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Re-locative guided search optimized self-sparse attention enabled deep learning decoder for quantum error correction
Abstract Heavy hexagonal coding is a type of quantum error-correcting coding in which the edges and vertices of a low-degree graph are assigned auxiliary and physical qubits. While many topological code decoders have been presented, it is still difficult to construct the optimal decoder due to leakage errors and qubit collision. Therefore, this research proposes a Re-locative Guided Search optimized self-sparse attention-enabled convolutional Neural Network with Long Short-Term Memory (RlGS2-DCNTM) for performing effective error correction in quantum codes. The integration of the self-sparse attention mechanism in the proposed model increases the feature learning ability of the model to selectively focus on informative regions of the input codes. In addition, the use of statistical features computes the statistical properties of the input, thus aiding the model to perform complex tasks effectively. For model tuning, this research utilizes the RIGS nature-inspired algorithm that mimics the re-locative, foraging, and hunting strategies, which avoids local optima problems and improves the convergence speed of the RlGS2-DCNTM for Quantum error correction. When compared with other methods, the proposed RlGS2-DCNTM algorithm offers superior efficacy with a Minimum Mean Squared Error (MSE) of 4.26, Root Mean Squared Error of 2.06, Mean Absolute Error of 1.14 and a maximum correlation and $$R^2$$ of 0.96 and 0.92 respectively, which shows that the proposed model is highly suitable for real-time error decoding tasks.
Comparison of mNGS with conventional methods for diagnosis of cryptococcal meningitis: a retrospective study
Considerations for establishment of a private virtual hospital identified using an implementation science approach
Abstract Virtual hospitals are rapidly being implemented internationally. Research has predominantly focused on clinical outcomes not implementation. We aimed to identify pre-implementation determinants to enable health services to tailor virtual hospital models, increasing likelihood of suitability, acceptability, uptake, clinical effectiveness, and sustainability. We aimed to inform the design and implementation of a private Australian virtual hospital by identifying contextual barriers, enablers, and considerations. We conducted a qualitative pre-implementation determinant study using snowball sampling and semi-structured interviews ( n = 37) between February and July 2023 with consumers/carers/both ( n = 11), clinicians (doctors/allied health/nursing/personal carers), hospital, health service and aged care leadership ( n = 22), and public health stakeholders (n=4). Deductive framework analysis based on the PERCS implementation science framework was used. The following key determinants were identified: Enablers – strong executive leadership support; enthusiasm for expanding rural and remote services; need for a clear vision; strong tension for change; commitment to high-quality healthcare. Major barrier: restrictive funding models that stifle opportunities for innovation. Other barriers: technological limitations; communication challenges; workforce issues; clinicians’ opinions varied on safety and suitability of virtual healthcare. This implementation science approach enabled identification of a broad set of determinants not previously reported, relevant locally and for an international audience. Evaluation of implementation outcomes is necessary.
Effects of thermal aging on the performance of ordinary and novel superhydrophobic and oleophobic ultra-fine dry powder extinguishing agent
Leveraging survival analysis and machine learning for accurate prediction of breast cancer recurrence and metastasis
Abstract Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence to improve treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasis using Recurrence-Free Survival Analysis and machine learning techniques. We merged datasets from the Molecular Taxonomy of Breast Cancer International Consortium, Memorial Sloan Kettering Cancer Center, Duke University, and the SEER program, creating a comprehensive dataset of 272, 252 rows and 23 columns. Our methodology utilized three predictive strategies: assessing recurrence risk, differentiating local from distant recurrences, and identifying potential metastatic sites. Key prognostic factors were identified through survival analysis. LightGBM, XGBoost, and Random Forest models were employed and validated against data from the Baheya Foundation. The models demonstrated strong performance; the survival analysis achieved a C-index of 0.837. The LightGBM model reached an AUC of 92% in predicting recurrences, while XGBoost and Random Forest models distinguished recurrence types with up to 86% accuracy, and they effectively differentiated between bone metastasis and all other locations combined (brain, liver, and lungs). This study highlights the significant potential of machine learning in advancing breast cancer management and sets a new benchmark for predictive analytics. Future research will integrate genetic data to further enhance these models.
An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energy
Examining physical and technical performance among youth basketball national team development program players: a multidimensional approach
From relaxed beliefs under psychedelics (REBUS) to revised beliefs after psychedelics (REBAS)
Abstract The Relaxed Beliefs Under pSychedelics (REBUS) model proposes that serotonergic psychedelics decrease the precision weighting of neurobiologically-encoded beliefs. We conducted a preliminary examination of two psychological assumptions of REBUS: (a) psychedelics foster acute relaxation and post-acute revision of confidence in mental-health-relevant beliefs; which (b) facilitate positive therapeutic outcomes and are associated with the entropy of EEG signals. Healthy individuals (N = 11) were administered 1 mg and 25 mg psilocybin 4-weeks apart. Confidence ratings for personally held beliefs were obtained before, during, and 4-weeks post-psilocybin. Acute entropy and subjective experiences were measured, as was well-being (before and 4-weeks post-psilocybin). Confidence in negative self-beliefs decreased following 25 mg psilocybin. Entropy and subjective effects under 25 mg psilocybin correlated with decreases in negative self-belief confidence (acutely and at 4-weeks). Particularly strong evidence was seen for a relationship between decreases in negative self-belief confidence and increases in well-being. We report the first empirical evidence that the relaxation and revision of negative self-belief confidence mediates psilocybin's positive psychological outcomes, and provide tentative evidence for a neuronal mechanism, namely, increased neuronal entropy. Replication within larger and clinical samples is necessary. We also introduce a new measure for examining the robustness of these preliminary findings and the utility of the REBUS model.
Unveiling microbial diversity in slightly and moderately magnesium deficient acidic soils
Revolutionizing load harmony in edge computing networks with probabilistic cellular automata and Markov decision processes
Abstract In general, edge computing networks are based on a distributed computing environment and hence, present some difficulties to obtain an appropriate load balancing, especially under dynamic workload and limited resources. The conventional approaches of Load balancing like Round-Robin and Threshold-based load balancing fails in scalability and flexibility issues when applied to highly variable edge environments. To solve the problem of how to achieve steady-state load balance and provide dynamic adaption to edge networks, this paper proposes a new framework that using PCA and MDP. Taking advantage of the stochasticity of PCA classification our model describes interactions between neighboring nodes in terms of a local load thus allowing for a distributed, self-organizing approach to load balancing. The MDP framework then determines each node’s decision-making with the focus on load offloading policies that are aligned with rewards that promote per node balance and penalties for offloading a larger load than it can handle.These models are then incorporated into our proposed PCA-MDP system to achieve dynamic load balancing with low variability in resource usage among nodes. By conducting a large number of experiments, we prove that the proposed PCA-MDP model yields a higher efficiency in the distribution of the load, higher stabilities of the reward function, and a faster convergence speed compared to the existing approaches. Key performance parameters, such as load variance, convergence time, and scalability, validate the robustness of the proposed model. Besides optimizing resource exploitation, load harmony in edge computing networks helps provide efficient work progression and minimize latency, thereby contributing to the advancement of the field with respect to real-time applications such as self-driving vehicles and the Internet of Things. The presented work offers an excellent foundation for the next-generation edge-computing load-balancing solution that can be easily scaled up.
Confinement-induced Ni-based MOF formed on Ti3C2Tx MXene support for enhanced capacitive deionization of chromium(VI)
Understanding Burnout among Surgical residents: a mixed method study
Statistical flaws of the fitness-fatigue sports performance prediction model
The effect of sarpogrelate compared to aspirin in high- or very-high-risk diabetes for primary prevention
Elucidating the potential of EGFR mutated NSCLC and identifying its multitargeted inhibitors
Dimensional structure of the Brief Illness Perception Questionnaire and Association with adverse childhood experiences in patients with an implantable cardioverter-defibrillator
Spatial accuracy of dose delivery significantly impacts the planning target volume margin in linear accelerator-based intracranial stereotactic radiosurgery
Abstract The impact of three-dimensional (3D) dose delivery accuracy of C-arm linacs on the planning target volume (PTV) margin was evaluated for non-coplanar intracranial stereotactic radiosurgery (SRS). A multi-institutional 3D starshot test using beams from seven directions was conducted at 22 clinics using Varian and Elekta linacs with X-ray CT-based polymer gel dosimeters. Variability in dose delivery accuracy was observed, with the distance between the imaging isocenter and each beam exceeding 1 mm at one institution for Varian and nine institutions for Elekta. The calculated PTV margins for Varian and Elekta linacs that could cover the gross tumor volume with 95% probability at 95% of the institutions were 2.3 and 3.5 mm, respectively, in the superior–inferior direction. However, with multifactorial system management (i.e., high-accuracy 3D dose delivery with rigorous linac quality assurance, strict patient immobilization, and high intra-fractional positioning accuracy), these margins could be reduced to 1.0 mm and 1.5 mm, respectively. The findings indicate significant millimeter-level variability in 3D dose delivery accuracy among linacs installed in clinical settings. Thus, maximizing a linac’s 3D dose delivery accuracy is essential to achieve the required PTV margin in intracranial SRS.