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This paper aims to present a new design in the area of basal osseointegrated implant (BOI) for oral and maxillofacial surgery using a patient-specific computer-aided design (CAD) and additive manufacturing (AM) approach. The BOI was designed and fabricated according to the patient’s specific requirement, of maxilla stabilisation and dental fixation, a capacity not currently available in conventional BOI. The combination of CAD and AM techniques provides a powerful approach for optimisation and realisation of the implant in a design which helps to minimise blood loss and surgery time, translating into better patient outcomes and reduced financial burdens on healthcare providers.

The current study integrates the capabilities of conventional medical imaging techniques, CAD and metal AM to realise the BOI. The patient’s anatomy was scanned using a 128-slice spiral computed tomography scanner into a standard Digital Imaging and Communication in Medicine (DICOM) data output. The DICOM data are processed using MIMICS software to construct a digital representative patient model to aid the design process, and the final customised implant was designed using Creo software. The final, surgically implanted BOI was fabricated using direct metal laser sintering in titanium (Ti-64).

The current approach assisted us to design BOI customised to the patient’s unique anatomy to improve patient outcomes. The design realises a nerve relieving option and placement of porous structure at the required area based up on the analysis of patient bone structural data.

The novelty in this work is that developed BOI comprises a patient-specific design that allows for custom fabrication around the patients' nerves, provides structural support to the compromised maxilla and comprises a dual abutment design, with the capacity of supporting fixation of up to four teeth. Conventional BOIs are only available for a signal abutment capable of holding one or two teeth only. Given the customised nature of the design, the concept could easily be extended to explore a greater number of fixation abutments, abutment length/location, adjusted dental fixation size or greater levels of maxilla support. The study highlights the significance of CAD packages to construct patient-specific solution directly from medical imaging data, and the efficiency of metal AM to translate designs into a functional implant.

The COVID-19 pandemic emphasises the need for antiviral materials that can reduce airborne and surface-based virus transmission. This study aims to propose the use of additive manufacturing (AM) and surrogate modelling for the rapid development and deployment of novel copper-tungsten-silver (Cu-W-Ag) microporous architecture that shows strong antiviral behaviour against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

The research combines selective laser melting (SLM), in-situ alloying and surrogate modelling to conceive the antiviral Cu-W-Ag architecture. The approach is shown to be suitable for redistributed manufacturing by representing the pore morphology through a surrogate model that parametrically manipulates the SLM process parameters: hatch distance (h_d), scan speed (S_s) and laser power (L_p). The method drastically simplifies the three-dimensional (3D) printing of microporous materials by requiring only global geometrical dimensions solving current bottlenecks associated with high computed aided design data transfer required for the AM of porous materials.

The surrogate model developed in this study achieved an optimum parametric combination that resulted in microporous Cu-W-Ag with average pore sizes of 80 µm. Subsequent antiviral evaluation of the optimum architecture showed 100% viral inactivation within 5 h against a biosafe enveloped ribonucleic acid viral model of SARS-CoV-2.

The Cu-W-Ag architecture is suitable for redistributed manufacturing and can help reduce surface contamination of SARS-CoV-2. Nevertheless, further optimisation may improve the virus inactivation time.

The study was extended to demonstrate an open-source 3D printed Cu-W-Ag antiviral mask filter prototype.

The evolving nature of the COVID-19 pandemic brings new and unpredictable challenges where redistributed manufacturing of 3D printed antiviral materials can achieve rapid solutions.

The papers present for the first time a methodology to digitally conceive and print-on-demand a novel Cu-W-Ag alloy that shows high antiviral behaviour against SARS-CoV-2.

Nonperforming assets in any banking system have stressed the economic health of nations. Resultantly, literature has given considerable impetus to predict failures and bankruptcy. Past studies have focused on the outcome of failures, while, there is a dearth of studies focusing on ongoing firms in bad shape. We plug this gap and attempt to identify underlying communication patterns for firms witnessing prolonged underperformance. Using text mining, we extract and analyze semantic, linguistic, emotional, and sentiment-based features in non-numeric communication channels of these poor-performing firms and their peers. These uncovered patterns highlight the use of vocabulary and tone of communication, in correspondence to their financial well-being. Furthermore, using such patterns, we deploy various Machine Learning algorithms to identify loser firm(s) way ahead in time. We observe promising accuracy over a time window of five years. Such early warning signals can be of critical importance to various stakeholders of a firm. Exploration of writing style-related features for any firm would help its investors, lending agencies to assess the likelihood of future underperformance. Firm management can use them to take suitable precautionary measures and preempt the future possibility of distress. While investors and lenders can be benefitted from this incremental information to identify the likelihood of future failures.

The purpose of this paper is to investigate experimentally the effect of several input process factors, namely, feed rate, spindle speed, ultrasonic power and coolant pressure, on hole quality measures (penetration rate [PR] and chipping diameter [CD]) in rotary mode ultrasonic drilling of macor bioceramic material.

The main experiments were planned using the response surface methodology (RSM). Scanning electron microscopy was also used to examine and study the microstructure of machined samples. This study revealed the existence of dominant brittle fracture and little plastic flow that resulted in a material loss from the base work surface. Experiment findings have shown the dependability and adequacy of the proposed mathematical model.

The percentage of brittle mode deformation rises as the penetration depth of abrasives increases (at increasing levels of feed rate). This was due to the fact that at greater depths of indentation, material loss begins in the form of bigger chunks and develops inter-granular fractures. These stated causes have provided an additional advantage to increasing the CD over the machined rod of bioceramic. The desirability method was also used to optimize multi-response measured responses (PR and CD). The mathematical model created using the RSM method will be very useful in industrial revelation. Furthermore, the investigated answers’ particle swarm optimization (PSO) and teacher-learner-based optimization (TLBO) make the parametric analysis more relevant and productive for real-life industrial practices.

Macor bioceramic has been widely recognized as one of the most highly demanded innovative dental ceramics, receiving expanded industry approval because of its outstanding and superior characteristics. However, effective and efficient processing remains a problem. Among the available contemporary machining methods introduced for processing typical and advanced materials, rotary mode ultrasonic machining has been identified as one of the best suitable candidates for precise processing of macor bioceramics, as this process produces thermal damage-free profiles, as well as high accuracy and an increased material removal rate. The optimized combined setting obtained using PSO is feed rate = 0.16 mm/s, spindle speed = 4,500 rpm, ultrasonic power = 60% and coolant pressure = 280 kPa with the value of fitness function is 0.0508. The optimized combined setting obtained using TLBO is feed rate = 0.06 mm/s, spindle speed = 2,500 rpm, ultrasonic power = 60% and coolant pressure = 280 kPa with the value of fitness function is 0.1703.

The environmental performance of organizations has come under public policy limelight since the phenomenal increase of natural resource degradation and industrial waste. Thus, green concepts have been put forward, but the implementation of green practices faces many barriers and challenges. These barriers require attention as organizational practices are negatively affecting the environment leading to global warming and climate change. Therefore, this chapter systematically identifies four internal barriers including inadequate management commitment and support, insufficient technology competence and infrastructure, financial constraints, the uncertainty of economic benefits, and eight sub barriers within an organization concerning green concepts implementation. Moreover, this chapter also identifies four external barriers including lack of stakeholder's interest, inadequate environmental administrative support, scarce academic research, and lack of green collaborative practices, and eight sub barriers outside an organizational context concerning green concept implementation. The barriers in this research were identified by reviewing the existing literature on the topic. This chapter advances the green literature by identifying multiple barriers and challenges to the successful implementation of green concepts in organizations. This is of significance as if these barriers are tackled strategically, it would reduce environmental degradation problems and help make financial gains. Moreover, this research can help managers understand the key barriers to green concept implementation and provide guidance to them when attempting to implement green practices in their organizations. This research would also motivate researchers to extend further investigation on how to overcome such barriers and find out strategies to mitigate the barriers to green concept implementation to effectively address environmental issues.

This study describes a series of experiments investigating the upper hot layer temperature profile in a confined space under different ventilation conditions for porosity-controlled wood crib fires for pre-flashover conditions.

Full-scale compartment (4 m × 4 m × 4 m) experiments were carried out for four-door openings, i.e. 100%, 75%, 50% and 25% of the total vent area (2 m × 1 m) with the wood crib as a fuel load. The temperature of the upper hot smoke layers of the compartment was recorded with the help of four layers of thermocouples for varying vent areas.

The effect of ventilation on the properties, i.e. mass loss rate, enclosure temperature, heat release rate and carbon monoxide (CO) gas concentration, has been measured and analyzed. The effect of ventilation on heat flux and flame temperature has also been studied. Compartment gas temperature has been examined by five wood crib burning stages: Ignition, growth, steady burning, recess and collapse.

Findings demonstrate that the influence of vent openings varies for the burning parameters and upper layer temperature of the compartment. The current results are beneficial in analyzing thermal risks concerning compartment fire and fire safety engineering projects.

In today’s uncertain business environment, Industry 4.0 is regarded as a viable strategic plan for addressing a wide range of manufacturing-related challenges. However, it appears that its level of adoption varies across many countries. In the case of a developing economy like India, practitioners are still in the early stages of implementation. The implementation of Industry 4.0 appears to be complex, and it must be investigated holistically in order to gain a better understanding of it. Therefore, an attempt has been made to examine the Industry 4.0 implementation for the Indian manufacturing organization in a detailed way by analyzing the complexities of relevant variables.

SAP-LAP (situation-actor-process and learning-action-performance) and an efficient interpretive ranking process (e-IRP) were used to analyze the various variables influencing Industry 4.0 implementation. The variables were identified, as per SAP-LAP, through a thorough review of the literature and based on the perspectives of various experts. The e-IRP has been used to prioritize the selected elements (i.e. actors with respect to processes and actions with respect to performance) of SAP-LAP.

This study ranked five stakeholders according to their priority in Industry 4.0 implementation: government policymakers, industry associations, research and academic institutions, manufacturers and customers. In addition, the study also prioritized important actions that need to be taken by these stakeholders.

The results of this study would be useful in identifying and managing the various actors and actions related to Industry 4.0 implementation. Accordingly, their prioritized sequence would be useful to the practitioners in preparing the well-defined and comprehensive strategic roadmap for Industry 4.0.

This study has adopted qualitative and quantitative approaches for identifying and prioritizing different variables of Industry 4.0 implementation. This, in turn, helps the stakeholder to comprehend the concept of Industry 4.0 in a much simpler way.

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

This paper aims to provide a critical review of water generation from atmospheric air by using desiccant materials. Over the past few years, there has been very high stress on water scarcity, especially in Asian and African countries. Because of this insecurity, many countries are focusing on their research in the field of water technologies. Water generation from atmospheric air by using desiccant materials is one of the techniques among the air-to-water generators (AWGs).

A structured and systematic literature review has been presented to observe and understand the past trend/patterns in the field of water generation from atmospheric air by using desiccant materials. To understand the water generation technologies based on desiccant materials, the research papers from the years 1987 to 2022 have been studied and included.

The properties of the different and most probable desiccant materials in the field of AWGs have been discussed. A detailed review of testing reports of collected water samples has also been presented in tabular form. Finally, the economic analysis has been done and future prospects have been discussed. It is also found that the capacity of solid desiccant materials to adsorb the water is less as compared to liquid desiccant materials. But, the adsorption capacity can be improved by using composite desiccant materials.

The uniqueness of this manuscript lies in the compiling and examination of the existed published research papers, including variables such as author, year and geographical location, experimental/simulative, types of desiccant material, type of setup, desiccant material type and quantity and type of concentrator. This manuscript provides critique to the empirical and conceptual research in AWG technologies and also stimulates researchers to explore the topic very carefully.

This study seeks to understand the connection of methodology by finding relevant papers and their full review using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA).

Concrete-filled steel tubular (CFST) columns have gained popularity in construction in recent decades as they offer the benefit of constituent materials and cost-effectiveness. Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Gene Expression Programming (GEP) and Decision Trees (DTs) are some of the approaches that have been widely used in recent decades in structural engineering to construct predictive models, resulting in effective and accurate decision making. Despite the fact that there are numerous research studies on the various parameters that influence the axial compression capacity (ACC) of CFST columns, there is no systematic review of these Machine Learning methods.

The implications of a variety of structural characteristics on machine learning performance parameters are addressed and reviewed. The comparison analysis of current design codes and machine learning tools to predict the performance of CFST columns is summarized. The discussion results indicate that machine learning tools better understand complex datasets and intricate testing designs.

This study examines machine learning techniques for forecasting the axial bearing capacity of concrete-filled steel tubular (CFST) columns. This paper also highlights the drawbacks of utilizing existing techniques to build CFST columns, and the benefits of Machine Learning approaches over them. This article attempts to introduce beginners and experienced professionals to various research trajectories.