Analysis of the Impact of Digital Transformation on Non-Financial Performance of Manufacturing Enterprises

As one of the important pillars of China’s national economy,the development of the manufacturing industry has attracted much attention.The manufacturing industry has become a key development area due to its high technology content,capital-intensive,knowledge-intensive,and many more characteristics.With the rapid development of big data information technology,emerging technologies such as artificial intelligence have brought a lot of convenience to people’s lives,and manufacturing enterprises have also realized the importance of reform and innovation,and have begun to carry out digital transformation.Based on the study of the motivation of digital transformation of manufacturing enterprises,this paper explores the transformation path of manufacturing enterprises to meet the needs of social development,and deeply analyzes the impact of digital transformation of manufacturing enterprises on enterprise performance,with a view to improving the unbalanced industrial structure and lagging technological development at the present stage through digital transformation.

Information Conductivity:Universal Performance Measure for Semantic Communications

As a novel paradigm,semantic communication provides an effective solution for breaking through the future development dilemma of classical communication systems.However,it remains an unsolved problem of how to measure the information transmission capability for a given semantic communication method and subsequently compare it with the classical communication method.In this paper,we first present a review of the semantic communication system,including its system model and the two typical coding and transmission methods for its implementations.To address the unsolved issue of the information transmission capability measure for semantic communication methods,we propose a new universal performance measure called Information Conductivity.We provide the definition and the physical significance to state its effectiveness in representing the information transmission capabilities of the semantic communication systems and present elaborations including its measure methods,degrees of freedom,and progressive analysis.Experimental results in image transmission scenarios validate its practical applicability.

Comparative Performance Measurement of the Pareto Optimal Combination and Multi-Objective Combination Models for Controller Placement in Software-Defined Networks

The evolution of the current network has challenges of programmability, maintainability and manageability, due to network ossification. This challenge led to the concept of software-defined networking (SDN), to decouple the control system from the infrastructure plane caused by ossification. The innovation created a problem with controller placement. That is how to effectively place controllers within a network topology to manage the network of data plane devices from the control plane. The study was designed to empirically evaluate and compare the functionalities of two controller placement algorithms: the POCO and MOCO. The methodology adopted in the study is the explorative and comparative investigation techniques. The study evaluated the performances of the Pareto optimal combination (POCO) and multi-objective combination (MOCO) algorithms in relation to calibrated positions of the controller within a software-defined network. The network environment and measurement metrics were held constant for both the POCO and MOCO models during the evaluation. The strengths and weaknesses of the POCO and MOCO models were justified. The results showed that the latencies of the two algorithms in relation to the GoodNet network are 3100 ms and 2500 ms for POCO and MOCO respectively. In Switch to Controller Average Case latency, the performance gives 2598 ms and 2769 ms for POCO and MOCO respectively. In Worst Case Switch to Controller latency, the performance shows 2776 ms and 2987 ms for POCO and MOCO respectively. The latencies of the two algorithms evaluated in relation to the Savvis network, compared as follows: 2912 ms and 2784 ms for POCO and MOCO respectively in Switch to Controller Average Case latency, 3129 ms and 3017 ms for POCO and MOCO respectively in Worst Case Switch to Controller latency, 2789 ms and 2693 ms for POCO and MOCO respectively in Average Case Controller to Controller latency, and 2873 ms and 2756 ms for POCO and MOCO in Worst Case Switch to Controller latency respectively. The latencies of the two algorithms evaluated in relation to the AARNet, network compared as follows: 2473 ms and 2129 ms for POCO and MOCO respectively, in Switch to Controller Average Case latency, 2198 ms and 2268 ms for POCO and MOCO respectively, in Worst Case Switch to Controller latency, 2598 ms and 2471 ms for POCO and MOCO respectively, in Average Case Controller to Controller latency, 2689 ms and 2814 ms for POCO and MOCO respectively Worst Case Controller to Controller latency. The Average Case and Worst-Case latencies for Switch to Controller and Controller to Controller are minimal, and favourable to the POCO model as against the MOCO model when evaluated in the Goodnet, Savvis, and the Aanet networks. This simply indicates that the POCO model has a speed advantage as against the MOCO model, which appears to be more resilient than the POCO model.

Traffic Incident Management Performance Measures: Ranking Agencies on Roadway Clearance Time

This study develops a procedure to rank agencies based on their incident responses using roadway clearance times for crashes. This analysis is not intended to grade agencies but to assist in identifying agencies requiring more training or resources for incident management. Previous NCHRP reports discussed usage of different factors including incident severity, roadway characteristics, number of lanes involved and time of incident separately for estimating the performance. However, it does not tell us how to incorporate all the factors at the same time. Thus, this study aims to account for multiple factors to ensure fair comparisons. This study used 149,174 crashes from Iowa that occurred from 2018 to 2021. A Tobit regression model was used to find the effect of different variables on roadway clearance time. Variables that cannot be controlled directly by agencies such as crash severity, roadway type, weather conditions, lighting conditions, etc., were included in the analysis as it helps to reduce bias in the ranking procedure. Then clearance time of each crash is normalized into a base condition using the regression coefficients. The normalization makes the process more efficient as the effect of uncontrollable factors has already been mitigated. Finally, the agencies were ranked by their average normalized roadway clearance time. This ranking process allows agencies to track their performance of previous crashes, can be used in identifying low performing agencies that could use additional resources and training, and can be used to identify high performing agencies to recognize for their efforts and performance.

PerfMon: Measuring Application-Level Performance in a Large-Scale Campus Wireless Network

WiFi has become one of the most popular ways to access the Internet.However,in large-scale campus wireless networks,it is challenging for network administrators to provide optimized access quality without knowledge on fine-grained traffic characteristics and real network performance.In this paper,we implement PerfMon,a network performance measurement and diagnosis system,which integrates collected multi-source datasets and analysis methods.Based on PerfMon,we first conduct a comprehensive measurement on application-level traffic patterns and behaviors from multiple dimensions in the wireless network of T university(TWLAN),which is one of the largest campus wireless networks.Then we systematically study the application-level network performance.We observe that the application-level traffic behaviors and performance vary greatly across different locations and device types.The performance is far from satisfactory in some cases.To diagnose these problems,we distinguish locations and device types,and further locate the most crucial factors that affect the performance.The results of case studies show that the influential factors can effectively characterize performance changes and explain for performance degradation.

Research and Analysis on the Application of Strategic Performance Management in Enterprises

In today’s competitive business environment,strategic performance management(SPM)is crucial for continuous growth and innovation.Therefore,it is important to realize the advantages of SPM in enterprise applications.This paper analyzes the theoretical basis,implementation framework,and application effects of SPM in enterprises by examining actual cases from different industries.It reveals the core elements of an effective performance management system,including clear goal setting,reasonable performance indicators,and periodic performance evaluations.Additionally,the paper examines the impact of China’s specific economic policy environment on the implementation of SPM and proposes strategies to optimize performance management practices,ultimately promoting the achievement of strategic goals.This paper provides specific and personalized practical guidance for enterprises.

A Nationwide Evaluation of the State of Practice of Performance Measurements for Intelligent Transportation Systems

State departments of transportation’s (DOTs) decisions to invest resources to expand or implement intelligent transportation systems (ITS) programs or even retire existing infrastructure need to be based on performance evaluations. Nonetheless, an apparent gap exists between the need for ITS performance measurements and the actual implementation. The evidence available points to challenges in the ITS performance measurement processes. This paper evaluated the state of practice of performance measurement for ITS across the US and provided insights. A comprehensive literature review assessed the use of performance measures by DOTs for monitoring implemented ITS programs. Based on the gaps identified through the literature review, a nationwide qualitative survey was used to gather insights from key stakeholders on the subject matter and presented in this paper. From the data gathered, performance measurement of ITS is fairly integrated into ITS programs by DOTs, with most agencies considering the process beneficial. There, however, exist reasons that prevent agencies from measuring ITS performance to greater detail and quality. These include lack of data, fragmented or incomparable data formats, the complexity of the endeavor, lack of data scientists, and difficulty assigning responsibilities when inter-agency collaboration is required. Additionally, DOTs do not benchmark or compare their ITS performance with others for reasons that include lack of data, lack of guidance or best practices, and incomparable data formats. This paper is relevant as it provides insights expected to guide DOTs and other agencies in developing or reevaluating their ITS performance measurement processes.

Evaluation of Arterial Signal Coordination with Commercial Connected Vehicle Data: Empirical Traffic Flow Visualization and Performance Measurement

Emerging connected vehicle (CV) data sets have recently become commercially available, enabling analysts to develop a variety of powerful performance measures without deploying any field infrastructure. This paper presents several tools using CV data to evaluate traffic progression quality along a signalized corridor. These include both performance measures for high-level analysis as well as visualizations to examine details of the coordinated operation. With the use of CV data, it is possible to assess not only the movement of traffic on the corridor but also to consider its origin-destination (O-D) path through the corridor. Results for the real-world operation of an eight-intersection signalized arterial are presented. A series of high-level performance measures are used to evaluate overall performance by time of day, with differing results by metric. Next, the details of the operation are examined with the use of two visualization tools: a cyclic time-space diagram (TSD) and an empirical platoon progression diagram (PPD). Comparing flow visualizations developed with different included O-D paths reveals several features, such as the presence of secondary and tertiary platoons on certain sections that cannot be seen when only end-to-end journeys are included. In addition, speed heat maps are generated, providing both speed performance along the corridor and locations and the extent of the queue. The proposed visualization tools portray the corridor’s performance holistically instead of combining individual signal performance metrics. The techniques exhibited in this study are compelling for identifying locations where engineering solutions such as access management or timing plan change are required. The recent progress in infrastructure-free sensing technology has significantly increased the scope of CV data-based traffic management systems, enhancing the significance of this study. The study demonstrates the utility of CV trajectory data for obtaining high-level details of the corridor performance as well as drilling down into the minute specifics.

Diverging Diamond Interchange Performance Measures Using Connected Vehicle Data

Since the first Diverging Diamond Interchange (DDI) implementation in 2009, most of the performance studies developed for this type of interchange have been based on simulations and historical crash data, with a small number of studies using Automated Traffic Signal Performance Measures (ATSPM). Simulation models require considerable effort to collect volumes and to model actual controller operations. Safety studies based on historical crashes usually require from 3 to 5 years of data collection. ATSPMs rely on sensing equipment. This study describes the use of connected vehicle trajectory data to analyze the performance of a DDI located in the metropolitan area of Fort Wayne, IN. An extension of the Purdue Probe Diagram (PPD) is proposed to assess the levels of delay, progression, and saturation. Further, an additional PPD variation is presented that provides a convenient visualization to qualitatively understand progression patterns and to evaluate queue length for spillback in the critical interior crossover. Over 7000 trajectories and 130,000 GPS points were analyzed between the 7th and the 11th of June 2021 from 5:00 AM to 10:00 PM to estimate the DDI’s arrivals on green, level of service, split failures, and downstream blockage. Although this technique was demonstrated for weekdays, the ubiquity of connected vehicle data makes it very easy to adapt these techniques to analysis during special events, winter storms, and weekends. Furthermore, the methodologies presented in this paper can be applied by any agency wanting to assess the performance of any DDI in their jurisdiction.

Diamond Interchange Performance Measures Using Connected Vehicle Data

Diamond interchanges are frequently used where a freeway intersects a two-way surface street. Most of the techniques to evaluate the performance of diamond interchanges rely on the Highway Capacity Manual (HCM), simulation, Automated Traffic Signal Performance Measures (ATSPMs), and historical crash data. HCM and simulation techniques require on-site data collection to obtain models’ inputs. ATSPMs need high-resolution controller event data acquired from roadway sensing equipment. Safety studies typically need 3 to 5 years of crash data to provide statistically significant results. This study utilizes commercially available connected vehicle (CV) data to assess the performance and operation of a three- and four-phase diamond interchange located in Indianapolis, Indiana, and Dallas, Texas, respectively. Over 92,000 trajectories and 1,400,000 GPS points are analyzed from August 2020 weekdays CV data. Trajectories are linear-referenced to generate Purdue Probe Diagrams (PPDs) from which arrivals on green (AOG), split failures, downstream blockage, and movement-based control delay are estimated. In addition, an extension of the PPD is presented that characterizes the complete journey of a vehicle travelling through both signals of the diamond interchange. This enhanced PPD is a significant contribution as it provides an analytical framework and graphical summary of the operational characteristics of how the external movements traverse the entire system. The four-phase control showed high internal progression (99% AOG) compared to the moderate internal progression of the three-phase operation (64% AOG). This is consistent with the design objectives of three- and four-phase control models, but historically these quantitative AOG measures were not possible to obtain with just detector data. Additionally, a graphical summary that illustrates the spatial distribution of hard-braking and hard-acceleration events is also provided. The presented techniques can be used by any agency to evaluate the performance of their diamond interchanges without on-site data collection or capital investments in sensing infrastructure.

Can composite performance measures predict survival of patients with colorectal cancer?

AIM: To assess the relationship between long-term colorectal patient survival and methods of calculating composite performance scores.

Implications of Financial and Non-financial Measures on the Refinement of the Assessments of Audit Risk： An Empirical Investigation

Identifying and assessing audit risk is a key part of the audit process. Prior research documented that auditors primarily look at financial data, information, and measures when assessing the audit risk for an audit engagement. However, professional standard setters, regulators, and academic researchers have discussed the potential for non-financial data, information, and measures to provide a powerful and independent benchmark for evaluating the validity of the numbers of financial statements of an audit client. A field study was conducted in Egypt during the years 2013 and 2014 to explore how auditors perceive and assess audit risk for an audit engagement in the period following the Egyptian revolution of January 25, 2011. The results of the field study indicated that auditors appear not to give sufficient attention to non-financial data, information, and measures when assessing the audit risk during an audit engagement. Auditors seem to rely on financial data, information, and measures when assessing the audit risk of an audit engagement. Furthermore, auditors do not seem to consider the inconsistencies between financial and non-financial data, information, and measures of an audit client as an indicator of the existence of fraud or material misstatements in the financial statements of an audit client.

Congruity of Multiple Performance Measures

In multi-action situation, there are losses of efficiency due to noise effect and incongruity effect. To isolate noise effect, we prove that the loss of efficiency is zero if and only if the objective of the principal is a linear representation of the performance measures when the performance measures are noiseless. Using this result, several definitions of incongruity are given. Our definitions extend the single performance definition given in Feltham and Xie （1994） to multiple performance measures situation, and reconcile the conflict between the definition given in Datar et al. （2001） and that in Feltham and Xie （1994）. A numeric example is analyzed to show the way that the variances of measures and correlation coefficient between measures affect the utility of the principal.

Continuous Flow Intersection Performance Measures Using Connected Vehicle Data

Continuous flow intersections (CFIs), also known as displaced left turns (DLTs), are a type of alternative intersection designed to improve operations at locations with heavy left-turn movements by reallocating these vehicles to the left side of opposing traffic. Currently, simulation is commonly used to evaluate operational performance of CFIs. However, this approach requires significant on-site data collection and is highly dependent on the analyst’s ability to correctly model the intersection and driver behavior. Recently, connected vehicle (CV) trajectory data has become widely available and presents opportunities for the direct measurement of traffic signal performance measures. This study utilizes CV trajectory data to analyze the performance of a CFI located in West Valley City, UT. Over 4500 trajectories and 105,000 GPS points are analyzed from August 2021 weekday data. Trajectories are linear-referenced to generate Purdue Probe Diagrams (PPDs) and extended PPDs to estimate split failures (SF), arrivals on green (AOG), traditional Highway Capacity Manual (HCM) level of service (LOS), and the distribution of stops. The estimated operational performance showed effective progression during the PM peak period at all the critical internal storage areas with AOG levels at exit traffic signals between 83% and 100%. In contrast, all external approaches with longer queue storage areas had AOG values ranging from 2% to 81% during the same time period. The presented analytical techniques and summary graphics provide practitioners with tools to evaluate the performance of any CFI where CV trajectories are available without the need for on-site data collection.

Stock Market Measures and Market Performance

Sri Lanka is considered a highly fluctuating economy in the South Asian region.Understanding the behavior of economics is of utmost important to obtain the maximum benefit.Stock market can be considered as one of the key influencers to the economy whereas the behavior of the stock market would highly define the behaviors of the economic system.It is required to identify the stock market measures and their contribution for the market development to recognize the influence of stock market.The immense importance of its actions on the market performance leads to find more about the stock market’s measures.This research contains the evidence of the study conducted to identify the stock markets development and behavior measures such as all share price index,market capitalization,dividend yield,price to earnings ratio and shares traded equity.All of these variables were used to obtain a model to describe and predict performance of stock market over the time.The secondary data from the CSE(Colombo Stock Exchange)is studied which the trend analysis was conducted for each series of data and results were used for the analysis.A statistical analysis was carried out to identify the measures of stock market depicts that all the measures of the stock market have influences on the stock market development except for the dividend yield,a useful fact in the process of decision making in many aspects.

A New Technology to Measure the Thermoelectric Performance of Nanowire Array Structure

Recently, the study on one-dimensional thermoelectric materials is getting more and more attention. For those one-dimensional thermoelectric materials with nanowire array structure fabricated with alumina film as template, its thickness is often in the range of 10 to several tens micrometers, and the conventional measurement cannot be used. The key difficulties of the thermoelectric performance measurement for nanowire array materials include two aspects: 1) How to heat the two sides of the specimen uniformly and keep the temperature difference constantly at the same time; 2) How to measure the temperature of the two sides of the specimen with the thickness of 10 to several tens micrometers. A new type heating and temperature measuring technology has been used, and it can be simply described as liquid heating and separate temperature measurement. According to this principle, a thermoelectric performance measurement system has been established.

New Measurement Method for Spline Shaft Rolling Performance Evaluation using Laser Displacement Sensor

In order to control the quality of spline shaft in rolling process, an efficient measurement method for rolling performance evaluation is essential. Here, a newly developed on-machine non-contact measurement prototype based on laser displacement sensor and rotary encoder is proposed. The prototype is intended for the automated evaluation of the spline shaft rolling performance by measuring the dimensional change of tooth root, which is correlated with the surface residual stress and micro-hardness. Laser displacement sensor and rotary encoder are used to record the polar radius and polar angle of each point on measuring section. Data are displayed in a polar coordinate system and fitted in a gear. Through multipoint curvature method, the roots of spline shaft are recognized automatically. Then, the dimensional change can be calculated by fitting the radius of the tooth root circle before and after rolling. Systematic error covering offset error is also analyzed and calibrated. At last, measurement test results show that the system has advantages of simple structure, high measurement precision(radius error < 0.6 μm), high measurement efficiency(measuring time < 2 s) and automatic control ability, providing a new opportunity for the efficient evaluation of various spline shafts in high-precision mechanical processing.

Rates of surgical site infection as a performance measure:Are we ready?

With the introduction of quality assurance in health care delivery,there has been a proliferation of research studies that compare patient outcomes for similar conditions among many health care delivery facilities.Since the 1990s,increasing interest has been placed in the incorporation of clinical adverse events as quality indicators in hospital quality assurance programs.Adverse post-operative events,and very especially surgical site infection(SSI) rates after specif ic procedures,gained popularity as hospital quality indicators in the 1980s.For a SSI rate to be considered a valid indicator of the quality of care,it is essential that a proper adjustment for patient case mix be performed,so that meaningful comparisons of SSI rates can be made among surgeons,institutions,or over time.So far,a signif icant impediment to developing meaningful hospital-acquired infection rates that can be used for intra-and inter-hospital comparisons has been the lack of an adequate means of adjusting for case mix.This paper discusses what we have learned in the last years regarding risk adjustment of SSI rates for provider performance assessment,and identif ies areas in which signif icant improvement is still needed.

Detection performance and inversion processing of logging-while-drilling extra-deep azimuthal resistivity measurements

We present systematic investigations on the physics,detection performance and inversion of logging-while-drilling extradeep azimuthal resistivity measurements(EDARM).First,the definitions of EDRAM measurements are discussed,followed by the derivation of the attenuation and phase-shift geometrical factors to illustrate the relative contributions of formation units to the observed signals.Then,a new definition of detection depth,which considers the uncertainty of inversion results caused by the data noise,is proposed to quantify the detection capability of ED ARM.Finally,the B ayesian theory associated with Markov chain Monte Carlo sampling is introduced for fast processing of EDARM data.Numerical results show that ED ARM is capable of detecting the azimuth and distance of remote bed boundaries,and the detection capability increases with increasing spacing and resistivity contrast.The EDARM tool can accommodate a large range of formation resistivity and is able to provide the resistivity anisotropy at arbitrary relative dipping angles.In addition,multiple bed boundaries and reservoir images near the borehole are readily obtained by using the Bayesian inversion.

Application and dynamical performance simulation of tooth surface measured data of hypoid gear

The tooth surface shape of hypoid gear is very complicated, and tooth surface accuracy of hypoid gear can be measured by using the latticed measurement and scanning measurement. Advantages and disadvantages of the two measurement patterns are compared and application of their measurement data on hypoid gear＇s quality management is analyzed. How to use these measurement data to simulate the dynamical performance of hypoid gear is researched, and the intelligent predicton of the dynamical performance indexes of contact spot, root stress, vibration exciting forces and load distribution and hertz contact stress on the tooth surface are carried out. This research work has an important guiding sense to design and ma- chine hypoid gear with low vibration and noise.