Search

In artificial intelligence (AI), optimization techniques are used to solve several problems in different fields. One of these areas is software reliability verification, which is an important part of software products, as it helps determine how reliable the software is to complete its functions. This is done by estimating the parameters of software reliability growth models (SRGMs). SRGMs predict the expected number of failures after completion, while also serving as an indicator of software readiness for delivery. Therefore, this study aims to optimize the estimation of these parameters based on the available failure data using one of the stochastic optimization algorithms, the simulated annealing algorithm (SA) due to its power and effectiveness. Three SRGMs' models are studied: delayed S-shaped, Musa-Okumoto logarithmic and Power models, to examine the feasibility of the proposed algorithm using five different data sets. The results were compared and analyzed with several algorithms: Particle swarm optimization (PSO), cuckoo search (CS), modify whale optimization algorithm (MWOA), S-shaped model with logistic TEF and social spider algorithm (SSA). A comparison was also made with recent SRGMs that do not rely on AI techniques. The results showed that the proposed algorithm based on SA outperformed all other methods in finding the optimal parameters. [ABSTRACT FROM AUTHOR]

Solar radiation is one of the main inputs to crop growth models to estimate growth and yield. This study was conducted with the aim of evaluating the impact of radiation estimation accuracy by different models on the accuracy of simulating wheat evapotranspiration and yield by CSM-CERES-Wheat model in Razavi Khorasan province. The efficiency of simple models including Angstrom-Prescott, Angstrom-Prescott suggested by FAO, Hargreaves-Samani, Hargreaves-Samani suggested by FAO, Kermani, Hunt and Power have been evaluated in this study. The results of the radiation estimation for different models in the calibration stage showed that Kermani, Angstrom-Prescott calibrated, Angstrom-Prescott models with FAO recommended coefficients, Power, Hunt and Hargreaves-Samani gave the closest estimates compared to the measured radiation in Mashhad, respectively. In the validation stage of the calibrated models, it is found that the Power, Kermani, Angstrom-Prescott models had the most accurate estimation of daily radiation compared to the measured radiation in the study areas, respectively. Furthermore, the lowest difference between simulated yield and evapotranspiration of wheat using the observed radiation data and the estimated radiation data is obtained from the models of Power, calibrated Angstrom-Prescott, Angstrom-Prescott with FAO suggested coefficients and finally the Kermani, respectively. Considering the accuracy and extensive spatial coverage of the radiation data in the Power model, it is recommended to use it as the best radiation estimation method for use in the crop models. [ABSTRACT FROM AUTHOR]

Objective: To optimize the operating parameters of wheat milling in roller mill I skin mill to reduce the energy consumption of milling. Methods: Using EDEM discrete element simulation software, Box-Behnken test was designed for key milling factors such as roll pitch, feeding amount, rotational speed and rotational speed ratio to investigate the relationship between operating parameters and milling power. Results: The significance rankings of the effects on the toothed roll crushing power were, in order, speed ratio, roll pitch, feed volume, speed ratio quadratic term, roll pitch quadratic term, interaction of roll pitch and speed ratio, interaction of feed volume and speed ratio, and interaction of roll pitch and feed volume. According to the different requirements of the I-skin mill, the optimal parameter combinations were obtained: the finer powder output with a rolling pitch of 0.67 mm, a feeding amount of 803.60 kg/（cm·d）, a fast roller speed of 537.68 r/min, and a rotational speed ratio of 2.64, and the coarser powder output with the optimal low-power consumption of 0.79 mm, a feeding amount of 803.83 kg/（cm·d）, a fast roller speed of 576.08 r/min, and a rotational speed ratio of 2.23. Conclusion: The error between the simulation results and the measured results is within a reasonable range, and the established discrete element milling model can be used for power prediction in wheat milling.

Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The challenges of innovative IC technology typically come with various new design constraints in terms of circuit implementation, behaviour, scaling, and an accurate power-delay model to evaluate the circuit's performance. The circuit realization technique using GDI is gaining popularity because of its power and transistor utilization factors. Considering the core advantage of the GDI technique, this research presents the creation of new GDI library cells implemented using the MUX-based algorithm and its delay-power model. This research defines two goals; the former goal depicts the proposal of GDI library cells with full swing using a MUX-based signal connectivity model, and the later presents the mathematical delay-power model for the proposed GDI library cells. The number of attributes defined in the delay and power model incorporates minimum variables without sacrificing precision. It calculates the delay for simple RC networks and combinational circuits with multiple paths. The power model is given using the node activity factor and the power factor related to the internal node capacitances, wiring, and gate capacitances of the driving and receiving GDI nodes. The experimental results of this study, which conform to the specifications of the sub-micron library supported for the SilTerra 130 nm 6-metal layer fabricated for the CMOS n-well process, demonstrate that the proposed GDI library is indeed superior in terms of delay-transistor and power utilisation to PTL and CMOS technology. The simulation results reveal that there is 55 to 65 % improvement in terms of power and delay factor with the existing CMOS and PTL logic. The proposed delay model demonstrates that GDI cells require less logical effort than CMOS technology. The proposed power model shows that the node activity factor of the proposed GDI cells lies between 0.1 and 0.2, while in CMOS, it is between 0.1 and 0.3. Doi: 10.28991/ESJ-2023-07-04-022 Full Text: PDF

As an important cultural institution for urban cultural inheritance and construction, the complementarity, interaction, and development between university culture and urban culture are also getting more and more attention. This study analyzes the dynamic mechanism of the integration of university culture and urban culture by combining the dynamics model. Then, it constructs a dynamics model for the integration of urban culture into university culture based on the group dynamics theory and the airplane dynamics model. Then, a data-driven decision-making optimization scheme is proposed based on the BASM model, and a data-driven decision-making model oriented to the development of cultural integration is constructed with the data related to the artistic integration of universities as the driving basis. Finally, an empirical analysis of the integration development of culture between University M culture and Guangzhou city culture is carried out in Guangzhou city as an example, with a comprehensive average score of 92.70 for cultural recognition, and the correlation coefficients of the dimensions are highly correlated between 0.812 and 0.891. Meanwhile, the regression coefficients corresponding to the seven dimensions of cultural integration and development are all less than 0.05, and the VIF values are all less than 10. Moreover, the comprehensive average score of each dimension after optimization is 4.193, which is 0.79 points higher compared with that before optimization, and the satisfaction level is 1.297 points higher. In this paper, the optimization of the development strategy for the integration of university culture and urban culture can promote cultural integration and provide a specific optimization path for the integration of university culture and urban culture.

Strategic human resource management is a concept that aims to explain what is involved in examining the core issues of HRM from a startegic perspective. Organizations in an open system aim to adapt to the environment by trying to manage scarce resources and gain power by gaining competitive advantage. In this context, it is important that strategic HRM practices are in line with the strategy determined by the organization in order to gain competitive advantage. Resource dependence theory tries to explain the power relations between and within organizations in order for organizations to survive and gain competitive advantage, and offers strategies for organizations to manage these relations. Organizations’ need for skilled and non-substituble employees throughout the strategic planning process makes the role of human resources very important. Therefore, the power of the HR department, which is located on the power and political plane, may increase in the organization due to its control over valuable and scarce resources, and in this context, it is necessary to try to understand the non-strategic determinants of HRM practices on the basis of resource dependency theory. In this article, it is aimed to examine strategic HRM based on Resource Dependence Theory to comprehend the nonstrategic determinants of HRM practices. [ABSTRACT FROM AUTHOR]

A new grey power model is proposed for modeling periodic small sample systenis. In the modeling process, a trigonometric tutiction is introduced to identifj, the periodicity characteristics of the data, and the specific time response formula and modeling steps are given. At the same time, in order to minimize the average relative error, the least square method and genetic algorithm are used to optimize and solve each parameter. Finally, the model is applied to forecast the traffic flow of the Herigcla Expressway iii Hebei Province and the PM2. 5 coment of air pollutants in Tianjin. The results are compared with those of other models. The results show that the proposed model has higher prediction accuracy, which further verifies the validity of the model. [ABSTRACT FROM AUTHOR]

The power model of cloud server is one of the important contents of the research on the energy consumption optimization of cloud data center.The CPU power model is an important part of the power models of cloud servers.However,the existing CPU power models do not consider the CPU heterogeneity,such as lack of research on the CPU power model of ARM architecture cloud servers.Based on the investigation and analysis of existing ARM architecture CPU power models,this paper proposes a new CPU power model oriented to the ARM architecture,namely the hybrid based model(HBM).HBM comprehensively considers modeling features such as CPU utilization and CPU performance events.Compared with existing PMC based model with high measurement accuracy,HBM has similar measurement accuracy and lower model training cost.Thus,HBM is more suitable for CPU power modeling of ARM servers.This paper uses the Sysbench benchmark to verify HBM,and experimental results show that the mean relative error(MRE) of HBM is within 1%,which means HBM has high measurement accuracy.Cross-experiments are also conducted for x86 and ARM architecture servers.,and experimental results show that the CPU power beha-viors of servers with different architectures are not the same,thus different CPU power modeling methods should be used.

Dynamic voltage frequency scaling (DVFS) has been widely adopted for runtime power management of various processing units. In the case of neural processing units (NPUs), power management of neural network applications is required to adjust the frequency and voltage every layer to consider the power behavior and performance of each layer. Unfortunately, DVFS is inappropriate for layer-wise run-time power management of NPUs due to the long latency of voltage scaling compared with each layer execution time. Because the frequency scaling is fast enough to keep up with each layer, we propose a layerwise dynamic frequency scaling (DFS) technique for an NPU. Our proposed DFS exploits the highest frequency under the power limit of an NPU for each layer. To determine the highest allowable frequency, we build a power model to predict the power consumption of an NPU based on a real measurement on the fabricated NPU. Our evaluation results show that our proposed DFS improves frame per second (FPS) by 33% and saves energy by 14% on average, compared with DVFS.

Choosing the most appropriate mathematical model for establishing a simple regression between two variables which represent two economic indicators or from other scientific fields is an approach that involves many and often quite difficult actions. In the article we rigorously presented the stages to be followed in order to obtain the defining parameters for nine models: linear, power (double logarithmic), exponential, logarithmic, three hyperbolic models (the third is also a Tӧrnqvist-type model) and two exponential-hyperbolic models. The final stage in establishing the most appropriate model is less discussed in the paper. Thus, the simple regression for which the model has the highest Pearson correlation coefficient is chosen as optimal in the article.

As cloud computing technologies and applications develop rapidly in recent years, the quantity and size of cloud datacenters have been ever-increasing, making the overconsumption of energy in datacenters become a widespread concern. To reduce the energy cost by servers, we must first build an accurate power model to achieve flexible, device-free power consumption measuring. However, most of the previous work related to server power modeling solely apply to the server and virtual machine levels, and the existing power models fail to take into account the heterogeneity in workload. Therefore, we first propose separate power consumption models based on the distinction of workload types including CPU-intensive, I/O-intensive, memory-intensive, and mixed workload. Then, we present an adaptive workload-aware power consumption measuring method (WSPM) for cloud servers. Our method proactively selects an appropriate power model for the upcoming workload through workload clustering, forecasting and classification, which are implemented using K-means, ARIMA, and threshold-based methods, respectively. We conducted several experiments to evaluate the performance of the key components of our method. The result shows: (1) the accuracy of our future workload forecasting on real traces of requests to our servers, (2) the accuracy of the power consumption measured by WSPM, and (3) the effectiveness of our workload-aware method in reducing real-time power estimation lag. Overall, the proposed method simplifies power modeling under diverse workloads without losing accuracy, making it a general and highly available solution for cloud data centers. [ABSTRACT FROM AUTHOR]

An accurate host power model is necessary for effective power management in data centers which is crucial for reducing energy consumption and cost. One should evaluate the power models for different workloads and host configurations. We have analyzed several existing power models by varying the workload type (CPU, memory, and disk-intensive) and host configurations. By analyzing the system performance and nature of the power consumption of the hosts, we have identified some performance counter parameters that determine the system power consumption. We have proposed three power models based on multi-variable Linear Regression, Support Vector Regression (SVR), and Artificial Neural Network (ANN). Experimental results show that compared to the existing models, our proposed power models, especially those based on SVR and ANN, more accurately predict the power consumption of the hosts. We have also conducted simulation experiments to show the importance of the power model in the energy-efficient resource management of the hosts in the data center. Results show that the use of our SVR-based and ANN-based power models in a resource management approach can effectively decrease the energy consumption of the data center. Moreover, we have proposed an energy-efficient virtual machine (VM) placement and consolidation algorithm that further reduces energy consumption. At first, we formulated a model using integer linear programming. Then, we designed a heuristic based on Vogel’s Approximation Method. Extensive simulation on the CloudSim platform with benchmark workload data and the Google Cloud trace logs shows that our approach outperforms the state-of-the-art algorithms under comparison in terms of energy efficiency and quality of service (QoS). The results also highlight the importance of a suitable VM placement and consolidation approach and an accurate power model in reducing energy consumption. [ABSTRACT FROM AUTHOR]

Bifacial photovoltaic (PV) modules, capable of capturing solar energy from both sides of the cells, are becoming increasingly popular as their manufacturing costs approach those of traditional monofacial modules. Accurate estimation of their power generation capacity is essential for optimizing their use. This study evaluates a power production model for bifacial PV modules using local irradiance data from Razon+ in Sherbrooke, Canada, and Solcast irradiance data derived from satellite imagery and weather models. The model's performance was assessed throughout the year, with particular attention to the impact of snow coverage during winter. To address computational efficiency, the study evaluated ray tracing and a 2D view factor model, selecting the more time-efficient method. Experimental validation showed that, using local irradiance data, the model achieved Normalized Root Mean Square Errors (NRMSE) of 18.77%, 4.94%, 3.93%, and 6.22% for winter, spring, summer, and fall, respectively. With Solcast data, the NRMSEs were 22.76%, 15.32%, 14.72%, and 17.78% for the corresponding seasons. While the model performed satisfactorily in spring, summer, and fall, it was less accurate in winter. To enhance winter accuracy, the model incorporated snow coverage, using snow depth as a metric to detect snow on the front surface. This adjustment improved the accuracy by 51.1%.

Copyright of Terra Latinoamericana is the property of Sociedad Mexicana de la Ciencia del Suelo A.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The high computational capability provided by a data center makes it possible to solve complex manufacturing issues and carry out large-scale collaborative cloud manufacturing. Accurately, real-time estimation of the power required by a data center can help resource providers predict the total power consumption and improve resource utilization. To enhance the accuracy of server power models, we propose a real-time energy consumption prediction method called IECL that combines the support vector machine, random forest, and grid search algorithms. The random forest algorithm is used to screen the input parameters of the model, while the grid search method is used to optimize the hyperparameters. The error confidence interval is also leveraged to describe the uncertainty in the energy consumption by the server. Our experimental results suggest that the average absolute error for different workloads is less than 1.4% with benchmark models. [ABSTRACT FROM AUTHOR]

Dynamic voltage frequency scaling (DVFS) has been widely adopted for run‐time power management of various processing units. In the case of neural processing units (NPUs), power management of neural network applications is required to adjust the frequency and voltage every layer to consider the power behavior and performance of each layer. Unfortunately, DVFS is inappropriate for layer‐wise run‐time power management of NPUs due to the long latency of voltage scaling compared with each layer execution time. Because the frequency scaling is fast enough to keep up with each layer, we propose a layer‐wise dynamic frequency scaling (DFS) technique for an NPU. Our proposed DFS exploits the highest frequency under the power limit of an NPU for each layer. To determine the highest allowable frequency, we build a power model to predict the power consumption of an NPU based on a real measurement on the fabricated NPU. Our evaluation results show that our proposed DFS improves frame per second (FPS) by 33% and saves energy by 14% on average, compared with DVFS. [ABSTRACT FROM AUTHOR]

The doubly-fed induction wind turbine has gradually become the mainstream model of wind power generation. Power control is one of the advantages of doubly-fed induction wind generators and has been widely studied. However, existing power control strategies for these turbines have issues with low efficiency. An adaptive control method is proposed in this work, aiming at the power control system of doubly-fed induction wind turbines. Firstly, the power model is obtained, and then the adaptive controller is designed according to the model. Finally, simulation results verify the effectiveness of this method.

IEEE802.11, known as WiFi has proliferated in the last decade. It can be found in smartphones, laptops, smart TVs and surveillance cameras. This popularity has revealed many issues in health, data privacy and security. In this work, a WiFi measurement study has been conducted in Amman, the capital city of Jordan. An Android App has been written to harvest WiFi information of the transmitted frames of any surrounding Access points (APs). More than 240,000 APs information has been harvested in this work. The harvested data have been analyzed to find statistics ofWiFi devices in this city. Moreover, three power distribution models have been derived from the data for three different areas, closed, open and hybrid areas. In addition, the collected data revealed that the SSID can be leveraged as a landmark for the access points (APs). To this end, SSIDtrack algorithm is proposed to track shoppers/walkers in closed areas, such as malls to find their walking route utilizing only the SSID information collected from the surrounding area. The algorithm has been tested in two different malls that consist of four different floors. The accuracy recorded for the algorithm acceded 95%. [ABSTRACT FROM AUTHOR]

Choosing the most appropriate mathematical model for establishing a simple regression between two variables which represent two economic indicators or from other scientific fields is an approach that involves many and often quite difficult actions. In the article we rigorously presented the stages to be followed in order to obtain the defining parameters for nine models: linear, power (double logarithmic), exponential, logarithmic, three hyperbolic models (the third is also a T?rnqvist-type model) and two exponential-hyperbolic models. The final stage in establishing the most appropriate model is less discussed in the paper. Thus, the simple regression for which the model has the highest Pearson correlation coefficient is chosen as optimal in the article. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the increasing demand of cloud resources, the ever-increasing number and scale of cloud data centers make their massive power consumption a prominent issue today. Evidence reveals that the behaviors of cloud servers make the major impact on data centers' power consumption. Although extensive research can be found in this context, a systematic review of the models and modeling methods for the entire hierarchy (from underlying hardware components to the upper-layer applications) of the cloud server is still missing, which is supposed to cover the relevant studies on physical and virtual cloud server instances, server components, and cloud applications. In this article, we summarize a broad range of relevant studies from three perspectives: power data acquisition, power models, and power modeling methods for cloud servers (including bare-metal, virtual machine (VM), and container instances). We present a comprehensive taxonomy on the collection methods of server-level power data, the existing mainstream power models at multiple levels from hardware to software and application, and commonly used methods for modeling power consumption including classical regression analysis and emerging methods like reinforcement learning. Throughout the work, we introduce a variety of models and methods, illustrating their implementation, usability, and applicability while discussing the limitations of existing approaches and possible ways of improvement. Apart from reviewing existing studies on server power models and modeling methods, we further figure out several open challenges and possible research directions, such as the study on modeling the power consumption of lightweight virtual units like unikernel and the necessity of further explorations toward empowering server power estimation/prediction with machine learning. As power monitoring is drawing increasing attention from cloud service providers (CSPs), this survey provides useful guidelines on server power modeling and can be inspiring for further research on energy-efficient data centers. [ABSTRACT FROM AUTHOR]

In multi-core processors, energy efficiency and performance consideration are essential issues. Usually, energy-saving techniques result in performance loss and vice versa. Therefore, energy delay product (EDP) is used broadly in many applications as a trade-off between energy saving and performance improvement. This paper presents a technique to perform work-stealing scheduling in the operating system kernel without needing any modification to the user-space program. The proposed scheduling uses predictive models to determine the optimal active number of cores and clock frequency of the processor as an optimum configuration at runtime for any running program to achieve the minimum EDP value. Since EDP is considered as a long-term metric, at runtime, in each specific time frame, PEPS uses the instruction per watt (IPW) to determine the best configuration. By using performance and power predicting models, PEPS finds the optimal configuration in terms of energy efficiency for the next time interval. Because different workloads at runtime have different behaviors and programs with different degrees of parallelization acted variously, the proposed method uses performance counters as a factor for workload characterization. Compared to the Linux scheduler, the proposed algorithm has up to 25% improvement in energy saving at the cost of 7% performance loss. Moreover, while reducing the temperature by 24%, it results in 19% improvement in EDP. [ABSTRACT FROM AUTHOR]

As wave energy conversion technology advances, recharge of autonomous underwater vehicles has emerged as a promising application for this at-sea power. We bring together an interdisciplinary team to create a simulation framework linking hydrodynamic modeling, autonomous docking and navigation algorithms, and a power tracking model to better understand how a full wave energy converter–autonomous underwater vehicle system could be modeled. A floating point absorber wave energy converter is modeled and analyzed under various wave conditions. We incorporate three different dock designs, using the modeled dock motion and simulated wave-induced currents to test our autonomous docking algorithm. We couple the output of this algorithm to the hydrodynamic model to simulate autonomous docking. This shows that docking with a floating third body is successful in most sea states, while a dock rigidly mounted to the wave energy converter presents difficulty for autonomous docking. Finally, we incorporate a power model to better understand the feasibility and capabilities of a wave energy converter–underwater vehicle system in simulated wave environments. This shows that this system is comfortably supported in the majority of sea states, and provides an estimate of the on-board power storage required to maximize vehicle mission time. • Wave energy converter and autonomous vehicle dock modeled using conditions found at Oregon Pacific Ocean test site. • Autonomous underwater vehicle docking algorithm demonstrated for three different modeled docks under various wave conditions. • Power model demonstrates vehicle mission capabilities with modeled wave energy converter power output. [ABSTRACT FROM AUTHOR]

Kohlrabi is used for consumption in various regions of the world as, raw, cooked or fried. Heat and mass transfer phenomena take place during frying cause physicochemical changes, which affect the color and surface of the fried products. The effect of frying temperature on the color changes and center temperature change during deep fat frying of kohlrabi was investigated. The color scale parameters redness (a*), yellowness (b*) and lightness (L*), and color change intensity (∆E) were used to estimate color changes during frying as a function of oil temperature and time. L* value of fried kohlrabi decreased during frying but a* and b* values were increased. A first-order kinetic equation was used for each one of the three color parameters, in which the rate constant is a function of oil temperatures. The results showed that oil temperature has a significant effect on the color parameters. Different kinetic models were used to fit the experimental data, and the results revealed that the power model was the most suitable to describe the color change intensity (∆E). Center temperature of kohlrabi slice increased with an increase in oil temperature and time during frying.

In order to efficiently manage display power, there is a need for a system that can accurately predict the power consumed according to the displayed image. Existing AMOLED display power models do not consider the effects of I–R drop, which is a voltage drop along the VDD line. Therefore, when the existing power model is applied to a recent AMOLED display panel, which has a large physical size and uses a large current, power consumption cannot be accurately predicted. In this article, we propose a new model to compensate for the I–R drop and present a more accurate method for predicting AMOLED display power consumption by applying it to existing AMOLED display power models. To verify this, we conducted extensive experiments for various image databases on AMOLED display panels of Samsung Galaxy S7 and S9. When comparing the proposed display power model connected with the I–R drop model with conventional power models, it is confirmed that the proposed power model improves the accuracy by up to 70.3% over them. [ABSTRACT FROM AUTHOR]

Recently, active matrix organic light-emitting diode (AMOLED) displays have widely been used for user-interface applications on smartphones. Since frequent interactions between a user and a display which come from such applications tend to incur big display power consumption, the display power efficiency should be managed exactly and fast. For this purpose, a high-accurate and fast power model for AMOLED displays is greatly required for efficient power management. In this paper, we propose a novel high-accurate and fast power model for AMOLED displays, which is developed using a multiple regression technique to account for the dependency between R, G, and B channels and is polished using various performance optimization techniques. Then, the proposed model is applied to five AMOLED display panels of a CHIMEI panel and Galaxy S1, 3, 5, and 7 panels from SAMSUMG. The average error rates are obtained on these panels for four well-known image datasets. The proposed model shows the lowest error rate of 1.29% on Galaxy S7, while the simple model and Park’s model show the error rates of 6.91% and 6.57%, respectively. In addition, in order to guarantee the fast execution time of the proposed power model on smartphones, we implement performance optimizations to the model. For runtime application, the proposed power model is implemented at the operating system level of the Android smartphone equipped with Exynos 4412. The optimized power model shows an average execution time of approximately 7.77 milli-seconds per image displayed on the panel with 720x1280 resolution. Consequently, the proposed power model is shown to be very fast and accurate, and thus it is highly useful in smartphones with AMOLED displays.

Sweet pepper purees (red, yellow and green) were examined for FTIR (Fourier-transform infrared spectroscopy), chemical, bioactive, color and rheological parameters. FTIR technique was used to evaluate the functional groups. FTIR wave numbers are associated with the absorption bands that depicted the presence of several phytocompounds in the purees. Among the chemical parameters, water activity varied non-significantly whereas, total soluble sugars (TSS), sugars and pH increased after processing of the fruits into purees. The presence of bioactive compounds depends on the variety of sweet pepper. The red puree had significantly higher carotenoids, phenolics and antioxidant capacity followed by yellow and green pepper purees. The minimal change was observed in the color of purees during processing. The purees were subjected to different shear rate (1 to 50 s−1) to evaluate the effect on viscosity and shear stress that is desirable for its end use in different food products. All purees show the shear thinning behavior as shear rate increased. Results revealed that heat processing of sweet peppers didn't affect color, sugars, carotenoids, phenolics and antioxidant capacity to a greater extent. The finding will be helpful to manage seasonal bulk production efficiently and make them available as an ingredient in various food products. [ABSTRACT FROM AUTHOR]

Background: Xenograft mouse tumor models are used to study mechanisms of tumor growth and metastasis formation and to investigate the efficacy of different therapeutic interventions. After injection the engrafted cells form a local tumor nodule. Following an initial lag period of several days, the size of the tumor is measured periodically throughout the experiment using calipers. This method of determining tumor size is error prone because the measurement is two-dimensional (calipers do not measure tumor depth). Primary tumor growth can be described mathematically by suitable growth functions, the choice of which is not always obvious. Growth parameters provide information on tumor growth and are determined by applying nonlinear curve fitting.Methods: We used self-generated synthetic data including random measurement errors to research the accuracy of parameter estimation based on caliper measured tumor data. Fit metrics were investigated to identify the most appropriate growth function for a given synthetic dataset. We studied the effects of measuring tumor size at different frequencies on the accuracy and precision of the estimated parameters. For curve fitting with fixed initial tumor volume, we varied this fixed initial volume during the fitting process to investigate the effect on the resulting estimated parameters. We determined the number of surviving engrafted tumor cells after injection using ex vivo bioluminescence imaging, to demonstrate the effect on experiments of incorrect assumptions about the initial tumor volume.Results: To select a suitable growth function, measurement data from at least 15 animals should be considered. Tumor volume should be measured at least every three days to estimate accurate growth parameters. Daily measurement of the tumor volume is the most accurate way to improve long-term predictability of tumor growth. The initial tumor volume needs to have a fixed value in order to achieve meaningful results. An incorrect value for the initial tumor volume leads to large deviations in the resulting growth parameters.Conclusions: The actual number of cancer cells engrafting directly after subcutaneous injection is critical for future tumor growth and distinctly influences the parameters for tumor growth determined by curve fitting. [ABSTRACT FROM AUTHOR]

Energy autonomy is an important aspect that needs to be improved in order to increase efficiency in mobile robotic tasks. Having accurate power models allows the estimation of energy consumption along different trajectories. This article proposes a power model for two-wheel differential drive mobile robots. The proposed model takes into account the dynamic parameters of the robot and its motors, and predicts the energy consumption for trajectories with variable accelerations and variable payloads. The experimental validation of the proposed model was performed with a Nomad Super Scout II mobile robot which was driven along straight and curved trajectories, with different payloads and accelerations. The experiments using the proposed model showed accuracies of 96.67% along straight trajectories and 81.25% along curved trajectories in the estimation of energy consumption. [ABSTRACT FROM AUTHOR]

Each year, the NCAA Division I Men's Basketball Tournament attracts popular attention, including bracket challenges where fans seek to pick the winners of the tournament's games. However, the quantity and unpredictable nature of games suggest a single bracket will likely select some winning teams incorrectly even if created with insightful and sophisticated methods. Hence, rather than focusing on creating a single bracket to perform well, a challenge participant may wish to create a pool of brackets that likely contains at least one high-scoring bracket. This paper proposes a power model to estimate tournament outcome probabilities based on past tournament data. Bracket pools are generated for the 2013–2019 tournaments using six generators, five using the power model and one using the Bradley-Terry model. The generated brackets are assessed by the ESPN scoring system and compared to those produced by a traditional pick favorite approach as well as the highest scoring brackets in the ESPN Tournament Challenge for each year. [ABSTRACT FROM AUTHOR]

The pervasive use of virtualization techniques in today's datacenters poses challenges in power monitoring since it is not possible to directly measure the power consumption of a virtual entity such as a virtual machine (VM) and a container. In this paper, we present cWatts++, a lightweight virtual power meter that enables accurate power usage measurement in virtualized computing environments such as VMs and containers of Cloud data centers. At the core of cWatts++ is its application-agnostic power model. To this end, we devise two power models (eventModel and raplModel) that are driven by CPU event counters and the Running Average Power Limit (RAPL) feature of modern Intel CPUs, respectively. While eventModel is more generic and, thus, applicable to a wide range of workloads, raplModel is particularly good for CPU-bound workloads. We have evaluated cWatts++ with its two power models in a real system using the PARSEC benchmark suite and our in-house benchmarks. Our evaluation study demonstrates that these power models have an average error of 4.55 and 1.25 percent, respectively, compared with actual power usage measurements of a real power meter, Cabac Power-Mate. [ABSTRACT FROM AUTHOR]