan Zahid, Yue Tian and Wenda Li

Sensors 2026, 26(4), 1289; https://doi.org/10.3390/s26041289 (registering DOI) - 16 Feb 2026

Abstract

People counting and occupancy monitoring play a vital role in applications such as intelligent building management, safety control, and resource optimization in future smart cities. Conventional camera and infrared-based methods often suffer from privacy risks, lighting dependency, and limited robustness in complex indoor [...] Read more.

People counting and occupancy monitoring play a vital role in applications such as intelligent building management, safety control, and resource optimization in future smart cities. Conventional camera and infrared-based methods often suffer from privacy risks, lighting dependency, and limited robustness in complex indoor environments. In this paper, we present a 60 GHz millimeter-wave (mmWave) radar-based occupancy monitoring system that enables accurate and privacy-preserving people counting. The proposed system leverages echo signals processed through Doppler and range spectrogram and analyzed by an enhanced ResNet-50 deep learning model to classify motion states and count individuals. Experimental results collected in a typical indoor environment demonstrate that the system achieves 95.45% accuracy across 6 classes of movements and 98.86% accuracy for people counting (0–3 persons). The method also shows strong adaptability under limited data and robustness to Gaussian blur interference, providing an efficient and reliable solution for intelligent indoor occupancy monitoring. Full article

(This article belongs to the Special Issue Human Signal Processing Based on Wearable Non-Invasive Device: 2nd Edition)

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17 pages, 307 KB

Open AccessArticle

The Social Distribution of Climate Change Risk Perception: Unraveling Intersectional Patterns of Concern in the United States

by Musabber Ali Chisty

Climate 2026, 14(2), 58; https://doi.org/10.3390/cli14020058 (registering DOI) - 16 Feb 2026

Abstract

The escalating frequency and severity of extreme weather events globally have underscored the critical importance of addressing anthropogenic climate change. Countries that contribute disproportionately to global warming relative to their population size bear an urgent responsibility to mitigate climate risks. However, effecting substantive [...] Read more.

The escalating frequency and severity of extreme weather events globally have underscored the critical importance of addressing anthropogenic climate change. Countries that contribute disproportionately to global warming relative to their population size bear an urgent responsibility to mitigate climate risks. However, effecting substantive policy change requires a broad public consensus to compel legislative action, a process fundamentally dependent on risk perception. It is theorized that individuals, households, and communities with higher levels of climate change risk perception are more inclined to adopt mitigation behaviors and support collective action. Such perception, however, varies considerably across social dimensions. This study aims to examine how sociodemographic factors shape climate change risk perception among Americans and how intersectionality reveals nuanced patterns beyond single-axis analysis. Using data from the 2023 National Survey of Health Attitudes, the analysis demonstrates that gender, race/ethnicity, educational attainment, religiosity, marital status, and geographic region serve as strong predictors of climate risk perception. Further intersectional analysis reveals that individuals with multiple marginalized social identities, such as race, gender, and socioeconomic status, perceive climate risk distinctly from those without such compounded identities. The study concludes that effective climate communication and policy interventions must prioritize sociodemographic diversity and integrate an intersectional lens to address differential vulnerabilities and perceptions equitably. Full article

18 pages, 3887 KB

Open AccessArticle

The Interplay Between Topographic Gradients and Lake Effects on the Spatiotemporal Dynamics of Surface Environmental Variables in the Qinghai Lake Riparian Zone

by Fei Li, Minghao Liu, Zekun Ding, Chen Shi, Maoding Zhou and Yafeng Guo

Remote Sens. 2026, 18(4), 620; https://doi.org/10.3390/rs18040620 (registering DOI) - 16 Feb 2026

Abstract

As a critical climate regulator on the Qinghai–Xizang Plateau, Qinghai Lake exerts important influences on surrounding surface environmental conditions. Using MODIS remote sensing data and topographic information from 2000 to 2024, this study analyzed the spatiotemporal variations in land surface temperature (LST), normalized [...] Read more.

As a critical climate regulator on the Qinghai–Xizang Plateau, Qinghai Lake exerts important influences on surrounding surface environmental conditions. Using MODIS remote sensing data and topographic information from 2000 to 2024, this study analyzed the spatiotemporal variations in land surface temperature (LST), normalized difference vegetation index (NDVI), and temperature vegetation dryness index (TVDI) in the 10-km riparian zone. The buffer was subdivided into five 2-km distance gradients to quantify the attenuation of lake effects and their interaction with topographic factors. The results indicate pronounced seasonal contrasts and distance-dependent differentiation of surface variables. LST exhibited clear seasonal variability, with peak values in the second and third quarters (Q2 and Q3). During Q2, the near-shore zone (0–2 km) remained notably cooler by approximately 2–3 °C (23.8 °C) than intermediate and distal zones (25.4–26.8 °C), indicating a moderate lake-related cooling effect during the early warm season. NDVI showed consistent seasonal phenology across all buffers, reaching maximum values in Q3, while mean NDVI values increased gradually with distance from the lake, ranging approximately from 0.48 in the near-shore zone to 0.51 in the distal zone. TVDI displayed distinct seasonal and spatial patterns, with relatively low and stable values in the near-shore zone throughout the year and a pronounced seasonal minimum in the distal zone during Q3 (0.57). These findings highlight strong seasonal and spatial heterogeneity of surface environmental conditions in the Qinghai Lake riparian zone. The observed patterns suggest that lake proximity and topographic gradients jointly influence hydrothermal conditions and vegetation dynamics at the landscape scale, providing quantitative evidence for understanding surface–environmental gradients in alpine lake systems. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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13 pages, 647 KB

Open AccessArticle

Endometriosis Is Associated with Increased Serum and Peritoneal Fluid Concentrations of Chromogranin A and Its Derivatives

by Alicja Sztokfisz-Ignasiak, Maja Owe-Larsson, Maciej Maj, Hubert Rytel, Kateryna Shevchenko, Filip Dąbrowski, Piotr Laudański, Mikołaj Pater, Izabela Róża Janiuk and Jacek Malejczyk

J. Clin. Med. 2026, 15(4), 1567; https://doi.org/10.3390/jcm15041567 (registering DOI) - 16 Feb 2026

Abstract

Background/Objectives: Endometriosis is a prevalent gynecological illness associated with chronic pain, inflammation, and infertility, as ectopic endometrial lesions are formed. No fully effective treatment is available, and the pathogenesis of this disease is unclear. The survival of ectopic endometrial cells is facilitated by [...] Read more.

Background/Objectives: Endometriosis is a prevalent gynecological illness associated with chronic pain, inflammation, and infertility, as ectopic endometrial lesions are formed. No fully effective treatment is available, and the pathogenesis of this disease is unclear. The survival of ectopic endometrial cells is facilitated by their low susceptibility to apoptosis, an immunosuppressive environment, and local angiogenesis. Chromogranin A (CgA), a glycoprotein prohormone, modulates various processes including angiogenesis and innate immunity, and its higher levels are detected in neuroendocrine tumors and inflammatory disorders. Since endometriosis may be considered an autoinflammatory disorder, this study aimed to evaluate serum and peritoneal fluid concentrations of CgA and its derivatives, catestatin and pancreastatin, and to correlate these levels with disease severity. Methods: The study was conducted on samples of serum and peritoneal fluid (PF) obtained from 65 women diagnosed with endometriosis and from 60 control individuals who underwent surgery for other reasons. The concentrations of CgA, catestatin, and pancreastatin were assessed in the collected samples by specific enzyme-linked immunosorbent assays. Results: CgA, catestatin, and pancreastatin concentrations were significantly higher in the sera and PF of endometriosis patients compared to controls. There was a correlation between their serum and PF levels, and all tested factors were correlated with each other in both serum and PF. Serum concentrations of CgA, catestatin, and pancreastatin were also associated with disease progression. Receiver operating characteristic (ROC) analysis further confirmed that endometriosis is associated with increased circulating CgA, catestatin, and pancreastatin levels, suggesting that they may be considered markers of endometriosis. Conclusions: The upregulation of CgA and its derivatives in endometriosis may indicate their role in the disease pathogenesis and implicate them as potential diagnostic markers and/or therapeutic targets. Full article

(This article belongs to the Special Issue Management of Endometriosis: Evidence-Based Updated by Collective Clinical Experience and New Perspectives from Research)

20 pages, 9096 KB

Open AccessArticle

Beam Drift Mitigation and Wide-Range Measurement in a Miniaturized Ultrasonic Gas Flowmeter

by Shanfeng Hou, Xueying Xiu, Chengguang Liu, Haochen Lyu and Songsong Zhang

Micromachines 2026, 17(2), 254; https://doi.org/10.3390/mi17020254 (registering DOI) - 16 Feb 2026

Abstract

To mitigate acoustic beam drift, which degrades the signal-to-noise ratio (SNR) and limits the measurement range in ultrasonic gas flowmeters (USFMs), we present a miniaturized transit-time USFM that integrates a single piezoelectric micromachined ultrasonic transducer (PMUT) with a non-axisymmetric conical cavity. This design [...] Read more.

To mitigate acoustic beam drift, which degrades the signal-to-noise ratio (SNR) and limits the measurement range in ultrasonic gas flowmeters (USFMs), we present a miniaturized transit-time USFM that integrates a single piezoelectric micromachined ultrasonic transducer (PMUT) with a non-axisymmetric conical cavity. This design increases acoustic transmission gain and produces anisotropic directivity across orthogonal radiation planes, thereby broadening acoustic coverage along the flow direction and reducing beam steering. With an optimized cavity angle combination of (50°, 70°), the system achieves a 7.4 dB transmission gain and a half-power beamwidth (HPBW) of 29.1°. Experimental validation demonstrates a sound pressure attenuation of only 0.72 dB at 18.74 m/s. Within the 0.06–12 m³/h flow range, the USFM exhibits indication errors of ±2% (<1 m³/h) and ±1.5% (≥1 m³/h), with repeatability below 0.5%. The performance meets the Class 1.5 accuracy standard specified in CJ/T 477-2015, offering an innovative solution for wide-range miniaturized gas flow measurement. Full article

(This article belongs to the Special Issue Acoustic Transducers and Their Applications, 3rd Edition)

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10 pages, 390 KB

Open AccessArticle

Characteristics, Prognosis and Reasons for Opting-Out of Treatment in Patients with Untreated Pancreatic Cancer

by Morten Ladekarl and Mogens Tornby Stender

Curr. Oncol. 2026, 33(2), 116; https://doi.org/10.3390/curroncol33020116 (registering DOI) - 16 Feb 2026

Abstract

Background: About 40% of patients with pancreatic cancer (PC) are left untreated. Identification of the modifiable factors for opting out could increase the number eligible for treatment. Methods: We first assessed the completeness of registration. Next, we identified patients residing in the North [...] Read more.

Background: About 40% of patients with pancreatic cancer (PC) are left untreated. Identification of the modifiable factors for opting out could increase the number eligible for treatment. Methods: We first assessed the completeness of registration. Next, we identified patients residing in the North Denmark Region, included 2023/24 in the Danish Pancreas Cancer Database (DPCD), registered as “no treatment”. We supplemented register data with health record data, including reasons for opting out of treatment. Results: Registration in DPCD was complete compared to the National Clinical Cancer Database, except for one patient. Six patients had other tumors. Of a total of 91 patients, 79% were >75 years old, 2/3 were in performance status (PS) > 2, more than half were socially or physically fragile, while 42% had significant comorbidity. Only 20% were referred to an oncologist. The median overall survival was 2 months, and the 1-year survival was 6%. Clinical stage and PS were prognostic in multivariable analysis. In 70%, poor PS was a reason for opting out of treatment, while 11% declined treatment without objective reasons. Conclusions: Poor PS, frailty, or patients’ wishes explained 89% opting out of treatment. On a patient level, modifiable factors seem limited in this population. Full article

(This article belongs to the Section Gastrointestinal Oncology)

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21 pages, 4286 KB

Open AccessFeature PaperArticle

Synthesis of Carbon Nanotubes on Active Silica for Enhanced Cementitious Mortars

by Alaíde Marta dos Santos, Cláudio Ernani Martins Oliveira, Viviany Geraldo, Jaqueline do Carmo Lima Carvalho and Wanna Carvalho Fontes

Processes 2026, 14(4), 676; https://doi.org/10.3390/pr14040676 (registering DOI) - 16 Feb 2026

Abstract

The incorporation of carbon nanotubes (CNT) into cementitious composites has shown strong potential for enhancing mechanical performance. However, conventional dispersion methods, such as ultrasonication and chemical functionalization, are costly, complex, and difficult to scale for construction applications. This study introduces an alternative approach [...] Read more.

The incorporation of carbon nanotubes (CNT) into cementitious composites has shown strong potential for enhancing mechanical performance. However, conventional dispersion methods, such as ultrasonication and chemical functionalization, are costly, complex, and difficult to scale for construction applications. This study introduces an alternative approach based on the in situ synthesis of CNT on active silica grains, which enables their direct incorporation into mortar formulations. The material was produced via chemical vapor deposition and characterized by scanning electron microscopy, thermogravimetric analysis, energy-dispersive spectroscopy, and Fourier-transform infrared spectroscopy. The resulting nanostructured active silica (NAS) exhibited high carbon content (80.7%) and a 1350% yield, confirming efficient nanotubular deposition. Residual oxygen (9.12%), Mg (0.75%), and Al (0.17%) indicated partial retention of catalytic species, while Fe–Co promoters with Mg–Al modifiers enabled a catalytically active surface favorable to CNT growth. Mortars incorporating NAS restored the flexural strength losses associated with cement replacement by silica, achieving values comparable to the reference mixture and outperforming the silica-only sample; compressive strength increased by ~16.5%. These results demonstrate that NAS promotes effective CNT dispersion at the composite scale without additional dispersion techniques, reduces process complexity, and adds value to commercial silica, providing a scalable route for developing nanostructured cementitious composites for construction applications. Full article

(This article belongs to the Special Issue Production, Purification and Applications of Carbon Nanomaterials)

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20 pages, 1781 KB

Open AccessArticle

Effect of Pyrolysis Temperature on Chemical Structure and Thermal Stability of Digestate-Based Biochar

by Justyna Kujawska, Wojciech Cel, Barbara Charmas and Dorota Szala

Energies 2026, 19(4), 1043; https://doi.org/10.3390/en19041043 (registering DOI) - 16 Feb 2026

Abstract

Biochar obtained from digestate is a promising material in the context of digestate management. However, it is important to note that the properties of the resulting material are largely dependent on the parameters of the pyrolysis process, with temperature being a particularly significant [...] Read more.

Biochar obtained from digestate is a promising material in the context of digestate management. However, it is important to note that the properties of the resulting material are largely dependent on the parameters of the pyrolysis process, with temperature being a particularly significant factor. The objective of this study was to evaluate the impacts of the digestate pyrolysis temperature on the chemical structure, thermal stability, and thermal decomposition characteristics of biochar produced at temperatures of 400, 500, 600, and 800 °C in an inert nitrogen atmosphere. Material characterization was performed using a range of analytical techniques, including elemental analysis, FTIR spectroscopy, thermogravimetric analysis (TGA/DTG), and coupled TGA–FTIR analysis, in order to identify volatile products released during the heating process. The results demonstrated that elevating the pyrolysis temperature results in progressive carbonization and aromatization of the carbon structure. Concurrently, functional groups containing oxygen and hydrogen were eliminated, as evidenced by declines in the H/C and O/C atomic ratios. FTIR analysis confirmed the disappearance of aliphatic and hydroxyl bands, as well as the dominance of aromatic structures and mineral components in biochar subjected to high-temperature treatment. The TGA results demonstrated an enhancement in thermal stability with increasing pyrolysis temperature. Concurrently, the TGA–FTIR analysis revealed a substantial decline in the emission of volatile decomposition products from biochar obtained at temperatures ≥600 °C. Overall, the pyrolysis temperature of digestate determines the utilization potential of the resulting biochar; in particular, low-temperature biochar can be used as a soil amendment and methane fermentation stimulant, while high-temperature biochar can be used for contaminant immobilization in soil and long-term carbon sequestration. Full article

(This article belongs to the Special Issue Advances in Waste-to-Energy Technologies)

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24 pages, 11174 KB

Open AccessArticle

JMSC: Joint Spatial–Temporal Modeling with Semantic Completion for Audio–Visual Learning

by Xinfu Xu, Fan Yang and Zhibin Yu

Sensors 2026, 26(4), 1288; https://doi.org/10.3390/s26041288 (registering DOI) - 16 Feb 2026

Abstract

‌Audio–visual learning‌ seeks to achieve holistic scene understanding by integrating auditory and visual cues. Early research focused on fully fine-tuning pre-trained models, incurring high computational costs. Consequently, recent studies have adopted ‌parameter-efficient tuning‌ methods to adapt large-scale vision models to the audio–visual domain. [...] Read more.

‌Audio–visual learning‌ seeks to achieve holistic scene understanding by integrating auditory and visual cues. Early research focused on fully fine-tuning pre-trained models, incurring high computational costs. Consequently, recent studies have adopted ‌parameter-efficient tuning‌ methods to adapt large-scale vision models to the audio–visual domain. Despite the competitive performance of existing methods, several challenges persist. Firstly, effectively leveraging the ‌complementary semantics‌ between the audio and visual modalities remains difficult, as these two modalities capture fundamentally different aspects of a video. Secondly, comprehending ‌dynamic video context is challenging because both spatial attributes (such as scale) and temporal characteristics (such as motion) of objects co-evolve over time, making semantic comprehension more complex. To address these challenges, we propose a novel framework, named Joint Spatial–Temporal Modeling with Semantic Completion (JMSC). JMSC introduces cross-modal latent reconstruction, which moves beyond shallow correlation by encouraging the model to reconstruct one modality’s complete semantic summary from a masked version of its counterpart. Furthermore, JMSC learns a unified representation of video spatial attributes and temporal changes by jointly modeling them under audio guidance, enabling accurate localization and consistent tracking in dynamic video scenes. Experimental results demonstrate that JMSC achieves state-of-the-art performance across multiple downstream tasks while maintaining high computational efficiency. Full article

(This article belongs to the Special Issue Deep Learning Technologies and Their Applications in Image Processing, Computer Vision, and Computational Intelligence)

31 pages, 2938 KB

Open AccessArticle

Reactive Power Optimal Configuration in Distribution Networks with UPQC Dynamic Voltage Regulation

by Bin Lin, Zhensong Zeng, Yan Huang, Xiangyu Wang, Cheng Lin and Yan Zhang

Energies 2026, 19(4), 1042; https://doi.org/10.3390/en19041042 (registering DOI) - 16 Feb 2026

Abstract

To address voltage violation issues in active distribution networks, this paper proposes a reactive power optimal configuration method that considers the dynamic voltage regulation strategy of a unified power quality conditioner (UPQC). First, an adaptive network partitioning model based on reactive power–voltage modularity [...] Read more.

To address voltage violation issues in active distribution networks, this paper proposes a reactive power optimal configuration method that considers the dynamic voltage regulation strategy of a unified power quality conditioner (UPQC). First, an adaptive network partitioning model based on reactive power–voltage modularity is developed. This model identifies high-sensitivity regions and determines preferred siting locations for UPQCs. Next, a voltage stability index is derived using a two-bus equivalent decoupled model. The classical holomorphic embedding method is then applied to track the variation in the voltage stability index, with respect to the embedding factor. Based on these results, weak buses are identified as key buses for evaluating the effectiveness of voltage control measures. Subsequently, a bi-level optimisation model that integrates planning and operation is formulated by incorporating the dynamic voltage regulation strategy of the UPQC. The model aims to minimise the equivalent annual total cost of the deployment scheme while reducing bus voltage deviations, thereby enabling proactive mitigation of voltage violations in active distribution networks. Finally, case studies conducted on an extended IEEE-33 bus power system and real-world distribution networks demonstrate that the proposed UPQC optimal configuration method effectively alleviates voltage violation problems. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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