bluuebunny/crossref_metadata_2025 huggingface dataset:
Brave spaces: Indigenous children in Canada plan for a different tomorrow
Authors: Cooper Elizabeth
Published: March 2022

When considering the impacts of historical trauma and colonization on the lived realities of Indigenous young people within Canada, it is essential that research uses strength-based, capacity-building approaches to ensure that the voices are heard and that their perspectives and advice can be actioned. A series of participatory workshops with adults and children were conducted within urban geographies to explore health-seeking behaviours, health knowledge, and community resiliency. Research took place in Manitoba, Canada (2015–2016, n = 36 girls and 24 adults) with First Nations and Métis community members and British Columbia, Canada (2017, n = 11 children and 15 adults) with Métis community members. Children participated in community transect walks, photo elicitation activities, discussion circles (with adult participant contribution), and projected community mapping exercises where they drew their ideal, imagined community. Community consensus processes were used for member checking as well as initial evaluation of research findings, including the establishment of key themes. Field notes, discussion transcripts, and images were analyzed for similarities and differences between ages, genders, cultural identifiers, and geographies. A key finding was the need for safe spaces that can also be brave spaces, or moments when the community can be free to push for change without reprimand. Children were particularly concerned with sustainable, appropriate housing, safe methods of transportation, access to green spaces, and environmental stewardship within their day-to-day lives.

Novelty: Brave spaces are essential for achieving/maintaining wellness within Indigenous communities. This article responds to a need to shift to a determinants of life focus.
Dynamic performance of flax fiber-reinforced polymer
Authors: Mo Zonglai , Wang Wenjie , Chouw Nawawi , Jayaraman Krishnan
Published: March 2022

The study addresses the dynamic performance of flax fiber-reinforced polymer (FFRP) composites via a series of drop-weight impact tests. The experiment was performed based on ASTM standards by using an instrumented falling weight impact machine. Increasing impact energy was applied on the FFRP specimens until an occurrence of perforation. The effect of composite thickness and impact striker diameter were considered. The results showed that the striker diameter and composite thickness have a significant effect on the impact properties of the composites. It is also revealed the influence factors that control the impact behavior of FFRP composite that will be useful for developing future protective fiber-reinforced composites.
Riding with the Surging Tide: A Review of MCDM’s Evolution
Authors: Li Weishu , Yu Liying , Xia Wenying , Zhou Jian , Zhao YvXiu , Du Mei
Published: March 2022

Multi-criteria decision making (MCDM) is a sub-discipline of operations research aimed at evaluating alternatives in consideration of various criteria. It is used by practically everyone in their daily lives and professional settings. Seeing the great value of MCDM, we conduct a comprehensive review to propel its innovation and development forward. Compared with any other reviews, we do not focus on introducing its methods, but on tracing its evolution and characteristics. As a general rule, every discipline has its own developing laws. They can be understood on various levels and can point out the direction the discipline is heading for. We firmly believe that our work can bring insight into MCDM’s frontiers and trend, which can eventually provide guidance on how to conduct the future research. In the first half of the paper, we investigate MCDM’s paradigm through literature review and colloquially divide its story into four stages: the Stone Age, the Iron Age, the Industrial Age and the New Stage. The first three stages symbolise its debut, growth and prosperity. Each one has distinctive thoughts, techniques and application, and to some extent hints the major works of the next stage. Since the turn of the 21st century, the paradigm has been once again experiencing dramatic changes, suggesting that it is on the threshold of a new era. Thus, in the second half of the work, a bibliometric analysis of the present stage ensues. Put simply, we take a global view of the stage by visualizing the stage’s publication quantity, publication distribution, and research categories. Then we design a snowballing co-citation method to explore its movements. The results demonstrate that MCDM is a dynamic, worldwide study for which China is the most productive country and the USA plays a pivot role in scholar communication. Method reviews, straightforward methods and MCDM-oriented fuzzy sets are predominant frontiers. The application, however, always changes with the requirements of the times. Now, it mainly refers to energy, environment, and supplier selection, while issues like sociology, tourism, education, etc. also emerge at a fast speed. Apart from this, experts have gradually shaken off the fetters of the traditional research style and are increasingly willing to structure methods and select application areas with a more personal touch. In the future, how to improve reviews, methods and fuzzy sets, how to understand and draw inspirations from society needs, and how experts can tailor MCDM to accommodate specific problems might be the pressing concerns.
An Interval Type-2 Fuzzy Model of Computing with Words via Interval Type-2 Fuzzy Finite Rough Automata with Application in COVID-19 Deduction
Authors: Yadav Swati , Tiwari S. P. , Kumari Mausam , Yadav Vijay K.
Published: March 2022

Classical automata, fuzzy automata, and rough automata with input alphabets as numbers or symbols are formal computing models with values. Fuzzy automata and rough automata are computation models with uncertain or imprecise information about the next state and can only process the string of input symbols or numbers. To process words and propositions involved in natural languages, we need a computation model to model real-world problems by capturing the uncertainties involved in a word. In this paper, we have shown that computing with word methodology deals with perceptions rather than measurements and allows the use of words in place of numbers and symbols while describing the real-world problems together with interval type-2 (IT2) fuzzy sets which have the capacity to capture uncertainties involved in word using its footprint of uncertainty. The rough set theory, which has potential of modeling vagueness in the imprecise and ill-defined environment, introduces a computation model, namely, IT2 fuzzy rough finite automata, which is efficient to process uncertainties involved in words. Further, we have shown the application of introduced IT2 fuzzy finite rough automaton in the medical diagnosis of COVID-19 patients.
Characterization Summary of Performance, Reliability, and Threshold Voltage Distribution of 3D Charge-Trap NAND Flash Memory
Authors: Liu Weihua , Wu Fei , Chen Xiang , Zhang Meng , Wang Yu , Lu Xiangfeng , Xie Changsheng
Published: March 2022

Solid-state drive (SSD) gradually dominates in the high-performance storage scenarios. Three-dimension (3D) NAND flash memory owning high-storage capacity is becoming a mainstream storage component of SSD. However, the interferences of the new 3D charge-trap (CT) NAND flash are getting unprecedentedly complicated, yielding to many problems regarding reliability and performance. Alleviating these problems needs to understand the characteristics of 3D CT NAND flash memory deeply. To facilitate such understanding, in this article, we delve into characterizing the performance, reliability, and threshold voltage (

V
th

) distribution of 3D CT NAND flash memory. We make a summary of these characteristics with multiple interferences and variations and give several new insights and a characterization methodology. Especially, we characterize the skewed (

V
th

) distribution, (

V
th

) shift laws, and the exclusive layer variation in 3D NAND flash memory. The characterization is the backbone of designing more reliable and efficient flash-based storage solutions.
Intelligent Data Analysis using Optimized Support Vector Machine Based Data Mining Approach for Tourism Industry
Authors: Sharma Ms Promila , Meena Uma , Sharma Girish Kumar
Published: March 2022

Data analysis involves the deployment of sophisticated approaches from data mining methods, information theory, and artificial intelligence in various fields like tourism, hospitality, and so on for the extraction of knowledge from the gathered and preprocessed data. In tourism, pattern analysis or data analysis using classification is significant for finding the patterns that represent new and potentially useful information or knowledge about the destination and other data. Several data mining techniques are introduced for the classification of data or patterns. However, overfitting, less accuracy, local minima, sensitive to noise are the drawbacks in some existing data mining classification methods. To overcome these challenges, Support vector machine with Red deer optimization (SVM-RDO) based data mining strategy is proposed in this article. Extended Kalman filter (EKF) is utilized in the first phase, i.e., data cleaning to remove the noise and missing values from the input data. Mantaray foraging algorithm (MaFA) is used in the data selection phase, in which the significant data are selected for the further process to reduce the computational complexity. The final phase is the classification, in which SVM-RDO is proposed to access the useful pattern from the selected data. PYTHON is the implementation tool used for the experiment of the proposed model. The experimental analysis is done to show the efficacy of the proposed work. From the experimental results, the proposed SVM-RDO achieved better accuracy, precision, recall, and F1 score than the existing methods for the tourism dataset. Thus, it is showed the effectiveness of the proposed SVM-RDO for pattern analysis.
Incremental Graph Computations: Doable and Undoable
Authors: Fan Wenfei , Tian Chao
Published: March 2022

The incremental problem for a class

\( {\mathcal {Q}} \)

of graph queries aims to compute, given a query

\( Q \in {\mathcal {Q}} \)

, graph
G
, answers
Q
(
G
) to
Q
in
G
and updates
ΔG
to
G
as input, changes
ΔO
to output
Q
(
G
) such that
Q
(
G
⊕
ΔG
) =
Q
(
G
)⊕
ΔO
. It is called
bounded
if its cost can be expressed as a polynomial function in the sizes of
Q
,
ΔG
and
ΔO
, which reduces the computations on possibly big
G
to small
ΔG
and
ΔO
. No matter how desirable, however, our first results are negative: For common graph queries such as traversal, connectivity, keyword search, pattern matching, and maximum cardinality matching, their incremental problems are unbounded.

In light of the negative results, we propose two characterizations for the effectiveness of incremental graph computation: (a)
localizable
, if its cost is decided by small neighbors of nodes in
ΔG
instead of the entire
G
; and (b)
bounded relative to
a batch graph algorithm

\( {\mathcal {T}} \)

, if the cost is determined by the sizes of
ΔG
and changes to the affected area that is necessarily checked by any algorithms that incrementalize

\( {\mathcal {T}} \)

. We show that the incremental computations above are either localizable or relatively bounded by providing corresponding incremental algorithms. That is, we can either reduce the incremental computations on big graphs to small data, or incrementalize existing batch graph algorithms by minimizing unnecessary recomputation. Using real-life and synthetic data, we experimentally verify the effectiveness of our incremental algorithms.
Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists
Authors: Varde Aparna S.
Published: March 2022

Experimental results are often plotted as 2-dimensional graphical plots (aka graphs) in scientific domains depicting dependent versus independent variables to aid visual analysis of processes. Repeatedly performing laboratory experiments consumes significant time and resources, motivating the need for computational estimation. The goals are to estimate the graph obtained in an experiment given its input conditions, and to estimate the conditions that would lead to a desired graph. Existing estimation approaches often do not meet accuracy and efficiency needs of targeted applications. We develop a computational estimation approach called AutoDomainMine that integrates clustering and classification over complex scientific data in a framework so as to automate classical learning methods of scientists. Knowledge discovered thereby from a database of existing experiments serves as the basis for estimation. Challenges include preserving domain semantics in clustering, finding matching strategies in classification, striking a good balance between elaboration and conciseness while displaying estimation results based on needs of targeted users, and deriving objective measures to capture subjective user interests. These and other challenges are addressed in this work. The AutoDomainMine approach is used to build a computational estimation system, rigorously evaluated with real data in Materials Science. Our evaluation confirms that AutoDomainMine provides desired accuracy and efficiency in computational estimation. It is extendable to other science and engineering domains as proved by adaptation of its sub-processes within fields such as Bioinformatics and Nanotechnology.
DexDeepFM: Ensemble Diversity Enhanced Extreme Deep Factorization Machine Model
Authors: Chen Ling , Shi Hongyu
Published: March 2022

Predicting user positive response (e.g., purchases and clicks) probability is a critical task in Web applications. To identify predictive features from raw data, the state-of-the-art extreme deep factorization machine model (xDeepFM) introduces a new interaction network to leverage feature interactions at the vector-wise level explicitly. However, since each hidden layer in the interaction network is a collection of feature maps, it can be viewed essentially as an ensemble of different feature maps. In this case, only using a single objective to minimize the prediction loss may lead to overfitting and generate correlated errors. In this article, an ensemble diversity enhanced extreme deep factorization machine model (DexDeepFM) is proposed, which designs the ensemble diversity measure in each hidden layer and considers both ensemble diversity and prediction accuracy in the objective function. In addition, the attention mechanism is introduced to discriminate the importance of ensemble diversity measures with different feature interaction orders. Extensive experiments on three public real-world datasets are conducted to show the effectiveness of the proposed model.
Polynomial Multiplication over Finite Fields in Time \( O(n \log n \)
Authors: Harvey David , van der Hoeven Joris
Published: March 2022

Assuming a widely believed hypothesis concerning the least prime in an arithmetic progression, we show that polynomials of degree less than 

\( n \)

over a finite field

\( \mathbb {F}_q \)

with 

\( q \)

elements can be multiplied in time

\( O (n \log q \log (n \log q)) \)

, uniformly in

\( q \)

. Under the same hypothesis, we show how to multiply two

\( n \)

-bit integers in time

\( O (n \log n) \)

; this algorithm is somewhat simpler than the unconditional algorithm from the companion paper [
22
]. Our results hold in the Turing machine model with a finite number of tapes.
Corrosion of Linear-Friction-Welded AZ91 and AZX912 Mg-Al Alloys
Authors: Nakatsugawa Isao , Wanjara Priti , Villegas-Armenta Luis Angel , Gholipour Javad , Pekguleryuz Mihriban , Chino Yasumasa
Published: March 2022

The corrosion performance of the AZ91 and AZ91 + 2%Ca (AZX912) magnesium alloys joined using linear friction welding was investigated. For similar and dissimilar metal combinations—namely AZ91/AZ91, AZX912/AZX912, and AZ91/AZX912, the performance was evaluated by mass loss analysis and the scanning vibrating electrode technique in 1 wt% NaCl solution. Galvanic behavior between AZ91 and AZX912 was examined by potentiostatic polarization and galvanic current measurements. Approximately 0.3–0.6 mm thick seamless weld zones were generated in all of the welds, which did not show any appreciable changes in the surface profiles acquired after the corrosion test. The corrosion-induced mass loss rate of the AZ91/AZX912 dissimilar weld was 10% higher than those of the AZ91/AZ91 and AZX912/AZX912 similar welds. The galvanic test indicated that AZ91 and AZX912 were the anodic and cathodic regions, respectively, with a difference in the corrosion potential of 20–30 mV. The corrosion of the AZ91/AZX912 weld was presumed to proceed via a macro-galvanic cell between the base alloys, whereas the weld acted as a bridge and did not influence the corrosion.
Review—Chemical Structures and Stability of Carbon-doped Graphene Nanomaterials and the Growth Temperature of Carbon Nanomaterials Grown by Chemical Vapor Deposition for Electrochemical Catalysis Reactions
Authors: Chadha Utkarsh , Sinha Sanyukta , Jonna Jaidhitya , Goswami Maitreya , Ghani Hammad , Nair Karan , Pandey Neelesh , Kataray Tarun , Selvaraj Senthil Kumaran , Bhardwaj Preetam , Banavoth Murali , Sonar Prashant
Published: March 2022

Carbon nanotubes (CNTs) have been studied extensively utilizing the catalytic chemical vapor deposition (CCVD) process for several decades. CCVD is seen to have a better degree of control and scalability. CNTs have proved to be useful in single-molecule transistors, Scanning Electron Microscope (SEM) tips, gas and electrochemical storage, electron field emitting flat panel displays, and sensors. This paper summarizes various stabilizing agents such as cobalt ferrite and molybdenum disulphide that can increase the electrochemical activity of the Carbon Doped-Graphene Nanomaterials as Graphene doped with carbon shows a significant improvement in the properties in various aspects. We also investigated the electrochemical applications where CNTs are used as a prerequisite. Carbon nanotubes are seen in biosensors, energy storage, conductive plastics, and power fuel cells. Carbon nanomaterials’ influence on symmetrical and asymmetrical supercapacitors, carbon nanomaterials to power dye-synthesized solar cells, and the importance of CVD in the synthesis of carbon nanomaterials were also investigated.
Inhibition of phosphate transport by NAD+/NADH in brush border membrane vesicles
Authors: Lucea Susana , Guillén Natalia , Sosa Cecilia , Sorribas Víctor
Published: March 2022

Nicotinamide is an important regulator of Pi homeostasis after conversion into NAD+/NADH. In this work, we have studied the classical inhibition of Pi transport by these compounds in the brush border membrane vesicles (BBMV) of rat kidney and rat intestine, and we examined the effects in opossum kidney (OK) cells and in phosphate transporter-expressing Xenopus laevis oocytes. In BBMV, NAD+ required preincubation at either room temperature or on ice to inhibit Pi uptake in BBMV. However, no effects were observed in the known Slc34 or Slc20 Pi transporters expressed in Xenopus oocytes, in OK cells, or in isolated rat cortical nephron segments. In BBMV from jejunum or kidney cortex, the inhibition of Pi transport was specific, dose-related, and followed a competitive inhibition pattern, as shown by linear transformation and nonlinear regression analyses. A Ki value of 538 µM NAD+ in kidney BBMV was obtained. Ribosylation inhibitors and ribosylation assays revealed no evidence that this reaction was responsible for inhibiting Pi transport. An analysis of the persistence of NAD+/NADH revealed a half-life of just 2 min during preincubation. Out of several metabolites of NAD degradation, only ADP-ribose was able to inhibit Pi uptake. Pi concentration also increased during 30 min of preincubation, up to 0.67 mM, most likely as a metabolic end product. In conclusion, the classical inhibition of Pi transport by NAD+/NADH in BBMV seems to be caused by the degradation metabolites of these compounds during the preincubation time.
Housing Rental Scheme Based on Redactable Blockchain
Authors: Wang Chunli , Jia Wensheng , Chen Yuling
Published: March 2022

With the increase of the housing population, it is very important for renters to find credible housing information through rental platforms. However, due to the imperfection of relevant laws and insufficient supervision of the housing rental market, problems have emerged one after another. In this article, we propose a housing rental scheme based on redactable blockchain, introducing a trusted third party to verify the homeowner rental listings, and fundamentally preventing false listings from flowing into the housing rental market. Specifically, a trusted third party similar to the housing authority will issue a verifiable claim for the homeowners listing, and then the accounting node of the consortium blockchain will publish the verified listing information on the blockchain so as tenants to make inquiries according to their needs. At the same time, we use the new chameleon hash to build a redactable blockchain. After the homeowner proposes to change the housing price or housing information, the content of the historical block is modified as required. Thus, to a certain extent, the changes in housing prices can be monitored to curb chaos in the housing rental market.
Control Strategies for Flight Stability of Trajectory Correction Projectile with Air-Ducts Structure
Authors: Cui Silin , Liu Xing , Jiang Shoushan , Guo Jiachong
Published: March 2022

Aiming at the controllability of spin-stabilized projectile trajectory correction, a new two-dimensional trajectory correction projectile model with a controllable air-ducts structure suitable for the spin-stabilized projectile is proposed in this paper. Furthermore, control strategies of the projectile in the corrected configuration are studied to ensure a stable flight. The correction scheme of the projectile is innovated on the basis of the ram air control mechanism. The air-ducts structure is designed inside the projectile body, and the internal air valve is used to control the lateral air-jet to achieve trajectory correction of the projectile. This is a new two-dimensional trajectory correction scheme with a relatively simple control method. Firstly, the correction mechanism of the new air-ducts structure projectile is analyzed. Secondly, based on the dynamic equation of the projectile, the key parameters of the impulse air valve control, including the safe range of the working pulse width and frequency, are calculated, and the corresponding control strategies are proposed. Finally, the flow field of the projectile in the corrected configuration of the terminal trajectory is numerically simulated, and aerodynamic parameters of the projectile are obtained. Through stability conditions established in this paper and combined with aerodynamic parameters, the validity of the proposed projectile stable flight control strategies is confirmed. The innovations of this paper include: (1) a proposed new aerodynamic configuration model with the air-ducts structure for the spin-stabilized projectile and (2) proposed innovative stable flight control strategies of the corrected configuration projectile. The numerical simulation results show that, compared with the reference projectile without air-ducts structure, the lateral force of the air-ducts structure projectile can produce a radial correction effect on the moving projectile. The simulation results of control strategies indicate that the control strategies can ensure stable flight of the corrected configuration projectile under different conditions.
Forecast of Chemical Export Trade Based on PSO‐BP Neural Network Model
Authors: Li Na , Li Meng
Published: March 2022

With the gradual deepening of China’s reform and opening up, the degree of foreign development has been deepened, and its dependence on foreign trade has increased. The “export‐oriented” economic development has achieved results. Export trade is introducing advanced technology and equipment, expanding employment opportunities, and increasing government revenue. The export trade is affected by various domestic and international factors and is a complex nonlinear system. Although the traditional linear prediction method has the advantages of intuitiveness, simplicity, and strong interpretability, it is difficult to deal with the prediction problem of dynamic and complex nonlinear systems. The neural network is a nonlinear dynamic system, with strong nonlinear mapping ability, strong robustness, and fault tolerance. It has unique advanced advantages for solving nonlinear problems and is very suitable for solving nonlinear problems.
Evaluation of Natural Peptides to Prevent and Reduce the Novel SARS-CoV-2 Infection
Authors: Paredes-Ramos María , Conde Piñeiro Enma , Pérez-Sánchez Horacio , López-Vilariño José M.
Published: March 2022

In a preventive context, natural peptides can play a major role against SARS-CoV-2, so their character of GRAS (generally recognized as safe) means they would not need innocuity analyses to be employed. This study analyses the potential of pea peptides, LSDRFS and SDRFSY, and amaranth peptides, GGV, IGV, IVG, VGVL, and VIKP, against the SARS-CoV-2 hosts, ACE2 (angiotensin-converting enzyme 2), ACE (angiotensin-converting enzyme), and CD26 (cluster of differentiation 26), and SARS-CoV-2 enzymes, spike glycoprotein and 3CLpro (3-chymotrypsin-like protease). Also, currently used drugs were analysed to contrast drug and peptide behaviour. Employing docking, virtual screening, and molecular dynamics assays, SDRFSY, LSDRFS, and VIKP were detected as potential bioactive peptides by blocking ACE2 and CD26 or reducing the inflammation associated with COVID-19. Enzyme inhibition analyses were also performed, proving the ability of SDRFSY and LSDRFS as ACE2-blocking agents against the spike glycoprotein with inhibition capacities above 80%.
The Perception of Allied Health Professionals on Occupational Therapy
Authors: Wan Yunus Farahiyah , Ahmad Ridhuwan Nuralia Fatiha , Romli Muhammad Hibatullah
Published: March 2022

Occupational therapy is a client-centered health profession and is a part of an interdisciplinary team. Effective interdisciplinary practice occurs when each professional understands the role of another professional in the team. This resulted in optimal referral among the professionals that can benefit the clients to receive good care and service. However, it seems that referrals to occupational therapy are becoming lesser by day in specific settings and hospitals in Malaysia. This brings a perspective if other professions in the healthcare team understand the role of occupational therapists. This study is aimed at exploring the perception and misconceptions of allied health professionals on the roles of occupational therapists. A focus group discussion was conducted among seven allied health professionals using a heuristic approach. The interview data were analyzed thematically. Themes developed were (i) awareness of occupational therapy ecosystem, (ii) in cooperating togetherness for the benefit of the client, and (iii) addressing the limitation to enhance the occupational therapy visibility. Findings found that the knowledge of other healthcare practitioners and clients on the occupational therapist’s role is still limited. Role confusion and overlapping are common among other allied health professionals. Occupational therapy needs to continually promote the profession’s role and identity for the maximum benefit to the client and overall improvement.
Prediction Model for Geologically Complicated Fault Structure Based on Artificial Neural Network and Fuzzy Logic
Authors: Li Ye , Liu Xiao , Yang Zhenliang , Zhang Chao , Song Mingchun , Zhang Zhaolu , Li Shiyong , Zhang Weiqiang
Published: March 2022

The development and distribution of geologically complicated fault structure have the characteristics of uncertainty, randomness, ambiguity, and variability. Therefore, the prediction of complicated fault structures is a typical nonlinear problem. Neither fuzzy logic method nor artificial neural network alone can solve this problem well because the fuzzy method is generally not easy to realize adaptive learning function, and the neural network method is not suitable for describing sedimentary microfacies or geophysical facies. Therefore, taking the marginal subsags in the Jiyang Depression, Eastern China, as a study case, this paper uses the method of combining artificial neural network and fuzzy logic to study geologically complicated fault structure prediction model. This paper expounds on the research status and significance of geologically complicated fault structure prediction model, elaborates the development background, current status, and future challenges of artificial neural networks and fuzzy logic, introduces the method and principle of fuzzy neural network structure and fuzzy logic analysis algorithm, conducts prediction model design and implementation based on fuzzy neural network, proposes the learning algorithm of fuzzy neural network, analyzes the programming realization of fuzzy neural network, constructs complicated fault structure prediction model based on the artificial neural network and fuzzy logic, performs the fuzzy logic system selection of complicated fault structure prediction model, carries out the artificial neural network structure design of complicated fault structure prediction model, compares the prediction effects of the geologically complicated fault structure model based on artificial neural networks and fuzzy logic, and finally discusses the system design and optimization of the prediction model for geologically complicated fault structures. The study results show that the fuzzy neural network fully integrates the advantages of artificial neural network and fuzzy logic system; based on the clear physical background of fuzzy logic system, it effectively integrates powerful knowledge expression ability and fuzzy reasoning ability into the network knowledge structure of neural network, which greatly improves the prediction accuracy of geologically complicated fault structure.
New Progress in Artificial Intelligence Algorithm Research Based on Big Data Processing of IOT Systems on Intelligent Production Lines
Authors: Shi He , Cao Guohua , Ma Guoqing , Duan Jingsong , Bai Jimeng , Meng Xiangyin
Published: March 2022

Intelligent production line is the abbreviation of intelligent production line. Intelligent production line refers to a form of production organization that uses intelligent manufacturing technology to realize the production process of products. The actual manufacturing process includes different levels and links, and each step cooperates to create a high-efficiency production line. The intelligent production line includes 3 levels covering automation equipment, digital workshops, and intelligent factories and runs through 6 major links of intelligent manufacturing (intelligent management, intelligent monitoring, intelligent processing, intelligent assembly, intelligent inspection, and intelligent logistics). The emergence of the Internet of Things system has changed the way of information dissemination. The system combines radio frequency automatic identification and global positioning system technologies to achieve functions such as information exchange and processing, enabling information processing to be intelligent and improving resource utilization. Big data processing includes multiple data processing procedures, but data quality is the most important link in the entire process, and each data processing link will have an impact on the quality of big data. The big data processing process mainly includes data collection, data preprocessing, data storage, data processing and analysis, data display, data visualization, data application, and other links. This article aims to study the new progress of artificial intelligence algorithms for big data processing of IOT systems on intelligent production lines. It is hoped that through the development of intelligent production lines and big data processing technologies, ways to optimize artificial intelligence algorithms can be found. This study proposes a metadata replication method based on a separate replication strategy, which separates the replication process of the data operation log, each is independent, and shortens the data replication time. Combining the existing intelligent production line network platform in the laboratory and carrying out the research of the intelligent production line network state prediction system based on the neural network to design a network prediction system can prejudge the operation status of the intelligent production line network. The experimental results in this article show that when the Namenode mode is used to read data and when the number of clients reaches 8, the data processing basically remains unchanged. When the NCluster system reads data and when the number of clients is 6, the data is processed 1256. When the number of clients is 20, the data is processed 2100, the NCluster system will remain stable when the number of clients reaches 12, and compared with the Namenode system, it has obvious advantages.
Path Planning of Industrial Wheeled Robots Based on Wireless Communication and Machine Learning Algorithms
Authors: Li Jingmin
Published: March 2022

With the advancement of science and technology, robotics has made considerable progress. Robots can free humans from heavy repetitive labor. From the industrial field to the lives of the general public, robots are playing an increasingly important role. Path planning is one of the core contents of industrial wheeled robotics and has very important significance. Based on the reinforcement Q learning and BP network, this work studies the path planning of industrial wheeled robots. According to task requirements of path planning, design learning strategies, and control rules, use wireless communication to transmit environmental perception information and propose corresponding control strategies. The main researches are as follows: (1) Based on grid map environment, a path planning algorithm with Q-CM learning is designed. The algorithm first designs robot states and actions based on reinforcement Q learning and grid map and establishes Q matrix. Secondly, a coordinate matching (CM) obstacle avoidance control rule is designed to improve the efficiency of robot avoidance. Then, a reward function is designed for the evaluation problem of action execution. (2) Based on the map environment of free space and the generalization ability of BP neural network, a robot path planning with Q-BP learning is designed. The algorithm first designs the sensor detection mechanism and action selection strategy according to the state of the robot in the map environment. Secondly, a dynamic reward function is designed. Then, according to the obstacle avoidance requirements of special obstacles, the obstacle avoidance rules after three shocks were designed. The experimental results show robots can perform better path planning in a discrete and continuous free space map, and the obstacle avoidance effect is good.
Wear Analysis of Support Spring of Sprag Clutch during State of Overrunning
Authors: Yan Hongzhi , Wen Bo , Wang Zhibiao , Zhu Chu , Ni De , Lin Minghao
Published: March 2022

This paper analyzes the wear of the support spring of sprag clutches for different structural parameters during overrunning state. Specifically, the stiffness model of the support spring is established based on the characteristics of the variable helix angle, and also the supporting force model is established according to the energy conservation law during the working process of the sprag clutch. Meanwhile, the relative rotation speed between the support spring and the wedge is analyzed by dynamic simulation method. Furthermore, the wear model of the support spring in process of overrunning is established based on Archard wear theory. The results show that there is a linear relationship between supporting force and spring radial displacement compression. In addition, increasing free length and wire diameter of springs will aggravate the wear of springs, whereas enlarging pitch diameter of springs will reduce it. When free length is increased from 270 to 290 mm, the wear time is reduced by 20.88%; when wire diameter is increased from 0.5 to 0.7 mm, the wear time is reduced by 42.75%; when pitch diameter is increased from 2 to 2.4 mm, the wear time is increased by 3.35%. The degree of influence of the structural parameters on the spring wear from large to small is free length, wire diameter, and pitch diameter. Exactly speaking, when wear time is 100 hr, the relative sensitivity of structural parameters affecting the wear of the support spring is in order of free length (2.2), wire diameter (0.97), and pitch diameter (0.08). The research provides a reference for the design and optimization of the support spring for highly reliable sprag clutches.
Carbon Transfer Decision Model Based on LMDI Method
Authors: Qi Yawei , Rao Guangping , Zha Lei , Chen Lu , Niu Yuping
Published: March 2022

Establishing a coordinated governance mechanism for regional carbon emissions is an essential way to achieve carbon peak and carbon neutrality, while the study of interprovincial carbon emissions transfer is one of the important foundations of regional carbon emissions coordinated governance research. Based on the multiregional input-output (MRIO) model, this study calculated the carbon emissions from both the producers’ perspective and the consumers’ perspective and analyzed the interprovincial net carbon emissions transfer decision. Furthermore, the logarithmic mean Divisia index (LMDI) method was adopted to decompose the factors that affect the province’s net carbon emissions into technological effect, structural effect, input-output effect, and scale effect. It was revealed that the input-output effect was the primary influencing factor of the net carbon transfer at the provincial level.
Investigation of Mechanical and Physical Behaviours of Polyester Resin Matrix from Recycled Polyethylene Terephthalate with Bamboo Fibre
Authors: Kaliappan Nandagopal , Govindarajan Venkatesan , Anil Kumar T CH , Vishnu Kumar R. , Muthukumaran S. , Ahamed Maqusood , Mahadik Mahadeo A. , Markos Mebratu
Published: March 2022

In this research article, we investigate the physical and mechanical properties of composites comprised of unsaturated polyester resin (UPR) and recycled polyethylene terephthalate (PET) with 10% to 40% volume of bamboo fibre (BF). Chemical evaluation of BF revealed that BF has a cellulose content of 49.86%, hemicellulose content of 25.17%, and lignin content of 7.14%. As the UPR’s different connections, FTIR identified an interconnecting framework between the styrene monomer (ST) and the unsaturated polyester (UP). It was found by TGA-DTG that there were two breakdown phases. UPR’s physical and mechanical properties were found to be affected by increasing the amount of fibre in the material, with the water absorption rising from 0.7% to 2.81% and the density (1214.38 to 1168.83 kg/m), flexural strength (51.81 to 28.92 MPa), flexural modulus (2.78 to 2.83 GPa), and tensile strength (9.71 to 3.86 MPa) all decreasing at the same time. On the other hand, the hardness increased from 82.4 Shore D to 67.9 Shore D. Fibre distribution flaws in the UPR were found, affecting the composites' mechanical characteristics. By repurposing two waste products, this study helps create new materials that are better for the surroundings.
A Study of Blast Vibration Propagation Law under Negative Altitude Terrains
Authors: Gao Jing , Huang Cong , Huang Xiaomin , Ren Jian-Jun , Wang Na
Published: March 2022

The propagation characteristics of blast waves and the prediction accuracy of blast vibration velocities along negative slopes are important because those can be used to guide engineering application and theoretical research. In this paper, the wave theory was applied to better understand the propagation mechanism of blast waves along negative slopes. Regression analysis was used for the Sadovsky and the CRSRI blast vibration velocity prediction models during on-site operations. The magnification of peak blast vibration velocity along a negative slope was introduced to determine the threshold altitude difference for the magnification effect to occur. Based on this parameter, the relative errors between the two prediction models were compared. The obtained results indicate that the superposition of incident and reflected blast waves on a negative slope creates the “slope effect” which locally amplifies the blast vibration velocity. The relative error of the CRSRI prediction model was as small as 17.53%, demonstrating a greater accuracy than Sadovsky’s prediction model. The magnification effect of a negative slope was observed at specific altitude differences and was more noticeable in the perpendicular direction. This paper creates a theoretical basis for studying the propagation mechanisms of blast vibration waves along negative slopes as well as predicting the blast vibration velocities.
Design and Exploration of College English Reading, Writing, and Translation Teaching Classroom Based on Machine Learning
Authors: Ruan Xushan
Published: March 2022

The aims are to ameliorate the dull classroom atmosphere and unsatisfactory teacher-student interaction and cultivate students’ reading, writing, and translation (R-W-T) English proficiency level (EPL). Specifically, this paper designs an optimized polynomial kernel-based support vector machine (SVM) classification algorithm based on the machine learning (ML) theory. Firstly, this paper expounds on the correlation between ML and teaching classrooms to analyze the optimization direction in the application of ML in classroom teaching. Afterward, SVM and RF algorithms are selected for data normalization optimization, and their optimal hyperparameters are analyzed. Consequently, an experiment is designed to compare the classification accuracy of the algorithms before and after optimization, and the polynomial kernel-based SVM algorithm is proved to present the most remarkable improvement and accuracy after optimization, which is as high as 95.23% or 66.96% improvement. Therefore, the polynomial kernel-based SVM algorithm is chosen for the college English (R-W-T) classroom-oriented human pose recognition (HPR) system. Thus, English teachers can better grasp the students’ psychological state and classroom atmosphere and ameliorate unsatisfactory teacher-student interaction in students’ English (R-W-T). The proposal plays a positive role in cultivating college students’ strong (R-W-T) EPL, which is of great significance in improving the English classroom.
Flexible Light Field Angular Superresolution via a Deep Coarse-to-Fine Framework
Authors: Wang Qian , Fang Li , Ye Long , Zhong Wei , Hu Fei , Zhang Qin
Published: March 2022

Acquisition of densely-sampled light fields (LFs) is challenging. In this paper, we develop a coarse-to-fine light field angular superresolution that reconstructs densely-sampled LFs from sparsely-sampled ones. Unlike most of other methods, which are limited by the regularity of sampling patterns, our method can flexibly deal with different scale factors with one model. Specifically, a coarse restoration on epipolar plane images (EPIs) with arbitrary angular resolution is performed and then a refinement with 3D convolutional neural networks (CNNs) on stacked EPIs. The subaperture images in LFs are synthesized first horizontally, then vertically, forming a pseudo 4DCNN. In addition, our method can handle large baseline light field without using geometry information, which means it is not constrained by Lambertian assumption. Experimental results over various light field datasets including large baseline LFs demonstrate the significant superiority of our method when compared with state-of-the-art ones.
Artificial Intelligence Monitoring System Using ZigBee Wireless Network Technology in Warehousing and Logistics Innovation and Economic Cost Management
Authors: Zhao Lingjiang , Xu Yufa
Published: March 2022

The study expects to reduce the development cost and energy consumption of the storage environment monitoring and improve the efficiency of the storage monitoring environment. Firstly, the storage environment monitoring system is designed in detail using ZigBee. It includes the design of terminal nodes, router nodes, coordinator nodes, and upper computer software. Here is a detailed description of the process of establishing a communication network for three different types of devices in the ZigBee network. The address allocation mechanism of the ZigBee network is introduced. Then, through the application of activity-based costing, the cost reduction is achieved as expected. Finally, the evaluation model of the storage logistics cost level is established, and the various reasons that affect the storage logistics cost are analyzed. The completed warehouse environment monitoring system is a tree topology network composed of a ZigBee coordinator, several ZigBee routing nodes, and terminal devices through the above design. The coordinator in the network is responsible for establishing the network, and it can communicate with the host computer through the serial port. The terminal node collects environmental parameter information and feeds the data back to the ZigBee coordinator through wireless multihop. The ZigBee routing node is mainly used to expand the range of the network to achieve the purpose of wireless monitoring of the entire storage environment. This provides a reference for the combination of wireless communication technology and sensor technology.
Training Program to Modify Manual Wheelchairs to Simplified Power Wheelchairs for Community Dwelling Elderly People and Caregivers
Authors: Lersilp Suchitporn , Putthinoi Supawadee , Lersilp Theeratorn , Panyo Kewalin , Punyakaew Autchariya
Published: March 2022

Mobility aids, particularly power wheelchairs, are necessary for elderly individuals who have health problems and disabilities. However, there is a limitation in providing power wheelchairs for such people in the community. The objectives of this study were to develop a prototype for a simplified power wheelchair and develop and evaluate a training program that has the potential to encourage evaluation and modification of the wheelchair for the elderly and their caregivers in the community. Twenty-four participants consist of elderly people and caregivers who were interested in the training program that comprised two sessions: theory and fieldwork experience. Results showed that the elderly people and caregivers, who had no knowledge or experience of wheelchair modification, were able to learn and provide suggestions for wheelchair users in their community. Two themes emerged from evaluating the training program, which included “benefits from the training program” and “improvement of the training program in the future.” Key concepts were elicited and considered in six categories: sufficient knowledge and practical learning to build confidence, values of fieldwork experiences, team support, organizational support, expansion of various contexts, and system of continued connection and services after training.
Aldehyde Dehydrogenase 1 in Gastric Cancer
Authors: Wang Ling , Wang Luosha , Yu Yang , Su Ruiliang , Zhang Yating , Li Yumin , Zang Mingde
Published: March 2022

Gastric cancer (GC) is a disease that threatens human health. It is thus crucial to clarify the mechanisms involved in GC development and discover diagnostic biomarkers and therapeutics. As a cancer stem cell marker, aldehyde dehydrogenase 1 (ALDH1) is involved in the development, progression, and treatment of GC. This review evaluated the prognostic value of ALDH1 and explored its mechanism of action in GC. Importantly, ALDH1 is an informative biomarker in clinical practice as it has specific relationships with indicators, such as metastasis and overall survival. Additionally, ALDH1 interacts with genes and exhibits properties that mimic stem cell characteristics amongst other mechanisms employed in the occurrence and progression of GC. Our results, therefore, provide evidence of possible clinical utility of ALDH1 as a GC therapeutic target.