The Application of the Development of Customary Inheritance Law According to the Jurisprudence of the Supreme Court

Customary inheritance law is influenced by the three kinship system. The Indonesian indigenous peoples, if there is a dispute about inheritance customs, completed the family council, if the hearts of deliberation families not bring results, then the Settlement shown to the Indigenous Institute, but when hearts division of inheritance still feel less satisfied BY Decision Traditional Leader Then Settlement of inheritance can be resolved in the court. Application of norms The jurisprudence of the Supreme Court Third hearts kinship system can be implemented yet, due to lack of knowledge of indigenous peoples against jurisprudence. Jurisprudence singer known only hearts Verdict The heritage dispute resolved by the Court InstituteIntisariHukum waris adat masih dipengaruhi tiga sistem kekerabatan Pada masyarakat adat jika terjadi sengketa waris adat, diselesaikan musyawarah keluarga, apabila dalam musyawarah keluarga tidak membawa hasil, maka penyelesaian kepada lembaga adat, namun apabila dalam pembagian harta waris masih merasa kurang puas dengan putusan ketua adat maka penyelesaian waris dapat diselesaikan di pengadilan. Penerapan norma Yurisprudensi Mahkamah Agung dalam ketiga sistem kekerabatan belum dapat dilaksanakan, disebabkan kurangnya pengetahuan masyarakat adat terhadap yurisprudensi. Yurisprudensi ini hanya dikenal dalam putusan sengketa warisan yang diselesaikan oleh lembaga pengadilan

Autonomous assessment of spontaneous retinal venous pulsations in fundus videos using a deep learning framework

The presence or absence of spontaneous retinal venous pulsations (SVP) provides clinically significant insight into the hemodynamic status of the optic nerve head. Reduced SVP amplitudes have been linked to increased intracranial pressure and glaucoma progression. Currently, monitoring for the presence or absence of SVPs is performed subjectively and is highly dependent on trained clinicians. In this study, we developed a novel end-to-end deep model, called U3D-Net, to objectively classify SVPs as present or absent based on retinal fundus videos. The U3D-Net architecture consists of two distinct modules: an optic disc localizer and a classifier. First, a fast attention recurrent residual U-Net model is applied as the optic disc localizer. Then, the localized optic discs are passed on to a deep convolutional network for SVP classification. We trained and tested various time-series classifiers including 3D Inception, 3D Dense-ResNet, 3D ResNet, Long-term Recurrent Convolutional Network, and ConvLSTM. The optic disc localizer achieved a dice score of 95% for locating the optic disc in 30 milliseconds. Amongst the different tested models, the 3D Inception model achieved an accuracy, sensitivity, and F1-Score of 84 ± 5%, 90 ± 8%, and 81 ± 6% respectively, outperforming the other tested models in classifying SVPs. To the best of our knowledge, this research is the first study that utilizes a deep neural network for an autonomous and objective classification of SVPs using retinal fundus videos

Photoelectron spectra of finite-thickness layers

A method of computing x-ray photoemission spectra in the wide range of energy losses and different sighting angles is presented. Photoemission spectra for layers of finite thickness are investigated. Angular and energy spectra are analyzed using the invariant imbedding principle. They are computed using small-angle approximation and the exact numerical solution of the multiple photoelectron scattering events in solids. The presented methods of x-ray photoemission spectra analysis are compared regarding their efficiencies. Comparison of the exact numerical solution to those based on straight line approximation and small-angle approximation reveals an error in straight line approximation of about 50%. Numerical solutions are compared with the experimental data and Monte-Carlo simulations

ENTRY AND EXIT FROM FARMING IN NORTH CAROLINA, 1978-87

Herein, we report the first site-selective, Pd(II)-catalyzed, cross-dehydrogenative Heck reaction of indoles in micro flow. By use of a capillary microreactor, we were able to boost the intrinsic kinetics to accelerate former hour-scale reaction conditions in batch to the minute range in flow. The synergistic use of microreactor technology and oxygen, as both terminal oxidant and mixing motif, highlights the sustainable aspect of this process.Hannes P. L. Gemoets, Volker Hessel and Timothy Noë

SSP: Early prediction of sepsis using fully connected LSTM-CNN model.

Background: Sepsis is a life-threatening condition that occurs due to the body's reaction to infections, and it is a leading cause of morbidity and mortality in hospitals. Early prediction of sepsis onset facilitates early interventions that promote the survival of suspected patients. However, reliable and intelligent systems for predicting sepsis are scarce.Methods: This paper presents a novel technique called Smart Sepsis Predictor (SSP) to predict sepsis onset in patients admitted to an intensive care unit (ICU). SSP is a deep neural network architecture that encompasses long short-term memory (LSTM), convolutional, and fully connected layers to achieve early prediction of sepsis. SSP can work in two modes; Mode 1 uses demographic data and vital signs, and Mode 2 uses laboratory test results in addition to demographic data and vital signs. To evaluate SSP, we have used the 2019 PhysioNet/CinC Challenge dataset, which includes the records of 40,366 patients admitted to the ICU.Results: To compare SSP with existing state-of-the-art methods, we have measured the accuracy of the SSP in 4-, 8-, and 12-h prediction windows using publicly available data. Our results show that the SSP performance in Mode 1 and Mode 2 is much higher than existing methods, achieving an area under the receiver operating characteristic curve (AUROC) of 0.89 and 0.92, 0.88 and 0.87, and 0.86 and 0.84 for 4 h, 8 h, and 12 h before sepsis onset, respectively.Conclusions: Using ICU data, sepsis onset can be predicted up to 12 h in advance. Our findings offer an early solution for mitigating the risk of sepsis onset