DeepRoute+: Modeling Couriers’ Spatial-temporal Behaviors and Decision Preferences for Package Pick-up Route Prediction

Over 10 billion packages are picked up every day in China. A fundamental task raised in the emerging intelligent logistics systems is the couriers’ package pick-up route prediction, which is beneficial for package dispatching, arrival-time estimation and overdue-risk evaluation, by leveraging the predicted routes to improve those downstream tasks. In the package pick-up scene, the decision-making of a courier is affected by strict spatial-temporal constraints (e.g., package location, promised pick-up time, current time, and courier’s current location). Furthermore, couriers have different decision preferences on various factors (e.g., time factor, distance factor, and balance of both), based on their own perception of the environments and work experience. In this article, we propose a novel model, named DeepRoute+, to predict couriers’ future package pick-up routes according to the couriers’ decision experience and preference learned from the historical behaviors. Specifically, DeepRoute+ consists of three layers: (1) The representation layer produces experience- and preference-aware representations for the unpicked-up packages, in which a decision preference module can dynamically adjust the importance of factors that affects the courier’s decision under the current situation. (2) The transformer encoder layer encodes the representations of packages while considering the spatial-temporal correlations among them. (3) The attention-based decoder layer uses the attention mechanism to generate the whole pick-up route recurrently. Experiments on a real-world logistics dataset demonstrate the state-of-the-art performance of our model.

Perception of public school students about animal welfare and the occurrence of the topic in education books

In current time, it is evident the necessity of animal welfare education policies. Animal welfare is defined as the state of an individual attempting to adjust to the environment and education material can encourage dialogue inside schools. We aimed to verify the presence or absence of animal welfare related content in school books and survey the perceptions of the students about the subject, associating with the socioeconomic profile of the school district and with the overall school performance in the SARESP test. This study was performed between July and November, with seven of the twenty-two public schools in the city of Araçatuba, São Paulo, Brazil. Seven books were collected for analysis from each school, totalizing 49 books, and 430 students answered a survey with 10 questions. The statistical analysis did not show relevant difference between biological gender, age, grade and socioeconomic profile and animal welfare knowledge perception. This study concluded that in most of the analyzed books, animal welfare topics are absent, and that there is no difference between the schools’ socioeconomic profile and animal welfare knowledge perception of the students.

A novel multi-information decision fusion based on improved random forests in HVCB fault detection application

Evaluating the mechanical state of high-voltage circuit breakers (HVCBs) based on vibration information has currently become an important research direction. In contrast to the unicity of the travel–time and current–time curves, the vibration information from the different positions is diverse. These differences are often overlooked in HVCB fault identification applications. Additionally, the fault recognition results based on different location information often vary, and conflicting diagnosis results directly cause the accurate identification of the fault type to fail. Therefore, in this paper, a novel multi-information decision fusion approach is proposed based on the improved random forest (RF) and Dempster-Shafer evidence theory. In the proposed method, the diagnostic distribution of all classification regression trees (CART) in the RF is considered to solve the conflicts among the multi-information diagnosis results. Experimental results show that the proposed method eases the contradiction of multi-position diagnostic results and improves the accuracy of fault identification. Furthermore, compared to the common classifiers and probability generation methods, the effectiveness and superiority of the proposed method are verified.

Antarctic sea ice types from active and passive microwave remote sensing

Polar sea ice is one of the Earth’s climate components that has been significantly affected by the recent trend of global warming. While the sea ice area in the Arctic has been decreasing at a rate of about 4 % per decade, the multi-year ice (MYI), also called perennial ice, is decreasing at a faster rate of 10 %–15 % per decade. On the other hand, the sea ice area in the Antarctic region was slowly increasing at a rate of about 1.5 % per decade until 2014 and since then it has fluctuated without a clear trend. However, no data about ice type areas are available from that region, particularly of MYI. Due to differences in physical and crystalline structural properties of sea ice and snow between the two polar regions, it has become difficult to identify ice types in the Antarctic. Until recently, no method has existed to monitor the distribution and temporal development of Antarctic ice types, particularly MYI throughout the freezing season and on decadal time scales. In this study, we have adapted a method for retrieving Arctic sea ice types and partial concentrations using microwave satellite observations to fit the Antarctic sea ice conditions. The first circumpolar, long-term time series of Antarctic sea ice types; MYI, first-year ice and young ice is being established, so far covering years 2013–2019. Qualitative comparison with synthetic aperture radar data, with charts of the development stage of the sea ice, and with Antarctic polynya distribution data show that the retrieved ice types, in particular the MYI, are reasonable. Although there are still some shortcomings, the new retrieval for the first time allows insight into the evolution and dynamics of Antarctic sea ice types. The current time series can in principle be extended backwards to start in the year 2002 and can be continued with current and future sensors.

Measuring the Impact of Operational Stress: The Relevance of Assessing Stress-related Health Across the Deployment Cycle

ABSTRACT Introduction Mental health issues from intense or prolonged stress are a common concern in regard to military deployment. Deployments can objectively vary in stress exposure, but it is the individuals’ perception of that stress that affects sustainability, mental health, and combat fitness, which calls for the need of a protocol to evaluate and maintain a current estimation of stress impact. So, how can we assess the impact of stressors during different phases of deployment? Materials and Methods We used three psychological self-rating forms, the PSS14—Perceived Stress Scale, SMBM—Shirom Melamed Burnout Measure, and KSQ—Karolinska Sleep Questionnaire, to measure the impact of stress before (T1), during (T2), and at homecoming (T3). We also wanted to see if T1 or T2 results could predict T3 results to be able to better prepare the homecoming program.The forms were handed out to Swedish soldiers deployed in Mali in 2017. The forms were collected as a way to assess the status of the mental health load at three timepoints based on the personnel function as a way to assess the current “psychological fitness level”. Results The results show that stress measured using PSS14 was high at homecoming. The same result was observed for SMBM. No measures from T1 or T2 could however predict the T3 results. Conclusions Taken together, we found that screening of all contingent staff is relatively easy and provides personnel with relevant data on mental health and stress at the current time. We also found that test results correlated between T1 and T2 but not with T3. This indicates that there might be different stressors that affect staff at different timepoints.

Vehicle diagnostic technology

In the article, using the example of a mechatronic control system for the engine and transmission of vehicles (automobiles), the features of the technology of their diagnosis are shown. In an electronic transmission control system, the object of regulation is mainly an automatic transmission. Also, the laws of control (programs) of gear shifting in an automatic transmission ensure the optimal transfer of engine energy to the wheels of the vehicle (TC), taking into account the required traction and speed properties and fuel economy. At the same time, the programs for achieving optimal traction-speed properties and minimum fuel consumption differ from each other, since the simultaneous achievement of these goals is not always possible. Therefore, depending on the driving conditions and the desire of the driver, using a special switch, you can select the "economy" program to reduce fuel consumption, the "power" program - to improve traction and speed properties, or the "manual" program to switch gears by the driver. In turn, self-diagnostic capabilities include: system identification and electronic control units (ECU) (ECU); recognition, storage and reading of information about static and single malfunctions; reading current real data, including environmental conditions and specifications; modeling of system functions; programming of system parameters. The individual programs for the test block are stored in the plug-in modules, while the correction and data transfer in the system is carried out via the data interface. Note also that the diagnostic process begins with the initialization of the systems - their detection in the electrical equipment of the vehicle. Upon successful initialization, it is possible to: read the error memory; erase the error memory; view the data of the next detected system or exit to the main menu; change the readings of the selected category; correct the current time; correct the current date and perform a number of additional functions.

Debunking the essential effect of temperature and voltage on the current curve and the nanotube morphology

In this paper, the change of nanotubes and the current–time curve under different temperature are explained clearly. Also, ginseng shaped nanotubes were found in experiments, which proved the irrationality of field assisted dissolution theory.

Debunking six stages of current-time curve and the bilayer nanotubes obtained by one-step anodization of Zr

The application and growth mechanism of anodic TiO2 nanotubes have been a hot topic in recent ten years, but the formation mechanism of anodic ZrO2 nanotubes is rarely studied. In...

Comparative study on performance of different artificial neural network methods for prediction of the Covid19

Purpose This study aims to compare many artificial neural network (ANN) methods to find out which method is better for the prediction of Covid19 number of cases in N steps ahead of the current time. Therefore, the authors can be more ready for similar issues in the future. Design/methodology/approach The authors are going to use many ANNs in this study including, five different long short-term memory (LSTM) methods, polynomial regression (from degree 2 to 5) and online dynamic unsupervised feedforward neural network (ODUFFNN). The authors are going to use these networks over a data set of Covid19 number of cases gathered by World Health Organization. After 1,000 epochs for each network, the authors are going to calculate the accuracy of each network, to be able to compare these networks by their performance and choose the best method for the prediction of Covid19. Findings The authors concluded that for most of the cases LSTM could predict Covid19 cases with an accuracy of more than 85% after LSTM networks ODUFFNN had medium accuracy of 45% but this network is highly flexible and fast computing. The authors concluded that polynomial regression cant is a good method for the specific purpose. Originality/value Considering the fact that Covid19 is a new global issue, less studies have been conducted with a comparative approach toward the prediction of Covid19 using ANN methods to introduce the best model of the prediction of this virus.

Improper Disposal of Non-biodegradable Chewing Gum is One of the Biggest Threats to Our Ecology: A Review

One of the oldest habits of human beings is chewing gum. It has continued from ancient civilizations to the current time. Gum chewing provides a relaxing experience that individuals enjoy for a long time. The non-food item, chewing gum, has a long history. The gradual progression of its development has provided us with a greater flavour as well as extra medicinal properties. Chewing gum is known for its stress-relieving qualities as well as its ability to keep our mouths fresh. Soon, ‘chewing gum’ will be included as part of the drug delivery mechanism. Unfortunately, it has had some negative consequences. Modern chewing gum is made of non-biodegradable hydrophobic polymers together with artificial sweeteners and flavours. So, chewing this sort of synthetic material over a long time could produce some adverse effects. After chewing, most individuals throw the waste part of chewing gum everywhere, resulting in environmental trash known as 'gum pollution. Each year, chewing gum generates more than 105 tonnes of "plastic" garbage. Thus, the discarded non-biodegradable residue of the gum produces plastic pollution. Every year, enormous sums of money are spent to clean up the abandoned gum from the streets. Again, it has a high potential to trap bacteria inside. Therefore, this widespread habit causes an additional nuisance in this pandemic situation. As a result, the waste part of the gum has multiple dimensions to pollute our environment. Gum disposal has become a major problem all across the world. Gum litter can only be reduced by properly disposing of gum. As a result, it's time to reconsider the role of chewing gum in terms of human health and the environment. This article emphasizes the importance of proper waste (gum) disposal and calls for increased awareness to safeguard our environment from "gum pollution."