Circadian rhythms regulate a wide range of biological processes, and their precise characterization is essential for understanding behavioral and physiological fluctuations. However, existing tools to analyze circadian data often require coding expertise or rely on specific data acquisition software, limiting their general applicability. Here, we present easyClock, an intuitive and interactive application designed to streamline circadian rhythm analysis and visualization. The easyClock application enables simultaneous processing of multiple files, allowing users to batch-analyze and visualize diverse sets of time series data. To enhance data analysis efficiency and provide comparable results, this application integrates comprehensive methods for handling data with various waveforms and noises. Additionally, easyClock can assess inter-individual variability and group differences using linear mixed-effects modeling. All statistical results and graphs are easily viewed and exported for any selected range of data. As a demonstration, we present a re-analysis of a time-series transcriptomic dataset, highlighting the value of easyClock as an accessible, open-source tool. This easy-to-use application requires no programming expertise and can be directly installed on Windows and macOS machines in a single step.

Abstract To address the issue of low coverage resulting from the challenge of acquiring the optimal deployment position in commonly used distributed deployment algorithms, this study presents a three‐dimensional deployment algorithm for Unmanned Aerial Vehicles (UAVs) based on potential games. First, a local mutually beneficial game model is designed to demonstrate the existence of exact potential games and Nash equilibrium. The Nash equilibrium solution corresponds to the maximum coverage. Next, drawing inspiration from exploration, a solution method called Exploration Spatial Adaptive Play is proposed. It utilizes the maximum utility function value from multiple step sizes in the exploration direction to update the action selection probability, thereby ensuring the optimal deployment position in each decision cycle. To address the issue of sensor position error, a method for processing sensor position errors is proposed. The simulation results demonstrate that the proposed distributed deployment algorithm achieves higher coverage compared to commonly used methods.

Abstract This article investigates a problem involving the collection of data from sensor nodes (SNs) with heterogeneous information aging speed (IAS) by an unmanned aerial vehicle (UAV) in a dense obstacle environment. The objective is to minimize the average age of information (AoI) of the SNs through UAV path planning. The problem is challenging due to the tight coupling of obstacle avoidance, information timeliness, and the heterogeneity of SNs. Directly solving this path planning problem is difficult, and the conventional approach involves planning the access sequence without considering obstacle avoidance and then optimizing the UAV trajectory while incorporating safety constraints. However, optimizing the trajectory for safe flight introduces changes in the flight time cost, resulting in the average AoI not reaching its minimum value. To address this, a UAV safe flight network is first established by generating trajectories using a combination of A*‐based and successive convex approximation (SCA)‐based algorithms. Subsequently, a genetic algorithm (GA)‐based method is employed and compared with the time greedy strategy. The numerical results demonstrate that the time greedy strategy, which aligns with intuitive understanding, can achieve a smaller total UAV flight time, while the proposed method effectively minimizes the average AoI of SNs.

With the development of urbanization, the number of buildings has increased significantly. Efficiently identifying the collapse risk of these buildings is a key challenge in creating smart cities. Traditional methods that rely on expert judgment can no longer meet the needs, and the Internet of Things (IoT) is considered a promising solution. However, there’s a lack of IoT platforms dedicated to the risk identification of building collapse. From this point of view, this paper proposes a novel IBRI platform, which attempts to provide the first holistic IoT-based solution for building collapse risk identification. We propose the cloud-edge-device platform architecture and design a cloud-edge collaboration mechanism that supports intelligent application deployment. On this basis, this paper proposes a large-scale survey model and risk assessment rules and then achieves two important applications, including overall and individual monitoring of buildings. In an exemplary and real-world case study at Shenzhen, satellite-based overall monitoring and sensor-based individual monitoring are further conducted to demonstrate the efficiency and versatility of our platform.

Abstract In this study, the problem of estimating multiple frequency‐hopping (FH) signal parameters in situations in which random observations are missing is addressed. A uniform linear array (ULA) is utilized to receive FH signals, and the missing observations are considered to be equivalent to randomly missing elements in the space‐time matrix obtained from the ULA. Bayesian compressed sensing (BCS) estimates the hopping time of received FH signals by the spatial information from the space‐time matrix and restores the missing observations. The estimated hopping time is implemented as the boundary to divide the signals such that each segment contains a superposition of time‐invariant multiple components. Then, the instantaneous frequency (IF) of multiple components can be estimated precisely by atomic norm soft thresholding (AST) within each segment. After estimating the hopping time and IF, the direction of arrival (DOA) of multiple signals is directly calculated using these two parameters. The simulation results show that the proposed method is superior to existing approaches. Provided that sufficient array elements are available, multi‐FH signal parameters can be estimated with satisfactory accuracy even when a large portion of observations is missing.

Abstract This paper studies unmanned aerial vehicles (UAV) enabled industrial Internet of Things while a UAV dispatched to collect data of low‐power ground sensor nodes (SNs) in multi‐obstacle environment. The authors aim to minimize the completion time while satisfying the communication link constraints of each SN and obstacle avoidance, data collection requirements etc. To this end, the authors first formulate the completion time minimization problem by jointly optimizing the UAV trajectory and collection sequence of SNs. The problem is difficult to be optimally solved, as it is non‐convex. To tackle this problem, the authors first transform the original problem to a Traveling Salesman Problem‐like (TSP‐like) problem based on a hover point that can naturally satisfy the communication link constraints of data collection. The dynamic programming (DP) algorithm to figure out the order in which the UAV collects each SN, which gives the initial path of the UAV traversing each SN from the beginning point to the end point. Next, the authors consider the general scenarios of data collection tasks where the UAV also communicates while flying. The authors construct an equivalent problem with integer variable constraints for the original problem with indicative function constraints. The authors rewrite the non‐convex constraints of the equal problem by introducing slack variables and leveraging the SCA, and add the discrete region threat constraints for the traditional path discretization method. Finally, the simulation results verify the effectiveness of the proposed algorithm under different parameter configurations.

The de novo assembly tools aim at reconstructing genomes from next-generation sequencing (NGS) data. However, the assembly tools usually generate a large amount of contigs containing many misassemblies, which are caused by problems of repetitive regions, chimeric reads, and sequencing errors. As they can improve the accuracy of assembly results, detecting and correcting the misassemblies in contigs are appealing, yet challenging. In this study, a novel method, called MEC, is proposed to identify and correct misassemblies in contigs. Based on the insert size distribution of paired-end reads and the statistical analysis of GC-contents, MEC can identify more misassemblies accurately. We evaluate our MEC with the metrics (NA50, NGA50) on four datasets, compared it with the most available misassembly correction tools, and carry out experiments to analyze the influence of MEC on scaffolding results, which shows that MEC can reduce misassemblies effectively and result in quantitative improvements in scaffolding quality. MEC is publicly available at https://github.com/bioinfomaticsCSU/MEC.

The de novo assembly aims to reconstruct the genome of the unknown species. Many algorithms have been proposed for de novo assemblies. Due to problems of repetitive regions and sequencing errors, contigs usually contain a large amount of misassemblies. Consequently, the misassembly correction of contigs is a challenging and significant work, which receives considerable attentions from researchers. In this study, we propose a novel method, called MEC, to identify and correct misassemblies in contigs. Firstly, MEC takes fragment coverage as the feature to detect the candidate misassemblies. Then, it can distinguish a large number of false positives from the candidate misassemblies based on the distribution of paired-end reads and the statistical analysis of GC-contents. We apply MEC to four real contig datasets, and carry out experiments to analyze the influence of MEC on scaffolding results, which shows that MEC can reduce misassemblies effectively and result in quantitative improvements in scaffolding quality. MEC is publicly available for download at https://github.com/bioinfomaticsCSU/MEC.

The downlink capacity performance of distributed antenna systems (DAS) with antennas selection is investigated in a composite fading channel which takes path loss, Rayleigh fading and lognormal shadowing into account. According to the performance analysis, and using log-normal distribution approximation, the tightly approximate closed-form expressions of cumulative distribution function (CDF) and probability density function (PDF) of the effective SNR are obtained, respectively. Based on these results, the outage capacity performance of DAS is analyzed. By utilizing the Gaussian distribution approximation, a simple approximate outage capacity is derived. As a result, the closed-form expression is achieved. All these expressions will provide good theoretical performance evaluation for DAS. Simulation results show that the derived outage capacity is effective, and can match the corresponding simulation well.

Based on the asymptotic analysis in spatially correlated Rician fading channel, the precoders for space‐time block‐coded multiple‐input multiple‐output system at high and low signal‐to‐noise ratios (SNRs) are, respectively, designed to minimise the system bit error rate (BER). The precoding for high SNR provides closed‐form positive power allocation by using the Langrage optimisation method, and has the BER performance close to that of the optimal scheme, especially at high SNR. The Langrage multiplier does exist and is unique, and practical Newton's method is proposed to find its optimal value. Moreover, it has a closed‐form solution for some special cases. Thus, it avoids the iterative calculation of some existing precoding schemes. The precoding for low SNR has a closed‐form solution similar to onedirectional (1‐D) beamforming, and can obtain the performance very close to that of the optimal scheme at low SNR. By combining the above two schemes, a simple and effective suboptimal precoding scheme can be achieved for different SNRs. Simulation results show that the proposed scheme derived from the integration of high and low SNR can provide BER lower than the equal power precoding, and obtain the BER performance close to that of the existing optimal scheme with low complexity.

Free-space optical communication (FSOC) is a method by which one transmits a modulated beam of visible or infrared light through the atmosphere for broadband applications. FSOC makes connectivity possible between high-rise buildings and metropolitan and intercity communication infrastructures. While radio frequency (RF) communication still dominates the commercial communications networks, it has not been able to meet the increasing demand for larger data rates. Reliable communications require a good understanding of the surrounding environment that affects the communications channel. The fundamental limitation of FSOC arises from the environment through which light propagates. The presence of particles, such as fog and clouds in the propagation channel introduces spatial and angular dispersion to the transmitted light signals. Attenuation of the light signals through the propagation medium is a combined effect of dispersion and absorption by particles. A mode of ray tracing is developed to simulate light going through the medium. A 2-D histogram is presented to investigate correlation of spatial and angular dispersion of the received light.