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The analytical constant modulus algorithm (ACMA) is an effective tool for separating and recovering statistically independent constant modulus (CM) signals. In previous ACMA studies, source signals are usually restricted to be statistically independent or statistically orthogonal. In this article, we no longer impose these restrictions on CM signals, but only require the CM signals to be partially independent or orthogonal. However, this will lead to the inability of ACMA to successfully separate these partially correlated source signals from the observed data. This article introduces a new method for separating partially correlated sources based on ACMA. We first analyzed that the biggest shortcoming of the original algorithm when dealing with partially correlated sources was that it could not estimate the number of source signals. Later, we found that the best range of the threshold value was a curve about SNR, which would not change greatly with the increase in the correlation coefficient. Therefore, we adopted the method of mixed estimation of the SNR of source signals and improved the threshold formula. The threshold performance of estimating the number of partially correlated sources is improved, and the ability of ACMA to estimate the number of partially correlated constant modulus sources is improved. We then estimate the source signal by reconstructing the covariance matrix of the signal to eliminate the associated effects. Simulation results show that this method can be used for nonindependent constant modulus signals with a correlation coefficient of less than 0.5.

Multifocus image fusion aims to merge source images with distinct focused areas into a single, fully focused fused image. Sparse representation (SR) stands out as a robust signal modeling technique that has achieved remarkable success in multifocus image fusion. Numerous SR-based fusion methods have been proposed over the years, underscoring the importance of SR in enhancing fusion quality. However, a fundamental problem appearing in most existing SR models is the absence of directionality. This deficiency restricts their capacity to extract intricate details. To address this issue, we propose the complex SR ( $\mathbb {C}$ SR) model for image fusion. This model utilizes the properties of hypercomplex signals to extract directional information from real-valued signals through complex extension. Subsequently, the directional components of the input signal are decomposed into sparse coefficients over corresponding directional dictionaries. The key advantage of our design over conventional SR models is the ability to capture the geometrical image structures effectively, since $\mathbb {C}$ SR coefficients can provide precise measurements of detailed information along specific directions. Experimental results conducted on three widely used multifocus image fusion datasets substantiate the superiority of our method over 17 representative multifocus image fusion methods in terms of both visual quality and objective evaluation.

Real-time fraud detection remains a pressing issue for many financial and e-commerce platforms. $\mathsf {Grab}$Grab, a prominent technology company in Southeast Asia, addresses this by constructing a transactional graph. This graph aids in pinpointing dense subgraphs, possibly indicative of fraudster networks. Notably, prevalent methods are designed for static graphs, neglecting the evolving nature of transaction graphs. This static approach is ill-suited to the real-time necessities of modern industries. In our earlier work, $\mathsf {Spade}$Spade, the focus was mainly on edge insertions. However, $\mathsf {Grab}$Grab's operational demands necessitated managing outdated transactions. Persistently adding edges without a deletion mechanism might inadvertently lead to densely connected legitimate communities. To resolve this, we present $\mathsf {Spade+}$Spade+, a refined real-time fraud detection system at $\mathsf {Grab}$Grab. Contrary to $\mathsf {Spade}$Spade, $\mathsf {Spade+}$Spade+ manages both edge additions and removals. Leveraging an incremental approach, $\mathsf {Spade+}$Spade+ promptly identifies suspicious communities in large graphs. Moreover, $\mathsf {Spade+}$Spade+ efficiently handles batch updates and employs edge packing to diminish latency. A standout feature of $\mathsf {Spade+}$Spade+ is its user-friendly APIs, allowing for tailored fraud detection methods. Developers can easily integrate their specific metrics, which $\mathsf {Spade+}$Spade+ autonomously refines. Rigorous evaluations validate the prowess of $\mathsf {Spade+}$Spade+; fraud detection mechanisms powered by $\mathsf {Spade+}$Spade+ were up to a million times faster than their static counterparts.

Hybrid rice, widely planted in Asia, is pathogen resistant and has superior yields, making it a major contributor to global food security. The two-line hybrid rice system, which utilizes mutants exhibiting photo-/thermo-sensitive genic male sterility (P/TGMS), is the leading hybrid rice breeding technology. Mutations in THERMO-SENSITIVE GENIC MALE STERILE 5(TMS5) accounts for over 95% of current TGMS lines. We previously found that tms5carries a mutation in ribonuclease ZS1. Despite its importance for breeding robust rice lines, the mechanism underlying tms5-mediated TGMS remains elusive. Here, we demonstrate that TMS5 is a tRNA 2′,3′-cyclic phosphatase. The tms5mutation leads to accumulation of 2′,3′-cyclic phosphate (cP)-ΔCCA-tRNAs (tRNAs without 3′ CCA ended with cP), which is exacerbated by high temperatures, and reduction in the abundance of mature tRNAs, particularly alanine tRNAs (tRNA-Alas). Overexpression of tRNA-Alas in the tms5mutant restores male fertility to 70%. Remarkably, male fertility of tms5mutant is completely restored at high temperatures by knocking out OsVms1which encodes the enzyme for cP-ΔCCA-tRNA generation. Our study reveals the mechanism underlying tms5-mediated TGMS in rice and provides mechanistic insight into the further improvement of TGMS in hybrid crop development.

The aim of this study was to determine the osteogenic activity and mechanism of soybean peptide VVELLKAFEEKF (SOP) and the potential relationship between SOP and transforming growth factor-β1 (TGF-β1). The results show that SOP promotes MC3T3-E1 cell proliferation by altering cell progression. SOP induced cell differentiation and mineralization in a dose-dependent manner at 0.7–7 μM. Moreover, SOP stimulates osteoblast differentiation, which may be achieved through the activation of p38-MAPK and Smad2/3 signaling pathways. Furthermore, treatment with a TβRI inhibitor (SB525334) inhibited the phosphorylation levels of p38 and Smad2/3, which indicates the involvement of TβRI in the process of osteoblast differentiation caused by SOP. Besides, in non-FBS-cultured MC3T3-E1 cells, SOP and TGF-β1 promoted the phosphorylation of Smad2/3 and alkaline phosphatase (ALP) activity, but the effect was lost when SOP was incubated separately, indicating that SOP stimulated osteoblast differentiation by promoting TGF-β1 activity. In vivo, SOP significantly restores bone mineral density loss and behavioral deficits in a model of glucocorticoid-induced osteoporosis (GIOP) in zebrafish. These results suggest that SOP may have the function of promoting bone remodeling and may be used as a potential active factor for functional food development to prevent osteoporosis.

Ultra-massive multiple-input multiple-output (UM-MIMO) is the enabler of Terahertz (THz) communications in next-generation wireless networks. In THz UM-MIMO systems, a new paradigm of cross-field communications spanning from near-field to far-field is emerging, since the near-field range expands with higher frequencies and larger array apertures. Precise beam alignment in cross-field is critical but challenging. Specifically, unlike far-field beams that rely only on the angle domain, the incorporation of dual-domain (angle and distance) training significantly increases overhead. A natural question arises of whether far-field beam training can be deployed for cross-field beam alignment. In this paper, this question is answered, by demonstrating that the far-field training enables sufficient signal-to-noise ratio (SNR) in both far- and near-field scenarios, while exciting all channel dimensions. Based on that, we propose a subarray-coordinated hierarchical (SCH) training with greatly reduced overhead. To further obtain high-precision beam designs, we propose a two-phase angle and distance beam estimator (TPBE). Extensive simulations demonstrate the effectiveness of the proposed methods. Compared to near-field exhaustive search, the SCH possesses 0.2% training overhead. The TPBE achieves 0.16 degrees and 0.01 meters estimation root-mean-squared errors for angle and distance. Furthermore, with the estimated beam directions, a near-optimal SNR with 0.3 dB deviation is attained after beam alignment.

Due to the complexity of current variation of the multifrequency and multiload (MFML) wireless power transfer (WPT) system based on hysteresis current (HC) control, resulting in a wide range of switching frequency variation, high frequency, and high switching losses of the inverter. This article proposes a blocking delay switching frequency control strategy. Based on the design of blocking delay module, the time division blocking delay and full-time fixed frequency blocking delay control modes are investigated, which can reduce the inverter switching frequency fluctuation range and fix the inverter switching frequency respectively. First, the structure and working principle of the MFML WPT system based on HC control containing passive compensation network are analyzed. Second, the system switching frequency characteristics are analyzed. Third, for different system switching frequency limitation requirements, time division blocking delay control mode and full-time fixed-frequency blocking delay control mode are proposed. Finally, an experimental platform is built to verify the proposed control strategy, the experimental results show that the system switching frequency of different parameters is reduced to within 200 kHz and 180 kHz in the time division blocking delay control, and the system switching frequency is fixed to 150 kHz and 250 kHz in the full-time fixed-frequency blocking delay control.

As the most mature printing technology of additive manufacturing (AM), SLA has been favoured by the market due to its high-precision process and excellent adaptability to high-volume products manufacturing. However, it is still a challenge to design the stable auxiliary support structure of SLA production with less material but successful printing. Although most of the research and current commercial software have designed a tree-like support topology based on geometric algorithms, there is a lack of global evolutionary ideas for support structure optimization. Herein, a new kind of SLA hierarchical clustering evolutionary tree support (HCETS) is proposed through an efficient and economical solution. This HCETS combines the principles of hierarchical clustering and parallel configuration to adapt two different types of support points. It uses an improved multi-objective decomposition algorithm specifically for tree support, and uses KD-tree acceleration method in the process of special inclined support structure to reduce the algorithm load. In addition, HCETS adopts intra-group selection strategy (IGSS) and environment selection strategy (ESS) to alleviate the selection pressure and diversity loss caused by population expansion. These strategies could greatly improve the convergence speed in large-scale populations through fast non-dominated sorting algorithms. The overall results show that our presented HCETS algorithm is superior to the known algorithms in terms of support creating speed and material waste for stable and low-volume tree-like support structures, which is of great significance for the further improvement of the support performance of SLA additive manufacturing.

In the field of medical imaging, the fusion of data from diverse modalities plays a pivotal role in advancing our understanding of pathological conditions. Sparse representation (SR), a robust signal modeling technique, has demonstrated noteworthy success in multi-dimensional (MD) medical image fusion. However, a fundamental limitation appearing in existing SR models is their lack of directionality, restricting their efficacy in extracting anatomical details from different imaging modalities. To tackle this issue, we propose a novel directional SR model, termed complex sparse representation (ComSR), specifically designed for medical image fusion. ComSR independently represents MD signals over directional dictionaries along specific directions, allowing precise analysis of intricate details of MD signals. Besides, current studies in medical image fusion mostly concentrate on addressing either 2D or 3D fusion problems. This work bridges this gap by proposing a MD medical image fusion method based on ComSR, presenting a unified framework for both 2D and 3D fusion tasks. Experimental results across six multi-modal medical image fusion tasks, involving 93 pairs of 2D source images and 20 pairs of 3D source images, substantiate the superiority of our proposed method over 11 state-of-the-art 2D fusion methods and 4 representative 3D fusion methods, in terms of both visual quality and objective evaluation.

Aiming at the problems that existing multifrequency and multiload (MFML) magnetic coupling resonant wireless power transfer (MCR-WPT) systems either the output power frequencies are limited or a large amount of reactive power in them leads to decreased power factor (PF). This article proposes an MFML MCR-WPT system based on hysteresis current control, which controls multifrequency current superposition output and has equivalent current source characteristics. The system outputs multifrequency current by tracking the command current, which consists of subcommand currents designed according to load frequency and power requirements and raises PF without affecting power transfer quality by adding power compensating capacitor (CP) with lower cost and design complexity than existing technologies. First, the system structure and working principle are introduced. Then, the criterion of equivalent current source is deduced, based on which the system model is established, and its characteristics are analyzed. After that, the system parameter design methods are given. Finally, an experimental platform is built to verify the proposed theory. The simulation and experimental results show that the proposed system can output multifrequency power, switch the frequency freely, and raise PF by adding Cp without affecting power transfer.

Landfills are the final stage of urban wastes containing perfluoroalkyl and polyfluoroalkyl substances (PFASs). PFASs in the landfill leachate may contaminate the surrounding groundwater. As major environmental pollutants, emerging PFASs have raised global concern. Besides the widely reported legacy PFASs, the distribution and potential toxic effects of numerous emerging PFASs remain unclear, and unknown PFASs still need discovery and characterization. This study proposed a comprehensive method for PFAS screening in leachate samples using suspect and nontarget analysis. A total of 48 PFASs from 10 classes were identified; nine novel PFASs including eight chloroperfluoropolyether carboxylates (Cl-PFPECAs) and bistriflimide (HNTf2) were reported for the first time in the leachate, where Cl-PFPECA-3,1 and Cl-PFPECA-2,2 were first reported in environmental media. Optimized molecular docking models were established for prioritizing the PFASs with potential activity against peroxisome proliferator-activated receptor α and estrogen receptor α. Our results indicated that several emerging PFASs of N-methyl perfluoroalkyl sulfonamido acetic acids (N-MeFASAAs), n:3 fluorotelomer carboxylic acid (n:3 FTCA), and n:2 fluorotelomer sulfonate (n:2 FTSA) have potential health risks that cannot be ignored.

Most of the existing research for cross-domain fault diagnosis of rotating machineries focuses on the single-source–single-target domain adaptation. The single-source–single-target domain adaptation refers to applying the model and knowledge learned from the source domain from a certain working condition to the target domain from another working condition, which is different from the source domain. In addition, only one working condition is considered in the source domain and target domain of a fault diagnosis task. Compared with the fault diagnosis task of single-source–single-target domain adaptation, the multisource–multitarget domain adaptation is more complicated and insufficiently researched. The differences between the source and target domains have to be considered, but they are seldom modeled in the existing methods. To solve the multisource–multitarget fault diagnosis problem, this article proposes a two-stage training multibranch network (TTMN). In the source-only learning stage of the TTMN, a 1-D convolutional neural network (1D-CNN) is trained by all the source domain data, and the similarity of each one-source–one-target pair is measured. In the similarity-weighted domain adaptation stage of the TTMN, each target domain is assigned to one branch transferred from the well-trained 1D-CNN in the source-only learning stage and fine-tuned by the cosine similarity-weighted loss. Thus, the fault diagnosis with multisource–multitarget domain adaptation is transformed into a single-source–single-target domain diagnosis. The proposed TTMN is validated on the Donghua University (DHU) and the Case Western Reserve University (CWRU) bearing datasets. The results show the effectiveness and outperformance of the proposed method compared with some state-of-the-art methods.

The catalytic oxidation of ethylbenzene (EB) into acetophenone (AP) is a vibrant area, with a growing number of researchers paying attention to this thematic investigation. Herein, we demonstrate that spinel-type (Co,Mn)(Co,Mn)2O4can function as an efficient catalyst for the solvent-free oxidation of EB with molecular oxygen. The incorporation of Mn into the Co3O4network can break the local structural symmetry of Co–O–Co linkages due to the bond competition, inducing the formation of an asymmetrical Co–O–Mn configuration with an electron local exchange interaction. The Co–O–Mn sites can facilitate the perturbation of nonpolar O2and thus contribute to the generation of abundant •O2–species for initiating the oxidation of EB. We envision that this study not only provides a promising catalyst for EB oxidation but also affords a new insight into the design of advanced spinel oxides for selective oxidation reactions.

With the growing data on microRNA (miRNA) expression in tissues and circulation, there is increasing evidence for the potential of microRNAs to serve as biomarkers in cancer diagnosis and prognosis, as well as novel therapeutic targets. The expression level of miRNA-301a (miR-301a) is altered in a wide range of human tumor types, and numerous studies have revealed the roles of miR-301a in tumorigenesis and tumor progression. Herein, we comprehensively summarize, compare, and contrast the research advancements on the role of miR-301a in different cancers. Differential expression patterns of miR-301a in tissues and biofluids are implicated in cancer diagnosis, treatment response, and prognosis. MiR-301a modulates the expression of multiple genes, other noncoding RNAs, and signaling cascade via direct or indirect regulation in human cancer proliferation, migration, invasion, angiogenesis, and radio- or chemotherapy resistance. Cancer cell-associated miR-301a affects the tumor microenvironment through the alteration of immune function and cancer metabolism. These findings highlight the functional roles, clinical implications, and therapeutic relevance of miR-301a in various human cancers.

It is practically challenging for the output power in the magnetic field energy harvesting systems around high-voltage transmission lines. Consequently, a system of isolated orthogonal magnetic energy harvesting is proposed, which is consisted of two mutually orthogonal magnetic field energy harvesting modules and subsequent rectification circuits. According to the magnetic field distribution around the power transmission lines based on the catenary model at an existing 500 kV double circuit transmission tower, an H-type magnetic field energy harvesting module is designed based on the isolated magnetic field energy harvesting principle. Through two H-type magnetic energy harvesters, the energy is harvested and converted into voltage. The corresponding voltage is connected in series to power the load after being rectified respectively by the Cockcroft–Walton voltage multiplier circuit. Compared with the existing harvesting modules, the output power of the H-type magnetic field energy harvesting module is significantly improved in the condition of the same external magnetic flux density. The system proposed is modeled and analyzed, then a prototype was designed. The experimental results show that the DC output power of the system can reach up to 13 mW in an environment with an external magnetic flux density of 18 μT. And the correctness and feasibility of the system of isolated orthogonal magnetic field energy harvesting are verified.

Selectively and efficiently capturing trace titanium dioxide nanoparticles (TiO2NPs) in environmental waters is a prerequisite for their determination to understand their occurrence, behavior and effects in the environment. Herein, we report the determination of TiO2NPs by using magnetic microextraction with phosphorylated Fe3O4as a selective adsorbent, followed by inductively coupled plasma mass spectrometry (ICP-MS) detection. The phosphorylated Fe3O4was prepared by modifying phosphoric acid groups on the surface of synthesized Fe3O4, added into the water samples to selectively adsorb and agglomerate with TiO2NPs, and collected using a permanent magnet. Under the optimized extraction parameters, the factors including sizes and concentrations of TiO2NPs, volumes of water samples, and dissolved organic matter (0–30 mg C L−1) showed little effects on the extraction process. The feasibility of the method was further validated by the analysis of natural water samples, with spiked recoveries of 81.9–99.1% and an extremely low detection limit of 0.4 ng L−1. This method is capable of accurately detecting and quantifying the concentration of trace TiO2NPs in aquatic systems, which contributes to monitoring the fate of TiO2NPs in the environment and assessing their hazards.

The Terahertz (THz) band with abundant multiten-GHz bandwidth is capable of supporting terabit-per-second wireless communications, which is a pillar technology for 6G and beyond systems. With sub-millimeter-long antennas, ultra-massive (UM)-MIMO and reflective intelligent surface (RIS) systems with thousands of array elements can be exploited to effectively combat the distance limitation and blockage problems of a promising THz ultra-large antenna array (ULAA) system. As a combined effect of wavelength and array aperture, the resulting coverage of THz systems ranges from near-field to far-field, leading to a new paradigm of cross-field communications. Although channel models, communications theories, and networking strategies have been studied for far-field and near-field separately, the unified design of cross-field communications that achieve high spectral efficiency and low complexity is still missing. In this article, the challenges and features of THz ULAA cross-field communications are investigated. Furthermore, cross-field solutions in three perspectives are presented, including a hybrid spherical- and planar-wave channel model, cross-field channel estimation, and widely-spaced multi-subarray hybrid beamforming, where a subarray as a basic unit in THz ULAA systems is exploited. The approximation error of channel modeling accuracy, spectral efficiency, and estimation error of these designs are numerically evaluated. Finally, as a roadmap of THz ULAA cross-field communications, multiple open problems and potential research directions are elaborated.

Integrated ultra-massive multiple-input multiple-output (UM-MIMO) and intelligent reflecting surface (IRS) systems are promising for 6G and beyond Terahertz (0.1-10 THz) communications, to effectively bypass the barriers of limited coverage and line-of-sight blockage. However, excessive dimensions of UM-MIMO and IRS enlarge the near-field region, while strong THz channel sparsity in the far-field is detrimental to spatial multiplexing. Moreover, channel estimation (CE) requires recovering the large-scale channel from severely compressed observations due to limited RF-chains. To tackle these challenges, a hybrid spherical- and planar-wave channel model (HSPM) is introduced for the cascaded channel of the integrated system. The spatial multiplexing gains under near-field and far-field regions are analyzed, which are found to be limited by the segmented channel with a lower rank. Furthermore, a compressive sensing-based CE framework is developed, including a sparse channel representation method, a separate-side estimation (SSE) and a dictionary-shrinkage estimation (DSE) algorithms. Numerical results verify the effectiveness of the HSPM, the capacity of which is only $5\times 10^{-4}$ bits/s/Hz deviated from that obtained by the ground-truth spherical-wave-model, with 256 elements. While the SSE achieves improved accuracy for CE than benchmark algorithms, the DSE is more attractive in noisy environments, with 1 dB lower normalized-mean-square error than SSE.

Phase-locked loop (PLL) is essential for the grid-connected inverter to ensure grid synchronization. Since the PLL introduces a negative-resistive admittance to be in parallel with the original inverter output admittance, it poses an instability risk for the grid-connected inverter under weak grid. The system stability can be evaluated by the return ratio matrix, which is the ratio of the inverter output admittance to the grid admittance. Due to the nonsingular and nondiagonal properties of this matrix, its eigenvalues are irrational functions, leading to inconvenience of stability analysis and PLL parameters design. To address this issue, this article reconstructs the return ratio matrix to make it singular. Thus, the eigenvalues become simpler, including a zero eigenvalue and a nonzero eigenvalue. Based on the nonzero eigenvalue, the relation between the gain margin and the PLL parameters is established, and an optimizing design method of the PLL parameters is proposed to achieve system stability and satisfactory PLL dynamic response. Finally, experimental results from a 10-kVA prototype of three-phase grid-connected inverter are provided to verify the validity of the proposed design method.

Approximate computing is an emerging paradigm for error-tolerant applications. By introducing a reasonable amount of inaccuracy, both the area and delay of a circuit can be reduced significantly. To produce approximate circuits automatically, many approximate logic synthesis (ALS) algorithms are proposed. However, they mainly focus on area reduction and are not optimal in reducing the circuit delay. In this article, we propose HEDALS, a Highly Efficient Delay-driven ALS framework, which supports various types of local approximate changes (LACs), circuit representations, and average error metrics. To reduce delay, HEDALS builds a critical error graph (CEG) consisting of nodes on the critical paths and error information, and finds an optimized set of LACs in the CEG by either a maximum flow-based method or a priority cut-based method. The resulting set of LACs is applied to shorten all critical paths simultaneously so that the circuit delay is reduced. Besides, the simultaneous application of multiple LACs also makes HEDALS extremely fast. Compared to a state-of-the-art method, on average, HEDALS can reduce the circuit delay by 32.3%, while being $167\times $ faster. The code of HEDALS is made open-source.

Millimeter-wave (mmWave) and Terahertz (THz)-band communications exploit the abundant bandwidth to fulfill the increasing data rate demands of 6G wireless communications. To compensate for the high propagation loss with reduced hardware costs, ultra-massive multiple-input multiple-output (UM-MIMO) with a hybrid beamforming structure is a promising technology in the mmWave and THz bands. However, channel estimation (CE) is challenging for hybrid UM-MIMO systems, which requires recovering the high-dimensional channels from severely few channel observations. In this paper, a Pruned Approximate Message Passing (AMP) Integrated Deep Convolutional-neural-network (DCNN) CE (PRINCE) method is firstly proposed, which enhances the estimation accuracy of the AMP method by appending a DCNN network. Moreover, by truncating the insignificant feature maps in the convolutional layers of the DCNN network, a pruning method including training with regularization, pruning and refining procedures is developed to reduce the network scale. Simulation results show that the PRINCE achieves a good trade-off between the CE accuracy and significantly low complexity, with normalized-mean-square-error (NMSE) of −10 dB at signal-to-noise-ratio (SNR) as 10 dB after eliminating 80% feature maps.

Graphical abstract

Metasurface provides a novel way to modulate light energy at specific wavelengths, namely resonances, where there is a sharp drop in the transmission spectrum. Based on the relationship between the resonant position and the environmental condition, various refractive index detection methods have been developed. However, the resonance spectrum is strongly affected by the environmental and instrumental fluctuations, and current researches usually focus on the improvement of a single sensing performance metric, such as the Q factor, sensitivity, detection range, etc. In this work, we proposed an all-dielectric metasurface for environmental refractive index sensing based on quasi-BIC with an enhanced stability, simultaneously taken into account an enlarged detection range, a high Q factor and a relatively high sensitivity. With this designed metasurface, dual-resonance sensing is realized because the interval between the two resonance peaks in the transmission spectrum decreases near linearly with the environmental refractive index. We experimentally demonstrated that compared to traditional single-resonance sensing, the errors caused by environmental and instrumental fluctuations can be minimized, and the stability can be improved. This metasurface has great potential for applications such as refractive index sensing, concentration detection, biomacromolecule identification, and cancerous cell screening.

The bridge pavement subjected to vehicle loads is the most vulnerable component in the highway bridge and overloaded vehicles can result in serious damage to the bridge pavement more easily. Therefore, it is necessary to consider the pavement in vehicle-bridge interaction (VBI) system. However, the bridge pavement is not considered in the traditional VBI system or the viscoelasticity of bridge pavement is ignored. In this study, a new vehicle-pavement-bridge interaction (VPBI) system is established. The viscoelastic asphalt pavement is simulated with the continuously and uniformly distributed spring-damper. The equivalent stiffness coefficient and equivalent damping coefficient of the pavement are determined through dynamic mechanical analysis (DMA) test and finite element method. The equations of motion for VPBI system can be derived using Lagrange equation and the modal superposition method. Then the crucial parameters such as displacement at mid-span of bridge, acceleration of vehicle body, tire contact force, and pavement deformation are obtained. The effects of vehicle velocity, bridge span, tire contact area, pavement stiffness coefficient, and pavement damping coefficient on VPBI responses are analyzed. The results show that (1) The pavement is compressive during vehicle passing bridge and its dynamic deformation fluctuates around initially static deformation of pavement. (2) Increasing the stiffness of pavement can rapidly reduce the deformation of pavement, while the pavement damping has the opposite effect. (3) The increase of bridge span can conduct exponentially growth of dynamic responses of VPBI system. The pavement deformation will increase by 7.9% if the bridge span is lengthened by 5 m. This study provides a reliability response analysis method for VPBI system.

Reconfigurable intelligent surfaces (RISs) have emerged as a prospective technology for next-generation wireless networks due to their potential in coverage and capacity enhancement. Previous works on achievable rate analysis of RIS-assisted communication systems have mainly focused on the rich-scattering environment where Rayleigh and Rician channel models can be applied. This work studies the ergodic achievable rate of RIS-assisted multiple-input multiple-output communication systems in the millimeter-wave band with limited scattering under the Saleh-Valenzuela channel model. Firstly, we derive an upper bound of the ergodic achievable rate by means of majorization theory and Jensen’s inequality. The upper bound shows that the ergodic achievable rate increases logarithmically with the number of antennas at the base station (BS) and user, the number of the reflection units at the RIS, and the eigenvalues of the steering matrices associated with the BS, user and RIS. Then, we aim to maximize the ergodic achievable rate by jointly optimizing the transmit covariance matrix at the BS and the reflection coefficients at the RIS. Specifically, the transmit covariance matrix is optimized by the water-filling algorithm and the reflection coefficients are optimized using the Riemannian conjugate gradient algorithm. Simulation results validate the effectiveness of the proposed optimization algorithms.

Phase unwrapping is the core of phase shift profilometry. Conventional phase unwrapping usually requires the projection of additional patterns or other constrains to unwrap the high-frequency wrapped phase into absolute phase, which increases time consumption and complicates measurement procedures. To solve this problem, we propose a self-coding phase shift (SCPS) method for retrieving the absolute phase. Two sets of codes are encoded into the projection patterns, namely, the De-Bruijn sequence code and the phase gradient code. The fringe order can then be determined by combining these two different encodings. The proposed method does not require any prior information and only demands four phase shift fringes. Experimental results demonstrate that the SCPS method is highly robust and accurate in measuring both complex and separated objects.

The increasing interdependency between power system and natural gas systems, making the energy transaction and operation conditions of power system more complicated, threatens the security and reliability of power system. Transmission interconnections play a crucial role in the propagation of major power system disturbances. Understanding the impact of natural gas system on the transmission interconnections is important for enhancing monitoring and taking appropriate actions. This paper proposes a transmission interconnections identification method considering the impact of natural gas system. The criticality index is constructed to rank the critical transmission lines consisting of the transmission interconnections by simultaneously considering generator-load energy transaction and line-line power flow transferring, as well as topological characteristics of the power system. Furthermore, the self-organizing feature map neural network model is used to identify transmission lines with similar power flow components of the selected critical transmission line to form a transmission interconnection. Specially, a co-optimization model of interdependent natural gas-electricity system is proposed to derive energy transaction between generators and loads, and power flow transferring among transmission lines. Numerical results on the 24-bus electricity/20-node natural gas interdependent system illustrate effectiveness of the proposed method against different operation conditions.

The solubility data of nimesulide (form I) in different mono- and mixed solvents over the temperature range from 273.15 to 318.15 K are determined. In this work, it is further studied to improve the solubility data of nimesulide (form I) and analyze the intermolecular force. The solubility profiles in mono- and mixed solvents are correlated by some thermodynamic models. The results of statistical analysis show that all selected models could provide a good correlation of the dissolution profile for nimesulide in selected solvents at all investigated temperatures. At 318.15 K, the largest solubility in mole fraction is obtained in acetone (7.812 × 10–2), followed by 2-butanone (6.334 × 10–2), cyclohexanone (4.977 × 10–2), ethyl acetate (3.803 × 10–2), tetrahydrofuran (3.651 × 10–2), acetonitrile (2.975 × 10–2), ethanol (2.169 × 10–3), toluene (1.788 × 10–3), isobutanol (1.348 × 10–3), isopropanol (1.201 × 10–3), and propylene glycol (PG, 6.764 × 10–4). However, in mixtures of (ethyl acetate + isopropanol), the solubility data increase with the increase of the mass fraction of ethyl acetate at a given temperature. The effect of solute–solvent interactions on the solid–liquid phase equilibrium of the solute is discussed with the solvent properties. The calculation of preferential solvation parameters shows that solute molecules are preferentially solvated by isopropanol in isopropanol-rich regions and as the mass fraction of ethyl acetate increases, nimesulide is preferentially solvated by ethyl acetate. Studying the dissolution process of nimesulide is of great practical significance for its purification.