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  • Open Access

    ARTICLE

    Convolutional Neural Network Model for Fire Detection in Real-Time Environment

    Abdul Rehman, Dongsun Kim*, Anand Paul

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2289-2307, 2023, DOI:10.32604/cmc.2023.036435

    Abstract Disasters such as conflagration, toxic smoke, harmful gas or chemical leakage, and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent. The calamities are causing massive fiscal and human life casualties. However, Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco. The authors have proposed an early fire detection system uses machine and/or deep learning algorithms. The article presents an Intelligent Industrial Monitoring System (IIMS) and introduces an Industrial Smart Social Agent (ISSA) in the Industrial SIoT (ISIoT) paradigm. The proffered ISSA empowers smart… More >

  • Open Access

    ARTICLE

    Strategic Contracting for Software Upgrade Outsourcing in Industry 4.0

    Cheng Wang1,2,*, Zhuowei Zheng1

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1563-1592, 2024, DOI:10.32604/cmes.2023.031103

    Abstract The advent of Industry 4.0 has compelled businesses to adopt digital approaches that combine software to enhance production efficiency. In this rapidly evolving market, software development is an ongoing process that must be tailored to meet the dynamic needs of enterprises. However, internal research and development can be prohibitively expensive, driving many enterprises to outsource software development and upgrades to external service providers. This paper presents a software upgrade outsourcing model for enterprises and service providers that accounts for the impact of market fluctuations on software adaptability. To mitigate the risk of adverse selection due to asymmetric information about the… More >

  • Open Access

    VIEWPOINT

    Future of the current anticoronaviral agents: A viewpoint on the validation for the next COVIDs and pandemics

    AMGAD M. RABIE*

    BIOCELL, Vol.47, No.10, pp. 2133-2139, 2023, DOI:10.32604/biocell.2023.030057

    Abstract Despite the global decline in the severity of the coronavirus disease 2019 (COVID-19) cases, the disease still represents a major concern to the relevant scientific and medical communities. The primary concern of drug scientists, virologists, and other concerned specialists in this respect is to find ready-to-use suitable and potent anticoronaviral therapies that are broadly effective against the different species/strains of the coronaviruses in general, not only against the current and previous coronaviruses (e.g., the recently-appeared severe acute respiratory syndrome coronavirus 2 “SARS-CoV-2”), i.e., effective antiviral agents for treatment and/or prophylaxis of any coronaviral infections, including those of the coming ones… More > Graphic Abstract

    Future of the current anticoronaviral agents: A viewpoint on the validation for the next COVIDs and pandemics

  • Open Access

    ARTICLE

    Optimal Concentration of the Bubble Drainage Agent in Foam Drainage Gas Recovery Applications

    Shaopeng Liu1, Guowei Wang2,3,*, Pengfei Liu1, Dong Ye1, Jian Song1, Xing Liu1, Yang Cheng2,3

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.12, pp. 3045-3058, 2023, DOI:10.32604/fdmp.2023.029810

    Abstract Foam drainage is the flow of liquid through the interstitial spaces between bubbles driven by capillarity and gravity and resisted by viscous damping. The so-called foam drainage gas recovery technology is a technique traditionally used to mitigate the serious bottom-hole liquid loading in the middle and late stages of gas well production. In this context, determining the optimal concentration of the bubble drainage agent is generally crucial for the proper application of this method. In this study, a combination of indoor experiments and theoretical analysis have been used to determine the pressure drop related to the foam-carrying capacity in a… More >

  • Open Access

    ARTICLE

    Characterization of Endophytic Microorganisms of Rice (Oryza sativa L.) Potentials for Blast Disease Biocontrol and Plant Growth Promoting Agents

    Shugufta Parveen1, Fayaz A. Mohiddin2,*, M. Ashraf Bhat3, Zahoor Ahmed Baba4, Fehim Jeelani5, M. Anwar Bhat6, Sajad Un Nabi7, Burhan Hamid2, Saba Bandey8, Farhanaz Rasool9, Zakir Amin1, Ibrahim Al-Ashkar10,*, Muhammad Adnan11, Ayman El Sabagh12

    Phyton-International Journal of Experimental Botany, Vol.92, No.11, pp. 3021-3041, 2023, DOI:10.32604/phyton.2023.030921

    Abstract One hundred twenty-five endophytic microorganisms were isolated from the roots, stems, and leaves of four prominent rice cultivars growing in temperate regions. Their potential to combat rice blast disease and promote plant growth was investigated. The dual culture tests highlighted the strong antagonistic activity of five fungal (ranging from 89%–70%) and five bacterial (72%–61%) endophytes. Subsequent examination focused on volatile compounds produced by selected isolates to counter the blast pathogen. Among these, the highest chitinase (13.76 µg mL−1) and siderophore (56.64%), was exhibited by Aspergillus flavus, and the highest HCN production was shown by Stenotrophomonas maltophilia (36.15 µM mL−1). In… More >

  • Open Access

    ARTICLE

    Multi-Agent Deep Reinforcement Learning for Efficient Computation Offloading in Mobile Edge Computing

    Tianzhe Jiao, Xiaoyue Feng, Chaopeng Guo, Dongqi Wang, Jie Song*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3585-3603, 2023, DOI:10.32604/cmc.2023.040068

    Abstract Mobile-edge computing (MEC) is a promising technology for the fifth-generation (5G) and sixth-generation (6G) architectures, which provides resourceful computing capabilities for Internet of Things (IoT) devices, such as virtual reality, mobile devices, and smart cities. In general, these IoT applications always bring higher energy consumption than traditional applications, which are usually energy-constrained. To provide persistent energy, many references have studied the offloading problem to save energy consumption. However, the dynamic environment dramatically increases the optimization difficulty of the offloading decision. In this paper, we aim to minimize the energy consumption of the entire MEC system under the latency constraint by… More >

  • Open Access

    ARTICLE

    Deoxynortryptoquivaline: A unique antiprostate cancer agent

    YOHKO YAMAZAKI1,*, MANABU KAWADA2, ISAO MOMOSE1

    Oncology Research, Vol.31, No.6, pp. 845-853, 2023, DOI:10.32604/or.2023.030266

    Abstract The androgen receptor (AR) is a critical target in all the clinical stages of prostate cancer. To identify a new AR inhibitor, we constructed a new screening system using the androgen-dependent growth of prostate cancer cell lines as a screening indicator. We screened 50,000 culture broths of microorganisms using this screening system and found that the fermentation broth produced by a fungus inhibited androgen-dependent growth of human prostate cancer LNCaP cells without cytotoxicity. Purification of this culture medium was performed, and this resulted in deoxynortryptoquivaline (DNT) being identified as a novel inhibitor of AR function. DNT showed potent inhibition of… More > Graphic Abstract

    Deoxynortryptoquivaline: A unique antiprostate cancer agent

  • Open Access

    ARTICLE

    Identification of a dihydroorotate dehydrogenase inhibitor that inhibits cancer cell growth by proteomic profiling

    MAKOTO KAWATANI1,2,*, HARUMI AONO2, SAYOKO HIRANUMA3, TAKESHI SHIMIZU3, MAKOTO MUROI1,2, TOSHIHIKO NOGAWA4, TOMOKAZU OHISHI5, SHUN-ICHI OHBA5, MANABU KAWADA5, KANAMI YAMAZAKI6, SHINGO DAN6, NAOSHI DOHMAE1, HIROYUKI OSADA2,7,*

    Oncology Research, Vol.31, No.6, pp. 833-844, 2023, DOI:10.32604/or.2023.030241

    Abstract Dihydroorotate dehydrogenase (DHODH) is a central enzyme of the de novo pyrimidine biosynthesis pathway and is a promising drug target for the treatment of cancer and autoimmune diseases. This study presents the identification of a potent DHODH inhibitor by proteomic profiling. Cell-based screening revealed that NPD723, which is reduced to H-006 in cells, strongly induces myeloid differentiation and inhibits cell growth in HL-60 cells. H-006 also suppressed the growth of various cancer cells. Proteomic profiling of NPD723-treated cells in ChemProteoBase showed that NPD723 was clustered with DHODH inhibitors. H-006 potently inhibited human DHODH activity in vitro, whereas NPD723 was approximately… More >

  • Open Access

    REVIEW

    Molecular dynamics-driven exploration of peptides targeting SARS-CoV-2, with special attention on ACE2, S protein, Mpro, and PLpro: A review

    MOHAMAD ZULKEFLEE SABRI1, JOANNA BOJARSKA2, FAI-CHU WONG3,4, TSUN-THAI CHAI3,4,*

    BIOCELL, Vol.47, No.8, pp. 1727-1742, 2023, DOI:10.32604/biocell.2023.029272

    Abstract Molecular dynamics (MD) simulation is a computational technique that analyzes the movement of a system of particles over a given period. MD can provide detailed information about the fluctuations and conformational changes of biomolecules at the atomic level over time. In recent years, MD has been widely applied to the discovery of peptides and peptide-like molecules that may serve as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) inhibitors. This review summarizes recent advances in such explorations, focusing on four protein targets: angiotensin-converting enzyme 2 (ACE2), spike protein (S protein), main protease (Mpro), and papain-like protease (PLpro). These four proteins are… More > Graphic Abstract

    Molecular dynamics-driven exploration of peptides targeting SARS-CoV-2, with special attention on ACE2, S protein, M<sup>pro</sup>, and PL<sup>pro</sup>: A review

  • Open Access

    ARTICLE

    AI Safety Approach for Minimizing Collisions in Autonomous Navigation

    Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed*

    Journal on Artificial Intelligence, Vol.5, pp. 1-14, 2023, DOI:10.32604/jai.2023.039786

    Abstract Autonomous agents can explore the environment around them when equipped with advanced hardware and software systems that help intelligent agents minimize collisions. These systems are developed under the term Artificial Intelligence (AI) safety. AI safety is essential to provide reliable service to consumers in various fields such as military, education, healthcare, and automotive. This paper presents the design of an AI safety algorithm for safe autonomous navigation using Reinforcement Learning (RL). Machine Learning Agents Toolkit (ML-Agents) was used to train the agent with a proximal policy optimizer algorithm with an intrinsic curiosity module (PPO + ICM). This training aims to improve AI… More >

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