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This paper aims to further develop stator permanent magnet (PM) type memory machines by providing generalized design guidelines for double-stator memory machines (DSMMs) with hybrid PMs. This paper discusses the design experience of DSMMs and presents a comparative study of radial magnetization (RM) and circumferential magnetization (CM) types.

It begins with an introduction to RM and CM operating principles and magnetization mechanisms. Then, a comparative study is conducted for one of the RM-DSMM rotor pole pairs, inner and outer stator clamping angles and low coercive force PMs thickness. Finally, the two machines’ finite element simulation performance is compared. The validity of the proposed machine structure is demonstrated.

In this paper, the double-stator structure is extended to parallel hybrid PM memory machines, and two novel DSMMs with RM and CM configurations are proposed. Two types of DSMMs have PMs and magnetizing windings on the inner stator and armature windings on the outer stator. The main difference between the two is the arrangement of PMs on the inner stator.

Conventional stator PM memory machines have geometrical space conflicts between the PM and armature windings. The proposed double-stator structure can alleviate these conflicts and increase the torque density accordingly. In addition, this paper contributes to comparing the arrangement of hybrid PMs for DSMMs.

The purpose of this study is to create an extended equivalent circuit model for a compound DC motor, consisting completely of electrical parameters and quantities.

The dynamic model of the compound DC motor is obtained by establishing the voltage equations for the armature and excitation circuit and the mechanical equation for the mechanical part. The mechanical parameters in the dynamic model are converted into electrical parameters with an electrical circuit proposed for the mechanical part. By combining the armature and excitation circuits with the electrical circuit created for the mechanical part, the extended equivalent circuit model of the compound DC motor is obtained. Because the proposed extended equivalent model is completely an electrical circuit, simulations can be made in the circuit simulation programme. Simulations of the proposed compound DC motor circuit were carried out, and the accuracy of the proposed circuit was verified by performing experimental studies with an existing compound motor.

When comparing speed and current profiles in experiments and simulations, it is seen that compound DC motor can be modelled with the proposed equivalent circuit including completely electrical elements in a simulation programme for the circuits. The results show that the proposed equivalent circuit satisfies the dynamic model of the compound motor.

In DC machine models, armature and excitation circuits are given as an electrical circuit, and mechanical part of the machine is modelled by only mechanical equations. The originality of this study is converting the dynamic model of an electrical machine consisting of electrical and mechanical equations into a completely electrical circuit. With the proposed method, the dynamic model of many motors can be converted into a completely electrical circuit. In this way, motors can be simulated as an electrical circuit in simulation programmes for the circuits, and the dynamic behaviour of motors can be obtained.

This paper aims to propose a novel simple and efficient structure for line-start axial-flux permanent magnet (LSAFPM) synchronous motor, especially regarding the permanent magnets (PMs) demagnetization reduction.

At first, a primitive raw scheme of the new structure for the LSAFPM motor is introduced. Considering this raw scheme, the levels of irreversible demagnetization in various regions throughout the entire volume of each PM are evaluated using 3 dimensional (3D) finite elements analysis (3D FEA) in full loading condition during startup until reaching steady state. Based on the results of these analyses, the primitive structural scheme is then modified through segmenting (cutting into four pieces) each PM from where the worst irreversible demagnetization levels occurred.

As will be demonstrated by the results of 3D FEA, the proposed modified structure is not only capable of successful startup and synchronization of the motor but also it considerably reduces the PM demagnetization level. Thus, the performance of the motor is significantly improved.

The demagnetization of PMs is an important effect in PM synchronous motors, which can greatly affect motor performance. Therefore, it is necessary to be considered in the motor design processes. This effect becomes much more significant in the line-start PM motors because the usual high-magnitude startup induction current produces a strong armature-reaction magnetic field, which may cause the PMs to be irreversibly demagnetized. The approach proposed in this paper provides a structural solution to mitigate the PM demagnetization effect and thereby improve the performance of an LSAFPM motor through modifying the structure of the LSAFPM motor according to an FEA-based PM demagnetization analysis. As a considerable contribution, in this analysis, the variation of demagnetization level between different areas inside each PM is computed and is considered as a basis for proposing an appropriate structural modification to mitigate the PM demagnetization effect as much as possible.

Consumers' perceptions of product intelligence affect their willingness to accept smart offerings. This paper explores how people perceive various smart products based on their smartness profiles, composed of five distinct smartness facets. Additionally, the study investigates how these perceptions of product intelligence impact consumers' evaluation of factors that either promote or impede the adoption of smart products. These factors are examined as potential mediators in the adoption process. This paper aims to determine if the value-based adoption model can be applied to a broad range of smart service systems.

Consumers assessed one of 28 smart products in a scenario-based quantitative study. Multilevel structural equation modeling (SEM) is used to test the conceptual model, taking the nested data structure into account.

The findings show that product smartness essentially enhances usage intention via adoption drivers (enjoyment and usefulness) and reduces usage intention via adoption barriers (intrusiveness). In particular, the ability to interact in a humanlike manner increases the benefits consumers perceive, which in turn increases consumer acceptance. Only the smartness characteristic of awareness impairs usage intention, mediated by the perceived benefits of enjoyment and usefulness.

In contrast to previous research, which usually focuses on single smart products, this work examines a variety of different products, which allows for better transferability of the results to other smart offerings. Furthermore, prior research has mainly focused on single facets of product smartness or researched smartness on an aggregated level. By considering the consumer perception of each smartness facet, the authors gain deeper insights into the perceptual differences regarding product smartness and how this affects technology adoption via conflicting key acceptance drivers and barriers.

India generates around two million tonnes of e-waste every year, and it is increasing at a very high rate of 30%. However, due to inefficient handling of infrastructure and limited number of collection centres along with the absence of proper incentive structure for producer and recyclers, 95% of e-waste reaches to unorganized sector for disposal. Consumers are not aware of the need of proper e-waste disposal and in absence of proper motivation and they are not inclined towards recycling process. Therefore, this paper aims to identify the best practices of e-waste take adopted all over the world to implement effective policy interventions for e-waste management in India and other emerging economies.

This paper has recommended preventive as well as curative policy interventions on the basis of best e-waste management practices of Germany, Italy and Japan; life cycle assessment of e-waste; and SWOT analysis of Indian electronic product industry.

Preventive measures include a deposit refund scheme wherein a consumer will be responsible for depositing a refundable fees during the purchase of the product. The amount should be arrived at keeping in mind cost involved in handling e-waste and ensure some motivation for the consumers to give back used product. To ensure proper tracking of the product, Radio frequency identification (RFID) tags can be used which will be activated at the time of sale of product and remain so until product reaches some designated recycling space or recycler and consumer is returned back his deposit fee. Subsidy to the producers and recyclers can also be provided by the government to further incentivize the whole process. An example of mobile phones has been used to understand the proposed deposit fees and associated cost structure. Curative measures to reduce the generation of e-waste in long run for managing the discussed issue have also been proposed.

This study is an initiative for proposing and implementing best e-waste take back techniques in a developing economy like India by acquiring learnings from best/advanced economies in terms of e-waste take back.

The purpose of the present study is to identify the active and dead links of uniform resource locators (URLs) associated with web references and to compare the effectiveness of Chrome, Google and WayBack Machine in retrieving the dead URLs.

The web references of the Library Hi Tech from 2004 to 2008 were selected for analysis to fulfill the set objectives. The URLs were extracted from the articles to verify their accessibility in terms of persistence and decay. The URLs were then executed directly in the internet browser (Chrome), search engine (Google) and Internet Archive (WayBack Machine). The collected data were recorded in an excel file and presented in tables/diagrams for further analysis.

From the total of 1,083 web references, a maximum number was retrieved by the WayBack Machine (786; 72.6 per cent) followed by Google (501; 46.3 per cent) and the lowest by Chrome (402; 37.1 per cent). The study concludes that the WayBack Machine is more efficient, retrieves a maximum number of missing web citations and fulfills the mission of preservation of web sources to a larger extent.

A good number of studies have been conducted to analyze the persistence and decay of web-references; however, the present study is unique as it compared the dead URL retrieval effectiveness of internet explorer (Chrome), search engine giant (Google) and WayBack Machine of the Internet Archive.

The web references of a single journal, namely, Library Hi Tech, were analyzed for 5 years only. A major study across disciplines and sources may yield better results.

URL decay is becoming a major problem in the preservation and citation of web resources. The study has some healthy recommendations for authors, editors, publishers, librarians and web designers to improve the persistence of web references.

The purpose of this paper is to provide a new procedure for in-plane compensation of geometric errors that often appear in the layers deposited by an additive manufacturing (AM) process when building a part, regardless of the complexity of the layer geometry.

The procedure is based on comparing the real layer contours to the nominal ones extracted from the STL model of the part. Considering alignment and form deviations, the compensation algorithm generates new compensated contours that match the nominal ones as closely as possible. To assess the compensation effectiveness, two case studies were analysed. In the first case, the parts were not manufactured, but the distortions were simulated using a predictive model. In the second example, the test part was actually manufactured, and the distortions were measured on a coordinate measuring machine.

The geometric deviations detected in both case studies, as evaluated by various quality indicators, reduced significantly after applying the compensation procedure, meaning that the compensated and nominal contours were better matched both in shape and size.

Although large contours showed deviations close to zero, dimensional overcompensation was observed when applied to small contours. The compensation procedure could be enhanced if the applied compensation factor took into account the contour size of the analysed layer and other geometric parameters that could have an influence.

The presented method of compensation is applicable to layers of any shape obtained in any AM process.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.

We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.

The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.

Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.

Because many cutting fluids contain hazardous chemical constituents, industries and researchers are looking for alternative methods to reduce the consumption of cutting fluids in machining operations due to growing awareness of ecological and health issues, government strict environmental regulations and economic pressures. Therefore, the purpose of this study is to raise awareness of the minimum quantity lubrication (MQL) technique as a potential substitute for environmental restricted wet (flooded) machining situations.

The methodology adopted for conducting a review in this study includes four sections: establishment of MQL technique and review of MQL machining performance comparison with dry and wet (flooded) environments; analysis of the past literature to examine MQL turning performance under mono nanofluids (M-NF); MQL turning performance evaluation under hybrid nanofluids (H-NF); and MQL milling, drilling and grinding performance assessment under M-NF and H-NF.

From the extensive review, it has been found that MQL results in lower cutting zone temperature, reduction in cutting forces, enhanced tool life and better machined surface quality compared to dry and wet cutting conditions. Also, MQL under H-NF discloses notably improved tribo-performance due to the synergistic effect caused by the physical encapsulation of spherical nanoparticles between the nanosheets of lamellar structured nanoparticles when compared with M-NF. The findings of this study recommend that MQL with nanofluids can replace dry and flood lubrication conditions for superior machining performance.

Machining under the MQL regime provides a dry, clean, healthy and pollution-free working area, thereby resulting the machining of materials green and environmentally friendly.

This paper describes the suitability of MQL for different machining operations using M-NF and H-NF.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0131/