Niche Technology Research Articles

Theorising the shift to video consulting in the UK during the COVID-19 pandemic: Analysis of a mixed methods study using practice theory

We studied video consulting in the National Health Service during 2020–2021 through video interviews, an online survey and online discussions with people who had provided and participated in such consultations.Video consulting had previously been used for selected groups in limited settings in the UK. The pandemic created a seismic shift in the context for remote consulting, in which video transformed from a niche technology typically introduced by individual clinicians committed to innovation and quality improvement to offering what many felt was the only safe way to deliver certain types of healthcare. A new practice emerged: a co-constitution of technology and healthcare made possible by new configurations of equipment, connectivity and physical spaces. Despite heterogeneous service settings and previous experiences of video consulting, we found certain kinds of common changes had made video consulting possible. We used practice theory to analyse these changes, interpreting the commonalities found in our data as changes in purpose, material arrangements and a relaxing of rules about security, confidentiality and location of consultations.The practice of video consulting was equivocal. Accounts of, and preferences for, video consulting varied as did the extent to which it was sustained after initial take-up. People made sense of video consulting in different ways, ranging from interpreting video as offering a new modality of healthcare for the future to a sub-optimal, temporary alternative to in-person care. Despite these variations, video consulting became a recognisable social phenomenon, albeit neither universally adopted nor consistently sustained. The nature of this social change offers new perspectives on processes of implementation and spread and scale-up. Our findings have important implications for the future of video consulting. We emphasise the necessity for viable material arrangements and a continued shared interpretation of the meaning of video consulting for the practice to continue.

The Configuration Design of Electronic Products Based on improved NSGA-III with Information Feedback Models

<p>The configuration of electronic products is an important means to meet the diverse and personalized needs of users and achieve mass customization, and one of its goals is to recommend an excellent bill of material to users according to users’ individualized needs and preferences. Current research describes the configuration of electronic products as a single-objective optimization model, which suffers from the problems of single recommended configuration and difficulty in meeting the dynamic adjustment of user preferences. Therefore, we describe it as a multi-objective optimization model and propose an NSGA-III-FR algorithm to solve the model. In order to balance the convergence and diversity of the algorithm, NSGA-III-FR has made two improvements on the basis of NSGA-III with information feedback models: introducing adaptive parameters to balance NSGA-III-F1 and NSGA-III-R1; and using Angle Penalized Distance (APD) to improve the niche technology. The experimental results show that our modified meth-od can achieve better performance compared with the other three algorithms.</p> <p> </p>

Operation optimization for park with integrated energy system based on integrated demand response

Industrial parks in China have a large energy consumption with a noticeable peak valley difference between energy consumption and colossal carbon emission. This causes a significant contradiction between energy consumption and energy supply in the country. To solve the above-mentioned problems, an optimization method is proposed for the park integrated energy system based on integrated demand response. First, the energy supply model of the typical day integrated with the energy system in the industrial park during summer was constructed. Second, the price demand response model was established according to the substitution elasticity coefficient. Considering the dynamic characteristics of temperature, the equivalent thermal parameter model and the predicted mean vote (PMV) index model were used to select the adjustable demand response model. Then, aiming at the minimum operation cost and carbon emission, the adaptive chaotic drosophila melanogaster optimization algorithm with niche technology was used to solve this problem, whereby the Pareto optimal solution set was obtained. The VIKOR method was used to select the optimal result comprehensively. Finally, the integrated energy system of an north industrial park was analyzed, and the results were regarded as an example. The results showed that the method proposed by this paper improved the economy considerably and provided environmental protection effectively.

Optimization scheduling of home appliances in smart home: A model based on a niche technology with sharing mechanism

Smart appliances bring convenience to people’s lives, but the high electricity cost and high peak load are still drawbacks. The scheduling optimization of household electricity tasks can effectively alleviate those drawbacks. Therefore, this paper proposes a scheduling optimization model of smart home appliances to reduce the load peak value and electricity cost. Herein, the electricity tasks are defined by the set theory and classified by the operational characteristics of the loads. Then, a hybrid coding genetic algorithm based on the niche technology is proposed for scheduling optimization problems of electricity tasks. Different coding and cross-mutation operation strategies are designed for different electricity tasks. Finally, a tracing Pareto method based on weighted sum approach is proposed to solve the multi-objective optimization problem. The experimental results show that the proposed algorithm has a good performance in reducing load peaks and electricity costs. In cases A, B, and C, the load peak value is reduced by 54.7%, 45.7%, and 20.7%, respectively; the electricity cost is reduced by 5.98%, 12.00%, and 16.12%, respectively. Furthermore, a series of Pareto optimal solutions are also identified, which can help users better understand the trade-off relation between the load peak value and electricity cost.

Industry 4.0 Engineering Product Life Cycle Management Based on Multigranularity Access Control Model.

In order to improve the management efficiency of the safety status of Industry 4.0 engineering products, the multigranularity access control model (MGACM) Industry 4.0 engineering product life cycle management (PLM) is adopted to optimize the safety management mode of Industry 4.0 engineering products in this paper. The multigranularity access control model is constructed in this paper, which has strong nonlinearity and better fault tolerance. In addition, the parameters of PLM are optimized through the multiparticle access control model, and PLM search is enabled. Taking into account the slow and easy convergence of the multigranular access control model, a niche technology with full life cycle heterogeneity and elimination mechanism is proposed to solve the premature convergence problem of the multigranular access control model. The final simulation results of this paper show that, compared with traditional algorithms, the proposed multigranularity access control model is more reliable and effective and has faster convergence speed and higher management efficiency.

Optimization of Multi-Objective Unequal Area Facility Layout

The unequal area facility layout problem (UA-FLP) refers to the reasonable arrangement of a certain number of facilities in a given layout area. The facility layout should satisfy given layout constraints and optimize given optimization objectives as far as possible. In this paper, a method combining improved lowest horizontal line method and particle swarm optimization algorithm is proposed to solve UA-FLP, which achieves multi-objective optimization of material handling cost, the adjacent value and the utilization rate of floor shop. On the one hand, the algorithm formulates facility packing rules through the improved lowest horizontal line method, which simplifies the legalization of facility layout. On the other hand, a modified particle swarm optimization (PSO) algorithm combining objective space division method (OSD) and niche technology is used for multi-objective optimization. The proposed algorithm overcomes the shortcomings of previous facility layout methods such as complex overlapping interference process, large amount of calculation and long time for multi-objective optimization. Compared with the comparison methods, the results show that material handling cost is reduced by 1%–6% and the utilization ratio of floor shop is increased by 2%–7%.

Understanding the Perception of Information System Niche: Conceptualization, Measurement, and Validation

The number of niche technologies is growing, and they are getting noticed. However, the usage dynamics of such systems have not been explored. This study introduces the “niche” information systems (IS) construct to differentiate between mainstream and niche technologies and to illustrate how such differences potentially influence a person’s IS usage behavior. We posit that niche-ness is perceptual, and a measurement to gauge an individual’s niche perception in the domain of systems usage is desired. Resting on Optimal Distinctiveness theory, the study develops a novel conceptualization and operationalization of niche technology perceptions, focusing on social networking systems. Our main contribution includes the examination of the typology of the perceived niche, which results in the development and validation of an eight-item instrument for perceived technology niche through three studies. In addition, by placing the construct in a nomological network in a fourth study, we demonstrate that the perceived technology niche construct is related to IS continuance intention.

Colored optic filters on c‐Si IBC solar cells for building integrated photovoltaic applications

AbstractBuilding Integrated Photovoltaic systems can produce a significant portion of the energy demand of urban areas. Despite their potential, they remain a niche technology that architects and project engineers still find esthetically limited. The dark blue or black color of standard photovoltaic panels is considered inappropriate for restoration projects of historic buildings and represents a major constraint on the development of new projects. This work will provide insight into how the use of optic filters can offer new pathways for architectural acceptance of photovoltaic panels. Optic filters selectively reflect or transmit light by interference and can be designed and fabricated using cost‐effective and industrially compatible processes. By using in‐house developed ray tracing software coupled with TCAD Sentaurus, more than 400 colors were obtained, and their impact on the opto‐electrical performance of interdigitated back‐contacted solar cells was studied. Results show a maximum efficiency loss of 1.6% absolute at the perpendicular incidence of light on the range of obtained colors when compared with a standard dark blue solar cell. Simulations for different angles of incidence showed that the current reduction on the standard device could be modeled using a cosine relationship. The colored cells, however, deviated significantly from this relationship. We propose that the angular behavior of any cell (colored or standard) could be simulated by modifying the effective irradiance with scaling factors equal to the ratios of the photogenerated current at any angle with respect to the value at normal incidence. We demonstrate that this approach accurately models the effect of the color filter and allows for an easy transition from a bare cell to an encapsulated one. Due to the spectral effect of the filter, we developed both a spectrally resolved optical model and a two‐dimensional finite volume transient thermal model. In case of the optical model, we demonstrate an accuracy in the prediction of the reflectance produced by the color with values of mean bias error (MBE) between 2.0% and 3.9%. As for the thermal model, it was validated by first analyzing a standard model under conditions of nominal operating cell temperature and then comparing its results with published scientific literature. Later, we compare its prediction against 2 weeks of measurements. In both cases the thermal model proves an adequate accuracy, yielding differences below 1.5°C with respect to other scientific works and an MBE value of 0.89°C as well as a root‐mean‐square error value of 2.10°C for the entire measurement period. With the validated models, we studied the effect of the encapsulation on the color perception. We present two options of color filters. The first one produces relatively low reflectance losses and presents relative annual direct current (DC) energy losses of up to 6.4% for Delft, in the Netherlands, and up to 5.9% for Alice Springs in Australia. However, this first option has very poor color brightness. The second studied filter produces highly saturated bright colors. Improving brightness can increase the annual DC relative losses up to 13.7% and 13.5% for Delft and Alice Springs, respectively. Overall, we demonstrate that colored filters based on multilayer optical stacks are a versatile option for coloring cells that allow a good compromise between esthetics and performance.

Dynamic Optimization Algorithm Based on Multi-modal Niche Lion Swarm by Sensitive Individuals

Aiming at the problem that the model of complex dynamic system which is difficult to track its global optimal solution, this paper proposes a multi-modal niche lion swarm optimization algorithm based on sensitive individuals to solve the optimization problem of complex dynamic system. Sensitive individuals are set in the lion swarm algorithm to perceive changes in the dynamic environment, and to track the global optimal value in time. It combines with niche technology to increase the diversity of the population and avoids falling into local extremes. The dynamic environment is simulated by the dynamic function generator and compared with other optimization algorithm to verify the superiority of the algorithm proposed in this paper.

Sustainable Desalination and Water Reuse

Over the past half century, reverse osmosis (RO) has grown from a nascent niche technology into the most versatile and effective desalination and advanced water treatment technology available. However

Dual Resource Constrained Flexible Job Shop Scheduling Based on Improved Quantum Genetic Algorithm

Aiming at solving the problem of dual resource constrained flexible job shop scheduling problem (DRCFJSP) with differences in operating time between operators, an artificial intelligence (AI)-based DRCFJSP optimization model is developed in this paper. This model introduces the differences between the loading and unloading operation time of workers before and after the process. Subsequently, the quantum genetic algorithm (QGA) is used as the carrier; the process is coded through quantum coding; and the niche technology is used to initialize the population, adaptive rotation angle, and quantum mutation strategy to improve the efficiency of the QGA and avoid premature convergence. Lastly, through the Kacem standard calculation example and the reliability analysis of the factory workshop processing process example, performance evaluation is conducted to show that the improved QGA has good convergence and does not fall into premature ability, the improved QGA can solve the problem of reasonable deployment of machines and personnel in the workshop, and the proposed method is more effective for the DRCFJSP than some existing methods. The findings can provide a good theoretical basis for actual production and application.

Integrating virtual reality in qualitative research methods: Making a case for the VR-assisted interview

Virtual reality (VR) is still very much a niche technology despite its increasing popularity since recent years. VR has now reached a point where it can offer photorealistic experiences, while also being consumer-friendly and affordable. However, so far only a very limited amount of software has been developed for the specific purpose of conducting (social science) research. In this article, we illustrate that integrating virtual reality to good effect in social science research does not necessarily require specialized hardware or software, an abundance of expertise regarding VR-technology or even a large budget. We do this by discussing our use of a method we have come to call ‘VR-assisted interviews’: conducting a (semi-structured) interview while respondents are confronted with a virtual environment viewed via a VR-headset. This method allows respondents to focus on what they are seeing and experiencing, instead of having them worry about how to operate a device and navigate an interface they are using for the first time. ‘VR-assisted interviews’ are very user-friendly for respondents but also limits options for interactiveness. We believe this method can be a valuable alternative, both because of methodological and practical considerations, for more complex applications of VR-technology in social science research.

Fundamental insights into the rejection behavior of polyimide-based OSN membranes

Organic solvent nanofiltration (OSN) is still a niche technology in the chemical industry due to time and money consuming process development, as organic solvents cause complex interactions between membrane, solvent, and solute. These interactions are not fully identified, and a fundamental understanding of mass transfer through OSN membranes is not established so far. Accordingly, mathematical models in OSN lack a general predictive capability. To overcome this problem, we aim to extend the current knowledge by a comprehensive experimental investigation of two polyimide membranes in combination with 17 solutes with molecular weights of 114–339 g/mol covering the range from nonpolar to polar, and five solvents with various properties. The results indicate solvent-dependent rejection phenomena that may be explained by a difference in the impact of solute properties on the rejection in dependency of the solvent. Based on these findings, three solvent-dependent solute sensitivity regions are derived similarly to previous results for PDMS membranes. The investigation of membrane–solvent interactions revealed an impact of the spatial distance in the 3D space of the total Hansen solubility parameter between solvent and membrane with a specific impact on membrane–solute sensitivity. These new findings contribute to a better understanding of transport phenomena and can serve as a new basis for predicting the separation performance in OSN.

AI‐Driven Multiobjective Scheduling Algorithm of Flood Control Materials Based on Pareto Artificial Bee Colony

Considering the competition between rescue points, we use artificial intelligence (AI) driven Internet of Thing (IoT) and regional material storage data to propose a multiobjective scheduling algorithm of flood control materials based on Pareto artificial bee colony (MSA_PABC). To address the scheduling of flood control materials, the multiple types of flood control materials, the multiple disaster sites, and entertain both emergency and fairness of rescue need to be considered comprehensively. The MSA_PABC has the constraints such as storage quantity constraint of warehouse materials, material demand constraint, and maximum transportation distance of flood control materials. We establish the scheduling optimization model of flood control materials for each disaster rescue point and the total scheduling optimization model for all flood control materials. Then, MSA_PABC uses the modified Pareto artificial bee colony algorithm to solve the multiobjective models. Three types of initialization strategies are proposed to calculate the fitness of each rescue point and the overall evaluation value of the food source. We propose the employ bee operations such as niche technology and local search of the variable neighborhood, the onlooker bee operations such as Pareto nondominated sorting and crossover operation, the scout bee operations such as maximum evolutionary threshold, and end elimination mechanism. Finally, our proposed solution obtains the nondominated solution set and its optimal solution. The experimental results show that no matter how the number of rescue points changes, MSA_PABC can find the nondominated solution set and optimal solution quickly. It improves the convergence rate of MSA_PABC and material satisfaction rate. Our solution also reduces the average maximum transportation distance, the standard deviation of maximum transportation distance, and the standard deviation of material satisfaction rate. The evaluation also demonstrates MSA_PABC outperforms the state‐of‐arts such as ABC (artificial bee colony), NSGA2 (nondominated sorting genetic algorithm 2), and MOPSO (multiobjective particle swarm optimization).

Hybrid Niche Immune Genetic Algorithm for Fault Detection Coverage in Industry Wireless Sensor Network

The industry wireless sensor network (IWSN) technology, which is used to monitor industrial equipment, has attracted more and more attention in recent years. Sensor nodes in IWSN can spontaneously complete distributed networking and carry out monitoring tasks under random deployment conditions. Therefore, a self‐organized IWSN is particularly suitable for the fault detection and diagnosis of industrial equipment in complex environments. However, due to the detection, ability of a single sensor node is limited, and the monitoring distribution problem is a typical multidimensional discrete NP‐hard combinatorial stochastic optimization problem, which is challenging to solve for the traditional mathematical methods. With the purpose of improving the target monitoring capability and prolonging lifetime of IWSN, a novel hybrid niche immune genetic algorithm (HNIGA) for optimizing the target coverage model of fault detection is proposed. It uses the genetic operation to evolve antibody groups and applies niche technology to maintain the diversity of antibody groups. As a result, HNIGA can effectively reduce the failure rate of detection targets. To verify the performance of HNIGA, a series of simulations under different simulation conditions are carried out. Specifically, HNIGA is compared with genetic algorithm (GA) and simulated annealing (SA). Simulation results show that HNIGA has a faster convergence speed and more robust global search capability than the other two algorithms.

A Modified MOEA/D Algorithm for Solving Bi-Objective Multi-Stage Weapon-Target Assignment Problem

In command of modern intelligent operations, in addition to solving the problem of multi-unit coordinated task assignment, it is also necessary to obtain a suitable plan according to the needs of decision makers. Based on these requirements, we established a multi-stage bi-objective weapon-target assignment model, and designed a new algorithm with niche and region self-adaptive aggregation (named MOEA/ D-NRSA) based on the decomposition-based multi-objective evolutionary algorithm (MOEA/D) to obtain richer solutions that meet the preferences of different decision makers. Compared with MOEA/D, MOEA/ D-NRSA has advantages in improving the convergence and maintaining the distribution of the solution. On the one hand, it contains a population evolution method based on niche technology to obtain better offspring; on the other hand, it has a new neighborhood selection and update strategy. This strategy first clusters the individuals in the objective space to divide into different regions, in which the subproblems can independently select the appropriate aggregation mode according to the clustering density of the region and update its neighborhood. This strategy can improve the uneven distribution of individuals and maintain the diversity and distribution of the population. Numerical experiments selected state-of-the-art algorithms for comparison, which proved the superiority of MOEA/D-NRSA.

Biocatalysis in the Swiss Manufacturing Environment

Biocatalysis has undergone a remarkable transition in the last two decades, from being considered a niche technology to playing a much more relevant role in organic synthesis today. Advances in molecular biology and bioinformatics, and the decreasing costs for gene synthesis and sequencing contribute to the growing success of engineered biocatalysts in industrial applications. However, the incorporation of biocatalytic process steps in new or established manufacturing routes is not always straightforward. To realize the full synthetic potential of biocatalysis for the sustainable manufacture of chemical building blocks, it is therefore important to regularly analyze the success factors and existing hurdles for the implementation of enzymes in large scale small molecule synthesis. Building on our previous analysis of biocatalysis in the Swiss manufacturing environment, we present a follow-up study on how the industrial biocatalysis situation in Switzerland has evolved in the last four years. Considering the current industrial landscape, we record recent advances in biocatalysis in Switzerland as well as give suggestions where enzymatic transformations may be valuably employed to address some of the societal challenges we face today, particularly in the context of the current Coronavirus disease 2019 (COVID-19) pandemic.

NGA-Inspired Nanorobots-Assisted Detection of Multifocal Cancer.

We propose a new framework of computing-inspired multifocal cancer detection procedure (MCDP). Under the rubric of MCDP, the tumor foci to be detected are regarded as solutions of the objective function, the tissue region around the cancer areas represents the parameter space, and the nanorobots loaded with contrast medium molecules for cancer detection correspond to the optimization agents. The process that the nanorobots detect tumors by swimming in the high-risk tissue region can be regarded as the process that the agents search for the solutions of an objective function in the parameter space with some constraints. For multimodal optimization (MMO) aiming to locate multiple optimal solutions in a single simulation run, the niche technology has been widely used. Specifically, the niche genetic algorithm (NGA) has been shown to be particularly effective in solving MMO. It can be used to identify the global optima of multiple hump functions in a running, effectively keep the diversity of the population, and prematurely avoid the genetic algorithm. Learning from the optimization procedure of NGA, we propose the NGA-inspired MCDP in order to locate the tumor targets efficiently while taking into account realistic in vivo propagation and controlling of nanorobots, which is different from the use scenario of the standard NGA. To improve the performance of the MCDP, we also modify the crossover operator of the original NGA from crossing within a population to crossing between two populations. Finally, we present comprehensive numerical examples to demonstrate the effectiveness of the NGA-inspired MCDP when the biological objective function is associated with the blood flow velocity profile caused by tumor-induced angiogenesis.

Multi-strategy synergy-based backtracking search optimization algorithm

In order to solve the problems of backtracking search optimization algorithm with slow convergence speed and easy to fall into local optimum, an improved backtracking search optimization algorithm based on multi-strategy synergy is proposed. Foremost, a combination mutation strategy based on chaotic map and gamma distribution is introduced. The poor individuals are mutated to generate better quality individuals under a certain probability. Next, the global optimal individual information is introduced into the cross equation to guide the population update. Last but not least, a small habitat displacement method based on simulated annealing is designed. The poor individual is found by the niche radius, and the rich individuals are reconstructed by the global optimal individual information and the Gaussian distribution random function, and the convergence speed of the algorithm is improved. The simulated annealing algorithm is integrated on the niche technology to ensure the diversity of the new population and improve the convergence speed of the algorithm. In this paper, some standard test functions are selected, and numerical simulations are carried out in low-dimensional and high-dimensional states, compared with seven well-performing algorithms. The improved algorithm was analyzed by complexity, T test and ANOVA test. Simulation experiments on 20 standard test functions show that the improved algorithm has a faster convergence speed and higher convergence accuracy. Even in a high-dimensional multi-peak function, the convergence accuracy of the improved algorithm after the same number of iterations is 15 times higher than the original algorithm above the magnitude.

Research on Multi-Alternatives Problem of Finite Element Model Updating Based on IAFSA and Kriging Model.

Due to insufficient test data, insufficient constraint equations and uncertain objective function, the local optimal solution and the global optimal solution of the objective function in finite element model updating may represent the actual parameters of the structure. Based on this, this paper proposes an improved artificial fish school algorithm. By combining the niche technology with the artificial fish school algorithm, the improved algorithm can systematically find multiple global optimal solutions and local optimal solutions of the objective function. Aiming at the difficulty of determining the niche radius, an adaptive niche radius mechanism is proposed. The improved algorithm is used to study the multi-alternatives problem of finite element model updating after verifying its feasibility through numerical simulation analysis. In the case of benchmark framework model updating, it is confirmed that multi-alternative problems exist and the global optimal solution of the objective function does not necessarily represent the true parameters of the structure. In case 2, the improved algorithm combined with the Kriging model is applied to the model updating of a cable-stayed footbridge, and 15 sets of solutions are obtained, in which the error objective function values of the measured and theoretical values of the bridge modes are close but the solutions are completely different. Combining with the actual bridge condition and reanalysis technology, the author takes the suboptimal solution 2 as the most representative solution of the bridge parameters, which reduces the possibility of misjudgment of structural parameters.