Dendritic cells (DCs) are professional antigen-presenting cells (APCs) that play a critical role to activate immune response. They may be targeted for immunomodulation by microbes, including probiotics. In this study, chicken bone marrow dendrite cells (chi-BMDCs) were stimulated with lipopolysachride (LPS), Saccharomyces boulardii (Sb), Bacillus subtilis B10 (Bs), co-culture of Sb + Bs and phosphate buffer saline (PBS) as a control group (Ctr) at 3, 6, and 12 h intervals. Results revealed that treatment groups modulated the phenotype and biological functions of chi-BMDCs. Scan electron microscopy showed attachment of probiotics on the surface of chi-BMDCs. Additionally transmission electron microscopy (TEM) revealed efficiently engulfing and degradation of probiotics. Gene expression levels of MHC-II, CD40, CD80 and CD86 up-regulated in stimulated groups. Furthermore, toll-like receptors TLR1, TLR2, TLR4, and chicken specific TLR15 expressions were improved and downstream associated factors MyD88, TRAF6, TAB1, and NFκ-B mRNA levels increased in all treatment groups as compared to control. Surprisingly, NFκ-B response was noted significant higher in LPS treatment among all groups. Moreover, IL-1β, IL-17, IL-4, TGF-β, and IL-10 production levels were found higher, and lower concentration of INF-γ and IL-8 were observed in Sb, Bs, and Sb + Bs treatment groups. In contrast, LPS groups showed prominent increase in IL-12, INF-γ, and IL-8 concentration levels as compared to control group. Altogether, these results emphasize a potentially important role of Saccharomyces boulardii and Bacillus subtilis B10 in modulating immunological functions of chi-BMDCs by targeting specific toll like receptors (TLRs) and associated factors. The role of probiotics on chi-BMDCs functionality in a non-mammalian species have been presented for the first time.

No abstract

Flying Ad-hoc Network (FANET) is a new class of Mobile Ad-hoc Network in which the nodes move in three-dimensional (3-D) ways in the air simultaneously. These nodes are known as Unmanned Aerial Vehicles (UAVs) that are operated live remotely or by the predefined mechanism which involves no human personnel. Due to the high mobility of nodes and dynamic topology, link stability is a research challenge in FANET. From this viewpoint, recent research has focused on link stability with the highest threshold value by maximizing Packet Delivery Ratio and minimizing End-to-End Delay. In this paper, a hybrid scheme named Delay and Link Stability Aware (DLSA) routing scheme has been proposed with the contrast of Distributed Priority Tree-based Routing and Link Stability Estimationbased Routing FANET's existing routing schemes. Unlike existing schemes, the proposed scheme possesses the features of collaborative data forwarding and link stability. The simulation results have shown the improved performance of the proposed DLSA routing protocol in contrast to the selected existing ones DPTR and LEPR in terms of E2ED, PDR, Network Lifetime, and Transmission Loss. The Average E2ED in milliseconds of DLSA was measured 0.457 while DPTR was 1.492 and LEPR was 1.006. Similarly, the Average PDR in %age of DLSA measured 3.106 while DPTR was 2.303 and LEPR was 0.682. The average Network Lifetime of DLSA measured 62.141 while DPTR was 23.026 and LEPR was 27.298. At finally, the Average Transmission Loss in dBm of DLSA measured 0.975 while DPTR was 1.053 and LEPR was 1.227.

Car crashes are among the top ten leading causes of death; they could mainly be attributed to distracted drivers. An advanced driver-assistance technique (ADAT) is a procedure that can notify the driver about a dangerous scenario, reduce traffic crashes, and improve road safety. The main contribution of this work involved utilizing the driver’s attention to build an efficient ADAT. To obtain this “attention value”, the gaze tracking method is proposed. The gaze direction of the driver is critical toward understanding/discerning fatal distractions, pertaining to when it is obligatory to notify the driver about the risks on the road. A real-time gaze tracking system is proposed in this paper for the development of an ADAT that obtains and communicates the gaze information of the driver. The developed ADAT system detects various head poses of the driver and estimates eye gaze directions, which play important roles in assisting the driver and avoiding any unwanted circumstances. The first (and more significant) task in this research work involved the development of a benchmark image dataset consisting of head poses and horizontal and vertical direction gazes of the driver’s eyes. To detect the driver’s face accurately and efficiently, the You Only Look Once (YOLO-V4) face detector was used by modifying it with the Inception-v3 CNN model for robust feature learning and improved face detection. Finally, transfer learning in the InceptionResNet-v2 CNN model was performed, where the CNN was used as a classification model for head pose detection and eye gaze angle estimation; a regression layer to the InceptionResNet-v2 CNN was added instead of SoftMax and the classification output layer. The proposed model detects and estimates head pose directions and eye directions with higher accuracy. The average accuracy achieved by the head pose detection system was 91%; the model achieved a RMSE of 2.68 for vertical and 3.61 for horizontal eye gaze estimations.

It is critical for intelligent vehicles to be capable of monitoring the health and well-being of the drivers they transport on a continuous basis. This is especially true in the case of autonomous vehicles. To address the issue, an automatic system is developed for driver’s real emotion recognizer (DRER) using deep learning. The emotional values of drivers in indoor vehicles are symmetrically mapped to image design in order to investigate the characteristics of abstract expressions, expression design principles, and an experimental evaluation is conducted based on existing research on the design of driver facial expressions for intelligent products. By substituting a custom-created CNN features learning block with the base 11 layers CNN model in this paper for the development of an improved faster R-CNN face detector that detects the driver’s face at a high frame per second (FPS). Transfer learning is performed in the NasNet large CNN model in order to recognize the driver’s various emotions. Additionally, a custom driver emotion recognition image dataset is being developed as part of this research task. The proposed model, which is a combination of an improved faster R-CNN and transfer learning in NasNet-Large CNN architecture for DER based on facial images, enables greater accuracy than previously possible for DER based on facial images. The proposed model outperforms some recently updated state-of-the-art techniques in terms of accuracy. The proposed model achieved the following accuracy on various benchmark datasets: JAFFE 98.48%, CK+ 99.73%, FER-2013 99.95%, AffectNet 95.28%, and 99.15% on a custom-developed dataset.

The present study was conducted to compare the achievement based performance of the public and private secondary schools on different variables such as leader’s leadership styles, management practices and physical facilities provided in these schools. Two public and two private secondary schools were selected as case study from Tehsil Lal Qilla on the basis of convenience and accessibility. The stakeholder’s views approach was adopted for case study. The secondary school principals, teachers, students of 9th and 10th classes and their parents were identified as stakeholders of these schools. Interview protocol for each stakeholder, observation and document analysis were used as instruments of data collection. In this way interviews were conducted for these cases including interviews from public and private secondary schools. The school management/leadership, practices of heads, implementation of departmental policies, teaching learning environment and discipline in schools, lesson planning, available facilities, co-curricular activities and future developmental plans etc. were explored in different interview protocols from each stakeholder. Data was analyzed by using qualitative analysis methods. It was found that, the public schools have better facilities, spacious buildings, highly qualified staff and people oriented management styles as compared to private schools. The heads and teachers of private schools desired to shift in public schools. It is recommended that Government should bound the private sector to provide infrastructure and facilities to the students similar to the public schools.

No abstract

Unmanned Aerial Ad-hoc Network (UAANET) also knows as by the name of Flying Ad-hoc NETwork (FANET) is a new class of Mobile Ad-hoc NETwork (MANET) in which the nodes move in three dimensional (3-D) ways in the air simultaneously. These nodes are known as Unmanned Aerial Vehicles (UAVs) that are operated live remotely or by pre-defined mechanism which involve no human personnel. Due to high mobility of nodes and dynamic topology, the link stability is a research challenge in FANET. From this viewpoint, recent research has focused on link stability with highest threshold value by maximizing Packet Delivery Ratio (PDR) and minimizing End-to-End Delay (E2ED). In this research, a hybrid scheme named Delay and Link Stability Aware (DLSA) routing scheme has been proposed with the contrast of Distributed Priority Tree-based Routing (DPTR) and Link Stability Estimation-based Routing (LEPR) FANET’s existing routing schemes. Unlike existing schemes, the proposed scheme possesses the features in collaborative data forwarding and link stability by merging the positive features of DPTR and LEPR. The link stability via maximum threshold value has been introduced to acquire and select the most feasible route from source to destination. The simulation was carried out using Matrix Laboratory (MATLAB) tool for the concerned research. Simulation results have showed improved performance of the proposed protocol in contrast to the selected existing ones in terms of E2ED, PDR, Network Lifetime and Transmission Loss. Average E2ED in (milliseconds) of DLSA measured 0.457, while DPTR was 1.492 and LEPR was 1.006. Similarly, Average PDR (in %age) of DLSA measured 3.106, while DPTR was 2.303 and LEPR was 0.682. Average Network Lifetime in (seconds) for DLSA measured 62.141, while DPTR was 23.036 and LEPR was 27.298. Average Transmission Loss in (dBm) for DLSA measured 0.975, while DPTR was 1.053 and LEPR was 1.227.