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WiFi backscatter has attracted intensive attention because the large population of WiFi radios can provide plenty of excitation signals. However, WiFi backscatter communication has imposed unwanted constraints on either exciters or receivers since its inception. In this paper, we present Chameleon, a native WiFi backscatter system, where WiFi tags can generate native WiFi packets using uncontrolled productive WiFi signals as carriers. Our tag-only design requires no particular excitation patterns and no changes in software/hardware on WiFi network interface cards (NICs). The key idea is for the Chameleon tag to demodulate the productive WiFi signal and backscatter it into a full-function packet using on-the-fly modulation. To align tag decoding and modulation with excitation symbols, we design a time synchronization and clock compensation scheme suitable for low-power tags. We prototype WiFi tags using ultra-low-power FPGAs and evaluate them in real-world scenarios where excitations are ambient traffic and backscatter receivers are a wide range of commercial off-the-shelf (COTS) NICs. Comprehensive field studies show that the maximal backscatter throughput of Chameleon is almost 1 Mbps, which is over $125\times $ and $1000\times $ higher than what WiTAG and FS-Backscatter tags could achieve, respectively. We also show that Chameleon can natively communicate with various COTS WiFi devices on Windows, iOS, and Android platforms. We believe that this design will enable ubiquitous WiFi connectivity for billions of IoT devices via widely available mobile gadgets and existing wireless infrastructure.

In the past decade, backscatter communications have drawn significant attention as they are an ultra-low-power solution to transmit IoT sensor data, including video and audio. However, most of the state-of-the-art backscatter systems that are fully compatible with commodity radios suffer from poor synchronization accuracy and low throughput, being unable to support various multimedia sensors. In this paper, we propose Bitalign, a Bluetooth backscatter system that can make use of uncontrolled ambient signals as excitations and deliver high throughput for multimedia streaming applications. To do so, we identify several backscatter bottlenecks and employ a set of techniques to considerably boost backscatter throughput. In particular, we introduce an identification-based synchronization method that can efficiently distinguish various ambient signals and accurately decide where to modulate. We further propose a matching-based synchronization method with higher synchronization accuracy. In addition, we propose header reconstruction to make Bitalign truly compatible with commercial Bluetooth devices. Finally, we implement a tag prototype using FPGAs and conduct extensive experiments. Results show that the minimum bit error rate of Bitalign is 0.5%, which is 60 times better than that of FreeRider, a state-of-the-art Bluetooth system that features uncontrolled excitors. The maximal theoretical throughput of Bitalign is 1.98 Mbps.

Mechanoluminescent (ML) fibers and textiles enable stress visualization without auxiliary power, showing great potential in wearable electronics, machine vision, and human-computer interaction. However, traditional ML devices suffer from inefficient stress transfer in soft-rigid material systems, leading to low luminescence brightness and short cycle life. Here, we propose a tendon-inspired scale-bridging mechanics transfer mechanism for ML composites, which employs molecular-scale copolymerized cross-linking and nanoscale inorganic nanoparticles as hierarchical stress transfer sites. This strategy effectively reduces the dissipation of stress in molecular chain segments and alleviates local stress concentration, increases luminescence by 9 times, and extends cycle life to more than 10,000 times. Furthermore, a scalable (kilometer-scale) anti-Plateau–Rayleigh instability manufacturing technology is developed for thermoset ML fibers, compatible with various existing textile techniques. We also demonstrate its system-level applications in motion capture, underwater interaction, etc., providing a feasible strategy for the next generation of smart visual textiles.

As a specific type of shape gradient descent algorithm, shape gradient flow is widely used for shape optimization problems constrained by partial differential equations. In this approach, the constraint partial differential equations could be solved by finite element methods on a domain with a solution-driven evolving boundary. Rigorous analysis for the stability and convergence of such finite element approximations is still missing from the literature due to the complex nonlinear dependence of the boundary evolution on the solution. In this article, rigorous analysis of numerical approximations to the evolution of the boundary in a prototypical shape gradient flow is addressed. First-order convergence in time and $k$th order convergence in space for finite elements of degree $k\geqslant 2$are proved for a linearly semi-implicit evolving finite element algorithm up to a given time. The theoretical analysis is consistent with the numerical experiments, which also illustrate the effectiveness of the proposed method in simulating two- and three-dimensional boundary evolution under shape gradient flow. The extension of the formulation, algorithm and analysis to more general shape density functions and constraint partial differential equations is also discussed.

Backscatter communication (BackCom) technology represents a low-cost and battery-free technology for Internet-of-Things (IoT) applications. One variant of BackCom, termed as ambient BackCom, which uses ambient signals to eliminate the need for a dedicated carrier source is more advantageous due to reduced system complexity and cost. Previous studies on ambient BackCom have primarily relied on amplitude modulation-based systems like ON–OFF-keying (OOK) modulation, which achieved a maximum tag-to-receiver (Rx) distance of less than five meters. Alternatively, frequency-shift-keying (FSK)-based ambient BackCom systems have demonstrated improved distance, but with limitations. This work introduces an ambient broadcast frequency-modulation (FM) BackCom system utilizing in-band 2FSK-based modulation, designed to be compatible with both mono and stereo radio systems and extend communication distance. In order to successfully detect the backscatter 2FSK symbols, two pivotal technologies are employed: first, a quadrature demod-based in-phase/quadrature ( $I/Q$ ) sample processing method enhances the signal-to-interference-plus-noise ratio (SINR). This enhancement has been substantiated through theoretical analysis, simulations, and cable-connected experiments. Second, a moving window-based 2FSK demodulation strategy is implemented, exhibiting resilience to tag frequency instability. The proposed system significantly enhances the tag-to-Rx communication distance, achieving an impressive range of over 100 m, a considerable improvement over the current state-of-the-art. The performance of the proposed system is evaluated in both cable-connected and outdoor environments, achieving a maximum bit rate of 333 bits per second (bps) at a distance of 58 m and a maximum communication distance of 107 m at a bit rate of 200 bps.

Purpose: Variants in the Aryl hydrocarbon receptor-interacting protein (AIP) gene have been identified in sporadic acromegaly and pituitary gigantism, especially in young patients, with a predisposition to aggressive clinical phenotype and poor treatment efficacy. The clinical characteristics of patients with sporadic acromegaly and pituitary gigantism as well as AIPvariants in Han Chinese have been rarely reported. We aimed to identify AIPgene variants and analyze the clinical characteristics of patients with sporadic acromegaly and pituitary gigantism in Han Chinese. Methods: The study included 181 sporadic acromegaly (N = 163) and pituitary gigantism (N = 18) patients with an onset age of no more than 45 years old, who were diagnosed, treated, and followed up in Huashan Hospital. All 6 exons and their flanking regions of the AIPgene were analyzed with Sanger sequencing or NGS. The clinical characteristics were compared between groups with and without AIPvariants. Results: Germline AIPvariants were found in 15/181 (8.29%) cases. In patients with an onset age ≤30 years old, AIPvariants were identified in 12/133 (9.02%). Overall, 13 variants were detected. The pathogenic (P) variants p.R304X and p.R81X were identified in four cases, with two instances of each variant. Six exon variants (p.C254R, p.K103fs, p.Q228fs, p.Y38X, p.Q213*, and p.1115 fs) have not been reported before, which were likely pathogenic (LP). Patients with P/LP variants had younger onset ages, a higher prevalence of pituitary gigantism, larger tumor volumes, and a higher percentage of Ki-67-positive cells in tumors. In addition, the group with P/LP variants showed a less significant reduction of GH levels in an acute octreotide suppression test (OST) [17.7% (0, 65.0%) vs. 80.5% (63.9%, 90.2%), P= 0.001], and a trend of less GH decrease after the 3-month treatment with long-acting somatostatin analogs (SSAs). Conclusion: Germline AIPvariants existed in sporadic Chinese Han acromegaly and pituitary gigantism patients and were more likely to be detected in young patients. AIPvariants were associated with more aggressive tumor phenotypes and less response to SSA treatment.

The accurate assessment of running safety during earthquakes is of significant importance for ensuring the safety of railway lines. Currently, assessment methods based on a single index suffer from issues such as misjudgment of operational safety and difficulty in evaluating operational margin, making them unsuitable for assessing train safety during earthquakes. Therefore, in order to propose an effective evaluation method for the running safety of trains during earthquakes, this study employs three indexes, namely lateral displacement of the wheel–rail contact point, wheel unloading rate, and wheel lift, to describe the lateral and vertical contact states between the wheel and rail. The corresponding evolution characteristics of the wheel–rail contact states are determined, and the derailment forms under different frequency components of seismic motion are identified through dynamic numerical simulations of the train–track coupled system under sine excitation. The variations in the wheel–rail contact states during the transition from a safe state to the critical state of derailment are analyzed, thereby constructing the evolutionary path of train derailment and seismic derailment risk domain. Lastly, the wheel–rail contact and derailment states under seismic conditions are analyzed, thus verifying the effectiveness of the evaluation method for assessing running safety under earthquakes proposed in this study. The results indicate that the assessment method based on the derailment risk domain accurately and comprehensively reflects the wheel–rail contact states under seismic conditions. It successfully determines the forms of train derailment, the risk levels of derailment, and the evolutionary paths of derailment risk.

In high-dimensional environment, the interaction among particles significantly affects their movements in searching the vast solution space and thus plays a vital role in assisting particle swarm optimization (PSO) to attain good performance. To this end, this article designs a random contrastive interaction (RCI) strategy for PSO, resulting in RCI-PSO, to tackle large-scale optimization problems (LSOPs) effectively and efficiently. Unlike existing interaction mechanisms for low-dimensional problems, RCI randomly chooses several different peers from the current swarm to construct a random interaction topology for each particle. Then, it lets the particle interact with the selected peers based on their current evolutionary information instead of their historical evolutionary information. Within the topology, RCI only propagates the evolutionary information of two contrastive dominators with the largest difference in fitness to direct the evolution of the particle. Therefore, particles with no more than two dominators in their topologies are not updated. Furthermore, a dynamic topology size adjustment scheme is devised to gradually enlarge the interaction topology. In this way, the swarm gradually switches from exploring the immense search space dispersedly to exploiting the found optimal regions intensively as the evolution continues. With these two strategies, RCI-PSO expectedly compromises search diversity and search convergence well at the swarm level and the particle level. Finally, extensive experiments executed on two public LSOP suites verify that RCI-PSO performs competitively with or even much better than totally 40 state-of-the-art large-scale approaches and preserves a good capability and scalability in tackling complex LSOPs.

With the rapid development of edge computing technology, edge-assisted unmanned aerial vehicle (UAV) networks have become popular, helping with fast and cost-effective data collection in mobile crowdsensing (MCS) environments. This paper investigates the online data collection problem for MCS over an edge-assisted UAV network architecture, where UAVs work to collect the data required by tasks at different on-ground point-of-interests (PoIs) in an autonomous and cooperative manner. Different from conventional edge-assisted UAV networks, edge nodes in our paper help distribute, aggregate, share, and update the task state information (TSI, e.g., if a task has been completed, or if it still requires more data), to support efficient and cost-effective data collection, e.g., avoiding repetitive and ineffective task execution. In particular, a UAV can exchange TSI with an edge node when it is within the signal coverage of the edge node, while making decisions on PoI selection and path planning in an online and decentralized manner. To address the issue of edge-assisted UAV data collection, we propose a multi-agent deep reinforcement learning-based algorithm using personalized training with decentralized executing (PTDE) architecture. Different from the traditional centralized training with decentralized executing (CTDE) architecture, our considered architecture adopts the agent-specific state for critic networks instead of the joint observation, thus achieving effective utilization of environmental information. Furthermore, we propose an observation enhancement algorithm based on artificial potential field (APF). Extensive simulation results demonstrate that our proposed algorithm greatly outperforms baseline algorithms in terms of total profit, data collection ratio, geographical fairness, and energy efficiency.

ABSTRACTAfter emergency surgery for intestinal obstruction caused by colorectal cancer, postoperative ileus (POI) is more likely to occur in the early-stage oral intake. POI incited the occurrence of postoperative complications and prolongs hospital stay. Reducing the occurrence of POI will Enhance Recovery After Surgery (ERAS). Aim: The aim of this study is to observe and evaluate the preventive effect of postoperative oral administration of 76% Meglumine Diatrizoate in reducing the incidence of POI and promoting intestinal absorption during the recovery of intestinal peristalsis in patients after intestinal obstruction surgery. Methods: From October 2018 to December 2021, 94 patients (47 vs 47) with intestinalobstruction were rolled. Patients with an ASA score of 4 or higher and gastrointestinal perforation with peritonitis were excluded. After 24 hours of surgery, the patients were divided into experimental group and control group disposed of with an opaque airtight envelope method, patient-side single blind. After intestinal peristalsis recovery (2.45 ± 0.62 d vs 2.60 ± 0.68 d, P > 0.05), the experimental group was given 76% Meglumine Diatrizoate 20 ml orally 9am and the control group was given 10% glucose 20 ml for three consecutive days. POI cases, the time taken to achieve full daily oral calorie and discharge days were counted. Results: The time required to achieve full daily oral calorie (11.04 ± 2.70 d vs 14.09 ± 3.74 d, p < 0.05), POI cases (10/47 vs 20/47, p < 0.05) and discharge days (14.00 ± 4.89 d vs 16.77 ± 5.94 d, p < 0.05) are significantly different between the two groups. Conclusions: Oral 76% Meglumine Diatrizoate is safe and effective, which can reduce the occurrence of POI, promote the recovery of intestinal absorption and shorten the length of hospital stay effectively.

We pay attention to the topological properties and skin effects in the one-dimensional Su-Schrieffer-Heeger (SSH) structure, by considering the presence of different-density non-reciprocal couplings. We find that when only one single non-reciprocal coupling term is introduced, e.g., in the middle of the SSH structure, edge states can be induced around the sites suffering from the non-reciprocal coupling. As one non-reciprocal coupling term is introduced in each two cells, the topological phase transition is modified. Besides, new topological edge states and skin effects appear at the ends of the SSH structure, and the edge states can be differentiated by their localities, localization degrees, and the imaginary part of energy. Furthermore, if two neighboring cells of the SSH structure are coupled by the non-reciprocal coupling, the topological edge states and skin effect more well-defined, accompanied by the further change of the topological phase transition condition. These results suggest that the non-reciprocal coupling plays a unique and important role in modulating the topological properties and the skin effects of the one-dimensional SSH structure.

In this paper, the certain relation between threshold voltage and flat‐band voltage to improve the image sticking for AMOLED display was investigated. Flat band state generally refers to a state in which the energy bands of each region in an ideal MOSFET system are flattened. However, it is requiring an additional voltage to flatten the energy band for actual MOS systems due to the difference in metal and semiconductor work function, which is called the flat‐band voltage. It is investigated that this flat‐band voltage is strongly linked to the short‐term image sticking.

In this work, phase transitions of the non-Hermitian tetramerized Su–Schrieffer–Heeger (TSSH) chain are investigated by periodically introducing the non-Hermitian on-site imaginary potentials U1=(iγ1,iγ2,−iγ1,−iγ2)or U2=(iγ1,−iγ2,−iγ1,iγ2), with γ1and γ2being the imaginary-potential strengths. Numerical results show that these imaginary potentials induce the topologically nontrivial transitions of the topological properties of the TSSH chain in different ways. Namely, U1only modifies the two-fold degenerated topologically nontrivial region, without affecting the other topological phases. By contrast, U2can reduce the topologically trivial region and modify the four-fold degenerated topologically nontrivial region by introducing the new topological phase transition. Moreover, when the imaginary potentials are influenced by the Anderson disorder, the bulk states display obvious localization. Disordered U2leads to the widening of the topologically nontrivial regions. Based on these results, we believe this work provides insights into the influences of on-site imaginary potentials on the topological properties of non-Hermitian TSSH structures.

The disposal of contaminated water from Japan’s Fukushima nuclear power plant is a significant international nuclear safety issue with considerable cross-border implications. This matter requires compliance not only with the law of the sea but also with the principles of nuclear safety under international law. These principles serve as the overarching tenet of international and China’s domestic nuclear laws, applicable to nuclear facilities and activities. The principle of safety in nuclear activities is fully recognized in international and domestic laws, carrying broad legal binding force. Japan’s discharge of nuclear-contaminated water into the sea violates its obligations under the principle of safety in nuclear activities, including commitments to optimum protection, as low as reasonably practicable, and prevention. The Japanese government and the International Atomic Energy Agency (IAEA) have breached the obligation of optimum protection by restricting the scope of assessments, substituting core concepts, and shielding dissenting views. In the absence of clear radiation standards, they have acted unilaterally without fulfilling the obligation as low as reasonably practicable principle. The discharge of Fukushima nuclear-contaminated water poses an imminent and unpredictable risk to all countries worldwide, including Japanese residents. Japan and the IAEA should fulfill their obligations under international law regarding disposal, adhering to the principles of nuclear safety, including optimum protection, the obligation as low as reasonably practicable, and prevention through multilateral cooperation. Specifically, the obligation to provide optimum protection should be implemented by re-evaluating the most reliable disposal technologies and methods currently available and comprehensively assessing various options. The standard of the obligation as low as reasonably practicable requires that the minimization of negative impacts on human health, livelihoods, and the environment should not be subordinated to considerations of cutting costs and expenses. Multilateral cooperation should be promoted through the establishment of sound multilateral long-term monitoring mechanisms for the discharge of nuclear-contaminated water, notification and consultation obligations, and periodic assessments. These obligations under international law were fulfilled after the accidents at the Three Mile Island and Chernobyl nuclear power plants. The implications of the principles of nuclear safety align with the concept of building a community of shared future for nuclear safety advocated by China. In cases of violations of international law regarding the disposal of nuclear-contaminated water that jeopardize the concept of a community of a shared future for nuclear safety, China can also rely on its own strength to promote the implementation of due obligations through self-help.

ABSTRACTIn view of the serious erosion of concrete in salt lake regions, the properties (the corrosion resistance coefficient, relative dynamic elastic modulus, and spalling amount) of concrete, including ordinary Portland [...]

Bidirectional links are widely adopted in many active radios to provide efficient data exchanges, e.g., WiFi and Bluetooth. However, they are underexplored for general-purpose backscatter communications. The downlinks of state-of-the-art Bluetooth backscatter systems are based on packet length modulation, which is inefficient and unreliable. In this paper, we propose BiBlue, a bidirectional Bluetooth backscatter system that uses an edge server to enable fast and reliable downlinks. Specifically, our edge server acts like a bridge that can translate commercial Bluetooth signals to Amplitude Shift Keying (ASK) signals. In addition, we design a reliable edge-to-tag link that adaptively decodes ASK signal under variance. Finally, we propose a low-power uplink design that enables connection-based bidirectional communication with commodity devices. We prototype BiBlue using FPGA and Software Defined Radio (SDR). Extensive experimental results demonstrate that BiBlue achieves more than 62x downlink throughput gains over FreeRider and meanwhile supports an 80 cm communication range of edge-to-tag with BER at around 1%.

Control of humidity within confined spaces is critical for maintaining air quality and human well-being, with implications for environments ranging from international space stations and pharmacies to granaries and cultural relic preservation sites. However, existing techniques rely on energy-intensive electrically driven equipment or complex temperature and humidity control (THC) systems, resulting in imprecision and inconvenience. The development of innovative techniques and materials capable of simultaneously meeting the stringent requirements of practical applications holds the key to creating intelligent and energy-efficient humidity control devices. In this study, we introduce chiral reticular chemistry as a tailored synthetic approach, targeting a highly porous heatopological framework characterized by intrinsic interpenetrating pore architecture. This groundbreaking design successfully circumvents the traditional compromise between the pore volume and hydrolytic stability. Our metal–organic framework (MOF) exhibits an extraordinary working capacity, setting a new record at 1.35 g g–1within the relative humidity (RH) range of 40–60%, without exhibiting hysteresis. Consequently, it emerges as a state-of-the-art candidate for intelligent humidity regulation within confined spaces. Utilizing single-crystal X-ray measurements and molecular simulations, we unequivocally elucidate the mechanism of water clustering and pore filling, underscoring the pivotal role of the linker functionality in governing the water seeding process. Our findings represent a significant advancement in the field, paving the way for the development of highly efficient humidity control technologies and offering promising solutions for diverse real-world scenarios.

In addressing the insufficiencies of feature insertion, inaccurate positioning, and incompatible feature sizes in data augmentation algorithms based on deep learning for detecting microscopic defects on printed circuit boards (PCBs), this paper proposes a novel approach incorporating multiple strategies for small target alignment insertion. First, traditional linear feature extraction methods are transformed into a multiscale comprehensive analysis process. Subsequently, point-to-point matching calculations are converted into region-wise traversals to enhance accuracy and constrain the matching region. Next, geometric correspondences are determined through the computation of a transfer matrix, effectively eliminating perspective distortions. Finally, by constructing a top-down pyramid optical flow module, size limitations are overcome while enhancing features of small target defects. Experimental results demonstrate that this method significantly improves the recognition accuracy of the network model for small target defects on PCB surfaces.

The use of controlled excitation makes pervasive backscatter communication difficult to achieve and the redundant modulation severely limits the performance of the system. We present a novel WiFi backscatter system that can take uncontrolled OFDM WiFi signals as excitations and efficiently embed tag data at the single-symbol rate. Specifically, we are the first to discover the fundamental reason why the previous systems have to rely on multi-symbol modulation, which makes it possible to demodulate tag data on the single-symbol level. Further, we design deinterleaving-twins decoding that can reuse any uncontrolled WiFi signals as carriers to backscatter tag data. Moreover, we present how to robustly handle high-order excitations, including different demapping rules for diverse excitations and three different bit-translation methods for decoding. To verify the effectiveness of our proposal, we prototype our solution using various FPGAs and SDRs. Comprehensive evaluations show that our solution’s maximum throughput is 3.92x and 1.97x better than FreeRider and MOXcatter. In addition, with 16QAM excitations, the decoding BERs of majority voting are around 5%, which is 10x better than subsequence matching and jaccard similarity methods. Meanwhile, the throughput of deinterleaving-level demodulation is 2x better than payload-level demodulation with 16QAM ambient traffic.

Addressing the challenges of complex backgrounds, minute defects, and irregular shapes in PCB defect images that often lead to missed detections, inaccurate localizations, and false positives, this article introduces an improved you only look once (YOLO) model, termed YOLO-Biformer, to enhance the network’s ability to detect surface defects on PCBs. First, YOLO-Biformer incorporates a hybrid attention module to differentiate the importance among various channels, thus strengthening the extraction of small target defect features and preventing the loss of minor target information often caused by deep convolutional networks. Second, the model introduces a jumping hollow space convolutional pyramid aimed at preserving more image details and interrelated information, thereby boosting the network’s defect localization capability. Finally, the Enhanced Intersection Over Union-FocalLoss loss function is employed to enhance the network’s ability to distinguish between defects with similar features. Experimental results demonstrate that the proposed algorithm increases the mean average precision by 4.1%, showing excellent performance in recognizing small target defects on printed circuit board surfaces and achieving both high accuracy and real-time capabilities.

AI-aided financial regulation (AIFR) is a practical and significant task, but current solutions have yet to be optimized with customized model designs. Given the privacy concerns surrounding financial data, we aim to employ neural architecture search (NAS) to help nonexpert end-users automatically design architectures. The genetic algorithm (GA)-based NAS stands out due to its relatively low hardware requirements and robust theoretical foundation. However, constrained by limited data, the model would undergo architecture search on a general regulatory dataset while being deployed on private one owned by each organization. The data distribution of the private dataset may vary from that of public datasets, giving rise to the challenge of data domain shift. To alleviate this problem, we propose a novel fitness evaluation method. When scoring the fitness, we take into account both the architecture’s validation accuracy and its potential for generalization by the metric of loss landscape. In addition, we improve the training paradigm for evaluation, utilizing a prototype-based training paradigm based on embedding distances for classification, allowing for rapid domain adaptation and improving performance on the distribution-shift data. We further introduce GA-TextCNN, a GA-based NAS framework specifically designed for text recognition, enhancing its suitability for text data within AIFR tasks. To demonstrate the effectiveness of our approach, we collect two related datasets and evaluate our method on it. The extensive experiments demonstrate that our method significantly improves baseline models and is effective in solving the AIFR problem.

ObjectiveThe pathogenesis of sepsis is complex, and the sepsis-induced systemic proinflammatory phase is one of the key drivers of organ failure and consequent mortality. Akkermansia muciniphila(AKK) is recognised as a functional probiotic strain that exerts beneficial effects on the progression of many diseases; however, whether AKK participates in sepsis pathogenesis is still unclear. Here, we evaluated the potential contribution of AKK to lethal sepsis development.DesignRelative abundance of gut microbial AKK in septic patients was evaluated. Cecal ligation and puncture (CLP) surgery and lipopolysaccharide (LPS) injection were employed to establish sepsis in mice. Non-targeted and targeted metabolomics analysis were used for metabolites analysis.ResultsWe first found that the relative abundance of gut microbial AKK in septic patients was significantly reduced compared with that in non-septic controls. Live AKK supplementation, as well as supplementation with its culture supernatant, remarkably reduced sepsis-induced mortality in sepsis models. Metabolomics analysis and germ-free mouse validation experiments revealed that live AKK was able to generate a novel tripeptide Arg-Lys-His (RKH). RKH exerted protective effects against sepsis-induced death and organ damage. Furthermore, RKH markedly reduced sepsis-induced inflammatory cell activation and proinflammatory factor overproduction. A mechanistic study revealed that RKH could directly bind to Toll-like receptor 4 (TLR4) and block TLR4 signal transduction in immune cells. Finally, we validated the preventive effects of RKH against sepsis-induced systemic inflammation and organ damage in a piglet model.ConclusionWe revealed that a novel tripeptide, RKH, derived from live AKK, may act as a novel endogenous antagonist for TLR4. RKH may serve as a novel potential therapeutic approach to combat lethal sepsis after successfully translating its efficacy into clinical practice.

The boundary layer height (BLH) is a vital parameter that affects the vertical distribution of matter within the atmospheric boundary layer (ABL). However, the traditional algorithms determine the BLH based on changes in gradient within the signal-to-noise ratio (SNR) profile. It often leads to significant uncertainty under complex atmospheric conditions. Here, a convolutional neural network (CNN) algorithm considering multiple atmospheric parameter profiles is proposed for determining the BLH from radar wind profiler (RWP) data. The CNN algorithm is applied to the RWP dataset of atmospheric radiation measurement (ARM) site at Southern Great Plains (SGP) from August 2019 to July 2023. The sensitivity analysis shows that the CNN algorithm overcomes the shortcomings of the traditional algorithms that are susceptible to multiple local peaks. Moreover, the CNN algorithm performs well under complex conditions. It exhibits strong consistency with the BLH estimated by radiosonde (RS), with correlation coefficients, mean absolute error (MAE), and root-mean-square error (RMSE) of 0.81, 0.24, and 0.34 km, respectively. The CNN algorithm is then compared with the covariance wavelet transform (CWT) algorithm and the peak detection algorithm (PDA) using the BLH estimated by RS as a reference. The results indicate that the accuracy of BLH estimated by the CNN algorithm is higher than that of the two traditional algorithms. The MAE and RMSE of the CNN algorithm reduce from $0.53~\pm ~0.56$ km ( $0.57~\pm ~0.60$ km) and 0.77 km (0.83 km) of CWT (PDA) to $0.24~\pm ~0.25$ and 0.34 km, respectively. Finally, the diurnal and seasonal variation patterns of BLH are explored. The BLH shows a high correlation with solar radiation, rising from sunrise and then decreasing after sunset. Regarding seasonal variation, BLH peaks in summer and troughs in winter. Overall, the CNN algorithm proposed here can improve the accuracy and stability of BLH estimation. This study verifies the great potential of deep learning algorithms in the BLH estimation.

Different from intermittent ISM signals like Bluetooth and WiFi, LTE signals are continuous in time and more pervasive in space, which makes them suitable carriers for ambient backscatter systems. However, due to the continuous LTE traffic and complex frame structures, existing ambient backscatter systems, such as HitchHike and LScatter, cannot reliably backscatter LTE signals in a standard-compatible way. We observe that the primary cause of their failures is that tags cannot accurately synchronize with LTE excitations. To address this issue, we propose SyncLTE, an LTE backscatter system that achieves high-accuracy synchronization and standard-compatible backscatter communication. The key novelty is a new tag design that uses the periodicity of LTE signals for synchronization and provides a customized single-symbol modulation scheme for LTE carriers. Our design is prototyped using FPGAs, SDR LTE eNBs and UEs. Comprehensive experiments have been done in various scenarios, including LoS, NLoS, indoor and outdoor. Results show that SyncLTE is 22.4x and 7.4x better than LScatter and Multiscatter in terms of the 80th percentiles of synchronization errors. Also, SyncLTE can deliver throughputs of up to 200 bps using BPSK and 400 bps using QPSK while other systems suffer from failures.

Purpose: To investigate the effect of nicotinamide (Nam) on diabetic kidney disease (DKD) in mice and explore its mechanism. Methods: Thirty DBA/2 J mice were randomly assigned to three groups. After 8 weeks of hyperglycemia induced by streptozocin (STZ), Nam and saline were administrated to STZ + Nam and STZ + NS mice, respectively, for 8 weeks. Non-diabetic mice (NDM) were used as control group. Twenty In2−/−Akita mice were randomly divided into two groups. After 8 weeks of hyperglycemia, Nam and saline were administered to Akita + Nam and Akita + NS mice, respectively, for 6 weeks. Wild-type littermates were used as control group. Markers of renal injury were analyzed, and the molecular mechanisms were explored in human proximal tubular HK2 cells. Results: Urinary albumin-to-creatinine ratio (UACR) and kidney injury molecule 1 (KIM-1) decreased in the STZ + Nam and Akita + Nam groups. Pathological analysis showed that Nam improved the structure of glomerular basement membrane, ameliorated glomerular sclerosis, and decreased the accumulation of extracellular matrix and collagen. Compared to the diabetic control group, renal fibrosis, inflammation, and oxidative stress were reduced in the Nam-treated mice. The expression of sirtuin 1 (Sirt1) in human proximal tubular HK2 cells was inhibited by high glucose and Nam treatment enhanced its expression. However, in HK2 cells with Sirt1 knockdown, the protective effect of Nam was abolished, indicating that the beneficial effect of Nam was partially dependent on Sirt1. Conclusions: Nam has a renoprotective effect against renal injury caused by hyperglycemia and may be a potential target for the treatment of DKD.

Converter slag is a by-product of the steelmaking process and contains a large amount of Ca, Fe, P and other elements. If the phosphorus in the converter slag can be effectively extracted, the resulting phosphorus can be used as a phosphate fertilizer. Phosphorus in converter slag is mainly enriched in 2CaO·SiO2–3CaO·P2O5(C2S–C3P) solid solution and is easily dissolved in water. Therefore, acid leaching method was used to dissolve the solid solution to extract phosphorus in converter slag, so as to realize the recycling of phosphorus resources in converter slag. The leaching behavior of three actual converter slags from different steel mills in acid leaching solutions composed of citric acid, sodium hydroxide, hydrochloric acid and deionized water was studied by X-ray diffraction, scanning electron microscopy combined with energy dispersive spectrometry, Fourier transform infrared spectroscopy and Raman spectroscopy to reveal the change in object image structure as well as SiO4and PO4tetrahedron before and after acid leaching. The results show that only a small amount of phosphorus in converter slag with too low basicity is enriched in C2S–C3P solid solution, resulting in poor leaching effect. When the converter slag has a high basicity, the removal effect of phosphorus is 70.64%–81.88%. In addition, when the basicity of converter slag is roughly the same, the dephosphorization effect of slag with high FeO content is relatively poor. And acid leaching will cause depolymerization behavior of SiO4and PO4tetrahedron, so that the mole fraction of Q0(Si) (asymmetric stretching vibration of Si–O bond in SiO4tetrahedron with bridge oxygen number of 0) and NBO/Si (non-bridging oxygen in silicate tetrahedra) will increase. Phosphate-containing solids can be obtained from the solution after acid leaching through static precipitation, and the solids have the potential to make phosphate fertilizers.

In this letter, an efficient method is proposed for analyzing electromagnetic scattering from complex targets coated with anisotropic metal composites. The method discretizes electromagnetic scattering on the complex target's surface into a superposition of triangular surface element scattering. Initially, the plane wave spectrum expansion method derives the dual-spectral-domain integral representation of the scattering from triangular surface elements on the anisotropic metal composites coated slab model. Then, the saddle point evaluation is utilized to perform the asymptotic evaluation of the integral; the reflection coefficient can be obtained in the spatial domain. Finally, the equivalent electromagnetic current of the triangular surface elements is solved using the Stratton–Chu equation. Furthermore, the electromagnetic scattering of complex targets coated with anisotropic metal composites is obtained through the physical optics method. The numerical results, including radar cross section comparisons for typical slab, cube, and fighter models, demonstrate the accuracy and efficiency of the proposed method. The anisotropic metal composites can enhance the stealth capability of the fighter, which possesses excellent structural stealth performance.

This paper presents GLAC, the first 3D localization system that enables millimeter-level object manipulation for robotics using only COTS RFID devices. The key insight of GLAC is that mobility reduces ambiguity (One-to-many mapping relationship between phase and distance) and thus improves accuracy. Unlike state-of-the-art systems that require extra time or hardware to boost performance, it draws the power of modeling mobility in a delicate way. In particular, we build a novel framework for real-time tracking using the Hidden Markov Model (HMM). In our framework, multiple Kalman filters are designed to take a single phase observation for updating mobility states, and a fast inference algorithm is proposed to efficiently process an exponentially large number of candidate trajectories. We prototype GLAC with only UHF tags and a commercial reader of four antennas. Comprehensive experiments show that the median position accuracies of x/y/z dimensions are within 1 cm for both LoS and NLoS cases. The median position accuracy for slow-moving targets is 0.41 cm, which is $2.2\times $ , $17.3\times $ , and $14.9\times $ better than TurboTrack, Tagoram, and RF-IDraw, respectively. Also, its median velocity accuracy is at least $20\times $ better than all three competitors for fast-moving targets. Besides accuracy, it achieves more than $4\times $ localization time gains over state-of-the-art systems.

Since backscatter communication has the advantage of low power, it is promising to be widely used in IoT applications. For a space-time stream backscatter, we envision it can efficiently leverage ready-to-use multiform space-time streams in the environment. Previous works have struggled to achieve this because they use various tag-data modulations on various types of ambient space-time stream signals. What’s worse is that it is challenging to identify the properties of a space-time stream in a passive way. To this end, we present STScatter, a WiFi backscatter that is universally applicable for ambient space-time streams. Besides, STScatter achieves efficient tag-data modulation at the symbol-level, allowing space-time stream backscatter to reach high tag-data throughput with multiform excitation. Additionally, unlike previous systems that decoded tag data from both the original ambient and backscattered signal, STScatter can decode ambient data and backscattered data simultaneously from the backscattered signal. STScatter was prototyped using commercial FPGAs and SDRs. Our extensive experiments show that the STScatter is transmitter-agnostic and universal with different ambient space-time streams. STScatter can achieve a goodput of up to 455.9 kbps in single-stream excitation and 460.0 kbps in multi-stream excitation, which is 4.2x and 157.5x better than MOXcatter. Besides, STScatter successfully uses signals from various types of WiFi hardware for excitation and achieves an average tag-data goodput of 293.62 kbps with real-life file-downloading traffic generated by a household WiFi router.

Over the past decade, backscatter nodes have received booming interest for many emerging mobile applications, such as sports analytics and interactive gaming. However, backscatter networks are not ready to provide a high-throughput and stable communication platform for billions of such mobile nodes due to two main factors in rate adaptation. First, the common mapping paradigm that chooses the optimal rate based on RSSIs is hardly adaptable to hardware diversity. Second, the current probing processes are not optimized for mobile scenarios due to inefficient probing trigger, inaccurate channel estimation, and unique self-interference. To address those issues, we propose MobiRate, a mobility-aware rate adaptation link-layer that fully exploits the mobility hints from PHY information to deliver a high-throughput link-layer for mobile backscatter networks. The key insight is that mobility-hints can greatly benefit link-layer design, including rate selection and channel probing. Specifically, we introduce a novel velocity-based loss-rate estimation module, a mobility-assisted probing trigger, a selective probing module, and a robust self-interference detection module, significantly saving probing time and improving probing accuracy. As MobiRate is fully compatible with the current standard, we prototype it using COTS RFID readers and commercial tags. Our extensive experiments demonstrate that MobiRate can successfully identify self-interference with detection accuracy over 90% for tags of different velocities. Moreover, it achieves up to 3.8x throughput gain over the state-of-the-art methods across a wide range of mobility, channel conditions, and tag types.

The addition of small amount of nickel to hot-working die steel has been found to be beneficial for hardenability and toughness. In this paper, the addition of nickel to Cr–Mo–V hot-working die steel was studied to improve hardenability, and the inherent mechanism of enhancing toughness was further explored. The research results indicate that nickel as a non-carbide-forming element does not alter the type and total amount of carbide precipitation at equilibrium, but it can dissolve into the matrix and form substitutional lattice distortions with Fe atoms, promoting the dispersion of secondary carbides during the tempering process. This mechanism can reduce intergranular precipitation and inhibit grain boundary embrittlement, thereby improving toughness. However, it can also accelerate the precipitation, aggregation, and growth of secondary carbides during prolonged tempering, which deteriorates the high-temperature anti-softening properties of die steels. The role of Ni has been revealed through thermodynamic and kinetic calculations. The results show that Ni significantly enhances the diffusion coefficient of Cr atoms in the face centered cubic (fcc) Fe matrix during tempering and also improves the phase transition driving force of various precipitates. This work provides new insights into the mechanism of Ni in hot-working die steels, and can provide guidance for designing novel hot-working die steel.

Scholars who use English as an additional language (EAL) worldwide are under increasing pressure to write and publish in English due to the pervasive publish‐or‐perish culture and the growing dominance of English in academia. While previous studies have focused either on textual differences between EAL scholars' writing and native writers', or explored the difficulties EAL scholars meet and strategies they employ in the process, this paper examines a longitudinal case of a Chinese engineering scholar using online machine translation (MT) to support his academic writing and publishing in English. Drawing from the technology acceptance model as the theoretical background, this study shows that online MT can be a crucial support for scholars with limited English proficiency in the process of preparing a manuscript, submitting it and communicating with editors and reviewers. Frequently occurring problems in machine‐translated academic texts include improper use of technical terms, use of informal language and the inclusion of grammatical mistakes. However, in the last decade, online MT has made great improvements in academic translation. We believe these findings can empower EAL scholars to effectively use online MT in academic English writing and inspire educators to help novice scholars mobilize various high‐tech tools such as online MT to support their writing and international publishing.

Proteins play important roles in the therapeutic, medical diagnostic, and chemical catalysis industries. However, their potential is often limited by their fragile and dynamic nature outside cellular environments. The encapsulation of proteins in solid materials has been widely pursued as a route to enhance their stability and ease of handling. Nevertheless, the experimental investigation of protein interactions with rationally designed synthetic hosts still represents an area in need of improvement. In this work, we leveraged the tunability and crystallinity of metal–organic frameworks (MOFs) and developed a series of crystallographically defined protein hosts with varying chemical properties. Through systematic studies, we identified the dominating mechanisms for protein encapsulation and developed a host material with well-tailored properties to effectively encapsulate the protein ubiquitin. Specifically, in our mesoporous hosts, we found that ubiquitin encapsulation is thermodynamically favored. A more hydrophilic encapsulation environment with favorable electrostatic interactions induces enthalpically favored ubiquitin–MOF interactions, and a higher pH condition reduces the intraparticle diffusion barrier, both leading to a higher protein loading. Our findings provide a fundamental understanding of host–guest interactions between proteins and solid matrices and offer new insights to guide the design of future protein host materials to achieve optimal protein loading. The MOF modification technique used in this work also demonstrates a facile method to develop materials easily customizable for encapsulating proteins with different surface properties.

This paper reports printable metallic gels (pendular suspensions) consisting of an aqueous suspension of copper particles connected by bridges of liquid eutectic gallium indium alloy (EGaIn). Pendular suspensions rely on capillary forces to form networks between solid particles with a composition-dependent rheology, but prior studies have focused on insulating suspensions. Here, the rheology of a conductive solid-liquid-liquid suspension is tuned for 3D printing by varying the composition and the pH; the latter promotes metallic wetting. The dry printed parts have metallic electrical conductivity (1.05×105S/m) without requiring a sintering step. Drying at elevated temperatures can accelerate the removal of water while creating stress that drives shape change (i.e., 4D printing). As a demonstration, we print a conductive spider that lifts and assembles its own body from an initially flat shape. Such conductive inks are promising for printing metallic structures under ambient conditions.

The interplay of primary organic ligands and inorganic secondary building units (SBUs) has led to a continual boom of reticular chemistry, particularly metal–organic frameworks (MOFs). Subtle variations of organic ligands can have a significant impact on the ultimate structural topology and consequently, the material’s function. However, the role of ligand chirality in reticular chemistry has rarely been explored. In this work, we report the organic ligand chirality-controlled synthesis of two zirconium-based MOFs (Spiro-1 and Spiro-3) with distinct topological structures as well as a temperature-controlled formation of a kinetically stable phase (Spiro-4) based on the carboxylate-functionalized inherently axially chiral 1,1′-spirobiindane-7,7′-phosphoric acid ligand. Specifically, Spiro-1 is a homochiral framework comprising only enantiopure S-spiro ligands and has a unique 4,8-connected sjttopology with large 3D interconnected cavities, while Spiro-3 contains equal amounts of S- and R-spiro ligands, resulting in a racemic framework of 6,12-connected edge-transitive albtopology with narrow channels. Interestingly, the kinetic product Spiro-4 obtained with racemic spiro ligands is built of both hexa- and nona-nuclear zirconium clusters acting as 9- and 6-connected nodes, respectively, giving rise to a newly discovered azsnet. Notably, the preinstalled highly hydrophilic phosphoric acid groups combined with large cavity, high porosity, and outstanding chemical stability endow Spiro-1 with remarkable water vapor sorption performance, whereas Spiro-3 and Spiro-4 show poor performances due to inappropriate pore systems and structural fragility upon the water adsorption/desorption process. This work highlights the important role of ligand chirality in manipulating the framework topology and function and would further enrich the development of reticular chemistry.

Mucoadhesive hydrogels with multifunctional properties such as gastric acid resistance and sustained drug release in the intestinal tract are highly desirable for the oral treatment of inflammatory bowel diseases (IBDs). Polyphenols are proven to have great efficacies compared with the first-line drugs for IBD treatments. We recently reported that gallic acid (GA) was capable of forming a hydrogel. However, this hydrogel is prone to easy degradation and poor adhesion in vivo. To tackle this problem, the current study introduced sodium alginate (SA) to form a gallic acid/sodium alginate hybrid hydrogel (GAS). As expected, the GAS hydrogel showed excellent antiacid, mucoadhesive, and sustained degradation properties in the intestinal tract. In vitrostudies demonstrated that the GAS hydrogel significantly alleviated ulcerative colitis (UC) in mice. The colonic length of the GAS group (7.75 ± 0.38 cm) was significantly longer than that of the UC group (6.12 ± 0.25 cm). The disease activity index (DAI) value of the UC group was (5.5 ± 0.57), which was markedly higher than that of the GAS group (2.5 ± 0.65). The GAS hydrogel also could inhibit the expression of inflammatory cytokines, regulating macrophage polarization and improving the intestinal mucosal barrier functions. All these results indicated that the GAS hydrogel was an ideal candidate for oral treatment of UC.

Hydatid disease is a chronic zoonotic parasitic disease caused by Echinococcus. This paper proposes and analyzes a mathematical model of Echinococcosis disease system with a piecewise control function concerning threshold policy. The model is represented the control measures being triggered once the total number of infected hosts and human reaches the tolerant level. Model solutions are able to approach either one real equilibrium or the pseudo-equilibrium, depending on the tolerant threshold. Our results suggest that in order to diminish the outbreak of Echinococcosis or lead the number of infecteds to an expected level, it requires not only adequate hospital resources and certain government interventions, but also a good threshold policy. [ABSTRACT FROM AUTHOR]

We present multiscatter, a novel battery-free backscatter design that can simultaneously work with multiple excitation signals for personal IoT sensors. Specifically, we show for the first time that the backscatter tag can identify various excitation signals in an ultra-low-power way, including WiFi, Bluetooth, and ZigBee. Further, we employ a new modulation approach, overlay modulation, that can leverage those excitation signals to convey tag data on top of productive data, which makes decoding both data possible with only a single personal radio. Moreover, we introduce a low-power listening scheme to improve energy efficiency. Since 2.4 GHz signals and personal radios are everywhere, multiscatter is readily deployable in our everyday IoT applications. We prototype multiscatter using an FPGA and various commodity radios. Extensive experiments show that for mixed 802.11b&n, Bluetooth and ZigBee signals, the average identification accuracy of four protocols is more than 93%. The maximal aggregate throughput of both productive and tag data is 278.4 kbps with a single Bluetooth radio. When the transmitter-to-tag distance is increased from 0.2 to 1.8 m, the maximal communication for BLE drops from 71 m to 29 m. And it can leverage excitation diversity to provide uninterrupted communication and greater throughput gains, whereas the single-protocol tag being idle when carrier signals are unavailable. With indoor office light as harvesting sources, the low-power listening scheme can support backscatter rate at 12 pkts/s.

Rarely are bionic robots capable of rapid multi-dimensional deformation and object identification in the same way as animals and plants. This study proposes a topological deformation actuator for bionic robots based on pre-expanded polyethylene and large flake MXene, inspired by the octopus predation behavior. This unusual, large-area topological deformation actuator (easily reaching 800 cm2but is not constrained to this size) prepared by large-scale blow molding and continuous scrape coating exhibits different distribution states of molecular chains at low and high temperatures, causing the actuator's deformation direction to change axially. With its multi-dimensional topological deformation and self-powered active object identification capabilities, the actuator can capture objects like an octopus. The contact electrification effect assists the actuator to identify the type and size of the target object during this multi-dimensional topological deformation that is controllable and designable. This work demonstrates the direct conversion of light energy into contact electrical signals, introducing a new route for the practicality and scaling of bionic robots.

Research on pedestrian movement is of great significance for public safety management and the formulation of safe evacuation strategies. The compatibility effect of approaching the positive and avoiding the negative is a common phenomenon in pedestrian movements, there is a compatibility relationship between emotional stimuli and approach avoidance behavior, individuals are faster in avoiding than in approaching negative stimuli, they are faster in approaching than in avoiding positive stimuli, and emotional difference is an important reason for this phenomenon. However, current pedestrian movement models cannot simulate the movement phenomenon caused by emotional differences, the application of sensors mainly focuses on real-time navigation and scene monitoring, and research on sensor-based pedestrian movement simulation is lacking. These reduce the effect of pedestrian movement simulation and the crowd evacuation efficiency. For this purpose, a sensor-based pedestrian movement simulation and evacuation method is proposed in this article. First, image sensors are used to collect multiple media data (images and videos), and an emotional perception model is proposed to associate pedestrians’ emotional states with their movement characteristics. Then, an emotional difference-based pedestrian movement model (ED-PMM) is constructed to describe the compatibility effect of approaching the positive and avoiding the negative in pedestrian movements. Finally, based on wireless sensors, a personalized path planning method considering the emotional differences of pedestrians is designed to guide pedestrians to evacuate. The experiment results show that the ED-PMM can improve the simulation effect of pedestrian movement and the evacuation strategy can improve the evacuation efficiency.

High temporal resolution aerosol optical depth (AOD) observations derived from new-generation geostationary (GEO) satellites possess unique advantages in analyzing aerosol fast variation processes and thereby providing more accurate assessments of their climate effects and health risks. Unfortunately, the expected advantages and values are dramatically limited by a relatively large proportion of data missing in the GEO AOD products due to cloud obscuration and intrinsic retrieval algorithm. Although several data recovery algorithms have been proposed in recent years to improve the spatial coverage for GEO AOD products, most of them aim at filling up the data blanks rather than reconstructing the temporally continuous variation of aerosol. Accordingly, in this study, a novel framework of nested spatiotemporal fusion blending GEO with the sun-synchronous orbit (SSO) satellite observations based on the Bayesian maximum entropy (BME) theorem is developed for GEO Himawari-8 Advanced Himawari Imager (AHI) AOD recovery with the sufficient excavation of complementary information from GEO and SSO satellite observations, where the minute-stage and hour-stage BME fusion are jointly employed to reconcile temporal inconsistency and data discrepancies between GEO and SSO observations. The results demonstrate that the AOD spatial coverage is dramatically increased by 240.9% (from 20.5% to 70%) with ensured accuracy after Nested-BME fusion. Additionally, two case analyses, during the development and dispersion processes of haze respectively, both demonstrate that the proposed Nested-BME fusion framework could reconstruct the reliable aerosol diurnal variation trends on the basis of recovering missing data for Himawari-8 AHI AOD datasets, while the AHI official level-2 and level-3 AOD products fail to capture these key trends. Furthermore, the developed Nested-BME AOD fusion framework is also applicable for other GEO satellites over other regions, which could substantially enhance the availability and value of high temporal resolution AOD products for better scientific applications.

Graphical abstract:

AimTo assess the role of the corneal radius of curvature (CR) in the identification of fundus tessellation in children with low myopia.MethodsIn the cross-sectional study, students aged 9–12 years from 24 primary schools in Shanghai were enrolled by cluster sampling. Participants underwent measurements including cycloplegic refraction and axial length. Fundus images and choroidal thickness were obtained by swept-source optical coherence tomography. Fundus tessellation was classified into four grades according to fundus photographs.ResultsA total of 1127 children with low myopia (spherical equivalence (SE) >−3.00 dioptre (D) but ≤−0.50 D) were included, with a mean age of 10.29±0.60 years and a mean SE of −1.44±0.69 D. Fundus tessellation was found in 591 (52.4%) cases (grade 1: 428, 38.0%; grade 2: 128, 11.4%; grade 3: 35, 3.1%). Choroidal thickness decreased as fundus tessellation grade increased (p trend <0.001). According to regression analysis, higher fundus tessellation grade was independently associated with larger CR (OR, 7.499; 95% CI 2.279 to 24.675, p=0.001). For those with CR >7.9 mm, along with CR, degree and proportion of fundus tessellation increased sharply.ConclusionFundus tessellation existed in more than half of children with low myopia. Preliminary fundus photography conducted in children with low myopia with large CR would be necessary and beneficial to the early management of myopic fundus changes.Trial registration numberNCT02980445.

Low noise ( ${i}_{\text {noise}}$ ) and high responsivity ( ${R}$ ) are significant factors for Lead sulfide (PbS) colloidal quantum dots (CQDs) photodetectors implementing high specific detectivity ( ${D}^{\ast} $ ). However, the simultaneous achievement of the above two factors is still challenging due to the complicated roles of interfaces and the difficulties in comprehensive interface modification by individual strategy. Here, we propose a well-designed PbS CQD near-infrared (NIR) photodiode with high performance by multi-interface engineering. First, the well-passivated interface of a photo-active layer is obtained by high-quality n-type CQD inks and gentle ligands cross-linked p-type CQD solid films, which leads to low defect density and thus extremely low device dark current ( $\sim $ 70 nA $\cdot $ cm $^{-{2}}$ ). Second, the construction of LiF and MoOx carrier-selective layers in the electrode interfaces greatly increases the photogenerated charge carrier extraction efficiency due to the enhanced built-in electric field, which promotes a high ${R}$ to 0.61 $\text{A}\cdot \text{W}^{-{1}}$ at 1100 nm. Both the two modifications enable low ${i}_{\text {noise}}$ and high ${R}$ . Hence the device exhibits an ultrahigh ${D}^{\ast} $ up to $1.42\times 10^{{12}}$ Jones. The further demonstration of high-precision biological health monitoring in both visible and infrared bands by this device illustrates its huge potential in ultra-sensitive broadband optoelectronic applications.

The planetary boundary layer height (PBLH) has a significant impact on the energy and material exchange in the atmosphere. The traditional gradient method (GM) determines the PBLH based on the gradient change of the aerosol profile. It is susceptible to the effect of complex atmospheric conditions, which leads to uncertainties in the estimation of PBLH. Here, a random forest (RF) algorithm that considers the vertical distribution of aerosols is proposed to find the PBLH under complex atmospheric conditions. The height of the three minimum local peaks of the range correction signal (RCS) profile and seven other variables, such as aerosol layer number (ALN), relative humidity (RH), solar radiation, and other meteorological parameters, from January 2017 to December 2021 is used as an RF model input. The radiosonde estimated PBLH (PBLHRS) is used as a reference value. The sensitivity analysis indicates that the relative error of RF-estimated PBLH (PBLHRF) is smaller than that of GM-estimated PBLH (PBLHGM), and it decreases with an increase in aerosol optical depth (AOD). Moreover, RF achieves good performance under different atmospheric conditions. It can effectively overcome the effects of complex atmospheric conditions in PBLH estimation. Based on the correlation analysis, it is found that the estimation accuracy of the RF algorithm is greatly improved compared with the GM. The correlation coefficient between the PBLHRF and the PBLHRS reaches 0.8, which is much larger than that of the PBLHGM (0.47). Finally, long-term PBLHRF analysis shows that there are obvious diurnal and seasonal variations of PBLH. It increases and then decreases from early morning to late evening. It is highest in summer and lowest in winter. Overall, RF can effectively overcome the shortcomings of traditional GM and has high accuracy and robustness for various atmospheric conditions. The findings obtained here have great potential for lidar application in obtaining reliable PBLH estimations.

High-dimensional optimization problems are increasingly pervasive in real-world applications nowadays and become harder and harder to optimize due to increasingly interacting variables. To tackle such problems effectively, this paper designs a random elite ensemble learning swarm optimizer (REELSO) by taking inspiration from human observational learning theory. First, this optimizer partitions particles in the current swarm into two exclusive groups: the elite group consisting of the top best particles and the non-elite group containing the rest based on their fitness values. Next, it employs particles in the elite group to build random elite neighbors for each particle in the non-elite group to form a positive learning environment for the non-elite particle to observe. Subsequently, the non-elite particle is updated by cognitively learning from the best elite among the neighbors and collectively learning from all elites in the environment. For one thing, each non-elite particle is directed by superior ones, and thus the convergence of the swarm could be guaranteed. For another, the elite learning environment is randomly formed for each non-elite particle, and hence high swarm diversity could be maintained. Finally, this paper further devises a dynamic partition strategy to divide the swarm into the two groups dynamically during the evolution, so that the swarm gradually changes from exploring the immense solution space to exploiting the found optimal areas without serious diversity loss. With the above mechanisms, the devised REELSO is expected to explore the search space and exploit the found optimal areas properly. Abundant experiments on two popularly used high-dimensional benchmark sets prove that the devised optimizer performs competitively with or even significantly outperforms several state-of-the-art approaches designed for high-dimensional optimization.