Miracle-dz/UniFAR-FTUs huggingface dataset:
A hierarquical distributed fault diagnosis algorithm based on clusters with Detours

A distributed system-level diagnosis algorithm allows the fault-free nodes of a system to diagnose the state of all nodes in the system. Diagnosis has been successfully employed for dependable network fault management. In this paper we present a new hierarchical adaptive distributed system-level diagnosis algorithm, Hi-ADSD with Detours which has latency at most log22N, but requires less tests and less diagnostic information than other hierarchical diagnosis algorithms. Nodes running the new algorithm are grouped in clusters. If a tested node is faulty, instead of executing more tests, the tester tries to obtain information about the rest of the cluster from nodes tested fault-free outside the cluster, such that the diagnosis of the system is not delayed. Each such alternative path to a cluster is called a detour. An extra test is executed on a given cluster only when no detour is available. The worst case of the algorithm's latency is formally proved. Simulation results are presented.
Shifts in flowering phenology reshape a subalpine plant community

Significance Seasonal timing of biological events, phenology, is one of the strongest bioindicators of climate change. Our general understanding of phenological responses to climate change is based almost solely on the first day on which an event is observed, limiting our understanding of how ecological communities may be responding as a whole. Using a unique long-term record of flowering phenology from Colorado, we find that the number of species changing their flowering times likely has been underestimated and the magnitude of phenological change overestimated. In addition to earlier first flowering, we document a diverse assortment of other changes, such as delayed last flowering, as temperatures warm. This variety of species-level phenological shifts has ultimately reshaped various temporal components of the plant community. Phenology—the timing of biological events—is highly sensitive to climate change. However, our general understanding of how phenology responds to climate change is based almost solely on incomplete assessments of phenology (such as first date of flowering) rather than on entire phenological distributions. Using a uniquely comprehensive 39-y flowering phenology dataset from the Colorado Rocky Mountains that contains more than 2 million flower counts, we reveal a diversity of species-level phenological shifts that bring into question the accuracy of previous estimates of long-term phenological change. For 60 species, we show that first, peak, and last flowering rarely shift uniformly and instead usually shift independently of one another, resulting in a diversity of phenological changes through time. Shifts in the timing of first flowering on average overestimate the magnitude of shifts in the timing of peak flowering, fail to predict shifts in the timing of last flowering, and underrepresent the number of species changing phenology in this plant community. Ultimately, this diversity of species-level phenological shifts contributes to altered coflowering patterns within the community, a redistribution of floral abundance across the season, and an expansion of the flowering season by more than I mo during the course of our study period. These results demonstrate the substantial reshaping of ecological communities that can be attributed to shifts in phenology.
Maximum likelihood reconstruction of ancestral amino-acid sequences

Maximum-likelihood methods are used extensively in phylogenetic studies [3]. In particular, aminoacid sequences of ancestral species have been inferred using these methods [7]. Such ancestral reconstruction tasks aim at identifying either the most likely sequence in a specific ancestor species (marginal reconstruction), or the most likely set of ancestral states corresponding to all the ancestral taxa in a given phylogeny (joint reconstruction [6]). Joint reconstruction is motivated by studies of phenomena involving several independent lineages, like [8], and is implemented in [6]. However, existing algorithms for this task are exhaustive, and take exponential time. Furthermore, these algorithms assume a naive model of evolution, i.e., a constant substitution rate, whereas [5] shows that models incorporating rate variation among sites are statistically superior. In this work we: (a) Devise a dynamic programming algorithm for joint reconstruction. The complexity of this algorithm is linear in the number of sequences, but assumes no rate variation among sites.1 (b) Present a greedy heuristic for joint reconstruction assuming rate variation among sites. (c) Introduce a speed-up for calculating the replacement probabilities between any two states.
Functional Differences between Keratins of Stratified and Simple Epithelia

Dividing populations of stratified and simple epithelial tissues express keratins 5 and 14, and keratins 8 and 18, respectively. It has been suggested that these keratins form a mechanical framework important to cellular integrity, since their absence gives rise to a blistering skin disorder in neonatal epidermis, and hemorrhaging within the embryonic liver. An unresolved fundamental issue is whether different keratins perform unique functions in epithelia. We now address this question using transgenic technology to express a K16-14 hybrid epidermal keratin transgene and a K18 simple epithelial keratin transgene in the epidermis of mice null for K14. Under conditions where the hybrid epidermal keratin restored a wild-type phenotype to newborn epidermis, K18 partially but not fully rescued. The explanation does not appear to reside in an inability of K18 to form 10-nm filaments with K5, which it does in vitro and in vivo. Rather, it appears that the keratin network formed between K5 and K18 is deficient in withstanding mechanical stress, leading to perturbations in the keratin network in regions of the skin that are subjected either to natural or to mechanically induced trauma. Taken together, these findings suggest that the loss of a type I epidermal keratin cannot be fully compensated by its counterpart of simple epithelial cells, and that in vivo, all keratins are not equivalent.
Autoantibody to MDM2: A potential serological marker of primary Sjogren's syndrome

Introduction Primary Sjogrens Syndrome (pSS) is one of the autoimmune diseases characterized by polyclonal autoantibody production. The human homologue of the mouse double minute 2 (MDM2) is an important negative regulator of p53. Our previous study indicated that autoantibody to MDM2 can be detected in systemic lupus erythematosus patients. The purpose of this study is to study anti-MDM2 autoantibody in pSS patients. Methods Anti-MDM2 autoantibody in sera from 100 pSS patients and 74 normal controls was investigated by ELISA. Positive samples were further confirmed by western blotting. Expression of MDM2 in labial gland tissue from pSS patients and normal controls was checked by immunohistochemistry. The difference in clinical characteristics and laboratory findings between anti-MDM2 positive and anti-MDM2 negative pSS patients was analyzed. Results The presence of anti-MDM2 autoantibody in pSS patients was 21.0%, significantly higher than normal controls (5.40%). MDM2 was overexpressed in labial gland from pSS patients. pSS patients with positive anti-MDM2 were characterized by longer disease duration and more lymphocytes focal gathering in labial gland. Prevalence of anemia, thrombocytopenia and anti-SSB was significantly higher in pSS patients with anti-MDM2 autoantibody. Titer of anit-MDM2 was negatively associated with hemoglobin level, platelet count, complement 3 level and complement 4 level, positively associated with European Sjogrens syndrome disease activity index (ESSDAI) and level of IgG. Conclusions Anti-MDM2 autoantibody may be used as a potential serological biomarker in pSS disease activity evaluation. Study on the role of anti-MDM2 or MDM2 in pSS may help us know the pathogenesis mechanism of pSS better.
Energy efficiency optimization through THD and switching losses reduction in continuous and discontinuous digital modulation techniques

This paper analyzes the way to increase the energy efficiency of three phase converters applied to control AC motor drives, through the reduction of the total harmonic distortion (THD) of the output voltage and the reduction in the switching losses in the PWM inverter. The aim is to develop a system which selects the most adequate modulation technique required to simultaneously optimize the harmonic distortion and the commutation losses in the electronic switches that constitute the power converter. The developed modulation techniques are the sinusoidal pulse width modulation (SPWM), the sinusoidal pulse width modulation with the injection of a third harmonic of zero sequence (THIPWM), the space vector pulse width modulation (SVPWM) and different discontinuous modulation techniques (DPWM2, DPWMMAX and PWMMIN). The validation of the proposed method has been carried out on an experimental system based on the digital signal processor DSP56F807 from the commercial firm Motorola.
Dialectics of Knowledge Representation in a Granular Rough Set Theory

The concepts of rough and definite objects are relatively more determinate than those of granules and granulation in general rough set theory (RST) [1]. Representation of rough objects can however depend on the dialectical relation between granulation and definiteness. In this research, we make this exact in the context of RST over proto-transitive approximation spaces. This approach can be directly extended to many other types of RST. These are used for formulating an extended concept of knowledge interpretation (KI)(relative the situation for classical RST) and the problem of knowledge representation (KR) is solved. These will be of direct interest in granular KR in RST as developed by the present author [2] and of rough objects in general. In [3], these have already been used for five different semantics by the present author. This is an extended version of [4] with key examples and more results.
Understanding the Evolution and Co-evolution of Classes in Object-oriented Systems

As software systems evolve over a long time, non-trivial and often unintended relationships among system classes arise, which cannot be easily perceived through source-code reading. As a result, the developers' understanding of continuously evolving, large, long-lived systems deteriorates steadily. A most interesting relationship is class co-evolution: because of implicit design dependencies clusters of classes change in "parallel" ways and recognizing such co-evolution is crucial in effectively extending and maintaining the system. In this paper, we propose a data-mining method for recovering "hidden" co-evolutions of system classes. This method relies on our UML-aware structural differencing algorithm, UMLDiff, which, given a sequence of UML class models of an object-oriented software system, produces a sequence of "change records" that describe the design-level changes over its life span. The change records are analyzed from the perspective of each individual system class to extract "class change profiles". Each phase of a class change profile is then discretized and classified into one of two general change types: function extension or refactoring. Finally, the Apriori association-rule mining algorithm is applied to the database of categorical class change profiles, to elicit co-evolution patterns among two or more classes, which may be as yet undocumented and unknown. The recovered knowledge facilitates the overall understanding of system evolution and the planning of future maintenance activities. We report on one real world case study evaluating our approach.
Microprocessor Based Firing Circuit for Static Slip Energy Recovery Controlled Slip Ring Induction Motor Drive

Microprocessor based firing control scheme of static slip energy recovery (SER) controlled slip ring induction motor drive is described. In the scheme the microprocessor generates firing pulses and also decides the pair of thyristors of the controlled converter to be gated. It uses less number of hardware components. The oscillograms at different points of the firing circuit and at the output of the controlled converter are presented. These oscillograms resemble with the theoretical wave forms.
The Use and Usability of Satellite Data for tropospheric research

The newly observed global distributions of tropospheric species such as NO2, SO2, HCHO and BrO, obtained by observations from space platforms, mark the opening of a new era in tropospheric chemistry. Both the treatment of the data presently available, and of data from the missions planned for the near future, will add a new dimension to the field. The new data offer the possibility of studying concentration distributions over large areas, and in locations and at levels in the troposphere, which were only accessible to occasional individual observations in the past. They will lead to a significant enhancement of our capability for investigating the chemistry and physics of the troposphere. An example of a global distribution for NO2 is given in Fig. 1 [1–9].
A Comparison of Mandani and Sugeno Inference Systems for a Space Fault Detection Application

This research provides a comparison between the performances of TSK (Takagi, Sugeno, Kang)-type versus Mandani-type fuzzy inference systems. The main motivation behind this research was to assess which approach provides the best performance for a gyroscope fault-detection application, developed in 2002 for the European Space Agency (ESA) satellite ENVISAT. Due to the importance of performance in online systems we compare the application, developed with Mamdani model, with a TSK formulation using three types of tests: processing time for both systems, robustness in the presence of randomly generated noise; and sensitivity analysis of the systems' behaviors to changes in input data. The results show that the TSK model perform better in all three tests, hence we may conclude that replacing a Mamdani system with an equivalent TSK system could be a good option to improve the overall performance of a fuzzy inference system.
RINSE: Interactive Data Series Exploration with ADS+

Numerous applications continuously produce big amounts of data series, and in several time critical scenarios analysts need to be able to query these data as soon as they become available. An adaptive index data structure, ADS+, which is specifically tailored to solve the problem of indexing and querying very large data series collections has been recently proposed as a solution to this problem. The main idea is that instead of building the complete index over the complete data set up-front and querying only later, we interactively and adaptively build parts of the index, only for the parts of the data on which the users pose queries. The net effect is that instead of waiting for extended periods of time for the index creation, users can immediately start exploring the data series. In this work, we present a demonstration of ADS+; we introduce RINSE, a system that allows users to experience the benefits of the ADS+ adaptive index through an intuitive web interface. Users can explore large datasets and find patterns of interest, using nearest neighbor search. They can draw queries (data series) using a mouse, or touch screen, or they can select from a predefined list of data series. RINSE can scale to large data sizes, while drastically reducing the data to query delay: by the time state-of-the-art indexing techniques finish indexing 1 billion data series (and before answering even a single query), adaptive data series indexing can already answer 3 * 105 queries.
Microwave Properties of an Inhomogeneous Optically Illuminated Plasma in a Microstrip Gap

The optical illumination of a microstrip gap on a thick semiconductor substrate creates an inhomogeneous electron-hole plasma in the gap region. This allows the study of the propagation mechanism through the plasma region. This paper uses a multilayer plasma model to explain the origin of high losses in such structures. Measured results are shown up to 50 GHz and show good agreement with the simulated multilayer model. The model also allows the estimation of certain key parameters of the plasma, such as carrier density and diffusion length, which are difficult to measure by direct means. The detailed model validation performed here will enable the design of more complex microwave structures based on this architecture. While this paper focuses on monocrystalline silicon as the substrate, the model is easily adaptable to other semiconductor materials such as GaAs.
A Compact 23 GHz Hartley VCO in 0.13μm CMOS Technology

A 23 GHz balanced Hartley VCO is designed and manufactured in 0.13 mum COMS technology. In this VCO design, a pair of coupled inductors are employed to make the whole circuit compact. Its size is only 0.17 mm2. Very low phase noise, -108 dBc/Hz at 1 MHz offset frequency, is measured and a wide tuning range of 13% is achieved. In addition, the VCO consumes only 5.7 mW dc power at 0.79 V supply voltage*.
The impact of natural selection on health and disease: uses of the population genetics approach in humans

Investigations of the legacy of natural selection in the human genome have proved particularly informative, pinpointing functionally important regions that have participated in our genetic adaptation to the environment. Furthermore, genetic dissection of the intensity and type of selection acting on human genes can be used to predict involvement in different forms and severities of human diseases. We review here the progress made in population genetics studies toward understanding the effects of selection, in its different forms and intensities, on human genome diversity. We discuss some outstanding, robust examples of genes and biological functions subject to strong dietary, climatic and pathogen selection pressures. We also explore the possible relationship between cancer and natural selection, a topic that has been largely neglected because cancer is generally seen as a late‐onset disease. Finally, we discuss how the present‐day incidence of some diseases of modern societies may represent a by‐product of past adaptation to other selective forces and changes in lifestyle. This perspective thus illustrates the value of adopting a population genetics approach in delineating the biological mechanisms that have played a major evolutionary role in the way humans have genetically adapted to different environments and lifestyles over time.
Disability and economy in Japan

This paper shows that, at least in Japan, the perception of homogeneity affects people’s attitudes toward disabled people, inducing prejudices and stigma, and that these attitudes are not only real, but are also related to economic variables. Through this work, we argued that disability studies can provide economics and game theory--that have developed tools to study not only markets, but also customs and institutions as endogenous and integral parts of economies and societies--with new insights and can in turn make use of their methods in forming economic and social policies.
Connectionist models for tracing user-task profiles

This paper discusses an approach of modeling users’ task related characteristics in interactive systems. Connectionist models are used to trace the on-going interaction and user-task profiles. These models function as associative memories that can capture the causal relationships among users’ characteristics for the system adaptation. This approach can be used for stereotyping users’ characteristics.
A new telomerase RNA element that is critical for telomere elongation

The stability of chromosome ends, the telomeres, is dependent on the ribonucleoprotein telomerase. In vitro, telomerase requires at least one RNA molecule and a reverse transcriptase-like protein. However, for telomere homeostasis in vivo, additional proteins are required. Telomerase RNAs of different species vary in size and sequence and only few features common to all telomerases are known. Here we show that stem-loop IVc of the Saccharomyces cerevisiae telomerase RNA contains a structural element that is required for telomerase function in vivo. Indeed, the distal portion of stem-loop IVc stimulates telomerase activity in vitro in a way that is independent of Est1 binding on more proximal portions of this stem-loop. Functional analyses of the RNA in vivo reveal that this distal element we call telomerase-stimulating structure (TeSS) must contain a bulged area in single stranded form and also show that Est1-dependent functions such as telomerase import or recruitment are not affected by TeSS. This study thus uncovers a new structural telomerase RNA element implicated in catalytic activity. Given previous evidence for TeSS elements in ciliate and mammalian RNAs, we speculate that this substructure is a conserved feature that is required for optimal telomerase holoenzyme function.
Knowledge, attitudes and practice of breast sonography among females at a rural health facility in Uganda

It was a cross-sectional study in which data from women was collected using interviewer-administered questionnaires. The qualitative component of the data was analysed using thematic analysis. For the quantitative component, SPSS was used for analysis. Association between socio-demographic characteristics and adequacy of knowledge, attitudes and practice of breast sonography were determined using both bivariate and logistic regression analysis. A Chi-square was used to test statistical significance.
Race, Rejection Sensitivity, and Identity Centrality Among Young Sexual Minority Women

RACE, REJECTION SENSITIVITY, AND IDENTITY CENTRALITY AMONG YOUNG SEXUAL MINORITY WOMEN Denise M. Calhoun Virginia Consortium Program in Clinical Psychology, 2018 Director: Dr. Robin Lewis This study examined to what extent the centrality of sexual and racial/ethnic identities were associated with rejection sensitivity in young adult sexual minority women. The relationships between sexual identity centrality and current alcohol use, alcohol-related problems, and sexual minority stress outcomes were also examined with race as a potential moderator. African American/Black and Non-Hispanic White sexual minority women 18-25 years old (N = 676) were recruited through online social media platforms, community organizations, and email advertisements. Participants completed an online survey that included measures of three types of rejection sensitivity (interpersonal, race-based, and sexual orientationbased), sexual minority stress, alcohol use and related problems, and identity centrality (both sexual identity and racial identity). Data were collected in the United States in March to July of 2017. Results indicate that sexual identity centrality and racial identity centrality are associated with personal anxiety and expectations of discrimination among young, racially diverse LB women. Lesbian women reported significantly more sexual identity centrality than bisexual women, and White lesbian women reported the greatest amount of anxiety and expectations of discrimination based on their sexual minority status compared to all other groups. African-American/Black women reported less hazardous and risky drinking behaviors than White women. These findings demonstrate that multiple minority statuses, and the importance of those identities to young sexual minority women, impact the amount of anxiety they experience with regards to how they will be treated or accepted by others. The findings also highlight the importance of diversity considerations among young lesbian and bisexual women with regards to health and psychosocial outcomes.