Search Results (29)

Modern multi-tiered data storage systems optimize file access by managing data across a hybrid composition of caches and storage tiers while using policies whose decisions can severely impact the storage system’s performance. Recently, different Machine-Learning (ML) algorithms have been used to model access patterns from complex workloads. Yet, current approaches train their models offline in a batch-based approach, even though storage systems are processing a stream of file requests with dynamic workloads. In this manuscript, we advocate the streaming ML paradigm for modeling access patterns in multi-tiered storage systems as it introduces various advantages, including high efficiency, high accuracy, and high adaptability. Moreover, representative file access patterns, including temporal, spatial, length, and frequency patterns, are identified for individual files, directories, and file formats, and used as features. Streaming ML models are developed, trained, and tested on different file system traces for making two types of predictions: the next offset to be read in a file and the future file hotness. An extensive evaluation is performed with production traces provided by Huawei Technologies, showing that the models are practical, with low memory consumption (<1.3 MB) and low training delay (<1.8 ms per training instance), and can make accurate predictions online (0.98 F1 score and 0.07 MAE on average). Full article

The increasing frequency and severity of disasters in urban areas demand sustainable, smart disaster management strategies to leverage technological advancements. This study provides a comprehensive review of mobile apps for disaster awareness available in the Google Play Store, with a particular emphasis on addressing flood disaster readiness and response. Mobile apps have become indispensable tools for disseminating immediate notifications, facilitating emergency communication, and coordinating response activities. A total of 77 mobile apps in the Google Play Store were identified and evaluated using a systematic search. The evaluation criteria included user ratings, download counts, and key crisis management functionalities such as real-time alerts, emergency contact directories, preparedness checklists, and user reporting capabilities. The findings emphasised the following: (a) the importance of integrating cutting-edge technologies, i.e., AI and IoT, to enhance functionality, accuracy, and capacity in mobile applications; (b) the use of crowdsourcing as a valuable mechanism for enriching inclusive and responsible data; (c) enabling timely updates and fostering community engagement; and (d) establishing agency engagements, gamified elements, and real-time reciprocal communication tools, i.e., push-to-talk features to ensure the long-term sustainability of mobile apps. By incorporating these insights, disaster management apps can significantly enhance community resilience and improve the effectiveness of responding to natural disasters in this digital age. Full article

Background/Objectives: Dementia is not a single disease but an umbrella term that encompasses a range of symptoms, such as memory loss and cognitive impairments, which are severe enough to disrupt daily life. One of the most common forms of dementia is Alzheimer’s Disease (AD), a complex neurodegenerative condition influenced by both genetic and environmental factors. Recent research has highlighted diet as a potential modifiable risk factor for AD. Decades of research have explored the role of dietary patterns, including the Mediterranean Diet (MD) and its components, in neuroprotection and cognitive health. Systematic review examines studies investigating the impact of the Mediterranean Diet, Mediterranean-like diets, the Nordic Diet (ND), dietary intake patterns, and specific components such as extra virgin olive oil and rapeseed oil on cognitive function, disease onset, and progression in AD and dementia. Methods: A comprehensive search of PubMed, the Directory of Open Access Journals, and the Social Science Research Network was conducted independently by two reviewers using predefined search terms. The search period included studies from 2006 to 2024. Eligible studies meeting the inclusion criteria were systematically reviewed, yielding 88 studies: 85 focused on the MD and its relationship to AD and dementia, while only 3 investigated the ND. Results: The findings suggest that adherence to the Mediterranean and Nordic diets is generally associated with improved cognitive function and delayed cognitive decline and that adherence to both these diets can improve cognitive function. Some studies identified that higher legume consumption decreased dementia incidence, while fruits and vegetables, carbohydrates, and eggs lowered dementia prevalence. Most studies demonstrated that high MD or ND adherence was associated with better cognitive function and a lower risk of poor cognition in comparison to individuals with lower MD or ND adherence. However, some studies reported no significant benefits of the MD on cognitive outcomes, while two studies indicated that higher red meat consumption was linked to better cognitive function. Conclusion: Despite promising trends, the evidence remains varying across studies, underscoring the need for further research to establish definitive associations between diet and cognitive function. These findings highlight the essential role of dietary interventions in the prevention and management of dementia and AD, therefore offering critical insights into the underlying mechanisms by which the diet may impact brain health. Full article

Accurate identification of surgical instruments is crucial for efficient workflows and patient safety within the operating room, particularly in preventing complications such as retained surgical instruments. Artificial Intelligence (AI) models have shown the potential to automate this process. This study evaluates the accuracy of publicly available Large Language Models (LLMs)—ChatGPT-4, ChatGPT-4o, and Gemini—and a specialized commercial mobile application, Surgical-Instrument Directory (SID 2.0), in identifying surgical instruments from images. The study utilized a dataset of 92 high-resolution images of 25 surgical instruments (retractors, forceps, scissors, and trocars) photographed from multiple angles. Model performance was evaluated using accuracy, weighted precision, recall, and F1 score. ChatGPT-4o exhibited the highest accuracy (89.1%) in categorizing instruments (e.g., scissors, forceps). SID 2.0 (77.2%) and ChatGPT-4 (76.1%) achieved comparable accuracy, while Gemini (44.6%) demonstrated lower accuracy in this task. For precise subtype identification of instrument names (like “Mayo scissors” or “Kelly forceps”), all models had low accuracy, with SID 2.0 having an accuracy of 39.1%, followed by ChatGPT-4o (33.69%). Subgroup analysis revealed ChatGPT-4 and 4o recognized trocars in all instances. Similarly, Gemini identified surgical scissors in all instances. In conclusion, publicly available LLMs can reliably identify surgical instruments at the category level, with ChatGPT-4o demonstrating an overall edge. However, precise subtype identification remains a challenge for all models. These findings highlight the potential of AI-driven solutions to enhance surgical-instrument management and underscore the need for further refinements to improve accuracy and support patient safety. Full article

Traditional identity management (IdM) solutions based on centralised protocols, such as Lightweight Directory Access Protocol (LDAP) and Security Assertion Markup Language (SAML), are where a central authority manages all the processes. This risks a single point of failure and other vulnerabilities. In response, decentralised techniques like blockchain and decentralised identities (DIDs) are being explored. This review paper performs a comparison of popular decentralised identity management (DIM) protocols, such as self-sovereign identity (SSI), against traditional centralised approaches such as LDAP and SAML. These decentralised identity management systems are being developed, keeping users’ identity data as its highest priority. Additionally, this method eliminates the need for a central authority to manage and secure the system. To further explore the potential of decentralised identity management, this study delves into popular blockchain-based decentralised identity management systems such as uPort, Sovrin, EverID, Blockstack, ShoCard, and Hyperledger Indy. We analyse their underlying principles and compare them with the well-established centralised identity management solutions, focusing on key aspects such as scalability, security, and feasibility. However, despite their benefits and several worthy developments in this field, decentralised approaches are still not widely used. Through this study, we investigate both centralised and decentralised methods and review their strengths and weaknesses. By reviewing multiple research papers, this survey aims to provide an understanding and aid in selecting the most suitable identity management system for different use cases. Full article

Background/Objectives: As congenital syphilis incidence continues to increase yearly in the United States (US), recommendations from government and professional organizations aim to inform effective clinical practice, although it is unclear how closely these recommendations are followed. This study surveyed US pediatric specialists regarding their approach to congenital syphilis diagnosis and treatment to examine decision-making relative to practice guidelines and subspecialty. Methods: US pediatric physicians recruited from subspecialty directories were sent an online survey conducted in March–April 2024. The case-based survey elicited diagnostic and treatment decisions for different case definitions of congenital syphilis (proven or highly probable, possible, and less likely). Results: Among 442 respondents (56.8% women, 74.2% age 40–69, 57.7% 15+ years since training completion), 94.1% chose to evaluate and manage proven or highly probable congenital syphilis as recommended whereas only 45.8% did so for congenital syphilis considered less likely. Diagnostic and treatment decisions by infectious disease specialists and other subspecialists differed across case definitions. Conclusions: Physicians’ approaches to congenital syphilis workup and management, including the decision to treat, varied with case presentation where decision-making seemed to diverge from published recommendations and between subspecialists as infection became less likely by case definition. Full article

To maintain high-level economic development, protect the ecological environment, and achieve carbon peaking and carbon neutrality goals, the construction of green mines has become a critical issue in China. In this study, the importance of mineral resources to human society is discussed, and the construction experiences and sustainable development directions of green mines are summarized, which can provide valuable references for the global mining industry. The entry and management process in China was introduced to help understand green mines’ construction objectives and tasks. Moreover, based on the successful construction cases of green mines, four typical green mine models are concluded: the green technology mining model, operation modernization mining model, stability mining model, and ecological restoration mining model. In addition, the key construction elements of green mines are concluded, for example, the mining environment, mining methods, comprehensive utilization of resources, energy conservation, emission reduction, scientific and technological innovation and intelligence, and enterprise-land stability, which provided the directions and guidance for green mine construction. Full article

Mongolian women enjoy equal rights and actively participate in various sectors of the national economy, including the mineral and mining industry. The Mongolian University of Science and Technology (MUST), the largest university in Mongolia, plays a crucial role in preparing engineers for the Mongolian industry. Within MUST, the School of Geology and Mining Engineering (SGME) stands out as one of the largest schools, boasting a dedicated team of 136 staff members. Impressively, 92 of these staff members are female, constituting a remarkable 67.65% of the total staff. The directorial board of SGME, consisting of 12 members, also demonstrates a noteworthy level of gender diversity, with 5 of its members being female. This represents a proportion of 41.67% and highlights the inclusion of women in decision-making positions. Additionally, it is worth noting that the Geology and Hydrogeology department, one of the five departments within the School, is led by a capable female leader. However, despite the encouraging representation of women among staff and in leadership roles, there is a noticeable disparity in the enrollment and graduation rates of students at SGME. Currently, these rates stand at only about 20–24 percent, indicating the need for further efforts to encourage and support female students in pursuing geology and mining engineering studies. Outside of academia, within the mining industry, the Oyu Tolgoi large-scale mine, which in 2022 employed 20,328 workers, faces a significant gender imbalance. Out of this workforce, only 3577 are women, comprising a mere 18% of the total employees, while the remaining 82% are men. Among the 2997 total employees in the open pit mine, 737 women are employed in various roles, including 66 engineers and technicians, with the remaining 671 in other positions. In the newly opened underground mine, the total number of women employees stands at 2840, including 248 engineers and technicians and 2592 in other roles. Furthermore, on the Board of Directors, there are only 2 women out of a total of 23 managers, and a mere 104 women hold positions as senior staff and superintendents. A comparative analysis between Asia and other global regions reveals that female employment in Mongolia’s mining sector in general, at 18%, closely aligns with Oceania’s rates (17%) and surpasses those of both the broader Asian region (13%) and South America (11%). Addressing these statistical imbalances is crucial to improving gender equality in geoscience and mining. Historically, the mining industry has been male-dominated, but women-led professional geoscience and mining organizations in Mongolia play a vital role in promoting the recruitment, retention, and advancement of women in these industries. Recognizing the significance of gender diversity, these organizations strive to increase the representation of women in leadership positions. Women in leadership bring unique perspectives that contribute to well-rounded decision-making processes within organizations. By acknowledging the importance of gender dynamics, promoting inclusivity, and supporting the professional growth of Mongolian women in geoscience and mining, the overall development and sustainability of these sectors in the country will be greatly enhanced. Full article

Cloud services provided by Microsoft are growing rapidly in number and importance. Azure Active Directory (AAD) is becoming more important due to its role in facilitating identity management for cloud-based services. However, several risks and security issues have been associated with cloud systems due to vulnerabilities associated with identity management systems. In particular, misconfigurations could severely impact the security of cloud-based systems. Accordingly, this study identifies and experimentally evaluates exploitable misconfiguration vulnerabilities in Azure AD which can eventually lead to the risk of privilege escalation attacks. The study focuses on two scenarios: dynamic group settings and the activation of the Managed Identity feature on virtual devices. Through experimental evaluation, the research demonstrates the successful execution of these attacks, resulting in unauthorized access to sensitive information. Finally, we suggest several approaches to prevent such attacks by isolating sensitive systems to minimize the possibility of damage resulting from a misconfiguration accident and highlight the need for further studies. Full article

Over the years, Malaysian manufacturers struggled to mitigate the widened gap among the three aspects of TBL performance, e.g., economic, social, and environmental. Although, the economic performance is relatively elevated compared to environmental performance based on environmental performance index (EPI) data reports. Similarly, less than twenty per cent (20%) of manufacturers are ISO14001-certified out of the total registered firms in the 52nd FMM directory. The firms must employ green HRM practices to foster pro-environmental behaviour and support their managers to adopt the environmental management system (EMS) framework to reap the benefits of TBL performance. To resolve these issues, sustainability has become an essential strategy for manufacturers in addressing environmental problems due to consistent ecological awareness among stakeholders that compels firms to adopt EMS and green HRM practices to foster organizational citizenship behaviour for the environment (OCBE) and improve triple bottom line (TBL) performance. This research aimed to investigate the impact of the EMS on TBL performance through green HRM practices and OCBE via a serial mediation approach among ISO14001-certified manufacturing firms. A quantitative methodology was employed based on a positivist paradigm. The sample of 350 respondent firms was randomly targeted via standard questionnaires. Around 248 manufacturers responded with a response rate of 70%, which is sufficient for data analysis. After outliers and normality assessment, the clean data of 216 manufacturers were finally analysed using SmartPLS 4.0. Structural equation modelling (SEM) analysis revealed that EMS is positively related to OCBE, and OCBE is positively associated with TBL. EMS is positively related to green HRM practices, and green HRM practices are positively associated with OCBE. Furthermore, it was confirmed that green HRM practices and OCBE serially mediated the relationship between EMS and TBL performance among ISO14001-certified manufacturing firms. The current study also presents vital organizational and managerial implications. Full article

Telegram, an Industry 4.0 style communication service, is one of the world’s most widespread communication platforms. The availability of channels and bots has opened as a broadcast channel for any media outlet. We asked the following questions: Do media outlets from Spain use Telegram channels? Which media outlets? Are they verified? What is their volume of subscribers? Can this information be used to rank media outlets? We identified many media channels and data were collected from each one. We present the results in a ranking. Forty-two media based in Spain have Telegram channels, 26 of which are ranked in the directory. Less than half of these channels are verified by the platform, and only three are linked to their website. This lack of verification could lead to the proliferation of fake channels. The article ends with a series of recommendations for channel managers to make it easier for the end user to identify and verify each media outlet. Full article

This paper investigates the views of healthcare researchers and professionals on the implementation of the Quality Management System (QMS) approach using a 5-point Likert scale survey. Researchers and healthcare professionals who observed or participated in QMS implementation were surveyed. Multiple channels, including occupational societies, social networking, i.e., LinkedIn, hospital’s directories, award recipients, academic researchers, and professional connections, made it possible to reach this particular sample. Participants were surveyed using a series of questions with a total of 56 questions. The survey was administrated through the web portal of Qualtrics and then analyzed both on Qualtrics and SPSS software packages. Descriptive Statistics, Exploratory Factor Analysis (EFA), and Linear Regression were employed to draw conclusions. The final sample group consisted of 71 participants representing a range of healthcare settings. EFA was conducted, producing a model of 10 emergent factors and an outcome for total improvement. Regression modeling revealed the Critical Success Factors (CSFs) and the interaction between emergent factors. The results indicated that QMS Implementation Culture, Structure, and Managerial Training are critical to the QMS implementation success. This research helps quality professionals enhance their ability to prioritize elements affecting the successful implementation of the QMS. Full article

The number of customers transferring information to cloud storage has grown significantly, with the rising prevalence of cloud computing. The rapidly rising data volume in the cloud, mostly on one side, is followed by a large replication of data. On the other hand, if there is a single duplicate copy of stored symmetrical information in the de-duplicate cloud backup the manipulation or lack of a single copy may cause untold failure. Thus, the deduplication of files and the auditing of credibility are extremely necessary and how they are achieved safely and effectively must be addressed in academic and commercial contexts urgently. In order to tune in this task by using application recognition, data similitude, and locality to simplify decentralized deduplication with two-tier internode and application deduction, we suggest a flexible direct decentralized symmetry deduplication architecture in a cloud scenario. It first distributes application logic to the contents of the directory through implementation-oriented steering to maintain a deployment location and also attributes the same kind of information to the cloud backup node with the storage node specificity by means of a hand printing-based network model to attain adequate global deduplication performance. We build up a new ownership mechanism during file deduplication to ensure continuity of tagging and symmetrical modeling and verify shared ownership. In addition, we plan an effective ownership policy maintenance plan. In order to introduce a probabilistic key process and reduce key storage capacity, a user-helped key is used for in-user block deduplication. Finally, the protection and efficiency audit demonstrate that the data integrity and accuracy of our system are ensured and symmetrically effective in the management of data ownership. Full article

Leukemia is a form of blood cancer that develops when the human body’s bone marrow contains too many white blood cells. This medical condition affects adults and is considered a prevalent form of cancer in children. Treatment for leukaemia is determined by the type and the extent to which cancer has developed across the body. It is crucial to diagnose leukaemia early in order to provide adequate care and to cure patients. Researchers have been working on advanced diagnostics systems based on Machine Learning (ML) approaches to diagnose leukaemia early. In this research, we employ deep learning (DL) based convolutional neural network (CNN) and hybridized two individual blocks of CNN named CNN-1 and CNN-2 to detect acute lymphoblastic leukaemia (ALL), acute myeloid leukaemia (AML), and multiple myeloma (MM). The proposed model detects malignant leukaemia cells using microscopic blood smear images. We construct a dataset of about 4150 images from a public directory. The main challenges were background removal, ripping out un-essential blood components of blood supplies, reduce the noise and blurriness and minimal method for image segmentation. To accomplish the pre-processing and segmentation, we transform RGB color-space into the greyscale 8-bit mode, enhancing the contrast of images using the image intensity adjustment method and adaptive histogram equalisation (AHE) method. We increase the structure and sharpness of images by multiplication of binary image with the output of enhanced images. In the next step, complement is done to get the background in black colour and nucleus of blood in white colour. Thereafter, we applied area operation and closing operation to remove background noise. Finally, we multiply the final output to source image to regenerate the images dataset in RGB colour space, and we resize dataset images to [400, 400]. After applying all methods and techniques, we have managed to get noiseless, non-blurred, sharped and segmented images of the lesion. In next step, enhanced segmented images are given as input to CNNs. Two parallel CCN models are trained, which extract deep features. The extracted features are further combined using the Canonical Correlation Analysis (CCA) fusion method to get more prominent features. We used five classification algorithms, namely, SVM, Bagging ensemble, total boosts, RUSBoost, and fine KNN, to evaluate the performance of feature extraction algorithms. Among the classification algorithms, Bagging ensemble outperformed the other algorithms by achieving the highest accuracy of 97.04%. Full article

The Android platform accounts for 85% of the global smartphone operating-system market share, and recently, it has also been installed on Internet-of-Things (IoT) devices such as wearable devices and vehicles. These Android-based devices store various personal information such as user IDs, addresses, and payment information and device usage data when providing convenient functions to users. Insufficient security for the management and deletion of data stored in the device can lead to various cyber security threats such as personal information leakage and identity theft. Therefore, research on the protection of personal information stored in the device is very important. However, there is a limitation that the current research for protection of personal information on the existing Android platform was only conducted on Android platform 6 or lower. In this paper, we analyze the deleted data remaining on the device and the possibility of recovery to improve user privacy for smartphones using Android platforms 9 and 10. The deleted data analysis is performed based on three data deletion scenarios: data deletion using the app’s own function, data deletion using the system app’s data and cache deletion function, and uninstallation of installed apps. It demonstrates the potential user privacy problems that can occur when using Android platforms 9 and 10 due to the leakage of recovered data. It also highlights the need for improving the security of personal user information by erasing the traces of deleted data that remain in the journal area and directory entry area of the filesystem used in Android platforms 9 and 10. Full article