Otolith phenotypic analysis for the endemic Anatolian fish species, Caucasian bleak Alburnus escherichii Steindachner, 1897 (Teleostei, Leuciscidae), from Selevir Reservoir, Akarçay Basin, Turkey

Otolith phenotypic variability was analyzed in the Caucasian bleak (Alburnus escherichii) from the Selevir Reservoir in Turkey. Utricular (lapillus) and lagenar (asteriscus) otoliths were removed, while distinguishing between left and right otoliths. All otoliths were photographed on the distal (for asterisci) and dorsal surface (for lapilli) using a Leica DF295 digital camera. Otolith morphometrics were measured to the nearest 0.001 mm using Leica Imaging Software. Linear and nonlinear (power) models were applied to determine the relationships between otolith measurements and total length of fish individuals. Two length classes (Class I: 6.7–10.9 cm Lt ; Class II: 11.0–15.0 cm Lt ) were established to analyze the shape of otoliths. The Form Factor, Circularity, Roundness, Rectangularity, Aspect Ratio and Ellipticity were used to analyze the shape of otoliths. A standardized model was used to remove the effect of size on otolith measurements. Multivariate analysis was performed to detect differences in otolith shape variation. The results of discriminant function analysis showed that 79.9% of A. escherichii specimens were correctly classified by length classes. In this study, intraspecific variation of asteriscus and lapillus otoliths in A. escherichii is reported for the first time. The results of this study provide the first comprehensive data on otolith shape analysis and the relationship between otolith morphometrics and total length in the Caucasian bleak.

Wheat Grain Width: A Clue for Re-Exploring Visual Indicators of Grain Weight

Background Mean grain weight (MGW) is among the most frequently measured parameters in wheat breeding and physiology. Although in the recent decades, various wheat grain analyses (e.g. counting, and determining the size, color, or shape features) have been facilitated thanks to the automated image processing systems, MGW estimations has been limited to using few number of image-derived indices; i.e. mainly the linear or power models developed based on the projected area (Area). Following a preliminary observation which indicated the potential of grain width in improving the predictions, the present study was conducted to explore potentially more efficient indices for increasing the precision of image-based MGW estimations. For this purpose, an image archive of the grains was processed, which was harvested from a two-year field experiment carried out with 3 replicates under two irrigation conditions and included 15 cultivar mixture treatments (so the archive was consisted of 180 images taken from an overall number of more than 72000 grains). Results It was observed that among the more than 30 evaluated indices of grain size and shape, indicators of grain width (i.e. Minor & MinFeret) along with 8 other empirical indices had a higher correlation with MGW, compared with Area. The most precise MGW predictions were obtained using the Area×Circularity, Perimeter×Circularity, and Area/Perimeter indices. In general, two main common factors were detected in the structure of the major indices, i.e. either grain width or the Area/Perimeter ratio. Moreover, comparative efficiency of the superior indices almost remained stable across the 4 environmental conditions. Eventually, using the selected indices, ten simple linear models were developed and validated for MGW prediction, which indicated a relatively higher precision than the current Area-based models. The considerable effect of enhancing image resolution on the precision of the models has been also evidenced. Conclusions It is expected that the findings of the present study, along with the simple predictive linear models developed and validated using the new image-derived indices, could improve the precision of the image-based MGW estimations, and consequently facilitate wheat breeding and physiological assessments.

A Solution to Design Semi-static Null Convention Logic Cell Libraries

The Null Convention Logic (NCL) based asynchronous circuits have eliminated the disadvantages of the synchronous circuits, including noise, glitches, clock skew, power, and electromagnetic interference. However, using NCL based asynchronous designs was not easy for students and researchers because of the lack of standard NCL cell libraries. This paper proposes a solution to design a semi-static NCL cell library used to synthesize NCL based asynchronous designs. This solution will help researchers save time and effort to approach a new method. In this work, NCL cells are designed based on the Process Design Kit 45nm technology. They are simulated at the different corners with the Ocean script and Electronic Design Automation (EDA) environment to extract the timing models and the power models. These models are used to generate a *.lib file, which is converted to a *.db file by the Design Compiler tool to form a complete library of 27 cells. In addition, we synthesize the NCL based full adders to illustrate the success of the proposed library and compare our synthesis results with the results of the other authors. The comparison results indicate that power and delay are improved significantly.

Study of a Modified Kumaraswamy Distribution

In this article, a structural modification of the Kumaraswamy distribution yields a new two-parameter distribution defined on (0,1), called the modified Kumaraswamy distribution. It has the advantages of being (i) original in its definition, mixing logarithmic, power and ratio functions, (ii) flexible from the modeling viewpoint, with rare functional capabilities for a bounded distribution—in particular, N-shapes are observed for both the probability density and hazard rate functions—and (iii) a solid alternative to its parental Kumaraswamy distribution in the first-order stochastic sense. Some statistical features, such as the moments and quantile function, are represented in closed form. The Lambert function and incomplete beta function are involved in this regard. The distributions of order statistics are also explored. Then, emphasis is put on the practice of the modified Kumaraswamy model in the context of data fitting. The well-known maximum likelihood approach is used to estimate the parameters, and a simulation study is conducted to examine the performance of this approach. In order to demonstrate the applicability of the suggested model, two real data sets are considered. As a notable result, for the considered data sets, statistical benchmarks indicate that the new modeling strategy outperforms the Kumaraswamy model. The transmuted Kumaraswamy, beta, unit Rayleigh, Topp–Leone and power models are also outperformed.

Declarative Power Sequencing

Modern computer server systems are increasingly managed at a low level by baseboard management controllers (BMCs). BMCs are processors with access to the most critical parts of the platform, below the level of OS or hypervisor, including control over power delivery to every system component. Buggy or poorly designed BMC software not only poses a security threat to a machine, it can permanently render the hardware inoperative. Despite this, there is little published work on how to rigorously engineer the power management functionality of BMCs so as to prevent this happening. This article takes a first step toward putting BMC software on a sound footing by specifying the hardware environment and the constraints necessary for safe and correct operation. This is best accomplished through automation: correct-by-construction power control sequences can be efficiently generated from a simple, trustworthy model of the platform’s power tree that incorporates the sequencing requirements and safe voltage ranges of all components. We present both a modeling language for complex power-delivery networks and a tool to automatically generate safe, efficient power sequences for complex modern platforms. This not only increases the trustworthiness of a hitherto opaque yet critical element of platform firmware: regulator and chip power models are significantly simpler to produce than hand-written power sequences. This, combined with model reuse for common components, reduces both time and cost associated with platform bring-up for new hardware. We evaluate our tool using a new high-performance 2-socket server platform with >100W per socket TDP, tight voltage limits and 25 distinct power regulators needing configuration, showing both fast (<10s) tool runtime, and correct power sequencing of a live system.

Wheat grain width: A clue for re-exploring visual indicators of grain weight

Mean grain weight (MGW) is among the most frequently measured parameters in wheat breeding and physiology. Although in the recent decades, various wheat grain analyses (e.g. counting, and determining the size, color, or shape features) have been facilitated thanks to the automated image processing systems, MGW estimations has been limited to using few number of image-derived indices; i.e. mainly the linear or power models developed based on the projected area (Area). Following a preliminary observation which indicated the potential of grain width in improving the predictions, the present study was conducted to explore potentially more efficient indices for increasing the precision of image-based MGW estimations. For this purpose, an image archive of the grains was processed, which was harvested from a two-year field experiment carried out with 3 replicates under two irrigation conditions and included 15 cultivar mixture treatments (so the archive was consisted of 180 images taken from an overall number of more than 72000 grains). It was observed that among the more than 30 evaluated indices of grain size and shape, indicators of grain width (i.e. Minor & MinFeret) along with 8 other empirical indices had a higher correlation with MGW, compared with Area. The most precise MGW predictions were obtained using the Area*Circularity, Perimeter*Circularity, and Area/Perimeter indices. In general, two main common factors were detected in the structure of the major indices, i.e. either grain width or the Area/Perimeter ratio. Moreover, comparative efficiency of the superior indices almost remained stable across the 4 environmental conditions. Eventually, using the selected indices, ten simple linear models were developed and validated for MGW prediction, which indicated a relatively higher precision than the current Area-based models. The considerable effect of enhancing image resolution on the precision of the models has been also evidenced. It is expected that the findings of the present study improve the precision of the image-based MGW estimations, and consequently facilitate wheat breeding and physiological assessments.

A Categorization of Power Measurement and Power Consumption Models in the Telco Cloud

This article surveys the literature, over the period 2010-2020, on measurement of power consumption and relevant power models of virtual entities as they apply to the telco cloud. Hardware power meters are incapable of measuring power consumption of individual virtual entities co-hosted on a physical machine. Thus, software power meters are inevitable, yet their development is difficult. Indeed, there is no direct approach to measurement and, therefore, modeling through proxies of power consumption must be used. In this survey, we present trends, fallacies and pitfalls. Notably, we identify limitations of the widely-used linear models and the progression towards Artificial Intelligence / Machine Learning techniques as a means of dealing with the seven major dimensions of variability: workload type; computer virtualization agents; system architecture and resources; concurrent, co-hosted virtualized entities; approaches towards attribution of power consumption to virtual entities; frequency; and temperature.

Empirical Models for the Correlations Between Total Ozone Column and Latitude in different regions in IRAQ: النماذج الرياضية لعلاقات الارتباط بين طول عمود الأوزون وخط العرض في مناطق مختلفة من العراق

This Research aimed at find a correlation between Total Ozone Column (TOC) and Latitude in different regions in Iraq using several Mathematical Models. Models were used for that [Linear Models, Quadratic Models, Exponential Models, Logarithmic Models, Power Models]. Several statistical tests [R2, R, MAE, RMSE] were used to control the validation and goodness of these Models. Quadratic Model gave the highest R2 among the other models in all stations. R2 obtained between (TOC) & Latitude in Winter & Spring months were very high and ranged between (0.953 – 0. 976). Summer months show a good correlation in June & July and week correlation in August. In Autumn months a good correlation was obtained in October & November and week correlation was obtained in September. The highest R2 means that there is a highly significance correlations between Total Ozone Column and Latitude. This mean that these Models gave a very good results to estimate (TOC) from Latitude.

Ground Cover—Biomass Functions for Early-Seral Vegetation

Vegetation biomass is commonly measured through destructive sampling, but this method is time-consuming and is not applicable for certain studies. Therefore, it is necessary to find reliable methods to estimate vegetation biomass indirectly. Quantification of early-seral vegetation biomass in reforested stands in the United States Pacific Northwest (PNW) is important as competition between the vegetation community and planted conifer seedlings can have important consequences on seedling performance. The goal of this study was to develop models to indirectly estimate early-seral vegetation biomass using vegetation cover, height, or a combination of the two for different growth habits (ferns, forbs, graminoids, brambles, and shrubs) and environments (wet and dry) in reforested timber stands in Western Oregon, USA. Six different linear and non-linear regression models were tested using cover or the product of cover and height as the only predicting variable, and two additional models tested the use of cover and height as independent variables. The models were developed for six different growth habits and two different environments. Generalized models tested the combination of all growth habits (total) and sites (pooled data set). Power models were used to estimate early-seral vegetation biomass for most of the growth habits, at both sites, and for the pooled data set. Furthermore, when power models were preferred, most of the growth habits used vegetation cover and height separately as predicting variables. Selecting generalized models for predicting early-seral vegetation biomass across different growth habits and environments is a good option and does not involve an important trade-off by losing accuracy and/or precision. The presented models offer an efficient and non-destructive method for foresters and scientists to estimate vegetation biomass from simple field or aerial measurement of cover and height. Depending on the objectives and availability of input data, users may select which model to apply.