Miracle-dz/UniFAR-FTUs huggingface dataset:
Klebsiella pneumoniae disassembles host microtubules in lung epithelial cells

Klebsiella pneumoniae raises significant concerns to the health care industry as these microbes are the source of widespread contamination of medical equipment, cause pneumonia as well as other multiorgan metastatic infections and have gained multidrug resistance. Despite soaring mortality rates, the host cell alterations occurring during these infections remain poorly understood. Here, we show that during in vitro and in vivo K. pneumoniae infections of lung epithelia, microtubules are severed and then eliminated. This destruction does not require direct association of K. pneumoniae with the host cells, as microtubules are disassembled in cells that are distant from the infecting bacteria. This microtubule dismantling is dependent on the K. pneumoniae (Kp) gene ytfL as non‐pathogenic Escherichia coli expressing Kp ytfL disassemble microtubules in the absence of K. pneumoniae itself. Our data points to the host katanin catalytic subunit A like 1 protein (KATNAL1) and the katanin regulatory subunit B1 protein (KATNB1) as the gatekeepers to the microtubule severing event as both proteins localise specifically to microtubule cut sites. Infected cells that had either of these proteins knocked out maintained intact microtubules. Taken together, we have identified a novel mechanism that a bacterial pathogen has exploited to cause microtubule destruction within the host epithelia.
Application of the Lean Startup Methodology in Project Management at Launching New Innovative Products

It is analyzed the Lean startup methodology and its application in launching new competitive, innovative products or business models. The particular attention is paid to methods for identifying the target audience and methods for confirming the hypotheses of the business model's effectiveness. The possibility of integration of Lean Startup methods is described, the essence of which is to maximize risk prevention and adaptation of marketing strategy, flexible methodology of Agile, breakdown of laborious tasks into simpler subtasks and Customer Development model, as well as testing a business model.
Validation of Safety Necessities for a Safety-Bag Component in Experimental Autonomous Vehicles

This work presents a study to improve the safety of experimental autonomous vehicles in the Heudiasyc laboratory. This work presents risk analyses showing that the use of our vehicles involves significant risks during experiments, and that integrating an Independent Safety Component called Safety-Bag in the vehicle architecture can significantly reduce these risks. The Safety-Bag carries out the on-line verification of safety necessities by checking the vehicle's current state with safety rules and taking or disabling actions to ensure a safe behavior. In our work, we present and we apply two methods for risk analysis (FMEA and HazOp-UML) to design these safety necessities in the case of experimental autonomous vehicles. We also present the validation of two safety necessities through fault injection experiments with a robotized Fluence vehicle and a vehicle in the loop testbed.
Joint Resource Allocation, Computation Offloading, and Path Planning for UAV Based Hierarchical Fog-Cloud Mobile Systems

In this paper, the computation offloading problem for the hierarchical fog-cloud computing (FCC) system with unmanned aerial vehicles (UAVs) is studied. The hierarchical FCC, which exploits both centralized and distributed computing architectures, is very promising to support computation offloading in emerging computation-demanding mobile applications. In our design, UAVs integrating computing platforms act as small distributed clouds while the macro base station (BS) integrates a more powerful central cloud server. Furthermore, the multiple input multiple output (MIMO) technology is employed for data communication. We assume that mobile users (UEs) and (UAVs) can change their locations over time and we consider the joint task offloading, user-cloud/cloudlet association, transmit power allocation, and path planning to minimize the total weighted consumed power of the system. To tackle the underlying non-convex mixed integer non-linear program (MINLP), we propose an iterative two-phase algorithm. Specifically, we iteratively solve the user-cloud/cloudlet association problem in the first phase and address the joint resource allocation, path planning problem in the second phase. Furthermore, we employ the difference of convex (DC) optimization method in the second phase to approximate the non-convex bilinear functions and propose to transform the non-convex INLP to the integer linear program (ILP) in the first phase. Numerical studies confirm that the proposed design for the FCC architecture achieves great performance benefits for executing mobile computation tasks.
An Improved Localization Algorithm Based on CHAN with High Positioning Accuracy in NLOS-WGN Environment

The CHAN algorithm is a non-recursive hyperbolic equations solution with an analytic expression solution.The advantage of this algorithm is that the positioning accuracy is high under the ideal environment where the noise obeys the Gaussian distribution(WGN). However, in the actual environment with non-line-of-sight (NLOS) factors, the positioning accuracy of the CHAN algorithm is seriously degraded.To solve the above problems, this paper proposes an improved RWCHAN-GA positioning algorithm based on the principle of residual weighting and genetic algorithm.The simulation experiments show that in the NLOS-WGN environment, the algorithm has a significant improvement in positioning accuracy with respect to the CHAN algorithm.
A novel algorithm for a grammar model checking using statistical Markov model

The sign language research area shows many significant results in recent years. Mostly, the research of sign language is built for signer independent are focuses in sign and video corpus. Rarely, the research discusses about ‘pola’ grammar of sign language. The purpose of this paper is to contribute the topic of ‘pola’ grammar for sign language. The ‘pola’ grammar is used with a rule based algorithm and a Markov model and rule based. The model is used to validate the Malay sign language. The input of words and sentences of Malay sign language are taken from books, newspapers, and magazines by users either in form of single words or a paragraph of sentences. Training and testing words and sentences to check the ‘pola’ grammar of Malay sign language is implemented to gain the results of experiences. Simple sentences and complex sentences of Malay sign language are designed for a test case. The comparison of two models between rule based algorithm and a Markov model and rule based has been investigation based on the accuracy of tagset of Malay sign language. The accuracy of a Markov model and rule based shown a better result than rule based algorithm when used to check simple and complete sentences of Malay sign language.
A Blockchain-based Architecture Framework for Secure Sharing of Personal Health Data

Health information exchanges have been popular for some time with their advantages known and widely researched. In spite of their utility in increasing provider efficiency and decreasing administrative costs, one challenge that has persisted is the data owners inability to control data after transmission. The lack of technical mechanisms to effectively control patients’ health data in the network significantly affects participation of health and medical institutions while perpetrating the silo-based data management that locks value and potential inherent in the data. This not only affects researchers due to the lack of data for research and analysis but the quality of life of patients.We present a blockchain-supported architectural framework for secure control of personal data in a health information exchange by pairing user-generated acceptable use policies with smart contracts. We highlight the merits of our system, its user-centric focus and also show experimental results along with directions for extending our work. The framework introduces minimal risk to data by architecting a mechanism for controlling data after sharing. In adopting our framework, health service providers can deliver a stronger assurance for data management than is possible with current systems.
Criso: An Incremental Scalable and Cost-Effective Data Center Interconnection by Using 2-Port Servers and low-end Switches

With the data growing explosively, data center networks (DCN) have to possess the characteristics of incrementally scalable, cost-efficient, high network capacity and fault tolerance. However, the widely used DCNs can not meet the demands above. In this paper, we propose a new type of data center topology named Criso to settle the challenges. Different from the existed works, Criso has the advantages of both switch-centric topologies (servers do not participate in routing) and the server-centric topologies (the scalability is not limited by the ports of switches). It is constructed based on pods, The internal structure of each pod is the same and there are only four external interfaces. By applying such structure, a pod-based and fault-tolerant routing algorithm is designed to handle multiple types of failures. Criso is hierarchically, recursively defined and high-network capacity which can scale up to millions of nodes. The analysis results demonstrate that the Criso model is significantly superior to four state-of-the-art data center structures in terms of the network capacity, scalability, cost, power consumption and other static characteristics. Criso achieves the target of low-cost, low-energy consumption and highly-scalability simultaneously.
‘We must ignore ePrescription’: An empirical analysis of Czech physicians’ attitudes against eHealth

The use of Information Technologies (IT) for drug prescribing and dispensing is commonly referred to as ePrescription. This paper focuses on the negative attitudes of Czech physicians towards the upcoming stage of ePrescription ("eRecept") adoption. The goal of this stage had been to make ePrescription mandatory throughout the Czech Republic from January 2018. However, this was reconsidered due to physicians’ resistance that culminated in the last few months of 2017. Our qualitative, discourse-oriented analysis focused on this period shows that physicians’ criticism can be categorized into five main themes. These are as follows: Deterioration of care; Missing benefits; Medicine is not about IT; High costs of adoption & contract overpricing; State despotism. In addition, the paper contributes to Health IT research by giving some initial hints on the role of specific contextual factors that possibly influence eHealth adoption patterns in the Visegrad group of countries (i.e. Czech Republic, Hungary, Poland, and Slovakia).
SEM PLS Models for Performance Analysis of Manufacturing Companies

This study examines the influence of external factors on the financial performance of manufacturing companies. In this study, three external factors allegedly affect the financial performance of manufacturing companies. These factors were derived from several aspects, namely macroeconomics, import of manufacturing products, and prices of energy fuels. In analyzing to measure the relationship is used Structural Equation Modeling Partial Least Squares (SEM PLS). This study uses secondary data based on the company's financial statements with the criteria of manufacturing companies listed on the Indonesia Stock Exchange and has published the company's annual report in 2010–2015. The results of this study indicate that the import of manufacturing products negatively and significantly affect the financial performance of manufacturing companies. Macroeconomics and energy fuel prices have no significant effect on the performance of manufacturing companies. The results of this study can be used by companies as a basis for consideration in decision making.
Droop Control Strategy with a Novel Characteristic Model

Droop control strategy with a novel characteristic model is proposed in this paper. Using a communication line to control the parallel system, the flexibility of the system is limited and error can occur due to the external disturbance. The droop is the key function to control the parallel operating system. Conventional droop control model is generally based on the linear function. The reference value changes depending on the droop coefficient and power variation, and also the droop coefficient is related to the steady-state errors and dynamic response. Proposed droop characteristic model consists of the linear and quadratic functions. It can minimize the variation of the output reference in a specific power range and also improve the transient dynamic response. The proposed droop characteristic model is verified by the simulation result.
The Impact of Timing of Tympanostomy Tube Placement on Sequelae in Children With Cleft Palate

Objective: To describe the impact of timing of tympanostomy tube insertion on the number of tubes received and complications in children with routine tube placement. Design: Retrospective case series. Setting: Tertiary care children’s hospital. Participants: Records from a consecutive sample of 401 children with cleft palate were reviewed. Sixty-five patients with isolated cleft palate and 82 patients with cleft lip and palate had follow-up until 5 years of age and were included. Interventions: Tympanostomy tubes. Main Outcome Measure(s): Number of tubes received and tube-related complications. The hypothesis was formulated prior to data collection. Results: Males comprised 55.8% of included patients, and tubes were placed in 98.6% of patients at a median age of 6.5 months. Effusion was documented at first tube placement for 96.5% of patients. Most (67.4%) patients required replacement of tubes, and 10.6% required long-term tubes. Complications included otorrhea (71.0%), myringosclerosis (35.2%), granulation (22.8%), perforation (17.9%), retained tubes (5.5%), and cholesteatoma (1.4%). Cleft lip and palate (P < .001) and otorrhea (P = .023) were associated with tube placement before palatoplasty. Patients with tube placement before palatoplasty (P = .033), genetic disorders (P = .007), failed newborn hearing screen (P = .012), otorrhea (P < .001), and granulation (P < .001) received more tubes. Conclusions: Nearly universal effusion in patients with cleft palate supports the need for routine tube placement. The potential for otorrhea and requiring more tubes should be weighed against the risks associated with prolonged effusion when considering tube placement before palatoplasty.
Visual Descriptors Based Real Time Hand Gesture Recognition

Vision based gesture recognition technologies based on structural shape descriptors and contour analysis methods have been described in this paper. Gesture have been the basic mode of communication since ancient times, until vocal communication was developed. Skin color detection technique for hand segmentation based on YCbCr methods has proved effective enough in real environment. Haar based classifier for face detection is used to remove one of the largest contour besides hand. Median filtering along with morphological operations applied here alleviates the effects of noise to a great extend without losing the boundary information in the image. The binary image formed after this only consists of two largest skin colored contours, mostly one or two hand contour image. Over this resultant contour, structural analysis based on contour shape along with convex hull and convexity defect formation together with geometrical analysis based on angle between convexity defect point and that of hand centroid helps in determining the number of fingertips. Based on number of finger-tips, the gestures can be classified into various gestures. Various applications in real time hand gesture recognition include American Sign Language (ASL) recognition, human computer interaction (HCI), robotics, real time traffic signal control and remotely controlling devices such as TVs, air conditioners, fan using hand gestures.
Enhanced Approach of Using Custom Heuristic Rules with Stanford and Naive Bayes Classification Technique

Expressive nature of user provides opinions, feedbacks, ideas or suggestions for some entities on the internet and hence there is a constant growth in the amount of knowledge provided for those entities. To get to know about the entities on the internet site, user will go through reviews of the other users provided for entities. The systems these days cannot only depend on user's star rating, so the opinions have to be analyzed using comments and natural language processing. Opinion mining works for classifying the reviews based on polarity. The basic problem of opinion mining is identifying the required aspect in order to find the exact opinion. In this paper, reviews are collected for smart phone and hotel entities. Heuristic rules for noun and adjective phrases from the reviews are created using regular expression on parts-of-speech (POS) tags. Enhanced heuristic rules are implemented with Stanford natural language processing (SNLP) classification and are compared with Naive Bayes (NB) classification. The results proved the high accuracy of enhanced Heuristic rules approaches using SNLP classifier over NB for the similar entities.
Studying Permission Related Issues in Android Wearable Apps

Wearable devices are becoming increasingly popular; these devices host software that is known as wearable apps. Wearable apps could be packaged alongside handheld apps, hence they must be installed on the accompanying device (e.g., smartphone). This device dependency causes both apps to be also tightly coupled. Most importantly, when a wearable app is distributed by embedded it in a handheld app, Android Wear platform requires to include the wearable permission also in the handheld app which is error-prone. In this paper, we defined two permission issues related to wearable apps-namely permission mismatches and superfluous features. To study the permission related issues, we propose a technique to detect permission issues in wearable apps. We implement our technique in a tool called Permlyzer, which automatically detects these permission issues from an app's APK. We run Permlyzer on a dataset of 2,724 apps that have embedded wearable version and 339 standalone wearable app. Our result shows that I) 6% of wearable apps that request permissions are suffering from the permission mismatching problem; II) out of the apps that requires underlying features, 523 (52.4%) of handheld apps and 66 (80.5%) of standalone wearable apps have at least one superfluous feature; III) all the studied apps missed a declaration of underlying features for one or more of their permissions, which shows that developers may not know the mapping between the permissions they request and the hardware features. Additionally, in a survey of wearable app developers, all of the developers that responded mention that having a tool like Permlyzer, that detect permission related issues would be useful to them. Our results contribute to the understanding of permissions related issues in wearable apps, in particular, proposing a technique to detect permission mismatch and superfluous features.
Modeling Enzyme Controlled Metabolic Networks in Rapidly Changing Environments by Robust Optimization

Constraint-based methods, such as the flux balance analysis (FBA), are widely used to model cellular growth processes without relying on knowledge of regulatory features. Regulation is instead substituted by an optimization problem to maximize a biological objective such as biomass accumulation. A recent extension to these methods is called dynamic enzyme-cost FBA (deFBA). This fully dynamic modeling method allows to predict optimal enzyme levels and reaction fluxes under changing environmental conditions. However, this method was designed for well-defined deterministic settings in which dynamics of the environment are exactly known. In this letter, we present a theoretical framework called the robust deFBA which extends the deFBA to handle uncertainty in nutrient availability. We achieve this by combining deFBA with multi-stage model predictive control which explicitly captures the evolution of uncertainty by a scenario tree. The resulting method is capable of predicting robust optimal gene expression levels for rapidly changing environments. We apply these algorithms to a model of the core metabolic process in bacteria under alternating oxygen availability.
Computing Multiplicative Spanners Efficiently for a Class of Simple Graphs

The notion of a multiplicative $t$ – spanner was defined by David Peleg and Alejandro Schäfer in 1989. A vertex-spanning sub graph $S$, of a simple graph $H(V, E)$ in which the pairwise distance between any two vertices is less than or equal to $t$ times that in $H$ is called a puerly multiplicative $t$ – spanner. The real valued parameter $t(> 1)$ denotes the maximum possible multiplicative stretch of $u-v$ distances in the spanner $S$. Although the combinatorial construction of a t-spanner with $m$ or fewer edges for a given $m$ and $t$ (MTS problem henceforth) is known to be difficult, it is possible to construct MTS efficiently for graphs which satisfies certain properties. In this paper we characterize an infinite family of graph for which the MTS problem can be solved efficiently. Based on the characterization we posit a $\text{O}(n)$ algorithm for constructing an MTS.
Analyzing the Shared Information in Social Networks

We use a concise stochastic calculus to describe the interactions among the participants in web-based social networks. The stochastic nature of these interactions is given by the fact that the duration of the interaction can be represented by a stochastic rate of an exponential distribution. To ensure a more secure sharing of information in the systems described by our stochastic calculus, we use a probabilistic model checker to verify various (quantitative) properties of their dynamics.
Soil Bacteria Isolated From Tunisian Arid Areas Show Promising Antimicrobial Activities Against Gram-Negatives

Arid regions show relatively fewer species in comparison to better-watered biomes, but the competition for the few nutrients is very distinct. Here, in total 373 bacterial strains were isolated from rhizospheric soils obtained from three different sampling sites in Tunisia. Their potential for the production of antimicrobial compounds was evaluated. Bacterial strains, showing antibacterial activity against pathogenic bacteria, were isolated from all three sites, one strain from the Bou-Hedma national park, 15 strains from Chott-Djerid, and 13 strains from Matmata, respectively. The dominant genus was Bacillus, with 27 out of 29 strains. Most interestingly, 93% of the isolates showed activity against Gram-positive and Gram-negative test bacteria. Strain Bacillus sp. M21, harboring high inhibitory potential, even against clinical isolates of Gram-negative bacteria, was analyzed in detail to enable purification and identification of the bioactive compound responsible for its bioactivity. Subsequent HPLC-MS and NMR analyses resulted in the identification of 1-acetyl-β-carboline as active component. Furthermore, fungicides of the bacillomycin and fengycin group, which in addition show antibiotic effects, were identified. This work highlights the high potential of the arid-adapted strains for the biosynthesis of specialized metabolites and suggest further investigation of extreme environments, since they constitute a promising bioresource of biologically active compounds.