Search Results (10,012)

Multiphase pumps are key equipment for the efficient transport of multiphase fluids in the petroleum industry, and their transient stability under variable-speed conditions directly affects system reliability. By combining numerical simulation with experimental validation, this study systematically investigates the evolution of external characteristics, energy conversion mechanisms, and the dynamic response of the internal flow field during a 0.4 s variable-frequency speed regulation cycle at inlet gas volume fractions (IGVFs) of 10% and 20%. The numerical model was validated against experimental measurements of a four-stage multiphase pump under pure-water steady-state conditions, with deviations in head, efficiency, and power all within 5%. The results show that during acceleration, the increase in hydraulic efficiency at the lower IGVF is greater than that at the higher IGVF; once deceleration begins, IGVF has no significant effect on hydraulic efficiency. At the investigated IGVFs of 10% and 20%, a higher IGVF increases the transient sensitivity of the internal flow field to speed variation, and increasing IGVF suppresses energy conversion in the impeller. The principal novelty of this work lies in the temporal decomposition of impeller work into dynamic and static pressure components during transient speed variation, revealing that static pressure power consistently accounts for more than 50% of the total power throughout the speed regulation cycle. As rotational speed increases, dynamic pressure power rises because the circumferential velocity of the fluid increases with impeller peripheral speed, while static pressure power also increases continuously owing to the enhanced static pressure work of the blades. During deceleration, the impeller’s energy transfer capability weakens with decreasing rotational speed, and both dynamic and static pressure power decline. These findings elucidate the coupled evolution of gas–liquid two-phase flow under variable-speed conditions and provide a theoretical basis for the operational optimization and speed control of multiphase pumps. Full article

The Antioxidant Food Table is the largest open collection of measured food antioxidant capacity. It covers 3139 products assayed by the ferric reducing ability of plasma (FRAP) method. The table has served mainly as a dietary lookup source and has never been machine-readable or benchmarked. Here it was extracted into a validated open dataset (3135 records, 99.9%). A leakage-aware benchmark of antioxidant capacity prediction from product description and category was then constructed. Eighteen predictors, from naïve baselines to deep networks and fusions, were evaluated under two partitioning regimes with the same five seeds and permutation controls. Since 39.4% of records share a product name, the conventional random split rewards memorization. A learning-free duplicate lookup explained 45% of the apparent k-nearest-neighbor advantage over the category median. In grouped evaluation, ridge regression on term frequency–inverse document frequency (TF–IDF) features ($R^2=0.674$) outperformed both deep networks. Pretrained word vectors did not close this gap. An equal-weight fusion of all eight models performed best ($R^2=0.684$) with 2.6-fold lower variability. Protocol and representation, rather than architecture, dominated the outcome on this benchmark. The dataset, code, and predictions are released openly. Full article

Background: The characterisations of highly diluted preparations impregnated onto solid carriers remains analytically challenging, particularly when residual material is present at very low levels. Electrophotonic analysis (EPA), based on corona-discharge imaging and quantitative image analysis, has recently been explored as a possible complementary approach, but its applicability to impregnated pilules requires further investigation. Purpose: This exploratory study examined whether EPA-derived image parameters could reveal measurable differences among size-6 pilules impregnated with stepwise diluted and dynamised preparations of Cuprum metallicum and Gelsemium sempervirens and whether such differences might vary according to source material and manufacturing protocol. Methods: Size-6 pilules were impregnated with Hahnemannian and Korsakovian preparations of Cuprum metallicum and Gelsemium sempervirens. Control samples included non-impregnated pilules, pilules impregnated with pure solvent, and pilules impregnated with simply diluted, non-dynamised preparations. Images were acquired in randomised and blinded conditions using a prototype EPA device. Image intensity, contrast, entropy, and fast Fourier transform (FFT)-derived period/spatial-frequency parameters were analysed. Results: The EPA images showed patterns that may indicate differences between impregnated pilules and controls, between the two source materials, and between manufacturing protocols under the experimental conditions used here. FFT-based analysis suggested that some spatial-period/spatial-frequency indices may contribute to describing these preliminary differences. Among the parameters examined, the smallest diameter of the selected high-intensity FFT-image regions remained statistically significant after correction for multiple comparisons, whereas other parameters should be interpreted cautiously. Observations of aged samples also suggested possible differences from reference preparations, but these findings remain preliminary and require confirmation. Conclusions: These results suggest that EPA combined with FFT-based image analysis may provide exploratory information on EPA-derived image patterns in potentised preparations impregnated onto solid carriers. However, the present data are not sufficient to establish EPA as a reliable discriminatory analytical tool. Further measurements, larger sample sets, independent replication, improved control of environmental variables, and comparison with complementary analytical methods are required before firm conclusions can be drawn about its analytical value. Full article

Despite major advances in exercise physiology, biomechanics, and mechanobiology, exercise science still lacks a clear conceptual definition of the mechanical stimulus experienced by skeletal muscle during resistance training. Current approaches rely on diverse external, internal, and biomechanical variables (e.g., volume-load, force, power, velocity, time under tension, or muscle architecture), yet none individually captures the muscle-specific mechanical exposure relevant to muscular adaptation. This conceptual inconsistency limits comparisons across studies, complicates training prescription, and hinders the development of individualized monitoring strategies. This integrative review critically synthesizes current evidence from mechanobiology, skeletal muscle physiology, biomechanics, and resistance training to examine how mechanical stimuli are currently conceptualized, quantified, and interpreted. Based on this synthesis, we propose a working definition of Mechanical Dose as the cumulative, muscle-specific mechanical exposure experienced over a defined time window, characterized by loading magnitude, rate, duration, frequency, and spatial distribution, and conditioned by contraction mode and muscle–tendon geometry. Rather than representing a directly measurable variable, mechanical dose is presented as a latent conceptual construct that can only be estimated through combinations of biomechanical, physiological, and morphological indicators. Building upon this definition, we introduce an integrative conceptual framework linking external load, movement biomechanics, mechanical dose, mechanotransduction, and tissue adaptation. We further discuss how this framework may guide future research on the interpretation of field-based monitoring, resistance training prescription, recovery management, and future explainable artificial intelligence approaches in sport science. By reframing mechanical dose as the central construct connecting biomechanics and biological adaptation, this review provides a unified conceptual basis for future research and contributes toward a more biologically informed paradigm of exercise prescription and monitoring. Full article

Multivariate time series forecasting requires modeling both hierarchical temporal dynamics and complex inter-variable dependencies, a dual requirement that often degrades predictive performance and incurs high computational costs in standard Transformer architectures. Unlike current channel-independent models that ignore vital cross-variable synergies, or dense-attention frameworks that suffer from quadratic computational noise, our approach extracts structurally sparse dependencies. To address these specific limitations, this study introduces the FCA-Transformer. The proposed framework integrates a Feature Pyramid Network (FPN) to isolate macroscopic trends from high-frequency localized fluctuations via hierarchical downsampling. Concurrently, a structured Transformer-based Cross-Attention (TCA) mechanism employs Dimensional Segmentation with Weighting (DSW) and a Two-Stage Attention (TSA) layer to map topological variable interactions, effectively extracting robust cross-variable pathways and mitigating distributional noise. Extensive empirical evaluations across three real-world multivariate benchmarks (ETTh1, Electricity, and Exchange Rate) demonstrate that the FCA-Transformer achieves an average reduction of up to 4.39% in MSE and 5.11% in MAE compared to leading baselines. These findings indicate that the proposed architecture successfully reconciles multi-scale feature extraction with lightweight dependency modeling, enhancing structural generalization and providing a scalable framework for real-time temporal analysis in complex industrial environments. Full article

The Biophilic Quality Matrix (BQM) evaluates natural-element criteria in relation to intervention scales. This study applied its published 10 × 10 structure to examine document-supported biophilic integration in three completed Singapore hospital environments. Twenty-five trained raters independently assessed case-specific public documentation, organized under a common template, for all 100 C × S cells. Median ratings yielded hospital-specific BQM profiles for the three cases, and cross-hospital screening identified 67 shared candidate cells. Rater-level bootstrap analysis retained 47 high-stability cells at an inclusion-frequency cutoff of 0.95, and the study-defined high-priority rule further retained 18 cells with a median of 4 in at least two hospitals. Exploratory semantic organization of the retained cells distinguished criterion-anchored groups centered on plants, ecological systems, sunlight, and earthly materials from scale-anchored groups centered on direct use and on sensory perception and variability. The retained relationships were then traced across the three hospitals to documented spatial configurations, including pond–courtyard–bridge systems, water-and-plant respite gardens, planted terraces and roof gardens, landscape-facing building interfaces, and landscaped routes across the hospital campus. These spatial comparisons support candidate design interpretations for precedent analysis. The findings describe what the reviewed records support and do not establish measured environmental performance, realized user experience, restorative effects, or clinical outcomes. Full article

Financial time-series forecasting remains challenging because of high volatility, nonlinear market dynamics, and the growing volume of heterogeneous information available to market participants. This study investigates Bitcoin forecasting in a big-data setting by combining high-frequency market data aggregated to hourly forecasting features, news headlines, Bitcoin on-chain variables, and broader macro-financial indicators. Rather than treating headline sentiment as a predefined categorical property, we construct continuous headline-conditioned market-impact labels from short-horizon Bitcoin price responses, directional volume imbalance, and volatility conditions. A chronology-controlled expanding-window FinBERT procedure generates scores without reusing each headline’s own future-derived target. The scores are integrated into multivariate Bitcoin forecasting using iTransformer, with LSTM as a benchmark. Evaluation uses repeated runs, benchmarks, Diebold–Mariano tests, backtesting, and forecast-free momentum controls. The headline-derived signal exhibits a measurable but temporally heterogeneous association with subsequent Bitcoin movements. Adding the headline score yields small, statistically non-significant error reductions for iTransformer, whereas it significantly worsens LSTM forecasts. Backtesting shows no consistent improvement in terminal portfolio value, but the headline feature alters the risk–return profile in several strategy configurations. Overall, the study provides a chronology-aware evaluation of whether headline-conditioned market-impact signals add predictive or economic value, while acknowledging residual dependence from exploratory iTransformer architecture selection. Full article

This study develops an analytical–evolutionary framework for optimizing the dynamic efficiency of bidirectional porous functionally graded beams. Touratier’s higher-order shear deformation theory is coupled with a real-coded genetic algorithm. The material-gradation indices in the thickness and width directions, the porosity coefficient, and the cross-sectional aspect ratio are treated as four coupled design variables. Dynamic efficiency is defined as the first modal frequency per unit mass, J = f₁/m (Hz/kg), with f₁ evaluated at β₁ = π/L for the simply supported finite beam. Uniform and non-uniform porosity laws are examined under three admissible design domains. After correcting and consistently implementing the modified rule of mixtures, the restricted-domain efficiencies are 117.819 and 96.766 Hz/kg for uniform and non-uniform porosity, respectively. Extending the material-gradation bounds increases them to 920.823 and 328.627 Hz/kg, while extension of the geometric domain gives 2302.058 and 821.568 Hz/kg. Thirty independent GA runs for each case yield 100% success under a 0.5% tolerance. Deterministic corner and one-at-a-time sampled checks confirm the observed boundary-directed trends within the investigated boxes. The results are mathematical optima for the stated objective and constraints, not production-ready designs. Full article

In high-speed drive applications, including drone propulsion systems and high-speed spindle drives, switching frequency is often limited by thermal constraints or cost considerations when the adoption of wide-bandgap power devices is not economically justified. Under these conditions, natural-sampled PWM offers significant advantages over regular-sampled PWM, particularly at low switching-to-fundamental frequency ratios, by improving output waveform quality and reducing control-loop delay. This paper proposes an adaptive modulation strategy for two-level three-phase voltage-source inverters, enabling a seamless transition from space-vector PWM (SVPWM) to generalized discontinuous PWM (GDPWM). The proposed approach preserves the number of switching events by synchronizing the discontinuities of the modulation signals with the corresponding carrier peaks, thereby ensuring a consistent switching pattern while exploiting the benefits of discontinuous modulation. Full article

Background/Objectives: Temporomandibular disorders (TMDs) affect the masticatory muscles, temporomandibular joints, and related tissues and are frequently associated with pain and functional limitation. Patients with chronic TMD may present multiple comorbidities that complicate clinical management and contribute to symptom persistence. This study aimed to identify comorbid conditions associated with chronic TMD, compare their distribution between chronic and acute TMD presentations, and propose a conceptual interdisciplinary assessment framework. Methods: A retrospective observational study included 82 patients with TMD and at least one symptomatic or documented comorbidity identified during interdisciplinary clinical assessment or within the previous three months. Of these, 58 patients had chronic TMD and 24 had acute TMD. Categorical variables were compared using Fisher’s exact test, with odds ratios and 95% confidence intervals calculated for each comparison. False discovery rate (FDR) correction was applied, and age- and sex-adjusted Firth-penalized logistic regression models were also fitted. Results: In direct group comparisons, chronic TMD was associated with higher frequencies of primary headache, secondary headache, neck pain, lower back pain, endocrine/hormonal disorders, Axis II findings, and kinesiophobia. After age- and sex-adjusted Firth-penalized logistic regression analyses and FDR correction, primary headache, secondary headache, neck pain, lower back pain, endocrine/hormonal disorders, and kinesiophobia remained significantly associated with chronic TMD. No significant differences were observed for sleep bruxism, awake bruxism, or poor sleep quality. Conclusions: Given the retrospective design and limited sample size, the results should be interpreted as exploratory associations rather than causal relationships. Further prospective studies are needed to evaluate and validate the proposed framework and assess its clinical applicability. Full article

This article presents the results of the classification of measurement errors of electromyography signals caused by interference and noise. It is shown that interference and measurement noise mainly occur during the registration of electromyography signals and significantly reduce the measurement accuracy. To classify measurement errors, the article analyzes and identifies the features of interference and measurement noise. This analysis is based on the mechanism of occurrence and the physical nature of interference and noise, as well as their spectral, frequency, and statistical characteristics. Initially, to analyze and identify the features of interference and noise, the article examines the characteristics of electromyography signals recorded during the movement of a human limb. It is shown that the informative components of the motor units of electromyography signals during limb movement are variable. The analysis results revealed that measurement noise and interference are slowly variable and have a Gaussian distribution. The frequency composition of low- and high-frequency interference and measurement noise is distinct from that of the measured electromyography signals. To systematize the results of the analysis, a classification scheme for measuring errors in electromyography signals is presented. It is recommended to use the measurement error classification scheme when solving the problem of signal filtering, where it is necessary to identify informative components for measuring and evaluating the movement of human limbs using electromyography signals. Furthermore, a comparative evaluation of band-pass filters of different orders was conducted to eliminate interference. A lower-order band-pass filter, even when implemented unidirectionally, improves the signal-to-noise ratio and highlights informative components. Full article

This work presents an experimental investigation of droplet evaporation dynamics using a step-index multimode fiber-tip (MMF) interferometer. Distilled water, ethanol, isopropyl alcohol (IPA), and their binary mixtures with water were analyzed through complementary frequency- and time-domain approaches. Fast Fourier Transform (FFT) analysis was used to identify the dominant spectral components over selected evaporation intervals, while the instantaneous phase obtained from the analytic signal was used to track time-dependent variations in the optical response. For water, dominant components at 8.34 and 9.87 Hz corresponded to thickness-variation rates of −4.85 and −5.75 µm/s, respectively. Ethanol exhibited a dominant component at 15.8 Hz, corresponding to −9.01 µm/s, whereas IPA showed components at 13.2 and 34.8 Hz, associated with rates of −7.46 and −19.6 µm/s. Binary mixtures exhibited multiple spectral components and stronger temporal variability, indicating a nonstationary optical response during evaporation. The frequency- and time-domain results therefore provide complementary descriptions: the FFT identifies the dominant components over the selected interval, whereas instantaneous-phase analysis reveals their temporal evolution. Because the analysis was performed over short, selected evaporation intervals, the refractive index was assumed to remain approximately constant, and the measured phase variations were therefore attributed predominantly to changes in droplet thickness. The retrieved values are consequently interpreted as thickness-variation rates rather than direct mass-loss rates. The proposed approach provides a simple and compact method for monitoring droplet evaporation. Full article

Rainfed smallholder farming systems in semi-arid tropical climates are highly vulnerable to drought, threatening food security and rural livelihoods. This study evaluates drought variability, severity, and temporal periodicity from 1981 to 2021 across representative Sudano-Sahelian locations in the Centre-Est and Centre-Sud regions of Burkina Faso and the Upper East Region of Ghana. Monthly precipitation and temperature data were used to calculate the Standardized Precipitation–Evapotranspiration Index (SPEI) at 1-, 3-, 6-, and 12-month timescales. Continuous Wavelet Transform (CWT) was applied to identify recurrent drought periodicities, while temporal trend analysis and spatial interpolation assessed spatiotemporal variability. Drought frequency (months with SPEI < −0.5) ranged from 6% to 35%, while the spatial frequency of prolonged drought episodes lasting at least four consecutive months ranged from 11% to 39%. Wavelet analysis revealed dominant drought cycles between 15 and 64 months, together with shorter intra-seasonal oscillations. Seasonal analysis showed that drought also occurs during the wet season, coinciding with critical crop growth stages. Wet-season drought occurrence was slightly higher than dry-season occurrence, highlighting the potential for moisture deficits during critical crop growth periods. Integrating multi-timescale drought monitoring with periodicity analysis into early-warning systems can strengthen drought preparedness and support climate-resilient agricultural management in semi-arid West Africa. Full article

High-frequency meteorological observations from automatic weather stations (AWS) are frequently affected by substantial data gaps in data-sparse regions such as West Africa, limiting their usability for climate analysis and decision-making. This study presents a machine learning-based framework for reconstructing missing hourly observations by integrating in situ AWS measurements with ERA5-Land reanalysis fields and Global Precipitation Measurement (GPM) satellite-derived products. The framework was applied to a network of over 50 AWS across 10 West African countries over the period 2017–2025, targeting seven meteorological variables: air temperature, relative humidity, global solar radiation, atmospheric pressure, precipitation, wind speed, and wind direction. Gradient boosting models (XGBoost, LightGBM, and CatBoost) were trained following a station-wise and variable-wise strategy, yielding over 300 variable-specific models. Detailed quantitative results are reported for four representative stations spanning distinct agro-climatic zones (Sahelian, Sudanian, coastal, and humid tropical). Air temperature and atmospheric pressure exhibit the highest reconstruction skill, with R² values typically exceeding 0.90, while relative humidity and global solar radiation achieve R² between 0.80 and 0.92. Precipitation and wind speed showed lower reconstruction skill than thermodynamic variables, reflecting their intermittency and sensitivity to local-scale processes. Wind direction, evaluated separately using circular statistics after recombination into degrees, exhibited the largest angular errors, highlighting the difficulty of reconstructing directional variability from large-scale predictors alone. Full article

Background/Objectives: Pharmacogenetic testing of the dihydropyrimidine dehydrogenase gene (DPYD) is increasingly incorporated into clinical practice to identify patients at increased risk of fluoropyrimidine-related toxicity. However, most routine assays target a limited number of well-established variants, whereas the DPYD gene demonstrates substantial population variability and may harbor rare potentially functional alleles that are not detected by conventional targeted testing. Data describing coding and splice-region DPYD variants detectable by whole-exome sequencing in Russian oncology populations remain limited. This study aimed to characterize the frequency and distribution of common, clinically relevant, and rare DPYD variants in a Russian cohort of patients receiving fluoropyrimidine-containing chemotherapy and to compare the observed allele frequencies with European and East Asian reference populations. Methods: Descriptive pharmacogenetic analysis was performed in 339 patients with malignant tumors treated with fluorouracil, leucovorin, oxaliplatin, and docetaxel (FLOT), folinic acid, fluorouracil, and oxaliplatin (FOLFOX), or folinic acid, fluorouracil, irinotecan, and oxaliplatin (FOLFIRINOX) regimens. Whole-exome sequencing was performed using Illumina technology with exome enrichment by KAPA HyperExome and a sequencing depth of at least 100×. Sequence reads were aligned to the Genome Reference Consortium Human Build 38 (GRCh38) reference genome, germline variants were called using Genome Analysis Toolkit (GATK), HaplotypeCaller, and functional annotation was performed with Ensembl Variant Effect Predictor. DPYD variants were classified according to their population frequency, predicted functional effect, ClinVar annotations, and current pharmacogenetic recommendations. Allele and genotype frequencies were calculated and descriptively compared with Genome Aggregation Database (gnomAD) v4.1.1 Non-Finnish European and East Asian populations. Results: Seventeen DPYD variants were identified. The most frequent alternative alleles were rs1801265 (24.93%), rs1801159 (17.70%), rs2297595 (11.06%), rs1801160 (7.08%), and rs17376848 (5.16%). Their distribution was generally closer to that observed in the Non-Finnish European population than in East Asian populations. The established reduced-function variant rs67376798 (c.2846A>T, p.Asp949Val) was detected in one heterozygous patient, corresponding to a carrier frequency of 0.29% and an allele frequency of 0.15%. The HapB3 proxy variant rs56038477 (c.1236G>A) was identified in 13 heterozygous patients, with a carrier frequency of 3.83% and an allele frequency of 1.92%; confirmation of the functional intronic variant rs75017182 would be required for definitive HapB3 assignment. Overall, rs67376798 or rs56038477 was detected in 14 patients (4.13%). In addition, rare variants with a cohort allele frequency below 1% were identified in 11 patients (3.24%). Among these, p.Thr65Ala, p.Thr65Met, p.Asn151Asp, and p.Val691Leu represented potentially relevant findings requiring further functional validation. Conclusions: Whole-exome analysis revealed a heterogeneous spectrum of DPYD variants in the studied Russian oncology cohort, including both established pharmacogenetic markers and rare variants that would not be captured by limited targeted panels. The overall allele-frequency pattern was predominantly similar to that of European reference populations, although several rare variants demonstrated distinct distributions. These findings support the value of population-specific characterization of DPYD and suggest that expanded sequencing approaches may complement conventional pharmacogenetic testing by identifying rare potentially functional alleles. Full article