Miracle-dz/UniFAR-FTUs huggingface dataset:
Customized shaping of vibration modes by acoustic metamaterial synthesis

Acoustic metamaterials have attractive potential in elastic wave guiding and attenuation over specific frequency ranges. The vast majority of related investigations are on transient waves. In this research we focus on stationary wave manipulation, i.e., shaping of vibration modes. Periodically arranged piezoelectric transducers shunted with inductive circuits are integrated to a beam structure to form a finite-length metamaterial beam. We demonstrate for the first time that, under a given operating frequency of interest, we can facilitate a metamaterial design such that this frequency becomes a natural frequency of the integrated system. Moreover, the vibration mode corresponding to this natural frequency can be customized and shaped to realize tailored/localized response distribution. This is fundamentally different from previous practices of utilizing geometry modification and/or feedback control to achieve mode tailoring. The metamaterial design is built upon the combinatorial effects of the bandgap feature and the effective resonant cavity feature, both attributed to the dynamic characteristics of the metamaterial beam. Analytical investigations based on unit-cell dynamics and modal analysis of the metamaterial beam are presented to reveal the underlying mechanism. Case illustrations are validated by finite element analyses. Owing to the online tunability of circuitry integrated, the proposed mode shaping technique can be online adjusted to fit specific requirements. The customized shaping of vibration modes by acoustic metamaterial synthesis has potential applications in vibration suppression, sensing enhancement and energy harvesting.
ANT COLONY OPTIMIZATION FOR OPTIMIZING TRAVELING SALESMAN PROBLEM

— Ant Colony Optimization (ACO) is an optimization technique for designing meta-heuristic algorithms to solve combinatorial optimization problems. The essential trait of ACO algorithms is the combination of a priori information about the structure of a promising solution with posterior information about the structure of previously obtained good solutions. Travelling salesman problem is real-world combinatorial optimization problem which determine the optimal route for travelling salesman. Generally, to provide the efficient vehicle serving to the customer through different services by visiting the number of cities or stops, the VRP follows the Travelling Salesman Problem (TSP), in which each of vehicle visiting a set of cities such that every city is visited by exactly one vehicle only once. In this project I implement the ant colony algorithm on the travelling salesman problem to obtain optimal results.
Molecular analysis of multifoca l papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) frequently presents as a multifocal process. To study the importance of separating independent primary (IP) from intrathyroid metastatic (ITM) PTC, we examined 19 molecular markers on 42 separate tumors from 18 multifocal PTC cases. In 12 of 18 (66.7%) cases, including 6 of 12 (50%) papillary microcarcinoma cases, the same or similar profile of loss of heterozygosities (LOH) and v-raf murine sarcoma viral oncogene homolog B1 (BRAF) mutation was demonstrated, indicating that they were from the same primary and represented ITM. Different profiles of LOHs and BRAFmutation were detected in separate tumors of 6 of 18 cases, indicating that they represented IP. Patients with ITM, including papillary microcarcinoma, had significantly increased lymph node metastasis. The frequencies of LOHs of 17q21, 17p13, 10q23, and 22q13 were higher in tumors with lymph node metastasis, suggesting that these LOHs may be important in increased lymph node metastasis. LOH of 9p21 was found at the highest frequency in PTC (53.8%), followed by 1p36 (46.2%), 10q23 (34.6%), and 22q13 (34.6%). Papillary microcarcinoma had acquired similar genomic mutations as conventional PTC, but higher frequencies of mutations of BRAF, 1p36, 18q, and 22q13 were found in the larger PTC, suggesting that they might play a role in the aggressiveness of PTC. Different profiles of mutations were observed in conventional, follicular variants, and diffuse sclerosing variant of PTC, which might influence the different morphological appearances and clinical courses. In conclusion, molecular analysis can separate multifocal IP PTC from ITM PTC, andmay be more important than tumor size in predicting lymph nodemetastasis, aggressiveness, and prognosis of PTC. Journal of Molecular Endocrinology (2008) 41, 195–203
METRICS FOR SOFTWARE MAINTAINABILITY AND USABILITY IN AGILE ENVIRONMENT

: It is not easy to comprehend the quality and features of software unless we are familiar of its software development process and software products. Some measurements process should be there to predict the development of the software, and to evaluate the software products. In the conventional technique for the product advancement, there are a number of measurements to compute the maintenance and utilization of programming. This investigation is to understand whether the same measures apply to Agile, or there is a need to modify a few measurements utilized for the agile environment. This paper gives a brief view on Maintainability and Usability by which the specified quality factors of software can be predicted. Maintainability and Usability are emerging attributes of software quality, which play a very important role in determining the quality and excellence of a software system. Consequently, the usage of software metrics improves quality and excellence of software.
Deep Learning with Minimal Coding Effort: A Tutorial on Theory Deep Learning with Minimal Coding Effort: A Tutorial on Theory and Implementation of Deep Artificial Neural Networks and Implementation of Deep Artificial Neural Networks

Advances in computer technologies in the past couple of decades has enabled data and computer scientists to employ deep neural networks to detect and analyze complex patterns in large and varied data repositories from a wide variety of application domains. Given the interest in big data and analytics coursework in most information systems departments, this paper provides a step-by-step tutorial on the design and implementation of deep neural networks using an open-source, low-code, intuitive analytics platform. This platform (KNIME) suits well for both technical and non-technical users. Although this tutorial focuses on an image processing (classification) project in the popular context of healthcare, we believe the provided guidelines, with slight modifications, can be applied to the design and implementation of various deep learning architectures built to analyze a wide variety of data types.
Traffic Monetization on Community E-commerce Platforms: Trust in Virtual Communities and Its Effect on Actual Purchase

: In China, major community e-commerce websites are focusing on increasing customer bases by converting members in their virtual communities into online buyers, which is one of the important ways of monetizing social traffic. Based on the Stimulus-Organism-Response (S-O-R) theory, this paper proposes and empirically tests a model of trust in virtual communities. We will collect data through online questionnaire surveys with users of Little Red Book, followed by structural equation modeling to analyze the influence mechanism of community e-commerce platform characteristics on consumers’ trust. The expected results will show the full mediation effects of interactivity and presence in the relationship between the quality of user-generated content (UGC) and the influence of key opinion leader (KOL) in virtual communities and trust. And consumers’ identity trust, cognitive trust as well as emotional trust will all significantly promote their actual purchases. This paper fills the research gap of trust mechanism on community e-commerce platforms. It also guides the community e-commerce platforms to encourage users to generate more high-quality content and promote major brands to cooperate with KOLs to accelerate the monetization of social traffic in virtual communities.
Herding Behavior in Online Restaurant Ratings: Moderating Herding Behavior in Online Restaurant Ratings: Moderating Effects of Reviewer Popularity and Observed Review Volume Effects of Reviewer Popularity and Observed Review Volume

Understanding the generation of online reviews is fundamental work for retailers to better utilize them. Review rating is the most important component of an online review; therefore, our study tries to investigate the antecedents of online reviews by figuring out the relationship between a reviewer’s observed review rating and his/her rating. More specifically, this study aims to answer the following three research questions: (1) Does herding behavior exist in online ratings, i.e., is a reviewer’s rating affected by his/her observed ratings given by other reviewers? (2) Does the observed review number moderate the direct effect of observed review ratings on a reviewer’s rating behavior? (3) Does a reviewer’s popularity moderate the direct effect of observed review ratings on his/her rating behavior? To answer these research questions, we conduct multiple empirical analyses using online restaurant reviews obtained from the most popular review platform in China. The results show that herding behavior does exist in online rating behavior. To be more specific, a reviewer’s observed review rating while authoring reviews are positively related to his/her review rating; The observed review volume of the rated restaurant can mitigate the positive relationship between a reviewer’s observed review rating and his/her rating. A reviewer’s popularity can also mitigate the positive relationship between his/her observed review rating and his/her own rating. Our study makes contributions to both academic literature and managerial practice by demonstrating the presence of herding behavior in online review ratings. Our findings offer important implications for online review platform managers, product retailers, and consumers.
Sounds Full of Meaning and the Evolution of Language

Imagine that you are a child again and smell freshbaked cake when you come home after school. Maybe it’s your grandmother’s apple cake or your neighbor’s famous cheesecake. You audibly inhale through the nose and your eyes roll because the cake smells delicious. You open the door. There is a piece waiting for you right there, still warm and fresh from the oven! You take a bite. Now, with this memory in place, how could you let someone experience it with you? You could try describing the taste with words. Maybe the cake is mild, maybe zesty, or maybe it’s just delightful! However, it might be difficult to describe a sensory experience like that using conventional language.
Exploring the connectivity of memes and emotions of images through the use of Tagasaurus

Images have proven to be catalysts of emotions and ideals at a personal level and for collectives hoping to make change. Political mobilization can be triggered and directed by images which share ideological position, and politicians can use them to signal to followers where they position themselves within the political spectrum. As content becomes easier to create and disseminate from the populace those seeking to study the images will find themselves in need of tools to improve the efficiency and organization of their work. This study presents an analysis of the datasets that the application Tagasaurus can produce. Tagasaurus, is a cross platform application for providing semantic information as annotations for images. The annotations guide the user to input descriptions for the production of hashtags, emotional variables and memes. The export facility allows a JSON to be produced for the collection of annotations and subsequently a researcher can investigate associations. Based on a dataset of over a 100 images, a set of diagrams displays the capabilities for investigating cross community meme adoption in order to see the overlaps and distinctions in the symbolism used between groups sharing content. The investigation also shows how emotions and hashtag co-occurrences can be studied.
Effects of Technostress in the Role Stress Context on Proximity Managers’ Performance

Managers are currently facing the challenge of information and communication technology (ICT) diversification from a managerial perspective and because of their role as an intermediary between employees and the organization. The purpose of our research is to examine the specificity of “technostress” experienced by proximity managers (PM). We develop the following research questions: What are the factors that create technostress and role stress among PMs? How do the creators of technostress influence the performance of PMs? The data were collected through questionnaires distributed to the PMs (managers or team managers) of organizations that extensively use ICT to perform their professional tasks. The return rate is almost 40%. We believe that what we consider to be the technostress creator, role stress, and PMs’ performance are significantly linked. Therefore, role stress emphasizes that technostress creators negatively influence the performance of PMs when the level of role stress increases.
CONTENT DISTRIBUTION VIA P2P NETWORKS: A SURVEY

— today the major portion of internet traffic is comprised of content such as videos, images and music files. Distributing such content to the end users efficiently is a challenge which has been addressed by various client-server architecture models such as server farms, web proxies and CDNs (Content distribution System). This paper surveys how an alternative architecture called p2p (peer to peer) can be used in assisting the client server architecture to make the delivery more efficient. This paper presents the state of content distribution in the internet, p2p architectures available, and working of Bit Torrent and Web RTC methods.
BIG DATA MINING AND TOOLS: A REVIEW

— Big data term can be used for large amount of data, generated from various different resources. With the difficulty of storing and processing. Big data is now expanding in all area of science and engineering including, physical, biomedical and biological sciences. Big Data mining is the capability of extracting useful information from large datasets or streams of data, that due to its volume, veracity, and velocity, it was not possible before to do it. In this paper we will characterize the Big Data processing with the view of data mining. The Big Data challenge is becoming one of the most exciting deal for the next years. This paper presents an overview of Big data, challenges and tools for processing this big data’s content. This paper also introduces the HACE theorem that characterizes the Big Data rising and represents the Big Data Processing Model. This data-driven model has lot of information resources, analysis and mining, user interest and privacy and security.
CAM: CHAOTIC ADAPTIVE MODULATION SECURE SYSTEM

— This paper discusses the uses of chaotic signals in secure wireless communication systems. A novel system, called chaotic adaptive modulation, CAM, has been developed. The system is mainly designed to simplify the chaotic secure communication system by adding conventional QAM scheme at transmitter end where the states of QAM is chosen randomly due to the beforehand data-chaotic stream key Xoring block. The performance of the proposed system under imperfect fading channel is evaluated.
Neonatal Cholestasis: The Changing Etiological Spectrum in Pakistani Children

Objectives: To determine the frequency of clinical presentation and laboratory profile in the diagnosis of the etiological spectrum of neonatal cholestasis. Material and methods: In this prospective cross-sectional study, we recruited children who presented with jaundice and direct hyperbilirubinemia with onset in the first three months of life. The study was conducted between April 2019 to March 2021 (24 months) at the Government Lady Reading Hospital of Khyber Pakhtunkhwa province in Pakistan. The diagnosis was based on history and clinical findings that included jaundice, stool color, itching, abdominal distention, and deranged liver function tests and confirmed on liver biopsy and specific diagnostic tests. Data was recorded and analyzed using SPSS version 20 (IBM Corp., Armonk, NY). Results: A total of 90 children were included in the study, out of which 65.6% were male. The average age was recorded as 118.01 days + 118.1 SD. Jaundice, dark urine, and hepatomegaly were found in 85.6% of children while ophthalmologic disorder, congenital heart disease, and itching were the least common symptoms. Laboratory findings of the cholestasis patients showed high bilirubin (mean: 8.88 mg/dL), alanine transaminase (ALT) (mean: 177.48 IU/mL), aspartate transaminase (AST) (mean: 187.11 IU/mL), gamma-glutamyl transpeptidase (GGT) (mean: 187.66 IU/mL) and prolonged international normalized ratio (INR) (mean: 2.20) in majority of patients. The genetic and metabolic disorder was the leading cause found in the majority of children, which was 43.8%. Conclusion: The common causes of neonatal cholestasis in this study are galactosemia, idiopathic hepatitis, and biliary atresia. The common presentation includes jaundice, hepatomegaly, direct hyperbilirubinemia, raised liver enzymes, and INR.
Communication Between Automated Vehicles and Drivers in Manual Driving Vehicles: Using a Mechanical Arm to Produce Gestures

Abstract Effective communication between automated vehicles and human drivers in manual driving vehicles is of great importance for traffic safety during the transition phase of automated vehicles. Gestures, which were widely used in road users’ communication, were promising in conveying the intentions of automated vehicles naturally and intuitively without extra learning costs. However, the effect of gestures in conveying the automated vehicles’ intentions on human understanding remains unknown. This study proposed the idea of adopting mechanical arms to produce gestures. An experiment based on video recordings was conducted to explore the effect of arms type (slow-waved mechanical arm (80 beats/min) vs. fast waved mechanical arm (120 beats/min) vs. human arm) and gesture type (taking the road vs. giving the road) on the participants’ objective responses and subjective opinions. A total of 30 participants were recruited as human drivers in a manual driving vehicle, who received and responded to the gestures transferred by an encountering automated vehicle. Results indicated that regardless of the gesture type, the slow-waved mechanical arm led to a longer response time (mean ± SD: 4.871 ± 0.947 s) and lower response accuracy (88.3 ± 32.4%) when compared with the human arm (response time: 4.457 ± 0.727 s, response accuracy: 95.0 ± 22.0%). It was also rated less understandable and comfortable than the human arm. Nevertheless, the fast-waved mechanical arm not only exerted as fast (4.484 ± 0.818 s) and accurate responses (98.3 ± 12.9%) as the human arm but was also rated as understandable, polite, and comfortable as the human arm. This indicated the implication of conveying gestures by utilizing the fast-waved mechanical arm (120 beats/min) to facilitate effective communication from automated vehicles to human drivers in manual driving vehicles. The present study’s findings provided reference implications for manufacturers and designers to adopt this gesture-based communication method to develop safe and user-friendly automated vehicles.
Antiinflamatórios não esteróides inibidores da ciclooxigenase-2 (COX-2): aspectos atuais

BACKGROUND AND OBJECTIVES: Due to the high incidence of NSAID-related side-effects, the discovery of two cycloxygenase isoforms, classified as: COX-1 or constitutive and COX-2 or inductive, has formulated the paradigm that NSAIDs anti-inflammatory properties would be mediated by COX-2 inhibition, and side-effects, by COX-1 blockade. However, COX-2 has been constitutively detected in normal tissues raising the question of how really safe are specific inhibitors of such enzyme. This review aimed to report new clinical and experimental evidences involving COX-2 and its specific inhibitors. CONTENTS: New concepts on structural differences between COX-1 and COX-2, the existence of these isoforms in different organic tissues, and animal and human experiments are reported, in addition to clinical observations of specific COX-2 inhibitors (coxibs). Potential new therapeutic indications of NSAIDS, mainly coxibs, for Alzheimers disease and cancer are emphasized. CONCLUSIONS: Coxibs are an important pharmacological advance for anti-inflammatory treatment, decreasing the incidence of gastrointestinal adverse effects and probably playing a role in the prevention of cancer and neurological diseases. However, similar side-effects from conventional NSAIDs still exist, and also, coxibs are high-cost drugs. Like any new medication, further evaluations are needed to determine the actual safety profile of such compounds.
Deep Learning-Assisted Power Minimization in Underlay MISO-SWIPT Systems Based On Rate-Splitting Multiple Access

In this article, we consider a multi-user multiple-input single-output underlay cognitive radio system with simultaneous wireless information and power transfer (SWIPT) based on the rate-splitting multiple access (RSMA) framework. The system model is composed of a set of secondary users that only decode information, and another set of secondary users that simultaneously decode information and harvest energy based on a power-splitting (PS) ratio. Precoders are designed to minimize the transmission power of the secondary transmitter subject to a minimum rate requirement, an energy harvesting requirement, and maximum allowable interference with the primary network. The optimization problem is non-convex and challenging. Thus, we divide it into two subproblems where the outer problem is solved by a deep neural network (DNN)-based scheme with an autoencoder, and the inner problem is solved based on the semidefinite relaxation (SDR) technique. The inner problem takes the solution of the DNN-based scheme to provide the precoder vectors and PS ratios based on SDR, where a penalty function is proposed to guarantee feasible solutions to the problems. Our simulation results prove that the proposed framework based on RSMA outperforms the conventional methods and can achieve performance close to that of the optimal solutions, with a significant reduction in computational complexity.
Sensor-Reduction Control for Dual Active Bridge Converter under Dual-Phase-Shift Modulation

This paper proposes a sensor-reduction control in order to control the output voltage of the dual active bridge (DAB) converter under dual-phase-shift (DPS) modulation. The deadbeat control-based Lagrange multiplier method (LMM) is adopted to achieve current stress optimization (CSO). A first-order extended state observer (ESO) is established to observe the load current based on the basis of the dynamic equation of the output capacitor. Compared to the existing ESO for the DAB converter, the proposed observer is able to reject disturbances even when output capacitor mismatches occur, resulting in better observer performance. Besides, the proposed method exhibits good steady-state performance without using any compensating controller. Consequently, the proposed method enables current sensorless control and significantly decreases the number of control parameters, resulting in increased simplicity and cost-saving. The simulation and experimental results obtained with a 300 W prototype demonstrate that the proposed method outperforms the existing methods under changing load current and input voltage conditions.