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To compare the screening value of the Reflux Symptom Score (RSS) and the Reflux Symptom Index (RSI) for laryngopharyngeal reflux (LPR).All included patients attending the Department of Otolaryngology at the Sixth Medical Center of the PLA General Hospital from February 2022 to August 2022, completed the RSS and the RSI and underwent 24-hour hypopharyngeal-esophageal multichannel intraluminal impedance-pH (24 h HEMII-pH) monitoring. The results of 24 h HEMII-pH were used as a diagnostic criterion for LPR, consistency between two questionnaires (RSS, RSI) and 24 h HEMII-pH was compared by the weighted Cohen's kappa statistic and the screening value of RSS and RSI for LPR was compared by receiver operating characteristics analysis.A total of 77 patients, 58 males, and 19 females, were included. The Kappa values of between RSS, RSI, and 24 h HEMII-pH were 0.663 (p 0.001) and 0.213 (p 0.05), respectively. The sensitivity of RSS and RSI were 92.8% and 48.2%, respectively, the specificity was 71.4% and 80.9%. The negative predictive value and positive predictive value of RSI were 36.9%, 87.1%, and that of RSS were 78.9%, 89.6%. The area under curve of the RSS (0.783; 95% CI = 0.53, 0.75) is significantly higher than RSI (0.633; 95% CI = 0.72, 0.90).The RSS has a higher consistency and better screening value for LPR compared to RSI. In addition, the RSS includes more reflux symptoms, which may reduce the number of missed diagnoses in patients with LPR to some extent.3 Laryngoscope, 2022.

To investigate the mutually relationship between gastroesophageal reflux disease (GERD) and laryngopharyngeal reflux disease (LPRD).All included patients completed simultaneous 24-hour hypopharyngeal intraluminal multichannel impedance pH monitoring (24h-MII-pH), Reflux Symptom Index (RSI), and Reflux Finding Score (RFS). The LPRD diagnosis was based on the occurrence of ≥1 acid or non-acid hypopharyngeal proximal reflux episode(HRE), GERD was defined as a length of time4.0% of the 24-hour recording spent below pH 4.0 or a DeMeester score14.72. Patients with both positive LPRD and GERD were classified as LPRDGERD group, patients with positive LPRD and negative GERD were classified as ILPRD group, patients with negative LPRD and positive GERD were classified as IGERD group, and patients with both negative LPRD and GERD were classified as N group. The differences in clinical characteristics of reflux between the groups were statistically analyzed.A total of 437 patients were included, including 248 (56.75%) in the ILPRD group, 98 (22.43%) in the LPRDGERD group, 23 (5.26%) in the IGERD group, and 68 (15.56%) in the N group. There was no significant difference between the types of gastroesophageal reflux in patients with GERD. The number of weak acid/acid/gas/liquid HREs was significantly more in LPRDGERD patients than in ILPRD patients (P0.01), and the number of distal acid reflux events and Longest distal acid clearance time were significantly higher in LPRDGERD patients than in IGERD patients (P0.01).GERD and LPRD are not the same disease but can mutually influence. Combined GERD increased all types of laryngopharyngeal reflux events in patients with LPRD, whereas combined LPRD only increased acidic distal reflux events and acid clearance time in patients with GERD.

To investigate the optimal time point for diagnosing laryngopharyngeal reflux (LPR) through combining 24-h hypopharyngeal-esophageal multichannel intraluminal impedance-pH (24-h HEMII-pH) monitoring and the multi-time point salivary pepsin test (MTPSPT).Prospective uncontrolled trial.Patients with and without LPR symptoms were included as the test group and the control group, respectively. The patients in the test group underwent 24-h HEMII-pH and MTPSPT. The results of 24-h HEMII-pH were used as a diagnostic criterion for LPR, and the diagnostic value of salivary pepsin tests performed at different time points was compared by receiver operating characteristic (ROC) analysis.A total of 153 patients were included. Based on 24-h HEMII-pH, the positive rate of LPR in the test group of patients was 84.00%. In the control group, only one person (3.57%) had a positive salivary pepsin test result. The area under the curve (AUC) of the MTPSPT was 0.827. In addition, we separately calculated the AUC of the combined salivary pepsin test at different time points, and found good diagnostic value (AUC = 0.799) when the test was combined with the waking, 1 and 2 h after breakfast and lunch, and 1 h after dinner tests. However, when the number of tests were further increased, the diagnostic value did not improve significantly.Salivary pepsin testing combined with waking, 1 h and 2 h after breakfast and lunch, and 1 h after dinner has almost the same diagnostic value as MTPSPT, and testing at these time points can be an effective method for diagnosing LPR.3 Laryngoscope, 2022.

To evaluate the necessity of multiple salivary pepsin tests within a day when diagnosing laryngopharyngeal reflux.Prospective cohort study.Tertiary hospitals.A total of 138 patients with signs and/or symptoms associated with laryngopharyngeal reflux were included. Salivary pepsin was detected on the day of 24-hour pH monitoring, and the results of salivary pepsin detected once in the morning and multiple times in 1 day were compared with the results of pH monitoring.Among the 138 patients, pH monitoring results were positive in 112. Salivary pepsin was positive in 47 cases in the morning, which was not consistent with the results of pH monitoring (kappa value = 0.117). With the pH monitoring results as the standard, the salivary pepsin detected once in the morning had a sensitivity of 38.4% (43/112) and a specificity of 84.6% (22/26) for the diagnosis of laryngopharyngeal reflux. When salivary pepsin was detected multiple times per day, 102 patients tested positive. The consistency with pH monitoring was moderate (kappa value = 0.587). The sensitivity was 86.6% (97/112), and the specificity was 80.8% (21/26). Of the 97 patients with positive results from pH monitoring and salivary pepsin detected multiple times a day, 54 had negative findings for a single detection in the morning, indicating that 55.7% (54/97) of the true positive cases were missed.Although a single detection of salivary pepsin in the morning is more economical, the sensitivity is too low, and it is necessary to detect it multiple times a day.

To investigate the profiles of laryngopharyngeal reflux (LPR) and the relationship between hypopharyngeal-proximal reflux episodes (HREs) and the positive rate of salivary pepsin testing in patients with vocal fold lesions (VFLs).The enrolled patients were divided into vocal fold cancer group (VFCG), vocal fold leukoplakia group (VFLG), benign vocal fold lesion group (BVFLG) and control group (CG). Patients benefited from multichannel intraluminal impedance-pH monitoring (MII-pH) and multi-time point salivary pepsin testing (MTPSPT). The LPR profiles of patients with VFLs were studied by analyzing the MII-pH findings. The relationship between HREs and positive rate of MTPSPT was investigated through the nonparametric test.177 patients were included. According to 24 h MII-pH, the occurrence of HREs tended to increase after meals. 55.75%, 63.98%, 66.82% and 55.77% of the HREs in the VFCG, VFLG, BVFLG and CG occurred within 3 h after meals, respectively. The overall positive rate of MTPSPT was higher in the VFCG than the remaining groups. In the VFCG, there was a significant correlation between overall positive results of MTPSPT and HREs occurring within 3 h after meals.In the Chinese population, the occurrence of HREs tend to more frequently after meals in patients with VFLs, and most HREs occur within 3 h after meals. By analyzing the 24h MII-pH findings, we can develop a more individualized anti-reflux therapeutic strategy for LPR patients.

To analyze the role of nonacid reflux in laryngopharyngeal reflux diseases (LPRD). From January 2014 to April 2019, 344 patients associated with LPRD underwent 24-h multichannel intraluminal impedance–pH monitoring, and their reflux symptom index (RSI) and reflux finding score (RFS) were recorded. The numbers of acid, weakly acidic and alkaline reflux events in the laryngopharynx were counted, and the consistency analysis of the results with the results of the RSI and RFS was conducted. Among the 344 patients, nonacid reflux events accounted for 74.1% (1367/1845) of the all reflux events. There were 111 patients with ≥ 3 acid reflux events, 218 patients with ≥ 3 any kinds of reflux events, and 257 patients with positive results of RSI or RFS. Taking the results of the RSI and or RFS as a reference, the sensitivity, specificity and consistency test Kappa value for the diagnosis of LPRD according to the existence of ≥ 3 acid reflux events were 41.2%, 94.2% and 0.228, respectively. With the existence of three or more all kinds of reflux events as the standard, the sensitivity, specificity and consistency test Kappa value were 76.7%, 74.7% and 0.449, respectively. The nonacid reflux events account for the highest proportion of laryngopharyngeal reflux events, and the consistency of the results of RSI and or RFS with all reflux events is higher than that with only acid reflux events, that indicates nonacid reflux may play an important role in LPRD.

This paper addresses task assignment (TA) for multivehicle systems. Multivehicle TA problems are formulated as a combinatorial optimization problem and further as a global optimization problem. To fulfill heterogeneous tasks, cooperation among heterogeneous vehicles is incorporated in the problem formulations. A collaborative neurodynamic optimization approach is developed for solving the TA problems. Experimental results on four types of TA problems are discussed to substantiate the efficacy of the approach.

Mitochondrial disease (MD) is genetically a heterogeneous group of disorders with impairment in respiratory chain complexes or pathways associated with the mitochondrial function. Nowadays, it is still a challenge for the genetic screening of MD due to heteroplasmy of mitochondrial genome and the complex model of inheritance. This study was designed to investigate the feasibility of whole exome sequencing (WES)-based testing as an alternative option for the diagnosis of MD.A Chinese Han cohort of 48 patients with suspect MD features was tested using nanoWES, which was a self-designed WES technique that covered the complete mtDNA genome and 21,019 nuclear genes. Fourteen patients were identified with a single genetic variant and three with single deletion in mtDNA.The heteroplasmy levels of variants in mitochondrial genome range from 11% to 100%. NanoWES failed to identify multiple deletions in mtDNA compared with long range PCR and massively parallel sequencing (LR-PCR/MPS). However, our testing showed obvious advantages in identifying variations in nuclear DNA. Based on nanoWES, we identified two patients with nuclear DNA variation. One of them showed Xp22.33-q28 duplication, which indicated a possibility of Klinefelter syndrome.NanoWES yielded a diagnostic rate of 35.4% for MD. With the rapid advances of next generation sequencing technique and decrease in cost, we recommend the usage of nanoWES as a first-line method in clinical diagnosis.

PURPOSE Laryngoscopy, the most common diagnostic method for vocal cord lesions (VCLs), is based mainly on the visual subjective inspection of otolaryngologists. This study aimed to establish a highly objective computer-aided VCLs diagnosis system based on deep convolutional neural network (DCNN) and transfer learning. METHODS To classify VCLs, our method combined DCNN backbone with transfer learning on a system specifically finetuned for a laryngoscopy image dataset. Laryngoscopy image database was collected to train the proposed system. The diagnostic performance was compared with other DCNN based model. Analysis of F1 score and receiver operating characteristic (ROC) curves were conducted to evaluate the performance of the system. RESULTS Beyond existing VCLs diagnosis method, the proposed system achieved an overall accuracy of 80.23%, an F1 score of 0.7836, and an AUC of 0.9557 for four fine-grained classes of VCLs, namely normal, polyp, keratinization, and carcinoma. It also demonstrated robust classification capacity for detecting urgent (keratinization, carcinoma) and non-urgent (normal, polyp), with an overall accuracy of 0.939, a sensitivity of 0.887, a specificity of 0.993, and an AUC of 0.9828. The proposed method also outperformed clinicians in the classification of normal, polyps, and carcinoma at an extremely low time cost. CONCLUSION The VCLs diagnosis system succeeded in using DCNN to distinguish the most common VCLs and normal cases, holding a practical potential for improving the overall diagnostic efficacy in VCLs examinations. The proposed VCLs diagnosis system could be appropriately integrated into the conventional work-flow of VCLs laryngoscopy as a highly objective auxiliary method. This article is protected by copyright. All rights reserved.

The effect of impingement jet on heat transfer in a rotating wedge-shaped ribbed trailing edge was experimentally investigated in this study. The inlet Reynolds number varied from 10000 to 61000, a...

In this paper, a method is presented for antidisturbance constrained control of autonomous underwater vehicles subject to uncertainties and constraints. The uncertainties stem from uncertain hydrodynamic parameters, modeling errors, and unknown forces due to the ocean currents in an underwater environment. An antidisturbance constrained controller is developed by designing a command governor and a disturbance observer. Specifically, the disturbance observer is developed to estimate the lumped disturbance composed of parametric model uncertainties, modeling errors, and unknown environmental forces. The command governor is designed for optimizing command signals in the receding horizon within the state and input constraints. The command governor is formulated as a quadratically constrained quadratic programming problem. To facilitate online implementations, a neurodynamic optimization method based on a one-layer recurrent neural network is employed for solving the quadratic optimization problem subject to inequality constraints with finite-time convergence. The efficacy of the proposed antidisturbance constrained control method for autonomous underwater vehicles is substantiated via simulations and comparisons.

With the development of green agriculture, the demand of farmers for operation loans is increasing. Supply chain finance is becoming a new way to solve the problem of difficult credit in agricultural development. As the importance of sustainability issues continues to rise, there are growing numbers of practical examples of combining agricultural supply chain finance (ASCF) with sustainability, and the attendant risks are emerging. The objectives of this study are first to construct a risk indicator system for sustainable ASCF, then to propose a fuzzy decision method that considers the confidence of decision-makers, and finally to perform a risk assessment of a credit case in the coffee bean supply chain. A combination of the neutrosophic enhanced best–worst method (NE-BWM) and combined compromise solution (COCOSO) is used to evaluate risk problems. The practicality and effectiveness of this research method is verified by a numerical simulation and a comparison with the method. The results show that the credit rating of core companies is the most important indicator. In the context of green and sustainable development, this indicator system is more suitable for the current green transformation development of agriculture and can help decision-makers scientifically and reasonably assess the risk level of ASCF. When loans are needed to transform green agriculture, this study provides new ideas for credit models for various actors in the agricultural supply chain and offers a new entry point to the issue of sustainable agricultural development.

This article presents a collaborative neurodynamic optimization (CNO) approach to multivehicle task assignments (TAs). The original combinatorial quadratic optimization problem for TA is reformulated as a quadratic unconstrained binary optimization (QUBO) problem with a quadratic utility function and a penalty function for handling load capacity and cooperation constraints. In the framework of CNO with a population of discrete Hopfield networks (DHNs), a TA algorithm is proposed for solving the formulated QUBO problem. Superior experimental results in four typical multivehicle operation scenarios are reported to substantiate the efficacy of the proposed neurodynamics-based TA approach.

This paper addresses the travelling salesman problem (TSP) based on collaborative neurodynamic optimization (CNO). In the CNO approach to TSP, a population of discrete Hopfield networks are employed for searching local optimal solutions and repeatedly reinitialized by using the particle swarm optimization rule towards a global optimal solution. Experimental results for solving four TSP benchmarks are reported to substantiate the efficacy of the CNO approach.

Flip chip technology has been rapidly developed and widely used in the field of microelectronic packaging, and defect detection has also received more and more attention. Aiming at the problem that noise affects the location and extraction of signal defects in ultrasonic testing, the sparse Bayesian learning based on generalized approximate message passing (GAMP-SBL) algorithm is used to extract signal defects, and the over-complete Gabor dictionary is used to reconstruct signal defects to effectively improve Sparse decomposition algorithm. The precision experiment tested the defect simulation signal and the actual ultrasonic signal respectively, and compared with the greedy algorithm. The experimental results show that the GAMP-SBL algorithm can more effectively extract the defect signal under the noise background.

This article presents a neurodynamic approach to nonlinear programming. Motivated by the idea of sequential quadratic programming, a class of two-timescale multilayer recurrent neural networks is presented with neuronal dynamics in their output layer operating at a bigger timescale than in their hidden layers. In the two-timescale multilayer recurrent neural networks, the transient states in the hidden layer(s) undergo faster dynamics than those in the output layer. Sufficient conditions are derived on the convergence of the two-timescale multilayer recurrent neural networks to local optima of nonlinear programming problems. Simulation results of collaborative neurodynamic optimization based on the two-timescale neurodynamic approach on global optimization problems with nonconvex objective functions or constraints are discussed to substantiate the efficacy of the two-timescale neurodynamic approach.

Backgound: Pepsin immunohistochemical (IHC) staining is a promising diagnostic approach of laryngopharyngeal reflux (LPR). The interarytenoid mucosa has been proved to be an effective biopsy area.O...

This article contains a new subnet training method for modular neural networks, proposed with the inspiration of the principle of “an expert with other capabilities”. The key point of this method is that a subnet learns the neighbor data sets while fulfilling its main task: learning the objective data set. Additionally, a relative distance measure is proposed to replace the absolute distance measure used in the classical subnet learning method and its advantage in the general case is theoretically discussed. Both methodology and empirical study of this new method are presented. Two types of experiments respectively related with the approximation problem and the prediction problem in nonlinear dynamic systems are designed to verify the effectiveness of the proposed method. Compared with the classical subnet learning method, the average testing error of the proposed method is dramatically decreased and more stable. The superiority of the relative distance measure is also corroborated.

Background Purposeful completion (fidelity) more than simple adherence to items in the surgical safety checklist may improve operating room efficiency and patient safety. The purpose of this study was to evaluate intraoperative delays and correlate them with adherence and fidelity to the preincision surgical safety checklist. Methods Trained observers evaluated surgical safety checklist compliance during 3 observation periods from 2014–2016. Degree of adherence, checkpoint verbalization, fidelity, and meaningful completion were assessed. Delays were categorized as missing or malfunctioning equipment, staff error, and medication issues. Descriptive statistics, analysis of variance, logistic regression, χ2 and Student t test were used to analyze results. Results Of the 591 cases observed, 19% (n = 110) had at least one documented, intraoperative delay. The majority of delays were related to missing (50%) or malfunctioning (30%) equipment. Compared with cases without delays, cases with delays did not have a different mean degree of adherence (96.3 ± 7.6% vs 95.6 ± 5.8%, P = .36). Degree of fidelity was different between cases with and without delays (mean fidelity 77.1 ± 14.9% vs 80.5 ± 7.14.2%, P = .03). Conclusion The preincision SSC is a communication tool offering an opportunity to discuss potential concerns and anticipated intraoperative needs. Fidelity rather than adherence to the surgical safety checklist seems to diminish intraoperative delays.

Impingement cooling is applied on the turbine blade leading edge which suffers the highest heat transfer and needs priority protection. The current work focuses on the rotational effects in an impingement cooling channel with film extraction, in which heat transfer is obtained with experiment whereas the flow field is predicted by numerical simulation. The dimensionless spacing of jet-to-jet (s/Dj) and jet-to-target surface (l/Dj) are both 3. The jet Reynolds number and the channel orientation (the angle between jet direction and rotating orientation) are 5,000 and 135°, respectively. The jet rotation number changes from 0 to 0.24, and the maximum jet buoyancy number reaches 0.57. The results show that the heat transfer on the suction side is better than that on the pressure side where the recirculation region generates. The heat transfer deteriorates from high to low radius due to the non-uniform mass flow rate distribution. Once the channel rotates, the non-uniformity of mass flow rate increases because the vortex occurs in the supply channel, resulting in the heat transfer increase by 140% in the low radius region on the pressure side. On the pressure side in the impingement channel, the rotation-induced secondary flow breaks the recirculation region, promoting the heat transfer. On the suction side, however, the secondary flow and rotation-driven jet deflection decrease the heat transfer. Besides, the jet buoyancy number is inappropriate to describe the combined influences of jet rotation number and wall-to-fluid temperature ratio at high jet buoyancy number in current experimental conditions.

Dynamic modularity is one of the fundamental characteristics of the human brain. Cooperative divide and conquer strategy is a basic problem solving approach. This chapter proposes a new subnet training method for modular neural networks with the inspiration of the principle of “an expert with other capabilities.” The key point of this method is that a subnet learns the neighbor data sets while fulfilling its main task: learning the objective data set. Additionally, a relative distance measure is proposed to replace the absolute distance measure used in the classical method and its advantage is theoretically discussed. Both methodology and empirical study are presented. Two types of experiments respectively related with the approximation problem and the prediction problem in nonlinear dynamic systems are designed to verify the effectiveness of the proposed method. Compared with the classical learning method, the average testing error is dramatically decreased and more stable. The superiority of the relative distance measure is also corroborated. Finally, a mind-gut frame is proposed.

This paper discusses dynamically weighted model predictive control based on two-timescale neurodynamic optimization. Minimax optimization problems with dynamic weights in objective functions are used in the model predictive control. The minimax optimization problems are solved by using a two-timescale neurodynamic optimization approach. Examples on controlling HVAC (heating, ventilation, and air-conditioning) and CSTR (cooling continuous stirred tank reactor) systems are elaborated to substantiate the efficacy of the control approach.

In this paper, neural networks based on linearly translated features (LTFs) are presented. LTFs including uniform, non-uniform, and multiple translation vectors are embedded into feedforward neural networks. Learning algorithms are presented for the neural networks. Learning capabilities of the neural networks are analyzed. Experimental results on approximation’ identification, and evaluation problems are reported to substantiate the efficacy of the neural networks and learning algorithms.

The heat transfer characteristics in a rectangular channel under rotating conditions with impingement jet and film holes was studied. The key features of the geometry are multiple impingement jets and film holes in the leading edge model. The jet Reynolds number (Rej), jet rotation number (Roj) and buoyancy number (Buo) varied from 5000 to 10,000, from 0 to 0.24, and from 0 to 0.57, respectively. Four temperature ratio (TR) cases (0.07, 0.10, 0.13, and 0.16) and three channel orientations (β = 90, 135, and 180) are selected. For all experiment cases, the spacing of jet-to-jet (s/d = 3) and jet-to-target surface (l/d = 3) were held constant. In the non-rotating cases, the heat transfer level of the T2 region (stagnation point), impinged by the jet directly, is the strongest and about twice to the other regions. The overall heat transfer distribution almost does not change significantly as the Rej varied from 5000 to 10,000. The jet rotation number plays an important role under the rotation state. For the orientation of 135°, the Coriolis force deflects the jet and weakens the heat transfer of the medium radius in the T2 region. No obvious variation in the heat transfer distribution for different temperature ratio under non-rotating and rotating cases was observed. The Coriolis force generated by different rotation directions causes two influences on the flow structure, leading to the different heat transfer distribution. The overall heat transfer level for the orientation of 90° and 135° varies little (5%) with the rotation number, while that of 180° decreases much (25%).

This paper addresses receding horizon control of a heating, ventilation, and air-conditioning (HVAC) system based on neurodynamic optimization. The receding horizon control problem for the HVAC system is formulated as sequential quadratic programs, which are solved by using a neurodynamic optimization model. Simulation results on temperature setpoint regulation of the HVAC system are discussed to substantiate the efficacy of the approach.

OBJECTIVE To observe the clinical effect of catheter-directed thrombolysis on deep venous thrombosis (DVT) of lower extremity after burn. STUDY DESIGN An observational study. PLACE AND DURATION OF STUDY Invasive Technology Department, Binzhou City Center Hospital, China, from January 2015 to November 2017. METHODOLOGY Eighty-two patients with lower extremity DVT after burn were selected as the study object. All patients received catheter-directed thrombolysis and the clinical efficacy was evaluated. The blood coagulation parameters including plasma prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrin original (FIB) and inflammatory response factors including interleukin (IL)-6, C-reactive protein (CRP) and D-dimer were compared before and after treatment. RESULTS All interventional surgeries that the patients received were successful. The effective rate of catheter-directed thrombolysis was 98.78% (81 cases). After 6 days of treatment, compared with that before treatment, serum PT, APTT and TT were up-regulated (all p

The degeneration mechanism of viscoelastic food material in specific processing mode affects the formulation of food material processing technology. On the other hand, it determines the taste of food in the chewing process. The viscoelastic food material was taken as the research object, and experimental data were obtained through stress relaxation experiments and strain relaxation experiments of texture analyzer material. Based on Maxwell model and Kelvin model, describing small deformation of the nonlinear viscoelastic constitutive model, building a composite model was proposed. By making analysis and comparison between constructed composite model and Maxwell model and Kelvin model, it was verified that the constructed composite model can be better described as the mechanical behavior of viscoelastic food material under the mode of compressed chewing, which is also providing a more precise theoretical model for the processing and development of viscoelastic food material.

This article addresses neurodynamics-based model predictive control of continuous-time under-actuated mechatronic systems. The control problem is formulated as a global optimization problem based on sampled data, which is solved by using a collaborative neurodynamic approach. The closed-loop system is proven to be asymptotically stable. Specific applications on control of autonomous surface vehicles and unmanned wheeled vehicles are elaborated to substantiate the efficacy of the approach.

In this paper, we design an adaptive iterative learning control method for a class of high-order nonlinear output feedback discrete-time systems with random initial conditions and iteration-varying desired trajectories. An n-step ahead predictor approach is employed to estimate future outputs. The discrete Nussbaum gain method is incorporated into the control design to deal with unknown control directions. The proposed control algorithm ensures that the tracking error converges to zero asymptotically along the iterative learning axis except for the beginning outputs affected by random initial conditions. A numerical simulation is carried out to demonstrate the efficacy of the presented control laws.

An adaptive iterative learning control method is proposed for a class of nonlinear strict-feedback discrete-time systems with random initial conditions and iteration-varying desired trajectories. An n-step ahead predictor approach is employed to estimate the future states in the control design. Discrete Nussbaum gain method is utilised to deal with the lack of a priori knowledge of control directions. The proposed control algorithm guarantees the boundedness of all the signals in the controlled system. The tracking error converges to zero asymptotically along the iterative learning axis except for beginning states affected by random initial conditions. The effectiveness of the proposed control scheme is verified through numerical simulation.

Background The debriefing phase of the surgical safety checklist (SSC) provides the operative team an opportunity to share pertinent intraoperative information and communicate postoperative plans. Prior quality improvement initiatives at our institution focused on the preincision phase of the SSC; however, the debriefing phase has not been evaluated. We aimed to assess adherence to the debrief checklist at our institution and identify areas for improvement. Materials and methods An observational study was conducted from 2014 to 2016 with a convenience sample of pediatric surgery cases at an academic children's hospital over 8-wk periods annually to evaluate the debriefing checklist across 14 subspecialties. Intraoperative team members' adherence to eight prespecified checkpoints was assessed. Descriptive statistics, Pearson's chi square, Kruskal–Wallis rank test, and Cohen's kappa for interrater reliability were used ( P Results A total of 603 cases were observed (2014 n = 191; 2015 n = 195; 2016 n = 217). The debriefing checklist was conducted in 90.6%, 90.3%, and 94.9% of observed cases each year respectively with the median number of checklist items completed relatively unchanged (8, 7, and 7, range 0-8). However, the checklist was only fully completed in 55%, 48%, and 50% of cases over the study period ( P = 0.001) with no debriefing at all in approximately 9% of cases in 2014 and 2015 versus 5% in 2016 ( P 0.65. Conclusions Despite slight increases annually in overall compliance to the debriefing checklist, only half of all checklists were completed in full. Future efforts to augment adherence are needed and will include interventions targeting the debriefing phase and increasing operating room efficiency.

Laryngopharyngeal reflux has become increasingly prevalent and received more and more attention. The "gold standard" for identifying and measuring laryngopharyngeal reflux disease is 24-hour multichannel intraluminal impedance pH monitoring. In this paper, we review the clinical application of this monitoring technology.

In this paper, a switching periodic adaptive control approach is proposed for a class of nonlinear systems with periodic parametric uncertainties whose period and bound are not known. A fully saturated periodic adaptation law is utilized to estimate the unknown parameter vector. A logic switching based algorithm is provided to tune the unknown period and bound of the parameter vector online. By virtue of Lyapunov energy function, asymptotic convergence can be ensured for the tracking error and all the signals in the system is guaranteed bounded. A simulation to a one-link robotic manipulator is carried out to demonstrate the effectiveness of the switching learning control algorithm.

This paper mainly aims at reducing relative learning complexity of subnets originate from “be Expert in Multiple aspects and Good at Many” (EMGM) modular neural network (MNN). Firstly, subnet learning algorithm which has a pure sequential execution style is built and convergence analysis is given. Secondly, in EMGM MNN system, an equivalent learning condition, which satisfies the criterion needed for the efficient training algorithm designed before, is founded for every subnet. Three identification problems have been involved to test the effectiveness and efficiency of the new framework in dealing with low dimension data. Both theoretical and experimental results show the new framework will reduce relative learning complexity of every subnet. The experiment result also shows new framework can achieve comparable generalization capability with original one. Furthermore, Bias Variance analysis shows maximum ability of generalization performance improvement of EMGM MNN may exist and the improvement comes from the improvement of bias estimation accuracy.

Objective: As a member of the S100 proteins family, the involvement of S100A11 has been suggested in a wide range of biological processes such as cell growth and motility, cell-cycle progression, transcription, differentiation and smooth muscle cell migration. However, the expression of S100A11 and its exact function in laryngeal squamous cell carcinoma (LSCC) have not been elucidated. Methods: The protein and mRNA expression levels of S100A11 were analyzed in primary tumors and matched tumor-adjacent tissues of LSCC by western blotting and semi-quantitative reverse transcription polymerase chain reaction (RT-PCR) or quantitative real time PCR (Q-RT-PCR), respectively. Cell proliferation, colony formation, migration and wound-healing assays were performed to assess whether the knockdown of S100A11 by small interfering RNA (siRNA) could influence the biological behavior of human laryngeal carcinoma Hep-2 cells in vitro. Results: We found that both protein and mRNA levels of S100A11 were overexpressed in laryngeal tumor tissues when compared to the corresponding noncancerous tissues. Further, it was demonstrated that the expression of S100A11 could induce migration but not proliferation of Hep-2 cells. Additionally, S100A11 altered a series of intracellular events, including the down-regulation of epidermal growth factor receptor (EGFR), CD44 and MMP2. Conclusions: These results highlight the significance of S100A11 in LSCC progression and suggest that the dysregulation of S100A11 might contribute to the metastatic progression of LSCC.

An adaptive iterative learning control method is developed for a class of high-order nonlinear output feedback discrete-time systems with random initial conditions and iteration-varying desired trajectories. The n-step ahead predictor approach is employed to estimate the future outputs. The discrete Nussbaum gain method is incorporated into the control design to deal with the lack of a priori knowledge of the control directions. The proposed control algorithm guarantees that the tracking error converges to zero asymptotically along the iterative learning axis except for the beginning outputs affected by the random initial conditions with all the signals in the controlled system bounded. A numerical simulation is carried out to demonstrate the effectiveness of the proposed control laws.

Based on the thought of """"to be expert in one aspect and good at many"""", a new training method of modular neural network (MNN) is presented. The key point of this method is a subnet learns the neighbor data sets while fulfiling its main task : learning the objective data set. Both methodology and empirical study of this new method are presented. Two examples (static approximation and nonlinear dynamic system pre-diction) are tested to show the new method's effectiveness: average testing error is dramatically decreased compared to original algorithm.