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Simple contest schemes, which tend to use extreme contemporaneous peer benchmarking, are generally thought to be less efficient at motivating productive and cooperative effort than explicit bonus contracts that optimally weight observed own and peer outputs. We show, however, that contest schemes can outperform explicit bonus contracts in a two-period dynamic setting if periodic output is correlated over time, and contract terms can be renegotiated. In our model, all parties update their beliefs about ex ante unknown time-invariant noise upon observing first-period outputs, and second-period wage offers are updated accordingly. This creates first-period shirking incentives for the agents. We show that relative to bonus contracts, the extreme contemporaneous peer benchmarking in contest schemes can weaken or even eliminate the adverse incentives created by prior-period benchmarking, even though the available information and the learning process are the same under both schemes. This dynamic advantage allows contest schemes to outperform repeated bonus contracts for a wide range of parameter values, despite imposing higher static efficiency losses in each period. If competing agents are assigned to sufficiently similar tasks, a contest scheme's ability to sever the statistical link between periods can yield not only higher early productive effort compared with bonus contracts, but also less uncooperative behavior, despite putting the agents in a more contest-like situation. This paper was accepted by Brian Bushee, accounting. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.00035. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis is a crucial tool for evaluating the performance of insurance companies, considering its ability to handle multiple inputs and outputs. This study provides a comprehensive bibliometric analysis of Data Envelopment Analysis (DEA) application in the insurance industry from 2010 to 2023, examining 405 documents from 432 sources. Materials from academic databases (Web of Science and Scopus) were used for the analysis. The methodological flow included three stages. For analysis, two sets of keywords were identified: one set oriented toward DEA and the other tailored to the Insurance Industry domain. To analyze and visualize the data, VOSviewer software, version 1.6.19, and RSTUDIO were used. This paper highlights the evolution of DEA methodologies, incorporating advanced techniques like Artificial Intelligence and Machine Learning, and addresses emerging trends such as digital transformation, customer-centric assessments, and sustainability. The analysis reveals significant geographical and sectoral differences in efficiency assessments, with higher efficiency levels typically found in developed markets such as North America and Europe compared to emerging markets in Asia and Africa. It also notes the distinctive efficiency patterns between life and non-life insurance firms, influenced by product complexity and market competition. The findings indicate that DEA remains versatile and essential for performance evaluation in the insurance industry, adapting to challenges through methodological advancements.

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Abstract We published a paper in BMC Bioinformatics comprehensively evaluating the performance of structural variation (SV) calling with long-read SV detection methods based on simulated error-prone long-read data under various sequencing settings. Recently, C.Y.T. et al. wrote a correspondence claiming that the performance of NanoVar was underestimated in our benchmarking and listed some errors in our previous manuscripts. To clarify these matters, we reproduced our previous benchmarking results and carried out a series of parallel experiments on both the newly generated simulated datasets and the ones provided by C.Y.T. et al. The robust benchmark results indicate that NanoVar has unstable performance on simulated data produced from different versions of VISOR, while other tools do not exhibit this phenomenon. Furthermore, the errors proposed by C.Y.T. et al. were due to them using another version of VISOR and Sniffles, which caused many changes in usage and results compared to the versions applied in our previous work. We hope that this commentary proves the validity of our previous publication, clarifies and eliminates the misunderstanding about the commands and results in our benchmarking. Furthermore, we welcome more experts and scholars in the scientific community to pay attention to our research and help us better optimize these valuable works.

Modular construction has become exceedingly relevant against the current scenario of increasing housing demand and can respond to this need in Canada due to its advantages of rapid construction. However, a holistic environmental sustainability framework for this construction method is still lacking. This paper proposes a hierarchical-based framework to benchmark the environmental performance of modular buildings versus conventional buildings. The framework identifies criteria and indicators affecting environmental sustainability, makes composite indicators, and establishes performance benchmarks. Based on expert survey, six criteria (construction waste management, energy performance and efficiency strategies, site disruption and appropriate strategies, renewable and environmentally preferable products, regional materials, and renewable energy use) are first identified. The related indicators and sub-indicators are combined to develop indices using Technique for Order of Preference by Similarity to Ideal Solution. Robust benchmark scales were established through application of Monte Carlo analysis on data collected through experts. Finally, the developed indices were compared against the benchmark scales at different assessment levels. The proposed framework was examined for two single-family modular buildings in British Columbia, Canada. The overall environmental performance benchmarking informs the policy makers on the usefulness of modular buildings, while the criteria-level indices direct the construction industry practitioners to improve the low performing indicators. The proposed framework will enhance the sustainability of modular and offsite construction in Canada. [ABSTRACT FROM AUTHOR]

We published a paper in BMC Bioinformatics comprehensively evaluating the performance of structural variation (SV) calling with long-read SV detection methods based on simulated error-prone long-read data under various sequencing settings. Recently, C.Y.T. et al. wrote a correspondence claiming that the performance of NanoVar was underestimated in our benchmarking and listed some errors in our previous manuscripts. To clarify these matters, we reproduced our previous benchmarking results and carried out a series of parallel experiments on both the newly generated simulated datasets and the ones provided by C.Y.T. et al. The robust benchmark results indicate that NanoVar has unstable performance on simulated data produced from different versions of VISOR, while other tools do not exhibit this phenomenon. Furthermore, the errors proposed by C.Y.T. et al. were due to them using another version of VISOR and Sniffles, which caused many changes in usage and results compared to the versions applied in our previous work. We hope that this commentary proves the validity of our previous publication, clarifies and eliminates the misunderstanding about the commands and results in our benchmarking. Furthermore, we welcome more experts and scholars in the scientific community to pay attention to our research and help us better optimize these valuable works. [ABSTRACT FROM AUTHOR]

There are increasing numbers of scientific articles dedicated to developments in the field of redox flow batteries. To date it is most common to provide efficiency values as a measure of performance. However, there are no agreed standard experimental conditions for these measurements, and so their merit as a tool for comparing different innovations among research groups is put into question. In the following manuscript, various experimental precautions are outlined to reduce experimental artefacts. Original experimental measurements on vanadium flow cells, together with data from the literature, are examined to explore efficiencies and two alternative benchmarking metrics: resistivity and self-discharge current density. The sensitivity of these parameters to current density, temperature, flow rate and state-of-charge range are examined, from which it is concluded that resistivity and self-discharge current density exhibit superior properties to efficiencies for quantifying flow battery improvements. [ABSTRACT FROM AUTHOR]

A concept of distributed replicated NoSQL data storages Cassandra-like, HBase, MongoDB has been proposed to effectively manage Big Data set whose volume, velocity and variability are difficult to deal with by using the traditional Relational Database Management Systems. Tradeoffs between consistency, availability, partition tolerance and latency is intrinsic to such systems. Although relations between these properties have been previously identified by the well-known CAP and PACELC theorems in qualitative terms, it is still necessary to quantify how different consistency settings, deployment patterns and other properties affect system performance.This experience report analysis performance of the Cassandra NoSQL database cluster and studies the tradeoff between data consistency guaranties and performance in distributed data storages. The primary focus is on investigating the quantitative interplay between Cassandra response time, throughput and its consistency settings considering different single- and multi-region deployment scenarios. The study uses the YCSB benchmarking framework and reports the results of the read and write performance tests of the three-replicated Cassandra cluster deployed in the Amazon AWS. In this paper, we also put forward a notation which can be used to formally describe distributed deployment of Cassandra cluster and its nodes relative to each other and to a client application. We present quantitative results showing how different consistency settings and deployment patterns affect Cassandra performance under different workloads. In particular, our experiments show that strong consistency costs up to 22 % of performance in case of the centralized Cassandra cluster deployment and can cause a 600 % increase in the read/write requests if Cassandra replicas and its clients are globally distributed across different AWS Regions.

Gastrointestinal endoscopy revolutionized the diagnostic as well as therapeutic paradigms of many gastrointestinal diseases. Advanced endoscopy procedures like ERCP and EUS have gained paramount importance in recent times. Structured training in basic as well as advanced endoscopy techniques is essential to boost the confidence of the trainee and ensure positive patient outcomes. This review article used the databases, published literature, and a large number of research journals to find out the latest data on the standards of advanced endoscopy training to improve patient care. To excel in the field of endoscopy, studies have established that an endoscopy trainee should be assessed based on his skill and expertise; not based on a particular number of procedures. The endoscopists must benchmark their performance against their peers to assess their competency. The training of endoscopists should be on real patients, in addition to practice on simulators. Also, training programs and curricula should be well structured. With advanced endoscopy being established across the world in patient-care, it is the need of the hour to establish standards and mechanisms for endoscopy training in Pakistan. The UK systems can offer a roadmap to achieve this goal. [ABSTRACT FROM AUTHOR]

The performance of one computer relative to another is traditionally characterized through benchmarking, a practice occasionally deficient in statistical rigor. The performance is often trivialized through simplified measures, such as the approach of central tendency, but doing so risks a loss of perspective of the variability and non-determinism of modern computer systems. Authentic performance evaluations are derived from statistical methods that accurately interpret and assess data. Methods that currently exist within performance comparison frameworks are limited in efficacy, statistical inference is either overtly simplified or altogether avoided. A prevalent criticism from computer performance literature suggests that the results from difference hypothesis testing lack substance. To address this problem, we propose a new framework, SPARC, that pioneers a synthesis of difference and equivalence hypothesis testing to provide relevant conclusions. It is a union of three key components: (i) identifying either superiority or similarity through difference and equivalence hypotheses (ii) scalable methodology (based on the number of benchmarks), and (iii) a conditional feedback loop from test outcomes that produces informative conclusions of relevance, equivalence, trivial, or indeterminant. We present an experimental analysis characterizing the performance of a trio of RISC-V open-source processors to evaluate SPARC and its efficacy compared to similar frameworks.

This article employs the new IBM INC-3000 prototype FPGA-based neural supercomputer to implement a widely used model of the cortical microcircuit. With approximately 80,000 neurons and 300 Million synapses this model has become a benchmark network for comparing simulation architectures with regard to performance. To the best of our knowledge, the achieved speed-up factor is 2.4 times larger than the highest speed-up factor reported in the literature and four times larger than biological real time demonstrating the potential of FPGA systems for neural modeling. The work was performed at Jülich Research Centre in Germany and the INC-3000 was built at the IBM Almaden Research Center in San Jose, CA, United States. For the simulation of the microcircuit only the programmable logic part of the FPGA nodes are used. All arithmetic is implemented with single-floating point precision. The original microcircuit network with linear LIF neurons and current-based exponential-decay-, alpha-function- as well as beta-function-shaped synapses was simulated using exact exponential integration as ODE solver method. In order to demonstrate the flexibility of the approach, additionally networks with non-linear neuron models (AdEx, Izhikevich) and conductance-based synapses were simulated, applying Runge–Kutta and Parker–Sochacki solver methods. In all cases, the simulation-time speed-up factor did not decrease by more than a very few percent. It finally turns out that the speed-up factor is essentially limited by the latency of the INC-3000 communication system. [ABSTRACT FROM AUTHOR]

A concept of distributed replicated data storages like Cassandra, HBase, MongoDB has been proposed to effectively manage the Big Data sets whose volume, velocity, and variability are difficult to deal with by using the traditional Relational Database Management Systems. Trade-offs between consistency, availability, partition tolerance, and latency are intrinsic to such systems. Although relations between these properties have been previously identified by the well-known CAP theorem in qualitative terms, it is still necessary to quantify how different consistency and timeout settings affect system latency. The paper reports results of Cassandra's performance evaluation using the YCSB benchmark and experimentally demonstrates how to read latency depends on the consistency settings and the current database workload. These results clearly show that stronger data consistency increases system latency, which is in line with the qualitative implication of the CAP theorem. Moreover, Cassandra latency and its variation considerably depend on the system workload. The distributed nature of such a system does not always guarantee that the client receives a response from the database within a finite time. If this happens, it causes so-called timing failures when the response is received too late or is not received at all. In the paper, we also consider the role of the application timeout which is the fundamental part of all distributed fault tolerance mechanisms working over the Internet and used as the main error detection mechanism here. The role of the application timeout as the main determinant in the interplay between system availability and responsiveness is also examined in the paper. It is quantitatively shown how different timeout settings could affect system availability and the average servicing and waiting time. Although many modern distributed systems including Cassandra use static timeouts it was shown that the most promising approach is to set timeouts dynamically at run time to balance performance, availability and improve the efficiency of the fault-tolerance mechanisms.

Research background: In the context of constantly changing business environment, the financial sector is focusing on new trends in financial management systems. Nowadays, there is a need to achieve long-term financial growth, so financial managers try to develop new models for managing and improving the financial performance of businesses in economic practice. Purpose of the article: This article aims to determine the financial performance of travel agencies by applying modern business performance evaluation methods in order to create a performance portfolio (ranking) for the years 2013–2017, subsequently to reveal the concordance rate of order of the selected business entities by comparing applied financial methods in the context of performance benchmarking. The research question is as follows: Does the multidimensional PCA method in the form of the performance portfolio of travel agencies provide similar financial results compared to the EVA indicator? Methods: For measuring the financial performance of businesses, the method of Principal Component Analysis (PCA) and the indicator Economic Value Added (EVA) were chosen. Spearman’s rank-order correlation was applied in order to reveal the concordance rate of the analyzed travel agencies. Findings & Value added: The results indicate that by applying the PCA method, 6 key performance factors can be identified. Moreover, the findings revealed that the assessment of travel agencies using the PCA method and EVA indicator did not lead to the same financial results. Individual financial methods identified a different number of strong-performing and inefficient business entities. In this backdrop, we concluded that the business performance measurement based on the PCA method is not a suitable alternative to measuring performance using the EVA indicator.

This article employs the new IBM INC-3000 prototype FPGA-based neural supercomputer to implement a widely used model of the cortical microcircuit. With approximately 80,000 neurons and 300 Million synapses this model has become a benchmark network for comparing simulation architectures with regard to performance. To the best of our knowledge, the achieved speed-up factor is 2.4 times larger than the highest speed-up factor reported in the literature and four times larger than biological real time demonstrating the potential of FPGA systems for neural modeling. The work was performed at Jülich Research Centre in Germany and the INC-3000 was built at the IBM Almaden Research Center in San Jose, CA, United States. For the simulation of the microcircuit only the programmable logic part of the FPGA nodes are used. All arithmetic is implemented with single-floating point precision. The original microcircuit network with linear LIF neurons and current-based exponential-decay-, alpha-function- as well as beta-function-shaped synapses was simulated using exact exponential integration as ODE solver method. In order to demonstrate the flexibility of the approach, additionally networks with non-linear neuron models (AdEx, Izhikevich) and conductance-based synapses were simulated, applying Runge–Kutta and Parker–Sochacki solver methods. In all cases, the simulation-time speed-up factor did not decrease by more than a very few percent. It finally turns out that the speed-up factor is essentially limited by the latency of the INC-3000 communication system.

There are increasing numbers of scientific articles dedicated to developments in the field of redox flow batteries. To date it is most common to provide efficiency values as a measure of performance. However, there are no agreed standard experimental conditions for these measurements, and so their merit as a tool for comparing different innovations among research groups is put into question. In the following manuscript, various experimental precautions are outlined to reduce experimental artefacts. Original experimental measurements on vanadium flow cells, together with data from the literature, are examined to explore efficiencies and two alternative benchmarking metrics: resistivity and self-discharge current density. The sensitivity of these parameters to current density, temperature, flow rate and state-of-charge range are examined, from which it is concluded that resistivity and self-discharge current density exhibit superior properties to efficiencies for quantifying flow battery improvements.

NoSQL database management systems are very diverse and are known to evolve very fast. With so many NoSQL database options available nowadays, it is getting harder to make the right choice for certain use cases. Also, even for a given NoSQL database management system, performance may vary significantly between versions. Database performance benchmarking shows the actual performance for different scenarios on different hardware configurations in a straightforward and precise manner. This paper presents a NoSQL database performance study in which two of the most popular NoSQL database management systems (MongoDB and Apache Cassandra) are compared, and the analyzed metric is throughput. Results show that Apache Cassandra outperformes MongoDB in an update heavy scenario only when the number of operations is high. Also, for a read intensive scenario, Apache Cassandra outperformes MongoDB only when both number of operations and degree of parallelism are high. [ABSTRACT FROM AUTHOR]

Space heating is the largest energy end use, consuming more than 7 quintillion joules of site energy annually in the U.S. building sector. A few recent studies showed discrepancies in simulated space-heating energy use among different building energy modeling programs, and the simulated results are suspected to be underpredicting reality. While various uncertainties are associated with building simulations, especially when simulations are performed by different modelers using different simulation programs for buildings with different configurations, it is crucial to identify and evaluate key driving factors to space-heating energy use in order to support the design and operation of low-energy buildings. In this study, 10 design and operation parameters for space-heating systems of two prototypical office buildings in each of three U.S. heating climates are identified and evaluated, using building simulations with EnergyPlus, to determine the most influential parameters and their impacts on variations of space-heating energy use. The influence of annual weather change on space-heating energy is also investigated using 30-year actual weather data. The simulated space-heating energy use is further benchmarked against those from similar actual office buildings in two U.S. commercial-building databases to better understand the discrepancies between simulated and actual energy use. In summary, variations of both the simulated and actual space-heating energy use of office buildings in all three heating climates can be very large. However these variations are mostly driven by a few influential parameters related to building design and operation. The findings provide insights for building designers, owners, operators, and energy policy makers to make better decisions on energy-efficiency technologies to reduce space-heating energy use for both new and existing buildings.

Evaluating the performance of complex systems, such as air traffic management (ATM), is a challenging task. When regarding aviation as a time-continuous system measured in value-discrete time series via performance indicators and certain metrics, it is important to use sufficiently targeted mathematical models within the analysis. A consistent identification of system dynamics at the evaluation level, without dealing with the actual physical events of the system, transforms the analysis of time series into a system identification process, which ensures control of an unknown (or only partially known) system. In this paper, the requirements for mathematical modeling are presented in the form of a step-by-step framework, which can be derived from the formal process model of ATM. The framework is applied to representative datasets based on former experiments and publications, for whose prediction of boarding times and classification of flight delays with machine learning (ML) the framework presented here was used. While the training process of neural networks was described in detail there, the paper shown here focuses on the control options and optimization possibilities based on the trained models. Overall, the discussed framework represents a strict guideline for addressing data and machine learning (ML)-based analysis and metaheuristic optimization in ATM.

Recently, the development of distributed renewable energy resources, smart devices, and smart grids empowers the emergence of peer-to-peer energy trading via local energy markets. However, due to security and privacy concerns in energy trading, sensitive information of energy traders could be leaked to an adversary. In addition, malicious users could perform attacks against the energy market, such as collusion, double spending, and repudiation attacks. Moreover, network attacks could be executed by external attackers against energy networks, such as eavesdropping, data spoofing, and tampering attacks. To tackle the abovementioned attacks, we propose a secure and privacy-preserving energy trading system (SPETS). First, a permissioned energy blockchain is presented to perform secure energy transactions between energy sellers and buyers. Second, a discrete-time double auction is proposed for energy allocation and pricing. Third, the concept of reputation scores is adopted to guarantee market reliability and trust. The proposed energy system is implemented using Hyperledger Fabric (HF) where the chaincode is utilized to control the energy market. Theoretical analysis proves that SPETS is resilient to several security attacks. Simulation results demonstrate the increase in sellers’ and buyers’ welfare by approximately 76.5% and 26%, respectively. The proposed system ensures trustfulness and guarantees efficient energy allocation. The benchmark analysis proves that SPETS consumes few resources in terms of memory and disk usage, CPU, and network utilization.

This article introduces two general ways to derive single-phase bridgeless power factor correction (PFC) topologies and then, 15 accessible bridgeless topologies are derived based on the configuration of different basic types of dc–dc converter cells. Although the majority of the topologies have been previously proposed, other possible research points are reviewed. Besides, a consistent component sizing procedure with electric, thermal, and cost models is applied to conduct the performance benchmarking of these PFC topologies in terms of power loss, volume, and cost. Three boost-type PFC topologies are chosen as examples to demonstrate the procedure and the corresponding benchmarking results with both theoretical analyses and experimental verification. Finally, mission profiles of one specific application are introduced to show the material cost payback period of adopting one modified bridgeless boost topology instead of conventional one in different scenarios. [ABSTRACT FROM AUTHOR]

Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future. [ABSTRACT FROM AUTHOR]

Research background: In the context of constantly changing business environment, the financial sector is focusing on new trends in financial management systems. Nowadays, there is a need to achieve long-term financial growth, so financial managers try to develop new models for managing and improving the financial performance of businesses in economic practice. Purpose of the article: This article aims to determine the financial performance of travel agencies by applying modern business performance evaluation methods in order to create a performance portfolio (ranking) for the years 2013-2017, subsequently to reveal the concordance rate of order of the selected business entities by comparing applied financial methods in the context of performance benchmarking. The research question is as follows: Does the multidimensional PCA method in the form of the performance portfolio of travel agencies provide similar financial results compared to the EVA indicator?. Methods: For measuring the financial performance of businesses, the method of Principal Component Analysis (PCA) and the indicator Economic Value Added (EVA) were chosen. Spearman's rank-order correlation was applied in order to reveal the concordance rate of the analyzed travel agencies. Findings & Value added: The results indicate that by applying the PCA method, 6 key performance factors can be identified. Moreover, the findings revealed that the assessment of travel agencies using the PCA method and EVA indicator did not lead to the same financial results. Individual financial methods identified a different number of strong-performing and inefficient business entities. In this backdrop, we concluded that the business performance measurement based on the PCA method is not a suitable alternative to measuring performance using the EVA indicator. [ABSTRACT FROM AUTHOR]

Supply and maintenance of urban drinking water utilities are the most important priorities of people in the world especially in urban areas and it is very clear for urban planners or decision makers to evaluate the costs of action or weigh them against the problems of inaction. Also, specific annual budget is essential for ensuring people welfare and using water utilities with good quality. There are different issues in relation to managing of urban water utilities in terms of cultural, social, physical, environmental and even political and it is necessary to assess the existing conditions of utility by authorities and experts for making decision about those applications. So, we introduce Performance Benchmarking method for reaching this aim. This method is one of the best and update solutions in analyzing drinking water utility in developed countries especially in United State of America. So, in this paper, seven drinking water utilities of United State have been compared with Tabriz metropolitan drinking water utility that is located in North West of Iran and results of indicators’ performance have been comparatively explained. Also, Results show that Tabriz metropolitan drinking water utilities are low advanced in terms of many indicators’ performance than seven United States drinking water utilities. But, in some indexes almost equal to and in certain other cases are advanced than it. However, this methodology is very effective for decision makers, responsible and other experts in all regions and this model can be applied for other cities and urban areas.

An experimental study regarding the performance of NoSQL database management systems is presented in this paper. In this study, two NoSQL database management systems (MongoDB and Cassandra) were compared, and the following factors were considered: degree of par- allelism and workload. Two distinct workloads (mostly read and update heavy) were used, and various numbers of client threads. The measured results refer to total runtime and they confirm that MongoDB outperforms Cassandra in almost all tests considered. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study evaluating the performance of NoSQL database management systems. The study compares two NoSQL database management systems (Cassandra and MongoDB) and considers the following parameters/factors: workload and degree of parallelism. Two different workloads (update heavy and mostly read) were used, and different numbers of threads. The measured results are related to average latency: update latency and read latency. Our study shows that with the only exception of 1000 operations, both latency indicators have a quasi-parabolic behavior, where the minimum (i.e. the best performance) depends mainly on the number of threads and slightly varies with the increase in the number of operations. In the case of 1000 operations, there is also a maximum point (i.e. worst performance) case, after which the latency decreases. [ABSTRACT FROM AUTHOR]

This paper presents a uniform benchmarking methodology for non-Boolean computation based on the cellular neural network (CNN) for a variety of beyond-CMOS device technologies, including charge-based and spintronic devices. Three types of CNN implementations are investigated using analog, digital, and spintronic circuits. Monte Carlo simulations are performed to quantify the impact of the input noise, thermal noise, and the number of bits representing the weights of synapses on the overall recall probability and delay. The results demonstrate that the recall probability improves significantly as the number of synapses increase. Using a 4-b resolution for synapse weights provides the best tradeoff between the required numbers of synapses and synapse bits for a target recall rate. Finally, three types of CNN implementations with various device technologies are benchmarked for a given input noise and recall accuracy target. It is shown that spintronic devices are promising candidates to implement CNNs, where up to 3× energy-delay product improvement is predicted in domain wall devices compared to its conventional CMOS counterpart.

This paper describes an experimental study regarding NoSQL database performance. Two NoSQL databases were considered (MongoDB and Cassandra), two different workloads (update heavy and mostly read) and several degrees of parallelism. The results refer to throughput performance metric. Statistical analysis performed is referring to throughput results. Conclusions confirm that MongoDB performs better than Cassandra in the context of a mostly read workload, while Cassandra outperformed MongoDB in the context of an update heavy workload where the number of operations was high. [ABSTRACT FROM AUTHOR]

Identifying, designing, and measuring performance metrics is critical to securing customer value, but can be a difficult task. This article examines the use of benchmarks based on publicly available performance data to set challenging, yet fair, metrics and targets. [ABSTRACT FROM AUTHOR]

The past decade has witnessed the rapid boom of cloud computing. Many public cloud infrastructures have been implemented and serve millions of tenants. Cloud file systems, which take charge of petabyte-scale data storage, play a crucial role in the performance of cloud infrastructures. Typical cloud file systems, including GFS, HDFS and Ceph, have attracted notable research efforts for performance evaluation and optimization. However, due to the heterogeneity and complexity of I/O workload characteristics in cloud environments, it is still challenging to conduct an accurate and efficient performance evaluation. To address this problem, we collected a two-week I/O workload trace from a 2,500-node production cluster in AliCloud, which is one of the largest cloud providers in Asia. Using the AliCloud trace, we characterized the I/O workload and data distribution, and compared two cloud services in multiple perspectives, including the request arrival pattern, request size, data population and so on. A list of observations and implications were derived and applied to help design a cloud file system benchmarking suite, called Porcupine. Porcupine aims to deploy a scalable and efficient performance evaluation on cloud file systems using realistic I/O workloads. We conducted a group of validation experiments, which demonstrated that Porcupine can achieve high accuracy and scalability. This paper provides our experiences and lessons in generating I/O workloads and deploying performance tests on cloud file systems, which we believe will be insightful to the cloud computing community in general. [ABSTRACT FROM PUBLISHER]

This article examines the use of publicly-available financial and operating data to benchmark distribution performance of U.S. IOUs. The authors describe a methodology addressing characteristics beyond IOU control commonly held to impact performance. Econometric analyses refute many commonly-held beliefs about the influence of various characteristics on various performance measures, and support other beliefs only weakly. The authors then apply appropriate adjustments quantified through regression analyses to create a distribution business performance ranking of U.S. IOUs. [ABSTRACT FROM AUTHOR]

Traditional approaches for benchmarking drinking water systems are binary, based solely on the compliance and/or non-compliance of one or more water quality performance indicators against defined regulatory guidelines/standards. The consequence of water quality failure is dependent on location within a water supply system as well as time of the year (i.e., season) with varying levels of water consumption. Conventional approaches used for water quality comparison purposes fail to incorporate spatiotemporal variability and degrees of compliance and/or non-compliance. This can lead to misleading or inaccurate performance assessment data used in the performance benchmarking process. In this research, a hierarchical risk-based water quality performance benchmarking framework is proposed to evaluate small drinking water systems (SDWSs) through cross-comparison amongst similar systems. The proposed framework ( R framework) is designed to quantify consequence associated with seasonal and location-specific water quality issues in a given drinking water supply system to facilitate more efficient decision-making for SDWSs striving for continuous performance improvement. Fuzzy rule-based modelling is used to address imprecision associated with measuring performance based on singular water quality guidelines/standards and the uncertainties present in SDWS operations and monitoring. This proposed R framework has been demonstrated using data collected from 16 SDWSs in Newfoundland and Labrador and Quebec, Canada, and compared to the Canadian Council of Ministers of the Environment WQI, a traditional, guidelines/standard-based approach. The study found that the R framework provides an in-depth state of water quality and benchmarks SDWSs more rationally based on the frequency of occurrence and consequence of failure events. [ABSTRACT FROM AUTHOR]

Thin-film transistors (TFT) in hydrogenated amorphoussilicon, amorphousmetal oxide, andsmallmolecule and polymer organic semiconductors would all hold promise as potential device candidates to large area flexible electronics applications. A universal compact dc model was developed with a proper balance between the physical and mathematical approaches for these thin-film transistors (TFTs). It can capture the common key parameters used for device performance benchmarking of the different TFTs while being applicable to a wide range of TFT technologies in different materials and device structures. Based on this model, a user-friendly tool was developed to provide an interactive way for convenient parameter extraction. The model is continuous from the off-state and subthreshold regimes to the above-threshold regime, avoiding the convergence problems when being used in SPICE circuit simulations. Finally, for verification, it was implemented into a SPICE circuit simulator using Verilog-A to simulate a TFT circuit examplewith the simulated results agreeing verywell with the experimental measurements. [ABSTRACT FROM AUTHOR]

In Canada, small to medium sized water utilities (SMWU) do not often participate in National Water and Wastewater Benchmarking Initiative due to lesser economies of scale, lack of resources, and data limitations. Consequently, such SMWU are managing their functional components (i.e., environmental, personnel, operational, physical assets, customer satisfaction, public health, and financial) without quantitatively assessing and knowing if they are meeting their performance objectives. A multilevel performance management framework, consisting of five modules has been developed and implemented for SMWU in BC, Canada. The framework provides an approach to identify and select the suitable performance indicators for SMWU, and to use them for inter-utility performance benchmarking under limited data. The subsequent modules can be used for detailed performance management at utility, system, and sub-component levels. The utility managers can effectively employ this framework to identify the underperforming functional components and can rationally take corrective actions, and address customer satisfaction with efficient inventory management and data analyses. [ABSTRACT FROM AUTHOR]

Commercial buildings, which are ubiquitous in metropolises, are typically equipped with a wide range of facilities. Satisfactory operation of the facilities necessitates provision of effective maintenance works in the long run. Since, fully computerized maintenance management systems are seldom adopted for facilities in commercial buildings, the dearth of proper maintenance data makes it difficult to investigate their maintenance performance. Reported in this paper is an examination of the maintenance data of a large commercial building, which were recorded semi-automatically by a computer-aided maintenance system. The maintenance works were found to be mainly corrective in nature, dominated by the electrical trade. Existing occupancy and business operation were influential to the issue time of the work orders. Benchmarks of response time, repair time, and downtime were established for evaluation and benchmarking purposes. Further works are needed to probe into the factors that affect maintenance performance, especially the effectiveness of maintenance manpower deployment. [ABSTRACT FROM AUTHOR]

By convention, the petroleum industry relies on thermal equivalence to summarize the results of upstream oil and gas operations—measuring outputs in terms of barrels of "oil equivalent." This despite the fact that the two commodities trade at nothing like thermal parity. Drawing on a well-known exponential production model of petroleum reserves, we demonstrate the potential for thermal equivalence to substantially distort common measures of exploration and development success. Drawing on a recent survey of actual upstream results, and relative to a proposed measure based on economic equivalence, we show that the extent of bias in estimates of value, cost, and profitability is indeed large. [ABSTRACT FROM AUTHOR]