Search Results (12,402)

Wildfires increasingly threaten human life, ecosystems, and infrastructure, with events like the 2025 Palisades and Eaton fires in Los Angeles County underscoring the urgent need for more advanced prediction frameworks. Existing physics-based and deep-learning models struggle to capture dynamic wildfire spread across both 2D and 3D domains, especially when incorporating real-time, multimodal geospatial data. This paper explores how generative artificial intelligence (AI) models—such as GANs, VAEs, and transformers—can serve as transformative tools for wildfire prediction and simulation. These models offer superior capabilities in managing uncertainty, integrating multimodal inputs, and generating realistic, scalable wildfire scenarios. We adopt a new paradigm that leverages large language models (LLMs) for literature synthesis, classification, and knowledge extraction, conducting a systematic review of recent studies applying generative AI to fire prediction and monitoring. We highlight how generative approaches uniquely address challenges faced by traditional simulation and deep-learning methods. Finally, we outline five key future directions for generative AI in wildfire management, including unified multimodal modeling of 2D and 3D dynamics, agentic AI systems and chatbots for decision intelligence, and real-time scenario generation on mobile devices, along with a discussion of critical challenges. Our findings advocate for a paradigm shift toward multimodal generative frameworks to support proactive, data-informed wildfire response. Full article

Network security courses rely on hands-on labs where students configure virtual Linux networks to practice attack and defense. Automated feedback is scarce because no standard exists for exchanging detailed configurations—interfaces, bridging, routing tables, iptables policies—between exercise software and large language models (LLMs) that could serve as tutors. We address this interoperability gap with an exercise-oriented YANG profile that augments the Internet Engineering Task Force (IETF) ietf-network module with a new network-devices module. The profile expresses Linux interface settings, routing, and firewall rules, and tags each node with roles such as linux-server or linux-firewall. Integrated into our LiNeS Cloud platform, it enables LLMs to both parse and generate machine-readable network states. We evaluated the profile on four topologies—from a simple client–server pair to multi-subnet scenarios with dedicated security devices—using ChatGPT-4o, Claude 3.7 Sonnet, and Gemini 2.0 Flash. Across 1050 evaluation tasks covering profile understanding (n = 180), instance analysis (n = 750), and instance generation (n = 120), the three LLMs answered correctly in 1028 cases, yielding an overall accuracy of 97.9%. Even with only minimal follow-up cues (≦3 turns) —rather than handcrafted prompt chains— analysis tasks reached 98.1% accuracy and generation tasks 93.3%. To our knowledge, this is the first exercise-focused YANG profile that simultaneously captures Linux/iptables semantics and is empirically validated across three proprietary LLMs, attaining 97.9% overall task accuracy. These results lay a practical foundation for artificial intelligence (AI)-assisted security labs where real-time feedback and scenario generation must scale beyond human instructor capacity. Full article

Aiming at the problems of low withstand voltage and poor RF performance of traditional HEMT devices, a new AlGaN/GaN high electron mobility transistor device with a π-gate (NπGS HEMT) is designed in this paper. The new structure incorporates a π-gate design along with a PN-junction field plate and an AlGaN back-barrier layer. The device is modeled and simulated in Silvaco TCAD 2015 software and compared with traditional t-gate HEMT devices. The results show that the NπGS HEMT has a significant improvement in various characteristics. The new structure has a higher peak transconductance of 336 mS·mm⁻¹, which is 13% higher than that of the traditional HEMT structure. In terms of output characteristics, the new structure has a higher saturation drain current of 0.188 A/mm. The new structure improves the RF performance of the device with a higher maximum cutoff frequency of about 839 GHz. The device also has a better performance in terms of voltage withstand, exhibiting a higher breakdown voltage of 1817 V. These results show that the proposed new structure could be useful for future research on high voltage withstand and high RF HEMT devices. Full article

A new, simple machine was developed to address a long-standing challenge in biomedical and mechanical engineering: how to enhance the primary stability and long-term integration of screws and implants in low-density or heterogeneous materials, such as bone or composite substrates. Traditional screws often rely solely on external threading for fixation, leading to limited cohesion, poor integration, or early loosening under cyclic loading. In response to this problem, we designed and built a novel device that leverages a unique mechanical principle to simultaneously perforate, collect, and compact the substrate material during insertion. This mechanism results in an internal material interlock, enhancing cohesion and stability. Drawing upon principles from physics, chemistry, engineering, and biology, we evaluated its biomechanical behavior in synthetic bone analogs. The maximum insertion (MIT) and removal torques (MRT) were measured on synthetic osteoporotic bones using a digital torquemeter, and the values were compared directly. Experimental results demonstrated that removal torque (mean of 21.2 Ncm) consistently exceeded insertion torque (mean of 20.2 Ncm), indicating effective material interlocking and cohesive stabilization. This paper reviews the relevant literature, presents new data, and discusses potential applications in civil infrastructure, aerospace, and energy systems where substrate cohesion is critical. The findings suggest that this new simple machine offers a transformative approach to improving fixation and integration across multiple domains. Full article

Antibiotic resistance is one of the most significant public health issues today. As a consequence, there is an urgent need for novel classes of antibiotics. This necessitates the development of highly efficient screening methods for the rapid identification of antibiotic-producing bacteria. Here, we describe a new method for high-throughput screening of antimicrobial compounds (AMC) producing halophilic bacteria. Our methodology used a newly designed 3D-printed Petri plate replicator used for drop deposition and colony replication. We employed this device in combination with a modified agar overlay assay to screen more than 7400 bacterial colonies. A total of 54 potential AMC producers were discovered at a success rate of 0.7%. Although 40% of them lost their antibacterial activity during the secondary screening, 22 strains retained inhibitory activity and were able to suppress the growth of one or more safe relatives of the ESKAPE group pathogens. The ethyl acetate extract from the most potent strain, Virgibacillus salarius POTR191, demonstrated moderate antibacterial activity against Enterococcus faecalis, Acinetobacter baumanii, and Staphylococcus epidermidis with minimal inhibitory concentrations of 128 μg/mL, 128 μg/mL, and 512 μg/mL, respectively. We propose that our replica plate assay could be used for target-based antimicrobial screening of various extremophilic bacteria. Full article

The accurate measurement of the tree diameter is vital for forest inventories, urban tree quality assessments, the management of roadside and railway vegetation, and various other applications. It also plays a crucial role in evaluating tree growth dynamics, which are closely linked to tree health, structural stability, and vulnerability. Although a range of devices and methodologies are currently under investigation, the widespread adoption of laser scanners remains constrained by their high cost. This study therefore aimed to compare high-end laser scanners (Trimble TX8 and GeoSLAM ZEB Horizon) with cost-effective alternatives, represented by the Apple iPhone 14 Pro and the LA03 scanner developed by mapry Co., Ltd. (Tamba, Japan). It further sought to evaluate the feasibility of employing these more affordable devices, even for small-scale forest owners or managers. Given the growing availability of 3D-based forest inventory algorithms, a selection of such processing pipelines was used to assess the practical potential of the scanning devices. The tested low-cost device produced moderate results, achieving a tree detection rate of up to 78% and a relative root mean square error (rRMSE) of 19.7% in diameter at breast height (DBH) estimation. However, performance varied depending on the algorithms applied. In contrast, the high-end mobile laser scanning (MLS) and terrestrial laser scanning (TLS) systems outperformed the low-cost alternative across all metrics, with tree detection rates reaching up to 99% and DBH estimation rRMSEs as low as 5%. Nevertheless, the low-cost device may still be suitable for scanning small sample plots at a reduced cost and could potentially be deployed in larger quantities to support broader forest inventory initiatives. Full article

Transparent conductive oxides (TCOs) have become essential components in a broad range of modern devices, including smartphones, flat-panel displays, and photovoltaic cells. Currently, indium tin oxide (ITO) is used in approximately 90% of these devices. However, ITO prices continue to rise due to the limited supply of indium (In), making the development of alternative materials for TCOs indispensable. Therefore, this study highlights the latest advances in creating new, affordable materials, with a focus on aluminum-doped zinc oxide (AZO). Over the last few decades, this material has been widely studied to improve its physical properties, particularly its low electrical resistivity, which can affect the performance of various devices. Now, it is close to replacing ITO due to several advantages including cost-effectiveness, stability under hydrogen plasma, low processing temperatures, and lack of toxicity. Besides that, in comparison to other TCOs such as IZO, IGZO, or IZrO, AZO achieved a low electrical resistivity (10⁻⁵ ohm cm) while maintaining a high transparency across the visible spectrum (over 85%). Additionally, due to the increasing development of technologies utilizing such materials, it is essential to develop more effective techniques for producing TCOs on a larger scale. Additionally, due to the increasing development of technologies utilizing such materials, it is essential to develop more effective techniques for producing TCOs on a larger scale. This review emphasizes the potential of AZO as a cost-effective and scalable alternative to ITO, highlighting key advancements in deposition techniques such as pulsed laser deposition (PLD). Full article

Water quality should be tested to ensure it is acceptable for the healthy growth of plants and animals, and water hardness is one of the important testing indexes. Herein, a novel approach was proposed to achieve high accuracy and rapid quantitative analyses of water hardness by combining one-point calibration laser-induced breakdown spectroscopy (OPC–LIBS) and aerosolization. First, the water samples are aerosolized via the aerosol generation device and the LIBS spectra of aerosols are obtained. Then, a modified OPC–LIBS model is used to determine the elemental contents of the aerosols via LIBS spectra, in which the plasma temperature is calculated using the Multi-Element Saha–Boltzmann (ME–SB) plot. One suitable standard liquid sample (the concentrations of Ca, Mg, and Sr were 50 mg/L, 50 mg/L, and 500 mg/L, respectively) was selected to evaluate the quantitative performance of the modified OPC–LIBS. Then, the Ca and Mg concentrations in the three real water samples (from the Yangtze River, reservoir, and underground) were detected and quantified by the proposed method, and the quantitative results of three LIBS calibration methods were compared with that of inductively coupled plasma optical emission spectroscopy (ICP–OES). The average relative error of Ca and Mg found in the OPC–LIBS results was lower by 22.23% than the internal standard method and 14.50% lower than the external standard method. The method combining modified OPC–LIBS and aerosolization can achieve high-precision rapid quantification of water hardness detection, which provides a new path for rapid detection of water hardness and is expected to make online detection a reality in the water quality testing field. Full article

Digital images have been widely applied in fields such as mobile devices, the Internet of Things, and medical imaging. Although significant progress has been made in image encryption technology, it still faces many challenges, such as attackers using powerful computing resources and advanced algorithms to crack encryption systems. To address these challenges, this paper proposes a novel image encryption algorithm based on one-dimensional sawtooth wave chaotic system (1D-SAW) and the three-strand structure of DNA. Firstly, a new 1D-SAW chaotic system was designed. By introducing nonlinear terms and periodic disturbances, this system is capable of generating chaotic sequences with high randomness and initial value sensitivity. Secondly, a new diffusion rule based on the three-strand structure of DNA is proposed. Compared with the traditional DNA encoding and XOR operation, this rule further enhances the complexity and anti-attack ability of the encryption process. Finally, the security and randomness of the 1D-SAW and image encryption algorithms were verified through various tests. Results show that this method exhibits better performance in resisting statistical attacks and differential attacks. Full article

Recently, a new class of magnetic material, termed altermagnets, has caught the attention of the magnetism and spintronics community. The magnetic phenomenon arising from these materials differs from traditional ferromagnetism and antiferromagnetism. It generally lacks net magnetization and is characterized by unusual non-relativistic spin-splitting and broken time-reversal symmetry. This leads to novel transport properties, such as the anomalous Hall effect, the crystal Nernst effect, and spin-dependent phenomena. Spin-dependent phenomena such as spin currents, spin-splitter torques, and high-frequency dynamics emerge as key characteristics in altermagnets. This paper reviews the main aspects pertaining to altermagnets by providing an overview of theoretical investigations and experimental realizations. We discuss the most recent developments in altermagnetism and prospects for exploiting its unique properties in next-generation devices. Full article

Accurate measurement of high voltages is required to guarantee the safe and stable operation of power systems. Modern power systems, which are mainly based on new energy sources, require high-voltage measurement instruments and equipment with characteristics such as high accuracy, wide frequency bandwidth, broad operating ranges, and ease of operation and maintenance. However, it is difficult for traditional electromagnetic measurement transformers to meet these requirements. To address the limitations of conventional Rydberg atomic measurement methods in low-frequency applications, this paper proposes an enhanced Rydberg measurement approach featuring high sensitivity and strong traceability, thereby enabling the application of Rydberg-based measurement methodologies under power frequency conditions. In this paper, a 110 kV high-voltage measurement method based on Rydberg atoms is studied. A power-frequency electric field measurement device is designed using Rydberg atoms, and its internal electric field distribution is analyzed. Additionally, a decoupling method is proposed to facilitate voltage measurements under multi-phase overhead lines in field conditions. The feasibility of the proposed method is confirmed, providing support for the future development of practical measurement devices. Full article

The widespread and persistent occurrence of micropollutants—such as pesticides, pharmaceuticals, heavy metals, personal care products, microplastics, and per- and polyfluoroalkyl substances (PFAS)—has emerged as a critical environmental and public health concern, necessitating the development of highly sensitive, selective, and field-deployable detection technologies. Microfluidic sensors, including biosensors, have gained prominence as versatile and transformative tools for real-time environmental monitoring, enabling precise and rapid detection of trace-level contaminants in complex environmental matrices. Their miniaturized design, low reagent consumption, and compatibility with portable and smartphone-assisted platforms make them particularly suited for on-site applications. Recent breakthroughs in nanomaterials, synthetic recognition elements (e.g., aptamers and molecularly imprinted polymers), and enzyme-free detection strategies have significantly enhanced the performance of these biosensors in terms of sensitivity, specificity, and multiplexing capabilities. Moreover, the integration of artificial intelligence (AI) and machine learning algorithms into microfluidic platforms has opened new frontiers in data analysis, enabling automated signal processing, anomaly detection, and adaptive calibration for improved diagnostic accuracy and reliability. This review presents a comprehensive overview of cutting-edge microfluidic sensor technologies for micropollutant detection, emphasizing fabrication strategies, sensing mechanisms, and their application across diverse pollutant categories. We also address current challenges, such as device robustness, scalability, and potential signal interference, while highlighting emerging solutions including biodegradable substrates, modular integration, and AI-driven interpretive frameworks. Collectively, these innovations underscore the potential of microfluidic sensors to redefine environmental diagnostics and advance sustainable pollution monitoring and management strategies. Full article

Design and use of wearable technology have grown exponentially, particularly in consumer products and service sectors, e.g., healthcare. However, there is a lack of a comprehensive understanding of wearable technology in consumer acceptance. This systematic review utilized a PRISMA on peer-reviewed articles published between 2014 and 2024 and collected on WoS, Scopus, and ScienceDirect. A total of 38 full-text articles were systematically reviewed and analyzed using bibliometric, thematic, and descriptive analysis to understand the technical functions of digital wearable products (DWPs) in consumer acceptance. The findings revealed four key functions: (i) wearable technology, (ii) appearance and design, (iii) biomimetic innovation, and (iv) security and privacy, found in eight types of DWPs, among them smartwatches, medical robotics, fitness devices, and wearable fashions, significantly predicted the customers’ acceptance moderated by the behavioral factors. The review also identified five key outcomes: health and fitness, enjoyment, social value, biomimicry, and market growth. The review proposed a comprehensive acceptance model that combines biomimetic principles and AI-driven features into the technical functions of the technical function model (TAM) while addressing security and privacy concerns. This approach contributes to the extended definition of TAM in wearable technology, offering new pathways for biomimetic research in smart devices and robotics. Full article

In device-independent (DI) quantum protocols, security statements are agnostic to the internal workings of the quantum devices—they rely solely on classical interactions with the devices and specific assumptions. Traditionally, such protocols are set in a non-local scenario, where two non-communicating devices exhibit Bell inequality violations. Recently, a new class of DI protocols has emerged that requires only a single device. In this setting, the assumption of no communication is replaced by a computational one: the device cannot solve certain post-quantum cryptographic problems. Protocols developed in this single-device computational setting—such as for randomness certification—have relied on ad hoc techniques, making their guarantees difficult to compare and generalize. In this work, we introduce a modular proof framework inspired by techniques from the non-local DI literature. Our approach combines tools from quantum information theory, including entropic uncertainty relations and the entropy accumulation theorem, to yield both conceptual clarity and quantitative security guarantees. This framework provides a foundation for systematically analyzing DI protocols in the single-device setting under computational assumptions. It enables the design and security proof of future protocols for DI randomness generation, expansion, amplification, and key distribution, grounded in post-quantum cryptographic hardness. Full article

With the large-scale integration of renewable energy sources, traditional distribution networks are gradually evolving into a new form of flexible interconnection distribution networks. To enhance the rapidity and accuracy of power flow control through flexible interconnection devices, there is an increasing demand for precise grid equivalent models. Existing grid equivalent models predominantly adopt single-port configurations for radial networks, while there is limited research on two-port network equivalent models tailored for flexible interconnection distribution networks. Focusing on the scenario of flexible interconnection distribution networks integrated with Rotary Power Flow Controllers (RPFCs), this paper proposes an equivalent modeling method of two-port networks based on the superposition theorem under small disturbance conditions. A flexible interconnection distribution network model incorporating RPFCs and its corresponding two-port equivalent model are developed. The parameters of the two-port equivalent model are derived through superposition theorem calculations, enabling the realization of power decoupling control functionality for RPFCs. The simulation results show that the deviations between the set value of active power and the actual value remains at about 3%, and the deviations between the set value of reactive power and the actual value is between 4% and 7%, thereby verifying the effectiveness of the constructed two-port model in power flow control and further supporting the accuracy of the proposed method. Full article