Miracle-dz/UniFAR-FTUs huggingface dataset:
The Small Round Blue Cell Tumors of the Sinonasal Area: Histological and Immunohistochemical Findings

Background Primary Small round blue cell tumors (SRBCT) in sinonasal comprise histogenetically diverse entities with overlapping morphologic features. Because of the limited initial biopsy tissue materials, differential diagnostic difficulties may arise, and as they have different management, exact diagnosis and classification are very important. Objectives In this study, we analyzed the immunohistochemical expression of a panel of markers in the classification and diagnosis of sinonasal SRBCTs. Material and Methods This cross sectional study was performed on 36 paraffin embedded tissue samples. Histologic and immunohistochemical slides from 36 patients with SRBCT were analyzed retrospectively. The patients were admitted in Khalili hospital, Shiraz from 1383 to 1388. Results There were 13 women and 23 men with the mean age of 53 ±12.1. There were 9 malignant melanoma, seven poorly differentiated SCC; six lymphoma (DLBL); 4 SCNEC; three SNUC; two ON; two Ewing/PNET; two embryonal rhabdomyosarcoma, and one plasmacytoma. Pan-cytokeratin was strongly expressed poorly differentiated SCC and all cases of SNUC. Coexpression of desmin and nuclear myoD1 was only detected in rhabdomyosarcoma. HMB45 was only expressed in sinonasal melanoma. CD99 expression was identified only in Ewing/PNET. FLI-1 was detected in 50% of PNET. P63 was expressed in poorly differentiated SCC (2/7) and SNUC (1/3). Conclusions The results of our study indicate that the integration of histopathologic findings with application of limited but highly specific markers led to the separation of carcinomas, lymphoma and melanomas from other small cell tumors. Using a panel of keratin, LCA, desmin, and HMB45 is the most practical and economic approach to accurately classify these tumors.
System dynamics modelling in supply chain management: research review

The use of system dynamics modeling in supply chain management has only recently re-emerged after a lengthy slack period. Current research on system dynamics modelling in supply chain management focuses on inventory decision and policy development, time compression, demand amplification, supply chain design and integration, and international supply chain management. The paper first gives an overview of recent research work in these areas, followed by a discussion of research issues that have evolved, and presents a taxonomy of research and development in system dynamics modelling in supply chain management.
The Travelling Agent Problem

This paper considers a sequencing problem which arises naturally in the scheduling of software agents. We are given n sites at which a certain task might be successfully performed. The probability of success is pi at the ith site and these probabilities are independent. Visiting site i and trying the task there requires time (or some other cost metric) ti whether successful or not. Latencies between sites i and j are lij , that is, the travel time between those two sites. Should the task be successfully completed at a site then any remaining sites do not need to be visited. The Travelling Agent Problem is to nd the sequence which minimizes the expected time to complete the task. The general formulation of this problem is NP -Complete. However, if the latencies are constant we show that the problem can be solved in polynomial time by sorting the ratios pi=ti according to decreasing value and visiting the sites in that order. This result then leads to an e cient algorithm when groups of sites form subnets in which latencies within a subnet are constant but can vary across subnets. We also study the case when there are deadlines for solving the problem in which case the goal is to maximize probability of success subject to satisfying the deadlines. Applications to mobile and intelligent agents are described.
Cardiometabolic Risk Profiles in Patients With Impaired Fasting Glucose and/or Hemoglobin A1c 5.7% to 6.4%: Evidence for a Gradient According to Diagnostic Criteria

AbstractIt has been suggested that the early detection of individuals with prediabetes can help prevent cardiovascular diseases. The purpose of the current study was to examine the cardiometabolic risk profile in patients with prediabetes according to fasting plasma glucose (FPG) and/or hemoglobin A1c (HbA1c) criteria.Cross-sectional analysis from the 2022 patients in the Cohort study in Primary Health Care on the Evolution of Patients with Prediabetes (PREDAPS Study) was developed. Four glycemic status groups were defined based on American Diabetes Association criteria. Information about cardiovascular risk factors–body mass index, waist circumference, blood pressure, cholesterol, triglycerides, uric acid, gamma-glutamyltransferase, glomerular filtration–and metabolic syndrome components were analyzed. Mean values of clinical and biochemical characteristics and frequencies of metabolic syndrome were estimated adjusting by age, sex, educational level, and family history of diabetes.A linear trend (P < 0.001) was observed in most of the cardiovascular risk factors and in all components of metabolic syndrome. Normoglycemic individuals had the best values, individuals with both criteria of prediabetes had the worst, and individuals with only one–HbA1c or FPG–criterion had an intermediate position. Metabolic syndrome was present in 15.0% (95% confidence interval: 12.6–17.4), 59.5% (54.0–64.9), 62.0% (56.0–68.0), and 76.2% (72.8–79.6) of individuals classified in normoglycemia, isolated HbA1c, isolated FPG, and both criteria groups, respectively.In conclusion, individuals with prediabetes, especially those with both criteria, have worse cardiometabolic risk profile than normoglycemic individuals. These results suggest the need to use both criteria in the clinical practice to identify those individuals with the highest cardiovascular risk, in order to offer them special attention with intensive lifestyle intervention programs.
Female Putty-Nosed Monkeys Use Experimentally Altered Contextual Information to Disambiguate the Cause of Male Alarm Calls

Many animal vocal signals are given in a wide range of contexts which can sometimes have little in common. Yet, to respond adaptively, listeners must find ways to identify the cause of a signal, or at least rule out alternatives. Here, we investigate the nature of this process in putty-nosed monkeys, a forest primate. In this species, adult males have a very restricted repertoire of vocalizations which are given in response to a wide variety of events occurring under conditions of limited visibility. We carried out a series of field playback experiments on females (N = 6) in a habituated group in Gashaka Gumti National Park, Nigeria, in which male alarm/loud calls were presented either alone, or following acoustic information that simulated the occurrence of natural disturbances. We demonstrate that listeners appear to integrate contextual information in order to distinguish among possible causes of calls. We conclude that, in many cases, pragmatic aspects of communication play a crucial role in call interpretation and place a premium on listeners' abilities to integrate information from different sources.
Computational intelligence methods for information understanding and information management

Information management relies on knowledge acquisition methods for extraction of knowledge from data. Statistical methods traditionally used for data analysis are satisfied with predictions, while understanding of data and extraction of knowledge from data are challenging tasks that have been pursued using computational intelligence (CI) methods. Recent advances in applications of CI methods to data understanding are presented, implementation of methods in the GhostMiner data mining package [1] developed in our laboratory described, new directions outlined and challenging open problems posed. To illustrate the advantages of different techniques, a single dataset is exposed to the many-sided analysis.
Why are poorer children at higher risk of obesity and overweight? A UK cohort study

Background: There is limited evidence on which risk factors attenuate income inequalities in child overweight and obesity; whether and why these inequalities widen as children age. Method: Eleven thousand nine hundred and sixty five singletons had complete data at age 5 and 9384 at age 11 from the Millennium Cohort Study (UK). Overweight (age 5 : 15%; age 11 : 20%) and obesity (age 5 : 5%; age 11 : 6%) were defined using the International Obesity Taskforce body mass index cut-points. To measure socioeconomic inequalities, we used quintiles of family income and as risk factors, we considered markers of maternal health behaviours and of children’s physical activity, sedentary behaviours and diet. Binary and multinomial logistic regression models were used. Results: The unadjusted analyses revealed stark income inequalities in the risk of obesity at age 5 and 11. At age 5, children in the bottom income quintile had 2.0 (95% CI: 1.4–2.8) increased relative risk of being obese whilst at age 11 they had 3.0 (95% CI: 2.0–4.5) increased risk compared to children in the top income quintile. Similar income inequalities in the risk of overweight emerged by age 11. Physical activity and diet were particularly important in explaining inequalities. Income inequalities in obesity and overweight widened significantly between age 5 and 11 and a similar set of risk factors protected against upward and promoted downward movements across weight categories. Conclusions: To reduce income inequalities in overweight and obesity and their widening across childhood the results support the need of early interventions which take account of multiple risk factors.
The greedy algorithm for strict cg-matroids

A matroid-like structure defined on a convex geometry, called a cg-matroid, is defined by S. Fujishige, G. A. Koshevoy, and Y. Sano in [6]. Strict cg-matroids are the special subclass of cg-matroids. In this paper, we show that the greedy algorithm works for strict cg-matroids with natural weightings, and also show that the greedy algorithm works for a hereditary system on a convex geometry with any natural weighting if and only if the hereditary system is a strict cg-matroid.
Si pu‘o dire che i calcolatori automatici pensano? Universal Turing Machine

Knowledge-base epistemologies and hash tables have garnered improbable interest from both leading analysts and hackers worldwide in the last several years. After years of theoretical research into online algorithms, we show the understanding of the locationidentity split, which embodies the confusing principles of cyberinformatics. We validate that despite the fact that link-level acknowledgements and telephony are often incompatible, the famous concurrent algorithm for the deployment of the Internet by I. Daubechies runs in Ω(n) time.
Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine

Time series analysis is an important and complex problem in machine learning and statistics. In the existing system, Support Vector Machine (SVM) and Association Rule Mining (ARM) is introduced to implement the time series data. However it has issues with lower accuracy and higher time complexity. Also it has issue with optimal rules discovery and segmentation on time series data. To avoid the above mentioned issues, in the proposed research Sliding Window Technique based Improved ARM with Enhanced SVM (SWT-IARM with ESVM) is proposed. In the proposed system, the preprocessing is performed using Modified K-Means Clustering (MKMC). The indexing process is done by using R-tree which is used to provide faster results. Segmentation is performed by using SWT and it reduces the cost complexity by optimal segments. Then IARM is applied on efficient rule discovery process by generating the most frequent rules. By using ESVM classification approach, the rules are classified more accurately.
On the control of complementary-slackness juggling mechanical systems

Studies the feedback control of a class of complementary-slackness hybrid mechanical systems. Roughly, the systems we study are composed of an uncontrollable part (the "object") and a controlled one (the "robot"), linked by a unilateral constraint and an impact rule. A systematic and general control design method for this class of systems is proposed. The approach is a nontrivial extension of the one degree-of-freedom (DOF) juggler control design. In addition to the robot control, it is also useful to study some intermediate controllability properties of the object's impact Poincare mapping, which generally takes the form of a nonlinear discrete-time system. The force input mainly consists of a family of dead-beat feedback control laws, introduced via a recursive procedure, and exploiting the underlying discrete-time structure of the system. The main goal of the paper is to highlight the role of various physical and control properties characteristic of the system on its stabilizability properties and to propose solutions in certain cases.
Efficient rule scoring for improved grapheme-based lexicons

For many languages, an expert-defined phonetic lexicon may not exist. One popular alternative is the use of a grapheme-based lexicon. However, there may be a significant difference between the orthography and the pronunciation of the language. In our previous work, we proposed a statistical machine translation based approach to improving grapheme-based pronunciations. Without knowledge of true target pronunciations, a phrase table was created where each individual rule improved the likelihood of the training data when applied. The approach improved recognition accuracy, but required significant computational cost. In this work, we propose an improvement that increases the speed of the process by more than 80 times without decreasing recognition accuracy.
Remote sensing for UREA Spraying Agricultural (UAV) system

New technologies are launching on every day in Indian agriculture. But it is expensive and high power consumption. We proposed a reliable system with low power usage and introduced an agricultural engineering project that is known as UREA spraying system using Unmanned Aerial Vehicle. That is rapidly upcoming method for remote-sensing data acquisition, mostly aerial green field images and derived products. By now, the systems are less weight and cost-effective, the improvement of the sensors and their good enable a relative safe operation with good satisfaction of customer. This Project introduced a high-performance quad-rotor model that is built around a high implementation, which is all direction (0°-360°) supports with high payloads through less weight materials and advanced brush-less BLDC motors. The UAV is 1.8m in diameter, weighs 3litre in total, and generates a maximum lift of 8 kg. Without payload the maximum flying time is around 60min. The UAV programmed using Arduino through network. The UAV solve the problem using RTOS for any critical situation. The paper also focuses to land edge detection, atmospheric climate detection through camera required to obtain accurate remote sensing products that are useful for plants monitoring and Urea Spraying equally to each ground in sector-wise. UAV speed is 100m/minute. UAV spraying time is 1 hour/acre with 4 rounds. UAV carrying weight is 5 liter of urea content. It has Manual mode for manual operation and auto mode for repeated operations. Arduino Controller is used for UAV lifting control and BLDC motor controller. We have designed four BLDC motor and designed a micro-strip patched antenna of RF transmitter with 2.919GHz for controlling the payload. This paper mainly proposed to Improvement in weight lifting capacity may leads to adding a more function in account of UAVs. The payload of our Quad-rotor is around from 1 litre to 5 litre water content which adds function of weight lifting in Agricultural fields for Urea spraying operations.
Fast Adaptive Weight Noise

Marginalising out uncertain quantities within the internal representations or parameters of neural networks is of central importance for a wide range of learning techniques, such as empirical, variational or full Bayesian methods. We set out to generalise fast dropout (Wang & Manning, 2013) to cover a wider variety of noise processes in neural networks. This leads to an efficient calculation of the marginal likelihood and predictive distribution which evades sampling and the consequential increase in training time due to highly variant gradient estimates. This allows us to approximate variational Bayes for the parameters of feed-forward neural networks. Inspired by the minimum description length principle, we also propose and experimentally verify the direct optimisation of the regularised predictive distribution. The methods yield results competitive with previous neural network based approaches and Gaussian processes on a wide range of regression tasks.
Aqueous cytokine and chemokine analysis in uveitis associated with tuberculosis

Purpose The aim of this study was to study the aqueous cytokine and chemokine composition in patients with uveitis associated with tuberculosis (TAU). Methods We present a prospective case series of consecutive new patients with active uveitis presenting at a single tertiary center (January 1, 2008-January 1, 2010). Patients with no ocular pathology other than cataracts were enrolled as non-inflammatory controls. Aqueous samples were taken from all study subjects and analyzed using a magnetic color-bead-based multiplex assay for cytokine and chemokine concentrations. Results Twenty-five eyes of 25 patients with active uveitis with suspected tuberculosis (TB) and 23 non-inflammatory controls were enrolled. Ten patients tested positive on a tuberculin skin test and interferon-gamma release assay; all ten patients responded to anti-TB treatment with no recurrences (TAU). The remaining 15 eyes were negative for the above tests and had no other underlying causes for uveitis found on clinical evaluation and investigations; therefore, they were classified as “idiopathic uveitis” (IU). The TAU group showed significantly higher levels of interleukin-6 (IL-6; p=0.047), interleukin-8 (CXCL8; p=0.001), monokine induced by interferon-gamma (CXCL9; p=0.001), and interferon-gamma-induced protein 10 (IP-10 or CXCL10; p=0.002), compared to the controls. The IU group showed significantly higher levels of IL-6 (p=0.008), monocyte chemotactic protein-1 (CCL2; p=0.036), CXCL8 (p=0.001), and IL-9 (p=0.045), and significantly lower levels of IL-2 (p=0.011), IL-12 (p=0.001), and tumor necrosis factor (TNF)-α (p=0.001), compared to the controls. Heat map analysis revealed significant differences in aqueous cytokine and chemokine concentrations among the TAU patients, the IU patients, and the controls. Conclusions In our study population, aqueous cytokine and chemokine analyses suggest that subjects with uveitis associated with TB who respond to anti-TB therapy do not have an active ocular tuberculous infection, but rather an autoimmune-related ocular inflammation that may be triggered by TB.
Border adaptive micro-base-station for the wireless communications

This article presents a novel architecture, namely, the border adaptive micro-base-station network, which can sufficiently meet the bandwidth requirement of future wireless networks. From the three screens convergence point, our goal is to make wireless services' performance match that of wired systems, instead of downgrading wired network applications to wireless level. Based on the analysis of Shannon theory from a new angle, the only way to build future wireless networks is to adopt this micro-base-station approach, but not progressively improving the traditional large cell base station systems, such as the long term evolution (LTE).
The Relationship Between Intrinsic Motivation and Reading Comprehension: Mediating Effects of Reading Amount and Metacognitive Knowledge of Strategy Use

ABSTRACT The goal of the present study was to investigate the mediating effects of reading amount and metacognitive knowledge of strategy use in the relationship between intrinsic motivation and reading comprehension among secondary school students. We hypothesized that reading amount and metacognitive knowledge of strategy use in Grade 6 will mediate the relationship between intrinsic motivation in Grade 5 and reading comprehension in Grade 7 while taking into account the reciprocal relationship between intrinsic motivation and reading comprehension in Grades 5 and 7. Within the framework of the German National Educational Panel Study, 3,829 secondary school students were included in the analyses. Based on the analyses of structural equation models, we found significant indirect effects of intrinsic motivation on reading comprehension through reading amount and metacognitive knowledge of strategy use. The results of exploratory analyses on moderating effects of sex and the levels of reading skills are also discussed.
Mining the Factors Affecting the Quality of Wikipedia Articles

In order to observe the variation of factors affecting the quality of Wikipedia articles during the information quality improvement process, we proposed 28 metrics from four aspects, including lingual, structural, historical and reputational features, and then weighted each metrics indifferent stages by using neural network. We found lingual features weighted more in the lower quality stages, and structural features, along with historical features, became more important while article quality improved. However, reputational features did not act as important as expected. The findings indicate that the information quality is mainly affected by completeness, and well-written is a basic requirement in the initial stage. Reputation of authors or editors is not so important in Wikipedia because of its horizontal structure.
A Broadband and Vialess Vertical Microstrip-to-Microstrip Transition

A novel, broadband, vialess, and vertical microstrip-to-microstrip transition is proposed in this paper. The transition consists of two open-circuited microstrip resonators and a U-shaped resonant-slot on the common ground plane. A physics-based equivalent-circuit model is developed for interpreting its working mechanism and facilitating the design process. The transition is analogous to a three-pole resonator filter. Based on the equivalent-circuit model, the coupling coefficients of the physical circuit can be calculated from the group delay information of two segregated electromagnetic models. To effectively control the couplings, a modified configuration is also proposed. A prototype transition is designed using the proposed design formulas. The fabricated circuit is measured to validate the proposed transition and the equivalent-circuit model. Good agreement is obtained between not only the measured and the simulated performance, but also the designed and the extracted-circuit model. In addition to the wide bandwidth, the features of vialess and easy fabrication make the novel transition very attractive for system-on-package applications.