bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Three-Layer Stacked LSTM Model Assisted by Educational Robots in Children’s Learning
Authors: Che Lixuan , Zuo Yuanyuan , Zhang Liping
Published: June 2022

To enable educational robots to better accompany children’s learning and growth, first, the encoder part of the image description model is established based on the convolutional neural network (CNN), and the decoder part of the image description model is built by the long short-term memory (LSTM). The attention mechanism is combined with the input stage, LSTM, and the output stage of the model. Second, three-layer stacked LSTM is used to improve and optimize the model. Artificial intelligence (AI) challenger data set is used to test the constructed model, and bilingual evaluation understudy (BLEU) and metric for evaluation of translation with explicit ordering (METEOR) are used to evaluate the model. Finally, 48 children are used as the research objects to study the application effect of the model. The results show that the BLEU-1, BLEU-2, BLEU-3, and BLEU-4 values of the constructed model are higher than those of the neural image caption (NIC) model during the entire iteration process. This model begins to converge when the number of iterations is 20,000. However, the NIC model starts to converge when the number of iterations is 45,000. The constructed model has a faster convergence speed. The scores of BLEU-1, BLEU-2, BLEU-3, and BLEU-4 of the constructed model are 0.687, 0.54, 0.432, and 0.348, respectively, and the score of METEOR is 0.187, which is higher than other models. In the actual application, the learning efficiency and effect of children who use this model for learning are better than the traditional teaching effect. The research results can help to improve the auxiliary role of educational robots in children’s learning, which has very important practical significance.
A Novel Object Recognition Algorithm Based on Improved YOLOv5 Model for Patient Care Robots
Authors: Wang Yina , Fu Guoqiang
Published: June 2022

The rapid development of computer vision raises a new research area involving patient care robots. Such robotic systems require fast target recognition at long ranges, where detecting smaller objects is notoriously challenging due to the cameras’ low resolution and noise. Spurred by these concerns, this paper develops a novel object recognition algorithm that solves these problems. Specifically, we amend YOLOv5 with our proposed sparse detection algorithm aiming to improve detection efficiency by separating the most significant context features and constructing smaller and less computational expensive models. Furthermore, we extend FReLU and suggest a novel activation function to improve recognition accuracy, which presents an extended nonlinearity increasing the expressiveness of the activation function. Finally, we propose sine annealing, which affords a trajectory that tends to cross over barriers and escape from local minima during training phase, addressing the challenging small object detection problem. The experimental results highlight that our algorithm has a lower memory consumption (Mem) value than the traditional YOLOv5 with a 5% boost down. Additionally, our method runs twice as fast as the traditional YOLOv5 while preserving accuracy, achieving more than 14.5 FPS on a medium-capability CPU. Overall, the detection results prove that our method can faster and accurately classifies and localizes most small-scaled objects.
The Role of Data-Driven Solutions for SMEs in Responding to COVID-19
Authors: Almeida Fernando , Wasim Jahangir
Published: June 2022

This study aims to explore the role of small and medium-sized enterprises (SMEs) in developing data-driven solutions to address the direct and indirect challenges posed by COVID-19. A sample of six case studies of SMEs from the UK and Portugal were selected to explore in-depth the experience of these companies in proposing innovative solutions in the pandemic context. The findings reveal that the pandemic caused amplifying effects on the digitalization of organizations and the emergence of data-driven solutions. However, the development of a data-driven approach involves not only technologies but also the digitalization of processes and highly skilled human resources. The pandemic was also a catalyst for the emergence of collaborative initiatives that have enabled the development of solutions involving diverse players from science, business, and civilian society. This study offers innovative contributions by focusing exclusively on companies developing data-driven solutions supported by technologies such as the internet of things (IoT), big data, and artificial intelligence.
The Linkage Between Intellectual Property and Innovation in the Global Innovation Ecosystem
Authors: Reis Diego , Moura Fábio , Aragão Iracema
Published: June 2022

The protection of intellectual property rights is a mechanism of economic appropriation that seeks to reward innovative efforts. The intellectual property ecosystem structure is particular to each country, generating different investment stimuli during the innovation life cycle. Our objective is to assess the connection between intellectual property and innovation production. We estimate an unconditional panel quantile regression model using annual country data for the intellectual property (IPI) and innovation ecosystems. The pooling specification results show that the effect of intellectual property on innovation output is significant at higher quantiles. However, when controlling for time and individual fixed effects, the intellectual property ecosystem shows a positive effect at the left tail of the innovation output distribution. We also identify the predominance of non-significant effects of IPI on innovation production. The absence of a significant relationship between IPI and innovation output in more developed countries suggests that interaction between these ecosystems needs to be revised to generate better benefits.
Fisher Scoring with Condition-Based Ensemble Supervised Learning Classification Technique for Prediction in PFZ
Authors: Vinston Raja R. , Ashok Kumar K.
Published: June 2022

Potential fishing zone (PFZ) alerts are critical in anticipating fishing places. Earlier PFZ predictions are based on NOAA’s advanced very high-resolution radiometer (AVHRR). To a significant degree, the expansion of the fishing industry may be attributed to the influence of research on fish growers, fishermen, fisheries planners, and managers. Artificial intelligence (AI) technologies are increasingly being used to improve the sustainability of smart fishing. While sustainability is frequently touted to be the intended consequence of AI applications, there is no data currently on how AI contributes to sustainable fishing. The purpose of this paper is to perform a feature selection using the fisher’s score (FS) technique to find the optimal features for final classification. Normalization is used as a preprocessing technique to remove missing and irrelevant data. Here, the collected features, financial derivatives, and geometrical features are used, which leads to poor classification accuracy for predicting the PFZ. Therefore, to improve the accuracy of the condition-based ensemble machine learning and deep learning classification technique (CECT), FS is used and provides the minimum number of features for classification. The experiment is carried out on collected data and tested with existing techniques in terms of accuracy, sensitivity, specificity, and F-measure. The simulation results proved that the proposed technique achieved 96.11% accuracy and 96% specificity compared to the FS technique.
Analysis of Citrus Diseases and Classification Based on Deep Learning Models
Authors: Gondal Shailesh , Agrawal Shweta , Kumar Anil
Published: June 2022

The citrus industry depends on the early identification of fungal infections, since a few infected fruits may spread the disease to a whole batch, resulting in substantial economic losses. In recent years, deep learning has played a significant role in the automated identification and categorization of illnesses in vegetables and fruits. This has increased the quality and quantity of vegetables and fruits. Numerous illnesses have a negative influence on the quality of citrus crops. Different pre-trained CNN models were employed in this research to identify and classify citrus diseases. The different CNN Models are compared with five pre-trained CNN models for detecting citrus diseases. Different combinations of training and learning methods are used with pre-trained architectures, such as VGG16, InceptionNet, ResNet, NasNet, MobileNet, and CNN for disease detection. A dataset of around 1500 images of diseases and healthy citrus leaves has been collected from different sources. The simulation shows that, of all the models, the MobileNet architecture is the most accurate, with an accuracy rate of 96%.
Spider Monkey Optimization Algorithm-Based Optimal Design of Wideband EBG Structure for Certain Uncertainty Parameters
Authors: Venkateshwar Rao B. , Panda Sunita
Published: June 2022

EBG structures have evolved as an emerging technology in recent years in the domain of microstrip antenna performance enhancement for wireless applications. They contain innovative structures, shapes, and materials that can be found in numerous industrial and military utilizations. The dimensions considered for the substrate of a microstrip antenna which propagates by using metal molecules (cells) are smaller than its kinetic wavelength ([Formula: see text]). This uncertainty in the arrangement increases both bandwidth and efficiency of an antenna when placed on the modified surface. Uttermost the literature surveys on the computational features of the EBG architecture, uncertainly there is little discussion on the development of the design and its effectiveness in challenging external conditions. This research is aimed to have optimized design values and respective control factors of an antenna like substrate height (h), breadth of EBG patch (p) and width of the gap between adjoining cells of EBG (d). Hence, an optimal design of wideband EBG architecture is done utilizing spider monkey optimization (SMO) process to advance the performance of micro-strip antenna. The result values highlighted through simulation outcomes exhibit the performance of the proposed optimal EBG structure outperforms than the standard EBG in the terms of reflection phase, transmission loss, magnitude of [Formula: see text], [Formula: see text], and gain comparison. The outcomes of this technique provide enhance in its bandwidth up to 11.25 GHz, return loss of −31 dB, gain of 5.5 dB and from [Formula: see text]comparison the magnitude of measured with EBG to magnitude of measured with optimal EBG is increased by 7.5% is observed.
Determination of breath isoprene in 109 suspected lung cancer patients using cavity ringdown spectroscopy
Authors: Wei Xin , Li Qingyuan , Wu Yinghua , Li Jing , Zhang Guangkuo , Sun Meixiu , Li Yingxin
Published: June 2022

Background: Lung cancer is one of the most common malignant tumors worldwide. Currently, effective screening methods for early lung cancer are still scarce. Breath analysis provides a promising method for the pre-screening or early screening of lung cancer. Isoprene is a potential and important breath biomarker of lung cancer. Material and Methods: To investigate the clinical value of isoprene for diagnosing lung cancer patients, a cavity ringdown spectroscopy (CRDS) based near-real time, sensitive analysis method of breath isoprene is developed in our lab. In this paper, 92 breath samples from lung cancer patients, 17 breath samples from patients with benign lesions, and 107 breath samples from healthy people were collected. Results: Research indicates that breath isoprene concentration is significantly higher in healthy individuals ([Formula: see text][Formula: see text]ppbv) than in patients with lung cancer ([Formula: see text][Formula: see text]ppbv) and benign lung lesions ([Formula: see text][Formula: see text]ppbv). The result of Receiver Operating Characteristic (ROC) curve suggests that the concentration of isoprene is meaningful for the diagnosis of lung cancer ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]). Conclusion: This study demonstrates that the CRDS breath isoprene analysis system can effectively analyze a large sample of human breath isoprene, and preliminarily confirms the use of breath isoprene as a biomarker for lung diseases.
Signposts and contributions of Leonid Kantorovich
Authors: Kutateladze S. S.
Published: June 2022

This is an overview of the life and contributions of Leonid Kantorovich, a renowned Russian mathematician and economist.
𝒪-operators of ternary Jordan algebras and ternary Jordan Yang–Baxter equations
Authors: Chtioui Taoufik , Hajjaji Atef , Mabrouk Sami
Published: June 2022

In this paper, we study the representation of ternary Jordan algebras which allows us to introduce the notion of coherent ternary Jordan algebras. Then the [Formula: see text]-operators of ternary Jordan algebras are introduced and the solutions of ternary Jordan Yang–Baxter equation are discussed involving [Formula: see text]-operators. Moreover, ternary pre-Jordan algebras are studied as the algebraic structure behind the [Formula: see text]-operators. Finally, the relations among ternary Jordan algebras and ternary pre-Jordan algebras are established and illustrated by examples.
Nordhaus–Gaddum problem in terms of G-free coloring
Authors: Rowshan Yaser
Published: June 2022

Let [Formula: see text] be a graph. A [Formula: see text]-coloring of [Formula: see text] is a mapping [Formula: see text], if each color class induces a [Formula: see text]-free subgraph. For a graph [Formula: see text] of order at least [Formula: see text], a [Formula: see text]-free [Formula: see text]-coloring of [Formula: see text], is a mapping [Formula: see text], so that the induced subgraph by each color class of [Formula: see text], contains no copy of [Formula: see text]. The [Formula: see text]-free chromatic number of [Formula: see text], is the minimum number [Formula: see text], so that it has a [Formula: see text]-free [Formula: see text]-coloring, and denoted by [Formula: see text]. Suppose that [Formula: see text] be a family of graphs, we say a graph [Formula: see text] has a [Formula: see text]-free [Formula: see text]-coloring, if there exists a map [Formula: see text], such that each color class [Formula: see text] does not contain any members of [Formula: see text]. In this paper, we give some bounds and attributes on the [Formula: see text]-free chromatic number of graphs in terms of the number of vertices, maximum degree, minimum degree, and chromatic number. Our main results are the Nordhaus–Gaddum-type theorem for the [Formula: see text]-free chromatic number of a graph.
An Efficient Temporal Inter-Object Association Rule Mining Algorithm on Time Series
Authors: Vu Nguyen Thanh , Chau Vo Thi Ngoc
Published: June 2022

Time series is acknowledged as one of the most common crucial data types in our daily lives. Among the time series mining tasks, rule discovery is important to provide valuable knowledge that brings us a profound insight view of relationships between different objects through time. One challenge is that when the number of objects and their lengths increase, it easily leads to a combinatorial explosion. Therefore, we propose a temporal inter-object association rule mining algorithm, NPTR, to discover new informative temporal inter-object association rules from time series and overcome the challenge with parallelization. Another remarkable point is that NPTR defines a concurrent approach by performing the frequent pattern mining process and rule mining one simultaneously. From the experiments on real-world data, NPTR returns the rules exactly with less time and memory costs than others do. Those rules can be further utilized for other tasks such as prediction, classification, and clustering.
Affective Analysis of Materials Choice in Clothing Based on Network Semantics and Big Data
Authors: He Yanan , Liang Pei , Cheng Kai , Shi Danqing
Published: June 2022

Consumer emotion analysis is one of the rising research topics in the retail clothing materials market. The e-commerce internet platform is becoming an enormous data resource, which can be collected and evaluated for the consumer’s preference. Here, we collected the online comments for clothing materials and applied the Software Kismet Engine to perform the big data semantic cluster analysis. Different materials’ design elements were analyzed and associated with the affective elements of the customers. The emotional words in the comment text are extracted and categorized. Such association and categorization between clothing materials and design elements based on consumer’s emotion analysis can be very important and helpful for choosing the materials in the retail market.
Control of a Flapping Wing Aerial Vehicle in the Presence of Matched and Mismatched Disturbances
Authors: Wissa Bishoy E. , Hermina Shady G. , El-Badawy Ayman A.
Published: June 2022

The work presented in this paper addresses trajectory tracking and position control of a six-degrees-of-freedom flapping wing micro aerial vehicle (FWMAV) in the presence of matched and mismatched disturbances. FWMAV is an unstable system which is affected by various disturbances. These disturbances can be classified into matched and mismatched disturbances according to their entrance via the same or different channels from control inputs. In order to handle these disturbances, an integral command-filtered block backstepping controller is applied to a nonlinear high-fidelity time-variant dynamic model. The stability of the proposed controller and the ultimate boundedness of the tracking error is derived based on Lyapunov stability arguments. Finally, the control law is validated by various simulation studies on the high-fidelity model. The proposed controller performance proves its ability to successfully track the arbitrary desired reference trajectories with feasible control effort in the presence of both constant and time-varying matched and mismatched disturbances.
Posterior Reversible Encephalopathy (PRES) as a Presentation Form of Classical Polyarteritis Nodosa (PAN)
Authors: Troncoso Jorge Álvarez , Ballester Laura Lacruz , Mozo Ana Noblejas , Diéguez Manuel Lorenzo , Marhuenda Ángel Robles , Marín Rafael Genaro Martínez , Blanco Juan José Ríos
Published: June 2022

A 21-year-old woman with unremarkable past health presented with an intense headache and visual disturbance, followed by a secondary generalized tonic-clonic focal seizure. She had a history of arthritis, livedo reticularis, myalgia, elevated erythrocyte sedimentation rate, and nonspecific constitutional manifestations (fever and weight loss). Examination revealed new-onset severe hypertension. Complete immunological studies were negative. Electroencephalogram showed abnormalities compatible with secondarily generalized tonic–clonic focal occipital seizures. Brain magnetic resonance imaging (MRI) revealed high signal intensity on T2 in occipital lobes, cerebellum, and brainstem and renal imaging multiple hypoenhancing parenchymal images (renal infarction). Follow-up brain MRI at 3 months showed marked improvement. A diagnosis of polyarteritis nodosa with posterior reversible encephalopathy syndrome was made.
Doping Effects in the Thiolate-Protected Gold Nanoclusters from the Perspective of Balance Between Ligands and the Core
Authors: Jia Hui , Liu Changlong , Li Yonghui
Published: June 2022

The atomically precise method has become an important technique for adjusting the core of thiolate-protected gold nanoclusters to improve biological, physical, and chemical properties. But, the doping effect on the structural stability across different elements has not been modeled within the scope of a bigger picture. Therefore, in this work, the H2S–nanoalloy complexes with different doping metal atoms have been investigated to elucidate the effects of the dopant on the structures. Indicated by simplified model complexes and density functional theory (DFT) simulation results, the zinc group atoms as dopants may be influenced by surrounded gold atoms, and binding to the thiolate units is enhanced by the addition of gold atoms. The reactivity of the zinc group atoms may be activated by a gold nanocluster. The simulated zinc group data when combined with the gold group and platinum group data can be summarized in the perspective of balance between the ligand–core binding and core cohesive energies. Most dopants move the modeled nanoclusters away from the balance, especially when the metal atom replaced the gold atom in gold–sulfur bindings. Besides, when cores of the nanoclusters are dominated by gold atoms, the dopants may achieve “saturation” such that the balance in the doped clusters may be corrected. This work provides a simple profile to understand the internal shift of the structure introduced by the atomically precise method.
Match Outcome Uncertainty and Sports Fan Demand: An Agnostic Review and the Standard Economic Theory of ports Leagues
Authors: Johnson Sidney , Fort Rodney
Published: June 2022

One of Rottenberg’s (1956, The baseball players’ labor market. Journal of Political Economy, 64, 242–258) foundational sports economics hypotheses is that fans prefer their team to win but with more match outcome uncertainty (MOU) rather than less. The testable implication is that increase in home team win chances and match ‘tightness’ should lead to increased fan demand for live attendance. It is safe to say that the verdict by those that voice one, especially recently, is that empirical results offer only occasional and weak support for Rottenberg’s hypothesis. We show that a more agnostic examination of the literature suggests that there is much more evidence failing to reject Rottenberg’s hypothesis than previously acknowledged. We also show that Rottenberg’s hypothesis is only one alternative generated by the standard economics of sports leagues. Rejecting Rottenberg’s hypothesis does not necessarily reject the importance of MOU with a different predicted sign as a demand determinant. This suggests additional testing tasks for the body of work that has moved on to loss aversion based on the prospect theory.
[Retracted] Rural Workplace Sustainable Development of Smart Rural Governance Workplace Platform for Efficient Enterprise Performances
Authors: Wu Yingli , Ma Wanying
Published: June 2022

In the long developmental process, China’s agriculture has transformed from organic agriculture to inorganic agriculture. New technologies have made the modernization of agriculture possible. However, most older people who are engaged in agriculture may not completely understand the modernization of agriculture. Based on the limitations of traditional image target detection methods, a deep learning‐based pest target detection and recognition method is proposed from a blockchain perspective, to analyze and research agricultural data supervision and governance and explore the effectiveness of deep learning methods in crop pest detection and recognition. The comparative analysis demonstrates that the average precision (AP) of GA‐CPN‐LAR (global activation‐characteristic pyramid network‐local activation region) increases by 4.2% compared with other methods. Whether under the Inception or ResNet‐50 backbone networks, the AP of GA‐CPN‐LAR is significantly better than other methods. Compared with the ResNet‐50 backbone network, GA‐CPN‐LAR has higher accuracy and recall rates under Inception. Precision‐recall curve measurement shows that the proposed method can significantly reduce the false detection rate and missed detection rate. The GA‐CPN‐LAR model proposed here has a higher AP value on the MPD dataset than the other target detection methods, which can be increased by 4.2%. Besides, the accuracy and recall of the GA‐CPN‐LAR method corresponding to two representative pests under the initial feature extractor are higher than the MPD dataset baseline. In addition, the research results of the MPD dataset and AgriPest dataset also show that the pest target detection method based on convolutional neural networks (CNNs) has a good presentation effect and can significantly reduce false detection and missed detection. Moreover, the pest regulation based on blockchain and deep learning comprehensively considers global and local feature extraction and pattern recognition, which positively impacts the conscientization of agricultural data processing and promotes the sustainable development of rural areas.
An Effective Bootstrapping Framework for Web Services Discovery Using Trigram Approach
Authors: Senthil Kumar G. , Ramana Kadiyala , Madana Mohana Rasineni , Aluvalu Rajanikanth , Gunjan Vinit Kumar , Singh Ninni
Published: June 2022

Web services are progressively being used to comprehend service-oriented architectures. Web services facilitate the integration of applications and simplify interoperability. Additionally, it assists in wrapping accessible applications in order for developers to access them using standard languages and protocols. The user faces a difficult challenge in selecting the appropriate service in accordance with the user request as the behavior of the participating service affects the overall performance in discovery, selection, and composition. As a result, it is critical to select a high-quality service provider for these activities. Existing approaches rely on nonfunctional qualities for discovery and selection, but the user cannot always rely on these features, and these QoS values cannot be used to determine the user’s or quality perspective. Additionally, the user indicates an interest in a high-quality service based on quality attributes or service with a good reputation throughout the selection process rather than a newly registered service. As a result, a proper bootstrapping mechanism is required to evaluate newly registered services prior to their use by service requestors. This paper proposes a novel bootstrapping mechanism. The contribution of this paper involves (a) a method for evaluating the quality of service (QoS) by focusing on performance-related indicators such as response time, execution time, throughput, latency, and dependability; (b) a methodology for evaluating the QoE attributes based on user reviews that take into account both attributes and opinions; (c) bootstrap the newly registered service based on quality of service and quality of experience; and (d) building a recommender system that suggests the top-rated service for composition. The evaluation results are used to augment currently available online services by providing up-to-date quality of service and quality of experience attributes for discovery, selection, and composition.
MEMS Sensors in Tennis Teaching and Training under the Background of Big Data Intelligent Communication
Authors: Li Fen , Li Zhenzhong
Published: June 2022

Racket sports (tennis, pawns, tennis, etc.) are currently very popular sports, and they are a very intense aerobic exercise. The climatic conditions of the competition venue, time difference factors, the size of the competition venue, background, lighting, wind direction, etc. may all have an impact on the performance of the game. In order to fully control and eliminate these risk factors, it is recommended to go to the competition site for adaptive training one month in advance. If you can participate in the prematch training camp organized by the general administration, it is of course the best choice. This research mainly discusses the development of MEMS sensors in tennis teaching and training under the background of big data intelligent communication. Using a single inertial sensor fixed on the bottom of the racket for data collection, a real-time data stream window segmentation method based on the combination of sliding window and action window is proposed. Finally, based on the above recognition method, an intelligent tennis training system software is developed, introduces the processing flow of MEMS sensor data, and gives a specific implementation plan for data preprocessing through in-depth study of the characteristics of inertial navigation data. The tennis training system collects the raw data of tennis players during the training process through the MEMS sensor hardware module placed at the bottom of the tennis racket handle, and the sensor module can upload these raw data to the smart phone terminal through wireless communication. The smart phone terminal can analyze and process the collected raw data and finally feedback the number and proportion of each technical action of the tennis player in the training process and the maximum racquet speed through the mobile phone software in real time. After the practice training, the average CTN score of the control group was 141.73, and the standard deviation of the data was 4.185. After the practice training, the average CTN score of the experimental group was 161.67, and the standard deviation was 6.042. This research will improve the efficiency of tennis teaching and training.
Removal of Hematite Water-Based Filter Cake Using Hydrochloric Acid
Authors: Siddig Osama , Alafnan Saad , Elkatatny Salaheldin
Published: June 2022

The filter cake layer, which is essential to reduce the fluid invasion to the drilled formation, has been reported to be composed mainly of the weighting materials. The complete removal of this layer is recommended to restore the rock permeability. Hematite is one material that is used to create the filter cake. However, the cleanup of hematite-based filter cake has not been studied thoroughly. The objective of the work presented in this paper is to investigate the removal of hematite water-based filter cake using hydrochloric acid. Numerous solubility tests were conducted to study the different factors affecting the treatment of hematite such as temperature and particle size. Based on these results, the treatment duration and acid concentration were selected. The high-pressure high-temperature filtration cell was used to build the filter cake and to soak it in the treatment solution at 100°C. Even though the solubility is increasing with acid concentration, however, no significant improvement was noticed above 12.5 vol.% of HCl. The particle size distribution has a considerable effect on the treatment, and the solubility was found to be 86 wt.% for particles finer than 25 microns and 47 wt.% for particle sizes above 75 microns, all in 10 vol.% HCl. The temperature has a significant influence as the solubility doubled when the temperature increased from 75°C to 100°C. The solubility rate decreased with time and reached the maximum in 16 hours which has been taken as treatment duration. Using 12.5 vol.%, 91 wt.% of the filter cake was successfully removed. In the literature comprehensive study on the cleanup of hematite-based filter cakes is lacking. This research provides new insights into hematite removal and address the different factors that impact the treatment efficiency.
Challenges of Learning Postgraduate Class with No Prior Work Experience: A Phenomenological Study
Authors: Birhanu Ermiyas Tsehay , Assefa Yalalem , Tilwani Shouket Ahmad
Published: June 2022

The main purpose of this research was to explore the lived experience of students who learn postgraduate classes without having prior work experience. To achieve the intended objective, the team of researchers used a qualitative research approach. The phenomenological research design was used to have the real lived experience of novice scholarship students. Data were collected through in-depth interviews, focus group discussion, and unstructured participant observation. The participants were ten postgraduate students who enrolled in the College of Education and Behavioural Sciences at Bahir Dar University. Data analysis was guided by Smith’s Interpretative Phenomenological Analysis, and we construct themes based on data we obtain from participants. The study revealed that postgraduate students with no prior work experience have both good and bad experiences. The good experiences were related to the opportunity they have to learn at a young age, and the bad experiences were the challenges they faced as a result of being novices in the work environment. They faced psychological challenges, economic challenges, language-related challenges, lack of information about the world of work, sexual harassment, and other challenges. At the end of the research, we infer implications based on the findings of the research.
Automatic Image Processing Algorithm for Light Environment Optimization Based on Multimodal Neural Network Model
Authors: Chen Mujun
Published: June 2022

In this paper, we conduct an in-depth study and analysis of the automatic image processing algorithm based on a multimodal Recurrent Neural Network (m-RNN) for light environment optimization. By analyzing the structure of m-RNN and combining the current research frontiers of image processing and natural language processing, we find out the problem of the ineffectiveness of m-RNN for some image generation descriptions, starting from both the image feature extraction part and text sequence data processing. Unlike traditional image automatic processing algorithms, this algorithm does not need to add complex rules manually. Still, it evaluates and filters through the training image collection and finally generates image automatic processing models by m-RNN. An image semantic segmentation algorithm is proposed based on multimodal attention and adaptive feature fusion. The main idea of the algorithm is to combine adaptive and feature fusion and then introduce data enhancement for small-scale multimodal light environment datasets by extracting the importance between images through multimodal attention. The model proposed in this paper can span the semantic differences of different modalities and construct feature relationships between different modalities to achieve an inferable, interpretable, and scalable feature representation of multimodal data. The automatic processing of light environment images using multimodal neural networks based on traditional algorithms eliminates manual processing and greatly reduces the time and effort of image processing.
Design and Experimental Study of Spoon-Clamping Type Garlic Precision Seeding Device
Authors: Zhang Xinyan , Yi Shujuan , Tao Guixiang , Zhang Dongming , Chong Jun
Published: June 2022

In order to realize the mechanized sowing operation of garlic and ensure that the position of garlic scales is planted upright-position, a spoon-clamping type garlic precision seeding device is designed. The device is composed of seed box, seeder, righting fittings, and so on. This paper adopts the method of theoretical analysis; combined with the characteristics of garlic, the key components of the sowing device have been optimized and designed; and the final form of the device is determined. The one-factor experiment and multifactor experimental studies were carried out, and the optimal working parameter combination of the device was obtained by using the response surface methodology and mathematical modeling method, and finally, the optimized working parameter combination was verified. The results showed that the spoon-clamping type garlic sowing device could meet the operational requirements of garlic mechanized sowing. This study has certain theoretical and practical significance for the mechanization development of garlic.
A New Nonlinear Controller Design for a TCP/AQM Network Based on Modified Active Disturbance Rejection Control
Authors: Aljuboury Anwer S. , Zeebaree Subhi R. M. , Abedi Firas , Hashim Zahraa Sabah , Malik Rami Q. , Ibraheem Ibraheem Kasim , Alkhayyat Ahmed
Published: June 2022

The main aim of this study was to address the problem of congestion in TCP nonlinear systems in the presence of mismatched exogenous disturbances. To achieve this problem, two methods are proposed: the first is active queuing management, based on two proposed controllers, an NLPID and STC‐SM, while the second is the application of active queuing management‐based anti‐disturbance techniques such as active disturbance rejection control (ADRC) and the nonlinear disturbance observer (NLDO). The proposed ADRC consists of a new NLPID and a new super‐twisting sliding mode controller (STC‐SM), which functions as a novel NLSEF, and a proposed NLESO estimates the applied disturbance and cancels it in a responsive manner. A new tracking differentiator with a novel function is also used to generate a smooth and accurate reference signal and derivative. The NLDO is proposed to estimate the disturbance and combine this with the control signal of the designed nonlinear controller as a way to compensate for the disturbance. The simulation results for the proposed scheme (ADRC) as applied to a nonlinear model of the TCP network are thus found to provide smoother and more accurate tracking of the desired value, with high robustness against applied disturbance, as compared to the other schemes introduced in this study. The proposed scheme also shows a noticeable improvement in terms of the utilized performance indices and the OPI.
Levofloxacin Use in Patients with Suspected Tuberculosis in a Community Hospital, Thailand: A Pilot Study
Authors: Khongyot Thanawat , Laopaiboonkun Sawitree , Kawpradid Throngpon , Jitkamrop Kannuwat , Chanphakphoom Tawee , Uitrakul Suriyon
Published: June 2022

Background. Levofloxacin is one of the broad-spectrum antibiotics that is indicated for the second-line treatment of tuberculosis (TB). However, using levofloxacin as an empirical therapy for patients without confirmation of TB could still be observed. This descriptive retrospective study, therefore, aimed to investigate the number of levofloxacin use in patients suspected TB in a community hospital in Thailand. Methods. Patient medical charts of all patients who were admitted to a community hospital in Nakhon Si Thammarat, Thailand, from 2016 to 2017, were reviewed. Patients who were suspected TB and received any levofloxacin-containing regimens were included. Data on patient characteristics and the received regimens were descriptively analyzed and reported as percentage and frequency. Results. There were a total of 21 patients who received levofloxacin in the hospital. Six of them (28.57%) had the diagnosis of hepatitis. The most prescribed regimen as empirical therapy was levofloxacin, ethambutol, and amikacin (66.67%). After the confirmation of TB using acid-fast bacilli (AFB) test, ten patients (47.62%) still received levofloxacin-containing regimens. Conclusion. The results from this study indicated high usage of levofloxacin despite no evidence of drug-resistant TB or negative AFB results in a community hospital in Thailand. The results from this study will be further used for the investigation of the prevalence of antibiotic resistance and clinical outcomes of using second-line regimens for TB treatment.
Layout Optimization of Urban Cultural Space Construction Based on Forward Three-Layer Neural Network Model
Authors: Zhou Ye
Published: June 2022

The change of urban cultural space layout is a multi-variable, multi-objective, and restricted research process. The optimization of urban cultural space construction and layout is a multi-objective decision-making problem that needs to be solved urgently. Based on the forward three-layer neural network theory, this paper constructs an optimization model for the construction and layout of urban cultural space evaluation of the layout of cultural space. This paper first analyzes the feasibility of combining the forward three-layer neural network model with the optimization and adjustment of cultural space layout structure. Taking the three-layer feedforward network as an example, the structure optimization model based on the forward three-layer neural network is selected, and the established model is used to reflect the internal environment of the objective world. Structure and perform dynamic simulation. In the process of simulation modeling, from the aspects of system description, model structure, logical analysis, reasoning, and interpretation, two effective computer dynamic simulation methods, namely, forward three-layer neural network model and system dynamics SD model, were carried out for theoretical comparison and identification. The experimental results show that the feasibility and calculation error of the application of the optimization model are relatively good, reaching 0.897 and 6.21%, respectively. The number of newly added cultural spaces and the expansion speed show an increasing trend, expanding at an average annual speed of about 35 km2, effectively increasing the quality of regional planning and construction layout.
Image Model and Algorithm of Human Resource Optimal Configuration Based on FPGA and Microsystem Analysis
Authors: Gu Jingjing
Published: June 2022

Human resources are the most dynamic factor among all productivity factors. The goal of human resource management is to achieve effective and optimal allocation of resources, while the goal of optimal allocation is to promote the realization of corporate goals. At present, the overall domestic human resource management is relatively backward, and most of it is nonstandardized management, and the level of informatization and technology is not high. Therefore, this paper proposes the image model and algorithm research of human resource optimization configuration based on FPGA and microsystem analysis. First, through the data analysis method, in-depth study of traditional resource allocation algorithms: genetic algorithm and particle swarm optimization algorithm, combined with machine learning algorithms, established a human resource optimization configuration model based on FPGA and microsystem analysis, and then optimized the configuration of human resources for the impact The standard deviation and variance measurement of 18 factors of the company, as well as the analysis of the matching degree between employees and positions, and required capabilities. Finally, it is concluded that the sample average is higher and the top three are employees’ positive spirit A12, employees’ executive ability A15, and the relationship between salary and performance A11. The sample averages are 4.615, 4.522, and 4.479, respectively; standard deviation of the three factors with the smallest values are good values A8, strong employee cooperation ability A17, and perfect reward and punishment system A16. This shows that the above considerations should be considered in the optimization of enterprise human resources. Moreover, the algorithm in this paper is easier to obtain the optimal solution, and it is easier to obtain the optimal allocation result of human resources.
64-Slice Spiral Computerized Tomography under Algebraic Reconstruction Algorithm in the Surgical Treatment of Acquired Immune Deficiency Syndrome Complicated with Gastric Cancer
Authors: Yang Ke , Chen Zheng , Xu Dayong , Peng Fang
Published: June 2022

In order to deeply analyze the application of CT images based on artificial intelligence algorithm in clinical treatment of AIDS patients with gastric cancer, and to provide reference for intervention of AIDS patients with gastric cancer, a total of 100 AIDS patients with gastric cancer were included as the research objects. The patients with CD4+ T lymphocyte count less than 200 cells/µL were in the control group (50 cases), whereas those higher than 200 cells/µL were in the experimental group (50 cases). All the patients underwent 64-slice spiral CT scanning. An improved algebraic reconstruction technology (ART) under L0 algorithmic approach (L0-ART) was proposed, and compared with the total variation (TV), filtered back projection (FBP), and weighted total variation (WTV) models. The standard deviation (STD) and average processing time of the L0-ART algorithm were significantly lower than those of the TV, FBP, and WTV algorithms (

P

 < 0.05). The operation time of the experimental group was longer than that of the control group; the intraoperative blood loss, the diameter of the surgical wound, the time of first farting, the length of hospital stays, and the incidence of adverse reactions were all greatly lower than those of the control group (

P

 < 0.05). Postoperatively, the total adipose tissue (TAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) in the abdominal area were higher in the experimental group than those in the control group (

P

 < 0.05). In conclusion, the improved L0-ART algorithm proposed in this study had an excellent processing effect on CT images with a clinical promotion value. Patients with CD4+ T lymphocytes over 200 cells/µL had better surgical outcomes and prognosis than those with less than 200 cells/µL.
The Design of Psychological Education Intervention System in Universities Based on Deep Learning
Authors: Wang Yuefen , Yang Dianyi
Published: June 2022

With the rapid development of Chinese society and economy as well as the deepening of the reform of the higher education management system and the change of employment mode of graduates, college students face various challenges of frustration and pressure in the areas of value and ethical concepts, interpersonal relationships, behavior, life, and employment. Some students who are relatively fragile psychologically are unable to bear the heavy pressure of frustration and challenges, and are prone to psychological crisis, overreacting, and even hurting others or self-injury or suicide. How to solve the current psychological problems of college students and help them become adults and talents is a new task and a serious challenge for college students’ mental health education under the new situation. With the development of the Internet, more and more people are expressing their emotions in social networks, including suicidal intentions, which creates new opportunities for suicide prevention. If suicide risk can be automatically identified using microblogs, it can open up new directions for suicide prevention efforts. This paper is based on the use of deep learning to build a social media suicide identifier to explore the possibility of assessing individual users’ suicide in real time through social platforms. To verify the effectiveness of this algorithmic model, the keyword attributes used by the algorithm are statistically analyzed and compared with the prediction results of two other algorithmic models. The experimental results show that the algorithmic model based on deep learning can be more effective in predicting the suicide risk of microblog users.
The Experimental Validation of a Digital Filter Mathematical Modelling for Nuclear Fuel Vibration Monitoring System
Authors: Yong Han , Shiyong Huang , MengXuan Han , Tao Zhao , Guang Wen , Mingfei Gu , Qi Zhang
Published: June 2022

Aiming at vehicles will produce shocks and vibrations for various reasons in the process of transportation and handling, any shock and vibration that exceeds the regulations would cause damage to the nuclear fuel being transported. In order to monitor and analyze the shock and vibration data scientifically, a monitoring system based on MEMS acceleration sensor technology is developed to measure shocks and vibrations. The measured result involves some noise caused by the sensitivity of the sensor and the electrical noise, and a digital filter mathematical modelling is introduced to reduce noise and improve the measured result accuracy. In order to verify the effectiveness of the device designed in this paper, the device and the high-precision seismic acceleration sensor AC73 are, respectively, used for field tests; the results show that the test precision of the designed device can meet the requirements, and also, the designed shock and vibration monitoring system can record and store the three-axis acceleration, temperature, humidity, pressure, time, and other parameters that exceed the set threshold during nuclear fuel transportation in real time and complete the fuel assembly transportation status assessment and promptly remind the transportation personnel to take safety precautions and treatment measures. Therefore, it can be widely used in the monitoring, recording, and postevent data analysis of the transportation process of important products such as nuclear fuel.