Latent Prototype-Based Clustering: A Novel Exploratory Electroencephalography Analysis Approach.

Electroencephalography (EEG)-based applications in brain-computer interfaces (BCIs), neurological disease diagnosis, rehabilitation, etc., rely on supervised approaches such as classification that requires given labels. However, with the ever-increasing amount of EEG data, incomplete or incorrectly labeled or unlabeled EEG data are increasing. It likely degrades the performance of supervised approaches. In this work, we put forward a novel unsupervised exploratory EEG analysis solution by clustering based on low-dimensional prototypes in latent space that are associated with the respective clusters. Having the prototype as a baseline of each cluster, a compositive similarity is defined to act as the critic function in clustering, which incorporates similarities on three levels. The approach is implemented with a Generative Adversarial Network (GAN), termed W-SLOGAN, by extending the Stein Latent Optimization for GANs (SLOGAN). The Gaussian Mixture Model (GMM) is utilized as the latent distribution to adapt to the diversity of EEG signal patterns. The W-SLOGAN ensures that images generated from each Gaussian component belong to the associated cluster. The adaptively learned Gaussian mixing coefficients make the model remain effective in dealing with an imbalanced dataset. By applying the proposed approach to two public EEG or intracranial EEG (iEEG) epilepsy datasets, our experiments demonstrate that the clustering results are close to the classification of the data. Moreover, we present several findings that were discovered by intra-class clustering and cross-analysis of clustering and classification. They show that the approach is attractive in practice in the diagnosis of the epileptic subtype, multiple labelling of EEG data, etc.

How do global value chains respond to climate change? A case study of Chinese manufacturing firms.

This study investigates climate risk and its effects on global value chain (GVC) participation, with a focus on the impact of drought on the export value-added ratio (DVAR) of Chinese manufacturing firms. Using fixed effects (FE) and system GMM models, the main findings are: Drought significantly reduces manufacturing firms' DVAR, with the lagged dependent variable showing a strong persistence effect and an even greater impact in the second lag period. This impact varies based on the firm's location, the complexity of its value chain, and its ability to adapt to and mitigate climate change effects. Strategies such as improving operational efficiency, investing in sustainable technologies, and enhancing competitiveness in developed markets may help mitigate or reverse the adverse effects of climate change on these firms. Additionally, significant industry and regional differences are observed, with the Northeast, East, and South China regions being most severely affected by drought. Global innovation value chains and regional processing value chains are significantly negatively impacted, while labor-intensive value chains are affected only in the current period. These findings provide new insights into the economic impacts of climate change and offer a basis for policymakers to develop strategies that help firms adapt to and mitigate climate risks.

New developments in biotechnology applied to microorganisms.

EFSA was requested by the European Commission (in accordance with Article 29 of Regulation (EC) No 178/2002) to provide a scientific opinion on the application of new developments in biotechnology (new genomic techniques, NGTs) to viable microorganisms and products of category 4 to be released into the environment or placed on the market as or in food and feed, and to non-viable products of category 3 to be placed on the market as or in food and feed. A horizon scanning exercise identified a variety of products containing microorganisms obtained with NGTs (NGT-Ms), falling within the remit of EFSA, that are expected to be placed on the (EU) market in the next 10 years. No novel potential hazards/risks from NGT-Ms were identified as compared to those obtained by established genomic techniques (EGTs), or by conventional mutagenesis. Due to the higher efficiency, specificity and predictability of NGTs, the hazards related to the changes in the genome are likely to be less frequent in NGT-Ms than those modified by EGTs and conventional mutagenesis. It is concluded that EFSA guidances are 'partially applicable', therefore on a case-by-case basis for specific NGT-Ms, fewer requirements may be needed. Some of the EFSA guidances are 'not sufficient' and updates are recommended. Because possible hazards relate to genotypic and phenotypic changes introduced and not to the method used for the modification, it is recommended that any new guidance should take a consistent risk assessment approach for strains/products derived from or produced with microorganisms obtained with conventional mutagenesis, EGTs or NGTs.

Commodity prices and economic growth: Empirical evidence from countries with different income groups.

The purpose of this paper is to examine the effect of global commodity prices such as beverage, energy, fertilizer, food, metal and mineral, precious metal and agricultural raw material on GDP per capita of countries with different income levels which are low, lower-middle, upper-middle, and high. The results of the study using panel system GMM method over the period 2007-2021 showed that for all income group countries, the impact of energy and fertilizer prices on GDP per capita is negative, while the impact of food and metal and mineral prices is positive on GDP per capita. The study also found that rising prices of agricultural raw materials reduces GDP per capita of all income group countries except lower-middle income countries. Moreover, according to the results of the study, rising beverage prices increased the GDP per capita only of high-income countries, while rising precious metal prices decreased the GDP per capita of lower-middle and high-income countries. The study revealed that price changes in all commodity groups have an impact on the GDP per capita of high-income countries. It is demonstrated that price changes in all commodity groups have an impact in both directions on the GDP per capita of all income groups, depending on whether they are net producers or net consumers. The results of the study showed that, contrary to the literature, the countries most affected by commodity prices are high-income countries. Based on the empirical findings, this study point to the need for international cooperation to minimize the adverse effects of commodity price changes.

Effects of human inequality and urbanization on ecological well-being performance: A System-GMM analysis.

Enhancing the efficiency with which ecological consumption is transformed into human well-being is a necessary condition for achieving sustainable development. However, the current literature lacks systematic methods and applications for scientifically assessing Ecological Well-being Performance (EWP). How to value and index EWP is crucial to improve EWP. This study combines the Human Development Index (HDI), Life Satisfaction (LS), and Ecological Footprint (EF) to construct a new Index of Ecological Well-being Performance (IEWP). Meanwhile, human inequality and urbanization are two common and profound socio-economic phenomena with potential impacts on EWP. Therefore, this study uses panel data for 129 countries from 2010 to 2021 and applies the System-GMM approach to explore the impact of human inequality, urbanization, and the interaction between these two factors on EWP. Our results show that EWP has a cumulative effect in the long run. Human inequality has a negative effect on EWP, while the effect of urbanization is positive. Compared to developed countries, the negative impact of human inequality and the positive impact of urbanization are more pronounced in emerging and developing countries. This paper further reveals that the interaction term inhibits EWP, which indicates that urbanization exacerbates the negative effect of human inequality and that human inequality weakens the positive effect of urbanization. This paper contributes to understanding how human inequality and urbanization affect sustainable development from the perspective of EWP.

Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions.

The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability. In this research, we discover the uniqueness of applying STFT (Short Term Fourier Transform), LASSO (Least Absolute Shrinkage and Selection Operator), and EHO (Elephant Herding Optimisation) for extracting significant features from lung cancer and reducing the dimensionality of the microarray gene expression database. The classification of lung cancer is performed using the following classifiers: Gaussian Mixture Model (GMM), Particle Swarm Optimization (PSO) with GMM, Detrended Fluctuation Analysis (DFA), Naive Bayes classifier (NBC), Firefly with GMM, Support Vector Machine with Radial Basis Kernel (SVM-RBF) and Flower Pollination Optimization (FPO) with GMM. The EHO feature extraction with the FPO-GMM classifier attained the highest accuracy in the range of 96.77, with an F1 score of 97.5, MCC of 0.92 and Kappa of 0.92. The reported results underline the significance of utilizing STFT, LASSO, and EHO for feature extraction in reducing the dimensionality of microarray gene expression data. These methodologies also help in improved and early diagnosis of lung cancer with enhanced classification accuracy and interpretability.

Source-free domain adaptation for semantic image segmentation using internal representations.

Semantic segmentation models trained on annotated data fail to generalize well when the input data distribution changes over extended time period, leading to requiring re-training to maintain performance. Classic unsupervised domain adaptation (UDA) attempts to address a similar problem when there is target domain with no annotated data points through transferring knowledge from a source domain with annotated data. We develop an online UDA algorithm for semantic segmentation of images that improves model generalization on unannotated domains in scenarios where source data access is restricted during adaptation. We perform model adaptation by minimizing the distributional distance between the source latent features and the target features in a shared embedding space. Our solution promotes a shared domain-agnostic latent feature space between the two domains, which allows for classifier generalization on the target dataset. To alleviate the need of access to source samples during adaptation, we approximate the source latent feature distribution via an appropriate surrogate distribution, in this case a Gaussian mixture model (GMM).

Role of investor attention and executive green awareness on environmental information disclosure of Chinese high-tech listed companies.

Against the backdrop of growing public concern about environmental disclosure, and despite this concern, the level of environmental disclosure by high-tech firms remains low, necessitating a heightened emphasis on corporate environmental disclosure. This study delves into the impact of investor attention on the environmental information disclosure of Chinese high-tech firms, analyzing data from 463 firms between 2011 and 2022. Utilizing dynamic panel GMM, our findings highlight a significant negative correlation between investor attention and environmental information disclosure. We also introduced executive green awareness, exploring their moderating role. The results show that improved executive green awareness mitigates the adverse impact of investor attention on environmental information disclosure. However, heterogeneity analysis revealed that this moderating effect does not exist in IT service and non-polluting high-tech enterprises. This research offers policy implications for enhancing transparency and environmental governance through targeted investor engagement and executive training programs. The findings underscore the importance of a comprehensive regulatory framework tailored to sector-specific challenges in high-tech industry.

Market response to environmental policy via public procurement: An empirical analysis of bids and prices.

This paper studies the effect of Green Public Procurement (GPP) on competition, bids, and winning bids under two different regulation periods where the latter include more explicitly expressed GPP ambitions. Based on detailed data from Swedish internal cleaning service procurements, our results imply that environmental considerations might not influence the bids as required for GPP to be considered an effective environmental policy instrument. Over time, lower degree of competition and increased bids are found. This phenomenon can be attributed, at least in part, to regulatory influences, signifying an escalating complexity in the process of submitting bids.

Does international trade improve carbon productivity? Evidence from countries along the Belt and Road.

The Belt and Road Initiative proposed by China has significantly increased trade in countries along the Belt and Road (B&R). Since most of these countries are developing and emerging economies, the pressure to reduce carbon emissions poses a leading challenge for them. Carbon productivity has become a key indicator for assessing the degree of low-carbon development, as it can link economic development with CO2 emission reduction. However, few studies have investigated how international trade affects carbon productivity. Based on panel data from 43 countries along the B&R during 2001-2019, this paper uses a system GMM model to explore the impact of international trade on carbon productivity. Then, we divide the 43 countries in the sample into two groups according to their income levels to compare the different effects of international trade on carbon productivity. The results show that, first, the carbon productivity of the examined B&R countries has an overall increasing trend, and there is a significant heterogeneity of carbon productivity among countries with different income levels. Second, the effects of international trade, export, and import on carbon productivity are all significantly positive, and export's effect is higher than import. In the high-income group, carbon productivity is more likely to be improved by trade than in the middle (low)-income group. Third, economic development level, urbanization, and energy productivity are positively associated with carbon productivity, while CO2 per capita and government size inhibit carbon productivity improvement. Insight into the impact of international trade on carbon productivity provides theoretical support for B&R countries to better leverage foreign trade activities to achieve a green economy.

A novel Gaussian sum quaternion constrained cubature Kalman filter algorithm for GNSS/SINS integrated attitude determination and positioning system.

The quaternion cubature Kalman filter (QCKF) algorithm has emerged as a prominent nonlinear filter algorithm and has found extensive applications in the field of GNSS/SINS integrated attitude determination and positioning system (GNSS/SINS-IADPS) data processing for unmanned aerial vehicles (UAV). However, on one hand, the QCKF algorithm is predicated on the assumption that the random model of filter algorithm, which follows a white Gaussian noise distribution. The noise in actual GNSS/SINS-IADPS is not the white Gaussian noise but rather a ubiquitous non-Gaussian noise. On the other hand, the use of quaternions as state variables is bound by normalization constraints. When applied directly in nonlinear non-Gaussian system without considering normalization constraints, the QCKF algorithm may result in a mismatch phenomenon in the filtering random model, potentially resulting in a decline in estimation accuracy. To address this issue, we propose a novel Gaussian sum quaternion constrained cubature Kalman filter (GSQCCKF) algorithm. This algorithm refines the random model of the QCKF by approximating non-Gaussian noise with a Gaussian mixture model. Meanwhile, to account for quaternion normalization in attitude determination, a two-step projection method is employed to constrain the quaternion, which consequently enhances the filtering estimation accuracy. Simulation and experimental analyses demonstrate that the proposed GSQCCKF algorithm significantly improves accuracy and adaptability in GNSS/SINS-IADPS data processing under non-Gaussian noise conditions for Unmanned Aerial Vehicles (UAVs).

Exploitation of Bio-Inspired Classifiers for Performance Enhancement in Liver Cirrhosis Detection from Ultrasonic Images.

In the current scenario, liver abnormalities are one of the most serious public health concerns. Cirrhosis of the liver is one of the foremost causes of demise from liver diseases. To accurately predict the status of liver cirrhosis, physicians frequently use automated computer-aided approaches. In this paper, through clustering techniques like fuzzy c-means (FCM), possibilistic fuzzy c-means (PFCM), and possibilistic c means (PCM) and sample entropy features are extracted from normal and cirrhotic liver ultrasonic images. The extracted features are classified as normal and cirrhotic through the Gaussian mixture model (GMM), Softmax discriminant classifier (SDC), harmonic search algorithm (HSA), SVM (linear), SVM (RBF), SVM (polynomial), artificial algae optimization (AAO), and hybrid classifier artificial algae optimization (AAO) with Gaussian mixture mode (GMM). The classifiers' performances are compared based on accuracy, F1 Score, MCC, F measure, error rate, and Jaccard metric (JM). The hybrid classifier AAO-GMM, with the PFCM feature, outperforms the other classifiers and attained an accuracy of 99.03% with an MCC of 0.90.

Optimisation of garlic mustard oil macerate with respect to its antifungal activity against Candida albicans MTCC 183 and in-silico molecular docking of the volatile compounds with N-myristoyltransferase.

Candida albicans infections are widespread in people and cause cutaneous and systemic infections. Optimisation of garlic mustard oil macerate (GMM) based on antifungal activity against C. albicans was done using agar diffusion method. Upon vapour diffusion assay, the volatile organic compounds of both GMM and MO were found to eradicate C. albicans. During agar diffusion, MO did not inhibit fungal growth, while undiluted GMM oil demonstrated a 26.33 ± 0.33 mm zone of inhibition. The minimum inhibitory concentration and minimum fungicidal concentration against C. albicans were 12.5%, v/v of GMM oil and 25%, v/v of GMM oil, respectively. Scanning electron microscopy analysis showed cell membrane disintegration of fungal cells by 50%, v/v of GMM oil, and MO caused no cell wall damage. In-silico analysis revealed strong binding affinity of sinigrin, ajoene, dithiin with N-myristoyltransferase. In conclusion, the optimised GMM preparation can be a potential antifungal agent against tropical C. albicans infections.

Research on gradual changes in incomplete technical information and optimization mechanism of innovation factor allocation efficiency.

The innovation factor allocation efficiency (IFAE), indicated by R&D institutions' technical input information and manufacturers' technical output information, is mainly optimized by transformation of incomplete technical information into relatively complete information and finally complete technical information. There are two optimization mechanisms, namely single optimization and continuous optimization. The empirical analysis selected data from 73 listed companies in software development and 596 listed companies in processing and manufacturing, and testified its hypotheses with GMM regression results. The research finds that the differences in benefits are caused by gradual changes in technical information. With shared goals of benefit maximization between R&D institutions and manufacturers, a correct strategy for technical information can optimize the IFAE. Single optimization refers to the one-time recognition or identification of relatively complete technical information and complete information, acquisition of technical information from one-time cognition, and thorough application of resource input, thus realizing high-level technology progress gradually. Continuous optimization, based on single optimization, involves the prioritized reporting of complete technical information by participants, thus achieving optimal IFAE. Therefore, it is necessary to accelerate the improvement in the incentive mechanisms for technology innovation efficiency. R&D institutions should promote original innovation and integrated innovation, expand technical information increment, deepen the cooperation with manufacturers actively, and facilitate the technical information sharing.

A data driven oil spill mapping using GMM clustering and damping ratio on X-Press Pearl ship disaster in the Indian Ocean.

The work presented in this paper is focused on the largest marine disaster to have occurred in the Indian Ocean due to the breakup of the container tanker ship X-Press Pearl. In order to identify the oil spill and its temporal evolution, a recently proposed damping ratio (DR) index is employed. To derive the DR, a data-driven GMM-EM clustering method optimized by stochastic ordering of the resulting classes in Sentinel 1 SAR time series imagery is proposed. A ship-born oil spill site is essentially considered to consist of three subsites: oil, open sea, and ship. The initial site probability densities were determined by using k-means clustering. In addition to the clustering method, two histogram-based approaches, namely contextual peak thresholding (CPT) and contextual peak ordering (CPO), were also formulated and presented. The improved histogram peak detection methods take into account spatial and contextual dependencies. The similarity of the marginal probability densities of the open sea and the oil classes makes it difficult to quantify the DR values to show the level of dampening. In the study, we show that reasonable class separability to correctly determine the σVV0,seaθ is possible by using GMM clustering. Resulting class separability's are also reported using JM and ML distances. The methods tested show the range of derived DR values stays significantly within similar ranges to each other. The outcomes were tested with the ground-based surveys conducted during the disaster for oil spill sites and other chemical compounds. The proposed methods are simple to execute, robust, and fully automated. Further, they do not require masking the oil or the selection of high-confidence water pixels manually.

Impact of regulatory capital on bank interest margins: Moderating role of default risk.

The recent pandemic and aftermath debate regarding bank interest margins deserve special attention and have become policy dialogue in emerging economies. However, the previous literature's findings were largely inconclusive and ignored influential variables such as the impact of default risk on bank interest margins. Using a two-step system GMM estimation considering 32 Bangladeshi commercial banks from 2000 to 2022, we produce robust evidence that higher regulatory capital restrictions reduce the bank interest margin, while increased default risk induces the bank interest margin. The impact intensity during the COVID pandemic is higher than in the pre-COVID period. Moreover, we find the synergy effect of regulatory capital and default risk assists in reducing the bank interest margin. Bank margin persistently fell during the capital market crash period, whereas it rose in the financial crisis period. We cast several robustness tests to confirm our main findings. These findings could generate important implications for bank stakeholders and policymakers.

Does the transition to low-carbon energy alleviate urban-rural energy inequality? The case of China.

This paper aims to investigate whether China can reduce urban-rural energy inequality during its transition to low-carbon energy. Using data from 30 Chinese provinces between 2006 and 2019, we employ the system generalized method of moments (SYS-GMM) to investigate the correlation between low-carbon energy transition (LET) and urban-rural energy inequality. Furthermore, to investigate the mechanism, this study also considers energy service accessibility and industrial structure upgrading. The results of the study show that the degree of LET in China is increasing but with uneven spatial distribution. Moreover, LET is effective in reducing urban-rural energy inequality in China. Specifically, 1 % increase in LET corresponds to 0.045 % reduction in the urban-rural energy inequality index. Additionally, energy service accessibility and industrial structure upgrading are identified as effective channels for LET to mitigate urban-rural energy inequality. Furthermore, our study demonstrates that the alleviating impact of LET on energy inequality is more significant in regions where LET and urban-rural energy inequality levels are high. Drawing on our research results, we suggest policy recommendations to encourage the adoption of low-carbon energy sources and diminish urban-rural energy inequality.

Enhancing prediction and analysis of UK road traffic accident severity using AI: Integration of machine learning, econometric techniques, and time series forecasting in public health research.

This research project explored into the intricacies of road traffic accidents severity in the UK, employing a potent combination of machine learning algorithms, econometric techniques, and traditional statistical methods to analyse longitudinal historical data. Our robust analysis framework includes descriptive, inferential, bivariate, multivariate methodologies, correlation analysis: Pearson's and Spearman's Rank Correlation Coefficient, multiple logistic regression models, Multicollinearity Assessment, and Model Validation. In addressing heteroscedasticity or autocorrelation in error terms, we've advanced the precision and reliability of our regression analyses using the Generalized Method of Moments (GMM). Additionally, our application of the Vector Autoregressive (VAR) model and the Autoregressive Integrated Moving Average (ARIMA) models have enabled accurate time series forecasting. With this approach, we've achieved superior predictive accuracy and marked by a Mean Absolute Scaled Error (MASE) of 0.800 and a Mean Error (ME) of -73.80 compared to a naive forecast. The project further extends its machine learning application by creating a random forest classifier model with a precision of 73%, a recall of 78%, and an F1-score of 73%. Building on this, we employed the H2O AutoML process to optimize our model selection, resulting in an XGBoost model that exhibits exceptional predictive power as evidenced by an RMSE of 0.1761205782994506 and MAE of 0.0874235576229789. Factor Analysis was leveraged to identify underlying variables or factors that explain the pattern of correlations within a set of observed variables. Scoring history, a tool to observe the model's performance throughout the training process was incorporated to ensure the highest possible performance of our machine learning models. We also incorporated Explainable AI (XAI) techniques, utilizing the SHAP (Shapley Additive Explanations) model to comprehend the contributing factors to accident severity. Features such as Driver_Home_Area_Type, Longitude, Driver_IMD_Decile, Road_Type, Casualty_Home_Area_Type, and Casualty_IMD_Decile were identified as significant influencers. Our research contributes to the nuanced understanding of traffic accident severity and demonstrates the potential of advanced statistical, econometric, machine learning techniques in informing evidence based interventions and policies for enhancing road safety.

Spatial information extraction of fishing grounds for light purse seine vessels in the Northwest Pacific Ocean based on AIS data.

Ecological fishery management requires high-precision fishery information to support resource management and marine spatial planning. In this paper, the Automatic Identification System (AIS) was adopted to extract the spatial information on the fishing grounds of light purse seine vessels in the Northwest Pacific Ocean. The spatial distributions of fishing grounds mapped by the data mining, kernel density analysis and hotspot analysis methods were compared. The spatial similarity index was applied to determine the spatial consistency between the computed spatial information and fisheries resource information. Finally, the spatial information derived by the best method was used to investigate the characteristics of fishing activity. The results showed that: the speed of light purse seine vessels related to operations was lower than 1.6 knots. The spatial information extracted by the three methods was consistent with the catch data distribution, and the spatial similarity between the fishing effort and catch data was the highest. The spatial variation in fishing activity was similar to that in the chub mackerel migration route. AIS data could be used to provide high-resolution fishery information. Light purse seine fishing vessels typically operate and travel along the exclusive economic zone boundary, and increased attention must be given to fishing vessel operation supervision. A comprehensive supervision system can be employed to monitor the operations of fishing vessels more effectively. The results of this study can provide technical support for the management of fishing activities and conservation of marine resources in this region using AIS data.

Revisiting the relationship between natural resources and environmental quality- can ICT break the "resource curse" in the environment?

Despite the prevalence of discussions on the "resource curse", the impact of natural resources on environmental quality for better or for worse has not been clearly answered, this study aims to answer the question by introducing the role of Information and Communication Technologies (ICT). To that end, by using the Instrumental Variable Generalized Method of Moments (IV GMM) estimator and a sample of 102 developing and emerging economies from 2006 to 2016, this paper studies the impact of ICT on the relationship between natural resources and environmental quality. Specially, the Environmental Performance Index (EPI) captures the environmental quality. The results show that natural resources have a significant negative effect on EPI, specially, EPI decreases by one unit with a 1% increase in natural resource rents. ICT significantly mitigates this adverse effect, and marginal effects analysis further confirms its positive moderate effects. The results proved to be robust by Lewbel 2SLS and Driscoll-Kraay techniques or other robust tests. It is noteworthy that the adverse effect of natural resources on EPI is greater and the mitigating effect of ICT is more pronounced in low-income countries and lower-middle income countries. Overall, these results remind resource-based countries to vigorously develop ICT, and apply intelligent exploration, digital monitoring, or other digital technologies to realize the high-efficiency use of natural resources, reducing environmental pollution and ecological damage.