Bilingual Publishing Co. (BPC): E-Journals
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Cambrian Explosion: A Complex Analysis of Facts
No full textMost researchers attribute the appearance of skeletons to some arbitrarily chosen factors. Many aspects of the phenomenon (the diversity of the composition of the remains, the mass nature of the phenomenon, geological immediacy, the role of geological and biotic factors, etc.) remain unexplained in this case. A comprehensive analysis of facts from different branches of science (lithology, tectonics, chemistry, biology, paleontology) allows us to explain (in addition to the listed) the smallness of Cambrian organisms, the replacement of chemical precipitation by biological, as well as the widespread development of bilaterality, the emergence of new taxa of high rank, and the morphological gap between the Ediacaran and Cambrian faunas. Both abiotic and biotic factors were important: Without the active participation of the living in the precipitation of salts, the formation of skeletons would not have been possible
Impact of Cooling Rate on the Results of Vibration Treatment of the Aluminum Casting
No full textThe effect of vibration (50 Hz) on the formation of aluminum castings of 99.5% purity at various cooling rates was studied. It was found that the presence of vibration leads to an increase in the cooling rate of the castings. It was found that the higher the speed without vibration, the stronger the effect of increasing the speed when vibration was applied. Apparently, this effect is associated with additional mixing of the melt by free-floating crystals
Development of New Machine Learning Based Algorithm for the Diagnosis of Obstructive Sleep Apnea from ECG Data
No full textIn this study, a machine learning algorithm is proposed to be used in the detection of Obstructive Sleep Apnea (OSA) from the analysis of single-channel ECG recordings. Eighteen ECG recordings from the PhysioNet Apnea-ECG dataset were used in the study. In the feature extraction stage, dynamic time warping and median frequency features were obtained from the coefficients obtained from different frequency bands of the ECG data by using the wavelet transform-based algorithm. In the classification phase, OSA patients and normal ECG recordings were classified using Random Forest (RF) and Long Short-Term Memory (LSTM) classifier algorithms. The performance of the classifiers was evaluated as 90% training and 10% testing. According to this evaluation, the accuracy of the RF classifier was 82.43% and the accuracy of the LSTM classifier was 77.60%. Considering the results obtained, it is thought that it may be possible to use the proposed features and classifier algorithms in OSA classification and maybe a different alternative to existing machine learning methods. The proposed method and the feature set used are promising because they can be implemented effectively thanks to low computing overhead.
Enhancing Human-Machine Interaction: Real-Time Emotion Recognition through Speech Analysis
No full textHumans, as intricate beings driven by a multitude of emotions, possess a remarkable ability to decipher and respond to socio-affective cues. However, many individuals and machines struggle to interpret such nuanced signals, including variations in tone of voice. This paper explores the potential of intelligent technologies to bridge this gap and improve the quality of conversations. In particular, the authors propose a real-time processing method that captures and evaluates emotions in speech, utilizing a terminal device like the Raspberry Pi computer. Furthermore, the authors provide an overview of the current research landscape surrounding speech emotional recognition and delve into our methodology, which involves analyzing audio files from renowned emotional speech databases. To aid incomprehension, the authors present visualizations of these audio files in situ, employing dB-scaled Mel spectrograms generated through TensorFlow and Matplotlib. The authors use a support vector machine kernel and a Convolutional Neural Network with transfer learning to classify emotions. Notably, the classification accuracies achieved are 70% and 77%, respectively, demonstrating the efficacy of our approach when executed on an edge device rather than relying on a server. The system can evaluate pure emotion in speech and provide corresponding visualizations to depict the speaker's emotional state in less than one second on a Raspberry Pi. These findings pave the way for more effective and emotionally intelligent human-machine interactions in various domains
Effects of Bush Fires on Biodiversity in West Africa Sahel: A Review
No full textBush fire is one of the drivers of biodiversity loss worldwide. However, the impact of bush fires on biodiversity in the West Africa Sahel is not well documented. Therefore, this study reviewed the effects of bush fires on biodiversity, the typology of the bush fire drivers and bush fires solutions in the West Africa Sahel via a systematic review. The authors used many research engines such as Google Scholar and Mendeley from 2010 to 2022 with some keywords in French and English. It comes from the analysis of the data that Mali is the most country affected by bush fires with an average of 35,000,000 ha burned. In Burkina Faso, bush fires burned more than 2 million hectares each year. The analysis showed also a loss of 1,675,157 ha in Niger and 56,568.10 ha in Senegal. The study recommends that climate actions should target bush fires prevention and fighting as climate response in order to promote sustainable biodiversity managementin the West Africa Sahel. The study recommends also that West Africa Sahel countries develop bushfire community education programs for fire prevention
A Proposed Method for Evaluating Management Feasibility When Determining Weed Control Priorities after Major Fires and Floods
No full textMajor fires and floods have enormous impacts on natural ecosystems and are predicted to increase in frequency with global warming. Land managers need to make decisions on the prioritisation of weeds for control in post-disturbance landscapes, but little is available in the way of guidance to support timely decision making. Semi-quantitative models (e.g., scoring systems) have been employed routinely in weed risk assessment, which considers the potential impacts posed by weeds, as well as the likelihood of these impacts being realised. Some progress has been made in the development of similar models addressing the topic of weed risk management. Under conditions prevailing after major disturbances, changes (both positive and negative) can be expected in the multiple factors that determine weed management feasibility, relative to pre-disturbance conditions. A semi-quantitative model is proposed that is based on the key factors that contribute to weed management feasibility in post-disturbance environments, along with annotated modules that could be used by land managers in both post-fire and post-flood situations. The fundamental challenge for weed management in these scenarios lies in the identification of differences between weeds and native species in relation to (1) patterns of seedling emergence; and (2) detectability relative to the growth stage. These two factors will determine the timing of control actions that are designed to address the trade-off between weed control and off-target damage during the period when both types of plants are recovering from a major disturbance event. The model is intuitively sound, but field testing is required to determine both its practical value and any necessary improvement
Some Features of Black Carbon Aerosols Connected with Regional Climate Over Pristine Environment: Carbonaceous Aerosols over Pristine Environment
No full textThe authors report the results of aethalometer black carbon (BC) aerosol measurements carried out over a rural (pristine) site, Panchgaon, Haryana State, India during the winter months of 2021–2022 and 2022–2023. They are compared with collocated and concurrent observations from the Air Quality Monitoring Station (AQMS), which provides synchronous air pollution and surface meteorological parameters. Secular variations in BC mass concentration are studied and explained with variations in local meteorological parameters. The biomass burning fire count retrievals from NASA-NOAA VIIRS satellite, and backward airmass trajectories from NOAA-ERL HYSPLIT Model analysis have also been utilized to explain the findings. They reveal that the north-west Indian region contributes maximum to the BC mass concentration over the study site during the study period. Moreover, the observed BC mass concentrations corroborate the synchronous fire count, primary and secondary pollutant concentrations. The results were found to aid the development of mitigation methods to achieve a sustainable climate system
Research on Well-being: Measuring “Good Life”, Shifting Values, and Cross-cultural Applicability
No full textResearch on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble
No full textPrecipitation is a significant index to measure the degree of drought and flood in a region, which directly reflects the local natural changes and ecological environment. It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy. In order to accurately predict precipitation, a new precipitation prediction model based on extreme learning machine ensemble (ELME) is proposed. The integrated model is based on the extreme learning machine (ELM) with different kernel functions and supporting parameters, and the submodel with the minimum root mean square error (RMSE) is found to fit the test data. Due to the complex mechanism and factors affecting precipitation change, the data have strong uncertainty and significant nonlinear variation characteristics. The mean generating function (MGF) is used to generate the continuation factor matrix, and the principal component analysis technique is employed to reduce the dimension of the continuation matrix, and the effective data features are extracted. Finally, the ELME prediction model is established by using the precipitation data of Liuzhou city from 1951 to 2021 in June, July and August, and a comparative experiment is carried out by using ELM, long-term and short-term memory neural network (LSTM) and back propagation neural network based on genetic algorithm (GA-BP). The experimental results show that the prediction accuracy of the proposed method is significantly higher than that of other models, and it has high stability and reliability, which provides a reliable method for precipitation prediction