AI-Based Vehicular Network toward 6G and IoT: Deep Learning Approaches

In recent years, vehicular networks have become increasingly large, heterogeneous, and dynamic, making it difficult to meet strict requirements of ultralow latency, high reliability, high security, and massive connections for next generation (6G) networks. Recently, deep learning (DL ) has emerged as a powerful artificial intelligence (AI ) technique to optimize the efficiency and adaptability of vehicle and wireless communication. However, rapidly increasing absolute numbers of vehicles on the roads are leading to increased automobile accidents, many of which are attributable to drivers interacting with their mobile phones. To address potentially dangerous driver behavior, this study applies deep learning approaches to image recognition to develop an AI-based detection system that can detect potentially dangerous driving behavior. Multiple convolutional neural network (CNN )-based techniques including VGG16, VGG19, Densenet, and Openpose were compared in terms of their ability to detect and identify problematic driving.

The Footprint of Factorization Models and Their Applications in Collaborative Filtering

Factorization models have been successfully applied to the recommendation problems and have significant impact to both academia and industries in the field of Collaborative Filtering ( CF ). However, the intermediate data generated in factorization models’ decision making process (or training process , footprint ) have been overlooked even though they may provide rich information to further improve recommendations. In this article, we introduce the concept of Convergence Pattern, which records how ratings are learned step-by-step in factorization models in the field of CF. We show that the concept of Convergence Patternexists in both the model perspective (e.g., classical Matrix Factorization ( MF ) and deep-learning factorization) and the training (learning) perspective (e.g., stochastic gradient descent ( SGD ), alternating least squares ( ALS ), and Markov Chain Monte Carlo ( MCMC )). By utilizing the Convergence Pattern, we propose a prediction model to estimate the prediction reliability of missing ratings and then improve the quality of recommendations. Two applications have been investigated: (1) how to evaluate the reliability of predicted missing ratings and thus recommend those ratings with high reliability. (2) How to explore the estimated reliability to adjust the predicted ratings to further improve the predication accuracy. Extensive experiments have been conducted on several benchmark datasets on three recommendation tasks: decision-aware recommendation, rating predicted, and Top- N recommendation. The experiment results have verified the effectiveness of the proposed methods in various aspects.

An Overview on Actual Knowledge About Immunohistochemical and Molecular Features of Vitality, Focusing on the Growing Evidence and Analysis to Distinguish Between Suicidal and Simulated Hanging

In forensic practice, the pathologist is often asked to determine whether a hanging was committed as suicide or as a simulated hanging (when a dead body is suspended after death). When exterior evidence of violence is absent and the crime scene investigation fails to identify useful proof, it is nearly impossible to tell whether the dead body was suspended or not. As a result, determining whether the ligature mark was created during life or not should rely on the research and demonstration of vital reactions on the ligature mark. The main purpose of this review article is to provide a summary of current knowledge about the histological and immunohistochemical characteristics of vitality in hanging. The authors also aim to identify the most significant vitality markers on ligature marks for further scientific validation and to propose a standardized diagnostic protocol for hanging. The study was conducted according to the Preferred Reporting Items for Systematic Review (PRISMA) Protocol. Relevant scientific papers were found from PubMed up to April 2021, using the following keywords: hanging AND skin AND vitality. Three main points were studied: ligature mark dehydration, immunological response to mechanical injury, and apoptosis induction as a result of the previous points. An increase in apoptosis is evident in the ligature mark (due to physical and chemical processes involved), as demonstrated by FLICE-inhibitory protein (FLIP) depletion. Immunohistochemical detection of Aquaporin 3 (AQP3) and increase in the concentration of different electrolytes rely solely on ligature mark dehydration. Also, microRNAs (MiRNAs) could become reliable forensic biomarkers for ligature mark vitality diagnosis in the near future. To ensure high reliability in court cases, forensic investigation in hanging should rely on modern and proven markers, even a mix of several markers.

Artificially created interfacial states enabled van der Waals heterostructure memory device

Two-dimensional (2D) interface plays a predominate role in determining the performance of a device that is configured as a van der Waals heterostructure (vdWH). Intensive efforts have been devoted to suppressing the emergence of interfacial states during vdWH stacking process, which facilitates the charge interaction and transfer between the heterostructure layers. However, the effective generation and modulation of the vdWH interfacial states could give rise to a new design and architecture of 2D functional devices. Here, we report a 2D non-volatile vdWH memory device enabled by the artificially created interfacial states between hexagonal boron nitride (hBN) and molybdenum ditelluride (MoTe2). The memory originates from the microscopically coupled optical and electrical responses of the vdWH, with the high reliability reflected by its long data retention time over 10^4 s and large write-erase cyclic number exceeding 100. Moreover, the storage currents in the memory can be precisely controlled by the writing and erasing gates, demonstrating the tunability of its storage states. The vdWH memory also exhibits excellent robustness with wide temperature endurance window from 100 K to 380 K, illustrating its potential application in harsh environment. Our findings promise interfacial-states engineering as a powerful approach to realize high performance vdWH memory device, which opens up new opportunities for its application in 2D electronics and optoelectronics.

The Scale of Quantitative Assessment Adherence to Treatment «QAA-25»: Updating of Formulations, Constructive and Factor Validity and a Measure of Consent

Aim: To update the definitions of selected questions of the "QAA-25" (quantitative adherence assessment) scale and evaluate it according to the criteria of validity and measure of agreement.Materials and Methods. In a descriptive cross-sectional study including 200 patients with coronary heart disease, adherence was determined using traditional and alternative versions of selected questions of the QAA-25 scale, followed by assessment of construct validity, factor validity, and measure of agreement.Results. Alternative question versions did not significantly affect test results, with 81% of respondents in the outpatient sample and 69% in the inpatient sample rating them as "more acceptable." The QAA-25 scale has good construct and internal validity (α – 0.818, αst – 0.832), with moderate agreement (κ – 0.562) and demonstrates high reliability of internal validity – when scale items are consistently excluded, α values remain in the 0.801-0.839 range.Conclusion. The QAA-25 scale with modified question definitions should be used instead of the previous version of the scale. Good construct validity and factor validity, sufficient measure of agreement and specificity, high sensitivity and reliability of the QAA-25 scale allow to recommend it as a tool for assessing adherence to drug therapy, medical support, lifestyle modification and integral adherence to treatment in scientific and clinical practice.

Improving the Stability of Islanded DC Microgrid with Constant Power Loads

The AC power system is leading due to its established standards. The depleting thread of fossil fuels, the significant increase in cost and the alarming environmental situation raises concerns. An Islanded DC microgrid, due to its novel characteristics of being able to withstand faulty conditions, has increased the reliability, accuracy, ease of integration, and efficiency of the power system. Renewable energy sources, characteristically DC, have wide usability in a distributive network and, accordingly, less circuitry and conversion stages are required, eliminating the need of reactive power compensation and frequency sync. Constant power loads (CPLs) are the reason for instability in the DC microgrid. Various centralized stability techniques have been proposed in the literature; however, the grid system collapses if there is a fault. To compensate, an efficient distributive control architecture, i.e., droop control method is proposed in this research. The significant advantage of using the droop control technique includes easy implementation, high reliability and flexibility, a reduced circulating current, a decentralized control with local measurements, the absence of a communication link and, thus, it is economic. Moreover, it offers local control for each individual power source in the microgrid. To investigate the stability of the islanded DC microgrid with constant power loads using the droop control technique, a small signal model of the islanded DC microgrid was developed in MATLAB/Simulink. Simulations were carried out to show the efficiency of the proposed controller and analyze the stability of the power system with constant power loads.

Validated tests for language research with university students: Tests of vocabulary, general knowledge, author recognition, reading comprehension, reading frequency, attention check

We present five studies aimed at developing a new vocabulary test for university students. Such a test isuseful as an indication of crystallized intelligence and because vocabulary size correlates well withreading comprehension. In the first study, a list of 100 words based on Nation’s Vocabulary Size Test waspresented to 195 participants and compared to other tests of crystallized intelligence. Analysis suggestedthe presence of two distinct factors, which we interpreted as evidence for the possible existence of twotypes of difficult words: Unfamiliar words for general knowledge and unfamiliar words for specializedknowledge. In the subsequent studies we tried to develop vocabulary tests for each type of words, at thesame time trying out various reading comprehension tests to use as validation criterion. However, in thefinal study a high correlation (r =.82) was found between our two vocabulary tests, indicating that theymeasure the same latent factor, contrary to our initial assumption. Both tests have high reliability (r >.85) and correlate well (r > .4) with general knowledge, author recognition, and reading comprehension.As part of our research efforts, a collection of new and existing tests was used and (often) improved toverify the validity of the vocabulary tests. An exploratory factor analysis on all tests established 3 factors(text comprehension, crystallized intelligence, and reading rate), with the vocabulary tests loading on thefactor of crystallized intelligence, which in turn correlated with reading comprehension. Structuralequation modeling corroborated the interpretation. We end by providing an overview of the differenttests that were developed or improved throughout the studies. They are freely available for researchpurposes at https://osf.io/ef3s4/.

Damage to the Cardiovascular System in Patients with SARS-CoV-2 Coronavirus Infection. Part 1: Predictors of the Development of an Unfavorable Prognosis

Aim. To evaluate the effect of sinus tachycardia and reduced left ventricular ejection fraction (LVEF) on the prognosis of patients with a verified diagnosis of a new coronavirus infection SARS-CoV-2.Material and methods. The study included 1,637 patients with a verified diagnosis of a new coronavirus infection SARS-CoV-2. The average age of the patients was 58.8±16.1 years. More than half of the patients admitted to the hospital had a history of cardiovascular diseases: hypertension was diagnosed in 915 (56%) patients, coronary artery disease – in 563 (34%), chronic heart failure – in 410 (25%). 294 (17.9%) patients suffered from diabetes mellitus. The unfavorable course of new coronavirus infection was assessed by the fact of being in the intensive care unit (ICU), the use of mechanical ventilation and death.Results. An unfavorable course of coronavirus infection was observed in 160 (9.8%) patients. Statistical analysis revealed that 341 (20.8%) patients with COVID-19 were diagnosed with sinus tachycardia, which required the appointment of pulse-reducing therapy. The occurrence of sinus tachycardia in patients with COVID-19 significantly increased the risk of death (odds ratio [OR] 1.248, confidence interval [CI] 1.038-1.499, p=0.018), increased the likelihood of mechanical ventilation use (OR 1.451, CI 1.168-1.803, p<0.001) and stay in the ICU (OR 1.440, CI 1.166-1.778, p<0.001).In 97 (5.9%) patients during hospital stay during echocardiography, a decrease in LVEF of less than 50% was diagnosed. A decrease in myocardial contractile function in patients with COVID-19 with high reliability increased the risk of death (OR 1.744, CI 1.348-2.256, p<0.001), increased the likelihood of using the mechanical ventilation (OR 1.372, CI 1.047-1.797, p=0.022) and stay in the ICU (OR 1.360, CI 1.077-1.716, p=0.010).Conclusion. The appearance of sinus tachycardia and reduced LVEF are in dependent predictors of the unfavorable course of COVID-19 in relation to factors such as death, the use of mechanical ventilation and the stay of patients in the ICU. Early pharmacological correction of cardiovascular lesions should be one of the goals of the management theese patients.

Multimarker Approach to Evaluate the Exposure to Electromagnetic Fields at 27 GHz (5g) On Danio rerio larvae

5G technology is evolving to satisfy several service requirements favoring high data-rate connections and lower latency times than current ones (&lt; 1ms). 5G systems use different frequency bands of the radio wave spectrum, taking advantage of higher frequencies than previous mobile radio generations. In order to guarantee a capillary coverage of the territory for high reliability applications, it will be necessary to install a large number of repeaters because higher frequencies waves have a lower capacity to propagate in free space. Following the introduction of this new technology, there has been a growing concern about possible harmful effects on human health. The aim of this study is investigating possible short term effects induced by 5G-millimeter waves on embryonic development of Danio rerio. We have exposed fertilized eggs to 27 GHz frequency, 9.7 mW/cm2 incident power density, 23 dbm and have measured several endpoints every 24 hours. The exposure to electromagnetic fields at 27 GHz (5G) caused no significant impacts on mortality nor on morphology because the exposed larvae showed a normal detachment of the tail, presence of heart-beat and well-organised somites. A weak positivity on exposed larvae has been highlighted by immunohistochemical analysis.

The impact of AI-enabled service attributes on service hospitableness: the role of employee physical and psychological workload

Purpose Hospitableness lies in the center of hospitality services. With the infusion of artificial intelligence (AI) technology in the hospitality industry, managers are concerned about how AI influences service hospitableness. Previous research has examined the consequences of AI technology based on customers’ assessment while ignoring the key players in service hospitableness – frontline employees (FLEs). This study aims to reveal how AI technology empowers FLEs physically, mentally and emotionally, facilitating hospitableness provision. Design/methodology/approach As the starting point, the instrument for AI-enabled service attributes was designed based on previous literature, hotel FLE interviews, expert panel and a pilot survey, and then validated using survey data. After that, a paired supervisor-employee sample was recruited in 15 hotels, and 342 valid questionnaires covering the constructs were obtained. Findings Factor analyses and measurement model evaluation suggest that the four factors, including anthropomorphic, entertainment, functional and information attributes, explain the construct of AI-enabled service attributes well, with high reliability and validity. Additionally, anthropomorphic, functional and information attributes of AI technology have been found to enable FLEs physically, mentally and emotionally, which further lead to increased service hospitableness. The entertainment attributes do not significantly reduce physical and mental fatigue but lead to positive emotions of FLEs significantly. Additionally, psychological job demand moderates the effects of AI-enabled service attributes on physical fatigue. Practical implications Practical implications can be made for AI technology application and hospitableness provision, in terms of AI technology analysis, job design and employee workload management. Originality/value This research contributes to understanding AI-enabled service attributes and their consequences, extends the conservation of resources theory to AI application context and promotes the research on service hospitableness.