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When the nursing department of our hospital checked the ward round records of the head nurse, it was found that some problems could not be found during rounds, and most of the problems found were superficial; Some studies have also found that most nursing managers are only going through the motions during rounds or are unable to identify problems due to a mismatch between their professional competence and the supervised department, or fail to identify superficial or deep-seated problems, making the quality of nursing supervision unsatisfactory. To sort out the ideas and ways for nursing managers to identify problems in on-site supervision of nursing quality and to provide ideas and directions for being able to identify problems, identifying problems quickly, and identifying high-quality problems in the supervision. A descriptive qualitative study was used to conduct face-to-face semi-structured interviews with 14 nursing managers from Bethune Hospital in Shanxi, China. The current problems of nursing quality supervision were combined with the 5W1H analysis method to form an interview outline, and a total of 8 questions were set up to interview 14 nursing managers to understand how nursing managers identified problems in supervision. Thematic analysis was used to analyze the data and extract themes. Three views and six ways of problem identification were crystallized: three views were direct access, comparison discovery, and deep excavation; and six ways were listening, looking, asking, checking, visiting, and tracking. The \"Pathways for identifying problems in nursing quality on-site supervision\" was finally formed. The pathway for identifying problems in nursing quality supervision is scientific, reliable, and can be used as a guideline for identifying problems in nursing quality supervision. The connotation of nursing quality supervision can be improved through this pathway, and the development of nursing discipline can be promoted. The \"Pathways for identifying problems in nursing quality on-site supervision\" is a clear pathway that helps nursing managers to quickly identify problems, especially during interdepartmental nursing quality inspections.

A standard approach to estimating structural parameters in life-cycle models imposes sufficient assumptions on the data to identify the 'age profile' of outcomes, then chooses model parameters so that the model's age profile matches this empirical age profile. I show that this approach is both incorrect and unnecessary: incorrect, because it generally produces inconsistent estimators of the structural parameters, and unnecessary, because consistent estimators can be obtained under weaker assumptions. I derive an estimation method that avoids the problems of the standard approach. I illustrate the method's benefits analytically in a simple model of consumption inequality and numerically by reestimating the classic life-cycle consumption model of Gourinchas and Parker (2002).

In many different fields, social scientists desire to understand temporal variation associated with age, time period, and cohort membership. Among methods proposed to address the identification problem in age-period-cohort analysis, the intrinsic estimator (IE) is reputed to impose few assumptions and to yield good estimates of the independent effects of age, period, and cohort groups. This article assesses the validity and application scope of IE theoretically and illustrates its properties with simulations. It shows that IE implicitly assumes a constraint on the linear age, period, and cohort effects. This constraint not only depends on the number of age, period, and cohort categories but also has nontrivial implications for estimation. Because this assumption is extremely difficult, if not impossible, to verify in empirical research, IE cannot and should not be used to estimate age, period, and cohort effects.

The slime mold algorithm (SMA) is a metaheuristic optimization algorithm that simulates the foraging behavior of slime molds. Compared to other optimization algorithms, SMA has fewer parameters, faster convergence speed, and stronger optimization capabilities. However, the standard SMA uses two randomly selected individuals to guide the search direction of the population, which results in excessive randomness during the search process. This can lead to the loss of valuable information and waste computational resources. To overcome these limitations, this study proposes an enhanced slime mold algorithm (S2SMA) based on a spiral sensing search mechanism. The main contributions of this study are as follows: Firstly, a fitness–distance balanced oscillation search mechanism is introduced to solve the issue of lack of guidance in the individual oscillatory search phase in the original SMA, thus enhancing the global exploration ability of the algorithm. Secondly, the spiral sensing search mechanism is introduced, reshaping the random redistribution behavior in SMA. This aims to fully utilize the effective information in the existing population, improve search efficiency, and enhance population diversity. Finally, the computational logic of SMA is restructured based on the existing parameters, improving the algorithm’s performance while avoiding additional computational overhead. To validate the effectiveness of the proposed S2SMA, experiments were conducted on 71 test instances from the IEEE CEC2017 and IEEE CEC2021 benchmark sets, as well as three engineering problems. The algorithm was compared with classical algorithms, high‐performance algorithms, and advanced SMA variants. Experimental results show that S2SMA outperforms the classical algorithms, high‐performance algorithms, and other SMA variants in terms of both performance and robustness, demonstrating its potential application in engineering optimization.

As a prerequisite and foundation for production and manufacturing, the design of product manufacturing process plays a crucial role in improving production efficiency, controlling resource consumption, shortening production cycles, and reducing processing costs. With the rapid development of advanced manufacturing technologies, product manufacturing process design is increasingly characterized by ambiguity, multi-solution possibilities, and cross-disciplinary integration, posing higher demands on enterprises’ capabilities in process design and innovation. To identify and innovatively address these issues within complex product manufacturing process, this study proposes a problem-driven innovation design strategy model for product manufacturing process. In the problem identification phase, opportunities are identified and categorized based on the product context and manufacturing process, followed by problem identification and the construction of problem elements using problem analysis methods. After identifying the problems, the type of innovation required is determined, and corresponding methods are applied to generate a set of solutions with innovation potential. These solutions are then evaluated and validated to determine the optimal innovation design for the product manufacturing process. If a solution fails validation, the problem is re-identified or re-solved, and the above steps are repeated until a validated final solution is achieved. Finally, the feasibility of the proposed strategy is demonstrated through a case study on the innovation design of the wax pattern manufacturing process for gas turbine blades.

For more than a century, researchers from a wide range of disciplines have sought to estimate the unique contributions of age, period, and cohort (APC) effects on a variety of outcomes. A key obstacle to these efforts is the linear dependence among the three time scales. Various methods have been proposed to address this issue, but they have suffered from either ad hoc assumptions or extreme sensitivity to small differences in model specification. After briefly reviewing past work, we outline a new approach for identifying temporal effects in population-level data. Fundamental to our framework is the recognition that it is only the slopes of an APC model that are unidentified, not the nonlinearities or particular combinations of the linear effects. One can thus use constraints implied by the data along with explicit theoretical claims to bound one or more of the APC effects. Bounds on these parameters may be nearly as informative as point estimates, even with relatively weak assumptions. To demonstrate the usefulness of our approach, we examine temporal effects in prostate cancer incidence and homicide rates. We conclude with a discussion of guidelines for further research on APC effects.

This paper identifies a source term depending on spatial variable in a heat equation from just part of the boundary conditions. The measurement data are specified at an internal moment of time. The ill-posedness of the problem is higher than most of the previous source identification problems. This is because the problem becomes a noncharacteristic Cauchy problem for the heat equation if the source term is given, which is known as severely ill-posed. The method of fundamental solutions (MFS) in conjunction with the classical Tikhonov regularization method is proposed to reconstruct a stable approximation. The fundamental solutions for the heat equation are spherically symmetric in spatial variable and satisfy the equation automatically, and thus only the boundary conditions need to be satisfied. This characteristic allows the discretization to be performed only on boundary-like geometry and improve the computational efficiency. In this paper, several numerical examples are listed to show the feasibility and effectiveness of the suggested method.

In recent years, a considerable amount of effort has been devoted, both in industry and academia, towards the development of advanced methods of control theory with focus on its practical implementation in various fields of human activity such as space control, robotics, control applications in marine systems, control processes in agriculture and food production. Control Systems: Theory and Applications consists of selected best papers which were presented at XXIV International conference on automatic control “Automatics 2017” (September 13–15, 2017, Kyiv, Ukraine) organized by Ukrainian Association on Automatic Control (National member organization of IFAC – International Federation on Automatic Control) and National University of Life and Environmental Sciences of Ukraine. More than 120 presentations where discussed at the conference, with participation of the scientists from the numerous countries. The book is divided into two main parts, a first on Theory of Automatic Control (5 chapters) and the second on Control Systems Applications (8 chapters). The selected chapters provide an overview of challenges in the area of control systems design, modeling, engineering and implementation and the approaches and techniques that relevant research groups within this area are employing to try to resolve these. This book on advanced methods of control theory and successful cases in the practical implementation is ideal for personnel in modern technological processes automation and SCADA systems, robotics, space and marine industries as well as academic staff and master/research students in computerized control systems, automatized and computer-integrated systems, electrical and mechanical engineering.

This article studies identification problems of multiple linear regression models, which may be described a class of multi-input multi-output systems (i.e. multivariable systems). Based on the coupling identification concept, a novel coupled-least-squares (C-LS) parameter identification algorithm is introduced for the purpose of avoiding the matrix inversion in the multivariable recursive least-squares (RLS) algorithm for estimating the parameters of the multiple linear regression models. The analysis indicates that the C-LS algorithm does not involve the matrix inversion and requires less computationally efforts than the multivariable RLS algorithm, and that the parameter estimates given by the C-LS algorithm converge to their true values. Simulation results confirm the presented convergence theorems.

Random effects (RE) models have been widely used to study the contextual effects of structures such as neighborhoods or schools. The RE approach has recently been applied to age-period-cohort (APC) models that are unidentified because the predictors are exactly linearly dependent. However, research has not fully explained how the RE specification identifies these otherwise unidentified APC models. We address this challenge by first making explicit that RE-APC models have greater—not less—rank deficiency than the traditional fixed-effects model, followed by two empirical examples. We then provide intuition and a mathematical proof to explain that for APC models with one RE, treating one effect as an RE is equivalent to constraining the estimates of that effect’s linear component and the random intercept to be zero. For APC models with two REs, the effective constraints implied by the model depend on the true (i.e., in the data-generating mechanism) nonlinear components of the effects that are modeled as REs, so that the estimated linear components of the REs are determined by the true nonlinear components of those effects. In conclusion, RE-APC models impose arbitrary although highly obscure constraints and thus do not differ qualitatively from other constrained APC estimators.