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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.

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.

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.

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.

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.

In this article, the authors consider the following nonlinear singular boundary value problem (SBVP) known as Lane–Emden equations, −u″(t)-(α/t) u′(t) = g(t, u), 0 < t < 1 where α ≥ 1 subject to two-point and three-point boundary conditions. The authors propose to develop a novel method to solve the class of Lane–Emden equations.

The authors improve the modified variation iteration method (VIM) proposed in [JAAC, 9(4) 1242–1260 (2019)], which greatly accelerates the convergence and reduces the computational task.

The findings revealed that either exact or highly accurate approximate solutions of Lane–Emden equations can be computed with the proposed method.

Novel modification is made in the VIM that provides either exact or highly accurate approximate solutions of Lane-Emden equations, which does not exist in the literature.

In today’s competitive market, product demand and its mix frequently vary due to various uncertainties, which thus imparts the overall manufacturing cost. Furthermore, uncertainties also impart the layout design in manufacturing industries in the long run. Therefore, the layout design needs to capture the possibility of uncertainties, and these uncertainties must be captured while designing the layout of a facility. Hence, an efficient facility layout design minimizes the manufacturing cost and lead time. The purpose of this paper is to propose a cellular layout design for a tower manufacturing industry.

The paper develops an embedded simulated annealing-based meta-heuristic to solve proposed cellular layout under different scenarios considering single and multi-time periods for tower manufacturing industry. A comparative study is also performed to analyze comparison among static cellular layout, a dynamic cellular layout or a robust stochastic cellular layout for the tower manufacturing industry.

The current layout of the industry is a process layout. Here, the layout for a tower manufacturing industry is proposed under SCFLP, DCFLP and RSCFLP. The proposed models and solution methodology is tested using six scenarios with different combination of time periods. Lastly, OFV value obtained for all the scenarios is compared, and it is found that RSCFLP outruns other SCFLP and DCFLP for a tower manufacturing industry. Based on the above study, it is also concluded that RSCFLP is an efficient and effective layout in tower manufacturing industry.

The paper proposes a cellular layout design for a tower manufacturing industry. The cellular layout design is found to be preferred over the traditional layout as it reduces material handling cost, manufacturing lead time and hazards. Moreover, it enhances productivity and quality.

This paper aims to develop supply chain strategies for the fashion retail supply chain (FRSC), likely to be disrupted by the current pandemic (COVID-19) under physical and online retail stores. The resilient retail supply chain design is proposed under budget allocation and merchandise capacity constraints.

This paper utilises the theory of constraint (ToC) and goal programming (GP) to address the COVID-19 impact on FRSC. The budgetary and capacity constraints are formulated with a constraint optimisation model and tested with six different priorities to deal with the physical and online stores. Next, all priorities are developed under different FRSC business scenarios. The ToC-GP-based optimisation model is validated with one of the Indian fashion retail supply chains.

The proposed optimisation model presents the optimal retailing strategies for selling fashion goods over physical and online platforms. The multiple scenarios are presented for developing trade-offs among different strategies to maximise the retailer's merchandise performance. This paper also highlighted the strategic movement from high merchandise density stores to low merchandise density stores. This implies a reduction of sales targets and aspiration levels of both online and physical fashion stores.

The proposed model is validated with one of the fashion retailers in India. Other nations or multiple fashion retailers might be considered for more generalisation of findings in the future.

This research helps fashion retail supply chain managers deal with consumer demand uncertainty over physical and online stores in pandemic times. Limitation: Other nations or multiple fashion retailers might be considered for more generalisation of findings in the future.

This is the first study that considered the impact of COVID-19 on the retail fashion supply chain. The effect of physical and online platforms is mainly discussed from consumer marketing perspectives, but an inventory and resilience perspective is missing in earlier studies. The role of merchandise planning is highlighted in this study.