With the rapid increase in demand for fresh products, cold chain logistics has become an important mode of transportation. Logistics enterprises are faced with the problem of cost control and improvement of customer satisfaction. In light of this, we present a bi-objective optimization vehicle routing problem model in cold chain logistics, which aims to reduce the total costs and improve customer satisfaction.

<p>This dataset consists of 200 occurrences extracted from three fields of a web analytics tool over time, along with labels indicating the service availability status at that moment. The data pertains to customer accesses of a real financial institution. The columns are named with a type and a unique identifier number. The column TX_ACAO_EVT represents an action performed by the customer, such as a click, system message, or background application action. The column TX_CTGR_EVT represents the category of the action, such as an error message or a specific type of action.

Adverse climatic events like heat stress, floods, unseasonal rainfall, and droughts frequently hinder crop productivity. Long-term crop yield data plays a crucial role in food security planning. This study presents historical wheat yield data at the satellite pixel level from 2001 to 2019 in Uttar Pradesh, India. We use various satellite indicators to develop wheat yield models, including the normalized difference vegetation index and gridded weather data, such as precipitation, temperature, and evapotranspiration.

During our research in generating or optimizing molecules to be drug candidates by extending deep reinforcement learning and graph neural networks algorithms, we used GEOM data [1], and we had an idea to make a dataset obtained from molecules from GEOM to predit the activity towards COVID and the drug linkeness. We calculated over 200 descriptors for the molecules using RDKit [2]. We hope you enjoy using it.

References:

Nowadays, how to non-destructively obtain the bending strength of the bronze-based matrix fabricated by low-temperature hot pressing sintering (HPS) is still difficult. The main contribution of our research is a proposed visual quantization model based on microstructure features of metallographic microscopy images and machine learning to predict the bending strength of bronze-based material. Exploring the interrelationship between microstructure features and mechanical properties will guide the modulation of grinding wheel composition and HPS parameters.

The "Multi-modal Sentiment Analysis Dataset for Urdu Language Opinion Videos" is a valuable resource aimed at advancing research in sentiment analysis, natural language processing, and multimedia content understanding. This dataset is specifically curated to cater to the unique context of Urdu language opinion videos, a dynamic and influential content category in the digital landscape.

Dataset Description:

The process of dataset generation comprises three integral components: "Account Profiles," responsible for creating detailed account representations; "Transaction Generation," which simulates a diverse range of financial transactions; and the "Generation of Fraud Scenarios," which introduces predefined templates for identifying potential fraudulent transactions based on various criteria. Together, these components collaboratively construct a dynamic and realistic dataset, mirroring real-world financial systems.

Since meteorological satellites can observe the Earth’s atmosphere from a spatial perspective at a large scale, in this paper, a dust storm database is constructed using multi-channel and dust label data from the Fengyun-4A (FY-4A) geosynchronous orbiting satellite, namely, the Large-Scale Dust Storm database based on Satellite Images and Meteorological Reanalysis data (LSDSSIMR), with a temporal resolution of 15 minutes and a spatial resolution of 4 km from March to May of each year during 2020–2022.

The following videoes use the video streaming completion model, which combines static and dynamic information for real-time processing. The proposed model is solved using the alternating direction method of multipliers (ADMM), and using MATLAB for solution recovery.

Gray video suzie: This video is restored in the case of the missing rates set to 70%, 80%, 90%, respectively

Color Video Hall: This video is restored in the case of the missing rates set to 70%, 80%, and 90%, respectively

It is observed from the pattern of Electricity Demand in the months of November 2022 and March 2023 that switchover from Summer Time to Winter Time and vice versa causes a shift in demand pattern corresponding to the time shifts. The hourly electricity demands of US48 states have been shown in separate excel files for November 2022 and March 2023. In sheet 2 of  both the excel files, a graphical plot of the average hourly demands in the week before the switchover has been compared with a graphical plot of the average hourly demands in the week after the switchover.

Cemented total hip arthroplasty (THA) demonstrates superior survival rates compared to uncemented procedures. Nevertheless, most younger patients opt for uncemented THA, as removing well-fixed bone cement in the femur during revisions is complex, particularly the distal cement plug. This removal procedure often increases the risk of femoral fracture or perforation, haemorrhage and weakening bone due to poor drill control and positioning. Aim of this study was to design a novel drill guide to improve drill positioning.

Learning SAR Dataset for Wet Snow Detection - Full Analysis Version. 

As the harmful effects of climate change on human society increase, the analysis of abnormal weather is becoming an important issue. Therefore, this work provides the Korean weather dataset, including the anomaly score measurements by using seven different methods. In this dataset, seven types of weather data for each day in 64 Korean cities from 2010 to 2020 are provided by Weather Radar Center in Korea Meteorological Administration.

This dataset consists of 3500 images of beach litter and 3500 corresponding pixel-wise labelled images. Although performing such pixel-by-pixel semantic masking is expensive, it allows us to build machine-learning models that can perform more sophisticated automated visual processing. We believe this dataset may be of significance to the scientific communities concerned with marine pollution and computer vision, as this dataset can be used for benchmarking in the tasks involving the evaluation of marine pollution with various machine learning models.

In the wake of marine oil exploration and transportation, the accidents of oil spills have occurred
frequently around the world, which leads to the severe pollution of the marine environment and the
huge damage of coastal species [1–6]. On April 20, 2010, the explosion of Deepwater Horizon oil
drilling platform led to a severe leakage. Million barrels of oil polluted the Gulf of Mexico with the
area of about 10,000 square kilometers [7, 8]. Due to this accident, the marine ecosystems, such as fish

ERA5 derived time series of European country-aggregate electricity demand, wind power generation and solar power generation: hourly data from 1979-2019.  The ERA5 reanalysis data (1979-2019) has been used to calculate the hourly country aggregated wind and solar power generation for 28 European countries based on a distribution of wind and solar farms which is considered to be representative of the 2017 situation. In addition a corresponding daily time series of nationally aggregated electricity demand is provided.

This work presents a large-scale three-fold annotated, low-cost microscopy image dataset of potato tubers for plant cell analysis in deep learning (DL) framework which has huge potential in the advancement of plant cell biology research. Indeed, low-cost microscopes coupled with new-generation smartphones could open new aspects in DL-based microscopy image analysis, which offers several benefits including portability, ease of use, and maintenance.

The rapid evolution of communication networks and the ever-increasing demand for efficient data transfer have led to the development of cognitive networking, which aims to enhance network performance through intelligent and adaptive protocols. To facilitate research and development in this domain, we present a comprehensive dataset detailing the parameters of a Network Protocol Stack which can be used to develop a Cognitive Network Protocol Stack designed for efficient networking.

The Transport-level pAcket RouTing ANalysis Tool for Cloud-native Applications (TARTAN) Dataset contains TARTAN/HiPerConTracer Traceroute runs between an endpoint in Oslo, Norway and the public Comprehensive TeX Archive Network (CTAN, https://www.ctan.org) and Comprehensive R Archive Network (CRAN, https://cran.r-project.org) mirror we

The paper presented by Samar Mahmoud; and Yasmine Arafaf et, al a novel dataset called the "Abnormal High-Density Crowd Dataset," addresses the challenge of anomaly detection in crowded environments, particularly focusing on high-density crowds—an area that has received limited exploration in computer vision and crowd behaviour understanding. The dataset is introduced with considerations for privacy, annotation accuracy, and preprocessing.

Deficient domestic wastewater management, industrial waste, and floating debris are some leading factors that contribute to inland water pollution. The surplus of minerals and nutrients in overly contaminated zones can lead to the invasion of different invasive weeds. Lemnaceae, commonly known as duckweed, is a family of floating plants that has no leaves or stems and forms dense colonies with a fast growth rate. If not controlled, duckweed establishes a green layer on the surface and depletes fish and other organisms of oxygen and sunlight.

For modern electric powertrain applications (wind, electric vehicles/ships/aircrafts,…), the vibration analysis of the electric motor is one of