Statistical Characteristics Research Articles

Disentangled global and local features of multi-source data variational autoencoder: An interpretable model for diagnosing IgAN via multi-source Raman spectral fusion techniques.

A single Raman spectrum reflects limited molecular information. Effective fusion of the Raman spectra of serum and urine source domains helps to obtain richer feature information. However, most of the current studies on immunoglobulin A nephropathy (IgAN) based on Raman spectroscopy are based on small sample data and low signal-to-noise ratio. If a multi-source data fusion strategy is directly adopted, it may even reduce the accuracy of disease diagnosis. To this end, this paper proposes a data enhancement and spectral optimization method based on variational autoencoders to obtain reconstructed Raman spectra with doubled sample size and improved signal-to-noise ratio. In the diagnosis of IgAN in multi-source domain Raman spectra, this paper builds a global and local feature decoupled variational autoencoder (DMSGL-VAE) model based on multi-source data. First, the statistical features after spectral segmentation are extracted, and the latent variables obtained by the variational encoder are decoupled through the decoupling module. The global representation and local representation obtained represent the global shared information and local unique information of the serum and urine source domains, respectively. Then, the cross-source reconstruction loss and decoupling loss are used to constrain the decoupling, and the effectiveness of the decoupling is proved quantitatively and qualitatively. Finally, the features of different source domains were integrated to diagnose IgAN, and the results were analyzed for important features using the SHapley Additive exPlanations algorithm. The experimental results showed that the AUC value of the DMSGL-VAE model for diagnosing IgAN on the test set was as high as 0.9958. The SHAP algorithm was used to further prove that proteins, hydroxybutyrate, and guanine are likely to be common biological fingerprint substances for the diagnosis of IgAN by serum and urine Raman spectroscopy. In summary, the DMSGL-VAE model designed based on Raman spectroscopy in this paper can achieve rapid, non-invasive, and accurate screening of IgAN in terms of classification performance. And interpretable analysis may help doctors further understand IgAN and make more efficient diagnostic measures in the future.

Spatial correlation and urban collaborative governance of air pollution in Anhui province based on complex networks

To promote the greening of the economy and sustainable development, and to achieve a win-win situation for the economy and the environment.By constructing and analyzing the complex network, this study provides a new perspective for understanding the spatial and temporal dynamics of air pollution, and provides a scientific basis for formulating effective air quality improvement measures.This study investigated the seasonal evolution and spatial correlation of air pollutants and AQI in 16 cities in Anhui Province. A weighted undirected complex network for AQI was constructed based on the average correlation coefficients of AQI between different cities, using the ratio of the average correlation coefficient to the distance of the cities as weights. By analyzing the overall statistical characteristics, node importance of the network, and dividing the network into communities, the results show that the atmospheric environmental quality in Anhui Province has significantly improved from 2018 to 2023. Bengbu, Chuzhou, and Hefei occupy the core positions in the network correlation, exhibiting the strongest ability to spread air pollution. The relationship between GDP and AQI between different cities was analysed using the grey correlation method. By combining the results of community division, the correlation between GDP and AQI, and the consideration of policy factors, it is expected that this comprehensive analysis will provide more effective strategies for pollution management, such as optimising and upgrading the industrial structure, perfecting the synergistic management system of air pollution prevention and control among cities, and strengthening regional cooperation for coordinated urban development.

Numerical investigation on droplet lateral movement in post-dryout region

This paper presents a numerical study of droplet lateral movement and droplet deposition behaviors in the post-dryout region using the Discrete Particle Model (DPM) in ANSYS FLUENT. The investigation focuses on the analysis of droplet lateral movement, including single droplet trajectory, the statistic distribution and average value of droplets radial velocity, and droplet transverse between central and near wall regions. The single droplet trajectory shows the droplet dynamic behavior in the near wall region vividly. The droplet radial velocities are plotted as histogram to display the droplets statistic characteristics. The average value of droplets radial velocities toward the wall are plotted along the radial distance. In addition, to quantitatively display the size and evaporation effect on droplet deposition, three new parameters presenting overall movement behavior, i.e. droplet deposition mass flux, droplet deposition velocity as well as droplet entrainment velocity are introduced and derived from the CFD results. The comparison of results with varying initial droplet size indicates that droplets with smaller sizes exhibit higher average velocities toward the wall. A smaller initial droplet size leads to a higher deposition velocity, lower entrainment velocity, and thus a higher droplet deposition mass flux. The histogram of droplet radial velocities reveals that the droplet evaporation significantly reduces the number of droplets with small radial velocities toward the wall, resulting in a decrease in overall droplet deposition mass flux. Furthermore, through the comparison of overall movement behavior parameters, evaporation not only inhibits droplet motion toward the wall but also noticeably enhances droplet movement away from the wall. This inhibition effect on droplet deposition and enhancement effect on droplet entrainment become more pronounced with increasing evaporation intensity.

A comparative study of statistical, radiomics, and deep learning feature extraction techniques for medical image classification in optical and radiological modalities.

Feature extraction in ML plays a crucial role in transforming raw data into a more meaningful and interpretable representation. In this study, we thoroughly examined a range of feature extraction techniques and assessed their impact on the binary classification models for medical images, utilizing a diverse and rich set of medical imaging modalities. Using H&E-stained, chest X-ray, and retina OCT images, we applied methods to extract statistical, radiomics, and deep features. These features were then used to develop PCA-LDA models as the employed classifier. We evaluated the models based on two decisive metrics: latency and performance. Latency measured the time taken for feature extraction and prediction, while mean sensitivity (balanced accuracy) characterizes the model performance. Our comparative study revealed that statistical and radiomics features were less effective for medical image classification, as they showed high latency and lower performance scores. In contrast, pre-trained DL networks performed efficiently, with high sensitivity and low latency. For H&E-stained images, the statistical feature extraction took about an hour and achieved 90.8% sensitivity, while ResNet50 reduced processing time fourfold and increased sensitivity to 96.9%. For chest X-rays, radiomics features were time-intensive with 92.2% sensitivity, while ResNet50 improved sensitivity to 96% with faster extraction time. For retina OCT images, radiomics yielded a sensitivity of 91%, while DenseNet121 achieved 98.6% sensitivity in 15min. These findings underscore the superior performance of DL techniques over the statistical and radiomics features, highlighting their potential for real-world applications where accurate and rapid diagnostic decisions are essential.

HYBRID SHEPARD CONVOLUTIONAL KRONECKER NETWORK-BASED GLAUCOMA DISEASE DETECTION SYSTEM USING RETINAL FUNDUS IMAGE

A neuro-degenerative eye disease called Glaucoma is expanded due to the development of ocular pressure inside the retina. Worldwide, glaucoma is the second widest reason for increased vision loss. Moreover, glaucoma affects millions of people, and early intervention is crucial to prevent irreversible vision loss. The absence of an early diagnosis causes complete blindness to the individuals. Furthermore, screening entire populations for glaucoma is impractical due to its low prevalence in communities, the lack of cost-effectiveness, and various logistical challenges. Therefore, the above-mentioned issues are overcome by this investigation proposing an effectual detection scheme called Shepard Convolutional Kronecker Network (ShCKN) for glaucoma detection. Initially, the input glaucoma image undergoes preprocessing with a Gaussian filter to eliminate unwanted noise. The preprocessed image is then fed into the segmentation module, where optic disc segmentation is achieved using Bayesian fuzzy clustering (BFC), followed by blood vessel detection through a sparking process. Significant features, such as local gradient binary pattern (LGBP), convolutional neural network (CNN) features, and statistical features, are then extracted. Finally, glaucoma detection is conducted using the ShCKN, which integrates Shepard convolutional neural networks (ShCNN) and deep Kronecker network (DKN), with layer modifications based on fuzzy concepts. Furthermore, the proposed ShCKN model achieved the highest accuracy of 91.8%, specificity of 91.4%, sensitivity of 91.2%, and Matthews correlation coefficient (MCC) of 88.3%.

EEG-derived brainwave patterns for depression diagnosis via hybrid machine learning and deep learning frameworks

In the fields of engineering, science, technology, and medicine, artificial intelligence (AI) has made significant advancements. In particular, the application of AI techniques in medicine, such as machine learning (ML) and deep learning (DL), is rapidly growing and offers great potential for aiding physicians in the early diagnosis of illnesses. Depression, one of the most prevalent and debilitating mental illnesses, is projected to become the leading cause of disability worldwide by 2040. For early diagnosis, a patient-friendly, cost-effective approach based on readily observable and objective indicators is essential. The objective of this research is to develop machine learning and deep learning techniques that utilize electroencephalogram (EEG) signals to diagnose depression. Different statistical features were extracted from the EEG signals and fed into the models. Three classifiers were constructed: 1D Convolutional Neural Network (1DCNN), Support Vector Machine (SVM), and Logistic Regression (LR). The methods were tested on a dataset comprising EEG signals from 34 patients with Major Depressive Disorder (MDD) and 30 healthy subjects. The signals were collected under three distinct conditions: TASK, when the subject was performing a task; Eye Close (EC), when the subject’s eyes were closed; and Eye Open (EO), when the subject’s eyes were open. All three classifiers were applied to each of the three types of signals, resulting in nine (3 × 3) experiments. The results showed that TASK signals yielded the highest accuracies of 88.4%, 89.3%, and 90.21% for LR, SVM, and 1DCNN, respectively, compared to EC and EO signals. Additionally, the proposed methods outperformed some state-of-the-art approaches. These findings highlight the potential of EEG-based approaches for the clinical diagnosis of depression and provide promising avenues for further research. Additionally, the proposed methodology demonstrated statistically significant improvements in classification accuracy, with p-values < 0.05, ensuring robustness and reliability.

Re-locative guided search optimized self-sparse attention enabled deep learning decoder for quantum error correction

Heavy hexagonal coding is a type of quantum error-correcting coding in which the edges and vertices of a low-degree graph are assigned auxiliary and physical qubits. While many topological code decoders have been presented, it is still difficult to construct the optimal decoder due to leakage errors and qubit collision. Therefore, this research proposes a Re-locative Guided Search optimized self-sparse attention-enabled convolutional Neural Network with Long Short-Term Memory (RlGS2-DCNTM) for performing effective error correction in quantum codes. The integration of the self-sparse attention mechanism in the proposed model increases the feature learning ability of the model to selectively focus on informative regions of the input codes. In addition, the use of statistical features computes the statistical properties of the input, thus aiding the model to perform complex tasks effectively. For model tuning, this research utilizes the RIGS nature-inspired algorithm that mimics the re-locative, foraging, and hunting strategies, which avoids local optima problems and improves the convergence speed of the RlGS2-DCNTM for Quantum error correction. When compared with other methods, the proposed RlGS2-DCNTM algorithm offers superior efficacy with a Minimum Mean Squared Error (MSE) of 4.26, Root Mean Squared Error of 2.06, Mean Absolute Error of 1.14 and a maximum correlation and of 0.96 and 0.92 respectively, which shows that the proposed model is highly suitable for real-time error decoding tasks.

Adaptive Kalman filter-based time synchronization algorithm for civil aviation communication

Abstract The traditional synchronization algorithms often struggle to meet the stringent requirements of civil aviation communication. This paper proposes an adaptive time synchronization algorithm based on Kalman filtering (KF) to enhance the synchronization performance of civil aviation communication systems. Firstly, a dynamic clock model is established considering the characteristics of civil aviation communication, with clock offset and drift as state variables. Moreover, a step-by-step detection strategy is employed to adaptively adjust the detection range based on the statistical characteristics of synchronization errors, thereby improving the algorithm's robustness and convergence speed. To evaluate the algorithm's performance, a civil aviation communication system-level simulation platform is built based on ICAO standards, and various typical scenarios, such as take-off, landing, and cruising, are designed. Simulation results demonstrate that the proposed algorithm achieves significant improvements in synchronization accuracy, convergence speed, and robustness compared to classical synchronization algorithms based on phase-locked loops (PLL) and early late gates (ELD).

Alzheimer’s Disease Diagnosis from EEG Signals using Recurrence Qualification Analysis (RQA) Features

Alzheimer’s disease (AD) is a neurological disorder that causes memory decline and loss of cognitive abilities. AD directly impacts the brain activity of affected individuals, which can be reflected in electroencephalogram (EEG) signals. Previous studies have typically relied on statistical, spectral, and wavelet features in order detect AD using EEG signals. Recurrence Qualification Analysis (RQA) is a non-linear technique that has been successfully used to analyze EEG signals in several domains including emotion recognition and autism detection. However, RQA features have not yet been fully investigated for AD diagnosis. The aim of this work is to thoroughly investigate the usefulness of RQA features for AD diagnosis. Fifteen RQA features were computed along with statistical measures, Hjorth parameters, and relative power in order to compare the performance of the RQA features to other commonly utilized EEG features. All features were computed from the different brain regions. Experimental results indicated that RQA features outperformed all other feature types regardless of the considered brain region. RQA features achieved accuracies ranging from 89.6% to 98.0% using a support machine vector (SVM) classifier with leave-one-subject-out (LOSO) cross-validation. These results are between 25% to 40% higher than the three other considered feature groups. Feature ranking was performed to find the most relevant RQA features, identifying seven of the fifteen computed RQA features. This work sheds light on the potential of RQA features for reliable AD diagnosis and paves the way for their integration in computer-aided AD diagnostic tools using EEG signals.

Modeling Urban Microclimates for High-Resolution Prediction of Land Surface Temperature Using Statistical Models and Surface Characteristics

Surface properties in complex urban environments can significantly impact local-level temperature gradients and distribution on several scales. Studying temperature anomalies and identifying heat pockets in urban settings is challenging. Limited high-resolution datasets are available that do not translate into an accurate assessment of near-surface temperature. This study developed a model to predict land surface temperature (LST) at a high spatial–temporal resolution in urban areas using Landsat data and meteorological inputs from NLDAS. This study developed an urban microclimate (UC) model to predict air temperature at high spatial–temporal resolution for inner urban areas through a land surface and build-up scheme. The innovative aspect of the model is the inclusion of micro-features in land use characteristics, which incorporate surface types, urban vegetation, building density and heights, short wave radiation, and relative humidity. Statistical models, including the Generalized Additive Model (GAM) and spatial autoregression (SAR), were developed to predict land surface temperature (LST) based on surface characteristics and weather parameters. The model was applied to urban microclimates in densely populated regions, focusing on Manhattan and New York City. The results indicated that the SAR model performed better (R2 = 0.85, RMSE = 0.736) in predicting micro-scale LST variations compared to the GAM (R2 = 0.39, RMSE = 1.203) and validated the accuracy of the LST prediction model with R2 ranging from 0.79 to 0.95.

An object detection algorithm based on deep learning and salient feature fusion for roadside surveillance camera

AbstractIn intelligent transportation systems, object detection for a surveillance video is one of the important functions. The performance of existing surveillance video object detection algorithms is affected by the conflict between the features of the objects, which leads to a decline in precision. Therefore, an object detection algorithm based on deep learning and salient feature fusion is proposed. The proposed method introduces a non‐weight‐sharing network to process the salient features of the image and fuse them with the features extracted from the red blue green branch. Different from the previous solutions, the salient feature extraction branch uses the boundary features and statistical features of the image and fuses the features of the two branches in the efficient layer aggregation networks structure. At the same time, the attention module is used in efficient layer aggregation networks with convolutional block attention module to improve the efficiency of feature utilisation. The training and evaluation are carried out in the constructed surveillance video feature conflict dataset, and eight scenes are constructed in the way of orthogonal experiments. The experimental results show that the performance of object detection can be significantly improved by using the proposed method in the object detection task of the intelligent transportation system surveillance video feature conflict scene.

Vibration-Based Anomaly Detection for Induction Motors Using Machine Learning

Predictive maintenance of induction motors continues to be a significant challenge in ensuring industrial reliability and minimizing downtime. In this study, machine learning techniques are utilized to enhance fault diagnosis through the use of the Machinery Fault Database (MAFAULDA). A detailed extraction of statistical features was performed on multivariate time-series data to capture essential patterns that could indicate potential faults. Three machine learning algorithms—deep neural networks (DNNs), support vector machines (SVMs), and K-nearest neighbors (KNNs)—were applied to the dataset. Optimization strategies were carefully implemented along with oversampling techniques to improve model performance and handle imbalanced data. The results achieved through these models are highly promising. The SVM model demonstrated an accuracy of 95.4%, while KNN achieved an accuracy of 92.8%. Notably, the combination of deep neural networks with fast Fourier transform (FFT)-based autocorrelation features produced the highest performance, reaching an impressive accuracy of 99.7%. These results provide a novel approach to machine learning techniques in enhancing operational health and predictive maintenance of induction motor systems.

Extensive benchmarking of a method that estimates external model performance from limited statistical characteristics

Predictive model performance may deteriorate when applied to data sources that were not used for training, thus, external validation is a key step in successful model deployment. As access to patient-level external data sources is typically limited, we recently proposed a method that estimates external model performance using only external summary statistics. Here, we benchmark the proposed method on multiple tasks using five large heterogeneous US data sources, where each, in turn, plays the role of an internal source and the remaining—external. Results showed accurate estimations for all metrics: 95th error percentiles for the area under the receiver operating characteristics (discrimination), calibration-in-the-large (calibration), Brier and scaled Brier scores (overall accuracy) of 0.03, 0.08, 0.0002, and 0.07, respectively. These results demonstrate the feasibility of estimating the transportability of prediction models using an internal cohort and external statistics. It may become an important accelerator of model deployment.

Стійкість до емоційного стресу при моделюванні «конфлікту аферентних збуджень» у щурів

The constant and regular correlates of emotional stress states are ethological and vegetative reactions that serve regulatoryadaptive functions. The cardiovascular system plays a key role in the body’s adaptation to emotional and stressful challenges. The study aimed to study heart rate variability to identify indicators that have prognostic value regarding the occurrence of cardiac disorders in the form of arrhythmias. Using electrocardiography and interval cardiometry, the occurrence of heart rhythm disorders was investigated, depending on the duration of stress exposure and the individual typological characteristics of animals in the process of forming an emotional and stressful state. The article presents the results of studies of the emotional stress state under the conditions of the “afferent excitation conflict” model in 70 sexually mature male Wistar rats weighing 200-250 g and taking into account the basic principles of bioethics. It has been established that a prognostic factor unfavorable for the occurrence of heart rhythm disturbances is the absence of discrepancy in changes in the statistical characteristics of the heart rhythm after shortterm stress exposure in combination with the predominance of sympathetic extracardial regulatory influences. It has been shown that animals with a predominance of parasympathetic regulatory influences on heart rate and a decrease in centralization of control (decrease in the tension index) during the formation of neurogenic stress were more resistant to heart rhythm disturbances.

Population: It’s Spatial Dimensions and Attributes in Ranchi District

Population geography is an important branch of Human Geography that focuses on the study of Human population, its density, growth, composition and distribution. The study of population distribution refers to the statistical characteristics of a population including age, gender, race ethnicity, income, education and geographic location. Analyzing demographic distribution is crucial for understanding the social dynamics, economic trends and public policy implications. This policy can inform businesses, governments and organizations about the need and preferences of different population segments. The spatial distribution of population across geographic areas exhibits certain patterns which are of primary interest to both the demographers and geographers. This paper attempts to analyze the regional disparity of population at block-level in the most populous district of Jharkhand. Ranchi district is contributed by combination of various factors that has developed it into the most populous district of Jharkhand using the concentration index component. Apart from the trends in population growth, various methods have been adopted to gain a better understanding of the spatial dimension of the population.

Classification of Flying Drones Using Millimeter-Wave Radar: Comparative Analysis of Algorithms Under Noisy Conditions

This study evaluates different machine learning algorithms in detecting and identifying drones using radar data from a 60 GHz millimeter-wave sensor. These signals were collected from a bionic bird and two drones, namely DJI Mavic and DJI Phantom 3 Pro, which were represented in complex form to preserve amplitude and phase information. The first benchmarks used four algorithms, namely long short-term memory (LSTM), gated recurrent unit (GRU), one-dimensional convolutional neural network (Conv1D), and Transformer, and they were benchmarked for robustness under noisy conditions, including artificial noise types like white noise, Pareto noise, impulsive noise, and multipath interference. As expected, Transformer outperformed other algorithms in terms of accuracy, even on noisy data; however, in certain noise contexts, particularly Pareto noise, it showed weaknesses. For this purpose, we propose Multimodal Transformer, which incorporates more statistical features—skewness and kurtosis—in addition to amplitude and phase data. This resulted in a improvement in detection accuracy, even under difficult noise conditions. Our results demonstrate the importance of noise in processing radar signals and the benefits afforded by a multimodal presentation of data in detecting unmanned aerial vehicle and birds. This study sets up a benchmark for state-of-the-art machine learning methodologies for radar-based detection systems, providing valuable insight into methods of increasing the robustness of algorithms to environmental noise.

Three‐dimensional characterization of particle morphology in natural gravel and blasted rock fragments using SfM‐MVS photogrammetry

AbstractThis study aims to develop a high‐precision and cost‐efficient method for the three‐dimensional reconstruction of large particles in natural gravel and blasted rock fragments, utilizing Structure from Motion (SfM) and Multi‐View Stereo (MVS) techniques. The proposed approach was applied to characterize the three‐dimensional morphology of rockfill dam materials at a real construction site. Particle shape was quantitatively analyzed using shape indices of sphericity, convexity, and angularity. The predominant morphology of natural gravel is characterized as slightly elongated and slightly flat, while rock fragments are slightly elongated and not flat. Probability density distributions of shape indices follow a skewed normal distribution: sphericity and convexity show leftward skewness, whereas angularity is right‐skewed. Skewness parameters of sphericity and angularity are consistent between natural gravel and blasted rock fragments, indicating comparable shape asymmetry. Convexity skewness is significantly higher in natural gravel compared to rock fragments, by approximately an order of magnitude. The relationship between size and particle shape shows that form ratios and associated shape descriptors change linearly with the logarithm of size; larger particles approach spherical or cubic forms. The innovative measurements contribute to the particle shape data set of rockfill dam materials, providing valuable insights into the three‐dimensional and statistical morphological characteristics of relatively large particles in natural gravel and blasted rock fragments. This approach enhances understanding of particle morphology's impact on the mechanical behavior of granular materials.

Markov chain-based method for degree distribution of evolving networks

Abstract Challenges often arise when solving the degree distribution of evolving networks with node preference deletion using empirical equations, such as the introduction of additional variables, increased computational complexity, and the inability to derive theoretical results. We propose the Enhanced Stochastic Process-based Rule (ESPR) method to address these limitations. By redesigning the evolution rules, ESPR preserves the network’s topological structure and statistical properties throughout its evolution. This approach effectively solves the theoretical challenges of degree distribution under the node preference deletion mechanism. Additionally, ESPR can handle degree distribution under uniform node deletion, providing a more comprehensive framework for analyzing evolving network structures. Finally, the characteristics of ESPR extend to the study of temporal networks and can even resolve more complex statistical features, such as degree-degree correlation.&amp;#xD;

Modeling chikungunya virus infection with Black–Karasinski process: stationary distribution, probability density function, and extinction

Incorporating stochastic processes into biological models is crucial for capturing the inherent variability and uncertainties within biological systems. This paper explores the benefits of introducing Black–Karasinski process into chikungunya virus infection modeling. By utilizing the Black–Karasinski process researchers can capture the inherent variability in biological processes and account for uncertainties. This paper highlights the advantages of Black–Karasinski processes in biological modeling. We investigate the dynamical behavior of a stochastic model for chikungunya virus infection incorporating a Black–Karasinski process. Firstly, we establish sufficient conditions for the existence of a stationary distribution in the model. By solving the corresponding Fokker–Planck equation we obtain the local probability density function near the quasi-endemic equilibrium, which provides insights into the statistical characteristics of the stochastic system. Additionally, we present sufficient conditions for the extinction of infected host cells and chikungunya virus particles. Finally, we supplement the analytical results with numerical simulations to investigate the impact of random noise.

Statistical Characteristic of the Transition Temperature Shift for Reactor Pressure Vessel Steel

Abstract In the ductile?brittle transition temperature range, the Charpy absorbed energy exhibits an inherent variation, which causes uncertainty in the transition temperature shift. In the current codes for the evaluation of fracture toughness of reactor pressure vessel steels, margins are considered to account for the uncertainty in the transition temperature shift. Although they have been statistically determined, their adequacy is only inductively assured by specific surveillance program databases. In this study, a model was proposed to describe the statistical characteristics of the transition temperature shift. An equation to describe the standard deviation of the associated transition temperature was theoretically derived from a previously proposed variation model of the Charpy absorbed energy according to the law of propagation of uncertainty. The obtained standard deviations were then compiled into procedures to estimate the transition temperature shift according to the sufficiency of the parameters required for the evaluation (shape of Charpy curve and sample size). The standard deviations of the transition temperature shift assumed in past studies/present codes were compared with those predicted using the proposed model. In the range of upper shelf energy greater than 100 J, the standard deviations in past studies/present codes.