Self-Combustion Induced Hierarchical Nanoporous Alloy Transition toward High Area Property Electrode for Supercapacitor

An “explosive extratropical cyclone” is an atmospheric phenomenon that occurs when there is a very rapid drop in central atmospheric pressure. This phenomenon, with its characteristic of rapidly lowering the pressure in its interior, generates very intense winds and for this reason it is called explosive cyclone, bomb cyclone. With gusts recorded of 116 km/h, atmospheric phenomenon – “cyclone bomb” (CB) hit southern Brazil on June 30, the beginning of winter 2020, causing destruction in its influence over. One of the cities most affected was Chapecó, west of the state of Santa Catarina. The satellite images show that the CB generated a low pressure (976 mbar) inside it, generating two atmospheric currents that moved at high speed. In a northwest-southeast direction, Bolivia and Paraguay, crossing the states of Parana and Santa Catarina, and this draft that hit the south of Brazil, which caused the destruction of the affected states. Another moving to Argentina, southwest-northeast direction, due to high area of high pressure (1022 mbar). Both enhanced the phenomenon.

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Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18105110 ◽

2021 ◽

Vol 18 (10) ◽

pp. 5110

Author(s):

Shu-Farn Tey ◽

Chung-Feng Liu ◽

Tsair-Wei Chien ◽

Chin-Wei Hsu ◽

Kun-Chen Chan ◽

...

Keyword(s):

Neural Network ◽

Early Stage ◽

Characteristic Curve ◽

Patient Discharge ◽

Ann Model ◽

Healthcare Settings ◽

High Area ◽

Icd 10 ◽

Artificial Neural ◽

Artificial Neural Network Ann

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

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Quality of wheat breads enriched with flour from germinated amaranth seeds

Food Science and Technology International ◽

10.1177/10820132211016577 ◽

2021 ◽

pp. 108201322110165

Author(s):

Luciano M Guardianelli ◽

María V Salinas ◽

María C Puppo

Keyword(s):

Relaxation Time ◽

Elastic Modulus ◽

Wheat Flour ◽

Specific Volume ◽

Bread Quality ◽

Maximum Volume ◽

High Area ◽

Amaranth Flour ◽

Fermentation Parameters

Amaranth flour from germinated (GA) and non-germinated (A) seeds (0%-C, 5%, 15%, 25%) were mixed with wheat flour for breadmaking. Fermentation parameters of dough (time-tf, maximum volume-Vmax) were obtained. Specific volume (Vsp) of breads, crust color, texture and relaxation of crumb were analyzed. A high amount of germinated amaranth flour decreased Vmax and increased tf, obtaining breads with low Vsp and darkness crust. A firmed and chewy crumb, although with a more aerated structure (high area occupied by alveoli) was obtained. The GA25 bread presented the softer crumb. The elastic modulus-E1 of crumb increased and the relaxation time-T1 decreased with higher amounts of amaranth flour, suggesting the formation of a more structured crumb; mainly in the case of non-germinated amaranth flour. Wheat flour resisted the inclusion of 25% of germinated amaranth seeds (GA25) without substantial changes in bread quality.

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A Slot-Die Technique for the Preparation of Continuous, High-Area, Chitosan-Based Thin Films

Polymers ◽

10.3390/polym13101566 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1566

Author(s):

Oliver J. Pemble ◽

Maria Bardosova ◽

Ian M. Povey ◽

Martyn E. Pemble

Keyword(s):

Thin Films ◽

Mechanical Properties ◽

High Volume ◽

Mechanical Anisotropy ◽

Diverse Range ◽

Direction Parallel ◽

High Area ◽

Wide Range ◽

Slot Die ◽

Potential Applications

Chitosan-based films have a diverse range of potential applications but are currently limited in terms of commercial use due to a lack of methods specifically designed to produce thin films in high volumes. To address this limitation directly, hydrogels prepared from chitosan, chitosan-tetraethoxy silane, also known as tetraethyl orthosilicate (TEOS) and chitosan-glutaraldehyde have been used to prepare continuous thin films using a slot-die technique which is described in detail. By way of preliminary analysis of the resulting films for comparison purposes with films made by other methods, the mechanical strength of the films produced was assessed. It was found that as expected, the hybrid films made with TEOS and glutaraldehyde both show a higher yield strength than the films made with chitosan alone. In all cases, the mechanical properties of the films were found to compare very favorably with similar measurements reported in the literature. In order to assess the possible influence of the direction in which the hydrogel passes through the slot-die on the mechanical properties of the films, testing was performed on plain chitosan samples cut in a direction parallel to the direction of travel and perpendicular to this direction. It was found that there was no evidence of any mechanical anisotropy induced by the slot die process. The examples presented here serve to illustrate how the slot-die approach may be used to create high-volume, high-area chitosan-based films cheaply and rapidly. It is suggested that an approach of the type described here may facilitate the use of chitosan-based films for a wide range of important applications.

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High Area-Efficient Parallel Encoder with Compatible Architecture for 5G LDPC Codes

Symmetry ◽

10.3390/sym13040700 ◽

2021 ◽

Vol 13 (4) ◽

pp. 700

Author(s):

Yufei Zhu ◽

Zuocheng Xing ◽

Zerun Li ◽

Yang Zhang ◽

Yifan Hu

Keyword(s):

High Performance ◽

Ldpc Codes ◽

Low Complexity ◽

Cmos Technology ◽

Area Efficiency ◽

Prior Art ◽

New Radio ◽

High Area ◽

Significant Area ◽

Area Efficient

This paper presents a novel parallel quasi-cyclic low-density parity-check (QC-LDPC) encoding algorithm with low complexity, which is compatible with the 5th generation (5G) new radio (NR). Basing on the algorithm, we propose a high area-efficient parallel encoder with compatible architecture. The proposed encoder has the advantages of parallel encoding and pipelined operations. Furthermore, it is designed as a configurable encoding structure, which is fully compatible with different base graphs of 5G LDPC. Thus, the encoder architecture has flexible adaptability for various 5G LDPC codes. The proposed encoder was synthesized in a 65 nm CMOS technology. According to the encoder architecture, we implemented nine encoders for distributed lifting sizes of two base graphs. The eperimental results show that the encoder has high performance and significant area-efficiency, which is better than related prior art. This work includes a whole set of encoding algorithm and the compatible encoders, which are fully compatible with different base graphs of 5G LDPC codes. Therefore, it has more flexible adaptability for various 5G application scenarios.

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MOF/PEDOT/HPMo-based polycomponent hierarchical hollow micro-vesicles for high performance flexible supercapacitors

Journal of Materials Chemistry A ◽

10.1039/d0ta10603h ◽

2021 ◽

Vol 9 (5) ◽

pp. 2948-2958

Author(s):

Bing Wang ◽

Shuo Liu ◽

Lin Liu ◽

Wen-Wei Song ◽

Yue Zhang ◽

...

Keyword(s):

Electrode Material ◽

High Performance ◽

Cycle Stability ◽

Supercapacitor Electrode ◽

Flexible Supercapacitors ◽

High Area

The three-component PCN-224/PEDOT/PMo12 supercapacitor electrode material is designed to offer high area capacitance, good cycle stability and mechanical flexibility.

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Simba

Communications of the ACM ◽

10.1145/3460227 ◽

2021 ◽

Vol 64 (6) ◽

pp. 107-116

Author(s):

Yakun Sophia Shao ◽

Jason Cemons ◽

Rangharajan Venkatesan ◽

Brian Zimmer ◽

Matthew Fojtik ◽

...

Keyword(s):

Deep Learning ◽

Large Scale ◽

Data Locality ◽

Coarse Grained ◽

Batch Size ◽

Peak Performance ◽

Large Scale Systems ◽

High Area ◽

On Chip ◽

And Storage

Package-level integration using multi-chip-modules (MCMs) is a promising approach for building large-scale systems. Compared to a large monolithic die, an MCM combines many smaller chiplets into a larger system, substantially reducing fabrication and design costs. Current MCMs typically only contain a handful of coarse-grained large chiplets due to the high area, performance, and energy overheads associated with inter-chiplet communication. This work investigates and quantifies the costs and benefits of using MCMs with finegrained chiplets for deep learning inference, an application domain with large compute and on-chip storage requirements. To evaluate the approach, we architected, implemented, fabricated, and tested Simba, a 36-chiplet prototype MCM system for deep-learning inference. Each chiplet achieves 4 TOPS peak performance, and the 36-chiplet MCM package achieves up to 128 TOPS and up to 6.1 TOPS/W. The MCM is configurable to support a flexible mapping of DNN layers to the distributed compute and storage units. To mitigate inter-chiplet communication overheads, we introduce three tiling optimizations that improve data locality. These optimizations achieve up to 16% speedup compared to the baseline layer mapping. Our evaluation shows that Simba can process 1988 images/s running ResNet-50 with a batch size of one, delivering an inference latency of 0.50 ms.

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