The Digitization of Public Services and Its Contribution to the Quality of Service in Relation to User Satisfaction

In recent years, some governments have been working on solutions to transform public administrations and provide high-quality services to users. The United Nations has recommended integrating digital technology into various aspects of life, including public services. The objective is to offer real-time, high-quality digital services, improve the delivery of public services, enhance their effectiveness and efficiency, while achieving objectives such as transparency, interoperability and citizen satisfaction. However, most governments in developing countries are unable to keep up with this trend. As a result, they often create digital public services that fall short of users’ expectations. This study aims to identify the fundamental factors influencing the quality of service in relation to user satisfaction and to explore the relevance of digital technology in implementing this quality within Cameroon public administrations. To achieve this, we used a qualimetric research method, which included non-directive interviews, questionnaires, and structured observations. The results showed that the public services offered in our study environment did not meet users’ expectations. However, the study identified the fundamental aspects to be considered in providing quality public services connected to user satisfaction, as well as the strengths of digitization in achieving this quality.

Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text.This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus.Furthermore,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic criteria.Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus.Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to baselines.Finally,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances.The proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText alone.The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweets with impactful performances.

An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and Dice.The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by researchers for improved segmentation processes of medicalimage datasets for identifying the targeted region of interest.

Federated Learning on Internet of Things:Extensive and Systematic Review

The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation.However,FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios.The paper systematically reviewed the available literature using the PRISMA guiding principle.The study aims to provide a detailed overview of the increasing use of FL in IoT networks,including the architecture and challenges.A systematic review approach is used to collect,categorize and analyze FL-IoT-based articles.Asearch was performed in the IEEE,Elsevier,Arxiv,ACM,and WOS databases and 92 articles were finally examined.Inclusion measures were published in English and with the keywords“FL”and“IoT”.The methodology begins with an overview of recent advances in FL and the IoT,followed by a discussion of how these two technologies can be integrated.To be more specific,we examine and evaluate the capabilities of FL by talking about communication protocols,frameworks and architecture.We then present a comprehensive analysis of the use of FL in a number of key IoT applications,including smart healthcare,smart transportation,smart cities,smart industry,smart finance,and smart agriculture.The key findings from this analysis of FL IoT services and applications are also presented.Finally,we performed a comparative analysis with FL IID(independent and identical data)and non-ID,traditional centralized deep learning(DL)approaches.We concluded that FL has better performance,especially in terms of privacy protection and resource utilization.FL is excellent for preserving privacy becausemodel training takes place on individual devices or edge nodes,eliminating the need for centralized data aggregation,which poses significant privacy risks.To facilitate development in this rapidly evolving field,the insights presented are intended to help practitioners and researchers navigate the complex terrain of FL and IoT.

Stress Detector Supported Galvanic Skin Response System with IoT and LabVIEW GUI

Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world.In general,it is difficult for a person to know if they are under stress.According to previous research,temperature,heart rate variability(HRV),humidity,and blood pressure are used to assess stress levels with the use of instruments.With the development of sensor technology and wireless connectivity,people around the world are adopting and using smart devices.In this study,a bio signal detection device with Internet of Things(IoT)capability with a galvanic skin reaction(GSR)sensor is proposed and built for real-time stress monitoring.The proposed device is based on an Arduino controller and Bluetooth communication.To evaluate the performance of the system,physical stress is created on 10 different participants with three distinct tasks namely reading,visualizing the timer clock,and watching videos.MATLAB analysis is performed for identifying the three different levels of stress and obtaining the threshold values as if the person GSR voltage i.e.,relaxed for<1.75 volts;Normal:between 1.75 and 1.44 volts and stressed:>1.44 volts.In addition,LabVIEW is used as a data acquisition system,and a Blueterm mobile application is also used to view the sensor reading received from the device through Bluetooth communication.

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications

Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications.Internet of Things(IoT),fog computing,edge computing,cloud computing,and the edge of things are the spine of all real-time and scalable applications.Conspicuously,this study proposed a novel framework for a real-time and scalable application that changes dynamically with time.In this study,IoT deployment is recommended for data acquisition.The Pre-Processing of data with local edge and fog nodes is implemented in this study.The thresholdoriented data classification method is deployed to improve the intrusion detection mechanism’s performance.The employment of machine learningempowered intelligent algorithms in a distributed manner is implemented to enhance the overall response rate of the layered framework.The placement of respondent nodes near the framework’s IoT layer minimizes the network’s latency.For economic evaluation of the proposed framework with minimal efforts,EdgeCloudSim and FogNetSim++simulation environments are deployed in this study.The experimental results confirm the robustness of the proposed system by its improvised threshold-oriented data classification and intrusion detection approach,improved response rate,and prediction mechanism.Moreover,the proposed layered framework provides a robust solution for real-time and scalable applications that changes dynamically with time.

Strategic Analysis to Develop an Internationalization Plan for a Small Construction Company in Guanajuato,Mexico

Nowadays internationalization is no longer a luxury for companies and becomes a necessity in order to survive in the market and ensure its durability over time.This justifies the structure of a internationalization plan(IP)for a small company(SEC),in order to modify its value curve against the competition and guarantee a sustained growth development in new markets.This paper presents a strategic analysis of a small construction company dedicated to the elaboration of finishes for walls,floors,and concrete architecture.A situational analysis was carried out to see what would be the most appropriate international strategy for the company.Tools,such as:(a)SWOT(strengths,weaknesses,opportunities,threats)Analysis,(b)PEST(political,economic,social,and technological)Analysis,(c)Internal Factors Evaluation Matrix(IFE),and(d)External Factors Evaluation Matrix(EFE Assessment Matrix),were used to identify the current standing of the SEC.The canvas model and the competitive profile matrix were used to make the connection with the strategic position of the SEC against the desire of internationalization.Likewise,an analysis of the external factors was carried out showing a significant growth of 4.6%in the specialized sub-sector,as well as a tense business landscape driven by the political situation between Mexico and the United States.

EU＇s Association Agreements ＆ Central America： No Milk Until the Cows Come Home

Why did the European Union （EU） conclude an Association Agreement （A.A）, rather than a free-trade agreement with Central American （CA） countries in 2010？ A CA-EU AA content analysis within the broader EU policy approach towards Latin America suggests it to be （1） the only feasible option for each side at a time of increasing commercial flux; （2） mutually attractive against globalizing threats, thus converting an institutional innovation into a double-edged instrument; and （3） illustrative of the subtle but significant shift of farm-protection demands from endogenous dynamics to exogenous. They carry important implications： （1） empirically： weak CA-EU linkages further dilute West Europe＇s broader Latin ambitions; （2） theoretically： AA analyses better reflect trading realities than pure regional economic integration theories, suggesting the corrosive impact of globalizing forces on regional pursuits; and （3） historically： the continued role of the farm jinx in trade.

Data Mining of Historic Hydrogeological and Socioeconomic Data Bases of the Toluca Valley, Mexico

In this paper we used several data mining techniques to analyze the coevolution of hydrogeological and socioeconomical data for the Toluca Valley in Mexico. We found non trivial relations between two historic data bases that make clear that groundwater and economy may be much more linked than it was thought before. In particular, we found that hydrogeological data trends change during economical crisis and election years in Mexico. This shows that different macroeconomical policies implemented by several administrations have a direct impact in the way groundwater is used. We also found that hydrogoelogical data evolve in the direction of population transformation from rural to urban, which could represent a whole paradigm shift in groundwater management with profound repercussions in policy making.

Synthesis of Amine Terminated Pegylated Iron Oxide Nanoparticles for Prospective Astrocytoma Resection Grade Improvement

Having a survival rate to 5 years of only 3%,Glioblastoma’s(GBM)main treatment is surgical excision.Iron oxide nanoparticles have been proved to be a magnetic resonance imaging contrast agent and,if synthesized and tuned correctly,could be used to improve complete GBM resection.In this work monodisperse iron oxide nanoparticles were synthesized using thermal decomposition method,then a ligand exchange reaction with 3-aminopropyl trimethoxysilane(APS)was performed,following Pegylation of the particles using dicarboxylic acid PEG(PEG-diacid)and finally aminating with 2,2’-(ethylenedioxy)bis(ethylamine),last two by amide reactions.STEM and DLS demonstrate monodispersity(log σ<0.2)and desired size range to penetrate the blood-brain barrier(BBB);FT-IR shows the reactions were executed correctly and finally stability in deionized water,0.07 M NaCl and PBS 1X,as a function of 0-30 days,was tested.Results revealed the importance that the oleic acid/iron oleate molar ratio and the growth stage time represents for determining iron oxide nanoparticles’ size;as well as APS concentration and nucleation time influence on silica coating when performing the ligand exchange reaction.The produced iron oxide nanoparticles exhibit stability and proper amine terminated groups which are needed to allow easy incorporation of Chlorotoxin,a 36-amino acid peptide that binds specifically to astrocytoma cells,and a fluorescent molecule,which enables real time visualization of the tumor during surgery.

Real Time Monitoring of Muscle Fatigue with IoT and Wearable Devices

Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise.Moreover,it is widely utilizing for preventing injuries of athletes during a practice session and in few cases,it leads to muscle fatigue.At present,emerging technology like the internet of things(IoT)and sensors is empowering to monitor and visualize the physical data from any remote location through internet connectivity.In this study,an IoT-enabled wearable device is proposing for monitoring and identifying the muscle fatigue condition using a surface electromyogram(sEMG)sensor.Normally,the EMG signal is utilized to display muscle activity.Arduino controller,Wi-Fi module,and EMG sensor are utilized in developing the wearable device.The Time-frequency domain spectrum technique is employed for classifying the threemuscle fatigue conditions including meanRMS,mean frequency,etc.A real-time experiment is realized on six different individuals with developed wearable devices and the average RMS value assists to determine the average threshold of recorded data.The threshold level is analyzed by calculating the mean RMS value and concluded three fatigue conditions as>2V:Extensive);1–2V:Moderate,and<1V:relaxed.The warning alarm system was designed in LabVIEW with three color LEDs to indicate the different states of muscle fatigue.Moreover,the device is interfaced with the cloud through the internet provided with a Wi-Fi module embedded in wearable devices.The data available in the cloud server can be utilized for forecasting the frequency of an individual to muscle fatigue.

Technologically Advanced Reusable 3D Face Shield for Health Workers Confronting COVID19

The probability of medical staff to get affected from COVID19 is much higher due to their working environment which is more exposed to infectious diseases.So,as a preventive measure the body temperature monitoring of medical staff at regular intervals is highly recommended.Infrared temperature sensing guns have proved its effectiveness and therefore such devices are used to monitor the body temperature.These devices are either used on hands or forehead.As a result,there are many issues in monitoring the temperature of frontline healthcare professionals.Firstly,these healthcare professionals keep wearing PPE(Personal Protective Equipment)kits during working hours and as a result it would be very difficult to monitor their body temperature.Secondly,these healthcare professionals also wear face shields and in such cases monitoring temperature by exposing forehead needs removal of face shield.Doing so after regular intervals is surely uncomfortable for healthcare professionals.To avoid such issues,this paper is disclosing a technologically advanced face shield equipped with sensors capable of monitoring body temperature instantly without the hassle of removing the face shield.This face shield is integrated with a built-in infrared temperature sensor.A total of 10 such face shields were printed and assembled within the university lab and then handed over to a group of ten members including faculty and students of nursing and health science department.This sequence was repeated four times and as a result 40 healthcare workers participated in the study.Thereafter,feedback analysis was conducted on questionnaire data and found a significant overall mean score of 4.59 out of 5 which indicates that the product is effective and worthy in every facet.Stress analysis is also performed in the simulated environment and found that the device can easily withstand the typically applied forces.The limitations of this product are difficulty in cleaning the product and comparatively high cost due to the deployment of electronic equipment.

A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis

Coronavirus(COVID-19)has impacted nearly every person across the globe either in terms of losses of life or as of lockdown.The current coronavirus(COVID-19)pandemic is a rare/special situation where people can express their feelings on Internet-based social networks.Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions.This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown.The research is based on the logic expressed by people in this perspective and emotions for the suffering of COVID-19 and lockdown.In this research,we have used a Long Short-Term Memory(LSTM)network model with Convolutional Neural Network using Keras python deep-learning library to determine whether social media platform users are depressed in terms of positive,negative,or neutral emotional out bust based on their Twitter posts.The results showed that the model has 88.14%accuracy(representation of the correct prediction over the test dataset)after 10 epochs which most tweets showed had neutral polarity.The evaluation shows interesting results in positive(1),negative(–1),and neutral(0)emotions through different visualization.

Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome

In December 2019,a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia.The outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9,2020,named Novel Coronavirus 2019(nCoV-2019).This nCoV-2019 is now known as COVID-19.There is a big list of infections of this coronavirus which is present in the form of a big family.This virus can cause several diseases that usually develop with a serious problem.According to the World Health Organization(WHO),2019-nCoV has been placed as the modern generation of Severe Acute Respiratory Syndrome(SARS)and Middle East Respiratory Syndrome(MERS)coronaviruses,so COVID-19 can repeatedly change its internal genome structure to extend its existence.Understanding and accurately predicting the mutational properties of the genome structure of COVID-19 can form a good leadership role in preventing and fighting against coronavirus.In this research paper,an analytical approach has been presented which is based on the k-means cluster technique of machine learning to find the clusters over the mutational properties of the COVID-19 viruses’complete genome.This method would be able to act as a promising tool to monitor and track pathogenic infections in their stable and local genetics/hereditary varieties.This paper identifies five main clusters of mutations with k=5 as best in most cases in the coronavirus that could help scientists and researchers develop disease control vaccines for the transformation of coronaviruses.

Aquablation in men with benign prostate hyperplasia:A systematic review and meta-analysis

Objective:The aim of the study is to investigate improvements in lower urinary tract symptoms in men with benign prostatic hyperplasia(BPH)treated with prostatic Aquablation.Materials and methods:We performed a literature search of clinical trials using the MEDLINE,Embase,and Cochrane Library databases and retrieved published works on Aquablation for the treatment of BPH up to August 2021.Unpublished works,case reports,conference proceedings,editorial comments,and letters were excluded.Risk of bias was assessed using the ROBINS-I tool.Raw means and mean differences were meta-analyzed to produce summary estimates for pre-versus post-international Prostate Symptom Scores,maximum flow rate,and male sexual health questionnaire value changes.An inverse-variance weighted random effects model was used.Results:Seven studies were included in this review(n=551 patients)that evaluated various urological parameters.At 3 months,the International Prostate Symptom Scores raw mean difference from baseline was-16.475(95%confidence interval[CI],-15.264 to-17.686;p<0.001),with improvements sustained for 12 months.Similarly,maximum flow rate improved by+1.96(95%CI,10.015 to 11.878;p<0.001)from pre to 3 months postoperatively.In addition,the male sexual health questionnaire change pooled effect size was-0.55(95%CI,-1.621 to 0.531;p=0.321)from preintervention to postintervention at 3 months.Meta-analyses of some outcomes showed large statistical heterogeneity or evidence of publication bias.Conclusions:Aquablation seems to improve lower urinary tract symptoms in men with BPH while providing relatively preserved sexual function.Further research is required to confirm these preliminary results.

Research and development of the back-end electronics for the two-dimensional improved resistive plate chambers in CMS upgrade

Purpose To complement and ensure redundancy in the endcap muon system of the Compact Muon Solenoid(CMS)detector and to extend the Resistive Plate Chamber(RPC)system coverage,improved RPCs(iRPCs)with either orthogonal layer strips with one-end electronics or single layer strips with two-end electronics providing more precise time measurement will be installed in the very forward pseudorapidity region of|η|<2.4.The iRPC readout system needs to support twodimensional(2D)or two-end readout.In addition,it must combine detector data with Timing,Trigger and fast Control(TTC)and Slow Control(SC)into one data stream over a bi-directional optical link with a line rate of 4.8 Gb/s between the Front-End Electronics(FEE)and the Back-End Electronics(BEE).To fulfill these requirements,a prototype BEE for the iRPC 2D chamber has been researched and designed.Methods A Micro-Telecommunication and Computing Architecture(μTCA)-based processing card was designed in this study to establish a prototype system together with aμTCA crate.The Giga-Bit Transceiver(GBT)protocol is integrated to provide bi-directional communication between the FEE and BEE.A server is connected with the BEE by a Gigabit Ethernet(GbE)link for SC and a 10-GbE link for Data AcQuisition(DAQ).Results The Bit Error Rate(BER)test of the back-end board and a joint test with the iRPC 2D prototype chamber were performed.ABERof less than 1.331×10−16 was obtained.The timemeasurement with a resolution of 3.05 nswas successfully realized,and detector efficiencies of 97.7%for longitudinal strips and 96.0%for orthogonal strips were measured.Test results demonstrate the correctness and reliability of the prototype BEE.Conclusion The BEE prototype satisfies the requirements for the iRPC 2D chamber,and it worked stably and reliably during a long-term joint test run.

R&D of back-end electronics for improved resistive plate chambers for the phase 2 upgrade of the CMS end-capmuon system

Purpose The Large Hadron Collider(LHC)at European Organization for Nuclear Research is planned to be upgraded to the high luminosity LHC.Increasing the luminosity makes muon triggering reliable and offline reconstruction very challenging.To enhance the redundancy of the Compact Muon Solenoid(CMS)Muon system and resolve the ambiguity of track reconstruction in the forward region,an improved Resistive Plate Chamber(iRPC)with excellent time resolution will be installed in the Phase-2 CMS upgrade.The iRPC will be equipped with Front-End Electronics(FEE),which can perform high-precision time measurements of signals from both ends of the strip.New Back-End Electronics(BEE)need to be researched and developed to provide sophisticated functionalities such as interacting with FEE with shared links for fast,slow control(SC)and data,in addition to trigger primitives(TPs)generation and data acquisition(DAQ).Method The BEE prototype uses a homemade hardware board compatible with the MTCA standard,the back-end board(BEB).BEE interacts with FEE via a bidirectional 4.8 Gbps optical paired-link that integrates clock,data,and control information.The clock and fast/slow control commands are distributed from BEB to the FEE via the downlink.The uplink is used for BEB to receive the time information of the iRPC’sfired strips and the responses to the fast/slow control commands.To have a pipelined detector data for clusterfinding operation,recover(DeMux)the time relationship of which is changed due to the transmission protocol for the continuous incoming MUXed data from FEE.Then at each bunch crossing(BX),clusteringfired strips that satisfy time and spatial constraints to generate TPs.Both incoming raw MUXed detector data and TPs in a time window and latency based on the trigger signal are read out to the DAQ system.Gigabit Ethernet(GbE)of SiTCP and commercial 10-GbE are used as link standards for SC and DAQ,respectively,for the BEB to interact with the server.Results The joint test results of the BEB with iRPC and Front-End Board(FEB)show a Bit Error Rate of the transmission links less than 1×10-16,a time resolution of the FEB Time-to-Digital Converter of 16 ps,and the resolution of the time difference between both ends of 160 ps which corresponding a spatial resolution of the iRPC of approximately 1.5 cm.Conclusion Test results showed the correctness and stable running of the BEB prototype,of which the functionalities fulfill the iRPC requirements.