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Investigation on the dynamic evolution and evaluation of floor microseismic responses during extra-thick coal seam mining
Customs fraud detection using a gradient boosting approach for joint classification and risk estimation
Physicochemical characteristics and microbial community analysis of a wetland system treating acid mine drainage
Exposure to 5G-NR electromagnetic fields affects larval development of Aedes aegypti mosquito
Abstract Telecommunication networks, including 5G New Radio (5G-NR), emit these fields and consequently expose many insects. To quantify the potential effect of RF-EMF exposure on insects, a study was designed examining the development of the Aedes aegypti mosquito, a major vector of dengue and other pathogens, as model organism exposed to RF-EMFs at 3.6 GHz. A custom exposure setup, a reverberation chamber, was designed, built, and characterized. Numerical simulations made it possible to calculate doses received by the larvae during the exposure. Larvae were reared on two feeding regimes, differing in nutritional value, and exposed for 5 days. At an RF exposure level of 46.2 V/m and absorbed power of 1.2 $$\upmu$$ W, a slower development occurred, especially for weakened larvae. At an RF exposure level of 182.6 V/m and 18.7 $$\upmu$$ W absorbed power, dielectric heating changed development timing and adult size.
An innovative technique and laboratory protocol for CO2 storage in water disposal wells and monitoring asphaltene deposition
Abstract Injecting CO 2 into water-disposal wells is a promising strategy for geological carbon storage. However, this process can destabilize asphaltenes in residual oil blobs—primarily of the emulsified type – trapped within trapped in the porous rock, leading to precipitation that threatens storage integrity and operational safety. This study introduces a novel high-pressure laboratory apparatus and protocol designed to directly quantify asphaltene precipitation during CO 2 injection into oil-in-water emulsions, which represent water-flooded formations. The system operates at reservoir-relevant conditions (up to 11,000 psi and 210 °C) and utilizes in situ near-infrared (NIR) light transmission to monitor asphaltene precipitation in real-time. Additionally, this research investigates the behavior of the oil-in-water emulsion (EM) phase as the medium hosting CO 2 gas under different conditions. Quantitative results, expressed as the percentage reduction in NIR transmission, showed that asphaltene precipitation was minimized to 0.8% under optimal conditions (2DSW, 120 °C, 50 mol% CO 2 ), compared to a peak of 25.1% in the worst-case scenario (FW, 30 °C, 35 mol% CO 2 ). Regarding the CO 2 injection rate, less asphaltene precipitation occurred at higher injection rates. In this case, crude oil vaporized in the EM phase at high CO 2 injection rates (above 35 mol%), resulting in fewer crude oil droplets available to interact with CO 2 . Notably, EMs prepared with twice-diluted seawater (2DSW) exhibited the least asphaltene precipitation, a finding strongly correlated with lower oil/water interfacial tension. Overall, the developed protocol provides a critical tool for screening and de-risking CO 2 storage sites in water-disposal zones by enabling accurate prediction of asphaltene-related damage.
Analysis of volatile organic compounds in biological samples of colorectal cancer patients using electronic nose-based machine learning techniques
Abstract Colorectal cancer (CRC) is a significant global health burden characterized by prolonged asymptomatic progression and high mortality. CRC curability improves with early-stage detection, and removing precancerous adenomas allows for prevention, emphasizing the significance of screening. This prospective study, conducted between 2024 and 2025 with 100 randomly recruited participants, investigates eNose-based analysis of volatile organic compounds (VOCs) in biological matrices for CRC diagnosis using both unsupervised and supervised machine learning (ML) techniques. After detailed medical examinations, laboratory tests, and colonoscopy, 50 patients with confirmed stage III CRC and 50 healthy controls agreed to have their blood, urine, and stool samples analyzed by the eNose technique. Principal component analysis (PCA), logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and gradient boosting (GB) were used to analyze eNose VOC patterns in all biological matrices. Clinical and hematological alterations in CRC patients were consistent with systemic malignancy, including reduced weight, mild anemia, leukopenia, thrombocytopenia, and hypoalbuminemia, all of which are established indicators of disease severity and prognostic markers. Elevated VOC responses in CRC patients across all matrices, with blood and stool proving most informative due to favorable signal-to-noise ratios. Ensemble- and proximity-based models GB and KNN were found to be superior to LR classifiers, with GB exhibiting balanced and adaptable performance across different biological matrices. Limiting the study to stage III CRC patients improved VOC signal clarity but limited early-stage generalizability, a constraint effectively mitigated by Gaussian augmentation, which enriched data variability and boosted model performance for screening applications. Thus, eNose-based ML systems provide a globally accessible, innovative, non-invasive, and affordable solution for CRC detection, combining high sensitivity and specificity to support widespread early diagnosis.
Transcriptomics-based analysis of key genes and potential mechanism and therapeutic agents in cadmium-induced esophageal squamous cell carcinoma progression
Efficient calculation and analysis of dynamic RCS characteristics for moving target using the shooting and bouncing rays method
Nanoarchaeosomes for synergistic photochemotherapy in triple-negative breast cancer
Immunoinformatics-based design of a next generation multi-epitope vaccine candidate against Shigella boydii using a hierarchical subtractive proteomics approach
Prostate cancer diagnosis using sensitive and sophisticated machine learning classifiers based on non-invasive urinary RNA biomarkers (PCASSO)
Uricosuric, antioxidant, and anti-inflammatory properties of Pandanus amaryllifolius Roxb. extract against potassium oxonate-induced hyperuricemia in rats
Systematic hyperparameter analysis of GRU and LSTM across demand pattern types: a demand-characteristic-driven meta-learning framework for rapid optimization
Abstract Deep Learning (DL) offers powerful tools for demand forecasting by capturing complex nonlinear patterns and adapting to dynamic market conditions. Accurate forecasts are vital for optimizing production planning, reducing costs, aligning with customer demand, and efficient resource allocation. Forecast accuracy depends heavily on both dataset characteristics and DL hyperparameters, which influence model complexity and learning behavior. Although research efforts are focused on using data properties in demand classification and hyperparameter tuning for better DL accuracies, the efforts exerted in analyzing their impacts are few. This paper investigates how demand characteristics, such as variability, zero demand frequency, and spikiness, and DL hyperparameters of Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) affect multi-period forecast accuracy. Three types of demand patterns are analyzed: smooth demand, erratic demand without spikes, and erratic demand with spikes. Demand Complexity Index (DCI) is proposed as an integrated metric of demand characteristics, including demand variability, the amount of zero demands, and the degree of spikiness of the demand. To handle zero-demand periods and normalize accuracy across datasets, Weighted Mean Absolute Percentage Error (WMAPE%) is used to assess forecasting accuracy. Results show that the Coefficient of Variation (CV) is the most influential data feature, while Learning Rate is the most impactful hyperparameter affecting forecast accuracy. Demand complexity significantly influences forecasting accuracy, with WMAPE increasing by up to 14.6% per unit rise in DCI for GRU and 11.3% for LSTM, highlighting the need for complexity-driven model optimization. The main contribution of this work is introducing an integrated framework to tailor hyperparameter selection to input demand characteristics, enabling improved accuracy and faster processing.”
Design framework and optimization of portable biomedical waste decomposition systems using ANN and MOPSO
Abstract Biomedical waste (BMW) incineration requires accurate prediction of energy demand and efficiency due to its heterogeneous composition. In this study, material and energy balance calculations were combined with design of experiments (DOE), analysis of variance (ANOVA), artificial neural network (ANN) modeling, and multi-objective particle swarm optimization (MOPSO). The novelty of the work presents the integration of metaheuristic optimization (MOPSO) into the biomedical waste incineration process. Results showed cellulose content as the most significant determinant of auxiliary energy requirement, with higher cellulose reducing LPG demand, while tissue and moisture exerted secondary but measurable effects. Efficiency ranged between 95.5 and 95.6%, with efficiency decreasing at higher moisture levels. The ANN model achieved near-perfect prediction accuracy (R² > 0.9999), enabling robust surrogate-based optimization. MOPSO analysis identified Pareto-optimal operating conditions where auxiliary energy demand reduced from 99.7 MJ/h to 97.2 MJ/h while efficiency improved from 95.52% to 95.60%. Under optimal waste composition identified by the ANN-MOPSO hybrid, auxiliary LPG consumption reduced from 33.8 to 27.4 kg/h, indicating strong potential for energy savings within the studied domain.
LncRNA PVT1 promotes proliferation, migration and invasion of cholangiocarcinoma by regulating the expression of SOCS2
Sirtuin 3 promotes osteogenic differentiation of bone marrow mesenchymal stem cells by regulating macrophage polarization under high glucose
Assessment of volatile component stability in Guanxin Jieyu granules through gas chromatography in accordance with Q14 guidelines
Abstract Guanxin Jieyu Granules, a novel traditional Chinese medicine (TCM) in development, targets stable angina pectoris with accompanying anxiety. To improve stability, its volatile oil components—including borneol—are stabilized via β-cyclodextrin inclusion. In compliance with ICH Q14 guidelines, a gas chromatography (GC) analytical method was developed, incorporating knowledge management, risk management, experimental design, and results analysis, as well as performance evaluation of the analytical method. Accelerated Destructive Degradation Tests (ADDTs) using this method revealed a dramatic stability enhancement from β-cyclodextrin encapsulation. At 25 °C over 730 days, the inclusion complex showed only a 1.10% probability of failure (defined as ≥ 5% content loss), compared to 88.50% for the physical mixture. These results underscore the critical role of cyclodextrin inclusion in preserving volatile TCM constituents.