This study evaluates factors influencing pregnancy rates per artificial insemination (P/AI) and pregnancy loss in Lohi ewes undergoing laparoscopic AI with frozen–thawed semen under sub‐tropical ...conditions. Data from three experiments comprising ewes (n = 358) of mixed parity (nulliparous; NP and parous; P), various body condition score (BCS) and assigned to long‐term (LTP, 11 days) and short‐term (STP, 5 days) oestrus synchronization regimen across high breeding season (HBS) and low breeding season (LBS) were analysed. Laparoscopic insemination was conducted 54 h post‐sponge removal. Pregnancy diagnosis and loss were evaluated on days 35 and 90 post‐insemination via ultrasonography. Results showed parity significantly influenced P/AI, with nulliparous ewes achieving higher pregnancy ratios than parous ewes (p = .001). BCS significantly influenced P/AI (p < .05), with a quadratic relationship observed between BCS and season (BCS*BCS*Season; p = .07). Progestin treatment did not significantly influence the ratio of pregnant ewes (p = .07). Pregnancy losses were significantly higher during LBS than HBS (p < .05), irrespective of progestin treatment. In conclusion, parity and BCS significantly influenced P/AI, with BCS demonstrating a quadratic association with season. Ewes bred during LBS experienced higher pregnancy losses than HBS, irrespective of progestin treatment.
With the rapid advancements of ubiquitous information and communication technologies, a large number of trustworthy online systems and services have been deployed. However, cybersecurity threats are ...still mounting. An intrusion detection (ID) system can play a significant role in detecting such security threats. Thus, developing an intelligent and accurate ID system is a non-trivial research problem. Existing ID systems that are typically used in traditional network intrusion detection system often fail and cannot detect many known and new security threats, largely because those approaches are based on classical machine learning methods that provide less focus on accurate feature selection and classification. Consequently, many known signatures from the attack traffic remain unidentifiable and become latent. Furthermore, since a massive network infrastructure can produce large-scale data, these approaches often fail to handle them flexibly, hence are not scalable. To address these issues and improve the accuracy and scalability, we propose a scalable and hybrid IDS, which is based on Spark ML and the convolutional-LSTM (Conv-LSTM) network. This IDS is a two-stage ID system: the first stage employs the anomaly detection module, which is based on Spark ML. The second stage acts as a misuse detection module, which is based on the Conv-LSTM network, such that both global and local latent threat signatures can be addressed. Evaluations of several baseline models in the ISCX-UNB dataset show that our hybrid IDS can identify network misuses accurately in 97.29% of cases and outperforms state-of-the-art approaches during 10-fold cross-validation tests.
Temporal and surgical risk dependent associations with clinical outcomes in patients receiving transcatheter versus surgical aortic valve implantation (TAVI vs SAVI) are uncertain. In this ...meta-analysis, 7 randomized controlled trials (7,771 patients) were included to investigate trends in outcomes in TAVI versus SAVI up to 5 years, and variation in outcomes with respect to low-, intermediate-, and high-surgical risk of the patients up to 1 year. Estimates were calculated as random effects hazard ratios (HRs) with 95% confidence intervals (CI). All-cause mortality was similar in TAVI and SAVI at 30 days (HR 0.81, 95% CI 0.55 to 1.21, p = 0.31), 1 year (HR 0.97, 95% CI 0.89 to 1.06, p = 0.49), 2 years (HR 0.96, 95 CI 0.85 to 1.09, p = 0.54), and 5 years (HR 1.04, 95% CI 0.89 to 1.21, p = 0.62). Cardiac mortality, myocardial infarction and stroke were similar in both interventions up to 5 years. TAVI was associated with lower risk of atrial fibrillation, but higher risk of vascular complications, pacemaker implantation, and paravalvular leak up to 5 years. The lower risks of major bleeding and acute kidney injury with TAVI versus SAVI were limited to 1 and 2 years, respectively. Compared with SAVI, TAVI was superior in reducing all-cause mortality in low surgical risk patients at 30 days only, whereas TAVI was noninferior to SAVI in intermediate- and high-risk patients at 30 days and across all risks at 1 year. In conclusion, TAVI was noninferior to SAVI in terms of mortality, myocardial infarction, and stroke up to 5 years. TAVI improved survival versus SAVI in low-risk patients at 30 days.
The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows ...the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.
Current research proposed a new auto system for skin lesions detection, recognition using pixel‐based seed segmented images fusion, and multilevel features reduction. Finally, using a SVM via cubic kernel functions, skin lesions are classified.
Immunotherapy, with an increasing number of therapeutic dimensions, is becoming an important mode of treatment for cancer patients. The inhibition of immune checkpoints, which are the source of ...immune escape for various cancers, is one such immunotherapeutic dimension. It has mainly been aimed at T cells in the past, but NK cells are a newly emerging target. Simultaneously, the number of checkpoints identified has been increasing in recent times. In addition to the classical NK cell receptors KIRs, LIRs, and NKG2A, several other immune checkpoints have also been shown to cause dysfunction of NK cells in various cancers and chronic infections. These checkpoints include the revolutionized CTLA-4, PD-1, and recently identified B7-H3, as well as LAG-3, TIGIT & CD96, TIM-3, and the most recently acknowledged checkpoint-members of the Siglecs family (Siglec-7/9), CD200 and CD47. An interesting dimension of immune checkpoints is their candidacy for dual-checkpoint inhibition, resulting in therapeutic synergism. Furthermore, the combination of immune checkpoint inhibition with other NK cell cytotoxicity restoration strategies could also strengthen its efficacy as an antitumor therapy. Here, we have undertaken a comprehensive review of the literature to date regarding NK cell-based immune checkpoints.
Virtual reality (VR) has been widely used as a tool to assist people by letting them learn and simulate situations that are too dangerous and risky to practice in real life, and one of these is road ...safety training for children. Traditional video- and presentation-based road safety training has average output results as it lacks physical practice and the involvement of children during training, without any practical testing examination to check the learned abilities of a child before their exposure to real-world environments. Therefore, in this paper, we propose a 3D realistic open-ended VR and Kinect sensor-based training setup using the Unity game engine, wherein children are educated and involved in road safety exercises. The proposed system applies the concepts of VR in a game-like setting to let the children learn about traffic rules and practice them in their homes without any risk of being exposed to the outside environment. Thus, with our interactive and immersive training environment, we aim to minimize road accidents involving children and contribute to the generic domain of healthcare. Furthermore, the proposed framework evaluates the overall performance of the students in a virtual environment (VE) to develop their road-awareness skills. To ensure safety, the proposed system has an extra examination layer for children’s abilities evaluation, whereby a child is considered fit for real-world practice in cases where they fulfil certain criteria by achieving set scores. To show the robustness and stability of the proposed system, we conduct four types of subjective activities by involving a group of ten students with average grades in their classes. The experimental results show the positive effect of the proposed system in improving the road crossing behavior of the children.
This paper aims to analyze the flow of second grade nanoliquid by a rotating disk. Nanofluid under investigation strongly depends upon Brownian motion and thermophoresis. Heat transfer is studied ...subject to dissipation and Joule heating. Governing problems are made dimensionless. After this the out coming problems for momentum, temperature and concentration are solved. The convergence criteria related to solutions is spelled out. Convergence interval for solutions is analyzed. Impact of different variables on velocity, concentration and temperature is elucidated by plotting graphs. Velocity and temperature gradients are calculated and discussed. The obtained results demonstrate that velocity field enhances for larger estimation of viscoelastic variable. Further results also demonstrate that velocity gradient has opposite effects for Hartman number and viscoelastic parameter. Temperature gradient is more for higher estimation of Reynolds number.
In recent years, the synthesis of ammonia (NH3) has been developed by electrocatalytic technology that is a potential way to effectively replace the Haber–Bosch process, which is an industrial ...synthesis of NH3. Industrial ammonia has caused a series of problems for the population and environment. In the face of sustainable green synthesis methods, the advantages of electrocatalytic nitrogen reduction for synthesis of NH3 in aqueous media have attracted a great amount of attention from researchers. This review summarizes the recent progress on the highly efficient electrocatalysts based on 2D non-metallic nanomaterial and provides a brief overview of the synthesis principle of electrocatalysis and the performance measurement indicators of electrocatalysts. Moreover, the current development of N2 reduction reaction (NRR) electrocatalyst is discussed and prospected.
The purpose of this work is to present the quantum Hermite–Hadamard inequality through the Green function approach. While doing this, we deduce some novel quantum identities. Using these identities, ...we establish some new inequalities in this direction. We contemplate the possibility of expanding the method, outlined herein, to recast the proofs of some known inequalities in the literature.
Smart cities have been developed over the past decade, and reducing traffic congestion has been the top concern in smart city development. Short delays in communication between vehicles and Roadside ...Units (RSUs), smooth traffic flow, and road safety are the key challenges of Intelligent Transportation Systems (ITSs). The rapid upsurge in the number of road vehicles has increased traffic congestion and the number of road accidents. To fix this issue, Vehicular Networks (VNs) have developed many new ideas, including vehicular communications, navigation, and traffic control. Machine Learning (ML) is an efficient approach to finding hidden insights into ITS without being programmed explicitly by learning from data. This research proposed a fusion-based intelligent traffic congestion control system for VNs (FITCCS-VN) using ML techniques that collect traffic data and route traffic on available routes to alleviate traffic congestion in smart cities. The proposed system provides innovative services to the drivers that enable a view of traffic flow and the volume of vehicles available on the road remotely, intending to avoid traffic jams. The proposed model improves traffic flow and decreases congestion. The proposed system provides an accuracy of 95% and a miss rate of 5%, which is better than previous approaches.
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