Miracle-dz/UniFAR-FTUs huggingface dataset:
Roadside bombs and routes to life: The experience of Turkey

An improvised explosive device (IED) is a kind of homemade bomb frequently deployed in roadside bombings and not used in conventional warfare. IEDs consist of explosives (which may be conventional explosives such as artillery, or a number of other materials) connected to a detonator. These bombs are usually hidden along the edges or in the ditches of main roads and are detonated when a vehicle passes by. In general, all explosives produce their detrimental effects in one of several well-defined ways (1,2). However, roadside bombs differ from other explosives in that their victims are most often in vehicles, which presents not one but two potential dangers: blast and blunt traumas.
Evaluation of Brain Response during Head Impact in Youth Athletes Using an Anatomically Accurate Finite Element Model.

During normal participation in football, players are exposed to repetitive subconcussive head impacts, or impacts that do not result in signs and symptoms of concussion. To better understand the effects of repetitive subconcussive impacts, the biomechanics of on-field head impacts and resulting brain deformation need to be well characterized. The current study evaluates local brain response to typical youth football head impacts using the atlas-based brain model (ABM), an anatomically accurate brain finite element (FE) model. Head impact kinematic data were collected from three local youth football teams using the Head Impact Telemetry (HIT) System. The azimuth and elevation angles were used to identify impacts near six locations of interest, and low, moderate, and high acceleration magnitudes (5th, 50th, and 95th percentiles, respectively) were calculated from the grouped impacts for FE simulation. Strain response in the brain was evaluated by examining the range and peak maximum principal strain (MPS) values in each element. A total of 40,538 impacts from 119 individual athletes were analyzed. Impacts to the facemask resulted in 0.18 MPS for the high magnitude impact category. This was 1.5 times greater than the oblique impact location, which resulted in the lowest strain value of 0.12 for high magnitude impacts. Overall, higher strains resulted from a 95th percentile lateral impact (41.0g, 2556 rad/sec2) with two predominant axes of rotation than from a 95th percentile frontal impact (67.6g, 2641 rad/sec2) with a single predominant axis of rotation. These findings highlight the importance of accounting for directional dependence and relative contribution of axes of rotation when evaluating head impact response.
Apoptosis Induction of Agave lechuguilla Torrey Extract on Human Lung Adenocarcinoma Cells (SK-LU-1)

In this study, an ethanol extract of Agave lechuguilla was evaluated against six carcinogenic cell lines (HCT-15, MCF-7, PC-3, U-251, SK-LU-1 and K-562) with an inhibition of 75.7 ± 2.3% against the SK-LU-1 line. Based on the previous result, the extract was hydrolyzed and fractionated, to which the IC50 was determined; the cell line was more sensitive to the fractionated extract with an IC50 6.96 ± 0.15 µg/mL. Characterization by mass spectrometry showed the presence of kaempferol, quercetin and a flavonoid dimer formed by afzelechin-4β-8-quercetin, according to the generated fragmentation pattern. The fractionated extract presented cell death by apoptosis with 39.8% at 24 h. Molecular docking was performed with the molecules found to try to describe cell death by apoptosis through death receptors such as FasCD95, TNF-R1, DR4/5 and blocking signaling on the EGFR and K-Ras MAPK/ERK pathway, as well as through the intrinsic pathway activating tBID, which promotes the amplification of the apoptotic signal due to the activation of caspase-3, and consequently caspase-7. In addition to the activation of the IIb complex associated with cell death due to necroptosis.
Markov chain Monte Carlo for integrated face image analysis

This PhD thesis is about the integration of different methods to fit a statistical model of human faces to a single image. I propose to take a probabilistic view on the problem and implement and evaluate an integrative framework for face image explanation based on a class of methods known as Data-Driven Markov Chain Monte Carlo. The framework is based on the propose-and-verify architecture of the Metropolis-Hastings algorithm. Probabilistic inference replaces traditional optimization methods and conceptually shifts the goal of face explanation from obtaining the optimal parameter set to extracting measures of the posterior distribution. The probabilistic view opened the process for deeper insights like the need of a background model and richer likelihood models. Within this framework, different methods are implemented and evaluated specifically for face image explanation with the 3D Morphable Model and face and feature point detection. The Markov Chain Monte Carlo integration method is able to algorithmically reproduce existing fitting algorithms as well as capable of dealing with unreliable and differently shaped information sources. The integration of Bottom-Up information into the adaption process leads to more robust results than a simple feed-forward combination of the methods and culminates into a fully automatic face image explanation method, independent of user-provided initialization. A full-system application leads to a fully automatic and general face recognition application with state of the art results.
Myocardial constructive work is impaired in hypertrophic cardiomyopathy and predicts left ventricular fibrosis

The estimation of myocardial work by pressure strain loops (PSLs) is a totally new non‐invasive approach to assess myocardial performance, and its role in patients with hypertrophic cardiomyopathy is unknown. The aims of the present study are therefore: (a) to compare myocardial work in patients with non‐obstructive hypertrophic cardiomyopathy (HCM) and in a subset of age‐matched healthy controls and (b) to assess the correlation between myocardial work and left ventricular (LV) fibrosis.
Alterations of immunoglobulin G and immunoglobulin M levels in the breast milk of mothers with exclusive breastfeeding compared to mothers with non‐exclusive breastfeeding during 6 months postpartum: The Jordanian cohort study

The purpose of this study was to measure changes in the concentration of immunoglobulin G (IgG) and immunoglobulin M (IgM) in the mature breast milk of Jordanian mothers during the first 6 months after giving birth between exclusively breastfeeding (EBF) mothers and non‐exclusively breastfeeding (non‐EBF) mothers.
Coverage and Rate Probability in Hexagonal Cell Structure

Fractional Frequency Reuse (FFR) is an interference coordination well suited to OFDMA based wireless network where in cells is divided into special region with various frequency reuse factors. The research focuses at understanding the concept as well working of FFR in Hexagonal geometrical structure for analytical frame work and comparison. The project plan includes the extensive literature review of both concepts as well as previous research works. The study leads to the need of a simulator for examining and analyzing FFR in orthogonal and synchronous cellular systems. In comparison with the traditional grid model for FFR in cellular system, the hexagonal geometry is much reliable with omni base stations. For this, designing a simulator for the same proves to be very useful in the industry.
Review of Image Classification Methods and Techniques

Unsupervised region become most challenging area in image processing. Data, object and image classification is a very important task in image processing. If any image has noisy content or its contain blurry data, so it is very difficult to classify these kinds of images. Classification is nothing but just the categorization of same kind of data in same category. Digital image processing introduces many techniques which can classify the data, but if image is blurry or noisy so they can not able to provide the satisfactory results. In this survey paper three main classification methods consider, Supervised learning, unsupervised learning, Semi-supervised Learning. The main motive of this literature survey is to give a brief comparison between different image classification techniques and methods. Finally it has shown that Semi-Supervised Biased Maximum Margin Analysis classifies the images more accurately even if they contain blurry or noisy image.
Relational transfer across reinforcement learning tasks via abstract policies.

When designing intelligent agents that must solve sequential decision problems, often we do not have enough knowledge to build a complete model for the problems at hand. Reinforcement learning enables an agent to learn behavior by acquiring experience through trial-and-error interactions with the environment. However, knowledge is usually built from scratch and learning the optimal policy may take a long time. In this work, we improve the learning performance by exploring transfer learning; that is, the knowledge acquired in previous source tasks is used to accelerate learning in new target tasks. If the tasks present similarities, then the transferred knowledge guides the agent towards faster learning. We explore the use of a relational representation that allows description of relationships among objects. This representation simplifies the use of abstraction and the extraction of the similarities among tasks, enabling the generalization of solutions that can be used across different, but related, tasks. This work presents two model-free algorithms for online learning of abstract policies: AbsSarsa(λ) and AbsProb-RL. The former builds a deterministic abstract policy from value functions, while the latter builds a stochastic abstract policy through direct search on the space of policies. We also propose the S2L-RL agent architecture, containing two levels of learning: an abstract level and a ground level. The agent simultaneously builds a ground policy and an abstract policy; not only the abstract policy can accelerate learning on the current task, but also it can guide the agent in a future task. Experiments in a robotic navigation environment show that these techniques are effective in improving the agent’s learning performance, especially during the early stages of the learning process, when the agent is completely unaware of the new task.
A Primate View on Multiple Sclerosis

textabstractMS is a chronic disease affecting the CNS with an average disease onset in the third or fourth decade of life. Clinical symptoms are caused by changes in the motor, sensory, visual, and autonomic systems. Most common symptoms include visual and balance disturbances, spasticity, bladder dysfunction, pain, and fatigue. At a later disease stage, paralysis may occur. Other symptoms are Lhermitte’s symptom, an electrical sensation running down the spine or limbs when the neck bends, and Uhthoff phenomenon, worsening of symptoms when the core body temperature increases.
A Diffe rential Power Analysis Resistant Randomized Algorithm using Multiple AES Ciphers

A Differential Power Analysis Resistant Randomized Algorithm using Multiple AES Ciphers by Richard Tran Differential power analysis (DPA) side channel attacks have been shown to have great effectiveness in breaking ciphers (such as the Advanced Encryption Standard or AES) that were previously though to be unbreakable. There are currently many methods published that prevent differential power analysis on AES. The method proposed for this project is based on the increased usage of multiprocessors and multicore processors. By using multiple copies of the same AES cipher, a randomly chosen cipher is used to encrypt each plaintext. The other ciphers are then used to obfuscate the data made available to the attacker for DPA in the hope of making DPA impossible or require a statistically large amount of data that it is practically impossible to run successfully.
Towards A Unified Model for Workflow Processes

References [1] C. A. R. Hoare. Communicating Sequential Processes. Prentice-Hall, 1985. [2] W. M. P. van der Aalst et al. Workflow Patterns. Distributed and Parallel Databases, 14(3):5–51, July 2003. [3] P. Y. H. Wong. A Case Study on the Formal Verification of Workflow Choreography, 2006. [4] P. Y. H. Wong. A Process Algebraic Approach to Workflow Verification, 2006. Submitted for publication. Aim Our aim is to develop a unified model in CSP [1] for generic workflow specification, refinement and verification
A study on the efficacy of extracts of Boerhavia diffusa L on bacterial isolates of finger tip infections (whitlow)

This research was conducted to determine the efficacy of crude extract of Boerhavia diffusa L (BHD L) on bacterial isolates of fingertip infection (Whitlow). Seventeen patients with whitlow were studied within 18 months and swabs of active fingertip infections were taken from the patients at Crossing-Kachia in Kaduna State for analysis at the microbiology laboratory of Federal University of Technology Minna, Niger State Nigeria. Associated bacteria were isolated and identified using standard microbiological and biochemical tests. The isolates were tested against extracts of BHD L and commercially available antibiotics using the Kirby Bauer agar well diffusion method. Phytochemical analysis was also conducted in order to determine the bioactive compounds in BHD L that may be responsible for its effectiveness in treatment. The results showed that more males (76.5%) were affected while the mean age of people affected was 28.6years. The predominant causative agent was Staphylococcus epidermidis (89.5%) while Staphylococcus epidermidis confirmed its resistance to commercial antibiotics, hence difficulty of treatment of whitlow with orthodox medicines. Similarly, extracts of BHD L had no antibacterial activity against Staphylococcus epidermidis . Thus, the efficacy of BHD L on the isolates of fingertip infections may be due to some other reasons yet unknown. Keywords - Antibacterial activity, Antibiotics, Efficacy, Photochemical, Whitlow.
Defect-Type-Dependent Near-Infrared-Driven Photocatalytic Bacterial Inactivation by Defective Bi2 S3 nanorods.

Defect engineering is crucial in tailoring photocatalytic efficiency, but it suffers from uncertainty in determining the vacancy type and in which type of the vacancy can better promote the photocatalytic efficiency. In this study, Bi2 S3 nanorods with bismuth or sulfur vacancies were synthesized to investigate their distinct effects on the electronic structure, electron-hole separation characteristics, and near-infrared (NIR)-driven photocatalytic bacterial inactivation activity. Both bismuth and sulfur vacancies can enhance the light absorption ability of Bi2 S3 . However, the lifetime of photoinduced electrons is extended by bismuth vacancies but shortened by sulfur vacancies. Owing to these tendencies, Bi2 S3 with Bi vacancies fully inactivated 7 log E. coli cells within 40 min of NIR irradiation, displaying better NIR-driven photocatalytic bacterial inactivation efficiency than Bi2 S3 with S vacancies. This study discloses the defect-type-dependent photocatalytic behaviors, providing new insights into designing highly efficient photocatalysts.
Interactions between the sexual identity of the nervous system and the social environment mediate lifespan in Drosophila melanogaster

Sex differences in lifespan are ubiquitous, but the underlying causal factors remain poorly understood. Inter- and intrasexual social interactions are well known to influence lifespan in many taxa, but it has proved challenging to separate the role of sex-specific behaviours from wider physiological differences between the sexes. To address this problem, we genetically manipulated the sexual identity of the nervous system—and hence sexual behaviour—in Drosophila melanogaster, and measured lifespan under varying social conditions. Consistent with previous studies, masculinization of the nervous system in females induced male-specific courtship behaviour and aggression, while nervous system feminization in males induced male–male courtship and reduced aggression. Control females outlived males, but masculinized female groups displayed male-like lifespans and male-like costs of group living. By varying the mixture of control and masculinized females within social groups, we show that male-specific behaviours are costly to recipients, even when received from females. However, consistent with recent findings, our data suggest courtship expression to be surprisingly low cost. Overall, our study indicates that nervous system-mediated expression of sex-specific behaviour per se—independent of wider physiological differences between the sexes, or the receipt of aggression or courtship—plays a limited role in mediating sex differences in lifespan.
Issues in data mining: A comprehensive survey

Data mining has attained marvelous triumph in almost every domain such as health care, wireless sensor network, social network etc with development of its various algorithms. Every data mining algorithm has its inherent limitations. The application domain and the actual data, both together, heavily influence the particular choice as well as performance of any data mining, machine learning or statistical algorithm. The contribution that this paper makes is that it elaborates a number of data mining issues along with the metrics to measure the data quality and algorithm performance under a single hood. This paper has elaborated seven most vital issues in data mining, i.e., Missing Value Imputation, Feature Selection, Outlier Detection, Cluster Analysis of high dimensional data, Imbalanced classes in classification, Privacy of data, Mining from complex/distributed data. It not only presents these issues but also discusses their existing solutions. Survey also throws light on the limitations and research gaps for prospective researchers. These issues are identified after an extensive study on 50 different papers. Selection of these papers is carefully done so as to investigate core issues in data mining that still need to be addressed. This work has also grouped thirty data quality and algorithm performance metrics used in literature into three categories. This comprehensive understanding of the issues and metrics can be a treat for beginners in the research of data mining. Survey shows that the most frequently used algorithm performance measures are accuracy and time complexity.
Urinary Proteomics for the Early Diagnosis of Diabetic Nephropathy in Taiwanese Patients

Diabetic nephropathy (DN) is a major complication in diabetic patients. Microalbuminuria testing is used to identify renal disease; however, its predictive value is questionable. We aimed to identify urinary biomarkers to early diagnosis nephropathy before identifiable alternations in kidney function or urine albumin excretion occurs. Proteomic approaches were used to identify potential urinary biomarkers and enzyme-linked immunosorbent assay was performed to verify the results. The data identified haptoglobin (HPT) and α-1-microglobulin/bikunin precursor (AMBP) as two biomarkers with the highest ability to distinguish between healthy individuals and patients with nephropathy, and between diabetic patients with and without DN. Further, the HPT-to-creatinine ratio (HCR) was evaluated as an independent predictor of early renal functional decline (ERFD) in a cohort with an average follow-up of 4.2 years. The area under the curve (AUC) value for ERFD prediction was significantly improved when the HCR biomarker was included in the model with albumin to creatinine ratio (ACR) and baseline characteristics (AUC values were 0.803 and 0.759 for HCR and ACR, respectively; p value was 0.0423 for difference between models). In conclusion, our results suggest that HCR represents an early indicator of nephropathy, and a marker related to ERFD among diabetic patients in Taiwan.
Darunavir Population Pharmacokinetic Model Based on HIV Outpatient Data

Background: Darunavir is a second-generation protease inhibitor and is registered for the treatment of HIV-1 infection. The aim of this study was to develop and validate a darunavir population pharmacokinetic model based on data from daily practice. Methods: Data sets were obtained from 2 hospitals: ASST Fatebenefratelli Sacco University Hospital, Italy (hospital A), and University Medical Center Groningen, the Netherlands (hospital B). A pharmacokinetic model was developed using data from the largest data set using the iterative two-stage Bayesian procedure within the MWPharm software package. External validation was conducted using data from the smaller data set with Passing–Bablok regression and Bland–Altman analyses. Results: In total, data from 198 patients from hospital A and 170 patients from hospital B were eligible for inclusion. A 1-compartment model with first-order absorption and elimination resulted in the best model. The Passing–Bablok analysis demonstrated a linear correlation between measured concentration and predicted concentration with r2 = 0.97 (P < 0.05). The predicted values correlated well with the measured values as determined by a Bland–Altman analysis and were overestimated by a mean value of 0.12 mg/L (range 0.23–0.94 mg/L). A total of 98.2% of the predicted values were within the limits of agreement. Conclusions: A robust population pharmacokinetic model was developed, which can support therapeutic drug monitoring of darunavir in daily outpatient settings.
Mixed Watermarking-Fingerprinting Approach for Integrity Verification of Audio Recordings

Abstract— W e introduce a method for audio-integrityveriﬁcation basedon a combination of watermarking and ﬁnger printing. An audio ﬁnger -print is a perceptual digest that holds content information of a recordingand allows one to identify it from other recordings. Integrity veriﬁcationis performed by embedding the ﬁnger print into the audio signal itself bymeans of a watermark. The original ﬁnger print is reconstructed from thewatermark and compared with a new ﬁnger print extracted from the ob-served signal. If they are identical, the signal has not been modiﬁed; if not,the system is able to determine the approximate locations where the signalhas been corrupted. The watermarked signal may undergo content pre-serving transformations, such as resampling or D/A and A/D conversion,without triggering the corruption alarm. I. I NTRODUCTION using N many applications, the integrity of an audio recording mustbe unquestionably established before the signal can actuallybe used, i.e. one must be sure that the recording has not beenmodiﬁed without authorization.When dealing with speech, some application contexts inwhich integrity must be ensured are: protection of previously recorded testimonies that are to beused as evidence before a court of law; protection of recorded interviews, which could be edited formalicious purposes.Regarding music applications, some examples are: integrity veriﬁcation of radio or television commercials to en-sure they air as negotiated;
A fixed point implementation of the backpropagation learning algorithm

In hardware implementations of digital artificial neural networks, the amount of logic that can be utilized is limited. Due to this limitation, learning algorithms that are to be executed in hardware must be implemented using fixed point arithmetic. Adapting the backpropagation learning algorithm to a fixed point arithmetic system requires many approximations, scaling techniques and the use of lookup tables. These methods are explained. The convergence results for a test example using fixed point, floating point and hardware implementations of the backpropagation algorithm are presented. <<ETX>>