Miracle-dz/UniFAR-FTUs huggingface dataset:
On triple g transformation and its properties

In this paper, we defined new triple transformation, which is called the fractional triple g-transformation of the order αl ,0<α≤1 for fractional of differentiable functions. This transformation is generalized to double g-transformation. Which has the following form;Tg_α (u(ξ,τ,μ)=p(s)∫_0^∞▒∫_0^∞▒∫_0^∞▒〖E_α 〖(-(q_1 (s)ξ+q_2 (s)τ+q_3 (s)μ)〗^α 〖(dξ)〗^α 〖(dτ)〗^α 〖(dμ)〗^α 〗
A new method for shear‐wave logging

Reliable evaluation of shear (S) wave characteristics in boreholes may be facilitated by the system proposed here, in a wide variety of geologic conditions and depths. The measurements can be done with a sonde, suspended freely in water contained in a borehole. The main part of the sonde consists of a source, a filter tube, and receivers. In this system, the wave field is treated approximately as that in an infinite homogeneous solid medium, because the wavelength is sufficiently longer than the borehole diameter. The source behaves as a single point force. The direct S‐wave is detected on a line (borehole axis) perpendicular to the force axis, in which pre‐dominant radiation of shear wave is expected. This fact is completely different from some modified systems of sonic log, in which (shear wave) is the converted‐refracted wave propagating as the shear wave along a borehole wall. The proposed source system is the (indirect‐excitation type), wherein the force is applied to a borehole wall indirectly throu.
3D LiDAR localization and mapping method incorporating inertial measurement unit

Simultaneous localization and mapping (SLAM) technology is widely used in smart factories, logistics, unmanned vehicles, high precision mapping and other fields, where inaccurate positioning or even failure is the key problem to be improved and solved by LiDAR SLAM algorithm. In this paper, we propose a loosely coupled SLAM algorithm based on lidar and inertial measurement unit (IMU). Firstly, linear interpolation of the laser point cloud is performed by introducing IMU data to correct the point cloud distortion. Second, a height threshold and a dynamic clustering threshold are introduced in the point cloud segmentation part to determine the classification. Again, the cumulative error is effectively avoided by using Scan-Context descriptors, and the next step is executed to find matching loopback frames based on the Euclidean distance, and the loopback constraints are added after the successful verification of both. Finally, a local map consisting of key frames is aligned with the current frame to obtain the inter-frame matching error, and then a joint optimization function is constructed jointly with the IMU pre-integration error and the loopback detection constraint to solve the laser odometer pose. The results on the KITTI dataset show that the improved laser odometer accuracy is higher than that of the LeGO-LOAM scheme in compliance with the odometer real-time requirements.
Experimental Study on Monitoring Damage Progression of Basalt-FRP Reinforced Concrete Slabs Using Acoustic Emission and Machine Learning

Basalt fiber-reinforced polymer (BFRP) reinforced concrete is a new alternative to conventional steel-reinforced concrete due to its high tensile strength and corrosion resistance characteristics. However, as BFRP is a brittle material, unexpected failure of concrete structures reinforced with BFRP may occur. In this study, the damage initiation and progression of BFRP-reinforced concrete slabs were monitored using the acoustic emission (AE) method as a structural health monitoring (SHM) solution. Two simply supported slabs were instrumented with an array of AE sensors in addition to a high-resolution camera, strain, and displacement sensors and then loaded until failure. The dominant damage mechanism was concrete cracking due to the over-reinforced design and adequate BFRP bar-concrete bonding. The AE method was evaluated in terms of identifying the damage initiation, progression from tensile to shear cracks, and the evolution of crack width. Unsupervised machine learning was applied to the AE data obtained from the first slab testing to develop the clusters of the damage mechanisms. The cluster results were validated using the k-means supervised learning model applied to the data obtained from the second slab. The accuracy of the K-NN model trained on the first slab was 99.2% in predicting three clusters (tensile crack, shear crack, and noise). Due to the limitation of a single indicator to characterize complex damage properties, a Statistical SHapley Additive exPlanation (SHAP) analysis was conducted to quantify the contribution of each AE feature to crack width. Based on the SHAP analysis, the AE duration had the highest correlation with the crack width. The cumulative duration of the AE sensor near the crack had close to 100% accuracy to track the crack width. It was concluded that the AE sensors positioned at the mid-span of slabs can be used as an effective SHM solution to monitor the initiation of tensile cracks, sudden changes in structural response due to major damage, damage evolution from tensile to shear cracks, and the progression of crack width.
L-Shaped Double Gate Bipolar Impact Ionization MOSFET Based Energy Efficient Leaky Integrate and Fire Neuron for Spiking Neural Network

In this article, an L-shaped double gate bipolar impact ionization MOSFET (L-DG BIMOS) is proposed and demonstrated as an ultra-low energy artificial leaky integrate and fire (LIF) neuron for spiking neural network. The L-shaped double gate structure offers a higher rate of impact ionization (II) essential for triggering LIF action and hence can efficiently be applicable to mimic a biological neuron. Using a well-calibrated 2D TCAD tool, the functioning of the proposed device as an LIF neuron is manifested by exhibiting a low threshold voltage of 0.2 V to fire a spike, reduced breakdown voltage of 0.68 V and 0.1050 pJ of energy per spike, making the device more energy efficient as compared to conventional neurons. In addition, the spiking frequency of L-DG BIMOS is calculated to be in the gigahertz (Gz) range when the drain is biased at 1.5 V, which is much higher than biological neurons.
Comparative Analysis of PWM AC Choppers with Different Loads with and Without Neural Network Application

In this paper, we focus on the "Artificial Neural Network (ANN) based PWM-AC chopper". This system is based on the PWM AC chopper-encouraged single-phase induction motor. The main purpose of this paper is to design and implement an ideal technique regarding speed control. Here analyzed PWM-based AC-AC converter with resistive load, R-L load and finally, the PWM AC chopper is fed to single phase induction for speed control. Using other soft computing and optimization techniques such as Artificial Neural Networks, Fuzzy Logic, Convolution algorithm, PSO, and Neuro Fuzzy can control the Speed. We used Artificial Neural Network to control the Speed of the PWM-AC Single phase induction motor drive. The Neural Network toolbox has been further used for getting desired responses. Neural system computer programs are executed in MATLAB. The performance of the proposed method of ANN system of PWM AC Chopper fed single phase induction motor drive is better than other traditional and base methods for controlling the Speed, based on the MOSFET.
New Fusarin Derivatives from the Marine Algicolous Fungus Penicillium steckii SCSIO41040

Five new fusarin derivatives, steckfusarins A–E (1–5), and two known natural products (6, 7), were isolated and identified from the marine algicolous fungus Penicillium steckii SCSIO 41040. The new compounds, including absolute configurations, were determined by spectroscopic analyses and calculated electronic circular dichroism (ECD). All new compounds were evaluated for their antioxidant, antibacterial, antifungal, antiviral, cytotoxic, anti-inflammatory, antioxidant, cholesterol-lowering, acetyl cholinesterase (AChE) enzyme and 6-phosphofructo-2-kinase (PFKFB3) and phosphatidylinositol-3-kinase (PI3K) inhibitory activities. The biological evaluation results revealed that compound 1 exhibited radical scavenging activity against 2,2-diphenyl-1-picrylhydrazylhydrate (DPPH), with an IC50 value of 74.5 µg/mL. In addition, compound 1 also showed weak anti-inflammatory activity at a concentration of 20 µM.
mTORC1 Signaling in AgRP Neurons Is Not Required to Induce Major Neuroendocrine Adaptations to Food Restriction

Hypothalamic mTORC1 signaling is involved in nutrient sensing. Neurons that express the agouti-related protein (AgRP) are activated by food restriction and integrate interoceptive and exteroceptive signals to control food intake, energy expenditure, and other metabolic responses. To determine whether mTORC1 signaling in AgRP neurons is necessary for regulating energy and glucose homeostasis, especially in situations of negative energy balance, mice carrying ablation of the Raptor gene exclusively in AgRP-expressing cells were generated. AgRPΔRaptor mice showed no differences in body weight, fat mass, food intake, or energy expenditure; however, a slight improvement in glucose homeostasis was observed compared to the control group. When subjected to 5 days of food restriction (40% basal intake), AgRPΔRaptor female mice lost less lean body mass and showed a blunted reduction in energy expenditure, whereas AgRPΔRaptor male mice maintained a higher energy expenditure compared to control mice during the food restriction and 5 days of refeeding period. AgRPΔRaptor female mice did not exhibit the food restriction-induced increase in serum corticosterone levels. Finally, although hypothalamic fasting- or refeeding-induced Fos expression showed no differences between the groups, AgRPΔRaptor mice displayed increased hyperphagia during refeeding. Thus, some metabolic and neuroendocrine responses to food restriction are disturbed in AgRPΔRaptor mice.
The Impact of Blockchain Adoption on Financial Performance in Fintech Firms: A Review of The Literature

This write-up explores the impact of blockchain adoption on financial performance in fintech firms. Fintech's integration of blockchain technology enhances revenue, reduces costs, and augments competitiveness. Key findings reveal revenue growth through asset tokenization and efficient cross-border payments. Cost savings arise from intermediary removal and smart contract automation. Fintech firms collaborating with blockchain startups gain market share. Enhanced profitability and risk management are observed. Challenges include scalability, regulatory uncertainty, and interoperability issues. Limited long-term data availability and industry-specific variations pose limitations. Implications suggest further research into long-term effects, subsector-specific investigations, and comparative analyses. Fintech firms are encouraged to strategically adopt blockchain while emphasizing compliance and risk management.
The effect of regulations on the efficiency of bus and taxi services in Windhoek.

This project was conducted in order to analyse the effect of regulations on the efficiency of bus and taxi services in Windhoek. As the public transport indeed plays a vital role in the economy, it is important to bring this sector to order and efficiency. Proper regulation of this sector is not only important for the income it generates to a country and its contribution to GDP, but as it has been observed elsewhere, when the transport sector goes on strike due to dissatisfaction in one
Optimization of Gradient-Echo Echo-Planar Imaging for T2* Contrast in the Brain at 0.5 T

Gradient-recalled echo (GRE) echo-planar imaging (EPI) is an efficient MRI pulse sequence that is commonly used for several enticing applications, including functional MRI (fMRI), susceptibility-weighted imaging (SWI), and proton resonance frequency (PRF) thermometry. These applications are typically not performed in the mid-field (<1 T) as longer T2* and lower polarization present significant challenges. However, recent developments of mid-field scanners equipped with high-performance gradient sets offer the possibility to re-evaluate the feasibility of these applications. The paper introduces a metric “T2* contrast efficiency” for this evaluation, which minimizes dead time in the EPI sequence while maximizing T2* contrast so that the temporal and pseudo signal-to-noise ratios (SNRs) can be attained, which could be used to quantify experimental parameters for future fMRI experiments in the mid-field. To guide the optimization, T2* measurements of the cortical gray matter are conducted, focusing on specific regions of interest (ROIs). Temporal and pseudo SNR are calculated with the measured time-series EPI data to observe the echo times at which the maximum T2* contrast efficiency is achieved. T2* for a specific cortical ROI is reported at 0.5 T. The results suggest the optimized echo time for the EPI protocols is shorter than the effective T2* of that region. The effective reduction of dead time prior to the echo train is feasible with an optimized EPI protocol, which will increase the overall scan efficiency for several EPI-based applications at 0.5 T.
Optimal directional view angles for remote-sensing missions

Abstract Directional reflectance measurements spanning the entire exitance hemisphere for a number of vegetation and bare soil sites were measured from the ground in NOAA-7 AVHRR bands 1 (0-58-0-68 μm) and 2 (0-73-1-1 μm) for various solar zenith angles. The normalized difference and ratio transformations of these bands were calculated. The effects of the atmosphere on the directional radiance above the atmosphere was explored by applying the average mid-latitude atmospheric model of Dave to a series of ground measurements. These data and simulations were analysed for the optimal directional view angles with respect to two strategies. The first strategy views the utility of off-nadir measurements as extending spatial and temporal coverage while the second strategy views the utility of off-nadir measurements as increasing information content about the physical characteristics of the target(s). The optimum view angles for the first strategy are view angles which minimize the change in sensor response of tar.
MODELLING EXTREME SEA LEVELS DUE TO SEA LEVEL RISE AND STORM SURGE IN THE SETO INLAND SEA, JAPAN

The extreme sea level due to storm surge and future sea-level rise (SLR) in the year of 2050 and 2100 are estimated by using ensemble empirical mode decomposition (EEMD) and extreme value analysis (EVA) with long-term sea level records in and around the Seto Inland Sea, Japan. Ensemble empirical mode decomposition, an adaptive data analysis method, can separate the tidal motions and the non-linear trend from the sea level records to reconstruct the storm surge levels, and then the reconstructed storm surge levels are applied to statistical model, EVA, to obtain the extreme storm surges at 95% confidence interval in the target return periods. The SLR trend at Tokuyama in the Seto Inland Sea obtained from EEMD is 3.58 mm/yr over 1993-2010, which is slightly larger than the recent altimetry-based global rate of 3.3 ± 0.4 mm/yr over 1993-2007. The resulting SLR in 2050 and 2100 estimated are 0.18 m and 0.49 m, respectively. The 30-, 50-, and 100-yr return levels at Tokuyama obtained by EVA are 1.30 m, 1.43 m and 1.64 m. Therefore, the extreme sea level in 2050 and 2100 due to future SLR and storm surge with 100-yr return level would be 1.82 m (1.35 m ~ 2.26 m with 95% confidence intervals) and 2.13 m (1.75 m ~ 3.10 m with 95% confidence intervals), respectively. The SLR is not only due to mass and volume changes of sea water, but also due to other factors such as local subsidence, river discharge and sediments, and vegetation effect. The non-linear trend of SLR, which is the residue from EEMD, can be regarded as a final consequential sea level after considering those factors and their nonlinearity. The combined EEMD-EVA method can be useful tool not only for the extreme sea level estimation under climate change, but also for many cases in coastal engineering and hydrology.
Long range technology for internet of things: review, challenges, and future directions

New networking issues are presented by the increasing need for a wide variety of applications, which has spurred the creation of a new internet of things (IoT) paradigm, such as long range (LoRa). The LoRa protocol uses a patented kind of spread spectrum modulation to provide low-power, long-range communication. In this paper, we provide a comprehensive review of LoRa-IoT in terms of IoT applications, LoRa class, security and privacy requirements, and the evolution of LoRa technology. This review analysis and compares long range wide area network (LoRaWAN) to wireless technology (e.g., Bluetooth, LoRa, 5G, Sigfox, long term evolution-M (LTE-M), Wi-Fi, Z-wave, Zigbee) and provides a list of environment simulators (e.g., OMNeT++, MATLAB, ns-3, SimPy) to carry out experiment for LoRa-IoT. Finally, this review does not only review literature recently studied for LoRa-IoT but also discusses challenges and future directions.
Análisis de las relaciones biológicas entre poblaciones del N.O. argentino por medio de caracteres métricos

The structure of the prehistoric population of Northwest Argentina is studied through the analysis of the phenotypic variability at a regional scale. The sample includes 961 artificially deformed and not deformed individuals, of both sexes, of post-reproductive ages belonging to four subregions (Puna, Quebrada of Humahuaca, Valliserrana and Selvas Occidentales). Thirty-five craneometric traits were examined. The variability among populations was evaluated by different (*) Departamento de Ciencias Naturales, Facultad de Ciencias Exactas, Fisico-Quimicas y Naturales, Universidad Nacional de Rio Cuarto-CONICET. (**) Departamento de Ciencias Naturales, Facultad de Ciencias Exactas, Fisico-Quimicas y Naturales, Universidad Nacional de Rio Cuarto.
Molecular Characteristics and Polymorphisms of Buffalo (Bubalus bubalis) ABCG2 Gene and Its Role in Milk Fat Synthesis

Simple Summary This study focuses on the ABCG2 gene, which is known to play a crucial role in secreting vitamins into milk and transporting xenotoxic and cytostatic drugs across the plasma membrane in cattle, mice, and humans. However, the specific role of this gene in buffaloes, especially its effect on milk fat synthesis in buffalo mammary epithelial cells (BuMECs), remains inadequately understood. In this study, we isolated and identified the full-length coding region of the buffalo ABCG2 gene from the mammary gland in buffalo and analyzed its physicochemical characteristics, gene structure, conserved domains and motifs, and polymorphisms. This study found that the ABCG2 gene is highly expressed in buffalo mammary glands and plays an important role in milk fat synthesis in BuMECs. These findings contribute to our understanding of milk fat synthesis and could have important implications for the dairy industry. This could benefit both farmers and consumers by helping to provide high-quality milk products. Abstract The ATP-binding cassette subfamily G member 2 (ABCG2) serves crucial roles in secreting riboflavin and biotin vitamins into the milk of cattle, mice, and humans, as well as in the transportation of xenotoxic and cytostatic drugs across the plasma membrane. However, the specific role of the ABCG2 gene in water buffaloes (Bubalus bubalis), especially its effect on milk fat synthesis in buffalo mammary epithelial cells (BuMECs), remains inadequately understood. In this study, the full-length CDS of the buffalo ABCG2 gene was isolated and identified from the mammary gland in buffaloes. A bioinformatics analysis showed a high degree of similarity in the transcriptional region, motifs, and conservative domains of the buffalo ABCG2 with those observed in other Bovidae species. The functional role of buffalo ABCG2 was associated with the transportation of solutes across lipid bilayers within cell membranes. Among the 11 buffalo tissues detected, the expression levels of ABCG2 were the highest in the liver and brain, followed by the mammary gland, adipose tissue, heart, and kidney. Notably, its expression in the mammary gland was significantly higher during peak lactation than during non-lactation. The ABCG2 gene was identified with five SNPs in river buffaloes, while it was monomorphic in swamp buffaloes. Functional experiments revealed that ABCG2 increased the triglyceride (TAG) content by affecting the expression of liposynthesis-related genes in BuMECs. The results of this study underscore the pivotal role of the ABCG2 gene in influencing the milk fat synthesis in BuMECs.
Mobile Proton-Exchange Membrane Fuel Cell Powered by Diesel Fuel: System Simulation and Life Cycle Analysis

Diesel engine generators used at construction sites generate noise, vibration, and large amounts of pollutant emissions. With the strengthening of emission standards for construction equipment, technologies must be developed to meet new requirements. We proposed and analyzed a mobile proton-exchange membrane fuel cell diesel-powered system to address these issues. The proposed system consisted of an autothermal reformer, a proton-exchange membrane fuel cell, and a balance of plant components. Previous studies on the system have not explored the optimal design and operating condition of the system and have not shown whether the proposed system is superior to the system using the hydrogen-fueled proton-exchange membrane fuel cell system in the life-cycle greenhouse gas emissions point of view. In this study, we clarified system operation characteristics, determined the operational design point, and evaluated system performance and life-cycle greenhouse gas emissions. The system was analyzed by constructing a zero-dimensional simulation model. Several control parameters were varied in parametric studies to determine the operational design point (steam-to-carbon ratio of 2, oxygen-to-carbon ratio of 0.5, fuel utilization factor of 0.85, and heat exchanger effectiveness of 0.85). Considering system performance, the determined design point achieved a 32.3% efficiency. Additionally, we assessed the life-cycle greenhouse gas emissions of the system and compared them with those of an alternative system, which is a hydrogen-fueled proton exchange membrane fuel cell system. It was confirmed that in the United States of America, the proposed system emits 1010.2 g-CO2-eq/kWh of greenhouse gas at a 300 km fuel transportation distance, which is similar to a hydrogen-fueled proton-exchange membrane fuel cell system (1001.1 g-CO2-eq/kWh). The proposed system emits less greenhouse gas emissions than the hydrogen-fueled proton-exchange membrane fuel cell system if the distance from the hydrogen production site to the construction site is more than 318 km. Therefore, for a construction site far from a hydrogen production plant, the proposed diesel-fueled proton-exchange membrane fuel cell system is preferable from both the greenhouse gas emissions and convenience perspectives.