LLukas22/scidocs huggingface dataset:
The Waiting-Time Distribution for the GI/G/1 Queue under the D-Policy

We study a generalization of the GI/G/1 queue in which the server is turned off at the end of each busy period and is reactivated only when the sum of the service times of all waiting customers exceeds a given threshold of size D. Using the concept of a “randomly-selected” arriving customer, we obtain as our main result a relation that expresses the waitingtime distribution of customers in this model in terms of characteristics associated with a corresponding standard GI/G/1 queue, obtained by setting D = 0. If either the arrival process is Poisson or the service times are exponentially distributed, then this representation of the waiting-time distribution can be specialized to yield explicit, transform-free formulas; we also derive, in both of these cases, the expected customer waiting times. Our results are potentially useful, for example, for studying optimization models in which the threshold D can be controlled. AMS 1980 subject classification. Primary: 90B22; Secondary: 60K25. IAOR 1973 subject classification. Main: Queues. OR/MS Index 1978 subject classification. Primary: 681 Queues.
Face Recognition Using Neural Network: A Review

Face recognition from the real data, capture images, sensor images and database images is challenging problem due to the wide variation of face appearances, illumination effect and the complexity of the image background. Face recognition is one of the most effective and relevant applications of image processing and biometric systems. In this paper we are discussing the face recognition methods, algorithms proposed by many researchers using artificial neural networks (ANN) which have been used in the field of image processing and pattern recognition. How ANN will used for the face recognition system and how it is effective than another methods will also discuss in this paper. There are many ANN proposed methods which give overview face recognition using ANN. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included, and also the performance analysis of different ANN approach and algorithm is analysing in this research study.
Analysis of Cockcroft-Walton Voltage Multiplier

In this paper a high D.C. voltage is based on Cockcroft-Walton cascade rectifier, without using transformer. The purpose of this paper is to get an output of a high dc voltage by applying input as an low dc voltage. It provides continuous input current to load with low ripple voltage and current. Moreover, based on the n-stage CW voltage multiplier, the proposed converter can provide a suitable dc source for an n + 1-level multilevel inverter. In this paper, control strategy employs two independent frequencies, one of which operates at high frequency while the other one operates at relatively low frequency.
Online Stator Interturn Short Circuits Monitoring in the DFOC Induction-Motor Drive

This paper deals with monitoring of interturn short circuits of induction-motor (IM) stator windings operating under the direct field-oriented control (DFOC). Time transients are presented to illustrate the performance of the field-oriented speed control under the stator-winding damage. Novel detection algorithm is introduced, based on an analysis of the internal signals from the control structure: controller outputs and control path decoupling variables. The spectral analysis of these signals is used to detect the stator-winding incipient damages. Significant changes of the constant component fdc and the double-fundamental harmonic component 2fs observed in these signals' spectra under the stator-winding failure become the basis for the failure diagnostics in various operating conditions of the DFOC IM drive. The superiority of the proposed detection algorithm over the classical motor current signature analysis (MCSA) method is described. The online monitoring process is shown. Simulation and experimental results are presented to validate the proposed detection algorithm.
Audio Analysis using the Discrete Wavelet Transform

The Discrete Wavelet Transform (DWT) is a transformation that can be used to analyze the temporal and spectral properties of non-stationary signals li ke audio. In this paper we describe some applications of the DWT to the problem of extracting information from non-speech audio. More specifically automatic classification of various types of audio using the DWT is described and compared with other traditional feature extractors proposed in the literature. In addition, a technique for detecting the beat attributes of music is presented. Both synthetic and real world stimuli were used to evaluate the performance of the beat detection algorithm. Key-Words: audio analysis, wavelets, classification, beat extraction
Demand seasonality in retail inventory management

We investigate the value of accounting for demand seasonality in inventory control. Our problem is motivated by discussions with retailers who admitted to not taking perceived seasonality patterns into account in their replenishment systems. We consider a single-location, single-item periodic review lost sales inventory problem with seasonal demand in a retail environment. Customer demand has seasonality with a known season length, the lead time is shorter than the review period and orders are placed as multiples of a fixed batch size. The cost structure comprises of a fixed cost per order, a cost per batch, and a unit variable cost to model retail handling costs. We consider four different settings which differ in the degree of demand seasonality that is incorporated in the model: with or without within-review period variations and with or without across-review periods variations. In each case, we calculate the policy which minimizes the long-run average cost and compute the optimality gaps of the policies which ignore part or all demand seasonality. We find that not accounting for demand seasonality can lead to substantial optimality gaps, yet incorporating only some form of demand seasonality does not always lead to cost savings. We apply the problem to a real life setting, using Point-of-Sales data from a European retailer. We show that a simple distinction between weekday and weekend sales can lead to major cost reductions without greatly increasing the complexity of the retailer’s automatic store ordering system. Our analysis provides valuable insights on the tradeoff between the complexity of the automatic store ordering system and the benefits of incorporating demand seasonality. 2014 Elsevier B.V. All rights reserved.
Minimalistic CNN-based ensemble model for gender prediction from face images

Despite being extensively studied in the literature, the problem of gender recognition from face images remains difficult when dealing with unconstrained images in a cross-dataset protocol. In this work, we propose a convolutional neural network ensemble model to improve the state-of-the-art accuracy of gender recognition from face images on one of the most challenging face image datasets today, LFW (Labeled Faces in the Wild). We find that convolutional neural networks need significantly less training data to obtain the state-of-the-art performance than previously proposed methods. Furthermore, our ensemble model is deliberately designed in a way that both its memory requirements and running time are minimized. This allows us to envision a potential usage of the constructed model in embedded devices or in a cloud platform for an intensive use on massive image databases. c © 2015 Elsevier Ltd. All rights reserved.
Isoflurane Sedation on the ICU in Cardiac Arrest Patients Treated With Targeted Temperature Management: An Observational Propensity-Matched Study.

OBJECTIVE
Targeted temperature management after cardiac arrest requires deep sedation to prevent shivering and discomfort. Compared to IV sedation, volatile sedation has a shorter half-life and thus may allow more rapid extubation and neurologic assessment.


DESIGN
Observational analysis of clinical data.


SETTING
University hospital, medical ICU.


PATIENTS
Four hundred thirty-two cardiac arrest survivors underwent targeted temperature management; of those, 110 were treated with volatile sedation using an anesthetic conserving device and isoflurane, and 322 received standard IV sedation.


INTERVENTION
No intervention.


MEASUREMENT AND MAIN RESULTS
A matched pairs analysis revealed that time on ventilator (difference of median, 98.5 hr; p = 0.003) and length of ICU stay (difference of median, 4.5 d; p = 0.006) were significantly shorter in patients sedated with isoflurane when compared with IV sedation although no differences in neurologic outcome (45% of patients with cerebral performance category 1-2 in both groups) were observed. Significant hypercapnia occurred more frequently during anesthetic conserving device use (6.4% vs 0%; p = 0.021).


CONCLUSIONS
Volatile sedation is feasible in cardiac arrest survivors. Prospective controlled studies are necessary to confirm the beneficial effects on duration of ventilation and length of ICU stay observed in our study. Our data argue against a major effect on neurologic outcome. Close monitoring of PaCO2 is necessary during sedation via anesthetic conserving device.
Machine vision

This text is intended to provide a balanced introduction to machine vision. Basic concepts are introduced with only essential mathematical elements. The details to allow implementation and use of vision algorithm in practical application are provided, and engineering aspects of techniques are emphasized. This text intentionally omits theories of machine vision that do not have sufficient practical applications at the time.
DEVELOPMENT AND TEMPERATURE CONTROL OF SMART EGG INCUBATOR SYSTEM FOR VARIOUS TYPES OF EGG

This work is aimed at modeling, designing and developing an egg incubator system that is able to incubate various types of egg within the temperature range of 35 – 40 0 C. This system uses temperature and humidity sensors that can measure the condition of the incubator and automatically change to the suitable condition for the egg. Extreme variations in incubation temperature affect the embryo and ultimately, post hatch performance. In this work, electric bulbs were used to give the suitable temperature to the egg whereas water and controlling fan were used to ensure that humidity and ventilation were in good condition. LCD is used to display status condition of the incubator and an interface (Keypad) is provided to key in the appropriate temperature range for the egg. To ensure that all part of the eggs was heated by the lamp, DC motor was used to rotate iron rod at the bottom side and automatically change position of the egg. The entire element is controlled using AT89C52 Microcontroller. The temperature of the incubator is maintained at the normal temperature using PID controller implemented in microcontroller. Mathematical model of the incubator, actuator and PID controller were developed. Controller design based on the models was developed using Matlab Simulink. The models were validated through simulation and the Zeigler-Nichol tuning method was adopted as the tuning technique for varying the temperature control parameters of the PID controller in order to achieve a desirable transient response of the system when subjected to a unit step input. After several assumptions and simulations, a set of optimal parameters were obtained at the result of the third test that exhibited a commendable improvement in the overshoot, rise time, peak time and settling time thus improving the robustness and stability of the system. Keyword: Egg Incubator System, AT89C52 Microcontroller, PID Controller, Temperature Sensor.
Identification of unexpected respiratory abnormalities in patients with amyotrophic lateral sclerosis through electromyographic analysis using intramuscular electrodes implanted for therapeutic diaphragmatic pacing.

BACKGROUND
Amyotrophic lateral sclerosis patients have significant respiratory abnormalities with incomplete understanding of respiratory control. This study analyzes electromyography (EMG) of the diaphragm (dEMG) using implanted diaphragm pacing (DP) electrodes.


METHODS
Retrospective analysis of dEMG data were obtained during Institutional Review Board and US Food and Drug Administration approved trials. The electrodes were used to analyze epochs of dEMG during multiple respiratory cycles.


RESULTS
Fifty-three patients were implanted. Thirty-six had bilateral dEMG assessments, 18 had continuous overnight readings with pulse oximetry, and 19 had serial analysis. Several findings revealed an alteration in the central respiratory drive including central apnea, hypoventilation, and hypercarbia. The electrodes showed unilateral dysfunction and demonstrated noninvasive ventilation suppression of diaphragm activity. DP can be used for serial monitoring, to decrease hypercarbia, improve sleep, and decrease atrophy.


CONCLUSIONS
Multiple abnormalities of respiratory control can be seen in amyotrophic lateral sclerosis patients using dEMG through therapeutic DP electrodes. DP is used to overcome instability of respiratory control when there are intact diaphragm motor units leading to improved survival.
Psychological treatment for insomnia in the management of long-term hypnotic drug use: a pragmatic randomised controlled trial.

OBJECTIVE
To evaluate the clinical and cost impact of providing cognitive behaviour therapy (CBT) for insomnia (comprising sleep hygiene, stimulus control, relaxation and cognitive therapy components) to long-term hypnotic drug users in general practice.


DESIGN
A pragmatic randomised controlled trial with two treatment arms (a CBT treated 'sleep clinic' group, and a 'no additional treatment' control group), with post-treatment assessments commencing at 3 and 6 months.


SETTING
Twenty-three general practices in Sheffield, UK.


PARTICIPANTS
Two hundred and nine serially referred patients aged 31-92 years with chronic sleep problems who had been using hypnotic drugs for at least 1 month (mean duration = 13.4 years).


RESULTS
At 3- and 6-month follow-ups patients treated with CBT reported significant reductions in sleep latency, significant improvements in sleep efficiency, and significant reductions in the frequency of hypnotic drug use (all P<0.01). Among CBT treated patients SF-36 scores showed significant improvements in vitality at 3 months (P<0.01). Older age presented no barrier to successful treatment outcomes. The total cost of service provision was 154.40 per patient, with a mean incremental cost per quality-adjusted life-year of 3416 (at 6 months). However, there was evidence of longer term cost offsets owing to reductions in sleeping tablet use and reduced utilisation of primary care services.


CONCLUSIONS
In routine general practice settings, psychological treatments for insomnia can improve sleep quality and reduce hypnotic consumption at a favourable cost among long-term hypnotic users with chronic sleep difficulties.
Current trends in hardware verification and automated theorem proving

1 Correctness Properties of the Viper Block Model: The Second Level.- 2 Formal Verification of the Sobel Image Processing Chip.- 3 Specification-Driven Design of Custom Hardware in HOP.- 4 Formal Verification of a Microprocessor Using Equational Techniques.- 5 OBJ as a Theorem Prover with Applications to Hardware Verification.- 6 Formal Verification in m-EVES.- 7 The Interactive Proof Editor: An Experiment in Interactive Theorem Proving.- 8 An Overview of the Edinburgh Logical Framework.- 9 Automating Recursive Type Definitions in Higher Order Logic.- 10 Mechanizing Programming Logics in Higher Order Logic.- 11 Automated Theorem Proving for Analysis and Synthesis of Computations.- 12 What Do Computer Architects Design Anyway?.
The diagnosis and incidence of allergic fungal sinusitis.

OBJECTIVE
To reevaluate the current criteria for diagnosing allergic fungal sinusitis (AFS) and determine the incidence of AFS in patients with chronic rhinosinusitis (CRS).


METHODS
This prospective study evaluated the incidence of AFS in 210 consecutive patients with CRS with or without polyposis, of whom 101 were treated surgically. Collecting and culturing fungi from nasal mucus require special handling, and novel methods are described. Surgical specimen handling emphasizes histologic examination to visualize fungi and eosinophils in the mucin. The value of allergy testing in the diagnosis of AFS is examined.


RESULTS
Fungal cultures of nasal secretions were positive in 202 (96%) of 210 consecutive CRS patients. Allergic mucin was found in 97 (96%) of 101 consecutive surgical cases of CRS. Allergic fungal sinusitis was diagnosed in 94 (93%) of 101 consecutive surgical cases with CRS, based on histopathologic findings and culture results. Immunoglobulin E-mediated hypersensitivity to fungal allergens was not evident in the majority of AFS patients.


CONCLUSION
The data presented indicate that the diagnostic criteria for AFS are present in the majority of patients with CRS with or without polyposis. Since the presence of eosinophils in the allergic mucin, and not a type I hypersensitivity, is likely the common denominator in the pathophysiology of AFS, we propose a change in terminology from AFS to eosinophilic fungal rhinosinusitis.
Mining LMS data to develop an "early warning system" for educators: A proof of concept

Earlier studies have suggested that higher education institutions could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk students and allow for more timely pedagogical interventions. This paper confirms and extends this proposition by providing data from an international research project investigating which student online activities accurately predict academic achievement. Analysis of LMS tracking data from a Blackboard Vista-supported course identified 15 variables demonstrating a significant simple correlation with student final grade. Regression modelling generated a best-fit predictive model for this course which incorporates key variables such as total number of discussion messages posted, total number of mail messages sent, and total number of assessments completed and which explains more than 30% of the variation in student final grade. Logistic modelling demonstrated the predictive power of this model, which correctly identified 81% of students who achieved a failing grade. Moreover, network analysis of course discussion forums afforded insight into the development of the student learning community by identifying disconnected students, patterns of student-to-student communication, and instructor positioning within the network. This study affirms that pedagogically meaningful information can be extracted from LMS-generated student tracking data, and discusses how these findings are informing the development of a customizable dashboardlike reporting tool for educators that will extract and visualize real-time data on student engagement and likelihood of success. 2009 Elsevier Ltd. All rights reserved.
A decision tree to assess short-term mortality after an emergency department visit for an exacerbation of COPD: a cohort study

BACKGROUND
Creating an easy-to-use instrument to identify predictors of short-term (30/60-day) mortality after an exacerbation of chronic obstructive pulmonary disease (eCOPD) could help clinicians choose specific measures of medical care to decrease mortality in these patients. The objective of this study was to develop and validate a classification and regression tree (CART) to predict short term mortality among patients evaluated in an emergency department (ED) for an eCOPD.


METHODS
We conducted a prospective cohort study including participants from 16 hospitals in Spain. COPD patients with an exacerbation attending the emergency department (ED) of any of the hospitals between June 2008 and September 2010 were recruited. Patients were randomly divided into derivation (50%) and validation samples (50%). A CART based on a recursive partitioning algorithm was created in the derivation sample and applied to the validation sample.


RESULTS
Two thousand four hundred eighty-seven patients, 1252 patients in the derivation sample and 1235 in the validation sample, were enrolled in the study. Based on the results of the univariate analysis, five variables (baseline dyspnea, cardiac disease, the presence of paradoxical breathing or use of accessory inspiratory muscles, age, and Glasgow Coma Scale score) were used to build the CART. Mortality rates 30 days after discharge ranged from 0% to 55% in the five CART classes. The lowest mortality rate was for the branch composed of low baseline dyspnea and lack of cardiac disease. The highest mortality rate was in the branch with the highest baseline dyspnea level, use of accessory inspiratory muscles or paradoxical breathing upon ED arrival, and Glasgow score <15. The area under the receiver-operating curve (AUC) in the derivation sample was 0.835 (95% CI: 0.783, 0.888) and 0.794 (95% CI: 0.723, 0.865) in the validation sample. CART was improved to predict 60-days mortality risk by adding the Charlson Comorbidity Index, reaching an AUC in the derivation sample of 0.817 (95% CI: 0.776, 0.859) and 0.770 (95% CI: 0.716, 0.823) in the validation sample.


CONCLUSIONS
We identified several easy-to-determine variables that allow clinicians to classify eCOPD patients by short term mortality risk, which can provide useful information for establishing appropriate clinical care.


TRIAL REGISTRATION
NCT02434536.
Level of Awareness, Perception and Screening Behavior Regarding Prostate Cancer Among Men in a Rural Community of Ikenne Local Government Area, Nigeria

The objective of the study was to measure the level of awareness, specific knowledge, perception and screening behavior of prostate cancer among males in a rural community of the Ikenne local government area of south-western Nigeria. The study was a cross-sectional design utilizing a pre-tested 36-item questionnaire (Cronbach’s alpha of 0.62) to collect information about knowledge regarding prostate cancer, perceived susceptibility and seriousness, perceived benefits of screening and screening behavior among men in a rural community in south-western Nigeria. Three hundred and ninety eight participants were enrolled for the study by systematic random selection of men in the community of the Ikenne local government area. Results indicated that the mean age of participants was 44.24 (standard error of mean (SEM) 0.47) years. Knowledge about prostate cancer as an important disease in men measured on a 12-point scale recorded a mean score of 4.97 (SEM 0.15) and perception of prostate cancer considered in three sub-domains of susceptibility, seriousness and benefit, measured on a 30-point scale, similarly recorded a mean score of 17.65 (SEM 0.18); while screening behavior, measured on a 11-point scale, showed that participants in the study recorded a mean scored of 2.40 (SEM 0.071). Furthermore, 156 (39.2%) of the respondents reported having heard about prostate cancer while 377 (94.7%) had heard of breast cancer as a condition affecting women. The findings suggest that level of awareness about prostate cancer among men in this study was low while their level of perception was just above average and screening behavior was very low. Again, perception variables positively and significantly correlated with screening behaviour among the participants. We conclude that in order to stimulate regular screening among men, there should be an aggressive health promotion intervention designed to increase awareness and to correct impressions about prostate cancer in the community.