Photovoltaic sample-and-hold circuit enabling MPPT indoors for low-power systems

Photovoltaic (PV) energy harvesting is commonly used to power autonomous devices, and maximum power point tracking (MPPT) is often used to optimize its efficiency. This paper describes an ultra low-power MPPT circuit with a novel sample-and-hold and cold-start arrangement, enabling MPPT across the range of light intensities found indoors, which has not been reported before. The circuit has been validated in practice and found to cold-start and operate from 100 lux (typical of dim indoor lighting) up to 5000 lux with a 55cm2 amorphous silicon PV module. It is more efficient than non-MPPT circuits, which are the state-of-the-art for indoor PV systems. The proposed circuit maximizes the active time of the PV module by carrying out samples only once per minute. The MPPT control arrangement draws a quiescent current draw of only 8uA, and does not require an additional light sensor as has been required by previously-reported low-power MPPT circuits

Biological activities of aerial parts of Tylophora hirsuta Wall

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Factors associated with seizure severity among children with epilepsy in Northern Nigeria

Objective: To describe how seizure severity in children with epilepsy may be affected by certain socio-demographic and clinical variablesDesign: A cross-sectional studySetting: At the Abubakar Tafawa Balewa University Teaching Hospital, Bauchi, NigeriaParticipants: Sixty children and adolescents who were being followed up for seizure disorder at the child neurology clinicIntervention: Information on socio-demographic characteristics was obtained with a questionnaire, details of neuro-logical co-morbidities were extracted from the participants’ records, and seizure severity was assessed with the Na-tional Hospital Seizure Severity Score 3 tool.Main Outcome Measure: Chi-square test was used to establish the relationship between categorical variables, while the Independent t-test was used in describing the differences between means. Simple linear regression was calculated to assess the predictability of seizure severity.Result: The median age was ten years (IQR = 6-13 years), with a male dominance (1.5:1). The Seizure Severity Score (SSS) ranged between 3 and 24 units, with a mean of 12.22 ± 4.29 units. The only characteristic that had a significant association with SSS on bivariate analysis was the “presence of co-morbidities” (p=0.019). A simple linear regression revealed that the presence of a neurological co-morbidity predicted an increase in the SSS by 2.67 units. [R2 = 0.091, F (1, 58)= 5.837, p = 0.019. ꞵ = 2.67, t= 2.42, p= 0.019.]Conclusion: This study shows that neurological co-morbidities predict worsening seizure severity. This knowledge may influence prognostication and the charting of a treatment trajectory

Energy Harvesting and Management for Wireless Autonomous Sensors

Wireless autonomous sensors that harvest ambient energy are attractive solutions, due to their convenience and economic benefits. A number of wireless autonomous sensor platforms which consume less than 100?W under duty-cycled operation are available. Energy harvesting technology (including photovoltaics, vibration harvesters, and thermoelectrics) can be used to power autonomous sensors. A developed system is presented that uses a photovoltaic module to efficiently charge a supercapacitor, which in turn provides energy to a microcontroller-based autonomous sensing platform. The embedded software on the node is structured around a framework in which equal precedent is given to each aspect of the sensor node through the inclusion of distinct software stacks for energy management and sensor processing. This promotes structured and modular design, allowing for efficient code reuse and encourages the standardisation of interchangeable protocols

Architectural blueprint for heterogeneity-resilient federated learning

This paper proposes a novel three-tier architecture for federated learning to optimize edge computing environments. The proposed architecture addresses the challenges associated with client data heterogeneity and computational constraints. It introduces a scalable, privacy-preserving framework that enhances the efficiency of distributed machine learning. Through experimentation, the paper demonstrates the architecture’s capability to manage non-IID data sets more effectively than traditional federated learning models. Additionally, the paper highlights the potential of this innovative approach to significantly improve model accuracy, reduce communication overhead, and facilitate broader adoption of federated learning technologies

Synthesis and characterization of silver nanoarticles from extract of Eucalyptus citriodora

The primary motivation for the study to develop simple eco-friendly green synthesis of silver nanoparticles using leaf extract of Eucalyptus citriodora as reducing and capping agent. The green synthesis process was quite fast and silver nanoparticles were formed within 0.5 h. The synthesis of the particles was observed by UV-visible spectroscopy by noting increase in absorbance. Characterization of the particles was carried out by X-ray diffraction, FTIR and electron microscopy. The developed nanoparticles demonstrated that E. citriodora is good source of reducing agents. UV-visible absorption spectra of the reaction medium containing silver nanoparticles showed maximum absorbance at 460 nm. FTIR analysis confirmed reduction of Ag+ to Ag0 atom in silver nanoparticles. The XRD pattern revealed the crystalline structure of silver nanoparticles. The SEM analysis showed the size and shape of the nanoparticles. The method being green, fast, easy and cost effective can be recommended for large scale production of AgNPs for their use in food, medicine and materials