Securing patient data in the healthcare industry: A blockchain-driven protocol with advanced encryption.

BACKGROUND: Ensuring the security and privacy of patient data is a critical concern in the healthcare industry. The growing utilization of electronic data transmission and storage in medical records has amplified apprehensions about data security. However, due to varying stakeholder interests, not all data can be freely shared, necessitating the development of secure protocols. MATERIALS AND METHODS: This study presents a highly secure protocol that integrates blockchain technology, patient biometric information, and robust cryptographic algorithms (elliptic curve cryptography (ECC) and advanced encryption algorithm (AEC)) to facilitate data encryption and decryption. The protocol encompasses secure login, secure key sharing, and data sharing mechanisms among miners, offering comprehensive security measures. To validate the effectiveness of the proposed protocol, both informal and formal security analyses are conducted. The security protocol description language in Scyther is utilized to evaluate the protocol's resilience against attacks. RESULTS: The culmination of this research is a secure protocol that leverages blockchain technology and ECC for the secure storage and sharing of medical records. The protocol covers all stages, including system setup, user registration, login mechanisms, key exchange between users and blockchain, communication between blockchains, and interaction with other miners, with a steadfast emphasis on security. Furthermore, the protocol's communication and computation costs are assessed, with a comparison to existing blockchain-based schemes. Informal proofs establish the protocol's security against common attacks faced by medical institutions. Formal simulation of the protocol using the Scyther tool provides definitive evidence of its resistance to attacks. CONCLUSIONS: As a result, this protocol presents a viable real-time implementation solution for safeguarding patient data within the healthcare domain, representing a significant contribution to data security.

A new wide input voltage DC-DC converter for solar PV systems with hybrid MPPT controller.

The present working conventional power generation systems utilization is reducing day by day because of their demerits are more functioning cost, high carbon dioxide emission, more complexity in handling, and required high installation area. So, the current power generation company focuses on Renewable Energy Sources (RES) which are wind, tidal, and solar. Here, the solar power network is utilized for supplying electricity to the electrical vehicle battery charging system. The Solar photovoltaic (PV) modules supply nonlinear power which is not useful for automotive systems. To maximize the supply power of the solar PV system, an Adaptive Step Genetic Algorithm Optimized (ASGAO) Radial Basis Functional Network (RBFN) is utilized for tracking the working point of the solar PV module thereby enhancing the operating efficiency of the overall system. The features of this proposed hybrid Maximum Power Point Tracking (MPPT) controller are quick system dynamic response, easy operation, quick convergence speed, more robustness, and high operating efficiency when equalized with the basic MPPT controllers. The major issue of solar PV modules is low supply voltage which is increased by introducing the wide input voltage DC-DC converter. The merits of this introduced converter are low-level voltage stress on diodes, good quality supply power, high voltage gain, plus low implementation cost. Here, the introduced converter along with the AGAO-RBFN controller is analyzed by selecting the MATLAB/Simulink environment. Also, the proposed converter is tested with the help of a programable DC source.

Cell behaviors that pattern developing tissues: the case of the vertebrate nervous system.

Morphogenesis from cells to tissue gives rise to the complex architectures that make our organs. How cells and their dynamic behavior are translated into functional spatial patterns is only starting to be understood. Recent advances in quantitative imaging revealed that, although highly heterogeneous, cellular behaviors make reproducible tissue patterns. Emerging evidence suggests that mechanisms of cellular coordination, intrinsic variability and plasticity are critical for robust pattern formation. While pattern development shows a high level of fidelity, tissue organization has undergone drastic changes throughout the course of evolution. In addition, alterations in cell behavior, if unregulated, can cause developmental malformations that disrupt function. Therefore, comparative studies of different species and of disease models offer a powerful approach for understanding how novel spatial configurations arise from variations in cell behavior and the fundamentals of successful pattern formation. In this chapter, I dive into the development of the vertebrate nervous system to explore efforts to dissect pattern formation beyond molecules, the emerging core principles and open questions.

Heritable epigenetic changes are constrained by the dynamics of regulatory architectures.

Interacting molecules create regulatory architectures that can persist despite turnover of molecules. Although epigenetic changes occur within the context of such architectures, there is limited understanding of how they can influence the heritability of changes. Here, I develop criteria for the heritability of regulatory architectures and use quantitative simulations of interacting regulators parsed as entities, their sensors, and the sensed properties to analyze how architectures influence heritable epigenetic changes. Information contained in regulatory architectures grows rapidly with the number of interacting molecules and its transmission requires positive feedback loops. While these architectures can recover after many epigenetic perturbations, some resulting changes can become permanently heritable. Architectures that are otherwise unstable can become heritable through periodic interactions with external regulators, which suggests that mortal somatic lineages with cells that reproducibly interact with the immortal germ lineage could make a wider variety of architectures heritable. Differential inhibition of the positive feedback loops that transmit regulatory architectures across generations can explain the gene-specific differences in heritable RNA silencing observed in the nematode Caenorhabditis elegans. More broadly, these results provide a foundation for analyzing the inheritance of epigenetic changes within the context of the regulatory architectures implemented using diverse molecules in different living systems.

Mutational robustness and the role of buffer genes in evolvability.

Organisms rely on mutations to fuel adaptive evolution. However, many mutations impose a negative effect on fitness. Cells may have therefore evolved mechanisms that affect the phenotypic effects of mutations, thus conferring mutational robustness. Specifically, so-called buffer genes are hypothesized to interact directly or indirectly with genetic variation and reduce its effect on fitness. Environmental or genetic perturbations can change the interaction between buffer genes and genetic variation, thereby unmasking the genetic variation's phenotypic effects and thus providing a source of variation for natural selection to act on. This review provides an overview of our understanding of mutational robustness and buffer genes, with the chaperone gene HSP90 as a key example. It discusses whether buffer genes merely affect standing variation or also interact with de novo mutations, how mutational robustness could influence evolution, and whether mutational robustness might be an evolved trait or rather a mere side-effect of complex genetic interactions.

Anomalous-Chern Steering of Topological Nonreciprocal Guided Waves.

Nonreciprocal topological edge states based on external magnetic bias have been regarded as the last resort for genuine unidirectional wave transport, showing superior robustness over topological states with preserved time-reversal symmetry. However, fast and efficient reconfigurability of their trajectory has remained a formidable challenge due to the difficulty in controlling the spatial distribution of magnetic fields over large areas and short times. Here, this persistent issue is solved by leveraging the rich topology of unitary scattering networks, and achieve fast steering of nonreciprocal topological transport at an interface between a Chern and an anomalous topological insulator, without having to control a magnetic field. Such interface can be drawn by doping the network with scatterers located at the center of each link, whose level of reflection is electrically tuned. With experiments in the GHz range, the possibility to actively steer the way of unidirectional edge states is demonstrated, switching the transmission path thousands of times per second in a fully-robust topological heterostructure. The approach represents a significant step towards the realization of practical reconfigurable topological meta-devices with broken time-reversal symmetry, and their application to future robust communication technologies.

An emerging role for tissue plasticity in developmental precision.

Reproducible tissue morphology is a fundamental feature of embryonic development. To ensure such robustness during tissue morphogenesis, inherent noise in biological processes must be buffered. While redundant genes, parallel signaling pathways and intricate network topologies are known to reduce noise, over the last few years, mechanical properties of tissues have been shown to play a vital role. Here, taking the example of somite shape changes, I will discuss how tissues are highly plastic in their ability to change shapes leading to increased precision and reproducibility.

Neural dynamics of robust legged robots.

Legged robot control has improved in recent years with the rise of deep reinforcement learning, however, much of the underlying neural mechanisms remain difficult to interpret. Our aim is to leverage bio-inspired methods from computational neuroscience to better understand the neural activity of robust robot locomotion controllers. Similar to past work, we observe that terrain-based curriculum learning improves agent stability. We study the biomechanical responses and neural activity within our neural network controller by simultaneously pairing physical disturbances with targeted neural ablations. We identify an agile hip reflex that enables the robot to regain its balance and recover from lateral perturbations. Model gradients are employed to quantify the relative degree that various sensory feedback channels drive this reflexive behavior. We also find recurrent dynamics are implicated in robust behavior, and utilize sampling-based ablation methods to identify these key neurons. Our framework combines model-based and sampling-based methods for drawing causal relationships between neural network activity and robust embodied robot behavior.

Reinforcement learning for decision-making under deep uncertainty.

Planning under complex uncertainty often asks for plans that can adapt to changing future conditions. To inform plan development during this process, exploration methods have been used to explore the performance of candidate policies given uncertainties. Nevertheless, these methods hardly enable adaptation by themselves, so extra efforts are required to develop the final adaptive plans, hence compromising the overall decision-making efficiency. This paper introduces Reinforcement Learning (RL) that employs closed-loop control as a new exploration method that enables automated adaptive policy-making for planning under uncertainty. To investigate its performance, we compare RL with a widely-used exploration method, Multi-Objective Evolutionary Algorithm (MOEA), in two hypothetical problems via computational experiments. Our results indicate the complementarity of the two methods. RL makes better use of its exploration history, hence always providing higher efficiency and providing better policy robustness in the presence of parameter uncertainty. MOEA quantifies objective uncertainty in a more intuitive way, hence providing better robustness to objective uncertainty. These findings will help researchers choose appropriate methods in different applications.

Does "National Civilized City" policy mitigate air pollution in China? A spatial Durbin difference-in-differences analysis.

"National Civilized City" (NCC) is regarded as China's highest honorary title and most valuable city brand. To win and maintain the "golden city" title, municipal governments must pay close attention to various key appraisal indicators, mainly environmental ones. In this study we verify whether cities with the title are more likely to mitigate SO2 pollution. We adopt the spatial Durbin difference-in-differences (DID) model and use panel data of 283 Chinese cities from 2003 to 2018 to analyze the local (direct) and spillover effects (indirect) of the NCC policy on SO2 pollution. We find that SO2 pollution in Chinese cities is not randomly distributed in geography, suggesting the existence of spatial spillovers and possible biased estimates. Our study treats the NCC policy as a quasi-experiment and incorporates spatial spillovers of NCC policy into a classical DID model to verify this assumption. Our findings show: (1) The spatial distribution of SO2 pollution represents strong spatial spillovers, with the most highly polluted regions mainly situated in the North China Plain. (2) The Moran's I test results confirms significant spatial autocorrelation. (3) Results of the spatial Durbin DID models reveal that the civilized cities have indeed significantly mitigated SO2 pollution, indicating that cities with the honorary title are acutely aware of the environment in their bid to maintain the golden city brand. As importantly, we notice that the spatial DID term is also significant and negative, implying that neighboring civilized cities have also mitigated their own SO2 pollution. Due to demonstration and competition effects, neighboring cities that won the title ostensibly motivates local officials to adopt stringent policies and measures for lowering SO2 pollution and protecting the environment in competition for the golden title. The spatial autoregressive coefficient was significant and positive, indicating that SO2 pollution of local cities has been deeply affected by neighbors. A series of robustness check tests also confirms our conclusions. Policy recommendations based on the findings for protecting the environment and promoting sustainable development are proposed.

Comparative finite element analysis of the first thoracic vertebra in artiodactyls.

Artiodactyls exhibit a striking diversity of the cervical vertebral column in terms of length and overall mobility. Using finite element analysis, this study explores the morphology at the cervico-thoracic boundary and its performance under loads in artiodactyls with different habitual neck postures and body sizes. The first thoracic vertebra of 36 species was loaded with (i) a compressive load on the vertebral body to model the weight of the head and neck exerted onto the trunk; and (ii) a tensile load at the spinous process to model the pull via the nuchal ligament. Additional focus was laid on the peculiar shape of the first thoracic vertebra in giraffes. We hypothesized that a habitually upright neck posture should be reflected in the greater ability to withstand compressive loads compared to tensile loads, whereas for species with a habitually suspended posture it should be the opposite. In comparison to species with a suspended posture, species with an upright posture exhibited lower stress (except Giraffidae). For compressive loads in larger species, stress surprisingly increased. Tensile loads in larger species resulted in decreased stress only in species with an intermediate or suspensory neck posture. High stress under tensile loads was mainly reflecting the relative length of the spinous process, while high stress under compressive loads was common in more "bell"-shaped vertebral bodies. The data supports a stability-mobility trade-off at the cervico-thoracic transition in giraffes. Performance under load at the cervico-thoracic boundary is indicative of habitual neck posture and is influenced by body size.

Improving Adversarial Robustness of ECG Classification Based on Lipschitz Constraints and Channel Activation Suppression.

Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturbations. This vulnerability can lead to potential medical accidents. This poses new challenges for the application of DNNs in the medical diagnosis of ECG signals. This paper proposes a novel network Channel Activation Suppression with Lipschitz Constraints Net (CASLCNet), which employs the Channel-wise Activation Suppressing (CAS) strategy to dynamically adjust the contribution of different channels to the class prediction and uses the 1-Lipschitz's ℓ∞ distance network as a robust classifier to reduce the impact of adversarial perturbations on the model itself in order to increase the adversarial robustness of the model. The experimental results demonstrate that CASLCNet achieves ACCrobust scores of 91.03% and 83.01% when subjected to PGD attacks on the MIT-BIH and CPSC2018 datasets, respectively, which proves that the proposed method in this paper enhances the model's adversarial robustness while maintaining a high accuracy rate.

Flexible and Compressible Nanostructure-Assembled Aramid Nanofiber/Silica Composites Aerogel.

The Applications of silica aerogel are limited due to its brittleness and low strength. As a result, it is essential to strengthen and toughen it. Organic nanofibers are one of the preferred reinforcement materials. In this work, we designed and fabricated flexible and compressible nanostructure-assembled aramid nanofiber/silica composites aerogel (ANF/SiO2 aerogel) to improve the mechanical strength and flexibility of silica aerogel without compromising thermal insulation properties. The aramid nanofiber/silica composite aerogels were prepared by immersing the aramid nanofiber wet gel into the silica sol for a certain period of time followed by freeze drying without solvent replacement. The surface modifier 3-aminopropyltriethoxysilane (APTES) was used as a coupling agent to form chemical linkage between the ANF fiber and silica gel. It was observed that APTES can effectively drive the silica sol to infuse into ANF hydrogel, promoting the assembly of silica gel onto the fiber surface and a uniform distribution in the network of ANF. The compressive resilience, thermal stability, and thermal insulation properties of the composite aerogels were evaluated by inducing the silica aerogel into the ANF network to form a protective layer on the fiber and change the pore structure in the ANF network.

Robust prostate disease classification using transformers with discrete representations.

PURPOSE: Automated prostate disease classification on multi-parametric MRI has recently shown promising results with the use of convolutional neural networks (CNNs). The vision transformer (ViT) is a convolutional free architecture which only exploits the self-attention mechanism and has surpassed CNNs in some natural imaging classification tasks. However, these models are not very robust to textural shifts in the input space. In MRI, we often have to deal with textural shift arising from varying acquisition protocols. Here, we focus on the ability of models to generalise well to new magnet strengths for MRI. METHOD: We propose a new framework to improve the robustness of vision transformer-based models for disease classification by constructing discrete representations of the data using vector quantisation. We sample a subset of the discrete representations to form the input into a transformer-based model. We use cross-attention in our transformer model to combine the discrete representations of T2-weighted and apparent diffusion coefficient (ADC) images. RESULTS: We analyse the robustness of our model by training on a 1.5 T scanner and test on a 3 T scanner and vice versa. Our approach achieves SOTA performance for classification of lesions on prostate MRI and outperforms various other CNN and transformer-based models in terms of robustness to domain shift and perturbations in the input space. CONCLUSION: We develop a method to improve the robustness of transformer-based disease classification of prostate lesions on MRI using discrete representations of the T2-weighted and ADC images.

Changes in the land-use landscape pattern and ecological network of Xuzhou planning area.

Ongoing rapid urbanization has triggered significant changes in land use, rendering landscape patterns adversely impacted and certain habitat patches degraded. Ecological networks have consequently contracted overall. As such, an investigation into how land-use landscape patterns and ecological networks change over time and space is of major significance for ecological restoration and regional sustainability. Taking Xuzhou Planning Area as a case study, we examined spatiotemporal changes and features of the landscape pattern by employing the land-use change degree, the land-use transition matrix, and quantified landscape pattern indices. An ecological network analysis, which studies the changes in network connectivity and robustness, as well as their causes and contributors, was undertaken to probe into the features and trends of spatiotemporal changes in the land-use landscape pattern and ecological network amid expeditious urbanization. Analysis results unveiled the following: (1) From 1985 to 2020, there was a decline in the area of farmland, forest, and grassland, accompanied by an increase in land for construction, water bodies, and unused land. The southwestern research area witnessed farmland substantially give way to land for construction for this period, and the most dramatic change in land use occurred between 2000 and 2010. (2) The area of dominant patches in the research area shrank, along with more fragmented, complex landscapes. The land for construction was emerging as the dominant landscape by area, whereas patches of farmland, forest, grassland, and water bodies became less connected. (3) The ecological network was densely linked in the northeast, with sparser connections in the southwest. Spatial shrinkage was observed in the research area's southwestern and central ecological corridors. Overall, the number of ecological sources and corridors rose and subsequently dropped before a rebound. (4) The ecological network grew more connected and robust from 1985 through 1990, as portions of farmland were converted into water bodies, which led to an increase in ecological sources. Given a reduction in ecological sources and corridors in the southwestern and central regions between 1990 and 2010, network connectivity and robustness declined, which was reversed from 2010 onward with the addition of two ecological sources-Pan'an Lake and Dugong Lake. With an optimal ecological network in 1990, however, it deteriorated significantly by 2010. The research area saw the minimum value of its network connectivity indices of network stability index (α), evenness index (ß), and connectivity index (γ), in 2010, when its ecological network was highly fragmented and vulnerable, attributing to a strong contrast between the maximal connected subgraph's relative size and connectivity robustness. The research findings can lay scientific groundwork for addressing ecological issues, restoring landscape patterns, and developing ecological networks amid urbanization.

Debunking the idea of biological optimisation: quantitative biology to the rescue.

The idea that plants would be efficient, frugal or optimised echoes the recurrent semantics of 'blueprint' and 'program' in molecular genetics. However, when analysing plants with quantitative approaches and systems thinking, we instead find that plants are the results of stochastic processes with many inefficiencies, incoherence or delays fuelling their robustness. If one had to highlight the main value of quantitative biology, this could be it: plants are robust systems because they are not efficient. Such systemic insights extend to the way we conduct plant research and opens plant science publication to a much broader framework.

RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic.

Recently, Machine Learning (ML)-based solutions have been widely adopted to tackle the wide range of security challenges that have affected the progress of the Internet of Things (IoT) in various domains. Despite the reported promising results, the ML-based Intrusion Detection System (IDS) proved to be vulnerable to adversarial examples, which pose an increasing threat. In fact, attackers employ Adversarial Machine Learning (AML) to cause severe performance degradation and thereby evade detection systems. This promoted the need for reliable defense strategies to handle performance and ensure secure networks. This work introduces RobEns, a robust ensemble framework that aims at: (i) exploiting state-of-the-art ML-based models alongside ensemble models for IDSs in the IoT network; (ii) investigating the impact of evasion AML attacks against the provided models within a black-box scenario; and (iii) evaluating the robustness of the considered models after deploying relevant defense methods. In particular, four typical AML attacks are considered to investigate six ML-based IDSs using three benchmarking datasets. Moreover, multi-class classification scenarios are designed to assess the performance of each attack type. The experiments indicated a drastic drop in detection accuracy for some attempts. To harden the IDS even further, two defense mechanisms were derived from both data-based and model-based methods. Specifically, these methods relied on feature squeezing as well as adversarial training defense strategies. They yielded promising results, enhanced robustness, and maintained standard accuracy in the presence or absence of adversaries. The obtained results proved the efficiency of the proposed framework in robustifying IDS performance within the IoT context. In particular, the accuracy reached 100% for black-box attack scenarios while preserving the accuracy in the absence of attacks as well.

Fundamental and Practical Feasibility of Electrocardiogram Reconstruction from Photoplethysmogram.

Electrocardiogram (ECG) reconstruction from contact photoplethysmogram (PPG) would be transformative for cardiac monitoring. We investigated the fundamental and practical feasibility of such reconstruction by first replicating pioneering work in the field, with the aim of assessing the methods and evaluation metrics used. We then expanded existing research by investigating different cycle segmentation methods and different evaluation scenarios to robustly verify both fundamental feasibility, as well as practical potential. We found that reconstruction using the discrete cosine transform (DCT) and a linear ridge regression model shows good results when PPG and ECG cycles are semantically aligned-the ECG R peak and PPG systolic peak are aligned-before training the model. Such reconstruction can be useful from a morphological perspective, but loses important physiological information (precise R peak location) due to cycle alignment. We also found better performance when personalization was used in training, while a general model in a leave-one-subject-out evaluation performed poorly, showing that a general mapping between PPG and ECG is difficult to derive. While such reconstruction is valuable, as the ECG contains more fine-grained information about the cardiac activity as well as offers a different modality (electrical signal) compared to the PPG (optical signal), our findings show that the usefulness of such reconstruction depends on the application, with a trade-off between morphological quality of QRS complexes and precise temporal placement of the R peak. Finally, we highlight future directions that may resolve existing problems and allow for reliable and robust cross-modal physiological monitoring using just PPG.

Thermal-Robust Phenoxyimine Titanium Catalysts Bearing Bulky Sidearms for High Temperature Ethylene Homo-/Co- Polymerizations.

A family of titanium complexes (Ti1-Ti7) with the general formula LTiCl3, supported by tridentate phenoxyimine [O-NO] ligands (L1-L7) bearing bulky sidearms, were synthesized by treating the corresponding ligands with stoichiometric amount of TiCl4. All the ligands and complexes were well characterized by 1H and 13C NMR spectroscopies, in which ortho- methoxyl groups on N-aryl moieties shifted to downfield, corroborating the successful coordination reaction. Structural optimization by DFT calculations revealed that one of the phenyl groups on dibenzhydryl moiety could form π-π stacking interaction with the salicylaldimine plane, because of which the obtained titanium complexes revealed good thermal stabilities for high-temperature polymerization of ethylene. The thermal robustness of the complexes was closely related to the strength of π-π stacking interactions, which were mainly influenced by the substituents on the dibenzhydryl moieties; Ti1, Ti4 and Ti5 emerged as the three best-performing complexes at 110 °C. With the aid of such π-π stacking interactions, the complexes were also found to be active at >150 °C, although decreased activities were witnessed. Besides homopolymerizations, complexes Ti1-Ti7 were also found to be active for the high-temperature copolymerization of ethylene and 1-octene, but with medium incorporation percentage, demonstrating their medium copolymerization capabilities.

"Expression of concern": publication bias for positive preclinical cardioprotection studies.

The present analysis reports on the robustness of preclinical cardioprotection studies with infarct size as endpoint which were published in Basic Research in Cardiology, Cardiovascular Research, and Circulation Research between January 2013 and December 2023. Only 26 out of 269 papers with technically robust analysis of infarct size by triphenyltetrazolium chloride staining, magnetic resonance imaging or single photon emission tomography applied a prospective power analysis. A retrospective power calculation revealed that only 75% of the reported data sets with statistically significant positive results from all these studies had a statistical power of ≥ 0.9, and an additional 9% had a statistical power ≥ 0.8. The remaining 16% of all significant positive data sets did not even reach the 0.8 threshold. Only 13% of all analyzed data sets were neutral. We conclude that neutral studies are underreported and there is indeed a significant lack of robustness in many of the published preclinical cardioprotection studies which may contribute to the difficulties of translating cardioprotection to patient benefit.