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This paper discusses the main scientific results of the XXV International Conference on Data Analytics and Management in Data-Intensive DomainsDomains, held on October 24–27, 2022 in Moscow, Russia. The motivation and goals of the conference, main areas of focus, related conferences, and associated events are considered. The program of the current year's conference is described, including the topics of invited and sectional reports. Conclusions are drawn based on the results of the analysis of the scientific contribution of the conference. A list of papers selected by the conference program committee for the special issue of the journal is attached. The papers relate to the areas of image analysis and processing, the application of machine learning methods in medicine, astronomy, materials science, and extracting information from texts. [ABSTRACT FROM AUTHOR]

This paper considers theoretical foundations and mathematical methods of data analysis under the conditions of the Rice statistical distribution. The problem involves joint estimation of the signal and noise parameters. It is shown that this estimation requires the solution of a complex system of essentially nonlinear equations with two unknown variables, which implies significant computational costs. This study is aimed at mathematical optimization of computer algebra methods for numerical solution of the problem of Rician data analysis. As a result of the optimization, the solution of the system of two nonlinear equations is reduced to the solution of one equation with one unknown variable, which significantly simplifies algorithms for the numerical solution of the problem, reduces the amount of necessary computational resources, and enables the use of advanced methods for parameter estimation in information systems with priority of real-time operation. Results of numerical experiments carried out using Wolfram Mathematica confirm the effectiveness of the developed methods for two-parameter analysis of Rician data. The data analysis methods considered in this paper are useful for solving many scientific and applied problems that involve analysis of data described by the Rice statistical model. [ABSTRACT FROM AUTHOR]

Serverless computing has gained significant popularity due to its scalability, cost-effectiveness, and ease of deployment. With the exponential growth of data, organizations face the challenge of efficiently processing and analyzing vast amounts of data in a serverless environment. Data pipelines play a crucial role in managing and transforming data within serverless architectures. This paper provides a taxonomy of data pipeline approaches in serverless computing. Classification is based on architectural features, data processing techniques, and workflow orchestration mechanisms, these approaches are categorized into three primary methods: heuristic-based approach, Machine learning-based approach, and framework-based approach. Furthermore, a systematic review of existing data pipeline frameworks and tools is provided, encompassing their strengths, limitations, and real-world use cases. The advantages and disadvantages of each approach, also the challenges and performance metrics that influence their effectuality have been examined. Every data pipeline approach has certain advantages and disadvantages, whether it is framework-based, heuristic-based, or machine learning-based. Each approach is suitable for specific use cases. Hence, it is crucial assess the trade-offs between complexity, performance, cost, and scalability, while selecting a data pipeline approach. In the end, the paper highlights a number of open issues and future investigations directions for data pipeline in the serverless computing, which involve scalability, fault tolerance, data real time processing, data workflow orchestration, function state management with performance and cost in the serverless computing environments. [ABSTRACT FROM AUTHOR]

Globally, there is a growing momentum toward achieving universal access to clean energy, particularly electricity, as it plays a crucial role in improving the overall quality of life. Addressing the challenge of universal electricity access requires the development of various mechanisms that enable individuals to access electricity at affordable rates while having the freedom to choose their service providers. In many countries, governments are actively involved in enhancing electricity access, but this effort can put strain on state finances, leading to increased fiscal deficits. This paper aims to investigate the relationship between power expenditure and state finances in Jammu and Kashmir, India, in light of the push for improved electricity access. Using data from the RBI Bulletins for the period 1990–91 to 2019–20, the paper employs panel data analysis to investigate the relationship between power expenditure and gross fiscal deficit. Results indicate a positive and significant correlation between expenditure on power and state gross fiscal deficit in Jammu and Kashmir, indicating lower fiscal sustainability with higher power expenditure. For the purpose of reducing the pressure on state finances caused by enormous domestic power subsidies, the study recommended targeted subsidies with a focus on small energy projects utilizing renewable energy sources and energy-saving technologies, as well as incentives and regulations to promote energy conservation. [ABSTRACT FROM AUTHOR]

This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal conditions to compute teacher-centered weights are detailed, including mathematical equations. We provide a proposal on how to define the targeted populations as well as how to collect data that is needed to derive teacher-centered weights, yet currently unavailable. We also tackle the issue of teacher nonresponse by proposing a respective adjustment factor, as well as mentioning the challenge of multiple selection probabilities when teachers teach in multiple schools. The core part of the paper focuses on studying the level of accuracy that can be expected when estimating teacher population characteristics. We use TIMSS 2019 data and simulate likely scenarios regarding the variance in weights. The results show that (i) the different weighting scenarios lead to relatively similar estimates; however, the differences between the scenarios are sufficient to justify the recommendation to use correctly derived teacher weights; (ii) differences between estimated standard errors based on complex sampling and corresponding estimates based on simple random sampling are sufficiently consistent to support use of a procedure to estimate standard errors that accounts for both sample weights and the complex sampling design; (iii) sample sizes and variance in weights significantly limit estimate precision, so that total population estimates with sufficient precision are available in the majority of countries but subpopulation features are generally not sufficiently precise. To provide a critical evaluation of our results, we recommend implementation of the proposed method in one or more countries. This recommended study will permit examination of logistical considerations in implementation of required changes in data acquisition and will provide data to replicate the analysis with teacher-centered weights. [ABSTRACT FROM AUTHOR]

Massive teaching resources will cause serious teaching efficiency problems for online teaching, and traditional online teaching models are even inferior to traditional classroom teaching in terms of teaching effects. Based on this, this paper analyzes massive educational resources and builds a scalable computer interactive education system based on large-scale multimedia data analysis. Moreover, this paper sets the role of the system according to the actual teaching situation, and constructs the functional module of the system structure. In addition, this paper uses computer simulation technology to analyze interactive technology and make technical improvements to make interactive technology the core technology of the computer interactive education system, and get an extensible interactive education system based on the characteristics of network teaching. Then helps to monitor and access the performance of an interactive educational system. Furthermore, this paper designs an experiment to evaluate the performance of the computer interactive education system, which is mainly carried out from two aspects: interactive evaluation and teaching evaluation. From the experimental research results, we can see that this system can effectively improve the quality of teaching. [ABSTRACT FROM AUTHOR]

Global cities play a significant role in world economy as they serve as key hubs of economic activity and trade. These cities are centers of innovation, finance, culture, and commerce, attracting businesses and entrepreneurs from all over the world. They are characterized by their openness, diversity, and their ability to attract and retain talent. This paper includes a bibliometric analysis of the structure of global cities through examining the literature on global cities, including the document type, country/territory distribution, institution distribution, geographical distribution of authors, specially most active authors and their interests or research areas, relationships between principal authors and more relevant journals, and the research hot spots. The input data consists of journal articles archived by the Web of Science from 1991 to 2023, and the analysis is performed using SciMAT and VOS Viewer. The result of this paper would provide valuable insights into the state of research on this topic, including who is conducting research, where it is being conducted, what types of publications are being produced, and which themes are having the most impact on the field. Such an analysis would be useful for researchers, policymakers, and other stakeholders interested in understanding the role of global cities in the world economy. [ABSTRACT FROM AUTHOR]

The latent block model is used to simultaneously rank the rows and columns of a matrix to reveal a block structure. The algorithms used for estimation are often time consuming. However, recent work shows that the log-likelihood ratios are equivalent under the complete and observed (with unknown labels) models and the groups posterior distribution to converge as the size of the data increases to a Dirac mass located at the actual groups configuration. Based on these observations, the algorithm Largest Gaps is proposed in this paper to perform clustering using only the marginals of the matrix, when the number of blocks is very small with respect to the size of the whole matrix in the case of binary data. In addition, a model selection method is incorporated with a proof of its consistency. Thus, this paper shows that studying simplistic configurations (few blocks compared to the size of the matrix or very contrasting blocks) with complex algorithms is useless since the marginals already give very good parameter and classification estimates. [ABSTRACT FROM AUTHOR]

Survival analysis studies time to event data, also called survival data in biomedical research. The main challenge in the analysis of survival data is to develop inferential methods that take into account the incomplete information contained in censored observations. The seminal paper of Kaplan and Meier (J Am Stat Assoc 53:457–481,1958) gave a boost to the development of statistical methods for time to event data subject to right censoring; methods that have been applied in a broad variety of scientific fields including health, engineering and economy. A basic quantity in survival analysis is the survival distribution: S (t) = P (T > t) , with T the time to event or, in case of a bivariate vector of lifetimes (T 1 , T 2) , S (t 1 , t 2) = P (T 1 > t 1 , T 2 > t 2 ). Nonparametric estimation of these basic quantities received, since Kaplan and Meier (J Am Stat Assoc 53:457–481,1958), considerable attention resulting in many publications scattered over a large period of time and a large field of applications. The purpose of this paper is to review, in a unified way, nonparametric estimation of S(t) and S (t 1 , t 2) for time to event data subject to right censoring. Interesting to realize is that, in the multivariate setting, the form of the nonparametric estimator for S (t 1 , t 2) is determined by the actual censoring scheme. In this survey we focus, for the proposed (implicitly) existing or new nonparametric estimators, on the asymptotic normality. By doing so we fill some gaps in the literature by introducing some new estimators and by providing explicit expressions for the asymptotic variances often not yet available for some of the existing estimators. [ABSTRACT FROM AUTHOR]

Purpose: This paper explores how Educational Big Data Analytics can enhance student learning. It investigates the role of active learning in improving students' data analysis skills and critical thinking. By actively engaging students in data analysis assessments, the aim is to equip them with the skills to navigate the data-rich educational landscape. Methods: The study uses a teaching strategy that combines structured and unstructured data analysis using Python tools and ChatGPT APIs. It presents five assignments, each highlighting data analysis skills and encouraging critical thinking. Results: The paper offers insights into how the teaching strategy effectively enhances students' data analysis and critical thinking skills. It investigates the specific impact of active learning on students' engagement with educational data. The study reveals that all students can complete a comprehensive project, integrating the skills they have learned in the five assignments related to educational big data while incorporating the educational implications from their respective disciplines. Conclusion: The key lies in instructors being able to design individual assignments that link practical experiences, enabling each teaching session's effectiveness to accumulate in students' personal experiences and practical skills, ultimately empowering them with the abilities necessary to work effectively with Educational Big Data Analytics. The findings of this study make a valuable contribution to the ongoing conversation about enhancing the educational experience for students in this data-rich era. [ABSTRACT FROM AUTHOR]

In this journal, Sciffer et al. (Large-scale Assessments in Education 10:1–22, 2022), hereafter SP&M, conclude that school socioeconomic compositional (SEC) or school socioeconomic status (school-SES) effects in Australia are substantial and substantively important for research and policy. This paper demonstrates that these claims are unwarranted. Their SEC estimates are much larger than estimates from comparable studies and a metastudy. Despite plausible theoretical reasons and empirical evidence, SP&M do not consider that school academic composition is a significant predictor of student achievement independent of SEC. SEC effects are confounded by academic composition and are typically trivial when considering academic composition. The second part of this paper compares SP&M's estimates with analysis of the same data, from the Australian National Assessments in Performance—Literacy and Numeracy (NAPLAN). In a model corresponding to SP&M analyses comprising demographics, SES, school-SES, and student-level prior achievement, the effects of school-SES are small, with standardized effects mostly less than 0.10. With the addition of academic composition measured by school-level prior achievement, school-SES effects are effectively zero. In contrast, academic composition has significant, albeit small, impacts on student achievement. Therefore, contrary to SP&M's (2022) conclusion, school-SES effects on student achievement in NAPLAN are negligible, whereas school-level prior achievement has small effects. That is not to say that school-SES is always irrelevant, but any assessment of its importance must consider both student- and school-level prior achievement. [ABSTRACT FROM AUTHOR]

Background: Multi-nozzle nebulisers for pressurised intraperitoneal aerosol chemotherapy (PIPAC) are implemented in clinical practice to improve the homogeneity of tissue drug delivery. Nonetheless, the advantages of such devices over one-nozzle nebulisers have not been demonstrated thus far. In this study, we compared the performance of multi- and one-nozzle nebulisers by conducting physical and ex vivo pharmacological experiments. Methods: The one-nozzle nebuliser Capnopen® and the multi-nozzle nebuliser were the subjects of this study. In physical experiments, the aerosol droplet size was measured by laser diffraction spectroscopy. Spatial spray patterns were depicted on blotting paper. Pharmacological experiments were performed on the enhanced inverted bovine urinary bladder model, demonstrating real-time tissue drug delivery, aerosol sedimentation and homogeneity of doxorubicin and cisplatin tissue distribution. Results: The multi-nozzle nebuliser had a sixfold greater aerosolisation flow and a threefold greater angle of aerosolisation than Capnopen®. The aerosol particle size and distribution range were higher than that of Capnopen®. Spray patterns on blotting paper were more extensive with the multi-nozzle nebuliser. Real-time tissue drug delivery with the multi-nozzle nebuliser was over 100 ml within 1 min, and the aerosol sedimentation was 48.9% ± 21.2%, which was not significantly different from that of Capnopen®. The doxorubicin and cisplatin tissue concentrations were greater with Capnopen®. Although there was no significant difference in the homogeneity of doxorubicin distribution between the two devices, the homogeneity of cisplatin distribution was significantly higher with Capnopen®. Conclusion: The multi-nozzle PIPAC nebuliser did not fulfil expectations. Even though the surface spray patterns were broader with the multi-nozzle nebuliser, the tissue drug homogeneity and concentration were greater with Capnopen®. [ABSTRACT FROM AUTHOR]

The tail conditional allocation plays an important role in a number of areas, including economics, finance, insurance, and management. Fixed-margin confidence intervals and the assessment of their coverage probabilities are of much interest. In this paper, we offer a convenient way to achieve these goals via resampling. The theoretical part of the paper, which is technically demanding, is rigorously established under minimal conditions to facilitate the widest practical use. A simulation-based study and an analysis of real data illustrate the performance of the developed methodology. [ABSTRACT FROM AUTHOR]

Trajectory similarity search is one of the most fundamental tasks in spatial-temporal data analysis. Classical methods are based on predefined trajectory similarity measures, consuming high time and space costs. To accelerate similarity computation, some deep metric learning methods have recently been proposed to approximate predefined measures based on the learned representation of trajectories. However, instead of predefined measures, real applications may require personalized measures, which cannot be effectively learned by existing models due to insufficient labels. Thus, this paper proposes a transfer-learning-based model FTL-Traj, which addresses this problem by effectively transferring knowledge from several existing measures as source measures. Particularly, a ProbSparse self-attention-based GRU unit is designed to extract the spatial and structural information of each trajectory. Confronted with diverse source measures, the priority modeling assists the model for the rational ensemble. Then, sparse labels are enriched with rank knowledge and collaboration knowledge via transfer learning. Extensive experiments on two real-world datasets demonstrate the superiority of our model. [ABSTRACT FROM AUTHOR]

This study explored the impact of Human-Elephant Conflict (HEC) on the communities' livelihood in Sarpang District in Bhutan. The main aim of the paper was to study the factors influencing the HEC and its impact on the livelihood of the selected Chiwogs (group of villages within sub district) of Gelephu gewog (one of the sub-districts in southern Bhutan). The study used explanatory sequential mixed method design involving a survey questionnaire and focus group discussions (FGD). Employing purposive sampling method, a total of 110 households were selected for the survey and 24 participants for FGD. For the qualitative data collection, a total of 3 FGDs were carried out with 8 participants each in a group. The data collected were analyzed using descriptive statistics such as mean and standard deviation for quantitative data, and deductive thematic analysis for the qualitative data. The study has shown that crop raiding, property damage, and reports of casualties by elephants as some of the major problems affecting the socio-economic wellbeing of the residents in the study area. Similarly, the study has found out that the current HEC mitigation strategies adopted as less effective. Study also highlights the key suggestive measures expressed by the participants to reduce the future conflicts. The study thus recommends revision of appropriate land-use plan, proper monitoring of implemented mitigation measures and inclusion of compensation schemes among other. [ABSTRACT FROM AUTHOR]

Background: Glycoprotein A repetitions predominant (GARP) is a novel transmembrane protein highly expressed on the surface of regulatory T cells (Tregs), which are a subset of immunosuppressive T lymphocytes that play a major role in inhibiting the antitumor immune response. Many studies documented increased GARP expression in various tumors, which is related to a poorer prognosis, and only one single paper investigated its expression in thyroid tumors. Aim: To evaluate the immunohistochemical expression of GARP in differentiated thyroid carcinomas and their tumor-infiltrating lymphocytes (TILs) in comparison to its expression in other benign and low-risk lesions. Methods: Sixty-nine cases of different thyroid lesions were subgrouped into 37 cases of malignant thyroid neoplasms, 25 cases of benign thyroid lesions, and 7 cases of low-risk neoplasms collected from the Pathology Department Laboratories of Ain Shams University Hospitals during the period from January 2017 to December 2021 and stained immunohistochemically for GARP. Immunohistochemical (IHC) results were evaluated in thyroid epithelial cells and TILs. The expression of GARP was correlated with the different clinicopathological parameters. Results: GARP expression discloses a significant statistical difference between the three studied groups (P < 0.001). High GARP expression was detected in 89.19% of the malignant cases and in 28.57% of low-risk neoplasms, while all benign lesions exhibited low GARP expression. High GARP expression of TILs was detected in 60% of the malignant cases. Synchronous high GARP expression in tumor tissue and in the surrounding TILs was detected in 63.16% of the malignant cases, yet these results did not reach statistical significance. Conclusion: GARP is a marker of Tregs, whose high expression is increased in malignant over benign and low-risk lesions. It might be a potential novel target for anticancer immunotherapy. [ABSTRACT FROM AUTHOR]

E-mental health (EMH) interventions gain increasing importance in the treatment of mental health disorders. Their outpatient efficacy is well-established. However, research on EMH in inpatient settings remains sparse and lacks a meta-analytic synthesis. This paper presents a meta-analysis on the efficacy of EMH in inpatient settings. Searching multiple databases (PubMed, ScienceGov, PsycInfo, CENTRAL, references), 26 randomized controlled trial (RCT) EMH inpatient studies (n = 6112) with low or medium assessed risk of bias were included. A small significant total effect of EMH treatment was found (g = 0.3). The effect was significant both for blended interventions (g = 0.42) and post-treatment EMH-based aftercare (g = 0.29). EMH treatment yielded significant effects across different patient groups and types of therapy, and the effects remained stable post-treatment. The results show the efficacy of EMH treatment in inpatient settings. The meta-analysis is limited by the small number of included studies. [ABSTRACT FROM AUTHOR]

Spherical data arise widely in various settings. Spherical statistics is an analysis of data on a unit hyper-spherical domain. In this paper, we mainly consider the local kernel estimators for regression models with a binary response and the predictors including spherical variables. We apply the random forests kernel to nonparametric binary regression models with spherical predictors. Simulation experiments and real examples are used to validate the performance of the new models. Compared with the classical von Mises–Fisher kernel and the linear-spherical kernel, the random forests kernel has better fitting effect and faster computation speed. Compared with other classifiers, the models proposed in this paper have better classification performance in both low and high dimensional cases. [ABSTRACT FROM AUTHOR]

This paper delves into the late-time accelerated expansion of the universe and the evolution of cosmic structures within the context of a specific f (R , L m) gravity model, formulated as f (R , L m) = λ R + β L m α + η . To study the cosmological viability of the model, we employed the latest cosmic measurement datasets: (i) 57 observational Hubble parameter data points (OHD); (ii) 1048 distance moduli data points (SNIa); (iii) a combined dataset (OHD+SNIa); and large scale structure datasets, including (iv) 14 growth rate data points (f); and (v) 30 redshift space distortion data points (f σ 8 ). These datasets facilitated the constraint of the f (R , L m) -gravity model via MCMC simulations, followed by a comparative analysis with the Λ CDM model. A comprehensive statistical analysis has been conducted to evaluate the f (R , L m) -gravity model's efficacy in explaining both the accelerated expansion of the universe and the growth of cosmic structures. Using large-scale structure data, we find the best-fit values of Ω m = 0. 242 - 0.032 + 0.016 , α = 1. 15 - 0.20 + 0.20 , β = 1. 12 - 0.30 + 0.13 , λ = 0. 72 - 0.13 + 0.30 and γ = 0.555 ± 0.014 using f-data and Ω m = 0. 284 - 0.049 + 0.035 , σ 8 = 0. 799 - 0.086 + 0.045 , α = 0. 766 - 0.064 + 0.026 , β = 1. 08 - 0.16 + 0.40 , and λ = 0. 279 - 0.11 + 0.078 using f σ 8 -data at the 1 σ and 2 σ confidence levels, respectively, with the model showing substantial observational support based on Δ AIC values but less observational support based on the Δ BIC values on Jeffreys' statistical criteria. On the other hand, from the joint analysis of the OHD+SNIa data, we obtain α = 1. 091 - 0.042 + 0.035 , β = 1. 237 - 0.16 + 0.056 and λ = 0. 630 - 0.050 + 0.031 with the Jeffreys' scale statistical criteria showing the f (R , L m) model having substantial support when using OHD data, less observational support with the joint analysis OHD+SNIa, and rejected using SNIa data, compared with Λ CDM at the background level. [ABSTRACT FROM AUTHOR]

Deep learning-based approaches for understanding and analyzing 3D flow field grids have been extensively studied in recent years due to their importance in exploring the physical mechanisms of flow fields. However, these methods have shown significant success, which fail to fully utilize the available information and robustly accomplish the 3D flow field grid data analysis. Specifically, the limitation of grid information hampers flow field-based methods from analyzing global velocity distance. Conversely, the absence of velocity information poses challenges for grid-based methods to understand local grid structure. To address the aforementioned issues and cater to most downstream tasks, this paper introduces a flow field-aware network (FFANet). The main innovations of FFANet include: (i) constructing a multi-scale feature learning module using the self-attention mechanism to independently learn features of different scales for velocity distribution and grid structure information. This module aims to generate a global feature with enhanced discriminative representation to improve overall performance; (ii) building a co-attention module to adaptively learn the co-matrix between the aforementioned two features, enhancing effective information utilization of the global feature; (iii) proposing a wall identification method based on the classification module of FFANet, which facilitates generating surface cloud maps and streamlining. Experimental results demonstrate the superior performance of FFANet compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

For many years, spectral remote sensing has been essential for research on the Earth's surface. The data from a single satellite sensor is sometimes insufficient to fulfil the expanding needs of remote sensing applications. Spatial-temporal fusion techniques have become an effective way for merging spectral data from many sources and times, enabling improved data analysis and interpretation. The goal of this review paper is to offer a thorough examination of the historical growth of spatio-temporal fusion techniques for spectral remote sensing. The classification of all currently used fusion approaches, such as Unmixing, Weight-based, Bayesian-based, machine learning-based, and hybrid methods, is covered in detail. Additionally, it evaluates pixel-level, decision-level, and feature-level-based data fusion techniques and compares and contrasts their advantages and disadvantages. The report also discusses spatiotemporal fusion's difficulties and recommends future advances. For those working in remote sensing research and practice, it offers an invaluable resource. In conclusion, this review paper provides a comprehensive overview of spatio-temporal fusion systems for spectral remote sensing, including an analysis of their comparative benefits and drawbacks and a description of their historical development. It aims to stimulate further research and development of spatio-temporal fusion methods for spectral remote sensing. In summary, this review paper presents a comprehensive overview of spatio-temporal fusion methods for spectral remote sensing, including their historical development, categorization of existing techniques and applications, and a comparative analysis of their strengths and limitations. It also discusses the current challenges and future research directions, providing a valuable resource for the remote sensing community. [ABSTRACT FROM AUTHOR]

This paper introduces a bounded probability distribution which is derived from the Muth distribution. The main statistical properties are studied and analytical expressions are provided for the moments, incomplete moments, inverse of the cumulative distribution function, extropy, Lorentz and Bonferroni curves, among others. Moreover, it possesses both monotone and non-monotone hazard rate functions so the new distribution is rich enough to model real data. Different estimation methods are applied to estimate the parameters of the model and a Monte Carlo simulation study assesses their performances. The usefulness in practical applications is illustrated using two real data sets and the results show that the proposed distribution provides better fits than other competing distributions commonly used to model data with bounded support. [ABSTRACT FROM AUTHOR]

Innovation is increasingly recognized as a linchpin for CSR transformation within organizations. Beyond mere compliance, businesses are embracing innovation as a strategic imperative to address societal challenges. Using panel-data analysis spanning from 2014 to 2023, this paper investigates the relationship between innovation strategies and Corporate Social Responsibility (CSR) performance, with a special emphasis on the moderating effect of Green Innovation (GI). We examine how panel data analysis shows, green innovation is found to be an important moderator that lessens the possible harm that China's high-tech may cause to CSR. Results further shows that green innovations can positively moderate relationship between China's high-tech and CSR. Further, we found several other key variables (state equity, innovation level, market value) to improve firm's innovations and CSR performance. The findings of this study have important implications for practitioners to give sustainable innovation strategies top priority, and policymakers are advised to encourage ecologically friendly behaviour. [ABSTRACT FROM AUTHOR]

Ambient Intelligence deployments are very vulnerable to Cyber-Physical attacks. In these attacking strategies, intruders try to manipulate the behavior of the global system by affecting some key elements within the deployment. Typically, attackers inject false information, integrate malicious devices within the deployment, or infect communications among sensor nodes, among other possibilities. To protect Ambient Intelligence deployments against these attacks, complex data analysis algorithms are usually employed in the cloud to remove anomalous information from historical series. However, this approach presents two main problems. First, it requires all Ambient Intelligence systems to be networked and connected to the cloud. But most new applications for Ambient Intelligence are supported by isolated systems. And second, they are computationally heavy and not compatible with new decentralized architectures. Therefore, in this paper we propose a new decentralized security solution, based on a Blockchain ledger, to protect isolated Ambient Intelligence deployments. In this ledger, new sensing data are considered transactions that must be validated by edge managers, which operate a Blockchain network. This validation is based on reputation metrics evaluated by sensor nodes using historical network data and identity parameters. Through information theory, the coherence of all transactions with the behavior of the historical deployment is also analyzed and considered in the validation algorithm. The relevance of edge managers in the Blockchain network is also weighted considering the knowledge they have about the deployment. An experimental validation, supported by simulation tools and scenarios, is also described. Results show that up to 93% of Cyber-Physical attacks are correctly detected and stopped, with a maximum delay of 37 s. [ABSTRACT FROM AUTHOR]

Background: Cognitive impairment associated with schizophrenia (CIAS) represents a distinct, persistent, and core group of schizophrenia symptoms. Cognitive symptoms have been shown to have an impact on quality of life. There are several published CIAS measures, but none based on direct patient self-report. It is important to capture the patient's perspective to supplement performancebased outcome measures of cognition to provide a complete picture of the patient's experience. This paper describes additional validation work on the Patient-Reported Experience of Cognitive Impairment in Schizophrenia (PRECIS) instrument. Methods: Data from two large, international, pharmaceutical clinical trials in medically and psychiatrically stable English-speaking patients with schizophrenia and 88 healthy controls were analyzed. An exploratory factor analysis (EFA) was conducted in one trial (n = 215), using the original 35-item PRECIS. The factor structure suggested by EFA was further evaluated using item response theory (IRT; Samejima's graded response model), and tested using confirmatory factor analysis (CFA). Both EFA and CFA results were tested in a second trial with similar inclusion/exclusion characteristics (n = 410). Additional statistical properties were evaluated in healthy controls. Results: EFA suggested that the best solution after item reduction suggested a factor structure of 6 factors based on 26 items (memory, communication, self-control, executive function, attention, sharpness of thought), supporting a total score, with an additional 2-item bother score (28 items in all). IRT analysis indicated the items were well-ordered within each domain. The CFA demonstrated excellent model fit, accounting for 69% of the variance. The statistical properties of the 28-item version of the PRECIS were confirmed in the second trial. Evidence for internal consistency and test-retest reliability was robust. Known-groups validity was supported by comparison of healthy controls with patients with schizophrenia. Correlations indicated moderate associations between PRECIS and functioning instruments like the Schizophrenia Cognition Rating Scale (SCoRS), but weak correlations with performance-based outcomes like MATRICS Consensus Cognitive Battery (MCCB). Discussion: Using two clinical trial samples, we identified a robust factor structure for the PRECIS and were able to replicate it in the second sample. Evaluation of the meaningful score difference (MSD) should be repeated in future studies, as these samples did not show enough change for it to be evaluated. Conclusions: This analysis provides strong evidence for the reliability and validity of the PRECIS, a 28-item, patient-reported instrument to assess cognitive impairment associated with schizophrenia. The correlation with functioning and the weak correlation with performance on cognitive tasks suggests that patient reports of cognitive impairment measure a unique aspect of patient experience. [ABSTRACT FROM AUTHOR]

Spectroscopic techniques generate one-dimensional spectra with distinct peaks and specific widths in the frequency domain. These features act as unique identities for material characteristics. Deep neural networks (DNNs) has recently been considered a powerful tool for automatically categorizing experimental spectra data by supervised classification to evaluate material characteristics. However, most existing work assumes balanced spectral data among various classes in the training data, contrary to actual experiments, where the spectral data is usually imbalanced. The imbalanced training data deteriorates the supervised classification performance, hindering understanding of the phase behavior, specifically, sol-gel transition (gelation) of soft materials and glycomaterials. To address this issue, this paper applies a novel data augmentation method based on a generative adversarial network (GAN) proposed by the authors in their prior work. To demonstrate the effectiveness of the proposed method, the actual imbalanced spectral data from Pluronic F-127 hydrogel and Alpha-Cyclodextrin hydrogel are used to classify the phases of data. Specifically, our approach improves 8.8%, 6.4%, and 6.2% of the performance of the existing data augmentation methods regarding the classifier's F-score, Precision, and Recall on average, respectively. Specifically, our method consists of three DNNs: the generator, discriminator, and classifier. The method generates samples that are not only authentic but emphasize the differentiation between material characteristics to provide balanced training data, improving the classification results. Based on these validated results, we expect the method's broader applications in addressing imbalanced measurement data across diverse domains in materials science and chemical engineering. [ABSTRACT FROM AUTHOR]

Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods can be used for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies within real-world datasets. In this paper, we present Multi-representation DeepInsight (MRep-DeepInsight), a novel extension of the DeepInsight method designed to enhance the analysis of tabular data. By generating multiple representations of samples using diverse feature extraction techniques, our approach is able to capture a broader range of features and reveal deeper insights. We demonstrate the effectiveness of MRep-DeepInsight on single-cell datasets, Alzheimer's data, and artificial data, showcasing an improved accuracy over the original DeepInsight approach and machine learning methods like random forest, XGBoost, LightGBM, FT-Transformer and L2-regularized logistic regression. Our results highlight the value of incorporating multiple representations for robust and accurate tabular data analysis. By leveraging the power of diverse representations, MRep-DeepInsight offers a promising new avenue for advancing decision-making and scientific discovery across a wide range of fields. [ABSTRACT FROM AUTHOR]

Air quality prediction technology can provide effective technical means for environmental governance. In recent years, due to the strong nonlinearity of data, there has been extensive research on data analysis and preprocessing techniques. This paper aims to comprehensively summarize and analyze the methods used in air quality forecasting, specifically focusing on four categories: data decomposition, dimensionality reduction, data correction, and spatial interpolation. Each method's purpose, characteristics, improvements, and implementation details are described in detail. The evaluation of data preprocessing methods is based on popularity, accuracy improvements, time consumption, maturity, and implementation difficulty. Among the existing methods, data decomposition and feature selection are commonly used and well-developed. However, outlier detection and spatial interpolation have limited applications and require further research. Furthermore, this paper discusses current challenges in applying these methods and future development trends, providing a valuable reference for future research. [ABSTRACT FROM AUTHOR]

This paper presents a conceptual framework that accurately represents the current and future perspectives of data-driven companies in tourism by means of an analysis of the data sources used in the data-driven tourism research literature, as well as the research topics to which they are applied. For this purpose, a bibliometric analysis of data-driven tourism research is carried out. The framework of the study is all tourism-related publications whose research was based on data sources during the period 1982–2020. The results show some of the basic bibliometric performance indicators and the maps of science. The main themes of research interest are identified, and the conceptual evolution is obtained based on these maps. Three major thematic areas are identified: tourism research topics, information sources, and data analysis techniques. Based on these three thematic areas, the conceptual model of data architecture and processes of a data-driven organization in the tourism sector are obtained. An additional qualitative analysis of the three thematic areas is performed. [ABSTRACT FROM AUTHOR]

This paper analyses Twitter data to detect the political lean of a profile by extracting and classifying sentiments expressed through tweets. The work utilizes natural language processing, augmented with sentiment analysis algorithms and machine learning techniques, to classify specific keywords. The proposed methodology initially performs data pre-processing, followed by multi-aspect sentiment analysis for computing the sentiment score of the extracted keywords, for precisely classifying users into various clusters based on similarity score with respect to a sample user in each cluster. The proposed technique also predicts the sentiment of a profile towards unknown keywords and gauges the bias of an unidentified user towards political events or social issues. The proposed technique was tested on Twitter dataset with 1.72 million tweets taken from over 10,000 profiles and was able to successfully identify the political leniency of the user profiles with 99% confidence level, and also on a synthetic dataset with 2500 tweets, where the predicted accuracy and F1 score were 0.99 and 0.985 respectively, and 0.97 and 0.975 when neutral users were also considered for classification. The paper could also identify the impact of political decisions on various clusters, by analyzing the shift in the number of users belonging to the different clusters. [ABSTRACT FROM AUTHOR]

In order to improve the effectiveness of tourism management classroom teaching, this article intelligentizes the teaching process of tourism management, and combines multimedia technology and big data technology to improve the teaching process. The crisis early warning system based on the image monitoring index system monitors and observes the components of the tourist destination from the perspective of tourists, and calculate the indexes based on the monitored results, so as to discover the possibility of image crisis events in time and better serve tourists. In addition, this article builds a tourism management classroom teaching system based on multimedia and big data technology on this basis, and tests the system. Through research, it can be seen that the crisis early warning system based on the image monitoring index system and the tourism management classroom teaching system based on multimedia and big data technology proposed in this paper have good practical effects. [ABSTRACT FROM AUTHOR]

Clustering streaming data is challenging due to many temporal dynamics, such as concept drift, concept evolution, and feature evolution. Concept evolution is the most challenging of these. Due to concept evolution, new classes may emerge or existing classes may disappear, so it is crucial to process streaming data continuously. This paper proposes a novel online clustering method, specifically for streaming data with concept evolution. It consists of three phases: initialization, clustering and outlier handling. To identify recurrences of previous data in streaming data, it is critical to preserve the sequential properties of data chunks. In the proposed model, representatives from previous windows are added to the current window, making it distinct from existing models. The detection and handling of outliers are very challenging tasks in streaming data analysis. Outliers are often the first instances of a new cluster. The proposed model stores the outliers from each data window. When the number of outliers exceeds a certain threshold, the representatives of outliers are added to the next window to identify new classes. The lack of data sets made it necessary for us to create a synthetic data set with 22020 data instances and test the model on both synthetic and real datasets. Using Silhouette Coefficient, Calinski–Harabasz index, and Davies–Bouldin index analysis, this model yielded the most favourable results. [ABSTRACT FROM AUTHOR]

Engineering education has slowly been making its way into schools with the aim of promoting engineering literacy, which is central to learning and working in a technology-oriented society. Educators and policy makers advocate the need for developing students' understanding of the nature of engineering (NOE); yet, there is an ongoing debate on the heuristics that should be applied. In this article, we review and discuss current studies on engineering education in schools and the integration of engineering into the science curriculum. We describe four aspects of engineering fields: Structures, Machines, Materials, and Data, each uniquely characterized by the technology used and the artefact produced. We discuss the application of the Family Resemblance Approach (FRA) to the characterization of NOE, focusing on the cognitive and epistemic domain. Accordingly, we describe NOE through four categories: Aims & Values, Engineering Practices, Methods & Methodological Rules, and Engineering Knowledge, which can guide teaching and learning about NOE. Building on the FRA, this paper provides a framework for a continuous discussion on NOE and the theoretical and practical relationships between science and engineering. [ABSTRACT FROM AUTHOR]

The paper attempts to answer a very obstinate fundamental problem—is literary meaning determinable at all? Would it be determinable if it were constructed by the language of the text? Or is this meaning open-ended, constantly deferred or shifted as a result of the very nature of signification? In this paper, I argue that the levels of dhvani-ṣ Avivakṣitavācya dhvani and Vivakṣitavācya dhvani and their sub-levels are far more comprehensive than the concept of 'differance', both based on the play of meanings. Ānandvardhana seems to anticipate Derrida in postulating the infinite suggested meaning over and above the expressed literal meaning in the levels of dhvani, though the admission of any definite literal meaning itself goes against the deconstructive approach of 'differance'. The paper also deals with the analysing a few literary works with the levels of dhvani and also how meaning of a text is revealed to the sahrdaya (reader). [ABSTRACT FROM AUTHOR]

Data releasing and sharing between several fields has became inevitable tendency in the context of big data. Unfortunately, this situation has clearly caused enormous exposure of sensitive and private information. Along with massive privacy breaches, privacy-preservation issues were brought into sharp focus and privacy concerns may prevent people from providing their personal data. To meet the requirements of privacy protection, such a problem has been extensively studied. However, privacy protection of sensitive information should not prevent data users from conducting valid analyses of the released data. We propose a novel algorithm in this paper, named Data Release under Adjustable Privacy-utility Equilibrium (DRAPE), to address this problem. We handle the privacy versus utility tradeoff in the data release problem by breaking sensitive associations among variables while maintaining the correlations of nonsensitive variables. Furthermore, we quantify the impact of the proposed privacy-preserving method in terms of correlation preservation and privacy level, and thereby develop an optimization model to fulfil data privacy and data utility constraints. The proposed approach is not only able to provide a better privacy levels control scheme for data publishers, but also provides personalized service for data requesters with different utility requirements. We conduct experiments on one simulated dataset and two real datasets, and the simulation results show that DRAPE efficiently achieves a guaranteed privacy level while simultaneously effectively preserving data utility. [ABSTRACT FROM AUTHOR]

Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications. [ABSTRACT FROM AUTHOR]

This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis. [ABSTRACT FROM AUTHOR]

The lives of caregivers can be deeply impacted by having a child with a developmental disability (DD). To offset those impacts, caregivers may engage in accommodations, or strategies to bolster everyday functioning. The nature and extent of these accommodations can provide insight into how the family is doing and what supports are needed from a family-centered perspective. This paper presents the development and preliminary validation of the Accommodations & Impact Scale for Developmental Disabilities (AISDD). The AISDD is a rating scale that measures day-to-day accommodations and impacts of raising a child with a DD. A sample of 407 caregivers of youth with DDs (Mage = 11.7 years; 63% males) completed the AISDD, along with measures of caregiver strain, daily challenges, child adaptive behavior, and behavior and emotional regulation. The AISDD is a unidimensional, 19-item scale with excellent internal consistency (ordinal alpha =.93) and test–retest (ICC =.95) reliability. Scores were normally distributed and sensitive to age (r = −.19), diagnosis (ASD + ID > ASD > ID), adaptive functioning (r = −.35), and challenging behaviors (r =.57). Finally, the AISDD showed excellent convergent validity with similar measures of accommodations and impacts. These findings support the use of the AISDD as a valid and reliable tool for measuring accommodations among caregivers of individuals with DDs. This measure shows promise in its ability to identify which families may need additional support for their children. [ABSTRACT FROM AUTHOR]

In this paper, we consider the partial linear regression model yi = xiβ* + g(ti) + εi, i = 1, 2, ..., n, where (xi, ti) are known fixed design points, g(·) is an unknown function, and β* is an unknown parameter to be estimated, random errors εi are (α, β)-mixing random variables. The p-th (p > 1) mean consistency, strong consistency and complete consistency for least squares estimators of β* and g(·) are investigated under some mild conditions. In addition, a numerical simulation is carried out to study the finite sample performance of the theoretical results. Finally, a real data analysis is provided to further verify the effect of the model. [ABSTRACT FROM AUTHOR]

This paper delves into a novel micro-substance tracer test in fractured horizontal well C-15. The experimental results are highly encouraging as they demonstrate that the trace material tracer is capable of satisfying the testing demands, even when there are large numbers of fracturing stages involved. Data interpretation process involved dividing the test duration into two stages-fluid flowback period and stable production period. The tracer test data were employed to analyze the production profile of the well. The findings made it evident that the primary production stage underwent alterations in different production stages. Moreover, the degree of heterogeneity pertaining to each fracturing stage was characterized by employing the residence time distribution method. It was observed that the Lorentz coefficient lying between the primary production stage and the remaining fracturing stages ranged from 0.46 to 0.68. This study expands the application of the residence time distribution method for evaluating tracer testing. Through a comprehensive analysis of heterogeneity data within the fracturing stages and the production dynamics of the well, the effectiveness of the fracturing process can be assessed. This research enables reservoir operators to gain deeper insights into the dynamics of test wells, ultimately leading to increased production efficiency. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Cerebrospinal Fluid from Aneurysmal Subarachnoid Hemorrhage Patients Leads to Hydrocephalus in Nude Mice" published in the journal Neurocritical Care. The authors have identified an error in Figure 1A of the manuscript, where one MRI image was inadvertently duplicated. The corrected version of Figure 1A is provided in the document. The error does not affect any of the data analysis in the paper. The publisher, Springer Nature, maintains a neutral stance on jurisdictional claims and institutional affiliations. [Extracted from the article]

This paper presents an analysis of the impact of the indoor classroom environment on students' learning efficiency. The research is based on a classroom smart monitoring and a questionnaire about the students' assessment of the comfort conditions and learning efficiency. Multisensor devices are used to measure the indoor temperature, relative humidity, and CO2 concentration at the students' desks. Data analysis concerned an investigation of the spatial and temporal variation of the comfort parameters and their correlation with students' assessment of comfort conditions and learning efficiency. The results show a significant spatial variation in the indoor comfort conditions, particularly for temperature and CO2 concentration. The indoor temperature could exceed by up to 5 °C, the temperature threshold limits value in France's public buildings. At the beginning of the class, the learning efficiency correlates well with the students' assessment of comfort conditions. At the end of the class, the results show a weak correlation with both recorded comfort parameters and the students' assessment of the indoor conditions. The results indicate a decrease in learning efficiency during the class. However, students do not mainly attribute this decrease to the degradation in indoor conditions. [ABSTRACT FROM AUTHOR]

Terahertz frequency domain spectroscopy provides a superior resolution in the broad range from 50 GHz to 5 THz. However, temporal frequency drift and the standing wave pattern in the spectrometer, which often act simultaneously, produce intensive coherent noise in the transmittance spectra of the measured samples. The paper presents a spectrum processing method allowing to reduce the coherent noise and thus significantly enhance the accuracy of spectral data analysis. The idea of the method is to remove the small-period (0.25 GHz) oscillations in the standing wave pattern of the measured signal by applying the windowed Fourier filtering. The large-period (4 GHz) oscillations in the measured spectra are broadened and then used to compensate the frequency drift of the two consecutive measurements of the signal with the sample and without it. The proposed approach is tested on the measured spectra of metamaterial and silicon wafer. Its advantage over the classical method based on the averaging of adjacent data points is confirmed. Our work benefits to optimization of the frequency domain terahertz systems and paves the way for fast and accurate analysis of spectral data. [ABSTRACT FROM AUTHOR]

Deterioration of water infrastructure is a global challenge that jeopardizes water system ability to deliver water safely. While various factors affect watermain failure, previous studies have focused on common pipe attributes or general protection strategies. The main objective of this study is to examine the relationship between pipe break characteristics and system properties. Comprehensive data from thirteen Canadian water systems (over 60,000 failures) are examined with correlation and chi-squared analyses. Joint and fitting failures are most likely for pipes aged 20 years or less, and universal joints are most associated with joint failure. Pipes in clay and sand soils are more likely to break due to improper bedding and differential settlement, respectively. Furthermore, in the summer, accidental breaks of asbestos cement pipes are more likely, as are failures of pipes with collar joints and coal tar lined pipes. By exploring these relationships, the paper provides insights into opportunities for reducing water main failure, through improved design, maintenance and rehabilitation. [ABSTRACT FROM AUTHOR]

In recent years, experimental economics has seen a rise in the collection and analysis of choice-process data, such as team communication transcripts. The main purpose of this paper is to understand whether the collection of team communication data influences how individuals reason and behave as they enter the team deliberation process, i.e. before any communication exchange. Such an influence would imply that team setups have limited validity to speak to individual reasoning processes. Our treatment manipulations allow us to isolate the effects of (1) belonging to a team, (2) actively suggesting an action to the team partner, and (3) justifying the suggestion in a written text to the team partner. Across three different tasks, we find no systematic evidence of changed suggestions and altered individual sophistication due to changes in aspects (1)–(3) of our experimental design. We thus find no threat to said validity of team setups. In addition to investigating how the team setup affects individual behavior before communication, we also investigate the sophistication of decisions after the communication. We find that sophisticated strategies are more persuasive than unsophisticated strategies, especially when communication includes written justifications, thereby explaining why teams are more sophisticated and proving rich communication to be fruitful. [ABSTRACT FROM AUTHOR]

This paper contributes to the research on the development of comparable composite indicators by introducing a Functional Weighted Malmquist Productive Index that allows for comparative trend analysis. In analogy with entropy-based weighted methods, this novel dynamic indicator is derived by measuring the degree of diversification of the single method through a family of diversity indices. The paper has the merit of proposing a new dynamic composite indicator that supplements the analysis with Functional Data Analysis (FDA) tools that provide us with useful information about the order and dynamics of the composite index trajectories. The simulation study set up in this paper raises doubts about the robustness of the entropy-based weighted methods while the application of the new index to well-being dataset highlights its practical appeal. [ABSTRACT FROM AUTHOR]

Valid inference can be drawn from a random-effects model for repeated measures that are incomplete if whether the data are missing or not, known as missingness, is independent of the missing data. Data that are missing completely at random or missing at random are two data types for which missingness is ignorable. Given ignorable missingness, statistical inference can proceed without addressing the source of the missing data in the model. If the missingness is not ignorable, however, recommendations are to fit multiple models that represent different plausible explanations of the missing data. A popular choice in methods for evaluating nonignorable missingness is a random-effects pattern-mixture model that extends a random-effects model to include one or more between-subjects variables that represent fixed patterns of missing data. Generally straightforward to implement, a fixed pattern-mixture model is one among several options for assessing nonignorable missingness, and when it is used as the sole model to address nonignorable missingness, understanding the impact of missingness is greatly limited. This paper considers alternatives to a fixed pattern-mixture model for nonignorable missingness that are generally straightforward to fit and encourage researchers to give greater attention to the possible impact of nonignorable missingness in longitudinal data analysis. Patterns of both monotonic and non-monotonic (intermittently) missing data are addressed. Empirical longitudinal psychiatric data are used to illustrate the models. A small Monte Carlo data simulation study is presented to help illustrate the utility of such methods. [ABSTRACT FROM AUTHOR]

Control charts are widely used as a tool in process quality monitoring to detect anomalies and to improve the quality of a process and product. Nevertheless, their limitations have increased in the face of increasingly complex manufacturing processes. They do not have capability of handling large streams of non-normal and autocorrelated multivariate data, which is in most real applications. This may lead to an increase in false alarm signals and/or missed detection of anomalies. They are not designed to automatically identify the root causes of an anomaly when the process is out-of-control. Several machine-learning techniques were integrated with control charts to improve the sensitivity and specificity of anomaly detection. Nevertheless, some existing techniques still produce a high false alarm rate and/or missed detection. The root cause analysis is seldom performed. In this paper, we propose a new integration that combines the logical analysis of data regression technique (LADR) and the exponential weighted moving average (EWMA) as a new model-based control chart. LADR is based on the traditional LAD methodology, which is a supervised data mining technique for pattern generation. LADR transforms the original independent variables into pattern variables by using cbmLAD software to develop a regression model. The LADR–EWMA increases the sensitivity of anomaly detection in the process and uses the patterns to perform root cause analysis of that anomaly. We applied LADR–EWMA to a real application: a concrete manufacturing process. We compared its performance with Linear regression, Support vector regression, Partial Least Square regression, and Multivariate adaptive regression Spline. The results demonstrate that the LADR–EWMA, which is based on pattern recognition, performs better compared to the other techniques in terms of a reduction of false alarms and missed detection. In addition, LADR–EWMA facilitates interpretation and identification of the root cause of the detected anomaly. [ABSTRACT FROM AUTHOR]

Assessment of study success using conditional power (CP), the predictive power of success (PPoS) and probability of success (PoS) is becoming increasingly common for resource optimization and adaption of trials in clinical investigation. Determination of these measures is often a non-trivial mathematical task. Further, the terminologies used across the literature are not consistent, and there is no consolidated presentation on this. We have made a structured presentation on these measures for both trial success and clinical success: first, we have summarized the expressions of CP, PPoS and PoS in a general setting and subsequently, expressions for these measures are obtained for continuous, binary, and time-to-event endpoints in single-arm and two-arm trial settings. Many of these expressions are previously published; however, some of the expressions are very new including the expressions for testing median of time-to-event endpoint in a single-arm trial. We have also shown that 1 / (no. of events) consistently underestimates the variance of log(median) and alternative expression for variance was derived. Examples are given along with the comparison of CP and PPoS. Expressions presented in this paper are implemented in LongCART package in R and in R shiny app https://ppos.shinyapps.io/public/. [ABSTRACT FROM AUTHOR]

This study analyzes the impact of e-governance on corruption using a panel dataset comprising 110 countries over the period from 2003 to 2021. The specific focus of the research is on the relationship between the E-government Development Index (EGDI) and its four components, namely the Online Service Index (OSI), Human Capital Index (HCI), Telecommunication Infrastructure Index (TII), and E-Participation Index (EPI), and their effects on the Corruption Perceptions Index (CPI). To examine these relationships, the empirical analysis employs a fixed effect model, which is a suitable statistical approach for panel data analysis. The findings of the study reveal significant negative associations between the EGDI, OSI, HCI, and TII with corruption levels across countries worldwide. This implies that higher levels of e-government development, online service provision, human capital, and telecommunication infrastructure are associated with lower corruption perceptions. However, the EPI does not demonstrate a statistically significant relationship with the CPI. Based on these results, the paper provides recommendations for governments to prioritize two key areas. Firstly, governments should invest in the development of e-government infrastructure to enhance transparency, accountability, and efficiency in public administration, thereby reducing corruption. Secondly, initiatives aimed at enhancing public awareness and understanding of e-governance should be prioritized, as they contribute to increased citizen engagement and participation, which can ultimately lead to improved governance practices and reduced corruption levels. [ABSTRACT FROM AUTHOR]