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World Journal of Clinical Cases 2024年第4期12卷 758-765页

作者： amal S Shaiban Department of Restorative Dental Science College of DentistryKing Khalid UniversityAbha 61471Saudi Arabia

BACKGROUND Accreditation processes are extensively employed to guarantee the quality of higher education institutions.However,this process can potentially generate a stressful atmosphere for staff,thus impacting their ability to perform optimally under pressure.AIM To examine the knowledge of the National Commission for Academic Assessment and Accreditation(NCAAA)and its effects on perceived stress among dental college staff before and after the program accreditation process.METHODS The present cross-sectional questionnaire-based study included 300 employees from three dental colleges.The same employees had to fill out the questionnaire 1 mo after the NCAAA process.RESULTS Approximately 51.33%of the included employees believed that NCAAA is essential to attract quality students to join the program before and after the program.A total of 44.67%felt that their stress was high before,and 31.33%had low stress levels before and after NCAAA.These findings were statistically significant(P≤0.05).CONCLUSION NCAAA raised the prestige of the institution and specific programmes and increased prospects to attract better students,staff,and money,according to the employees.In addition to their needed teaching,research,and other service responsibilities,this work requires a tremendous amount of faculty effort and may cause them stress.

关键词： National Commission for Academic Assessment and Accreditation Accreditation process Perceived stress Staff

Development of sustainable thermal insulation based on bio-polyester filled with date pits

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Journal of Bioresources and Bioproducts 2024年第1期9卷 74-89页

作者： amal Mlhem Thomas Teklebrhan Evenezer Bokuretsion Basim Abu-Jdayil Chemical&Petroleum Engineering Department United Arab Emirates UniversityPO Box 15551United Arab Emirates National Water and Energy Center United Arab Emirates UniversityPO Box 15551United Arab Emirates

Date palm pit(DPP)-filled poly(-hydroxybutyrate)(PHB)composites were prepared,evaluated,and characterized to determine their thermal insulation ability.Thermal conductivity values ranged between 0.086 and 0.100 W/(m·K).At a maximum filler concentration(50%(w)),the specific heat capacity and thermal diffusivity were 1183 J/(kg·K)and 0.0689 mm^(2)/s,respec-tively.The DPP increased the thermal stability,and the highest compressive strength obtained was 80 MPa at 30%filler content.The PHB-DPP composites exhibited promising water absorption(less than 6%)and tensile strength(6-14 MPa).Date-pit-based PHB composites could be used in sustainable building engineering and cleaner production.

关键词： Green construction material Sustainable thermal insulator Date pit Polyhydroxybutyrate

Quaternionic Monge–Amp`ere Measure on Pluripolar Set

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Acta Mathematica Sinica,English Series 2024年第6期40卷 1431-1457页

作者： Hichame amal Saïd ASSERDA Fadoua BOUKHARI Department of Mathematics Laboratory LaREAMIRegional Centre of Trades of Education and TrainingKenitraMorocco Ibn Tofail University Faculty of SciencesDepartment of MathematicsPO 242 KenitraMorroco

In this paper,we prove that in a hyperconvex domainΩin H^(n),if a non-negative Borel measure is dominated by a quaternionic Monge-Amp`ere measure,then it is a quaternionic Monge-Amp`ere measure of a function in the c... 详细信息

关键词： Quaternion space Monge-Amp`ere operator plurisubharmonic function

Nanozyme‑Engineered Hydrogels for Anti‑Inflammation and Skin Regeneration

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Nano-Micro Letters 2024年第6期16卷 127-179页

作者： amal George Kurian Rajendra K.Singh Varsha Sagar Jung‑Hwan Lee Hae‑Won Kim Institute of Tissue Regeneration Engineering(ITREN) Dankook UniversityCheonan 31116Republic of Korea Department of Nanobiomedical Science&BK21 NBM Global Research Center for Regenerative Medicine Dankook UniversityCheonan 31116Republic of Korea Department of Biomaterials Science School of DentistryDankook UniversityCheonan 31116Republic of Korea UCL Eastman‑Korea Dental Medicine Innovation Centre Dankook UniversityCheonan 31116Republic of Korea Cell and Matter Institute Dankook UniversityCheonan 31116Republic of Korea Mechanobiology Dental Medicine Research Center Dankook UniversityCheonan 31116Republic of Korea

Inflammatory skin disorders can cause chronic scarring and functional impairments,posing a significant burden on patients and the healthcare system.Conventional therapies,such as corticosteroids and nonsteroidal anti-inflammatory drugs,are limited in efficacy and associated with adverse effects.Recently,nanozyme(NZ)-based hydrogels have shown great promise in addressing these challenges.NZ-based hydrogels possess unique therapeutic abilities by combining the therapeutic benefits of redox nanomaterials with enzymatic activity and the water-retaining capacity of hydrogels.The multifaceted therapeutic effects of these hydrogels include scavenging reactive oxygen species and other inflammatory mediators modulating immune responses toward a pro-regenerative environment and enhancing regenerative potential by triggering cell migration and differentiation.This review highlights the current state of the art in NZ-engineered hydrogels(NZ@hydrogels)for anti-inflammatory and skin regeneration applications.It also discusses the underlying chemo-mechano-biological mechanisms behind their effectiveness.Additionally,the challenges and future directions in this ground,particularly their clinical translation,are addressed.The insights provided in this review can aid in the design and engineering of novel NZ-based hydrogels,offering new possibilities for targeted and personalized skin-care therapies.

关键词： Nanozymes Hydrogels ROS scavenging Anti-inflammation Skin regeneration

A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan Kumar Neeraj Kumar Bander Alzahrani amal Almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human lives.The detonation of these landmines results in thousands of casualties reported worldwide annually.Therefore,there is a pressing need to employ diverse landmine detection techniques for their removal.One effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic field.It can generate a contour plot or heat map that visually represents the magnetic field strength.Despite the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith risks.Edge computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine detection.By processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field data.It enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the process.Furthermore,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during transmission.This paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and localization.We have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset traces.By simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry images.The trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

The Impact of Network Topologies and Radio Duty Cycle Mechanisms on the RPL Routing Protocol Power Consumption

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Computers, Materials & Continua 2024年第5期79卷 1835-1854页

作者： amal Hkiri Hamzah Faraj Omar Ben Bahri Mouna Karmani Sami Alqurashi Mohsen Machhout Physic Department Electronics and Micro-Electronics LaboratoryFaculty of Sciences of MonastirMonastir5000Tunisia Department of Science and Technology College of RanyahTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

The Internet of Things(IoT)has witnessed a significant surge in adoption,particularly through the utilization of Wireless Sensor Networks(WSNs),which comprise small internet-connected devices.These deployments span various environments and offer a multitude of benefits.However,the widespread use of battery-powered devices introduces challenges due to their limited hardware resources and communication capabilities.In response to this,the Internet Engineering Task Force(IETF)has developed the IPv6 Routing Protocol for Low-power and Lossy Networks(RPL)to address the unique requirements of such networks.Recognizing the critical role of RPL in maintaining high performance,this paper proposes a novel approach to optimizing power consumption.Specifically,it introduces a developed sensor motes topology integrated with a Radio Duty Cycling(RDC)mechanism aimed at minimizing power usage.Through rigorous analysis,the paper evaluates the power efficiency of this approach through several simulations conducted across different network topologies,including random,linear,tree,and elliptical topologies.Additionally,three distinct RDC mechanisms—CXMAC,ContikiMAC,and NullRDC—are investigated to assess their impact on power consumption.The findings of the study,based on a comprehensive and deep analysis of the simulated results,highlight the efficiency of ContikiMAC in power conservation.This research contributes valuable insights into enhancing the energy efficiency of RPL-based IoT networks,ultimately facilitating their widespread deployment and usability in diverse environments.

关键词： WSN IoT radio duty cycles topologies

RPL-Based IoT Networks under Decreased Rank Attack:Performance Analysis in Static and Mobile Environments

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Computers, Materials & Continua 2024年第1期78卷 227-247页

作者： amal Hkiri Mouna Karmani Omar Ben Bahri Ahmed Mohammed Murayr Fawaz Hassan Alasmari Mohsen Machhout Physic Department Electronics and Micro-Electronics LaboratoryFaculty of Sciences of MonastirMonastir5000Tunisia Department of Science and Technology College of RanyahTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

The RPL(IPv6 Routing Protocol for Low-Power and Lossy Networks)protocol is essential for efficient communi-cation within the Internet of Things(IoT)ecosystem.Despite its significance,RPL’s susceptibility to attacks remains a concern.This paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the decreased rank attack in both static andmobilenetwork environments.We employ the Random Direction Mobility Model(RDM)for mobile scenarios within the Cooja simulator.Our systematic evaluation focuses on critical performance metrics,including Packet Delivery Ratio(PDR),Average End to End Delay(AE2ED),throughput,Expected Transmission Count(ETX),and Average Power Consumption(APC).Our findings illuminate the disruptive impact of this attack on the routing hierarchy,resulting in decreased PDR and throughput,increased AE2ED,ETX,and APC.These results underscore the urgent need for robust security measures to protect RPL-based IoT networks.Furthermore,our study emphasizes the exacerbated impact of the attack in mobile scenarios,highlighting the evolving security requirements of IoT networks.

关键词： RPL decreased rank attacks mobility random direction model

Heavy metal pollution of river water and eco-friendly remediation using potent microalgal species

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Water Science and Engineering 2024年第1期17卷 41-50页

作者： Amudham Radha amal Raj Prabhakaran Mylsamy V.Sivasankar B.Sathish Kumar Kiyoshi Omine T.G.Sunitha Phycological Research Laboratory Research Department of BotanyPachaiyappa's College(Affliated to University of Madras)Chennai 600030India Research Department of Chemistry Pachaiyappa's College(Affliated to University of Madras)Chennai 600030India Department of Botany Sri Vidya Mandir Arts&Science College(Autonomous)KatteriUthangarai 636902India Department of Civil Engineering School of Engineering.Nagasaki UniversityNagasaki-Daigaku1-14 Bunkyo-machiNagasaki 8528521Japan

Pollution of rivers is mainly caused by anthropogenic activities such as discharge of effluent from industrial facilities,maintenance of sewage/effluent treatment plants,and dumping of solid waste on river banks.This study dealt with the pollution issues of the Cooum River in the well-known city of Chennai in South India.Water samples from 27 locations were collected and analyzed for 12 elements,including Ba,B,and Al,as well as heavy metals such as Pb,Cr,Mn,Fe,Co,Ni,Cu,Zn,and Cd.The samples showed levels of these elements that exceeded World Health Organization recommendations.Pearson correlation analysis revealed the inter-dependency among elements,and the contribution of each element based on factor loadings showed its percentage contribution compared to others.Water samples from six significant locations were chosen for remediation with three algae:Chlorella vulgaris,Scenedesmus dimorphus,and Phormedium sp.The uptake of pollutants led to the continuous growth of algae during the incubation period of 15 d,effectively removing heavy metals from the river water.The increasing levels of algal counts and the chlorophyll a content confirmed the algal growth during the incubation period,followed by a declining stage after the incubation period.The scanning electron microscopic images of algae before and after the remediation showed no remarkable modification of morphological patterns.This study showed that the uptake of heavy metals using algae is an effective water pollution remediation measure,making the process practicable in the field on a large scale in the near future.

关键词： Heavy metal Cooum river Phycoremediation Microalgae Factor analysis

Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3513-3534页

作者： Nazik Alturki Abdulaziz Altamimi Muhammad Umer Oumaima Saidani amal Alshardan Shtwai Alsubai Marwan Omar Imran Ashraf Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department College of Computer Science and Engineering University of Hafr Al-BatinHafarAl-Batin39524Saudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurP.O.Box 63100BahawalpurPakistan Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Information Technology and Management Illinois Institute of TechnologyChicagoIL 60616-3793USA Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.

关键词： Precisionmedicine chronic kidney disease detection SMOTE missing values healthcare KNNimputer ensemble learning

Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3387-3415页

作者： Nazik Alturki Raed Alharthi Muhammad Umer Oumaima Saidani amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Engineering University of Hafr Al-BatinHafar Al-BatinSaudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device itself.Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features.This paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in homes.We have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT devices.Our system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing devices.We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server.The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings.It is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation systems.Additionally,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber threats.The trial results support the proposed system and demonstrate its potential for use in everyday life.

关键词： Blockchain Internet of Things(IoT) smart home automation cybersecurity