Miracle-dz/UniFAR-FTUs huggingface dataset:
Identificação de Processo Não-linear Usando Rede Neural com Método de Treinamento Híbrido Baseado em Filtro de Kalman e Máxima Descida

There are a wide variety of identification methods and model structures forms to choose for system nonlinear identification applications. Neural networks have proved to be versatile and useful nonlinear models for industrial processes. This paper presents a comparative study of learning methods for training of radial function neural networks (RBF-NN). Simulations involving the RBF-NNs with training methods based on Kalman filter, gradient descent, and a Kalman-gradient descent hybrid approach. The simulation results indicate the potentialities of the hybrid approach in RBF-NNs learning for one step ahead identification of a pH neutralization case study.
Pathologic Profile of Hysterectomy Cases in Saudi Arabia: A Tertiary Center Experience

Background: Hysterectomies are increasingly being replaced by more conservative management modalities and are now only used for limited clinical indications. The agreement between these indications and the final pathology is understudied. Objectives: This study aimed to correlate the preoperative clinical diagnoses with the pathological findings detected in hysterectomies in Saudi women of different age groups. Materials and Methods: This retrospective study analyzed the preoperative clinical indications and the subsequent clinical and pathological data of all hysterectomies conducted at a single tertiary care hospital in the Eastern Province of Saudi Arabia between January 2010 and December 2021. Results: There was no decline in the frequency of hysterectomies across the study period. Abnormal uterine bleeding was the most common clinical indication across all age groups, followed by symptomatic fibroid and uterine prolapse in women aged <50 and ≥50 years, respectively. A total of 9.2% of the cases were indicated for therapeutic reasons, either as an emergency procedure for peripartum hemorrhage or for suspected uterine premalignant and malignant lesions. Besides these, 41.2% of patients had more than one pathology, with the most common combination being leiomyoma and adenomyosis (43.4%). The difference in age between malignant and benign cases (52.3 vs. 48.8 years, respectively) was not statistically significant (P = 0.109). Conclusions: Most of our cohort had benign lesions. Perceptible misuse of the procedure in this cohort was unlikely, as a large proportion of the patients had more than one pathology corresponding to each clinical indication.
Continental and island arc segmentation: Implications for retro‐arc and pro‐arc foreland basin and backarc basin geometries and sediment distributions

Segmented magmatic arcs create arc‐parallel variable loads and influence associated foreland basins through flexural isostasy along strike, in addition to standard subduction‐perpendicular foredeep/forebulge/backbulge models. Segmentation occurs in both continental and island arcs (Aleutians, Calabria, Japan, Kuril/Kamchatka, Lesser Antilles, Solomon Islands and Sumatra/Java). Some segments have variable gravity anomalies and elastic thicknesses (Te). In standard theory, the load created by an orogenic belt/magmatic arc depresses the lithosphere modelled as a thin elastic plate floating on a fluid mantle substrate, leading to an arc‐perpendicular foredeep, forebulge and backbulge. Arc‐parallel topographic and gravitational features should influence loading along strike in a similar way, resulting in a checkerboard pattern of foredeeps, forebulges and backbulges in both pro‐arc and retro‐arc foreland basins. This pattern exercises profound controls on sediment and facies distribution in a here‐to‐fore un‐envisaged manner, with implications for resource exploration in foreland basins.
The Effect of Advertising Attraction and Brand Embassy on Shopee Purchase Decisions

: Through the development of technology and the increasing number of internet usage at this time, it is very interesting that many various business businesses to market their products through e-commerce, resulting in considerable business competition for participating companies. In e-commerce business competition, companies use various marketing strategies related to communication, one of which is advertising, because advertising attracts many consumers to see their products on the e-commerce site. In the use of advertising strategies, companies must have a variety of creative concepts in pairing their advertisements, one of which is by requiring ad stars who will present their products to consumers who can be said to be brand ambassadors. Companies must be smart in choosing brand ambassadors who will be used as weapons to market their products in these advertisements, in order to attract many consumers to view these products and buy them. So, the appeal of advertising and brand ambassadors is very important in making purchasing decisions. Because of the existence of attractive advertisements and brand ambassadors that can attract consumers to buy makes the company's best strategy to market its products. The research I did was to analyze the effect of ad appeal on Shopee's online purchasing decisions and also analyze the influence of brand ambassadors on Shopee's online purchasing decisions. The method used in this study is a survey method with a questionnaire as primary data. The results of research that uses the attractiveness of advertising and brand ambassadors find that the attractiveness of advertisements has a significant effect on purchasing decisions and brand ambassadors also show a significant influence on purchasing decisions. So it can be concluded that advertising and brand ambassadors have a very important role in the company's strategy in marketing its products.
Experience Report: Putting an Oxymoron to Work Using established functional-programming technology to implement TTCN-3

The functional-programming community has produced mature tools for writing compilers: This paper describes our experience implementing TTCN-3, a programming language designed for writing test suites for programs and protocol implementations. Our TTCN-3 compiler is written in Scheme; it builds upon the Transfor-mational Compiler that originated in the T and Scheme 48 projects, as well as the Essence functional LR parser generator, which is implemented using partial evaluation—both of which have existed for more than 20 and 10 years, respectively. Development was a straightforward experience—the only source of problems was the considerable complexity of the source language.
Aging mechanisms analysis of

Lithium-ion batteries (LIBs) are widely used in electric vehicles, while capacity fading happens due to unwanted side reactions during cycling. Therefore, it is of great significance to study the aging mechanisms of LIBs among the whole life cycle for the use and design of LIBs. In this study, the aging experiments of Graphite/LiNi 0.80 Co 0.15 Al 0.05 O 2 (NCA) batteries were conducted at 25 ℃ and 45 ℃ , the aging mechanisms were examined by differential voltage analysis method and electrochemical impedance spectroscopy analysis method, and verified by microscopic morphology observation. The results show that the loss of anode active material and lithium ions are the main degradation modes, and the lithium plating side reaction at the late aging stage is the inducement of capacity plunge both at 25 ℃ and 45 ℃ . But the causes of lithium plating are different, at 25 ℃ , the growth of solid electrolyte interphase (SEI) leads to lithium plating, while at 45 ℃ , the accumulation of gas leads to lithium plating.
Rule-Based Synthesis of Ternary Reversible Up/Down Counters

—A ternary bijective logic function can be implemented as a reversible circuit using ternary reversible gates. These gates can be constructed using existing technologies as well as quantum technology. Considerable amount of works have been reported in the literature on synthesis of ternary reversible combinational circuits. In comparison, synthesis of ternary reversible sequiential circuits is in the infancy. In this paper, we present a truth table based rule generation and then rule based synthesis of n -qutrit ternary reversible level-triggered synchronous up/down counters. The presented method produces better circuits than those reported in the literature in terms of both quantum cost and ancilla inputs needed.
Journal of Nutritional Biology Antioxidant, Antibacterial, Antiviral and Antifungal properties of Virgin Coconut Oil (VCO)

Coconut can be the source for five types of food products: coconut water, coconut milk, sugar, oil, and meat. Medical uses of coconut have been known in India since ancient times. Many coconut parts have medicinal properties. It has antiseptic, anthelmintic, antidotal, diuretic, hemostat, aphrodisiac, astringent, bactericidal, depurative, pediculicide. It is used as a folk remedy for abscesses, alopecia, amenorrhea, asthma, blennorrhagia, bronchitis, constipation, cough, debility, dropsy, dysentery, bruises, burns, dysmenorrhea, earache, phthisis, pregnancy, rash, erysipelas, fever, flu, gingivitis, gonorrhea, cachexia, calculus, colds, hematemesis, hemoptysis, jaundice, menorrhagia, nausea, scabies, scurvy, sore throat, stomachache, swelling, syphilis, toothache, tuberculosis, tumors, typhoid, venereal diseases, and wounds. It is also believed to be anti-blennorrhagia, antibronchitis, febrifugal, and anti-gingivitis. VCO has been brought up as a possible adjuvant therapy in the treatment of COVID-19, and several countries are conducting trials in this area. As highly relevant as this potential application is within the context of the current global COVID-19 pandemic, it is just as important to continue assessment of VCO for its myriad antibacterial, antiviral, antifungal, and anti-inflammatory capabilities in potential adjuvant therapies for a wide range of diseases. This paper aims to provide a comprehensive overview of these capabilities as well as clinical trials in the existing literature that have assessed VCO’s many therapeutic properties.
Ähnliche Weisheitsstrategien für unterschiedliche Lebensprobleme

Zusammenfassung Ziel Weisheit kann verstanden werden als Fähigkeit, unlösbare Probleme zu lösen. Weisen Menschen gelingt es besser, verschiedene schwierige Lebenssituationen zu bewältigen. Dabei helfen ihnen Weisheitsstrategien. Fraglich ist, ob in verschiedenen schwierigen Lebenssituationen die gleichen oder verschiedene Weisheitsstrategien benötigt werden. Diese Studie geht deshalb der Frage nach, ob Menschen die gleichen oder unterschiedliche Weisheitsstrategien für sinnvoll halten, wenn sie mit zwei verschiedenen Lebensproblemen konfrontiert werden. Methodik Insgesamt 416 Personen (40,4 Jahre alt) lasen jeweils zwei Vignetten, eine zu einer beruflichen Problemsituation und eine zu einer privaten Problemsituation. Für jede Situation schätzten sie ein, wie stark sie den angebotenen Weisheitsstrategien, bezogen auf die jeweilige Situation, zustimmten. Die Weisheitsstrategien wurden mittels Selbstbeurteilungsfragebogen erfasst. Ergebnisse Die Befragten stimmten den Weisheitsstrategien für beide Situationen ähnlich stark zu. Für beide Situationen stimmten sie am stärksten der Bedeutung von Fakten- und Problemlösewissen, Emotionswahrnehmung und -akzeptanz, Wertrelativismus sowie Problem- und Anspruchsrelativierung zu. Die Reihenfolge der übrigen acht Weisheitsstrategien ist für beide Situationen dieselbe. Diskussion und Schlussfolgerung Menschen tendieren dazu, allgemeinen Weisheitsstrategien zuzustimmen, ungeachtet der situationsspezifischen Hinweisreize. Weisheitsstrategien werden über verschiedene Lebenssituationen hinweg für sinnvoll erachtet. Das ist bedeutsam für die Gestaltung von Präventions- und Interventionsmaßnahmen zur Förderung psychischer Resilienz.
Text classification model based on CNN and BiGRU fusion attention mechanism

This model proposes a text classification model with deep learning algorithm, which combines the characteristics of Convolutional Neural Network （ CNN ） and Gate Recurrent Unit (GRU) in cyclic neural network, extracts local and global features of text feature words respectively, and calculates the importance of words to text classification task after fusing attention mechanism (Attention). Make the model focus on the feature words with high weight. Through the fusion of models, the accuracy of text classification is improved. The experimental results on IMDB film review dataset, Fudan University Chinese dataset and THUCNews dataset show that the proposed model has different degrees of improvement compared with the previously proposed models based on CNN, or LSTM and related fusion models in terms of accuracy, recall rate and F1 value.
trend in new car technology?

We compare the performance and level of distraction of expressive directional gesture input in the context of in-vehicle system commands. Center console touchscreen swipes and midair swipe-like movements are tested in 8 -directions, with 8 -button touchscreen tapping as a baseline. Participants use these input methods for intermittent target selections while performing the Lane Change Task in a virtual driving simulator. Input performance is measured with time and accuracy, cognitive load with deviation of lane position and speed, and distraction from frequency of off-screen glances. Results show midair gestures were less distracting and faster, but with lower accuracy. Touchscreen swipes and touchscreen tapping are comparable across measures. Our work provides empirical evidence for vehicle interface designers and manufacturers considering midair or touch directional gestures for centre console input.
MULTI-CYCLE LARGE EDDY SIMULATIONS IN A REALISTIC HUMAN LEFT HEART

A numerical framework designed to perform numerical simulations of the blood flow in patient-specific human hearts is presented. The heart cavities and their wall dynamics are extracted from medical images. The flow equations are written on a conformal moving computational domain, using an arbitrary Lagrangian-Eulerian approach. Additional structures (the valves) of the heart are modelled with an immersed boundary method. The resulting equations are solved numerically using a fourth-order finite-volume solver. Application of this framework on a left patient heart is presented as well. Flow dynamics is analysed in order to show the ability of the numerical procedure to reproduce the main fluid phenomena commonly observed in the left heart. The flow is characterised by its transitional nature, resulting in a complex cyclic flow. Development of weak turbulence is observed and analysed, presenting phase-averaged results.
National Geospatial Policy, 2022: An urban perspective

The vulnerabilities associated with modern systems relying on Global Navigation Satellite Systems (GNSS) due to intentional and unintentional interference is an increasing threat. Since radio frequency interference (RFI) significantly degrades the performance of a GNSS receiver. Several traditional critical applications such as aviation, maritime and rail transport systems to more recent applications such as autonomous vehicles, can be severely affected by such undetected nor mitigated RFIs. More-over, critical infrastructures such as power supply and money transfer, are becoming more and more dependent on the accurate timing information provided by GNSS. Thus, interference detection and management techniques are crucial to be utilised in order to reduce interference effects. This paper offers a state-of-the-art review of several proposed methods for interference detection and mitigation with solutions ranging from traditional to machine learning-based approaches. In addition, to be able to characterise the RFI threats and develop mitigation techniques, it is essential to monitor RFI systematically and share recorded data with interested entities. Therefore, three GNSS threat monitoring systems are briefly described. This White paper is a compilation of the seminar presentations given at a seminar on ”Protecting GNSS against intentional interference” in March 2022 in Finland.
Global Patterns of Spatial and Temporal Variability in Salinity from Multiple Gridded Argo Products

Salinity is one of the fundamental ocean state variables and has been used to infer important information about climate change and variability. Previous studies have found inconsistent salinity variations in various objective ocean analyses that are based on the Argo measurements. However, as far as we are aware, a comprehensive assessment of those inconsistencies, as well as robust spatial and temporal features of salinity variability among the Argo-based products, has not been conducted. Here we compare and evaluate ocean salinity variability from ﬁve objective ocean analyses that are solely or primarily based on Argo measurements for their overlapping period from 2005 to 2015. We examine the salinity variability at the sea surface and within two depth intervals (0–700 and 700–2000 m). Our results show that the climatological mean is generally consistent among all examined products, although regional discrepancies are evident in the sub-surface ocean. The time evolution,verticalstructure, and leading EOF modes of salinity variations show good agreement among most of the examined products, indicating that a number of robust features of the salinity variability can be obtained by examining gridded Argo products. However, signiﬁcant discrepancies in these variations exist, particularly in the subsurface North Atlantic and Southern Oceans. Also, despite the increasing number of Argo ﬂoats deployed in the ocean, the discrepancies were not signiﬁcantly reduced over time. Our analyses, particularly those of the discrepancies between products, can serve as a useful reference for utilizing and improving the existing objective ocean analyses that are based on Argo measurements.
On modeling of a thickness‐shear vibrating quartz crystal plate attached with micro‐beams immersed in liquid with considering couple stress effects

A coupling model for a compound quartz crystal resonator (QCR) system, consisting of a QCR under the thickness‐shear mode (TSM) vibrations and surface micro‐beam arrays immersed in liquid, is developed using the modified couple stress theory and the Hamilton's variational principle. Couple stresses are included in both governing equations of micro‐beams and the quartz crystal plate. Incorporating the boundary conditions, a set of homogeneous equations is numerically solved to get the frequency shift caused by micro‐beams for different material parameters, such as Young's modulus and material characteristic length. The normalized beam deflections are plotted and discussed. Influence of couple stresses of both micro‐beams and QCR together with their interaction on frequency shift is discussed in detail. The obtained results are useful in measuring geometrical and physical properties of micro‐beams using plate‐like acoustic wave sensors.
Multi-level Feature Representation and Multi-layered Fusion Contrast for Few-Shot Classification

—Training a model that can quickly adapt to new tasks keeps a crucial challenge for few-shot learning. Approaches based on metric-learning are very popular and promising. However, the existing such approaches usually only rely on the feature information obtained in the last layer of the feature extraction backbone for similarity metric, and do not consider that the feature information obtained in multi-layered can be fully utilized. Furthermore, the targeted differentiation of features and the selection and construction of loss functions are usually ignored by these approaches, which will become important factors that limit the performance of the model. Therefore, a few-shot learning approach MFR-MFC with attention mechanism based on multi-level feature representation and multi-layered fusion contrast is proposed in this paper. First, multi-level feature representations are introduced when extracting features, feature information of multi-layers are used to perform information fusion, and then this information is utilized for subsequent metric. Then, when training the model, multi-level features are also used to introduce multi-layered fusion contrast. Additionally, an attention module is introduced in the feature extraction process to make the model obtain more discriminative information. Experiments have shown that the approach proposed in this paper has achieved excellent performance in few-shot classification and has significant advantages compared with advanced technologies.
DEVIATIONS BETWEEN OSTENSIVE AND PERFORMATIVE ASPECTS IN ORGANIZATIONAL ROUTINES: A PROPOSAL OF A REVIEW CYCLE FOR PUBLIC ADMINISTRATION

Objective of the study: To propose a revision cycle for organizational routines in public administration organization based on the analysis of deviations between the ostensive and performative aspects of the contracts management and supervision routine of the Instituto Federal Catarinense. Methodology/approach: Qualitative, descriptive, single case study research, with data from documentary research and interviews. Originality/Relevance: The research helps to appropriate the topic of organizational routines to public administration by analyzing deviations between agents' behavior and the rules that govern them. Additionally, a revision cycle of these deviations is proposed. Main results: Deviations arising from the outdatedness or inadequacy of what is foreseen in the ostensive aspect were identified. Moreover, agents tend to base performativity on something other than what is foreseen in documents and instructions since their habits, competencies, and skills force them to inertia in how they act. Social/management contributions: The proposition of an organizational routine review cycle for public administration that curbs unwanted variations in routine execution.
The Performance of Classiﬁcation Method in Telco Customer Trouble Ticket Dataset

—A customer trouble ticketing system (CTT) is an organization’s tool to track the detection, reporting, and resolution of tickets submitted by customers. It also comprises a summary of the issue reported, the status of the ticket, the incident information, and the approach that was previously utilized to resolve the problems. The technician’s skill set and experience rely solely on completing the task without the right direction on which area to focus on ﬁrst. As a result of this manual classiﬁcation of a trouble ticket, it will be necessary to build methodologies for predicting future resolution codes. The research for this report is mainly focused on one of the telco companies in Malaysia. This study result assists the telco engineer, and the specialists resolve each issue in a very short amount of time. Additionally, the classiﬁcation of the trouble ticket resolution code method used in this study will indicate the characteristics of each issue that is being investigated. The relationship between events is feasible to discover by exploring the root cause. It is critical to establish a link between recent events and events in the previous. Because of current data mining limitations, the study needs to be more comprehensive. Data processing methods are being implemented within big data platforms to overcome the limitation of data scalability, enhance classiﬁcation accuracy, and increase computation speed. The research work will continue to progress in the direction of big data centricity. Some of the most effective approaches for big data integration and machine learning will be discussed in this paper. Throughout the experiment, any problems will be explained, as well as the solutions to each situation. A wide range of research subjects will be discussed, including construction classiﬁcation models for trouble tickets. To achieve reasonable accuracy, a few customized transformations are required. The data set’s custom parameter optimization process will further increase the classiﬁcation trouble ticket’s efﬁciency. However, greater processing capacity is necessitated to use multiple parallel classiﬁers such as Bayes, Decision-Tree, and Rule-Based with help of bigdata framewrks such as Spark.