Search Results (15,646)

This study examines how international educators come to understand Lesson Study as a form of professional learning through participation in the Lesson Study Immersion Program in Japan (LSIP-JR). While prior research has documented the impact of Lesson Study on individual teachers’ knowledge and instructional practices, less attention has been paid to how teachers recognize the norms of learning communities and how they conceptualize curriculum and instructional tasks as objects of collective inquiry. Drawing on reflective journals produced by program participants, this study analyzes how these often-implicit dimensions of Lesson Study were interpreted through engagement with Japanese classroom practices and professional learning discourse. The findings suggest that participants did not view research lessons as polished demonstrations but rather as provisional inquiries shaped by uncertainty, shared responsibility, and openness to critique. Such interpretations brought into focus norms that are deeply embedded—and often taken for granted—within the Japanese educational context. In addition, participants came to recognize curriculum materials and instructional tasks not simply as tools for implementation but as shared research objects through which hypotheses about student learning are generated and examined, within both normative and institutional conditions. Rather than presenting Japanese Lesson Study as a model to be replicated, this study clarifies the conditions under which Lesson Study functions as collective inquiry. By making these typically unarticulated elements visible, the study offers a conceptual foundation for teachers and professional development leaders seeking to design and sustain meaningful Lesson Study across diverse educational contexts. Full article

This study examines two wildfire events in the southern Amazon in August 2021, addressing the challenges in investigating the development of pyro-convective clouds in tropical regions. The analysis combines the Normalized Difference Vegetation Index, Fire Radiative Power derived from the Suomi-NPP and NOAA-20 satellites, and meteorological conditions from thermodynamic profiles and atmospheric modeling. The Meso-NH model was applied exploratorily with two simulations that allow convection, at a 2.5 km resolution. In the first case, a pyro-convective cloud (PyroCu) formed directly from active fires. In the second, a deep convective cloud developed over dispersed fire hotspots, exhibiting characteristics compatible with pyro-convective activity, although uncertainties remain regarding its classification as a true PyroCb. The results indicate that background thermodynamic instability primarily controls vertical plume development, modulating the influence of fire intensity. Incorporating high-resolution thermodynamic profiles into coupled atmospheric and chemical dispersion models can improve estimates of smoke injection height, complementing information on fire power. The results provide a basis for future developments related to understanding tropical pyro-convective clouds, showing how smoke dispersion may occur in the tropical region depending on the vertical structure of the atmosphere and fire intensity. Full article

The integration of distributed renewable energy sources into distribution networks is a key approach to achieving sustainable and low-carbon power systems. However, high renewable penetration significantly increases the volatility and uncertainty of distribution systems, posing challenges to renewable energy accommodation and reliable operation. To address these challenges, active control of distribution networks is required, which in turn relies on accurate system states. In practice, the limited number and accuracy of measurement devices in distribution networks make dynamic state estimation a critical technology for sustainable distribution systems. In this paper, a novel dynamic state estimation method for sustainable distribution systems is proposed, incorporating spatiotemporal data correlation and adaptiveness to process and measurement noise. A CNN-BiGRU-Attention model is developed to reconstruct high-accuracy real-time pseudo-measurements, compensating for insufficient sensing infrastructure. Furthermore, a noise adaptive dynamic state estimation method is proposed based on an improved unscented Kalman filter. An amplitude modulation factor (AMF) is applied to track time-varying process noise, while an evaluation method based on robust Mahalanobis distance (RMD) is embedded to deal with non-Gaussian measurement noise. Finally, simulation studies on the IEEE 33-bus three-phase unbalanced distribution network demonstrate the effectiveness and robustness of the proposed method. Full article

Manufacturing industries are increasingly applying sustainable closed-loop supply chains (CLSCs) to meet economic, environmental, and societal goals. The increasing complexity and interdependence associated with the sustainability CLSCs make them highly vulnerable to disruption risks that threaten continuity and sustainability. However, prior studies fall short of guiding how disruption risks in sustainable CLSCs can be systematically prioritized under uncertainty in a stable and decision-relevant manner. To fill this literature void, this study develops a hybrid of the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) method and the genetic algorithm (GA) technique to prioritize disruption risks under uncertainty. Triangular fuzzy numbers are used to capture the imprecision of 13 experts from industry and academia, whereas the GA technique used aimed to improve stability and reduce the variability commonly observed in conventional fuzzy multi-criteria decision-making methods. The method is validated through a real-world case study, identifying supplier disruption risk, route disruption risk, and industrial accidents as the most critical risks. Moreover, sensitivity analysis is conducted to validate the robustness of GA-based Fuzzy-TOPSIS, demonstrating its superior stability and reliability compared to the classical Fuzzy-TOPSIS method in uncertain environments. The novelty of this study lies in embedding a GA-driven approach within the fuzzy-TOPSIS structure to explicitly address ranking instability under uncertainty in sustainable CLSCs. The study provides significant theoretical contributions by enhancing multi-attribute decision-making regarding disruption risk in sustainable CLSC literature, as well as practical insights for decision-makers to efficiently allocate resources by focusing mitigation investments on consistently high-priority risks instead of low-priority ones. Full article

Wireless sensor networks ($WSNs$) are extensively used in $IoT$ applications. Secure access control and data protection are essential. Nonetheless, the wireless environment has an open nature. The limited resources of sensor devices render $WSNs$ susceptible to a variety of security attacks, causing significant difficulties in the design phase of efficient authentication and key agreement ($AKA$) protocols. This study proposes a physically unclonable function ($PUF$)-based lightweight and secure $AKA$ protocol for $WSNs$ based on elliptic curve cryptography ($ECC$). A secure password update scheme is offered, which would allow legitimate users to reset forgotten passwords without re-registration. According to formal security analysis using $BAN$ logic and $ProVerif$, the proposed protocol is secure against common attacks. Moreover, from an entropy perspective, the use of dynamic pseudonyms and fresh session randomness increase an adversary’s uncertainty about user identities, thereby limiting identity-related information leakage. Performance evaluation shows that the proposed protocol achieves lower computational and communication overhead than the existing ones, making it suitable for $WSNs$ with resource constraints. Full article

The sustainable transformation of electronics supply chains (ESCs) increasingly relies on effective green supply chain planning under carbon pricing and demand uncertainty. However, prior studies often lack an integrated framework that jointly considers carbon taxation, green technology investment, and profitability—environment trade-offs in forward and reverse supply chains. To address this gap, this study proposes a fuzzy multi-goal optimization model using linear goal programming under progressive carbon taxation. The model incorporates fuzzy demand (triangular fuzzy numbers), carbon emissions, carbon taxes, and green investment costs and is converted into a solvable linear form via a defuzzification-based procedure to simultaneously achieve multiple aspiration levels for economic and environmental objectives. A real-world ESC case validates the model. The results show that carbon taxation and green investments can reduce emissions while maintaining profitability, with total cost and emission sensitivity of ±10–20% across different policies and demand uncertainty settings. The findings support adaptive, policy-aware planning by guiding green investment intensity and forward–reverse logistics decisions to balance cost efficiency and emissions reduction and provide actionable insights for managers facing progressive carbon pricing regulations. Full article

Capillary tubes are widely used as essential expansion devices in small refrigeration and air-conditioning systems. Accurate prediction of mass flow rate through adiabatic capillaries is a critical aspect of system design and optimization. While there are currently numerous models capable of predicting mass flow through capillaries, most rely on experimental data containing uncertainties, resulting in suboptimal generalization performance. Unlike previous ANN models and empirical correlations that rely on experimental data, this study addresses this limitation by introducing neural networks based on the homogeneous equilibrium model (HEM) of adiabatic capillaries. Two neural networks—a traditional multi-layer perceptron (MLP) and a deep residual network (ResNet)—are developed using a dataset generated by the HEM. The models are subsequently validated and compared against established models using experimental data for various refrigerants and operating conditions collected from the open literature. The results demonstrate that both neural networks exhibit exceptional generalization ability. The average deviations on the experimental dataset are 5.2% for the MLP and 4.5% for the ResNet, outperforming existing models. Their performance across different refrigerants is stable, with the ResNet demonstrating superior overall performance. Furthermore, the trained neural networks achieve a computational speed substantially superior to that of the HEM. Full article

Golden pompano (Trachinotus ovatus) ranks among the most commercially important and high-yield marine finfish species in Chinese mariculture. In response to the requirement for monitoring fish in aquaculture, this study employed Adaptive Resolution Imaging Sonar (ARIS) to observe the body length, swimming speed, and spatial distribution of untreated (GI group), anesthetized (GII group), and injured (GIII group) T. ovatus in a small offshore cage (1.5 × 1.5 × 2.5 m³). The results demonstrated that the relative error range of the length measurement of the T. ovatus spanned from −3.24 to 4.35, and there was no significant difference between the observed and actual body lengths (ANOVA, p > 0.05). We failed to detect a significant difference in the average speed between the untreated group and the anesthetized group (ANOVA, p > 0.05; Tukey’s HSD, p > 0.05). The injured fish exhibited a significantly lower swimming speed compared to untreated and anesthetized individuals (ANOVA, p < 0.01; Tukey’s HSD, p < 0.01). Untreated individuals and fish with physical injuries exhibited mean vertical distribution depths of 1.06 ± 0.47 m and 1.70 ± 0.51 m, respectively, with the injured fish occupying a significantly greater water depth than the untreated conspecifics (one-way ANOVA, p < 0.01). There was a highly significant association between the treatment status of the fish (untreated/injured) and the frequency of water layer distribution (χ²(2) = 196.78, p < 0.01). The findings of the present study can furnish specific methodological references for the imaging sonar-based monitoring of T. ovatus within aquaculture cage systems. Nevertheless, the study is subject to several inherent limitations, including a small sample size for the injured group (n = 3), the employment of an artificial injury model, and the confinement of experimental subjects to a closed cage environment; these factors may introduce statistical uncertainty and thus exert a considerable impact on the external validity of the study’s results. Full article

The cruise shipbuilding industry faces significant disruptions stemming from escalating trade frictions and regional conflicts which threaten its operational and economic sustainability. Enhancing supply chain resilience is thus crucial for sustainable development. This study identifies critical resilience factors and examines their interrelationships within growth-stage cruise shipbuilding supply chains. Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Modeling (ISM), and Matrix Cross-Reference Multiplication Method (MICMAC) analysis are integrated to explore causal linkages, hierarchical structures, and driver-dependence dynamics. The analysis reveals that customized demand responsiveness, learning organization, specialized industrial clusters, and inter-industry collaboration are fundamental causal drivers. In contrast, knowledge stock, risk culture, and final-assembly orchestration serve as critical mediators. Based on these findings, we propose distinct resource-contingent strategic pathways for managers. This study provides an actionable framework for building resilience, offering critical guidance for securing the sustainable development of the cruise shipbuilding industry amid uncertainty. Full article

Sixth-generation (6G) networks will operate with pervasive autonomy and minimal centralised control, imposing stringent requirements on security and trust. This short communication presents a hybrid trust evaluation approach that combines fuzzy inference for uncertainty management, bidirectional long short-term memory (BiLSTM) networks for temporal prediction, and blockchain for immutable verification. The pipeline first maps multi-source interaction and context metrics into linguistic trust values via fuzzy rules, then leverages BiLSTM to anticipate trust fluctuations under dynamic conditions, and finally anchors trust updates on a permissioned blockchain to ensure integrity and traceability. Using CIC-IoT2023, the proposed approach attains high accuracy and F1-score while reducing Execution Time (ET) and energy demands relative to a recent spatial-temporal trust model for 6G IoT. Results indicate that jointly addressing uncertainty, temporal evolution, and ledger-backed validation yields stable trust trajectories suitable for resource-constrained devices. The study outlines a practical path toward explainable, adaptive, and tamper-resistant trust management for 6G ecosystems. Full article

The accurate and non-invasive measurement of moisture content in wood is essential for the preservation of historical and artistic artifacts. This study presents the calibration of a planar Microwave Planar Capacitive Coupled Ring Resonator (MPCCRR) designed to indirectly and non-destructively assess the water content in wood samples. The method relies on analyzing shifts in the resonant frequencies and variations in the transmission parameter |S21| resulting from changes in the material’s dielectric permittivity. After preliminary characterization via parametric simulations (εr = 1–10) and validation with low-permittivity reference materials, the sensor was tested on three wood species (poplar, fir, beech), including measurements at two sensor positions and with different grain orientations. The results demonstrate a monotonic, repeatable response to increasing moisture content with frequency shifts up to ≈220 MHz and normalized sensitivities ranging from 3 to 9 MHz/% water content, depending on species and measurement position. Position 2 showed the greatest sensitivity due to stronger field–sample interaction, while Position 1 provided a quasi-isotropic response with excellent repeatability. Linear regression analyses revealed good correlations between the frequency shifts and the gravimetric water content (R² ≥ 0.85). The MPCCRR sensor therefore proves to be a promising tool for the non-invasive monitoring of wood moisture, which is particularly suitable for the low-moisture range encountered in cultural heritage conservation, with an estimated moisture uncertainty of 0.12–0.35% under controlled laboratory conditions. Full article

Land-use and land-cover (LULC) change is one of the most pervasive drivers of socioenvironmental transformation worldwide. Given its impacts on ecosystems and climate, the systematic analysis of LULC dynamics remains a central objective of land-change science. Despite major advances in Earth observation capabilities, robust, flexible, and scalable algorithms for long-term monitoring remain unevenly adopted, particularly in remote, forested tropical regions. This study introduces the Multivariate Random Forest Land-Use and Land-Cover (MuRaF-LULC) framework, a supervised and generalizable framework that produces annual, multi-class LULC maps from Landsat time series, with interannual change derived through year-to-year comparisons. A key methodological component of the framework is its predictor-selection strategy, in which variable-importance rankings are used to identify an optimized subset of predictors prior to final model training. MuRaF-LULC was implemented in Google Earth Engine (GEE) and evaluated in Guatemala’s Maya Biosphere Reserve (MBR) for the 2018–2024 period using probability-based sampling and uncertainty-aware accuracy assessment and area estimation. Results show that MuRaF-LULC generates robust annual LULC classifications across multiple years (overall accuracy = 0.90–0.92) and reliable estimates of agropecuario expansion (the dominant transition in the study area) when change is assessed over longer temporal windows where transitions signals stabilize and for which the framework is best suited (producer’s accuracy = 0.97 ± 0.03; user’s accuracy = 0.69 ± 0.05). By prioritizing consistent annual, multiclass LULC trajectories, MuRaF-LULC complements breakpoint- and disturbance-oriented approaches commonly used in land-change studies. Implemented in publicly available, well-documented GEE scripts, MuRaF-LULC facilitates policy-relevant LULC assessment by remote sensing practitioners in governmental and private organizations, where reproducibility, clarity, and ease of deployment are as important as methodological sophistication. Full article

Background/Objectives: Hyperbaric Oxygen Therapy (HBOT) involves breathing oxygen at pressures greater than atmospheric levels and is used to treat diverse clinical conditions. However, little is known about the lived experiences and perceived needs of patients undergoing scheduled treatment in multiplace hyperbaric chambers, where nurses play a key role in support, safety, and communication. This study aimed to explore the perceptions, expectations, and needs of patients receiving scheduled HBOT sessions in a multiplace chamber in a hospital setting. Methods: A qualitative phenomenological design was used. Participants were recruited consecutively among adults who had completed at least 10 HBOT sessions and demonstrated adequate cognitive function. Individual semi-structured interviews were conducted between January and March 2023 in locations chosen by participants. Interviews were audio-recorded, transcribed, and validated by participants. Results: Twelve participants (eight men, four women; aged 25–84 years) were included. Four thematic areas emerged: (1) Biopsychosocial lived experiences, including initial uncertainty, physical discomfort such as ear pressure or mask-related issues, and progressive recognition of therapeutic benefits. (2) Interpersonal relationships, highlighting trust, security, and emotional support provided mainly by nurses. (3) Communication experiences, with participants expressing satisfaction but requesting clearer, earlier information on procedures, risks, and expected sensations. (4) Structural and organizational factors, where transportation logistics and treatment scheduling were significant sources of fatigue and discomfort. Conclusions: Patients valued HBOT and perceived notable health improvements, while identifying specific unmet informational and organizational needs. These findings suggest the importance of nurse-led educational interventions to enhance preparation, reduce anxiety, and optimize patient experience during HBOT. Full article

The emerging scope of the Industrial Internet of Services (IIoS) requires a robust intrusion detection system to detect malicious attacks. The increasing frequency of sophisticated and high-impact cyber attacks has resulted in financial losses and catastrophes in IIoS-based manufacturing industries. However, existing solutions often struggle to adapt and generalize to new cyber attacks. This study proposes a unique approach designed for known and zero-day network attack detection in IIoS environments, called Denoising Adaptive Multi-Branch Architecture (DA-MBA). The proposed approach is a smart, conformal, and self-adjusting cyber attack detection framework featuring denoising representation learning, hybrid neural inference, and open-set uncertainty calibration. The model merges a denoising autoencoder (DAE) to generate noise-tolerant latent representations, which are processed using a hybrid multi-branch classifier combining dense and bidirectional recurrent layers to capture both static and temporal attack signatures. Moreover, it addresses challenges such as adaptability and generalizability by hybridizing a Multilayer Perceptron (MLP) and bidirectional LSTM (BiLSTM). The proposed hybrid model was designed to fuse feed-forward transformations with sequence-aware modeling, which can capture direct feature interactions and any underlying temporal and order-dependent patterns. Multiple approaches have been applied to strengthen the dual-branch architecture, such as class weighting and comprehensive hyperparameter optimization via Optuna, which collectively address imbalanced data, overfitting, and dynamically shifting threat vectors. The proposed DA-MBA is evaluated on two widely recognized IIoT-based datasets, Edge-IIoT set and WUSTL-IIoT-2021 and achieves over 99% accuracy and a near 0.02 loss, underscoring its effectiveness in detecting the most sophisticated attacks and outperforming recent deep learning IDS baselines. The solution offers a scalable and flexible architecture for enhancing cybersecurity within evolving IIoS environments by coupling feature denoising, multi-branch classification, and automated hyperparameter tuning. The results confirm that coupling robust feature denoising with sequence-aware classification can provide a scalable and flexible framework for improving cybersecurity within the IIoS. The proposed architecture offers a scalable, interpretable, and risk sensitive defense mechanism for IIoS, advancing secure, adaptive, and trustworthy industrial cyber-resilience. Full article

Current agriculture faces the challenge of producing sufficient food from diminishing land resources, due to deteriorating soil quality and accelerated population growth. Numerous studies have demonstrated that biochar/hydrochar can serve as efficient soil amendments by improving soil fertility and enhancing crop productivity. Various food wastes are promising raw materials for biochar/ hydrochar production due to their abundant organic matter. Recently, thermochemical techniques such as pyrolysis, hydrothermal carbonization (HTC), and microwave-assisted pyrolysis (MAP) have been widely proposed for converting food waste into biochar/hydrochar for soil amendment. However, the composition of food waste is complex and the parameters for its thermal treatment are highly variable, leading to uncertainties in the performance of the derived biochar/hydrochar for soil applications. This study aims to establish a structure–activity relationship linking food waste carbonization technology, the properties of the obtained biochar/hydrochar, and its functions as a soil amendment. Furthermore, the detailed mechanisms by which biochar improves plant growth or poses potential ecological risks to agricultural land are discussed. This review is intended to provide a guideline for the large-scale application of food waste-derived char for soil amendment. Full article