Key Challenges Research Articles

A Systematic Review of Medical Expert Systems for Cardiac Arrest Prediction

Background:: Predicting cardiac arrest is crucial for timely intervention and improved patient outcomes. Machine learning has yielded astounding results by offering tailored prediction analyses on complex data. Despite advancements in medical expert systems, there remains a need for a comprehensive analysis of their effectiveness and limitations in cardiac arrest prediction. This need arises because there are not enough existing studies that thoroughly cover the topic. Objective:: The systematic review aims to analyze the existing literature on medical expert systems for cardiac arrest prediction, filling the gaps in knowledge and identifying key challenges. Methods:: This paper adopts the PRISMA methodology to conduct a systematic review of 37 publications obtained from PubMed, Springer, ScienceDirect, and IEEE, published within the last decade. Careful inclusion and exclusion criteria were applied during the selection process, resulting in a comprehensive analysis that utilizes five integrated layers- research objectives, data collection, feature set generation, model training and validation employing various machine learning techniques. Results and Conclusion:: The findings indicate that current studies frequently use ensemble and deep learning methods to improve machine learning predictions’ accuracy. However, they lack adequate implementation of proper pre-processing techniques. Further research is needed to address challenges related to external validation, implementation, and adoption of machine learning models in real clinical settings, as well as integrating machine learning with AI technologies like NLP. This review aims to be a valuable resource for both novice and experienced researchers, offering insights into current methods and potential future recommendations.

Building Confidence in a World of Eroding Trust

Trust is an important pillar in digital transformation. It is the key to fostering effective communications and collaborations. It is the foundational element that exists in the interactions between humans and technologies. However, the rapid innovation of technologies has accelerated their widespread exploitation, resulting in trust relationships among people and technologies becoming increasingly complex. In this age of eroding trust, exploring how to steer away from this trend towards sustaining and enhancing trust becomes critical. Through our review of the varying definitions and applications of trust in existing literature, we believe that there is another perspective to address the challenges of trust. In our article, we propose a trust pyramid to investigate the causality factors that influence the decision to trust. We classify the trust pyramid into three classifications (Foundational, Supplemental, and Innovative) according to the order of importance and deliberations. Within each classification, we also propose the causality factors that serve as key trust elements to influence any decision-making. We identify key challenges (e.g., the need for ethical technologies, regulations to keep pace with technological advancement) and address them by proposing viable initiatives that stakeholders can consider to build trust in a systematic manner.

Benefits, Challenges, and Opportunities in Addressing Medicaid Beneficiaries' Non-Medical Risks to Health: A Qualitative Analysis.

North Carolina's Healthy Opportunities Pilots (HOP) is a Medicaid 1115 Waiver program that seeks to address nonmedical risks to health for Medicaid beneficiaries through multisector collaboration. Among other stakeholders, HOP involves collaboration between human services organizations that deliver interventions, network leads, which establish and oversee the human services organizations within a region of the state. To understand how employees at human services organizations and network leads prepared to deliver HOP services. Qualitative analysis of semistructured interviews. Interviews were conducted between April and June 2022. Interviews were recorded, transcribed verbatim, coded thematically, and analyzed using a conceptual model derived from the consolidated framework for implementation research. Organizations within North Carolina counties participating in HOP. Employees of human services and network lead organizations across all 3 HOP regions of North Carolina. The researchers interviewed 37 participants. Overall, organizations experienced benefits from HOP participation, including capacity-building resources, flexibility in allocating resources, and creating community-wide enthusiasm for addressing nonmedical risks to health. There were also key challenges. These included the time needed to build capacity, adjustments to the work processes and regulations inherent to multisector collaboration, geographic variation in availability of services to offer, and the difficulty of addressing different needs. Finally, participants recognized substantial opportunities that HOP presented, including membership in a more extensive network, exposure to a learning community, and a more sustainable funding source. The perspectives of individuals preparing to deliver HOP services offer important lessons for those developing and implementing large-scale programs that can address nonmedical threats to health.

Matching Tasks and Workers under Known Arrival Distributions: Online Task Assignment with Two-sided Arrivals

Efficient allocation of tasks to workers is a central problem in crowdsourcing. In this article, we consider a setting inspired by spatial crowdsourcing platforms, where both workers and tasks arrive at different times, and each worker-task assignment yields a given reward. The key challenge is to address the uncertainty in the stochastic arrivals from both workers and the tasks. In this work, we consider a ubiquitous scenario where the arrival patterns of worker “types” and task “types” are not erratic but can be predicted from historical data. Specifically, we consider a finite time horizon T and assume that in each time-step the arrival of a worker and a task can be seen as an independent sample from two (different) distributions. Our model, called Online Task Assignment with Two-sided Arrival (OTA-TSA), is a significant generalization of the classical online task assignment problem when all the tasks are statically available. For the general case of OTA-TSA, we present an optimal non-adaptive algorithm (NADAP), which achieves a competitive ratio (CR) of at least 0.295. For a special case of OTA-TSA when the reward depends only on the worker type, we present two adaptive algorithms, which achieve CRs of at least 0.343 and 0.355, respectively. On the hardness side, we show that (1) no non-adaptive can achieve a CR larger than that of NADAP, establishing the optimality of NADAP among all non-adaptive algorithms; and (2) no (adaptive) algorithm can achieve a CR better than 0.581 (unconditionally) or 0.423 (conditionally on the benchmark linear program), respectively. All aforementioned negative results apply to even unweighted OTA-TSA when every assignment yields a uniform reward. At the heart of our analysis is a new technical tool, called two-stage birth-death process , which is a refined notion of the classical birth-death process. We believe it may be of independent interest. Finally, we perform extensive numerical experiments on a real-world rideshare dataset collected in Chicago and a synthetic dataset, and results demonstrate the effectiveness of our proposed algorithms in practice.

A Short Appraisal of Magnetic Nanoparticles for Breast Cancer: In vitro and In vivo Research.

The increasing incidence of breast cancer and the associated morbidity due to higher metastasis created the urge to develop a nanocarrier that can be used as a potent therapeutic carrier with targeting efficacy. The use of superparamagnetic nanoparticles in breast cancer research and treatment has gained considerable attention in recent years. Magnetic nanoparticles (MNPs) can be used to construct nanocarriers since they possess superior properties such as superparamagnetism, easy surface functionalization to attach ligands, and non-immunogenic. MNPs are superior carriers that are used to target cancer cells without harming the normal cells in the body, which leads to therapeutic efficacy in the body. Along with their established anticancer potential and enhanced drug concentration at endosomal pH, the superparamagnetic property of MNPs was further exploited for their applications in reticuloendothelial uptake, drug delivery, medical imaging, and theranostics applications in breast cancer. Moreover, the clinical translational of MNPs, along with future prospects and key challenges in vivo, have been duly presented in the final review. The scientists preferred the ongoing research in MNPs due to their high biocompatibility and ease of targeting at molecular and cellular levels. The review highlighted the in vitro and in vivo research and patent supported data for potential use of MNPs for the treatment of breast cancer.

Nursing Competence as a Developmental Task of Nursing Students in Nursing Education: A Qualitative Panel Study

Nursing Competence as a Developmental Task of Nursing Students in Nursing Education: A Qualitative Panel Study Abstract: Background: One of the key challenges of nursing education is to foster the development of professional nursing competency. Currently, there is a lack of empirical knowledge about nursing students' subjective educational processes to be able to specifically support them in this development. Method: To reconstruct the developmental processes of nursing students, a qualitative panel study was conducted with 26 students of the three-year nursing training in Germany. Data were collected at the end of the 1st, 2nd, and 3rd year of nursing students' training through episodic interviews, and analysed using reconstructive-hermeneutical analysis (Kruse, 2015). Results: 'Developing nursing competency' was identified as one of the five developmental tasks. In the perception of the students, this development task focuses on the acquisition of medical knowledge, skills to carry out nursing actions and the ability to organise processes. In doing so, they ignore the subjective perspective of the people to be cared for. Conclusion: Through a cross-training overarching analyses, it becomes clear that nursing students do not succeed in developing a patient-oriented understanding of nursing regarding nursing competency. Hence, it must be examined whether the perspectives of the nursing students have changed due the stronger process orientation in the new legal nursing requirements.

Cross-modality semantic guidance for multi-label image classification

Multi-label image classification aims to predict a set of labels that are present in an image. The key challenge of multi-label image classification lies in two aspects: modeling label correlations and utilizing spatial information. However, the existing approaches mainly calculate the correlation between labels according to co-occurrence among them. While the result is easily affected by the label noise and occasional co-occurrences. In addition, some works try to model the correlation between labels and spatial features, but the correlation among labels is not fully considered to model the spatial relationships among features. To address the above issues, we propose a novel cross-modality semantic guidance-based framework for multi-label image classification, namely CMSG. First, we design a semantic-guided attention (SGA) module, which applies the label correlation matrix to guide the learning of class-specific features, which implicitly models semantic correlations among labels. Second, we design a spatial-aware attention (SAA) module to extract high-level semantic-aware spatial features based on class-specific features obtained from the SGA module. The experiments carried out on three benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art algorithms on multi-label image classification.

A study of concept similarity in Wikidata

Robust estimation of concept similarity is crucial for applications of AI in the commercial, biomedical, and publishing domains, among others. While the related task of word similarity has been extensively studied, resulting in a wide range of methods, estimating concept similarity between nodes in Wikidata has not been considered so far. In light of the adoption of Wikidata for increasingly complex tasks that rely on similarity, and its unique size, breadth, and crowdsourcing nature, we propose that conceptual similarity should be revisited for the case of Wikidata. In this paper, we study a wide range of representative similarity methods for Wikidata, organized into three categories, and leverage background information for knowledge injection via retrofitting. We measure the impact of retrofitting with different weighted subsets from Wikidata and ProBase. Experiments on three benchmarks show that the best performance is achieved by pairing language models with rich information, whereas the impact of injecting knowledge is most positive on methods that originally do not consider comprehensive information. The performance of retrofitting is conditioned on the selection of high-quality similarity knowledge. A key limitation of this study, similar to prior work lies in the limited size and scope of the similarity benchmarks. While Wikidata provides an unprecedented possibility for a representative evaluation of concept similarity, effectively doing so remains a key challenge.

Constrained Tiny Machine Learning for Predicting Gas Concentration with I4.0 Low-cost Sensors

Low-cost gas sensors (LCS) often produce inaccurate measurements due to varying environmental conditions that are not consistent with laboratory settings, leading to inadequate productivity levels compared to high-quality sensors. To address this issue, we propose the use of Machine Learning (ML) to predict accurate concentrations of pollutant gases acquired by LCS integrated into an embedded Internet of Things platform. However, a key challenge is to optimize an accurate ML design under low memory and computation power constraints of microcontrollers (MCUs) while maintaining accurate ML scores. After data analysis and pre-processing, we assess and analyze the performance of five ML algorithms to predict the concentration of pollutants gases from multiple specifications (weather, presence of other gases, etc.). To support the experiments, datasets from three sources are used: (1) VOCSens, (2) Belgian Interregional Environment Agency cell, and (3) Visual-Crossing. Once the best model was optimized and validated, multiple hard constraints were added to the selected ML structure to satisfy material and expert requirements. Trained models were ported to be implemented locally in a MCU after comparing several porting libraries. The assembled code obtained is evaluated based on two metrics: storage memory consumption and inference time, relative to the highest attainable capacities. The improved random forest is the best ML model for the used dataset with an R2 score meeting of 0.72 and Root Means Square Error of 0.0028 ppm. The best generated Tiny-ML model needs 3% of RAM and 98% of Flash storage. The empirical results prove that the developed ML algorithm applied to LCS provides high accuracy to predict pollutant gases. This algorithm can also be used to adjust the LCS systems to provide calibrated data in real time, even if the platform being used is not particularly advanced or powerful.

Corr-Track: Category-Level 6D Pose Tracking with Soft-Correspondence Matrix Estimation.

Category-level pose tracking methods can continuously track the pose of objects without requiring any prior knowledge of the specific shape of the tracked instance. This makes them advantageous in augmented reality and virtual reality applications. The key challenge is how to train neural networks to accurately predict the poses of objects they have never seen before and exhibit strong generalization performance. We propose a novel category-level 6D pose tracking method Corr-Track, which is capable of accurately tracking objects belonging to the same category from depth video streams. Our approach utilizes direct soft correspondence constraints to train a neural network, which estimates bidirectional soft correspondences between sparsely sampled point clouds of objects in two frames. We first introduce a soft correspondence matrix for pose tracking tasks and establish effective constraints through direct spatial point-to-point correspondence representations in the sparse point cloud correspondence matrix. We propose the "point cloud expansion" strategy to address the "point cloud shrinkage" problem resulting from soft correspondences. This strategy ensures that the corresponding point cloud accurately reproduces the shape of the target point cloud, leading to precise pose tracking results. We evaluated our approach on the NOCS-REAL275 and Wild6D dataset and observed superior performance compared to previous methods. Additionally, we conducted cross-category experiments that further demonstrated its generalization capability.

Compressed-SDR to HDR Video Reconstruction.

The new generation of organic light emitting diode display is designed to enable the high dynamic range (HDR), going beyond the standard dynamic range (SDR) supported by the traditional display devices. However, a large quantity of videos are still of SDR format. Further, most pre-existing videos are compressed at varying degrees for minimizing the storage and traffic flow demands. To enable movie-going experience on new generation devices, converting the compressed SDR videos to the HDR format (i.e., compressed-SDR to HDR conversion) is in great demands. The key challenge with this new problem is how to solve the intrinsic many-to-many mapping issue. However, without constraining the solution space or simply imitating the inverse camera imaging pipeline in stages, existing SDR-to-HDR methods can not formulate the HDR video generation process explicitly. Besides, they ignore the fact that videos are often compressed. To address these challenges, in this work we propose a novel imaging knowledge-inspired parallel networks (termed as KPNet) for compressed-SDR to HDR (CSDR-to-HDR) video reconstruction. KPNet has two key designs: Knowledge-Inspired Block (KIB) and Information Fusion Module (IFM). Concretely, mathematically formulated using some priors with compressed videos, our conversion from a CSDR-to-HDR video reconstruction is conceptually divided into four synergistic parts: reducing compression artifacts, recovering missing details, adjusting imaging parameters, and reducing image noise. We approximate this process by a compact KIB. To capture richer details, we learn HDR representations with a set of KIBs connected in parallel and fused with the IFM. Extensive evaluations show that our KPNet achieves superior performance over the state-of-the-art methods.

Bioactive Exploration in Functional Foods: Unlocking Nature's Treasures.

Functional foods offer an appealing way to improve health and prevent chronic diseases, and this subject has received much attention lately. They are effective in preventing chronic diseases like cancer, diabetes, heart disease, and obesity, according to research. This work presents an in-depth analysis of functional foods, covering key challenges from a scientific, legal, and commercial perspective. Multiple databases were searched to find studies on functional foods included in the systematic literature review. Various aspects of functional foods, from their classification, impact on human wellness, effectiveness in inhibiting chronic diseases, the regulatory environment, global market trends, and industry challenges, are all clarified in this thorough review. This study aims to enhance understanding and establish a pathway for functional foods to be acknowledged as valid choices in the field of dietary supplements. It provides a thorough investigation of bioactive compounds present in functional foods, including but not limited to polyphenols, carotenoids, omega fatty acids, prebiotics, probiotics, and dietary fiber, along with an overview of their potential to mitigate chronic illnesses. We engage in an in-depth exploration of regulatory frameworks, shed light on groundbreaking research advancements, and meticulously examine strategies for commercialization and the variety of global challenges that accompany them. Establishing scientific consensus, navigating complex regulatory processes, dealing with skeptical consumers, and rising levels of competition are all problems that need to be solved in this field. The field of functional foods can advance further, promoting better public health outcomes, by deeply comprehending and addressing these complex dimensions.

Nanomedicine Tumor Targeting.

Nanomedicines are extensively explored for cancer therapy. By delivering drug molecules more efficiently to pathological sites and by attenuating their accumulation in healthy organs and tissues, nanomedicine formulations aim to improve the balance between drug efficacy and toxicity. More than 20 cancer nanomedicines are approved for clinical use, and hundreds of formulations are in (pre)clinical development. Over the years, several key pitfalls have been identified as bottlenecks in nanomedicine tumor targeting and translation. These go beyond materials- and production-related issues, and particularly also encompass biological barriers and pathophysiological heterogeneity. In this manuscript, the author describes the most important principles, progress, and products in nanomedicine tumor targeting, delineates key current problems and challenges, and discusses the most promising future prospects to create clinical impact.

Fairness-Aware Graph Neural Networks: A Survey

Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks. Despite this success, GNNs suffer from fairness issues that arise as a result of the underlying graph data and the fundamental aggregation mechanism that lies at the heart of the large class of GNN models. In this article, we examine and categorize fairness techniques for improving the fairness of GNNs. We categorize these techniques by whether they focus on improving fairness in the pre-processing, in-processing (during training), or post-processing phases. We discuss how such techniques can be used together whenever appropriate and highlight the advantages and intuition as well. We also introduce an intuitive taxonomy for fairness evaluation metrics, including graph-level fairness, neighborhood-level fairness, embedding-level fairness, and prediction-level fairness metrics. In addition, graph datasets that are useful for benchmarking the fairness of GNN models are summarized succinctly. Finally, we highlight key open problems and challenges that remain to be addressed.

Direct C-H Sulfuration: Synthesis of Disulfides, Dithiocarbamates, Xanthates, Thiocarbamates and Thiocarbonates.

In light of the important biological activities and widespread applications of organic disulfides, dithiocarbamates, xanthates, thiocarbamates and thiocarbonates, the continual persuit of efficient methods for their synthesis remains crucial. Traditionally, the preparation of such compounds heavily relied on intricate multi-step syntheses and the use of highly prefunctionalized starting materials. Over the past two decades, the direct sulfuration of C-H bonds has evolved into a straightforward, atom- and step-economical method for the preparation of organosulfur compounds. This review aims to provide an up-to-date discussion on direct C-H disulfuration, dithiocarbamation, xanthylation, thiocarbamation and thiocarbonation, with a special focus on describing scopes and mechanistic aspects. Moreover, the synthetic limitations and applications of some of these methodologies, along with the key unsolved challenges to be addressed in the future are also discussed. The majority of examples covered in this review are accomplished via metal-free, photochemical or electrochemical approaches, which are in alignment with the overraching objectives of green and sustainable chemistry. This comprehensive review aims to consolidate recent advancements, providing valuable insights into the dynamic landscape of efficient and sustainable synthetic strategies for these crucial classes of organosulfur compounds.

An All-Inclusive Taxonomy and Critical Review of Blockchain-Assisted Authentication and Session Key Generation Protocols for IoT

Authentication and Session Key Generation Protocols (SKGPs) play an essential role in securing the communication channels of connected Internet of Things (IoT) devices. Recently, through blockchain integration, scholars have tried to enhance the security and applicability of SKGPs. In brief, blockchain is a distributed ledger technology that can provide interesting features such as immutability, transparency, and accountability without any need for the active participation of trusted parties. This survey presents a comprehensive critical review of blockchain-assisted authentication and SKGPs, suggested for different IoT domains, including Internet of Vehicles, Internet of Drones, and Industrial IoT. Our survey categorizes existing schemes based on several criteria, including IoT application domains, security aspects, and blockchain components. By presenting an unbiased critical review and taxonomy of protocols, we aim to clarify the key challenges. Our review will specifically indicate what properties authors gained or lost through the integration of blockchain. To our best knowledge, this survey is the only one that offers all prerequisites for interested readers in blockchain-integrated SKGPs, such as security features and attacks, attack models, verification tools, blockchain types, blockchain platforms, and consensus mechanisms. Further, our survey elaborates existing research gaps in blockchain-assisted SKGPs. In doing so, we aim to guide future research in this field and provide researchers with the essential information they require.

Single-Transmitter-Controlled Multiple-Channel Constant Current Outputs for In-Flight Wireless Charging of Drones

This paper proposes a single-transmitter-controlled multiple-channel constant current outputs scheme, which can energize multiple hovering drones over the air simultaneously. As an unmanned and cost-effective solution to extend the cruising range of drones, the multiple-drone in-flight wireless charging has to deal with two key technical challenges, namely the continuous disturbance of multiple mutual inductances ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> ) caused by the hovering attitude of drones, as well as the limit of drone-side payload. Accordingly, this paper adopts the multi-frequency resonating compensation (MFRC) to establish multiple transmission channels and achieves the single-transmitter-controlled multiple-channel constant current outputs based on a communication-free multiple- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> estimation method. The simulated and experimental results are both given in this paper, which shows that the accuracy of the multiple- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> estimation is up to 97% and the steady-state accuracy of the current output control is up to 95%. What is more, the current output control can effectively restrain the output current fluctuation of the battery load under continuous multiple- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> disturbance, indicating that the proposed scheme is feasible to improve the stability and reliability of the multiple-drone in-flight wireless charging system.

Multiparameter screen optimizes immunoprecipitation.

Immunoprecipitation (IP) coupled with mass spectrometry effectively maps protein-protein interactions when genome-wide, affinity-tagged cell collections are used. Such studies have recorded significant portions of the compositions of physiological protein complexes, providing draft 'interactomes'; yet many constituents of protein complexes still remain uncharted. This gap exists partly because high-throughput approaches cannot optimize each IP. A key challenge for IP optimization is stabilizing in vivo interactions during the transfer from cells to test tubes; failure to do so leads to the loss of genuine interactions during the IP and subsequent failure to detect. Our high-content screening method explores the relationship between in vitro chemical conditions and IP outcomes, enabling rapid empirical optimization of conditions for capturing target macromolecular assemblies.

SwinPA-Net: Swin Transformer-Based Multiscale Feature Pyramid Aggregation Network for Medical Image Segmentation.

The precise segmentation of medical images is one of the key challenges in pathology research and clinical practice. However, many medical image segmentation tasks have problems such as large differences between different types of lesions and similar shapes as well as colors between lesions and surrounding tissues, which seriously affects the improvement of segmentation accuracy. In this article, a novel method called Swin Pyramid Aggregation network (SwinPA-Net) is proposed by combining two designed modules with Swin Transformer to learn more powerful and robust features. The two modules, named dense multiplicative connection (DMC) module and local pyramid attention (LPA) module, are proposed to aggregate the multiscale context information of medical images. The DMC module cascades the multiscale semantic feature information through dense multiplicative feature fusion, which minimizes the interference of shallow background noise to improve the feature expression and solves the problem of excessive variation in lesion size and type. Moreover, the LPA module guides the network to focus on the region of interest by merging the global attention and the local attention, which helps to solve similar problems. The proposed network is evaluated on two public benchmark datasets for polyp segmentation task and skin lesion segmentation task as well as a clinical private dataset for laparoscopic image segmentation task. Compared with existing state-of-the-art (SOTA) methods, the SwinPA-Net achieves the most advanced performance and can outperform the second-best method on the mean Dice score by 1.68%, 0.8%, and 1.2% on the three tasks, respectively.

Computational Experiments for Complex Social Systems—Part III: The Docking of Domain Models

Powered by advanced information technology, more and more complex systems are exhibiting characteristics of the cyber–physical–social systems (CPSS). In consideration of the cost, legal, and institutional constraints on the study of CPSS in real world, computational experiments have emerged as a new method for quantitative analysis of CPSS. However, with the increase of application scenarios, how to map complex and diverse domain models to artificial society models has become a key challenge to hinder the wide use of computational experiments. In this article, the docking framework between the domain model and the artificial society model was proposed in this article, and the model docking specification is given from three aspects: the agent model, the environmental model, and the rules model. In addition, the effectiveness of the framework was verified by two classic cases: artificial stock market and epidemic prevention and control. The result showed that the proposed model docking framework can provide technical support for the multidisciplinary applications of computational experiments and significantly reduce the difficulty of using the method.