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Directed Acyclic Graph (DAG) blockchain applications represent an evolution from traditional linear blockchain solutions, addressing their inherent scalability issues. The analysis of the transactions fee market in these applications proves particularly insightful, given their demonstrated ability to manage very high transaction throughputs, and the crucial role transaction fees play in blockchain sustainability, efficiency and security, as a mechanism to incentivize validators and regulate network congestion.

This dataset includes conjunctival and retinal images collected from both diabetic and healthy individuals to support research on diabetes-related vascular changes. For each subject, eight conjunctival images (four per eye: looking left, right, up, and down) are provided. Subjects with diabetes additionally have corresponding left and right retinal fundus images. Metadata for diabetic participants includes classification into subgroups: diabetes only, diabetes with retinopathy, or diabetes with related complications such as hypertension.

We also provide a new data set CA on chassis assembly for further research in this field. CA is mainly used to study possible logical anomalies in assembly chassis. It has a total of 364 samples for the training set and 191 samples for the test set. The training set contains only normal samples, and the test set contains 93 normal samples and 91 abnormal samples. The main causes of logical anomalies contains several types of logical anomalies, such as quantity anomalies, location anomalies, size anomalies, matching anomalies and mixed anomalies, which poses additional challenges.

We also provide a new data set CA on chassis assembly for further research in this field. CA is mainly used to study possible logical anomalies in assembly chassis. It has a total of 364 samples for the training set and 191 samples for the test set. The training set contains only normal samples, and the test set contains 93 normal samples and 91 abnormal samples. The main causes of logical anomalies contains several types of logical anomalies, such as quantity anomalies, location anomalies, size anomalies, matching anomalies and mixed anomalies, which poses additional challenges.

 We also provide a new data set CA on chassis assembly for further research in this field. CA is mainly used to study possible logical anomalies in assembly chassis. It has a total of 364 samples for the training set and 191 samples for the test set. The training set contains only normal samples, and the test set contains 93 normal samples and 91 abnormal samples. The main causes of logical anomalies contains several types of logical anomalies, such as quantity anomalies, location anomalies, size anomalies, matching anomalies and mixed anomalies, which poses additional challenges.

 We also provide a new data set CA on chassis assembly for further research in this field. CA is mainly used to study possible logical anomalies in assembly chassis. It has a total of 364 samples for the training set and 191 samples for the test set. The training set contains only normal samples, and the test set contains 93 normal samples and 91 abnormal samples. The main causes of logical anomalies contains several types of logical anomalies, such as quantity anomalies, location anomalies, size anomalies, matching anomalies and mixed anomalies, which poses additional challenges.

 We also provide a new data set CA on chassis assembly for further research in this field. CA is mainly used to study possible logical anomalies in assembly chassis. It has a total of 364 samples for the training set and 191 samples for the test set. The training set contains only normal samples, and the test set contains 93 normal samples and 91 abnormal samples. The main causes of logical anomalies contains several types of logical anomalies, such as quantity anomalies, location anomalies, size anomalies, matching anomalies and mixed anomalies, which poses additional challenges.

These datasets of synthetic workflows (task graphs) were generated to evaluate the performance and scalability of a multi-objective and multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) based on the edge-hub-cloud paradigm.

<p><span style="font-family: 'Times New Roman'; font-size: medium;">This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99 The Fifth International Conference on Knowledge Discovery and Data Mining. The competition task was to build a network intrusion detector, a predictive model capable of distinguishing between ``bad'' connections, called intrusions or attacks, and ``good'' normal connections.

The use of technology in cricket has seen a significant increase in recent years, leading to overlapping computer vision-based research efforts. This study aims to extract front pitch view shots in cricket broadcasts by utilizing deep learning. The front pitch view (FPV) shots include ball delivery by the bowler and the stroke played by the batter. FPV shots are valuable for highlight generation, automatic commentary generation and bowling and batting techniques analysis. We classify each broadcast video frame as FPV and non-FPV using deep-learning models.

Short-range association fibers located in the superficial white matter play an important role in mediating higher-order cognitive function in humans. Detailed morphological characterization of short-range association fibers at the microscopic level promises to yield important insights into the axonal features driving cortico-cortical connectivity in the human brain yet has been difficult to achieve to date due to the challenges of imaging at nanometer-scale resolution over large tissue volumes.

MSD is a infrared and visible image dataset collected in real-world road environments. It comprises aligned infrared and visible images captured in multiple scenarios, such as dense fog, dusk, nighttime, intersection, and tunnel.

Since 2018, our team has used coherent Doppler lidar to conduct extensive aircraft wake detection experiments at Chengdu Shuangliu International Airport and Mianyang Nanjiao Airport, and collected wake data of mainstream commercial aircraft including A320/A330/A350/B737/B747/B757/B767/B777/B787 under different meteorological conditions. These data include radial wind speed, spectral width Pitch, RadialWind (m/s), SpectralWidth, SpectralIntensity, etc.

This dataset encompasses crucial power - related information. It includes network parameters, which are fundamental for grid analysis. Thermal generator parameters detail the characteristics of thermal power units. Load power data reflects consumption demands, while the wind power output upper limit defines the potential of wind energy. Dispatch results summarize the optimized power distribution, all valuable for power system research and operation.

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another.

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another.

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another.

This dataset presents a curated collection of 9,000 English verbs annotated with normalized fuzzy values across four cognitive-behavioral quadrants of the BEET-M (Behavior Engagement Emotion Trigger Modes) model: Value & Credibility (NW), Relationship & Human Impact (NE), Process & Information (SE), and Time Urgency (SW). Each verb is assigned fuzzy scores summing to 1.0, along with a corresponding binary vector marking its dominant influence quadrant.

We collect metadata including published year and keywords for 84,725 papers published in 42 statistical journals from 1992 to 2021 from the Web of Science (www.webofscience.com). After combining different expressions of the same keyword and filtering out keywords with low frequency, we finally obtain 5,037 keywords. Multiple keywords co-exist within a paper, and this co-occurrence relationship can be utilized to construct the keyword co-occurrence network.

This dataset captures various aspects of parenting styles, child behavior, and family demographics to explore the relationship between caregivers’ approaches and children’s emotional and social development. It includes 22 variables covering parental age, education, number of children, emotional and disciplinary parenting behaviors, and the child's emotional responses and prosocial behaviors. Additionally, demographic factors such as the primary caretaker's gender, urban or rural environment, and generational identity (Gen X, Millennial, Gen Z) are included.

Sensitivity (Se) is the proportion of correctly identified actual abnormal intelligence C&A by the models. Specificity (Sp) is the proportion of correctly identified normal intelligence C&A by the models. Positive predictive value (PV+) is the proportion of correctly identified C&A predicted to have abnormal intelligence. Negative predictive value (PV–) is the proportion of correctly identified C&A predicted to have normal intelligence. Odds ratio (OR) represents the ability of the models to distinguish between C&A with normal and abnormal intelligence.

Real-time tracking of electricians in distribution rooms is essential for ensuring operational safety. Traditional GPS-based methods, however, are ineffective in such environments due to complex non-line-of-sight (NLOS) conditions caused by dense cabinets and thick walls that obstruct satellite signals. Existing solutions, such as video-based systems, are prone to inaccuracies due to NLOS effects, while wearable devices often prove inconvenient for workers.

Human Body is an extremely intricate and modern structure and involves a huge number of capacities. All these muddled capacities have been comprehended by man him, part-by-part their exploration and tests. As science and innovation advanced, pharmaceutical turned into a necessary part of the exploration. Continuously, restorative science turned into an altogether new branch of science. Starting today, the Health Sector involves Medical establishments i.e. Healing facilities, HOSPITALs and so forth innovative work foundations and medicinal universities.