Transforming education:Developing a practice-ready workforce

The well-known question,why do people keep doing the same thing but expect a different result,applies to nursing education.Healthcare delivery systems are in constant evolution yet much of nursing education operates with traditional teaching approaches expecting to produce graduates suitable for rapidly changing healthcare delivery systems.Nursing is a practice-based discipline with its own body of knowledge situated within the context of dy-namic nursing practice.It is the responsibility of nursing academia to prepare graduates suitable for practice.If we are to change prac-tice,we must rethink education.The impact and interruptions of the global COVID-19 pandemic gave rise to questions on best prac-tices to guide nursing education into the future.

Kinematic calibration under the expectation maximization framework for exoskeletal inertial motion capture system

This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79%and 7.16%respectively in comparison to the traditional calibration method.

Optimization of Generator Based on Gaussian Process Regression Model with Conditional Likelihood Lower Bound Search

The noise that comes from finite element simulation often causes the model to fall into the local optimal solution and over fitting during optimization of generator.Thus,this paper proposes a Gaussian Process Regression(GPR)model based on Conditional Likelihood Lower Bound Search(CLLBS)to optimize the design of the generator,which can filter the noise in the data and search for global optimization by combining the Conditional Likelihood Lower Bound Search method.Taking the efficiency optimization of 15 kW Permanent Magnet Synchronous Motor as an example.Firstly,this method uses the elementary effect analysis to choose the sensitive variables,combining the evolutionary algorithm to design the super Latin cube sampling plan;Then the generator-converter system is simulated by establishing a co-simulation platform to obtain data.A Gaussian process regression model combing the method of the conditional likelihood lower bound search is established,which combined the chi-square test to optimize the accuracy of the model globally.Secondly,after the model reaches the accuracy,the Pareto frontier is obtained through the NSGA-II algorithm by considering the maximum output torque as a constraint.Last,the constrained optimization is transformed into an unconstrained optimizing problem by introducing maximum constrained improvement expectation(CEI)optimization method based on the re-interpolation model,which cross-validated the optimization results of the Gaussian process regression model.The above method increase the efficiency of generator by 0.76%and 0.5%respectively;And this method can be used for rapid modeling and multi-objective optimization of generator systems.

Lottery Numbers and Ordered Statistics

The lottery has long captivated the imagination of players worldwide, offering the tantalizing possibility of life-changing wins. While winning the lottery is largely a matter of chance, as lottery drawings are typically random and unpredictable. Some people use the lottery terminal randomly generates numbers for them, some players choose numbers that hold personal significance to them, such as birthdays, anniversaries, or other important dates, some enthusiasts have turned to statistical analysis as a means to analyze past winning numbers identify patterns or frequencies. In this paper, we use order statistics to estimate the probability of specific order of numbers or number combinations being drawn in future drawings.

Parental Educational Expectations,Academic Pressure,and Adolescent Mental Health:An Empirical Study Based on CEPS Survey Data

Background:This study aimed to investigate the relationship between parental educational expectations and adolescent mental health problems,with academic pressure as a moderating variable.Methods:This study was based on the baseline data of the China Education Panel Survey,which was collected within one school year during 2013–2014.It included 19,958 samples from seventh and ninth graders,who ranged from 11 to 18 years old.After removing missing values and conducting relevant data processing,the effective sample size for analysis was 16344.The OLS(Ordinary Least Squares)multiple linear regression analysis was used to examine the relationship between parental educational expectations,academic pressure,and adolescents’mental health problems.In addition,we established an interaction term between parents’educational expectations and academic pressure to investigate the moderating effect of academic stress.Results:The study found that adolescents whose parents had high educational expectations reported less mental health problems.(β=−0.195;p<0.001).Additionally,adolescents who had high academic pressure reported more mental health problems.(β=0.649;p<0.001).Furthermore,the study found that academic pressure had a significant moderating effect on the relationship between parental educational expectations and adolescents’mental health problems(β=0.082;p<0.001).Conclusion:Parental educational expectations had a close relationship with adolescents’mental health problems,and academic pressure moderated this relationship.For those adolescents with high levels of academic pressure,the association between high parental educational expectations and mental health problems became stronger.On the contrary,for those adolescents with low levels of academic pressure,the association between high parental educational expectations and mental health problems became weaker.These findings shed new light on how parental educational expectations affected adolescent mental health problems and had significant implications for their healthy development.

Exact controllability of rational expectations model with multiplicative noise and input delay

This paper considers the rational expectations model with multiplicative noise and input delay,where the system dynamics rely on the conditional expectations of future states.The main contribution is to obtain a sufficient condition for the exact controllability of the rational expectations model.In particular,we derive a sufficient Gramian matrix condition and a rank condition for the delay-free case.The key is the solvability of the backward stochastic difference equations with input delay which is derived from the forward and backward stochastic system.

Modeling Expected Failure Considering Repair Time and Degradation: A Rail System Case Study

The repeated failures of any equipment or systems are modeled as a renewal process. The management needs an assessment of the number of future failures to allocate the resources needed for fast repairs. Based on the idea of expectation by conditioning, special Volterra-type integral equations are derived for general types of repairs, considering the length of repair and reduced degradation of the idle object. In addition to minimal repair and failure replacement, partial repairs are also discussed when the repair results in reduction of the number of future failures or decreases the effective age of the object. Numerical integration-based algorithm and simulation study are performed to solve the resulting integral equation. Since the geometry degradation in different dimensions of a rail track and controlling and maintaining defects are of importance, a numerical example using the rail industry data is conducted. Expected number of failures of different failure type modes in rail track is calculated within a two-year interval. Results show that within a two-year period, anticipated occurrences of cross level failures, surface failures, and DPI failures are 2.4, 3.8, and 5.8, respectively.

Research Progress and Prospects of Magnetic Nanomaterials

Nanomaterials are one of the research and development hotspots in the field of cutting-edge new materials,and also an important strategic emerging industry.Magnetic nanomaterials have broad application prospects in fields such as chemical engineering,new materials,electronic information,and biomedicine.This article introduces the application progress and preparation methods of magnetic nanomaterials,and puts forward suggestions for further optimizing the preparation process of magnetic nanomaterials and developing new magnetic materials with better performance.

Exceeding Expectations

The Chinese economy continued to rebound and made solid progress in 2023,despite the complex international situation and numerous risks and challenges.The accelerated economic structural transformation and upgrade,coupled with increasing economic resilience,has resulted in various highlights.

Vector Approximate Message Passing with Sparse Bayesian Learning for Gaussian Mixture Prior

Compressed sensing(CS)aims for seeking appropriate algorithms to recover a sparse vector from noisy linear observations.Currently,various Bayesian-based algorithms such as sparse Bayesian learning(SBL)and approximate message passing(AMP)based algorithms have been proposed.For SBL,it has accurate performance with robustness while its computational complexity is high due to matrix inversion.For AMP,its performance is guaranteed by the severe restriction of the measurement matrix,which limits its application in solving CS problem.To overcome the drawbacks of the above algorithms,in this paper,we present a low complexity algorithm for the single linear model that incorporates the vector AMP(VAMP)into the SBL structure with expectation maximization(EM).Specifically,we apply the variance auto-tuning into the VAMP to implement the E step in SBL,which decrease the iterations that require to converge compared with VAMP-EM algorithm when using a Gaussian mixture(GM)prior.Simulation results show that the proposed algorithm has better performance with high robustness under various cases of difficult measurement matrices.

A Novel Adaptive Kalman Filter Based on Credibility Measure

It is quite often that the theoretic model used in the Kalman filtering may not be sufficiently accurate for practical applications,due to the fact that the covariances of noises are not exactly known.Our previous work reveals that in such scenario the filter calculated mean square errors(FMSE)and the true mean square errors(TMSE)become inconsistent,while FMSE and TMSE are consistent in the Kalman filter with accurate models.This can lead to low credibility of state estimation regardless of using Kalman filters or adaptive Kalman filters.Obviously,it is important to study the inconsistency issue since it is vital to understand the quantitative influence induced by the inaccurate models.Aiming at this,the concept of credibility is adopted to discuss the inconsistency problem in this paper.In order to formulate the degree of the credibility,a trust factor is constructed based on the FMSE and the TMSE.However,the trust factor can not be directly computed since the TMSE cannot be found for practical applications.Based on the definition of trust factor,the estimation of the trust factor is successfully modified to online estimation of the TMSE.More importantly,a necessary and sufficient condition is found,which turns out to be the basis for better design of Kalman filters with high performance.Accordingly,beyond trust factor estimation with Sage-Husa technique(TFE-SHT),three novel trust factor estimation methods,which are directly numerical solving method(TFE-DNS),the particle swarm optimization method(PSO)and expectation maximization-particle swarm optimization method(EM-PSO)are proposed.The analysis and simulation results both show that the proposed TFE-DNS is better than the TFE-SHT for the case of single unknown noise covariance.Meanwhile,the proposed EMPSO performs completely better than the EM and PSO on the estimation of the credibility degree and state when both noise covariances should be estimated online.

The study of a neutron spectrum unfolding method based on particle swarm optimization combined with maximum likelihood expectation maximization

The neutron spectrum unfolding by Bonner sphere spectrometer(BSS) is considered a complex multidimensional model,which requires complex mathematical methods to solve the first kind of Fredholm integral equation. In order to solve the problem of the maximum likelihood expectation maximization(MLEM) algorithm which is easy to suffer the pitfalls of local optima and the particle swarm optimization(PSO) algorithm which is easy to get unreasonable flight direction and step length of particles, which leads to the invalid iteration and affect efficiency and accuracy, an improved PSO-MLEM algorithm, combined of PSO and MLEM algorithm, is proposed for neutron spectrum unfolding. The dynamic acceleration factor is used to balance the ability of global and local search, and improves the convergence speed and accuracy of the algorithm. Firstly, the Monte Carlo method was used to simulated the BSS to obtain the response function and count rates of BSS. In the simulation of count rate, four reference spectra from the IAEA Technical Report Series No. 403 were used as input parameters of the Monte Carlo method. The PSO-MLEM algorithm was used to unfold the neutron spectrum of the simulated data and was verified by the difference of the unfolded spectrum to the reference spectrum. Finally, the 252Cf neutron source was measured by BSS, and the PSO-MLEM algorithm was used to unfold the experimental neutron spectrum.Compared with maximum entropy deconvolution(MAXED), PSO and MLEM algorithm, the PSO-MLEM algorithm has fewer parameters and automatically adjusts the dynamic acceleration factor to solve the problem of local optima. The convergence speed of the PSO-MLEM algorithm is 1.4 times and 3.1 times that of the MLEM and PSO algorithms. Compared with PSO, MLEM and MAXED, the correlation coefficients of PSO-MLEM algorithm are increased by 33.1%, 33.5% and 1.9%, and the relative mean errors are decreased by 98.2%, 97.8% and 67.4%.

Iterative Receiver for Orthogonal Time Frequency Space with Index Modulation via Structured Prior-Based Hybrid Belief and Expectation Propagation

Orthogonal Time Frequency Space(OTFS)signaling with index modulation(IM)is a promising transmission scheme characterized by high transmission efficiency for high mobility scenarios.In this paper,we study the receiver for coded OTFS-IM system.First,we construct the corresponding factor graph,on which the structured prior incorporating activation pattern constraint and channel coding is devised.Then we develop a iterative receiver via structured prior-based hybrid belief propagation(BP)and expectation propagation(EP)algorithm,named as StrBP-EP,for the coded OTFS-IM system.To reduce the computational complexity of discrete distribution introduced by structured prior,Gaussian approximation conducted by EP is adopted.To further reduce the complexity,we derive two variations of the proposed algorithm by using some approximations.Simulation results validate the superior performance of the proposed algorithm.

JONES TYPE C^(*)-BASIC CONSTRUCTION IN NON-EQUILIBRIUM HOPF SPIN MODELS

Let H be a finite dimensional Hopf C^(*)-algebra,and let K be a Hopf^(*)-subalgebra of H.Considering that the field algebra■K of a non-equilibrium Hopf spin model carries a D(H,K)-invariant subalgebra ■K,this paper shows that the C^(*)-basic construction for the inclusion ■K×■K can be expressed as the crossed product C^(*)-algebra■KD(H,K).Here,D(H,K)is a bicrossed product of the opposite dual H^(op) and K.Furthermore,the natural action of D(H,K)on D(H,K)gives rise to the iterated crossed product■KD(H,K)×D(H,K),which coincides with the C^(*)-basic construction for the inclusion■K×■KD(H,K).In the end,the Jones type tower of field algebra■Kis obtained,and the new field algebra emerges exactly as the iterated crossed product.

Behavioral Decision-Making of Key Stakeholders in Public-Private Partnerships:A Hybrid Method and Benefit Distribution Study

Public-private partnerships(PPPs)have been used by governments around the world to procure and construct infrastructural amenities.It relies on private sector expertise and funding to achieve this lofty objective.However,given the uncertainties of project management,transparency,accountability,and expropriation,this phenomenon has gained tremendous attention in recent years due to the important role it plays in curbing infrastructural deficits globally.Interestingly,the reasonable benefit distribution scheme in a PPP project is related to the behavior decisionmaking of the government and social capital,aswell as the performance of the project.In this paper,the government and social capital which are the key stakeholders of PPP projects were selected as the research objects.Based on the fuzzy expected value model and game theory,a hybrid method was adopted in this research taking into account the different risk preferences of both public entities and private parties under the fuzzy demand environment.To alleviate the problem of insufficient utilization of social capital in a PPP project,this paper seeks to grasp the relationship that exists between the benefit distribution of stakeholders,their behavioral decision-making,and project performance,given that they impact the performance of both public entities and private parties,as well as assist in maximizing the overall utility of the project.Furthermore,four game models were constructed in this study,while the expected value and opportunity-constrained programming model for optimal decision-making were derived using alternate perspectives of both centralized decision-making and decentralized decision-making.Afterward,the optimal behavioral decision-making of public entities and private parties in four scenarios was discussed and thereafter compared,which led to an ensuing discussion on the benefit distribution system under centralized decision-making.Lastly,based on an example case,the influence of different confidence levels,price,and fuzzy uncertainties of PPP projects on the equilibrium strategy results of both parties were discussed,giving credence to the effectiveness of the hybrid method.The results indicate that adjusting different confidence levels yields different equilibriumpoints,and therefore signposts that social capital has a fair perception of opportunities,as well as identifies reciprocal preferences.Nevertheless,we find that an increase in the cost coefficient of the government and social capital does not inhibit the effort of both parties.Our results also indicate that a reasonable benefit distribution of PPP projects can assist them in realizing optimum Pareto improvements over time.The results provide us with very useful strategies and recommendations to improve the overall performance of PPP projects in China.

Dimensionality reduction model based on integer planning for the analysis of key indicators affecting life expectancy

Purpose:Exploring a dimensionality reduction model that can adeptly eliminate outliers and select the appropriate number of clusters is of profound theoretical and practical importance.Additionally,the interpretability of these models presents a persistent challenge.Design/methodology/approach:This paper proposes two innovative dimensionality reduction models based on integer programming(DRMBIP).These models assess compactness through the correlation of each indicator with its class center,while separation is evaluated by the correlation between different class centers.In contrast to DRMBIP-p,the DRMBIP-v considers the threshold parameter as a variable aiming to optimally balances both compactness and separation.Findings:This study,getting data from the Global Health Observatory(GHO),investigates 141 indicators that influence life expectancy.The findings reveal that DRMBIP-p effectively reduces the dimensionality of data,ensuring compactness.It also maintains compatibility with other models.Additionally,DRMBIP-v finds the optimal result,showing exceptional separation.Visualization of the results reveals that all classes have a high compactness.Research limitations:The DRMBIP-p requires the input of the correlation threshold parameter,which plays a pivotal role in the effectiveness of the final dimensionality reduction results.In the DRMBIP-v,modifying the threshold parameter to variable potentially emphasizes either separation or compactness.This necessitates an artificial adjustment to the overflow component within the objective function.Practical implications:The DRMBIP presented in this paper is adept at uncovering the primary geometric structures within high-dimensional indicators.Validated by life expectancy data,this paper demonstrates potential to assist data miners with the reduction of data dimensions.Originality/value:To our knowledge,this is the first time that integer programming has been used to build a dimensionality reduction model with indicator filtering.It not only has applications in life expectancy,but also has obvious advantages in data mining work that requires precise class centers.

Complete moment convergence for ND random variables under the sub-linear expectations

In this article,we establish a general result on complete moment convergence for arrays of rowwise negatively dependent(ND)random variables under the sub-linear expectations.As applications,we can obtain a series of results on complete moment convergence for ND random variables under the sub-linear expectations.

Experimental Evaluation of Trilateration-Based Outdoor Localizationwith LoRaWAN

Long Range Wide Area Network (LoRaWAN) in the Internet ofThings (IoT) domain has been the subject of interest for researchers. Thereis an increasing demand to localize these IoT devices using LoRaWAN dueto the quickly growing number of IoT devices. LoRaWAN is well suited tosupport localization applications in IoTs due to its low power consumptionand long range. Multiple approaches have been proposed to solve the localizationproblem using LoRaWAN. The Expected Signal Power (ESP) basedtrilateration algorithm has the significant potential for localization becauseESP can identify the signal’s energy below the noise floor with no additionalhardware requirements and ease of implementation. This research articleoffers the technical evaluation of the trilateration technique, its efficiency,and its limitations for the localization using LoRa ESP in a large outdoorpopulated campus environment. Additionally, experimental evaluations areconducted to determine the effects of frequency hopping, outlier removal, andincreasing the number of gateways on localization accuracy. Results obtainedfrom the experiment show the importance of calculating the path loss exponentfor every frequency to circumvent the high localization error because ofthe frequency hopping, thus improving the localization performance withoutthe need of using only a single frequency.

The Effects of Job Insecurity, Emotional Exhaustion, and Met Expectations on Hotel Employees’ Pro-Environmental Behaviors: Test of a Serial Mediation Model

There are a plethora of empirical pieces about employees’pro-environmental behaviors.However,the extant literature has either ignored or not fully examined various factors(e.g.,negative or positive non-green workplace factors)that might affect employees’pro-environmental behaviors.Realizing these voids,the present paper proposes and tests a serial mediation model that examines the interrelationships of job insecurity,emotional exhaustion,met expectations,and proactive pro-environmental behavior.We used data gathered from hotel customer-contact employees with a time lag of one week and their direct supervisors in China.After presenting support for the psychometric properties of the measures via confirmatory analysis in LISREL 8.30,the abovementioned linkages were gauged using the PROCESS plug-in for statistical package for social sciences.The findings delineated support for the hypothesized associations.Specifically,emotional exhaustion and met expectations partly mediated the effect of job insecurity on proactive pro-environmental behavior.More importantly,emotional exhaustion and met expectations serially mediated the influence of job insecurity on proactive pro-environmental behavior.These findings have important theoretical implications as well as significant implications for diminishing job insecurity,managing emotional exhaustion,increasing met expectations,and enhancing ecofriendly behaviors.

Risk factors for anastomotic fistula development after radical colon cancer surgery and their impact on prognosis

BACKGROUND Colon cancer is a common malignant tumor in the gastrointestinal tract that is typically treated surgically.However,postradical surgery is prone to complic-ations such as anastomotic fistulas.AIM To investigate the risk factors for postoperative anastomotic fistulas and their impact on the prognosis of patients with colon cancer.METHODS We conducted a retrospective analysis of 488 patients with colon cancer who underwent radical surgery.This study was performed between April 2016 and April 2019 at a tertiary hospital in Wuxi,Jiangsu Province,China.A t-test was used to compare laboratory indicators between patients with and those without postoperative anastomotic fistulas.Multiple logistic regression analysis was performed to identify independent risk factors for postoperative anastomotic fistulas.The Functional Assessment of Cancer Therapy-Colorectal Cancer was also used to assess postoperative recovery.RESULTS Binary logistic regression analysis revealed that age[odds ratio(OR)=1.043,P=0.015],tumor,node,metastasis stage(OR=2.337,P=0.041),and surgical procedure were independent risk factors for postoperative anastomotic fistulas.Multiple linear regression analysis showed that the development of postoperative anastomotic fistula(P=0.000),advanced age(P=0.003),and the presence of diabetes mellitus(P=0.015),among other factors,independently affected CONCLUSION Postoperative anastomotic fistulas significantly affect prognosis and survival rates.Therefore,focusing on the clinical characteristics and risk factors and immediately implementing individualized preventive measures are important to minimize their occurrence.