Miracle-dz/UniFAR-FTUs huggingface dataset:
Estrogen Receptor, Progesterone Receptor and Glucocorticoid Receptor Expression in Normal Myometrium and in Leomyoma

Uterine leiomyoma is the most common benign smooth muscle tumour that originates from the myometrium. Ovarian sex steroids are responsible for the growth of such tumours, though estrogen and progesterone are not the only modulators involved in the tumourigenesis and growth of leiomyomata. The activation of glucocorticoid receptor (GR) can induce the expression and activity of estrogen sulfotransferase, an enzyme that generally inactivates estrogen, because of the effects of sulfonated estrogen fails to activate the estrogen receptor, it is of interest to know whether GR is present or not in leiomyomata and in the normal myometrium. The aim of this study is to analyse the expression of GR in leiemyoma and myometrium. Method: The immunohistochemistry of 47 patients was tested to find the expression status of estrogen receptor (ER), progesterone receptor (PR) and GR in leiemyoma and normal myometrium. Results: GR, ER and PR were expressed in both normal myometrium and in leiomyoma. There was a significant difference in the expression of GR found in normal myometrium compared with those expressed in leiomyoma (Allred Score, 256±29 versus 91 ±23). The GR expressed was significantly lower than the ER and PR expressed in leiomyoma. Conclusion: This presence of GR in the normal myometrium and in myomas is interesting and could be used for therapeutic action. But, first, the roles of G on myomas have to be understand. Journal of Gynecology & Reproductive Medicine Frederic Buxant1*, Sven Saussez2 and Jean Christophe Noël3 *Corresponding author Dr Frederic Buxant. Department of Gynecology. Iris South Hospital. Brussels. Belgium. Rue Jean Paquot 63. 1050 Bruxelles. AD Andorra. E-mail: fbuxant@his-izz.be. Submitted: 27 Sep 2017; Accepted: 07 Oct 2017; Published: 15 Oct 2017
A Novel QB Leach Based Heterogeneous Wireless Sensor Network Using IOT

- Heterogeneous wireless networks (HWNs) provide flexible and diversified wireless network access by integrating cellular networks, wireless LANs, and ad hoc networks with the Internet. Heterogeneous wireless sensor network is a network connecting different applications and protocols. The smart sensor nodes are used in applications like medical health care systems, industrial monitoring,environmental/earth sensing, air pollution monitoring etc. But these nodes are energy constraint devices. Efﬁcient clustering and appropriate cluster head selection schemes are necessary to improve energy saving of sensor nodes. In this paper, dynamic cluster head selection method (DCHSM) is used where CHs are selected in two phases and network coding algorithm is implemented. Initially, QB-LEACH based Clustering is used to divide the monitoring area in polygonal shaped clusters. Then, CH election is performed in two phases. First class of CH is elected based on perceived probability and the second class is elected on the based on the residual energy estimation. Energy consumption is reduced using this algorithm. Simulation analysis show that dynamic cluster head selection methodout performs the conventional methods in terms of network lifetime.
The Development of a Microcontroller Based Low- Cost Heart Rate Counter for Health Care Systems

The heart rate is one of the significant physiological parameters of the human cardiovascular system. Heart rate is the number of times the heart beats per minute. Heart rate data reflects various physiological states such as biological workload, stress at work and concentration on tasks, drowsiness and the active state of the autonomic nervous system. Human cardiac dynamics are driven by the complex nonlinear interactions of two competing forces: sympathetic regulation increases and parasympathetic regulation decreases the heart rate. Thus, monitoring of heart rate plays a significant role in providing the status of cardiovascular system and clinically correlated information to medical professionals. Heart rate measurement is also regarded as an essential parameter in patient care monitoring system. Heart rate can be measured either by the ECG waveform or by sensing the pulse the rhythmic expansion and contraction of an artery as blood is forced through it by the regular contractions of the heart. The pulse can be felt from those areas where the artery is close to the skin. This paper highlights on the design of a microcontroller (PIC series) based heart rate counter that is able to capture the pulse from finger tip by sensing the change in blood volume. The heart rates of fifteen healthy normal subjects (students of age 21-22 yrs.) both in relaxed and excited states were measured using the designed device and a standard heart rate measuring device. The outputs of the measured device were satisfactory. Also, the designed device, being noninvasive one, can easily find its place in health care monitoring system. Keywords— Heart rate measurement, cardiovascular system, patient care monitoring, microcontroller, health care systems
Tracking and Recognition: A Unified Approach on Tracking and Recognition

— This paper proposes a unified approach on tracking and recognition . Object tracking is done at low level and recognition is done at high level. Traditional tracking methods give importance to low level image correspondences between frames. High level image correspondences are used for reliable tracking. Online and Offline models are used for both tracking and recognition which is done simultaneously. Thus high level offline model is combined with low level online model to increase the tracking performance. Onine model used for tracking is given to the video based recognition and at same time offline model plays important role to recognize the category of the object. This method is useful to handle difficult scenarios like abrupt change, background clutter, pose variations, occlusion and morphable objects. This is based on study of different IEEE papers
CLUSTERING HIGH DIMENSIONAL COMBINING HUBNESS AND KERNEL MAPPING

: Clustering high dimensional data becomes challenging due to the increasing sparsity of such data. One of the inherent properties of high dimensional data is hubness phenomenon, which is used for clustering such data. Hubness is the tendency of high-dimensional data to contain points (hubs) that occurs frequently in k-nearest neighbor lists of other data points. The k-nearest-neighbor lists are used to measure the hubness score of each data point. The simple hub based clustering algorithms detect only hyperspherical clusters in the high dimensional dataset. But the real time high dimensional dataset contains more number of arbitrary shaped clusters. To improve the performance of clustering, a new algorithm is proposed which is based on the combination of kernel mapping and hubness phenomenon. The proposed algorithm detects arbitrary shaped clusters in the dataset and also improves the performance of clustering by reducing the intra-cluster distance and maximizing the inter-cluster distance thus improving the cluster quality.
Improved Underwater Image Enhancement using L*A*B on CLAHE and Gradient based Smoothing

The underwater images usually suffer from non-uniform lighting, low contrast, blur and diminished color. This paper proposed an image based preprocessing technique to enhance the quality of underwater images. The mixed CLAHE has neglected the use of L*A*B color space to enhance the image in efficient manner. Also the problem of the uneven illuminate is also neglected by the most of the researchers. To overcome the problems of existing technique a new L*A*B color space and CLAHE based image enhancement algorithm is proposed in this research work. To overcome the issue of the uneven illuminate issue in the output image of the CLAHE output has been further removed using the use of image gradient based smoothing. The main scope of the proposed algorithm is to improve the accuracy of the underwater image enhancement techniques. Different type of the images will be taken for experimental purposes to estimate the effectiveness of the image enhancement techniques. Different kind of image quality metrics has been used to find the significant improvement of the proposed technique over the available techniques. The comparative analysis has shown the significant improvement of the proposed technique over the mixed CLAHE.
A Review: Wireless Sensor Networks and Its Application, Platforms, Standards and Tools

: Wireless Sensor Networks (WSN) is formed by a large number of networked sensing nodes. The main goal of a WSN is to produce meaningful information from raw local data collected by individual sensors. The low cost, flexibility, fault tolerance, high sensing fidelity, and rapid deployment characteristics of sensor networks create many new and exciting application areas for remote sensing. In this paper, a survey on Wireless Sensor Networks (WSN) and their architecture, applications, standards in WSN are carried out. Further, the test beds used in WSNs and network tools are surveyed.
On using Adaptive Hybrid Intelligent Systems in PM10 Prediction

A comparative study based on producing two intelligence systems applied to PM 10 prediction was presented in this work. Adaptive Network –based Fuzzy Inference System (ANFIS) used in build a system has three weather elements as input variables (Wind Speed, Wind Direction and Temperature) and the PM 10 as output variable for PM 10 nowcast model. Another technique used ANFIS in prediction of chaotic time series to get 6 hours forecast for PM 10 from the present data. For developing the models, thirteen years hourly data for Mansoria station coordinates 29° 300 ′ 0 ″ N, 45° 45 ′ 0 ″ E from 1995to 2007 has been used. Different models employing a different training and testing data sets had been studied. The criteria of performance evaluation are calculated for estimating and comparing the performances of the two techniques. The results show that the two presented models success tools in PM 10 prediction with acceptable root mean square error (RMSE); the model built on using ANFIS for chaotic time series prediction has smaller error compared with the adaptive network fuzzy inference system.
Administration of Medication

a. The Health Contacts have completed a formal instructional class, offered by Polk County School Board Health Services Registered Nurses (PCSB Health Services RNs), on medication administration, including practical training in the recommended procedures for administration of medication as outlined in “Medication Administration at School: A Handbook for Polk County School Personnel” (Appendix). This handbook will be provided to each individual who receives this training. Upon completion of the training, the Health Contacts will receive Acknowledgement of Training in Medication Administration at School.
EXPERIMENTAL AND FEA OF PARTICLE IMPACT EROSION FOR POLYMER COMPOSITES

The main aim of this paper is to study solid particle erosion of PMCs such as vinylester/glass and vinylester/carbon using orthogonal array experimental approach. The controllable parameters selected for the study included impingement velocity (50, 75 and 100 m/sec), impact angle (45o, 60o and 75o), erodent size (200, 300 and 400μm) and standoff-distance (120, 180, 240 mm). S/N ratio analysis was performed to find the most influencing factor. The laminates with [0/90] and [-55/+55] stacking sequence were characterized for erosion damage. Amongst the four parameters selected for the study, impingement velocity had the greatest influence on the erosion rate followed by impact angle. The other two parameters showed marginal significance. Erosion rates were lower at lower values of S/N ratio. Also, erosion wear rates of laminate having stacking sequence [-55/+55] is lower than that of [0/90] stacking sequence.
Cognizable Recommendation System using Spatial Ratings with Collaborative Filter

- Traditional recommender systems do not consider spatial properties of users nor items; proposed system cognizable recommender system is based on locations of item and user also referred as location aware recommender system that exploits community opinions or ratings to produce suggestions for other items also referred as recommendations. CRS (Cognizable Recommender System) uses recent transactions to avoid excessive processing of data which is old and not interested by user. It also supports taxonomy of three novel classes of location-based ratings, namely, non-spatial ratings for spatial items, spatial ratings for non-spatial items, and spatial ratings for spatial items. The technique that is user partitioning exploits user rating locations, influences recommendations with ratings spatially close to querying users. This will help to achieve maximum system scalability without giving-up quality of recommendations. CRS exploits location of item using a technique referred as travel penalty that helps recommendation candidates closer in travel distance to querying users in a way that helps to avoid exhaustive access to all spatial items. These three techniques can be applied separately, or together, depending on the type of location-based rating available. CRS works to provide recommendations which are more accurate than existing approaches and also proven to be efficient and scalable.
DoS ATTACK DETECTION SYSTEM BASED ON MULTIVARIATE CORRELATION ANALYSIS

: In today’s world the number of online applications are increasing. With the increasing number of online applications the threat to the security of these applications is also increasing. The increasing number of cyber attacks have challenged the security of these online applications. DoS is one such type of cyber-attack which aims at making the website and the resources of the server unavailable to the intended users. In this paper we propose an anomaly based detection for both known and unknown attacks. We propose an MCA based system for accurate traffic characterization by extracting the geometrical correlation between network traffic. We also propose a triangle area based technique which enhances and speedup the MCA process. For evaluating the system KDD Cup 99 dataset is used. KDD Cup 99 dataset has 41 features out of which 10 features with maximum statistical variance are selected.
Performance Evolution of Proactive and Reactive Routing Protocols in Mobile Ad Hoc Network

-The Mobile Ad hoc Network (MANET) is an autonomous system of mobile nodes connected by wireless links. Each node operates not only as an end system, but also as a router to forward packets. The nodes are free to move about and organize themselves into a networ main classes of routing protocols are proactive, reactive and hybrid. A reactive (on a popular routing category for wireless ad hoc routing. It is a relatively new routing philosoph scalable solution to relatively large network topologies. The design follows the idea that each node tries to reduce routing overhead by sending routing packets whenever a communication is to evaluate AODV,DSR and DSDV theoretically. We have done on the simulation on AODV,DSR and DSDV and analyze the results through the simulation based on some quantitative performance metrics throughput, packet delivery ratio, end to end delay and average routing overhead improvements.
THERAPEUTIC ACTIVITY OF BEE-STINGS THERAPY IN RHEUMATOID ARTHRITIS CAUSES INFLAMMATION AND OXIDATIVE STRESS IN FEMALE PATIENTS

Here the present study aimed to evaluate the therapeutic activity of bee venom acupuncture in rheumatoid arthritis (RA) which causes inflammation and oxidative stress in female patients. 75 female patients were divided into 5 groups as control, bee venom acupuncture, rheumatoid arthritis, treated rheumatoid arthritis and rheumatoid arthritis stung with bee venom groups. Serum rheumatoid factor, erythrocyte sedimentation rate, C-reactive protein, prostaglandins E2 and F2α, lipid peroxidation, nitric oxide, glutathione and total antioxidant capacity levels were determined in all groups. Rheumatoid arthritis in female patients was resulted in a significant elevation in serum rheumatoid factor, erythrocyte sedimentation rate, C-reactive protein, prostaglandins E2 and F2α, lipid peroxidation and nitric oxide levels (p < 0.05) compared to control group. In addition, rheumatoid arthritis caused a significant reduction in serum glutathione and total antioxidant capacity levels. On the other hand, bee venom stings alleviated rheumatoid arthritis inflammation and oxidative stress effects, where all investigated parameters were statistically significant compared to rheumatoid arthritis group. Moreover, bee venom therapy was more potent than the routine treatment of rheumatoid arthritis in patients treated group. Bee venom acupuncture in RA patient may have therapeutic, anti-inflammatory and antioxidant activities.
Robust RLS Wiener Fixed-Interval Smoother in Linear Discrete-Time Stochastic Systems with Uncertain Parameters

This paper proposes the robust RLS Wiener filter and fixed-interval smoothing algorithms based on the innovation approach. As a result, the robust RLS Wiener filtering algorithm is same as the existing robust RLS Wiener filtering algorithm. The estimation accuracy of the fixed-interval smoother is compared with the robust RLS Wiener filter and the following fixed-interval smoothers. In the proposed robust RLS Wiener fixed-interval smoother, the case, where the observed value is replaced with the robust filtering estimate of the signal, is also simulated. (1) The RLS Wiener fixed-interval smoother in which the filtering estimate of the state is replaced with the robust RLS Wiener filtering estimate. (2) The RTS (Rauch-Tung-Striebel) fixed-interval smoother in which the filtering estimate of the state is replaced with the robust RLS Wiener filtering estimate. (3) The RLS Wiener fixed-interval smoother and the RLS Wiener filter. (4) The RLS Wiener fixed-interval smoother in which the filtering estimate of the state is replaced with the robust RLS Wiener filtering estimate and the observed value is replaced with the robust RLS Wiener filtering estimate of the signal. From the simulation results, the most feasible estimation technique for the fixed-interval smoothing estimate is the RLS Wiener fixed-interval smoother. Here, the robust filtering estimate is used and the observed value is replaced with the robust filtering estimate.
Relationship between Perceived Stress, Psychological Wellbeing and Emotional Competence among Baccalaureate Nursing Students: an Egyptian Sample

Nursing education is acknowledged as one of the stressful and emotionally demanding profession. Stress among nursing students is an inevitable issue that might lead to poor psychological wellbeing. Having a good emotional competence helps university nursing students to decrease their stressand enhance their psychological well-being. Aim : this study was aimed to determine the relationship between the perceived stress, psychological wellbeing and emotional competence among baccalaureate nursing students. Design : A descriptive correlational research design was used in this study. Baccalaureate nursing students who were enrolled at Psychiatric Nursing and Mental Health coursewere the study subjects. Their number amounted 135 students. Four tools were used namely; Bio-socio demographic data structured interview schedule, Ryff’s Psychological Wellbeing (RPWB) scale, The Emotional Skills/Competence questionnaire and Perceived Stress Scale (PSS). Results :A positive significant relation between the students’ psychological well-being and their Emotional competence was noted and there was a negative significant correlation between them and students’ perceived stress.It can be concluded that nursing students have high level of stress and their emotional skills/competence in addition to their psychological well-being can help them to decrease their level of stress. Implementation of emotional hygiene program, to teach the nursing students to care for their own emotions and didn’t leave them injured or untreated is recommended. Key words: Stress, Psychological well-being. Emotional competence, Nursing students. DOI : 10.7176/JHMN/68-09 Publication date : November 30th 2019
Immune-detection of Entamoeba histolytica in symptomatic and asymptomatic infection

High prevalence of intestinal amoebiasis is commonly reported by microscopy in Gaza.To determine the misdiagnosis of intestinal amoebiasis associated with microscopic examination of faces, two tests were applied: 1-a non-specific antigen Entamoeba (ELISA) to differentiate Entamoeba histolytica/Entamoeba dispar complex from other non-pathogenic intestinal amoebae.2An enzyme-linked immunosorbent assay (ELISA) for detecting antibody against E. histolytica, 53 stool and serum samples were used in this study: 32 (60.38%) samples were symptomatic (diarrheal) patients and 21 (39.62%) were asymptomatic. Data from these two immunological diagnostic tests were compared with those obtained by microscopic examination. A routine microscopic examination detected 7(13.25) prevalence of Giardia lamblia, 21(39.6%) prevalence of Entamoeba spp. 13(40.6%; X=0.578; P-value= 0.749) in symptomatic stool samples and 8(38.1%) in asymptomatic stool samples. ELISA antigen detected only 2(6.2%; X=1.364; P-value= 0.243) prevalence of E. histolytica/ dispar complex in symptomatic samples and none in asymptomatic samples. The two positive symptomatic samples by ELISA/Ag were also positive by microscopy, and no negative subject by microscopy was positive by ELISA. ELISA antibody detected 4 (7.5%; X=0.195; P-value= 0.659) prevalence of E. histolytica in all subjects: 2(6.2%) in symptomatic samples which were positive by both microscopy and ELISA antigen and 2(9.5%) in asymptomatic samples, that were negative by both microscopy and ELISA antigen. The ratio of E. histolytica/ dispar was very low (3.7%) suggesting that the vast majority of Entamoeba infections in this area were nonpathogenic. The microscopic examination is less sensitive than both ELISA/Ag and ELISA/IgG tests. [Fayez Muhammad Shaldoum, Nahla Mezeid and Adnan I. Al-Hindi Immune-detection of Entamoeba histolytica in symptomatic and asymptomatic infection. J Am Sci 2015;11(5):55-61]. (ISSN: 1545-1003). http://www.jofamericanscience. org. 8
A Novel Approach for Improving Similarity Search Using SVM Classification Algorithm

- The World Wide Web has transcended from a read-only to a read-write web. The problem of identifying important online or real life events from large textual document streams that are freely available on the World Wide Web is increasingly gaining popularity, given the flourishing of the social web. Earlier work used efficient algorithm for detecting all important events from a document stream through named entity recognition and topic modelling. In existing scenario, the learning algorithms are considered to identify and detect the unknown important topics from the disaster document. The problem found in this work is that the effective recognition of interesting named entities in previously unknown free text documents remains an open problem. The problem of finding similar literals with different meaning, or different literals that describe the same entity should be addressed. Hence to overcome all these issues the Support Vector Machine (SVM) algorithm is utilized in the current work. The SVM is used to create a model which helps to identify most random and important events from the given document. It is also used to recognize the high similarity sentences and semantic events from the specified document. The required information is retrieved based on the ranking events. The top most ranked events will identify the important event as well as produce the rich semantic meaning. Ranking SVM can be successfully applied for the task of finding and learning a similarity function for the event identification problem.