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The purpose of this paper is to develop a statistical model for delamination and thrust forcing during drilling of carbon-fibre reinforced polymer (CFRP) composites using response surface methodology (RSM) to determine the input parameters (drill speed, drill diameter and feed rate) that influences the output response (delamination and thrust force) in the machining of CFRP composite using solid carbide drill cutter.

Three factors, three levels central composite face centred (CCFC) design, is used to conduct the experiments on CFRP by carbide drill. The whole quality evaluation (delamination) was done by video measuring system to measure the width of maximum damage of the machined CFRP composite. The thrust forces during drilling are measured using digital multi-component cutting force (Make: IEICOS, Model: 652) dynamometer. The “Design Expert 7.0” is used to analyse the data collected graphically. An analysis of variance is carried out to validate the model and for determining the most significant parameter.

The response surface model is used to predict the input factors influencing the delamination and thrust force on the drilled surfaces of CFRP composite at different cutting conditions with the chosen range of 95 per cent confidence intervals. The analysis on the influences of the entire individual input machining parameters on the delamination and thrust force has been carried out using RSM. This investigation revealed that the drill diameter is the eminent factor which affects the responses.

In all, 0.3, 0.4 and 0.5 mm holes have been successfully made on CFRP using vertical machining center, whereas the previous researchers have not drilled hole size less than 1 mm in CFRP using vertical machining center.

This paper aims to propose a system to acquire images automatically for digital reconstruction of heritage artifacts using a six-degree of freedom industrial manipulator.

A virtual environment is created using Robot Studio® software to integrate the trajectory and differential motion of the robot manipulator and the motion of camera while acquiring images. A new area similarity matrix method is proposed to reduce the number of images required for digital reconstruction using Autodesk Recap® software. Real-time experiments have been performed using objects such as minion, ultimaker robot and cube. Evaluation of the digital reconstruction is conducted using the contour area matching method.

The number of images required for reconstruction based on area similarity matrix method is reduced to 63 per cent when compared with the random selection method. Quality parameters such as surface area, volume, number of defect holes, vertices and faces are enhanced for the proposed method.

Digital reconstruction of large-sized heritage artifacts cannot be performed in this setup. But this can be overcome by fixing the manipulator on a mobile platform or overhead crane. This paper does not discuss the reconstruction of partially damaged heritage artifacts, which could be accomplished based on deep learning techniques.

Using this approach, off-the-shelf heritage artifacts and large-scale objects can be reconstructed digitally with a minimum number of images and without compromising the quality of original models.

To the best of the authors’ knowledge, area similarity-based approach in 3D digital reconstruction by coupling the kinematics of an industrial manipulator and camera is proposed for the first time. A fully automated digital reconstruction technology to preserve valuable heritage artifacts has been developed. It also highlights the space constraints of the industrial manipulator in digital reconstruction.

In recent times, the construction industry is being influenced by technological innovations when delivering a better, more effective and efficient desired project, cyber-physical systems (CPSs) offer a coupling of the physical and engineered systems by monitoring, coordinating, controlling and integrating their operations. This study aims to examine the level of awareness of professionals and usage of CPSs for construction projects in Nigerian construction industry.

The target population for this study was the professionals in the construction industry consisting Architects, Quantity Surveyors, Engineers and Builders. Data collection was through the use of a structured questionnaire administered to the target population. The data was analyzed by using statistical tools.

This study concluded that the construction professionals in the Nigerian construction industry are mostly aware about the heating, ventilation and air conditioning (HVAC) systems, global positioning system, microphone, speakers and camera as the most widely used CPSs in construction industry. HVAC systems was also found to be the mostly adopted technologies in the construction industry.

This study recommended that platforms that increase the awareness and encourage the usage of CPSs in construction industry should be encouraged by stakeholders concerned with management of construction projects. Such include electronic construction and adoption of blockchain technology.

The continuous failure of construction projects notwithstanding appreciable increase in project management knowledge has necessitated a proactive approach of assessing early warning signs (EWS) of building projects failure. Building projects are expected to show warning signs before experiencing crises, comparable to a patient displaying symptoms of a disease. Thus, this study aims to examine the EWS that predisposed building projects to failure in Nigeria to provide empirical data for enhancing projects delivery.

Primary data were used for the study. Structured questionnaire was administered to consultants and contractors’ personnel within Lagos State, Nigeria. A total of 180 copies of questionnaire were administered and 134 copies (combined response rate of 74.44 per cent) were retrieved. Frequency distribution, percentages, mean item score and Mann–Whitney test were used to analyse the data.

Most construction professionals applied the EWS approach from project planning and early construction phase. The most significant EWS predisposing building projects to failure were “Management inability and incompetence to proactively detect and manage problems at early project stages”, “Actual expenditure is constantly shooting beyond cost estimates” and “Incurred costs already getting higher than the anticipated benefits”. Project/construction management-related symptoms are most significant to predisposing building projects to failure.

The study provided implications for effective project management of building projects through proactive approach which is very paramount to improving the delivery of building projects in Nigeria.

The study provides implications for proactive management of building projects, thereby enhancing the delivery of building projects.

The purpose of this research is to undertake a bibliometric analysis of digital marketing research in small and medium enterprises (SMEs). The study examines papers over the last two decades and performed performance analysis, co-citation analysis, bibliographic coupling and scientific mapping.

The study examines 247 documents retrieved from the Scopus database using bibliometric analysis, performance analysis and thematic clustering. The study looked at the scientific productivity of papers, prolific authors, most influencing papers, institutions and nations, keyword co-occurrence, thematic mapping, co-citations and authorship and country collaborations. VOSviewer was employed as a tool in the research to conduct the performance analysis and thematic clustering.

The most productive year was 2021 with 56 publications and the most impactful institute and countries are the University of Birmingham, UK, and the country is United Kingdom, respectively. Similarly, the most influential journal is “Industrial Marketing Management”, and the most productive journal is “International Journal of Internet Marketing and Advertising”. Furthermore, the most cited article is “Usage, barriers and measurement of social media marketing: An exploratory investigation of small and medium B2B brands”. The authors also identified five thematic clusters of digital marketing research in SMEs.

It informs and directs researchers on the current state of study in the field of digital marketing literature in SMEs. It also outlines future research directions in this field.

This is the first study which provides the performance analysis and scientific mapping of the digital marketing literature in SMEs.

The purpose of this paper is the physico-chemical, microbiological, colorimetric, sensorial characterization of choris, a traditional smoked pork sausage produced in the state of Goa (India), including its storage stability (0–180 days) at room temperature, aiming at the geographical indication certification and entrepreneurship prospects.

A total of 48 samples of “choris” were collected from 24 villages of Goa and were analyzed at different intervals (0–180 days) for physico-chemical, proximate, microbiological, colorimetric and sensory attributes during the storage at room temperature.

The mean pH of 4.45 and water activity (a_w) of 0.78 were recorded. The pH, tyrosine value, fat percentage and free fatty acid content registered a significant increase, whereas moisture content, a_w and moisture–protein ratio reduced as storage progresses. The colorimetric values lightness (L*), hue angle (H*) and redness index values reduced significantly during storage. The mean total viable count, yeast and mold, and lactic acid bacteria count were found to proliferate significantly as storage advanced. Nevertheless, the hedonic scores not reaching an unacceptable level (= 4) at the end of the storage period.

In accordance with the various parameters adopted during the study, choris could be characterized as “naturally fermented dry smoked” sausages, which were shelf-stable at room temperature for 180 days. The product characterization of choris is essential to guarantee the genuineness, safety and consumer's acceptability. This study will also rebound in an augmented uniformity of the product, which will favor the geographical indication certification and the entrepreneurship prospects of this traditional product.

The purpose of this paper is to assess food safety knowledge, choice behavior and health hazard concerns of ethnic Goan pork sausage consumers through various consumer food safety indices and also to evaluate socio-demographic influences on various consumer food safety indices.

Primary data in the form of 210 questionnaire responses were collected from Goan pork sausage consumers. Based on the responses, seven various consumer food safety indices were derived. The cumulative score of all the indices were normalized (0–1). The association between socio-demographic variables and consumer indices were evaluated using linear regression analysis, Pearson's correlation matrix, principal component analysis (PCA) and correspondence analysis (CA).

The results of the analysis indicate that the consumption of Goan pork sausages is most prevalent amongst younger people with high education having higher income. A majority of consumers have high score for the four consumer indices. The results of correlation analysis, regression analysis, PCA and CA revealed a significant association of consumer indices with socio-demographic variables.

The results of this study will provide a way forward for the evaluation of existing overall food safety concerns in the target population and in the future would be used for the promotion of good handling practices and health of Goan pork sausage consumers.

Bundled payments for care are an efficient mechanism to align payer, provider, and patient incentives in the provision of health care services for an episode of care. In this chapter, we use agency theory to examine the evolution of bundled payment programs in private and public payer arrangements, and postulate future directions for bundled payment development as a key component in the provision and payment of health care services.

Purpose: Green computing is a way of using the computer resource in an eco-friendly while maintaining and decreasing the harmful environmental impact. Minimising toxic materials and reducing energy usage can also be used to recycle the product.

Need for the Study: The motivation of the study is to use green computing resources to decrease carbon emissions and their adverse effect on the environment.

Methodology: The study uses a qualitative method of collecting resources and data to address the opportunities, challenges, and future trends in green computing for Sustainable Future Technologies. The study focusses on multiple kinds of cloud computing services collected and executed into single remote servers. The service demand processor offers these services to the client per their needs. The simultaneous requests to access the cloud services, processing and expertly managing these requests by the processors are discussed and analysed.

Findings: The findings suggest that green computing is an upcoming and most promising area. The number of resources employed for green computing can be beneficial for lowering E-waste so that computing can be environmentally friendly and self-sustainable.

Practical Implications: Green computing applies across all industries and service sectors like healthcare, entertainment, tourism, and education. The convergence of technologies like Cloud Computing, AI, and Internet of Things (IoT) is greatly impacting Green Supply Chain Management (GSCM) market.

Affective states in learning have gained immense attention in education. The precise affective-states prediction can increase the learning gain by adapting targeted interventions that can adjust the changes in individual affective states of students. Several techniques are devised for predicting the affective states considering audio, video and biosensors. Still, the system that relies on analyzing audio and video cannot certify anonymity and is subjected to privacy problems.

A new strategy, termed rider squirrel search algorithm-based deep long short-term memory (RiderSSA-based deep LSTM) is devised for affective-state prediction. The deep LSTM training is done by the proposed RiderSSA. Here, RiderSSA-based deep LSTM effectively predicts the affective states like confusion, engagement, frustration, anger, happiness, disgust, boredom, surprise and so on. In addition, the learning styles are predicted based on the extracted features using rider neural network (RideNN), for which the Felder–Silverman learning-style model (FSLSM) is considered. Here, the RideNN classifies the learners. Finally, the course ID, student ID, affective state, learning style, exam score and course completion are taken as output data to determine the correlative study.

The proposed RiderSSA-based deep LSTM provided enhanced efficiency with elevated accuracy of 0.962 and the highest correlation of 0.406.

The proposed method based on affective prediction obtained maximal accuracy and the highest correlation. Thus, the method can be applied to the course recommendation system based on affect prediction.