The Evaluation of Artificial Intelligence Technology for the Differentiation of Fresh Human Blood Cells From Other Species' Blood in the Investigation of Crime Scenes.

OBJECTIVES: The current study used the deep machine learning approach to differentiate human blood specimens from cow, goat, and chicken blood stains based on cell morphology. METHODS: A total of 1,955 known Giemsa-stained digitized images were acquired from the blood of humans, cows, goats, and chickens. To train the deep learning models, the well-known VGG16, Resnet18, and Resnet34 algorithms were used. Based on the image analysis, confusion matrices were generated. RESULTS: Findings showed that the F1 score for the chicken, cow, goat, and human classes were all equal to 1.0 for each of the three algorithms. The Matthews correlation coefficient (MCC) was 1 for chickens, cows, and humans in all three algorithms, while the MCC score was 0.989 for goats by ResNet18, and it was 0.994 for both ResNet34 and VGG16 algorithms. The three algorithms showed 100% sensitivity, specificity, and positive and negative predictive values for the human, cow, and chicken cells. For the goat cells, the data showed 100% sensitivity and negative predictive values with specificity and positive predictive values ranging from 98.5% to 99.6%. CONCLUSION: These data showed the importance of deep learning as a potential tool for the differentiation of the species of origin of fresh crime scene blood stains.

Occupational Stress Among Middle-Aged Professionals in India.

Background: Professional world nowadays is very competitive, and surviving the cutthroat competition while sustaining work-related stress and pressure is an important task for employees. Professionals are required to meet daily and monthly objectives and may encounter work-related stressors. Purpose: The study aims to explore occupational stress among middle-aged professionals in the age range of 45-60 years from the marketing, banking, and teaching sectors. Methods: A total sample of 180 consented middle-aged professionals in the age range of 45-60 years from the banking, teaching, and marketing sectors were included in the study using a purposive and snowball sampling technique. Professionals having serious medical or psychiatric conditions and undergoing treatment for the same were excluded. The Occupational Stress Index was administered to assess different types of occupational stressors. The statistical analysis was done using the Statistical Package for Social Sciences version 20 software. A descriptive analysis and a one-way analysis of variance (ANOVA) were used to get meaningful results. Results: Results revealed that 40% of the middle-aged professionals reported experiencing minimal levels of occupational stress, followed by 32.2% experiencing moderate levels and 27.8% experiencing high levels of occupational stress. Additionally, it was found that a significantly higher percentage (91.6%) of banking professionals reported low levels of occupational stress compared to their counterparts. Eighty percent of marketing professionals reported experiencing high levels of occupational stress, whereas a majority (73.3%) of teaching professionals reported moderate levels of occupational stress. Conclusion: Occupational stress with different severity levels is found to be common among middle-aged professionals, which is a risk factor to develop mental health problems and affects well-being. Large-scale primary and secondary interventions are required to manage stress and facilitate professional growth and development in India.

A scalable blockchain based framework for efficient IoT data management using lightweight consensus.

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required.

Work Climate, Improved Communication, and Cohesive Work Linked with Patient Safety Culture: Findings from a Sports Medicine Hospital.

BACKGROUND: This study aims to investigate the patient safety culture at a sports medicine hospital and explore the quality of healthcare and associated factors. METHODS: In a cross-sectional study design, the Hospital Survey on Patient Safety Culture (HSOPC) tool was administered online among staff at a sports medicine hospital in Doha, Qatar. Out of 898 staff who received an email invitation, 504 participated (56.1%). RESULTS: The results showed that 48.0% of the staff rated the patient safety grade as excellent and 37.5% as very good, totaling 85.5%. Factors associated with excellent or very good patient safety grades were management support OR 4.7 95% CI (1.8 to 12.3); team communication OR 3.0 95% CI (1.4 to 6.3), supervisor action supporting patient safety OR 3.5 95% CI (1.7 to 7.0) and other items related to work area such as working together: OR 3.0 95% CI (1.2 to 7.6), helping out busy areas OR 2.5 95% CI (1.1 to 5.5) and having good procedures and systems: OR 2.8 95% CI (1.4 to 5.8). CONCLUSIONS: Addressing management support, enhancing communication, and cohesive work within the work area facilitates a culture of trust that improves patient safety grades.

Design of Multiplexers for IoT-Based Applications Using Stub-Loaded Coupled-Line Resonators.

This paper presents the design of microstrip-based multiplexers using stub-loaded coupled-line resonators. The proposed multiplexers consist of a diplexer and a triplexer, meticulously engineered to operate at specific frequency bands relevant to IoT systems: 2.55 GHz, 3.94 GHz, and 5.75 GHz. To enhance isolation and selectivity between the two passband regions, the diplexer incorporates five transmission poles (TPs) within its design. Similarly, the triplexer filter employs seven transmission poles to attain the desired performance across all three passbands. A comprehensive comparison was conducted against previously reported designs, considering crucial parameters such as size, insertion loss, return loss, and isolation between the two frequency bands. The fabrication of the diplexer and triplexer was carried out on a compact Rogers Duroid 5880 substrate. The experimental results demonstrate an exceptional performance, with the diplexer exhibiting a low insertion loss of 0.3 dB at 2.55 GHz and 0.4 dB at 3.94 GHz. The triplexer exhibits an insertion loss of 0.3 dB at 2.55 GHz, 0.37 dB at 3.94 GHz, and 0.2 dB at 5.75 GHz. The measured performance of the fabricated diplexer and triplexer aligns well with the simulated results, validating their effectiveness in meeting the desired specifications.

Hardware Trojan Mitigation Technique in Network-on-Chip (NoC).

Due to globalization in the semiconductor industry, malevolent modifications made in the hardware circuitry, known as hardware Trojans (HTs), have rendered the security of the chip very critical. Over the years, many methods have been proposed to detect and mitigate these HTs in general integrated circuits. However, insufficient effort has been made for hardware Trojans (HTs) in the network-on-chip. In this study, we implement a countermeasure to congeal the network-on-chip hardware design in order to prevent changes from being made to the network-on-chip design. We propose a collaborative method which uses flit integrity and dynamic flit permutation to eliminate the hardware Trojan inserted into the router of the NoC by a disloyal employee or a third-party vendor corporation. The proposed method increases the number of received packets by up to 10% more compared to existing techniques, which contain HTs in the destination address of the flit. Compared to the runtime HT mitigation method, the proposed scheme also decreases the average latency for the hardware Trojan inserted in the flit's header, tail, and destination field up to 14.7%, 8%, and 3%, respectively.

A Systematic Review of Chronic Diseases and Their Prevalence Among the Population of Northern Borders Province (NBP) in Saudi Arabia.

Estimation of the prevalence of chronic conditions is pivotal to effective healthcare planning and management. Therefore, our objective was to systemically review previous literature about the prevalence of chronic diseases among residents of Northern Borders Province (NBP) in Saudi Arabia. The electronic search has been done using scientific databases (PubMed, Ebsco, SciFinder, and Web of Science) and search engines up to September 2021. The following main key terms: chronic disease OR chronic conditions AND prevalence AND Northern Borders Province OR Northern Borders AND Saudi Arabia were applied. Other related terms with a more specific search were done with names of the main cities in the province and the most common diseases in Saudi Arabia. Duplicates were removed electronically by Endnote and manually. Extracted data were tabulated in the literature matrix. The risk of bias and quality of included studies were assessed using the "Strengthening the Reporting of Observational Studies in Epidemiology" (STROBE) checklist. Out of 63 observational studies that were assessed for eligibility, 21 observational studies were included to synthesize the evidence. These studies were conducted on Arar (n=16), Turaif (n=2), and Rafha (n=1), while the remaining were national studies in which NBP was one of the included regions (n=2). The most frequently studied diseases were diabetes (4 records), psychological diseases (4 records), and obesity (3 records). The most prevalent disease was gastroesophageal reflux disease (GERD), with an estimated prevalence of 61% among adults in Arar city. In conclusion, although some research is conducted about chronic diseases somewhere in NBP, further studies are needed to study chronic diseases using a representative sample of the whole NBP population.

Finclusion: The nexus of Fintech and financial inclusion against banks' market power.

The banking literature does not provide consensus over the impact of Fintech on banks. On the one hand, Fintech advancements are poised to enhance the accessibility of financial services; on the other, it can lead to alterations in market structure. Thus, it is important to ascertain the impact of Fintech entry from both perspectives. We examine the impact of Fintech entry on financial inclusion (FI) and banking competition by introducing conditionalities and non-linearity to uncover the potential transmission channels for Fintech to affect inclusion and market structure. Findings suggest episodes of low and medium inclusion from 2005 till 2018. However, post 2018, there has been a significant increase in FI. Similarly, persistent monopolistic tendencies were observed with most banks enjoying higher Lerner margins. The extent of Fintech reveals highly sluggish growth over 2005 to 2015. However, post 2016, drastic increase is observed commensurate with the central bank's regulatory push. Further, Fintech is inversely related to banks' market power indicating a diminishing effect. We propose three transmission mechanisms for Fintech effects: the inclusion channel, the growth channel, and the regulatory environment. In addition, we find a significant and positive impact of Fintech on FI however, the relationship is essentially non-linear.

Malware Detection in Internet of Things (IoT) Devices Using Deep Learning.

Internet of Things (IoT) devices usage is increasing exponentially with the spread of the internet. With the increasing capacity of data on IoT devices, these devices are becoming venerable to malware attacks; therefore, malware detection becomes an important issue in IoT devices. An effective, reliable, and time-efficient mechanism is required for the identification of sophisticated malware. Researchers have proposed multiple methods for malware detection in recent years, however, accurate detection remains a challenge. We propose a deep learning-based ensemble classification method for the detection of malware in IoT devices. It uses a three steps approach; in the first step, data is preprocessed using scaling, normalization, and de-noising, whereas in the second step, features are selected and one hot encoding is applied followed by the ensemble classifier based on CNN and LSTM outputs for detection of malware. We have compared results with the state-of-the-art methods and our proposed method outperforms the existing methods on standard datasets with an average accuracy of 99.5%.

An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals.

Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions. Due to the unbiased nature of physiological signals, this field has become a great motivation in recent research as physiological signals are generated autonomously from human central nervous system. Researchers have developed multiple methods for the classification of these signals for emotion detection. However, due to the non-linear nature of these signals and the inclusion of noise, while recording, accurate classification of physiological signals is a challenge for emotion charting. Valence and arousal are two important states for emotion detection; therefore, this paper presents a novel ensemble learning method based on deep learning for the classification of four different emotional states including high valence and high arousal (HVHA), low valence and low arousal (LVLA), high valence and low arousal (HVLA) and low valence high arousal (LVHA). In the proposed method, multimodal signals (EEG, ECG, and GSR) are preprocessed using bandpass filtering and independent components analysis (ICA) for noise removal in EEG signals followed by discrete wavelet transform for time domain to frequency domain conversion. Discrete wavelet transform results in spectrograms of the physiological signal and then features are extracted using stacked autoencoders from those spectrograms. A feature vector is obtained from the bottleneck layer of the autoencoder and is fed to three classifiers SVM (support vector machine), RF (random forest), and LSTM (long short-term memory) followed by majority voting as ensemble classification. The proposed system is trained and tested on the AMIGOS dataset with k-fold cross-validation. The proposed system obtained the highest accuracy of 94.5% and shows improved results of the proposed method compared with other state-of-the-art methods.

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications.

Traditional advertising techniques seek to govern the consumer's opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers' actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods.

Presentation Of Children With Masturbation.

Childhood masturbations (CM) is stimulation of genital by pre-adolescent children with accompanying symptoms including sweating, tachycardia, blushing, muscle contraction and increase rate of breathing. We are presenting case series of three patients, who presented with history of vague symptoms and ultimately diagnosed and managed as case of CM. A 2 years old girl presented with history of to and fro movements. A 3 years old girl presented with history of rubbing of inner thighs and 3 years old boy presented with history of holding and rubbing genitalia with forward bending and symptoms of increase breathing, flushing and sweating. Video recording was available with two patients, which helped in making final diagnosis. Parents were counselled and patients referred for behavioural therapy. Conclusion: In young child CM should be considered in differential diagnosis whenever history is not fully suggestive of seizures.

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers.

Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.

An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain.

In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN.

Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction.

A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network's security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network's integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. In the first part of this survey paper, we offer an introduction to the NIDS theory, as well as recent research that has been conducted on the topic. After that, we conduct a thorough analysis of the most recent ML- and DL-based NIDS approaches to ensure reliable identification of potential security risks. Finally, we focus on the opportunities and difficulties that lie ahead for future research on SDN-based ML and DL for NIDS.

Anomaly Detection in Traffic Surveillance Videos Using Deep Learning.

In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and accidents. This research deals with accidents in traffic videos. In the modern world, video traffic surveillance cameras (VTSS) are used for traffic surveillance and monitoring. As the population is increasing drastically, the likelihood of accidents is also increasing. The VTSS is used to detect abnormal events or incidents regarding traffic on different roads and highways, such as traffic jams, traffic congestion, and vehicle accidents. Mostly in accidents, people are helpless and some die due to the unavailability of emergency treatment on long highways and those places that are far from cities. This research proposes a methodology for detecting accidents automatically through surveillance videos. A review of the literature suggests that convolutional neural networks (CNNs), which are a specialized deep learning approach pioneered to work with grid-like data, are effective in image and video analysis. This research uses CNNs to find anomalies (accidents) from videos captured by the VTSS and implement a rolling prediction algorithm to achieve high accuracy. In the training of the CNN model, a vehicle accident image dataset (VAID), composed of images with anomalies, was constructed and used. For testing the proposed methodology, the trained CNN model was checked on multiple videos, and the results were collected and analyzed. The results of this research show the successful detection of traffic accident events with an accuracy of 82% in the traffic surveillance system videos.

A Computationally Efficient Online/Offline Signature Scheme for Underwater Wireless Sensor Networks.

Underwater wireless sensor networks (UWSNs) have emerged as the most widely used wireless network infrastructure in many applications. Sensing nodes are frequently deployed in hostile aquatic environments in order to collect data on resources that are severely limited in terms of transmission time and bandwidth. Since underwater information is very sensitive and unique, the authentication of users is very important to access the data and information. UWSNs have unique communication and computation needs that are not met by the existing digital signature techniques. As a result, a lightweight signature scheme is required to meet the communication and computation requirements. In this research, we present a Certificateless Online/Offline Signature (COOS) mechanism for UWSNs. The proposed scheme is based on the concept of a hyperelliptic curves cryptosystem, which offers the same degree of security as RSA, bilinear pairing, and elliptic curve cryptosystems (ECC) but with a smaller key size. In addition, the proposed scheme was proven secure in the random oracle model under the hyperelliptic curve discrete logarithm problem. A security analysis was also carried out, as well as comparisons with appropriate current online/offline signature schemes. The comparison demonstrated that the proposed scheme is superior to the existing schemes in terms of both security and efficiency. Additionally, we also employed the fuzzy-based Evaluation-based Distance from Average Solutions (EDAS) technique to demonstrate the effectiveness of the proposed scheme.

Plasma α-Glutathione S-Transferase in Patients with Chronic Mesenteric Ischemia and Median Arcuate Ligament Syndrome.

Background: Chronic mesenteric ischemia (CMI) due to either atherosclerosis of the mesenteric arteries or median arcuate ligament syndrome (MALS) is an underdiagnosed entity. The etiology of MALS and its existence have been debated and questioned. We aimed to identify plasma biomarkers indicating mesenteric ischemia in patients with CMI and MALS. Methods: Plasma α-glutathione S-transferase (α-GST), intestinal fatty acid-binding protein (I-FABP), citrulline, and ischemia modified albumin (IMA) were analyzed in fifty-eight patients with CMI (Group A, n=44) and MALS (Group B, n=14) before and after revascularization. The plasma levels of these potential biomarkers were compared with those of healthy individuals (Group C, n=16). Group comparison was performed with the Mann-Whitney U-test. Cross-tabulation and its derivatives were obtained. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were calculated. Results: Plasma levels of α-GST were significantly raised in the patients with CMI (7.8 ng/mL, p<0.001) and MALS (8.4 ng/mL, p<0.001), as compared with the control Group C (3.3 ng/mL). The threshold for normal median plasma α-GST levels of 4 ng/mL yielded a sensitivity of 93% and 86%, specificity of 86% and 88%, respectively, for the diagnosis of CMI due to atherosclerosis and MALS. AUC of ROC curves was 0.96 (p<0.0001) for CMI and 0.85 (p<0.002) for MALS. The patient groups did not differ from the healthy controls in any other biomarkers. Conclusion: Plasma α-GST levels are elevated in CMI and MALS patients. Elevated plasma levels of α-GST suggest ischemia as the etiology of MALS.

Early Identification of Chronic Mesenteric Ischemia with Endoscopic Duplex Ultrasound.

Introduction: Due to diagnostic delay, chronic mesenteric ischemia (CMI) is underdiagnosed. We assumed that the patients suspected of CMI of the atherosclerotic origin or median arcuate ligament syndrome (MALS) could be identified earlier with endoscopic duplex ultrasound (E-DUS). Patients and Methods: Fifty CMI patients with CTA-verified stenosis of either ≥50% and ≥70% of celiac artery (CA) and superior mesenteric artery (SMA) were examined with E-DUS and transabdominal duplex ultrasound (TA-DUS). Peak systolic velocities (PSV) of ≥200cm/s and ≥275cm/s for CA and SMA, respectively, were compared with CTA. Subgroup analysis was performed for the patients with (n=21) and without (n=29) prior revascularization treatment of CMI. The diagnostic ability of E-DUS and TA-DUS was tested with crosstabulation analysis. Receiver operating characteristics (ROC) curve analysis was performed, and the area under the curve (AUC) was calculated to investigate the test accuracy. Results: In the patients with ≥70% stenosis, E-DUS had higher sensitivity than TA-DUS (91% vs 81% for CA and 100% vs 92% for SMA). AUC for SMA ≥70% in E-DUS was 0.75 and with TA-DUS 0.68. The sensitivity of E-DUS for CTA-verified stenosis ≥70% for CA was 100% in the patients without prior treatment. E-DUS demonstrated higher sensitivity than TA-DUS for both arteries with stenosis ≥50% and ≥70% in the treatment-naive patients. Conclusion: E-DUS is equally valid as TA-DUS for the investigation of CMI patients and should be used as an initial diagnostic tool for patients suspected of CMI.