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Reliability is a crucial index of the system, and many safety-critical applications have reliability requirements and deadline constraints. In addition, in order to protect the environment and reduce system operating costs, it is necessary to minimize energy consumption as much as possible. This paper considers parallel applications on heterogeneous distributed systems and proposes two algorithms to minimize energy consumption for meeting the deadline and satisfying the reliability requirement of the applications. The first algorithm is called minimizing scheduling length while satisfying the reliability requirement (MSLSRR). It first transforms the reliability requirement of the application into the reliability requirement of the task and then assigns the task to the processor with the earliest finish time. Since the reliability generated by MSLSRR is often higher than the reliability requirement of the application, and the scheduling length is also less than the deadline, an algorithm called improving energy efficiency (IEE) is designed, which redefined the minimum reliability requirement for the task and applied dynamic voltage and frequency scaling (DVFS) technique for energy conservation. The proposed algorithms are compared with existing algorithms by using real parallel applications. Experimental results demonstrate that the proposed algorithms consume the least energy. [ABSTRACT FROM AUTHOR]

In this study, we introduce a cutting-edge gas sensor exploiting surface plasmon resonance (SPR) in a novel D-shaped photonic crystal fiber (PCF) configuration, tailored for methane detection. The sensor’s innovative design incorporates dual-core polished gas access microstructures, optimizing methane-gas interaction surfaces. A gold nanofilm was strategically selected to induce SPR, augmented by a polysiloxane-doped cryptane E overlay for heightened methane sensitivity. To meticulously evaluate sensor performance, we employed a combination of finite element analysis (FEA) and rigorous numerical modeling. These analyses revealed the profound impact of structural parameters on sensor attributes. Our findings highlight a remarkable methane sensing capability, evidenced by a sensitivity of 11.52 nm/% and a figure of merit (FOM) reaching 0.409 within the tested 0–3.5% concentration range, accompanied by a linear response profile—underscoring the system’s precision and reliability. This pioneering sensor technology, distinguished by its affordability, simplicity, and real-time monitoring capabilities, charts a new course in the realm of gas leak detection systems, poised to make significant contributions to future safety and environmental monitoring endeavors. [ABSTRACT FROM AUTHOR]

• This paper proposes an efficient high-order harmonics correction method for phase measuring profilometry. • The addition of the ideal phase and the residual harmonics is converted to the multiplication based on Hilbert transform. • Simulations and experiments have validated the proposed method compared with conventional methods. Gamma nonlinearity severely affects the accuracy of phase measuring profilometry (PMP). Through averaging two phase maps with inverse errors, conventional methods including double phase-shifting (DPS) and Hilbert transform (HT) only compensate the fundamental harmonic while leaving high-order harmonics. To address this issue, this paper presents an efficient high-order harmonics correction (HHC) method. The averaged phase is encoded into factitious fringes on the computer. Then we directly apply HT on these factitious fringes to generate transformed fringes, while the addition of the ideal phase and residual harmonics is converted to multiplication. Finally, the residual high-order harmonics are suppressed, and the corrected phase is calculated using the phase-shifting algorithm. In comparison to conventional DPS and HT methods, the proposed HHC method can efficiently correct the residual high-order harmonics, thereby enhancing the measuring accuracy. [ABSTRACT FROM AUTHOR]

Background: The overall survival (OS) for patients with recurrent glioma is meager. Also, the effect of radionecrosis and prognostic factors for recurrent glioma remains controversial. In this regard, developing effective predictive models and guiding clinical care is crucial for these patients. Methods: We screened patients with recurrent glioma after radiotherapy and those who received surgery between August 1, 2013, and December 31, 2020. Univariate and multivariate Cox regression analyses determined the independent prognostic factors affecting the prognosis of recurrent glioma. Moreover, nomograms were constructed to predict recurrent glioma risk and prognosis. Statistical methods were used to determine the prediction accuracy and discriminability of the nomogram prediction model based on the area under the curve (AUC), the C-index, the decision curve analysis (DCA), and the calibration curve. In order to distinguish high-risk and low-risk groups for OS, the X-Tile and Kaplan-Meier (K-M) survival curves were employed, and the nomogram prediction model was further validated by the X-Tile and K-M survival curves. Results: According to a Cox regression analysis, independent prognostic factors of recurrent glioma after radiotherapy with radionecrosis were World Health Organization (WHO) grade and gliosis percentage. We utilized a nomogram prediction model to analyze results visually. The C-index was 0.682 (95% CI: 0.616–0.748). According to receiver operating characteristic (ROC) analysis, calibration plots, and DCA, the nomogram prediction model was found to have a high-performance ability, and all patients were divided into low-risk and high-risk groups based on OS (P <.001). Conclusion: WHO grade and gliosis percentage are prognostic factors for recurrent glioma with radionecrosis, and a nomogram prediction model was established based on these two variables. Patients could be divided into high- and low-risk groups with different OS by this model, and it will provide individualized clinical decisions for future treatment. [ABSTRACT FROM AUTHOR]

Introduction: The prognosis of acute lymphoblastic leukemia (ALL) in adolescents and adults is poor, and recurrence is an important cause of their death. Changes of genetic information play a vital role in the pathogenesis and recurrence of ALL; however, the impact of molecular genetic mutations on disease diagnosis and prognosis remains unexplored. This study aimed to explore the frequency spectrum of gene mutations and their prognostic significance, along with the minimal residual disease (MRD) level and hematopoietic stem cell transplantation (HSCT), in adolescent and adult patients aged ≥15 years with ALL. Methods: The basic characteristics, cytogenetics, molecular genetics, MRD level, treatment regimen, and survival outcome of patients with untreated ALL (≥15 years) were collected, and the correlation and survival analysis were performed using the SPSS 25.0 and R software. Results: This study included 404 patients, of which 147 were selected for next-generation sequencing (NGS). NGS results revealed that 91.2% of the patients had at least one mutation, and 67.35% had multiple (≥2) mutations. NOTCH1, PHF6, RUNX1, PTEN, JAK3, TET2, and JAK1 were the most common mutations in T-ALL, whereas FAT1, TET2, NARS, KMT2D, FLT3, and RELN were the most common mutations in B-ALL. Correlation analysis revealed the mutation patterns, which were significantly different between T-ALL and B-ALL. In the prognostic analysis of 107 patients with B-ALL, multivariate analysis showed that the number of mutations ≥5 was an independent risk factor for overall survival and the RELN mutation was an independent poor prognostic factor for event-free survival. Discussion: The distribution of gene mutations and the co-occurrence and repulsion of mutant genes in patients with ALL were closely related to the immunophenotype of the patients. The number of mutations ≥5 and the RELN mutation were significantly associated with poor prognosis in adolescent and adult patients with ALL. [ABSTRACT FROM AUTHOR]

• Invasive and non-invasive CBT measurements were concurrently assessed and compared. • Non-invasive CBT measurement can serve as a general reference for sleep research. • Body fat rate and heat flux significantly affect the reliable of non-invasive CBT measurement. • Further optimization is possible in the algorithms for non-invasive CBT measurements. Core body temperature (CBT) emerges as a pivotal physiological marker in sleep research, and the commonly employed invasive CBT measurements significantly disrupt the sleep process itself. Non-invasive CBT measurement offers a viable solution to this challenge but lack validation. In this sleep study, simultaneous invasive and non-invasive measurements of CBT were conducted on 14 subjects. Ingestible capsule was chosen to represent invasive measurement, while external GreenTeg patch was selected as representative device for non-invasive measurement. Quantitative comparisons of two measurements were conducted from four perspectives: correlation, consistency, difference, and stability, and results indicated: (1) A significant correlation was observed between two real-time measurements, and correlation became stronger as ambient temperature decreased. (2) Consistency of most data group can be acceptable as most of them fell within the 95% agreement range. (3) Non-invasive instrument exhibited substantial error for unstable core temperature, yet displayed relatively small error for stable core temperature. (4) Stability of difference between two measurements was poor during 64.3% of the time intervals, and this performance deteriorated further in unstable state. In summary, existing non-invasive measurement can serve as a reference for sleep research, but caution is warranted when applying it to real-time and unstable analyses. Additionally, feasibility of algorithm optimization makes the future of non-invasive measurements promising. [ABSTRACT FROM AUTHOR]