The Role of Artificial Intelligence of Things in Achieving Sustainable Development Goals: State of the Art.

With the environmental and societal changes, the achievement of sustainable development goals (SDGs) and the realization of sustainability in general is now more important than ever. Through a bibliometric analysis and scientific mapping analysis, this study aims to explore and provide a review regarding the role of artificial intelligence (AI), the Internet of Things (IoT), and artificial intelligence of things (AIoT) in realizing sustainable development and achieving SDGs. AIoT can be defined as the combination of AI with IoT to create more efficient and data-driven interconnected, intelligent, and autonomous IoT systems and infrastructure that use AI methods and algorithms. The analysis involved 9182 documents from Scopus and Web of Science (WoS) from 1989 to 2022. Descriptive statistics of the related documents and the annual scientific production were explored. The most relevant and impactful authors, articles, outlets, affiliations, countries, and keywords were identified. The most popular topics and research directions throughout the years and the advancement of the field and the research focus were also examined. The study examines the results, discusses the main findings, presents open issues, and suggests new research directions. Based on the results of this study, AIoT emerged as an important contributor in ensuring sustainability and in achieving SDGs.

A deep learning framework for non-functional requirement classification.

Analyzing, identifying, and classifying nonfunctional requirements from requirement documents is time-consuming and challenging. Machine learning-based approaches have been proposed to minimize analysts' efforts, labor, and stress. However, the traditional approach of supervised machine learning necessitates manual feature extraction, which is time-consuming. This study presents a novel deep-learning framework for NFR classification to overcome these limitations. The framework leverages a more profound architecture that naturally captures feature structures, possesses enhanced representational power, and efficiently captures a broader context than shallower structures. To evaluate the effectiveness of the proposed method, an experiment was conducted on two widely-used datasets, encompassing 914 NFR instances. Performance analysis was performed on the applied models, and the results were evaluated using various metrics. Notably, the DReqANN model outperforms the other models in classifying NFR, achieving precision between 81 and 99.8%, recall between 74 and 89%, and F1-score between 83 and 89%. These significant results highlight the exceptional efficacy of the proposed deep learning framework in addressing NFR classification tasks, showcasing its potential for advancing the field of NFR analysis and classification.

Contextualizing the BEST-CLI Trial Results in Clinical Practice.

Background: Chronic limb-threatening ischemia (CLTI) is associated with poor long-term outcomes. Although prompt revascularization is recommended, the optimal revascularization strategy remains uncertain. The BEST-CLI trial compared endovascular and open surgical revascularization for CLTI, but the generalizability of this study to the clinical population with CLTI has not been evaluated. Methods: We included Medicare beneficiaries aged 65-85 years with CLTI who underwent revascularization and would be eligible for enrollment in BEST-CLI between 2016 and 2019. The primary exposure was type of revascularization (endovascular vs autologous graft [cohort 1] vs nonautologous graft [cohort 2]), and the primary outcome was a composite of major adverse limb events (MALE) and death. MALE included above-ankle amputation and major intervention, which was defined as new bypass of index limb, thrombectomy, or thrombolysis. Results: A total of 66,153 patients were included in this study (10,125 autologous grafts; 7867 nonautologous grafts; 48,161 endovascular). Compared with those enrolled in BEST-CLI cohort 1, patients in this study were older (mean age, 73.5 ± 5.7 vs 69.9 ± 9.9 years), more likely to be female (38.3% [22,340/58,286] vs 28.5% [408/1434]), and presented with more comorbidities. Endovascular operators for the study population vs BEST-CLI cohort 1 were less likely to be surgeons (55.9% [26,924/48,148] vs 73.0% [520/708]) and more likely to be cardiologists (25.5% [5900/48,148] vs 14.5% [103/78]). When assessing long-term outcomes, the crude risk of death or MALE in this cohort was higher with surgery (56.6% autologous grafts vs 42.6% BEST-CLI cohort 1 at a median of follow-up 2.7 years; 51.6% nonautologous grafts vs 42.8% BEST-CLI cohort 2 at a median follow-up of 1.6 years) but similar with the endovascular cohort (58.7% Medicare vs 57.4% cohort 1 at 2.7 years; 47.0% Medicare vs 47.7% cohort 2 at 1.6 years). Of those who received endovascular treatment, the risk of incident major intervention was less than half in this cohort compared with the trial cohort (10.0% Medicare vs 23.5% cohort 1 at 2.7 years; 8.6% Medicare vs 25.6% cohort 2 at 1.6 years), although technical endovascular failures were not captured. Conclusions: These results suggest that the findings of the BEST-CLI trial may not be applicable to the entirety of the Medicare population of patients with CLTI undergoing revascularization.

Racial and Ethnic Disparities in Treatment of Critical Limb Ischemia: A National Perspective.

Background Recent guidelines have emphasized the use of medical management, early diagnosis, and a multidisciplinary team to effectively treat patients with critical limb ischemia (CLI). Previous literature briefly highlighted the current racial disparities in its intervention. Herein, we analyze the trend over a 14-year time period to investigate whether the disparities gap in CLI management is closing. Methods and Results The National Inpatient Sample was queried between 2005 and 2018 for hospitalizations involving CLI. Nontraumatic amputations and revascularization were identified. Utilization trends of these procedures were compared between races (White, Black, Hispanic, Asian and Pacific Islander, Native American, and Other). Multivariable regression assessed differences in race regarding procedure usage. There were 6 904 562 admissions involving CLI in the 14-year study period. The rate of admissions in White patients who received any revascularization decreased by 0.23% (P<0.001) and decreased by 0.25% (P=0.025) for Asian and Pacific Islander patients. Among all patients, the annual rate of admission in White patients who received any amputation increased by 0.21% (P<0.001), increased by 0.19% (P=0.001) for Hispanic patients, and increased by 0.19% (P=0.012) for the Other race patients. Admissions involving Black, Hispanic, Asian and Pacific Islander, or Other race patients had higher odds of receiving any revascularization compared with White patients. All races had higher odds of receiving major amputation compared with White patients. Conclusions Our analysis highlights disparities in CLI treatment in our nationally representative sample. Non-White patients are more likely to receive invasive treatments, including major amputations and revascularization for CLI, compared with White patients.

IN.PACT AV Access Randomized Trial of Drug-Coated Balloons for Dysfunctional Arteriovenous Fistulae: Clinical Outcomes through 36 Months.

PURPOSE: To present the 36-month outcomes of the prospective randomized IN.PACT AV Access study of participants with obstructive de novo or restenotic native upper extremity arteriovenous dialysis fistula lesions treated with drug-coated balloon (DCBs) or standard percutaneous transluminal angioplasty (PTA) following successful high-pressure PTA. MATERIALS AND METHODS: Participants at 29 international sites were randomized 1:1 to receive an IN.PACT AV DCB (n = 170) or undergo PTA (n = 160). The outcomes through 36 months included target lesion primary patency (TLPP) and access circuit primary patency (ACPP) (composites of clinically driven target lesion or access circuit revascularization and/or access circuit thrombosis), number of reinterventions, and serious adverse events involving the access circuit. RESULTS: TLPP was 52.1% in the DCB group compared with 36.7% in the PTA group through 24 months and 43.1% in the DCB group compared with 28.6% in the PTA group through 36 months (both log-rank P < .001). ACPP was 39.4% in the DCB group compared with 25.3% in the PTA group through 24 months and 26.4% in the DCB group compared with 16.6% in the PTA group through 36 months (both log-rank P < .001). Cumulative incidence of access circuit thrombosis through 36 months was 8.2% in the DCB group compared with 18.3% in the PTA group (log-rank P = .040). Cumulative incidence of mortality through 36 months was 26.6% in the DCB group compared with 30.8% in the PTA group (log-rank P = .71). CONCLUSIONS: This study demonstrated superior TLPP and ACPP with DCBs compared with PTA, with no difference in mortality through 3 years. Access circuit thrombosis was statistically significantly higher in the PTA group at 3 years.

A Bibliometric Analysis of the Evolution of State-of-the-Art Blockchain Technology (BCT) in the Agrifood Sector from 2014 to 2022.

In recent years, BCT has garnered significant attention from researchers worldwide. The technology in question is a distributed database system characterised by its decentralised nature and lack of reliability. BCT has been widely adopted by numerous governments and scholars across various sectors for a number of years. Blockchain technology also involves highly innovative and advanced concepts. Given the increasing interest among scholars in the academic community regarding the agrifood supply chain, the objective of this study was to investigate BCT and its potential for application in the fields of food and agriculture. This research paper presents a bibliometric analysis of articles on the utilisation of BCT in the fields of food and agriculture. This study discusses scholarly articles that have been published in esteemed academic journals and conferences. Through our bibliometric analysis, we aimed to discern the recurring trends and themes within the research on BCT in relation to agrifood systems. Furthermore, this study examines a diverse array of research domains, prominent scholarly publications, leading publishing platforms, prominent funding institutions, and the prospective trajectory of future research. This study also presents the prominent patterns and themes within this field through an analysis of the most influential scholarly articles, authors, countries, and keywords found in the existing literature. Hence, this research employed various analytical techniques, including analyzing the co-occurrence of author keywords, bibliographic coupling analysis, network view map analysis, and co-citation analysis. This study holds promise as a valuable learning resource for aspiring researchers seeking to acquire compelling and pertinent information about research outcomes from studies on the utilisation of BCT in the field of smart agriculture.

A blockchain-based evaluation approach to analyse customer satisfaction using AI techniques.

Due to technological advancements and consumer demands, online shopping creates new features and adapts to new standards. A robust customer satisfaction prediction model concerning trust and privacy platforms can encourage an organization to make better decisions about its service and quality. This study presented an approach to predict consumer satisfaction using the blockchain-based framework combining the Multi-Dimensional Naive Bayes-K Nearest Neighbor (MDNB-KNN) and the Multi-Objective Logistic Particle Swarm Optimization Algorithm (MOL-PSOA). A regression model is employed to quantify the impact of various production factors on customer satisfaction. The proposed method yields better levels of measurement for customer satisfaction (98%), accuracy (95%), necessary time (60%), precision (95%), and recall (95%) compared to existing studies. Measuring consumer satisfaction with a trustworthy platform facilitates to development of the conceptual and practical distinctions influencing customers' purchasing decisions.

National Trends in Complications of Vascular Access for Hemodialysis and Analysis of Racial Disparities Among Patients With End-Stage Renal Disease in the Inpatient Setting.

Introduction: The aim of this study is to assess the trends in access-related complications, as well as the impact of race on these complications, among admitted patients with end-stage kidney disease (ESKD) receiving hemodialysis. Methods: A retrospective cohort study between 2005 and 2018 was performed using the National Inpatient Sample (NIS). Hospitalizations involving ESKD and hemodialysis were identified. There were 9,246,553 total admissions involving ESKD and hemodialysis, of which 1,167,886 (12.6%) had complications. Trends in complications were assessed and compared among races. Results: There was a decreasing trend in rates of mechanical (trend: -0.05% per year; P < 0.001), inflammatory or infectious (-0.48%; P < 0.001), and other (-0.19%; P < 0.001) complications from 2005 to 2018. Non-White patients had a greater magnitude in the decrease in trends in rates of complications compared to White patients (-0.69% per year vs. -0.57%; P < 0.001). Compared to the White patients, Black patients (odds ratio [OR]: 1.26; P < 0.001) and those of the other races (OR: 1.11; P < 0.001) had higher odds of complications. These differences were also statistically significant among lower socioeconomic classes (75 percentile vs. 0-25 percentile: P = 0.009) and within southern states (vs. Northeast: P < 0.001). Conclusion: Although there was an overall decrease in the trends of dialysis-associated complications requiring hospitalization among ESKD patients receiving hemodialysis, non-White patients have higher odds of complications compared to White patients. The findings in this study emphasize the need for more equitable care for hemodialysis patients.

Health Disparities in Peripheral Artery Disease: A Scientific Statement From the American Heart Association.

Peripheral artery disease (PAD) affects 200 million individuals worldwide. In the United States, certain demographic groups experience a disproportionately higher prevalence and clinical effect of PAD. The social and clinical effect of PAD includes higher rates of individual disability, depression, minor and major limb amputation along with cardiovascular and cerebrovascular events. The reasons behind the inequitable burden of PAD and inequitable delivery of care are both multifactorial and complex in nature, including systemic and structural inequity that exists within our society. Herein, we present an overview statement of the myriad variables that contribute to PAD disparities and conclude with a summary of potential novel solutions.

A bibliometric review of grid parity, energy transition and electricity cost research for sustainable development.

As the topic of sustainable development continues to prominence in global affairs, the case for renewable energy has never been stronger. To be regarded as a perfect alternative to conventional (non-renewable) energy sources in many climes, renewable energy, such as solar and wind, shows promise when considering concepts like grid parity. A significant number of studies have been devoted to understanding the concept. However, only a few studies have committed themselves to analysing the research activity carried out on it. This paper will present a bibliometric and empirical review of worldwide grid parity, energy transition, and electricity cost research. To situate the progress in this research area, a detailed search of Scopus was used to identify and situate research development in the field from 1965 until 2021. Using the data extracted from Scopus and VOSviewer for analysis, we explore different aspects of the publications, such as the volume, growth rate, and coverage of published documents, the most influential research papers and journals in this research area, and the most studied research themes in recent years. We also discuss Governmental policies in developed and developing economies that have accelerated the attainment of Grid parity in certain countries. Also, an empirical review of top-down, bottom-up, and artificial neural network approaches to evaluating grid parity was conducted. The study revealed a steady increase in the research articles focused on grid parity, energy transition, and electricity cost research from 2006. The geographic distribution of the publications shows that most of the publications on the subject originated from the USA, Germany, China, United Kingdom, and Spain, raking in 42.2% of the publications. Also, the top 7 authors with the highest document count from Scopus are from Finland, which coincidentally is one of the countries making significant progress in Grid parity attainment. Of the total document count from Scopus, only 0.02% are papers published from African Countries. Could this reluctance to publish research findings on energy transition be one of the reasons for the slow progression of sustainable energy for all in Africa? Therefore, it is imperative now more than ever for more research focusing on the attainment of grid parity, energy transition, and electricity costs for developing countries to be brought to the fore. This article provides a review of state-of-the-art research on the attainment of grid parity and energy transition with a focus on the Levelized Cost of Electricity (LCOE) models of renewable energy sources.

The Neurovasculome: Key Roles in Brain Health and Cognitive Impairment: A Scientific Statement From the American Heart Association/American Stroke Association.

BACKGROUND: Preservation of brain health has emerged as a leading public health priority for the aging world population. Advances in neurovascular biology have revealed an intricate relationship among brain cells, meninges, and the hematic and lymphatic vasculature (the neurovasculome) that is highly relevant to the maintenance of cognitive function. In this scientific statement, a multidisciplinary team of experts examines these advances, assesses their relevance to brain health and disease, identifies knowledge gaps, and provides future directions. METHODS: Authors with relevant expertise were selected in accordance with the American Heart Association conflict-of-interest management policy. They were assigned topics pertaining to their areas of expertise, reviewed the literature, and summarized the available data. RESULTS: The neurovasculome, composed of extracranial, intracranial, and meningeal vessels, as well as lymphatics and associated cells, subserves critical homeostatic functions vital for brain health. These include delivering O2 and nutrients through blood flow and regulating immune trafficking, as well as clearing pathogenic proteins through perivascular spaces and dural lymphatics. Single-cell omics technologies have unveiled an unprecedented molecular heterogeneity in the cellular components of the neurovasculome and have identified novel reciprocal interactions with brain cells. The evidence suggests a previously unappreciated diversity of the pathogenic mechanisms by which disruption of the neurovasculome contributes to cognitive dysfunction in neurovascular and neurodegenerative diseases, providing new opportunities for the prevention, recognition, and treatment of these conditions. CONCLUSIONS: These advances shed new light on the symbiotic relationship between the brain and its vessels and promise to provide new diagnostic and therapeutic approaches for brain disorders associated with cognitive dysfunction.

Impact of Mobile Received Signal Strength (RSS) on Roaming and Non-roaming Mobile Subscribers.

Mobile phones have transitioned from voice-centric devices to smart devices supporting functionalities like high-definition video and games, web browsers, radio reception, and video conferencing. Mobile phones are used in telemedicine, health monitoring applications, navigation tools, and gaming devices, among other applications. Given the above, Mobile broadband connectivity affects mobile access to the internet and voice communications. This paper assesses the impact of the Reference Signal Received Power (RSRP) and broadband connectivity around Covenant University. LTE, GSM, and HSPA mobile signal measurement campaigns were conducted around Covenant University in Ota, Ogun state, Nigeria. To investigate the best optimized mobile network for mobile subscribers on roaming services and subscriber's high performance and data rates. After the experiment, exploratory data analysis was used to visualize the best mobile network; GSM proved as stable than LTE and HSPA.

LSTM-DGWO-Based Sentiment Analysis Framework for Analyzing Online Customer Reviews.

Sentiment analysis furnishes consumer concerns regarding products, enabling product enhancement development. Existing sentiment analysis using machine learning techniques is computationally intensive and less reliable. Deep learning in sentiment analysis approaches such as long short term memory has adequately evolved, and the selection of optimal hyperparameters is a significant issue. This study combines the LSTM with differential grey wolf optimization (LSTM-DGWO) deep learning model. The app review dataset is processed using the bidirectional encoder representations from transformers (BERT) framework for efficient word embeddings. Then, review features are extracted by the genetic algorithm (GA), and the optimal review feature set is extracted using the firefly algorithm (FA). Finally, the LSTM-DGWO model categorizes app reviews, and the DGWO algorithm optimizes the hyperparameters of the LSTM model. The proposed model outperformed conventional methods with a greater accuracy of 98.89%. The findings demonstrate that sentiment analysis can be practically applied to understand the customer's perception of enhancing products from a business perspective.

MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors.

Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients' recoveries. By using IoMT to diagnose and examine BreakHis v1 400× breast cancer histology (BCH) scans, disorders may be quickly identified and appropriate treatment can be given to a patient. Imaging equipment having the capability of auto-analyzing acquired pictures can be used to achieve this. However, the majority of deep learning (DL)-based image classification approaches are of a large number of parameters and unsuitable for application in IoMT-centered imaging sensors. The goal of this study is to create a lightweight deep transfer learning (DTL) model suited for BCH scan examination and has a good level of accuracy. In this study, a lightweight DTL-based model "MobileNet-SVM", which is the hybridization of MobileNet and Support Vector Machine (SVM), for auto-classifying BreakHis v1 400× BCH images is presented. When tested against a real dataset of BreakHis v1 400× BCH images, the suggested technique achieved a training accuracy of 100% on the training dataset. It also obtained an accuracy of 91% and an F1-score of 91.35 on the test dataset. Considering how complicated BCH scans are, the findings are encouraging. The MobileNet-SVM model is ideal for IoMT imaging equipment in addition to having a high degree of precision. According to the simulation findings, the suggested model requires a small computation speed and time.

User and Professional Aspects for Sustainable Computing Based on the Internet of Things in Europe.

The commonly accepted definition of sustainability considers the availability of relevant resources to make an activity feasible and durable while also recognizing users' support as an essential part of the social side of sustainability. IoT represents a disruption in the general scenario of computing for both users and professionals. The real expansion and integration of applications based on IoT depend on our capacity of exploring the necessary skills and professional profiles that are essential for the implementation of IoT projects, but also on the perception of relevant aspects for users, e.g., privacy, legal, IPR, and security issues. Our participation in several EU-funded projects with a focus on this area has enabled the collection of information on both sides of IoT sustainability through surveys but also by collecting data from a variety of sources. Thanks to these varied and complementary sources of information, this article will explore the user and professional aspects of the sustainability of the Internet of Things in practice.

SCLAVOEM: hyper parameter optimization approach to predictive modelling of COVID-19 infodemic tweets using smote and classifier vote ensemble.

Fake COVID-19 tweets are dangerous since they are misinformative, completely inaccurate, as threatening the efforts for flattening the pandemic curve. Thus, aside the COVID-19 pandemic, dealing with fake news and myths about the virus constitute an infodemic issue, which must be tackled by ensuring only valid information. In this context, this study proposed the Synthetic Minority Over-Sampling Technique (SMOTE) and the classifier vote ensemble (SCLAVOEM) method as a fake news classifier and a hyper parameter optimization approach for predictive modelling of COVID-19 infodemic tweets. Hyper parameter optimization variables were deployed across specific points of the proposed model and a minority oversampling of training sets was applied within imbalanced class representations. Experimental applications by the SCLAVOEM for COVID-19 infodemic prediction returned 0.999 and 1.000 weighted averages for F-measure and area under curve (AUC), respectively. Thanks to the SMOTE, the performance increases of 3.74 and 1.11%; 5.05 and 0.29%; 4.59 and 8.05% was seen in three different data sets. Eventually, the SCLAVOEM provided a framework for predictive detecting 'fake tweets' and three classifiers: 'positive', 'negative' and 'click-trap' (piège à clics). It is thought that the model will automatically flag fake information on Twitter, hence protecting the public from inaccurate and information overload.

Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic.

Introduction: Vaccines are the most important instrument for bringing the pandemic to a close and saving lives and helping to reduce the risks of infection. It is important that everyone has equal access to immunizations that are both safe and effective. There is no one who is safe until everyone gets vaccinated. COVID-19 vaccinations are a game-changer in the fight against diseases. In addition to examining attitudes toward these vaccines in Africa, Asia, Oceania, Europe, North America, and South America, the purpose of this paper is to predict the acceptability of COVID-19 vaccines and study their predictors. Materials and methods: Kaggle datasets are used to estimate the prediction outcomes of the daily COVID-19 vaccination to prevent a pandemic. The Kaggle data sets are classified into training and testing datasets. The training dataset is comprised of COVID-19 daily data from the 13th of December 2020 to the 13th of June 2021, while the testing dataset is comprised of COVID-19 daily data from the 14th of June 2021 to the 14th of October 2021. For the prediction of daily COVID-19 vaccination, four well-known machine learning algorithms were described and used in this study: CUBIST, Gaussian Process (GAUSS), Elastic Net (ENET), Spikes, and Slab (SPIKES). Results: Among the models considered in this paper, CUBIST has the best prediction accuracy in terms of Mean Absolute Scaled Error (MASE) of 9.7368 for Asia, 2.8901 for America, 13.2169 for Oceania, and 3.9510 for South America respectively. Conclusion: This research shows that machine learning can be of great benefit for optimizing daily immunization of citizens across the globe. And if used properly, it can help decision makers and health administrators to comprehend immunization rates and create strategies to enhance them.