bluuebunny/crossref_metadata_2025 huggingface dataset:
Multidisciplinary Forensic Approach in “Complex” Bodies: Systematic Review and Procedural Proposal
Authors: Baldino Gennaro , Mondello Cristina , Sapienza Daniela , Stassi Chiara , Asmundo Alessio , Gualniera Patrizia , Vanin Stefano , Ventura Spagnolo Elvira
Published: January 2023

The recovery of severely altered cadavers (i.e., extensively decomposed, mummified, charred or dismembered) can be a challenge for forensic pathologists due to the difficulties in identification, PMI estimation and manner and cause of death determination. In such cases, integrating routine approaches (autopsy, histology, toxicology) to more specific forensic branches can be fundamental to improving the investigative process. In this paper a systematic review using PubMed, Scopus and Web of Science databases has been performed. The aim was to evaluate the forensic approaches implemented in the management of severely altered bodies due to decomposition, mummification, skeletonization, charring or dismemberment (to which we refer to as “complex”), and the role of each approach in the solution of a case. Then, the literature revision results were used to propose a schematic flowchart summarizing the post mortem activities that can be performed in forensic practice, adaptable in relation to each case.
Application of Teeth in Toxicological Analysis of Decomposed Cadavers Using a Carbamazepine-Administered Rat Model
Authors: Ichioka Hiroaki , Saito Urara , Shintani-Ishida Kaori , Shirahase Takahira , Idota Nozomi , Kanamura Narisato , Ikegaya Hiroshi
Published: January 2023

In a regular autopsy, blood and organs are used to quantify drug and toxicant concentrations; however, specimens such as blood cannot be collected from highly decomposed corpses, making the quantification of drug and toxicants impossible. This study aimed to estimate the blood carbamazepine (CBZ) concentration from teeth, a part of the human body that is best preserved after death. We sampled teeth and blood of rats administered CBZ. The correlation between the tooth and serum CBZ concentrations was analyzed. Rats were euthanized after CBZ administration and kept at 22 °C for 0 to 15 days before sampling the teeth and measuring the CBZ concentration. Undecalcified, fresh, frozen sections of rat teeth were prepared, and CBZ localization was evaluated. CBZ concentrations in both teeth and cardiac blood peaked at 60 min after administration and increased in a dose-dependent manner. CBZ concentration in teeth did not substantially change after death, with high CBZ distribution being observed in the pulp cavity. The tooth and serum CBZ concentrations were highly correlated, suggesting that the measurement of toxicant concentration in sampled teeth would allow for the estimation of blood toxicant concentration in highly decomposed corpses.
Derived Neutrophil-Lymphocyte Ratio and C-Reactive Protein as Prognostic Factors for Early-Stage Non-Small Cell Lung Cancer Treated with Stereotactic Body Radiation Therapy
Authors: Dong Baiqiang , Zhu Xuan , Chen Runzhe , Wu Qing , Jin Jia’nan , Wang Lin , Xu Yujin , Chen Ming
Published: January 2023

Objectives: To explore the relationship between peripheral blood inflammation parameters and overall survival (OS) and progression-free survival (PFS) of early-stage non-small cell lung cancer patients who underwent stereotactic body radiotherapy (SBRT). Patients and methods: In this study, eligible patients treated with SBRT from 2013 to 2018, and both serum complete blood count and blood biochemical results were available prior to (within 60 days) radiotherapy were included. Results: A review of hospital registries identified 148 patients, and the 5-year OS and PFS of the entire cohort were 69.8% and 65.6%, respectively, with the median follow-up time was 52.8 months. Multivariable analysis showed that derived neutrophil-lymphocyte ratio (dNLR) ≥1.4 and C-reactive protein (CRP) ≥2.9 were statistically and independently associated with worse OS (HR = 4.62, 95% CI 1.89–11.27, p = 0.001; HR = 2.92, 95% CI 1.49–5.70, p = 0.002, respectively). The 5-year OS for patients with dNLR below and equal to or above the 1.4 were 85.3% and 62.9% (p = 0.002), respectively, and 76.7% for the low CRP group versus 58.5% for the high CRP group (p = 0.030). Higher serum level of post-treatment CRP also independent parameters for inferior PFS (HR = 4.83, 95% CI 1.28–18.25, p = 0.020). Conclusions: Our results demonstrate that dNLR and CRP are associated with the outcomes of early-stage NSCLC patients treated with SBRT, which may assist in selecting optimal nursing care and therapeutic scheme for every individual.
Evaluation of the Effect of Duration on Retinal Nerve Fiber Layer Thickness and Choroidal Thickness in Exfoliation Syndrome and Exfoliative Glaucoma
Authors: Toptan Müslüm , Simsek Ali
Published: January 2023

Aims: To compare retinal nerve fiber layer (RNFL) thickness and choroidal thickness in patients with exfoliation syndrome (XFS) and exfoliative glaucoma (XFG) for 1–5 years or 6–10 years compared to healthy volunteers. Methods and Material: Seventy-eight eyes of 78 patients with XFG, 78 eyes of 78 patients with XFS, and 83 eyes of 83 healthy individuals were included in this prospective study. SD-OCT data for choroid thickness and RNFL were recorded. Results: RNFL thickness was statistically significantly lower in eyes with XFG and XFS than in the control group (p < 0.001). Macular choroidal thickness decreased significantly in the XFG group compared to the XFS and control groups (p < 0.001). No significant difference was observed between the XFS and control groups (p > 0.05). In terms of choroidal and RNFL thicknesses by years in XFG and XFS patients, values were lower in the patients diagnosed 6–10 years previously than in those diagnosed 1–5 years previously. However, the difference was not statistically significant (p > 0.05). Conclusions: Thinning of both choroidal and RNLF thickness in XFG and XFS patients may mean that PEX material is an important risk factor for the progression of XFS to XFG. In addition, thinner choroid and RNLF thickness in the 6–10 years groups show the effect of PEX material and the importance of time.
Clinical Significance of Maximum Intensity Projection Method for Diagnostic Imaging of Thoracic Outlet Syndrome
Authors: Ogawa Takeshi , Onishi Shinzo , Mamizuka Naotaka , Yoshii Yuichi , Ikeda Kazuhiro , Mammoto Takeo , Yamazaki Masashi
Published: January 2023

The aim of this study was to use the magnetic resonance imaging maximum-intensity projection (MRI-MIP) method for diagnostic imaging of thoracic outlet syndrome (TOS) and to investigate the stricture ratios of the subclavian artery (SCA), subclavian vein (SCV), and brachial plexus bundle (BP). A total of 113 patients with clinically suspected TOS were evaluated. MRI was performed in a position similar to the Wright test. The stricture was classified into four grades. Then, the stricture ratios of the SCA, SCV, and BP in the sagittal view were calculated by dividing the minimum diameter by the maximum diameter of each structure. Patients were divided into two groups: surgical (n = 22) and conservative (n = 91). Statistical analysis was performed using the Mann–Whitney U test. The stricture level and ratio in the SCV were significantly higher in the surgical group, while the stricture level and the ratio of SCA to BP did not show significant differences between the two groups. The MRI-MIP method may be helpful for both subsidiary and severe diagnoses of TOS.
Could Spinal Epidural Lipomatosis Be the Hallmark of Metabolic Syndrome on the Spine? A Literature Review with Emphasis on Etiology
Authors: D’Agostino Valerio , Petrera Miriana Rosaria , Tedesco Giuseppe , Pipola Valerio , Ponti Federico , Spinnato Paolo
Published: January 2023

Spinal epidural lipomatosis is defined by an excessive amount of epidural fat in the spinal canal, usually in the lumbosacral tract: a well-known cause of lumbar pain and spinal stenosis with a possible wide range of neurological symptoms. Recent research data reveal that, nowadays, obesity has become the main cause of spinal epidural lipomatosis. Moreover, this condition was recently recognized as a previously unknown manifestation of metabolic syndrome. Radiological studies (CT and MRI) are the only tools that are able to diagnose the disease non-invasively. Indeed, radiologists play a key role in disease recognition, with subsequent possible implications on patients’ systemic health assessments. Despite its clinical importance, the condition is still underreported and neglected. The current literature review summarizes all the main etiologies of spinal epidural lipomatosis, particularly regarding its linkage with metabolic syndrome. An overview of disease characteristics from diagnosis to treatment strategies is also provided.
Location-Based Augmented Reality for Cultural Heritage Education: Creating Educational, Gamified Location-Based AR Applications for the Prehistoric Lake Settlement of Dispilio
Authors: Kleftodimos Alexandros , Moustaka Maria , Evagelou Athanasios
Published: January 2023

Today, augmented reality (AR) applications are being used in many fields, such as advertising, entertainment, tourism, and education. Location-based augmented reality (AR) is a technology where interactive digital content is associated with real-world locations and their geo-based markers. The merging of the real-world environment with the digital content occurs when the user reaches these locations. Location-based AR applications are typically experienced using mobile devices with the ability to report the user’s location via GPS. These applications are increasingly used in education, since it has been shown that they positively affect student satisfaction and engagement. Furthermore, it is known in the literature that learner satisfaction and engagement increase when gamification and storytelling techniques are incorporated into the educational process. The aim of the study is to present two location-based, educational augmented-reality applications that utilize gamification and storytelling to provide cultural heritage knowledge about a prehistoric lake settlement. The study also aims to provide ideas and guidance to educators who wish to create applications that transform educational visits to archeological sites and museums into engaging augmented-reality experiences. Both applications underwent a preliminary evaluation using a sample of 71 higher-education students and a sample of 58 school students. The findings showed that the applications scored well in aspects such as ease of use, student satisfaction, and perceived educational usefulness.
Acknowledgment to the Reviewers of Digital in 2022
Authors:   Digital Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Applying the ‘Human Rights Model of Disability’ to Informed Consent: Experiences and Reflections from the SHAPES Project
Authors: Lombard-Vance Richard , Soye Evelyn , Ferri Delia , McEvoy Emma , MacLachlan Malcolm , Sarlio-Siintola Sari
Published: January 2023

Understanding the complexity of informed consent processes is critically important to the success of research that requires participants to test, develop, or inform research data and results. This is particularly evident in research involving persons experiencing neurodegenerative diseases (e.g., Alzheimer’s disease, dementia) that impair cognitive functioning, who according to national law are considered to have a diminished capacity, or to lack the capacity, to consent to research participation. Those who would potentially benefit most from applied research participation may be excluded from participating and shaping data and outcomes. This article offers insights into challenges faced by the Smart and Healthy Ageing through People Engaging in Supportive Systems (SHAPES) Project in obtaining the consent of older persons, including older persons with disabilities. The promotion of continuing health, active ageing, and independent living is central to SHAPES, requiring project partners to reflect on traditional informed consent approaches to encourage the full, cognisant participation of older persons with disabilities. We examine how this issue may be addressed, with reference to the inclusive approach of SHAPES. In respecting the inalienable legal capacity of all legal persons, SHAPES uses the UN Convention on the Rights of Persons with Disabilities (CRPD) and the human rights model of disability as part of the theoretical framework. A novel, inclusive, representative informed consent framework was designed and is detailed herein. This framework provides significant opportunity to advance the inclusion of persons with disabilities or those experiencing neurodegenerative diseases in innovative research and is readily transferable to other research studies. The SHAPES approach is a substantial contribution to research on informed consent, demonstrating the utility of the human rights model of disability in facilitating the full research participation of target populations.
Numerical Calculation of the Irreversible Entropy Production of Additively Manufacturable Off-Set Strip Fin Heat-Transferring Structures
Authors: Fuchs Marco , Lubos Nico , Kabelac Stephan
Published: January 2023

In this manuscript, off-set strip fin structures are presented which are adapted to the possibilities of additive manufacturing. For this purpose, the geometric parameters, including fin height, fin spacing, fin length, and fin longitudinal displacement, are varied, and the Colburn j-factor and the Fanning friction factor are numerically calculated in the Reynolds number range of 80–920. The structures are classified with respect to their entropy production number according to Bejan. This method is compared with the results from partial differential equations for the calculation of the irreversible entropy production rate due to shear stresses and heat conduction. This study reveals that the chosen temperature difference leads to deviation in terms of entropy production due to heat conduction, whereas the dissipation by shear stresses shows only small deviations of less than 2%. It is further shown that the variation in fin height and fin spacing has only a small influence on heat transfer and pressure drop, while a variation in fin length and fin longitudinal displacement shows a larger influence. With respect to the entropy production number, short and long fins, as well as large fin spacing and fin longitudinal displacement, are shown to be beneficial. A detailed examination of a single structure shows that the entropy production rate due to heat conduction is dominated by the entropy production rate in the wall, while the fluid has only a minor influence.
Synchronization Transition of the Second-Order Kuramoto Model on Lattices
Authors: Ódor Géza , Deng Shengfeng
Published: January 2023

The second-order Kuramoto equation describes the synchronization of coupled oscillators with inertia, which occur, for example, in power grids. On the contrary to the first-order Kuramoto equation, its synchronization transition behavior is significantly less known. In the case of Gaussian self-frequencies, it is discontinuous, in contrast to the continuous transition for the first-order Kuramoto equation. Herein, we investigate this transition on large 2D and 3D lattices and provide numerical evidence of hybrid phase transitions, whereby the oscillator phases θi exhibit a crossover, while the frequency is spread over a real phase transition in 3D. Thus, a lower critical dimension dlO=2 is expected for the frequencies and dlR=4 for phases such as that in the massless case. We provide numerical estimates for the critical exponents, finding that the frequency spread decays as ∼t−d/2 in the case of an aligned initial state of the phases in agreement with the linear approximation. In 3D, however, in the case of the initially random distribution of θi, we find a faster decay, characterized by ∼t−1.8(1) as the consequence of enhanced nonlinearities which appear by the random phase fluctuations.
A Pseudorandom Number Generator Based on the Chaotic Map and Quantum Random Walks
Authors: Zhao Wenbo , Chang Zhenhai , Ma Caochuan , Shen Zhuozhuo
Published: January 2023

In this paper, a surjective mapping that satisfies the Li–Yorke chaos in the unit area is constructed and a perturbation algorithm (disturbing its parameters and inputs through another high-dimensional chaos) is proposed to enhance the randomness of the constructed chaotic system and expand its key space. An algorithm for the composition of two systems (combining sequence based on quantum random walks with chaotic system’s outputs) is designed to improve the distribution of the system outputs and a compound chaotic system is ultimately obtained. The new compound chaotic system is evaluated using some test methods such as time series complexity, autocorrelation and distribution of output frequency. The test results showed that the new system has complex dynamic behavior such as high randomicity, unpredictability and uniform output distribution. Then, a new scheme for generating pseudorandom numbers is presented utilizing the composite chaotic system. The proposed pseudorandom number generator (PRNG) is evaluated using a series test suites such as NIST sp 800-22 soft and other tools or methods. The results of tests are promising, as the proposed PRNG passed all these tests. Thus, the proposed PRNG can be used in the information security field.
Image Registration for Visualizing Magnetic Flux Leakage Testing under Different Orientations of Magnetization
Authors: Li Shengping , Zhang Jie , Liu Gaofei , Chen Nanhui , Tian Lulu , Bai Libing , Chen Cong
Published: January 2023

The Magnetic Flux Leakage (MFL) visualization technique is widely used in the surface defect inspection of ferromagnetic materials. However, the information of the images detected through the MFL method is incomplete when the defect (especially for the cracks) is complex, and some information would be lost when magnetized unidirectionally. Then, the multidirectional magnetization method is proposed to fuse the images detected under different magnetization orientations. It causes a critical problem: the existing image registration methods cannot be applied to align the images because the images are different when detected under different magnetization orientations. This study presents a novel image registration method for MFL visualization to solve this problem. In order to evaluate the registration, and to fuse the information detected in different directions, the mutual information between the reference image and the MFL image calculated by the forward model is designed as a measure. Furthermore, Particle Swarm Optimization (PSO) is used to optimize the registration process. The comparative experimental results demonstrate that this method has a higher registration accuracy for the MFL images of complex cracks than the existing methods.
From Nonlinear Dominant System to Linear Dominant System: Virtual Equivalent System Approach for Multiple Variable Self-Tuning Control System Analysis
Authors: Pan Jinghui , Peng Kaixiang , Zhang Weicun
Published: January 2023

The stability and convergence analysis of a multivariable stochastic self-tuning system (STC) is very difficult because of its highly nonlinear structure. In this paper, based on the virtual equivalent system method, the structural nonlinear or nonlinear dominated multivariable self-tuning system is transformed into a structural linear or linear dominated system, thus simplifying the stability and convergence analysis of multivariable STC systems. For the control process of a multivariable stochastic STC system, parameter estimation is required, and there may be three cases of parameter estimation convergence, convergence to the actual value and divergence. For these three cases, this paper provides four theorems and two corollaries. Given the theorems and corollaries, it can be directly concluded that the convergence of parameter estimation is a sufficient condition for the stability and convergence of stochastic STC systems but not a necessary condition, and the four theorems and two corollaries proposed in this paper are independent of specific controller design strategies and specific parameter estimation algorithms. The virtual equivalent system theory proposed in this paper does not need specific control strategies, parameters and estimation algorithms but only needs the nature of the system itself, which can judge the stability and convergence of the self-tuning system and relax the dependence of the system stability convergence criterion on the system structure information. The virtual equivalent system method proposed in this paper is proved to be effective when the parameter estimation may have convergence, convergence to the actual value and divergence.
Precision Machine Learning
Authors: Michaud Eric J. , Liu Ziming , Tegmark Max
Published: January 2023

We explore unique considerations involved in fitting machine learning (ML) models to data with very high precision, as is often required for science applications. We empirically compare various function approximation methods and study how they scale with increasing parameters and data. We find that neural networks (NNs) can often outperform classical approximation methods on high-dimensional examples, by (we hypothesize) auto-discovering and exploiting modular structures therein. However, neural networks trained with common optimizers are less powerful for low-dimensional cases, which motivates us to study the unique properties of neural network loss landscapes and the corresponding optimization challenges that arise in the high precision regime. To address the optimization issue in low dimensions, we develop training tricks which enable us to train neural networks to extremely low loss, close to the limits allowed by numerical precision.
Acknowledgment to the Reviewers of Earth in 2022
Authors:   Earth Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Smart Polymeric Wound Dressing for Treating Partial-Thickness Burns: A Preliminary Preclinical Study on the Porcine Model
Authors: Beylin Dmitry , Haik Josef , Biros Erik , Kornhaber Rachel , Cleary Michelle , Harats Moti , Cohn Daniel , Sapir Yair , Weisberg Ori
Published: January 2023

Several so-called “smart” dressings are available for burn injuries to promote faster wound healing, and this technology has recently reported substantial advancements. However, the selection of an appropriate dressing for partial-thickness burns requires consideration of several crucial elements, including exudate management, conformability, antimicrobial properties, ease of application and removal, patient comfort, and cost-effectiveness. This preliminary feasibility study uses a porcine model to test the INTELIGELS product (Smart Bandage) for partial-thickness burns treatment. Artificially made wounds, mimicking partial-thickness burns, were assessed in two studies with and without antimicrobial additives, where wounds were randomly assigned to the experimental group treated with Smart Bandage and two control groups treated with a simple saline gauze dressing or Aquacel® products with and without silver additives. In addition, all dressings were evaluated for their ability to reduce wound size, quantified by histological analysis using punch biopsies. This study demonstrates comparable healing properties of Smart Bandage and Aquacel® dressings that are superior to the simple saline gauze dressing. The superiority is demonstrated by better regeneration, less inflammation of the epidermis and dermis, and better dermis remodeling with more granulation tissue maturation within the wound area when Smart Bandage/Aquacel® dressings are applied as compared with the simple gauze dressing.
Compulsive Gambling in the Stock Market: Evidence from an Emerging Market
Authors: Kamolsareeratana Atcha , Kouwenberg Roy
Published: January 2023

During the COVID-19 pandemic, many new individual investors globally entered the stock markets, often pursuing speculative investment strategies that resemble gambling. A concern is that trading as a form of gambling can become addictive for some people, as documented by several recent studies in developed markets. We contribute to this literature by adding new evidence from a large emerging market, Thailand, where most forms of traditional gambling are illegal. We field a diagnostic checklist from the American Psychiatric Association for compulsive gambling, changing the content of each item to refer to stock market trading instead of gambling. In a survey of 285 Thai investors, we document that 9.5% are potential problem gamblers, while 4.9% meet the stricter criteria for addiction. The trading addiction score explains speculative trading behavior such as frequent trading, day trading and buying high-risk “lottery” stocks, beyond common factors such as overconfidence and high risk-tolerance. Further, the trading addiction score is positively related to high levels of stress and alcohol use, problems often associated with gambling disorders. Our results raise awareness about investors whose objectives are more related to gambling than long-term investment, and the associated problems when such behavior becomes compulsive.
Impact of Inflation in Different States of Unemployment: Evidence with the Phillips Curve in South Africa from 2008 to 2022
Authors: Buthelezi Eugene Msizi
Published: January 2023

This paper investigates the impact of inflation in different states of unemployment: evidence with the Phillips curve in South Africa. The contribution of this paper is to examine the impact of inflation on different states of unemployment in South Africa. The Paper employs Markov-switching dynamic regression and data from 2008 to 2022. It was found that there are 2 states of unemployment mean rates of 25.55% and 33.59%, expected to run for 67 and 7 quarters, respectively. There is a 98.51% and 86.99% chance of a move and then returning to the same state, respectively. In states 1 and 2, a 1% increase in inflation results in a 2.61% increase and a 0.06% decrease in unemployment, respectively. There are times when the Phillips curve rationale is not holding. The government needs to increase the channels of employment opportunities. There is a need to re-look at the trade-offs between inflation and unemployment in the economy.
How to Cultivate the Modern Self: Development of the Concept of Mental Discipline in the University History of the United States
Authors: Cao Chun-Ping , Liu Si-Jing , Ren Yi-Ming
Published: January 2023

The development of the concept of mental discipline can be understood from three clues, as a puritanical phenomenon, as a phenomenon rooted in the classics, and, on the other hand, as one rooted in faculty psychology. The aim of this research is to explore whether there has been a fracture in the evolution of the concept of mental discipline and explain how and why it evolved in three stages. Centered on three figures, Timothy Dwight, William Torrey Harris, and Irving Babbitt, it can be found that mental discipline evolved into three contexts during its development. Harris’s ideas, to a large extent, reflected his absorption of the ideas of Hegelianism and the transcendental philosophy of Kant, and they also included Unitarianism. Babbitt hoped to solve the problem of how the general will formed by the individual of modern society not only guaranteed the public interest but also avoided excessively eroded individual spiritual freedom. Babbitt’s issue horizon was similar to that of Harris, but they slightly differed. A brief overview of the research may imply that, using the perspective of intellectual history, mental discipline is no longer regarded as a static and continuous ideal but as a concept embedded in different contexts and facing different issue horizons.
Factors Contributing to Teachers’ Self-Efficacy: A Case of Nepal
Authors: Shah Dhurba Bahadur , Bhattarai Prakash C.
Published: January 2023

A key issue for teachers working in public secondary schools in Nepal is their confidence in their capability to complete the tasks associated with their professional activities. With this consideration, a study was carried out to explore the factors contributing to Nepali teachers’ self-efficacy. To find out the factors, the study administered the NTSE tool, developed through the e-Delphi technique. A sample of 390 public school teachers was drawn from a population of 3427 teachers in the Kathmandu, Lalitpur, and Bhaktapur districts of Nepal. A cluster sampling technique was used to draw a local government body in each district. The exploratory factor analysis (EFA) was applied using a principal component matrix with varimax rotation. The EFA extraction was further validated by confirmatory factor analysis (CFA). The study found four factors, viz., efficacy in student engagement and efficacy in instructional preparation, which contribute to teachers’ self-efficacy in Nepali public schools. The study helps streamline teachers’ self-efficacy as a key psychological construct in their professional development opportunities, directly impacting students’ academic learning and achievement.
The Breaking News Effect and Its Impact on the Credibility and Trust in Information Posted on Social Media
Authors: Pelau Corina , Pop Mihai-Ionut , Stanescu Mihaela , Sanda Grigorie
Published: January 2023

The development of social media has triggered important changes in our society and in the way consumers read and trust online information. The presence of consumers in the online environment exposes them to a greater extent to various instances of fake news, which are spread more or less intentionally. Sensational and breaking-news-style information are one of the ways in which consumers’ attention is attracted, by posting exaggerated or distorted information. The objective of our research is to determine the impact of sensational and breaking news headlines on content credibility. In a mediation model, we show that the perception of sensationalism mediates the relation between the presence of breaking news headlines and trust in the content of the information. Based on our proposed model, the existence of breaking news headlines increases the consumers’ perception of sensationalism and reduces trust in news content. These results have important implications for patterns of news consumption. If a piece of information is presented in a sensational way, it might attract more consumers’ attention in the short term, but in the long run it will reduce the credibility of its content. Based on our research, we recommend using sensational headlines with caution to maintain credibility.
IoMT with Deep CNN: AI-Based Intelligent Support System for Pandemic Diseases
Authors: Thandapani Sujithra , Mahaboob Mohamed Iqbal , Iwendi Celestine , Selvaraj Durai , Dumka Ankur , Rashid Mamoon , Mohan Senthilkumar
Published: January 2023

The Internet of Medical Things (IoMT) is an extended version of the Internet of Things (IoT). It mainly concentrates on the integration of medical things for servicing needy people who cannot get medical services easily, especially rural area people and aged peoples living alone. The main objective of this work is to design a real time interactive system for providing medical services to the needy who do not have a sufficient medical infrastructure. With the help of this system, people will get medical services at their end with minimal medical infrastructure and less treatment cost. However, the designed system could be upgraded to address the family of SARs viruses, and for experimentation, we have taken COVID-19 as a test case. The proposed system comprises of many modules, such as the user interface, analytics, cloud, etc. The proposed user interface is designed for interactive data collection. At the initial stage, it collects preliminary medical information, such as the pulse oxygen rate and RT-PCR results. With the help of a pulse oximeter, they could get the pulse oxygen level. With the help of swap test kit, they could find COVID-19 positivity. That information is uploaded as preliminary information to the designed proposed system via the designed UI. If the system identifies the COVID positivity, it requests that the person upload X-ray/CT images for ranking the severity of the disease. The system is designed for multi-model data. Hence, it can deal with X-ray, CT images, and textual data (RT-PCR results). Once X-ray/CT images are collected via the designed UI, those images are forwarded to the designed AI module for analytics. The proposed AI system is designed for multi-disease classification. It classifies the patients affected with COVID-19 or pneumonia or any other viral infection. It also measures the intensity level of lung infection for providing suitable treatment to the patients. Numerous deep convolution neural network (DCNN) architectures are available for medical image classification. We used ResNet-50, ResNet-100, ResNet-101, VGG 16, and VGG 19 for better classification. From the experimentation, it observed that ResNet101 and VGG 19 outperform, with an accuracy of 97% for CT images. ResNet101 outperforms with an accuracy of 98% for X-ray images. For obtaining enhanced accuracy, we used a major voting classifier. It combines all the classifiers result and presents the majority voted one. It results in reduced classifier bias. Finally, the proposed system presents an automatic test summary report textually. It can be accessed via user-friendly graphical user interface (GUI). It results in a reduced report generation time and individual bias.
Trust-Degree-Based Secure Relay Selection in SWIPT-Enabled Relay Networks
Authors: Gao Ran , Xu Ling , Xu Dan , Bao Jianrong
Published: January 2023

In this paper, we investigate the trust-degree-based secure relay selection in simultaneous wireless information and power transfer (SWIPT)-enabled relay networks. In particular, we optimize the interference power by selecting the appropriate relay and time division ratio on the condition of both the secure transmission and trust degree. First, by applying the security capacity constraint, we derive the expressions of the time division ratio and relay interference power concerning the trust degree. Then, we discuss the different results for different trust degrees and determine the set of trust degrees to guarantee secure communications. Finally, according to the different trust degrees, we compute the needed interference power for each relay and make a selection to obtain the optimal performance under relay networks. Simulation results show that with the known trust degree the proposed algorithm obtains the appropriate relay and time division ratio, reduces energy consumption, and guarantees an achievable security rate.
Federated Auto-Meta-Ensemble Learning Framework for AI-Enabled Military Operations
Authors: Demertzis Konstantinos , Kikiras Panayotis , Skianis Charalabos , Rantos Konstantinos , Iliadis Lazaros , Stamoulis George
Published: January 2023

One of the promises of AI in the military domain that seems to guarantee its adoption is its broad applicability. In a military context, the potential for AI is present in all operational domains (i.e., land, sea, air, space, and cyber-space) and all levels of warfare (i.e., political, strategic, operational, and tactical). However, despite the potential, the convergence between needs and AI technological advances is still not optimal, especially in supervised machine learning for military applications. Training supervised machine learning models requires a large amount of up-to-date data, often unavailable or difficult to produce by one organization. An excellent way to tackle this challenge is federated learning by designing a data pipeline collaboratively. This mechanism is based on implementing a single universal model for all users, trained using decentralized data. Furthermore, this federated model ensures the privacy and protection of sensitive information managed by each entity. However, this process raises severe objections to the effectiveness and generalizability of the universal federated model. Usually, each machine learning algorithm shows sensitivity in managing the available data and revealing the complex relationships that characterize them, so the forecast has some severe biases. This paper proposes a holistic federated learning approach to address the above problem. It is a Federated Auto-Meta-Ensemble Learning (FAMEL) framework. FAMEL, for each user of the federation, automatically creates the most appropriate algorithm with the optimal hyperparameters that apply to the available data in its possession. The optimal model of each federal user is used to create an ensemble learning model. Hence, each user has an up-to-date, highly accurate model without exposing personal data in the federation. As it turns out experimentally, this ensemble model offers better predictability and stability. Its overall behavior smoothens noise while reducing the risk of a wrong choice resulting from under-sampling.
Blockchain-Based Authentication Scheme for Collaborative Traffic Light Systems Using Fog Computing
Authors: Namane Sarra , Ahmim Marwa , Kondoro Aron , Dhaou Imed Ben
Published: January 2023

In the era of the Fourth Industrial Revolution, cybercriminals are targeting critical infrastructures such as traffic light systems and smart grids. A major concern is the security of such systems, which can be broken down into a number of categories, such as the authentication of data collection devices, secure data transmission, and use of the data by authorized and authenticated parties. The majority of research studies in the literature have largely focused on data integrity and user authentication. So far, no published work has addressed the security of a traffic light system from data collection to data access. Furthermore, it is evident that the conventional cloud computing architecture is incapable of analyzing and managing the massive amount of generated data. As a result, the fog computing paradigm combined with blockchain technology may be the best way to ensure data privacy in a decentralized manner while reducing overheads, latency, and maintaining security. This paper presents a blockchain-based authentication scheme named VDAS using the fog computing paradigm. The formal and informal verifications of the proposed solution are presented. The evaluation of the proposed scheme VDAS showed that it has low communication and computation costs compared to existing lightweight authentication techniques.
Ka-Band Three-Stack CMOS Power Amplifier with Split Layout of External Gate Capacitor for 5G Applications
Authors: Yang Junhyuk , Lee Jaeyong , Jang Seongjin , Jeong Hayeon , Park Changkun
Published: January 2023

In this study, we designed a Ka-band two-stage differential power amplifier (PA) using a 65 nm RFCMOS process. To enhance the output power of the PA, a three-stack structure was utilized in the power stage, while the driver stage of the PA was designed with a common-source structure to minimize power consumption in the driver stage. The layout of an external gate capacitor for the stacked power stage was split to maximize the performance of the power transistor. With the proposed split layout of the external capacitor, gain, output power, and power-added efficiency (PAE) were improved. Additionally, a capacitive neutralization technique was applied to the power and driver stages to ensure the stability and enhance the gain of the PA. The measured P1dB and the saturation power were 22.0 dBm and 23.3 dBm, respectively, while the peak PAE was 27.8% at 28.5 GHz.
A Target Detection Method of Distributed Passive Radar without Direct-Path Signal
Authors: Zhu Huijie , Wang Changlong , Wang Lu
Published: January 2023

At present, there are many technical methods in the field of target detection, but the detection methods are greatly affected by direct-path signals and need technical support such as extracting pure direct-path signals, so they cannot be used under the condition of without-direct-path signals. In this paper, a distributed target detection method is studied under a without-direct-path signals system. In the case of without-direct-path signals, target detection is achieved by the generalized likelihood ratio test (GLRT). At the same time, the input echo signals in target detection need time synchronization, so the impact of time delay should be eliminated. However, the traditional time delay estimation method is realized through coherent processing between echoes, which requires a high signal-to-noise ratio (SNR). Therefore, a time delay estimation method is proposed in this paper. Finally, the experimental results show that the accuracy of target detection by GLRT is improved, and the signal-to-noise ratio is also improved under the condition of without-direct-path signals. Moreover, the accuracy of delay detection is improved.
Increasing the Accuracy Characteristics of Focused Electromagnetic Devices for Non-Destructive Testing and Technical Diagnostics by Implementing Sum–Difference Signal Processing
Authors: Vedenkin Denis , Morozov Oleg , Sedelnikov Yuri , Agliullin Timur , Nasybullin Aydar
Published: January 2023

Nowadays, the classical aperture theory is supplemented by data on the properties of electromagnetic fields in the near radiated field zone. A fundamental feature of the functioning of antennas in this area is the possibility of spatial focusing of electromagnetic energy. In this case, it is possible to realize the maximum field strength at a given point in space and reduce the field strength outside this point. The focusing effect itself is provided by the in-phase addition of fields from elementary sources. Also, in addition to the formation of the maximum field strength at the focusing point, it is also possible to realize a minimum field strength at the focusing point by implementing antiphase addition of partial fields at the focusing point. The fundamental possibility of forming sum and difference amplitude distributions of electromagnetic fields in the near radiated field zone makes it possible to realize sum–difference signal processing in order to reduce the level of the secondary maximum of the focused field. This article discusses the features of the formation of the sum and difference amplitude distributions and the principles of the sum–difference processing of focused electromagnetic fields, along with the implementation of technical solutions for non-destructive testing and technical diagnostic tasks.
Extractive Arabic Text Summarization-Graph-Based Approach
Authors: AL-Khassawneh Yazan Alaya , Hanandeh Essam Said
Published: January 2023

With the noteworthy expansion of textual data sources in recent years, easy, quick, and precise text processing has become a challenge for key qualifiers. Automatic text summarization is the process of squeezing text documents into shorter summaries to facilitate verification of their basic contents, which must be completed without losing vital information and features. The most difficult information retrieval task is text summarization, particularly for Arabic. In this research, we offer an automatic, general, and extractive Arabic single document summarizing approach with the goal of delivering a sufficiently informative summary. The proposed model is based on a textual graph to generate a coherent summary. Firstly, the original text is converted to a textual graph using a novel formulation that takes into account sentence relevance, coverage, and diversity to evaluate each sentence using a mix of statistical and semantic criteria. Next, a sub-graph is built to reduce the size of the original text. Finally, unwanted and less weighted phrases are removed from the summarized sentences to generate a final summary. We used Recall-Oriented Research to Evaluate Main Idea (RED) as an evaluative metric to review our proposed technique and compare it with the most advanced methods. Finally, a trial on the Essex Arabic Summary Corpus (EASC) using the ROUGE index showed promising results compared with the currently available methods.
FPGA Implementation of a Chaotic Map with No Fixed Point
Authors: García-Grimaldo Claudio , Bermudez-Marquez Ciro Fabián , Tlelo-Cuautle Esteban , Campos-Cantón Eric
Published: January 2023

The employment of chaotic maps in a variety of applications such as cryptosecurity, image encryption schemes, communication schemes, and secure communication has been made possible thanks to their properties of high levels of complexity, ergodicity, and high sensitivity to the initial conditions, mainly. Of considerable interest is the implementation of these dynamical systems in electronic devices such as field programmable gate arrays (FPGAs) with the intention of experimentally reproducing their dynamics, leading to exploiting their chaotic properties in real phenomena. In this work, the implementation of a one-dimensional chaotic map that has no fixed points is performed on an FPGA device with the objective of being able to reproduce its chaotic behavior as well as possible. The chaotic behavior of the introduced system is determined by estimating the Lyapunov exponents and its chaotic behavior is also analyzed using bifurcation diagrams. Simulations of the system are realized via Matlab, as well as in C and the very high-speed integrated circuit (VHSIC) hardware description language (VHDL). Experimental results on FPGA show that they are like those obtained in the simulations; therefore, this chaotic dynamical system could be used as an element in some encryption schemes such as in the generation of cryptographically secure pseudorandom numbers.
Recent Advances and Applications of AI-Based Mathematical Modeling in Predictive Control of Hybrid Electric Vehicle Energy Management in China
Authors: Zhang Qian , Tian Shaopeng , Lin Xinyan
Published: January 2023

Artificial intelligence is widely used in mathematical modeling. The technical means in mathematical modeling are more and more diversified, especially the application of artificial intelligence algorithm greatly promotes the development of mathematical modeling. In recent years, because of its great influence on the fuel consumption, output power and exhaust performance of automobiles, the control strategy has become a research hotspot and focus in automobile R&D industry. Therefore, based on the relevant research results in recent years, after studying and analyzing the typical control strategies of hybrid vehicles, this paper finally puts forward the energy management strategy of hybrid vehicles based on model predictive control (MPC), and strives to contribute to the academic research of energy management strategies of hybrid vehicles.
Design of Generalized Enhanced Static Segment Multiplier with Minimum Mean Square Error for Uniform and Nonuniform Input Distributions
Authors: Di Meo Gennaro , Saggese Gerardo , Strollo Antonio G. M. , De Caro Davide
Published: January 2023

In this paper, we analyze the performances of an Enhanced Static Segment Multiplier (ESSM) when the inputs have both uniform and non-uniform distribution. The enhanced segmentation divides the multiplicands into a lower, a middle, and an upper segment. While the middle segment is placed at the center of the inputs in other implementations, we seek the optimal position able to minimize the approximation error. To this aim, two design parameters are exploited: m, defining the size and the accuracy of the multiplier, and q, defining the position of the middle segment for further accuracy tuning. A hardware implementation is proposed for our generalized ESSM (gESSM), and an analytical model is described, able to find m and q which minimize the mean square approximation error. With uniform inputs, the error slightly improves by increasing q, whereas a large error decrease is observed by properly choosing q when the inputs are half-normal (with a NoEB up to 18.5 bits for a 16-bit multiplier). Implementation results in 28 nm CMOS technology are also satisfactory, with area and power reductions up to 71% and 83%. We report image and audio processing applications, showing that gESSM is a suitable candidate in applications with non-uniform inputs.