Nonlinear interaction of head-on solitary waves in integrable and nonintegrable systems

This study numerically investigates the nonlinear interaction of head-on solitary waves in a granular chain(a nonintegrable system)and compares the simulation results with the theoretical results in fluid(an integrable system).Three stages(the pre-in-phase traveling stage,the central-collision stage,and the post-in-phase traveling stage)are identified to describe the nonlinear interaction processes in the granular chain.The nonlinear scattering effect occurs in the central-collision stage,which decreases the amplitude of the incident solitary waves.Compared with the leading-time phase in the incident and separation collision processes,the lagging-time phase in the separation collision process is smaller.This asymmetrical nonlinear collision results in an occurrence of leading phase shifts of time and space in the post-in-phase traveling stage.We next find that the solitary wave amplitude does not influence the immediate space-phase shift in the granular chain.The space-phase shift of the post-in-phase traveling stage is only determined by the measurement position rather than the wave amplitude.The results are reversed in the fluid.An increase in solitary wave amplitude leads to decreased attachment,detachment,and residence times for granular chains and fluid.For the immediate time-phase shift,leading and lagging phenomena appear in the granular chain and the fluid,respectively.These results offer new knowledge for designing mechanical metamaterials and energy-mitigating systems.

Hepatomegaly and jaundice as the presenting symptoms of systemic light-chain amyloidosis: A case report

BACKGROUND Light chain(AL)amyloidosis is a plasma cell dyscrasia characterized by the pathologic production and extracellular tissue deposition of fibrillar proteins derived from immunoglobulin AL fragments secreted by a clone of plasma cells,which leads to progressive dysfunction of the affected organs.The two most commonly affected organs are the heart and kidneys,and liver is rarely the dominant affected organ with only 3.9%of cases,making them prone to misdia-gnosis and missed diagnosis.CASE SUMMARY A 65-year-old woman was admitted with a 3-mo history of progressive jaundice and marked hepatomegaly.Initially,based on enhanced computed tomography scan and angiography,Budd-Chiari syndrome was considered and balloon dilatation of significant hepatic vein stenoses was performed.However,addi-tional diagnostic procedures,including liver biopsy and bone marrow-exami-nation,revealed immunoglobulin kapa AL amyloidosis with extensive liver involvement and hepatic vascular compression.The disease course was progre-ssive and fatal,and the patient eventually died 5 mo after initial presentation of symptoms.CONCLUSION AL amyloidosis with isolated liver involvement is very rare,and can be easily misdiagnosed as a vascular disease.

Risk-Balanced Routing Strategy for Service Function Chains of Cyber-Physical Power System Considering Cross-Space Cascading Failure

Cyber-physical power system(CPPS)has significantly improved the operational efficiency of power systems.However,cross-space cascading failures may occur due to the coupling characteristics,which poses a great threat to the safety and reliability of CPPS,and there is an acute need to reduce the probability of these failures.Towards this end,this paper first proposes a cascading failure index to identify and quantify the importance of different information in the same class of communication services.On this basis,a joint improved risk-balanced service function chain routing strategy(SFC-RS)is proposed,which is modeled as a robust optimization problem and solved by column-and-constraint generation(C-CG)algorithm.Compared with the traditional shortest-path routing algorithm,the superiority of SFC-RS is verified in the IEEE 30-bus system.The results demonstrate that SFC-RS effectively mitigates the risk associated with information transmission in the network,enhances information transmission accessibility,and effectively limits communication disruption from becoming the cause of cross-space cascading failures.

Evaluation of the Reliability of a System: Approach by Monte Carlo Simulation and Application

The objective of this paper is to evaluate the reliability of a system in its different states (absence of failures, partial failure and total failure) and to propose actions to improve this reliability by an approach based on Monte Carlo simulation. It consists of a probabilistic evaluation based on Markov Chains. In order to achieve this goal, the functionalities of Markov Chains and Monte Carlo simulation steps are deployed. The application is made on a production system. .

An overview of recent progress in high-order nonholonomic chained system control and distributed coordination

This paper reviews some main results and progress concerning with nonholonomic system control,especially focusing on the networked chained system coordination.The controllability of nonholonomic system,the control method of nonholonomic system,the chained form transformation,the basic graph theory for multi-agent systems are recalled,respectively.Some important definitions,lemmas,theorems and dynamics are elaborated.Both the consensus and formation control problems for networked nonholonomic chained systems are summarised.Finally,some open questions are proposed.

Robust Exponential Stabilization of Nonholonomic Chained Systems with Unknown Parameters

The exponential stabilization problem of a robot-camera system with unknown camera parameters is investigated. Based on the visual feedback and the state-input transformation, an uncertain chained form model is presented for a type of nonholonomic mobile robots. Then, a new time-varying feedback controller is proposed to stabilize the uncertain system exponentially with the help of the stabilization theorems, state-scaling and switching techniques. The exponential stability of the closed-loop system is rigorously proved. Simulation results are given to demonstrate the effectiveness of the proposed strategies.

Dynamic Simulation and Test Verifcation of Hydraulic Automatic Tensioner for an Engine Timing Chain Drive System

As a fundamental component of an automobile engine’s timing chain drive system, the hydraulic automatic tensioner signifcantly enhances fuel economy while minimizing system vibrations and noise. However, there is a noticeable lack of research on automatic hydraulic tensioners. This study presents a comprehensive calculation approach for the principal parameters of a hydraulic automatic tensioner. An efective method, grounded in hydraulics and multibody dynamics, was introduced for estimating the dynamic response of such a tensioner. The simulation model developed for the hydraulic tensioner is characterized by its rapid dynamic response, consistent operation, and high accuracy. The dynamic behavior of the tensioner was analyzed under varying boundary conditions, using sinusoidal signal excitation. It was observed that the maximum damping force increases with a decreasing leakage gap. Conversely, an increase in oil temperature or air content leads to a decrease in the maximum damping force. The reaction forces derived from these calculations align well with experimental results. This calculation and simulation approach ofers considerable value for the design of innovative hydraulic tensioners. It not only streamlines the design phase, minimizes the number of trials, and reduces product costs, but also provides robust insights for evaluating the performance of hydraulic tensioners.

Off-diagonal approach to the exact solution of quantum integrable systems

We investigate the t–W scheme for the anti-ferromagnetic X X X spin chain under both periodic and open boundary conditions. We propose a new parametrization of the eigenvalues of the transfer matrix. Based on it, we obtain the exact solution of the system. By analyzing the distribution of zero roots at the ground state, we obtain the explicit expressions of the eigenfunctions of the transfer matrix and the associated W operator(see Eqs.(10) and(70)) in the thermodynamic limit. We find that the ratio of the quantum determinant with the eigenvalue of W operator for the ground state exhibits exponential decay behavior. Thus this fact ensures that the so-called inversion relation(the t–W relation without the W-term) can be used to study the ground state properties of quantum integrable systems with/without U(1)-symmetry in the thermodynamic limit.

Hybrid Algorithm-Driven Smart Logistics Optimization in IoT-Based Cyber-Physical Systems

Effectively managing complex logistics data is essential for development sustainability and growth,especially in optimizing distribution routes.This article addresses the limitations of current logistics path optimization methods,such as inefficiencies and high operational costs.To overcome these drawbacks,we introduce the Hybrid Firefly-Spotted Hyena Optimization(HFSHO)algorithm,a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm(FO)with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm(SHO).HFSHO aims to improve logistics path optimization and reduce operational costs.The algorithm’s effectiveness is systematically assessed through rigorous comparative analyses with established algorithms like the Ant Colony Algorithm(ACO),Cuckoo Search Algorithm(CSA)and Jaya Algo-rithm(JA).The evaluation also employs benchmarking methodologies using standardized function sets covering diverse objective functions,including Schwefel’s,Rastrigin,Ackley,Sphere and the ZDT and DTLZ Function suite.HFSHO outperforms these algorithms,achieving a minimum path distance of 546 units,highlighting its prowess in logistics path optimization.This comprehensive evaluation authenticates HFSHO’s exceptional performance across various logistic optimization scenarios.These findings emphasize the critical significance of selecting an appropriate algorithm for logistics path navigation,with HFSHO emerging as an efficient choice.Through the synergistic use of FO and SHO,HFSHO achieves a 15%improvement in convergence,heightened operational efficiency and substantial cost reductions in logistics operations.It presents a promising solution for optimizing logistics paths,offering logistics planners and decision-makers valuable insights and contributing substantively to sustainable sectoral growth.

Reliability Analysis of Satellite Turntable System under Multiple Operation Modes Based on Multi-Valued Decision Diagrams

As a payload support system deployed on satellites,the turntable system is often switched among different working modes during the on-orbit operation,which can experience great state changes.In each mode,the missions to be completed are different,consecutive and non-over-lapping,from which the turntable system can be considered to be a phased-mission system(PMS).Reliability analysis for PMS has been widely studied.However,the system mode cycle characteristic has not been taken into account before.In this paper,reliability analysis method of the satellite turntable system is proposed considering its multiple operation modes and mode cycle characteristic.Firstly,the multi-valued decision diagrams(MDD)manipulation rules between two adjacent mission cycles are proposed.On this basis,MDD models for the turntable system in different states are established and the reliability is calculated using the continuous time Markov chains(CTMC)method.Finally,the comparative study is carried out to show the effectiveness of our proposed method.

Delivery Service Management System Using Google Maps for SMEs in Emerging Countries

This paper proposes a Delivery Service Management(DSM)system for Small and Medium Enterprises(SMEs)that own a delivery fleet of pickup trucks to manage Business-to-Business(B2B)delivery services.The proposed DSM system integrates four systems:Delivery Location Positioning(DLP),Delivery Route Planning(DRP),Arrival Time Prediction(ATP),and Communication and Data Sharing(CDS)systems.These systems are used to pinpoint the delivery locations of customers,plan the delivery route of each truck,predict arrival time(with an interval)at each delivery location,and communicate and share information among stakeholders,respectively.The DSM system deploys Google applications,a GPS tracking system,Google Map APIs,ATP algorithms(embedded in Excel Macros),Line,and Telegram as supporting tools.To improve the accuracy of the ATP system,three tech-niques are applied considering driver behaviors.The proposed DSM system has been implemented in a Thai SME.From the process perspective,the DSM system is a systematic procedure for end-to-end delivery services.It allows the interactions between planner-driver decisions and supporting tools.The supporting tools are simple,can be easily used with little training,and require low capital expenditure.The statistical analysis shows that the ATP algorithm with the three techniques provides high accuracy.Thus,the proposed DSM system is beneficial for practitioners to manage delivery services,especially for SMEs in emerging countries.

Modeling the Dynamics of the Random Demand Inventory Management System

At any given time, a product stock manager is expected to carry out activities to check his or her holdings in general and to monitor the condition of the stock in particular. He should monitor the level or quantity available of a given product, of any item. On the basis of the observation made in relation to the movements of previous periods, he may decide to order or not a certain quantity of products. This paper discusses the applicability of discrete-time Markov chains in making relevant decisions for the management of a stock of COTRA-Honey products. A Markov chain model based on the transition matrix and equilibrium probabilities was developed to help managers predict the likely state of the stock in order to anticipate procurement decisions in the short, medium or long term. The objective of any manager is to ensure efficient management by limiting overstocking, minimising the risk of stock-outs as much as possible and maximising profits. The determined Markov chain model allows the manager to predict whether or not to order for the period following the current period, and if so, how much.

Impacts of Transparency and Traceability on Fashion Supply Chain System

The level of fashion consumer awareness and communication regarding sustainable consumption is rising. Organizations are working to provide clarity and guidance on fashion consumption. Brands are experimenting with new materials and supply chain strategies, and suppliers are improving the manufacturing processes and quality of products. However, given the size and complexity of the industrial process, these efforts are not adequate in ensuring a sustainable fashion supply chain. Transparency and traceability in the fashion supply chain are needed to improve the fashion industry by supporting sustainable and ethical practices in the apparel supply chain. Key gaps include a lack of comprehensive and transparent information about how, where, and by whom materials are sourced, processed, and assembled;a lack of transparency in the supply chain practices and procedures affects the environment, working conditions, and human health. The industry has to build the capacity to manage its supply chain, more effectively and responsibly, by improving transparency and traceability as the top goals. So, in this context, the main purpose of this research paper is to study the impacts of transparency and traceability on the dimensions of sustainability in fashion supply chain. The researchers have applied descriptive research methods in which secondary data are collected and analyzed through a literature review of peer-reviewed research papers and the primary data are collected through the survey method by distributing a semi structured questionnaire. The data collected are analyzed using statistical tools and techniques. Finally, the results are discussed and presented.

基于NEATM和WISE数据的小尺寸近地小行星物理特性研究

小行星是太阳系中广泛分布的金属或岩石天体,直径从米级跨越到几百公里.它们蕴含了太阳系早期的信息,同时也可能会与地球轨道相交且撞击地球,因此研究小行星的物理参数、物质成分和表面性质对于了解太阳系行星的形成演化和近地天体防御具有重要意义.以国际小行星中心(Minor Planet Center,MPC)获取直径D<160 m的小尺寸近地小行星共67颗作为研究对象,其中包含部分潜在威胁小行星(Potentially Hazardous Asteroids,PHA).基于NEATM(Near-Earth Asteroid Thermal Model),使用广域红外巡天望远镜(Wide-field Infrared Survey Explorer,WISE)的观测数据,利用反射光模型对太阳反射光进行了修正,使用动力学模型计算WISE观测历元的小行星轨道数据,计算了这67颗小尺寸近地小行星的直径和反照率.拟合过程采用马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)方法,与WISE的研究结果和MPC的数据进行了比较分析,给出了其分类特征.研究为小行星的观测和理论提供了有力的支持,可以更好地了解近地小行星的特征和演化.

A novel mechanism of PHB2-mediated mitophagy participating in the development of Parkinson's disease

Endoplasmic reticulum stress and mitochondrial dysfunction play important roles in Parkinson s disease,but the regulato ry mechanism remains elusive.Prohibitin-2(PHB2)is a newly discove red autophagy receptor in the mitochondrial inner membrane,and its role in Parkinson’s disease remains unclear.Protein kinase R(PKR)-like endoplasmic reticulum kinase(PERK)is a factor that regulates cell fate during endoplasmic reticulum stress.Parkin is regulated by PERK and is a target of the unfolded protein response.It is unclear whether PERK regulates PHB2-mediated mitophagy thro ugh Parkin.In this study,we established a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine(MPTP)-induced mouse model of Parkinson’s disease.We used adeno-associated virus to knockdown PHB2 expression.Our res ults showed that loss of dopaminergic neurons and motor deficits were aggravated in the MPTP-induced mouse model of Parkinson’s disease.Ove rexpression of PHB2 inhibited these abnormalities.We also established a 1-methyl-4-phenylpyridine(MPP+)-induced SH-SY5Y cell model of Parkinson’s disease.We found that ove rexpression of Parkin increased co-localization of PHB2 and microtubule-associated protein 1 light chain 3,and promoted mitophagy.In addition,MPP+regulated Parkin involvement in PHB2-mediated mitophagy through phosphorylation of PERK.These findings suggest that PHB2 participates in the development of Parkinson’s disease by intera cting with endoplasmic reticulum stress and Parkin.

Regulation of the quantum barrier and carrier transport toward high-efficiency quasi-2D Dion-Jacobson tin perovskite solar cells

Quasi-2D Dion-Jacobson(DJ)tin halide perovskite has attracted much attention due to its elimination of Van der Waals gap and enhanced environmental stability.However,the bulky organic spacers usually form a natural quantum well structure,which brings a large quantum barrier and poor film quality,further limiting the carrier transport and device performance.Here,we designed three organic spacers with different chain lengths(ethylenediamine(EDA),1,3-propanediamine(PDA),and 1,4-butanediamine(BDA))to investigate the quantum barrier dependence.Theoretical and experimental characterizations indicate that EDA with short chain can reduce the lattice distortion and dielectric confinement effect,which is beneficial to the effective dissociation of excitons and the inhibition of trap-free non-radiative relaxation.In addition,EDA cation shows strong interaction with the inorganic octahedron,realizing large aggregates in precursor solution and high-quality films with improved structural stability.Furthermore,femtosecond transient absorption proves that EDA cations can also weaken the formation of small n-phases with large quantum barrier to achieve effective carrier transport between different nphases.Finally,the quasi-2D DJ(EDA)FA_(9)Sn_(10)I_(31)solar cells achieves a 7.07%power conversion efficiency with good environment stability.Therefore,this work sheds light on the regulation of the quantum barrier and carrier transport through the chain length of organic spacer for qua si-2D DJ lead-free perovskites.

揭示氨对正己酸生产影响的机制——影响途径、关键酶和微生物

n-Caproate,which is produced via chain elongation(CE)using waste biomass,can supply various fossilderived products,thus advancing the realization of carbon neutrality.Ammonia released from the degradation of nitrogen-rich waste biomass can act as a nutrient or an inhibitor in anaerobic bioprocesses,including CE,with the distinction being primarily dependent on its concentration.Currently,the optimal concentration of ammonia and the threshold of toxicity for open-culture n-caproate production using ethanol as an electron donor,along with the underlying mechanisms,remain unclear.This study revealed that the optimal concentration of ammonia for n-caproate production was 2.0 g∙L^(-1),whereas concentrations exceeding this threshold markedly suppressed the CE performance.Exploration of the mechanism revealed the involvement of two forms of ammonia(i.e.,ammonium ions and free ammonia)in this inhibitory behavior.High ammonia levels(5.0 g∙L^(-1))induced excessive ethanol oxidation and suppressed the reverse β-oxidation(RBO)process,directly leading to the enhanced activities of enzymes(phosphotransacetylase and acetate kinase)responsible for acetate formation and diminished activities of butyryl-coenzyme A(CoA):acetyl-CoA transferase,caproyl-CoA:butyryl-CoA transferase,and caproyl-CoA:acetyl-CoA transferase that are involved in the syntheses of n-butyrate and n-caproate.Furthermore,the composition of the microbial community shifted from Paraclostridium dominance(at 0.1 g∙L^(-1)ammonia)to a co-dominance of Fermentimonas,Clostridium sensu stricto 12,and Clostridium sensu stricto 15 at 2.0 g∙L^(-1)ammonia.However,these CE-functional bacteria were mostly absent in the presence of excessive ammonia(5.0 g∙L^(-1)ammonia).Metagenomic analysis revealed the upregulation of functions such as RBO,fatty acid synthesis,K^(+)efflux,adenosine triphosphatase(ATPase)metabolism,and metal cation export in the presence of 2.0 g∙L^(-1)ammonia,collectively contributing to enhanced n-caproate production.Conversely,the aforementioned functions(excluding metal cation export)and K^(+)influx were suppressed by excessive ammonia,undermining both ammonia detoxification and n-caproate biosynthesis.The comprehensive elucidation of ammonia-driven mechanisms influencing n-caproate production,as provided in this study,is expected to inspire researchers to devise effective strategies to alleviate ammonia-induced inhibition.

A Tutorial Review of the Solar Power Curve: Regressions, Model Chains, and Their Hybridization and Probabilistic Extensions

Owing to the persisting hype in pushing toward global carbon neutrality,the study scope of atmospheric science is rapidly expanding.Among numerous trending topics,energy meteorology has been attracting the most attention hitherto.One essential skill of solar energy meteorologists is solar power curve modeling,which seeks to map irradiance and auxiliary weather variables to solar power,by statistical and/or physical means.In this regard,this tutorial review aims to deliver a complete overview of those fundamental scientific and engineering principles pertaining to the solar power curve.Solar power curves can be modeled in two primary ways,one of regression and the other of model chain.Both classes of modeling approaches,alongside their hybridization and probabilistic extensions,which allow accuracy improvement and uncertainty quantification,are scrutinized and contrasted thoroughly in this review.

Application of Stork Optimization Algorithm for Solving Sustainable Lot Size Optimization

The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and transparency.To address these challenges and improve operations in green manufacturing,optimization algorithms play a crucial role in supporting decision-making processes.In this study,we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms,notably the Stork Optimization Algorithm(SOA).The SOA draws inspiration from the hunting and winter migration strategies employed by storks in nature.The theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases:exploration,based on migration simulation,and exploitation,based on hunting strategy simulation.To tackle the green lot size optimization issue,our methodology involved gathering real-world data,which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO_(2) emissions.This function served as input for the SOA model.Subsequently,the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and sustainability.Through extensive experimentation,we compared the performance of SOA with twelve established metaheuristic algorithms,consistently demonstrating that SOA outperformed the others.This study’s contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma,thereby reducing environmental impact and enhancing supply chain efficiency.The simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies,making it a promising approach for green manufacturing and sustainable supply chain management.

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.