bluuebunny/crossref_metadata_2025 huggingface dataset:
Laser, Intense Pulsed Light, and Radiofrequency for the Treatment of Burn Scarring: A Systematic Review and Meta-Analysis
Authors: Bai Yubing , Zhang Yiqiu , Ni Wei , Yao Min
Published: March 2023

Burns and scarring are considered some of the greatest problems in public health because of their frequent occurrence. Today, photo-electric technology shows promising results in the treatment of burn scars. Over the years, more clinical trials and more technologies for scarring have emerged. The aim of this study was to determine better timing and methods of photo-electric therapy for burn scars. This study was registered in PROSPERO (CRD42023397244), following the PRISMA statement, and was carried out in concordance with the PRISMA checklist. In October 2022, we searched PubMed.gov, Embase, and the Cochrane library (1980–present) for published studies related to the photo-electric treatment of burn scars. Two review authors independently selected the studies, extracted the data, assessed the risk of bias among the studies included, and carried out NIH assessments to assess the certainty of the evidence. A third review author arbitrated any disagreements. Our research included 39 studies. We found evidence suggesting that photo-electric therapy between six months and one year offers significantly better outcomes than treatment of scarring after one year. The evidence also suggests the use of IPL for the treatment of early burn scarring. However, it is important to emphasize that the scientific evidence remains insufficient. We need more clinical trials of higher quality and with less heterogeneity to confirm our results.
Impact of Patent Signal on Firm’s Performance at IPO: An Empirical Analysis of Japanese Firms
Authors: An Le Thuy Ngoc , Matsuura Yoshiyuki , Tareq Mohammad Ali , Issa Nurhayati Md , Che-Yahya Norliza
Published: March 2023

This study investigates whether patents can be a useful signaling tool for the IPO performances among high- and low-tech firms. Literature has provided a wealth of evidence confirming a significant relationship between patent signal and capital-raising success for US and EU venture capital-backed firms and start-ups in specific industries. Therefore, this paper focuses on the IPO firms from a more risk-averse market, Japan, to fill in the gaps in the literature, examining the signaling effect of patent applications prior to initial public offering (IPO) to the amount raised at IPO. Moreover, we examine whether patent applications prior to IPO from high-tech have relatively weaker signaling effects to compare with low-tech IPOs. Using the OLS model for 338 Japanese IPOs listed between 2000 and 2015, the result shows a robust and positive association between the number of patents before an IPO and the amount of cash raised during the IPO. The finding confirms that patents are a reliable signal for IPOs in the Japanese context. Using OECD industry categorization to classify high-tech and low-tech IPOs, our OLS result found that the interaction impact between the high-tech dummy and the quantity of patent applications before IPO is significantly negative to the amount of cash generated at IPO. The findings hold for a new set of high-tech and low-tech firms when we used a new industrial categorization proposed by Thomson Reuters, leading us to conclude that for the Japanese companies that belong to the high-tech industry sector, patenting activities fail to have a positive signal for the IPO.
When an Exchange Semester Is No Longer Enough: Why and How the Bologna-Reforms Changed the Behavior of High-Ability Students?
Authors: Frick Bernd , Lensing Fabian , Beck-Werz Lisa
Published: March 2023

Emphasizing the existence of information asymmetries between, e.g., young academics and potential employers, signaling theory has shaped our understanding of how high-ability students try to document their superior skills in a competitive environment such as the labor market: high-ability individuals benefit from a relative cost advantage compared to low-ability individuals when producing a credible signal of superior ability. When this cost advantage decreases, the signal’s value also decreases. We analyze how the signal ‘international qualification’ has changed due to increasing overall student mobility, driven by the effect of a massive change in the institutional framework, namely the implementation of the Bologna reforms. Using a large and hitherto not accessible dataset with detailed information on 9096 German high-ability students, we find that following the Bologna reforms, high-ability students extended their stays and completed degrees abroad (instead of doing exchange semesters). No such changes in behavior are to be observed in the overall student population. We conclude that completing a degree abroad is the new labor market signal for the ‘international qualification’ of high-ability students.
The Work Environment and Its Influence on Learning Transfer in Virtual In-Service ICT Training for Teachers and University Professors
Authors: Fauth Fernanda , González-Martínez Juan
Published: March 2023

This study seeks to analyse which factors in the work environments of students undergoing virtual in-service ICT teacher training facilitate the initial transfer of learning. Deductive categories were first defined based on previous studies. A qualitative analysis was then conducted to triangulate three data sources—two reflective training activities and interviews with students—using NVivo 12 software. This enabled us to determine how important some of these factors are for learning transfer within the context of techno-pedagogical distance teacher training, such as the predominance of social support over organizational categories.
Collaborative Learning: A Design Challenge for Teachers
Authors: Pozzi Francesca , Manganello Flavio , Persico Donatella
Published: March 2023

This study, focused on collaborative learning approaches, aims to contribute to our understanding of whether and how teachers propose these kinds of activities in their daily practice. Particularly, this study aims to explore teachers’ behaviour when designing such activities for their learners with respect to different learning settings (i.e., face-to-face and/or blended settings vs. fully online settings). With reference to fully online settings, the Emergency Remote Teaching that took place during the COVID-19 outbreak is used as a reference case. The results of a self-reported survey of Italian teachers (N = 268) are presented. Our conclusions indicate that Italian teachers do propose collaborative learning activities to some extent in face-to-face and/or blended settings as well as in fully online settings, with statistically significant differences both in reference to the approaches adopted and to the (technological) tools used. Nonetheless, the data also indicate that teachers’ design decisions are not always in line with recommendations widely proposed by the collaborative learning research community.
University-Wide Digital Skills Training: A Case Study Evaluation
Authors: Raji Nabila A. S. , Busson-Crowe David A. , Dommett Eleanor J.
Published: March 2023

Digital competencies and confidence are thought to be critical to success in higher education. However, despite learning frequently taking place online through the use of virtual learning environment and tools such as lecture capture, and evidence to counter the idea of digital nativity, these critical skills are often not explicitly taught at university. In the present study, we describe the development and evaluation of our Essential Digital Skills programme, which is a university-wide digital training programme designed and implemented at a large London university, aimed at new students but open to all students at the University. Using Kirkpatrick’s evaluation model, we demonstrate that the programme provided effective training in digital skills for all students but that individual differences exist in the training experience, notably around ethnicity and student status, with Black and Minority Ethnic (BAME) students and international students feeling that the training made a greater contribution to their skill levels and resulted in greater behaviour change and impact, as well as intention to undertake further training.
Using Digital Learning Platforms to Enhance the Instructional Design Competencies and Learning Engagement of Preservice Teachers
Authors: Gameil Azza Ali , Al-Abdullatif Ahlam Mohammed
Published: March 2023

Given the effects of the COVID-19 pandemic and the associated radical changes to the teaching landscape, employing digital learning platforms in the professional training of teachers has become imperative. The instructional design competencies are among the most important competencies for today’s teachers. Therefore, it is necessary to test the effectiveness of digital learning platforms to provide teachers with the required competencies, both cognitive and practical, and to help them improve these competencies. Accordingly, in this study, we aimed to measure the effects of using the Google Classroom platform to develop instructional design competences and learning engagement among preservice teachers in Saudi Arabia. We used a quasi-experimental approach, with a one-group design (pre- and posttest) in this study, which included 61 female student teachers. Three measurement instruments were utilised, namely, the achievement test, the product evaluation card, and the learning engagement scale. The results of our study showed that using Google Classroom as a digital learning platform was effective in helping the preservice teachers acquire and develop their cognitive and practical competencies in instructional design. Our results further revealed high levels of learning engagement at cognitive, behavioural, and social levels among the preservice teachers who participated in learning instructional design through the Google Classroom platform. Our findings emphasise the need for greater investment in digital learning platforms to support preservice teachers’ professional training, as these students can benefit from the potential these platforms offer in developing their instructional design competencies.
An Image Object Detection Model Based on Mixed Attention Mechanism Optimized YOLOv5
Authors: Sun Guangming , Wang Shuo , Xie Jiangjian
Published: March 2023

As one of the more difficult problems in the field of computer vision, utilizing object image detection technology in a complex environment includes other key technologies, such as pattern recognition, artificial intelligence, and digital image processing. However, because an environment can be complex, changeable, highly different, and easily confused with the target, the target is easily affected by other factors, such as insufficient light, partial occlusion, background interference, etc., making the detection of multiple targets extremely difficult and the robustness of the algorithm low. How to make full use of the rich spatial information and deep texture information in an image to accurately identify the target type and location is an urgent problem to be solved. The emergence of deep neural networks provides an effective way for image feature extraction and full utilization. By aiming at the above problems, this paper proposes an object detection model based on the mixed attention mechanism optimization of YOLOv5 (MAO-YOLOv5). The proposed method fuses the local features and global features in an image so as to better enrich the expression ability of the feature map and more effectively detect objects with large differences in size within the image. Then, the attention mechanism is added to the feature map to weigh each channel, enhance the key features, remove the redundant features, and improve the recognition ability of the feature network towards the target object and background. The results show that the proposed network model has higher precision and a faster running speed and can perform better in object-detection tasks.
Periodontal Disease Classification with Color Teeth Images Using Convolutional Neural Networks
Authors: Park Saron , Erkinov Habibilloh , Hasan Md. Al Mehedi , Nam Seoul-Hee , Kim Yu-Rin , Shin Jungpil , Chang Won-Du
Published: March 2023

Oral health plays an important role in people’s quality of life as it is related to eating, talking, and smiling. In recent years, many studies have utilized artificial intelligence for oral health care. Many studies have been published on tooth identification or recognition of dental diseases using X-ray images, but studies with RGB images are rarely found. In this paper, we propose a deep convolutional neural network (CNN) model that classifies teeth with periodontal diseases from optical color images captured in front of the mouth. A novel network module with one-dimensional convolutions in parallel was proposed and compared to the conventional models including ResNet152. In results, the proposed model achieved 11.45% higher than ResNet152 model, and it was proved that the proposed structure enhanced the training performances, especially when the amount of training data was insufficient. This paper shows the possibility of utilizing optical color images for the detection of periodontal diseases, which may lead to a mobile oral healthcare system in the future.
Design and Non-Uniform Current Analysis of a 0.35-THz Extended Interaction Oscillator Based on Pseudospark-Sourced Multiple Sheet Electron Beams
Authors: Peng Ruibin , Li Hailong , Yin Yong , Wang Bin , Xu Xiaotao , Bi Liangjie , Qin Yu , Meng Lin
Published: March 2023

A novel method, which combines a multiple-beam extended interaction oscillator (EIO) with pseudospark-sourced (PS) sheet electron beams, is applied to generate high-power terahertz sources. For a multiple-beam EIO, the beam cross-section is significantly improved by replacing the commonly used pencil electron beams with sheet electron beams. The PS electron beams have the advantage of high current density and operate without a focus magnetic field. The volume of the cavity is larger when the EIO operates in the TM31-3π mode than in the conventional TM01-2π mode at the same operating frequency. The EIO operating at the terahertz frequency has a larger cavity volume, which means greater power capacity and lower manufacturing difficulty. For a PS multiple-beam EIO, the non-uniformity of electron beam currents is a common problem. In order to study this problem, an original high-order mode EIO driven by PS multiple sheet electron beams is presented with enhanced output power at 0.35 THz. The authors analyze electron beams with different currents through particle-in-cell (PIC) simulations. Simulation results show that the EIO can operate stably even in the case of non-uniform PS electron beam currents. When each current is 1.4 A, simulation results show the EIO’s output power of 4.9 kW at 0.35 THz. Considering the low conductivity of 1.1 × 107 S/m, the efficiency is still 1.42%.
Usability of Inexpensive Optical Pulse Sensors for Textile Integration and Heartbeat Detection Code Development
Authors: Richter Niclas , Tuvshinbayar Khorolsuren , Ehrmann Guido , Ehrmann Andrea
Published: March 2023

Low-cost sensors and single circuit boards such as Arduino and Raspberry Pi have increased the possibility of measuring biosignals by smart textiles with embedded electronics. One of the main problems with such e-textiles is their washability. While batteries are usually removed before washing, single-board computers and microcontrollers, as well as electronic sensors, would ideally be kept inside a user-friendly smart garment. Here, we show results of washing tests with optical pulse sensors, which can be used in smart gloves not only for hospitalized patients, and ATtiny85 as an example of a single-board microcontroller, sewn onto different cotton fabrics. We report that even without any encapsulation, all tested sensors and microcontrollers endured 10 washing cycles at 30–60 °C without defects. For easier garment integration, we suggest using an ESP8266 with integrated Wi-Fi functionality and offer a new program code to measure beats per minute (BMP) with optimized accuracy.
A Novel Levy-Enhanced Opposition-Based Gradient-Based Optimizer (LE-OB-GBO) for Charging Station Placement
Authors: Raval Sanket , Natarajan Thangadurai , Deb Sanchari
Published: March 2023

Transportation modes are shifting toward electric vehicles from conventional internal combustion engines to reduce pollution and dependency on conventional fuels. This reduces the fuel cost, while charging stations must be distributed across the locations to minimize range anxiety. Installing charging stations randomly across the distribution system can lead to violation of active power loss, voltage deviation, and reliability parameters of the power system. The problem of the optimal location of charging stations is a nonlinear optimization problem that includes the parameters of the distribution system and road network with their respective constraints. This work proposes a new metaheuristic optimization algorithm, a levy-enhanced opposition-based gradient-based optimizer (LE-OB-GBO), to solve the charging station placement problem. It has a balance between exploration and exploitation and fast convergence rate. The performance of the proposed algorithm was evaluated by solving CEC 2017 benchmark functions and charging station problem. The performance of the proposed algorithm was also compared with that of other state-of-the-art optimization algorithms and was found to outperform 17 out of 29 CEC 2017 functions. Statistical analysis of the charging station placement problem indicates the lowest mean values of 1.4912, 1.4783, and 1.5217 for LE-OB-GBO for considered cases 1 to 3, respectively, thus proving the efficacy of the proposed algorithm.
Multi-USV Dynamic Navigation and Target Capture: A Guided Multi-Agent Reinforcement Learning Approach
Authors: Nantogma Sulemana , Zhang Shangyan , Yu Xuewei , An Xuyang , Xu Yang
Published: March 2023

Autonomous unmanned systems have become an attractive vehicle for a myriad of military and civilian applications. This can be partly attributed to their ability to bring payloads for utility, sensing, and other uses for various applications autonomously. However, a key challenge in realizing autonomous unmanned systems is the ability to perform complex group missions, which require coordination and collaboration among multiple platforms. This paper presents a cooperative navigating task approach that enables multiple unmanned surface vehicles (multi-USV) to autonomously capture a maneuvering target while avoiding both static and dynamic obstacles. The approach adopts a hybrid multi-agent deep reinforcement learning framework that leverages heuristic mechanisms to guide the group mission learning of the vehicles. Specifically, the proposed framework consists of two stages. In the first stage, navigation subgoal sets are generated based on expert knowledge, and a goal selection heuristic model based on the immune network model is used to select navigation targets during training. Next, the selected goals’ executions are learned using actor-critic proximal policy optimization. The simulation results with multi-USV target capture show that the proposed approach is capable of abstracting and guiding the unmanned vehicle group coordination learning and achieving a generally optimized mission execution.
Analysis of a p-i-n Diode Circuit at Radio Frequency Using an Electromagnetic-Physics-Based Simulation Method
Authors: Chen Zhenzhen , Chen Junquan , Chen Xing
Published: March 2023

Embracing the era of higher operating frequencies, expanding functionality, and increased integration scale, modern circuit design relies more and more on the accurate prediction of the electromagnetic (EM) effects resulting from undesired radiation and mutual coupling of digital electronic devices. In this paper, an electromagnetic-physics-based simulation method is proposed, to simulate semiconductor devices and circuits. It utilizes physics-based simulation to analyze semiconductor devices in a circuit and incorporates this physics-based simulation into electromagnetic simulation (e.g., the finite difference time domain (FDTD)), to simulate a circuit at high frequency. To validate the proposed method, sample numerical results on circuits containing a commercial p-i-n diode with model number mot_bal99lt1 at radio frequency (RF) were obtained and compared with measurement data. The comparison showed a good agreement between the two sets of data, which validated the feasibility and accuracy of the proposed algorithm. Moreover, the proposed method can provide a useful physical mechanism for understanding effects on semiconductor devices and circuits.
Attitude and Altitude Control Design and Implementation of Quadrotor Using NI myRIO
Authors: Hong Jun-Yao , Chiu Po-Jui , Pong Chun-Da , Lan Chen-Yang
Published: March 2023

Multi-rotor vehicles have demonstrated great potential in many applications, from goods delivery to military service. Its simple structure has drawn much attention in control research, with various feedback control design methods being tested on it. In this study, a quadrotor is constructed with National Instrument (NI) myRIO for its flight controller. Linear controllers are synthesized with a simple model and with a more detailed model, which also considers the actuator dynamic and system time delay, to exploit the limitations and trade-offs posted by hardware and its influence on linear control design and implementation. The simulation results match the real response better if the detailed model is used in the control design. In addition, the linear–quadratic–Gaussian (LQG) control provides the best response in the control design in this study. The constraints posted by the actuator and time delay are clearly observed in the control synthesis process and the experiment result. These constraints also led to poor control performance or even instability if not considered in the control implementation in advance.
A Freehand 3D Ultrasound Reconstruction Method Based on Deep Learning
Authors: Chen Xin , Chen Houjin , Peng Yahui , Liu Liu , Huang Chang
Published: March 2023

In the medical field, 3D ultrasound reconstruction can visualize the internal structure of patients, which is very important for doctors to carry out correct analyses and diagnoses. Furthermore, medical 3D ultrasound images have been widely used in clinical disease diagnosis because they can more intuitively display the characteristics and spatial location information of the target. The traditional way to obtain 3D ultrasonic images is to use a 3D ultrasonic probe directly. Although freehand 3D ultrasound reconstruction is still in the research stage, a lot of research has recently been conducted on the freehand ultrasound reconstruction method based on wireless ultrasonic probe. In this paper, a wireless linear array probe is used to build a freehand acousto-optic positioning 3D ultrasonic imaging system. B-scan is considered the brightness scan. It is used for producing a 2D cross-section of the eye and its orbit. This system is used to collect and construct multiple 2D B-scans datasets for experiments. According to the experimental results, a freehand 3D ultrasonic reconstruction method based on depth learning is proposed, which is called sequence prediction reconstruction based on acoustic optical localization (SPRAO). SPRAO is an ultrasound reconstruction system which cannot be put into medical clinical use now. Compared with 3D reconstruction using a 3D ultrasound probe, SPRAO not only has a controllable scanning area, but also has a low cost. SPRAO solves some of the problems in the existing algorithms. Firstly, a 60 frames per second (FPS) B-scan sequence can be synthesized using a 12 FPS wireless ultrasonic probe through 2–3 acquisitions. It not only effectively reduces the requirement for the output frame rate of the ultrasonic probe, but also increases the moving speed of the wireless probe. Secondly, SPRAO analyzes the B-scans through speckle decorrelation to calibrate the acousto-optic auxiliary positioning information, while other algorithms have no solution to the cumulative error of the external auxiliary positioning device. Finally, long short-term memory (LSTM) is used to predict the spatial position and attitude of B-scans, and the calculation of pose deviation and speckle decorrelation is integrated into a 3D convolutional neural network (3DCNN). Prepare for real-time 3D reconstruction under the premise of accurate spatial pose of B-scans. At the end of this paper, SPRAO is compared with linear motion, IMU, speckle decorrelation, CNN and other methods. From the experimental results, it can be observed that the spatial pose deviation of B-scans output using SPRAO is the best of these methods.
Acoustic Feedback Cancellation Algorithm for Hearing Aids Based on a Weighted Error Adaptive Filter
Authors: Yin Yunchao , Chen Fei
Published: March 2023

Acoustic feedback is a common phenomenon that occurs during hearing aid use, limiting the maximum gain that a hearing aid can provide. Effective cancellation of acoustic feedback is an essential feature of hearing aids. However, due to the complex environments in which hearing aids are used and the frequently changing acoustic feedback path, it is difficult for existing adaptive filter-based acoustic feedback cancellation algorithms to balance both convergence speed and steady-state error. For this reason, based on the nonparametric variable step size (NPVSS) algorithm, a weighted NPVSS algorithm that also introduces a prediction error method is proposed in this paper. First, by introducing the prediction error method, the adaptive filter bias caused by the nonwhite source signal is effectively reduced. Second, the proposed weighting mechanism weights the error signal according to the adaptive filter misalignment, which enhances the steady-state robustness of the algorithm while accelerating its convergence. In addition, a new low-complexity method is herein proposed for source signal energy estimation by reusing the misalignment information to solve the step size calculation problem of the NPVSS algorithm. Simulation results show that the new algorithm exhibits greater robustness and faster convergence than similar algorithms. The proposed algorithm is implemented with a real hearing aid and its performance is measured on a dummy head in a soundproof room. The test results demonstrate that the proposed algorithm achieves a 35% reduction in convergence time compared with PEM-IMLMS and a 60% reduction compared with PEM-NLMS. Furthermore, the proposed algorithm reduces the sound pressure level of acoustic feedback residues compared with PEM-IMLMS and PEM-NLMS by approximately 2 dB SPL and 6 dB SPL, respectively. These results indicate that the new algorithm can provide timely and stable cancellation of acoustic feedback.
An 18–19.2 GHz Voltage-Controlled Oscillator with a Compact Varactor-Only Capacitor Array
Authors: Kim Jusung , Mauludin Muhammad Fakhri , Azzahra Hapsah Aulia , Jhon Heesauk , Lee Sanghun , Cho Kunhee
Published: March 2023

This paper presents a K-band CMOS cross-coupled pair voltage-controlled oscillator (VCO) that utilizes the varactor-only capacitor network. The proposed circuit features low parasitics and a compact capacitor network, such that the varactor in the analog tuning path directly affects the oscillation frequency with a large gain of the VCO, and the varactor in the digital tuning path results in minimal noise sources, thus achieving low phase noise for the automotive FMCW radar application. This VCO was designed using TSMC 65 nm technology, and the post-layout simulation results show a stable oscillation across 18–19.2 GHz, a minimum phase noise of −105.9 and −128 dBc/Hz at 1- and 10-MHz offset frequencies while dissipating 21 mW of power from a 1 V supply. Furthermore, no notable variations in phase noise are observed across the oscillation frequency tuning range. It reveals a figure of merit (FOM) of −178.3 dBc/Hz while using a varactor-only capacitor array and occupying a small chip area.
A Review of Indoor Positioning Systems for UAV Localization with Machine Learning Algorithms
Authors: Sandamini Chamali , Maduranga Madduma Wellalage Pasan , Tilwari Valmik , Yahaya Jamaiah , Qamar Faizan , Nguyen Quang Ngoc , Ibrahim Siti Rohana Ahmad
Published: March 2023

The potential of indoor unmanned aerial vehicle (UAV) localization is paramount for diversified applications within large industrial sites, such as hangars, malls, warehouses, production lines, etc. In such real-time applications, autonomous UAV location is required constantly. This paper comprehensively reviews radio signal-based wireless technologies, machine learning (ML) algorithms and ranging techniques that are used for UAV indoor positioning systems. UAV indoor localization typically relies on vision-based techniques coupled with inertial sensing in indoor Global Positioning System (GPS)-denied situations, such as visual odometry or simultaneous localization and mapping employing 2D/3D cameras or laser rangefinders. This work critically reviews the research and systems related to mini-UAV localization in indoor environments. It also provides a guide and technical comparison perspective of different technologies, presenting their main advantages and disadvantages. Finally, it discusses various open issues and highlights future directions for UAV indoor localization.
MMATERIC: Multi-Task Learning and Multi-Fusion for AudioText Emotion Recognition in Conversation
Authors: Liang Xingwei , Zou You , Zhuang Xinnan , Yang Jie , Niu Taiyu , Xu Ruifeng
Published: March 2023

The accurate recognition of emotions in conversations helps understand the speaker’s intentions and facilitates various analyses in artificial intelligence, especially in human–computer interaction systems. However, most previous methods need more ability to track the different emotional states of each speaker in a dialogue. To alleviate this dilemma, we propose a new approach, Multi-Task Learning and Multi-Fusion AudioText Emotion Recognition in Conversation (MMATERIC) for emotion recognition in conversation. MMATERIC can refer to and combine the benefits of two distinct tasks: emotion recognition in text and emotion recognition in speech, and production of fused multimodal features to recognize the emotions of different speakers in dialogue. At the core of MATTERIC are three modules: an encoder with multimodal attention, a speaker emotion detection unit (SED-Unit), and a decoder with speaker emotion detection Bi-LSTM (SED-Bi-LSTM). Together, these three modules model the changing emotions of a speaker at a given moment in a conversation. Meanwhile, we adopt multiple fusion strategies in different stages, mainly using model fusion and decision stage fusion to improve the model’s accuracy. Simultaneously, our multimodal framework allows features to interact across modalities and allows potential adaptation flows from one modality to another. Our experimental results on two benchmark datasets show that our proposed method is effective and outperforms the state-of-the-art baseline methods. The performance improvement of our method is mainly attributed to the combination of three core modules of MATTERIC and the different fusion methods we adopt in each stage.
Design of Broadband Low-RCS Array Antennas Based on Characteristic Mode Cancellation
Authors: Han Jialiang , Jia Dan , Du Biao , Han Guodong , Jia Yongtao , Zhao Zekang
Published: March 2023

In this letter, a design method for low radar cross section (RCS) array antennas based on characteristic mode cancellation (CMC) is presented. Based on the characteristic mode theory (CMT), two novel microstrip elements are designed by introducing rectangular slots and cross slots, which produce 180° scattering phase difference by adjusting the size of slots. The dominant characteristic modes of the two elements achieve broadband dual-linear polarization CMC and similar radiation performances. The 4 × 4 array antenna consisting of these two antenna elements is designed. The operating band is from 4.55 GHz to 5.49 GHz (relative bandwidth 18.7%). The gain loss of the proposed array is about 0.1 dB compared to the reference array. The monostatic RCS is reduced for dual−linear polarized waves, and the 6 dB radar cross section reduction (RCSR) bandwidths are 62.3% and 35.7%, respectively. The prototype is fabricated and measured. The measured results of radiation pattern and RCS are in good agreement with the simulated results.
An Unobtrusive, Wireless and Wearable Single-Site Blood Pressure Monitor Based on an Armband Using Electrocardiography (ECG) and Reflectance Photoplethysmography (PPG) Signal Processing
Authors: Silverio Angelito A. , Suarez Consuelo G. , Silverio Lean Angelo A. , Dino Joseph Y. , Duran Justine B. , Catambing Giuseppe Edgardo G.
Published: March 2023

Wearable medical devices (WMDs) for healthcare applications have become ubiquitous, allowing remote, at-home, and real-time chronic monitoring that have significantly decongested clinics. These WMDs permitted the monitoring of several physiological parameters, such as heart and respiration rates, SPO2, temperature, and energy expenditure during activities of daily living (ADLs) or fitness activities. While the measurement of these parameters has become common, full noninvasive, unobtrusive, and real-time blood pressure (BP) monitoring remains elusive owing to BP’s complex dynamics. To bring this into fruition, several works have been conducted combining different biosignals to indirectly extract BP by using PTT. Unlike previous works, we considered PTT variability by averaging it over discrete durations to account for BP variability for a more accurate estimation. PTTs were obtained using electrocardiograph (ECG) and reflective photoplethysmograph (rPPG) signals extracted by a wearable device attached to a single site on the upper arm. Our results show a significant correlation between average PTT and the BP measured using auscultation in a trial study. The developed system has potential for chronic, noninvasive, and cuff-less blood pressure monitors (BPMs) for localized and single-site implementations. Meanwhile, real-time data from the wearable device may be accessed via a remote desktop or a mobile phone application.
A Survey on Ambient Sensor-Based Abnormal Behaviour Detection for Elderly People in Healthcare
Authors: Wang Yan , Wang Xin , Arifoglu Damla , Lu Chenggang , Bouchachia Abdelhamid , Geng Yingrui , Zheng Ge
Published: March 2023

With advances in machine learning and ambient sensors as well as the emergence of ambient assisted living (AAL), modeling humans’ abnormal behaviour patterns has become an important assistive technology for the rising elderly population in recent decades. Abnormal behaviour observed from daily activities can be an indicator of the consequences of a disease that the resident might suffer from or of the occurrence of a hazardous incident. Therefore, tracking daily life activities and detecting abnormal behaviour are significant in managing health conditions in a smart environment. This paper provides a comprehensive and in-depth review, focusing on the techniques that profile activities of daily living (ADL) and detect abnormal behaviour for healthcare. In particular, we discuss the definitions and examples of abnormal behaviour/activity in the healthcare of elderly people. We also describe the public ground-truth datasets along with approaches applied to produce synthetic data when no real-world data are available. We identify and describe the key facets of abnormal behaviour detection in a smart environment, with a particular focus on the ambient sensor types, datasets, data representations, conventional and deep learning-based abnormal behaviour detection methods. Finally, the survey discusses the challenges and open questions, which would be beneficial for researchers in the field to address.
Technology for Hot Spring Cooling and Geothermal Heat Utilization: A Case Study for Balneology Facility
Authors: Vondra Marek , Buzík Jiří , Horňák David , Procházková Michaela , Miklas Václav , Touš Michal , Jegla Zdeněk , Máša Vítězslav
Published: March 2023

Reducing energy costs in Europe is more challenging than before due to extreme price increases. The use of local renewable energy sources is one way to contribute to this effort. In the case of spa resorts, the use of heat from hot springs for therapeutic baths is an option. It is necessary to cool down this thermal mineral water to a temperature acceptable to the human body. However, due to the high mineral content of this water, heavy fouling can be a problem for conventional heat exchangers. The purpose of this study is to identify the suitable cooling technology in terms of required cooling capacity and waste heat recovery capability. The cooling technology was selected on the basis of a literature search. A pilot cooling unit consisting of vacuum cooler and plate heat exchanger was designed and tested in a real spa resort for six months. Both selected technologies have demonstrated the ability to cool thermal mineral water in long-term operation, as well as the possibility to utilize waste heat for domestic hot water heating. However, fouling problems occur in the plate heat exchanger. The vacuum cooler demonstrated greater operational robustness and resistance to encrustation.
Predicting Thermoelectric Power Plants Diesel/Heavy Fuel Oil Engine Fuel Consumption Using Univariate Forecasting and XGBoost Machine Learning Models
Authors: Siqueira-Filho Elias Amancio , Lira Maira Farias Andrade , Converti Attilio , Siqueira Hugo Valadares , Bastos-Filho Carmelo J. A.
Published: March 2023

Monitoring and controlling thermoelectric power plants (TPPs) operational parameters have become essential to ensure system reliability, especially in emergencies. Due to system complexity, operating parameters control is often performed based on technical know-how and simplified analytical models that can result in limited observations. An alternative to this task is using time series forecasting methods that seek to generalize system characteristics based on past information. However, the analysis of these techniques on large diesel/HFO engines used in Brazilian power plants under the dispatch regime has not yet been well-explored. Therefore, given the complex characteristics of engine fuel consumption during power generation, this work aimed to investigate patterns generalization abilities when linear and nonlinear univariate forecasting models are used on a representative database related to an engine-driven generator used in a TPP located in Pernambuco, Brazil. Fuel consumption predictions based on artificial neural networks were directly compared to XGBoost regressor adaptation to perform this task as an alternative with lower computational cost. AR and ARIMA linear models were applied as a benchmark, and the PSO optimizer was used as an alternative during model adjustment. In summary, it was possible to observe that AR and ARIMA-PSO had similar performances in operations and lower error distributions during full-load power output with normal error frequency distribution of −0.03 ± 3.55 and 0.03 ± 3.78 kg/h, respectively. Despite their similarities, ARIMA-PSO achieved better adherence in capturing load adjustment periods. On the other hand, the nonlinear approaches NAR and XGBoost showed significantly better performance, achieving mean absolute error reductions of 42.37% and 30.30%, respectively, when compared with the best linear model. XGBoost modeling was 8.7 times computationally faster than NAR during training. The nonlinear models were better at capturing disturbances related to fuel consumption ramp, shut-down, and sudden fluctuations steps, despite being inferior in forecasting at full-load, especially XGBoost due to its high sensitivity with slight fuel consumption variations.
Literature Review of Frost Formation Phenomena on Domestic Refrigerators Evaporators
Authors: Krasota Daria , Błasiak Przemysław , Kolasiński Piotr
Published: March 2023

The topic of frost formation on the heat exchanger surface has been gaining interest since the late 1940s. Scientists and industrial engineers from many scientific and R&D units around the world have been trying to understand the nature of frosting and implement solutions to prevent such an unwanted phenomenon from having a significant impact on the performance of heat exchangers (such as a decrease in heat transfer efficiency, mechanical damage, and condensation risk). The aim of this article is to summarize the present state of knowledge dedicated to frost formation types and morphology, review, and discuss the most recent studies relevant to the challenge of frost formation, focusing on the evaporator of the domestic refrigerator. The different types of domestic refrigerators are summarized, as are the different types of evaporators inside them. Common methods of testing frost formation phenomena on the evaporator are revisited in this article, and the analysis of the most recent mathematical models is presented as well. The input and output parameters of these models are grouped, and a similar analysis is conducted for the CFD models.
Aerothermal Performance and Soot Emissions of Reacting Flow in a Micro-Gas Turbine Combustor
Authors: Wang Heyu , Luo Kai Hong
Published: March 2023

Micro-gas turbines are used for power generation and propulsion in unmanned aerial vehicles. Despite the growing demand for electric engines in a world striving for a net zero carbon footprint, combustion gas turbines will continue to play a critical role. Hence, there is a need for improved micro-gas turbines that can meet stringent environmental regulations. This paper is the first part of a comprehensive study focused on understanding the fundamental performance and emission characteristics of a micro-gas turbine model, with the aim of finding ways to enhance its operation. The study used a multidisciplinary CFD model to simulate the reacting flow in the combustion chamber and validated the results against experimental data and throughflow simulations. The present work is one of the few work that attempts to address both the aerothermal performance and emissions of the gas turbine. The findings highlight that parameters such as non-uniform outlet pressure, fuel-to-air ratio, and fuel injection velocity can greatly influence the performance and emissions of the micro-gas turbine. These parameters can affect the combustion efficiency, the formation of hot spots at the combustor–turbine interface, and the soot emissions. The results provide valuable insights for optimizing the performance and reducing the emissions of micro-gas turbines and serve as a foundation for further research into the interaction between the combustor and the turbine.
Enhancement in Efficiency of CIGS Solar Cell by Using a p-Si BSF Layer
Authors: Chadel Meriem , Chadel Asma , Benyoucef Boumediene , Aillerie Michel
Published: March 2023

Copper–indium–gallium–diselenide Cu(In,Ga)Se2 (CIGS) is a semiconductor compound belonging to group I-III-VI, with a chalcopyrite crystal structure. CIGS is promising for the development of high-performance photovoltaic applications in terms of stability and conversion efficiency. It is one of the main candidates to rival the efficiency and stability of conventional crystalline silicon cells, due to its high light absorption coefficient, lower material cost, and high stability. The limitation of its use is that CIGS integrates indium (In) and gallium (Ga), which are rare and expensive materials. The amount of these materials in the CIGS cell can be reduced by optimizing the thickness of the absorber. We show that the introduction of a layer of highly doped silicon in the structure of the solar cell between the absorber layer and the back surface field layer effectively allows for decreasing the thickness of the absorber. Within the same objective, we focus on the danger of cadmium in the CdS buffer layer. In the first optimizations, we replaced the n-type CdS buffer layer with a n-type Zn(O,S) buffer layer. For this work, we used a one-dimensional simulation program, named Solar Cell Capacitance Simulator in one Dimension (SCAPS-1D), to investigate this new CIGS solar cell structure. After optimization, a maximum conversion efficiency of 24.43% was achieved with a 0.2 μm CIGS absorber layer and a 1 µm Si BSF layer.
Early Warning Method and Fire Extinguishing Technology of Lithium-Ion Battery Thermal Runaway: A Review
Authors: Wang Kuo , Ouyang Dongxu , Qian Xinming , Yuan Shuai , Chang Chongye , Zhang Jianqi , Liu Yifan
Published: March 2023

Lithium-ion batteries (LIBs) are widely used in electrochemical energy storage and in other fields. However, LIBs are prone to thermal runaway (TR) under abusive conditions, which may lead to fires and even explosion accidents. Given the severity of TR hazards for LIBs, early warning and fire extinguishing technologies for battery TR are comprehensively reviewed in this paper. First, the TR reaction mechanism and hazards of LIBs are discussed. Second, the TR early warning and monitoring methods of LIBs are summarized in five aspects consisting of acoustic, heat, force, electricity, and gas. In addition, to reduce the fire and explosion hazards caused by the TR of LIBs, the highly efficient extinguishing agents for LIBs are summarized. Finally, the early warning technology and fire extinguishing agent are proposed, which provides a reference for the hazard prevention and control of energy storage systems.
Simple Loss Model of Battery Cables for Fast Transient Thermal Simulation
Authors: Fedele Emanuele , Di Noia Luigi Pio , Rizzo Renato
Published: March 2023

In electric vehicles, currents with high-frequency ripples flow in the power cabling system due to the switching operation of power converters. Inside the cables, a strong coupling between the thermal and electromagnetic phenomena exists, since the temperature and Alternating Current (AC) density distributions in the strands affect each other. Due to the different time scales of magnetic and heat flow problems, the computational cost of Finite Element Method (FEM) numeric solvers can be excessive. This paper derives a simple analytical model to calculate the total losses of a multi-stranded cable carrying a Direct Current (DC) affected by a high-frequency ripple. The expression of the equivalent AC cable resistance at a generic frequency and temperature is derived from the general treatment of multi-stranded multi-layer windings. When employed to predict the temperature evolution in the cable, the analytical model prevents the use of complex FEM models in which multiple heat flow and magnetic simulations have to be run iteratively. The results obtained for the heating curve of a 35 mm2 stranded cable show that the derived model matches the output of the coupled FEM simulation with an error below 1%, whereas the simple DC loss model of the cable gives an error of 2.4%. While yielding high accuracy, the proposed model significantly reduces the computational burden of the thermal simulation by a factor of four with respect to the complete FEM routine.