Hasil untuk "Information technology"

Shaoqi He, Fei Yu, Rongyao Guo et al.

To model the response of neural networks to electromagnetic radiation in real-world environments, this study proposes a memristive dual-wing fractional-order Hopfield neural network (MDW-FOMHNN) model, utilizing a fractional-order memristor to simulate neuronal responses to electromagnetic radiation, thereby achieving complex chaotic dynamics. Analysis reveals that within specific ranges of the coupling strength, the MDW-FOMHNN lacks equilibrium points and exhibits hidden chaotic attractors. Numerical solutions are obtained using the Adomian Decomposition Method (ADM), and the system’s chaotic behavior is confirmed through Lyapunov exponent spectra, bifurcation diagrams, phase portraits, and time series. The study further demonstrates that the coupling strength and fractional order significantly modulate attractor morphologies, revealing diverse attractor structures and their coexistence. The complexity of the MDW-FOMHNN output sequence is quantified using spectral entropy, highlighting the system’s potential for applications in cryptography and related fields. Based on the polynomial form derived from ADM, a field programmable gate array (FPGA) implementation scheme is developed, and the expected chaotic attractors are successfully generated on an oscilloscope, thereby validating the consistency between theoretical analysis and numerical simulations. Finally, to link theory with practice, a simple and efficient MDW-FOMHNN-based encryption/decryption scheme is presented.

Nikolaos Pavlidis, Andreas Sendros, Theodoros Tsiolakis et al.

In an era of increasing reliance on digital health solutions, safeguarding user privacy has emerged as a paramount concern. Health applications often need to balance advanced AI functionalities with sufficient privacy measures to ensure user engagement. This paper presents the architecture of FLORA, a privacy-first ovulation-tracking application that leverages federated learning (FL), privacy-enhancing technologies (PETs), and blockchain to protect user data while delivering accurate and personalized health insights. Unlike conventional centralized systems, FLORA ensures that sensitive information remains on users’ devices, with predictive algorithms powered by local computations. Blockchain technology provides immutable consent tracking and model update transparency, further improving user trust. In addition, FLORA’s design incentivizes participation through a token-based reward system, fostering collaborative data contributions. This work illustrates how the integration of cutting-edge technologies creates a secure, scalable, and user-centric health application, setting a new standard for privacy-preserving digital health platforms.

Ya Zhang, Ravie Chandren Muniyandi, Faizan Qamar

In the rapid development of the Internet of Things (IoT) and large-scale distributed networks, Intrusion Detection Systems (IDS) face significant challenges in handling complex spatiotemporal features and addressing data imbalance issues. This article systematically reviews recent advancements in applying deep learning techniques in IDS, focusing on the core challenges of spatiotemporal feature extraction and data imbalance. First, this article analyzes the spatiotemporal dependencies of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in network traffic feature extraction and examines the main methods these models use to solve this problem. Next, the impact of data imbalance on IDS performance is explored, and the effectiveness of various data augmentation and handling techniques, including Generative Adversarial Networks (GANs) and resampling methods, in improving the detection of minority class attacks is assessed. Finally, the paper highlights the current research gaps and proposes future research directions to optimize deep learning models further to enhance the detection capabilities and robustness of IDS in complex network environments. This review provides researchers with a comprehensive perspective, helping them identify the challenges in the current field and laying a foundation for future research efforts.

Kun Li, Yingqian Wang, Qiang Ling et al.

Hyperspectral anomaly detection (HAD) is challenging especially when anomalies are presented in sub-pixel form.The spectral signatures of anomalies in mixed pixels are mixed with those of background, making anomalies difficult to be distinguished from background. Most existing methods detect sub-pixel targets in abundance space by spectral unmixing. However, since abundance feature extraction and anomaly detection are decoupled, the learned features are not well-suitable for the subsequent detection. Moreover, these methods neglect the negative effect of anomalies on spectral unmixing, which leads to degradation of detection performance. To tackle these problems, we propose a cascaded autoencoder (AE) unmixing network for HAD. First, based on anomalies have larger spectral reconstruction errors than background, a background estimation approach is proposed to alleviate the negative effect of anomalies on spectral unmixing. Second, a cascaded AE is designed to achieve spectral unmixing from the estimated background to simultaneously obtain the endmembers and abundance vectors. Third, a deep Gaussian mixture model is leveraged to estimate the density distributions of spectral features since anomalies usually lie in the low-density areas. In this way, spectral unmixing and detection are jointly optimized to construct a unified detection framework. Experimental results demonstrate that our method achieves superior detection performance to existing state-of-the-art HAD methods.

Angga Iryanto Pratama, Dwi Rantini, Ratih Ardiati Ningrum et al.

In 2022, tobacco was the top export commodity, generating $106.3 million in sales. East Java Province contributes significantly to Indonesia's tobacco production, with an annual output of 188.6 thousand tons. Jember Regency is the area in East Java Province which is a Kasturi tobacco cultivation area. The productivity of Kasturi tobacco continues to decrease due to several factors that can affect the survival of the tobacco. This study aims to analyze the survival of Kasturi tobacco by creating stratified Cox and extended Cox models in order to handle factors that cannot fulfill the proportional hazard assumption. Based on the results of the analysis, the stratified Cox model is the best model with AIC and BIC values of 798.108 and 805.5748 respectively, while the extended Cox has AIC and BIC values of 840.2186 and 850.1732 respectively. Variables that can significantly affect the survival of Kasturi tobacco are the variable concentration of ZA fertilizer, pesticide concentration. The addition of ZA fertilizer must be appropriate, because if excessive it can cause poisoning of tobacco plants. Likewise with pesticides, if excessive it will cause damage to the leaves. This policy can increase the productivity of Kasturi tobacco. Then, Jember Regency contributes greater export capacity. • This paper aims to determine the factors that affect the survival of Kasturi tobacco plants in Jember • By using the extended Cox model, the best one is obtained using the Heaviside function with AIC and BIC values of 840.2186 and 850.1732, respectively • The stratified Cox model is better than the extended Cox model, which has AIC and BIC values of 798.108 and 805.5748, respectively

O. V. Shcherbakova

The subject. Economic, technological and geopolitical changes are leading to the digitalization of virtually all structures of the labor market: from the process of production and human resources management to the organization of the workplace. The use of new digital technologies makes it possible to give up routine human labor, contribute to improving the quality of working life of employees and employers, and increase industrial production, which means economic growth of the state. Thus, in accordance with the National Security Strategy, approved by the Decree of the President of the Russian Federation dated July 2, 2021 No. 400, the situation in the production industry is one of the key criteria of Russia's competitiveness and contributes to the strengthening of the state's defense capability. Ensuring Russia's independence  and competitiveness was also announced to be the main goal of the Strategy for Scientific and Technological Development of the Russian Federation, approved by Presidential Decree No. 642 dated December 1, 2016. On the other hand, the use of new technologies may have time-delayed risks. The most important risk today is the increasing release of labor force and mass cuts of jobs requiring average qualifications, as well as dismissal of employees due to failure to pass tests because of the lack of skills in digital tools.The purpose of the study was to substantiate the urgent character of the implementation of digital profile programs as a part of the employer's personnel policy to achieve the objectives set in the National Security Strategy of the Russian Federation dated 2021.The methodology of comprehensive research, including methods of document analysis, comparative analysis, secondary use of sociological and economic data were used.Main results. The study shows that the use of the employee digital profile programs will allow the employer to identify weaknesses in any of the employee’s skills well in advance, and to pave individual learning pathway, based on his/her preferences, hobbies  and intentions, in order to upgrade the skills. It is deemed that the competence of employees is a factor for transfer of any business to digitalization. This policy of the employer will allow to cover for low-quality job cut and give personnel the minimum knowledge that makes it possible to acquire information on modern information technologies, be able to use it to solve the set problems and have necessary skills and technology, which will facilitate solution of the problems. Ultimately, these are tools to achieve the tasks set by the state in the framework of the state's defensive capability and competitiveness. At the same time, the lack of normative methodologies for the creation and operation of employee digital profiles and comprehensive scientific research predetermine increasing risks of violation of personal data of employees, privacy of employees, as well as discrimination in making legally significant decisions. Today there are no normative standards of data processing and system interaction, which leads to the diminution of guarantees of employees' rights in terms of respect for personal data and other data in terms of classified information.

Jasman Pardede, Desita Nurrohmah

Background: Hepatitis is a contagious inflammatory disease of the liver and is a public health problem because it is easily transmitted. The main factors causing hepatitis are viral infections, disease complications, alcohol, autoimmune diseases, and drug effects. Some hepatitis variants such as B, C, and D can also cause liver cancer if left untreated. Objective: This research aims to determine the effect of Backward Elimination feature selection on the performance of hepatitis disease identification compared to cases where Backward Elimination is not applied. Methods: XGBoost classification, capable of handling machine learning problems, was utilized. Additionally, Backward Elimination was used as a featured selection to increase accuracy by reducing the number of less important features in the data classification process. Results: The results for training XGBoost model with Backward Elimination, and applying Random Search for hyperparameter optimization, achieved an accuracy of 98.958% at 0.64 seconds. This performance was better than using Bayesian search, which produced the same accuracy of 98.958% but required a longer training time of 0.70 seconds. Conclusion: The use of features obtained from Backward Elimination process as well as the use of feature average values for missing value treatment, produced an accuracy of 98.958%.the precision in training XGBoost model with hyperparameter Bayesian search achieved accuracy, recall, and F1 score of 98.934%, 98.934%, and 98.934%, respectively. Consequently, the use of Backward Elimination in XGBoost model led to faster training, improved accuracy, and decreased overfitting.   Keywords: Hepatitis, Backward Elimination, XGBoost, Bayesian Search, Random Search

Awang Herjunie Nurdy, Abdul Rahim, Arbansyah

Perkembangan teknologi yang pesat mempermudah akses ke berbagai hiburan digital, termasuk game online seperti Stumble Guys, yang telah diunduh lebih dari 163 juta kali dan mendapatkan ulasan beragam di Google Play Store. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna Stumble Guys menggunakan algoritma Naïve Bayes. Metode penelitian melibatkan tahapan Knowledge Discovery in Databases (KDD), meliputi pemilihan data, preprocessing, transformasi dengan CountVectorizer dan TF-IDF, serta pengklasifikasian dengan Naïve Bayes. Dengan menggunakan 1.500 ulasan dari Google Play Store, model Naïve Bayes mencapai akurasi 86%, dengan precision, recall, dan f1 score masing-masing sebesar 86%. Hasil penelitian menunjukkan bahwa Naïve Bayes efektif dalam mengklasifikasikan sentimen ulasan game Stumble Guys.

Agata P. Perlinska, Mai Lan Nguyen, Mai Lan Nguyen et al.

We have been aware of the existence of knotted proteins for over 30 years—but it is hard to predict what is the most complicated knot that can be formed in proteins. Here, we show new and the most complex knotted topologies recorded to date—double trefoil knots (31#31). We found five domain arrangements (architectures) that result in a doubly knotted structure in almost a thousand proteins. The double knot topology is found in knotted membrane proteins from the CaCA family, that function as ion transporters, in the group of carbonic anhydrases that catalyze the hydration of carbon dioxide, and in the proteins from the SPOUT superfamily that gathers 31 knotted methyltransferases with the active site-forming knot. For each family, we predict the presence of a double knot using AlphaFold and RoseTTaFold structure prediction. In the case of the TrmD-Tm1570 protein, which is a member of SPOUT superfamily, we show that it folds in vitro and is biologically active. Our results show that this protein forms a homodimeric structure and retains the ability to modify tRNA, which is the function of the single-domain TrmD protein. However, how the protein folds and is degraded remains unknown.

Ali Raza, Mohammad Rustom Al Nasar, Essam Said Hanandeh et al.

Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturing, caring for the elderly. Computer vision-based activity recognition is challenging due to problems such as partial occlusion, background clutter, appearance, lighting, viewpoint, and changes in scale. Our research aims to detect human kinematic motions such as walking or running using smartphones’ sensor data within a high-performance framework. An existing dataset based on smartphones’ gyroscope and accelerometer sensor values is utilized for the experiments in our study. Sensor exploratory data analysis was conducted in order to identify valuable patterns and insights from sensor values. The six hyperparameters, tunned artificial indigence-based machine learning, and deep learning techniques were applied for comparison. Extensive experimentation showed that the ensemble learning-based novel ERD (ensemble random forest decision tree) method outperformed other state-of-the-art studies with high-performance accuracy scores. The proposed ERD method combines the random forest and decision tree models, which achieved a 99% classification accuracy score. The proposed method was successfully validated with the k-fold cross-validation approach.

Matipa Ricky Ngandu, David Risinamhodzi, Godwin Pedzisai Dzvapatsva et al.

Abstract ICT tools in education are widely used to support the aim of achieving learning outcomes by improving critical areas such as student engagement, participation, and motivation. In this study, we examine literature to explore how game elements are used in capturing students’ interest, which the study suggests is fundamental to the teaching and learning of Software Engineering in higher education. Given the potential of alternative ICT tools such as flipped classrooms to increase interest in learning activities, there is a gap in similar literature on capturing interest in gamified environments, which has the potential to improve the achievement of learning outcomes. We applied flow theory to provide a guiding frame for our study. Following a systematic literature review for our data, we analysed 15 papers from the initial 342 articles, which were extracted from IEEE Xplore and Science Direct databases. The main finding in the reviewed papers underscores the positive impact of gamified learning environments on capturing student interest when teaching and learning Software Engineering. While the reviewed papers were not conclusive in identifying the best game elements for capturing students’ interest, we found, that game elements such as points and leaderboards were the most common mechanisms used to advance students' interest when studying Software Engineering courses. The findings also suggest that different game elements are used in gamified environments to increase participation and engagement. The paper adds voice to the practical implications of gamification for teaching and learning. Although our study requires empirical evidence to validate our claims, we believe it sets the stage for further discussion. In the future, comparative studies of game elements in similar environments will be beneficial for identifying the ones that are more engaging and assessing their long-term impacts.

Lan Geng, Genyan Jiang, Lingling Yu et al.

BackgroundMany smartphone apps designed to assist individuals in managing their weight are accessible, but the assessment of app quality and features has predominantly taken place in Western countries. Nevertheless, there is a scarcity of research evaluating weight management apps in China, which highlights the need for further investigation in this area. ObjectiveThis study aims to conduct a comprehensive search for the most popular commercial Chinese smartphone apps focused on weight management and assess their quality, behavior change techniques (BCTs), and content-related features using appropriate evaluation scales. Additionally, the study sought to investigate the associations between the quality of various domains within weight management apps and the number of incorporated BCTs and app features. MethodsIn April 2023, data on weight management apps from the iOS and Android app stores were downloaded from the Qimai Data platform. Subsequently, a total of 35 weight management apps were subjected to screening and analysis by 2 researchers. The features and quality of the apps were independently assessed by 6 professionals specializing in nutrition management and health behavioral change using the Mobile Application Rating Scale (MARS). Two registered dietitians, who had experience in app development and coding BCTs, applied the established 26-item BCT taxonomy to verify the presence of BCTs. Mean (SD) scores and their distributions were calculated for each section and item. Spearman correlations were used to assess the relationship between an app’s quality and its technical features, as well as the number of incorporated BCTs. ResultsThe data set included a total of 35 apps, with 8 available in the Android Store, 10 in the Apple Store, and 17 in both. The overall quality, with a mean MARS score of 3.44 (SD 0.44), showed that functionality was the highest scoring domain (mean 4.18, SD 0.37), followed by aesthetics (mean 3.43, SD 0.42), engagement (mean 3.26, SD 0.64), and information (mean 2.91, SD 0.52), which had the lowest score. The mean number of BCTs in the analyzed apps was 9.17 (range 2-18 BCTs/app). The most common BCTs were “prompt review of behavioral goals” and “provide instruction,” present in 31 apps (89%). This was followed by “prompt self-monitoring of behavior” in 30 apps (86%), “prompt specific goal setting” in 29 apps (83%), and “provide feedback on performance” in 27 apps (77%). The most prevalent features in the analyzed apps were the need for web access (35/35, 100%), monitoring/tracking (30/35, 86%), goal setting (29/35, 83%), and sending alerts (28/35, 80%). The study also revealed strong positive correlations among the number of BCTs incorporated, app quality, and app features. This suggests that apps with a higher number of BCTs tend to have better overall quality and more features. ConclusionsThe study found that the overall quality of weight management apps in China is moderate, with a particular weakness in the quality of information provided. The most prevalent BCTs in these apps were reviewing behavioral goals, providing guidance, self-monitoring of behavior, goal setting, and offering performance feedback. The most common features were the need for web access, monitoring and tracking, goal setting, and sending alerts. Notably, higher-quality weight management apps in China tended to incorporate more BCTs and features. These findings can be valuable for developers looking to improve weight management apps and enhance their potential to drive behavioral change in weight management.

Stéphanie Pinard, Carolina Bottari, Catherine Laliberté et al.

BackgroundAlthough assistive technology for cognition (ATC) has enormous potential to help individuals who have sustained a severe traumatic brain injury (TBI) prepare meals safely, no ATC has yet been developed to assist in this activity for this specific population. ObjectiveThis study aims to conduct a needs analysis as a first step in the design of an ATC to support safe and independent meal preparation for persons with severe TBI. This included identifying cooking-related risks to depict future users’ profiles and establishing the clinical requirements of the ATC. MethodsIn a user-centered design study, the needs of 3 future users were evaluated in their real-world environments (supported-living residence) using an ecological assessment of everyday activities, a review of their medical files, a complete neuropsychological test battery, individual interviews, observational field notes, and log journals with the residents, their families, and other stakeholders from the residence (eg, staff and health professionals). The needs analysis was guided by the Disability Creation Process framework. ResultsThe results showed that many issues had to be considered for the development of the ATC for the 3 residents and other eventual users, including cognitive issues such as distractibility and difficulty remembering information over a short period of time and important safety issues, such as potential food poisoning and risk of fire. This led to the identification of 2 main clinical requirements for the ATC: providing cognitive support based on evidence-based cognitive rehabilitation to facilitate meal preparation and ensuring safety at each step of the meal preparation task. ConclusionsThis needs analysis identified the main requirements for an ATC designed to support meal preparation for persons with severe TBI. Future research will focus on implementing the ATC in the residence and evaluating its usability.

Haiming Li, Haiming Li, Haiming Li et al.

The development and adoption of agriculture has been investigated for decades, and remains a central topic within archaeology. However, most previous studies focus on the crop’s domestication centers, leading to gaps in knowledge, particularly in transitional zones between these centers. This paper reviews published archaeobotanical evidence and historical documents to reconstruct the trajectory of agricultural systems in Holocene Jiangsu Province. Comparing these new results to paleoclimate information, historical documents, and archaeological data enables us to better understand the underlying influences of past agricultural development. Our results indicate that a warm and wet climate may have promoted ancient peoples to first settle in Jiangsu between 8,500 and 6,000 BP and adopt rice farming. The continuous warm and wet climate may have facilitated the rapid development and expansion of rice agriculture, ultimately contributing to large-scale human settlement in 6,000–4,000 BP in Jiangsu Province. Between 4,000 and 2,300 BP during a cooler and drier climate millet agriculture diffused southward, facilitating a mixed rice and millet agricultural system. This mixed farming supported a continuesd widespread settlement and population growth in Jiangsu. After 2,300 BP, political instability in north China resulted in further southeastward migration, advanced planting technology was brought about to south China, facilitating highly developed agricultural systems and rapid population expansion in Jiangsu. Population growth led to the establishment of Jiangnan as the regional economic center, where people chose high-yielding rice and wheat rather than millet.

Diego Noé Rodríguez-Sánchez, Giovana Boff Araujo Pinto, Luciana Politti Cartarozzi et al.

Abstract Background Nerve injuries are debilitating, leading to long-term motor deficits. Remyelination and axonal growth are supported and enhanced by growth factor and cytokines. Combination of nerve guidance conduits (NGCs) with adipose-tissue-derived multipotent mesenchymal stromal cells (AdMSCs) has been performing promising strategy for nerve regeneration. Methods 3D-printed polycaprolactone (PCL)-NGCs were fabricated. Wistar rats subjected to critical sciatic nerve damage (12-mm gap) were divided into sham, autograft, PCL (empty NGC), and PCL + MSCs (NGC multi-functionalized with 106 canine AdMSCs embedded in heterologous fibrin biopolymer) groups. In vitro, the cells were characterized and directly stimulated with interferon-gamma to evaluate their neuroregeneration potential. In vivo, the sciatic and tibial functional indices were evaluated for 12 weeks. Gait analysis and nerve conduction velocity were analyzed after 8 and 12 weeks. Morphometric analysis was performed after 8 and 12 weeks following lesion development. Real-time PCR was performed to evaluate the neurotrophic factors BDNF, GDNF, and HGF, and the cytokine and IL-10. Immunohistochemical analysis for the p75NTR neurotrophic receptor, S100, and neurofilament was performed with the sciatic nerve. Results The inflammatory environment in vitro have increased the expression of neurotrophins BDNF, GDNF, HGF, and IL-10 in canine AdMSCs. Nerve guidance conduits multi-functionalized with canine AdMSCs embedded in HFB improved functional motor and electrophysiological recovery compared with PCL group after 12 weeks. However, the results were not significantly different than those obtained using autografts. These findings were associated with a shift in the regeneration process towards the formation of myelinated fibers. Increased immunostaining of BDNF, GDNF, and growth factor receptor p75NTR was associated with the upregulation of BDNF, GDNF, and HGF in the spinal cord of the PCL + MSCs group. A trend demonstrating higher reactivity of Schwann cells and axonal branching in the sciatic nerve was observed, and canine AdMSCs were engrafted at 30 days following repair. Conclusions 3D-printed NGCs multi-functionalized with canine AdMSCs embedded in heterologous fibrin biopolymer as cell scaffold exerted neuroregenerative effects. Our multimodal approach supports the trophic microenvironment, resulting in a pro-regenerative state after critical sciatic nerve injury in rats.

Nuno António , Gustavo Sá

Hospitality is a highly competitive market that struggles to improve its performance. Today, the use of technology is a critical factor for more efficient performance. To understand the perception that decision-makers have of information systems’ influence and importance in their organizations, we conduct a case study in Portugal. The objective was to assess information systems' maturity level of independent hotels and small hotel chains, mapping the level to the hotel's characteristics. We surveyed 86 companies, representing a total of 195 hotels. We designed the survey to evaluate the adoption of information systems and the perception decision-makers had of those systems' importance accordingly to the Network Exploitation Capability (NEC) model (Piccoli et al., 2011), with results of 2.7 on a scale of 1 to 5, differing by hotel characteristics. Generally, hoteliers consider that their companies take more advantage of technology and information systems than they do. In addition, this study explores the types of systems used and hoteliers' main factors, drivers, and limitations to invest in technology and influencing information systems' maturity.

Aijun Hu, Chujin Li, Jing Wu

Large-scale data presents great challenges to data analysis due to the limited computer storage capacity and the heterogeneous data structure. In this article, we propose a distributed expectile regression model to resolve the challenges of large-scale data by designing a surrogate loss function and using the Iterative Local Alternating Direction Method of the Multipliers (IL-ADMM) algorithm, which is developed for the calculation of the proposed estimator. To obtain nice performance only after fewer rounds of communications, the proposed method only needs to solve an M-estimation problem on the master machine while the other working machines only to compute the gradients based on local data. Moreover, we show the consistency and the asymptotic normality of the proposed estimator, and illustrate the efficient proof by numerical simulations and positive analysis on the superconductor data.