With its origin in sociology, Social Network Analysis (SNA), quickly emerged and spread to other areas of research, including anthropology, biology, information science, organizational studies, political science, and ...
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Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We ...
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Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad
Indonesia is one of the countries whose people are most affected by the 2019 corona virus (COVID-19). When the corona virus spreads faster, the Indonesian government imposed large-scale social restrictions in big citi...
Indonesia is one of the countries whose people are most affected by the 2019 corona virus (COVID-19). When the corona virus spreads faster, the Indonesian government imposed large-scale social restrictions in big cities to villages in Indonesia. With the changing situation in carrying out work activities during this pandemic, it will certainly affect the quality of organization in companies and the quality of education in schools and colleges. In order to identify all processes in teaching and learning activities that are running well or not, schools need to monitor and measure employee performance. Key performance indicators (KPIs) are generally used as benchmarks to determine how well a company's performance can be represented by employee performance. In this study, the key performance indicator preparation process was carried out through the stages of designing a questionnaire, distributing questionnaires to employees in educational institutions, conducting factor analysis and then developing a KPI model. The output is in the form of a KPI model that can be used as a reference in the formation of KPIs in educational institutions, namely Y = 5,360 + 1,380 X1-1, 068 X3.
In this paper strategies to control the speed of a generic DC motor system are discussed. They range from the conventional, like PID and state-feedback, to reference model adaptive algorithms such as MRAC, VS-MRAC and...
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Magnetic Resonance Images of the brain provide detailed anatomical information that allows morphological analysis of the different brain structures. The analysis of the cortical folding patterns variation is inspiring...
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Magnetic Resonance Images of the brain provide detailed anatomical information that allows morphological analysis of the different brain structures. The analysis of the cortical folding patterns variation is inspiring great interest, as this relates to cognitive function. The sulci are a depression in the cerebral cortex and represent ≈ 70% of the cortical surface. The sulcal width, depth and cortical thickness are the most frequent morphological descriptors applied to the sulci. In this work, the 3D tortuosity is proposed as new descriptor to capture information about the twist and turns of the sulci. 1 The 3D tortuosity of the central sulcus of both hemispheres was measured using the Minimal Interval Resonance Imaging in the Alzheimer's Disease (MIRIAD) database, for a set of high-resolution MRI of 66 subjects: 43 patients with Alzheimer Disease (AD) and 23 control subjects. As it is known AD causes significant gray matter loss which cause morphological changes in the cortical structure. It is expected for the tortuosity to capture these changes and serve as a biomarker to differentiate between populations. The result of the Wilcoxon tests show that the tortuosity values of the central sulci are significantly larger for the AD patients (p < 0.05) for the left hemisphere, which is consistent with the hypothesis. The evaluation of the proposed tortuosity measurement of the Central Sulcus as a potential biomarker was performed and the results indicate its effectiveness to extract additional anatomical information and discriminate between AD patients and Controls.
This paper presents a novel modification on the Phenyl-C61-butyric acid methyl ester (PCBM) for better performance as an acceptor material by introducing a Heterocyclic ring (Pyrazine) in the place of the Phenyl group...
This paper presents a novel modification on the Phenyl-C61-butyric acid methyl ester (PCBM) for better performance as an acceptor material by introducing a Heterocyclic ring (Pyrazine) in the place of the Phenyl group of the PCBM to form a new acceptor material, Heterocyclic-C61-butyric Acid Methyl Ester/ Pyrazine (HCBM-Pyrazine). The electronic properties for the new material are obtained theoretically through the use of the Density Functional Theory (DFT) method with the help of the Gaussian 16 program. The results show that the HCBM-Pyrazine can create more voltage if used with any donor material. Besides that, it has a considerably higher electron affinity, making it a more efficient electron acceptor than the original PCBM. Furthermore, the results show that both molecules have closer absorption spectrums and reorganization energies.
This paper reports on the basic findings and future perspectives of a capacity building project funded by the European Union. The International Master of Science on Cyber Physical systems (MS@CPS) is a collaborative p...
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This paper reports on the basic findings and future perspectives of a capacity building project funded by the European Union. The International Master of Science on Cyber Physical systems (MS@CPS) is a collaborative project that aims to establish a master program in cyber physical systems (CPS). A consortium composed of nine partners proposed the project. Three partners are European and from Germany, UK and Sweden; while the other six partners are from the South Mediterranean region and include: Palestine, Jordan and Tunisia. The consortium is led by the University of Siegen in Germany who also manages the implementation of the work packages. CPS is an emerging engineering subject with significant economic and societal implications, which motivated the consortium to propose the establishment of a master program to offer educational and training opportunities at graduate level in the fields of CPS. In this paper, CPS as a field of study is presented with an emphasis on its importance, especially with regard to meeting local needs. A brief description of the project is presented in conjunction with the methodology for developing the courses and their learning outcomes.
Dynamically encircling exceptional points (EPs) can lead to chiral mode switching as the system parameters are varied along a path that encircles EP. However, conventional encircling protocols result in low transmitta...
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Dynamically encircling exceptional points (EPs) can lead to chiral mode switching as the system parameters are varied along a path that encircles EP. However, conventional encircling protocols result in low transmittance due to path-dependent losses. Here, we present a paradigm to encircle EPs that includes fast Hamiltonian variations on the parameter boundaries, termed Hamiltonian hopping, enabling ultrahigh-efficiency chiral mode switching. This protocol avoids path-dependent loss and allows us to experimentally demonstrate nearly 90% efficiency at 1550 nm in the clockwise direction, overcoming a long-standing challenge of non-Hermitian optical systems and powering up new opportunities for EP physics.
With the progress of sensor technologies, there has been an increase in the number of connected computing devices capable of collecting information and interacting with the environment in which they are inserted, form...
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With the progress of sensor technologies, there has been an increase in the number of connected computing devices capable of collecting information and interacting with the environment in which they are inserted, forming the basis of the Internet of Things (IoT). Such devices enable the development of new applications capable of making cities more intelligent, providing improvements to society and urban infrastructure. As a result of the interaction between different applications, there is a large set of data, from which useful knowledge can be extracted. An important problem to be faced is the recurrent occurrence of arbovirus outbreaks in big cities with tropical climate. This research proposes an architecture capable of aggregating data from different types of IoT devices, and manipulating them using data analytics techniques to assist the surveillance of arbovirus outbreaks. As an example of case study, we present an exploratory analysis of climatology data and cases of arbovirus diseases in the city of Fortaleza, Ceara, Brazil, between the years of 2011 and 2017.
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