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The seasonal dynamics, sources, and ecological risks of polycyclic aromatic hydrocarbons (PAHs) in inland freshwater ports remain largely limited, despite extensive research on coastal port PAH pollution. Here, we investigated sixteen U.S. EPA priority PAHs in surface waters of Jiujiang Port, a major inland hub along the Yangtze River, China. Total PAH concentrations ranged from 21.8 to 121.0 ng·L⁻¹ (mean: 65.0 ng L⁻¹), which represents relatively low levels compared with coastal ports worldwide. In this study, significant seasonal variations were also observed, with higher concentrations during the dry season than the wet season. Diagnostic ratios and multivariate analyses indicated petroleum combustion as the dominant source, while PAH levels showed positive correlations with turbidity and COD_Mn, underscoring the role of suspended particulates and organic load. Ecological risk assessment revealed low to moderate risks, with elevated risks in the dry season. These findings provide novel insights into PAH pollution in inland port systems and offer a scientific basis for pollution control and ecological management under the Yangtze River Protection framework. Full article

(This article belongs to the Special Issue Legacy and Emerging Contaminants in the Water Environment Under Contemporary Global Changes)

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24 pages, 6868 KB

Open AccessArticle

Study on Multi-Parameter Collaborative Optimization of Motor-Pump Stator Slotting for Cogging Torque and Noise Suppression Mechanism

by Geqiang Li, Xiaojie Guo, Xiaowen Yu, Min Zhao and Shuai Wang

World Electr. Veh. J. 2026, 17(1), 39; https://doi.org/10.3390/wevj17010039 (registering DOI) - 13 Jan 2026

Abstract

As a highly integrated and compact power unit, the motor-pump finds critical applications in emerging electric vehicle (EV) domains such as electro-hydraulic braking and steering systems, where its vibration and noise performance directly impacts cabin comfort. A key factor limiting its NVH (Noise, Vibration, and Harshness) performance is the electromagnetic vibration and noise induced by the cogging torque of the built-in brushless DC motor (BLDCM). Traditional suppression methods that rely on stator auxiliary slots exhibit certain limitations. To address this issue, this paper proposes a collaborative optimization method integrating multi-parameter scanning and response surface methodology (RSM) for the design of auxiliary slots on the motor-pump’s stator teeth. The approach begins with a multi-parameter scanning phase to identify a promising region for global optimization. Subsequently, an accurate RSM-based prediction model is established to enable refined parameter tuning. Results demonstrate that the optimized stator structure achieves a 91.2% reduction in cogging torque amplitude for the motor-pump. Furthermore, this structure effectively suppresses radial electromagnetic force, leading to a 5.1% decrease in the overall sound pressure level. This work provides a valuable theoretical foundation and a systematic design methodology for cogging torque mitigation and low-noise design in motor-pumps. Full article

(This article belongs to the Section Propulsion Systems and Components)

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18 pages, 5889 KB

Open AccessArticle

High-Resolution Mapping Coastal Wetland Vegetation Using Frequency-Augmented Deep Learning Method

by Ning Gao, Xinyuan Du, Peng Xu, Erding Gao and Yixin Yang

Remote Sens. 2026, 18(2), 247; https://doi.org/10.3390/rs18020247 (registering DOI) - 13 Jan 2026

Abstract

Coastal wetland vegetation exhibits pronounced spectral mixing, complex mosaic spatial patterns, and small target sizes, posing considerable challenges for fine-grained classification in high-resolution UAV imagery. At present, remote sensing classification of ground objects based on deep learning mainly relies on spectral and structural features, while the frequency domain features of ground objects are not fully considered. To address these issues, this study proposes a vegetation classification model that integrates spatial-domain and frequency-domain features. The model enhances global contextual modeling through a large-kernel convolution branch, while a frequency-domain interaction branch separates and fuses low-frequency structural information with high-frequency details. In addition, a shallow auxiliary supervision module is introduced to improve local detail learning and stabilize training. With a compact parameter scale suitable for real-world deployment, the proposed framework effectively adapts to high-resolution remote sensing scenarios. Experiments on typical coastal wetland vegetation including Reeds, Spartina alterniflora, and Suaeda salsa demonstrate that the proposed method consistently outperforms representative segmentation models such as UNet, DeepLabV3, TransUNet, SegFormer, D-LinkNet, and MCCA across multiple metrics including Accuracy, Recall, F1 Score, and mIoU. Overall, the results show that the proposed model effectively addresses the challenges of subtle spectral differences, pervasive species mixture, and intricate structural details, offering a robust and efficient solution for UAV-based wetland vegetation mapping and ecological monitoring. Full article

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11 pages, 406 KB

Open AccessArticle

Effect of Different Types of Propolis on the Quality Characteristics of Fermented Sucuk

by Zeyneb Sadıgzade, Recep Kara and Ali Sorucu

Fermentation 2026, 12(1), 46; https://doi.org/10.3390/fermentation12010046 (registering DOI) - 13 Jan 2026

Abstract

Fermented sucuk is a fermented food product widely consumed and enjoyed by people in Türkiye. Spices and other additives are used in the production of fermented sucuk. Due to the increasing demand for natural and healthy food consumption, the need for natural additives is also growing. Propolis is naturally obtained from honeybee hives and consists of bioactive compounds with antimicrobial and antioxidant properties. Studies have been conducted on the addition of propolis to various meat and meat products, as well as other food products. However, no studies have been found on the addition of propolis to fermented sucuk. The aim was to investigate the effect of different types of propolis (red, green, brown) on the quality characteristics of fermented sucuks. Microbiological, physicochemical, textural and sensory analyses were performed on the sucuk samples produced. It was observed that propolis did not have an adverse effect on the analysis results of propolis-added sucuks. In particular, it was determined that the growth of pathogenic bacteria was inhibited in propolis-added sucuk, resulting in low TBARS values, and other analyses yielded results in line with these standards. Based on these findings, the addition of propolis has been shown to have a positive effect on the quality of fermented sucuk. Full article

(This article belongs to the Special Issue Advances in Fermented Foods and Beverages)

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22 pages, 4957 KB

Open AccessArticle

Machine Learning-Based Algorithm for the Design of Multimode Interference Nanodevices

by Roney das Mercês Cerqueira, Vitaly Félix Rodriguez-Esquerre and Anderson Dourado Sisnando

Nanomanufacturing 2026, 6(1), 3; https://doi.org/10.3390/nanomanufacturing6010003 (registering DOI) - 13 Jan 2026

Abstract

Multimode interference photonic nanodevices have been increasingly used due to their broad functionality. In this study, we present a methodology based on machine learning algorithms for inverse design capable of providing the output port position (x-axis coordinate) and MMI region length (y-axis coordinate) for achieving higher optical signal transfer power. This is sufficient to design Multimode Interference 1 × 2, 1 × 3, and 1 × 4 nanodevices as power splitters in the wavelength range between 1350 and 1600 nm, which corresponds to the E, S, C, and L bands of the optical communications window. Using Multilayer Perceptron artificial neural networks, trained with k-fold cross-validation, we successfully modeled the complex relationship between geometric parameters and optical responses with high precision and low computational cost. The results of this project meet the requirements for photonic device projects of this nature, demonstrating excellent performance and manufacturing tolerance, with insertion losses ranging from 0.34 dB to 0.58 dB. Full article

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22 pages, 1147 KB

Open AccessFeature PaperArticle

Toward Objective Assessment of Positive Affect: EEG and HRV Indices Distinguishing High and Low Arousal Positive Affect

by Yuri Nakagawa, Tipporn Laohakangvalvit, Toshitaka Matsubara, Keiko Tagai and Midori Sugaya

Sensors 2026, 26(2), 521; https://doi.org/10.3390/s26020521 (registering DOI) - 13 Jan 2026

Abstract

Positive affect comprises distinct affective states that differ in arousal level, such as high-arousal positive affect (HAPA) and low-arousal positive affect (LAPA), which have been shown to be associated with different effects and effective contexts. In studies of positive affect, it is therefore important not only to assess overall positivity but also to distinguish between different types of positive affect. Existing assessments rely mainly on self-reports, which may be unreliable for individuals with limited self-report abilities. The aim of this study was to examine whether physiological indices can discriminate between HAPA and LAPA. Participants were presented with eight video stimuli designed to elicit either HAPA or LAPA, and self-report measures were used as manipulation checks to define the affective conditions, while heart rate variability (HRV) and electroencephalography (EEG) were recorded. HRV indices did not show significant differences between the two affective conditions. In contrast, analyses of EEG relative power revealed significant differences between the HAPA and LAPA conditions. These findings demonstrate that, under the present experimental conditions, physiological differences between low- and high-arousal positive affect can be captured in EEG signals using relative power, a simple and reproducible analytical index, whereas no such differences were observed in HRV indices. Full article

(This article belongs to the Special Issue Feature Papers in Smart Sensing and Intelligent Sensors 2025)

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25 pages, 8211 KB

Open AccessArticle

EMG-Spectrogram-Empowered CNN Stroke-Classifier Model Development

by Katherine, Riries Rulaningtyas and Kalaivani Chellappan

Life 2026, 16(1), 114; https://doi.org/10.3390/life16010114 (registering DOI) - 13 Jan 2026

Abstract

Stroke is a leading cause of death and long-term disability worldwide, with ischemic stroke accounting for approximately 62.4% of all cases. This condition often results in persistent motor dysfunction, significantly reducing patients’ productivity. The effectiveness of rehabilitation therapy is crucial for post-stroke motor recovery. However, limited access to rehabilitation services particularly in low- and middle-income countries remains a major barrier due to a shortage of experienced professionals. This challenge also affects home-based rehabilitation, an alternative to conventional therapy, which primarily relies on standard evaluation methods that are heavily dependent on expert interpretation. Electromyography (EMG) offers an objective and alternative approach to assessing muscle activity during stroke therapy in home environments. Recent advancements in deep learning (DL) have opened new avenues for automating the classification of EMG data, enabling differentiation between post-stroke patients and healthy individuals. This study introduces a novel methodology for transforming EMG signals into time–frequency representation (TFR) spectrograms, which serve as input for a convolutional neural network (CNN) model. The proposed Tri-CCNN model achieved the highest classification accuracy of 93.33%, outperforming both the Shallow CNN and the classic LeNet-5 architecture. Furthermore, an in-depth analysis of spectrogram amplitude distributions revealed distinct patterns in stroke patients, demonstrating the method’s potential for objective stroke assessment. These findings suggest that the proposed approach could serve as an effective tool for enhancing stroke classification and rehabilitation procedures, with significant implications for automating rehabilitation monitoring in home-based rehabilitation (HBR) settings. Full article

(This article belongs to the Special Issue Etiology, Prediction and Prognosis of Ischemic Stroke)

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20 pages, 17607 KB

Open AccessArticle

Parasitic Inductance Assessment of E-GaN DPT Circuit Through Finite Element Analysis

by Xing-Rou Chen, Huang-Jen Chiu, Yun-Yen Chen, Yi-Xuan Yang and Yu-Chen Liu

Energies 2026, 19(2), 383; https://doi.org/10.3390/en19020383 (registering DOI) - 13 Jan 2026

Abstract

This article explores the high-frequency characteristics of gallium nitride (GaN) power-switching devices and evaluates their application performance using a double-pulse test (DPT) circuit model. With the increasing adoption of GaN power-switching devices in high-performance and miniaturized electronic products, their low junction capacitance makes them highly suitable for high-frequency applications. However, parasitic inductance in the power loop can introduce resonance phenomena, impacting system stability and switching performance. To address this, this study integrates the parasitic parameters of printed circuit boards (PCBs) with the nonlinear junction capacitance characteristics of GaN devices. Finite element analysis (FEA) is employed to extract PCB parasitic inductance values and analyze their effects on GaN power-switching behavior. The findings indicate that precise extraction and analysis of parasitic inductance are critical for optimizing the performance of GaN switching devices. Additionally, this study investigates mitigation strategies to minimize parasitic inductance, ultimately enhancing GaN device design and reliability. The insights from this research provide valuable guidance for the development of GaN power devices in high-frequency applications. Full article

(This article belongs to the Section F3: Power Electronics)

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12 pages, 2079 KB

Open AccessCommunication

Synthesis, Structure, and Physical Properties of RbCr₂Se₂O

by Xiaoning Sun, Pindu Chen, Xiaochun Wen and Hongxiang Chen

Crystals 2026, 16(1), 56; https://doi.org/10.3390/cryst16010056 (registering DOI) - 13 Jan 2026

Abstract

Layered compounds containing the T₂O plane (T = transition metal), which is the anti-type of the CuO₂ plane in cuprate superconductors, have been explored widely because of their diverse physical properties. Among them, KV₂Se₂O has attracted much attention due to its interesting physical properties, especially the magnetic order. In this work, we report a new isostructural chromium oxyselenide, RbCr₂Se₂O. It was synthesized using a solid-state method using Rb₂CO₃ as the source of Rb and O for the title compound, with the assistance of Ba. The compound crystallizes in the space group P4/mmm with lattice parameters a = 4.01123(8) Å and c = 7.49357(18) Å. Magnetic susceptibility measurements indicate an antiferromagnetic transition at 345 K for RbCr₂Se₂O and also above room temperature, as the Néel temperature is T_N ≈ 400 K for KV₂Se₂O. The analysis of variable temperature XRD data reveals the anisotropic thermal expansion of the RbCr₂Se₂O lattice. The almost unchanged lattice parameter a near the transition temperature and the broad peak with an onset temperature of ~360 K in the differential scanning calorimetry data may have a relationship with the magnetic ordering. The measurement of electrical resistivity demonstrates the semiconducting behavior of RbCr₂Se₂O. The thermal activation model and variable-range hopping model are proposed to describe the conduction mechanism in the high- and low-temperature ranges, respectively. Full article

(This article belongs to the Section Inorganic Crystalline Materials)

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14 pages, 2720 KB

Open AccessArticle

Hollow-Core Fiber Properties and System-Level Specifications for Next-Generation Optical Transport Networks

by Bruno Correia and João Pedro

Photonics 2026, 13(1), 71; https://doi.org/10.3390/photonics13010071 (registering DOI) - 13 Jan 2026

Abstract

In light of the recent advances in hollow-core fiber (HCF) design and manufacturing, wide-scale deployments of this fiber type to realize next-generation optical transport networks may become viable in the foreseeable future, with benefits in terms of lower latency and improved capacity/reach. Nevertheless, several uncertainties remain regarding the properties of HCF that can be manufactured at scale, as well as the specifications of optical amplifiers developed to leverage the negligible low linearity of this fiber type. This work evaluates the performance of HCFs considering a wide range of potential fiber and amplifier parameters and compares them with traditional standard single-mode fiber (SSMF) and pure-silica-core fiber (PSCF). The resulting analysis allows us to determine, at a system and network level, the combination of fiber and amplifier parameters that will allow HCF to become a competitive transmission medium for next-generation optical transport networks. Full article

(This article belongs to the Special Issue Specialty Optical Fibers: Advances in Design, Fabrication, Performance and Applications)

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21 pages, 4794 KB

Open AccessArticle

Heat Transfer and Mechanical Performance Analysis and Optimization of Lattice Structure for Electric Vehicle Thermal Management

by Xiaokang Ye, Xiaoxia Sun, Zhixuan Liang, Ran Tian, Mingshan Wei, Panpan Song and Lili Shen

Electronics 2026, 15(2), 347; https://doi.org/10.3390/electronics15020347 (registering DOI) - 13 Jan 2026

Abstract

With the trend toward integrated development in electric vehicles, thermal management components are becoming more compact and highly integrated. This evolution, however, leads to complex spatial layouts of high- and low-temperature fluid circuits, causing localized heat accumulation and unintended heat transfer between channels, which compromises cooling efficiency. Concurrently, these compact components must possess sufficient mechanical strength to withstand operational loads such as vibration. Therefore, designing structures that simultaneously suppress heat transfer and ensure mechanical intensity presents a critical challenge. This study introduces Triply Periodic Minimal Surface (TPMS) and Body-Centered Cubic (BCC) lattice structures as multifunctional solutions to address the undesired heat transfer and mechanical support requirements. Their thermal and mechanical performances are analyzed, and a feedforward neural network model is developed based on CFD simulations to map key structural parameters to thermal and mechanical outputs. A dual-objective optimization approach is then applied to identify optimal structural parameters that balance thermal and mechanical requirements. Validation via CFD confirms that the neural network-based optimization effectively achieves a trade-off between heat transfer suppression and structural strength, providing a reliable design methodology for integrated thermal management systems. Full article

(This article belongs to the Special Issue Electro-Thermal Modelling, Status Estimation and Thermal Management of Electric Vehicles, 2nd Edition)

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30 pages, 1982 KB

Open AccessPerspective

Microfluidic Paper-Based Devices at the Edge of Real Samples: Fabrication Limits, Hybrid Detection, and Perspectives

by Hsing-Meng Wang, Sheng-Zhuo Lee and Lung-Ming Fu

Micromachines 2026, 17(1), 105; https://doi.org/10.3390/mi17010105 (registering DOI) - 13 Jan 2026

Abstract

Microfluidic paper-based analytical devices (µPADs) convert ordinary cellulose into an active analytical platform where capillary gradients shape transport, surface chemistry guides recognition, and embedded electrodes or optical probes translate biochemical events into readable signals. Progress in fabrication—from wax and stencil barriers to laser-defined grooves, inkjet-printed conductive lattices, and 3D-structured multilayers—has expanded reaction capacity while preserving portability. Detection strategies span colorimetric fields that respond within porous fibers, fluorescence and ratiometric architectures tuned for low abundance biomarkers, and electrochemical interfaces resilient to turbidity, salinity, and biological noise. Applications now include diagnosing human body fluids, checking food safety, monitoring the environment, and testing for pesticides and illegal drugs, often in places with limited resources. Researchers are now using learning algorithms to read minute gradients or currents imperceptible to the human eye, effectively enhancing and assisting the measurement process. This perspective article focuses on the newest advancements in the design, fabrication, material selection, testing methods, and applications of µPADs, and it explains how they work, where they can be used, and what their future might hold. Full article

(This article belongs to the Special Issue Microfluidics in Biomedical Research)

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22 pages, 884 KB

Open AccessArticle

Sentiment-Augmented RNN Models for Mini-TAIEX Futures Prediction

by Yu-Heng Hsieh, Keng-Pei Lin, Ching-Hsi Tseng, Xiaolong Liu and Shyan-Ming Yuan

Algorithms 2026, 19(1), 69; https://doi.org/10.3390/a19010069 - 13 Jan 2026

Abstract

Accurate forecasting in low-liquidity futures markets is essential for effective trading. This study introduces a hybrid decision-support framework that combines Mini-TAIEX (MTX) futures data with sentiment signals extracted from 13 financial news sources and PTT forum discussions. Sentiment features are generated using three domain-adapted large language models—FinGPT-internLM, FinGPT-llama, and FinMA—trained on more than 360,000 finance-related texts. These features are integrated with technical indicators in four deep learning models: LSTM, GRU, Informer, and PatchTST. Experiments from June 2024 to June 2025 show that sentiment-augmented models consistently outperform baselines. Backtesting further demonstrates that the sentiment-enhanced PatchTST achieves a 526% cumulative return with a Sharpe ratio of 0.407, highlighting the value of incorporating sentiment into AI-driven futures trading systems. Full article

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16 pages, 528 KB

Open AccessArticle

Assessment of the Treatment of Natural Hazards in the Spanish School Curriculum (Secondary Education and Baccalaureate)

by Álvaro-Francisco Morote, Jorge Olcina and Alberto Alfonso-Torreño

Geosciences 2026, 16(1), 42; https://doi.org/10.3390/geosciences16010042 (registering DOI) - 13 Jan 2026

Abstract

The cut-off low that struck Valencia (Spain) on 29 October 2024, causing 229 deaths, underscored the pressing need to promote awareness and strengthen education on natural hazards, particularly among school-aged students. In this scenario, revising the school curriculum becomes essential to ensure that future generations are prepared to confront the challenges posed by climate change. This study examines how knowledge related to natural hazards is incorporated into the official curricula of Secondary (ages 12 to 16) and Baccalaureate education (ages 16 to 19), based on the Royal Decrees enacted since 2022. The study aims to determine which contents are included, the Specific Competencies addressed, and the pedagogical approaches employed (descriptive, preventive, or critical), while also evaluating the coherence of these elements across subjects and educational levels. Findings reveal a scarce and often fragmented presence of such contents, with a predominance of descriptive approaches and limited emphasis on prevention or critical reflection. The study concludes that risk education should be transversal, contextually grounded, and transformative. Current curricular gaps and that current gaps and overlaps represent an opportunity to reinforce territorial literacy and enhance students’ resilience. Full article

(This article belongs to the Collection Education in Geosciences)

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17 pages, 3406 KB

Open AccessArticle

Study on Microstructure and Properties of Micron Copper Powder-Liquid Metal Gallium Composite Interconnect Joint

by Bo Wang, Siliang He, Guopei Zhang, Menghao Liu, Kaixuan He, Wei Huang and Kailin Pan

Materials 2026, 19(2), 314; https://doi.org/10.3390/ma19020314 (registering DOI) - 13 Jan 2026

Abstract

Liquid gallium (Ga) enables low-temperature transient liquid phase bonding (TLPB), but optimizing microstructure and joint performance remains challenging. Here, we developed a copper (Cu)-powder/liquid-Ga composite paste for Cu/Cu interconnects and systematically studied the effects on the interconnect joint performance of Cu powder particle size (Cu_PS, 10–20, 20–30 and 30–40 μm) and Cu mass fraction (Cu_MF, 10–30 wt%). The microstructure, electrical conductivity, and shear strength of the joint were evaluated, followed by an assessment of bonding temperature, pressure, and time. Under bonding conditions of 220 °C, 5 MPa and 720 min, a dense intermetallic compound (IMC) microstructure predominantly composed of Cu₉Ga₄ and CuGa₂ was formed, yielding an electrical conductivity of approximately 1.1 × 10⁷ S·m⁻¹ and a shear strength of 52.2 MPa, thereby achieving a synergistic optimization of electrical and mechanical properties; even under rapid bonding conditions of 220 °C, 5 MPa and 1 min, the joint still attained a shear strength of 39.2 MPa, demonstrating the potential of this process for high-efficiency, short-time interconnection applications. These results show that adjusting the composite paste formulation and dosage enables Cu–Ga TLPB joints that combine high conductivity with robust mechanical integrity for advanced packaging. Full article

(This article belongs to the Special Issue Advanced Materials Processing Technologies for Lightweight Design)

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