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Machine learning for survival outcome in head and neck squamous cell carcinoma: a multicenter validation study
Comparison study for yield and nutritional value in white and colored purple and blue wheat varieties
Fabrication of microcolumn arrays for high-throughput oligonucleotide synthesis using 3D printing
Research on the dust transportation law and cyclone air curtain dust control technology of coalface
Missing links of melioidosis in India: a cross-sectional analysis of case reports, agrometeorological and socioeconomic factors
Abstract Melioidosis, an emerging tropical infectious disease and a global threat, lacks a disease prediction model owing to the limited data on case incidences and associated factors. This article focuses on spatial data analysis of melioidosis patients in India, a tropical country considered to be endemic for the disease, to identify gaps in disease reporting. In this study, we screened over 20,000 articles and identified 1,694 patients diagnosed with melioidosis in India between 1953 and 2023. We performed a correlative analysis of patient profiles, case-reporting centres, common misdiagnosed etiologies, susceptible populations, agrometeorological and socioeconomic factors. Our findings suggest that melioidosis can affect individuals of all ages, with a higher prevalence among farmers and individuals with diabetes mellitus, especially adults aged 40–55 years. Most cases are reported during the monsoon season (June to September). Numerous favorable conditions for Burkholderia pseudomallei growth exist across India. However, most reported cases are from the south, suggesting under-reporting, under-diagnosis, or misdiagnosis. A “Melioidosis Checklist Index” has been developed as a surveillance tool to enhance case reporting. The melioidosis checklist index combines patient demographics, clinical symptoms, medical and family history, exposure risks, occupation, travel history, and lifestyle factors to assess infection likelihood. Probability scores derived from these parameters provide a structured aid for early diagnosis and case reporting. The study also highlighted the need to enhance regional data collection by raising awareness among vulnerable populations, healthcare providers, and paramedical staff through government initiatives.
Effects of non-magnetic impurities on transport and spectral properties of a hole-doped Mott insulator
Reduced-order linearized dynamic model for induction motor-driven centrifugal fan-pump system
Continental scale geographic analysis of hospital exposure to natural hazards in the Americas
Oyster recruitment and growth increases wave attenuation by breakwaters
Improving performance of electric vehicle drive system based a five-phase PMSM under fault using ANN and MPC
Abstract This study presents an advanced speed tracking control strategy designed to handle open-phase motor faults in EV drive systems. The proposed control strategy is evaluated using two distinct drive cycles. A five-phase interior permanent magnet synchronous motor is employed due to its notable advantages, including high efficiency, reliability, power density, and inherent fault tolerance. The control strategy leverages a multi-layer perceptron artificial neural network for online tuning of proportional-integral controller gains, enabling adaptive performance. This approach is benchmarked against a recent metaheuristic optimization algorithm known as Honey Badger. To further enhance performance, model predictive control is applied using a tailored cost function to minimize current harmonics and torque ripple. Simulation results, conducted in MATLAB Simulink, validate the effectiveness of the proposed method. Compared to conventional technique, the new approach achieves lower values for motor torque ripples and speed percentage overshoot, mean square error and integral absolute error across both test drive cycles. Additionally, the proposed method gives lower THD and attains energy saving.
Explainable OptiCNN-SLSTM hybrid model for enhanced lithium-ion battery state of health prediction
AFP promotes cancer multidrug resistance through activating PI3K/Akt/NF-κB signaling pathway
Comparative structural, thermal, and fatigue analysis of connecting rod materials for ural-4320 military diesel engines using finite element method
A MF-ConvLSTM-XAI model integrating multi-feature and fuzzy control for financial time series forecasting
Preparatory use of neurodynamics to enhance upper limb function in patients with acquired brain injury: a randomized controlled trial
Optimal time for collateral channel wiring in retrograde chronic total occlusion percutaneous coronary intervention
Sequence stratigraphy of the syn-rift miocene succession in the Abu Rudeis-Sidri Field, Gulf of Suez, Egypt
Abstract The syn-rift Miocene succession of the Gulf of Suez remains poorly constrained, with persistent uncertainties in rift initiation timing, paleoenvironmental reconstruction, and the interplay between tectonics and eustasy—factors that complicate stratigraphic correlation and hydrocarbon exploration. This study aims to refine the chronostratigraphic framework, reconstruct depositional environments, and develop a detailed sequence stratigraphic model for the Abu Rudeis–Sidri Field in the east-central Gulf of Suez through integration of high-resolution foraminiferal biostratigraphy, wireline logs, and seismic data from four wells (ARM-7, ARS-6, SIDRI-20, SIDRI-9). Planktonic and benthic foraminifera constrain the succession to the early Burdigalian–early Langhian, encompassing the Globigerinoides altiaperturus–Catapsydrax dissimilis , Trilobatus trilobus , and Praeorbulina glomerosa zones. Ten benthic biofacies define paleodepths from inner shelf (< 50 m) to upper slope (> 150 m), enabling reconstruction of depositional environments and relative sea-level trends. Four third-order depositional sequences (SQ1–SQ4) are recognized, bounded by regionally mappable sequence boundaries and maximum flooding surfaces. Stacking patterns reveal deepening from SQ1 to SQ3 followed by shoaling and lagoonal restriction in SQ4. Tectonic subsidence and block tilting exerted primary control on accommodation, whereas eustatic influence is expressed mainly at flooding surfaces. Correlation with regional and global sea-level curves shows partial alignment with Burdigalian–Langhian cycles, underscoring the dominance of local tectonics. This refined framework enhances understanding of syn-rift sedimentation and provides predictive insights for hydrocarbon exploration in rift-related basins.
Purification of vegetable oils from acrylamide pollutants using metal-organic frameworks
Abstract Acrylamide is a chemical compound that can form in certain foods during high-temperature cooking processes like frying, roasting, and baking. The presence of acrylamide in used cooking oil has environmental impact; therefore, the purification of used oils may reduce the environmental risks. Nowadays, biochar, a porous carbonaceous material derived from biomass pyrolysis can added to crystalline porous materials called metal-organic framework (MOF), these combination lead to fantastic properties for adsorption of contaminants. Here, ZIF-8@Biocharas adsorbents were synthesized for purification of the used frying oil from many pollutants, including, acrylamides, acids and peroxides. First, surface and pore properties, and morphologies of the adsorbents were determined using different characterization techniques. The used frying oil was treated with 0.5% (w/w) ZIF-8@Biochar and ZIF-8 MOF to remove free fatty acid (FFA), the results showed reduction by 80.6% and 32.2%, respectively. Likewise, peroxide value reductions ranged from 70.6% with the same adsorbent. The residues of acrylamide were determined by GC-MS/MS. whereas acid and peroxide values were determined by titration methods. This study showed an economical solution for reducing acrylamide, acid value, and peroxide value in used cooking oil for improving safety and quality of used oil.