Large Volume Research Articles

Knowledge-based Data Processing for Multilingual Natural Language Analysis

Natural Language Processing (NLP) aids the empowerment of intelligent machines by enhancing human language understanding for linguistic-based human-computer communication. Recent developments in processing power, as well as the availability of large volumes of linguistic data, have enhanced the demand for data-driven methods for automatic semantic analysis. This paper proposes multilingual data processing using feature extraction with classification using deep learning architectures. Here, the input text data has been collected based on various languages and processed to remove missing values and null values. The processed data has been extracted using Histogram Equalization based Global Local Entropy (HEGLE) and classified using Kernel-based Radial basis Function (Ker_Rad_BF). These architectures could be utilized to process natural language. We present solutions to the multilingual sentiment analysis issue in this research article by implementing algorithms, and we compare precision factors to discover the optimum option for multilingual sentiment analysis. For the HASOC dataset, the proposed HEGLE_ Ker_Rad_BF achieved an accuracy of 98%, a precision of 97%, a recall of 90.5%, an f-1 score of 85%, RMSE of 55.6%, and a loss curve analysis attained 44%. For the TRAC dataset, the accuracy of 98%, the precision attained is 97%, the Recall is 91%, the F-1 score is 87%, and the RMSE of the proposed neural network is 55%.

Phosphate Recovery Mechanism from Low P-Containing Wastewaters via CaP Crystallization Using Apatite as Seed: Seed Adsorption, Surface-Induced Crystallization, or Ion Clusters Aggregation?

Low P-containing wastewaters (LPWs) exhibit huge P recovery potential, considering their larger volume. P recovery via CaP crystallization using apatite as seed is documented as being potentially well suited for LPWs. However, its responsible mechanisms remain a subject for debate. Taking hydroxyapatite (HAP) as the seed of LPWs, this paper conducted HAP adsorption/dissolution experiments, titration experiments, and P recovery experiments to distinguish the primary responsible mechanism. Results showed that it was HAP dissolution, not P adsorption, that occurred when the initial P concentration was no higher than 5 mg/L, ruling out adsorption mechanism of P recovery from LPWs using HAP as the seed. Significant OH− consumption and rapid P recovery occurred simultaneously within the first 60 s in titration experiments, suggesting CaP crystallization should be responsible for P recovery. Moreover, the continuous increase in P recovery efficiency with seed dosages observed in P recovery experiments seemed to follow well the mechanism of pre-nucleation ion clusters (PNCs) aggregation. During PNCs aggregation, P aggregates with Ca2+ quickly, generating CaP PNCs; then, CaP PNCs aggregate with seed particles, followed by CaP PNCs fusion, and ultimately transform into fines attached to the seed surface. PNCs’ aggregation mechanism was further supported by a comparison of seed SEM images before and after P recovery, since denser and smaller rod-shaped fines were observed on the seed surface after P recovery. This study suggests that PNCs’ aggregation is the dominant mechanism responsible for the recovery of P from LPWs via CaP crystallization using HAP as the seed.

Painting robot with three axis motion

Paint was applied directly to the painted surface while painting with a brush. There are occasionally unsatisfactory outcomes, such as paint stacking or streaks remaining. Spray guns yield better paint results, therefore a robot was developed that can regulate the spray gun's movement in order to apply paint. A painting robot that resembles an arm is one that has three axes of movement. The robot can move left-right on the x axis, up-down on the y axis, and back-front on the z axis. Its end effector is a spray cannon. An ultrasonic sensor is used to measure the distance between components and the spray gun; when the sensor measures near distances, such as between 4 and 8 cm, the test sensor distance measurements have a larger average error. When the test reading distance is increased from 10 cm to 14 cm, the average error reduces. Superior painting outcomes can be achieved with a paint distance of 10 cm, an air pressure of 80 psi, and a spray gun arm movement speed of 25 rpm. With average x-axis movement of 74.6 cm, y-axis movement of 14.8 cm, and z-axis movement of 88.2 cm, the largest volume that can be painted is 97,517.5 cm3. The rotation of the arm is used to determine the angular rotation needed to paint components. Painting the left side of the component at an angle of 10°, the back side at an angle of 100°, the right side at an angle of 190°, and only painting the front side of the component at the required angle of 90° require the rotational movement of the arm to the left.

Synthesis of Metal-Organic Framework (MOF) Cr-PTC-HIna for Heavy Metal Ion Adsorption

As an adsorbent material, Metal-Organic Framework (MOF) provides several advantages, such as large surface area and pore volume, as well as stability in aqueous systems both in acidic, basic, and neutral conditions. The research successfully made a metal organic framework (MOF) from chromium and perylene that was modulated by isonicotinic acid. This was done using three different methods: hydrothermal (Cr-PTC-HIna-HT), solvothermal (Cr-PTC-HIna-ST), and sonochemical (Cr-PTC-HIna-SC). The Cr-PTC-HIna-SC showed the greatest Pb2+ ion adsorption capacity. The optimum adsorption of Pb2+ ions occurred at 150 ppm Pb2+ ion concentration, pH 5, 90 minutes of contact time, and an adsorption capacity of 149.95 mg/g at 35 °C. The dominant adsorption isotherm model is the Langmuir isotherm model with R2 = 0.9867 and follows the pseudo-second-order. The selectivity test showed that Cr-PTC-HIna-SC MOF could adsorb more Cd2+ ions than Pb2+ ions, with 250 mg/g being the best amount.

Air entrapment modelling in water supply networks during pipe filling events

ABSTRACT Intermittent water supply systems are prone to air entrapments during the pipe filling phase. This work aims to analyse and discuss the numerical results obtained by applying the recently developed AirSWMM model, an extension of SWMM incorporating air phase, to a laboratory network. Experimental data consisting of pressure-head at multiple locations and video recordings of air entrapments are collected in a single loop network with a high point, for different pipe-filling conditions, system layouts and node elevations. Experimental tests have shown that the air entrapment occurred not only at the high point but also throughout the pipe network, creating air pockets with elongated shapes and larger volumes than for single pipes. AirSWWM model with air-entrapment formation, growth and transport is tested in the pipe network, and results are compared with measurements. AirSWWM model can correctly locate large air pockets but underestimates their volume.

Efficient data management in Internet of Things: A survey of data aggregation techniques

The Internet of Things (IoT) refers to a vast network of interconnected devices, objects, and systems powered by sensors, software, and connectivity capabilities. The interconnectivity of IoT devices has led to a substantial increase in data production. Efficiently managing and analyzing large data volumes is a significant challenge for IoT systems. To address this challenge, data aggregation is the primary process. IoT data aggregation aims to provide high-quality service by ensuring fast data transmission, high reliability, minimal energy consumption, and data priority consideration. Data aggregation involves collecting data from multiple sensors and devices and then integrating it using a function to minimize system traffic. This paper thoroughly examines data aggregation techniques in the IoT context. Techniques are grouped according to underlying principles, and their potential applications, advantages, and limitations are discussed.

Few sex differences in regional gray matter volume growth trajectories across early childhood

Abstract Sex-specific developmental differences in brain structure have been documented in older children and adolescents, with females generally showing smaller overall brain volumes and earlier peak ages than males. However, sex differences in gray matter structural development in early childhood are less studied. We characterized sex-specific trajectories of gray matter volume development in children aged 2-8 years. We acquired anatomical magnetic resonance imaging (MRI) of the brain at the Alberta Children's Hospital in 123 typically developing children. Most children were scanned multiple times, for a total of 393 scans (mean=3.2 scans/subject). We segmented T1-weighted structural MRI with MaCRUISE to define 116 regions and measure both absolute volumes (mm3) and proportional volumes (percent of intracranial volume). We characterized growth trajectories of gray matter volume for these brain regions between 2 and 8 years using mixed effects models, showing volume increases, with most posterior and temporo-parietal regions peaking before 8 years. We found widespread main effects of sex, with males having larger volumes in 86% of brain regions. However, there were no significant sex differences in trajectories (age or age2 terms) for absolute volume. Proportional volumes of the right occipital fusiform gyrus and left medial postcentral gyrus showed significant age-by-sex interactions where females had steeper volume decreases than males. This study also confirms regional patterns observed in previous studies of older children, such as a posterior-to-anterior timing of brain maturation. These results provide a comprehensive picture of gray matter volume development across early childhood, and suggest that sex differences do not emerge until later in development.

Current Sharing Analysis and Evaluation of Parallel DC-Link Capacitors in Vehicle Motor Drive

In electric vehicle inverter systems, the DC-link capacitor bank becomes a critical obstacle to high power density due to its large volume. DC-link capacitor bank commonly adopts a multi-core parallel structure. The challenges exist in the current sharing of parallel capacitor cores because of the imbalance of stray parameters in the busbars. A current sharing analysis method aiming at thermal stress for capacitor banks in high-power-density inverters based on the characteristic matrix is proposed in this paper. First, the design basis of DC-link capacitors is given theoretically. Then, the complex coupling impedance model between multiple ports on capacitor busbars is mathematically derived by the matrix method, revealing the current resonance phenomenon of parallel capacitor cores affected by stray parameters. Next, three capacitor banks are evaluated by the proposed current sharing model. The influence of capacitor core arrangement and terminal position on current sharing is explored, which can guide capacitor banks’ high-power-density structure and heat dissipation design. Finally, an experimental platform is built with customized capacitor samples. The experimental results are consistent with the analysis results of the model, verifying the proposed method.

Particle-level insights into the powder flow behavior of the Fette 1200i tablet press feed frame using the discrete element method (DEM)

This study employs the DEM to simulate powder flow in the Fette-1200i tablet press three-chambered feed-frame system under varying operating conditions. It examines the influence of operational speeds on parameters like residence time, particle travel distance, shear work, paddle torque, tablet weight, and API concentration. Utilizing continuous spatial surface maps, the research comprehensively analyzes powder flow behavior. Results indicate that particle residence time increases with paddle speed but decreases with turret speed. Similar trends are observed for particle travel distance and shear work. Larger particle residence time can lead to overlubrication. The torque of the paddles is related to the volume of material in each chamber, with the distributing chamber having the largest volume. Tablet weight rises with paddle speed but drops with turret speed. The smallest particle (API) tends to collect near the paddle hub due to limited centrifugal force and on the chamber floor due to percolation. However, the tablet's API content eventually stabilizes at the target concentration.

The first degree-scale starlight-polarization-based tomography map of the magnetized interstellar medium

We present the first degree-scale tomography map of the dusty magnetized interstellar medium (ISM) from stellar polarimetry and distance measurements. We used the RoboPol polarimeter at Skinakas Observatory to conduct a survey of the polarization of starlight in a region of the sky of about four square degrees. We propose a Bayesian method to decompose the stellar-polarization source field along the distance to invert the three-dimensional (3D) volume occupied by the observed stars. We used this method to obtain the first 3D map of the dusty magnetized ISM. Specifically, we produced a tomography map of the orientation of the plane-of-sky component of the magnetic field threading the diffuse, dusty regions responsible for the stellar polarization. For the targeted region centered on Galactic coordinates (l, b) ≈ (103.3°, 22.3°), we identified several ISM clouds. Most of the lines of sight intersect more than one cloud. A very nearby component was detected in the foreground of a dominant component from which most of the polarization signal comes and which we identified as being an intersection of the wall of the Local Bubble and the Cepheus Flare. Farther clouds, with a distance of up to 2 kpc, were similarly detected. Some of them likely correspond to intermediate-velocity clouds seen in H I spectra in this region of the sky. We found that the orientation of the plane-of-sky component of the magnetic field changes along distance for most of the lines of sight. Our study demonstrates that starlight polarization data coupled to distance measures have the power to reveal the great complexity of the dusty magnetized ISM in 3D and, in particular, to provide local measurements of the plane-of-sky component of the magnetic field in dusty regions. This demonstrates that the inversion of large data volumes, as expected from the PASIPHAE survey, will provide the necessary means to move forward in the modeling of the Galactic magnetic field and of the dusty magnetized ISM as a contaminant in observations of the cosmic microwave background polarization.

Collusion-resistant multiparty data sharing in social networks

The number of users on online social networks (OSNs) has grown tremendously over the past few years, with sites like Facebook amassing over a billion users. With the popularity of OSNs, the increase in privacy risk from the large volume of sensitive and private data is inevitable. While there are many features for access control for an individual user, most OSNs still need concrete mechanisms to preserve the privacy of data shared between multiple users. The proposed method uses metrics such as identity leakage (IL) and strength of interaction (SoI) to fine-tune the scenarios that use privacy risk and sharing loss to identify and resolve conflicts. In addition to conflict resolution, bot detection is also done to mitigate collusion attacks. The final decision to share the data item is then ascertained based on whether it passes the threshold condition for the above metrics.

Gap Self-Sensing Method for Maglev System Using Partial Electromagnet Coil

The use of dedicated sensor to measure levitation gap in maglev system leads to the problems of large volume, inconvenient installation and high cost. In this article, a gap self-sensing method using partial electromagnet coil is proposed to solve these problems. The entire electromagnet coil provides levitation force, while a small part of the electromagnet coil (the subcoil) on the side facing the levitated object is injected with high frequency excitation for gap self-sensing. The theoretical derivation shows the effects of levitation gap on the impedance of the subcoil. The frequency of the injected excitation and the structure of the subcoil are determined with finite element analysis for high sensitivity. The crosstalk between the gap self-sensing system and the levitation system is suppressed by the designed signal processing based on series resonance. The prototype in this article can cover a gap measurement range of 0–20 mm for maglev ball, with a nonlinearity error of 0.2% and a measurement accuracy of 0.2 mm. The closed-loop feedback control experiment shows that the performances of the proposed gap self-sensing system meet the control requirements.

Finding Medicine's Moneyball: How Lessons From Major League Baseball Can Advance Assessment in Precision Education.

Precision education (PE) leverages longitudinal data and analytics to tailor educational interventions to improve patient, learner, and system-level outcomes. At present, few programs in medical education can accomplish this goal as they must develop new data streams transformed by analytics to drive trainee learning and program improvement. Other professions, such as Major League Baseball (MLB), have already developed extremely sophisticated approaches to gathering large volumes of precise data points to inform assessment of individual performance.In this perspective, the authors argue that medical education-whose entry into precision assessment is fairly nascent-can look to MLB to learn the possibilities and pitfalls of precision assessment strategies. They describe 3 epochs of player assessment in MLB: observation, analytics (sabermetrics), and technology (Statcast). The longest tenured approach, observation, relies on scouting and expert opinion. Sabermetrics brought new approaches to analyzing existing data in a way that better predicted which players would help the team win. Statcast created precise, granular data about highly attributable elements of player performance while helping to account for nonplayer factors that confound assessment such as weather, ballpark dimensions, and the performance of other players. Medical education is progressing through similar epochs marked by workplace-based assessment, learning analytics, and novel measurement technologies. The authors explore how medical education can leverage intersectional concepts of MLB player and medical trainee assessment to inform present and future directions of PE.

Research Paper on Role of Data Features and Data Collection Tools in Artificial Intelligence

Abstract: Artificial intelligence (AI) is revolutionizing various industries by enabling machines to learn, reason, and make decisions autonomously. However, the success of AI systems depends heavily on the quality and quantity of data used for training and testing. Therefore, data collection tools have become essential in AI development. In this paper, we will discuss some popular data collection tools in AI that facilitate the process of gathering large volumes of high-quality data for training and testing AI models. Robotics and sensors are increasingly being used to collect data for AI applications in various industries like healthcare, manufacturing, and agriculture. For instance, in healthcare, robots equipped with sensors can collect medical data like vital signs, blood pressure, and heart rate from patients. In agriculture, drones equipped with sensors can collect crop data like moisture levels, temperature, and nutrient content. These tools provide high-quality data that can be used to train AI models for diagnosis, prediction, and decision-making. Mobile apps are increasingly being used to collect user data for AI applications. Apps like Google Maps, Waze, and Uber collect location data that can be used to train AI models for navigation and traffic prediction. Healthcare apps like MyFitnessPal and Fitbit collect user health data that can be used to train AI models for personalized health recommendations. The Internet of Things is enabling the collection of vast amounts of real-time data from various devices like smart homes, smart cities, and smart factories. This data can be used to train AI models for predictive maintenance, energy management, and resource optimization.

Machine learning based Cyber Attack detection on Internet Traffic

Cyber attacks on the internet have become increasingly sophisticated and frequent, posing significant challenges to cybersecurity. Traditional rule-based methods for detecting these attacks often struggle to keep pace with the evolving tactics of malicious actors. In this context, machine learning (ML) techniques have emerged as a promising approach for cyber attack detection due to their ability to analyze large volumes of data and identify patterns indicative of malicious behavior. The proposed framework for utilizing machine learning in cyber attack detection on the internet. The framework integrates various ML algorithms, including supervised, unsupervised, and reinforcement learning techniques, to enhance the detection capabilities against different types of cyber threats. Moreover, the framework incorporates feature engineering and selection methods to optimize the performance of ML models in identifying malicious activities.

A comprehensive review of strategic management practices in satellite telecommunications, highlighting the role of data analytics in driving operational efficiency and competitive advantage

Satellite telecommunications play a crucial role in modern connectivity, enabling global communication, broadcasting, and internet access. This paper presents a comprehensive review of strategic management practices in the satellite telecommunications industry, with a particular focus on the role of data analytics in enhancing operational efficiency and achieving competitive advantage. The satellite telecommunications sector is characterized by rapid technological advancements, evolving market dynamics, and increasing demand for bandwidth-intensive services. To thrive in this competitive landscape, companies in the industry must adopt strategic management practices that enable them to effectively leverage their resources, capabilities, and market opportunities. One of the key strategic management practices in satellite telecommunications is the integration of data analytics into business operations. Data analytics encompasses the collection, analysis, and interpretation of large volumes of data to extract valuable insights and inform decision-making. By harnessing the power of data analytics, satellite telecommunications companies can optimize their network performance, enhance customer experience, and drive innovation. Data analytics can also help companies in the satellite telecommunications industry to identify new market trends, customer preferences, and competitive threats. By analyzing data from various sources, including satellite sensors, customer feedback, and market research, companies can gain a deeper understanding of their operating environment and make informed strategic decisions. Furthermore, data analytics can enable satellite telecommunications companies to enhance their operational efficiency by optimizing resource allocation, minimizing downtime, and improving network reliability. By using predictive analytics, companies can anticipate equipment failures and proactively address potential issues, thereby reducing maintenance costs and improving service quality. In conclusion, this paper highlights the importance of strategic management practices in the satellite telecommunications industry, with a focus on the role of data analytics in driving operational efficiency and competitive advantage. By adopting data-driven decision-making processes, satellite telecommunications companies can position themselves for long-term success in a rapidly evolving industry landscape.

Culture, prefrontal volume, and memory.

Prior cross-cultural studies have demonstrated differences among Eastern and Western cultures in memory and cognition along with variation in neuroanatomy and functional engagement. We further probed cultural neuroanatomical variability in terms of its relationship with memory performance. Specifically, we investigated how memory performance related to gray matter volume in several prefrontal lobe structures, including across cultures. For 58 American and 57 Taiwanese young adults, memory performance was measured with the California Verbal Learning Test (CVLT) using performance on learning trial 1, on which Americans had higher scores than the Taiwanese, and the long delayed free recall task, on which groups performed similarly. MRI data were reconstructed using FreeSurfer. Across both cultures, we observed that larger volumes of the bilateral rostral anterior cingulate were associated with lower scores on both CVLT tasks. In terms of effects of culture, the relationship between learning trial 1 scores and gray matter volumes in the right superior frontal gyrus had a trend for a positive relationship in Taiwanese but not in Americans. In addition to the a priori analysis of select frontal volumes, an exploratory whole-brain analysis compared volumes-without considering CVLT performance-across the two cultural groups in order to assess convergence with prior research. Several cultural differences were found, such that Americans had larger volumes in the bilateral superior frontal and lateral occipital cortex, whereas Taiwanese had larger volumes in the bilateral rostral middle frontal and inferior temporal cortex, and the right precuneus.

Strain Monitoring of Concrete Using Carbon Black-Based Smart Coatings.

Given the challenges we face of an ageing infrastructure and insufficient maintenance, there is a critical shift towards preventive and predictive maintenance in construction. Self-sensing cement-based materials have drawn interest in this sector due to their high monitoring performance and durability compared to electronic sensors. While bulk applications have been well-discussed within this field, several challenges exist in their implementation for practical applications, such as poor workability and high manufacturing costs at larger volumes. This paper discusses the development of smart carbon-based cementitious coatings for strain monitoring of concrete substrates under flexural loading. This work presents a physical, electrical, and electromechanical investigation of sensing coatings with varying carbon black (CB) concentrations along with the geometric optimisation of the sensor design. The optimal strain-sensing performance, 55.5 ± 2.7, was obtained for coatings with 2 wt% of conductive filler, 3 mm thickness, and a gauge length of 60 mm. The results demonstrate the potential of applying smart coatings with carbon black addition for concrete strain monitoring.

Overcoming data utilization challenges for built environment flood resilience: Strategies and best practices

AbstractBuilt environment flood resilience is a critical challenge facing communities worldwide. Amongst various efforts to resilience, the conception towards data utilization becomes popular with enormous technological advancements. Built environment creates varieties of data at larger volumes throughout their life cycle signifying that the importance of these data in the context of flood resilience cannot be ignored. However, despite the power of data, the greatest opportunities that exist for flood resilience enhancement have been mired by numerous and complex unidentified challenges. Thus, identifying these challenges with timely relevant strategies is a significant need. One of the best ways to tackle these challenges is by viewing them through the lens of data life cycle stages. This study, therefore, aimed to identify these challenges in each stage of the data life cycle with strategies to overcome them. Semi‐structured interviews conducted with 12 experts revealed the significant challenges allied with built environment data with potential future strategies. The qualitative content analysis was conducted to analyse the findings. The use of advanced sensing technologies, cloud‐based storage solutions, data governance policies and the development of predictive models are some of the consequential strategies outlined in this study. These findings provide valuable insights and guidance to facilitate built environment data utilization for flood resilience.

Bio-based aerogel composites of coconut pith-derived carbon and chitosan for efficient anionic dye-polluted water treatment

Abstract Biomass-based aerogels are of interest due to their abundant precursors, ease of functionalization, eco-friendliness, low cost, and effectiveness in dye-contaminated water treatment. Coconut pith (CP) known as a by-product after coconut processing is a sponge-like and lightweight material discarded in large volumes. For the first time, carbon micro-particles resulting from two-step treatment and pyrolysis of CP have been successfully incorporated with chitosan to produce aerogel composites by high-speed homogenization and freeze-drying. After pretreatment, holocellulose content and crystallinity index of the recovered CP pulp are respectively 75.4 and 58.6 % along with a solid recovery rate of 27.5 %. Characteristic properties of the aerogel composites are featured by their density as low as 15.23–28.17 mg/cm3, remarkably high porosity of 98.17–99.05 %, and Young’s modulus of 1.64–12.23 kPa. Synergistic effects of the porous network, electrostatic interactions between both amine groups in chitosan and carbon surface with methyl orange (MO) cause the as-fabricated aerogel composites to achieve an extremely high adsorption capacity of 454.13 mg/g and removal efficiency of 92.32 % at initial MO concentration of 500 mg/L. Therefore, the CP-derived carbon/chitosan aerogel composites synthesized from a feasible procedure exhibit their great potential in enhancing the value of coconut waste and dealing with dye-contaminated water pollution by simple and economical adsorption.