Miracle-dz/UniFAR-FTUs huggingface dataset:
Acinetobacter spp in a Third World Country with Socio-economic and Immigrants Challenges.

INTRODUCTION In the last decade, Acinetobacter species have taken a major public health concern. This is mainly due the increased resistance to a wide range of antibiotics causing treatment challenges. In view of the constant population mobilization and the economic crisis that Lebanon is currently facing, it becomes a necessity to re-evaluate the real threat of Acinetobacter spp and its implication in the one health. METHODOLOGY This review was conducted through the analysis of 45 research papers and reports pertaining to Acinetobacter spp performed in Lebanon. More than 82% of the papers consulted were published in international journals and more than 70 percent of them had received impact factor. RESULTS An in depth description of the involvement of this organism in human infection and its role as potential pathogen or simple colonizer was performed. In addition, the different aspects of resistance, mostly to carbapenems and colistin was studied and summarized. While in animals and environment, susceptible strains were mostly isolated, OXA-23/OXA-24 were predominant in humans. Recently, NDM-1 producing Acinetobacter spp was detected in a Syrian refugee which then was reported in Lebanese patients. The bacterial identification procedures are non-systematic and not always reliable in the Lebanese studies presenting sometimes discrepancies an inconsistency. CONCLUSION Acinetobacter is commonly isolated Lebanon. In view of the spread of resistance among these isolated and their dissemination, Infection control measures attempting to control the spread of this genus in and outside hospitals are lacking and thus require more attention and stewardship activities.
The Effect of Financial Structure and Audit Quality on Operating Cash Flow (OCF) Management Behavior and Its Effect on OCF Predictive Ability: Evidence from Egypt

This study develops a model for measuring operating cash flow (OCF) management, and investigates the effect of financial structure and audit quality (OCF) management behavior in the Egyptian market. In addition it investigates the effect of OCF management on the predictive ability of OCF. To capture the OCF management, I developed two models depending on both direct and indirect approaches of preparing cash flow statement. The results of the study provide evidence that heavy dependence on loans as a financing source (higher leverage ratio) leads to more OCF management. In addition, the results support the proposition that audit quality decreases OCF management. Moreover, the study provides evidence on regarding the negative effect of OCF management on the predictive ability of OCF.
Node Relocation Techniques for Wireless Sensor Networks: A Short Survey

Sensor nodes in a sensor network often operate in harsh and challenging environments and this leads to frequent failure of sensor nodes. Failure of sensor nodes leads to partitioning in the network connectivity. For significant effectiveness of applications of sensor networks, the inter-sensor connectivity among sensors is vital. Some sensors are also involved in sustaining the flow of information from the sensor to unapproachable end users. The network can be split up into multiple incoherent blocks and cease working due to physical damage or onboard energy depletion. To deal with such scenarios, a plethora of node repositioning techniques are proposed in the literature. In this article, the recent and up to date mode of research on dynamic sensor repositioning in WSN is discussed. This article classifies sensor repositioning methods into on-demand and post-deployment repositioning based on whether the optimization is accomplished at deployment time or while the network is functioning.
Development and validation of a simple and sensitive HPLC method for the determination of liquid form of therapeutic substances

Background:Caffeine is the most consumed psychostimulant in the world that is found in numerous foods and drinks with coffee and tea have the highest concentration of caffeine per weight. Caffeine has a number of reported physiological benefits and clinical uses such as coma recovery. Caffeine can prove dangerous and its consumption, the amount in foods and drinks should be closely monitored, by the Food and Drug Administration.Material and Methods:The availability of sensitive, reliable, and simple testing methods with rapid turnover time is essential for testing any laboratories. In this manuscript, we describe the development and validation of a simple, sensitive and economical high performance liquid chromatographic (HPLC) method with ultraviolet detection for the quantification of the amount of caffeine in different beverages.Results:HPLC method was validated and tested to measure caffeine contents in different beverages that showed high precision, reliability, and sensitivity that makes it suitable for routine measurement of caffeine as well as other therapeutic or chemicals in liquid forms.Conclusions:The proposed method recovered caffeine without the need for any extraction step for recovering the caffeine from the formulation excipients matrices, therefore decreased the degree of error, time for estimation of caffeine and the overall cost of the analysis.
Yerel Yönetimlerdeki Coğrafi Bilgi Sistemleri İçin Yeni Bir Yazılım Mimari Önerisi

Cografi Bilgi Sistemleri (CBS) gunumuzde her alanda karsimiza cikmaktadir. Kullaniciya problemlerini veya verilerini gorsellestirme ve farkli acilardan bakma imkâni tanidigi icin de onemli bir aractir. Gunumuzde CBS iceren projeleri olusturabilmek icin bircok acik/ticari yazilim, meslek dali, insan kaynagi ve de fiziksel sunuculara ihtiyac duyulabilmektedir. Kullanilan mevcut yontemler, bu projelerin uzun surede tamamlanmasina ve maliyetli olmasina sebebiyet vermektedir. Farkli dallarda uzman personellerin bulunamamasi da CBS icin yine problem teskil edebilmektedir. Tum bu problem ve riskleri azaltabilmek icin bu calismada yerel yonetimlerde cografi veri tabani, yazilim, harita sunucusu, veri toplama ve analizini iceren acik kaynak ve katmanli bir yazilim mimari onerisi sunulmustur.
LIGHTWEIGHT EXPANDED CLAY AS A SPACE HOLDER IN ALUMINUM MATRIX COMPOSITES: EFFECT OF PARTICLE SIZE IN DENSITY AND COMPRESSION BEHAVIOR

Composite metal foams are an attractive solution due to their high specific energy absorption, thermal and sound insulation properties. Most manufacturing processes involve the inclusion of air or solid particles, that are posteriorly, removed to generate internal voids. However, there is a recent trend for the use of materials as permanent space holders. In this study, Lightweight Expanded Clay (LECA) is used as a space holder to obtain a metal matrix composite foam manufactured by casting (AlSi7Mg). Results show that smaller LECA particles promote lower yield strength, strain densification and crushing energy. Generally, it is found that this kind of composites show high values of specific compressive strength and may be a viable solution for structural applications.
Mathematical Physics Problems: Thesaurus and Ontology

The work is devoted to the study of knowledge representation in the subject area “Equations of mixed type in the sections of mathematical physics”. Such comprehensive resources as Wikipedia, claim to be encyclopedic knowledge, but cannot provide informational support for in-depth research. This is a field of activity for specialists in specific areas of knowledge. The thesaurus and ontology are considered as a formal means of describing the subject area. This approach takes into account the peculiarities of knowledge representation in the domain, namely the presence and use of formulas as independent objects. Consideration of the domain features leads to a reduction in search noise and a reduction in search time within the framework of the constructed library. The use of thesaurus and ontology in the design of a digital semantic library is considered.
TESTING THE PERCEPTUAL CONTRAST BETWEEN /o/ AND /ɔ/, AND /e/ AND /ɛ/ IN KINSHASA LINGALA

This research tests the claim that the perceptual contrast between the vowels /ɔ/ and /o/, and /ɛ/ and /e/ have been neutralized in Kinshasa Lingala (KL). Previous studies have claimed that the vowels /ɔ/ and /ɛ/ have merged into /o/ and /e/ respectively (Campbell and King 2013, Montingea 2006, Bokamba 2012). This assumed merger has resulted in the claim that KL has 5 vowels as opposed to Makanza Lingala (ML), which has 7. However, this claim has never been tested empirically, that is, no study has ever empirically demonstrated the merger of those vowels through robust findings. Also, it has never been specified whether this merger is perceptual or articulatory. Most studies, which have formulated the aforementioned claim on the vowel system in KL were conducted without any support of the new technology in linguistics. None of those studies have used any phonetic software to analyze those sounds in order to back their claims. In most of those studies, researchers relied upon their intuition as native speakers of KL and upon their auditory skills to discriminate between the pairs of vowels /ɔ/ and /o/, and /ɛ/ and /e/, and on the basis of their intuition as native speakers of Lingala, they concluded that vowels /ɔ/ and /ɛ/ have been merged to /o/ and /e/ respectively. I believe that reliance on only the native auditory skills is not enough to back up one’s claim. An analysis of the acoustic characteristics of those targeted vowel sounds would help to provide an effective answer to the concern of this research. In this study, however, I aim to test the perceptual abilities of KL speakers on the contrast between the pairs of vowels /ɔ/ and /o/, and /ɛ/ and /e/. I administered a pictureauditory contrastive task to the subjects to test their ability to discriminate between those pairs of vowel sounds. This test will determine whether KL speakers perceptually discriminate between the pairs of aforementioned vowels. I further use the Signal Detection Theory (SDT) to determine their sensitivity in discriminating between those pairs of vowels.
Design of an Educational Electronic Game System

The dynamics of learning have evolved over time and continue to change to date because of several reasons that include but not limited to competition for time between formal school curriculum learning and entertainment media such as television and social media, and the growing student teacher ratio that limits interaction of the teacher and his/her students, and as such the notion of a teacher attending to different unique needs of his/her learners remains a pipeline dream for most African governments. As such, we design a system that performs mathematical operation involving addition, subtraction, multiplication, and division (4 mathematical operations) whereby the person who has the highest score will win the game. The system is divided into different sub-circuits. This division includes; Power supply, user input, timing circuit, counter circuit, and display circuits. In the end, a successful prototype was built using an efficient and simple circuit.
Authentication for e-health applications in IoT enabled 5G and proposed solution

As one of the performant technologies in 5G, the internet of things has become more attractive by the day. Therefore, as one of Iot's most important domain E-health plays a sensitive role in the human's life. In addition, E-health serves the patients to be remotely monitored by their doctors and get some medical consultations without direct meeting, by collecting data from the patient's body using some medical sensors and things. However, protecting the authenticated users and their data has become a main task that faces many challenges. Therefore, through our research we have found several RFID and NFC authentication protocols in order to ensure security for the 5G-iomt and especially E-health applications, then we discuss the performance and security analysis of these protocols: LMAPC, ULMAPC, ULMAP, and RSAR. Our aim in this paper, is to choose the potential scheme among the four presented protocols which verify reliability, computational time and robust security analysis, to provide a high level of security for e-health applications over 5G.
Emotional Speech Generator by using Generative Adversarial Networks

In this paper, we propose an affective voice conversion method that can generate an emotional phonation from neutral speech by using cycle-consistent generative adversarial networks (CycleGAN). Our method uses the Mel-cepstral coefficients (MCEPs), which are extracted from speech signal as an input. Next, we apply the modified network model which is comprised two components with a generator and discriminator. In this generator network, the pairing structure with an encoder and decoder is used for an accurate and fast calculation in the learning process. Furthermore, we construct two types of encoder; the one equips a content encoder for the linguistic-information, and the other equips a domain encoder for the emotional-information. This separation is enable to reproduce the smooth speech with emotional information. Finally, we evaluate the emotion expression and sound quality of speeches by using the subjective evaluation for an accuracy of emotional change. As the result, although it is necessary to improve the deterioration of sound quality, our method has accomplished to convert the emotion of the speech.
Post-Operative Electrode Placement Prediction in Deep Brain Stimulation Using Support Vector Regression

Deep brain stimulation (DBS) is a neurosurgical procedure for treating neurodegenerative diseases and neurological disorders such as Parkinson's disease (PD) and epilepsy. Image guidance is crucial for the accurate placement of DBS electrodes. However, current surgical planning systems based on pre-operative MR and CT images of the brain cannot take into account the intra-operative brain-shift, resulting in suboptimal electrode placement undermining clinical outcomes. In this study, a support vector regression (SVR) model was constructed based on 114 patient-specific data of PD patients. Two target nuclei were manually delineated based on pre-operative MR and CT images. Spatial coordinates of the two nuclei were collected and compared to the post-surgical electrode position from CT images. Analysis of a total of 45 features showed that the pre-operative target coordinates are the parameters mainly influencing the model prediction for both nuclei. The mean absolute error (MAE) of the prediction of the electrodes on unseen patients was 0.76mm. This study demonstrates the potential of using SVR modelling to improve current DBS surgical planning procedure and pre-operative risk-assessment.
Adaptation of the random forest method: solving the problem of pulsar search

Random Forest Algorithm is a method of machine learning that refers to train individual classifiers and aggregates their predictors. It is specifically reserved to decision tree classifiers and used for classification and regression problems in several areas. Beyond the choice of the most appropriate algorithm to the study context and the database criteria, another challenge can be faced on the input parameters of each algorithm, which can have a significant influence on the result performance. In the context of the pulsar detection candidates, this work aims to study the influence of the parameters on the result performance and suggest an optimum scenario. We explore our result experiments using the R language.
Automatic Degree Evaluation of Facial Nerve Paralysis Based on Triple-stream Long Short Term Memory

Facial nerve paralysis adversely affects both mental and physical health of patients. Several degree evaluation methods for facial paralysis have been put forward based on the static facial asymmetry and computer vision technologies. However, traditional methods still suffer from two drawbacks: (1) the facial movement information is ignored which could be important for facial paralysis analysis; (2) the shallow machine learning models used by them may not well extract effective facial features for facial paralysis of different grades. In this paper, we present Triple-stream Long Short Term Memory (LSTM) to evaluate the severity degree of facial paralysis automatically. Hence, the Triple-stream LSTM employs two LSTM sub-networks to extract local movement features from the two involved facial regions and one LSTM sub-network to extract global features from the combination of the two regions. Then, the extracted high level representations from the three LSTMs are fused for final severity degree evaluation. By extracting both local and global movement features from the involved regions in an optimal sense, the final fused features could express the facial features for different degrees of facial nerve paralysis more effectively. The experimental results have verified the effectiveness of Triple-stream LSTM for evaluating the severity of facial paralysis.
Training road crossing skills for young people with intellectual disabilities using virtual reality: a feasibility study

Virtual Reality (VR) has three main advantages: allowing safe simulations, experimenting different conditions for the same scenario, and providing the perfect replicability of the scenarios. We present a feasibility study on the use of VR and VR immersive headsets to assist young adults (10-18) with intellectual disabilities in learning new skills. We focused on the scenario of a pedestrian crossing without traffic lights, and we considered several environmental conditions (day/night, weather, kindness of drivers, etc.). Our study is not limited to young people with autism spectrum disorder but takes into account young adults with intellectual disability with an associated disorder. 15 young people participated in the study showing a very good acceptability of immersive headsets and a noticeable learning effect already after a short training session. However, a longer and more extensive study is needed to evaluate the transfer of learning to reality.
Expanded Mask R-CNN's Retinal Edema Detection Network

To better assist doctors in rapid recognition of lesions in OTC edema images, an expended Mask R-CNN method was proposed to detect and recognize the retinal edema. We also design a software to label 8960 images automatically. A large number of images can be labeled in a few minutes. For the backbone of Resnet101, we add a parallel 1*1 convolution to the basic residual block, the residual blocks are widens in its width, the receptive field of feature maps are enlarged, and more richer feature information can be extracted. By adding the filters with the size of 1*1, the outputs of the filters are equivalent to multiplying a coefficient for feature maps, which can enhance the representation of feature maps and facilitate the location of the edema area. In our experiments, 8960 retinal edema images were used to train the model and 1920 images were kept to test. Compared with Faster R-CNN and Mask R-CNN, the expended Mask R-CNN achieved more accurate location of the edema lesion area. The recognition accuracy of the expanded Mask R-CNN, Faster R-CNN and Mask R-CNN can reach are 92.27%,87.95%,90.65%, respectively.
Towards Including Software Tools for e-motivation in the Learning Process

In this paper, we present a new approach for increasing motivation, specially for students, by integrating new software and e-learning concepts. We focused on including the students and their needs and preferences within the software to be used. We defined the principle of Gamification elements and also the model based approach for generating the targeted software application. We focused on adapting the software to the needs of students rather than having one software for all.
Robust Prototypical Networks for Small-Intestine Polyp Recognition in Wireless Capsule Endoscopy Images

Wireless capsule endoscopy (WCE) is a gastrointestinal examination technology, which can help find the polyps in small bowel noninvasively. The computer-aided polyp recognition systems based on deep learning require large amounts of manually annotated data, which is often unavailable for WCE images. Meanwhile, there is a serious imbalance between normal and polypoid samples in WCE image database. We proposed a few-shot learning method of automatic polyp recognition under the circumstance of absolute lack of data, named Robust Prototypical Networks (RPNs), and RPNs made adjustments to polyp position changing. Trained with an imbalanced polypoid WCE image dataset and a polypoid colon endoscopy (CE) image dataset, RPNs can extract their common features by introducing a multi-task learning scheme, separating-diffusing, to overcome imbalance problem. RPNs outperforms the previous work, and the best average AUC-PR score is 0.87.
Vibrotactile cues for conveying directional information during blind exploration of digital graphics

Graphics are useful in many contexts of daily life (education, mobility, etc.) and spread widely in digital media. However, accessing to digital graphical information remains a challenging work to people with visual impairments. In this study, we were interested in the transmission of vibrotactile cues allowing users to explore digital graphics more easily and faster. We have designed a vibrotactile matrix fixed on the hand for presenting directional information. Two vibrotactile displays - Spatiotemporal Vibrotactile Pattern (SVP) and Apparent Tactile Motion (ATM) - were compared. A study with sixteen blindfolded participants examined the efficiency and user preferences of proposed interaction techniques and showed that the recognition accuracy with SVP is significantly better. Final study involving six participants with visual impairments confirmed the improvement of digital graphics exploration with vibrotactile directional cues.
The Effects of a Virtual Environment and Robot-Generated Haptic Forces on the Coordination of the Lower Limb During Gait in Chronic Stroke Using Planar and 3D Phase Diagrams

Previous studies have combined robot-controlled haptic tensile forces with virtual reality (VR) to produce gait adaptation and post-adaptation effects in spatiotemporal gait and postural outcomes in healthy young, elderly, and chronic stroke individuals. The present study focuses on kinematic and dynamic adaptation and post-adaptation effects of lower limb segment coordination, during and after a 15 N tensile force exposure by presenting two representations of 3D coordination - planar and phase diagrams. One chronic stroke subject (73 y.o., 8 months post-stroke, RH) and one age-matched control subject (71 y.o, RH) walked on a self-paced treadmill in a virtual environment holding a robot-controlled haptic leash. The paradigm consisted of a 30 s pre-force baseline epoch, followed by a 60 s tensile force and a 60 s post-force epoch. Both the chronic stroke and control subject showed evidence of changes in bilateral intersegmental coordination of the lower limb to accompany gait speed increases during force and post-force epochs. In particular, both subjects increased dorsiflexion of the non-dominant leg during and after the 15 N force exposure. Changes in limb segment coordination also corresponded to bilateral increases in 3D intersegmental trajectory areas. While there was no evidence of increased symmetry based on left and right leg plane comparisons, slight increases in angular velocity were noted just prior to and during the swing phase of the paretic leg during force and post-force epochs. These findings were further substantiated by Sobolev norms which increased bilaterally and proportionally for force and post-force epochs. Adaptation and post-adaptation effects seen in bilateral lower limb coordination when haptic forces were present and released suggest proportional increases in the kinematic and dynamic outcomes. Further investigation involving a wider range of chronic stroke functional levels should be conducted.