Search

In the e-commerce last-mile delivery process, the asset operators (logistics service providers who own parcel locker facilities) support their delivery service with parcel lockers, while the non-asset operators (logistics service providers without parcel lockers) perform door-to-door delivery. Due to demand fluctuation, asset operators' parcel-locker slots may be left vacant, while non-asset operators are stuck with the high-cost door-to-door service. The exclusiveness of parcel-locker usage reduces resource utilization and service efficiency in last-mile delivery. Therefore, this paper proposes a parcel-locker-sharing model in which these two parties share the parcel-locker capacity in last-mile delivery. The asset operator rents the unused parcel lockers to the non-asset operator by charging a rental fee,while the non-asset operator rents the parcel lockers for delivery to save logistics costs. The motivation of this alliance is to increase the profits of both parties and that of the total supply chain. This study establishes the supply-chain profit model for the parcel-locker-sharing framework and finds that the profit or loss depends on the comparison of the operation cost savings and delivery-cost savings. A numerical analysis is conducted to validate the final result. The research further suggests the optimal rental quantity and price interval. This paper is the first to study the operational mechanism of sharing the parcel locker between two distinct types of logistics service providers and to offer recommendations for industrial application. [ABSTRACT FROM AUTHOR]

This paper delves into the historical evolution of spinal surgery, focusing on the pivotal role of the Harrington rod in treating spinal deformities. Introduced in 1955, the Harrington rod marked a significant breakthrough in neurosurgery, especially for scoliosis treatment, by offering a novel approach to spinal stabilization. Through a retrospective analysis, this study examines the development and impact of the Harrington rod, highlighting Dr. Paul Harrington's contributions to spinal surgery. His innovative technique revolutionized the management of spinal deformities, laying the groundwork for future advancements in spinal instrumentation. Despite initial skepticism, Harrington's methods gained acceptance, significantly influencing neurosurgical practices and patient outcomes. This study also explores subsequent advancements that built on Harrington's work, including the transition to long-segment spine constructs and the introduction of segmental pedicle screws, which allowed for more precise deformity correction. Reflecting on Harrington's legacy, this paper acknowledges the continuous evolution of spinal surgery, driven by the interplay between clinical challenges and technological innovations. Harrington's pioneering spirit exemplifies the ongoing pursuit of better surgical outcomes, underscoring the importance of innovation in the field of neurosurgery. [ABSTRACT FROM AUTHOR]

Background: Liquid biopsy is a method that could potentially improve the management of thyroid cancer (TC) by enabling the detection of circulating tumor DNA and RNA (ctDNA, ctRNA). The BRAFV600E mutation appears to be the most representative example of a biomarker in liquid biopsy, as it is the most specific mutation for TC and a target for molecular therapeutics. The aim of this review is to summarize the available data on the detection of the BRAFV600E mutation in liquid biopsy in patients with TC. Methods: A comprehensive analysis of the available literature on the detection of the BRAFV600E mutation in liquid biopsy in TC was performed. Thirty-three papers meeting the inclusion criteria were selected after full-text evaluation. Results: Eleven papers discussed correlations between BRAF mutation and clinicopathological characteristics. Nine studies tested the utility of BRAFV600E detection in the assessment of residual or recurrent disease. Seven studies investigated BRAF-mutated circulating tumor nucleic acids (ctNA) as a marker of response to targeted therapy. In seven studies the method did not detect the BRAFV600E mutation. Conclusions: This review shows the potential of BRAFV600E-mutated ctNA detection in monitoring disease progression, particularly in advanced TC. The diagnostic value of BRAFV600E-mutated ctNA detection appears to be limited to advanced TC. The choice of the molecular method (quantitative PCR [qPCR], droplet digital polymerase chain reaction [ddPCR], and next-generation sequencing [NGS]) should be made based on the turnaround time, sensitivity of the test, and the clinical indications. Despite the promising outcomes of some studies, there is a need to verify these results on larger cohorts and to unify the molecular methods. [ABSTRACT FROM AUTHOR]

The risk of post-operatory hypothyroidism and hypocalcaemia, along with recurrent laryngeal nerve injury, is lower following a less-than-total thyroidectomy; however, a previously unsuspected carcinoma or a disease progression might be detected after initial surgery, hence indicating re-intervention as mandatory (so-called "redo" surgery) with completion. This decision takes into consideration a multidisciplinary approach, but the surgical technique and the actual approach is entirely based on the skills and availability of the surgical team according to the standard protocols regarding a personalised decision. We aimed to introduce a review of the most recently published data, with respect to redo thyroid surgery. For the basis of the discussion, a novel vignette on point was introduced. This was a narrative review. We searched English-language papers according to the key search terms in different combinations such as "redo" and "thyroid", alternatively "thyroidectomy" and "thyroid surgery", across the PubMed database. Inclusion criteria were original articles. The timeframe of publication was between 1 January 2020 and 20 July 2024. Exclusion criteria were non-English papers, reviews, non-human studies, case reports or case series, exclusive data on parathyroid surgery, and cell line experiments. We identified ten studies across the five-year most recent window of PubMed searches that showed a heterogeneous spectrum of complications and applications of different surgeries with respect to redo interventions during thyroid removal (e.g., recurrent laryngeal nerve monitoring during surgery, other types of incision than cervicotomy, the use of parathyroid fluorescence, bleeding risk, etc.). Most studies addressing novel surgical perspectives focused on robotic-assisted re-intervention, and an expansion of this kind of studies is expected. Further studies and multifactorial models of assessment and risk prediction are necessary to decide, assess, and recommend redo interventions and the most adequate surgical techniques. [ABSTRACT FROM AUTHOR]

Objectives: This systematic review aimed to analyse the published evidence for the use of non-invasive methods for the early detection of oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs). Methods: The literature was systematically searched through several databases: PubMed, Cochrane Library, and Web of Science. Additional exploration was performed through cross-checks on the bibliographies of selected reviews. The inclusion criteria involved studies assessing the application of non-invasive tests on humans in the screening, diagnosis, or surveillance of OSCC or OPMDs and reporting sensitivity (SE) and specificity (SP). The Newcastle–Ottawa scale (NOS) was applied to assess the quality of the studies included. Results: The search strategy resulted in 8012 preliminary records. After a duplicate check, 116 titles remained. After abstract analysis, 70 papers remained. After full text analysis, only 54 of the 70 papers fit the inclusion criteria (28 were original articles and 26 were reviews). Those 26 reviews were used to manually search for further original articles. From this last search, 33 original articles were found. Thus, a total of 61 original studies were included and investigated. Findings from this systematic review indicate useful information, such as a description of the mechanisms, ease of use, limitations, and SE and SP values, to drive the choice of the optimal minimally invasive method to be utilized as an adjunctive tool to examine the suspicious lesions. Conclusions: Each of the analysed tools can be improved or implemented, considering their high SE and low SP. Despite advancements, incisional biopsy continues to be the gold standard for the definitive diagnosis of oral cancer and precancerous lesions. Further research and development are essential to improving the sensitivity, specificity, and reliability of non-invasive tools for widespread clinical application. [ABSTRACT FROM AUTHOR]

Simple Summary: One may think that the number of pigs that can fit on a truck is a simple scientific question. It is not. It is vitally important that scientists can describe their work simply and concisely to the public, especially when that work is providing the basis for regulation in the public interest. Successful regulation of human behavior requires the regulated to agree with the rule. Commercial livestock transporters have an interest in loading a livestock compartment to the maximum to minimize costs. Livestock production academics, veterinarians, and animal welfare activists have been working for decades to determine the level of livestock crowding in transport containers that would be an appropriate threshold for regulatory enforcement. To date, there is no real consensus on this issue across species or animal size within a species. The EU countries agreed to a maximum floor pressure for market pigs of around 110 kg body weight to be 235 kgm−2 in 2005, while North American regulators have no legal standard. Current practice in North America allows for significant crowding of pigs in excess of the EU standard. Using the pig as an example, this paper examines the practical barriers that have for decades prevented emergence of a consensus on the "Is this truck full?" question. This paper focuses on the problem of numeracy when writing regulations, specifically how to describe a threshold for crowding of pigs during transport, considering transported pigs range in body mass from 5 to 500 kg. When scientific findings provide the basis for regulation in the public interest, those findings must be communicated in a consistent way to regulators and policymaking bodies. Numeracy is the ability to understand, reason with, and apply appropriate numerical concepts to real-world questions. Scientific understanding is almost always based on rational understanding of numerical information, numeracy. The threshold of administrative offenses is often a numerical description. Commercial livestock transporters have an interest in loading livestock compartments to the maximum to achieve the largest payload allowed by axle weight laws, as is the case in all bulk commodity transport. Maximizing payload minimizes costs and environmental hazards of fuel exhaust and can benefit the public with lower pork prices, but has a serious animal welfare risk. Livestock production academics, veterinarians, and animal welfare activists have been working for decades to determine the level of livestock crowding in transport containers that would be appropriate for regulatory enforcement. The scientific discourse has been plagued by a lack of numerical standardization when describing results of trials and forming recommendations. Exceeding specific numerical thresholds is the core to implementing enforcement actions. This paper examines the communication and other barriers that have prevented emergence of a consensus on this question and provides a direction toward resolution. Further confirmation of effects of crowding livestock in transit is needed. This paper suggests that articulating an enforceable standard in pig transport is possible. In inspection for compliance, discovering the LP50 (lethal pressure—50) for slaughter-weight pigs is an initial global benchmark goal. The LP50 is the loading floor pressure in a commercial transport compartment, under field conditions, that would result in the death of at least one pig in the group 50% of the time. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper aims to review the development of the cow–calf bonding process in water buffalo (Bubalus bubalis) via olfactory, tactile, auditory, and visual stimuli. It will also discuss the neuroendocrine factors motivating buffalo cows to care for the calf. To develop cow–calf bonding, several olfactory, tactile, auditory, and visual cues need to be interchanged and processed by both the mother and the newborn. Sniffing, licking, grooming, and listening to the calf's vocalizations are the first inputs during calving. Oxytocin is the main hormone that mediates selective maternal behavior. Understanding the importance of the sensitive period and the endocrine changes required to elicit maternal behavior could help provide the appropriate stimulus to both the buffalo cow and the calf during calving. The cow–calf bonding is a process that must be developed within the first six hours after calving. Both the buffalo dam and the newborn calf receive a series of sensory cues during calving, including olfactory, tactile, auditory, and visual stimuli. These inputs are processed in the brain to develop an exclusive bond where the dam provides selective care to the filial newborn. The limbic system, sensory cortices, and maternal-related hormones such as oxytocin mediate this process. Due to the complex integration of the maternal response towards the newborn, this paper aims to review the development of the cow–calf bonding process in water buffalo (Bubalus bubalis) via the olfactory, tactile, auditory, and visual stimuli. It will also discuss the neuroendocrine factors motivating buffalo cows to care for the calf using examples in other ruminant species where dam–newborn bonding has been extensively studied. [ABSTRACT FROM AUTHOR]

Simple Summary: Infrared thermography is a rapid, contactless technology that can provide real-time temperature measurements, with various applications in livestock science within the framework of precision livestock farming. The aim of the current review paper is to present the updated state of knowledge regarding the detection of mastitis in dairy ruminants using infrared thermography. Therefore, it summarizes the technological advancements, the diagnostic capacity, and the operational and analytical challenges of the method when applied under real-world conditions as a diagnostic tool for mastitis, while factors associated with the performance of the method are critically described and discussed to justify recognized strengths, weaknesses, opportunities, and challenges of the method. Among the health issues of major concern in dairy ruminants, mastitis stands out as being associated with considerable losses in productivity and compromised animal health and welfare. Currently, the available methods for the early detection of mastitis are either inaccurate, requiring further validation, or expensive and labor intensive. Moreover, most of them cannot be applied at the point of care. Infrared thermography (IRT) is a rapid, non-invasive technology that can be used in situ to measure udder temperature and identify variations and inconsistencies thereof, serving as a benchmarking tool for the assessment of udders' physiological and/or health status. Despite the numerous applications in livestock farming, IRT is still underexploited due to the lack of standardized operation procedures and significant gaps regarding the optimum settings of the thermal cameras, which are currently exploited on a case-specific basis. Therefore, the objective of this review paper was twofold: first, to provide the state of knowledge on the applications of IRT for the assessment of udder health status in dairy ruminants, and second, to summarize and discuss the major strengths and weaknesses of IRT application at the point of care, as well as future challenges and opportunities of its extensive adoption for the diagnosis of udder health status and control of mastitis at the animal and herd levels. [ABSTRACT FROM AUTHOR]

Simple Summary: Genomic selection (GS) is defined as a marker-assisted selection method that uses high-density molecular markers covering the entire genome combined with phenotypic data to estimate genomic estimated breeding values (GEBV). This paper reviews the principles, methods, and influencing factors of GS for breeding by using phenotypic information and genetic relationships simultaneously. It also covers the research progress of GS in meat, fiber, dairy, and reproductive traits in sheep and goats. Compared to other livestock, especially cattle, sheep and goat breeding is relatively small-scale and has lagged behind. GS combined with genomic information can help promote the breeding process and development in commercial farming of sheep and goats. We highlight the potential for phenotypic information and genetic relationships to work together to drive the breeding process, providing valuable insights for implementing GS in sheep and goat breeding. The advancement of sequencing technology and molecular breeding methods has provided technical support and assurance for accurate breeding. Genomic Selection (GS) utilizes genomic information to improve livestock breeding, and it is more accurate and more efficient than traditional selection methods. GS has been widely applied in domestic animal breeding, especially in cattle. However, there are still limited studies on the application and research of GS in sheep and goats. This paper outlines the principles, analysis methods, and influential factors of GS and elaborates on the research progress, challenges, and prospects of applying GS in sheep and goat breeding. Through the review of these aspects, this paper is expected to provide valuable references for the implementation of GS in the field of sheep and goat breeding. [ABSTRACT FROM AUTHOR]

Simple Summary: Through gesture recognition and detection of sika deer, farmers can observe the gestures of sika deer without physical contact, providing data and technical support for the intelligent and welfare-oriented breeding of sika deer. This study is based on the YOLOv8 network model. By optimizing the convolution module, incorporating the attention mechanism, and enhancing the detection head module, a new method for detecting sika deer poses was developed. The method was assessed using four behavioral datasets, which included standing, lying, eating, and attacking. The pose-recognition accuracy of sika deer significantly improved to an average of 91.6%, laying a foundation for the health assessment and information management of sika deer. As the sika deer breeding industry flourishes on a large scale, accurately assessing the health of these animals is of paramount importance. Implementing posture recognition through target detection serves as a vital method for monitoring the well-being of sika deer. This approach allows for a more nuanced understanding of their physical condition, ensuring the industry can maintain high standards of animal welfare and productivity. In order to achieve remote monitoring of sika deer without interfering with the natural behavior of the animals, and to enhance animal welfare, this paper proposes a sika deer individual posture recognition detection algorithm GFI-YOLOv8 based on YOLOv8. Firstly, this paper proposes to add the iAFF iterative attention feature fusion module to the C2f of the backbone network module, replace the original SPPF module with AIFI module, and use the attention mechanism to adjust the feature channel adaptively. This aims to enhance granularity, improve the model's recognition, and enhance understanding of sika deer behavior in complex scenes. Secondly, a novel convolutional neural network module is introduced to improve the efficiency and accuracy of feature extraction, while preserving the model's depth and diversity. In addition, a new attention mechanism module is proposed to expand the receptive field and simplify the model. Furthermore, a new pyramid network and an optimized detection head module are presented to improve the recognition and interpretation of sika deer postures in intricate environments. The experimental results demonstrate that the model achieves 91.6% accuracy in recognizing the posture of sika deer, with a 6% improvement in accuracy and a 4.6% increase in mAP50 compared to YOLOv8n. Compared to other models in the YOLO series, such as YOLOv5n, YOLOv7-tiny, YOLOv8n, YOLOv8s, YOLOv9, and YOLOv10, this model exhibits higher accuracy, and improved mAP50 and mAP50-95 values. The overall performance is commendable, meeting the requirements for accurate and rapid identification of the posture of sika deer. This model proves beneficial for the precise and real-time monitoring of sika deer posture in complex breeding environments and under all-weather conditions. [ABSTRACT FROM AUTHOR]

This paper investigates the impact characteristics of the 4 November 2021 magnetic storm across different frequency bands based on the electric field data (EFD) from the China Seismo-Electromagnetic Satellite (CSES), categorized into four frequency bands: ULF (Ultra-Low-Frequency, DC to 16 Hz), ELF (Extremely Low-Frequency, 6 Hz to 2.2 kHz), VLF (Very Low-Frequency, 1.8 to 20 kHz), and HF (High-Frequency, 18 kHz to 3.5 MHz). The study reveals that in the ULF band, magnetic storm-induced electric field disturbances are primarily in the range of 0 to 5 Hz, with a significant disturbance frequency at 3.9 ± 1.0 Hz. Magnetic storms also enhance Schumann waves in the ULF band, with 8 Hz Schumann waves dominating in the southern hemisphere and 13 Hz Schumann waves dominating in the northern hemisphere. In the ELF band, the more pronounced anomalies occur at 300 Hz–900 Hz and above 1.8 kHz, with the 300 Hz–900 Hz band anomalies around 780 Hz being the most significant. In the VLF band, the electric field anomalies are mainly concentrated in the 3–15 kHz range. The ELF and VLF bands exhibit lower absolute and relative disturbance increments compared to the ULF band, with the relative perturbation growth rate in the ULF band being approximately 10% higher than in the ELF and VLF bands. Magnetic storm-induced electric field disturbances predominantly occur in the ULF, ELF, and VLF bands, with the most significant disturbances in the ULF band. The electric field perturbations in these three frequency bands exhibit hemispheric asymmetry, with strong perturbations in the northern hemisphere occurring earlier than in the southern hemisphere, corresponding to different Dst minima. No electric field disturbances were observed in the HF band (above 18 kHz). The conclusions of this paper are highly significant for future anti-jamming designs in spacecraft and communication equipment, as well as for the further study of magnetic storms. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) ship target detection has been extensively researched. However, most methods use the same dataset division for both training and validation. In practical applications, it is often necessary to quickly adapt to new loads, new modes, and new data to detect targets effectively. This presents a cross-domain detection problem that requires further study. This paper proposes a method for detecting SAR ships in complex backgrounds using fusion tensor and cross-domain adversarial learning. The method is designed to address the cross-domain detection problem of SAR ships with large differences between the training and test sets. Specifically, it can be used for the cross-domain detection task from the fully polarised medium-resolution ship dataset (source domain) to the high-resolution single-polarised dataset (target domain). This method proposes a channel fusion module (CFM) based on the YOLOV5s model. The CFM utilises the correlation between polarised channel images during training to enrich the feature information of single-polarised images extracted by the model during inference. This article proposes a module called the cross-domain adversarial learning module (CALM) to reduce overfitting and achieve adaptation between domains. Additionally, this paper introduces the anti-interference head (AIH) which decouples the detection head to reduce the conflict of classification and localisation problems. This improves the anti-interference and generalisation ability in complex backgrounds. This paper conducts cross-domain experiments using the constructed medium-resolution SAR full polarisation dataset (SFPD) as the source domain and the high-resolution single-polarised ship detection dataset (HRSID) as the target domain. Compared to the best-performing YOLOV8s model among typical mainstream models, this model improves precision by 4.9%, recall by 3.3%, AP by 2.4%, and F1 by 3.9%. This verifies the effectiveness of the method and provides a useful reference for improving cross-domain learning and model generalisation capability in the field of target detection. [ABSTRACT FROM AUTHOR]

Lunar exploration is of significant importance in the development and utilization of in situ lunar resources, water ice exploration, and astronomical science. In recent years, ground-based radar (GBR) has gained increasing attention in the field of lunar exploration due to its flexibility, low cost, and penetrating capabilities. This paper reviews the scientific research on lunar exploration using GBR, outlining the basic principles of GBR and the progress made in lunar exploration studies. Our paper introduces the fundamental principles of lunar imaging using GBR and systematically reviews studies on lunar surface/subsurface detection, the dielectric properties inversion of the lunar regolith, and polar water ice detection using GBR. In particular, the paper summarizes the current development status of the Chinese GBR and forecasts future development trends in China. This review will enhance the understanding of lunar exploration results using GBR radar, systematically demonstrate the main applications and scientific achievements of GBR in lunar exploration, and provide a reference for GBR radar in future lunar exploration missions. [ABSTRACT FROM AUTHOR]

This paper aims to improve a small-scale object detection model to achieve detection accuracy matching or even surpassing that of complex models. Efforts are made in the module design phase to minimize parameter count as much as possible, thereby providing the potential for rapid detection of maritime targets. Here, this paper introduces an innovative Anchor-Free-based Multi-Scale Feature Fusion Network (AFMSFFNet), which improves the problems of missed detection and false positives, particularly in inshore or small target scenarios. Leveraging the YOLOX tiny as the foundational architecture, our proposed AFMSFFNet incorporates a novel Adaptive Bidirectional Fusion Pyramid Network (AB-FPN) for efficient multi-scale feature fusion, enhancing the saliency representation of targets and reducing interference from complex backgrounds. Simultaneously, the designed Multi-Scale Global Attention Detection Head (MGAHead) utilizes a larger receptive field to learn object features, generating high-quality reconstructed features for enhanced semantic information integration. Extensive experiments conducted on publicly available Synthetic Aperture Radar (SAR) image ship datasets demonstrate that AFMSFFNet outperforms the traditional baseline models in detection performance. The results indicate an improvement of 2.32% in detection accuracy compared to the YOLOX tiny model. Additionally, AFMSFFNet achieves a Frames Per Second (FPS) of 78.26 in SSDD, showcasing superior efficiency compared to the well-established performance networks, such as faster R-CNN and CenterNet, with efficiency improvement ranging from 4.7 to 6.7 times. This research provides a valuable solution for efficient ship detection in complex backgrounds, demonstrating the efficacy of AFMSFFNet through quantitative improvements in accuracy and efficiency compared to existing models. [ABSTRACT FROM AUTHOR]

Monitoring aircraft using synthetic aperture radar (SAR) images is a very important task. Given its coherent imaging characteristics, there is a large amount of speckle interference in the image. This phenomenon leads to the scattering information of aircraft targets being masked in SAR images, which is easily confused with background scattering points. Therefore, automatic detection of aircraft targets in SAR images remains a challenging task. For this task, this paper proposes a framework for speckle reduction preprocessing of SAR images, followed by the use of an improved deep learning method to detect aircraft in SAR images. Firstly, to improve the problem of introducing artifacts or excessive smoothing in speckle reduction using total variation (TV) methods, this paper proposes a new nonconvex total variation (NTV) method. This method aims to ensure the effectiveness of speckle reduction while preserving the original scattering information as much as possible. Next, we present a framework for aircraft detection based on You Only Look Once v8 (YOLOv8) for SAR images. Therefore, the complete framework is called SAR-NTV-YOLOv8. Meanwhile, a high-resolution small target feature head is proposed to mitigate the impact of scale changes and loss of depth feature details on detection accuracy. Then, an efficient multi-scale attention module was proposed, aimed at effectively establishing short-term and long-term dependencies between feature grouping and multi-scale structures. In addition, the progressive feature pyramid network was chosen to avoid information loss or degradation in multi-level transmission during the bottom-up feature extraction process in Backbone. Sufficient comparative experiments, speckle reduction experiments, and ablation experiments are conducted on the SAR-Aircraft-1.0 and SADD datasets. The results have demonstrated the effectiveness of SAR-NTV-YOLOv8, which has the most advanced performance compared to other mainstream algorithms. [ABSTRACT FROM AUTHOR]

Rapid advancements in satellite technology have led to a significant increase in high-resolution remote sensing (RS) images, necessitating the use of advanced processing methods. Additionally, patent analysis revealed a substantial increase in deep learning and machine learning applications in remote sensing, highlighting the growing importance of these technologies. Therefore, this paper introduces the Kolmogorov-Arnold Network (KAN) model to remote sensing to enhance efficiency and performance in RS applications. We conducted several experiments to validate KAN's applicability, starting with the EuroSAT dataset, where we combined the KAN layer with multiple pre-trained CNN models. Optimal performance was achieved using ConvNeXt, leading to the development of the KonvNeXt model. KonvNeXt was evaluated on the Optimal-31, AID, and Merced datasets for validation and achieved accuracies of 90.59%, 94.1%, and 98.1%, respectively. The model also showed fast processing speed, with the Optimal-31 and Merced datasets completed in 107.63 s each, while the bigger and more complicated AID dataset took 545.91 s. This result is meaningful since it achieved faster speeds and comparable accuracy compared to the existing study, which utilized VIT and proved KonvNeXt's applicability for remote sensing classification tasks. Furthermore, we investigated the model's interpretability by utilizing Occlusion Sensitivity, and by displaying the influential regions, we validated its potential use in a variety of domains, including medical imaging and weather forecasting. This paper is meaningful in that it is the first to use KAN in remote sensing classification, proving its adaptability and efficiency. [ABSTRACT FROM AUTHOR]

For missile-borne platforms, traditional SAR technology consistently encounters two significant shortcomings: geometric distortion of 2D images and the inability to achieve forward-looking imaging. To address these issues, this paper explores the feasibility of using a maneuvering trajectory to enable forward-looking and three-dimensional imaging by analyzing the maneuvering characteristics of an actual missile-borne platform. Additionally, it derives the corresponding resolution characterization model, which lays a theoretical foundation for future applications. Building on this, the paper proposes a three-dimensional super-resolution imaging algorithm that combines axis rotation with compressed sensing. The axis rotation not only realizes the dimensionality reduction of data, but also can expand the observation scenario in the cross-track dimension. The proposed algorithm first focuses on the track-vertical plane to extract 2D position parameters. Then, a compressed sensing-based process is applied to extract reflection coefficients and super-resolution cross-track position parameters, thereby achieving precise 3D imaging reconstruction. Finally, numerical simulation results confirm the effectiveness and accuracy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Angle domain common image gathers (ADCIGs) play a crucial role in seismic exploration, offering prestack underground illumination information that aids in validating migration velocity and conducting prestack amplitude versus angle (AVA) analysis for reservoir characterization. This paper introduces an innovative approach for compensating amplitude errors caused by irregular seismic acquisition geometries in ADCIGs. By incorporating an angle domain illumination compensation factor, the proposed method effectively modifies these errors, preserving the amplitude of seismic reflectivity in the prestack angle domain. The effectiveness of the proposed approach is validated through comprehensive tests conducted on synthetic and field data examples. The results demonstrate the capability of the method to enhance the quality of ADCIGs derived from 3D reverse time migration (RTM), yielding accurate and reliable amplitude preservation. While the illumination compensation factor assumes a vertically linear velocity model, the method holds promise for extension to more complex media and diverse migration techniques. This suggests its applicability and adaptability beyond the specific assumptions considered in this study. In conclusion, this paper presents an innovative angle domain illumination compensation factor that significantly improves the quality of ADCIGs by addressing amplitude errors arising from irregular seismic acquisition geometries. The experimental validation using synthetic and field data confirms the effectiveness of the proposed method within the context of 3D RTM. Furthermore, the technique holds potential for broader application in more complex subsurface scenarios and various migration methodologies. [ABSTRACT FROM AUTHOR]

The eastern slope of Gongga Mountain is located in the mountainous region of Southwestern China, which has strong geologic tectonics that leads to frequent landslide hazards. A large number of such landslides were induced by the 2022 Luding Ms 6.8 earthquake. Therefore, it is necessary to identify the spatial distribution of landslides in the region. In this paper, the Google Earth platform and GF-1 and GF-6 satellite imagery were used to construct new pre-earthquake and co-seismic landslides. Then, we analyzed the relationship between the conditioning factors of the pre-earthquake and co-seismic landslide inventories and the spatial distribution of landslides, as well as the main controlling factors of landslide development. The main conclusions are as follows: (i) Through remote-sensing interpretation and field investigation, 1198 and 4284 landslides were recognized before and after the earthquake, respectively, and the scale was mainly small- and medium-sized. (ii) In two kinds of inventories, landslides are primarily distributed along the banks of the Dadu River basin, within elevations of 1200–1400 m and slopes of 30–50°. (iii) The distribution of pre-earthquake and co-seismic landslides was influenced by engineering geological layer combinations and earthquake intensity, with these two factors being the most significant. This paper plays an important role in hazard prevention and reconstruction planning in the Gongga Mountains. [ABSTRACT FROM AUTHOR]

The compatibility of seal materials with the working fluid is crucial for the flawless, energy-saving, environmentally sustainable, and safe operation of any technical system. This is especially true for hydraulic systems operating under high operating pressure. The problem of materials compatibility comes into play when either a new type of seal material or a new type of fluid comes into use. The paper discusses the research findings regarding material compatibility testing of new high-tech ionic hydraulic fluids with commonly used seal materials. Due to the completely different chemical composition of these new fluids compared to the classical mineral-based oil, for these fluids, there are no standardized testing procedures. In these cases, we can only lean on the Standards that apply to classical fluids, which can lead to incorrect results. In the forefront of the paper is the discrepancy between the results obtained by the standardized test, and the test under real operating conditions. FKM, an excellent material for seals, proved to be the most suitable in the case of using ionic hydraulic fluid, according to a standardized test. However, it failed in the comparison test under real operating conditions, as the cylinder leaked. NBR seals proved to be a better solution. [ABSTRACT FROM AUTHOR]

Multi-junction solar cells are vital in developing reliable, green, sustainable solar cells. Consequently, the computational optimization of solar cell architecture has the potential to profoundly expedite the process of discovering high-efficiency solar cells. Copper indium gallium selenide (CIGS)-based solar cells exhibit substantial performance compared to those utilizing cadmium sulfide (CdS). Likewise, CIGS-based devices are more efficient according to their device performance, environmentally benign nature, and thus, reduced cost. Therefore, the paper introduces an optimization process of three-layered n-CdS/p-CIGS/p-GaAs (NPP)) solar cell architecture based on thickness and carrier charge density. An in-depth investigation of the numerical analysis for homojunction PPN-junction with the 'GaAs' layer structure along with n-ZnO front contact was simulated using the Solar Cells Capacitance Simulator (SCAPS-1D) software. Subsequently, various computational optimization techniques for evaluating the effect of the thickness and the carrier density on the performance of the PPN layer on solar cell architecture were examined. The electronic characteristics by adding the GaAs layer on the top of the conventional (PN) junction further led to optimized values of the power conversion efficiency (PCE), open-circuit voltage (VOC), fill factor (FF), and short-circuit current density (JSC) of the solar cell. Lastly, the paper concludes by highlighting the most promising results of our study, showcasing the impact of adding the GaAs layer. Hence, using the optimized values from the analysis, thickness of 5 (μm) and carrier density of 1 × 10 20 (1/cm) resulted in the maximum PCE, VOC, FF, and JSC of 45.7%, 1.16 V, 89.52%, and 43.88 (mA / m 2) , respectively, for the proposed solar cell architecture. The outcomes of the study aim to pave the path for highly efficient, optimized, and robust multi-junction solar cells. [ABSTRACT FROM AUTHOR]

The electricity sector plays a key role in achieving zero emissions targets. The required transition will lead to substantial changes in the supply, demand, and distribution of electricity, as well as in stakeholder roles. Future market designs may change substantially to accommodate these changes, address challenges, and take advantage of new opportunities. This paper reviews the characteristics of future carbon-neutral electricity systems and electricity market design options. To provide a guiding framework for the literature review, we transfer the complexity of electricity systems into a three-layer structure: Firstly, we analyze papers that rely on techno-economic modeling of the physical electricity system. As a case study, we analyze various studies focusing on a decarbonized European electricity system in 2050. Secondly, we review papers that investigate the economic behavior and effects of self-interest-seeking stakeholders such as producers, network operators, and consumers. Finally, we review papers focusing on policy and market design questions that guide policymakers in achieving a target physical asset combination while considering the behavior of stakeholders. We highlight common trends and disagreements in the literature, review the main drivers of future markets, and finally provide a mapping between those drivers, challenges, and opportunities. The review concludes that the most promising next step toward a fully comprehensive assessment approach is to combine existing approaches across topical and disciplinary boundaries. [ABSTRACT FROM AUTHOR]

The rapid expansion of the Industrial Internet-of-Things (IIoT) has spurred significant research interest due to the growth of security-aware, vehicular, and time-sensitive applications. Unmanned aerial vehicles (UAVs) are widely deployed within wireless communication systems to establish rapid and reliable links between users and devices, attributed to their high flexibility and maneuverability. Leveraging UAVs provides a promising solution to enhance communication system performance and effectiveness while overcoming the unprecedented challenges of stringent spectrum limitations and demanding data traffic. However, due to the dramatic increase in the number of vehicles and devices in the industrial wireless networks and limitations on UAVs' battery storage and computing resources, the adoption of energy-efficient techniques is essential to ensure sustainable system implementation and to prolong the lifetime of the network. This paper provides a comprehensive review of various disruptive methodologies for addressing energy-efficient issues in UAV-assisted industrial wireless networks. We begin by introducing the background of recent research areas from different aspects, including security-enhanced industrial networks, industrial vehicular networks, machine learning for industrial communications, and time-sensitive networks. Our review identifies key challenges from an energy efficiency perspective and evaluates relevant techniques, including resource allocation, UAV trajectory design and wireless power transfer (WPT), across various applications and scenarios. This paper thoroughly discusses the features, strengths, weaknesses, and potential of existing works. Finally, we highlight open research issues and gaps and present promising potential directions for future investigation. [ABSTRACT FROM AUTHOR]

The present paper reviews the hot topics at the 18th Conference on Sustainable Development of Energy, Water, and Environment Systems (SDEWES) held from 24 to 29 September 2023 in Dubrovnik, Croatia. The selected papers, including aspects of biomass energy application, energy saving in building, low-carbon development and climate change adaptation, hybrid application of sustainable energy, energy storage system, and other measures in sustainable development, are published in this Special Issue of Energies. The 12 selected papers in Energies and corresponding literature that relates to the above topics and has been published in the most recent year are reviewed. In particular, diversifying energy applications are trending towards comprehensive integration with more efficient clean energy capacities for global energy procurement, which can also offer increased resilience and sustainability, reduce reliance on fossil fuels, mitigate environmental impacts, and enhance energy security through a mix of renewable sources and innovative technologies. Meanwhile, energy storage has become increasingly important, which can manage the intermittent of renewable energy, stabilize the grid, and improve energy security and resilience against disruptions. In addition to conventional solar energy storage, biomass energy storage, building energy storage, water storage, etc., can also reduce the overall energy costs, deferring investments in additional transmission infrastructure. [ABSTRACT FROM AUTHOR]

This paper presents an optimization method for scheduling a multi-energy VPP (Virtual Power Plant) supply–demand balance in the power market environment of Jiangxi Province. The primary objective of this method is to improve the operational efficiency of the power grid, reduce energy costs, and facilitate economical and efficient energy distribution in the power market. The method takes into account the characteristics and uncertainties of renewable energy sources such as solar and wind energy, and incorporates advanced multi-objective optimization algorithms. Furthermore, it integrates real-time market price feedback to achieve the accurate allocation of power supply and demand. Through a case study of a multi-energy VPP in Jiangxi Province, this paper examines the optimal combination model for various energy sources within VPP, and analyzes the impact of different market environments on supply–demand balance. The results demonstrate that the proposed scheduling optimization method significantly improves economic benefits while ensuring grid stability. Compared with traditional power supply models, it reduces average electricity costs by 15% and increases renewable energy utilization efficiency by 20%. [ABSTRACT FROM AUTHOR]

Due to the continuous growth of global energy demand and the urgent pursuit of sustainable development goals, renewable energy development has become a vital strategy to deal with energy challenges and environmental issues. Renewable energy generation efficiency (REGE) around the world has begun to be examined, and ambitious goals with a sense of mission within a predetermined timeline have been set. The goal of this paper is to use the dynamic slacks-based measure (DSBM) data envelopment analysis (DEA) method to obtain the REGE for 44 Asian economies from 2010 to 2021. This paper also uses Tobit regression analysis to explore the factors that may affect the REGE. The empirical results indicate that the REGE in 17 economies reached the efficiency target during this period. When classified by income level, differences in average REGE are observed among high-income, upper-middle-income, lower-middle-income, and low-income economies. Additionally, differences in average REGE exist between tropical and temperate economies when classified by geographic latitude. Furthermore, through the Tobit regression model, we determine that information digitalization, financial openness, technological innovation ability, and renewable energy device capacity share all have significant positive effects on REGE, but life quality and democracy degree have significant negative impacts on REGE. Moreover, it has been found that the REGE scores of Asian economies exhibit a status similar to the middle-income trap. The outcome of the research provides Asian governments and those middle-income economies with ways to enhance REGE. Due to data limitations, this study cannot estimate the convergent solution based on the data of the research sample, and a new advanced Panel Tobit model is required. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources (RESs) is rapidly increasing within energy systems worldwide. However, this shift introduces intermittency and uncertainty on the supply side. To hedge against RES intermittency, demand-side flexibility introduces a practical solution. Therefore, further studies are required to unleash demand-side flexibility in power systems. This flexibility is relevant across various sectors of power systems, including residential, industrial, commercial, and agricultural sectors. This paper reviews the key aspects of demand-side flexibility within the residential sector. To achieve this objective, a general introduction to demand flexibility across the four sectors is provided. As a contribution of this paper, and in comparison with previous studies, household appliances are classified based on their flexibility and controllability. The flexibility potential of key residential demands, including heat pumps, district heating, electric vehicles, and battery systems, is then reviewed. Another contribution of this paper is the exploration of demand-side flexibility scheduling under uncertainty, examining three approaches: stochastic programming, robust optimization, and information-gap decision theory. Additionally, the integration of demand flexibility into short-term electricity markets with high-RES penetration is discussed. Finally, the key objective functions and simulation software used in the study of demand-side flexibility are reviewed. [ABSTRACT FROM AUTHOR]

Accurate electrical load forecasting is crucial for the stable operation of power systems. However, existing forecasting models face limitations when handling multidimensional features and feature interactions. Additionally, traditional metaheuristic algorithms tend to become trapped in local optima during the optimization process, negatively impacting model performance and prediction accuracy. To address these challenges, this paper proposes a short-term electrical load forecasting method based on a parallel Temporal Convolutional Network–Gated Recurrent Unit (PTCN-GRU) model, optimized by an improved Dung Beetle Optimization algorithm (IDBO). This method employs a parallel TCN structure, using TCNs with different kernel sizes to extract and integrate multi-scale temporal features, thereby overcoming the limitations of traditional TCNs in processing multidimensional input data. Furthermore, this paper enhances the optimization performance and global search capability of the traditional Dung Beetle Optimization algorithm through several key improvements. Firstly, Latin hypercube sampling is introduced to increase the diversity of the initial population. Next, the Golden Sine Algorithm is integrated to refine the search behavior. Finally, a Cauchy–Gaussian mutation strategy is incorporated in the later stages of iteration to further strengthen the global search capability. Extensive experimental results demonstrate that the proposed IDBO-PTCN-GRU model significantly outperforms comparison models across all evaluation metrics. Specifically, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were reduced by 15.01%, 14.44%, and 14.42%, respectively, while the coefficient of determination (R2) increased by 2.13%. This research provides a novel approach to enhancing the accuracy of electrical load forecasting. [ABSTRACT FROM AUTHOR]

Energy communities are a transformative force in the electricity markets and ancillary services, reshaping the energy landscape through collective action. This paper explores the successful strategies adopted by these communities, highlighting real-world cases where they have participated directly in the market, or through aggregators, or sold their energy to retailers, which is of paramount importance because it serves as a foundation for those countries that wish to implement these entities as part of their decarbonization plan. It also serves as a model for the development of future citizen initiatives that aim to turn citizens into active users of the electricity system. The paper examines collaborative dynamics within the energy sector, highlighting how these communities optimize resource sharing and contribute to a more resilient and sustainable energy system. The study emphasizes the potential of energy communities in driving innovation and fostering a participatory approach to energy management. The results show that some pilot projects are being developed and several electricity cooperatives, one of the most common forms of energy communities, are participating in energy trading with their members and other entities. More efforts are also needed for energy communities to participate more directly in the market and/or through aggregators. [ABSTRACT FROM AUTHOR]

The shared energy storage device acts as an energy hub between multiple microgrids to better play the complementary characteristics of the microgrid power cycle. In this paper, the cooperative operation process of shared energy storage participating in multiple island microgrid systems is researched, and the two-stage research on multi-microgrid operation mode and shared energy storage optimization service cost is focused on. In the first stage, the output of each subject is determined with the goal of profit optimization and optimal energy storage capacity, and the modified grey wolf algorithm is used to solve the problem. In the second stage, the income distribution problem is transformed into a negotiation bargaining process. The island microgrid and the shared energy storage are the two sides of the game. Combined with the non-cooperative game theory, the alternating direction multiplier method is used to reduce the shared energy storage service cost. The simulation results show that shared energy storage can optimize the allocation of multi-party resources by flexibly adjusting the control mode, improving the efficiency of resource utilization while improving the consumption of renewable energy, meeting the power demand of all parties, and realizing the sharing of energy storage resources. Simulation results show that compared with the traditional PSO algorithm, the iterative times of the GWO algorithm proposed in this paper are reduced by 35.62%, and the calculation time is shortened by 34.34%. Compared with the common GWO algorithm, the number of iterations is reduced by 18.97%, and the calculation time is shortened by 22.31%. [ABSTRACT FROM AUTHOR]

The integration of numerous distributed energy sources into the power system offers exciting opportunities to enhance the resilience of distribution networks. It is worth noting that the renewable-to-ammonia system has the potential to alleviate the multi-temporal and spatial imbalance of the power system. Therefore, this paper proposes a mathematical model for a renewable-to-ammonia system, taking into account the material balance and power balance of each unit. Based on this, this paper further explores the optimization scheduling method for flexible ammonia loads in distribution networks. A relaxation method for branch flow models in distribution networks based on second-order cone programming is proposed. An optimization scheduling model for flexible ammonia loads in distribution networks is constructed to minimize network loss. Moreover, considering the environmental advantages of the renewable-to-ammonia system, this paper compares the changes in hydrogen production technologies under different carbon emission constraints. Finally, a case study of the IEEE 33-node system is adopted to verify the effectiveness of the proposed model and method. It indicates that the renewable-to-ammonia system has environmental benefits and can reduce network loss to a certain extent. [ABSTRACT FROM AUTHOR]

A reduction in greenhouse gases has become an inescapable requirement. An effective scenario for achieving carbon neutrality is to develop a hydrogen economy. Its success, however, requires strict control of the different processes involved in planned hydrogen chains. The energy chain considered in this paper is a stationary application which involves the production of hydrogen by electrolysis (a power-to gas process) and its combustion in gas turbine combined cycles to generate electricity (a gas-to-power process). In such applications, the need is twofold: (i) to control the risk of explosive atmospheres by performing safe gas detection in the presence of hydrogen and (ii) to secure the reliability of all chain processes using hydrogen-rich gases by achieving reliable analyses of these gases. This paper is dedicated to the development of hydrogen energy to decarbonize the thermal production of electricity. We will first describe the hydrogen chain that would best suit the power generation sector. Then, we will highlight the properties of hydrogen that are critical for its reliable operation. Finally, we will review the sensing technologies suitable for hydrogen-containing fuels. This review paper was published as part of a Joint Industrial Project (JIP) aimed at enabling the safe and reliable deployment of hydrogen energy. [ABSTRACT FROM AUTHOR]

This paper presents the results of experimental studies on the effects of temperature and time of annealing on the elastoplastic properties of bimetallic aluminium–copper sheets. Mechanical tests were carried out on flat samples previously heated to temperatures of 250, 350, 450, and 500 °C for 40, 90, and 150 min. At the beginning of the tests, the elastic constants and internal friction energy were determined after thermal exposure using the impulse vibration exposure method. Further tests were carried out on the same samples using the three-point bending test. Based on the tests, the following quantities were determined and analysed: elasticity angles, translocations of the neutral axes of the cross-sections of samples, and changes in the values of bending moments plasticizing the extreme layers of bimetallic Al/Cu samples resulting from thermal interactions. The final part of this paper presents the results of measurements of the thickness of diffusion zones at the interface and their effect on the stability of the joint after annealing. The studies that were conducted indicate the dominant influence of the thermal factor on the properties of the Al/Cu bimetal above the temperature of 350 °C, which leads to the weakening of its strength and the degradation of the structure at the metallic phase boundary. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate a method of measuring the flexural elastic modulus of ceramics at an intermediate (~millimeter) scale at high temperatures. We used a picosecond laser to precisely cut microbeams from the location of interest in a bulk ceramic. They had a cross-section of approximately 100 μm × 300 μm and a length of ~1 cm. They were then tested in a thermal mechanical analyzer at room temperature, 500 °C, 800 °C, and 1100 °C using the four-point flexural testing method. We compared the elastic moduli of high-purity Al2O3 and AlN measured by our method with the reported values in the literature and found that the difference was less than 5% for both materials. This paper provides a new and accurate method of characterizing the high-temperature elastic modulus of miniature samples extracted from representative/selected areas of bulk materials. [ABSTRACT FROM AUTHOR]

This paper presents a type of acoustic metamaterial that combines a labyrinth channel with a Helmholtz cavity and a thin film. The labyrinth-opening design and thin-film combination contribute to the metamaterial's exceptional sound-insulation performance. After comprehensive research, it is observed that in the frequency range of 20–1200 Hz, this acoustic metamaterial exhibits multiple sound-insulation peaks, showing a high overall sound-insulation quality. Specifically, the first sound-insulation peak is 26.3 Hz, with a bandwidth of 13 Hz and giving a transmission loss of 56.5 dB, showing excellent low-frequency sound-insulation performance. To further understand the low-frequency sound-insulation mechanism, this paper uses the equivalent model method to conduct an acoustic–electrical analogy, construct an equivalent model of the acoustic metamaterial, and delve into the sound-insulation mechanism at the first sound-insulation peak. To confirm the validity of the theoretical calculations, physical experiments are carried out by 3D printing experimental samples. The analysis of the experimental data has yielded results that are consistent with the simulation data, providing empirical evidence for the accuracy of the theoretical model. The material has significant practical application value. Finally, various factors are studied in depth based on the established equivalent model, which can provide valuable insights for the design and practical engineering application of acoustic metamaterials. [ABSTRACT FROM AUTHOR]

In salt lake areas, the cast-in-situ concrete structure has been corroded by the combination of sulfate and chloride for a long time. The incorporation of basalt fiber materials into concrete helps to improve the durability of concrete. In this paper, experiments were conducted to study the corrosion deterioration mechanisms of basalt fiber-reinforced cast-in-situ concrete under sulfate, chloride, and combined attack. The appearance, size, mass, flexural, and compressive strength of specimens were investigated during the immersion period to determine the changes in the physical and mechanical properties of specimens. Moreover, the microstructure and mineral changes of specimens during the immersion period were observed by Scanning Electron Microscope (SEM), Energy Dispersive Spectrometer (EDS), X-ray diffraction (XRD), and Thermogravimetric (TG)/ Derivative Thermogravimetric (DTG) analyses. Results show that premixed chloride has a significant detrimental influence on the strength development of cast-in-situ concrete, with concrete powder spalling occurring on the surface of the specimen. Severe corrosion degradation of specimens occurs under the external sulfate and internal chloride combined attack, resulting in lower flexural and compressive strength. The compressive strength and flexural strength of the corroded specimens decreased by 15.4% and 24.8%, respectively, compared with the control group at 28 days. Moreover, premixed basalt fiber has a beneficial influence on cast-in-situ concrete. When the basalt fiber content is 0.5%, the flexural strength of the specimen is increased by 16.2%. The filling and bridging effect of basalt fiber alleviates the negative effects caused by corrosion. In addition, increasing fiber content is beneficial for enhancing its effectiveness when the fiber content is less than 0.5%. This paper provides a valuable reference for the application of basalt fiber-reinforced cast-in-situ concrete under the condition of sulfate–chloride compound corrosion. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) technology, as a non-destructive testing methodology, is extensively utilized to monitor various materials' structural integrity. However, AE signals captured during experimental processes are often tainted with assorted noise factors that degrade the signal clarity and integrity, complicating precise analytical evaluations of the experimental outcomes. In response to these challenges, this paper introduces an unsupervised deep learning-based denoising model tailored for AE signals. It juxtaposes its efficacy against established methods, such as wavelet packet denoising, Hilbert transform denoising, and complete ensemble empirical mode decomposition with adaptive noise denoising. The results demonstrate that the unsupervised skip autoencoder model exhibits substantial potential in noise reduction, marking a significant advancement in AE signal processing. Subsequently, the paper focuses on applying this advanced denoising technique to AE signals collected during the tensile testing of steel fiber-reinforced concrete (SFRC), the tensile testing of steel, and flexural experiments of reinforced concrete beam, and it meticulously discusses the variations in the waveform and the spectrogram of the original signal and the signal after noise reduction. The results show that the model can also remove the noise of AE signals. [ABSTRACT FROM AUTHOR]

An adaptive Nesterov quasi-Newton acceleration (an-QNA)-optimized low-noise amplifier (LNA) is proposed in this paper. An optimized single-ended-to-differential two-stage LNA circuit is presented. It includes an improved post-linearization (IPL) technique to enhance the linearity. Traditional methods like conventional quasi-Newton (c-QN) often suffer from slow convergence and the tendency to get trapped in local minima. However, the proposed an-QNA method significantly accelerates the convergence speed. Furthermore, in this paper, modifications have been made to the an-QNA algorithm using a quadratic estimation to guarantee global convergence. The optimized an-QNA-based LNA, using standard 65 nm CMOS technology, achieves a simulated gain of 17.5 dB, a noise figure (NF) of 3.7 dB, and a 1 dB input compression point (IP1dB) of −13.1 dBm. It is also noted that the optimized LNA achieves a measured gain of 12.9 dB and an NF of 4.98 dB, and the IP1dB is −17.8 dB. The optimized LNA has a chip area of 0.67 mm2. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission distance, leading to "double near and far effect" in the joint transmission of wireless energy and data, which affects the quality of the data processing service for end users. Consequently, it is essential to design a reasonable system model to overcome the "double near and far effect" and make reasonable scheduling of multi-dimensional resources such as energy, communication and computing to guarantee high-quality data processing services. First, this paper designs a relay collaboration WPT-MEC resource scheduling model to improve wireless energy utilization efficiency. The optimization goal is to minimize the normalization of the total communication delay and total energy consumption while meeting multiple resource constraints. Second, this paper imports a BK-means algorithm to complete the end terminals cluster to guarantee effective energy reception and adapts the whale optimization algorithm with adaptive mechanism (AWOA) for mobile vehicle path-planning to reduce energy waste. Third, this paper proposes an immune differential enhanced deep deterministic policy gradient (IDDPG) algorithm to realize efficient resource scheduling of multiple resources and minimize the optimization goal. Finally, simulation experiments are carried out on different data, and the simulation results prove the validity of the designed scheduling model and proposed IDDPG. [ABSTRACT FROM AUTHOR]

In modern urban traffic, vehicles and pedestrians are fundamental elements in the study of traffic dynamics. Vehicle and pedestrian detection have significant practical value in fields like autonomous driving, traffic management, and public security. However, traditional detection methods struggle in complex environments due to challenges such as varying scales, target occlusion, and high computational costs, leading to lower detection accuracy and slower performance. To address these challenges, this paper proposes an improved vehicle and pedestrian detection algorithm based on YOLOv8, with the aim of enhancing detection in complex traffic scenes. The motivation behind our design is twofold: first, to address the limitations of traditional methods in handling targets of different scales and severe occlusions, and second, to improve the efficiency and accuracy of real-time detection. The new generation of dense pedestrian detection technology requires higher accuracy, less computing overhead, faster detection speed, and more convenient deployment. Based on the above background, this paper proposes a synchronous end-to-end vehicle pedestrian detection algorithm based on improved YOLOv8, aiming to solve the detection problem in complex scenes. First of all, we have improved YOLOv8 by designing a deformable convolutional improved backbone network and attention mechanism, optimized the network structure, and improved the detection accuracy and speed. Secondly, we introduced an end-to-end target search algorithm to make the algorithm more stable and accurate in vehicle and pedestrian detection. The experimental results show that, using the algorithm designed in this paper, our model achieves an 11.76% increase in precision and a 6.27% boost in mAP. In addition, the model maintains a real-time detection speed of 41.46 FPS, ensuring robust performance even in complex scenarios. These optimizations significantly enhance both the efficiency and robustness of vehicle and pedestrian detection, particularly in crowded urban environments. We further apply our improved YOLOv8 model for real-time detection in intelligent transportation systems and achieve exceptional performance with a mAP of 95.23%, outperforming state-of-the-art models like YOLOv5, YOLOv7, and Faster R-CNN. [ABSTRACT FROM AUTHOR]

The spatial target motion model exhibits a high degree of nonlinearity. This leads to the fact that it is easy to diverge when the conventional Kalman filter is used to track the spatial target. The Unscented Kalman filter can be a good solution to this problem. This is because it conveys the statistical properties of the state vector by selecting sampling points to be mapped through the nonlinear model. In practice, however, the measurement noise is often time-varying or unknown. In this case, the filtering accuracy of the Unscented Kalman filter will be reduced. In order to reduce the influence of time-varying measurement noise on the spatial target tracking, while accurately representing the a posteriori mean and covariance of the spatial target state vector, this paper proposes an adaptive noise factor method based on the Unscented Kalman filter to adaptively adjust the covariance matrix of the measurement noise. In this paper, numerical simulations are performed using measurement models from a space-based infrared satellite and a ground-based radar. It is experimentally demonstrated that the adaptive noise factor method can adapt to time-varying measurement noise and thus improve the accuracy of spatial target tracking compared to the Unscented Kalman filter. [ABSTRACT FROM AUTHOR]

There is a growing need to implement modern technologies, such as digital twinning, to improve the efficiency of transport fleet maintenance processes and maintain company operational capacity at the required level. A comprehensive review of the existing literature is conducted to address this, offering an up-to-date analysis of relevant content in this field. The methodology employed is a systematic literature review using the Primo multi-search tool, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The selection criteria focused on English studies published between 2012 and 2024, resulting in 201 highly relevant papers. These papers were categorized into seven groups: (a) air transportation, (b) railway transportation, (c) land transportation (road), (d) in-house logistics, (e) water and intermodal transportation, (f) supply chain operation, and (g) other applications. A notable strength of this study is its use of diverse scientific databases facilitated by the multi-search tool. Additionally, a bibliometric analysis was performed, revealing the evolution of DT applications over the past decade and identifying key areas such as predictive maintenance, condition monitoring, and decision-making processes. This study highlights the varied levels of adoption across different transport sectors and underscores promising areas for future development, particularly in underrepresented domains like supply chains and water transport. Additionally, this paper identifies significant research gaps, including integration challenges, real-time data processing, and standardization needs. Future research directions are proposed, focusing on enhancing predictive diagnostics, automating maintenance processes, and optimizing inventory management. This study also outlines a framework for DT in transportation systems, detailing key components and functionalities essential for effective maintenance management. The findings provide a roadmap for future innovations and improvements in DT applications within the transportation industry. This study ends with conclusions and future research directions. [ABSTRACT FROM AUTHOR]

The small area of a printed circuit board (PCB) results in densely distributed defects, leading to a lower detection accuracy, which subsequently impacts the safety and stability of the circuit board. This paper proposes a new YOLO-BFRV network model based on the improved YOLOv8 framework to identify PCB defects more efficiently and accurately. First, a bidirectional feature pyramid network (BIFPN) is introduced to expand the receptive field of each feature level and enrich the semantic information to improve the feature extraction capability. Second, the YOLOv8 backbone network is refined into a lightweight FasterNet network, reducing the computational load while improving the detection accuracy of minor defects. Subsequently, the high-speed re-parameterized detection head (RepHead) reduces inference complexity and boosts the detection speed without compromising accuracy. Finally, the VarifocalLoss is employed to enhance the detection accuracy for densely distributed PCB defects. The experimental results demonstrate that the improved model increases the mAP by 4.12% compared to the benchmark YOLOv8s model, boosts the detection speed by 45.89%, and reduces the GFLOPs by 82.53%, further confirming the superiority of the algorithm presented in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a novel centrifugal microfluidic approach (so-called lab-on-a-CD) for magnetic circulating tumor cell (CTC) separation from the other healthy cells according to their physical and acquired chemical properties. This study enhances the efficiency of CTC isolation, crucial for cancer diagnosis, prognosis, and therapy. CTCs are cells that break away from primary tumors and travel through the bloodstream; however, isolating CTCs from blood cells is difficult due to their low numbers and diverse characteristics. The proposed microfluidic device consists of two sections: a passive section that uses inertial force and bifurcation law to sort CTCs into different streamlines based on size and shape and an active section that uses magnetic forces along with Dean drag, inertial, and centrifugal forces to capture magnetized CTCs at the downstream of the microchannel. The authors designed, simulated, fabricated, and tested the device with cultured cancer cells and human cells. We also proposed a cost-effective method to mitigate the surface roughness and smooth surfaces created by micromachines and a unique pulsatile technique for flow control to improve separation efficiency. The possibility of a device with fewer layers to improve the leaks and alignment concerns was also demonstrated. The fabricated device could quickly handle a large volume of samples and achieve a high separation efficiency (93%) of CTCs at an optimal angular velocity. The paper shows the feasibility and potential of the proposed centrifugal microfluidic approach to satisfy the pumping, cell sorting, and separating functions for CTC separation. [ABSTRACT FROM AUTHOR]

In response to the issue that the fusion process of infrared and visible images is easily affected by lighting factors, in this paper, we propose an adaptive illumination perception fusion mechanism, which was integrated into an infrared and visible image fusion network. Spatial attention mechanisms were applied to both infrared images and visible images for feature extraction. Deep convolutional neural networks were utilized for further feature information extraction. The adaptive illumination perception fusion mechanism is then integrated into the image reconstruction process to reduce the impact of lighting variations in the fused images. A Median Strengthening Channel and Spatial Attention Module (MSCS) was designed to be integrated into the backbone of YOLOv8. In this paper, we used the fusion network to create a dataset named ivifdata for training the target recognition network. The experimental results indicated that the improved YOLOv8 network saw further enhancements of 2.3%, 1.4%, and 8.2% in the Recall, mAP50, and mAP50-95 metrics, respectively. The experiments revealed that the improved YOLOv8 network has advantages in terms of recognition rate and completeness, while also reducing the rates of false negatives and false positives. [ABSTRACT FROM AUTHOR]

Heart diseases remain one of the leading causes of morbidity and mortality worldwide, necessitating innovative diagnostic methods for early detection and intervention. An electrocardiogram (ECG) is a well-known technique for the preliminary diagnosis of heart conditions. However, it can not be used for continuous monitoring due to skin irritation. It is well known that every body organ generates a magnetic field, and the heart generates peak amplitudes of about 10 to 100 pT (measured at a distance of about 3 cm above the chest). This poses challenges to capturing such signals. This paper reviews the different techniques used to capture the heart's magnetic signals along with their limitations. In addition, this paper provides a comprehensive review of the different approaches that use the heart-generated magnetic field to diagnose several heart diseases. This research reveals two aspects. First, as a noninvasive tool, the use of the heart's magnetic field signal can lead to more sensitive advanced heart disease diagnosis tools, especially when continuous monitoring is possible and affordable. Second, its current use is limited due to the lack of accurate, affordable, and portable sensing technology. [ABSTRACT FROM AUTHOR]

This research focuses on the development of a machine learning-based approach for the early diagnosis of blade rubbing in rotary machinery. In this paper, machine learning-based diagnostic methods are used for blade rubbing early diagnosis, and the faults are simulated using experimental models. The experimental conditions were simulated as follows: Excessive rotor vibration is generated by an unbalance mass, and blade rubbing occurs through excessive rotor vibration. Additionally, the severity of blade rubbing was also simulated while increasing the unbalance mass. And then, machine learning-based diagnostic methods were applied and the trends according to the severity of blade rubbing were compared. This paper provides a signal processing method through feature analysis to diagnose blade rubbing conditions in machine learning. It was confirmed that the results of the unbalance and blade rubbing represent different trends, and it is possible to distinguish unbalance from blade rubbing before blade rubbing occurs. The diagnosis using machine learning methods will be applicable to rotating machinery faults like blade rubbing; furthermore, the early diagnosis of blade rubbing will be possible. [ABSTRACT FROM AUTHOR]

Multi-spectral temperature measurement technology has been found to have extensive applications in engineering practice. Addressing the challenges posed by unknown emissivity in multi-spectral temperature measurement data processing, this paper adds emissivity constraints to the objective function. It proposes a multi-spectral radiation temperature measurement data processing model realized through a particle swarm optimization algorithm improved based on multiple strategies. This paper simulates six material models with distinct emissivity trends. The simulation results indicate that the algorithm calculates an average relative temperature error of less than 0.3%, with an average computation time of merely 0.24 s. When applied to the temperature testing of silicon carbide and tungsten, experimental data further confirmed its accuracy: the absolute temperature error for silicon carbide (tungsten) is less than 4 K (7 K), and the average relative error is below 0.4% (0.3%), while two materials maintain an average computation time of 0.33 s. In summary, the improved particle swarm optimization algorithm demonstrates strong performance and high accuracy in multi-spectral radiation thermometry, making it a feasible solution for addressing multi-spectral temperature measurement challenges in practical engineering applications. Additionally, it can be extended to other multi-spectral systems. [ABSTRACT FROM AUTHOR]

Azimuth resolution and swath width are two crucial parameters in spaceborne synthetic aperture radar (SAR) systems. However, it is difficult for conventional spaceborne SAR to simultaneously achieve high-resolution wide-swath (HRWS) due to the minimum antenna area constraint. To mitigate this limitation, some representative HRWS SAR imaging techniques have been investigated, e.g., the azimuth multichannel technique, digital beamforming (DBF) technique, and pulse repetition interval (PRI) variation technique. This paper focus on a comprehensive review of the three techniques with respect to their latest developments. First, some key parameters of HRWS SAR are presented and analyzed to help the reader establish the general concept of SAR. Second, three techniques are introduced in detail, roughly following a simple-to-complex approach, i.e., start with the basic concept, then move to the core principles and classic technical details, and finally report the technical challenges and corresponding solutions. Third, some in-depth insights on the comparison among the three techniques are given. The purpose of this paper is to provide a review and brief perspective on the development of HRWS SAR. [ABSTRACT FROM AUTHOR]

In recent years, the accuracy of visual SLAM (Simultaneous Localization and Mapping) technology has seen significant improvements, making it a prominent area of research. However, within the current RGB-D SLAM systems, the estimation of 3D positions of feature points primarily relies on direct measurements from RGB-D depth cameras, which inherently contain measurement errors. Moreover, the potential of triangulation-based estimation for ORB (Oriented FAST and Rotated BRIEF) feature points remains underutilized. To address the singularity of measurement data, this paper proposes the integration of the ORB features, triangulation uncertainty estimation and depth measurements uncertainty estimation, for 3D positions of feature points. This integration is achieved using a CI (Covariance Intersection) filter, referred to as the CI-TEDM (Triangulation Estimates and Depth Measurements) method. Vision-based SLAM systems face significant challenges, particularly in environments, such as long straight corridors, weakly textured scenes, or during rapid motion, where tracking failures are common. To enhance the stability of visual SLAM, this paper introduces an improved CI-TEDM method by incorporating wheel encoder data. The mathematical model of the encoder is proposed, and detailed derivations of the encoder pre-integration model and error model are provided. Building on these improvements, we propose a novel tightly coupled visual-inertial RGB-D SLAM with encoder integration of ORB triangulation and depth measurement uncertainties. Validation on open-source datasets and real-world environments demonstrates that the proposed improvements significantly enhance the robustness of real-time state estimation and localization accuracy for intelligent vehicles in challenging environments. [ABSTRACT FROM AUTHOR]