Search Results (1,701)

Background and Objectives: Non-pharmaceutical interventions such as cognitive training, transcranial electrical stimulation (tES), and repetitive transcranial magnetic stimulation (rTMS) have shown promise in improving cognitive outcomes in Alzheimer’s disease (AD) and dementia. However, the long-term effects of repeated non-invasive interventions remain unknown. This study investigated whether repeated non-invasive interventions administered over a span of 1 to 3 years were associated with slower cognitive decline compared to typical AD progression, and whether longer no-treatment intervals between treatments predicted greater post-treatment decline. Materials and Methods: Seventy-three participants living with dementia or AD received 2 to 9 blocks of non-invasive treatments (including tES, rTMS, cognitive training). Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) scores were collected longitudinally up to 3 years (36 months), across multiple intervention and assessment sessions. A mixed-effects model was used to estimate the rate of cognitive decline, adjusting for baseline age, sex, and baseline cognition (MoCA) with participants being the random effect. The observed rate of change was compared to a meta-analysis estimate of AD progression. Additionally, a linear mixed-effects model using robust sandwich estimation of standard errors was employed to assess whether the no-treatment interval was associated with changes in ADAS-Cog scores. Results: Participants showed a significantly slower rate of cognitive decline than expected from the AD reference rate (p < 0.001), with many demonstrating stabilized ADAS-Cog scores during their respective treatment periods, ranging from 1 to 3 years. Medication analyses revealed no significant effect of AD medications, antidepressants, antihypertensives, or cholesterol-lowering agents on cognitive outcomes. Furthermore, longer no-treatment intervals were significantly associated with greater post-treatment decline (p < 0.001). Conclusions: Repeated non-invasive treatments seem to slow the rate of cognitive decline in individuals living with dementia when administered over a prolonged period. This study provides evidence supporting the feasibility and effects of personalized long-term non-invasive treatment strategies for dementia. Full article

This study presents a comprehensive construction monitoring program for a geometrically complex, large-span spatial tubular truss system within a typical center steel exhibition hall. To ensure construction quality and structural integrity throughout the entire process, the monitoring strategy was rigorously aligned with the actual construction sequence. Real-time vertical displacement measurements were acquired at critical structural members and joints. A detailed finite element model of the entire structure was developed to systematically analyze the structural behavior of herringbone columns, primary and secondary trusses, and temporary supports during both installation and removal phases. Displacement patterns at key locations were investigated, and a global stability assessment was performed. Results demonstrate close agreement between finite element predictions and field measurements, confirming the rationality and reliability of the construction scheme. The structural system exhibited excellent stability across all construction stages, satisfying both architectural aesthetics and structural safety requirements. This study provides practical insights for construction control of similar large-span steel structures. Full article

The Chesapeake Bay (CB) region faces significant risks from relative sea-level change (RSLC), driven by global mean sea-level rise (GMSLR), regional sea-level rise (RSLR), and local land subsidence (LS). This study introduces a methodology to decipher RSLC trends in the CB area by integrating these components. We develop trend equations spanning 1900–2100, incorporating acceleration for GMSLR and RSLR since 1992, with linear LS estimation using tide gauge, satellite altimetry, and InSAR data. Our approach employs dynamic RSLC equations, Maclaurin series expansions, and inverse simulations to project RSLC trends through 2100. Stable RSLC rates require over 122 years of data for reliable linear trend estimation, with the Baltimore tide gauge providing the necessary long-term dataset. Similarity in monthly mean sea-level variations within a coastal region enables a new method to identify LS from short-term tide gauge data by correlating it with corresponding long-term data at Baltimore. LS is categorized into bedrock-surface subsidence (BSS) and compaction subsidence (CS), with methods proposed to map BSS contours and estimate CS. CS is further classified into primary consolidation, secondary consolidation, construction-induced, and negative subsidence to determine specific compaction types. The projection model highlights the dominant influence of GMSLR acceleration since 1992, with local LS and RSLR influenced by ocean circulation, density changes, and gravitational, rotational, and deformational (GRD) effects. This integrated approach enhances understanding and predictive reliability for RSLC trends, supporting resilience planning and infrastructure adaptation in coastal CB communities. Full article

Permeability evolution is one of the key parameters influencing the efficient exploitation of deep unconventional energy resources, as it reflects the dynamic development of pore-fracture structures under complex engineering effects. Using fractal geometry to describe the pore-fracture system, rock permeability enhancement can be quantitatively evaluated. In this study, fractured coal specimens were analyzed under simulated mining-induced stress relief and CH₄ release conditions based on fractal geometry theory. The permeability-enhancement rate was derived and verified through CT (Computed Tomography) characterization of the pore-fracture network. The fractal dimension of the fracture aperture distribution and the tortuosity of fracture paths were determined to establish a fractal permeability-enhancement model, and its sensitivity was analyzed. The results indicate that permeability evolution undergoes four distinct stages: a stable stage, a slow-growth stage, a rapid-growth stage, and a stable or declining stage. The mining-induced stress relief and gas desorption effects significantly accelerate permeability enhancement, providing new insights into the mechanisms governing gas flow and pressure relief in deep coal seams. The proposed model, highly sensitive to the fracture aperture ratio (λ_min/λ_max), reveals that a smaller aperture span leads to greater permeability enhancement during the damage and fracture stage. These findings offer practical guidance for predicting permeability evolution, optimizing gas drainage design, and enhancing the safety and efficiency of coal mining and methane extraction operations. Full article

Docosahexaenoic acid (DHA), an omega-3 polyunsaturated fatty acid essential for human health, is primarily produced at scale using Schizochytrium sp. Mutagenesis-based strain improvement has increased DHA yields, but the genetic and metabolic mechanisms underlying high productivity remain poorly understood. Here, we conducted the comparative whole-genome sequencing and transcriptomic profiling of a high-DHA-yielding mutant strain (HS01) and its parental strain (GS00). The GS00 genome assembly spans 62.4 Mb and encodes 14,886 predicted genes. Functional annotation highlighted pathways involved in central metabolism, saturated fatty acid (SFA) synthesis, and polyunsaturated fatty acid (PUFA)/DHA biosynthesis. Comparative genomics identified 40 insertions/deletions and 396 single-nucleotide polymorphisms between HS01 and GS00, including mutations in the coding and regulatory regions of key metabolic genes. Transcriptomic analysis revealed extensive metabolic reprogramming in HS01, including the upregulation of glycolysis and tricarboxylic acid (TCA) cycle genes, along with a distinct fatty acid profile and the altered expression of fatty acid metabolism genes compared with GS00. Collectively, the integrated genomic and transcriptomic analyses not only pinpointed specific mutations potentially associated with the HS01 high-DHA phenotype but also revealed substantial transcriptional and metabolic remodeling, providing valuable insights into the mechanisms that drive enhanced DHA biosynthesis. Full article

The fast-increasing penetration of photovoltaic (PV) power raises the issue of grid stability due to its intermittency and lack of inertia in power systems. Solar irradiance forecasting effectively supports advanced control, mitigates power intermittency, and improves grid resilience. Irradiance forecasting based on data-driven methods aims to predict the direction and level of power variation and indicate quick action. This article presents a comprehensive review and comparative analysis of data-driven approaches for time-series solar irradiance forecasting. It systematically evaluates nineteen representative models spanning from traditional statistical methods to state-of-the-art deep learning architectures across multiple performance dimensions that are critical for practical deployment. The analysis aims to provide actionable insights for researchers and practitioners when selecting and implementing suitable forecasting solutions for diverse solar energy applications. Full article

Grid-scale lithium-ion storage must deliver fast, reliable thermal control during dynamic grid services, yet high-fidelity thermal models are too slow for real-time use and inefficient cooling inflates energy and safety costs. This study develops and validates a reduced-order thermal modeling framework for grid-scale lithium-ion battery energy storage, targeting real-time thermal management. The framework uses proper orthogonal decomposition to capture dominant thermal dynamics across frequency regulation, peak shaving, and fast charging. Across scenarios, it delivers 15.2–22.3× computational speedups versus a detailed model while maintaining RMS temperature errors of 7.8 °C (frequency regulation), 34.4 °C (peak shaving), and 23.3 °C (fast charging). Spatial analysis identifies inter-zone temperature gradients up to 1.0 °C under severe loading, motivating targeted cooling strategies. Cooling energy scales nonlinearly with load intensity, from 5.44 kWh in frequency regulation to over 300 kWh in peak shaving, with cooling efficiencies spanning 17.27% to 8.94%. The reduced-order model achieves sub-0.1 s computational solve time per control cycle, suggesting feasibility for real-time integration into industrial battery-management systems under the tested simulation settings. Collectively, the results show that reduced-order thermal models can balance accuracy and computational efficiency for several grid services in the simulated scenarios, while high-power operation benefits from scenario-specific calibration and controller tuning. Practically, the benchmarks and workflow support decisions on predictive cooling schedules, temperature limits, and service prioritization to minimize parasitic energy. Full article

The integration of artificial intelligence (AI) is rapidly transforming the field of breast reconstruction, with applications spanning surgical planning, complication prediction, patient-reported outcome assessment, esthetic evaluation, and patient education. A comprehensive narrative review was performed to evaluate the integration of AI technologies in breast reconstruction, encompassing preoperative planning, intraoperative use, and postoperative care. Emerging evidence highlights AI’s growing utility across these domains. Machine learning algorithms can predict postoperative complications and patient-reported outcomes by leveraging clinical, surgical, and patient-specific factors. Neural networks provide objective assessments of breast esthetics following reconstruction, while large language models enhance patient education by guiding consultation questions and reinforcing in-clinic discussions with accessible medical information. As these tools continue to advance, their adoption in everyday practice is becoming increasingly relevant. Staying current with AI applications is essential for plastic surgeons, as AI is not only reshaping breast reconstruction today, but is also poised to become an integral component of routine clinical care. Full article

Dynamic wind-induced vibrations of structures will cause cyclic stresses in structural elements, potentially leading to fatigue damage accumulation or structural failure. Existing research on wind-induced fatigue mainly focuses on tower and large-span steel structures, such as chimneys, signal towers, transmission towers, long-span bridges, and wind turbines. However, existing studies on wind-induced fatigue damage in tall steel buildings remain limited. To determine whether and under what conditions wind-induced fatigue damage needs to be considered in tall steel structures, this study investigates wind-induced fatigue failure through wind tunnel tests and numerical simulations. Specifically, six real tall steel buildings were examined to assess their fatigue life under dynamic wind loads. First, wind tunnel tests using synchronous pressure models were conducted to obtain wind load time histories of these six buildings. Subsequently, time histories of wind-induced displacements and component stresses were calculated. The wind-induced fatigue life of each building was evaluated using the rain-flow counting method and the Palmgren–Miner rule, revealing that the fatigue life generally exceeds 400 years. The results demonstrate that tall steel structures designed according to current standards perform well in resisting wind-induced fatigue damage. Furthermore, when the ratio of the wind-induced root mean square (RMS) stress to the ultimate strength of a structural element reaches 0.125–0.164, the fatigue life of components may fall below the design life, indicating the necessity of considering potential fatigue damage. The RMS stress ratio can be preliminarily compared with the RMS stress ratio threshold proposed in this study to determine whether wind-induced fatigue damage needs to be considered in tall steel buildings. Finally, a simplified fatigue life prediction formula is established to provide approximate estimates for the fatigue life of tall steel buildings. Full article

Geographic Named Entity Recognition (Geo-NER) is a crucial task for extracting geography-related entities from unstructured text, and it plays an essential role in geographic information extraction and spatial semantic understanding. Traditional approaches typically treat Geo-NER as a sequence labeling problem, where each token is assigned a single label. However, this formulation struggles to handle nested entities effectively. To overcome this limitation, we propose Geo-MRC, an improved model based on a Machine Reading Comprehension (MRC) framework that reformulates Geo-NER as a question-answering task. The model identifies entities by predicting their start positions, end positions, and lengths, enabling precise detection of overlapping and nested entities. Specifically, it constructs a unified input sequence by concatenating a type-specific question (e.g., “What are the location names in the text?”) with the context. This sequence is encoded using BERT, followed by feature extraction and fusion through Gated Recurrent Units (GRU) and multi-scale 1D convolutions, which improve the model’s sensitivity to both multi-level semantics and local contextual information. Finally, a feed-forward neural network (FFN) predicts whether each token corresponds to the start or end of an entity and estimates the span length, allowing for dynamic inference of entity boundaries. Experimental results on multiple public datasets demonstrate that Geo-MRC consistently outperforms strong baselines, with particularly significant gains on datasets containing nested entities. Full article

Background/Objectives: Bacterial blight (BB) represents one of the most devastating diseases threatening global rice production. Exploring and characterizing disease resistance (R) genes provides an effective strategy for controlling BB and enhancing rice resilience. Common wild rice (Oryza rufipogon) serves as a valuable reservoir of genetic diversity and disease resistance resources. In this study, we identified and functionally characterized a novel NLR gene, YPR1, from common wild rice (Oryza rufipogon), which exhibited significant spatial, temporal, and tissue-specific expression patterns. Methods: Using a combination of conventional PCR, RT-PCR, bioinformatics, transgenic analysis, and CRISPR/Cas9 gene-editing approaches, the full-length YPR1 sequence was successfully cloned. Results: The gene spans 4689 bp with a coding sequence (CDS) of 2979 bp, encoding a 992-amino acid protein. Protein domain prediction revealed that YPR1 is a typical CNL-type NLR protein, comprising RX-CC_like, NB-ARC, and LRR domains. The predicted molecular weight of the protein is 112.43 kDa, and the theoretical isoelectric point (pI) is 8.36. The absence of both signal peptide and transmembrane domains suggests that YPR1 functions intracellularly. Furthermore, the presence of multiple phosphorylation sites across diverse residues implies a potential role for post-translational regulation in its signal transduction function. Sequence alignment showed that YPR1 shared 94.02% similarity with Os09g34160 and up to 96.47% identity with its closest homolog in the NCBI database, confirming that YPR1 is a previously unreported gene. To verify its role in disease resistance, an overexpression vector (Ubi–YPR1) was constructed and introduced into the BB-susceptible rice cultivar JG30 via Agrobacterium tumefaciens-mediated transformation. T₁ transgenic lines were subsequently inoculated with 15 highly virulent Xanthomonas oryzae pv. oryzae (Xoo) strains. The transgenic plants exhibited strong resistance to eight strains (YM1, YM187, C1, C5, C6, T7147, PB, and HZhj19), demonstrating a broad-spectrum resistance pattern. Conversely, CRISPR/Cas9-mediated knockout of YPR1 in common wild rice resulted in increased susceptibility to most Xoo strains. Although the resistance of knockout lines to strains C7 and YM187 was comparable to that of the wild type (YP^WT), the majority of knockout plants exhibited more severe symptoms and significantly lower YPR1 expression levels compared with YP^WT. Conclusions: Collectively, these findings demonstrate that YPR1 plays a crucial role in bacterial blight resistance in common wild rice. As a novel CNL-type NLR gene conferring specific resistance to multiple Xoo strains, YPR1 provides a promising genetic resource for the molecular breeding of BB-resistant rice varieties. Full article

The structural safety of multi-span ultra-high-voltage (UHV) substation gantries is a cornerstone for the reliability and resilience of sustainable energy grids. The wind-resistant design of the structures is complicated by their complex modal behaviors and highly non-uniform wind load distributions. This study proposes a novel hybrid framework that integrates segmented high frequency force balance (HFFB) testing with a multi-modal stochastic vibration analysis, enabling the precise assessment of wind load distribution and dynamic response. Five representative segment models are tested to quantify both mean and dynamic wind loads, a strategy rigorously validated against whole-model HFFB tests. Key findings reveal significant aerodynamic disparities among structural segments. The long-span beam, Segment 5, exhibits markedly higher and direction-dependent responses. Its mean base shear coefficient reaches 4.34 at β = 75°, which is more than twice the values of 1.74 to 2.27 for typical tower segments. Furthermore, its RMS wind force coefficient peaks at 0.65 at β = 60°, a value 2.5 to 4 times higher than those of the tower segments, all of which remained below 0.26. Furthermore, a computational model incorporating structural modes, spatial coherence, and cross-modal contributions is developed to predict wind-induced responses, validated through aeroelastic model tests. The proposed framework accurately resolves spatial wind load distribution and dynamic wind-induced response, providing a reliable and efficient tool for the wind-resistant design of multi-span UHV lattice gantries. Full article

Rett syndrome (RTT) presents with a wide range of symptoms spanning various clinical areas. Capturing symptom change as the disorder progresses is challenging. Wearable sensors offer a non-invasive and objective means of monitoring disease states in neurodevelopmental disorders. The goal of this study was to conduct a systematic literature review to critically appraise the literature on the use of wearable sensors in individuals with RTT. The PRISMA criteria were used to search four databases without time restriction and identified 226 records. After removing duplicates, the titles and abstracts of 184 records were screened, 147 were excluded, and 37 were assessed for eligibility. Ten (10) articles remained, and a further two were included after additional searching. In total, 12 articles were included in the final analysis. The sample size ranged from 7 to 47 subjects with an age range of 1 to 41 years. Different wearable biosensor devices were used across studies, with the Empatica E4 wearable device being most frequently used in 33% (4/12) of the studies. All the studies demonstrated a high methodological quality with a low risk of bias. Evidence from wearable sensors, combined with machine learning methods, enabled the prediction of different sleep patterns and clinical severity in RTT. Given the small sample size and the limitations of available data for training machine learning models, we highlight areas for consideration. The review emphasises the need to enhance research on the application of wearable sensors in epilepsy and gastrointestinal manifestations/morbidity in RTT. Increased electrodermal activity (EDA), % of maximum heart rate (HRmax%) and the heart rate to low-frequency power (HR/LF) ratio were identified as physiological measures potentially associated with disease states. Based on the evidence synthesis, the role of physiological parameters and their association with symptom management in RTT is discussed. Full article

Power outages during extreme heat events threaten occupant safety by exposing buildings to rapid indoor overheating. However, current building thermal resilience assessments rely mainly on physics-based simulations or IoT sensor data, which are computationally expensive and slow to scale. This study develops an Artificial Intelligence (AI)-driven workflow that integrates Building Information Modeling (BIM)-based residential models, automated EnergyPlus simulations, and supervised Machine Learning (ML) algorithms to predict indoor thermal trajectories and calculate thermal resilience against power failure events in hot seasons. Four representative U.S. residential building typologies were simulated across fourteen ASHRAE climate zones to generate 16,856 scenarios over 45.8 h of runtime. The resulting dataset spans diverse climates and envelopes and enables systematic AI training for energy performance and resilience assessment. It included both time-series of indoor thermal conditions and static thermal resilience metrics such as Passive Survivability Index (PSI) and Weighted Unmet Thermal Performance (WUMTP). Trained on this dataset, ensemble boosting models, notably XGBoost, achieved near-perfect accuracy with an average R² of 0.9994 and nMAE of 1.10% across time-series (indoor temperature, humidity, and cooling energy) recorded every 3 min for a 5-day simulation period with 72 h of outage. It also showed strong performance for predicting static resilience metrics, including WUMTP (R² = 0.9521) and PSI (R² = 0.9375), and required only 1148 s for training. Feature importance analysis revealed that windows contribute 74.3% of the envelope-related influence on passive thermal response. This study demonstrates that the novelty lies not in the algorithm itself, but in applying the model to resilience context of power outages, to reduce computations from days to seconds. The proposed workflow serves as a scalable and accurate tool not only to support resilience planning, but also to guide retrofit prioritization and inform building codes. Full article

Ectropis grisescens Warren (Lepidoptera: Geometridae) is a destructive pest that has severely impacted major tea-growing regions in recent years; as such, it is vital to determine how climate change influences its areas of distribution. In this study, we employed a parameter-optimized maximum entropy (MaxEnt) model, integrating 170 E. grisescens occurrence records and seven selected environmental variables, to predict the pest’s current and future potential distribution in China. Parameter optimization was conducted with the ENMeval package in R, identifying the optimal feature combination as “linear—L, quadratic—Q” and the regularization multiplier as 0.5. These results indicated that the mean diurnal range (bio2), precipitation of driest month (bio14), and elevation were the key variables contributing to the suitable area for E. grisescens. Currently, the total potential suitable area for E. grisescens in China spans approximately 1.969 × 10⁶ km², covering 20.51% of the country’s land area, of which 5.121 × 10⁵ km², 7.385 × 10⁵ km², and 7.185 × 10⁵ km² possess low, medium, and high suitability, respectively. Notably, the high-suitability regions are predominantly concentrated in southeastern China, encompassing the provinces and municipalities of Zhejiang, Anhui, Hunan, Jiangsu, Chongqing, Jiangxi, Guangxi, Hubei, and Sichuan. Under future climate scenarios, it is projected that the suitable habitats for this pest will undergo varying degrees of change. Specifically, under the SSP1-2.6 scenario, the suitable habitat area is estimated to increase by up to 12.21% by the 2070s. Under the SSP2-4.5 scenario, the centroid of the suitable habitat will be displaced northwest by up to 238.4 km by the 2030s. Our findings provide valuable insights into the future management of E. grisescens and will aid in mitigating its ecological and economic impacts. Full article