bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling Decision-Making in Schizophrenia: Associations Between Computationally Derived Risk Propensity and Self-Reported Risk Perception
Authors: Herms Emma , Brown Joshua W. , Wisner Krista , Hetrick William P. , Zald David , Purcell John R.
Published: September 2023

Schizophrenia is associated with decreased pursuit of risky rewards during uncertain-risk decision-making. However, putative mechanisms subserving this disadvantageous risky reward pursuit, such as contributions of cognition and relevant traits, remain poorly understood. Participants (30 schizophrenia/schizoaffective disorder [SZ]; 30 comparison participants [CP]) completed the Balloon Analogue Risk Task (BART). Computational modeling captured subprocesses of uncertain-risk decision-making: Risk Propensity, Prior Belief of Success, Learning Rate, and Behavioral Consistency. IQ, self-reported risk-specific processes (i.e., Perceived Risks and Expected Benefit of Risks), and non-risk specific traits (i.e., defeatist beliefs; hedonic tone) were examined for relationships with Risk Propensity to determine what contributed to differences in risky reward pursuit.On the BART, the SZ group exhibited lower Risk Propensity, higher Prior Beliefs of Success, and comparable Learning Rates. Further, Risk Propensity was positively associated with IQ across groups. Linear models predicting Risk Propensity revealed two interactions: one between group and Perceived Risk, and one between IQ and Perceived Risk. Specifically, in both the SZ group and individuals with below median IQ, lower Perceived Risks was related to lower Risk Propensity. Thus, lower perception of financial risks was associated with less advantageous pursuit of uncertain-risk rewards.Findings suggest consistently decreased risk-taking on the BART in SZ may reflect risk imperception, the failure to accurately perceive and leverage relevant information to guide advantageous pursuit of risky rewards. Additionally, our results highlight the importance of cognition in uncertain-risk decision-making.
Students’ Decision-Making at the University Level: A Phenomenological Study
Authors: Vaidya Prabina Rajbhandari , Acharya Khagendra
Published: September 2023

Students’ decision making is a dynamic and complex process. A number of quantitative studies have identified the elements that play their role in this process, particularly in the contexts of the developed regions. However, in the developing countries, especially in the countries of South-Asian region where collectivist societies exist, such studies occur in a very negligible number. To fill this gap, the present study explored the elements that are perceived to affect undergraduate and graduate students’ decision making on academic as well as non-academic contexts. Given that today’s students are tomorrow’s leaders, understanding their decision making may contribute in anticipating the nature of decisions they would make in the future. To understand this phenomenon, interpretive phenomenological analysis was used. The study found two major themes – personal elements, and external elements. The discussion on these themes and their sub-themes highlight that students make decisions centered on consequences such as loss of position and prestige, fear of individual punishments. Students decision making were also found to reiterate the theory of utilitarianism and relative truth theory.
PERANCANGAN GEDUNG PUSAT PENDIDIKAN BAHASA DI KOTA SAMARINDA
Authors: Prasetyo Kukuh
Published: September 2023

ABSTRACT The foreign language skills of both students and students in Samarinda are still below average because of the mixing of contemporary language trends, making them forget a good and correct grammar. The ability to be able to speak foreign languages in the world of work and education is needed, therefore students and students are required to master a foreign language, coupled with the existence of student exchange programs from within and outside the country that require foreign language skills. However, the current problem is that the learning institutions in the city of Samarinda are quite large, such as Primagama, Briton, Slikey, providing subjects according to the existing subjects at school and focusing on the subjects tested on graduation exams and college entrance exams, so this is a concern for students. and Samarinda city students who want to learn foreign languages The application of the concept of organic architecture in educational buildingscan show how educational buildings harmonize with nature without destroying the function of the building itself, can last all the time with its dynamic form, harmonious with nature,and functional towards the function of the building. Keywords: Language Education Center Building, Samarinda, Organic Architecture
PERENCANAAN PASAR LOAK DI KOTA SAMARINDA
Authors: Hidayat Defriandy
Published: September 2023

Samarinda City, is the capital of the province of East Kalimantan, Indonesia and the city with the largest population on the entire island of Kalimantan with a population of 812,597 people. Samarinda has an area of 718 km2 with hilly geographical conditions with altitudes varying from 10 to 200 meters above sea level. In big cities and regencies in Indonesia, especially in Samarinda, flea markets are often found. Some people make it a hunting ground for used goods that are hard to find on the market, which is located at the Segiri market on Jl. Heroes, Sidodadi, Samarinda City, East Kalimantan. This is the main market that carries out loading and unloading activities in the city of Samarinda. From the description above, the flea market is located in a traditional market that is not planned, resulting in not being neatly organized, looking shabby, unkempt, and uncomfortable. So that people find it difficult to find the items they are looking for, and access to the place is always jammed and parking is difficult when getting in and out of the vehicle. Based on the problems that occur above at this time, a new building is needed for a flea market in the city of Samarinda. the need for a better quality building or place such as a large parking lot, a neat building arrangement and a kiosk or stall that has been arranged according to the types of goods to be sold and based on aspects of postmodern architecture. which can be the center point of orientation and its presence can indicate the characteristics, image or image of an area or land mark.Keywords: Planning, Flea Market, Post Modern Architecture.
A RETROSPECTIVE EVALUATION OF GRAMMAR & VOCABULARY TEACHING PRACTICES FOR MORE EFFECTIVE FOREIGN LANGUAGE EDUCATION
Authors: ÖZGÜR KÜFİ Elmaziye
Published: September 2023

As grammar was once perceived to be the essence of language instruction, for many years foreign language teachers have emphasized grammar rules in their English lessons. This practice was first challenged by text linguists who claimed that the context, the mind or achieving meaning are significant factors that need to be considered in language teaching. The second challenge came from researchers who conducted corpus studies. Their findings, based on the analysis of large sizes of corpora, started the ongoing ‘grammar’ or ‘lexis’ debate which is still valid today. This study investigates these two opposing views by providing a historical review of grammar and vocabulary teaching practices in foreign language education.   By sharing different views related to the ‘grammar over lexis’ or ‘lexis over grammar’ dilemma, this reflective review discusses that grammar and vocabulary teaching should have equal importance in language classes in light of recent developments in foreign language education. To this end, this article discusses that in this era undertaking a lexico-grammatical approach, which blends grammar and vocabulary teaching with holistic attitudes, is a prerequisite for evidence-based contemporary foreign language teaching practices.
CLASSROOM MANAGEMENT QUALIFICATION OF SPECIAL EDUCATION TEACHERS WORKING IN SPECIAL EDUCATION SCHOOLS
Authors: KAYA Dilek
Published: September 2023

This research was conducted to determine the classroom management competencies of special education teachers working in special education schools. The study group of the research consists of 25 special education teachers working in three different types of special education schools in Üsküdar district of Istanbul. The participants of the study were determined by the maximum diversity method. The data were obtained with a semi-structured interview form developed by the researcher by taking expert opinion. The following questions were asked to the teachers: “As a teacher working in special education schools, what is your competence in managing the physical environment?” “As a teacher working in special education schools, what is your ability to carry out education and training in a healthy way?” “As a teacher working in special education schools, what is your ability to use time effectively and efficiently?” “As a teacher working in special education schools, what is your competence in communicating effectively with students?”, “What is your competence in managing student behavior as a teacher working in special education schools?” questions were posed. According to the research, it has been observed that the most important issue that teachers pay attention to while managing the physical environment is the characteristics such as the type of disability and behavioral problems of the students. It has been observed that the way for teachers to carry out education and training in a healthy way is to determine the performance of students, to prepare individualized education plans, activities and materials suitable for their performance and individual differences. It has been determined that teachers prepare lesson plans, activities, methods, techniques and materials in advance in order to use time effectively and efficiently while managing the classroom. It has been determined that the teachers determine the communication method according to the student's characteristics in terms of communicating effectively with the students. It has been observed that teachers prepare behavior change plans while managing student behaviors, use reinforcements for the continuity of positive behaviors, use methods such as ignoring to extinguish negative behaviors, and also determine the classroom rules. According to the findings, the most common problems in classroom management were determined as "student's behavior problems", "inadequate physical conditions of the classroom", "program density", "different learning levels of the students ", "lack of family support". In the classroom management process, the physical conditions of the classrooms should be improved, equipment deficiencies should be eliminated, and material diversity should be increased. Teachers should be supported, and the content and number of in-service trainings should be increased for the development of teachers.
ابن هشام اللخمي وتوجيهاته في الإبدال وحركة بنية الألفاظ في ردّه على ابن مكي الصقلي
Authors: بالخير أحمد عبدالرحمن
Published: September 2023

يتمحور هذا البحث حول دراسة التوجيهات الصوتية لابن هشام اللخميّ في كتابه (المدخل إلى تقويم اللسان وتعليم البيان)، فيما ردّ فيه على ابن مكيّ الصقليّ في كتابه (تثقيف اللسان وتلقيح الجنان).
حيث صوّر ابن مكيّ الصقليّ حال العربية في صقلية في القرن الخامس الهجري، ولحّنَ عامّة زمانه وخطّأهم فيما استعملوه.
وذهب ابن هشام اللخميّ إلى أنّ كثيراً مما لحّنَ فيه ابن مكيّ عامّة أهل صقلية إنّما هو صحيح وليس بلحن، ووصف ما ذكره ابن مكيّ بأنه أوهام.
ومن هنا فإن هذا البحث يهدف إلى حصر الظواهر الصوتية لعربية أهل صقلية في القرن السادس الهجري من خلال تناول نماذج ممّا ذكره ابن هشام اللخميّ في كتابه السابق الذكر، ومعرفة موقفه من تلحين ابن مكيّ لهم في تلك الظواهر الصوتية.
La traducción del chiste cultural del español al árabe
Authors: Hameed Sura , Jasim Reyadh
Published: September 2023

El presente trabajo se centra en la traducción de los chistes de componente cultural del español al árabe, intentamos demostrar en qué medida el chiste cultural puede ser traducido al árabe, y probar la eficacia de las estrategias propuestas por los autores y lingüistas para traducir el chiste cultural, al aplicarlas en la traducción de chistes españoles al árabe. Para hacer nuestro trabajo más elaborado, hemos incluido un conjunto de chistes en la parte práctica donde hemos aplicado los métodos de traducción correspondientes para cada ejemplo de chistes, estos ejemplos los hemos obtenido de diferentes sitios en la Web, por la falta de fuentes o libros que incluyen todos los elementos culturales que queremos exponer en nuestro estudio.  Después de la traducción determinamos si la estrategia ha tenido éxito en transmitir el mensaje del chiste original y en producir un efecto humorístico equivalente.
Comparison of Robotic vs Open Cystectomy: A Systematic Review
Authors: Sathianathen Niranjan J. , Pan Henry Y.C. , Furrer Marc , Thomas Benjamin , Dundee Philip , Corcoran Niall , Weight Christopher J. , Konety Badrinath , Nair Rajesh , Lawrentschuk Nathan
Published: September 2023

BACKGROUND:

The benefits of a robot-assisted radical cystectomy (RARC) compared to an open approach is still under debate. Initial data on RARC were from trials where urinary diversion was performed by an extracorporeal approach, which does not represent a completely minimally invasive procedure. There are now updated data for RARC with intracorporeal urinary diversion that add to the evidence profile of RARC.

OBJECTIVE:

To perform a systematic review and meta-analysis of the effectiveness of RARC compared with open radical cystectomy (ORC).

MATERIALS AND METHODS:

Multiple databases were searched up to May 2022. We included randomised trials in which patients underwent RARC and ORC. Oncological and safety outcomes were assessed.

RESULTS:

Seven trials of 907 participants were included. There were no differences seen in primary outcomes: disease progression [RR 0.98, 95% CI 0.78 to 1.23], major complications [RR 0.95, 95% CI 0.72 to 1.24] and quality of life [SMD 0.05, 95% CI -0.13 to 0.38]. RARC resulted in a decreased risk of perioperative blood transfusion [RR 0.57, 95% CI 0.43 to 0.76], wound complications [RR 0.34, 95% CI 0.21 to 0.55] and reduced length of hospital stay [MD -0.62 days, 95% CI -1.11 to -0.13]. However, there was an increased risk of developing a ureteric stricture [RR 4.21, 95% CI 1.07 to 16.53] in the RARC group and a prolonged operative time [MD 70.4 minutes, 95% CI 34.1 to 106.7]. The approach for urinary diversion did not impact outcomes.

CONCLUSION:

RARC is an oncologically safe procedure compared to ORC and provides the benefits of a minimally invasive approach. There was an increased risk of developing a ureteric stricture in patients undergoing RARC that warrants further investigation. There was no difference in oncological outcomes between approaches.
Effects of breathing exercises on chronic low back pain: A systematic review and meta-analysis of randomized controlled trials
Authors: Jiang Xiaoyu , Sun Wenyu , Chen Qiang , Xu Qiling , Chen Guoming , Bi Hongyan
Published: September 2023

BACKGROUND: A range of studies concerning the effects of breathing exercises on chronic low back pain (CLBP) have been proven inconclusive. OBJECTIVE: The study aimed to evaluate the effectiveness of breathing exercises for the treatment of CLBP. METHODS: We considered randomized controlled trials in English or Chinese that used breathing exercises for the treatment of CLBP. An electronic search was performed in the MEDLINE, EMBASE, Web of Science, Cochrane Library, CNKI, Wan Fang, and CBM databases for articles published up to November 2022. Two reviewers independently screened the articles, assessed the risk of bias using the Cochrane risk of bias tool, and extracted the data. The outcomes included pain, lumbar function and pulmonary function post-intervention. RESULTS: A total of thirteen studies (n= 677) satisfied the inclusion criteria. The meta-analysis results demonstrated a significant effect of breathing exercises on the Visual Analog Scale (VAS) score (SMD =-0.84, 95% CI: -1.24 to -0.45, P< 0.0001), the Oswestry Disability Index (ODI) score (SMD =-0.74, 95% CI: -0.95 to -0.54, P< 0.00001), Forced Vital Capacity (FVC) score (MD = 0.24, 95% CI: 0.10 to 0.37, P= 0.0006), Forced Expiratory Volume in 1 second /Forced Vital Capacity (FEV1/FVC) (MD = 1.90, 95% CI: 0.73 to 3.07, P= 0.001), although there was no significant difference between the breathing exercises and control interventions for Forced Expiratory Volume in the first second (FEV1) score (MD = 0.22, 95% CI = [0.00, 0.43], P= 0.05), and Maximal Voluntary Ventilation (MVV) score (MD = 8.22, 95% CI = [-4.02, 20.45], P= 0.19). CONCLUSION: Breathing exercises can reduce pain, assist people with lumbar disabilities, and improve pulmonary function, and could be considered as a potential alternative treatment for CLBP.
Blood-based protein biomarkers and red blood cell aggregation in pancreatic cancer
Authors: Wiewiora Maciej , Jopek Janusz , Świętochowska Elżbieta , Sławomir Gregorczyn , Piecuch Jerzy , Gąska Mateusz , Piecuch Jerzy
Published: September 2023

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is highly malignant with a low 5-year survival rate. Blood biomarkers may be of value for the noninvasive diagnosis of pancreatic cancer. OBJECTIVE: This study assessed blood-based biomarkers and disturbances in red blood cell aggregation associated with pancreatic cancer. METHODS: We studied 61 patients who underwent pancreatic resection. Of these 61 patients, 46 patients had PDAC, and 15 patients had inflammatory tumours. Serum VEGF, hypoxia-inducible factor (HIF-1α), elastin-derived peptides (EDPs), total sialic acid (TSA) and resistin levels were measured. Red blood cell aggregation was assessed by a laser-assisted optical rotational cell analyser. RESULTS: VEGF (p < 0.000001), HIF-1α (p = 0.000002), resistin (p = 0.000349), EDP (p = 0.000089) and TSA (p = 0.000013) levels were significantly higher in the PDAC group than in the inflammatory tumour group. The aggregation index (AI), syllectogram amplitude (AMP) and threshold shear rate (γthr) were significantly higher in the PDAC group, whereas the aggregation half-time (t1/2) was lower than in the inflammatory tumour group. Multivariate analyses revealed that VEGF, TSA and EDP levels were variables that predicted PDAC. VEGF levels were the most powerful predictor of PDAC independent of CA 19-9 levels. The cut-off points for VEGF, TSA and EDP levels were 134.56 pg/ml, 109.11 mg/dl and 36.4 ng/ml, respectively, with sensitivities of 97.8%, 87% and 69.6%, respectively, and specificities of 86.7%, 86.7% and 93.3%, respectively. CONCLUSION: This study indicated that there are significant differences in blood-based biomarkers for differentiating between PDAC and inflammatory tumours of the pancreas. We also confirmed that PDAC is associated with the excessive aggregation of RBCs.
Algorithmically random series
Authors: Downey Rodney G. , Greenberg Noam , Tanggara Andrew
Published: September 2023

Rademacher (Mathematische Annalen 87 (1922) 112–138), Steinhaus (Mathematische Zeitschrift 31 (1930) 408–416) and Paley and Zygmund (Mathematical Proceedings of the Cambridge Philosophical Society 26 (1930) 337–257, Mathematical Proceedings of the Cambridge Philosophical Society 26 (1930) 458–474, Mathematical Proceedings of the Cambridge Philosophical Society 28 (1932) 190–205) initiated the extensive study of random series. Using the theory of algorithmic randomness, which is a mix of computability theory and probability theory, we investigate the effective content of some classical theorems. We discuss how this is related to an old question of Kahane and Bollobás. We also discuss how considerations of such algorithmic questions about random series seem to lead to new notions of algorithmic randomness.
A multi-domain adaptive neural machine translation method based on domain data balancer
Authors: Xu Jinlei , Wen Yonghua , Huang Shuanghong , Yu Zhengtao
Published: September 2023

Most methods for multi-domain adaptive neural machine translation (NMT) currently rely on mixing data from multiple domains in a single model to achieve multi-domain translation. However, this mixing can lead to imbalanced training data, causing the model to focus on training for the large-scale general domain while ignoring the scarce resources of specific domains, resulting in a decrease in translation performance. In this paper, we propose a multi-domain adaptive NMT method based on Domain Data Balancer (DDB) to address the problems of imbalanced data caused by simple fine-tuning. By adding DDB to the Transformer model, we adaptively learn the sampling distribution of each group of training data, replace the maximum likelihood estimation criterion with empirical risk minimization training, and introduce a reward-based iterative update of the bilevel optimizer based on reinforcement learning. Experimental results show that the proposed method improves the baseline model by an average of 1.55 and 0.14 BLEU (Bilingual Evaluation Understudy) scores respectively in English-German and Chinese-English multi-domain NMT.
Hybrid unstructured text features for meta-heuristic assisted deep CNN-based hierarchical clustering
Authors: Jyothi Bankapalli , Sumalatha L. , Eluri Suneetha
Published: September 2023

The text clustering model becomes an essential process to sort the unstructured text data in an appropriate format. But, it does not give the pave for extracting the information to facilitate the document representation. In today’s date, it becomes crucial to retrieve the relevant text data. Mostly, the data comprises an unstructured text format that it is difficult to categorize the data. The major intention of this work is to implement a new text clustering model of unstructured data using classifier approaches. At first, the unstructured data is taken from standard benchmark datasets focusing on both English and Telugu languages. The collected text data is then given to the pre-processing stage. The pre-processed data is fed into the model of the feature extraction stage 1, in which the GloVe embedding technique is used for extracting text features. Similarly, in the feature extraction stage 2, the pre-processed data is used to extract the deep text features using Text Convolutional Neural Network (Text CNN). Then, the text features from Stage 1 and deep features from Stage 2 are all together and employed for optimal feature selection using the Hybrid Sea Lion Grasshopper Optimization (HSLnGO), where the traditional SLnO is superimposed with GOA. Finally, the text clustering is processed with the help of Deep CNN-assisted hierarchical clustering, where the parameter optimization is done to improve the clustering performance using HSLnGO. Thus, the simulation findings illustrate that the framework yields impressive performance of text classification in contrast with other techniques while implementing the unstructured text data using different quantitative measures.
A novel multi-level clustering mechanism using heuristic approach for secure data transmission in WSN sector with various trust computation
Authors: Hareesha D. , Ahamed Shaik Fayaz , Priyanka Brahmaiah V. , Kalpana Dingari , Arun M. , Satyanarayana P.
Published: September 2023

Several sensor nodes are used in Wireless Sensor Networks (WSNs). A multi-level clustering-based multi-trust model is introduced in WSN. The model’s main intent is to compute trust value for performing secure transmission. Initially, to verify vulnerability, the watchdog counter provides the required trust output. Further, this is intended to build a multi-level trust clustering process. Here, multi-trust is carried out by energy trust, communication trust, and data trust. Hence, multi-trust is compared with a threshold value. Once the trust value is generated, it is given for processing the cluster groups. Due to more multi-trusting, it creates multi-level clustering for security level enhancement. After the cluster group is formed, the major aspect of CH is optimally obtained with a Modified Exploration-based Pelican Optimization Algorithm (ME-POA). Finally, performance is carried out in multi-objective functions, where parameters are defined as distance, delay, energy, and multi-trust. Thus, with the determination of optimal results, the proposed multi-level clustering proves that it offers secure data transmission over WSN.
Taobao transaction data mining based on time series evaluation under the background of big data
Authors: Zhang Yanmin
Published: September 2023

With the emergence of e-commerce, more and more people conduct transactions through the Internet, thus resulting in a large number of transaction data. Data mining is to decompose a large amount of data according to data rules, and analyze network transaction data, so as to provide necessary digital links for companies to analyze the market and develop business. Although time series data mining is smaller than other types of data mining, it is also an important issue of data mining. In the real world, the correlation between data and time is very common. The study of time series model plays a very important role in data mining. Due to different purposes, Taobao data analysis is also different. In addition to statistics, at present, the in-depth research and analysis of Taobao data are relatively insufficient, and the analysis of Taobao transaction data based on time series is rare. In order to improve the accuracy of Taobao transaction data mining and better formulate Taobao marketing strategy, this paper used time series data mining technology to mine Taobao transaction data. This paper first introduced the role of Taobao transaction data mining, and then described the calculation method of time series data mining, including the re-description of time series and the similarity measurement of time series. Finally, through a series of processes such as data collection, data processing and data feature extraction, the data mining model for Taobao transaction was established, and two data prediction evaluation indicators, namely prediction accuracy and entropy, were proposed. The experimental part verified the effect of Taobao transaction data mining. The experimental results showed that the data mining model moment had good data prediction accuracy and entropy. The average data prediction accuracy was 94.26%, and the data mining ability was strong.
A novel dataset and local interpretable model-agnostic explanations (LIME) for monkeypox prediction
Authors: Sharma Nonita , Mohanty Sachi Nandan , Mahato Shalini , Pattanaik Chinmaya Ranjan
Published: September 2023

In image-based diagnosis, machine learning recently showed great potential, particularly in the detection of cancer, the identification of tumour cells, and the diagnosis of COVID-19. Similar methods could be used to detect monkeypox on human skin, however there isn’t a public dataset with data on monkeypox that can be used to train and evaluate machine learning models. In order to address this, the dataset “Monkeypox2022” has been created and made available on GitHub (https://github.com/Angmo21720/data-set). Images from internet and open-source sources were used to construct the collection; these sources did not impose any restrictions on use, including commercial use. Additionally, the enhanced VGG16 model was proposed and ran two Studies for comparison. According to the findings, the model’s AUC Score for identifying monkeypox patients in Study One was 0.972, while its AUC Score for identifying such patients in Study Two was 0.748. Deeper understanding of the characteristics that distinguish a monkeypox infection is provided by using Local Interpretable Model-Agnostic Explanations (LIME), which is used to decipher predictions and reveal the features utilized by a model in decision-making.
Heuristic approach for bandwidth consecutive multi-coloring problem
Authors: Srivastava Stuti , Bansal Richa , Thapar Antika
Published: September 2023

Bandwidth consecutive multicoloring problem is also known as a b-coloring problem. Let G = (V, E) be a graph where V is the set of vertices and E is the set of edges. Let each vertex v of V has a positive integer weight b (v) and each edge (v, w) of E has a non-negative integer weight b (v, w). A bandwidth consecutive multicoloring of a graph is a problem of assigning b (v) consecutive positive integers to each vertex v of V in such a manner that the difference between all the integers of vertex v and all the integers of vertex w is greater than b (v, w). The maximum integer assigned in this coloring is called the span of the coloring. The b-coloring is the problem of minimizing this span. No metaheuristic is proposed for general graphs so far for this problem till date as it is strongly NP-hard. In this paper, we proposed three heuristics for the problem including a greedy randomized adaptive search procedure (GRASP). The efficiency of these algorithms is tested on benchmark graphs and their performance is compared among themselves. Experimental results showed that among all three proposed heuristics, GRASP performed well for the given problem.
Heterogeneous domain adaptation method for tomato leaf disease classification base on CycleGAN
Authors: Cho Seung-Beom , Jeong Si-Hwa , Yu Jae-Wook , Choi Jae-Boong , Kim Moon Ki
Published: September 2023

Despite the significant improvements in the detection and diagnosis of plant diseases at an early stage facilitated by deep learning technology, there are challenges associated with the generalization performance of deep learning models. These problems from the differences between in-field and in-lab data, as well as the heterogeneity of training and prediction data features. In the case of tomato leaf diseases, the PlantVillage dataset is widely used and has already demonstrated accuracy of more than 99%. However, using trained model based on this dataset to predict in-field data results in low accuracy due to domain differences and heterogeneous features. In this paper, we propose a domain adaptation method based on CycleGAN to solve this problem, followed by a preprocessing technique that utilizes both the OpenCV module and a segmentation model based on U-Net for the best generalization performance. The classification accuracy is evaluated by applying the DenseNet121 model trained on the PlantVillage dataset to the images generated by CycleGAN. Our results demonstrate, with an F1-score of 95.6%, that our domain adaptation method between the two domains is effective in mitigating the effect of domain shift.
Optimization of supervision mechanism for public cooperation considering limited supervision ability and heterogeneous preferences
Authors: Guo Peng , Wang Xiaonan , Zhang Duo
Published: September 2023

Punishment promotes cooperation among selfish agents. Unlike previous studies, we propose a new supervision (heterogeneous preference supervision, HPS) mechanism based on the original random supervision (ORS) mechanism considering regulators’ limited supervision ability and agents’ heterogeneous preferences. The concepts of exemption list capacity, observation period, and removal time are introduced as the variables under the HPS mechanism. A public goods game model is built to verify the supervision effects under the ORS and HPS mechanisms. The simulation results show that the HPS mechanism can promote cooperation more than the ORS mechanism. Increasing the exemption list capacity can make regulators pay more attention to defectors and improve the cooperation level. Setting a relatively moderate observation period benefits a better supervision effect, while a too-small or too-large observation period leads to the collapse of cooperation. Shortening the removal time can increase the updating speed of the exemption list and enhance the role of the exemption list, resulting in improving the fraction of cooperators.
Unreliable server with Non-Markovian Retrial Queueing System, Bernoulli Vacation and Fortuitous breakdown
Authors: Bharathi J. , Nandhini S.
Published: September 2023

This paper explores the behaviour of a Bulk Arrival Retrial Queue Model (BARQ) with two phases of service under the Bernoulli Vacation schedule and Breakdown (BVSB). Each batch of customers arriving the system finds if the server is available, instantly utilizes the service. If the server is busy, under breakdown, or taking a vacation, then the customers enter into the orbit. After completing both service stages, the server will either take a vacation with probability p or wait until the next customer arrives with probability 1 - p or q. Our approach considers the nature of the customer as balking and also takes into account the breakdown of server, which may occur instantaneously during any stage of service. Significant performance measures have been derived and presented. A numerical study of the proposed model is carried out using MATLAB and results were reported.
Breast cancer classification using hybrid deep neural networks: staging and grading of cancer
Authors: Sri Geetha M. , Grace Selvarani A.
Published: September 2023

Breast cancer is responsible for the deaths of hundreds of women every year. The manual identification of breast cancer has more difficulties, and have the possibility of error. Many imaging approaches are being researched for their potential to identify breast cancer (BC). Incorrect identification might sometimes result in unneeded therapy and diagnosis. Because of this, accurate identification of breast cancer may save a great number of patients from needing unneeded surgery and biopsies. Deep learning’s (DL) performance in the processing of medical images has substantially increased as a result of recent breakthroughs in the sector. Because of their improved capacity to anticipate outcomes, deep learning algorithms are able to reliably detect BC from ultrasound pictures. Transfer learning is a kind of machine learning that reuses knowledge representations from public models that were built with the use of large-scale datasets. Transfer learning has been shown to often result in overfitting. The primary purpose of this research is to develop and provide suggestions for a deep learning model that is effective and reliable in the detection and classification of breast cancer. A tissue biopsy is obtained from the suspicious region in order to ascertain the nature of a breast tumor and whether or not it is cancerous. Tumors may take any of these forms. When the images have been reconstructed with the help of a variational autoencoder (VAE) and a denoising variational autoencoder (DVAE), a convolutional neural network (CNN) model is used. This will be the case because it opens up a new area of the field to be investigated. The histological subtypes of breast cancer are used in conjunction with the degree of differentiation to execute the task of breast cancer categorization.
Parameter estimation in multifactor uncertain population model
Authors: Gao Caiwen , Zhang Zhiqiang , Liu Baoliang
Published: September 2023

This paper extends the traditional multifactor population model from a probabilistic background to an uncertain background, allowing us to better account for the fact that the birth and death rates are sometimes affected by different, independent uncertain noises. The theory of multifactor uncertain differential equation is applied to analyze the novel model. First, a multifactor uncertain population model is developed and the solution of the model is obtained. Secondly, the stability in measure and stability in distribution of the multifactor uncertain population model are studied respectively, and the relationship between the two kinds of stability is further discussed. Then, a method of moment estimation based on residuals is proposed for the unknown parameters in the multifactor uncertain population model. Finally, an example is given to illustrate the validity and rationality of the parameter estimation method.
Cattle face detection method based on channel pruning YOLOv5 network and mobile deployment
Authors: Weng Zhi , Liu Ke , Zheng Zhiqiang
Published: September 2023

The detection and identification of individual cattle plays an integral role in precision feeding and insurance claims, among others. Most current research is based on high-performance computing devices, which limits the application of deep learning techniques to mobile terminals. To this end, in this paper, we propose a channel-pruned YOLOv5 network-based method for cattle face detection on mobile terminals, referred to as NS-YOLO. First, the original model is sparsely trained and a sparse regularization penalty term is applied to the BN layers, then the corresponding mask values are labeled according to different weight thresholds, and the channels are pruned with global thresholds. Second, the detection precision is recovered by fine-tuning the model. Finally, the NCNN forward inference framework is used to quantize the model and an Android-based cattle face detection application is developed. Experimental results show that the model size, number of parameters and FLOPs are reduced by 86.10%, 88.19% and 63.25%, respectively, and the inference time is reduced by 35.53% compared to the original model, while mAP0.5 is reduced by only 1.6%. In particular, the 16-bit quantized model reduces the model size by 93.97% and the inference time by 34.97% compared to the original model on the mobile side. The results show that the proposed method can be easily deployed in resource-constrained mobile devices and has great advantages in practical engineering applications.
Cubical fuzzy Einstein Bonferrori mean averaging aggregation operators and their applications to multiple criteria group decision making problems
Authors: Thilagavathy A. , Mohanaselvi S.
Published: September 2023

Consolidating cubical fuzzy numbers (CFNs) is essential in an uncertain decision-making process. This study focuses on creating innovative cubical fuzzy aggregation operators based on the newly proposed Einstein operational laws, utilizing the Bonferroni mean function to capture the interrelationships among the aggregated CFNs. The first contribution of this paper is introducing a novel cubical fuzzy Einstein Bonferroni mean averaging operator. Building upon this operator, we extend our research to develop cubical fuzzy Einstein Bonferroni mean weighted, ordered weighted, and hybrid averaging operators, taking into account the weights of the aggregated CFNs. To ensure their effectiveness, we thoroughly investigate the desirable properties of these proposed operators. Furthermore, we leverage the introduced operators to establish a new approach known as the cubical fuzzy linear assignment method, which proves valuable in resolving multiple criteria group decision-making problems. As a practical demonstration of the method’s utility, we apply it to address a real-life challenge: identifying the optimal location for constructing a wind power plant under a cubical fuzzy environment. To validate the effectiveness of our approach, we compare its results with those obtained using existing methods from the literature. Additionally, we conduct a statistical analysis to visualize the correlative conjunction between the ranking outcomes obtained by different operators
Runtime verification of self-adaptive multi-agent system using probabilistic timed automata
Authors: Mu Yongan , Liu Wei , Lu Tao , Li Juan , Gao Sheng , Wang Zihao
Published: September 2023

The self-adaptive multi-agent system requires adaptive adjustments based on the dynamic environment during its runtime. Heterogeneous agent can accomplish different task goals, enhance the efficiency of system operation, but its complex collaboration problem poses new challenges to the study of verification of adaptive policies for heterogeneous multi-agents. This paper proposes a runtime verification method for self-adaptive multi-agent systems using probabilistic timed automata. The method constructs a probabilistic timed automaton model by formally describing the functional characteristics of heterogeneous agents and integrating random factors in the environment to simulate the operation process of the self-adaptive multi-agent system. Regarding the collaboration logic among heterogeneous agents, security constraints are established to ensure the security of state transition processes during system operation. Combining model checking with runtime quantitative verification methods to conduct experiment and applying it in the case of an intelligent unmanned parking system. Experimental results manifest the correctness of the cooperation logic between agents can effectively ensure the stability of the system at runtime. Significant improvement in system uptime and efficiency compared to the initial system without runtime quantitative validation.
Indoor fire emergency evacuation path planning based on improved NavMesh algorithm
Authors: Cao Xianghong , Wu Kunning , Geng Xin , Wang Yongdong
Published: September 2023

With the acceleration of urbanization, the frequency of building fire incidents has been increasing year by year. Therefore, rapid, efficient, and safe evacuation from buildings has become an urgent and important task. A construction fire escape path planning method based on an improved NavMesh algorithm is proposed in this paper. Firstly, by using the method of local updates in the navigation grid, redundant computation is reduced, and the update time of the improved algorithm is about 6.8% of that of the original algorithm, immediate generation of navigation is achieved. Secondly, the heuristic function of the pathfinding algorithm is improved, and a multi-exit path planning mechanism is proposed to achieve more efficient, which can quickly plan a safe evacuation path away from the spreading fire and smoke in the event of a fire. Finally, a new evaluation index called Navigation Grid Complexity (NGC) is proposed and demonstrated to measure the quality of navigation grids. The feasibility and effectiveness of the proposed method are validated through simulation experiments on actual building models, which can provide real-time, efficient, intelligent, and safe path planning for rapid evacuation of evacuees in the fire scene.
Applying the improved EBM and spatial statistical models to examining carbon emission performance: Evidence from Yellow River Basin urban agglomerations
Authors: Li Jing , Li Chengyu , Meng Lusha
Published: September 2023

Global warming caused by excessive emissions of carbon dioxide (CO2) has become a hot topic globally in today’s society, and optimizing carbon emission performance (CEP) is an effective way to alleviate CO2 emissions. Many studies have explored CEP at the global, national, provincial and sector levels. However, due to the difficulty in obtaining energy consumption data, there is a lack of studies at the urban agglomeration and city levels. Taking the urban agglomeration dimension as the starting point, this paper constructs an improved epsilon-based measure (EBM) model to measure the CEP of the Yellow River Basin. A spatial data analysis model was introduced to explore the regional spatial characteristics of CEP. The newly developed spatial statistical model was used to study the driving factors of CEP. The results showed that: (1) The overall CEP of the Yellow River Basin was relatively high, showing an upward trend of volatility. There were significant differences between the seven urban agglomerations and 69 cities. (2) The CEP of the Yellow River Basin showed a trend of spatial agglomeration. The urban agglomerations of the eastern region showed a low-value agglomeration phenomenon, and the urban agglomerations of the central and western regions showed a trend of high-value agglomeration. (3) Economic development level (PGGDP), technological progress (TP), industrialization level (IND) and human capital (HC) can play a positive role in promoting the improvement in CEP, and population structure (PD) and energy structure (ES) can play a negative role in promoting the improvement in CEP. Industrial agglomeration (IA) and CEP show a “U"-shaped relationship that first inhibits and then promotes. In addition, foreign direct investment (FDI), IND, and HC have significant spatial spillover effects on neighboring cities.
Automated Tuberculosis classification using Egret Swarm Optimization with deep learning based fusion model on chest X-ray images
Authors: Manivannan K. , Sathiamoorthy S.
Published: September 2023

In the last decades, Tuberculosis (TB) can be considered a serious illness affecting people over the globe and it leads to mortality when left untreated. Chest X-Ray (CXR) is the topmost selection for the recognition of pulmonary diseases in hospitals since it can be cost-efficient and easily available in many nations. But, manual CXR image screening is a huge load for radiologists, which results in a maximum inter-observer discrepancy rate. At present, Computer-Aided Detection (CAD) is a powerful imaging equipment for detecting and screening dangerous ailments. In recent times, Deep Learning (DL) based CAD schemes have demonstrated positive outcomes in the recognition of TB diseases. This study introduces an Egret Swarm Optimization Algorithm with Deep Feature Fusion based Tuberculosis Classification (ESOA-DFFTC) technique on CXR Images. The presented ESOA-DFFTC technique utilizes feature fusion and tuning processes for the classification of TB. To accomplish this, the ESOA-DFFTC model first exploits the Gaussian Filtering (GF) approach for image denoising purposes. Next, the ESOA-DFFTC model performs a feature fusion process using three DL models namely ResNeXt-50, MobileNetv2, and Xception. To enhance the achievement of the DL models, the ESOA-based hyperparameter optimizer is implemented in the study. For TB classification, the ESOA-DFFTC methodology uses an Arithmetic Optimization Algorithm (AOA) with Weight-Dropped Long Short-Term Memory (WDLSTM) methodology. The investigational output of the ESOA-DFFTC system was examined on a benchmark medical imaging dataset. A wide comparative investigation stated the greater achievement of the ESOA-DFFTC system over other current algorithms.
Interactive modeling of painting art communication structure from the perspective of integrated media
Authors: Shu Mingming , Liu Xiaoyu , Zhou Hongming
Published: September 2023

In order to better realize the effective display of painting art, this paper puts forward an interactive modeling method of structural sense of painting art communication from the perspective of media integration. From the perspective of comprehensive media, the painting art is spread and displayed, and the interactive evaluation index of painting art communication structure sense is constructed, and the interactive behavior evaluation model of painting art communication structure sense is constructed to realize the interactive modeling of communication structure sense. The experimental results show that from the perspective of integrating media, the somatosensory interaction mode of the communication structure of painting art is highly practical in the practical application process, which meets the research requirements and can realize the effective display of painting art in a modified way.