Delay Tolerant Network (DTN) can be characterized as a heterogeneous network of nodes in which nodes mobility is high and resources are limited to transmit messages. In DTN, nodes use store, carry, ...and forward principle for delivering messages to the destination node. To forward messages through intermediate nodes may cause security issues in the network since there may exist a few selfish nodes. These nodes can affect the network's performance as they may drop messages due to their limited resources such as energy and storage capacity. To handle this kind of hazard, it is necessary to propose a mechanism that may decrease the degree of the selfishness of nodes and improve the network's delivery ratio by fully distributing credits to nodes. In this article, a credit-based mechanism has been proposed based on Combined Trust Value (CTV) of nodes in DTN. In the proposed mechanism, an agent is used to compute each node's trust value grounded of the number of messages relayed by sensor nodes. This trust value is used to distribute credits to the nodes in a distributed manner without any partiality with nodes. Backtracking approach is used to distribute credits to boundary nodes deserving credits but didn’t get credits by agent node. The proposed mechanism is implemented using ONE simulator, and the performance of the projected scheme is analyzed in comparison to existing techniques Dynamic Trust, SMART (Secure Multilayer credit based incentive Technique) and Credit-based. The results exhibit that the suggested mechanism is superior than existing techniques with reference to various performance metrics like 25% higher delivery ratio, 41% less overhead, 21% less average message delay, and 28% less packets dropped. The proposed mechanism might be helpful in scenarios where the degree of selfishness is high, and the distribution of credit follows a fully distributed approach rather than an existing partial distribution used in existing techniques.
Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and ...quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.
Security is one of the main objectives while designing protocols for underwater wireless sensor networks (UWSN), since the sensors in UWSN are vulnerable to malicious attack. So it becomes easy for ...opponents to manipulate the communication channel of UWSN and its nodes. Authentication and data integrity play important roles in the context of security to make network scalable and survivable. Hence in this paper, a secure authentication and protected data aggregation method for the cluster based structure of UWSN is proposed as because cluster based arrangement produces a concise and stable network. In this technique, the cluster head in each cluster is authenticated by the gateway to ensure that all the clusters are being handled by valid nodes. Also, the data being communicated in the network will be securely handled to ensure that it will not get compromised during network operations. In this way, the security of all the nodes is ensured to maintain safe network communication. The proposed technique improves the data reliability in the network by reducing the energy consumption and delay. Here, the proposed method is moreover compared with the state of the art techniques to prove the validity and effectiveness.
Smart city surveillance systems are the battery operated light weight Internet of Things (IoT) devices. In such devices, automatic face recognition requires a low powered memory efficient visual ...computing system. For these real time applications in smart cities, efficient visual recognition systems are need of the hour. In this manuscript, efficient fast subspace decomposition over Chi Square transformation is proposed for IoT based on smart city surveillance systems. The proposed technique extracts the features for visual recognition using local binary pattern histogram. The redundant features are discarded by applying the fast subspace decomposition over the Gaussian distributed Local Binary Pattern (LBP) features. This redundancy is major contributor to memory and time consumption for battery based surveillance systems. The proposed technique is suitable for all visual recognition applications deployed in IoT based surveillance devices due to higher dimension reduction. The validation of proposed technique is proved on the basis of well-known databases. The technique shows significant results for all databases when implemented on Raspberry Pi. A comparison of the proposed technique with already existing/reported techniques for the similar applications has been provided. Least error rate is achieved by the proposed technique with maximum feature reduction in minimum time for all the standard databases. Therefore, the proposed algorithm is useful for real time visual recognition for smart city surveillance.
Distributed computing workflow is an effective paradigm to express a range of applications with cloud computing platforms for scientific research explorations. One of the most difficult application ...areas of cloud computing technology is task scheduling. In a cloud, heterogeneous context, job scheduling with minimal execution cost and time, as well as workflow reliability, are critical. While working in the heterogeneous cloud environment, tasks that are successfully executed are widely identified by considering the failure of the processor or any communication technologies link. It will also have an impact on the workflow's reliability as well as the user's service quality expectations. This research paper proposes a Critical Parent Reliability-based Scheduling (CPRS) method that uses the reliability parameter to plan the task while taking into account the user-defined cost and deadline metrics. The effectiveness of the algorithm is compared to current algorithms utilizing scientific workflows as a benchmark, such as Cybershake, Sipht, and Montage. The simulation results supported the assertions by efficiently allocating resources to the cloudlets and stabilizing all of the aforementioned parameters using sufficient performance metrics growth.
Introduction: The use of chest Computed Tomography (CT) scans has significantly increased in recent times, and it is also considered the preferred investigation method in various cases, including ...occult pneumothorax and interstitial lung diseases. Aim: To assess the relationship between chest CT scan findings/outcomes, clinicians’ expectations, and their influence on treatment outcomes. Materials and Methods: The present retrospective analytical observational study was conducted in the Department of Radiodiagnosis and Respiratory Medicine at a tertiary-level hospital, Adesh Medical College and Hospital, Kurukshetra, Haryana, India. The findings of the selected chest CT scans were classified as normal, incidental, or pathological. The response of the consulting physicians to these scans was divided into three grades: highly expected, moderately expected, and unexpected. The impact of these scans on patients’ treatment was divided into three categories: major, minor, and none. The collected data were analysed using the Statistical Package for Social Sciences (SPSS) version 28.0, and the Chi-square test was used to assess the association between different variables. Results: The mean age of study participants was approximately 58±15 years. Out of the total 74 scans (each belonging to a different individual), the findings of the chest CT scans revealed that 59 (79.7%) scans were pathological, 11 (14.9%) were incidental, and only 4 (5.4%) were normal. The outcomes of these scans were highly expected in 42 (56.7%) cases, moderately expected in 25 (33.8%), and unexpected in 7 (9.5%). These scans had a major impact on the patient’s treatment course in 28 (37.8%) cases, minor impact in 40 (54%), and no influence in 6 (8.1%) cases. The Chi-square test showed a significant association between chest CT scan outcomes and clinicians’ expectations, chest CT scan outcomes and their influence on treatment, as well as clinicians’ expectations and the effect of scans on treatment. The p-value <0.05 was considered statistically significant for all three cases. Conclusion: Chest CT scans significantly impact the diagnostic and treatment pathway for patients.
Purpose of Review
Acute internal carotid artery occlusion (ICAO) is associated with large infarcts and poor clinical outcomes and contributes to morbidity and mortality worldwide. In this review, we ...discuss various etiologies and pathophysiology of clinical presentations of ICAO, different radiographic patterns, and management of patients with ICAO.
Recent Findings
Recanalization rates remain suboptimal with systemic thrombolysis amongst patients with acute ICAO. Recent success of endovascular therapy for vessel occlusion in anterior circulation has expanded the horizons; however, few patients with cervical dissections and ICAO were included in these landmark trials.
Summary
Acute ICAO responds poorly to intravenous thrombolysis and portends worse clinical outcomes. Extracranial and intracranial ICAOs have varied clinical course and imaging patterns, with discrete cervical ICAO usually associated with better clinical outcomes while tandem occlusions predispose poor outcomes. Diagnostic catheter-based angiogram is often required since appearances of ICAO using non-invasive neuroimaging modalities are often deceiving. Repeated vascular imaging in acute to subacute phase to determine recanalization of ICAO is critical for secondary prevention. Recent success of endovascular procedures will continue to expand the horizons to improve the management of ICAO.
Background
Various randomized-controlled clinical trials (RCTs) have investigated the neuroprotective role of minocycline in acute ischemic stroke (AIS) or acute intracerebral hemorrhage (ICH) ...patients. We sought to consolidate and investigate the efficacy and safety of minocycline in patients with acute stroke.
Methods
Literature search spanned through November 30, 2017 across major databases to identify all RCTs that reported following efficacy outcomes among acute stroke patients treated with minocycline vs. placebo: National Institute of Health Stroke Scale (NIHSS), Barthel Index (BI), and modified Rankin Scale (mRS) scores. Additional safety, neuroimaging and biochemical endpoints were extracted. We pooled mean differences (MD) and risk ratios (RR) from RCTs using random-effects models.
Results
We identified 7 RCTs comprising a total of 426 patients. Of these, additional unpublished data was obtained on contacting corresponding authors of 5 RCTs. In pooled analysis, minocycline demonstrated a favorable trend towards 3-month functional independence (mRS-scores of 0–2) (RR = 1.31; 95% CI 0.98–1.74,
p
= 0.06) and 3-month BI (MD = 6.92; 95% CI − 0.92, 14.75;
p
= 0.08). In AIS subgroup, minocycline was associated with higher rates of 3-month mRS-scores of 0–2 (RR = 1.59; 95% CI 1.19–2.12,
p
= 0.002;
I
2
= 58%) and 3-month BI (MD = 12.37; 95% CI 5.60, 19.14,
p
= 0.0003;
I
2
= 47%), whereas reduced the 3-month NIHSS (MD − 2.84; 95% CI − 5.55, − 0.13;
p
= 0.04; I
2
= 86%). Minocycline administration was not associated with an increased risk of mortality, recurrent stroke, myocardial infarction and hemorrhagic conversion.
Conclusions
Although data is limited, minocycline demonstrated efficacy and seems a promising neuroprotective agent in acute stroke patients, especially in AIS subgroup. Further RCTs are needed to evaluate the efficacy and safety of minocycline among ICH patients.
The rapid growth in the number of vehicles has led to traffic congestion, pollution, and delays in logistic transportation in metropolitan areas. IoT has been an emerging innovation, moving the ...universe towards automated processes and intelligent management systems. This is a critical contribution to automation and smart civilizations. Effective and reliable congestion management and traffic control help save many precious resources. An IoT-based ITM system set of sensors is embedded in automatic vehicles and intelligent devices to recognize, obtain, and transmit data. Machine learning (ML) is another technique to improve the transport system. The existing transport-management solutions encounter several challenges resulting in traffic congestion, delay, and a high fatality rate. This research work presents the design and implementation of an Adaptive Traffic-management system (ATM) based on ML and IoT. The design of the proposed system is based on three essential entities: vehicle, infrastructure, and events. The design utilizes various scenarios to cover all the possible issues of the transport system. The proposed ATM system also utilizes the machine-learning-based DBSCAN clustering method to detect any accidental anomaly. The proposed ATM model constantly updates traffic signal schedules depending on traffic volume and estimated movements from nearby crossings. It significantly lowers traveling time by gradually moving automobiles across green signals and decreases traffic congestion by generating a better transition. The experiment outcomes reveal that the proposed ATM system significantly outperformed the conventional traffic-management strategy and will be a frontrunner for transportation planning in smart-city-based transport systems. The proposed ATM solution minimizes vehicle waiting times and congestion, reduces road accidents, and improves the overall journey experience.
Background
Randomized controlled clinical trials (RCT) have demonstrated varied efficacy of glucagon-like peptide-1 receptor (GLP-1R) agonists for cardiovascular outcomes. We sought to evaluate the ...efficacy and safety of GLP-1R agonists among patients with Type 2 diabetes mellitus (DM) for stroke prevention.
Methods
We conducted a systematic review and meta-analysis of RCTs reporting the following outcomes among patients with Type 2 DM treated with GLP-1R agonists (vs. placebo): nonfatal or fatal strokes, all-cause or cardiovascular mortality, myocardial infarction (MI) and major adverse cardiovascular events (MACE). The protocol of our systematic review and meta-analysis was registered to the PROSPERO database. We pooled odds ratios (OR) using random-effect models, and assessed the heterogeneity using Cochran
Q
and
I
2
statistics.
Results
We identified 8 RCTs, comprising 56,251 patients. In comparison to placebo, GLP-1R agonists reduced nonfatal strokes (OR 0.84; 95% CI 0.76–0.94,
p
= 0.002;
I
2
= 0%) and all strokes (OR 0.84; 95% CI 0.75–0.93,
p
= 0.001;
I
2
= 0%) by 16%. Overall, GLP-1R agonists reduced MACE by 13% (OR 0.87; 95% CI 0.81–0.94,
p
= 0.0003;
I
2
= 42%), cardiovascular mortality by 12% (OR 0.88; 95% CI 0.81–0.95;
p
= 0.002;
I
2
= 0%) and all-cause mortality by 12% (OR 0.88; 95% CI 0.82–0.95,
p
= 0.0007;
I
2
= 15%). Additional analyses demonstrated that GLP-1R agonists reduced the risk of incident MACE (OR 0.86; 95% CI 0.80–0.92;
p
< 0.0001;
I
2
= 0%) among patients with prior history of MI or nonfatal strokes.
Conclusions
Among patients with type 2 DM, GLP-1R agonists are beneficial for primary stroke, MACE, and cardiovascular mortality prevention. Further RCTs are needed to evaluate their role for secondary stroke prevention.
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