bluuebunny/crossref_metadata_2025 huggingface dataset:
Cohort Study on the Effect of Psychological Education for Nurses in Psychiatric Department
Authors: Chu Lifang , Qian Guoying
Published: August 2022

With the deepening of the medical and health system, high-quality nursing service with the reform of nursing service model and the provision of holistic nursing care for patients as the core connotation is being comprehensively carried out. With the continuous improvement of medical quality, people’s awareness of health has gradually increased. They have put forward higher requirements for medical quality, nursing service quality, and medical care safety. Under the influence of these conditions, the workload and work pressure of clinical nurses continue to increase, and mental health problems become increasingly prominent. According to relevant data, the detection rate of occupational stress among nurses was 100%, and the proportion of nurses who perceived considerable stress was 60.9%, which seriously affected their work efficiency and quality of life. However, the physical and mental health of the frontline nurses working in psychiatric hospitals is not optimistic. This study explored the effects of psychological education among nurses in the department of psychiatry. The results showed that psychological education intervention among nurses in the department of psychiatry could alleviate their professional tiredness, effectively improve their psychological elasticity and happiness index, and thus improve sleep quality and promote their physical and mental health development.
Label Propagation Clustering Algorithm Based on Adaptive Angle
Authors: Du Hui , Zhang Manjie , Wang Zhihe , Zhai Qiaofeng , Cao Xuyan
Published: August 2022

The direction-based label propagation clustering (DBC) algorithm needs to set the number of neighbors (

k

) and the angle value (degree), which are highly sensitive. Moreover, DBC algorithm is not suitable for datasets with uneven neighbor density distribution. To overcome above problems, we propose an improved DBC algorithm based on adaptive angle and label redistribution (ALR-DBC). The ALR-DBC algorithm no longer input parameter degree, but dynamically adjusts the deviation angle through the concept of high-low density region to determine the receiving range. This flexible receiving range is no longer affected by the uneven distribution of neighbor density. Finally, those points that do not meet the expectations of the main direction are redistributed. Experiments show that the ALR-DBC algorithm performs better than DBC algorithm in most artificial datasets and real datasets. It is also superior to the classical algorithms listed. It also has good experimental results when applied to wireless sensor data annotation.
Design of Classroom Intelligent Assistance System under Wireless Network and Deep Learning
Authors: Meng Bin , Yang Fan
Published: August 2022

With the development of science and technology, a variety of computer technologies have emerged. The disadvantages of traditional education appear one after another. It is difficult for teachers and students to interact synchronously, and classroom efficiency is not high. Therefore, it is urgent to improve classroom efficiency and the interaction between teachers and students. In this paper, the hardware and software modules are analyzed and studied using wireless network technology, and deep learning convolutional neural network architecture is constructed. The neural network is trained until the optimal model is obtained. The system consists of many scientific front-end technologies. It is a system structure that integrates various information technologies such as computer vision, network communication, wireless sensors, analog electronic technology, and digital electronic technology. The results show that the model generates the most network structures irregularly when the loss rate of hidden nodes is 0.5. Besides, the training effect of the model is the best. In addition, the recognition accuracy of the training dataset can reach 0.98 after the model iteration round is ten. The recognition accuracy of the validation dataset is 0.6. After model iteration, the recognition accuracy can be improved by 8.6%. The performance of the system can be further optimized. In addition, the intelligent assistance system has completed multiple data iterative updates, and the performance of the system can be optimal. The system can ensure the quality of teachers’ teaching, improve the quality of students’ classroom learning, and adjust the classroom atmosphere. This paper has important reference value for enhancing the interaction between teachers and students and improving learning efficiency.
One Person Plays Multiple Roles: The Diversified Roles and Functions of Convention Project Hosts by SEM Methods
Authors: Zhou Jie , Zhao Mengjie , Yang Qi
Published: August 2022

In a convention project, the relationship between the host and professional conference organizers has an important impact on the convention project promotion. However, as an important stakeholder of the convention, the role of hosts in the project is less concerned and studied. Therefore, based on theories of project governance, this paper combines the role of the convention project hosts from the perspective of the project sponsors and formulates the convention hosts’ role measuring scale with the project sponsor measuring scale. Then, through content validity analysis and exploratory and confirmatory factor analysis, the scale was optimized and nine items were finally determined. The study found that the hosts of the convention project play three roles in the convention project, the customer role, the controller role, and the supporter role, and verified through the SEM that all three roles have a positive effect on the convention project performance.
In Vitro and In Vivo Investigation on the Effectiveness of Alginate‐Based Gastric Mucosal Protective Gel
Authors: Zeng Hang , He Muye , Yang Minyi , Meng Zhu , Wang Han , Wang Chunren
Published: August 2022

Objective. To investigate the feasibility and effectiveness of an alginate‐based gastric mucosal protective gel on the gastric ulcer. Methods. (1) In the physical protection model, after GES‐1 cell attachment add the gel to transwell chamber, add different concentrations of HCl to the gel. Absorbance was measured to assess proliferation and images of the cells migrating into the wound were taken; then the migration rate of the cells was quantified by comparing images. (2) In the gastric ulcer model, excise the gastric mucosal of SD rats; the gel and fixative were applied on the artificial ulcer immediately. Dissect rats after 10 days, and calculate the wound healing rate and analyzed histology changes. Results. The effect of hydrochloric acid on cells in the lower layer was significantly reduced after the use of gastric mucosal protection gel. The protective gel had an isolation effect on different concentrations of acid. A number of GES‐1 were significantly higher than those in the control group at 24 h to 72 h (P < 0.01). The migration was observed compared with the control group. The average healing rate of ulcer in the gel group was about 50%, and the control group was about 30%. Inflammation occurred in all wound regions after ten days. In the gel group, inflammatory infiltration depth was lower than that of the control, and part of SD rats’ new muscle layer appeared without inflammatory infiltration. The connective tissue proliferation promoted tissue repair. In the control group, necrosis marginal, mucosal hyperplasia, marginal lymphocyte aggregation, and bleeding were observed. Conclusion. This novel gel mainly has an isolating and shielding effect to prevent the wound from being exposed to gastric acid for a long time, and it can reduce the inflammatory reaction on the wounds to promote the healing of the ulcer. The gastric mucosal protective gel cannot only promote the speed of wound healing but also improve the quality of wound healing.
Groundwater Quality: The Application of Artificial Intelligence
Authors: Al-Adhaileh Mosleh Hmoud , Aldhyani Theyazn H. H. , Alsaade Fawaz Waselallah , Al-Yaari Mohammed , Albaggar Ali Khalaf Ahmed
Published: August 2022

Humans and all other living things depend on having access to clean water, as it is an indispensable essential resource. Therefore, the development of a model that can predict water quality conditions in the future will have substantial societal and economic value. This can be accomplished by using a model that can predict future water quality circumstances. In this study, we employed a sophisticated artificial neural network (ANN) model. This study intends to develop a hybrid model of single exponential smoothing (SES) with bidirectional long short‐term memory (BiLSTM) and an adaptive neurofuzzy inference system (ANFIS) to predict water quality (WQ) in different groundwater in the Al‐Baha region of Saudi Arabia. Single exponential smoothing (SES) was employed as a preprocessing method to adjust the weight of the dataset, and the output from SES was processed using the BiLSTM and ANFIS models for predicting water quality. The data were randomly divided into two phases, training (70%) and testing (30%). Efficiency statistics were used to evaluate the SES‐BiLSTM and SES‐ANFIS models’ prediction abilities. The results showed that while both the SES‐BiLSTM and SES‐ANFIS models performed well in predicting the water quality index (WQI), the SES‐BiLSTM model performed best with accuracy (R = 99.95% and RMSE = 0.00910) at the testing phase, where the performance of the SES‐ANFIS model was R = 99.95% and RMSE = 2.2941 × 100‐07. The findings support the idea that the SES‐BilSTM and SES‐ANFIS models can be used to predict the WQI with high accuracy, which will help to enhance WQ. The results demonstrated that the SES‐BiLSTM and SES‐ANFIS models’ forecasts are accurate and that both seasons’ performances are consistent. Similar investigations of groundwater quality prediction for drinking purposes should benefit from the proposed SES‐BiLSTM and SES‐ANFIS models. Consequently, the results demonstrate that the proposed SES‐BiLSTM and SES‐ANFIS models are useful tools for predicting whether the groundwater in Al‐Baha city is suitable for drinking and irrigation purposes.
Detecting ShadowsocksR User Based on Intelligence of Cyber Entities
Authors: Zhang Jiancong , Dong Ping , Jin Minyu , Tang Yuting
Published: August 2022

ShadowsocksR (SSR), as a typical emerging anonymous communication tool, may record user information on the SSR client or server, leading to the theft of the user’s privacy, and may be used by attackers to anonymize their internal network environment and organization, which will cause serious damage to data security and bring severe challenges to security defense and threat assessment within organizations. To solve the problem of accurately and effectively discovering SSR users within an organization in a real traffic environment, in this paper, we propose an SSR user detection method based on network entity intelligence as follows: (1) According to the communication characteristics of SSR users, relevant network entity intelligence information from inside and outside the organization is obtained, such as the distribution of IP addresses within and outside the organization, and the differences between SSR and non-SSR users are analyzed to construct a feature space. (2) The communication behaviors of SSR and non-SSR users are further analyzed and features are extracted from the perspective of traffic behavior analysis, and the feature space of the SSR user detection model is expanded. (3) A data-driven machine-learning-based approach is designed and implemented to provide suggestions for the automatic identification of SSR users based on the extracted feature vectors. Results show that the detection method proposed in this paper has a detection accuracy of more than 95% for SSR users in the experimental environment, can accurately distinguish between SSR communication and normal communication, and can achieve accurate SSR user detection.
Theater Music Data Acquisition and Genre Recognition Using Edge Computing and Deep Brief Network
Authors: Wang Xiaohua , Cheng Lei , Cheng Ding , Zhou Qinlin
Published: August 2022

Artificial intelligence (AI) and the Internet of Things (IoT) make it urgent to push the frontier of AI to the network edge and release the potential of edge big data. The model’s accuracy in data acquisition and music genre classification (MGC) is further improved based on theater music data acquisition. First, machine learning and AI algorithms are used to collect data on various devices and automatically identify music genres. The data collected by edge devices are safe and private, which shortens the time delay of data processing and response. In addition, the deep belief network (DBN)-based MGC algorithm has better overall recognition and classification effect on music genres. The MGC accuracy of the proposed improved DBN algorithm is nearly 80%, compared to 30%–40% of the traditional algorithms. The DBN algorithm is more accurate than the traditional classical algorithm in MGC. The research has an important reference value for developing Internet technology and establishing a music recognition model.
Implementation of Rules and Routines in Physical Education in China through IOT Systems
Authors: Tu Bogong , Fu Wenqing , Tai Yang
Published: August 2022

The goal of this research is to determine how norms and practices are implemented in physical education teaching in China’s special region (DIY). This study used a descriptive method study as the design and systematic technique. The analytical quantitative research was applied in this study. Practice in teaching the number of inhabitants in the review is comprised of educators who show in various schools in China, public middle schools, and public senior secondary schools. Using realistic efficient sampling strategy, this concentrate just included educators who work in state-funded schools as members. Furthermore, the proportional sampling was used in this study; the researchers surveyed only 12.5% of primary school instructors at each grade level. A questionnaire was utilized to obtain the data. The survey includes 233 educators who teach at various levels: 185 primary teachers, 27 middle teachers, and 21 secondary school educators. The information investigation results demonstrate that the ID of standards and schedules in actual schooling educating and learning in China overall is acceptable, with a normal worth (mean) of 35.57 (64%). Recognizable proof of rule treatment is sufficient, with a mean of 20.45 (54.1 percent), whereas routine execution is acceptable, with a mean of 14.91 percent (54.1 percent).
The Efficacy and Safety of Dingkun Pill in Women with Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Authors: Jin Bao , Zhang Yang , Zhang Zongyu , Yang Guorong , Pan Yujia , Xie Liangzhen , Liu Jiarui , Shen Wenjuan
Published: August 2022

Objective. Dingkun Pill (DKP) is a proprietary Chinese medicine that has been utilized for patients with gynecological diseases, and its clinical application has been widely accepted in China. However, the effects of DKP on reproduction and metabolism in women with polycystic ovary syndrome (PCOS) have never been systematically evaluated. Our objective was to evaluate the efficacy and safety of DKP in treating reproductive and metabolic abnormalities with PCOS. Methods. We searched in PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, VIP Database, and Chinese Biomedical Literature Database up until January 2022 to identify randomized controlled trials (RCTs). The methodological quality of the included RCTs was estimated using the Cochrane collaboration risk-of-bias instrument, and the meta-analysis was performed using RevMan. Results. A total of 22 RCTs (including 1994 participants) were identified. DKP, combined with ovulation-inducing drugs (OID) or combined oral contraceptives (COC) was superior to OID or COC alone in improving the pregnancy rate (relative risk (RR) 1.84, 95% CI 1.62 to 2.11 and RR 1.38, 95% CI 1.16 to 1.64, respectively), ovulation rate (RR 1.38, 95% CI 1.03 to 1.84 and RR 1.23, 95% CI 1.11 to 1.37, respectively), endometrial thickness (weighted mean difference (WMD) 2.50, 95% CI 1.91 to 3.09 and WMD 0.62, 95% CI 0.08 to 1.16, respectively), luteinizing hormone (WMD −1.93, 95% CI −2.80 to−.07 and WMD −1.79, 95% CI −2.66 to−0.92, respectively), and testosterone (standardized mean difference (SMD) −2.12, 95% CI −3.01 to−1.24 and SMD −1.21, 95% CI −1.64 to−0.78, respectively). DKP combined with COC led to a greater improvement in homeostasis model assessment-β (WMD 20.42, 95% CI 16.85 to 23.98) when compared with COC alone. There was a significant difference between DKP and COC in terms of decreasing total cholesterol (WMD −0.37, 95% CI −0.72 to−0.02), triacylglycerol (WMD −0.85, 95% CI −1.50 to−0.20), and free fatty acid (WMD −130.00, 95% CI −217.56 to−42.22). However, DKP did not affect the follicle stimulating hormone, fasting blood glucose, fasting insulin, body mass index, waist-to-hip ratio, high-density lipoprotein cholesterol, or low-density lipoprotein cholesterol. Adverse reactions were more common in COC alone compared to DKP and COC in combination (RR 0.22, 95% CI 0.07 to 0.63). Conclusion. DKP shows promise in modifying reproductive and metabolic parameters in patients with PCOS and may be used as a primary choice in conventional or complementary therapies for PCOS. The quality of the evidence analyzed was suboptimal, and therefore, our results should be interpreted cautiously. More prospective large-scale and well-designed RCTs, as well as longer intervention durations are required in the future to draw more reliable conclusions.
Understanding Waiting Time from Graduation to First Employment: Survival Analysis-Based Evidence from Ethiopia
Authors: Alemu Mengistu , Yismaw Bedilu
Published: August 2022

Most students considered that having a university degree is sufficient to be employed. However, obtaining a job after graduation becomes challenging, especially in developing countries. As a result, some graduates have to wait a considerable amount of time to get their first job. We estimated the average waiting time for first employment for 2019 first-degree graduates from Debre Markos University (Burie campus), Ethiopia. The median waiting time for the first employment of graduates was 35 weeks, showing that 50% of graduates managed to secure their first job 35 weeks after graduation. In addition, Cox proportional hazard and log-normal accelerated failure time models were fitted to model waiting time for first employment with various factors. It was found that cumulative grade point average, gender, internship, and age of the graduates were significant factors that affect the waiting time for first employment of graduates. Finally, we documented that though cumulative grade point average is important to pass the first phase of elimination criteria, it is not the only metric that employers consider.
Next Generation IoT and Blockchain Integration
Authors: Tanwar Sarvesh , Gupta Neelam , Iwendi Celestine , Kumar Karan , Alenezi Mamdouh
Published: August 2022

The Internet of Things (IoT) refers to the interconnection of smart devices to collect data and make intelligent decisions. However, a lack of intrinsic security measures makes next generation IoT more vulnerable to privacy and security threats. With its “security by design,” Blockchain (BC) can help in addressing major security requirements in IoT. Blockchain is an ever-growing list of records that are linked and protected using cryptographic methods. It offers its users the flexibility to conduct transactions with lower costs and faster speeds. Blockchain ledgers are also decentralized and a ledger is maintained at each node in the network. Blockchain’s security and adaptability help in making even entire systems on it a much easily task with the benefit of decentralization. BC capabilities like immutability, transparency, auditability, data encryption, and operational resilience can help solve most architectural shortcomings of IoT. In the vision of the Internet of Things, traditional devices are becoming smarter and more autonomous. This vision is becoming reality as technology advances but there are still challenges to be resolved. This is especially true in a security domain like data trust, and with the expected evolution of the IoT in the coming years, it is important to ensure that this great source of data arrives. This paper began with an overview of blockchain and IoT, as well as explore the IoT blockchain application challenges. This article also focuses to review the most relevant tasks to analyse how IoT blockchain can improve and examine current research concerns and developments in the use of blockchain-related techniques and technologies in the context of IoT security in depth. One of the best parts of working or learning about blockchain and its application is the curiosity about how it can impact the things that we have been accustomed to without trying to improve and make things more efficient and productive.
Optimal Power Flow Incorporating Thyristor-Controlled Series Capacitors Using the Gorilla Troops Algorithm
Authors: Ginidi Ahmed , Elattar Ehab , Shaheen Abdullah , Elsayed Abdallah , El-Sehiemy Ragab , Dorrah Hassen
Published: August 2022

The optimal power flow issue (OPFI) can be solved in this work using the recently developed algorithm, gorilla troops algorithm (GTA). The goal of OPFI is to reduce numerous functions such as minimizing fuel costs, emissions, and power losses and improving the voltage stability related to electric power networks (EPNs). The GTA is inspired by gorillas’ social habits, which include migration to a strange region, migration toward a specified spot, traveling to other gorillas, competing for adult females, and escorting the silverback. The developed GTA is tested with and without the inclusion of the Thyristor-Controlled Series Capacitor (TCSC) devices in the system. The proposed GTA is applied on a practical Egyptian West Delta-EPN (WD-EPN) and the standard IEEE 57-bus EPN and with and without the inclusion of the TCSC devices to appraise the GTA algorithm’s performance in the OPFI. In addition, the proposed GTA is applied on a large-scale IEEE 118 bus system with higher outperformance compared to particle swarm optimization. The results illustrate that the fuel costs, emissions, voltage stability, and power losses are reduced for the standard IEEE 57-bus EPN with and without TCSC devices by a percentage of (18.847% and 18.818%), (59% and 58.97%), (13.405% and 11.507%), and (64.337% and 65.178%), respectively, while fuel costs, emissions, voltage stability, and power losses are reduced for WD-EPN with and without TCSC devices by a percentage of (8.547%, 8.565%), (13.641%, 13.6%), and (61.949%, 61.954%), respectively. A comparison study is conducted to demonstrate the GTA’s effectiveness when compared with other recently developed algorithms such as improved Salp Swarm Algorithm, quasi-reflection jellyfish search, Salp Swarm Algorithm, improved heap-based algorithm, bat search algorithm, social network search algorithm, electromagnetic field optimization, and other well-known algorithms as well. According to the comparison with these algorithms, the GTA demonstrates the best results among the attained results.
Optimization and Combination Analysis of English Multimodule Learning Strategies based on Computational Intelligence
Authors: Bao Xiaodan
Published: August 2022

Due to the stagnation of foreign language teaching method research and the rapid development of cognitive psychology, in the early 1970s, the research focus of foreign language teaching changed from teacher to student research. In this context, the research of English learning strategy entered the historical stage of foreign language teaching research, and the global research of foreign language learning strategy was launched. In the past 30 years, the study of English learning strategy has been a hot research topic in the field of education in China. Finally, we conclude that English learning strategy training is the result of learning strategy training. In addition, the learning strategy training also effectively improves the English learning level of the students in the experimental class. This study aims to solve the problem of optimization and combination of English learning strategies, find effective ways and implement feasible programs, and propose theoretical and practical ways in college English teaching reforms. The connotation of “multimodule” has not been given due attention, so its role has not been fully brought into play. The following questions are answered, such as “teach students complete skills,” “train students in strategies,” “treat the teaching process correctly,” “Chinese computational intelligence has many patterns, but few practical effects” and “get rid of the strange theory that cannot be taught on the basis of computational intelligence” and “let students experience the practical process of English multimodule learning strategies and applications based on computational intelligence. The problems all seem to be related to “multimodules.”
Chile, ¿un país ecológico o sostenible?
Authors: Geldes Cristian
Published: August 2022

En el Artículo 1 de la Propuesta de Constitución elaborada por la Convención Constituyente y que será sometida a un Plebiscito Nacional el 4 de septiembre próximo, se menciona que “Chile es un Estado social y democrático de derecho. Es plurinacional, intercultural, regional y ecológico”. Declaración que nos invita a reflexionar sobre el futuro que se propone para Chile y cómo se administrará el Estado y todas sus organizaciones para lograr lo propuesto. En este caso, se discutirá específicamente del alcance de un Chile “ecológico” en relación al enfoque de “sostenibilidad”. Continuar leyendo...
Sobre los cambios en salud de la nueva Constitución
Authors: De Elejalde Ramiro
Published: August 2022

El texto de la nueva Constitución incluye cambios significativos en los “derechos sociales” de los ciudadanos. Uno de los más importantes para las personas es el derecho a la salud. En este sentido, el artículo 44 de la nueva Constitución se refiere a este y es el que se analiza en esta columna. Continuar leyendo...
¿Debe ser el rol del Estado crear empresas?
Authors: Castillo Mauricio
Published: August 2022

La propuesta de convención establece que el Estado podrá crear empresas. El artículo 182 expresa que este puede participar en la economía, incluyendo el fomento y desarrollo de actividades económicas, el artículo 214 permite que las comunas autónomas puedan crear empresas y el artículo 220 les otorga las mismas competencias a las regiones autónomas. En el artículo 224 se permite que los gobiernos regionales propongan la creación de empresas. A mi juicio, estas iniciativas suponen varios errores en su diseño. Continuar leyendo...
Incertidumbre institucional y emprendimiento
Authors: Muñoz Victor
Published: August 2022

Los emprendedores están familiarizados con la incertidumbre, es decir, con situaciones en que no es posible anticipar o predecir con precisión el futuro. Los emprendedores en Chile están habituados a la incertidumbre que proviene de factores económicos, por ejemplo, las alzas del precio del petróleo o del dólar y también con la incertidumbre propia de los negocios que operan, por ejemplo, respecto a las condiciones de la demanda o las acciones de sus competidores. Sin embargo, el proceso constitucional ha expuesto a los emprendedores a un tipo diferente de incertidumbre. Ahora enfrentan incertidumbre institucional que los acompañará por un largo tiempo.
Derechos de propiedad en la nueva Constitución
Authors: Ponce Carlos
Published: August 2022

La propuesta de nueva Constitución instaura modificaciones al sistema vigente de derechos de propiedad. Sin ambigüedad, muchos de ellos se eliminan mientras que otros se debilitan. Continuar leyendo...
Dll1-Mediated Notch Signaling Drives Tumor Cell Cross-talk with Cancer-Associated Fibroblasts to Promote Radioresistance in Breast Cancer
Authors: Nandi Ajeya , Debnath Rahul , Nayak Anupma , To Tsun Ki Jerrick , Thacker Gatha , Reilly Megan , Gumber Sanjeev , Karagounis Ilias , Li Ning , Lengner Christopher J. , Haldar Malay , Welm Alana L. , Blanco Andres M. , Thomas Christoforos , Chakrabarti Rumela
Published: August 2022

Resistance to radiotherapy is a major obstacle for effective cancer treatment. Both cancer-associated fibroblasts (CAF) within the tumor microenvironment (TME) and Notch signaling are implicated in radioresistance, but their potential interrelationship is unclear. Here, we report that irradiated samples obtained from luminal breast cancer patient tumors express higher levels of the Notch ligand Dll1 and have a greater number of αSMA- and FAP-expressing activated CAFs. Single cell transcriptomic profiles further revealed enrichment of an αSMA+ myofibroblastic subpopulation of CAF in Dll1+ tumors. In murine and human patient-derived xenograft models, Dll1+ tumor cells were more radioresistant than Dll1– tumor cells, and genetic and pharmacologic blocking of Dll1-mediated Notch signaling decreased the number of Dll1+ cancer stem cells (CSC) and CAFs and increased Dll1+ tumor cell radiosensitivity. Dll1+ cells recruited CAFs in an IL6-dependent fashion and promoted Wnt ligand secretion by Notch2/3-expressing CAFs, thereby driving Wnt/β-catenin–dependent increases in Dll1+ CSC function to promote metastasis. Treatment with the porcupine inhibitor LGK-974 that inhibits Wnt ligand secretion or pharmacologic blockade of IL6 or Dll1 suppressed CAF-dependent enhancement of Dll1+ CSC function and metastasis in radioresistant tumors. Together, these findings reveal an essential cross-talk between Dll1+ cancer cells and CAFs, which promotes metastasis and radioresistance, which could be therapeutically exploited to improve the outcome of patients with breast cancer.

Significance:

Dll1+ breast cancer cells activate Notch signaling in cancer-associated fibroblasts that increases Wnt ligand secretion and leads to β-catenin–driven radioresistance and metastasis, opening new therapeutic avenues for breast cancer.
Pathway-based Approach Reveals Differential Sensitivity to E2F1 Inhibition in Glioblastoma
Authors: Alvarado Alvaro G. , Tessema Kaleab , Muthukrishnan Sree Deepthi , Sober Mackenzie , Kawaguchi Riki , Laks Dan R. , Bhaduri Aparna , Swarup Vivek , Nathanson David A. , Geschwind Daniel H. , Goldman Steven A. , Kornblum Harley I.
Published: August 2022

Analysis of tumor gene expression is an important approach for the classification and identification of therapeutic vulnerabilities. However, targeting glioblastoma (GBM) based on molecular subtyping has not yet translated into successful therapies. Here, we present an integrative approach based on molecular pathways to expose new potentially actionable targets. We used gene set enrichment analysis to conduct an unsupervised clustering analysis to condense the gene expression data from bulk patient samples and patient-derived gliomasphere lines into new gene signatures. We identified key targets that are predicted to be differentially activated between tumors and were functionally validated in a library of gliomasphere cultures. Resultant cluster-specific gene signatures associated not only with hallmarks of cell cycle and stemness gene expression, but also with cell type–specific markers and different cellular states of GBM. Several upstream regulators, such as PIK3R1 and EBF1 were differentially enriched in cells bearing stem cell like signatures and bear further investigation. We identified the transcription factor E2F1 as a key regulator of tumor cell proliferation and self-renewal in only a subset of gliomasphere cultures predicted to be E2F1 signaling dependent. Our in vivo work also validated the functional significance of E2F1 in tumor formation capacity in the predicted samples. E2F1 inhibition also differentially sensitized E2F1-dependent gliomasphere cultures to radiation treatment. Our findings indicate that this novel approach exploring cancer pathways highlights key therapeutic vulnerabilities for targeting GBM.

Significance:

Molecular classification of GBM has not yet resulted in the development of effective therapies. We have developed an integrative approach to identify molecular targets differentially utilized by individual tumors. This approach could lead to patient- and tumor-specific therapeutics.
The impact of weather data on traffic flow prediction models
Authors: Al-Selwi Hatem Fahd , Bin Abd Aziz Azlan , Abas Fazly Salleh , Amir Hamzah Nur Asyiqin , Mahmud Azwan Bin
Published: August 2022

Traffic flow prediction is an integral part of the intelligent transportation system (ITS) that helps in making well-informed decisions. Traffic flow prediction helps in alleviating traffic congestion as well as in some connected vehicles applications such as resources allocation. However, most of the existing models do not consider external factors such as weather data. Traffic flow in road networks is affected by weather conditions which affects the periodicity of traffic. These effects introduce some irregularity to the traffic pattern, making traffic flow prediction a challenging issue. In this paper, we present a detailed investigation on the impact of weather data on different traffic flow prediction models. The investigation presented in this paper demonstrates how adding weather data could improve the models’ prediction accuracy and efficiency.
Cognitive and academic-based probability models for predicting campus placements
Authors: Alfana Shaik , Rama Jammalamadaka Sastry Kodanda , Prakash Vudatha Chandra , Reddy Burramukku Tirapathi
Published: August 2022

Industrial organizations select the students for placement by conducting tests based on the academic content and targeting students' cognitive levels, such as the problem-solving ability. Educational institutes are mostly dependent on the students' academic performance to judge the likelihood of Employing the students. Cognitive and academic-based models are required to accurately predict the students' employment and assess the areas of improvement required. The interrelationships must be established to achieve coherence between the models. In this paper, three predictive models have been presented, which are based on: cognitive factors, Academic factors with and without anomaly correction. The models will help the educational institutions prepare the students for the highest number of placements. The models provide the basis for prediction on the individual subject/factor basis and the overall prediction considering all the subjects/cognitive factors. 98% accuracy in predicting the placement of the students has been achieved considering both the cognitive and Academic models with a built-in anomaly correction mechanism. The anomaly correction mechanism presented in the paper improved the accuracy of prediction from 92% to 98%. The positive correlation between the cognitive and Academic model helps inferencing one model from the other.
Predictive linguistic cues for fake news: a societal artificial intelligence problem
Authors: Aneja Sandhya , Aneja Nagender , Kumaraguru Ponnurangam
Published: August 2022

Media news are making a large part of public opinion and, therefore, must not be fake. News on web sites, blogs, and social media must be analyzed before being published. In this paper, we present linguistic characteristics of media news items to differentiate between fake news and real news using machine learning algorithms. Neural fake news generation, headlines created by machines, semantic incongruities in text and image captions generated by machine are other types of fake news problems. These problems use neural networks which mainly control distributional features rather than evidence. We propose applying correlation between features set and class, and correlation among the features to compute correlation attribute evaluation metric and covariance metric to compute variance of attributes over the news items. Features unique, negative, positive, and cardinal numbers with high values on the metrics are observed to provide a high area under the curve (AUC) and F1-score.
Bidirectional long-short term memory and conditional random field for tourism named entity recognition
Authors: Zahra Annisa , Hidayatullah Ahmad Fathan , Rani Septia
Published: August 2022

The common thing to do when planning a trip is to search for a tourist destination. This process is often done using search engines and reading articles on the internet. However, it takes much time to search for such information, as to obtain relevant information, we have to read some available articles. Named entity recognition (NER) can detect named entities in a text to help users find the desired information. This study aims to create a NER model that will help to detect tourist attractions in an article. The articles used for the dataset are English articles obtained from the internet. We built our NER model using bidirectional long-short term memory (BiLSTM) and conditional random fields (CRF), with Word2Vec as a feature. Our proposed model achieved the best with an average F1-Score of 75.25% compared to all scenarios tested.
Improving RepVGG model with variational data imputation in COVID-19 classification
Authors: Trang Kien , Nguyen An Hoang , TonThat Long , Vuong Bao Quoc
Published: August 2022

Millions of fatal cases have been reported worldwide as a result of the Coronavirus disease 2019 (COVID-19) outbreak. In order to stop the spreading of disease, early diagnosis and quarantine of infected people are one of the most essential steps. Therefore, due to the similar symptoms of SARS-CoV-2 virus and other pneumonia, identifying COVID-19 still exists some challenges. Reverse transcription-polymerase chain reaction (RT-PCR) is known as a standard method for the COVID-19 diagnosis process. Due to the shortage of RT-PCR toolkit in global, Chest X-Ray (CXR) image is introduced as an initial step to support patient’s classification. Applying deep learning in medical imaging becomes an advanced research trend in many applications. In this research, RepVGG pre-trained model is considered to be used as the main backbone of the network. Besides, variational autoencoder (VAE) is firstly trained to perform lung segmentation. Afterwards, the encoder part in VAE is preserved as an additional feature extractor to combine with RepVGG performing classification. A COVID-19 radiography database consisting of 3 classes COVID-19, Normal and Viral Pneumonia is conducted. The obtained average accuracy of the proposed model is 95.4% and other evaluation metrics also show better results compared with the original RepVGG model.
Classification of nutrient deficiency in oil palms from leaf images using convolutional neural network
Authors: Hafiz Razali Muhammad Ikmal , Hairuddin Muhammad Asraf , Jahidin Aisyah Hartini , Mat Som Mohd Hanafi , Megat Ali Megat Syahirul Amin
Published: August 2022

Oil palm is a perennial plant that thrives well in tropical climate. Apart from humid environment, the plant also requires a wide variety of nutrients. Any deficiencies will directly affect its growth and palm oil production. These can often be detected from the change of leaf colour and texture. Deviations from the standard dark green colour indicates lack of certain nutrients. Therefore, this study proposes convolutional neural network (CNN) to classify nutrient deficiency in oil palms using leaf images. A total of 180 leaf images are acquired using standardized protocol. The samples are evenly distributed into healthy, nitrogen-deficient, and potassium-deficient groups. Residual network (ResNet)-50, visual geometry group-16 (VGG16), Densely connected network (DenseNet)-201, and AlexNet are trained and tested using the randomized samples. Each attained classification accuracies of 96.7%, 100%, 98.3%, and 100% respectively. Despite yielding similar performance, AlexNet is the more computational efficient architecture with less convolutional layers compared to VGG-16.
Classification of jackfruit and cempedak using convolutional neural network and transfer learning
Authors: Sumari Putra , Kassim Azleena Mohd , Ong Song-Quan , Nair Gomesh , Ragheed Al Dabbagh , Aminuddin Nur Farihah
Published: August 2022

Jackfruit (Artocarpus integer) and Cempedak (Artocarpus heterophyllus) are two different Southeast Asian fruit species from the same genus that are quite similar in their external appearance, therefore, sometimes difficult to be recognized visually by humans, especially in the form of pictures. Convolutional neural networks (CNN) and transfer learning can provide an excellent solution to recognize fruits, where the methods are known to be able to classify objects with high accuracy. In this study, several models were proposed and constructed to recognize the Jackfruit and Cempedak using a deep convolutional neural network (DCNN). We proposed our custom-made own CNN model and modify five transfer learning models on pre-trained VGG16, VGG19, Xception, ResNet50, and InceptionV3. The experiment used our own dataset and the result showed that the proposed CNN architecture was able to provide an accuracy between 89% to 93.67% compared to the other CNN transfer learning.
Real-time implementation of an enhanced proportional-integral-derivative controller based on sparrow search algorithm for micro-robotics system
Authors: Ghith Ehab Saif , Aziz Tolba Farid Abdel
Published: August 2022

This paper presents a new approach to control the position of the microrobotics system with a proportional-integral-derivative (PID) controller. By using sparrow search algorithm (SSA), the optimal PID controller indicators were obtained by applying a new objective function namely, integral square time multiplied square error (ISTES). The effeciency of the proposed SSAbased controller was verified by comparisons made with grey wolf optimization (GWO) algorithm-based controllers in terms of time. Each control technique will be applied to the identified model using MATLAB Simulink and the experimental test facility was conducted using LabVIEW software. The simulation and experimental results show that the performance of SSA-PID controller based on ISTES cost function achieves the best performance among various techniques. Moreover, the SSA technique had the highest performance compared to GWO technique based on rising and setting time and many other performance measurements. Thus, it is recommended to apply SSA for tuning the parameters of PID as it can enhance its performance in micro-robotic systems. It was found that the amount of error is reduced by 50% using SSA than other former experiments.
Topographical distribution of the human brain to Plukenetia volubilis-based omega-3,6,9 enriched egg consumption
Authors: Sittiprapaporn Phakkharawat , Suyajai Prayoon
Published: August 2022

Docosahexaenoic acid (DHA) is related to cognitive development. Consumers' brain health could benefit from changes in the omega 6:3 ratio. This study aimed to examine how the Plukenetia volubilis-based omega-3,6,9 enriched egg supplementation affects electrical brain activity during the attention/inhibition (Go/NoGo) test. Healthy subjects (n = 20) were chosen at random to eat 2 boiled Plukenetia volubilis-based omega-3,6,9 enriched eggs for 12 weeks. The ePrime v.3.0 application recorded behavioral performance during the Go/NoGo test during the electroencephalographic recording. The finding of this study was that twelve weeks of boiled Plukenetia volubilis-based omega-3,6,9 enriched egg consumption significantly decreased the reaction time responses compared to the baseline. The topographical distribution revealed that the mean amplitude of N1 produced a slightly larger amplitude in the 12th week compared to the baseline. The P3 component, following N1, was also larger in the 12th week compared to the baseline. After 12 weeks of consuming the Plukenetia volubilis-based omega-3,6,9 enriched eggs, the central nervous system activities during a Go/NoGo test were believed to be enhanced. 
Detection of traffic congestion based on twitter using convolutional neural network model
Authors: Almassar Rifqi Ramadhani , Girsang Abba Suganda
Published: August 2022

Microblogging is a form of communication between users to socialize by describing the state of events in real-time. Twitter is a platform for microblogging. Indonesia is one of the countries with the largest Twitter users, people can share information about traffic jams. This research aims to detect traffic jams by extracting tweets in the form of vectors and then inserting them into the Convolution neural network (CNN) model and getting the best model from CNN+Word2Vec, CNN+FastText, and support vector machine (SVM). Data retrieval was conducted using the Rapidminer application. Then, the context of the tweets was checked so that there were 2777 data consisting of 1426 congestion road data and 1351 smooth road data. The data was taken from certain coordinate points in around Jakarta, Indonesia. Then, preprocessing and changes to vector form were carried out using the Word2Vec and FastText methods, then inserted into the CNN model. The results of CNN+Word2Vec and CNN+FastText were compared to the SVM method. The evaluation was done manually using the actual traffic conditions. The highest result obtained using test data by the CNN+FastText method are 86.33% while CNN+Word2Vec is 85.79% and SVM is 67.62%.
Classification technique for real-time emotion detection using machine learning models
Authors: Sawangwong Chanathip , Puangsuwan Kritsada , Boonnam Nathaphon , Kajornkasirat Siriwan , Srisang Wacharapong
Published: August 2022

This study aimed to explore models to identify a human by using face recognition techniques. Data were collected from Cohn-Kanade dataset composed of 398 photos having face emotion labeled with eight emotions (i.e., neutral, angry, disgusted, fearful, happy, sad, and surprised). Multi-layer perceptron (MLP), support vector machine (SVM), and random forest were used in model accuracy comparisons. Model validation and evaluation were performed using Python programming. The results on F1 scores for each class in the dataset revealed that predictive classifiers do not perform well for some classes. The support vector machine (RBF kernel) and random forest showed the highest accuracies in both datasets. The results could be used to extract and identify emotional expressions from the Cohn-Kanade dataset. Furthermore, the approach could be applied in other contexts to enhance monitoring activities or facial assessments. 
Machine learning based chroma phase offset detection and correction in motion video
Authors: Mogre Advait , Madnani Shekhar
Published: August 2022

Generally, chroma phase or hue offset issues within a scene are hard to detect, without a reference or context (i.e. some apriori knowledge about how certain objects within the scene should actually appear in terms of their hue). Moreover, when it comes to skin/flesh tones, hue deviation can be noticeable and can markedly degrade the viewer quality of experience(QoE), whenever it does occur. However a lot of research has gone into flesh tone detection, specifically, the color gamut within which flesh tone is present. This topic has been well documented in the literature with respect to various color spaces: red, green, blue (RGB) and YIQ. Therefore, overall issues with chroma offset or hue within the video content could potentially be approached by extracting and analyzing a reliable reference, such as skin or flesh tone (if present), within some allowable deviation. This involves machine learning (ML) based facial recognition and tracking followed by skin tone region recognition within the detected facial sequence (i.e. Region of Interest). The skin region serves as a ‘self-reference’ in order to discern any inherent phase offset within the content. Finally, the angular chroma deviation discerned can then be used for subsequent correction as well.
 