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3D printed and paper-based microfluidics are promising formats for applications that require portable miniaturized fluid handling such as point-of-care testing. These two formats deployed in isolation, however, have inherent limitations that hamper their capabilities and versatility. Here, we present the convergence of 3D printed and paper formats into hybrid devices that overcome many of these limitations, while capitalizing on their respective strengths. Hybrid channels were fabricated with no specialized equipment except a commercial 3D printer. Finger-operated reservoirs and valves capable of fully-reversible dispensation and actuation were designed for intuitive operation without equipment or training. Components were then integrated into a versatile multicomponent device capable of dynamic fluid pathing. These results are an early demonstration of how 3D printed and paper microfluidics can be hybridized into versatile lab-on-chip devices. [ABSTRACT FROM AUTHOR]

We reported a scalable and modular method to prepare a new type of sandwich-structured graphene-based nanohybrid paper and explore its practical application as high-performance electrode in flexible supercapacitor. The freestanding and flexible graphene paper was firstly fabricated by highly reproducible printing technique and bubbling delamination method, by which the area and thickness of the graphene paper can be freely adjusted in a wide range. The as-prepared graphene paper possesses a collection of unique properties of highly electrical conductivity (340 S cm−1), light weight (1 mg cm−2) and excellent mechanical properties. In order to improve its supercapacitive properties, we have prepared a unique sandwich-structured graphene/polyaniline/graphene paper by in situ electropolymerization of porous polyaniline nanomaterials on graphene paper, followed by wrapping an ultrathin graphene layer on its surface. This unique design strategy not only circumvents the low energy storage capacity resulting from the double-layer capacitor of graphene paper, but also enhances the rate performance and cycling stability of porous polyaniline. The as-obtained all-solid-state symmetric supercapacitor exhibits high energy density, high power density, excellent cycling stability and exceptional mechanical flexibility, demonstrative of its extensive potential applications for flexible energy-related devices and wearable electronics. [ABSTRACT FROM AUTHOR]

Optical chemosensors and ionochromic cellulosic papers based on oxazolidine chromophores were developed for selective photosensing of metal ions and information encryption as security tags, respectively. The oxazolidine molecules have been displayed highly intense fluorescent emission and coloration characteristics that are usable in sensing and anticounterfeiting applications. Obtained results indicated that oxazolidine molecules can be used for selective detection of pb2+ (0.01 M), and photosensing of Fe3+, Co2+ and Ag+ metal ion solutions by colorimetric and fluorometric mechanisms with higher intensity and sensitivity. Also, oxazolidine derivatives were coated on cellulosic papers via layer-by-layer method to prepare ionochromic papers. Prepared ionochromic papers were used for printing and handwriting of optical security tags by using of metal ion solutions as a new class of anticounterfeiting inks with dual-mode fluorometric and colorimetric securities. The ionochromic cellulosic papers can be used for photodetection of metal ions in a fast and facile manner that presence of metal ions is detectable by naked eyes. Also, key-lock anticounterfeiting technology based on ionochromic papers and metal ion solution as ink is the most significant strategy for encryption of information to optical tags with higher security. [ABSTRACT FROM AUTHOR]

Chirality at the nanoscale has emerged as a key area of interest in materials science and engineering, with significant implications for various fields such as spintronics, photonics, optoelectronics, quantum computing, and biomedicine. With their unique properties such as enantioselective interactions with light and spin-polarized electron transport, chiral nanomaterials are opening a new window of opportunities for the design of advanced functional devices. This editorial provides an overview of the current state of research in chirality in nanomaterials. We also showcase several papers from this collection that exemplify the breadth of current work, offering insights into the future directions of this rapidly evolving field. [ABSTRACT FROM AUTHOR]

Microfluidic paper-based analytical devices (µPADs) have provided a breakthrough in portable and low-cost point-of-care diagnostics. Despite their significant scope, the complexity of fabrication and reliance on expensive and sophisticated tools, have limited their outreach and possibility of commercialization. Herein, we report for the first time, a facile method to fabricate µPADs using a commonly available laser printer which drastically reduces the cost and complexity of fabrication. Toner ink is used to pattern the µPADs by printing, without modifying any factory configuration of the laser printer. Hydrophobic barriers are created by heating the patterned paper which melts the toner ink, facilitating its wicking into the cross-section of the substrate. Further, we demonstrate the utilization of the fabricated device by performing two assays. The proposed technique provides a versatile platform for rapid prototyping of µPADs with significant prospect in both developed and resource constrained region. [ABSTRACT FROM AUTHOR]

The recent Zika virus (ZIKV) outbreak has prompted the need for field-ready diagnostics that are rapid, easy-to-use, handheld, and disposable while providing extreme sensitivity and specificity. To meet this demand, we developed a wax-printed paper microfluidic chip utilizing reverse transcription loop-mediated isothermal amplification (RT-LAMP). The developed simple and sensitive ZIKV assay was demonstrated using undiluted tap water, human urine, and diluted (10%) human blood plasma. Paper type, pore size, and channel dimension of various paper microfluidic chips were investigated and optimized to ensure proper filtration of direct-use biological samples (tap water, urine, and plasma) during capillary action-driven flow. Once ZIKV RNA has flowed and reached to a detection area of the paper microfluidic chip, it was excised for the addition of an RT-LAMP mixture with a pH indicator, then placed on a hot plate at 68 °C. Visible color changes from successful amplification were observed in 15 minutes and quantified by smartphone imaging. The limit of detection was as low as 1 copy/μL. The developed platform can also be used for identifying other flaviviruses, such as Chikungunya virus (CHIKV) and Dengue virus (DENV), and potentially other quickly transmitted virus pathogens, towards field-based diagnostics. [ABSTRACT FROM AUTHOR]

This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life. [ABSTRACT FROM AUTHOR]

Collaboration is a key driver of science and innovation. Mainly motivated by the need to leverage different capacities and expertise to solve a scientific problem, collaboration is also an excellent source of information about the future behavior of scholars. In particular, it allows us to infer the likelihood that scientists choose future research directions via the intertwined mechanisms of selection and social influence. Here we thoroughly investigate the interplay between collaboration and topic switches. We find that the probability for a scholar to start working on a new topic increases with the number of previous collaborators, with a pattern showing that the effects of individual collaborators are not independent. The higher the productivity and the impact of authors, the more likely their coworkers will start working on new topics. The average number of coauthors per paper is also inversely related to the topic switch probability, suggesting a dilution of this effect as the number of collaborators increases. [ABSTRACT FROM AUTHOR]

With the advancement of society, ensuring the safety of personnel involved in municipal construction projects, particularly in the context of pandemic control measures, has become a matter of utmost importance. This paper introduces a security measure for municipal engineering, combining deep learning with object detection technology. It proposes a lightweight artificial intelligence (AI) detection method capable of simultaneously identifying individuals wearing masks and safety helmets. The method primarily incorporates the ShuffleNetv2 feature extraction mechanism within the framework of the YOLOv5s network to reduce computational overhead. Additionally, it employs the ECA attention mechanism and optimized loss functions to generate feature maps with more comprehensive information, thereby enhancing the precision of target detection. Experimental results indicate that this algorithm improves the mean average precision (mAP) value by 4.3%. Furthermore, it reduces parameter and computational loads by 54.8% and 53.8%, respectively, effectively striking a balance between lightweight operation and precision. This study serves as a valuable reference for research pertaining to lightweight target detection in the realm of municipal construction safety measures. [ABSTRACT FROM AUTHOR]

With the increasing number and types of global power loads and the development and popularization of smart grid technology, a large number of researches on load-level non-intrusive load monitoring technology have emerged. However, the unique power characteristics of the load make NILM face the difficult problem of low robustness of feature extraction and low accuracy of classification and identification in the recognition stage. This paper proposes a structured V-I mapping method to address the inherent limitations of traditional V-I trajectory mapping methods from a new perspective. In addition, for the verification of the V-I trajectory mapping method proposed in this paper, the complexity of load characteristics is comprehensively considered, and a lightweight convolutional neural network is designed based on AlexNet. The experimental results on the NILM dataset show that the proposed method significantly improves recognition accuracy compared to existing VI trajectory mapping methods. [ABSTRACT FROM AUTHOR]

The segmentation and extraction on color features can provide useful information for many different application domains. However, most of the existing image processing algorithms on feature extraction are gray image-based and consider only one-dimensional parameters. In order to carry out a fast and accurate color feature extraction, this paper proposes a color extraction algorithm by segmentation that is called a color extraction algorithm This algorithm is compared under different color distribution situations, and the extraction effect on color is also shown by the combination of the segmentation and feature extraction algorithms. Experimental results show that such segmentation algorithm has some advantages for color segmentation. In the fuzzy color image preprocessing, this paper gives the location method of region of interest. Moreover, compared with other existing extraction algorithms, the presented segmentation extraction algorithm in this paper not only has higher accuracy, shorter extraction time and stronger anti-interference ability, but also has better effect on more divergent color edge. Experimental evaluation of the proposed color extraction algorithm demonstrates dominance over existing algorithms for feature extraction. These researches in this paper provide a new way of thinking for color feature extraction by segmentation, which has an important theoretical references and practical significance. [ABSTRACT FROM AUTHOR]

Historical documents contain essential information about the past, including places, people, or events. Many of these valuable cultural artifacts cannot be further examined due to aging or external influences, as they are too fragile to be opened or turned over, so their rich contents remain hidden. Terahertz (THz) imaging is a nondestructive 3D imaging technique that can be used to reveal the hidden contents without damaging the documents. As noise or imaging artifacts are predominantly present in reconstructed images processed by standard THz reconstruction algorithms, this work intends to improve THz image quality with deep learning. To overcome the data scarcity problem in training a supervised deep learning model, an unsupervised deep learning network (CycleGAN) is first applied to generate paired noisy THz images from clean images (clean images are generated by a handwriting generator). With such synthetic noisy-to-clean paired images, a supervised deep learning model using Pix2pixGAN is trained, which is effective to enhance real noisy THz images. After Pix2pixGAN denoising, 99% characters written on one-side of the Xuan paper can be clearly recognized, while 61% characters written on one-side of the standard paper are sufficiently recognized. The average perceptual indices of Pix2pixGAN processed images are 16.83, which is very close to the average perceptual index 16.19 of clean handwriting images. Our work has important value for THz-imaging-based nondestructive historical document analysis. [ABSTRACT FROM AUTHOR]

Stabilizing reagents that can be deposited onto paper is an important issue for researchers who depend on paper-based analytical devices (PADs), because long-term stability of the devices is essential in point-of-care testing. Here, we found that poly(vinyl alcohol) (PVA) would stabilize hydrogen peroxide placed on a paper substrate following exposure to air. Horseradish peroxidase was employed as a sample in colorimetric measurements of PADs after hydrogen peroxide and 3,3′,5,5′-tetramethylbenzidine were deposited as substrates in an enzymatic reaction. The addition of PVA to hydrogen peroxide significantly suppressed its degradation. Concentrations of PVA that ranged from 0.5 to 2%, increased the duration of the stability of hydrogen peroxide, and the results for a PVA concentration of 1% approximated those of 2% PVA. Storage of the PADs at 4 °C in a refrigerator extended the stability of the hydrogen peroxide containing 2% PVA by as much as 30 days. The stability of hydrogen peroxide without PVA was degraded after one day under room temperature. [ABSTRACT FROM AUTHOR]

Metal oxides based graphene nanocomposites were used for ammonia vapour sensing. The self-assembly process was adopted to prepare freestanding flexible pure rGO, CeO2-rGO and SnO2-rGO composite papers. The structural studies confirmed the formation of rGO composite papers. The ammonia vapor sensing was demonstrated using an impedance analyzer at different humidity levels as well as concentration. The CeO2-rGO composite paper achieved a sensitivity of 51.70 ± 1.2%, which was higher than that of pure rGO and SnO2-rGO composite paper. Both the surfaces (top and bottom) of the papers are active in efficiently sensing ammonia, which makes the present work unique. The results reveal that metal oxide/rGO papers can be effectively utilized in real time sensor application. [ABSTRACT FROM AUTHOR]

Microfluidic paper-based analytical devices (microPADs) are emerging as cost-effective and portable platforms for point-of-care assays. A fundamental limitation of microPAD fabrication is the imprecise nature of most methods for patterning paper. The present work demonstrates that paper patterned via wax printing can be miniaturized by treating it with periodate to produce higher-resolution, high-fidelity microPADs. The optimal miniaturization parameters were determined by immersing microPADs in various concentrations of aqueous sodium periodate (NaIO4) for varying lengths of time. This treatment miniaturized microPADs by up to 80% in surface area, depending on the concentration of periodate and length of the reaction time. By immersing microPADs in 0.5-M NaIO4 for 48 hours, devices were miniaturized by 78% in surface area, and this treatment allowed for the fabrication of functional channels with widths as small as 301 µm and hydrophobic barriers with widths as small as 387 µm. The miniaturized devices were shown to be compatible with redox-based colorimetric assays and enzymatic reactions. This miniaturization technique provides a new option for fabricating sub-millimeter-sized features in paper-based fluidic devices without requiring specialized equipment and could enable new capabilities and applications for microPADs. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

With an increasing number of new scientific papers being released, it becomes harder for researchers to be aware of recent articles in their field of study. Accurately classifying papers is a first step in the direction of personalized catering and easy access to research of interest. The field of Density Functional Theory (DFT) in particular is a good example of a methodology used in very different studies, and interconnected disciplines, which has a very strong community publishing many research articles. We devise a new unsupervised method for classifying publications, based on topic modeling, and use a DFT-related selection of documents as a use case. We first create topics from word analysis and clustering of the abstracts from the publications, then attribute each publication/paper to a topic based on word similarity. We then make interesting observations by analyzing connections between the topics and publishers, journals, country or year of publication. The proposed approach is general, and can be applied to analyze publication and citation trends in other areas of study, beyond the field of Density Function Theory. [ABSTRACT FROM AUTHOR]

In recent years, highway accidents occur frequently, the main reason is that there is always foreign body invasion on the highway, which makes people unable to respond to emergencies in time. In order to reduce the occurrence of highway incidents, an object detection algorithm for highway intrusion was proposed in this paper. Firstly, a new feature extraction module was proposed to better preserve the main information. Secondly, a new feature fusion method was proposed to improve the accuracy of object detection. Finally, a lightweight method was proposed to reduce the computational complexity. We compare the algorithm in this paper with existing algorithms, the experimental results showed that: On the Visdrone dataset (small size targets), (a) the CS-YOLO was 3.6% more accurate than the YOLO v8. (b) The CS-YOLO was 1.2% more accurate than the YOLO v8 on the Tinypersons dataset (minimal size targets). (c) CS-YOLO was 1.4% more accurate than YOLO v8 on VOC2007 data set (normal size). [ABSTRACT FROM AUTHOR]

Optically transparent nanofiber paper containing silver nanowires showed high electrical conductivity and maintained the high transparency, and low weight of the original transparent nanofiber paper. We demonstrated some procedures of optically transparent and electrically conductive cellulose nanofiber paper for lightweight and portable electronic devices. The nanofiber paper enhanced high conductivity without any post treatments such as heating or mechanical pressing, when cellulose nanofiber dispersions were dropped on a silver nanowire thin layer. The transparent conductive nanofiber paper showed high electrical durability in repeated folding tests, due to dual advantages of the hydrophilic affinity between cellulose and silver nanowires, and the entanglement between cellulose nanofibers and silver nanowires. Their optical transparency and electrical conductivity were as high as those of ITO glass. Therefore, using this conductive transparent paper, organic solar cells were produced that achieved a power conversion of 3.2%, which was as high as that of ITO-based solar cells. [ABSTRACT FROM AUTHOR]

This paper describes a novel approach to fabricate paper-based electric circuits consisting of a paper matrix embedded with three-dimensional (3D) microchannels and liquid metal. Leveraging the high electric conductivity and good flowability of liquid metal, and metallophobic property of paper, it is possible to keep electric and mechanical functionality of the electric circuit even after a thousand cycles of deformation. Embedding liquid metal into paper matrix is a promising method to rapidly fabricate low-cost, disposable, and soft electric circuits for electronics. As a demonstration, we designed a programmable displacement transducer and applied it as variable resistors and pressure sensors. The unique metallophobic property, combined with softness, low cost and light weight, makes paper an attractive alternative to other materials in which liquid metal are currently embedded. [ABSTRACT FROM AUTHOR]

This article explores the use of online negative emotions to predict public risk-coping behaviors during urban relocation. Through a literature review, the paper proposes hypotheses that anticipate advanced prediction of public risk-coping behaviors based on online negative emotions. The study's empirical focus is on the relocation of the Beijing municipal government, using time series data for Granger causality analysis in EViews 10.0 software. Data on online negative emotions is sourced from Sina Weibo. After data cleaning, 1420 pieces of data related to the relocation policy of the Beijing Municipal Government within the period from June 9, 2015 to April 28, 2019 are retained. while risk-coping behaviors are measured through public information search behaviors and the incidence of violent crimes, the data coverage is also from June 9, 2015 to April 28, 2019. The results indicated that: (1) Online negative emotions regarding the relocation policy predict public risk-coping behaviors in advance. (2) Negative comments are more effective predictors than negative feelings; (3) Negative emotions about relocation policy formulation predict risk-coping behaviors better than those related to policy effectiveness and implementation; (4) Negative emotions from individuals better predict public risk-coping behaviors than those from institutions; (5) Negative emotions from key stakeholders better predict public risk-coping behaviors than those from non-key or marginal stakeholders. It is recommended that relevant departments establish a real-time monitoring system to track negative public opinions and emotions expressed online, adopt a stakeholder-centric approach to facilitate communication, and promote transparency and educational campaigns to address the challenges of urban relocation. In future studies, methods such as expanding the sample size and adding indicators will be used to address the limitations of potential bias in sample data. [ABSTRACT FROM AUTHOR]

The accurate detection of tunnel lining cracks and prompt identification of their primary causes are critical for maintaining tunnel availability. The advancement of deep learning, particularly in the domain of convolutional neural network (CNN) for image segmentation, has made tunnel lining crack detection more feasible. However, the CNN-based technique for tunnel lining crack detection commonly prioritizes increasing algorithmic complexity to enhance detection accuracy, posing a challenge in balancing the accuracy of detection and the efficiency of the algorithm. Motivated by the superior performance of Unet in image segmentation, this paper proposes a lightweight tunnel lining crack detection model named Mini-Unet, which refined the Unet architecture and utilized depthwise separable convolutions (DSConv) to replace some standard convolution layers. In the optimization of the proposed model parameters, applying a hybrid loss function that integrated dice loss and cross-entropy loss effectively tackled the imbalance between crack and background categories. Several models were set up to contrast with Mini-Unet and the experimental results were analyzed. Mini-Unet achieves a mean intersection over union (MIoU) of 60.76%, a mean precision of 84.18%, and a frame per second (FPS) of 5.635, respectively. Mini-Unet outperforms several mainstream models, enabling rapid detection while maintaining identified accuracy and facilitating the practical application of AI power for real-time tunnel lining crack detection. [ABSTRACT FROM AUTHOR]

The goal of the paper is to examine how convolutional neural network (CNN) is used in the immersive world of digital media art. In order to do this, this paper first explains digital media art in the context of smart cities and the use of immersive scenarios. Next, a brief analysis of the Unet network model and deep aggregation structure is provided. Then, a projector, camera, and computer-based immersive projector-camera display system is constructed. Based on the mathematical reflection model of the system, this paper discusses the method of using CNN to solve the photometric compensation of the projection picture. Meanwhile, a Projection Compensation Network (PCN) is designed, and a multi-scale perceptual loss is added to this compensation network, and the content information of the compensated image is improved by calculating the loss of feature maps of different scales. The final network is named Perceptual Loss-Projection Compensation Network (PL-PCN). Experiments are used to confirm the PL-PCN model's efficacy. The outcomes demonstrate that the SSIM and PSNR of the projected picture compensated by PL-PCN are boosted by 35.8% and 31.6%, respectively. While the RMSE is reduced by 40.9%, demonstrating an improvement in the compensated image's quality. Additionally, utilizing a CNN makes it possible to do cross-reflection compensation. Additionally, compared to the network without deep polymerization, the PL-PCN with deep polymerization structure boosts the projected image's SSIM and PSNR by 6% and 7.57%, respectively, and lowers the RMSE by 13.3% This demonstrates that the addition of deep polymerization structure can have a stronger compensatory effect. This paper can offer a theoretical framework for improving the immersive scene quality of digital media art. [ABSTRACT FROM AUTHOR]

The utilization of motor imagery-based brain-computer interfaces (MI-BCI) has been shown to assist stroke patients activate motor regions in the brain. In particular, the brain regions activated by unilateral upper limb multi-task are more extensive, which is more beneficial for rehabilitation, but it also increases the difficulty of decoding. In this paper, self-attention convolutional neural network based partial prior transfer learning (SACNN-PPTL) is proposed to improve the classification performance of patients' MI multi-task. The backbone network of the algorithm is SACNN, which accords with the inherent features of electroencephalogram (EEG) and contains the temporal feature module, the spatial feature module and the feature generalization module. In addition, PPTL is introduced to transfer part of the target domain while preserving the generalization of the base model while improving the specificity of the target domain. In the experiment, five backbone networks and three training modes are selected as comparison algorithms. The experimental results show that SACNN-PPTL had a classification accuracy of 55.4%±0.17 in four types of MI tasks for 22 patients, which is significantly higher than comparison algorithms (P < 0.05). SACNN-PPTL effectively improves the decoding performance of MI tasks and promotes the development of BCI-based rehabilitation for unilateral upper limb. [ABSTRACT FROM AUTHOR]

Blockchain is a decentralized electronic account book that is secure, open, and impervious to manipulation, and records transactions across numerous computers. Internet of Things (IoT) deployed sensors are limited in bandwidth, processing power, and battery life. Energy efficiency is a vital factor and a decisive matter for energy-bound IoT-based networks. This paper provides an idea of the working principle of blockchain technology in IoT-enabled energy systems. It investigates the fundamentals of blockchain technology, clarifying its decentralized nature, cryptographic mechanisms, and consensus algorithms that ensure data immutability and transparency. It explores how blockchain technology can be integrated with various IoT fields for energy efficiency. The paper investigates the blockchain-based energy consumption approaches in IoT qualitatively, and quantitatively with future research directions and challenges. It is seen that the hybrid blockchain model is suitable for energy efficiency in IoT. [ABSTRACT FROM AUTHOR]

Microgrids require efficient energy management systems to optimize the operation of microgrid sources and achieve economic efficiency. Bi-level energy management model is proposed in this paper to minimize the operational cost of a grid-tied microgrid under load variations and uncertainties in renewable sources while satisfying the various technical constraints. The first level is day ahead scheduling of generation units based on day ahead forecasting of renewable energy sources and load demand. In this paper, a recent meta-heuristic algorithm called Coronavirus Herd Immunity Optimizer (CHIO) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem, while the Lagrange multiplier method is used to determine the set-point of the Diesel Generator (DG). The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. The obtained results show that the CHIO-based bi-level EMS demonstrates an optimal economic operation for a grid-connected microgrid in real-time when there are uncertainties in weather, utility tariffs, and load forecasts. [ABSTRACT FROM AUTHOR]

In order to solve the problems of long reconstruction time and low reconstruction accuracy of compressed sensing reconstruction algorithm in the measurement of transient signals, a deep unfolding network reconstruction method based on scalable sampling is proposed to achieve fast and high-quality reconstruction of transient signal under low number of measurements. Firstly, the measurement process of compressed sensing is embedded into the neural network to realize automatic design and optimization of the observation matrix, which can reduce the number of measurements. Secondly, scalable sampling is introduced into the measurement process of compressed sensing, which can realize the training of data with different sampling ratios in the same model. Finally, a deep unfolding network model is designed to reconstruct the transient signal, which not only realizes the interpretability of the reconstructed network, but also achieves fast and high-quality reconstruction of the transient signal under the low number of measurements. Experimental results show that compared with the traditional compressed sensing reconstruction algorithms, the proposed method can obtain high-quality reconstruction accuracy with lower measurement times, and the reconstruction time is greatly reduced. The algorithm in this paper also obtains good reconstruction results under different sampling ratios, which shows that the method in this paper has good adaptability and effectiveness. [ABSTRACT FROM AUTHOR]

The continuous advancement of autonomous driving technology imposes higher demands on the accuracy of target detection in complex environments, particularly when traffic targets are occluded. Existing algorithms still face significant challenges in detection accuracy and real-time performance under such conditions. To address this issue, this paper proposes an improved YOLOX algorithm based on adaptive deformable convolution, named OCC-YOLOX. This algorithm enhances the feature extraction network's ability to focus on occluded targets by incorporating a coordinate attention mechanism. Additionally, it introduces the Overlapping IoU (OL-IoU) loss function to optimize the overlap between predicted and ground truth bounding boxes, thereby improving detection accuracy. Furthermore, the adoption of Fast Spatial Pyramid Pooling (Fast SPP) reduces computational complexity while maintaining real-time performance. Experiments on fused public datasets demonstrate that OCC-YOLOX achieves improvements in accuracy, recall, and average precision by 2.76%, 1.25%, and 1.92%, respectively. In addition to testing on the KITTI, CityPersons, and BDD100K datasets, the effectiveness of the OCC-YOLOX algorithm is further validated through comparisons with self-collected occlusion scene data. The experimental results indicate that OCC-YOLOX outperforms existing mainstream detection algorithms, particularly in handling complex occlusion scenarios, significantly enhancing the accuracy and efficiency of object detection. This study provides new insights for addressing the challenges of occluded target detection in intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Good and optimal rheology is a primary requisite for a drilling fluid to achieve better hole-cleaning and barite sag resistance. Conventional thickeners like organophilic clay that provide rheology to invert emulsion fluids degrade with time and thereby fail to maintain sufficient rheology of the drilling fluid. Generally, excess amount of organophilic clay or low gravity solids (LGS) is added to the drilling fluid to enhance rheology. However, addition of excess amount of organoclay or low gravity solids not only increases the cost of drilling but also may severely affect other drilling fluid properties, which will require further treatment. In addition, addition of organoclay or low gravity solids increases the plastic viscosity, decreases the rate of penetration thereby ultimately increasing the cost of drilling. Thus, there was a need to develop a drilling fluid with optimal rheology sufficient to give good hole cleaning and barite sag resistance. This paper describes the formulation of 70pcf, 90pcf and 120pcf organoclay-free invert emulsion drilling fluids formulated with a novel rheology modifier combination of RM1 and RM2. The paper describes the synergistic effect of RM1 and RM2 which is a combination of fatty acid and fatty amine respectively. The fluids were hot rolled at 250°F, 250°F and 350°F respectively and the temperature effect on the rheological and filtration properties were studied. The paper also describes the static aging and contamination studies of 90pcf and 120pcf fluids at 250°F and 350°F respectively. Rheology of the 90pcf invert emulsion fluid was measured across high temperature and high pressure (HTHP) conditions to observe their effects on the fluid properties. 70pcf, 90pcf and 120pcf organoclay-free invert emulsion drilling fluids formulated with the novel rheology modifier combination of RM1 and RM2 showed optimal rheology and low HTHP fluid loss. Static aging studies of 90pcf IEFs at 250°F respectively showed that the fluids are sag-resistant with low top-oil separation. Contamination studies of 90pcf fluid showed that the contaminants have minimal effect on the rheology and filtration properties of the invert emulsion fluid. HTHP rheology of the 90pcf invert emulsion fluid shows consistent rheology across high temperatures and pressures. The paper thus demonstrates the superior performance of the rheology modifier combination to achieve good rheological and filtration properties. [ABSTRACT FROM AUTHOR]

Minimally invasive procedures for endovascular interventions involve manual navigation of a guidewire. Endovascular interventions encompassing highly tortuous vessels would benefit from guidewires which exhibit higher dexterity. This paper introduces a version of the COAST (COaxially Aligned STeerable) guidewire system capable of exhibiting higher dexterity. The system presented in this paper consists of three coaxially aligned tubes with a tendon to actuate the middle tube. Furthermore, it is possible to independently rotate the middle tube with respect to the outer tube. This variation enables the guidewire to achieve curvature in different planes while avoiding rotation of the entire structure. We also present the simulated stability of the guidewire with different outer tube geometries and experimentally validate the model. Experimental analysis and modeling of the kinematic behavior of the system is presented. A model to calculate the curvature vs. tendon stroke relationship for the optimal notch geometry is presented with an average RMSE of 0.16 mm. A control strategy addressing the snapping instabilities to ensure reliable operation is discussed. A custom phantom vessel and an aortic arch phantom model were used to demonstrate the ability of the system to safely navigate through tortuous pathways without exhibiting these elastic instabilities. [ABSTRACT FROM AUTHOR]

This paper introduces the DeNet-Mamba-DC-SCSSA network, an advanced solution for predicting the Remaining Useful Life (RUL) of lithium-ion batteries, crucial for the safety and efficiency management of electric vehicles. Combining the robust Denoising Enhancement Network (DeNet), the Improved Sparrow Optimization Algorithm (SCSSA), the adept Mamba time-series model, and the proficient Dilated Convolution (DC), this model excels in precise noise handling and sophisticated feature extraction. DeNet diligently refines input data, mitigating noise interference, while Mamba skillfully captures sequential intricacies. DC, on the other hand, adeptly extracts features over varying time scales, ensuring meticulous RUL predictions.The model's efficacy was rigorously tested on NASA and CALCE datasets and was benchmarked against cutting-edge algorithms. Remarkably, it reduced average RE and RMSE by 48.59% and 21.45%, respectively, showcasing its superior performance and accuracy. Further evaluation on the CALCE dataset against the latest methods affirmed its leading predictive precision and stability.The model's robustness and practical applicability were further validated using real vehicle data from a new energy vehicle platform. In a challenging test, it accurately predicted the charging capacities corresponding to the mileage of four vehicles with minimal errors: 0.52 Ah, 1.03 Ah, 0.84 Ah, and 0.71 Ah. These results significantly surpassed those of other recent methods, highlighting the model's exceptional generalizability and potential for real-world applications in electric vehicle battery management. [ABSTRACT FROM AUTHOR]

The aim of this work was to develop an experimental protocol that takes into account the influence of experimental conditions on these perineal tissues, before determining their mechanical properties. Samples of each perineal tissue layer were obtained from the skin, the vagina, the external anal sphincter (EAS), the internal anal sphincter (IAS) and anal mucosa of freshly dead sows. They were tested in quasi-static uniaxial tension using the Mach-1 testing machine. Stress-strain curves of each perineal tissue layer before the first damage for each sow were obtained and modeled by hyperelastic laws described by three coefficients: C1, C2, and C3 (Yeoh model). The influence of sample preparation conditions such as tissue freezing, hygrometry and sample orientation were evaluated, and the conditions under which the tests were performed such as the displacement velocity during testing were also evaluated by analysing C1-coefficient. This study suggested that sample preparation conditions such as tissue freezing for 24 h, storage in cellophane paper for two hours and the strain rate did not statistically affect the C1-hyperelastic coefficient for each perineal layer (p > 0.05). Samples should not be stored in saline for 2 h (p < 0.05). Sample orientation did not influence C1-hyperelastic coefficient (p > 0.05). This experimental protocol could be used to study in vitro biomechanical properties of perineal tissues in order to better understand perineal tears during delivery. [ABSTRACT FROM AUTHOR]

Optimization problems are common across various fields, and one effective solution is the swarm intelligence algorithm.It is essential for the algorithm to deliver high-quality solutions for problems with varying characteristics. However, most existing swarm intelligence rely on fixed and monotonic search strategies, which limits their ability to handle the diverse and complex situations encountered when solving real-world optimization problems with unknown fitness landscapes. To extend the applicability of swarm intelligence and thus offer users an efficient black-box optimizer for various applications, a novel self-learning mechanism is proposed and applied to the Salp Swarm Algorithm (SSA) to develop the self-learning salp swarm algorithm (SLSSA) in this paper. In SLSSA, four distinct search strategies, including a novel multiple food sources search strategy, are adopted to strengthen the search agents' abilities to conquer various difficulties in the search space. To improve the efficiency of the search process, the self-learning strategy dynamically determines the execution probability of each search strategy according to the quality of solutions it produced previously. Moreover, a parameter setting method is proposed in this paper, which eliminates the need for a trial-and-error approach and allows for straightforward configuration of the parameters that optimize the performance of SLSSA. In comparison with several highly regarded state-of-the-art peer algorithms, the performance of SLSSA in solving the CEC2014 benchmark functions was thoroughly examined. Subsequently, SLSSA was applied to train multi-layer perceptron classifiers and test on the UCI machine-learning datasets. The experimental results and analysis on benchmark functions and multi-layer perceptron classifier training problems demonstrate that SLSSA outperforms the competing algorithms in terms of solution accuracy, stability, and overall convergence speed. Moreover, computational time comparisons reveal that SLSSA achieves significant performance improvement with only a marginal increase in time cost compared to the original SSA. [ABSTRACT FROM AUTHOR]

This paper introduces a Hybrid Plasmonic Nano-Antenna (HPNA) with a gradient-index dielectric flat lens modeled with different materials to enhance and steer the radiation in a particular direction based on a phase shift array. Firstly, the design of hybrid plasmonic Nano-Antenna (NA) is introduced and analyzed considering different horn-shapes such as diamond, hexagonal, circular, rectangular, and square shapes. The commercial software Computer Simulation Technology-Microwave Studio (CST-MWS) is used to analyze the radiation characteristics of the plasmonic NAs at the standard telecommunication wavelength of 1,550 nm. The produced horn-shaped nano-antenna made up from gold cladding with low- and high-index dielectric materials of SiO2 and InGaAs, respectively. The gain of the Square Horn shape Hybrid Plasmonic Nano-Antenna (SHHPNA) achieves the greatest gain with a value of 10.7 dBi at the desired frequency and the return loss reached -18.09 dB due to the wide aperture area for SHHPNA, which results in a narrower beam-width and higher gain. Moreover, by using two different shapes of dielectric flat lens to enhance the antenna's performance by improving directivity while correspondingly reducing beam-width, the gain is enhanced and reaches 16.7 for SHHPNA with a circular lens and 16.9 for SHHPNA with a rectangular lens compared with the traditional NA that equal to 9.03 dBi. The main lobe for SHHPNA with each lens is more directed, with Side Lobe Level (SLL) and Half Power Beam-Width (HPBW) of -13.1 dB and 16.5° for SHHPNA with a circular lens and -15.1 dB and 15.4° for SHHPNA with a rectangular lens, respectively. In addition, the array configuration was investigated, and the gain was found to be 21 dBi for the single row array of 4×1 and 23.2 dB for the array of 3×3. Moreover, the array of 4×1 and 3×3 with +90° showed gains of 18.6 dBi and 20.7 dBi, respectively, compared to traditional paper with gains of 11.20 dBi and 13.1 dBi. [ABSTRACT FROM AUTHOR]

Nuclear fusion is entering the era of power plant-scale devices, which are now undergoing extensive studies to support the design phase. Plasma disruptions pose a high risk to these classes of devices because of the large stored thermal and magnetic energy which might jeopardize machine integrity and availability. Therefore, disruptions within these devices must be virtually eliminated, and any disruptions which do happen must be highly mitigated. However, the characterisation, prediction and technology used to mitigate disruptions is still an area of active development. In this paper, the authors investigate the disruptions within ST40, with particular attention at the identification of causes and effects associated with disruptions, both from a physics basis and an engineering standpoint. This paper aims at presenting preliminary predictive analyses of ST40 plasma scenarios by exploiting Machine Learning techniques applied to an experimental database populated by plasma pulses executed during the ST40 2021–2022 experimental campaign. The database contains both disrupted and non-disrupted pulses. Using Machine Learning, common features within disruptions are automatically classified and identified, mapping the controllable operational space in terms of plasma displacement and variation of specific plasma internal parameters. The classification was validated by benchmarking the numerical reconstruction of the plasma dynamics with experimental data recovered from the plasma diagnostics. Subsequent Machine Learning analyses allowed the extrapolation of new disrupted plasma configurations for preliminary predictive simulations of the plasma column displacement. Thanks to the numerical simulations performed in MAXFEA environment, it is possible to investigate the plasma vertical displacement both during disrupted and regularly terminated plasma scenarios and to provide lessons to be learnt for the next ST40 experimental campaign and for the design of future ST devices. [ABSTRACT FROM AUTHOR]

This study introduces an approach to validate sampling points for air monitoring in environments where operations generate significant airborne particulate radioactivity. A case study of repair works in a nuclear spent fuel reprocessing facility is used to synthesis and demonstrate the methodology. We use the probability distribution of air activity measurements and the correlation between two sampling points near high particulate generating operations and the ventilation ducts. The methodology developed in this paper can be applied in task-related air monitoring scenarios for validation of sampling points. This will augment internal exposure control measures during works involving physical entries in areas with high potential of escalation of air activity (e.g., during major repairs, during decommissioning of nuclear facilities etc.) and to evaluate the sufficiency of respiratory protection. [ABSTRACT FROM AUTHOR]

Traditional English corpora mainly collect information from a single modality, but lack information from multimodal information, resulting in low quality of corpus information and certain problems with recognition accuracy. To solve the above problems, this paper proposes to introduce depth information into multimodal corpora, and studies the construction method of English multimodal corpora that integrates electronic images and depth information, as well as the speech recognition method of the corpus. The multimodal fusion strategy adopted integrates speech signals and image information, including key visual information such as the speaker's lip movements and facial expressions, and uses deep learning technology to mine acoustic and visual features. The acoustic model in the Kaldi toolkit is used for experimental research.Through experimental research, the following conclusions were drawn: Under 15-dimensional lip features, the accuracy of corpus A under monophone model was 2.4% higher than that of corpus B under monophone model when the SNR (signal-to-noise ratio) was 10dB, and the accuracy of corpus A under the triphone model at the signal-to-noise ratio of 10dB was 1.7% higher than that of corpus B under the triphone model at the signal-to-noise ratio of 10dB. Under the 32-dimensional lip features, the speech recognition effect of corpus A under the monophone model at the SNR of 10dB was 1.4% higher than that of corpus B under the monophone model at the SNR of 10dB, and the accuracy of corpus A under the triphone model at the SNR of 10dB was 2.6% higher than that of corpus B under the triphone model at the SNR of 10dB. The English multimodal corpus with image and depth information has a high accuracy, and the depth information helps to improve the accuracy of the corpus. [ABSTRACT FROM AUTHOR]

Conveyor belt deviation is a commmon and severe type of fault in belt conveyor systems, often resulting in significant economic losses and potential environment pollution. Traditional detection methods have obvious limitations in fault localization precision and analysis accuracy, unable to meet the demands of efficient and real-time fault detection in complex industrial scenarios. To address these issues, this paper proposes an improved detection algorithm based on YOLOv8, aiming to achieve efficient and accurate detection during the operation of the belt. Firstly, an Enhanced Squeeze-and-Excitation (ESE) module is incorporated into C2f to boost feature extraction for rollers and belts. Secondly, the construction of the BiFPN_DoubleAttention module in the neck network enhances bidirectional feature fusion and attention mechanism, thereby improving multi-scale object localization accuracy under complex environments. Then, a Multi-Head Self-Attention (MHSA) mechanism is introduced in the head network, better capturing positional features of small roller targets and belt areas in various environments, thus enhancing detection performance. Finally, extensive experiments are conducted on a self-built dataset, achieving an accuracy of 98.1%, mAP0.5 of 99.0%, and a detection speed of 46 frames per second (FPS). This method effectively handles variations and disturbances in the conveyor belt transportation environment, meeting real-time diagnostic needs for belt misalignment in the industry. [ABSTRACT FROM AUTHOR]

The present paper contributes to the literature in two ways. First, it develops a novel p, q Quasirung Orthopair Fuzzy (p, q QOF) based group decision making framework to modify a recently developed multi-criteria decision making (MCDM) model such as Comparisons between Ranked Criteria (COBRAC). Second, the paper ruminates on the Strength-Weakness-Opportunity-Threat (SWOT) of the sugarcane supply chain (SSC) in India vis-à-vis adaptation of the advanced technologies featuring Industry 4.0. To set the sub-factors of various dimensions of SWOT, the theoretical ground of Technology-Organization-Environment (TOE) framework has been used. The sub-factors of SWOT have been derived through an informal in-depth discussion with the experts of the sugar industry. Then using a Likert five-point linguistic scale the experts rated the sub-factors based on their relative importance. To determine the weights the modified COBRAC method has been applied. In subsequent stages the reliability of the model has been tested and sensitivity analysis has been carried out to check the stability of the result. The analysis reveals that while experience, by-product utilization and high demand provides strength and create opportunities for SSC, the areas of concern are lack of variety, fragmented nature of supply chains, shortage of next-gen talent and inadequate infrastructure. However, there are enough promises for SSC. The paper shall provide impetus to strategic decision makers for the sugar industry and puts forth a new decision-making framework for the analysts. [ABSTRACT FROM AUTHOR]

This paper reports on the development of a method for enhanced non-destructive assay (NDA) of radioactive waste using the novel technique neutron-gamma emission tomography (NGET). The technique relies on the detection of correlated fast neutrons and gamma rays emitted in spontaneous or induced fission. It is based on fast organic scintillators and enables sensitive detection and three-dimensional (3D) localization of the fission events. The technique is passive and does not require moving components. In this work, we apply the NGET technique to the category of radioactive waste which is often referred to as historic or legacy waste. This can include mixed wastes encased in shielded containers many decades ago, before the advent of detailed waste description criteria. These low or intermediate level wastes are often associated with lacking, limited or conflicting documentation. This poses a challenge when assigning the waste to the proper disposal route as well as in deciding whether the waste needs to undergo sorting and conditioning to fulfil waste acceptance criteria both with regards to safe interim storage and to its ultimate disposal. Actinides, such as isotopes of uranium and plutonium, with their typically long half-lives and decay chains are of special interest in this regard since they may challenge the long-term safety assessment in repositories predicated on mainly shorter half-life radionuclides if undetected. Accurate identification and localisation of actinides is also important from a safeguards perspective, especially since they are generally difficult to detect and localise by established passive means due to their relatively weak radiation emissions, in particular in shielded containments and in the presence of strong radiation fields from other radioactive materials. In this paper we present findings of measurements on shielded containments of long-lived radioactive waste performed at the Studsvik site in Sweden, as well as measurements on a laboratory assembly simulating a grouted waste drum. Similarities and differences between the novel NGET technique and a commercially available gamma imaging system are also briefly discussed. [ABSTRACT FROM AUTHOR]

Spurred by the simultaneous need for data privacy protection and data sharing, federated learning (FL) has been proposed. However, it still poses a risk of privacy leakage in it. This paper, an improved Differential Privacy (DP) algorithm to protect the federated learning model. Additionally, the Fast Fourier Transform (FFT) is used in the computation of the privacy budget , to minimize the impact of limited arithmetic resources and numerous users on the effectiveness of training model. Moreover, instead of direct analyses of the privacy budget through various methods, Privacy Loss Distribution (PLD) and privacy curves are adopted, while the number of artificial assignments hyperparameters is reduced, and the grid parameters delineated for FFT use are improved. The improved algorithm tightens parameter bounds and minimizes human factors' influence with minimal efficiency impact. It decreases the errors caused by truncation and discreteness of PLDs while expanding the discreteness interval to reduce the calculation workload. Furthermore, an improved activation function using a temper sigmoid with only one parameter , smooths the accuracy curve and mitigates drastically fluctuating scenarios during model training. Finally, simulation results on real datasets show that our improved DP algorithm, which accounts for long trailing, facilitates a better balance between privacy and utility in federated learning models. [ABSTRACT FROM AUTHOR]

In the financial field, constructing efficient investment portfolios is a focal point of research, encompassing asset selection and optimization of asset allocation. With the advancements in Large Language Models (LLMs), generative Artificial Intelligence (AI) tools have showcased capabilities never seen before. However, the black-box nature of these tools renders their outputs difficult to interpret directly, often necessitating iterative fine-tuning to align with users' expected outcomes. This study presents a structured prompt framework specifically designed for stock selection, aiming to provide direct and interpretable stock-selecting tools for investors of various levels. By creating representative scenarios and combining them into different cases for experimentation, we can explore how the construction of prompts influences the responses generated by generative AI tools. Additionally, this paper proposes a novel algorithm that combines the Nonlinear-Activated Beetle Antennae Search strategy with the Egret Swarm Optimization Algorithm (NBESOA) to address the Mean-Variance Portfolio Selection problem with Transaction Costs and Cardinality Constraints (MVPS-TCCC), utilizing real stock market data to construct portfolios based on generative AI tools recommendations. Simulation results indicate that, compared to other algorithms, NBESOA prefers optimizing portfolio configurations to achieve the highest Sharpe Ratio with the strictest constraints, bringing the outcomes closer to the portfolio's efficient frontier. [ABSTRACT FROM AUTHOR]

Low-frequency noise (20–200 Hz) radiated from concrete box girder bridges on urban viaduct railway lines is a significant source of disturbance to nearby residents. Many researchers have investigated methods which can be used to effectively reduce this bridge noise. Currently, most of the methods are based on the principles of vibration isolation and/or absorption. This paper, after modelling and analysing the contributions of various parts of a concrete box girder bridge to the total noise radiated from the bridge, presents a study on the potential in bridge noise control of making holes to create acoustic short circuits on the bridge wings. It is found that by this means the bridge noise can be reduced by up to 6.8 dB at standard-specified measurement points. Additionally, a 3dB-effectiveness distance ratio design method for acoustic short-circuiting is proposed, which effectively reduces bridge noise and achieves stable noise reduction outcomes. [ABSTRACT FROM AUTHOR]

Weeding is an important part of agricultural production. With the development of science and technology, automated weeding is regarded as the future development direction, and how to accurately and efficiently detect plants in the field is one of the key points. Corn seedlings and weeds are similar in color, shape and other characteristics, which brings serious challenges to plant detection. In this paper, we propose an improved model based on YOLOv7-tiny, called DC-YOLO. To improve the extraction of key features in the model, we propose Dual Coordinate Attention model (DCA). In addition, we introduce the Content-Aware ReAssembly of FEatures (CARAFE) operator to represent the up-sampling process as a learnable feature reorganization, which enriches the feature information of the sampled images. Finally, we decoupled the detection head to minimize conflicts between features from different tasks. The results show that applying the proposed method to corn and weed datasets, the detection accuracy of the model reaches 95.7% mean Average Precision (mAP@0.5), the computational effort of the model is 13.083 Giga Floating-point Operations (GFLOPs), and the parameter size is 5.223 Millon (M), which is better than the rest of the mainstream light-weight target detection model. [ABSTRACT FROM AUTHOR]

This article presents the unique research results of the comprehensive analysis of the porous structure of activated carbons obtained from biomass waste materials from the wood industry during activation in an air atmosphere. The porous structure was analysed on the basis of nitrogen and argon adsorption isotherms via complementary multi-method analysis, i.e. the new numerical clustering-based adsorption analysis, the non-local density functional theory and the quenched solid density functional theory methods. The analytical results for the prepared activated carbons were compared with analogous results obtained for commercial activated carbon. On the basis of the conducted studies it has been determined that the new numerical clustering-based adsorption analysis method gives credible and valuable information on the textural properties of activated carbons which are in strict correlation and mutually complement with the results of the analysis with the use of the quenched solid density functional theory method. The research results obtained in this paper, it has also been shown that from waste materials of the wood industry, in a relatively cheap and cleaner production process, it is possible not only to obtain carbonaceous materials almost comparable to commercial activated carbon, but also to manage the waste in accordance with the principles of a closed-loop economy and sustainable development. The paper pays also attention to the often overlooked economic and ecological aspects, which should nevertheless be taken into account when comparing different adsorbents, rather than their textural properties alone. [ABSTRACT FROM AUTHOR]

The accurate recognition of apples in complex orchard environments is a fundamental aspect of the operation of automated picking equipment. This paper aims to investigate the influence of dense targets, occlusion, and the natural environment in practical application scenarios. To this end, it constructs a fruit dataset containing different scenarios and proposes a real-time lightweight detection network, ELD(Efficient Lightweight object Detector). The EGSS(Efficient Ghost-shuffle Slim module) module and MCAttention(Mix channel Attention) are proposed as innovative solutions to the problems of feature extraction and classification. The attention mechanism is employed to construct a novel feature extraction network, which effectively utilizes the low-latitude feature information, significantly enhances the fine-grained feature information and gradient flow of the model, and improves the model's anti-interference ability. Eliminate redundant channels with SlimPAN to further compress the network and optimise functionality. The network as a whole employs the Shape-IOU loss function, which considers the influence of the bounding box itself, thereby enhancing the robustness of the model. Finally, the target detection accuracy is enhanced through the transfer of knowledge from the teacher's network through knowledge distillation, while ensuring that the overall network is sufficiently lightweight. The experimental results demonstrate that the ELD network, designed for fruit detection, achieves an accuracy of 87.4%. It has a relatively low number of parameters ( 4.3 × 10 5 ), a GLOPs of only 1.7, and a high FPS of 156. This network can achieve high accuracy while consuming fewer computational resources and performing better than other networks. [ABSTRACT FROM AUTHOR]

The ubiquitous presence of electronic devices demands robust hardware security mechanisms to safeguard sensitive information from threats. This paper presents a physical unclonable function (PUF) circuit based on magnetoresistive random access memory (MRAM). The circuit utilizes inherent characteristics arising from fabrication variations, specifically magnetic tunnel junction (MTJ) cell resistance, to produce corresponding outputs for applied challenges. In contrast to Arbiter PUF, the proposed effectively satisfies the strict avalanche criterion (SAC). Additionally, the grid-like structure of the proposed circuit preserves its resistance against machine learning-based modeling attacks. Various machine learning (ML) attacks employing multilayer perceptron (MLP), linear regression (LR), and support vector machine (SVM) networks are simulated for two-array and four-array architectures. The MLP-attack prediction accuracy was 53.61% for a two-array circuit and 49.87% for a four-array circuit, showcasing robust performance even under the worst-case process variations. In addition, deep learning-based modeling attacks in considerable high dimensions utilizing multiple networks such as convolutional neural network (CNN), recurrent neural network (RNN), MLP, and Larq are used with the accuracy of 50.31%, 50.25%, 50.31%, and 50.31%, respectively. The efficiency of the proposed circuit at the layout level is also investigated for simplified two-array architecture. The simulation results indicate that the proposed circuit offers intra and inter-hamming distance (HD) with a mean of 0.98% and 49.96%, respectively, and a mean diffuseness of 49.09%. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) radar technology has potential applications in vital signs detection and medicine. In order to minimize the influence of human micro-movements and respiratory harmonics on heart rate estimation, the vital signs detection method based on mmWave radar is studied in this paper. First, we use median filtering to eliminate baseline drift caused by human micromotion. Next, a differential recursive least squares multiple classification (DR-MUSIC) algorithm is proposed based on the combination of recursive least squares-based adaptive filter (RLS) and multiple signal classification (MUSIC) algorithm. This algorithm effectively suppresses respiratory harmonics and separates respiratory and heartbeat signals. Finally, heart rate value can be precisely estimated using spectral peak search. We invite a number of people to participate in the experiment, which demonstrate that the method successfully suppresse the impact of respiratory harmonics at low SNR. The error rate between the estimated heart rate and the reference heart rate is only 1.69% to 2.61%, which is significantly better than the existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we performed microseismicity detection and location using the deep learning method and obtained a high-precision earthquake catalog in the Changning gas field, which is one of the largest shale gas production demonstration areas in China. We found that the spatial and temporal characteristics of seismicity in the region are indicative of its correlation with industrial operations. The distribution of earthquakes at depth reflected variations in reservoir depth and provided valuable constraints on it. The horizontal layered distribution of earthquakes at depth is due to the formation of fracture surfaces from interconnected fractures near the reservoir during hydraulic fracturing (HF) operations, which clearly demonstrates how HF operations impact seismicity. We suggested that the horizontally layered distribution was driven by two fundamental mechanisms: reactivation of pre-existing faults during HF and injection of high-pressure fluids into the reservoir, leading to fracture creation. Several ML ≥4 earthquakes, which did not occur on well-defined seismogenic faults, may have been triggered by pore elastic coupling resulting from regional stress accumulation and fluid injection. Significantly, the ML 4.9 seismic sequence occurring at the basement indicates that fracking has reactivated and promoted pre-existing faults, highlighting the need for further investigation into potential seismic hazards in the region. [ABSTRACT FROM AUTHOR]

Accurate detection of asphalt pavement distress is crucial for road maintenance and traffic safety. However, traditional convolutional neural networks usually struggle with this task due to the varied distress patterns and complex background in the images. To enhance the accuracy of asphalt pavement distress identification across various scenarios, this paper introduces an improved model named SMG-YOLOv8, based on the YOLOv8s framework. This model integrates the space-to-depth module and the multi-scale convolutional attention mechanism, while optimizing the backbone's C2f structure with a more efficient G-GhostC2f structure. Experimental results demonstrate that SMG-YOLOv8 outperforms the YOLOv8s baseline model, achieving Pmacro and mAP50 scores of 81.1% and 79.4% respectively, marking an increase of 8.2% and 12.5% over the baseline. Furthermore, SMG-YOLOv8 exhibits clear advantages in identifying various types of pavement distresses, including longitudinal cracks, transverse cracks, mesh cracks, and potholes, when compared to YOLOv5n, YOLOv5s, YOLOv6s, YOLOv8n, and SSD models. This enhancement optimizes the network structure, reducing the number of parameters while maintaining excellent detection performance. In real-world scenarios, the SMG-YOLOv8 model, when applied to image data collected from projects, achieves a Pmacro of 80.5% and an Rmacro of 86.2%. This result demonstrates its excellent generalization capability and practicality. The model provides significant technical support for the intelligent detection of pavement distress. [ABSTRACT FROM AUTHOR]