Search Results (725)

Elastic springback is a critical challenge in sheet metal bending that directly affects dimensional accuracy and manufacturing efficiency. This study presents a comparative experimental and machine learning-based analysis of elastic springback behavior in three widely used sheet metals like stainless steel, aluminum, and copper, which are subjected to folding bending. The influence of key process parameters, namely sheet thickness (0.5 to 1.5 mm) and bending temperature (room temperature to 200 °C), was systematically examined under cold working. A cost-effective localized heating approach using a direct flame was introduced to enhance process control and reduce elastic recovery without the complexity associated with heated dies. Experimental results revealed substantial variability in elastic springback, ranging from 0.15% to 12.41%, emphasizing the fact that they are nonlinear in nature. Statistical evaluation confirmed that sheet thickness is the dominant factor governing elastic springback, while material type and temperature exhibit secondary yet meaningful effects. To improve predictive capability, five regression models (Linear, Polynomial, Support Vector, Random Forest, and Gradient Boosting) were developed and assessed. Among them, Random Forest demonstrated superior performance with the lowest prediction errors and strongest explanatory power, achieving an R² of approximately 0.85. Cross-validation further validated its robustness and generalization capability. Feature importance and SHapley Additive exPlanations (SHAP) analyses reinforced the primary role of thickness in determining elastic recovery behavior. The findings provide practical insights for selecting materials and process conditions to minimize elastic springback while highlighting the effectiveness of ensemble learning techniques for accurate prediction. This work contributes a consistent framework for enhancing bending precision and supports data-driven decision-making in modern manufacturing environments. Full article

Transepithelial photorefractive keratectomy (Trans-PRK) offers superior re-epithelialization and visual recovery. This study evaluates the impact of preoperative refractive status on clinical outcomes and identifies prognostic factors across varying myopic severities. This retrospective observational study included 125 eyes [64 patients; age > 20 years; best-corrected visual acuity (BCVA) ≥ 20/25] that underwent Trans-PRK between March and December 2022. Patients were stratified into low myopia (LM: > −5.0 D), moderate-to-high myopia (MHM: −5.0 D to −8.0 D), and extremely high myopia (EHM: ≤ −8.0 D) groups. Analysis focused on preoperative refraction, intraoperative parameters, postoperative uncorrected visual acuity (UCVA), and corneal conditions of superficial punctate keratitis (SPKs) and haze. The mean age was 30.20 ± 6.34 years, with a mean initial manifest sphere (MS) of −6.42 ± 2.27 diopter (D) overall and −3.73 ± 0.15 D, −6.28 ± 0.13 D, and −9.17 ± 0.15 D in the LM, MHM, and EHM groups, respectively. At a mean follow-up of 6.69 ± 3.73 months, the overall mean final manifest spherical equivalent (MSE) was −0.12 ± 0.73 D, and the mean final UCVA was 0.01 [Snellen equivalent (SE), 205/200] ± 0.08 logMAR. Predictability was 94.4%, 88.88%, and 94.3% for the final MS ≤ −1.0 D, final MSE ≤ −1.0 D, and UCVA 0.8, respectively. In the LM and MHM groups, cycloplegic and subjective refractions showed the highest concordance with emmetropia, whereas initial manifest refractions were most accurate for the EHM group. Corneal SPK incidence declined from 32.2% (1 month) to 1.6% (6 months), primarily localized to EHM eyes. Corneal haze peaked at 28.2% at three months before receding to 9.4% by 6 months. Refractive and visual stability were achieved by the third month for the LM and MHM groups, whereas the EHM group (mean MSE: −9.59 ± 0.15 D) required six months to reach both refractive and visual plateaus. Despite transiently higher rates of corneal SPKs and haze in EHM eyes, final visual outcomes remained excellent, achieving a mean UCVA of 18/20. Full article

The Chang 8 tight oil reservoir in the Xifeng area of the Ordos Basin is characterized by poor reservoir properties, making conventional water flooding ineffective for efficient reservoir development. CO₂ flooding is therefore considered an important approach for enhancing oil recovery in tight reservoirs. However, suitable development strategies for direct CO₂ injection in undeveloped reservoir areas remain insufficiently understood. In this study, compositional numerical simulation combined with a single-factor sensitivity analysis was employed to investigate the effects of key parameters, including well pattern configuration, fracturing parameters, injection–production strategy, and gas injection modes. The results indicate that an inverted nine-spot well pattern with vertical well injection and vertical well production, a well spacing of 500 m, and a row spacing of 200 m can achieve relatively favorable areal and vertical sweep performance. A fracture half-length of 80 m, fracture widths of 0.003–0.005 m, and fracturing treatment before initial production help balance early-stage productivity and gas channeling control. Maintaining an injection rate of 0.03–0.04 PV/a, an oil production rate of 2–3 m³/d, and a bottomhole flowing pressure of 13–14 MPa is beneficial for maintaining reservoir energy and stabilizing displacement-front propagation. Based on neighboring field development experience, switching from continuous CO₂ injection to water–alternating–gas (WAG) injection during the mid-development stage can improve mobility control and enlarge the CO₂ swept volume. Under the current geological model and simulation conditions, the recommended development strategy predicts a recovery factor of 35.43% over a 30-year production period. The results provide reasonable parameter ranges and an engineering reference for direct CO₂ flooding development in the Chang 8 tight oil reservoir and similar reservoirs. Full article

Background: Perioperative outcomes in pelvic organ prolapse (POP) surgery remain difficult to predict due to substantial heterogeneity in both surgical techniques and patient characteristics. Existing studies typically evaluate these factors in isolation, limiting their ability to support individualized risk stratification. This study introduces a stratified analytical framework to disentangle the relative impact of procedural and patient-related determinants across common vaginal reconstructive approaches. Methods: A retrospective cohort of 376 women undergoing POP surgery between 2020 and 2025 was analyzed. Patients were stratified into three procedure groups: sacrospinous fixation with mid-urethral sling (SFM + TOT/TVT), anterior and posterior repair with sling (A&P + TOT/TVT), and isolated anterior and posterior repair (A&P alone). Key outcomes included intraoperative blood loss, length of hospitalization, postoperative hospital stay and catheterization time. Within-group predictors were assessed using stratified odds ratios and synthesized via a random-effects model. Results: Procedure type was consistently associated with recovery-related outcomes, although it explained only a modest proportion of outcome variability. Patients undergoing A&P repair exhibited significantly prolonged hospitalization (8.00 vs. 6.29 and 6.94 days), postoperative recovery (4.99 vs. 3.48 and 4.17 days), and catheterization duration (3.31 vs. 2.33 and 2.86 days) (all p < 0.001). In contrast, intraoperative blood loss was primarily driven by patient-specific factors, including concomitant hysterectomy, prolapse severity, obesity, age, and obstetric history. Prolonged hospitalization was strongly associated with combined procedural complexity and clinical burden, while catheterization duration was influenced by postoperative complications and parity. Conclusions: This study demonstrates that perioperative outcomes in POP surgery arise from distinct and interacting domains: procedural factors predominantly shape recovery trajectories, whereas patient characteristics govern intraoperative risk. The proposed stratified random-effects framework enables integrated evaluation across heterogeneous surgical groups and provides an exploratory basis for identifying domains that may inform future individualized perioperative risk models. Full article

Background/Objectives: Reconstruction of defects in the ankle, foot and heel is complex because of the limited availability of local tissue and multiple comorbidities like diabetes mellitus and peripheral vascular disease. Even though free and pedicled flaps are widely used, their comparative effectiveness remains incompletely defined. Methods: This study presents a narrative analysis of 21 studies. From each study, we extracted data related to flap type, characteristics of the patient, indications, and outcomes: flap survival, limb salvage, functional recovery and complications. Results: Free flaps were mainly used for the management of large, complex, infected, or weight-bearing plantar defects and generally showed high rates of survival (~95–97%) with good functional outcomes and limb salvage rates. On the other hand, pedicled flaps and perforator-based flaps were principally used for small-to-medium defects and showed comparable survival rates in selected cohorts (up to ~98–100%), although direct comparison is limited by differences in defect complexity and patient selection. Overall, the functional outcomes appeared comparable across techniques in appropriately selected patients. However, long-term complications, such as ulceration in weight-bearing heel regions, remained frequent (reported rates were up to 39–41% in some free flap series). Sensory recovery and vascular status were key elements of long-term success, often exceeding flap type in predicting outcomes. Conclusions: Both free and pedicled flaps are effective options for reconstructing lower limb defects when appropriately indicated. While pedicled flaps remain preferred for smaller defects and high-risk patients, free flaps are generally better suited for extensive and more complex defects. The outcomes are influenced by several factors: individualized reconstruction strategy, characteristics of the defects, vascular status and patient comorbidities. Full article

Agricultural land abandonment is widespread in high-latitude regions, yet its effects on soil microbial communities in permafrost ecosystems remain insufficiently understood. In this study, we used a 0–25 year chronosequence of abandoned soils in the Yamalo–Nenets Autonomous Okrug to analyze the succession of soil microbial communities and compared them with mature reference Podzols. Soil physicochemical properties, microbial community composition, and potential functional changes were systematically assessed using 16S rRNA gene sequencing, multivariate statistical analyses, and functional prediction. The results showed that, in mature soils, SOC was the key factor driving microbial community variation, whereas in agricultural and abandoned soils, available nutrients were the main factors influencing microbial community structure. The abandonment process also constrained soil microbial mineralization. The dominant microbial phyla mainly included Proteobacteria, Acidobacteriota, Verrucomicrobiota, Bacteroidota, and Actinobacteriota, while the relative abundances of other taxa differed markedly among land-use stages. Agricultural soils were dominated by copiotrophic microbial groups, whereas microbial communities in abandoned soils gradually shifted toward oligotrophic groups with increasing recovery time, and some taxa associated with the degradation of complex carbon substrates also increased in abundance. Functional analysis further indicated that carbon and phosphorus cycling functions in soil microbial communities exhibited a certain degree of functional redundancy, whereas nitrogen-cycling functions depended more strongly on specific microbial taxa. Land abandonment promoted an increase in the abundance of genes related to microbial carbon metabolism in soil. However, even after 25 years of abandonment, microbial community composition and functional potential had not fully recovered to the level of mature reference Podzols, indicating that agricultural disturbance exerts long-term legacy effects on soil microbiomes in permafrost-affected regions. 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

Humic acid (HA) production from crop straw is often limited by lignocellulosic recalcitrance and insufficient coordination among functional microorganisms. In this study, a data-driven strategy was developed to evaluate multi-fungal inoculation timing for HA recovery from pretreated straws. Three substrate platforms, namely raw wheat straw (SW), steam-exploded corn straw (SC-SE), and ammoniated steam-exploded rice straw (SR-SE-N), were comparatively evaluated across an 81-run experimental matrix. Pretreatment markedly improved lignocellulose degradation and precursor turnover, with SR-SE-N showing the best humification performance. Based on the selected substrate, a two-factor interaction (2FI) model was established to describe the effects of inoculation timing on HA yield. The model was significant for HA prediction (R² = 0.8768, adjusted R² = 0.8398, predicted R² = 0.7795). Inoculation timing strongly affected HA formation, and within the investigated timing range, the highest HA yield was obtained under simultaneous inoculation of Aspergillus niger, Phanerochaete chrysosporium, and Candida sp. Predicted and experimental HA yields were in close agreement, supporting the reliability of the model. These results indicate that favorable fungal inoculation timing is substrate-dependent and can be effectively identified through data-driven analysis within a bounded experimental range. The study provides a practical basis for improving HA biomanufacturing from pretreated agricultural residues. Full article

Objective: Rapid vertical manoeuvres and intermittent vibration in autonomous electric vertical take-off and landing (eVTOL) aircraft can provoke pronounced psychological anxiety in passengers. To address this, we propose a closed-loop adaptive system that integrates an in-ear wearable sensor with dynamic regulation of the cabin microenvironment, enabling real-time monitoring of each passenger’s autonomic state and delivering individualised mitigation through a continuous sense–analyse–intervene–feedback loop. Methods: The system is built around a pair of custom in-ear modules that integrate dual-wavelength photoplethysmography (PPG; 525 nm green and 940 nm infrared), galvanic skin response (GSR), and a six-axis inertial measurement unit (IMU) sampled at 200 Hz. To suppress the 20–80 Hz vibration generated by the distributed electric propulsion system, a compliant silicone damping sleeve attenuates high-frequency components at the hardware level, while a Kalman filter fuses the IMU and PPG streams and an adaptive notch filter removes residual rotor harmonics. The pipeline raises the heart-rate-variability (HRV) signal-to-noise ratio (SNR) to 24.1 dB, with a Pearson correlation of 0.96 against a medical-grade chest strap. A hybrid CNN–LSTM network—two convolutional layers (32 filters each) followed by two LSTM layers (128 hidden units)—predicts impending anxiety from HRV time-domain features (RMSSD, pNN50) and frequency-domain features (LF/HF ratio), triggering intervention 8.2 s in advance on average. According to the predicted anxiety level (mild/moderate/severe), a fuzzy controller modulates transcutaneous auricular vagus nerve stimulation (1–5 mA), the binaural-beat frequency (4–8 Hz, theta band), and the cabin lighting colour temperature (2700–6500 K) in real time. The intervention parameters are continuously refined by SPSA-based stochastic optimisation of the HRV recovery rate (step size 0.01; updated every 30 s). Results: In a randomised controlled experiment conducted in a simulated flight environment (N = 50; aged 22–45 years; 1:1 sex ratio), the active group reached physiological recovery in 52.3 s on average, compared with 98.6 s for the sham-controlled group—a 47% reduction (Cohen’s d = 1.24, p < 0.001). User acceptance reached 94%. Conclusions: The proposed in-ear platform enables closed-loop adaptive regulation of anxiety in the eVTOL cabin and overcomes the limitations of conventional passive mitigation strategies. By combining vibration-tolerant physiological sensing with multimodal environmental control, the work offers a practical pathway for improving passenger experience in urban air mobility and provides a useful reference for human-factors standards governing autonomous aircraft. Full article

Bioethanol production from lignocellulosic biomass remains energy-intensive, and its energy performance can be affected by equipment degradation, utility disturbances, and operating variability. This study developed a degradation-aware mechanistic–AI framework for energy forecasting, anomaly detection, maintenance-oriented interpretation, and multi-objective optimization in bioethanol production under limited-data conditions. Reduced-order energy models were formulated for pretreatment, hydrolysis–fermentation, and ethanol purification. Equipment deterioration was represented through heat-transfer fouling, column-efficiency decline, and pump-efficiency decay. Condition-dependent modifiers were introduced to account for load-related degradation and intervention-related partial recovery. Benchmark-constrained synthetic time-series datasets were generated under baseline, accelerated-degradation, condition-dependent, stress, and data-quality perturbation scenarios. Empirical baselines and machine-learning models were compared for specific energy consumption prediction, with uncertainty reported using confidence intervals. The long short-term memory model achieved the lowest prediction errors under both baseline and stress conditions. Robustness testing showed that sensor drift, missing values, and outliers increased forecasting and anomaly-detection uncertainty. Sensitivity analysis identified degradation coefficients, seasonal disturbance, and anomaly-threshold selection as influential factors. Multi-objective optimization revealed trade-offs among specific energy consumption, ethanol purity, and equipment-health penalty. The proposed framework should be interpreted as a benchmarked methodological platform rather than a plant-validated maintenance or control system. Plant-specific deployment requires calibration with operating records, maintenance logs, cleaning records, and sensor-quality assessment. Full article

Reclaimed water-receiving rivers face increased hypoxic and malodorous risks after stormwater runoff. To investigate how initial runoff drives the co-variation of pollutants and microbial communities at the sediment–water interface (SWI), this study constructed a four-channel simulated river system based on the Froude similarity criterion, including two low-intensity rainfall (R-L) treatments and two high-intensity rainfall (R-H) treatments. Each experiment consisted of a 48 h runoff disturbance stage followed by a 48 h recovery stage. The dynamics of carbon (C), nitrogen (N), and phosphorus (P) in both water and sediments were systematically analyzed, together with variations in dissolved organic matter (DOM) composition, microbial communities based on 16S rRNA, and predicted N-cycling functional potential. Results showed that R-H exerted a pronounced dilution effect on pollutants in water but significantly enhanced SWI disturbance, facilitating nutrient accumulation within the system. DOM profiles indicated active microbial metabolism, consistent with long-term reclaimed water inputs. Microbial analyses revealed that TN was a key environmental factor influencing community differences. Nitrification and denitrification potentials were higher under R-H, whereas ammonia assimilation was higher under R-L. These findings highlight the importance of managing N accumulation and transformation following rainfall events in reclaimed water-receiving rivers. Full article

This study aimed to optimize the extraction of total phenolic compounds from Cannabis sativa L. leaf powder cultivated in Tucumán, Argentina (INBIOFIV 00500 Tuc chemotype), using ultrasound-assisted extraction (UAE), and to evaluate the process at a screening level in terms of environmental performance. Although leaves are not the primary raw material in the medicinal cannabis industry—where inflorescences are preferentially used due to their cannabinoid content—they represent an underutilized biomass with potential for valorization as a source of bioactive phenolic compounds for cosmetic, food, and pharmaceutical applications. The leaves were dried and ground to a particle size between 74 and 840 µm. The effects of process parameters, including solid-to-liquid ratio, solvent composition, and extraction time, were evaluated using response surface methodology (RSM). The optimal conditions predicted by the model were 46% ethanol, a solid-to-liquid ratio of 1:10 (w/v), 70% amplitude, and 30 °C. Although the model indicated a short optimal sonication time (1 min), experimental results showed that a plateau in extraction yield was reached at 6 min, with no significant increase thereafter. Under these conditions, the extract exhibited high total phenolic content (1746.83 µg GAE/mL) and total flavonoids (858.41 µg QE/mL), along with strong antioxidant activity. The extract showed no significant toxicity in the Artemia salina assay. The environmental performance of the process was assessed at a laboratory scale. The total energy consumption was 0.329 ± 0.08 kWh per extraction batch, corresponding to a carbon footprint of 0.12 kg CO₂ per batch, based on Argentina’s electricity emission factor (0.387 kg CO₂/kWh). When normalized to extraction yield, the process exhibited a relative carbon footprint of 0.0052 kg CO₂/mg GAE, indicating favorable energy efficiency per unit of antioxidant recovered. These results demonstrate that UAE is a rapid and energy-efficient technique for the recovery of phenolic compounds from cannabis leaves, supporting their valorization within a circular economy framework. However, further studies at a pilot scale and full life cycle assessment are required to confirm the environmental performance of the process at industrial level. Full article

Heap bioleaching is widely used to extract copper from low-grade sulfide ores thanks to its operational simplicity, low cost, and environmental sustainability. However, current control strategies rely primarily on single-factor optimization and often overlook the synergistic interactions of multiple key parameters, such as ore particle size, pore structure, pH, temperature, microbial activity, and oxygen transfer efficiency. As a result, issues such as low recovery rates, extended leaching periods, and high operational costs persist. Moreover, the “gray-box” nature of heap systems impedes real-time monitoring of internal physical, chemical, and biological processes. In addition, empirical multi-parameter optimization is time-consuming and inadequate for capturing complex interdependencies. This review was conducted to systematically examine the key factors influencing heap bioleaching efficiency and critically evaluate recent advances in numerical simulation and intelligent control strategies. As a result, we identified a major research gap: the existing models—including microscale shrinking core models (SCMs), mesoscale pore-network models based on CT reconstruction, and macroscale continuum models—have inherent limitations. SCMs assume idealized spherical particles with uniform mineral distribution while neglecting pore structure evolution and biofilm dynamics. Mesoscale models offer detailed pore characterization but lack robust multi-physics coupling (thermal–hydro–mechanical–chemical–biological, or THMCB). Macroscale models rely on homogenization assumptions that oversimplify spatial heterogeneity and temporal variations in permeability. This analysis covers the relevant literature from 1985 to 2025, with a focus on three methodological scales (micro, meso, and macro) and their integration with machine learning approaches. A notable finding is that hybrid neural network models (e.g., BP and RBF architectures) outperform purely physics-based models in predicting leaching kinetics under varying operational conditions. However, their accuracy depends heavily on high-quality field data—a limitation rarely addressed in prior reviews. By clearly delineating these model-specific limitations and scale-dependent trade-offs, this review makes two unique contributions: a structured framework for selecting and coupling numerical methods according to process requirements and a roadmap for integrating artificial neural networks with multi-physics simulations to achieve real-time intelligent control of heap bioleaching. The findings offer both theoretical guidance and practical references for optimizing the processing of low-grade copper sulfide ores. Full article

Verticillium wilt (VW), caused by the soil-borne fungus Verticillium dahliae, is a major disease that markedly compromises both the yield and fiber quality of cotton. In this study, we explored the function and underlying mechanism of the cotton expansin gene GhEXLB2 in response to VW infection. Expression profiling revealed that members of the GhEXL family exhibit distinct patterns across tissues and under various biotic and abiotic stresses. Notably, GhEXLB2, which encodes an extracellular protein, showed the strongest induction following V. dahliae challenge. Ectopic expression of GhEXLB2 in Arabidopsis thaliana promoted root elongation and root hair formation, and was associated with improved resistance to the pathogen. In contrast, silencing GhEXLB2 in cotton via virus-induced gene silencing (VIGS) led to pronounced vascular browning, increased pathogen recovery, and a lower level of disease resistance. In addition, RNA-seq profiling of GhEXLB2-silenced (VIGS) cotton plants revealed that most differentially expressed genes were enriched in pathways related to phytohormone signaling and plant–pathogen interactions, with salicylic acid (SA) signaling and WRKY transcription factors emerging as central regulatory components. Analysis of the GhEXLB2 promoter further identified multiple cis-acting elements associated with stress and hormone responsiveness. When integrated with protein–protein interaction (PPI) prediction data, these results suggest that GhEXLB2 may be modulated by a network of transcription factors and signaling pathways. Collectively, the evidence supports a positive association between GhEXLB2 and VW resistance. This study provides a framework for understanding expansin functions in cotton defense against VW. Full article

Human milk is highly rich in miRNAs, with differential expression amongst its fractions, including cells, fat, and skim milk. Various factors, such as the stage of lactation or milk removal during breastfeeding, have been shown to influence the miRNA content of. Here, we sought to determine the effect of maternal and/or infant infection on the miRNA profile of cell and fat fractions analyzed using next-generation sequencing. Breastfeeding mother/infant dyads (n = 18) were followed during one or more infection episodes as well as upon recovery. Cells and fat together contain 1780 known miRNA species, which is the highest number of known miRNAs assayed in human body fluids to date. In addition, 592 novel miRNAs were predicted, of which 95 were of high confidence. Comparisons between samples collected when the participants were healthy and when infected yielded 453 differentially expressed (p < 0.05) known miRNAs. Of these, 70 were highly expressed and differentially regulated during infection, with 62 upregulated and 8 downregulated known miRNAs during infection. Most of the highly and differentially expressed miRNAs are known to play critical roles in immunity and immune system development. These findings support the use of miRNAs as biomarkers of the health status of the lactating breast and the breastfeeding mother/infant dyad. Full article