信息工程最新文献

Protecting private data in smart homes, a popular Internet-of-Things (IoT) application, remains a significant data security and privacy challenge due to the large-scale development and distributed nature of IoT networks. Recently, smart healthcare has leveraged smart home systems, thereby compounding security concerns in terms of the confidentiality of sensitive and private data and by extension the privacy of the data owner. However, proof-of-authority (PoA)-based blockchain distributed ledger technology (DLT) has emerged as a promising solution for protecting private data from indiscriminate use and thereby preserving the privacy of individuals residing in IoT-enabled smart homes. This review elicits some concerns, issues, and problems that have hindered the adoption of blockchain and IoT (BCoT) in some domains and suggests requisite solutions using the aging-in-place scenario. Implementation issues with BCoT were examined as well as the combined challenges BCoT can pose when utilised for security gains. The study discusses recent findings, opportunities, and barriers, and provides recommendations that could facilitate the continuous growth of blockchain applications in healthcare. Lastly, the study explored the potential of using a PoA-based permission blockchain with an applicable consent-based privacy model for decision-making in the information disclosure process, including the use of publisher-subscriber contracts for fine-grained access control to ensure secure data processing and sharing, as well as ethical trust in personal information disclosure, as a solution direction. The proposed authorisation framework could guarantee data ownership, conditional access management, scalable and tamper-proof data storage, and a more resilient system against threat models such as interception and insider attacks.

Background

The prognosis and survival of patients with lung cancer are likely to deteriorate with metastasis. Using deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of the likelihood of such metastasis, thereby providing clinicians with crucial information to enhance diagnostic precision and ultimately improve patient survival and prognosis

Methods

In total, 623 eligible patients were recruited from two medical institutions. Seven deep learning models, namely Alex, GoogLeNet, Resnet18, Resnet101, Vgg16, Vgg19, and MobileNetv3 (small), were utilized to extract deep image histological features. The dimensionality of the extracted features was then reduced using the Spearman correlation coefficient (r ≥ 0.9) and Least Absolute Shrinkage and Selection Operator. Eleven machine learning methods, namely Support Vector Machine, K-nearest neighbor, Random Forest, Extra Trees, XGBoost, LightGBM, Naive Bayes, AdaBoost, Gradient Boosting Decision Tree, Linear Regression, and Multilayer Perceptron, were employed to construct classification prediction models for the filtered final features. The diagnostic performances of the models were assessed using various metrics, including accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Calibration and decision-curve analyses were also performed.

Results

The present study demonstrated that using deep radiomic features extracted from Vgg16, in conjunction with a prediction model constructed via a linear regression algorithm, effectively distinguished the status of mediastinal lymph nodes in patients with lung cancer. The performance of the model was evaluated based on various metrics, including accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value, which yielded values of 0.808, 0.834, 0.851, 0.745, 0.829, and 0.776, respectively. The validation set of the model was assessed using clinical decision curves, calibration curves, and confusion matrices, which collectively demonstrated the model's stability and accuracy

Conclusion

In this study, information on the deep radiomics of Vgg16 was obtained from computed tomography images, and the linear regression method was able to accurately diagnose mediastinal lymph node metastases in patients with lung cancer.

This research paper seeks to examine the possibilities of blockchain technology. For use in the field of restaurant food tracking and safety. Public health risks and economic costs are at stake when foodborne illness outbreaks occur, making food safety a top priority in the food industry. It can be difficult to quickly identify and address possible concerns about using traditional food traceability systems due to inefficiencies, data discrepancies, and a lack of transparency. In this study, we introduce a novel blockchain-based system developed especially for the purpose of tracking restaurant food. Blockchain decentralised consensus, immutability, and smart contracts are put to use in this system to provide trustworthy and transparent traceable infrastructure. Real-time monitoring and data collection along the food supply chain become possible when the blockchain architecture is combined with the Internet of Things (IoT) devices and RFID technology. We show that our proposed blockchain-based traceability solution is practical and efficient through a thorough assessment and validation procedure. The outcomes show that the system not only improves data quality and reliability but also drastically decreases the time and resources needed for food traceability. In addition, patrons are more likely to return to eateries that place a premium on food safety when they are given more information about the establishment’s practises. Additionally, we discuss scalability, data privacy, and interoperability concerns that may arise in future implementations and provide some initial ideas for overcoming these issues.

Background

This study represents the first Spanish investigation to rigorously evaluate compliance with the use of safety goggles and face masks as essential personal protective equipment (PPE) in companies with workplaces involving exposure to biological agents.

Objectives

This study aimed to examine the degree of use of face masks and safety goggles as personal protective equipment (PPE), the factors that influence their use, and the profile of workers exposed to occupational biological agents in Spanish companies in the health sector, farming sector, meat industry, waste treatment plants, food industry, and veterinary centers.

Methods

We conducted a cross-sectional descriptive study involving 590 Spanish workers from 51 companies. We developed a 34-item questionnaire to assess workers’ perception of risk related to exposure to biological agents in their workplaces. Among the questions, three were designed to measure the degree of use of key protective equipment in sectors with biological agent exposure: protective gloves, safety goggles or face masks. We only analyzed safety goggles and face masks. We performed various statistical analyses, including Cronbach’s alpha, frequency of endorsement, content validity ratio using Lawshe’s method, varimax rotation, the Kaiser-Meyer-Olkin test, and Bartlett’s sphericity test, to assess the internal consistency and reliability of the questionnaire. Additionally, we employed a chi-square automatic interaction detection (CHAID) segmentation analysis, using workers’ responses regarding their attitudes toward safety goggles and face mask usage as PPE for protection against biological risks, with demographic variables as independent factors.

Results

In the current study, CHAID analysis revealed that workers exposed to group 2 biological agents used more safety goggles and face shields compared with workers exposed to other groups of biological agents. Moreover, workers in laboratories and the food industry used face masks more than workers of other sectors.

Conclusion

The CHAID analysis in the current study indicated that workers exposed to biological agents from both group 2 and group 3 demonstrated satisfactory levels of compliance and utilization of protective masks, surpassing their counterparts in terms of usage. Workers in the food and laboratory industries had subpar compliance with preventive measures, and employees from companies with internal health and safety departments exhibited significant adherence to workplace mask usage, safeguarding themselves against biological risks.

Background

Atrial septal defect (ASD) is one of the most common congenital heart diseases. The diagnosis of ASD via transthoracic echocardiography is subjective and time-consuming.

Methods

The objective of this study was to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic static images using end-to-end convolutional neural networks. The proposed depthwise separable convolution model identifies ASDs with static color Doppler images in a standard view. Among the standard views, we selected two echocardiographic views, i.e., the subcostal sagittal view of the atrium septum and the low parasternal four-chamber view. The developed ASD detection system was validated using a training set consisting of 396 echocardiographic images corresponding to 198 cases. Additionally, an independent test dataset of 112 images corresponding to 56 cases was used, including 101 cases with ASDs and 153 cases with normal hearts.

Results

The average area under the receiver operating characteristic curve, recall, precision, specificity, F1-score, and accuracy of the proposed ASD detection model were 91.99, 80.00, 82.22, 87.50, 79.57, and 83.04, respectively.

Conclusions

The proposed model can accurately and automatically identify ASD, providing a strong foundation for the intelligent diagnosis of congenital heart diseases.

In today's corporate environment, Environmental, Social, and Governance (ESG) reports crucially reflect an organization's commitment to sustainability, environmental preservation, and social responsibility. As corporations share these detailed reports, the responsibility to validate and assure adherence to respected ESG benchmarks critically lies with third-party assurance organizations. However, the essential verification process often encounters challenges related to authenticity, credibility, and fairness, underscoring the need for a new solution. The selection of verifiers is a crucial aspect of this process, as their expertise and impartiality directly impact the validity and trustworthiness of the verification. Consequently, “Veri-Green,” an innovative blockchain-based incentive mechanism, has been introduced to improve the ESG data verification process. Considering potential risks in verification systems, such as reputational damage due to oversight or inadvertent approval of inaccurate data, and data security risks involving the management of sensitive organizational information, the verifier selection process needs to be thoroughly considered and designed. Through the utilization of advanced machine learning algorithms, potential verification candidates are precisely identified, followed by the deployment of the Vickrey Clarke Groves (VCG) auction mechanism. This approach ensures the strategic selection of verifiers and cultivates an ecosystem marked by truthfulness, rationality, and computational efficiency throughout the ESG data verification process. In this framework, verifiers are not only encouraged but also properly incentivized, developing a more transparent and equitable verification process, thereby driving the ESG agenda towards a future defined by genuine, impactful corporate responsibility and sustainability.