Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize

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From preparation to product: Factors influencing probiotic viability in spray drying

With growing health awareness, particularly amid the SARS-CoV-2 pandemic, consumers increasingly value nutrition, diet, and food safety. Probiotic-based foods and beverages are widely recognized for their health benefits, including improved gut health and immune function. Spray drying is a scalable and efficient method for encapsulating, enhancing the stability and shelf life of probiotics. This review explores strategies to optimize the spray drying process, with a particular focus on factors influencing probiotic viability during and after drying. Key considerations included strain-specific thermal tolerance, feed composition, and critical process parameters such as drying temperature and feed rate. Notably, encapsulating agents play a vital role in maintaining the physicochemical quality of the final product while protecting probiotics from environmental stress. Recent advancements in encapsulation technologies, including biopolymers, hybrid materials, and emerging nanotechnology-based solutions have shown significant potential for enhancing probiotic survival under harsh processing conditions. Future research should integrate molecular-level insights, such as omics-based approaches, to better understand stress responses and optimize encapsulation strategies. Genome-editing tools and high-throughput screening techniques could accelerate the creation of thermotolerant probiotic strains, enabling more robust formulations. In parallel, the development of environmentally sustainable encapsulating agents with superior protective properties is essential to advance both efficiency and scalability. By addressing these challenges, spray drying can be further refined to produce durable, high-quality probiotic formulations that meet the growing demand for functional foods and beverages, while aligning with evolving consumer health priorities

Most complicated lock pattern-based seismological signal framework for automated earthquake detection

BACKGROUND : Seismic signals record earthquakes and also noise from different sources. The influence of noise makes it difficult to interpret seismograph signals correctly. This study aims to develop a computationally lightweight, accurate, and explainable machine learning model for the automated detection of seismogram signals that could serve as an effective warning system for earthquake prediction. MATERIAL AND METHOD : We developed a handcrafted model for earthquake detection using a balanced dataset of 5001 earthquakes and 5001 non-earthquake signal samples. The model included multilevel feature extraction, selectorbased feature selection, classification, and post-processing. Input signals were decomposed using tunable Q wave transform and fed to a statistical and textural feature extractor based on the most complicated lock pattern (MCLP). Four feature selectors were used to choose the most valuable features for the support vector machine classifier. Additionally, voted vectors were generated using iterative hard majority voting. Finally, the best model was chosen using a greedy algorithm. RESULTS : The presented self-organized MCLP-based feature engineering model yielded 96.82% classification accuracy with 10-fold cross-validation using the seismic signal dataset. CONCLUSIONS : Our model attained high seismological signal detection performance comparable with more computationally expensive deep learning models. Our handcrafted explainable feature engineering model is computationally less expensive and can be easily implemented. Furthermore, we have introduced a competitive feature engineering model to the deep learning models for the seismic signal classification model.The South African National Library and Information Consortium (SANLiC).https://www.elsevier.com/locate/jagam2024Electrical, Electronic and Computer EngineeringSDG-09: Industry, innovation and infrastructureSDG-13:Climate actio

Determination of the strong coupling and its running from measurements of inclusive jet production

The value of the strong coupling S is determined in a comprehensive analysis at next-to-next-to-leading order accuracy in quantum chromodynamics. The analysis uses double-differential cross section measurements from the CMS Collaboration at the CERN LHC of inclusive jet production in proton-proton collisions at centre-of- mass energies of 2.76, 7, 8, and 13 TeV, combined with inclusive deep-inelastic data from HERA. The value $_S$($_Z$ ) = 0.1176 $^{+0.0014}_{-0.0016}$ is obtained at the scale of the Z boson mass. By using the measurements in different intervals of jet transverse momentum, the running of $_S$ is probed for energies between 100 and 1600 GeV

A systematic review of randomised controlled trials assessing effectiveness of prosthetic and orthotic interventions.

BACKGROUND: Assistive products are items which allow older people and people with disabilities to be able to live a healthy, productive and dignified life. It has been estimated that approximately 1.5% of the world's population need a prosthesis or orthosis. OBJECTIVE: The objective of this study was to systematically identify and review the evidence from randomized controlled trials assessing effectiveness and cost-effectiveness of prosthetic and orthotic interventions. METHODS: Literature searches, completed in September 2015, were carried out in fourteen databases between years 1995 and 2015. The search results were independently screened by two reviewers. For the purpose of this manuscript, only randomized controlled trials which examined interventions using orthotic or prosthetic devices were selected for data extraction and synthesis. RESULTS: A total of 342 randomised controlled trials were identified (319 English language and 23 non-English language). Only 4 of these randomised controlled trials examined prosthetic interventions and the rest examined orthotic interventions. These orthotic interventions were categorised based on the medical conditions/injuries of the participants. From these studies, this review focused on the medical condition/injuries with the highest number of randomised controlled trials (osteoarthritis, fracture, stroke, carpal tunnel syndrome, plantar fasciitis, anterior cruciate ligament, diabetic foot, rheumatoid and juvenile idiopathic arthritis, ankle sprain, cerebral palsy, lateral epicondylitis and low back pain). The included articles were assessed for risk of bias using the Cochrane Risk of Bias tool. Details of the clinical population examined, the type of orthotic/prosthetic intervention, the comparator/s and the outcome measures were extracted. Effect sizes and odds ratios were calculated for all outcome measures, where possible. CONCLUSIONS: At present, for prosthetic and orthotic interventions, the scientific literature does not provide sufficient high quality research to allow strong conclusions on their effectiveness and cost-effectiveness