Miracle-dz/UniFAR-FTUs huggingface dataset:
Mesothermal gold lodes in the north-western Alps: A review of genetic constraints from radiogenic isotopes.

Identifying the source of the hydrothermal fluid responsible for mesothermal gold lodes in orogenic belts has proven to be a formidable hurdle. As a consequence, several key aspects of the genesis of this worldwide class of deposits remain poorly understood. This article reviews a wide spectrum of published data on the Monte Rosa Gold District, a belt of mesothermal gold lodes in the Alpine orogen. The data include Sr-, Pb- and He-Ar isotopes, 40 Ar/ 39 Ar chronology, fluid-inclusion compositions, mineralogy, and the geological framework of the deposits. It is demonstrated that simultaneous consideration of several radiogenic isotope systems is highly valuable in reconstructing the source characteristics and processes of ore deposition in open hydrothermal systems. The genetic model suggested by these data involves prograde metamorphic devolatilization of Mesozoic calcschists during mid-Tertiary continental collision and orogenic uplift. The liberated fluids scavenged gold from metabasites interlayered with the calcschists, then ascended to form auriferous quartz-carbonate-sulphide veins in rocks undergoing retrograde metamorphism. Metamorphic hydrothermal systems of this type recurred along the district over a period of at least 20 Ma, their location and timing being controlled by the progress of differential uplift of the north-western Alps. The nature of this “temporal continuum” of mineralisation contrasts with that reported for Archean gold-lode deposits. As well as clarifying aspects of gold-lode genesis, the isotopic approaches discussed have great potential to constrain scenarios of large-scale fluid flow – with or without mineralising potential – in orogenic belts of all ages.
Vegetation response to rainfall seasonality and interannual variability in tropical dry forests

Projected changes in rainfall seasonality and interannual variability are expected to have severe impacts on arid and semi‐arid tropical vegetation, which is characterized by a fine‐tuned adaptation to extreme rainfall seasonality. To study the response of these ecosystems and the related changes in hydrological processes to changes in the amount and seasonality of rainfall, we focused on the caatinga biome, the typical seasonally dry forest in semi‐arid Northeast Brazil. We selected four sites across a gradient of rainfall amount and seasonality and analysed daily rainfall and biweekly Normalized Difference Vegetation Index (NDVI) data for hydrological years 2000 to 2014. Rainfall seasonal and interannual statistics were characterized by recently proposed metrics describing duration, timing and intensity of the wet season and compared to similar metrics of NDVI time series. The results show that the caatinga tends to have a more stable response with longer and less variable growing seasons (3.1 ± 0.1 months) compared to the duration wet seasons (2.0 ± 0.5 months). The ecosystem ability to buffer the interannual variability of rainfall is also evidenced by the stability in the timing of the growing season compared to the wet season, which results in variable delays (ranging from 0 to 2 months) between the peak of the rainfall season and the production of leaves by the ecosystem. The analyses show that the shape and size of the related hysteresis loops in the rainfall–NDVI relations are linked to the buffering effects of soil moisture and plant growth dynamics. Finally, model projections of vegetation response to different rainfall scenarios reveal the existence of a maximum in ecosystem productivity at intermediate levels of rainfall seasonality, suggesting a possible trade‐off in the effects of intensity (i.e. amount) and duration of the wet season on vegetation growth and related soil moisture dynamics and transpiration rates. Copyright © 2016 John Wiley & Sons, Ltd.
A New Algorithm for Generalization of Least Square Method for Straight Line Regression in Cartesian System for Fully Correlated Both Coordinates

This work presents the estimation of parameters and uncertainty of straight regression line using the least square method when both coordinates in Cartesian System XOY are affected by errors and fully correlated. The maximalization of the likelihood multivariate Gaussian function as equivalent of minimalization of objection function is derived for common vector variable Z included X and Y vectors random variable – vectors of coordinates of measurement points. In this way all kind of possible correlations are taken into account – within the metrological literature, there exist some works taking them into account. The core of the presentation is the mathematical manipulation based on linear algebra matrixes and vectors with elements of functional analysis. Any novelty claimed in this field will be carefully demonstrated. The problem is reduced to the determination of numerical one-dimensional characteristic e.g. objection function as function of slope a of straight regression line. The algorithm allows to determine numerically the covariance matrix and the coverage corridor for straight line regression. The implementation of the numerical method is applied in script running in MATLAB environment. Finally, the comparison of results based on the previous published tests are carried out.
Dynamic Semantically Guided Monocular Depth Estimation for UAV Environment Perception

Monocular depth estimation (MDE) is one of the most difficult tasks in computer vision. The problem becomes even more complicated in case of aerial images due to the high complexity and lack of structure present in such scenarios. State-of-the-art MDE methods can cope with such environments only by using high amounts of resources. In this work, we try to provide a more resource-aware alternative by dynamically inserting scene priors through semantic features into the network. To this end, we initially propose a novel dynamic semantic-aware module, that combines features extracted from the RGB image with a dynamically weighted semantic map. The weights are gradually modified, according to the iteration number inside the training process. By pursuing this methodology, we initially predict the depth for larger objects inside the scene. As the training resumes, smaller objects are further emphasized, thus increasing object boundary confidences. We prove that this dynamic adaptation mechanism leads to a better and faster convergence. The adaptation technique is applied for various learning frameworks, by introducing three novel semantic-aware losses for depth-based regression, classification, and ordinal regression. We show the results on a large set of synthetic and real-life aerial images, captured in various scenarios and thus we prove the effectiveness of our approach in terms of convergence time, depth accuracy, and prediction time.
Terahertz Metamaterials for Biosensing Applications: A Review

In recent decades, THz metamaterials have emerged as a promising technology for biosensing by extracting useful information (composition, structure and dynamics) of biological samples from the interaction between the THz wave and the biological samples. Advantages of biosensing with THz metamaterials include label-free and non-invasive detection with high sensitivity. In this review, we first summarize different THz sensing principles modulated by the metamaterial for bio-analyte detection. Then, we compare various resonance modes induced in the THz range for biosensing enhancement. In addition, non-conventional materials used in the THz metamaterial to improve the biosensing performance are evaluated. We categorize and review different types of bio-analyte detection using THz metamaterials. Finally, we discuss the future perspective of THz metamaterial in biosensing.
Computer-Assisted Qualitative Data Analysis in the Healthcare Cold Chain

Healthcare Cold Chain (HCC) involves a number of health-related stakeholders, in which procedures are needed to ensure the proper storage and distribution of health services and medical products at recommended temperatures to maintain their potency. In this study, we conduct the qualitative analysis for key issues and concerns in the HCC by using computer-assisted qualitative analysis tool called Atlas.ti program. Data are initially collected from 332 samples based on seven groups of HCC stakeholders in Thailand and then later analyzed using the content analysis by evaluating key themes of issues and concerns in the HCC. Our analysis using the Sankey diagram shows that key concerns can be classified and categorized to 9 group codes, where the top three ranks are 1) knowledge and training for key involved staffs, 2) an effectiveness of temperature control in the chain, and 3) equipment as well as storage resource, respectively. Finally, characteristics of concerns are examined for each type of stakeholders to obtain key insights in the study.
To Compare The Two Approaches Of Nerve Flossing Technique On Pain, Lumbar Mobility And Straight Leg Raise In Subjects With Sciatica

Background: Sciatica is one of the most common painful and disabling conditions. Nerve flossing technique commonly called as neural mobilization are the forms of manual therapy that are used in an effort to reduce radiating pain and improve range of motion. A wide range of physical therapy interventions have been proposed to be effective, however the efficacy of neural mobilization technique is still undetermined in developing countries like India. Purpose: A study to compare the two approaches of nerve flossing technique on pain, lumbar mobility and striaght leg raise in subjects with sciatica. Study design: Quasi- Experimental study, comparative in nature Methodology: A total of 45 subjects were selected for the study as per inclusion and exclusion criteria between age group of 20-55 years. Participants were divided into 3 groups of 15 subjects each. Group A (control), Group B (Experimental-1) and Group C (experimental-2). Group A was receive Transcutaneous Electrical Nerve Stimulation (TENS), Hydro-collateral pack, Group B was receive slider mobilization (Nerve Flossing Technique) and Group C was receive single joint mobilization on knee (Nerve Flossing Technique). The baseline data was recorded on 1th day (pre- intervention) and 5th day (post-intervention) and then on 10th day (post-intervention). Total 10 sessions per subject was given over 2 weeks i.e., 5 sessions per week. The collected Data was analyzed by using ANOVA and other statistical tests as appropriate Results: The result showed significant improvement in slider mobilization on pain, lumbar mobility and striaght leg raise in subjects with sciatica. Conclusion: The present study concludes that slider mobilization is most effective nerve flossing technique in improving pain, lumbar mobility and striaght leg raise in subjects with sciatica. Trial registration number (TRN): Not applicable.
Revolutionizing Farm Irrigation: A Smart IoT-based Solution for Water Conservation in Agriculture

Agriculture motor and pipe control system deploys innovative technology-based IoT platform to reduced water wastage in the agriculture field. Mobile application interfaced with the microcontroller. A mobile application is developed for the user to control a motor in smart way. The user will control motor in android application. There are many options provided by motor on and off, the user switches on the motor either automatically or manually. This operation performed in a motor can be operated and connected to an android application. In a microcontroller, a timer can be set based on user's desire. Here, the motor will operate based on option user can control, the microcontroller checks moisture and temperature present in the land, based on that water will flow to the land. Another option is that when the farm contains high yield crops when this motor will run three times longer than the other to increase water flow. The main motive of this proposed system is to provides a smart motor for farmers to reduce water flow in farming land. Thus, the objective of this work which is to check both moisture and temperature level of the land based on that water will flows. Hence it can reduce water wastage and manpower.
The accuracy of different mismatch negativity amplitude representations in predicting the levels of consciousness in patients with disorders of consciousness

Introduction The mismatch negativity (MMN) index has been used to evaluate consciousness levels in patients with disorders of consciousness (DoC). Indeed, MMN has been validated for the diagnosis of vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS). In this study, we evaluated the accuracy of different MMN amplitude representations in predicting levels of consciousness. Methods Task-state electroencephalography (EEG) data were obtained from 67 patients with DoC (35 VS and 32 MCS). We performed a microstate analysis of the task-state EEG and used four different representations (the peak amplitude of MMN at electrode Fz (Peak), the average amplitude within a time window −25– 25 ms entered on the latency of peak MMN component (Avg for peak ± 25 ms), the average amplitude of averaged difference wave for 100–250 ms (Avg for 100–250 ms), and the average amplitude difference between the standard stimulus (“S”) and the deviant stimulus (“D”) at the time corresponding to Microstate 1 (MS1) (Avg for MS1) of the MMN amplitude to predict the levels of consciousness. Results The results showed that among the four microstates clustered, MS1 showed statistical significance in terms of time proportion during the 100–250 ms period. Our results confirmed the activation patterns of MMN through functional connectivity analysis. Among the four MMN amplitude representations, the microstate-based representation showed the highest accuracy in distinguishing different levels of consciousness in patients with DoC (AUC = 0.89). Conclusion We discovered a prediction model based on microstate calculation of MMN amplitude can accurately distinguish between MCS and VS states. And the functional connection of the MS1 is consistent with the activation mode of MMN.
HotWire– Decreasing Latency Between Wake-Up Calls in Semi-Active WUR-Scenarios

Wireless Sensor Networks using Wake-up Radios (WURs) in dense deployments benefit from addressing at WUR-level. Many semi-active WURs rely on a simple frontend that is able to wake-up the back-end circuits in a relatively short time. However, once woken-up the design of these frontends prevents a quick consecutive receiving of Wake-up Calls (WUCs). So, whenever a non-matching address is received by the address decoder, it takes quite a while until a new address can be decoded. In this paper, we demonstrate how a significant increase in WUC frequency can be achieved with relatively simple adaptations to the WUR front-end. The dimensioning of the individual components is also considered and derived. In the respective evaluation, we show that by means of our adaptation, consecutive WUCs can be handled approximately 1000 times faster than without this adaptation.
Analysis of Modified Conveyor Skirt Board Geometry and Their Effects on Dust Generation Prediction

The issue with coal transportation is the dust that is produced and flies into the air, potentially posing a health risk to workers and the surrounding machinery. This study was carried out to evaluate the alteration of the skirt board's effectiveness in reducing the amount of coal dust produced. The geometric variations carried out in this analysis were skirt board height variations, transfer chute width variations, and skirt board length variations. The simulations carried out showed that the greatest decrease in airflow velocity occurred in the variation of the transfer chute width. While the findings of the material velocity simulation show a decrease in material velocity and a prediction that the greatest amount of dust will collect on the skirt board's outlet side at length variations.
Data-Driven Heart Disease Risk Prediction with Machine Learning on Healthcare Datasets

The Centers for Disease Control and Prevention (CDC) reports that heart disease remains a leading cause of death among people of all races. Identifying cardiovascular disease early on can help high-risk individuals make informed lifestyle choices, leading to reduced complications and improved health outcomes. To achieve this goal, this research utilizes data mining and analytics techniques to investigate healthcare datasets. The dataset under study includes 803,916 rows and 279 columns, comprising various health-related factors. The data was divided into training and test datasets, and four different machine learning models were employed, including Decision Tree, Binary Logistic Regression, K-Nearest Neighbors, and Random Forest, to compare their accuracy in predicting heart disease. The Binary Logistic Regression model performed the best with an accuracy score of 91.48%. The study's findings can be beneficial to both public and private sectors in developing preventive measures and interventions to reduce mortality rates.
DigitalCommons@UNO DigitalCommons@UNO

The advent of Blockchain and smart contracts is empowering many technologies and systems to automate commerce and facilitate the exchange, tracking and the provision of goods, data and services in a reliable and auditable way. Crowdsensing systems is one type of systems that have been receiving a lot of attention in the past few years. In crowdsensing systems consumer devices such as mobile phones and Internet of Things devices are used to deploy wide-scale sensor networks. We identify some of the major security and privacy issues associated with the development of crowdsensing systems based on smart contracts and Blockchain. We also explore possible solutions that can address major security concerns with these systems.
A Novel System to Map Reasonably Connected Internet Regions

Knowing geographical locations becoming necessity more than facility nowadays. Most IPgeolocation mapping schemes [14], [16], [17], [18] take delay-measurement approach, based on the assumption of a strong correlation b e t w e e n networking delay and geographical distance between the targeted client and the landmarks. In this paper, however, we investigate a large region of moderately connected Internet and find the delay-distance correlation is weak. But we discover a more probable rule—with high probability the shortest delay comes from the closest distance. Based on this closest- shortest rule, we develop a simple and novel IP- geolocation mapping scheme for moderately connected Internet regions, called GeoGet. In GeoGet, we take a large number of web servers as passive landmarks and map a targeted client to the geolocation of the landmark that has the shortest delay. We further use JavaScript at targeted clients to generate HTTP/Get probing for delay measurement. To control the measurement cost, we adopt a multistep probing method to refine the geolocation of a targeted client, finally to city level. The evaluation results show that when probing about 100 landmarks, GeoGet correctly maps 35.4 percent clients to city level, which outperforms current schemes such as GeoLim [16] and GeoPing [14] by 270 and 239 percent, respectively, and the median error distance in GeoGet is around 120 km, outperforming GeoLim and GeoPing by 37 and 70 percent, respectively.
A real-world pharmacovigilance study of mepolizumab in the FDA adverse event reporting system (FAERS) database

Mepolizumab is primarily used in the treatment of asthma, eosinophilic granulomatosis with polyangiitis, eosinophilia syndrome, and chronic rhinitis with nasal polyps. The information about its adverse drug reactions is mainly derived from clinical trials, and there is a shortage of real-world studies with extensive sample sizes. In this study, the U.S. FDA’s Adverse Event Reporting System (FAERS) database was analyzed to evaluate the side effects of mepolizumab. A total of 18,040 reports of mepolizumab-associated adverse events were identified from the FDA Adverse Event Reporting System database. Multiple disproportionality analysis algorithms were used to determine the significance of these AEs. The study identified 198 instances of mepolizumab-induced AEs, including some important AEs not mentioned in the product labeling. The time to onset of adverse reactions was also analyzed, with a median time of 109 days. Most AEs occurred within the first month of mepolizumab use, but some may still occur after 1 year of treatment. Gender-specific analysis showed different high-risk AEs for females (digestive and neurological side effects) and males (serious adverse effects leading to hospitalization and death). The findings mentioned provide valuable insights on optimizing the use of mepolizumab, enhancing its effectiveness, and minimizing potential side effects. This information will greatly contribute to the practical implementation of the drug in clinical settings.
Roles and Challenge of Social Media in E-Commerce Through Expert Review

Everyone and every business has been profoundly impacted by social media to the point where it can no longer be ignored. Its growth has been meteoric in every market around the globe. E-commerce sites' ability to facilitate social interaction between vendors and buyers is becoming increasingly important in the framework of the current digital revolution. The most popular type of app was social media, followed by games, shopping, and messaging. The primary objective is to provide a comprehensive overview of the research conducted on a particular topic. It also can establish context for a research topic by demonstrating how current research builds on past work. This paper has searched pertinent literature using databases such as Google Scholar, PubMed, Scopus, etc. They employ particular keywords and criteria to identify the most pertinent articles. The identified papers undergo a screening based on their titles and abstracts. The most relevant results are chosen for full-text viewing. The text emphasizes the significance of social media in the evolution of e-commerce and the need for businesses to adapt and utilize these platforms for successful consumer engagement, brand development, and overall growth.