Browse Articles

Improved multi-objective decision-making in manufacturing processes through uncertainty quantification and robust pareto front modelling

Huygens metasurface supporting quasi-bound states in the continuum for terahertz gas sensing

Abstract We investigate a terahertz (THz) gas sensing platform based on all-dielectric metasurfaces that support quasi-bound states in the continuum (quasi-BIC) with both electric and magnetic dipole resonances. The structure is designed to achieve the first Kerker condition, minimizing backscattering and maximizing light-matter interaction, which significantly enhances the sensitivity of the sensor. By optimizing structural parameters, this metasurface selectively resonates at characteristic absorption frequencies of target gases, facilitating detection even at low concentrations. We validate the approach using two gases with strong but distinct THz absorption profiles: hydrogen cyanide and sulfur dioxide ( $$\hbox {SO}_2$$ ). Furthermore, the free-standing design maximizes gas interaction on both sides of the metasurface, eliminating substrate-induced losses and enabling a reduced physical footprint. Our findings indicate that this metasurface outperforms standard THz sensing approaches in terms of compactness and sensitivity per path length unit, obtaining the same detection threshold as sensing in free space with a path length between 2 and 3 orders of magnitude shorter, underscoring its potential for industrial applications where the available space for sensing can be limited.

Telomere-related gene risk model predicts prognostic and immune microenvironment alterations in prostate cancer

Establishment and genetic characterization of zebrafish RW line

A novel, low-cost clay ceramic membrane for the separation of oil-water emulsions

Towards decentralized and sustainable water and wastewater treatment systems

A lightweight deep learning framework for transformer fault diagnosis in smart grids using multiple scale CNN features

Abstract Scheduled maintenance and condition monitoring of power transformers in smart grids is mandatory to reduce their downtimes and maintain economic benefits. However, to minimize energy losses during inspection, non-invasive fault diagnosis techniques such as thermogram imaging can enable continuous monitoring of transformer health with minimal out-of-service time. Deep learning (DL) has proven to be a fast and efficient intelligent diagnostic tool. In this paper, a DL-based thermography method is proposed called Trans-Light for transformers’ interturn faults detection and short-circuit severity identification. Trans-light extracts deep features from two deep layers of a convolutional neural network (CNN) rather than depending on one layer, thus obtaining more intricate patterns. Moreover, a Dual-tree Complex Wavelet Transform method is adopted which offers two enhancements. First, it acquires time–frequency knowledge besides the already obtained spatial information and second, it reduces the huge deep features dimensionality. Trans-light combines extracted deep features, then a feature selection process is applied to further reduce features’ size, thus decreasing computation burden and reducing classification and training time. To validate the proposed scheme’s diagnosis performance and robustness, different combinations of two CNN models, two feature selection methods, and six classifiers were tested, applying the proposed Trans-light framework, under noise-free and noise-existing conditions. Experimental results indicated that the combination of the LDA classifier, applied with the ResNet-18 CNN model and trained with merged deep features undergoing the chi-square (χ2) selection approach, attained superior performance under noise-free conditions. Compared to its counterparts in previous work, this configuration outperforms their performance since it uses the fewest features’ number yet maintains 100% classification accuracy. Besides, it attained robust performance under two different noise natures again with minimal features’ dimension, thus minimizing computational load and implementation complexity.

Physicochemical characteristics of ferroalloys of the Cr–C–Si–Fe system

Author Correction: Genetic algorithm type 2 fuzzy logic controller of microgrid system with a fractional-order technique

Differential treatment response to virtual reality in high-impact chronic pain: secondary analysis of a randomized trial

Simulation and experimental research on drilling and rock breaking mechanisms of anchor drill rigs with analysis of drilling feedback signals

Impact of single dose of pegfilgrastim on peripheral blood stem cell harvest in patients with multiple myeloma or malignant lymphoma

Abstract This phase 2 study evaluated the impact of pegfilgrastim, a single-dose, long-acting granulocyte colony-stimulating factor, on the steady-state mobilization of hematopoietic stem cells into peripheral blood in patients with multiple myeloma (MM) or malignant lymphoma (ML). Efficacy and safety, along with CD34-positive cell mobilization outcomes were assessed in patients with MM, who were randomly assigned to pegfilgrastim (n = 30) or daily filgrastim (n = 31), and ML (pegfilgrastim only, n = 13) cohorts. In the MM cohort, CD34-positive cell counts ≥ 2 × 106/kg were achieved in 100% of patients in the pegfilgrastim group and 96.7% in the filgrastim group (difference: 3.3%; 80% confidence interval: −0.9–7.5%), demonstrating the non-inferiority of pegfilgrastim to filgrastim. All patients in the ML cohort achieved ≥ 2 × 106/kg CD34-positive cell counts. The plerixafor administration rates in the MM cohort were 50.0% and 63.3% in the pegfilgrastim and filgrastim groups, respectively, and 91.7% in the ML cohort. There were no major differences in safety measures between the two groups. Although the sample size was small, particularly in the ML cohort, a single dose of pegfilgrastim demonstrated comparable efficacy and safety to daily doses of filgrastim, indicating its potential for clinical use while reducing patient burden. Trial Registration: jRCT2011210029, NCT05007652.

Alvespimycin is identified as a novel therapeutic agent for diabetic kidney disease by chemical screening targeting extracellular vesicles

Research on blasting technology for thick and hard roof fracturing in isolated island coal mine working face

Abstract In order to study the prevention and control technology for hard roof type coal burst in the isolated working face of the Chenjiashan coal mine, the 418 isolated working face was selected as the engineering case study. Based on the different impact danger zones and mining areas, a roof breaking blasting pressure relief technical scheme was proposed. The anti-impact effect was verified through hole peeping and infrared radiation data. The research shows: (1) According to the geological conditions of the 418 working face of the Chenjiashan coal mine, the working face is divided into weak impact hazard areas and moderate impact hazard areas. Targeted roof blasting schemes were proposed for the initial square area of the working face, moderate danger area, weak danger area, initial mining and initial caving area, and the strike area of the working face. (2) On-site borehole data show that after blasting, a large number of fractures and delaminations were formed in the roof, and some fractures further developed into delaminations, with local areas showing crushed zones. This proves the formation of a “buffer zone” in the roof and floor, achieving pre-cracking of the thick and hard roof, full development of fractures, significant reduction in stress concentration, and the roof blasting can achieve good pressure relief effect. (3) The temperature monitoring near the blasting point and the infrared radiation temperature shows that within an hour after the implementation of the roof blasting, the coal mass at the breaking position experienced a process of heating up and then cooling down, with the temperature at the monitoring point rising by 0.5–0.7 °C. After the implementation of the roof blasting, the key layer above the working face was destroyed, and the stress was released and transmitted to the corresponding area of the coal mass, the stress of the coal increased, and the infrared radiation temperature increased, proving that the blasting pressure relief achieved the expected effect.

Transformer-based language-independent gender recognition in noisy audio environments

Peer-driven task scheduling and resource allocation for enhanced performance in industrial IoT systems

Identification of prognostic and therapeutic biomarkers associated with macrophage and lipid metabolism in pancreatic cancer

Anticollision communication ontology technique implementation with MANET networking

Evidence of reduced birthweight in Ukraine following the Russian invasion

Abstract The impact of armed conflict on birth weight has been shown in various studies, but the effects of the full-scale Russian invasion of Ukraine in 2022 have been overlooked until now. This study investigates the influence of war on birth-weight using data and follow-up of pregnancies among women having prenatal screening. Maternal factors, outcomes, and birth weights were analyzed for 706 screened women who delivered between 2020 and 2023, excluding cases of spontaneous abortion, termination, and multiple pregnancies. Of these, 330 deliveries occurred before the invasion and 376 afterward. The only statistically significant maternal factors were parity − 22% nulliparous before and 48% after invasion (P < 0.0001) and maternal weight − 60 versus 62 kg. (P < 0.05). Birth-weight was significantly reduced: median 3500 g before and 3350 g after (P < 0.0001); proportions < 2500 g, 1.2% and 2.4%, respectively (P = 0.24). The ratio of birth-weight to infant length was also significant (P < 0.0001). Analysis of variance showed birth-weight significances remained after allowing for parity and gender. These findings highlight a significant decline in birth weight associated with armed conflict, emphasizing the need for targeted interventions to support maternal and neonatal health during periods of war.

Session interest model for CTR prediction based on feature co-action network