Miracle-dz/UniFAR-FTUs huggingface dataset:
Geochemical characteristics and petrogenesis of four Palaeoproterozoic mafic dike swarms and associated large igneous provinces from the eastern Dharwar craton, India

Palaeoproterozoic mafic dike swarms of different ages are well exposed in the eastern Dharwar craton of India. Available U-Pb mineral ages on these dikes indicate four discrete episodes, viz. (1) ~2.37 Ga Bangalore swarm, (2) ~2.21 Ga Kunigal swarm, (3) ~2.18 Ga Mahbubnagar swarm, and (4) ~1.89 Ga Bastar-Dharwar swarm. These are mostly sub-alkaline tholeiitic suites, with ~1.89 Ga samples having a slightly higher concentration of high-field strength elements than other swarms with a similar MgO contents. Mg number (Mg#) in the four swarms suggest that the two older swarms were derived from primary mantle melts, whereas the two younger swarms were derived from slightly evolved mantle melt. Trace element petrogenetic models suggest that magmas of the ~2.37 Ga swarm were generated within the spinel stability field by ~15–20% melting of a depleted mantle source, whereas magmas of the other three swarms may have been generated within the garnet stability field with percentage of melting lowering from the ~2.21 Ga swarm (~25%), ~2.18 Ga swarm (~15–20%), to ~1.89 Ga swarm (~10–12%). These observations indicate that the melting depth increased with time for mafic dike magmas. Large igneous province (LIP) records of the eastern Dharwar craton are compared to those of similar mafic events observed from other shield areas. The Dharwar and the North Atlantic cratons were probably together at ~2.37 Ga, although such an episode is not found in any other craton. The ~2.21 Ga mafic magmatic event is reported from the Dharwar, Superior, North Atlantic, and Slave cratons, suggesting the presence of a supercontinent, ‘Superia’. It is difficult to find any match for the ~2.18 Ga mafic dikes of the eastern Dharwar craton, except in the Superior Province. The ~1.88–1.90 Ga mafic magmatic event is reported from many different blocks, and therefore may not be very useful for supercontinent reconstructions.
EEG-based biomarkers for optimizing deep brain stimulation contact configuration in Parkinson’s disease

Objective Subthalamic deep brain stimulation (STN-DBS) is a neurosurgical therapy to treat Parkinson’s disease (PD). Optimal therapeutic outcomes are not achieved in all patients due to increased DBS technological complexity; programming time constraints; and delayed clinical response of some symptoms. To streamline the programming process, biomarkers could be used to accurately predict the most effective stimulation configuration. Therefore, we investigated if DBS-evoked potentials (EPs) combined with imaging to perform prediction analyses could predict the best contact configuration. Methods In 10 patients, EPs were recorded in response to stimulation at 10 Hz for 50 s on each DBS-contact. In two patients, we recorded from both hemispheres, resulting in recordings from a total of 12 hemispheres. A monopolar review was performed by stimulating on each contact and measuring the therapeutic window. CT and MRI data were collected. Prediction models were created to assess how well the EPs and imaging could predict the best contact configuration. Results EPs at 3 ms and at 10 ms were recorded. The prediction models showed that EPs can be combined with imaging data to predict the best contact configuration and hence, significantly outperformed random contact selection during a monopolar review. Conclusion EPs can predict the best contact configuration. Ultimately, these prediction tools could be implemented into daily practice to ease the DBS programming of PD patients.
An Improved Convolutional Neural Network for Churn Analysis

—The significance of customer churn analysis has escalated due to the increasing availability of relevant data and intensifying competition. Researchers and practitioners are focused on enhancing prediction accuracy in modeling approaches, with deep neural networks emerging as appealing due to their robust performance across domains. However, the computational demands surge due to the challenges posed by dimensionality and inherent characteristics of the data. To address these issues, this research proposes a novel hybrid model that strategically integrates Convolutional Neural Networks (CNN) and a modified Variational Autoencoder (VAE). By carefully adjusting the parameters of the VAE to capture the central tendency and range of variation, the study aims to enhance the effectiveness of classifying high-dimensional churn data. The proposed framework's efficacy is evaluated using six benchmark datasets from various domains, with performance metrics encompassing accuracy, f1-score, precision, recall, and response time. Experimental results underscore the prowess of the hybrid technique in effectively handling high-dimensional and imbalanced time series data, thus offering a robust pathway for enhanced churn analysis.
An Enhanced Threshold RSA-Based Aggregate Signature Scheme to Reduce Blockchain Size

The transformative potential of blockchain technology has resulted in its widespread adoption, bringing about numerous advantages such as enhanced data integrity, transparency, and decentralization. Blockchain has effectively proven its ability to establish trustworthy systems across a multitude of applications. As the number of transactions recorded into a blockchain grows, the blockchain’s size expands significantly, posing challenges to the network, particularly in terms of storage capacity and processing power. To address this problem, we present a cryptosystem based on RSA to provide aggregate signatures in blockchains. The aggregate signature replaces all transaction signatures of a block. In this scheme, all participating blockchain nodes use the same modulus <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>, each with its own private and public key pair generated from <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>. Regardless of the number of transactions, nodes, and signers, the aggregate signature size is always <inline-formula> <tex-math notation="LaTeX">$O(k)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> is a security parameter. The miner that constructs a candidate block computes the aggregate signature <inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula>, replaces all transaction signatures by <inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula>, and transmits the block with only one aggregate signature. The proposed scheme incorporates a flexible and accountable subgroup aggregate signature mechanism, allowing any subset <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> total elements to sign data, where <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> is the required number of signers. To verify that a set of elements signed the block, the verifier requires the aggregate signature, the aggregate public key, and the data hash. This approach requires minimal interaction between the signers, which results in reduced network traffic. Regardless of the network size, there are always <inline-formula> <tex-math notation="LaTeX">$t + n$ </tex-math></inline-formula> exchanged messages. Experimental analysis shows the proposed aggregate signature scheme’s effectiveness in increasing security robustness and reducing block size and overall network traffic.
VARIABILITY OF MELTWATER AND SOLUTE FLUXES FROM HOMOGENEOUS MELTING SNOW AT THE LABORATORY SCALE

Four experiments were performed to examine the relationship between the meltwater flow field and ion release from melting snow. A 0.4 m 3 volume of snow was placed in a Plexiglass box and melted from above using a heating plate. The meltwater and solute fluxes issuing from the bottom of the snow were monitored. In experiments with NaCI tracer added to the snow, the solute concentrations were generally lower in the flow fingers than in the background wetting front. Dye tracer experiments revealed contemporaneous areas of concentrated dye and dilute meltwater in flow fingers. This suggests that the meltwater in flow fingers is diluted by low concentration water from the top of the snowpack. Flow fingers contribute more meltwater flux primarily because the flow is maintained for a longer period of time than in the non-finger areas ; however, the relative contribution of flow fingers to solute flux was apparently not as great as that of the background wetting front because of dilution of solute in the flow finger areas.
Redox state of the Archean atmosphere: Evidence from detrital heavy minerals in ca. 3250–2750 Ma sandstones from the Pilbara Craton, Australia

The presence of detrital uraninite and pyrite in fluvial placers of the Witwatersrand basin, South Africa, has been used to infer low levels of atmospheric oxygen during the Archean (>2500 Ma). However, recent studies advocate a hydrothermal origin for these minerals, thereby limiting their value as constraints on the composition of the early atmosphere. In contrast, ca. 3250–2750 Ma fluvial siliciclastic sediments from the Pilbara Craton in Australia have never undergone significant hydrothermal alteration, and their heavy minerals are of unequivocal detrital origin. These heavy minerals include the redox-sensitive phases pyrite, uraninite, and gersdorffite, along with more inert zircon, rutile, chromite, and monazite. Locally, siderite is a major constituent (to 90%) of the heavy mineral population, with grains displaying evidence for several episodes of erosion, rounding, and subsequent authigenic overgrowth. Detrital siderite is very rare in post-Archean sandstones, largely due to its instability in oxidizing environments. However, its frequent survival of prolonged transport in well-mixed and therefore well-aerated Archean river waters that contained little organic matter strongly implies that the contemporary atmosphere was indeed much less oxidizing than at present. Moreover, concentrations of reduced sulfur species must have been very low in surface fluids for siderite to survive repeated transportation events without pyritization.
Light dark matter and flavour

Flavour physics and dark matter physics have a long and fruitful history of shared interests. We briefly review in this work these links for the case of light dark matter models and present some of the recent developments. A particular emphasis is put on the case of flavour non-universal or flavour-violating couplings between a new dark sector and the Standard Model fields
Modelling the Determinants of Students’ Performance in Mathematics

Mathematics is a subject most students perceived to be difficult and have consequently developed a phobia towards. This study aimed to model the determinants of students’ performance in core and elective mathematics and to unearth the various factors that make students perceive mathematics as a difficult subject. Self-administered questionnaires were used to collect primary data from a total of 170 students in five (5) selected senior high schools (SHS). Whereas convenience non-probabilistic sampling was used to select the SHS and elective mathematics students, simple random sampling was employed in selecting core mathematics students. Results from a multiple linear regression analysis indicated that characteristics of students such as students’ fear of mathematics, parents’ level of education, students’ perception of the subject as being difficult, and students’ residential type (boarding or day) were significant determinants of students’ performance in mathematics. Results of a binary logistic regression showed younger students are about 4 times more likely to perceive mathematics to be a difficult subject compared to older students while students who generally fear the subject are about 19 times more likely to perceive the subject as difficult than those who have no mathematics anxiety or fear. Similarly, a student whose father had only basic education could perceive mathematics about 18 times difficult compared to those whose fathers went beyond basic education. The study recommends that in order to improve mathematics performance, students should be desensitized to misconceptions that the subject is for only top-notched students, more boarding facilities should be built, and students studying mathematics should be motivated by teachers and parents.
Working Memory Ability Evaluation Based on Fuzzy Support Vector Regression

One’s working memory process is a fundamental cognitive activity which often serves as an indicator of brain disease and cognitive impairment. In this research, the approach to evaluate working memory ability by means of electroencephalography (EEG) analysis was proposed. The result shows that the EEG signals of subjects share some characteristics when performing working memory tasks. Through correlation analysis, a working memory model describes the changes in EEG signals within alpha, beta and gamma waves, which shows an inverse tendency compared to Zen meditation. The working memory ability of subjects can be predicted using multi-linear support vector regression (SVR) with fuzzy C-mean (FCM) clustering and knowledge-based fuzzy support vector regression (FSVR), which reaches the mean square error of 0.6 in our collected data. The latter, designed based on the working memory model, achieves the best performance. The research provides the insight of the working memory process from the EEG aspect to become an example of cognitive function analysis and prediction.
A circumpolar gravest empirical mode for the Southern Ocean hydrography

Historical hydrographic data from the Southern Ocean are projected into a baroclinic stream function space, generating a three-dimensional gravest empirical mode (GEM) parameterized by pressure, geopotential height, and longitude. The low-dimensional GEM projection is intrinsically steady, vertically coherent, and equivalent barotropic. The GEM fields capture more than 97% of the total density and temperature variance in the Antarctic Circumpolar Current region.
Ayurvedic Management of Diabetic Complications - A Case Report

Diabetes mellitus can fall under Prameha in Ayurveda . Long-standing Prameha leads to several complications. Some of the subsequent complications of diabetes mellitus are diabetic osteomyelitis, charcot foot, peripheral neuropathy, diabetic ulcer, periarticular arthritis, etc. This case will be discussing a patient who had some of the above complications. Diabetic complications were badly affecting his day-to-day activities. Hence, the treatment was mainly focused on reducing his symptoms. 73-year-old upper-middle-class man resided at Vijayawada for the past 70 years and worked as a businessman (egg cellar) for 50 years there, admitted to Vaidyaratnam Ayurveda College hospital on 2020 January 21 st , presented with numbness of both upper limbs and lower limbs and difficulty in walking for the past 2 years and difficulty to raise left upper limb for the past 1 year. The case was diagnosed as Prameha Upadrava . Accordingly, the basic Ayurvedic principles were used for the treatment. Along with internal medicines, Rookshana, Snehana, Swedana, Sodhana, Rasayana, and Samana therapies were also administered. After 47 days of treatment, he was able to walk without moving to one side like he used to. He was also relieved of tremors and the numbness had begun subsiding. His left shoulder joint flexion and abduction were possible up to 120*. This case categorically emphasizes the need for positively constructive and effective results if two systems of medicine go hand in hand at different stages of treatment to achieve optimum healing of the patient. This case also tells us the importance and effectiveness of Rasayana therapy in Prameha .
Seismic potential for large and great interplate earthquakes along the Chilean and Southern Peruvian Margins of South America: A quantitative reappraisal

The seismic potential of the Chilean and southern Peruvian margins of South America is reevaluated to delineate those areas or segments of the margin that may be expected to experience large or great interplate earthquakes within the next 20 years (1984–2004). Long-term estimates of seismic potential (or the conditional probability of recurrence within a specified period of time) are based on (1) statistical analysis of historic repeat time data using Weibull distributions and (2) deterministic estimates of recurrence times based on the time-predictable model of earthquake recurrence. Both methods emphasize the periodic nature of large and great earthquake recurrence, and are compared with estimates of probability based on the assumption of Poisson-type behavior. The estimates of seismic potential presented in this study are long-term forecasts only, as the temporal resolution (or standard deviation) of both methods is taken to range from ±15% to ±25% of the average or estimated repeat time. At present, the Valparaiso region of central Chile (32°–35°S) has a high potential or probability of recurrence in the next 20 years. Coseismic uplift data associated with previous shocks in 1822 and 1906 suggest that this area may have already started to rerupture in 1971–1973. Average repeat times also suggest this area is due for a great shock within the next 20 years. Flanking segments of the Chilean margin, Coquimbo-Illapel (30°–32°S) and Talca-Concepcion (35°–38°S), presently have poorly constrained but possibly quite high potentials for a series of large or great shocks within the next 20 years. In contrast, the rupture zone of the great 1960 earthquake (37°–46°S) has the lowest potential along the margin and is not expected to rerupture in a great earthquake within the next 100 years. In the north, the seismic potentials of the Mollendo-Arica (17°–18°S) and Arica-Antofagasta (18°–24°S) segments (which last ruptured during great earthquakes in 1868 and 1877) are also high, but poorly constrained.
Introduction: Knowledge and Geography

Geography is currently penetrating economics. New and technically advanced versions of growth and trade economics are under development (e.g. Romer, 1990; Krugman, 1991; Fujita et al., 1999), while empirical studies of industries and innovation are increasingly undertaken as studies of geographical systems of different varieties, such as clusters, national or regional innovation systems (e.g. Lundvall, 1992; Nelson, 1996; Edquist, 1997; Klepper, 2002; Malerba, 2004). What has happened so far on the theory side is mainly the application of economic method to geographical issues. It seems that, being a mainly empirical discipline, economic geography has not yet had much to offer economics, other than the handing over of its main concern—the geographical location of economic activity—to be treated by economists. The coming together of economics and geography, however, has more potential than simply using economic method on stylized geographical issues. What has been a weakness of geography as far as theory building is concerned, namely, its preoccupation with empirical, often qualitative, studies, has also supplied the discipline a noteworthy empirical insight into meso-level patterns of economic activity. For decades, economic geographers have studied real-life clusters and regional specialization and growth patterns—in fact, taking over the empirical study of these phenomena after classical economists such as Smith, Marshall and Veblen (a heritage largely disregarded by 20th-century economists). Geographers can now offer economists empirical insights into the organization of economic activity—including the influence of the institutions and mechanisms that society and business firms apply in order to change the geographical distribution of economic activity.
The ecology and biodiversity of urban ponds

Recent research has demonstrated that ponds contribute a great deal to biodiversity at a regional level as networks of habitat patches that also act as ‘stepping stones’ to facilitate the movement of species through the landscape. Similarly, a great deal of biodiversity persists in urban environments where synanthropic communities are supplemented by species that thrive in disturbed environments. Aquatic urban biodiversity appears to persist despite anthropogenic stressors: an array of anthropogenic pollutants (road salt and heavy metals), invasive species, and active mismanagement—particularly the removal of riparian vegetation. Optimizing urban ponds for different ecosystem services results in conflicting priorities over hydrological, geochemical, ecological, aesthetic, and cultural functions. The socio‐ecosystem approach to environmental management opens a path to greater incorporation of biodiversity into town planning and sustainability, while accounting for cultural attitudes to urban ecosystems. I identify a range of research needs: (1) the roles of design and location of urban ponds in influencing biodiversity, (2) the function of urban wetlands for stormwater and pollution management, and (3) public perceptions of urban ecosystems and how those perceptions are influenced by interactions with natural systems. Urban wetlands offer an important opportunity to educate the general public on natural systems and science in general using a resource that is located on their doorstep. In the face of increasing pressures on natural systems and increasing extent and intensity of urbanization, a more comprehensive appreciation of the challenges and opportunities provided by urban ponds could play a substantial role in driving sustainable urban development.
Sensitivity Analysis of Geomechanics Influence on The Success of Hydraulic Fracturing in Shale Gas Reservoir

Shale gas has a permeability of <0.1 mD and a porosity of around 2% - 8% to produce gas that rises to the surface through hydraulic fracturing and horizontal drilling. Geomechanics is one of the important factors that influence the success of a hydraulic fracturing job. Technology in fractures makes geomechanics a clear factor in predicting the success or failure of rocks in deformation and knowing the properties that will be faced by fracture fluids which will later be used to see the effectiveness of fracture fluids in resisting fractures. High operational costs need to be studied further to determine the parameters that affect hydraulic fracturing work, especially from the geomechanical aspect to minimize production failures and work safety. The research conducted this time focuses on the sensitivity of geomechanical parameters by using CMG (GEM) reservoir simulations for reservoir models and conducting Response Surface Methodology (RSM) in selection and ease when applied in the field prior to the hydraulic fracturing process. In this sensitivity study carried out on 5 parameters namely stress, Poisson's ratio, Young's modulus, biot coefficient, and pore pressure. The geomechanical parameter that has the most influence on hydraulic fracturing work based on the sensitivity results carried out through 500 data sets using the Analysis of Variance obtained R2 = 0.99 with the results based on the importance value of the pore pressure variable of 3.8. Then Young's modulus is 0.28, stress is 0.12, Poisson's ratio is 0.08, and biot coefficient is 0.04.
Influential Factors Affecting Customer Loyalty among BTPN Jenius Mobile Banking User’s Factors

Study This aim For now influence Trust Customer, Perception Convenience Use and Perception Security to Loyalty Customers of BTPN Jenius mobile banking users. Research design carried out that is study quantitative with method associative. Population study this is BTPN Jenius mobile banking users, meanwhile technique sample use purposive sampling sample with amount respondents as many as 100 people. Analytical tools used namely SPSS version 26. Results from study This show that Trust Customer, Perception Convenience Use and Perception Security influential positive and significant to Loyalty Jenius BTPN mobile banking customers.