Browse Articles
Discover research articles across all indexed journals
LDPC-cat codes for low-overhead quantum computing in 2D
Abstract The main obstacle to large scale quantum computing are the errors present in every physical qubit realization. Correcting these errors requires a large number of additional qubits. Two main avenues to reduce this overhead are (i) low-density parity check (LDPC) codes requiring very few additional qubits to correct errors (ii) cat qubits where bit-flip errors are exponentially suppressed by design. In this work, we combine both approaches to obtain an extremely low overhead architecture. Assuming a physical phase-flip error probability ϵ ≈ 0.1% per qubit and operation, one hundred logical qubits can be implemented on a 758 cat qubit chip, with a total logical error probability per cycle and per logical qubit ϵ L ≤ 10−8. Our architecture also features two major advantages. First, the hardware implementation of the code can be realised with short-range qubit interactions in 2D and low-weight stabilizers, under constraints similar to those of the popular surface code architecture. Second, we demonstrate how to implement a fault-tolerant universal set of logical gates with an additional layer of routing cat qubits stacked on top of the LDPC layer, while maintaining the local connectivity. Furthermore, our architecture benefits from a high capacity of parallelization for these logical gates.
Composition and liquid-to-solid maturation of protein aggregates contribute to bacterial dormancy development and recovery
High-temperature structural disorders stabilize hydrous aluminosilicates in the mantle transition zone
Crucial role of subsurface ocean variability in tropical cyclone genesis
Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm
Label-free proteomic analysis of Duchenne and Becker muscular dystrophy showed decreased sarcomere proteins and increased ubiquitination-related proteins
Abstract Muscular dystrophies (MD) are a group of hereditary diseases marked by progressive muscle loss, leading to weakness and degeneration of skeletal muscles. These conditions often result from structural defects in the Dystrophin–Glycoprotein Complex (DGC), as seen in Duchenne Muscular Dystrophy (DMD) and Becker Muscular Dystrophy (BMD). Since MDs currently have no cure, research has focused on identifying potential therapeutic targets to improve patients’ quality of life. In this study, skeletal muscle tissue samples from DMD and BMD patients, as well as non-dystrophic controls, were analyzed using label-free mass spectrometry (MS/MS) to characterize the proteomic profile of these conditions and identify biomarkers for differential diagnosis. In-silico analysis revealed that dystrophic muscle tissues are linked to biological processes related to cellular energy metabolism, including oxidation of organic compounds, energy production, and cellular respiration. Enrichment of functions associated with cell structure and RNA binding was also observed, including cytoskeletal protein binding and RNA binding. The human phenotypes most related to the proteomic signature were abnormal circulating metabolites, muscle physiology, and weakness. Quantitative analysis identified significant changes in proteins associated with sarcomere organization and protein ubiquitination, such as myomesin, myozenin, and E3 ubiquitin-protein ligase rififylin, suggesting these as potential therapeutic targets.
DLCDroid an android apps analysis framework to analyse the dynamically loaded code
Abstract To combat dynamically loaded code in anti-emulated environments, DLCDroid is an Android app analysis framework. DL-CDroid uses the reflection API to effectively identify information leaks due to dynamically loaded code within malicious apps, incorporating static and dynamic analysis techniques. The Dynamically Loaded Code (DLC) technique employs Java features to allow Android apps to dynamically expand their functionality at runtime. Unfortunately, malicious app developers often exploit DLC techniques to transform seemingly benign apps into malware once installed on real devices. Even the most sophisticated static analysis tools struggle to detect data breaches caused by DLC. Our analysis demonstrates that conventional tools areill-equipped to handle DLC. DLCDroid leverages dynamic code interposition techniques for API hooking to expose concealed malicious behavior without requiring modifications to the Android framework. DLCDroid can unveil suspicious behavior that remains hidden when relying solely on static analysis. We evaluate DLCDroid’s performance using a dataset comprising real-world benign and malware apps from reputed repositories like VirusShare and the Google Play Store. Compared to state-of-the-art approaches, the results indicate a significant improvement in detecting sensitive information leaks, more than 95.6% caused by reflection API. Furthermore, we enhance DLCDroid’s functionality by integrating it with an event-based trigger solution, making the framework more scalable and fully automated in its analysis process.
Remaining useful life prediction of lithium-ion batteries using a novel particle flow filter framework with grey model
Abstract Remaining useful life (RUL) prediction is a crucial aspect of the prognostics health management of lithium-ion batteries (LIBs). Owing to the influence of resampling technology, particle degradation is often observed in the particle filter-based RUL prediction of LIBs, resulting in a low prediction accuracy and large uncertainty. In this paper, a novel particle flow filter with the grey model method (GM-PFF) is proposed to forecast the RUL and state of health of batteries. First, the least squares method is employed to obtain the initial values for double exponential empirical model parameters. Subsequently, the grey model is used to predict the current cycle capacity of LIBs as an observation value for the particle flow filter, solving the inaccurate estimation problem of the state of particle flow filter observation values, and the particle flow filter method is employed to update model parameters. Finally, a test dataset is divided into early, middle, and late stages to predict the RUL of LIBs and obtain the probability distributions. On the CALCE and NASA PCoE LIB dataset, GM-PFF reduces RMSE by 1% compared to PFF, exhibiting a higher prediction accuracy and effectively addressing the particle degradation problem.
Global, regional, and national burden of esophageal cancer using the 2019 global burden of disease study
An improved sparrow search algorithm with multi-strategy integration
Digital transformation project risks assessment using hybrid picture fuzzy distance measure-based additive ratio assessment method
Abstract Digital transformation (DT) has become vital for companies trying to remain competitive in the recent ever-changing technological environment. DT is the integration of digital technologies into all disciplines of business from regular activities to strategic decision making. Risk management planning requires projects to assess possible risks that may negatively or positively affect a DT project. The purpose of the study is to introduce a hybridized decision support system (DSS) by combining the distance measure, ranking comparison (RANCOM) model and additive ratio assessment (ARAS) approach in the context of a picture fuzzy set (PFS). In this framework, the decision experts’ significance values are computed using a picture fuzzy score function-based formula. With the combination of objective weight using distance measure and subjective weight through the RANCOM model, a combined weight-determining approach is developed to determine the significance values of considered DT risks under picture fuzzy environment, while a hybrid ARAS model is developed to evaluate and rank DT projects from the risks perspective. To exhibit the feasibility of the introduced framework, a case study of a DT projects assessment problem is discussed in the context of picture fuzzy sets. A sensitivity study is also discussed over different values of the strategy coefficient, which confirms the strength of the proposed model. Further, a comparison with the existing picture fuzzy information-based methods is presented to prove the robustness of the developed decision-making framework.
Global transcriptional modulation and nutritional status of soybean plants following foliar application of zinc borate as a suspension concentrate fertilizer
Abstract The management of micronutrients, such as boron (B) and zinc (Zn), is critical for plant growth and crop yields. One method of rapid intervention crop management to mitigate nutritional deficiency is the foliar supply of B and Zn. Our study investigates the effect of foliar-supplied B and Zn availability on the global transcriptional modulation in soybean (Glycine max). The transcriptional response to B was more widespread compared to Zn. RNA-Seq of leaves under different B levels revealed modulated genes with potential roles in nutritional homeostasis and stress response that may be key to controlling B status in the plant. We also identified putative B transporters whose expression levels were significantly affected by B supplementation with foliar fertilization of plants growing under low B conditions. Furthermore, a gene lacking functional annotation (Glyma.03G180300) emerged as a novel potential marker of B status. Two genes (Glyma.16G118000, Glyma.16G199000) were consistently induced by Zn availability, highlighting their potential as biomarkers for assessing its status in soybean plants. This work advances our understanding of nutritional homeostasis in soybean plants and identifies target genes and potential molecular mechanisms involved in nutritional response. Our study informs fertilizer design targeting specific nutrient transporters, thereby enhancing nutrient efficiency in crops.