A Survey on the Role of Industrial IoT in Manufacturing for Implementation of Smart Industry.

The Internet of Things (IoT) is an innovative technology that presents effective and attractive solutions to revolutionize various domains. Numerous solutions based on the IoT have been designed to automate industries, manufacturing units, and production houses to mitigate human involvement in hazardous operations. Owing to the large number of publications in the IoT paradigm, in particular those focusing on industrial IoT (IIoT), a comprehensive survey is significantly important to provide insights into recent developments. This survey presents the workings of the IoT-based smart industry and its major components and proposes the state-of-the-art network infrastructure, including structured layers of IIoT architecture, IIoT network topologies, protocols, and devices. Furthermore, the relationship between IoT-based industries and key technologies is analyzed, including big data storage, cloud computing, and data analytics. A detailed discussion of IIoT-based application domains, smartphone application solutions, and sensor- and device-based IIoT applications developed for the management of the smart industry is also presented. Consequently, IIoT-based security attacks and their relevant countermeasures are highlighted. By analyzing the essential components, their security risks, and available solutions, future research directions regarding the implementation of IIoT are outlined. Finally, a comprehensive discussion of open research challenges and issues related to the smart industry is also presented.

Blockchain based transparent and reliable framework for wheat crop supply chain.

The wheat crop that fulfills 35% of human food demand is facing several problems due to a lack of transparency, security, reliability, and traceability in the existing agriculture supply chain. Many systems have been developed for the agriculture supply chain to overcome such issues, however, monopolistic centralized control is the biggest hurdle to realizing the use of such systems. It has eventually gained consumers' trust in branded products and rejected other products due to the lack of traceable supply chain information. This study proposes a blockchain-based framework for supply chain traceability which provides trustable, transparent, secure, and reliable services for the wheat crop. A crypto token called wheat coin (WC) has been introduced to keep track of transactions among the stakeholders of the wheat supply chain. Moreover, an initial coin offering (ICO) of WC, crypto wallets, and an economic model are proposed. Furthermore, a smart contract-based transaction system has been devised for the transparency of wheat crop transactions and conversion of WC to fiat and vice versa. We have developed the interplanetary file system (IPFS) to improve data availability, security, and transparency which stores encrypted private data of farmers, businesses, and merchants. Lastly, the results of the experiments show that the proposed framework shows better performance as compared to previous crop supply chain solutions in terms of latency to add-blocks, per-minute transactions, average gas charge for the transaction, and transaction verification time. Performance analysis with Bitcoin and Ethereum shows the superior performance of the proposed system.

Aspect based sentence segregated dataset of hybrid car's consumers online reviews.

Dataset presented in this paper is obtained from the top online automobile selling and purchasing websites. A total of 1000 reviews related to hybrid cars in the form of text reviews are extracted with the help of the Web Scraper tool. The dataset presents the customers sentiments in the form of reviews related to hybrid cars. Various aspects are taken into consideration while annotating the reviews such as driving, performance, comfort, safety features, interior, exterior and accessories. The annotation of data is done at three levels by three annotators i.e., (1) overall polarity of a review, (2) segregation of the sentence term in which aspect is discussed, (3) polarity of the discussed aspect. Cohen's Kappa score of 0.90 was achieved among the authors while annotating the reviews. Dataset can be used for sentiment analysis, information retrieving, lexicon analysis, and grammatical and morphological analysis.

Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features.

In Alzheimer's disease (AD) progression, it is imperative to identify the subjects with mild cognitive impairment before clinical symptoms of AD appear. This work proposes a technique for decision support in identifying subjects who will show transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD) in the future. We used robust predictors from multivariate MRI-derived biomarkers and neuropsychological measures and tracked their longitudinal trajectories to predict signs of AD in the MCI population. Assuming piecewise linear progression of the disease, we designed a novel weighted gradient offset-based technique to forecast the future marker value using readings from at least two previous follow-up visits. Later, the complete predictor trajectories are used as features for a standard support vector machine classifier to identify MCI-to-AD progressors amongst the MCI patients enrolled in the Alzheimer's disease neuroimaging initiative (ADNI) cohort. We explored the performance of both unimodal and multimodal models in a 5-fold cross-validation setup. The proposed technique resulted in a high classification AUC of 91.2% and 95.7% for 6-month- and 1-year-ahead AD prediction, respectively, using multimodal markers. In the end, we discuss the efficacy of MRI markers as compared to NM for MCI-to-AD conversion prediction.

Right Iliac fossa pain due to torsion and ischaemia of accessory liver lobe.

An accessory liver lobe (ALL) is the presence of ectopic hepatic tissue which has developed as a result of excessive hepatic development. The majority of patients with ALL remain asymptomatic and their findings are incidental, either after undergoing radiological imaging or during surgery for other pathology. However, in a small number of patients who have a pedunculated ALL, torsion of the lobe on its axis can cause pain, often due to ischaemia of the lobe. We report a 26-year-old female who presented with right iliac fossa pain mimicking acute appendicitis and preceded by recent vigorous exercising activity.

Predicting Progression From Mild Cognitive Impairment to Alzheimer's Disease Using Autoregressive Modelling of Longitudinal and Multimodal Biomarkers.

Mild cognitive impairment is a preclinical stage of Alzheimer's disease (AD). For effective treatment of AD, it is important to identify mild cognitive impairment (MCI) patients who are at a high risk of developing AD over the course of time. In this study, autoregressive modelling of multiple heterogeneous predictors of Alzheimer's disease is performed to capture their evolution over time. The models are trained using three different arrangements of longitudinal data. These models are then used to estimate future biomarker readings of individual test subjects. Finally, standard support vector machine classifier is employed for detecting MCI patients at risk of developing AD over the coming years. The proposed models are thoroughly evaluated for their predictive capability using both cognitive scores and MRI-derived measures. In a stratified five-fold cross validation setup, our proposed methodology delivered highest AUC of 88.93% (Accuracy = 84.29%) and 88.13% (Accuracy = 83.26%) for 1 year and 2 year ahead AD conversion prediction, respectively, on the most widely used Alzheimer's disease neuroimaging initiative data. The notable conclusions of this study are: 1) Clinical changes in MRI-derived measures can be better forecasted than cognitive scores, 2) Multiple predictor models deliver better conversion prediction than single biomarker models, 3) Cognitive score boosted by MRI-derived measures delivers better short-term ahead conversion prediction, and 4) Neuropsychological scores alone can deliver good accuracy for long-term conversion prediction.

A Nonparametric Approach for Mild Cognitive Impairment to AD Conversion Prediction: Results on Longitudinal Data.

The goal of this study is to introduce a nonparametric technique for predicting conversion from Mild Cognitive impairment (MCI)-to-Alzheimer's disease (AD). Progression of a slowly progressing disease such as AD benefits from the use of longitudinal data; however, research till now is limited due to the insufficient patient data and short follow-up time. A small dataset size invalidates the estimation of underlying disease progression model; hence, a supervised nonparametric method is proposed. While depicting a real-world setting, longitudinal data of three years are employed for training, whereas only the baseline visit's data is used for validation. The train set is preprocessed for extraction of two dense clusters representing the subjects who remain stable at MCI or progress to AD after three years of the baseline visit. Similarity between these clusters and the test point is calculated in Euclidean space. Multiple features from two modalities of biomarkers, i.e., neuropsychological measures (NM) and structural magnetic resonance imaging (MRI) morphometry are also analyzed. Due to the limited MCI dataset size (NM: 145, MRI: 52, NM+MRI: 29), leave-one-out cross validation setup is employed for performance evaluation. The algorithm performance is noted for both unimodal case and bimodal cases. Superior performance (accuracy: 89.66%, sensitivity: 87.50%, specificity: 92.31%, precision: 93.33%) is delivered by multivariate predictors. Three notable conclusions of this study are: 1) Longitudinal data are more powerful than the temporal data, 2) MRI is a better predictor of MCI-to-AD conversion than NM, and 3) multivariate predictors outperform single predictor models.

Meckel's Diverticulitis as a Cause of an Acute Abdomen in the Second Trimester of Pregnancy: Laparoscopic Management.

Introduction. Meckel's diverticulitis is an extremely rare cause of an acute abdomen in pregnancy. Its clinical presentation tends to be rather unusual and therefore commonly delaying diagnosis. The surgical method of exploration can be either by laparoscopy or through an open incision. Case Report. We report a case of a 34-year-old, P1 with previous Caesarean section, who presented at 20 weeks with worsening right-sided abdominal pain, distention, and peritonism. Ultrasound scan showed an area of a possibly thickened loop of bowel inconsistent with an appendicitis. The findings at laparoscopy were purulent fluid in the pelvis, a congested appendix, and inflamed Meckel's diverticulum. An appendectomy and excision of the diverticulum was performed using stapler technique. Discussion. Meckel's diverticulitis in pregnancy can have nonspecific presentation and poses difficulties for preoperative diagnosis. Delay in diagnosis and management poses significant maternal and fetal risks. The use of laparoscopy if the gestational age and uterine size permit its use allows a thorough exploration of the abdominal cavity and management of rarer and unexpected pathology. Laparoscopic management of acute abdomen in the midtrimester of pregnancy has been found to be safe and effective.