Search Results (773)

With the advancement of low-carbon and sustainable development initiatives, electric scooters, recognized as essential transportation tools and leisure products, have gained significant popularity, particularly among young people. However, the current electric scooter market is plagued by severe product similarity. Once the initial novelty fades for users, the usage frequency declines, resulting in considerable resource wastage. This research collected user needs via surveys and employed the KJ method (affinity diagram) to synthesize fragmented insights into cohesive thematic clusters. Subsequently, a hierarchical needs model for electric scooters was constructed using analytical hierarchy process (AHP) principles, enabling systematic prioritization of user requirements through multi-criteria evaluation. By establishing a house of quality (HoQ), user needs were transformed into technical characteristics of electric scooter products, and the corresponding weights were calculated. After analyzing the positive and negative correlation degrees of the technical characteristic indicators, it was found that there are technical contradictions between functional zoning and compact size, lightweight design and material structure, and smart interaction and usability. Then, based on the theory of inventive problem solving (TRIZ), the contradictions were classified, and corresponding problem-solving principles were identified to achieve a multi-functional innovative design for electric scooters. This research, leveraging a systematic industrial design analysis framework, identified critical pain points among electric scooter users, established hierarchical user needs through priority ranking, and improved product lifecycle sustainability. It offers novel methodologies and perspectives for advancing theoretical research and design practices in the electric scooter domain. Full article

Background/Objectives: The Plague of Justinian marked the beginning of the First Pandemic (541–750 CE), yet no genomic evidence of Yersinia pestis has previously been recovered from the Eastern Mediterranean, where the outbreak was first recorded. This study aimed to determine whether Y. pestis was present in a mid-6th to early 7th century mass grave in Jerash, Jordan, and to characterize its genome within the broader context of First Pandemic strains. Methods: We analyzed samples from multiple individuals recovered from the Jerash mass grave. Initial screening for potential pathogen presence was conducted using proteomics. Select samples were subjected to ancient DNA extraction and whole genome sequencing. Comparative genomic and phylogenetic analyses were conducted to assess strain identity and evolutionary placement. Results: Genomic sequencing recovered Y. pestis DNA from five individuals, revealing highly similar genomes. All strains clustered tightly with other First Pandemic lineages but were notably recovered from a region geographically close to the pandemic’s historical epicenter for the first time. The near-identical genomes across diverse individuals suggest an outbreak of a single circulating lineage at the time of this outbreak. Conclusions: This study provides the first genomic evidence of Y. pestis in the Eastern Mediterranean during the First Pandemic, linking archaeological findings with pathogen genomics near the origin point of the Plague of Justinian. Summary Sentence: Genomic evidence links Y. pestis to the First Pandemic in an ancient city. Full article

Infants discriminate faces early in the first year, but research on infants’ discrimination of bodies is plagued by mixed findings. Using a familiarisation novelty preference method, we investigated 7- and 9-month-old infants’ discrimination of body postures presented in upright and inverted orientations, and with and without heads, along with relationships with gross and fine motor development. In our initial studies, 7-month-old infants discriminated upright headless postures with forward-facing and about-facing images. Eye tracking revealed that infants looked at the bodies of the upright headless postures the longest and at the heads of upright whole figures for 60–70% of the time regardless of the presence of faces, suggesting that heads detract attention from bodies. In a more stringent test, with similarly complex limb positions between test items, infants could not discriminate postures. With longer trials, the 7-month-olds demonstrated a familiarity preference for the upright whole figures, and the 9-month-olds demonstrated a novelty preference, albeit with a less robust effect. Unlike previous studies, we found that better gross motor skills were related to the 7-month-olds’ better discrimination of upright headless postures compared to inverted postures. The 9-month-old infants’ lower gross and fine motor skills were associated with a stronger preference for inverted compared to upright whole figures. This is further evidence of a configural representation of bodies in infancy, but it is constrained by an upper bias (heads in upright figures, feet in inverted), the test item similarity, and the trial duration. The measure and type of motor development reveals differential relationships with infants’ representations of bodies. Full article

The global coffee supply chain is a complex multi-stakeholder ecosystem plagued by fragmented records, unverifiable origin claims, and limited real-time visibility. These limitations pose risks to ethical sourcing, product quality, and consumer trust. To address these issues, this paper proposes a blockchain and IoT-enabled framework for secure and transparent coffee supply chain management. The system integrates simulated IoT sensor data such as Radio-Frequency Identification (RFID) identity tags, Global Positioning System (GPS) logs, weight measurements, environmental readings, and mobile validations with Ethereum smart contracts to establish traceability and automate supply chain logic. A Solidity-based Ethereum smart contract is developed and deployed on the Sepolia testnet to register users and log batches and to handle ownership transfers. The Internet of Things (IoT) data stream is simulated using structured datasets to mimic real-world device behavior, ensuring that the system is tested under realistic conditions. Our performance evaluation on 1000 transactions shows that the model incurs low transaction costs and demonstrates predictable efficiency behavior of the smart contract in decentralized conditions. Over 95% of the 1000 simulated transactions incurred a gas fee of less than ETH 0.001. The proposed architecture is also scalable and modular, providing a foundation for future deployment with live IoT integrations and off-chain data storage. Overall, the results highlight the system’s ability to improve transparency and auditability, automate enforcement, and enhance consumer confidence in the origin and handling of coffee products. Full article

Power batteries and their management technology are crucial for the safe and efficient operation of electric vehicles (EVs). The life and safety issues of power batteries have always plagued the EV industry. To achieve an intelligent battery management system (BMS), it is crucial to accurately estimate the internal state of the power battery. The purpose of this review is to analyze the current status of research on multi-state estimation of power batteries, which mainly focuses on the estimation of state of charge (SOC), state of energy (SOE), state of health (SOH), state of power (SOP), state of temperature (SOT), and state of safety (SOS). Moreover, it also analyzes and prospects the research hotspots, development trends, and future challenges of battery state estimation. It is a significant guide for designing BMSs for EVs, as well as for achieving intelligent safety management and efficient power battery use. Full article

Studies of prehistoric burials are fundamental for understanding cultural human evolution. Those found in the San Teodoro cave (northeastern Sicily) are significant for the discovery at the turn of the 1930s and 1940s of at least four individuals (ST1–ST4). About 15–16 kyr ago, the bodies of ST1–ST4 were intentionally buried, apparently in a manner original to the context of prehistoric burials, namely by covering them with a continuous layer of red ochre found to connect the graves. Since the earliest excavations, plagued by clandestine digging, there is no material memory of the stratigraphic transition from the burial layer to the subsequent anthropogenic deposit through the red ochre, and nothing certain is known about the orientation of ST3, the presence of grave goods and the ochre cover related to this burial. Moreover, there is no exhaustive knowledge of how much is actually left of the anthropogenic layers described in the old literature. Based on field observations and 3D reconstruction of ST3’s skull position and deposits at the San Teodoro site, we provide insights into anthropological issues, such as the rediscovery of the red ochre vestiges that reasonably covered the ST3 burial, and the burial context of this individual, and shed light on what actually remains of the stratigraphic units described in the 1940s. Full article

In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. Traditional artificial potential field (APF) methods are plagued by issues such as getting stuck in local minima and inadequate handling of dynamic obstacles. This paper introduces a layered obstacle avoidance structure that merges vortex artificial potential (VAPF) fields with reinforcement learning (RL) for motion control. This approach dynamically adjusts the target position through VAPF, strategically guiding the drone to avoid obstacles indirectly. Additionally, it employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to facilitate the training of the motion controller. Simulation experiments demonstrate that the incorporation of the VAPF effectively mitigates the issue of local minima and significantly enhances the success rate of drone navigation, reduces the average arrival time and the number of sharp turns, and results in smoother paths. This solution harmoniously combines the flexibility of VAPF methods with the precision of RL for motion control, offering an effective strategy for autonomous navigation of quadrotor drones in complex environments. Full article

The rapid progress of Large Language Models (LLMs) has greatly improved natural language tasks like code generation, boosting developer productivity. However, challenges persist. Generated code often appears “pseudo-correct”—passing functional tests but plagued by inefficiency or redundant structures. Many models rely on outdated methods like greedy selection, which trap them in local optima, limiting their ability to explore better solutions. We propose AnnCoder, a multi-agent framework that mimics the human “try-fix-adapt” cycle through closed-loop optimization. By combining the exploratory power of simulated annealing with the targeted evolution of genetic algorithms, AnnCoder balances wide-ranging searches and local refinements, dramatically increasing the likelihood of finding globally optimal solutions. We speculate that traditional approaches may struggle due to narrow optimization focuses. AnnCoder addresses this by introducing dynamic multi-criteria scoring, weighing functional correctness, efficiency (e.g., runtime/memory), and readability. Its adaptive temperature control dynamically modulates the cooling schedule, slowing cooling when solutions are diverse to encourage exploration, then accelerating convergence as they stabilize. This design elegantly avoids the pitfalls of earlier models by synergistically combining global exploration with local optimization capabilities. After conducting thorough experiments with multiple LLMs analyses across four problem-solving and program synthesis benchmarks—AnnCoder showcased remarkable code generation capabilities—HumanEval 90.85%, MBPP 90.68%, HumanEval-ET 85.37%, and EvalPlus 84.8%. AnnCoder has outstanding advantages in solving general programming problems. Moreover, our method consistently delivers superior performance across various programming languages. Full article

For many years, staphylococci have been detected mainly in infections of the skin and soft tissues, organs, bone inflammations, and generalized infections. Thromboembolic diseases have also become a serious plague of our times, which, as it turns out, are closely related to the toxic effects of staphylococci. Staphylococcus aureus, because of the presence of many different kinds of virulence factors, is capable of manipulating the host’s innate and adaptive immune responses. These include toxins and cofactors that activate host zymogens and exoenzymes, as well as superantigens, which are highly inflammatory and cause leukocyte death. Coagulases and staphylokinases can control the host’s coagulation system. Nucleases and proteases inactivate various immune defense and surveillance proteins, including complement components, peptides and antibacterial proteins, and surface receptors that are important for leukocyte chemotaxis. On the other hand, secreted toxins and exoenzymes are proteins that disrupt the endothelial and epithelial barrier as a result of cell lysis and disintegration of linking proteins, which ultimately increases the risk of thromboembolism. In this review, we discuss various virulence factors and substances that may inhibit their activity. Full article

The Architecture, Engineering, and Construction (AEC) industry is plagued by persistent challenges such as low productivity, cost overruns, and frequent project delays. Integrated Project Delivery (IPD) has emerged as a potential solution, offering collaborative approaches to improve project outcomes. This study proposes a two-tiered methodology for evaluating the disruptiveness of innovations in the AEC industry, with a particular focus on IPD as a disruptive innovation. In the first tier, a multidimensional framework is developed to systematically assess the disruptiveness of innovations in the AEC sector. This framework, informed by a thorough literature review and disruptive innovation theory, includes dimensions such as business models, processes, and anticipated outcomes. The second tier applies the framework by analyzing the disruptiveness of IPD. The assessment draws on data from three comprehensive studies, including ethnographic research, interviews, and focus groups, which examine IPD’s impact on different stakeholder groups such as clients, consultants, and contractors. Findings reveal that IPD has the potential to significantly disrupt traditional business models, processes, and project outcomes, particularly at the project level. Notable disruptive characteristics include shifts in collaboration dynamics, redefined project financing models, and improved efficiency. However, several barriers hinder IPD adoption, including resistance to change and misalignment with conventional contractual structures. Expert interviews support these results, indicating that IPD represents a fundamental shift in the AEC industry. This research contributes to the existing body of knowledge by offering a structured framework for assessing the disruptiveness of AEC innovations and demonstrating its practical application. In this way, AEC organizations, projects, and practitioners can better strategize for the adoption of any new disruptive innovation and thus pursue a strategic advantage in the highly competitive industry market. Full article

Huperzine A (HupA) can alleviate Alzheimer’s disease due to its reversible inhibition of acetylcholinesterase (AChE). The chemical synthesis and plant extraction of HupA is plagued by route complexity and resource scarcity, respectively. Although some endophytic fungi from Huperzia serrata can independently biosynthesize HupA, their yields are trivial. After a comprehensive investigation of HupA-producing H. serrata across China, we focused on the endophytic fungi from Hunan and Hubei provinces, which demonstrated high-level HupA. Morphological characteristics and internal transcriptional sequence (ITS) analysis revealed their diversity. Among the four HupA-producing endophytic fungi, Colletotrichum kahawae is the best-performing and was thus subjected to fermentation optimization. When its fermentation medium was supplemented with H. serrata flavonoids daidzein and apigenin, HupA yields reached 58.38 μg/g (dry cell weight, dcw) and 72.21 μg/g dcw, respectively. In contrast, the addition of L-lysine and H. serrata extracts led to yields of 50.17 μg/g dcw and 255.32 μg/g dcw, respectively. Transcriptomic analysis revealed that H. serrata extracts substantially upregulated the expression of HupA biosynthesis genes in C. kahawae. Overall, H. serrata extracts outperformed L-lysine, daidzein, and apigenin in boosting HupA production, as they encompass all the necessary nutrients for C. kahawae growth. This study not only connotes a nutritional exchange between H. serrata and C. kahawae during long-term coevolution but also offers insights for harnessing plant extracts for the overproduction of desired metabolites in endophytic fungi. Full article

Outbreaks of locust plagues are becoming increasingly frequent against the backdrop of climate change. Locust outbreaks in the Caucasus and Central Asia, especially in Kazakhstan, pose continuous threats to neighboring countries, including China, Kyrgyzstan, and more. However, locust outbreak forecasts and migration movement are yet to be studied in this area. In our study, we collected water level data in major lakes and water bodies, as well as annual average precipitation in the past 15 years in Kazakhstan, to analyze their contributions to locust outbreaks. Multiple linear regression analysis revealed a significant negative correlation between overall lake water level and the following year’s locust outbreak area in Kazakhstan. Considering that the overall lake water levels in 2023 and 2024 reached a quite low level historically, we predicted heavy locust outbreaks in 2025. Furthermore, through wind field analysis and wind-born trajectory modeling, we identified two migration routes of locusts from Kazakhstan into Xinjiang, China, riding the northwest wind, with lakes near the Sino-Kazakhstan border as the main sources. Overall, our study identified high locust outbreak challenges in Kazakhstan in recent years and determined two wind-supported migration routes of locusts invading China, which are significant for guiding monitoring and prevention efforts in the Sino-Kazakhstan border area. Full article

In time-sensitive aerial missions such as urban surveillance, emergency response, and adversarial airspace operations, achieving rapid and reliable formation control of multi-UAV systems is crucial. This paper addresses the challenge of ensuring robust and efficient formation control under stringent time constraints. The proposed singularity-free prescribed-time formation (PTF) control scheme guarantees task completion within a user-defined time, independent of initial conditions and control parameters. Unlike existing scaling-based prescribed-time methods plagued by unbounded gains and fixed-time strategies with non-tunable convergence bounds, the proposed scheme uses fixed-time stability theory and systematic parameter tuning to avoid singularity issues while ensuring robustness and predictable convergence. The method also accommodates directed communication topologies and unknown external disturbances, allowing follower UAVs to track a dynamic leader and maintain the desired geometric formation. Finally, some simulation results demonstrate the effectiveness of the proposed control strategy, showcasing its superiority over existing methods and validating its potential for practical applications. Full article

Video anomaly detection (VAD) faces significant challenges in multimodal semantic alignment and long-term temporal modeling within open surveillance scenarios. Existing methods are often plagued by modality discrepancies and fragmented temporal reasoning. To address these issues, we introduce MT-CMVAD, a hierarchically structured Transformer architecture that makes two key technical contributions: (1) A Context-Aware Dynamic Fusion Module that leverages cross-modal attention with learnable gating coefficients to effectively bridge the gap between RGB and optical flow modalities through adaptive feature recalibration, significantly enhancing fusion performance; (2) A Multi-Scale Spatiotemporal Transformer that establishes global-temporal dependencies via dilated attention mechanisms while preserving local spatial semantics through pyramidal feature aggregation. To address the sparse anomaly supervision dilemma, we propose a hybrid learning objective that integrates dual-stream reconstruction loss with prototype-based contrastive discrimination, enabling the joint optimization of pattern restoration and discriminative representation learning. Our extensive experiments on the UCF-Crime, UBI-Fights, and UBnormal datasets demonstrate state-of-the-art performance, achieving AUC scores of 98.9%, 94.7%, and 82.9%, respectively. The explicit spatiotemporal encoding scheme further improves temporal alignment accuracy by 2.4%, contributing to enhanced anomaly localization and overall detection accuracy. Additionally, the proposed framework achieves a 14.3% reduction in FLOPs and demonstrates 18.7% faster convergence during training, highlighting its practical value for real-world deployment. Our optimized window-shift attention mechanism also reduces computational complexity, making MT-CMVAD a robust and efficient solution for safety-critical video understanding tasks. Full article

An adequate food supply is a core issue for sustainable development worldwide. Amid greater instability in the food supply triggered by more armed conflicts, trade disputes, and climate change, a decline in grain cultivation area still plagues many regions. China, a major food producer globally, is a case in point. The truth is that at the moment, the formulation and implementation of policies as well as academic discussions regarding this issue are predominantly based on the sown area of grains, overlooking the fundamental role co-played by population, yield efficiency, and sown area in determining food supply. Furthermore, the commonly used indicator, the non-grain cultivation rate, fails to directly reflect the impact of the phenomenon on the grain supply. To address these gaps, this study introduces trend-change detection and factor-contribution analysis, uses long-term grain sown area data to identify regions with significant grain retreat, and quantifies the relative influence of population shifts, crop yield improvements, and sown area changes on food supply. Key findings include the following: China’s total grain production maintained steady growth from 2003 to 2023, far exceeding conventional food security thresholds. Temporary reductions in grain sown area (2015–2019, 2021–2022) were offset by rising yields, with no substantial decline in supply. Twelve provinces/municipalities, Beijing, Shanghai, Zhejiang, Fujian, Guangdong, Guangxi, Guizhou, Shaanxi, Ningxia, Sichuan, Chongqing, and Hainan, exhibited substantial declines in grain plantation. However, Sichuan and Shaanxi achieved counter-trend growth in food supply, while Ningxia and Guizhou experienced frequent fluctuations. The sown area was not always the dominant factor in per capita grain availability. Yield increases neutralized cropland reduction in Sichuan, Shaanxi, Guizhou, and Ningxia, whereas population inflows outweighed the sown area effect in the other eight provinces. The study concludes that China’s grain cropland reduction has not yet posed a threat to national food security. That said, the spatial concentration of these affected regions and their ongoing output reductions may raise domestic grain redistribution costs and intensify inter-regional conflicts over cropland protection. Meanwhile, population influx plays a similarly important role to that of grain plantation decline in the grain supply. Considering that, we believe that more moderate measures should be adopted to address the shrinkage of grain planting areas, with pre-set food self-sufficiency standards. These measures include, but are not limited to, improving productivity and adopting integrated farming. Methodologically, this work lowers distortions from normal annual cropland fluctuations, enabling more precise identification of non-grain production zones. By quantifying the separate impacts of population, crop yield, and sown area changes, it supplements existing observations on grain cropland decline and provides better targeted suggestions on policy formulation and coordination. Full article