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Abstract Uncertainty in operating parameters during laser thermal pain treatment can yield unreliable results. To ensure reliability and effectiveness, we performed uncertainty analysis and optimization on these parameters. Firstly, we conducted univariate analysis to identify significant operational parameters. Next, an agent model using RBNN regression determined the relationship between these parameters, the constraint function, and the target function. Using interval uncertainty analysis, we obtained confidence distributions and established a nonlinear interval optimization model. Introducing RPDI transformed the model into a deterministic optimization approach. Solving this with a genetic algorithm yielded an optimal solution. The results demonstrate that this solution significantly enhances treatment efficacy while ensuring temperature control stability and reliability. Accounting for parameter uncertainties is crucial for achieving dependable and effective laser thermal pain treatment. These findings have important implications for advancing the clinical application of this treatment and enhancing patient outcomes.

The difunctionalization of diazo compounds, traditionally with one nucleophile and one electrophile, is a powerful strategy. Here, the authors develop a path of electrochemical oxidative difunctionalization of diazo compounds with two different nucleophiles.

Abstract Background There has been controversy about how obesity affects the clinical prognosis for patients with atrial fibrillation (AF), and the relationship between obesity and outcomes in critically ill patients with AF remains unclear. The purpose of this study was to explore the association between obesity and short- and medium-term mortality in critically ill patients with AF. Methods The Medical Information Mart for Intensive Care-IV (MIMIC-IV) database was used to conduct a retrospective cohort analysis on 9282 critically ill patients with AF. Patients were categorized into four groups based on their body mass index (BMI) values: underweight, normal-weight, overweight, and obese. The outcomes of this study were 30-day, 90-day, and 1-year all-cause mortality. Cox proportional-hazards models and restricted cubic spline analyses were performed to investigate the association between BMI and mortality. Results For 30-day mortality, after adjustment for all confounding factors, the hazard ratio (HR) with 95% confidence interval (CI) for the underweight, overweight, and obese categories were 1.58 (1.21, 2.07), 0.82 (0.72, 0.93), and 0.79 (0.68, 0.91), respectively, compared to the normal-weight category. Using multivariable-adjusted restricted cubic spline analysis, an “L-shaped” correlation was observed between BMI and 30-day mortality. For each 1 kg/m2 increase in BMI when BMI was less than 30 kg/m2, the risk of 30-day mortality decreased by 6.4% (HR, 95% CI: 0.936 [0.918, 0.954]; P

Abstract To further improve the frequency regulation stability of wind farm, and optimize the state of charge (SOC) basepoint, charge and discharge rate and recovery capacity of energy storage. In an isolated, off‐grid state, a two‐layer optimization method is proposed, taking into account the frequency regulation reliability and SOC adaptive adjustment of the wind storage. In the upper layer, the model predictive control theory is adopted to optimize the energy‐storage frequency regulation output power with the goal of minimizing the wind storage frequency regulation power deviation. While solving the problem of low‐frequency regulation reliability of wind farm, the SOC recovery basepoint and frequency regulation power of energy storage are optimized. At the SOC optimization layer, based on the upper SOC recovery basis point, we propose a dynamic recovery method for energy‐storage SOC that considers both the SOC recovery demand and grid‐bearing capacity, to determine the energy‐storage recovery power. By determining the frequency regulation or recovery power, we propose a calculation method to optimize the energy‐storage charge and discharge coefficients as per the SOC for avoiding excessive charging and discharging. The simulation results show that, under continuous disturbance, the root‐mean‐square deviations of the proposed method is 80.13% and 62.63% lower than those of the fixed K and SOC methods, respectively. Furthermore, the proposed method exhibits the best SOC maintenance effect.

Polar codes constructed by large kernels can attain better finite length performance than those originating from Arıkan’s $2\times 2$ kernel. However, the successive cancellation (SC) decoding for these polar codes is impractical even for relatively small kernel size of $m$ because complexity of the kernel computation grows exponentially with $m$ . This research shows when $m>2$ , there exists a large amount of like terms in the kernel computation which yields a ground for facilitating the decoding. By transferring the kernel computation from the probability domain to the likelihood ratio domain ( $l$ -domain), the so- called $l$ -formula method provides an efficient way to combine the like terms in the kernel computation for kernels up to size 11. However, the $l$ -formula method becomes intractable for kernel size beyond 11. To further reduce the computational complexity, this paper proposes a $W$ -formula method which transforms the kernel computation into the probability pair domain ( $W$ -domain). Advanced from the $l$ -domain, the numerator and denominator of the likelihood ratio are considered separately, which eases the restrictions of combining like terms. The $W$ -formula method can combine much more like terms resulting in a significant reduction on the number of sub-formulas for medium kernels ( $m\leq 16$ ). Furthermore, in the $W$ -domain, sub-formulas become regular and there exist many common sub-formulas whose computations can be shared. Being able to handle kernels of size up to 16, we show that the $W$ -formula based SC decoding achieves a significant complexity reduction over the existing non-approximate SC decoding (the $l$ -formula based SC decoding).

To improve the short-term wind power output prediction accuracy and overcome the model prediction instability problem, we propose a combined prediction model based on variational modal decomposition (VMD) combined with the improved whale algorithm (GSWOA) to optimize the long short-term memory network (LSTM) short-term wind power. First, VMD is utilized to decompose the wind power input sequence into modal components of different complexities, and the components are reconstructed into subcomponents with typical characteristics through approximate entropy, which reduces the computational scale of non-smooth sequence analysis. Second, the GSWOA is used to optimize the main influencing parameters of the LSTM model in order to obtain the weights and thresholds under the optimal LSTM model and to use the reconstructed individual subsequences. Finally, the actual data from two wind farms in Xinjiang and Northeast China are taken to verify the generalizability of the proposed model. The comparative analysis of the prediction results under different scenarios demonstrates that the improved model shows higher performance than the original model.

1,2-Aminoxygenation of alkenes without extra oxidant is a practical yet challenging way to prepare β-amino alcohols. Here, the authors report an electro-oxidative route achieving such a goal with H2 evolution, exhibiting broad scope and application potential.

A novel magnetically-coupled energy storage inductor boost inverter circuit for renewable energy and the dual-mode control strategy with instantaneous value feedback of output voltage are proposed. In-depth research and analysis on the circuit, control strategy, voltage transmission characteristics, etc., providing the parameter design method of magnetically-coupled energy storage inductors and output filter. The circuit topology is cascaded by the input source ��in, the input filter ��in, a single-phase inverter bridge with a magnetically-coupled energy storage inductor, and a CL filter; The control strategy serves the output voltage as a reference to achieve the switch of step-down and step-up modes smoothly. The simulation results of a 1000 VA 100–200 VDC, 220 V 50 Hz AC inverter show that the proposed inverter can realize single-stage boost power conversion, which can adapt to resistive, capacitive and inductive loads, has high power density and low output waveform distortion. It has good application prospects in small and medium-capacity single-phase inverter occasions with low input voltage.

As the largest target in remote sensing images, buildings have important application value in urban planning and old city reconstruction. However, most networks have poor recognition ability on high resolution images, resulting in blurred boundaries in the segmented building maps. Then, the similarity between buildings and backgrounds will lead to inter-class indistinction. Finally, the diversity of buildings brings difficulties to segmentation, which requires the network to have better generalization ability. To address these problems, we propose Fusion Multi-scale Attention Mechanism Network (FMAM-Net). Firstly, we design Feature Refine Compensation Module(FRCM) to improve the boundary ambiguity problem, including Feature Refinement Module(FRM) and Feature Compensation Module(FCM). FRM utilizes the densely connected architecture to refine features and increase recognition capabilities. FCM introduces low-level features to make up for the lack of boundary information in high-level features. Secondly, to handle inter-class indistinction, we design Tandem Attention Module(TAM) and Parallel Attention Module(PAM). TAM is designed to sequentially filter some features from channels and spaces for adaptive feature refinement. PAM combines context information and uses high-level features to guide low-level features to select more distinguishable features. Finally, based on the binary cross entropy loss function, we add an evaluation index to reduce the error caused by determining the optimization direction only through cross entropy. On the Inria Aerial Image Labeling Dataset, FMAM-Net achieves mean IoU of 85.34%, which is 5.58% higher than AMUNet and 3.77% higher than our baseline(U-Net ResNet-34). On the WHU Dataset, IoU reached the maximum value of 91.06% on FMAM-Net, 1.67% higher than SARB-UNet and 0.2% higher than MAP-Net. The visualization results show that FMAM-Net improves the fuzzy boundary of building segmentation and reduces the inter-class indistinction.

During mild moxibustion treatment, uncertainties are involved in the operating parameters, such as the moxa-burning temperature, the moxa stick sizes, the stick-to-skin distance, and the skin moisture content. It results in fluctuations in skin surface temperature during mild moxibustion. Existing mild moxibustion treatments almost ignore the uncertainty of operating parameters. The uncertainties lead to excessive skin surface temperature causing intense pain, or over-low temperature reducing efficacy. Therefore, the interval model was employed to measure the uncertainty of the operation parameters in mild moxibustion, and the uncertainty optimization design was performed for the operation parameters. It aimed to provide the maximum thermal penetration of mild moxibustion to enhance efficacy while meeting the surface temperature requirements. The interval uncertainty optimization can fully consider the operating parameter uncertainties to ensure optimal thermal penetration and avoid patient discomfort caused by excessive skin surface temperature. To reduce the computational burden of the optimization solution, a high-precision surrogate model was established through a radial basis neural network (RBNN), and a nonlinear interval model for mild moxibustion treatment was formulated. By introducing the reliability-based possibility degree of interval (RPDI), the interval uncertainty optimization was transformed into a deterministic optimization problem, solved by the genetic algorithm. The results showed that this method could significantly improve the thermal penetration of mild moxibustion while meeting the skin surface temperature requirements, thereby enhancing efficacy.

An optical fiber sensor for the simultaneous measurement of microdisplacement and temperature based on balloon-shaped single-mode fibers cascaded with a fiber Bragg grating with two core-offset joints is proposed. The interference between the core mode and cladding mode is caused by the stimulation of the cladding mode by the core-offset joints’ structure. The cladding of the core has a distinct refractive index, which causes optical path differences and interference. The balloon-shaped structure realizes mode selection by bending. As the displacement increases, the radius of the balloon-shaped interferometer changes, resulting in a change in the interference fringes of the interferometer, while the Bragg wavelength of the fiber grating remains unchanged. Temperature changes will cause the interference fringes of the interferometer and the Bragg wavelength of the fiber grating to shift. The proposed optical fiber sensor allows for the simultaneous measurement of microdisplacement and temperature. The results of the experiment indicate that the sensitivity of the interferometer to microdisplacement is 0.306 nm/µm in the sensing range of 0 to 200 μm and that the temperature sensitivity is 0.165 nm/°C, respectively. The proposed curvature sensor has the advantages of a compact structure, extensive spectrum of dynamic measurement, high sensitivity, and simple preparation, and has a wide range of potential applications in the fields of structural safety monitoring, aviation industry, and resource exploration.

Fused silica is produced by melting high-purity silica and then rapidly cooling it. It has good physical and chemical properties, which also make it the raw material for most components in the optical industry, and is widely used in optical fiber communications, semiconductors, and aerospace. The content of SiO2 in fused silica is as high as 99.995% or more, but some impurities will still be generated during production and processing. The impurity elements of its optical curved surface can induce damage to the fused silica and adversely affect its performance, thereby affecting the performance of the fabricated element. In this paper, the impurities in the nanostructure of the fused silica optical surface are mainly analyzed, and the impurities on the surface are qualitatively and quantitatively analyzed by the characterization methods of secondary ion mass spectrometry (SIMS) and X-ray diffraction analysis, respectively. The main component type and content of impurities were measured. The experimental results have an important impact on the production and preparation of optical components using fused silica as raw materials, which can remove surface impurities in a targeted manner and better utilize the advantages of fused silica materials.

Nanosecond pulsed electric field (nsPEF) is a novel ablation technique that is based on high-intensity electric voltage to achieve tumour-killing effect in the target region, and increasingly considered for treating tumours of the liver, kidneys and other organs with rich blood supply. This study aims to observe effect of nsPFE treatment on serum metabolites and gut microbiota. The serum and faecal specimens of the pigs were collected pre- and post-treatment. The gut microbiota of pigs was sequenced by Illumina Miseq platform for analysing the diversity and alterations of gut microbiota. Liquid chromatography-mass spectrometry (LC-MS)-based metabonomic analysis and Pearson coefficient method were also used to construct the interaction system of different metabolites, metabolic pathways and flora. A total of 1,477 differential metabolites from the serum were identified by four cross-comparisons of different post-operative groups with the control group. In addition, an average of 636 OTUs per sample was detected. Correlation analysis also revealed the strong correlation between intestinal bacteria and differential metabolites. The nsPEF ablation of the liver results in a degree of liver damage that affects various metabolic pathways, mainly lipid metabolism, as well as gut microbiota. In conclusion, our study provided a good point for the safety and feasibility of applying nsPEF on liver through the integrated analysis of metabolomics and microbiomes, which is beneficial for the improvement of nsPEF in clinical use.

Objects: Inflammation is one of the hallmarks of cancer. Tumor-associated inflammatory response plays a crucial role in enhancing tumorigenesis. This study aimed to establish an effective predictive nomogram based on inflammation factors in patients with advanced non-small cell lung cancer (NSCLC). Methods: We retrospectively evaluated 887 patients with advanced NSCLC between November 2004 and December 2015 and randomly divided them into primary (n = 520) and validation cohorts (n = 367). Cox regression analysis was used to identify prognostic factors for building the nomogram. The predictive accuracy and discriminative ability of the nomogram were determined using a concordance index (C-index), calibration plot, and decision curve analysis and were compared to the TNM staging system. Results: The nomogram was established using independent risk factors ( P < 0.05): age, TNM stage, C reaction protein-to-albumin ratio (CAR), and neutrophils (NEU). The C-index of the model for predicting OS had a superior discrimination power compared to that of the TNM staging system both in the primary [0.711 (95% CI: 0.675-0.747) vs 0.531 (95% CI: 0.488-0.574), P < 0.01] and validation cohorts [0.703, 95% CI: 0.671 -0.735 vs 0.582, 95% CI: 0.545-0.619, P < 0.01]. Decision curves also demonstrated that the nomogram had higher overall net benefits than that of the TNM staging system. Subgroup analyses revealed that the nomogram was a favorable prognostic parameter in advanced NSCLC ( P < 0.05). The results were internally validated using the validation cohorts. Conclusions: The proposed nomogram with inflammatory factors resulted in an accurate prognostic prediction in patients with advanced NSCLC.

Introduction: The purpose of this study is to evaluate the diagnostic value of macrophage migration inhibitory factor in patients with nasopharyngeal carcinoma. Materials and Methods: The expression levels of macrophage migration inhibitory factor in nasopharyngeal carcinoma cell lines, tumor tissues, and plasma were measured by real-time polymerase chain reaction, Western blotting, enzyme-linked immunosorbent assay, and immunohistochemistry. Plasma Epstein-Barr virus viral capsid antigen was determined by immunoenzymatic techniques. Results: Both the messenger RNA and protein expression levels of macrophage migration inhibitory factor were upregulated in nasopharyngeal carcinoma cell lines and nasopharyngeal carcinoma tissues. Macrophage migration inhibitory factor in plasma was significantly elevated in patients with nasopharyngeal carcinoma compared to Epstein-Barr virus viral capsid antigen–negative and Epstein-Barr virus viral capsid antigen–positive healthy donors. The combination of macrophage migration inhibitory factor and Epstein-Barr virus viral capsid antigen was better for diagnosing nasopharyngeal carcinoma (area under receiver operating characteristic curve = 0.925, 95% CI: 0.898-0.951) than macrophage migration inhibitory factor (area under receiver operating characteristic curve = 0.778, 95% CI: 0.732-0.824) and Epstein-Barr virus viral capsid antigen. Combining macrophage migration inhibitory factor and Epstein-Barr virus viral capsid antigen had higher specificity (82.40% vs 69.96%) and higher positive predictive value (79.17% vs 67.44%) without an obvious reduction in sensitivity (95.25%) compared to Epstein-Barr virus viral capsid antigen alone. Macrophage migration inhibitory factor was highly expressed in nasopharyngeal carcinoma cell lines, whereas it was not associated with Epstein-Barr virus infection. The level of macrophage migration inhibitory factor in plasma was not related to the titer of Epstein-Barr virus viral capsid antigen. Conclusion: The combination of macrophage migration inhibitory factor and Epstein-Barr virus viral capsid antigen increases the specificity and positive predictive value of detecting nasopharyngeal carcinoma and improves the diagnostic accuracy of nasopharyngeal carcinoma in high-risk individuals.

We propose a two-phase Monte Carlo (TPMC) method to accelerate the original Monte Carlo (MC) method for constructing polar codes with high-dimensional kernels. In the TPMC method, some of the most reliable and unreliable bits are obtained by Gaussian approximate-density evolution (GA-DE) method in the first phase; in the second phase, these most reliable and unreliable bits are viewed as frozen bits. Then, the MC method are used to evaluate the remaining bits and select some best bits from the remaining bits. Finally, these best bits and the most reliable bits are combined as the information bits of the constructed polar code. By our investigation, most bits can be fixed as frozen bits in the second phase without error performance loss of the constructed polar codes. Because computation of frozen bits can be saved and computation of the GA-DE method can be ignored in contrast to that of the MC method, the TPMC method substantially reduces the complexity of the MC method. Simulation results show that 1) For a G⊗315 polar code with block length 3375 and code rate 1/2, the TPMC method can fix 3200 bits as frozen bits in the second phase without error performance loss, which reduces the computation cost by approximately 92.6% over the MC method; 2) Polar codes with high-dimensional kernels constructed by the TPMC method outperforms polar codes with the G2 kernel constructed by the Tal-Vardy method in terms of error performance; 3) With the same computational cost, the TPMC method can construct better polar codes than the MC method.

A method is proposed, called l-formula, to design efficient successive cancellation (SC) decoding of polar codes with medium-dimensional binary kernels (dimensions up to 16). Our l-formula method obtains the simplified recursive formulas of the SC decoder in the likelihood ratio domain for any binary kernel. We confirm that the complexity of the SC decoder based on l-formulas achieves considerable advantages over the straightforward SC decoder for polar codes with medium-dimensional binary kernels.

In cognitive radio networks, cooperative spectrum sensing (CSS) is generally adopted for improving spectrum sensing accuracy to increase spectrum utilization and avoid interference with the primary users. However, some malicious secondary users (SUs) may affect the CSS performance by inducing false observation bits for fusion. The message authentication code (MAC) is a promising technique to avoid the damage from the spectrum sensing data falsification (SSDF) attacks. In this paper, as both the more spectrum sensing nodes and the MAC reporting bits result in extra energy consumption, we propose an energy efficiency model to capture the effects of the length of MAC and the number of cooperative SUs under independent and collaborative SSDF attacks, respectively, and analyze the existence of the optimal length of MAC and the optimal number of cooperative SUs that can achieve the maximum value of energy efficiency, respectively. Simulation results are provided to show that the CSS scheme based on MAC can resist SSDF attacks and the accuracy of the theoretical analysis is also validated.

Conventional decoupling approaches usually employ first-order reliability method to deal with probabilistic constraints in a reliability-based design optimization problem. In first-order reliability method, constraint functions are transformed into a standard normal space. Extra non-linearity introduced by the non-normal-to-normal transformation may increase the error in reliability analysis and then result in the reliability-based design optimization analysis with insufficient accuracy. In this article, a decoupling approach is proposed to provide an alternative tool for the reliability-based design optimization problems. To improve accuracy, the reliability analysis is performed by first-order asymptotic integration method without any extra non-linearity transformation. To achieve high efficiency, an approximate technique of reliability analysis is given to avoid calculating time-consuming performance function. Two numerical examples and an application of practical laptop structural design are presented to validate the effectiveness of the proposed approach.

The energy management strategy has a great influence on the fuel economy of hybrid electric vehicles, and the equivalent consumption minimization strategy (ECMS) has proved to be a useful tool for the real-time optimal control of Hybrid Electric Vehicles (HEVs). However, the adaptation of the equivalent factor poses a major challenge in order to obtain optimal fuel consumption as well as robustness to varying driving cycles. In this paper, an adaptive-ECMS based on driving pattern recognition (DPR) is established for hybrid electric vehicles with continuously variable transmission. The learning vector quantization (LVQ) neural network model was adopted for the on-line DPR algorithm. The influence of the battery state of charge (SOC) on the optimal equivalent factor was studied under different driving patterns. On this basis, a method of adaptation of the equivalent factor was proposed by considering the type of driving pattern and the battery SOC. Besides that, in order to enhance drivability, penalty terms were introduced to constrain frequent engine on/off events and large variations of the continuously variable transmission (CVT) speed ratio. Simulation results showed that the proposed method efficiently improved the equivalent fuel consumption with charge-sustaining operations and also took into account driving comfort.

The conventional engineering robustness optimization approach considering uncertainties is generally based on a probabilistic model. However, a probabilistic model faces obstacles when handling problems with epistemic uncertainty. This paper presents an evidence-theory-based robustness optimization (EBRO) model and a corresponding algorithm, which provide a potential computational tool for engineering problems with multi-source uncertainty. An EBRO model with the twin objectives of performance and robustness is formulated by introducing the performance threshold. After providing multiple target belief measures (Bel), the original model is transformed into a series of sub-problems, which are solved by the proposed iterative strategy driving the robustness analysis and the deterministic optimization alternately. The proposed method is applied to three problems of micro-electromechanical systems (MEMS), including a micro-force sensor, an image sensor, and a capacitive accelerometer. In the applications, finite element simulation models and surrogate models are both given. Numerical results show that the proposed method has good engineering practicality due to comprehensive performance in terms of efficiency, accuracy, and convergence.

This paper presents a new latency reduction method for successive-cancellation (SC) decoding of polar codes that performs a frozen-bit checking on the rate-other (R-other) nodes of the Fast Simplified SC (Fast-SSC) pruning tree. The proposed method integrates the Fast-SSC algorithm and the Improved SSC method (frozen-bit checking of the R-other nodes). We apply a recognition-based method to search for as many constituent codes as possible in the decoding tree offline. During decoding, the current node can be decoded directly, if it is a special constituent code; otherwise, the frozen-bit check is executed. If the frozen-bit check condition is satisfied, the operation of the R-other node is the same as that of the rate-one node. In this paper, we prove that the frame error rate (FER) performance of the proposed algorithm is consistent with that of the original SC algorithm. Simulation results show that the proportion of R-other nodes that satisfy the frozen-bit check condition increases with the signal-to-noise-ratio (SNR). Importantly, our proposed method yields a significant reduction in latency compared to those given by existing latency reduction methods. The proposed method solves the problem of high latency for the Improved-SSC method at a high code rate and low SNR, simultaneously.

In this paper, the valence change and distribution of iron elements in octahedral vacancies of biotite were studied in the oxidation process. The biotite and saturated barium nitrate solution were mixed in dilute hydrochloric acid under hydrothermal reaction conditions, the solid after reaction was used as the test sample. Firstly, the remainder potassium and iron content were measured by atomic absorption spectrometry(AAS). Secondly, the state of iron along with oxidation degree increased was analyzed, in addition, the phase composition and the change of layer spacing in samples was detected by X-ray diffraction(XRD). Thirdly, The variation mode of Si-O bond were characterized by Fourier transform infrared spectroscopy(FT-IR).This research was adopted hydrogen ions in diluted hydrochloric acid and nitrate ions in barium nitrate to provide oxidation environment for reaction, and the oxidation degree was controlled by adjusted the amount of hydrogen ion introduced. We found out that the amount of hydrogen ion is positively correlated with oxidation degree in biotite, and the deeper oxidation degree in biotite, the lower electronegativity of singer layer. Potassium and iron element would be release out of micaceous structure during the biotite oxidation. The higher the oxidation degree is, the greater the releasing happen. The charge density combining Fe oxidation and releasing firstly increased then decreased with the oxidation degree turned greater. During the oxidation, the Si-O vibrated would change from parallel layer vibration model to vertical vibration model.