Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensitivity analysis of an availability model for disaster tolerant cloud computing system

Because of the dependence on Internet‐based services, many efforts have been conceived to mitigate the impact of disasters on service provision. In this context, cloud computing has become an interesting alternative for implementing disaster tolerant services due to its resource on‐demand and pay‐as‐you‐go models. This paper proposes a sensitivity analysis approach to assess the parameters that most impact the availability of cloud data centers, taking into account disaster occurrence, hardware and software failures, and disaster recovery mechanisms for cloud systems. The analysis adopts continuous‐time Markov chains, and the results indicate that disaster issues should not be neglected. Hardware failure rate and time for migration of virtual machines (VMs) are the critical factors pointed out for the system modeled in our analysis. Moreover, the location where data centers are placed has a significant impact on system availability, due to time for migrating VMs from a backup server.
Chemical Structure Elucidation from Mass Spectrometry by Matching Substructures

Chemical structure elucidation is a serious bottleneck in analytical chemistry today. We address the problem of identifying an unknown chemical threat given its mass spectrum and its chemical formula, a task which might take well trained chemists several days to complete. Given a chemical formula, there could be over a million possible candidate structures. We take a data driven approach to rank these structures by using neural networks to predict the presence of substructures given the mass spectrum, and matching these substructures to the candidate structures. Empirically, we evaluate our approach on a data set of chemical agents built for unknown chemical threat identification. We show that our substructure classifiers can attain over 90% micro F1-score, and we can find the correct structure among the top 20 candidates in 88% and 71% of test cases for two compound classes.
Fuzzy autopilot design using a multiobjective evolutionary algorithm

This paper details a Fuzzy-Feedback Linearisation controller applied to a non-linear missile. The design uses an evolutionary algorithm optimisation approach to a multiple model description of the airframe aerodynamics. A set of convex models is produced that map the vertex points in a high order parameter space (of the order of 16 variables). These are used to determine the membership function distribution within the outer loop control system by using a multi-objective evolutionary algorithm. This produces a design that meets objectives related to closed loop performance such as: steady state error, overshoot, settling and rising time. The evolutionary algorithm uses non-dominated sorting for forming a Pareto front of possible solutions. This paper shows that fuzzy controllers can be produced for engineering problems, with the multiobjective algorithm allowing the designer the freedom to choose solutions and investigate the properties of the system.
Factors Affecting Adherence to Antihypertensive Medication

Background Hypertension is a major contributor to the global disease burden of cardiovascular and cerebrovascular disease. The aim of this study was to determine demographic and clinical factors associated with adherence to antihypertensive medication. Methods From August 2012 to February 2015, we recruited 1,523 Korean patients with hypertension who visited family physicians. The study was conducted in 24 facilities located in urban and metropolitan areas. Of these facilities, two were primary care clinics and 22 were level 2 or 3 hospitals. Adherence was assessed using the pill count method; a cut-off value of 80% was used as the criterion for good adherence. Sociodemographic and lifestyle factors were compared between the adherent and nonadherent groups using the chi-square test for categorical variables and t-test for continuous variables. Binary logistic regression analysis was performed with medication adherence as the outcome variable. Results Of the 1,523 patients, 1,245 (81.7%) showed good adherence to antihypertensive medication. In the multivariate logistic analysis, age ≥65 years, exercise, treatment in a metropolitan-located hospital, being on ≥2 classes of antihypertensive medication and concomitant medication for diabetes, and a family history of hypertension or cardiovascular diseases were associated with good adherence. Patients who had a habit of high salt intake were less adherent to medication. Conclusion Multiple classes of antihypertensive medications, concomitant medication, and exercise were associated with good adherence to antihypertensive medication, and high salt intake was associated with poor adherence to antihypertensive medication. These factors should be considered to improve hypertension control.
Title-Based Extraction of News Contents for Text Mining

As a vital measure to obtain valuable information and intelligence, web news are flooding all corners of the Internet anytime, anywhere. Traditionally, templates or hand-designed features are utilized to extract the content from web pages, but these models have higher time cost and lower extensibility. Recently, many scholars leverage DOM-tree-based or text-density-based models to extract the contents which have better extensibility and lower time cost, but most of them are hard to extract the content accurately and completely and are easy to introduce the noises. In this paper, we propose a title-based web content extracting model TWCEM to extract the contents of each web page, which leverage the title information to extract the web content. Compared with other extraction model, TWCEM can filter the noises effectively and locate the content positions more accurately. In this experiment, we evaluate the proposed model on real-life websites, and TWCEM achieves state-of-the-art results and outperforms its competitors on both extraction performance and time cost.
A Deadly Disparity: A Comprehensive Assessment of the Black-White Infant Mortality Gap

We provide a comprehensive assessment of a striking disparity in the United States: The differential rate at which white and black infants die. We separate the overall mortality gap into three temporal components - fitness at birth, neonatal mortality, and post-neonatal mortality - and quantify the extent to which each of the components can be explained using a flexible reweighting method. Roughly 90 percent of the overall mortality gap is due to differential fitness at birth, little of which can be explained by racial differences in background characteristics. The remaining mortality gap stems from post-neonatal mortality differences, nearly all of which can be explained by background characteristics. The amount of the mortality gap that can be explained by background characteristics has declined sharply over the past two decades, largely due to background characteristics explaining less of the racial disparity in extremely low-fitness births.
The Effects of Natural Antioxidants on Protein Oxidation, Lipid Oxidation, Color, and Sensory Attributes of Beef Patties during Cold Storage at 4℃

Abstract This study aimed to investigate the use of golden thread extract (GTE), clove extract (CE), and commercially available nitrite for retarding lipid and protein oxidation and for maintaining color stability and sensory attributes in beef patties stored at 4℃. GTE, CE, and nitrite treatment samples were found to be efficient in retarding lipid oxidation as all three treatments resulted in low thiobarbituric acid reactive substance (TBARS) content (p<0.05). By using GTE, CE, and nitrite into beef patties, protein oxidation was not developed. Incorporation of GTE and CE into beef patties maintained color stability by protecting against the decrease of L*, a*, b*, chroma, and hue angle values and exhibited significant influence on sensory characteristics, including color and odor of beef patties (p<0.05). Compared to commercially available nitrite, GTE and CE were more effective as antioxidants for inhibiting lipid oxidation, and preserving color stability of fresh beef patties. The study indicated that GTE and CE could be utilized efficiently to extend the shelf life of beef patties.
MIR sequences recruit zinc finger protein ZNF768 to expressed genes

Abstract Mammalian-wide interspersed repeats (MIRs) are retrotransposed elements of mammalian genomes. Here, we report the specific binding of zinc finger protein ZNF768 to the sequence motif GCTGTGTG (N20) CCTCTCTG in the core region of MIRs. ZNF768 binding is preferentially associated with euchromatin and promoter regions of genes. Binding was observed for genes expressed in a cell type-specific manner in human B cell line Raji and osteosarcoma U2OS cells. Mass spectrometric analysis revealed binding of ZNF768 to Elongator components Elp1, Elp2 and Elp3 and other nuclear factors. The N-terminus of ZNF768 contains a heptad repeat array structurally related to the C-terminal domain (CTD) of RNA polymerase II. This array evolved in placental animals but not marsupials and monotreme species, displays species-specific length variations, and possibly fulfills CTD related functions in gene regulation. We propose that the evolution of MIRs and ZNF768 has extended the repertoire of gene regulatory mechanisms in mammals and that ZNF768 binding is associated with cell type-specific gene expression.
GUV-AP: multifunctional FIJI-based tool for quantitative image analysis of Giant Unilamellar Vesicles

Motivation Giant Unilamellar Vesicles (GUVs) are widely used synthetic membrane systems that mimic native membranes and cellular processes. Various fluorescence imaging techniques can be employed for their characterization. In order to guarantee a fast and unbiased analysis of imaging data, the development of automated recognition and processing steps is required. Results We developed a fast and versatile Fiji-based macro for the analysis of digital microscopy images of GUVs. This macro was designed to investigate membrane dye incorporation and protein binding to membranes. Moreover, we propose a fluorescence intensity-based method to quantitatively assess protein binding. Availability The ImageJ distribution package FIJI is freely available online: https://imagej.net/Fiji. The macro file GUV-AP.ijm is available at https://github.com/AG-Roemer/GUV-AP. Supplementary information Supplementary data are available at Bioinformatics online.
Detection and Classification of Retinal Diseases in Spectral Domain Optical Coherence Tomography Images based on SURF descriptors

Optical Coherence Tomography (OCT) is a non-invasive eye-imaging modality for detecting macular edema both in its early and advanced stages. The main aim of this work is to present the automatic detection of edema of the retinal layers particularly around the macula in diabetic patients. After detection and extracting certain features in the OCT retinal images a classification of the type of Diabetic Macular Edema is done. In this method during preprocessing stage we remove the speckle noise followed by flattening and cropping of the image is done. Then this is followed by Speeded up robust feature extraction. The extracted features are then classified using Support Vector Machine binary classifier as normal or abnormal and thus having Diabetic Macular Edema. This technique has been applied for 25 normal and 45 abnormal OCT images. The results show that this method accurately detected edema diseases in between the layers in the retinal. Then we could classify them using Support Vector Machine as normal or abnormal. Experimental results shows that an average retinal disease detection accuracy of 99% for Support Vector Machine (SVM) classifier. Thus, this algorithm can be used by ophthalmologists in early detection of Macular Edema.
Advanced Processing Techniques for Detection and Classification of Skin Lesions

The paper presents how the skin cancer in forms of melanoma can be identified based on the digital image processing of the Iesion. The solution is based on the extraction of seven features (deterministic and statistic type) from the image of a skin lesion: perimeter, area, diameter, fractal dimension, lacunarity, histogram of oriented gradients, and local binary patterns. Each feature has attached a specific classifier and the diagnosis is obtained by using a voting scheme in the final classifier. The experimental results on a free database demonstrate that the method provides a high accuracy.
Impaired bone healing following treatment of established nonunion correlates with serum cytokine expression

Delayed union and nonunion are a significant concern in long bone fractures and spinal fusions. Treatment of nonunion often entails multiple revision surgeries that further increase the financial, physical, and emotional burden on patients. The optimal treatment strategy for nonunions remains unclear in many cases, and the risk of complications after revision procedures remains high. This is in part due to our limited understanding of the biological mechanisms that inhibit proper bone healing and lead to nonunion. And yet, few preclinical models directly investigate how healing is impacted after establishment of nonunion, with most instead primarily focusing on treatment immediately after a fresh bone injury. Here, we utilized a critical size femoral defect model in rats where treatment was delayed 8 weeks post‐injury, at which time nonunion was established. In this study, acute and delayed treatments with bone morphogenetic protein‐2 (BMP‐2) were assessed. We found that delayed treatment resulted in decreased bone formation and reduced mechanical strength compared to acute treatment, even when BMP‐2 dose was increased by 2.5 times the acute treatment dose. Interestingly, serum cytokine analysis at 12 weeks post‐treatment revealed signs of chronic immune dysregulation after delayed treatment. In particular, non‐responders (rats that did not exhibit defect bridging) demonstrated higher overall expression of inflammatory cytokines, including TNFα and IL‐1β, compared to responders. These findings suggest that re‐establishing long‐term immune homeostasis may be critical for successful bone healing, particularly after nonunion. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:299–307, 2019.
A 120 pJ/bit  $\Delta\Sigma$ -Based 2.4-GHz Transmitter Using FIR-Embedded Digital Power Amplifier

An energy efficient <inline-formula> <tex-math notation="LaTeX">${\Delta \Sigma }$ </tex-math></inline-formula>-based transmitter using the digital power amplifier (DPA) with the embedded FIR filtering is proposed in this brief. Compared to the conventional <inline-formula> <tex-math notation="LaTeX">${\Delta \Sigma }$ </tex-math></inline-formula>-based transmitters, the proposed architecture has the advantage of low power consumption with the DPA serving as the mixer, filter, and RF amplifier simultaneously. The embedded FIR filter provides improved suppression on the out-of-band quantization noise. The transmitter has been implemented in 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS technology with a die area of 1.5 mm<sup>2</sup>. The fabricated transmitter consumes 2.37 mW power from 1.2-V power supply when outputting 20 Mb/s 64 QAM signal centered at 2.4 GHz with a power level of –15 dBm. The overall energy efficiency is 120 pJ/bit, which outperforms most of those recently published low power 2.4-GHz transmitters.
Investigating an A-star Algorithm-based Fitness Function for Mobile Robot Evolution

One of the factors that affect the success of Evolutionary Robotics (ER) is the way fitness functions are designed to operate. While needs-based custom fitness functions have been developed, most of the time they have been defined in simpler mathematical functions to reduce the computation time. In this paper, we hypothesize that an incremental fitness function based on established techniques in specific task domains in robotics will aid the evolution process. An A-star algorithm-based fitness function for path planning is designed and implemented for evolving the body plans and controllers of robots for navigation and obstacle avoidance tasks. It has been shown that using this concept, fitter robots have evolved in most cases when compared to simple distance-only based fitness functions. However, due to variable performance of the evolver with the A-star fitness function, the results are inconclusive. We also identify problems associated with the fitness function and make recommendations for designing future fitness functions based on observations of the experiments.
Multipath SQRT Congestion Control for Multimedia Streaming

In this paper, we introduce a multipath congestion control algorithm for audio/video streaming, called MPSQRT. MPSQRT is derived from single-path Square-Root TCP (SQRT TCP) designed for multimedia streaming, where a lower variation of sending rate is important. Based on the fluid model of SQRT for single-path, we extend it towards spreading concurrently packets across multiple paths ensuring load-balancing and fairness to SQRT at shared bottleneck. Through simulations, we evaluate the proposed protocol under various network conditions.
Designing Interaction for Chatbot-Based Conversational Commerce with User-Centered Design

Chatbot-based conversational commerce which allows buyers to do online shopping by conversing with chatbot through messaging application has been growing in the e-commerce industry. However, based on studies conducted in this paper, findings show that interaction design of the existing conversational commerce are still lacking in various areas. Therefore, an interaction and interface design for chatbot-based conversational commerce are developed in this study using user-centered design. The outcome of this study is a prototype that fulfills the usability goal and the user experience goal defined for chatbot-based conversational commerce on mobile platform especially for Indonesian users. We conduct usability testing to evaluate the prototype. The results show that the prototype fulfills the defined usability goals and user experience goals as 100% users agree that the prototype is effective to use, efficient to use, easy to learn, enjoyable and helpful, although only 83.3% users agree that the prototype is safe to use.
Joint Rate and BER Scheduling Resource Allocation for Wireless Communication Systems

Most resource allocation algorithms used in wireless communication networks arrange each user’s transmission rate based on the type of user data but neglect the quality of the users’ communication channels. In this paper, the relationship between rate and bit error rate (BER) was established by considering both the communication channel decay and the error control mechanism. A resource allocation based on joint rate and BER scheduling (JRBS) was proposed to satisfy the transmission power requirement generated by the quality change in communication channels. The JRBS analyzes the maximum transmission capacity requirement in each time slot, determines the variable capacity and the available channels in each time slot, and decides the transmission priority of data packets based on various quality of service (QoS). Ultimately, the JRBS algorithm improves system capacity to satisfy the BER and QoS of various services by the simulations.
Molecular mechanism of fusion pore formation driven by the neuronal SNARE complex

Significance SNARE proteins facilitate the synaptic vesicle fusion with the plasma membrane. The structure of the fusion pore and the mechanism of its formation are not clearly understood. Using coarse-grained molecular dynamics simulations of a nanodisc bridged by four SNARE proteins to a bilayer, we identify the importance of the C termini of synaptobrevin 2 and syntaxin 1 in facilitating fusion pore formation. The conductances of the simulated fusion pores agree with experimental values, and SNARE mutants that inhibit fusion experimentally fail to form fusion pores in the simulations. Confinement of SNARE complexes is essential for rapid fusion pore formation, but release of the SNARE complexes from this confinement is required for fusion pore expansion. Release of neurotransmitters from synaptic vesicles begins with a narrow fusion pore, the structure of which remains unresolved. To obtain a structural model of the fusion pore, we performed coarse-grained molecular dynamics simulations of fusion between a nanodisc and a planar bilayer bridged by four partially unzipped SNARE complexes. The simulations revealed that zipping of SNARE complexes pulls the polar C-terminal residues of the synaptobrevin 2 and syntaxin 1A transmembrane domains to form a hydrophilic core between the two distal leaflets, inducing fusion pore formation. The estimated conductances of these fusion pores are in good agreement with experimental values. Two SNARE protein mutants inhibiting fusion experimentally produced no fusion pore formation. In simulations in which the nanodisc was replaced by a 40-nm vesicle, an extended hemifusion diaphragm formed but a fusion pore did not, indicating that restricted SNARE mobility is required for rapid fusion pore formation. Accordingly, rapid fusion pore formation also occurred in the 40-nm vesicle system when SNARE mobility was restricted by external forces. Removal of the restriction is required for fusion pore expansion.
Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressions

With more than 300 million people depressed worldwide, depression is a global problem. Due to access barriers such as social stigma, cost, and treatment availability, 60% of mentally-ill adults do not receive any mental health services. Effective and efficient diagnosis relies on detecting clinical symptoms of depression. Automatic detection of depressive symptoms would potentially improve diagnostic accuracy and availability, leading to faster intervention. In this work, we present a machine learning method for measuring the severity of depressive symptoms. Our multi-modal method uses 3D facial expressions and spoken language, commonly available from modern cell phones. It demonstrates an average error of 3.67 points (15.3% relative) on the clinically-validated Patient Health Questionnaire (PHQ) scale. For detecting major depressive disorder, our model demonstrates 83.3% sensitivity and 82.6% specificity. Overall, this paper shows how speech recognition, computer vision, and natural language processing can be combined to assist mental health patients and practitioners. This technology could be deployed to cell phones worldwide and facilitate low-cost universal access to mental health care.