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9 pages, 1378 KB

Open AccessBrief Report

Debris Flow Detection System Based on Tree-Topology Optical Fiber Network

by Keiji Kuroda

Sensors 2026, 26(14), 4409; https://doi.org/10.3390/s26144409 (registering DOI) - 11 Jul 2026

This report proposes a debris flow detection system based on an optical fiber network. Optical communication devices, including a distributed feedback laser, fiber amplifier, and amplified photodetector are used in the detection system. A tree topology fiber network formed by multiple optical couplers [...] Read more.

This report proposes a debris flow detection system based on an optical fiber network. Optical communication devices, including a distributed feedback laser, fiber amplifier, and amplified photodetector are used in the detection system. A tree topology fiber network formed by multiple optical couplers and delay fibers is employed to increase the number of sensor lines in the sensing port. Fiber patch cables or fiber mirrors are used as sensor heads instead of electric cables. The time-division multiplexing technique is used to simultaneously monitor all sensors in the time domain. It is demonstrated that this setup can be used to realize a passive and robust sensor network for debris flow detection by performing simulation experiments at the laboratory level. Results for a five-stage tree-topology are presented, and the extensibility of the proposed approach to a seven-stage network is outlined. Full article

(This article belongs to the Section Sensor Networks)

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20 pages, 1621 KB

Open AccessReview

Numerical Simulation of Die Forging Processes: A Review of Finite Element Modelling Approaches, Material Models and Process Parameters

by Mayar Abdullah Taleb, Géza Husi and Sándor Pálinkás

Appl. Sci. 2026, 16(14), 6968; https://doi.org/10.3390/app16146968 (registering DOI) - 11 Jul 2026

Abstract

Die forging is a widely used production method for manufacturing high-strength components with high accuracy and good mechanical properties. Since the die forging process involves many complicated thermo-mechanical coupled field physical problems, such as large plastic deformation, high temperature, friction, and heat transfer, [...] Read more.

Die forging is a widely used production method for manufacturing high-strength components with high accuracy and good mechanical properties. Since the die forging process involves many complicated thermo-mechanical coupled field physical problems, such as large plastic deformation, high temperature, friction, and heat transfer, etc., experimental studies are difficult and expensive to perform. The numerical simulation method has become the main method of study and optimal design for the die forging process. This paper reviews the published papers on numerical simulation of the die forging process from 2016 to 2026, in a structured literature review of computational simulations dealing with die forging processes. The literature search was conducted using the Scopus and Web of Science databases. After screening and full-text assessment, 24 relevant journal articles were selected for this paper. The articles studied were analyzed in terms of finite element modelling strategies, constitutive and material models used, friction and thermal boundary conditions considered, and process parameters. Typical results obtained from the studies discussed in the paper include stress, strain, temperature, forging load, and metal flow. The current state-of-the-art research has evolved from simple metal-flow predictions to more complex thermo-mechanical models, and even optimization-based models. Most of the current studies are based on experimentally derived constitutive equations, as well as more complex friction and heat-transfer models. Furthermore, studies applying optimization methods (Taguchi methods, design of experiments, machine learning, artificial intelligence) are increasingly common. The growing interest in the digital twin concept and real-time process control is observed. However, experimental validation, thermal contact modelling, and simulation of stress, strain, temperature, and microstructure in one simulation remain key challenges. Full article

(This article belongs to the Section Mechanical Engineering)

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19 pages, 4216 KB

Open AccessArticle

Land-Use Types Regulate Microbial Carbon-Use Efficiency Through Stoichiometric Balance and Resource Limitation in Coastal Saline–Alkaline Soils of the Yellow River Delta

by Haidong Xu, Hongyang Jing, Jianni Sun, Haifei Lu, Rongjia Wang, Qun Gao, Guai Xie, Yiming Wang and Ling Peng

Biology 2026, 15(14), 1130; https://doi.org/10.3390/biology15141130 (registering DOI) - 11 Jul 2026

Abstract

Coastal saline–alkaline land has considerable potential for soil carbon sequestration, but how different land-use types affect microbial resource limitation and carbon-use efficiency (CUE) in coastal saline–alkaline soils remains unclear. Four representative land-use types, namely bare land (BL), wetland (WL), grassland (GL), and forest [...] Read more.

Coastal saline–alkaline land has considerable potential for soil carbon sequestration, but how different land-use types affect microbial resource limitation and carbon-use efficiency (CUE) in coastal saline–alkaline soils remains unclear. Four representative land-use types, namely bare land (BL), wetland (WL), grassland (GL), and forest land (FL), were investigated in the coastal saline–alkaline soils of the Yellow River Delta. Soil physicochemical properties, microbial biomass, and extracellular enzyme activities were measured, and ecoenzymatic stoichiometry, microbial resource limitation, and CUE were subsequently calculated. Compared with BL, vegetated land-use types decreased electrical conductivity by 52.1–95.8%, while soil water content, soil nutrient indicators, and microbial biomass indicators increased by 47.1–77.5%, 2.6–136.8%, and 2.2–274.4%, respectively. WL was mainly phosphorus-limited, whereas BL, GL, and FL were primarily nitrogen-limited. Despite relatively high soil organic carbon and nutrient availability, GL showed the strongest N limitation and was the only land-use type showing C limitation. Model-estimated CUE ranged from 0.544 to 0.579 and followed the order FL > BL > WL > GL. Random forest analysis showed that soil physicochemical properties contributed most to CUE variation (42.9%). Structural equation modeling further indicated that soil physicochemical properties were indirectly associated with CUE, mainly through stoichiometric characteristics and microbial resource limitation, showing positive and negative associations, respectively. These findings provide microbial evidence for optimizing land-use patterns, vegetation restoration, and carbon-oriented ecological restoration in coastal saline–alkaline land. Full article

(This article belongs to the Section Ecology)

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17 pages, 3237 KB

Open AccessArticle

A SAR-Based Classification Model for Assessing Pesticide Toxicity to Apis mellifera

by Nadia Iovine, Anna Lombardo, Alessandra Roncaglioni and Emilio Benfenati

J. Xenobiot. 2026, 16(4), 130; https://doi.org/10.3390/jox16040130 (registering DOI) - 11 Jul 2026

Abstract

Pollinators are essential for maintaining ecosystem stability and agricultural productivity, yet their populations are in decline due to various stressors, including parasites, pathogens, climate change and pesticide exposure. Protecting pollinators has become a priority for environmental safety and food security. Regulatory authorities, including [...] Read more.

Pollinators are essential for maintaining ecosystem stability and agricultural productivity, yet their populations are in decline due to various stressors, including parasites, pathogens, climate change and pesticide exposure. Protecting pollinators has become a priority for environmental safety and food security. Regulatory authorities, including the European Food Safety Authority (EFSA), the Environmental Protection Agency (EPA) and the Organisation for Economic Co-operation and Development (OECD), have guidelines for pesticide risk assessment, but conventional testing methods are costly and time-consuming, limiting their applicability to large chemical datasets. Computational approaches, such as Structure–Activity Relationship (SAR) models, offer efficient alternatives by enabling the rapid screening of pesticides for potential toxicity to pollinators. In this study, we used a dataset of 357 compounds to develop a classification model based on structural alerts to predict oral acute toxicity in Apis mellifera. The model showed a higher Matthews Correlation Coefficient in the training set (0.82), with a moderate decay in the test set (0.56) likely due to applicability domain limits. Despite this, high balanced accuracy (0.80) and sensitivity (0.79) in the test set confirm the model as a reliable tool for the toxicological screening of pesticides. Full article

(This article belongs to the Section Ecotoxicology)

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19 pages, 1175 KB

Open AccessArticle

Tourist Flow Forecasting for Sustainable Scenic Area Management Using a Seasonal Trend Decomposition-Enhanced ConvLSTM Framework

by Lin Zhan, Xiaoyu Sun, Xiaonan Shi and Tingting Wu

Sustainability 2026, 18(14), 7099; https://doi.org/10.3390/su18147099 (registering DOI) - 11 Jul 2026

Abstract

Accurate daily tourist flow prediction is crucial for optimizing tourism management, allocating public resources, and ensuring sustainable ecological development in scenic areas. However, forecasting visitor volume at the Jiuzhaigou Scenic Area poses significant challenges for conventional deep learning architectures due to the data’s [...] Read more.

Accurate daily tourist flow prediction is crucial for optimizing tourism management, allocating public resources, and ensuring sustainable ecological development in scenic areas. However, forecasting visitor volume at the Jiuzhaigou Scenic Area poses significant challenges for conventional deep learning architectures due to the data’s high volatility, strong seasonality, and complex non-stationarity. To address these limitations, this study proposes a novel integrated forecasting framework, SD-ConvLSTM-Attn (Seasonal Trend Decomposition-Enhanced Convolutional Long Short-Term Memory with Attention). Using daily visitor data spanning 30 March 2015 to 10 April 2026, collected from the official website of the Jiuzhaigou Scenic Area on 11 April 2026, the model employs a “divide-and-conquer” strategy. A Seasonal Trend decomposition layer first decouples the non-stationary raw sequence into a deterministic trend and stochastic seasonal components. Subsequently, a ConvLSTM module is utilized to extract local features, while a Multi-Head Attention mechanism is integrated to capture long-range temporal dependencies, including recurring Golden Week demand spikes. The experimental results demonstrate that the SD-ConvLSTM-Attn architecture achieves competitive predictive performance against six benchmark architectures, including LSTM, CNN-LSTM, ConvLSTM, TimeMixer, DLinear, and PatchTST, exhibiting superior accuracy in peak flow capture. Furthermore, a hierarchical capacity warning system and predictive resource scheduling protocol are proposed to support dynamic scenic area management. Full article

(This article belongs to the Special Issue Advancements and Innovation in Tourism Management and Strategic Management: Sustainable Development Perspective)

21 pages, 553 KB

Open AccessArticle

The Blind Spot of Extension Security: WebAssembly–JavaScript Collaborative Attacks on Chrome

by Yeongmin Moon, Minhyuk Hong and Jeman Park

Electronics 2026, 15(14), 3049; https://doi.org/10.3390/electronics15143049 (registering DOI) - 11 Jul 2026

Abstract

Chrome extensions are increasingly exploited as an attack surface, yet existing static malware detectors share a critical blind spot: they analyze JavaScript but cannot inspect WebAssembly (Wasm) or reason across the Wasm–JS boundary. We exploit this gap by embedding malicious logic in Wasm [...] Read more.

Chrome extensions are increasingly exploited as an attack surface, yet existing static malware detectors share a critical blind spot: they analyze JavaScript but cannot inspect WebAssembly (Wasm) or reason across the Wasm–JS boundary. We exploit this gap by embedding malicious logic in Wasm modules while confining JavaScript to minimal glue code, rendering the core of each attack invisible to static analysis. Grounded in this collaborative architecture, we implement eight proof-of-concept attack scenarios across six categories—adware, malicious file delivery, forced redirection, keylogging, credential theft, and ransomware—as functioning Manifest V3 extensions. Evaluated against four representative static detectors, none achieves genuine detection: three register the samples as benign or raise no alert, and the fourth flags every sample only through systematic false positives on generated glue code. An analysis of 165,314 live extensions further shows that every permission our attacks require is already in widespread legitimate use, so such extensions would not be distinguishable from benign ones by permission-based screening alone. Full article

(This article belongs to the Special Issue Offensive Cybersecurity: Novel Attack Techniques, Red Teaming and Adversarial AI)

31 pages, 2508 KB

Open AccessReview

From Ecological Monitoring to Prevention Decision Support: A Critical Review of Artificial Intelligence for Forest Fire Prevention

by Shuwei Feng, Hao Liang and Xiaodong Liu

Forests 2026, 17(7), 817; https://doi.org/10.3390/f17070817 (registering DOI) - 11 Jul 2026

Abstract

Forest fire prevention increasingly depends on translating ecological monitoring into earlier, more reliable decisions about ignition risk, fuel condition, spread potential, and management intervention. This critical review evaluates artificial intelligence (AI) for forest fire prevention through full-text extraction of core studies and contextual [...] Read more.

Forest fire prevention increasingly depends on translating ecological monitoring into earlier, more reliable decisions about ignition risk, fuel condition, spread potential, and management intervention. This critical review evaluates artificial intelligence (AI) for forest fire prevention through full-text extraction of core studies and contextual synthesis of foundational fire-science literature. The evidence base contains 179 unique references, including an AI-focused corpus, classical deterministic and probabilistic fire-danger and spread models, global ignition and lightning studies, remote-sensing and fuel-moisture foundations, decision-support tools, and governance literature. We define prevention-facing AI as systems that support pre-ignition or pre-escalation decisions and compare studies by data source, model design, validation protocol, forecast horizon, transferability, interpretability, and management action. The synthesis shows that AI is most mature for multimodal sensing, smoke/fire detection, susceptibility mapping, and short-horizon forecasting, but less mature for prospective decision-support validation, cross-ecosystem transfer, and operational accountability. AI is therefore most useful when it is hybrid, interpretable, and deployment-aware: it should complement established fire-weather and spread-model baselines while converting ecological observations into timely and actionable prevention judgments. Full article

(This article belongs to the Special Issue Ecological Monitoring and Forest Fire Prevention)

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24 pages, 905 KB

Open AccessArticle

Resource Barrier Versus Resource Drain: Adult Attachment Differentially Moderates the Impact of Social Support on Well-Being Through Need Satisfaction

by Xi Chen, Azlina Mohd Khir, Hanina Halimatusaadiah Hamsan and Nik Ahmad Sufian Burhan

Behav. Sci. 2026, 16(7), 1170; https://doi.org/10.3390/bs16071170 (registering DOI) - 11 Jul 2026

Abstract

Perceived social support consistently predicts subjective well-being (SWB), yet the underlying psychological mechanisms and boundary conditions remain insufficiently understood. Integrating Conservation of Resources (COR) theory with Self-Determination Theory (SDT), this study tested a moderated mediation model in which basic psychological need satisfaction (autonomy, [...] Read more.

Perceived social support consistently predicts subjective well-being (SWB), yet the underlying psychological mechanisms and boundary conditions remain insufficiently understood. Integrating Conservation of Resources (COR) theory with Self-Determination Theory (SDT), this study tested a moderated mediation model in which basic psychological need satisfaction (autonomy, competence, relatedness) mediates the social support–SWB relationship, with adult attachment orientations (anxiety, avoidance) as moderators. A sample of 488 Chinese university students (52.0% female; M_age = 20.15) completed self-report measures. Parallel mediation analysis showed that all three needs significantly mediated the social support–SWB link, with competence as the strongest mediator. Moderated mediation analyses showed that attachment avoidance significantly weakened the indirect effect through competence satisfaction, whereas attachment anxiety, despite significant negative main effects, did not moderate any pathway. These findings suggest that avoidance may function as a “resource barrier” that attenuates the internalization of support into need satisfaction, whereas anxiety may operate as a “resource drain,” associated with depleted baseline resources while the support-uptake mechanism remains intact. This study advances theory by proposing distinct functional roles for attachment dimensions within an integrated COR-SDT framework. Practical implications for attachment-informed, need-supportive interventions among university students are discussed. Full article

(This article belongs to the Section Social Psychology)

18 pages, 996 KB

Open AccessReview

Artificial Intelligence-Driven Nanomedicine: From Drug Formulation and Nanocarrier Design to Clinical Translation

by Abdulrahman A. Alsaqabi, Abdulaziz A. Almoutairi, Faisal Alnehari, Abdulaziz N. Alanazi, Rema Aldugiem, Yara Alsaeed and Sarah Alotaibi

Pharmaceutics 2026, 18(7), 845; https://doi.org/10.3390/pharmaceutics18070845 (registering DOI) - 11 Jul 2026

Abstract

The integration of artificial intelligence (AI) and machine learning (ML) is fundamentally transforming pharmaceutical sciences, shifting drug formulation and nanocarrier design from traditional empirical approaches toward predictive, data-driven methodologies. By enabling the analysis of large, complex datasets, AI technologies are accelerating decision-making, improving [...] Read more.

The integration of artificial intelligence (AI) and machine learning (ML) is fundamentally transforming pharmaceutical sciences, shifting drug formulation and nanocarrier design from traditional empirical approaches toward predictive, data-driven methodologies. By enabling the analysis of large, complex datasets, AI technologies are accelerating decision-making, improving formulation efficiency, and supporting the development of more effective therapeutic systems. Despite these advances, the successful clinical translation of advanced nanomedicines, including polymeric nanoparticles and mRNA–lipid nanoparticle platforms, remains limited by challenges such as biological barriers, highly sensitive formulation parameters, scalability issues, and the limited interpretability of many computational models. This review provides a comprehensive overview of AI applications throughout the pharmaceutical development lifecycle. It explores how classical machine learning algorithms and deep learning architectures optimize conventional dosage forms, enhance formulation development, and enable the rational design of targeted nanocarriers. Particular emphasis is placed on predicting critical quality attributes, encapsulation efficiency, physicochemical properties, drug-release behavior, therapeutic efficacy, and early-stage nanotoxicity. Furthermore, we critically assess the regulatory considerations, manufacturing constraints, data quality issues, and tumor microenvironment heterogeneity that continue to impede bench-to-clinic translation. Ultimately, overcoming these challenges requires moving beyond isolated algorithmic optimization toward an integrated framework that combines computational intelligence, robust experimental validation, and continuous clinical feedback. Such a synergistic approach is expected to drive the next generation of precision nanomedicine and facilitate the safe and effective translation of AI-enabled pharmaceutical innovations into clinical practice. Full article

(This article belongs to the Section Nanomedicine and Nanotechnology)

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22 pages, 1742 KB

Open AccessArticle

Structural Characteristics and Controllability Analysis of China’s Provincial-Industrial Embodied Carbon Emission Transfer Network

by Yixin Bao, Wenxia Chen, Chenhao Qian, Titi Zhang and Zidan Zhou

Entropy 2026, 28(7), 785; https://doi.org/10.3390/e28070785 (registering DOI) - 11 Jul 2026

Abstract

In the context of global climate change and China’s “Dual Carbon” target, the misallocation of carbon emission reduction responsibilities and low regulatory efficiency urgently require analysis and resolution. Based on China’s 2020 MRIO and carbon emission inventory data, this study integrates multi-regional input–output [...] Read more.

In the context of global climate change and China’s “Dual Carbon” target, the misallocation of carbon emission reduction responsibilities and low regulatory efficiency urgently require analysis and resolution. Based on China’s 2020 MRIO and carbon emission inventory data, this study integrates multi-regional input–output models and complex network theory to construct an embodied carbon emission (ECE) transfer network at the provincial-industrial level and analyze its structural characteristics. Drawing on complex network control theory, this paper proposes a heuristic node-ranking strategy to identify driver nodes for full controllability of the ECE transfer network and compare its regulatory effect with other topological indicators. The findings reveal: (1) At the provincial level, embodied carbon emissions show a distinct transfer pattern from central provinces to southeast coastal or economically developed regions. Jiangxi, Anhui, Shandong, etc., are net outflow provinces, while Jiangsu, Beijing, Guangdong, etc., are net inflow provinces. (2) At the industrial level, secondary industry is the main net inflow industry, and primary industry is the main net outflow industry. The secondary industries in Guangdong, Henan, etc., have high betweenness centrality, acting as “hub” nodes for carbon transmission. Community detection shows that the largest community in China is centered on the secondary and tertiary industries of Jiangsu, Henan, Guangdong, etc., and the network overall exhibits small-world characteristics. (3) Compared with other control strategies, the designed algorithm achieves the best control effect: it realizes full network controllability with the minimum number of control nodes (26), and the shortest reachable paths from the control node set to non-control nodes, meaning policy signals imposed on control nodes transmit at the fastest speed. (4) Among the control node set, 22 key control nodes are mostly secondary and tertiary industries, located at the center of the transfer network and ranking high in net outflow or inflow, belonging to the core nodes of the ECE transfer network. This study provides a scientific basis and methodological support for clarifying the attribution of carbon transfer responsibilities and formulating differentiated collaborative regulatory policies. This paper establishes a qualitative matching mechanism between network control inputs and carbon tax, emission quotas and industrial regulation to connect controllability theory and practical carbon governance. Full article

(This article belongs to the Section Complexity)

29 pages, 10432 KB

Open AccessArticle

A Physical-Layer Threat Detection Framework for Secure IoT and Smart Grid Networks Using HHT-Based Multimodal Deep Learning

by Jie Ren, Chunhai Zhou, Chuyang Tan and Yan Wang

Technologies 2026, 14(7), 423; https://doi.org/10.3390/technologies14070423 (registering DOI) - 11 Jul 2026

Abstract

Secure IoT and smart grid networks depend on reliable hardware operation to maintain continuous service and system availability. Physical-layer abnormalities such as partial discharge (PD) can weaken infrastructure components and disrupt connected systems before conventional monitoring methods detect the problem. PD is one [...] Read more.

Secure IoT and smart grid networks depend on reliable hardware operation to maintain continuous service and system availability. Physical-layer abnormalities such as partial discharge (PD) can weaken infrastructure components and disrupt connected systems before conventional monitoring methods detect the problem. PD is one of the earliest indicators of abnormal hardware activity in electrical infrastructure. If it is not detected in time, it can damage equipment, reduce system reliability, and increase the risk of service interruption in intelligent network environments. Existing detection methods often struggle with PD signals because these signals are non-stationary, vary over time, and frequently contain noise. This limits reliable physical-layer threat detection in secure IoT and smart grid networks. This study presents an integrated physical-layer threat-detection framework for secure IoT and smart grid networks that combines adaptive HHT-based signal decomposition with multimodal deep learning for early hardware threat identification. The framework first applies the Hilbert–Huang Transform (HHT) to decompose PD signals and extract time–frequency features that describe discharge behavior. A convolutional neural network with an attention-based fusion mechanism then learns patterns from electrical and acoustic signals. The model classifies hardware condition into normal operation, early abnormal activity, and critical discharge states associated with potential hardware threats. The framework is evaluated using two public datasets: the Dataset of Partial Discharge and Noise Signals and the Partial Discharge Localization (PD-Loc) dataset available through the IEEE DataPort. Experimental evaluation shows that the proposed framework achieves 97.8% detection accuracy, a 97.0% F1-score, and an average AUC of 0.98. The framework maintains 94.6% accuracy under severe noise conditions (10 dB SNR) and performs inference in approximately 12 ms per sample. Furthermore, component-wise analysis further shows that HHT-based feature extraction improves detection accuracy from 91.8% to 95.6%, while multimodal learning increases the final accuracy to 97.8%. Full article

(This article belongs to the Special Issue Research on Security and Privacy of Data and Networks)

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25 pages, 821 KB

Open AccessArticle

Event-Triggered Adaptive Time Synchronization for Industrial Internet of Things

by Zhaowei Wang and Lei Zhou

Appl. Sci. 2026, 16(14), 6967; https://doi.org/10.3390/app16146967 (registering DOI) - 11 Jul 2026

Abstract

Time synchronization plays a critical role in enabling coordinated control and accurate data fusion in the Industrial Internet of Things (IIoT). However, most existing time-triggered synchronization protocols rely on periodic information exchange, which leads to considerable communication and energy consumption, particularly in large-scale [...] Read more.

Time synchronization plays a critical role in enabling coordinated control and accurate data fusion in the Industrial Internet of Things (IIoT). However, most existing time-triggered synchronization protocols rely on periodic information exchange, which leads to considerable communication and energy consumption, particularly in large-scale and resource-constrained deployments. To address these limitations, this study proposes an adaptive event-triggered time synchronization scheme that eliminates the need for periodic communication. Unlike conventional approaches that employ fixed or predefined time-varying thresholds, the proposed method constructs a fully distributed triggering mechanism based on both local clock evolution and synchronization discrepancies observed from neighboring nodes. The triggering threshold evolves automatically according to the network synchronization state and does not require additional coordination messages. Theoretical analysis shows that the logical clock skews asymptotically converge to a common value, while the logical clock offset disagreement is ultimately bounded within an explicitly characterized neighborhood. Simulation results demonstrate that the proposed scheme achieves a more effective balance between synchronization accuracy and communication overhead, while producing more evenly distributed triggering events than several representative event-triggered synchronization methods. Full article

(This article belongs to the Special Issue Deployment and Control of Wireless Sensor Networks (WSNs))

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13 pages, 2349 KB

Open AccessArticle

Haemocyte-Derived Innate Immune Reactions of the Giant African Snail (Lissachatina fulica) Against the Lungworm Angiostrongylus vasorum

by Alena Dusch, Iván Conejeros, Zahady D. Velásquez, Carlos Hermosilla and Anja Taubert

Animals 2026, 16(14), 2150; https://doi.org/10.3390/ani16142150 (registering DOI) - 11 Jul 2026

Abstract

The metastrongyloid parasite Angiostrongylus vasorum is currently considered an emerging gastropod-borne lungworm species of canids and has gained growing scientific attention in recent years. Nonetheless, knowledge of gastropod innate immune reactions and how A. vasorum larvae are attacked by gastropod-derived haemocytes is still [...] Read more.

The metastrongyloid parasite Angiostrongylus vasorum is currently considered an emerging gastropod-borne lungworm species of canids and has gained growing scientific attention in recent years. Nonetheless, knowledge of gastropod innate immune reactions and how A. vasorum larvae are attacked by gastropod-derived haemocytes is still very sparse. The current study aims to investigate gastropod haemocyte innate immune reactions in response to A. vasorum L1 larvae and antigen (AvAg) in vitro. Gastropod haemocytes were isolated from the giant African snail (Lissachatina fulica) via cardiac puncture, confronted with AvAg or A. vasorum L1, and thereafter assessed for cell activation status, ROS production and extrusion of invertebrate extracellular phagocyte traps (InEPT). Haemocyte–parasite interactions were assessed via flow cytometry, live cell 3D-holotomographic microscopy as well as scanning electron microscopy (SEM). Overall AvAg stimulation activated haemocytes and induced a time-dependent increase in haemocyte ROS production. Moreover, SEM analyses illustrated the formation of ‘InEPT-like’ structures in response to A. vasorum L1. These novel findings on neglected invertebrate innate immune responses will help to better understand the complex mollusc–metastrongyloid parasite interactions, which contribute to the epizootiology of not only canine angiostrongylosis but also of closely related metastrongyloid nematodes of public and veterinary health concern. Full article

(This article belongs to the Section Veterinary Clinical Studies)

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17 pages, 1104 KB

Open AccessArticle

Determinants of Demand for Exports of Brazilian Sawnwood

by Júlia de Oliveira Carneiro, Humberto Angelo, Alexandre Nascimento de Almeida, Eraldo Aparecido Trondoli Matricardi, Evelyn Bianca Almeida Vaz and Laura de Castro Silva

Forests 2026, 17(7), 816; https://doi.org/10.3390/f17070816 (registering DOI) - 11 Jul 2026

Abstract

The Brazilian non-coniferous sawnwood sector, despite producing 255.7 million m³ between 1995 and 2024 and ranking among the world’s largest producers, remains predominantly oriented toward the domestic market. This study analyzes the sectoral panorama and the determinants of export demand using a [...] Read more.

The Brazilian non-coniferous sawnwood sector, despite producing 255.7 million m³ between 1995 and 2024 and ranking among the world’s largest producers, remains predominantly oriented toward the domestic market. This study analyzes the sectoral panorama and the determinants of export demand using a time-series econometric approach. A log-log model was estimated by Ordinary Least Squares and corrected for serial autocorrelation using the Cochrane–Orcutt procedure. The results indicate that export demand is strongly influenced by external economic factors. World income showed a positive and highly significant elasticity (1.80), indicating that non-coniferous sawnwood is a superior good with demand highly sensitive to global economic growth. The price of coniferous sawnwood, a substitute product, presented a positive elasticity (0.27), while the world price of non-coniferous sawnwood showed a negative elasticity (−0.98), suggesting increased competition among exporters. Engineered wood prices exhibited a negative elasticity (−0.18), indicating limited influence on demand. The trend variable was negative and significant (−0.013), revealing a gradual decline in demand over time. Overall, export performance is highly dependent on world income and relative prices, while domestic production and regulatory constraints limit the sector’s international competitiveness. Full article

(This article belongs to the Special Issue Forest Economics and Policy Analysis)

30 pages, 7658 KB

Open AccessReview

Extracting Phase Structure and Stability of the Magnetic Dual Chiral Density Wave from a Ginzburg–Landau Expansion

by William Gyory

Universe 2026, 12(7), 208; https://doi.org/10.3390/universe12070208 (registering DOI) - 11 Jul 2026

Abstract

We review some recent findings on thermal properties of the magnetic dual chiral density wave (MDCDW) condensate in the Nambu–Jona-Lasinio (NJL) model of dense quark matter, as well as a convenient method for investigating this phase with a high-order Ginzburg–Landau (GL) expansion. We [...] Read more.

We review some recent findings on thermal properties of the magnetic dual chiral density wave (MDCDW) condensate in the Nambu–Jona-Lasinio (NJL) model of dense quark matter, as well as a convenient method for investigating this phase with a high-order Ginzburg–Landau (GL) expansion. We show how a recently discovered formula for the GL coefficients can be used to compute key physical properties of the condensate, such as its ground state order parameters and critical temperature in the mean-field approximation and its stability against thermal phonon fluctuations. We find that magnetic fields of order

10^{18}

G significantly increase the condensate magnitude and critical temperature, eventually making the condensate favored up to temperatures a few times 10 MeV over the entire range of densities in the model. At much smaller fields, the condensate is still preferred and thermally stable over a range of densities relevant to cold neutron stars. We emphasize how the topological features of MDCDW are encoded in certain terms of the GL expansion, which can be used to show that the preceding effects have a topological origin. Finally, we present a new result on the convergence properties of the GL expansion, proving that it converges when

{| m |}^{2} + {| b |}^{2} < \sqrt{μ^{2} + {(π T)}^{2}},

where m and b are order parameters proportional to the condensate magnitude and spatial modulation, respectively. This condition holds over a large region of parameter space, including the region of interest for neutron star applications. Full article

(This article belongs to the Special Issue Magnetized Dense Matter in Compact Stars: From Fundamental Properties to Astrophysical Observables)

30 pages, 42623 KB

Open AccessArticle

Effect of Non-Periodic Leading-Edge Wear on Aerodynamic Performance and Stall-Precursor Coherence in Centrifugal Compressor

by Hong Xie, Zhibiao Cai, Bo Yang and Chunrong Wang

Aerospace 2026, 13(7), 630; https://doi.org/10.3390/aerospace13070630 (registering DOI) - 11 Jul 2026

Abstract

Non-periodic leading-edge wear near the impeller tip is investigated with respect to the aerodynamic performance, steady flow organization, and near-stall unsteady evolution of a centrifugal compressor. A full-annulus three-dimensional impeller–vaned-diffuser model is established for a baseline configuration (O-M) and a non-periodically worn configuration [...] Read more.

Non-periodic leading-edge wear near the impeller tip is investigated with respect to the aerodynamic performance, steady flow organization, and near-stall unsteady evolution of a centrifugal compressor. A full-annulus three-dimensional impeller–vaned-diffuser model is established for a baseline configuration (O-M) and a non-periodically worn configuration (W-M). The two configurations are compared in terms of performance characteristics, near-tip pressure coefficient, static pressure, entropy, relative Mach number, three-dimensional vortical structures, and pressure fluctuation signals. The W-M generally produces a lower total pressure ratio than the O-M, with a maximum reduction of approximately 0.7%. Nevertheless, the isentropic efficiency is slightly improved over the main operating range, with a peak increase of about 0.6%, and the near-stall flow rate shifts toward a lower value. Pressure coefficient distributions at 95% span show that leading-edge wear weakens both the pressure-side pressure peak and the suction-side suction peak of the worn blades, redistributing the near-tip loading from a highly leading-edge-concentrated form to a broader chordwise distribution. The steady flow fields indicate that wear does not eliminate local low-pressure or high-entropy regions; rather, it reorganizes their circumferential arrangement, converting originally synchronized low-pressure zones, high-entropy bands, and high-speed shear layers into a non-uniform pattern with alternating strong and weak passages. Near-stall unsteady results further reveal that pressure cells, high-entropy zones, and large-scale vortical structures in the O-M exhibit clear cross-passage propagation, whereas the corresponding disturbances in the W-M remain predominantly localized, dispersed, and asynchronous. These results demonstrate that, for the wear location and blade-to-blade distribution considered here, non-periodic leading-edge wear affects stability primarily by weakening the circumferentially coherent amplification of disturbances, rather than by simply reducing all local loss sources. Full article

(This article belongs to the Section Aeronautics)

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19 pages, 1956 KB

Open AccessArticle

Discriminating Marine Macroalgae by Volatilomic Fingerprint and Bioactivity: A Chemometric Approach

by Gonçalo Jasmins, Rosa Perestrelo, Ricardo Luís, Rodrigo Silva, Pedro Sousa, Carlos A. P. Andrade and José Câmara

Biology 2026, 15(14), 1129; https://doi.org/10.3390/biology15141129 (registering DOI) - 11 Jul 2026

Abstract

Marine macroalgae are now considered potential sources of renewable feedstocks to produce valuable bioactive compounds. However, the species-specific volatilomes and their correlation with antioxidant capacity remains largely unknown. This study aimed to investigate the volatilomic fingerprint, total phenolic content (TPC), total flavonoids content [...] Read more.

Marine macroalgae are now considered potential sources of renewable feedstocks to produce valuable bioactive compounds. However, the species-specific volatilomes and their correlation with antioxidant capacity remains largely unknown. This study aimed to investigate the volatilomic fingerprint, total phenolic content (TPC), total flavonoids content (TFC) and antioxidant capacity of four macroalgae belonging to distinct taxonomic groups: Rugulopteryx okamurae (brown algae), Asparagopsis taxiformis (red algae), Caulerpa webbiana (green algae), and coralline algae. To achieve this purpose, the volatilomic fingerprint was established using headspace solid-phase microextraction tandem gas chromatography–mass spectrometry (HS-SPME/GC-MS), and the TPC, TFC, and antioxidant capacity were evaluated using in chemico assays. A total of 59 volatile organic compounds (VOCs) were identified across the marine macroalgae, encompassing carbonyl compounds (on average, 40.7 ± 13.4% of total volatilomic fraction), hydrocarbons (17.0 ± 8.3%), organohalogens (23.0 ± 10.8%), terpenoids (6.5 ± 2.0%), alcohols (4.5 ± 2.0%), and the remaining chemical families showed a contribution lower than 5% to the total volatilomic fingerprint. Multivariate statistical analyses revealed significant differences (p < 0.05) in the volatilomic fingerprint among the macroalgae investigated, allowing clear discrimination between species. Organohalogen compounds emerged as key discriminant features for C. webbiana and A. taxiformis algae, while hydrocarbons were characteristic of the R. okamurae. Despite taxonomic differences, hexanal, benzaldehyde and β-ionone were VOCs found across all samples. Regarding bioactivity, R. okamurae exhibited the highest TPC (144 mgGAE/100 g DW), TFC (69 ± 6 mg QE/100 g DW), and antioxidant capacity. These findings confirm the large variability of the volatilomes of the different species of macroalgae and demonstrate the power of the HS-SPME/GC-MS, chemometric, and antioxidant analysis strategy for the differentiation of species and the identification of VOCs with valorization potential, helping to transform invasive or unused biomass of these species into new, sustainable bioproducts through emerging technological and biorefinery applications. Full article

(This article belongs to the Section Biochemistry and Molecular Biology)

20 pages, 4880 KB

Open AccessArticle

Development of a Cre-Inducible Rabl6a Transgenic Mouse Model That Enhances Sarcoma Growth In Vivo

by Ellen M. Voigt, Alexandra L. Isaacson, Mariah R. Leidinger, James A. Goeken, Quinn Hanigan, Deng Fu Guo, Rachel M. Gasser, Makenna Eadie, Isabella Babor, Benjamin W. Darbro, William Paradee, Kamal Rahmouni, Tian Zhao, Patrick Breheny, Eunhyeong Lee, Minah Kim, David K. Meyerholz, Mohammed Milhem, Rebecca D. Dodd and Dawn E. Quelle

Cancers 2026, 18(14), 2230; https://doi.org/10.3390/cancers18142230 (registering DOI) - 11 Jul 2026

Abstract

Background: Malignant peripheral nerve sheath tumors (MPNSTs) are deadly sarcomas that arise from Schwann cells and lack effective therapies. RABL6A is an oncogenic Rab-like GTPase whose expression is associated with worse survival in many human cancers. It is required for human MPNST cell [...] Read more.

Background: Malignant peripheral nerve sheath tumors (MPNSTs) are deadly sarcomas that arise from Schwann cells and lack effective therapies. RABL6A is an oncogenic Rab-like GTPase whose expression is associated with worse survival in many human cancers. It is required for human MPNST cell survival, and its expression is dramatically increased in patient MPNSTs compared to benign precursor lesions. Methods: To model elevated expression of RABL6A in vivo, we developed transgenic mice expressing Cre-inducible Rabl6a. These Rabl6a-tg mice express the murine Rabl6a cDNA with a 5′ hemagglutinin [HA] epitope sequence downstream of a CMV enhancer and separated by a lox–stop–lox cassette. Double transgenic DhhCre–Rabl6a-tg mice were generated to achieve Schwann-cell specific Cre expression from the Desert hedgehog (Dhh) promoter. De novo MPNSTs were induced by CRISPR editing of Nf1, Ink4a, and Arf genes in the mouse sciatic nerve. Results: Cre-dependent expression of transgenic Rabl6a was verified at the mRNA and protein levels in Cre-positive mouse embryo fibroblasts and tissues. Increased Rabl6a expression in DhhCre–Rabl6a-tg mice had no effect on de novo MPNST initiation but significantly accelerated tumor progression relative to DhhCre control mice. The Rabl6a phenotype was associated with increased tumor angiogenesis but not proliferation. Interestingly, many MPNSTs in the DhhCre background exhibited varying levels of rhabdomyoblastic (RMB) features. That immature muscle cell phenotype is a hallmark of malignant Triton tumors, a rare histological variant of human MPNSTs associated with worse outcomes. Conclusions: These data provide direct evidence that Rabl6a is a functional driver of MPNSTs while establishing Rabl6a-tg mice as a suitable model for investigating Rabl6a’s role in other lethal RABL6A-high tumors. Full article

(This article belongs to the Section Molecular Cancer Biology)

16 pages, 9210 KB

Open AccessArticle

Asymmetric Residual Stress Distribution in Friction Stir Welded Magnesium Alloy: A Sequentially Coupled Thermo-Mechanical Analysis

by Huiting Wu, Sili Feng, Zhe Liu and Renlong Xin

Metals 2026, 16(7), 774; https://doi.org/10.3390/met16070774 (registering DOI) - 11 Jul 2026

Abstract

Friction stir welding (FSW) is an effective solid-state joining technique for magnesium alloys such as AZ31, owing to its ability to minimize conventional welding defects. Nevertheless, the process generates significant residual stresses that can impair the fatigue performance and dimensional stability of welded [...] Read more.

Friction stir welding (FSW) is an effective solid-state joining technique for magnesium alloys such as AZ31, owing to its ability to minimize conventional welding defects. Nevertheless, the process generates significant residual stresses that can impair the fatigue performance and dimensional stability of welded structures. In this study, a sequentially coupled thermo-mechanical finite element model was employed to characterize the residual stress distribution in FSW AZ31 Mg alloy. The calculated near-surface longitudinal residual stress was assessed against XRD measurements at five locations on the top surface, giving a root mean square error of about 10.34 MPa. The results revealed an M-shaped longitudinal residual stress profile with marked asymmetry between the advancing and retreating sides, associated with non-uniform heat input and the resulting asymmetric temperature history. Among the three stress components, the longitudinal residual stress was the largest, followed by the transverse component, while the normal stress was the smallest. The thermo-mechanically affected zone and the crown zone exhibited higher residual stresses compared to the heat-affected zone. In addition, the influences of welding speed and tool rotational speed on residual stress evolution were systematically evaluated. The longitudinal residual stress increased with welding speed up to 350 mm/min and subsequently decreased, while a peak value was observed at 1200 rpm. These numerical results provide useful guidance, within the studied parameter range, for welding-parameter selection and residual-stress control in magnesium alloy joints. Full article

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18 pages, 17095 KB

Open AccessArticle

Subacute Octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine Exposure Induces Neurobehavioral Deficits and Hippocampal Demyelination in Mice

by Xiaoqiang Lv, Cunzhi Li, Yinan Zhang, Qian Luo, Ting Gao, Hui Deng, Huan Li, Xinying Peng, Jiachen Shen, Siqi Liu, Junhong Gao and Zhiyong Liu

Toxics 2026, 14(7), 605; https://doi.org/10.3390/toxics14070605 (registering DOI) - 11 Jul 2026

Abstract

Octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) is a nitramine explosive widely used in military and industrial fields. While emerging evidence suggests the neurotoxicity of HMX, the mechanisms underlying central nervous system (CNS) damage remain largely unknown. In the present study, we established a mouse model of 28-day [...] Read more.

Octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) is a nitramine explosive widely used in military and industrial fields. While emerging evidence suggests the neurotoxicity of HMX, the mechanisms underlying central nervous system (CNS) damage remain largely unknown. In the present study, we established a mouse model of 28-day subacute HMX exposure to explore HMX-induced neurotoxicity and underlying mechanisms in vivo. Behavioral assessments revealed that HMX increased spontaneous locomotor activity and central exploration in the open field test, and reduced immobility time in the forced swimming test, indicating abnormal emotional regulation. The Morris water maze further demonstrated impaired hippocampus-dependent spatial learning and memory in HMX-treated mice, as evidenced by prolonged platform latency. Histopathological analysis showed hippocampal demyelination in HMX-treated mice, accompanied by downregulation of myelin structural proteins (MBP, PLP1) and oligodendrocyte lineage proteins (OLIG2, CNPase). Additionally, proteomic analysis identified 173 differentially expressed proteins in the HMX-exposed hippocampus, which were enriched in myelination, synaptic transmission and neuroactive ligand–receptor interaction pathways. Collectively, our findings demonstrate that subacute HMX exposure induces behavioral deficits and demyelination in mice hippocampus, providing a novel mechanistic insight into HMX neurotoxicity and a theoretical basis for occupational health protection against HMX exposure. Full article

(This article belongs to the Section Neurotoxicity)

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24 pages, 25280 KB

Open AccessArticle

Study on Failure and Articulated Anti-Dislocation Fortification Parameters of Tunnels Crossing Active Faults

by Xiangyu Zhang, Abudureyimujiang Aosimanjiang, Qunyi Huang and Bin He

Appl. Sci. 2026, 16(14), 6966; https://doi.org/10.3390/app16146966 (registering DOI) - 11 Jul 2026

Abstract

By systematically conducting seismic damage investigations of tunnels crossing active faults, this study summarizes the failure characteristics of typical damage cases and performs model tests at a similarity ratio of 1:50 to examine the dislocation-induced failure mechanisms of tunnel structures subjected to reverse [...] Read more.

By systematically conducting seismic damage investigations of tunnels crossing active faults, this study summarizes the failure characteristics of typical damage cases and performs model tests at a similarity ratio of 1:50 to examine the dislocation-induced failure mechanisms of tunnel structures subjected to reverse strike-slip faulting, reverse faulting, and strike-slip faulting, respectively. In view of the lack of systematic theoretical calculations in existing articulated anti-dislocation fortification methods, a displacement-pattern-based calculation method for the articulated fortification width is proposed. The main conclusions are as follows: The failure of tunnels crossing active faults results from the coupling of fault dislocation, strong-motion inertial forces, and surrounding rock restraint, and is jointly controlled by multiple factors including fault type, movement mode, geometric relationship, structural stiffness, and surrounding rock properties. Under reverse strike-slip faulting, the tunnel failure is dominated by a combination of oblique shear and compression, exhibiting an overall spatial “S”-shaped deformation; under strike-slip faulting, the tunnel experiences combined shear and bending failure with an overall planar “S”-shaped deformation; under reverse faulting, shear failure prevails, locally accompanied by tensile failure. The proposed calculation method for the articulated fortification width not only fills the gaps in previous studies but also broadens its scope of application. It is applicable not only to the displacement patterns addressed in this paper but also to other displacement patterns that conform to tunnel dislocation. Full article

(This article belongs to the Section Civil Engineering)

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11 pages, 8291 KB

Open AccessSystematic Review

Atrioventricular Block Potentially Associated with Melatonin Supplementation: A Case Report and Systematic Review of Cardiac Rhythm Adverse Events

by Emma Sola, Eva Ramos, Alejandro Romero and Diego Monzón

Pharmaceuticals 2026, 19(7), 1069; https://doi.org/10.3390/ph19071069 (registering DOI) - 11 Jul 2026

Abstract

Background/Objectives: Melatonin is widely used as a sleep aid and is generally considered safe; moreover, it is well known for its cardioprotective properties. Nonetheless, little is known about the underlying mechanisms of melatonin. Indeed, some potential undesirable cardiovascular effects associated with melatonin supplementation [...] Read more.

Background/Objectives: Melatonin is widely used as a sleep aid and is generally considered safe; moreover, it is well known for its cardioprotective properties. Nonetheless, little is known about the underlying mechanisms of melatonin. Indeed, some potential undesirable cardiovascular effects associated with melatonin supplementation have been observed. Methods: We conducted a systematic review to evaluate evidence relevant to a case involving a 63-year-old man who developed ventricular arrhythmia while taking 10 mg of exogenous melatonin daily, exhibiting cardiovascular symptoms, and was then diagnosed with ventricular arrhythmia. After discontinuing melatonin, the patient fully recovered from all cardiovascular symptoms. Results: Following withdrawal of melatonin, the patient experienced complete resolution of symptoms and improvement in electrocardiographic findings. The systematic review identified reports describing similar arrhythmic events associated with melatonin supplementation, with symptom resolution or clinical improvement after discontinuation of the supplement. Conclusions: Although melatonin is generally regarded as safe, with cardioprotective effects, in this review we discuss possible mechanisms involving melatonin receptors, sleep architecture, pharmacokinetic variability, and genetic predisposition which could be associated with cardiac rhythm and conduction disturbances. Herein, we highlight the necessity for further studies to identify risk factors and underlying mechanisms and guide the safe use of this molecule. Full article

(This article belongs to the Special Issue New Advances in Antiarrhythmic Drugs)

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17 pages, 2165 KB

Open AccessArticle

Demodulation-Oriented Neural Denoising for Long-Sequence I/Q Wireless Signals in Data-Link Receiver Chains

by Mingdi Li, Yanbin Li and Qi Feng

Sensors 2026, 26(14), 4406; https://doi.org/10.3390/s26144406 (registering DOI) - 11 Jul 2026

Abstract

Reliable demodulation of low-signal-to-noise ratio (SNR) data-link signals is challenging when thermal noise, multipath fading, and structured interference overlap with the target waveform in time and frequency. This paper studies neural denoising as a front-end module in a receiver chain for long-sequence in-phase/quadrature [...] Read more.

Reliable demodulation of low-signal-to-noise ratio (SNR) data-link signals is challenging when thermal noise, multipath fading, and structured interference overlap with the target waveform in time and frequency. This paper studies neural denoising as a front-end module in a receiver chain for long-sequence in-phase/quadrature (I/Q) wireless signals. A residual one-dimensional denoising autoencoder (DAE) is inserted before a conventional demodulator to recover the interference-free waveform from corrupted inputs. The denoised outputs are assessed using the waveform-level mean squared error (MSE), output SNR, and demodulated bit error rate (BER). The relative bit error rate (RBER) quantifies the residual BER penalty after denoising within the receiver transition region. Extensive experiments across three practical communication-signal classes and various disturbance/channel-effect conditions demonstrate that neural preprocessing significantly improves low-SNR demodulation accuracy. Crucially, the results show that traditional waveform metrics do not fully reflect receiver-level bit recovery. Compared with convolutional, attention-based, adversarial, and pooling-based baselines, the proposed receiver front end provides lower RBER in the operating region where demodulation is most sensitive to waveform distortion. The results support demodulation-oriented evaluation for neural receiver preprocessing and show that waveform restoration should be verified through the downstream bit-recovery task. Full article

(This article belongs to the Special Issue Intelligent Signal Processing Techniques for Wireless Communications)

16 pages, 1198 KB

Open AccessReview

Cannabis-Based Nanolipid Formulations for Pain Management

by Ana Clara Santiago Bastos, Arissa De Oliveira Sato, Luana Carvalho de Oliveira, Fernanda Nervo Raffin, Túlio F. A. L. Moura, Leandro S. Ferreira, Marco V. Navarro and Lígia Nunes de Morais Ribeiro

Pharmaceutics 2026, 18(7), 844; https://doi.org/10.3390/pharmaceutics18070844 (registering DOI) - 11 Jul 2026

Abstract

Medicinal cannabis has gained increasing attention from both the scientific community and clinical practice, due to the therapeutic potential of its major phytocannabinoids, particularly cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC), for pain management. This review compiled and analyzed the available evidence regarding the antinociceptive [...] Read more.

Medicinal cannabis has gained increasing attention from both the scientific community and clinical practice, due to the therapeutic potential of its major phytocannabinoids, particularly cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC), for pain management. This review compiled and analyzed the available evidence regarding the antinociceptive effects of nanoencapsulated cannabinoids compared to free compounds. The published works have explored some pharmaceutical formulations and administration routes on different acute, chronic and neuropathic pain experimental models. The findings indicated that cannabinoids exhibited promising analgesic effects, while nanoencapsulation could enhance its stability and bioavailability. Despite these advances, the number of reports investigating nanostructured cannabinoid-based systems remains limited, with a predominance of preclinical research. A recurrent lack of structural information and quality control data for such works was also noted. Furthermore, there were not identified any research regarding the nanoencapsulation of full-spectrum cannabis oils or whole cannabis extracts, highlighting a significant gap in the current literature. Overall, nanoencapsulation emerges as a versatile strategy to overcome the intrinsic limitations of cannabinoids and expand its clinical applicability for pain treatment. Nevertheless, further efforts are required to determine standardized methodologies, facilitating the translation of preclinical findings into clinical practice, in order to provide stable, safe, effective and more accessible cannabinoid-based therapies. Full article

(This article belongs to the Special Issue Advanced Nanoencapsulation Platforms for Bioactive Compounds and Drugs in Pain Therapy)

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27 pages, 885 KB

Open AccessArticle

Reconfigurable Transmission Design for PASS-MIMO via Waveguide Indexing

by Yaxian Wang, Songjie Yang and Juhong Peng

Sensors 2026, 26(14), 4407; https://doi.org/10.3390/s26144407 (registering DOI) - 11 Jul 2026

Abstract

To address the stringent requirements of 6G industrial Internet of Things (IoT) and ultra-dense networks on spectral efficiency, hardware cost, and transmission reliability, this paper investigates waveguide index modulation based on the pinching-antenna system (PASS), a promising flexible multiple-input multiple-output (MIMO) architecture featuring [...] Read more.

To address the stringent requirements of 6G industrial Internet of Things (IoT) and ultra-dense networks on spectral efficiency, hardware cost, and transmission reliability, this paper investigates waveguide index modulation based on the pinching-antenna system (PASS), a promising flexible multiple-input multiple-output (MIMO) architecture featuring large-scale reconfigurability and robust line-of-sight (LoS) link establishment. A two-stage sparse transmission framework is proposed, where a Simulated Annealing-based Constrained Discrete Optimization (SA-CDO) algorithm is first employed to optimize pinching-antenna (PA) positions and construct a near-orthogonal equivalent channel dictionary for inter-waveguide interference suppression. Subsequently, an Orthogonal Least Squares-based Constellation-Constrained (OLS-CC) detector is developed to jointly recover active waveguide indices and modulation symbols with low computational complexity. Monte Carlo simulations demonstrate that the proposed scheme consistently outperforms conventional antenna index modulation under both LoS and Rician fading channels across the entire SNR range. The SA-CDO optimization significantly reduces the bit error rate (BER), while the OLS-CC detector further improves sparse recovery accuracy and reduces the detection complexity from exponential to polynomial order. These results provide valuable insights for the design of highly reliable 6G IoT communication systems. Full article

(This article belongs to the Special Issue MIMO Systems for Future Wireless Communications)

26 pages, 1308 KB

Open AccessArticle

Design of Trivariate Random Preventive Maintenance for Systems Under Random Discrete Warranty with Cost Charging

by Shenmiao Zhao, Yuemei Mao and Xiaojian Ma

Mathematics 2026, 14(14), 2501; https://doi.org/10.3390/math14142501 (registering DOI) - 11 Jul 2026

Abstract

In product lifecycle management, warranty and post-warranty maintenance are critical for system reliability but suffer from notable limitations: Traditional warranty models lack profit-generating mechanisms and ignore user usage intensity data, while post-warranty strategies fail to account for system heterogeneity (e.g., diverse usage habits [...] Read more.

In product lifecycle management, warranty and post-warranty maintenance are critical for system reliability but suffer from notable limitations: Traditional warranty models lack profit-generating mechanisms and ignore user usage intensity data, while post-warranty strategies fail to account for system heterogeneity (e.g., diverse usage habits and reliability degradation patterns). To fill these gaps, this paper proposes two random warranty models: random discrete repair warranty considering cost charging first (RDRW-CCF) and random discrete repair warranty considering cost charging last (RDRW-CCL). Both employ failure thresholds and time spans to classify user usage rates, enabling manufacturers to control costs by providing free repairs for low-usage users and charging high-usage users to generate profits. Additionally, this study develops a trivariate random preventive maintenance (TRPM) strategy for post-warranty systems. Drawing on three metrics (failure threshold, limited mission cycles, and time span threshold) from RDRW-CCF, TRPM categorizes systems into usage-rate and reliability scenarios and customizes maintenance actions accordingly. Numerical experiments show that RDRW-CCF is more profitable than RDRW-CCL, and TRPM outperforms the bivariate random preventive maintenance (BRPM) strategy in both cost efficiency and reliability. Full article

(This article belongs to the Special Issue Advances of Applied Probability and Statistics)

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