Dynamic Matching Research Articles

Dynamic power matching for wheel loader based on power reflux hydrodynamic transmission system

Dynamic power matching for wheel loader based on power reflux hydrodynamic transmission system

The Cost of Impatience in Dynamic Matching: Scaling Laws and Operating Regimes

We study matching queues with abandonment. The simplest of these is the two-sided queue with servers on one side and customers on the other, both arriving dynamically over time and abandoning if not matched by the time their patience elapses. We identify nonasymptotic and universal scaling laws for the matching loss due to abandonment, which we refer to as the “cost of impatience.” The scaling laws characterize the way in which this cost depends on the arrival rates and the (possibly different) mean patience of servers and customers. Our characterization reveals four operating regimes identified by an operational measure of patience that brings together mean patience and utilization. The four regimes subsume the regimes that arise in asymptotic (heavy-traffic) approximations. The scaling laws, specialized to each regime, reveal the fundamental structure of the cost of impatience and show that its order of magnitude is fully determined by (i) a “winner-take-all” competition between customer impatience and utilization, and (ii) the ability to accumulate inventory on the server side. Practically important is that when servers are impatient, the cost of impatience is, up to an order of magnitude, given by an insightful expression where only the minimum of the two patience rates appears. Considering the trade-off between abandonment and capacity costs, we characterize the scaling of the optimal safety capacity as a function of costs, arrival rates, and patience parameters. We prove that the ability to hold inventory of servers means that the optimal safety capacity grows logarithmically in abandonment cost and, in turn, slower than the square-root growth in the single-sided queue. This paper was accepted by Baris Ata, stochastic models and simulation. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01513 .

A ±0.15 °C (3σ) Inaccuracy CMOS Smart Temperature Sensor from −40 °C to 125 °C with a 10 ms Conversion Time-Leveraging an Adaptative Decimation Filter in 65 nm CMOS Technology

This paper presents the design and implementation of a highly accurate smart temperature sensor designed in 65 nm CMOS technology. The sensor exhibits a ±0.15 °C (3σ) error across a wide temperature range from −40 °C to 125 °C, catering to diverse application needs. Leveraging advanced CMOS technology, the sensor employs an adaptive decimation filter that allows us to control the conversion time, ensuring that the accuracy of the conversion is maintained even in challenging conditions. The proposed sensor architecture integrates advanced techniques for temperature sensing for improved accuracy and reliability. Through meticulous circuit design and the usage of dynamic element matching, chopping, and calibration/trimming, the sensor demonstrates exceptional performance characteristics, making it suitable for various industrial, automotive, and consumer electronics applications demanding high precision temperature monitoring.

Mobile Apps, Trading Behaviors, and Portfolio Performance: Evidence from a Quasi-Experiment in China

Mobile Apps, Trading Behaviors, and Portfolio Performance: Evidence from a Quasi-Experiment in China How do mobile apps influence individual investors’ financial decisions and performance? This study answers this timely and important question by using rare archival data from a large securities company in China using a sample of 20,665 investors. Authors find that mobile app adoption does not affect investors’ portfolio performance when one examines aggregate impacts using a binary indicator of mobile app use. Their additional analyses suggest that adopting mobile apps results in a noticeable decrease in time constraints, a proxy for transaction friction, and a modest increase in trend-chasing bias, reflecting tendencies toward myopic decision making. Because the reduction in time constraints can benefit investors’ performance, the increase in trend chasing can be detrimental to investors’ performance, these findings explain why mobile app adoption has no overall effect on portfolio performance. Further analyses of adopters’ postadoption behaviors provide interesting insights and show that the mobile app usage intensity has an inverted U–shaped relationship with portfolio performance. The results are robust to using different samples or excluding high market volatility periods and by using a variety of methods, such as propensity score matching, dynamic matching, stacked difference-in-differences, or an instrumental variable approach.

PM-profiler: a high-resolution and fast tool for taxonomy annotation of amplicon-based microbiome.

Amplicon sequencing stands as a cornerstone in microbiome profiling, yet concerns persist regarding its resolution and accuracy. The enhancement of reference databases and annotations marks a new era for 16S rRNA-based profiling. Capitalizing on this potential, we introduce PM-profiler, a novel tool for profiling amplicon short reads. PM-profiler is implemented by C++-based advanced algorithms, such as pre-allocated hash for reference construction, hybrid and dynamic short-read matching, big-data-guided dual-mode hierarchical taxonomy annotation strategy, and full-procedure parallel computing. This tool delivers species-level resolution and ultrafast speed for large-scale microbiomes, surpassing alignment-based approaches and the Naïve-Bayesian model. Furthermore, recognizing the global uneven distribution of microbes, we delineate optimal annotation strategies for each sampling habitat based on microbial patterns over 270,000 microbiomes. Integrated with the established workflow of Parallel-Meta Suite and the latest curated reference databases, this endeavor offers a swift and dependable solution for high-precision microbiome surveys.IMPORTANCEOur study introduces PM-profiler, a new tool that deciphers the complexity of microbial communities. With advanced algorithms, flexible annotation strategies, and well-organized big-data, PM-profiler provides a faster and more accurate way to study on microbiomes, paving the way for discoveries that could improve our understanding of microbiomes and their impact on the world.

Enhancing Cybersecurity Posture through Dynamic Vulnerability Matching and Threat Intelligence Integration Precious

As the digital landscape continues to evolve, organizations face increasingly sophisticated cyber threats that challenge traditional cybersecurity measures. In response, this paper proposes a novel approach to bolstering cybersecurity posture by integrating dynamic vulnerability matching and threat intelligence. The proposed framework combines proactive identification of vulnerabilities within an organization's network with real-time threat intelligence feeds. Leveraging advanced analytics and machine learning algorithms, the system dynamically matches vulnerabilities to relevant threat intelligence, allowing for prioritized remediation efforts. This dynamic matching ensures that resources are allocated efficiently, focusing on mitigating the most imminent threats to the organization's security. Furthermore, the integration of threat intelligence enriches the vulnerability management process by providing contextual information about emerging threats, attack vectors, and adversary tactics. This contextual awareness enables organizations to anticipate and proactively defend against potential cyber attacks, thereby reducing the window of vulnerability and minimizing the impact of security breaches. Through empirical evaluation and case studies, we demonstrate the efficacy of the proposed framework in enhancing cybersecurity posture across diverse organizational environments. By empowering organizations to adaptively respond to evolving cyber threats, this approach enables them to stay ahead of adversaries and effectively safeguard their critical assets and data.

Enhancing predictive understanding and accuracy in geological carbon dioxide storage monitoring: Simulation and history matching of tracer transport dynamics

Co-injection of conservative tracers with carbon dioxide (CO2) is a viable tool for monitoring subsurface processes during geological CO2 storage (GCS). This research investigates the simulation and history-matching of a gas tracer (sulfur hexafluoride, SF6) during CO2 flooding, employing a core flooding result in Berea sandstone. Four extensively used saturation functions are assessed for their efficacy in history matching of CO2/SF6 injection at the core scale. The history-matching process incorporates particle swarm optimization (PSO) to fine-tune constitutive relationships parameters. Next, employing transport models at the aquifer scale, we interrogate the impact on tracer transport and mixing of saturation function uncertainties, arising from the non-uniqueness of constitutive relationships parameters and saturation function types. To assess the effects of geological heterogeneity on behavior of tracer breakthrough curves (BTCs), we employ two normalized parameters assessing the degree of mixing and SF6 breakthrough time. The aquifer-scale investigation encompasses both homogeneous and heterogeneous systems with and without capillary heterogeneity effects. Our findings underscore the critical importance of addressing saturation function uncertainties, emphasizing the significance of auxiliary experiments and innovative methodologies to enhance predictive accuracy. The findings highlight significant disparities in arrival times, BTC peaks, tails, and mixing levels, even under optimal conditions. Heterogeneity, with or without capillary heterogeneity, plays a crucial role in shaping BTC variations, resulting in accelerated SF6 breakthrough times and reduced BTC peaks. Evaluation of monitoring points distant from the injector reveals a dampening effect on the SF6 BTC peak, particularly in heterogeneous systems with capillary heterogeneity, where the peak is halved. These insights underscore the challenges associated with tracer monitoring and the necessity for enhanced methodologies to improve predictive accuracy in subsurface processes during GCS.

Constructing and Realising an Employment Platform for Slash Youth in the Age of Digital Intelligence

The current employment environment is becoming increasingly complex, with many job seekers competing with each other in more concentrated and narrower fields, worsening the job market as well as inhibiting the career potential of job seekers. There is a need to provide better employment guidance and employment quality assessment for slash youth. This study attempts to design a job recommendation model for slash youths by combining an improved collaborative filtering algorithm and a dynamic bilateral matching algorithm (BMA). The test results show that the precision rate of the BMA is always the largest with the increase of the number of clusters, with the highest value reaching 90.04%; the average ranking inverse curve of bilateral matching has the fastest growth rate, with the maximum value of 62.04%, which is 34.26% and 10.06% higher than the other two maximum values, and the optimal number of clusters is set to 24. The highest precision rate of the algorithm is 82.17% when the number of recommendations is 10. The algorithm also performed better in terms of recommendation diversity, with a maximum value of around 0.28. The recommendation success rate and satisfaction value reached 87.72% and 47.86%, respectively. The recommendation precision of the model designed in this study is high. It is conducive to solving problems such as difficulty in recruiting and finding jobs, and promotes the healthy development of the recruitment market.

Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control

Constrained by the computing power, adaptive active noise control systems often have a low sampling rate. Therefore, reconstruction filters and anti-aliasing filters with fixed parameters are generally adopted to eliminate the mirror noise and aliasing noise, respectively; however, they may boost the group delay of the system. A dynamic matching method based on dual sampling rates is proposed to dynamically adjust the parameters of the reconstruction and anti-aliasing filters, according to the characteristics of the primary sound source, for a compromise between high-frequency noise and group delay. In digital high-sampling-rate regions, data that include high-frequency information are analyzed regularly, following which the parameters of the reconstruction filters and those of the anti-aliasing filters are dynamically matched. In digital low-sampling-rate regions, the estimation of the secondary path transfer function is updated. The results of laboratory experiments show that the proposed method not only can suppress the mirror and aliasing noise for primary sound sources with different spectra, but can also effectively reduce the group delay and improve the noise reduction performance of a system.

A Platform Ecosystem Evolution Model With Service Dynamic Supply and Matching

A Platform Ecosystem Evolution Model With Service Dynamic Supply and Matching

Direct reconstruction of tissue temperature fields during thermal therapy by dynamic response matching and steady-state correction

In the field of oncology thermotherapy technology, online and real-time reconstruction of biological tissue temperature field is a fundamental work with important practical needs. In this paper, we proposed a new method for direct reconstruction of biological tissue temperature field by dynamic response feature matching and steady-state correction. Based on the bioheat transfer equation, the step response model of tissue temperature field during thermal therapy is established, and the dynamic response eigenvectors of biological tissue temperature and the steady-state gain of temperature response are determined offline. According to the optimal matching principle between the temperature dynamic response of the observation point and the internal space point of tissue, the optimal matching matrix of the dynamic response features between the observation point temperature and the internal temperature field of tissue is determined with the use of the dynamic response eigenvectors. Further, through the weighted synthesis of observation point temperature response by using the optimal matching matrix of dynamic response features, the tissue transient temperature field pre-estimation model is established. Finally, by correcting the tissue temperature field pre-estimation model with steady-state gain correction factor, the direct reconstruction scheme of temperature field based on dynamic response feature matching and steady-state gain correction is established. In this work, the non-destructive reconstruction of the internal temperature field of biological tissues during laser-induced thermal therapy is achieved based on the above direct reconstruction scheme. Numerical simulation experiments are performed to investigated the effects of pre-estimation time domain, the number of measurement points, laser irradiation form and measurement error on the temperature field reconstruction results.

Dynamic resource matching in manufacturing using deep reinforcement learning

Matching plays an important role in the logical allocation of resources across a wide range of industries. The benefits of matching have been increasingly recognized in manufacturing industries. In particular, capacity sharing has received much attention recently. In this paper, we consider the problem of dynamically matching demand-capacity types of manufacturing resources. We formulate the multi-period, many-to-many manufacturing resource-matching problem as a sequential decision process. The formulated manufacturing resource-matching problem involves large state and action spaces, and it is not practical to accurately model the joint distribution of various types of demands. To address the curse of dimensionality and the difficulty of explicitly modeling the transition dynamics, we use a model-free deep reinforcement learning approach to find optimal matching policies. Moreover, to tackle the issue of infeasible actions and slow convergence due to initial biased estimates caused by the maximum operator in Q-learning, we introduce two penalties to the traditional Q-learning algorithm: a domain knowledge-based penalty based on a prior policy and an infeasibility penalty that conforms to the demand–supply constraints. We establish theoretical results on the convergence of our domain knowledge-informed Q-learning providing performance guarantee for small-size problems. For large-size problems, we further inject our modified approach into the deep deterministic policy gradient (DDPG) algorithm, which we refer to as domain knowledge-informed DDPG (DKDDPG). In our computational study, including small- and large-scale experiments, DKDDPG consistently outperformed traditional DDPG and other RL algorithms, yielding higher rewards and demonstrating greater efficiency in time and episodes.

Fairness and competition in a bilateral matching market

This paper analyzes fairness and bargaining in a dynamic bilateral matching market. Traders from both sides of the market are pairwise matched to share the gains from trade. The bargaining outcome depends on the traders' fairness attitudes. In equilibrium fairness matters because of market frictions. But, when these frictions become negligible, the equilibrium approaches the Walrasian competitive equilibrium, independently of the traders' inequity aversion. Fairness may yield a Pareto improvement; but also the contrary is possible. Overall, the market implications of fairness are very different from its effects in isolated bilateral bargaining.

An offloading and pricing mechanism based on virtualization in edge–cloud computing

Integrate users, edge servers and cloud into a system can improve resource utilization and quality of service (QoS). The users can send the same request to multiple Edge Servers. However, edge servers have limited scope, which may result in repeated offloading tasks for the same user. In this paper, we consider the cloud resource migration in the form of virtual machines (VMs) to achieve high computing efficiency and low latency requirements. Obviously, the migration of VMs causes changes in the number and price of edge resources, which further affects the task offloading strategy. Therefore, it is necessary to optimize the VMs migration strategy and resource pricing strategy successively to achieve the optimal offloading strategy suitable for users. First, based on the competition between edge servers, a static VMs migration algorithm (SVMMA) is designed to formulate the strategy and price of VMs migration. Then, according to the users’ resource needs and the resource possession of different edge servers, a resource partitioning approach (RPA) have been proposed. Finally, two different QoS functions are evaluated, representing the QoS matching degree between tasks and multiple resource combination blocks (MRCBs). Wherein, a dynamic matching and pricing of tasks algorithm (DMPT) is conducted to obtain the optimal offloading strategy and price of the task. Experiments show that our algorithms can improve the offloading rate of tasks and achieve the relative balance among user, cloud server, and edge server benefits.

Peer-to-peer electricity trading model for urban virtual power plants considering prosumer preferences and power demand heterogeneity

Peer-to-peer electricity trading has emerged as a critical way to meet the varied demands of prosumers within urban virtual power plants. To better support supply-demand balance and ensure the stable, low-carbon and efficient peer-to-peer electricity trading, both prosumer trading preferences and power demand heterogeneity need to be considered. This study proposes a peer-to-peer electricity trading model for urban virtual power plants that considers both preference of prosumers and power demand heterogeneity. First, to effectively meet the trading demands of diverse prosumers and enhance the penetration of renewable energy, prosumers are classified based on variations in financial returns, green electricity preferences and risk avoidance. Power demand is further classified based on the trading preferences of prosumers. Then, a peer-to-peer electricity trading model incorporating a game model and a market bidding mechanism is proposed to enable efficient and dynamic matching between supply and demand. Finally, to mitigate the impact of renewable energy uncertainty on peer-to-peer electricity trading, real-time auctions are implemented across multiple virtual power plants. The results show that the proposed model is capable of more effectively accommodating the diverse demands of prosumers, and facilitates a more stable, low-carbon and efficient peer-to-peer electricity trading.

A novel method for multiple targets localization based on normalized cross-correlation adaptive variable step-size dynamic template matching

The template matching method has been widely utilized in the defect detection of wafer surfaces. However, the traditional matching approaches are limited by illumination, noise, and deformation, which cannot meet the requirements of accuracy and robustness. In this paper, a novel multiple targets localization method, named Normalized Cross-correlation Adaptive Variable Step-Size Dynamic Template (NCC-AVSSDT) matching, is proposed to improve the accuracy and efficiency of image localization, which combines the advantages of NCC and AVSSDT. The AVSSDT method is utilized to dynamically adjust the scanning step size based on the NCC matching coefficients. This approach optimizes the scanning process, accelerating convergence toward the optimal matching position. Experimental results verify the accuracy and robustness of the proposed method under different conditions, especially when dealing with rotational variations and variations in noise textures. Therefore, NCC-AVSSDT can be used to perform multiple targets localization of chip image in nearly real-time. Three experiment types were used for comprehensive evaluations, including multiple targets, noise, and rotation angles. Experimental results show that NCC-AVSSDT is much better than the sequential similarity detection algorithm and mean absolute deviation methods in terms of multiple targets (0.667 vs 0.811 s, 0.832 s) and success rate (100% vs 35%, 20%).

Dimensions of corporate culture and financial performance: Evidence from Chinese publicly listed companies

Corporate culture is the soul of the enterprise and plays a vital role in creating corporate cohesion and improving business performance; in many cases, the role of corporate culture on business performance is not direct, but there are a series of intermediary influencing mechanisms. Most of the current academic circles have studied the influencing mechanism between corporate culture and business performance from the perspective of dynamic matching. Still, there is a lack of research results based on the theoretical perspective of Edgar H. Schein's corporate culture dimensions. Based on Edgar H. Schein's theoretical perspective of corporate culture dimensions, this study explores the influencing mechanism between corporate culture and the business performance of Chinese listed companies from the dimensions of corporate governance culture in the external environment and corporate governance culture in the internal environment. It conducts empirical research using the data of A-share listed companies in China's Shanghai Stock Exchange and Shenzhen Stock Exchange as samples. The results are twofold. First, the adaptability of the enterprise's external environment has a significant and positive effect on the Weighted average return on net assets in the financial dimension; it has a substantial and positive impact on customer satisfaction in the non-financial dimension. Second, internal corporate governance culture has a significant and positive effect on weighted average return on net assets, Debt-to-Equity Ratio, Earnings per share in financial dimensions, Percentage of highly educated employees, customer satisfaction survey, and non-financial dimensions. Percentage of highly educated employees, Customer Satisfaction, Customer retention rate, and R&D investment as a percentage of operating income in non-financial dimensions have significant positive effects. The findings of this study provide theoretical and practical guidance for Chinese listed companies to strengthen the construction of external environment adaptive culture and internal corporate governance culture.

Literature Survey for In-Vivo Reynolds and Womersley Numbers of Various Arteries and Implications for Compliant In-Vitro Modelling

PurposeIn-vitro modelling can be used to investigate haemodynamics of arterial geometry and stent implants. However, in-vitro model fidelity relies on precise matching of in-vivo conditions. In pulsatile flow, velocity distribution and wall shear stress depend on compliance, and the Reynolds and Womersley numbers. However, matching such values may lead to unachievable tolerances in phantom fabrication.MethodsPublished Reynolds and Womersley numbers for 14 major arteries in the human body were determined via a literature search. Preference was given to in-vivo publications but in-vitro and in-silico values were presented when in-vivo values were not found. Subsequently ascending aorta and carotid artery case studies were presented to highlight the limitations dynamic matching would apply to phantom fabrication.ResultsSeven studies reported the in-vivo Reynolds and Womersley numbers for the aorta and two for the carotid artery. However, only one study each reported in-vivo numbers for the remaining ten arteries. No in-vivo data could be found for the femoral, superior mesenteric and renal arteries. Thus, information derived in-vitro and in-silico were provided instead. The ascending aorta and carotid artery models required scaling to 1.5× and 3× life-scale, respectively, to achieve dimensional tolerance restrictions. Modelling the ascending aorta with the comparatively high viscosity water/glycerine solution will lead to high pump power demands. However, all the working fluids considered could be dynamically matched with low pump demand for the carotid model.ConclusionThis paper compiles available human haemodynamic information, and highlights the paucity of information for some arteries. It also provides a method for optimal in-vitro experimental configuration.

Dynamic behavior modelling of the silicone-ethanol actuator based on principal dynamic modes of the Laguerre model

The silicone-ethanol actuator is an emerging artificial muscle gaining attention because of its simple construction/stimulation and high-force generation. Most related researches are focused on structural modification methods, while behavior modeling has received less attention. Although the previous models are highly accurate, they suffer from the lack of ethanol content as an input, highly feedback dependency, and fixed output range. In this research, to address the mentioned concerns, the actuator was made according to the conventional procedure and subjected to stimulation, and displacement/blocked-force recording by special custom hardware. The displacement/blocked-force was not limited to a fix amount in order to evaluate ethanol leakage. To predict displacement/blocked-force, a non-autoregressive model was developed with the applied energy, temperature, and ethanol content as inputs. The Laguerre functions was applied to ensure dynamic matching between input-output. Finally, Principal Dynamic Mode analysis was used to optimize the number of filter banks in the model. The results showed that the proposed model provide a reasonable balance between accuracy, interpretability, and simplicity. Also, we found that the prediction of displacement/blocked-force is mainly influenced by the core-temperature/applied-energy, respectively. The proposed model focuses on the effect of stimulation signal and disregards the structural parameters due to their variety in different applications. By considering the both structural parameters and excitation signal, a more comprehensive model can be developed to predict actuator dynamic behavior.

Designing Service Menus for Bipartite Queueing Systems

Designing Fair and Incentive-Compatible Matching Systems Many service systems face the challenge of dynamically matching customers who have heterogeneous preferences with an available pool of service providers in a fair and equitable manner. The first come, first served (FCFS) service discipline offers a straightforward and fair approach to allocate arriving customers to available servers and manage congestion. However, FCFS may limit the ability of service providers to reach an efficient allocation based on the individual preferences of customers and the characteristics of servers. In “Designing Service Menus for Bipartite Queuing Systems,” Caldentey, Hillas, and Gupta develop a queueing framework to address this dynamic matching problem. Their proposed methodology enables service providers to offer a menu of different service classes, each served by a distinct pool of servers. By thoughtfully designing this menu, a service provider can encourage self-interested customers to select service classes in a way that optimizes the trade-off between quality of matching and waiting time delays.