Miracle-dz/UniFAR-FTUs huggingface dataset:
26. AGE DISTRIBUTION OF VOLCANISM ALONG ASEISMIC RIDGES IN THE EASTERN INDIAN OCEAN

Concordant plateau and isochron ages are obtained from 40Ar-39Ar incremental heating experiments on volcanic rocks recovered by drilling at three Leg 121 sites along the Ninetyeast Ridge and two dredge locations on the southern scarp of the Broken Ridge, eastern Indian Ocean. The new data confirm a northerly increase in the age of volcanism along the Ninetyeast Ridge, from 38 to 82 Ma; this lineament links current hotspot volcanism near the Kerguelen islands with the Rajmahal flood basalt eruptions at MO time (117 ± 1 Ma). The Broken Ridge was formed over the same hotspot at 88-89 Ma, but later experienced rift-related volcanism in Paleocene time (63 Ma). The geometry and distribution of ages along these prominent volcanic ridges and the MascareneChagos-Laccadive-Maldive ridge system in the western Indian Ocean are most compatible with plate motions over fixed hotspots near Kerguelen and Reunion islands, respectively.
Prediction of rainfall variation through flowering phenology of night-flowering jasmine ( Nyctanthes arbor-tristis L.; Verbenaceae) in Tripura

Folk people of Tripura, a small state of Northeast India, rely on their own traditional knowledge of phenological indicators for predicting the weather, to help plan their agroforestry activities and for disaster prevention. One such prediction relates to the phenology of Nyctanthes arbor-tristis L. , night-flowering jasmine, which helps them to forecast the onset of heavy rainfall. During 2007-2009, a comprehensive study was initiated to document the predictive accuracy of this indicator, through conversations and interviews with village members, observation and reviewing the older literature. The researchers found that the flowering of Nyctanthes arbor-tristis is a good indicator of weather lore for the prediction of both shortand long-range precipitation. The information about the prediction of rainfall by observing the flowering phenology was recorded through the interview with village elders. The researchers use interview schedule while interacting with elders. Several small groups of knowledge holders including elder men and women (as focus group) were selected randomly from a wider population of study areas and sampled. A questionnaire was also prepared for knowledge gathering through open conversation and discussion. Prior Informed Consent (PIC) was taken from the knowledge providers time to time.
Examination of mean top height definitions and height estimation equations for Pinus radiata in New Zealand

For Pinus radiata in New Zealand mean top height is essentially defined and calculated via one definition and the so-called Petterson equation. A study shows that changing the definition to one used overseas has very little impact on top height values. The Petterson model appears to give unbiased estimates of tree height. The basic relationship between tree height and diameter is not particularly precise, especially at low stockings. To ensure a satisfactory model, it is important that the sample trees have a wide range in diameter. Changing the power parameter in the Petterson equation has a negligible effect. Adopting alternative height/diameter equations gives a minimal improvement in precision in one case but significantly less precision with the other.
Temperature time series from Icelandic coastal stations

As a response to growing mariculture in Iceland during the 1980s, the Marine Research Institute initiated a project in 1986 for monitoring the sea temperature at selected locations along the coast. The longest of these records, from seven stations along the west, north and east coast of Iceland, are discussed for the period 1987-1996. The time series contain several gaps, mainly due to battery failure, causing problems in the analysis. The mean for each calender month at each location was calculated, as well as the deviations of the monthly mean temperature. The years 1987, the latter part of 1990, 1991, 1994 and 1996 were relatively warm, while the remainder of the period was relatively cold. The longer term variations seem to be coherent at all sites. The data can be used for evaluating the temperature conditions at a potential mariculture location and there is probably sufficient data material avalaible for a first evaluation of sites anywhere in Iceland. However, more accurate evaluation would require further measurements at each proposed site. It is therefore suggested that the project should now focus on long term monitoring of the marine climate.
Diurnal activities of green monkeys Cercopithecus aethiops in Old Oyo National Park, Nigeria : short communications

The activity budget of green monkeys Cercopithecus aethiops was studied in the Guinea Savanna Forest of Old Oyo National Park, Nigeria for 18 months, during both the dry and wet seasons. A focal troop was randomly chosen from the four troops which are resident in the park. The activities studied included travelling, feeding, resting, drinking and grooming. Green monkeys were more active in the morning and evenings than in the afternoons. They travelled and drank more during the dry season than during the wet season, while the least time was spent on grooming during both seasons. Food availability influenced green monkeys' daily activity during all seasons.
Neural Network-Based Model for Osteoporosis Prediction: A Novel Approach for Enhanced Accuracy

Osteoporosis is a degenerative bone disease that has major health consequences worldwide due to decreased bone density and increased fracture risk. Diagnostic procedures such as bone mineral density (BMD) measures employing techniques such as DXA scans, QUS, or pQCT are used to detect osteoporosis. However, the cost of these testing is prohibitively expensive. While the cost of osteoporosis detection is an issue, early detection is critical for preventing fractures and lowering long-term healthcare costs connected with the illness. To address the aforementioned difficulty, we offer an osteoporosis prediction model that employs a neural network with L2 regularisation to improve prediction accuracy. This model's performance is compared to that of various major machine learning methods, such as KNN, SVM, Logistic Regression, XGBoost, and Random Forest. The NHANES dataset is used in the analysis, which takes into account demographics and living conditions. Our suggested model has an accuracy of 87.2%, proving its efficiency in the prediction of osteoporosis. These findings emphasise the ANN model's potential as a useful tool for identifying people at high risk of osteoporosis and guiding preventative efforts. By correctly identifying at-risk patients, appropriate treatments can be implemented, thereby minimising the need for costly surgeries and hospitalisations associated with fractures and sequelae.
A Review on Automatic Image Caption Generation for Various Deep Learning Approaches

In machine learning, image captioning is a process of extracting information as per the action performed in an image. An image may contain various objects that may be associated with certain actions; image captioning is responsible to translate the action into text by the help of deep learning algorithms. Image captioning is a challenging task in the field of image processing because real world data is bit complex in action and may contain unidentified objects. It has been implemented in various deep learning algorithms such as CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), DNN (Deep Neural Network), LSTM (Long Short Term Memory) and many more. These networks use distinct datasets such as Flickr8K, Flickr30K, MSCOCO and many more to obtain the result accordingly. Flickr8K contains eight thousand images for training; similarly Flickr30K contains thirty thousand images and MSCOCO contains 82783 images that is why MSCOCO is considered as large dataset and it is preferred by various researchers. The result of the machine translation is based on syntactic and semantic traits. The quality of machine translation can be measured by BLEU (BiLingual Evaluation Understudy) in the reference of ground truth captions. The intension of this paper is to review various approaches related to the image captioning and it also intended to find out the best deep neural network model among various implemented models.
Study of Various Deep Learning Models for COVID-19 Detection Based on Fractional Order Optical Flow

The world entered into a new phase in the wake of Covid-19, which is declared as a global threat because countless people were losing their lives worldwide due to this disease every day. Even the best medical facilities were crumbling due to the rise of COVID-19 and lack of funding. However the use of nucleic acid-based testing generates an excessive amount of bio-waste and increases the risk of infection to the healthcare personnel. These tests take a lot of time, which contributes to the high mortality rate. To address these challenges, vision based techniques have been demonstrated to be beneficial in the early and swift diagnosis of COVID-19. Earlier imaging techniques used X-ray or CT-scan images to detect COVID-19. Consequently, these techniques do not filter out non-COVID cases, resulting in longer processing time. In this paper, COVID-19 identification is accomplished through the dynamic characteristics obtained from chest X-ray (CXR) images. These dynamic characteristics are derived as optical flow (OF). Moreover a fractional order-based variational model is used to estimate OF from two consecutive images of lungs. The novelty of this work is to use OF instead of images in COVID-19 detection. Further, the classification of lung dynamic features is performed with a convolutional neural network (CNN) in coordination of various machine learning (ML) classifiers. A detailed comparison is provided to show the significance of the algorithm.
Restoration of eroded stratal thickness in key periods of tectonic change in a multi-stage superimposed Tarim Basin in China

Abstract With an area of 56×104 km2, the Tarim Basin is the largest inland basin in China and is also generally acknowledged as one of the most important areas for potential oil and gas exploration. On the basis of data from 22 regional seismic profiles and 40 drilling wells, 15 important first-order and second-order regional unconformities were defined. Almost all the main unconformities are superimposed unconfomities. Since the Cambrian, 5 key periods of tectonic change have occurred during the evolution of the Tarim Basin. The total eroded stratal thickness of the above-mentioned unconformities was calculated by using the method of virtual extrapolation of seismic reflection. The results indicate that the total eroded stratal thickness of different periods is quite different in different locations of the basin. Taking the Upper-Middle Ordovician as an example, its thickness restoration of eroded strata was calculated into individual stages i.e. its thickness restoration of eroded strata was calculated to different tectonic periods. Otherwise, as for the specific period of tectonic change, the underlying strata were, respectively eroded and thus the thickness restoration of eroded strata was calculated into individual intervals. Taking the Early Hercynian period as an example, the eroded stratal thickness was calculated into individual intervals to calculate the ratio of intervals of various ages occupying the total eroded thickness. The results show that for the same stratum, its degree of erosion is quite different in different periods and at different locations, due to the varying influence of tectonic movement. The unconformities of some key periods of tectonic change have different controls on the degree of erosion and the eroded range of the individual period of the underlying strata which are the typical characteristics of multi-stage superimposition of unconformities in the Tarim Basin.
J The Effect of Physical Therapist’s Expertise and Interactivity on Revisit Intention Based on Trust

Purpose: This study aimed to examine how the expertise and interactivity of a physical therapist impact a patient’s intention to revisit a hospital based on trust. Methods: We surveyed 274 patients who received physical therapy in Seoul and Gyeonggi Province to assess their reliability and revisit intentions based on their expertise and interactivity. SPSS 22.0 was used for frequency analysis and reliability verification, while AMOS 18.0 was used for confirmatory factor analysis and model verification. Results: Physical therapist interactivity significantly impacted patients’ intentions to revisit based on trust. The physical therapist’s expertise had a significant effect on trust but did not demonstrate a significant effect on the intention to revisit. Conclusion: The interactivity of physical therapists has an important effect on patients’ intentions to revisit a hospital based on trust. Although therapist-centered expertise can generate trust in patients, it positively affects the intention to revisit the hospital. Therefore, it is suggested that physical therapists’ patient-centered expertise and interactivity build patients’ trust and are important for revisiting intention.
Recognition of Obstructive Sleep Apnea from a Single Lead Electrocardiogram Signal Using a ResNet Model

Obstructive sleep apnea (OSA) is a prevalent sleep disorder that induces respiratory disturbances during the state of sleep. Furthermore, it is associated with several comorbidities, such as cardiovascular ailments like coronary heart disease and hypertension. Nocturnal polysomnography (PSG) is a clinical diagnostic modality utilized for the diagnosis of OSA. This method is associated with significant costs and discomfort for patients, as it necessitates manual interpretation by trained professionals and is time-consuming. Methods utilizing electrocardiogram (ECG) signals for the diagnosis of OSA have been proposed as a means of mitigating associated challenges. Nevertheless, the vast majority of the currently available solutions are based on feature engineering, which requires a large amount of specialized knowledge and skill. The present study introduces an innovative technique for the categorization of OSA through the utilization of a ResNet model on a solitary-lead ECG signal. In order to train and test the proposed model, the PhysioNet Apnea-ECG database was used throughout the process. ReseNet18 and ResNet50 are both deep learning models, and they are used to predict how well the model will perform. For a batch size of 256, after 2, and before 2, the ResNet 18 proved its maximum performance by achieving an accuracy of 89% and a recall of 89%. In addition, the ResNet 50 was able to attain its maximum performance by doing the following: obtaining an accuracy of 89%, a precision of 89%, an F1-score of 89%, and a recall of 89% for the batch size of 512, after 1, and before 1.
EM-coupling removal from time-domain IP data

Electromagnetic (EM) coupling of frequency-domain induced polarisation (IP) data has been the subject of many studies, and a number of ‘de-coupling’ procedures have been devised. However, there has been far less emphasis on coupling in the time domain, the normal approaches being to wait until late times and assume the EM contribution is insignificant or, less frequently, to invoke a Cole-Cole model to account for the EM-coupling response. A fast and simple procedure has been devised for suppression of EM-coupling effects in time-domain IP data. The essence of the approach is to represent the EM-coupling as a half-space decay. The half-space resistivity (EM apparent resistivity) is adjusted via inversion until the fit to the observed transient voltage decay is optimal in the least squares sense. The EM voltages associated with this best-fitting EM half-space decay are then subtracted from the measured voltages to yield a de-coupled ‘IP transient’. Transients that are well represented by an EM half-space decay are deemed ‘non-responsive’ in the context of IP. Transients that deviate markedly from an EM half-space decay are indicative of high apparent chargeability. The application of the new procedure is illustrated on dipole-dipole IP data from the Yandal greenstone belt of Western Australia.
The Use of Diatoms to Infer Environmental Change of the Lower White River, Southeastern Arkansas

This dissertation describes the limnology of the lower White River oxbow lakes in southeast Arkansas, inundated by the major 2008 spring flood. The flood water and sediment deposited in oxbow lakes were analyzed for water chemistry, sediment geochemistry and diatom assemblages and used as an analogue for past floods. The 2008 spring flood inundated and homogenized the entire floodplain; also represented by the predominantly planktonic diatom assemblage suggesting riverine input. Indicator species for eutrophication were also recorded consistent with the high nutrient content in the lakes. However, the variance in the surface diatom assemblage was best explained by the lake sill height and was used to develop a transfer function. The transfer function was applied to fossil diatom assemblage from Adams Bayou Lake sediment core (36 cm length) to test it as an archive for past White River floods. Cross validation of the developed model was done with river stage and the groundwater level data (proxy for increasing agricultural activity). The diatom inferred sill height was not successful in identifying the flood events in the lower section of the sediment core due to lack of modern analogue. The fossil assemblages show that the Adams Bayou was a stable, less disturbed system abundant in benthic diatoms prior to 1996 (estimated age) later converting into an open, nutrient rich system abundant in planktonic, eutrophic indicator diatoms. This shift in lake conditions towards the nutrient rich system also coincided with the lowering of groundwater due to increased agricultural practices. Finally, the history of flooding and land use for the past 750 years ( 137 Cs and AMS used for chronology) was investigated from a 400 cm long sediment core from the Adams Bayou using sediment geochemistry. The lake was a steady system from 1300-1600 AD, but experienced major land use changes and flooding from 1800 AD till present. This study provides insights about current and past environmental conditions of the lower White River floodplain. It underlines the limitations of working in floodplain systems and also serves as a foundation for future paleolimnological studies in the study site as well as other floodplain river
SDN-based Firewall using Machine Learning

Due to the widespread use of the Internet, the traditional destination-based forwarding which includes two steps: (1) match and (2) action, has caused much stress on the network due to its traffic and security issues. With SDN, network control has become simpler. In addition to simplification, network security has always been a critical aspect of networking, where a firewall helps to secure traffic by enforcing some security policies. Based on the packet headers, this firewall can detect malicious packets and then drop or block them. In this paper, we present a firewall that uses an intuitive machine-learning algorithm to detect malicious packets based on the header. The firewall has been implemented in two SDN controllers: (1) RYU controller and (2) POX, for a simple mesh topology and compared based on packet loss and delay.
Atmospheric Simulations of Extreme Surface Heating Episodes on Simple Hills

A two-dimensional nonhydrostatic atmospheric model was used to simulate the circulation patterns (wind and vorticity) and turbulence energy fields associated with lines of extreme surface heating on simple two- dimensional hills. Heating-line locations and ambient crossflow conditions were varied to qualitatively deter- mine the impact of terrain geometry on the development of buoyancy-induced horizontal roll vortices and their turbulence structures. The model simulations indicate that the type of induced circulation that develops near a line of extreme surface heating on a simple hill is very dependent on the location of the heating line. Heating lines located on the crests of simple hills produced sym- metric horizontal roll vortices with large values of turbu- lent kinetic energy. Symmetric vortices did not develop over heating lines located on the slopes of the hills. The introduction ofa light ambientcrossflowradically changed the circulation and turbulence structures. Simulated vorticity and turbulence energy values over a heating line located on the windward slope were very different than those observed over heating lines located on the crest or leeward slope. A low-level vortex developed just down- wind of the windward-slope heating line when a light ambient crossflow was introduced, and this vortex be- came stronger as the steepness of the hill was increased. Although field dataare not available toconfirm the model results, the simulations suggest that terrain effects play an important role in the development and destruction of vortices near lines of extreme surface heating, especially when a light ambient crossflow is introduced. These effects have implications for fire-fighter safety in actual wildland fire episodes.
A New Image Processing System to Diagnose the Orange Fruit Disease

Citrus fruits are among the most important food components. Orange is one of the most important citrus fruits because of its vitamin C content and delicious taste, and it is planted and harvested in most countries. Aphids, pests, and fungi cause the destruction of orange fruits. Therefore, it is very important to quickly identify diseases. Several factors are important to improve the ripening of the orange fruit, so that it does not suffer from viruses or fungi. First, several important viruses and fungi and their causes in oranges should be investigated, and ways to improve orange ripening should be made easy; here, we used an image processing system and Red, Green, Blue (RGB) to Hue, Saturation, Lightness (HSL) conversion to identify several important viruses and fungi from the color of orange peel. In addition, the proposed algorithm detects both healthy and diseased oranges. The accuracy of the present study was 93.6%. In this study, an algorithm for diagnosing orange-fruit diseases was proposed. This algorithm is performed using Python programming and executed by the Raspberry Pi. Therefore, oranges with Melanosis, Canker, and Black Spot disease were detected. The defective part of the orange fruit is shown in the form of mask and HSL images as well as the integration of the mask and HSL images in the output. After running the program, the severity of the disease is shown through the number of pixels of the defective part of the orange, and guidance for the prevention and treatment of the disease is described.
Effects of crop rotation on soil macronutrient content and pH in potato producing areas in Kenya: Case study of KARI Tigoni station

Proper crop rotation might help to conserve soil fertility among small scale potato farmers in Kenya. A study was carried out at KARI Tigoni station between April 2007 and May 2010 to evaluate the effect of crop rotation on soil pH and macro nutrient levels. Rotations involved maize, potatoes and cabbages. Results showed that, soils at KARI Tigoni station are generally acidic with pH ranging from 4.3 (extreme acidity) to 5.59 (medium acidity). Cultivation reduced pH further probably due to increased organic matter decomposition. Percent organic carbon was low and was significantly (P = 0.05) increased by the rotation. Percent total nitrogen was adequate for potato production. Phosphorus was generally low. Proper crop rotation should be employed which should include a legume. This study should be carried out over a longer period of time to get more comprehensive results. Key words: Crop rotation, soil pH, soil macronutrients, potato production, KARI Tigoni.