Browse Articles

CT-radiomics combined with inflammatory indicators for prediction of progression free survival of resectable esophageal squamous cell carcinoma

Topological phase transition in monolayer 1$$\hbox {T}^\prime$$-$$\hbox {MoS}_2$$

Abstract 1 $$\hbox {T}^{\prime }$$ phase of the monolayer transition metal dichalcogenides has recently attracted attention for its potential in nanoelectronic applications. We theoretically prove the topological behavior and phase transition of 1 $$\hbox {T}^{\prime }$$ - $$\hbox {MoS}_2$$ using k . p Hamiltonian and linear response theory. The spin texture in momentum space reveals a strong spin-momentum locking with different orientations for the valence and conduction bands. Also, Berry curvature distributions around the Dirac points highlight the influence of $$\alpha$$ parameter demonstrating a topological phase transition in 1 $$\hbox {T}^\prime$$ - $$\hbox {MoS}_2$$ . For $$\alpha <1$$ the spin Hall conductivity is the only non-zero term $$(C_s=1$$ and $$C_v=0)$$ , corresponding to a quantum spin Hall insulator (QSHI) phase, while for $$\alpha>1$$ , valley Hall conductivity prevails, indicating a transition to a band insulator (BI). Further analysis explores the spin-valley-resolved Hall conductivity and Chern numbers across varying values of $$\alpha$$ , V , and Fermi energy, uncovering regions of non-trivial and trivial topological phases (TTP) and the role of the edge modes. The zero total Nernst coefficient across energy ranges suggests strong cancellation between spin and valley contributions, providing insights into the material’s potential for thermoelectric applications and spintronic devices.

Temporal patterns of cognitive decline after hypertension onset among middle-aged and older adults in China

Enhancing shooting performance and cognitive engagement in virtual reality environments through brief meditation training

Anchoring effect of NPR cables under dynamic conditions and its engineering applications

Dietary inflammation and its impact on congestive heart failure in older adults with depression

Real world study on efficacy and safety of surufatinib in advanced solid tumors evaluation

Machine learning model to predict sepsis in ICU patients with intracerebral hemorrhage

Variations of the chemical components and biological activities of Thymus capitatus essential oil from three regions in Palestine

Improved biosynthesis and characteristics of silver nanoparticles using marine endophytic fungi exposed to hypo-osmotic stress

The impact of radiotherapy on the prognosis of metastatic clear cell renal cell carcinoma after surgery

Forecasting water quality indices using generalized ridge model, regularized weighted kernel ridge model, and optimized multivariate variational mode decomposition

Research and application of deep learning object detection methods for forest fire smoke recognition

Plastic failure and deformation calculation of shaft structure in soil under lateral explosion

Prediction and optimization of stretch flangeability of advanced high strength steels utilizing machine learning approaches

Abstract Advanced high strength steels (AHSS) exhibit diverse mechanical properties due to their complex chemical compositions and microstructures. Existing machine learning (ML) studies often focus on specific steel grades, limiting generalizability in predicting and optimizing AHSS properties. Here, an ML framework was presented to predict and optimize the stretch-flangeability of AHSS based on composition-microstructure-property correlations, using datasets from 212 steel conditions. Support vector machine, symbolic regression, and extreme gradient boosting models accurately predicted hole expansion ratio (HER), ultimate tensile strength (UTS), and total elongation (TE). Shapley additive explanations revealed the importance of bainite volume fraction (VB), carbon content (C), and chromium content (Cr) for HER, UTS, and TE, respectively. Multi-objective optimization generated 252 optimized conditions with improved comprehensive mechanical properties. The best optimized chemical compositions (0.12wt.% C-1.10Mn-0.15Si-0.47Cr) along with the carbon equivalent (CE) of 0.44 wt.%, and microstructural features (7.2% ferrite, 44.5% bainite, 40.5% martensite, and 7.8% tempered martensite) yielded HER of 119.8%, UTS of 1013.5 MPa, and TE of 22.7%. This systematic framework enables efficient prediction and optimization of material properties (especially HER), with potential applications across various fields of materials science.

Retraction: Carbapenem resistance and Acinetobacter baumannii in Senegal: The paradigm of a common phenomenon in natural reservoirs

Noninvasive blood glucose monitoring using a dual band microwave sensor with machine learning

Abstract The potential for continuous non-invasive blood glucose monitoring has attracted a lot of interest in the field of medical diagnostics. This paper provides a new shape of a dual-band bandpass filter (DBBPF) acting as a microwave transmission line sensor for continuous non-invasive blood glucose monitoring operating at 2.45 and 5.2 GHz. The proposed system uses the interaction between biological tissues and microwave signals to correctly assess blood glucose levels. The proposed dual-band bandpass filter (DBBPF), comprises three split ring resonator (SRR) cells with different dimensions. It is designed to operate as a sensor with improved sensitivity, compact dimensions, and a high-quality factor. It also ensures a reasonable bandwidth for lower and higher bands of 8.6 and 2%, respectively in the industrial, scientific, medical band, and the wireless local area network (ISM and WLAN) Bands. A dual-band filter enhances measurement sensitivity and specificity by targeting specific frequency ranges where glucose exhibits distinctive dielectric responses, thereby providing redundant data points for accurate glucose level determination. Glucose concentrations can be evaluated by measuring the changes in the dielectric properties of blood by sending microwave waves through the body and assessing the collected S-parameter signals. The measurement parameters encompass the reflection, phase, magnitude, as well as transmission parameters. This yields multiple evaluations of the glucose-induced alterations. Simulations are validated through laboratory measurements incorporating a phantom finger model for capturing realistic outcomes. Machine learning models are employed to analyze the sensor data, improving the accuracy of diabetes detection. Simulations are validated through laboratory measurements incorporating a phantom finger model for capturing realistic outcomes. A Cole-Cole model, implemented using MATLAB, is utilized for the phantom finger model. The main results reveal the success of the proposed transmission-based microwave glucose sensing, with a remarkable sensitivity of 1 ~ 1.5 dB for glucose level change up to 200 mg/dL.

The development of a brain injury survivor patient and public involvement group by a brain injury survivor

Background Patient and public involvement (PPI) in research is seen as key to ensuring applicability and impact. Undertaking PPI in people after brain injury has long been seen to be a challenge. In 2020 The NIHR Brain Injury MedTech Cooperative developed a programme with the aim of improving PPI involvement, impact and diversity in this population. Methods Through a process of iterative development, a PPI programme was created. It built on an existing underutilised database of people after brain injury and their carers who were interested in engaging with PPI and utilised video-calling software. It was led by a Brain injury Survivor acting as Facilitator with admin support from the MedTech Cooperative. Results To date 14 PPI sessions were completed supporting a total of 17 projects. The diversity of the panel members was comparable to that of the population at large. However, further work is needed, especially in engaging people experiencing homelessness, people living outside of England and those with communication impairments. Feedback from researchers was positive and specific impacts are stated. Conclusion Through the leadership of a facilitator who has an understanding of the lived experience of brain injury a PPI programme has been developed. The use of a video-calling platform enabled a wider representation then a face-to-face group would have and techniques such as shortened sessions and single project presentations ensured engagement and impact.

Intelligent fault tolerance control using long short-term memory for efficient system performance under fault conditions

Abstract Fault-Tolerant Control (FTC) is a crucial field within control systems engineering that focuses on designing systems capable of maintaining desired performance and stability even in the presence of faults. This study introduces a data-driven fault-tolerant control system that enhances the operation of control systems in the presence of faults. The system is designed on a single Long Short-Term Memory (LSTM), which replaces the units responsible for diagnosis and control reconfiguration. The LSTM-FTC system does not require diagnostic and process models, which is a significant advantage over traditional model-based methods. The factory I/O is interfaced with MATLAB through the implementation of the digital twin idea, which allows for the simulation and validation of the suggested approaches. These approaches are then applied to an assembler case study that included both faultless and multiple faulty sensors. The training process reaches 6553 iterations with Root Mean Square Error (RMSE) equal to $$\:5\times\:{10}^{-3}$$ at six minutes and 17 s. The results of the simulation demonstrate the effectiveness of the proposed approaches. The accuracy of the system outputs in the faultless and worst-case scenarios are 92.81% and 67.16% respectively.

Sample selection bias due to omitting short trees for tree height estimation in forest inventories: A case study on Pinus koraiensis plantations in South Korea

This study investigates the impact of omitting short tree data on tree height estimation in conventional forest inventories, focusing on Pinus koraiensis plantations in South Korea. Twenty height-diameter models were tested on both datasets: the complete data and the short tree-free data. The models were divided into Group 1 (with two model parameters) and Group 2 (with three model parameters) to examine whether the omission of short tree data affects model performance based on the number of parameters. Results demonstrated that excluding short tree data led to significant overestimation of tree height in small diameter ranges, with Group 2 models showing greater sensitivity to the omission. This omission also caused substantial variations in model rankings between the Full and short tree-free datasets, leading to specification errors and suboptimal model selection. Despite the small sample size difference, half of the Group 2 models produced non-significant parameter estimates when fitted to the short tree-free data, underscoring the influence of sample distribution on statistical outcomes. While most models maintained consistent height-diameter relationships during extrapolation, some generated unrealistic results, including negative or excessively large tree height estimates and inverse relationships in small diameter ranges. These findings emphasize the necessity of including short trees in forest inventory samples to mitigate biases in tree height estimation, which is critical for accurate biomass and carbon stock assessments.