In this systematic umbrella review we aggregate the current knowledge of how virtual and augmented reality technologies are applicable to and impact remote learning in higher education; specifically, how they impact such learning outcomes as performance and engagement in all stages of higher education from course preparation to student evaluation and grading. This review was done as part of a state wide research effort of Latvia, to mitigate the impact of COVID-19 and specifically to provide a framework for a technological transformation of education in this context. In this work we search the Scopus and Web of Science databases for articles describing the use of virtual and/or augmented reality technologies in remote learning for higher education and their impact on learning outcomes. We identified 68 articles from which, after multiple screening and eligibility phases, nine review articles were left for extraction phase in which 30 structural elements with corresponding interventions and measured effects were extracted. Of these, 24 interventions had a measured effect on student performance (11 positive, seven negative, six no impact) and six interventions had a measured effect on student engagement (all six positive).

Narrowband Internet of Things (NB-IoT) is widely used for connecting low-power devices that must operate for years without maintenance. To design reliable systems, it is essential to understand how much energy these devices consume under different conditions and which power sources can support long lifetimes. This study presents a detailed experimental evaluation of NB-IoT power consumption using a commercial System-on-Module (LMT-SoM). We measured various transmissions across different payload sizes, signal strengths, and temperatures. The results show that sending larger packets is far more efficient: a 1280-byte message requires about 7 times less energy per bit than an 80-byte message. However, standby currents varied widely between devices, from 6.7 µA to 23 µA, which has a major impact on battery life. Alongside these experiments, we compared different power sources for a 5-year deployment. Alkaline and lithium-thionyl chloride batteries were the most cost-effective solutions for indoor use, while solar panels combined with supercapacitors provided a sustainable option for outdoor applications. These findings offer practical guidance for engineers and researchers to design NB-IoT devices that balance energy efficiency, cost, and sustainability.

Signal loss models are frequently utilized by wireless communication researchers and engineers to predict received signal strength, optimize system parameters, and conduct feasibility studies. However, novel communication methods such as Body-Coupled Communication (BCC) that are suitable for Body Area Networks formed by wearable devices currently lack readily available signal propagation models. In this data article, we present a galvanic-coupled BCC signal loss and bioimpedance dataset, which serves as a foundation for building such models. This extensive dataset consists of experimental data recorded from 30 volunteer test subjects. The experimental setup involves a tunable signal generator transmitting continuous wave signals, along with two oscilloscopes recording the transmitter-side and receiver-side voltages. From these measurements, we compute the signal loss over the body, and the transmitter-side impedance. The transmitted signal frequencies range from 50 kHz to 20 MHz, with discrete steps. The primary application of this dataset is to enable empirical-data-supported modeling in the human body as a BCC signal propagation medium, which will help to explore how the properties of the human body, the measurement locations, and the signal frequency impact the sig-nal loss.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

This paper presents a thermal and power characterization of the NVIDIA Jetson AGX Orin deployed in a realistic outdoor edge AI enclosure, integrated with power supplies, a Long Term Evolution (LTE) module, and a thermostat-controlled fan, across an ambient temperature range from −20 °C to +40 °C. The device was tested in a climate chamber under two workloads: a synthetic CPU and GPU stress test, and a YOLOv8s inference workload (TensorRT FP16, 640 × 640 input). Internal temperatures were recorded using four calibrated platinum Resistance Temperature Detectors (RTD)—PT100/PT1000, while Jetson chip temperatures and power consumption were logged via tegrastats and jtop. At sub-zero ambient temperatures, heat dissipated by the device itself kept all components within their operating ranges down to −20 °C. At the +40 °C setpoint, the stress test triggered Jetson thermal throttling at GPU and CPU temperatures of +95.6 °C and +99.0 °C, respectively. Under the same conditions, the YOLOv8s inference workload sustained 108.8 frames per second (FPS) at 19.1 W average power, approximately half of the 36.5 W consumed under the stress test, with chip temperatures well below the throttling threshold. These findings indicate that synthetic stress tests substantially overestimate the thermal and power demands of the tested inference workload, and that the enclosure retains sufficient thermal headroom for outdoor edge device deployment.

Narrowband Internet of Things (NB-IoT) is widely used for connecting low-power devices that must operate for years without maintenance. To design reliable systems, it is essential to understand how much energy these devices consume under different conditions and which power sources can support long lifetimes. This study presents a detailed experimental evaluation of NB-IoT power consumption using a commercial System-on-Module (LMT-SoM). We measured various transmissions across different payload sizes, signal strengths, and temperatures. The results show that sending larger packets is far more efficient: a 1280-byte message requires about 7 times less energy per bit than an 80-byte message. However, standby currents varied widely between devices, from 6.7 µA to 23 µA, which has a major impact on battery life. Alongside these experiments, we compared different power sources for a 5-year deployment. Alkaline and lithium-thionyl chloride batteries were the most cost-effective solutions for indoor use, while solar panels combined with supercapacitors provided a sustainable option for outdoor applications. These findings offer practical guidance for engineers and researchers to design NB-IoT devices that balance energy efficiency, cost, and sustainability.