Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Criteria and Model-Based Analysis for Project Selection: An Integration of Capability-Based Planning, Project Portfolio Management and Enterprise Architecture

Nowadays, organizations are faced with many investment decisions, many of which are related to the selection of projects the organization should undertake in order to achieve its strategic goals. However, these projects are often influenced by several drivers, such as specific concerns of the stakeholders, availability of resources, and varying levels of risk. Thus, organizations often encounter difficulties when having to make such investment decisions. To address this issue, we propose a method which integrates aspects of Project Portfolio Management, of Enterprise Architecture and of Capability-based planning. Our method also covers a comprehensive selection process, based on organizational capabilities, and analytical techniques, such as AHP and linear programming. We demonstrate the application of the method in the case of a large European energy supplier.
Improving FISTA’s Speed of Convergence via a Novel Inertial Sequence

The FISTA (fast iterative shrinkage-thresholding algorithm) is a well-known and fast (theoretical $\mathrm{O}(k^{-2})$ rate of convergence) procedure for solving optimization problems composed by the sum of two convex functions, such that one is smooth (differentiable) and the other is possible nonsmooth. FISTA can be understood as a first order method with one important aspect: it uses a suitable extragradient rule, i.e.: the gradient is evaluated at a linear combination of the past two iterates, whose weights, are usually referred to as the inertial sequence. While problem dependent, it has a direct impact on the FISTA’s practical computational performance. In this paper we propose a novel inertial sequence; when compared to well-established alternative choices, in the context of convolutional sparse coding and Wavelet-based inpainting, our proposed inertial sequence can reduce the number of FISTA’s global iterations (and thus overall computational time) by 30% $\sim 50$% to attain the same level of reduction in the cost functional.
STUDY OF DHATU SARATA AND ITS ASSOCIATION WITH BLOOD INDICES

Ashta Sarata parikshana is explained especially to determine the strength of person. It is important to add modern parameter to giv e authenticity to the ancient subjective parameter . 50 healthy male students between the ages of 18 - 25 years were selected for study . Sarata parikshan was done with the help of standard sarata parikshan proforma speci ally prepared by AYUSOFT C - DAC Centre for Development of Advance Computing Pune , India to find out percentage of each dhatu separately. Blood indices hav e been formulated by modern science to detect type of anaemia by extracting information about structure, morphology of Red Blood Cells and about haemoglobin. Blood indices give complete information of Red Blood Cells. Blood sample were collected for analys is of blood indices and this was done by auto analyser . This was s urvey type co relational research . The s tatistical analysis shows that “ there is a relation between Rakta sarata and Blood indices” , it’s a positive relation. The person having higher percen tage of Ra kta sarata possesses good value of Blood indices .
Management of High Convexity, Parasagittal and Falcine Meningiomas

Background: Neurosurgery has witnessed steady change in both technological capacity and in conceptualization of certain diseases. High convexity and falco/parasagittal Meningiomas are good examples of this change. Objectives: We aimed to analyze 25 consecutive cases of high convexity, parasagittal and falcine meningiomas with respect to surgical technique, image-guidance, complication rate, and pathological factors leading to recurrence in this particular midline location. Patients and Methods: We retrospectively reviewed 25 cases of closely related meningiomas by location operated by the first author over 6 years in two centers in KSA between 2007 and 2013. The median follow-up time was 29.7 months (range, 12–42 Ms). Results: High convexity and falco/parasagittal meningiomas represented 30 % of all meningiomas operated by the first author. Median age was 58.8 years (range, 48–72 yr), there was a female: male ratio of 1.8:1(16 female and 9 male). Image guided surgery was used on 10 cases (40%) operated at the second center (2010-2013). 8 cases (32%) presented with preoperative neurological deficit showed improvement during the postoperative follow up period. The incidence of new neurological deficits was 8% (2 cases), No permanent deficit and the overall complication rate was 16% (4 cases). The 30-day mortality rate was one case (4%). The pathology of the tumors was benign in 21 (84%), atypical in 4 (16%), and no cases diagnosed as anaplastic/malignant. In 3/21 cases designated “benign, "there were borderline atypical features with Ki-67 LI more than 5%. No cases of recurrence within the follow –up period for purely benign meningioma (72%). We reported two cases of recurrence (8%), one case of falcine benign meningioma (Grade I) with features of atypia and another case of atypical parasagittal meningioma (Grade II) recurred in 39 months and 21 months respectively. Both were sent to radiosurgery. Conclusion: High convexity, parasagittal and falcine meningiomas can be safely removed using modern image guided surgical techniques with acceptable operative morbidity and mortality. The conservative surgical approach with saving the sinus and the major veins with adjuvant radiation therapy for the misbehaving residual has a very satisfactory longterm effect. The real behavior of the borderline tumors (Grade I with atypia) needs more cases, deep research and long term follow up.
Estimating Quality of Service on Wi-Fi Stations Using Recurrent Neural Networks

Wireless networks are the most common way to access the Internet, with more than 10 billion Wi-Fi devices already sold. Wireless connections suffer from problems related to spectrum overuse, such as transmission errors and loss of information. Intelligent control systems can be used for network management and to improve Quality of Service (QoS). However, the first step towards such systems is a way to correlate current Wi-Fi readings to a QoS value. This work proposes a model using Recurrent Neural Networks (RNN) to infer such relation, based on real Wi-Fi data, and compares two RNN types: Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM). The model predicts four network metrics: throughput, loss, delay, and jitter, based only in traffic data obtained at the AP. The average Root Mean Square Error is of the order of 10−2 for throughput, 10−4 for delay, and 10−5 for jitter and packet loss using both methods.
Assessment of Conflict Area Parameters Measurements by Using Photographic Methods

This paper presents a case study for measuring luminance and uniformity by using a modern method. The presented method, also known as ”imaging method” uses a conventional digital photo camera calibrated to measure luminance and present it as a luminance map presenting the values for the entire area for which the photo is taken. The particular study presented is for a conflict area, but this method can be used for straight road measurements as well. In this particular study it is presented a very important conflict area in Iaşi City, Romania Country, on Bucsinescu road for which a lighting calculation is made using DIALux 4.13 and the results are compared to the values measured by the camera. One of the conclusions is that values are very sensible to the properties of the asphalt and the position of the observer, but still very close to the values calculated to the DIALux Software. This is a strong statement of the accuracy and usability of this method and a clear example of the benefit one would encounter if this measurement method would be considered as a standard for reception in street lighting systems upgrades and extensions.
Complexity Reduction Opportunities in the Future VVC Intra Encoder

The Joint Video Expert Team (JVET) is developing the next-generation video coding standard called Versatile Video Coding (VVC) and their ultimate goal is to double the coding efficiency over the current state-of-the-art standard HEVC without letting complexity get out of hand. This work addresses the complexity of the VVC reference encoder called VVC Test Model (VTM) under All Intra coding configuration. The VTM3.0 is able to improve intra coding efficiency by 21% over the latest HEVC reference encoder HM16.19. This coding gain primarily stems from three new coding tools. First, the HEVC Quad-Tree (QT) structure extension with Multi-Type Tree (MTT) partitioning. Second, the duplication of intra prediction modes from 35 to 67. And third, the Multiple Transform Selection (MTS) scheme with two new discrete cosine/sine transforms (DCT-VIII and DST-VII). However, these new tools also play an integral part in making VTM intra encoding around 20 times as complex as that of HM. The purpose of this work is to analyze these tools individually and specify theoretical upper limits for their complexity reduction. According to our evaluations, the complexity reduction opportunity of block partitioning is up to 97%, i.e., the encoding complexity would drop down to 3% for the same coding efficiency if the optimal block partitioning could be directly predicted. The respective percentages for intra mode reduction and MTS optimization are 65% and 55%. We believe these results motivate VVC codec designers to develop techniques that are able to take most out of these opportunities.
Design and Evaluation of a Healthcare Management Terminology Mobile Learning Application

The present study develops a healthcare management terminology mobile learning application, called the EZ-HCM App. Based on the technology acceptance model (TAM), we design a questionnaire using a five-point Likert scale to investigate students’ perceptions of the usefulness, ease of use, attitude toward use, and intention to use the EZ-HCM App. Research subjects were university students from the fourth largest Department of Health Care Management in Taiwan between July 2016 and June 2017. We adopt the persona method to understand students’ design requirements for a healthcare management terminology translation dictionary and explanation. After developing the EZ-HCM App, 113 students from the department were invited to participate in pre-testing and post-testing surveys without and with using the app and to complete the EZ-HCM App Acceptance Questionnaire. Seventy valid questionnaires were returned. The results showed that participating students have a high level of acceptance of the usefulness (mean= 4.40, SD= 0.46), ease of use (mean= 4.49, SD= 0.48), attitude toward use (mean= 4.42, SD= 0.49), and intention to use (mean= 4.15, SD=0.58) the EZ-HCM App. Participants in different levels of smartphone use frequency do not lead to the different perception toward usefulness and ease of use of the EZ-HCM App. The acceptance of the EZ-HCM App is also consistent with that of other m-learning systems, with its usefulness and ease of use producing positive attitudes toward use ($\beta$ =0.77, p <.0001; $\beta$ =0.47, p =.0002) and intention to use ($\beta$= 0.87, p <.0001; =0.98, p <.0001) the EZ-HCM App among participating students.
Sentiment Classification And Personality Detection Via Galvanic Skin Response Based on Deep Learning Models

Sentiment and personality have great impact on our lives; they affect our cognition, creativity, and decision making. Many approaches have been proposed to automatically recognize users' sentiment through images and speech. Accurately predicting a person's sentiment and personality traits can be beneficial for certain situations, such as interviews and polygraphs. In this work, several models including Convolutional Neural Network-Long Short-Term Memory Joint Learning Model, and spatiotemporal hybrid models are proposed to automatically learn galvanic skin response (GSR) and video clips rating for the sentiments recognition and personality detection tasks. Comparisons are made between the proposed model and state-of-the-art models in related works. The experimental results demonstrate that better results of sentiment recognition and personality detection are gained in terms of precision, recall, and F1 score.
Prevalence and Risk Factors of Post-Partum Depression among Women in Khartoum Maternity Hospital

Background : Unfortunately, many women will experience short- or long-term mood disturbances postpartum. Post-partum depression can transform this period into a nightmare. Objectives : To determine the prevalence of post-partum depression among women during post-partum follow up and to determined risk factorsof post partum depression. Patients and Methods : It was cross-sectional and hospital based studyconducted in Khartoum Maternity hospital, during one year (March 2012 – March 2013). A total of 500women were chosen as proportion of total number of women attending the refer clinic for postpartum follow up. A systemic random sampling technique was used to select the study sample. A detailed and structured questionnaire was filled directly by women. Results : The mean ±SD of age was 31.5 + 3.1 years. Fifty five (11.0%) were had depression, while 445 (89.0%) were not. Sixteen (3.2%) had mild form, 30 (6.0%) had moderate and 9 (1.8%) had severe depression.The study find significant statistical association between depression and the following variables: p value < 0.05, age, parity, unwanted pregnancy, number of miscarriages, loss of husband support and personal or family history of psychiatric illness.Conclusion: The present study concluded that almost 11.0% of these women are experiencing moderate levels of depressive symptoms. As well, there are implications for practice, theory, policy, and knowledge translation.
On the Rules for Inheritance in LML

Although classification relationships play the dominant role in structuring multi-level models, inheritance relationships, which are restricted to clabjects at the same level, are also important. As in traditional two-level modeling, the basic principle of inheritance in multi-level modeling is to ensure the substitutability of instances of sub-clabjects for instances of super-clabjects. However, for multi-level modeling languages that rely on potency and endurance properties to control classification, the rules governing inheritance can become complicated if "deep substitutable" is desired and these properties are regraded as part of clabjects' intensions. This paper clarifies these rules in the context of the Level-Agnostic Modeling Language supported by the Melanee multi-level modeling environment.
An Ethnobotanical Survey of Herbal Markets and Medicinal Plants in Lagos State of Nigeria

An ethno-botanical survey of three largest herbal markets and medicinal plants used for treating ailments in Lagos state of Nigeria was undertaken by means of oral and written questionnaire. Traditional medicine is undoubtedly a reliable alternative approach to health care delivery in the metropolis because it is cheap, easily accessible, and efficacious. Herbs are obtained from bushes around the neighbourhood and forests from adjacent states. Recipes for the treatment of common ailments which are reported almost on daily basis such as hypertension, dysentery, low sperm count and weak erection, coated tongue, pile, menstrual disorder, leucorrhoea and fevers were given by the practitioners. Moreover, a list of medicinal plants and their reported folkloric uses was compiled during the survey. Apparently, daily health issues of Lagosians are partly taken care of by traditional medicine and its use is on the increase in Lagos state. The efforts of the Lagos State Traditional Medicine Board and Nigeria Natural Medicine Development Agency are praised in facilitating recognition of herbal medicine in the metropolis. Government should help in the area of conservation of medicinal plant and protect patent right of the Traditional Medicine Practitioners to encourage release of information.
Monaural Speech Separation with Deep Learning Using Phase Modelling and Capsule Networks

The removal of background noise from speech audio is a problem with high practical relevance. A variety of deep learning approaches have been applied to it in recent years, most of which operate on a magnitude spectrogram representation of a noisy recording to estimate the isolated speaking voice. This work investigates ways to include phase information, which is commonly discarded, firstly within a convolutional neural network (CNN) architecture, and secondly by applying capsule networks, to our knowledge the first time capsules have been used in source separation. We present a Circular Loss function, which takes into account the periodic nature of phase. Our results show that the inclusion of phase information leads to an improvement in the quality of speech separation. We also find that in our experiments convolutional neural networks outperform capsule networks at speech separation.
Experimental Validation of a Non-Redundant NF-FF Transformation for Long AUTs Mounted in Offset Configuration in a Spherical NF Facility

The experimental validation of an accurate and fast near-field - far-field (NF-FF) transformation technique with spherical scan, suitable for long antennas under test (AUTs) mounted in offset configuration, is provided in this work. The main feature of such a NF-FF transformation is to require, unlike the traditional spherical (TS) one, an amount of NF samples, which is minimum and results to be practically the same in both cases of offset and onset mountings of the AUT. To this end, an optimal sampling interpolation formula, developed by properly exploiting the non-redundant sampling representations and modeling an offset mounted long AUT by a cylinder ended by two half-spheres, is employed to precisely recover the massive input NF data for the TS NF-FF transformation from the collected non-redundant samples. A considerable measurement time-saving can be so achieved. Experimental results assessing the validity and the practical feasibility of such a technique are shown.
Low-Power Wireless With Denseness: The Case of an Electronic Shelf Labeling System—Design and Experience

One of the most important and recurrent tasks in managing a store is to provide accurate information of products to customers by maintaining up-to-date price tags of every item. Till today, however, updating price tags is done manually in most markets, which is labor-intensive, time-consuming, and error-prone. Adopting wirelessly reconfigurable electronic price tags could be of significant benefit to this problem. A key requirement of such a system, then, is to establish a robust, reliable, and scalable wireless communication network while spending minimal energy on thousands of densely-deployed and battery-powered price tags. In this work, we present a comprehensive study of electronic shelf labeling (ESL) system including its design and real-world deployment experiences. While the overall design turns out to be relatively straightforward, our preliminary study using 1000 real commercial e-tags reveals technical challenges in reliable and low-power wireless communication with ultra-high node density in scatter-rich indoor environments. To address the problem, we adopt multi-radio and multi-channel diversity techniques which need to modify only the gateways but significantly improve overall system performance. Our evaluation from a real-world deployment of 550 tags at an actual convenience store shows that our proposal achieves 98.51% network connectivity, 590 ms average latency for price updates, and over 5 years of e-tag lifetime. ESL system is part of the upcoming smart market era, and we see this work as a practical industrial application case study of Internet of Things technologies.
CONTROLLED DRUG DELIVERY THROUGH MICROENCAPSULATION

An appropriately designed controlled release drug delivery system can be a major advance towards solving problems concerning to the targeting of drug to a specific organ or tissue and controlling the rate of drug delivery to the target site. The development of oral controlled release systems has been a challenge to formulation scientist due to their inability to restrain and localize the system at targeted areas of gastrointestinal tract. Microparticulate drug delivery systems are an interesting and promising option when developing an oral controlled release system. The objective of this paper is to take a closer look at microparticles as drug delivery devices for increasing efficiency of drug delivery, improving the release profile and drug targeting. In order to appreciate the application possibilities of microcapsules in drug delivery, some fundamental aspects are briefly reviewed.
An Epidemiological Study On Foot And Mouth Disease Outbreak In Cattle Of Erode District Of Tamilnadu

Foot and Mouth Disease (FMD) is one of the most important viral diseases of livestock especially cattle causing heavy economic losses to farmers. FMD outbreak in cattle during the period from 2002 to 2008 in Erode District of Tamil Nadu is reported. The incidence of FMD in cattle and buffaloes, season, morbidity and mortality were investigated. The results revealed that the disease generally occurred fr om August to Febr uary of the calendar year with very high prevalence from November to Febr uary. The disease caused an average mortality of 16.1 per cent in cattle, with highest mortality rate of 32.6 per cent during 2003-04, followed by 16 per cent mortality in 2007-08, 4.2 per cent in 2002-03 and a lowest mortality rate of 2.5 per cent during 2004-05. In 2007-08, a total of 2352 cattle and buffalo were affected with a mortality of 16 per cent among morbid animals. The disease affected 1578 cows, 349 calves, 349 buffaloes and 76 buffalo calves with a mor tality percentage of 14.3, 29.8, 10.6 and 13.2 respectively. The disease caused 17.1 per cent mortality in white cattle and 11.1 per cent mortality in buffaloes. Among morbid animals, the mortality in adults (cow and buffalo) and young ones (calves and buffalo calves) were 13.7 per cent and 26.8 per cent, respectively. Higher morbidity and mortality were observed in ar eas irrigated with Lower Bhavani Project (LBP) Channel and Cauvery river water irrigation than dry land area of the district.
Reduced-bias complex adaptive notch filter

This paper develops first-order complex adaptive notch filters (CANFs) which are computationally simple, in order to minimise resource usage in an ASIC firmware implementation, but when connected in cascade can achieve substantial attenuation of multiple interference tones with slowly varying frequencies. When existing first-order CANFs are used in this way the adaptation is biased due to the presence of multiple tones at the filter's input; some algorithms are also un-normalised, which is unsatisfactory when the interfering tones may have a wide range of amplitudes. This paper describes a normalised algorithm for first order adaptive filters which is computationally simple and offers significantly reduced bias when used in a multi-notch cascade.