Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality of Service (QoS)-aware Application Management in Fog Computing Environments

In recent years, the Internet of Things (IoT) paradigm is being rapidly adopted in the creation of applications for various smart-city, healthcare, Industry 4.0, and Agtechbased Cyber-Physical Systems (CPS). Usually, the IoT-enabled CPSs reside at a multihop distance from the Cloud datacentres. As a consequence, the Cloud-centric execution of IoT applications often fails to meet their Quality of Service (QoS) requirements in real-time. Fog computing, an extension of Cloud at the edge network, can execute the IoT applications closer to the data sources. Thus, it can improve the application service delivery time and reduce network congestion. However, the Fog computing nodes are highly distributed, heterogeneous, and most of them are constrained in resources and spatial sharing. Therefore, without efficient management of applications, it is complicated to harness the capabilities of Fog computing for different IoT-driven use cases. Application management is as an integral part of computing resource management. It can be ensured by finding suitable placement options for the applications within the computing infrastructure. In IoT-enabled CPSs, different entities including, applications, Fog nodes, IoT devices, users, and service providers, continuously interact with each other. This thesis focuses on application placement in Fog environments considering a. the characteristics of the applications, b. the communication delay among the Fog nodes, c. the context of the IoT devices, d. the service expectations of the users, and e. the operational cost of the providers. It demonstrates how the placement of applications from the perspectives of different system entities can improve the application’s QoS, the user’s Quality of Experience (QoE), and the provider’s profit. This thesis advances the state-of-the-art by making the following contributions: 1. A comprehensive taxonomy and literature review on application management approaches in Fog computing environments.
A π–π Stacked High-Performance Organic Anode for Durable Rocking-Chair Zinc-Ion Battery

Sustainable organic materials have gained considerable attention as electrodes for zinc-ion batteries (ZIB) due to their high theoretical capacity, structural versatility, and environmental friendliness. However, issues of inferior capacities and poor rate performance owing to limited inherent electronic conductivity and severe dissolution still persist. Herein, sandwich-structured perylene diimide-ethylene diamine/graphene (PDI-EDA/EG) composites are judiciously designed and synthesized. The two-dimensional graphene host can interact with the PDI-EDA polymer through π–π stacking, endowing accelerated ion/electron transfer, abundant active sites, excellent structural integrity, and mitigated solubility of the hybrid electrodes. When evaluated as an anode in ZIB, the hybrid electrode delivers a high capacity (110.2 mAh g−1 at 0.1 A g−1), superior rate capability (88.9 mAh g−1 at 5 A g−1), and exceptional durability (93.4% capacity retained after 1000 cycles). The structure evolution of the hybrid electrode during the insertion/extraction cycle was investigated by ex-situ Fourier transform infrared spectra (FTIR) and X-ray photoelectron spectroscopy (XPS), revealing the reversible Zn2+ storage at carbonyl sites. In addition, a prototype rocking-chair ZIB cell was constructed with a zinc pre-intercalated MnO2 cathode, displaying an ultrahigh energy density of 54.9 Wh kg−1 at a power density of 42.5 W kg−1 and excellent stability with negligible capacity decay after 1000 cycles.
The Factors Associated with the Development of COVID-19 Symptoms among Employees in a U.S. Healthcare Institution

Healthcare workers have experienced increased occupational health risks caused by COVID-19 disease. The purpose of this project was to examine the relationships between reporting COVID-19 symptoms by employees in a healthcare institution in the United States and employees’ demographics, vaccination status, co-morbid conditions, and body mass index (BMI). This project employed a cross-sectional design. It involved the analysis of data on COVID-19 exposure and infection incidents among employees in the healthcare institution. The dataset contained more than 20,000 entries. The results indicate that being female, African American, between 20 and 30 years old, diagnosed with diabetes, diagnosed with chronic obstructive pulmonary disease (COPD), or on immunosuppressive medicines is associated with greater reporting of COVID-19 symptoms by the employees. Furthermore, BMI is associated with reporting COVID-19 symptoms; the higher the BMI, the greater the likelihood of reporting a symptomatic infection. Moreover, having COPD, being 20–30 years old, being 40–50 years old, BMI, and vaccination status were significantly associated with employees reporting symptoms while controlling for other variables associated with reporting the symptoms among the employees. These findings may be applicable to other infectious disease outbreaks or pandemics.
The Yield of Packchoi and Lettuce under Different Nutrient Formulations with a Hydroponic Raft System

- This study examines the yield and quality of packcoy and lettuce with several nutritional formulations. The study was conducted with a 2 x 2 factorial arrangement based on a completely randomized design (CRD) with three replications. This study used Packcoy and Lettuce plants as the first factor, then four nutritional formulations: AB mix (control), Hoagland & Arnond, and Hewitt, Huett's Lettuce. The research focused on observing the weight of dry plant biomass, crop yields, and plant quality as seen from the accumulation of chlorophyll and carotenoid leaves. The results showed that Hoagland & Arnond's nutritional formulations increased plant leaf area weight, plant dry weight, and plant production yield. Meanwhile, Packcoy plants, given AB mix nutrition, could accumulate high levels of leaf chlorophyll and carotenoids. Hoagland & Arnond's nutritional formulation is ideal because it optimizes plant production and accumulates relatively better chlorophyll and carotenoids.
Teachers Perspectives on English Language Teaching (ELT) in Libyan Public Schools

- This research paper explores the perspectives of English language teachers in Libyan public schools on English Language Teaching (ELT) and the challenges they face in the classroom. The study aims to identify the factors influencing the effectiveness of ELT, the role of pedagogical strategies, and the impact of socio-political context on the teaching process. The research uses a mixed-method approach, involving a combination of qualitative interviews and quantitative surveys, to gather data from teachers working in various regions in Libya. The findings suggest that teachers face numerous challenges, including lack of resources, inadequate training, and socio-political instability, which negatively impact ELT in Libyan public schools. The paper concludes with recommendations for policy and practice to improve the quality of ELT in Libya.
Long Term Dynamic Monitoring of a PSC Box Girder Bridge

Structural Health Monitoring (SHM) aims to determine whether damage is present or not based on the analysis of measured dynamic characteristics of a monitored system. Normal changes are usually caused by modifications in environmental conditions such as humidity, wind and most important, temperature. Conversely, abnormal changes are generally caused by the presence of damage i.e. loss of structural mass and/or stiffness. This paper reports on the effect of temperature changes on the natural frequencies of the PI-57 motorway bridge located at Senlis, France. Dynamic measurements were carried out during almost two years and the goal is to develop a methodology for separating environmental influences from possible damage events. Hence, several regression and model fitting methods are used such as multiple linear regression and neural networks. It is observed that possible structural modifications show up as outliers in the statistical frequency-temperature relations.
Carpal Tunnel Syndrome: Neuropathic Pain Associated or Not with a Nociplastic Condition

Carpal tunnel syndrome (CTS) has been traditionally classified as primarily a neuropathic condition with or without pain. Precision medicine refers to an evidence-based method of grouping patients based on their susceptibility to biology, prognosis of a particular disease, or in their response to a specific treatment, and tailoring specific treatments accordingly. In 2021, the International Association for the Study of Pain (IASP) proposed a grading system for classifying patients into nociceptive, neuropathic, or nociplastic phenotypes. This position paper presents data supporting the possibility of subgrouping individuals with specific CTS related-pain into nociceptive, neuropathic, nociplastic or mixed-type phenotypes. Carpal tunnel syndrome is a neuropathic condition but can also be comorbid with a nociplastic pain condition. The presence of extra-median symptoms and the development of facilitated pain processing seem to be signs suggesting that specific CTS cases can be classified as the nociplastic pain phenotype. The clinical responses of therapeutic approaches for the management of CTS are inconclusive. Accordingly, the ability to identify the predominant pain phenotype in patients with CTS could likely be problematic for producing efficient treatment outcomes. In fact, the presence of a nociplastic or mixed-type pain phenotype would explain the lack of clinical effect of treatment interventions targeting the carpal tunnel area selectively. We propose a clinical decision tree by using the 2021 IASP classification criteria for identifying the predominant pain phenotype in people with CTS-related pain, albeit CTS being a priori a neuropathic pain condition. The identification of a nociplastic-associated condition requires a more nuanced multimodal treatment approach to achieve better treatment outcomes.
Kriging Metamodeling in Simulation

This article reviews Kriging (also called spatial correlation modeling). It presents the basic Kriging assumptions and formulas(cid:151) contrasting Kriging and classic linear regression metamodels. Furthermore, it extends Kriging to random simulation, and discusses bootstrapping to estimate the variance of the Kriging predictor. Besides classic one-shot statistical designs such as Latin Hypercube Sampling, it reviews sequentialized and customized designs. It ends with topics for future research
Applying Convolutional Neural Network and Transfer Learning to Mel-Spectrograms for COVID-19 Diagnosis via Cough Sounds

- COVID-19, an extremely contagious disease caused by the coronavirus, has led to a global pandemic. Among its prominent symptoms is a persistent cough. Previous studies have suggested that cough sounds carry specific signatures associated with COVID-19. This study highlights the potential of utilizing AI models, specifically deep learning techniques, by collecting a large amount of audio data and divided into training, transfer learning, and testing datasets. The data is pre-processed to ensure a consistent length and transformed into Mel-spectrograms. A CNN model is trained using open-source datasets with different categories they are wind, breath, and cough to create a pre-trained model. Deep transfer learning is then applied by adding new layers to the pre-trained model and adapting it to the new task of binary classification for COVID-19 (positive or negative). Finally, the performance of the model is evaluated using a separate test dataset. The NoCoCoDa dataset, curated by M. Cohen-McFarlane, proved to be a valuable resource in this study. It offered diverse coughing samples and included an additional 73 positive cough sound records and 100 randomly selected negative cough sound records from other datasets to evaluate the model's effectiveness. The proposed model achieves impressive results and exhibits an area under the curve (AUC) value of 0.98 on the previously unseen test set. and illustrates that the model achieved an accuracy of approximately 98%, with a precision of 95% for negative samples and 100% for positive samples. These results highlight the model's high accuracy and precision in identifying both positive and negative cases of COVID-19 based on cough sounds.
Molecular Dynamics Simulation from Symmetry Breaking Changing to Asymmetrical Phospholipid Membranes Due to Variable Capacitors during Resonance with Helical Proteins

Biological symmetry breaking is a mechanism in biosystems that is necessary for human survival, and depends on chemical physics concepts at both microscopic and macroscopic scales. In this work, we present a few mechanisms of the signaling phenomenon that have been studied in various tissues of human origin. We exhibit that anatomical asymmetry in the structure of a membrane can produce a flow of extracellular fluid. Furthermore, we exhibit that membrane asymmetry is a misbalance in the composition of the aqueous phases and interaction forces with the protein trans-membrane. Various biological membranes such as DPPC, DMPC, DLPC, and so on, have considerable electrostatic voltages that extend across the phosphor lipids bilayer. For studying these phenomena, we modeled DPPC, DMPC, and DLPC lipid bilayers with a net charge misbalance across the phospholipids. Because asymmetric membranes create the shifted voltages among the various aqueous tissues, this effect makes the charge misbalances cause a voltage of 1.3 V across the DPPC bilayer and 0.8 V across the DMPC bilayer. This subject exhibits the importance of membrane structures on electrostatic potential gradients. Finally, we exhibited that a quantum effect was created in small parts of the cell’s thickness due to the symmetry breaking of asymmetrical phospholipid bilayers.
A systematic review of digital interventions for smoking cessation in patients with serious mental illness

Abstract Tobacco smoking is highly prevalent among patients with serious mental illness (SMI), with known deleterious consequences. Smoking cessation is therefore a prioritary public health challenge in SMI. In recent years, several smoking cessation digital interventions have been developed for non-clinical populations. However, their impact in patients with SMI remains uncertain. We conducted a systematic review to describe and evaluate effectiveness, acceptability, adherence, usability and safety of digital interventions for smoking cessation in patients with SMI. PubMed/MEDLINE, EMBASE, CINAHL, Web of Science, PsychINFO and the Cochrane Tobacco Addiction Group Specialized Register were searched. Studies matching inclusion criteria were included and their information systematically extracted by independent investigators. Thirteen articles were included, which reported data on nine different digital interventions. Intervention theoretical approaches ranged from mobile contingency management to mindfulness. Outcome measures varied widely between studies. The highest abstinence rates were found for mSMART MIND (7-day point-prevalent abstinence: 16–40%). Let's Talk About Quitting Smoking reported greater acceptability ratings, although this was not evaluated with standardized measures. Regarding usability, Learn to Quit showed the highest System Usability Scale scores [mean (s.d.) 85.2 (15.5)]. Adverse events were rare and not systematically reported. Overall, the quality of the studies was fair to good. Digitally delivered health interventions for smoking cessation show promise for improving outcomes for patients with SMI, but lack of availability remains a concern. Larger trials with harmonized assessment measures are needed to generate more definitive evidence and specific recommendations.
Dynamic Offloading Loading Optimization in Distributed Fault Diagnosis System with Deep Reinforcement Learning Approach

Artificial intelligence and distributed algorithms have been widely used in mechanical fault diagnosis with the explosive growth of diagnostic data. A novel intelligent fault diagnosis system framework that allows intelligent terminals to offload computational tasks to Mobile edge computing (MEC) servers is provided in this paper, which can effectively address the problems of task processing delays and enhanced computational complexity. As the resources at the MEC and intelligent terminals are limited, performing reasonable resource allocation optimization can improve the performance, especially for a multi-terminals offloading system. In this study, to minimize the task computation delay, we jointly optimize the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection under a dynamic environment with stochastic task arrivals. The challenging dynamic joint optimization problem is formulated as a reinforcement learning (RL) problem, which is designed as the computational offloading policies to minimize the long-term average delay cost. Two deep RL strategies, deep Q-learning network (DQN) and deep deterministic policy gradient (DDPG), are adopted to learn the computational offloading policies adaptively and efficiently. The proposed DQN strategy takes the MEC selection as a unique action while using the convex optimization approach to obtain the local content splitting ratio and the transmission/computation power allocation. Simultaneously, the actions of the DDPG strategy are selected as all dynamic variables, including the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection. Numerical results demonstrate that both proposed strategies perform better than the traditional non-learning schemes. The DDPG strategy outperforms the DQN strategy in all simulation cases exhibiting minimal task computation delay due to its ability to learn all variables online.
AI and Big Data in Healthcare: Impacts and Challenges in Covid-19 Outbreak Prediction and Management

A disease outbreak is challenging to predict. However, with the recent Covid-19 case, many governments are trying to develop a model and use data to predict its effects. As a result, AI is most widespread in the healthcare field, particularly with the spread of Coronavirus. Indeed, technology influences healthcare across many sectors by automating procedures, improving operations, and altering traditional approaches. Artificial intelligence in health care is essential in manufacturing drugs, offering significant discoveries by analyzing big data and making clear decisions with fewer errors. This article will provide an overview of the sound impacts of using AI and big data to tackle several pandemic challenges and investigate how AI impacts the health sciences business. Since the Covid-19 virus spread throughout the country, many countries, including China and US, have deployed big data, AI, and other automated tools to monitor and contain the outbreak. The article provided an in-depth analysis of the use of big data and AI to draw patterns of disease outbreaks.
Intelligent Scheduling Method for Bulk Cargo Terminal Loading Process Based on Deep Reinforcement Learning

: Sea freight is one of the most important ways for the transportation and distribution of coal and other bulk cargo. This paper proposes a method for optimizing the scheduling efﬁciency of the bulk cargo loading process based on deep reinforcement learning. The process includes a large number of states and possible choices that need to be taken into account, which are currently performed by skillful scheduling engineers on site. In terms of modeling, we extracted important information based on actual working data of the terminal to form the state space of the model. The yard information and the demand information of the ship are also considered. The scheduling output of each convey path from the yard to the cabin is the action of the agent. To avoid conﬂicts of occupying one machine at same time, certain restrictions are placed on whether the action can be executed. Based on Double DQN, an improved deep reinforcement learning method is proposed with a fully connected network structure and selected action sets according to the value of the network and the occupancy status of environment. To make the network converge more quickly, an improved new epsilon-greedy exploration strategy is also proposed, which uses different exploration rates for completely random selection and feasible random selection of actions. After training, an improved scheduling result is obtained when the tasks arrive randomly and the yard state is random. An important contribution of this paper is to integrate the useful features of the working time of the bulk cargo terminal into a state set, divide the scheduling process into discrete actions, and then reduce the scheduling problem into simple inputs and outputs. Another major contribution of this article is the design of a reinforcement learning algorithm for the bulk cargo terminal scheduling problem, and the training efﬁciency of the proposed algorithm is improved, which provides a practical example for solving bulk cargo terminal scheduling problems using reinforcement learning.
People Centric Innovation Ecosystem

How can knowledge management function well in a highly dynamic VUCA context? This Element focuses on the context of Japanese management and practices to present the concept of people-centric innovation ecosystem. An overview of Japanese management is provided, from publications in English to the insiders' view of Japanese scholars, combining these sources with interviews and dynamic groups with local managers and case studies to illustrate the state and evolution of Japanese management and practices. Highlighting the people-centricity in Japanese management, its networked innovative capability sustains enterprise development in a highly dynamic VUCA context. The interconnectedness and mutual influence of Japanese and Western management have the potential to generate more general management advancements. This Element aims to contribute to the debate on generalization and contextualization, culture and metaculture, and the coexistence of convergence and divergence. Japanese womenomics and its implications for Asian emerging economic powers are also discussed.
A coalgebraic model of graphs

For a set-endofunctor $F$, a graph is triple $(V,E,g)$ with a structure map $g:E\rightarrow F V$. This model is a generalized coalgebra over the category of sets. In this note, we model graphs as coalgebras over $Set\times Set$ and use the theory of coalgebras over arbitrary categories to conclude properties of the category of graphs.
The Implementation of Number Envelope

In this paper,the envelope of signal is extracted by Hilbert transform.The real part of analytic signal,which gotten by the Hilbert transform is itself.The imaginary part is its Hilbert transformation,and the amplitude is signal envel-ope.The envelope demodulated by Hilbert method is the envelope of signal absolute value,its demodulated amplitude re-presents the real envelope.