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Addressing metallosis in knee arthroplasty:From diagnostic challenges to innovative treatments

World Journal of Orthopedics 2024年第5期15卷 386-389页

作者： Yvon Maurice Bogdonoff farid amirouche Department of Orthopedics Surgery University of Illinois at ChicagoChicagoIL 60612United States Department of Orthopedics Surgery Northshore University Health SystemSkokieIL 60076United States

In this editorial,we comment on the article by Toro et al published in the recent issue of World Journal of Orthopedics.This editorial review provides a comprehensive exploration of the landscape surrounding knee arthroplasty metallosis,focusing on key aspects ranging from the mechanisms influencing susceptibility to clinical implications and advanced treatment strategies.We elucidate the complex interplay of implant design,patient-specific variables,and wear-related processes contributing to metallosis.Furthermore,we seek to shed light on diagnostic challenges,the necessity of a multidisciplinary approach,and the imperative for vigilant implant surveillance.Uni-on-uni revision,as a targeted treatment modality,is discussed,highlighting its potential to address metallosis in unicompartmental knee arthroplasty(UKA).There is a need for heightened awareness among clinicians regarding the subtle presentations of metallosis,coupled with the limitations of traditional imaging techniques.Addressing metallosis requires a collaborative,multidisciplinary approach to effectively navigate the complexities associated with this complication.Furthermore,the review emphasizes the evolving paradigm of personalized care,with uni-on-uni revision emerging as a promising surgical solution.In conclusion,the editorial outlines the dynamic nature of knee arthroplasty metallosis and its multifaceted impact on clinical practice.It calls for ongoing collaboration,education,and integration of innovative solutions to enhance diagnostic accuracy,proactive management,and overall patient outcomes in the realm of UKA.

关键词： Ultrasound X-ray Total knee arthroplasty Case report Review Editorial

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Deep learning automation of radiographic patterns for hallux valgus diagnosis

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World Journal of Orthopedics 2024年第2期15卷 105-109页

作者： Angela Hussain Cadence Lee Eric Hu farid amirouche Department of Orthopaedic Surgery University of Illinois College of MedicineChicagoIL 60612United States Department of Orthopaedics Surgery University of Illinois at ChicagoChicagoIL 60612United States Department of Orthopaedic Surgery Northshore University Health SystemSkokieIL 6007United States

Artificial intelligence(AI)and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine.Deep learning has already been utilized for automated detection of pneumonia from chest radiographs,diabetic retinopathy,breast cancer,skin carcinoma classification,and metastatic lymphadenopathy detection,with diagnostic reliability akin to medical experts.In the World Journal of Orthopedics article,the authors apply an automated and AIassisted technique to determine the hallux valgus angle(HVA)for assessing HV foot deformity.With the U-net neural network,the authors constructed an algorithm for pattern recognition of HV foot deformity from anteroposterior highresolution radiographs.The performance of the deep learning algorithm was compared to expert clinician manual performance and assessed alongside clinician-clinician variability.The authors found that the AI tool was sufficient in assessing HVA and proposed the system as an instrument to augment clinical efficiency.Though further sophistication is needed to establish automated algorithms for more complicated foot pathologies,this work adds to the growing evidence supporting AI as a powerful diagnostic tool.

关键词： Artificial intelligence Hallux valgus Deep learning Automated radiography

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Recurrent cyclops lesion after primary resection of fibroreactive nodule following anterior cruciate ligament reconstruction

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World Journal of Orthopedics 2024年第6期15卷 495-497页

作者： Cadence Lee farid amirouche Department of Orthopedic Surgery University of Illinois College of MedicineChicagoIL 60612United States

In this case report featured in World Journal of Orthopedics,Kelmer et al describe a rare finding of a 28-year-old female patient who presented with a recurrent fibroreactive nodule 7 months following the resection of a primary cyclops lesion,suggesting recurrent cyclops syndrome.The patient had undergone an initial anterior cruciate ligament reconstruction for a non-contact right knee injury and reported successful recovery.Two years later,the patient sustained a repeat right knee injury followed by a positive McMurray test and acute pain with terminal extension.Arthroscopic synovectomy confirmed magnetic resonance imaging(MRI)finding of a cyclops lesion,which was surgically removed.Seven months postoperatively,the patient reported stiffness and difficulty with terminal extension.Repeat MRI indicated a recurrent cyclops lesion,which was surgically resected.Following resection of the second lesion,the patient underwent physical therapy and achieved full range of motion,maintaining complete recovery 19 months postoperatively.Recurrent cyclops lesions have rarely been reported in the literature,and this article is novel in its report of recurrent cyclops syndrome following a bone-patellar tendon-bone allograft.The presentation of this unusual finding exposes a need for further investigation of cyclops lesion pathology,which will aid its prevention and treatment.

关键词： Cyclops lesion Anterior cruciate ligament reconstruction Fibrous nodule Bone-patellar-tendon-bone allograft Cyclops syndrome

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Numerical study of the near-wall vortical structures in particle-laden turbulent flow by a new vortex identification method-Liutex

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Journal of Hydrodynamics 2024年第1期36卷 53-60页

作者： farid Rousta Goodarz Ahmadi Bamdad Lessani Chaoqun Liu Department of Mechanical and Aerospace Engineering Clarkson UniversityPotsdamNYUSA Mechanical Engineering and Engineering Science The University of North Carolina at CharlotteNCUSA Department of Mathematics University of Texas at ArlingtonArlingtonTXUSA

This study investigates turbulent particle-laden channel flows using direct numerical simulations employing the Eulerian-Lagrangian method.A two-way coupling approach is adopted to explore the mutual interaction between particles and fluid flow.The considered cases include flow with particle Stokes number varying from St=2 up to St=100 while maintaining a constant Reynolds number of Reτ=180 across all cases.A novel vortex identification method,Liutex(Rortex),is employed to assess its efficacy in capturing near-wall turbulent coherent structures and their interactions with particles.The Liutex method provides valuable information on vortex strength and vectors at each location,enabling a detailed examination of the complex interaction between fluid and particulate phases.As widely acknowledged,the interplay between clockwise and counterclockwise vortices in the near-wall region gives rise to low-speed streaks along the wall.These low-speed streaks serve as preferential zones for particle concentration,depending upon the particle Stokes number.It is shown that the Liutex method can capture these vortices and identify the location of low-speed streaks.Additionally,it is observed that the particle Stokes number(size)significantly affects both the strength of these vortices and the streaky structure exhibited by particles.Furthermore,a quantitative analysis of particle behavior in the near-wall region and the formation of elongated particle lines was carried out.This involved examining the average fluid streamwise velocity fluctuations at particle locations,average particle concentration,and the normal velocity of particles for each set of particle Stokes numbers.The investigation reveals the intricate interplay between particles and near-wall structures and the significant influence of particles Stokes number.This study contributes to a deeper understanding of turbulent particle-laden channel flow dynamics.

关键词： Turbulent channel flow particle-laden flow direct numerical simulation(DNS) Eulerian-Lagrangian vortex identification turbulence coherent structures Liutex

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An integrated computational method for calculating dynamic thermal bridges of building facades in tropical countries

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Frontiers of Architectural Research 2024年第1期13卷 201-218页

作者： Miktha farid Alkadri Muhammad Rafif Cahyadi Agung Francesco De Luca Research Cluster of Architectural Sciences and Building Technology Department of ArchitectureFaculty of EngineeringUniversitas IndonesiaDepok 16425Indonesia Department of Civil Engineering and Architecture Tallinn University of TechnologyTallinn 19086Estonia

Identifying thermal bridges on building façades has been a great challenge for architects,especially during the conceptual design stage.This is not only due to the complexity of parameters when calculating thermal bridges,but also lack of feature integration between building energy simulation(BES)tools and the actual building conditions.For example,existing BES tools predominantly calculate thermal bridges only in steady state without considering the temperature dynamic behaviour of building outdoors.Consequently,relevant features such as thermal delay,decrement factor,and operative temperature are often neglected,and this can lead to miscalculation of energy consumption.This study then proposes an integrated method to calculate dynamic thermal bridges under transient conditions by incorporating field observations and computational simulations of thermal bridges.More specifically,the proposed method employs several measurement tools such as HOBO data logger to record the actual conditions of indoor and outdoor room temperature and thermal cameras to identify the surface temperature of selected building junctions.The actual datasets are then integrated with the simulation workflow developed in BES tools.This study ultimately enables architects not only to identify potential thermal bridges on existing building façades but also to support material and geometric exploration in early design phase.

关键词： Thermal bridges Dynamic calculation Thermal delay Cooling load Computational design method

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Integrated hazard assessment of rockfall incidents in the Cap Aokas Cliff Region

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Journal of Mountain Science 2024年第6期21卷 1916-1929页

作者： Zohra LADJEL farid ZAHRI Riheb HADJI Younes HAMED Karim ZIGHMI Kaddour BENMARCE Department of Earth Sciences Institute of Architecture and Earth SciencesFerhat Abbas UniversitySetif 19137Algeria Unit of Emerging Materials Research Ferhat Abbes UniversitySétif 19137Algeria Laboratory of Applied Research in Engineering Geology GeotechnicsWater Sciencesand EnvironmentFerhat Abbas UniversitySetif 19137Algeria International Association of Water Resources in the Southern Mediterranean Basin Gafsa 2112Tunisia Department of Earth Sciences Faculty of SciencesUniversity of GafsaGafsa 2112Tunisia

Rock fall accidents in mountainous cliff areas have significant consequences for human life and transportation.This study aimed to evaluate the rockfall hazard in the Cap Aokas cliff region located along the northeast coast of Algeria by identifying the key factors contributing to rockfall occurrence.We employed a combination of kinematic analysis,Matterocking method,and 3D trajectory simulations to determine zones that are susceptible to rockfall mobilization.By using a probabilistic and structural approach in conjunction with photogrammetry,we identified the controlling factors.The kinematic analysis revealed the presence of five discontinuity families,which indicated both plane and wedge failure modes.The 3D trajectory simulations demonstrated that the falling blocks followed the stream direction.We then validated the susceptibility maps generated from the analysis using aerial photos and historical rockfall events.The findings of this study enhance our understanding of rockfall phenomena and provide valuable insights for the development of effective strategies to mitigate rockfall hazards.

关键词： Trajectory simulations Probabilistic analysis Structural approach Rockfall hazard Cap Aokas cliff

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Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods

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Journal of Magnesium and Alloys 2024年第5期12卷 1931-1943页

作者： farid Bahari-Sambran Fernando Carre?o C.M.Cepeda-Jiménez Alberto Orozco-Caballero Physical Metallurgy Department CENIM-CSIC Department of Mechanical Engineering Chemistry and Industrial Design Polytechnic University of Madrid

The aim of this work is to predict, for the first time, the high temperature flow stress dependency with the grain size and the underlaid deformation mechanism using two machine learning models, random forest（RF） and artificial neural network（ANN）. With that purpose, a ZK30 magnesium alloy was friction stir processed（FSP） using three different severe conditions to obtain fine grain microstructures（with average grain sizes between 2 and 3 μm） prone to extensive superplastic response. The three friction stir processed samples clearly deformed by grain boundary sliding（GBS） deformation mechanism at high temperatures. The maximum elongations to failure, well over 400% at high strain rate of 10-2s-1, were reached at 400 ℃ in the material with coarsest grain size of 2.8 μm, and at 300 ℃ for the finest grain size of 2 μm. Nevertheless, the superplastic response decreased at 350 ℃ and 400 ℃ due to thermal instabilities and grain coarsening, which makes it difficult to assess the operative deformation mechanism at such temperatures. This work highlights that the machine learning models considered, especially the ANN model with higher accuracy in predicting flow stress values, allow determining adequately the superplastic creep behavior including other possible grain size scenarios.

关键词： Machine learning Artificial intelligence Magnesium alloys Superplasticity Friction stir processing Grain coarsening

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The combined effects of farm yard manure and boron application on growth,and oil quality of Canola grown under newly reclaimed soils

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Oil Crop Science 2024年第1期9卷 53-59页

作者： farid Hellal Saied El Sayed Amany Abdel Mohsen Ramadan Doaa M.Abobasha Plant Nutrition Department National Research Centre33 El-Behouth St12622DokkiCairoEgypt Botany Department National Research Centre33 El-Behouth St12622DokkiCairoEgypt

Two field experiments were conducted during the main seasons of 2021/2022 at the Research and Production Station of National Research Centre in Egypt to investigate the effects of farmyard manure(FYM)and boron on Canola growth,yield,oil yield,and quality.The results unequivocally demonstrated that the combined application of FYM at a rate of 14.4 ton ha^(-1)with a foliar spray of boron at 100 ppm positively influenced plant characteristics,leading to enhanced growth rates and higher yields compared to the control group.Moreover,this integrated approach significantly improved nutrient content by enhancing levels of oil content,carbohydrates,proteins,phenolics,flavonoids,and total soluble sugars.These findings provide compelling evidence that utilizing farm manure along with boron can effectively enhance Canola properties in newly reclaimed soils while promoting sustainable agricultural practices.

关键词： Oil quality Farmyard manure Phytochemical content Canola Yield production

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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Computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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Tensile Strain Capacity Prediction of Engineered Cementitious Composites (ECC) Using Soft Computing Techniques

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2925-2954页

作者： Rabar H.Faraj Hemn Unis Ahmed Hardi Saadullah Fathullah Alan Saeed Abdulrahman farid Abed Civil Engineering Department University of HalabjaHalabjaIraq University of Sulaimani College of EngineeringCivil Engineering DepartmentSulaimaniIraq Civil Engineering Department Komar University of Science and TechnologySulaimaniIraq Department of Engineering Kurdistan Institution for Strategic Studies and Scientific ResearchesSulaimaniIraq Department of Civil Engineering American University of SharjahSharjah26666UAE

Plain concrete is strong in compression but brittle in tension,having a low tensile strain capacity that can significantly degrade the long-term performance of concrete structures,even when steel reinforcing is present.In order to address these challenges,short polymer fibers are randomly dispersed in a cement-based matrix to forma highly ductile engineered cementitious composite(ECC).Thismaterial exhibits high ductility under tensile forces,with its tensile strain being several hundred times greater than conventional concrete.Since concrete is inherently weak in tension,the tensile strain capacity(TSC)has become one of the most extensively researched properties.As a result,developing a model to predict the TSC of the ECC and to optimize the mixture proportions becomes challenging.Meanwhile,the effort required for laboratory trial batches to determine the TSC is reduced.To achieve the research objectives,five distinct models,artificial neural network(ANN),nonlinear model(NLR),linear relationship model(LR),multi-logistic model(MLR),and M5P-tree model(M5P),are investigated and employed to predict the TSCof ECCmixtures containing fly ash.Data from115 mixtures are gathered and analyzed to develop a new model.The input variables include mixture proportions,fiber length and diameter,and the time required for curing the various mixtures.The model’s effectiveness is evaluated and verified based on statistical parameters such as R2,mean absolute error(MAE),scatter index(SI),root mean squared error(RMSE),and objective function(OBJ)value.Consequently,the ANN model outperforms the others in predicting the TSC of the ECC,with RMSE,MAE,OBJ,SI,and R2 values of 0.42%,0.3%,0.33%,0.135%,and 0.98,respectively.

关键词： Engineered cementitious composites fly ash curing time tensile strain capacity modeling

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