Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Unbalance Detection and Diagnosis on Large Flexible Rotors by SVR and ANN trained by Dynamic Multibody Simulations

Multiple-stage steam turbine generators, like those found in nuclear power plants, pose special challenges with regards to mechanical unbalance diagnosis. Several factors contribute to a complex vibrational response, which can lead to incorrect assessments if traditional condition monitoring strategies are used without considering the mechanical system as a whole. This, in turn, can lead to prolonged machinery downtime. Several machine learning techniques can be used to integrally correlate mechanical unbalance along the shaft with transducer signals from rotor bearings. Unfortunately, this type of machinery has scarce data regarding faulty behavior. However, a variety of fault conditions can be simulated in order to generate these data using computational models to simulate the dynamic response of individual machines. In the present work, a multibody model of a 640 MW steam turbine flexible rotor is employed to simulate mechanical unbalance in several positions along the shaft. Synchronous components of the resulting vibration signals at each bearing are obtained and utilized as training data for two regression models designed for mechanical unbalance diagnosis. The first approach uses an artificial neural network and the second one utilizes a support vector regression algorithm. In order to test their performance, the stiffness of each bearing in the multibody simulation was altered between 50% and 150% of the training model values, random noise was added to the signal and several dynamic unbalance conditions were simulated. Results show that both approaches can reliably diagnose dynamic rotor unbalance even when there is a typical degree of uncertainty in bearing stiffness values.
Seismic Imaging of Crust Beneath the Western Tibet‐Pamir and Western Himalaya Using Ambient Noise and Earthquake Data

We present a new high‐resolution image of the crust beneath the western Himalaya‐Asia convergence zone encompassing the geographical domain of western Himalaya‐western Tibet‐Ladakh‐Karakoram‐Pamir‐Hindu Kush, using ambient noise cross‐correlations from 530 seismological stations along with surface wave observations from 1,261 earthquakes recorded over the network. The 3‐D shear wave velocity image is created using 22,726 inter‐station Rayleigh wave dispersion measurements from 5 to 60 s period at a horizontal resolution of less than 0.5° × 0.5° following the Bayesian Trans‐dimensional Tree tomography approach. The Moho beneath the Himalayas and south Tibet correlates with a velocity transition of 4.4–4.6 km/s and a reduced velocity transition of 4.0–4.2 km/s in northern Tibet and the Pamir. We used the Moho depth and the nature of high‐velocity lower crust (Vs > 4.0 km/s) to map the northern limit of the Indian crust that extends beyond the Qiangtang block in western Tibet (77–82°E) from its previously assumed boundary in the Lhasa block and till the central Pamir farther west. The velocity image reveals discontinuous low‐velocity zones (LVZs; Vs < 3.4 km/s) in the mid‐crust of western Tibet and the Pamir that do not support the existence of the channel flow model. The LVZs in the Pamir correlate with the surface distribution of gneiss domes. The lowest velocities (Vs < 3.2 km/s) are observed over the Ladakh‐Karakoram batholith and the Nanga Parbat region. The study suggests a continuation of LVZs across the Karakoram Fault at a depth below 20 km, indicating the fault's upper crustal depth extent.
Identifying COVID-19 Personal Health Mentions from Tweets Using Masked Attention Model

Twitter has been an important platform for people to discuss and share health-related information. It provides a massive amount of data for real-time monitoring of infectious diseases (such as COVID-19) and freeing disease-prevention organizations from the tedious labor involved in public health surveillance. Personal health mention (PHM) detection is one of the critical methods to keep up-to-date on an epidemic’s condition; it attempts to identify a person’s health condition based on online text information. This paper explores PHM identification for COVID-19 through Twitter. We built a COVID-19 PHM data set containing tweets annotated with four types of COVID-19-related health conditions. A masked attention model was devised to classify the tweets as self-mention, other-mention, awareness, and non-health. We obtained promising results on the PHM identification task. The classification results facilitate timely health monitoring and surveillance for digital epidemiology. We also evaluate how the attention mechanism and training method affect the model’s predictive performance.
The Effects of a Complex Training Protocol on Vertical Jump Performance in Male High School Basketball Players

The purpose of this study was to investigate the effects of CT (complex training) on VJ (vertical jump). It was hypothesized that VJ height would be enhanced through CT and even greater increases would occur as a result of higher intensity resistance exercise. Twenty male high school basketball players were divided into two training groups: a HI (high intensity), low repetition group and a medium intensity, HR (high repetition) group. Each training group trained twice per week for six weeks. Training protocols involved three sets of 8-10 squat repetitions (80%-85% 1RM (repetition maximum)) for the HI group followed by ten countermovement jumps and three sets of 12-15 squat repetitions (60%-70% 1RM) for the HR group followed by ten countermovement jumps. A three minute rest period was utilized between each set. Participants were tested at weeks 1, 3, and 6. The results of this study revealed that there was not a significant difference between the two CT protocols with respect to VJ (p = 0.077). At week 6 both groups increased VJ significantly; HI group (4.0 ± 1.8 cm, p < 0.01) and the HR group (2.7 ± 1.6 cm, p < 0.01). Within the parameters of this study CT with either HI or HR protocols are effective at improving VJ.
Current Challenges, Constraints and Future Strategies for Development of Successful Market for Biopesticides

Biopesticides are revolutionary crop protectants that protect crops from vast array of pests and pathogens in eco-friendly manner. They show plethora of benefits over synthetic pesticides, such as target specificity, less toxicity and biodegradability; however, they lack position in crop protection market and represent only 3.5% of global pesticides market (Olson et al., 2013). Though biopesticides lag behind in commercial pesticide market, they have great deals to offer for development of sustainable agriculture. The successful market for biopesticides can be achieved through understanding the major challenges and constraints faced by them and developing new strategies such as optimising delivery system; screening new and better strains; educating farmers and others to cope up with encountering hurdles.
Multi-input Memristor Rationed Logic Full Adder Circuit for Efficient Processing Time

This research proposes a multi-input memristor rationed logic (MRL) 1-bit full adder circuit as an alternative to conventional multi-stage MRL full adders. The proposed multi-input 1-bit full adder circuit consisted of 25 memristors without CMOS transistor. Ripple carry adder was utilized to realize multi-input MRL 8-bit full adder circuit by cascading eight blocks of multi-input MRL 1-bit full adder. The advantages of the multi-input MRL 8-bit full adder include straightforwardness, compactness, and improved delay time. Simulations were carried out by using LTspice simulator and results compared with multi-stage full adders. The multi-input MRL 8-bit full adder circuit achieved higher processing-time efficiency and thereby holds great potential as an alternative to multi-stage full adders. The novelty of this research lies in the use of multi-input MRL technique to realize full adder circuits that effectively mitigate voltage degradation and improve delay time. Streszczenie. Badania te proponują wielowejściowy układ memristor rationed logic (MRL) 1-bitowego pełnego sumatora jako alternatywę dla konwencjonalnych wielostopniowych sumatorów MRL. Proponowany wielowejściowy 1-bitowy układ pełnego sumatora składał się z 25 memrystorów bez tranzystora CMOS. Sumator przenoszenia Ripple został wykorzystany do realizacji wielowejściowego 8-bitowego pełnego sumatora MRL poprzez kaskadowanie ośmiu bloków wielowejściowego 1-bitowego pełnego sumatora MRL. Zalety wielowejściowego, 8-bitowego pełnego sumatora MRL obejmują prostotę, zwartość i poprawiony czas opóźnienia. Symulacje przeprowadzono przy użyciu symulatora LTspice, a wyniki porównano z wielostopniowymi pełnymi sumatorami. Obwód wielowejściowego 8-bitowego pełnego sumatora MRL osiągnął wyższą wydajność w czasie przetwarzania, a tym samym ma ogromny potencjał jako alternatywa dla wielostopniowych pełnych sumatorów. Nowość tych badań polega na wykorzystaniu wielowejściowej techniki MRL do realizacji pełnych obwodów sumatorów, które skutecznie łagodzą degradację napięcia i poprawiają czas opóźnienia. (Wielowejściowy układ logiczny Memristor racjonowany z pełnym sumatorem dla wydajnego czasu przetwarzania)
Frequency of transposable elements and fungicide resistance in Botrytis Cinerea Pers. populations on strawberries from Aydin and Mersin provinces in Turkey

Strawberry is a delicious fruit that can be consumed fresh or/and can be used in the industry. One of the important factors that affect yield is the plant protection problems. Fungicides because of the metabolites they produce, especially their presence in foods, pose a threat to health and also cause economic problems. Botrytis cinerea causes grey mold disease in over 220 plant species. Samples were collected from five different localities from Aydın and Mersin province. Samples were incubated on um and then isolated from mixed cultures. Morphological and molecular identification of the samples were made. Transposon sites were amplified with PCR. In total 154 B.cinerea samples were isolated of which Transposa 20.1%, Boty 46.1%, Flipper 10.4% and Vacuma 23.4%, in Aydın population Transposa 19.5%, Boty 48.3%, Flipper 9.2% and Vacuma 23.0%, in Mersin Transposa 20.9%, Boty 43.3%, Flipper 11.9% and Vacuma 23.9% were found. In the fungicide resistant test, in spore tests, Cyprodinil was found most effective while fenhexamid was found less effective. In mycelium te sts, fe nhexamid was mo st effective at the lowest concentration applied while carbendazim was found less effective even at the highest concentration. Although short-term results are limited, in long-term transposon profiling can help with disease management. Cite this article as: Bahadır T, Halil H B. Frequency of transposable elements and fungicide resistance in Botrytis Cinerea Pers. populations on strawberries from Aydin and Mersin provinces in Türkiye. Sigma J Eng Nat Sci 2022;40(2):281–291. Sigma Journal of Engineering and Natural Sciences Web page info: https://sigma.yildiz. edu.tr DOI: 10.14744/sigma.2022. 00032
Prosumption for Social Sustainability: Social-media Posting of DIY-cooking Outcomes During COVID-19

Sharing of Do-It-Yourself (DIY) cooking outcomes on various social media platforms was one of the most visible phenomena on the digital landscapes during COVID-19 led lockdowns. Since the prosumption of food is not considered as a source of alternative food system only, but also as a source of pleasure, DIY-cooking-related social media posts during the lockdown were prevalent among internet users. This paper examines variations in the social media posting behavior of food prosumers based on four individual and three social factors of gender, age, marital status, and family structure. Responses from 198 Facebook food community members were used to test the statistical hypotheses. The analyses report that the need for entertainment value while posting on social media was different among different demographic factors, whereas self-discovery and social enhancement did not exhibit variations across demographics. The need for social presence mattered more for unmarried people during social isolation whereas females used DIY-cooking posting to fulfil the need for uniqueness. The implications for social sustainability and business practices are also discussed.
The Pareto-Exponential Distribution: Theory and Real-life Applications

In this article, Pareto-Exponential (PE) distribution, a sub model of the Pareto-X family which is introduced by Rana et al. [1], is explained. The maximum likelihood estimators of the model parameters are find out, and a simulation study is also done. Various distributional properties of the proposed model are illustrated. Some practical- life applications are appraised from different types of fields for examining the applicability of proposed model. The empirical implementation exhibits that the developed model allow greater suitability than any other models applied in this study.
Study on road sign recognition in LabVIEW

Road and traffic sign identification is a field of study that can be used to aid the development of in-car advisory systems. It uses computer vision and artificial intelligence to extract the road signs from outdoor images acquired by a camera in uncontrolled lighting conditions where they may be occluded by other objects, or may suffer from problems such as color fading, disorientation, variations in shape and size, etc. An automatic means of identifying traffic signs, in these conditions, can make a significant contribution to develop an Intelligent Transport Systems (ITS) that continuously monitors the driver, the vehicle, and the road. Road and traffic signs are characterized by a number of features which make them recognizable from the environment. Road signs are located in standard positions and have standard shapes, standard colors, and known pictograms. These characteristics make them suitable for image identification. Traffic sign identification covers two problems: traffic sign detection and traffic sign recognition. Traffic sign detection is meant for the accurate localization of traffic signs in the image space, while traffic sign recognition handles the labeling of such detections into specific traffic sign types or subcategories [1].
The Applicability of LandTrendr to Surface Water Dynamics: A Case Study of Minnesota from 1984 to 2019 Using Google Earth Engine

The means to accurately monitor wetland change over time are crucial to wetland management. This paper explores the applicability of LandTrendr, a temporal segmentation algorithm designed to identify significant interannual trends, to monitor wetlands by modeling surface water presence in Minnesota from 1984 to 2019. A time series of harmonized Landsat and Sentinel-2 data in the spring is developed in Google Earth Engine, and calculated to sub-pixel water fraction. The optimal parameters for modeling this time series with LandTrendr are identified by minimizing omission of known surface water locations, and the result of this optimal model of sub-pixel water fraction is evaluated against reference images and qualitatively. Accuracy of this method is high: overall accuracy is 98% and producer’s and user’s accuracies for inundation are 82% and 88% respectively. Maps summarizing the trendlines of multiple pixels, such as frequency of inundation over the past 35 years, also show LandTrendr as applied here can accurately model long-term trends in surface water presence across wetland types. However, the tendency of omission for more variable prairie pothole wetlands and the under-prediction of inundation for small or emergent wetlands suggests the algorithm will require careful development of the segmented time series to capture inundated conditions more accurately.
Predictors of Therapy Response in Chronic Aphasia: Building a Foundation for Personalized Aphasia Therapy

Chronic aphasia, a devastating impairment of language, affects up to a third of stroke survivors. Speech and language therapy has consistently been shown to improve language function in prior clinical trials, but few clinicially applicable predictors of individual therapy response have been identified to date. Consequently, clinicians struggle substantially with prognostication in the clinical management of aphasia. A rising prevalence of aphasia, in particular in younger populations, has emphasized the increasing demand for a personalized approach to aphasia therapy, that is, therapy aimed at maximizing language recovery of each individual with reference to evidence-based clinical recommendations. In this narrative review, we discuss the current state of the literature with respect to commonly studied predictors of therapy response in aphasia. In particular, we focus our discussion on biographical, neuropsychological, and neurobiological predictors, and emphasize limitations of the literature, summarize consistent findings, and consider how the research field can better support the development of personalized aphasia therapy. In conclusion, a review of the literature indicates that future research efforts should aim to recruit larger samples of people with aphasia, including by establishing multisite aphasia research centers.
2D Gait Skeleton Data Normalization for Quantitative Assessment of Movement Disorders from Freehand Single Camera Video Recordings

Overlapping phenotypic features between Early Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) can complicate the clinical distinction of these disorders. Clinical rating scales are a common way to quantify movement disorders but in children these scales also rely on the observer’s assessment and interpretation. Despite the introduction of inertial measurement units for objective and more precise evaluation, special hardware is still required, restricting their widespread application. Gait video recordings of movement disorder patients are frequently captured in routine clinical settings, but there is presently no suitable quantitative analysis method for these recordings. Owing to advancements in computer vision technology, deep learning pose estimation techniques may soon be ready for convenient and low-cost clinical usage. This study presents a framework based on 2D video recording in the coronal plane and pose estimation for the quantitative assessment of gait in movement disorders. To allow the calculation of distance-based features, seven different methods to normalize 2D skeleton keypoint data derived from pose estimation using deep neural networks applied to freehand video recording of gait were evaluated. In our experiments, 15 children (five EOA, five DCD and five healthy controls) were asked to walk naturally while being videotaped by a single camera in 1280 × 720 resolution at 25 frames per second. The high likelihood of the prediction of keypoint locations (mean = 0.889, standard deviation = 0.02) demonstrates the potential for distance-based features derived from routine video recordings to assist in the clinical evaluation of movement in EOA and DCD. By comparison of mean absolute angle error and mean variance of distance, the normalization methods using the Euclidean (2D) distance of left shoulder and right hip, or the average distance from left shoulder to right hip and from right shoulder to left hip were found to better perform for deriving distance-based features and further quantitative assessment of movement disorders.
Seawater signatures in the supracrustal Lewisian Complex, Scotland

Abstract Marble in the supracrustal rocks of the Lewisian Complex, Tiree, includes chlorine-bearing amphiboles, chlorine-rich apatite, sulphur-rich scapolite, albite and phlogopite, all of which are regarded as evidence for evaporites in other metamorphosed sequences. Titanite yields U–Pb ages of ∼1.6 Ga, i.e. late Laxfordian, which excludes a younger imprint of sodium metasomatism. Traces of anhydrite, and isotopically heavy pyrite, also indicate deposition from seawater. Elsewhere in the Hebrides, tourmaline in Lewisian Complex marbles may represent seafloor exhalative deposits. Combined, the evidence suggests Lewisian Complex supracrustal marbles formed in an evaporative environment, like other Palaeoproterozoic successions across the North Atlantic region.
Studi Penerapan E-System dan Pelaksanaan Self Assesment System terhadap Kepatuhan Wajib Pajak

Penelitian ini bertujuan untuk melihat pengaruh penerapan e-System dan pelaksanaan self assesment system terhadap kepatuhan wajib. Populasi yang digunakan dalam penelitian ini adalah pegawai (staf dan dosen) dilingkungan universitas serang raya. Metode pengolahan data menggunakan software Partial Least Square (PLS) sebagai pembeda dari penelitian sebelumnya. Hasil penelitian menunjukan terdapat pengaruh yang signifikan antara variabel e-Registration terhadap kepatuhan wajib pajak yang di tunjukan dengan nilai origin sampel estimate sebesar 0,384 dan t-statistik 4.500 (lebih besar dari pada t-tabel ± 1,660). Hubungan antara e-Billing dan kepatuhan wajib pajak, berdasarkan hasil data yang diperoleh memiliki hubungan yang signifikan di tunjukan dengan nilai origin sampel estimate sebesar 0,380 dan t-statistik 4.299 (lebih besar dari pada t-tabel ± 1,660). Sedangkan hubungan antara e-Filling dan kepatuhan wajib pajak di tunjukan dengan nilai origin sampel estimate sebesar 0.610 dan t-statistik 8.070 (lebih besar dari pada t-tabel ± 1,660). Hubungan antara self assesment sytem dan kepatuhan wajib pajak berdasarkan hasil data yang diperoleh memiliki hubungan yang signifikan di tunjukan dengan nilai origin sampel estimate sebesar 0,392 dan t-statistik 5.211 (lebih besar dari pada t-tabel ± 1,660).
Pilot Study of Maternal Autoantibody–Related Autism

ABSTRACT: Objective: The objective of this study was to investigate the presence of maternal autoantibody–related autism spectrum disorder (MAR-ASD) in 2 geographically distinct DBPNet clinical sites (Pennsylvania and Arkansas). MAR-ASD is a biologically defined subtype of ASD that is defined by the presence of autoantibodies specific to proteins in the fetal brain and present in approximately 20% of a Northern California sample but has not been studied in other states. Methods: Sixty-eight mothers of children with ASD were recruited from 2 DBPNet clinics and provided blood samples. Mothers also completed behavioral questionnaires about their children, and data from the child's clinical diagnostic assessment were abstracted. Results: The mean age of mothers was 38.5 ± 6.1 years, and the mean age of children was 8.3 ± 2.7 years. MAR-ASD was present in 24% of the sample and similar across sites. Children of +MAR mothers had more severe autism symptoms as measured by Autism Diagnostic Observation Schedule comparison scores (W = 3604; p < 0.001) and the Social Communication Questionnaire (W = 4556; p < 0.001). There were no differences in IQ, adaptive function, or aberrant behavior. Conclusion: MAR-ASD is a subtype of autism that is present in similar frequencies across 3 states and related to autism severity.