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ObjectiveTo characterize dynamic transmission of prevalent subtype strains of human immunodeficiency virus type 1 (HIV-1) through molecular network among HIV/AIDS patients aged 50 years and above in a district of Shanghai. MethodsBlood samples and demographic information were collected from a total of 580 first diagnosed HIV/AIDS patients during 2018 – 2021 in Pudong New Area, Shanghai municipality. After RNA nucleic acid extraction and the amplification of HIV-1 pol gene segment, a phylogenetic tree was constructed with maximum likelihood method and molecular transmission networks were also established with Tamura-Nei 93 pairwise gene distance method for the patients. The characteristics of the molecular transmission networks for the patients of different ages was analyzed. Results HIV-1 strains were successfully sequenced for a total of 518 patients (302 under 50 years and 216 aged ≥ 50 years). At the optimal genetic distance of 1.20%, the overall molecular clustering rate was 49.07% (106/216) for the patients aged ≥ 50 years and significantly higher than 28.48% (86/302) for the patients under 50 (χ2 = 22.903, P < 0.001). For the networks of degree one (or only one sexual partner involved in a network), the proportion of network involvement in the patients aged ≥ 50 years was 23.45% (53/216) ; the proportion was not significantly different between the patients with homosexual transmission (29.21%, 26/89) and those with heterosexual transmission (21.60% , 27/125) (χ2 = 1.617, P = 0.203) but the proportion was significantly higher in the patients with homosexual transmission between spouses or fixed partners (χ2 = 70.166, P < 0.001); similar involvement of the patients aged ≥ 50 years was observed for the networks of degree two and above, with an overall proportion of 23.45% (53/216) and not significantly different proportions of 23.60% (21/89) and 25.60% (32/125) for the patients with homosexual and heterosexual transmission (χ2 = 0.112, P = 0.738). In terms of HIV-1subtype-specific molecular transmission network involvement, the proportion was 63.64% (7/ 11) for subtype C, 33.33% (33/100) for CRF07_BC, 21.43% (3/14) for CRF08_BC, and 14.93% (10/67) for CRF01_AE, with significant difference among the involvement proportions (χ2 = 14.381, P = 0.002). The proportion of the involved patients aged ≥ 50 years and with delayed diagnosis was significantly higher (61.11%, 22/36) for the larger molecular transmission networks of degree four and above (χ2 = 8.176, P = 0.004). The results of univariate logistic regression analysis indicated that the over 50 years old patients having both homosexual and extramarital heterosexual behaviors, with 50 and more sexual partners, and with a sexual partner in the same residential region were at a significantly increased risk of to be involved in a larger molecular transmission network (P < 0.05 for all) . ConclusionFor the HIV-1 molecular transmission networks with the involvement of patients aged 50 years and above, the characteristics of the networks differs by transmission route of the involved patients and the virus subtype; a part of the involved high risk virus carriers could play an important transmission role in large or larger networks.

Abstract Evidence of the triglyceride‐glucose (TyG) index as an independent predictor of arterial stiffness in stage 1 hypertension patients is scarce. This study aimed to explore the association between TyG index and arterial stiffness in this population. A total of 1041 individuals from 32 centers with normal/elevated blood pressure (BP,

Abstract Highly stretchable and robust strain sensors are rapidly emerging as promising candidates for a diverse of wearable electronics. The main challenge for the practical application of wearable electronics is the energy consumption and device aging. Energy consumption mainly depends on the conductivity of the sensor, and it is a key factor in determining device aging. Here, we design a liquid metal (LM)‐embedded hydrogel as a sensing material to overcome the barrier of energy consumption and device aging of wearable electronics. The sensing material simultaneously exhibits high conductivity (up to 22 S m−1), low elastic modulus (23 kPa), and ultrahigh stretchability (1500%) with excellent robustness (consistent performance against 12 000 mechanical cycling). A motion monitoring system is composed of intrinsically soft LM‐embedded hydrogel as sensing material, a microcontroller, signal‐processing circuits, Bluetooth transceiver, and self‐organizing map developed software for the visualization of multi‐dimensional data. This system integrating multiple functions including signal conditioning, processing, and wireless transmission achieves monitor hand gesture as well as sign‐to‐verbal translation. This approach provides an ideal strategy for deaf‐mute communicating with normal people and broadens the application of wearable electronics.

Abstract Background Both obesity and subclinical hypothyroidism (SCH) have adverse effects on human body, but the relationship between these two conditions remains inconsistent. The presence of thyroid autoantibodies influences thyroid hormone levels, and may further mediate the interaction between obesity and SCH. This study aimed to explore the association among obesity, SCH and thyroid autoantibodies. Methods This study was a cross-sectional survey of 2505 subjects. Obesity was defined as a body mass index ≥28 kg/m2. Serum concentrations of thyroid hormones, thyroid peroxidase antibody (TPO-Ab) and thyroglobulin antibody (Tg-Ab) were examined. Logistic analysis was used to explore the relation among obesity, SCH and thyroid autoantibodies. Results A proportion of 11.54% (289/2505) subjects were obese, and 165 subjects had SCH. The positive rates of thyroid autoantibodies, TPO-Ab and Tg-Ab were 17.64% (442/2505), 11.02% (276/2505) and 14.13% (354/2505), respectively. The proportion of SCH was significantly higher in obese than nonobese subjects among those with positive thyroid autoantibodies [22.41% (13/58) vs. 11.72% (45/384), p = 0.025, χ2 test]. Moreover, obesity was significantly associated with SCH in the presence of thyroid autoantibodies after adjusting for confounding factors (OR 2.212, 95% CI 1.103 to 4.433, p = 0.025). A higher proportion of subjects with obesity had Tg-Ab positivity [17.99% (52/289) vs. 13.63% (302/2216), p = 0.045, χ2 test], and obesity remained significantly associated with Tg-Ab positivity by multiple logistic analysis (OR 1.504, 95% CI 1.077 to 2.101, p = 0.017). Conclusions Obesity was associated with SCH in the presence of thyroid autoantibodies. Examination of SCH is recommended in obese subjects with thyroid autoantibody positivity.

As drones are widely employed in various industries and daily life, concerns regarding their safety have been gradually emerging. Denial of service (DoS) attacks have become one of the most significant threats to the stability of resource-constrained sensor nodes. Traditional brute-force and high-rate distributed denial of service (DDoS) attacks are easily detectable and mitigated. However, low-rate TCP DoS attacks can considerably impair TCP throughput and evade DoS prevention systems by inconspicuously consuming a small portion of network capacity, and whereas the literature offers effective defense mechanisms against DDoS attacks, there is a gap in defending against Low-Rate TCP DoS attacks. In this paper, we introduce AccFlow, an incrementally deployable Software-Defined Networking (SDN)-based protocol designed to counter low-rate TCP DoS attacks. The main idea of AccFlow is to make the attacking flows accountable for the congestion by dropping their packets according to their loss rates. AccFlow drops their packets more aggressively as the loss rates increase. Through extensive simulations, we illustrate that AccFlow can effectively safeguard against low-rate TCP DoS attacks, even when attackers employ varying strategies involving different scales and data rates. Furthermore, whereas AccFlow primarily addresses low-rate TCP DoS attacks, our research reveals its effectiveness in defending against general DoS attacks. These general attacks do not rely on the TCP retransmission timeout mechanism but rather deplete network resources, ultimately resulting in a denial of service for legitimate users. Additionally, we delve into the scalability of AccFlow and its viability for practical deployment in real-world networks. Finally, we demonstrate the effectiveness of AccFlow in safeguarding network resources.

Ternary composite with great modulation of electron transfers has attracted a lot of attention from the field of high-performance room-temperature (RT) gas sensing. Herein, walnut-like WO3-Ni–graphene ternary composites were successfully synthesized by the hydrothermal method for formaldehyde (HCHO) sensing at RT. The structural and morphological analyses were carried out by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). SEM and TEM studies confirmed that walnut-like WO3 nanostructures with an average size of 53 ± 23 nm were functionalized. The Raman and XPS results revealed that, due to the deformation of the O-W-O lattice, surface oxygen vacancies Ov and surface-adsorbed oxygen species Oc were present. The gas-sensing measurement shows that the response of the WO3-Ni-Gr composite (86.8%) was higher than that of the Ni-Gr composite (22.7%) for 500 ppm HCHO at RT. Gas-sensing enhancement can be attributed to a p-n heterojunction formation between WO3 and Ni-Gr, Oc, spill-over effect of Ni decoration, and a special walnut-like structure. Moreover, long term stability (%R = 61.41 ± 1.66) for 30 days and high selectivity in the presence of other gases against HCHO suggested that the proposed sensor could be an ideal candidate for future commercial HCHO-sensing in a real environment.

Formation problem of multi-agents systems has been investigated studied by more and more scientists. This paper studies the formation problem of the first-order multi-agent systems with region constraint. Not only formation control but also the constraint area is considered. The state of each agent is constrained by a common convex set. A formation control protocol is proposed based on the local information of neighbourhood and region constraint. If the communication topology is connected, and the target framework is rigid, all agents would enter the target area while reaching the desired formation. The convergence of the formation control to the final formation is guaranteed as proven by Lyapunov framework. Compared with the leader follower controller, the controller does not depend on the leader and has good robustness. In addition, the controller can quickly and accurately control all agents to all agents to enter the constrained area. Finally, the results are illustrated by numerical simulations.

As formaldehyde is an extremely toxic volatile organic pollutant, a highly sensitive and selective gas sensor for low-concentration formaldehyde monitoring is of great importance. Herein, metal-organic framework (MOF) derived Pd/PdO@ZnO porous nanostructures were synthesized through hydrothermal method followed by calcination processes. Specifically, porous Pd/PdO@ZnO nanomaterials with large surfaces were synthesized using MOFs as sacrificial templates. During the calcination procedure, an optimized temperature of 500°C was used to form a stable structure. More importantly, intensive PdO@ZnO inside the material and composite interface provides lots of p-n heterojunction to efficiently manipulate room temperature sensing performance. As the height of the energy barrier at the junction of PdO@ZnO exponentially influences the sensor resistance, the Pd/PdO@ZnO nanomaterials exhibit high sensitivity (38.57% for 100 ppm) at room temperature for 1-ppm formaldehyde with satisfactory selectivity towards (ammonia, acetone, methanol, and IPA). Besides, due to the catalytic effect of Pd and PdO, the adsorption and desorption of the gas molecules are accelerated, and the response and recovery time is as small as 256 and 264 s, respectively. Therefore, this MOF-driven strategy can prepare metal oxide composites with high surface area, well-defined morphology, and satisfactory room-temperature formaldehyde gas sensing performance for indoor air quality control.

The distributed optimization for multi-agent systems with time delay and first-order is investigated in this paper. The objective of the distributed optimization is to optimize the objective function composed of the sum of local objective functions, which can only be known by its corresponding agents. Firstly, a distributed algorithm for time-delay systems is proposed to solve the optimization problem that each agent depends on its own state and the state between itself and its neighbors. Secondly, Lyapunov-Krasovskii function is used to prove that the states of each agent can be asymptotically the same, and the states are optimal. Finally, an example is given for illustrating the analytical results and a comparison is also gave to illustrate the differences between the algorithm of this paper and other results.

Fast recognition of flammable and toxic gas species within very short response time is a challenging task for the gas sensing devices adopted in a wide range of applications. The recognition accuracies of the previous implementations are always constrained by the limited feature or dynamic information extracted from the short transient gas response curves. In order to address this issue, in this paper, we propose a novel hybrid approach with both convolutional and recurrent neural networks combined, which is based on the long short-term memory module. Featuring the capability of learning the correlations of time-series data, the proposed deep learning method is well-suited for extracting the valuable transient feature contained in the very beginning of the response curve. As a result, within a response time as short as 0.5 s, the proposed implementation is capable of recognizing the gas species with an accuracy of 84.06%. In addition, the aforesaid accuracy can be further improved by increasing the response time with the step of 0.5 s. According to our extensive experimental results, the recognition accuracy can be elevated up to 98.28% at the response time of 4 s, where it typically needs 40 s for the response curve to achieve saturation. The reported accuracy dramatically outperforms the previous algorithms, including gradient tree boosting (GTB), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and linear discriminant analysis (LDA). Moreover, various standard drift related experiments are conducted, of which the results validate our proposed algorithm's superior robustness for the wide range of real-life applications.

In this paper, we present a novel one-dimensional deep convolutional neural network (1D-DCNN) with a multi-label-way-based algorithm for comprehensively and automatically extracting features and classifying mixture gases. Although a number of pattern recognition methods have been used to analyze the mixed gases, the performances of these methods highly depend on the hand-crafted feature engineering. By contrast, the proposed implementation, based on one-dimensional convolution, is capable of automatically extracting features and distinguishing the individual component of binary mixture gases composed of ethylene, CO, and methane. To the best of our knowledge, the proposed 1D-DCNN algorithm is first applied in the mixture gases' recognition. In addition, the proposed 1D-DCNN with multi-label way not only significantly reduces the label dimension but also quantifies the probability of each component in mixed gases. Compared with the conventional pattern recognition algorithms including support vector machine, artificial neural network, k-nearest neighbor, and random forest, the proposed 1D-DCNN exhibits a higher recognition accuracy (96.30%) based on our extensive experimental results using ten-fold cross validation.

This paper presents an energy-efficient level shifter design that is capable of converting an extremely low input voltage to the supply voltage level. Featuring a core area as compact as 32.99 μm2, the proposed design comprises a front-end current mirror and an output cross-coupled structure. Concretely, the current mirror is used to boost the complementary input signals to a proper level, with the operations of pull-up and pull-down networks well-balanced. The cross-coupled structure is used at the second stage to achieve a full-swing output. In addition, multi-threshold CMOS transistors are employed and optimized to elevate the circuit performance. The prototype was fabricated using a commercial 65-nm CMOS process. Experiments show that a 90-mV low input voltage can be successfully converted to 1.2 V. The energy consumed per conversion is reported to be 31.47 fJ for converting a 0.2-V input to 1.2 V at 10 MHz, and the corresponding propagation delay is measured to be 23.98 ns.

This paper presents a new type of miniaturized base station antenna array, which consists of a metal reflector, six upper-band dipoles, and a couple of ladder sidewalls, operating over 1.71-2.69 GHz (DCS/PCS/UMTS/LTE). Meanwhile, this paper also proposes a novel miniaturized phase shifter with one input and five outputs to enable the antenna array to achieve electrical downtilt (0°-10°) in the operating frequency band. Three different sidewalls are applied in the proposed antenna array to compare their performance. Simulated results demonstrate that the antenna array with ladder sidewalls can achieve an excellent performance, and this proposed phase shifter also has good electrical properties to help the proposed antenna array realize downtilt. The proposed antenna array and phase shifter are fabricated and measured, and the measured results show that the antenna array has good radiation characteristics, including low-voltage standing wave ratio (31 dB), low third-order passive intermodulation (3 30 dB), and high cross-polarization discrimination at ±60° azimuth (>11.5 dB) in all frequency bands and electrical downtilt angles. The proposed antenna array exhibits an acceptable gain of 15 ± 1.1 dBi across the operational frequency band.

In this paper, we report an on-chip gas sensor based on novel zinc oxide (ZnO) nanocombs for carbon monoxide (CO) sensing. With ZnO gas sensing nanocombs fully integrated on a single silicon chip, the concept of low cost complementary-metal-oxide-semiconductor (CMOS) microsensor capable of on-chip gas sensing and processing is enabled. Compared with all previous implementations, the proposed ZnO nanocombs feature much larger effective sensing area and exhibit ultra-high sensitivity even at the room temperature. Specifically, at room temperature, we demonstrate peak sensitivities as high as 7.22 and 8.93 for CO concentrations of 250 ppm and 500 ppm, respectively. As a result, by operating the proposed ZnO-nanocomb-based gas sensor at the room temperature, the widely adopted power consuming heating components are completely removed. This leads to not only great power saving, but also full compatibility between the gas sensor and the on-chip circuitry in term of acceptable operating temperature. In addition, the reported fast response/recovery time of ~200 s/~50 s (250 ppm CO) makes it well suited to real-life applications.

In this paper, a novel analog gamma correction scheme with a logarithmic image sensor dedicated to minimize the quantization noise of the high dynamic applications is presented. The proposed implementation exploits a non-linear voltage-controlled-oscillator (VCO) based analog-to-digital converter (ADC) to perform the gamma correction during the analog-to-digital conversion. As a result, the quantization noise does not increase while the same high dynamic range of logarithmic image sensor is preserved. Moreover, by combining the gamma correction with the analog-to-digital conversion, the silicon area and overall power consumption can be greatly reduced. The proposed gamma correction scheme is validated by the reported simulation results and the experimental results measured for our designed test structure, which is fabricated with 0.35 μm standard complementary-metal-oxide-semiconductor (CMOS) process.

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

Authentication is a crucial security service for the wireless sensor networks (WSNs) in versatile domains. The deployment of WSN devices in the untrusted open environment and the resource-constrained nature make the on-chip authentication an open challenge. The strong physical unclonable function (PUF) came in handy as light-weight authentication security primitive. In this paper, we present the first ring oscillator (RO) based strong physical unclonable function (PUF) with high resilience to both the electromagnetic (EM) side-channel attack and the support vector machine (SVM) modelling attack. By employing an RO based PUF architecture with the current starved inverter as the delay cell, the oscillation power is significantly reduced to minimize the emitted EM signal, leading to greatly enhanced immunity to the EM side-channel analysis attack. In addition, featuring superior reconfigurability due to the conspicuously simplified circuitries, the proposed implementation is capable of withstanding the SVM modelling attack by generating and comparing a large number of RO frequency pairs. The reported experimental results validate the prototype of a 9-stage RO PUF fabricated using standard 65 nm complementary-metal-oxide-semiconductor (CMOS) process. Operating at the supply voltage of 1.2 V and the frequency of 100 KHz, the fabricated RO PUF occupies a compact silicon area of 250 μ m 2 and consumes a power as low as 5.16 μ W per challenge-response pair (CRP). Furthermore, the uniqueness and the worst-case reliability are measured to be 50.17% and 98.30% for the working temperature range of −40∼120 ∘ C and the supply voltage variation of ±2%, respectively. Thus, the proposed PUF is applicable for the low power, low cost and secure WSN communications.

In this paper, an approach that can fast classify the data from the electronic nose is presented. In this approach the gradient tree boosting algorithm is used to classify the gas data and the experiment results show that the proposed gradient tree boosting algorithm achieved high performance on this classification problem, outperforming other algorithms as comparison. In addition, electronic nose we used only requires a few seconds of data after the gas reaction begins. Therefore, the proposed approach can realize a fast recognition of gas, as it does not need to wait for the gas reaction to reach steady state.