High brachial artery bifurcation: a report of 2 cases

High division of the brachial artery was observed in two cadavers, during routine dissection of upper extremities. In the first case, the brachial artery of the right upper extremity was divided into its two terminal branches immediately after passing between the lateral and medial roots of the median nerve and just below the origin of the profunda brachii artery. The lateral branch was the radial artery, located in the space normally occupied by the brachial artery and the medial one was the ulnar artery. In the second case, the brachial artery was divided into its two terminal branches just below the origin of the profunda brachii artery. Accurate knowledge of the relationships and course of these major arterial conduits and particularly of their variational patterns, is of considerable practical importance in the conduct of reperative surgery in the arm, forearm and hand.     

Morphological evolution of edge-hillocks on single-crystal films having anisotropic drift-diffusion under the capillary and electromigration forces

The morphological evolution of hillocks at the unpassivated sidewalls of single-crystal metallic thin film interconnects is investigated via computer simulations using the free-moving boundary value problem. The effect of drift-diffusion anisotropy on the development of surface topographical scenarios is fully explored under the action of electromigration and capillary forces, utilizing numerous combinations of the surface texture, the drift-diffusion anisotropy and the direction of the applied electric field. The simulation studies yield analytical relationships for the velocity of the surface solitary waves and the drift velocity of electromigration-induced internal voids as a function of the applied current densities, which contain intrinsic and structural properties of the single-crystal thin films. The threshold value of the applied current density, above which electromigration-induced internal voids can be formed and may cause the catastrophic failure of interconnects by breaching, also appears explicitly in this relationship.     

Serologic evidence of previous Campylobacter jejuni infection in patients with the Guillain-Barré syndrome

OBJECTIVE: To determine if patients with the Guillain-Barré syndrome are likely to have had Campylobacter jejuni infection before onset of neurologic symptoms. DESIGN: A case-control study. SETTING: Several university medical centers. PATIENTS: Case patients met clinical criteria for the Guillain-Barré syndrome between 1983 and 1990 and had a serum sample collected and frozen within 3 weeks after onset of neurologic symptoms (n = 118). Disease controls were patients with other neurologic illnesses (n = 56); healthy controls were hospital employees or healthy family members of patients (n = 47). MEASUREMENTS: Serum IgA, IgG, and IgM antibodies to C. jejuni were determined by enzyme-linked immunosorbent assays. Assays were done in a blinded manner. RESULTS: Optical density ratios > or = 2 in two or more immunoglobulin classes were seen in 43 (36%) of patients with the Guillain-Barré syndrome and in 10 (10%) of controls (odds ratio, 5.3; 95% CI, 2.4 to 12.5; P < 0.001). Increasing the optical density ratio or the number of immunoglobulin classes necessary to yield a positive result increased the strength of the association. The number of patients with the Guillain-Barré syndrome who had positive serologic responses was greatest from September to November (P = 0.02). Male patients were three times more likely to have serologic evidence of C. jejuni infection (P = 0.009); the proportion of patients with the syndrome who had a positive serologic response increased with age. CONCLUSIONS: Patients with the Guillain-Barré syndrome are more likely than controls to have serologic evidence of C. jejuni infection in the weeks before onset of neurologic symptoms. Campylobacter jejuni may play a role in the initiation of the Guillain-Barré syndrome in many patients.     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

Variance sensitive adaptive threshold-based PCA method for fault detection with experimental application

Principal Component Analysis (PCA) is a statistical process monitoring technique that has been widely used in industrial applications. PCA methods for Fault Detection (FD) use data collected from a steady-state process to monitor T² and Q statistics with a fixed threshold. For the systems where transient values of the processes must be taken into account, the usage of a fixed threshold in PCA method causes false alarms and missing data that significantly compromise the reliability of the monitoring systems. In the present article, a new PCA method based on variance sensitive adaptive threshold (T_vsa) is proposed to overcome false alarms which occur in the transient states according to changing process conditions and the missing data problem. The proposed method is implemented and validated experimentally on an electromechanical system. The method is compared with the conventional monitoring methods. Experimental tests and tabulated results confirm the fact that the proposed method is applicable and effective for both the steady-state and transient operations and gives early warning to operators.     

Unsupervised and simultaneous training of multiple object detectors from unlabeled surveillance video

Object detection is an essential component in automated vision-based surveillance systems. In general, object detectors are constructed using training examples obtained from large annotated data sets. The inevitable limitations of typical training data sets make such supervised methods unsuitable for building generic surveillance systems applicable to a wide variety of scenes and camera setups. In our previous work we proposed an unsupervised method for learning and detecting the dominant object class in a general dynamic scene observed by a static camera. In this paper, we investigate the possibilities to expand the applicability of this method to the problem of multiple dominant object classes. We propose an idea on how to approach this expansion, and perform an evaluation of this idea using two representative surveillance video sequences.     

DeepClassRooms: a deep learning based digital twin framework for on-campus class rooms

Neural Computing and Applications - A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart...     

In Situ Electrical Network Activation and Deactivation in Short Carbon Fiber Composites via 3D Printing

Prior studies on carbon-filler based, conductive polymer composites have mainly investigated how conductive filler morphology and concentration can tailor a material's electrical conductivity and overlooks the effects of filler alignment due to the difficulty to control and quickly quantify the filler alignment. Here, direct ink write 3D printing's unique ability is utilized to control carbon fiber alignment with a single process parameter, velocity ratio, to instantaneously activate or deactivate the electrical network in composites. Maximum electrical conductivity is achieved by randomly aligning carbon fibers that enhances the chance of direct fiber-to-fiber contact and, thus, activating the electrical network. However, aligning the fibers by increasing the velocity ratio disrupts the electrical network by minimizing fiber-to-fiber contact that resulted in a drastic decrease in electrical conductivity by as much as five orders of magnitude in both short and long carbon fiber composites. With this study, this study demonstrates that electrically conductive or insulative composites can be fabricated sequentially with a single ink. This novel ability to instantaneously control the electrical conductivity of carbon fiber reinforced composites allow to directly embed conductive pathways into designs to 3D print multifunctional composites that are capable of localized heating and self-sensing.