bluuebunny/crossref_metadata_2025 huggingface dataset:
Geometric Morphometrics of the Cranium and Mandible in Social Voles of the “Guentheri” Group (Arvicolinae: Sumeriomys)
Authors: Zorenko Tanya , Kagainis Ugis , Golenishchev Fedor , Barashkova Lubova
Published: January 2023

We analyzed the cranium dorsal projection and the mandible lateral projection in bone specimens from five Microtus guentheri and Microtus hartingi forms by geometric morphometrics (GM) methods (generalized Procrustes analysis, principal component analysis, canonical variance analysis, and discriminant function analysis). Analyses of the linear size and shapes of the cranium and lower jaw showed clear-cut differentiation among the forms into an eastern cluster and western cluster, matching M. guentheri and M. hartingi, respectively. Differences were revealed both between two subspecies of M. guentheri and between the subspecies M. h. strandzensis and Rhodopean M. hartingi, whose subspecies status has not yet been determined. M. h. ankaraensis bone specimens differ in many parameters of GM from the studied European specimens and to a lesser extent from M. g. guentheri and M. g. philistinus. Calculated morpho-ecological indices of the lower jaw revealed significant differences among all these forms, thereby possibly indicating adaptation of each to a specific habitat and dietary habits. Because of the emergence of impenetrable barriers for voles (the Anatolian Diagonal in the east and the Dardanelles and Bosporus in the west), the resultant vole groups have evolved independently.
Design of Medical Devices with Usability in Mind: A Theoretical Proposal and Experimental Case Study Using the LEPRE Device
Authors: Formicola Raffaele , Amici Cinzia , Mor Maurizio , Bissolotti Luciano , Borboni Alberto
Published: January 2023

Usability is a critical product feature and is required for widespread market adoption. Standards on usability are highly focused on evaluation procedures and specific aspects, such as software issues or human–machine interaction, whereas the relative scientific literature is very normative oriented. The few methodological works dealing with usability either consider it as one of the many attributes that a particular project must satisfy or present very general methods. No design methods systematically oriented toward the integration of usability and usability-related constraints have been developed to date. This paper presents a usability-oriented model for the design of medical devices and its application to the design of LEPRE, a medical device for upper- and lower-limb robotic rehabilitation. Two methods were used to assess the device’s usability: interviews with experts to outline qualitative evaluations and System Usability Scale (SUS) questionnaires on eight physiotherapists, two physiatrists, and 12 patients, enabling a quantitative assessment. Results support the intention of providing an integrated methodological approach to be applied from the early stages of the project, thus saving time and costs, leading to a more linear product development for this application.
Technological Advancements in Interventional Oncology
Authors: Posa Alessandro , Barbieri Pierluigi , Mazza Giulia , Tanzilli Alessandro , Natale Luigi , Sala Evis , Iezzi Roberto
Published: January 2023

Interventional radiology, and particularly interventional oncology, represents one of the medical subspecialties in which technological advancements and innovations play an utterly fundamental role. Artificial intelligence, consisting of big data analysis and feature extrapolation through computational algorithms for disease diagnosis and treatment response evaluation, is nowadays playing an increasingly important role in various healthcare fields and applications, from diagnosis to treatment response prediction. One of the fields which greatly benefits from artificial intelligence is interventional oncology. In addition, digital health, consisting of practical technological applications, can assist healthcare practitioners in their daily activities. This review aims to cover the most useful, established, and interesting artificial intelligence and digital health innovations and updates, to help physicians become more and more involved in their use in clinical practice, particularly in the field of interventional oncology.
In Vivo Preclinical Assessment of the VEGF Targeting Potential of the Newly Synthesized [52Mn]Mn-DOTAGA-Bevacizumab Using Experimental Cervix Carcinoma Mouse Model
Authors: Csikos Csaba , Vágner Adrienn , Nagy Gábor , Kálmán-Szabó Ibolya , Szabó Judit P. , Ngo Minh Toan , Szoboszlai Zoltán , Szikra Dezső , Krasznai Zoárd Tibor , Trencsényi György , Garai Ildikó
Published: January 2023

Among humanized monoclonal antibodies, bevacizumab specifically binds to vascular endothelial growth factor A (VEGF-A). VEGF-A is an overexpressed biomarker in cervix carcinoma and is involved in the development and maintenance of tumor-associated neo-angiogenesis. The non-invasive positron emission tomography using radiolabeled target-specific antibodies (immuno-PET) provides the longitudinal and quantitative assessment of tumor target expression. Due to antibodies having a long-circulating time, radioactive metal ions (e.g., 52Mn) with longer half-lives are the best candidates for isotope conjugation. The aim of our preclinical study was to assess the biodistribution and tumor-targeting potential of 52Mn-labeled DOTAGA-bevacizumab. The VEGF-A targeting potential of the new immuno-PET ligand was assessed by using the VEGF-A expressing KB-3-1 (human cervix carcinoma) tumor-bearing CB17 SCID mouse model and in vivo PET/MRI imaging. Due to the high and specific accumulation found in the subcutaneously located experimental cervix carcinoma tumors, [52Mn]Mn-DOTAGA-bevacizumab is a promising PET probe for the detection of VEGF-A positive gynecological tumors, for patient selection, and monitoring the efficacy of therapies targeting angiogenesis.
Prognostic Value of BRAF, Programmed Cell Death 1 (PD1), and PD Ligand 1 (PDL1) Protein Expression in Colon Adenocarcinoma
Authors: Ibrahiem Afaf T. , Eladl Entsar , Toraih Eman A. , Fawzy Manal S. , Abdelwahab Khaled , Elnaghi Khaled , Emarah Ziad , Shaalan Aly A. M. , Ehab Ziad , Soliman Nahed A.
Published: January 2023

Patients with colorectal cancer in different stages show variable outcomes/therapeutic responses due to their distinct tumoral biomarkers and biological features. In this sense, this study aimed to explore the prognostic utility of BRAF, programmed death-1 (PD1), and its ligand (PDL1) protein signatures in colon adenocarcinoma. The selected protein markers were explored in 64 archived primary colon adenocarcinomas in relation to clinicopathological features. BRAF overexpression was found in 39% of the cases and was significantly associated with grade 3, N1, advanced Dukes stage, presence of relapse, and shorter overall survival (OS). PD1 expression in the infiltrating immune cells (IICs) exhibited significant association with T2/T3, N0/M0, early Dukes stage, and absence of relapse. PDL1 expression in IICs is significantly associated with advanced nodal stage/distant metastasis, advanced Dukes stage, and shorter OS. Meanwhile, PDL1 expression in neoplastic cells (NC) was associated with the advanced lymph node/Dukes stage. A positive combined expression pattern of PDL1 in NC/IICs was associated with poor prognostic indices. Tumor PDL1 expression can be an independent predictor of OS and DFS. The multivariate analyses revealed that short OS was independently associated with the RT side location of the tumor, PD1 expression in stromal IICs, and PDL1 expression in NC. In conclusion, overexpression of BRAF in colon adenocarcinoma is considered a poor prognostic pathological marker. In addition, PDL1 expression in NC is considered an independent prognostic factor for DFS/OS. Combined immunohistochemical assessment for BRAF and PD1/PDL1 protein expressions in colon adenocarcinoma might be beneficial for selecting patients for future targeted therapy.
Development and Validation of a Dual-Language (English and Malay) Needs Assessment Tool for Breast Cancer (NeAT-BC)
Authors: Kong Yek-Ching , Danaee Mahmoud , Kaur Ranjit , Thiagarajan Muthukkumaran , Zaharah Hafizah , Sener Mustafa , Rajah Harenthri Devy Alagir , Taib Nur Aishah , Chinna Karuthan , Yip Cheng-Har , Bhoo-Pathy Nirmala
Published: January 2023

Background: Needs assessment tools may be used to guide the optimisation of cancer survivorship services. We sought to develop and validate a dual-language needs assessment tool for women with breast cancer. Methods: The study comprised two phases; (I) co-design, and (II) psychometric testing. In Phase I, items were generated based on focus group discussions with patients and a literature review. These items were then translated into the Malay language. Content and face validation were undertaken with an expert panel. In phase II, 315 Malaysian women living with breast cancer were recruited. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to determine construct validity and reliability. Criterion validity was assessed using the EORTC QLQ-C30 questionnaire. Results: We co-designed and validated a 48-item dual-language needs assessment tool for breast cancer (NeAT-BC). Five underlying constructs were revealed in the EFA: (1) emotional support, (2) social and intimate relationships, (3) hospital appointments, (4) personal care and health, and (5) information and services. The NeAT-BC demonstrated good reliability across all constructs (Cronbach’s alpha: 0.90 to 0.96). CFA also demonstrated acceptable convergent and divergent validity, composite reliability ≥ 0.87, and Heterotrait–Monotrait index < 0.85 for all constructs. Criterion validity was established given the significant negative correlation between overall needs and quality of life (r = −0.14; p = 0.02). The NeAT-BC took approximately 25 min to be completed and could be interviewer-administered or self-administered. Conclusion: The utilization of the NeAT-BC is expected to guide establishment of evidence-based cancer survivorship services in Malaysia, with wider potentials for adoption in other multi-ethnic and/or low-and-middle income settings.
Identification of BMP10 as a Novel Gene Contributing to Dilated Cardiomyopathy
Authors: Gu Jia-Ning , Yang Chen-Xi , Ding Yuan-Yuan , Qiao Qi , Di Ruo-Min , Sun Yu-Min , Wang Jun , Yang Ling , Xu Ying-Jia , Yang Yi-Qing
Published: January 2023

Dilated cardiomyopathy (DCM), characterized by left ventricular or biventricular enlargement with systolic dysfunction, is the most common type of cardiac muscle disease. It is a major cause of congestive heart failure and the most frequent indication for heart transplantation. Aggregating evidence has convincingly demonstrated that DCM has an underlying genetic basis, though the genetic defects responsible for DCM in a larger proportion of cases remain elusive, motivating the ongoing research for new DCM-causative genes. In the current investigation, a multigenerational family affected with autosomal-dominant DCM was recruited from the Chinese Han population. By whole-exome sequencing and Sanger sequencing analyses of the DNAs from the family members, a new BMP10 variation, NM_014482.3:c.166C > T;p.(Gln56*), was discovered and verified to be in co-segregation with the DCM phenotype in the entire family. The heterozygous BMP10 variant was not detected in 268 healthy volunteers enrolled as control subjects. The functional measurement via dual-luciferase reporter assay revealed that Gln56*-mutant BMP10 lost the ability to transactivate its target genes NKX2.5 and TBX20, two genes that had been causally linked to DCM. The findings strongly indicate BMP10 as a new gene contributing to DCM in humans and support BMP10 haploinsufficiency as an alternative pathogenic mechanism underpinning DCM, implying potential implications for the early genetic diagnosis and precision prophylaxis of DCM.
Small Fixed-Wing UAV Radar Cross-Section Signature Investigation and Detection and Classification of Distance Estimation Using Realistic Parameters of a Commercial Anti-Drone System
Authors: Kapoulas Ioannis K. , Hatziefremidis Antonios , Baldoukas A. K. , Valamontes Evangelos S. , Statharas J. C.
Published: January 2023

Various types of small drones constitute a modern threat for infrastructure and hardware, as well as for humans; thus, special-purpose radar has been developed in the last years in order to identify such drones. When studying the radar signatures, we observed that the majority of the scientific studies refer to multirotor aerial vehicles; there is a significant gap regarding small, fixed-wing Unmanned Aerial Vehicles (UAVs). Driven by the security principle, we conducted a series of Radar Cross Section (RCS) simulations on the Euclid fixed-wing UAV, which has a wingspan of 2 m and is being developed by our University. The purpose of this study is to partially fill the gap that exists regarding the RCS signatures and identification distances of fixed-wing UAVs of the same wingspan as the Euclid. The software used for the simulations was POFACETS (v.4.1). Two different scenarios were carried out. In scenario A, the RCS of the Euclid fixed-wing UAV, with a 2 m wingspan, was analytically studied. Robin radar systems’ Elvira Anti Drone System is the simulated radar, operating at 8.7 to 9.65 GHz; θ angle is set at 85° for this scenario. Scenario B studies the Euclid RCS within the broader 3 to 16 Ghz spectrum at the same θ = 85° angle. The results indicated that the Euclid UAV presents a mean RCS value (σ ¯) of −17.62 dBsm for scenario A, and a mean RCS value (σ ¯) of −22.77 dBsm for scenario B. These values are much smaller than the values of a typical commercial quadcopter, such as DJI Inspire 1, which presents −9.75 dBsm and −13.92 dBsm for the same exact scenarios, respectively. As calculated in the study, the Euclid UAV can penetrate up to a distance of 1784 m close to the Elvira Anti Drone System, while the DJI Inspire 1 will be detected at 2768 m. This finding is of great importance, as the obviously larger fixed-wing Euclid UAV will be detected about one kilometer closer to the anti-drone system.
Retrieval of Fractional Vegetation Cover from Remote Sensing Image of Unmanned Aerial Vehicle Based on Mixed Pixel Decomposition Method
Authors: Du Mengmeng , Li Minzan , Noguchi Noboru , Ji Jiangtao , Ye Mengchao (George)
Published: January 2023

FVC (fractional vegetation cover) is highly correlated with wheat plant density in the reviving period, which is an important indicator for conducting variable-rate nitrogenous topdressing. In this study, with the objective of improving inversion accuracy of wheat plant density, an innovative approach of retrieval of FVC values from remote sensing images of a UAV (unmanned aerial vehicle) was proposed based on the mixed pixel decomposition method. Firstly, remote sensing images of an experimental wheat field were acquired by using a DJI Mini UAV and endmembers in the image were identified. Subsequently, a linear unmixing model was used to subdivide mixed pixels into components of vegetation and soil, and an abundance map of vegetation was acquired. Based on the abundance map of vegetation, FVC was calculated. Consequently, a linear regression model between the ground truth data of wheat plant density and FVC was established. The coefficient of determination (R2), RMSE (root mean square error), and RRMSE (Relative-RMSE) of the inversion model were calculated as 0.97, 1.86 plants/m2, and 0.677%, which indicates strong correlation between the FVC of mixed pixel decomposition method and wheat plant density. Therefore, we can conclude that the mixed pixel decomposition model of the remote sensing image of a UAV significantly improved the inversion accuracy of wheat plant density from FVC values, which provides method support and basic data for variable-rate nitrogenous fertilization in the wheat reviving period in the manner of precision agriculture.
Processing Real-Life Recordings of Facial Expressions of Polish Sign Language Using Action Units
Authors: Irasiak Anna , Kozak Jan , Piasecki Adam , Stęclik Tomasz
Published: January 2023

Automatic translation between the national language and sign language is a complex process similar to translation between two different foreign languages. A very important aspect is the precision of not only manual gestures but also facial expressions, which are extremely important in the overall context of a sentence. In this article, we present the problem of including facial expressions in the automation of Polish-to-Polish Sign Language (PJM) translation—this is part of an ongoing project related to a comprehensive solution allowing for the animation of manual gestures, body movements and facial expressions. Our approach explores the possibility of using action unit (AU) recognition in the automatic annotation of recordings, which in the subsequent steps will be used to train machine learning models. This paper aims to evaluate entropy in real-life translation recordings and analyze the data associated with the detected action units. Our approach has been subjected to evaluation by experts related to Polish Sign Language, and the results obtained allow for the development of further work related to automatic translation into Polish Sign Language.
Physical Layer Security in Two-Way SWIPT Relay Networks with Imperfect CSI and a Friendly Jammer
Authors: Hayajneh Maymoona , Gulliver Thomas Aaron
Published: January 2023

In this paper, the security of two-way relay communications in the presence of a passive eavesdropper is investigated. Two users communicate via a relay that depends solely on energy harvesting to amplify and forward the received signals. Time switching is employed at the relay to harvest energy and obtain user information. A friendly jammer is utilized to hinder the eavesdropping from wiretapping the information signal. The eavesdropper employs maximal ratio combining and selection combining to improve the signal-to-noise ratio of the wiretapped signals. Geometric programming (GP) is used to maximize the secrecy capacity of the system by jointly optimizing the time switching ratio of the relay and transmit power of the two users and jammer. The impact of imperfect channel state information at the eavesdropper for the links between the eavesdropper and the other nodes is determined. Further, the secrecy capacity when the jamming signal is not perfectly cancelled at the relay is examined. The secrecy capacity is shown to be greater with a jammer compared to the case without a jammer. The effect of the relay, jammer, and eavesdropper locations on the secrecy capacity is also studied. It is shown that the secrecy capacity is greatest when the relay is at the midpoint between the users. The closer the jammer is to the eavesdropper, the higher the secrecy capacity as the shorter distance decreases the signal-to-noise ratio of the jammer.
FCKDNet: A Feature Condensation Knowledge Distillation Network for Semantic Segmentation
Authors: Yuan Wenhao , Lu Xiaoyan , Zhang Rongfen , Liu Yuhong
Published: January 2023

As a popular research subject in the field of computer vision, knowledge distillation (KD) is widely used in semantic segmentation (SS). However, based on the learning paradigm of the teacher–student model, the poor quality of teacher network feature knowledge still hinders the development of KD technology. In this paper, we investigate the output features of the teacher–student network and propose a feature condensation-based KD network (FCKDNet), which reduces pseudo-knowledge transfer in the teacher–student network. First, combined with the pixel information entropy calculation rule, we design a feature condensation method to separate the foreground feature knowledge from the background noise of the teacher network outputs. Then, the obtained feature condensation matrix is applied to the original outputs of the teacher and student networks to improve the feature representation capability. In addition, after performing feature condensation on the teacher network, we propose a soft enhancement method of features based on spatial and channel dimensions to improve the dependency of pixels in the feature maps. Finally, we divide the outputs of the teacher network into spatial condensation features and channel condensation features and perform distillation loss calculation with the student network separately to assist the student network to converge faster. Extensive experiments on the public datasets Pascal VOC and Cityscapes demonstrate that our proposed method improves the baseline by 3.16% and 2.98% in terms of mAcc, and 2.03% and 2.30% in terms of mIoU, respectively, and has better segmentation performance and robustness than the mainstream methods.
A Learning Environment to Promote Awareness of the Experiential Learning Processes with Reflective Writing Support
Authors: Kingkaew Chanakarn , Theeramunkong Thanaruk , Supnithi Thepchai , Chatpreecha Pronsiri , Morita Kai , Tanaka Koji , Ikeda Mitsuru
Published: January 2023

Promoting reflective thinking is becoming increasingly important in helping learners develop strategies to apply new information to unpredictable situations during their daily activities. Reflective writing, based on Kolb’s experiential learning cycle, could be one method for promoting reflective thinking that allows learners to consider their experiences and transform them into transferable knowledge, which can be applied to new contexts. However, learners cannot sufficiently practice reflective writing; thus, they cannot learn from their experiences. Therefore, our primary goal is to support reflective thinking by providing writing support. This study presents a computerized learning-environment design that helps learners to master experiential learning concepts using reflective writing. The study demonstrates how the learning-support function enhances experiential learning and enables the desired learning process to be captured. This helps mentors to provide suitable support toward understanding experiential learning. We also demonstrate how the learning environment can help learners to master experiential learning. This design, which has the dual role of supporting and observing implicit thinking behavior, can then be applied to a meta-level thinking support framework in other problem domains.
The 5G-FR1 Signals: Beams of the Phased Antennas Array and Time-Recurrence of Emissions with Consequences on Human Exposure
Authors: Deaconescu Delia Bianca , Miclaus Simona
Published: January 2023

The fifth generation (5G) of mobile communication technology poses lots of questions while introducing significant improvements compared with previous generations. The most sensitive question is related to the safety of human exposure. The aim of present work was to analyze, with a few chosen examples, two of the most significant features of 5G emissions: the extreme spatial variability of the exposure and the nonlinear dynamics characteristics of the temporal variability of the exposure. Two models of patch antenna arrays operating at 3.7 GHz with varying beam forming and beam steering capabilities were considered for an analysis of the specific absorption rate of electromagnetic energy deposition in tissues of a head model. This allowed clear emphasis on the influence of the antenna geometry and feeding peculiarities on the spatial variability of exposure. The second approach implemented the original idea of following the nonlinear recurrence behavior of exposure in time, and underlined the time variability characteristics of emissions with a real-life mobile phone running different 5G applications. Time series of the emitted electric-field strengths were recorded by means a real-time spectrum analyzer and two near-field probes differently positioned in the beam. The presence of laminar emissions, chaotic emissions, determinism and recurrence in the exposures prove the potential for recurrence quantification in predicting time variability features of 5G exposure. Overall, the impact of 5G signals on living bodies, with the highest possible man-made spatial and temporal variability, may have very unpredictable bio-medical consequences.
Distributed Deep Neural-Network-Based Middleware for Cyber-Attacks Detection in Smart IoT Ecosystem: A Novel Framework and Performance Evaluation Approach
Authors: Bhandari Guru , Lyth Andreas , Shalaginov Andrii , Grønli Tor-Morten
Published: January 2023

Cyberattacks always remain the major threats and challenging issues in the modern digital world. With the increase in the number of internet of things (IoT) devices, security challenges in these devices, such as lack of encryption, malware, ransomware, and IoT botnets, leave the devices vulnerable to attackers that can access and manipulate the important data, threaten the system, and demand ransom. The lessons from the earlier experiences of cyberattacks demand the development of the best-practices benchmark of cybersecurity, especially in modern Smart Environments. In this study, we propose an approach with a framework to discover malware attacks by using artificial intelligence (AI) methods to cover diverse and distributed scenarios. The new method facilitates proactively tracking network traffic data to detect malware and attacks in the IoT ecosystem. Moreover, the novel approach makes Smart Environments more secure and aware of possible future threats. The performance and concurrency testing of the deep neural network (DNN) model deployed in IoT devices are computed to validate the possibility of in-production implementation. By deploying the DNN model on two selected IoT gateways, we observed very promising results, with less than 30 kb/s increase in network bandwidth on average, and just a 2% increase in CPU consumption. Similarly, we noticed minimal physical memory and power consumption, with 0.42 GB and 0.2 GB memory usage for NVIDIA Jetson and Raspberry Pi devices, respectively, and an average 13.5% increase in power consumption per device with the deployed model. The ML models were able to demonstrate nearly 93% of detection accuracy and 92% f1-score on both utilized datasets. The result of the models shows that our framework detects malware and attacks in Smart Environments accurately and efficiently.
SEDG-Yolov5: A Lightweight Traffic Sign Detection Model Based on Knowledge Distillation
Authors: Zhao Liang , Wei Zhengjie , Li Yanting , Jin Junwei , Li Xuan
Published: January 2023

Most existing traffic sign detection models suffer from high computational complexity and superior performance but cannot be deployed on edge devices with limited computational capacity, which cannot meet the direct needs of autonomous vehicles for detection model performance and efficiency. To address the above concerns, this paper proposes an improved SEDG-Yolov5 traffic sign detection method based on knowledge distillation. Firstly, the Slicing Aided Hyper Inference method is used as a local offline data augmentation method for the model training. Secondly, to solve the problems of high-dimensional feature information loss and high model complexity, the inverted residual structure ESGBlock with a fused attention mechanism is proposed, and a lightweight feature extraction backbone network is constructed based on it, while we introduce the GSConv in the feature fusion layer to reduce the computational complexity of the model further. Eventually, an improved response-based objectness scaled knowledge distillation method is proposed to retrain the traffic sign detection model to compensate for the degradation of detection accuracy due to light-weighting. Extensive experiments on two challenging traffic sign datasets show that our proposed method has a good balance on detection precision and detection speed with 2.77M parametric quantities. Furthermore, the inference speed of our method achieves 370 FPS with TensorRT and 35.6 FPS with ONNX at FP16-precision, which satisfies the requirements for real-time sign detection and edge deployment.
A Composite Right/Left-Handed Phase Shifter-Based Cylindrical Phased Array with Reinforced Particles Responsive to Magneto-Static Fields
Authors: Ayaz Muhammad , Iftikhar Adnan , Braaten Benjamin D. , Khalil Wesam , Ullah Irfan
Published: January 2023

A conformal cylindrical phased array antenna excited with composite right/left-handed (CRLH) phase shifters is proposed. The phase tuning of the CRLH phase shifter is achieved by embedding novel magneto-static field-responsive micron-sized particles in its structure. It is shown that through the tiny magnet activation of these novel magneto-static particles at appropriate locations along the length of CRLH stub and inter-digital fingers, variable phase shifts are obtained. The proposed particle-based CRLH phase shifter operates in C-band (5–6) GHz with a low insertion loss and phase error. The 1 × 4 cylindrical phased array is excited with the four unit cells of the proposed particle-embedded CRLH transmission line phase shifters to scan the main beam at desired scan angles. A prototype of a 1 × 4 cylindrical phased array excited with the particle-based CRLH phase shifters was fabricated, and the results show that the simulated results are in close agreement with the measured results. The conformal cylindrical array with the proposed particle-based CRLH phase shifters has great potential for use in printed and flexible electronics design where commercially available phase shifters have a definite drawback.
Minimally Persistent Graph Generation and Formation Control for Multi-Robot Systems under Sensing Constraints
Authors: Zhao Xinyue , Yang Qingkai , Liu Qi , Yin Yuhan , Wei Yue , Fang Hao
Published: January 2023

This paper presents a minimally persistent graph generation and formation control strategy for multi-robot systems with sensing constraints. Specifically, each robot has a limited field of view (FOV) and range sensing capability. To tackle this problem, one needs to construct an appropriate interaction topology, namely assign neighbors to each robot such that all their sensing constraints are satisfied. In addition, as a stringent yet reasonable guarantee for the visual constraints, it is also required that the prescribed neighbors always stay within its visual field during the formation evolution. To this end, given a set of feasible initial positions, we first present a depth-first-search (DFS)-based algorithm to generate a minimally persistent graph, which encodes the sensing constraints via its directed edges. Then, based on the resultant graph, by invoking the gradient-based control technique and control barrier function (CBF), we propose a class of distributed formation control laws, rendering not only the convergence to the desired formation but also the satisfaction of sensing constraints. Simulation and experimental results are presented to verify the effectiveness of the proposed approach.
A Wireless Intelligent Motion Correction System for Skating Monitoring Based on a Triboelectric Nanogenerator
Authors: Lu Zhuo , Wen Yuzhang , Yang Xu , Li Dan , Liu Bocong , Zhang Yaotian , Zhu Jiabin , Zhu Yongsheng , Zhang Shouwei , Mao Yupeng
Published: January 2023

Smart sport and big data have become inextricably linked with new technologies and devices to monitor sport-related information in real time. In this paper, a lightweight, portable and self-powered triboelectric nanogenerator (LPS-TENG) has been developed to monitor the frequency and force of skaters’ pedaling. Friction layers are formed of polytetrafluoroethylene (PTFE) and nylon films. Based on the triboelectric effect, LPS-TENG does not require an external power supply, and it can be used to monitor biomechanical motion independently. Under the conditions of 1 Hz and 17.19 N, the outputting voltage of LPS-TENG is stabilized at 14 V. Wireless data transmission is achieved with the help of the LPS-TENG and AD module. Visual feedback is provided by the upper computer system in the process of processing data. The wireless intelligent motion correction system is composed of an LPS-TENG, an AD module and a back-end computer. It can clearly analyze the changes between different frequencies and forces during skating. Results showed that the signal of tester’s high-frequency and great-force motion, was transmitted to the computer, and its feedback was given after analysis and processing successfully. The system may help coaches develop training methods, means and tactics to increase athletes’ performance and competitive level in athletic sport. The purpose of this study is to provide new ideas for monitoring skaters’ sport techniques, promote the use of force sensors in the monitoring of sport and develop intelligent assistant training systems.
A Customized Efficient Deep Learning Model for the Diagnosis of Acute Leukemia Cells Based on Lymphocyte and Monocyte Images
Authors: Ansari Sanam , Navin Ahmad , Sangar Amin , Gharamaleki Jalil , Danishvar Sebelan
Published: January 2023

The production of blood cells is affected by leukemia, a type of bone marrow cancer or blood cancer. Deoxyribonucleic acid (DNA) is related to immature cells, particularly white cells, and is damaged in various ways in this disease. When a radiologist is involved in diagnosing acute leukemia cells, the diagnosis is time consuming and needs to provide better accuracy. For this purpose, many types of research have been conducted for the automatic diagnosis of acute leukemia. However, these studies have low detection speed and accuracy. Machine learning and artificial intelligence techniques are now playing an essential role in medical sciences, particularly in detecting and classifying leukemic cells. These methods assist doctors in detecting diseases earlier, reducing their workload and the possibility of errors. This research aims to design a deep learning model with a customized architecture for detecting acute leukemia using images of lymphocytes and monocytes. This study presents a novel dataset containing images of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML). The new dataset has been created with the assistance of various experts to help the scientific community in its efforts to incorporate machine learning techniques into medical research. Increasing the scale of the dataset is achieved with a Generative Adversarial Network (GAN). The proposed CNN model based on the Tversky loss function includes six convolution layers, four dense layers, and a Softmax activation function for the classification of acute leukemia images. The proposed model achieved a 99% accuracy rate in diagnosing acute leukemia types, including ALL and AML. Compared to previous research, the proposed network provides a promising performance in terms of speed and accuracy; and based on the results, the proposed model can be used to assist doctors and specialists in practical applications.
Optical Fibers as Dosimeter Detectors for Mixed Proton/Neutron Fields—A Biological Dosimeter
Authors: Niedermeier Jana , Penner Crystal , Usherovich Samuel , Bélanger-Champagne Camille , Paulssen Elisabeth , Hoehr Cornelia
Published: January 2023

In recent years, proton therapy has gained importance as a cancer treatment modality due to its conformality with the tumor and the sparing of healthy tissue. However, in the interaction of the protons with the beam line elements and patient tissues, potentially harmful secondary neutrons are always generated. To ensure that this neutron dose is as low as possible, treatment plans could be created to also account for and minimize the neutron dose. To monitor such a treatment plan, a compact, easy to use, and inexpensive dosimeter must be developed that not only measures the physical dose, but which can also distinguish between proton and neutron contributions. To that end, plastic optical fibers with scintillation materials (Gd2O2S:Tb, Gd2O2S:Eu, and YVO4:Eu) were irradiated with protons and neutrons. It was confirmed that sensors with different scintillation materials have different sensitivities to protons and neutrons. A combination of these three scintillators can be used to build a detector array to create a biological dosimeter.
ST-PBFT: An Optimized PBFT Consensus Algorithm for Intellectual Property Transaction Scenarios
Authors: Zhong Wang , Feng Wenlong , Huang Mengxing , Feng Siling
Published: January 2023

For the current Intellectual Property (IP) transaction scenario, consensus nodes need to simultaneously consensus transactions of the same transaction type, resulting in low consensus efficiency, accuracy, and reliability, which seriously hinders the development of intellectual property. Based on the consortium chain, this paper proposes a secure and efficient blockchain-distributed consensus algorithm, ST-PBFT (Shard Transaction Practical Byzantine Fault Tolerance), applied to the IP transaction scenario. The main contributions of ST-PBFT include the following: first, a grouping method based on the principle of consistent hashing is proposed to group consensus nodes, and nodes group consensus, which reduces the complexity of communication. Second, the transaction consensus group can process IP transactions in parallel, which improves the throughput of the algorithm. Third, a node reputation evaluation model is proposed, which can prevent byzantine nodes from being repeatedly elected as primary nodes. The experimental results show that ST-PBFT can significantly improve the consensus efficiency and reliability and reduce consensus latency.
Depression Detection in Speech Using Transformer and Parallel Convolutional Neural Networks
Authors: Yin Faming , Du Jing , Xu Xinzhou , Zhao Li
Published: January 2023

As a common mental disorder, depression becomes a major threat to human health and may even heavily influence one’s daily life. Considering this background, it is necessary to investigate strategies for automatically detecting depression, especially through the audio modality represented by speech segments, mainly due to the efficient latent information included in speech when describing depression. However, most of the existing works focus on stacking deep networks in audio-based depression detection, which may lead to insufficient knowledge for representing depression in speech. In this regard, we propose a deep learning model based on a parallel convolutional neural network and a transformer in order to mine effective information with an acceptable complexity. The proposed approach consists of a parallel convolutional neural network (parallel-CNN) module used to focus on local knowledge, while a transformer module is employed as the other parallel stream to perceive temporal sequential information using linear attention mechanisms with kernel functions. Then, we performed experiments on two datasets of Distress Analysis Interview Corpus-Wizard of OZ (DAIC-WOZ) and Multi-modal Open Dataset for Mental-disorder Analysis (MODMA). The experimental results indicate that the proposed approach achieves a better performance compared with the state-of-the-art strategies.
TEZE: A System to Enhance Safety in Highway Tunnels as a New Smartphone-Based Emergency Call Paradigm
Authors: Giuliano Romeo , Mazzenga Franco , Tenaglia Emilio , Vatalaro Francesco , Vizzarri Alessandro , Zei Gherardo
Published: January 2023

In the European Union, the eCall system has been mandatory since 31 March 2018. The system enabled a significant increase in safety on roads and highways, making help faster in the event of an accident. However, based on circuit-switched 2G/3G communications and an onboard device called IVS, it is generally unavailable on legacy vehicles. Some of its limitations tend to be remedied by the future NG eCall based on 4G/5G packet-switched communications. This paper discusses why the IVS may be an Achilles’ heel of any future IVS-based eCall and analyzes the advantages of a smartphone-based system. The TEZE system, starting to be implemented and installed in Italian highway tunnels, is one first general-purpose safety system, allowing highly reliable smartphone-based emergency calls. It is based on a dedicated low-cost ground infrastructure that allows monitoring of the availability of emergency call service through the mobile radio networks of the operators present inside a tunnel. The system complies with the ISO/IEC 30141:2018 reference standard. Identifying functional/non-functional requirements and their verification criteria provides an overall validation of the analyzed system. The TEZE system has been implemented and tested on the field. We report and comment on some experimental results. The paper also examines some key functionalities for vehicular services that can be implemented in an integrated system based on smartphones and heterogeneous networks.
Partial Discharge Detection Technology for Switchgear Based on Near-Field Detection
Authors: Suo Chunguang , Zhao Jingjing , Wu Xuehua , Xu Zhipeng , Zhang Wenbin , He Mingxing
Published: January 2023

In view of the fact that the partial discharge (PD) signal energy is mainly concentrated below hundreds of megahertz, the ultra-high frequency part of the energy is weak, and the interior space of the switchgear is narrow, this paper proposes a new method for PD detection of the switchgear based on near-field detection. Firstly, based on the principle of PD, the field characteristics of the signal in the switchgear are analyzed. After that, the probe is designed with an electric small loop structure. Based on its equivalent circuit, its measurement principle and amplitude frequency characteristics are analyzed. The influence of probe size and material on amplitude frequency characteristics is obtained by using simulation software High Frequency Structure Simulator (HFSS), and the probe parameters suitable for PD detection in the switchgear are determined. Finally, the performance of the probe is measured by network analyzer, and the PD signal is tested on the simulated PD test platform. The results show that the probe works in the frequency band of 10–200 MHz and can receive PD signals containing more energy information. In the operating frequency band, the reflection coefficient of the probe port is very large, and its interference to the signal near field is particularly small. The probe also has good frequency response characteristics, and the fluctuation in the frequency band is less than 5 dB, which can obtain more accurate PD signal characteristics in subsequent processing. In addition, the probe is passive, with dimensions of 166 mm in length, 104 mm in width, and 2 mm in thickness, which is suitable for placing in the switchgear with small internal space. The results of PD receiving test show that the probe can reflect the occurrence of PD remarkably and accurately.
Design and Implementation of Embedded Controller-Based Energy Storage and Management System for Remote Telecom
Authors: Nalina B. S. , Chilambarasan M. , Tamilselvi S. , Al Alahmadi Ahmad Aziz , Alwetaishi Mamdooh , Mujtaba M. A. , Kalam M. A.
Published: January 2023

The source of energy extracted in renewable form has turned out to be a primary mainstream energy source, especially in the telecom sectors. Rapid growth of renewable sources has led to telecom operators concentrating more on designing the system with appropriate energy storage elements, providing control facilities, improving system efficiency and verifying uninterrupted power supplies. Therefore, this paper gives a novel approach of utilizing embedded control in energy generation consisting of a solar-wind hybrid energy system placed in isolated areas. For the purpose of integration of wind, together with the solar energy sources, into an increasingly efficient system, a single Cuk-Luo integrated DC-DC converter has been put forward. The proposed system has been modeled using MATLAB/Simulink and verified under various combinations of solar-wind energy sources without compromising the required power. In order to verify the proposed Cuk-Luo integrated converter with the energy management controller system, a prototype hardware is implemented and tested.
Influence of Dead-Time on the Input Current Ripple of Three-Phase Voltage Source Inverter
Authors: Arrozy Juris , Retianza Darian V. , Duarte Jorge L. , Ilhan Caarls Esin , Huisman Henk
Published: January 2023

The DC-link capacitor in power electronic systems is one of the most vulnerable components in terms of reliability. Since a reliable design of the DC-link capacitor depends on an accurate estimation of its current ripple, this paper proposes analytical equations to model the influence of dead-time on the input current ripple of a three-phase voltage source inverter. The effect of dead-time is modeled as a delay in the rising edges of the input current waveform. The proposed analytical equations are derived and then verified by simulations and experiments. The proposed equations generally provide better accuracy in predicting the input current ripple value compared to the benchmark equations. From the simulation and experimental results, the proposed equations are optimized for dead-time values more than 0.7 μs and modulation indices less than or equal to 0.7. Limitations of the proposed equations are also discussed. For small phase displacements and high modulation indices (0.8 to 1), the accuracy decreases because of the influence of AC output current ripple. For small modulation indices (less than 0.2) and a high value of dead-time (2 μs), the accuracy also decreases due to distortion in the phase current waveforms.
A Comprehensive Review on Utilizing Nanomaterials in Enhanced Oil Recovery Applications
Authors: El-Masry Jamil Fadi , Bou-Hamdan Kamel Fahmi , Abbas Azza Hashim , Martyushev Dmitriy A.
Published: January 2023

Chemicals are a pivotal part of many operations for the oil and gas industry. The purpose of chemical application in the subsurface reservoir is to decrease the mobility ratio between the displaced fluid and the displacing one or to increase the capillary number. These have been the favorable mechanisms for Enhanced Oil Recovery (EOR). Recently, it became a mainstay with EOR researchers looking for effective and efficient materials that can be economically feasible and environmentally friendly. Therefore, when the development of chemicals reached a peak point by introducing nanosized materials, it was of wondrous interest in EOR. Unlike other sizes, nanoparticles display distinct physical and chemical properties that can be utilized for multiple applications. Therefore, vast amounts of nanoparticles were examined in terms of formulation, size effect, reservoir condition, viscosity, IFT, and wettability alteration. When a holistic understanding of nanoparticles is aimed, it is necessary to review the recent studies comprehensively. This paper reviews the most recently published papers for nanoparticles in oil in general, emphasizing EOR, where most of these publications are between the years 2018 and 2022. It covers a thorough comparison of using nanoparticles in different EOR techniques and the expected range of oil recovery improvements. Moreover, this paper highlights the gaps existing in the field-scale implementation of NPs in EOR and opens space for research and development. The findings of this review paper suggest that the selection of the best NPs type for an EOR application is critical to the reservoir rock properties and conditions, reservoir fluids type, EOR mechanism, chemicals type (surfactant/polymer/alkaline), chemicals concentration used in the flooding process, and NPs properties and concentration.