Efficacy of stromal vascular fraction for knee osteoarthritis:A prospective,single-centre,non-randomized study with 2 years follow-up

BACKGROUND Current osteoarthritis(OA)treatments focus on symptom relief without addressing the underlying disease process.In regenerative medicine,current treatments have limitations.In regenerative medicine,more research is needed for intra-articular stromal vascular fraction(SVF)injections in OA,including dosage optimization,long-term efficacy,safety,comparisons with other treatments,and mechanism exploration.AIM To compare the efficacy of intra-articular SVF with corticosteroid(ICS)injections in patients with primary knee OA.METHODS The study included 50 patients with Kellgren-Lawrence grades II and III OA.Patients were randomly assigned(1:1)to receive either a single intra-articular SVF injection(group A)or a single intra-articular ICS(triamcinolone)(group B)injection.Patients were followed up at 1,3,6,12,and 24 months.Visual analog score(VAS)and International Knee Documentation Committee(IKDC)scores were administered before the procedure and at all followups.The safety of SVF in terms of adverse and severe adverse events was recorded.Statistical analysis was performed with SPSS Version 26.0,IBM Corp,Chicago,IL,United States.RESULTS Both groups had similar demographics and baseline clinical characteristics.Follow-up showed minor patient loss,resulting in 23 and 24 in groups A and B respectively.Group A experienced a notable reduction in pain,with VAS scores decreasing from 7.7 to 2.4 over 24 months,compared to a minor reduction from 7.8 to 6.2 in Group B.This difference in pain reduction in group A was statistically significant from the third month onwards.Additionally,Group A showed significant improvements in knee functionality,with IKDC scores rising from 33.4 to 83.10,whereas Group B saw a modest increase from 36.7 to 45.16.The improvement in Group A was statistically significant from 6 months and maintained through 24 months.CONCLUSION Our study demonstrated that intra-articular administration of SVF can lead to reduced pain and improved knee function in patients with primary knee OA.More adequately powered,multi-center,double-blinded,randomised clinical trials with longer follow-ups are needed to further establish safety and justify its clinical use.

Effect of Cryogenic Treatment on Microstructure and Tribological Property Evolution of Electron Beam Melted Ti6Al4V

Cryogenic treatment was used to improve the tribological properties of Ti6Al4V artificial hip joint implants.Cryogenic treatment at-196℃with different holding time were carried out on Ti6Al4V specimens fabricated using electron beam melting(EBM),and their microstructure and tribological properties evolution were systematically analyzed by scanning electron microscopy(SEM),vickers hardness,and wear tests.The experimental results show that the as-fabricated specimen consists of lamellarαphase andβcolumnar crystal.While,the thickness of lamellarαphase decreased after cryogenic treatment.In addition,it can be found that the fineαphase was precipitated and dispersed between the lamellarαphase with the holding time increase.Vickers hardness shows a trend of first increasing and then decreasing.The wear rate of the specimen cryogenic treated for 24 h is the minimum and the average friction coefficient is 0.50,which is reduced by 14.61%compared with the as-fabricated.The wear mechanism of the as-fabricated specimen is severe exfoliation,adhesive,abrasive,and slight fatigue wear.However,the specimen cryogenic treated for 24 h shows slight adhesive and abrasive wear.It can be concluded that it is feasibility of utilizing cryogenic treatment to reduce the wear of EBMed Ti6Al4V.

Comparison of microwave- and thermal-assisted rock fragmentation methods at different temperatures and loading rates

Understanding the effects of microwave irradiation and thermal treatment on the dynamic compression and fragmentation properties of rocks is essential to quantify energy consumption in rock engineering.In this study,Fangshan granite(FG)specimens were exposed to microwave irradiation and heat treatment.The damage of FG specimens induced by these two methods was compared using X-ray CT scanning and ultrasonic wave method.The temperatures of FG after microwave irradiation and thermal treatment were effectively evaluated using a newly proposed technique.A novelty method for precisely determining the geometric features of fragments is developed to estimate the fragmentation energy.Thus,the dynamic uniaxial compressive strength(UCS),the dynamic fragmentation characteristics,and the fragmentation energy of FG after these two pretreatment methods can be reasonably compared.The noticeable distinction of loading rate effect on the dynamic UCS of FG between these two pretreatment methods is first observed.A relationship is established between the dynamic UCS and the damage induced by microwave irradiation and heat treatment.Moreover,fragmentation energy fan analysis is introduced to accurately compare the fragmentation properties of FG after two pretreatment methods in dynamic compression tests.

Quasi-static and dynamic compressive behaviour of additively manufactured Menger fractal cube structures

This paper presents the first-ever investigation of Menger fractal cubes'quasi-static compression and impact behaviour.Menger cubes with different void ratios were 3D printed using polylactic acid(PLA)with dimensions of 40 mm×40 mm×40 mm.Three different orders of Menger cubes with different void ratios were considered,namely M1 with a void ratio of 0.26,M2 with a void ratio of 0.45,and M3with a void ratio of 0.60.Quasi-static Compression tests were conducted using a universal testing machine,while the drop hammer was used to observe the behaviour under impact loading.The fracture mechanism,energy efficiency and force-time histories were studied.With the structured nature of the void formation and predictability of the failure modes,the Menger geometry showed some promise compared to other alternatives,such as foams and honeycombs.With the increasing void ratio,the Menger geometries show force-displacement behaviour similar to hyper-elastic materials such as rubber and polymers.The third-order Menger cubes showed the highest energy absorption efficiency compared to the other two geometries in this study.The findings of the present work reveal the possibility of using additively manufactured Menger geometries as an energy-efficient system capable of reducing the transmitting force in applications such as crash barriers.

Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development

With the projected global surge in hydrogen demand, driven by increasing applications and the imperative for low-emission hydrogen, the integration of machine learning(ML) across the hydrogen energy value chain is a compelling avenue. This review uniquely focuses on harnessing the synergy between ML and computational modeling(CM) or optimization tools, as well as integrating multiple ML techniques with CM, for the synthesis of diverse hydrogen evolution reaction(HER) catalysts and various hydrogen production processes(HPPs). Furthermore, this review addresses a notable gap in the literature by offering insights, analyzing challenges, and identifying research prospects and opportunities for sustainable hydrogen production. While the literature reflects a promising landscape for ML applications in hydrogen energy domains, transitioning AI-based algorithms from controlled environments to real-world applications poses significant challenges. Hence, this comprehensive review delves into the technical,practical, and ethical considerations associated with the application of ML in HER catalyst development and HPP optimization. Overall, this review provides guidance for unlocking the transformative potential of ML in enhancing prediction efficiency and sustainability in the hydrogen production sector.

Mechanical properties and energy evolution law of water bearing sandstone under cyclic loading

Due to excavation disturbances and the coupled hydro-mechanical effects,deep rock masses experience nonlinear large deformations in the surrounding rock,necessitating an urgent exploration of the rock damage and failure mechanisms from the perspectives of hydro-mechanical coupling and mechanical properties.Therefore,this study conducted uniaxial cyclic loading-unloading tests on sandstone samples with different water contents(0%,0.26%,0.52%,0.78%,and 1.04%)to investigate the microstructural evolution,energy evolution laws,and failure characteristics under varying water contents and cyclic loading conditions.The main conclusions are as follows:(1)Concerning micro-pore structures,as the water content increases,the porosity and maximum pore size of the sandstone first decrease and then increase.At 0%water content,the porosity is 4.82%and the maximum pore size is 31.94μm.At 0.26%water content,both porosity and maximum pore size decrease to 3.03%and 16.15μm,respectively.When the water content reaches 1.04%,the porosity and maximum pore size increase to 14.34%and 45.99μm,respectively.(2)Regarding energy evolution laws,the energy evolution of the specimens during cyclic loading-unloading mainly converts to elastic energy,showing a step-wise increase in energy.Further analysis reveals that the water content has a significant impact on the dissipation energy coefficient of the sandstone.At lower stress levels(<0.4σmax),the water content has a negligible effect,while at higher stress levels(>0.85σmax),an increase in water content leads to increased fluctuations in the dissipation energy coefficient.(3)In terms of failure characteristics,with increasing water content,the failure mode of the specimens shifts from primary crack failure to microcrack failure,corresponding to the energy evolution during cyclic loading-unloading processes.

List of 85 typical catastrophic landslides from March 2004 to February 2024

Landslides are one of the most dangerous natural hazards that cause significant property damage and loss of life.Landslides often destroy farmland,villages,houses,factories,schools,roads and other facilities,injuring humans and livestock.Sometimes,entire towns are devastated by landslides.Due to their pervasiveness,varied triggering factors,and sudden occurrence,landslides are currently one of the most challenging natural disasters to prevent and mitigate.

Enhancing landslide hazards survey and management to reduce the loss of human lives and properties

Landslides are pervasive geohazards that pose a serious threat to human lives,property,and crucial engineering constructions.Annually,landslides lead to tens of thousands of fatalities(see the paper of"List of typical catastrophic landslides from March 2004 to February 2024"in this issue,doi:10.31035/cg2024079)and cause economic damages amounting to billions of dollars around the world,as well as disrupting crucial infrastructures such as railways,highways。

Effects of fracture evolution and non-Darcy flow on the thermal performance of enhanced geothermal system in 3D complex fractured rock

In fractured geothermal reservoirs,the fracture networks and internal fluid flow behaviors can significantly impact the thermal performance.In this study,we proposed a non-Darcy rough discrete fracture network(NR-DFN)model that can simultaneously consider the fracture evolution and non-Darcy flow dynamics in studying the thermo-hydro-mechanical(THM)coupling processes for heat extraction in geothermal reservoir.We further employed the model on the Habanero enhanced geothermal systems(EGS)project located in Australia.First,our findings illustrate a clear spatial-temporal variation in the thermal stress and pressure perturbations,as well as uneven spatial distribution of shear failure in 3D fracture networks.Activated shear failure is mainly concentrated in the first fracture cluster.Secondly,channeling flow have also been observed in DFNs during heat extraction and are further intensified by the expansion of fractures driven by thermal stresses.Moreover,the combined effect of non-Darcy flow and fracture evolution triggers a rapid decline in the resulting heat rate and temperature.The NR-DFN model framework and the Habanero EGS's results illustrate the importance of both fracture evolution and non-Darcy flow on the efficiency of EGS production and have the potential to promote the development of more sustainable and efficient EGS operations for stakeholders.

Open-Source Software Defined Networking Controllers:State-of-the-Art,Challenges and Solutions for Future Network Providers

Software Defined Networking(SDN)is programmable by separation of forwarding control through the centralization of the controller.The controller plays the role of the‘brain’that dictates the intelligent part of SDN technology.Various versions of SDN controllers exist as a response to the diverse demands and functions expected of them.There are several SDN controllers available in the open market besides a large number of commercial controllers;some are developed tomeet carrier-grade service levels and one of the recent trends in open-source SDN controllers is the Open Network Operating System(ONOS).This paper presents a comparative study between open source SDN controllers,which are known as Network Controller Platform(NOX),Python-based Network Controller(POX),component-based SDN framework(Ryu),Java-based OpenFlow controller(Floodlight),OpenDayLight(ODL)and ONOS.The discussion is further extended into ONOS architecture,as well as,the evolution of ONOS controllers.This article will review use cases based on ONOS controllers in several application deployments.Moreover,the opportunities and challenges of open source SDN controllers will be discussed,exploring carriergrade ONOS for future real-world deployments,ONOS unique features and identifying the suitable choice of SDN controller for service providers.In addition,we attempt to provide answers to several critical questions relating to the implications of the open-source nature of SDN controllers regarding vendor lock-in,interoperability,and standards compliance,Similarly,real-world use cases of organizations using open-source SDN are highlighted and how the open-source community contributes to the development of SDN controllers.Furthermore,challenges faced by open-source projects,and considerations when choosing an open-source SDN controller are underscored.Then the role of Artificial Intelligence(AI)and Machine Learning(ML)in the evolution of open-source SDN controllers in light of recent research is indicated.In addition,the challenges and limitations associated with deploying open-source SDN controllers in production networks,how can they be mitigated,and finally how opensource SDN controllers handle network security and ensure that network configurations and policies are robust and resilient are presented.Potential opportunities and challenges for future Open SDN deployment are outlined to conclude the article.

Federated Learning on Internet of Things:Extensive and Systematic Review

The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation.However,FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios.The paper systematically reviewed the available literature using the PRISMA guiding principle.The study aims to provide a detailed overview of the increasing use of FL in IoT networks,including the architecture and challenges.A systematic review approach is used to collect,categorize and analyze FL-IoT-based articles.Asearch was performed in the IEEE,Elsevier,Arxiv,ACM,and WOS databases and 92 articles were finally examined.Inclusion measures were published in English and with the keywords“FL”and“IoT”.The methodology begins with an overview of recent advances in FL and the IoT,followed by a discussion of how these two technologies can be integrated.To be more specific,we examine and evaluate the capabilities of FL by talking about communication protocols,frameworks and architecture.We then present a comprehensive analysis of the use of FL in a number of key IoT applications,including smart healthcare,smart transportation,smart cities,smart industry,smart finance,and smart agriculture.The key findings from this analysis of FL IoT services and applications are also presented.Finally,we performed a comparative analysis with FL IID(independent and identical data)and non-ID,traditional centralized deep learning(DL)approaches.We concluded that FL has better performance,especially in terms of privacy protection and resource utilization.FL is excellent for preserving privacy becausemodel training takes place on individual devices or edge nodes,eliminating the need for centralized data aggregation,which poses significant privacy risks.To facilitate development in this rapidly evolving field,the insights presented are intended to help practitioners and researchers navigate the complex terrain of FL and IoT.

Design of an Efficient and Provable Secure Key Exchange Protocol for HTTP Cookies

Cookies are considered a fundamental means of web application services for authenticating various Hypertext Transfer Protocol(HTTP)requests andmaintains the states of clients’information over the Internet.HTTP cookies are exploited to carry client patterns observed by a website.These client patterns facilitate the particular client’s future visit to the corresponding website.However,security and privacy are the primary concerns owing to the value of information over public channels and the storage of client information on the browser.Several protocols have been introduced that maintain HTTP cookies,but many of those fail to achieve the required security,or require a lot of resource overheads.In this article,we have introduced a lightweight Elliptic Curve Cryptographic(ECC)based protocol for authenticating client and server transactions to maintain the privacy and security of HTTP cookies.Our proposed protocol uses a secret key embedded within a cookie.The proposed protocol ismore efficient and lightweight than related protocols because of its reduced computation,storage,and communication costs.Moreover,the analysis presented in this paper confirms that proposed protocol resists various known attacks.

Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

Toward Adaptation of Briquettes Making Technology for Green Energy and Youth Employment in Tanzania: A Review

Briquette technology is an alternative green energy source to offset the increasing demand for charcoal and firewood to save the forests and the environment while creating employment for youth and women. Using the scoping and realistic review techniques, a review study was conducted to establish the briquette technology’s existence, and its value chain, identify stakeholders and challenges along the value chain and explore the policies supporting the technology and potential employment opportunities for youth in the green energy sector. The review results indicated that the briquette technology value chain consists of sourcing raw materials, production process, distribution, and consumption as its components while transportation, storage or packaging, marketing, and training are its supporting services. In addition, it was found that stakeholders in the value chain are manufacturers, producers, and supporting service providers who differ based on their formalities, such as groups, companies, government organizations, Non-Governmental Organizations (NGOs), institutions, and enterprises. Furthermore, five challenges were identified that impair the briquette adoption. They include the technology, raw materials, and the quality of briquettes, promotion, and marketing. Also, the study found that there are limited policies that provide a conducive environment for briquette technology to flourish. The study concludes that briquette technology exists in Tanzania. However, it is not yet matured as compared to the developed countries, and the technology is not backstopped by existing policies. The study recommends the briquette technology as a viable employment opportunity, especially for youth and women;therefore, the formulated briquette value chain should be utilized for easy coordination of stakeholders and deployment of the technology. Also, there is a need to create awareness and innovative strategies for promoting and engaging more stakeholders in the technology through the policies that explicitly insist on adopting the briquette technology.

Direct ink writing of nickel oxide-based thin films for room temperature gas detection

The rapid industrial growth and increasing population have led to significant pollution and deterioration of the natural atmospheric environment.Major atmospheric pollutants include NO_(2)and CO_(2).Hence,it is imperative to develop NO_(2)and CO_(2)sensors for ambient conditions,that can be used in indoor air quality monitoring,breath analysis,food spoilage detection,etc.In the present study,two thin film nanocomposite(nickel oxide-graphene and nickel oxide-silver nanowires)gas sensors are fabricated using direct ink writing.The nano-composites are investigated for their structural,optical,and electrical properties.Later the nano-composite is deposited on the interdigitated electrode(IDE)pattern to form NO_(2)and CO_(2)sensors.The deposited films are then exposed to NO_(2)and CO_(2)gases separately and their response and recovery times are determined using a custom-built gas sensing setup.Nickel oxide-graphene provides a good response time and recovery time of 10 and 9 s,respectively for NO_(2),due to the higher electron affinity of graphene towards NO_(2).Nickel oxide-silver nanowire nano-composite is suited for CO_(2)gas because silver is an excellent electrocatalyst for CO_(2)by giving response and recovery times of 11 s each.This is the first report showcasing NiO nano-composites for NO_(2)and CO_(2)sensing at room temperature.

Study and Application of Heat Treat ment of Multi-Element Wear-Resistant Low-Alloy Steel

The effects of heat treatment on the properties of multi element wear-resistant low-alloy steel （MLAWS） which is used to make the liner of rolling mill torus were researched. The results show that when quenching temperature is lower than 900℃, the hardness increases with the increase of temperature, and when quenching temperature is higher than 900℃, the hardness decreases with the increase of temperature. As quenching temperature is lower than 920℃, the effect of quenching temperature on the impact toughness is not obvious. When quenching temperature is higher than 920℃ , impact toughness decreases with the increase of temperature. When tempering temperature is higher than 450 ℃ , the hardness begins to decrease obviously. After tempering at 350℃, the best wear resistance was obtained. According to the service condition of rolling mill torus liner, the MLAWS is quenched from 900-920 ℃ and tempered at 350-370℃.

Resilient and Safe Platooning Control of Connected Automated Vehicles Against Intermittent Denial-of-Service Attacks

Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumption and vehicle emissions.A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads.This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service(DoS)attacks that disrupt vehicle-to-vehicle communications.First,a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties,including diverse vehicle masses and engine inertial delays,unknown and nonlinear resistance forces,and a dynamic platoon leader.Then,a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability,attack resilience,platoon safety and scalability.Furthermore,a numerically efficient offline design algorithm for determining the desired platoon control law is developed,under which the platoon resilience against DoS attacks can be maximized but the anticipated stability,safety and scalability requirements remain preserved.Finally,extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.

Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County,Three Gorges Reservoir, China

Landslide susceptibility mapping is vital for landslide risk management and urban planning.In this study,we used three statistical models[frequency ratio,certainty factor and index of entropy(IOE)]and a machine learning model[random forest(RF)]for landslide susceptibility mapping in Wanzhou County,China.First,a landslide inventory map was prepared using earlier geotechnical investigation reports,aerial images,and field surveys.Then,the redundant factors were excluded from the initial fourteen landslide causal factors via factor correlation analysis.To determine the most effective causal factors,landslide susceptibility evaluations were performed based on four cases with different combinations of factors("cases").In the analysis,465(70%)landslide locations were randomly selected for model training,and 200(30%)landslide locations were selected for verification.The results showed that case 3 produced the best performance for the statistical models and that case 2 produced the best performance for the RF model.Finally,the receiver operating characteristic(ROC)curve was used to verify the accuracy of each model's results for its respective optimal case.The ROC curve analysis showed that the machine learning model performed better than the other three models,and among the three statistical models,the IOE model with weight coefficients was superior.

Effect of sulfate erosion on strength and leaching characteristic of stabilized heavy metal contaminated red clay

Solidification/stabilization(S/S)technology has been widely used for remediation of the heavy metal contaminated soils.The heavy metal ions will be leached from the stabilized contaminated soil under sulfate erosion conditions,which gives rise tosecondary contamination to the areas around the mine sites.The commonly used Portland cement,fly ash and quicklime were takenas binder raw materials with various mix proportions.And then,the sulphuric acid and nitric acid method was used to investigate theleaching characteristic of stabilized heavy metal contaminated soils.The effects of binder types and binder contents,sulfateconcentrations(1.5,3.0and6.0g/L)and erosion time(0,7,14and28d)on leached concentrations of heavy metal ions fromcontaminated soils were studied.Moreover,a parameter named immobilization percentage(IP)was introduced to evaluate theinfluence of erosion time and sulfate concentration on immobilization effectiveness for heavy metal ions.The results showed that,theleached heavy metal concentrations increased with sulfate concentration and erosion time.Comparatively speaking,the compositebinders that had calcium oxide in it exhibited the worst solidification effectiveness and the lowest immobilization percentage,withthe largest leached heavy metal concentration.

Failure mechanism of large-diameter shield tunnels and its effects on ground surface settlements

A new technique for the analysis of the three-dimensional collapse failure mechanism and the ground surface settlements for the large-diameter shield tunnels were presented.The technique is based on a velocity field model using more different truncated solid conical blocks to clarify the multiblock failure mechanism.Furthermore,the shape of blocks between the failure surface and the tunnel face was considered as an entire circle,and the supporting pressure was assumed as non-uniform distribution on the tunnel face and increased with the tunnel embedded depth.The ground surface settlements and failure mechanism above large-diameter shield tunnels were also investigated under different supporting pressures by the finite difference method.