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This article aims to present a direct solution to handle linear constraints in finite element (FE) analysis without penalties or the Lagrange multipliers introduced.

First, the system of linear equations corresponding to the linear constraints is solved for the leading variables in terms of the free variables and the constants. Then, the reduced system of equilibrium equations with respect to the free variables is derived from the finite-dimensional virtual work equation. Finally, the algorithm is designed.

The proposed procedure is promising in three typical cases: (1) to enforce displacement constraints in any direction; (2) to implement local refinements by allowing hanging nodes from element subdivision and (3) to treat non-matching grids of distinct parts of the problem domain. The procedure is general and suitable for 3D non-linear analyses.

The algorithm is fitted only to the Galerkin-based numerical methods.

The proposed procedure does not need Lagrange multipliers or penalties. The tangential stiffness matrix of the reduced system of equilibrium equations reserves positive definiteness and symmetry. Besides, many contemporary Galerkin-based numerical methods need to tackle the enforcement of the essential conditions, whose weak forms reduce to linear constraints. As a result, the proposed procedure is quite promising.

This study aims to modify a nature-based numerical method named the invasive weed optimization (IWO) method for mobile robot path planning in various complex environments.

The existing IWO method is quick in converging to a feasible solution but in a complex environment; it takes more time as well as computational resources. So, in this paper, the computational part of this artificial intelligence technique is modified with the help of recently developed evolution algorithms like particle swarm optimization, genetic algorithm, etc. Some conditional logic statements were used while doing sensor-based mapping for exploring complex paths. Implementation of sensor-based exploration, mathematical IWO method and prioritizing them for better efficiency made this modified IWO method take complex dynamic decisions.

The proposed modified IWO is better for dynamic obstacle avoidance and navigating a long complex map. The deviation of results in simulation and experiments is less than 5.5%, which validates a good agreement between simulation and real-time testing platforms.

As per a deep literature review, it has found that the proposed approach has not been implemented on the Khepera-III robot for smooth motion planning. Here a dynamic obstacle mapping feature is implemented. A method to selectively distribute seeds instead of a random normal distribution is also implemented in this work. The modified version of IWO is coded in MATLAB and simulated through V-Rep simulation software. The integration of sensors was done through logical conditioning. The simulation results are validated using real-time experiments.

Managing resources is crucial for firms to gain competitive advantages and succeed, particularly for startups with limited resources. It is important to understand how digital startups in general and digital academic spin-offs (ASOs) in particular may orchestrate their resources to optimize value. This paper integrates the resource-based perspective with digital entrepreneurship to analyze the resource configurations leading to success of digital ASOs.

The paper adopts an inductive approach and applies qualitative comparative analysis (QCA) on a longitudinal dataset of digital ASOs to identify the resource configurations for a successful outcome.

The authors' paper identifies two main paths to success among digital ASOs, consisting of five distinct resource configurations. The first path is termed “market exploiters” that operate in favorable market conditions where specific technological resources and research collaboration resources are lacking. The second path involves “technology explorers” that combines both technological and commercial resources to achieve success.

By outlining distinct pathways to the success of digital ASOs, this paper contributes to the digital academic entrepreneurship literature and the resource-based view of entrepreneurial firms. The paper also suggests implications for policymakers and managers in managing resources for the success of digital ventures.

By exploring the resource configurations leading to the success of ASOs commercializing digital technologies, the paper shows that favorable market conditions and complementary resource configurations can be alternative pathways to success.

In this paper, the author presents a hybrid method along with its error analysis to solve (1+2)-dimensional non-linear time-space fractional partial differential equations (FPDEs).

The proposed method is a combination of Sumudu transform and a semi-analytc technique Daftardar-Gejji and Jafari method (DGJM).

The author solves various non-trivial examples using the proposed method. Moreover, the author obtained the solutions either in exact form or in a series that converges to a closed-form solution. The proposed method is a very good tool to solve this type of equations.

The present work is original. To the best of the author's knowledge, this work is not done by anyone in the literature.

The calculation of electromagnetic fields can involve many degrees of freedom (DOFs) to achieve accurate results. The DOFs are directly related to the computational effort of the simulation. The effort is decreased by using the proper generalized decomposition (PGD) and proper orthogonalized decomposition (POD). The purpose of this study is to combine the advantages of both methods. Therefore, a hybrid enrichment strategy is proposed and applied to different electromagnetic formulations.

The POD is an a-priori method, which exploits the solution space by decomposing reference solutions of the field problem. The disadvantage of this method is given by the unknown number of solutions necessary to reconstruct an accurate field representation. The PGD is an a-priori approach, which does not rely on reference solutions, but require much more computational effort than the POD. A hybrid enrichment strategy is proposed, based on building a small POD model and using it as a starting point of the PGD enrichment process.

The hybrid enrichment process is able to accurately approximate the reference system with a smaller computational effort compared to POD and PGD models. The hybrid enrichment process can be combined with the magneto-dynamic T-Ω formulation and the magnetic vector potential formulation to solve eddy current or non-linear problems.

The PGD enrichment process is improved by exploiting a POD. A linear eddy current problem and a non-linear electrical machine simulation are analyzed in terms of accuracy and computational effort. Further the PGD-AV formulation is derived and compared to the PGD-T-Ω reduced order model.

This paper aims to answer report how mentors who onboard newcomers to a high-stress social work organization can learn about their onboarding practice by treating onboarding as a wicked problem that escapes definitive formulation and final solutions.

The authors follow an action research approach with three iterations of learning about onboarding with mentors in a Danish social work organization struggling with an employee turnover exceeding 30%.

The authors unfold the authors’ emerging sensitivity to wickedity over the iterations of learning about onboarding with the mentors. As the authors foreground the wickedity of the authors onboarding in the last iteration, three lessons learned could be derived: it warrants the mentors’ continuous inquiry; opens inquiry into the ambivalence of mentoring; and convenes responsibility for inquiry to a community of mentors.

This study of problematic onboarding to high-stress social work shows the value of fore-grounding wickedity instead of hiding it with a positive framing. This wickedity rests on situated grounding and is only transferrable to other organizations with the utmost caution.

High-stress social work organizations without the capacity to systematically sustain best practices for onboarding may, instead, increase attention to the wickedity of onboarding as a motivation for continuous inquiry by a broader community of mentors.

To the best of the authors’ knowledge, this paper is the first to present an action research study of problem wickedity to motivate mentors’ inquiry into onboarding newcomers to high-stress social work.

Although user stickiness has been studied for several years in the field of live e-commerce, little attention has been paid to the effects of streamer attributes on user stickiness in this field. Rooted in the stimulus-organism-response (S-O-R) theory, this study investigated how streamer attributes influence user stickiness.

The authors obtained 496 valid samples from Chinese live e-commerce users and explored the formation of user stickiness using partial least squares-structural equation modeling (PLS-SEM). Artificial neural network (ANN) was used to capture linear and non-linear relationships and analyze the normalized importance ranking of significant variables, supplementing the PLS-SEM results.

The authors found that attractiveness and similarity positively impacted parasocial interaction (PSI). Expertise and trustworthiness positively impacted perceived information quality. Moreover, streamer-brand preference mediated the relationship between PSI and user stickiness, as well as the relationship between perceived information quality and user stickiness. Compared to PLS-SEM, the predictive ability of ANN was more robust. Further, the results of PLS-SEM and ANN both showed that attractiveness was the strongest predictor of user stickiness.

This study explained how streamer attributes affect user stickiness and provided a reference value for future research on user behavior in live e-commerce. The exploration of the linear and non-linear relationships between variables based on ANN supplements existing research. Moreover, the results of this study have implications for practitioners on how to improve user stickiness and contribute to the development of the livestreaming industry.

The purpose of the study is distributed generation planning in a radial delivery framework to identify an appropriate location with a suitable rating of DG units energized by renewable energy resources to scale back the power loss and to recover the voltage levels. Though several algorithms have already been proposed through the target of power loss reduction and voltage stability enhancement, further optimization of the objectives is improved by using a combination of heuristic algorithms like DE and particle swarm optimization (PSO).

The identification of the candidate buses for the location of DG units and optimal rating of DG units is found by a combined differential evolution (DE) and PSO algorithm. In the combined strategy of DE and PSO, the key merits of both algorithms are combined. The DE algorithm prevents the individuals from getting trapped into the local optimum, thereby providing efficient global optimization. At the same time, PSO provides a fast convergence rate by providing the best particle among the entire iteration to obtain the best fitness value.

The proposed DE-PSO takes advantage of the global optimization of DE and the convergence rate of PSO. The different case studies of multiple DG types are carried out for the suggested procedure for the 33- and 69-bus radial delivery frameworks and a real 16-bus distribution substation in Tamil Nadu to show the effectiveness of the proposed methodology and distribution system performance. From the obtained results, there is a substantial decrease in the power loss and an improvement of voltage levels across all the buses of the system, thereby maintaining the distribution system within the framework of system operation and safety constraints.

A comparison of an equivalent system with the DE, PSO algorithm when used separately and other algorithms available in literature shows that the proposed method results in an improved performance in terms of the convergence rate and objective function values. Finally, an economic benefit analysis is performed if a photo-voltaic based DG unit is allocated in the considered test systems.

Supply chain risk management can effectively reduce the loss of retailers. In this regard, retailers need to consider the competition risks of competitors in addition to the disruption risks. This paper designs a resilient retail supply chain network for perishable foods under the dynamic competition to maximize retailer's profits.

A two-stage mixed-integer non-linear model is presented for designing the supply chain network. In the first stage, an equilibrium model that considers the characteristics of perishable foods is developed. In the second stage, a mixed integer non-linear programming model is presented to deal with the strategic decisions. Finally, an efficient memetic algorithm is designed to deal with large-scale problems.

The optimal the selection of suppliers, distribution centers and the order allocation are found among the supply chain entities. Considering the perishability of agri-food products, the equilibrium retail price and selling quantity are determined. Through a numerical example, the optimal inventory period under different maximum shelf life and the impact of three resilient strategies on retailer's profit, selling price and selling quantity are analyzed.

As for future research, the research can be extended in a number of directions. First, this paper studies the retail supply chain network design problem under competition among retailers. It can be an interesting direction to consider retailers competing with suppliers. Second, the authors can try to linearize the non-linear model and solve the large-scale integer programming problem by exact algorithm. Finally, the freshness of perishable foods gradually declines linearly to zero as the maximum shelf life approaches, and it would be a meaningful attempt to consider the freshness of perishable foods declines exponentially.

This paper innovatively designs the resilient supply chain network for perishable foods under dynamic competition. The retailer's dynamic competition and resilient strategies are considered simultaneously when designing supply chain network for perishable foods. In addition, this paper gives insights into how to obtain the optimal inventory period and compare the retailer's resilient strategies.

The Government’s investment in infrastructure projects is considerably high, especially in bridge construction projects. Government authorities must establish an initial forecasted budget to have transparency in transactions. Early cost estimating is challenging for Quantity Surveyors due to incomplete project details at the initial stage and the unavailability of standard cost estimating techniques for bridge projects. To mitigate the difficulties in the traditional preliminary cost estimating methods, there is a requirement to develop a new initial cost estimating model which is accurate, user friendly and straightforward. The research was carried out in Sri Lanka, and this paper aims to develop the artificial neural network (ANN) model for an early cost estimate of concrete bridge systems.

The construction cost data of 30 concrete bridge projects which are in Sri Lanka constructed within the past ten years were trained and tested to develop an ANN cost model. Backpropagation technique was used to identify the number of hidden layers, iteration and momentum for optimum neural network architectures.

An ANN cost model was developed, furnishing the best result since it succeeded with around 90% validation accuracy. It created a cost estimation model for the public sector as an accurate, heuristic, flexible and efficient technique.

The research contributes to the current body of knowledge by providing the most accurate early-stage cost estimate for the concrete bridge systems in Sri Lanka. In addition, the research findings would be helpful for stakeholders and policymakers to propose policy recommendations that positively influence the prediction of the most accurate cost estimate for concrete bridge construction projects in Sri Lanka and other developing countries.