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Abstract Structural studies of pyrene have been limited to below 2 GPa. Here, we report on investigations of pyrene up to ~35 GPa using in situ single-crystal synchrotron X-ray diffraction in diamond anvil cells and ab initio calculations. They reveal the phase transitions from pyrene-I to pyrene-II (0.7 GPa), and to the previously unreported pyrene-IV (2.7 GPa), and pyrene-V (7.3 GPa). The structure and bonding analysis shows that gradual compression results in continuous compaction of molecular packing, eventually leading to curvature of molecules, which has never been observed before. Large organic molecules exhibit unexpectedly high conformational flexibility preserving pyrene-V up to 35 GPa. Ab initio calculations suggest that the phases we found are thermodynamically metastable compared to pyrene-III previously reported at 0.3 and 0.5 GPa. Our study contributes to the fundamental understanding of the polymorphism of polycyclic aromatic hydrocarbons and calls for further theoretical exploration of their structure–property relationships.

Here, we present the synthesis of a novel quaternary compound, iodide carbonate Na5(CO3)2I, at 18(1) and 25.1(5) GPa in laser-heated diamond anvil cells. Single-crystal synchrotron X-ray diffraction provides accurate structural data for Na5(CO3)2I and shows that the structure of the material can be described as built of INa8 square prisms, distorted NaO6 octahedra, and trigonal planar CO32− units. Decompression experiments show that the novel iodide carbonate is recoverable in the N2 atmosphere to ambient conditions. Our ab initio calculations agree well with the experimental structural data, provide the equation of state, and shed light on the chemical bonding and electronic properties of the new compound.

Abstract Nitrogen catenation under high pressure leads to the formation of polynitrogen compounds with potentially unique properties. The exploration of the entire spectrum of poly- and oligo-nitrogen moieties is still in its earliest stages. Here, we report on four novel scandium nitrides, Sc2N6, Sc2N8, ScN5, and Sc4N3, synthesized by direct reaction between yttrium and nitrogen at 78-125 GPa and 2500 K in laser-heated diamond anvil cells. High-pressure synchrotron single-crystal X-ray diffraction reveals that in the crystal structures of the nitrogen-rich Sc2N6, Sc2N8, and ScN5 phases nitrogen is catenated forming previously unknown N6 6 − and N8 6 − units and $${\!\,}_{\infty }{\!\,}^{2}({{{{{\rm{N}}}}}}_{5}^{3-})$$ ∞ 2 ( N 5 3 − ) anionic corrugated 2D-polynitrogen layers consisting of fused N12 rings. Density functional theory calculations, confirming the dynamical stability of the synthesized compounds, show that Sc2N6 and Sc2N8 possess an anion-driven metallicity, while ScN5 is an indirect semiconductor. Sc2N6, Sc2N8, and ScN5 solids are promising high-energy-density materials with calculated volumetric energy density, detonation velocity, and detonation pressure higher than those of TNT.

The sustainable intensification (SI) of cultivated land use (CLU) is important for achieving global sustainable development goals (SDGs). However, in the rapid advancement of agglomeration economies, it remains unclear whether economic agglomeration (EA) affects SI. Combined with emergy theory, this study utilized socioeconomic and natural environment data from 31 provincial administrative regions from 1990 to 2017 to evaluate the SI level in China. It further analyzed the impact of EA on SI using a spatial econometric model that includes mediating effects. The results showed that the EA level in China continued to rise, whereas the SI level continued to decline from 1990 to 2017. The mediating effect analysis showed that fiscal support for agriculture and agricultural labor productivity have a suppression effect of |−4.186|% and |−2.002|% in the local region, while cultivated land-use intensity has partial mediation of 6.350% in the local region; and agricultural labor productivity has a suppression effect of |−4.680|% on the surrounding region, while cultivated land-use intensity has stronger partial mediation relative to the local region, even up to 10.402% on surrounding regions. This implies that the impact of EA on SI involves complex paths and spatial differences. This study provides a better understanding of the reasons for the continuous decline in the SI in agglomeration-type economic development and feasible policy proposals.

The chemical stability of alkali halides has caused them to be exploited as inert media in high-pressure, high-temperature experiments. Here, NaCl and KCl are unexpectedly found to react with yttrium, dysprosium and iron oxide in a laser-heated diamond anvil cell, producing Y2Cl, DyCl, Y2ClC and Dy2ClC at ~40 GPa and 2000 K and FeCl2 at ~160 GPa and 2100 K.

Abstract The major challenge to controlling the COVID pandemic is the rapid mutation rate of the SARS-CoV-2 virus, leading to the escape of the protection of vaccines and most of the neutralizing antibodies to date. Thus, it is essential to develop neutralizing antibodies with broad-spectrum activity targeting multiple SARS-CoV-2 variants. Here, we report a synthetic nanobody (named C5G2) obtained by phage display and subsequent antibody engineering. C5G2 has a single-digit nanomolar binding affinity to the RBD domain and inhibits its binding to ACE2 with an IC50 of 3.7 nM. Pseudovirus assays indicated that monovalent C5G2 could protect the cells from infection with SARS-CoV-2 wild-type virus and most of the viruses of concern, i.e., Alpha, Beta, Gamma and Omicron variants. Strikingly, C5G2 has the highest potency against Omicron BA.1 among all the variants, with an IC50 of 4.9 ng/mL. The cryo-EM structure of C5G2 in complex with the spike trimer showed that C5G2 binds to RBD mainly through its CDR3 at a conserved region that does not overlap with the ACE2 binding surface. Additionally, C5G2 binds simultaneously to the neighboring NTD domain of the spike trimer through the same CDR3 loop, which may further increase its potency against viral infection. Third, the steric hindrance caused by FR2 of C5G2 could inhibit the binding of ACE2 to RBD as well. Thus, this triple-function nanobody may serve as an effective drug for prophylaxis and therapy against Omicron as well as future variants.

Chemical reactions between dysprosium and carbon were studied in laser-heated diamond anvil cells at pressures of 19, 55, and 58 GPa and temperatures of ∼2500 K. In situ single-crystal synchrotron X-ray diffraction analysis of the reaction products revealed the formation of novel dysprosium carbides, Dy4C3 and Dy3C2, and dysprosium sesquicarbide Dy2C3 previously known only at ambient conditions. The structure of Dy4C3 was found to be closely related to that of dysprosium sesquicarbide Dy2C3 with the Pu2C3-type structure. Ab initio calculations reproduce well crystal structures of all synthesized phases and predict their compressional behavior in agreement with our experimental data. Our work gives evidence that high-pressure synthesis conditions enrich the chemistry of rare earth metal carbides.

Hand gesture recognition is an essential Human–Computer Interaction (HCI) mechanism for users to control smart devices. While traditional device-based methods support acceptable recognition performance, the recent advance in wireless sensing could enable device-free hand gesture recognition. However, two severe limitations are serious environmental interference and high-cost hardware, which hamper wide deployment. This paper proposes the novel system TaGesture, which employs an inaudible acoustic signal to realize device-free and training-free hand gesture recognition with a commercial speaker and microphone array. We address unique technical challenges, such as proposing a novel acoustic hand-tracking-smoothing algorithm with an Interaction Multiple Model (IMM) Kalman Filter to address the issue of localization angle ambiguity, and designing a classification algorithm to realize acoustic-based hand gesture recognition without training. Comprehensive experiments are conducted to evaluate TaGesture. Results show that it can achieve a total accuracy of 97.5% for acoustic-based hand gesture recognition, and support the furthest sensing range of up to 3 m.

Two new genera and species of Dermaptera are described from the Middle Jurassic Jiulongshan Formation of Daohugou, Inner Mongolia, China: Applanatiforceps angustus gen. et sp. nov. in the archidermapteran family Protodiplatyidae, and Ekpagloderma gracilentum gen et sp. nov. in the eodermapteran family Semenoviolidae. Applanatiforceps shares the typical characters of the extinct suborder Archidermaptera (e.g., pentamerous meta tarsi, filiform and multimerous cerci) and externalized ovipositor. The family identity of the Protodiplatyidae can be further distinguished by comparing this new genus with other genera of the Protodiplatyidae. As a result of its large compound eyes, tegmina without venation, body sparsely setose, legs rather short and slender, and shape of the veinless tegmina, Ekpagloderma is classified in the subfamily Aglyptodermatinae. Ekpagloderma not only has the typical features of the Aglyptodermatinae, but also exhibits a more primitive slender segmented cerci, which is different from all other genera of Eodermaptera. In fact, the diversity of Eodermaptera as known today indicates some of the challenges in understanding the suborder and whether or not it is monophyletic as historically construed, or if the separation of Turanodermaptera is justified.

This study proposes a new approach to gait recognition using LoRa signals, taking into account the challenging conditions found in underground coal mines, such as low illumination, high temperature and humidity, high dust concentrations, and limited space. The aim is to address the limitations of existing gait recognition research, which relies on sensors or other wireless signals that are sensitive to environmental factors, costly to deploy, invasive, and require close sensing distances. The proposed method analyzes the received signal waveform and utilizes the amplitude data for gait recognition. To ensure data reliability, outlier removal and signal smoothing are performed using Hampel and S-G filters, respectively. Additionally, high-frequency noise is eliminated through the application of Butterworth filters. To enhance the discriminative power of gait features, the pre-processed data are reconstructed using an autoencoder, which effectively extracts the underlying gait behavior. The trained autoencoder generates encoder features that serve as the input matrix. The Softmax method is then employed to associate these features with individual identities, enabling LoRa-based single-target gait recognition. Experimental results demonstrate significant performance improvements. In indoor environments, the recognition accuracy for groups of 2 to 8 individuals ranges from 99.7% to 96.6%. Notably, in an underground coal mine where the target is located 20 m away from the transceiver, the recognition accuracy for eight individuals reaches 93.3%.

Tyrosine sulfation plays a vital role in various biochemical reactions. Although sulfated tyrosine (sTyr) has a similar structure to phosphotyrosine (pTyr), the number of available sTyr sites is significantly less than that of pTyr sites, mainly because of the lack of effective sTyr probes. A few sTyr binders were identified on the basis of structural similarity by engineering the pTyr-binding pocket of an Src Homology 2 (SH2) domain through phage selections against sTyr peptides. Nevertheless, they still interact with pTyr peptides with comparable affinity. This study aims to identify sTyr superbinders using the SH2 domain as a template. We created a distinctive phage selection scheme that separately covered selections against sTyr and pTyr peptides, followed by next-generation sequencing (NGS). After selections, phage pools showed strong enzyme-linked immunosorbent assay (ELISA) signal intensities for both modified peptides, indicating that the variants evolved with a high affinity for these peptides, which causes difficulty in identifying sTyr-specific binders. In contrast, NGS data from selected pools showed significant differences, suggesting the enrichment of sTyr-specific variants during selections. Accordingly, we obtained the sTyr features based on NGS data analysis and prioritized a few potential sTyr binders. The variant SH2-4 showed a stronger affinity for sTyr than pTyr and was superior to previous sTyr binders as measured by the Biolayer Interferometry assay. In summary, we described the strategy of integrating NGS data mining with a novel selection scheme to identify sTyr superbinders.

Background: Metabolic diseases pose considerable burden on the healthcare system worldwide, indicating the significance of prevention and treatment. In constitution theory of traditional Chinese medicine, phlegm-dampness constitution (PDC) is the common basis of metabolic diseases. In clinical practice, Huatan Qushi (HTQS) decoction targeting on PDC can effectively improve metabolic indicators. However, its underlying biochemical mechanism still remains unclear. Methods: Eight PDC participants received HTQS decoction for three months. Their blood was collected at baseline and 1 and 3 months after intervention started. Related biomedical indicators were detected. High-throughput sequencing and RT-qPCR were used for validation. Due to the missing data, repeated measures with missing values in mixed models were used. Results: After 3-month treatment, HDL-C level increased (P

Improving the cultivated land use ecological efficiency (CLUE) to obtain greater agricultural output at a lower environmental cost, is an important part of effective management of land resources and sustainable intensification (SI). However, when evaluating CLUE, the existing research ignores the heterogeneity of the production environment of different agricultural production units, which leads to a “distortion effect”. To address this, this study focuses on the Yangtze River Economic Belt (YREB) in China as the study area and uses a three-stage super-efficiency slacks-based measure-undesirable (three-stage super-efficiency SBM-U) model to evaluate CLUE after excluding the interference of a heterogeneous agricultural production environment. In addition, we constructed ordinary least squares (OLS) and geographically weighted regression (GWR) models to analyze the driving mechanism of CLUE. The results indicate that environmental factors have a significant impact on the evaluation results of CLUE. Ignoring environmental factors leads to an overestimation of CLUE in areas with better environmental conditions, and vice versa. Second, due to the difference in input–output structure and the varied difficulty in production factor substitution, the driving mechanism of CLUE is spatiotemporal non-stationarity. During the study period, gross domestic product per capita, and farmland management scale had a positive effect on CLUE, while industrialization level had a negative effect. The impact of fiscal support for agriculture changed from insignificant to significant, in terms of improving CLUE in the western YREB. The influence direction of the multiple cropping index and agricultural planting structure changes from positive to negative and is concentrated in the east of the YREB. As a result, a series of measures are proposed to promote the improvement of CLUE and realize SI.

Indoor localization of smartphones has received much attention recently and the smartphone localization is essential to a wide range of applications in office buildings, nursing homes, parking lots, and other public places. Existing solutions relying on inertial sensors or received signal strength suffer from large location errors and poor stability. We observe an opportunity in the recent trend of increasing numbers of wireless transmitters installed in indoor spaces to design a precise and robust indoor localization solution. We can extract fine-grained channel state information from wireless transmitters for indoor fingerprint localization. However, the accuracy of localization relying on a single physical quantity is limited and difficult to self-correct. This study proposes an integrated channel state information (CSI) and magnetic field strength (MFS) localization method (CSMS) that achieves sub-meter accuracy for smartphones. CSMS constructs an integrated fingerprint map of CSI and MFS and proposes the Local Dynamic Time Warping algorithm for geomagnetic tracking and the Multi-Module Data k-Nearest Neighbor algorithm for fusion fingerprint dynamic weighted comparison. By doing so, CSMS outputs enhanced accuracy with low cost, while overcoming the respective drawbacks of each individual sub-system. We conduct extensive experiments in two scenarios to validate the performance of CSMS. The results of experimental show that the mean distance error in both scenarios is less than 0.5m which is significantly superior to existing smartphone-based indoor positioning methods.

Abstract This letter proposes a fully domain‐independent WiFi‐based gesture recognition system based on the multi‐label adversarial network. Unlike pioneer machine learning works, our system does not require retraining in the target domain, which benefits from our key idea of eliminating fully domain information such as orientations, locations, environment information. The system proposed by us includes three parts: Feature extractor, domain discriminator, and gesture recogniser. The feature extractor attempts to deceive the domain discriminator based on the adversarial network structure so that it is difficult to judge the input domain label, thereby obtaining domain‐independent features and realising fully domain‐independent gesture recognition. Extensive experiments are conducted on the Widar 3.0 dataset and our dataset to evaluate system performance. The results show that our system can achieve an in‐domain recognition accuracy of 93.2% and a cross‐domain recognition accuracy of 87.1%, which is superior to other state‐of‐the‐art works.

Opportune and accurate position information of the ubiquitous shared bikes can help users rapidly find the available bicycle around them. In order to remove the professional locating device of bicycles and reduce industrial costs, a passive positioning method for shared bikes named PSA is proposed in this paper with the support of activity recognition based on deep learning. First, an action recognition module based on LSTM algorithm is constructed through training three-axis accelerator data collected by commercial mobile phone to distinguish user's riding state from other actions like walking or running; then, the GPS module embedded in the user's mobile phone will record the location when the state changes from riding to non-riding, which is regarded as the bike's return location. Furthermore, considering the variety of state changing during riding procedure, a duration threshold mechanism is added for the sake of fault tolerance processing. Several simulations and test-bed experiments were conducted to verify the feasibility and practicality of the proposed method, where the accuracy of activity recognition in practical data set can reach more than 95% and the detection time in public data set is less than 0.2s. In addition, this method reduces phone operations and enhances the convenience of the experience.

Accurate localization in underground coal mining is a challenging technology in coal mine safety production. This paper proposes a low-cost battery-free localization scheme based on depth images, called MineBL. The main idea is to utilize the battery-free low-cost reflective balls as position nodes and realize underground target localization with a series of algorithms. In particular, the paper designs a data enhancement strategy based on small-target reorganization to increase the identification accuracy of tiny position nodes. Moreover, a novel ranging algorithm based on multi-filter cooperative denoising has been proposed, and an optimized weighted centroid location algorithm based on multilateral location errors has been designed to minimize underground localization errors. Many experiments in the indoor laboratories and the underground coal mine laboratories have been conducted, and the experimental results have verified that MineBL has good localization performances, with localization errors less than 30 cm in 95% of cases. Therefore, MineBL has great potential to provide a low-cost and effective solution for precise target localization in complex underground environments.

COVID-19 is currently the biggest threat that challenges all of humankind’s health and property. One promising and effective way to control the rapid spreading of this infection is searching for primary close contacts of the confirmed cases. In response, we propose COVID-19 Tracer, a low-cost passive searching system to find COVID-19 patients’ close contacts. The main idea is utilizing ubiquitous WiFi probe requests to describe the location similarity, which is then achieved by two designed range-free judgment indicators: location similarity coefficient and close contact distance. We have carried out extensive experiments in a school office building, and the experimental results show an average accuracy of more than 98%, demonstrating our system’s effectiveness in judging close contacts. Last but not least, we have developed a prototype system for a school building to find potential close contacts.

Objective: In this study, we investigated the composition of the intestinal microflora associated with Yang-deficiency and the function-specific members of this microflora. Method: Subjects with normal and Yang-deficient constitutions were recruited in Beijing, with 30 subjects in each group. Illumina high-throughput sequencing was used to sequence the DNA of their fecal bacteria in the 16S rRNA V3–V4 region. The species abundance and distribution of the intestinal microflora in each specimen were determined with a statistical analysis of these sequences. We identified an underlying taxonomic trend with nonparametric PCoA and other statistical techniques. Results: (1) Subjects with Yang-deficiency displayed more uneven abundances of the taxa in their intestinal microflora than the subjects with normal Yang; (2) several differences were observed in the proportions of certain bacterial groups between the Yang-deficient and control groups; and (3) the following bacteria differed significantly between the two groups: (i) Brevundimonas, Leuconostoc, Turicibacter, Defluviitaleaceae incertae sedis, Rothia, and Butyricimonas were slightly higher in the Yang-deficient subjects, whereas (ii) Megasphaera, Ruminococcus, and Lachnospira were higher in the normal subjects than in the Yang-deficient subjects. Conclusions: The composition of the intestinal microflora is altered in Yang-deficient subjects.

Indoor localization provides robust solutions in many applications, and Wi-Fi-based methods are considered some of the most promising means for optimizing indoor fingerprinting localization accuracy. However, Wi-Fi signals are vulnerable to environmental variations, resulting in data across different times being subjected to different distributions. To solve this problem, this paper proposes an across-time indoor localization solution based on channel state information (CSI) fingerprinting via multi-domain representations and transfer component analysis (TCA). We represent the format of CSI readings in multiple domains, extending the characterization of fine-grained information. TCA, a domain adaptation method in transfer learning, is applied to shorten the distribution distances among several CSI readings, which overcomes various CSI distribution problems at different time periods. Finally, we present a modified Bayesian model averaging approach to integrate the multi-domain outcomes and give the estimated positions. We conducted test-bed experiments in three scenarios on both personal computer (PC) and smartphone platforms in which the source and target fingerprinting data were collected across different days. The experimental results showed that our method outperforms state-of-the-art methods in localization accuracy.

Clock synchronization is still a vital and challenging task for underground coal wireless internet of things (IoT) due to the uncertainty of underground environment and unreliability of communication links. Instead of considering on-demand driven clock synchronization, this paper proposes a novel Adaptive Robust Synchronization (ARS) scheme with packets loss for mine wireless environment. A clock synchronization framework that is based on Kalman filtering is first proposed, which can adaptively adjust the sampling period of each clock and reduce the communication overhead in single-hop networks. The proposed scheme also solves the problem of outliers in data packets with time-stamps. In addition, this paper extends the ARS algorithm to multi-hop networks. Additionally, the upper and lower bounds of error covariance expectation are analyzed in the case of incomplete measurement. Extensive simulations are conducted in order to evaluate the performance. In the simulation environment, the clock accuracy of ARS algorithm is improved by 7.85% when compared with previous studies for single-hop networks. For multi-hop networks, the proposed scheme improves the accuracy by 12.56%. The results show that the proposed algorithm has high scalability, robustness, and accuracy, and can quickly adapt to different clock accuracy requirements.

Node localization plays a key and basic role for wireless sensor network. Existing range-free localization approaches suffer from precision limit of positioning, while range-based solutions may obtain good accuracy but pay high costs for ranging hardware. Instead of directly mapping received signal strength values into physical distances, a novel localization scheme with signal-based signature distance estimation is proposed for wireless sensor network. More specifically, we first quantify the near-far relationship between neighbor nodes through received signal strength comparison, and then the distance between non-neighbor nodes is calculated by an improved shortest path algorithm. Based on the obtained node distance, a relative map can be constructed by multidimensional scaling technology. Eventually, the locations of nodes can be acquired by virtue of Procrustes analysis method. For purpose of verifying the effectiveness of our design, several simulations are made in a sensor network with 200 randomly deployed nodes, and two experiments are implemented in real outdoor environments: a linear network and a two-dimensional grid network with 25 nodes. It is remarkable from results that the proposed scheme performs better in positioning and reduces localization errors by as much as 37% compared with state-of-the-art methods.

Convenient indoor positioning has become an urgent need due to the improvement it offers to quality of life, which inspires researchers to focus on device-free indoor location. In areas covered with Wi-Fi, people in different locations will to varying degrees have an impact on the transmission of channel state information (CSI) of Wi-Fi signals. Because space is divided into several small regions, the idea of classification is used to locate. Therefore, a novel localization algorithm is put forward in this paper based on Deep Neural Networks (DNN) and a multi-model integration strategy. The approach consists of three stages. First, the local outlier factor (LOF), the anomaly detection algorithm, is used to correct the abnormal data. Second, in the training phase, 3 DNN models are trained to classify the region fingerprints by taking advantage of the processed CSI data from 3 antennas. Third, in the testing phase, a model fusion method named group method of data handling (GMDH) is adopted to integrate 3 predicted results of multiple models and give the final position result. The test-bed experiment was conducted in an empty corridor, and final positioning accuracy reached at least 97%.

Underground personnel localization is highly important in the operations of coal mines. Considering the special underground environment, this paper introduces a novel localization scheme based on step detection and image recognition technologies, which makes use of unique characteristics of the underground environment like the dark environment and the miner’s lamp. Since the underground topology is relatively simple, the miner can be located only by step information. However, the localization with step information always causes the problem of cumulative error. To solve this problem, we rebuild a special base station with a camera in a dark underground environment. A miner’s lamp, which every miner carries, can simply transform to irradiate unique shapes (such as triangles, rectangles, and circles) and every coal miner at the base station can identify these shapes based on image recognition technologies. Thus, we can obtain the miner’s precise position when he/she is passing by a base station. In that way, we can correct the localization results to solve cumulative error. We implemented our algorithm in indoor and underground environments. The experimental results show that 96% of spatial errors were 2.5 m or less.

Node localization is an essential issue in wireless sensor networks (WSNs). Many range-free localization methods have been proposed to satisfy the requirement of low-system cost. However, some range-free methods only depend on network connectivity, and others only utilize the proximity information attached in neighborhood ordering. To employ the strength of the above two aspects, this paper introduces a new metric system called Combined and Weighted Diffusion Distance (CWDD). CWDD is designed to obtain the relative distance among nodes based on both graph diffusion property and neighbor information. We implement our design by embedding CWDD into two well-known localization algorithms and evaluate it by extensive simulations. Results show that our design improves the localization performance in large scale and non-uniform sensor networks, which reduces positioning errors by as much as 26%.