B2C e-commerce logistics network optimization model

In this paper, considering the characteristics of e-commerce logistics and the problems arising from the construction of logistics network, based on the full investigation and research of the B2C electricity supplier operation mode, we have studied and summarized the research results of scholars at home and abroad aiming at the related problems, and summarized and summarized the methods and steps of logistics network optimization in the whole country. First, we established the logistics network structure.The definition of network level, node function and connection between nodes is carried out. Then, through the improved urban agglomeration order gravity model, the scope of self logistics city is screened. Finally, the link of logistics network structure is scheduled for completion with the goal of cost optimization.

B2C E-Commerce Logistics Network Optimization Model

In this paper, considering the characteristics of e-commerce logistics and the problems arising from the construction of logistics network, based on the full investigation and research of the B2C electricity supplier operation mode, we have studied and summarized the research results of scholars at home and abroad aiming at the related problems, and summarized and summarized the methods and steps of logistics network optimization in the whole country. First, we established the logistics network structure.The definition of network level, node function and connection between nodes is carried out. Then, through the improved urban agglomeration order gravity model, the scope of self logistics city is screened. Finally, the link of logistics network structure is scheduled for completion with the goal of cost optimization.

Design of a Modified-Bridge Circuit with a Master-Slave Input Supply Mechanism for Ozone-Driven System Applications

This study proposes a design of a modified-bridge circuit with a master–slave input supply mechanism for ozone-driven system applications. Because the single-source supply design is becoming the mainstream choice in the existing ozone-driven systems, the input supply reliability of the ozone-driven system is crucial. Therefore, this proposed design involves a modified-bridge circuit combined with inductors and transistors, which can be augmented with the energy storage device as a backup source to improve the reliability of the input supply for the ozone-driven system. In addition, considering that the original source directly connected to DC BUS can re-charge the energy storage device, the energy recycling operation mode is designed in this proposed system to extend the duration of the energy storage device, which improves the supply reliability of the ozone-driven system further. To validate this proposed system, both model formulation and hardware realization are assessed through different test scenarios. Experimental outcomes of these tests confirm the practicality of the proposed design.

Modeling Joule Heating Effect on Thermal Efficiency of Photovoltaic Thermal (PVT) Collectors with Operation Mode Factor (OMF)

The purpose of the present study is developing the operation mode factor (OMF) by remodeling the thermal efficiency model of a hybrid PVT collector during steady state. Joule heating occurs when the photovoltaic (PV) panel operates at a high current during maximum power point tracking (MPPT) on higher irradiation. Under these conditions, some electrical energy converts to thermal energy within the PV cells. Joule heating contributed to increasing the PVT thermal efficiency. The steps were to construct the OMF by remodeling the thermal efficiency involving the Joule heating effect and to validate the results using the model by comparing the simulation and experiment. The dimensionless OMF was responsible for changes in thermal efficiency for PVT-mode. The conductive heat transfer coefficient from the surface to the absorber was the most decisive component in the OMF. Heat removal factor and OMF might be interrelated at the mass flow rate by decreasing PV temperature to maintain Joule heating. The proposed model with OMF had explained PVT-mode and T-mode with the RMS value of less than 1%. This model complemented the results of the previous studies. The results may contribute from the initial design to the operational monitoring for thermal to electrical energy production.

Numerical modeling of pedestal stability and broadband turbulence of wide-pedestal QH-mode plasmas on DIII-D

Wide pedestal Quiescent High confinement (QH) mode discovered on DIII-D in recent years is a stationary and quiescent H-mode with the pedestal width exceeding EPED prediction by at least 25%. Its characteristics, such as low rotation, high energy confinement and ELM-free operation, make it an attractive operation mode for future reactors. Linear and nonlinear simulations using BOUT++ reduced two fluid MHD model are carried out to investigate the bursty broadband turbulence often observed in the edge of wide-pedestal QH-mode plasmas. Two kinds of MHD-scale instabilities in different spatial locations within the pedestal were found in the simulations: one mild peeling-ballooning (PB) mode (γ_PB<0.04ω_A) located near the minimum in Er well propagating in ion diamagnetic drift direction; and one drift-Alfvén wave (DAW) locates at smaller radius compared to Er well propagating in the electron diamagnetic drift direction and unstable only when the parallel electron dynamics is included in the simulation. The coupling between drift wave and shear Alfvén wave provides a possible cause of the experimentally observed local profile flattening in the upper-pedestal. The rotation direction, mode location, as well as the wavenumber of these two modes from BOUT++ simulations agree reasonably well with the experimental measurements, while the lack of quantitatively agreement is likely due to the lack of trapped electron physics in current fluid model. This work presents improved physics understanding of the pedestal stability and turbulence dynamics for wide-pedestal QH-mode.

API as a way to interact with social networks

In the realities of the pandemic, chat bots have become indispensable helpers. They do not need a lot of resources and constant human control. A method of interacting with social networks through a specialized software interface Web API, which is the basis of the REST architecture, is considered. The basic structure of requests for receiving and sending data to servers is presented. On the example of the implementation of a chatbot for vk.com, capable of automating a dialogue with users, the main design stages are presented, including the requirements for the implementation and operation mode based on the client-server architecture, implementation and testing. The project server is implemented on a Raspberry Pi4 single-board computer. Demonstrated code for performing basic queries and implemented a Long Polling approach to continuously track and distribute user messages. Methods were formed to obtain the necessary resources from the server, to declare a new resource on the server, to update information on the server, and to delete certain objects from the database. The result was a patented software product "Intelligent assistant of VSUIT for social networks".

Simulations of Heat Supply Performance of a Deep Borehole Heat Exchanger under Different Scheduled Operation Conditions

With the changing world energy structure, the development of renewable energy sources is gradually accelerating. Among them, close attention has been given to geothermal energy because of its abundant resources and supply stability. In this article, a deep borehole heat exchanger (DBHE) is coupled with a heat pump system to calculate the heat supply and daily electricity consumption of the system. To make better use of the peaks and valleys in electricity prices, the following three daily operating modes were studied: 24-h operation (Mode 1), 8-h operation plus 16-h non-operation (Mode 2), and two cycles of 4-h operation and 8-h non-operation (Mode 3). Simulation results show that scheduled non-continuous operation can effectively improve the outlet temperature of the heat extraction fluid circulating in the DBHE. The heat extraction rates of Mode 1 is 190.9 kW for mass flowrate of 9 kg/s; in Mode 2 and Mode 3 cases, the rates change to 304.7 kW and 293.0 kW, respectively. The daily operational electricity cost of Mode 1 is the greatest because of 24-h operation; due to scheduled non-continuous operation, the daily operational electricity cost of Mode 3 is only about 66% of that of Mode 2. After an 8-month period without heating, the formation-temperature can be restored within 4 °C of its original state; 90% recovery of the formation-temperature can be achieved by the end of the second month of the non-operation season.

Feasibility of Using Wearable EMG Armbands combined with Unsupervised Transfer Learning for Seamless Myoelectric Control

Myoelectric control uses electromyography (EMG) signals as human-originated input to enable intuitive interfaces with machines. As such, recent rehabilitation robotics employs myoelectric control to autonomously classify user intent or operation mode using machine learning. However, performance in such applications inherently suffers from the non-stationarity of EMG signals across measurement conditions. Current laboratory-based solutions rely on careful, time-consuming control of the recordings or periodic recalibration, impeding real-world deployment. We propose that robust yet seamless myoelectric control can be achieved using a low-end, easy-to-don and doff wearable EMG sensor combined with unsupervised transfer learning. Here, we test the feasibility of one such application using a consumer-grade sensor (Myo armband, 8 EMG channels @ 200 Hz) for gesture classification across measurement conditions using an existing dataset: 5 users x 10 days x 3 sensor locations. Specifically, we first train a deep neural network using Temporal-Spatial Descriptors (TSD) with labeled source data from any particular user, day, or location. We then apply the Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN), which automatically adjusts the trained TSD to improve classification performance for unlabeled target data from a different user, day, or sensor location. Compared to the original TSD, SCADANN improves accuracy by 12±5.2% (avg±sd), 9.6±5.0%, and 8.6±3.3% across all possible user-to-user, day-to-day, and location-to-location cases, respectively. In one best-case scenario, accuracy improves by 26% (from 67% to 93%), whereas sometimes the gain is modest (e.g., from 76% to 78%). We also show that the performance of transfer learning can be improved by using a better model trained with good (e.g., incremental) source data. We postulate that the proposed approach is feasible and promising and can be further tailored for seamless myoelectric control of powered prosthetics or exoskeletons.

Investigation of the Efficiency of Manufacturing Polymer Molds by FDM-Printing

The results of experimental studies of the efficiency of manufacturing polymer foundry models by the FDM method are presented. Rational consumables for 3D-printing are revealed. The optimal values of the shrinkage value of the 3D-model during printing and the parameters of the 3D-printer operation mode are revealed. It has been established that polymer casting molds are rationally manufactured by FDM printing, using ABS-plastic as a consumable, while making a correction to linear dimensions at the stage of creating a 3D-model to eliminate plastic shrinkage.