Miracle-dz/UniFAR-FTUs huggingface dataset:
Complementary Feature Extractions for Event Identification in Power Systems Using Multi-Channel Convolutional Neural Network

This paper presents an event identification process in complementary feature extractions via convolutional neural network (CNN)-based event classification. The CNN is a suitable deep learning technique for addressing the two-dimensional power system data as it directly derives information from a measurement signal database instead of modeling transient phenomena, where the measured synchrophasor data in the power systems are allocated by time and space domains. The dynamic signatures in phasor measurement unit (PMU) signals are analyzed based on the starting point of the subtransient signals, as well as the fluctuation signature in the transient signal. For fast decision and protective operations, the use of narrow band time window is recommended to reduce the acquisition delay, where a wide time window provides high accuracy due to the use of large amounts of data. In this study, two separate data preprocessing methods and multichannel CNN structures are constructed to provide validation, as well as the fast decision in successive event conditions. The decision result includes information pertaining to various event types and locations based on various time delays for the protective operation. Finally, this work verifies the event identification method through a case study and analyzes the effects of successive events in addition to classification accuracy.
Social Media and the Automatic Production of Memory: Classification, Ranking, and Sorting of the Past

How is social media archiving, filtering, and sorting the digital artifacts of our pasts? what do these algorithmic processes mean for our human conception of memory and memory practices in everyday life? Social Media and the Automatic Production of Memory offers an expansive series of provocations in a compact volume, one of Bristol University Press’s “Shorts” Research publications. Ben Jacobsen and David Beer are well-placed to pose these questions as sociologists at the University of York, whose work on memory, metrics and techno-social transformations moors empirical research to critical theory and to emerging theories of data and the self. The book interweaves a comprehensive literature review with a short overview of empirical findings from a larger qualitative research project exploring people’s experiences and perceptions of algorithmic systems in their remem-brance of the past. Highlighting the algorithmic interaction of classification and ranking that work together to define and resurface “memories” on digital devices and platforms, the book introduces a useful vocabulary for making sense of our abounding, algorithmically mediated personal repositories of remembering stored in social media archives. The introduction sets out core preoccupation the authors – namely, titular production memory. this automation, memories identified, certain meanings values, then at that social media authors walter Benjamin an “digging,” active past. when ourselves, result algorithmic
Fluorescent additive for estimation of compatibility of polyester blend by imipramine-containing polymer

ABSTRACT The effects of adding imipramine-containing polymer (PImip-Fl) in two kinds of polyester/lysine triisocyanate (LTI) blends were investigated. PImip-Fl was prepared by Suzuki–Miyaura coupling reaction of dibromoimipramine hydrochloride and dioctylfluorene diboronic acid. The addition of PImip-Fl gave fluorescence to resins [poly(lactic acid) (PLA) and poly(ε-caprolactone) (PCL)], and its fluorescent maximum wavelength (λmax) depended on the resin. PImip-Fl was used as a functional additive for the estimation of compatibility of PLA/PCL blend composite. The addition of LTI to the resins/PImip-Fl blend composites afforded a shorter wavelength shift of the fluorescent λmax. This suggested that the method using PImip-Fl is adaptive for the estimation of compatibility of the blend composite.
TRANSFORMING DATA INTO ADDED-VALUE INFORMATION: THE DESIGN OF SCIENTIFIC MEASUREMENT MODELS THROUGH THE LENS OF DESIGN THEORY

Abstract Transforming data into added-value information is a recurrent issue in the context of “big data” phenomenon, as new sources of data become increasingly available. This paper proposes to offer a fresh look on how data and added-value information are linked through the design of specific models. This investigation is based on design theory, used as an analysis framework, and on a historical example in the Earth science field. It aims at unveiling the reasoning logic behind the design process of models combining data science and domain knowledge in specific ways, especially involving not only knowledge about the physical phenomena but also on the measuring instrument itself. More specifically, this paper shows how specific efforts on exploring the originality of the new instrument compared to existing ones can result in designing performant models to transform new sources of data into information. This also suggests several important competencies to be involved in the model-design process: (1) a detailed understanding of the limitations of existing models (2) the ability to explore both the originality of the new source of data compared to existing ones (3) the ability of leveraging independent data sources.
Down-regulation of CatSper 1 and CatSper 2 genes by methamphetamine

Abstract Methamphetamine (METH) is a strong central nervous system stimulant that has toxic effects on the reproductive system. The aim of this study was to evaluate changes in gene and protein expression levels of CatSper1, 2, and oxidative stress after exposure to METH in the testis of mice. The mRNA and protein expression levels of CatSper1, 2 decreased in the experimental groups. The administration of METH caused a decrease of SOD and CAT activities and an increase in the MDA level. METH damaged the male reproductive system via the oxidative stress pathway and by reduction of CatSper1, 2 genes and protein expression. Mini-abstract: Administration of METH damaged the male reproductive system via the oxidative stress pathway and reduction of CatSper1, 2 genes and protein expression.
Social (–Ecological) Network Analysis in Environmental Governance: Central Publications, Important Concepts, and Areas of Application

Social and social–ecological network analysis (S(E)NA) have recently emerged as new methods in the environmental governance (EG) literature. By investigating networks of connections between actors, S(E)NA advances the understanding of who is involved in EG and how. We provide an overview of the EG literature applying S(E)NA and map (1) the citation network emerging from cross-references and (2) the similarity network emerging from word similarities between publications. We show that S(E)NA application in EG is in the process of developing into a field of research where publications frequently cite each other. We identify 20 publications which occupy positions as sources, storers, or bridges of knowledge in the citation network. While we see S(E)NA applied in diverse resource contexts, these are mainly discussed on the local spatial level, with a focus on “policy” or “collaboration. ” We discover that “power structures” and “the production of knowledge” are themes influencing the whole field.
Formation of the Traffic Flow Rate under the Influence of Traffic Flow Concentration in Time at Controlled Intersections in Tyumen, Russian Federation

Present experience shows that it is impossible to solve the problem of traffic congestion without intelligent transport systems. Traffic management in many cities uses the data of detectors installed at controlled intersections. Further, to assess the traffic situation, the data on the traffic flow rate and its concentration are compared. Latest scientific studies propose a transition from spatial to temporal concentration. Therefore, the purpose of this work is to establish the regularities of the influence of traffic flow concentration in time on traffic flow rate at controlled city intersections. The methodological basis of this study was a systemic approach. Theoretical and experimental studies were based on the existing provisions of system analysis, traffic flow theory, experiment planning, impulses, probabilities, and mathematical statistics. Experimental data were obtained and processed using modern equipment and software: Traficam video detectors, SPECTR traffic light controller, Traficam Data Tool, SPECTR 2.0, AutoCad 2017, and STATISTICA 10. In the course of this study, the authors analyzed the dynamics of changes in the level of motorization, the structure of the motor vehicle fleet, and the dynamics of changes in the number of controlled intersections. As a result of theoretical studies, a hypothesis was put forward that the investigated process is described by a two-factor quadratic multiplicative model. Experimental studies determined the parameters of the developed model depending on the directions of traffic flow, and confirmed its adequacy according to Fisher’s criterion with a probability of at least 0.9. The results obtained can be used to control traffic flows at controlled city intersections.
The Datafication of Law: How Technology Encodes Carceral Power and Affects Judicial Practice in the United States

This inquiry explores how data analyses about US Federal sentences have transformed sentencing practice beginning in the mid-1980s. I consider this inquiry an early case of the datafication of law, a pervasive process that translates legal practice into data and embeds it in digital networks so it can be tracked and analyzed in real time. To explore datafication historically and in relation to legal practice and power, I consider it not as an objective and passive undertaking but, rather, as an ideological and performative process that encodes and enacts normative presumptions and desirable futures. The empirical inquiry traverses “levels of analysis” and thus bridges prominent perspectives in sociolegal research. In so doing, I identify four mechanisms that mediate “large-scale” processes and “local” practices: field assembly, symbolic projection, material inscription, and boundaries spanning. Substantively, I show how datafication has not simply described, but also transformed, sentencing practice according to a colorblind-carceral imaginary that strives to fix the present in place. By relentlessly translating decisions into data forms that derive from this carceral imaginary, datafication affects judicial action and partakes in sustaining legacies of oppression. Yet, like other technologies, datafication also reveals dialectic dimensions in opening up to new actors and subjecting its ideological underpinnings to contestation and change.
New Radiocarbon Constraints on the Global Cycling of Solid‐Phase Extractable Dissolved Organic Carbon

Radiocarbon (∆14C) measurements suggest the deep ocean stores marine dissolved organic carbon (DOC) on millennial timescales. The mechanisms that mediate this residence time remain unconstrained. Solid‐phase extraction (SPE) has emerged as a widely used technique to isolate DOC for subsequent analyses. We present SPE‐DOC concentrations and ∆14C values for three GO‐SHIP Repeat Hydrography transects, spanning the Pacific, Southern and Indian Oceans. Comparisons of SPE‐DOC with total DOC ∆14C values are used with an isotopic mass‐balance to estimate the size of the refractory DOC (RDOC) reservoir and changes in RDOC relative abundance in the global ocean. Estimated RDOC abundance is similar across the deep Pacific and Indian Oceans (average = 93 ± 5%, 35 ± 6 μM), whereas RDOC in the surface ocean varies as a function of total DOC concentration. Our results fill in spatial SPE‐DOC ∆14C sampling gaps for the global ocean, and our mass‐balance RDOC estimates are consistent with previous observations.
Movescapes and eco‐evolutionary movement strategies in marine fish: Assessing a connectivity hotspot

Susan K. LowerreBarbieri1,2 | Claudia Friess2 | Lucas P. Griffin3 | Danielle Morley3,4 | Gregory B. Skomal5 | Joel W. Bickford2 | Neil Hammerschlag6 | Mitchell J. Rider6 | Matthew J. Smukall7 | Maurits P. M. van Zinnicq Bergmann7,8 | Tristan L. Guttridge9 | Andrea M. Kroetz10 | R. Dean Grubbs11 | Carissa L. Gervasi12 | Jennifer S. Rehage12 | Gregg R. Poulakis13 | Kim BassosHull14 | Jayne M. Gardiner15 | Grace A. Casselberry3 | Joy Young16 | Matt Perkinson17 | Debra L. Abercrombie18 | Dustin T. Addis2 | Barbara A. Block19 | Alejandro Acosta4 | Aaron J. Adams20,21 | Andy J. Danylchuk3 | Steven J. Cooke22 | Frederick G. Whoriskey23 | Jacob W. Brownscombe24
Early Detection of Hepatocellular Carcinoma Since the Introduction of the German Clinical Practice Guideline in 2013.

In Germany, more than 80% of cases of hepatocellular carcinoma (HCC) occur in patients with cirrhosis of the liver (1). Early detection is crucial for the overall prognosis in this setting (2). The German Clinical Practice (S3) Guideline—published in 2013 by the German Society for Gastroenterology, Digestive and Metabolic Diseases (DGVS)—included the recommendation for sonography screening of at-risk patients every six months, so as to improve the early detection rate (3). The current study compares the early detection rate and its effect on the overall prognosis before and after publication of the guideline.
DeepPhasePick: A method for Detecting and Picking Seismic Phases from Local Earthquakes based on highly optimized Convolutional and Recurrent Deep Neural Networks

Seismic phase detection, identification and first-onset picking are basic but essential routines to analyse earthquake data. As both the number of seismic stations, globally and regionally, and the number of experiments greatly increase due to ever greater availability of instrumentation, automated data processing becomes more and more essential. E.g., for modern seismic experiments involving 100s to even 1,000s instruments, conventional human analyst-based identification and picking of seismic phases is becoming unfeasible, and the introduction of automatic algorithms mandatory. In this paper, we introduce DeepPhasePick, an automatic two-stage method that detects and picks P and S seismic phases from local earthquakes. The method is entirely based on highly optimized deep neural networks, consisting of a first stage that detects the phases using a convolutional neural network, and a second stage that uses two recurrent neural networks to pick both phases. Detection is performed on three-component seismograms. P- and S-picking is then conducted on the vertical and the two-horizontal components, respectively. Systematic hyperparameter optimization was applied to select the best model architectures and to define both the filter applied to preprocess the seismic data as well as the characteristics of the window sample used to feed the models. We trained DeepPhasePick using seismic records extracted from two sets of manually-picked event waveforms originating from northern Chile (∼39,000 records for detection and ∼36,000 records for picking). In different tectonic regimes, DeepPhasePick demonstrated the ability to both detect P and S phases from local earthquakes with high accuracy, as well as predict P- and S-phase time onsets with an analyst level of precision. DeepPhasePick additionally computes onset uncertainties based on the Monte Carlo Dropout technique as an approximation of Bayesian inference. This information can then further feed an associator algorithm in an earthquake location procedure.
Written Corrective Feedback Beliefs and Practices in Thai as a Foreign Language Context: A Perspective from Experienced Teachers

Most studies on Written Corrective Feedback (WCF) have focused either on ESL or EFL teachers’ beliefs and practices. However, it is still not known how teachers who teach second or foreign languages other than English will provide information on the learners’ written language production. To bridge the gap in the literature, this current study reports on an interview study investigating nine university lecturers’ beliefs and their actual practices about WCF on an assignment done by the third-year exchange student of Thai as a foreign language (TFL). Quantitative and qualitative data were collected through feedback tasks and semi-structured interviews. The findings indicated that there was alignment between teachers’ beliefs of their WCF practice and their actual practice in terms of types of WCF and feedback techniques. However, the amount of feedback provided and the teachers’ time constraint, not the level of students’ ability as they thought, appeared to be the reasons for these misalignments of TFL teachers’ beliefs and practices.
Fuel Improvement Measures for Particulate Matter Emission Reduction during Corn Cob Combustion

Fuel-related measures and modernization of small-scale combustion units has become the focus of attention in the renewable heat generation sector, as a means to promote local biomass utilization and fuel-flexibility while meeting strict environmental legislative requirements. With the aim to mitigate total particulate matter emissions and ash-associated problems characteristic of crop residue combustion, (1) corn cob pellets (with and without kaolin and binder) as well as (2) fuel blends with wood pellets were combusted in a pellet oven under full load. Results show that additivation or fuel blending (e.g., 50 wt. % wood and 50 wt. % corn cob pellets) reduce total particulate and CO-emissions by 48 to 60 wt. % and 64 to 89 wt. %, respectively, in comparison to baseline emissions from non-additivized corn cob pellets. Kaolin prevented sintering of corn cob ash. However, considerable grate ash entrainment was observed. TPM consists of a “primary network”—polyhedral and spherical particles approximately 1 μm in diameter (mainly KCl), and a “secondary network” built on top of the primary network, consisting of square-prism-shaped particles of approximately 200 nm in diameter. KCl and K2SO4 are main compounds in particles from corn cob and wood pellet combustion, respectively. Effective measures demonstrated within this study should be complemented with low-cost coarse ash removal systems.
Belinurus Bronn, 1839 (Chelicerata, Xiphosura) has priority over Bellinurus Pictet, 1846

In the first half of the nineteenth century, a marked shift occurred in our understanding and treatment of the chelicerate fossil record, with the differentiation and recognition of entirely extinct genera for the first time. At the heart of this taxonomic revolution were the Eurypterida (sea scorpions) and Xiphosura (horseshoe crabs), although both groups were in fact considered crustaceans until Lankester's (1881) seminal comparative anatomical study of the extant xiphosuran Limulus Müller, 1785 and modern scorpions. The oldest available eurypterid genus is Eurypterus deKay, 1825; the oldest available fossil arachnid genus name is that of the scorpion Cyclophthalmus Corda, 1835. However, there has been considerable historical confusion over the oldest available fossil xiphosuran genus name, which has been recognized alternately as Belinurus König (with a publication date of either 1820 or 1851) or the synonymous Bellinurus Pictet, 1846. Most recent treatments (e.g., Selden and Siveter, 1987; Anderson and Selden, 1997; Anderson et al., 1997; Lamsdell, 2016, 2021; Bicknell and Pates, 2020) have favored Bellinurus Pictet, 1846 as the available name; however, Haug and Haug (2020) recently argued that Belinurus König, 1820 is valid and has priority, a position then followed by Lamsdell (2020), prompting a reinvestigation of the taxonomic history of the genus. Upon review, it is clear that neither of the previously recognized authorities for Belinurus are accurate and that the two candidate type species for each genus are, in fact, synonyms. Given the convoluted and at times almost illogical history of the competing names, along with the most recent controversy as to which has priority, we present a complete history of the treatment of the genus to resolve the issue.
ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering

We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA). ReTraCk is designed as a modular framework to maintain high flexibility. It includes a retriever to retrieve relevant KB items efficiently, a transducer to generate logical form with syntax correctness guarantees and a checker to improve transduction procedure. ReTraCk is ranked at top1 overall performance on the GrailQA leaderboard and obtains highly competitive performance on the typical WebQuestionsSP benchmark. Our system can interact with users timely, demonstrating the efficiency of the proposed framework.
Fishes of the Lower Lulua River (Kasai Basin, Central Africa): A Continental Hotspot of Ichthyofaunal Diversity under Threat

The ichthyofauna of the Lulua River, a large right bank tributary of the Kasai River in central Africa, is among the most poorly documented in the Kasai ecoregion. To remedy this lack of knowledge, sampling was carried out between 2007 and 2014 along the main channel and in many tributaries. A total of 201 species distributed in 81 genera, 24 families, and 12 orders are reported from the lower reaches of the Lulua. The species richness reported in this study represents a substantially improved documentation of the Lulua ichthyofauna (historically estimated at only 79 species). Here, 129 species are recorded for the first time, bringing the total number of known species to 208. Among these, five have recently been described: Raiamas brachyrhabdotos Katemo Manda, Snoeks, Choca Manda, and Vreven 2018, Distichodus kasaiensis Moelants, Snoeks, and Vreven, 2018, Distichodus polli Abwe, Snoeks, Choca Manda, and Vreven 2019, Epiplatys atractus Van Der Zee, Mbimbi, and Sonnenberg 2013, and Hypsopanchax stiassnyae Van Der Zee, Sonnenberg, and Mbimbi 2015; numerous additional taxa are currently under investigation. Recognized here as a continental hotspot of ichthyofaunal diversity, the Lulua is under extreme threat from exploitation of forest products for building materials, deforestation for shifting agriculture and charcoal exploitation, destructive fishing practices, and mining, all of which are rapidly increasing in the watershed. The present study provides baseline documentation for use in conservation planning and future developmental projects in the Kasai ecoregion in general and Lulua River basin in particular.