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Antimicrobial consumption (AMC) measures the level and types of antibiotics consumed in a specific setting. Monitoring AMC is critical component of antimicrobial resistance (AMR) containment strategies. However, AMC data at both facility and national-levels are scarce in Africa, which limits our understanding of the rate, patterns and drivers of antibiotic consumption, and prevents the establishment of evidence-based antimicrobial stewardship. We determined facility and national-level rates and patterns of AMC from data retrospectively collected between 2016 and 2019 in 327 pharmacies from 14 countries AMC data collection followed a backfilling strategy leveraging from public and private central medical stores, wholesalers, distributors or import services as data sources. Participating hospital and community pharmacies were selected based on their location in or proximity to hospitals capable of producing AMR data. Levels of AMC were determined as defined daily dose (DDD) and DDD per inhabitant (DID). AMC patterns were analysed according to the WHO Access, Watch, and Reserve (AWaRe) Categories, the Anatomical Therapeutic Chemical (ATC) classes and the individual antibiotic molecules included in the Drug Utilisation 75% (DU75). The availability of antibiotics was examined against the WHO and the National Essential Medicine Lists (EML). National AMC data was available in 11 of the 14 participating countries, revealing a collective AMC of 8.42 DID varying from 2.8 to 115.5 at individual country level. AMC was also determined in 327 hospital and community pharmacies. Nine of 11 (82%) countries with national data available, and 219 of the 327 (72%) participating pharmacies achieved the WHO AWaRe target of at least 60% of antibiotic consumption from Access drugs. Eighty percent of country-level AMC was accounted for by five ATC sub-classes classes of antibacterial for systemic use. Facility-level antibiotic consumption was dominated by a narrow scope of less than five drugs, taking advantage of only 10% of all possible WHO-recommended Access drugs within ATC classes. Collectively, the 14 national EML included 70% of Access, 60% of Watch and less than 5% of Reserve antibiotics listed in the WHO EML. Forty-eight uncategorized and 50 categorized non-WHO-recommended drugs were included in national EMLs or documented to be circulating in countries. The relatively low AMC and the poorly diversified subset of antibiotics available in countries underscores that strategies to expand equitable access to adequate treatment of bacterial infections should complement current efforts to promote the judicious use of antimicrobials. Interventions to increase the volume of analysable data on AMU, AMC and AMR, should be prioritized in national AMR action plans as well as in wider infrastructural and economic development plans.

This paper presents a novel machine learning-driven approach for designing and optimizing multi-band patch antennas tailored for next-generation Internet of Things applications in 5G and 6G wireless communication systems. Recognizing the limitations of traditional antenna design methods, this work leverages the power of ML to enhance antenna performance and design efficiency. We investigate various ML algorithms, including Decision Tree, Random Forest, ANN, KNN, Extra Tree, CatBoost, Gradient Boost, and XGBoost, for predicting antenna characteristics. Notably, the CatBoost algorithm demonstrates superior performance, achieving 77.4% accuracy in predicting antenna return loss. To validate the efficacy of this approach, a multi-band antenna operating across the 3.5–7.8 GHz, 8.5–10.2 GHz, and 11.8–15 GHz frequency bands was fabricated and evaluated. Results demonstrate a good agreement between predicted and measured performance, highlighting the accuracy and efficiency of the ML-driven design methodology. This approach holds significant promise for accelerating the development of high-performance antennas for a wide range of applications, including Wi-Fi, Fixed Wireless Access, Wideband Aeronautical Intranet Communications, IoT devices, Industrial IoT, smart cities, and remote monitoring systems. Article Highlights A novel machine learning-driven methodology is presented for the design and optimization of multi-band patch antennas, addressing the increasing demand for efficient and high-performance antennas in next-generation 5G and 6G IoT applications. This approach significantly reduces antenna design time and computational resources by minimizing reliance on iterative simulations. A multi-band antenna operating across the frequency bands of 3.5–7.8 GHz, 8.5–10.2 GHz, and 11.8–15 GHz was successfully developed and tested, demonstrating strong agreement between predicted and measured results. The CatBoost machine learning algorithm exhibited superior performance in predicting antenna return loss, achieving high accuracy, speed, and reliability with low error rates. This antenna design methodology, applicable to various IoT applications including Wi-Fi, Fixed Wireless Access, Wideband Aeronautical Intranet Communications, Industrial IoT, smart cities, and remote monitoring, has the potential to revolutionize antenna development for next-generation wireless communication systems.

The Himalayas are a crucial source of food, water, and habitat for a wide range of ecosystems in the northern Indian subcontinent. This research focuses on utilizing remote sensing data to analyze hydrological and meteorological factors—such as precipitation, temperature, runoff, evapotranspiration (ET), soil moisture and snow depth—for nine watersheds in the North West Himalayas (NWH): Jhelum, Tawi, Beas, Parbati, Suketi, Gangotri, Aglar, Asan, and Henval. The study employs near real-time (~1-day latency) hydrological flux data from the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) at a spatial resolution of 0.01°. To gain further insights, the trend in snow cover area (SCA) for the NWH region is analyzed using Interactive Multisensor Snow and Ice Mapping System (IMS) data from the United States National Ice Center (USNIC), disseminated by the National Oceanic and Atmospheric Administration (NOAA) via the National Snow and Ice Data Center (NSIDC). The analysis covers water balance components for these basins from 2001 to 2023. Monthly and annual averages are computed to reveal overall watershed behavior and distribution, which is closely linked to the land use and land cover (LULC) in the area. For instance, the Gangotri basin, with over 50% snow coverage, shows low ET throughout the year, a trend reflected in the long-term annual average ET values. Similarly, basins with higher SCA exhibit lower ET percentages, resulting in increased runoff and a strong correlation between rainfall and runoff patterns. A regional analysis of snow cover from 2014 to 2024, encompassing the union territories of Jammu and Kashmir, Ladakh, and the states of Himachal Pradesh and Uttarakhand, indicates a significant decrease in relative SCA. For the peak winter months of December and January, SCA dropped by about 50% from January 2023 (~2,94,635 km2) to January 2024 (~1,48,225 km2). Additionally, the Indian Meteorological Department's (IMD) daily rainfall data for Jammu and Kashmir from December 1, 2023, to January 31, 2024, shows lower rainfall compared to the previous year, likely due to reduced north-west disturbances. This prolonged snow drought poses challenges to water security and increases the risk of wildfire disasters in these regions.