A Qualitative Approach to Analyze Marketing Communication Based on AIDA Model

In present spirited business environment, marketers and advertisers are facing trouble in the selection of viable marketing channels. Obviously, mobile phone and electronic mail are the two interesting communication sources. This study aims to emphasize on the effectiveness of mobile and email marketing channels using AIDA model. The study applied qualitative approach to collect data through semi-structured interviews from the registered customers of a retailer and whole sales company “Metro-Habib Cash & Carry Pakistan (MHCCP)â€. The collected data were analyzed through matrix structure by defining themes and color codes. The sub-rows and columns were re-arranged with the help of iterative approach to properly manage the data. The study clarified that mobile marketing channel creates better market attention, interest, desire and purchase action as compared to email marketing channel

Role of Cardiopulmonary Exercise Testing in Predicting Perioperative Outcomes in Cancer Patients Undergoing Thoracoabdominal Surgeries; an Observational Cohort Study

Introduction: The cancer patients are at a high risk of developing perioperative complications. Cardiopulmonary exercise testing (CPET) is a non-invasive, perioperative risk stratification tool that predicts perioperative morbidity and mortality. Prior literature has concluded that CPET has a valuable role in predicting post-operative complications in major surgical procedures. However, the data on the effectiveness of CPET in evaluating the perioperative risk in cancer-specific populations are limited. This study assessed the usefulness of CPET in perioperative risk stratification of patients with thoracoabdominal cancer who underwent elective major thoracoabdominal surgeries. Materials and Methods: A retrospective observational cohort study was conducted on cancer patients that underwent pre-operative CPET at Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan, from September 2017 to September 2019. All adult male and female patients with a significant medical history for cancer of the thoracoabdominal region who underwent CPET before a major thoracoabdominal surgery were included in the study. Results: A total of 32 patients were included in the present investigation. The mean age of the sample was 62.75 ± 10.18 years, and the majority of the participants were female. Following surgery, 53% of the participants had post-operative complications in terms of morbidity and mortality. Fifteen participants had an anaerobic threshold (AT) of ≥11.0 ml/ kg/min. Among these, 12 participants had an uneventful surgery. On the contrary, among 17 participants that were considered to have a high risk (<11.0 ml/kg/min) for surgery, 14 subjects (82%) had at least one complication (including mortality). The sensitivity and specificity of CPET to anticipate complications during oncological surgery were calculated to be 82% and 80%, respectively. The mean AT of participants with uneventful surgery was calculated to be 11.83 ± 1.01 ml/kg/min. This was statistically greater than the AT of subjects that had morbidity (9.86 ± 1.20 ml/kg/min) or mortality (8.95 ± 0.35 ml/kg/min) (P < 0.001). Conclusion: CPET, when using AT alone as an indicator, can provide a good-excellent prediction of perioperative outcome among oncology patients undergoing major thoracoabdominal surgical procedures

A novel routing optimization strategy based on reinforcement learning in perception layer networks

Wireless sensor networks have become incredibly popular due to the Internet of Things’ (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for low-power and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm’s effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio

Saliency guided Siamese attention network for infrared ship target tracking

Due to the lack of discriminative features in infrared images, most of existing trackers cannot separate a target from its background. There are some studies on generating discriminative features where feature fusion and attention are applied to enhance targets. However, the saliency information and information interaction which assist in locating the targets is ignored. To improve the accuracy of infrared ship target tracking, we propose a saliency guided Siamese attention network (SGSiamAttn). The main contribution is to design a saliency prediction network that obtains the saliency map of a search region and followed by a saliency enhancement network to highlight the target. With the saliency information, our network is able to perceive the entire target, which improves the discriminative ability and the tracking accuracy. Meanwhile, a local-to-global correlation module is applied before the saliency prediction network, aiming to refine the correlation map while suppressing non-target interferences. We also impose a shared cross-correlation module on the region proposal network. By sharing the correlation map in the classification and regression branches, it enhances information interaction between the two tasks and reduces the computational cost. As there are limited number of infrared ship tracking datasets publicly available, we construct a new infrared ship dataset (ISD) which includes 16 different types of ships and 7,872 video frames with manual annotations. The experimental results on ISD and other three public datasets, namely VOT-TIR2015, PTB-TIR, and LSOTB-TIR, demonstrate that our tracker achieves superior performance in terms of accuracy, expected average overlap, success, and precision

Two-stage Domain Adaptation for Infrared Ship Target Segmentation

Ship target segmentation in infrared scenes has always been a hot topic, since it is an important basis and prerequisite for infrared-guided weapons to reliably capture and recognize ship targets in the sea-level background. However, given the small target and fuzzy boundary characteristics of infrared ship images, obtaining accurate pixel-level labels for them is hardly achievable, which brings difficulty to train segmentation networks. To improve the segmentation accuracy of infrared ship images, we propose a two-stage domain adaptation method for infrared ship target segmentation (T-DANet), where the segmentation model is trained using visible ship images with clear target boundaries. In this case, the source domain is the labeled visible ship images, while the target domain is the unlabeled infrared ship images. Specifically, in the first stage, we use an image style transfer network to convert the infrared ship images into those with visible light style, so that the visual disparity between the two domain images can be reduced. Next, the visible, infrared, and converted infrared images are input into the Deeplab-v2 segmentation network for training, thereby obtaining the initial network weights. At this time, random attention modules are added separately to the low- and high-level spaces of Deeplab-v2, in order to improve its feature extraction capability. In the second stage, we mix the visible and infrared images through region mixing to acquire the mixed domain images, as well as their corresponding labels. Subsequently, Deeplab-v2 is further trained using the mixed domain images to attain better segmentation accuracy. Experimental results on both the home-made visible-infrared ship (VI-Ship) image dataset and the public infrared image dataset are superior to those existing mainstream methods, demonstrating its effectiveness

A cross-domain trust model of smart city IoT based on self-certification

Smart city refers to the information system with Internet of things and cloud computing as the core technology and government management and industrial development as the core content, forming a large-scale, heterogeneous and dynamic distributed Internet of things environment between different Internet of things. There is a wide demand for cooperation between equipment and management institutions in the smart city. Therefore, it is necessary to establish a trust mechanism to promote cooperation, and based on this, prevent data disorder caused by the interaction between honest terminals and malicious terminals. However, most of the existing research on trust mechanism is divorced from the Internet of things environment, and does not consider the characteristics of limited computing and storage capacity and large differences of Internet of things devices, resulting in the fact that the research on abstract trust mechanism cannot be directly applied to the Internet of things; On the other hand, various threats to the Internet of things caused by security vulnerabilities such as collision attacks are not considered. Aiming at the security problems of cross domain trusted authentication of Intelligent City Internet of things terminals, a cross domain trust model (CDTM) based on self-authentication is proposed. Unlike most trust models, this model uses self-certified trust. The cross-domain process of internet of things (IoT) terminal can quickly establish a trust relationship with the current domain by providing its trust certificate stored in the previous domain interaction. At the same time, in order to alleviate the collision attack and improve the accuracy of trust evaluation, the overall trust value is calculated by comprehensively considering the quantity weight, time attenuation weight and similarity weight. Finally, the simulation results show that CDTM has good anti collusion attack ability. The success rate of malicious interaction will not increase significantly. Compared with other models, the resource consumption of our proposed model is significantly reduced

Identity-based edge computing anonymous authentication protocol

With the development of sensor technology and wireless communication technology, edge computing has a wider range of applications. The privacy protection of edge computing is of great significance. In the edge computing system, in order to ensure the credibility of the source of terminal data, mobile edge computing (MEC) needs to verify the signature of the terminal node on the data. During the signature process, the computing power of edge devices such as wireless terminals can easily become the bottleneck of system performance. Therefore, it is very necessary to improve efficiency through computational offloading. Therefore, this paper proposes an identity-based edge computing anonymous authentication protocol. The protocol realizes mutual authentication and obtains a shared key by encrypting the mutual information. The encryption algorithm is implemented through a thresholded identity-based proxy ring signature. When a large number of terminals offload computing, MEC can set the priority of offloading tasks according to the user’s identity and permissions, thereby improving offloading efficiency. Security analysis shows that the scheme can guarantee the anonymity and unforgeability of signatures. The probability of a malicious node forging a signature is equivalent to cracking the discrete logarithm puzzle. According to the efficiency analysis, in the case of MEC offloading, the computational complexity is significantly reduced, the computing power of edge devices is liberated, and the signature efficiency is improved