查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting originating in Salt Lake City, Utah, by NewsRx journalists, research stated, "We conducted an implementation planning process during the pilot phase of a pragmatic trial, which tests an intervention guided by artificial intelligence (AI) analytics sourced from noninvasive monitoring data in heart failure patients (LINK-HF2). A mixed-method analysis was conducted at 2 pilot sites." The news reporters obtained a quote from the research from Cardiology Section, "Interviews were conducted with 12 of 27 enrolled patients and with 13 participating clinicians. iPARIHS constructs were used for interview construction to identify workflow, communication patterns, and clinician's beliefs. Interviews were transcribed and analyzed using inductive coding protocols to identify key themes. Behavioral response data from the AI-generated notifications were collected. Clinicians responded to notifications within 24 hours in 95% of instances, with 26.7% resulting in clinical action. Four implementation themes emerged: (1) High anticipatory expectations for reliable patient communications, reduced patient burden, and less proactive provider monitoring. (2) The AI notifications required a differential and tailored balance of trust and action advice related to role. (3) Clinic experience with other home-based programs influenced utilization. (4) Responding to notifications involved significant effort, including electronic health record (EHR) review, patient contact, and consultation with other clinicians. Clinician's use of AI data is a function of beliefs regarding the trustworthiness and usefulness of the data, the degree of autonomy in professional roles, and the cognitive effort involved. The implementation planning analysis guided development of strategies that addressed communication technology, patient education, and EHR integration to reduce clinician and patient burden in the subsequent main randomized phase of the trial."
查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting from Bucharest, Romania, by NewsRx journalists, research stated, "Recently, there have been significant changes in the labour market and in the lives of employees, as modern society adapts increasingly easily to the implementation of artificial intelligence tools." Our news reporters obtained a quote from the research from Bucharest University of Economic Studies: "However, technological changes have also created challenges, including a gap between available and equired competencies in the use of artificial intelligence technologies. This study aims to analyse the relationships between employee competencies and effectiveness in the use of artificial intelligence tools, in order to highlight the set of essential competencies in effective interaction with artificial intelligence technology. Therefore, to achieve the purpose of the research, a questionnaire was created and completed by 209 Romanian employees between August and September 2023. For data analysis, two advanced techniques were applied: structural equation modelling (SEM) and necessary conditions analysis (NCA) using the SmartPLS v4 program. The results suggest that employee competencies are significantly associated with the effectiveness of using AI tools, and optimism and innovativeness positively mediate this relationship. The originality of the research stands out through the use of two advanced analysis methods (structural equation modelling and necessary conditions analysis), with the aim of identifying the set of sufficient and necessary skills in the use of artificial intelligence tools."
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting from Edinburgh, United Kingdom, by NewsRx journalists, research stated, "When modeling competing risks (CR) survival data, several techniques have been proposed in both the statistical and machine learning literature. State-of-the-art methods have extended classical approaches with more flexible assumptions that can improve predictive performance, allow high-dimensional data and missing values, among others." The news correspondents obtained a quote from the research from the University of Edinburgh, "Despite this, modern approaches have not been widely employed in applied settings. This article aims to aid the uptake of such methods by providing a condensed compendium of CR survival methods with a unified notation and interpretation across approaches. We highlight available software and, when possible, demonstrate their usage via reproducible R vignettes." According to the news reporters, the research concluded: "Moreover, we discuss two major concerns that can affect benchmark studies in this context: the choice of performance metrics and reproducibility." This research has been peer-reviewed.
查看更多>>摘要：Current study results on artificial intelligence have been published. According to news reporting from Nagasaki, Japan, by NewsRx journalists, research stated, "When obtaining specimens from pulmonary nodules in TBLB, distinguishing between benign samples and mis-sampling from a tumor presents a challenge." Financial supporters for this research include New Energy And Industrial Technology Development Organization. The news journalists obtained a quote from the research from Nagasaki University Graduate School of Biomedical Sciences: "Our objective is to develop a machine-learning-based classifier for TBLB specimens. Three pathologists assessed six pathological findings, including interface bronchitis/bronchiolitis (IB/B), plasma cell infiltration (PLC), eosinophil infiltration (Eo), lymphoid aggregation (Ly), fibroelastosis (FE), and organizing pneumonia (OP), as potential histologic markers to distinguish between benign and malignant conditions. A total of 251 TBLB cases with defined benign and malignant outcomes based on clinical follow-up were collected and a gradient-boosted decision-tree-based machine learning model (XGBoost) was trained and tested on randomly split training and test sets. Five pathological changes showed independent, mild-to-moderate associations (AUC ranging from 0.58 to 0.75) with benign conditions, with IB/B being the strongest predictor. On the other hand, FE emerged to be the sole indicator of malignant conditions with a mild association (AUC = 0.66). Our model was trained on 200 cases and tested on 51 cases, achieving an AUC of 0.78 for the binary classification of benign vs. malignant on the test set."
查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news originating from Glasgow, United Kingdom, by NewsRx correspondents, research stated, "This article explores the convergence of artificial intelligence and its challenges for precise planning of LoRa networks. It examines machine learning algorithms in conjunction with empirically collected data to develop an effective propagation model for LoRaWAN." The news correspondents obtained a quote from the research from Glasgow Caledonian University: "We propose decoupling feature extraction and regression analysis, which facilitates training data requirements. In our comparative analysis, decision-tree-based gradient boosting achieved the lowest root-mean-squared error of 5.53 dBm. Another advantage of this model is its interpretability, which is exploited to qualitatively observe the governing propagation mechanisms. This approach provides a unique opportunity to practically understand the dependence of signal strength on other variables. The analysis revealed a 1.5 dBm sensitivity improvement as the LoR's spreading factor changed from 7 to 12. The impact of clutter was revealed to be highly non-linear, with high attenuations as clutter increased until a certain point, after which it became ineffective."
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting out of Hohhot, People's Republic of China, by NewsRx editors, research stated, "How to use digitalization to support the green transformation of organizations has drawn much attention based on the rapid development of digitalization. However, digital transformation (DT) may be hindered by the 'IT productivity paradox.' Exploring the influence of DT on green innovation, we analyze panel data encompassing A-share listed companies in Shanghai and Shenzhen spanning the period from 2010 to 2018." Our news journalists obtained a quote from the research from Inner Mongolia University, "It tests the DT's non-linear impact, employing a random-forest and mediation effect models. The results reveal that (i) DT can promote green innovation; (ⅱ) regarding heterogeneity, the promotion effect is mainly manifested in enterprises in non-state-owned and highly competitive industries; (ⅲ) based on mechanism testing, DT relies on two routes to encourage green innovation: improving environmental information disclosure and reducing environmental uncertainty; and (ⅳ) random-forest analysis shows that DT exhibits an inverted Ushaped non-linear effect on green innovation, including the 'IT productivity paradox.' This study enhances the existing discourse on DT and green innovation by furnishing empirical substantiation for the non-linear influence exerted by DT on green innovation."
查看更多>>摘要：Current study results on Nanotechnology - Carbon Nanotubes have been published. According to news reporting out of Hunan, People's Republic of China, by NewsRx editors, research stated, "The sustainable development of the construction industry necessitates the utilization of multipurpose Cement Composites (CC). Therefore, the integration of nanomaterials has the potential to provide CC that exhibits superior performance and possesses several functionalities." Financial supporters for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Hunan Province. Our news journalists obtained a quote from the research from the Changsha University of Science and Technology, "Hence, the use of Carbon Nanotubes (CNTs) inside the concrete cementitious sector holds significant potential for implementing effective solutions toward creating a sustainable ecosystem characterized by versatile attributes. Nevertheless, the prediction of the characteristics of these composites is a significant challenge owing to their complex composite structure and non-linear response. Furthermore, the process of designing and executing experimental trials on diverse samples and across various age groups is arduous, time-consuming, and financially burdensome. There is currently a dearth of a predictive model capable of estimating the compressive strength of concrete including nanoparticles. The utilization of such models is of significant importance in the project and study of Reinforced Concrete (RC) structures including nanoparticles. Three machine learning algorithms, including Gene Expression Programming (GEP), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were utilized in this study to forecast the Compressive Strength (CS) of nanocomposites that incorporate CNTs. The evaluation of the models' reliability was conducted by the utilization of cross-validation with K-folding and subsequent statistical error analysis. According to the results of the coefficient of determination (R2), the XGB model achieved the highest R2 value (0.95), while the GB model and GEP model both earned R2 values of 0.94. Furthermore, the validation method for the models included the implementation of statistical analysis and k-fold cross-validation. Therefore, the XGB model exhibited much lower values for statistical metrics compared to the GEP and GB models. In addition, a GEP empirical equation and a Graphical User Interface (GUI) have been created for practical applications in predicting the strength of concrete. This streamlines the procedure and provides a valuable instrument for harnessing the model's potential in the field of civil engineering. Furthermore, the use of Shapley analysis is conducted to assess the predominant factors in concrete prediction."
查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting originating from Jinan, People's Republic of China, by NewsRx correspondents, research stated, "Federated learning (FL) is a kind of distributed machine learning framework, where the global model is generated on the centralized aggregation server based on the parameters of local models, addressing concerns about privacy leakage caused by the collection of local training data. With the growing computational and communication capacities of edge and IoT devices, applying FL on heterogeneous devices to train machine learning models is becoming a prevailing trend." Financial supporters for this research include National Key R&D Program of China, Australian Research Council.
查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Nanjing, People's Republic of China, by NewsRx correspondents, research stated, "Accurate prediction of particulate matter with aerodynamic diameter ＜= 2.5 mu m (PM2.5) is important for envi-ronmental management and human health protection. In recent years, many efforts have been devoted to develop air quality predictions using the machine learning and deep learning techniques." Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from the Nanjing University of Information Science and Technology (NUIST), "In this study, we propose a deep learning model for short-term PM2.5 predictions. The salient feature of the proposed model is that the convolution in the model architecture is causal, where the output of a time step is only convolved with components of the same or earlier time step from the previous layer. The model also weighs the spatial corre-lation between multiple monitoring stations. Through temporal and spatial correlation analysis, relevant in-formation is screened from the monitoring stations with a strong relationship with the target station. Information from the target and related sites is then taken as input and fed into the model. A case study is conducted in Nanjing, China from January 1, 2020 to December 31, 2020. Using historical air quality and meteorological data from nine monitoring stations, the model predicts PM2.5 concentrations for the next hour. The experimental results show that the predicted PM2.5 concentrations are consistent with observation, with correlation coefficient (R2) and Root Mean Squared Error (RMSE) of our model are 0.92 and 6.75 mu g/m3. Additionally, to better un-derstand the factors affecting PM2.5 levels in different seasons, a machine learning algorithm based on Principal Component Analysis (PCA) is used to analyze the correlations between PM2.5 and its influencing factors. By identifying the main factors affecting PM2.5 and optimizing the input of the predictive model, the application of PCA in the model further improves the prediction accuracy, with decrease of up to 17.2 % in RMSE and 38.6 % in mean absolute error (MAE)."
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting out of Guangzhou, People's Republic of China, by NewsRx editors, research stated, "Currently, the diagnostic testing for the primary origin of liver metastases (LMs) can be laborious, complicating clinical decision-making. Directly classifying the primary origin of LMs at computed tomography (CT) images has proven to be challenging, despite its potential to streamline the entire diagnostic workflow." Our news journalists obtained a quote from the research from Southern Medical University, "We developed ALMSS, an artificial intelligence (AI)-based LMs screening system, to provide automated liver contrast-enhanced CT analysis for distinguishing LMs from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), as well as subtyping primary origin of LMs as six organ systems. We processed a CECT dataset between January 1, 2013 and June 30, 2022 (n = 3105:840 HCC, 354 ICC, and 1911 LMs) for training and internally testing ALMSS, and two additional cohorts (n = 622) for external validation of its diagnostic performance. The performance of radiologists with and without the assistance of ALMSS in diagnosing and subtyping LMs was assessed. ALMSS achieved average area under the curve (AUC) of 0.917 (95% confidence interval [CI]: 0.899-0.931) and 0.923 (95% [CI]: 0.905-0.937) for differentiating LMs, HCC and ICC on both the internal testing set and external testing set, outperformed that of two radiologists. Moreover, ALMSS yielded average AUC of 0.815 (95% [CI]: 0.794-0.836) and 0.818 (95% [CI]: 0.790-0.842) for predicting six primary origins on both two testing sets. Interestingly, ALMSS assigned origin diagnoses for LMs with pathological phenotypes beyond the training categories with average AUCof 0.761 (95% [CI]: 0.657-0.842), which verify the model's diagnostic expandability. Our study establishedan AI-based diagnostic system that effectively identifies and characterizes LMs directly from multiphasicCT images."
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