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.

Tunneled dialysis catheter utilization and patency: A retrospectiveprospective study from a tertiary care hospital in Karachi Pakistan.

Objectives: To assess the patency of tunnel dialysis catheters and their results in patients in a tertiary care setting. METHODS: The retrospective-prospective, observational study was conducted at the Department of Interventional Radiology, Liaquat National Hospital, Karachi, from September 2021 to February 2022, and comprised records of patients who underwent tunnel dialysis catheters placement from July 2019 to December 2020. Data regarding age, gender, residence, comorbidity, catheter placement site, use of antibiotics before catheter insertion, reasons for catheter removal, and total catheter days was retrieved from the medical record. Data was analysed using STATA 14. RESULTS: Of the 134 patients, 74(56.9%) were males and 56(43.1) were females. The overall median age was 65 years (interquartile range: 56.75-70.25 years). Catheters were electively removed in 97(72.4%) patients, while in 19(14.2%), 16(11.9%) and 2(1.5%) cases, catheters were removed due to infections, blockage and physical damage, respectively. Mortality due to catheter-related complications was not found. Incidence per 10,000 catheter days of overall infection, bacteraemia and other infections during one year was 8.4, 5.3 and 3.1, respectively. Overall infection-free survival rate was seen for 19 patients (survival rate=67.6%). Survival rate from catheter removal was seen in 36 patients (survival rate=53.2%). CONCLUSIONS: When all precautions were followed to circumvent catheter-related issues, tunnel dialysis catheters were found to be a viable option for haemodialysis until permanent access for dialysis is gained or a kidney transplant is performed.

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.

A Novel Synthesis of Quasi-Chebyshev Ultra-Wideband Bandpass Filter Using Nth Order Stub Loaded Coupled-Line Resonator.

This paper presents a novel synthesis of a quasi-Chebyshev Nth order stub-loaded coupled-line ultra-wideband bandpass filter. A unit element of a proposed filter topology consists of two short-circuited stubs loaded at the edges of coupled lines. A distributed equivalent circuit model of a proposed topology is extracted and used to acquire a generalized filtering function. The extracted filtering function is of rational form. The denominator of the filtering function causes a mismatch with Chebyshev type-I polynomials. For conventional narrowband filters, the denominator term can be neglected because of the close vicinity of band-edge frequencies; however, for the ultra-wideband filter response, the factor in the denominator cannot be neglected and hence requires a new mathematical procedure to compensate for the effect of the frequency-dependent term in the denominator. The electrical parameters are calculated using the proposed synthesis and used to design an ideal filter topology on ADS. To validate the proposed design procedure, fabrication is performed on a high-frequency substrate. The proposed filter is miniaturized in size and has good out-of-band performance. The simulated and measured results provide good agreement.

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.

Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach.

Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique for estimating software costs. The limitation of the COCOMO models is that the values of these coefficients are constant for similar kinds of projects whereas, in reality, these parameters vary from one organization to another organization. Therefore, for accurate estimation, it is necessary to fine-tune the coefficients. The research community is now examining deep learning (DL) as a forward-looking solution to improve cost estimation. Although deep learning architectures provide some improvements over existing flat technologies, they also have some shortcomings, such as large training delays, over-fitting, and under-fitting. Deep learning models usually require fine-tuning to a large number of parameters. The meta-heuristic algorithm supports finding a good optimal solution at a reasonable computational cost. Additionally, heuristic approaches allow for the location of an optimum solution. So, it can be used with deep neural networks to minimize training delays. The hybrid of ant colony optimization with BAT (HACO-BA) algorithm is a hybrid optimization technique that combines the most common global optimum search technique for ant colonies (ACO) in association with one of the newest search techniques called the BAT algorithm (BA). This technology supports the solution of multivariable problems and has been applied to the optimization of a large number of engineering problems. This work will perform a two-fold assessment of algorithms: (i) comparing the efficacy of ACO, BA, and HACO-BA in optimizing COCOMO II coefficients; and (ii) using HACO-BA algorithms to optimize and improve the deep learning training process. The experimental results show that the hybrid HACO-BA performs better as compared to ACO and BA for tuning COCOMO II. HACO-BA also performs better in the optimization of DNN in terms of execution time and accuracy. The process is executed upto 100 epochs, and the accuracy achieved by the proposed DNN approach is almost 98% while NN achieved accuracy of up to 85% on the same datasets.

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.

Blockchain-Based Land Registration System: A Conceptual Framework.

Land registration authorities are frequently held accountable for the alleged mismanagement and manipulation of land records in various countries. Pakistan's property records are especially vulnerable to falsification and corruption because of the country's poverty. Different parties therefore claim varying degrees of authority over a specific piece of land. Given the fact that this data has been consolidated, it has become significantly more vulnerable to security threats. The goal of decentralized system research has been to increase the reliability of these systems. In order to fix the flaws of centralized systems, blockchain-based decentralized systems are currently in development. By using significant land record registration models as the basis for this research, we hope to create a proof-of-concept system or framework for future use. Pakistan's land registration agency will benefit from our proposed conceptual framework. For the Pakistani government to implement a decentralized land record registry system, we propose a conceptual framework that outlines the essential components.

A Comprehensive Survey on Signcryption Security Mechanisms in Wireless Body Area Networks.

WBANs (Wireless Body Area Networks) are frequently depicted as a paradigm shift in healthcare from traditional to modern E-Healthcare. The vitals of the patient signs by the sensors are highly sensitive, secret, and vulnerable to numerous adversarial attacks. Since WBANs is a real-world application of the healthcare system, it's vital to ensure that the data acquired by the WBANs sensors is secure and not accessible to unauthorized parties or security hazards. As a result, effective signcryption security solutions are required for the WBANs' success and widespread use. Over the last two decades, researchers have proposed a slew of signcryption security solutions to achieve this goal. The lack of a clear and unified study in terms of signcryption solutions can offer a bird's eye view of WBANs. Based on the most recent signcryption papers, we analyzed WBAN's communication architecture, security requirements, and the primary problems in WBANs to meet the aforementioned objectives. This survey also includes the most up to date signcryption security techniques in WBANs environments. By identifying and comparing all available signcryption techniques in the WBANs sector, the study will aid the academic community in understanding security problems and causes. The goal of this survey is to provide a comparative review of the existing signcryption security solutions and to analyze the previously indicated solution given for WBANs. A multi-criteria decision-making approach is used for a comparative examination of the existing signcryption solutions. Furthermore, the survey also highlights some of the public research issues that researchers must face to develop the security features of WBANs.

Electron Transfer-Mediated Photodegradation of Phototoxic Antipsychotic Drug Quetiapine.

Quetiapine (QTP) (1), a psychotropic agent belonging to a chemical class, dibenzothiazepine derivatives, is photosensitive and photolabile. Its photochemistry was studied in the presence of an electron donor N,N-dimethylaniline (DMA) and an electron acceptor 1,4-dicyanobenzene (DCB) under anaerobic conditions. This resulted in photoinduced electron transfer-mediated transformation of drug QTP. Irradiation of Quetiapine (QTP, 1) in the presence of electron donor N,N-dimethylaniline (DMA) under anaerobic conditions in a photochemical reactor afforded one major photoproduct 2 when irradiation of QTP (1) was carried out in the presence of electron acceptor 1,4-dicyanobenzene (DCB) under similar conditions; it afforded 3 as a major photoproduct. These photoproducts were isolated and characterized on the basis of their spectral (IR, UV, 1H NMR, 13C NMR, and mass spectra) studies. The photophysical properties of Quetiapine were also determined in several solvents to investigate the relevance of the molecular structure in their photophysics and consequently in their photochemistry.

Towards the Design of Efficient and Secure Architecture for Software-Defined Vehicular Networks.

Recently, by the rapid development of Vehicular Ad Hoc Networks (VANETs) and the advancement of Software Defined Networking (SDN) as an emerging technology, the Software-Defined Vehicular Network (SDVN) has a tremendous attraction in the academia and research community. SDN's unique properties and features, such as its flexibility, programmability, and centralized control, make the network scalable and straightforward. In VANETs, traffic management and secure communication of vehicle information using the public network are the main research dimensions in the current era for the researchers to be considered while designing an efficient and secure VANETs architecture. This paper highlights the possible identified threat vectors and efficiently resolves the network vulnerabilities to design a novel and secure hierarchic architecture for SDVN. To solve the above problem, we proposed a Public Key Infrastructure-based digital signature model for efficient and secure communication from Vehicle to Vehicle. We also used the public key authority infrastructure for Vehicle to Infrastructure and the three-way handshake method for secure session creation and secure data communication in the SDN controller. The proposed security is validated through the well-known simulation tool AVISPA. Additionally, a formal security model is applied to validate the design hierarchic architecture's fundamental security properties for SDVN in an efficient and desirable way. In a comparative analysis, we prove that our proposed scheme fulfills all the essential security properties compared to other states of the art schemes.

On the Design of Efficient Hierarchic Architecture for Software Defined Vehicular Networks.

Modern vehicles are equipped with various sensors, onboard units, and devices such as Application Unit (AU) that support routing and communication. In VANETs, traffic management and Quality of Service (QoS) are the main research dimensions to be considered while designing VANETs architectures. To cope with the issues of QoS faced by the VANETs, we design an efficient SDN-based architecture where we focus on the QoS of VANETs. In this paper, QoS is achieved by a priority-based scheduling algorithm in which we prioritize traffic flow messages in the safety queue and non-safety queue. In the safety queue, the messages are prioritized based on deadline and size using the New Deadline and Size of data method (NDS) with constrained location and deadline. In contrast, the non-safety queue is prioritized based on First Come First Serve (FCFS) method. For the simulation of our proposed scheduling algorithm, we use a well-known cloud computing framework CloudSim toolkit. The simulation results of safety messages show better performance than non-safety messages in terms of execution time.

Self-expanding Metallic Stent Placement in Malignant Terminal Ileal Stricture.

Malignancies of gastro-intestinal tract cause stricture formation that leads to intestinal obstruction. In such cases, either surgery or placement of self-expanding metallic stents (SEMS) are options of palliation. For left sided colorectal obstruction, SEMS have been widely used and reported. Luminal stenting is not always an easy task to perform because of altered anatomy of the surrounding structures, specially in the right side of colon and terminal ileum. SEMS placement, particularly in the ileocecal region, is technically difficult. Few studies on SEMS deployment in right sided colon have been reported till now. We report a case of metastatic signet ring cell carcinoma of rectosigmoid junction with malignant terminal ileal stricture palliation done with placement of SEMS.

Dyspepsia Amongst End Stage Renal Disease Undergoing Hemodialysis: Views from a Large Tertiary Care Center.

BACKGROUND AND OBJECTIVES: Gastrointestinal symptoms are common in patients with end stage renal disease (ESRD) among which dyspepsia is frequently observed. The aim of the study was to determine the frequency and associations of dyspepsia in ESRD patients using the Leeds questionnaire. METHODS: All ESRD patients on maintenance hemodialysis were consecutively enrolled in the study. Leeds questionnaire was used to interrogate the patients for the assessment of dyspepsia. Mean and standard deviation were calculated for age, body mass index (BMI), disease duration and number of hemodialysis sessions. Independent t-test and Chi square tests were used for statistical analysis. RESULTS: Total number of patients was 200, out which 118 (59.3%) were male. The mean age was of 41.4 years. According to the Leeds questionnaire, dyspepsia was present in 62 (63.9%) patients. Younger patients (age 20-40 years) more frequently had dyspeptic symptoms (61.5% patients), retrosternal pain (156 patients, 78.0%), regurgitation (127 patients, 63.5%), dysphagia (67 patients, 33.5%), and nausea (142 patients, 71.0%). Patients presented with intermittent pattern of symptoms in 179 (89.5%) cases, while continuous symptoms in 6 (3.0%). Dyspepsia was associated with aspartate aminotransferase (AST) levels > 25 U/L (P = 0.001), alanine aminotransferase (ALT) levels > 28U/L (P = 0.000) and gamma glutamyl transferase (GGT) levels > 34 U/L (P = 0.002). On multivariate analysis, urea, creatinine, and presenting symptoms of dysphagia and belching showed significant statistical association with dyspepsia. CONCLUSION: Dyspepsia is a common problem affecting patients with end stage renal disease and is associated with raised serum AST, ALT and GGT in such patients.

Diagnostic Efficiency of Multidetector Computed Tomography in the Evaluation of Clinically Equivocal Cases of Acute Appendicitis with Surgical Correlation.

Acute appendicitis is one of the most frequent causes of lower abdominal pain and requires immediate surgical intervention. The diagnosis often poses a lot of challenge even to experienced surgeon. Those patients with equivocal symptoms may require different imaging modalities like radiography, contrast examination and ultrasound with limited utility. Multidetector computed tomography (MDCT) used in suspected acute appendicitis has, however, resulted in improved diagnostic accuracy and also reduction of negative surgeries. Objective We intend to determine the diagnostic efficiency of MDCT in clinically equivocal cases of acute appendicitis correlating it with surgical/histopathological findings. Materials and methods A group of 116 patients was included in this study. Spiral MDCT was performed in all these cases after administration of oral and intravenous contrast. All these patients underwent surgery and the CT findings were correlated with histopathology. Out of these 116 patients, 60 patients were male and 56 female. The age range was from three to seventy years and mean age was 28+1 years. Results The results proved that MDCT had a sensitivity of 97.5%, specificity of 97.0%, and accuracy of 97.4% for the diagnosis of appendicitis with one false positive and two false negative cases. The study showed 100% accuracy in diagnosing acute appendicitis in children. In 33 patients, an alternate cause was identified with CT. The alternate diagnosis made on CT findings was consistent with the final diagnosis in 27 (81.8%) of 33 patients in whom there was no evidence of acute appendicitis. The clinical diagnosis disagreed with the CT diagnosis in six patients (18.18%). Conclusion Our study verifies that MDCT plays an important role in evaluation and consequent management of equivocal cases of acute appendicitis. MDCT is also able to diagnose appendicitis or detect alternative diagnosis in pediatric population.

Role of Interventional Radiology in the Management of Peripheral Vascular Malformations: A Tertiary Care Center Experience.

Peripheral vascular malformations (PVMs) represent a wide spectrum of vascular abnormalities occurring due to anomalous connections between arteries, veins, capillaries, and lymphatic channels at the microscopic level, in different combinations. They are rare and challenging to treat. Different operators may have different approaches based on their experience and expertise. Sclerotherapy either alone or in combination with embolization has been used as an independent method for the treatment of PVMs. Purpose The aim of this study is to assess the safety and efficacy of sclerotherapy and embolization, with or without surgery, for the treatment of peripheral vascular malformations, based on our approach. Materials and methods A retrospective review of all patients with PVMs treated in our interventional radiology department from 2011 to 2017 was carried out. Medical records, imaging, and follow-up notes were reviewed to evaluate the response to treatment and post-procedure complications. Results Thirty-four sessions were performed in 15 patients (eight male, seven female) with PVMs. Low-flow lesions were identified in 10, intermediate flow in one, and high flow in four patients. Sodium tetradecyl sulfate (STS) was used as the sclerotherapeutic agent in 10 (66.67%), glue with lipoidal in three (20.0%), and bleomycin in one patient (6.67%). Coils with PVA and a covered stent were used in one and a combination of coil, PVA, and gel foam was used in one patient. A marked response was seen in 11 and a partial response in four patients. One patient developed foot gangrene. Stent thrombosis was noted in one patient with no clinical consequences. Recurrence was seen in two patients, who were lost to follow up. Conclusion PVMs are complex lesions. Sclerotherapy with or without embolization is a safe and effective treatment modality, with clinical response approaching 100%.