Deployment Issues Research Articles

Revolutionising Osseous Biopsy: The Impact of Artificial Intelligence in the Era of Personalised Medicine.

In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalised medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted. We provide a brief review of AI in image- guided biopsy of bone tumours (primary and secondary) and specimen handling, in the era of personalised medicine. This paper explores AI's role in enhancing diagnostic accuracy, improving safety in biopsies, and enabling more precise targeting in bone lesion biopsies, ultimately contributing to better patient outcomes in personalised medicine. We dive into various AI technologies applied to osseous biopsies, such as traditional machine learning, deep learning, radiomics, simulation and generative models. We explore their roles in tumour board meetings, communication between clinicians, radiologists, and pathologists. Additionally, we inspect ethical considerations associated with the integration of AI in bone biopsy procedures, technical limitations, and we delve into health equity, generalisability, deployment issues, and reimbursement challenges in AI-powered healthcare. Finally, we explore potential future developments and offer a list of open-source AI tools and algorithms relevant to bone biopsies, which we include to encourage further discussion and research.

Adaptive Probabilistic Neural Network based Edge Data Center Authentication for Secure Load Balancing in Fog Computing

The growth of current technological advances has promising of fog computing, that increases the processing capacity of devices and provides fresh approaches for conventional applications in industry. Fog computing has become increasingly vital given the huge and sophisticated system of sensors to manage the information stream. Node authentication and secure load balancing are important in fog computing. The rationale behind developing this model is that the existing deep learning models are affected by the node authentication performance and also generate issues like high latency developed by the cloud-based architecture while processing huge amount of data, and node employment. Thus, the objective of the developed model is to implement an advanced deep learning method for better node authentication and an appropriate enhanced optimization scheme for performing optimal resource allocation. Additionally, secure load balancing is done at edge node centers in fog computing to allocate the workload for each node securely. Initially, the network initialization attributes in fog computing are provided to the authentication process. Here, the deep learning-based Edge Data Centre (EDC) authentication takes place with the help of the Adaptive Probabilistic Neural Network (A-PNN) model. The major intention of introducing an A-PNN method is to check whether the node is authenticated or not. If the EDC node is authenticated, then the A-PNN model suggests the particular EDC node for the resource allocation process, or else the node will be ignored or removed from the process. Here, the new hybrid approaches as Hybrid Heap with African Buffalo Optimization Algorithm (HH-ABO) are suggested for execute the process of optimization. Here, the parameters of the A-PNN model are tuned for maximizing accuracy, precision, and NPV and also minimizing the False Positive Rate (FPR) rate. Then the authenticated node is subjected to the load-balancing scheme. The allocation of allocation is carried out in the load balancing scheme. Here, the diverse constraints like resource allocation, energy consumption, cost, the execution time of the task, security by authentication, latency, and task completion time are considered for formulating the objective function using the HH-ABO algorithm. At last, it is performed well than the traditional methods using diverse parameters. The outcome demonstrate that the developed model achieved better results on load balancing at edge nodes in fog computing. The proposed model effectively resolves the load balancing issues and also it distributes workload for each node accurately. It effectively resolves the latency and deployment issues. In addition, the modern applications based on the proposed scheme will allow million of workloads from one node to another in a fast and reliable manner.

Optimizing Bus Bridging Service Considering Passenger Transfer and Reneging Behavior

This paper addresses the design of bus bridging services in response to urban rail disruption, which plays a critical role in enhancing the resilience and sustainability of urban transportation systems. Specifically, it focuses on unplanned urban rail disruptions that result in temporary closure of line sections, including transfer stations. Under this “transfer scenario”, a heuristic-rule based method is firstly presented to generate candidate bus bridging routes. Non-parallel bridging routes are introduced to facilitate transfer passengers affected by the disruption. Meanwhile, the bridging stops visited by parallel routes are extended beyond the disrupted section, mitigating passenger congestion and bus bunching at turnover stations. Then, we propose an integrated optimization model that collaboratively addresses bus route selection and vehicle deployment issues. Capturing passenger reneging behavior, the model aims to maximize the number of served passengers with tolerable waiting times and minimize total passenger waiting times. A two-stage genetic algorithm is developed to solve the model, which incorporates a multi-agent simulation method to demonstrate dynamic passenger and bus flow within a time–space network. Finally, a case study is conducted to validate the effectiveness of the proposed methods. Sensitivity analyses are performed to explore the impacts of fleet size and route diversity on the overall bridging performance. The results offer valuable insights for transit agencies in designing bus bridging services under transfer scenarios, supporting sustainable urban mobility by promoting efficient public transit solutions that mitigate the social impacts of sudden service disruptions.

Integrating collaborative robots in manufacturing, logistics, and agriculture: Expert perspectives on technical, safety, and human factors.

This study investigates the implementation of collaborative robots across three distinct industrial sectors: vehicle assembly, warehouse logistics, and agricultural operations. Through the SESTOSENSO project, an EU-funded initiative, we examined expert perspectives on human-robot collaboration using a mixed-methods approach. Data were collected from 31 technical experts across nine European countries through an online questionnaire combining qualitative assessments of specific use cases and quantitative measures of attitudes, trust, and safety perceptions. Expert opinions across the use cases emphasized three primary concerns: technical impacts of cobot adoption, social and ethical considerations, and safety issues in design and deployment. In vehicle assembly, experts stressed the importance of effective collaboration between cobots and exoskeletons to predict and prevent collisions. For logistics, they highlighted the need for adaptable systems capable of handling various object sizes while maintaining worker safety. In agricultural settings, experts emphasized the importance of developing inherently safe applications that can operate effectively on uneven terrain while reducing workers' physical strain. Results reveal sector-specific challenges and opportunities: vehicle assembly operations require sophisticated sensor systems for cobot-exoskeleton integration; warehouse logistics demand advanced control systems for large object handling; and agricultural applications need robust navigation systems for uneven terrain. Quantitative findings indicate generally positive attitudes toward cobots, particularly regarding societal benefits, moderate to high levels of trust in cobot capabilities and favorable safety perceptions. The study highlights three key implications: (1) the need for comprehensive safety protocols tailored to each sector's unique requirements, (2) the importance of user-friendly interfaces and intuitive programming methods for successful cobot integration, and (3) the necessity of addressing workforce transition and skill development concerns. These findings contribute to our understanding of human-robot collaboration in industrial settings and provide practical guidance for organizations implementing collaborative robotics while considering both technological advancement and human-centered design principles.

Investigating Deployment Issues of DNS Root Server Instances From a China-Wide View

Investigating Deployment Issues of DNS Root Server Instances From a China-Wide View

The Research Advance in Ethical Issues of AI Application

The topic of AI has become more and more discussed ever since the release of ChatGPT, as its astonishing intelligence has made humans to challenge the boundary. This vagueness then contributes to the emergence of discussions on the ethics of AI. Aiming to clarify the many current academic discussions on the ethics of artificial intelligence and to recognize different ethical perspectives, this study conducts a literature review of the research and discussions among different scholars on ethical issues in the application of artificial intelligence technologies. It is found that the current relevant discussion extends from a micro perspective emphasizing the individual to a macro perspective emphasizing the human race. Research advances on ethical issues in AI applications can be categorized into three levels: personal, social, and global. Among which, the individual level focuses on the ethical issues in algorithmic logic and user data management. The social level focuses on the social employment and public accountability systems. While the global level discusses intellectual property disputes and the social identity of AI robots. Finally, the author gave an evaluation of the gaps and challenges of studying AI ethics and emphasize the importance of addressing ethical issues in AI development and deployment. The paper aims to serve as information for scholars and policymakers, fostering ethical awareness in the AI research community and helping AI technologies to be developed and used responsibly.

Sir Giles Gilbert Scott’s K2 Telephone Kiosk — A Design Classic

ABSTRACT The red telephone kiosk or box has become synonymous with Britain, standing as an icon of national identity. Yet, of all the versions of kiosk that have been introduced, only two have achieved this exalted status. This paper explores how the telephone box evolved within the UK, details the circumstances that led to an architect becoming involved in their development, and describes how Sir Giles Gilbert Scott’s design was transformed into Britain’s K2 kiosk, the first of the iconic red telephone boxes. Thereafter the paper considers issues of kiosk deployment, explains why few K2s can be found outside London and concludes with a description of the onward development of the K2 and its eventual replacement by a second Scott design, the K6. Sir Giles Gilbert Scott’s K2 kiosk forms an important part of Industrial Heritage for it not only enabled the growth of public telephones in London but also established the humble telephone box as a thing of utility combined with beauty.

If the Body Keeps the Score, What Happens When You Bring the Body to Work? Exploring the Health Effects of Trauma on Human Capital

Data reveal that the physical effects of trauma exposure increasingly surface in business, social, and other settings. Exposure to trauma at any point in life can cause employee health concerns, yet many firms do not acknowledge or address this. Herein, we combine trauma theory with human capital theory to explain how manifestations of trauma exposure— hyperarousal, intrusion, and constriction—impact employee health and performance. This article outlines how each manifestation affects human capital deployment, and thus employee performance. It further demonstrates how these human capital deployment issues have individual- and unit-level performance implications. This article offers a theory linking health effects of trauma to performance outcomes at work. It suggests how managerial awareness of trauma manifestations is a necessary step toward workplaces becoming supportive or healing. Our model offers new explanations related to why some individuals behave as they do at work and connects trauma to employee behavior and value creation.

Sponsored senior computers never happened – a 20-year perspective on ICT deployment of healthcare at home!

PurposeThe article studies the utilization of ICT within healthcare at home in Sweden overlooking a perspective of 20 + years. Starting point are articles by the authors from 2002 and 2003. These outlined an already feasible ICT supported healthcare at home as a tool to address healthcare’s identified challenges—a growing elderly population and general demand for more healthcare in combination with issues on financing and lack of human resources. Despite early signals the care transition has been unexpectedly slow. Why?MethodsWith the articles as take-off the journey for ICT based healthcare at home up to today is discussed. In the articles five areas considered as critical for a successful transition were pinpointed and are now used to support the authors considerations, reasoning, and conclusions.ResultsResults indicate that technology has never been a limiting factor. Nor has selected medical issues or expected benefits normally been a limitation. Instead limitations and hindrances are found in other areas and activities.ConclusionsTechnology is not the main challenge when it comes to implementation, instead a focus on business models, deployment, and scaling issues is now advocated. Initiatives also must make more use of gained knowledge and experience. Not doing so delays utilization and deployment. The impact on healthcare effects and expected savings may be over-optimistic. A thorough analysis of all associated costs, consequences, investments, and benefits is recommended. Non-digital alternatives must continue to be offered to these who cannot go digital. In communication improved medical outcome and patient satisfaction must be emphasised.

Three-dimensional deployment strategy for a multi-BWBUG cooperative system at deep depths

ABSTRACT This study investigates the issue of the three-dimensional deployment for blended-wing-body underwater gliders (BWBUGs) at deep depths. Initially, an underwater three-dimensional deployment optimisation model is formulated, considering factors such as the three-dimensional coverage ratio and the balance of node communication energy consumption. Subsequently, a sine-logistic chaos strategy with excellent ergodicity is proposed. Thereafter, the dynamic decision-assisted heuristic method (DDHM) is proposed based on the dynamic decision framework, sine-logistic chaos strategy, and opposition-based learning. The well-designed dynamic decision framework effectively avoids the premature convergence and local optimum of the algorithm. Additionally, the multi-objective variant of DDHM, referred to as MDDHM, is established utilising the archival mechanism. Lastly, the performance of the DDHM and MDDHM is evaluated by simulation experiments. Statistical analysis demonstrates that DDHM is highly effective for the underwater three-dimensional deployment of the multi-BWBUG cooperative system. Furthermore, its overall performance surpasses that of other comparative methods.

A Multiscale Computational Modeling Framework to Quantify Microstructural Effects on Nucleation of Li Metal on Carbon Scaffold Architectures

Carbon materials with scaffold architecture have been shown to mitigate the non-uniform Li plating/stripping issues during cycling, which is critical for achieving significantly enhanced energy densities and improved safety of Li metal batteries. However, optimal design of the architecture and microstructure of the carbon materials requires fundamental understanding of the Li metal nucleation behavior at different length scales. In this poster, we present a multiscale computational modeling framework to quantify the microstructural effects of carbon materials on the tendency of Li metal nucleation during plating by integrating atomic-scale simulation, mesoscale microstructure-based homogenization, continuum-scale electrochemistry modeling, and classical nucleation theory. We modeled the architecture of the carbon scaffold and the electrochemistry of Li transport across the electrode and electrolyte by finite element simulation to determine boundary conditions for our mesoscale models. We then generated different porous microstructures of various carbon materials by stochastic and physics-based simulations informed from experiments and performed multiphysics-based simulations at mesoscale to identify the electro-chemo-mechanical hotspots which are sensitive to the microstructure morphology. We calculated the energy barriers for Li to form clusters on graphene surfaces as a function of local Li concentrations and electric fields by ab initio molecular dynamics simulations. The synergistic deployment of different models across scales allows us to theoretically estimate the Li metal nucleation tendency based on a modified classical nucleation theory for different microstructures under various electroplating conditions. Comparison of the theoretical predictions and experimental results will be briefly discussed. The insights obtained from this multiscale framework offer new understanding and strategies for the design of carbon scaffold architectures to mitigate the non-uniformity of Li plating and issues in Li metal anode deployment in solid-state batteries.This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and 22-ERD-043.

Safety of TNF-α inhibitors: A real-world study based on the US FDA Adverse Event Reporting System Database.

As a common treatment for rheumatoid arthritis (RA), the adverse reactions of TNF-α inhibitors (TNFis) in practical application have garnered attention. This study aims to investigate the adverse drug events (ADEs) associated with TNFi in RA patients as reported in the FDA Adverse Event Reporting System, to offer insights for clinical use. Cases related to RA and primarily involving TNFi were extracted from the FDA Adverse Event Reporting System database and compared by gender stratification. Screening was conducted based on reporting odds ratio and information component to identify positive ADEs for different TNFis and evaluate common and unique ADEs among various TNFis. There are 4 common ADEs among TNFis, including pulmonary tuberculosis, infection, hypersensitivity, and herpes zoster, as described in the package inserts. However, each TNFi has unique positive ADEs. Adalimumab has 63 unique positive ADEs, including lower respiratory tract inflammation, systemic lupus erythematosus rash, vascular dementia, ovarian neoplasm, adhesion, sarcoma, coccidioidomycosis, etc. Golimumab has 6 unique positive ADEs, including pneumonia cryptococcal, device deployment issue, pneumonia bacterial, polyneuropathy, device malfunction, device issue, etc; certolizumab has 24 unique positive ADEs, including maternal exposure before pregnancy, premature rupture of membranes, exposure via breast milk, staphylococcal sepsis, erysipelas, low birth weight baby, herpes virus infection, premature delivery, etc; etanercept has 180 unique positive ADEs, including joint destruction, chondrolysis, finger deformity, ankle deformity, joint warmth, etc; infliximab has 60 unique positive ADEs, including Hodgkin's disease, metastatic neoplasm, non-Hodgkin's Lymphoma, etc. Although the aforementioned 5 TNFis share common ADEs such as herpes zoster, clinicians must exercise caution when selecting specific medications, especially for RA patients concurrently suffering from malignancies. The analysis indicates that infliximab is associated with 60 unique positive ADEs, including Hodgkin's disease, metastatic neoplasm, and non-Hodgkin's lymphoma; therefore, these patients should use infliximab with greater caution. Similarly, certolizumab should be used with increased caution in pregnant and postpartum women.

Use of artificial intelligence to address health disparities in low- and middle-income countries: a thematic analysis of ethical issues

ObjectivesArtificial intelligence (AI) is reshaping health and medicine, especially through its potential to address health disparities in low- and middle-income countries (LMICs). However, there are several issues associated with the use of AI that may reduce its impact and potentially exacerbate global health disparities. This study presents the key issues in AI deployment faced by LMICs. Study designThematic analysis. MethodsPubMed, Scopus, Embase and the Web of Science databases were searched, from the date of their inception until September 2023, using the terms “artificial intelligence”, “LMICs”, “ethic∗” and “global health”. Additional searches were conducted by snowballing references before and after the primary search. The final studies were chosen based on their relevance to the topic of this article. ResultsAfter reviewing 378 articles, 14 studies were included in the final analysis. A concept named the ‘AI Deployment Paradox’ was introduced to focus on the challenges of using AI to address health disparities in LMICs, and the following three categories were identified: (1) data poverty and contextual shifts; (2) cost-effectiveness and health equity; and (3) new technological colonisation and potential exploitation. ConclusionsThe relationship between global health, AI and ethical considerations is an area that requires systematic investigation. Relying on health data inherent with structural biases and deploying AI without systematic ethical considerations may exacerbate global health inequalities. Addressing these challenges requires nuanced socio-political comprehension, localised stakeholder engagement, and well-considered ethical and regulatory frameworks.

Infrared Dim Small Target Detection Networks: A Review.

In recent years, with the rapid development of deep learning and its outstanding capabilities in target detection, innovative methods have been introduced for infrared dim small target detection. This review comprehensively summarizes public datasets, the latest networks, and evaluation metrics for infrared dim small target detection. This review mainly focuses on deep learning methods from the past three years and categorizes them based on the six key issues in this field: (1) enhancing the representation capability of small targets; (2) improving the accuracy of bounding box regression; (3) resolving the issue of target information loss in the deep network; (4) balancing missed detections and false alarms; (5) adapting for complex backgrounds; (6) lightweight design and deployment issues of the network. Additionally, this review summarizes twelve public datasets for infrared dim small targets and evaluation metrics used for detection and quantitatively compares the performance of the latest networks. Finally, this review provides insights into the future directions of this field. In conclusion, this review aims to assist researchers in gaining a comprehensive understanding of the latest developments in infrared dim small target detection networks.

Development of Random Forest Model for Stroke Prediction

Stroke is a significant cause of mortality and morbidity worldwide, and early detection and prevention of stroke are essential for improving patient outcomes. Machine learning algorithms have been used in recent years to predict the risk of stroke by leveraging large amounts of clinical and demographic data. The development of a stroke prediction system using Random Forest machine learning algorithm is the main objective of this thesis. The primary goal of the project is to increase the accuracy of stroke detection while addressing the shortcomings of the current system, which include real- time deployment and interpretability issues with logistic regression. The development and use of an ensemble machine learning-based stroke prediction system, performance optimization through the use of ensemble machine learning algorithms, performance assessment, and real-time model deployment through the use of Python Django are among the goals of the research. The study's potential to improve public health by lessening the severity and consequences of strokes through early diagnosis and treatment makes it significant. Data collection, preprocessing, model selection, evaluation, and real-time deployment using Python Django are all part of the research technique. Our dataset consists of 5110 rows of tuples and columns with total size of 69kg. The performance of our stroke prediction algorithm was evaluated using confusion metrics-consisting of accuracy, precision, recall and F1-score. At the end of the research, Random Forest model gave an accuracy of 98.5% compared to the existing model logistic regression which has 86% accuracy.

CACTUS: A Comprehensive Abstraction and Classification Tool for Uncovering Structures

The availability of large datasets is providing the impetus for driving many current artificial intelligent developments. However, specific challenges arise in developing solutions that exploit small datasets, mainly due to practical and cost-effective deployment issues, as well as the opacity of deep learning models. To address this, the Comprehensive Abstraction and Classification Tool for Uncovering Structures (CACTUS) is presented as a means of improving secure analytics by effectively employing explainable artificial intelligence. CACTUS achieves this by providing additional support for categorical attributes, preserving their original meaning, optimising memory usage, and speeding up the computation through parallelisation. It exposes to the user the frequency of the attributes in each class and ranks them by their discriminative power. Performance is assessed by applying it to various domains, including Wisconsin Diagnostic Breast Cancer, Thyroid0387, Mushroom, Cleveland Heart Disease, and Adult Income datasets.

Amphibious Robots Locomotion Strategies in Unstructured Complex Environments: A Review

In the previous literature, amphibious robots focused mainly on locomotion in underwater and flat land surface manoeuvring. Few amphibious robots focused on unstructured land environments. The amphibious robot designs were more emphasised in academics, leading to more work done in building amphibious robots that mimic biological amphibians, imitating the geometry and overall functionality of the amphibious robots. Developing amphibious robots with propulsive mechanisms for manoeuvring in a water environment received more attention than other functionalities like adaptability on rough natural terrain and obstacle repositioning capability. However, practical applications like reconnaissance and surveying posed challenges in the ground environment, which had unstructured and complex terrain profiles, especially in the transition area. Therefore, reviewing the amphibious robots focused on manoeuvring complex uneven surfaces was essential. The literature had comprehensive review papers on navigation strategies encompassing manoeuvring on flat ground surfaces and underwater locomotion. There was a need for a focused study that highlighted the amphibious robot that manoeuvred in an unstructured land environment. The open challenges and recent solutions by designing new mechanisms and deployment issues were highlighted and reviewed. Hence, the paper addressed a more specific review of amphibious robot locomotion in an unstructured environment. The paper also discussed a case study of an amphibious robot capable of locomotion in unstructured environments. It was envisaged that the review would provide directions and insights to researchers and robotic system designers on developing robust propulsive mechanisms for amphibious robots capable of locomotion in unstructured environments.Keywords: manoeuvrability, mobility, terrain, unified locomotion, mechanism, propulsion

Concentrating Solar Power (CSP) Plant Optimization Study for the California Power Market (CalCSP)

In the United States, many states are implementing goals to achieve 100% carbon free power generation by 2050 or sooner. California has one of the more aggressive goals to achieve 100% carbon free generation of its retail power sales by 2045. California has excellent solar resources, but to accomplish this goal, California’s power utilities will need new zero carbon resources that can replace the natural gas units that they currently rely on for suppling power at night. Appropriately configured concentrating solar power plants with thermal energy storage are an option to serve this nighttime load. These plants would be designed to take advantage of CSP’s low-cost thermal energy storage and collect and store energy during the day and then be dispatched to produce power at night. The U.S. Department of Energy has funded a study to identify the design of a CSP plant that optimally meets the evolving CA grid needs. This will be done by taking a fresh look at how CSP technologies can best be designed to meet the emerging nighttime market for carbon free power generation or zero-carbon firm resources. The paper provides an overview of the study and present preliminary findings on the California power market requirements, CSP configurations, technoeconomic analysis, siting opportunities, and key issues for CSP deployment in this market.

Neural Bookmarks: Information Retrieval with Deep Learning and EEG Data

In neural memory decoding, a concept being mentally recalled is identified using brain data. Recently, the feasibility of neural memory decoding with EEG data has been demonstrated. Here we propose a new application – neural information retrieval – that uses neural memory decoding to allow a document to be retrieved merely by thinking about it. In this paper we describe neural memory decoding, define the application of neural information retrieval, present experimental results related to the practicality of the application, and discuss issues of deployment and data privacy.

Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News Recommendation

The emergence of online media has facilitated the dissemination of news, but has also introduced the problem of information overload. To address this issue, providing users with accurate and diverse news recommendations has become increasingly important. News possesses rich and heterogeneous content, and the factors that attract users to news reading are varied. Consequently, accurate news recommendation requires modeling of both the heterogeneous content of news and the heterogeneous user-news relationships. Furthermore, users’ news consumption is highly dynamic, which is reflected in the differences in topic concentration among different users and in the real-time changes in user interests. To this end, we propose a Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News Recommendation (DivHGNN). DivHGNN first represents the heterogeneous content of news and the heterogeneous user-news relationships as an attributed heterogeneous graph. Then, through a heterogeneous node content adapter, it models the heterogeneous node attributes into aligned and fused node representations. With the proposed attributed heterogeneous graph neural network, DivHGNN integrates the heterogeneous relationships to enhance node representation for accurate news recommendations. We also discuss relation pruning, model deployment, and cold-start issues to further improve model efficiency. In terms of diversity, DivHGNN simultaneously models the variance of nodes through variational representation learning for providing personalized diversity. Additionally, a time-continuous exponentially decaying distribution cache is proposed to model the temporal dynamics of user real-time interests for providing adaptive diversity. Extensive experiments on real-world news datasets demonstrate the effectiveness of the proposed method.