The sustainable reuse of batteries after their first life in electric vehicles requires accurate state-of-health (SoH) estimation to ensure safe and efficient repurposing. This study applies the systematic ProKnow-C methodology to analyze the state of the art in SoH estimation using machine learning (ML). A bibliographic portfolio of 534 papers (from 2018 onward) was constructed, revealing key research trends. Public datasets are increasingly favored, appearing in 60% of the studies and reaching 76% in 2023. Among 12 identified sources covering 20 datasets from different lithium battery technologies, NASA’s Prognostics Center of Excellence contributes 51% of them. Deep learning (DL) dominates the field, comprising 57.5% of the implementations, with LSTM networks used in 22% of the cases. This study also explores hybrid models and the emerging role of transfer learning (TL) in improving SoH prediction accuracy. This study also highlights the potential applications of SoH predictions in energy informatics and smart systems, such as smart grids and Internet-of-Things (IoT) devices. By integrating accurate SoH estimates into real-time monitoring systems and wireless sensor networks, it is possible to enhance energy efficiency, optimize battery management, and promote sustainable energy practices. These applications reinforce the relevance of machine-learning-based SoH predictions in improving the resilience and sustainability of energy systems. Finally, an assessment of implemented algorithms and their performances provides a structured overview of the field, identifying opportunities for future advancements.

It is challenging to manufacture complex and intricate shapes and geometries with desired surface characteristics using a single manufacturing process. Parts often need to undergo post-processing and must be transported from one machine into another between steps. This makes the whole process cumbersome, time-consuming, and inaccurate. These shortcomings play a major role during the manufacturing of micro and nano products. Hybrid manufacturing (HM) has emerged as a favorable solution for these issues. It is a flexible process that combines two or more manufacturing processes, such as additive manufacturing (AM) and subtractive manufacturing (SM), into a single setup. HM works synergistically to produce complex, composite, and customized components. It makes the process more time efficient and accurate and can prevent unnecessary transportation of parts. There are still challenges ahead regarding implementing and integrating sensors that allow the machine to detect defects and repair or customize parts according to needs. Even though modern hybrid machines forecast an exciting future in the manufacturing world, they still lack features such as real-time adaptive manufacturing based on sensors and artificial intelligence (AI). Earlier reviews do not profoundly elaborate on the types of laser HM machines available. Laser technology resolutely handles additive and subtractive manufacturing and is capable of producing groundbreaking parts using a wide scope of materials. This review focuses on HM and presents a compendious overview of the types of hybrid machines and setups used in the scientific community and industry. The study is unique in the sense that it covers different HM setups based on machine axes, materials, and processing parameters. We hope this study proves helpful to process, plan, and impart productivity to HM processes for the betterment of material utilization and efficiency.

This paper presents the design and development of a proof of concept (PoC) open-source data logger system for wireless data acquisition via Wi-Fi aimed at bench testing and fault detection in combustion and electric engines. The system integrates multiple sensors, including accelerometers, microphones, thermocouples, and gas sensors, to monitor critical parameters, such as vibration, sound, temperature, and CO₂ levels. These measurements are crucial for detecting anomalies in engine performance, such as ignition and combustion faults. For combustion engines, temperature sensors detect operational anomalies, including diesel engines operating beyond the normal range of 80 °C to 95 °C and gasoline engines between 90 °C and 110 °C. These readings help identify failures in cooling systems, thermostat valves, or potential coolant leaks. Acoustic sensors identify abnormal noises indicative of issues such as belt misalignment, valve knocking, timing irregularities, or loose parts. Vibration sensors detect displacement issues caused by engine mount failures, cracks in the engine block, or defects in pistons and valves. These sensors can work synergistically with acoustic sensors to enhance fault detection. Additionally, CO₂ and organic compound sensors monitor fuel combustion efficiency and detect failures in the exhaust system. For electric motors, temperature sensors help identify anomalies, such as overloads, bearing problems, or excessive shaft load. Acoustic sensors diagnose coil issues, phase imbalances, bearing defects, and faults in chain or belt systems. Vibration sensors detect shaft and bearing problems, inadequate motor mounting, or overload conditions. The collected data are processed and analyzed to improve engine performance, contributing to reduced greenhouse gas (GHG) emissions and enhanced energy efficiency. This PoC system leverages open-source technology to provide a cost-effective and versatile solution for both research and practical applications. Initial laboratory tests validate its feasibility for real-time data acquisition and highlight its potential for creating datasets to support advanced diagnostic algorithms. Future work will focus on enhancing telemetry capabilities, improving Wi-Fi and cloud integration, and developing machine learning-based diagnostic methodologies for combustion and electric engines.

Salinity measurement in water is typically performed with conductivity sensors. However, for long-term marine deployments, loss of precision is observed, mainly due to electrode drift (oxidation and degradation occurs in the presence of water, salts and bio-fouling), which results in inaccuracy of measurements. A cost-effective, low-power, four-probe salinity sensor is presented, to accurately measure long-term deployments in oceans, rivers and lakes. The four-probe methodology overcomes many of the drift problems, and the use of low-cost stainless-steel electrodes (avoiding platinum or titanium materials) can still achieve good long-term stability, in the practical salinity scale range from 2 to 42 PSU. Low-power electronics (200 μA in sleep-mode and 1 mA in active-mode) based on a ratiometric ADC conversion, and a low-power microcontroller with non-volatile memory, complements the proposed sensor, to achieve an autonomous salinity sensor for long-term marine deployments, with autonomy above 1 year with a 1 min ^-1 sample rate, using a common 2400 mA x 3.7 V lithium battery.

In this ongoing work, the team intends to present a conceptual serious game as an educational tool to promote and demonstrate the applicability of statistical concepts in day-to-day life and in the decision-making process. The serious game will provide learning content in a game-based environment where students/players interact by applying knowledge in Statistics. There will be no right or wrong answers. Based on the students' answers and choices, the serious game, by a design mechanism, will lead them to different challenges until they reach the end. Students' answers must use concepts acquired in the classroom.

LoRa is one of the most popular low-power wireless network technologies for implementation of the Internet of Things, with the advantage of providing long-range communication, but lower data rates, when compared with technologies such as Zigbee or Bluetooth. LoRa is a single-channel physical layer technology on top of which LoRaWAN implements a more complex multi-channel network with enhanced functionalities, such as adaptive data rate. However, LoRaWAN relies on expensive hardware to support these functionalities. This paper proposes a LoRa data-link-layer architecture based on a multi-layer star network topology that adapts relevant LoRa parameters for each end node dynamically taking into account its link distance and quality in order to balance communication range and energy consumption. The developed solution is comprised of multiple components, including a LoRa parameter calculator to help the user to configure the network parameters, a contention-free MAC protocol to avoid collisions, and an adaptive spreading factor and transmission power mechanism. These components work together to ensure a more efficient use of the chosen ISM band and end node resources, but with low-cost implementation and operation requirements.