Browse Articles
Discover research articles across all indexed journals
Predicting soil organic carbon with ensemble learning techniques by using satellite images for precision farming
Adaptive laboratory evolution of Blakeslea trispora under acetoacetanilide stress leads to enhanced β-carotene biosynthesis
Process optimization and modeling research for the defluoridation of water using a novel adsorbent of cellulose and hydroxyapatite nanocomposite
Machine learning-driven framework for realtime air quality assessment and predictive environmental health risk mapping
Abstract This research introduces a practical and innovative approach for real-time air quality assessment and health risk prediction, focusing on urban, industrial, suburban, rural, and traffic-heavy environments. The framework integrates data from multiple sources, including fixed and mobile air quality sensors, meteorological inputs, satellite data, and localised demographic information. Using a combination of machine learning techniques such as Random Forest, Gradient Boosting, XGBoost, and Long Short-Term Memory (LSTM) networks the system predicts pollutant concentrations and classifies air quality levels with high temporal accuracy. Interpretability is achieved through SHAP analysis, which provides insight into the most influential environmental and demographic variables behind each prediction. A cloud-based architecture enables continuous data flow and live updates through a web dashboard and mobile alert system. Visual risk maps and health advisories are generated every five minutes to support timely decision-making. The framework not only forecasts pollution trends but also identifies vulnerable populations through spatial overlays. Future validation will include real-world sensor deployment and comparison with health impact records to ensure both scientific accuracy and community relevance.
Distal Scaffold Flexibility Modulates Eyring Activation Parameters in Re(I) Substitution Reactions: A Case for Dynamics as the “Thematic Third Coordination Sphere”
Crystalline Peptoid Nanofibers with a Single-Unit Cell Cross Section
Anatomic insights into the vascularized supraclavicular lymph node flap and a novel design for enhanced lymphedema surgery
Switchable linear to circular polarization conversion in reflection and transmission modes based on vanadium-dioxide
Abstract A design of a switchable dual-mode linear-to-circular polarization converter (LTC-PC) in the terahertz (THz) band is reported based on vanadium dioxide (VO2). Adjusting the VO2 state allows the converter to alternate between the transmission and reflection modes. In the insulating state, VO2 enables transmission mode operation for a forward x- or y-polarized wave. LTC polarization conversion occurs within the frequency bands of 1.26–1.47 THz and 1.83–1.85 THz. Moreover, this mode yields an LTC polarization conversion at a frequency of 1.7 THz. The polarizer operates in reflection mode when VO2 is in the metallic state. Two conversion bands are identified for circular polarization within the frequency bands of 0.93–1.67 THz and 1.80–1.86 THz. The dual-mode polarization converter achieves axial ratios below 3 dB and a polarization conversion efficiency greater than 0.8. Surface current distributions reveal the polarization conversion mechanisms. Furthermore, we analyze the polarization ellipses of both reflected and transmitted waves at various frequencies across the operational bands. We anticipate that the proposed design, featuring high performance and dual functionality, will be applicable in THz communication systems and sensors.
Robust direct voltage control of stand-alone DFIG wind systems using a fractional-order fuzzy logic approach
Well-Aligned Liquid Crystal Interface and Expanded Solvation Sheath Accelerate Zn<sup>2+</sup> Desolvation Kinetics for Stable Zinc Batteries
Zearalenone and nanozeolite improve Arugula (Eruca sativa) drought tolerance by enhancing photosynthesis and water relations
The effect of cooling rate and content of niobium on the structure, wear and corrosion resistance of CoCrFeNiNbx high entropy alloys
Abstract In this work, CoCrFeNiNbx (x = 0.25, 0.45 and 0.65) high entropy alloys were prepared by two different methods to determine the effect of cooling rate and the niobium content on the structure and properties of ingots and plates. The structure was investigated extensively using X-ray diffraction, scanning electron microscopy, and Mössbauer spectroscopy. The results confirmed the dual-phase structure, consisting of the FCC solid solution and the Laves phase. The increase in niobium content changed the microstructure from hypoeutectic (x = 0.25 and 0.45) to hypereutectic (x = 0.65). The high cooling rate during solidification from the liquid state enabled the formation of ultrafine eutectic structures with an average lamellae thickness of only 130 ± 9 nm in the CoCrFeNiNb0.65 plate. The corrosion behaviour of the alloys was studied in solutions of 3.5% NaCl and 3.5% NaCl + H3BO3. The beneficial effect of increasing the niobium content in as-cast CoCrFeNiNbx alloys on the corrosion resistance was confirmed in both environments. Furthermore, the alloys solidified with a higher cooling rate exhibited a lower corrosion susceptibility in the 3.5% NaCl solution. The results of the EIS study indicated that a higher content of niobium contributed to the formation of a more stable and compact passivation layer. The hardness of the CoCrFeNiNbx alloys increased with a higher niobium content, achieving the highest value of 669 HV1 for the CoCrFeNiNb0.65 plate. The increase in the cooling rate positively affected the tribological properties of the CoCrFeNiNbx alloys, contributing to the decrease in the friction coefficient for the CoCrFeNiNb0.25 and CoCrFeNiNb0.45 plates.
Identification of Ni–N<sub>4</sub> Active Sites in Atomically Dispersed Ni Catalysts for Efficient Chlorine Evolution Reaction
Development and validation of a CT-measured body composition radiomics model for prognostic assessment in resected pancreatic adenocarcinoma
Cross-species comparison reveals therapeutic vulnerabilities halting glioblastoma progression
Abstract The growth of a tumor is tightly linked to the distribution of its cells along a continuum of activation states. Here, we systematically decode the activation state architecture (ASA) in a glioblastoma (GBM) patient cohort through comparison to adult murine neural stem cells. Modelling of these data forecasts how tumor cells organize to sustain growth and identifies the rate of activation as the main predictor of growth. Accordingly, patients with a higher quiescence fraction exhibit improved outcomes. Further, DNA methylation arrays enable ASA-related patient stratification. Comparison of healthy and malignant gene expression dynamics reveals dysregulation of the Wnt-antagonist SFRP1 at the quiescence to activation transition. SFRP1 overexpression renders GBM quiescent and increases the overall survival of tumor-bearing mice. Surprisingly, it does so through reprogramming the tumor’s stem-like methylome into an astrocyte-like one. Our findings offer a framework for patient stratification with prognostic value, biomarker identification, and therapeutic avenues to halt GBM progression.