Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of fault-tolerant concurrent programs

Methods for mechanically synthesizing concurrent programs from temporal logic speciﬁcationsobviate the need to manually construct a program and compose a proof of its correctness. A seriousdrawback of extant synthesis methods, however, is that they produce concurrent programs formodels of computation that are often unrealistic. In particular, these methods assume completelyfault-free operation, that is, the programs they produce are fault-intolerant. In this paper, we showhow to mechanically synthesize fault-tolerant concurrent programs for various fault classes. Weillustrate our method by synthesizing fault-tolerant solutions to the mutual exclusion and barriersynchronization problems.Categories and Subject Descriptors: C.2. 4 [
Physician education programme improves quality of diabetes care

Background. Diabetes mellitus is a common chronic disease needing long-term glycaemic control to prevent complications. Guidelines are available for achievement of optimal glycaemic control, but these are seldom properly instituted. Objectives. To determine if a physician education programme and a structured consultation schedule would improve the quality of diabetes patient care in a diabetes clinic. Setting. Two tertiary care diabetes clinics at Kalafong Hospital, Pretoria. Study design. Quasi-experimental controlled before-and after study. Methods. A baseline audit of the quality of care in two comparable diabetes clinics was performed. Three hundred patients were randomly selected for an audit of their hospital records: 141 from the intervention and 159 from the control clinics. Thereafter a physician training programme and a structured consultation schedule were introduced to the intervention clinic and maintained for a 1-year period. The control clinic continued with care as usual. Process and outcome measures were determined at a post-intervention audit and compared between the two groups. Consultation time was measured for both the intervention and control groups and data were compared. Results. At baseline the intervention and control groups did not differ significantly with regard to process and outcome measures. After intervention the intervention group had significantly higher process measure scores than the control group (p < 0.01). HbA1c did not significantly differ between the two groups (p = 0.60). The average number of clinic visits reduced over time for the intervention group compared with the control group (p < 0.01), but the average consultation times were significantly longer (p < 0.01). Conclusion. The introduction of a physician education programme and a structured consultation schedule improved the quality of care delivered at a tertiary care diabetes clinic.
Quality Improvement: Combining ProMES and SPC to Work Smarter

Quality improvement programmes often aim at letting people work smarter. This article compares two different approaches, the Productivity Measurement and Enhancement System ProMES and Statistical Process Control SPC. It turns out that both approaches try to stimulate people in working smarter, although focusing on different issues and having different starting points in the process of implementation. A case study illustrates that in practice both approaches can be combined.
Reducing Undesirable Hepatic Clearance of a Tumor-Targeted Vinca Alkaloid via Novel Saccharopeptidic Modifications

During a phase I trial of EC145 (a folate-targeted vinca alkaloid conjugate), constipation was identified as the dose-limiting toxicity, probably from a nonfolate receptor-related liver clearance process capable of releasing unconjugated vinca alkaloid from EC145 and shuttling it to the bile. Here, we report on the selective placement of novel carbohydrate segments (1-amino-1-deoxy-glucitolyl-γ-glutamate) spaced in-between the folate and vinca alkaloid moieties of EC145, which yielded a new agent (EC0489) that is equipotent but less toxic than EC145. Whereas both compounds could cure tumor-bearing mice reproducibly, EC0489 differed from EC145 with i) a shorter elimination half-life, ii) approximately 70% decrease in bile clearance, iii) a 4-fold increase in urinary excretion, and iv) improved tolerability in rodents. This combination of improvements justified the clinical evaluation of EC0489 where currently administered dose levels have exceeded the maximal tolerated dose of EC145 by approximately 70%, thereby reflecting the translational benefits to this new approach.
A Stochastic Model for Analyzing Tail Latency of Multi-Tier Online Cloud Services

Tail latency is an important performance metric to characterize the quality of experience (QoE) of online cloud services. However, there still lacks an efficient and easy-to-use analytical model to analyze the tail latency of response time of online cloud services. A big challenge is that online cloud service typically consists of multiple tiers of components that interacts with each other, which is difficult to be accurately modeled. In this paper, we propose an efficient stochastic model for analyzing tail latency of online cloud services. First, a stochastic reward net (SRN) is used to model the online cloud services by taking the interaction between multi-tiers into account. A tagged customer model is then introduced into the SRN to compute the cumulative distribution of response time, based on which tail latency can be directly derived. We further implement an e-commerce site on both a private cloud and a public cloud to verify the accuracy and effectiveness of the proposed model without and with interferences from other applications, respectively. A series of experiments show that the differences between the tail latency obtained from the model and that from the actual experiments are generally less than 20% in both cases.
SUMMARIZED: Efficient Framework for Analyzing Multidimensional Process Traces under Edit-distance Constraint

Domains such as scientific workflows and business processes exhibit data models with complex relationships between objects. This relationship is typically represented as sequences, where each data item is annotated with multi-dimensional attributes. There is a need to analyze this data for operational insights. For example, in business processes, users are interested in clustering process traces into smaller subsets to discover less complex process models. This requires expensive computation of similarity metrics between sequence-based data. Related work on dimension reduction and embedding methods do not take into account the multi-dimensional attributes of data, and do not address the interpretability of data in the embedding space (i.e., by favoring vector-based representation). In this work, we introduce Summarized, a framework for efficient analysis on sequence-based multi-dimensional data using intuitive and user-controlled summarizations. We introduce summarization schemes that provide tunable trade-offs between the quality and efficiency of analysis tasks and derive an error model for summary-based similarity under an edit-distance constraint. Evaluations using real-world datasets show the effectives of our framework.
The infrared retina

As infrared imaging systems have evolved from the first generation of linear devices to the second generation of small format staring arrays to the present ‘third-gen’ systems, there is an increased emphasis on large area focal plane arrays (FPAs) with multicolour operation and higher operating temperature. In this paper, we discuss how one needs to develop an increased functionality at the pixel level for these next generation FPAs. This functionality could manifest itself as spectral, polarization, phase or dynamic range signatures that could extract more information from a given scene. This leads to the concept of an infrared retina, which is an array that works similarly to the human eye that has a ‘single’ FPA but multiple cones, which are photoreceptor cells in the retina of the eye that enable the perception of colour. These cones are then coupled with powerful signal processing techniques that allow us to process colour information from a scene, even with a limited basis of colour cones. Unlike present day multi or hyperspectral systems, which are bulky and expensive, the idea would be to build a poor man's ‘infrared colour’ camera. We use examples such as plasmonic tailoring of the resonance or bias dependent dynamic tuning based on quantum confined Stark effect or incorporation of avalanche gain to achieve embodiments of the infrared retina.
An Optimized Stacked Support Vector Machines Based Expert System for the Effective Prediction of Heart Failure

About half of the people who develop heart failure (HF) die within five years of diagnosis. Over the years, researchers have developed several machine learning-based models for the early prediction of HF and to help cardiologists to improve the diagnosis process. In this paper, we introduce an expert system that stacks two support vector machine (SVM) models for the effective prediction of HF. The first SVM model is linear and $L_{1}$ regularized. It has the capability to eliminate irrelevant features by shrinking their coefficients to zero. The second SVM model is $L_{2}$ regularized. It is used as a predictive model. To optimize the two models, we propose a hybrid grid search algorithm (HGSA) that is capable of optimizing the two models simultaneously. The effectiveness of the proposed method is evaluated using six different evaluation metrics: accuracy, sensitivity, specificity, the Matthews correlation coefficient (MCC), ROC charts, and area under the curve (AUC). The experimental results confirm that the proposed method improves the performance of a conventional SVM model by 3.3%. Moreover, the proposed method shows better performance compared to the ten previously proposed methods that achieved accuracies in the range of 57.85%–91.83%. In addition, the proposed method also shows better performance than the other state-of-the-art machine learning ensemble models.
An Iterative Delay Chain based Impedance to Digital Converter using 0.18μm CMOS

Majority of the Capacitance to Digital converters reported till date use charge sharing or charge transfer methods to convert capacitance to voltage value. A fully digital Capacitance to Digital Converter is proposed in this paper. The proposed architecture is based on the principle of iterative delay-chain discharge. Significant improvements in terms of measuring time and power consumption is observed. The performance parameters of the CDC proposed here are: resolution is 0.617 pF, range is 5 pF to 645 nF, measuring time is $0.7\mu {s}$ and power consumption is $54\mu {W}$. Further, the CDC architecture is extended to function as Resistance to Digital Converter which exhibits a range of $30{K} \Omega $ to $70{K} \Omega $ with resolution of $130\Omega $.
Feature Extraction and Classification of Spam Emails

Emails are a popular and preferred way of written communication in our daily life. The problem with emails is spam. These spam emails are sent with different intentions, but advertisement and fraud are the main reasons. As being inexpensive to send, it causes many problems to the internet society. This paper discusses the use of different feature extraction methods coupled with two different supervised machine learning classifiers evaluated using four performance metrics on two publicly available spam email datasets for spam filtering. We highlight the importance of the correct coupling of feature extraction and classifier, and the merits of using two independent datasets.
Autonomy and bureaucratic accountability in primary care: what English general practitioners say

Abstract Clinical autonomy has long been seen as conceptually central to the analysis of the occupational status of the medical profession, though the implications for this of recent developments in health care managerialism have been disputed by theorists. In particular, the question has arisen as to whether ‘restratification’, that is, the active involvement of physicians in this process, should be construed as medical elites exerting control over the rank and file in order to protect the profession as a whole, or as an incursion from outside it. This paper uses interview data from 49 general medical practitioners in Northern England. It investigates their perceptions of how current government policies, and the new institutions and governance arrangements that they have created impact on physicians’ ability to set their own limits and to judge their own work. We found a clear acceptance by GPs of the need to discharge ‘bureaucratic accountability’, in particular to maintain records of their clinical decisions. This provides the possibility of external surveillance of medical work, and thus implies a clear reduction in autonomy over the content of medical work on the part of rank-and-file GPs, who may regret this situation but offer little resistance to it. Our findings illustrate a form of restratification; the most frequently reported immediate source of pressure to modify casenote recording was the Primary Care Group (PCG), an organisation constitutionally dominated by physicians acting in a managerial capacity. Nevertheless, the agendas of PCGs are largely driven by central government and our study thus provides further evidence of the intermediary or contingent (rather than independent) character of professional autonomy.
A Technique for Behavior Testing of SET Payment Based on Petri Nets

ABSTRACT By secure electronic transaction (SET) protocol, business and customers can construct different electronic commerce models on the Internet. In this paper, we propose a method based on Petri net models and techniques of software engineering to detect unusual electronic commerce transactions. Users can construct all electronic commerce transactions of business and customers by SET. Because of features and functions of SET, users can get security and verification on the network transactions. Using our proposed technique, electronic commerce designers can detect potential security flaws in transaction behavior on the Internet and find any electronic transaction problems that need to solve.
Sublingual misoprostol prior to insertion of a T380A intrauterine device in women with no previous vaginal delivery

Abstract Objective To investigate whether sublingual misoprostol administered one hour before intrauterine device (IUD) insertion reduces failed insertions, insertion-related complications and pain in parous women delivered only by elective caesarean section (CS). Methods Single-blind randomised controlled trial conducted in Ismailia, Egypt, between July 2010 and December 2011. Women who had never delivered otherwise than by elective CS and desirous of using an IUD were randomly allocated to receive sublingually 400 μg misoprostol and 100 mg diclofenac (misoprostol group) or 100 mg diclofenac alone (control group) one hour before IUD insertion. Outcome measures were failed insertion, ease of insertion judged by the investigators, insertion-related complications, and patients’ satisfaction. Results In all, 255 women (130 and 125 in the study and control groups, respectively) had an IUD inserted. Seven insertions failed: five in the control group, and two in the study group. Ease of insertion and patients’ satisfaction were comparable in both groups. Abdominal pain and nausea were the commonest side effects reported in the misoprostol group. Conclusion Sublingual administration of misoprostol one hour before IUD insertion in parous women with no previous vaginal delivery does not facilitate the procedure and may cause undesirable side effects. This approach is not recommended as a standard treatment.
Ecosystem Adversity as Setting Factors in Mothers' Judgment of Child Behavior and Indiscriminate Mothering

Several studies have shown that troubled mothers are not accurate observers of their children's behavior. These mothers have a global and usually blame-oriented reporting style coloured by disphoric views of social isolation, social coercion and socioeconomic disadvantage. The dsyphoric view describes a number of contextual or setting factors that seem to influence a mother's observational reports about her child more than the child's actual behavior does. Of course, when this observational bias exists, the mother's parenting performance is bound to be comprised. According to the considerations above, this study was designed with a group of 21 mother-child dyads characterized by their coercive interactions and child abuse (physical and emotional). The children were school-aged children ranging from 4 to 14 years of age and all mother-child dyads were observed at home. Two sets of analyses were conducted, one related to the maternal perception and one related to maternal behavior. The maternal judgment of .
Molecular Descriptors Selection and Machine Learning Approaches in Protein-Ligand Binding Affinity with Applications to Molecular Docking

In this paper, we propose algorithms for biomolecular docking sites selection problem by various machine learning approaches with selective features reduction. The proposed method can reduce the number of various amino acid features before constructing machine learning prediction models. Given frame boxes with features, the proposed method analyzes the important features by correlation coefficients to LE values. The algorithm ranks these possible candidate locations on the receptor before launching AutoDock. Given a small molecular, namely ligand, it is a time-consuming task to compute the molecular docking against a large, relatively stationary molecule, or receptor. Our methods divide the surface area of receptor to several subspaces and evaluate these subspaces before choosing the promising subspaces to speed up the molecular docking simulation. The method is implemented upon the widely employed automated molecular docking simulation software package, AutoDock. The paper examines three different machine learning prediction models including the support vector machines (LIBSVM), deep neural networks (H2O), and the logistic regression model (AOD). The proposed affinity estimation algorithm, incorporated with a ligand-specific SVM prediction model, achieves about 4 folds faster comparing with original Autodock searching the whole surface of the receptor with similar binding energy score (LE, lowest engery) measurement. Furthermore, the proposed method can be easily parallelized in the implementation. Hadoop MapReduce frameworks are used in our experiments to parallelize the underlying massive computation works corresponding to ligand-receptor pairs examined under the experiment.
The Devil's Staircase in the Frequency and Amplitude Locking of Nonlinear Oscillators with Continuous Periodic Forcing

We study the emergence of a devil’s staircase resonance structure in the locking behavior of driven continuous systems by periodic modulation of the driving force. In particular, we concentrate on systems that, for a constant sinusoidal forcing term, show only Adler-type locking around a single main resonance tongue. As an exemplary system, we consider a semiconductor laser that is driven by a modulated optical signal. We observe a clear devil’s staircase structure in both the lasing frequency and the induced oscillations in the electric field amplitude. We show that the emergence of higher harmonic resonance tongues in the lasing frequency is enabled by the locking of the amplitude oscillations, but find also regions of unlocked oscillations in the field amplitude where the optical frequency remains locked. Our results show that by proper implementation of an external drive, additional regions of stable frequency locking can be introduced in systems which originally show only a single Adler-type resonance tongue.
SEFL: Selective Ensemble Fuzzy Learner for Cognitive Detection of Bio-Modality Spoofing in MCPS

User authentication in a Medical Cyber Physical Systems (MCPS) can be effectively done using biometric features. Biometric features, widely used for user authentication, are equally important to national and global technology systems. Biometric features, such as face, iris, fingerprint, are commonly used while more recently palm, vein and gait are also getting attention. To fail the traditional biometric detection systems, various spoofing approaches have also been developed over time. Among various methods, image synthesis with play-doh, gelatin, ecoflex etc. are some of the more common ways for spoofing bio-modalities. Success of traditional detection systems are related to custom tailored solutions where feature engineering for each attack type must be developed. However, this is not a feasible process when we consider countless attack possibilities. Also, a slight change in the attack can cause the whole system to be redesigned and therefore becomes a limiting constraint. The recent success of machine learning inspires this paper to explore weak and strong learners with ensemble learning approaches using AdaBoost. In essence, the paper proposes a selective ensemble fuzzy learner approach using Ada Boost, feature selection and combination of weak and strong learners to enhance the detection of bio-modality spoofing for MCPS. Our proposal was experimented on real datasets and verified on the fingerprint and iris benchmark.
Preferential Methylation of Unmethylated DNA by Mammaliande Novo DNA Methyltransferase Dnmt3a*

DNA methylation is an epigenetic modification of DNA. There are currently three catalytically active mammalian DNA methyltransferases, DNMT1, -3a, and -3b. DNMT1 has been shown to have a preference for hemimethylated DNA and has therefore been termed the maintenance methyltransferase. Although previous studies on DNMT3a and -3b revealed that they act as functional enzymes during development, there is little biochemical evidence about how new methylation patterns are established and maintained. To study this mechanism we have cloned and expressed Dnmt3a using a baculovirus expression system. The substrate specificity of Dnmt3a and molecular mechanism of its methylation reaction were then analyzed using a novel and highly reproducible assay. We report here that Dnmt3a is a true de novo methyltransferase that prefers unmethylated DNA substrates more than 3-fold to hemimethylated DNA. Furthermore, Dnmt3a binds DNA nonspecifically, regardless of the presence of CpG dinucleotides in the DNA substrate. Kinetic analysis supports an Ordered Bi Bi mechanism for Dnmt3a, where DNA binds first, followed byS-adenosyl-l-methionine.
Novel serotonin receptor 2 (5-HT2R) agonists and antagonists: a patent review (2004-2014)

Introduction: Serotonin or 5-hydroxytryptamine (5-HT) is a substance found in plasma, which increases smooth muscle contraction and mediates platelet aggregation. In addition, it is a monoamine neurotransmitter and is implicated in diverse behaviors. The serotonin receptor 2 (5-HT2) subfamily is best known for biased signaling and is strongly expressed mainly in the brain regions postulated to be involved in the modulation of higher cognitive and affective functions. Modulators of the 5-HT2 receptor are currently used to treat a variety of diseases including chronic pain and psychonosema. These properties suggest that 5-HT2 receptors may become an important therapeutic target for the treatment of various pathological conditions. Areas covered: This review highlights the significant progress that has been made in the discovery and development of 5-HT2 receptor agonists and antagonists based on an analysis of the patent literature between January 2004 and December 2014. Expert opinion: Cumulative evidence over the past decade supports the notion that the modulation of 5-HT2 receptors has a positive effect on human cognition and emotion. Therefore, we suggest that new agonists and antagonists may play an important role in the treatment of disorders such as schizophrenia, addiction and obesity.