Search Results (5,432)

The emergence of Artificial Neural Networks (ANNs) and their deep learning form called Artificial Intelligence (AI) opened a new path to improve energy efficiency and the indoor environment. A small collaborating network team is now extending the passive house approach, in a book entitled Retrofitting, the Energy and Environment of Buildings (Gruyter Publishers), and presenting generalized AI modeling in the following paper. This concept uses a long-term neural network with a short-term memory (LSTM) and three stages (training, validation, and test) for optimalization to hourly data collected for one full year. The non-residential buildings are less affected by the space occupants. This paper examines the feasibility of a uniform, climate modified technology, as our objective is to create a universal and affordable approach to buildings assisting in slowing the rate of climate change. Hence, the idea of creating a generalized neural network for predicting electricity consumption linked with weather conditions was born. This network is to forecast the electricity consumption for buildings linked to the local weather conditions, but different categories of buildings are put together in one set. While this will lower the large set precision, still our question is if such a network would work. If so, in the future we will create multi-variant, local residential systems with the capability of predicting energy use. Full article

The frequent occurrence of extreme climate events poses a severe challenge to the reliability of building energy systems. Hydrogen energy, with its long-term storage capacity, has become a key technology carrier for enhancing building resilience. This study constructs a resilience–environment–economy co-optimization framework that couples dynamic simulation and emergy analysis. Through a five-in-one approach of physical modeling, climate scenario generation, resilience quantification, emergy accounting, and multi-objective optimization, the resilience performance of building hydrogen energy systems under the scenario of extreme heat waves combined with grid failure is evaluated. The results show that the thermal time constant deviation of the electrolyzer is 4.06%, the correlation coefficient between the generated heat wave scenario sequence and the historical measured data is 0.94, the prediction deviation of the once-in-a-century extreme temperature is 0.5%, the environmental load rate is 4.33, the Pareto front contains 127 non-dominated solutions, and the comprehensive performance of the co-optimal solution is improved by 42% to 88%. Engineering suggestions: For public buildings in hot summer and cold winter regions, the hydrogen energy system should adopt a configuration of 50–60 kW electrolyzers and 50–70 kg hydrogen storage tanks, with a key load guarantee rate of no less than 95%, and the ecological cost is 35% lower than that of diesel backup. This study provides a quantitative decision-making tool for the resilience planning of building hydrogen energy systems under extreme climate conditions and can be extended to other high climate risk areas. Full article

Dental implant placement has evolved from conventional freehand techniques toward digitally guided workflows integrating cone-beam computed tomography (CBCT), computer-aided design/computer-aided manufacturing (CAD/CAM), and dynamic navigation systems. Although guided surgery improves positional accuracy, its clinical relevance compared with freehand placement remains debated. This narrative review, based on a systematic and structured literature search following predefined selection criteria, analyzes studies published between 2000 and 2025 comparing guided and freehand implant placement regarding accuracy, survival, complications, biological outcomes, and workflow efficiency. Searches of PubMed/MEDLINE, Embase, and Web of Science identified 40 eligible human clinical studies for qualitative synthesis. Guided placement consistently demonstrated greater positional accuracy, with angular deviations of approximately 2–4° versus 5–9° for freehand placement and linear deviations reduced by about 1 mm. Nevertheless, implant survival rates were high and comparable for both techniques, generally exceeding 95% across short- and medium-term follow-up. Overall complication rates were low; guided approaches reduced anatomical risk and improved prosthetic predictability in complex or multi-implant cases, while freehand placement allowed greater intraoperative flexibility and tactile feedback, potentially optimizing primary stability in variable bone conditions. Marginal bone loss and peri-implant tissue outcomes were similar between approaches. Guided workflows required additional planning time and costs but enhanced reproducibility in complex rehabilitations. Guided and freehand implant placement should therefore be considered complementary strategies, with optimal outcomes depending on case selection, surgical expertise, and the balanced integration of digital technologies into contemporary implant practice. Full article

Background/Objectives: Shenfu Decoction (SFD) is a traditional Chinese herbal formula composed of Panax ginseng and Aconitum carmichaelii that can revive and counteract shock. However, how SFD can mitigate hypothermia caused by seawater immersion is poorly understood. Methods: Three commonly used ratios of SFD (Panax ginseng:Aconitum carmichaelii = 1:1, 1:2, 2:1) were prepared, and their chemical properties were analyzed with UPLC-Q-TOF-MS. A rat model of hypothermia caused by seawater immersion at 15 °C was utilized. Survival analysis was used to evaluate the prophylactic effect of single intragastric administration of SFD with different ratios and doses on the survival time of rats, and to identify the optimal intervention conditions. Network pharmacology analysis based on the absorbed constituents of SFD was performed to preliminarily predict the underlying mechanisms, which were subsequently validated using RT-PCR, Western blotting, ELISA, and H&E staining. Results: SFD contained 54 compounds, including ginsenosides and aconitine alkaloids, whose relative concentrations varied across different ratios of SFD. Animal studies showed that pretreatment of SFD (1:1) administered at a dose of 1.35 g/kg was very effective in increasing rats’ survival time in hypothermia and slowed down core body temperature decline. Based on the 28 plasma-absorbed compounds of SFD, network pharmacology identified 503 targets, enriched in cAMP and MAPK signaling pathways. SFD (1:1, 1.35 g/kg) resulted in larger lipid droplets in brown adipose tissue (BAT) and enhanced the respiratory metabolic rate in seawater-immersion-induced hypothermia rats. Furthermore, its thermogenic effect is likely associated with the upregulation of uncoupling protein 1 (UCP1) via activating p38 MAPK/PGC1α/PPARγ and NE-(β3-AR)-cAMP-PKA pathways. Conclusions: The results of this study demonstrate that a single prophylactic administration of the traditional Chinese medicine formula SFD prior to cold seawater exposure significantly prolongs the survival time of rats. This effect is associated with the upregulation of UCP1 and the subsequent enhancement of thermogenesis in BAT. These findings highlight the great potential of SFD as a promising intervention for the management of hypothermia. Full article

The rapid advancement of photovoltaic (PV) technologies has transformed solar energy systems into intelligent, high-efficiency platforms. This review systematically examines next-generation PV materials, hybrid system architectures, and intelligent control strategies. Key technologies include perovskite-based tandem cells, N-type TOPCon, bifacial, heterojunction (HJT), and photovoltaic-thermal (PVT) systems. These innovations overcome the intrinsic limitations of conventional P-type silicon panels by reducing recombination losses, mitigating light- and temperature-induced degradation, and enhancing energy yield under real-world operating conditions. At the system level, AI-enabled inverters, adaptive maximum power point tracking (MPPT), predictive maintenance, and real-time grid interaction enable dynamic optimization under variable irradiance, thermal stress, and load fluctuations. A critical comparison across diverse deployment environments highlights current challenges, including manufacturing complexity, material stability, and AI data-quality limitations. Despite higher upfront costs and system complexity, these advanced PV systems offer superior long-term performance, improved reliability, and reduced levelized cost of electricity through lower degradation rates and enhanced operational resilience. Collectively, intelligent, material-optimized PV technologies represent a scalable, sustainable, and grid-compatible solution for solar energy deployment across diverse climates, supporting the global transition toward low-carbon energy infrastructures. Full article

Composite laminates experience static and fatigue delamination, presenting significant challenges for failure prediction. This is critical in curved composites, where delamination behavior is complex to predict. In this study, fatigue tests were conducted on curved composite laminates under non-constant mixed-mode conditions. The testing setup involved a four-point bending test using L-shaped, unidirectional carbon-fiber-reinforced polymer curved beam specimens. A Teflon insert placed at the bend was used to initiate delamination. Experimental data acquisition included digital image correlation (DIC) to monitor delamination length during testing. This is important since it enhances subsequent model correlation. A virtual crack closure technique (VCCT)-based method for simulating fatigue-driven delamination under variable mixed-mode conditions was validated against experiments. Delamination growth was modeled using a Paris-like power–law relationship based on the strain energy release rate. The approach was implemented in Abaqus as a user subroutine, incorporating load ratio and mode mixity effects through VCCT-based mode separation. This study demonstrates accurate fatigue delamination prediction and highlights the role of optical measurements in experiments. The model improves our understanding of delamination propagation under varying mode mixity and contributes to structural integrity analysis. The results show how mode mixity influences delamination, impacting the performance and lifecycle of composite structures. Full article

Accurate prediction of invasive species diffusion is essential for effective management and ecological conservation. Existing spatio-temporal Cox process models face limitations due to the separability assumption, which fails to capture spatio-temporal coupling dynamics inherent in biological diffusion processes. This study proposes a Spatio-Temporal Interaction Kernel Cox (STIK-Cox) model that constructs a non-separable conditional intensity function integrating baseline intensity, spatial and temporal proximity kernels, seasonal fluctuation, and a spatio-temporal interaction term. The model employs maximum likelihood estimation with Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Bounds (L-BFGS-B) optimisation and incorporates SHapley Additive exPlanations (SHAP) for interpretability analysis. Using the Vespa mandarinia (Hymenoptera, Vespidae) monitoring dataset from Washington State, the model achieves a comprehensive accuracy score of 0.957, a capture rate of 98.74% at a 0.5° threshold, and a mean prediction error of 0.0802°. K-function analysis confirms effective capture of spatial clustering patterns, while SHAP analysis reveals longitude as the primary predictive driver. The non-separable design outperforms conventional methods including inverse distance weighting and Poisson point processes. This framework demonstrates the potential of non-separable spatio-temporal point processes for invasive species early warning, providing a scientific basis for targeted monitoring and resource allocation in ecological management. Full article

Concrete crack segmentation in bridge health monitoring is crucial for ensuring the safety and longevity of infrastructure. However, this task is complicated by challenges such as weak contrast, background interference, and multi-scale crack structures, which hinder traditional methods’ accuracy. This study introduces a novel Boundary-Sensitive Hybrid Attention Network (BSA-Net) designed to address these issues by combining a hierarchical Transformer encoder (Hiera-A), a multi-scale context module (Light-ASPP), and a boundary-aware decoder (BAD). The hierarchical encoder effectively captures multi-scale features, while Light-ASPP enhances the network’s ability to aggregate contextual information with minimal computational cost, making it suitable for large-scale applications. The dual-branch decoder explicitly decouples the learning of semantic segmentation and boundary prediction, ensuring more accurate boundary detection and crack continuity. The extensive experiments on multiple benchmark datasets demonstrate that BSA-Net consistently outperforms existing crack detection models, particularly in complex, noisy environments. The model achieves competitive performance in terms of segmentation accuracy, boundary clarity, and recall rates, particularly for fine-scale and weak contrast cracks. The results indicate that BSA-Net not only enhances the performance of crack segmentation in real-world conditions but also provides a scalable and reliable solution for automated infrastructure monitoring and defect detection. Full article

Ultraviolet (UV) disinfection is widely used in water treatment; however, its effectiveness strongly depends on water optical quality (e.g., turbidity, total dissolved solids, and UV transmittance, UVT). This study investigates an integrated RO–UV scheme in which reverse osmosis (RO) pretreatment improves UVT and thereby increases the effective UV dose available for microbial inactivation. First, UV-only reactor performance is characterized using literature data to fit an intensity-specific dose response relationship. The RO contribution is then incorporated at the process level through a UVT based coupling and evaluated using deterministic low/central/high scenarios (p05/p50/p95) constructed from assumed input ranges. Finally, a multi-objective optimization solved with the Grey Wolf Optimizer (GWO) is used to identify operating conditions that maximize predicted bacterial log-inactivation while limiting a UV-equivalent energy proxy based on nominal UV dose. Across the investigated flow-rate and intensity ranges, RO pretreatment yields a systematic increase in effective dose (median gain $\approx 6.8\%$) and a corresponding improvement in predicted inactivation, with the marginal benefit depending on the dose response regime. Full article

Gliomas represent the most prevalent type of brain tumor, with their most aggressive variant, glioblastoma multiforme, associated with high mortality rates. Due to their elevated molecular heterogeneity, accurate classification of gliomas has presented significant challenges. Therefore, considerable effort has been dedicated to identifying relevant biomarkers that improve early diagnosis and unveil new areas for treatment. Advances in high-throughput sequencing technology have enabled public resources such as The Cancer Genome Atlas (TCGA) to provide large-scale data from various cancers, allowing researchers to perform more comprehensive analysis of this disease. In this study, we introduce MOHVAE-B, a comprehensive framework designed for the integration of multi-omics data and biomarker discovery using data from TCGA. MOHVAE-B employs a supervised hierarchical variational autoencoder integrated with SHAP-based interpretability to effectively integrate high-dimensional multi-omics data and extract the most influential features driving the model’s predictions. Subsequently, Bayesian Networks (BNs) are constructed to model conditional dependencies between the selected features, providing insights into their possible relations. Applied to the TCGA glioma cohorts, MOHVAE-B achieved a near-perfect AUC of $0.9993$ and successfully identified high-impact features related to glioma classification. For glioblastoma multiforme, this included six novel candidates: LINC02172, NACA2, LINC01114, HNRNPA1P48, PPIAL4G, and LINC01558. For low-grade gliomas, the model highlighted AMER2 as a promising marker. Across both cohorts, PMP2 stood out as a particularly strong candidate for a potential role in glioma pathogenesis. The constructed BNs provided an additional layer of validation, reinforcing NACA2 as a candidate of interest in glioma biology. Full article

Modern UAV swarm operations face strict onboard bandwidth and autonomy constraints, making simultaneous multi-target interception under limited communication a critical unsolved challenge. This paper addresses three-dimensional cooperative interception of maneuvering targets by multiple unmanned aerial vehicles (UAVs) at prescribed line-of-sight (LOS) angles under limited communication resources. In the LOS direction, a fixed-time consensus-based guidance law is designed with remaining flight time as the coordination variable, synchronizing each UAV’s impact time to a freely specified desired value with bounded gains throughout the engagement. Unlike most existing fixed-time cooperative guidance works, the consensus convergence time is rigorously proven to be strictly less than the maximum initial predicted flight time, guaranteeing impact-time agreement is reached before any UAV intercepts the target—a necessary condition for genuine simultaneous salvo attack. A dynamic event-triggered (DET) mechanism is incorporated to reduce inter-UAV communication frequency by adaptively updating the triggering threshold according to consensus state evolution. In the LOS normal directions, a piecewise nonsingular terminal sliding-mode law ensures fixed-time convergence of the LOS angle and its rate to desired values under impact-angle constraints. Fixed-time stability and Zeno-behavior exclusion are rigorously established via Lyapunov analysis. Comparative simulations against existing methods demonstrate clear advantages in impact-time accuracy, guidance smoothness, and communication efficiency. Full article

Physics-based electrochemical models such as the Doyle–Fuller–Newman (DFN) framework provide high predictive accuracy for lithium-ion batteries but are computationally intensive, limiting their applicability in large-scale and real-time simulations. Reduced-order models such as the Newman–Tiedemann–Gu–Kim (NTGK) model offer improved computational efficiency but typically require experimentally fitted parameters, restricting their scalability across chemistries and operating conditions. This work proposes a physics-informed parameter transfer framework in which NTGK model parameters are derived directly from experimentally validated DFN simulation outputs using a regression-based formulation, thereby reducing dependence on direct experimental parameterization. The approach is applied to LCO–graphite and NMC–graphite cells across multiple discharge rates. The DFN model shows good agreement with experimental data at low to moderate C-rates, with mean absolute errors (MAE) in the range of 20–35 mV at 0.5C. The NTGK model parameterized using DFN-generated synthetic data accurately reproduces the DFN voltage response, with model reduction MAE values as low as 4.5 mV for LCO and 7.17 mV for NMC cells under low-rate operating conditions. Validation against experimental data yields MAE values up to 74 mV for LCO cells and 98 mV for NMC cells at higher C-rates. The proposed framework establishes a direct and physically consistent mapping between high-fidelity electrochemical models and reduced-order representations, enabling scalable and computationally efficient battery simulations while minimizing reliance on extensive experimental parameterization. This approach provides a practical pathway for integrating electrochemical fidelity into system-level and multi-physics battery simulations. Full article

Hydraulic fracture network initiation and propagation in tight sandstone are strongly controlled by mineral heterogeneity and fluid–solid interaction. However, existing numerical models still have limited capability in simultaneously representing multi-mineral distributions and dynamically coupled fracture-fluid processes. In this study, a two-dimensional polygonal discrete element fluid–solid coupled model was established based on mineralogical images of tight sandstone. Compared with conventional continuum-based approaches, the proposed model is better suited to describing fracture initiation, branching, and network evolution in multi-mineral granular media. Under dimensionless operating conditions calibrated against field data, coupled and uncoupled formulations were systematically compared to evaluate the role of hydro-mechanical interaction during hydraulic fracturing. The coupled simulations generated consistently more fractures than the uncoupled simulations over the investigated injection-rate range, with an average increase of 28.7% and a maximum increase of 67.2%. Compared with the uncoupled model, the coupled model also predicted higher breakdown pressures and stronger fracture-tip pressure concentrations, and the breakdown pressure increased with injection rate. Under low injection rates, the coupled formulation reproduced pressure-buildup-driven fracture-tip advance, whereas the uncoupled formulation failed to sustain fracture propagation. Under higher injection rates, the coupled formulation produced multilayered and highly branched fracture networks, while the uncoupled formulation mainly generated simple first-order branching. These results demonstrate that hydro-mechanical coupling is a controlling mechanism for fluid-energy dissipation, fracture-tip pressure evolution, and complex fracture network formation in tight sandstone. This study provides an image-based polygonal DEM framework for evaluating hydro-mechanical fracture network evolution in multi-mineral tight sandstone. Full article

Cemented backfill material is an important technical means for improving the safety, efficiency, and environmental sustainability of underground mining. In tailings-free mining conditions, however, suitable aggregates for cemented backfill are often limited, making it necessary to identify alternative aggregates and optimize their mix proportions. To address this issue, clay-bearing crushed stone was selected as the primary aggregate for a tailings-free bauxite mine, and its effects on the mechanical properties, slurry stability, and rheological properties of cemented backfill material were systematically investigated. Crushed stone ratio, mass concentration, and fly ash ratio were used as experimental factors, and 24 experimental mixes were designed to determine the 3-day compressive strength, bleeding rate, and yield stress. Based on the experimental results, response surface regression models were established, and multi-objective optimization was performed using cost analysis, NSGA-II, and entropy-weighted TOPSIS. The results showed that the system containing clay-bearing crushed stone exhibited better stability than the clay-free crushed stone system. The response surface models for 3-day compressive strength, bleeding rate, and yield stress were all significant, with p-values below 0.0001 and R² values of 0.9658, 0.9306, and 0.8704, respectively. Comprehensive optimization gave the optimal mix proportions as a crushed stone ratio of 6.9721, a mass concentration of 0.82, and a fly ash ratio of 1, corresponding to a predicted 3-day compressive strength of 0.9629 MPa, a bleeding rate of 3.73%, and a cost of 68.225 RMB/t. For engineering application, the recommended mix proportions were adjusted to X₁ = 7, X₂ = 0.82, and X₃ = 1. Parallel tests gave a 3-day compressive strength of 0.99 MPa and a bleeding rate of 3.52%, both within the 95% prediction interval. These results demonstrate that clay-bearing crushed stone can serve as a feasible alternative aggregate for cemented backfill material under tailings-free conditions and that the proposed method combining response surface modeling with multi-objective optimization can effectively balance early strength, slurry stability, and material cost. Full article

Petroleum refinery wastewaters are highly recalcitrant and recognized as one of the most challenging industrial effluents requiring advanced treatment strategies. This study aims to investigate the synergistic performance of a sequential electrocoagulation (EC) and electro-Fenton (EF) process for the mineralization of this complex effluent. The EC pretreatment was optimized using response surface methodology via Doehlert design, establishing optimal conditions at pH 6.0, 0.8 A, and a 75 min electrolysis time. Under these conditions, 39% of total organic carbon (TOC) and 56% of chemical oxygen demand (COD) were removed. The quadratic polynomial model developed for the EC stage presented an excellent fit with the experimental data (R² = 0.99, R²_adj = 0.97, p < 0.05), confirming its strong predictive robustness. In order to degrade the remaining recalcitrant organic pollutants, the pretreated effluent underwent EF oxidation (0.01 M ferrous ion, 0.8 A, pH 3), leading to TOC and COD removal rates of 68% and 76%, respectively, after a 360 min electrolysis time. The integrated EC-EF process achieved an overall mineralization of 81% and an oxidation efficiency of 89%. Finally, a comprehensive evaluation of the system’s energy consumption and economic viability established a solid techno-economic baseline for this sequential approach, indicating a competitive total operating cost of USD 0.036 per gram of TOC removed. Full article