Dynamics of Advantageous Mutant Spread in Spatial Death-Birth and Birth-Death Moran Models

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.

Interplay of laser power and pore characteristics in selective laser melting of ZK60 magnesium alloys:A study based on in-situ monitoring and image analysis

This study offers significant insights into the multi-physics phenomena of the SLM process and the subsequent porosity characteristics of ZK60 Magnesium(Mg)alloys.High-speed in-situ monitoring was employed to visualise process signals in real-time,elucidating the dynamics of melt pools and vapour plumes under varying laser power conditions specifically between 40 W and 60 W.Detailed morphological analysis was performed using Scanning-Electron Microscopy(SEM),demonstrating a critical correlation between laser power and pore formation.Lower laser power led to increased pore coverage,whereas a denser structure was observed at higher laser power.This laser power influence on porosity was further confirmed via Optical Microscopy(OM)conducted on both top and cross-sectional surfaces of the samples.An increase in laser power resulted in a decrease in pore coverage and pore size,potentially leading to a denser printed part of Mg alloy.X-ray Computed Tomography(XCT)augmented these findings by providing a 3D volumetric representation of the sample internal structure,revealing an inverse relationship between laser power and overall pore volume.Lower laser power appeared to favour the formation of interconnected pores,while a reduction in interconnected pores and an increase in isolated pores were observed at higher power.The interplay between melt pool size,vapour plume effects,and laser power was found to significantly influence the resulting porosity,indicating a need for effective management of these factors to optimise the SLM process of Mg alloys.

Tailoring the microstructure of Mg-Al-Sn-RE alloy via friction stir processing and the impact on its electrochemical discharge behaviour as the anode for Mg-air battery

Constructing the magnesium alloy with fine grains,low density of dislocations,and weak crystal orientation is of crucial importance to enhance its comprehensive performance as the anode for Mg-air battery.However,this unique microstructure can hardly be achieved with conventional plastic deformation such as rolling or extrusion.Herein,we tailor the microstructure of Mg-Al-Sn-RE alloy by using the friction stir processing,which obviously refines the grains without increasing dislocation density or strengthening crystal orientation.The Mg-air battery with the processed Mg-Al-Sn-RE alloy as the anode exhibits higher discharge voltages and capacities than that employing the untreated anode.Furthermore,the impact of friction stir processing on the electrochemical discharge behaviour of Mg-Al-Sn-RE anode and the corresponding mechanism are also analysed according to microstructure characterization and electrochemical response.

Anomalous{1012}tensile twinning and subsequent detwinning in a friction stir processed carbon fiber-reinforced Mg composite

1.Main text Owing to their low density and high specific strength,magnesium alloys and magnesium-based composites have great potential as structure metal materials in applications where lightweight matters[1–4].Deformation twins[5],especially the{1012}tension twins(also called tensile or extension twins)with a low critical resolved shear stress(CRSS)[6],are commonly observed in Mg alloys.They can provide the much-needed deformation along the c-axis in their hcp structure resulting from the very few easily activated slip systems in this crystal structure[7].The tensile twinning activation usually follows the macroscopic Schmid factor law[2],i.e.,the twin variant with the highest Schmid factor occurs,and it only appears when its Schmid factor is positive.

Modeling Expected Failure Considering Repair Time and Degradation: A Rail System Case Study

The repeated failures of any equipment or systems are modeled as a renewal process. The management needs an assessment of the number of future failures to allocate the resources needed for fast repairs. Based on the idea of expectation by conditioning, special Volterra-type integral equations are derived for general types of repairs, considering the length of repair and reduced degradation of the idle object. In addition to minimal repair and failure replacement, partial repairs are also discussed when the repair results in reduction of the number of future failures or decreases the effective age of the object. Numerical integration-based algorithm and simulation study are performed to solve the resulting integral equation. Since the geometry degradation in different dimensions of a rail track and controlling and maintaining defects are of importance, a numerical example using the rail industry data is conducted. Expected number of failures of different failure type modes in rail track is calculated within a two-year interval. Results show that within a two-year period, anticipated occurrences of cross level failures, surface failures, and DPI failures are 2.4, 3.8, and 5.8, respectively.

Soft Computing Based Procurement Planning of Time-variable Demand in Manufacturing Systems

Procurement planning with discrete time varying demand is an important problem in Enterprise Resource Planning （ERP）. It can be described using the non-analytic mathematical programming model proposed in this paper. To solve the model we propose to use a fuzzy decision embedded genetic algorithm. The algorithm adopts an order strategy selection to simplify the original real optimization problem into binary ones. Then, a fuzzy decision quantification method is used to quantify experience from planning experts. Thus, decision rules can easily be embedded in the computation of genetic operations. This approach is applied to purchase planning problem in a practical machine tool works, where satisfactory results have been achieved.

Friction behaviors in the metal cutting process:state of the art and future perspectives

Material removal in the cutting process is regarded as a friction system with multiple input and output variables.The complexity of the cutting friction system is caused by the extreme conditions existing on the tool–chip and tool–workpiece interfaces.The critical issue is significant to use knowledge of cutting friction behaviors to guide researchers and industrial manufacturing engineers in designing rational cutting processes to reduce tool wear and improve surface quality.This review focuses on the state of the art of research on friction behaviors in cutting procedures as well as future perspectives.First,the cutting friction phenomena under extreme conditions,such as high temperature,large strain/strain rates,sticking–sliding contact states,and diverse cutting conditions are analyzed.Second,the theoretical models of cutting friction behaviors and the application of simulation technology are discussed.Third,the factors that affect friction behaviors are analyzed,including material matching,cutting parameters,lubrication/cooling conditions,micro/nano surface textures,and tool coatings.Then,the consequences of the cutting friction phenomena,including tool wear patterns,tool life,chip formation,and the machined surface are analyzed.Finally,the research limitations and future work for cutting friction behaviors are discussed.This review contributes to the understanding of cutting friction behaviors and the development of high-quality cutting technology.

Improving Energy and Power Efficiency Using NComputing and Approaches for Predicting Reliability of Complex Computing Systems

Opting to follow the computing-design philosophy that the best way to reduce power consumption and increase energy efficiency is to reduce waste, we propose an architecture with a very simple ready-implementation by using an NComputing device that can allow multi-users but only one computer is needed. This intuitively can save energy, space as well as cost. In this paper, we propose a simple and realistic NComputing architecture to study the energy and power-efficient consumption of desktop computer systems by using the NComputing device. We also propose new approaches to estimate the reliability of k-out-of-n systems based on the delta method. The k-out-of-n system consisting of n subsystems works if and only if at least k-of-the-n subsystems work. More specificly, we develop approaches to obtain the reliability estimation for the k-out-of-n systems which is composed of n independent and identically distributed subsystems where each subsystem （or energy-efficient usage application） can be assumed to follow a two-parameter exponential lifetime distribution function. The detailed derivations of reliability estimation of k-out-of-n systems based on the biased-corrected estimator, known as delta method, the uniformly minimum variance unbiased estimate （UMVUE） and maximum likelihood estimate （MLE） are discussed. An energy-management NComputing application is discussed to illustrate the reliability results in terms of the energy consumption usages of a computer system with qua（t-core, 8 GB of RAM, and a GeForce 9800GX-2 graphics card to perform various complex applications. The estimated reliability values of systems based on the UMVUE and the delta method differ only slightly. Often the UMVUE of reliability for a complex system is a lot more difficult to obtain, if not impossible. The delta method seems to be a simple and better approach to obtain the reliability estimation of complex systems. The results of this study also show that, in practice, the NComputing architecture improves both energy cost saving and energy efficient living spaces.

Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map

With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of data.The major concerns in this domain are security and privacy.Therefore,attaining a reliable level of confidentiality in the fog computing environment is a pivotal task.Among different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing technologies.Hence,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog data.Chaotic Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,etc.To boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat map.The sequence generated by this map is used to transform the coordinates of the fog data.The improved range of the proposed map is depicted using bifurcation analysis.The quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate entropy.We also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical attack.The experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

Water, Air Emissions, and Cost Impacts of Air-Cooled Microturbines for Combined Cooling, Heating, and Power Systems： A Case Study in the Atlanta Region

城市化进程的加快意味着城市和国际组织需要去寻找各种能够提高能源效率和减少空气中污染物排放的方法。冷热电联产(CCHP)系统可以同时供暖、制冷和发电,具有提高城市或城市区域能源发电效率的潜力。本研究的目的是在满足建筑热需求(供热和制冷)的各种运行条件下,对亚特兰大大都市区内的五种常见建筑类型在采用CCHP系统时的发电耗水、CO_2和NO_x排放,及其经济性进行评价。对于大多数采用或不采用净计量策略的建筑类型来说,以满足每小时热需求去运行CCHP系统均可减少CO_2的排放量。该系统能否对这些建筑类型产生经济效益,主要取决于天然气的价格、净计量策略的采用和假定的CCHP系统的成本结构。当建筑物采用净计量策略并且CCHP系统是以满足建筑物每年的最大热需求而运行时,CCHP系统的发电耗水量和NO_x的排放量均有最大限度的减少,尽管此时该运行情景会增加温室气体排放和发电成本。CCHP系统对中型办公楼、大型办公楼和多户型住宅建筑更经济、实用。

Extreme Engineering: Polarization in Product Development and Manufacturing

1.Introduction Products and solutions are usually developed to meet customer needs at a cost justified by the market.New technologies are likely to be adopted by pr omising applications involving tolerable risks.over time,some application domains become saturated with new technologies.This naturally occurring market phenomenon and progress in the technology itself are likely to lead to newd evelopments that normally would not be considered.

Prognostics and Remaining Useful Life Prediction of Machinery:Advances,Opportunities,and Challenges

As the fundamental and key technique to ensure the safe and reliable operation of vital systems,prognostics with an emphasis on the remaining useful life(RUL)prediction has attracted great attention in the last decades.In this paper,we briefly discuss the general idea and advances of various prognostics and RUL prediction methods for machinery,mainly including data-driven methods,physics-based methods,hybrid methods,etc.Based on the observations fromthe state of the art,we provide comprehensive discussions on the possible opportunities and challenges of prognostics and RUL prediction of machinery so as to steer the future development.

Microcurvature landscapes induce neural stem cell polarity and enhance neural differentiation

Tissue curvature has long been recognized as an important anatomical parameter that affects intracellular behaviors,and there is emerging interest in applying cell-scale curvature as a designer property to drive cell fates for tissue engineering purposes.Although neural cells are known to undergo dramatic and terminal morphological changes during development and curvature-limiting behaviors have been demonstrated in neurite outgrowth studies,there are still crucial gaps in understanding neural cell behaviors,particularly in the context of a three-dimensional(3D)curvature landscape similar to an actual tissue engineering scaffold.In this study,we fabricated two substrates of microcurvature(curvature-substrates)that present a smooth and repeating landscape with focuses of either a concave or a convex pattern.Using these curvature-substrates,we studied the properties of morphological differentiation in N2a neuroblastoma cells.In contrast to other studies where two-dimensional(2D)curvature was demonstrated to limit neurite outgrowth,we found that both the concave and convex substrates acted as continuous and uniform mechanical protrusions that significantly enhanced neural polarity and differentiation with few morphological changes in the main cell body.This enhanced differentiation was manifested in various properties,including increased neurite length,increased nuclear displacement,and upregulation of various neural markers.By demonstrating how the micron-scale curvature landscape induces neuronal polarity,we provide further insights into the design of biomaterials utilizing the influence of surface curvature in neural tissue engineering.

Exceptional thermal stability and enhanced hardness in a nanostructured Mg-Gd-Y-Zn-Zr alloy processed by high pressure torsion

A Mg-8.2Gd-3.8Y-1.0Zn-0.4Zr(wt.%) alloy is processed by solution treatment and high pressure torsion(HPT) at room temperature to produce a nanostructured light material with high hardness. The stability of this alloy is subsequently tested through isochronal annealing for 0.5 h at 373 K to 673 K. The results reveal a thermal stability that is vastly superior to that of conventional Mg-based alloys processed by severe plastic deformation: the grain size remains at around 50 nm on heating to 573 K, and as the temperature is increased to 673 K,grain growth is restricted to within 500 nm. The stability of grain refinement of the present alloy/processing combination allowing grain size to be limited to 55 nm after exposure at 573 K, appears to be nearly one order of magnitude better than for the other SPD processed Mg-RE type alloys, and 2 orders of magnitude better than those of SPD processed RE-free Mg alloys. This superior thermal stability is attributed to formation of co-clusters near and segregation at grain boundaries, which cause a thermodynamic stabilization of grain size, as well as formation of β-Mg_(5)RE equilibrium phase at grain boundaries, which impede grain growth by the Zener pinning effect. The hardness of the nanostructured Mg-Gd-Y-Zn-Zr alloy increases with increasing annealing temperature up to 573 K, which is quite different from the other SPD-processed Mg-based alloys. The high hardness of 136 HV after annealing at 573 K is mainly due to solute segregation and solute clustering at or near grain boundaries.

Energy Consumption Analysis and Characterization of Aerospace Manufacturing Facilities in the United States–A Step towards Sustainable Development

In this study,information on energy usage in the United States(U.S.)aerospace manufacturing sector has been analyzed and then represented as energy intensities(kWh/m2)to establish benchmark data and to compare facilities of varying sizes.First,public sources were identified and the data from these previously published sources were aggregated to determine the energy usage of aerospace manufacturing facilities within the U.S.From this dataset,a sample of 28 buildings were selected and the energy intensity for each building was estimated from the data.Next,as a part of this study the energy data for three additional aerospace manufacturing facilities in the U.S.were collected firsthand.That data was analyzed and the energy intensity(kWh/m2)for each facility was calculated and then compared with the energy intensities of the 28 buildings from the sample.Three different indicators of energy consumption in aerospace manufacturing facilities were used as comparators to assist facility managers with determining potential energy savings and help in the decision-making process.On average,aerospace manufacturing facilities in the United States spent 4 cents for each dollar of sale on energy.The energy intensity(kWh/m2)and the power intensity(W/m2)for each facility were calculated based on the actual facility energy bills.The power intensity for these facilities ranges from 34 to 134 W/m2.The energy intensity ranged from 232 to 949 kWh/m2.We found that the power intensity could be used to estimate energy consumption when the annual operating hours of the facility are considered.and to estimate the energy-related carbon dioxide emissions.

Vibration-assisted vat photopolymerization for pixelated-aliasing-free surface fabrication

Mask image projection-based vat photopolymerization(MIP-VPP)offers advantages like low cost,high resolution,and a wide material range,making it popular in industry and education.Recently,MIP-VPP employing liquid crystal displays(LCDs)has gained traction,increasingly replacing digital micromirror devices,particularly among hobbyists and in educational settings,and is now beginning to be used in industrial environments.However,LCD-based MIP-VPPsuffers from pronounced pixelated aliasing arising from LCD’s discrete image pixels and itsdirect-contact configuration in MIP-VPP machines,leading to rough surfaces on the 3D-printed parts.Here,we propose a vibration-assisted MIP-VPP method that utilizes a microscalevibration to uniformize the light intensity distribution of the LCD-based mask image on VPP’s building platform.By maintaining the same fabrication speed,our technique generates asmoother,non-pixelated mask image,reducing the roughness on flat surfaces and boundary segments of 3D-printed parts.Through light intensity modeling and simulation,we derived an optimal vibration pattern for LCD mask images,subsequently validated by experiments.We assessed the surface texture,boundary integrity,and dimensional accuracy of componentsproduced using the vibration-assisted approach.The notably smoother surfaces and improved boundary roughness enhance the printing quality of MIP-VPP,enabling its promisingapplications in sectors like the production of 3D-printed optical devices and others.

Research on Driver’s Fatigue Detection Based on Information Fusion

Driving fatigue is a physiological phenomenon that often occurs during driving.After the driver enters a fatigued state,the attentionis lax,the response is slow,and the ability todeal with emergencies is significantly reduced,which can easily cause traffic accidents.Therefore,studying driver fatigue detectionmethods is significant in ensuring safe driving.However,the fatigue state of actual drivers is easily interfered with by the external environment(glasses and light),which leads to many problems,such as weak reliability of fatigue driving detection.Moreover,fatigue is a slow process,first manifested in physiological signals and then reflected in human face images.To improve the accuracy and stability of fatigue detection,this paper proposed a driver fatigue detection method based on image information and physiological information,designed a fatigue driving detection device,built a simulation driving experiment platform,and collected facial as well as physiological information of drivers during driving.Finally,the effectiveness of the fatigue detection method was evaluated.Eye movement feature parameters and physiological signal features of drivers’fatigue levels were extracted.The driver fatigue detection model was trained to classify fatigue and non-fatigue states based on the extracted features.Accuracy rates of the image,electroencephalogram(EEG),and blood oxygen signals were 86%,82%,and 71%,separately.Information fusion theory was presented to facilitate the fatigue detection effect;the fatigue features were fused using multiple kernel learning and typical correlation analysis methods to increase the detection accuracy to 94%.It can be seen that the fatigue driving detectionmethod based onmulti-source feature fusion effectively detected driver fatigue state,and the accuracy rate was higher than that of a single information source.In summary,fatigue drivingmonitoring has broad development prospects and can be used in traffic accident prevention and wearable driver fatigue recognition.

Extending the solid solution range of sodium ferric pyrophosphate:Off‐stoichiometric Na_(3)Fe(2.5)(P_(2)O_(7))_(2)as a novel cathode for sodium‐ion batteries

Iron‐based pyrophosphates are attractive cathodes for sodium‐ion batteries due to their large framework,cost‐effectiveness,and high energy density.However,the understanding of the crystal structure is scarce and only a limited candidates have been reported so far.In this work,we found for the first time that a continuous solid solution,Na_(4−α)Fe_(2+α)_(2)(P_(2)O_(7))_(2)(0≤α≤1,could be obtained by mutual substitution of cations at center‐symmetric Na3 and Na4 sites while keeping the crystal building blocks of anionic P_(2)O_(7) unchanged.In particular,a novel off‐stoichiometric Na_(3)Fe(2.5)(P_(2)O_(7))_(2)is thus proposed,and its structure,energy storage mechanism,and electrochemical performance are extensively investigated to unveil the structure–function relationship.The as‐prepared off‐stoichiometric electrode delivers appealing performance with a reversible discharge capacity of 83 mAh g^(−1),a working voltage of 2.9 V(vs.Na^(+)/Na),the retention of 89.2%of the initial capacity after 500 cycles,and enhanced rate capability of 51 mAh g^(−1)at a current density of 1600 mA g^(−1).This research shows that sodium ferric pyrophosphate could form extended solid solution composition and promising phase is concealed in the range of Na_(4−α)Fe_(2+α)_(2)(P_(2)O_(7))_(2),offering more chances for exploration of new cathode materials for the construction of high‐performance SIBs.

The Periodic Table of Primes

Over millennia, nobody has been able to predict where prime numbers sprout or how they spread. This study establishes the Periodic Table of Primes (PTP) using four prime numbers 2, 3, 5, and 7. We identify 48 integers out of a period 2×3×5×7=210 to be the roots of all primes as well as composites without factors of 2, 3, 5, and 7. Each prime, twin primes, or composite without factors of 2, 3, 5, and 7 is an offspring of the 48 integers uniquely allocated on the PTP. Three major establishments made in the article are the Formula of Primes, the Periodic Table of Primes, and the Counting Functions of Primes and Twin Primes.

A Review on the 3D Printing of Functional Structures for Medical Phantoms and Regenerated Tissue and Organ Applications

医学模型或模具已广泛应用于医学培训和医患互动领域,同时也日益应用于手术规划、医学计算模型、算法核实和验证以及医疗器械的开发等方面。这种新应用需要高保真度、患者特异性及模拟组织结构的医学模具,其不仅能够模拟人体器官的几何结构,而且还具有器官的功能。随着三维(3D)打印和3D生物打印技术的快速发展,许多研究人员已经开始使用增材制造技术来生产具有多种功能的医学模具。本文综述了3D打印和3D生物打印技术在制作功能性医学模具和生物结构方面的应用。特别讨论了3D打印功能性医学模具(即组织模拟医学模具、放射性医学模具和生理医学模具)及被用于再生组织和器官的3D生物打印模具的制备(即混合模式支架材料、可转换支架和集成传感器)工艺、发展现状以及未来发展趋势。