bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting the success of transfer learning for genetic programming using DeepInsight feature space alignment
Authors: Trujillo Leonardo , Nation Joel , Muñoz Luis , Galván Edgar
Published: July 2023

In Transfer Learning (TL) a model that is trained on one problem is used to simplify the learning process on a second problem. TL has achieved impressive results for Deep Learning, but has been scarcely studied in genetic programming (GP). Moreover, predicting when, or why, TL might succeed is an open question. This work presents an approach to determine when two problems might be compatible for TL. This question is studied for TL with GP for the first time, focusing on multiclass classification. Using a set of reference problems, each problem pair is categorized into one of two groups. TL compatible problems are problem pairs where TL was successful, while TL non-compatible problems are problem pairs where TL was unsuccessful, relative to baseline methods. DeepInsight is used to extract a 2D projection of the feature space of each problem, and a similarity measure is computed by registering the feature space representation of both problems. Results show that it is possible to distinguish between both groups with statistical significant results. The proposal does not require model training or inference, and can be applied to problems from different domains, with a different a number of samples, features and classes.
Prediction-based channel assignment for minimizing channel switching in mobile WBANs
Authors: Pradhan Prajna Paramita , Bhattacharjee Sanghita
Published: July 2023

As the world’s population rises, the healthcare system experiences significant changes. Wireless body area network (WBAN) is an emerging technology that has considerable impact on medical and non-medical applications. However, two crucial challenges in WBANs are interference minimization and channel assignment. High interference may increase collision probability, transmission delay, and energy consumption. Multichannel schemes are proposed to reduce the data transmission latency and improve the system throughput by allowing simultaneous transmission of sensors in coexisting WBANs. When WBAN users move, they need to switch the channels frequently to avoid potential channel conflicts and to maintain the Quality of Service (QoS). However, frequent switching may raise energy consumption and aggravate delay. Existing multichannel assignment schemes failed to perform well in highly dynamic and densely deployed WBANs environments. In contrast to existing studies, this paper proposes a Prediction-based Channel Assignment (PCA) algorithm that selects the channels for WBANs to remain valid for future time instances and thus minimizes the delay and number of channel switches for dynamic and coexisting WBANs. When a WBAN needs to switch a channel, the proposed method predicts the future neighbors of that WBAN based on its history. It explores the channel information of present and future neighbors to select a suitable channel with higher resilience in a dynamic environment. Thus, our algorithm minimizes channel interference by avoiding unnecessary channel switching. We have used machine learning algorithms to predict the future neighbors of a WBAN. Experiment results show that the proposed algorithm performs better than an existing algorithm and random channel assignment in delay and throughput.
Causal Effects of Plasma Haptoglobin Levels on Alzheimer’s Disease: A Two-Sample Mendelian Randomization Study
Authors: Lin Yijia , Hu Tingjun , Cheng Lizhen , Chen Yixin , Li Wei , Guo Qihao , Miao Ya
Published: July 2023

Background:

A connection between plasma levels of haptoglobin (Hp) and Alzheimer’s disease (AD) has been shown in several observational studies. It is debatable, nonetheless, how the two are related causally.

Objective:

To establish the causal relationship between Hp and AD using a two-sample Mendelian randomization (MR) study.

Methods:

From the extensive genome-wide association studies and FinnGen dataset, summaries and statistics pertaining to AD were gathered. We investigated the possibility of a causal link between Hp and AD using a two-sample MR study. Inverse variance weighting was used as the primary analytical technique, and it was supported by the joint application of complementary analyses and fixed effects meta-analysis to combine results from various sources.

Results:

Genetically determined Hp was causally associated with AD [odds ratio (OR), 1.05; 95% confidence interval (CI), 1.02 to 1.09; p = 8.96×10–4]; Inverse variance-weighted estimates coming from different data sources were combined in a meta-analysis with consistent findings (OR, 1.03; 95% CI, 1.01 to 1.05; p = 2.00×10–3). The outcomes of the inverse MR analysis showed that AD had no appreciable causal impact on Hp.

Conclusion:

The present MR analysis shows that higher plasma Hp leads to an increased risk of AD. Strategies for plasma Hp testing may open up new doors for the early diagnosis and prevention of AD.
The inclusion degrees of skill maps and knowledge structures
Authors: Yang Taoli , Li Jinjin , Li Zhaowen , Zhou Yinfeng , Feng Danlu
Published: July 2023

Knowledge and learning assessment is a popular topic. In existing models for constructing the knowledge structure of an individual, it is often considered whether an individual has mastered the skills to solve the corresponding item. However, the relationship between the number of skills an individual has mastered and the item is ignored. It is not reasonable to explain the phenomenon that individuals solve the same item but have different knowledge structures behind it. This paper introduces the concept of skill inclusion degree and constructs the variable precision α-models to delineate knowledge structures. The skill inclusion degree takes into account an individual’s mastery of the number of skills assigned to each item. Firstly, the concept of the skill inclusion degree is given, and some of its properties are discussed. Then, the variable precision α-model is constructed. Moreover, the relationship between knowledge structures delineated via the variable precision α-models by a skill map is studied, and the algorithm of knowledge structures delineated via these models by a skill map is designed. Finally, the experimental results on a real dataset demonstrate the feasibility and effectiveness of the proposed algorithm.
A secure fractional-order polar harmonic transforms based image watermarking scheme using funk singular value decomposition
Authors: Thirugnanam G. , Sahul Hameed Jennathu Beevi , Bharathidasan B.
Published: July 2023

In addition to existing cryptographic systems, watermarking technologies have been developed to add extra security. Digital watermarking utilizes embedding or hiding techniques to protect multimedia files from copyright violations. Fundamental procedures of digital watermarking techniques are embedding and extraction. Singular value decomposition (SVD) based Image watermarking schemes become popular owing to its better trade-off among robustness and imperceptibility. Nevertheless, false positive problem (FPP) is a major issue of SVD-based watermarking schemes. The singular value that is a fixed value and does not contain structural information about image is the primary cause of FPP problem. Therefore, Message Digest algorithm image watermarking scheme based on Funk Singular Value Decomposition and Fractional-Order Polar Harmonic Transform (FSVD-FOPHT) is proposed in this paper to address this problem. The MD-5 algorithm is used to extract data from the host and watermark imageries and then create secret key. The FSVD-FOPHT method is utilized to hide watermark information in host image. The secret keys are extracted from hided image using inverse process of Fractional-Order Polar Harmonic Transforms with Funk Singular Value Decomposition algorithm. By using the extraction procedure, watermark image is extracted, and then reconstructs original watermarked image. During extraction procedure, the secret key is used for authentication to address FPP. Then, the proposed method is implemented in MATLAB and performance is analyzed with evaluation metrics, such as Embedding capacity, MSE, PSNR, and NC. The proposed method provide 14.6%, 17.34%, 19.53%, 21.46% and 23.89% high PSNR for cold-snow-landscape-water test image, 14.29%, 16.47%, 18.39%, 20.16% and 21.93% high PSNR for landscape-nature-sky-blue Test image, 16.85%, 19.99%, 22.70%, 27.22% and 29.16% high Embedding Capacity for cold-snow-landscape-water test image 22.83%, 24.64%, 27.92%, 29.60% and 31.77% high Embedding Capacity for landscape-nature-sky-blue Test image 35.38%, 32.63%, 30.95%, 28.61% and 26.08% low extraction time compared with existing methods SVD-CMSF-SIW, FE-IWS-DNN, AR-IWS-DNN, BBET-SHA1-SIW and LSB-DWT-SIW respectively.
A comprehensive review on the development of copulas in financial field
Authors: Ismail Isaudin , Abd Mutalip Fatin Noor Najihah , Jacob Kavikumar
Published: July 2023

The Copula concept has long been used in many applications, especially in the financial field. This concept was first used in 1959 by Sklar in his mathematical work and greatly assisted in the applications of financial and insurance areas. The copula functions have been widely used in dependence modeling. In this study, we look at how the copula began to develop from a basic form to a more advanced form through studies that previous researchers have made. Throughout this study, we find various types of the copula, and each exhibits its own characteristics lying under two main families, Elliptical and Archimedean copulas. Our findings suggest that copula is vital in solving problems in statistical dependence measures and joint marginal distribution functions. This comprehensive study served as a review paper on the development of copulas from their initial existence to their latest evolution.
Performance analysis of study material recommendation system to reduce dropout in online learning using optimal behavior prediction cluster and Online Poll Bot
Authors: Sageengrana S. , Selvakumar S. , Srinivasan S.
Published: July 2023

Students are termed “multitaskers,” and it is likely that they easily fall prey to other subjects or topics that most interest them. In the current educational system, our young generations receive materials from their leftovers, and their constant behavioral classification has decided the material to learn. The rate at which many students gave up on their studies was predominantly higher in online classroom than in offline classroom due to the lack of direct interaction between the students and teachers. To eradicate this and to make online classroom an effective one, the proposed model can be put forth in each class to predict the student’s behavior based on their keen interests. The model predicts and recommends their live session-wise apt course materials to learn. The intelligent Online Poll Bot (OPB) acts as a teacher in this virtual learning environment by engaging in live interactions during class time. It is developed using GAN and the IBM Watson Framework. This paper analyzes the time complexity and accuracy of the developed poll bot, and 96.82% accuracy was achieved with the proposed GAN-based poll bot. Students can be categorized according to their learning behavior by using the Optimal Behavior Prediction Cluster (OBPC). According to the model, the study materials are preferred based on the students’ performance in each class. In online learning environments, the Live Behavior Analysis (LBA) method using the proposed OBPC and OPB can create interactive learning environments and deliver behavior-based study materials to learners, thus reducing dropout rates. The proposed experiments show that the accuracy of the OBPC-based system is 97.43%, and LBA produces 96.52% accuracy, 95.13% F-Score, 97.13% recall, and 96.14% precision compared to existing approaches. This technology will reduce the number of dropouts and can effectively predict the behavior of all students in the virtual environment where they are placed.
Research on loom on-board condition monitoring technology based on fuzzy rough set and improved DSmT theory1
Authors: Xiao Yanjun , Zhao Yue , Li ShiFang , Song Weihan , Wan Feng
Published: July 2023

The foundation of textile machinery digitization and intelligence is condition monitoring and identification. Online condition monitoring of looms is of great significance to ensure their long-term stable operation and improve their digital management level. However, the existing loom condition monitoring methods have problems such as insufficient depth of information mining, low condition recognition rate and poor system versatility. As a result, the loom on-board condition monitoring technology based on fuzzy rough set and improved DSmT theory is studied. To begin, we examine the loom operation mechanism, loom state characterization, and loom state feature data composition. Then, using the fuzzy rough set method, we analyze and make decisions on the loom state feature data, apply the theory of uncertainty and importance improvement DSmT fusion to solve the uncertainty problem of the rough set method’s decision rules, and build the loom state feature decision network on the embedded terminal using the decision rules. Meanwhile, to collect, communicate, display, and alarm loom characteristic data, this paper employs the STM32F407ZET6 microcontroller and designs a loom system status data collection platform with the AD7730 as the core, as well as tests loom status monitoring data collection and loom status data analysis and decision method based on this platform. The experimental findings show the usefulness of attribute data gathering as well as data analysis and decision-making processes. The technology enhances the precision of loom condition identification and decision making, as well as the safety and quality of manufacturing. It is critical for carrying out applications like as problem detection, remote monitoring, efficiency optimization, and intelligent weaving machine management.
Analysis of learning behaviour in immersive virtual reality
Authors: Wang Hejin , He Mingzhao , Zeng Chengli , Qian Lei , Wang Jun , Pan Wu
Published: July 2023

Immersive virtual reality technology has been widely used in teaching and learning scenarios because of its unique visual and interactive experiences that bring learners a sense of immersive reality. However, how to better apply immersive virtual reality technology to learning environments to promote learning effectiveness is a direction that has been studied and explored by many scholars. Although a growing number of studies have concluded that immersive virtual reality technology can enhance learners’ attention in teaching and learning, few studies have directly linked both learning behaviors and attention to investigate the differences in behavioral performance across attention. In this study, attention data monitored by EEG physiological brainwaves and a large number of videos recorded during learning were used to explore the differences in the sequence of high attention behaviors across performance levels in an immersive virtual reality environment using behavioral data mining techniques. The results found that there was a strong correlation between attention and performance in immersive virtual reality, that thinking and looking may be more conducive to learners’ concentration, and that high concentration behaviors in the high-performing group accompanied the test and appeared after the monitoring, while the action continued to be repeated after the high concentration behaviors in the low-performing group. Based on this, this study provides a reference method for the analysis of the learning process in this environment, and provides a theoretical basis and practical guidance for the improvement of participants’ attention and learning effectiveness.
Anomaly detection for high-dimensional data using a novel autoencoder-support vector machine
Authors: Jiang Zhuo , Huang Xiao , Wang Rongbin
Published: July 2023

Aiming at anomaly detection upon a high-dimensional space, this paper proposed a novel autoencoder-support vector machine. The key thought is that using the autoencoder extracts the features from high-dimensional data, and then the support vector machine achieves the separation of abnormal features and normal features. To increase the precision of identifying anomalies, Chebyshev’s theorem was used to estimate the upper of the number of abnormal features. Meanwhile, the dot product operation was implemented in order to strengthen the learning of the model for class labels. Experiment results show that the detected accuracy of the proposed method is 0.766 when the data dimensionality is 5408, and also wins over competitors in detected performance for the considered cases. We also demonstrate that the strengthened learning of class labels can improve the ability of the model to detect anomalies. In terms of noise resistance and overcoming the curse of dimensionality, the former can carry out more efforts than the latter.
Improving Data Processing Speed on Large Datasets in a Hadoop Multi-node Cluster using Enhanced Apriori Algorithm
Authors: Sundarakumar M.R. , Sharma Ravi , Fathima S.K. , Gokul Rajan V. , Dhayanithi J. , Marimuthu M. , Mohanraj G. , Sharma Aditi , Johny Renoald A.
Published: July 2023

For large data, data mining methods were used on a Hadoop-based distributed infrastructure, using map reduction paradigm approaches for rapid data processing. Though data mining approaches are established methodologies, the Apriori algorithm provides a specific strategy for increasing data processing performance in big data analytics by applying map reduction. Apriori property is used to increase the efficiency of level-wise creation of frequent itemsets by minimizing the search area. A frequent itemset’s subsets must also be frequent (Apriori property). If an itemset is rarely, then all of its supersets are infrequent as well. We refined the apriori approach by varying the degree of order in locating frequent item sets in large clusters using map reduction programming. Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC) is a classical algorithm which are used for data processing from the huge datasets but their accuracy is not up to the mark. In this article, updated Apriori algorithms such as multiplied-fixed-pass combined counting (MFPC) and average time-based dynamic combined counting (ATDFC) are used to successfully achieve data processing speed. The proposed approaches are based on traditional Apriori core notions in data mining and will be used in the map-reduce multi-pass phase by ignoring pruning in some passes. The optimized-MFPC and optimized-ATDFC map-reduce framework model algorithms were also presented. The results of the experiments reveal that MFPC and ATDFC are more efficient in terms of execution time than previously outmoded approaches such as Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC). In a Hadoop multi-node cluster, this paradigm accelerates data processing on big data sets. Previous techniques were stated in terms of reducing execution time by 60–80% through the use of several passes. Because of the omitted trimming operation in data pre-processing, our proposed new approaches will save up to 84–90% of that time.
Construction of theoretical model of academic entrepreneurship behavior map: based on grounded theory analysis
Authors: Duan Qi , Zong Yi
Published: July 2023

It is worth exploring how “novices in academic entrepreneurship” can more clearly judge their performance in academic entrepreneurship process, self-diagnose the interaction effect between internal and external factors, and improve the effectiveness of entrepreneurial activities. This research takes Chinese academic entrepreneurs as the object, through the qualitative research method of grounded theory analysis, constructs the behavior map of academic entrepreneurship. The main stages of academic entrepreneurship chain are divided, including four stages: starting point, finding technology application, stabilizing technology application, and enterprise mature development. The common decision logic of academic entrepreneurs in each stage is explained. At the same time, the map shows the main influencing factors of academic entrepreneurial behavior and the logic of these factors’ influence on academic entrepreneurial behavior. The above results not only enrich the research theory in the field of academic entrepreneurship process, but also have guiding significance for the practical activities of “novices in academic entrepreneurship”.
A survey of few-shot knowledge graph completion
Authors: Zhang Chaoqin , Li Ting , Yin Yifeng , Ma Jiangtao , Gan Yong , Zhang Yanhua , Qiao Yaqiong
Published: July 2023

With the continuous development of knowledge graph completion (KGC) technology, the problem of few-shot knowledge graph completion (FKGC) is becoming increasingly prominent. Traditional methods for KGC are not effective in addressing this problem due to the lack of sufficient data samples. Therefore, completing the task of knowledge graph with few-shot data has become an urgent issue that needs to be addressed and solved. This paper first presents a concise introduction to FKGC, which covers relevant definitions and highlights the advantages of FKGC techniques. We then categorize FKGC methods into meta-learning-based, metric-based, and graph neural network-based methods, and analyze the unique characteristics of each model. We also introduced the research on FKGC in a specific domain - Temporal Knowledge Graph Completion (TKGC). Subsequently, we summarized the commonly used datasets and evaluation metrics in existing methods and evaluated the completion performance of different models in TKGC. Finally, we presented the challenges faced by FKGC and provided directions for future research.
Enhancing classification performance through multi-source online transfer learning algorithm with oversampling
Authors: Liao Yi , Ning Kuangfeng
Published: July 2023

Multi-source online transfer learning uses the tagged data from multiple source domains to enhance the classification performance of the target domain. For unbalanced data sets, a multi-source online transfer learning algorithm that can oversample in the feature spaces of the source domain and the target domain is proposed. The algorithm consists of two parts: oversampling multiple source domains and oversampling online target domains. In the oversampling phase of the source domain, oversampling is performed in the feature space of the support vector machine (SVM) to generate minority samples. New samples are obtained by amplifying the original Gram matrix through neighborhood information in the source domain feature space. In the oversampling phase of the online target domain, minority samples from the current batch search for k-nearest neighbors in the feature space from multiple batches that have already arrived, and use the generated new samples and the original samples in the current batch to train the target domain function together. The samples from the source domain and the target domain are mapped to the same feature space through the kernel function for oversampling, and the corresponding decision function is trained using the data from the source domain and the target domain with relatively balanced class distribution, so as to improve the overall performance of the algorithm. Comprehensive experiments were conducted on four real datasets, and compared to other baseline algorithms on the Office Home dataset, the accuracy improved by 0.0311 and the G-mean value improved by 0.0702.
Data augmentation based face anti-spoofing (FAS) scheme using deep learning techniques
Authors: Lakshminarasimha Kasetty , Ponniyin Selvan V.
Published: July 2023

Recent years have seen a rise in interest in face anti-spoofing (FAS) owing to the critical function it plays in protecting face recognition systems against presentation assaults (PAs). Early-stage FAS approaches relying on handmade characteristics become inaccurate when steadily realistic PAs of unique sorts emerge. Thus, face anti-spoofing algorithms are gaining increasing relevance in such setups. A very innovative method called deep learning has shown remarkable success in difficult computer vision problems. The proposed method uses deep acquisition and transfer of learning to extract characteristics from people’s faces. This is why the authors of this study recommend using the Faster RCNN classifier with a face-liveness detection approach. Two distinct components— the data augmentation module for assessing sparse information as well as the faster RCNN classifier module— make up the anti-spoofing approach. We may use any publicly accessible dataset to train our quicker RCNN classifier. We successively fused these two components and used the Android platform to create a basic face recognition app. The results of the tests demonstrate that the developed module can identify several types of face spoof assaults, such as those carried out with the use of posters, masks, or cell phones. Testing the proposed architecture both across and inside databases using three benchmarking (Idiap Replay Attack, CASIA- FASD, & 3DMAD) demonstrate its ability to deliver outcomes on par with cutting-edge techniques.
Evaluation of Nocturnal Symptoms in Chinese Parkinson’s Disease Patients Based on the PDSS-2 Scale: A Multicenter Cross-Sectional Study
Authors: Su Wen , Huang Pei , Ma Xinxin , Shang Huifang , Ye Qinyong , Cheng Oumei , Chan Piu , Liu Chunfeng , Liu Weiguo , Tang Beisha , Wang Lijuan , Zhang Baorong , Wang Tao , Shao Ming , Xie Anmu , Chen Xianwen , Zhu Xiaodong , Wang Jian , Tao Enxiang , Chen Shengdi , Chen Haibo
Published: July 2023

Background:

Nocturnal symptoms have a significant effect on the quality of life in Parkinson’s disease (PD) patients.

Objective:

This study aimed to investigate the prevalence and associated factors of nocturnal symptoms in Chinese PD patients.

Methods:

This multicenter cross-sectional study included 1,500 patients with primary PD from 18 centers in China was carried out between February 2019 and February 2020. Questionnaires including Parkinson’s disease sleep scale 2 (PDSS-2), Parkinson’s disease questionnaire 8 (PDQ-8), Beck depression inventory (BDI), and generalized anxiety disorder scale 7 (GAD-7) were used to assess nocturnal symptoms, quality of life, depression, and anxiety.

Results:

Among 1,500 Chinese PD patients, 576 (38.4%) reported nocturnal symptoms. Of them, 59.2% were older than 65 years. The PDQ-8 total score was higher in patients with nocturnal symptoms ( p < 0.01). Moderate and severe depression was reported more often in patients with nocturnal symptoms ( p < 0.01), and the occurrence and severity of anxiety were higher as well ( p < 0.01). Longer disease duration and higher Hoehn-Yahr (HY) stage were independently associated with nocturnal symptoms ( p < 0.01). Education level, depression, disease course, HY stage, and nocturnal symptoms were related to the quality of life in Chinese PD patients ( p < 0.01).

Conclusion:

Our study found that 38.4% of Chinese PD patients have nocturnal symptoms, even in early and mid-stage PD. Nocturnal symptoms were associated with worse quality of life and higher incidences of depression and anxiety. Nocturnal symptoms should be included in the assessment and care plan, especially in patients with longer disease courses and higher HY stages.
May Bradykinesia Features Aid in Distinguishing Parkinson’s Disease, Essential Tremor, And Healthy Elderly Individuals?
Authors: Paparella Giulia , Cannavacciuolo Antonio , Angelini Luca , Costa Davide , Birreci Daniele , Alunni Fegatelli Danilo , Guerra Andrea , Berardelli Alfredo , Bologna Matteo
Published: July 2023

Background:

Bradykinesia is the hallmark feature of Parkinson’s disease (PD); however, it can manifest in other conditions, including essential tremor (ET), and in healthy elderly individuals.

Objective:

Here we assessed whether bradykinesia features aid in distinguishing PD, ET, and healthy elderly individuals.

Methods:

We conducted simultaneous video and kinematic recordings of finger tapping in 44 PD patients, 69 ET patients, and 77 healthy elderly individuals. Videos were evaluated blindly by expert neurologists. Kinematic recordings were blindly analyzed. We calculated the inter-raters agreement and compared data among groups. Density plots assessed the overlapping in the distribution of kinematic data. Regression analyses and receiver operating characteristic curves determined how the kinematics influenced the likelihood of belonging to a clinical score category and diagnostic group.

Results:

The inter-rater agreement was fair (Fleiss K = 0.32). Rater found the highest clinical scores in PD, and higher scores in ET than healthy elderly individuals ( p < 0.001). In regard to kinematic analysis, the groups showed variations in movement velocity, with PD presenting the slowest values and ET displaying less velocity than healthy elderly individuals (all ps < 0.001). Additionally, PD patients showed irregular rhythm and sequence effect. However, kinematic data significantly overlapped. Regression analyses showed that kinematic analysis had high specificity in differentiating between PD and healthy elderly individuals. Nonetheless, accuracy decreased when evaluating subjects with intermediate kinematic values, i.e., ET patients.

Conclusion:

Despite a considerable degree of overlap, bradykinesia features vary to some extent in PD, ET, and healthy elderly individuals. Our findings have implications for defining bradykinesia and categorizing patients.
Analysis of multilayer OLED for improvement in drive current and luminescent power
Authors: Chandra Dinesh , Sahu Anupam , Kannaujiya Aryan , Kumar Brijesh
Published: July 2023

This research paper discusses the performance enhancement techniques of multi-layered OLED structures. OLED’s electrical behaviour is evaluated using an analytical model using benchmarked industry-standard Atlas 2-D numerical device simulator. It highlights the extraction of emission and charge transport phenomena and charges injection role. Multilayer OLED device has been investigated by extracting the key performance parameters, including the current density and luminescent power as a function of anode voltage. There is an improvement of 15% in the current density. Furthermore, this work has proceeded with internal analysis and using a mathematical model, where the electric field, total current density, electron and hole concentration, and internal device parameters have been extracted to understand the performance of OLED devices better. Additionally, the internal physics and behaviour of the device have been studied in terms of Langevin recombination. Internal analysis is performed by making cutlines in a vertical fashion so that device physics and the process occurring in the internal part can be clearly understood. The OLED device behaviour analysis is performed using the industry standard state of art device simulation tool.
Tri-butyltin(IV)phenoxyacetohydroxamate: Synthesis, characterization, biological evaluation and molecular docking studies
Authors: Sharma Vandna , Choudhary Vineet Kumar
Published: July 2023

The tri-organotin(IV) complex of composition [n-Bu3Sn(C6H5OCH2CONHO)] has been synthesized by using Bu3SnCl and potassium phenoxyacetohydroxamate ligand (KPAH) in pre-determined molar ratio of methanol and tetrahydrofuran. These are characterized by spectral techniques like FTIR, 1 H NMR and mass spectrometry. Studies on electrochemical processes are carried out using cyclic voltammetry. The in vitro antibacterial and antifungal activities performed by using minimum inhibitory concentration (MIC) method has revealed complex as biologically active. Molecular docking of the ligand and complex has been performed against the fungal receptor (PDB ID: 1UKC) to study the interactions between compound and receptor.
Training on Making Rat Empos Explosions, Application, and Introduction of Active Rat Holes in Binturu Village, Kelua District, Tabalong Regency, South Kalimantan
Authors: Sepe Muslimin , Orbani Rosa Helda , Aidawati Noor
Published: July 2023

Rats are the main pests on rice plantations. The presence of rat pests in rice plantations begins at the time of vegetative age rice plants and peaks during the generative phase. The availability of feed sources in the form of rice grains that contain high carbohydrates causes rice planting to be inseparable from the presence of rat pests. Efforts to suppress rat pest populations around rice plantations are the first step in increasing production yields. Improper rat pest control will cause rats to bait strap. The nature of bait jerks by rats makes repeated use of rodenticides no longer effective. The utilization of rat empos explosions in active holes is expected to have a significant influence in suppressing rat pest populations in rice plantations. The results of community service show the enthusiastic attitude of farmer groups in Binturu Village, Kelua District, Tabalong Regency, South Kalimantan.
Counseling on the Utilization of Fruit Waste as an Alternative to Liquid Organic Fertilizer in the Tangga Takat Village, District of Seberang Ulu II, Palembang City
Authors: Amir Nurbaiti , Palmasari Berliana , Paridawati Ika , Astuti Dessy Tri , Sofian Ahmad , Marlina Neni
Published: July 2023

Fruit waste is considered a waste material that cannot be used anymore, so that its existence has not been properly utilized by the community. Even though fruit waste can still be reused as material for making liquid organic fertilizer (POC). Liquid organic fertilizer made from fruit waste is a type of fertilizer that has many benefits, both for plants and the environment. Liquid organic fertilizer is environmentally friendly and safe for humans and animals. Liquid organic fertilizer also improves soil structure, increases soil fertility, and increases soil water holding capacity, besides being environmentally friendly, reduces soil pollution, and protects soil health. This community service activity aims to provide knowledge and skills to the community about reusing fruit waste by processing it into liquid organic fertilizer (POC). The location of the community service was carried out at the Tangga Takat village head office, Seberang Ulu II District, Palembang City, on February 24 2022. The method used was counseling and training for the community about processing fruit waste into liquid organic fertilizer. This activity brought together 25 enthusiastic participants in a discussion and question and answer session during the training. The results of this community service show that the method of community service in the form of consultation, training and question and answer is very suitable for motivating the community in processing fruit waste into liquid organic fertilizer.
Contactless methods to acquire heart and respiratory signals—A review
Authors: Pal Pushparaj , Kumar Amod , Saini Garima
Published: July 2023

The vital sign is the most important parameter for the internal health status of any subject in time. Every person is witnessed of COVID-19 global pandemic viruses. The world population has faced this problem globally. Collecting the infected person’s sample data in a contact-based approach may lead to the spreading of the disease. On the other hand, if we use a non-contact-based approach for the collection, it is somehow far better and breaks the chain of virus spreading. This radar-based technique is preferred in non-contact vital sign detection so that any person gets to their health status prior and according to that doctor can diagnose the proper treatment. The radar-based signal is targeted to the subject’s chest. Due to the chest wall displacement main vital sign parameters of the heart and respiration of the individual’s health are being captured. These captured signals are called vital signs, with this it is very helpful that the pre-diagnosis and treatment can be recommended by doctors or health service providers. Some patients due to their movement may be older or children for a long-time use skin irritation or allergy type of problems may face. On the other hand, some patients may be COVID-19 infected disease and burn patients. Hence, it is not possible to connect as both cases are unexpected for the required purpose. For constant and continuous measurement, existing contact-based methods are not fruitful hence non-contact-based approach is adopted. Non-contact-based vital sign detection is preferably due to several problems occurring. This paper presents a state-of-the-art review of recent monitoring methods and techniques for health monitoring in medical fields of operations. These methods and techniques are used as a tool to acquire, visualize and analyze the sampled data collected in any environment either indoor or outdoor.
Pengaruh Persepsi dan Media Sosial Instagram Terhadap Keputusan Berkunjung ke Museum Konperensi Asia Afrika Bandung
Authors: Naseng Jane Angelina , Riana Nova , Fajri Khoirul
Published: July 2023

The level of tourists visits at Museum of The Asia-Africa Conference (MKAA), Bandung had been fluctuated in the last 3 years from 2019-2022. The factor that influenced this fluctuation were the Covid-19 Pandemic over the past few years which decreased the level of tourists visits, also, as well as the tourists perception about the service of MKAA. The purpose of this study was to determine visitor perceptions, Instagram social media, visiting decisions and find out the influence between visitor perceptions and Instagram social media on the decision to visit The Museum of Asia-African Conference. This research was conducted at Museum of The Asia-Africa Conference with number of sample at 100 respondents taken from the population. This study conducted in descriptive, verifiable and multiple linear regression methods, counted by using SPSS 25.0 software. As results of research, visitor perception is in the "Very good" category, Instagram social media is in the "Very good" category, and visiting decisions are in the "Very good" category. This research also found that visitors' perceptions influenced at 26.7% on the visiting decisions. Whilst the Instagram social media affected the visiting decisions for 23.3%. Simultaneously, visitors' perceptions and Instagram social media influenced the visiting decisions at 50%. The better the visitors’ perception and Instagram social media managed by The Museum of The Asia-Africa Conference, the higher tourists’ visiting decisions to The Museum of Asia-Africa Conference (MKAA) Bandung.
Penggunaan Aplikasi QRIS Pembayaran Non Tunai untuk Meningkatkan Transaksi Penjualan Outlet di Wisata Belanja Jeans Cihampelas Bandung
Authors: Pamungkas Panji , Syamsiah Nurul , Budikartini Adiarti
Published: July 2023

This study aims to determine and explain the use of the QRIS application to increase outlet sales in jeans shopping tours in Cihampelas Bandung. This research uses quantitative methods with descriptive and verification analysis. The sample was a jeans outlet that used the QRIS application with as many as 68 respondents. The analysis methods used are instrument tests (validity tests, reliability tests, normality tests and linearity tests), multiple regression analysis, coefficient of determination, t-test and F test using IBM SPSS 24 for Windows. The results showed a coefficient of determination (R-square) value of 0.938 or 93.8% that all independent variables influenced the dependent variable by 93.8%. While the remaining 6.2% was influenced by other variables that were not studied in this study. QRIS purchase variables, QRIS promos, QRIS usage, QRIS usage satisfaction, partial and simultaneous influence on Cihampelas jeans outlet transactions. The conclusion of this research is the purchase of QRIS, its usefulness of QRIS, and satisfaction with the use of QRIS has a positive and significant effect on outlet sales transactions in Cihampelas Bandung jeans shopping tours.
Struggling Learners’ Mathematics Achievement Level using Quick Response Embedded Strategic Intervention Material
Authors: Abuda Ben Fermin Quitorio , Balazo Glory Fe Oraller , Orque Jestoni C. , Cabili Mariel Clarisse D. , Maestre Ma. Fe Angeilene M.
Published: July 2023

This study focused on the effect of a QR-embedded Strategic Intervention Material (QRSIM) on the achievement level of 11th Grade Students in General Mathematics via Solomon-four group research design, focusing on the least mastered competencies in solving exponential equations. A total of 120 students participated in the experiment at Dolores National High School during the school year 2019-2020. Findings revealed that; the students were unable to reach the expected level of achievement based on the pretest; the experimental and control groups with pretest performed better in the posttest assessment; no significant difference was observed between the pretest achievement level of the students; the pretest and posttest achievement levels of the experimental and control group shows a significant achievement; and that the achievement levels among the four groups significantly improved in the posttest.  The findings of this study highlight the use of an innovative SIM based instruction and its counterpart in teaching frequently least mastered competencies in the subject, General Mathematics that paves the way in developing mathematical competencies among 11th grade learners.  Furthermore, the researcher recommends that;  assessing the learners’ level of achievement prior to instruction will aid teachers in developing instructional tools and approaches suited to their scholastic needs; Mathematics teachers should be geared with appropriate knowledge on the use of 21st century teaching and learning resources such as Strategic Intervention Material to help a student who finds mathematics concepts difficult to comprehend; and that a similar study be conducted to cover other least learned competencies in the Eastern Samar division to further probe and enrich the findings of this study.

Keywords: Least Mastered Competencies, Solomon Four Group Design, Achievement Level, Pretest, Posttest
Alignment of Software Development Life Cycle and Product Life Cycle (Lesson Learned from BBM Fall Down)
Authors: Wicaksono Soetam , Setiawan Rudy
Published: July 2023

Blackberry Messenger (BBM) has already announced its shutdown on May 31, 2019. With their massive growth in 2014, BBM slowly decreased the number of users due to their competitor’s growth, such as WhatsApp and Telegram. In the marketing life cycle, when a product already reaches its maturity level, it will enter a decline gate, which means that if a product wants longer existence in the market, it should prevent that decline level, or it will be gone just like BBM. While the product life cycle is related to marketing strategy (in this case BBM is a software product), the software development life cycle (SDLC) is related to information system design and analysis. Thus, this research tries to align both of life cycle and find out which step or level that should be anticipated by the board of director or project manager in software development. The result of this research finally declared that the mature stage should be aligned with the test and operation stage. It also stated that there are some suggestions on avoiding the decline stage, such as implementing change management, take customers’ suggestions and critics seriously for future development and execute strategy into action immediately. Thus, it can delay or even avoid software products entering the decline stage just like the lesson learned from BBM fall down.

Keywords: product life cycle, software development life cycle, mature stage, BBM
The educational value of the Sol Y Agua digital game into middle school science class
Authors: Rangel Diana Minerva Camberos , Akbar Monika
Published: July 2023

This paper focuses on finding the added educational value that the Sol y Agua game provides to science students in the El Paso TX context. Hong et al. (2009) created an evaluation index for assessing the educational value of digital games which includes seven categories: Mentality change, emotional fulfillment, knowledge enhancement, thinking skill development, interpersonal skills, spatial ability development, and bodily coordination. Sol y Agua is an educational digital game developed at the CyberShare Center of Excellence at the University of Texas at El Paso. The game is a role-playing adventure game that focuses on the environmental and water sustainability problems in the El Paso area. The game was created to be part of a lesson plan for middle school science classrooms. Along with practices that include computational thinking skills, the game provides students with opportunities to develop reasoning, simulation, and a better understanding of the soil and water properties as well as their usage within the community. Based on the Evaluation Indices for Assessing Educational Digital Games, a game provides elements that allow players (students) a mentality change, emotional fulfillment, knowledge enhancement, development of thinking skills, and spatial ability. The ultimate goal of the game is for students to design a park for a specific population, children and elder people. To achieve this, the game provides students with the opportunity to identify the different types of soil (desert, mountain) different types of trees, and their water and soil requirements.

Keywords: digital games, assessment, science class, middle school
The Advent of Disruptive Innovation in the New Normal Educational Landscape: Challenges and Future Directions
Authors: Carreon Joseph R. , Aquino Jocelyn T.
Published: July 2023

The advent of disruptive innovation is gradually penetrating education and changing the way learning is delivered in the new normal educational landscape. The study aims to describe the models of flexible learning modalities, the challenges amidst the advent of disruptive innovation in the new normal educational landscape, and the proposed future directions toward the new normal educational landscape. The study used content analysis of literature reviewed in understanding, analyzing, and synthesizing significant knowledge and ideas. The findings revealed that flexible learning modalities continuously invade and redefine the educational system which promotes learning continuity according to learners' needs. The study suggests to intensify readiness for the delivery of a technology-enhanced learning environment efficiently and effectively in the new normal educational landscape not only in the midst of crisis. Thus, strengthen parent's and stakeholder's support; provisions of fast and reliable internet connectivity; appropriate budget and procurement; right attitude and highly motivated users; current technical training; and availability of adequate yet more inclusive technology resources and facilities.

Keywords: Disruptive Innovation, New Normal, Flexible Learning Modalities, Information Communication Technology, Technology Enhanced Learning Environment