Search Results (5,164)

Sleep-related respiratory disturbances are difficult to monitor continuously outside specialized laboratories because conventional polysomnography is resource-intensive and intrusive. This study presents a contactless edge-AI engineering prototype for detecting controlled voluntary respiratory-motion suppression and motion artifacts using a 60 GHz frequency-modulated continuous-wave radar. The system integrates a 60 GHz radar front end, lightweight local preprocessing, an INT8 one-dimensional convolutional neural network deployed on the Analog Devices MAX78000 CNN accelerator (Analog Devices Thailand, Chon Buri, Thailand), and an event-driven Raspberry Pi Zero 2W gateway for alert transmission. Evaluation was performed using a controlled healthy-volunteer dataset consisting of normal breathing, voluntary breath-holding-induced respiratory suppression, and deliberate motion artifact. The final valid test set contained 270 technically valid 30 s windows balanced across the three classes. The INT8 model achieved an overall accuracy of 92.6% (95% confidence interval: 88.8–95.2%), with a macro-averaged precision, recall, and F1-score of 92.6%, 92.6%, and 92.5%, respectively. Active CNN inference on the MAX78000 consumed 0.152 ± 0.011 mJ and was completed in 5.20 ± 0.11 ms, corresponding to approximately 280-fold lower active inference energy than Python 3.14.6/TensorFlow Lite 2.21.0-based execution on the Raspberry Pi Zero 2W. These results demonstrate the feasibility of privacy-aware, low-power respiratory-pattern classification at the edge. However, the study should be interpreted strictly as an engineering proof-of-concept based on controlled voluntary breathing and movement tasks in healthy volunteers. It is not a clinically validated apnea or obstructive sleep apnea detection system and did not include polysomnography, oxygen saturation measurement, airflow sensing, sleep staging, or diagnosed patient cohorts. Full article

Lithium has become one of the key raw materials for the energy transition due to the central role of lithium-ion batteries in electromobility, energy storage, and the integration of renewable energy sources. However, the rapid increase in demand reveals growing environmental, social, geopolitical, and market tensions. The aim of the paper is a critical synthesis of global lithium utilization from the perspective of challenges, technological innovations, and integrative strategies supporting a more sustainable material–energy system. A broad, systematic literature review covering the entire value chain was applied: resources, extraction, processing, end-use applications, second life of batteries, recycling, and governance. The analysis shows that the strategic importance of lithium arises from the increasing demand pressure from electric vehicles and stationary storage, while the sustainability of the current model is constrained by supply concentration, uneven control over downstream stages, the water–carbon footprint of extraction and processing, social conflicts, and incomplete integration of secondary loops. At the same time, innovations such as direct lithium extraction (DLE), recovery from geothermal brines, design for recycling, second life, and battery passports can partially alleviate these tensions, but they do not eliminate the need for primary supply in the short term. The conclusion of the work is that sustainable global lithium utilization requires simultaneous diversification of sources, development of circular value chains, and multi-level governance integrating resource security, environmental efficiency, and social legitimacy. Full article

Driven by the high-end furniture industry’s demand for skin-tactile decorative boards, UV inkjet printing shows potential for wood-based surface finishing. Using primer-treated inkjet-printed melamine-faced panels, this study compared traditional UV varnish coatings with different thicknesses and UV curing intensities and 254 nm UV excimer-cured coatings with different radiant energies. Varnish thickness significantly affected surface roughness, 20° gloss, 85° gloss, and color difference, indicating a trade-off between matte tactile appearance and color fidelity. Thinner varnish coatings exhibited higher roughness and lower gloss but larger color differences, whereas thicker coatings better preserved color fidelity but resulted in higher gloss. For the UV excimer-cured system, one-way ANOVA showed significant treatment effects on acrylate conversion, water contact angle, 85° gloss, surface roughness, and abrasion mass loss. The coating prepared at an excimer radiant energy of 827.9 mJ/cm² showed the lowest 85° gloss of 5.28 GU and a pencil hardness of 3H, but also exhibited the highest abrasion mass loss in the short-cycle abrasion screening test. For both coating systems, three independently prepared specimens were tested for each processing condition. The UV varnish system was analyzed using two-way ANOVA, whereas the UV excimer-cured system was analyzed using one-way ANOVA. Friedman tests of sensory evaluation data showed significant differences among the eight selected samples for fineness, smoothness, and elasticity, with the excimer-cured coatings generally receiving higher fineness and smoothness scores than the UV varnish coatings. These results indicate that 254 nm UV excimer curing is a promising route for producing low-gloss, micro-wrinkle-induced skin-tactile surfaces on inkjet-printed melamine-faced panels, although optimization should balance tactile quality, gloss reduction, and abrasion resistance. Full article

Bisphenol A type epoxy resin has the problem of relatively high brittleness after curing. Although traditional polysiloxane toughening methods can improve toughness, they often come at the expense of strength. In this paper, methylphenyl dimethoxysilane (MPS) was used as a monomer to synthesize end-hydroxyl poly(methylphenyl)siloxane (PMPS), which was then used to modify E51 epoxy resin. The structure and reaction degree were characterized by infrared spectroscopy, proton nuclear magnetic resonance spectroscopy, matrix-assisted laser desorption/ionization time-of-flight/time-of-flight mass spectrometry and viscosity tests. The mechanical test results show that when the PMPS content is 20 wt%, the tensile, flexural, compressive and impact strengths of the modified resin increase by 31.26%, 26.16%, 18.53% and 98.66%, respectively, compared with the unmodified resin, and the tensile and flexural elastic moduli increase by 38.36% and 32.25%, respectively. The fracture toughness increases by 60.29%, indicating that the strength, stiffness and toughness of the material have all been improved. Dynamic mechanical analysis shows that the glass transition temperature and crosslinking density of the system gradually decrease with increasing PMPS content. Thermogravimetric analysis shows that the introduction of PMPS increases the char yield and decreases the maximum thermal decomposition rate, thereby enhancing the thermal stability of the system. Microscopic morphology analysis by optical microscopy, scanning electron microscopy and atomic force microscopy shows that the system has good compatibility, and the internal different modulus phases are distributed in a network-like manner, forming a uniform co-continuous or bicontinuous phase structure. This structure effectively promotes stress transfer and energy dissipation, alleviates local stress concentration, and thus comprehensively improves the mechanical properties of the resin system. Full article

The increasing frequency of extreme weather events, particularly hailstorms, driven by climate change, poses growing threats to the resilience, environmental sustainability, and long-term performance of photovoltaic (PV) systems. This study evaluates the environmental impacts of a 12 kWp rooftop PV installation in Brescia, northern Italy, through a comparative Life Cycle Assessment (LCA) of three system configurations: a standard unprotected system (Scenario A), one equipped with a retractable polycarbonate hail-protection panel with automated weather-sensor activation (Scenario B), and one using thicker reinforced front-glass modules (Scenario C). The analysis follows a cradle-to-gate plus operational maintenance phase (30-year horizon, excluding end-of-life) system boundary and employs the ReCiPe 2016 Midpoint (H) methodology across 18 environmental impact categories. A novel integration of the Social Discount Rate (SDR) to the LCA framework—constituting a Discounted LCA (D-LCA)—incorporates both temporal discounting and risk dimensions into the environmental evaluation. A structured PESTEL-based risk taxonomy is applied to derive scenario-specific SDRs, with the Environmental risk category as the key differentiator between configurations. The static LCA identifies Scenario A as the lowest-impact option, while the D-LCA framework reverses this ranking: Scenario C achieves the highest Net Present Value of Emissions, followed by Scenario A. A negative NPV-E for Scenario B reflects the temporal cost of a large, front-loaded construction debt rather than absolute environmental harm. D-LCA framework should be interpreted as a complement to the full 18-category static LCIA profile, not a replacement. These results demonstrate that risk-informed D-LCA provides a more policy-relevant environmental sustainability assessment than static LCA for long-lived energy infrastructure subject to climate-driven operational risks. Full article

The rapid scaling of large language model (LLM) training has intensified demand for Graphics Processing Unit (GPU) clusters balancing throughput with energy efficiency. While NVIDIA’s H100 and B200 architectures are increasingly deployed in production datacenters, their comparative behavior under distributed training remains insufficiently characterized beyond vendor specifications, leaving datacenter operators without empirical guidance on metrics such as TFLOPs/kW and tokens-per-kilojoule. This work presents a system-level evaluation of single-node 8× H100 and 8× B200 configurations using Distributed Data Parallel (DDP) training across LLMs and vision–language models (VLMs) ranging from 7B to 32B parameters, spanning various real AI workload scenarios. We benchmark end-to-end throughput, utilization, power, energy, TFLOPs/kW, and tokens-per-kilojoule, complemented by architectural analysis explaining observed behavioral differences. Across LLM workloads, B200 achieves higher utilization (1–6%), faster training (up to 15%), and greater compute efficiency (up to 32% higher TFLOPs/GPU), attributable to higher memory bandwidth and large streaming multiprocessor (SM) count. However, B200 exhibits lower TFLOPs/kW and tokens-per-kilojoule, revealing a fundamental trade-off: throughput gains come at a measurable energy cost per useful token. VLM results further expose model-dependent asymmetries, with B200 consuming disproportionately more energy for lighter compute kernels due to elevated baseline power draw. These findings provide an empirical framework distinguishing compute efficiency from energy efficiency across next-generation GPU nodes, offering practical guidance for energy-aware AI datacenter design. Full article

Waste-to-energy (WtE) systems are frequently proposed as complementary waste-management strategies; however, their climate performance depends on the interaction between thermodynamic efficiency, material circularity, and electricity-system characteristics. Existing life-cycle assessments generally provide static comparisons between landfill and WtE but rarely identify the operating conditions under which WtE remains environmentally competitive. To address this gap, a parametric life cycle–energy framework was developed by integrating attributional LCA with an analytical energy model capable of evaluating critical efficiency thresholds under varying recovery rates and electricity-grid conditions. Four representative thermoplastics (PET, HDPE, PP, and LDPE) were evaluated using ReCiPe 2016 Midpoint (H) in SimaPro under Mexican electricity conditions ($E{F}_{\mathrm{grid}}=0.444$ kg CO₂eq/kWh). Results indicate that total life-cycle climate impacts are dominated by upstream polymer production, whereas end-of-life management contributes only marginally to overall GWP. Critical-efficiency analysis revealed strong sensitivity to both recovery rate and electricity-grid carbon intensity. For PET, the minimum efficiency required for WtE to outperform landfill increased from 13.1% to 73.5% across the evaluated scenarios, whereas HDPE remained competitive at efficiencies below 1.3%. Monte Carlo simulations (10,000 realizations) further demonstrated that avoided emissions decline systematically with increasing recovery rates, with LDPE exhibiting the highest mean avoided emissions (1735 kg CO₂eq) and PET the lowest (811 kg CO₂eq). These results demonstrate that WtE climate performance is governed primarily by residual waste availability and electricity-system evolution rather than thermodynamic efficiency alone. Consequently, WtE should be interpreted as a transitional residual-waste management strategy whose long-term climate relevance decreases as material circularity and electricity-grid decarbonization advance. Full article

Cobalt occupies an irreplaceable strategic position in renewable energy and high-end advanced industries. As high-grade mineral resources gradually deplete, associated sulfide minerals have attracted increasing attention as alternative sources of cobalt. This study investigated a selective extraction of cobalt from low-grade cobalt-bearing pyrite using oxygen-pressure acid leaching. The Gibbs free energy (ΔG) of key chemical reactions in the leaching system was calculated to verify the thermodynamic feasibility of the process. The effects of critical parameters, including oxygen pressure, initial acidity, stirring speed, leaching time, and temperature, on cobalt leaching efficiency and phase transformation characteristics were systematically investigated. Under optimal conditions of oxygen pressure 1.5 MPa, H₂SO₄ initial acidity 7.36 g·L⁻¹ (0.82 mol/L), stirring speed 300 rpm, leaching duration 120 min, and temperature 230 °C, the cobalt leaching rate reached 98.2%, whereas the leaching rates of iron and aluminum were only 19.79% and 28.11%, respectively. Combined with SEM-EDS, XRD, and XPS characterization results, oxygen pressure acid leaching effectively destroyed the lattice structure of cobalt-bearing pyrite and liberates lattice-hosted cobalt, thereby facilitating efficient cobalt leaching. At high-temperature and oxygen pressure conditions, Fe³⁺ underwent hydrolysis and precipitated as hematite (Fe₂O₃) or hydronium jarosite (H₃O)Fe₃(SO₄)₂(OH)₆, enabling the selective extraction of cobalt. Aluminum in cobalt-bearing pyrite primarily occurred as the stable boehmite (AlOOH) phase, exhibiting excellent acid resistance and low dissolution during leaching. This study broadens the utilization pathway of low-grade cobalt resources and provides valuable insights and a scientific theoretical basis for the efficient treatment of cobalt-containing sulfide concentrates and tailings. Full article

Elopomorpha is one of the most primitive teleost lineages and is characterised by a unique trait, the leptocephalus larva. Its leaf-like and transparent body is largely composed of glycosaminoglycan (GAG), which makes it distinctive from other fish larvae. Hyaluronic acid (HA) has been identified as the principal GAG present in these larvae and is thought to play several physiological roles, such as floatability, osmoregulation, and energy storage. During the larval stage, HA reaches a concentration of up to 40 to 50% of the body composition due to a HA synthesis peak. Later in metamorphosis, HA concentration decreases and, after this, adults show a normal HA metabolism. These observations could be explained if the larva has an alternative HA metabolism compared to the adult. It is known that HA synthesis is done by three hyaluronan synthase (HAS) enzymes, which differ in the length of HA synthetized, codified by three genes: HAS1, HAS2 and HAS3. Therefore, to test the hypothesis of a larval-specific metabolism, the genomic basis of these genes in Elopomorpha will be analysed. To this end, HAS coding and protein sequences from nine Elopomorpha, four Osteoglossomorpha and 21 Clupeocephala species were mined from the NCBI database to ensure a broad phylogenetic coverage. To guarantee a complete retrieval of HAS genes, the BLAST tool (https://blast.ncbi.nlm.nih.gov/Blast.cgi, accessed on 1 March 2026) and synteny analysis were performed. Then, Pseudochecker was used to confirm the functionality of the candidate genes obtained. Functional genes were used for phylogenetic analyses, maximum likelihood and Bayesian inference to test orthology. Both lineage and species duplications of HAS1 and HAS2 were found in Elopomorpha, which would be responsible for the synthesis of HA during the larval stage. These new genetic patterns provide the first evidence of a possible alternative metabolism of HA during the larval stage of Elopomorpha. Full article

Free vibrations of axially moving beam-like nanostructures have been investigated in recent years; however, vibrations of moving nanochassis traveling over a surface with arbitrarily small irregularities have not been displayed yet due to some complexities in modeling. To address this challenge, a nonlinear, nonlocal surface energy-based composite beam-like model is established to fairly accurately capture the nanochassis’ vibrations. The nanocar consists of a composite-like nanochassis and the ends’ wheels, where the nanochassis is modeled by an appropriate beam model and the wheels are simulated as rigid solid elements that are attached to the beam’s ends. Both differential- and integral-based formulations are presented, and their nonlinear stiffness, as well as the procedure for capturing the nonlocal elastic field, is carefully explained using the assumed mode approach. For several particular cases, the predicted results by the suggested models are verified with those of several analytical solutions, and reasonably good agreements are achieved. Beyond the aforementioned comparison studies, the possible instabilities of the nanochassis that travels over a straight route were also identified and explained under a small deformation regime. Through conducting a fairly comprehensive parametric study, the roles of amplitude and frequencies of the harmonic route, axial velocity, length, diameter, nonlocality, surface energy, and geometrical nonlinearity on maximum deformations and internal forces are examined comprehensively. This study could be considered as basic scrutiny for the nonlinear analysis of more complex traveling nanostructures over arbitrarily shaped surfaces. Full article

Natural gas metering is shifting from volume-based measurement to energy-based assessment as gas sources diversify, pipeline networks become more interconnected, and gas quality varies more strongly across time and space. This review examines the key technologies required for natural gas energy metering and evaluates how they support full-chain traceability from production to end use. The reviewed topics include flow measurement, gas composition analysis, calorific value determination, temperature-pressure compensation, state correction, uncertainty evaluation, intelligent data acquisition, and metrological traceability. The literature shows that individual technologies have advanced substantially. Ultrasonic flowmeters, rapid gas-quality sensing methods, dynamic calorific value allocation models, high-accuracy equations of state, and digital metering platforms have improved the technical basis of energy metering. However, these advances remain more mature at the level of individual links than at the level of the complete metering chain. Under multi-source supply, gas-quality fluctuation, hydrogen blending, and digitalized operation, the main challenge is to maintain consistency, uncertainty control, online verification, data credibility, and auditability across different metering stages. Future development should therefore focus on dynamic calorific value allocation, robust state correction under variable gas quality, full-chain uncertainty propagation, online verification, and secure data management for traceable natural gas energy metering. Full article

Low-dose radiation (LDR) has emerged as a promising therapeutic modality for Alzheimer’s Disease (AD). Although different irradiation protocols have been explored, the optimal parameters for maximizing therapeutic efficacy remain unclear. Radiation energy has been shown to influence radiobiological responses, with more pronounced effects at lower energy ranges. We therefore investigated whether kilovoltage LDR (KLDR) provides superior therapeutic efficacy compared with megavoltage LDR (MLDR) in a murine model of AD(3xTg-AD). To this end, we directly compared the efficacy of MLDR and KLDR in AD model mice to identify an optimal irradiation strategy for LDR treatment with potential relevance to clinical translation in AD. X-rays with 110-kV or 6-MV energy were applied to the brain of AD model mice at an early-stage of disease progression (26–28 weeks age; 0.6 Gy × 5 fractions for 2.5 weeks). After LDR treatment, cognitive function was assessed in AD model mice using passive avoidance (PA) test and novel object recognition (NOR) test. In addition, different molecular markers associated with inflammation, amyloid-beta (Aβ) plaques, tau burden, and neuronal and synaptic degeneration were analyzed in the brain of AD model mice. KLDR (110 kV) significantly inhibited cognitive decline in AD model mice, as demonstrated by both the PA and NOR tests. In addition, KLDR significantly reduced hippocampal levels of GFAP, Iba-1, and pro-inflammatory cytokines (TNF-α, IL-6, and IL-1β), while increasing anti-inflammatory cytokines (TGF-α, TGF-β, and IL-10), and was associated with marked reductions in Aβ and tau levels. Furthermore, the expression levels of Aβ40 and Aβ42 were quantified by ELISA following KLDR and MLDR treatment, revealing a statistically significant reduction in the KLDR group. The degeneration of neurons and synapses was significantly suppressed also at the kilovoltage energy level. Conversely, MLDR (6 MV) exerted minimal effects and did not produce statistically significant improvements. Taken together, our findings demonstrate that radiation energy level is a key determinant of LDR therapeutic efficacy in AD model mice, with KLDR showing significantly greater effectiveness in improving AD-related pathological features than MLDR. Therefore, KLDR may be recommended as a novel radiation protocol for AD treatment. Full article

High-precision rolling bearing applications are widely used in aerospace, new energy vehicles and high-end equipment. However, high-precision bearing manufacturing has always been one of the hot topics of research. The main problem is that the grinding accuracy of rolling bearings is too divergent. In this study, the improvement of grinding accuracy of high-precision rolling bearings was mainly studied. An on-line roundness measurement system was developed and its accuracy was analyzed. The same bearing precision grade, different bearing brands, different bearing sizes and different measurement methods were mainly used for cross-precision measurement comparison. Meanwhile, a static analysis was conducted on the measuring claw. Results indicate that the on-line measurement system can achieve an accuracy of 3 µm. The error rate was less than 11% compared with the current mature measurement technology. Under the action of the same normal measuring force, the deformation of the measuring claw of invar was larger than that of the measuring claw of 45 steel, which was relatively increased by 31%. The conclusion of this study will provide reliable data analysis and a theoretical basis for research in the field of bearing. Full article

Recirculating organic byproducts like food waste, wastewater and manure efficiently and at scale in a circular bioeconomy will be critical to ensuring future food security, energy security, climate resilience, water security and environmental health. Ultimately, we will not be able to live within the safe operating space of our planetary boundaries if we do not stop our wasteful and inefficient habits. Our food, waste, energy and water sectors are starting to transform towards circularity, driven by a diverse range of drivers, from net zero emissions targets, to food waste policies, and to rising fertiliser prices and geopolitical risks. However, these sectors are often not transforming in a coordinated manner, risking unintended consequences like competition between end-uses, technology lock-in, the prevention of scalability, or failure to achieve key sustainability targets, causing rebound effects. For example, society’s organic waste is being earmarked for the production of bioenergy, sustainable aviation fuels, biomaterials, and biofertilisers; however, it is not clear if there will be a sufficient supply of organic waste to meet these diverse demands. Phosphorus flow analyses indicate that we will need to secure almost all of the nutrients in organic waste as fertiliser raw material to produce food. There are some existing pockets of innovation within sectors related to food waste, water and wastewater, fertilisers and agriculture, and bioenergy. However, many initiatives are being driven by short-term challenges, are not operating at scale, or are not sufficiently integrated across sectors. In this paper, we provide examples of innovations and challenges from around the world, including Italy, Australia, Sri Lanka, the UK, Japan, and Malawi. This paper identifies a pathway to navigate tensions to achieve co-existing sustainability goals, including key enablers and barriers, ranging from overcoming regulatory fragmentation to a lack of capital investments. Creating a truly viable circular economy for organic byproducts requires the integration of policies, markets, technologies and people. This means engaging diverse stakeholders, from local councils and private waste contractors, farmers, and fertiliser companies to energy retailers and wastewater utilities, NGOs, informal collectors, and environmental regulators and policy-makers. Full article

Smartwatches are increasingly used in safety-critical scenarios, yet their optical heart-rate (HR) measurements often contain noise, artifacts, and missing data, undermining clinical trust. This paper presents Edge Beats, a data-curation layer and end-to-end architecture that enables the low-cost, open source PineTime smartwatch to function as a practical HR sensing node for distributed wearable systems. Heart-rate packets are streamed from PineTime to an ESP32 at the edge layer over Bluetooth Low Energy (BLE), then forwarded via an embedded Message Queuing Telemetry Transport (MQTT) broker to an edge server laptop for processing and visualization. A lightweight multi-stage algorithm cleans and smooths the HR stream using physiological boundary checks, a configurable data imputation technique, and exponential moving average (EMA) smoothing, all designed for real-time operation on resource-constrained hardware. We have evaluated the system over long monitoring sessions and compared the processed PineTime output against a commercial Huawei GT Pro 2 smartwatch. The system suppresses extreme spikes and short-term oscillations, yielding a more stable HR trace with qualitative agreement to the reference trends while keeping values in a physiologically plausible range. Network measurements show low latency (almost 3 ms one-way, 15 ms RTT) and stable throughput, and power measurements (100–450 mW for ESP32 and 3–70 mW for PineTime watch) confirm that continuous HR streaming over BLE and MQTT is feasible within the PineTime’s energy budget. These results imply that data stream processing combined with a modest publish–subscribe architecture improves the stability and usability of HR streams obtained from commodity wearable sensors, making PineTime a candidate as a complementary component for mission-critical health and safety systems. Full article