bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the “Bottleneck” Effect in an Isolated Population of Microtus hartingi (Rodentia, Arvicolinae) from the Eastern Rhodopes (Bulgaria) by Methods of Integrative Analysis
Authors: Golenishchev Fedor N. , Zorenko Tanya A. , Petrova Tatyana V. , Voyta Leonid L. , Kryuchkova Lyudmila Yu. , Atanasov Nasko
Published: August 2022

An integrative analysis of an isolated population of Harting’s vole (Microtus hartingi) from the Eastern Rhodope Mountains (Bulgaria) was carried out by morphological and morphometric methods, computed tomography, Cytb variation data, and experimental hybridization. Substantial changes in the development of the skull and teeth were found. Nevertheless, those voles can live to the senex stage. A phylogenetic reconstruction based on Cytb sequences showed that M. hartingi from the Eastern Rhodopes forms a separate clade, which is a sister clade to the voles from Northeastern Greece (also from the foothills of the Rhodopes). M. hartingi from the Rhodopes is mostly isolated reproductively from M. h. ankaraensis from Anatolia (Turkey), as evidenced by a decrease in the proportion of pairs that started breeding, relatively high mortality of pups, and increased sterility of hybrid males. Possible time of isolation of the Rhodopean population and consequences of the “bottleneck” effect on its current state are discussed. At the same time, we believe that, at present, it is impossible to determine its taxonomic status. It is necessary to recognize M. hartingi from the Bulgarian Rhodopes as an endangered population and to include it in regional Red Lists of Bulgaria.
Genome-Wide Characterization of Remorin Genes in Terms of Their Evolution and Expression in Response to Hormone Signals and Abiotic Stresses in Foxtail Millet (Setaria italica)
Authors: Wang Yingqi , Li Jiaqi , Li Mengyu , Li Yuntong , Zhao Zibo , Li Cong , Yue Jing
Published: August 2022

Remorin genes encode plant-specific and plasma-membrane-associated proteins that play important roles in several plant physiological processes and adaptations to environmental adaptations. However, little is known regarding the remorin gene family in foxtail millet (Setaria italica), a traditional drought-resistant crop that grows in China. In this study, 21 remorin genes in the foxtail millet genome were identified, renamed according to their chromosomal distribution, and classified into four main groups based on their phylogenetic relationships and structural features. Additionally, we found that SiREM gene family expansion was primarily because of whole- genome duplication and segmental duplication events. Evolutionary changes in the remorin family in Poaceae crops were clarified via synteny analyses. Gene expression analyses through RT-PCR and qRT-PCR indicated that SiREM genes influenced millet growth and development, particularly SiREM1, 4, 11, and 12. Concurrently, SiREM genes expression showed inconsistent response to phytohormone treatments and abiotic stresses, suggesting that they are regulated by different signaling pathways. This systematic reanalysis remorin gene family in the foxtail millet provides fundamental information about the biological functions associated with growth, development, and stress tolerance and evolutionary characteristics, thus helping in elucidating the molecular mechanism and improving the agricultural traits of this crop in the future.
Small Vessel Impact on the Whistle Parameters of Two Ecotypes of Common Bottlenose Dolphin (Tursiops truncatus) in La Paz Bay, Mexico
Authors: Antichi Simone , Jaramillo-Legorreta Armando M. , Urbán R. Jorge , Martínez-Aguilar Sergio , Viloria-Gómora Lorena
Published: August 2022

Vessel traffic is one of the major sources of underwater anthropogenic noise. Dolphins can modify their vocal repertoire, especially whistles, in presence of vessels to facilitate their communication. Acoustic data were collected (sampling rate 96 kHz) in La Paz Bay, Gulf of California, Mexico. Whistle rate and parameters of the coastal and oceanic ecotypes of common bottlenose dolphins (Tursiops truncatus) were measured in absence of vessels and in presence of moving small vessels (size 5–10 m). The peak noise difference was calculated between the two frequency bands dominated by the whistles (2000–20,000 Hz) and the small vessel (500–2000 Hz). In presence of vessels the oceanic ecotype decreased whistle frequencies while the coastal ecotype increased them. Both ecotypes raised whistle frequencies with the decreasing of the peak noise difference. The differences in habitat and group structure could have driven the two ecotypes to react in a different way to the vessel presence.
Two-Scale Topology Optimization with Isotropic and Orthotropic Microstructures
Authors: Rastegarzadeh Sina , Wang Jun , Huang Jida
Published: August 2022

Advances in additive manufacturing enable the fabrication of complex structures with intricate geometric details, which bring opportunities for high-resolution structure design and transform the potential of functional product development. However, the increasingly delicate designs bring computational challenges for structural optimization paradigms, such as topology optimization (TO), since the design dimensionality increases with the resolutions. Two-scale TO paves an avenue for high-resolution structural design to alleviate this challenge. This paper investigates the efficacy of introducing function-based microstructures into the two-scale TO. Both isotropic and orthotropic microstructure are considered to develop this TO framework. Implicit functions are exploited to model the two classes of cellular materials, including triply periodic minimal surfaces (TPMS) and Fourier series-based functions (FSF). The elasticity tensor of microstructures is computed with numerical homogenization. Then, a two-scale TO paradigm is formulated, and a gradient-based algorithm is proposed to simultaneously optimize the micro-scale structures and macro-scale material properties. Several engineering benchmark cases are tested with the proposed method, and experimental results reveal that using proposed microstructures leads to, at most, a 36% decrease in the compliance of optimal structures. The proposed framework provides achievable directionality and broader design flexibility for high-resolution product development.
Research on Indoor Spatial Behavior Perception IoT Smart System for Solitary Elderly at Home
Authors: Lim Chor-Kheng
Published: August 2022

This research aims at contributing to a seamless, integrated technology intelligent living system for solitary older adults at home. The capacitive intimate sensing module, that can be easily pasted to the existing home space element surfaces, daily objects, or home furniture, such as a wall, door, stairs, a chair, cabinet, table, sofa, etc, is developed in this research. This 30 × 30 cm sensing module can actively sense people’s physical behaviors and body movements in spaces. The signals acquired from the sensing modules in indoor spaces will then integrate into the controller system through the IoT application and logically define the behavior classification. From the preliminary analysis of observing the 80-year-old elderly subject’s daily activities, the movement trajectory of the ‘Move–Stop’ pattern is found. There will be a touch (T) and a touchless (TL) relationship between the body and the space elements or objects. The touchless or non-contact intimate relationship also can be divided into two types: 1. the body ‘Passes by’ (P) the spatial elements or objects, and 2. the body ‘Stays’ (S) in front of the object and performs activities. This research pasted eight sensing modules on nine objects in six spaces. Finally, the specific actions and life pattern can be recognized and analyzed through the developed IoT spatial behavior smart system and provide the customized intelligent application function for the elderly.
Correction: Liu et al. A Predictive Model for the Risk of Posterior Circulation Stroke in Patients with Intracranial Atherosclerosis Based on High Resolution MRI. Diagnostics 2022, 12, 812
Authors: Liu Zhenxing , Zhong Feiyang , Xie Yu , Lu Xuanzhen , Hou Botong , Ouyang Keni , Fang Jiabin , Liao Meiyan , Liu Yumin
Published: August 2022

Errors occurred in the number of patients in the posterior circulation ischemic stroke (PCIS) group and non-PCIS group described in the original publication [...]
Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems
Authors: Alrayes Fatma S. , Alotaibi Saud S. , Alissa Khalid A. , Maashi Mashael , Alhogail Areej , Alotaibi Najm , Mohsen Heba , Motwakel Abdelwahed
Published: August 2022

Unmanned Aerial Vehicles (UAVs), or drones, provided with camera sensors enable improved situational awareness of several emergency responses and disaster management applications, as they can function from remote and complex accessing regions. The UAVs can be utilized for several application areas which can hold sensitive data, which necessitates secure processing using image encryption approaches. At the same time, UAVs can be embedded in the latest technologies and deep learning (DL) models for disaster monitoring areas such as floods, collapsed buildings, or fires for faster mitigation of its impacts on the environment and human population. This study develops an Artificial Intelligence-based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems (AISCC-DE2MS). The proposed AISCC-DE2MS technique majorly employs encryption and classification models for emergency disaster monitoring situations. The AISCC-DE2MS model follows a two-stage process: encryption and image classification. At the initial stage, the AISCC-DE2MS model employs an artificial gorilla troops optimizer (AGTO) algorithm with an ECC-Based ElGamal Encryption technique to accomplish security. For emergency situation classification, the AISCC-DE2MS model encompasses a densely connected network (DenseNet) feature extraction, penguin search optimization (PESO) based hyperparameter tuning, and long short-term memory (LSTM)-based classification. The design of the AGTO-based optimal key generation and PESO-based hyperparameter tuning demonstrate the novelty of our work. The simulation analysis of the AISCC-DE2MS model is tested using the AIDER dataset and the results demonstrate the improved performance of the AISCC-DE2MS model in terms of different measures.
Using Semicircular Sampling to Increase Sea-Wind Retrieval Altitude with a High-Altitude UAV Scatterometer
Authors: Nekrasov Alexey , Khachaturian Alena , Fidge Colin
Published: August 2022

Currently, unmanned aerial vehicles (UAVs) are widely used due to their low cost and flexibility. In particular, they are used in remote sensing as airborne platforms for various instruments. Here, we investigate the capability of a conical scanning radar operated as a scatterometer mounted on a high-altitude UAV to perform sea surface wind retrieval based on an appropriate geophysical model function (GMF). Increasing the maximum altitude of the wind retrieval method’s applicability is an important problem for UAV or manned aircraft scatterometers. For this purpose, we consider the possibility of increasing the method’s maximum altitude by applying a semicircular scheme for azimuth normalized radar cross section (NRCS) sampling instead of a whole 360° circular scheme. We developed wind retrieval algorithms for both semicircular and circular NRCS sampling schemes and evaluated them using Monte Carlo simulations. The simulations showed that the semicircular scheme for azimuth NRCS sampling enables twice the maximum altitude for wind retrieval compared to a 360° circular scheme. At the same time, however, the semicircular scheme requires approximately three times the number of integrated NRCS samples in each azimuth sector to provide equivalent wind retrieval accuracy. Nonetheless, our results confirm that the semicircular azimuth NRCS sampling scheme is well-suited for wind retrieval, and any wind retrieval errors are within the typical range for scatterometer wind recovery. The obtained results can be used for enhancing existing UAV and aircraft radars, and for the development of new remote sensing systems.
An Entropy-Based Architecture for Detection of Sepsis in Newborn Cry Diagnostic Systems
Authors: Khalilzad Zahra , Kheddache Yasmina , Tadj Chakib
Published: August 2022

The acoustic characteristics of cries are an exhibition of an infant’s health condition and these characteristics have been acknowledged as indicators for various pathologies. This study focused on the detection of infants suffering from sepsis by developing a simplified design using acoustic features and conventional classifiers. The features for the proposed framework were Mel-frequency Cepstral Coefficients (MFCC), Spectral Entropy Cepstral Coefficients (SENCC) and Spectral Centroid Cepstral Coefficients (SCCC), which were classified through K-nearest Neighborhood (KNN) and Support Vector Machine (SVM) classification methods. The performance of the different combinations of the feature sets was also evaluated based on several measures such as accuracy, F1-score and Matthews Correlation Coefficient (MCC). Bayesian Hyperparameter Optimization (BHPO) was employed to tailor the classifiers uniquely to fit each experiment. The proposed methodology was tested on two datasets of expiratory cries (EXP) and voiced inspiratory cries (INSV). The highest accuracy and F-score were 89.99% and 89.70%, respectively. This framework also implemented a novel feature selection method based on Fuzzy Entropy (FE) as a final experiment. By employing FE, the number of features was reduced by more than 40%, whereas the evaluation measures were not hindered for the EXP dataset and were even enhanced for the INSV dataset. Therefore, it was deduced through these experiments that an entropy-based framework is successful for identifying sepsis in neonates and has the advantage of achieving high performance with conventional machine learning (ML) approaches, which makes it a reliable means for the early diagnosis of sepsis in deprived areas of the world.
Adaptive Problem Solving Dynamics in Gate-Model Quantum Computers
Authors: Gyongyosi Laszlo
Published: August 2022

Gate-model quantum computer architectures represent an implementable model used to realize quantum computations. The mathematical description of the dynamical attributes of adaptive problem solving and iterative objective function evaluation in a gate-model quantum computer is currently a challenge. Here, a mathematical model of adaptive problem solving dynamics in a gate-model quantum computer is defined. We characterize a canonical equation of adaptive objective function evaluation of computational problems. We study the stability of adaptive problem solving in gate-model quantum computers.
An Alternative Study about the Geometry and the First Law of Thermodynamics for AdS Lovelock Gravity, Using the Definition of Conserved Charges
Authors: Aros Rodrigo , Estrada Milko , Pereira Pablo
Published: August 2022

In this work, we introduce an extension of the study of the first law of thermodynamics of black holes based on the geometry of the extended phase space for AdS Lovelock gravities, which includes changes in scales. As expected, the result obtained coincides with the previously known four-dimensional case. For higher dimensions, the result is the rise of two new contributions to the first law of thermodynamics. The first term corresponds to corrections of the usual definition of thermodynamic volumes at the horizon due to the presence of the higher curvature terms. The second term arises in odd dimensions, comes from the asymptotic region, and corresponds to a scale transformation of the form ∝δ^ln(l/ℓ), with l the AdS radius and ℓ a parameter. A particularly interesting case corresponds to the Chern Simons gravity where the change scale does not generate a contribution at the asymptotic region, likely due to the Chern Simons AdS local symmetry.
An Enhanced Steganography Network for Concealing and Protecting Secret Image Data
Authors: Chen Feng , Xing Qinghua , Sun Bing , Yan Xuehu , Cheng Jingwen
Published: August 2022

The development of Internet technology has provided great convenience for data transmission and sharing, but it also brings serious security problems that are related to data protection. As is detailed in this paper, an enhanced steganography network was designed to protect secret image data that contains private or confidential information; this network consists of a concealing network and a revealing network in order to achieve image embedding and recovery separately. To reduce the system’s computation complexity, we constructed the network’s framework using a down–up structure in order to compress the intermediate feature maps. In order to mitigate the input’s information loss caused by a sequence of convolution blocks, the long skip concatenation method was designed to pass the raw information to the top layer, thus synthesizing high-quality hidden images with fine texture details. In addition, we propose a novel strategy called non-activated feature fusion (NAFF), which is designed to provide stronger supervision for synthetizing higher-quality hidden images and recovered images. In order to further boost the hidden image’s visual quality and enhance its imperceptibility, an attention mechanism-based enhanced module was designed to reconstruct and enhance the salient target, thus covering up and obscuring the embedded secret content. Furthermore, a hybrid loss function that is composed of pixel domain loss and structure domain loss was designed to boost the hidden image’s structural quality and visual security. Our experimental results demonstrate that, due to the elaborate design of the network structure and loss function, our proposed method achieves high levels of imperceptibility and security.
Reflective Processes Promoted in the Practicum Tutoring and Pedagogical Knowledge Obtained by Teachers in Initial Training
Authors: Flores-Lueg Carolina
Published: August 2022

This research focuses on the formative contributions made by tutor teachers in the university to the acquisition of pedagogical knowledge based on the reflective processes that they manage to implement, from the perspective of future teachers in professional practice or a practicum. A qualitative method was employed. Twenty practitioners of pedagogy programs who developed a practicum in the year 2020 or 2021 took part in this process, matriculated at three universities found in the Biobío and Ñuble regions in the central–south zones of Chile. A semi-structured interview was used as a technique and the analysis was carried out through categories of meaning with a phenomenological basis, whose topics were the following: (1) appreciation of the support of the tutor teacher, (2) strategies and resources used to promote reflection, (3) reflective actions linking theory and practice, and (4) types of knowledge obtained from the reflective processes implemented. Among the results, what stands out is that future teachers legitimize the guidance provided by the tutor and value the generation of reflective processes, but these are situated in an experiential and emotional dimension, stressing the formation of pedagogical knowledge. In conclusion, there is a need to redefine tutorial action and promote its professionalization.
A Qualitative Analysis of the Educational Value of Commercial Video Games
Authors: Reyes-de-Cózar Salvador , Ramírez-Moreno Carlos , Barroso-Tristán Jose María
Published: August 2022

Video games have evolved from a mere object of consumption to a cultural artifact of our time. Each cultural milieu is connected to the dominant technologies of its time, and video games today present narratives that allow for meaningful dialogues between the younger generations and the themes or content they find interesting. The main objective of this research was to highlight the value of commercial video games with an ideal potential for transmitting pedagogical and curricular content in educational contexts. The scientific methodology used in this study followed a mixed approach divided into three stages (qualitative–qualitative–quantitative). The wide variety of themes and settings found among the 75 top-rated games on the Steam digital distribution platform sheds light on the interests of young people, representing a broad and rich range that highlights their interest in topics that are close and relevant to them. Video games can no longer be ignored, as they represent one of our most widespread cultural values. Their narratives connect directly with the way our young people think, feel, and dialogue. Therefore, embracing video games, both culturally and educationally, is a leap forward with an eye to progress.
PEARLS (Perspectives on Equity Advancement: Research and Learning Symposium), a Case Report in Promoting DEI in a Medical School Setting
Authors: Yoder Sarah R. , Lonstein Allison B. , Sharma Arth , Garcia-Munoz John , Moreno Rafael , Chen Alice Y. , Orben Grace , Clemons Tiana , Masters Madison , Forrest Lala L. , Ukhuedoba Ikhianosen , Hall Julianne M.
Published: August 2022

PEARLS (Perspectives on Equity Advancement: Research and Learning Symposium) was a student-led, faculty-mentored, year-long initiative culminating in a symposium with three components: (1) “Change Agents”, where faculty leaders presented their research and advances in DEI and the impact on the community; (2) The “15% Better” initiative, which promoted individual commitments to improving DEI in the Frank H. Netter School of Medicine at Quinnipiac University through reflection on a monthly prompt and review of selected educational resources; and (3) “Grand Rounds Pitches’’ where student-designed scholarly DEI initiatives received competitive seed grants alongside a mentorship team that help support their project to completion. The 2022 post-event survey (RR 58%) showed that 100% of respondents felt inspired to engage more deeply in DEI after seeing the faculty’s work, felt committed to at least one action that furthers their learning or promotes DEI broadly, and were better able to describe student-led DEI projects. In total, 94% of respondents felt more connected to faculty and students advancing DEI in the school of medicine community and 91% felt a greater sense of belonging. The 15% Better initiative strengthened the belief that small individual DEI commitments can result in substantial positive changes for 73%, while 27% already believed this. In total, 61% were more likely to engage in DEI efforts this year and 36% already planned to engage. Qualitative data highlighted themes of inspiration, positive impact, and actionable change. PEARLS stands as an easily reproducible way for higher education institutions to engage their community in DEI strategies through its foundational elements of promoting belonging, fostering inspiration, and encouraging commitment to action.
Writing in Geography Lessons—An Unreflected Routine?
Authors: Steingrübl Saskia , Budke Alexandra
Published: August 2022

Writing is an integral part of everyday school life and is a relevant didactic tool in geography lessons. Nevertheless, there is still little research on the topic of writing in geography lessons. The study aims to investigate the attitudes towards writing and school practice of geography teachers by analysing qualitative guided interviews. It will reflect on the extent to which teachers ascribe meanings and didactic significance to this language action and implement it in their geography lessons. Teachers see writing in particular as a function of performance assessment and material evaluation and less as a means of subject-specific learning. In addition, the integration of writing tasks in geography lessons is a challenge for them. Research and university teaching should support teachers in cultivating a didactically justified approach to writing tasks and raise awareness among them to the diverse functions of writing.
MetaEmotions at School: A Program for Promoting Emotional and MetaEmotional Intelligence at School; a Research-Intervention Study
Authors: D’Amico Antonella , Geraci Alessandro
Published: August 2022

MetaEmotions at Schools is a SEL program that aims at promoting the culture of emotional and metaemotional intelligence in schools, and at improving emotional awareness both among teachers and students. It is a train-the-trainers program that unfolds in five steps, respectively, aimed at stimulating teachers and students to: (1) develop emotional literacy; (2) create emotionally inclusive environments; (3) build tools, materials and methods for emotionally inclusive classes; (4) develop didactic method mediated by emotions; (5) become ambassadors of the metaemotional intelligence at school and beyond. In this study, we report some results of the first application of the training program, focusing in particular on the effect of the program on emotional and metaemotional intelligence of participants. A total of 264 pupils from lower secondary schools of five Italian cities were recruited and divided in 9 intervention and 9 comparison classes. All participants were administered with emotional and metaemotional intelligence assessment tools before and after the training program. The results showed that emotional abilities scores in the intervention groups tended to be stable over time compared to the comparison groups ones which tended to decrease. Moreover, the pupils showed a reduced tendency to overestimate their emotional abilities. These results pave the way to further applications of the program and shed the light on areas of improvement.
A Systematic Survey on Fog and IoT Driven Healthcare: Open Challenges and Research Issues
Authors: Kashyap Vijaita , Kumar Ashok , Kumar Ajay , Hu Yu-Chen
Published: August 2022

Technological advancements have made it possible to monitor, diagnose, and treat patients remotely. The vital signs of patients can now be collected with the help of Internet of Things (IoT)-based wearable sensor devices and then uploaded on to a fog server for processing and access by physicians for recommending prescriptions and treating patients through the Internet of Medical Things (IoMT) devices. This research presents the outcome of a survey conducted on healthcare integrated with fog computing and IoT to help researchers understand the techniques, technologies and performance parameters. A comparison of existing research focusing on technologies, procedures, and findings has been presented to investigate several aspects of fog computing in healthcare IoT-based systems, such as increased temporal complexity, storage capacity, scalability, bandwidth, and latency. Additionally, strategies, tools, and sensors used in various diseases such as heart disease, chronic disease, chikungunya viral infection, blood pressure, body temperature, pulse rate, diabetes, and type 2 diabetes have been compared.
API Message-Driven Regression Testing Framework
Authors: Demircioğlu Emine Dumlu , Kalipsiz Oya
Published: August 2022

With the increase in the number of APIs and interconnected applications, API testing has become a critical part of the software testing process. Particularly considering the business-critical systems using API messages, the importance of repetitive API tests increases. Successfully performing repetitive manual API testing for a large number of test scenarios in large business enterprise applications becomes even more difficult due to the fact that human errors may prevent performing thousands of human-written tests with high precision every time. Furthermore, the existing API test automation tools used in the market cannot be integrated into all business domains due to their dependence on applications. These tools generally support web APIs over the HTTP protocol. Hence, this study is motivated by the fact that there is a lack of API message-driven regression testing frameworks in a particular area in which API messages are used in client-server communication. This study has been prepared to close the gap in a specific domain which uses business domain APIs, rather than HTTP, in client-server communication. We propose a novel approach based on the use of network packets for regression testing. We developed a proof-of-concept test automation tool implementing our approach and evaluated it in a financial domain. Unlike prior studies, our approach can provide the use of real data packets in software testing. The use of network packets increases the generalization of the framework. Overall, our study reports remarkable reuse capacity and makes a significant impact on a real-world business-critical system by reducing effort and increasing the automation level of API regression testing.
Res-CDD-Net: A Network with Multi-Scale Attention and Optimized Decoding Path for Skin Lesion Segmentation
Authors: Song Zian , Luo Wenjie , Shi Qingxuan
Published: August 2022

Melanoma is a lethal skin cancer. In its diagnosis, skin lesion segmentation plays a critical role. However, skin lesions exhibit a wide range of sizes, shapes, colors, and edges. This makes skin lesion segmentation a challenging task. In this paper, we propose an encoding–decoding network called Res-CDD-Net to address the aforementioned aspects related to skin lesion segmentation. First, we adopt ResNeXt50 pre-trained on the ImageNet dataset as the encoding path. This pre-trained ResNeXt50 can provide rich image features to the whole network to achieve higher segmentation accuracy. Second, a channel and spatial attention block (CSAB), which integrates both channel and spatial attention, and a multi-scale capture block (MSCB) are introduced between the encoding and decoding paths. The CSAB can highlight the lesion area and inhibit irrelevant objects. MSCB can extract multi-scale information to learn lesion areas of different sizes. Third, we upgrade the decoding path. Every 3 × 3 square convolution kernel in the decoding path is replaced by a diverse branch block (DBB), which not only promotes the feature restoration capability, but also improves the performance and robustness of the network. We evaluate the proposed network on three public skin lesion datasets, namely ISIC-2017, ISIC-2016, and PH2. The dice coefficient is 6.90% higher than that of U-Net, whereas the Jaccard index is 10.84% higher than that of U-Net (assessed on the ISIC-2017 dataset). The results show that Res-CDD-Net achieves outstanding performance, higher than the performance of most state-of-the-art networks. Last but not least, the training of the network is fast, and good results can be achieved in early stages of training.
Analysis and Verification of a Half-Dual Bridge Resonant Converter with Voltage Match Modulation
Authors: Wang Rui , Li Yinan , Sun Chuan , Hu Song , Li Xiaodong , Chen Wu , Lv Gang
Published: August 2022

To overcome the weakness of the narrow conversion gain of dual bridge resonant converter, a half-dual bridge resonant converter (H-DBRC) with voltage match modulation (VMM) is proposed and implemented for wide voltage range applications. The H-DBRC, including a full bridge on the primary side and a half bridge on the secondary side, is adopted to realize the bidirectional power flow and wide voltage operation. Owing to the synergy between the H-DBRC structure and VMM strategy, the converter can operate in full-bridge state and half-bridge state with maximized ZVS (zero-voltage switching) operation and minimized circulating current. The steady-state analysis is performed and the solutions for both forward and backward modes could be obtained uniformly by using the fundamental harmonics approximation approach. The soft-switching characteristics, implementation process and design example are analyzed in detail. Finally, to confirm the theoretical analysis and feasibility, both simulation and experimental verifications are demonstrated in this paper. Since the converter consistently achieves voltage-matching operation, the optimal ZVS range can be obtained when the voltage gain M is between 0.5 to 1. Moreover, the economic cost and control complexity are greatly reduced because only six switches are required in the converter.
Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective
Authors: Bzai Jamal , Alam Furqan , Dhafer Arwa , Bojović Miroslav , Altowaijri Saleh M. , Niazi Imran Khan , Mehmood Rashid
Published: August 2022

Machine learning (ML) allows the Internet of Things (IoT) to gain hidden insights from the treasure trove of sensed data and be truly ubiquitous without explicitly looking for knowledge and data patterns. Without ML, IoT cannot withstand the future requirements of businesses, governments, and individual users. The primary goal of IoT is to perceive what is happening in our surroundings and allow automation of decision-making through intelligent methods, which will mimic the decisions made by humans. In this paper, we classify and discuss the literature on ML-enabled IoT from three perspectives: data, application, and industry. We elaborate with dozens of cutting-edge methods and applications through a review of around 300 published sources on how ML and IoT work together to play a crucial role in making our environments smarter. We also discuss emerging IoT trends, including the Internet of Behavior (IoB), pandemic management, connected autonomous vehicles, edge and fog computing, and lightweight deep learning. Further, we classify challenges to IoT in four classes: technological, individual, business, and society. This paper will help exploit IoT opportunities and challenges to make our societies more prosperous and sustainable.
Rapid and Accurate Diagnosis of COVID-19 Cases from Chest X-ray Images through an Optimized Features Extraction Approach
Authors: Kumar K. G. Satheesh , Venkatesan Arunachalam , Selvaraj Deepika , Raj Alex Noel Joseph
Published: August 2022

The mutants of novel coronavirus (COVID-19 or SARS-Cov-2) are spreading with different variants across the globe, affecting human health and the economy. Rapid detection and providing timely treatment for the COVID-19 infected is the greater challenge. For fast and cost-effective detection, artificial intelligence (AI) can perform a key role in enhancing chest X-ray images and classifying them as infected/non-infected. However, AI needs huge datasets to train and detect the COVID-19 infection, which may impact the overall system speed. Therefore, Deep Neural Network (DNN) is preferred over standard AI models to speed up the classification with a set of features from the datasets. Further, to have accurate feature extraction, an algorithm that combines Zernike Moment Feature (ZMF) and Gray Level Co-occurrence Matrix Feature (GF) is proposed and implemented. The proposed algorithm uses 36 Zernike Moment features with variance and contrast textures. This helps to detect the COVID-19 infection accurately. Finally, the Region Blocking (RB) approach with an optimum sub-image size (32 × 32) is employed to improve the processing speed up to 2.6 times per image. The performance of this implementation presents an accuracy (A) of 93.4%, sensitivity (Se) of 72.4%, specificity (Sp) of 95%, precision (Pr) of 74.9% and F1-score (F1) of 72.3%. These metrics illustrate that the proposed model can identify the COVID-19 infection with a lesser dataset and improved accuracy up to 1.3 times than state-of-the-art existing models.
Design and Field Test of a Mobile Augmented Reality Human–Machine Interface for Virtual Stops in Shared Automated Mobility On-Demand
Authors: Hub Fabian , Oehl Michael
Published: August 2022

Shared automated mobility on-demand (SAMOD) is considered as a promising mobility solution in the future. Users book trips on-demand via a smartphone, and service algorithms set up virtual stops (vStop) where users then need to walk to board the automated shuttle. Navigation and identification of the virtual pickup location, which has no references in the real world, can be challenging. Providing users with an intuitive information system in that situation is essential to achieve high user acceptance of new automated mobility services. Our novel vStop human–machine interface (HMI) prototype for mobile augmented reality (AR) supports users with information in reference to the street environment. This work firstly presented the results of an online interview study (N = 21) to conceptualize an HMI. Secondly, the HMI was prototyped by means of AR and evaluated (N = 45) regarding user experience (UX), workload, and acceptance. The results show that the AR prototype provided high rates of UX especially in terms of high pragmatic quality. Furthermore, cognitive workload when using the HMI was low, and acceptance ratings were high. The results show the positive perception of AR for navigation tasks in general and the highly assistive character of the vStop prototype in particular. In the future, SAMOD services can provide customers with vStop HMIs to foster user acceptance and smooth operation of their service.
Rider in the Loop Dynamic Motorcycle Simulator: An Instrumentation Strategy Focused on Human Acceptability
Authors: Michel Pauline , Bouaziz Samir , Delgehier Flavien , Espié Stéphane
Published: August 2022

Human-in-the-loop driving simulation aims to create the illusion of driving by stimulating the driver’s sensory systems in as realistic conditions as possible. However, driving simulators can only produce a subset of the sensory stimuli that would be available in a real driving situation, depending on the degree of refinement of their design. This subset must be carefully chosen because it is crucial for human acceptability. Our focus is the design of a physical dynamic (i.e., motion-based) motorcycle-riding simulator. For its instrumentation, we focused on the rider acceptability of all sub-systems and the simulator as a whole. The significance of our work lies in this particular approach; the acceptability of the riding illusion for the rider is critical for the validity of any results acquired using a simulator. In this article, we detail the design of the hardware/software architecture of our simulator under this constraint; sensors, actuators, and dataflows allow us to (1) capture the rider’s actions in real-time; (2) render the motorcycle’s behavior to the rider; and (3) measure and study rider/simulated motorcycle interactions. We believe our methodology could be adopted by future designers of motorcycle-riding simulators and other human-in-the-loop simulators to improve their rendering (including motion) quality and acceptability.
Digital Technology Implementation in Battery-Management Systems for Sustainable Energy Storage: Review, Challenges, and Recommendations
Authors: Krishna Gopal , Singh Rajesh , Gehlot Anita , Akram Shaik Vaseem , Priyadarshi Neeraj , Twala Bhekisipho
Published: August 2022

Energy storage systems (ESS) are among the fastest-growing electrical power system due to the changing worldwide geography for electrical distribution and use. Traditionally, methods that are implemented to monitor, detect and optimize battery modules have limitations such as difficulty in balancing charging speed and battery capacity usage. A battery-management system overcomes these traditional challenges and enhances the performance of managing battery modules. The integration of advancements and new technologies enables the provision of real-time monitoring with an inclination towards Industry 4.0. In the previous literature, it has been identified that limited studies have presented their reviews by combining the literature on different digital technologies for battery-management systems. With motivation from the above aspects, the study discussed here aims to provide a review of the significance of digital technologies like wireless sensor networks (WSN), the Internet of Things (IoT), artificial intelligence (AI), cloud computing, edge computing, blockchain, and digital twin and machine learning (ML) in the enhancement of battery-management systems. Finally, this article suggests significant recommendations such as edge computing with AI model-based devices, customized IoT-based devices, hybrid AI models and ML-based computing, digital twins for battery modeling, and blockchain for real-time data sharing.
Network Mobility Management Challenges, Directions, and Solutions: An Architectural Perspective
Authors: Al-Khalidi Mohammed , Al-Zaidi Rabab , Hammoudeh Mohammad
Published: August 2022

Efficient mobility management solutions are essential to provide users with seamless connectivity and session continuity during movement. However, user mobility was not envisaged as one of the early Internet’s use cases due to the early adoption of destination based routing and the assumption that end-nodes are static. This has become a critical hinder for providing efficient mobility support. This paper presents the challenges, drivers, and solutions that aim to overcome the drawbacks of current mobility management approaches. Furthermore, it introduces a promising solution that builds on emerging path-based forwarding architectures that identify network links rather than end nodes. Delivery path information is stored inside the packet while forwarding is achieved by performing a simple set membership test rather than the current destination-based routing approach. Mobility management in these architectures simply requires partial recomputation of the delivery path allowing for efficient mobility support over an optimal path. Evaluation results show significant cost savings in terms of delivery paths and end-to-end packet delay when using a path forwarding architecture.
Recognition of Dorsal Hand Vein in Small-Scale Sample Database Based on Fusion of ResNet and HOG Feature
Authors: Li Jindi , Li Kefeng , Zhang Guangyuan , Wang Jiaqi , Li Keming , Yang Yumin
Published: August 2022

As artificial intelligence develops, deep learning algorithms are increasingly being used in the field of dorsal hand vein (DHV) recognition. However, deep learning has high requirements regarding the number of samples, and current DHV datasets have few images. To solve the above problems, we propose a method based on the fusion of ResNet and Histograms of Oriented Gradients (HOG) features, in which the shallow semantic information extracted by primary convolution and HOG features are fed into the residual structure of ResNet for full fusion and, finally, classification. By adding Gaussian noise, the North China University of Technology dataset, the Shandong University of Science and Technology dataset, and the Eastern Mediterranean University dataset are extended and fused to from a fused dataset. Our proposed method is applied to the above datasets, and the experimental results show that our proposed method achieves good recognition rates on each of the datasets. Importantly, we achieved a 93.47% recognition rate on the fused dataset, which was 2.31% and 26.08% higher than using ResNet and HOG alone.
Design of Filtering Magic-T with Wideband and Wide Stopband Based on HMSIW and Spoof Surface Plasmon Polaritons
Authors: Liu Hao , Xue Bing , Xu Jun
Published: August 2022

A novel filtering magic-T (FMT) with a compact size, a broad bandwidth, and a wide stopband rejection based on the dielectric-covered L-shaped groove (DCLSG) half-mode substrate integrated waveguide and the spoof surface plasmon polariton (HMSIW-SSPP) structure is proposed for the first time. A HMSIW magic-T (HMT) based on dual-layer substrates is first designed. Then, we construct the proposed FMT by periodically etching the subwavelength DCLSG SSPP structure into the HMT. The proposed FMT achieves a bandpass filtering response and a nearly 50% reduction of longitudinal dimension attributed to the bandpass characteristics and strong slow-wave property of the DCLSG HMSIW-SSPP structure. In addition, beneficial from the regulable cut-off frequencies of the DCLSG HMSIW-SSPP structure, the proposed FMT provides a wide impedance bandwidth and independently adjustable lower and upper cut-off frequencies of the passband. Finally, a prototype of the proposed FMT is fabricated to validate this design idea. The measured results illustrate that the FMT has a 3-dB fractional bandwidth of 40.23% and a 20-dB stopband rejection up to 2.12 f0 (f0: center frequency of the passband).
Improving Thermal Efficiency of Internal Combustion Engines: Recent Progress and Remaining Challenges
Authors: Dahham Rami Y. , Wei Haiqiao , Pan Jiaying
Published: August 2022

Improving thermal efficiency and reducing carbon emissions are the permanent themes for internal combustion (IC) engines. In the past decades, various advanced strategies have been proposed to achieve higher efficiency and cleaner combustion with the increasingly stringent fuel economy and emission regulations. This article reviews the recent progress in the improvement of thermal efficiency of IC engines and provides a comprehensive summary of the latest research on thermal efficiency from aspects of thermodynamic cycles, gas exchange systems, advanced combustion strategies, and thermal and energy management. Meanwhile, the remaining challenges in different modules are also discussed. It shows that with the development of advanced technologies, it is highly positive to achieve 55% and even over 60% in effective thermal efficiency for IC engines. However, different technologies such as hybrid thermal cycles, variable intake systems, extreme condition combustion (manifesting low temperature, high pressure, and lean burning), and effective thermal and energy management are suggested to be closely integrated into the whole powertrains with highly developed electrification and intelligence.
Modeling of a Hybrid Fuel Cell Powertrain with Power Split Logic for Onboard Energy Management Using a Longitudinal Dynamics Simulation Tool
Authors: Zecchi Laura , Sandrini Giulia , Gadola Marco , Chindamo Daniel
Published: August 2022

This work aims to develop a mathematical model for the simulation of a fuel cell (FC) hybrid powertrain. The work starts from modeling a single cell to obtain information on the entire FC stack. The model obtained was integrated into a simulation tool presented in the literature that simulates the longitudinal dynamics of auxiliary power unit hybrid electric vehicles and fully electric vehicles. Therefore, the integrated model allows the simulation of hybrid vehicles equipped with FC and a battery pack that acts as a peak power source. The tool simulates the mechanical and electrical behavior of the vehicle, introducing an investigation of the power flows relating to the FC and batteries. An appropriate power split logic has been implemented, allowing the correct management of the power distribution between the FC and the batteries. The importance of analyzing FC vehicles’ behavior arises from the recent necessity to find alternative propulsion systems, overcoming the range problems associated with fully electric vehicles. The innovation lies in the versatility and modularity of the model, which is open to modifications and features a low computational burden, making it suitable for testing new solutions by performing first design and sizing calculations.