Search Results (62,600)

Background: The WRKY transcription factor family represents one of the most crucial transcription factor families in plants, regulating diverse physiological processes. The heartwood of Dalbergia odorifera is a prized material for both high-quality rosewood and traditional medicinal applications, exhibiting exceptional economic value. However, the roles of WRKY transcription factors in the growth and development of D. odorifera, particularly their correlation with heartwood formation, remain unexplored. Methods: WRKY transcription factors were identified through bioinformatics analysis using the published genome data of D. odorifera. Phylogenetic comparative analysis was performed based on the Arabidopsis classification system. Collinearity analysis was conducted to investigate the evolutionary dynamics and expansion mechanisms of the WRKY gene family, and differential expression analysis was performed across tissues. Results: A total of 94 WRKY genes were unevenly distributed across 10 chromosomes and systematically designated as DodWRKY1 to DodWRKY94 according to their chromosomal positions. The WRKY family was classified into three major clades (Groups I, II, and III), with Group II further subdivided into five subgroups (IIa–IIe). Purifying selection served as the primary force shaping the WRKY family, with whole-genome or segmental duplication acting as the dominant expansion mechanism; these duplication events contributed to functional divergence, whereas genes within the same subgroup retained conserved structural features and motif compositions. DodWRKY14 (subgroup IIb) and DodWRKY58/68 (subgroup IIc) were highly expressed in the transition zone, suggesting a potential involvement in heartwood formation. Conclusions: This study provides a comprehensive characterization of the DodWRKY family and identifies candidate genes associated with heartwood formation, thereby establishing a foundation for further investigation into the molecular mechanisms underlying heartwood development. Full article

Temporal monitoring of bioprocesses is inherently complex because process variables do not evolve independently over time, and their interpretation changes as the reaction progresses. In many existing abnormality detection methods, sensor signals are analyzed at isolated time points or temporal characteristics are only weakly reflected through model structures. As a result, such approaches struggle to explain or detect abnormal behavior that emerges differently across reaction states. This study proposes TPP-TimeNet, a time-aware artificial intelligence framework developed to improve abnormality detection in bioprocess monitoring. Unlike conventional methods, the proposed framework explicitly incorporates reaction time as contextual information. Multivariate process signals are reorganized into sliding windows that reflect reaction-state transitions rather than uniform time segmentation. Temporal behavior inside each window is captured using a sequential encoding model, and reaction-state information is subsequently integrated to form state-dependent representations. Through this design, the model can distinguish between temporal patterns that are similar in shape but occur at different points in the reaction timeline. This capability leads to improved sensitivity to abnormal events that may otherwise remain undetected. Abnormality is evaluated at the window level using a probabilistic scoring scheme with a fixed threshold, enabling consistent and reproducible decision-making. The performance of TPP-TimeNet was evaluated using publicly available process control datasets from Kaggle. The datasets were reinterpreted in a bioprocess context by mapping variables such as temperature, pH, and pressure. Experimental results show that the proposed method outperforms traditional machine learning models as well as deep learning approaches that focus only on temporal features, achieving higher accuracy, sensitivity, and F1-score. These findings suggest that incorporating explicit reaction-state awareness is essential for effective abnormality detection in bioprocess monitoring systems. Full article

The transition to sustainable energy systems requires the effective integration of offshore wind energy with hydrogen production. In this context, the paper assesses the potential for offshore hydrogen production in eight locations, three of which are located in the Black Sea, using data from the ERA5 database (period 2016–2025) at a height of 10 m and then extrapolated to a height of 150 m. The methodology includes estimating the annual energy production for four types of offshore turbines (Siemens Gamesa (Zamudio, Spain) SG 14-236 DD, Vestas (Aarhus, Denmark) V236-15.0, GE (Rotterdam, The Netherlands) Haliade-X 13, and MingYang (Guangdong, China) MySE12-242) and correlating it with six electrolyzer configurations (PEM and AWE) in gross and net scenarios, as well as analyzing the energy compatibility related to the number of electrolyzers. The novelty of the study lies in the integrated multi-site approach and in the direct quantification of the relationship between wind production and electrolysis requirements for different turbine–electrolyzer combinations. The results indicate a variation in gross annual energy production (AEP) in the range of 45.65 to 81.11 GWh/year, while the net scenario, accounting for operational losses, ranged from 37.75 to 67.05 GWh/year, and hydrogen production between 327 and 1075 t/year, highlighting that the optimal performance is determined by the compatibility between turbine and electrolyzer and the specific energy consumption rather than the nominal power. The <!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ --> Full article

Accidental crushing by sows is the primary cause of pre-weaning piglet mortality in intensive production, often due to the spatiotemporal lag of manual inspection. While Internet of Things (IoT) solutions exist, they frequently face challenges such as vision occlusion, high hardware costs, and latency. To address these, this study developed a low-cost multi-modal edge computing system based on TinyML. Using an ESP32-S3 microcontroller, the system employs a “Motion-Gated Acoustic Detection” strategy, activating a lightweight 1D-CNN model to identify piglet screams only when an IMU detects high-risk postural transitions of the sow. Results show the quantized model (5.1 KB) achieves 95.56% accuracy and 2 ms inference latency. The total end-to-end response latency is within 179 ms, ensuring intervention within the early “golden rescue window.” The low-power design enables the battery life to cover the entire lactation period. Field tests demonstrated that the system intercepted identified crushing risks within the monitored cohort, supporting its potential for significantly improving piglet survival probability. This research overcomes the limitations of single-modal monitoring and provides a scalable, cost-effective engineering intervention for enhancing animal welfare and achieving intelligent, unattended supervision in precision livestock farming. Full article

The hybrid parallel operation of the grid-following (GFL) converter and the grid-forming (GFM) converter has become a typical scenario in distribution networks. The vastly different control philosophies and dynamics between the two give rise to complex small-signal stability issues, especially under weak grids. Traditional methods primarily rely on equivalent models or impedance-based approaches at fixed operating points, which struggle to reveal the system instability mechanisms when the capacity ratio between the two types of converters changes. This paper establishes a three-port dynamic average model for a grid-connected system with heterogeneous GFL-GFM converters. Using the participation factor analysis method, the system's dominant modes are identified, and the key parameters influencing oscillations at different frequencies, as well as their formation processes, are revealed. Furthermore, a stability analysis method for variable capacity ratios is proposed. This method re-performs modal analysis based on the varying capacities of the GFM and GFL converters, revealing the dominant factors and influencing mechanisms of system instability during capacity transitions. Finally, a simulation model is built in PSCAD/EMTDC to verify the correctness of the proposed three-port model and the theoretical analysis results. Full article

In ecologically fragile and geomorphologically complex mountainous regions, ensuring a smooth transition from poverty alleviation to multidimensional sustainable rural development remains a key issue in regional governance. Focusing on the Qinba Mountains, a typical former contiguous poverty-stricken region in China covering 18 prefecture-level cities in six provinces, this study uses 2009–2023 prefecture-level panel data to examine the spatiotemporal evolution and driving mechanisms of coordinated rural revitalization. An integrated framework of “multi-dimensional evaluation–spatiotemporal tracking–attribution diagnosis” is developed by combining the improved AHP–entropy-weight TOPSIS method, the Coupling Coordination Degree (CCD) model, spatial Markov chains, spatial autocorrelation, and the Geodetector. The results show pronounced subsystem asynchrony. Livelihood and Well-being Security (U5) improves steadily, while Level of Industrial Development (U1), Civic Virtues and Cultural Vibrancy (U3), and Rural Governance (U4) also rise but with clear spatial differentiation; by contrast, Quality of Human Settlements (U2) fluctuates in stages under ecological fragility. Overall, the coupling coordination level advances from the Verge of Imbalance to Intermediate Coordination, yet the regional pattern remains uneven, with eastern basin cities leading and western deep mountainous cities lagging. State transitions display both policy responsiveness and path dependence: the probability of retaining the original state ranges from 50.0% to 90.5%; low-level neighborhoods reduce the upward transition probability to 25%, whereas medium-to-high-level neighborhoods raise the upward transition probability of low-level cities from 36.36% to 53.33%. Spatial dependence is also evident, with Global Moran’s I increasing, with fluctuations, from 0.331 in 2009 to 0.536 in 2023; high-value clusters extend along the Guanzhong Plain–Han River Valley corridor, while low-value clusters remain relatively locked in mountainous border areas. Driving mechanisms show clear stage-wise succession. At the single-factor level, the explanatory power of Road Network Density (F6) declines from 0.639 to 0.287, whereas Terrain Relief Amplitude (F1) becomes the dominant background constraint in the later stage (q = 0.772). Multi-factor interactions are generally enhanced. In particular, the traditional infrastructure-led pathway weakens markedly, with F1 ∩ F6 = 0.055 in 2023, while the interaction between terrain and consumer market vitality becomes dominant, with F1 ∩ F7 = 0.987 in 2023. On this basis, three major pathways are identified: government fiscal intervention and transportation accessibility improvement, capital agglomeration and market demand stimulation, and human–earth system adaptation and ecological value realization. These findings provide quantitative evidence for breaking spatial lock-in and improving cross-regional resource allocation in ecologically constrained mountainous regions. Full article

In this work, we propose a modified Schwarzschild geometry inspired by the Asymptotic Safety approach to quantum gravity, in which the Newtonian coupling becomes a running quantity depending on the radial coordinate. We employ an infrared cutoff at the proper distance and obtain a new quantum-corrected black hole metric. We provide a thermodynamical analysis, first using standard methods and then proceeding to a geometrothermodynamical study of the phase space and to a topological analysis of phase transitions. We also calculate the grey-body factors of our solution, providing exact lower bounds in the quantum-corrected transmission coefficients. Finally, we present the shadow size and intensity profile of our solution, showing its consistency with current observational constraints. Full article

Photochromic molecules, capable of reversible isomerization under specific light irradiation, are pivotal for developing advanced photo-responsive materials. Azobenzene derivatives, in particular, are renowned for their significant conformational change, excellent reversibility, and high photostability. This study presents a novel cyclic diazo compound (CDTA) comprising two azobenzene units connected via flexible glycol chains. The photo-responsive behavior of CDTA doped into the liquid crystal 4-cyano-4′-octylbiphenyl (8CB) was systematically investigated. The composite exhibits a pronounced photo-induced phase transition from a liquid crystalline to an isotropic state under 365 nm UV irradiation, accompanied by a reversible change in light transmittance. The response kinetics were found to be highly dependent on temperature and dopant concentration. At 35 °C, the UV response time was accelerated to 6.8 s, attributed to the transition of the host 8CB from a smectic to a nematic phase. Furthermore, the composite demonstrated dual responsiveness: optical switching under UV light and electrical switching under an applied field in its nematic state. This work elucidates the interaction between molecular structure and photo-response in a liquid crystalline matrix, offering insights for designing next-generation smart windows and adaptive optical devices. Full article

Historical small towns (HSTs) embody irreplaceable cultural heritage and territorial identity, facing depopulation, economic marginalization, and infrastructure decay. Improving their liveability and attractiveness is essential to reverse these trends and boost sustainable development. In this context, HSTs are potential drivers of circular and sustainable socio-technical systems, where the circular economy (CE) offers a framework for local sustainability. However, HSTs lack adequate sustainable CE implementation tools. This study, the culmination of the H-SMA-CE project, develops a Decision Support System (DSS) to assist local policymakers in planning CE transitions in Italian HSTs. The DSS integrates three building blocks: context analysis (metabolic flows, stakeholder networks), an intervention library with cost–benefit data, and a composite Municipal Circular Economy Index (MCEI). The tool enables users to assess baseline circularity, simulate scenarios, and identify optimal investment portfolios through multi-objective optimization. This approach allows for the simultaneous evaluation of the benefits of each sustainability aspect, i.e., environmental, economic and social. Tested on the municipality of Taurasi (Italy), an HST with a wine-based economy, the results show that balanced intervention strategies yield greater circularity improvements than single-objective approaches. The paper contributes to the discourse on digital tools for sustainability transitions, offering a replicable model for evidence-based CE governance in heritage-rich territorial contexts. Full article

This study develops a unified multiscale–machine learning framework to interpret and predict thermo-mechanical wear regime transitions in MWCNT- and nanoclay-reinforced bio-based epoxy composites. A physics-informed master wear formulation integrating real contact mechanics, geometry-dependent shear transfer, interfacial adhesion energetics, and fracture-controlled matrix detachment was combined with interpretable machine learning analytics on a unified tribological dataset. In the CNT system, increasing loading from 0.1 to 0.4 wt.% enhanced interfacial adhesion energy density from 0.00813 to 0.01906 J/m², resulting in a monotonic reduction in the wear rate from 0.00918 to 0.00613 mm³/N·m (~33% reduction). In contrast, nanoclay exhibited an optimum behavior, with a minimum wear at 0.25 wt.% (0.000093 mm³/N·m; 7.9% reduction vs. neat clay baseline), followed by deterioration at a higher loading due to dispersion loss. The unified probabilistic regime classification of low-wear conditions (k < 0.007 mm³/N·m) achieved an ROC − AUC = 0.9256 and balanced accuracy = 94.3%, with thermo-mechanical severity identified as the dominant regime-switching driver. Reinforcement identity significantly modulated regime stability, confirming distinct shear transfer (Carbon Nano Tubes(CNT)) and confinement/tribofilm (clay) mechanisms within a common mathematical framework. By enabling the durability-oriented design of bio-based tribological systems and extending component service life through predictive stability mapping, this work contributes to resource-efficient materials engineering and reduced lifecycle waste, supporting Sustainable Development Goals SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). Full article

The Qinghai–Tibet Plateau and surrounding mountain regions form one of the world’s most distinctive freshwater environmental gradients. Schizothoracinae are among the most representative endemic fish lineages in these systems and provide a useful model for studying how drainage history, genome evolution, adaptation, and conservation interact. In this review, we synthesize schizothoracine research within an environment–evolution–conservation framework. We examine how drainage history and connectivity shape divergence and gene exchange, how polyploidy and genome remodeling provide the genomic background for adaptive inference, and how phenotypic and population-genomic evidence can be translated into conservation units and management priorities. Across current studies, cold-associated metabolic remodeling and UV-related DNA damage response and repair emerge as the most recurrent molecular themes, whereas hypoxia-related signals are more context-dependent. We further show that morphology, otolith chemistry, age–growth traits, and population structure can strengthen MU/ESU interpretation when integrated with genomic evidence. Future progress will depend on broader chromosome-level genome coverage, more systematic comparison of structural genomic variation, standardized stressor-linked designs, and denser sampling in geomorphic transition zones and putative hybrid regions. Full article

Recent space missions demand higher pointing accuracy, smoother attitude transitions and lower energy consumption than those typically achievable with conventional control approaches. This motivates the exploration of intelligent and nonlinear control methods. The FuzzyBuzz experiment investigates the application of fuzzy logic for spacecraft attitude control using NASA’s Astrobee robotic system aboard the International Space Station. Unlike traditional control methods, fuzzy logic introduces a rule-based approach capable of handling uncertainties and nonlinearities inherent in space environments, making it particularly suited for autonomous operations in microgravity. The objective of FuzzyBuzz is to evaluate the effectiveness of fuzzy controllers compared to traditional linear ones, such as Proportional–Integral–Derivative (PID) and $H_{\infty }$ controllers. In addition, a comparison with a nonlinear controller based on a Model Predictive Control (MPC) strategy is considered. The controllers will be tested through predefined attitude maneuvers, evaluating precision, energy efficiency, and real-time adaptability. This work presents the design of the FuzzyBuzz experiment, including the software architecture, simulation environment, experiment protocol, and the development of a fuzzy logic-based attitude control system for Astrobee robots. The proposed fuzzy controller and a PID controller are optimized using a Multi-Objective Particle Swarm Optimization (MOPSO) method, providing a range of operational points with different trade-offs between two metrics, related to convergence time and energy consumption. Results show that the PID controller is better suited for scenarios demanding low convergence times, whereas the fuzzy controller provides smoother responses, reduced steady-state error, and maintains convergence under significant parametric uncertainties. Results from $H_{\infty }$ and MPC controllers will be reported once the in-orbit experiment is performed. Full article

The twin-blade planetary mixer is critical for processing highly viscous materials in the chemical and polymer industries, yet optimizing its mixing characteristics alongside energy efficiency remains challenging. This study investigates the twin-blade planetary mixer, using computational fluid dynamics simulation methods to analyze the operating parameters and multi-objective optimization of performance in viscous systems. First, the multi-axis stirring process was simulated numerically based on the Planetary Motion Method, revealing the working process at the cross-section and of the blades, thereby unveiling a mixing mechanism driven by cyclic transitions between local shear-intensive kneading and global convective circulation. Then, through orthogonal experiments and ANOVA, the dominant role of the hollow blade’s self-rotation speed on performance was clarified. Furthermore, based on Kriging and NSGA-II, with LINMAP employed for decision making, an optimal parameter combination, specifically a hollow blade self-rotation speed of 94.86 rpm, a speed ratio of 0.063, and a blade-to-bottom height of 2.79 mm, successfully achieved an 8.15% reduction in power consumption, a 20.03% increase in global axial flow, and a 5.01% enhancement in maximum kneading pressure. Full article

This study investigates the relationship between general and problematic social media use, and young adults’ future time perspectives during their school-to-work-transition. It also explores how parents perceive the influence of their children’s media use on career perspectives. Utilizing longitudinal data from a quantitative study, 443 parent–youth dyads at t1 and 355 at t2 were surveyed on their practices concerning daily social media use, problematic social media engagement, transition and moratorium orientations, and parental assessments. Open-ended responses from parents indicated that the majority perceived either a positive effect or no influence of media use on career opportunities, while a smaller proportion reported negative impacts. Adolescents whose parents expressed positive views demonstrated significantly stronger transition orientations. Cross-sectional analyses demonstrated that problematic social media use was negatively associated with transition orientation and positively related to moratorium orientation. General usage time, however, showed no meaningful associations. Longitudinal regression analyses indicated that neither general nor problematic social media use predicted subsequent levels of transition or moratorium orientation after controlling for baseline orientations, pointing to substantial stability in these dispositions. The findings suggest that problematic social media engagement coincides with less future-oriented mindsets, while future orientations remain stable over time. Full article

This paper examines several portfolio rules for comparing performance against the Shanghai Composite Index. The investor can use mutual funds or sector-based Exchange-Traded funds (ETFs). Five different approaches are applied for analysis. Two core approaches are discussed in detail and compared to passive investing: The top-N strategy and the sector rotation strategy. The Top-N strategy shifts capital each period into the last period rank-N fund, and the sector rotation strategy ranks funds based on their performance in the preceding investment period, forming three baskets: “Winners”, “Median”, and “Losers”. Extensive statistical tests on more than 300 equity mutual funds are performed for the top-N strategy to evaluate performance persistence using quintile sorts, winner–loser spreads, and transition tests. In contrast, the sector-rotation strategy and a holdings-based replication strategy (constructed from annual reports and sector funds) are implemented as case studies using the ten largest funds. Their performance is evaluated using multiple return and risk metrics. Full article