Data Processing Research Articles

Natural Language Processing (NLP) Application For Classifying and Managing Tacit Knowledge in Revolutionizing AI-Driven Library

The rapid evolution of technology has transformed library systems, with Natural Language Processing (NLP) emerging as a pivotal tool for enhancing knowledge management. This study aims to examine how NLP can improve the classification and management of tacit knowledge within AI-driven libraries, addressing the challenge of handling large volumes of unstructured data. The objective is to explore how NLP can optimize the retrieval, organization, and access to tacit knowledge, thus enhancing decision-making processes in libraries. The research adopts a conceptual design, synthesizing existing literature and theoretical models, including Information Processing Theory and Constructivist Theory, to propose a framework that integrates NLP with traditional knowledge management practices. Methodologies include a thorough review of recent advancements in NLP technologies and their applications within knowledge management systems. The study’s findings demonstrate that NLP significantly improves the accuracy and efficiency of knowledge retrieval by automating the processing of natural language data. This allows better access to tacit knowledge, supporting more informed decision-making. The outcomes of the study are twofold: it enhances existing knowledge management frameworks theoretically, and it provides practical insights for libraries to leverage NLP for greater operational efficiency and improved user experience. The study also underscores the need for future research on the real-world application of NLP and its ethical implications, such as data privacy and algorithmic bias.

SISTEM INFORMASI PERPUSTAKAAN MENGGUNAKAN QR CODE PADA SMP NEGERI 6 KEMPAS BERBASIS WEB

Information technology has developed very rapidly nowadays, this has had a significant influence on human activities, such as making various fields of human work easier. One of these technological developments is the Library Information System. The Library of SMP Negeri 6 Kempas currently still uses media in the form of master books as storage media to manage library data. Storage that uses master books has the potential to experience many problems such as data loss because master books are easily torn, faded or lost, inaccuracies in reports, the length of the book management process and many other problems. The aim of this research is to produce a website-based library information system using QR Codes that can be used to handle the service process and book data management at the SMP Negeri 6 Kempas library and overcome the problems and weaknesses that occur when using the old system that is currently in use. The result of this research is to produce a Web-based QR Code Library Information System and has passed system Functionality testing with a score of 100% and Usability with a score of 81.6%.

KENDALI BERBASIS WEB PADA ANOMALI NEUTRAL GROUND RESISTOR (NGR)

A real-time monitoring and control system for Neutral Grounding Resistor (NGR) has been developed utilizing Internet of Things (IoT) technology. The system employs the PZEM-004T sensor for measuring current and voltage, and the NodeMCU ESP8266 for data processing and communication. Measurement data is displayed in real-time on an LCD screen and a website, with early warning alerts provided through a buzzer/alarm if abnormal conditions are detected. This system addresses the limitations of traditional monitoring methods, which are typically visual and periodic, by enabling continuous monitoring. Integration with a circuit breaker adds an additional layer of protection by disconnecting the power supply when necessary. Testing results demonstrate the effectiveness of the device in measuring voltages ranging from 5V to 220V and current, with a reliance on internet connectivity to ensure measurement accuracy. This system offers an efficient and proactive solution for maintaining the safety and reliability of electrical equipment at substations

FLEXIBLE WORKING ARRANGEMENT: DOES REALLY GIVE JOB SATISFACTION TOWARD EMPLOYEE?

This study aims to examine the impact of Flexible Working Arrangement (FWA) on employee job satisfaction with wellbeing as a mediating variable. Data processing techniques used SMART PLS 3.0, with mediation testing based on Hayes' process. The total number of respondents in this study was 108. The results of the study revealed that FWA does not have a direct influence on employee job satisfaction. However, after the mediation process, FWA does have an impact on job satisfaction through employee wellbeing. Based on this, the practical implication is that companies need to pay attention to the limitations of working hours and workload to ensure that flexible working hours do not make employees feel like they are working endlessly with unlimited time, so that employee wellbeing can be maintained, and job satisfaction can be achieved.

Opportunity or opportunism? Blockchain technology adoption and corporate default risk

Blockchain technology plays a significant role in new technology infrastructure and has a significant influence on artificial intelligence, big data, and 5 G, and its application has been extended to the Internet of Things, digital asset transactions, digital finance, and so on. In financial systems, it could improve the efficiency of data processing, reduce errors, and detect fraud. However, blockchain adoption could increase the stock price and induce manipulation opportunisms. Managers could also use this advantage to mask their performance and increase corporate default risk. Using the data of Chinese listed companies from 2001 to 2021, we investigate whether blockchain technology adoption could increase or decrease corporate default risk. Using the expected default risk model to estimate the risk, the results show that the default risk increases once the firm adopts blockchain technology, which indicates managerial opportunism behavior. The effect works in firms with CEO duality and higher power consolidation. And similar results are also shown in the dual-duty companies. The results are also robust in instrumental variable tests, difference-in-difference tests (DIDs), firm-fixed effect, placebo tests, and other robustness tests.

Natural language processing‐based deep transfer learning model across diverse tabular datasets for bond strength prediction of composite bars in concrete

AbstractAs conventional machine learning models often struggle with scarcity and structural variation of training data, this paper proposes a novel regression transfer learning framework called transferable tabular regressor (TransTabRegressor) to address this challenge. The TransTabRegressor integrates natural language processing (NLP) for feature encoding, transformer for enhanced feature representation, and deep learning (DL) for robust modeling, facilitating effective transfer learning across tabular datasets using reducing input parameters. By leveraging the NLP data processor, the framework embeds both parameter names and values, enabling it to recognize and adapt to different expressions of similar parameters. For instance, the bond strength of fiber‐reinforced polymer (FRP) bars embedded in ultra‐high‐performance concrete (UHPC) is critical for ensuring the integrity of FRP‐UHPC structures. While pullout tests are widely adopted for their simplicity to generate substantial data, beam tests provide a closer approximation to actual stress conditions but are more complex thus resulting in limited data size. As a verification, the framework is applied to predict the bond strength of FRP bars embedded in UHPC using limited beam test data. A pre‐trained model is first established using 479 pieces of pullout test data. Subsequently, two transfer learning models are developed by fine‐tuning on 115 pieces of beam test data, where 66 correspond to concrete splitting failure and 49 correspond to pullout failure. For comparative analysis, XGBoost and neural network models are directly trained on the beam test data. Evaluation results demonstrate that the transfer learning models achieve significantly improved prediction accuracy and generalization capability. This study significantly highlights the effectiveness of the proposed TransTabRegressor in handling data scarcity and variability in input parameters across various engineering applications.

Multi-Layered Unsupervised Learning Driven by Signal-to-Noise Ratio-Based Relaying for Vehicular Ad Hoc Network-Supported Intelligent Transport System in eHealth Monitoring

Every year, about 1.19 million people are killed in traffic accidents; hence, the United Nations has a goal of halving the number of road traffic deaths and injuries by 2030. In line with this objective, technological innovations in telecommunication, particularly brought about by the rise of 5G networks, have contributed to the development of modern Vehicle-to-Everything (V2X) systems for communication. A New Radio V2X (NR-V2X) was introduced in the latest Third Generation Partnership Project (3GPP) releases which allows user devices to exchange information without relying on roadside infrastructures. This, together with Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC), has led to the significantly increased reliability, coverage, and efficiency of vehicular communication networks. The use of artificial intelligence (AI), especially K-means clustering, has been very promising in terms of supporting efficient data exchange in vehicular ad hoc networks (VANETs). K-means is an unsupervised machine learning (ML) technique that groups vehicles located near each other geographically so that they can communicate with one another directly within these clusters while also allowing for inter-cluster communication via cluster heads. This paper proposes a multi-layered VANET-enabled Intelligent Transportation System (ITS) framework powered by unsupervised learning to optimize communication efficiency, scalability, and reliability. By leveraging AI in VANET solutions, the proposed framework aims to address road safety challenges and contribute to global efforts to meet the United Nations’ 2030 target. Additionally, this framework’s robust communication and data processing capabilities can be extended to eHealth monitoring systems, enabling real-time health data transmission and processing for continuous patient monitoring and timely medical interventions. This paper’s contributions include exploring AI-driven approaches for enhanced data interaction, improved safety in VANET-based ITS environments, and potential applications in eHealth monitoring.

Integrated Curriculum Model: Reformulation and Actualization in the Basics of Islamic Science Course

Reformulation and actualization of the integrated curriculum has become one of the characteristics of every State Islamic University. However, conceptually the terminology for integrating science and curriculum has not yet reached an agreement among experts. Even now, it is still a discourse that has not ended. This research tries to reveal two main problems: first, how the integrated curriculum is applied in the learning process of the basics of Islamic science in the chemistry education study program. Second, the extent to which the application of the integrative curriculum is appropriate in relation to learning the basics of Islamic science in the chemistry education study program. This research uses a qualitative descriptive-analysis approach with purposive sampling consisting of head of the study program, 2 lecturers, 5 students from unit 01 and 5 students from unit 02 in semester 2 (two) who took the basics of Islamic science course in the Chemistry Education Study Program. Data were collected using direct observation techniques, in-depth interviews, and document studies and questionnaires. The data analysis technique goes through the stages of data reduction, data presentation, data processing and interpretation and drawing conclusions (verification). The research results show that the implementation of the integrated curriculum in the basics of Islamic science courses has been realized at the planning and presentation stage of integrated content in line with CPL and the material taught in class is in sync with CPMK and RPS. Meanwhile, the model used is an integration model based on monotheism and fiqh. At the implementation stage in unit 01 the lecturer did not carry out apperception. Meanwhile, in unit 02 the lecturer begins learning by praying together, and after that the lecturer conducts an apperception and explores the students' learning experiences. The suitability of the curriculum implemented in the Chemistry Education Study Program with the integrated curriculum in the Basics of Islamic Science course has been realized adequately although it still needs improvement. It is hoped that the results of this research will become an initial reference for lecturers, observers and educational policy makers.

Measuring Environmental and Behavioral Drivers of Chronic Diseases Using Smartphone-Based Digital Phenotyping: Intensive Longitudinal Observational mHealth Substudy Embedded in 2 Prospective Cohorts of Adults.

Previous studies investigating environmental and behavioral drivers of chronic disease have often had limited temporal and spatial data coverage. Smartphone-based digital phenotyping mitigates the limitations of these studies by using intensive data collection schemes that take advantage of the widespread use of smartphones while allowing for less burdensome data collection and longer follow-up periods. In addition, smartphone apps can be programmed to conduct daily or intraday surveys on health behaviors and psychological well-being. The aim of this study was to investigate the feasibility and scalability of embedding smartphone-based digital phenotyping in large epidemiological cohorts by examining participant adherence to a smartphone-based data collection protocol in 2 ongoing nationwide prospective cohort studies. Participants (N=2394) of the Beiwe Substudy of the Nurses' Health Study 3 and Growing Up Today Study were followed over 1 year. During this time, they completed questionnaires every 10 days delivered via the Beiwe smartphone app covering topics such as emotions, stress and enjoyment, physical activity, access to green spaces, pets, diet (vegetables, meats, beverages, nuts and dairy, and fruits), sleep, and sitting. These questionnaires aimed to measure participants' key health behaviors to combine them with objectively assessed high-resolution GPS and accelerometer data provided by participants during the same period. Between July 2021 and June 2023, we received 11.1 TB of GPS and accelerometer data from 2394 participants and 23,682 survey responses. The average follow-up time for each participant was 214 (SD 148) days. During this period, participants provided an average of 14.8 (SD 5.9) valid hours of GPS data and 13.2 (SD 4.8) valid hours of accelerometer data. Using a 10-hour cutoff, we found that 51.46% (1232/2394) and 53.23% (1274/2394) of participants had >50% of valid data collection days for GPS and accelerometer data, respectively. In addition, each participant submitted an average of 10 (SD 11) surveys during the same period, with a mean response rate of 36% across all surveys (SD 17%; median 41%). After initial processing of GPS and accelerometer data, we also found that participants spent an average of 14.6 (SD 7.5) hours per day at home and 1.6 (SD 1.6) hours per day on trips. We also recorded an average of 1046 (SD 1029) steps per day. In this study, smartphone-based digital phenotyping was used to collect intensive longitudinal data on lifestyle and behavioral factors in 2 well-established prospective cohorts. Our assessment of adherence to smartphone-based data collection protocols over 1 year suggests that adherence in our study was either higher or similar to most previous studies with shorter follow-up periods and smaller sample sizes. Our efforts resulted in a large dataset on health behaviors that can be linked to spatial datasets to examine environmental and behavioral drivers of chronic disease.

Global obesity epidemic and rising incidence of early-onset cancers.

Incidence of early-onset cancers at multiple organ sites has increased worldwide in recent decades. We investigated whether such increasing trends could be explained by trends in obesity. We obtained incidence data for 21 common cancers among 25-49-year-olds during 2000-2012 in 42 countries from the Cancer Incidence in Five Continents database. Nine cancers we examined have been classified as obesity-related by the International Agency for Research on Cancer. Estimates of overweight and obesity prevalence came from the Non-communicable Disease Risk Factor Collaboration. Using country-level data, we examined whether changes in the prevalence of overweight and obesity combined were correlated with changes in cancer incidence, after accounting for various time lags (0-15 years) between exposure and cancer diagnosis. To test the validity of our approach, we conducted negative control analyses (using non-obesity-related cancers as the outcome variable, and per-capita gross national income as the exposure variable), and sensitivity and supplemental analyses using alternative data streams or processing. We found increased incidence for six of nine obesity-related and seven of twelve non-obesity-related cancers in 25-49-year-olds. These increases were more predominant in Western countries (particularly Australia, the USA, Canada, Norway, the Netherlands, and Lithuania). For four obesity-related cancers displaying increased incidence (colon, rectum, pancreas, kidney), changes in cancer incidence were positively correlated with changes in overweight and obesity prevalence. When accounting for a 15-year lag, the estimated correlation was 0.27 (95% confidence interval (CI) = -0.04, 0.53; P = 0.090) for colon cancer, 0.33 (95% CI = 0.02, 0.58; P = 0.036) for rectal cancer, 0.39 (95% CI = 0.08, 0.64; P = 0.018) for pancreatic cancer, and 0.22 (95% CI = -0.10, 0.50; P = 0.173) for kidney cancer. Similar correlations were found in the sensitivity and supplemental analyses. We did not find similar correlations with excess body weight for the non-obesity-related early-onset cancers, nor correlations with per-capita gross national income for any cancer types, in the negative control analyses. Worldwide increases in early-onset colon, rectal, pancreatic, and kidney cancers may have been partly driven by increases in excess body weight. The increases in other early-onset cancers, however, were likely driven by other factors deserving of further investigation.

Real-time non-invasive control of ultrasound hyperthermia using high-frequency ultrasonic backscattered energy in ex vivo tissue and in vivo animal studies

Objective.A reliable, calibrated, non-invasive thermometry method is essential for thermal therapies to monitor and control the treatment. Ultrasound (US) is an effective thermometry modality due to its relatively high sensitivity to temperature changes, and fast data acquisition and processing capabilities.Approach.In this work, the change in backscattered energy (CBE) was used to control the tissue temperature non-invasively using a real-time proportional-integral-derivative (PID) controller. A clinical high-frequency US scanner was used to acquire radio-frequency echo data fromex vivoporcine tissue samples andin vivomice hind leg tissue while the tissue was treated with mild hyperthermia by a focused US applicator. The PID controller maintained the focal temperature at approximately 40 °C for about 4 min.Main results.The results show that the US thermometry based on CBE estimated by a high-frequency US scanner can produce 2D temperature maps of a localized heating region and to estimate the focal temperature during mild hyperthermia treatments. The CBE estimated temperature varied by an average of ±0.85 °C and ±0.97 °C, compared to a calibrated thermocouple, inex vivoandin vivostudies, respectively. The mean absolute deviations of CBE thermometry during the controlled hyperthermia treatment were ±0.45 °C and ±0.54 °C inex vivoandin vivo,respectively.Significance.It is concluded that non-invasive US thermometry via backscattered energies at high frequencies can be used for real-time monitoring and control of hyperthermia treatments with acceptable accuracy. This provides a foundation for an US mediated drug delivery system.

Endless optical domain polarization demultiplexing in 2000 km simplified coherent transceiver for long-distance transmission

We demonstrate an endless optical domain polarization demultiplexing scheme that was realized in a 96 Gbaud-PS-16QAM coherent transmission system to alleviate the data processing pressure of the DSP in the receiver. It can be effectively employed in the 140 km fiber link (Metro DCI) and extended to the 2000 km optical fiber networks (Long haul DCI or backbone networks transmission). It can track 10 krad/s state of polarization (SOP) changes following Rayleigh distribution which is the general statistical case for real fibers. The proposed method has a noise tolerance equivalent to that induced by a 2000 km transmission, with OSNR as low as 20 dB, with a maximum PMD of 4 ps and a mean DGD of 2.5 ps.

Analysis of Studies on Digital Strategy: Bibliometric Research of Three Decades

This article empirically examines the research on digital strategy, addressing its temporal evolution, sources of scientific production, countries and organizations generating knowledge, and the topics investigated. It highlights key authors and journals from 1991 to 2023, contributing to Sustainable Development Goal (SDG) 9. Methodologically, traditional bibliometric laws and computational tools (VOSviewer software version 1.6.18) were applied for data processing. The results show an exponential increase in publications from 2005 to 2022 with a critical mass in digital strategy studies, concentrated in 11 journals and 83 authors with two or more publications. The main organizations leading scientific production in this field are in the USA. Finally, the primary Web of Science categories for the leading journals are Business, Management, and Psychology. The research examines the use and adoption of digital strategies in marketing, the impact of digital transformation on business models, and the challenges it poses for human resources. In addition, the challenges arising from the impact of COVID-19, the rise of artificial intelligence, and the integration of digital strategies in public administration should continue to be addressed. Finally, digital transformation is a topic of increasing interest in digital strategy studies.

ECG signal analysis and detection system based on deep learning

In view of the problems of large workload and easy error in traditional manual recognition of ECG signals, and the existing ECG monitoring equipment still has few ECG signal recognition types, low diagnostic accuracy, and excessive reliance on network services, in order to improve the performance of ECG monitoring equipment, an ECG (Electrocardiogram Signals) analysis and detection system is designed based on deep learning technology. The SENet-LSTM (Squeeze-and-Excitation Networks Long Short Term Memory) network model is built to realize automatic diagnosis of 7 categories of ECG signals. The model is built on an intelligent hardware platform that uses ADS1292R as ECG acquisition module, STM32F103 as data processing module, and Raspberry Pi as central processing module. The system uses the integrated high- performance microcomputer Raspberry Pi for calculation and analysis, providing users with offline artificial intelligence (AI: Artificial Intelligence) services. At the same time, the accuracy and precision of the model are and respectively 98.44%, 90.00%thereby realizing real-time detection and accurate classification of ECG, providing patients with accurate disease diagnosis.

An Information Ecosystem Map of Resources Supporting the Mobilization and Discovery of Paleontological Specimen Data

Over the last decade, the United States paleontological collections community has invested heavily in the digitization of specimen-based data, including over 10 million USD funded through the National Science Foundation’s Advancing Digitization of Biodiversity Collections program. Fossil specimen data—9.0 million records and counting (Global Biodiversity Information Facility 2024)—are now accessible on open science platforms such as the Global Biodiversity Information Facility (GBIF). However, the full potential of this data is far from realized due to fundamental challenges associated with mobilization, discoverability, and interoperability of paleontological information within the existing cyberinfrastructure landscape and data pipelines. Additionally, it can be difficult for individuals with varying expertise to develop a comprehensive understanding of the existing landscape due to its breadth and complexity. Here, we present preliminary results from a project aiming to explore how we might address these problems. Funding from the US National Science Foundation (NSF) to the University of Colorado Museum of Natural History, Smithsonian National Museum of Natural History, and Arizona State University will result in, among other products, an “ecosystem map” for the paleontological collections community. This map will be an information-rich visualization of entities (e.g. concepts, systems, platforms, mechanisms, drivers, tools, documentation, data, standards, people, organizations) operating in, intersecting with, or existing in parallel to our domain. We are inspired and informed by similar efforts to map the biodiversity informatics landscape (Bingham et al. 2017) and the research infrastructure landscape (Distributed System of Scientific Collections 2024), as well as by many ongoing metadata cataloging projects, e.g. re3data and the Global Registry of Scientific Collections (GRSciColl). Our strategy for developing this ecosystem map is to model the existing information and systems landscape by characterizing entities, e.g. potentially in a graph database as nodes with relationships to other nodes. The ecosystem map will enable us to provide guidance for communities working across different sectors of the landscape, promoting a shared understanding of the ecosystem that everyone works in together. We can also use the map to identify points of entry and engagement at various stages of the paleontological data process, and to engage diverse members within the paleontological community. We see three primary user types for this map: people new(er) to the community, people with expertise in a subset of the community, and people working to integrate initiatives and systems across communities. Each of these user types needs tailored access to the ecosystem map and its community knowledge. By promoting shared knowledge with the map, users will be able to identify their own space within the ecosystem and the connections or partnerships that they can utilize to expand their knowledge or resources, relieving the burden on any single individual to hold a comprehensive understanding. For example, the flow of taxonomic information between publications, collections, digital resources, and biodiversity aggregators is not straightforward or easy to understand. A person with expertise in collections care may want to use the ecosystem map to understand why taxonomic identifications associated with their specimen occurrence records are showing up incorrectly when published to GBIF. We envision that our final ecosystem map will visualize the flow of taxonomic information and how it is used to interpret specimen occurrence data, thereby highlighting to this user where problems may be happening and whom to ask for help in addressing them (Fig. 1). Ultimately, development of this map will allow us to identify mobilization pathways for paleontological data, highlight core cyberinfrastructure resources, define cyberinfrastructure gaps, strategize future partnerships, promote shared knowledge, and engage a broader array of expertise in the process. Contributing domain-based evidence FAIRly*2 requires expertise that bridges the content (e.g. paleontology) and the mechanics (e.g. informatics). By centering the role of humans in open science cyberinfrastructure throughout our process, we hope to develop systems that create and sustain such expertise.

Detailed lipid investigation of edible seaweeds by photochemical derivatization and untargeted lipidomics.

Seaweeds are macrophytic algae that have been gaining interest as alternative healthy foods, renewable drug sources, and climate change mitigation agents. In terms of their nutritional value, seaweeds are renowned for their high content of biologically active polyunsaturated fatty acids. However, little is known about the regiochemistry-the geometry and position of carbon-carbon double bonds-of free and conjugated fatty acids in seaweeds. In the present work, a detailed characterization of the seaweed lipidome was achieved based on untargeted HRMS-based analysis and lipid derivatization with a photochemical aza-Paternò-Büchi reaction. A triple-data processing strategy was carried out to achieve high structural detail on the seaweed lipidome, i.e., (i) a first data processing workflow with all samples for aligning peak and statistical analysis that led to the definition of lipid sum compositions (e.g., phosphatidylglycerol (PG) 34:1), (ii) a second data processing workflow in which the samples of each seaweed were processed separately to annotate molecular lipids with known fatty acyl isomerism (e.g., PG 16:0_18:1), and (iii) the annotation of lipid regioisomers following MS/MS annotation of the lipid derivatives obtained following the aza-Paternò-Büchi reaction(e.g., PG 16:0_18:1 ω-9). Once the platform was set up, the lipid extracts from 8 seaweed species from different seaweed families were characterized, describing over 900 different lipid species, and information on the regiochemistry of carbon-carbon double bonds uncovered unknown peculiarities of seaweeds belonging to different families. The overall analytical approach helped to fill a gap in the knowledge of the nutritional composition of seaweeds.

Enhanced Input-Doubling Method Leveraging Response Surface Linearization to Improve Classification Accuracy in Small Medical Data Processing

Currently, the tasks of intelligent data analysis in medicine are becoming increasingly common. Existing artificial intelligence tools provide high effectiveness in solving these tasks when analyzing sufficiently large datasets. However, when there is very little training data available, current machine learning methods do not ensure adequate classification accuracy or may even produce inadequate results. This paper presents an enhanced input-doubling method for classification tasks in the case of limited data analysis, achieved via expanding the number of independent attributes in the augmented dataset with probabilities of belonging to each class of the task. The authors have developed an algorithmic implementation of the improved method using two Naïve Bayes classifiers. The method was modeled on a small dataset for cardiovascular risk assessment. The authors explored two options for the combined use of Naïve Bayes classifiers at both stages of the method. It was found that using different methods at both stages potentially enhances the accuracy of the classification task. The results of the improved method were compared with a range of existing methods used for solving the task. It was demonstrated that the improved input-doubling method achieved the highest classification accuracy based on various performance indicators.

ICT in biology education: teachers’ readiness and implementation challenges in the context of Bangladesh

Information and Communication Technology (ICT) is commonly employed in biology classes to facilitate students' comprehensive comprehension of biological processes and enable them to see living phenomena. The purpose of the study was to explore the use of ICT in enhancing the teaching and learning process of biology in the secondary schools of Bangladesh. In our study, sequential explanatory mixed method design has been followed. Data was collected from purposively selected ten secondary schools of Dhaka city. The study reveals that teachers have a positive attitude towards incorporating ICT in biology lessons and possess operational understanding of data processing tools and online browsing. Nevertheless, it also emphasizes the fact that teachers are devoid of personal computers in their houses and have restricted availability to school computers. Furthermore, the existing curriculum and teachers' guides lack adequate instructions for integrating ICT into biology lectures. The study also suggests that the process of developing ICT-based curriculum is complicated, which deters teachers from incorporating ICT into their classes. Furthermore, there is a limited availability of educational materials related to ICT in the Bangla language. This study recommends continuous in-servicing training on ICT to teachers, interest free loan to teachers for purchasing laptops, developing interactive biology educational ICT contents in Bangla and include specific instructions in the curriculum and teachers’ guide on how to use ICT in biology classes. These findings have important implications for policy makers, educators, and researchers who are dedicated to expanding the use of ICT in education.

Using Online Algorithms to Solve NP-Hard Problems

This paper explores the application of online algorithms to tackle NP-hard problems, a class of computational challenges characterized by their intractability and wide-ranging real-world implications. Unlike traditional offline algorithms that have access to complete input data, online algorithms make decisions sequentially, often under constraints of incomplete information. We investigate various strategies, including greedy approaches, randomization, and competitive analysis, to assess their effectiveness in solving NP-hard problems such as the Traveling Salesman Problem, the Knapsack Problem, and the Set Cover Problem. Our analysis highlights the trade-offs between solution quality and computational efficiency, emphasizing the significance of the competitive ratio in evaluating algorithm performance. Additionally, we discuss the practical applications of online algorithms in dynamic environments, such as real-time systems and streaming data processing. Through a comprehensive review of existing literature and novel algorithmic designs, we aim to provide insights into the viability of online algorithms as a robust framework for addressing NP-hard problems in scenarios where immediate decision-making is crucial. The findings underscore the potential for future research to enhance these algorithms, making them increasingly applicable in complex, real-world contexts.

The Transitivity of Jungkook Song Lyrics “Hate You”

A song is a series of poems that have a certain meaning. Through a song, listeners can feel different emotions. In a song, songwriters usually include their feelings into the lyrics, so that listeners can understand the meaning of the song. However, it is not uncommon for people to just listen to a song without understanding the meaning of the song. Therefore, this research discusses the transitivity system type process in Jungkook's song lyrics. This research is a descriptive qualitative research. The purpose of this research is to find out the most dominating type of transitivity process in the song "Hate You" by Jungkook BTS. The data used in this research are Jungkook's song lyrics. Based on the study results, 31 transitivity processes were identified in the lyrics of the song "Hate You" by Jungkook. The most widely used transitivity in Jungkook's song lyrics is the mental process consisting of affective, cognitive, and perceptive. They were followed by the material process as the second largest process with 8 data points. Then, in the third position, is the existential process with five data points. Furthermore, the behavioral process occupies the 4th position with 2 data points. And finally, there is one verbal process data. This shows that the theme raised in a song will affect the purpose of its transitivity.