Search Results (509)

Background/Objectives: Psoriatic disease is a systemic inflammatory condition associated with increased cardiometabolic risk, but the impact of contemporary systemic therapies and narrowband ultraviolet B (NB-UVB) phototherapy on vascular and systemic inflammatory markers remains incompletely characterized. We aimed to systematically synthesize prospective evidence on treatment-associated changes in vascular inflammation and systemic inflammatory biomarkers in adults with moderate-to-severe psoriatic disease. Specifically, we evaluated changes assessed by 18F-FDG PET/CT imaging and circulating biomarkers following biologic therapies or NB-UVB phototherapy. Methods: We systematically searched MEDLINE, Embase, Web of Science, Scopus, and CENTRAL from inception to 31 January 2026 for prospective interventional and observational studies in adults with psoriasis or psoriatic arthritis treated with biologic agents targeting TNF-α, IL-12/23, IL-17, or IL-23, or with NB-UVB phototherapy. Eligible studies were required to report serial assessments of vascular inflammation by 18F-FDG PET/CT (typically aortic target-to-background ratio) and/or systemic inflammatory markers (high-sensitivity C-reactive protein, interleukin-6, TNF-α, GlycA, or hematologic indices such as the neutrophil-to-lymphocyte ratio) over at least 8 weeks of follow-up. We imposed no language restrictions and included only full-text, peer-reviewed prospective studies. Risk of bias was evaluated using RoB 2 for randomized trials and ROBINS-I for nonrandomized studies. Random-effects meta-analyses were prespecified for outcomes reported by at least two clinically comparable studies; however, because of substantial heterogeneity in reporting and methodology, effect estimates were summarized using a structured narrative synthesis. Results: Thirteen prospective studies (n ≈ 900 adults, published 2015–2025) met inclusion criteria, including four studies with serial 18F-FDG PET/CT imaging and one additional PET/CT study providing baseline observational data on vascular inflammation, as well as eight biomarker-focused prospective cohorts. Across randomized mechanistic trials and observational studies, biologic therapies reduced aortic target-to-background ratio by approximately 6–12% over 12–24 weeks (e.g., mean change from 2.42 to 2.18 with TNF-α inhibition and from 2.51 to 2.20 with IL-17 blockade), and no study reported worsening of PET-derived vascular indices under effective systemic treatment. Biologic and other systemic therapies produced concordant reductions in hs-CRP (typically by 30–50%), IL-6, TNF-α, GlycA, and blood-count-derived indices including neutrophil-to-lymphocyte ratio, with biomarker improvements frequently paralleling reductions in cutaneous disease activity and cardiometabolic risk markers. Two NB-UVB cohorts demonstrated significant hs-CRP reductions of roughly 20–30% and modulation of vitamin D-related inflammatory proteins, suggesting systemic anti-inflammatory effects, although these changes appeared less pronounced than with biologic therapy and were not accompanied by vascular imaging. Conclusions: Contemporary systemic psoriasis therapies, particularly biologic agents targeting the IL-23/Th17 axis and TNF-α, are associated with consistent reductions in aortic vascular inflammation and broad improvements in systemic inflammatory biomarkers, whereas NB-UVB phototherapy confers more modest but measurable systemic anti-inflammatory effects, although the current evidence does not allow differentiation between individual biologic classes in terms of magnitude of effect. Although reductions in vascular and systemic inflammatory markers were observed across therapies targeting TNF-α, IL-12/23, IL-17, and IL-23, the small number of mechanistic imaging studies and absence of head-to-head comparisons do not allow robust differentiation between biologic classes or support a uniform class effect. The convergence of imaging and biomarker data reinforces psoriasis as a clinically relevant model of inflammation-driven atherosclerosis and supports the concept that effective control of psoriatic inflammation may contribute to cardiovascular risk modification, highlighting the need for integrated cardiovascular risk assessment in routine care. However, the imaging evidence base remains limited to four small mechanistic PET/CT studies with relatively short follow-up, which constrains the strength and generalizability of conclusions regarding vascular inflammation. Larger, adequately powered, event-driven prospective trials with standardized imaging and biomarker endpoints are needed to determine whether these vascular and systemic anti-inflammatory effects translate into reduced cardiovascular events in psoriatic disease; because of methodological and reporting heterogeneity across the 13 included studies, these conclusions are based on a structured narrative synthesis rather than a formal quantitative meta-analysis. PROSPERO registration number: CRD420261296646. Full article

Accurate yaw alignment is critical for maximizing power capture in horizontal-axis wind turbines, as even moderate yaw misalignment leads to significant aerodynamic losses, increased actuator usage, and accelerated mechanical wear. This research paper proposes a hybrid smart yaw control system for small-scale wind turbines that combines real-time measurements with short-term wind direction prediction to improve alignment accuracy, operational reliability, and energy efficiency under realistic operating conditions. The system integrates four wind direction information sources, such as physical wind vane sensing, live online weather data, forecast data, and a data-driven prediction module within a structured priority framework (VANE → LIVE → FORECAST → AI), to ensure continuous yaw control during sensor or communication unavailability. The prediction module is based on a long short-term memory (LSTM) neural network trained in MATLAB using live data from an online platform, with sine–cosine encoding employed to address the circular nature of directional data. The yaw controller incorporates a ±15° deadband, dwell-time logic, shortest-path rotation, and cable-safe constraints to reduce unnecessary actuation while maintaining effective alignment. The proposed system is validated through MATLAB/Simulink simulations and real-time microcontroller-based experiments using a stepper motor-driven nacelle. Compared with conventional vane-based yaw control, the hybrid AI-assisted approach reduces the average yaw error by approximately 35–45%, maintains a yaw error within ±15° for more than 90% of the operating time, increases average electrical power output by 3–5%, and reduces yaw motor energy consumption by 10–15%, while decreasing corrective yaw actuation events by 30–40%. These results demonstrate that integrating an LSTM-based wind direction predictor with multi-source wind data provides a robust, low-cost, and practically deployable yaw control solution that enhances energy capture and mechanical durability in small-scale wind turbines. Full article

Omni-directional mobile manipulators (OMMs) are inherently nonlinear, strongly coupled, and multiple-input multiple-output systems, posing significant challenges in developing accurate mechanistic models due to their complexity. Koopman operator theory offers a data-driven modeling framework that leverages input–output data to characterize system dynamics, but there often exist modeling errors. In this paper, an event-triggered data-driven linear model predictive control (MPC) framework is proposed for an OMM, without using any prior knowledge of the robot system. A finite-dimensional approximate linear Koopman model is established for an OMM using input–output data. The Gaussian process regression (GPR) is employed to estimate the model’s errors, while an extended state observer (ESO) is designed to estimate external disturbances. Since the introduction of GPR increases the computational burden, an event-triggered (ET) mechanism is introduced to reduce unnecessary controller recomputations and controller update frequency. Finally, comparative experiments are carried out to verify the effectiveness and performance superiority of the proposed control scheme. Full article

Cells rely heavily on DNA repair networks to survive genomic damage. For repairing double-strand breaks, Non-Homologous End Joining (NHEJ) remains the primary pathway, which is largely controlled by the DNA-dependent protein kinase catalytic subunit (DNA-PKcs). Researchers have long studied how phosphorylation drives this kinase. However, recent data point to an important additional layer of control. Drawing on evidence accumulated over the past two decades, we propose a “Spatiotemporal Logic Circuit” model for DNA-PKcs regulation. In this model, SUMO-associated interactions may help stabilize synaptic assembly, HUWE1-mediated neddylation may facilitate kinase activation at Lys4007, and K48-linked ubiquitination—potentially involving RNF144A—may contribute to the turnover of persistent repair complexes. Importantly, we frame these UBL-mediated events within the broader autophosphorylation-driven conformational cycle of DNA-PKcs, which remains central to NHEJ progression. Additionally, we highlight the structural interface where activation and degradation signals may converge and the extraction barrier posed by the massive DNA-PKcs scaffold. From a translational perspective, we argue that the exceptional size of DNA-PKcs (~470 kDa) and its topological entrapment on DNA render it an unusually challenging PROTAC target—one that may require p97/VCP-assisted extraction before proteolysis can proceed. We also highlight the underappreciated risk that E3 ligase loss-of-function, already documented in BET-PROTAC resistance, may similarly undermine DNA-PKcs degrader strategies. Full article

White mold caused by Sclerotinia sclerotiorum (Lib.) de Bary continues to threaten yield and quality and remains a stubborn, sometimes unpredictable constraint in many cropping systems. The pathogen’s broad host range and its capacity to persist for years as sclerotia mean that fields can carry risk long after visible symptoms fade. Disease development is often driven by short windows of favorable temperature and moisture that promote germination and ascospore release and dispersal, while myceliogenic infection from soil-borne sclerotia can also initiate disease directly. Yet dependable control is still undermined by durable inoculum, limited stable host resistance, variable biocontrol performance, and shrinking chemical options together with fungicide resistance risk. Here we consolidate current understanding and ongoing uncertainties around sclerotial formation and germination cues, the environmental drivers that shape epidemic onset, and the processes governing host colonization, including the roles of cell wall-degrading enzymes, oxalic acid, and redox regulation, as well as the continuing debate over necrotrophic versus hemibiotrophic phases. Management is considered from a practical perspective, covering cultural risk reduction, forecasting-guided fungicide programmes supported by resistance-management principles, and biological control strategies targeting sclerotia. Across systems, the evidence points to the same lesson: single tactics rarely remain reliable under field variability, whereas integrated packages that reduce soil inoculum and align interventions with risk are more durable. Future priorities include resolving early infection events, improving prediction of carpogenic germination under changing climates, increasing the consistency of biocontrol, and accelerating resistance breeding supported by genomic resources. Full article

Fault Detection and Diagnosis (FDD) in complex industrial systems requires methods that can handle uncertain operating conditions, soft thresholds, evolving sensor behavior, and increasing volumes of heterogeneous data. Traditional model-based or rule-driven approaches offer interpretability but lack adaptability, while purely data-driven and Large Language Model (LLM)-based methods often struggle with consistency, traceability, and causal grounding. Dynamic Master Logic (DML) provides a causal and temporal reasoning structure with fuzzy rules that capture gradual drift, soft limits, and asynchronous sensor signals while preserving traceability and deterministic evidence propagation. Building on this foundation, this paper presents a DML–LLM hybrid architecture that integrates targeted LLM inference to interpret unstructured information such as logs, notes, or retrieved documents under controlled prompts that maintain domain constraints. The combined system integrates Bayesian updating, deterministic routing, and semantic interpretation into a unified FDD pipeline. In a semiconductor manufacturing case study, the proposed framework reduced time to detection (TTD) from 7.4 h to 1.2 h and improved the F1 score from 0.59 to 0.83 when compared with conventional Statistical Process Control (SPC) and Fault Detection and Classification (FDC) workflows. Provenance completeness increased from 18% to 96%, while engineer triage time was reduced from 72 min to 18 min per event. These results demonstrate that the hybrid framework provides a scalable and explainable approach to anomaly detection and fault diagnosis in sensor-rich industrial environments. Full article

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by genetic heterogeneity. Post-transcriptional regulation—particularly alternative polyadenylation (APA)—plays a critical role in the pathogenesis of ASD. APA controls mRNA stability, translational efficiency, and subcellular localization through modulating the length of the 3′ untranslated region of mRNA. APA profiling can uncover functionally relevant post-transcriptional alterations often missed by conventional gene expression analyses. However, current ASD analyses still largely rely on differential gene expression or individual APA event detection, which ignores the collective explanatory power of ASD risk genes or co-dysregulated functional gene modules within specific cell types. In this study, we present an integrative computational framework that combines matrix factorization and machine learning to identify ASD-associated gene modules driven by APA and to predict cell-type-specific ASD-related cells. Applied to human brain single-nucleus RNA sequencing (snRNA-seq) data, our approach systematically uncovers APA regulatory patterns that are specific to cell type, brain region, and sex in ASD. The identified APA modules are significantly enriched in pathways related to synaptic function, neurodevelopment, and immune response, with the strongest signals observed in excitatory neurons of the prefrontal cortex. Using APA genes from these modules as features, we built a classification model that effectively distinguishes ASD cells from normal cells. Moreover, we found that integrating APA with gene expression—two complementary modalities—substantially improves prediction accuracy, underscoring APA as an independent and biologically informative regulatory layer. Our work delineates a high-resolution APA regulatory landscape in ASD, offering novel insights and potential therapeutic avenues beyond transcriptional abundance. Full article

Surgical resection remains a cornerstone in the multidisciplinary management of central nervous system (CNS) tumors, particularly diffuse gliomas. Traditionally, the role of surgery has been evaluated primarily through quantitative metrics such as extent of resection and its association with survival outcomes. However, despite maximal and radiologically complete resections, recurrence remains nearly universal in malignant CNS tumors, suggesting that surgical cytoreduction alone does not fully account for post-surgical disease dynamics. Emerging biological and molecular evidence indicates that surgery represents not merely a technical intervention, but a biologically active event that profoundly reshapes tumor evolution and treatment response. In this review, we propose a conceptual framework that redefines surgery as a key biological driver in CNS tumor progression. We synthesize evidence demonstrating that surgical trauma induces inflammation, hypoxia, vascular remodeling, immune modulation, and extracellular matrix reorganization, collectively reprogramming the residual tumor microenvironment. These changes create selective pressures that favor the survival and expansion of adaptive tumor cell subpopulations, including invasive and stem-like phenotypes. From an evolutionary perspective, surgical resection functions as an acute selective bottleneck acting on heterogeneous tumor ecosystems, contributing to clonal selection and molecular divergence at recurrence. We further examine the dissociation between surgical (anatomical) margins and molecular (biological) margins, highlighting how biologically active tumor cells infiltrate beyond radiologically defined boundaries. This discrepancy provides a biological explanation for marginal and distant recurrences and challenges anatomy-based paradigms of surgical completeness. Importantly, we discuss how surgery-induced biological changes influence postoperative radiotherapy and systemic therapies, affecting radiosensitivity, target delineation, and therapeutic vulnerability. Finally, we outline future directions toward surgery-integrated precision neuro-oncology, emphasizing the potential of spatial profiling, liquid biopsy, advanced imaging, and artificial intelligence to capture perioperative tumor evolution. By reframing surgery as a biological inflection point rather than a neutral prelude to adjuvant treatment, this review advocates for a dynamic, biology-driven continuum of care aimed at anticipating tumor adaptation and improving long-term disease control in CNS tumors. Full article

The increasing complexity and uncertain system of modern discrete event system (DES) challenge traditional model-based control approaches, while artificial intelligence (AI) techniques offer powerful data-driven decision-making capabilities but lack formal guarantees. This review surveys recent research on the integration of AI with DES and supervisory control theory. Following a systematic literature mapping methodology, the literature is organized using a taxonomy based on three orthogonal perspectives: control and decision paradigm, system capability and property, and application and operational objectives. The review highlights how learning-based methods enhance adaptability and performance in DES, while also exposing persistent challenges related to safety, nonblocking behavior, data efficiency, and interpretability. By structuring existing approaches and identifying open issues, this review provides a coherent overview of the current research landscape and outlines key directions for future work on AI-enabled DES. Full article

Future 6G-enabled smart hospital infrastructures will support latency-critical medical operations such as robotic surgery, autonomous monitoring, and real-time clinical decision systems, which require communication mechanisms that ensure both ultra-low latency and long-term cryptographic security. Existing security solutions either rely on classical cryptographic protocols that are vulnerable to quantum attacks or deploy isolated post-quantum primitives without providing a unified framework for secure real-time medical command transmission. This research presents a latency-aware, multi-layered post-quantum security architecture for 6G-enabled smart hospital environments. The proposed framework establishes an end-to-end secure command transmission pipeline that integrates hardware-rooted device authentication, post-quantum key establishment, hybrid payload protection, dynamic access enforcement, and tamper-evident auditing within a coherent system design. In contrast to existing approaches that focus on individual security mechanisms, the architecture introduces a structured integration of Kyber-based key encapsulation and Dilithium digital signatures with hybrid AES-based encryption and legacy-compatible key transport, while Physical Unclonable Function authentication provides hardware-bound device identity verification. Zero Trust access control, metadata-driven anomaly detection, and blockchain-style audit logging provide continuous verification and traceability, while threshold cryptography distributes cryptographic authority to eliminate single points of compromise. The proposed architecture is evaluated using a discrete-event simulation framework representing adversarial conditions in realistic 6G medical communication scenarios, including replay attacks, payload manipulation, and key corruption attempts. Experimental results demonstrate improved security and operational efficiency, achieving a 48% reduction in detection latency, a 68% reduction in false-positive anomaly detection rate, and a 39% improvement in end-to-end round-trip latency compared to conventional RSA-AES-based architectures. These results demonstrate that the proposed framework provides a practical and scalable approach for achieving post-quantum secure and low-latency command transmission in next-generation 6G smart hospital systems. Full article

Accurate forecasting of equity returns remains fundamentally constrained by weak short-horizon predictability, pronounced noise, and structural non-stationarity. While deep learning models have been widely applied to financial time series, most studies prioritize point prediction and provide limited guidance on reliable uncertainty quantification, particularly in emerging markets. This study developed an uncertainty-aware forecasting framework for the South African equity market by integrating variational mode decomposition (VMD), gated recurrent units (GRUs), and temporal conformal prediction (TCP) to construct distribution-free prediction intervals with finite-sample coverage guarantees. Using daily returns from the FTSE/JSE All Share Index, we first confirmed that baseline recurrent models applied directly to raw returns exhibited negligible out-of-sample explanatory power, consistent with weak-form market efficiency. Incorporating VMD enhanced representation learning and improved point forecast accuracy by isolating latent frequency components. However, model-based predictive variance alone proved insufficient for reliable calibration. Embedding the models within a rolling conformal prediction framework restored near-nominal coverage across multiple confidence levels while allowing interval widths to adapt dynamically to changing volatility regimes. Robustness analyses, including walk-forward validation, stress-regime evaluation, and block permutation negative control experiments, indicated that the observed performance was not driven by temporal leakage or alignment artifacts. The results further highlight a trade-off between interval sharpness and tail-risk protection, particularly during extreme market events. Overall, the findings support a shift from return-level prediction toward calibrated uncertainty estimation as a more stable and economically meaningful objective in non-stationary financial environments. Full article

Background: The need for autonomous aerial surveillance originates from weaknesses in manual monitoring, such as late response, low scalability and rigid patrol plans. AI and GPS-driven smart aerial monitoring present an attractive solution for continuous adaptive wide-area surveillance. Objective: In this paper, we aim at designing and validating experimentally a low-cost drone-based unmanned autonomous mission patrolling system with waypoint navigation, real-time video backhauling, AI-based human/object detection and GPS path re-planning when an event occurs to ensure the safety of patrol missions under battery constraints. Methods: The proposed architecture combines autonomous navigation and embedded flight-control with online analog video streaming and ground-station-based computer vision processing. Object detection based on deep learning for live aerial video is used, and the proposed system’s performance is tested at different altitudes, lighting states and GPS patrol plans. Results: Experimental results show that the proposed method can obtain stable waypoint tracking with a clear real-time video downlink in patrol missions. The system is able to adaptively modify paths as a reaction to detected events and commence safe return-to-home functionality during low-battery conditions. The proposed detection model obtains a mean average precision of 87.4%, with an F1-score of 0.89 and real-time inference latency (20–25 ms per frame) that enables fast service without any interruption in practice during surveillance deployment. Conclusions: Experimental results show that the proposed method can obtain stable waypoint tracking with a clear real-time video downlink in patrol missions. The system can adaptively modify paths as a reaction to detected events and commence safe return-to-home functionality during low-battery conditions. The proposed detection model obtains a mean average precision of 87.4%, with an F1-score of 0.89 and real-time inference latency (20–25 ms per frame) that enables fast service without any interruption in practice during surveillance deployment. Full article

The transformation of the labor market driven by digital technology has profoundly affected workers’ income. Based on data from the China Family Panel Studies (CFPS) 2014–2022 and the China Labor-force Dynamic Survey (CLDS) 2012–2018, this paper systematically examines the causal effects of standard employment, traditional non-standard employment (labor dispatch), and new non-standard employment (non-contract employment) on income within a unified framework. This study adopts a progressive identification strategy combining the two-way fixed-effects model, individual fixed-effects model, and event study methodology. The findings are as follows: First, new non-standard employment exhibits a significant “income penalty” effect, with its wage level being 14–15% lower than that of standard employment. This effect remains robust after controlling for individual heterogeneity. Second, dynamic analysis shows that transitioning from standard employment to new non-standard employment leads to sustained income loss, with a decline of nearly 10.8% after four years. Third, mechanism testing reveals that workers increase part-time work to compensate for income loss, but job satisfaction significantly declines, leading to a dual dilemma of “exchanging time for income” and “welfare discount.” Fourth, heterogeneity analysis shows that less educated and rural workers suffer greater shocks. The study concludes that new non-standard employment has inherent income suppression characteristics, and its effects are persistent and heterogeneous. It calls for the improvement of a labor rights protection system that adapts to new forms of employment, as well as the implementation of targeted support policies for vulnerable groups, in order to build a more equitable and secure labor market. Full article

This paper presents the design of an event-triggered state estimator for neural sliding mode control (SMC) in networked nonlinear Markov jump systems (MJSs) with incomplete mode information. To improve communication efficiency, a simulated annealing-based event-triggering mechanism is introduced for observer design over the network. This mechanism is enhanced by a neural-based adaptive compensator that effectively addresses unknown nonlinearities. An integral sliding surface is then constructed in the state estimation space, serving as the foundation for deriving the sliding mode dynamics and ensuring robustness to uncertainties. In light of uncertain transition rates (TRs), a unified sliding mode controller is developed to accommodate various mode types, ensuring both the reachability condition and the maintenance of sliding motion. The stochastic stability of the system is analyzed for each transition rate scenario. Finally, simulation results are provided to validate the effectiveness and performance of the proposed approach. Full article

Background and Clinical Significance: Deep thalamic and periventricular lesions are uncommon in adults but can result in significant loss of function because of their convergence on three interdependent processes: thalamocortical state regulation, throughput of periventricular long association systems, and ventricular compartmental compliance. The resulting combination of executive control collapse, retrieval-weighted language fragility, and load-sensitive gait instability may occur early after a lesion forms an atrial/posterior horn interface, and pressure-linked autonomic symptoms may be late to develop. Screening deficits will likely be minimal and therefore underreported. Objective/Aim: To present a thalamic–atrial/posterior horn tumor case with quantified load-sensitive cognitive–language–gait dysfunction and to detail a physiology-guided, sequence-driven decompression approach emphasizing ventricular relaxation and perforator-preserving, interface-limited thalamic resection. Case Presentation: A 56-year-old female patient experienced a 3-month, rapidly progressive decline in her cognitive and language abilities. The clinical progression was not stepwise or punctuated by a single “sentinel” event. She had a moderate level of cognitive impairment consistent with both Broca’s and Wernicke’s aphasias (MoCA: 22/30) and suffered from significant interference effects and increased cost of task-switching. Her ability to generate novel responses and name objects was significantly impaired; however, she was able to repeat words and phrases appropriately. In addition, she exhibited a severe sustained attention signature and a high error rate during dual-task performance, indicating severe gait instability, although her overall global anchors were nearly neutral (GCS 15; FOUR 15/16; NIHSS 2). Nausea and vomiting occurred simultaneously with the cognitive and language decline, suggesting decreased intracranial compliance. MRI revealed a heterogeneous left-sided thalamic tumor extending into the posterior horn of the lateral ventricle. The tumor caused deformation of the lateral ventricle and midline displacement. The patient underwent microsurgical intervention using a physiology-conscious sequence of graded cerebrospinal fluid (CSF) equilibration and primary mechanical removal of the tumor from the ventricular system. Additionally, decompression of the thalamus was performed in a manner that was cognizant of the boundaries formed by the perforating arteries of the thalamus. Early resolution of pressure symptoms was noted postoperatively. Objective measures demonstrated significant improvement in the patient’s executive functioning, language skills, attentional errors, and dual-task performance stability. The patient remained functionally independent at discharge and at subsequent follow-up visits. Surveillance imaging did not demonstrate any evidence of tumor recurrence. Conclusions: The clinical presentation described above is supportive of a model in which the synergy between deep network damage and distortion of the posterior ventricular compartment amplifies network dysfunction. Additionally, the use of quantitative stress-phenotyping makes it possible to identify deep network pathology early in its course. Finally, the physiology-guided decompression approach that was used in this case has the potential to increase functional reserve in patients with pathology that requires millimeter transitions. Full article