Hasil untuk "Information technology"

F. V. Perevoshchikov, V. G. Bukreev

Currently, the development of new control approaches for asynchronous electric drives with stringent requirements for vibration-acoustic performance and spectral composition of autonomous inverter output currents represents a highly relevant research challenge. The key challenges in designing this class of electric drives stem from the relatively low effectiveness of existing technical solutions. This limitation arises either from constraints in current controller synthesis methods or from rigorous demands regarding power-to-weight and dimensional parameters. This paper presents an original method for generating control signals in an alternating current electric drive autonomous inverter. The proposed approach utilizes regulation based on the deviation of the generalized output voltage vector amplitude in the autonomous inverter. The synthesis procedure for such a controller begins with defining the desired closed-loop system transfer function. The system dynamic processes are determined by a characteristic polynomial that can be of arbitrary type. For comparative analysis, two controller types are examined: one based on a Butterworth filter and another utilizing a Newton polynomial. The study proposes employing bilinear transformation to implement the derived continuous functions in discrete form, enabling software implementation in Simulink and subsequent microprocessor-based execution. The developed model, which accounts for discrete control signal generation, has yielded the spectral composition of the drive converter output currents and voltage-frequency characteristics under parametric disturbances introduced by the control object. Results demonstrate that the Butterworth filter-based controller shows superior efficiency compared to both open-loop systems and closed-loop systems with Newton polynomial-based controllers. The obtained results can be effectively applied in the development of low-noise electric drives for specialized applications.

Xin Shi, Zhengchao Xu, Sunan Ge et al.

Abstract In computer vision, zero-shot image restoration—a technique enabling degraded image restoration without large-scale paired training data—has emerged as a pivotal technique for scenarios where data is limited or paired training data is challenging to obtain. However, existing methods face two key limitations: data consistency preservation remains challenging for out-of-domain data, and degradation process alignment is difficult when the degradation mechanism is not mathematically predetermined. To address these issues, this paper presents a novel zero-shot image restoration method (FIR-SDE). Traditional generation-oriented diffusion models (designed for image creation) are replaced with restoration-oriented models (specialized for degradation repair), expanding the range of effectively restorable images. To mitigate the noise offset (discrepancies between real and model-simulated degradation) and to enhance the alignment, a multi-step optimization strategy is employed, which evaluates the distance between real and simulated degraded images via frequency domain distribution. Experiments were conducted on two image restoration tasks (image deraining and inpainting) using three public datasets (AFHQ-dog, CelebA, and FFHQ), with Gaussian blur and motion blur superimposed as noise offsets. Results demonstrate that FIR-SDE method outperforms competitive methods in restoration quality and noise resistance. By eliminating data space constraints and exhibiting robustness against noise offsets, FIR-SDE offers a more flexible and efficient solution to broaden the practical applicability of zero-shot image restoration.

Bambang Budiwiranto, Jasmadi Jasmadi, Dewi Maryam et al.

The need for inclusive communication in farmer empowerment is becoming increasingly urgent in the digital era, especially in rural areas such as Sumberejo Sub-district, Tanggamus Regency, and Lampung, where most people depend on the agricultural sector as the primary source of livelihood. This research explores how inclusive communication can increase farmers' participation in digital technology-based socio-economic activities. Using a qualitative research method with a case study approach, data was collected through semi-structured interviews with farmers' group association administrators, village heads, and agricultural extension officers in 13 villages, as well as direct observation and documentation studies. The results show that inclusive communication that considers the level of digital literacy and local cultural sensitivity can improve farmers' access to information, strengthen social networks, and expand economic opportunities. Farmer group associations (Gapoktan) and agricultural extension officers act as key intermediaries in the technology adoption process, but there are still constraints, such as limited digital infrastructure and lack of technical training. Collaboration between the local government, private sector, and extension agencies is important in creating sustainable empowerment synergies. This research provides theoretical contributions to developing community-based inclusive communication models in the agricultural sector, as well as practical implications for local policies that support digital development in rural areas. Further studies are recommended to explore variables such as gender roles and the influence of social trust in the successful implementation of inclusive communication in farming communities.

Oyewole Oyekoya, Kwame Agyemang Baffour

Using look-alike avatars may enhance the likeability and realism of avatars in 3D virtual learning environments. This paper explores perception of the features of the look-alike avatar representations of an instructor in virtual environments in two studies. In a pilot study, an instructor was represented as a look-alike, stick, and video avatar, allowing us to investigate students’ perceptions of teaching effectiveness in virtual and augmented reality environments. The main study seeks to determine the influence of three specific features of a look-alike avatar (head shape, texture fidelity and head orientation) on perception of likeability and visual realism, especially when judged by other people. Two textured look-alike avatars were generated using: (i) three-dimensional (3D) stereophotogrammetry; and (ii) 3D face reconstruction from a single full-face image. Participants compared three different head orientations (0°, 45°, 90°) of the look-alike avatars’ textured heads to their corresponding head silhouettes, to emphasize the differences in head shapes. Results suggest that participants prefer geometrically-accurate photorealistic avatars of the instructor due to the accuracy of the head shape and texture fidelity. In line with studies on face recognition, participants ranked the likeability and realism of the look-alike avatars similarly regardless of the head orientation. We discuss the implications of these findings for 3D virtual learning environments.

Ali Dorosti, Ali Asghar Alesheikh, Mohammad Sharif

Advancements in navigation and tracking technologies have resulted in a significant increase in movement data within road networks. Analyzing the trajectories of network-constrained moving objects makes a profound contribution to transportation and urban planning. In this context, the trajectory similarity measure enables the discovery of inherent patterns in moving object data. Existing methods for measuring trajectory similarity in network space are relatively slow and neglect the temporal characteristics of trajectories. Moreover, these methods focus on relatively small volumes of data. This study proposes a method that maps trajectories onto a network-based space to overcome these limitations. This mapping considers geographical coordinates, travel time, and the temporal order of trajectory segments in the similarity measure. Spatial similarity is measured using the Jaccard coefficient, quantifying the overlap between trajectory segments in space. Temporal similarity, on the other hand, incorporates time differences, including common trajectory segments, start time variation and trajectory duration. The method is evaluated using real-world taxi trajectory data. The processing time is one-quarter of that required by existing methods in the literature. This improvement allows for spatio-temporal analyses of a large number of trajectories, revealing the underlying behavior of moving objects in network space.

Samia Nasr, Sohail Rehman, Naeem Ullah et al.

The study of boundary layer flow (BLF) with heat-mass transfer of binary chemical processes and nanofluids (NF) over a wedge is essential for improving heat transfer and reaction kinetics in applications including processing of material technologies, chemical reactors, and energy-efficient cooling mechanisms. This paper examines the entropy optimized BLF of silver Ag− water based nanofluid with binary chemical species over a wedge surface. The Tiwari-Das model is executed in this model which account the load of Ag− nanomaterials. The flow of NF over a moving wedge subject to favorable and adverse pressure differential is addressed by Naiver-Stokes equation. This model accounts the homogeneous heat reaction, viscous dissipation, joule heating and thermal radiations. The dimensionless equations for flow, for heat, and concentration are formulated and solved numerically using the fourth ordered Rung-Kutta approach. The findings suggest that fluid concentration is lowered with a rise in Schmidt number and homogenous chemical reaction. Thermal distribution improve with heterogonous reaction, magnetic parameter and deteriorate with wedge parameter. The skin friction rises from 25.277 % to 26.455 % with a material load of 3 % and magnetic parameter. The Nusselt decline with a radiative parameter from 10.984 % to 2.9748 % when particle load of 3 % is accounted.

Tan Yan Lin, Pang Ying Han, Ooi Shih Yin et al.

In the telecom industry, predicting customer churn is crucial for improving customer retention. In literature, the use of single classifiers is predominantly focused. Customer data is complex data due to class imbalance and contain multiple factors that exhibit nonlinear dependencies. In these complex scenarios, single classifiers may be unable to fully utilize the available information to capture the underlying interactions effectively. In contrast, ensemble learning that combines various base classifiers empowers a more thorough data analysis, leading to improved prediction performance. In this paper, a heterogeneous ensemble model is proposed for churn prediction in the telecom industry. The model involves exploratory data analysis, data pre-processing and data resampling to handle class imbalance. In this proposed model, multiple trained base classifiers with different characteristics are integrated through a stacking ensemble technique. Specifically, convolutional-based neural network, logistic regression, decision tree and Support Vector Machine (SVM) are considered as the base classifiers in this work. The proposed stacking ensemble model utilizes the unique strengths of each base classifier and leverages collective knowledge to improve prediction performance with a meta-learner. The efficacy of the proposed model is assessed on a real-world dataset, i.e., Cell2Cell. The empirical results demonstrate the superiority of the proposed model in churn prediction with 62.4% f1-score and 60.62% recall.

Zhuo Wang, Bo Zhang, Dezhi Tan et al.

Long-term optical data storage (ODS) technology is essential to break the bottleneck of high energy consumption for information storage in the current era of big data. Here, ODS with an ultralong lifetime of 2×107 years is attained with single ultrafast laser pulse induced reduction of Eu3+ ions and tailoring of optical properties inside the Eu-doped aluminosilicate glasses. We demonstrate that the induced local modifications in the glass can stand against the temperature of up to 970 K and strong ultraviolet light irradiation with the power density of 100 kW/cm2. Furthermore, the active ions of Eu2+ exhibit strong and broadband emission with the full width at half maximum reaching 190 nm, and the photoluminescence (PL) is flexibly tunable in the whole visible region by regulating the alkaline earth metal ions in the glasses. The developed technology and materials will be of great significance in photonic applications such as long-term ODS.

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Halaman Sampul dan Daftar Isi

Paolo Graniero, Paolo Graniero, Mark Khenkin et al.

Perovskite solar cells are the most dynamic emerging photovoltaic technology and attracts the attention of thousands of researchers worldwide. Recently, many of them are targeting device stability issues–the key challenge for this technology–which has resulted in the accumulation of a significant amount of data. The best example is the “Perovskite Database Project,” which also includes stability-related metrics. From this database, we use data on 1,800 perovskite solar cells where device stability is reported and use Random Forest to identify and study the most important factors for cell stability. By applying the concept of learning curves, we find that the potential for improving the models’ performance by adding more data of the same quality is limited. However, a significant improvement can be made by increasing data quality by reporting more complete information on the performed experiments. Furthermore, we study an in-house database with data on more than 1,000 solar cells, where the entire aging curve for each cell is available as opposed to stability metrics based on a single number. We show that the interpretation of aging experiments can strongly depend on the chosen stability metric, unnaturally favoring some cells over others. Therefore, choosing universal stability metrics is a critical question for future databases targeting this promising technology.

Inam Bari, Javed Iqbal, Haider Ali et al.

A wideband circularly polarized rectangular dielectric resonator antenna (DRA) fed by a single feeding mechanism has been studied theoretically and experimentally. The purpose of the study is to determine how adding a parasitic strip next to the flat surface metallic feed would affect various far- and near-field antenna characteristics. Initially, the basic antenna design, i.e., the T-shape feed known as antenna A, produced a 4.81% impedance matching bandwidth (|S₁₁| −10 dB). Due to the narrow and undesirable results of the initial antenna design, antenna-A was updated to the antenna-B design, i.e., Yagi-Uda. The antenna-B produced a decent result (7.89% S₁₁) as compared to antenna-A but still needed the bandwidth widened, for this, a parasitic patch was introduced next to the Yagi-Uda antenna on the rectangular DRA at an optimized location to further improve the results. This arrangement produced circular polarization (CP) waves spanning a broad bandwidth of 28.21% (3.59–3.44 GHz) and a broad impedance |S₁₁| bandwidth of around 29.74% (3.71–3.62 GHz). These findings show that, in addition to producing CP, parasite patches also cause the return loss to rise by a factor of almost three times when compared to results obtained with the Yagi-Uda-shape feed alone. Computer simulation technology was used for the simulation (CST-2017). The planned antenna geometry prototype was fabricated and measured. Performance indicators show that the suggested antenna is a good fit for 5G applications. The simulated outcomes and measurements match up reasonably.

Amey P. Pasarkar, Tyler A. Joseph, Itsik Pe’er

ABSTRACT The gut microbiome is spatially heterogeneous, with environmental niches contributing to the distribution and composition of microbial populations. A recently developed mapping technology, MaPS-seq, aims to characterize the spatial organization of the gut microbiome by providing data about local microbial populations. However, information about the global arrangement of these populations is lost by MaPS-seq. To address this, we propose a class of Gaussian mixture models (GMM) with spatial dependencies between mixture components in order to computationally recover the relative spatial arrangement of microbial communities. We demonstrate on synthetic data that our spatial models can identify global spatial dynamics, accurately cluster data, and improve parameter inference over a naive GMM. We applied our model to three MaPS-seq data sets taken from various regions of the mouse intestine. On cecal and distal colon data sets, we find our model accurately recapitulates known spatial behaviors of the gut microbiome, including compositional differences between mucus and lumen-associated populations. Our model also seems to capture the role of a pH gradient on microbial populations in the mouse ileum and proposes new behaviors as well. IMPORTANCE The spatial arrangement of the microbes in the gut microbiome is a defining characteristic of its behavior. Various experimental studies have attempted to provide glimpses into the mechanisms that contribute to microbial arrangements. However, many of these descriptions are qualitative. We developed a computational method that takes microbial spatial data and learns many of the experimentally validated spatial factors. We can then use our model to propose previously unknown spatial behaviors. Our results demonstrate that the gut microbiome, while exceptionally large, has predictable spatial patterns that can be used to help us understand its role in health and disease.

Xiaoqing Lv, Ming Zhao

In view of the problem that Bloch Quantum Genetic Algorithm (BQGA) is easy to fall into local optimum, an improved BQGA is proposed. The algorithm can control the step size and the mutation probability in real time in the iterative process, avoiding over the optimal solution and guaranteeing search efficiency. In addition, the improved algorithm further completes the anti-degradation mechanism, which maintains the diversity of the population while preserving the dominant gene to the maximum extent, so that the algorithm is not easy to fall into the local extremum and finally approaches the global optimal solution. The application in the inverse solution of robot kinematics shows that the improved BQGA effectively avoids the premature problem and accelerates the convergence of understanding and the search result is close to the complete solution, which provides a new idea for solving complex nonlinear and multivariate functional equations.

M. M. Mirzemagomedova, M. M. Muradov

Objectives. The article is devoted to the development of a software application that allows you to automate methods for collecting and processing information, as well as perform time-consuming analytical calculations. Embarcadero C ++ Builder XE, a visual object-oriented programming language, was used to implement a software application. The software being developed is created to solve the following tasks: a comparative analysis of innovative development indicators for the selected years; selection of a projected indicator, building a regression model; making a forecast with a lead time of 3; determination of the confidence interval; the formation of a graphical display of observable and calculated values, the selected indicator of innovative development.Method. In a software application, formalized methods were used as a mathematical model, one of which is multiple regression. Regression analysis consists in defining an analytical expression of a relationship in which a change in a single quantity, called a dependent or productive attribute, is due to the influence of one or several independent quantities (factors).Result. With the help of the developed software product, you can not only automate time-consuming methods of collecting and processing information, but also per-form complex analytical calculations using the multiple regression method.Conclusion. Embarcadero C ++ Builder XE is by far the latest state-of-the-art technology and C ++ programming environment. With C ++ Builder, XE has become faster to do the job of creating high-quality applications for Windows-based applications, due to the rapid writing of code, new tools and components.

Jo Howard, Sophie A. Lee, Baba Inusa et al.

Abstract Design This randomised crossover trial compared nocturnal auto-adjusting continuous positive airway pressure (APAP) and nocturnal oxygen therapy (NOT) in adults and children with sickle cell anaemia, with patient acceptability as the primary outcome. Secondary outcomes included pulmonary physiology (adults), safety, and daily pain during interventions and washout documented using tablet technology. Methods Inclusion criteria were age > 8 years and the ability to use an iPad to collect daily pain data. Trial participation was 4 weeks; week 1 involved baseline data collection and week 3 was a washout between interventions, which were administered for 7 days each during weeks 2 and 4 in a randomised order. Qualitative interviews were transcribed verbatim and analysed for content using a funnelling technique, starting generally and then gaining more detailed information on the experience of both interventions. Safety data included routine haematology and median pain days between each period. Missing pain day values were replaced using multiple imputation. Results Ten adults (three female, median age 30.2 years, range 18–51.5 years) and eleven children (five female, median age 12 years, range 8.7–16.9 years) enrolled. Nine adults and seven children completed interviews. Qualitative data revealed that the APAP machine was smaller, easier to handle, and less noisy. Of 16 participants, 10 preferred APAP (62.5%, 95% confidence interval (CI) 38.6–81.5%). Haemoglobin decreased from baseline on APAP and NOT (mean difference −3.2 g/L (95% CI −6.0 to −0.2 g/L) and −2.5 g/L (95% CI −4.6 to 0.3 g/L), respectively), but there was no significant difference between interventions (NOT versus APAP, 1.1 (−1.2 to 3.6)). Pulmonary function changed little. Compared with baseline, there were significant decreases in the median number of pain days (1.58 for APAP and 1.71 for NOT) but no significant difference comparing washout with baseline. After adjustment for carry-over and period effects, there was a non-significant median difference of 0.143 (95% CI −0.116 to 0.401) days additional pain with APAP compared with NOT. Conclusion In view of the point estimate of patient preference for APAP, and no difference in haematology or pulmonary function or evidence that pain was worse during or in washout after APAP, it was decided to proceed with a Phase II trial of 6 months APAP versus standard care with further safety monitoring for bone marrow suppression and pain. Trial registration ISRCTN46078697. Registered on 18 July 2014

Saleh Albahli

Since the introduction of just-in-time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods, which can predict the defect inducing changes in a software product. In order to predict these changes, it is important for a learning model to consider the nature of the dataset, its unbalancing properties and the correlation between different attributes. In this paper, we evaluated the importance of these properties for a specific dataset and proposed a novel methodology for learning the effort aware just-in-time prediction of defect inducing changes. Moreover, we devised an ensemble classifier, which fuses the output of three individual classifiers (Random forest, XGBoost, Multi-layer perceptron) to build an efficient state-of-the-art prediction model. The experimental analysis of the proposed methodology showed significant performance with 77% accuracy on the sample dataset and 81% accuracy on different datasets. Furthermore, we proposed a highly competent reinforcement learning technique to avoid false alarms in real time predictions.

Daisuke Mashima, Aidana Serikova, Yao Cheng et al.

Thanks to the roll-out of smart meters, availability of fine-grained electricity usage data has rapidly grown. Such data has enabled utility companies to perform robust and efficient grid operations. However, at the same time, privacy concerns associated with sharing and disclosure of such data have been raised. In this paper, we first demonstrate the feasibility of estimating privacy-sensitive household attributes based solely on the energy usage data of residential customers. We then discuss a framework to measure privacy gain and evaluate the effectiveness of customer-centric privacy-protection schemes, namely redaction of data irrelevant to services and addition of bounded artificial noise. Keywords: Privacy, Smart meter data, Quantitative evaluation

V. V. Skalozub, I. V. Klymenko

Purpose. The article is aimed to improve the automated systems operation of the railway technical system parks and switch D.C. electric motors (EMs), taking into account all uncertainties. Methodology. Solution of the problem was obtained through the development of the model and the method for optimal planning for the EMs set operation. The method is based on the information technology with the possibility to assess the parameters of the current and the predicted state of EMs based on their individual models. The models are built both for individual EMs and for the specified groups. The factors of non-determinism in the model are calculated based on the Hurst index. The task of planning is solved as calculating the optimal sequence of the EM facilities services, which provides a minimum of the total expected operating costs. Findings. The analysis of the main known models, the automated technologies and the systems of EM (ASEM) park operation on the basis of the remote monitoring was done in the research. Based on the practice of the EM park maintenance the new category of the analysis objects was proposed – the service group (SG). The new procedure for the processes classification was developed based on using the Hurst index to improve the reliability of EM and SG individual models forecasting. The technological and the economic model for planning the EM parks operation was created. The article presents the results of the developed automated data management system based on the improved model for the operation planning of the D.C. EM parks. The optimal planning model ensures the minimization of the expected operating costs for the EMs operation, due to the selection of the EM groups service queue. The specialized procedure is used to classify non-deterministic EM remote monitoring data during planning, which allows increasing the accuracy of forecasting the object state parameters. Origilnality. The article describes development of the mathematical model and the information technology for the remote monitoring of the railway technical systems park operation, the railway switch EMs based on the formation of EM and SG individual models, as well as on the evaluation of their current and predicted states, taking into account random factors. The proposed model of the optimal planning as the possibility to choose the SG service queue differs by the group maintenance of the EM facilities, as well as application of the specialized procedure for classifying EM monitoring data. Practical value. The practical value of the results is determined by the provision of the new opportunities for the group optimal planning of the EM service based on the criterion of the minimum expected costs. The procedure for the monitoring data classification of the operational processes makes it possible to increase the reliability of the forecasting antipersistent time sequences results. It also provides an interpretation of the observational data classification results based on the need for practical usage.

Tahirou Koné

African societies are experiencing since the beginning of the 21st century transformations brought by information technology and digital. With the rise of new media, it is developing a digital journalism in Africa, particularly in Francophone West Africa, which often does not respect the standards and professional practices. What comes pose acutely the question of freedom adjusted to their social responsibility in a fragile sector, but where excess risks are real because of the specificity of the medium. Therefore, our approach attempts to capture the ratio of regulatory mechanisms and the editorial offers digital information sites with the aim of quality content. This text is, therefore, within a perspective of social constructivism, to assess the impact of technological developments on information, to encourage digital journalists to adhere to a set of values and rules to guide their daily practice, and stimulate the establishment of effective mechanisms of regulation of new media.

Keppner Sarah, Proschak Ewgenij, Schneider Gisbert et al.