A new design of 3D-printed orthopedic bone plates with auxeticstructures to mitigate stress shielding and improve intra-operative bending

Orthopedic bone plates are most commonly used for bone fracture fixation for more than 100 years.The bone plate design had evolved over time overcoming many challenges such as insufficient strength and excessive plate–bone contact affecting the blood circulation.However,it is only made of two materials,either stainless steel(AISI 316L)or titanium(Ti–6Al–4V).There are two main limitations of metallic bone implants,namely stress shielding and the problem of malocclusion caused by the displacement of the fracture site during healing.To overcome the two problems,a new bone plate design with the incorporation of auxetic structures is proposed in this work.This study aims to use auxetic structure section in the bone plate that would decrease the stiffness of the region,thereby mitigating the stress-shielding effect and at the same time act as a deformable section to enable intra-operative bending for effective alignment while having enough bending strength and stiffness.Two different auxetic structures namely re-entrant honeycomb and missing rib structures were considered.The auxetic structure incorporated bone plates were designed,finite element analysis was done,fabricated using direct metal laser sintering technique,and tested.The results indicate that the re-entrant honeycomb structure incorporated bone plates serve as an effective bone design compared to the conventional bone plate design,in terms of stress shielding and intra-operative bending while offering similar mechanical and bending strength.

ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model

The ChatGPT,a lite and conversational variant of Generative Pretrained Transformer 4(GPT-4)developed by OpenAI,is one of the milestone Large Language Models(LLMs)with billions of parameters.LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks,which profoundly impact various fields.This paper mainly discusses the future applications of LLMs in dentistry.We introduce two primary LLM deployment methods in dentistry,including automated dental diagnosis and cross-modal dental diagnosis,and examine their potential applications.Especially,equipped with a cross-modal encoder,a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations.We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application.While LLMs offer significant potential benefits,the challenges,such as data privacy,data quality,and model bias,need further study.Overall,LLMs have the potential to revolutionize dental diagnosis and treatment,which indicates a promising avenue for clinical application and research in dentistry.

Consensus of second-order nonlinear multi-agent systems via sliding mode observer and controller

This paper investigates the consensus problem of second-order nonlinear multi-agent systems (MASs) via the sliding mode control (SMC) approach. The velocity of each agent is assumed to be unmeasurable. A second-order sliding mode observer is designed to estimate the velocity. Then a distributed discontinuous control law based on first-order SMC is presented to solve the consensus problem. Moreover, to overcome the chatting problem, two controllers based on the boundary layer method and the super-twisting algorithm respectively are presented. It is shown that the MASs will achieve consensus under some given conditions. Some examples are provided to demonstrate the effectiveness of the proposed control laws.

A Fast Image Matching Algorithm Using a Combination of Line Segment Features

The scene matching navigation is a research focus in the field of autonomous navigation,but the real-time performance of image matching algorithm is difficult to meet the needs of real navigation systems.Therefore,this paper proposes a fast image matching algorithm.The algorithm improves the traditional line segment extraction algorithm and combines with the Delaunay triangulation method.By combining the geometric features of points and lines,the image feature redundancy is reduced.Then,the error with confidence criterion is analyzed and the matching process is completed.The simulation results show that the proposed algorithm can still work within 3°rotation and small scale variation.In addition,the matching time is less than 0.5 s when the image size is 256 pixel×256 pixel.The proposed algorithm is suitable for autonomous navigation systems with multiple feature distribution and higher real-time requirements.

Traffic Engineering in Dynamic Hybrid Segment Routing Networks

The emergence of Segment Routing(SR)provides a novel routing paradigm that uses a routing technique called source packet routing.In SR architecture,the paths that the packets choose to route on are indicated at the ingress router.Compared with shortest-path-based routing in traditional distributed routing protocols,SR can realize a flexible routing by implementing an arbitrary flow splitting at the ingress router.Despite the advantages of SR,it may be difficult to update the existing IP network to a full SR deployed network,for economical and technical reasons.Updating partial of the traditional IP network to the SR network,thus forming a hybrid SR network,is a preferable choice.For the traffic is dynamically changing in a daily time,in this paper,we propose a Weight Adjustment algorithm WASAR to optimize routing in a dynamic hybrid SR network.WASAR algorithm can be divided into three steps:firstly,representative Traffic Matrices(TMs)and the expected TM are obtained from the historical TMs through ultrascalable spectral clustering algorithm.Secondly,given the network topology,the initial network weight setting and the expected TM,we can realize the link weight optimization and SR node deployment optimization through a Deep Reinforcement Learning(DRL)algorithm.Thirdly,we optimize the flow splitting ratios of SR nodes in a centralized online manner under dynamic traffic demands,in order to improve the network performance.In the evaluation,we exploit historical TMs to test the performance of the obtained routing configuration in WASAR.The extensive experimental results validate that our proposed WASAR algorithm has superior performance in reducing Maximum Link Utilization(MLU)under the dynamic traffic.

Loss of Notch signaling in skeletal stem cells enhances bone formation with aging

Skeletal stem and progenitor cells(SSPCs) perform bone maintenance and repair. With age, they produce fewer osteoblasts and more adipocytes leading to a loss of skeletal integrity. The molecular mechanisms that underlie this detrimental transformation are largely unknown. Single-cell RNA sequencing revealed that Notch signaling becomes elevated in SSPCs during aging. To examine the role of increased Notch activity, we deleted Nicastrin, an essential Notch pathway component, in SSPCs in vivo. Middle-aged conditional knockout mice displayed elevated SSPC osteo-lineage gene expression, increased trabecular bone mass, reduced bone marrow adiposity, and enhanced bone repair. Thus, Notch regulates SSPC cell fate decisions, and moderating Notch signaling ameliorates the skeletal aging phenotype, increasing bone mass even beyond that of young mice. Finally, we identified the transcription factor Ebf3 as a downstream mediator of Notch signaling in SSPCs that is dysregulated with aging, highlighting it as a promising therapeutic target to rejuvenate the aged skeleton.

Generation and Detection of a Directed Monoenergetic Neutrino Beam with Hydrogen-Like Ions

We consider two possible schemes for generation and detection of a monoenergetic directed beam of neutrinos which may have application to neutrino communication. First, we consider generation of a directed neutrino beam using electron capture beta decay in hydrogen-like ions. Next, we suggest detection of a directed neutrino beam using resonant absorption of a neutrino by a bare nucleus with the generation of a bound electron. This reaction is inverse to electron capture beta decay, and we call it “Bound State Inverse Beta Decay (BSIBD)”. We show that the recoil effect can be eliminated by an appropriate choice of velocities for the ions and bare nuclei. Finally, we consider a combination of a solid state source of a directed mono-energetic neutrino beam and its detection using BSIBD.

A New Unified Electro-Gravity Theory for the Electron, and the Fundamental Origin of the Fine Structure Constant and the Casimir Effect

A rigorous model for the electron is presented by generalizing the Coulomb’s Law or Gauss’s Law of electrostatics, using a unified theory of electricity and gravity. The permittivity of the free-space is allowed to be variable, dependent on the energy density associated with the electric field at a given location, employing generalized concepts of gravity and mass/energy density. The electric field becomes a non-linear function of the source charge, where the concept of the energy density needs to be properly defined. Stable solutions are derived for a spherically symmetric, surface-charge distribution of an elementary charge. This is implemented by assuming that the gravitational field and its equivalent permittivity function is proportional to the energy density, as a simple first-order approximation, with the constant of proportionality, referred to as the Unified Electro-Gravity (UEG) constant. The stable solution with the lowest mass/energy is assumed to represent a “static” electron without any spin. Further, assuming that the mass/energy of a static electron is half of the total mass/energy of an electron including its spin contribution, the required UEG constant is estimated. More fundamentally, the lowest stable mass of a static elementary charged particle, its associated classical radius, and the UEG constant are related to each other by a dimensionless constant, independent of any specific value of the charge or mass of the particle. This dimensionless constant is numerologically found to be closely related to the fine structure constant. This possible origin of the fine structure constant is further strengthened by applying the proposed theory to successfully model the Casimir effect, from which approximately the same above relationship between the UEG constant, electron’s mass and classical radius, and the fine structure constant, emerges.

Understanding the correlation between energy-state mismatching and open-circuit voltage loss in bulk heterojunction solar cells

Photoinduced intermolecular charge transfer(PICT)determines the voltage loss in bulk heterojunction(BHJ)organic photovoltaics(OPVs),and this voltage loss can be minimized by inducing efficient PICT,which requires energy-state matching between the donor and acceptor at the BHJ interfaces.Thus,both geometrically and energetically accessible delocalized state matching at the hot energy level is crucial for achieving efficient PICT.In this study,an effective method for quantifying the hot state matching of OPVs was developed.The degree of energy-state matching between the electron donor and acceptor at BHJ interfaces was quantified using a mismatching factor(MF)calculated from the modified optical density of the BHJ.Furthermore,the correlation between the open-circuit voltage(Voc)of the OPV device and energy-state matching at the BHJ interface was investigated using the calculated MF.The OPVs with small absolute MF values exhibited high Voc values.This result clearly indicates that the energy-state matching between the donor and acceptor is crucial for achieving a high Voc in OPVs.Because the MF indicates the degree of energy-state matching,which is a critical factor for suppressing energy loss,it can be used to estimate the Voc loss in OPVs.

3D-bioprinted tri-layered cellulose/collagen-based drug-eluting fillers for the treatment of deep tunneling wounds

Tunneling wounds create passageways underneath the skin surface with varying sizes and shapes and can have twists and turns,making their treatment extremely difficult.Available wound care solutions only cater to superficial wounds,and untreated tunneling wounds pose major health concerns.This study aims to fulfill this challenge by fabricating tunnel wound fillers(TWFs)made of natural polymers that mimic the dermal extracellular matrix.In this study,cellulose microfibers(CMFs)derived from banana stem and fish skin-derived collagen were used to formulate bio-inks with varying CMF contents(25,50,and 75 mg).Tri-layered(CMFs,primary and secondary collagen coatings),drug-eluting(Baneocin),and cell-laden(human mesenchymal stem cells)TWFs were three-dimensional(3D)-printed and extensively characterized.CMFs showed the most suitable rheological properties for 3D printing at 50 mg concentration.The Alamar Blue data showed significantly increased cell proliferation from Day 1 to Day 7,and scratch tests used to evaluate in vitro wound healing revealed that the best coverage of the wound area was achieved using CMFs in combination with collagen and alginate.Finally,the TWF showed promising capability and tunability in terms of wound shape and size upon testing on a chicken tissue model.The results demonstrate the tremendous potential of TWFs in treating deep tunneling wounds with unique advantages,such as patient-specific customization,good wound exudate absorption capability while releasing wound healing drugs,and the inclusion of stem cells for accelerated healing and tissue regeneration.

Reinforcement Learning for Linear Continuous-time Systems: an Incremental Learning Approach

In this paper, we introduce a novel reinforcement learning(RL) scheme for linear continuous-time dynamical systems. Different from traditional batch learning algorithms,an incremental learning approach is developed, which provides a more efficient way to tackle the on-line learning problem in realworld applications. We provide concrete convergence and robust analysis on this incremental-learning algorithm. An extension to solving robust optimal control problems is also given. Two simulation examples are also given to illustrate the effectiveness of our theoretical result.

Directional neighbor discovery in mmWave wireless networks

The directional neighbor discovery problem,i.e,spatial rendezvous,is a fundamental problem in millimeter wave(mmWave)wireless networks,where directional transmissions are used to overcome the high attenuation.The challenge is how to let the transmitter and the receiver beams meet in space under deafness caused by directional transmission and reception,where no control channel,prior information,and coordination are available.In this paper,we present a Hunting based Directional Neighbor Discovery(HDND)scheme for ad hoc mmWave networks,where a node follows a unique sequence to determine its transmission or reception mode,and continuously r0-tates its directional beam to scan the neighborhood for other mmWave nodes.Through a rigorous analysis,we derive the conditions for ensured neighbor discovery,as well as a bound for the worst-case discovery time and the impact of sidelobes.We validate the analysis with extensive simulations and demonstrate the superior perfor-mance of the proposed scheme over several baseline schemes.

Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale

Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability.Despite the scientific interest suggested by such assumptions,the relationships between descriptive time series features(e.g.,temporal dependence,entropy,seasonality,trend and linearity features)and actual time series forecastability(quantified by issuing and assessing forecasts for the past)are scarcely studied and quantified in the literature.In this work,we aim to fill in this gap by investigating such relationships,and the way that they can be exploited for understanding hydroclimatic forecastability and its patterns.To this end,we follow a systematic framework bringing together a variety of–mostly new for hydrology–concepts and methods,including 57 descriptive features and nine seasonal time series forecasting methods(i.e.,one simple,five exponential smoothing,two state space and one automated autoregressive fractionally integrated moving average methods).We apply this framework to three global datasets originating from the larger Global Historical Climatology Network(GHCN)and Global Streamflow Indices and Metadata(GSIM)archives.As these datasets comprise over 13,000 monthly temperature,precipitation and river flow time series from several continents and hydroclimatic regimes,they allow us to provide trustable characterizations and interpretations of 12-month ahead hydroclimatic forecastability at the global scale.We first find that the exponential smoothing and state space methods for time series forecasting are rather equally efficient in identifying an upper limit of this forecastability in terms of Nash-Sutcliffe efficiency,while the simple method is shown to be mostly useful in identifying its lower limit.We then demonstrate that the assessed forecastability is strongly related to several descriptive features,including seasonality,entropy,(partial)autocorrelation,stability,(non)linearity,spikiness and heterogeneity features,among others.We further(i)show that,if such descriptive information is available for a monthly hydroclimatic time series,we can even foretell the quality of its future forecasts with a considerable degree of confidence,and(ii)rank the features according to their efficiency in explaining and foretelling forecastability.We believe that the obtained rankings are of key importance for understanding forecastability.Spatial forecastability patterns are also revealed through our experiments,with East Asia(Europe)being characterized by larger(smaller)monthly temperature time series forecastability and the Indian subcontinent(Australia)being characterized by larger(smaller)monthly precipitation time series forecastability,compared to other continental-scale regions,and less notable differences characterizing monthly river flow from continent to continent.A comprehensive interpretation of such patters through massive feature extraction and feature-based time series clustering is shown to be possible.Indeed,continental-scale regions characterized by different degrees of forecastability are also attributed to different clusters or mixtures of clusters(because of their essential differences in terms of descriptive features).

Improved Eigenstructure-Based 2D DOA Estimation Approaches Based on Nystrom Approximation

In this paper,we propose improved approaches for two-dimensional(2 D) direction-of-arrival(DOA) estimation for a uniform rectangular array(URA).Unlike the conventional eigenstructure-based estimation approaches such as Multiple Signals Classification(MUSIC) and Estimation of Signal Parameters via Rotational Invariance Technique(ESPRIT),the proposed approaches estimate signal and noise subspaces with Nystr?m approximation,which only need to calculate two sub-matrices of the whole sample covariance matrix and avoid the need to directly calculate the eigenvalue decomposition(EVD) of the sample covariance matrix.Hence,the proposed approaches can improve the computational efficiency greatly for large-scale URAs.Numerical results verify the reliability and efficiency of the proposed approaches.

Adaptive dynamic programming for finite-horizon optimal control of linear time-varying discrete-time systems

This paper studies data-driven learning-based methods for the finite-horizon optimal control of linear time-varying discretetime systems. First, a novel finite-horizon Policy Iteration (PI) method for linear time-varying discrete-time systems is presented. Its connections with existing in finite-horizon PI methods are discussed. Then, both data-drive n off-policy PI and Value Iteration (VI) algorithms are derived to find approximate optimal controllers when the system dynamics is completely unknown. Under mild conditions, the proposed data-driven off-policy algorithms converge to the optimal solution. Finally, the effectiveness and feasibility of the developed methods are validated by a practical example of spacecraft attitude control.

Learning-based adaptive optimal output regulation of linear and nonlinear systems:an overview

This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance rejection.The proposed framework aims to bring together two separate topics—output regulation and adaptive dynamic programming—that have been under extensive investigation due to their broad applications in modern control engineering.Under this framework,one can solve optimal output regulation problems of linear,partially linear,nonlinear,and multi-agent systems in a data-driven manner.We will also review some practical applications based on this framework,such as semi-autonomous vehicles,connected and autonomous vehicles,and nonlinear oscillators.

A non-ideal solution theory for the mechanics and electrochemistry of charged membranes

Understanding how ions and solvent molecules migrate within charged membranes is fundamental for advancing the analysis of biological membranes and the design of energy storage and production devices.Recent efforts highlighted a significant interplay between mechanics and electrochemistry in charged membranes,calling for the development of high-fidelity models to describe their interaction.Here,we propose a continuum theory of the chemoelectromechanics of charged membranes,accounting for potentially large deformations and non-idealities of the solution permeating the membrane.We demonstrate the potential applications of our theory within the study of ionic polymer actuators.Our theory predicts sizeable effects of non-idealities and mechanical deformations,enabling insight into the role of mechanics on solute and solvent transport within charged membranes.

Spike-and nucleocapsid-based gold colloid assay toward the development of an adhesive bandage for rapid SARS-CoV-2 immune response detection and screening

Immunoglobulin M(IgM)and immunoglobulin G(IgG)antibodies are important biomarkers used for the diagnosis and screening of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infections in both symptomatic and asymptomatic individuals.These antibodies are highly specific to the spike(S)and nucleocapsid(N)proteins of the SARS-CoV-2 virus.This paper outlines the development steps of a novel hybrid(vertical-lateral-vertical)flow assay in the form of a finger-stick point-of-care device,similar to an adhesive bandage,designed for the timely detection and screening of IgM and IgG immune responses to SARS-CoV-2 infections.The assay,comprising a vertically stacked plasma/serum separation membrane,conjugate pad,and detection(readout)zone,utilizes gold nanoparticles(AuNPs)conjugated with SARS-CoV-2 S and N proteins to effectively capture IgM and IgG antibodies from a pinprick(~15µL)of blood in just one step and provides results of no immune IgM−/IgG−,early immune IgM+/IgG−,active immune IgM+/IgG+or immune IgM−/IgG+in a short amount of time(minutes).The adhesive bandage-like construction is an example of the design of rapid,low-cost,disposable,and easy-to-use tests for large-scale detection and screening in households.Furthermore,the bandage can be easily adjusted and optimized to detect different viral infections as they arise by simply selecting appropriate antigens related to pandemics and outbreaks.

Hydrodynamic Performance of Euplectella aspergillum:Simulating Real Life Conditions in the Abyss

We detail some of the understudied aspects of the flow inside and around the Hexactinellid Sponge Euplectella aspergillum.By leveraging the flexibility of the Lattice Boltzmann Method,High Performance Computing simulations are performed to dissect the complex conditions corresponding to the actual environment at the bottom of the ocean,at depths between 100 and 1,000 m.These large-scale simulations unveil potential clues on the evolutionary adaptations of these deep-sea sponges in response to the surrounding fluid flow,and they open the path to future investigations at the interface between physics,engineering and biology.

Strain-Rate-Dependent In-Plane Compressive Properties of 3D Fine Weave Pierced C/C Composite:Failure Mechanism and Constitutive Model

In-plane compression experiments are performed on 3D fine weave pierced C/C composite at a wide strain rate range of 0.0001/s-1000/s.The in-plane compressive failure mechanism of the composite at quasi-static and high strain rates is analyzed by a scanning electron microscope.The results show that the in-plane compressive modulus,maximum stress and the corresponding strain increase with increasing strain rate.The quasi-static in-plane compressive failure mode of the 3D fine weave pierced C/C composite is characterized by the shear failure at the angle of 45°and the local buckling of the^direction fiber bundles.In comparison,the high strain rate in-plane compression failure mode of the composite is characterized by the compressive fracture of the interlaminar matrix and the progressive compression failure of the a>direction fiber bundles.A strain-rate-dependent in-plane compressive constitutive model is proposed to predict the dynamic in-plane compressive response of the composite.The proposed constitutive model is verified by experimental data.