Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer-aided decision system for the clubfeet deformities

Clubfoot is one of the congenital orthopaedic abnormalities for children (Bensahel et al. 2003). A computeraided decision system (CADS) is developed in order to assess the abnormalities of the clubfeet deformities. The first version of our system was presented in the previous study with the rotational abnormalities (Dao et al. 2008). Our current system integrates a universal scoring system and a remote treatment programme of the clubfeet deformities.
Infrared active nanoprobes for bio-medical imaging based on inorganic nanocrystals

In this work we will show the results of optical imaging with a different types of inorganic nanocrystals. The list of probes including hydrophilic NaGdF4:Yb, Er, NaGdF4:Eu and PbS nanocrystals all synthesised and modified in our group. We will report on their optical porperties and discuss examples for perspective work for these types of markers.
A New Definition of Fairness for Non-Orthogonal Multiple Access

Fairness in non-orthogonal multiple access (NOMA) may be defined in different ways, taking into account the physical layer alone or also including the process of radio resource management. The latter has been frequently used in the recent literature, implying a significant advantage of this technique compared with conventional orthogonal multiple access (OMA). In this letter, we look at the fairness issue from another angle and introduce a fundamental definition of fairness, which measures the difference between the rates that can be achieved by the users and the fair rates suggested by the power distribution among them. With this definition, the fairness issue is inexistent in OMA and it appears as a NOMA-specific problem due to interference between users and the detection process. The new fairness index we advocate incorporates the power distribution of different users. Thus, in a cell with a non-uniform power distribution, fairness implies that a user with a higher power should get a higher rate, and hence, the index should measure the rate of each user by accounting for the fraction of total power allocated to it. Our index achieves its maximum value of 1 only when all users get their fair rates, and it goes to zero when one user gets all resources in the case of uniform power distribution. We provide an asymptotic analysis of fairness at high and low signal-to-noise ratio (SNR) values and give a comprehensive illustration in the two-user case.
Automated delineation of non‐small cell lung cancer: A step toward quantitative reasoning in medical decision science

Quantitative reasoning in medical decision science relies on the delineation of pathological objects. For example, evidence‐based clinical decisions regarding lung diseases require the segmentation of nodules, tumors, or cancers. Non‐small cell lung cancer (NSCLC) tends to be large sized, irregularly shaped, and grows against surrounding structures imposing challenges in the segmentation, even for expert clinicians. An automated delineation tool based on spatial analysis was developed and studied on 25 sets of computed tomography scans of NSCLC. Manual and automated delineations were compared, and the proposed method exhibited robustness in terms of the tumor size (5.32–18.24 mm), shape (spherical or irregular), contouring (lobulated, spiculated, or cavitated), localization (solitary, pleural, mediastinal, endobronchial, or tagging), and laterality (left or right lobe) with accuracy between 80% and 99%. Small discrepancies observed between the manual and automated delineations may arise from the variability in the practitioners' definitions of region of interest or imaging artifacts that reduced the tissue resolution.
Dynamic Time Window Adjustment

To improve customer satisfaction in a delivery network with uncertain travel times, we propose to communicate time window adjustments to the customers throughout the day. We refer to these updates as dynamic time window adjustments. Dynamic time window adjustments are often used in practice, but have not yet been considered in the scientific literature. We provide a general model and we present the Dynamic Time Window Adjustment Problem (DTWAP). The DTWAP is the problem of optimizing the dynamic time window adjustments to maximize the expected customer satisfaction for a given route. Instead of solving the DTWAP in a specific setting, we derive general properties and we present three different solution methods. We also introduce the simple DTWAP, which is a special case that we analyze in more detail. The use of our results is demonstrated with an illustrative example concerning attended home delivery.
A study of interpretability mechanisms for deep networks

Deep neural networks are traditionally considered to be “black-box” models where it is generally difficult to interpret a certain decision made by such models given a test instance. However, as deep learning is increasingly becoming the tool of choice in making many safetycritical and time-critical decisions such as perception for self-driving cars, the machine learning community has been extremely interested recently to build interpretation mechanisms for these so called black box deep learning models primarily to build users’ trust with the models. Many such mechanisms have been developed to explain behavior of deep models such as convolutional neural networks (CNNs) and provide visual interpretations of their classification decisions. However, there is still no consensus in the community about the specific goals and performance metrics for the interpretability mechanisms. In this thesis, we review the recent literature to arrive at a formal definition for the “Interpretability-problem” for CNNs with the help of different axioms. We observe that many recently proposed mechanisms do not adhere to the axioms of interpretability and hence not quite robust in performance. In this context, we propose a framework to test the interpretation algorithms under model perturbation and data perturbation. This framework tests the “sensitivity” of the algorithms and helps in evaluating “implementation invariance”, which are desired characteristics for any interpretability mechanism. We demonstrate our framework using two well-known algorithms namely “Saliency Maps” and “Grad-CAM” and introduce a new interpretability technique called “Forward-Backward Interpretability algorithm” that provides a systematic framework for visualizing information flow in deep networks. Finally, we also present visualization and interpretability results for an impactful scientific application involving microstructure-property mapping in material science.
Effective Approach for Extracting Noise from Digital Image and Real Time Data using Filtering Technique

Digital image are made out of pixels and we know pixel is the smallest component of a picture. Every pixel speaks to the dark level for highly contrasting photographs at a solitary point in the image, so a pixel can be spoken to by a small speck of particular shading. In an image pixel having intensity in range of 0-255 and if pixel having intensity zero it means black and having intensity 255 it means white and in between them then considered as gray level. There are various types of images and various types of noises occurred and to remove them diverse filters are available and every filter are having own advantage and disadvantages and suitable for a particular types of noised which it can remove efficiently. In our research work our main target is to fetch out salt and pepper noise. In base paper at two levels of S&P noise filter is used to denoise the image to get various parameters. But in our research work salt and pepper noise at various levels targeted and removed efficiently with parameters PSNR, MSE and IEF. Noise is random in nature and it can be mixed with image anywhere therefore diverse noise models were studied deeply. Restoration efficiency was checked by PSNR and mean square error (MSE) into considerations.
Synthesis and Characterization of Zirconia Supported Silicotungstic Acid for Ethyl Levulinate Production

A Keggin silicotungstic acid (HPS) catalyst was heterogenized by loading (10–40 wt %) over zirconia support, and the resulting catalysts were named ESZN-1 (10 wt %), ESZN-2 (20 wt %), ESZN-3 (30 wt %), and ESZN-4 (40 wt %). After that, synthesized catalysts were characterized using several tools and techniques which revealed that the Keggin structure of the parent HPS catalyst remained intact after heterogenization. Eventually, synthesized catalyst performance evaluation tests were performed for the production of ethyl levulinate from biomass-derived levulinic acid under microwave heating irradiations. Under optimum operating conditions, more than 90% LA conversion with 100% EL selectivity was obtained at a 110 °C temperature in 30 min in the presence of 100 mg of ESZN-4 in a solution containing levulinic acid and ethanol in a 1:43 ratio. A kinetic study on LA conversion in the presence of the ESZN-4 catalyst revealed a pseudo-first-order mechanism for ethyl levulinate synthesis.
Reading and Misreading 8-oxoguanine, a Paradigmatic Ambiguous Nucleobase

7,8-Dihydro-8-oxoguanine (oxoG) is the most abundant oxidative DNA lesion with dual coding properties. It forms both Watson–Crick (anti)oxoG:(anti)C and Hoogsteen (syn)oxoG:(anti)A base pairs without a significant distortion of a B-DNA helix. DNA polymerases bypass oxoG but the accuracy of nucleotide incorporation opposite the lesion varies depending on the polymerase-specific interactions with the templating oxoG and incoming nucleotides. High-fidelity replicative DNA polymerases read oxoG as a cognate base for A while treating oxoG:C as a mismatch. The mutagenic effects of oxoG in the cell are alleviated by specific systems for DNA repair and nucleotide pool sanitization, preventing mutagenesis from both direct DNA oxidation and oxodGMP incorporation. DNA translesion synthesis could provide an additional protective mechanism against oxoG mutagenesis in cells. Several human DNA polymerases of the X- and Y-families efficiently and accurately incorporate nucleotides opposite oxoG. In this review, we address the mutagenic potential of oxoG in cells and discuss the structural basis for oxoG bypass by different DNA polymerases and the mechanisms of the recognition of oxoG by DNA glycosylases and dNTP hydrolases.
“Numerical determination of Chern numbers and critical exponents for Anderson localization in tight-binding and related models”

Author(s): Talkington, Spenser | Advisor(s): Roy, Rahul | Abstract: Computational modules were developed to numerically determine electronic band structures, berry curvatures, Chern numbers, localization lengths, and critical exponents for tight- binding and related models. These modules were applied to a variety tight-binding and related models including the Hofstadter Model, Anderson Model, and the Chalker- Coddington model. When analytic solutions were available, numeric energy bands agreed with analytic solutions to within machine precision. In addition Chern numbers for well known models were reproduced, and localization lengths and critical exponents agreed with values in the literature.
Inverted encoding models estimate sensible channel responses for sensible models

In a commentary published in eNeuro, Gardner & Liu (2019) discuss the role of model specification in interpreting the output of complex models of neural data. As a case study, they suggest that one variant of such analyses, the inverted encoding model (IEM) analysis framework, should not be used to assay properties of “stimulus representations” because the ability to apply linear transformations at various stages of the analysis procedure renders results ‘arbitrary’. As we discuss, the specification of all models is arbitrary to the extent that an experimenter makes choices based on current knowledge of the model system. However, the results derived from any given model, such as the reconstructed channel response profiles obtained from an IEM analysis, are uniquely defined and are arbitrary only in the sense that changes in the model can predictably change results. Moreover, with knowledge of the model used for IEM analyses, the results remain informative as comparisons between reconstructed channel response profiles across task conditions using a fixed encoding model – the most common use of the IEM technique – can generally capture changes in population-level representation magnitude across linear transformations. Thus, changes in the magnitude of the response profiles across conditions are preserved, even across unprincipled linear transforms. IEM-based channel response profiles should therefore not be considered arbitrary when the model is clearly specified and guided by our best understanding of neural population representations in the brain regions being analyzed. Intuitions derived from this case study are important to consider when interpreting results from all model-based analyses, which are similarly contingent upon the specification of the models used.
Implementation Of Classroom Attendance Monitoring Using Face Detection And Raspberry-Pi

Attendance for the students is an important task in class. Thus the drawbacks arise as it consumes time, needs manual work and the most important, information or the attendance can be manipulated. Also, there are chances of students not responding to their attendance and later claiming for the attendance. So, we proposed an automation of attendance system by using face recognition. The primary identification is Face for any human. This paper describes the method of detecting and recognizing the face in real-time by utilizing Raspberry Pi. This project describes an efficient algorithm using open source image processing framework known as OpenCV. Our approach has five modules – Face Detection, Face Preprocessing, Face Training, Face Recognition and Attendance Database. The face database is collected to recognize the faces of the students. Initially, The system is trained with the student`s faces which is collectively called student database. This project can be used for many other applications where face recognition can be used for authentication.
Performance of the PROMIS After Operative Interventions for Shoulder Instability

Background: The Patient-Reported Outcomes Measurement Information System (PROMIS) was developed to capture patient-reported outcomes (PROs) in an efficient manner. Few studies have assessed this instrument postoperatively. Purpose: To compare the PROMIS Physical Function computer adaptive test (PROMIS PF CAT) and Upper Extremity (PROMIS UE) item bank to other previously validated PRO instruments and to evaluate ceiling and floor effects and construct validity responsiveness in patients who underwent operative interventions for shoulder instability. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A total of 72 patients who underwent operative interventions for shoulder instability completed the American Shoulder and Elbow Surgeons (ASES) assessment form, Marx shoulder activity scale (Marx), 36-Item Short Form Health Survey physical function (SF-36 PF) and general health (SF-36 GH), Western Ontario Shoulder Instability Index (WOSI), PROMIS PF CAT, and PROMIS UE before surgery and then at 6 weeks and 6 months postoperatively. Correlation coefficients were calculated among these tools. The effect size of change was also calculated for each tool at each time point. A total of 91 patients who had also undergone surgery for shoulder instability completed these PRO instruments 2 years postoperatively. The percentage of patients hitting the ceiling and floor effects of each of the PRO instruments was calculated at all time points. Results: The PROMIS PF CAT demonstrated excellent-good correlation with the SF-36 PF at all postoperative time points (0.61 at 6 weeks, 0.68 at 6 months, and 0.64 at 2 years; P < .01 for all). The PROMIS UE showed excellent correlation with the ASES at 6 weeks postoperatively (0.73, P < .01). Both the PROMIS PF CAT and PROMIS UE demonstrated the ability to detect change after surgical interventions with a medium to large effect size. The PROMIS UE demonstrated a ceiling effect at 6 months (68.1%) and 2 years (67.0%) postoperatively. The PROMIS PF CAT demonstrated no ceiling effect at any time point. Conclusion: The PROMIS PF CAT demonstrated good to excellent correlation with other previously validated PRO instruments that assess physical function in patients with shoulder instability postoperatively. The PROMIS UE demonstrated good correlation with other PRO tools but had a significant ceiling effect and is not recommended for this patient population. Both tools demonstrated an ability to detect change after surgical interventions with a good effect size.
Hypoglycemic effect of black tea extract attenuates carbohydrate metabolic enzymes in streptozotocin-induced diabetic rats

Aim: To evaluate the beneficial effects of black tea extract (BTE) on key enzymes of carbohydrate metabolism in streptozotocin -induced diabetic rats. Materials and Methods: Diabetes was induced in male albino Wistar rats by intraperitoneal administration of STZ (40 mg/kg b.wt). Results and Discussion: BTE was administered to diabetic rats at a dose of 100 mg/kg b.wt for 30 days significantly decreased the level of glucose, glycated hemoglobin and increased the levels of insulin. In addition, Black tea administration reinstated the altered carbohydrate metabolizing enzymes to near normal levels. These findings suggest that the administration of black tea extract was potentially ameliorated the carbohydrate metabolizing enzymes in addition to its antihyperglycemic effect. Conclusion: The effect produced by black tea extract on various parameters was comparable to that of glibenclamide- an antidiabetic drug used as a reference drug.
Proportional–integral-derivative controller with inlet derivative filter fine-tuning of a double-pendulum gantry crane system by a multi-objective genetic algorithm

ABSTRACT Overhead cranes are widely used in industry to move extremely heavy and bulky loads through the overhead space in a facility instead of through aisles or on the floor. Unfortunately, the natural sway of a crane's payload is a huge safety problem which needs more research by control engineers, as the sway of the payload affects both the safety and productivity of a factory. This article introduces an optimized mathematical model of a double-pendulum overhead crane with control schemes: scheme 1 uses three proportional -integral-derivative (PID) controllers with inlet derivative filters; scheme 2 uses one PID controller for accurate trolley positioning and two anti-swing proportional -derivative controllers to eliminate hooks and payload sway, all with inlet derivative filters. The controllers include up to nine gains which are tuned using the multi-objective non-dominated sorting genetic algorithm-II with five fitness functions and weighted function to choose a suitable solution from Pareto solutions.
Intracohort Trends in Ethnic Earnings Gaps: The Role of Education

This study demonstrates that studying ethnic/racial inequality on the basis of cross-sectional data conceals how such inequality might unfold over the life course. Moving beyond a snapshot perspective, we ask, Do Israel’s Jewish ethnic groups differ in their long-term earnings trajectories? Analyzing nearly 20 years of registered earnings data, the authors find that for the same cohort (25- to 32-year-old Jews in 1995), the ethnic earnings gap has widened over these years. This trend, we demonstrate, is explained largely by increasing wage premiums for college degree, even when these premiums are ethnicity blind. That is, ethnic inequality in educational attainment is translated to increasing ethnic earnings inequality over the life course. This pattern cannot be detected in previous research in Israel, which relied on the snapshot perspective on the basis of cross-sectional data. The consequences of these findings for changes in inequality in divided societies are discussed.
A Waterway Monitoring Method of Unmanned Surface Vehicle Based on Deep Learning

A waterway monitoring method of unmanned surface vehicle based on deep learning is proposed to help identify ship accidents in real time. Compared with the traditional waterway monitoring method, the use of unmanned surface vehicle is more flexible and the recognition method of deep learning is more real-time. First, we build a dataset of ship accidents, and then use this dataset to train the Faster R-CNN network, we optimize the RPN in the fast R-CNN framework for the problem of missed detection at the same time, and finally obtain the target detection model. Experiments show that the method can effectively improve the efficiency of the waterway monitoring, greatly reduce the labour cost, and facilitate the management personnel to grasp the waterway situation in real time.
Towards bioinspired in vitro models of intestinal mucus

Intestinal mucus is a biological structure that acts as a barrier between the external environment and the epithelium. It actively selects nutrient and drug intake, regulates the symbiosis with the intestinal microbiota and keeps the epithelium protected from the attack of pathogens. All these functions are closely connected to the chemical and structural complexity of this biological material, on which its viscoelastic and diffusive properties depend. Many models have been proposed to replicate these characteristics using glycoproteins in solution and possibly the addition of other mucus components, such as lipids and other proteins. In the field of mucus modelling, an overall view of the mucus as a material, having its own viscous, rheological and diffusive characteristics, has been undersized with respect to a pure biological-functional analysis. In this review, we propose a description of the mucus as a biomaterial, including a presentation of its chemical and structural complexity, and of its main viscoelastic-diffusive properties, in order to provide a synthesis of the characteristics necessary for the engineering of more advanced mucus models.
Comparison of Depression and Anxiety Disorders among Native and Non-native Students of Islamic Azad University of Medical Sciences in Tehran

Introduction: Depression is one of the most common psychological problems which is not limited to a specific place or time, and involves people of different social classes and age groups. Anxiety may even cause physical illnesses such as blood pressure and cardiovascular disease, as well as psychiatric disorders such as depression. The aim of this study was to compare the level of depression and anxiety in native and non-native medical students. Methods: In this cross-sectional study, 21,800 students of the faculties of Islamic Azad University of medical sciences of Tehran were assessed for eligibility. A number of 280 students were selected. The Beck depression and anxiety questionnaires were used to prepare the items of the study. Results: Among the sample of 280 students, 184 students were female, of which 160 were native and 24 were non-native. Also 98 students were male, of which 38 were native and 60 were non-native students.Also 116 students (41%) were in normal condition in terms of depression degree, 103 students (36%) had mild depression, 48 students (17%) had moderate depression, and 13 students (4%) suffered from major (severe) depression.Also, the results of the anxiety test indicated that 121 students (43%) were in the healthy condition, 100 students (35%) had mild anxiety, 43 students (15%) had moderate anxiety, and 16 students (5%) suffered from severe anxiety. Conclusion: Findings of the present study show that there is no significant difference between depression and anxiety scores between native and non-native students.