Search Results (74,581)

This study investigates hybrid fiber-reinforced polymer (FRP)–steel-reinforced concrete (RC) beams by using three-dimensional finite element models. The research systematically analyzes the influence of key parameters, including FRP type, FRP bar ratio (ρ_f), the ratio of FRP to total reinforcement (ρ_f/ρ_t), and concrete strength. The load–deflection response of the hybrid RC beams is analyzed in detail. The results show that the investigated parameters have a relatively limited influence on the cracking moment, but significantly affect both the yield and ultimate moments. When ρ_f/ρ_t increases from 0 to 0.75, the yield moment decreases by up to 44.34%. When ρ_f increases from 0.55% to 0.88%, the yield moment increases by 50.63%. Meanwhile, increasing the concrete strength significantly enhances the ultimate moment, with a maximum increase of 38.46%. In addition, an energy ductility index is adopted to quantitatively evaluate the structural ductility. The results indicate that the energy ductility index is consistently lower than the conventional ductility index. Finally, to improve the accuracy of theoretical predictions, a semi-empirical simplified formula is proposed for estimating the FRP bar stress at the ultimate state of hybrid beams. The verification results show that the proposed prediction method agrees well with the experimental data, demonstrating that the simplified formula has good applicability and reliability within the parameter range investigated in this study. Full article

Despite advances in multimodal treatment, head and neck squamous cell carcinoma (HNSCC) remains a major clinical problem owing to its high recurrence rate and frequent development of treatment resistance. Abnormal histone modifications, particularly lysine methylation regulated by methyltransferases (KMTs) and demethylases (KDMs), have emerged as key drivers of HNSCC initiation, progression, and cellular plasticity. This review aims to comprehensively evaluate the role of selected KMTs and KDMs in HNSCC biology, with a focus on their contribution to resistance to immunotherapy, radiotherapy, and cytotoxic chemotherapy. We summarize and critically analyze preclinical and clinical studies investigating histone methylation dynamics in HNSCC, with particular emphasis on enzymes such as KMT2C/D, EZH2, NSD1/NSD2, SMYD3, G9a/EHMT2, LSD1, KDM2A/B, KDM3, KDM4, KDM5, KDM6, KDM7, and KDM8. Attention is given particularly to pharmacological approaches targeting these proteins: we discuss small-molecule inhibitors of EZH2, LSD1, KDM4/5/6, and other KMT/KDMs that are currently in preclinical development or in early clinical trials, and we highlight completed and ongoing studies testing EZH1/2 inhibitors and epigenetic combinations in patients with recurrent and metastatic HNSCC. The deregulation of specific KMTs and KDMs reshapes histone methylation at key residues, thereby controlling cell cycle progression, epithelial–mesenchymal transition (EMT), stem cell phenotypes, DNA damage responses, and multiple interactions with the immune system in HNSCC. Targeting disrupted histone methylation pathways may partially reverse the epigenetic reprogramming of HNSCC cells and represents a promising strategy to improve treatment efficacy in patients with advanced disease. We also summarize the preclinical evidence and the currently limited clinical data on targeting histone methylation dynamics in HNSCC and discuss their therapeutic implications. Full article

This study examines the modelling and forecasting of South African macroeconomic and financial time series using a comparative framework based on Vector Autoregressive (VAR), Vector Autoregressive Moving Average (VARMA), and GARCH-type models. Quarterly data spanning 1970 to 2024 were analysed to determine GDP growth, exchange rates, interest rates, and household consumption expenditure. VAR and VARMA models were employed to capture conditional mean dynamics, while GARCH, EGARCH, and GJR-GARCH models, including ARMA-GARCH extensions, were used to model volatility behaviour. Optimal model specifications were selected using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan–Quinn Criterion (HQ), and the Extended Cross-Correlation Matrix (ECCM), resulting in the estimation of VAR (4) and VARMA (1,1) models. The results reveal strong dynamic interdependencies among the variables. However, diagnostic tests indicate that the VAR (4) and VARMA (1,1) models do not fully capture the underlying data-generating process, as evidenced by residual autocorrelation, heteroskedasticity, and non-normality. Although the VARMA (1,1) model improved forecasting performance relative to the VAR (4) model, important nonlinear and higher-order dynamics remained unexplained. Volatility modelling revealed substantial persistence and clustering, particularly in exchange rates and interest rates. Initial GARCH, EGARCH, and GJR-GARCH specifications exhibited residual autocorrelation and remaining ARCH effects, suggesting model misspecification. The incorporation of an ARMA (1,1) term into the asymmetric GARCH models significantly improved model adequacy by eliminating residual autocorrelation and heteroskedasticity. Limited evidence of asymmetric volatility effects was found. Overall, the findings demonstrate that GARCH-ARMA specifications provide a more robust framework for modelling South Africa’s macroeconomic and financial dynamics. This study recommends future research incorporating nonlinear, regime-switching, and exogenous-variable models to enhance forecasting accuracy and policy relevance. Full article

Shale gas production is governed by complex geological and engineering factors, and its production dynamics are often highly variable. Conventional methods, which can incorporate only a limited number of production-related variables, often struggle to provide accurate forecasts under fluctuating operating conditions. Focusing on the natural flowing stage of shale gas wells, this study proposes a probabilistic forecasting framework that integrates physical decline characteristics with dynamic production data. A dual-branch TCN–LSTM network constrained by decline features is constructed, and Student’s t-distribution is introduced to quantify the uncertainty caused by short-term production fluctuations. The results show that embedding physical decline constraints into the deep learning architecture helps bridge the gap between conventional models with limited parameter representation and purely data-driven models with insufficient interpretability. The proposed method improves forecasting accuracy while preserving the physical meaning of the predictions, and it can generate noise-robust confidence intervals with stable coverage. This method provides decision support for short-term production tracking and production-regime adjustment in shale gas wells. Full article

The large-scale integration of renewable energy has significantly improved the low-carbon performance of power systems, but has also increased operational uncertainty. Under extreme weather conditions, wind and solar power may experience consecutive days of simultaneous output shortfalls—referred to as “renewable energy drought”—leading to persistently high net load and severe challenges to supply guarantee. To address this issue, this paper proposes a multi-source coordinated supply-guarantee dispatch strategy for consecutive-day renewable energy drought scenarios. First, net load is defined as the total system load minus the available wind and solar output. Based on magnitude and duration thresholds, renewable energy drought events are extracted from historical data to generate representative scarcity scenarios. Second, a multi-source coordinated optimization dispatch model is constructed, incorporating wind power, solar power, thermal units, battery energy storage, and pumped-storage hydro. The objective is to minimize the total system operating cost, which includes thermal fuel cost, start-up/shut-down costs, storage cycling cost, wind/solar curtailment penalty cost, and load shedding penalty cost. The load shedding penalty coefficient is set to a magnitude much higher than conventional costs to highlight the priority of supply guarantee. The model accounts for operational constraints such as minimum up/down times, deep regulation capability, ramping limits of thermal units, and charge/discharge power limits of storage. Taking a provincial power system in China for the year 2030 as a case study, a dispatch case covering four consecutive days (96 time periods) is designed. Based on a baseline scenario, eight groups of sensitivity analyses are conducted to comprehensively investigate the impacts of key factors on the supply-guarantee strategy, including: the minimum up/down time of thermal units, deep regulation capability, load shedding penalty cost, load level, rated energy capacity and charge/discharge efficiency of battery energy storage, rated energy capacity and pumping/generating efficiency of pumped-storage hydro, thermal fuel cost coefficient, and renewable energy capacity. Simulation results show that the proposed strategy can effectively coordinate multiple resources under consecutive-day drought conditions; reducing the minimum up/down time of thermal units improves supply flexibility but increases start-up/shut-down costs; enhancing deep regulation capability optimizes storage utilization and reduces total system cost; the load shedding penalty cost directly determines the trade-off between supply guarantee and economic efficiency; and as load level decreases by 5%, 10%, and 15%, the total system operating cost reduces by approximately 6.3%, 12.5%, and 18.8%, respectively. This study provides a quantitative method and technical support for supply-guarantee dispatch decisions and resource allocation in high-renewable power systems under persistent drought conditions. Full article

Growing up without parental care may negatively affect adolescents’ behavioral and emotional development, particularly among adolescents in conflict with the law. In a selected secure care center in the Vhembe District, limited research has explored the lived experiences and behavioral impact of growing up without parents. This study aimed to explore and describe the behavioral and emotional impact of growing up without parents among adolescents in conflict with the law in a child and adolescent secure care center in Limpopo Province. A qualitative, explorative, descriptive, and contextual research design was employed. Purposive sampling was used to recruit twelve (12) adolescents aged 15–17 years admitted to a secure care center in the Vhembe District. Data was collected through individual semi-structured interviews conducted in Xitsonga or Tshivenda, depending on participants’ preferred language. Interviews were audio-recorded, transcribed, translated into English, and analyzed using Tesch’s eight steps of data analysis. Ethical considerations and measures to ensure trustworthiness were observed throughout the study. The findings revealed that adolescents experienced low self-esteem, diminished self-confidence, early initiation of substance use, poor educational engagement, survival-oriented delinquent behavior, and feelings of neglect. These findings highlight the need for an integrated intervention approach to ensure coordinated psychosocial, educational, behavioral, and socio-economic support for this population. Full article

For soil processes that are known to be temporally dynamic, such as soil respiration, methanogenesis, and nitrification–denitrification, it is challenging to capture temporal variations with field-portable greenhouse gas (GHG) analyzers to provide the most accurate estimates of season-long GHG emissions and global warming potentials (GWPs). The objective of this field study was to evaluate the effects of measurement frequency (i.e., weekly, every other week, and every third week), replication (i.e., three, four, or five), and their interaction on the consistency of season-long carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) emissions and GWP estimates across multiple ecosystems. Results are based on direct, in-field measurements with a field-portable gas analyzer. Field research was conducted throughout the 2024 growing season in a minimally grazed pasture, tallgrass prairie, soybean under conventional and conservation management practices, and cotton under conservation management in Arkansas, USA. Season-long CO₂ emissions and GWP from the tallgrass prairie were 1.1 times (12%) greater from the weekly and every-other-week (16.9 and 17.0 Mg ha⁻¹, respectively), which did not differ, than the every-third-week (14.2 and 14.2 Mg ha⁻¹, respectively) measurement frequencies. Season-long CH₄ emissions from the minimally grazed pasture and conservation-tilled soybean system were ≥7.5 times greater with four and five replications, which did not differ, than with three replications. Global warming potential in the conservation-tilled soybean (13.9 Mg ha⁻¹) and conservation-tilled cotton (21.1 Mg ha⁻¹) systems were ≥1.1 times (13%) greater with the every-third-week than with the weekly data set. Though this study was somewhat limited due the data sub-setting approach used, even using current, state-of-the-art, field-portable GHG analyzers, an appropriate in-field measurement frequency and number of spatial replications should be considered to reliably quantify whole-field, season-long GHG emissions and GWP estimates. Full article

As an information-guided environmental regulation method that can effectively improve farming practices, environmental training is widely used in the agricultural field. However, evidence on whether and how such training improves the green production efficiency of livestock farmers remains limited. This study investigates the effect of environmental training on the green production efficiency of hog farmers by explicitly accounting for spatial spillovers and exploring technology adoption as a mechanism pathway. Specifically, green production efficiency is first measured using the Super-SBM DEA model, and the spatial Durbin model is then employed to estimate both the direct effect and spatial spillover effect of training. The results of survey data from 371 hog farmers in China show that participation in training significantly enhances the green production efficiency of farmers, with positive spillover effects from neighboring farmers’ participation in training. Further mechanism analysis indicates that training promotes the adoption of clean production technologies, which in turn enhances green production efficiency. The positive effect of training is more pronounced among large-scale and better-educated farmers. Therefore, these findings suggest that policies should strengthen environmental protection training, promote the diffusion of clean production technologies, make better use of the demonstration households mechanism, and customize strategies to support the green transformation of hog farming. Full article

Post-dissection thoracoabdominal aortic aneurysm (PD-TAAA) is a late sequela of chronic aortic dissection. Complex endovascular aneurysm repair (EVAR), including fenestrated and branched techniques (F/B-EVAR), enables aneurysm exclusion while preserving visceral perfusion; however, bridging stents are not specifically designed for PD-TAAA and are frequently used off-label. Evidence on bridging stent performance is largely derived from degenerative aneurysm cohorts, and PD-TAAA-specific data remain limited. This study evaluated outcomes of the VBX Stent Graft when used as a bridging stent during F/B-EVAR for PD-TAAA. This retrospective analysis included patients with PD-TAAA from the EMBRACE registry (ClinicalTrials.gov: NCT05143138), a multicenter, single-arm registry with retrospective and prospective components, with all outcomes core-laboratory-adjudicated. Procedural, early (thirty-day), and midterm outcomes at one and three years were assessed. The primary endpoints were all-cause mortality and freedom from target vessel instability, defined as loss of durable target vessel reconstruction. Twenty-one patients (mean age 61.5 years; range, 28–77 years) underwent F/B-EVAR with at least one VBX Stent Graft. In total, 82 visceral arteries were treated, of which 51 were bridged with a VBX Stent Graft. Technical success was 100%. Two serious adverse events occurred perioperatively, one requiring reintervention, with no thirty-day mortality or major adverse events. Freedom from all-cause mortality was 95.2% at one year and 90.5% at three years, with two deaths during follow-up. Freedom from target vessel instability at the patient level was 85.7% at both one and three years (95% CI, 62.0–95.2%). VBX Stent Grafts used as bridging stents during F/B-EVAR for PD-TAAA demonstrated high technical success, low early morbidity and mortality, and acceptable mid-term survival and target vessel stability, supporting their use in this challenging anatomical setting within the limitations of a small PD-TAAA cohort. Full article

Uncertainty quantification of well-log interpretation is essential to derisking subsurface exploration and development decision-making by providing possible scenarios for reservoir property distribution, fluid flow behaviors, and hydrocarbon potential. Well-log interpretation offers crucial insights into permeability variations, reservoir compartmentalization, mineral composition, and fluid mobility. Inherent uncertainties, such as those arising from geological heterogeneity, limited sampling, and non-uniform distribution of rock properties, can lead to inaccuracies that compromise petrophysical interpretation and formation evaluation. However, traditional data-driven well-log interpretation methods, which map well logs to formation properties based on core measurements, are primarily deterministic and fail to quantify uncertainty accurately. By leveraging deep learning and generative models, we introduce a probabilistic approach that significantly improves permeability estimation and uncertainty quantification. Our methodology integrates co-kriging techniques with Conditional Generative Adversarial Networks (cGANs) and Conditional Variational Autoencoders (cVAEs), establishing a quantitative relationship between kriged core, well-log data and permeability. Our approach enhances petrophysical property uncertainty estimations based on geostatistics by establishing a quantitative relationship between kriged estimates and flow-related properties. Training features are constructed using collocated co-kriging, capturing the cross-correlation between well logs (input features) and core data (output formation properties). Core bulk density, calculated from grain density, is kriged to well-log resolution to enable porosity estimation, while permeability is similarly kriged. A low-pass filter is then applied to smooth the kriged core bulk density, permeability, and estimated porosity, ensuring more accurate interpretations. The results reveal that cGANs and cVAEs consistently produce lower uncertainty estimates compared to traditional machine learning models. High-permeability zones exhibit lower uncertainty (approximately 3–5%), while low-permeability zones show higher uncertainty (10–15%). Traditional deep learning models tend to overestimate uncertainty, whereas generative models provide more reliable estimates. Additionally, applying kriged permeability data improves uncertainty estimations, further reducing uncertainty to 3% in high-permeability zones and 10% in low-permeability zones. To ensure broad applicability, the methods were tested on datasets from both carbonate and clastic reservoirs. In carbonate formations, prior classification steps are necessary to achieve accurate permeability predictions. The interpretation workflow improves permeability estimation accuracy and enhances uncertainty quantification across conventional and unconventional reservoirs. Additionally, this method is adaptable for CO₂ injection and H₂ storage wells, demonstrating versatility across various reservoir types. Full article

Data-constrained island power systems with increasing shares of variable renewable energy (VRE) face growing challenges in maintaining reliability while preserving market efficiency. Existing reserve sizing practices typically rely on either fixed deterministic rules or data-intensive probabilistic methods, both presenting practical limitations in Small Island Developing States (SIDS). This paper develops a market-based framework for the co-optimization of energy and operating reserves in low-inertia island power systems, in which reserve requirements are established using historically observed extreme generation or load deviations that represent operationally validated high-risk system conditions, while reserve allocation and pricing emerge from the co-optimization process. By relying on observed operational variability, the proposed approach avoids explicit probabilistic uncertainty modeling while retaining sensitivity to system stress conditions. The approach is evaluated using a stylized island power system representative of Caribbean SIDS. Results show that reserve requirements are highly sensitive to operating conditions, reaching up to 26.7% of demand under high variability and significantly exceeding conventional fixed reserve criteria. The framework reduces non-served energy, improves reserve allocation efficiency, and generates scarcity-consistent reserve prices under stressed conditions. These findings demonstrate that the proposed methodology provides a practical intermediate solution between deterministic and probabilistic reserve sizing approaches while remaining suitable for data-constrained island power systems. Full article

Low-frequency mobile bands remain essential for wide-area and penetration-limited wireless coverage, but their limited channel bandwidth constrains the achievable capacity. This paper presents a measurement-supported extrapolation framework for assessing how lowband MIMO and future AAS-assisted operation can enhance coverage and single-user throughput-oriented capacity in wireless networks. The motivation is to evaluate whether such deployments can strengthen the lower-frequency layer as a robust coverage-and-capacity support layer for general traffic and reduce the load on midband and higher-frequency resources. Controlled radiated SISO and $2\times 2$ MIMO measurements were performed with a base-station simulator and commercial user equipment in representative lowband and midband frequency bands. Measured RSRP, CQI, BLER, MAC-layer throughput, and IP-layer throughput thresholds for a 25 Mbit/s downlink target were used for coverage estimation and conditional extrapolation. Under the Extended Hata model, the measured $2\times 2$ MIMO thresholds yielded a 43% larger estimated radius at 800 MHz than at 1800 MHz, while the same model indicated a 93% radius increase for a representative 10 dB AAS-related beamforming gain scenario. Conditional $4\times 4$ MIMO extrapolations indicated data rates above 100 Mbit/s in 10 MHz and above 200 Mbit/s with 10 MHz two-component-carrier aggregation under ideal high-CQI conditions. The results support the potential of future lowband AAS deployments. The AAS and higher-order MIMO results are scenario-based estimates rather than direct field validation. Full article

Breast cancer (BC) is a major global public health problem. Classical therapies have limited success on the treatment of BC; therefore, new therapeutic options are needed. Proteolysis targeting chimeras (PROTACs) are heterobifunctional molecules that represent a revolutionary class of new drug candidates because they induce the degradation of harmful, undruggable proteins by activating the ubiquitination machinery of cells. Their unique mechanism of action offers several advantages over conventional drugs, but also disadvantages, as most of them are large molecules with unfavorable pharmacokinetic properties, which limits their bioavailability. Vepdegestrant (Veppanu^TM) is an orally administered, estrogen receptor (ER) targeting chimera that was approved by the FDA on 1 May 2026, for the treatment of adults with ESR1-mutated advanced or metastatic breast cancer. Thus, vepdegestrant became the first-ever approved PROTAC drug. In this article, we briefly summarize the structure, mechanism of action, and key available pharmacokinetic and pharmacological data of vepdegestrant. Full article

Background/Objectives: Adult degenerative lumbar deformity is a heterogeneous condition in which outcome depends on radiographic correction, patient-related risk factors, and surgical burden. This study evaluated spinopelvic realignment, clinical outcomes, complications, and predictors of unfavorable postoperative course after surgical treatment of adult degenerative lumbar deformity. Methods: This three-center retrospective cohort study included adult patients who underwent posterior decompression and instrumented fusion, with or without interbody fusion, for adult degenerative lumbar deformity between January 2021 and December 2024. Of 136 screened patients, 113 completed final follow-up and were included in the analysis. The mean follow-up duration was 31.0 ± 12.9 months. Radiographic parameters were assessed preoperatively, immediately postoperatively, and at final follow-up. Patient-reported outcome measures were analyzed using available paired data. Unfavorable postoperative course was defined as persistent or worsened pain with functional limitation, symptomatic mechanical complication, deep infection requiring surgical treatment, or revision/reoperation. Results: Surgery produced significant immediate improvement in coronal and sagittal alignment. Cobb angle improved from 29.8 ± 13.1° to 13.7 ± 6.7°, lumbar lordosis increased from 28.8 ± 15.5° to 40.3 ± 16.0°, PI–LL mismatch decreased from 21.7 ± 10.0° to 10.1 ± 11.5°, and SVA decreased from 58.8 ± 31.4 mm to 32.5 ± 36.0 mm. Partial loss of correction was observed at final follow-up, although alignment generally remained improved compared with baseline. ODI improved from 57.8 ± 12.6 to 34.7 ± 8.7 in patients with available paired data. Any postoperative complication occurred in 42.5% (n = 48) of patients, revision/reoperation in 23.9% (n = 27), and unfavorable postoperative course in 35.4% (n = 40). In multivariable analysis, osteoporosis, greater fusion length, and residual immediate postoperative PI–LL mismatch were independently associated with unfavorable postoperative course. Conclusions: In this three-center retrospective cohort, surgery for adult degenerative lumbar deformity was associated with significant radiographic correction and meaningful clinical improvement in patients with available paired outcome data. However, the substantial complication and revision/reoperation burden highlights the morbidity of adult degenerative lumbar deformity surgery. Osteoporosis, fusion length, and residual immediate postoperative PI–LL mismatch may help identify patients at higher risk for unfavorable postoperative course. Full article

To investigate the sensitivity and underlying mechanisms of key parameters for gas extraction, a three-dimensional numerical model was established. Using the control variable method, the effects of critical extraction parameters on gas pressure evolution, the effective extraction radius of drilling, and cumulative gas production were systematically analyzed. The results indicate the following: Extraction time is the primary factor controlling the expansion of the pressure disturbance zone, and gas extraction exhibits a significant characteristic of diminishing marginal returns. Increasing the extraction negative pressure and drilling diameter mainly improves near-drilling flow conditions and contributes only marginally to the overall extraction effectiveness. Drilling length determines the gas resource volume controlled by a single drilling operation and its sustained extraction capacity while exerting only a limited influence on the effective extraction radius. The initial porosity of the coal seam is the dominant factor controlling both the effective extraction radius and extraction efficiency. Field extraction data verified the model’s reliable representation of extraction patterns and parameter influence characteristics. A synergistic gas control strategy integrating long drilling coverage, enhanced permeability, reasonable negative pressure, and continuous extraction was proposed. These research results can provide a theoretical basis and technical support for the optimization of gas extraction parameters. Full article