Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence of intrapreneurship on accelerating digital transformation in a bank's credit operations center

Objective of the study: The objective of this study was to analyze how intrapreneurship accelerated the digital transformation in a credit operations center of a Brazilian bank, linked to an innovation laboratory in Silicon Valley. Methodology/approach: The research method was the in-depth case study and the data analysis technique was the content analysis. Main results: The original results indicated that intrapreneurship accelerated digital transformation through digitalization and digitization of processes, implementing and improving technological applications. Theoretical/methodological contributions: Leaders encouraged intrapreneurship by supporting creative ideas, using agile methodologies and digital innovation, and showing that rigidity in accepting failures and risks can be overcome. Relevance/originality: We conclude that intrapreneurship can accelerate incremental technological innovations, in products and processes, in order to promote digital transformation in the financial sector. Social/management contributions: We also conclude that digital transformation can significantly advance from the institutional incentive to intrapreneurship, acceptance of failures and risks, and creation of innovation poles connected to international technological networks. Future studies may investigate intrapreneurial solutions for the banking sector, based on open banking, artificial intelligence and blockchain.
Vibration Analysis of Elevator Rope (Comparison Between Experimental Results and Calculated Results)

An elevator rope in a high-rise building is forcibly excited by the displacement of the building caused by earthquakes and wind forces. This paper presents experiments involving free vibration and forced vibration of a rope for obtaining its natural frequency and damping coefficient. Experiments involving forced vibration of a rope whose length varies with time due to the up-and-down movement of a cage are also presented. Finite difference analyses of the rope vibration were performed by considering the time-varying length of the rope, based on the assumption that the movement velocity is constant. The calculated results of the finite difference analyses are in fairly good agreement with the experimental results.
Pure azimuth passive localization in attempted formation flight of UAVs under different constraints

Each UAV in a UAV formation has its own team number and maintains the same relative position to the rest of the UAVs during the formation flight. The UAV cluster needs to choose a suitable formation shape, a suitable navigator and an excellent position adjustment scheme to ensure flexible maintenance of the cluster formation in various complex environments. In this paper, on the basis of maintaining circular formation, the flight positioning model of UAVs passively receiving signals is established when signals are transmitted by the central UAV and two other UAVs and the position of the UAV transmitting signals is not deviated and the number is known, and the position of the remaining UAVs is deviated; on the basis of maintaining circular formation, a certain position UAV receives signals FY00 and FY01 as transmitting signals when it is also On the basis of maintaining circular formation, when all UAVs deviate from their positions, how to reasonably and accurately develop a UAV position adjustment plan to ensure that each time the number FY00, FY01 and any maximum of two remaining UAVs are selected to transmit signals, the remaining UAVs can adjust their positions according to the agreed direction information received The UAV will adjust its position according to the agreed direction information received.
The Impact of Cavity Size on Electric Field Distribution and PD Inception Voltage in Epoxy-resin Insulation

Cavities in insulation systems of active high voltage (HV) equipment affect their performance, reliability and useful life periods. Cavities serve as flash points for enhanced field activity leading to ageing from partial discharge (PD) events and subsequent breakdown of the insulation material. In this paper, the impact of cavity diameter on the electric field distribution as well as on PD inception voltage are investigated in an Epoxy-resin insulation sample. A 3D adequate model of the sample with a spherical cavity was created and simulated in COMSOL for different cavity sizes and applied voltage stresses respectively. The simulation results indicate that both field distribution and the PD inception voltages are strongly influenced by cavity size as well as the applied voltage magnitude. The electric field intensity was observed to be higher in cavities with smaller diameter relative to the insulation bulk, while the inception voltage decreases with increase in the cavity diameter and vice-versa.
Piecewise Support Vector Machines

A novel piecewise support vector machines(PSVM) model is provided in this paper,which used the traditional piecewise linear recognition method for reference.In this new model,the feature space was firstly partitioned into several subspaces,then the piecewise classification surface was developed by linking the optimal classification surfaces in each subspaces based on SVM.As the experimental results shown,validated with the Two Spiral Data and the actually measuring data,the performance of PSVM such as computational efficiency,classification capability and generalized capability are improved obviously in contrast to SVM.
ATRIAL ACTIVATION PATTERN FROM SURFACE LAPLACIAN ELECTROCARDIOGRAMS OF HUMANS

The goal was to determine whether atrial activation patterns could be obtained with our Laplacian electrocardiogram (LECG) system. Body surface maps from humans were generated depicting such patterns. Unprocessed atrial LECG activity coincided with P-waves in the Lead II ECG (L2). Partial atrial flutter activation patterns were also constructed. The system promises to reveal underlying cardiac activation patterns, including those of the atria, which are far less prominent than those from the ventricles.
Reliable Estimation of Automotive States Based on Optimized Neural Networks and Moving Horizon Estimator

Accurate estimation of vehicle sideslip angle and attitude angles are essential for the safety control and lateral behaviour of driving performance. In this article, the variation of wheels cornering stiffness is considered for sideslip estimation and addressed by introducing a recursive least squares approach. Based on the nonlinear vehicle dynamic model and the investigated coupling effect between lateral and longitudinal velocity, an optimized moving horizon estimator is proposed to obtain the vehicle sideslip angle, in which an iteration decent algorithm is integrated. Furthermore, a framework, consisting of inertial navigation system measurements, a dual neural network and a square-root cubature Kalman filter, is designed, such that the influence of sensor noise and varied maneuvers are alleviated when estimating the system states. Finally, extensive simulation and field experiments are carried out on different driving scenarios to verify the effectiveness of the developed method. The obtained results clearly indicate the satisfactory estimation accuracy of the designed strategy, superior to the existing estimation methods, such as sole neural networks methods and Kalman-based filters.
Solving nonlinear soliton equations using improved physics-informed neural networks with adaptive mechanisms

Partial differential equations (PDEs) are important tools for scientific research and are widely used in various fields. However, it is usually very difficult to obtain accurate analytical solutions of PDEs, and numerical methods to solve PDEs are often computationally intensive and very time-consuming. In recent years, Physics Informed Neural Networks (PINNs) have been successfully applied to find numerical solutions of PDEs and have shown great potential. All the while, solitary waves have been of great interest to researchers in the field of nonlinear science. In this paper, we perform numerical simulations of solitary wave solutions of several PDEs using improved PINNs. The improved PINNs not only incorporate constraints on the control equations to ensure the interpretability of the prediction results, which is important for physical field simulations, in addition, an adaptive activation function is introduced. By introducing hyperparameters in the activation function to change the slope of the activation function to avoid the disappearance of the gradient, computing time is saved thereby speeding up training. In this paper, the mKdV equation, the improved Boussinesq equation, the Caudrey–Dodd–Gibbon–Sawada–Kotera equation and the p-gBKP equation are selected for study, and the errors of the simulation results are analyzed to assess the accuracy of the predicted solitary wave solution. The experimental results show that the improved PINNs are significantly better than the traditional PINNs with shorter training time but more accurate prediction results. The improved PINNs improve the training speed by more than 1.5 times compared with the traditional PINNs, while maintaining the prediction error less than 10−2 in this order of magnitude.
Adding Zero Trust in BYOD Environments through Network Inspection

The integration of new enterprise computing practices and the emergence of sophisticated adversarial tactics has severely threatened the established security model of perimeter defense, in which controls placed at the organization’s or segment boundary shall fend off or contain attacks. While zero trust networking promises angles to cope with new workplace habits and advanced adversaries, it requires a comprehensive collection and evaluation of sensor data on network devices. While from the organization’s and end user’s perspective it is desirable to integrate personal devices as part of a bring-your-own-device policy, collecting the necessary sensor data on user-owned devices raises severe privacy concerns for many users.In this paper, we investigate how zero trust may be applied in BYOD environment without requiring users to install monitoring software, but indirectly infer the device’s status from sensor information passively collected in the network. We find that this collected information can provide high visibility, and can thus balance both the requirement for security and user acceptance.
Peanut Seed Germination Detection from Aerial Images

Peanut is an essential economic oil crop around the world. Therefore, accurate and real-time detection of peanut seed germination is necessary for peanut field management. However, traditional peanut seedlings’ germination monitoring is time-consuming and labor intense, especially for large fields. In this work, we propose to reduce the time lag in detecting peanut germination failures by combining the power of deep learning-based object detection (OD) and unmanned aerial systems (UAS) to identify early in-field peanut germination. To find the most suitable object detection model, we first compared the performance of two representative OD models, Faster RCNN and SSD, to identify peanut seedlings from UAS imagery obtained through a multispectral camera setup (MicaSense Rededge). The results showed that the F1 score of the SSD model is 0.82, while it is 0.85 for Faster RCNN at an Intersection over Union (IoU) of 0.5. Through extensive ablations, we find that deeper models only marginally improved the performance but were more expensive in computation and inference times. Interestingly we find that the performance of RGB-based seedling detection (0.917 mAP) is comparable to that of R-RedEdge-NIR (0.919 mAP), indicating that a remote sensing setup with a regular RGB camera can perform as well as a more expensive, multispectral camera system to detect peanut seedlings. With extensive experimentation, we infer that cheaper remote sensing mechanisms with the rapid acquisition of UAS-based imagery and the efficiency of OD methods are practical for early peanut germination detection.
Investigation of the Effect of Students’ Nodding on Their Arousal Levels in On-Demand Lectures

Due to COVID-19, various lecture styles are being explored. On-demand lectures are attracting increasing attention due to advantages such as being able to watch without restrictions due to location and time. In contrast, on-demand lectures have disadvantages, such as no interaction with the lecturer, so the quality of on-demand lectures should be improved. Our previous study showed that when participants nod without showing their faces in a real-time remote lecture, their heart rate state changes to arousal and nodding can increase arousal. In this paper, we hypothesize that nodding during on-demand lectures increases participants’ arousal levels, and we investigate the relationship between natural and forced nodding and the level of arousal based on heart rate information. Students taking on-demand lectures rarely nod naturally, so we used entrainment to encourage nodding by showing a video of another participant nodding, and by forcing the participants to nod when the other participant nodded in the video. The results showed that only participants who nodded spontaneously changed the value of pNN50, an index of the arousal level, which reflected a state of high arousal after one minute. Thus, participants’ nodding in on-demand lectures can increase their arousal levels; however, the nodding must be spontaneous, not forced.
Mesures de Hausdorff d'ensembles fractals

On etudie une construction generale d'ensembles fractals dans R N dont la propriete caracteristique est d'etre decomposable d'une infinite de manieres en portions geometriquement semblables. La dimension de Hausdorff de ces ensembles «parfaits de similitude» est obtenue a partir des parametres de leur construction
Iron Deficiency and Iron Deficiency Anemia: Potential Risk Factors in Bone Loss

Iron is one of the essential mineral elements for the human body and this nutrient deficiency is a worldwide public health problem. Iron is essential in oxygen transport, participates in many enzyme systems in the body, and is an important trace element in maintaining basic cellular life activities. Iron also plays an important role in collagen synthesis and vitamin D metabolism. Therefore, decrease in intracellular iron can lead to disturbance in the activity and function of osteoblasts and osteoclasts, resulting in imbalance in bone homeostasis and ultimately bone loss. Indeed, iron deficiency, with or without anemia, leads to osteopenia or osteoporosis, which has been revealed by numerous clinical observations and animal studies. This review presents current knowledge on iron metabolism under iron deficiency states and the diagnosis and prevention of iron deficiency and iron deficiency anemia (IDA). With emphasis, studies related to iron deficiency and bone loss are discussed, and the potential mechanisms of iron deficiency leading to bone loss are analyzed. Finally, several measures to promote complete recovery and prevention of iron deficiency are listed to improve quality of life, including bone health.
Regulatory Requirements for Exporting Cosmetic Products to Extra-EU Countries

In this study, an overview of the regulations in force in some extra-EU states belonging to different geoeconomic areas is provided, starting from the current EU legislation on cosmetic products. We focused on their legislative frameworks and the location of the relevant regulatory documentation. Furthermore, for each state considered, our analysis examined the notification/authorization processes, approaches to animal testing, and allowed/prohibited ingredient lists, as these aspects are considered to be among the primary restrictions hindering the cosmetic market. It can be observed that many states are working towards standardising their regulations to promote greater international trade. However, it is essential to recognize that different countries belonging to distinct geoeconomic areas may have unique requirements, and harmonization may not necessarily be the best solution. It is crucial to consider different needs and preferences when approaching the global regulation of the cosmetic industry.
Small field dosimetry: What have we learnt?

Small field x-ray beam dosimetry is difficult due to a number of challenges that include a lack of lateral electronic equilibrium, source occlusion, high dose gradients, and detector volume averaging. This has become more apparent with a rapid increase in the number of treatment machines that deliver small x-ray fields which are used for precision radiotherapy techniques such as stereotactic radiosurgery (SRS) and SBRT. A large body of literature is now available for dosimetry in small fields, but with a lot of contradiction. There is also a large collection of micro-detectors that are being advocated for dosimetry. This review provides an update on small field dosimetry, recommendations for measurements and updates on recent commercial detectors on the market. It is recommended that detectors that are small volume and tissue equivalent are best suited for small field dosimetry which are plastic scintillators, synthetic diamond detectors and possibly Gafchromic films.
APRENDIZAGEM BASEADA EM PROBLEMAS NO ENSINO REMOTO: UM ESTUDO COM DISCENTES DE UMA DISCIPLINA DO CURSO DE ADMINISTRAÇÃO

A preocupação em adotar metodologias de ensino e estratégias pedagógicas adequadas à formação de discentes é constante do cotidiano de docentes de instituições de ensino superior, pois podem tanto contribuir para aumentar as chances de evasão escolar quanto de motivar e envolver os alunos na relação de ensino e aprendizagem. É neste cenário que esta pesquisa se insere, uma vez que busca analisar como ocorreu a aprendizagem dos discentes, a partir da utilização da aprendizagem baseada em problemas em uma disciplina do curso de Administração, ministrada de forma remota. Para isso, realizou-se uma pesquisa qualitativa, de natureza descritiva e delineada a partir da perspectiva de discentes da disciplina em questão. Os resultados permitiram observar a relevância da metodologia ativa utilizada para associar a teoria à prática, para resgatar conhecimentos já adquiridos de outras disciplinas e de experiências prévias, para a busca de novos conhecimentos em fontes diversas, para estimular a interação dos alunos entre si e também com o docente mediador do ensino, para o envolvimento e como todas essas ações também contribuem para a motivação do aluno durante seu processo de aprendizagem.
Quantification of radiation damage for proton craniospinal irradiation using magnetic resonance imaging

Radiation doses delivered to entire vertebral bodies are current standard practice for the growing pediatric proton craniospinal irradiation (CSI) patients who are growing children. This procedure prevents patients from developing radiation-induced growth impairment, but it will cause hematopoietic marrow suppression. We aim to develop a noninvasive method to verify radiation damage to the marrow in spine vertebrae during fractional treatment using multiple magnetic resonance imaging (MRI) scans. We identified five pediatric patients who received proton CSI treatment with prescription relative biological effectiveness doses of 36 Gy for the spine. Each patient underwent multiple MRI scans during the treatment using T1-weighted sequences. Sagittal MR images were analyzed and focused on lumbar spine regions. Multi-Gaussian models were used to fit histograms from different MR images to quantify the radiation-induced damage to the bone marrow. MR images acquired before the treatment served as the reference to ensure no radiation-induced damage was found. After the treatment started, radiation-induced fatty marrow filtration showed in the vertebral bodies. We defined the radiation-induced damage based on the ratio between fatty marrow imaging pixels and total pixels in spine marrow, L1-L5 level. Damage fractions increased rapidly when the vertebral bodies received doses between 14 Gy and 34 Gy. The maximum damage happened approximately 40 days from the treatment start. After that, bone marrow regeneration was observed, and the damage fractions decreased. The proposed method can potentially achieve adaptative proton plan modification on the fly.
Prediction and functional characterization of transcriptional activation domains

Gene expression is induced by transcription factors (TFs) through their activation domains (ADs). However, ADs are unconserved, intrinsically disordered sequences without a secondary structure, making it challenging to recognize and predict these regions and limiting our ability to identify TFs. Here, we address this challenge by leveraging a neural network approach to systematically predict ADs. As input for our neural network, we used computed properties for amino acid (AA) side chain and secondary structure, rather than relying on the raw sequence. Moreover, to shed light on the features learned by our neural network and greatly increase interpretability, we computed the input properties most important for an accurate prediction. Our findings further highlight the importance of aromatic and negatively charged AA and reveal the importance of unknown AA properties. Taking advantage of these most important features, we used an unsupervised learning approach to classify the ADs into 10 subclasses, which can further be explored for AA specificity and AD functionality. Overall, our pipeline, relying on supervised and unsupervised machine learning, shed light on the non-linear properties of ADs.
Error Detection Strategies for CRC-Concatenated Polar Codes under Successive Cancellation List Decoding

In this work we introduce a framework to study the trade-off between the undetected error rate (UER) and overall frame error rate (FER) of CRC-concatenated polar codes in the short blocklength regime. Three approaches to improve the tradeoff under successive cancellation list (SCL) decoding are outlined. Two techniques are based on the optimum threshold test introduced by Forney in 1968, whereas a third technique partitions the CRC code parity bits in two sets, where one set is used to prune the SCL decoder list, and the other set is used for error detection. The performance of the three schemes is analyzed via Monte Carlo simulations, and compared with a finite-length achievability bound based on Forney's random coding bound.