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Currently, massive multiple-input multiple-output (m-MIMO) antennas are typically designed using complex trial-and-error methods. The purpose of this study is to determine an effective optimization method to achieve more efficient antenna design processes.

This paper presents the design stages of a m-MIMO antenna array compatible with 5G smartphones operating in long term evolution (LTE) bands 42, 43 and 46, based on a specific algorithm. Each antenna element in the designed 10-port m-MIMO antenna array is intended to perfectly cover the three specified LTE bands. The optimization methods used for this purpose include the Nelder–Mead simplex algorithm, covariance matrix adaptation evolution strategy, particle swarm optimization and trust region framework (TRF).

Among the primary optimization algorithms, the TRF algorithm met the defined objectives most effectively. The achieved antenna efficiency values exceeded 60.81% in the low band and 68.39% in the high band, along with perfect coverage of the desired bands, demonstrating the success of the design with the TRF algorithm. In addition, the potential electromagnetic field exposure caused by the designed m-MIMO antenna array is elaborated upon in detail using computational human models through specific absorption rate analysis.

The comparison of four different algorithms (two local and two global) for use in the design of a 10-element m-MIMO antenna array with a complex structural configuration and the success of the design implemented with the selected algorithm distinguish this study from others.

In response to the challenge of reduced efficiency or failure of robot motion planning algorithms when faced with end-effector constraints, this study aims to propose a new constraint method to improve the performance of the sampling-based planner.

In this work, a constraint method (TC method) based on the idea of cross-sampling is proposed. This method uses the tangent space in the workspace to approximate the constrained manifold pattern and projects the entire sampling process into the workspace for constraint correction. This method avoids the need for extensive computational work involving multiple iterations of the Jacobi inverse matrix in the configuration space and retains the sampling properties of the sampling-based algorithm.

Simulation results demonstrate that the performance of the planner when using the TC method under the end-effector constraint surpasses that of other methods. Physical experiments further confirm that the TC-Planner does not cause excessive constraint errors that might lead to task failure. Moreover, field tests conducted on robots underscore the effectiveness of the TC-Planner, and its excellent performance, thereby advancing the autonomy of robots in power-line connection tasks.

This paper proposes a new constraint method combined with the rapid-exploring random trees algorithm to generate collision-free trajectories that satisfy the constraints for a high-dimensional robotic system under end-effector constraints. In a series of simulation and experimental tests, the planner using the TC method under end-effector constraints efficiently performs. Tests on a power distribution live-line operation robot also show that the TC method can greatly aid the robot in completing operation tasks with end-effector constraints. This helps robots to perform tasks with complex end-effector constraints such as grinding and welding more efficiently and autonomously.

This study aims to optimize the levels of factors for a green supply chain (GSC) while concurrently gaining valuable insights into the dynamic interrelationships among several factors, leading to reductions in CO₂ emissions and the maximization of the average service level, thereby enhancing overall supply chain performance.

Response surface methodology (RSM) is employed as a technique for multiple response optimization. This study uses a supply chain simulation model that includes decision variables related to the level of inventory control parameters and vehicle capacity. The desirability approach is adopted to achieve optimization objectives by focusing on minimizing CO₂ emissions and maximizing service levels while simultaneously determining the optimum levels of considered decision variables.

The high R² values of 97.38% for CO₂ and 97.28% for service level, along with adjusted R² values reasonably close to predicted values, affirm the models' capability to predict responses accurately. Key significant model terms for CO₂ encompassed reorder point, order up to quantity, vehicle capacity, and their interaction effects, while service level is notably influenced by reorder point, order up to quantity, and their interaction effects. The study successfully achieved a high level of desirability value of %99.1 and the validated performance levels confirmed that the results fall within the prediction interval.

This study introduces a metamodel framework designed to optimize various design parameters for a GSC combining discrete event simulation (DES) and RSM in the form of a simulation optimization model. In contrast to the literature, the current study offers an exhaustive and in-depth analysis of the structural elements of the supply chain, particularly the inventory control parameters and vehicle capacity, which are crucial for comprehending its performance and environmental impact.

The purpose of this study aims to synthesize a novel donor–acceptor dye based on phenothiazine as a donor (D) and nonconjugated spacer was devised and synthesized by condensing of 2,2'-(1H-indene-1,3(2H)-diylidene) dimalononitrile with aldehyde and the practical synthesis methodology as given in Scheme 1.

The prepared phenothiazine dye was systematically experimentally and theoretically examined and characterized using nuclear magnetic resonance spectroscopy (1H,13C NMR), Fourier-transform infrared spectroscopy (IR) and high-resolution mass spectrometry. Density functional theory (DFT) and time-dependent density functional theory DT-DFT calculations were implemented to determine the electronic properties of the new dye

The UV-Vis absorption and fluorescence spectroscopy of the synthesized dye was investigated in a variety of solvents with varying polarities to demonstrate positive solvatochromism correlated with intramolecular charge transfer (ICT). The probe’s quantum yields (Фf) are experimentally measured in ethanol, and the Stokes shifts are found to be in the 4846–9430 cm⁻¹ range.

The findings depicted that the novel (D-π-A) chromophores may act as a significant factor in the organic optoelectronics.

This study aims to investigate the individual electrochemical transients arising from local anodic events on stainless steel, to uncover the potential mechanisms producing different types of transients and to derive appropriate parameters indicative of the corrosion severity of such transient events.

An equivalent circuit model was used for the transient analysis, which was performed using a local current allocation rule based on the relative instant cathodic resistance of the coupled electrodes, as well as the kinetic parameters derived from the electrochemical polarization measurement.

The shape and size of the electrochemical current transients arising from SS 316 L were influenced by the film stability, local anodic dissolution kinetics and the symmetry of the cathodic kinetics between the coupled electrodes, where the ultralong transient might correspond to the propagation of film damage with a slow anodic dissolution rate. The dynamic cathodic resistance during the final stage of transient current growth can serve as a characteristic parameter that reflects the loss of passive film protection.

Estimation of the local anodic current trace opens a new way for individual electrochemical transient analysis associated with the charges involved, local current densities and changes in film resistance throughout localized corrosion processes.

This study aims to minimize the transportation-related cost in distribution while utilizing a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach. An increased cost in distribution is a major problem for many companies due to the absence of efficient planning methods to overcome operational challenges in distinct distribution networks. The problem addressed in this study is to minimize the transportation-related cost in distribution while using a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach which has not gained the adequate attention in the literature.

This study formulated the transportation problem as a vehicle routing problem with a heterogeneous fixed fleet and workload balancing, which is a combinatorial optimization problem of the NP-hard category. The model was solved using both the simulated annealing and a genetic algorithm (GA) adopting distinct local search operators. A greedy approach has been used in generating an initial solution for both algorithms. The paired t-test has been used in selecting the best algorithm. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet compositions of the heterogeneous fleet. Results were analyzed using analysis of variance (ANOVA) and Hsu’s MCB methods to identify the best scenario.

The solutions generated by both algorithms were subjected to the t-test, and the results revealed that the GA outperformed in solution quality in planning a heterogeneous fleet for distribution with load balancing. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet utilization with different compositions of the heterogeneous fleet. Results were analyzed using ANOVA and Hsu’s MCB method and found that removing the lowest capacities trucks enhances the average vehicle utilization with reduced travel distance.

The developed model has considered both planning of heterogeneous fleet and the requirement of work load balancing which are very common industry needs, however, have not been addressed adequately either individually or collectively in the literature. The adopted solution methodologies to solve the NP-hard distribution problem consist of metaheuristics, statistical analysis and scenario analysis are another significant contribution. The planning of distribution operations not only addresses operational-level decision, through a scenario analysis, but also strategic-level decision has also been considered.

The planning of distribution operations not only addresses operational-level decisions, but also strategic-level decisions conducting a scenario analysis.

This study aims to investigate the interplay between environmental concerns, consumer attitudes and their influence on sustainable practices within the craft beer industry, exploring how consumers’ perception of environmental attributes impact their purchase intentions regarding craft beer.

The study used a quantitative approach, using a self-administered survey instrument comprised 28 Likert-scale items. Data was gathered from a convenience sample of 319 craft beer consumers. Confirmatory factor analysis was conducted to assess the measurement model’s validity and reliability. Subsequently, partial least squares structural equation modeling was used to test the hypothesized relationships.

This study’s findings challenge established notions within the field, demonstrating that water conservation, carbon footprint reduction, organic farming and local sourcing practices hold significant influence on consumers’ purchase intentions regarding sustainable craft beer. These findings depart from previous research that suggested a less pronounced impact of specific sustainability practices on consumer behavior.

This study contributes to the literature by revealing consumer insights into the environmental implications of craft beer choices, empowering them to make informed purchasing decisions aligned with their sustainability values. However, acknowledging potential limitations, sample size and demographic composition (young adults in Brazil) potentially impact the generalizability of the findings.

This study addresses the understanding of consumer attitudes and priorities regarding environmental sustainability within the craft beer industry. By focusing on the tangibility of specific sustainable attributes the research shows how these practices influence consumer perceptions and purchasing intentions.

The study explores the possibility of developing a valid scale for integrated management of heritage sites to bring a holistic approach to heritage properties' conservation practices.

The study followed the exploratory sequential method. An in-depth interview was adopted for exploring indicators, and a questionnaire survey was administered for descriptive analysis.

Cultural resources conservation strategies have been analyzed from a tourist, local communities and stakeholder's perspective with local-specific indicators. The study indicates that a multi-dimensional approach that integrates tourists, local communities and other stakeholders-based indicators can be developed at the destination level for the integrated management of heritage properties. Tourist-centric, local community-specific and stakeholder-oriented approaches could act as catalysts for more pragmatic conservation practices in the local areas based on the site-specific indicators.

The study is limited to lesser-known heritage sites located in the southern provincial states of India. The technical conservation strategies on the structure and architecture are not part of the study. Theoretical implications on the study of this kind can contribute to the literature as it throws light on future studies seeking local-centric conservation and management practices of heritage sites hitherto less explored in the domain of conservation science. The scale provides insight into the appropriate form of intervention that the local communities, tourists and other stakeholders can do at the heritage sites, hence the possibility of garnering the attention of other discipline strivings towards the conservation of heritage sites and to apply along with other relevant variables. It is expected that the study might expedite the knowledge accumulation in conservation science.

The scale can be used in a similar context for the integrated management of heritage sites. The study can assist the policymakers and planners in seeking the support of stakeholders, local communities and tourists for the implementation of heritage conservation and management programs. Such a local-centric management strategy promoting responsible consumption and production could contribute to SDG 12. Further, the study can also contribute towards SDG 11.4, which calls for strengthening the effort to protect and safeguard cultural and natural heritage. This scale can be a tool for destination management organizations (DMOs) to understand the level of intervention of local communities, tourists and other stakeholders at the heritage site.

The integrated management approach of heritage conservation immensely helps the lesser-known heritage sites the world over as such structures are out of the focal point of government funding and other conservation efforts. The synergy of the integrated approach could protect lesser-known unfunded heritage sites, and thereby, the cultural reflections of the community concerned can be made available for future visitors’ consumption.

The study attempted to understand the conservation approaches for lesser-known heritage sites with the support of both demand and supply-side stakeholders. Such a collaborative approach is the first of this kind in the conservation of heritage sites in India.

Nanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc.

In this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021.

Among these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

This is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

The purpose of this paper is to present a new approach to optimizing the design of 3D magnetic circuits. This approach is based on topology optimization, where derivative calculations are performed using the continuous adjoint method. Thus, the continuous adjoint method for magnetostatics has to be developed in 3D and has to be combined with penalization, filtering and homotopy approaches to provide an efficient optimization code.

To provide this new topology optimization code, this study starts from 2D magnetostatic results to perform the sensitivity analysis, and this approach is extended to 3D. From this sensitivity analysis, the continuous adjoint method is derived to compute the gradient of an objective function of a 3D topological optimization design problem. From this result, this design problem is discretized and can then be solved by finite element software. Thus, by adding the solid isotropic material with penalization (SIMP) penalization approach and developing a homotopy-based optimization algorithm, an interesting means for designing 3D magnetic circuits is provided.

In this paper, the 3D continuous adjoint method for magnetostatic problems involving an objective least-squares function is presented. Based on 2D results, new theoretical results for developing sensitivity analysis in 3D taking into account different parameters including the ferromagnetic material, the current density and the magnetization are provided. Then, by discretizing, filtering and penalizing using SIMP approaches, a topology optimization code has been derived to address only the ferromagnetic material parameters. Based on this efficient gradient computation method, a homotopy-based optimization algorithm for solving large-scale 3D design problems is developed.

In this paper, an approach based on topology optimization to solve 3D magnetostatic design problems when an objective least-squares function is involved is proposed. This approach is based on the continuous adjoint method derived for 3D magnetostatic design problems. The effectiveness of this topology optimization code is demonstrated by solving the design of a 3D magnetic circuit with up to 100,000 design variables.