Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Material Extrusion In-Process Failures via Deep Learning

Additive manufacturing (AM) is evolving rapidly and this trend is creating a number of growth opportunities for several industries. Recent studies on AM have focused mainly on developing new machines and materials, with only a limited number of studies on the troubleshooting, maintenance, and problem-solving aspects of AM processes. Deep learning (DL) is an emerging machine learning (ML) type that has widely been used in several research studies. This research team believes that applying DL can help make AM processes smoother and make AM-printed objects more accurate. In this research, a new DL application is developed and implemented to minimize the material consumption of a failed print. The material used in this research is polylactic acid (PLA) and the DL method is the convolutional neural network (CNN). This study reports the nature of this newly developed DL application and the relationships between various algorithm parameters and the accuracy of the algorithm.
Prevalence and antibiotic susceptibility profile of Staphylococcus aureus from milk and milk products in Nasarawa State, Nigeria

This study determined the prevalence and antibiotic susceptibility profile of Staphylococcus aureus isolated from milk and milk products in Nasarawa State, Nigeria. A total of 180 samples comprising of fresh raw milk, bulk milk, Nono, and Kindirmo were collected over a period of 6 months. Standard microbiological procedures were employed in the isolation, identification, characterization and determination of the antibiogram of S. aureus from the milk samples. Characterization was achieved by morphological, biochemical characteristics using conventional methods and Microgen® STAPH-ID kits. The isolates were tested for susceptibility or resistance to a panel of 11 commonly used antibiotics using the agar disc diffusion technique. Out of the 180 milk samples examined, nine (9) S. aureus were isolated giving a prevalence of 5.0%. The occurrence of S. aureus was higher in Nono (12.1%) and Kindirmo (10.6%) than in fresh raw milk (5.9%). The kind of water (well water) used for cleaning utensils at the Nono and Kindirmo selling points was found to be a risk factor associated with the occurrence of S. aureus in the products. All of the isolates were resistant to cefoxitin (100%), ampicillin (100%), and amoxicillin/clavulanic acid (100%). The isolates displayed various rates of resistance to erythromycin (22.2%), sulphamethoxazole/trimethoprim (22.2%), and tetracycline (44.4%). Five (5) antibiotic resistance patterns were recorded among the isolates an indication of different levels of use and misuse of antibiotics in the areas studied. The detection of Staphylococcus aureus in fresh and fermented milk in the areas studied suggests that consumption of dairy products especially those produced using traditional methods, constitute a hazard to consumers. It is recommended that since compliance with basic hygiene requirements is not guaranteed, hazard analysis and critical control points (HACCP) concepts should be seen as a part of an effective total hygiene concept at the selling points. Keywords: Antibiotic susceptibility profile, Milk, Nasarawa State, Nigeria, Prevalence, Staphylococcus aureus
Diferencias transculturales en la declaración de impuestos ante diferentes niveles de presión fiscal: Evidencia experimental con muestras de Argentina y España

© Los autores. Este artículo es publicado por la Revista de Investigación en Psicología de la Facultad de Psicología, Universidad Nacional Mayor de San Marcos. Este es un artículo de acceso abierto, distribuido bajo los términos de la licencia Creative Commons Atribucion No Comercia_Compartir Igual 4.0 Internacional. (http://creativecommons.org/licenses/by-nc-sa/4.0/) que permite el uso no comercial, distribución y reproducción en cualquier medio, siempre que la obra original sea debidamente citada. DOI: http://dx.doi. org/10.15381/rinvp.v23i1. 18093
Interpersonal Collaboration to Support Digitally Mediated Gig Workers’ Well-being in Pre- and Post-Pandemic Times

ABSTRACT The COVID-19 outbreak has raised concerns about its impact on gig workers, who perform on-demand flexible work that is mediated by digital work platforms (e.g., Uber and DoorDash), in terms of their safety, health, and well-being. The situation facing gig workers is also closely related to the public as the demand for the services provided by them has increased dramatically. However, the need for better supporting gig workers’ well-being and work-life balance is not new. Issues such as lack of self-awareness about personal health conditions or uncontrolled long working hours tend to be common among workers. Given the flexibility of gig work, it remains unclear what mechanisms or interventions may improve the well-being awareness and practices of workers. In this short paper, we discuss the potential may serve to fostering health collaboration between the workers and their significant others (e.g., family members) through technological medications. We present a pre-pandemic case study that deploys a social sensing mobile app to Uber and taxi drivers and their partners for a month. The app allows a partner to access a worker’s health sensing data, add interpersonal observations about the worker’s wellness, and participate in the process of their well-being management. We reflect on what we learned from this pre-pandemic study in order to shed light on ways to support well-being management for gig workers in the current and post-pandemic times. Key Words gig workers, work-life balance, well-being, health sensing, collaborative health management, computer-supported collaboration, sharing economy ABOUT THE AUTHOR/S Hao-Chuan Wang UC Davis hciwang@ucdavis.edu Hao-Chuan Wang is an acting Associate Professor in the Department of Computer Science, University of California, Davis. He studies how computer-mediated communication tools can be shaped to address collaborative and societal issues, such as bridging cultural and language gaps between global co-workers, and support expert-novice knowledge transfer to ensure the wellness and sustainability of work practices in platform and gig economies. Personal website: http://www.haochuanwang. info/ Chuang-Wen You National Tsing Hua University cwyou@mx.nthu. edu.tw Chien-Wen Yuan National Taiwan Normal University tinayuan@ntnu.edu.tw Nanyi Bi National Taiwan University nanyi.bi@gmail.com Fu-Yin Cherng UC Davis fuyincherng@gmail.com New Future of Work 2020, August 3–5, 2020 © 2020 Copyright held by the owner/author(s).
Post-Polymerization Heat Effect in the Production of Polyamide 6 by Bulk Quasiliving Anionic Ring-Opening Polymerization of ε-Caprolactam with Industrial Components: A Green Processing Technique

Bulk, solventless anionic ring-opening polymerization (AROP) of ε-caprolactam (CPL) with high yields, without side products and with short reaction times, initiated by caprolactamate-carbamoylcaprolactam initiating systems belong to green polymerization processes, leading to poly(ε-caprolactam) (Polyamide 6, PA6, Nylon 6). However, the effect of post-polymerization heat (i.e., slow, technically feasible cooling) on the fundamental characteristics of the resulting polymers such as yield and molecular weight distributions (MWDs) have not been revealed thus far. Significant post-polymerization effect was found by us in terms of both monomer conversions and MWDs by carrying out CPL polymerization with industrial components under conditions mimicking thermoplastic reaction transfer molding (T-RTM). Remarkably, higher monomer conversions and molecular weights (MWs) were obtained for Polyamide 6 samples prepared without quenching than that for the quenched polymers at the same reaction times. Independent of quenching or non-quenching, Mn of the resulting polymers as a function of conversion fell in the theoretical line of quasiliving AROP of CPL. At high monomer conversions, significant increase of the MW and broadening of the MWDs occurred, indicating pronounced chain–chain coupling. These findings have fundamental importance for designing processing conditions for in situ polymerization processes of ε-caprolactam by various techniques such as T-RTM, reaction injection molding (RIM), and other processing methods of Polyamide 6.
A Code Naturalness Based Defect Prediction Method at Slice Level

: Software defect prediction is an active research topic in the domain of software quality assurance. It can help developers find potential defects and make better use of resources. How to design more discriminative metrics for the prediction system, taking into account performance and interpretability, is a research direction that people devote to. Aiming at this challenge, a code naturalness feature based defect predictor method (CNDePor) is proposed. This method improves the language model by taking advantage of the bidirectional code-sequence measurement and weighting the samples by using the quality information, so as to increase the defect discrimination of the cross-entropy (CE) type metrics obtained from the model. Aiming at the shortcomings of coarse-grained defect prediction (e.g. difficulties in focusing on defect areas and high cost of code reviews), a new fine-grained defect prediction problem, statement-oriented slice level defect prediction, is studied. Four metrics are designed for this problem
The COVID-19 Spread Patterns in Italy and India: A Comparison of the Current Situation

Epidemiological mathematical models and time series models can be used to forecast about the spread of an infectious disease. In this article, without using such models, we are going to show how exactly the patter n evolves day by day once a pattern is seen to be approximately followed by the data. A lthough in Italy as well as in India the novel corona virus appeared on the same day, in Ita ly the spread is nearly logarithmic by now and in India it is nearly exponential even now, as on June 20, 2020.
Development Of Matrix Assisted Ionization Methods For Characterization Of Soluble And Insoluble Proteins From Native Environments By Mass Spectrometry

DEVELOPMENT OF MATRIX ASSISTED IONIZATION METHODS FOR CHARACTERIZATION OF SOLUBLE AND INSOLUBLE PROTEINS FROM NATIVE ENVIRONMENTS BY MASS SPECTROMETRY by ELLEN DELA VICTORIA INUTAN August 2013 Advisor: Prof. Sarah Trimpin Major: Chemistry (Analytical) Degree: Doctor of Philosophy Matrix-assisted laser/desorption ionization (MALDI) and electrospray ionization (ESI) have made a huge impact in the analysis of biological materials. ESI has gained its popularity involving liquid based analysis, efficient fragmentation, chromatographic and electrophoretic separations but has a limitation for solubility restricted materials and surface analysis. MALDI is applicable to large biomolecule analysis and for surface methods useful for tissue imaging but is limited for structural characterization due to poor fragmentation and is ill suited for liquid based separation methods. The research presented here relates to new ionization methods that encompass the benefits of ESI and MALDI. These novel ionization methods produce multiply charged ions similar to ESI but directly from surfaces similar to MALDI. The formation of multiply charged ion extends the mass range of high performance mass spectrometers with advanced features
A new short-circuit calculation method of power system with photovoltaic power sources

A large number of photovoltaic power sources connected to the grid will increase short-circuit current level of power system, and its fault transient process will change the fault characteristics of power grid. In this paper, the equivalent models of transient and steady state faults are obtained, by analysing the full current expression of photovoltaic power sources and its current characteristics in different fault stages. Based on the superposition theorem, the short-circuit current calculation method of power system with photovoltaic power sources is proposed. Finally, the effectiveness of the proposed method is verified by the analysis of actual power grid calculation examples, and the results can be used for power grid planning and relay protection setting.
An Alertness-Adjustable Cloud/Fog IoT Solution for Timely Environmental Monitoring Based on Wildfire Risk Forecasting

Internet of Things (IoT) appliances, especially those realized through wireless sensor networks (WSNs), have been a dominant subject for heavy research in the environmental and agricultural sectors. To address the ever-increasing demands for real-time monitoring and sufficiently handle the growing volumes of raw data, the cloud/fog computing paradigm is deemed a highly promising solution. This paper presents a WSN-based IoT system that seamlessly integrates all aforementioned technologies, having at its core the cloud/fog hybrid network architecture. The system was intensively validated using a demo prototype in the Ionian University facilities, focusing on response time, an important metric of future smart applications. Further, the developed prototype is able to autonomously adjust its sensing behavior based on the criticality of the prevailing environmental conditions, regarding one of the most notable climate hazards, wildfires. Extensive experimentation verified its efficiency and reported on its alertness and highly conforming characteristics considering the use-case scenario of Corfu Island’s 2019 fire risk severity. In all presented cases, it is shown that through fog leveraging it is feasible to contrive significant delay reduction, with high precision and throughput, whilst controlling the energy consumption levels. Finally, a user-driven web interface is highlighted to accompany the system; it is capable of augmenting the data curation and visualization, and offering real-time wildfire risk forecasting based on Chandler’s burning index scoring.
Differential Effects of the Valence and Volume of Online Reviews on Customer Share of Visits: The Case of US Casual Dining Restaurant Brands

Online customer reviews increasingly influence customer purchase decisions. Indeed, many customers have highlighted the significance of online reviews as an influential source of information. This study reports an investigation of the differential effects of online reviews, such as valence and volume, on the customer share of visits. Our findings suggest that valence (i.e., star rating) had more effect, giving a higher average check size to restaurants on the share of visits, while number reviews (volume) did not drive the share of visits to restaurants regardless of the average check size. Therefore, the ideal for casual dining restaurant brands would be to manage highly positive ratings to retain their customers.
Effects of Orthographic Forms on Second Language Speech Production and Phonological Awareness, With Consideration of Speaker‐Level Predictors

Orthographic forms (spellings) can affect pronunciation in a second language (L2); however, it is not known whether the same orthographic form can affect both L2 pronunciation and metalinguistic awareness. To test this, we asked 260 speakers of English—first‐language (L1) English speakers, L1 Italian and L2 English sequential bilinguals, and L1 Italian learners of L2 English—to perform word repetition tasks and rhyme judgment tasks for word pairs containing the same consonant or vowel spelled with a letter or a digraph. L1 Italian speakers established a long–short contrast and used consonant and vowel length contrastively in their L2 English, both in production and in an awareness task. This provides evidence for a direct link between the effects of the same orthographic phenomenon on speech production and on metalinguistic awareness. Results were strengthened by combining experimental and qualitative data in the study of orthographic effects. Finally, the results show that proficiency predicts orthographic effects, and that orthographic effect predictors vary in naturalistic and instructed contexts.
Inductively coupled Ar/CH₄/H₂plasmas for low-temperature deposition of ordered carbon nanostructures

The study of inductively coupled Ar/CH 4/H 2 plasmas in the plasma enhanced chemical vapor deposition (PECVD) of self-assembled carbon nanostructures (CN) was presented. A spatially averaged (global) discharge model was developed to study the densities and fluxes of the radical neutrals and charged species, the effective electron temperature, and methane conversion factors under various conditions. It was found that the deposited cation fluxes in the PECVD of CNs generally exceed those of the radical neutrals. The agreement with the optical emission spectroscopy (OES) and quadrupole mass spectrometry (QMS) was also derived through numerical results.
Building Damage Caused by the 2017 M5.4 Pohang, South Korea, Earthquake, and Effects of Ground Conditions

ABSTRACT The 2017 M5.4 Pohang, South Korea, earthquake damaged numerous buildings. Immediately after the main shock event, 1,880 professional engineers inspected the damaged buildings and assigned three damage grades (“Usable,” “Restricted use,” and “Unusable”). We investigate factors influencing the building damage. The built year, number of stories, and estimated peak ground acceleration are strongly related with the frequency of building damage. We also prove that the damage caused to the building is influenced by the basin geometry, based on site parameters such as N value, shear wave velocity, depth to rock, and site fundamental period.
Análisis termoenergético del sistema de generación de vapor de una central térmica de 49 MW

En el trabajo se realiza un análisis termoenergético integral del sistema de generación de vapor de una termoeléctrica de 49 MW, lo cual no ha sido estudiado con rigor desde el punto de vista energético. En un algoritmo se sintetiza la metodología para el cálculo de los rendimientos térmicos brutos y exergéticos de las calderas que lo integran, aspectos que no se han interrelacionado en estudios precedentes. Los resultados evidencian un elevado grado de aprovechamiento de la energía térmica y una baja capacidad de utilización de la exergía disponible en las instalaciones, provocados por el deterioro de algunos de sus parámetros de operación y por irreversibilidades inherentes al proceso de transformación del agua en vapor. Los rendimientos, térmico y exergético del sistema, ascendieron a 90.106 y 45.491 %, respectivamente. El algoritmo propuesto prevé el cálculo y la comparación de los parámetros termoenergéticos reales de las calderas con los nominales y las acciones científicas, y técnico-organizativas, a desarrollar para lograr rendimientos termoexergéticos superiores. Su implementación futura tendrá en cuenta el análisis de los rendimientos térmicos netos, los indicadores económico-ambientales y la optimización energético-operacional de las calderas.
Uplift of Anatolia

The Cenozoic history of the Anatolian Plateau is investigated using the distribution of the last Cenozoic marine strata, the ages of Neogene continental sediments and magmatic rocks, and thermochronology. In central and northern Anatolia, the youngest marine sediments are of Middle Eocene age and show that the region has been above the sea level since ca. 41 Ma. The preservation of marine Eocene sequences over large regions and widespread distribution of Neogene continental sediments point to minor erosion or subsidence, except in the Miocene core complexes, and indicate average surface uplift or subsidence rates of less than 0.05 km/Myr since 41 Ma. The Cenozoic mammal ages point to widespread continental deposition on the Anatolian Plateau from Early Miocene ca. 22 Ma to the present, and a similar pattern of semicontinuous magmatism has been observed in Anatolia since ca. 23 Ma. New thermochronological data from central Anatolia to west of Ankara have indicate a major exhumation phase during the Paleocene and Early Eocene, followed by minor uplift and/or subsidence. Miocene exhumation is restricted to the core complexes, such as the Kazdag Massif. The Anatolian Plateau has been a land area since 41 Ma and was characterized by continental sedimentation and volcanism in the last 22 Myr. In this period, subsidence and uplift were balanced so that central Anatolia was maintained above sea level. In contrast, its southern mountainous margin, the Taurides, is free of Neogene magmatism and has undergone a fast uplift above sea level since 8 Ma ca. 0.3 km/Myr . These differences indicate that the uplift of Anatolia cannot be ascribed to a single mechanism. Flat subduction, followed by mantle upwelling under Anatolia in the post-Middle Eocene period, maintained the region above the sea level, whereas the Late Miocene rupture of the subducting eastern Mediterranean oceanic slab have induced fast uplift of the Taurides.
Transitioning360: Content-aware NFoV Virtual Camera Paths for 360° Video Playback

Despite the increasing number of head-mounted displays, many 360° VR videos are still being viewed by users on existing 2D displays. To this end, a subset of the 360° video content is often shown inside a manually or semi-automatically selected normal-field-of-view (NFoV) window. However, during the playback, simply watching an NFoV video can easily miss concurrent off-screen content. We present Transitioning360, a tool for 360° video navigation and playback on 2D displays by transitioning between multiple NFoV views that track potentially interesting targets or events. Our method computes virtual NFoV camera paths considering content awareness and diversity in an offline preprocess. During playback, the user can watch any NFoV view corresponding to a precomputed camera path. Moreover, our interface shows other candidate views, providing a sense of concurrent events. At any time, the user can transition to other candidate views for fast navigation and exploration. Experimental results including a user study demonstrate that the viewing experience using our method is more enjoyable and convenient than previous methods.
A Study on the Workplace Implications of Incivility upon Worker’s Productivity

A growing body of research explores workplace incivility, defined as low-intensity deviant workplace behavior with an ambiguous intent to harm. In the 15 years since the theoretical introduction of the workplace incivility construct, research in this domain has taken off, albeit in a variety of directions.The present research aimed to figure out how incivility impacts the workplace behavior of the employees upon their mental health & their productivity. An exhaustive literature review was carried out to identify the broad dimensions demanding attention in this regard. Research was carried out in two segments , firstly , via a semi-structured questionnaire & then via a focused group interview which elicited certain information pertaining to the dispositions , behavioral antecedents , situational antecedents, affective outcomes , attitudinal outcomes & cognitive outcomes of incivility at workplace. IndexTerms Low – intensity defiant workplace behavior , Organizational Citizenship Behavior , Attrition , Behavioral Antecedents , Situational Antecedents , Affective outcome , Attitudinal outcomes ,Cognitive outcomes.