Search Results (331)

Lost circulation is a significant challenge in drilling operations, leading to fluid losses, increased non-productive time, and well instability. This paper develops a predictive model to quantify lost circulation in the Al Jeribe Formation in the Al-Omar field, one of the largest oilfields in the Middle East. Lost circulation is especially prevalent when drilling through the Al Jeribe formation due to the presence of vugs and caves. However, current models for predicting lost circulation often suffer from limited accuracy and efficiency due to the complexity of geological formations and the variability of drilling conditions, leading to unreliable predictions in challenging environments. This research aims to overcome these limitations by developing a more accurate and efficient predictive model tailored to the Al Jeribe Formation, providing valuable insights to mitigate fluid loss and improve drilling efficiency. This paper introduces a novel predictive model for lost circulation in the Al Jeribe Formation, utilizing artificial neural networks (ANNs) trained on extensive field data from over 100 wells. The model incorporates key drilling parameters such as mud weight (MW), yield point (Yp), equivalent circulation density (ECD), rate of penetration (ROP), revolutions per minute (RPM), strokes per minute (SPM), Plastic viscosity (PV), and weight on bit (WOB) as input parameters. The ANN achieved excellent predictive performance, with Training R² = 0.99 and Testing R² = 0.99. Error metrics also confirmed strong generalization, with RMSE = 1.70% (training) and 1.40% (testing), and AAPE = 11.0% (training) and 10.2% (testing). In addition, the model identified the most critical parameters influencing lost circulation and provided optimized parameter ranges to mitigate fluid loss during drilling operations. This study focuses on lost circulation prediction in the Al Jeribe Formation, identifying key drilling parameters and providing optimized ranges to reduce losses and improve wellbore stability. It offers insights not covered in previous research, specifically targeting the Al Jeribe Formation. The model predicts lost circulation and suggests practical adjustments to drilling parameter values. The findings are expected to enhance drilling efficiency and minimize downtime in the Al-Omar field. This methodology can also be applied to similar geological formations worldwide to reduce lost circulation in oil fields. Full article

Maximizing the agricultural output on inherently infertile land and minimizing the environmental cost remain central research imperatives. Albic black soil typifies such infertility. Conventional practice relies on fertilization and straw incorporation, but the albic layer’s impermeability funnels applied nutrients into adjacent aquatic systems. Therefore, this study developed deep placement fertilization by lodging fertilizer directly within the albic layer to block hydrologic loss. The feasibility of mechanization was first validated in pot experiments. Soybeans were allocated to six treatments simulating fertilizer placement at different soil depths: control (C), control and fertilizer (CF), surface soil mixing (SM), surface soil mixing and fertilizer (SMF), plow pan soil mixing (PM), and plow pan soil mixing and fertilizer (PMF). The treatments used 20 cm tillage, and the data were collected after 15, 25, and 35 days and at harvest. Integrative transcriptomic, proteomic, metabolomic, and soil microbiome profiling revealed that fertilizer positioned at 25 cm in the albic layer increased yield, restructured the rhizobiont community and promoted arbuscular mycorrhizal fungal colonization. Among the fertilizer treatments, CF had the best growth, and SMF was inhibited by a nutrient shortage. SMF and PMF lost water faster than CF. Abscisic acid (ABA) conveyed the subterranean fertilization signal to the leaf. The enrichment of Vicinamibacterales, Xanthobacteraceae, and Glomeromycota in soil lowered the ABA content in the roots, which upregulated thymidine kinase and peroxidase upon arrival in the leaf, increasing yield. These findings provide a transferable benchmark for any parent material exhibiting poor hydraulic conductivity. Full article

Automatic sleep-stage classification is a key component of long-term sleep monitoring and digital health applications. Although deep learning models trained on centralized datasets have achieved strong performance, their deployment in real-world healthcare settings is constrained by privacy, data-governance, and regulatory requirements. Federated learning (FL) addresses these issues by enabling decentralized training in which raw data remain local and only model parameters are exchanged; however, its effectiveness under realistic physiological heterogeneity remains insufficiently understood. In this work, we investigate a subject-level federated deep learning framework for sleep-stage classification using polysomnography data from the ISRUC-Sleep dataset. We adopt a realistic one subject = one client setting spanning three clinically distinct subgroups and evaluate a lightweight one-dimensional convolutional neural network (1D-CNN) under four training regimes: a centralized baseline and three federated strategies (FedAvg, FedProx, and FedBN), all sharing identical architecture and preprocessing. The centralized model, trained on a cohort with regular sleep architecture, achieves stable performance (accuracy 69.65%, macro-F1 0.6537). In contrast, naive FedAvg fails to converge under subject-level non-IID data (accuracy 14.21%, macro-F1 0.0601), with minority stages such as N1 and REM largely lost. FedProx yields only marginal improvement, while FedBN—by preserving client-specific batch-normalization statistics—achieves the best federated performance (accuracy 26.04%, macro-F1 0.1732) and greater stability across clients. These findings indicate that the main limitation of FL for sleep staging lies in physiological heterogeneity rather than model capacity, highlighting the need for heterogeneity-aware strategies in privacy-preserving sleep analytics. Full article

Background: Transportation delays pose significant challenges for perishable products by reducing freshness, shortening selling duration, and causing lost sales during the delay. Methods: Motivated by the growing importance of transportation delays on perishable products, this study develops a single-period analytical expected profit expression to determine the optimal order quantity that maximizes expected profit. The model incorporates deterioration-driven price reductions, lost sales opportunities occurring during the delay, and the shortened selling duration resulting from delayed delivery, without imposing a specific probability distribution on the transportation delay duration. Results: Numerical experiments illustrate how key parameters influence the optimal order quantity and the corresponding expected profit. Deterioration reduces expected profit by primarily reducing the selling price. In addition, a higher disruption probability reduces both the optimal order quantity and the expected profit, while longer selling durations result in larger order quantities and yield higher expected profits. A low initial selling price can result in negative expected profit, indicating cases where placing the order is inappropriate. Conclusions: The findings offer managerial implications for determining optimal order quantities that maximize profit under transportation delays for perishable products. Full article

Background: Climate change is intensifying extreme heat exposure in Mediterranean agricultural systems. Migrant workers engaged in outdoor fieldwork are a highly vulnerable population with limited access to resources. Crucially, there is a notable lack of data on how heat affects these workers in this specific region. Objective: This study aimed to analyze the physiological effects of high-temperature exposure by quantifying and correlating indirect indicators of dehydration and electrolyte imbalance (sodium and potassium losses, sweat, body weight, and blood pressure). Methods: An observational study was conducted over nine consecutive days involving ten agricultural participants, yielding 90 observations. Measurements of body weight, heart rate, blood pressure, skin temperature, sweat loss, and sodium and potassium concentrations were taken before, during, and after daily field activity. Results: Results showed considerable interindividual variability in thermophysiological responses. Participants lost an average of 0.8 kg (range –9.1 to +3.6 kg) and produced 3.91 L of sweat (range 1.9–6.4 L), with sodium and potassium losses of 4932 mg and 646 mg, respectively. Sweat loss correlated with sodium (r = 0.414, p = 0.001) and potassium (r = 0.791, p < 0.001), and diastolic blood pressure was moderately associated with weight loss (r = 0.576, p = 0.016). Conclusions: Sweat loss was the main driver of electrolyte depletion, with marked interindividual variability. Monitoring sweat-related indicators and diastolic blood pressure could help detect dehydration risk in agricultural workers exposed to extreme heat. Targeted hydration strategies and occupational health education are essential to mitigate these risks. Full article

Paleogene lacustrine shale is a key source rock for large oil reserves in China and a major target for shale oil exploration. However, differences in the chemical characteristics of felsic and carbonate shales during burial and thermal evolution remain poorly understood. This study evaluates hydrocarbon generation and expulsion efficiency in these shale types using pyrolysis experiments on lower Paleocene Kongdian Formation samples (Type I) from the Eastern China Sedimentary Basin. Results show that felsic shale has higher hydrocarbon generation capacity than carbonate shale. During pyrolysis, carbonate shale retained ~119 mg/g more oil but expelled 184 mg/g less than felsic shale. Felsic shale reached peak oil generation and retention faster but with lower retention efficiency. The larger volume of residual hydrocarbons in felsic shale facilitated earlier expulsion onset, higher yields of gaseous hydrocarbons, and superior gas expulsion efficiency. While both shales exhibited similar thermal evolution trends for hydrocarbon gases, methane proportions and gas-oil ratios (GOR) differed significantly. Carbon loss was comparable during the oil window, but felsic shale lost more carbon overall. At higher temperatures, n-alkanes in residual oil decreased sharply, with lighter oil retained at advanced maturity, increasing GOR and reducing heavy hydrocarbons. These findings demonstrate the effective hydrocarbon potential of medium-high TOC felsic and carbonate shales. Full article

The influence of synthesis method on the properties of Zn_1−xFe_xO nanoparticles with different Fe doping levels (x = 0, 0.01, 0.03, and 0.05) for Congo Red (CR) adsorption was investigated. Nanoparticles were prepared by sol–gel and coprecipitation and characterized by XRD, SEM-EDS, FTIR, and BET analyses. Sol–gel synthesis produced smaller particles (~13 nm) than coprecipitation (~35 nm), and both the method and calcination temperature strongly affected crystallite size. Sol–gel nanoparticles showed significantly higher adsorption efficiency (~90%) due to their larger BET surface area, greater BJH pore volume, and smaller particle size, which increased the number of accessible active sites. In contrast, coprecipitation nanoparticles exhibited a much lower adsorption capacity (~24%). Fe incorporation further enhanced performance by introducing lattice distortions and oxygen vacancies, as evidenced by XRD peak broadening and increased lattice strain. SEM images displayed particle growth and compaction after adsorption, particularly in doped samples. Temperature-dependent experiments indicated that undoped ZnO lost efficiency at 60 °C due to weak physical interactions, whereas Fe-doped nanoparticles maintained high adsorption, due to improved stability of the adsorbent-adsorbate bond. The combination of Fe doping and sol–gel synthesis significantly improved the properties of ZnO, yielding highly efficient adsorbents suitable for environmental remediation. Full article

Nanogels provide unique opportunities for stabilizing fragile enzymes through soft, hydrated polymer networks. Here, we report the development of a glutathione peroxidase (GPx)-loaded nanogel (GPxNG) engineered via a mild “grafting-to” epoxy–amine coupling strategy to enhance enzyme stability and antioxidant function. An amphiphilic copolymer composed of methacrylated 2,2,6,6-tetramethyl-4-piperidyl (PMA) and glycidyl methacrylate (GMA) was synthesized by controlled reversible addition–fragmentation chain-transfer (RAFT) polymerization using a poly(ethylene glycol) (PEG) macro-chain transfer agent (macro-CTA), yielding well-defined polymer chains with reactive epoxy groups. Covalent conjugation between polymer epoxides and GPx enzyme surface amines generated soft, PEGylated nanogels with high coupling efficiency, uniform particle sizes, and excellent colloidal stability. The engineered nanogels exhibited shear-thinning injectability, robust storage stability, and non-cytotoxic behavior in RAW 264.7 macrophages. Compared with native GPx enzyme, GPxNGs demonstrated significantly enhanced reactive oxygen species (ROS) scavenging activity, including strong inhibition of lipid peroxidation and copper-induced low-density lipoprotein (LDL) oxidation. Importantly, the nanogels preserved GPx enzyme activity after extended storage, freeze–thaw cycles, and repeated catalytic use, whereas the free enzyme rapidly lost function. This protective effect arises from the nanoscale confinement of the GPx enzyme within the flexible PEG-based network, which limits unfolding and aggregation. Overall, this work introduces a simple and biocompatible “grafting-to” nanogel platform capable of stabilizing redox-active enzymes without harsh conditions. The GPx nanogels combine high enzymatic preservation, potent antioxidant activity, and excellent handling properties, highlighting their potential as a therapeutic nanoplatform for mitigating oxidative stress-associated disorders such as atherosclerosis. Full article

The recycling of by-products from fish processing procedures has recently been attracting increased attention. In this study, three types of gelatin were isolated from grass carp skin, bone, and scales, named SKG, BG, and SCG, respectively, and their structural and functional characteristics were investigated. Compared with BG and SCG, SKG exhibited the highest extraction yield (18.30 ± 0.24%) and protein content (90.12 ± 0.21%) and the lowest ash content (1.50 ± 0.08%). Electrophoresis analysis revealed that SKG contained more α chains than BG and SCG. Fourier transform infrared spectra showed that the absorption peaks of gelatin were mainly positioned in amide band regions, whereas some of the triple helix structure was lost. More than 85% solubility was observed in all gelatin types with pH 3–10. Meanwhile, there was a higher gel strength in SKG (288.2 g) than in BG (270.2 g) and SCG (245.1 g). Furthermore, the water or oil absorption and emulsifying characteristics of SKG were also better than those of BG and SCG. The differences in functional properties between gelatin types appear to be related to protein distribution and composition. All the results indicate that grass carp skin is a material with the potential to extract gelatin with a higher yield and gel strength and better functional characteristics compared with bone and scales. Full article

Data loss is a recurring and critical issue in Structural Health Monitoring (SHM) systems, often arising from a range of factors including sensor malfunction, communication breakdown, and exposure to adverse environmental conditions. Such interruptions in data availability can significantly compromise the accuracy and reliability of structural performance assessments, thereby hindering effective decision-making in safety evaluation and maintenance planning. In this study, a novel deep learning-based framework is proposed for data reconstruction in SHM, employing a hybrid architecture that integrates one-dimensional convolutional neural networks (1D-CNNs) with recurrent neural networks (RNNs). By combining these complementary strengths, the hybrid 1D-CNN–RNN model demonstrates superior capacity for accurate signal reconstruction. A real-world case study was conducted using vibration data from the Trai Hut Bridge in Vietnam. Five network configurations with varying depths were examined under single- and multi-channel loss scenarios. The results confirm that the method can accurately reconstruct lost signals. For single-channel loss, the best configuration achieved an MAE = 0.019 m/s² and R² = 0.987, while for multi-channel loss, a deeper network yielded an MAE = 0.044 m/s² and R² = 0.974. Furthermore, the model exhibits robust and stable performance even under more demanding multi-channel data loss conditions, highlighting its resilience to practical operational challenges. The results demonstrate that the proposed CNN–RNN framework is accurate, robust, and adaptable for practical SHM data reconstruction applications. Full article

Following MI, massive cardiomyocytes are lost, and inflammatory cells such as monocytes and macrophages migrate into the damaged region to remove dead cells and tissue. While cardiac macrophages are abundant in the injured heart post-MI, the role of inflammation in cardiovascular disease has been under-appreciated in the past. Consequently, the contribution of specific macrophage subsets or macrophage-derived factors on cardiac cells is not well known. Thus, this study investigated the paracrine signaling between human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) and macrophages, with the focus on the effects of macrophage-derived osteopontin (OPN) on hiPSC-CM function. HiPSC-CM were first co-cultured with unpolarized (M0), pro-inflammatory (M1), or anti-inflammatory (M2) macrophages. The co-culture of hiPSC-CM with M2 macrophages specifically led to notable changes in the electrophysiological properties of hiPSC-CM, including prolonged contraction time (RT90), action potential duration (APD90), and calcium decay time (CSD RT90). Moreover, a significant upregulation of action potential-related genes such as CACNA1C and SCN5A was demonstrated, which coincided with the elevated OPN level in the hiPSC-CM with M2 macrophages co-culture. These functional changes were not observed in the hiPSC-CM-M0 and M1 co-culture groups, likely due to the OPN level remaining below the threshold required to induce detectable changes in hiPSC-CM. Subsequent experiments involving exogenous OPN supplementation and inhibition in hiPSC-CM culture yielded concordant results, further confirming the direct role of OPN in modulating hiPSC-CM gene expression. This study highlights the differential effect of specific macrophage subtypes on hiPSC-CM, as well as the potent bioactivity of OPN and its ability to directly modulate cardiomyocyte behavior, even in the absence of direct cell–cell interactions within a co-culture system. These findings further suggest that OPN could be a novel target for therapeutic intervention in cardiac diseases. Full article

Wellbore instability during drilling in soft formations often leads to unwanted hydraulic fractures and lost circulation, resulting in non-productive time and elevated costs. The fracture initiation pressure (FIP) and fracture propagation pressure (FPP) are critical for managing these risks, particularly in narrow mud weight windows, yet industrial models overlook post-plugging stress behaviors at plug locations, where changes in stress concentration may initiate secondary fractures. This study introduces a fully coupled hydro-mechanical plane-strain (KGD) finite element model to examine fluid diffusion and deformation in fractured formations, emphasizing plastic and poroelastoplastic effects for wellbore strengthening. Fluid flow in the fracture follows lubrication theory for incompressible Newtonian fluids, while Darcy’s law governs porous media diffusion. Rock deformation adheres to Biot’s effective stress principle, extended to poroelastoplasticity via the Mohr–Coulomb criterion with associative flow. Simulations yield fracture dimensions, fluid pressures, in situ stress changes, and principal stresses during propagation and plugging, for both plastic and poroplastic cases. A new yield factor is proposed, derived from the Mohr–Coulomb criterion, that quantifies the risk of failure and reveals that fracture tips resist propagation through plastic and poroelastoplastic deformation, with the poroelastoplastic coupling amplifying back-stresses and dilation after plugging. Pore pressure evolution critically influences the fracture growth and plugging efficiency. These findings advance wellbore strengthening by optimizing lost circulation material plugs, bridging the gaps from elastic and poroelastic models, and offer practical tools for safer and more efficient plugging in soft rocks through modeling. Full article

Background: Empagliflozin and dapagliflozin are the most widely prescribed sodium–glucose cotransporter-2 inhibitors (SGLT2i) with established cardioprotective benefits across the spectrum of heart failure (HF). However, direct comparative data remain limited, particularly in patients with a history of coronary revascularization—a population at persistently high cardiovascular (CV) risk. This study aimed to compare the long-term cardiovascular outcomes of empagliflozin versus dapagliflozin in revascularized HF patients who had undergone coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI). Methods: This retrospective cohort study included 631 HF patients who had undergone coronary revascularization (CABG or PCI) and were treated with an SGLT2 inhibitor (353 dapagliflozin, 278 empagliflozin) between 2014 and 2022 at a tertiary cardiovascular center. Patients were stratified by left ventricular ejection fraction (LVEF ≥ 50%: HFpEF; LVEF < 50%: HFrEF/HFmrEF). The primary outcomes were all-cause mortality, cardiac mortality, major adverse cardiovascular events (MACE), cardiac MACE, and HF-related hospitalization. Cox regression analyses—including time-dependent covariates—were performed to identify independent predictors of cardiac MACE. Results: Baseline demographic, clinical, and biochemical characteristics were comparable between groups. During a mean follow-up of 19.6 ± 1.5 months, there were no significant differences between dapagliflozin and empagliflozin in all-cause mortality (19.3% vs. 19.8%), cardiac mortality (11.0% vs. 12.2%), MACE (25.8% vs. 26.3%), cardiac MACE (23.8% vs. 21.9%), or hospitalization (23.8% vs. 23.7%) (all p > 0.05). Subgroup analyses by LVEF yielded consistent findings. In time-adjusted Cox modeling, age (HR = 2.089; 95% CI: 1.723–2.533; p < 0.001) and atrial fibrillation (AF) (log-rank p = 0.030) were identified as significant predictors of cardiac MACE, while creatinine and NT-proBNP lost significance after adjustment. Both age and AF showed time-varying hazard effects, with risk attenuation over time. Conclusions: In this real-world cohort of revascularized HF patients, empagliflozin and dapagliflozin demonstrated comparable long-term cardiovascular outcomes, supporting a class effect of SGLT2 inhibitors in this high-risk population. Beyond pharmacologic comparison, age and AF emerged as dynamic predictors of cardiac MACE, highlighting the importance of longitudinal, time-dependent risk assessment in heart failure management following coronary revascularization. Full article

Background: Quantitative urine culture (CFU/mL) remains the reference standard for diagnosing urinary tract infections (UTIs) but is limited by delayed turnaround times and sensitivity to pre-analytic factors. Multiplex PCR panels offer rapid detection; however, standardized mappings between molecular signals and viable bacterial burdens are insufficiently defined. We used the multicenter NCT06996301 paired dataset to evaluate the analytical validity (AV), clinical validity (CV), and pre-analytic robustness of ΔCt (Ct_target − IC_Ct) as a semi-quantitative indicator of bacterial load. Methods: We analyzed 1027 paired PCR and quantitative urine culture specimens from six sites. The primary molecular predictor was ΔCt (Ct_target − IC_Ct). Species-level Spearman and Pearson correlations, species-specific linear mixed-effects calibration models (log₁₀CFU ~ ΔCt + (1|site)), and ROC analyses were performed for the taxa meeting pre-specified sample thresholds. A pooled multilevel model assessed the collection method and time-to-processing (hours) effects (log₁₀CFU ~ ΔCt × collection_method + ΔCt × time_to_processing_h + (1|site) + (1|run)). AV was assessed via reproducibility, internal control normalization, and site run variance. CV was assessed by ΔCt calibration and discrimination. Clinical utility (CU) was contextualized using outcomes from the parent randomized trial. Results: PCR positivity exceeded culture positivity across all sites (PCR ~82–88% vs. culture ~66–70%); this excess likely reflects a combination of molecular detection of non-viable DNA, detection of fastidious taxa less readily recovered by culture, and pre-analytic effects. For six common uropathogens (n = 90 pairs/species), ΔCt correlated strongly with log₁₀CFU (Spearman ρ = −0.64 to −0.75; Pearson r = −0.75 to −0.83). Species-specific mixed models yielded slopes of −0.746 to −0.922 log₁₀CFU per ΔCt unit (all p < 0.001), indicating that each 1 unit ΔCt change corresponds to a ~5.6–8.4-fold CFU difference. ROC AUCs for ΔCt discrimination were 0.78–0.84 (interpreted as good discrimination, i.e., ΔCt meaningfully improves the clinician’s probability estimate of a high CFU but does not perfectly classify every specimen). The collection method (catheter vs. clean-catch) did not materially modify the ΔCt→CFU relationship, whereas the processing delay was associated with reduced recovered CFU (~0.048 log₁₀CFU lost per hour) and a significant ΔCt × time interaction, consistent with time-dependent viability loss driving the PCR⁺/culture⁻ discordance. Conclusions: ΔCt from the DOC Lab UTM 2.0 panel shows a reproducible, analytically valid semi-quantitative measure of urinary bacterial load. Laboratories can derive assay- and workflow-specific ΔCt cut points for semi-quantitative reporting, but thresholds must be validated prospectively and paired with operational controls for specimen handling. Full article

The saliva of the medicinal leech Hirudo medicinalis contains a wide range of biologically active compounds, including multiple anticoagulants. Previously, we identified a novel cysteine-rich anticoagulant protein (CRA) from leech saliva and produced it recombinantly in Escherichia coli, demonstrating its potential as a basis for new anticoagulant drugs. In this study, we developed an optimized procedure for scalable production and purification of recombinant CRA. The purified protein was analyzed for common contaminants originating from E. coli, such as endotoxins, bacterial proteins, and DNA, and its anticoagulant properties were evaluated using standard clotting assays. Across three independent experiments, the yield of purified CRA ranged from 3.7 to 5.5 mg per liter of bacterial culture, with impurity levels per milligram of protein ranging from 7.1–31.2 ng of bacterial proteins, 1.2–15.1 ng of DNA, and 60–1445 EU of endotoxins. The purified CRA displayed electrophoretic and chromatographic homogeneity and retained strong anticoagulant activity. Additionally, a truncated form of CRA lacking the C-terminal region was produced and characterized. This variant lost membrane affinity and showed altered activity profiles, with higher thrombin time activity but reduced prothrombin time and activated partial thromboplastin time activities compared with the full-length protein. Full article