Development Of System Research Articles

Enhancing service excellence: analyzing natural language question answering with advanced cosine similarity

<span lang="EN-US">Information related to student services in higher education must be produced and disseminated in various forms. Covid-19 pandemic, student services with a remote model related to this question and answer become very important. To carry out this automation process, the advanced cosine similarity method is used to check the similarity of the questions to the database and statistics to calculate the similarity value of each word. The proposed paper proceeds with three phases. The first stage to solve this problem is the data processed in question; the professional next step is word insertion. It converts alphanumeric words to vector format. Each word is a vector that represents a point in space with a certain dimension. The recommended advanced cosine similarity data still must be analyzed into a statistical approach. We will measure accuracy to get results so that optimal results and answers are obtained, research procedures are carried out based on literature study, initial data collection and observation, system development, system testing, system analysis, and system evaluation. This research implemented in universities with student chat automation applications providing an accuracy 83.90% given by natural language question answering system (NLQAS) so that it can improve excellent service in universities.</span>

An efficient and low cost realization of LoRa based real-time forest protection system

The forest is a natural habitat for a variety of fauna and flora, and helps to maintain the ecosystem equilibrium. However, wildfire incidents and deforestation lead to forest degradation. Moreover, most of the existing methods, to preserve the forest resources, are ineffective due to their large establishment cost, more power consumption, and poor coverage. This paper brings out a sustainable solution by developing a forest protection system (FPS) that uses internet of things (IoT) technology together with long range (LoRa) communication. The work focuses on the development of an IoT framework for the detection of any intrusion into the forest as well as the detection of fire incidents in the vicinity of the equipment. Powering the equipment through solar energy makes the system cost-effective. The system is examined in terms of acquisition of data from sensor nodes pertaining to forest protection, relaying the same to the cloud using LoRa wide area network (LoRaWAN) technology and analyzing using cloud based visualization tools. The developed system has been deployed at Eturnagaram Wildlife Sanctuary, Mulugu district, Telangana, India for validation in the forest environment. The obtained results have shown that the system has an accuracy of 97.14% for intrusion detection and 100% for fire detection.

Experimental investigation of a hybrid photovoltaic-thermal energy system for hot air production

Solar energy as a non-fossil alternative energy source has become the best choice to overcome the problem of energy demand in most countries in the world. There are two different techniques to convert solar energy: photovoltaic (PV) panels to produce electricity and thermal collectors to generate heat. The two technologies can be combined to provide electrical and thermal energy either simultaneously or separately. In order to optimize the performance of a hybrid photovoltaic-thermal (PVT) solar air heater, it is necessary to collect experimental data on solar irradiation and temperature. This paper emphasized the development of a PVT energy system for hot air production in a temperature range of 50-55 °C. Additionally, experiments were constructed to monitor the information acquired from the proposed PVT solar air heater and the environment, such as hot air temperature, ambient temperature, and solar irradiation. The real-time monitoring system was set for five sample days. A microcontroller unit was used to control the hot air temperature and save the measurement data into memory. The experimental results showed that the proposed PVT solar air heater is capable of maintaining a certain level of hot air temperature throughout the day and night.

Research Progress of Chitosan-based Multifunctional Nanoparticles in Cancer Targeted Therapy.

Conventional tumor therapeutic modalities, such as radiotherapy, chemotherapy, and surgery, involve low tumor inhibition efficiency, non-targeted drug delivery, and side effects. The development of novel and practical nano-drug delivery systems (DDSs) for targeted tumor therapy has become particularly important. Among various bioactive nanoparticles, chitosan is considered a suitable candidate for drug delivery due to its non-toxicity, good biocompatibility, and biodegradability. The amino and hydroxyl groups of chitosan endow it with the diverse function of chemical modification, thereby improving its physical and biological properties to meet the requirements of advanced biomedical applications. Therefore, it is necessary to review the property and applications of chitosan-based materials in biomedicine. In this review, the characteristics of chitosan related to its applications are first introduced, and then the preparation and modification of chitosan-based nanoparticles, including the function tailoring of chitosan-modified nanoparticles, are demonstrated and discussed. Finally, the opportunities and challenges of chitosan-based nanomaterials in this emerging field are proposed from the perspective of the rational and systematic design for the biomedicine field.

Comparative study on the flame retardancy, pyrolysis behavior, and flame retardant pathways of black phosphorus in polyurethane and polypropylene

Black phosphorus nanosheets (BPns) have gained widespread attention in the field of flame retardancy due to their highly efficient flame-retardant properties achieved with a low addition rate (e.g., 0.4%). However, the pyrolysis pathways of BP in polymers have not yet been elucidated. This work systematically studies the flame retardancy and pyrolysis behavior of BP in oxygen-containing (polyurethane, PU) and oxygen-free (polypropylene, PP) polymers, distinguishing between the gas-phase and the condensed-phase flame retardant effect of BP. Through slow heating and staged heating methods, the condensed phase and gas phase pyrolysis products of BP in PU and PP at different temperatures are compared in detail. The distinct flame retardant pathways of BP in PU and PP are revealed. The results indicate that the oxidation of BP commences with its reaction with atmospheric oxygen, implying that the condensed-phase flame-retardant effect of BP primarily occurs at the polymer surface. In PP, BP mainly functions as a gas-phase flame retardant. In the condensed phase, BP generates phosphate derivatives that cover the substrate surface, providing solely a physical barrier effect. In PU, while BP demonstrates an outstanding gas-phase flame-retardant effect, it also accelerates the early dehydration carbonization of alcohols within the substrate, resulting in the formation of phosphate ester compounds (P–O–C structure) to bolster the thermal stability of residual carbon. Additionally, the side reactions of BP during catalytic carbonization will decrease the thermal stability of ester bonds in PU. This work lays a theoretical foundation for the development and application of BP-based flame retardant systems.

Energy management in hybrid complexes based on wind generation and hydrogen storage

This work is devoted to the analysis of scientific articles in the field of hybrid energy complexes based on hydrogen technologies and renewable energy sources. A special attention is given to wind generation. The review of the topics of scientific publications indexed in the Scopus database in the field of research is carried out, the most frequently encountered topics and the rarest ones are highlighted. Brief statistics about publications selected for detailed analysis are given. The most interesting direction for studying is development of control systems for hybrid energy complexes. Several traditional approaches, which are commonly used in research on this topic and methods are highlighted. The existing shortcomings and inaccuracies in the overviewed works are identified. Conclusions are drawn about the need to transform existing methods and specific proposals are made to improve management systems to increase the efficiency of decision-making and achieve greater economic benefits. Promising areas of research that also require special attention are formulated.

Development of a soil electrical conductivity measurement system in paddy fields

This study aims to develop a data collection system for measuring soil electrical conductivity (EC) in Paddy Fields using the Wenner measurement principle, with one electrode configuration based on the Veris 3100 measuring machine, and a second electrode configuration with fixed electrodes. The result is a system that measures soil EC using the fixed electrode configuration for more stable results compared to the coulter electrode configuration on the Veris 3100. This system comprises three main components: a 7-inch industrial touchscreen monitor used for real-time monitoring and storing collected data on external memory, a controller that provides reverse direct current (DC) power supply to the electrodes and measures voltage and current parameters on the electrodes, two mechanical configurations including six electrodes in contact with the soil. The system is developed to operate best after soil preparation and before seed sowing. The collected data is processed and compared to actual measurements using an artificial neural network (ANN), resulting in an R2=0.7449, equivalent to linear regression. The system has been successfully installed and operated on a handheld tractor and tested in Paddy Fields on the outskirts of Can Tho City, Vietnam.

Guiding Parents of Children With Persistent Primitive Reflexes

Strong primitive reflexes in infants suggest proper development of the central nervous system. However, if not naturally integrated in the first year of life, these persistent primitive reflexes may interfere with further motor and cognitive development as well as emotional regulation. When nurse practitioners diagnose persistent primitive reflexes, parents are often eager to know what can be done at home. Described here is a program evaluation completed on a series of movement-to-music videos called Rockin’ Reflex that provide parents with inexpensive, easy-to-follow videos that they can do at home to help their children integrate these reflexes.

Cell-penetrating peptide and cationic liposomes mediated siRNA delivery to arrest growth of chronic myeloid leukemia cells in vitro

Gene silencing through RNA interference (RNAi) is a promising therapeutic approach for a wide range of disorders, including cancer. Non-viral gene therapy, using specific siRNAs against BCR-ABL1, can be a supportive or alternative measure to traditional chronic myeloid leukemia (CML) tyrosine kinase inhibitor (TKIs) therapies, given the prevalence of clinical TKI resistance. The main challenge for such approaches remains the development of the effective delivery system for siRNA tailored to the specific disease model.The purpose of this study was to examine and compare the efficiency of endosomolytic cell penetrating peptide (CPP) EB1 and PEG2000-decorated cationic liposomes composed of polycationic lipid 1,26-bis(cholest-5-en-3-yloxycarbonylamino)-7,11,16,20-tetraazahexacosane tetrahydrochloride (2Х3) and helper lipid 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE) for anti-bcr-abl siRNA delivery into the K562 human CML cell line. We show that both EB1 and 2Х3-DOPE-DSPE-PEG2000 (0.62 % mol.) liposomes effectively deliver siRNA into K562 cells by endocytic mechanisms, and the use of liposomes leads to more effective inhibition of expression of the targeted gene (BCR-ABL1) and cancer cell proliferation. Taken together, these findings suggest that PEG-decorated cationic liposomes mediated siRNA delivery allows an effective antisense suppression of certain oncogenes, and represents a promising new class of therapies for CML.

Development of Water Quality Control and Monitoring System for Hydroponic Plants with a Cloud-Based System

Development of Water Quality Control and Monitoring System for Hydroponic Plants with a Cloud-Based System

Analysis of the emotional coloring of text using machine and deep learning methods

The presented scientific article is a comprehensive study of machine learning and deep learning methods in the context of emotion recognition in text data. The main goal of the study is to conduct a comprehensive analysis and comparison of various machine learning and deep learning methods to classify emotions in text. During the work, special attention was paid to the analysis of traditional machine learning algorithms, such as multinomial naive Bayes (MNB), multilayer perceptron (MLP), and support vector machine (SVM), as well as the use of deep learning methods based on long short-term memory (LSTM). The experimental part of the study involves the analysis of different data sets covering a variety of text styles and contexts. The experimental results are analyzed in detail, identifying the advantages and limitations of each method. The article provides practical recommendations for choosing the optimal method depending on the specific tasks and context of the application. The data obtained is important for the development of intelligent systems that can effectively adapt to the emotional aspects of interaction with users. Overall, this work makes a significant contribution to the field of emotion recognition in text and provides a basis for further research in this area.

The Therapeutic Effect of Natural Compounds on Osteoporosis through Ferroptosis.

Ferroptosis is a newly discovered non-apoptotic cell death whose key is lipid peroxidation. It has been reported that ferroptosis is involved in the occurrence and development of tumors and nervous system and musculoskeletal diseases. Cellular ferroptosis contributes to the imbalance of bone homeostasis and is involved in the development of osteoporosis; however, the detailed mechanism of which is still unclear though it may provide a new direction for anti-osteoporosis. The current drugs used in the treatment of osteoporosis, such as bisphosphonates and teriparatide, have many side effects, increasing people's search for natural compounds to treat osteoporosis. This review paper briefly summarizes the current research regarding the mechanisms of ferroptosis and natural anti-osteoporosis compounds targeting its pathway.

Retro analysis of the development of the aviation security system in Ukraine

Various measures to ensure the safety of civil aviation flights in Ukraine from the time of its inception to the present are considered in a historical aspect at certain stages of activity, mainly organizational, scientific and technical and personal factors regarding the minimization of risks in civil aviation. The article confirms that at all stages of the more than 100-year history of civil aviation of Ukraine, various measures were constantly taken to ensure flight safety and minimize risks in aviation, especially since the second half of the 20th century. Innovative achievements of designers, scientists, engineers and technicians, training of highly qualified aviation specialists at the national and international level have become the achievement of global aviation. The integration of domestic aviation into the European and world community contributed to the strengthening of aviation safety in Ukraine.

Empowering geological data analysis with specialized software GIS modules

This research is devoted to the development of a geographic information system (GIS) for the analysis of geological data. It presents two specialized software modules designed to solve complex geological problems related to potential progress to disturbed masses and magnetotelluric sounding. These modules are integrated into the QGIS environment, offering efficient data processing and analysis capabilities, contributing to a deeper understanding of geological structures. The study presents a mathematical model for the problem of magnetotelluric sounding (MTS) and the continuation of potentials towards the perturbed masses, demonstrating numerical results using the developed algorithm. To confirm the accuracy of the model, a comparative analysis was carried out with empirical data for various chemical elements, which showed high accuracy, especially at shallow depths, with an error rate of less than 2%. In addition, the study highlights the importance of powerful GIS for the analysis and interpretation of geological data, including geochemical, geophysical and remote sensing information. The advanced functionality of QGIS simplifies data processing and visualization, which makes it an invaluable tool for geologists and researchers.

A modified sliding mode controller based on Fuzzy logic to control the longitudinal dynamics of the autonomous vehicle

This article delves into the intricate world of controlling the longitudinal dynamics of autonomous vehicles. In the first part, we studied two distinct controllers: the Super Twisting Sliding Mode Control and its modified version enriched with the integration of fuzzy logic, applied to the longitudinal dynamics of the autonomous automobile to follow a desired speed longitudinal profile, the two controllers are compared with a Neural Network-Based Non-singular Terminal Sliding-Mode Control, the system takes the throttle and brake as its inputs and delivers speed and acceleration as outputs. The overarching objective is to ensure that the controlled vehicle maintains a close and precise alignment with the desired speed profile. The second part of our research is dedicated to the development of adaptive cruise control systems and cruise control according to the safety conditions. This controller consists of two blocks low and upper controller, in upper controller the inputs are the speeds of the automobile in front and of the autonomous automobile itself, the safety distance, the measured distance. The output is the desired acceleration. The objective is to maintain a distance between the front vehicles, greater than or equal to the safety distance. For this, to achieve this task, we have implemented a control system known as the Proportional Integral Derivative (PID) controller in the adaptive cruise control system to control this system. In the low controller block, the same controllers used in the first part: the Super Twisting Sliding Mode Control and its modified version based on fuzzy logic, are applied, the system inputs are throttle and brake, and the outputs are speed and acceleration. This system is processed by MATLAB code, we obtained a better result with our proposed controller such that the maximum absolute speed error is equal to 0.0144 m/s in the first case of speed tracking, and to 0.006 m/s in the second case of the using adaptive cruise control, the illustrations below show the efficiency and robustness of these controllers.

Tailoring drug release from long-acting contraceptive levonorgestrel intrauterine systems

The wide array of polydimethylsiloxane (PDMS) variants available on the market, coupled with the intricate combination of additives in silicone polymers, and the incomplete understanding of drug release behavior make formulation development of levonorgestrel intrauterine systems (LNG-IUSs) formidable. Accordingly, the objectives of this work were to investigate the impact of excipients on formulation attributes and in vitro performance of LNG-IUSs, elucidate drug release mechanisms, and thereby improve product understanding. LNG-IUSs with a wide range of additives and fillers were prepared, and in vitro drug release testing was conducted for up to 12 months. Incorporating various additives and/or fillers (silica, silicone resins, silicone oil, PEG, etc.) altered the crystallization kinetics of the crosslinked polymer, the viscosity, and the microstructure. In addition, drug-excipient interactions can occur. Interestingly, additives which increased matrix hydrophobicity and hindered PDMS crystallization facilitated dissolution and permeation of the lipophilic LNG. The influence of additives and lubricants on the mechanical properties of LNG-IUSs were also evaluated. PDMS chemical substitution and molecular weight were deemed to be most critical polymer attributes to the in vitro performance of LNG-IUSs. Drugs with varying physicochemical characteristics were used to prepare IUSs, modeling of the release kinetics was performed, and correlations between release properties and the various physicochemical attributes of the model drugs were established. Strong correlations between first order release rate constants and both drug solubility and Log P underpin the partition and diffusion-based release mechanisms in LNG-IUSs. This is the first comprehensive report to provide a mechanistic understanding of material-property-performance relationships for IUSs. This work offers an evidence-based approach to rational excipient selection and tailoring of drug release to achieve target daily release rates in vivo. The novel insights gained through this research could be helpful for supporting development of brand and generic IUS products as well as their regulatory assessment.

Development of a decision support system for client acceptance in independent audit process

Intelligent Information Technology (IIT) applications are crucial in the audit process, enhancing quality, effectiveness, and efficiency. The client acceptance process (CAP), one of the critical audit steps, involves subjective evaluations where business managers' claims intersect with independent audit firm managers' expectations. This subjective nature introduces the potential for errors or misjudgments, impacting audit time and costs. In this paper, therefore, we propose a decision support system considering both auditors' subjective judgments and financial data variations for accepting or rejecting a client enterprise. The decision support system consisting of the Fuzzy Analytic Hierarchy Process (AHP), the logistic regression model, and the fuzzy inference system comprises four phases. In the first phase, a logistic regression model is developed using financial ratios to determine the client's probability of being in a close monitoring market (CMM) which represents publicly traded firms that are struggling to meet specific financial indicators or that are exposed to certain risks. In the second phase, the evaluation criteria used by the audit firm to measure the market reputation of the client enterprise are defined, and the weights of the evaluation criteria are obtained by using Fuzzy AHP. In the third phase, the Client Acceptance Score (CAS) representing market reputation of the client is calculated by incorporating the results of a reputation survey and applying the weights assigned to the evaluation criteria obtained in the second phase. Finally, client acceptance risk level (CARL) is obtained by using a fuzzy inference system and a rule-based defined by auditors. The CMM probability value and CAS score obtained in previous phases are used as input values of the fuzzy inference system. The CARL score guides the audit firm in deciding whether to engage with the client. To illustrate the applicability of the proposed model, a case study has been given in the paper.

Subsidence associated with dewatering and gas extraction from coal seams: Contribution of desorption-induced coal shrinkage

Several coal seams are contained within the formations of Great Artesian Basin (GAB), the largest natural underground water reservoir in Australia and the world. The extraction of coal seam gas (CSG) and its associated water from thousands of wells within the coal measures of the GAB has led to tens of millimetres of subsidence in CSG development areas. Since highly developed farming systems located in these areas rely on very low slope landforms, even this scale of subsidence has caused significant community concern about the potential for CSG extraction to impair farming operations and productivity through changes in land slope and drainage. Coal seam compaction associated with dewatering and gas extraction has two key components: poromechanical compaction and desorption-induced bulk shrinkage. The former results from pore pressure depletion (due to dewatering and gas extraction) and an increase of vertical effective stress, while the latter is induced by gas desorption from the coal matrix, leading to further deformation. While poromechanical compaction of fluid-bearing formations has been extensively addressed in the literature, relatively little research has been conducted on the role of coal shrinkage in CSG-induced subsidence. This paper introduces an innovative practical modelling approach for assessing CSG-induced subsidence at a subregional/regional scale. The approach utilises an analytical model for CSG-induced subsidence derived from constitutive stress–strain relations for poroelastic-sorptive media. This is a novel approach in the context of CSG-induced subsidence which considers both poromechanical compaction and desorption-induced shrinkage. A further distinction to previous work is the integration of the geomechanical-sorptive subsidence model with a numerical groundwater model. Based on this approach, this paper examines the effect of coal shrinkage on subsidence, and its proportions with respect to total compaction for one of the major coal measures in the Surat Basin. Input data are derived from three-dimensional geological, geomechanical, and groundwater models, as well as methane adsorption and desorption reports. Results show that (a) the impact of coal shrinkage on CSG-induced subsidence is likely to be significant in the study area and (b) the contribution of coal shrinkage to CSG-induced subsidence depends on gas content, Langmuir isotherm, shrinkage strain parameters, and the saturation state of coal. This study provides important insights into CSG-induced subsidence and lays the foundation for the development of robust, efficient, and localised predictive models to support environmental impact assessment and management.

Embedded machine learning-based road conditions and driving behavior monitoring

Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.

Unveiling Crash Casualties: A Neural Network Model for Crash Type Classification Using Traffic Datasets

Abstract: Traffic accidents, a significant public health burden with substantial economic costs, necessitate proactive measures for sustainable transportation. This study investigates the efficacy of neural networks in predicting crash types using traffic data. We propose a deep learning model that leverages the power of recurrent neural networks (RNNs) for sequential traffic pattern analysis and convolutional neural networks (CNNs) for extracting spatial features from traffic data. The model, trained on a labeled traffic dataset, learns to identify patterns and relationships between traffic characteristics (e.g., speed, volume, weather) and different crash types (e.g., rear-end collision, single-vehicle). The evaluation focuses on the model's accuracy in predicting crash types using metrics like precision, recall, and F1-score. This research contributes to the development of intelligent traffic management systems (ITMS) that can proactively identify and mitigate potential accidents, ultimately enhancing road safety and promoting a more sustainable transportation future.The implementation results indicate that the proposed method yields an accuracy of 82.93% approximately.