Search Results (2,963)

Quantum spin transport in integrable systems reveals a rich nonequilibrium phenomena that challenges the conventional hydrodynamic framework. Recent advances in ultracold atom experiments with state preparation and single-site addressing have enabled the understanding of this anomalous behavior. Particularly, the full universality characterization of exotic initial states, as well as their measurement representation, remain unknown. By employing tensor network and contrast methods, we systematically investigate spin transport in the quantum XXZ spin chain and extract dynamical scaling exponents emerging from two paradigmatic and experimentally attainable initial states, i.e., multi-periodic domain-wall (MPDW) and spin-helix (SH) states. Our results using different values of anisotropic parameters $\Delta \in [0,1.2]$ demonstrate the evident impeded transport and the difference between the two states with increasing $\Delta$ values. Large-scale and consistent simulations confirm the contrast method as a viable scaling extraction approach for exotic states with periodicity within experimentally accessible timescales. Our work establishes a foundation for studying initial memory and the corresponding relations of emergent transport behavior in nonequilibrium quantum systems, opening avenues for the identification of their unique universality classes. Full article

To accurately diagnose the potential faults such as jamming and incomplete opening and closing of high-voltage disconnectors during long-term operation, this paper proposes a fault diagnosis method based on the fusion of time-frequency domain energy features of the body-side vibration signal. This method extracts short-term energy in the time domain and the marginal spectral energy of the sub-signals processed by variational mode decomposition (VMD) as features in the frequency domain, and constructs a feature set that can effectively represent different states through feature fusion. This enables the distinction between six states, namely normal closing, normal opening, closing jam, opening jam, closing not in place, and opening not in place. On this basis, the particle swarm optimization (PSO) algorithm is adopted to optimize the hyperparameters of the support vector machine (SVM), and the fault diagnosis model is obtained. The fault simulation experiment was conducted on the ZF12B type disconnector, and the experimental results show that the recognition accuracy of the proposed method reaches 98.33%, which is superior to the compared method, verifying the effectiveness and superiority of the proposed method. Full article

Conventional classifiers are generally unable to identify samples from classes absent during the model’s training. However, such samples frequently emerge in real-world scenarios, necessitating the extension of classifier capabilities. Open-Set Recognition (OSR) models are designed to address this challenge. Previously, we developed a robust OSR method that employs generated—“fake”—features to model the space of unknown classes encountered during deployment. Like most OSR models, this method was initially designed for image datasets. However, it is essential to extend OSR techniques to other data types, given their widespread use in practice. In this work, we adapt our model to time-series data while preserving its core efficiency advantage. Thanks to the model’s modular design, only the feature extraction component required modification. We implemented three approaches: a one-dimensional convolutional network for accurate representation, a lightweight method based on predefined statistical features, and a frequency-domain neural network. Further, we evaluated combinations of these methods. Experiments on a biometric time-series dataset, used here as a case study, demonstrate that our model achieves excellent open-set detection and closed-set accuracy. Combining feature extraction strategies yields the best performance, while individual methods offer flexibility: CNNs deliver high accuracy, whereas handcrafted features enable resource-efficient deployment. This adaptability makes the proposed framework suitable for scenarios with varying computational constraints. Full article

This study presents a loop-shaping methodology for the attitude control of a fixed-wing unmanned aerial vehicle (UAV). The proposed controller design focuses on achieving desired frequency–domain characteristics—such as specified phase and gain margins—to ensure stability and robustness. Unlike many existing approaches that rely on oversimplified plant models or involve mathematically intensive robust-control formulations, this work develops controllers directly from a high-fidelity six-degree-of-freedom UAV model that captures realistic aerodynamic and actuator dynamics. The loop-shaping procedure translates multi-objective requirements into a transparent, step-by-step workflow by progressively shaping the plant’s open-loop frequency response to match a target transfer function. This provides an intuitive, visual design process that reduces reliance on empirical PID tuning and makes the method accessible for both hobby-scale UAV applications and commercial platforms. The proposed loop-shaping procedure is demonstrated on the pitch inner rate loop of a fixed-wing UAV, with controllers discretized and validated in nonlinear simulations as well as real flight tests. Experimental results show that the method achieves the intended bandwidth and stability margins on the desired design target closely. Full article

The increasing capabilities of Unmanned Aerial Vehicles (UAVs) or drones are opening up diverse business opportunities. Innovations in drones, U-space, and UTM systems are driving the rapid development of new air mobility applications, often outpacing current regulatory frameworks. These applications now span multiple sectors, from infrastructure monitoring to urban parcel delivery, resulting in a projected increase in drone traffic within shared airspace. This growth introduces significant safety concerns, particularly in managing the separation between drones and manned aircraft. Although various research efforts have addressed this deconfliction challenge, a critical need remains for improved automated solutions at both strategic and tactical levels. In response, our SESAR-funded initiative, AI4HyDrop, investigates the application of machine learning to develop an intelligent system for UAV deconfliction. As part of this effort, we conducted a comprehensive literature review to assess the application of Artificial Intelligence (AI) in this domain. The AI algorithms used in drone deconfliction can be categorized into three types: deep learning, reinforcement learning, and bio-inspired learning. The findings lay a foundation for identifying the key requirements of an AI-based deconfliction system for UAVs. Full article

Electronic health records (EHRs) are typically stored in relational databases, making them difficult to query for nontechnical users, especially under privacy constraints. We evaluate two practical clinical NLP workflows, natural language to SQL (NL2SQL) for EHR querying and retrieval-augmented generation for clinical question answering (RAG-QA), with a focus on privacy-preserving deployment. We benchmark nine large language models, spanning open-weight options (DeepSeek V3/V3.1, Llama-3.3-70B, Qwen2.5-32B, Mixtral-8 × 22B, BioMistral-7B, and GPT-OSS-20B) and proprietary APIs (GPT-4o and GPT-5). The models were chosen to represent a diverse cross-section spanning sparse MoE, dense general-purpose, domain-adapted, and proprietary LLMs. On MIMICSQL (27,000 generations; nine models × three runs), the best NL2SQL execution accuracy (EX) is 66.1% (GPT-4o), followed by 64.6% (GPT-5). Among open-weight models, DeepSeek V3.1 reaches 59.8% EX, while DeepSeek V3 reaches 58.8%, with Llama-3.3-70B at 54.5% and BioMistral-7B achieving only 11.8%, underscoring a persistent gap relative to general-domain benchmarks. We introduce SQL-EC, a deterministic SQL error-classification framework with adjudication, revealing string mismatches as the dominant failure (86.3%), followed by query-join misinterpretations (49.7%), while incorrect aggregation-function usage accounts for only 6.7%. This highlights lexical/ontology grounding as the key bottleneck for NL2SQL in the biomedical domain. For RAG-QA, evaluated on 100 synthetic patient records across 20 questions (54,000 reference–generation pairs; three runs), BLEU and ROUGE-L fluctuate more strongly across models, whereas BERTScore remains high on most, with DeepSeek V3.1 and GPT-4o among the top performers; pairwise t-tests confirm that significant differences were observed among the LLMs. Cost–performance analysis based on measured token usage shows per-query costs ranging from USD 0.000285 (GPT-OSS-20B) to USD 0.005918 (GPT-4o); DeepSeek V3.1 offers the best open-weight cost–accuracy trade-off, and GPT-5 provides a balanced API alternative. Overall, the privacy-conscious RAG-QA attains strong semantic fidelity, whereas the clinical NL2SQL remains brittle under lexical variation. SQL-EC pinpoints actionable failure modes, motivating ontology-aware normalization and schema-linked prompting for robust clinical querying. Full article

Mathematical creativity (MC) plays an important role in mathematics and education; however, its conceptualization and assessment remain inconsistent across empirical studies. This systematic review examined how MC has been defined, conceptualized, and assessed across 80 empirical studies involving K-12 populations. Through thematic analysis, the study identified three definition types: divergent thinking, problem-solving, and problem-posing, as well as affective–motivational emphasis. We organized theoretical frameworks into three categories: domain-general, domain-specific, and multidimensional frameworks. Results showed that the most common definitions emphasized divergent thinking components while fewer studies highlighted affective and dispositional factors. Domain-specific frameworks were the most frequently used, followed by multidimensional frameworks. Regarding assessment, studies predominantly relied on divergent-thinking scoring. Most assessments used criterion-referenced rubrics with norm-based comparisons. They were delivered mainly in paper-pencil format. Tasks were typically open-ended multiple-solution problems with fewer studies using self-reports or observational methods. Overall, the field prioritizes product-based scoring (e.g., fluency, flexibility, originality) over evidence about students’ solution processes (e.g., reasoning, metacognitive monitoring). To improve cross-context comparability, future work should standardize and transparently report age, grade, and country coding and scoring practices. Full article

Background/Objectives: Adults with type 1 diabetes (T1D) experience disproportionately high rates of depression, anxiety, and psychological distress, yet integration of behavioral health into diabetes care remains limited. The objective of this study was to identify barriers and facilitators to mental health treatment among adults with T1D, using the Behavior Change Wheel as a framework to inform future integrated care strategies. Methods: We conducted five online focus groups with 21 adults with T1D. Discussions were guided by a semi-structured guide, with questions on lived experience, accessibility of mental health treatment, and integrated service delivery informed by the COM-B model domains. Transcripts were analyzed using qualitative content analysis, whereby meaning units were open-coded and then deductively categorized into COM-B constructs of capability, opportunity, and motivation. Results: Participants described limited psychological capability to address mental health due to the heavy self-management burden of T1D, lack of knowledge about navigating care, and uncertainty about treatment interactions. Physical opportunities were constrained by fragmented systems, high costs, and competing responsibilities. However, co-located services and telehealth were viewed as facilitators. Social opportunity was shaped by stigma, isolation, and feeling burdensome, with peer communities providing critical support. Motivation was undermined by past traumatic encounters with psychiatric care and the burden of educating providers about diabetes, contributing to mistrust and avoidance of treatment. Conclusions: Findings highlight how capability, opportunity, and motivation interact to influence engagement with mental health care among adults with T1D. Addressing these barriers through tailored, integrated models of care may strengthen access, trust, and long-term treatment engagement. Full article

In open-network environments of smart buildings and urban infrastructure, abnormal traffic from security and energy monitoring systems is critical for operational safety and decision reliability. We can develop malware that exploits building automation protocols to simulate attacks involving the falsification or modification of chiller controller commands, thereby endangering the entire network infrastructure. Intrusion detection systems rely on abundant labeled abnormal traffic data to detect attack patterns, improving network system reliability. However, transmitting such data faces two major challenges: single-feature representations fail to capture comprehensive traffic features, limiting the information representation for artificial intelligence (AI)-based detection models, and unconcealed abnormal traffic is easily intercepted by firewalls or intrusion detection systems, hindering cross-departmental sharing. Existing methods struggle to balance feature integrity and transmission stealth, often sacrificing one for the other or relying on easily detectable spatial-domain steganography. To address these gaps, we propose a multi-channel imaging hiding method that reconstructs abnormal traffic into multi-channel images by combining three mappings to generate grayscale images that depict traffic state transitions, dynamic trends, and internal similarity, respectively. These images are combined to enhance feature representation and embedded into frequency-domain adversarial examples, enabling evasion of security devices while preserving traffic integrity. Experimental results demonstrate that our method captures richer information than single-representation approaches, achieving a PSNR of 44.5 dB (a 6.0 dB improvement over existing methods) and an SSIM of 0.97. The high-fidelity reconstructions enabled by these gains facilitate the secure and efficient sharing of abnormal traffic data, thereby enhancing AI-driven security in smart buildings. Full article

With the rapid development of artificial intelligence and machine learning technologies, automatic speech recognition (ASR) and text-to-speech (TTS) have become key components of the digital transformation of society. The Kazakh language, as a representative of the Turkic language family, remains a low-resource language with limited audio corpora, language models, and high-quality speech synthesis systems. This study provides a comprehensive analysis of existing speech recognition and synthesis models, emphasizing their applicability and adaptation to the Kazakh language. Special attention is given to linguistic and technical barriers, including the agglutinative structure, rich vowel system, and phonemic variability. Both open-source and commercial solutions were evaluated, including Whisper, GPT-4 Transcribe, ElevenLabs, OpenAI TTS, Voiser, KazakhTTS2, and TurkicTTS. Speech recognition systems were assessed using BLEU, WER, TER, chrF, and COMET, while speech synthesis was evaluated with MCD, PESQ, STOI, and DNSMOS, thus covering both lexical–semantic and acoustic–perceptual characteristics. The results demonstrate that, for speech-to-text (STT), the strongest performance was achieved by Soyle on domain-specific data (BLEU 74.93, WER 18.61), while Voiser showed balanced accuracy (WER 40.65–37.11, chrF 80.88–84.51) and GPT-4 Transcribe achieved robust semantic preservation (COMET up to 1.02). In contrast, Whisper performed weakest (WER 77.10, BLEU 13.22), requiring further adaptation for Kazakh. For text-to-speech (TTS), KazakhTTS2 delivered the most natural perceptual quality (DNSMOS 8.79–8.96), while OpenAI TTS achieved the best spectral accuracy (MCD 123.44–117.11, PESQ 1.14). TurkicTTS offered reliable intelligibility (STOI 0.15, PESQ 1.16), and ElevenLabs produced natural but less spectrally accurate speech. Full article

Defensins are a class of small cysteine-rich cationic antimicrobial peptides (AMPs) that play vital roles in immune-regulating insect–microbe interaction, offering great potential for developing pest control approaches using RNA interference (RNAi) and insect pathogens. However, the biocontrol potential of defensins from the destructive rice pest Nilaparvata lugens (brown planthopper, BPH) remains largely unexplored. Here, we identified and functionally characterized a defensin-encoding gene NldefB in BPH. The open reading frame (ORF) of NldefB is 315 bp in length, encoding 104 amino acids with a conserved Knot1 domain. The qRT-PCR results showed that the transcription level of NldefB went upward with the increasing developmental stages, with the highest expressions in the female adults and their fat body. The expression of NldefB was continuously induced by bacterial pathogens but exhibited a pattern of initial increase followed by a decrease when challenged by a fungal pathogen Metarhizium anisopliae. RNAi-mediated silencing of NldefB significantly decreased the host survival rate, egg production and hatchability, as well as the capability to resist fungal infection. Additionally, NldefB suppression resulted in a significant increase in microbial loads. Our findings underscored that NldefB plays essential roles in regulating host development, pathogen defense, and microbial maintenance, providing a potential target for RNAi- and microbe-mediated BPH biocontrol. Full article

Background: Optical coherence tomography (OCT) is the main diagnostic technology used to detect damage to the retinal ganglion cells (RGCs) in glaucoma. However, it remains unclear which OCT parameter demonstrates the best diagnostic performance for eyes with early, especially preperimetric glaucoma (PPG). We determined the diagnostic performance of ganglion cell–inner plexiform layer (GCIPL) parameters using spectral-domain OCT (SD-OCT) in primary open-angle preperimetric and early perimetric glaucoma and compared them with optic nerve head (ONH) and peripapillary retinal nerve fiber layer (pRNFL) parameters. Methods: We analyzed 101 eyes: 36 normal eyes, 33 with PPG, and 32 with early perimetric glaucoma. All patients underwent Topcon SD–OCT imaging using the Optic Disc and Macular Vertical protocols. The diagnostic abilities of the GCIPL, rim area, vertical cup-to-disc ratio (CDR), and pRNFL were assessed using the area under the receiver operating characteristic curve (AUC). Results: For PPG, the AUCs ranged from 0.60 to 0.63 (GCIPL), 0.82 to 0.86 (ONH), and 0.49 to 0.75 (pRNFL). For early perimetric glaucoma, the AUCs for GCIPL and pRNFL ranged from 0.81 to 0.88 and 0.57 to 0.91, respectively, whereas both ONH parameters demonstrated an AUC of 0.89. The GCIPL parameters were significantly lower than both ONH parameters in detecting preperimetric glaucoma (p < 0.05). For early perimetric glaucoma, comparisons between the AUCs of the best-performing mGCIPL parameters and those of the best-performing pRNFL and ONH parameters revealed no significant differences in their diagnostic abilities (p > 0.05). Conclusions: GCIPL parameters exhibited a diagnostic performance comparable to that of ONH and pRNFL parameters for early perimetric glaucoma. However, their ability to detect preperimetric glaucoma was significantly lower than the ONH parameters. Full article

Biomass gasification, a key waste-to-energy technology, is a complex thermochemical process with many input variables influencing the yield and quality of syngas. In this study, data-driven machine learning models are developed to capture the nonlinear relationships between feedstock properties, operating conditions, and syngas composition, in order to optimize process performance. Random Forest (RF), CatBoost (Categorical Boosting), and an Artificial Neural Network (ANN) were trained to predict key syngas outputs (syngas composition and syngas yield) from process inputs. The best-performing model (ANN) was then integrated into a multi-objective optimization framework using the open-source Optimization & Machine Learning Toolkit (OMLT) in Pyomo. An optimization problem was formulated with two objectives—maximizing the hydrogen-to-carbon monoxide (H₂/CO) ratio and maximizing the syngas yield simultaneously, subject to operational constraints. The trade-off between these competing objectives was resolved by generating a Pareto frontier, which identifies optimal operating points for different priority weightings of syngas quality vs. quantity. To interpret the ML models and validate domain knowledge, SHapley Additive exPlanations (SHAP) were applied, revealing that parameters such as equivalence ratio, steam-to-biomass ratio, feedstock lower heating value, and fixed carbon content significantly influence syngas outputs. Our results highlight a clear trade-off between maximizing hydrogen content and total gas yield and pinpoint optimal conditions for balancing this trade-off. This integrated approach, combining advanced ML predictions, explainability, and rigorous multi-objective optimization, is novel for biomass gasification and provides actionable insights to improve syngas production efficiency, demonstrating the value of data-driven optimization in sustainable waste-to-energy conversion processes. Full article

(1) Background: trays are surgery-specific sets of required materials and medical devices, assembled in consultation between manufacturer and user, and provided in a sterile package. (2) Methods: in a high-volume urological center performing 11,920 operations/procedures annually (2023), we prospectively evaluated the effect of trays compared with the standard approach in a comparative study of 64 operations conducted between 29 October and 30 November 2024. The primary endpoints were the amount of operating room (OR) waste (volume/cm³, weight/g) and setup time (minutes). The secondary endpoint was the workflow assessment by nursing staff, rated on a numerical score (0–10) across seven relevant domains. (3) Results: for endourological procedures, setup time was reduced by 35%, operating room (OR) waste by 34%, and waste volume by 19.0%. Workflow was positively rated with a mean score of 9.75/10. For major open procedures, setup time was reduced by 43%, waste weight by 24.8%, and waste volume by 32%. Workflow was positively rated with a mean score of 8.9/10. (4) Conclusions: Trays have a sustainable and significant impact on reducing OR waste, save nursing staff preparation time, and facilitate improved workflow in the operating room. Full article

Background: Depression has been identified as an important determinant of outcomes in hip and knee arthroplasty, but its impact on total ankle arthroplasty (TAA) remains unclear. Given the growing use of TAA as a treatment for end-stage ankle arthritis, understanding psychosocial risk factors is critical for optimizing surgical outcomes. This study aims to assess the effect of preoperative depression on clinical and functional outcomes following total ankle arthroplasty. Methods: A systematic review was conducted in accordance with PRISMA guidelines and prospectively registered with the Open Science Framework. PubMed, Cochrane Library, and CINAHL were searched through August 2025 for studies reporting outcomes of TAA stratified by depression status. Eligible designs included randomized trials, cohort studies and case series. Risk of bias was assessed using the Newcastle–Ottawa Scale (NOS). Given heterogeneity in study designs, depression definitions, and outcome measures, findings were synthesized narratively and summarized using a revised effect-direction plot. Results: Six unique studies involving approximately 9000 patients met inclusion criteria. Five studies were rated as good quality on the Newcastle–Ottawa Scale, while one study was judged to be of moderate quality. Four studies assessing pain outcomes consistently demonstrated worse postoperative pain or less improvement in patients with depression. Three of five studies assessing functional or disability outcomes reported reduced improvement, while two studies found no independent association. Two studies evaluating complications showed higher risks of adverse events, including prolonged hospital stay, non-home discharge, osteophytosis, and implant subsidence, among depressed patients. Revised effect-direction synthesis confirmed a consistent trend toward poorer outcomes across pain, function, and complication domains. Conclusions: Depression is associated with worse pain and higher complication rates following TAA, while its influence on functional recovery was not demonstrated uniformly. These findings support the importance of routine preoperative screening and targeted management of depression. Further prospective, multicenter studies and interventional trials are needed to clarify causality and optimize perioperative care. Full article