Search Results (5,587)

Systems that operate in dangerous environments are becoming essential in case of emergencies. This survey reviews the latest ground reconnaissance robots using computer vision (CV), machine learning (ML), MCU-based control, LoRa communication, DC motors, and dual-power systems. The analysis includes hardware and algorithms, and their performance in the field and lab. There has been clear progress in navigation, sensor fusion, and situational awareness. The main challenges which remain include the use of energy and standardization of benchmarks. This survey focuses exclusively on Unmanned Ground Vehicles (UGVs) for disaster reconnaissance, examining recent advances in hardware, software, and autonomy. The survey highlights the improvements in navigation, sensor fusion, and intelligence, and identifies remaining challenges such as energy limitations, robustness in harsh conditions, and the lack of standardized benchmarks. The analysis synthesizes findings from over 190 recent studies (2020–2025) in ground-based disaster robotics, providing a comprehensive overview of current capabilities and research gaps. It encapsulates all issues with their remedy for future disaster-response systems. Full article

(1) Since its introduction to China, pine wilt disease (PWD) has caused severe damage to coniferous forests in affected regions. The disease continues to expand northwestward, posing a significant threat to the ecological security of Xinjiang. (2) This study employed the maximum entropy (MaxEnt, version 3.4.4) model to predict potential areas for PWD transmission and suitable habitats for its vector insect, Monochamus saltuarius (Gebler, 1830). By integrating these results, the potential occurrence areas of PWD in Xinjiang were identified. (3) Human activities were the primary drivers of PWD spread, with factors related to scenic areas and overall human influence playing key roles. Altitude and isothermality were the main limiting factors for the vector insect. Potential PWD occurrence areas were identified, covering approximately 88% of the total coniferous forest area in Xinjiang. (4) High-risk regions include Urumqi City, Ili Kazakh Autonomous Prefecture, and Altay Prefecture. This study clarifies potential transmission routes and analyzes high-risk areas, providing a scientific basis for forestry authorities to implement targeted prevention and control measures. Full article

Azimuth multi-channel synthetic aperture radar (SAR) is a core technology for achieving high-resolution wide-swath (HRWS) imaging. However, inter-channel phase inconsistency causes image amplitude distortion and phase accuracy degradation, which severely affects subsequent applications. Existing phase error estimation methods face specific limitations: the performance of subspace-based approaches degrades in complex scenes due to unreliable covariance matrix estimation, while conventional frequency-domain correlation methods rely on manual selection of strong scatterers, introducing inefficiency and subjectivity that precludes autonomous deployment. To address these issues, this paper proposes a geometry-driven inter-channel phase error estimation framework based on Global Radar Landmark Control Point Library (GRL-CP). The proposed framework replaces scene-dependent target selection with geometric-prior-driven control point activation. The GRL-CP library stores only the geodetic coordinates and scattering stability attributes of globally persistent radar landmarks, rather than image patches. For a new SAR acquisition, the echo position of these landmarks are predicted using a range–Doppler geometric model, enabling fully automatic and reliable control point activation. Based on the activated radar landmarks, inter-channel phase error is estimated using a frequency-domain correlation scheme. Experimental results on multi-channel spaceborne SAR datasets demonstrate that the proposed method achieves improved stability and accuracy under complex terrain scenarios. Full article

Thyrotroph pituitary neuroendocrine tumors (PitNETs) are rare functional pituitary tumors characterized by autonomous secretion of thyroid-stimulating hormone (TSH), leading to central hyperthyroidism. Under the 2022 World Health Organization classification, these tumors are defined as PIT1-lineage PitNETs, reflecting lineage-specific differentiation and improving pathological accuracy. Clinically, thyrotroph PitNETs often present as macroadenomas with invasive growth, making complete surgical resection challenging and necessitating multimodal treatment strategies. From a molecular oncology perspective, thyrotroph PitNETs lack recurrent driver mutations and instead exhibit heterogeneous alterations involving dysregulated cell-cycle control, impaired thyroid hormone-mediated negative feedback, and aberrant growth factor signaling. Immunohistochemically, tumor cells express PIT1 and TSH and show strong membranous expression of somatostatin receptor subtype 2, providing a biological rationale for somatostatin receptor ligand -based therapy. Somatostatin receptor ligands play a central role in the management of thyrotroph PitNETs as preoperative, adjuvant, or primary treatment and achieve effective hormonal control and tumor stabilization or shrinkage in many patients. Accurate differentiation between thyrotroph PitNETs and resistance to thyroid hormone β is essential, as these entities share biochemical features but require fundamentally different management. Advances in lineage-based tumor classification, receptor profiling, and molecular pathology have refined diagnostic strategies and enabled a more personalized, tumor-oriented therapeutic approach. This review highlights current insights into the tumor biology and treatment of thyrotroph PitNETs and discusses future perspectives for receptor-targeted and molecularly informed therapies. Full article

The paper presents the design of an active seat suspension system for a medium-sized passenger vehicle (installation height of 180 mm), which is aimed at enhancing passenger comfort, with an emphasis on autonomous vehicle applications. The system is developed in two design variants based on Scott–Russell and Kempe mechanisms. The former is characterized by high rigidity and low friction, and it serves as a benchmark solution in this research. The latter is distinguished by cost-effectiveness and, thus, targeted for production vehicle applications once it is verified against the benchmark solution. Both designs are developed to satisfy the operational requirements derived from system computer simulations (suspension stroke of ±40 mm, speed of up to 0.5 m/s, and acceleration of up to 1 g), which are based on a half-car vehicle model extended with seat suspension dynamics and controlled by a linear quadratic regulator. The paper also outlines the electrical, measurement, and basic control subsystem of the overall active seat suspension mechatronic system. Finally, it presents experimental identification results to illustrate that the designed system complies with the specified requirements. Full article

Type 2 diabetes mellitus and prediabetes often develop silently and may remain undiagnosed for years. This is particularly relevant in regions where laboratory-based screening is not always readily accessible. Against this background, the present work explores whether multilead electrocardiography can provide physiologically meaningful markers potentially associated with disturbances in glucose metabolism. We developed and tested an upgraded wearable 12-lead ECG system capable of synchronized multichannel recording under controlled conditions. ECG signals were acquired in sitting and standing positions, with a sampling frequency of 500 Hz and a recording duration of one minute per posture. The hardware architecture included a high resolution analog front-end and wireless data transmission; the accompanying software provided acquisition control, preprocessing, visualization, and data storage within a unified framework. Signal processing focused on the extraction of rhythm-related and morphological parameters, with particular attention to ventricular repolarization indices. QT interval, heart rate–corrected QT (QTc), and QT dispersion (QTd) were calculated across leads, as these parameters are known to reflect heterogeneity of repolarization and autonomic influences on myocardial electrophysiology. The analysis was structured to ensure reproducible boundary detection and systematic feature formation rather than isolated parameter measurement. The study had a pilot character and included a limited and unbalanced sample (healthy n = 10; prediabetes n = 1; T2DM n = 1). For this reason, the results are presented descriptively and should be regarded as preliminary observations. In representative cases, differences in QT-related indices were noted between categories of glycemic status; however, the potential influence of age, sex, and other confounders cannot be excluded. A pilot expert comparison of T-wave end detection demonstrated close agreement between the automated algorithm and cardiologist assessment (mean Δ$T_{end}$ approximately −1 to −2 ms; MAE 10–24 ms). Diagnostic performance metrics such as ROC/AUC, sensitivity, and specificity were not calculated at this stage, as validation in a larger cohort with biochemical confirmation (HbA1c, OGTT) is required. The study demonstrates the technical feasibility of combining synchronized 12-lead wearable acquisition with structured multilead repolarization analysis. The proposed system should therefore be considered a research platform intended to support further clinical validation and methodological development rather than a finished screening solution. Full article

Inorganic nitrate (NO₃⁻) has demonstrated therapeutic efficacy in several populations characterised by cardiovascular risk. However, it is unknown whether increasing nitric oxide (NO) bioavailability affects vascular and cardiovascular responses in men with androgenic–anabolic steroid (AAS) abuse. Objective: To investigate the effects of dietary NO₃⁻ on cardiovascular, autonomic, and strength performance in men with AAS abuse. Methods: In this double-blind, randomised, placebo-controlled crossover trial, participants consumed beetroot juice (12.8 mmol [800 mg] NO₃⁻) or a placebo (0.3 mmol NO₃⁻). After two hours, assessments included saliva collection, endothelial function, heart rate, and systolic (SBP) and diastolic (DBP) blood pressure at rest, during, and after an isometric handgrip test. Results: Thirteen resistance-trained males [mean (standard deviation) age: 31 (9) y; body mass index (BMI): 30 (4) kg/m²; SBP: 132 (3) mmHg; DBP: 70 (2) mmHg] completed the protocol. NO₃⁻-rich juice significantly increased salivary NO₃⁻ (40.6 μM, p < 0.001) and nitrite (NO₂⁻) (3.1 μM, p = 0.002) versus placebo. Flow-mediated dilation was greater with NO₃⁻ both at pre-exercise (2.37%, p = 0.02) and post-exercise (2.57%, p = 0.01). No between-group differences were observed in isometric strength (0.02 kgf, p = 0.99) or systolic/diastolic blood pressure across conditions. Conclusions: Dietary NO₃⁻ enhanced salivary NO₂⁻ and NO₃⁻ concentrations and modestly improved endothelial function but did not reduce the elevated blood pressure or alter cardiac autonomic responses associated with AAS abuse. Full article

Sweeteners occupy a pivotal role in the global transition toward sustainable, health-aligned, and resource-efficient food systems. Conventional sucrose production carries significant environmental burdens, while escalating metabolic health concerns intensify demand for viable alternatives. This paper reframes sweeteners not as commodity ingredients, but as digitally engineered, biologically manufactured, and circularity-optimized materials within the emerging bioeconomy. Advances in artificial intelligence (AI), metabolic engineering, precision fermentation, and lignocellulosic valorization are fundamentally reshaping sweetener innovation. We introduce the Sweetener Innovation 4.0 framework, in which AI functions as the integrative engine linking molecular design, bioprocess optimization, and system-level sustainability. Across diverse sweetener classes, including steviol glycosides, mogrosides, rare sugars, sweet proteins, and forestry-derived polyols, AI accelerates discovery, improves metabolic flux control, optimizes downstream processing and enables more adaptive manufacturing systems. This digital–biological convergence is progressively decoupling sweetness production from land-intensive agriculture, reducing dependence on geographically constrained crops, and enabling resilient, low-carbon manufacturing pathways. Comparative life-cycle assessments highlight substantial sustainability gains, but also reveal persistent methodological gaps, particularly in accounting for downstream-processing energy and digital infrastructure emissions. Socioeconomic analysis further underscores the importance of equitable transitions, transparent labeling, and effective consumer communication as fermentation-derived sweeteners enter global markets. Looking forward, we identify key frontiers for Sweetener Innovation 4.0, including de novo AI-designed sweeteners, autonomous fermentation systems, carbon-negative feedstocks, personalized sweetness modulation, and integrated circular biorefineries. Together, these developments position sweeteners as a top domain for demonstrating how AI, biotechnology, and sustainability principles can jointly reshape ingredient development and industrial systems within the 21st-century circular-economy. Full article

Automating ICD-10 coding from discharge summaries remains demanding because coders analyze clinical narratives while justifying decisions. This study compares three automation patterns: PLM-ICD as a standalone deep learning system emitting 15 codes per case, LLM-only generation with full autonomy, and a hybrid approach where PLM-ICD drafts candidates for an agentic LLM audit to accept or reject. All strategies were evaluated on 19,801 MIMIC-IV summaries using four LLMs spanning compact (Qwen2.5-3B-Instruct, Llama-3.2-3B-Instruct, Phi-4-mini-instruct) to large-scale (Sonnet-4.5). Precision guided evaluation because coders still supply any missing diagnoses. PLM-ICD alone reached 55.8% precision while always surfacing 15 suggestions. LLM-only generation lagged severely (1.5–34.6% precision) and produced inconsistent output sizes. The agentic audit delivered the best trade-off: compact LLMs reviewed the 15 candidates, discarded weak evidence, and returned 2–8 high-confidence codes. Llama-3.2-3B-Instruct, for example, improved from 1.5% as a generator to 55.1% as a verifier while trimming false positives by 73%. These results show that positioning LLMs as quality controllers, rather than primary generators, yields reliable support for clinical coding teams, while formal recall/F1 reporting remains future work for fully autonomous implementations. Full article

Background/Objectives: Current musculoskeletal health assessment expanded beyond bone mineral density (BMD) at central DXA to include, for instance, trabecular bone score (TBS) and emergent biomarkers, such as adipokines and myokines (e.g., irisin) assays. A current gap in their application is reflected in limited research regarding adrenal tumors, especially non-functional adrenal tumors/mild autonomous cortisol secretion (NFATs/MACS). To assess this current gap, we aimed to explore beyond BMD, specifically, TBS and circulating irisin, in relation to the adrenal status in NFATs/MACS. Methods: This is a prospective, cross-sectional, single-center, exploratory study, conducted between October 2024 and December 2025. Results: A total of 81 menopausal women were included (mean age of 63.26 ± 8.82 years, 15.86 ± 9.5 years since menopause, average BMI of 30.69 ± 5.76 kg/sqcm. Out of them, 33.33% had NFATs/MCAS (group AI) and 66.67% were controls (group C), with similar age, years since menopause, and BMI. The prevalence of type 2 diabetes was 66.67% versus 68.52% (p = 0.865). TBS correlated with lumbar BMD/T-score (N = 33), while age and lumbar BMD were independent TBS predictors (N = 81), but not type 2 diabetes nor NFAs/MCAS. TBS correlated with the five-year age groups (r = −0.273, p = 0.003). Irisin correlated with osteocalcin (r = −0.252, p = 0.007), P1NP (r = −0.187, p = 0.049) and CrossLaps (r = −0.209, p = 0.026) in tumor-free controls. In the AI group, a higher irisin was associated with a higher second-day cortisol after 1 mg DST (r = 0.11, p = 0.584) and a lower ACTH (r = −0.716, p < 0.001). The rate of low TBS (based on 1.350 cutoffs) was 48.15% versus 38.89% in group AI versus C. In the AI group, patients with low TBS had lower osteocalcin, P1NP, and CrossLaps than those with normal TBS, with a similar rate of type 2 diabetes (which might reduce the bone turnover markers) and MACS-positive prevalence (between 25 and 28%). Conclusions: The median glycated hemoglobin A1c (5.78% versus 5.93%, p = 0.94) and median HOMA-IR (1.53 versus 1.42, p = 0.948) suggest a certain level of glucose control, which might not be reflected in severely damaged bone microarchitecture, as shown by TBS. Irisin may be one of the additional factors in these tumors reflecting the hormonal burden. Irisin was statistically significantly elevated with the increase in BMI groups. To our best awareness, this is the first synchronous analysis of TBS and irisin levels in this type of tumor to address the bone status in relation to the glucose profile and adrenal panel. Noting this is an exploratory, hypothesis-generating study, further research will highlight the true value of TBS and irisin for practitioners in the adrenal field, including multi-layered models of bone status prediction. Full article

Drones are expected to support applications such as emergency response, parcel delivery, and infrastructure monitoring in dense urban airspaces, creating traffic levels that are unmanageable for human operators. Autonomous separation management is therefore essential, combining strategic and tactical control to prevent conflicts. This paper addresses the tactical landing phase by introducing a centralized landing flow manager—a reinforcement learning (RL) agent that adjusts drone speed and heading to merge landing flows safely and efficiently prior to a final approach fix. The objective of the work was to demonstrate the potential of reinforcement learning in this novel context, by implementing and evaluating it in simulation and testing its capabilities with 10 concurrent landing drones. The RL agent learns to successfully separate traffic, thereby lowering intrusion counts compared to the baseline autopilot, but is outperformed in safety by the decentralized Modified Voltage Potential (MVP) method due to outlier scenarios. Nevertheless, the RL-based system achieves faster scenario completion and thus a higher overall throughput, by speeding up the vehicles towards the final approach fix. Future work will explore improved network architectures, transfer learning across varied scenarios, and algorithmic fine-tuning to further enhance safety performance. Full article

Casing collar locator (CCL) measurements are widely used as reliable depth markers for positioning downhole instruments in cased-hole operations, enabling accurate depth control for operations such as perforation. However, autonomous collar recognition in downhole environments remains challenging because CCL signals are often corrupted by toolstring- or casing-induced magnetic interference, while stringent size and power budgets limit the use of computationally intensive algorithms and specific operations require real-time, in situ processing. To address these constraints, we propose Collar Recognition Nets (CRNs), a family of domain-specific lightweight 1-D convolutional neural networks for collar signature recognition from streaming CCL waveforms. With depthwise separable convolutions and input pooling, CRNs optimize efficiency without sacrificing accuracy. Our most compact model achieves an F1-score of 0.972 on field data with only 1985 parameters and 8208 MACs, and deployed on an ARM Cortex-M7-based embedded system using the TensorFlow Lite for Microcontrollers (TFLM) library, the model demonstrates a throughput of 1000 inferences per second and 343.2 μs latency, confirming the feasibility of robust, autonomous, and real-time collar recognition under stringent downhole constraints. Full article

This paper proposes a novel method for synthesizing a discrete optimization algorithm based on the moth–flame paradigm for application to the architecture of deceptive systems incorporating decoys and traps. Unlike existing approaches that primarily rely on continuous search spaces or static deception strategies, the proposed method enables the formation of a discrete search space with a coordinate-based representation of deception objects and system states. A spiral search trajectory is synthesized by modeling the dynamic interaction between moths and flames, which allows the algorithm to balance exploration and exploitation effectively and to mitigate premature convergence to local optima. The problem of selecting subsequent operational steps of a deceptive system, which includes the control and reconfiguration of decoys and traps in response to detected events, is formulated as a discrete optimization problem. The objective of this optimization is to increase the effectiveness of cyberattack and malware detection in corporate network environments. The decision variables include the sequence of deception actions, process models, and architectural characteristics of the system, while the constraints are defined by the operational conditions, resource limitations, and structural features of corporate networks. The proposed method supports the identification of an optimal sequence of deception actions under dynamically changing conditions and provides mechanisms for operational adaptation to attacker behavior in real time. This adaptability enables the creation of deceptive systems capable of long-term autonomous operation without continuous administrative intervention, while simultaneously increasing their resistance to adversarial reconnaissance and reverse engineering of their operational principles. The experimental results confirm the feasibility and effectiveness of the proposed approach and demonstrate the potential of integrating population-based optimization algorithms into deceptive system architectures. Comparative analysis shows that the proposed method outperforms its closest competitor, the genetic algorithm, achieving an improvement of 4.82% in terms of the objective function value. Future research directions include deeper integration of population-based optimization methods into decoy-and-trap architectures and the development of a comprehensive framework for organizing their operation in accordance with the proposed conceptual model. Overall, the results contribute to enhancing the cyber-resilience of corporate networks through intelligent, adaptive, and autonomous systems for countering modern cyberattacks and malware. Full article

A crucial problem for autonomous aerial operations is to provide dependable and strong control of unmanned aerial vehicles (UAVs) in adverse environmental circumstances. The current paper provides an extensive analysis of the vehicle dynamics and control of drones in strong wind fields with altitude-dependent wind shear, wind gusts, and turbulence. A comparative evaluation of sliding mode control (SMC), linear quadratic regulator (LQR), model predictive control (MPC), adaptive constrained adaptive linear control (ACALC), and higher-order control barrier function (HOCBF)-based control in the context of trajectory tracking performance, control effort, and robustness is carried out. Simulation outcomes show that SMC exhibits superior robustness to sudden wind disturbances and the most consistent tracking accuracy under stochastic variations; HOCBF and ACALC provide comparable high precision with added constraint enforcement and adaptive capability, respectively; MPC has smooth control and minimal energy consumption; and LQR has a high level of computational efficiency with significantly tolerable tracking performance. Monte Carlo calculations are conducted to measure tracking errors and control energy under the stochastic wind variations, and the capability of the proposed control strategies to remain resilient in uncertain conditions is brought to light. The results provide useful information about the architecture of effective controllers used in UAVs during severe weather conditions and underline the compromises between the accuracy of tracking, the control effort, and the energy consumption. The suggested framework offers an effective and scalable system suitable for reliable autonomous drone activity in complicated reality settings. Full article

Blockchain technology is redefining the foundations of cybersecurity by introducing decentralized, tamper-resistant mechanisms for data integrity, trust management, and malware intelligence sharing. Traditional detection systems, which are dependent on centralized control and opaque validation, remain vulnerable to data manipulation and systemic compromise. The integration of blockchain transforms these paradigms because it provides verifiable provenance, distributed consensus, and autonomous enforcement through smart contracts. This review synthesizes fifteen years of progress (2010–2025) at the intersection of blockchain and malware detection and discusses core architectures, consensus protocols, and cryptographic properties that underpin decentralized defenses. The review follows a structured literature review methodology, which focuses on blockchain architectures, consensus protocols, and malware-detection pipelines reported in the cybersecurity literature. It also analyzes blockchain detection pipelines, performance tradeoffs, and data protection mechanisms in distributed learning systems and artificial intelligence models. Special attention is given to scalability constraints, regulatory compliance, and interoperability challenges that shape adoption. The review identifies three dominant design patterns: (i) decentralized threat-intelligence sharing with provenance guarantees, (ii) consensus-driven validation of malware artifacts, and (iii) on-chain trust and reputation mechanisms for detector accountability. Through the union of blockchain, artificial intelligence, edge computation, and federated learning, cybersecurity attains an auditable and adaptive architecture resilient to adversarial threats. The study concludes that blockchain provides a verifiable trust infrastructure for malware detection, but its practical deployment requires faster transaction validation and stronger protection of sensitive data; future research should address performance optimization and regulatory compliance. Full article