Miracle-dz/UniFAR-FTUs huggingface dataset:
OnRAMP for Regulating Artificial Intelligence in Medical Products

Medical artificial intelligence (AI) involves the application of machine learning (ML) algorithms to biomedical datasets to improve medical practices. Products incorporating medical AI require certification before deployment in most jurisdictions. To date, clear pathways for regulating medical AI are still under development. Below the level of formal pathways lies the actual practice of developing a medical AI solution. This Perspective proposes best practice guidelines for development compatible with the production of a regulatory package which, regardless of the formal regulatory path, will form a core component of a certification process. The approach is predicated on a statistical risk perspective, typical of medical device regulators, and a deep understanding of ML methodologies. These guidelines will allow all parties to communicate more clearly in the development of a common good machine learning practice (GMLP), and thus lead to the enhanced development of both medical AI products and regulations.
Arabic Handwriting Word Recognition Based on Scale Invariant Feature Transform and Support Vector Machine

Offline Arabic handwritten recognition lies in a major field of challenge due to the changing styles of writing from one individual to another. It is difficult to recognize the Arabic handwritten because of the same appearance of the different characters. In this paper a proposed method for Offline Arabic handwritten recognition. The proposed method for recognition hand-written Arabic word without segmentation to sub letters based on feature extraction scale invariant feature transform (SIFT) and support vector machines (SVMs) to enhance the recognition accuracy. The proposed method experimented using (AHDB) database. The experiment result show (99.08) recognition rate.
Landmark‐based multi‐region ensemble convolutional neural networks for bone age assessment

Bone age assessment based on hand X‐ray imaging is important in pediatry medicine. At present, prediction of bone age is mainly performed by the manual comparison with the existing atlas. To develop an automatic regression framework based on deep learning with high performance and efficiency. A landmark‐based multi‐region convolutional neural networks for automatic bone age assessment based on left hand X‐ray images was proposed. The deep alignment network localized multiple landmarks distributed over the hand, and cropped the local regions to establish the multi‐region ensemble convolutional neural networks with different sub‐network combinations. The modified loss function and the optimized bone sub‐regions were applied to train the networks. The experiments on Digital Hand Atlas Database revealed that the mean absolute error of bone age assessment was 0.52 ± 0.25 years. It is the first study to predict bone age using deep learning methods throughout the entire process including image preprocessing, landmark localization and bone age predication. The proposed method outperformed most of the existing state‐of‐the‐art deep learning methods and achieved good results compared with the expert's experience. It can improve the efficiency of the medical doctors while minimizing the subjective errors.
Acute Phase Proteins (Haptoglobin and Serum Amyloid A) and Pro-Inflammatory Cytokines (IL-12 and IL-10) in Cows Vaccinated with Prototype Killed S. aureus Mastitis Vaccine and Challenged with S. aureus Mastitis Infection.

Background The economic downturn experienced by farmers and the fear of milk borne infection are of a greater public health concern. Haptoglobin, Serum Amyloid A, IL-12 and IL-10 in lactating Friesian cows vaccinated with prototype killed S. aureus mastitis vaccine and challenged with S. aureus were evaluated. Bacterin concentration at 10 8 cfu /ml of the local isolate of S. aureus was adjuvanted with KAl(SO₄)₂. Six lactating Friesian cows were grouped into A= Negative control, B = Positive control and C = vaccine group. Group C was vaccinated intramuscularly with 2ml of the monovalent vaccine, groups A and B with physiologic normal saline. Groups B and C were later challenged with the live bacterium via intramammary route . Result There was a significant increase in IL-10 concentration in vaccinated group post primary vaccination (PPV), booster phase (PB) and during the bacterial challenge phase. There was also a significantly increased IL-12 concentration in the vaccinated group at 24 hours, weeks 1 and 2 PPV. Haptoglobin at 12 and 24 hours PPV had a significant difference in group C. During the PB at 8 and 12 hours there was a significant difference in group C. During the bacterial challenged phase at 0, 3, 24 hours and day 7 PC there was a significant difference in group B. At 8 hours PC there was a significant difference in group C. For Serum Amyloid A, during PPV at 0, 3, 8, 12, 24 hours and weeks 1 and 2, the concentrations was significantly different in groups C. During PB at 0, 3, 8 and 12 hours PB there was a significant difference in groups C. During the bacterial challenge phase at 3, 8, 12, 24 hours, days 7 and 14 PC there was a significant difference in group B. At 0 hour PC there was a significant increase observed in group C. Conclusion The developed prototype killed S. aureus mastitis vaccine using local isolates was able to stimulate acute phase proteins and pro-inflammatory cytokines. The pattern of responses PC indicated protection, thereby suggesting that vaccination can protect against mastitis infection in dairy cows.
Reliability-Optimal Cooperative Communication and Computing in Connected Vehicle Systems

The emergence of vehicular networking enables distributed cooperative computation among nearby vehicles and infrastructures to achieve various applications that may need to handle mass data by a short deadline. In this paper, we investigate the fundamental problems of a cooperative vehicle-infrastructure system (CVIS): how does vehicular communication and networking affect the benefit gained from cooperative computation in the CVIS and what should a reliability-optimal cooperation be? We develop an analytical framework of reliability-oriented cooperative computation optimization, considering the dynamics of vehicular communication and computation. To be specific, we propose stochastic modeling of V2V and V2I communications, incorporating effects of the vehicle mobility, channel contentions, and fading, and theoretically derive the probability of successful data transmission. We also formulate and solve an execution time minimization model to obtain the success probability of application completion with the constrained computation capacity and application requirements. By combining these models, we develop constrained optimizations to maximize the coupled reliability of communication and computation by optimizing the data partitions among different cooperators. Numerical results confirm that vehicular applications with a short deadline and large processing data size can better benefit from the cooperative computation rather than non-cooperative solutions.
Primjena
SNSQUAL modela u mjerenju kvalitete društvenih mreža

Društvene mreže postale su važan alat za prijenos informacija i povezivanje pojedinaca. Iako ne postoji dobna granica za korištenje društvenih mreža, najčešće ih koristi mlađa populacija. Pregled literature pokazuje da postoji niz istraživanja kvalitete usluga u on-line okruženju, ali su rijetka ona koja istražuju kvalitetu društvenih mreža. U ovom je istraživanju, u svrhu mjerenja kvalitete društvenih mreža, testirana primjena SNSQUAL modela, kojeg su razvili Phillips et al. (2016). Model obuhvaća varijable koje opisuju jednostavnost korištenja, povjerenje, personalizaciju, pouzdanost i integraciju usluga na društvenim mrežama. Istraživanje je provedeno na uzorku studenata, te je prikupljen 341 ispravno ispunjen anketni upitnik. Prikupljeni podaci su analizirani primjenom metoda deskriptivne statističke analize, faktorske analize i analize pouzdanosti. Rezultati istraživanja ukazuju na visoku razinu percipirane kvalitete društvenih mreža. Ujedno se kvaliteta društvenih mreža u Hrvatskoj percipira kroz sljedeće dimenzije: informacije, jednostavnost korištenja, pouzdanost i sigurnost, integriranost i prilagođavanje.
Parking management system and method

Various configurations for presenting the vehicle parking is disclosed. Usage data and interest data about parking spaces of the parking facility may be tracked and stored. Parking amount of time that the parking space is the use of facilities, based on the available data that has been stored for a parking space. The degree of interest to the parking space may be determined on the basis of the data of interest. According to a set of defined rules, the price of the parking space may be determined based on the least amount of time and attention. Parking spaces can be presented to the customer at a determined price. Field
Finding and tracking local communities in social networks using differential geometry

Since various complex systems are represented by networks, community detection in networks has gained a lot of attention . Community detection methods in networks can be classified into two domains, global methods and local methods. Many real-world networks are typically very large, making the global community detection methods impractical due to the computational expense. In addition to being large contemporary social networks tend to be in continuous flux. The constant stream of changes in the structure of networks makes it impossible for anyone to know the structure of the network fully. Therefore, local community detection algorithms, which are requiring less computation and do not need full knowledge of the structures of the network, have met with renewed interest. This thesis focuses on the problem of finding local communities and tracking them in dynamic networks using derivative-based geometric features. Mapping the concepts of derivatives into graph space in a practical manner poses a few challenges. For instance, in Euclidean space, every point has three dimensions, whereas in graph space, the dimension (or degree) of every node can be different. In this thesis, a general framework for finding derivatives in graph space is proposed. This framework enables us to apply derivative-based methods in graph theory. Using the derivative-approximation framework, inspired by the active contour algorithm in computer vision domain, a local derivative-based community detection method (DCD) is proposed. DCD is built upon concepts of curvature and gradient of the community’s boundary. DCD finds the local community around a seed node by expanding the boundary of the local community by means of a velocity function that determines whether the boundary should expand to include a node. The second task of this thesis is to track local communities in dynamic
Cooperative Beamforming and Efficient Resource Allocation for Large Scale Global Optimization

Cognitive radio is a software defined radio system that enables the cognitive users (secondary users) to stumble on white space (unused spectrum) in the primary user’s spectrum band and allowing them to make utilize of the detected spectrum of data transmission. As soon as the white space has been recognized, it will be worn by a number of secondary users. When the number of users exists it is alike as Multiple Input Multiple Output (MIMO). Stipulation a large number of users bring into play the detected spectrum for data transmission, this will forge mutual interference among them. The MIMO characteristics are improved by using the Cooperative Beamforming technique in large scale cases and Orthogonal Frequency Division Multiplexing (OFDM) by using its orthogonal subcarriers. But, OFDM method has the drawbacks such as the total throughput decreament and the coverage area limitation. The high Peak to Average Power Ratio (PAPR) is a crucial drawback for such multicarrier technique. This feature is also analyzed with Partial Transmit Sequence (PTS). Further the performance of the users is increased by using theBeamforming technique. In this paper we showed the performancecomparision graphs of MIMO-OFDM and the Beamformingfor different dimensions of MIMO antennas, PTS to reduce PAPR, and Cooperative Beamforming for enhanced performance.
A dynamic two‐level artificial neural network for estimation of parameters in combined mode conduction‐radiation heat transfer in porous medium: An application to handle huge dataset with noise

A dynamic two‐level artificial neural network (DTLANN) approach is used for the estimation of parameters in combined mode conduction–radiation heat transfer in a porous medium. Four commonly used neural networks: feed forward, cascade forward, fitnet, and radial basis are used in mapping artificial neural network (ANN), and their performance is compared under noisy big data (10,302 × 1300 matrix size). Governing equations for heat transfer in the porous medium through conduction and radiation modes are solved by finite volume method and discrete transfer method. This numerical model is called a direct model. A large amount of data is generated by using the direct model for different values of extinction coefficient β and convective coupling P2. These data were divided into different groups (class) based on the temperature difference between the gas and solid phase. In the inverse analysis, a new pair of temperature profiles for the solid and gas phase is taken as input and classified with the help of a pattern net artificial neural network model. On the basis of classification, data from that particular class and its neighbor class are used for training the mapping ANN model. After the training of the mapping ANN model, corresponding values of β and P2 are obtained as output for any new input. This DTLANN model has a high regression coefficient (R) of . 99131 and can predict highly accurate values of parameters under a huge dataset with noise, within much less CPU time.
Evaluation of a Low-cost Camera for Agricultural Applications

This study aimed to modify a webcam by replacing its near-infrared (NIR) blocking filter to a low-cost red, green and blue (RGB) filter for obtaining NIR images and to evaluate its performance in two agricultural applications. First, the sensitivity of the webcam to differentiate normalized difference vegetation index (NDVI) levels through five nitrogen (N) doses applied to the Batatais grass (Paspalum notatum Flugge) was verified. Second, images from maize crops were processed using different vegetation indices, and thresholding methods with the aim of determining the best method for segmenting crop canopy from the soil. Results showed that the webcam sensor was capable of detecting the effect of N doses through different NDVI values at 7 and 21 days after N application. In the second application, the use of thresholding methods, such as Otsu, Manual, and Bayes when previously processed by vegetation indices showed satisfactory accuracy (up to 73.3%) in separating the crop canopy from the soil.
Comparison of pathological clotting using haematological, functional and morphological investigations in HIV-infected and HIV-uninfected patients with deep vein thrombosis

Background Patients infected with the human immunodeficiency virus (HIV) are more prone to systemic inflammation and pathological clotting, and many may develop deep vein thrombosis (DVT) as a result of this dysregulated inflammatory profile. Coagulation tests are not routinely performed unless there is a specific reason. Methods We recruited ten healthy control subjects, 35 HIV negative patients with deep vein thrombosis (HIV negative-DVT), and 13 HIV patients with DVT (HIV positive-DVT) on the primary antiretroviral therapy (ARV) regimen- Emtricitabine, Tenofovir and Efavirenz. Serum inflammatory markers, haematological results, viscoelastic properties (using thromboelastography-TEG) and scanning electron microscopy (SEM) of whole blood (WB) were used to compare the groups. Results DVT patients (HIV positive and HIV negative) have raised inflammatory markers. The HIV positive-DVT group has anaemia in keeping with anaemia of chronic disorders. DVT patients have a hypercoagulable profile on the TEG but no significant difference between HIV negative-DVT and HIV positive-DVT groups. The TEG analysis compared well and supported our ultrastructural results. Scanning electron microscopy of DVT patient’s red blood cells (RBCs) and platelets demonstrates inflammatory changes including abnormal cell shapes, irregular membranes and microparticle formation. All the ultrastructural changes were more prominent in the HIV positive-DVT patients. Conclusions It is well-known that HIV infection is linked to inflammation and inflammation is linked with the presence of a hypercoagulable state. The presence of DVT is also associated with inflammation. Whether HIV is the cause of the DVT is not certain. Although there were trends that HIV infected patients were more hypercoagulable on functional tests (viscoelastic profile) compared to HIV uninfected patients, there were no significant differences between the 2 groups. Morphologically there were inflammatory changes in patients with DVT. These ultrastructural changes, specifically with regard to platelets, appear more pronounced in HIV infected patients which may contribute to increased risk for hypercoagulability and deep vein thrombosis.
Modified Single-Carrier Multilevel SPWM and Online Efficiency Enhancement for Single-Phase Asymmetrical NPC Grid-Connected Inverter

In this paper, a modified single-carrier and multimodulation sine pulse width modulation (MSCMM-SPWM) is proposed to simplify the implementation of the single-phase asymmetrical neutral-point clamped grid-connected inverter (ANPC-GCI) for the PV generation system application. Furthermore, the expressions of the total switching loss and grid current total harmonic distortion (THD) of ANPC-GCI controlled by the proposed MSCMM-SPWM are derived. Then taking the grid current THD meet the grid-connected standard in the entire operational range as the constraint, an efficiency enhancement scheme for ANPC-GCI based on online adjusting the carrier frequency and the obtained THD expression is proposed. The control configuration of ANPC-GCI, including the proposed MSCMM-SPWM and online efficiency enhancement scheme, is designed. The detailed experimental results verify the correctness and feasibility of the proposed MSCMM-SPWM and the efficiency enhancement scheme.
Module Partition Method for Complex Products Considering the Change Risk

： Modularization method can effectively control the cost of research and development for complex products, and module partition is the foundation of modularization. Impacted by the dynamic internal and external factors including customer requirements, key technologies, policy and market, continuous evolution is unavoidable for complex products. Design change is the basic means for an enterprise to establish product evolution. In order to reduce the impact of evolution, three factors of function, structure and change risk are considered in the module partition of complex products. Firstly, the complex network theory is applied to the structure modelling of complex products, with components as nodes and component relationships as edges, the network model of the components for complex products is established. Then the LinkRank community discovery algorithm is used to obtain the initial module partition scheme of the product. Afterwards, the network similarity-based community discovery algorithm is used to obtain the final partitioning scheme. Finally, a case study of a motorcycle engine is presented to verify the feasibility and validity of the proposed approach.
Update Vorhofflimmern – Was ist neu?

Was ist neu? Behandlungspfad im Management von Vorhofflimmerpatient*innen Die im vergangenen Jahr neu erschienen Vorhofflimmer-Leitlinien schlagen einen Diagnose- und Therapiealgorithmus vor, der eine strukturierte und erweiterte Charakterisierung ermöglichen soll und nach umfangreicher und „ganzheitlicher“ Einschätzung in einer gezielten Therapiestrategie mündet. Zentraler Bestandteil bleibt die Prävention thromboembolischer Komplikationen. Therapie von Begleiterkrankungen Eine konsequente Behandlung von Begleiterkrankungen, insbesondere klassischer kardiovaskulärer Risikofaktoren, wird in einem multimodalen Therapieansatz betont. Symptomkontrolle und die Rolle der Katheterablation Symptomatische Patient*innen sollten eine rhythmuserhaltende Therapie bekommen. Vor dem Hintergrund oft limitierter medikamentöser Optionen kommt der Katheterablation eine entscheidende Rolle zu. Prognostische Aspekte und das richtige Timing Mit der EAST-AFNET4-Studie konnte erstmals der prognostische Vorteil einer Rhythmuskontrolle bei Patient*innen mit Begleiterkrankungen gezeigt werden. Entscheidend scheint insbesondere der frühe Therapiebeginn nach Diagnosestellung. Aktuelle Studien demonstrieren die hohe Effektivität der Katheterablation auch als Erstlinienbehandlung. Bei Herzinsuffizienz geht die Katheterablation mit einer Prognoseverbesserung einher.
evolution

------------------------------------------------------------------------------------------3 Introduction------------------------------------------------------------------------------------5 Motivation----------------------------------------------------------------------------------------6 Concept and Focus-----------------------------------------------------------------------------7 Influence------------------------------------------------------------------------------------------9 Body of Work----------------------------------------------------------------------------------11 1. First Stage: Awakening-------------------------------------------------------------------------12 2. Second Stage: Equilibrium---------------------------------------------------------------------13 3. Third Stage: Death-----------------------------------------------------------------------------------------21 Conclusion--------------------------------------------------------------------------------------25 Installation--------------------------------------------------------------------------------------26 List of Images-----------------------------------------------------------------------------------29 Bibliography------------------------------------------------------------------------------------30
To Locate an Attacker in Wi-Fi Networks Using a Forensic Positioning Tool

Network Forensics plays a major role in fighting cyber terrorism. The important way to identify and locate attacker’s position in IEEE 802.11 is Surveillance tool, The Digital Marauder’s Map. Implement in Wireless network with Single based antenna, two techniques are used Active and Passive to monitor the various mobile devices and its localization algorithm. To maximize the coverage area and number of covered channels by combine the usage of high-gain antennas with a low noise amplifier(LNA) and a signal splitter,inorder to collect as much wireless traffic as possible. Second implement three Localization algorithms, M-Loc, AP-Rad and AP-Loc for law enforcement to accurately position a mobile device based on the set of APs communicable with it.The system proposed with the concept of APCL (Access point coordinated localization) which forces the attacker to reveal its information by gathering all the AP information . Then coordinated techniques is estimated by Modeling it as Finite Horizon discrete Markov decision process (MDP) solved by an approximation algorithm.