Optimizing the beacon exchange rate for proactive autonomic configuration in ubiquitous MANETs

Proactive self-configuration is indispensable for MANETs like ubiquitous sensor networks (USNs), as component devices of the network are usually exposed to natural or man-made disasters due to the hostile deployment and ad hoc nature of the USNs. Network state beacons (NSBs) are exchanged among the key nodes of the network for crucial and effective monitoring of the network for steady state operation. The rate of beacon exchange (F/sub E/) and its contents, define the time and nature of the proactive action. Therefore it is very important to optimize these parameters to tune the functional response of the USN. This paper presents a comprehensive model for monitoring and proactively reconfiguring the network by optimizing the F/sub E/. The results confirm the improved throughput while maintaining QoS over longer periods of network operation

Distributed and Load-Adaptive Self Configuration in Sensor Networks

Proactive self-configuration is crucial for MANETs such as sensor networks, as these are often deployed in hostile environments and are ad hoc in nature. The dynamic architecture of the network is monitored by exchanging so-called Network State Beacons (NSBs) between key network nodes. The Beacon Exchange rate and the network state define both the time and nature of a proactive action to combat network performance degradation at a time of crisis. It is thus essential to optimize these parameters for the dynamic load profile of the network. This paper presents a novel distributed adaptive optimization Beacon Exchange selection model which considers distributed network load for energy efficient monitoring and proactive reconfiguration of the network. The results show an improvement of 70% in throughput, while maintaining a guaranteed quality-of- service for a small control-traffic overhead

Feature-Oriented Modelling Using Event-B

Event-B is a formal method for specification and verification of reactive systems. Its Rodin toolkit provides comprehensive support for modelling, refinement and analysis using theorem proving, animation and model checking. There has always been a need to reuse existing models and their associated proofs when modelling related systems to save time and effort. Software product lines (SPLs) focus on the problem of reuse by providing ways to build software products having commonalities and managing variations within products of the same family. Feature modelling is a well know technique to manage variability and configure products within the SPLs. We have combined the two approaches to formally specify SPLs using Event-B. This will contribute the concept of formalism to SPLs and re-usability to Event-B. Existing feature modelling notations were adapted and extended to include refinement mechanism of Event-B. An Eclipse-based graphical feature modelling tool has been developed as a plug-in to the Rodin platform. We have modelled the "production cell" case-study in Event-B, an industrial metal processing plant, which has previously been specified in a number of formalisms. We have also highlighted future directions based on our experience with this framework so far

Investigation Into Laser Shock Processing

Laser shock processing is a good candidate for surface industry due to its rapid processing, localized ablation, and precision of operation. In the current study, laser shock processing of steel was considered. The numerical solutions for temperature rise and recoil pressure development across the interface of the ablating front and solid are presented. The propagation of elastic-plastic waves in the solid due to recoil pressure loading at the surface is analyzed and numerical solution for the wave propagation was obtained. An experiment was conducted to ablate the steel surfaces for shock processing. Scanning electron microscopy was carried out to examine the ablated surfaces shock processing while transmission electron microscopy was conducted to obtain dislocation densities after the shock processing. It was found that surface hardness of the workpiece increased in the order of 1.8 times of the base material hardness, and the dislocation was the main source of the shock hardening in the region affected by laser shock processing

Laser Induced Breakdown Spectroscopy For Detection Of Heavy Metals In Cancerous And Healthy Colon Tissues

Cancer mortalities are common due to the lack of diagnostic at the early stages in many countries. Recent studies discovered that the heavy metals in the human colon could cause the colon cancer. The conventional cancer detection techniques suffer from the insensitiveness, imprecision, slowness, cumbersomeness of sample preparation, and some time show conflicting results. Hence an accurate, reliable, and rapid detection technique is essential for the early diagnostic and prevention of heavy metals accumulation induced colon cancers. In this work, calibration-free laser-induced breakdown spectrometer (LIBS) was applied on several cancerous and normal colon tissues collected from the colon cancer infested patients aged 40 — 60 years. The results showed the presence of carcinogenic heavy metals including lead (Pb), chromium (Cr), and mercury (Hg) in the malignant colon tissues, while the healthy tissues were devoid of these elements. The accuracy of the LIBS results was validated by comparing the results obtained using a standard inductively coupled plasma atomic emission spectroscopy (ICP-OES). This study demonstrated that LIBS technique is very effective for rapid, precise early detection of the heavy metals accumulation in malignant colon tissues

Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images

Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly shown to work with medical image data which often poses a challenge as the decision for a class relies on different lesion areas scattered around the entire image. Using the DiaretDB1 dataset, we show that on retina images different lesion areas fundamental for diabetic retinopathy are detected on an image level with high accuracy, comparable or exceeding supervised methods. On lesion level, we achieve few false positives with high sensitivity, though, the network is solely trained on image-level labels which do not include information about existing lesions. Classifying between diseased and healthy images, we achieve an AUC of 0.954 on the DiaretDB1.Comment: Accepted in Proc. IEEE International Conference on Image Processing (ICIP), 201

Cross-compiler bipartite vulnerability search

Open-source libraries are widely used in software development, and the functions from these libraries may contain security vulnerabilities that can provide gateways for attackers. This paper provides a function similarity technique to identify vulnerable functions in compiled programs and proposes a new technique called Cross-Compiler Bipartite Vulnerability Search (CCBVS). CCBVS uses a novel training process, and bipartite matching to filter SVM model false positives to improve the quality of similar function identification. This research uses debug symbols in programs compiled from open-source software products to generate the ground truth. This automatic extraction of ground truth allows experimentation with a wide range of programs. The results presented in the paper show that an SVM model trained on a wide variety of programs compiled for Windows and Linux, x86 and Intel 64 architectures can be used to predict function similarity and that the use of bipartite matching substantially improves the function similarity matching performance. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

A Comparative Study of Personality Dynamics of Family and Non-Family Entrepreneurs and their Impact on Organizational Effectiveness

The current research focuses on the exploration of the big five personality traits model and its impact on organizational effectiveness in the restaurant industry entrepreneurs of Lahore, Pakistan. The personality traits of different entrepreneurs working in family and non-family owned enterprises are analyzed along with their effectiveness. Data collection mode was survey and data was collected through structured questionnaire. Purposive non-probability sampling technique was used for sample selection. Questionnaire was responded by 110 entrepreneurs (55 family entrepreneurs and 55 non-family entrepreneurs) of restaurant industry. The research findings reveal that all of the personality items have a positive correlation with the organizational effectiveness except neuroticism, yet these personality traits have variable impact on the effectiveness of restaurants. For family entrepreneurs, the traits of extrovert, neuroticism, openness to experience and agreeableness have significant impact on the effectiveness of restaurants whereas for non-family restaurants only conscientiousness trait has a significant impact on restaurants’ effectiveness. Keywords: family entrepreneurs, non-family entrepreneurs, conscientiousness, extrovert, neuroticism, openness to experience, agreeableness, organizational effectiveness.