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The robotic approach improves the feasibility of minimally invasive colectomy even where there may be an anatomic challenge with laparoscopy. Whether a failure in completing colectomy with this newer technology is associated with worse consequences needs to be considered when evaluating the relative benefit of robotic colectomy. The aim of this study is to evaluate rates of conversion to open surgery after robotic and laparoscopic colectomy and whether outcomes after conversion vary after the two techniques since this has not been well studied. From the American College of Surgeons (ACS) – National Surgical Quality Improvement Program (NSQIP) (2015–2016), patients who underwent elective minimally invasive colectomy were identified. Converted robotic were compared to laparoscopic procedures for patient demographics, co-morbidities; primary procedure and diagnosis, prolonged operation and postoperative complications. Of 36,046 colectomy procedures, 30,808 (85.5%) were laparoscopic, while 5238 (14.5%) were robotic-assisted. There were 3271 (9.1%) conversions to open surgery (laparoscopic: 2959 [9.6%]; robotic: 312 [6%]). Thirty-day postoperative surgical site infection, anastomotic leak, ileus, sepsis, bleeding requiring transfusion, urinary tract infection, reoperation; pulmonary, renal, cardiac/cerebrovascular complications; readmission, hospital stay, and mortality, were similar between the two groups. However, deep vein thrombosis/pulmonary embolism was higher after robotic conversion (4.5% vs. 2.2%, p = 0.01). Conversion was lower after robotic when compared to laparoscopic colectomy. Converted patients had similar outcomes except for vein thromboembolism which was higher after robotic surgery. Robotic technology seems to improve the feasibility of minimally invasive surgery without negatively affecting safety and efficacy even when conversion is required. [ABSTRACT FROM AUTHOR]

Blockchain technology can solve current interoperability challenges in health information systems and provide the technical standard which ensures a secure sharing of electronic health data between individuals, healthcare providers, medical care institutions, and medical experts. In healthcare management, IoMT devices can deliver real-time sensory data from patients to be analyzed and processed. In IoMT devices within a range of product areas, the confidentiality of patients' health data transmission and privacy is still a big problem. In this paper, the Multi-Modal Secure Data Dissemination Framework (MMSDDF) has been proposed based on blockchain in IoMT for secure patient data access and control. To meet the optimized security and privacy requirements of healthcare data management in IoMT devices, the proposed framework is effectively employed. Blockchain's key has been used to a healthcare application network where the patient's health data can create warnings that are significant to authenticated healthcare providers securely. The simulation outcomes show that the suggested MMSDDF method achieves a high accuracy ratio of 95.8%, a prediction ratio of 92.3%, less delay of 0.48 s, latency range of 0.5, and response time of 1.5% to other existing methods. [ABSTRACT FROM AUTHOR]

With the increased use of internet services for various public and private purposes, the threat of cyber-attacks is also increasing. Machine learning methods are effective against these threats. This paper aims to review these methods in terms of their utility for the detection, identification, and mitigation of cyber-attacks now and in the future. Many reviews described and discussed various machine learning methods and how each method can be used against specific types of cyber threats. However, empirical evidence did not show any clear superiority of one method over the others. The differences in research contexts and methodologies could have contributed to this uncertainty. However, deep learning methods might have an edge over shallow learning methods. Emerging new trends of cyber-attacks demand renewed research on the problem. The invention of an entirely new concept to deal with various cybersecurity issues cannot be ruled out. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has been a rapidly developing field since its inception in 1982. In order to assess the past and future trends in IoT research, a search was conducted on Google Scholar which yielded 25 papers. These papers were then analyzed and discussed, leading to the conclusion that IoT is not only here to stay, but also continuously evolving. This can be seen in the advancements in technology, applications, areas of use, benefits, problems, and challenges. It is expected that this pattern of development will continue in the future, with the emergence of new technologies, applications, software, and areas of use. However, as with any other field, there will be barriers and challenges that need to be overcome at each stage of progress. Furthermore, there is immense potential to integrate IoT with Artificial Intelligence (AI), leading to even more innovative and efficient applications. Overall, the future of IoT research looks promising, with continued growth and advancements in various aspects of the field. [ABSTRACT FROM AUTHOR]

Postpartum haemorrhage (PPH) is the loss of blood above 500 ml during vaginal or caesarean deliveries. It is difficult to find a PPH in an earlier stage, so pregnant women are exposed to excess blood loss that makes them suffer and die. Antenatal practices help in identifying risk factors, and modern technology is used to overcome the risk. Still, the morbidity rate and the mortality arise due to the unpredicted and unexpected cause. PPH is still a significant cause of maternal morbidity and mortality worldwide. The novelty of this research work is to alert medical practitioner about excessive bleeding of pregnant women during childbirth. We are proposing an automation system using wearable devices to prevent pregnant women from the PPH. These devices measure parameters like temperature, pulse rate, blood pressure, and sweat rate of pregnant women. Fuzzy neural technique-based rules are used for each parameter to predict the risk in developing PPH and to evaluate the performance of proposed system for reducing mortality and morbidity rates. Our findings of experiment are carried on metrics of HPPH (high-level postpartum haemorrhage), NPPH (normal-level postpartum haemorrhage), and MPPH (medium-level postpartum haemorrhage) for 15 patients. Fuzzy output value of 1 indicates patient state with NPPH, 0 indicates patient state with HPPH, and values in between 0 and 1 indicate MPPH. Based on the sensitivity of the predicted values, medical attention is taken from doctors or nurses in nearby locations using Internet of Things infrastructure. [ABSTRACT FROM AUTHOR]

Healthcare is a sensitive domain, and it is necessary to design an efficient solution in the healthcare system. Electronic Medical Records often have high privacy-sensitive data; unauthorized access to these patient data could hurt patients' reputations and cause financial losses to the organization. Most of the existing healthcare systems are based on a centralized architecture, which is more vulnerable such as hacking the healthcare system, arbitrary modification attacks, and single-point-of-failure. Also, these systems increase the cost due to various factors like—establishing a trusted network, cost per transaction, and limited access to patient health data, which are the problems that remain open until now. The blockchain is a general-purpose technology that seems to be an innovative and interesting technology that can improve the Health care domain by solving the above issues with a secure architecture. This paper address addresses the challenging problems. (1) How to provide secure data exchange and anonymity. (2) How to preserve the personal data privacy of the patient health records. The proposed solution leverages the Hyperledger Fabric, a permissioned blockchain that establishes a secured and trusted network to all stakeholders, ensuring the integrity of protected health information and providing authenticity and health access control. Further, the entire supply chain can be traced, decreasing duplicate tests and unnecessary service, increasing accountability, preserving the crucial documents, limiting the unauthorized sharing of the EHRs documents, and offering lower costs across the care continuum. [ABSTRACT FROM AUTHOR]

Background: Given its short procedure time and low morbidity, there is enthusiasm to perform sleeve gastrectomy (SG) in an outpatient setting. However, most relevant studies include an overnight stay at a medical facility (≤ 24-h). Hence, we investigated the feasibility and safety of a same-day discharge (SDD) protocol for laparoscopic SG. Methods: In a prospective pilot study (02/01/2021–02/28/2022), all patients planned for SG were screened for eligibility. Patients met the inclusion criteria if they were ≤ 65 years old, without major comorbidity, and lived close to the hospital. Postoperatively, patients who met discharge criteria were sent home directly from the recovery room. Patients were called the same night and the next morning. Feasibility was defined as discharge on the day of surgery without emergency department (ED) visit or readmission within 24-h. Secondary outcomes, including 90-day morbidity, were compared to patients who met inclusion criteria but chose a same-day admission (SDA) approach during the same study period. Descriptive statistics are displayed as count (percentage) and median (interquartile range). Results: A total of 320 patients were planned for SG during the study period, 229 of whom met eligibility criteria and underwent SG with 56 agreeing to SDD-SG while 173 opted for SDA-SG. Baseline characteristics were all similar between both groups except for obstructive sleep apnea being more prevalent in SDA-SG group (38.2% vs. 16.1%; P < 0.001). Operative characteristics including procedure time were similar between both groups. Successful SDD-SG was achieved in 54(96%) of patients with a median of 6.0(1.0) hours of stay in the recovery room. Ninety-day morbidity was similar between SDD-SG and SDA-SG groups (1.8% vs. 6.9%, respectively; P = 0.196). Conclusion: A SDD protocol for laparoscopic SG was feasible and safe in selected patients. Larger studies that evaluate patient reported outcomes and include bypass-type procedures may be needed to guide safe use of ambulatory bariatric surgery. [ABSTRACT FROM AUTHOR]

Triple Data Encryption Standard (also known as 3DES) is a symmetric encryption algorithm. European Traffic Management System popularly uses 3DES for authentication and encryption. However, as per a draft published by NIST in 2018, 3DES is officially being retired and not suggested to use for new applications. Several attacks were imposed on 3DES, and the biggest threat to 3DES is meet in the middle attack. Therefore for long term security, it is essential to enhance the security of such algorithms. This paper proposed a new cipher DeCrypt inspired by 3DES, an improved version of the 3DES algorithm, and secured against meet in the middle attack. As per the experiment performed, DeCrypt cipher is 61 per cent faster than 3DES, providing long-term and better security against sweet-32 attacks than other symmetric algorithms. The proposed algorithm is also faster than 3DES due to reduced encryption and decryption time. [ABSTRACT FROM AUTHOR]

Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today. For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to bring innovation in healthcare. Through all these innovations happening it is not wrong to establish that disease prediction with anticipation of its cure is no longer unrealistic. First, Dengue Fever (DF) and then Covid-19 likewise are new outbreak in infectious lethal diseases and diagnosing at all stages is crucial to decrease mortality rate. In case of Diabetes, clinicians and experts are finding challenging the timely diagnosis and analyzing the chances of developing underlying diseases. In this paper, Louvain Mani-Hierarchical Fold Learning healthcare analytics, a hybrid deep learning technique is proposed for medical diagnostics and is tested and validated using real-time dataset of 104 instances of patients with dengue fever made available by Holy Family Hospital, Pakistan and 810 instances found for infectious diseases including prognosis of; Covid-19, SARS, ARDS, Pneumocystis, Streptococcus, Chlamydophila, Klebsiella, Legionella, Lipoid, etc. on GitHub. Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data model derived from 15696 endocrine records of multiple visits of 100 patients identified by a unique ID. Accuracy for induced rules is evaluated by Laplace (Fig. 8) as 0.727, 0.701 and 0.203 for 41, 18 and 24 rules, respectively. Endocrine diagnostic data is made available by Shifa International Hospital, Islamabad, Pakistan. Our results show that in future this algorithm may be tested for diagnostics on healthcare big data. [ABSTRACT FROM AUTHOR]

Mobile ubiquitous computing has not only enriched human comfort but also has a deep impact on changing standards of human daily life. Modern inventions can be even more automated by using the Internet of Things (IoT) and Artificial Intelligence (AI). Mobile devices, body area networks, and embedded computing systems allow healthcare providers to continuously assess and monitor their patients but also bring privacy concerns. This paper proposes a smartphone-based end-to-end novel framework named PP-SPA for privacy-preserved Human Activity Recognition (HAR) and real-time activity functioning support using the smartphone-based virtual personal assistant. PP-SPA helps to improve the routine life functioning of the Cognitive Impaired individuals. PP-SPA uses a highly accurate machine learning model that takes input from smartphone sensors (i.e., accelerometer, gyroscope, magnetometer, and GPS) for accurate HAR and uses a digital diary to recommend real-time support for improvement in individual's health. PP-SPA yields a proficient accuracy of 90% with the Hoeffding Tree and Logistic Regression algorithm which endeavors reasonable models in terms of uncertainty. [ABSTRACT FROM AUTHOR]

Cloud computing provides a service-oriented computing model to cloud users on a metered basis. Most of the cloud data centers are running on fossil fuels. It elevates the carbon emissions to the environment. Green cloud computing is the fusion of greenness in cloud computing to address the issues related to energy consumption and environmental sustainability. Virtual machine (VM) consolidation and live migration can provide standard solutions to energy consumption. The selection of VM for migration is a vital task. It should be performed effectively to trade-off between energy consumption and service level agreement violation (SLAV). The research activity in this article focuses on a new VM selection policy that chooses VM with high energy consumption and small size. Real-world workload traces were used to evaluate the performance of the proposed MECpVmS VM selection policy. Using CloudSim simulation, the MECpVmS VM selection policy has been implemented and assessed with existing VM selection policies. The results show an overall improvement in energy efficiency, energy consumption, SLAV. [ABSTRACT FROM AUTHOR]

With increase in the demand of better data collection from various IoT devices, researchers are showing more interests in providing the enhanced data collection methods to the users. Many data collection methods have proposed, but still more efficiency and better results are required. In this paper, a novel and secure data collection method from IoT devices has proposed. In this paper, SEED (Secure and energy efficient data-collection) method has discussed. It includes creation of aggregator nodes and path discovery algorithms. Integrity of data is analysed by using MD5 hashing technique. This hashing technique is one of the most usable techniques as compared to other. In the regard of implementation of this novel secure and energy data collection method, MD5 is used to just hash the processing data in the entire process. In earlier scenarios, there were various issues regarding fault tolerance, congestion, energy wastage, path discovery and load balancing in a network. To resolve these issues, updating of the aggregator node is done after each failure or transmission. Transmission of huge amount of data can create different challenges in network. This research is completely different from other existing researches of data collection with routing as it deals with sink node by using a unique path discovery algorithm. This proposed mechanism provides the better results for all of the nodes and network in terms of energy efficiency and throughput. [ABSTRACT FROM AUTHOR]

Background: Indocyanine green, near infrared, fluorescence angiography (ICG-FA) is increasingly adopted in colorectal surgery for intraoperative tissue perfusion assessment to reduce anastomotic leakage rates. However, the economic impact of this intervention has not been investigated. This study is a cost analysis of the routine use of ICG-FA in colorectal surgery from the hospital payer perspective. Methods: A decision analysis model was developed for colorectal resections considering two scenarios: resection without using ICG-FA and resection with intraoperative ICG-FA for anastomotic perfusion assessment. Incorporated into the model were the costs of ICG agent, fluorescence angiography equipment, surgery, anastomotic leak, and the leak rates with and without ICG-FA. All input data were derived from recent publications. Results: The routine use of ICG-FA for colorectal anastomosis is cost saving when cost analysis is performed using the following base case assumptions: 8.6% leak rate without ICG-FA, odds ratio of 0.46 for reduction of leakage with ICG-FA (4.8% leak rate relative to 8.6% base case), cost of ICG-FA of $250, and incremental cost of leak, not requiring reoperation, of $9,934.50. In one-way sensitivity analyses, routine use of ICG-FA was cost saving if the cost of an anastomotic leak is more than $5616.29, the cost of ICG-FA is less than $634.44, the leak rate (without ICG-FA) is higher than 4.9%, or the odds ratio for reduction of leak with ICG-FA is less than 0.69. There is a per-case saving of $192.22 with the use of ICG-FA. Conclusion: Using the best available evidence and most conservative base case values, routine use of ICG-FA in colorectal surgery was found to be cost saving. Since the evidence suggests there is a reduction in leak rate, the routine use of ICG-FA is a dominating strategy. However, the overall quality of evidence is low and there is a clear need for prospective, randomized controlled trials. [ABSTRACT FROM AUTHOR]

Background: Laparoscopic intracorporeal continuous suturing is being employed in a growing number of minimally invasive procedures. However, there is a lack of adequate bench models for gaining proficiency in this complex task. The purpose of this study was to assess a novel simulation model for running suture. Methods: Participants were grouped as novice (LSN) or expert (LSE) at laparoscopic suturing based on prior experience and training level. A novel low-cost bench model was developed to simulate laparoscopic intracorporeal continuous closure of a defect. The primary outcome measured was time taken to complete the task. Videos were scored by independent raters for Global Operative Assessment of Laparoscopic Skills (GOALS). Results: Sixteen subjects (7 LSE and 9 LSN) participated in this study. LSE completed the task significantly faster than LSN (430 ± 107 vs 637 ± 164 seconds, P ≤.05). LSN scored higher on accuracy penalties than LSE (Median 30 vs 0, P ≤.05). Mean GOALS score was significantly different between the 2 groups (LSE 20.64 ± 2.64 vs LSN 14.28 ± 1.94, P <.001) with good inter-rater reliability (ICC ≥.823). An aggregate score using the formula: Performance Score = 1200-time(sec)-(accuracy penalties x 10) was significantly different between groups with a mean score of 741 ± 141 for LSE vs 285 ± 167 for LSN (P <.001). Conclusion A novel bench model for laparoscopic continuous suturing was able to significantly discriminate between laparoscopic experts and novices. This low-cost model may be useful for both training and assessment of laparoscopic continuous suturing proficiency. [ABSTRACT FROM AUTHOR]

Recent advancements in sensing and networking technology, such as the Internet of Things (IoT), provide a low-cost monitoring system that serves as the backbone of the smart city. In recent years, various real-time applications have developed to monitor and control physical objects using smart sensors. The use of smart sensors generates a massive amount of data in terms of text, numeric, image and video formats, etc. In IoT, transmitting an enormous amount of data consume more energy. So, clustering is the right option for collecting and exchanging data over the network. Many researchers have used the optimization algorithms to choose cluster head (CH) in the cluster. During the CH rotation, many of the optimization algorithms take more time to converge. To address this problem, this paper proposes a new caledonian crow learning algorithm (NCCLA) to identify the appropriate CH in the cluster. Generally, it has two processes, namely cluster formation and CH selection. The Euclidean distance is used to create a cluster within the network. The NCCLA algorithm is proposed to find the best CH on the cluster. Simulation is conducted on MATLAB R2019a. The efficacy of the proposed NCCLA algorithm is compared to the fuzzy clustering particle swarm optimization (FCPSO) and the Improved Fruit Fly Optimization Algorithm (IFFOA). The performance of NCCLA increases the network lifespan by 10% and increases the data packet delivery by 10%. [ABSTRACT FROM AUTHOR]

Nature inspired algorithm plays a very vibrant role in solving the different optimization problems these days. The fundamental attitude of naturalistic approaches is to boost the competence, improvement, proficiency, success in the task except from it to help in underrating the energy use, cost, size. Several computing techniques are taking the benefits from nature inspired algorithms for solving their problems related to load balancing, scheduling and many others. These algorithms have come up with lots of improvements in the results. The aim of this analysis is to make efforts in the betterment in different areas of computing and help in solving various problems related to load balancing, scheduling and energy efficiency. The structure of the paper includes an introduction, contribution to the work, background study, which includes the role of nature inspired techniques in a different computing environment, research challenges and its applications. The sustainable goal and objective of the article is to perform the energy efficiency, load balancing and scheduling on different computing systems which include grid, cloud, distributed, fog and edge computing by using various nature inspired algorithms. This comprehensive study gives the awareness and valuable provision for the researchers in this area by providing a thorough study of different computing techniques in different research fields. [ABSTRACT FROM AUTHOR]

The automated toll-tax collection system (ATCS) is advantageous to facilitate traffic management at the toll plaza and to save fuel for vehicles. The most advanced application of the electronic toll collection (ETC) system is to collect the toll-tax amount (TA) at toll plazas of the national highways. The few existing TA collection systems suffer from data security, transparency, privacy, and data immutability as these are centralized systems. As the Blockchain is a decentralized, transparent, secure, and low-cost technology. However, this paper presents an intelligent transportation management system (I-TMS) using Blockchain. The proposed I-TMS shows the way of implementation of blockchain technology for vehicle data management in various applications of I-TMS. Herein, a framework of blockchain-enabled ATCS (BATCS) is provided as a blockchain-based I-TMS application to collect TAs without stopping vehicles while they pass the toll plaza. Smart contracts are used to authenticate vehicles' data and to collect TAs automatically. An efficient algorithm is presented for data verification and TA collection in this paper. This research work provides a secure, transparent, and privacy-preserving framework in the field of the ETC system. The significant contributions of the BATCS compared with the RFID-based system are less fuel consumption and time-saving for a vehicle. The proposed framework can enhance data security and user privacy in the intelligent decentralized ETC system. [ABSTRACT FROM AUTHOR]

Purpose: Hemodynamically unstable trauma patients who would benefit from angioembolization (AE) typically also require emergent surgery for their injuries. The critical decision of transferring a patient to the operating room versus the interventional radiology (IR) suite can be bypassed with the advent of intra-operative AE (IOAE). Previously limited by the availability of costly rooms termed RAPTOR (resuscitation with angiography, percutaneous techniques and open repair) suites, it has been suggested that using C-arm digital subtraction angiography (DSA) is a comparable alternative. This case series aims to establish the feasibility and safety of IOAE. Methods: We conducted a retrospective analysis of all trauma patients at our level 1 trauma center who underwent IOAE with a concomitant surgical intervention from January 2011 to May 2019. Descriptive analyses were conducted. Results: A total of 49 patients (80% male, 44 ± 17 years, 92% blunt) underwent IOAE using the C-arm DSA during the study period. All but one patient underwent exploratory laparotomy, 56% of which underwent an additional surgical procedure (ex. exploratory thoracotomy, orthopedic). Either Gelfoam® (Pfizer, New York, USA) (90%), coils (2.0%), or a combination (8.2%) were used for embolization. Internal iliac embolization was performed in 88% of cases (59% bilateral). IOAE was successful in all but four cases (8.2%) and thirty-day mortality was 31%. Conclusion: IOAE appears to be a feasible and safe management option in severe trauma patients with the advantage of concurrent operative intervention and ongoing active resuscitation with good success in hemorrhage control. [ABSTRACT FROM AUTHOR]

Summary: The global demand for electricity has visualized high growth with the rapid growth in population and economy. It thus becomes necessary to efficiently distribute electricity to households and industries in order to reduce power loss. Smart Grids (SG) have the potential to reduce such power losses during power distribution. Machine learning and artificial intelligence techniques have been successfully implemented on SGs to achieve enhanced accuracy in customer demand prediction. There exists a dire need to analyze and evaluate the various machine learning algorithms, thereby identify the most suitable one to be applied to SGs. In the present work, several state‐of‐the‐art machine learning algorithms, namely Support Vector Machines (SVM), K‐Nearest Neighbor (KNN), Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the SG. The SG dataset used in the study is publicly available collected from UC Irvine (UCI) machine learning repository. The experimentation results highlighted the superiority of the Decision Tree classification algorithm, which outperformed the other state of the art algorithms yielding 100% precision, 99.9% recall, 100% F1 score, and 99.96% accuracy. [ABSTRACT FROM AUTHOR]

Energy conservation is a major challenge in the Internet of Things (IoT) as the number of resource‐constrained devices is connected to the network. Routing plays a vital role in IoT to extend the lifespan of the network. Routing protocol for low‐power and lossy networks (RPL) is a standard routing protocol in IoT. The parent selection is a crucial role in the routing process to exchange the data. In RPL, the researchers have introduced a single metric, composite metric, and multiobjective optimization algorithm for parent selection. However, the improper parent selection causes the packet losses, congestion among the network nodes, depletes more energy, and increases the convergence time. To overcome these issues, this article proposes energy efficient optimal parent selection in RPL (EEOPS‐RPL) using firefly optimization algorithm to extend the lifespan of the IoT network. In EEOPS‐RPL, each node in the network is considered to be firefly and also calculates the current location of firefly, attraction of the fireflies, random function, velocity, and the global best values in the network. Residual energy and expected transmission count are attractiveness parameters and distance is a movement parameter to choose the optimum parent in the destination‐oriented directed acyclic graph for data transmission. The simulation is conducted using COOJA. The EEOPS‐RPL provides better performance in comparison to the efficient parent selection for RPL (EPC‐RPL) and the E‐RPL. The EEOPS‐RPL improves the packet transmission ratio and lifespan of the network by 2% to 5%, and 5% to 10%, respectively, compared with EPC‐RPL and E‐RPL. In EEOPS‐RPL, each participant node applies the firefly algorithm over the parent node information such as distance, residual energy, and expected transmission count through DODAG information object control message to pick the best parent node in the DODAG. The firefly algorithm provides the fast convergence that able to choose the optimal parent quickly. Thus, it avoids the packet loss during the route establishment in network. Thus, EEOPS‐RPL extends the lifespan of the network and reduces the convergence time. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused hundreds of thousands of deaths, millions of infections worldwide, and the loss of trillions of dollars for many large economies. It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope. Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses. In the same vein, a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of, and role of age groups and gender on, the probability of COVID-19 infection. This study aimed to review, analyze, and critically appraise published works that report on various factors to explain their relationship with COVID-19. Such studies span a wide range, including descriptive analyses, ratio analyses, cohort, prospective and retrospective studies. Various studies that describe indicators to determine the probability of infection among the general population, as well as the risk factors associated with severe illness and mortality, are critically analyzed and these findings are discussed in detail. A comprehensive analysis was conducted on research studies that investigated the perceived differences in vulnerability of different age groups and genders to severe outcomes of COVID-19. Studies incorporating important demographic, health, and socioeconomic characteristics are highlighted to emphasize their importance. Predominantly, the lack of an appropriated dataset that contains demographic, personal health, and socioeconomic information implicates the efficacy and efficiency of the discussed methods. Results are overstated on the part of both exclusion of quarantined and patients with mild symptoms and inclusion of the data from hospitals where the majority of the cases are potentially ill. [ABSTRACT FROM AUTHOR]

Past international trade practices have been associated with opaque information flows that have hindered traceability and created hurdles in hassle‐free trade. Blockchain and allied technologies have been investigated as a panacea for the problems faced by supply chain and logistics industry. Network analysis also uncovered twenty types of legal and antisocial entities operating on bitcoin and provided a path for uncovering these antisocial entities. However, earlier works of literature have focused on limited aspects of a typical supply chain such as monitoring assets, securing traceability, data integrity negligence, and data access. To overcome such drawbacks, this article proposes permissioned Blockchain with relevant processes and functions to obtain a holistic framework for securing the supply chain and logistic operations. The efficacy of the proposed framework was demonstrated in the case study. Comparative study was performed with the existing frameworks where the proposed framework was found to be better at four counts compared on the basis of data integrity in supply chain, decentralized decision making and traceability. It was found that critical loopholes in a current supply chain can be overcome using the proposed framework. [ABSTRACT FROM AUTHOR]

Optical switching networks (OSN) rely on both hardware components and signals for performing efficient switching operations achieving service level requirements of the end-user. In this manuscript, an incremental learning-based wavelength assignment is introduced to minimize asynchronous channel selection and dispersion-free switch-over in OSN. This method accounts on the wavelength dispersion characteristics of the light path for establishing and reconnecting communications between end-to-end devices. The learning process segregates favorable and conflict channels based on blocking probability and channel capacity assigned to the optical users. A conventional wavelength division multiplexing technique is employed for improving data transmission rates. This method is appropriate for evading blocking rates in optical networks to improve throughput rate and to leverage OSN performance. Machine learning (ML) paradigm is designed for OSN leverages the communication rate influenced by channel conflicts and utilization capacity. Therefore, the number of transmissions likely requires optimal switch-over with the knowledge of channel constraints to improve the performance of OSN. [ABSTRACT FROM AUTHOR]

Background: Management of the post-traumatic open abdomen (OA) using negative pressure wound therapy (NPWT) alone is associated with low rates of primary fascial closure. The abdominal reapproximation anchor (ABRA) system exerts dynamic medial fascial traction and may work synergistically with NPWT to facilitate primary fascial closure. Methods: Patients with an OA following trauma laparotomy between 2009 and 2018 were identified from a prospectively maintained institutional database. Patients treated with ABRA in conjunction with NPWT (ABRA) versus NPWT alone (NPWT) were compared in terms of primary fascial closure rate, number of surgeries to closure, tracheostomy duration, length of stay and incidence of entero-atmospheric fistula. Multivariable linear regression was performed to identify predictors of tracheostomy duration. Results: We identified 48 patients [ABRA, 12 and NPWT, 36]. The ABRA group was significantly younger (25 vs. 37 years, p = 0.027) and included a lower proportion of males (58% vs. 89%, p = 0.032). Groups were similar with respect to the incidence of hollow viscus injury, injury severity score and abdominal abbreviated injury score. Compared to the NPWT group, the ABRA group had a significantly higher rate of primary fascial closure (100% vs. 28%, p < 0.001), fewer surgeries to abdominal closure (2 vs. 2.5, p = 0.023) and shorter duration of tracheostomy (15.5 vs. 36 days, p = 0.008). There were no differences in length of stay or incidence of entero-atmospheric fistula. On multivariable linear regression, ABRA placement was an independent predictor of shorter tracheostomy duration, after adjusting for covariates (β = − 0.294, p = 0.036). Conclusion: For the post-traumatic OA, ABRA coupled with NPWT achieves a higher rate of primary fascial closure compared to NPWT alone, while requiring fewer surgeries and a shorter duration of tracheostomy. [ABSTRACT FROM AUTHOR]

In this paper, we present a clustering-based energy aware secure routing protocol that computes trustworthy and energy efficient secure routes for wireless ad hoc networks (WANETs). Clustering in ad hoc networking paradigm is very useful for achieving scalability and robustness as it helps to save the bandwidth, to take simpler routing decisions, thus results in decreased energy dissipation of the network. WANETs are often deployed in such places where recharge or replace of energy sources is difficult. Further, unpredicted mobility and quasi-static nature of nodes and dynamic network topology yield a serious need for battery awareness during routing. In light of this, we first provide a definition of network lifetime based on application-specific criteria. Also a mathematical model for calculating residual battery power has been framed to propose an energy aware routing protocol. Trust-based scheme is used to assure secure end-to-end delivery of packets in the network. Extensive simulation has been carried out to show the efficacy of the proposed protocol to prolong the network lifetime. [ABSTRACT FROM PUBLISHER]

Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems. [ABSTRACT FROM AUTHOR]

In developed nations, the advent of distributed ledger technology is emerging as a new instrument for improving the traditional system in developing nations. Indeed, adopting blockchain technology is a necessary condition for the coming future of organizations. The distributed ledger technology provides better transparency and visibility. This study investigated the features that may influence the behavioral intention of energy experts to implement the distributed ledger technology for the energy management of developing countries. The proposed model is based on the Technology Acceptance Model construct and the diffusion of the innovation construct. Based on a survey of 178 experts working in the energy sector, the proposed model was tested using structural equation modeling. The findings showed that perceived ease of use, perceived usefulness, attitude, and cost saving had a positive and significant impact during the blockchain technology adoption. However, innovativeness showed a positive effect on the perceived ease of use whereas an insignificant impact on the perceived usefulness. The present study offers a holistic model for the implementation of innovative technologies. For the developers, it suggest rising disruptive technology solutions. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) is a heterogeneous network of different types of wireless networks such as wireless sensor networks (WSNs), ZigBee, Wi-Fi, mobile ad hoc networks (MANETs), and RFID. To make IoT a reality for smart environment, more attractive to end users, and economically successful, it must be compatible with WSNs and MANETs. In light of this, the present paper discusses a novel quantitative trust model for an IoT-MANET. The proposed trust model combines both direct and indirect trust opinion in order to calculate the final trust value for a node. A Beta probabilistic distribution is used to combine different trust evidences and direct trust has been calculated. The theory of ARMA/GARCH has been used to combine the recommendation trust evidences and predict the resultant trust value of each node in multi-step ahead. Further, a routing protocol has been designed to ensure the secure and reliable end-to-end delivery of packets by only considering trustworthy nodes in the path. Simulation results show that our proposed trust model outperforms similar existing trust models. [ABSTRACT FROM AUTHOR]