Search Results (255)

Paeonia ludlowii, a critically endangered species endemic to Tibet, China, possesses significant ornamental, culinary, and medicinal value. However, its mitochondrial genome remains understudied, limiting insights into its evolutionary mechanisms and constraining conservation genetics applications and molecular breeding programs. We present the first complete assembly and comprehensive analysis of the P. ludlowii mitochondrial genome. Most remarkably, we discovered that the P. ludlowii mitogenome exhibits an atypical dual-circular structure, representing the first documented occurrence of this architectural feature within the genus Paeonia. The assembled genome spans 314,371 bp and encodes 42 tRNA genes, 3 rRNA genes, and 31 protein-coding genes, with a pronounced adenine–thymine bias. This multipartite genome structure is characterized by abundant repetitive elements (112 functionally annotated SSRs, 33 tandem repeats, and 945 dispersed repeats), which potentially drive genome rearrangements and facilitate adaptive evolution. Analyses of codon usage bias and nucleotide diversity revealed highly conserved gene expression regulation with limited variability. Phylogenetic reconstruction confirms that P. ludlowii, P. suffruticosa, and P. lactiflora form a monophyletic clade, reflecting close evolutionary relationships, while extensive syntenic collinearity with other Paeonia species underscores mitochondrial genome conservation at the genus level. Extensive inter-organellar gene transfer events, particularly from chloroplast to mitochondrion, suggest that such DNA exchanges enhance genetic diversity and promote environmental adaptation. The discovery of the dual-circular architecture provides novel insights into plant mitochondrial genome evolution and structural plasticity. This study elucidates the unique structural characteristics of the P. ludlowii mitochondrial genome and establishes a crucial genetic foundation for developing targeted conservation strategies and facilitating molecular-assisted breeding programs for this endangered species. Full article

Nanodrugs hold great promise for targeted therapies, but their potential for cytotoxicity remains a major area of concern, threatening both patient safety and clinical translation. In this systematic review, we conducted a systematic investigation of nanotoxicity studies—identified through an AI-assisted screening procedure using Scopus, PubMed, and Elicit AI—to establish the molecular determinants of nanodrug-induced cytotoxicity. Our findings reveal three dominant and linked mechanisms that consistently act in a range of nanomaterials: oxidative stress, inflammatory signaling, and lysosomal disruption. Key nanomaterial properties like chemical structure, size, shape, surface charge, tendency to aggregate, and biocorona formation control these pathways, modulating cellular uptake, reactive oxygen species generation, cytokine release, and subcellular injury. Notably, the most frequent mechanism was oxidative stress, which often initiated downstream inflammatory and apoptotic signaling. By linking these toxicity pathways with particular nanoparticle characteristics, our review presents necessary guidelines for safer, more biocompatible nanodrug formulation design. This extensive framework acknowledges the imperative necessity for mechanistic toxicity assessment in nanopharmaceutical design and underscores the strength of AI tools in driving systematic toxicology studies. Full article

The lower-limb assistance exoskeleton is increasingly being utilized in various fields due to its excellent performance in human body assistance. As a crucial component of robots, the joint is expected to be designed with a high-output torque to support hip and knee movement, and lightweight to enhance user experience. Contrasted with the elastic actuation with harmonic drive and other flexible transmission, the non-elastic quasi-direct actuation is more promising to be applied in exoskeleton due to its advanced dynamic performance and lightweight feature. Moreover, robot joints are commonly driven electrically, especially by a permanent magnet synchronous motor which is rapidly developed because of its compact structure and powerful output. Based on different topological structures, numerous research focus on torque density, ripple torque suppression, efficiency improvement, and thermal management to improve motor performance. Furthermore, the elaborated joint with powerful motors should be controlled compliantly to improve flexibility and interaction, and therefore, popular complaint control algorithms like impedance and admittance controls are discussed in this paper. Through the review and analysis of the integrated design from mechanism structure to control algorithm, it is expected to indicate developmental prospects of lower-limb assistance exoskeleton joints with optimized performance. Full article

This study addresses the ’last-mile’ transportation challenges that arise in steep and narrow forest terrain by proposing a novel robotic palanquin system called roCaGo. It is inspired by the mechanical principles of two-wheel-steering and two-wheel-drive (2WS/2WD) bicycles. The roCaGo system integrates front- and rear-wheel-drive mechanisms, as well as a central suspension structure for carrying loads. Unlike conventional forestry machinery, which requires wide, well-maintained roads or permanent rail systems, the roCaGo system enables flexible, operator-assisted transport along narrow, unprepared mountain paths. A dynamic model of the system was developed to design a stabilization control strategy, enabling roCaGo to maintain transport stability and assist the operator during navigation. Numerical simulations and preliminary physical experiments demonstrate its effectiveness in challenging forest environments. Furthermore, the applicability of roCaGo has been extended to include use as a mobile third-person viewpoint platform to support the remote operation of existing forestry equipment; specifically the LV800crawler vehicle equipped with a front-mounted mulcher. Field tests involving LiDAR sensors mounted on roCaGo were conducted to verify its ability to capture the environmental data necessary for non-line-of-sight teleoperation. The results show that roCaGo is a promising solution for improving labor efficiency and ensuring operator safety in forest logistics and remote-controlled forestry operations. Full article

Pupil detection is a key technology in fields such as human–computer interaction, fatigue driving detection, and medical diagnosis. Existing pupil detection algorithms still face challenges in maintaining robustness under variable lighting conditions and occlusion scenarios. In this paper, we propose a novel pupil detection algorithm, ViMSA, based on the ViM model. This algorithm introduces weighted feature fusion, aiming to enable the model to adaptively learn the contribution of different feature patches to the pupil detection results; combines ViM with the MSA (multi-head self-attention) mechanism), aiming to integrate global features and improve the accuracy and robustness of pupil detection; and uses FFT (Fast Fourier Transform) to convert the time-domain vector outer product in MSA into a frequency–domain dot product, in order to reduce the computational complexity of the model and improve the detection efficiency of the model. ViMSA was trained and tested on nearly 135,000 pupil images from 30 different datasets, demonstrating exceptional generalization capability. The experimental results demonstrate that the proposed ViMSA achieves 99.6% detection accuracy at five pixels with an RMSE of 1.67 pixels and a processing speed exceeding 100 FPS, meeting real-time monitoring requirements for various applications including operation under variable and uneven lighting conditions, assistive technology (enabling communication with neuro-motor disorder patients through pupil recognition), computer gaming, and automotive industry applications (enhancing traffic safety by monitoring drivers’ cognitive states). Full article

The Fourth Industrial Revolution and artificial intelligence (AI) technology are driving the transformation of the meat industry from mechanization and automation to intelligence and digitization. This paper provides a systematic review of key technological innovations in this field, including physical technologies (such as smart cutting precision improved to the millimeter level, pulse electric field sterilization efficiency exceeding 90%, ultrasonic-assisted marinating time reduced by 12 h, and ultra-high-pressure processing extending shelf life) and digital technologies (IoT real-time monitoring, blockchain-enhanced traceability transparency, and AI-optimized production decision-making). Additionally, it explores the potential of alternative meat production technologies (cell-cultured meat and 3D bioprinting) to disrupt traditional models. In application scenarios such as central kitchen efficiency improvements (e.g., food companies leveraging the “S2B2C” model to apply AI agents, supply chain management, and intelligent control systems, resulting in a 26.98% increase in overall profits), end-to-end temperature control in cold chain logistics (e.g., using multi-array sensors for real-time monitoring of meat spoilage), intelligent freshness recognition of products (based on deep learning or sensors), and personalized customization (e.g., 3D-printed customized nutritional meat products), these technologies have significantly improved production efficiency, product quality, and safety. However, large-scale application still faces key challenges, including high costs (such as the high investment in cell-cultured meat bioreactors), lack of standardization (such as the absence of unified standards for non-thermal technology parameters), and consumer acceptance (surveys indicate that approximately 41% of consumers are concerned about contracting illnesses from consuming cultured meat, and only 25% are willing to try it). These challenges constrain the economic viability and market promotion of the aforementioned technologies. Future efforts should focus on collaborative innovation to establish a truly intelligent and sustainable meat production system. Full article

The advancement of artificial intelligence (AI) offers new opportunities for business model innovation in digital platform enterprises. Despite growing interest in AI applications, the specific mechanisms through which digital platform firms leverage AI to drive business model innovation remain insufficiently explored, particularly from the integrated perspective of resource mobilization and organizational capability reconfiguration. To address this gap, this study conducts a single-case analysis of a highly successful digital platform enterprise in China. This study explores how digital platform enterprises can effectively utilize AI technologies to support business model innovation. The findings reveal that AI technologies enable digital platform enterprises to develop organizational capabilities in intelligent connectivity, intelligent development, and intelligent governance. AI-enabled organizational capabilities in digital platform enterprises evolve through three progressive stages: AI-assisted, AI-augmented, and AI-integrated. At each stage, these capabilities are shaped through different types of resource actions—namely, entry-oriented resource patchwork, depth-oriented resource arrangements, and coordination-oriented resource orchestration. This study offers practical insights for digital platform enterprises seeking to leverage AI technologies for business model innovation. By integrating the concepts of resource actions and organizational capabilities, it provides a dynamic explanation of how AI drives innovation in digital platform business models. The research contributes to the theoretical advancement of human-AI integration and resource action frameworks, offering actionable intelligence for the broader industry. Full article

Sustainability has evolved from a new trend to an imperative and strategical necessity for companies. Despite the growing focus from economics and information systems (IS) research, practical solutions for integrating sustainability into business practices remain limited. Moreover, there is a growing demand for corporate sustainability (CS) and an increasing ability to implement digital technologies in companies. In our paper, we scrutinize how digital technologies promote corporate sustainability. We use the Delphi method to discuss future scenarios and assess the mechanisms of digitally assisted sustainability in companies. Our findings indicate that the synergy between sustainability measures and digital technologies, such as digital assistants, holds significant potential for improving sustainability, efficiency, and profitability across various use cases within businesses. For a company’s strategy, this means integrating sustainability as a core component, leveraging digital technologies to drive sustainable practices, enhance operational efficiency, and boost profitability. Full article

With the rapid development of intelligent transportation systems, voice assistants are increasingly integrated into driving environments, providing an effective means to mitigate the risks of fatigued driving. This study explored drivers’ interaction preferences with voice assistants under different fatigue states and proposed a fatigue-state-based dialogue-awakening mechanism. Using Grounded Theory and the Stimulus–Organism–Response (SOR) framework, in-depth interviews were conducted with 25 drivers from diverse occupational backgrounds. To validate the qualitative findings, a driving simulation experiment was carried out to examine the effects of different voice interaction styles on driver fatigue arousal across various fatigue levels. Results indicated that heavily fatigued drivers preferred highly stimulating and interactive voice communication; mildly fatigued drivers tended toward gentle and socially supportive dialogue; while drivers in a non-fatigued state preferred minimal voice interference, activating voice assistance only when necessary. Significant occupational differences were also observed: long-haul truck drivers emphasized practicality and safety in voice assistants, taxi drivers favored voice interactions combining navigation and social content, and private car owners preferred personalized and emotional support. This study enriches the theoretical understanding of fatigue-sensitive voice interactions and provides practical guidance for the adaptive design of intelligent voice assistants, promoting their application in driving safety. Full article

The advancement of modern lifestyles has precipitated excessive consumption of energy-dense foods, driving the escalating global burden of lipid metabolism dysregulation-related pathologies—including obesity, type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and cardiovascular disorders—which collectively pose a formidable challenge to global public health systems. The almond hull, as a by-product of almond processing, is rich in polyphenolic compounds with demonstrated antioxidant, anti-inflammatory, and lipid-lowering potential, though its precise hypo-lipidemic mechanisms remain elusive. In this study, polyphenols were extracted from almond hulls using 50% ethanol with ultrasound-assisted extraction, followed by preliminary purification via solvent partitioning. The ethyl acetate fraction was analyzed by liquid chromatography–mass spectrometry (LC-MS). Network pharmacology and molecular docking were employed to investigate the interactions between key bioactive constituents (e.g., quercetin, baicalein, and kaempferol) and targets in lipid metabolism-related pathways. Molecular dynamics (MD) simulations further evaluated the stability of the lowest-energy complexes. Results revealed that the ethyl acetate fraction exhibited potent pancreatic lipase inhibitory activity (IC50 = 204.2 µg/mL). At 0.1 mg/mL after 24 h treatment, it significantly reduced free fatty acids (FFAs)-induced intracellular triglyceride accumulation (p < 0.01) and enhanced cellular antioxidant capacity. Network pharmacology and in vitro studies suggest almond hull extract modulates PI3K-AKT signaling and improves insulin resistance, demonstrating lipid-lowering effects. These findings support its potential in functional foods and pharmaceuticals, though further in vivo validation and mechanistic investigations are required. Full article

In the field of active safety technology for intelligent connected vehicles (ICVs), the reliability and safety of the Automatic Emergency Braking (AEB) system is recognized as critical to driving safety. However, existing evaluation methods have been constrained by the inadequacy of static weight assessments in adapting to diverse driving conditions, as well as by the disconnect between conventional evaluation frameworks and experimental validation. To address these limitations, a comprehensive Vehicle-in-the-Loop (VIL) evaluation system based on the dynamic weight analytic hierarchy process (DWAHP) was proposed in this study. A two-tier dynamic weighting architecture was established. At the criterion level, a bivariate variable–weight function, incorporating the vehicle speed and road surface adhesion coefficient, was developed to enable the dynamic coupling modeling of road environment parameters. At the scheme level, a five-dimensional indicator system—integrating braking distance, collision speed, and other key metrics—was constructed to support an adaptive evaluation model under multi-condition scenarios. By establishing a dynamic mapping between weight functions and driving condition parameters, the DWAHP methodology effectively overcame the limitations associated with fixed-weight mechanisms in varying operating conditions. Based on this framework, a dedicated AEB system performance test platform was designed and developed. Validation was conducted using both VIL simulations and real-world road tests, with a Volvo S90L as the test vehicle. The experimental results demonstrated high consistency between VIL and real-world road evaluations across three dimensions: safety (deviation: 0.1833/9.5%), reliability (deviation: 0.2478/13.1%), and riding comfort (deviation: 0.05/2.7%), with an overall comprehensive score deviation of 0.0707 (relative deviation: 0.51%). This study not only verified the technical advantages of the dynamic weight model in adapting to complex driving environments and analyzing multi-parameter coupling effects but also established a systematic methodological framework for evaluating AEB system performance via VIL. The findings provide a robust foundation for the testing and assessment of AEB system, offer a structured approach to advancing the performance evaluation of advanced driver assistance systems (ADASs), facilitate the safe and reliable validation of ICVs’ commercial applications, and ultimately contribute to enhancing road traffic safety. Full article

Ultrafast-charging (UFC) technology for electric vehicles (EVs) and energy storage devices has brought with it an increase in demand for lithium-ion batteries (LIBs). However, although they pose advantages in driving range and charging time, LIBs face several challenges such as mechanical degradation, lithium dendrite formation, electrolyte decomposition, and concerns about thermal runaway safety. This review evaluates the key challenges and advances in LIB components (anodes, cathodes, electrolytes, separators, and binders), alongside innovations in charging protocols and safety concerns. Material-level solutions such as nanostructuring, doping, and composite architectures are investigated to improve ion diffusion, conductivity, and electrode stability. Electrolyte modifications, separator enhancements, and binder optimizations are discussed in terms of their roles in reducing high-rate degradation. Furthermore, charging protocols are addressed; adjustments can reduce mechanical and electrochemical stress on LIBs, decreasing capacity fade while providing rapid charging. This review highlights the key technological advancements that are enabling ultrafast charging and that are assisting us in overcoming severe limitations, paving the way for the development of next-generation high-performance LIBs. Full article

Long-term manual material handling (MMH) work leads to the trend of the younger onset of work-related musculoskeletal disorders (WMSDs), with low back pain (LBP) being the most common, which causes great trouble for both society and patients. To effectively prevent LBP and provide support for workers engaged in MMH work, wearable lumbar assistive exoskeletons have played a key role in industrial scenarios. This paper divides wearable lumbar assistive exoskeletons into powered, unpowered, and quasi-passive types, systematically reviews the research status of each type of exoskeleton, and compares and discusses the key factors such as driving mode, mechanical structure, control strategy, performance evaluation, and human–machine interaction. It is found that many studies focus on the assistive performance, human–machine coupling coordination, and adaptability of wearable lumbar assistive exoskeletons. At the same time, the analysis results show that there are many types of performance evaluation indicators, but a unified and standardized evaluation method and system are still lacking. This paper analyzes current research findings, identifies existing issues, and provides recommendations for future research. This study provides a theoretical basis and design ideas for the development of wearable lumbar assistive exoskeleton systems. Full article

High-temperature micro-electro-mechanical systems (MEMSs) are critical for applications in extreme environments and applications where the operating temperature can exceed 1000 °C, but their long-term performance is limited by complex failure mechanisms, including material degradation caused by atomic migration. This study introduces a simulation-assisted approach to analyze and predict the dominant failure modes, focusing on vacancy fluxes and their driving forces, within high-temperature MEMS structures. The focus is on tungsten-based structures operating at a temperature of 1580 °C. This approach couples electric-, stress- and temperature-dependent simulations to evaluate atomic migration pathways, which are key contributors to failure. This study demonstrates that void accumulation, driven by vacancy migration, results in localized current density increase, hotspot formation, and accelerated structural degradation. The mean time to failure (MTTF) is shown to have exponential dependence on temperature and inverse-square dependence on current density, highlighting the critical role of these parameters in device reliability. These findings provide a deeper understanding of the failure mechanisms in high-temperature MEMSs and underscore the need for design strategies that mitigate electromigration and stress-induced void growth to enhance device performance and longevity. Full article

Adopting building information modeling (BIM) within the architecture, engineering, and construction (AEC) industry presents an opportunity to tackle persistent challenges, such as chronic productivity deficits and emerging imperatives like sustainability. However, BIM implementation (BIMI) across European Union (EU) countries diverges due to different contexts and the complexity of BIM. This study aims to identify the main barriers to BIMI and recommend effective mitigation measures in Portugal, a late-adopting EU country. Initially, 28 BIMI barriers were identified through a literature review. Experts in a Delphi survey then selected 15 critical barriers. An interpretive structural modeling (ISM) model was developed with input from a focus group to clarify the hierarchical relationships among barriers, and an impact matrix cross-reference multiplication applied to a classification (MICMAC) analysis was performed to evaluate the barriers’ driving and dependence powers. The resulting main barriers to BIMI include a lack of evaluation mechanisms, ignorance of BIM benefits, a shortage of skilled professionals, limited experience and cooperation, resistance to change, and inadequate top management support. Finally, experts in a second focus group developed mitigation measures to address the main barriers while ensuring the measures affect the entire barrier system. These findings will assist researchers, policymakers, and practitioners in late-adopter EU countries in addressing these barriers effectively. Full article