Search Results (1,149)

Copy number variants (CNVs) are genomic rearrangements that carry a substantial risk for neurodevelopmental and neuropsychiatric disorders. Among these, recurrent deletions and duplications at the 16p11.2 locus are robustly associated with autism spectrum disorders, schizophrenia, epilepsy, and related conditions, yet also display marked variability in penetrance and phenotypic expression. Accumulating evidence indicates that 16p11.2 gene dosage influences multiple stages of brain development, from early progenitor dynamics and neuronal migration to synaptic formation, refinement, and plasticity. However, how disruptions across these processes are integrated over time, and how they relate to the observed variability and incomplete penetrance, remains poorly understood. In this review, we summarize the current evidence on the impact of 16p11.2 CNVs on brain development, focusing on cellular and circuit-level processes that shape neural connectivity. We discuss how gene dosage imbalance influences early developmental trajectories, synaptic formation and pruning, interneuron maturation, and activity-dependent plasticity, and consider how these processes interact across developmental stages. We suggest a conceptual framework wherein 16p11.2 CNVs do not impose fixed pathogenic outcomes, but rather they contribute towards developmental constraints that shape the timing and stability of neural circuit development. Consequently, these constraints increase vulnerability to neurodevelopmental and psychiatric outcomes in a context-dependent manner. Full article

Under weak-grid conditions, grid-following (GFL) control of doubly fed induction generators (DFIGs) suffers from reduced stability margins, deteriorated dynamic performance, and intensified oscillations near the stability boundary. To address these issues, a pre-synchronized switching strategy between GFL and grid-forming (GFM) modes, triggered by locally measured operating variables, is proposed. Based on the GFL control model, the evolution of system dynamics with decreasing short-circuit ratio is analyzed, thereby elucidating how reduced grid strength progressively weakens robustness and disturbance rejection and eventually leads to instability. To characterize this deterioration, a set of normalized indices is constructed to quantify the oscillation levels of active power, phase-locked loop frequency, and point of common coupling voltage, enabling reliable identification of control-performance deterioration. A pre-synchronization scheme based on a virtual power closed loop is then developed, allowing the target mode to converge to the current operating point prior to takeover and enabling smooth bidirectional switching between GFL and GFM modes. Hardware-in-the-loop results demonstrate that the proposed strategy accurately detects GFL performance deterioration and effectively suppresses boundary oscillations while mitigating switching transients, thereby enhancing the adaptability of DFIGs to variations in grid strength. Full article

Sharp wave–ripples (SWRs) are transient hippocampal population events that coordinate neuronal ensemble activity and play a central role in memory consolidation and affective processing. Although SWRs exhibit marked functional specialization along the dorsoventral axis of the hippocampus, and several cellular mechanisms underlying SWRs are sex-sensitive, systematic comparisons of SWR properties between females and males are lacking. Here, we examined sex- and region-dependent differences in SWRs and associated multiunit activity (MUA) in acute hippocampal slices from adult female and male rats. Spontaneous SWRs were recorded from the CA1 stratum pyramidale of the dorsal and ventral hippocampus, and SWR occurrence rate, amplitude, ripple oscillation properties, and SWR-locked neuronal firing were quantified. Linear mixed-effects analysis revealed robust region-dependent differences across multiple SWR parameters. In contrast, sex effects were selective. SWR occurrence rate and amplitude did not differ significantly between females and males, whereas SWR-associated MUA showed a significant main effect of sex, with higher values in males. Ripple power was also influenced by sex, with higher values in females, together with a significant effect of region, suggesting differences in oscillatory structure. Baseline MUA did not differ between sexes, indicating that sex-related effects are specific to the SWR state. These findings suggest that sex does not substantially alter the generation of SWRs per se but influences neuronal recruitment and oscillatory properties during these events. Our results reveal previously underappreciated dimensions of hippocampal network organization and provide a descriptive framework for future studies investigating how sex-dependent circuit properties may shape hippocampal contributions to cognition and affective regulation. They further highlight the importance of incorporating sex as a fundamental biological variable in studies of hippocampal network dynamics. Full article

Reasoning-intensive multimodal retrieval suffers from a counter-intuitive bottleneck: on MM-BRIGHT multimodal-to-text (Query+Image → Documents), the strongest dense multimodal encoder reaches only 27.6 nDCG@10 and the rest of the dense vision–language retrievers cluster between 10.0 and 23.0. The visual signal, encoded as a dense vector, adds noise rather than evidence; even augmenting strong text retrievers with raw image captions degrades performance by up to 12.0 points. We propose VISA, a Visual Symbolic Agent that re-casts multimodal-to-text as text retrieval over three parallel streams. A Vision LLM is dispatched in three roles via separate prompts: a zero-shot router that classifies the query image into up to three parser types from a fixed taxonomy of nine (chart, circuit, equation, screenshot, code, figure, diagram, map, photograph); typed parsers that extract structured text per type; and a holistic captioner. The agent constructs three text streams (raw query, query ⊕ symbolic, query ⊕ caption), scores each with a single frozen 4B-parameter retrieval LLM, and fuses the per-document scores via Reciprocal Rank Fusion or a confidence-weighted linear combination. The whole agent contains no trainable parameters. The key novelty is a change of substrate: rather than projecting the query image into a dense multimodal vector that competes with text, VISA is, to our knowledge, the first retrieval system to convert the image into typed symbolic text and keep retrieval entirely text-side, so that a frozen text retriever can match the literal tokens (axis values, variable names, function signatures) that answering documents actually contain. Across all 29 MM-BRIGHT multimodal-to-text domains, VISA achieves 32.4 nDCG@10, an absolute improvement of +4.8 over the strongest dense multimodal encoder and substantially larger margins over the remaining six dense vision–language baselines. Per-domain analysis shows VISA maintains its margin across STEM and software domains where image content is structure-heavy. In practical terms, VISA is training-free and model-agnostic: it requires no fine-tuning, reuses any off-the-shelf vision LLM and text retriever, caches all per-image parsing so re-runs cost only three query encodes, and can therefore be dropped into an existing text-retrieval stack to add reasoning-intensive multimodal capability without building or training a multimodal encoder. Full article

The rapid transition toward a converter-dominated power system, driven by high penetration of inverter-based resources (IBRs), the explosive growth of artificial intelligence (AI) technologies, and large power electronic loads, is fundamentally altering grid dynamics and exposing critical limitations in conventional stability, protection, and planning frameworks. Traditional metrics, such as the short-circuit ratio (SCR), have been shown to be insufficient for capturing impedance interactions, control coupling, and multi-timescale dynamics in such systems. This paper develops a unified, control-aware, and impedance-based modeling framework that accurately represents both grid-following and grid-forming behaviors. It highlights the increasingly active role of large-scale loads as grid-interactive resources with significant impacts on frequency and voltage stability, particularly in weak grids. In addition, battery energy storage systems (BESSs) are identified as a key enabler for providing fast dynamic support and mitigating variability across multiple timescales. A hierarchical assessment methodology combining system-strength screening, impedance-based stability analysis, Nyquist evaluation, and EMT-oriented validation is proposed to bridge conventional planning studies and converter-dominated system assessment. Key findings demonstrate that the reliable operation of future grids requires moving beyond steady-state and phasor-domain assumptions toward EMT-based validation, adaptive protection schemes, and coordinated grid-forming control strategies. The study further emphasizes the need for harmonized, performance-based grid codes to ensure the consistent integration of both generation and large loads. Overall, this work provides a comprehensive framework for the modeling, analysis, and control of inverter-dominated power systems, addressing critical gaps in current methodologies and supporting the secure evolution of modern power grids. Full article

The continued scaling of semiconductor devices at advanced technology nodes introduces significant challenges in maintaining performance, reliability, and design efficiency. This work presents a data-driven framework for the modeling and optimization of nanosheet (NS) and forksheet (FS) transistors using deep learning and Bayesian optimization. An extensive dataset is generated through LTSpice-based circuit simulations, enabling efficient exploration of the design space while incorporating key device parameters, including channel length, channel width, supply voltage, temperature, and threshold voltage, together with variability and noise effects. A deep neural network (DNN) is developed as a surrogate model to learn the nonlinear relationship between input parameters and transistor switching behavior, achieving strong predictive performance with a coefficient of determination ($R^2\approx 0.91$), mean absolute error (MAE $\approx 0.024$), and root mean square error (RMSE $\approx 0.031$) on unseen test data. To improve physical consistency, a bounded-output formulation is introduced to guarantee physically admissible voltage predictions, while device-level benchmarking is performed to assess agreement with expected transistor characteristics. The results demonstrate accurate modeling of transient behavior across the sampled operating conditions. Comparative analysis shows that NS devices achieve faster switching and lower propagation delay, whereas FS devices exhibit improved stability under certain conditions. Bayesian optimization is employed to efficiently explore the design space and identify high-performing transistor configurations without exhaustive simulation-based searches. The proposed framework provides a scalable and computationally efficient methodology for surrogate modeling, design-space exploration, and early-stage assessment of advanced transistor architectures. Full article

Most studies in the field of application technology have focused on the interaction between adjuvants and agrochemicals, highlighting the need for further research to evaluate the behavior of adjuvants in association with other classes of crop protection products. In this context, the objective of this study was to evaluate the influence of adjuvants and air velocity on spray drift deposition in simulated applications conducted in a wind tunnel using a bioinsecticide based on Bacillus thuringiensis. The experiment was carried out in an open-circuit, blower-type wind tunnel installed at the Agricultural Machinery Laboratory of the State University of Goiás—Central Campus. The study was conducted in a completely randomized design arranged in a 5 × 4 × 4 factorial scheme, with three replications. Treatments consisted of five horizontal distances from the spraying point (0.45, 0.75, 1.05, 1.35, and 1.65 m), four wind speeds inside the tunnel (1 m s⁻¹, 2 m s⁻¹, 3 m s⁻¹, and 4 m s⁻¹), and four spray solution formulations (water; Dipel^®, Dipel^® + Veget’Oil^®, and Dipel^® + Break Thru^®). Artificial targets positioned transversely to the airflow were used to collect spray deposition and, after spraying, were divided into lower, middle, and upper thirds according to the height of the test section. Data were obtained by spectrophotometry and, after verification of the ANOVA assumptions, were subjected to analysis of variance (p < 0.05). When significant effects were observed, regression analyses were applied. Statistical analyses were conducted using the R and Sisvar software packages. Mean deposition values were converted into deposition percentage as a function of the total sprayed volume. The experimental data were also subjected to geostatistical analysis using GS+ software (Version 7^®). After confirming spatial dependence, contour maps were generated using kriging. Higher wind speeds led to higher deposition percentages. The use of adjuvants affected spray deposition in the upper and middle thirds, with responses depending on the spray solution composition. Spray deposition in the wind tunnel can be analyzed using geostatistics, as this variable showed a high degree of spatial variability across all treatments evaluated. Full article

Maintaining stable operation and maximum power extraction in wind turbines under significant wind speed variations remains a key challenge in wind energy systems. This study aims to analyze the stability and maximum power point operation of a wind turbine equipped with a squirrel-cage induction generator using stator-side frequency control. This study examines the operational performance of medium-power wind turbines in the kilowatt range under significant wind speed variability. The analysis focuses on a turbine equipped with a squirrel-cage induction generator and a control architecture that incorporates a power converter integrated into the stator circuit. The findings show that adjusting the stator frequency through the converter allows the generator to track the optimal rotational speed, ensuring operation at the maximum power point across a wide range of wind conditions. Based on these results, the study defines the stable operating region of the turbine under time-varying wind speeds, making it suitable for distributed energy projects in coastal regions where wind can be highly variable. It also shows that, for a given electrical load, the generator must be calibrated to an appropriate maximum stator frequency to maintain stable and efficient energy conversion. Full article

Zoomafia is frequently invoked in Italian public, advocacy, and institutional discourse to describe profit-oriented animal-related crime, but the term remains analytically broad and insufficiently connected to criminological theory. This narrative criminological review examines zoomafia as a cautious social-scientific lens for studying organized animal-related crime across heterogeneous illicit markets. Keyword-driven searches in Scopus, Web of Science, PubMed, and targeted criminological, legal, policy, and institutional sources were complemented by citation tracking and qualitative source selection. Peer-reviewed scholarship forms the analytical core, while legal, institutional, and advocacy materials are used selectively and with explicit evidentiary limits. Findings suggest that organized animal-related crime is best understood through market governance, brokerage, legal-illegal interface management, digital mediation, logistics, facilitation, evidentiary visibility, and variable convergence with other illicit economies, rather than through generic offence labels alone. The Italian perspective is analytically useful because companion-animal trafficking, dog fighting and betting circuits, clandestine horse racing, illicit slaughtering, wildlife trafficking, and online-facilitated trade can be compared within a shared frame that also exposes the limits of rhetorical mafia labelling. The article argues that zoomafia should not be treated as a self-proving mafia label, a new legal category, or a synonym for wildlife trafficking, but as a comparative framework for identifying organizational features, enforcement constraints, and evidentiary thresholds. The evidence base remains stronger on strategic recommendations than on robust comparative evaluation of enforcement effectiveness. Full article

Aggressive technology scaling has led to a significant increase in manufacturing process variations and transistor aging effects, which critically degrade the performance of radio frequency (RF) circuits. These reliability challenges are particularly pronounced in voltage-controlled oscillators (VCOs), where phase noise and operating frequency stability are compromised. While design strategies incorporating micro-electromechanical systems (MEMS) actuators enhance VCO performance by leveraging MEMS varactors or inductors with substantially higher quality factors (Q), this benefit is progressively undermined over time by process variations and aging-induced shifts in the threshold voltage and carrier mobility of the VCO’s transistors. This work presents an ultra-low-power current-reuse voltage-controlled oscillator (VCO) designed to maintain stable performance under process variability and reliability-induced parameter shifts. Robust operation is achieved using a self-detecting–correcting (SDC) bias scheme that senses performance drift and applies corrective feedback through body-bias control in the VCO core. Analytical relations are derived to describe the impact of threshold voltage and mobility variations, and the approach is validated via post-layout simulations in a 130 nm complementary metal-oxide semiconductor (CMOS). Under 18% variations in threshold voltage and carrier mobility, the proposed SDC scheme preserves oscillation frequency, phase noise, and figure of merit (FoM) while also mitigating the intrinsic output amplitude imbalance of conventional current-reuse VCOs. Monte Carlo analysis (500 runs) demonstrates low sensitivity to fabrication uncertainty, with a standard deviation below 0.14 dBc/Hz for phase noise, 210 kHz for oscillation frequency, and 0.4 dBc/Hz for FoM. The VCO operates from a 0.9 V supply, consumes 175 μW, and achieves −124 dBc/Hz phase noise at 1 MHz offset near 2.4 GHz (FoM ≈ −199 dBc/Hz). Full article

Sex differences are increasingly recognized as key determinants of vulnerability, clinical presentation, and treatment response in depression. Rather than arising from a single mechanism, these differences emerge from the interplay of multiple biological and non-biological factors. Converging evidence points to the hippocampus as a central region where these processes intersect, with adult neurogenesis and astrogliogenesis representing a potential mechanistic link between sex-specific biological factors and behavioral outcomes in depression. In this review, we integrate findings from human studies and preclinical models to examine how sex impacts depression while considering the multiple origins of sexual differentiation in the central nervous system. We discuss the importance of studying sex as a biological variable and acknowledge current limitations in the field. Finally, we highlight how cytogenic processes in the adult hippocampus are modulated in a sex-dependent manner, how their disruption may contribute to the pathophysiology of depression, and their potential role in precision psychiatry. Adult cytogenesis provides a promising target for developing therapeutic strategies aimed at promoting the integration of these cells in neural circuits, which may counterbalance the cellular impairments observed in stress-induced depression, representing a therapeutic avenue for this disorder. Full article

Hydrogen-rich shaft furnaces operated with coke oven gas (COG) represent an important low-carbon ironmaking route. Conventional porous-medium CFD models, however, do not explicitly resolve geometry-dependent burden descent or downward advection of solid sensible heat in variable-cross-section moving beds. To address this gap, a user-defined-scalar (UDS)-based pseudo-fluid moving-bed dual-temperature CFD framework is developed in this study. The framework couples geometry-dependent pseudo-solid kinematics, UDS-based transport of pseudo-solid species and sensible enthalpy, and a 12-step reduction-reforming-carbon reaction network on a fixed Eulerian mesh. It is applied to a 0.5 Mt·a⁻¹ industrial reactor through one reference case and three parametric groups covering solid descent velocity, cooling-side back pressure, and CH₄ content. Mesh-independence and mass-conservation checks indicate that the medium mesh is adequate for the intended trend-level assessment; the fine-to-medium deviations are 0.54% for DRI metallization, 0.23% for DRI outlet temperature, and 0.20% for top-gas temperature, with a net global mass residual of 1.53 × 10⁻⁶ kg·s⁻¹; the baseline DRI metallization (96.3%), carbon content (1.1%), and combined H₂ + CO utilization (29.45%) all fall within the reported ranges of the HBIS demonstration line and Energiron-ZR projects. As the descent velocity increases from 2.88 to 6.72 × 10⁻⁴ m·s⁻¹, DRI metallization drops from 98.0% to 79.4% and the outlet temperature rises from 313.3 to 719.4 K. Increasing the cooling-gas outlet back pressure from 60 to 100 kPa reduces the cooling-outlet excess flow from 1.49 to 0.11 kg·s⁻¹, indicating a dynamic gas-seal control between the two gas circuits, whereas raising the inlet CH₄ fraction from 10 to 23 vol% lowers the apparent CH₄ conversion from 29.5% to 18.5% and broadens the carbon-deposition zone. The framework offers a continuum basis for proof-of-concept and trend-level analysis of variable-cross-section hydrogen-rich moving-bed shaft furnaces. Full article

Soccer performance is characterized by high motor and cognitive complexity, resulting from the interaction between, among others, physical and technical components. However, evidence regarding the relationships among physical performance, motor coordination and soccer-specific technical remains limited. Therefore, this cross-sectional study aimed to investigate the associations among these domains in youth soccer players. Forty-nine male U15 participants (age: 14.3 ± 0.5 years) underwent anthropometric assessments, physical fitness testing (10 m, 30 m sprint, CMJ, YYIRT1), a general motor coordination test (Harre Circuit Test), and soccer-specific technical evaluation (F-MARC test battery). Associations among variables were assessed using Spearman correlations and exploratory principal component analysis (PCA) based on a Spearman correlation matrix with oblimin rotation. Significant associations emerged between general motor coordination, physical performance variables, and several soccer-specific technical skills. The PCA identified three partially overlapping components, cumulatively explaining about 70% of the variance, highlighting the multidimensional and interconnected nature of soccer-related performance capacities. General motor coordination demonstrated relevant loadings in both coordinative/technical and physical-performance-oriented domains. These findings suggest that youth soccer performance should not be interpreted through isolated physical or technical characteristics, but rather as the result of interactions among coordinative, neuromuscular, and technical factors. Consequently, multidimensional and individualized training approaches integrating physical, coordinative, and technical stimuli may represent relevant strategies for youth soccer development. Full article

Perioperative anxiety is a common psychophysiological stress response experienced by patients before and after surgery, with a global prevalence of approximately 48%. Its occurrence is influenced by multiple factors including age, sex, type of surgery, and psychosocial determinants. The underlying pathophysiological mechanisms are complex, involving multi-system interactions such as autonomic nervous system imbalance, dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis, dysfunction of limbic system neural circuits, and neuroinflammation. Current assessment strategies are evolving from sole reliance on psychological scales toward multimodal approaches incorporating objective biomarkers including heart rate variability, cortisol, and electroencephalography. Management paradigms have shifted from traditional pharmacological premedication to integrated systems encompassing structured patient education, digital health tools, neuromodulation techniques, and cognitive behavioral therapy. However, significant gaps persist regarding standardized screening protocols, biomarker validation, and targeted intervention pathways for high-risk populations. Future management is likely to require more individualized risk assessment and intervention selection. Biomarker-based risk prediction, artificial intelligence-assisted intervention decision-making, and the deep integration of digital therapeutics such as virtual reality with existing enhanced recovery pathways will be key directions for improving patient outcomes and recovery quality. This structured narrative review summarizes current evidence on perioperative anxiety in adults, focusing on epidemiology, pathophysiological mechanisms, assessment tools, biomarkers, and multimodal management strategies. Full article

To address the issues of slow operation and high variability in traditional electromagnetic trip devices, this paper proposes a magnetic trip device based on the principle of “permanent magnet holding, spring driving, and electric reset,” thereby reducing the circuit breaker tripping time. However, its high material and manufacturing costs have limited its widespread adoption. To address this issue, this paper employs a method combining orthogonal experiments with BP-NSGA-II. Using permanent magnet dimensions, coil wire gauge, moving component mass, and spring initial force as variables, the number of simulations is reduced through orthogonal experiments, and transient electromagnetic simulation is utilized to analyze the trip mechanism’s dynamic performance; a BP neural network surrogate model was constructed to replace finite element simulation, and the NSGA-II algorithm was employed to perform weightless Pareto optimization, with the volume of the permanent magnet and the amount of copper used in the coil as the cost optimization objectives. Under the constraint of a trip time ≤ 15 ms, the permanent magnet dimensions were reduced from 8 × 44 × 5 mm to 6.5 × 44 × 5 mm (a 18.7% reduction in volume), and the coil wire diameter was optimized from 0.18 mm (2000 turns) to 0.14 mm (1400 turns) (a 22.3% reduction in copper usage). Test results show that the optimized trip time was reduced from 16.0 ms to 14.1 ms, with the prototype measuring 14.56 ms in actual testing; the discrepancy between the simulation and experiment was less than 5%. This method provides a reference for the design of a permanent magnet trip device and holds significant engineering value. Full article