Subtitle

A fully synthetic dataset simulating real-world medical billing scenarios, including claim status, denials, team allocation, and AR follow-up logic.

Description

This dataset represents a synthetic Account Receivable (AR) data model for medical billing, created using realistic healthcare revenue cycle management (RCM) workflows. It is designed for data analysis, machine learning modeling, automation testing, and process simulation in the healthcare billing domain.

The dataset includes realistic business logic, mimicking the actual process of claim submission, denial management, follow-ups, and payment tracking. This is especially useful for: ✔ Medical billing training ✔ Predictive modeling (claim outcomes, denial prediction, payment forecasting) ✔ RCM process automation and AI research ✔ Data visualization and dashboard creation

Key Features of This Dataset

✅ Patient & Claim Information:

• Visit ID: Unique alphanumeric ID in the format XXXXXZXXXXXX
• Patient Name: Randomly generated names
• Date of Service (DOS): In MM/DD/YYYY format
• Aging Days: Calculated as Today - DOS
• Aging Bucket: Categorized as 0-30, 31-60, 61-90, 91-120, 120+

✅ Claim Status & Denial Logic:

• Status Column: Indicates whether response received or not
• If No Response → Simulates a follow-up call → Claim may result in denial
• Status Code: Reflects actual denial reason (e.g., Dx inconsistent with CPT)
• Action Code: Required follow-up action (e.g., Need Coding Assistance)

• Team Allocation: Based on denial type

  • Coding-related denial → Coding Team
  • Submission/Claim-related denial → Billing Team
  • Payment-related denial → Payment Team

✅ Realistic Denial Scenarios Covered:

• Coding Errors (Dx inconsistent with CPT, Missing Modifier)
• Claim Issues (Duplicate Claim, Invalid Subscriber ID)
• Payment Issues (Allowed Amount Paid, No Coverage)

✅ Other Important Columns:

• Claim Amount, Paid Amount, Balance
• Insurance Details (Primary, Secondary, Tertiary)
• Notes explaining denial or next steps

Columns in the Dataset

Data Generation Rules Applied

• Date format: MM/DD/YYYY
• Aging Days: Calculated dynamically based on DOS
• Visit ID: Always follows the XXXXXZXXXXXX format

• Denial Workflow:

  • If claim denied → Status Code & Action Code updated
  • Team allocation based on denial type

• Payments: Realistic logic where payment may be partial, full, or none

• Insurance Flow: Balance moves from primary → secondary → tertiary → patient responsibility

Use Cases

• Predictive modeling for claim outcome
• Identifying high-risk claims for early intervention
• Denial pattern analysis for improving first-pass resolution rate
• Building RCM dashboards and AR management tools

License

CC BY 4.0 – Free to use, modify, and share with attribution.

Social rejection can lead to a variety of emotions. Two studies show that specific emotional reactions to social rejection can be understood by relying on the fundamental distinction between competence and warmth. Rejection that is perceived to be due to incompetence leads to anger, whereas rejection that is perceived to be due to lack of warmth leads to sadness. Study 1 measures perceptions of competence and warmth judgments. Study 2 manipulates those perceptions. In both studies, rejection that was perceived to be the result of incompetence led primarily to anger, while rejection that was perceived to be the result of lack of warmth led primarily to sadness. These results show that the effects of rejection can be better understood if we take into account how rejection is perceived. Highlights ► Adaptive emotional reactions to social rejection ► Compare effects of rejection due to lack of competence and due to lack of warmth ► Rejection due to lack of competence primarily leads to anger ► Rejection due to lack of warmth primarily leads to sadness

Contains detailed information on 4.4 million Office actions mailed from 2008 through June 2017 for 2.2 million publicly viewable patent applications. The data are sourced from the text of Non-Final Rejection and Final Rejection Office Actions issued by patent examiners to applicants during the patent examination process. The data files include information on grounds for rejection raised, the claims in question, and pertinent prior art.

Context

Textile weaving dataset for machine learning to predict rejection and production of a weaving factory

Abstract

Weaving is one of the most popular fabric manufacturing techniques. The weaving process consists of 3 major stages: warping, sizing, and weaving. The weaving factory henceforth involves a lot of data. But unfortunately, there is no attempt to utilize machine learning or data science in weaving production. Although a variety of scopes are there to implement statistical analysis, data science, and machine learning. The dataset was prepared by using the daily production report for 9 months. The final dataset contains 121,148 data with 18 parameters. Whereas the raw data contains the same number of entries with 22 columns. The raw data needs substantial work to combine the daily production report, treat the missing values, rename columns, and feature engineering to derive EPI, PPI, warp, weft count values, etc. The complete dataset is stored at https://data.mendeley.com/datasets/nxb4shgs9h/1. It is further processed to get the rejection dataset which is stored at https://data.mendeley.com/datasets/6mwgj7tms3/2. The future implementation of the dataset is to predict the weaving waste, investigate the statistical relations among various parameters, production prediction, etc.

https://i0.wp.com/textilelearner.net/wp-content/uploads/2016/04/weaving-mill.jpg" alt="">

Info

• Repository name: Mendeley Data
• Direct URL: https://data.mendeley.com/datasets/nxb4shgs9h/1
• Data identification number (DOI):10.17632/nxb4shgs9h.1
• Dataset name: Weaving rejection dataset
• Repository name: Mendeley Data
• Direct URL to rejection data: https://data.mendeley.com/datasets/6mwgj7tms3/2
• Data identification number (DOI):10.17632/6mwgj7tms3.2
• Dataset name: Reference dataset and preprocessing code
• Repository name : Zenodo
• Direct URL: https://zenodo.org/record/7498062
• Data Identification Number (DOI):10.5281/zenodo.7498062

Download

kaggle API Command

!kaggle datasets download -d azminetoushikwasi/textile-weaving-dataset-to-predict-rejection

Disclaimer

The data collected is all publicly available, and it's intended for educational purposes only.

A dataset of manually annotated pull request rejection reasons and their sentiment. Target project: Scapy (hosted on GitHub).

Parasite-host coevolution can lead to novel behavioural adaptations in hosts to resist parasitism. In avian obligate brood parasite and host systems, many hosts species have evolved diverse cognitive and behavioural traits to recognize and reject parasitic eggs. Our understanding of the evolution and ecology of these defences hinges on our understanding of the mechanisms that regulate them. We hypothesized that corticosterone, a hormone linked to the stress-response, vigilance, and the suppression of parental behaviour, stimulates the rejection of foreign eggs by brood parasite hosts. We experimentally reduced circulating glucocorticoid levels with mitotane injections in American robins Turdus migratorius and found that the mitotane-treated birds rejected foreign eggs at a lower frequency compared to the sham-treated subjects. This is the first study to causally identify a potential mechanism of a widespread defence behaviour, and it is consistent with egg rejection being mediated by stress physiology.

Methods Methods

Field site and species

We studied wild American robins Turdus migratorius, an occasional host to obligate brood-parasitic brown-headed cowbirds Molothrus ater, in Urbana, IL, USA, during the summer of 2019 (see details of the study area in [15,16]). For this study, we focused only on female robins, because it is the sex responsible for egg rejection in this species [17].

Treatment validation

Hormonal implants that result in supraphysiological hormone levels may result in ecologically irrelevant phenotypes [18]. We therefore opted to suppress glucocorticoid levels using mitotane, a glucocorticoid synthesis inhibitor, which has been shown to consistently reduce both baseline and stress-induced [19–22] corticosterone levels in birds. We first tested if the directional effect of mitotane on corticosterone in American robins parallels that already seen in other songbird species. We caught wild egg-laying or incubating robin (n=8) females and took a baseline blood sample from the brachial vein within 3 min of capture (mean start time = 130 sec; mean end time = 167 sec). Blood was stored on ice and centrifuged at 8000 RPM within 2 hrs to separate plasma. Plasma samples were kept on ice until frozen at –80 °C 4 hrs later. We then injected the pectoral muscle of 4 females with 34 mg mitotane (Sigma-Aldrich, Cat. No. 25925), dissolved in 400 µl sterile peanut oil (Acros Organics, Cat. No. 416855000, dosage 400 mg/kg), following guidelines for a high mitotane dosage in songbirds [19,20]. Four sham females were injected with peanut oil vehicle (400 µl).

In our population, robins become wary of mist nets and humans after capture. We therefore temporarily moved these birds into captivity, housing them singly overnight in 40×40×34 cm cages, and providing ad libitum water, earthworms, bananas, and crushed dog food. The following day, we again collected their blood within 3 min of capture (mean start time = 94 sec; mean end time = 120 sec).

Repeated administrations of mitotane can have adverse effects, such as lethargy, in the long term [23,24], but this effect has not been observed in birds using single injections [19-22], especially regarding self-maintenance behaviours within a 24 hr period [22]. Nonetheless, to assess the possibility of unintended side effects, we observed if mitotane treatment affected feeding behaviour in captivity by measuring the birds’ mass (nearest g; see Section (d) below for field-based assessment of possible lethargy).

To test the effect of mitotane on glucocorticoid levels, we analysed plasma corticosterone using an enzyme immunoassay (Cayman Chemical, Cat. No. 501320). Validation details and methods for this assay using robin plasma are published elsewhere [25]. All samples were assayed in duplicate on the same plate (intraplate coefficient of variation = 6.7%).

Hormone manipulation of wild birds

We captured incubating robin females (n=65) at their nests using a mist net between 6-10 am, after they had completed their clutches (median 2 days, range 0-5 days after clutch completion [26]). We first took a 450 µl blood sample as part of a different study. We then treated each bird randomly either with mitotane (n=38) or sham (n=27), as described above. We also took standard morphometric measurements, including age [27], mass (nearest g), and tarsus (nearest 0.1 mm). We fitted birds with a USGS band and 3 colour bands (Avinet), following which the birds were released. An unanticipatedly large number of birds abandoned their nests after the treatments (see Results and the Ethics statement).

Experimental parasitism

American robins reject the majority of natural cowbird [28] or cowbird-like model eggs [3], but they show variable responses to egg colours near their rejection threshold [29]. Importantly, robins also show intermediate and individually repeatable rejection rates to deep-blue cowbird-sized model eggs (figure 2 inset; for details see [30]). We therefore used deep-blue, 3D-printed eggs to investigate the effect of the injection treatment on egg rejection.

A day after the mitotane or sham injections, we added one deep-blue model egg to the nest. We did not remove any robin eggs, because Turdus thrushes show the same response to model eggs regardless of whether their own eggs are removed [31]. We returned to the nest one day later to record whether the model egg was accepted (present) or rejected (missing). On each visit, we verified the identity of the female using band colours. If the female was absent during these visits, we returned to the nest later to confirm female identity or nest abandonment (cold eggs). One female died and two nests were depredated and these were excluded from further analyses.

To assess if mitotane caused general loss of motor activity in the wild, we quantified vigilance behaviour and nest abandonment. We recorded vigilance before and after the injection by approaching the nest at a steady pace, typically from 20 m or greater distance, and recording the distance at which birds took flight (flight initiation distance (FID), measured by counting paces [32,33]). We also noted whether mitotane- or sham-treated birds differed in the probability of nest abandonment.

Statistical analyses

Hormone and mass data in the validation study, and FID data from the field experiment, were not normally distributed, therefore we used Mann-Whitney U-tests to assess the differences in these variables between treatments.

Life history, seasonal, and morphological variables did not differ between the treatment groups (all p>0.05, supplementary table 1). Because treatments were randomized across individuals and time, we assessed the effect of experimental injections on categorical behaviours (yes/no nest abandonment and egg rejection) using Fisher’s exact tests (a=0.05). Clutch size, previously associated with individual variation in egg rejection [25], did not explain significant variation in egg rejection in these data (supplementary table 2), and is not included in analyses.

We tested the existence of a rejection mind-set in online dating across three studies. In Study 1, we presented people with pictures of hypothetical partners, to test if and when people’s general choice behavior would change. In Study 2, we presented people with pictures of partners that were actually available and tested the gradual development of their choice behaviors as well as their success rate in terms of mutual interest (i.e., matches). In Study 3, we explored potential underlying psychological mechanisms. Specifically, and in line with choice overload literature, we explored whether the rejection mind-set may be due to people experiencing lower choice satisfaction and less success over the course of online dating. As an additional goal, we explored the potential moderating role of gender.

This repository contains the main data of the paper "Optimal Rejection-Free Path Sampling," and the source code for generating/appending the independent RFPS-AIMMD and AIMMD runs.

Due to size constraints, the data has been split into separate repositories. The following repositories contain the trajectory files generated by the runs:

all the WQ runs: 10.5281/zenodo.14830317chignolin, fps0: 10.5281/zenodo.14826023chignolin, fps1: 10.5281/zenodo.14830200chignolin, fps2: 10.5281/zenodo.14830224chignolin, tps0: 10.5281/zenodo.14830251chignolin, tps1: 10.5281/zenodo.14830270chignolin, tps2: 10.5281/zenodo.14830280

The trajectory files are not required for running the main analysis, as all necessary information for machine learning and path reweighting is contained in the "PatEnsemble" object files stored in this repository. However, these trajectories are essential for projecting the path ensemble estimate onto an arbitrary set of collective variables.

To reconstruct the full dataset, please merge all the data folders you find in the supplemental repositories.

Data structure and content

analysis (code for analyzing the data and generating the figures of the| paper)|- figures.ipynb (Jupyter notebook for the analysis)|- figures (the figures created by the Jupyter notebook) |- ...

data (all the AIMMD and reference runs, plus general info about the| simulated systems)|- chignolin |- *.py: (code for generating/appending AIMMD runs on a Workstation or | HPC cluster via Slurm; see the "src" folder below) |- run.gro (full system positions in the native conformation) |- mol.pdb (only the peptide positions in the native conformation) |- topol.top (the system's topology for the GROMACS MD engine) |- charmmm22star.ff (force field parameter files) |- run.mdp (GROMACS MD parameters when appending a simulation) |- randomvelocities.mdp (GROMACS MD parameters when initializing a | simulation with random velocities) |- signature.npy, r0.npy (parameters for defining the fraction of native | contacts involved in the folded/unfolded states | definition; used by params.py function | "states_function") |- dmax.npy, dmin.npy (parameters for defining the feature representation | of the AIMMD NN model; used by params.py | function "descriptors_function") |- equilibrium (reference long equilibrium trajectory files; only the | peptide positions are saved!) |- run0.xtc, ..., run3.xtc |- validation |- validation.xtc (the validation SPs all together in an XTC file) |- validation.npy (for each SP, collects the cumulative shooting results after 10 two-way shooting simulations) |- fps0 (the first AIMMD-RFPS independent run) |- equilibriumA (the free simulations around A, already processed | in PathEnsemble files) |- traj000001.h5 |- traj000001.tpr (for running the simulation; in that case, please | retrieve all the trajectory files in the right | supplemental repository first) |- traj000001.cpt (for appending the simulation; in that case, please | retrieve all the trajectory files in the right | supplemental repository first) |- traj000002.h5 (in case of re-initialization) |- ... |- equilibriumB (the free simulations around B, ...) |- ... |- shots0 |- chain.h5 (the path sampling chain) |- pool.h5 (the selection pool, containing the frames from which | shooting points are currently selected from) |- params.py (file containing the states and descriptors definitions, | the NN fit function, and the AIMMD runs hyperparameters; | if can be modified to allow for RFPS-AIMMD or the original | algorithm AIMMD runs) |- initial.trr (the initial transition for path sampling) |- manager.log (reports info about the run) |- network.h5 (NN weights of the model at different path | sampling steps) |- fps1, fps2 (the other RFPS-AIMMD runs) |- tps0 (the first AIMMD-TPS, or "standard" AIMMD, run) |- ... |- shots0 |- ... |- chain_weights.npy (weights of the trials in TPS; only the trials | with non zero weight had been accepted) |- tps1, tps2 (the other AIMMD runs, with TPS for the shooting simulations)|- wq (Wolfe-Quapp 2D system) |- *.py: (code for generating/appending AIMMD runs on a Workstation or | HPC cluster via Slurm) |- run.gro (dummy gro file produced for compatibility reasons) |- integrator.py (custom MD engine) |- equilibrium (reference long simulation) |- transition000001.xtc (extracted from reference long simulation) |- transition000002.xtc |- ... |- transitions.h5 (PathEnsemble file with all the transitions) |- reference |- grid_X.npy, grid_Y.npy (X, Y grid for 2D plots) |- grid_V.npy (PES projected on the grid) |- grid_committor_relaxation.npy (true committor on the grid solved | with the relaxation method on the | backward Kolmogorov equation; the | code for doing this is in utils.py) |- grid_boltzmann_distribution.npy (Boltzmann distribution on the grid) |- pe.h5 (equilibrium distribution processed as a PathEnsemble file) |- tpe.h5 (TPE distribution processed as a PathEnsemble file) |- ... |- uniform_tps (reference TPS run with uniform SP selection) |- chain.h5 (PathEnsemble file containin all the accepted paths | with their correct weight) |- fps0, ..., fps9 (the independent AIMMD-RFPS runs) |- ... |- tps0, ..., tps9 (the independent AIMMD-TPS, or "standard" AIMMD runs)

src (code for generating/appending AIMMD runs on a Workstation or HPC| cluster via Slurm)|- generate.py (on a Workstation: initializes the processes; on an HPC| cluster: creates the sh file for submitting a job)|- slurm_options.py (to customize and use in case of running on HPC)|- manager.py (controls SP selection; reweights the paths)|- shooter.py (performs path sampling simulations)|- equilibrium.py (performs free simulations)|- pathensemble.py (code of the PathEnsemble class)|- utils.py (auxiliary functions for data production and analysis)

Running/appending AIMMD runs

• To initialize a new RFPS-AIMMD (or AIMMD) run for the systems of this paper:

1. Create a "run directory" folder (same depth as "fps0")

2. Copy "initial.trr" and "params.py" from another AIMMD run folder. It is possible to change "params.py" to customize the run.

3. (On a Workstation) call:

python generate.py

where nsteps is the final number of path sampling steps for the run, n the number of independent path sampling chains, nA the number of independent free simulators around A, and nB that of free simulators around B.

1. (On a HPC cluster) call:

python generate.py -s slurm_options.pysbatch ._job.sh

• To append to an existing RFPS-AIMMD or AIMMD run

1. Merge the supplemental repository with the trajectory files into this one.

2. Just call again (on a Workstation)

python generate.py

or (on a HPC cluster)

sbatch ._job.sh

after updating the "nsteps" parameters.

• To run enhanced sampling for a new system: please keep the data structure as close as possible to the original. Different names for the files can generate incompatibilities. We are currently trying to make it easier.

Reproducing the analysis

Run the analysis/figures.ipynb notebook. Some groups of cells have to be run multiple times after changing the parameters in the preamble.

This data collection was designed to compare the differences between adult white males rejected by the Union Army and those accepted into the Union Army. Information includes each person's first and last name, date, place, and term of enlistment, place of birth, military identification number, occupation before enlistment, age at enlistment, and height. Summary of physical conditions, international classification of diseases code, and reason for rejection also are presented.

The behavioural data includes “old” responses to each type of word (studied word, related lure, new item). The ERP data includes electrical brain activity (in microvolts) to True Recognition, False Recognition, Correct Rejection of Related Lures, and Correct Rejection of New Items in each time window (300–500 ms, 500–800 ms, 1000–1500 ms), at the electrodes of interest. (XLS)

The black-headed duck (Heteronetta atricapilla) of South America is the only known avian obligate brood parasite with precocial offspring. In Argentina, it relies on two species of coots as primary hosts, which typically reject 35-65% of duck eggs. We show that environmentally-driven increases in host egg-rejection behavior lead to substantial reductions in the reproductive success of the brood parasite. Episodes of flooding and vegetation loss caused dramatic shifts in host egg rejection behavior, resulting in rejection (85-95%) of almost all duck eggs. Coots respond to fluctuating water levels by building up their nest, raising their own eggs but leaving duck eggs behind. Coots can apparently recognize parasitic duck eggs, but large-scale rejection is triggered only when hosts must actively make a choice. We use a simple population model to illustrate the unique demographic challenges that black-headed ducks face with their parasitic lifestyle, and to explore the potential impact of environmentally-induced escalation of egg rejection. Using best available estimates for key vital rates, we show that obligate parasitism may provide a demographically precarious existence for black-headed ducks, even under benign environmental conditions. Environmentally-mediated increases in egg rejection rates by hosts could impact significantly the viability of this enigmatic species of brood parasitic duck. Our results demonstrate that egg rejection rates are not fixed properties of host populations or individuals but are strongly influenced by social and ecological factors. Shifts in these environmental drivers could have important and unforeseen demographic consequences for brood parasites.

Methods Field observations and experiements in Argentina in 1993, 1994 and 1997.We conducted systematic surveys of the marshes every two to four days on foot or by canoe to find potential host nests and detect brood parasitism. Nests were identified to species by observing birds on or near nests. Parasitism was easily detected because the duck eggs differ dramatically from the eggs of both major hosts. Environmental effects on egg rejection were determined using four methods: natural cases of brood parasitism by the ducks (Mal Abrigo Gull and Tern Marshes and Real Viejo Marsh A, all 1993), experimental parasitism with real duck eggs swapped among host nests as soon as the eggs were laid (Real Viejo Marsh B 1993), experimental parasitism with painted domestic chicken eggs whose length and width (length: 50.5-63.5 mm, width: 41.3-47.6 mm, n = 314 eggs) overlapped the size range of duck eggs (Cari Lauquen 1994, Real Viejo Marshes A and B 1997), and experimental parasitism with real coot eggs from nests of conspecifics (Cari Lauquen 1994, Real Viejo Marshes A). The diversity of approaches was employed originally to examine different aspects of the host-parasite interaction (e.g. Lyon and Eadie 2004, 2013), but when the environmental changes occurred, we realized that we could leverage these experiments to evaluate how the changing environmental conditions affected rejection behavior of the hosts. We used nominal logistic regression analysis to evaluate the effects of flooding, species of host, and wetland site on rejection rates of parasitic eggs in 1993.We used a Fisher’s exact test to contrast rejection rates before and after a second flood event in 1997 on one wetland (Real Viejo Marsh B) in an experimental study of egg rejection; this comparison involved a single wetland (Marsh B), one host species (red-gartered coots) and a single egg type (painted hen egg). We used nominal logistic regression analysis to evaluate the effects of vegetation loss, wetland site, and egg type on rejection rates of parasitic eggs. For each wetland we pooled all egg rejections in that year.

Conspecific brood parasitism (CP) is a facultative breeding tactic whereby females lay their eggs in the nests of conspecifics. In some species, potential host individuals have evolved the ability to identify and reject foreign eggs from their nest. Previous studies suggest that the ubiquitous House Sparrow Passer domesticus in Spain and South Africa employs both CP and parasitic egg rejection, while a population in China does not. Given the species’ invasive range expansions, the House Sparrow represents a potentially excellent global model system for egg rejection across variable ecological conditions. The present study examines House Sparrow responses to experimental parasitism at three geographically distinct locations (in Israel, North America, and New Zealand) to provide a robust test of how general the findings of the previous studies are. In all three geographic regions egg rejection rates were negligible and not statistically different from background rates of disappearance of ...

The rejection threshold was calculated with the base method described in the Methods section (Method A). P-values from statistical analysis (Kruskal–Wallis test) provide the degree of association between the rejection threshold and the variable. The median rejection threshold (range (minimum-maximum) and standard deviation) is provided when the association is significant (p≤0.05). (XLSX)

The purpose of this study was to evaluate two aims. The first was whether patients with a history of keratoplasty who developed COVID-19 were at a higher risk of corneal graft rejection or failure. The second was examining whether patients who underwent a new keratoplasty during the first 2 years of the pandemic from 2020–2022 were at a higher risk of the same outcomes compared to those undergoing keratoplasty from 2017–2019 before the pandemic. A multicenter research network, TriNetX, was used to query for keratoplasty patients with or without a COVID-19 between January 2020 and July 2022. Additionally, the database was also queried to identify new keratoplasties performed from January 2020-July 2022 and compare it to keratoplasties performed during a similar pre-pandemic interval between 2017–2019. 1:1 Propensity Score Matching was utilized to adjust for confounders. Graft complication of either a rejection or failure was assessed within 120 day follow-up using the Cox proportional hazard model and survival analysis. A total of 21,991 patients with any keratoplasty history were identified from January 2020-July 2022, of which 8.8% were diagnosed with COVID-19. Matching revealed two balanced cohorts of 1,927 patients where no significant difference in risk of corneal graft rejection or failure among groups ((aHR [95% CI] = 0.76 [0.43,1.34]; p = .244)). Comparing first-time keratoplasties performed in a pandemic period of January 2020-July 2022 to a corresponding pre-pandemic interval from 2017–2019 also similarly revealed no differences in graft rejection or failure in matched analysis (aHR = 0.937[0.75, 1.17], p = .339). This study found no significant increase in the risk of graft rejection or failure in patients with a prior keratoplasty history following COVID-19 diagnosis nor in any patients who had a new keratoplasty done during 2020–2022 when compared to a similar pre-pandemic interval.

The Product Rejection Device market has emerged as a critical component in various industries, particularly in manufacturing and quality control processes, to ensure product excellence and compliance with industry standards. These advanced devices play a key role in identifying defective or non-compliant products an

Acceptance and avoidance can be socially transmitted, especially in the case of mate choice. When a Drosophila melanogaster female observes a conspecific female (called demonstrator female) choosing to mate with one of two males, the former female (called observer female) can memorize and copy the latter female’s choice. Traditionally in mate-copying experiments, demonstrations provide two types of information to observer females, namely the acceptance (positive) of one male, and the rejection of the other male (negative). To disentangle the respective roles of positive and negative information in Drosophila mate copying, we performed experiments in which demonstrations provided only one type of information at a time. We found that positive information alone is sufficient to trigger mate copying. Observer females preferred males of phenotype A after watching a female mating with a male of phenotype A in the absence of any other male. Contrastingly, negative information alone (provided by a demonstrator female actively rejecting a male of phenotype B) did not affect future observer females' mate choice. These results suggest that the informative part of demonstrations in Drosophila mate-copying experiments lies mainly, if not exclusively, in the positive information provided by the copulation with a given male. We discuss the reasons for such a result and suggest that Drosophila females learn to prefer the successful males, implying that the underlying learning mechanisms may be shared with those of appetitive memory in non-social associative learning. Methods We did behavioral observations of fruit fly mate choice decisions.

This dataset contains information about past loan applicants, including their income, loan amount, credit history, and other factors relevant to loan approval decisions.

The goal is to build a machine learning model that can analyze this data to predict whether future loan applications should be approved or rejected.

This is a modified version of https://www.kaggle.com/datasets/bhavikjikadara/loan-status-prediction with NaN value added to some of the row cells.

About the loan_data_added_nan.csv file:

• Loan_ID: A unique loan ID.
• Gender: Either male or female.
• Married: Weather Married(yes) or Not Marttied(No).
• Dependents: Number of persons depending on the client.
• Education: Applicant Education(Graduate or Undergraduate).
• Self_Employed: Self-employed (Yes/No).
• ApplicantIncome: Applicant income.
• CoapplicantIncome: Co-applicant income.
• LoanAmount: Loan amount in thousands.
• Loan_Amount_Term: Terms of the loan in months.
• Credit_History: Credit history meets guidelines.
• Property_Area: Applicants are living either Urban, Semi-Urban or Rural.
• Loan_Status: Loan approved (Y/N).

During every election cycle, election administrators validate voter registration applications submitted at different times and through various sources, with a notable peak in the demand for voter registration as Election Day approaches. The process of registering to vote, however, is error-prone and may depend on the voter’s capacity to fill a form correctly, or the election administrator’s capacity to successfully process applications as the voter registration window closes. Such errors can limit a prospective, and eligible, voter’s ability to cast a valid ballot. This study assesses the impact of time and registration source on the rates of rejected voter registration applications by analyzing monthly county-level voter registration reports during the 2012 election cycle in Florida. I find that there is a dynamic relationship between administrative and seasonal factors at the county-level, which condition the rates of rejected voter registrations as the registration deadline approaches. These findings suggest complications in the process of registering to vote that may stem from differences in voter engagement, but also the variation in administrative oversight throughout the election cycle.

This dataset is designed for research on acute graft rejection prediction in high-risk kidney transplant recipients. It includes demographic, clinical, immunological, and histopathological data to identify early biomarkers and predict the risk of acute rejection within 30 days post-transplantation. The dataset simulates real-world scenarios where imbalances in rejection cases are addressed to ensure practical applicability.

Description Objective: Predict acute graft rejection and identify contributing factors. Target Column: Acute_Rejection (Binary: 1 = Rejection, 0 = No Rejection). Key Features: Demographic Data: Patient age, gender, donor type (living/deceased). Clinical Information: Immunosuppressive regimen, biopsy scores. Immunological Markers: Cytokine levels (e.g., IL-2, TNFα). Histopathological Data: Biopsy score indicating rejection severity.

The E-Visa Processing System Database is designed to manage and analyze the processing of electronic visas for applicants. This dataset simulates real-world visa processing system operations, such as the Indian E-Visa platform. It includes data related to applicants, their visa applications, payments, document submissions, approvals, and rejections.

The dataset is structured into 19 interconnected tables, offering a comprehensive view of the entire visa lifecycle, from application submission to approval or rejection. This dataset can be used for various analytical tasks, such as tracking application trends, analyzing approval rates, and identifying bottlenecks in the visa processing system.

Purpose:

The primary purpose of the E-Visa Processing System Database is to:

Model the Visa Processing Workflow: Simulate the journey of an applicant through the visa process.

Enable Data Analysis: Facilitate insights into trends, performance metrics, and user behavior.

Support Automation Research: Aid in developing and testing automation or AI/ML models for process improvement.

Train Students and Analysts: Provide a real-world dataset for learning SQL and database management.

Key Features of the Dataset:

Comprehensive Coverage:Tracks every visa process step, including application submission, payment processing, document verification, approval, rejection, and feedback.

Interconnected Tables:Includes primary key and foreign key relationships, ensuring data integrity and enabling complex queries.

Realistic Data:Simulated but realistic data, mimicking a government-run visa processing system.

Versatile Use Cases:Suitable for analytics, AI/ML research, or database training purposes.