Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Team-Work and Supportive Working Environment of the Admission and Transfer Processes in Rural Mental Healthcare Services Using Lean Philosophy

Aim: To improve team work through joint development of clinical processes between regional, rural community mental health teams and rural hospitals using Lean philosophy. Background: There was lack of clarity of the roles and responsibilities of staff in the admission of mental health consumers in rural hospitals and in their transfer to regional acute mental health inpatient unit. This resulted in cross departmental animosity in Central Queensland health services which further led to compromised provision of consumer care. The impetus of this paper; therefore, is to present the application of Lean to the admission process in rural mental healthcare services as well as transfer to the regional acute inpatient unit in order to enhance teamwork and supportive working environment to improve consumer outcomes. Methods: The study utilized a qualitative approach where an online survey monkey was used to capture an in-depth understanding of the perceptions and experiences of the hospital and mental health staff pre- and post-implementation of the new processes. Key results: The participants reported improved collaborative working among the staff which resulted in more effective care provision for mental health consumers. Conclusion: Lean philosophy was successfully applied in creating effective administrative and clinical processes in rural and remote mental health services. Joint development of procedures and processes enhanced adherence to standards, improved teamwork and, consequently, ameliorated consumer outcomes.
Simulation of Forecasting Performance Comparison of a Hybrid Model Integrated By Binomial Smoothing and Bayesian Model Averaging Techniques

: Inthis study, a hybrid JPSN-AR model is proposed based on binomial smoothing (BS) and Bayesian model averaging (BMA)techniques. The aim is to determine the combination technique that produced the best forecasting performance for the proposed hybrid model. The forecasting performance measurements employed to ascertain the foregoing assertion are the mean absolute percentage error (MAPE) and the root mean square error (RMSE). The results revealed that the best performance of the proposed hybrid model was achieved through the combination of the Jordan pi-sigma neural (JPSN) network model and an autoregressive(AR) time series model by the Bayesian model averaging (BMA) technique. Although the combination that produced the hybrid model made by the binomial smoothing technique also produced good forecasting performance by the hybrid model, but the combination technique with the Bayesian model averaging(BMA) technique puts the proposed hybrid model at its best. Simulations in this study are made possible by using MATLABsoftware version 8.03
Comparison between biodegradable polymer drug eluting stents and durable polymer drug eluting stents on the outcome of percutaneous coronary intervention in patients of Acute Coronary Syndrome

Introduction: Patients with NSTEACS have a wide spectrum of risk of death and cardiac ischemic events. Biodegradable polymer-based DES have been established as a safe and effective alternative to durable polymer-based stent platforms as evidenced in several randomized clinical trials. Aim of the study: to assess of the role of biodegradable polymer DES in decreasing the incidence of major adverse cardiac events in comparison with durable polymer DES in patients with NSTEACS. Methods: 121 patients enrolled in the study divided into; group A: biodegradable polymer DES and group B: durable polymer DES. Patients presented with NSTEACS having TIMI risk score ≥ 3 were subjected to early invasive strategy. One year views after the index procedure coronary angiography were evaluated by quantitative coronary angiography (QCA) with edge detection method used for evaluation of coronary lesion in index and follow up procedures. Minimal luminal diameter (MLD), reference vessel diameter (RVD), percent diameter stenosis (%DS), acute gain, late loss and late loss index were estimated. Results: There were 155 targeted segments in 146 vessels in our patients with stenosis percentage ≥60%. TIMI 0 was achieved in 0.82% of treated segments, TIMI 1 was achieved in 1.7% of treated segments, TIMI 2 was achieved in 5.8% of treated segments, and TIMI 3 was achieved in 92.6% of treated segments (p-value NS). For biodegradable polymer DES group, 71 stents have been implanted. For durable polymer DES, 86 stents have been implanted. Restoring TIMI flow III after the PCI procedure was achieved in 92.6% of treated segments. Clinical success was achieved in 93.4% of cases. Thirty nine patients had repeated coronary angiography within 1 year (15 biodegradable DES and 24 durable DES). Eighteen patients experienced angina pains for which they were examined thoroughly (8 biodegradable DES and 10 durable DES). Only five patients needed TLR (2 biodegradable DES & 3 durable polymer DES). Three of them had a myocardial infarction with documented angiographic evidence of significant in-stent restenosis (1 biodegradable polymer DES & 2 durable polymer DES) (p-value NS). Conclusion: Biodegradable polymer and durable polymer DES are associated with similar clinical outcomes at 1 year. Biodegradable polymer DES is a safe alternative to durable polymer DES. Kew word: Non-ST segment elevation acute coronary syndrome, biodegradable polymer DES, major adverse cardiac events
Assessment of the Health-Related Quality of Life Impact of EUFLEXXA® (1% Sodium Hyaluronate) Using Short Form 36 (SF-36) Data Collected in aRandomized Clinical Trial Evaluating Treatment of Osteoarthritis Knee Pain

Background: Outcome measurements of clinical trials such as the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) have provided disease specific data on lower extremity osteoarthritis (OA). The impact of the disease, condition, or treatment on the dimensions of functioning and well-being has been supplemented by the use of general health-related quality of life (HRQoL) instruments.
Effects of Horticultural Therapy for the Korean Elderly : A Systematic Literature Review

Purpose: This study was conducted to ascertain the effectiveness of horticultural therapy by analyzing researches on horticultural therapy applied to elderly Koreans. Methods: We evaluated 401 research papers including dissertations on horticultural therapy applied to elderly Koreans from Jan. 2000 to Feb. 2016. We reviewed the appropriate 12 papers among them for the final analysis. Results: Horticultural therapy had physiological and psychosocial effects. First of all, it showed the physiological effects of reducing stress hormones. Outdoor horticultural therapy improved the gross motor movements of the body. Indoor horticulture therapy improved delicate cognitive & operating functions. Second, horticultural therapy showed the psychosocial effects of reducing depression and improving cognitive functions, language abilities, comprehension, daily activity, life satisfaction and sociality. But the effects appeared differently dependent on the implementation protocol & period of horticultural therapy, as well as the professionalism of the therapists. Therefore, if horticultural therapy were to be administered through nursing, it should be based on its key principle, in other words, the principle of action-interaction-response of horticultural therapy. Conclusion: The results of this study will be useful for developing therapeutic interventions through nursing. Also they will be helpful in applying horticultural therapy programs in nursing practice.
Research on GaAs-based RTD performance analyses and switching time characteristics

The paper designed and developed GaAs-based RTD (Resonant Tunneling Diode), which can achieve a high peak-to-valley current ratio of 7.6:1 at room temperature. Then it made on-wafer measurement for S parameter of RTD, analyzed frequency characteristics of RTD and the equivalent circuit parameters are obtained by fitting in order to calculate the maximum oscillation frequency. Resonant tunneling diode AC smallsignal model was established to be used for circuit simulation and the paper utilized PSPICE for analog simulation with analogue result coincidental with experimental data. Lastly it used negative resistance value to estimate switch time according to I-V characteristics and compared with other two methods, conclude that the reasonable switch time is about 0.8|RN|Cd.
Data Security using Genetic Algorithm and Artificial Neural Network

– By making use of Genetic Algorithm, Optimization problems can be solved and the best fit individual can be selected out of a given population. Artificial Neural Networks, a part of Artificial Intelligence, are used to simulate Human Intelligence on a machine. Cryptography is the science and art of encrypting data so that only the intended receiver can decrypt and retrieve the original data. This paper makes use of both Genetic Algorithm and Artificial Neural Networks for encryption and decryption of digital data. Genetic Algorithm is firstly used to generate two random numbers, both of which are later used as initial parameters for the Neural Network. The weights and bias of the Neural Network are created using the initial parameters by a specific procedure as decided by the user. The receiver who knows the initial parameters can decrypt and retrieve the original data, but the one without those parameters cannot retrieve the original data. This ensures optimal security of data over an open channel.
An adaptive segmentation based approach for image forgery detection using efficient feature matching

- In the today world digital images are popular sources of information. Digital images can be easily modified using powerful image editing software. The process of creating flexible or emulating documents with the aim of altering the details to earn the profit from the forged image is known as Image forgery. Copy move forgery is the process of creating a forged item by copying a region of the image and pasting it into another region in the same image. An adaptive segmentation based approach for image forgery detection using efficient feature matching the proposed scheme integrates both block-based and key point-based forgery detection methods. Adaptive segmentation devide image into non overlapping blocks and irregular blocks adaptively. Then, the feature points are extracted from each block as block features, and the block features are matched with one another to locate the labeled feature points; this procedure can approximately indicate the suspected forgery regions. To detect the forgery regions more accurately, we propose the Forgery Region Extraction algorithm, which replaces the feature points with small superpixels as feature blocks and then merges the neighboring blocks that have similar local color features into the feature blocks to generate the merged regions; finally, it applies the morphological operation to the merged regions to generate the detected forgery regions.
Evaluation of Effective Weed Management Strategy for Enhancing Productivity and Profitability of Chickpea (Cicer arietinum L.) under Rain- fed Condition of Southern Rajasthan

Chickpea is the premier pulse crop of India, shared about 70% area and 67% production of the world. In last 4 decade the area, production and productivity fluctuated widely. The area of chickpea is shifted from north India to central and southern India due to development of short duration genotypes which are better adapted to warmer, short-season environment, like central and southern Indian. Rajasthan is major part of north and central India that contributed about 15.37% area and 14.47% production of chickpea in India, but productivity of chickpea (888 kg ha -1 ) is recorded lower than national level (937 kg ha -1 ). The productivity of chickpea has fallen due to various constraints such as biotic and abiotic factors. The major abiotic factor is low moisture stress in rain-fed area. Among, the biotic constraints wilt, dry root rot and blight and pod borer are the major constraints International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 7 Number 11 (2018) Journal homepage: http://www.ijcmas. com
Abnormality Classification of Diabetic MacularEDEMA in Retinal Images

Diabetic Macular Edema disease leads to vision loss problem in Diabetic patients. Early detection of Macular Edema in diabetic patients pave a path for prevention from blindness. Diabetic Macular Edema (DME) occurs when blood vessels in the retina of patients with diabetes begin to leak into the macula region of eye. These leakages cause the macula to thicken and swell, progressively leads to vision loss. The automatic detection of Diabetic Macular Edema and classification of DME severity is done in this paper. The Hard Exudates (HE) presence in macula region is detected and the features are extracted. The extracted features are inputised to Adaptive Neuro Fuzzy Inference System (ANFIS) to classify the images as normal and the abnormal image severity levels as mild, moderate and severe.
Design and Analysis of Current Starved and Differential Pair VCO for low Power PLL Application

1Student, Department of Electronics Engineering, IET, Lucknow, India–226021 ------------------------------------------------------------------------***----------------------------------------------------------------------Abstract – This paper aims for the design and analysis of current starved voltage controlled oscillator (CSVCO) and differential pair voltage controlled oscillator (DAVCO) for low power PLL applications with 180nm CMOS technology. Every circuit has been designed and simulated inside tanner EDA by SPICE under similar environment. Various parameters like frequency response, tuning range, and power dissipation of CSVCO and DAVCO have been analyzed and compared. The supply voltage for CSVCO and DAVCO are 1.8V and 2.5V respectively. The control voltage for CSVCO is varied from 0.5V to 1.0V, while from 0.5V to 1.3V for DAVCO. The average power dissipations for CSVCO and DAVCO are 42 microwatts and 1.42 milliwatts respectively. The high pace and locking all outputs of DAVCO has been assessed towards the lower energy utilization advantage of CSVCO.
Evaluation of DEM, and orthoimage generated from Cartosat-1 with its potential for feature extraction and visualization

The present study evaluates the accuracy of Triangulation, Digital Elevation Model (DEM) and orthoimage generated by Cartosat-1 data by using Rational Polynomial Coefficient’s (RPC’s) provided with the Cartosat-1 satellite stereo (PAN) data. The study area is taken as Jaipur city, Rajasthan. The data acquired over Jaipur, on 18 May’ 2005 was analysed. The stereopair comprises of terrain ranging from plain area, agriculture fields, urban area, waterbody and Aravalli hill range. A Differential Global Positioning System (DGPS) survey was conducted to collect ground control points (GCPs) for satellite triangulation, DEM generation, orthoimage generation and subsequent analysis for accuracy assessment. Eighteen GCPs were measured through DGPS survey. Twelve GCPs are used for triangulation and six GCPs were used as check points. The overall RMSE for satellite triangulation is 0.753 pixel. Horizontal accuracy of 2.08 m and 1.74 m is achieved in X and Y direction, respectively. The vertical accuracy of 3.72 m has been assessed separately also by using independent check point in highly varying terrain.
On the Selection of Appropriate Kernel Function for SVM in Face Recognition

—Biometric researchers have paid critical attention to feature extraction since without this process it is virtually impossible to perform recognition. The weaker the features, the weaker the recognition rate, likewise, the stronger the features the more likely to achieve a better recognition rate. However, the later is not always the case since after feature extraction, a classifier is required to test how robust and invariant this features extracted. This classifiers depends highly on the choice of appropriate kernel functions. We determined an appropriate kernel function suitable for the face94 database. We showed that, the selection of Multilayer Perceptron (MLP) kernel function with weight 1 and bias -1 is appropriate for this selected database since it recorded the highest recognition rate. The Support Vector Machine was used as the classifier for this research.
A wearable antenna based on fabric materials with circular polarization for Body-centric wireless communications

A compact and simple structure antenna for wearable application at 2.4 GHz is presented and studied. The felt fabric material is used in this paper due to its suitable thickness and dielectric constant. This material provides high flexibility which can be easily worn on a body and incorporated into our daily clothes. In view of the fact that the design will work on a moving person, therefore a circularly polarized antenna is desired to optimize the off-body communication link. The Cicular Polarization (CP) is achieved by introducing truncated corners on the patch. The antenna size is 60 × 60 × 2 mm 3 . The Axial Ratio (AR), the Front to Back Ratio (FRB) and the realized gain are 0.96 dB, 10.5 dB, and 4.62 dB, respectively indicating a good performance of the antenna at the desired frequency. Furthermore, the antenna was investigated when operating near the body. The obtained result shows that the design has performance similar to the case of free space. This is due to the present of the full ground plane that acts as a shielding between the antenna and body. Finally, the Specific Absorption Rate (SAR) is carried out and showed that the antenna has a level less than the limits fixed by the FCC standard. Therefore, the antenna could be useful for wearable applications.
Multi Object Tracking Using Feature Selection Based Particle Swarm Optimization

— Vehicle detection and tracking play critical role in intelligent transportation system (ITS). This paper discuss the challenging of tracking arising from multiple object tracking, condition of scenes, occlusion, driving offenses, etc. The object tracking algorithm must be update in real time because of changing the scene. For example addition of pedestrians or new vehicles. In fact for multiple object tracking should find the object trajectories and classify them. We use the particle swarm optimization (PSO) algorithm to extract object trajectories. The results show the multi object tracking accuracy improved in our method.
CHUIs-Concise and Lossless representation of High Utility Itemsets

: Mining high utility itemsets (HUIs) from databases is an important data mining task, which refers to the discovery of itemsets with high utilities (e.g. high profits). However, it may present too many HUIs to users, which also degrades the efficiency of the mining process. To achieve high efficiency for the mining task and provide a concise mining result to users, this paper proposes a novel framework for mining (cid:1777)(cid:1812)(cid:1815)(cid:1819)(cid:1805)(cid:1804) (cid:2878) high utility itemsets (CHUIs), which serves as a compact and lossless representation of HUIs and three efficient algorithms named as AprioriHC (Apriori-based algorithm for mining High utility (cid:1777)(cid:1812)(cid:1815)(cid:1819)(cid:1805)(cid:1804) (cid:2878) itemsets), AprioriHC-D (AprioriHC algorithm with Discarding unpromising and isolated items) and CHUD ( (cid:1777)(cid:1812)(cid:1815)(cid:1819)(cid:1805)(cid:1804) (cid:2878) High Utility Itemset Discovery) to find this representation. Further, a method called DAHU (Derive All High Utility Itemsets) is proposed to recover all HUIs from the set of CHUIs without accessing the original database.
Optimization using Bio-Inspired Algorithm: Intelligent Waterdrop Algorithm

Nature inspired algorithms are Meta heuristics, these are used for solving the optimization problem. IWD is one of the best algorithms which contain Swarm intelligence is one of the closest field in nature inspired algorithms. IWD is based on behavior of water drops, which has been inspired from natural rivers. The process that happens in nature between the water drops of a river and the soil of the river bed. It is used to find the optimal solutions or paths from source to destination . In this work, offline signature and speech verification is combined which is widely used. For feature extraction in speech MFCC is used and in signature Gabor filters are used which is widely used for image and texture. Then, IWD is applied on the extracted features and fuse the optimized features of both modalities by using the average rule. Here, SVC2004 signature database is used for experimental results. Testing phase consists of 80(signature and speech) sample in which 60 samples are genuine and 20 are forgeries. The accuracy of the noiseless system is 100% with 0% of FAR and FRR and accuracy of the noisy system is 99.87% with FAR 0.025% and FRR 0.1%.
CLUSTER BASED PEER TO PEER UNPROMPTED MIDDLEWARE

In a peer to peer network there is no central coordinator to support the software packages in the network. Such network is said to be unstructured network. The project “Cluster based peer to peer unprompted middleware” is for creating middleware for the peer network and to cluster the peers in the network. While sharing the files among peers, query efficiency will be improved and middleware will provides application at the run time so there is no need for having the preinstalled file support software packages in the peer. The middleware contain the software, and it starts sending the software during runtime for each and every peer which is in need of that software for viewing the file .
An Optimization of Process Scheduling Based on Heuristic GA

: For the execution of the real time applications and to compute the high performance of each job, multiprocessor is the powerful tool. Such type of system highly depends on the parallel and distributed computing environment and generates a parallel and distributed network system. Consequently, several methods have been developed to optimally tackle the multiprocessor task scheduling problem which is called NP hard problem or NP complete problem. Scheduling in operating systems has a significant role in overall system performance and throughput. An efficient scheduling is vital for system performance. The concept of Parallel Processing is the speed-up of the execution of a large task by dividing the task into multiple segments that execute simultaneously. By doing the above concept each sub task has its own processor for a faster output. The effectiveness by doing so is beneficial for large number of calculations having some constraints like time constraints etc. Scheduling is the crucial step of parallel programming that deals with the assignment of a set of tasks in the parallel system environment and set the execution in such a way that the total execution time is minimized. The proposed algorithm has the efficient execution of the schedule on parallel system that takes the structure of the application and the performance characteristics. Number of appriximation and heuristics and algorithms have been proposed to fulfill the task scheduling problem. Here the study proposes a genetic based techniques to schedule parallel tasks on hetrogeneous parallel system. In this paper the scheduling problem considered includes - an optimal mapping of the task, search for an optimal configuration of the parallel system and their sequence of execution. The technique for the simultaneous optimization of all the three components of scheduling method using genetic algorithm is presented and its performance is evaluated in comparison with the First Come First Serve (FCFS), Shortest Job First (SJF), Round Robin (RR) and Largest Job First (LJF) scheduling methods. By comaring all the algorithms, optimized result can be evaluated