Original Data Research Articles

The role of the new institutional sociology in implementing the environmental agenda

The new institutional Sociology declares that organizations, whether public or private, are influenced by norms, customs and beliefs institutionalized in their structure. Therefore, it is possible to infer that these factors can also influence organizations' actions regarding the environment and sustainability. Based on this understanding, we sought to investigate the role of the new institutional Sociology in the implementation of the environmental agenda. Given the purpose of this work, it is possible to classify it as bibliographic and exploratory in nature. Scientific articles, books, reports and other secondary data of national and international origin were used as theoretical support for the research. In the context of the environmental agenda, institutions can materialize in the form of laws, regulations, public policies, positioning of the professional class, behavior of successful organizations, consumer preferences and many other factors. It was noticed that institutions pressure organizations to develop sustainable practices and these, in turn, enable environmental conservation without necessarily impeding economic development. It was also possible to identify that the pressures felt by organizations to adopt similar environmental practices are explained through the phenomenon understood as institutional isomorphism. This occurs in three ways: coercive; mimetic and normative. Furthermore, this study allowed us to understand the levels of implementation of an environmental agenda. However, it was observed that agendas can be implemented at global, national and local levels. And, the adoption of environmentally sustainable practices in organizations is motivated, above all, by the need to legitimize organizations.

Improving ventilation classification in under-actuated zones: a k-nearest neighbor and data preprocessing approach

This study investigates the use of k-nearest neighbors (k-NN) for classifying occupant positions in under-actuated zones, aiming to enhance ventilation control. The focus is on evaluating different data preprocessing techniques, particularly cumulative moving average (CMA), Kalman filtering (KF), and their combination, to boost the k-NN model's reliability and accuracy. The research uses received signal strength indicator (RSSI) data in a controlled setting. The methodology involves dividing the dataset into training and testing subsets and using root mean squared error (RMSE) to determine the best k value for model validation. The study performs a comparative analysis of the k-NN model's performance with both original and preprocessed RSSI data, focusing on metrics such as accuracy, precision, recall, F1-score, and RMSE. The findings emphasize the significant impact of the combined CMA-KF preprocessing technique in improving the model's accuracy and reliability. Specifically, this approach achieved an accuracy of 98.58%. The RMSE values are particularly noteworthy, exhibiting a perfect fit (RMSE of 0) for training data and a remarkably low RMSE of 0.119 for testing data, confirming the model's high accuracy and predictive capability.

Religious Leaders and Resistance to Repression: The Bishops Opposed to Argentina’s Dirty War

Can religious leaders who oppose state violence reduce its use? Communal elites, such as religious leaders, may oppose human rights violations. This article argues that these leaders, part of institutions embedded in local communities and with influence based on traditional power, reduce repression when they oppose dictatorships. The argument’s main implication is tested in Argentina during the Dirty War of its 1976???1983 military dictatorship, using original archival data on the country’s Catholic bishops. Opposed bishops are associated with reduced disappearances and killings. A variety of evidence is consistent with opposed bishops taking two types of actions to resist repression: assisting likeminded local agents and participating in human rights advocacy campaigns. The findings point to the importance of influential civil society actors in reducing state violence.

Reverse Graph Learning for Graph Neural Network.

Graph neural networks (GNNs) conduct feature learning by taking into account the local structure preservation of the data to produce discriminative features, but need to address the following issues, i.e., 1) the initial graph containing faulty and missing edges often affect feature learning and 2) most GNN methods suffer from the issue of out-of-example since their training processes do not directly generate a prediction model to predict unseen data points. In this work, we propose a reverse GNN model to learn the graph from the intrinsic space of the original data points as well as to investigate a new out-of-sample extension method. As a result, the proposed method can output a high-quality graph to improve the quality of feature learning, while the new method of out-of-sample extension makes our reverse GNN method available for conducting supervised learning and semi-supervised learning. Experimental results on real-world datasets show that our method outputs competitive classification performance, compared to state-of-the-art methods, in terms of semi-supervised node classification, out-of-sample extension, random edge attack, link prediction, and image retrieval.

LiFoL: An Efficient Framework for Financial Distress Prediction in High-Dimensional Unbalanced Scenario

Corporate financial distress will significantly damage the company’s and its stakeholders’ interests and even lead to a global financial crisis. Therefore, finding an efficient method for financial distress prediction (FDP) to avoid greater losses is essential. Although there is a lot of research and progress in this field, the existing methods rarely consider the problems of high dimensionality and class imbalance, which will largely limit the models to achieve satisfactory performance. To alleviate these problems, this article first proposes a novel Lightspace-SMOTE upsampling method, which can reduce the feature dimensionality and increase the signal-to-noise ratio (SNR) of the original data and then upsample it to increase the number of minor class samples. In addition, this article proposes an efficient ensemble framework (LiFoL) that combines Lightspace-SMOTE, focal loss (FL), and LightGBM, which can not only focus more on minor class and the hard-to-class samples but also obtain better performance. At the same time, the feature importance provided by the model can provide strong support for model interpretability. Experimental results show that the Lightspace-SMOTE upsampling method can help the model achieve higher scores in area under ROC curve (AUC) and recall, especially in the case of longer prediction periods. Compared with current methods, LiFoL can achieve more than 10% improvement in AUC and more than 20% in recall.

Linguistic diversity in media and state language proficiencies: evidence from Hungarian minority regions in central-Eastern Europe

ABSTRACT Can linguistic diversity in the media impede state language proficiency? In this paper, we test the extent to which the availability of minority languages in the media affects state language proficiency for ethnic minorities. We take advantage of an exogeneous development: The rapid expansion of Budapest-originating media outside Hungary in the mid-2000s transformed the media landscape in neighbouring states. Using an original survey data of ethnic Hungarians in minority regions in four Central-Eastern European states over two waves, we find (1) the availability of minority language media means minorities are consuming less media in the state language, which in turn induces low levels of state language proficiency – i.e. through an exogeneous exposure mechanism; and (2) minorities with weaker state language proficiency choose to consume minority language media – i.e. through an endogenous selection mechanism. The results suggest a strong relationship between the language of media consumption and linguistic proficiency.

Revitalising smallholder agriculture: the impact of technical training in rural Lebanon

AbstractThis paper provides new evidence about the impact of an agricultural development initiative focused on the cherry sector and implemented in rural areas of the Bekaa Valley (Lebanon). The initiative aims to enhance economic opportunities of smallholder farmers by strengthening technical skills, fostering sustainable productions and developing market linkages. Using original micro data from a sample of 118 smallholder cherry farmers, we explore, through a Difference-in-Differences approach, whether the development initiative, based on the provision of extension services and accompaniment through technical training, impacts on a broad set of alternative agricultural outcomes—namely, total cherry production, average market price, management competency and the adoption of improved agricultural practices. The results show that beneficiary small-scale farmers achieve better performances in three outcomes out of the four considered, with the adoption of improved and sustainable agricultural practices as the most remarkable result. Conversely, the management of the agricultural economic activity does not experience any statistically significant variation connected to the initiative implementation. The analysis of a limited source of treatment heterogeneity discloses the primary role of technical training, rather than other kinds of material support, to explain the main results.

Publication of pharmacy resident research projects: A systematic review and meta-analysis.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Pharmacy residents often aspire to develop research skills through conducting a research project. Project publication rates among pharmacy residents are variable and at times low; however, previous studies have been limited to specific geographic regions and timeframes. This study sought to conduct a systematic review and meta-analysis to determine the proportion of pharmacy resident research projects published in the peer-reviewed literature. A systematic review of PubMed MEDLINE, Embase, and the Web of Science Core Collection was performed from database inception to May 25, 2023. Articles were included if they were full-text, peer-reviewed manuscripts of original research presenting observational data regarding pharmacy resident research project publication rates. Data extraction and assessment of risk of bias were conducted by 2 independent reviewers. A proportional meta-analysis using a random effects model of the included studies was conducted to generate a pooled, overall proportion. The search yielded 5,225 records and 12 articles that met the inclusion criteria. All studies were retrospective and observational. Risk of selection and cohort identification biases was "high," whereas that of detection and timeframe biases was "low." The included studies represented 6,990 resident research projects, 777 of which were published in the peer-reviewed literature. Publication rates across individual studies ranged from 1.8% to 36.2%. The pooled proportion (scale of 0 to 1) of projects published was 0.13 (0.09-0.19). Pharmacy resident research project publication rates are low at 13%. Furthermore, studies reporting project publication rates over time suggest a neutral or negative trend in publication rates despite an exponential increase in the number of pharmacy residents.

The prevalence of gambling problems in prison populations: A systematic review and meta-analysis.

The overall prevalence of gambling problems across prison populations is currently unknown. The objective of the present study was therefore to quantitatively synthetize prevalence estimates of gambling problems in prison populations using a random effects meta-analytic model and to investigate if the estimates were moderated by time frame, cut-off levels, and sample size. To be included the studies had to report original data on the prevalence of gambling problems in a prison sample and to be written in a European language, whereas data based on abstracts or qualitative reports were excluded. The search ended on December 1, 2023 and were conducted in Web of Science, PubMed, Cinahl, PsycINFO, Embase, Google Scholar, Grey Literature Report, and GreyNet. Risk of bias was assessed with a standardized 10-item measure for epidemiological studies. A total of 26 studies comprising 9,491 participants were included. The vast majority of the participants were males. The most commonly used instrument for assessment of gambling problems was the South Oaks Gambling Screen. The pooled random-effects gambling problems prevalence estimate was 30.8% (95% CI = 25.1-37.3). The meta-regression analysis showed that none of the three moderator variables (criteria, timeframe, sample size) were related to the gambling problems prevalence. Common limitations of the included studies entailed not being representative nationally or for the target population, lack of randomization, and low response rate. The meta-analysis was restricted to studies published in a European language. Overall, the studies show that 1 in 3 prisoners has gambling problems and suggests that more emphasis on relevant prevention and treatment is warranted for this population. The study was funded by the Norwegian Competence Center for Gambling and Gaming Research and pre-registered at PROSPERO (CRD42023390552).

A review of convolutional neural networks in computer vision

In computer vision, a series of exemplary advances have been made in several areas involving image classification, semantic segmentation, object detection, and image super-resolution reconstruction with the rapid development of deep convolutional neural network (CNN). The CNN has superior features for autonomous learning and expression, and feature extraction from original input data can be realized by means of training CNN models that match practical applications. Due to the rapid progress in deep learning technology, the structure of CNN is becoming more and more complex and diverse. Consequently, it gradually replaces the traditional machine learning methods. This paper presents an elementary understanding of CNN components and their functions, including input layers, convolution layers, pooling layers, activation functions, batch normalization, dropout, fully connected layers, and output layers. On this basis, this paper gives a comprehensive overview of the past and current research status of the applications of CNN models in computer vision fields, e.g., image classification, object detection, and video prediction. In addition, we summarize the challenges and solutions of the deep CNN, and future research directions are also discussed.

Narrative medicine interventions for oncology clinicians: a systematic review.

As narrative medicine interventions are integrated increasingly into medical practice, growing evidence indicates benefits for healthcare professionals. Presently, the prevalence and impact of narrative medicine interventions in the field of oncology remain unknown. This systematic review synthesizes published data on narrative medicine interventions in oncology and reports current knowledge on feasibility, acceptability, and impact on cancer care professionals. Following PRISMA guidelines, we searched Ovid Medline, Embase, Scopus, Web of Science, Cochrane, and ClinicalTrials.gov databases from inception through February 2024. Eligible articles were published in English and contained original data on feasibility, acceptability, and/or impact of a narrative medicine intervention for oncology professionals. Database searches identified 2614 deduplicated articles, from which 50 articles were identified for full-text assessment and 11 articles met inclusion criteria. Two additional articles were identified through manual review of references. Thirteen articles described 12 unique narrative medicine interventions targeting cancer care professionals. All studies described their respective interventions as feasible, acceptable, and impactful for participants. Interventions involved writing, reading, reflection, and other narrative-based strategies. Standardized validated tools evaluated outcomes including burnout, empathy expression, secondary trauma, quality of humanistic care, and well-being. Participants reported appreciation of opportunities for reflection, perspective sharing, and bearing witness, which they perceived to strengthen wellness and community. Narrative medicine interventions are feasible and acceptable and may bolster oncology clinicians' functioning across domains. Multi-site, prospective, randomized studies are needed to investigate the broader impact of narrative medicine interventions and advance the science of narrative medicine in oncology. ClinicalTrials.gov Identifier: CRD42022369432.

Short-term power load forecasting method based on Bagging-stochastic configuration networks.

Accurate short-term load forecasting is of great significance in improving the dispatching efficiency of power grids, ensuring the safe and reliable operation of power grids, and guiding power systems to formulate reasonable production plans and reduce waste of resources. However, the traditional short-term load forecasting method has limited nonlinear mapping ability and weak generalization ability to unknown data, and it is prone to the loss of time series information, further suggesting that its forecasting accuracy can still be improved. This study presents a short-term power load forecasting method based on Bagging-stochastic configuration networks (SCNs). First, the missing values in the original data are filled with the average values. Second, the influencing factors, such as the weather- and week-type data, are coded. Then, combined with the data of influencing factors after coding, the Bagging-SCNs integration algorithm is used to predict the short-term load. Finally, by taking the daily load data of Quanzhou City, Zhejiang Province as an example, the program of the abovementioned method is compiled in Python language and then compared with the long short-term memory neural network algorithm and the single-SCNs algorithm. Simulation results show that the proposed method for medium- and short-term load forecasting has a high forecasting accuracy and a significant effect on improving the accuracy of load forecasting.

Research on vacuum glass insulation performance prediction based on unsteady state multivariate data screening and multi-model fusion self-optimization

A self-optimization regression method based on multivariate data screening and multi-model fusion is proposed for the regression prediction of vacuum glass insulation performance. Firstly, we analyzed the positive correlation between the temperature change rate and heat transfer coefficient. Secondly, we combined the technique of multi-distribution overall diffusion to generate virtual sample data with multiple variables corresponding to the small neighborhood of the target variable, addressing the issue of insufficient sample size. We conducted quantity and threshold screening on the generated data to enhance the effectiveness of the virtual sample data. The screened data and original training set data are integrated to form new training samples. Then, the limited-memory Broyden Fletcher Goldfarb Shanno with boundary constraints (L-BFGS-B) algorithm is introduced to optimize the parameters of multiple models to improve the accuracy of model regression prediction. Finally, through the self-searching optimization framework structure, the virtual sample data screening and optimization of various model parameters are updated. Eventually, the optimal model for predicting the heat transfer coefficient of vacuum glass insulation performance is determined from multiple models. In order to validate the effectiveness of self-optimization regression method, we conducted insulation performance heat transfer coefficient predictions under 30 different ways for vacuum glass. Experimental results demonstrate that the self-optimization regression method based on multivariate data screening and multi-model fusion can obtain the most effective regression prediction model, achieving accurate, rapid and intelligent prediction of vacuum glass insulation performance.

The deep-water, high-diversity Edgewood-Cathay brachiopod Fauna and its Hirnantian counterpart

The last phase of the end-Ordovician extinction event involved substantial sea-level changes. The Oslo/Asker District in Norway is a rare place where the deeper-water early Hirnantian fauna is succeeded by the equivalent deeper-water Edgewood-Cathay Fauna. Both faunas are highly diverse, with the same small-shelled brachiopods, Onniella, Leangella, and Eoplectodonta, dominating. They also share a large number of long-ranging and eurytopic genera. Taxa from contemporary shallower-water environments are rare. Near a third of the brachiopod genera in the Norwegian deeper-water early Hirnantian Fauna went extinct, including seven genera that survived into the Ordovician/Silurian boundary strata. Deeper-water early Hirnatian and Edgewood-Cathay collections are not well-known worldwide. Global quantitative samples from low latitudes, including the Norwegian samples, were compared against each other using NMDS (Non-metric Multidimensional Scaling) with the Bray-Curtis index. Qualitative samples used NMDS with the Raup-Crick index and Network Analysis. The Raup-Crick index sharply differentiated the early Hirnantian and Edgewood-Cathay faunas, possibly due to sensitivity to extinction and origination data. In contrast, the paleogeographic affinity between collections of the two faunas is pronounced using the Bray-Curtis index. Network Analysis also demonstrates regionality; the Edgewood-Cathay Fauna is especially heterogeneous. This contrasts with the shallower-water Hirnantia Fauna, which is more cosmopolitan. Quiet waters below the storm-wave base possibly hindered the spread of larvae, and anoxic plumes of water may have caused further barriers to the lateral spreading of the Edgewood-Cathay Fauna. As in Norway, long-ranging, eurytopic taxa were shared between the two faunas with few typical shallow-water taxa.

A hybrid data fusion approach with twin CNN architecture for enhancing image source identification in IoT environment

AbstractWith the proliferation of digital devices in internet of things (IoT) environment featuring advanced visual capabilities, the task of Image Source Identification (ISI) has become increasingly vital for legal purposes, ensuring the verification of image authenticity and integrity, as well as identifying the device responsible for capturing the original scene. Over the past few decades, researchers have employed both traditional and machine‐learning methods to classify image sources. In the current landscape, data‐driven approaches leveraging deep learning models have emerged as powerful tools for achieving higher accuracy and precision in source prediction. The primary focus of this research is to address the complexities arising from diverse image sources and variable quality in IoT‐generated multimedia data. To achieve this, a Hybrid Data Fusion Approach is introduced, leveraging multiple sources of information to bolster the accuracy and robustness of ISI. This fusion methodology integrates diverse data streams from IoT devices, including metadata, sensor information, and contextual data, amalgamating them into a comprehensive data set for analysis. This study introduces an innovative approach to ISI through the implementation of a Twin Convolutional Neural Network Architecture (TCA) aimed at enhancing the efficacy of source classification. In TCA, the first CNN architecture, referred to as DnCNN, is employed to eliminate noise from the original data set, generating 256 × 256 patches for both training and testing. Subsequently, the second CNN architecture is employed to classify images based on features extracted from various convolutional layers using a 3 × 3 filter, thereby enhancing prediction efficiency. The proposed model demonstrates exceptional accuracy in effectively classifying image sources, showcasing its potential as a robust solution in the realm of ISI.

AdverSPAM: Adversarial SPam Account Manipulation in Online Social Networks

In recent years, the widespread adoption of Machine Learning (ML) at the core of complex IT systems has driven researchers to investigate the security and reliability of ML techniques. A very specific kind of threats concerns the adversary mechanisms through which an attacker could induce a classification algorithm to provide the desired output. Such strategies, known as Adversarial Machine Learning (AML), have a twofold purpose: to calculate a perturbation to be applied to the classifier’s input such that the outcome is subverted, while maintaining the underlying intent of the original data. Although any manipulation that accomplishes these goals is theoretically acceptable, in real scenarios perturbations must correspond to a set of permissible manipulations of the input, which is rarely considered in the literature. In this article, we present AdverSPAM , an AML technique designed to fool the spam account detection system of an Online Social Network (OSN). The proposed black-box evasion attack is formulated as an optimization problem that computes the adversarial sample while maintaining two important properties of the feature space, namely statistical correlation and semantic dependency . Although being demonstrated in an OSN security scenario, such an approach might be applied in other context where the aim is to perturb data described by mutually related features. Experiments conducted on a public dataset show the effectiveness of AdverSPAM compared to five state-of-the-art competitors, even in the presence of adversarial defense mechanisms.

Water surface garbage detection based on lightweight YOLOv5

With the development of deep learning technology, researchers are increasingly paying attention to how to efficiently salvage surface garbage. Since the 1980s, the development of plastic products and economic growth has led to the accumulation of a large amount of garbage in rivers. Due to the large amount of garbage and the high risk of surface operations, the efficiency of manual garbage retrieval will be greatly reduced. Among existing methods, using YOLO algorithm to detect target objects is the most popular. Compared to traditional detection algorithms, YOLO algorithm not only has higher accuracy, but also is more lightweight. This article presents a lightweight YOLOv5 water surface garbage detection algorithm suitable for deployment on unmanned ships. This article has been validated on the Orca dataset, experimental results showed that the detection speed of the improved YOLOv5 increased by 4.3%, mAP value reached 84.9%, precision reached 88.7%, the parameter quantity only accounts for 12% of the original data. Compared with the original algorithm, the improved algorithm not only has higher accuracy, but also can be applied to more hardware devices due to its lighter weight.

Data-driven multivariate regression-based anomaly detection and recovery of unmanned aerial vehicle flight data

Abstract Flight data anomaly detection is crucial to ensuring the safe operation of unmanned aerial vehicles (UAVs) and has been extensively studied. However, the accurate modeling and analysis of flight data is challenging due to the influence of random noise. Meanwhile, existing methods are often inadequate in parameter selection and feature extraction when dealing with large-scale and high-dimensional flight data. This paper proposes a data-driven multivariate regression-based framework considering spatio-temporal correlation for UAV flight data anomaly detection and recovery, which integrates the techniques of correlation analysis (CA), one-dimensional convolutional neural network and long short-term memory (1D CNN-LSTM), and error filtering (EF), named CA-1DCL-EF. Specifically, correlation analysis is first performed on original UAV flight data to select parameters with correlation to reduce the model input and avoid the negative impact of irrelevant parameters on the model. Next, a regression model based on 1D CNN-LSTM is designed to fully extract the spatio-temporal features of UAV flight data and realize parameter mapping. Then, to overcome the effect of random noise, a filtering technique is introduced to smooth the errors to improve the anomaly detection performance. Finally, two common anomaly types are injected into real UAV flight datasets to verify the effectiveness of the proposed method.

Robust hyperspectral image classification using generative adversarial networks

This paper introduces Sill-Rgan, a novel Generative Adversarial Network (GAN) designed to improve hyperspectral image (HSI) classification under varying lighting conditions. Sill-Rgan uniquely maps different light condition domains, enhancing sample classification robustness and generating new virtual samples. Addressing challenges like high spectral dimensionality and noise in HSI classification, our approach utilizes a deep proxy-based learning framework. It integrates and improves advanced GAN models and multitask networks for optimal training stability and loss function optimization. The model's mapping network is adept at generating domain-specific latent codes, enabling the transformation of original hyperspectral data into enhanced versions. Extensive experiments conducted on hyperspectral datasets of agricultural products under diverse indoor and outdoor lighting conditions confirm the effectiveness of Sill-Rgan. The results highlight the model's adaptability in both supervised and semi-supervised learning scenarios, yielding exceptional classification accuracy and enhanced data quality. The versatile potential of Sill-Rgan extends its applicability to a broad range of spectral data classifications, underlining its significant contribution to hyperspectral imaging. This advancement opens new avenues in machine vision systems, particularly in scenarios with dynamic lighting challenges.

Research Progress on Spatiotemporal Interpolation Methods for Meteorological Elements

With the development of mathematical statistics, people have developed the spatiotemporal interpolation methods based on the spatial interpolation method or the temporal interpolation method. These methods fully consider the comprehensive effects of time series changes and spatial distribution to better handle complicated and changeable meteorological element data. This article systematically reviews the current research progress of spatiotemporal interpolation methods for spatiotemporal sampling data of meteorological origin. Spatiotemporal interpolation methods of meteorological elements are classified into three categories: spatiotemporal geostatistical interpolation methods, spatiotemporal deterministic interpolation methods, and spatiotemporal mixed interpolation methods. This article summarizes the theoretical concept and practical application of the spatiotemporal interpolation methods of meteorological elements, analyzes the advantages and disadvantages of using spatiotemporal interpolation methods for estimating or forecasting meteorological elements, combined through some measures and their application to explain the accuracy of the spatiotemporal interpolation methods; and discusses the problems and challenges of spatiotemporal interpolation. Finally, the future research focus of spatiotemporal interpolation methods is proposed. This article provides a valuable method reference for estimating or predicting meteorological elements such as precipitation in unsampled points.