Data Distribution Research Articles

Numerical solution of nanofluids mixing processes in T-mixer equipped titled gate

AbstractIn this study, a numerical simulation technique is employed to predicate the temperature distribution and velocity profile data of cold and hot nanofluids within a T-mixer was studied. The mixing of nanofluid flow with Al2O3 nanoparticles of 50 nm flows at Φ = 0.4 vol.% in a T-shaped mixer. The present numerical problem has been solved using the COMSOL Multiphysics version 5.4. Six angle of inclination was studied (θ = 15, 30, 45, 60, 75, and 90°) of the gate and evaluated its effects on the temperatures and velocity contour in the T-junction. The study's findings indicated that the presence of a gate in a stationary, non-rotating flow regime has a noteworthy impact on the stationary vortex flow. Also, the mixing occurs more quickly at angles of 45 or 60°. Mixing at a 30° or 90° angle took longer.

Skewness impact through distributional evaluation (SITDE) method: a new method in multi-criteria decision making

In this article, we propose the Skewness Impact Through Distributional Evaluation (SITDE) method for multi-criteria decision-making. Unlike traditional methods ignoring criteria distribution asymmetry, SITDE enhances the decision quality. We use a case-study on electric vehicle selection to illustrate the efficiency of SITDE relative to conventional methods. The comprehensive decision support with SITDE across multiple thresholds is an important aspect of a detailed and balanced decision-making virtue as our results show in case the skewness effects are included. Theoretical backgrounds, typologies practical applications and statistical justifications to confirm the accuracy of SITDE in regarding to limit complexities MCDM scenarios are explained this paper. SITDE enhances a robust decision-making process by accounting for the inherent biases of symmetric assumption models. Moreover, SITDE proves to be extremely useful in cases for dealing with asymmetrical data distributions and skewed datasets humanitarian cause as infected situations are always overwhelming. This broad applicability indicates the potential for wider use with respect to different domains, such as in environmental management and financial risk assessment. Future studies should investigate the adequacy or insufficiency of its fusion with different MCDM methods and explore if this approach would apply a more efficient and valid insight that might enhance decision making in multiple sectors.

SFDiff: Diffusion model with sufficient spatial‐Fourier frequency information interaction for low‐light image enhancement

AbstractDiffusion models are increasingly applied in low‐light image enhancement tasks due to their exceptional capability to model data distributions, but most current methods focus only on the original pixel space and neglect the potential of Fourier frequency information. In this article, SFDiff is proposed, a novel low‐light image enhancement method that integrates Fourier frequency information into the diffusion process. Specifically, Fourier transforms are applied at both the image and feature levels to separately enhance the amplitude and phase components, which restores global illumination degradation and positional information. Then a Spatial‐Frequency Fusion (SFF) block is used to fully integrate and interact with the information across spatial and frequency domains. Since illumination degradation is primarily manifested in the amplitude component, a loss function based on maximum likelihood learning is employed to constrain the amplitude component at each step of the sampling process, ensuring that the reverse process maintains an optimal trajectory. Owing to the streamlined network design and the fact that the Fourier transform requires no extra parameters, SFDiff achieves a reduction in parameters of over compared to several state‐of‐the‐art (SOTA) diffusion models and delivers high‐quality enhancement results on multiple real‐world datasets. The code is available at https://github.com/MrWan001/SFDiff.

Gyroscope in-assembly drift anomaly detection based on decision re-optimized deep auto-encoder

Abstract In gyroscope assembly, frequent drift performance overproof causes massive parts re-assembly and writing off, for which in-assembly gyro drift anomaly detection is required. However, when utilizing common anomaly detection methods, imbalanced assembly data distribution causes severe accuracy reduction and false alarms. To tackle these problems, first, we propose a decision re-optimized deep autoencoder (DRDAE) model to conduct the in-assembly drift anomaly detection under imbalanced assembly data distribution. Second, a decision-based training strategy is introduced to lower the false alarm rate in anomaly detection, for which models based on different training strategies are compared for better performance. Third, a modified SMOTE data augmentation method is utilized to settle the impact of data imbalance under small-sample condition. Experimental results show that the proposed method can achieve in-assembly drift anomaly detection under imbalanced data distribution in high precision and outperforms all other existing methods, lowering the assembly repetition rate and improving assembly efficiency.

Deep regression learning with optimal loss function

In this paper, we develop a novel efficient and robust nonparametric regression estimator under a framework of a feedforward neural network (FNN). There are several interesting characteristics for the proposed estimator. First, the loss function is built upon an estimated maximum likelihood function, which integrates the information from observed data as well as the information from the data distribution. Consequently, the resulting estimator has desirable optimal properties, such as efficiency. Second, different from the traditional maximum likelihood estimation (MLE), the proposed method avoids the specification of the distribution, making it adaptable to various distributions such as heavy tails and multimodal or heterogeneous distributions. Third, the proposed loss function relies on probabilities rather than direct observations as in least square loss, contributing to the robustness of the proposed estimator. Finally, the proposed loss function involves a nonparametric regression function only. This enables the direct application of the existing packages, simplifying the computational and programming requirements. We establish the large sample property of the proposed estimator in terms of its excess risk and minimax near-optimal rate. The theoretical results demonstrate that the proposed estimator is equivalent to the true MLE where the density function is known in terms of excess risk. Our simulation studies show that the proposed estimator outperforms the existing methods based on prediction accuracy, efficiency and robustness. Particularly, it is comparable to the MLE with the known density and even gets slightly better as the sample size increases. This implies that the adaptive and data-driven loss function from the estimated density may offer an additional avenue for capturing valuable information. We further apply the proposed method to four real data examples, resulting in significantly reduced out-of-sample prediction errors compared to existing methods.

Feasibility study of data-driven wall interference correction framework for subsonic wind tunnel

Although the classical method is widely used for wall interference correction in wind tunnel testing, its reliability and accuracy for complex and unconventional geometries are rather limited. Studies on the evaluation of wall interference and the improvement of correction methods are desirable to enhance the reliability and generality for various geometric configurations. This study proposes a wall interference correction framework based on a deep neural network (DNN) ensemble using data obtained from the numerical panel method. The panel method is validated by comparing the results with those of Reynolds-averaged Navier-Stokes simulations. An automated process was established to generate a large amount of training data, and 600,000 datasets were generated based on the geometric parameters of the wind tunnel, test model, and angles of attack. The input variables of the DNN were determined through sensitivity analysis of the data. To alleviate the randomness of the initial weights and data distribution in the generation process of the DNN model, 20 DNNs with the same multi-layer perceptron structure were trained, and a DNN ensemble model was constructed using five ensemble members with high predictability. The accuracy of the DNN-ensemble based correction models were evaluated by comparing the correction results for the testing data.

The Effect of TENS and Ultrasound Diathermy on Osteoarthritis Patients in Physical Medicine and Rehabilitation Unit of Patut Patuh Patju Hospital, West Nusa Tenggara

Osteoarthritis is generally recognized as a common musculoskeletal condition in Indonesia, characterized by the degeneration of joint cartilage and narrowing of the joint space. The form of therapy that can be given to knee OA patients is a combination of TENS therapy and ultrasound diathermy (USD). WOMAC is an index used to measure osteoarthritis patiens based on pain, stiffness, and physical function. Patut Patuh Patju General Hospital is the only hospital in West Lombok district where physical medicine and medical rehabilitation have recently been introduced. This research aims to determine the effects of administering TENS and ultra sound diathermy on OA patients in the physical medicine and rehabilitation unit of Patut Patuh Patju Hospital, a newly established district hospital in NTB, which has been operational for the past year. This type of research uses a pre-experimental design with a one group pretest-posttest method. Sampling was taken using a purposive sampling technique with a total sample of 30. The research population was knee osteoartritis patients undergoing physiotherapy treatment at the Medical Rehabilitation Unit of Patut Patuh Patju General Hospital. Data was collected using the WOMAC questionnaire before and after 6 until 8 times therapy. The distribution of research data was analyzed using the Shapiro-Wilk test. If the data distribution is normal, it is analyzed using the paired t-test, whereas if it is not normal, it is analyzed using the Wilcoxon test. The combination of TENS therapy and ultra sound diathermy results in improvement of WOMAC scores in knee osteoarthritis patients with p-value = 0.000 (p < 0.05).

LTFM: Long-tail few-shot module with loose coupling strategy for mineral spectral identification

In recent years, deep learning methods have exhibited superior performance in mineral identification when especially compared with conventional machine learning methods such as Support Vector Machine (SVM) and Partial Least Squares (PLS). Nevertheless, almost all of these deep learning methods pay more attention to improving and designing network structures, while neglecting the phenomenon of long-tail distribution in spectral data due to the inconsistency of ore distribution and the scarcity of several natural minerals. To alleviate the interference of majority categories on minority categories, we propose Long-Tail Few-shot Module (LTFM) which is inspired by rethinking the fashionable decoupling strategy that conducts primary representation learning and further classifier retrained on mineral spectral data. In particular, LTFM serves as a multi-expert mode, where these experts are respectively specialized in improving feature representation learning, mitigating the long-tail effect, and alleviating the interference of few shots. Additionally, the loose coupling learning strategy is introduced to facilitate primary representation learning and the subsequent additional experts to inherit this knowledge. Experiments on two publicly available spectral datasets show that the proposed LTFM significantly outperforms existing methods. In the end, extensive ablation studies are conducted to investigate the effectiveness, correctness, and robustness of our proposal.

Different copula types and reliability applications for a new fisk probability model

In this research endeavor, the authors introduce a novel lifetime probability model. This distribution is meticulously examined and characterized, offering insights into its behavior and applicability in various contexts. The proposed new density function of this distribution has various heavy tail forms that are useful in the field of reliability, insurance and statistical modeling. This allows for the representation and modeling of a wide range of data sets that are diverse in their form and nature. The new distribution is characterized by having different patterns of risk or failure rates. The researchers extend the new distribution to the bivariate domain through different methods, including the Morgenstern-Farley-Gumbel distribution, the modified Morgenstern-Farley-Gumbel distribution, the famous Clayton mathematical versions, and the Rennie versions. These extensions enhance the usefulness of the proposed distribution in modeling multivariate age and reliability data and dependencies between variables. The study presents some statistical modeling experiments on reliability data and some important comparisons are presented within the framework of some statistical comparison criteria.

Linking Thermal Conductivity to Equations of State Using the Residual Entropy Scaling Theory.

In recent years, the application of the residual entropy scaling (RES) method for modeling transport properties has become increasingly prominent. Based on Yang et al. (Ind. Eng. Chem. Res. 2021, 60, 13052) in modeling the thermal conductivity of refrigerants, we present here an RES model that extends Yang et al.'s approach to a wider range of pure fluids and their mixtures. All fluids available in the REFPROP 10.0 software, i.e., those with reference equations of state (EoS), were studied. A total of 71,554 experimental data of 125 pure fluids and 16,702 experimental data of 164 mixtures were collected from approximately 647 references, mainly based on the NIST ThermoData Engine (TDE) database 10.1. As a result, over 68.2% (corresponding to the standard deviation of a normal distribution) of the well-screened experimental data agree with the developed RES model within 3.1% and 4.6% for pure fluids and mixtures, respectively. Comparative analysis against the various models implemented in the REFPROP 10.0 (one of the state-of-the-art software packages for thermophysical property calculations) reveals that our RES model demonstrates analogous statistical agreement with experimental data yet with much fewer parameters. Regarding the average absolute value of the relative deviation (AARD) from experimental values to model predictions, the developed RES model shows a smaller or equal AARD for 74 pure fluids out of 125 and 76 mixtures out of 164. Besides, a detailed examination of the impact of the critical enhancement term on mixture calculations was conducted. To use our model easily, a software package written in Python is provided in the Supporting Information.

A multi-source domain feature-decision dual fusion adversarial transfer network for cross-domain anti-noise mechanical fault diagnosis in sustainable city

Rotating machinery forms the critical backbone of infrastructure in a sustainable city, with bearings playing a pivotal role as key mechanical transmission components. Therefore, the health status of these bearings directly influences the safe operation of the infrastructure. Accurate and reliable diagnosis of defects in these components minimizes downtime, reduces maintenance costs, and prevents major accidents, ultimately providing insights in the construction and management of a sustainable city. Typically, in actual industrial scenarios, varying working conditions and various types of machines can result in significant discrepancies in the distribution of sample data. Moreover, the non-negligible noise may degrade the diagnostic performance. Therefore, realizing an accurate and reliable bearing diagnosis considering the cross-domain and noise environment remains a challenge. Leveraging the merits of information fusion and multi-source domain transfer learning, this article proposes a multi-source domain feature-decision dual fusion adversarial transfer network (DFATN) to break through the aforesaid limitations. Initially, an adversarial transfer framework is developed, incorporating novel feature matching evaluation and joint distribution difference losses. This framework is designed to facilitate the learning of feature invariants across domains and to enhance the sharing of domain-specific knowledge, even in noise. Relying on channel-spatial interactive feature fusion, a multi-scale feature extractor (MFE) is constructed to share the interaction and enhance the modeling of complex features in multiple dimensions. Additionally, a fault state-related decision fusion mechanism (SDF) is also implemented to integrate diagnostic information, significantly enhancing the generalization performance and robustness of the proposed network. By employing both public Paderborn University (PU) and laboratory-collected (Lab) datasets, the effectiveness and superiority of the proposed DFATN on bearing fault diagnosis are validated. For cross-working condition tasks, the proposed method realizes impressive performance, with average accuracies of 96.52% and 98.76% for Paderborn University (PU) and laboratory-collected (Lab) datasets, respectively. For cross-machine tasks, the average accuracy is 83.36%, outperforming other latest cross-domain fault diagnosis techniques.

Clustering Functional Data With Measurement Errors: A Simulation-Based Approach.

Clustering analysis of functional data, which comprises observations that evolve continuously over time or space, has gained increasing attention across various scientific disciplines. Practical applications often involve functional data that are contaminated with measurement errors arising from imprecise instruments, sampling errors, or other sources. These errors can significantly distort the inherent data structure, resulting in erroneous clustering outcomes. In this article, we propose a simulation-based approach designed to mitigate the impact of measurement errors. Our proposed method estimates the distribution of functional measurement errors through repeated measurements. Subsequently, the clustering algorithm is applied to simulated data generated from the conditional distribution of the unobserved true functional data given the observed contaminated functional data, accounting for the adjustments made to rectify measurement errors. We illustrate through simulations show that the proposed method has improved numerical performance than the naive methods that neglect such errors. Our proposed method was applied to a childhood obesity study, giving more reliable clustering results.

Prevalence of and potential risk factors for multiple resistance to acaricides in Rhipicephalus (Boophilus) microplus ticks: A survey in the state of Rio Grande Do Sul, Brazil.

The cattle tick Rhipicephalus (Boophilus) microplus (Canestrini, 1887) (Ixodida, Ixodidae) is responsible for significant economic losses in bovine production in tropical and subtropical regions worldwide. Control of this tick predominantly involves the use of chemical acaricides; however, their indiscriminate use has led to the selection of resistant populations. A survey on tick populations was conducted in the state of Rio Grande do Sul, in Brazil, to assess the prevalence of multiple resistance to acaricides in cattle farms. Additionally, questionnaires were administered to identify potential risk factors associated with multiple resistance to acaricides. In total, 176 farms with a bovine population of ≥40 cattle were randomly assigned for tick sampling. The resistance to six acaricidal compounds was investigated by bioassays. A larval packet test was performed for amitraz, chlorpyrifos, cypermethrin, fipronil and ivermectin. Fluazuron was screened using an adult immersion test. Multiple resistance to acaricides (i.e., resistance to three or more compounds) was detected in 173 samples, representing 98% of the total samples. Among these samples, 125 (71%) showed resistance to all six compounds tested. Additionally, we classified the resistance intensity into four levels (I to IV) based on the quartile distribution of the bioassay data. Ten samples (6%) showed high and very high levels (III and IV) of resistance to all six compounds tested. Three variables were significantly associated with multiple resistance to the six acaricides tested: (i) use of injectable acaricides to control ticks, (ii) application of more than five acaricide treatments per year, and (iii) farms with larger herds (≥232 animals). These results regarding widespread resistance and the emergence of multiple resistance to acaricides ticks are alarming and highlight the significant challenge of tick control in southern Brazil.

Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ΔΔG≠ versus Enantiomeric Excess

AbstractWith the rise of machine learning (ML), the modeling of chemical systems has reached a new era and has the potential to revolutionize how we understand and predict chemical reactions. Here, we probe the historic dependence on utilizing enantiomeric excess (ee) as a target variable and discuss the benefits of using relative Gibbs free activation energies (ΔΔG≠), grounded firmly in transition‐state theory, emphasizing practical benefits for chemists. This perspective is intended to discuss best practices that enhance modeling efforts especially for chemists with an experimental background in asymmetric catalysis that wish to explore modelling of their data. We outline the enhanced modeling performance using ΔΔG≠, escaping physical limitations, addressing temperature effects, managing non‐linear error propagation, adjusting for data distributions and how to deal with unphysical predictions,in order to streamline modeling for the practical chemist and provide simple guidelines to strong statistical tools. For this endeavor, we gathered ten datasets from the literature covering very different reaction types. We evaluated the datasets using fingerprint‐, descriptor‐, and graph neural network‐based models. Our results highlight the distinction in performance among varying model complexities with respect to the target representation, emphasizing practical benefits for chemists.

Verdiff-Net: A Conditional Diffusion Framework for Spinal Medical Image Segmentation.

Spinal medical image segmentation is critical for diagnosing and treating spinal disorders. However, ambiguity in anatomical boundaries and interfering factors in medical images often cause segmentation errors. Current deep learning models cannot fully capture the intrinsic data properties, leading to unstable feature spaces. To tackle the above problems, we propose Verdiff-Net, a novel diffusion-based segmentation framework designed to improve segmentation accuracy and stability by learning the underlying data distribution. Verdiff-Net integrates a multi-scale fusion module (MSFM) for fine feature extraction and a noise semantic adapter (NSA) to refine segmentation masks. Validated across four multi-modality spinal datasets, Verdiff-Net achieves a high Dice coefficient of 93%, demonstrating its potential for clinical applications in precision spinal surgery.

Laboratory performances during proficiency tests for the analysis of active substances in pesticide formulations during 2021–2023

During the period 2021–2023, the Italian National Institute of Health (ISS) organised, in collaboration with the Italian Health Ministry, proficiency testing (PTs) for about 22 Italian and other EU laboratories involved in the monitoring programmes for Plant Protection Products (PPPs). The aim of the PTs was to determine the amount of active ingredient in various formulations of PPPs. Overall, the type of formulations to be analysed were soluble concentrate (SL), suspension concentrate (SC), capsule suspension (CS), emulsion water oil (EW), soluble water granule (SG), dustable powder (DP), water dispersible granule (WDG) and emulsion concentrate (EC). In 2021, the ISS organised PTs for the determination of Imazamox, Fenhexamid and Trinexapac-ethyl in three different PPPs as soluble concentrate (SL), suspension concentrate (SC), and capsule suspension (CS), respectively. In 2022, PTs for the determination of Bentazone, Spinosad and Tau-Fluvalinate were organised. For this PTs, commercial PPPs were obtained from the Italian market as SC, emulsion oil water (EW) and soluble water granule (SG). In 2023, the PT was organised to determine the active ingredient Trifloxystrobin, Deltamethrin and Cyprodinil in the commercial product as water dispersible granule (WDG), dustable powder (DP) and emulsion concentrate (EC), respectively. Homogeneity and stability, performed for all PT samples, fulfilled the acceptability criteria. The Jarque–Bera test was used to verify the hypothesis of normality of the data distributions before to calculate the z-score values. Laboratory performances, expressed in terms of z-scores, were satisfactory for almost all participants for all substances.

Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ▵▵G≠ versus Enantiomeric Excess.

With the rise of machine learning (ML), the modeling of chemical systems has reached a new era and has the potential to revolutionize how we understand and predict chemical reactions. Here, we probe the historic dependence on utilizing enantiomeric excess (ee) as a target variable and discuss the benefits of using relative Gibbs free activation energies (ΔΔG≠), grounded firmly in transition-state theory, emphasizing practical benefits for chemists. This perspective is intended to discuss best practices that enhance modeling efforts especially for chemists with an experimental background in asymmetric catalysis that wish to explore modelling of their data. We outline the enhanced modeling performance using ΔΔG≠, escaping physical limitations, addressing temperature effects, managing non-linear error propagation, adjusting for data distributions and how to deal with unphysical predictions,in order to streamline modeling for the practical chemist and provide simple guidelines to strong statistical tools. For this endeavor, we gathered ten datasets from the literature covering very different reaction types. We evaluated the datasets using fingerprint-, descriptor-, and graph neural network-based models. Our results highlight the distinction in performance among varying model complexities with respect to the target representation, emphasizing practical benefits for chemists.

New and rare bivalve species for the fauna of the Kuril Islands (northwestern Pacific Ocean): A study of materials collected over 70 years of expeditions (from 1949 to 2019)

Our study of bivalves collected during 24 marine expeditions between 1949 and 2019 at depths of 0–3,000 m off the Kuril Islands resulted in finding 16 new and 11 rare species to the fauna of this region. Their geographical and vertical distribution ranges have been significantly expanded. Species richness within the studied depths is thereby increased by 10%. Of the 27 species examined, three (Huxleyia pentadonta, Limopsis oliveri, and Samacar kurilensis) are endemic to the Kuril Islands. Photographs of shells and revised distribution data, as inferred from this study, are provided for all 27 species.

World of Crayfish™: a web platform towards real-time global mapping of freshwater crayfish and their pathogens

Freshwater crayfish are amongst the largest macroinvertebrates and play a keystone role in the ecosystems they occupy. Understanding the global distribution of these animals is often hindered due to a paucity of distributional data. Additionally, non-native crayfish introductions are becoming more frequent, which can cause severe environmental and economic impacts. Management decisions related to crayfish and their habitats require accurate, up-to-date distribution data and mapping tools. Such data are currently patchily distributed with limited accessibility and are rarely up-to-date. To address these challenges, we developed a versatile e-portal to host distributional data of freshwater crayfish and their pathogens (using Aphanomyces astaci, the causative agent of the crayfish plague, as the most prominent example). Populated with expert data and operating in near real-time, World of Crayfish™ is a living, publicly available database providing worldwide distributional data sourced by experts in the field. The database offers open access to the data through specialized standard geospatial services (Web Map Service, Web Feature Service) enabling users to view, embed, and download customizable outputs for various applications. The platform is designed to support technical enhancements in the future, with the potential to eventually incorporate various additional features. This tool serves as a step forward towards a modern era of conservation planning and management of freshwater biodiversity.

Semi‐supervised contextual cognitive augmentation‐based cross‐teaching network for multiclass medical image segmentation

AbstractThe application of medical image segmentation technology enables accurate localization of human tissues, providing doctors with a reliable foundation for diagnosis. While deep learning methods have proven effective in this task, most current approaches rely on a single prediction framework, which overlooks Edge semantic features and results in flawed texture features. Moreover, existing supervised methods face challenges due to limited availability of high‐quality annotations in the field of medical imaging. In this article, a Semi‐supervised Contextual Cognitive Augmentation‐based Cross‐teaching Network is proposed. A Contextual Cognitive Enhancement Module is introduced consisting of two components: data augmentation and information extraction. The data augmentation component provides multi‐level data distribution by incorporating diverse perturbation strategies such as Discrete Cosine Transform and Gaussian noise. The information extraction component employs the Comprehensive Information Extraction module, which consists of Global Perception Information Extraction module and Multi‐channel Information Extraction module to extract perceptual information from images and enhance interaction between image channels, respectively. Additionally, a cross‐teaching strategy is adopted and a hybrid loss function is utilized to encourage knowledge sharing among the networks, leveraging the advantages of dual networks for improved performance. Experimental results demonstrate significant enhancements in multiclass medical image segmentation compared to several state‐of‐the‐art single‐framework networks.