Development of an active fixture for ultrasonically assisted micro electro-discharge machining

Micromachining technologies have enjoyed a recent resurgence due to massive demands in many engineering, production and manufacturing sectors. Micro Electric Discharge Machining (μ-EDM) is one of the most popular techniques available to produce microscopic features and components for various industries. This technique can ensure better machining performance in terms of reduced Heat Affected Zones and surface finishing. It also comes with inherent disadvantages such as high machining time, low material removal rate (MRR) and unstable machining. To overcome these factors vigorous flushing of dielectric fluid is performed. The flushing is achieved through imparting ultrasonic vibration on either of the tool, dielectric fluid or workpiece. The vibration aids in carrying away the debris accumulated in the spark-gap region. In this paper, a novel design of an ultrasonic vibration fixture has been proposed. This fixture will facilitate vibration of the workpiece that is required to improve machining performance. Further enhancement of the design leads to better machining performance. System Identification helps to determine the nature of the system and model the input-output response. The oscillation of the system can be easily characterized and validated using System Identification. Machining results are compared to gain some more insight about the nature of ultrasonic vibration assisted μ-EDM

Target coverage through distributed clustering in directional sensor networks

Maximum target coverage with minimum number of sensor nodes, known as an MCMS problem, is an important problem in directional sensor networks (DSNs). For guaranteed coverage and event reporting, the underlying mechanism must ensure that all targets are covered by the sensors and the resulting network is connected. Existing solutions allow individual sensor nodes to determine the sensing direction for maximum target coverage which produces sensing coverage redundancy and much overhead. Gathering nodes into clusters might provide a better solution to this problem. In this paper, we have designed distributed clustering and target coverage algorithms to address the problem in an energy-efficient way. To the best of our knowledge, this is the first work that exploits cluster heads to determine the active sensing nodes and their directions for solving target coverage problems in DSNs. Our extensive simulation study shows that our system outperforms a number of state-of-the-art approaches

Hybrid multi-independent mmWave MNOs assessment utilising spectrum sharing paradigm for 5G networks

Spectrum sharing paradigm (SSP) has recently emerged as an attractive solution to provide capital expenditure (CapEx) and operating expenditure (OpEx) savings and to enhance spectrum utilization (SU). However, practical issues concerning the implementation of such paradigm are rarely addressed (e.g., mutual interference, fairness, and mmWave base station density). Therefore, in this paper, we proposed ultra-reliable and proportionally fair hybrid spectrum sharing access strategy that aims to address the aforementioned aspects as a function of coverage probability (CP), average rate distributions (ARD), and the number of mmWave base stations (mBSs). In this strategy, the spectrum is sliced into three parts (exclusive, semi-pooled, and fully pooled). A typical user that belongs to certain operator has the right to occupy a part of the spectrum available in the high and low frequencies (28 and 73 GHz) based on an adaptive multi-state mmWave cell selection scheme (AMMC-S) which associates the user with the tagged mBS that offers a highest SINR to maintain more reliable connection and enrich the user experience. Numerical results show that significant improvement in terms of ARD, CP, fairness among operators, and maintain an acceptable level of mBSs density

Energy-efficient user association mechanism enabling fully hybrid spectrum sharing among multiple 5G cellular operators

Spectrum sharing (SS) is a promising solution to enhance spectrum utilization in future cellular systems. Reducing the energy consumption in cellular networks has recently earned tremendous attention from diverse stakeholders (i.e., vendors, mobile network operators (MNOs), and government) to decrease the CO2 emissions and thus introducing an environment-friendly wireless communication. Therefore, in this paper, joint energy-efficient user association (UA) mechanism and fully hybrid spectrum sharing (EE-FHSS) approach is proposed considering the quality of experience QoE (i.e., data rate) as the main constraint. In this approach, the spectrum available in the high and low frequencies (28 and 73 GHz) is sliced into three portions (licensed, semi-shared, and fully-shared) aims to serve the users (UEs) that belong to four operators in an integrated and hybrid manner. The performance of the proposed QoE-Based EE UA-FHSS is compared with the well-known maximum signal-to-interference-plus-noise ratio (max-SINR UA-FHSS). Numerical results show that remarkable enhancement in terms of EE for the four participating operators can be achieved while maintaining a high degree of QoE to the UEs

A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification

Brain cancer classification is an important step that depends on the physician's knowledge and experience. An automated tumor classification system is very essential to support radiologists and physicians to identify brain tumors. However, the accuracy of current systems needs to be improved for suitable treatments. In this paper, we propose a hybrid feature extraction method with a regularized extreme learning machine (RELM) for developing an accurate brain tumor classification approach. The approach starts by preprocessing the brain images by using a min–max normalization rule to enhance the contrast of brain edges and regions. Then, the brain tumor features are extracted based on a hybrid method of feature extraction. Finally, a RELM is used for classifying the type of brain tumor. To evaluate and compare the proposed approach, a set of experiments is conducted on a new public dataset of brain images. The experimental results proved that the approach is more effective compared with the existing state-of-the-art approaches, and the performance in terms of classification accuracy improved from 91.51% to 94.233% for the experiment of the random holdout technique

Synthesis of Boron-Doped Zinc Oxide Nanosheets by Using Phyllanthus Emblica Leaf Extract: A Sustainable Environmental Applications

The use of Phyllanthus emblica (gooseberry) leaf extract to synthesize Boron-doped zinc oxide nanosheets (B-doped ZnO-NSs) is deliberated in this article. Scanning electron microscopy (SEM) shows a network of synthesized nanosheets randomly aligned side by side in a B-doped ZnO (15 wt% B) sample. The thickness of B-doped ZnO-NSs is in the range of 20–80 nm. B-doped ZnO-NSs were tested against both gram-positive and gram-negative bacterial strains including Staphylococcus aureus, Pseudomonas aeruginosa, Klebsiella pneumonia, and Escherichia coli. Against gram-negative bacterium (K. pneumonia and E. coli), B-doped ZnO displays enhanced antibacterial activity with 26 and 24 mm of inhibition zone, respectively. The mass attenuation coefficient (MAC), linear attenuation coefficient (LAC), mean free path (MFP), half-value layer (HVL), and tenth value layer (TVL) of B-doped ZnO were investigated as aspects linked to radiation shielding. These observations were carried out by using a PTW® electron detector and VARIAN® irradiation with 6 MeV electrons. The results of these experiments can be used to learn more about the radiation shielding properties of B-doped ZnO nanostructures

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Eosinophilic Enteritis: A Delayed Diagnosis

Eosinophilic gastrointestinal disorders are a rare and complex group of disorders that are characterized by eosinophilic infiltration of the gastrointestinal tract. Patients often present with a wide range of signs and symptoms as any length or layer of the GI tract can be involved such as mucosal, muscular, or serosal. As a part of the workup, patients frequently undergo computed tomography scans and multiple endoscopies before the diagnosis is finally made as was true in our case of a 59-year-old male patient presenting with 2 months of nausea, abdominal pain, and weight loss. He underwent esophagogastroduodenoscopies, colonoscopies, video capsule study, and balloon enteroscopy before the diagnosis was confirmed histologically. Endoscopic and radiographic findings can be variable and are usually unpredictable. The diagnosis is confirmed on histopathological examination of biopsies that must show >15-50 eosinophils/high-power field based on the location in the GI tract. In our patient, erythema, scalloping, whitish exudate, and patches of villous blunting were noted in the duodenum to proximal ileum endoscopically with >50 eosinophils/high-power field confirming the diagnosis of eosinophilic enteritis. This class of diseases is often found in patients with a history of allergic disorders suggestive of hypersensitivity in the etiology of the disease although our patient had no such known history. Elimination diets and steroids are the mainstay of therapy and often lead to complete resolution of symptoms as well as endoscopic and radiographic findings in up to 90% of patients as was seen in our patient, although some patients have a chronic remitting course