Miracle-dz/UniFAR-FTUs huggingface dataset:
Usefulness of combination of high-resolution ultrasonography and dual-phase dual-isotope iodine 123/technetium Tc 99m sestamibi scintigraphy for the preoperative localization of hyperplastic parathyroid glands in renal hyperparathyroidism.

BACKGROUND The usefulness of both dual-phase dual-isotope iodine 123 ( 123 I)/technetium Tc 99m ( 99m Tc) sestamibi scintigraphy and ultrasonography for the detection of hyperplastic parathyroid glands secondary to renal hyperparathyroidism is rarely addressed; most studies focus on primary hyperparathyroidism. However, it may be crucial to identify and accurately localize hyperplastic glands before surgery. METHODS To study the usefulness of high-resolution ultrasonography (performed by both the radiologist and surgeon) and dual-phase dual-isotope 123 I/ 99m Tc sestamibi scintigraphy in patients with renal hyperparathyroidism, a series of 20 patients consecutively referred for parathyroidectomy was studied prospectively. Results of both examinations, independently scored, were correlated with surgical and histopathologic findings for each hyperplastic parathyroid gland localization. RESULTS All parathyroid glands except 1 were found during primary surgery consisting of a subtotal parathyroidectomy (success rate, 99%). The missed gland was removed successfully 1 month later. Neither supernumerary nor ectopic glands were found. Mean weight of totally removed parathyroid glands was 633 mg. Ultrasonography detected 75% of hyperplastic parathyroid glands; dual-phase 123 I/ 99m Tc sestamibi scintigraphy, 66%; and a combination of both, 88%. Most missed glands at scintigraphy corresponded to superior glands, whereas false-negative results at ultrasonography correlated with low gland weight. CONCLUSION Combined ultrasonography and 123 I/ 99m Tc sestamibi scintigraphy should be considered for routine use to localize hyperplastic parathyroid glands in patients with renal hyperparathyroidism undergoing surgery. We suggest performing scintigraphy first, before ultrasonography, to guide the radiologist to areas of hyperfunctioning glands. In our experience, this proved very helpful in achieving a high surgical success rate in patients with renal hyperparathyroidism, especially when the surgeon visualizes the parathyroid glands at ultrasonography.
Influence of printing parameters on the transformation efficiency of 3D‐printed plaster of paris to hydroxyapatite and its properties

Purpose – The purpose of this paper is to study the influence of changing printing parameters (powder layer thickness and binder saturation) in a three dimensional printing machine (3DP) on the transformation of 3DP printed plaster of paris to hydroxyapatite by low temperature phosphorization.Design/methodology/approach – Plaster of paris‐based powder mixture was used to print specimens using different powder layer thickness (0.080, 0.10 and 0.20 mm) and saturation ratio (1 and 2). Subsequently, density, microstructure, mechanical properties, transformation rate and phase composition were analyzed to compare the influence of such printing parameters on properties.Findings – It was found that printing parameters strongly affect the transformation efficiency and properties of the samples. The sample printed at layer thickness of 0.10 mm and saturation ratio of 1 yielded the highest transformation rate, density and greatest flexural modulus and strength after conversion. This was related to the sufficiently .
The method of classification of user and system data based on the attributes

The paper presents method of classification of user and system data based on the attributes. The method is intended to solve the problem of data classification in the internal audit of computer equipment with a large volume of data stored on the media. The parametric space of informative features for the classification task is defined. To conduct internal audit of computer technologies in order to analyze the information security incidents we need data related directly with the user's action. While OS data are considered a priori as legitimate. With the growing volume of stored and processed information there is a need for accurate classification of user and system data to ensure that expert is analyzing only the information that can attribute itself with the information security incident. Thus, carrying out classification with high precision, it is possible to reduce the probability of the expert error during the internal audit of large amounts of data of operating system and application software. The method consists of several stages. The first stage involves the collecting of samples out of virtual machines running the Windows 10 operating system. Then the attributes of data stored on the hard disk are retrieved. Further, the received set of attributes is written to a “csv” file with a specific structure. On the final stage the informativity of features for further classification of files to user or system is determined. This method has the potential for development and showed its applicability in tasks of data classification to user or system. The feature informativity for accurate classification was identified. The comparison of three classification algorithms was carried out and the Naive Bayes classifier was selected. The method can be used not only in tasks of internal audit of computer equipment under the influence of information security violation threats, but for the purpose of computer forensics, which is investigation of information security incidents.
A plastid-localized glycogen synthase kinase 3 modulates stress tolerance and carbohydrate metabolism

Glycogen synthase kinase 3 (GSK-3) was originally identified as a regulator of glycogen synthesis in mammals. Like starch in plants, glycogen is a polymer of glucose, and serves as an energy and carbon store. Starch is the main carbohydrate store in plants. Regulation of starch metabolism, in particular in response to environmental cues, is of primary importance for carbon and energy flow in plants but is still obscure. Here, we provide evidence that MsK4, a novel Medicago sativa GSK-3-like kinase, connects stress signalling with carbon metabolism. MsK4 was found to be a plastid-localized protein kinase that is associated with starch granules. High-salt stress rapidly induced the in vivo kinase activity of MsK4. Metabolic profiling of MsK4 over-expressor lines revealed changes in sugar metabolism, including increased amounts of maltose, the main degradation product of starch in leaves. Plants over-expressing MsK4 showed improved tolerance to salt stress. Moreover, under high-salinity conditions, MsK4-over-expressing plants accumulated significantly more starch and showed modified carbohydrate content compared with wild-type plants. Overall, these data indicate that MsK4 is an important regulator that adjusts carbohydrate metabolism to environmental stress.
Can SMT and RBMT Improve each other’s Performance?- An Experiment with English-Hindi Translation

Rule-based machine translation (RBMT) and Statistical machine translation (SMT) are two well-known approaches for translation which have their own benefits. System architecture of SMT often complements RBMT, and the vice-versa. In this paper, we propose an effective method of serial coupling where we attempt to build a hybrid model that exploits the benefits of both the architectures. The first part of coupling is used to obtain good lexical selection and robustness, second part is used to improve syntax and the final one is designed to combine other modules along with the best phrase reordering. Our experiments on a English-Hindi product domain dataset show the effectiveness of the proposed approach with improvement in BLEU score.
Adaptive and hybrid genetic approaches for estimating the camera motion from image point correspondences

Rigid motion estimation from image point correspondences is an overconstrained problem that can be solved by minimizing an adequate cost function. Given the unreliable nature of image point correspondences, they must be divided into two categories: inliers and outliers. Finding the correct camera motion and discarding the outliers is a coupled problem usually solved by a random search of the solution space. This article proposes adaptive and hybrid genetic approaches to improve the efficiency of this search. We build on top of the GASAC algorithm that has been recently presented for solving problems in geometric computer vision. GASAC is modified to address the specific issues of camera motion estimation such as outlier ratios above 50% due to wide-baseline image acquisition and an adequate choice of a fitness function. In order to avoid local minima, we propose three adaptive strategies: varying the mutation probability, resampling the lowest ranked individuals, and using a hybrid approach that combines GASAC with simulated annealing. Results are validated on publicly available benchmark images, and it is shown that the proposed genetic approaches outperform the standard RANSAC search used among computer vision practitioners.
History , Models and Interpretations

In a relatively short period of time Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating performance. DEA has been successfully applied to a host of different types of entities engaged in a wide variety of activities in many contexts worldwide. This chapter discusses the fundamental DEA models and some of their extensions.
[Effect of behavioral training on learning and memory capacity and changes of hippocampal NR2B and GluR1 expressions in FGR offspring rats].

OBJECTIVE To study the effect of behavioral training on the learning and memory abilities and changes of NR2B and GluR1 expressions in the hippocampus of offspring rats with fetal growth retardation (FGR). METHODS A FGR model was established in SD rats by passive smoking. The offspring rats were divided into FGR group and control group, each then randomized into training and untrained group. Morris water maze behavioral training was carried out in postnatal months 2 and 4, and the learning and memory abilities of the young rats were assessed using dark-avoidance test and step-down test. NR2B and GluR1 expression in the hippocampus of the rats were detected by immunohistochemistry. RESULTS In the dark-avoidance and step-down tests, the FGR rats showed deteriorated learning and memory performance in comparison with the control group, but behavioral training resulted in improved performance of the rats. The performance in FGR group was much improved after behavioral training, and the model factor and the training factor showed a significant interaction (P<0.05). The expression of NR2B and GluR1 in CA1 and CA3 regions of the hippocampus decreased in FGR group, then the their expressions in the CA1 region increased after training in both FGR and control groups, and the increment was especially obvious in GluR1 expression in the CA1 region at postnatal month 2. The two factors showed a significant interaction (P<0.05). CONCLUSION Behavioral training can improve the learning and memory abilities of FGR offspring rats, the mechanism of which is probably related to increased expression of NR2B and GluR1 in the CA1 region of the hippocampus.
Extrusions and intrusions in fatigued metals. Part 2. AFM and EBSD study of the early growth of extrusions and intrusions in 316L steel fatigued at room temperature

Early stages of surface relief evolution of persistent slip markings (PSMs), formed in areas where persistent slip bands (PSBs) intersect the free surface, in polycrystalline 316L stainless steel cycled with constant plastic strain amplitude were studied using atomic force microscopy (AFM) and electron backscattering diffraction (EBSD). Focused ion beam (FIB) technique was employed to obtain additional, more detailed information on the shape of PSMs. To reveal true qualitative and quantitative information on the simultaneous growth of intrusions and extrusions within individual PSMs, identical areas both on the specimen surface and on its inverse copy obtained via plastic replica were studied using AFM. Intrusions are preceded by extrusions regardless of orientation of individual grains of the polycrystal. The first intrusions appear largely around 1% of fatigue life at the moment when ‘static’ extrusions are developed. They appear predominantly but not exclusively at the side of extrusions where the emerging active slip plane is inclined to the surface at an acute angle. They grow faster than the stage of stable extrusion growth. Typical morphology of mature PSMs developed at 15% of fatigue life consists of ribbon-like extrusions accompanied by two thin parallel intrusions along both PSB–matrix interfaces. Experimental data on the morphology and growth of extrusions and intrusions are discussed in relation to the theoretical models and computer simulations of surface relief evolution leading to fatigue crack initiation. †Present address: Institute of Material Science, Technische Universität Bergakademie Freiberg, Gustav-Zeuner-Str. 5, 09596 Freiberg, Germany
Robust abandoned object detection and analysis based on online learning

In this paper, we propose a novel approach based on online learning for accurate and effective detection of abandoned objects. Most existing methods for abandoned objects detection only detect abandoned objects without considering of the logic owner of the abandoned object. These methods need an advanced trained human detector to discriminate abandoned objects from still persons frequently. However, human detection is a challenge in robotic vision system, which always needs off-line training. The proposed framework without specific advanced trained human detector is able to detect abandoned objects and analyze their owners. The online framework is based on a valid assumption for objects and persons in natural scenes. Based on the assumptions that objects are moved by their logic owners and all the moving objects are humans in the scene, online classifiers are established with a certain moving objects just in the scene, which can assist us to detect abandoned objects and analyze their owner in true sense. Instead of a pixel based background model, a robust block based background model is established using online boosting method, which is able to adapt to a large variety of environment and complex changes. In the evaluation over the PETS 2006 and AVSS 2007 datasets, the proposed technique performs robustly and efficiently.
Evaluation of Different Data Mining Algorithms with KDD CUP 99 Data Set

Data mining is the modern technique for analysis of huge of data such as KDD CUP 99 data set that is applied in network intrusion detection. Large amount of data can be handled with the data mining technology. It is still in developing state, it can become more effect ive as it is growing rapidly. Our work in this paper survey is for the most algorithms Data Mining using KDD CUP 99 data set in the classification of attacks and compared their results which have been reached, and being used of the performance measurement such as, True Positive Rate (TP), False Alarm Rate(FP), Percentage of Successful Prediction (PSP) and training time (TT) to show the results, the reason for this survey is to compare the results and select the best system for detecting intrusion (classifica tion) . The results showed that the Data Mining algorithms differ in the proportion of determining the rate of the attack, according to its type. The algorithm Random Forest Classifier detection is the highest rate of attack of the DOS, While Fuzzy Logic al gorithm was the hi ghest in detection Probe attack. The two categories R2U and R2L attacks have been identified well by using an MARS , Fuzzy logic and Random Forest classifiers respectively . MARS getting higher accuracy in classification, while PART classi fication algorithm got less accuracy. OneR got the least training time, otherwise Fuzzy Logic algorithm and MLP algorithm got higher training time.
A parametric study on the behavior of slender reinforced concrete frames

By using a nonlinear computer analysis, a parametric study is developed in order to examine the accuracy of the Moment Magnifier Method of the American Concrete Institute Code (ACI 318-83). The variables used in the parametric study are: axial load intensity, P/P0\ column reinforcement ratio, p; slenderness ratio, klu/r\ shape of column cross section, flexural stiffness ratio, and distribution of axial loads. In the parametric study, 216 cases of single bay fixed-base portal frames are examined. The higher moment for each one of these frames at failure are then compared with the design moment predicted by the Moment Magnifier Method of the American Concret Institute Code (ACI 318-83). The Moment Magnifier Method proved to be very conservative when the columns are subjected to high level of axial loads and when the slenderness ratio is increased.
A Hybrid Heuristic for an Inventory Routing Problem

We consider an inventory routing problem in discrete time where a supplier has to serve a set of customers over a multiperiod horizon. A capacity constraint for the inventory is given for each customer, and the service cannot cause any stockout situation. Two different replenishment policies are considered: the order-up-to-level and the maximum-level policies. A single vehicle with a given capacity is available. The transportation cost is proportional to the distance traveled, whereas the inventory holding cost is proportional to the level of the inventory at the customers and at the supplier. The objective is the minimization of the sum of the inventory and transportation costs. We present a heuristic that combines a tabu search scheme with ad hoc designed mixed-integer programming models. The effectiveness of the heuristic is proved over a set of benchmark instances for which the optimal solution is known.
A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances

Nitrogen (N) plays a key role in the plant life cycle. It is the main plant mineral nutrient needed for chlorophyll production and other plant cell components (proteins, nucleic acids, amino acids). Crop yield is affected by plant N status. Thus, the optimization of nitrogen fertilization has become the object of intense research due to its environmental and economic impact. This article focuses on reviewing current methods and techniques used to determine plant N status. Kjeldahl digestion and Dumas combustion have been used as reference methods for N determination in plants, but they are destructive and time consuming. By using spectroradiometers, reflectometers, imagery from satellite sensors and digital cameras, optical properties have been measured to estimate N in plants, such as crop canopy reflectance, leaf transmittance, chlorophyll and polyphenol fluorescence. High correlation has been found between optical parameters and plant N status, and those techniques are not destructive. However, some drawbacks include chlorophyll saturation, atmospheric and soil interference, and the high cost of instruments. Electrical properties of plant tissue have been used to estimate quality in fruits, and water content in plants, as well as nutrient deficiency, which suggests that they have potential for use in plant N determination.
Revisiting with a Relative-Density Calibration Approach the Determination of Growth Rates of Microorganisms by Use of Optical Density Data from Liquid Cultures

ABSTRACT To solve the problems of measuring the growth rates of microorganisms from optical density (OD)-growth time plots, we used relative-density (RD) plots. The relationship of OD and RD was built from the diluted grown cultures. This method was satisfactorily applied to study the growth of Escherichia coli and the cyanobacterium Anabaena spiroides.
Mapping and Modelling the Geographical Distribution and Environmental Limits of Podoconiosis in Ethiopia

Background Ethiopia is assumed to have the highest burden of podoconiosis globally, but the geographical distribution and environmental limits and correlates are yet to be fully investigated. In this paper we use data from a nationwide survey to address these issues. Methodology Our analyses are based on data arising from the integrated mapping of podoconiosis and lymphatic filariasis (LF) conducted in 2013, supplemented by data from an earlier mapping of LF in western Ethiopia in 2008–2010. The integrated mapping used woreda (district) health offices’ reports of podoconiosis and LF to guide selection of survey sites. A suite of environmental and climatic data and boosted regression tree (BRT) modelling was used to investigate environmental limits and predict the probability of podoconiosis occurrence. Principal Findings Data were available for 141,238 individuals from 1,442 communities in 775 districts from all nine regional states and two city administrations of Ethiopia. In 41.9% of surveyed districts no cases of podoconiosis were identified, with all districts in Affar, Dire Dawa, Somali and Gambella regional states lacking the disease. The disease was most common, with lymphoedema positivity rate exceeding 5%, in the central highlands of Ethiopia, in Amhara, Oromia and Southern Nations, Nationalities and Peoples regional states. BRT modelling indicated that the probability of podoconiosis occurrence increased with increasing altitude, precipitation and silt fraction of soil and decreased with population density and clay content. Based on the BRT model, we estimate that in 2010, 34.9 (95% confidence interval [CI]: 20.2–51.7) million people (i.e. 43.8%; 95% CI: 25.3–64.8% of Ethiopia’s national population) lived in areas environmentally suitable for the occurrence of podoconiosis. Conclusions Podoconiosis is more widespread in Ethiopia than previously estimated, but occurs in distinct geographical regions that are tied to identifiable environmental factors. The resultant maps can be used to guide programme planning and implementation and estimate disease burden in Ethiopia. This work provides a framework with which the geographical limits of podoconiosis could be delineated at a continental scale.
System level modeling of a digital communication channel based on SystemC

In order to evaluate and investigate performance of a digital communication system, this paper presents a new approach that can be used to design, model and develop a Stochastic digital channel simulator to plug into any digital communication system under different modulation schemes. The data packet structure that has been used with this system is based on a High-Level Data Link Control (HDLC) standard. The simulation tool is a SystemC that is provide various features to perform system level modeling and simulation. The challenge was how to efficiently simulate this channel by using the hardware modeling features available in SystemC, and then how we have developed this channel. Also discussed how the modeling features can be used to simulate different noise sources that can be used to introduce noise events depends on specific distribution.