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The purpose of this paper is to introduce an innovative theoretical, numerical and experimental investigations on the HP NGD function. The identified HP NGD topology under study is constituted by first order passive RC-network. The simulations and measurements confirm in very good agreement the HP NGD behaviors of the tested circuits. NGD responses with optimal values of about -1 ns and cut-off frequencies of about 20 MHz are obtained.

The identified HP NGD topology understudy is constituted by a first-order passive Resistor-capacitor RC network. An innovative approach to HP NGD analysis is developed. The analytical investigation from the voltage transfer function showing the meaning of HP properties is established.

This paper introduces innovative theoretical, numerical and experimental investigations on the HP NGD function.

The NGD characterization as a function of the resistance and capacitance parameters is investigated. The feasibility of the HP NGD function is verified with proofs of concept constituted of lumped surface mounted components on printed circuit boards. The simulations and measurements confirm in very good agreement the HP NGD behaviors of the tested circuits. NGD responses with optimal values of about −1 ns and cut-off frequencies of about 20 MHz are obtained.

Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet users. Several features can be used for creating data mining and machine learning based spam classification models. Yet, spammers know that the longer they will use the same set of features for tricking email users the more probably the anti-spam parties might develop tools for combating this kind of annoying email messages. Spammers, so, adapt by continuously reforming the group of features utilized for composing spam emails. For that reason, even though traditional classification methods possess sound classification results, they were ineffective for lifelong classification of spam emails duo to the fact that they might be prone to the so-called “Concept Drift”. In the current study, an enhanced model is proposed for ensuring lifelong spam classification model. For the evaluation purposes, the overall performance of the suggested model is contrasted against various other stream mining classification techniques. The results proved the success of the suggested model as a lifelong spam emails classification method.

The development of the digital age has made data and information more transparent, enhancing the strategic perspectives of both buyers (strategic waiting) and sellers (price fluctuations) in their decision-making. This research investigates the optimal dynamic pricing strategy of the content product developer in relation to their consideration of consumer fairness concerns to elucidate the impact of consumer fairness concerns on the dynamic pricing strategy of the developer.

This paper assumes that monopolistic content developers implement a dynamic pricing strategy for the content product. Through constructing a two-period dynamic pricing game model, this research investigates the optimal decisions of the content developer, contingent upon their consideration or disregard of consumer fairness concerns. In the extension section, the authors additionally account for the influence of myopic consumers on these optimal decisions.

Our findings reveal that the degree of consumer fairness concerns significantly influences the developer’s optimal dynamic pricing decision. When a developer offers content products with lower depth, there is a propensity for the developer to refrain from incorporating consumer fairness concerns into a dynamic pricing strategy. Conversely, in cases where the developer offers a high-depth content product, consumer fairness concerns benefit the developer. Furthermore, our analysis reveals a consistent benefit for the developer from the inclusion of myopic consumers.

Few studies have delved into the conjoined influence of consumer fairness concerns and strategic behavior on dynamic pricing strategy. Our findings indicate that consumer fairness concerns can enhance the efficiency of the value chain for content products under specific conditions. This paper not only enriches the existing literature on dynamic pricing by incorporating consumer fairness concerns theoretically but also offers practical insights. The outcomes of this research can guide content product developers in devising optimal dynamic pricing strategies.

This study aims to determine whether or not fashion retail brands can maintain their essence by providing personalized care through conventional face-to-face interactions or the use of e-services.

An exploratory investigation is being conducted to attain this goal. According to the findings of this research, Chatbots have an impact on consumer loyalty. The quality of a Chatbot’s system, service and information are all critical to providing a positive consumer experience.

The study concluded that Chatbot e-services might potentially enable dynamic and fascinating interactions between firms and their consumers. To personalize a Chatbot, firms might change the tone of the language used. Customers are more likely to use a Chatbot if it resembles a real person, which increases their pleasure and confidence in the product.

More precisely, the emphasis of the inquiry was on Chatbot, a relatively new digital tool that offers user-friendly, personalized and one-of-a-kind support to customers. Using information supplied by consumers, the authors examine a five-dimensional model that gauges how customers feel about Chatbots in terms of their ability to communicate with users, offer amusement, be trendy, personalize interactions and solve problems.

Construction schedule delays and quality problems caused by construction errors are common in the field of prefabricated buildings. The effective monitoring of the construction project process is one of the key factors for the success of a project. How to effectively monitor the construction process of prefabricated building construction projects is an urgent problem to be solved. Aiming at the problems existing in the monitoring of the construction process of prefabricated buildings, this paper proposes a monitoring method based on the feature extraction of point cloud model.

This paper uses Trimble X7 3D laser scanner to complete field data collection experiments. The point cloud data are preprocessed, and the prefabricated component segmentation and geometric feature measurement are completed based on the PCL platform. Aiming at the problem of noisy points and large amount of data in the original point cloud data, the preprocessing is completed through the steps of constructing topological relations, thinning, and denoising. According to the spatial position relationship and geometric characteristics of prefabricated frame structure, the segmentation algorithm flow is designed in this paper. By processing the point cloud data of single column and beam members, the quality of precast column and beam members is measured. The as-built model and as-designed model are compared to realize the visual monitoring of construction progress.

The experimental results show that the dimensional measurement accuracy of beam and column proposed in this paper is more than 95%. This method can effectively detect the quality of prefabricated components. In the aspect of progress monitoring, the visualization of real-time progress monitoring is realized.

This paper proposed a new monitoring method based on feature extraction of the point cloud model, combined with three-dimensional laser scanning technology. This method allows for accurate monitoring of the construction process, rapid detection of construction information, and timely detection of construction quality errors and progress delays. The treatment process based on point cloud data has strong applicability, and the real-time point cloud data transfer treatment can guarantee the timeliness of monitoring.

Managing inventory continues to be a growing area of concern for many retailers due to the multitude of issues that arise from either an excess or shortage of inventory. This study aims to understand how a large-scale retail chain can improve its handling of excess seasonal inventory using three common strategies: information sharing, visibility, and collaboration.

This study has been designed utilizing a case study method focusing on one retail chain at three key levels: strategic (head office), warehouses, and retail stores. The data have been collected by conducting semi-structured interviews with senior-level employees at each of the three levels and employing a thematic analysis to examine the major themes.

The results show how three common strategies are being practiced by this retailer and how utilizing these strategies aids the retailer in improving its performance in regard to seasonal inventory. Among our research findings, some challenges were discovered in implementing the strategies, most notably: human errors, advanced forecasting deficiencies, and the handling of return merchandise authorizations.

This research takes a case study approach and focuses on one big-box retailer. The authors chose to study three levels (head office, warehouses, and retail stores) to gain a deeper understanding of the functions and processes of each level, and to understand the working relationships between them. Through the collection of primary data in a Canadian context, this study contributes to the literature by investigating supply chain strategies for managing inventory. The Canadian context is especially interesting due to the multi-cultural demographics of the country.

The objective of this study is threefold–first, to develop a Regional Competitiveness Index (RCI) for measuring competitiveness of sub-national regions for India; second, to test this index for its ability to explain regional growth, which validates usage and applicability of this index; and third, to further investigate if the competitiveness of states is in turn caused by economic growth, i.e. it is tested if there is a bidirectional causality between competitiveness and regional growth.

The data of indicators used in the index are from sources available freely in public domain. The competitiveness index is constructed using equal weightage supported by principal component analysis (PCA) technique. The causal relationship analysis is done using panel data of 10 years from 2008 to 2017 for 32 Indian states/union territories. The generalized method of moments (GMMs) is used for this dynamic regression estimation.

Based on RCI score, states have been ranked and through rank analysis, the authors observe the performance status of these sub-national regions and are able to categorize them as improving, no change or deteriorating in regional competitiveness. Using the GMM estimation, the association between RCI and economic growth is found to be significant at 10% level. This shows that regional competitiveness as captured through the RCI score is able to explain regional economic growth and economic disparity among the sub-national units. Further, that RCI score is found to Granger-cause growth, while growth does not lead to better RCI scores. This establishes the usefulness of RCI as an important policy variable to compare states and provide direction for sectoral reforms.

The limitations of the study include (1) broad assumption that these sub-national regions belong to a uniform macro-economic and technology environment, and (2) data constraints as it is a longitudinal study. The study implies that the composite index could capture differences in regional competitiveness explaining regional economic disparity and that competitiveness causes higher economic growth and not vice versa.

The RCI score can prove to be a useful indicator of economic performance of different states and can be used by national and state policymakers to compare and assess regional disparity among different states. The pillar-wise scores will be useful for in-depth study of weakness and strength of the sub-national territories.

Construction of an RCI for sub-national territories and analysis of panel data for longitudinal study of ten years is unique in the regional competitiveness literature.

In the context of promoting sustainable development in SMEs, the present study aims to investigate the relationship among solution dimensions based on the Industry 4.0 (I4.0) concept.

The study employs a comprehensive methodology that includes a systematic literature review, workshop, grounded theory and interpretive structural modeling. Various dimensions concerning I4.0 sustainability are tested and evaluated using a questionnaire design followed by hypothesis formulation. Further, grounded theory is used to extract the key solution dimensions that capture the essence of I4.0 implementation in SMEs. Finally, the solution dimensions for I4.0 sustainability are modeled using the ISM approach to understand the structural interdependencies among them, and Matrice d'Impacts Croisés Multiplication Applied to a Classification (MICMAC) analysis is done to understand the driving and dependence power among these dimensions.

The study identified 14 solution dimensions for the implementation of I4.0 in SMEs for sustainable development. Out of the 14 solution dimensions, human resource training programs (D4) appear at level 11, followed by top management commitment (D1), strategic collaborations (D3) and coordination among key stakeholders (D5) at level 2 in the hierarchical interpretive structural modeling (ISM) model. Also, these dimensions have an effect size of more than 0.50 which indicates a substantial correlation between the sustainability dimensions and Industry 4.0 implementation in SMEs.

The study contributes to the overall goal of fostering sustainability within the SME sector, which can pave the way for various stakeholders for the successful implementation of I4.0 sustainable solution dimensions.

Corrosion is a major concern for many industries that use metals as structural or functional materials, and the use of corrosion inhibitors is a widely accepted strategy to protect metals from deterioration in corrosive environments. Moreover, the toxic nature, non-biodegradability and price of most conventional corrosion inhibitors have encouraged the application of greener and more sustainable options, with natural and synthetic drugs being major actors. Hence, this paper aims to stress the capability of natural and synthetic drugs as manageable and sustainable, environmentally friendly solutions to the problem of metal corrosion.

In this review, the recent developments in the use of natural and synthetic drugs as corrosion inhibitors are explored in detail to highlight the key advancements and drawbacks towards the advantageous utilization of drugs as corrosion inhibitors.

Corrosion is a critical issue in numerous modern applications, and conventional strategies of corrosion inhibition include the use of toxic and environmentally harmful chemicals. As greener alternatives, natural compounds like plant extracts, essential oils and biopolymers, as well as synthetic drugs, are highlighted in this review. In addition, the advantages and disadvantages of these compounds, as well as their effectiveness in preventing corrosion, are discussed in the review.

This survey stresses on the most recent abilities of natural and synthetic drugs as viable and sustainable, environmentally friendly solutions to the problem of metal corrosion, thus expanding the general knowledge of green corrosion inhibitors.