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"On Complete Targets Coverage in Rechargeable IoT Networks: A Message-P" by Zilin Song, Kwan Wu Chin et al.

This article studies targets coverage in an energy harvesting (EH) Internet of Things (IoT) network. Specifically, it addresses the problem of activating subsets of EH sensor nodes to monitor targets, such as valuable assets or the ingresses and egresses of a building. A key requirement, so called complete targets coverage, is that all targets must be monitored by at least one sensor node at all times. To meet this requirement, we need to derive set covers, where each set cover is comprised of one or more sensor nodes that are activated simultaneously in each time slot. To this end, we show for the first time how the belief propagation message-passing framework can be used to derive these set covers. Advantageously, our approach does not require future energy arrivals information at devices. The results show that our message-passing approach is within 90% of the optimal coverage lifetime. ....

Causal Energy Arrivals , Graphical Models ,

"Optimization of operating conditions in the biological enzymes for eff" by Xu Kang, Chaolin Li et al.

The sludge dewaterability is essential for waste activated sludge (WAS) treatment and disposal in wastewater treatment plants (WWTPs). The biological enzyme conditioning process is a promising method to enhance the dewaterability of WAS. In this study, the optimal conditions in terms of pH, temperature, bio-enzyme dosage, and treatment time for five kinds of biological enzymes (α-amylase, cellulase, acidic protease, neutral protease, and alkaline protease) were investigated. Among them, α-amylase and neutral protease showed good performance in conditional optimization experiments. After biological enzyme conditioning, the sludge supernatant of proteins, polysaccharides, and SCOD contents increased. The sludge water content (Wc) decreased, while the capillary suction time (CST) increased. The optimal conditions for α-amylase were pH 6, 45 ℃ of temperature, 30 mg/g TSS of dosage, and 3 h of treatment time, under which the lowest Wc can reach 68.67%. The optimal conditions for neutra ....

Biological Enzyme , Operating Condition , Ludge Conditioning , Ludge Dewaterability ,

"Byproduct use optimization in a combined sinter plant and blast furnac" by C. McMahon, I. Cameron et al.

Integrated steelplants produce a range of byproducts. An increased focus on sustainability and a global drive to develop a circular economy has shifted focus from byproducts that can be reused without significant operational impact to byproducts that have physical or chemical characteristics that complicate reuse. A case study of byproduct reuse was undertaken using an existing integrated SP and BF model that included ten trace elements considered critical for the assessment of byproduct reuse: As, Cd, Cu, Hg, Ni, Pb, Ti, V, Se, and Zn. The case study presents work undertaken to optimize the use of biochar and the reuse of byproduct activated carbon in the BF. Scenarios also include the reuse of basic oxygen furnace (BOF) filter cake (FC) in the BF, and the use of BF flue dust in both the SP and BF. Significant rates of biochar use were noted, with maximum biochar use not limited by the assumed As and Pb concentrations. The activated carbon byproduct could be reused at its production r ....

Aspen Custom Modeler , Yproduct Reuse , Ntegrated Steelmaking , Process Integration , Trace Elements ,

"Enhanced framework for COVID-19 prediction with computed tomography sc" by Anand Motwani, Piyush Kumar Shukla et al.

Recent studies have shown that computed tomography (CT) scan images can characterize COVID-19 disease in patients. Several deep learning (DL) methods have been proposed for diagnosis in the literature, including convolutional neural networks (CNN). But, with inefficient patient classification models, the number of ‘False Negatives’ can put lives at risk. The primary objective is to improve the model so that it does not reveal ‘Covid’ as ‘Non-Covid’. This study uses Dense-CNN to categorize patients efficiently. A novel loss function based on cross-entropy has also been used to improve the CNN algorithm's convergence. The proposed model is built and tested on a recently published large dataset. Extensive study and comparison with well-known models reveal the effectiveness of the proposed method over known methods. The proposed model achieved a prediction accuracy of 93.78%, while false-negative is only 6.5%. This approach's significant advantage is accelerating the ....

False Negative , Chest Ct Images , Covid 19 , Deep Learning , Ense Convolutional Neural Network , Loss Function , Ars Cov 2 ,

"Alpha Harris Hawks Optimization based Overcurrent Relay Coordination w" by Mian Rizwan, Lucheng Hong et al.

The conventional protection schemes may lead towards the inadvertent operation of the protection devices with the increased penetration of renewable energy sources based distributed generation (RES-DG) in distribution networks (DN). The miscoordination and the malfunctioning of directional overcurrent relays (DOCR) may occur due to significant change in the fault current level (FCL) and the change in the network topology form the radial to ring topology caused by the RES-DG. Optimization algorithms (OA) and modification in relay characteristics were mostly used to tackle the DOCR miscoordination problem. However, in this paper, a fast and an accurate protection coordination scheme is proposed to eliminate the DOCR miscoordination and to reduce the overall operation time of DOCRs by employing an advance optimization algorithm along with an innovative modification in the relay characteristics. In the first step, time-current characteristics (TCC) of the conventional DOCR are modified to ....

Harris Hawk , Directional Overcurrent Relay , Ybrid Tcv , Es Dgs ,