bluuebunny/crossref_metadata_2025 huggingface dataset:
Validity of the CALERA Research Sensor to Assess Body Core Temperature during Maximum Exercise in Patients with Heart Failure
Authors: Kaltsatou Antonia , Anifanti Maria , Flouris Andreas D. , Xiromerisiou Georgia , Kouidi Evangelia
Published: January 2024

(1) Background: It is important to monitor the body core temperature (Tc) of individuals with chronic heart failure (CHF) during rest or exercise, as they are susceptible to complications. Gastrointestinal capsules are a robust indicator of the Tc at rest and during exercise. A practical and non-invasive sensor called CALERA Research was recently introduced, promising accuracy, sensitivity, continuous real-time analysis, repeatability, and reproducibility. This study aimed to assess the validity of the CALERA Research sensor when monitoring patients with CHF during periods of rest, throughout brief cardiopulmonary exercise testing, and during their subsequent recovery. (2) Methods: Twelve male CHF patients volunteered to participate in a 70-min protocol in a laboratory at 28 °C and 39% relative humidity. After remaining calm for 20 min, they underwent a symptom-limited stress test combined with ergospirometry on a treadmill, followed by 40 min of seated recovery. The Tc was continuously monitored by both Tc devices. (3) Results: The Tc values from the CALERA Research sensor and the gastrointestinal sensor showed no associations at rest (r = 0.056, p = 0.154) and during exercise (r = −0.015, p = 0.829) and a weak association during recovery (r = 0.292, p < 0.001). The Cohen’s effect size of the differences between the two Tc assessment methods for rest, exercise, and recovery was 1.04 (large), 0.18 (none), and 0.45 (small), respectively. The 95% limit of agreement for the CALERA Research sensor was −0.057 ± 1.03 °C. (4) Conclusions: The CALERA sensor is a practical and, potentially, promising device, but it does not provide an accurate Tc estimation in CHF patients at rest, during brief exercise testing, and during recovery.
Fall Risk Assessment in Stroke Survivors: A Machine Learning Model Using Detailed Motion Data from Common Clinical Tests and Motor-Cognitive Dual-Tasking
Authors: Abdollahi Masoud , Rashedi Ehsan , Jahangiri Sonia , Kuber Pranav Madhav , Azadeh-Fard Nasibeh , Dombovy Mary
Published: January 2024

Background: Falls are common and dangerous for stroke survivors. Current fall risk assessment methods rely on subjective scales. Objective sensor-based methods could improve prediction accuracy. Objective: Develop machine learning models using inertial sensors to objectively classify fall risk in stroke survivors. Determine optimal sensor configurations and clinical test protocols. Methods: 21 stroke survivors performed balance, Timed Up and Go, 10 Meter Walk, and Sit-to-Stand tests with and without dual-tasking. A total of 8 motion sensors captured lower limb and trunk kinematics, and 92 spatiotemporal gait and clinical features were extracted. Supervised models—Support Vector Machine, Logistic Regression, and Random Forest—were implemented to classify high vs. low fall risk. Sensor setups and test combinations were evaluated. Results: The Random Forest model achieved 91% accuracy using dual-task balance sway and Timed Up and Go walk time features. Single thorax sensor models performed similarly to multi-sensor models. Balance and Timed Up and Go best-predicted fall risk. Conclusion: Machine learning models using minimal inertial sensors during clinical assessments can accurately quantify fall risk in stroke survivors. Single thorax sensor setups are effective. Findings demonstrate a feasible objective fall screening approach to assist rehabilitation.
Intra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer’s Disease and Cognitive Impairment
Authors: Kolla Sahithi , Falakshahi Haleh , Abrol Anees , Fu Zening , Calhoun Vince D.
Published: January 2024

Network neuroscience, a multidisciplinary field merging insights from neuroscience and network theory, offers a profound understanding of neural network intricacies. However, the impact of varying node sizes on computed graph metrics in neuroimaging data remains underexplored. This study addresses this gap by adopting a data-driven methodology to delineate functional nodes and assess their influence on graph metrics. Using the Neuromark framework, automated independent component analysis is applied to resting state fMRI data, capturing functional network connectivity (FNC) matrices. Global and local graph metrics reveal intricate connectivity patterns, emphasizing the need for nuanced analysis. Notably, node sizes, computed based on voxel counts, contribute to a novel metric termed ‘node-metric coupling’ (NMC). Correlations between graph metrics and node dimensions are consistently observed. The study extends its analysis to a dataset comprising Alzheimer’s disease, mild cognitive impairment, and control subjects, showcasing the potential of NMC as a biomarker for brain disorders. The two key outcomes underscore the interplay between node sizes and resultant graph metrics within a given atlas, shedding light on an often-overlooked source of variability. Additionally, the study highlights the utility of NMC as a valuable biomarker, emphasizing the necessity of accounting for node sizes in future neuroimaging investigations. This work contributes to refining comparative studies employing diverse atlases and advocates for thoughtful consideration of intra-atlas node size in shaping graph metrics, paving the way for more robust neuroimaging research.
Small Target Detection in Refractive Panorama Surveillance Based on Improved YOLOv8
Authors: Zheng Xinli , Zou Jianxin , Du Shuai , Zhong Ping
Published: January 2024

Panoramic imaging is increasingly critical in UAVs and high-altitude surveillance applications. In addressing the challenges of detecting small targets within wide-area, high-resolution panoramic images, particularly issues concerning accuracy and real-time performance, we have proposed an improved lightweight network model based on YOLOv8. This model maintains the original detection speed, while enhancing precision, and reducing the model size and parameter count by 10.6% and 11.69%, respectively. It achieves a 2.9% increase in the overall mAP@0.5 and a 20% improvement in small target detection accuracy. Furthermore, to address the scarcity of reflective panoramic image training samples, we have introduced a panorama copy–paste data augmentation technique, significantly boosting the detection of small targets, with a 0.6% increase in the overall mAP@0.5 and a 21.3% rise in small target detection accuracy. By implementing an unfolding, cutting, and stitching process for panoramic images, we further enhanced the detection accuracy, evidenced by a 4.2% increase in the mAP@0.5 and a 12.3% decrease in the box loss value, validating the efficacy of our approach for detecting small targets in complex panoramic scenarios.
Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device
Authors: Gragnaniello Maria , Borghese Alessandro , Marrazzo Vincenzo Romano , Maresca Luca , Breglio Giovanni , Irace Andrea , Riccio Michele
Published: January 2024

Myocardial Infarction (MI), commonly known as heart attack, is a cardiac condition characterized by damage to a portion of the heart, specifically the myocardium, due to the disruption of blood flow. Given its recurring and often asymptomatic nature, there is the need for continuous monitoring using wearable devices. This paper proposes a single-microcontroller-based system designed for the automatic detection of MI based on the Edge Computing paradigm. Two solutions for MI detection are evaluated, based on Machine Learning (ML) and Deep Learning (DL) techniques. The developed algorithms are based on two different approaches currently available in the literature, and they are optimized for deployment on low-resource hardware. A feasibility assessment of their implementation on a single 32-bit microcontroller with an ARM Cortex-M4 core was examined, and a comparison in terms of accuracy, inference time, and memory usage was detailed. For ML techniques, significant data processing for feature extraction, coupled with a simpler Neural Network (NN) is involved. On the other hand, the second method, based on DL, employs a Spectrogram Analysis for feature extraction and a Convolutional Neural Network (CNN) with a longer inference time and higher memory utilization. Both methods employ the same low power hardware reaching an accuracy of 89.40% and 94.76%, respectively. The final prototype is an energy-efficient system capable of real-time detection of MI without the need to connect to remote servers or the cloud. All processing is performed at the edge, enabling NN inference on the same microcontroller.
Advances in Condition Monitoring of Railway Infrastructure
Authors: Mosleh Araliya , Ribeiro Diogo , Malekjafarian Abdollah , Martínez-Rodrigo Maria D.
Published: January 2024

In recent years, there has been a notable surge in investments directed towards developing new railway lines and revitalising existing ones, reflecting a global commitment to enhance transportation infrastructure [...]
Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning
Authors: Villalba-Meneses Fernando , Guevara Cesar , Lojan Alejandro B. , Gualsaqui Mario G. , Arias-Serrano Isaac , Velásquez-López Paolo A. , Almeida-Galárraga Diego , Tirado-Espín Andrés , Marín Javier , Marín José J.
Published: January 2024

Low back pain (LBP) is a highly common musculoskeletal condition and the leading cause of work absenteeism. This project aims to develop a medical test to help healthcare professionals decide on and assign physical treatment for patients with nonspecific LBP. The design uses machine learning (ML) models based on the classification of motion capture (MoCap) data obtained from the range of motion (ROM) exercises among healthy and clinically diagnosed patients with LBP from Imbabura–Ecuador. The following seven ML algorithms were tested for evaluation and comparison: logistic regression, decision tree, random forest, support vector machine (SVM), k-nearest neighbor (KNN), multilayer perceptron (MLP), and gradient boosting algorithms. All ML techniques obtained an accuracy above 80%, and three models (SVM, random forest, and MLP) obtained an accuracy of >90%. SVM was found to be the best-performing algorithm. This article aims to improve the applicability of inertial MoCap in healthcare by making use of precise spatiotemporal measurements with a data-driven treatment approach to improve the quality of life of people with chronic LBP.
Immediate Pose Recovery Method for Untracked Frames in Feature-Based SLAM
Authors: Dou Hexuan , Wang Zhenhuan , Wang Changhong , Zhao Xinyang
Published: January 2024

In challenging environments, feature-based visual SLAM encounters frequent failures in frame tracking, introducing unknown poses to robotic applications. This paper introduces an immediate approach for recovering untracked camera poses. Through the retrieval of key information from elapsed untracked frames, lost poses are efficiently restored with a short time consumption. Taking account of reconstructed poses and map points during local optimizing, a denser local map is constructed around ambiguous frames to enhance the further SLAM procedure. The proposed method is implemented in a SLAM system, and monocular experiments are conducted on datasets. The experimental results demonstrate that our method can reconstruct the untracked frames in nearly real time, effectively complementing missing segments of the trajectory. Concurrently, the accuracy and robustness for subsequent tracking are improved through the integration of recovered poses and map points.
UnSafengine64: A Safengine Unpacker for 64-Bit Windows Environments and Detailed Analysis Results on Safengine 2.4.0
Authors: Choi Seokwoo , Chang Taejoo , Park Yongsu
Published: January 2024

Despite recent remarkable advances in binary code analysis, malware developers still use complex anti-reversing techniques that make analysis difficult. Packers are used to protect malware, which are (commercial) tools that contain diverse anti-reversing techniques, including code encryption, anti-debugging, and code virtualization. In this study, we present UnSafengine64: a Safengine unpacker for 64-bit Windows. UnSafengine64 can correctly unpack packed executables using Safengine, which is considered one of the most complex commercial packers in Windows environments; to the best of our knowledge, there have been no published analysis results. UnSafengine64 was developed as a plug-in for Pin, which is one of the most widely used dynamic analysis tools for Microsoft Windows. In addition, we utilized Detect It Easy (DIE), IDA Pro, x64Dbg, and x64Unpack as auxiliary tools for deep analysis. Using UnSafengine64, we can analyze obfuscated calls for major application programming interface (API) functions or conduct fine-grained analyses at the instruction level. Furthermore, UnSafengine64 detects anti-debugging code chunks, captures a memory dump of the target process, and unpacks packed files. To verify the effectiveness of our scheme, experiments were conducted using Safengine 2.4.0. The experimental results show that UnSafengine64 correctly executes packed executable files and successfully produces an unpacked version. Based on this, we provided detailed analysis results for the obfuscated executable file generated using Safengine 2.4.0.
A Mood Semantic Awareness Model for Emotional Interactive Robots
Authors: Zhou Tiehua , Yu Zihan , Wang Ling , Ryu Keun Ho
Published: January 2024

The rapid development of natural language processing technology and improvements in computer performance in recent years have resulted in the wide-scale development and adoption of human–machine dialogue systems. In this study, the Icc_dialogue model is proposed to enhance the semantic awareness of moods for emotional interactive robots. Equipped with a voice interaction module, emotion calculation is conducted based on model responses, and rules for calculating users’ degree of interest are formulated. By evaluating the degree of interest, the system can determine whether it should transition to a new topic to maintain the user’s interest. This model can also address issues such as overly purposeful responses and rigid emotional expressions in generated replies. Simultaneously, this study explores topic continuation after answering a question, the construction of dialogue rounds, keyword counting, and the creation of a target text similarity matrix for each text in the dialogue dataset. The matrix is normalized, weights are assigned, and the final text score is calculated. In the text with the highest score, the content of dialogue continuation is determined by calculating a subsequent sentence with the highest similarity. This resolves the issue in which the conversational bot fails to continue dialogue on a topic after answering a question, instead waiting for the user to voluntarily provide more information, resulting in topic interruption. As described in the experimental section, both automatic and manual evaluations were conducted to validate the significant improvement in the mood semantic awareness model’s performance in terms of dialogue quality and user experience.
Field-Programmable Gate Array-Based Implementation of Zero-Trust Stream Data Encryption for Enabling 6G-Narrowband Internet of Things Massive Device Access
Authors: Tsai Wen-Chung
Published: January 2024

With the advent of 6G Narrowband IoT (NB-IoT) technology, IoT security faces inevitable challenges due to the application requirements of Massive Machine-Type Communications (mMTCs). In response, a 6G base station (gNB) and User Equipment (UE) necessitate increased capacities to handle a larger number of connections while maintaining reasonable performance during operations. To address this developmental trend and overcome associated technological hurdles, this paper proposes a hardware-accelerated and software co-designed mechanism to support streaming data transmissions and secure zero-trust inter-endpoint communications. The proposed implementations aim to offload processing efforts from micro-processors and enhance global system operation performance by hardware and software co-design in endpoint communications. Experimental results demonstrate that the proposed secure mechanism based on the use of non-repeating keys and implemented in FPGA, can save 85.61%, 99.71%, and 95.68% of the micro-processor’s processing time in key block generations, non-repeating checks, and data block transfers, respectively.
Pathogen Detection via Impedance Spectroscopy-Based Biosensor
Authors: Kandukuri Tharun Reddy , Prattis Ioannis , Oluwasanya Pelumi , Occhipinti Luigi G.
Published: January 2024

This paper presents the development of a miniaturized sensor device for selective detection of pathogens, specifically Influenza A Influenza virus, as an enveloped virus is relatively vulnerable to damaging environmental impacts. In consideration of environmental factors such as humidity and temperature, this particular pathogen proves to be an ideal choice for our study. It falls into the category of pathogens that pose greater challenges due to their susceptibility. An impedance biosensor was integrated into an existing platform and effectively separated and detected high concentrations of airborne pathogens. Bio-functionalized hydrogel-based detectors were utilized to analyze virus-containing particles. The sensor device demonstrated high sensitivity and specificity when exposed to varying concentrations of Influenza A virus ranging from 0.5 to 50 μg/mL. The sensitivity of the device for a 0.5 μg/mL analyte concentration was measured to be 695 Ω· mL/μg. Integration of this pathogen detector into a compact-design air quality monitoring device could foster the advancement of personal exposure monitoring applications. The proposed sensor device offers a promising approach for real-time pathogen detection in complex environmental settings.
A Ship Detection Model Based on Dynamic Convolution and an Adaptive Fusion Network for Complex Maritime Conditions
Authors: Li Zhisheng , Deng Zhihui , Hao Kun , Zhao Xiaofang , Jin Zhigang
Published: January 2024

Ship detection is vital for maritime safety and vessel monitoring, but challenges like false and missed detections persist, particularly in complex backgrounds, multiple scales, and adverse weather conditions. This paper presents YOLO-Vessel, a ship detection model built upon YOLOv7, which incorporates several innovations to improve its performance. First, we devised a novel backbone network structure called Efficient Layer Aggregation Networks and Omni-Dimensional Dynamic Convolution (ELAN-ODConv). This architecture effectively addresses the complex background interference commonly encountered in maritime ship images, thereby improving the model’s feature extraction capabilities. Additionally, we introduce the space-to-depth structure in the head network, which can solve the problem of small ship targets in images that are difficult to detect. Furthermore, we introduced ASFFPredict, a predictive network structure addressing scale variation among ship types, bolstering multiscale ship target detection. Experimental results demonstrate YOLO-Vessel’s effectiveness, achieving a 78.3% mean average precision (mAP), surpassing YOLOv7 by 2.3% and Faster R-CNN by 11.6%. It maintains real-time detection at 8.0 ms/frame, meeting real-time ship detection needs. Evaluation in adverse weather conditions confirms YOLO-Vessel’s superiority in ship detection, offering a robust solution to maritime challenges and enhancing marine safety and vessel monitoring.
CSI-F: A Human Motion Recognition Method Based on Channel-State-Information Signal Feature Fusion
Authors: Niu Juan , He Xiuqing , Fang Bei , Han Guangxin , Wang Xu , He Juhou
Published: January 2024

The recognition of human activity is crucial as the Internet of Things (IoT) progresses toward future smart homes. Wi-Fi-based motion-recognition stands out due to its non-contact nature and widespread applicability. However, the channel state information (CSI) related to human movement in indoor environments changes with the direction of movement, which poses challenges for existing Wi-Fi movement-recognition methods. These challenges include limited directions of movement that can be detected, short detection distances, and inaccurate feature extraction, all of which significantly constrain the wide-scale application of Wi-Fi action-recognition. To address this issue, we propose a direction-independent CSI fusion and sharing model named CSI-F, one which combines Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). Specifically, we have introduced a series of signal-processing techniques that utilize antenna diversity to eliminate random phase shifts, thereby removing noise influences unrelated to motion information. Later, by amplifying the Doppler frequency shift effect through cyclic actions and generating a spectrogram, we further enhance the impact of actions on CSI. To demonstrate the effectiveness of this method, we conducted experiments on datasets collected in natural environments. We confirmed that the superposition of periodic actions on CSI can improve the accuracy of the process. CSI-F can achieve higher recognition accuracy compared with other methods and a monitoring coverage of up to 6 m.
Joint Classification of Hyperspectral Images and LiDAR Data Based on Dual-Branch Transformer
Authors: Wang Qingyan , Zhou Binbin , Zhang Junping , Xie Jinbao , Wang Yujing
Published: January 2024

In the face of complex scenarios, the information insufficiency of classification tasks dominated by a single modality has led to a bottleneck in classification performance. The joint application of multimodal remote sensing data for surface observation tasks has garnered widespread attention. However, issues such as sample differences between modalities and the lack of correlation in physical features have limited the performance of classification tasks. Establishing effective interaction between multimodal data has become another significant challenge. To fully integrate heterogeneous information from multiple modalities and enhance classification performance, this paper proposes a dual-branch cross-Transformer feature fusion network aimed at joint land cover classification of hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data. The core idea is to leverage the potential of convolutional operators to represent spatial features, combined with the advantages of the Transformer architecture in learning remote dependencies. The framework employs an improved self-attention mechanism to aggregate features within each modality, highlighting the spectral information of HSI and the spatial (elevation) information of LiDAR. The feature fusion module based on cross-attention integrates deep features from two modalities, achieving complementary information through cross-modal attention. The classification task is performed using jointly obtained spectral and spatial features. Experiments were conducted on three multi-source remote sensing classification datasets, demonstrating the effectiveness of the proposed model compared to existing methods.
Research on the Sensing Characteristics of an Integrated Grid-like Sensor Based on a Triboelectric Nanogenerator
Authors: Zhao Shiyu , Han Guanghui , Deng Huaxia , Ma Mengchao , Zhong Xiang
Published: January 2024

With the development of the integration and miniaturization of sensing devices, the concept of self-sensing devices has been proposed. A motion state is self-sensed via the structure or integration of an actuator in the construction of a sensing unit. This device is then used to capture the perception and measurement of states such as position, displacement, and speed. A triboelectric nanogenerator converts mechanical energy into electrical energy through the coupling effect of contact generation and electrostatic induction, which represents one of the reliable ways through which to realize integrated sensing. In this world, the power generation technology of the TENG is applied to a sensing device. The sensing characteristics of a grid-like TENG are designed and analyzed in freestanding triboelectric mode. Firstly, a relation model of displacement, velocity, voltage, and charge is established. The charge-transfer increment and current amounts are linearly related to the velocity. The open-circuit voltage has a positive relationship with the displacement. The maximum open-circuit voltage and the maximum charge transfer are fixed values, and they are only related to the inherent parameters of a triboelectric nanogenerator. Next, the sensor model is constructed using COMSOL Multiphysics 6.0. The simulation results show that the relationships between output voltage and charge transfer, as well as those between the increments of charge transfer, velocity, and displacement, are consistent with the results derived from the formula. Finally, a performance test of the designed sensor is carried out, and the results are consistent with the theoretical deduction and simulation. After analysis and processing of the output electrical signal by the host computer, it can feedback the frequency and speed value of the measured object. In addition, the output signal is stable, and there is no large fluctuation or attenuation during the 521-s vibration test. Because the working unit of the sensor is thin filmed, it is small in size, easy to integrate, and has no external power supply; moreover, it can be integrated into a device to realize the self-sensing of a motion state.
Research on Magnetic Field-Based Damage Detection Technology for Ferromagnetic Microwires
Authors: Wang Haifei , Yin Junqing , Xin Cheng , Li Chan , Chen Yongdang
Published: January 2024

Composite materials are frequently exposed to external factors during their operational service, resulting in internal structural damage which subsequently impacts their structural performance. This paper employs ferromagnetic materials for their sensitivity to magnetic field strength. By detecting variations in the magnetic field within the embedded ferromagnetic microwires of composite materials, the aim is to indirectly assess the health status of the composite materials. Firstly, a theoretical numerical model for magnetic field intensity at the crack site was established. Subsequently, a finite element model was employed to analyze the variations in the magnetic characteristics of ferromagnetic microwires at the crack site. Under different parameter conditions, the patterns of magnetic signals at the crack site were determined. The results indicate that with an increase in the angle between the external magnetic field and the crack, the fitted curve of the magnetic signal shows a linear increase. The distance between the peak and valley of the radial magnetic signal in the axial direction decreases, and the axial magnetic signal transitions from double-peak to single-peak. With the increase in crack depth, the fitted curve of the magnetic signal shows a linear increase, and the magnetic signal at the crack tip also exhibits a linear increase. An increase in crack width leads to a non-linear decrease in the fitted curve of the magnetic signal, and after reaching a certain width, the magnetic signal stabilizes. For two identical cracks at different distances, the magnetic signal exhibits a transition from a complete pattern to two complete patterns. With the increase in the external magnetic field, the magnetic signal shows a completely regular linear increase. By analyzing and calculating the variations in magnetic signals, the patterns of magnetic characteristics under the damaged state of ferromagnetic microwires were obtained. This serves as a basis for assessing whether they can continue in service and for evaluating the overall health status of composite materials.
A Reformed PSO-Based High Linear Optimized Up-Conversion Mixer for Radar Application
Authors: Delwar Tahesin Samira , Aras Unal , Siddique Abrar , Lee Yangwon , Ryu Jee-Youl
Published: January 2024

A reformed particle swarm optimization (RPSO)-based up-conversion mixer circuit is proposed for radar application in this paper. In practice, a non-optimized up-conversion mixer suffers from high power consumption, poor linearity, and conversion gain. Therefore, the RPSO algorithm is proposed to optimize the up-conversion mixer. The novelty of the proposed RPSO algorithm is it helps to solve the problem of local optima and premature convergence in traditional particle swarm optimization (TPSO). Furthermore, in the RPSO, a velocity position-based convergence (VPC) and wavelet mutation (WM) strategy are used to enhance RPSO’s swarm diversity. Moreover, this work also features novel circuit configurations based on the two-fold transconductance path (TTP), a technique used to improve linearity. A differential common source (DCS) amplifier is included in the primary transconductance path (PTP) of the TTP. As for the subsidiary transconductance path (STP), the enhanced cross-quad transconductor (ECQT) is implemented within the TTP. A benchmark function verification is conducted to demonstrate the effectiveness of the RPSO algorithm. The proposed RPSO has also been compared with other optimization algorithms such as the genetic algorithm (GA) and the non-dominated sorting genetic algorithm II (NSGA-II). By using RPSO, the proposed optimized mixer achieves a conversion gain (CG) of 2.5 dB (measured). In this study, the proposed mixer achieves a 1 dB compression point (OP1dB) of 4.2 dBm with a high linearity. In the proposed mixer, the noise figure (NF) is approximately 3.1 dB. While the power dissipation of the optimized mixer is 3.24 mW. Additionally, the average time for RPSO to design an up-conversion mixer is 4.535 s. Simulation and measured results demonstrate the excellent performance of the RPSO optimized up-conversion mixer.
Res-NeuS: Deep Residuals and Neural Implicit Surface Learning for Multi-View Reconstruction
Authors: Wang Wei , Gao Fengjiao , Shen Yongliang
Published: January 2024

Surface reconstruction using neural networks has proven effective in reconstructing dense 3D surfaces through image-based neural rendering. Nevertheless, current methods are challenging when dealing with the intricate details of large-scale scenes. The high-fidelity reconstruction performance of neural rendering is constrained by the view sparsity and structural complexity of such scenes. In this paper, we present Res-NeuS, a method combining ResNet-50 and neural surface rendering for dense 3D reconstruction. Specifically, we present appearance embeddings: ResNet-50 is used to extract the appearance depth features of an image to further capture more scene details. We interpolate points near the surface and optimize their weights for the accurate localization of 3D surfaces. We introduce photometric consistency and geometric constraints to optimize 3D surfaces and eliminate geometric ambiguity existing in current methods. Finally, we design a 3D geometry automatic sampling to filter out uninteresting areas and reconstruct complex surface details in a coarse-to-fine manner. Comprehensive experiments demonstrate Res-NeuS’s superior capability in the reconstruction of 3D surfaces in complex, large-scale scenes, and the harmful distance of the reconstructed 3D model is 0.4 times that of general neural rendering 3D reconstruction methods and 0.6 times that of traditional 3D reconstruction methods.
Exploring Novel Sensor Design Ideas through Concentration-Induced Conformational Changes in PEG Single Chains
Authors: Yu Miao , Jiang Chong , Lai Bing , Zhang Kai
Published: January 2024

Polyethylene glycol (PEG) is an artificial polymer with good biocompatibility and a low cost, which has a wide range of applications. In this study, the dynamic response of PEG single chains to different ion concentrations was investigated from a microscopic point of view based on single-molecule force spectroscopy, revealing unique interactions that go beyond the traditional sensor-design paradigm. Under low concentrations of potassium chloride, PEG single chains exhibit a gradual reduction in rigidity, while, conversely, high concentrations induce a progressive increase in rigidity. This dichotomy serves as the cornerstone for a profound understanding of PEG conformational dynamics under diverse ion environments. Capitalizing on the remarkable sensitivity of PEG single chains to ion concentration shifts, we introduce innovative sensor-design ideas. Rooted in the adaptive nature of PEG single chains, these sensor designs extend beyond the traditional applications, promising advancements in environmental monitoring, healthcare, and materials science.
Residual Shrinkage ViT with Discriminative Rebalancing Strategy for Small and Imbalanced Fault Diagnosis
Authors: Zhang Li , Gu Shixing , Luo Hao , Ding Linlin , Guo Yang
Published: January 2024

In response to the challenge of small and imbalanced Datasets, where the total Sample size is limited and healthy Samples significantly outweigh faulty ones, we propose a diagnostic framework designed to tackle Class imbalance, denoted as the Dual-Stream Adaptive Deep Residual Shrinkage Vision Transformer with Interclass–Intraclass Rebalancing Loss (DSADRSViT-IIRL). Firstly, to address the issue of limited Sample quantity, we incorporated the Dual-Stream Adaptive Deep Residual Shrinkage Block (DSA-DRSB) into the Vision Transformer (ViT) architecture, creating a DSA-DRSB that adaptively removes redundant signal information based on the input data characteristics. This enhancement enables the model to focus on the Global receptive field while capturing crucial local fault discrimination features from the extremely limited Samples. Furthermore, to tackle the problem of a significant Class imbalance in long-tailed Datasets, we designed an Interclass–Intraclass Rebalancing Loss (IIRL), which decouples the contributions of the Intraclass and Interclass Samples during training, thus promoting the stable convergence of the model. Finally, we conducted experiments on the Laboratory and CWRU bearing Datasets, validating the superiority of the DSADRSViT-IIRL algorithm in handling Class imbalance within mixed-load Datasets.
Artificial Intelligence-Based Anomaly Detection Technology over Encrypted Traffic: A Systematic Literature Review
Authors: Ji Il Hwan , Lee Ju Hyeon , Kang Min Ji , Park Woo Jin , Jeon Seung Ho , Seo Jung Taek
Published: January 2024

As cyber-attacks increase in unencrypted communication environments such as the traditional Internet, protected communication channels based on cryptographic protocols, such as transport layer security (TLS), have been introduced to the Internet. Accordingly, attackers have been carrying out cyber-attacks by hiding themselves in protected communication channels. However, the nature of channels protected by cryptographic protocols makes it difficult to distinguish between normal and malicious network traffic behaviors. This means that traditional anomaly detection models with features from packets extracted a deep packet inspection (DPI) have been neutralized. Recently, studies on anomaly detection using artificial intelligence (AI) and statistical characteristics of traffic have been proposed as an alternative. In this review, we provide a systematic review for AI-based anomaly detection techniques over encrypted traffic. We set several research questions on the review topic and collected research according to eligibility criteria. Through the screening process and quality assessment, 30 research articles were selected with high suitability to be included in the review from the collected literature. We reviewed the selected research in terms of dataset, feature extraction, feature selection, preprocessing, anomaly detection algorithm, and performance indicators. As a result of the literature review, it was confirmed that various techniques used for AI-based anomaly detection over encrypted traffic were used. Some techniques are similar to those used for AI-based anomaly detection over unencrypted traffic, but some technologies are different from those used for unencrypted traffic.
Combined Displacement and Angle Sensor with Ultra-High Compactness Based on Self-Imaging Effect of Optical Microgratings
Authors: Zhang Mengdi , Yang Hao , Niu Qianqi , Zhang Xuye , Yang Jiaan , Lai Jiangbei , Fan Changjiang , Li Mengwei , Xin Chenguang
Published: January 2024

In this paper, an ultracompact combined sensor for displacement and angle-synchronous measurement is proposed based on the self-imaging effect of optical microgratings. Using a two-grating structure, linear and angular displacement can be measured by detecting the change of phase and amplitude of the optical transmission, respectively, within one single structure in the meantime. The optically transmitted properties of the two-grating structure are investigated in both theory and simulation. Simulated results indicate that optical transmission changes in a sinusoidal relationship to the input linear displacement. Meanwhile, the amplitude of the curve decreases with an input pitch angle, indicating the ability for synchronous measurement within one single compact structure. The synchronous measurement of the linear displacement and the angle is also demonstrated experimentally. The results show a resolution down to 4 nm for linear displacement measurement and a maximum sensitivity of 0.26 mV/arcsec within a range of ±1° for angle measurement. Benefiting from a simple common-path structure without using optical components, including reflectors and polarizers, the sensor shows ultra-high compactness for multiple-degrees-of-freedom measuring, indicating the great potential for this sensor in fields such as integrated mechanical positioning and semiconductor fabrication.
Characterisation of a Silicon Photomultiplier Based Oncological Brachytherapy Fibre Dosimeter
Authors: Caccia Massimo , Giaz Agnese , Galoppo Marco , Santoro Romualdo , Martyn Micheal , Bianchi Carla , Novario Raffaele , Woulfe Peter , O’Keeffe Sinead
Published: January 2024

Source localisation and real-time dose verification are at the forefront of medical research in brachytherapy, an oncological radiotherapy procedure based on radioactive sources implanted in the patient body. The ORIGIN project aims to respond to this medical community’s need by targeting the development of a multi-point dose mapping system based on fibre sensors integrating a small volume of scintillating material into the tip and interfaced with silicon photomultipliers operated in counting mode. In this paper, a novel method for the selection of the optimal silicon photomultipliers to be used is presented, as well as a laboratory characterisation based on dosimetric figures of merit. More specifically, a technique exploiting the optical cross-talk to maintain the detector linearity in high-rate conditions is demonstrated. Lastly, it is shown that the ORIGIN system complies with the TG43-U1 protocol in high and low dose rate pre-clinical trials with actual brachytherapy sources, an essential requirement for assessing the proposed system as a dosimeter and comparing the performance of the system prototype against the ORIGIN project specifications.
Inspection of Floating Offshore Wind Turbines Using Multi-Rotor Unmanned Aerial Vehicles: Literature Review and Trends
Authors: Zhang Kong , Pakrashi Vikram , Murphy Jimmy , Hao Guangbo
Published: January 2024

Operations and maintenance (O&M) of floating offshore wind turbines (FOWTs) require regular inspection activities to predict, detect, and troubleshoot faults at high altitudes and in harsh environments such as strong winds, waves, and tides. Their costs typically account for more than 30% of the lifetime cost due to high labor costs and long downtime. Different inspection methods, including manual inspection, permanent sensors, climbing robots, remotely operated vehicles (ROVs), and unmanned aerial vehicles (UAVs), can be employed to fulfill O&M missions. The UAVs, as an enabling technology, can deal with time and space constraints easily and complete tasks in a cost-effective and efficient manner, which have been widely used in different industries in recent years. This study provides valuable insights into the existing applications of UAVs in FOWT inspection, highlighting their potential to reduce the inspection cost and thereby reduce the cost of energy production. The article introduces the rationale for applying UAVs to FOWT inspection and examines the current technical status, research gaps, and future directions in this field by conducting a comprehensive literature review over the past 10 years. This paper will also include a review of UAVs’ applications in other infrastructure inspections, such as onshore wind turbines, bridges, power lines, solar power plants, and offshore oil and gas fields, since FOWTs are still in the early stages of development. Finally, the trends of UAV technology and its application in FOWTs inspection are discussed, leading to our future research direction.
An Analysis of the Demand for Tourist Accommodation to Travel with Dogs in Spain
Authors: Ramos-Ruiz José E. , Aguilar-Rivero Minerva , Aja-Valle Jaime , Castaño-Prieto Lucía
Published: January 2024

Pets, generally, and dogs have become an essential part of families. This situation implies that people consider their dogs when planning family holidays, excluding moving to a second home. This study aims to investigate the perceptions of dog owners according to the demand for tourist establishments where they can stay with their pets. A total of 1391 dog owners’ surveys were collected and analyzed, and various covariance-based structural equation modelling (CB-SEM) was developed to determine the suitability of the measurement model, the second-order factors, and the relationships between the different constructs. The main results of this research show that the motivations for traveling with the dog, the limitations this encounters, and, above all, the attachment that the family has with its pet significantly influence the choice of accommodation. The findings of this research will help hotel managers with the design of policies that meet the needs of families travelling with their dogs. The analysis of dog owners’ motivations for choosing tourist accommodation due to their attachment and the limitations for travelling allows us to obtain more accurate information.
Exploring the Immortological Imagination: Advocating for a Sociology of Immortality
Authors: Nowaczyk-Basińska Katarzyna , Kiel Paula
Published: January 2024

The digital age has rekindled popular and academic interest in immortality. While the idea of immortality has long been recognized as fundamental to human societies, unlike death, within the field of sociology, immortality has not yet established itself as a distinct and autonomous field of study. This paper contributes to the recently emerging scholarship promoting a sociology of immortality. Drawing inspiration from C. Wright Mills’s sociological imagination (1959) and building upon significant research in the field of immortality, we offer to use the concept of the immortological imagination as an analytical and conceptual tool for further developing a sociology of immortality. We refer to the immortological imagination as a complementary concept to Penfold-Mounce’s thanatological imagination, seeing both concepts as stemming from two different lineages and academic traditions. After defining the immortological imagination and how it differs from and complements the thanatological imagination, the paper moves to discuss examples in popular culture establishing the potential impacts and influences of the immortological imagination, particularly within the digital context.
Into the Second Decade
Authors: Parton Nigel Ashmore
Published: January 2024

The year 2023 saw Social Sciences enter its second decade after launching in 2012 [...]
Pollution Load Coordination and Eco-Compensation for Trans-Boundary Water Pollution Control: The Case of the Tri-Border Region of the Yangtze Delta
Authors: Wang Xiao , Wang Yumeng , Pang Yong , Wang Kan , Yu Jie
Published: January 2024

A partitioned governance mode, in the absence of multilateral cooperation, always culminates in recurring instances of trans-boundary conflicts and critical degradation of water bodies in border regions. Addressing the existing gaps in quantitative trans-boundary pollution control research in extensive river network, a new approach was designed to strategically guide water pollution control initiatives throughout the entire tri-border region of the Yangtze Delta (TBYD) via the following steps: (1) Building upon an analysis of the trans-boundary river hydrodynamics, the tri-border effective coordination scope (TECS), i.e., a strategic coordination scope for coordinated pollution control, was delineated, and 13 county-level administrative districts were identified as effective contributing regions for detailed coordination. (2) Considering water quality standard (WQS) attainment in the trans-boundary cross-sections, a one-dimensional mathematical model covering the complex river network was established. Then, the load capacities for all the contributing administrative regions were determined to facilitate coordinated pollution load reduction across the TECS. (3) Leveraging from the sewage treatment costs within the TECS, a standardized eco-compensation criterion was established to guide the coordinated compensation practices across the TECS. (4) By comparing the practical pollution discharging amount, the coordinated load reduction rates and eco-compensation payments of all 13 contributing administrative districts for trans-boundary pollution control were assessed. These assessments will guide policy promulgation and provide quantitative data support for harmonizing pollution control policymaking and addressing intricate trans-boundary pollution issues in complex river networks.
Trends in the Water–Energy–Food Nexus Research
Authors: Rhouma Ali , El Jeitany Jerome , Mohtar Rabi , Gil José Maria
Published: January 2024

This paper aims to analyze the scientific evolution and research trends concerning the water–energy–food (WEF) nexus, as well as its development within scientific databases. To achieve this, a bibliometric analysis has been conducted using publications sourced from Scopus and Web of science databases. This study examined key aspects such as primary journals, prominent authors, affiliated institutions, countries of origin, subject areas, and notable keywords. Furthermore, there is a dedicated section that delves into research and innovation gaps within publications related to the WEF nexus. Results reveal that research on the WEF nexus has experienced exponential growth over the past decade, with the majority of publications emerging between 2017 and 2023. The United States leads in this field, with engineering and environmental science being the predominant research categories, with Spain and Italy being the prominent countries in Europe. The WEF nexus concept in the agriculture sector is notably underdeveloped, particularly in its ties with the Sustainable Development Goals and the science–policy–society interface. The study stresses the importance of integrating health considerations into the WEF Nexus to understand the interconnections and their implications on public health, thereby enriching the Nexus approach with a critical dimension of human well-being. This situation underscores the urgent need to create a nexus community that bridges science and practice, and to incorporate this specialized discipline into university curricula.