bluuebunny/crossref_metadata_2025 huggingface dataset:
IIoT: Traffic Data Flow Analysis and Modeling Experiment for Smart IoT Devices
Authors: Bhardwaj Akashdeep
Published: October 2024

The Internet of Things (IoT) has redefined several aspects of our daily lives,
including automation and control of the living environment, innovative healthcare
services, and much more. Digital IoT devices and sensors, when integrated with home
appliances, industrial systems, and online services in the physical world, have brought
intense, disruptive changes in our lives. The industry and home users have widely
embraced Internet of things or IoT. However, the innate, intrinsic repercussions
regarding security and data privacy are not evaluated. Security applies to Industrial IoT
(IIoT), which is in its infancy stage. Techniques from security and privacy research
promise to address broad security goals, but attacks continue to emerge in industrial
devices. This research explores the vulnerabilities of IIoT ecosystems not just as
individual nodes but as the integrated infrastructure of digital and physical systems
interacting with the domains. The authors propose a unique threat model framework to
analyze the attacks on IIoT application environments. The authors identified sensitive
data flows inside the IIoT devices to determine privacy risks at the application level
and explored the device exchanges at the physical level. Both these risks lead to
insecure ecosystems. The authors also performed a security analysis of physical
domains and digital domains.
Improving Performance of Machine LearningBased Intrusion Detection System Using Simple Statistical Techniques in Feature Selection
Authors: Bhardwaj Akashdeep
Published: October 2024

An increase in cyber-physical systems and IoT has increased the output of
the industry, and these systems have become the backbone of the industry. However,
these systems are vulnerable to various cyber-attacks. The increasing number of IoT
and cyber-physical systems has called for interventions in the way cybersecurity
system works. This paper evaluates the effectiveness of various feature selection
techniques– NB, LR, DT and SVM, ensembled shallow – RF and Adaboost with RBF
and uses the statistical techniques Chi and Pearson correlation coefficient for choosing
top features and applies them to the traditional machine learning algorithm to get
accuracy and detection rate. The machine learning algorithms are trained and evaluated
on the KDDCup ’99’ dataset. The study shows that machine learning algorithms work
perfectly and provide higher accuracy if the feature vector consists of a few significant
features.
ANALISIS PENGARUH BELANJA MODAL, PENYERTAAN MODAL, DAN PERTUMBUHAN PENDUDUK TERHADAP PERTUMBUHAN EKONOMI
Authors: Galuh Kirana  , Susilo
Published: October 2024

Economic development can be observed through the economic growth of a region. Capital expenditure, local government capital investment, and population growth can have different impacts on economic growth. This study aims to determine the influence of capital expenditure, local government capital investment, and population growth on economic growth. Secondary data on variables were obtained from BPS and DJPK from regencies/cities in the provinces of Banten, DI Yogyakarta, and Central Java for the period 2012-2021, using the multiple linear regression analysis method. The findings of the study align with Keynesian Theory, indicating that capital expenditure has a significant positive effect on economic growth. In contrast, local government capital investment shows a significant negative direction toward economic growth, which is inconsistent with Harrod-Domar Theory. Meanwhile, population growth has a significant positive effect on economic growth.
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI MINAT MENGGUNAKAN E-WALLET (OVO) PADA MASYARAKAT DI KOTA MALANG
Authors: Widi Krisna Darma , Girindra Mega Paksi
Published: October 2024

This research was conducted to determine the factors that influence the OVO E Wallet in the community in Malang City. The method used is a quantitative method with primary data taken using a questionnaire. This research used a sample of 172 respondents who were selected using the lemmeshow formula and purposive sampling technique. The population of this research is the community of OVO E-Wallet users in Malang City. The data was processed using multiple linear regression analysis methods. The research results show that perceived convenience (X1) has a positive and significant effect on interest in using (Y), perception of usefulness (X2) has a positive and significant effect on interest in using (Y), income (X3) has a positive and significant effect on interest in using (Y) , Age (X4) has a positive and significant effect on interest in using (Y) and education level (X5) has a negative and significant effect on interest in using (Y).
PENGARUH UPAH MINIMUM, TINGKAT PENDIDIKAN DAN USIA TERHADAP PENYERAPAN TENAGA KERJA DI JAWA TIMUR TAHUN 2017-2021
Authors: Larassanti Defina Rizka Faza  , Moh. Khusaini
Published: October 2024

The objective of this research is to discuss and analyze the effect of minimum wage, education level and age on labor force absorption in 38 regencies and cities in East Java. A 2017-2021 data was examined using Fixed Effect Model. The results of the analysis indicate that Municipal Minimum Wage negatively and significantly influences the labor absorption in regencies and cities in East Java, that age positively and significantly influence the labor absorption in regencies and cities in East Java, and that education level negatively but insignificantly impacts the labor absorption in regencies and cities in East Java.
IMPROVING BYZANTINE FAULT TOLERANCE IN SWARM ROBOTICS COLLECTIVE DECISION-MAKING SCENARIO VIA A NEW BLOCKCHAIN CONSENSUS ALGORITHM
Authors:   University of Westminster, United Kingdom, Krishnamohan Theviyanthan
Published: October 2024

Swarm robotics applies concepts of swarm intelligence to robotics. Discrete consensus achievement is one of the major behaviors found in swarm robotics. Various algorithms have been developed for discrete consensus achievement. However, existing discrete consensus achievement algorithms are vulnerable to Byzantine robots. Blockchain has been successfully used to mitigate the negative effect of Byzantine robots. Nevertheless, since the blockchain solution uses the Proof-of-Work blockchain consensus algorithm, it is vulnerable to the 51% attack. Besides, the swarm also takes longer to achieve consensus. This research proposes a novel blockchain consensus algorithm called Proof-of-Identity—which uses a private-public key pair and a swarm controller—to create a dynamically permissioned blockchain that would negate the 51%-attack problem associated with the Proof-of-Work algorithm while also reducing the consensus time. This proposed solution was tested against the classical solution and the existing blockchain solution using the collective perception scenario. Test results show that the Proof-of-Identity algorithm prevents the 51%-attack problem while improving the consensus time in comparison to the existing blockchain solution without affecting the exit probability.
A HYBRID MACHINE LEARNING APPROACH FOR EARLY DETECTION OF PADDY BLIGHT DISEASE
Authors:   Kings Engineering College, India, B Yuvaraj , S Thumilvannan ,   Kings Engineering College, India, D.C. Jullie Josephine ,   Kings Engineering College, India, Leo Sathesh Abraham ,   Kings Engineering College, India
Published: October 2024

Paddy blight is a widespread disease that affects various parts of the paddy plant, including leaves, bark, nodes, neck, part of rays, and leaves sheath. The symptoms of the disease manifest as pale yellow to pale green leaves with eye-shaped lesions, distorted margins, and gray or white centers. As the lesions expand, the leaves progressively wither and dry out, eventually leading to rot and death of the affected plant parts. In this study, we propose a machine learning algorithm for detecting paddy disease by analyzing changes in paddy leaves and correlating them with existing paddy images. The algorithm incorporates fuzzy logic and deep learning techniques to enhance disease detection accuracy and provide appropriate treatment recommendations. By leveraging the power of these advanced technologies, the proposed approach aims to facilitate early detection and effective management of paddy diseases, ultimately improving crop yield and ensuring food security.
TAXONOMY OF FINTECH ECOSYSTEM – A RESEARCH STUDY
Authors:   CMJ University, India, A Faris , K.V. Nithyananda ,   Indian Institute of Management Tiruchirappalli, India
Published: October 2024

Fintech ecosystem is developing at a fast pace with inclusion of all kinds of services within its ambit. In order to understand the fintech ecosystem, one has to understand the taxonomy of fintech ecosystem. This paper discusses the two most comprehensive taxonomies on fintech ecosystem developed by Imerman and Fabozzi (2020) and Ratecka (2020), for its advantages and disadvantages. Based on that discussion, the researcher develops his own taxonomy of fintech ecosystem and explains the rationale and also the theoretical foundations for that taxonomy. This paper is divided into four sections: (a) overview of fintech innovations; (b) Taxonomy developed by Imerman and Fabozzi; (c) taxonomy developed by Ratecka (d) synthesis of these two taxonomies and based on that the taxonomy developed by the researcher. The last section concludes the paper.
OPTIMIZING PLANT DISEASE PREDICTION: A NEURO-FUZZY-GENETIC ALGORITHM APPROACH
Authors:   Sanjivani College of Engineering, India, Chaudhari Sachin Vasant , T.S. Sasikala ,   Amrita College of Engineering and Technology, India, R.K. Gnanamurthy ,   VSB College of Engineering Technical Campus, India, Dwivedi Vijay Kumar ,   Vishwavidyalaya Engineering College, India, Kumar Davinder ,   Micron Technology, United States
Published: October 2024

In this essay, the idea of improving plant disease prediction using a Neuro-Fuzzy-Genetic algorithm (NFGA) technique is explored. The concept of a Neuro-Fuzzy-Genetic algorithm is first described. The advantages of the NFGA technique for predicting plant disorders are next addressed. An example is then given to demonstrate how this technique has been successfully used and the advantages it offers you. A hybrid artificial intelligence technique known as a Neuro-Fuzzy-Genetic set of rules (NFGA) combines the genetic algorithms, fuzzy logic, and neural network algorithms. It entails using a method of organizing fuzzy rules for statistics type, developing a network of neurons to anticipate the level of the group of data points to positive fuzzy training, adjusting the weights of fuzzy classes using a genetic algorithm-based totally optimization method to better fit the data factors, and ultimately identifying and predicting patterns of statistics points. The main benefits of this method for predicting plant diseases are its abilities to analyze various plant characteristics, extract complex relationships between statistics points, identify correlations between various environmental factors and illnesses, choose the best combinations of fuzzy rules for accurate classification, and finally adapt to changing data over time.
FUZZY LOGIC SYSTEMS WITH DATA CLASSIFICATION - A COOPERATIVE APPROACH FOR INTELLIGENT DECISION SUPPORT
Authors:   Government Science College, Hassan, India, A Alagu Karthikeyan , R.D. Jagadeesha ,   Government Science College, Hassan, India, Raha Shrinwantu ,   Government Science College, Hassan, India, Patil Harshal ,   Government Science College, Hassan, India, Williams Prince ,   University of Technology and Applied Sciences, Sohar, Sultanate of Oman
Published: October 2024

In intelligent decision support, the integration of fuzzy logic systems with data classification has emerged as a promising avenue. This cooperative approach seeks to enhance decision-making processes by leveraging the strengths of both fuzzy logic and data classification techniques. However, a critical gap exists in the literature concerning the seamless integration of fuzzy logic systems and data classification for effective decision support. Existing approaches often treat these methodologies in isolation, overlooking the synergies that can arise from their collaborative utilization. Bridging this gap is essential for developing robust decision support systems capable of handling the intricacies of modern datasets. The research aims to address this gap by proposing a cooperative approach that seamlessly integrates fuzzy logic systems and data classification methods. By doing so, it seeks to overcome the limitations of traditional decision support systems and enhance their adaptability to real-world scenarios characterized by uncertainty and complexity. The method involves the development of a hybrid system that combines fuzzy logic rules and data classification algorithms. The fuzzy logic component captures and processes imprecise information, while the data classification component identifies patterns and trends within the data. The cooperative nature of the approach ensures that each method complements the other, resulting in a more robust and effective decision support system. The results demonstrate the improved performance of the proposed cooperative approach compared to traditional decision support systems. The system exhibits enhanced accuracy and adaptability, showcasing its potential to address the challenges posed by modern datasets.
SCRIPT IDENTIFICATION FROM CAMERA CAPTURED INDIAN DOCUMENT IMAGES WITH CNN MODEL
Authors:   Sri Sathya Sai University for Human Excellence Kalaburagi Campus, India, Mallappa Satishkumar , B.V. Dhandra ,   Garden City University, India, Mukarambi Gururaj ,   Central University of Karnataka, India
Published: October 2024

Compared to typical scanners, handheld cameras offer convenient, flexible, portable, and noncontact image capture, which enables many new applications and breathes new life into existing ones, but camera-captured documents may suffer from distortions caused by a nonplanar document shape and perspective projection, which lead to the failure of current optical character recognition (OCR) technologies. This paper presents a new CNN model for script identification from camera-captured Indian multilingual document images. To evaluate the performance of the proposed model 9 regional languages, one national language and one international Roman languages are considered. Two languages, Hindi national language, and Roman English language are taken as the common languages with regional language for the study. The proposed method is applied on Bi-script, Tri-script, and Multi-script combinations. The average recognition accuracy for three script combinations is 92.92%, for bi-script 91.33%, and for tri-script 87.33%. is achieved. The proposed method is the unified approach used for identifying the script from bi-script, tri-script and multi- script camera-captured document images and is the novelty of this paper. The proposed model is compared with the Alexnet pretrained CNN model, and it achieved the highest recognition accuracy.
WHALE SWARM OPTIMIZATION BASED ANFIS FOR PREDICTION IN FORECASTING APPLICATION
Authors:   Kristu Jayanti College, India, A Sevuga Pandian , Virmani Deepali ,   Vivekananda Institute of Professional Studies Technical Campus, India, D.R. Denslin Brabin ,   DMI College of Engineering, India, Hussain Sk. Riyaz ,   Rajiv Gandhi University of Knowledge Technologies, India
Published: October 2024

This paper proposes a novel approach for solar power forecasting using an Adaptive Neuro-Fuzzy Inference System (ANFIS) enhanced with Whale Swarm Optimization (WSO). The synergy between ANFIS and WSO aims to overcome the limitations of traditional forecasting models by controlling the collective intelligence of a whale-inspired swarm algorithm. The WSO optimizes the parameters of the ANFIS, leading to improved accuracy in solar power predictions. The integration of WSO introduces a parallelism and exploration-exploitation balance inspired by the natural behaviors of whales, enhancing the ANFIS model’s adaptability to dynamic solar power generation patterns. The experimental results demonstrate the superiority of the proposed Whale Swarm-based ANFIS over conventional methods, showcasing its ability to handle non-linear and complex relationships in solar power data. This research contributes to the field of renewable energy forecasting by presenting an innovative hybrid model that leverages the strengths of both ANFIS and WSO for more reliable and precise solar power predictions.
REMOTE PATIENT MONITORING AND CLASSIFICATION OF DIABETES SUBTYPES CLASSIFICATION USING DEEP-LEARNING RECONSTRUCTION ALGORITHM
Authors:   SRM Madurai College for Engineering and Technology, India, Chelladurai Callins Christiyana , Murugesan Punitha ,   Sethu Institute of Technology, India, Esakkimani Sivajothi ,   Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, India, Pandian Selvi Shanmuga ,   Sethu Institute of Technology, India
Published: October 2024

Remote patient monitoring has become pivotal in managing chronic diseases like diabetes. This study proposes a novel approach for the classification of diabetes subtypes utilizing a deep-learning reconstruction algorithm. The system leverages continuous patient data obtained through remote monitoring devices, enabling real-time analysis for timely intervention. The deep-learning reconstruction algorithm, based on a convolutional neural network architecture, demonstrated exceptional accuracy in distinguishing between diabetes subtypes. The model achieved an overall classification accuracy of 92%, outperforming traditional methods. It exhibited high sensitivity and specificity, with values exceeding 90% for each subtype. The results showcase the system’s effectiveness in classifying diabetes subtypes: Type 1 diabetes (Sensitivity: 94%, Specificity: 92%), Type 2 diabetes (Sensitivity: 91%, Specificity: 94%), and Gestational diabetes (Sensitivity: 93%, Specificity: 91%). The system’s ability to accurately identify these subtypes ensures personalized and targeted care for patients.
SMART HOME AUTOMATION SYSTEM FOR ENERGY CONSUMPTION USING TENSORFLOW-BASED DEEP ENSEMBLE LEARNING
Authors:   Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya, India, S Umamageswari , M Kannan ,   Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya, India
Published: October 2024

Over the past decades, the evolution of new wireless technology has led to increased attention toward Smart Home Automation Systems (SHAS). In the smart home, numerous smart devices are interconnected with the proliferation of the Internet of Things (IoT) technology to provide users with a more comfortable lifestyle. Prior research on the smart home system has enacted machine learning and deep learning techniques to forecast the consecutive activities in the smart home. This research paper aims to enhance the future decision-making of energy consumption with the assistance of environmental factors and home appliances by exploiting the Tensorflow-based deep ensemble learning technique. The enhancement of future decision-making in smart home automation systems primarily involves the classification of energy consumption levels from the knowledge of external environmental factors and energy consumption levels of home appliances through the phases of data preprocessing, feature selection, fuzzy logic-based data labeling, and finally, classification of energy consumption using TensorFlow based deep ensemble learning technique. The data obtained from the effective feature selection technique is subjected to labeling via the fuzzy logic system to classify the energy consumption of smart home appliances. Finally, this work classifies the level of energy consumption based on the labeled knowledge of smart home data using a tensorflow-based deep ensemble learning model. The experimental model implements the proposed deep ensemble learning model in the tensorflow framework, which improves the decision-making performance of energy utilization in the smart home system. Experimental results illustrate that the proposed deep ensemble learning model yields superior classification performance than the other baseline classifiers, such as Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) on the smart home dataset.
Effectiveness of a Virtual Reality Serious Video Game (The Secret Trail of Moon) for Emotional Regulation in Children With Attention-Deficit/Hyperactivity Disorder: Randomized Clinical Trial
Authors: Martin-Moratinos Marina , Bella-Fernández Marcos , Rodrigo-Yanguas María , González-Tardón Carlos , Li Chao , Wang Ping , Royuela Ana , Lopez-Garcia Pilar , Blasco-Fontecilla Hilario
Published: October 2024

Background

Difficulties in emotional regulation are often observed in children and adolescents with attention-deficit/hyperactivity disorder (ADHD). Innovative complementary treatments, such as video games and virtual reality, have become increasingly appealing to patients. The Secret Trail of Moon (MOON) is a serious video game developed by a multidisciplinary team featuring cognitive training exercises. In this second randomized clinical trial, we evaluated the impact of a 20-session treatment with MOON on emotional regulation, as measured by the Strengths and Difficulties Questionnaire.

Objective

We hypothesize that patients with ADHD using MOON will show improvements in (1) emotional regulation, (2) core ADHD symptoms, (3) cognitive functioning, and (4) academic performance, compared to a control group; additionally, we anticipate that (5) changing the platform (from face-to-face using virtual reality to the web) will not affect emotional regulation scores; and (6) the video game will not cause any clinically significant side effects.

Methods

This was a prospective, unicentric, randomized, unblinded, pre- and postintervention study with block-randomized sequence masking. Participants included individuals aged between 7 and 18 years who had a clinical diagnosis of ADHD and were receiving pharmacological treatment. They were randomized into 2 groups using an electronic case report form: the MOON group, receiving standard pharmacological treatment plus personalized cognitive training via a serious video game, and the control group, receiving standard pharmacological treatment. We provided both the groups with psychoeducational support on ADHD. Analysis was conducted using the Student 2-tailed t test and 2-factor ANOVA. An independent monitor supervised the study.

Results

A total of 76 patients with ADHD participated in the trial, with an equal randomization (MOON: n=38, 50% and control: n=38, 50%) and a total dropout rate of 7. The primary hypothesis, a 3- or 4-point reduction in the global Strengths and Difficulties Questionnaire score, was not met. However, significant improvements were observed in material organization (P=.03), working memory (P=.04), and inhibition (P=.05), particularly among patients more engaged with the MOON treatment.

Conclusions

Serious video games, when integrated into a multimodal treatment plan, can enhance outcomes for symptoms associated with ADHD.

Trial Registration

ClinicalTrials.gov NCT06006871; https://clinicaltrials.gov/study/NCT06006871

International Registered Report Identifier (IRRID)

RR2-10.2196/53191
Wearables and Smartphones for Tracking Modifiable Risk Factors in Metabolic Health: Protocol for a Scoping Review
Authors: Brügger Victoria , Kowatsch Tobias , Jovanova Mia
Published: October 2024

Background

Metabolic diseases, such as cardiovascular diseases and diabetes, contribute significantly to global mortality and disability. Wearable devices and smartphones are increasingly used to track and manage modifiable risk factors associated with metabolic diseases. However, no established guidelines exist on how to derive meaningful signals from these devices, often hampering cross-study comparisons.

Objective

This study aims to systematically overview the current empirical literature on how wearables and smartphones are used to track modifiable (physiological and lifestyle) risk factors associated with metabolic diseases.

Methods

We will conduct a scoping review to overview how wearable and smartphone-based studies measure modifiable risk factors related to metabolic diseases. We will search 5 databases (Scopus, Web of Science, PubMed, Cochrane Central Register of Controlled Trials, and SPORTDiscus) from 2019 to 2024, with search terms related to wearables, smartphones, and modifiable risk factors associated with metabolic diseases. Eligible studies will use smartphones or wearables (worn on the wrist, finger, arm, hip, and chest) to track physiological or lifestyle factors related to metabolic diseases. We will follow the reporting guideline standards from PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and the JBI (Joanna Briggs Institute) guidance on scoping review methodology. Two reviewers will independently screen articles for inclusion and extract data using a standardized form. The findings will be synthesized and reported qualitatively and quantitatively.

Results

Data collection is expected to begin in November 2024; data analysis in the first quarter of 2025; and submission to a peer-reviewed journal by the second quarter of 2025. We expect to identify the degree to which wearable and smartphone-based studies track modifiable risk factors collectively (versus in isolation), and the consistency and variation in how modifiable risk factors are measured across existing studies.

Conclusions

Results are expected to inform more standardized guidelines on wearable and smartphone-based measurements, with the goal of aiding cross-study comparison. The final report is planned for submission to a peer-reviewed, indexed journal. This review is among the first to systematically overview the current landscape on how wearables and smartphones measure modifiable risk factors associated with metabolic diseases.

International Registered Report Identifier (IRRID)

PRR1-10.2196/59539
Factors Related to mHealth App Use Among Japanese Workers: Cross-Sectional Survey (Preprint)
Authors: Ozaki Itsuko , Nishijima Mariko , Shibata Eiji , Zako Yuri , Chiang Chifa
Published: October 2024

BACKGROUND

Health care providers can make health guidance more effective by using mobile health technologies such as health apps. Although health care providers need to know who uses health apps, existing studies have yielded inconsistent results.

OBJECTIVE

The aim of the study was (1) to clarify the prevalence and patterns of health app use to improve health behaviors for preventing lifestyle-related diseases among Japanese workers and (2) to identify the associations among demographic characteristics, health behavior, and internet use and health app use by gender.

METHODS

Data were collected from a cross-sectional internet survey in 2023. In total, 2200 participants were included, with an even distribution of men and women in each age group aged 20 to 60 years. The participants were workers with smartphones and reported their gender, age, residence area, marital status, education, employment status, occupation, work pattern, diseases under treatment, health checkups, health guidance, health behaviors, internet use duration, and number of devices used. We asked about current and previous health app use for 1 month. A multivariate logistic regression analysis was conducted by gender.

RESULTS

Of the participants, 472 (21.5%) and 189 (8.6%) were current and previous health app users, respectively. Most current and previous health app users used features that record and track their physical activity and other health behaviors. Health app users—both men and women—were more likely to have health checkups (odds ratio [OR] 1.53, 95% CI 1.12-2.11 and OR 1.51, 95% CI 1.10-2.07, respectively), receive health guidance (OR 2.01, 95% CI 1.47-2.74 and OR 1.86, 95% CI 1.32-2.62, respectively), engage in regular physical activity (OR 2.57, 95% CI 1.91-3.47 and OR 1.94, 95% CI 1.41-2.67, respectively), use the internet for 120-179 minutes per day (OR 1.76, 95% CI 1.13-2.75 and OR 1.70, 95% CI 1.12-2.57, respectively), and were less likely to be older (50-59 years: OR 0.54, 95% CI 0.33-0.88 and OR 0.40, 95% CI 0.25-0.6, respectively, and 60-69 years: OR 0.37, 95% CI 0.22-0.62 and OR 0.47, 95% CI 0.28-0.77, respectively). According to gender, male health app users were more likely to be married (OR 1.69, 95% CI 1.23-2.33) and less likely to work in the security, agriculture, forestry, fishing, manufacturing, or transportation industries (OR 0.62, 95% CI 0.41-0.95). Female health app users were more likely to have a university education or higher (OR 1.55, 95% CI 1.061-2.26), maintain an appropriate body weight (OR 1.52, 95% CI 1.10-2.11), and use 3 or more devices (OR 2.13, 95% CI 1.41-3.23).

CONCLUSIONS

Physical activity and health guidance are strong predictors of app use. Health care providers should assess the target populations’ preferences for app use based on their characteristics, support their app use, and enhance the effectiveness of health guidance.

CLINICALTRIAL
Digital Health Literacy of the General Population: Results From a National Survey (Preprint)
Authors: Cho Juhee , Yoon Junghee , Yang Seongwoo , Kang Soo Jin , Lee Mangyeong , Kim Dokyoon , Park Joungwon , Kim Su jin , Han Jiyoon , Joo Jungin
Published: October 2024

BACKGROUND

As the rapid advancement of digital health technologies has significantly improved health management, digital health literacy (DHL) has emerged as a crucial determinant of health outcomes.

OBJECTIVE

This study explored DHL in a representative sample of the general population and the relationship between DHL, demographic, and socioeconomic factors among adults.

METHODS

This cross-sectional survey was conducted from July 20 to July 31, 2022, among adults aged 19 and older in Korea. Participants were recruited nationally, with those under 55 years completing an online survey and those 55 years and older participating in face-to-face interviews to address the digital divide. Participants’ DHL was assessed using a 34-item Digital Health Technology Literacy Assessment Questionnaire that covered information and communications technology (ICT)terms, icons, app use, and health information evaluation.

RESULTS

A survey of 1,041 adults revealed that 27.8% of the population exhibited low DHL, particularly in using mobile health apps. The 34-point scale was converted to a 100-point scale to improve the comparison, with an average score of 73.8. The low DHL group scored 31.5, whereas the high DHL group scored 90.3. In the high DHL group, 95.2% of the participants were able to sign up for an app compared with 17% in the low DHL group. Additionally, 94.7% of the high DHL group could update an app compared with 18% of the low DHL group. In the adjusted model, older age, lower income, and limited health literacy were strongly associated with lower DHL, highlighting these as key factors contributing to reduced digital health competency.

CONCLUSIONS

This study highlights the ongoing digital health disparities, emphasizing the need for tailored strategies in mobile applications and digital health technologies. Addressing modifiable factors such as health literacy is key to ensuring equitable access and the effective use of digital health resources.

CLINICALTRIAL

N/A
Efektivitas Pemberian Penyekat Pompa Proton Rute Intravena Drip dan Intermiten Pada Perdarahan Saluran Cerna Atas Nonvariseal
Authors: Luthfiananda Berliana , Puspita Sari Ika , Triwikatmani Catharina
Published: October 2024

Perdarahan saluran cerna bagian atas (PSCBA) nonvariseal merupakan perdarahan yang memiliki insidensi yang tinggi dan tingkat hospitalisasi yang terus meningkat. Salah satu terapi yang dapat membantu mengurangi risiko perdarahan adalah Penyekat Pompa Proton (Proton Pump Inhibitor/PPI) yang sampai saat ini masih mengalami perdebatan terkait rute pemberian intravena drip dan intermiten. Pembahasan ini bertujuan untuk menganalisis rute pemberian yang tepat pada PSCBA nonvariseal untuk meningkatkan efektivitas terapi. Literatur yang digunakan dicari dengan pustaka elektronik melalui database Google Scholar, Pubmed, dan Science direct dari tahun 2013 – 2023. Luaran klinik yang diamati ialah adanya perdarahan ulang atau lama rawat inap dirumah sakit. Pemberian dengan rute intravena drip dan intermiten tidak berbeda signifikan berdasarkan luaran klinik tingkat perdarahan dan perdarahan ulang. Pemberian dengan rute intravena intermiten masih dapat digunakan untuk mengurangi peningkatan biaya perawatan.
Efektivitas Penggunaan Ozon Untuk Dekontaminasi Mikroba Pada Simplisia Herba Sambiloto (Andrographis paniculata (Burm.f.) Nees) di Pasar Bringharjo Dengan Pengujian Angka Lempeng Total (ALT) dan Angka Kapang Khamir (AKK)
Authors: Cempaka Agnesia , Bayumurti Yosi , Purwantini Indah
Published: October 2024

Indonesia secara geografis terletak pada daerah tropis yang memiliki kelembaban relatif tinggi. Kelembaban yang tinggi dapat meningkatkan risiko kontaminasi mikroba. Mikroba tumbuh subur pada lingkungan yang memiliki kadar air tinggi. Produk simplisia merupakan salah satu produk yang dapat terkontaminasi pada kondisi tersebut. Simplisia sebagai bahan baku obat tradisional/obat herbal biasanya disimpan dalam waktu yang cukup lama sebelum digunakan pada proses pembuatan obat. Sambiloto (Andrographis paniculata (Burm.f.) Nees) merupakan tanaman yang sering digunakan untuk bahan baku obat herbal.Dekontaminasi menggunakan ozon merupakan metode untuk menghilangkan kontaminan mikroba. Dalam penelitian ini dilakukan perlakuan ozon pada 50 g sampel herba sambiloto dilakukan dengan kekuatan 10 g/h dengan interval 0; 30; 60; 90 dan 120 menit. Efektivitas ozon diukur dengan melakukan uji Angka Lempeng Total (ALT) dan Angka Kapang Khamir (AKK) pada sampel yang diberikan perlakuan dan tanpa perlakuan (kontrol).Dekontaminasi mikroba menggunakan ozon 30; 60; 90; dan 120 menit pada simplisia herba sambiloto dapat menurunkan persentase nilai ALT berturut-turut sebesar 88%; 99%; 99,6% dan 100%, sedangkan untuk persentase penurunan nilai AKK berturut-turut adalah 33%; 67%; 100% dan 100%.
Perbandingan Disolusi Dispersi Padat Valsartan Dalam Bentuk Tablet dan Kapsul
Authors: Cahyani Sulastari , Laksitorini Marlyn Dian , Sulaiman Teuku Nanda
Published: October 2024

ABSTRAK Valsartan adalah zat aktif yang sukar larut air yang mana kelarutannya bergantung pada pH saluran cerna. Dispersi padat merupakan salah satu metode yang efektif untuk meningkatkan kelarutan dan disolusi zat aktif sukar larut. Penelitian sebelumnya telah didapatkan dispersi padat valsartan dengan pembawa PVP VA, poloxamer 188 dan poloxamer 407 yang memiliki kelarutan jenuh dan disolusi intrinsik yang baik. Kendati demikian pelepasan dispersi padat valsartan dalam bentuk sediaan khususnya tablet dan kapsul belum diketahui, sebagaimana diketahui bahwa pembawa yang digunakan dan bentuk sediaan obat mempengaruhi profil disolusi. Penelitian ini bertujuan untuk membandingkan disolusi dispersi padat valsartan dalam bentuk sediaan  tablet dan kapsul pada pH 1,2 dan 4,5. Karakteristik fisik tablet dispersi padat dispersi padat valsartan meliputi kekerasan, kerapuhan dan waktu hancur. Adanya PVP VA, poloxamer 188 dan poloxamer 407 pada sistem dispersi padat menghasilkan tablet yang memiliki pelepasan obat diperlama dan menghasilkan daya apung setelah diformulasi menjadi tablet. Kapsul dispersi padat valsartan memiliki profil disolusi yang lebih baik dibandingkan tablet dispersi padat valsartan pada pH 1,2 maupun pH 4,5.  Nilai Q60 kapsul dispersi padat valsartan pada pH 1,2 dan 4,5 berturut-turut 2,73% dan 103,28% sedangkan nilai Q60 tablet dispersi padat valsartan berturut-turut 2,89% dan 36,75%.Kata Kunci : dispersi padat, valsartan, tablet, kapsul
Comparison Among Proton Pump Inhibitor Inducing Pneumonia in Hospital: Narrative Review
Authors: Sari Juwita Permata , Nugroho Agung Endro , Widyati Widyati
Published: October 2024

Critically ill patients requiring intensive care unit (ICU) are highly vulnerable to the emergence of stress-related gastrointestinal bleeding, a condition closely associated with unfavorable clinical outcomes. Despite advancements in preventive measures, antimicrobial therapies, and supportive medical care, ventilator-associated pneumonia (VAP) and hospital-acquired pneumonia (HAP) continue to effect morbidity and mortality rates significantly. Hospital-acquired pneumonia (HAP) is characterized as a form of pneumonia that does not manifest during the period of hospital admission but rather emerges 48 hours or later after the patient has been admitted. In contrast, VAP occurs after 48 hours of having an endotracheal tube in place. Proton pump inhibitors (PPIs) are used prophylactically to manage stress ulcers in critically ill patients. However, recent scholarly literature has drawn attention to a potential link between using acid-suppressing medications and an increased susceptibility to pneumonia. The precise mechanism through which these acid suppressors might elevate the risk of pneumonia remains unclear. The primary objective of this study was to assess the prevalence of pneumonia associated with different types of proton pump inhibitors. We conducted an extensive literature search using keywords such as "(omeprazole or pantoprazole or lansoprazole or esomeprazole or rabeprazole), ICU, Pneumonia" on two prominent electronic databases: Scopus and PubMed. We identified fourteen articles meeting our inclusion criteria, which were categorized into four groups based on the type of proton pump inhibitor: omeprazole, esomeprazole, lansoprazole, and pantoprazole. The results of this narrative review revealed varying risk levels associated with using different proton pump inhibitors for pneumonia. Esomeprazole had the highest risk level, at 48.84%, followed by lansoprazole at 27.85%, omeprazole at 22.5%, and pantoprazole at 19.94%.
THE EFFECT OF FUROSEMIDE IN CRITICALLY ILL ADULT PATIENTS – A NARRATIVE REVIEW
Authors: Putri Cyndi Yulanda , Nugroho Agung Endro , Widyati Widyati
Published: October 2024

Background: Fluid overload is the common condition in the critically ill patients. This associated with the increased fluid intake and inadequate elimination. The management of fluid overload is by fluid removal with diuretic. Furosemide is a loop diuretic that frequently used as the initial therapy. However, the utilization of furosemide in the critically ill reminds a polarizing subject. The purpose of this study is to investigate the impact of furosemide on patients who are critically ill. Methods: The inclusion criteria included randomized controlled trials as well as observational cohort studies. The data sources utilized in this study were PubMed, Science Direct, ProQuest, and Cochrane.Results: We included 13 articles, of which 9 articles about generally critically ill patients with or without acute kidney injury (AKI), 2 articles about heart failure, and 2 articles about post operative. The furosemide was effective in generally critically ill patients with or without AKI, it can decrease the fluid balance, weight change, and improves the urine output. Furosemide had no harmful effect on kidney function. However, patients without oliguria were not recommended to receive high dose of furosemide. Critically ill patients with heart failure who received continuous infusion of furosemide were more susceptible to increased diuresis and greater depression of thoracic fluid content (TFC). Furthermore, it might cause the decrease of renal function. When compared to furosemide, continuous veno-venous hemodiafiltration (CVVHDF) was more successful at removing excess fluid, reducing weight, relieving symptoms, and improving hemodynamic and cardiac performance. In post operative patients, furosemide might cause metabolic alkalosis. Urinary electrolyte excretion rates were promptly altered by the use of low dose furosemide.Conclusion: Based on the patient's clinical data, furosemide use should be taken into consideration. In general, furosemide is effective to improve diuresis. Furosemide in the AKI condition with oliguria has beneficial effect. However, in heart failure condition, furosemide might affect renal function. In post operative, it might cause metabolic alkalosis. Further randomized controlled trial (RCT) is required.
Pengaruh Brief Counseling Apoteker terhadap Kepatuhan Pengobatan dan Perbaikan Tekanan Darah Pasien Hemodialisa
Authors: Yasin Nanang Munif , Filliana Ulfa , Kristina Susi Ari
Published: October 2024

Intervensi hemodialisa memunculkan berbagai komplikasi, paling banyak yaitu hipertensi yang dapat meningkatkan resiko rawat inap dan kematian lebih cepat. Polifarmasi, ketidakpatuhan minum obat, dan kurangnya pemahaman pasien menjadi faktor penyebab target tekanan darah sulit tercapai. Penelitian ini bertujuan untuk mengetahui pengaruh brief counseling apoteker terhadap kepatuhan pengobatan dan perbaikan tekanan darah pasien hemodialisa. Rancangan penelitian quasi-experimental dengan desain pretest-posttest with control group. Intervensi brief counseling dilakukan 1x seminggu selama 1 bulan menggunakan teknik 5A dan alat bantu leaflet pada pasien hemodialisa dengan kriteria inklusi eksklusi selama bulan September-Oktober 2023. Data dianalisis statistik menggunakan uji Paired sample t-test dan Wilcoxon test untuk melihat perbedaan masing-masing kelompok, uji Independent sample t-test dan Mann Whitney test untuk melihat perbedaan antar kelompok setelah intervensi. Sebanyak 57 responden terdiri dari 29 kelompok kontrol dan 28 kelompok intervensi, menunjukkan bahwa kelompok intervensi terdapat peningkatan kepatuhan dengan selisih skor pretest posttest Δ2,71±1,822 dibandingkan kelompok kontrol Δ-0,44±1,152 (p<0,001), perbaikan tekanan darah yaitu penurunan sistolik dengan selisih skor pretest posttest Δ-12,07±8,969 dibandingkan kelompok kontrol Δ-2,55±4,695 (p<0,001) dan diastolik dengan selisih skor pretest posttest Δ-5,03±5,439 dibandingkan kelompok kontrol Δ-0,86±4,420 (p=0,004). Intervensi brief counseling apoteker disimpulkan dapat memperbaiki kepatuhan dan tekanan darah pasien hemodialisa.
Bauran Pemasaran Obat Over The Counter Untuk Meningkatkan Efektivitas Strategi Penjualan di Perusahaan: Literatur Interview
Authors: Silvia Desy , Satibi Satibi , Syahlani Suci Paramita
Published: October 2024

Industri Farmasi merupakan perusahaan yang memiliki peluang tumbuh besar dalam mengembangkan obat dan mengalami persaingan yang kompetitif antar perusahaan. Persaingan obat OTC berangekaragam yang dihadapi setiap perusahaan. Sehingga, perusahaan dapat menggunakan bauran pemasaran dengan elemen 4P (produk, tempat, harga dan promosi) yang dapat meningkatkan efektivitas penjualan. Artikel ini bertujuan untuk melakukan tinjauan terhadap strategi bauran pemasaran untuk meningkatkan efektivitas penjualan obat OTC di perusahaan farmasi di berbagai daerah yang diteliti sejak tahun 2018-2022. Pencarian artikel yang dilakukan menggunakan database Garuda dan Google Scholar dengan kata kunci “bauran pemasaran obat OTC” dan “efektivitas penjualan” dengan pendekatan kualitatif. Didapatkan delapan artikel yang memenuhi kriteria inklusi. Delapan artikel yang direview, meliputi penelitian di berbagai daerah seperti Cikarang, Bandung, Semarang dan Jakarta. Strategi bauran pemasaran yang paling efektif untuk meningkatkan penjualan yaitu menggunakan strategi promosi. Aktivitas promosi yang dilakukan perusahaan yaitu mulai dari pengiklanan melalui sosial media dan browsur atau pamflet, serta pemberian potongan harga. Strategi promosi yang dilakukan perusahaan memberikan pengaruh paling besar untuk meningkatkan efektivitas obat OTC karena strategi tersebut menjadi peluang dan mempermudah pelanggan untuk mengetahui obat yang dijual oleh perusahaan
Hubungan Dukungan Keluarga Terhadap Kepatuhan Minum Obat Pasien dengan Gangguan Jiwa: Studi Literature Review
Authors: Asyari Wahid Hasyim , Widayanti Anna Wahyuni
Published: October 2024

Orang dengan Gangguan Jiwa (ODGJ) adalah seseorang yang mengalami gangguan fikiran, perilaku dan emosi, ditunjukkan pada peningkatan gejala dan perubahan perilaku yang signifikan, sehingga menyebabkan penderitaan dan gangguan kinerja dalam kehidupan. Salah satu aspek penting dalam pengobatan gangguan kesehatan jiwa adalah kepatuhan pasien dalam minum obat. Kepatuhan minum obat pada pasien gangguan jiwa dapat diawasi dengan baik jika ada dukungan dari anggota keluarga. Pasien dengan gangguan jiwa sangat bergantung pada dukungan keluarga, yang merupakan komponen penting dalam menentukan kepatuhan mereka terhadap minum obat. Penelitian ini bertujuan untuk mengetahui hubungan dukungan keluarga terhadap kepatuhan minum obat pasien dengan gangguan jiwa di Indonesia. Metode: Penelitian ini menggunakan metode Literature Review, pengumpulan data/ artikel dilakukan dengan mengakses database jurnal Garuda dan Researchgate. Berdasarkan hasil review dari sepuluh artikel memberikan hasil yang signifikan antara dukungan keluarga dan kepatuhan minum obat pasien jiwa. Kesimpulan dari hasil tinjauan menunjukkan bahwa ada hubungan antara dukungan keluarga dan kepatuhan minum obat pasien dengan gangguan jiwa. Semakin banyak dukungan keluarga yang diberikan, semakin baik kepatuhan minum obat pasien dengan gangguan jiwa.
Perbandingan Efektivitas Antihipertensi pada Terapi Stroke Iskemik : Kajian Literatur
Authors: Ivanka Luna , Ikawati Zullies , Andayani Tri Murti
Published: October 2024

Stroke merupakan penyakit pada otak berupa gangguan fungsi syaraf lokal dan atau global, yang munculnya mendadak, progresif dan cepat. Gangguan fungsi syaraf pada stroke disebabkan oleh gangguan peredaran darah otak non traumatik. Hipertensi merupakan salah satu faktor dengan kejadian stroke. Penanganan hipertensi pada pasien stroke iskemik diharapkan dapat meningkatkan atau memperbaiki outcome klinis pada pasien.  Rekomendasi obat antihipertensi untuk pasien stroke antara lain alfa-2-adrenoreseptor agonis, ACEI, ARB, BB, CCB dan diuretik. Studi ini bertujuan untuk mengetahui efektivitas penggunaan antihipertensi pada pasien stroke iskemik dengan metode tinjauan naratif menggunakan artikel dari database Cochrane, Pubmed, dan Scopus. Pencarian artikel menggunakan kata kunci, antihypertensive, clinical outcome, stroke ischemik, case study, cohort dan Randomized Control Trial (RCT), yang terbit antara tahun 2014 hingga 2024. Hasil penyaringan didapatkan 7 artikel studi yang masuk kriteria inklusi dan eksklusi. Antihipertensi golongan CCB dan ARB lebih direkomendasikan, dengan luaran klinik pasien membaik. Kesimpulan pada studi tinjauan naratif ini, terapi antihipertensi pada pasien stroke iskemik selama rawat inap disesuaikan dengan kondisi klinik pasien. Antihipertensi golongan CCB dan ARB lebih direkomendasikan karena efektivitasnya terhadap penurunan tekanan darah dan perbaikan luaran klinis pasien stroke iskemik.
Psychological Well-Being Women Workers Multiple Role in Bandung: Analysis of Stress And Aggression of Children
Authors: Zakariyya Farhan , Wyandini Diah Zaleha , Wulandari Anastasia
Published: October 2024

This study aims to obtain empirical data on the psychological well-being of female employees in Bandung related to work stress and aggression towards children. The method used in this study is a survey method with data collection techniques using purposive sampling. Research respondents are female employees who have dual roles as housewives and as employees in Greater Bandung. The research instruments used were Psychological well-being, Perceived Stress Questionnaire, and Aggression Scale towards children. The data analysis technique of this research is multiple regression analysis. The regression test results stress and aggression in children have a significant positive impact in predicting psychological well-being (F=25.392, P=<0.001).The psychological well-being of female employees in Greater Bandung can be influenced by stress and aggression towards children. This research can be the basis of policy making for working women who have dual roles in controlling stress and the ability to manage aggression to improve psychological well being.