Search Results (155)

Advanced technologies, such as robotic process automation, blockchain, and machine learning, increase audit efficiency. Nonetheless, some Supreme Audit Institutions (SAIs) have not undergone digital transformation. This research aimed to develop a comprehensive framework for supreme audit institutions to adopt and integrate emerging technologies into their auditing processes using a hybrid theoretical approach based on the TAM (Technology Acceptance Model) and TOE (Technology–Organisation–Environment) models. The framework was informed by insights from nineteen highly experienced experts in the field from eight countries. Through a two-round Delphi questionnaire, the experts provided valuable input on the key factors, challenges, and strategies for successful technology adoption by public sector audit organisations. The findings of this research reveal that technology adoption in SAIs starts with solid management support led by the chief technology officer. They must evaluate the IT infrastructure and readiness for advanced technologies, considering the budget and funding. Integrating solutions like the SAI of Ghana’s Audit Management Information System can significantly enhance audit efficiency. Continuous staff training is essential to build a positive attitude toward new technologies, covering areas like data algorithm auditing and big data analysis. Assessing the complexity and compatibility of new technologies ensures ease of use and cost-effectiveness. Continuous support from technology providers and monitoring advancements will keep SAIs aligned with technological developments, enhancing their auditing capabilities. Full article

Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named CatBoostOpt, to estimate bathymetry based on high-resolution WorldView-2 (WV-2) multi-spectral optical satellite images. CatBoostOpt was demonstrated across the Florida Big Bend coastline, where the model learned correlations between in situ sound Navigation and Ranging (Sonar) bathymetry measurements and the corresponding multi-spectral reflectance values in WV-2 images to map bathymetry. We evaluated three different feature transformations as inputs for bathymetry estimation, including raw reflectance, log-linear, and log-ratio transforms of the raw reflectance value in WV-2 images. In addition, we investigated the contribution of each spectral band and found that utilizing all eight spectral bands in WV-2 images offers the best solution for handling complex water quality conditions. We compared CatBoostOpt with linear regression (LR), support vector machine (SVM), random forest (RF), AdaBoost, gradient boosting, and deep convolutional neural network (DCNN). CatBoostOpt with log-ratio transformed reflectance achieved the best performance with an average root mean square error (RMSE) of 0.34 and coefficient of determination (${\mathrm{R}}^2$) of 0.87. Full article

Background: Teaching methodology has recently undergone significant evolution from traditional apprenticeship models as we adapt to ever-increasing rates of technological advancement. Big data, artificial intelligence, and machine learning are on the precipice of revolutionising all aspects of surgical practice, with far-reaching implications. Robotic platforms will increase in autonomy as machine learning rapidly becomes more sophisticated, and therefore training requirements will no longer slow innovation. Materials and Methods: A search of published studies discussing surgeon training and computer-enhanced simulation robotics and emerging technologies using MEDLINE, PubMed, EMBASE, Scopus, CRANE, CINAHL, and Web of Science was performed in January 2024. Online resources associated with proprietary technologies related to the subject matter were also utilised. Results: Following a review of 3209 articles, 91 of which were published, relevant articles on aspects of robotics-based computer-enhanced simulation, technologies, and education were included. Publications ranged from RCTs, cohort studies, meta-analysis, and systematic reviews. The content of eight medical technology-based websites was analysed and included in this review to ensure the most up-to-date information was analysed. Discussion: Surgeons should aim to be at the forefront of this revolution for the ultimate benefit of patients. Surgical exposure will no longer be due to incidental experiences. Rather, surgeons and trainees will have access to a complete database of simulated minimally invasive procedures, and procedural simulation certification will likely become a requisite from graduation to live operating to maintain rigorous patient safety standards. This review provides a comprehensive outline of the current and future status of surgical training in the robotic and digital era. Full article

Growth traits are critical economic characteristics in aquaculture. This study aimed to identify the candidate genes associated with the growth of C. fuscus by integrating QTL mapping for growth traits and the RNA-seq analysis of differentially expressed genes (DEGs) between two extreme body size groups (big-sized group and small-sized group). QTL mapping was performed on eight growth traits—body weight, body height, body length, body width, orbital diameter, caudal peduncle length, caudal peduncle height, and pre-dorsal length—using 200 individuals from a full-sibling line. Seventeen growth-related QTL were identified across eight linkage groups, explaining phenotypic variance ranging from 8.00% to 11.90%. A total of 162 functional genes were annotated within these QTL intervals. RNA-seq analysis identified 3824 DEGs between the big-sized and small-sized groups, with 2252 genes upregulated and 1572 downregulated in the big group. By integrating QTL mapping and RNA-seq data, 27 candidate genes were identified, including myostatin (mstnb), epidermal growth factor receptor (egfr), and sarcoplasmic/endoplasmic reticulum calcium ATPase 1 (serca1). These findings provide crucial insights into the genetic regulation of growth in C. fuscus and lay a foundation for future genetic selection strategies. Full article

Introduction: Therapeutic footwear has been often prescribed in clinical practice for accommodating foot deformities and preventing the development of ulceration, yet scientific evidence is limited and outdated. This study aimed to investigate the effects of two types of Orthofeet therapeutic footwear in comparison to low-cost generic as well as participants’ own athletic shoes on plantar pressure as well as lower extremity kinematics and kinetics. Methods: Twenty healthy participants without foot disorders or pain walked at self-paced speeds under each of the four footwear conditions. In-shoe plantar pressures were measured using F-Scan, and the gait kinematics and kinetics in the sagittal plane were obtained. The foot was divided into eight anatomical zones and three combined zones (forefoot, mid-foot, and hind foot), with peak plantar pressures recorded in each zone. Results: The therapeutic footwear showed significantly greater ankle dorsiflexion during late midstance and less ankle plantar flexion during push-off than generic shoes. Similarly, larger ankle plantar flexor torques were shown when wearing therapeutic footwear. Therapeutic footwear modified the plantar pressure distribution, increasing the peak pressure under the big toe while slightly reducing the peak pressure under the medial heel. The participants’ own athletic shoes provided slightly distinct outcome measures yet comparable performance when compared to therapeutic footwear. Conclusions: This study suggests that therapeutic footwear offers some distinct biomechanical modifications compared with generic shoes. Future studies are needed to assess if these changes lead to meaningful clinical outcomes, such as reduced injury risk or improved foot health. Full article

As geological exploration technology advances, geoscience relies on digitization and intelligence to address challenges such as data fragmentation, multi-source heterogeneity, and visual analysis. This study develops a big data-driven 3D visual analysis system for regional-scale applications. The system integrates three core technological components: (1) a heterogeneous cloud resource scheduling method employing an optimized CMMN algorithm with unified cloud API standardization to enhance task distribution efficiency; (2) a block model-based dynamic data aggregation approach utilizing semantic unification and attribute mapping for multi-source geological data integration; (3) a GPU-accelerated rendering framework implementing occlusion culling and batch processing to optimize 3D visualization performance. Experimental validation shows the improved CMMN algorithm reduces cloud task completion time by 2.37% while increasing resource utilization by 0.652% compared with conventional methods. The dynamic data model integrates 12 geological data types across eight categories through semantic mapping. Rendering optimizations achieve a 93.7% memory reduction and 60.6% faster visualization compared with baseline approaches. This system provides robust decision support and reliable tools for the digital transformation of geoscience work. Full article

This article summarizes proceedings of the State of the Science (SOS) Conference on Information and Communication Technology (ICT) Access for Mobile Rehabilitation, convened by the Rehabilitation Engineering Research Center on Mobile Rehabilitation (mRehab RERC), which is funded by the U.S. National Institute on Disability, Independent Living and Rehabilitation Research (NIDILRR). The conference sought to assess the current state of the field and identify future research and development priorities for the field of mobile rehabilitation. The conference comprised four sessions addressing the following broad areas: (1) adherence to and effectiveness of home therapeutic exercise programs (HEPs); (2) technology for remote monitoring to support rehabilitation in the home and community (mRehab); (3) analytic techniques for using “big data” generated by remote monitoring to customize home exercise; and (4) barriers and facilitators to adoption of mRehab technology. Priorities for further research and development were identified using a three-stage process of gathering and refining expert opinions informed by the Delphi method for identifying future states in specific fields of inquiry. Results: Eight research and six technology development priorities were identified in the third and last stage of refinement of the initial set of priorities identified during the SOS Conference. Full article

Background: Lee Silverman Voice Treatment-BIG (LB) was developed for Parkinson’s disease patients to improve patients’ movement amplitude and accuracy through large movements and enhance movements through self-awareness and recalibration. This study aimed to review studies on LB for neurological diseases other than Parkinson’s disease and examine its potential as an intervention tool. Method: The main search databases included Google Scholar, PubMed, and ScienceDirect. ‘Neurological disease’, ‘LSVT-BIG’, ‘Treatment or Rehabilitation’, ‘Intervention’, and ‘Therapy’ were used as search keywords until December 2024, and eight articles were finally selected. Results: As a result of analyzing eight studies, there were four studies on stroke (all conducted by occupational therapists) and four studies on other diseases, including two studies on progressive supranuclear palsy, one study on idiopathic normal pressure hydrocephalus, and one study on Huntington’s disease (all conducted by physical therapists). Conclusions: LB had a positive effect on improving physical function and overall motor control in patients with neurological diseases other than Parkinson’s disease, indicating its potential as an intervention tool. In the future, studies that have high-level evidence-based study designs and complement small sample sizes are needed to demonstrate the effectiveness of LB. Full article

This study investigates the correlation between bluff body parameters and vortex flowmeter performance through big data modeling and machine learning techniques. Vortex flowmeters are widely used in industry due to their high accuracy and minimal pressure loss. Nonetheless, optimizing their design remains challenging due to the complex relationship between input and output parameters. Symmetry in bluff body design is crucial for vortex formation and stability. In this study, Latin Hypercube Sampling (LHS) was employed to generate 10,000 symmetry bluff bodies, and efficient serial simulations were conducted using Ansys Fluent, significantly reducing computational costs compared to traditional CFD methods. A regression model was developed using scikit-learn to map eight geometric parameters to eight performance indicators, achieving excellent fitting accuracy with residuals far smaller than the simulation accuracy of ANSYS Fluent. Through Grey Relational Analysis (GRA), objective function analysis, and in conjunction with CFD contour maps, this study has analyzed the relationships between input and output parameters and their impact on the Karman vortex street. This work has significantly improved the speed of big data collection and provided a solid theoretical foundation for data-driven optimization through big data analysis. In addition, the improvement of existing machine learning methods has achieved high-precision prediction and system parameter optimization, promoting the design of vortex flowmeters. Full article

In recent years, Internet of Things (IoT) systems are used in Industrial Internet of Things (IIoT) systems due to their widespread use in industrial sectors, providing convenience to users in SCADA systems, like other domains. In addition to the diverse technological advancements discussed, the inherent symmetry within the network structures of SCADA systems utilized in the IIoT echoes a fundamental balance sought in digital frameworks. However, along with the advantages of IIoT systems, there are also disadvantages, one major drawback being their vulnerability to attacks. It has been observed that advanced methods such as artificial intelligence, unlike traditional detection techniques, are more successful at detecting attacks on IIoT systems used in SCADA systems. The proposed model was developed to detect cyberattacks on SCADA systems using machine learning and deep learning models. The SCADA network traffic consists of over 7 million rows and has a size of 627 MB. Attack network traffic refers to the type of traffic aimed at causing damage to the system. The attack traffic in this study includes five different attacks. Normal traffic is the type of traffic that facilitates the system’s usual communication. Prepared network traffic is not a different type of traffic. Prepared network traffic, as named, is the state of the traffic dataset that has been made ready for analysis with models. The prepared network traffic was examined using eight machine learning models, including the CART, Decision Tree, KNN, Logistic Regression, Naive Bayes, Random Forest, SVM, and XGBoost models, as well as seven deep learning models, namely, CNN, GRU, LSTM, MLP, RNN, CNN-LSTM, and LSTM-CNN. During the evaluation of the models, performance parameters such as the accuracy, F-score, precision, and recall were considered, and the results are presented accordingly. Upon examining the dataset with various models, the highest outcomes were achieved using the MLP model. The investigation utilizing the MLP model resulted in an accuracy of 99.95%, a precision of 99.63%, a recall of 99.49%, and an F-score of 99.56%. These values were obtained with a batch-size combination of 100 and 20 epochs. By addressing cyberattack detection in SCADA systems used in the IIoT within a big data environment, the study encompasses a multidisciplinary approach, touching upon cybersecurity, big data analytics, AI, information security, and IoT-related concerns, all of which are focal points within the scope of the journal. This breadth and depth of coverage make the study highly relevant and aligned with the diverse interests of the journal. Full article

Droughts, intensified by climate change and human activities, pose a significant threat to winter wheat cultivation in the Huang-Huai-Hai (HHH) region. Soil moisture drought indices are crucial for monitoring agricultural droughts, while challenges such as data accessibility and soil heterogeneous necessitate the use of numerical simulations for their effective regional-scale applications. The existing simulation methods like physical process models and machine learning (ML) algorithms have limitations: physical models struggle with parameter acquisition at regional scales, while ML algorithms face difficulties in agricultural settings due to the presence of crops. As a more advanced and complex branch of ML, deep learning algorithms face even greater limitations related to crop growth and agricultural management. To address these challenges, this study proposed a novel hybrid monitoring system that merged ML algorithms with a physical process model. Initially, we employed the Random Forest (RF) regression model that integrated multi-source environmental factors to estimate soil moisture prior to the sowing of winter wheat, achieving an average coefficient of determination (R²) of 0.8618, root mean square error (RMSE) of 0.0182 m³ m⁻³, and mean absolute error (MAE) of 0.0148 m³ m⁻³ across eight soil depths. The RF regression models provided vital parameters for the operation of the Water Balance model for Winter Wheat (WBWW) at a regional scale, enabling effective drought monitoring and assessments combined with the Soil Moisture Anomaly Percentage Index (SMAPI). Subsequent comparative analyses between the monitoring system-generated results and the actual disaster records during two regional-scale drought events highlighted its efficacy. Finally, we utilized this monitoring system to examine the spatiotemporal variations in drought patterns in the HHH region over the past two decades. The findings revealed an overall intensification of drought conditions in winter wheat, with a decline in average SMAPI at a rate of −0.021% per year. Concurrently, there has been a significant shift in drought patterns, characterized by an increase in both the frequency and extremity of drought events, while the duration and intensity of individual drought events have decreased in a majority of the HHH region. Additionally, we identified the northeastern, western, and southern areas of HHH as areas requiring concentrated attention and targeted intervention strategies. These efforts signify a notable application of multi-source data fusion techniques and the integration of physical process models within a big data context, thereby facilitating effective drought prevention, agricultural management, and mitigation strategies. Full article

Air pollution remains a critical environmental and public health issue, requiring diverse research perspectives, including those related to energy production and consumption. This study examines the relationship between Poland’s energy mix and air pollution trends by integrating national statistical data on primary energy consumption and renewable energy sources over the past 15 years with air pollution measurements from the last eight years. The air pollution data, obtained from reference-grade monitoring stations, focus on particulate matter (PM). To address discrepancies in temporal resolution between daily PM measurements and annual energy sector reports, a bootstrapping method was applied within a regression framework to assess the overall impact of individual energy components on national air pollution levels. Seasonal decomposition techniques were employed to analyze the temporal dynamics of specific energy sources and their contributions to pollution variability. A key aspect of this research is the role of renewable energy sources in air quality trends. This study also investigates regional variations in pollution levels by analyzing correlations between geographic location, industrialization intensity, and the proportion of green areas across Poland’s administrative regions (Voivodeships). This spatially explicit approach provides deeper insights into the linkages between energy production and pollution distribution at a national scale. Poland presents a unique case due to its distinct energy mix, which differs significantly from the EU average, its persistently high air pollution levels, and recent regulatory changes. These factors create an ideal setting to assess the impact of energy sector transitions on environmental quality. By employing high-resolution spatiotemporal big data analysis, this study leverages measurements from over 100 monitoring stations and applies advanced statistical methodologies to integrate multi-scale energy and pollution datasets. From a PM perspective, the regression analysis showed that High-Methane Gas had a neutral impact on PM concentrations, making it a suitable transition energy source, while renewables exhibited negative regression coefficients and coal-based sources showed positive coefficients. The findings offer new perspectives on the long-term environmental effects of shifts in national energy policies. Full article

Clustering algorithms are widely used in statistical data analysis as a form of unsupervised machine learning, playing a crucial role in big data mining research for Maritime Intelligent Transportation Systems. While numerous studies have explored methods for optimizing ship trajectory clustering, such as narrowing dynamic time windows to prevent errors in time warp calculations or employing the Mahalanobis distance, these methods enhance DBSCAN (Density-Based Spatial Clustering of Applications with Noise) by leveraging trajectory similarity features for clustering. In recent years, machine learning research has rapidly accumulated, and multiple studies have shown that HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) outperforms DBSCAN in achieving accurate and efficient clustering results due to its hierarchical density-based clustering processing technique, particularly in big data mining. This study focuses on the area near Taichung Port in central Taiwan, a crucial maritime shipping route where ship trajectories naturally exhibit a complex and intertwined distribution. Using ship coordinates and heading, the experiment normalized and transformed them into three-dimensional spatial features, employing the HDBSCAN algorithm to obtain optimal clustering results. These results provided a more nuanced analysis compared to human visual observation. This study also utilized O notation and execution time to represent the performance of various methods, with the literature review indicating that HDBSCAN has the same time complexity as DBSCAN but outperforms K-means and other methods. This research involved approximately 293,000 real historical data points and further employed the Silhouette Coefficient and Davies–Bouldin Index to objectively analyze the clustering results. The experiment generated eight clusters with a noise ratio of 12.7%, and the evaluation results consistently demonstrate that HDBSCAN outperforms other methods for big data analysis of ship trajectory clustering. Full article

The present study focuses on analysis of the soil cover structure (SCS, SCSs), which is the most detailed level of soil organization in space. The detail in which complex SCS can be studied is often insufficient, since until now it has not been possible to map it over large areas at scales larger than 1:10,000. To increase the detail in which SCS can be studied, the methods of identifying the bare soil surface (BSS) and averaging its multitemporal spectral characteristics were used, which opens up new possibilities for mapping complex SCS over large areas. New SCSs of leached chernozems (Luvic Chernic Phaeozem) were discovered, which can produce patterns on satellite images similar to sections of Timan agate—agate-like soil cover structures (ASCS, ASCSs). ASCSs are formed on Quaternary sediments of varying thickness from 0.3 to 6 m, underlain by carbonate and red sediments of the Permian period. The ASCS pattern is formed by ring-shaped stripes (rings) of different colors and brightness, which are determined by the carbonate and red-colored inclusions involved in the arable horizon. Eight soil varieties were identified to describe ASCSs during the study. According to the WRB, there are six main soil types, and according to the classification of Russian soils in 1977, there are four types. ASCSs were identified over large areas and soil maps of ASCSs were constructed using multitemporal spectral characteristics of the BSS in the form of multitemporal soil line coefficients. Neural networks were used to identify BSS on big remote sensing data. ASCSs have contrasting soil properties and contrasting fertility (productivity of agricultural crops). ASCS maps can serve as the basis for task maps of precision farming systems. Perhaps ASCSs are unique objects for the area of chernozem distribution, where in one soil profile there are rocks with an age from the first thousand years (Quaternary) to 250 million years (Permian). Chernozems are fertile, studied, mercilessly exploited, but sometimes they are simply beautiful—agate-like. Full article

The world’s big cities, including Budapest, are becoming more crowded, with more and more people living in smaller and smaller spaces. There is an increasing demand for more green space and trees, with less vertical and less horizontal space. In addition, deteriorating environmental conditions are making it even more difficult for trees to grow and survive. Tree species in urban areas have multiple functions and high ecosystem services when in good health. Among taxa with diverse habits, sizes, crown shapes, growth vigor, longevity, urban tolerance, and canopy habit, our research aims to evaluate urban specimens of spherical species with smaller space requirements and sizes but have regular geometric crown shapes in public plantations in Budapest. In the restricted urban habitats, the city’s cadastral records include 4676 specimens with spherical crowns. Among the species examined, eight species with globular crowns (Acer platanoides ‘Globosum’, Catalpa bignonioides ‘Nana’, Celtis occidentalis ‘Globosa’, Fraxinus excelsior ‘Nana’, Fraxinus ornus ‘Mecsek’, Platanus × hispanica ‘Alphen’s Globe’, Prunus × eminens ‘Umbraculifera’ and Robinia pseudoacacia ‘Umbraculifera’) were evaluated in relation to age, health, wood type, crown size, and shade projection in order to show which species are or will be suitable in the future. Full article