Miracle-dz/UniFAR-FTUs huggingface dataset:
The Rate and Process of Mangrove Forest Expansion on Above- and Belowground Carbon Relations in Coastal Louisiana

Global climate change is a major environmental threat to natural and cultural resources in low-lying coastal zones and deltas worldwide. It is expected to have a significant effect on the development of coastal wetlands by changing species range expansion. In fact, recent studies have shown that there has been an increasing mangrove encroachment poleward in the Southern and Northern Hemispheres into temperate saltmarsh zones due to the lack of freeze events. This phenomenon could have a critical impact on the structure and function of tidal wetlands at critical latitudes, especially in carbon sequestration, since mangroves are thought to sequester more carbon than marshes due to their extensive roots, leaves, and branches. To understand this likely outcome of climate warming better, a study site was established in Port Fourchon, Louisiana, where mangroves are expanding into formerly saltmarsh-dominated habitat. The aim of this study was to estimate the rate and process of the conversion of marsh to mangrove and associated carbon sequestration of soils of historically saltmarsh or mangrove dominated cover in this locale. A chronosequence approach was used to compare carbon stores between mangrove and saltmarsh at different states of mangrove cover. Moreover, greenhouse experiments were conducted to understand the effects of different biotic properties and abiotic factors on propagule regeneration success and seedling growth. Results showed that tall Avicennia have higher aboveground biomass than Spartina and mixed mangrove/marsh zone. Tall Avicennia by habitat type also contributed the greater carbon storage and elevation compared to Spartina over the past 50 years based on 137 Cs dating method. Propagule size, floating time, and temperature thresholds had significant effects on mangrove regeneration, while elevated CO2 and sunlight had no effect on propagule regeneration success.
Calcium-Associated Proteins in Neuroregeneration

The dysregulation of intracellular calcium levels is a critical factor in neurodegeneration, leading to the aberrant activation of calcium-dependent processes and, ultimately, cell death. Ca2+ signals vary in magnitude, duration, and the type of neuron affected. A moderate Ca2+ concentration can initiate certain cellular repair pathways and promote neuroregeneration. While the peripheral nervous system exhibits an intrinsic regenerative capability, the central nervous system has limited self-repair potential. There is evidence that significant variations exist in evoked calcium responses and axonal regeneration among neurons, and individual differences in regenerative capacity are apparent even within the same type of neurons. Furthermore, some studies have shown that neuronal activity could serve as a potent regulator of this process. The spatio-temporal patterns of calcium dynamics are intricately controlled by a variety of proteins, including channels, ion pumps, enzymes, and various calcium-binding proteins, each of which can exert either positive or negative effects on neural repair, depending on the cellular context. In this concise review, we focus on several calcium-associated proteins such as CaM kinase II, GAP-43, oncomodulin, caldendrin, calneuron, and NCS-1 in order to elaborate on their roles in the intrinsic mechanisms governing neuronal regeneration following traumatic damage processes.
Smart pH Sensing: A Self-Sensitivity Programmable Platform with Multi-Functional Charge-Trap-Flash ISFET Technology

This study presents a novel pH sensor platform utilizing charge-trap-flash-type metal oxide semiconductor field-effect transistors (CTF-type MOSFETs) for enhanced sensitivity and self-amplification. Traditional ion-sensitive field-effect transistors (ISFETs) face challenges in commercialization due to low sensitivity at room temperature, known as the Nernst limit. To overcome this limitation, we explore resistive coupling effects and CTF-type MOSFETs, allowing for flexible adjustment of the amplification ratio. The platform adopts a unique approach, employing CTF-type MOSFETs as both transducers and resistors, ensuring efficient sensitivity control. An extended-gate (EG) structure is implemented to enhance cost-effectiveness and increase the overall lifespan of the sensor platform by preventing direct contact between analytes and the transducer. The proposed pH sensor platform demonstrates effective sensitivity control at various amplification ratios. Stability and reliability are validated by investigating non-ideal effects, including hysteresis and drift. The CTF-type MOSFETs’ electrical characteristics, energy band diagrams, and programmable resistance modulation are thoroughly characterized. The results showcase remarkable stability, even under prolonged and repetitive operations, indicating the platform’s potential for accurate pH detection in diverse environments. This study contributes a robust and stable alternative for detecting micro-potential analytes, with promising applications in health management and point-of-care settings.
Modern Education: Advanced Prediction Techniques for Student Achievement Data

—Enhancing educational outcomes across varied institutions like universities, schools, and training centers necessitates accurately predicting student performance. These systems aggregates the data from multiple sources—exam centers, virtual courses, registration departments, and e-learning platforms. Analyzing this complex and diverse educational data is a challenge, thus necessitating the application of machine learning techniques. Utilizing machine learning algorithms for dimensionality reduction simplifies intricate datasets, enabling more comprehensive analysis. Through machine learning, educational data is refined, uncovering valuable patterns and forecasts by simplifying complexities via feature selection and dimensionality reduction methods. This refinement significantly amplifies the efficacy of student performance prediction systems, empowering educators and institutions with data-driven insights and thereby enriching the overall educational landscape. In this particular research, the Decision Tree Classification (DTC) model is used for forecasting student performance. DTC stands out as a potent machine-learning method for classification purposes. Two optimization algorithms, namely the Fox Optimization (FO) and the Black Widow Optimization (BWO), are integrated to heighten the model's accuracy and efficiency further. The amalgamation of DTC with these pioneering optimization techniques underscores the study's dedication to harnessing the forefront of machine learning and bio-inspired algorithms, ensuring more precise and resilient predictions of student performance, ultimately culminating in improved educational outcomes. From the results garnered for G1 and G3, it is evident that the DTBW model demonstrated the most exceptional performance in both predicting and categorizing G1, achieving an Accuracy and Precision value of 93.7 percent. Conversely, the DTFO model emerged as the most precise predictor for G3, achieving an Accuracy and Precision of 93.4 and 93.5 percent, respectively, in the prediction task.
A Review on Automated Detection and Assessment of Fruit Damage Using Machine Learning

Automation improves the quality of fruits through quick and accurate detection of pest and disease infections, thus contributing to the country’s economic growth and productivity. Although humans can identify the fruit damage caused by pests and diseases, the methods used are inconsistent, time-consuming, and variable. The surface features of fruits typically observed by consumers who seek their health benefits affect their market value. The issue of pest and disease infections further deteriorates fruits’ quality, becoming a mounting stressor on farmers since they reduce the potential revenue from fruit production, processing, and export. This article reviews various studies on detecting and classifying damages in fruits. Specifically, we review articles where state-of-the-art approaches under segmentation, image processing, machine learning, and deep learning have proved effective in developing automated systems that address hurdles associated with manual methods of assessing damage using visual experiences. This survey reviews thirty-two journal and conference papers from the past thirteen years that were found electronically through Google Scholar, Scopus, IEEE, ScienceDirect, and standard online searches. This survey further presents a detailed discussion of previous research done in the past while emphasizing their strengths and limitations as well as outlining potential future research topics. It also reveals that much as the use of automated detection and classification of fruit damage has yielded promising results in the horticulture industry, more research is still needed with systems required to fully automate the detection and classification processes, especially those that are mobile phone-based towards addressing occlusion challenges.
Correlational Analysis of the Effective Optical Zone with Myopia, Myopic Astigmatism, and Spherical Equivalent in LASIK, PRK, and SMILE

Purpose We assess the relationship between preoperative myopic sphere, astigmatism, and spherical equivalent and effective optical zone (EOZ) size, shape, and decentration within individual populations of post-LASIK, PRK, and SMILE patients. Patients and Methods A retrospective chart review was conducted with 118 LASIK, 144 PRK, and 41 SMILE eyes from 179 total patients that underwent compound myopic ablation. One-year postoperative Pentacam tangential difference maps were used for EOZ data measurements. Correlational analysis between compound myopic measures [sphere, cylinder, manifest refractive spherical equivalent (MRSE)] and EOZ parameters was performed, and differences between groups of myopic sphere and cylinder within each surgery type were assessed. Results An increase in absolute myopic sphere (and subsequent MRSE) is associated with a smaller EOZ area in SMILE (r=0.454, p=0.003) and a more circular EOZ shape in LASIK (r=0.396, p<0.001) and PRK (r=0.563, p<0.001). An increase in absolute myopic cylinder is associated with an increased EOZ area in all three surgery types [LASIK (r=−0.459, p<0.001), PRK (r=−0.716, p<0.001), SMILE (r=−0.429, p=0.005)] and a more elliptical EOZ in LASIK (r=−0.491, p<0.001) and PRK (r=−0.538, p<0.001). Conclusion While astigmatism may be correlated to EOZ size within all three refractive surgery types, myopic sphere alone is insufficient to estimate EOZ size differences for procedures with a large blend zone of ablation like LASIK or PRK. Shape is just as important a factor as size to consider when examining corneal EOZ differences; reported correlative findings likely result from inherent differences in surgical technique and abruptness of planned surgical ablation borders.
Effects of menstrual disorders and dysmenorrhea on cardiovascular disease: a Mendelian randomization study

Background Observational studies have demonstrated associations between menstrual disorders, dysmenorrhea, and cardiovascular disease (CVD). However, it remains unclear whether these associations are causal. This study is to investigate whether menstrual disorders and dysmenorrhea causally affect the risk of CVD. Methods The summary data for menstrual disorders (excessive menstruation and irregular menses) and dysmenorrhea were obtained from FinnGen study, summary data for CVD were obtained from UK Biobank and meta-analysis. The inverse-variance-weighted method was mainly used in the Mendelian randomization for causality analysis. Sensitivity analyses were performed by several methods under different model assumptions. Results Genetic liability to excessive menstruation was associated with higher risk of atrial fibrillation (odds ratio (OR), 1.078 [95% confidence interval (CI), 1.015-1.145]; P=0.014), but a lower risk of hypertension (OR, 0.994 [95% CI: 0.989-0.999]; P=0.016). Irregular menses was associated with higher risk of atrial fibrillation (OR, 1.095 [95% CI: 1.015-1.182]; P=0.02), hypertension (OR, 1.007 [95% CI: 1.000-1.013]; P=0.047), myocardial infarction (OR, 1.172 [95% CI: 1.060-1.295]; P=0.02), ischemic heart disease, (OR, 1.005 [95% CI: 1.000-1.010]; P=0.037) and coronary heart disease (OR, 1.004 [95% CI: 1.001-1.008]; P=0.026). Dysmenorrhea was associated with higher risk of atrial fibrillation (OR, 1.052 [95% CI: 1.014-1.092]; P=0.008) and Ischemic stroke (cardioembolic) (OR, 1.122 [95% CI: 1.002-1.257]; P=0.046). After Benjamini-Hochberg correction, irregular menses was associated with higher risk of myocardial infarction. Conclusion We confirmed a causal relationship of excessive menstruation, irregular menses and dysmenorrhea on cardiovascular outcomes independent of sex hormone levels, with an emphasis on the link between irregular menses and myocardial infarction. These clinical features can be utilized as markers to identify women at higher risk of developing CVD in the future, recommending early clinical intervention of menstrual diseases.
Low salinity effect after sea water flooding in sandstone reservoirs

ABSTRACT Clays are the main wetting minerals and have permanently negative surface charges. The negative charges must be balanced by; active cat ions, polar components or H. pH changes is observed in the effluent by flooding in a sequence of FW-SW-LS-FW. The concentration of ions in the Low Salinity brine are lower than in the formation water and sea water brines, especially Ca, Mg.Clays are the main wetting minerals and have permanently negative surface charges. The negative charges must be balanced by; active cat ions, polar components or H. pH changes is observed in the effluent by flooding in a sequence of FW-SW-LS-FW. The concentration of ions in the Low Salinity brine are lower than in the formation water and sea water brines, especially Ca, Mg. In the proposed chemical mechanism on Low Sal EOR effects in sandstone reservoir, it is the effects of pH, both for the adsorption of acidic and basic organic components onto clay minerals, to create initial low water wetness, and also for the desorption of the polar components when the smart water is introduced. When injecting Low Salinity fluid with low Ca concentration, it will promote desorption of Ca from the clay surface which consequently creates a local increase in pH close to the brine-clay interface due to H from the water compensates the negative charges at the clay surface. A fast reaction between OH and the absorbed acidic and protonated basic material, it will cause desorption of organic material from the clay surface, and as the results, the water wetness of the rock is improved and increased in oil recovery is observed due to increased positive capillary pressure. Mostly all sandstone reservoirs in North Sea have already been flooded with Sea Water. Is it likely to observe Low Salinity EOR effect after the reservoir have been Sea Water flooded? Through the combination of theoretical knowledge, and detailed low salinity experiments carried out in the lab, both pH screening tests and oil recovery tests on reservoir cores confirmed the possibility to observe Tertiary LS EOR effects in a High Temperature Sandstone Reservoir.
Enhancing Respiratory Diseases Detection Using GAN Augmentation and Transfer Learning

Pneumonia is a potentially fatal bacterial disease that affects one or both lungs in humans and is frequently caused by the bacteria Streptococcus pneumonia. Chest X-rays provide an effective way to diagnose and detect this disease, it supports professionals to speed up detection and avoid deadly consequences, especially in vulnerable groups. Over the last few years, deep learning, particularly Convolutional neural network models, has improved medical image interpretation. Where an extensive quantity of labelled data is necessary to train a successful deep learning model, obtaining enough annotated images with balanced datasets is frequently problematic. In this work, we demonstrate how the suggested Generative Adversarial Network (GAN) can be efficiently used to supplement data and enhance disease detection precision in chest X-rays images. Later, we compare between several classification models using synthetic data, demonstrating that our GAN-based augmentation method can be a solution to the previous problems.
A UWB Vibrotactile Safety Monitoring System for Factory Environments

This paper presents a safety monitoring system for workers in factory environments. This aims to monitor the workers’ position in relation to hazardous zones and to issue timely alerts for potential safety risks. The suggested system uses Ultra-Wideband (UWB) positioning with vibrating vests to provide tactile proximity alerts. The block diagram of the system is detailed and subsequently implemented within the context of a control room. The findings show interesting positioning accuracy under static and dynamic conditions, as well. Hence, the proposed UWB vibrotactile safety monitoring system promises enhanced safety and productivity among the industrial workforce with a reduced risk of accidents and fatalities in the workplace.
Colletinin A and 2,2″-Diepicolletinin A: Two New Bisflavan-3-ols from Rhododendron Collettianum

Two new bisflavan-3-ols, named colletinin A (1) and 2,2″-diepicolletinin A (2) have been isolated from Rhododendron collettianum. Their structures were determined by means of 1D and 2D NMR analysis, aided by HREIMS data. The relative configuration was determined by means of 1H-1H NOESY correlation but the absolute configuration could not be established.
Removing the Reliance on Batteries: Energy Harvesting Based Power Supplies for Rail and Wearable Applications

Harvesting energy from ambient sources has been researched over many years but the application of the technology can prove challenging. Successful deployment in the rail industry is described where sufficient kinetic energy is present and the use of batteries is not acceptable. In this application, vibration energy harvesting is being used to power retrofittable wireless condition monitoring systems that give early warning of wheel bearing failure, an indication of wheel health and track condition. Energy harvesting technologies are also attractive in flexible and wearable electronic textile applications where conventional batteries are highly undesirable. A wide variety of textile energy harvesting sources have been demonstrated including kinetic, thermal, solar and wireless power transfer and the opportunities for energy harvesting powered e-textile systems are explored.
AMS 14C and Chemical Composition of Atmospheric Aerosols from Mexico City

Abstract Air pollution in Mexico City, which has more than 22 million inhabitants, continues to be one of the main environmental issues. Aerosol samples (PM10) collected in Mexico City and the city of Cuernavaca (a clean reference site) have been characterized using different techniques. This multifaceted approach addresses the source apportionment of the carbonaceous matter in PM10, as well as the airborne elements and ions. Accelerator mass spectrometry (AMS) radiocarbon analysis of total carbon, X-ray fluorescence (XRF), and ion chromatography were performed on aerosols collected at three sites in Mexico City and one site in Cuernavaca, during 2 months of the cold-dry season (November–December) in 2012. New results obtained for Mexico City are compared with previous reports. Average levels of PM10 were higher in Mexico City sites (43.3–60.8 μg/m3) than in Cuernavaca (32.2 μg/m3). According to the material balance, PM10 collected in Mexico City had a lower contribution of crustal material (31.2–36.8%) than Cuernavaca (46.9%). Average contributions of particulate carbonaceous matter to PM10 were similar in both cities, but much higher contributions of mineral salts, trace elements, and ions were observed in Mexico City in comparison to Cuernavaca. Total organic carbon (OC) and elemental carbon (EC) contents were higher in aerosols from Mexico City than those from Cuernavaca. The temporal variation results showed that within all locations studied the OC concentration was high compared to the EC. Results from a theoretical calculation of fossil carbon (FC) and biogenic carbon (BC) concentrations showed that FC and BC levels depend on the site: at Mexico City sites, FC was equal or higher than BC. At Cuernavaca, BC was always higher than FC.
Limitations of Perturbation-based Explanation Methods for Temporal Graph Neural Networks

Recently, there has been significant interest in Temporal Graph Neural Networks (TGNN) because of their capability to learn from time-evolving graph-related data. However, similar to Graph Neural Network (GNN), explaining the predictions of TGNN is non-trivial due to its black-box and complex nature. A major approach for this problem in GNNs is by analyzing the model’s responses to some perturbations of the model’s inputs, called perturbation-based explanation methods. These methods are convenient and flexible as they do not require access to the model’s internals. However, a question arises: Does the lack of internal access limit these methods from uncovering crucial information about the predictions? Motivated by the question, this study explores the limitations of some popular classes of perturbation-based explanation methods. By constructing specific instances of TGNNs, we show (i) Node-perturbation is not reliable for identifying the paths that carry out the prediction, (ii) Edgeperturbation cannot reliably determine all the nodes contributing to the prediction and (iii) perturbing both nodes and edges does not consistently help identify the graph components responsible for the temporal aggregation in TGNNs. Our experimental results further demonstrate situations for failures of explanations can occur frequently in both synthetic and real-world scenarios. Thus, they emphasize the importance of perturbation choices and the internal information of the explained model in determining faithful explanations of the model’s predictions.
Modeling Environmental Conditions in Poultry Production: Computational Fluid Dynamics Approach

Simple Summary The aim of this review is to provide researchers with a guide to simulating the environment of poultry houses using computational fluid dynamics. Through an extensive review of the literature in this area, it provides comprehensive insights into the common challenges encountered when applying this method, as well as a discussion of planned future research efforts. Abstract In recent years, computational fluid dynamics (CFD) has become increasingly important and has proven to be an effective method for assessing environmental conditions in poultry houses. CFD offers simplicity, efficiency, and rapidity in assessing and optimizing poultry house environments, thereby fueling greater interest in its application. This article aims to facilitate researchers in their search for relevant CFD studies in poultry housing environmental conditions by providing an in-depth review of the latest advancements in this field. It has been found that CFD has been widely employed to study and analyze various aspects of poultry house ventilation and air quality under the following five main headings: inlet and fan configuration, ventilation system design, air temperature–humidity distribution, airflow distribution, and particle matter and gas emission. The most commonly used turbulence models in poultry buildings are the standard k-ε, renormalization group (RNG) k-ε, and realizable k-ε models. Additionally, this article presents key solutions with a summary and visualization of fundamental approaches employed in addressing path planning problems within the CFD process. Furthermore, potential challenges, such as data acquisition, validation, computational resource requirements, meshing, and the selection of a proper turbulence model, are discussed, and avenues for future research (the integration of machine learning, building information modeling, and feedback control systems with CFD) are explored.