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Sensor nodes are critical components of the Internet of Things (IoT). Traditional IoT sensor nodes are typically powered by disposable batteries, making it difficult to meet the requirements for long lifetime, miniaturization, and zero maintenance. Hybrid energy systems that integrate energy harvesting, storage, and management are expected to provide a new power source for IoT sensor nodes. This research describes an integrated cube-shaped photovoltaic (PV) and thermal hybrid energy-harvesting system that can be utilized to power IoT sensor nodes with active RFID tags. The indoor light energy was harvested using 5-sided PV cells, which could generate 3 times more energy than most current studies using single-sided PV cells. In addition, two vertically stacked thermoelectrical generators (TEG) with a heat sink were utilized to harvest thermal energy. Compared to one TEG, the harvested power was improved by more than 219.48%. In addition, an energy management module with a semi-active configuration was designed to manage the energy stored by the Li-ion battery and supercapacitor (SC). Finally, the system was integrated into a 44 mm × 44 mm × 40 mm cube. The experimental results showed that the system was able to generate a power output of 192.48 µW using indoor ambient light and the heat from a computer adapter. Furthermore, the system was capable of providing stable and continuous power for an IoT sensor node used for monitoring indoor temperature over a prolonged period.

Wireless acoustic sensor networks (WASNs) and intelligent microsystems are crucial components of the Internet of Things (IoT) ecosystem. In various IoT applications, small, lightweight, and low-power microsystems are essential to enable autonomous edge computing and networked cooperative work. This study presents an innovative intelligent microsystem with wireless networking capabilities, sound sensing, and sound event recognition. The microsystem is designed with optimized sensing, energy supply, processing, and transceiver modules to achieve small size and low power consumption. Additionally, a low-computational sound event recognition algorithm based on a Convolutional Neural Network has been designed and integrated into the microsystem. Multiple microsystems are connected using low-power Bluetooth Mesh wireless networking technology to form a meshed WASN, which is easily accessible, flexible to expand, and straightforward to manage with smartphones. The microsystem is 7.36 cm3 in size and weighs 8 g without housing. The microsystem can accurately recognize sound events in both trained and untrained data tests, achieving an average accuracy of over 92.50% for alarm sounds above 70 dB and water flow sounds above 55 dB. The microsystems can communicate wirelessly with a direct range of 5 m. It can be applied in the field of home IoT and border security.

Information in conventional digital computing platforms is encoded in the steady states of transistors and processed in a quasi-static way. Memristors are a class of emerging devices that naturally embody dynamics through their internal electrophyiscal processes, enabling nonconventional computing paradigms with enhanced capability and energy efficiency, such as reservoir computing. Here, we report on a dynamic memristor based on LiNbO 3 . The device has nonlinear I-V characteristics and exhibits short-term memory, suitable for application in reservoir computing. By time multiplexing, a single device can serve as a reservoir with rich dynamics which used to require a large number of interconnected nodes. The collective states of five memristors after the application of trains of pulses to the respective memristors are unique for each combination of pulse patterns, which is suitable for sequence data classification, as demonstrated in a 5 × 4 digit image recognition task. This work broadens the spectrum of memristive materials for neuromorphic computing.

Information in conventional digital computing platforms is encoded in the steady states of transistors and processed in a quasi-static way. Memristors are a class of emerging devices that naturally embody dynamics through their internal electrophyiscal processes, enabling nonconventional computing paradigms with enhanced capability and energy efficiency, such as reservoir computing. Here, we report on a dynamic memristor based on LiNbO . The device has nonlinear I-V characteristics and exhibits short-term memory, suitable for application in reservoir computing. By time multiplexing, a single device can serve as a reservoir with rich dynamics which used to require a large number of interconnected nodes. The collective states of five memristors after the application of trains of pulses to the respective memristors are unique for each combination of pulse patterns, which is suitable for sequence data classification, as demonstrated in a 5 × 4 digit image recognition task. This work broadens the spectrum of memristive materials for neuromorphic computing.

Printed circuit board (PCB) defect detection is critical for ensuring the safety of electronic devices, especially in the space industry. Non-reference-based methods, typically the deep learning methods, suffer from a large amount of annotated data requirements and poor interpretability. In contrast, conventional reference-based methods achieve higher detection accuracy by comparing with a template image but rely on precise image alignment and face the challenge of fine defects detection. To solve the problem, we propose a novel Edge-guided Energy-based PCB Defect Detection method (EEDD). We focus on the salient edge characteristic of PCB images and regard the functional defects as contour differences and then propose a novel energy measurement method for PCB contour anomaly detection. We introduce the energy transformation using the edge information of the template and test image, then Speeded-Up Robust Features method (SURF) is used for image alignment, and finally achieve defect detection by measuring the energy anomaly score pixel by pixel with the proposed energy-based defect localization and contour flood fill methods. Our method excels in detecting multi-scale defects, particularly tiny defects, and is robust against interferences such as non-finely aligned images and edge spurs. Experiments on the DeepPCB-A dataset and our high-resolution PCB dataset (HDPCB) show that the proposed method outperforms state-of-the-art methods in PCB defect-detection tasks.

Stroke is the second leading cause of death worldwide and accounts for >2 million deaths annually in China1,2. Ischemic stroke (IS) and intracerebral hemorrhage (ICH) account for an equal number of deaths in China, despite a fourfold greater incidence of IS1,2. Stroke incidence and ICH proportion are higher in China than in Western populations3–5, despite having a lower mean low-density lipoprotein cholesterol (LDL-C) concentration. Observational studies reported weaker positive associations of LDL-C with IS than with coronary heart disease (CHD)6,7, but LDL-C-lowering trials demonstrated similar risk reductions for IS and CHD8–10. Mendelian randomization studies of LDL-C and IS have reported conflicting results11–13, and concerns about the excess risks of ICH associated with lowering LDL-C14,15 may have prevented the more widespread use of statins in China. We examined the associations of biochemically measured lipids with stroke in a nested case-control study in the China Kadoorie Biobank (CKB) and compared the risks for both stroke types associated with equivalent differences in LDL-C in Mendelian randomization analyses. The results demonstrated positive associations of LDL-C with IS and equally strong inverse associations with ICH, which were confirmed by genetic analyses and LDL-C-lowering trials. Lowering LDL-C is still likely to have net benefit for the prevention of overall stroke and cardiovascular disease in China.In a nested case-control study from the China Kadoorie Biobank, lowering blood low-density lipoprotein cholesterol levels confers lower risk for ischemic stroke but elevated risk for intracerebral hemorrhage, which was confirmed by genetic Mendelian randomization analyses.

Epidemiological evidence on the association of soy intake with breast cancer risk is still inconsistent due to different soy intake levels across previous studies and small number of breast cancer cases. We aimed to investigate this issue by analyzing data from the China Kadoorie Biobank (CKB) study and conducting a dose–response meta-analysis to integrate existing evidence. The CKB study included over 300,000 women aged 30–79 from 10 regions across China enrolled between 2004 and 2008, and followed-up for breast cancer events until 31 December 2016. Information on soy intake was collected from baseline, two resurveys and twelve 24-h dietary recalls. We also searched for relevant prospective cohort studies to do a dose–response meta-analysis. The mean (SD) soy intake was 9.4 (5.4) mg/day soy isoflavones among CKB women. During 10 years of follow-up, 2289 women developed breast cancers. The multivariable-adjusted relative risk was 1.00 (95% confidence interval [CI] 0.81–1.22) for the fourth (19.1 mg/day) versus the first (4.5 mg/day) soy isoflavone intake quartile. Meta-analysis of prospective studies found that each 10 mg/day increment in soy isoflavone intake was associated with a 3% (95% CI 1–5%) reduced risk of breast cancer. The CKB study demonstrated that moderate soy intake was not associated with breast cancer risk among Chinese women. Higher amount of soy intake might provide reasonable benefits for the prevention of breast cancer.

Exchange bias (EB) in antiferromagnetic (AFM)/ferromagnetic heterostructures is crucial for the advancement of spintronic devices and has attracted significant attention. The common EB effect in van der Waals heterostructures features a low blocking temperature (Tb) and a single polarity. In this work, a significant EB effect with a Tb up to 150 K is observed in FePS3/Fe3GaTe2 heterostructures, and in particular, the EB exhibits an unusual temperature‐dependent polarity‐reversal behavior. Under a high positive field‐cooling condition (e.g., μ0H ≥ 0.5 T), a negative EB field (HEB) is observed at low temperatures, and with increasing temperature, the HEB crosses zero at ≈20 K, subsequently becomes positive and later approaches zero again at Tb. A model composed of a top FePS3/interfacial FePS3/Fe3GaTe2 sandwich structure is proposed. The charge transfer from Fe3GaTe2 to FePS3 at the interface induces net magnetic moments (∆M) in FePS3. The interface favors AFM coupling, and thus the reversal of ∆M of the interfacial FePS3 leads to the polarity‐reversal of EB. Moreover, the EB can be extended to the bare Fe3GaTe2 region of the Fe3GaTe2 flake partially covered by FePS3. This work provides opportunities for a deeper understanding of the EB effect and opens a new route toward constructing novel spintronic devices. A significant EB effect with temperature‐dependent polarity‐reversal behavior is observed in FePS3/Fe3GaTe2 heterostructures under high cooling fields (e.g., μ0H ≥ 0.5 T). A model composed of a top FePS3/interfacial FePS3/Fe3GaTe2 sandwich structure is proposed. Charge transfer from Fe3GaTe2 to FePS3 induces net magnetic moments (∆M) in FePS3, and the reversal of ∆M of interfacial FePS3 leads to the polarity‐reversal of EB.

Manipulating the magnetic order transition of 2D magnetic materials is an important way for the application of spintronic devices, and carrier concentration modulation is a commonly used effective regulation method. Here the magnetic ground state of FePS3 is tuned from antiferromagnetic (AFM) to ferrimagnetic (FIM) and back to AFM by electron doping, which is achieved via the intercalation of various organic cations. The doped FePS3 with FIM order exhibits a Curie temperature Tc of ≈110 K, a strong out‐of‐plane magnetic anisotropy, and particularly an unusual hysteresis loop, where with increasing temperature, the area of magnetic hysteresis loop increases below 50 K, then decreases above 50 K and eventually disappears. Theoretical calculations indicate that at a doping concentration of 0.3–0.9 electrons per cell, spin splitting of energy bands occurs, leading to the FIM order; whereas at a doping concentration of ≥ 1.0 electrons per cell, the AFM order recovers. Such AFM‐FIM‐AFM transition is ascribed to the competition between the Stoner exchange‐dominated FM order and super‐exchange‐dominated AFM order. These results demonstrate an effective approach to engineering magnetism in 2D magnetic materials by purely electrical means for future device applications.

Systemic inflammation, reflected by increased plasma concentrations of C-reactive protein (CRP) and fibrinogen, is associated with increased risk of coronary heart disease, but its relevance for stroke types remains unclear. Moreover, evidence is limited in non-European populations. We investigated associations of CRP and fibrinogen with risks of incident major coronary events (MCE), ischemic stroke (IS) and intracerebral hemorrhage (ICH) in a cohort of Chinese adults. A nested case-control study within the prospective China Kadoorie Biobank included 1,508 incident MCE cases, 5,418 IS cases, 4,476 ICH cases, and 5,285 common controls, aged 30–79 years. High-sensitivity CRP and low-density lipoprotein cholesterol (LDL-C) were measured in baseline plasma samples from all participants, and fibrinogen in a subset (n = 9,380). Logistic regression yielded adjusted odds ratios (ORs) per SD higher usual levels of log-transformed CRP and fibrinogen. The overall mean (SD) baseline LDL-C was 91.6 mg/dL (24.0) and geometric mean (95% CI) CRP and fibrinogen were 0.90 mg/L (0.87–0.93) and 3.01 g/L (2.98–3.03), respectively. There were approximately log-linear positive associations of CRP with each outcome, which persisted after adjustment for LDL-C and other risk factors, with adjusted ORs (95% CI) per SD higher CRP of 1.67 (1.44–1.94) for MCE and 1.22 (1.10–1.36) for both IS and ICH. No associations of fibrinogen with MCE, IS, or ICH were identified. Adding CRP to prediction models based on established risk factors improved model fit for each of MCE, IS, and ICH, with small improvements in C-statistic and correct reclassification of controls to lower risk groups. Among Chinese adults, who have low mean LDL-C, CRP, but not fibrinogen, was independently associated with increased risks of MCE and stroke.