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This paper aims to propose a new solution for producing customized three-dimensional (3D)-printed flat-shaped splints, which are then thermoformed to fit the patient’s hand. The splint design process is automated and is available to clinicians through an online application.

Patient anthropometric data measured by clinicians are associated with variables of parametric 3D splint models. Once these variables are input by clinicians in the online app, customized stereo lithography (STL) files for both splint and half mold, in the case of the bi-material splint, are automatically generated and become available for download. Bi-materials splints are produced by a hybrid manufacturing process involving 3D printing and overmolding.

This approach eliminates the need for 3D CAD-proficient clinicians, allows fast generation of customized splints, generates two-dimensional (2D) drawings of splints for verifying shape and dimensions before 3D printing and generates the STL files. Automation reduces splint design time and cost, while manufacturing time is diminished by 3D printing the splint in a flat position.

The app could be used in clinical practice. It meets the demands of mass customization using 3D printing in a field where individualization is mandatory. The solution is scalable – it can be extended to other splint designs or to other limbs. 3D-printed tailored splints can offer improved wearing comfort and aesthetic appearance, while maintaining hand immobilization, allowing visually controlled follow-up for edema and rapidly observing the need for revision if necessary.

An online application was developed for uploading patient measurements and downloading 2D drawings and STL files of customized splints. Different models of splints can be designed and included in the database as alternative variants. A method for producing bi-materials flat splints combining soft and rigid polymers represents another novelty of the research.

The purpose of this paper is to extend an approach to solve linear programming problems with grey data and variables, based on a developed multi‐objective programming approach.

The proposed approach to generally solve the grey linear programming problems is based on the notion of order relation between interval grey numbers. This notion is applied to cascade the grey objective function to a bi‐objective problem based on the objective function of the original problem. The same approach is taken to transform grey constraints to a set of corresponding linear constraints. Finally, the obtained multi‐objective model can be solved by any existing methods in the literature.

One of the shortcomings of previous approaches to solve grey linear programming problems was that they required the grey coefficients of objective function to be both side negative or positive. The approach proposed here does not have such a requirement and guarantees the feasibility of solutions.

A different approach is developed in the paper that can be used to solve grey linear programming problems in general form. The method relaxes the limitation of existing approaches.

In contrast to point forecasts, interval forecasts provide information on future variability. This research thus aimed to develop interval prediction models by addressing two significant issues: (1) a simple average with an additive property is commonly used to derive combined forecasts, but this unreasonably ignores the interaction among sequences used as sources of information, and (2) the time series often does not conform to any statistical assumptions.

To develop an interval prediction model, the fuzzy integral was applied to nonlinearly combine forecasts generated by a set of grey prediction models, and a sequence including the combined forecasts was then used to construct a neural network. All required parameters relevant to the construction of an interval model were optimally determined by the genetic algorithm.

The empirical results for tourism demand showed that the proposed non-additive interval model outperformed the other interval prediction models considered.

The private and public sectors in economies with high tourism dependency can benefit from the proposed model by using the forecasts to help them formulate tourism strategies.

In light of the usefulness of combined point forecasts and interval model forecasting, this research contributed to the development of non-additive interval prediction models on the basis of combined forecasts generated by grey prediction models.

This paper describes the design of a scalable bio‐mimetic framework that addresses several key issues of autonomous agents in the functional management domain of complex Ubiquitous Service‐Oriented Networks.We propose an autonomous network service management platform ‐ SwarmingNet, which is motivated by observations of the swarm intelligence in biological systems (e.g., Termite, Ant/Bees colonies, or Locusts ). In this SwarmingNet architecture, the required network service processes are implemented by a group of highly diverse and autonomic objects. These objects are called TeleService Solons (TSSs) as elements of TeleService Holons (TSHs), analoguous to individual insects as members of the whole colony. A single TSS is only able to pursue simple behaviors and interactions with local neighbors, on the contrary, a group of TSSs have the capabilities of fulfilling the complex tasks relating to service discovery and service activation.We simulate a service configuration process for a Multimedia Messaging Service, and a performance comparison between the bio‐agents and normal agents is analyzed. Finally, we conclude that through bio‐swarming intelligence behaviors, this infrastructure develops the enhanced self‐X capabilities which give IP networks advantages of instinctive compatibility, efficiency and scalability.

Ranking and comparing grey numbers represent a very important decision-making procedure in any given grey environment. The purpose of this paper is to study the existing approaches of ordering interval grey numbers in the context of decision making by surveying existing definitions.

Different methods developed for comparing grey numbers are presented along with their disadvantages and advantages in terms of comparison outcomes. Practical examples are employed to show the importance and necessity of using appropriate methods to compare grey numbers.

Most the available methods are not suitable for pointing out which number is larger when the nuclei of the grey numbers of concern are the same. Also, these available methods are also considered in terms of partial order and total order. Kernel and degree of greyness of grey numbers method is more advantageous than other methods and almost eliminates the shortcomings of other methods.

Different methods for ranking grey numbers are presented where each of them has disadvantages and advantages. By using different methods, grey interval numbers are compared and the results show that some methods cannot make grey number comparisons in some situations. The authors intend to find a method that can compare grey numbers in any situation. The findings of this research can prevent errors that may occur based on inaccurate comparisons of grey numbers in decision making. There are various research studies on the comparison of grey numbers, but there is no research on the comparison of these methods and their disadvantages, advantages or their total or partial order.

The purpose of this paper is to contribute to emergent understandings in research into urban climate change-related disasters (such as extreme heat), which recognise that present-day actions or failures of cities to address climate risk are rooted in a historical context.

The paper analyses content of scientific journals produced by the not-for-profit Kyushu Environmental Evaluation Association in Fukuoka since the 1970s. The aim is to evaluate the shifting understanding and conception of a liveable urban environment within Fukuoka over time and assess how this narrative has informed capability to understand and manage extreme heat as an emergent disaster risk.

The strong technical competences enabling Fukuoka to undertake evidence-based management of risks from climate-related disasters today exist at least partially because of earlier environmental concerns within the city and an early emergence of techno-scientific competence within the city's research institutions working at the science–policy interface.

The findings suggest a need to avoid uncritically exporting “lessons” from apparent urban climate “success stories”, without full recognition of the historical context enabling production and utilisation of weather and climate knowledge in specific locations.

The purpose of this paper is to extend a methodology for solving multi‐objective linear programming (MOLP) problems, when the objective functions and constraints coefficients are stated as interval numbers.

The approach proposed in this paper for the considered problem is based on the maximization of the sum of membership degrees which are defined for each objective of multi objective problem. These membership degrees are constructed based on the deviation from optimal solutions of individual objectives. Then, the final model based on membership degrees is itself an interval linear programming which can be solved by current methods.

The efficiency of the solutions obtained by the proposed method is proved. It is shown that the obtained solution by the proposed method for an interval multi objective problem is Pareto optimal.

The proposed method can be used in modeling and analyzing of uncertain systems which are modeled in the context of multi objective problems and in which required information is ill defined.

The paper proposed a novel and well‐defined algorithm to solve the considered problem.

The purpose of this paper is to illustrate an alternative number comparison process to the original procedure utilized under the grey extent analysis.

Number comparison process is visualized on simple diagrams constructed on a Cartesian coordinate system. Discernable areas to assist related probability calculation were sought according to compliance with desired dominance conditions.

By visualization, the author was able to obtain exactly the same probability information as those attainable by using the original number comparison process.

Reach of this methodology is merely constrained by the limitation of original method where whitenization values of interest uniformly distribute over the domains of related grey numbers.

Number comparison with visualization is easy to comprehend and practice, does not require resorting to probability formulas and an understanding of probability concepts.

The purpose is to analyze the mechanical behavior of the arterial wall in the degraded region of the arterial wall and to determine the stress distribution, as an important factor for predicting the potential failure mechanisms in the wall. In fact, while the collagen fiber degradation process itself is not modeled, zones with reduced collagen fiber content (corresponding to the degradation process) are assumed. To do so, a local weakness in the media layer is considered by defining representative volume elements (RVEs) with different fiber collagen contents in the degraded area to investigate the mechanical response of the arterial wall.

A three-dimensional (3D) large strain hierarchical multiscale technique, based on the homogenization and genetic algorithm (GA), is utilized to numerically model collagen fiber degradation in a typical artery. Determination of material constants for the ground matrix and collagen fibers in the microscale level is performed by the GA. In order to investigate the mechanical degradation, two types of RVEs with different collagen contents in fibers are considered. Each RVE is divided into two parts of noncollagenous matrix and collagen fiber, and the part of collagen fiber is further divided into matrix and collagen fibrils.

The von Mises stress distributions on the inner and outer surfaces of the artery and the influence of collagen fiber degradation on thinning of the arterial wall in the degraded area are thoroughly studied. Comparing the maximum stress values on outer and inner surfaces in the degraded region shows that the inner surface is under higher stress states, which makes it more prone to failure. Furthermore, due to the weakness of the artery in the degraded area, it is concluded that the collagen fiber degradation considerably reduces the wall thickness in the degraded area, leading to an observable local inflation across the degraded artery.

Considering that little attention has been paid to multiscale numerical modeling of collagen fiber degradation, in this paper a 3D large strain hierarchical multiscale technique based on homogenization and GA methods is presented. Therefore, while the collagen fiber degradation process itself is not modeled in this study, zones with reduced collagen fiber content (corresponding to the degradation process) are assumed.

The purpose of this paper is to propose a method for solving multi-objective linear programming (MOLP) with interval coefficients using positioned programming and interactive fuzzy programming approaches.

In the proposed algorithm, first, lower and upper bounds of each objective function in its feasible region will be determined. Afterwards using fuzzy approach, considering a membership function for each objective function and finally using grey linear programming, the solution for this problem will be obtained.

According to the presented example, in this paper, the proposed method is both simple in use and suitable for solving different problems. In the numerical example mentioned in this paper, the proposed method provides an acceptable solution for such problems.

As in most real-world situations, the coefficients of decision models are not known and exact. In this paper, the authors consider the model of MOLP with interval data, since one of the solutions to cover uncertainty is using interval theory.

Based on using grey theory and interactive fuzzy programming approaches, an appropriate method has been presented for solving MOLP problems with interval coefficients. The proposed method, against the complex methods, has less effort and offers acceptable solutions.