Background: Irritable bowel syndrome (IBS) is a chronic condition prevalent in university students. It significantly affects quality of life (QOL) and requires effective interventions to appropriately manage its symptoms in this population. Objectives: To develop and evaluate the effectiveness of an integrated cognitive behavioral therapy (CBT) program using a mobile application to alleviate symptoms and enhance the QOL of university students with IBS. Methods: This quasi-experimental study included 58 participants from two universities in South Korea, who were divided into three groups: the CBT only group, the CBT with application group, and the control group that received a single educational session. An integrated eight-session CBT program and a mobile application for IBS self-management were developed and implemented. Outcomes, including the IBS Severity Scoring System (IBS-SSS), academic stress, depression, IBS-QOL, and heart rate variability, were evaluated at baseline, post-intervention, and at the 16-week follow-up. Results: Significant interactions between group and time were observed for IBS-SSS (Wald χ2 = 13.49, p = 0.009). Although the short-term effects for IBS-SSS were greater in the CBT group than in the control group, the long-term effects for IBS-SSS at 16 weeks were more sustained in the CBT with application group. Both the CBT and CBT with application groups demonstrated improvements in academic stress, depression, and QOL, but not in heart rate variability, whereas the control group demonstrated limited changes. Conclusions: The integrated CBT program, with or without a mobile application, effectively reduced the severity of IBS symptoms, depression, and academic stress among university students. This combined approach may provide long-term benefits for symptom management and psychosocial well-being. Further research is warranted to optimize the use of mobile device applications for CBT delivery.
Rainfall prediction efforts had been prevalent ever since the impact of climate change on occurrences of natural disasters globally. Implementation of machine and deep learning techniques on features that contribute to rainfall occurrences were conducted with aims of seeking greater prediction accuracy for rainfall occurrences with a lack of study for significance of features in rainfall occurrence prediction. This study presents a framework of rainfall prediction features' significance analysis in the case study of Peninsular Malaysia rainfall occurrences. Features investigated in this study consist of temperature, humidity and wind speed. The designed framework for the investigation includes phases of data collection, data preprocessing, integration of random forest (RF) for ensemble classification and feature importance (FI) for feature significance calculation and finally model evaluation based on the metrics of precision, recall, F1 score and receiver operating characteristic (ROC) curve. In the preliminary investigation, the prediction model demonstrated accuracy, precision, recall and F1-score of 80.65%, 80%, 81% and 0.80 respectively. Humidity was found to have highest significance to the model's predictive power as compared to temperature and wind speed. Rainfall occurrence correlation with lower temperature and higher humidity and vice versa was identified with further investigation of feature data distribution against rainfall occurrences.
Electronic computers. Computer science, Information technology
Marcelo T. Okano, William Aparecido Celestino Lopes, Sergio Miele Ruggero
et al.
This paper presents a lightweight and cost-effective computer vision solution for automated industrial inspection using You Only Look Once (YOLO) v8 models deployed on embedded systems. The YOLOv8 Nano model, trained for 200 epochs, achieved a precision of 0.932, an mAP@0.5 of 0.938, and an F1-score of 0.914, with an average inference time of ~470 ms on a Raspberry Pi 500, confirming its feasibility for real-time edge applications. The proposed system aims to replace physical jigs used for the dimensional verification of extruded polyamide tubes in the automotive sector. The YOLOv8 Nano and YOLOv8 Small models were trained on a Graphics Processing Unit (GPU) workstation and subsequently tested on a Central Processing Unit (CPU)-only Raspberry Pi 500 to evaluate their performance in constrained environments. The experimental results show that the Small model achieved higher accuracy (a precision of 0.951 and an mAP@0.5 of 0.941) but required a significantly longer inference time (~1315 ms), while the Nano model achieved faster execution (~470 ms) with stable metrics (precision of 0.932 and mAP@0.5 of 0.938), therefore making it more suitable for real-time applications. The system was validated using authentic images in an industrial setting, confirming its feasibility for edge artificial intelligence (AI) scenarios. These findings reinforce the feasibility of embedded AI in smart manufacturing, demonstrating that compact models can deliver reliable performance without requiring high-end computing infrastructure.
Maja Rajković, Ivana Jelić, Marija Janković
et al.
The increasing importance of waste materials utilization with the necessary modification to remove various pollutants from industrial wastewater has been a research focus over the past few decades. Using waste material from one industry to solve pollution problems in another ultimately leads toward sustainable and circular approaches in environmental engineering, solving waste management and wastewater treatment issues simultaneously. In contemporary research and industry, there is a notable trend toward utilizing industrial wastes as precursors for adsorbent formation with a wide application range. In line with this trend, red mud, a byproduct generated during alumina production, is increasingly viewed as a material with the potential for beneficial reuse rather than strictly a waste. One of the potential uses of red mud, due to its specific composition, is in the removal of heavy metal and radionuclide ions. This study summarizes red mud’s potential as an adsorbent for wastewater treatment, emphasizing techno-economic analysis and sorption capacities. An overview of the existing research includes a critical evaluation of the adsorption performance, factors influencing efficiency rather than efficacy, and the potential for specific pollutant adsorption from aqueous solutions. This review provides a new approach to a circular economy implementation in wastewater treatment while guiding future research directions for sustainable and cost-effective solutions.
Micro diamond tools are indispensable for the efficient machining of microstructured surfaces. The precision in tool manufacturing and cutting performance directly determines the processing quality of components. The manufacturing of high-quality micro diamond tools relies on scientific design methods and appropriate processing techniques. However, there is currently a lack of systematic review on the design and manufacturing methods of micro diamond tools in academia. This study systematically summarizes and analyzes modern manufacturing methods for micro diamond tools, as well as the impact of tool waviness, sharpness, and durability on machining quality. Subsequently, a design method is proposed based on the theory of cutting edge strength distribution to enhance tool waviness, sharpness, and durability. Finally, this paper presents current technical challenges faced by micro diamond tools along with potential future solutions to guide scientists in this field. The aim of this review is to contribute to the further development of the current design and manufacturing processes for micro diamond cutting tools.
Materials of engineering and construction. Mechanics of materials, Industrial engineering. Management engineering
Schizophrenia is a chronic mental illness that leads the patient to hallucinations and delusions with a prevalence of 0.4% worldwide. The importance early detection of Schizophrenia is tracking the pre-syndrome of Schizophrenia during the active phase, and could reduce psychosis symptomatic. However, the method sometimes cannot detect the symptoms accurately. As an alternative, machine learning can be implemented on microarray data for early detection. This study aimed to implement three ensemble methods, i.e., Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost) to identify Schizophrenia. Hyperparameter tuning was performed to improve the performance of the models. Based on the results, we found that the model 6, which is developed by the XGBoost method, performs better than other models with the value of accuracy and F1-score are 0.87 and 0.87, respectively.
Ikan mas koki sangat populer dan diminati untuk dipajang di dalam kolam kaca atau akuarium karena ikan tersebut memiliki warna yang cantik dan menarik. Tetapi banyak pembudidaya ikan hias yang kesulitan dalam memaksimalkan hasil budidaya bahkan sampai menimbulkan kerugian yang cukup besar. Pada saat ini para pembudidaya masih melakukan pemantauan dan mengontrol kolam secara langsung. Penelitian ini melakukan monitoring serta kontrol otomatis kadar keasaman (pH) dan suhu untuk perangkat yang dapat diakses secara online dengan menggunakan jaringan wifi supaya pengguna dapat dengan mudah untuk melakukan monitoring serta kontrol dari jarak jauh. Dengan monitoring air kolam ikan menggunakan sensor suhu DS18B20 dan Module 4502C dengan sensor pH electrode, untuk kontrol menggunakan 2 solenoid valve (asam dan basa), water heater dan fan cooler. Monitoring dan kontrol pH dan suhu dilakukan menggunakan aplikasi Blynk dengan program ada pada ESP32 serta koneksi jaringan Wi-Fi sebagai penghubung. Hasil penelitian dengan Alat Sensor DS18B20 adalah 0,631% hampir tidak ada kesalahan karena penempatan sensor yang tepat dan berdampingan dengan suhu digital dan pada sensor diberikan resistor 4,7 kOhm sehingga teganggan yang masuk pada sensor DS18B20 menjadi stabil. Pengamatan pH Module 4502C 4,128% hal ini didaptkan suatu kesalahan karena memang teganggan yang masuk pada Module 4502C kurang stabil dikarenakan jalur jumper yang dilewati arus dari Module 4502C mempengaruhi tegangan yang masuk ke sensor pH electrode. Pengamatan Water Heater dengan hasil suhu DS18B20 selama 5 menit 1,939°C dan suhu digital 1,950°C. pengamatan Fan Cooler dengan hasil suhu selama 10 menit 3,039°C dan suhu digital 3,097°C. Solenoid Valve asam dan basa hasil rata-rata dari keluaran Solenoid Valve selama 6 detik sama dengan 1mil air asam dan basa yang keluar dengan pengukuran pH Electrode yaitu 2,907 dan PH Digital yaitu 2,840.
The technology revolution has been a quantum leap in the creation of new teaching methods to bring special advantages to people who may find it difficult to enroll in the traditional education system. Algeria is among the countries that have adopted e-learning to optimize the use of modern technologies. Our study aims primarily at highlighting the experience of Algeria by following up the methods of e-learning and showing the most important features of this type of education.
Special aspects of education, Information technology
Diana Marcela Ramirez Bernal, Leticia Gorri Molina
Introdução: A pesquisa analisou a importância das competências exigidas para aplicação da gestão do conhecimento no Arquivo da Justiça do Trabalho de Londrina. Método: foi utilizado o estudo de caso, aplicando-se como fontes de evidência para coleta de dados o questionário com os servidores do Arquivo, observação direta e análise da documentação. Resultados: evidenciou-se que as atividades no Arquivo são rotineiras e muitas baseadas na legislação do judiciário, o que prejudica o desenvolvimento de ideias que criem um ambiente gerador de conhecimento. Em relação às competências comportamentais, verificou-se uma ordem de importância, apresentadas da mais para menos importante: autoconsciência, empatia, automotivação, autocontrole, e comunicação. Conclusão: embora os servidores desempenhem algumas das atividades voltadas à gestão do conhecimento, elas não são registradas, visto que o foco das atividades realizadas no Arquivo está voltado para ações relacionadas a normas e leis que regem tais atividades.
Management information systems, Information technology
BackgroundText message (short message service, SMS) interrogative reminders were adopted in population screening for the early detection of colorectal cancer (CRC).
ObjectiveThis study aims to examine responses to text message (SMS) reminders and associate responses with senders’ characteristics, message type (interrogative/declarative), and subsequent screening uptake.
MethodsWe conducted a prospective cohort intervention. Text message (SMS) reminders to undergo CRC screening, randomized into interrogative and declarative phrasing, were sent to nonadherent 40,000 women and men (age 50-74 years) at CRC average risk. We analyzed recipient responses by message phrasing, recipient characteristics, and for content, the latter predicting subsequent CRC screening per program database.
ResultsWhile interrogative text message (SMS) reminders elicited 7.67% (1475/19,227) responses, declarative ones elicited 0.76% (146/19,262) responses. Text message (SMS) responses were content analyzed and grouped into attitudes toward CRC screening (1237/1512, 81.8% positive) and intention to screen (1004/1512, 62.6%). Text message (SMS) respondents screened significantly more than nonrespondents after 6 months (415/1621, 25.6% vs 3322/36,868, 9.0%; χ12=487.5, P<.001); 1 year (340/1621, 21.0% vs 4711/36,868; χ12=91.5, P<.001); and 2 years (225/1621, 13.9% vs 3924/36,868; χ12=16.9, P<.001) following the reminders. In a multivariable logistic regression among text message (SMS) respondents, screening after 6 months was significantly predicted by older age, past sporadic screening, attitudes, and intentions.
ConclusionsInterrogative text message (SMS) reminders reached previously uninvolved sectors in the CRC target population—men, sporadic-screenees, and the “never-tested” before. This novel application resulted in a population-level, incrementally enhanced screening. Asking patients about their future health behavior may be relevant for enhancing other health behaviors in preventive medicine and clinical settings.
Information technology, Public aspects of medicine
Along with the widespread use of Enterprise Social Media (ESM) by various large companies in Indonesia, this research is conducted to discover what the factors that drive employees’ intention to use ESM as knowledge sharing media are, and what factor is the most dominant in driving employees’ intention. This research is a quantitative research which uses Innovation Diffusion Technology (IDT) and Extended Technology Acceptance Model (TAM) as the research model. Data collection in this research is conducted by the survey method. The questionnaires are distributed to 374 respondents. Based on the data collected, data processing and hypothesis testing are carried out using Partial Least Square Structural Equation Modelling (PLS-SEM). The result of this study indicates that relative advantage, compatibility, and perceived ease of use have a significant influence on perceived usefulness and perceived enjoyment. Meanwhile, perceived usefulness and perceived enjoyment have a significant influence on employees’ intention to use ESM. Furthermore, it is also found that the most dominant factor among those two variables is perceived enjoyment.
Maryam Ahmadi, Mashallah Torabi, Maryam Goodarzi
et al.
Background and Aim: The purpose of this study was to introduce a new model for indicator of letters in office automation of Tehran University of Medical Sciences.
Materials and Methods: The present study was an applied research and a developmental study in which old automation method has been modified to new model. Regarding to the dispersion of codes assigned to letters, there was no specific order in the codes of both old and new units defined in the system, and firstly, the letter indicators in the office automation system of university in combination with letters and numbers was done without classification, the decision was made to correct it in the office automation system. In new model, numbering the correspondence based on frequency of each university unitchr('39')s subdivision was described and proposed model was presented.
Results: According to the new numerical model, integrated codes were assigned which were entirely numerical or the combination of numbers. Due to the abundance of units covered by the university, the research centers allocate the largest number to themselves. Therefore, a larger range of indicator codes for these units was considered than for other sections.
Conclusion: This model provides a new model for implementation of office automation indicator code in Tehran University of Medical Sciences and facilitates the search of letters based on the defined number.
<p class="Abstract">Transition region based image segmentation is one of the simple and effective image segmentation methods. This method is capable to segment image contains single or multiple objects. However, this method depends on the background. It may produce a bad segmentation result if the gray level variance is high or the background is textured. So a method to repair the transition region is needed. In this study, a new method to repair the transition region with median filter based on the percentage of the adjacent transitional pixels is proposed. Transition region is extracted from the grayscale image. Transition region refinement is conducted based on the<strong> </strong>percentage of the adjacent transitional pixels. Then, several morphological operations and the edge linking process are conducted to the transition region. Afterward, region filling is used to get the foreground area. Finally, image of segmentation result is obtained by showing the pixels of grayscale image that are located in the foreground area.<strong> </strong>The value of misclassification error (ME), false negative rate (FNR), and false positive rate (FPR) of the segmentation result are calculated to measure the proposed method performance. Performance of the proposed method is compared with the other method. The experimental results show that the proposed method has average value of ME, FPR, and FNR: 0.0297, 0.0209, and 0.0828 respectively. It defines that the proposed method has better performance than the other methods. Furthermore, the proposed method works well on the image with a variety of background, especially on image with textured background.</p>
Makar Ghazaryan, Tatiana Sergeevna Yakushkina, David B. Saakian
Crow-Kimura model is one of the famous models of population genetics. Last decade models with low-dimensional fitness landscape have been investigated. We consider the Crow-Kimura model of evolutionary dynamics on multi-dimensional fitness landscape with a single peak. We deduce exact solution for the dynamics, confirmed well by the numerics.
The objective of road infrastructure safety management is to ensure that when roads are planned, designed, built and used traffic accident risks can be identified, assessed and mitigated. There is a number of approaches, methods and tools for road safety infrastructure management. European Union Directive 2008/96/EC regulates and proposes a list of tools for managing road infrastructure safety. The paper presents two of these tools - classification of dangerous sections and control of the infrastructure in the field of safety. The final section presents the necessary directions for further action, particularly scientific research, supporting the management of the existing road infrastructure.