Resistance spot welding (RSW) faces critical monitoring challenges in industrial applications due to nonlinear coupling characteristics and production line disturbances. This study developed a Zigbee-enabled real-time monitoring system to address the precision limitations of conventional methods in tracking RSW parameters. Using DP780/DP590 dual-phase steel specimens with thickness variations, we implemented a dedicated data acquisition system capturing welding current, voltage, and barometric pressure dynamics. The experimental results demonstrated measurement accuracies within ±0.49% for current, ±0.25% for voltage, and 3.72% average relative error for barometric pressure with stable operational deviations (0.017–0.024 MPa).

Image reconstruction plays an important role in modern electronic information systems and equipment and has attracted widespread intense attention in academic and industrial fields.Restoring delicate image particulars using traditional handcrafted prior image reconstruction methods is often difficult because of the inferior prior characterization abilities of these methods.In this article, an efficient image reconstruction model, named DGMR, is proposed based on deep Gaussian mixture learning.In particular, a channel attention mechanism is designed for image spatial correlation exploitation.Both the external and internal information are explored using deep Gaussian mixture.A residual Swin transformer module is constructed to learn Gaussian mixture priors, including the images ' means and variances; in contrast, existing methods calculate only the mean and ignore the variance.Moreover, sparse regularized united learning is developed to improve invariant representation learning ability, and the model is customized by internal learning with spatial constraints and regularization.Extensive qualitative and quantitative experiments are performed, confirming that DGMR is superior to the current advanced comparison approaches.

Porous gel beads have aroused considerable interest in the field of wastewater treatment because of their enhanced adsorption capacity and ease of separation. Herein, a facile yet efficient method to synthesize porous chitosan gel beads (PBCSB) through the amide reaction and integrated with a porogenic process was proposed and further optimized. The well-preserved honeycomb internal structure was beneficial for aquatic Hg(II) to approach the surface and diffuse into the interior of the gel beads. The as-prepared adsorbent at a diameter of 3-5 mm possessed ultra-high adsorption ability (1139.82 mg g-1) toward Hg(II), declining the concentration from 500 mg L-1 to 0.017 mg L-1. Furthermore, the PBCSB could attain a high removal Hg(II) rate (>80%) even at pH 1 and maintained excellent Hg(II) adsorption performance (94.24%-99.99%) within five regeneration cycles. In the multi-component solution, the K-d of Hg(II) was 4-5 orders of magnitude higher in comparison with that of others. The superior adsorption performance was attributed to the complexation between Hg(II) and the func-tional groups involving sulfur, nitrogen, and oxygen based on the XPS analysis and density functional theory (DFT) calculation. The fixed-bed column filled with PBCSB exhibited a stable and satisfactory performance under different conditions. The findings confirmed the inherent superior performance, renewability, and application potential for selective capture Hg(II) from real industrial wastewater.

Nanoscale zero-valent iron (nZVI) has been extensively adopted for reducing Cr(VI), but its effectiveness and applicability are limited by agglomeration and passivation layer caused by its strong magnetism and rapid oxidation. To address these problems, the polyethylene imide-grafted chitosan (PCS) was employed as the supporter composite for the in-situ growth of nZVI, and then they were covered with the Ni and FeS layers as the protective layers to synthesize the composite (FeS-nZVI/Ni@PCS). It was shown that the nZVI were uniformly dispersed and anchored on PCS, with the FeS coating on the surface, the composite possessed low aggregation and superior antioxidation ability. Adopting Ni accelerated the reaction rate and enhanced the removal capacity. Additionally, the surface-abundant functional groups involving amino and imino groups produced electrostatic attraction to Cr(VI), which was a minor contributor to the efficiency. Hence, the actual maximum capacity of 473.93 mg/g was achieved at 25 degrees C at 250 mg/L within 20 min. FeS-nZVI/Ni@PCS also owned exceptional selectivity and anti-interference when co-existing ion tests were performed. These excellent performances were primarily attributed to the redox reaction, electrostatic adsorption, and co-precipitation. Most importantly, the Cr(VI) content was rapidly reduced from 454.91 mg/L to 0.05 mg/L when the composite was applied to treat the actual electroplating wastewater, highlighting its practical promising potential. This study could provide valuable insights for the application extension and performance improvement of nZVI-based materials for the selective remediation of highly Cr(VI)-polluted wastewater.

The innovative application of Crowd Intelligent Devices (CIDS) in edge networks has garnered attention due to the rapid development of artificial intelligence and computer technology. This application offers users more reliable and low-latency computing services through computation offloading technology. However, the dynamic nature of network terminals and the limited coverage of edge servers pose challenges, such as data loss and service interruption. Furthermore, the high-speed mobility of intelligent terminals in the dynamic edge network environment further complicates the design of computation offloading and service migration strategies. To address these challenges, this paper explores the computation offloading model of cluster intelligence collaboration in a heterogeneous network environment. This model involves multiple intelligences collaborating to provide computation offloading services for terminals. To accommodate various roles, a switching strategy of split-cluster group collaboration is introduced, assigning the cluster head, the alternate cluster head, and the ordinary user are assigned to a group with different functions. Additionally, the paper formulates the optimal offloading strategy for group smart terminals as a Markov decision process, taking into account factors such as user mobility, service delay, service accuracy, and migration cost. To implement this strategy, the paper utilizes the deep reinforcement learning-based CCSMS algorithm. Simulation results demonstrate that the proposed edge network service migration strategy, rooted in groupwise cluster collaboration, effectively mitigates interruption delay and enhances service migration efficiency.

Aged microplastics are ubiquitous in the aquatic environment, which inevitably accumulate metals, and then alter their migration. Whereas, the synergistic behavior and effect of microplastics and Hg(II) were rarely reported. In this context, the adsorptive behavior of Hg(II) by pristine/aged microplastics involving polystyrene, polyethylene, polylactic acid, and tire microplastics were investigated via kinetic (pseudo-first and second-order dynamics, the internal diffusion model), Langmuir, and Freundlich isothermal models; the adsorption and desorption behavior was also explored under different conditions. Microplastics aged by ozone exhibited a rougher surface attached with abundant oxygen-containing groups to enhance hydrophilicity and negative surface charge, those promoted adsorption capacity of 4–20 times increment compared with the pristine microplastics. The process (except for aged tire microplastics) was dominated by a monolayer chemical reaction, which was significantly impacted by pH, salinity, fulvic acid, and co-existing ions. Furthermore, the adsorbed Hg(II) could be effectively eluted in 0.04% HCl, simulated gastric liquids, and seawater with a maximum desorption amount of 23.26 mg/g. An artificial neural network model was used to predict the performance of microplastics in complex media and accurately capture the main influencing factors and their contributions. This finding revealed that aged microplastics had the affinity to trap Hg(II) from freshwater, whereafter it released the Hg(II) once transported into the acidic medium, the organism's gastrointestinal system, or the estuary area. These indicated that aged microplastics could be the sink or the source of Hg(II) depending on the surrounding environment, meaning that aged microplastics could be the vital carrier to Hg(II).

High-resolution image reconstruction from partial observations plays an important role in modern electronic information systems and has attracted widespread intense attention in both the academic and industrial fields. It is often difficult for the traditional handcrafted prior image reconstruction methods to recover delicate image details because of the inferior prior characterization abilities of these methods; this is especially true in the presence of complex electromagnetic perturbations in real-world environment scenes. Therefore, in this paper, an efficient image high-resolution reconstruction and perturbation defense model, named DGMR, is proposed based on deep Gaussian mixture learning. In particular, a simultaneous maximum a posterior (MAP) reconstruction-defense framework is designed based on the learned deep Gaussian mixture prior. Moreover, a channel attention mechanism is designed for image spatial correlation exploitation. Both the external and internal information are explored using a deep Gaussian mixture. A deep residual Swin transformer module is constructed to further characterize the image and learn Gaussian mixture priors, including both the image means and variances; in contrast, existing methods calculate only the image means but ignore the variances. Furthermore, sparse regularized united learning is developed to improve invariant representation learning ability, and the model is customized by internal learning with spatial constraints and regularization. Extensive qualitative and quantitative experiments are performed, confirming that DGMR is superior to the existing state-of-the-art systems.

Ultra high-speed target recognition in complex electromagnetic environments is a critical and fundamental machine perception issue. It is difficult to ensure privacy protection and intelligent confrontation with centralized training in many practical situations. In this paper, an efficient ultra high-speed target recognition approach, named InVision, for robot systems is proposed using deep trustworthy federated learning (DTFL). InVision is accurate, safe, fast, and robust. Particularly, geometric component transformer (GCT) is presented to significantly promote neural element's complex representation description ability. And an ambiguity-aware cooperative learning (AACL) scheme is developed to relieve the noisy label problem. Moreover, decentralized federated training (DFT) is designed to mitigate the intractable privacy protection problem, to efficiently search for similarities and reduce the representation redundancy. Furthermore, to promote the running speed of the system in real-world environments, a lightweight deep architecture, called Mobile-XB, is developed. Extensive quantitative and qualitative experiments are carried out, and the results demonstrate that InVision greatly outperforms the outstanding comparison methods, establishing efficient connections and extraction, and providing security guarantees.

To improve the heterogeneous clutter suppression performance of airborne radar, a fast sparse reduced-dimension method based on multi-domain joint processing is proposed in this paper. Firstly, spatial diversity is adopted among transmitting sub-apertures to realize reduced dimension of spatial domain. Secondly, multiple equivalent samples are obtained by matching reconstruction. Thirdly, the dimension of these samples is further reduced based on energy transform domain. Finally, the clutter is suppressed by joint sparse recovery, and simulations demonstrate the effectiveness of proposal.

Weak feature enhancement is an important and intractable issue in machine vision fields. In this paper, an effective complex-scene inverse synthetic aperture radar (ISAR) image feature enhancement method is proposed based on spatial neighborhood sparse constraint hybrid clustering oversegmentation (SNSC-HCOS). A hybrid superpixel concept is first developed considering the consistency of pixel labels and the discrimination of perturbations. The scattering characteristic similarity, spatial proximity, neighborhood information, sparsity bias, and overall image features are simultaneously explored. The SNSC-HCOS algorithm is designed based on the hybrid superpixel concept, data reconstruction, and fuzzy c-means (FCM) clustering. Both sparsity constraints and local spatial information constraints are embedded into the oversegmentation framework, improving the perturbation defense capability and clustering robustness. To correct misclassified pixel membership and improve system efficiency, median membership filtering and area merging strategies are further applied. Extensive qualitative and quantitative experiments are conducted on a real-scene dataset, demonstrating that SNSC-HCOS greatly outperforms the outstanding comparison systems. The proposed system has good segmentation performance, can generate more pure superpixels with regular shapes and uniform sizes, and has perturbation defense and feature enhancement capabilities.

The two tricky problems: hard to dissolve and heat seal always hinder the step of cellulose materials for replacing plastic. Here, a binder-free method is proposed to realize the bonding of cellulose film via a green confined solvent.

Federated relationship recommendation is a growing technology trend in future intelligent system. Over the recent years, federated recommendation has recorded an exponential growth, leading to millions of intelligent equipments, and still increasing. However, privacy-preserving is an intractable problem in modern digital systems. In this paper, a comprehensive review of privacy-preserving federated recommendation is presented. Particularly, three kinds of federated recommendation methods are reviewed based on matrix factorization, neural network and other algorithms. Moreover, five authoritative datasets and three general evaluation metrics are mainly described. Simultaneously, a serious of outstanding federated recommender systems with related research discussions are provided. Finally, some potential future works will be tried to improve the application performance.

The polyvinyl alcohol (PVA)-based electronic skin with skin-like features and functions has shown great promise in wearable electronics, but traditional physical methods do not provide multiple interfacial bonds to result in poor mechanical properties, weak conductivity, low sensitivity, and healing ability for further hindering their practical application. In this work, novel alternating spraying technology driven by the Marangoni effect was used to address this challenge and took advantage of the interfacial bond and compatibility between nanocellulose-polyaniline (CNC-PANI) and PVA/CNC films to prepare self-assembled PVA/nanocellulose polyaniline (PVA/CP) electronic skin. Among them, the optimal PVA/CP electronic skin exhibited excellent mechanical performances (stress up to 78.2 MPa), large conductivity (648 mS/m), and high sensitivity (gauge factor = 22.7) with good self-healing abilities (healing efficiency 73.5%). Furthermore, it was designed and integrated into a multi-point sensing system for non-overlapping Morse code communication and real-time monitoring of human health during fitness exercise. Therefore, such high-performance PVA/CP electronic skin is expected to play a demonstration role in a new generation of sustainable wearable electronics.

Electronics Resurgence Initiative (ERI) 2.0 has been issued by Defense Advanced Research Projects Agency in the Fiscal Year 2024 budget estimates. ERI 2.0 consists of 32 programs with total cost of $710 million, indicating the development trend forward next generation microelectronics. First, the background, key funding parts (materials & integration, architectures and designs) and typical research findings of ERI are analyzed to present its overview and importance. Second, the overall program layout characteristics of ERI 2.0 are analyzed from different layout changes like being started or terminated. Third, all the programs are analyzed from five research areas including next generation microelectronics, advanced electronic components, microsystem integration technology, advanced computing architecture and facility, and security of electronic equipment and network. For every area, the budget funding is counted and contrasted with the past to show the research change. To further analyze program layout, the mission accomplishment, forward plan and application of all the programs from different technical areas are studied, to overview current advances and look ahead to the next generation microelectronics. The research of ERI 2.0 in this article will be great importance for following research, evaluation, layout and industrial development of future microelectronics.

Although silica (SiO2) cryogel possesses high surface area and physicochemical stability, the poor performance, high production cost, and difficult separation limit its capability to efficiently remove Cr(VI) from water. To address this issue, commercial water glass was chosen as the silica source, and Fe3O4 was used as the core to synthesize the magnetic SiO2 cryogel, which was then anchored with 3-(2-Aminoethylamino)propyltrimethoxysilane (APTMS) to prepare a novel cryogel named Fe3O4 @SiO2-APTMS. Successful amino functionalization was evidenced by the characterization of techniques such as SEM, TEM, FTIR, XRD, VSM, and BET. The adsorption performance of Fe3O4 @SiO2-APTMS was evaluated systematically under different conditions concerning the initial solution pH, adsorbent dosage, temperature, and coexisting anions and cations. Adsorption kinetics, isotherms, and thermodynamics revealed that the pseudo-second-order kinetic, Langmuir models preferably fitted the adsorption data, suggesting a single-layer, chemical sorption, and exothermic adsorption process occurred. The Fe3O4 @SiO2-APTMS exhibited a fast adsorption rate to Cr(VI) ions that it reached the equilibrium within 10 min when the initial Cr(VI) concentration was less than 200 mg/L, and the theoretical maximum capacity was estimated as high as 240.96 mg/g at 298 K and pH 2.0, which are superior than the majority magnetic adsorbents. Moreover, the amino-modified magnetic cryogel could be rapidly separated by the external magnetic field and maintain effective Cr(VI) removal efficiency even after 4 cycles. It was further expounded that the outstanding Cr(VI) removal performance could be attributable to electrostatic attraction coupled with reduction and chelation. The study could provide a promising alternative material for the Cr(VI)-polluted water purification and highlight the potential application of related magnetic cryogel material. The findings also implied the amino-decorated material could participate in the redox of Cr(VI) at nearly one-half of the total amount for the detoxification.

With the fast growth of wearable intelligent devices, flexible sensors with a broad strain sensing range and high sensitivity are in urgent demand. Furthermore, the sensitivity of flexible wearable electronic devices to various signal capture depends on multiple interfacial bond interactions and a microstructure. However, a flexible sensor without multiple interfacial bonds has poor mechanical properties, low sensitivity/conductivity, and single sensing function to limit commercial sensor application. In this work, the novel dip-coating technique was used to design a flexible sensor based on polyvinyl alcohol with multiple interfacial bond interactions and a microcrack structure. Interestingly, the sensor exhibits high sensitivity with gauge factor > 100, a high conductivity of 356 mS m-1, impressive thermal sensitivity (0.01071 degrees C-1), a large strain of 344.5%, and excellent wear-resistance and durability. Possible sensing mechanisms under multiple stimuli have been presented. Moreover, flexible sensors with multiple signal monitoring can monitor real-time full-range human body motion and physical signal collection, providing a promising strategy to develop this sensor with outstanding performance in flexible sensor and sporting applications.

Multi-antenna signal detection is one of the most critical and challenging issues for ambient backscatter communication (AmBC) systems. This paper proposes an efficient multi-antenna AmBC signal detection method, called Bayesian-MLE (maximum likelihood estimation). It shows good performance on high transmission rate, detection accuracy and low energy consumption. Particularly, a practical multi-antenna AmBC system model is developed to offer transmit-receive diversity, and then an efficient multi-antenna AmBC signal detection method is presented using Bayesian optimization and MLE theory. Furthermore, to maximize the detection performance, an optimal detection threshold selection scheme is developed. Particularly, a non-central chi-square distribution conditional probability density function (PDF) is considered instead of the conventional Gaussian PDF. Extensive qualitative and quantitative experiments are performed, showing that the proposed Bayesian-MLE detector achieves the state-of-the-art signal detection performance.

To deal with the problem that performance degradation of airborne phased array system is caused by the serious shortage of independent and identical distributed (IID) training samples in the nonhomogeneous clutter environment, an improved direct data domain method based on sparse Bayesian learning is proposed, which can only use single snapshot data of cell under test (CUT) to suppress the clutter. In this paper, the iterative formulas of three hyper-parameters are first given. Then, the sparsity solution of CUT is obtained. Lastly, with the approximate prior information of target, the clutter covariance matrix (CCM) is effectively estimated to calculate the adaptive filter weight and realize the clutter suppression. Simulation results verify that the proposed approach has more superior heterogeneous clutter suppression performance while dramatically decreasing the computational burden.

Cement-based composites reinforced with steel bars are the primary material in the construction of infrastructure. Their durability in the service environment is thus a crucial topic. The application of bio-mineralisation technology shows great prospects in improving the properties of cement-based materials (CBMs). In this study, the underlying mechanism was explored by investigating the effects of various factors on the mineralisation gradient at the surface layer of CBMs. Four factors were studied to investigate the process of bacterial action: the absorption of carbon dioxide from the air, the transformation of carbon dioxide into HCO3−, the adsorption of Ca2+ from the pore solution of CBMs and the formation of calcium carbonate in pores, which results in a decrease in porosity. The results of calcium carbonate content and porosity indicated that bacteria could regulate the mineralisation gradient at the surface of CBMs. Numerical calculations and experimental measurements were also conducted for further verification.

For remote malicious code injection attacks, the analysis of injected code behavior has always been the difficulty of malicious code dynamic analysis. In this paper, a remote code injection behavior analysis method based on code refactoring was proposed. By analyzing the behavior of remote code injection attack, extracting the injection behavior pattern rules, analyzing the malicious code by using the dynamic binary analysis platform, identifying the remote injection behavior in the execution process, obtaining the remote injected malicious data, then refactoring and executing the injected code, and finally triggering the hidden behavior of injected code, this method improves the integrity of malicious code behavior analysis. A series of malicious code samples were also used for experimental analysis. And the results showed that this method can obtain more comprehensive behavior information for malicious code with remote injection, and effectively improve the integrity of malicious code analysis.