Search Results (911)

Earthquakes are unpreventable natural disasters that result in many casualties and economic losses in the regions where they occur. Earthquake prediction and seismic risk assessments are essential in minimising these losses. Due to the complex nature of seismic events, it is necessary to use a cutting-edge methodology to predict earthquake occurrence effectively. Machine learning methods have been among the most efficient and current methods for solving complex nonlinear problems and analysing big datasets. Because of this feature, they are widely used for predicting earthquakes and earthquake parameters. This study focuses on applying machine learning methods to analyse seismic events in Western Turkey from 1975 to 2024. The aim is to compare the effectiveness of five machine learning approaches for predicting earthquake magnitudes: Long Short-Term Memory (LSTM), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Decision Tree (DT), Random Forest (RF), and Convolutional Neural Network (CNN). The outcomes of these applied methods are encouraging in terms of the prediction of magnitude. Among all the results, the LSTM method is slightly more successful than the other methods, with a Root Mean Square Error (RMSE) of 0.1391, Mean Square Error (MSE) of 0.0193, Mean Absolute Error (MAE) of 0.1046 and Mean Absolute Percentage Error (MAPE) of 3.0631%, respectively. Full article

Background/Objectives: Malnutrition, systemic inflammation, and immune dysfunction are key determinants of adverse outcomes in older adults following acute illness. Composite biomarkers integrating these domains could enhance early risk stratification. This study investigates, for the first time in acute geriatric care, the prognostic value of the C-reactive protein–albumin–lymphocyte (CALLY) index—a composite marker of nutritional, inflammatory, and immune status—in predicting short-term survival. Methods: We retrospectively analyzed 264 patients admitted to the acute geriatrics ward of Santa Maria della Misericordia Hospital in Perugia. The CALLY index was calculated as: (Albumin × Lymphocytes)/(CRP × 10⁴). The optimal prognostic cut-off was determined using receiver operating characteristic (ROC) curve analysis. Three-month survival was assessed by Kaplan–Meier analysis. Results: The cohort included 167 women (63.3%) and 97 men (36.7%), with a mean age of 88.0 ± 6.4 years. At 3-month follow-up, 80 patients (30.3%) had died. The CALLY index showed an area under the ROC curve of 0.647 (95% CI: 0.576–0.718; p < 0.001), with a cut-off of 0.055 (sensitivity: 68.5%, specificity: 46.3%). Among deceased patients, 42.5% had a CALLY index <0.055. After multivariable adjustment, a lower CALLY index remained independently associated with increased mortality (B = −0.805; OR = 0.45; 95% CI: 0.215–0.930; p = 0.031). Kaplan–Meier analysis demonstrated significantly higher survival in patients with a CALLY index ≥ 0.055 (Log-rank test: 13.71; p < 0.001). Conclusions: The CALLY index shows a modest but statistically significant discriminative ability for predicting short-term mortality in acutely ill older adults. As a simple, low-cost marker derived from routine laboratory tests, it holds potential for integration into clinical workflows to guide nutritional, metabolic, and prognostic management strategies in geriatric acute care. Full article

Canada goldenrod (Solidago canadensis L.), a short-day plant commonly cultivated as a cut flower, depends on proper lighting management to obtain long stems and higher commercial value. Thus, this study aimed to determine the effect of modifying the light spectrum through the installation of light-emitting diodes (LEDs) and the use of colored shade nets on the production and quality of Canada goldenrod stems. The treatments used were colored shade nets and different LED lighting treatments. Production per plant and productivity per square meter were determined. Twenty stems were selected and evaluated for: stem length; inflorescence length and width; number of floral ramets per inflorescence; number of leaves; stem base diameter (mm); and fresh stem biomass (g). Canada goldenrod plants require an extension of the light period with artificial lighting to produce higher-quality stems, regardless of whether the bulbs emit red or white light. The use of nets with 50% red and white shading promoted higher production and elongation of Canada goldenrod stems, with a production that reached up to 4.2 floral stems per plant and 100.3 floral stems per square meter using the red shade net and white LED. These floral stems were of high commercial standard, with a length of up to 81.35 cm with the red shade net and red LED, and were 31 cm in diameter for the inflorescences, approximately, under black or white shade nets and white or red LEDs. More robust floral stems with greater biomass were observed using any shade net color and LED lamps. Full article

Jujube (Ziziphus jujuba Mill.) is a nutritionally rich and economically significant fruit, highly valuable for its flavor, bioactive compounds, and therapeutic properties. However, it is highly perishable and has a short postharvest lifespan. This review aims to provide knowledge for preserving quality and improving postharvest storage by integrative strategies aimed at extending the shelf life of jujube. The literature was collected from recent peer-reviewed studies on postharvest physiology and handling technologies of jujube fruit. Key physiological factors, influencing postharvest deterioration such as water loss, softening, browning, and decay, are discussed, along with the underlying biochemical and enzymatic mechanisms driving quality decline. Conventional strategies such as cold storage, MAP, and CA effectively slow respiration, delay reddening, and extend storage up to 2–4 months, while emerging approaches such as ozone and cold plasma treatments reduce microbial decay and maintain antioxidant activity. Edible coatings like chitosan, aloe vera, and composites cut weight loss by 20–40%, and chemical regulators such as 1-MCP and calcium dips further delay ripening, preserve firmness, and enhance postharvest quality. Emphasis is placed on integrating innovative technologies with physiological insights to optimize storage conditions, control microbial contamination, and maintain nutritional integrity. The significance of this review lies in integrating physiological insights with innovative preservation methods, offering practical guidance for researchers, growers, and industry stakeholders to achieve sustainable, safe, and market-oriented solutions for jujube storage. Full article

Three-dimensional bulk fin-type field-effect transistors (FinFETs) have been the dominant devices since the sub-22 nm technology node. Electrical characteristics of scaled devices suffer from different process variation effects. Owing to the trapping and de-trapping of charge carriers, random interface traps (RITs) degrade device characteristics, and, to study this effect, this work investigates the impact of RITs on the DC/AC/RF characteristic fluctuations of FinFETs. Under high gate bias, the device screening effect suppresses large fluctuations induced by RITs. In relation to different densities of interface traps (D_it), fluctuations of short-channel effects, including potential barriers and current densities, are analyzed. Bulk FinFETs exhibit entirely different variability, despite having the same number of RITs. Potential barriers are significantly altered when devices with RITs are located near the source end. An analysis and a discussion of RIT-fluctuated gate capacitances, transconductances, cut-off, and 3-dB frequencies are provided. Under high D_it conditions, we observe ~146% variation in off-state current, ~26% in threshold voltage, and large fluctuations of ~107% and ~131% in gain and cut-off frequency, respectively. The effects of the random position of RITs on both AC and RF characteristic fluctuations are also discussed and designed in three different scenarios. Across all densities of interface traps, the device with RITs near the drain end exhibits relatively minimal fluctuations in gate capacitance, voltage gain, cut-off, and 3-dB frequencies. Full article

Climate change is transforming energy use at the residential level by increasing temperature fluctuations and sustaining extreme weather events. This study proposes a climate-reactive, multi-objective approach to integrate the demand response (DR) with distributed generation (DG) and power quality improvement under a multi-objective framework of an integrated climate-adaptive approach to residential energy management. A cognitive neural network combination model with bidirectional long short-term memory networks (bidirectional) and a self-attention mechanism was used to successfully predict temperature-sensitive loads. The hybrid deep learning solution, which applies convolutional and bidirectional long short-term memory (LSTM) networks with attention, predicted the temperature-dependent load profiles optimized with an enhanced modified grey wolf optimizer (MGWO). The results of the experimental studies indicated significant gains in performance: in energy expenditure, the studies reduced it by 32.7%; in peak demand, they were able to reduce it by 45.2%; and in self-generated renewable energy, the results were 28.9% higher. The solution reliability rate provided by the MGWO was 94.5%, and it converged more quickly, thus providing better diversity in the Pareto-optimal frontier than that of traditional metaheuristic algorithms. Sensitivity tests with climate conditions of +2 °C and +4 °C showed strategy changes as high as 18.3%, thus establishing the flexibility of the system. Empirical evidence indicates that the energy and peak demand are to be cut, renewable integration is enhanced, and performance is strong in fluctuating climate conditions, highlighting the adaptability of the system to future resilient smart homes. Full article

In this study, cement, short-cut carbon fibers, and polymer water-absorbing resin were used as the main materials, with high-performance water-reducing polycarboxylic acid agent as the modified material. A new conductive cement-based grouting material was developed by incorporating functional additives. Its mix design was optimized based on initial setting time, fluidity, bleeding rate, and compressive strength. The optimal ratio of the grouting material was determined as follows: 0.4 wt% of high water-absorbent resin, 0.25 wt% of high-efficiency water reducer, 0.8 wt% of short-cut carbon fibers, and a water–cement ratio of 0.8:1. The electrical conductivity of the grouting material was studied in depth under different dosages of short-cut carbon fibers, considering factors such as curing age, temperature, and pressure conditions. The results show that with the increase in curing age, the volume resistivity of the specimen gradually increases; the resistivity of the conductive cementitious grouting material decreases with the rise in temperature, showing a negative temperature coefficient effect; additionally, the doping of an appropriate amount of short-cut carbon fibers enables the conductive cementitious grouting specimen to exhibit good pressure-sensitive properties. Field test verification indicates that the new cementitious conductive grouting material has excellent conductive properties, and the grouting quality can be effectively evaluated via high-density electrical testing. Full article

To overcome the issues of excessive cutting force, poor chip segmentation, and premature tool wear during the drilling of Ti-6Al-4V titanium alloy. This study established the cutting edge motion trajectory function and instantaneous dynamic cutting thickness equation for ultrasonic vibration-assisted drilling through kinematic analysis. Based on this, an analytical model of drilling force was formulated, integrating tool geometry, cutting radius scale effects, dynamic chip thickness, and drilling depth. In parallel, a finite element model was constructed to achieve visual simulation analysis of chip deformation and cutting force. Finally, the accuracy of the model was verified through experiments, with a comprehensive analysis performed on how cutting parameters affect thrust force. The findings indicate that the average absolute prediction errors of thrust force and torque between the analytical model and finite element simulations were 7.87% and 6.26%, respectively, confirming the model’s capability to accurately capture instantaneous force and torque variations. Compared to traditional drilling methods, the application of ultrasonic vibration assistance resulted in reductions of 40.8% in thrust force and 41.7% in torque. The drilling force exhibited nonlinear growth as the spindle speed and feed rate were elevated, while it declined with greater vibration frequency and amplitude as drilling depth increased. Furthermore, the combined effect of optimized vibration parameters enhanced chip fragmentation, producing short discontinuous chips and effectively preventing entanglement. Overall, this research provides a theoretical and practical foundation for optimizing ultrasonic vibration-assisted drilling and improving precision hole making in titanium alloys. Full article

With the rapid development of modern industry, people’s living pressures are gradually increasing, and an increasing number of individuals are affected by sleep disorders such as insomnia, hypersomnia, and sleep apnea syndrome. Many cardiovascular and psychiatric diseases are also closely related to sleep. Therefore, the early detection, accurate diagnosis, and treatment of sleep disorders an urgent research priority. Traditional manual sleep staging methods have many problems, such as being time-consuming and cumbersome, relying on expert experience, or being subjective. To address these issues, researchers have proposed multiple algorithmic strategies for sleep staging automation based on deep learning in recent years. This paper studies MASleepNet, a sleep staging neural network model that integrates multimodal deep features. This model takes multi-channel Polysomnography (PSG) signals (including EEG (Fpz-Cz, Pz-Oz), EOG, and EMG) as input and employs a multi-scale convolutional module to extract features at different time scales in parallel. It then adaptively weights and fuses the features from each modality using a channel-wise attention mechanism. The integrated temporal features are integrated into a Bidirectional Long Short-Term Memory (BiLSTM) sequence encoder, where an attention mechanism is introduced to identify key temporal segments. The final classification result is produced by the fully connected layer. The proposed model was experimentally evaluated on the Sleep-EDF dataset (consisting of two subsets, Sleep-EDF-78 and Sleep-EDF-20), achieving classification accuracies of 82.56% and 84.53% on the two subsets, respectively. These results demonstrate that deep models that integrate multimodal signals and an attention mechanism offer the possibility to enhance the efficiency of automatic sleep staging compared to cutting-edge methods. Full article

We use unique hand-collected data to conduct an initial examination of the relationship between the information in dividend decreases and proxies of chief executive officer (CEO) entrenchment. The evidence suggests that CEO entrenchment weakens the negative stock market reaction to dividend decreases. However, the evidence relating CEO entrenchment to long-term firm outcomes is mixed. Following dividend cuts, CEO entrenchment is associated with better short-term profitability, but bankruptcy is more likely. Following dividend suspensions, long-term profitability is worse, but bankruptcy is less likely. Overall, the evidence is consistent with the notion that entrenched CEOs obscure the bad news in dividend announcements, which is later revealed in the long run. Full article

Background: ST-segment-elevation myocardial infarction (STEMI) continues to be associated with substantial short- and long-term cardiovascular (CV) mortality despite advances in treatment. Accurate early risk stratification remains critical for optimizing outcomes. Emerging biomarkers including CRP, sST2, and FABP may enhance predictive precision beyond classical markers. This study aimed to evaluate the prognostic value of these biomarkers for in-hospital and 18-month post-discharge CV mortality in STEMI patients. Methods: In this prospective, single-center study, 179 consecutive STEMI patients admitted September 2020–June 2021 underwent biomarker evaluation upon admission. Serum concentrations of CRP, sST2, and H-FABP were measured by ELISA. Patients were followed for in-hospital outcomes and post-discharge mortality during 18-month follow-up (FU) (last patient, last visit January 2023). ROC analysis was used to determine biomarker cut-off values. Cox regression and Kaplan-Meier analyses assessed associations with mortality. Results: In-hospital mortality was 7.8% (14/179). Elevated CRP (>11 mg/L) was significantly associated with higher in-hospital mortality (21.4% vs. 3.7%, p < 0.01). sST2 and H-FABP showed trends toward worse outcomes at higher levels, although their independent predictive value was less robust. Cox regression identified CRP > 11 mg/L (HR = 4.93, p < 0.01), admission glucose, and reduced GFR as independent predictors of in-hospital mortality. During FU, 18 of 165 discharged patients (10.1%) experienced CV death. Higher sST2 levels were significantly associated with post-discharge mortality in midterm FU (p = 0.041). Conclusions: We could show that CRP > 11 mg/L is a strong predictor of in-hospital mortality while elevated sST2 is associated with CV mortality during midterm FU in STEMI patients. Incorporating these biomarkers into clinical risk models may enhance early risk prediction and identify patients at higher risk for post-discharge events. Full article

Continuous-fibre-reinforced thermoplastics are attractive materials for industries to cut down on weight in structural components. Recycling these parts or trims generated during production is difficult due to the reduced properties in materials intended for high-performance applications. Our study investigates the recyclability of short-fibre-reinforced compounds made from shredded organosheets. The fibre share was varied by the addition of virgin polypropylene, and three recycling rounds via a reduced injection-moulding process and a full thermomechanical recycling process including a compounding step were compared. Organosheet cuttings were found to be able to be applied as a short-glass-fibre source for the production of composites with varying fibre shares. Up to 14,000 MPa of elastic modulus and 80 MPa of tensile strength could be achieved at a fibre content of 45 vol%. Fibre length was reduced with progressive processing, less so for lower fibre shares, and in the reduced process without the shear and stress of the compounding step. Fibres from organosheets might be present in bundles and disperse in the matrix with progressive processing, which is particularly the case without compounding processes and can also influence the mechanical properties. Full article

Reliable prognostic indicators for congestive heart failure (CHF) secondary to myxomatous mitral valve disease (MMVD) in dogs are limited. The hemoglobin, albumin, lymphocyte, and platelet (HALP) score, a prognostic marker in humans, has not been evaluated in veterinary medicine. This study aimed to assess the HALP score’s utility for predicting short-term mortality in dogs with CHF due to MMVD. This retrospective study included 54 small-breed dogs. The HALP score was calculated as: hemoglobin (g/L) × albumin (g/L) × lymphocytes (/L)/platelets (/L). Six-, nine-month, and one-year mortality were assessed. The HALP score was significantly higher in survivor groups. Receiver operating characteristic (ROC) analysis showed good predictive accuracy for six- and nine-month mortality (area under the curve > 0.7). A cut-off of 11.13 for six-month mortality yielded 44.44% sensitivity and 94.44% specificity. Kaplan–Meier analysis confirmed that a higher HALP score was associated with significantly longer survival. The HALP score appears to be a valuable, novel prognostic indicator for short-term mortality in dogs with CHF due to MMVD. Full article

The deep shale gas reservoirs of the southern Sichuan Basin exhibit high temperatures, high pressure, large stress differentials, and complex natural fracture systems. Since 2019, hydraulic fracturing technology in this region has evolved through four stages: exploratory fracturing, intensive limited-volume fracturing, tight spacing with controlled fluid and proppants, and balanced fracturing that combines long-section temporary plugging with short-section intensive cutting. Despite these advances, production remains suboptimal due to inefficient reserve utilization, a lack of quantitative methods for residual gas evaluation, and unclear identification of the remaining reserves. To address these challenges, we developed an integrated workflow combining dynamic production analysis, geomechanical modeling, and numerical simulation to evaluate representative fracturing techniques. Fracture propagation in the well group was modeled in the in-house hydraulic fracture simulator, ZFRAC, to assess fracture geometry, while production history and geological data were used to build calibrated reservoir simulation models. This enabled quantitative assessment of effective fracture parameters, reserve utilization, and residual gas distribution. The results show significant intra-stage heterogeneity driven by stress interference, effective fracture half-lengths of 60–105 m, and a cut-off ratio (proportion of effective fracture half-length to wetted fracture half-length) of 60–93%. Reserve utilization peaked at 60% for intensive limited-volume fracturing, while the efficacy of long-section temporary plugging was limited. These findings offer critical insights for optimizing infill strategies and enhancing sustainable shale gas development in southern Sichuan. Full article

This paper examines how smart city technologies can help promote sustainability in China by cutting energy use and carbon footprint, as well as how smart city technologies can help achieve urban sustainability. With the help of Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost) approaches to machine learning (ML), Long Short-Term Memory (LSTM), graph neural networks (GNNs) and SHapley Additive exPlanations (SHAP) value analysis, we have predicted urban energy consumption and have revealed the most powerful emission drivers. The findings indicate that smart grids could decrease energy use by 15 percent and renewable energy integration decreases per capita emissions by about 12 percent. The predictive model’s outstanding performance (R² = 0.996; RMSE = 13.63) confirms the reliability of the predictions. The major contributors to emissions, based on the SHAP analysis, are water heating and urban central heating systems, highlighting the critical significance of upgrading heating systems. Monte Carlo simulations and sensitivity analysis also illustrate that the possibility of optimization of heating infrastructure has the most significant potential of reducing the emissions. These results show that although renewable energy is needed, it is impossible to achieve a high level of de-carbonization without implementing ML-based prediction, smart grids, and building improvements on an integrated basis as part of urban development approaches. Full article