Effect of HIV/AIDS on labour productivity and the moderating role of literacy rate: A panel study of Africa and its sub-regions.

This study examined the effect of human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) on labour productivity in Africa and its sub-regions while controlling for the moderating effect of literacy rate. The study used the system Generalised Method of Moment estimation technique and annual panel data from 2010 to 2020 for 53 African economies. Labour productivity and literacy rate were measured by the ratio of gross domestic product to total employment and gross secondary school enrolment respectively. The results indicate that HIV/AIDS retards labour productivity, and that literacy rate can ease this depressing effect of HIV/AIDS in Africa. The sub-regional differences in Africa obtained in this study revealed that the depressing effect of HIV/AIDS on labour productivity is highest in Southern Africa and lowest in Northern and Central Africa. Interestingly, the study also established that per capita health expenditure, per capita income, gross capital formation, and information and communications technology are important drivers of labour productivity in Africa. The study, therefore, concludes that there is need for governments and other stakeholders to help to increase school enrolment and improve the quality of the content of education curriculum in Africa to increase the awareness of HIV/AIDS, especially as it relates to its channels of transmission like unprofessional blood transfusion, unprotected sexual activity, and genital mutilation, among others.

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.

Research on Pig Sound Recognition Based on Deep Neural Network and Hidden Markov Models.

In order to solve the problem of low recognition accuracy of traditional pig sound recognition methods, deep neural network (DNN) and Hidden Markov Model (HMM) theory were used as the basis of pig sound signal recognition in this study. In this study, the sounds made by 10 landrace pigs during eating, estrus, howling, humming and panting were collected and preprocessed by Kalman filtering and an improved endpoint detection algorithm based on empirical mode decomposition-Teiger energy operator (EMD-TEO) cepstral distance. The extracted 39-dimensional mel-frequency cepstral coefficients (MFCCs) were then used as a dataset for network learning and recognition to build a DNN- and HMM-based sound recognition model for pig states. The results show that in the pig sound dataset, the recognition accuracy of DNN-HMM reaches 83%, which is 22% and 17% higher than that of the baseline models HMM and GMM-HMM, and possesses a better recognition effect. In a sub-dataset of the publicly available dataset AudioSet, DNN-HMM achieves a recognition accuracy of 79%, which is 8% and 4% higher than the classical models SVM and ResNet18, respectively, with better robustness.

A New Permanent Scatterer Selection Method Based on Gaussian Mixture Model for Micro-Deformation Monitoring Radar Images.

The micro-deformation monitoring radar is usually based on Permanent Scatterer (PS) technology to realize deformation inversion. When the region is continuously monitored for a long time, the radar image amplitude and pixel variance will change significantly with time. Therefore, it is difficult to select phase-stable scatterers by conventional amplitude deviation methods, as they can seriously affect the accuracy of deformation inversion. For different regions studied within the same scenario, using a PS selection method based on the same threshold often increases the size of the deformation error. Therefore, this paper proposes a new PS selection method based on the Gaussian Mixture Model (GMM). Firstly, PS candidates (PSCs) are selected based on the pixels' amplitude information. Then, the amplitude deviation index of each PSC is calculated, and each pixel's probability values in different Gaussian distributions are acquired through iterations. Subsequently, the cluster types of pixels with larger probability values are designated as low-amplitude deviation pixels. Finally, the coherence coefficient and phase stability of low-amplitude deviation pixels are calculated. By comparing the probability values of each of the pixels in different Gaussian distributions, the cluster type with the larger probability, such as high-coherence pixels and high-phase stability pixels, is selected and designated as the final PS. Our analysis of the measured data revealed that the proposed method not only increased the number of PSs in the group, but also improved the stability of the number of PSs between groups.

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.

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.

Application of Gaussian mixture models to quantify the upper background threshold for perfluorooctane sulfonate (PFOS) in U.S. surface soil.

Studies on the occurrence and environmental distribution of per- and polyfluoroalkyl substances (PFAS) have clearly demonstrated their ubiquity in surface soil as a result of historic and ongoing emissions from various manufacturing and industrial activities worldwide. Given global efforts to characterize and mitigate risk from point source-impacted sites, there is, thus, an urgent need to quantify nonpoint source threshold concentrations (i.e., background) to support site management decisions particularly for perfluorooctane sulfonate (PFOS) as a top priority. Accordingly, this study evaluated the application of Gaussian mixture models (GMMs) fitted to log-transformed PFOS concentrations using nation-wide metadata consisting of thousands of surface soil samples representative of both background and aqueous film-forming foam (AFFF) impacts with unknown proportion. Multiple GMMs were fitted for a given number of components using different methods to account for bias associated with a marginal non-detect fraction (n = 8%) including exclusion, substitution, and imputation. Careful evaluation of the rate of change among multiple goodness-of-fit measures universally justified fitting a 2-component GMM; thus, discriminating between background and AFFF-impacted samples among the metadata. Background threshold PFOS concentrations were defined as the intersection of the probability density functions and ranged between 1.9 and 13.8 µg/kg within a broader concentration range extending up to ~ 50,000 µg/kg reflecting AFFF impacts. By demonstrating an innovative statistical approach that intelligently incorporates different criteria for model selection, this research makes significant contributions to risk mitigation efforts at point source-impacted sites and lays the groundwork for future targeted regulatory actions.

Bioinformatic and literature assessment of toxicity and allergenicity of a CRISPR-Cas9 engineered gene drive to control Anopheles gambiae the mosquito vector of human malaria.

BACKGROUND: Population suppression gene drive is currently being evaluated, including via environmental risk assessment (ERA), for malaria vector control. One such gene drive involves the dsxFCRISPRh transgene encoding (i) hCas9 endonuclease, (ii) T1 guide RNA (gRNA) targeting the doublesex locus, and (iii) DsRed fluorescent marker protein, in genetically-modified mosquitoes (GMMs). Problem formulation, the first stage of ERA, for environmental releases of dsxFCRISPRh previously identified nine potential harms to the environment or health that could occur, should expressed products of the transgene cause allergenicity or toxicity. METHODS: Amino acid sequences of hCas9 and DsRed were interrogated against those of toxins or allergens from NCBI, UniProt, COMPARE and AllergenOnline bioinformatic databases and the gRNA was compared with microRNAs from the miRBase database for potential impacts on gene expression associated with toxicity or allergenicity. PubMed was also searched for any evidence of toxicity or allergenicity of Cas9 or DsRed, or of the donor organisms from which these products were originally derived. RESULTS: While Cas9 nuclease activity can be toxic to some cell types in vitro and hCas9 was found to share homology with the prokaryotic toxin VapC, there was no evidence from previous studies of a risk of toxicity to humans and other animals from hCas9. Although hCas9 did contain an 8-mer epitope found in the latex allergen Hev b 9, the full amino acid sequence of hCas9 was not homologous to any known allergens. Combined with a lack of evidence in the literature of Cas9 allergenicity, this indicated negligible risk to humans of allergenicity from hCas9. No matches were found between the gRNA and microRNAs from either Anopheles or humans. Moreover, potential exposure to dsxFCRISPRh transgenic proteins from environmental releases was assessed as negligible. CONCLUSIONS: Bioinformatic and literature assessments found no convincing evidence to suggest that transgenic products expressed from dsxFCRISPRh were allergens or toxins, indicating that environmental releases of this population suppression gene drive for malaria vector control should not result in any increased allergenicity or toxicity in humans or animals. These results should also inform evaluations of other GMMs being developed for vector control and in vivo clinical applications of CRISPR-Cas9.

Trajectory of suicidal ideation among medical students during the COVID-19 pandemic: the role of childhood trauma.

BACKGROUND: The aim of this study was to understand the longitudinal trajectory of suicidal ideation (SI) among Chinese medical students and the role of childhood trauma (CT). METHODS: Using a whole-group sampling method, we assessed SI in 2192 (male = 834, female = 1358) medical students on three occasions over a period of one year. The Suicidal Ideation Self-Assessment Scale (SISAS) and the Childhood Trauma Questionnaire-Short Form (CTQ-SF) were used to assess SI and CT. The growth mixture modeling (GMM) was used to classify the developmental trajectory of SI. RESULTS: A greater number of medical students were experiencing suicidal ideation during the COVID-19 pandemic. The trajectory of SI among medical students was divided into two groups: a low risk, slowly rising group and a high risk, continuous group. The low risk, slowly rising group had a significant time effect (B = 1.57, p < 0.001) and showed a slowly increasing trend. Emotional neglect (EN), physical neglect (PN), emotional abuse (EA) and physical abuse (PA) all had significant positive predictive effects for the high risk, continuous group (B = 0.18-0.65, P < 0.01). CONCLUSION: The trajectory of SI among medical students can be divided into a low risk, slowly rising group and a high risk, continuous group; the more EN, PN, EA and PA experienced during childhood, the more likely medical students are to develop a high risk, continuous state of SI.

Proactive Fault Diagnosis of a Radiator: A Combination of Gaussian Mixture Model and LSTM Autoencoder.

Radiator reliability is crucial in environments characterized by high temperatures and friction, where prompt interventions are often required to prevent system failures. This study introduces a proactive approach to radiator fault diagnosis, leveraging the integration of the Gaussian Mixture Model and Long-Short Term Memory autoencoders. Vibration signals from radiators were systematically collected through randomized durability vibration bench tests, resulting in four operating states-two normal, one unknown, and one faulty. Time-domain statistical features of these signals were extracted and subjected to Principal Component Analysis to facilitate efficient data interpretation. Subsequently, this study discusses the comparative effectiveness of the Gaussian Mixture Model and Long Short-Term Memory in fault detection. Gaussian Mixture Models are deployed for initial fault classification, leveraging their clustering capabilities, while Long-Short Term Memory autoencoders excel in capturing time-dependent sequences, facilitating advanced anomaly detection for previously unencountered faults. This alignment offers a potent and adaptable solution for radiator fault diagnosis, particularly in challenging high-temperature or high-friction environments. Consequently, the proposed methodology not only provides a robust framework for early-stage fault diagnosis but also effectively balances diagnostic capabilities during operation. Additionally, this study presents the foundation for advancing reliability life assessment in accelerated life testing, achieved through dynamic threshold adjustments using both the absolute log-likelihood distribution of the Gaussian Mixture Model and the reconstruction error distribution of the Long-Short Term Memory autoencoder model.

Research on Robot Screwing Skill Method Based on Demonstration Learning.

A robot screwing skill learning framework based on teaching-learning is proposed to improve the generalization ability of robots for different scenarios and objects, combined with the experience of a human operation. This framework includes task-based teaching, learning, and summarization. We teach a robot to twist and gather the operation's trajectories, define the obstacles with potential functions, and counter the twisting of the robot using a skill-learning-based dynamic movement primitive (DMP) and Gaussian mixture model-Gaussian mixture regression (GMM-GMR). The hole-finding and screwing stages of the process are modeled. In order to verify the effectiveness of the robot tightening skill learning model and its adaptability to different tightening scenarios, obstacle avoidance trends and tightening experiments were conducted. Obstacle avoidance and tightening experiments were conducted on the robot tightening platform for bolts, plastic bottle caps, and faucets. The robot successfully avoided obstacles and completed the twisting task, verifying the effectiveness of the robot tightening skill learning model and its adaptability to different tightening scenarios.

Impact of CGIAR maize germplasm in Sub-Saharan Africa.

This study reports on the adoption and impacts of CGIAR-related maize varieties in 18 major maize-producing countries in sub-Saharan Africa (SSA) during 1995-2015. Of the 1345 maize varieties released during this timeframe, approximately 60% had a known CGIAR parentage. About 34% (9.5 million ha) of the total maize area in 2015 was cultivated with 'new' CGIAR-related maize varieties released between 1995 and 2015. In the same year, an additional 13% of the maize area was cultivated with 'old' CGIAR-related maize varieties released before 1995. The aggregate annual economic benefit of using new CGIAR-related maize germplasm for yield increase in SSA was estimated at US$1.1-1.6 billion in 2015, which we attributed equally to co-investments by CGIAR funders, public-sector national research and extension programs, and private sector partners. Given that the annual global investment in CGIAR maize breeding at its maximum was US$30 million, the benefit-cost ratios for the CGIAR investment and CGIAR-attributable portion of economic benefits varied from 12:1-17:1, under the assumption of a 5-year lag in the research investment to yield returns. The study also discusses the methodological challenges involved in large-scale impact assessments. Post-2015 CGIAR tropical maize breeding efforts have had a strong emphasis on stress tolerance.

Linking tourist's footprint and environmental tragedy through transportation, globalization and energy choice in BIMSTEC region: Directions for a sustainable solution using novel GMM-PVAR approach.

The rising temperature in the world's atmosphere is an outcome strongly linked to man-made manufactured interventions. Recreational activities in the form of tourism are such interventions that can unleash multidimensional negative externalities if not regulated properly. The Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) region has become one of the major hubs for recreational activities in the last few decades. However, the region's tourism-led environmental degradation has received scant attention in the literature. As such, this paper unveils how tourist footprint affects the region's environmental sustainability and explores potential solutions to encourage the tourism industry to be more pro-environmental. We have used the novel GMM-PVAR technique to assess how globalization, transportation, green energy, and economic growth have affected the tourism industry and carbon footprint in the BIMSTEC region from 1990 to 2019. We lean on the empirical outcomes to propose regional sustainable tourism development policies. The GMM-PVAR model indicates that renewable energy, economic growth, and the transportation sector's development positively affect the tourism industry's growth in the region. However, globalization and environmental degradation negatively influence tourists' arrival. Contrarily, transportation services, economic growth, and tourism boost the carbon footprint in the region. Although globalization and clean energy reduce carbon footprint, these indicators are insignificant, indicating that this region is still lagging in renewable energy generation and failed to reap the positive spillovers of globalization. Based on these outcomes, we propose that the region redesign its tourism industry to encourage eco-friendly tourism by leaning more on pro-environmental strategies (i.e., powering the tourism industry through the penetration of renewable energies) and tightening environmental regulations.

Impact of regional energy allocation distortion on carbon emission efficiency: Evidence from China.

The free flow of energy cannot be fully achieved in China's energy market because of incomplete market-oriented reform, resulting in energy allocation distortion, which has hampered carbon emissions reduction. However, the extent of energy allocation distortion and its role in carbon emission efficiency remain unexplored. Therefore, this study aims to measure energy allocation distortion and investigate its impact on carbon emission efficiency. For this purpose, first, we derive energy allocation distortion based on a production function and carbon emission efficiency using a meta-frontier non-radial Malmquist index. To effectively address the endogeneity issue, we use a generalized method of moments model to estimate the impact of energy allocation distortion on carbon emission efficiency. Second, we further explore the distortionary mechanism of carbon emission efficiency associated with energy allocation and analyze the asymmetric effect of energy allocation distortion on carbon emission efficiency. The results show a certain degree of energy allocation distortion throughout the country, and disparity exists among different regions. The average value of carbon emission efficiency in the eastern region is 1.0286, well ahead of the national average, demonstrating better performance than other regions. Energy allocation distortion negatively affects carbon emission efficiency, with a 1% increase in energy allocation distortion leading to a 0.251% decrease in carbon emission efficiency. Technological progress, the structure of energy consumption, and industrial structure are important transmission channels through which energy allocation affects carbon emission efficiency. The study contributes to uncovering regional energy allocation distortion and its impacts on carbon emission efficiency and providing strategic policy recommendations for improving energy allocation efficiency.

What motivates online community contributors to contribute consistently? A case study on Stackoverflow netizens.

Online Question and answer (Q&A) communities are the common and famous platforms to learn and share knowledge and are very useful for every knowledge seeker. Less knowledge contribution is a critical issue for the sustainability and future of these platforms. The motivation of inactive users to participate in Q&A communities is a real challenge. Based on the social cognitive and social exchange theory, we have studied the knowledge contribution patterns of active and consistent StackOverflow users over the last eleven years. We have used a difference generalized method of moments estimator to estimate the proposed model. Results revealed that reciprocation of knowledge and social interaction positively, whereas knowledge seeking of active and consistent users negatively influences knowledge contribution. Peer recognition and repudiation have partially positive and negative effects on users' knowledge contribution. This research offers theoretical and practical suggestions to encourage people to contribute their knowledge to online Q&A communities.

The role of education in modulating the effect of ICT on governance in Africa.

This study investigates the role of education in modulating the effect of ICT on governance in 53 African countries between 2002 and 2020. The Two-Step System Generalized Method of Moment (GMM) strategy is adopted to address the potential endogeneity problem. Governance is computed as a composite index that encompasses the six indicators of the Worldwide Governance Indicators (Control of corruption, rule of law, political stability, regulatory quality, government effectiveness, and voice and accountability). ICT is measured by the number of individuals using the internet, mobile cellular subscribers and fixed broadband subscription. The findings of the study reveal that the quality of governance in Africa is enhanced by growth in ICT. The findings further indicate that the interaction between ICT and education procure positive net effects on governance. In addition, we observed that ICT still enhances the quality of governance in African countries that have adopted the French civil law and the British common law system. The study suggests the design of policies for enhancing e-governance and ICT in African institutions, and are recommended to be used as part of the school curriculum for quality management.

Comparison of reference distributions acquired by direct and indirect sampling techniques: exemplified with the Pediatric Reference Interval in China (PRINCE) study.

BACKGROUND: Our study aimed to compare the reference distributions of serum creatinine and urea obtained by direct sampling technique and two indirect sampling techniques including the Gaussian Mixture Model (GMM) and the Self-Organizing Map (SOM) clustering based on clinical laboratory records, so that the feasibility as well as the potential limitations of indirect sampling techniques could be clarified. METHODS: The direct sampling technique was used in the Pediatric Reference Interval in China (PRINCE) study, in which 15,150 healthy volunteers aged 0 to 19 years were recruited from 11 provinces across China from January 2017 to December 2018. The indirect sampling techniques were used in the Laboratory Information System (LIS) database of Beijing Children's Hospital, in which 164,710 outpatients were included for partitioning of potential healthy individuals by GMM or SOM from January to December 2016. The reference distributions of creatinine and urea that were established by the PRINCE study and the LIS database were compared. RESULTS: The density curves of creatinine and urea based on the PRINCE data and the GMM and SOM partitioned LIS data showed a large overlap. However, deviations were found in reference intervals among the three populations. CONCLUSIONS: Both GMM and SOM can identify potential healthy individuals from the LIS data. The performance of GMM is consistent and stable. However, GMM relies on Gaussian fitting, and thus is not suitable for skewed data. SOM is applicable for high-dimensional data, and is adaptable to data distribution. But it is susceptible to sample size and outlier detection strategy.

Multi-Angle Optical Image Automatic Registration by Combining Point and Line Features.

Image registration is an important basis of image processing, which is of great significance in image mosaicking, target recognition, and change detection. Aiming at the automatic registration problem of multi-angle optical images for ground scenes, a registration method combining point features and line features to register images is proposed. Firstly, the LSD (Line Segment Detector) algorithm is used to extract line features of images. The obtained line segments whose length are less than a given threshold are eliminated by a visual significant algorithm. Then, an affine transform model obtained by estimating a Gaussian mixture model (GMM) is applied to the image to be matched. Lastly, Harris point features are utilized in fine matching to overcome shortages of methods based on line features. In experiments, the proposed algorithm is compared with popular feature-based registration algorithms. The results indicate that the proposed algorithm in this work has obvious advantages in terms of registration accuracy and reliability for optical images acquired at different angles.

sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning.

Conventional classification of hand motions and continuous joint angle estimation based on sEMG have been widely studied in recent years. The classification task focuses on discrete motion recognition and shows poor real-time performance, while continuous joint angle estimation evaluates the real-time joint angles by the continuity of the limb. Few researchers have investigated continuous hand action prediction based on hand motion continuity. In our study, we propose the key state transition as a condition for continuous hand action prediction and simulate the prediction process using a sliding window with long-term memory. Firstly, the key state modeled by GMM-HMMs is set as the condition. Then, the sliding window is used to dynamically look for the key state transition. The prediction results are given while finding the key state transition. To extend continuous multigesture action prediction, we use model pruning to improve reusability. Eight subjects participated in the experiment, and the results show that the average accuracy of continuous two-hand actions is 97% with a 70 ms time delay, which is better than LSTM (94.15%, 308 ms) and GRU (93.83%, 300 ms). In supplementary experiments with continuous four-hand actions, over 85% prediction accuracy is achieved with an average time delay of 90 ms.