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Fixed mode noise and random mode noise always exist in the image sensor, which affects the imaging quality of the image sensor. The charge diffusion and color mixing between pixels in the photoelectric conversion process belong to fixed mode noise. This study aims to improve the image sensor imaging quality by processing the fixed mode noise.

Through an iterative training of an ergoable long- and short-term memory recurrent neural network model, the authors obtain a neural network model able to compensate for image noise crosstalk. To overcome the lack of differences in the same color pixels on each template of the image sensor under flat-field light, the data before and after compensation were used as a new data set to further train the neural network iteratively.

The comparison of the images compensated by the two sets of neural network models shows that the gray value distribution is more concentrated and uniform. The middle and high frequency components in the spatial spectrum are all increased, indicating that the compensated image edges change faster and are more detailed (Hinton and Salakhutdinov, 2006; LeCun et al., 1998; Mohanty et al., 2016; Zang et al., 2023).

In this paper, the authors use the iterative learning color image pixel crosstalk compensation method to effectively alleviate the incomplete color mixing problem caused by the insufficient filter rate and the electric crosstalk problem caused by the lateral diffusion of the optical charge caused by the adjacent pixel potential trap.

Environmental pollution has increased as a result of modern settlements' expanding demand and rapid population growth. In Ebonyi State, Nigeria, quarrying is one of the activities that has an impact on the environment and ecosystem. The aim of the study is to assess stream water’s quality in order to ascertain how quarry operations affect the streams’ water quality. The present study investigated the environmental impact of quarrying on the physicochemical properties of surface water in Ebonyi State, Nigeria.

A total of 288 surface water samples were taken in 2018, 2019 and 2020 from quarry locations and a Control location for the determination of physicochemical properties and heavy metal contents using standard analytical methods. Datasets were analysed using Fisher’s significance least difference (F-LSD) at the 0.05 probability level.

This study discovered that surface waters around quarries are severely polluted, according to the results of the physico-chemical and heavy metal contents of the surface waters. Most of the physical and chemical properties of the water downstream of the Ishiagu, Umuoghara and Ngbo did not meet World Health Organisation (WHO) standards, such as colour and pH. The heavy metal levels in the Ishiagu, Umuoghara and Ngbo streams were above WHO criteria for Pb, Cd and Fe. The results point to the obvious conclusion, without prejudice to other unexplained factors that the pollution is most likely the result of quarry contamination. Strict measures should be taken to regularly monitor the water quality of the streams.

This study focused on the assessment of physicochemical properties using standard analytical methods to evaluate the environmental impact of quarrying on surface water qualities. The study used the World Health Organisation (WHO, 2011) water guideline as a standard to compare with the study dataset and control measures.

This paper aims to present two different methods to speed up a test used in the sanitary ware industry that requires to count the number of granules that remains in the commodity after flushing. The test requires that 2,500 granules are added to the lavatory and less than 125 remain.

The problem is approached using two deep learning computer vision (CV) models. The first model is a Vision Transformers (ViT) classification approach and the second one is a U-Net paired with a connected components algorithm. Both models are trained and evaluated using a proprietary data set of 3,518 labeled images, and performance is compared.

It was found that both algorithms are able to produce competitive solutions. The U-Net algorithm achieves accuracy levels above 94% and the ViT model reach accuracy levels above 97%. At this time, the U-Net algorithm is being piloted and the ViT pilot is at the planning stage.

To the best of the authors’ knowledge, this is the first approach using CV to solve the granules problem applying ViT. In addition, this work updates the U-Net-Connected components algorithm and compares the results of both algorithms.

The purpose of this paper is to optimize the key parameters (mesh count, paper type and ink type) in screen printing, which are affecting the printed ink volume. The objective of the optimization was to maximize the color reliability by decreasing the color difference (ΔE value) of the prints while minimizing the ink consumption. Screen printing is still dominating the printing industry to make cost-effective production when high volumes are needed.

The experiment was designed using the Taguchi method, and the samples were prepared with screen-printing by using the standard squeegee angle and pressure. The effect of mesh count, ink type and paper type on ink consumption was evaluated with using analysis of variances and main effects plots of S/N ratio and standard deviation.

The factors ink type, paper type and mesh count were found significant for ink consumption due to their Probability (P) values which were lower than 0.05. It was determined that the mesh count was the most critical variable with the analysis of variance. The analysis showed that the selection of an optimum mesh count was the key to controlling the amount of the deposited ink. Although mesh counts were inversely proportional with the ink consumptions, they did not affect the color differences as expected.

The optimization of process parameters, that are most effective on the print quality, is necessary to minimize the ink usage and lower the costs and environmental impact without exceeding the desired ΔE value limits.

This study aims to realize the multipurpose use of inorganic materials in adsorption treatment of pigment wastewater and preparation of core-modified Color Index Pigment Red 57:1 (C.I. Pigment Red 57:1, PR 57:1).

In this paper, the inorganic materials (sepiolite and SiO₂·nH₂O) were used in both PR 57:1 production wastewater treatment and its core-modification. The inorganic material firstly adsorbed 3-hydroxy-2-naphthoic acid (bon acid) in the pigment wastewater to reduce chemical oxygen demand. Then, the inorganic material adsorbed with bon acid was reused to prepare core-modified PR 57:1.

In the pigment wastewater adsorption experiment, it was found that under pH = 3, the adsorption percentage of bon acid by inorganic material can reached up to 46.00%. The pigment characterization results showed that the core-modified PR 57:1 had a core-shell structure. Under UV light irradiation for 1 h, the core-modified PR 57:1 prepared with sepiolite and SiO₂·nH₂O showed total color difference ΔE value of 1.43 and 2.05, respectively, which was lower than that of unmodified PR 57:1 (ΔE = 2.89). In addition, the transmittance of pigment water suspension test results showed that the core-modified PR 57:1 showed better water dispersibility.

This paper attempts to develop a synergistic strategy based on the multipurpose use of inorganic materials in adsorption treatment of pigment wastewater and preparation of core-modified PR 57:1.

The purpose of this paper is to provide an organizing lens for viewing the distinct contributions to knowledge production from those research communities addressing the impact of competitive strategy on company performance in general, and the influence of cost leadership and differentiation strategy on organizational performance in detail.

The research methodology was based on the PRISMA review, and thematic analysis based on an iterative process of open coding was analyzed and then the sample was analyzed by illustrating the research title, objectives, method, data analysis, sample size, variables and country.

The main factor that influenced the competitive strategy is strategic growth; strategic growth has a significant influence on competitive strategy. Furthermore, competitive strategy will boost firm network, performance measurement and organization behavior. In the same way, the internal goal factor will enhance organizational effectiveness. Also, a differentiation strategy will support management practice factors, strategic positions, product price, product characteristics and company performance.

This study contributes to the literature by identifying a framework of competitive strategy factors, company performance factors, cost leadership strategy factors, differentiation strategy factors and competitive strategy with global market factors. This study provides a complete picture and description of the resulting body knowledge in competitive strategy and organizational performance.

This study aims to investigate and propose guidelines to enhance the accessibility of virtual reality (VR) interfaces for all users within professional learning environments. Motivated by a lack of comprehensive accessibility guidelines for VR learning, the study delves into a case study of Edstutia’s VR campus.

This case study examines the intersection of VR platform development and learning experience design through the lens of accessibility in human-computer interaction. It elaborates on how user experience feedback from differently-abled learners who were unable to fully participate in the VR experience due to disability shaped the 2.0 version development of a VR platform.

The outcomes of this case study are (1) a description of an inclusive and empathetic design application to increase multi-learner VR platform accessibility, (2) a sample actionable path from a design/development perspective in ensuring the accessibility design of a VR learning platform, and recommendations to facilitate the design process.

This case is a foundation for further research on improving accessibility in VR. In future work on VR campuses, we recommend scholars undertake research with an inclusive approach that actively involves users with different abilities in shaping guidelines, particularly on how individuals with different abilities should be approached for their input during the design and development process in a sensitive manner.

The authors summarize the steps taken to enhance the accessible interplay between the end users and the interface of VR technology in the emergence of VR accessibility standards. Central to this case’s exploration is the integration of accessibility as a pivotal element into an academic VR campus, i.e. multilearner education platform.

Increasing access to VR has strong social implications in a world where 17% of people report a disability.

This case contributes to the limited research available on increasing access to VR on campuses.

The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO₂ emission, NO_x emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR.

In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO₂ emission and NO_x emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN).

From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NO_x and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R² correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel.

The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.

In this study, the authors sought to investigate how the implicit social ties of both project owners and potential backers are associated with crowdfunding project success.

Drawing on social ties theory and factors that affect crowdfunding success, in this research, the authors developed a model to study how project owners' and potential backers' implicit social ties are associated with crowdfunding projects' degrees of success. The proposed model was empirically tested with crowdfunding data collected from Kickstarter and social media data collected from Twitter. The authors performed the test using an ordinary least squares (OLS) regression model with fixed effects.

The authors found that project owners' implicit social ties (specifically, their social media activities, degree centrality and betweenness centrality) are significantly and positively associated with crowdfunding projects' degrees of success. Meanwhile, potential project backers' implicit social ties (their social media activities and degree centrality) are negatively associated with crowdfunding projects' degrees of success. The authors also found that project size moderates the effects of project owners' social media activities on projects' degrees of success.

This work contributes to the literature on crowdfunding by investigating how the implicit social ties of both potential backers and project owners on social media are associated with crowdfunding project success. This study extends the previous research on social ties' roles in explaining crowdfunding project success by including implicit social ties, while the literature explored only explicit social ties.

This paper explores how platforms reconfigure versatile digital resources to achieve marketing agility in international markets.

We draw on a case study of a Chinese digital platform to explore the processes and mechanisms of reconfiguring during marketing agility development. Data from different sources are collected, including interviews, informal dialogue and archival data.

Versatile digital resources create productive applications for previously less amendable marketing and nonmarketing resources to be malleable, editable and reconfigurable in marketing agility development. This study identifies and clarifies three versatile digital resource-enabled reconfiguration activities in marketing agility building: recombining digital artifacts, repurposing human capital and cross-pollinating markets.

Since our study adopts a case study method, future research can extend our insights by using quantitative methods to test and verify our theoretical framework.

First, we provide insights into how organizations can reconfigure versatile digital resources to achieve the benefits of marketing agility in international markets. Second, while recruiting new employees during internationalization is vital, we suggest that assisted by digital artifacts, firms can repurpose the existing workforce, such as via multitasking, swift task-switching and flexible job redirecting to satisfy dynamic international business requirements with lower adjustment costs. Third, we offer two localization approaches in which firms can use digital artifacts as the enabler to remix sociocultural elements with local adaptations to develop glocal content and decentralize content production to generate inclusive local content.

We provide a process model that specifies how platforms reconfigure versatile digital resources to achieve marketing agility in international markets. Furthermore, we provide novel insights into the literature on marketing agility in international markets and localization.