Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing the Prognostic Model of Cervical Cancer Based on Artificial Intelligence Algorithm and Data Mining Technology

With the accumulation and development of medical multimodal data as well as the breakthrough in the theory and practice of artificial neural network and deep learning algorithm, the deep integration of multimodal data and artificial intelligence based on the Internet has become an important goal of the Internet of Medical Things. The deep application of the latest technologies in the medical field, such as artificial intelligence, machine learning, multimodal data, and advanced sensors, has a profound impact on the development of medical research. Artificial intelligence can achieve low-consumption and high-efficiency screening of specific markers due to its powerful data integration and processing capabilities, and its advantages are fully demonstrated in the construction of disease-related risk prediction models. In this study, multi-type cloud data were used as research objects to explore the potential of alternative CpG sites and establish a high-quality prognosis model of cervical cancer DNA methylation big data. 14,419 strict differentially methylated CpG sites (DMCs) were identified by ChAMP methylation analysis and presented these distributions based on different genomic regions and relation to island. Further, rbsurv and Cox regression analyses were performed to construct a prognostic model integrating these four methylated CpG sites that could adequately predict the survival of patients ( AUC = 0.833 , P < 0.001 ). The low- and high-risk patient groups, divided by risk score, showed significantly different overall survival (OS) in both the training ( P < 0.001 ) and validation datasets ( P < 0.005 ). Moreover, the model has an independent predictive value for FIGO stage and age and is more suitable for predicting survival time in patients with histological type (SCC) and histologic grade (G2/G3). Finally, the model exhibited much higher predictive accuracy compared to other known models and the corresponding expression of genes. The proposed model provides a novel signature to predict the prognosis, which can serve as a useful guide for increasing the accuracy of predicting overall survival of cervical cancer patients.
Optimization of Ultrasonic Extraction of Triterpenes from Loquat Peel and Pulp and Determination of Antioxidant Activity and Triterpenoid Components

The aim of this paper was to study the optimal extraction process of total triterpenes from loquat peel and pulp assisted by ultrasound. The effects of solid–liquid ratio, ethanol concentration, ultrasonic time, ultrasonic power, and ultrasonic temperature on the yield of triterpenoid acid in loquat were investigated by single-factor and response surface methodology. FRAP (Ferric ion reducing antioxidant power) method, ABTS (2,2′-Azino-bis(3-ethylbenzthiazoline-6-sulfonic acid)) method, and DPPH (1,1-Diphenyl-2-picrylhydrazyl) method were used to determine the antioxidant capacity of peel and pulp at different stages. LC-MS (Liquid Chromatograph Mass Spectrometer) was used to qualitatively analyze different tissues of loquat. The optimal extraction conditions were as follows: ethanol concentration 71%, ultrasonic time 45 min, ultrasonic power 160 W, solid–liquid ratio 1:10, and ultrasonic temperature 30 °C. The total triterpenoid content of loquat peel was 13.92 ± 0.20 mg/g. The optimal extraction conditions were ethanol concentration 85%, ultrasonic time 51 min, ultrasonic power 160 W, solid–liquid ratio 1:8, and ultrasonic temperature 43 °C. The total triterpenoid content of loquat pulp was 11.69 ± 0.25 mg/g. The contents of triterpenes and antioxidant capacity in the peel and pulp of loquat at the three stages were the highest in the fruit ripening stage (S3). LC-MS analysis showed that most of the triterpenes belonged to ursolic acid derivatives and oleanolic acid derivatives, which laid the foundation for further utilization and development of loquat peel and pulp.
Trends in wildfire-related bills in California, 2001–2020

California has recently experienced severe and destructive wildfire seasons, prompting new policy proposals from the California State Legislature. Here, we examine proposed state wildfire-related legislation from the ten two-year legislative sessions between 2001–2002 and 2019–2020 to track trends and factors influencing wildfire-related legislative priorities. We first use Latent Dirichlet allocation topic modeling to identify 29 wildfire topics from the text of 294 bills. We next determine the degree to which legislators’ backgrounds, legislative procedures, and bill intent are correlated with bill topic. We find individual correlations but no overarching correlations between legislators’ personal and political backgrounds (e.g. political party, legislative experience, home district, bipartisanship), legislative procedures (i.e. committee review, state mandates, urgency measures), and bill intent (i.e. wildfire stage, commemorative bill, focus on specific geographic area or vulnerable group) with bill topics. However, we find that successful bills are correlated with review by Appropriations Committees, review by multiple committees, and a focus on specific geographic location. In the 2019–2020 legislative session, bill passage was correlated with Democratic sponsorship. Legislators tend to propose bills related to the dominant challenge or crisis of that legislative session (such as loss of homeowners’ insurance coverage or need for more fuels management), indicating the reactiveness of the legislature to statewide issues with new proposed legislation. These findings provide important insights into how legislative priorities change over time and the policy enablers and barriers for urgent climate change adaptation. Our results indicate a potentially fundamental policy shift in response to recent wildfires in California.
Using Domain Redefinition to explain Born Global Firms’ Behaviour

Many academics cite research gaps in the literature concerning the strategies used by Born Global (BG) organisations to enable competitive advantage and business continuance. (Andersson et al 2020; Eggers et al, 2020; Ibeh et al, 2019; Oyna and Alon, 2018). For the purposes of this paper, the definition of the Born Global is taken from Gabrielsson et al, (2008): “the organisation should be an independent small or medium sized business, have a global vision from inception and demonstrate internationalisation with precocity and speed”. Purpose This paper investigates Domain Redefinition (DR) a component of Corporate Entrepreneurship (CE), as a viable construct to explain competitive advantage and business sustainment in Born Global Firms (BG).
A deep learning-based approach for automatic detection of concrete cracks below the waterline

Convolutional neural networks have been created as deep learning-based approaches to automatically analyze photographs of concrete surfaces for crack diagnosis applications. Although deep learning-based systems assert to have extremely high accuracy, they frequently overlook how difficult it is to acquire images. Complex lighting situations, shadows, the irrationality of crack forms and widths, imperfections, and concrete spall frequently have an influence on real-world photos. The focus of the published research and accessible shadow databases is on photographs shot in controlled laboratory settings. In this research, we investigate the challenging underwater optical effects settings and the complexity of image classification for concrete crack detection. This research elaborates on difficulties encountered when using deep learning-based techniques to identify concrete cracks when optical effects are present. To improve the precision of automatically detecting concrete cracks on underwater surfaces, new optical effect augmentation techniques have been developed.
Simulation et optimisation de la consommation énergétique de réseaux de capteurs sans fil

The great technique developments of embedded system in recent years have successfully enabled the combination of sensing, data processing and various wireless communication technologies all in one node. Wireless sensor networks (WSNs) that consist of many of such node have gained worldwide attention from academic institutions and industrial communities, since their applications are widespread in such as environment monitoring, military fields, event tracing/tracking and disaster detection. Due to the reliance on battery, energy consumption of WSNs has always been the most significant concern. In this paper, a mixed method is employed for the accurate energy evaluation on WSNs, which involves the design of a transaction-level system-level based SystemC simulation environment for energy exploration, and the building of an energy measurement system platform for the real world testbed node measurements to calibrate and validate both node energy simulation model and operation model. Elaborate energy consumption of several different node platform based networks are investigated and compared under different kinds of scenarios, and then comprehensive energy-saving strategies are also given after each case scenario for the developers and researchers who focus on the energy-efficient WSNs design. A genetic algorithm (GA) based optimization framework is designed and implemented using MATLAB for the energy aware WSNs. Due to the global search property of genetic algorithms, the optimization framework is able to automatically and intelligently fine tune hundreds/thousands of potential solutions to find the most suitable tradeoff among energy consumption and other performance metrics. The framework’s high efficiency and reliability of finding the tradeoff solutions among node energy, network packet loss and latency have been proved by tuning unslotted CSMA/CA algorithm parameters (used by non-beacon mode of IEEE 802.15.4) in our SystemC-based simulation via a weighted sum cost function. Furthermore, the framework is also available for the multi-scenario and multi-objective based optimization task by studying a typical medical application on human body. Keywords: Wireless sensor networks (WSNs), energy consumption, simulation/emulation, SystemC, testbeds, measurements, calibration, optimization, genetic algorithms, performance metrics, weighted sum cost function, multi-scenario and multi-objective optimization, Pareto-front
On-Chip Refractive Index Sensor With Ultra-High Sensitivity Based on Sub-Wavelength Grating Racetrack Microring Resonators and Vernier Effect

An approach to optimize the sensitivity and the limit of detection of on-chip refractive index sensor is proposed and demonstrated based on sub-wavelength grating racetrack microring resonator and Vernier effect. The sub-wavelength grating waveguide can reduce the structure limitation of the light field, which is beneficial to enhancing the interaction between the photon and analyte. By optimizing the parameters of the sub-wavelength grating racetrack microring resonator, the sensitivity of the sensor could be significantly improved to 664 nm/RIU. Subsequently, capitalizing the Vernier effect, a two cascaded microring-based refractive index sensor is designed. Owing to the Vernier effect, the wavelength spacings among the overlapped peaks could be effectively amplified more than ten times, leading to a high performance. The results demonstrate that an ultra-high sensitivity of 7061 nm/RIU and a low limit of detection of 1.74 × 10−5 RIU. With the advantages of ultra-high sensitivity and low limit of detection, the integrated device has important value in the fields of environmental monitoring and biosensors.
A Time Series Analysis Using Shannon Index of Annual Domestic Crop Production and Area Planted in Jamaica from 2007 to 2021

- This paper aims to determine agricultural non-tree crop diversification with respect to crop production and area reaped in Jamaica from 2007 to 2021 using the Shannon-Wiener index. Shannon Index was calculated to determine diversification nationally and by parish. Time series analysis was done for the resulting transformed dataset and ten-year forecasts computed for national production and area reaped using linear and quadratic models. Two variables of the Shannon index, namely quantity and abundance, classified as the number of crops and the percent area reaped respectively, were regressed against the Shannon indices of area reaped and production for each parish. At the national level, a decreasing trend was unveiled for the Shannon indices for production and area reaped. This indicates that crop production and area reaped will suffer a decrease in diversification without intervention. Additionally, all parishes with the exception of Trelawny, Portland and St. Ann were diverse in crop production and area reaped. Despite moderate to high Shannon indices in most parishes, their forecasts showed decreases in production diversification in the next 10 years except for Portland, and Kingston and St Andrew which showed increase. All parishes showed decrease in diversification in area reaped with the exception of Portland, Kingston and St Andrew, St Catherine, and St Elizabeth. Regressions for Clarendon and St James were significant for the Shannon indices of production and area reaped however they showed decrease in the Shannon indices for an increase in percent area reaped implying that as agricultural land usage is increasing, the diversities in production and area reaped are still decreasing. For St James only, increasing in the number of crops will result in an increase in the Shannon indices for production and area reaped implying that as the number of crops increases, the diversification of production and area reaped increases.
Agile digitalization evolution in the energy sector taking into account innovative and disruptive technologies

The development of technology and science is decisive in the development of social welfare. We are experiencing the fourth revolution in technological development –after the invention of the steam engine; electricity and process automation; and then computer / IT technology – characterized by an interconnected information network. This revolution will affect all related industries, including the energy sector. New solutions, sensors, devices are available, but the main issue is the use of these devices. So-called smart grid technologies have many advantages, but they also present challenges, whether technologically, economically or sociologically and socially [1]. The answers to these challenges are often not traditional, but creative solutions can mean a breakthrough, changing and accelerating the current progress trends. Such solutions and methods can change and advance all segments and areas, from energy to medicine to the beauty industry. The digital transformation and its implications will significantly change the support for the basic functions of energy distribution. Nowadays, it is almost impossible to solve any task or complete a simple work phase without a computer or IT tool, location data, work schedule description, parameters, and so on. Long-term operation and design, proper architecture and design framework are crucial for efficient operation. Enterprise architecture management within large companies usually involves a very complex range of activities [2]: business, IT and organizational information infrastructure. The role of enterprise architecture is becoming increasingly important for developing and growing companies [3]. In our article, we show how recent decades have helped improve the efficiency of network management and how digitalization is becoming an indispensable player in a sustainable, renewablebased vision.
Extending the standard format of adaptive logics to the prioritized case

This paper introduces a new format for reasoning with prioritized standards of normality. It is applicable in a broad variety of contexts, e.g. dealing with (possibly conflicting) prioritized belief bases or combining different reasoning methods in a prioritized way. The format is a generalization of the standard format of adaptive logics (see [4]). Every logic that is formulated within it has a straightforward semantics in the style of Shoham's selection semantics (see [22]) and a dynamic proof theory. Furthermore, it can count on a rich meta-theory that inherits the attractive features of the standard format, such as soundness and completeness, reflexivity, idempotence, cautious monotonicity, and many other properties.
Use of Cannulated Screws for Primary Latarjet Procedures

Background: Rapid consolidation of the bone block is paramount for the success of the Latarjet procedure. Noncannulated screws have shown satisfactory long-term bone block fusion, while cannulated screws are challenged with inferior mechanical properties and fusion rates. Purpose: To report outcomes of the Latarjet procedure using cannulated screws, notably bone block fusion and complication rates at 3 months, as well as clinical scores and return to sports at minimum 2 years postoperatively. Study Design: Case series; Level of evidence, 4. Methods: We reviewed the records of 124 consecutive patients (124 shoulders) who underwent primary Latarjet procedures using cannulated screws between 2015 and 2018. All patients underwent clinical examination at 3 months postoperatively, including range of motion and radiographs to assess graft fusion. The Walch-Duplay score, Rowe score, Subjective Shoulder Value, and visual analog scale for pain were collected at a minimum follow-up of 2 years. Results: Overall, 9 patients (7%) required reoperation: 7 (6%) had screw removal for unexplained residual pain, 1 (0.8%) had lavage for deep infection, and 1 (0.8%) had evacuation to treat a hematoma. Furthermore, 3 patients (2.4%) had recurrent instability, none of whom underwent reoperation. At 3-month follow-up, all 124 shoulders showed complete radiographic graft fusion, and at 40 ± 11 months (mean ± SD; range, 24-64 months), 88% of patients had returned to sport (109/124), the Walch-Duplay score was 86 ± 14, Rowe was 84 ± 15, Subjective Shoulder Value was 88 ± 11, and visual analog scale for pain was 1 ± 1. Conclusion: The clinical relevance of these findings is that 4.5-mm cannulated screws are safe and effective for primary Latarjet procedures and grant adequate graft healing, with low recurrence of instability and a high rate of return to sports.
A machine learning‐based compressive spectrum sensing in 5G networks using cognitive radio networks

In recent times, evolution of communication technology and standard has grown in leaps and bounds from a conventional 1G communication technology towards the recent 5G and 6G technologies in a very short span of time. However, due to increasing scarcity of spectrum for these devices, cognitive radio networks (CRNs) have emerged to be promising solutions to allocate the required spectrum to the users in an intelligent manner. The method of compressive sensing‐based cyclo‐stationary feaure detection method is implemented based on a powerful CNN classifier to detect the presence or absence of PU activity. Detection probability and MSE have been improved, and the optimal detection has been reformulated for minimizing the possibility of error. Performance metrics have been compared against benchmark methods and superior performance reported. The sensing conduction and the accuracy of the proposed design are increased as 98.5%.
Identification of Changes in Rumination Behavior Registered with an Online Sensor System in Cows with Subclinical Mastitis

Simple Summary The aim of this study is to determine if there is a relationship between subclinical mastitis and rumination behavior, as measured by an online sensor system. According to the findings of our research, rumination time and rumination chews can act as biomarkers of subclinical mastitis. Moreover, changes in rumination behavior could be used to identify subclinical mastitis in dairy cows. Recognizing affected cows and treating udder inflammation at an early stage could be extremely beneficial to farmers. For the identification of subclinical mastitis, veterinarians and farmers could consider using an online sensor system that registers rumination behavior biomarkers. This system would allow early identification of sick cows and reduce economic losses to the farm caused by this disease. Abstract The aim of the present study is to determine the relationship between subclinical mastitis and rumination behavior registered with an online sensor system. Based on the findings of the general clinical examination of 650 milking cows, 10 cows with subclinical mastitis (SCM) and 10 clinically healthy cows (HG) were selected (without clinical signs of any diseases). Rumination behavior biomarkers were registered with RumiWatch noseband sensors (RWS; ITIN + HOCH GmbH, Fütterungstechnik, Liestal, Switzerland). Sensors were implanted on the first day after calving. Data from the RWS 13 days before diagnosis of SCM and 13 days after diagnosis of SCM were compared with HG data from the same period. Healthy cows were checked alongside SCM cows on the same days. In our study, we found that healthy cows spent more time engaging in rumination and drinking (p < 0.05) and also had more boluses per rumination. Moreover, among cows with subclinical mastitis, rumination time from day 4 to day 0 decreased by 60.91%, drinking time decreased by 48.47%, and the number of boluses per rumination decreased by 8.67% (p < 0.05). The results indicate that subclinical affects time and rumination chews registered with sensor systems. However, additional studies with larger numbers of animals are required to confirm these results. Furthermore, the impact of heat stress, estrus, and other effects on rumination behavior biomarkers should be evaluated.
Real-Time Drift-Driving Control for an Autonomous Vehicle: Learning from Nonlinear Model Predictive Control via a Deep Neural Network

A drift-driving maneuver is a control technique used by an expert driver to control a vehicle along a sharply curved path or slippery road. This study develops a nonlinear model predictive control (NMPC) method for the autonomous vehicle to perform a drift maneuver and generate the datasets necessary for training the deep neural network(DNN)-based drift controller. In general, the NMPC method is based on numerical optimization which is difficult to run in real-time. By replacing the previously designed NMPC method with the proposed DNN-based controller, we avoid the need for complex numerical optimization of the vehicle control, thereby reducing the computational load. The performance of the developed data-driven drift controller is verified through realistic simulations that included drift scenarios. Based on the results of the simulations, the DNN-based controller showed similar tracking performance to the original nonlinear model predictive controller; moreover, the DNN-based controller can demonstrate stable computation time, which is very important for the safety critical control objective such as drift maneuver.
Organizational learning: a basis for the incorporation of information and communication technologies in colombian microenterprises

This article develops a review of literature in different specialized databases where 54 articles related to models of technological acceptance, organizational learning, industries 4.0, and information and communication technologies were analyzed. The document initially presents a description of ICT (Information and Communication Technologies) and its advantages in the microenterprise sector, subsequently addresses some of the technology acceptance models, followed by conceptualizing organizational learning, and finally exposes the challenges of the world of industry 4.0 and what is necessary to achieve a process of incorporating ICT in Colombian microenterprises.