Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristic of Vibration Signal from Cutting Tool Against Steel with a Tensile Strength of 60 for CNC Turning Monitoring System

The condition of the insert significantly affects the product quality and manufacturing efficiency of lathe machining. The current study uses the power spectral density distribution of a signal vibration accelerometer machine built for CNC in developing a system to classify various conditions that can occur in a manufacturing environment. For four common lathe machining insert conditions (i.e., built-up edge, flank wear, standard, and fracture), In this case, the insert condition classification system is created with two stages—insert condition modeling and machining model fusion. At the stage of modeling the insert condition, the magnitude feature of the segmented frequency is captured according to the power spectral density distribution of the accelerometer vibration signal. Root mean square (RMS) and Fast Fourier Transform (FFT) was calculated to conduct vibration prediction studies using a turning cutting tool. The results of the raw signal in the experimental new conditions show the RMS value is in the range of 6.24-6.23 mV, and the FFT is 0.0007-0.0009 mV. In good condition, the raw signal condition shows that the RMS value is 54.36-67.08 mV and FFT 0.0068-0.0080 mV. In the middle state of the raw signal, the RMS value is 83.05-112.07mV, and the FFT value is 0.0089-0.0147mV. In poor condition raw signal, the RMS value is 125.14-152.09 mV, and the value FFT is at 0.0137-0.0178. The data clusters are well grouped and directly proportional between the damage to the tool blade and the increased voltage in RMS.
E-learning tools for enhanced teaching and learning of IsiNdebele in a rural context

In rural contexts, schools experience many challenges such as lack of electricity, unstable WIFI, not enough classrooms and inadequate classroom technology. These challenges resulted in many teachers shying away from the use of technology in the classrooms. As with other subject fields, IsiNdebele language teachers want to prepare their learners for new careers and make their lessons interesting. However, they encounter challenges integrating e-learning tools in their lessons. A qualitative case study was conducted to understand the rural contextual perspective obtained through exploring the experiences of the researcher, four observers, and a class group of 20 learners. Data were collected using a document analysis protocol, learners’ written questionnaire, observers’ interview protocol, and a teacher’s reflective journal. The Substitution, Augmentation, Modification, and Redefinition (SAMR) model was used as a frame of reference to integrate technology at various stages to enhance the quality of teaching and learning the IsiNdebele language in the rural context. In preparation for this study, six e-learning tools were found suitable for integration in teaching and learning, as well as enhancing classroom dynamics in the rural context. The data were analysed using content analysis. From this study, we suggest that rural IsiNdebele teachers should consider using e-learning tools daily, to ensure skills development and provide learners with real-world learning opportunities. Furthermore we recommend that similar research be conducted in different rural settings, using different official African languages to find more e-learning tools that can be used in rural contexts.
A Mechanism for Bitcoin Price Forecasting using Deep Learning

—Researchers and investors have recently become interested in forecasting the cryptocurrency price forecasting but the most important currency can take that it’s the bitcoin exchange rate. Some researchers have aimed at leveraging the technical and financial characteristics of Bitcoin to create predictive models, while others have utilized conventional statistical methods to explain these factors. This article explores the LSTM model for forecasting the value of bitcoins using historical bitcoin price series. Predict future bitcoin prices by developing the most accurate LSTM forecasting model, building an advanced LSTM forecasting model (LSTM-BTC), and comparing past bitcoin prices. This is the second step, if looking at the end of the model, it has very high accuracy in predicting future prices. The performance of the proposed model is evaluated using five different datasets with monthly, weekly, daily, hourly, and minute-by-minute bitcoin price data with total records from January 1, 2021, to March 31, 2022. The results confirm the better forecasting accuracy of the proposed model using LSTM-BTC. The analysis includes square error MSE, RMSE, MAPE, and MAE of bitcoin price forecasting. Compared to the conventional LSTM model, the suggested LSTM-BTC model performs better. The contribution made by this research is to present a new framework for predicting the price of Bitcoin that solves the issue of choosing and evaluating input variables in LSTM without making firm data assumptions. The outcomes demonstrate its potential use in applications for industry forecasting, including different cryptocurrencies, health data
An Entropy-based approach to Network Attack Classification with Deep Neural Network

Detecting and classifying attacks on computer networks is a significant challenge for network providers and users. This thesis project builds a deep neural network to detect and classify network attacks. Our approach based on the hypothesis that each type of network attacks generates a distinguishable change in the entropies of certain features of network flows. To generate a training and validation dataset, synthetic attacks of different types and levels of intensity are injected to the MAWI dataset, which contains captured raw network traffic from an Internet backbone link. Experimental results show that our machine learning model can achieve a high accuracy for network attacks which intensity is as low as 6% of the original traffic. This result is very promising, considering the fact that the amount of traffic in the Internet backbone link is substantial. This work also evaluates and quantifies the model performances on different attack types and the levels of intensity of these attacks. Thesis Supervisor: Dr. Vijay N. Gadepally Title: Senior Member of the Technical Staff, MIT Lincoln Laboratory
The Application of FIA-based Data to Wildlife Habitat Modeling: A Comparative Study

We evaluated the capability of two types of models, one based on spatially explicit variables derived from FIA data and one using so-called traditional habitat evaluation methods, for predicting the presence of cavity-nesting bird habitat in Fishlake National Forest, Utah. Both models performed equally well, in measures of predictive accuracy, with the FIA-based model having estimates of model sensitivity. The primary advantage of using the FIA data is the ability to convert the modeled relationships to spatially explicit depictions of bird habitat.
How Youth Are Put At Risk by Parents’ Low-Wage Jobs

It has been well documented that growing up poor is deeply harmful to children and youth. While some countries use social programs to reduce child poverty, US government policy has increasingly focused on employment of low-income parents as a key route to reduce the nations’ high rate of family poverty. In particular, government programs have focused on jobs for single mothers whose children experience the worst economic hardships of all. Yet jobs as the solution to young people’s poverty depend on the kind of work available to their parents. With the 30-year decline in higher-paying manufacturing jobs and, simultaneously, significant growth in low-wage service employment, many jobs do not provide the wages or flexibility that any parent needs to raise a family in safety and stability. In fact, there is evidence that low-wage jobs can cause harm to young people’s health, education, and overall development.
Subsurface temperature of the onshore Netherlands: new temperature dataset and modelling

Abstract Subsurface temperature is a key parameter for geothermal energy prospection in sedimentary basins. Here, we present the results of a 3D temperature modelling using a thermal-tectonic forward modelling method, calibrated with subsurface temperature measurements in the Netherlands. The first step involves the generation of a coherent dataset of temperature values for the calibration of the model. In the Netherlands, most of the available measurements (98.8%) are BHT measurements and therefore need to be corrected from the thermal perturbation created during drilling. The remaining 1.2% is composed of DST measurements, which closely resemble the formation temperature (i.e., ±5 °C). The resulting dataset, after correction, gives a total number of 1293 values in 454 wells. Included in this dataset are 412 highly reliable values corrected with the Instantaneous Cylinder Source (ICS) method and 829 values of a lower reliability corrected with the AAPG method. In addition to the corrected values, 52 DST values in 26 wells are available from the Dutch subsurface. The average thermal gradient of this whole dataset is 31.3 °C/km with a surface temperature of 10.1 °C. The second step in the modelling process was the generation of a 3D forward model that focuses on calculating the temperature distribution of the sedimentary basin fill, taking into account the basin evolution of the past 20 Myrs and thermal properties and processes of the whole lithosphere. From the 3D thermal model, we extracted 2D cross sections across well locations to compare model temperatures with calibration data. Furthermore, we present vertical profiles, isodepth maps and temperature projection on geological layers, to discuss the relationship between temperature and geology. Anomalies in this relationship can have several causes and include: 1) the extreme thermal conductivity and complex geometry of the Zechstein salt; 2) enhanced radiogenic heat production of the upper crust due to magmatic intrusions. In addition, our model supports earlier findings that shallow hydrothermal convection in highly permeable sediments can effectively lower thermal conductivity and temperatures in shallow sediments.
Fortified and bioengineered yoghurt: prospects for bacteriotherapy and nutrient deficiency management

One of the main vital processes for the enhancement of food nutrition quantity and quality is food fortification and bioengineering. Global utilization and functional properties of probiotics from yoghurts, make it a good candidate for fortification and bioengineering. Yoghurt is the most popular fermented milk product that is biochemically altered by activities of special microorganisms to achieve desired functional and sensory properties. Due to global food crisis, and health challenges from metabolic disorders and drug resistance. The prospect of fortified and bioengineered yoghurt as an effective bacterio-therapy and nutrient deficiencies management approach is new trend among stakeholders. The aim of this review is to underscore the prospect of fortified and bioengineered yoghurt for the bacterio-therapy and nutrition deficiency management. In this review paper fortification and bioengineering was defined and the major aims of prospect for bacterio-therapy and nutrition deficiency management were presented. Yoghurt fortified with different vitamins and minerals ingredients that exhibit functional properties were discussed. The main problems for human health in today’s world is infectious diseases, so yearly gastrointestinal infections resulted to sickness and death significantly worldwide. Therefore, drinking of fortified and bioengineered fermented dairy products increase host metabolism by keeping the balance of intestinal micro flora and thus on the host have a positive effect. The new dairy products designed to be fortified and bioengineered can resolved the problems of nutritional deficiencies. However, fortification of yoghurt with elemental and micronutrients are still developing challenges due to oxidation, solubility and sensory acceptance. Likewise, all genetically modified organisms products (GMO), acceptance of bioengineered yoghurts is still a surmountable challenge with adequate education.
Integrating Security in Process Control Loop: A Case for Proportional-Integral-Derivative Algorithm

This work presented mathematical models and flow charts for implementing secure Proportional-Integral-Derivative (PID) algorithm in a process control loop. A number of security solutions have been recommended and some deployed in a process control system. Majority of these solutions are network based while others leverage on good security policy. A security solution based on network can be effective for securing control system from external threat agents who have to first of all, gain access to the control network. But for an internal threat agent or a disgruntled insider who does not only have the right privilege but also has a good understanding of the control system's operation, a network security is definitely not going to be effective. This work used system analysis to identify the possible things an internal threat agent can do to manipulate a control system using PID control algorithm as a case study. A secured PID mathematical model is proposed as a proactive mitigation technique to embedding security in a process control loop. As ongoing research, future work will concentrate on simulation and prototyping of the secured algorithm presented in this work. The proposed secured algorithm will not only serve as an additional security layer in industrial control system (ICS) but will also be relevant in the control domain of Internet of Things.
Correlaciones genéticas, fenotípicas y ambientales en 81 genotipos de tomate de árbol (Cyphomandra betacea Cav. Sendt.)

El objetivo de este trabajo fue evaluar diferentes componentes de calidad de fruto,estimar las correlaciones fenotipicas, geneticas y ambientalesdel tomate de arbol (Cyphomandra betacea) considerando 14 variables relacionadas con el tamano y calidad del fruto, igualmente, establecer los efectos directos e indirectos de las variables componentes de calidad de fruto sobre el peso del fruto. Se utilizaron los datos de 81 hibridos con dos repeticiones, en condiciones del Municipio de Pasto, Colombia. Los resultados obtenidos, indicaron que las correlaciones genotipicas fueron superiores a las fenotipicas y ambientales.El peso de fruto (PF), presento las mayores correlaciones geneticas (rG >0,60) con peso de pulpa mas semilla (rG = 0,90), y el diametro ecuatorial (rG = 0,84). El analisis de sendero con base en correlaciones genotipicas, mostro que el espesor interno fue la variable que tuvo el mayor efecto directo sobre PF (1,63), esto demuestra que una seleccion por peso de fruto da como resultado un aumento en el espesor interno. Teniendo en cuenta las correlaciones fenotipicas, este analisis permitio establecer que los efectos directos de diametro ecuatorial y diametro polar (0,30 y 0,26) son los que mas aportan al PF.
Cu, Pu and Fe high Tc superconductors: all the same mechanism!

The new iron based high Tc superconductors with Tc up to 55 K have stirred new interest in this field. It is consensus that the BCS mechanism is not able to explain the high Tc's. In the following we propose that spin holes in antiferromagnetic clusters combine to make nonmagnetic bipolarons, which can condense and lead to superconductivity.
About the Governance Efficiency of Agricultural Farms. The Case of Bulgaria

The New Institutional Economics framework is incorporated and a practical approach for assessing the level and factors of governance efficiency of Bulgarian farms suggested. The evaluation is made on the basis of original micro-data collected by the managers of typical farms of different type and location. The "Nature of the problems in effective organization for major class farm transactions for securing needed factors of production and output marketing" is used as an indicator for the comparative efficiency and adaptability of individual farms. The study found that the governance efficiency of farms is at a Good level but 60% of all holdings are with a Low efficiency. Major factors for inferior governance efficiency of farms are unsatisfactory efficiency in supply of necessary labor, innovations and know-how, and funding. There is a huge variation in the level and factors of governance efficiency of farms with different juridical types and sizes as well as in the share of farms with different levels of efficiency in each group. A strong correlation is found between the level of governance efficiency of farms, and diverse critical internal and external market, technological, institutional, personal, etc. factors. There is a big discrepancy between the “new” assessment of Governance efficiency with dominating traditional approaches for farm efficiency assessments based on factors' productivity. There was an improvement in the overall governance efficiency of farms during 2016-2020 but the share of efficient farms significantly declined during the period.
Towards semi-automatic forest inventories using individual tree crown (ITC) recognition.

Some 24 million hectares of forest are mapped each year throughout Canada. Currently, forest management inventories are manually developed from aerial photo interpretation (at 1:10,000 or 1:20,000 scales) and field sampling. These visual processes and interpretations are labour intensive. Despite the resources expended, these inventories may be insufficient for some applications. In comparison with conventional aerial photography, high spatial resolution multispectral images have three basic advantages:they can make better use of the colour spectrum and show information not always present on aerial photography; they are captured digitally and therefore lend themselves to better and more direct computer analysis; and images and resulting information can be more readily tied to geographic locations, leading to direct transfers to the Geographic Information Systems (GIS) where most forest inventories now reside.
Prevalence and clinical profile of keratoconus in patients presenting at a provincial hospital in KwaZulu, Natal, South Africa: A case study

Keratoconus (KC) is a progressive, asymmetrical corneal disease, characterized by stromal thinning that leads to distortion, causing vision loss. The visual loss is secondary to corneal scarring, irregular astigmatism, and myopia. The prevalence of KC has been reported to differ in different parts of the world. The study aimed to determine the prevalence and profile of patients with KC presenting to a provincial hospital in KwaZulu-Natal, South Africa. A retrospective study design was used to review 412 clinical records of patients attending the McCord Provincial Eye Hospital (MPEH) during a five-year period (2016-2020). Data on age, race, refraction, clinical profile, treatment plan, and diagnosis were ascertained. The prevalence of KC in MPEH was found to be 13.7% with a mean age of 24.7±7.94 years. Black African and females had a higher frequency of KC compared to males and other ethnic groups. Most of the patients presented with a severe stage of KC and referral was the most common management. Central corneal thinning and Munson's sign were the most prevalent clinical signs. There was no statistically significant difference between the worse and better eye when comparing the clinical signs. The prevalence and clinical profile of patients with KC in this study was similar to that reported by previous studies and more in Blacks and females. Population based epidemiological studies are needed to determine the prevalence of KC in South Africa to enable early clinical interventions.
Responses of African mahogany seedlings to temperature, vapor pressure deficit and water availability

The genus Khaya holds significant value in the national and international forest market due to its noble wood. However, the limited understanding of species-climate interactions hinders its expansion in Brazil. To address this, we investigated the effects of different microclimates on the growth of Khaya ivorensis , Khaya senegalensis , and Khaya anthotheca seedlings, considering variations in mean air temperature (T), vapor pressure deficit (VPD), and water availability. Seedlings were cultivated in controlled environments representing three microclimates (I: 24.7 °C, 0.30 kPa VPD; II: 28.6 °C, 0.95 kPa VPD; III: 29.8 °C, 1.80 kPa VPD) with varying water availability levels (20%, 40%, 60%, and 90% of maximum water holding capacity). Growth variables were assessed. Our findings indicate that water restriction reduced seedling growth across all microclimates, leading to lower total dry mass production. Specifically, K. ivorensis exhibited robust growth under microclimates with higher water availability. K. senegalensis displayed optimal initial growth in microclimate III (29.8 °C, 1.80 KPa), particularly when subjected to higher water availability levels in the substrate.
Association of systemic immune inflammation index and system inflammation response index with clinical risk of acute myocardial infarction

Background Systemic immune inflammatory index (SII) and systemic inflammatory response index (SIRI) are combinations of non-specific inflammatory and adaptive immune response impairments associated with cardiovascular disease. Yet little analysis has been done on SII, SIRI and acute myocardial infarction (AMI) prognosis. The purpose of this study was to investigate the correlation of SII and SIRI with clinical risk factors such as GRACE, Gensini, and QTc after acute myocardial infarction. Methods This study enrolled 310 patients with AMI from February 1, 2018, to December 31, 2022, at our institution. Routine blood items calculated SII and SIRI. Two groups were divided according to whether MACE occurred: the MACE group (81 cases) and the NMACE group (229 cases); each group was divided into three groups according to the SII and SIRI tertiles. The relationship between SII, SIRI and MACE was analyzed using multifactorial logistic regression analysis after adjusting for confounders; ROC curves were plotted to examine the predictive value of SII and SIRI for MACE. The correlation between SII and SIRI and potential risk factors such as Gensini, QTc and GRACE was further analyzed. Results The study enrolled 310 patients, comprising 248 men (80%, mean age 60.73 ± 13.695 years) and 62 women (20%, mean age 69.79 ± 11.555 years). In the regression model completely adjusted for confounders, the risk of MACE was higher in AMI patients with SII > 11.00 [OR = 1.061,95% CI (1.018,1.105)] than in SII < 5.98; the risk of MACE was 115.3% higher in AMI patients with SIRI (1.72–3.68) [OR = 2.153, 95% CI (1.251, 3.705)] was 115.3% higher in AMI patients with SIRI < 1.72 and the risk of MACE was 25.1% higher in AMI patients with SIRI > 3.68 [OR = 1.251, 95% CI (1.123, 1.394)] than in AMI patients with SIRI < 1.72. In addition, SII, SIRI, and potential post-infarction risk factors (Gensini, QTc, and GRACE) were also associated. Conclusion SII and SIRI have been significantly associated with post-myocardial infarction MACE and the predictive potential clinically integrated risk factors in AMI patients, for which more attention should be paid to targeted anti-inflammatory therapy in AMI patients to further reduce the incidence of prognostic MACE in AMI patients.