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The purpose of this paper is to fabricate the scaffolds with different pore architectures using additive manufacturing and analyze its mechanical and biological properties for bone tissue engineering applications.

The polylactic acid (PLA)/composite filament were fabricated through single screw extrusion and scaffolds were printed with four different pore architectures, i.e. circle, square, triangle and parallelogram with fused deposition modelling. Afterwards, scaffolds were coated with hydroxyapatite (HA) using dip coating technique. Various physical and thermo-mechanical tests have been conducted to confirm the feasibility. Furthermore, the biological tests were conducted with MG63 fibroblast cell lines to investigate the biocompatibility of the developed scaffolds.

The scaffolds were successfully printed with different pore architectures. The pore size of the scaffolds was found to be nearly 1,500 µm, and porosity varied between 53% and 63%. The fabricated circular pore architecture resulted in highest average compression strength of 13.7 MPa and modulus of 525 MPa. The characterizations showed the fidelity of the work. After seven days of cell culture, it was observed that the developed composites were non-toxic and supported cellular activities. The coating of HA made the scaffolds bioactive, showing higher wettability, degradation and high cellular responses.

The research attempts highlight the development of novel biodegradable and biocompatible polymer (PLA)/bioactive ceramic (Al₂O₃) composite for additive manufacturing with application in the tissue engineering field.

The purpose of this paper is to argue for an integrative model of social enterprises (SE) and social marketing (SM) to usher in desirable change, instead of the currently adopted either/or approach. We offer the shadow framework to integrate these two paradigms in the context of peace-building organizations.

Using purposive sampling strategy, 19 cases of peacebuilding initiatives were identified and reviewed from secondary sources. Ashoka Fellows working in the domain of peacebuilding, along with a few other exemplary cases across the globe were considered.

We found an emerging typology of three forms in the organizational responses to peacebuilding initiatives: (a) pure charity-driven work, (b) dual structure of charity plus business enterprises, and (c) social enterprises with distinct revenue model.

Building upon previous theoretical research, we find a lot of merit in SEs adopting the SM toolkit. We contribute to theory building by showing the interaction between paradox theory and stakeholder marketing in the context of SEs dealing with wicked problems such as peacebuilding. Consequently, we propose a shadow social marketing (SSM) model that would camouflage the real offering of peace through an apparent offering that would be non-controversial in nature and result in moderate-importance small wins for the multiple stakeholders involved with conflicting interests.

From a managerial perspective, chances of success of the desired social change increases by complementing the efforts of SEs through the SM toolkit. Organizationally, although all the three forms of peacebuilding initiatives can benefit from systematic usage of the SSM, they need to reframe their efforts toward those that are not pro-peace, rather than preach to the converted. Consequently, the answer may lie in efforts at building cultural sensitivity to promote entrepreneurship amongst such target groups amongst such target groups in conflicting communities, with an organizational form that successfully marries SEs and SM.

Though previous scholarship mentions the need for finding complementarities between social marketing objectives and social enterprise missions, no paper yet has suggested a roadmap for achieving it. This paper highlights an integrative plan that, in this specific case of peacebuilding initiatives, or social enterprises in general, can leverage to evolve better organizational practices, improve financial sustainability and measurable impact to effect the desired social change.

Access to pure drinking water is considered as a basic human right and part of the United Nations' Sustainable Development Goals. India ranks poorly in terms of providing universal coverage of potable water to its citizens. This case highlights the challenges faced by the private sector in providing purified drinking water for a diverse country like India with many geographical regions (and their corresponding water impurities) and differing levels of economic prosperity (making sustainability tougher for private companies) by focusing on Waterlife India Private Limited (WLIP). WLIP is a for-profit social enterprise that sells drinking water in rural hinterlands and urban slums at a very affordable price of US$0.006 per liter. Since its inception in 2008, WLIP has evolved to become a major player in the fragmented Indian affordable drinking water industry. Sustainability of the WLIP business model is based on a unique public–private partnership template in which three parties come together—a corporate sponsor who bears the cost of the water filtration equipment; community governance bodies like panchayats or municipalities, which give a sense of operational legitimacy to the model; and WLIP as the driving force of the network. This business model is unique for three reasons: suitable incentive mechanisms with proper alignment of interests among various stakeholders; optimization of the water-filtration plant equipment to community demand; and achieving the delicate balance between standardization of processes and customization in offerings to the operating context. Alignment of partner interest is the principal differentiator that also ensures accountability and impact.

As enterprises focus on offering integrated product/service bundles, performance‐based contracts become ever so important in ensuring effective delivery. Performance‐based contracts fall under the result‐oriented category of industrial product service systems (PSSs). The paper aims to present a conceptual framework for operations strategy in performance‐based industrial PSSs that will help manufacturing companies configure their operations to support effective delivery of integrated product/service offering.

This paper first develops a conceptual framework for operations strategy in performance‐based contracts by identifying the key elements after a detailed systematic review of literature. A major shift in support and maintenance logistics for complex engineering systems over the past few years has been observed in the defence and aerospace industries. Availability contracting, a special type of performance‐based contracts, is replacing traditional service procurement practices. Two exploratory case studies involving defence availability contracts are conducted for making inferences regarding the operations strategy.

The important findings of this research are a set of elements of operations strategy guiding the development of a conceptual framework, a set of operating principles and processes supporting effective delivery of performance‐based service contracts.

The true value of this research is to open up the novel area of result‐oriented industrial PSSs operations strategy by capturing the key characteristics of operations using both literature and empirical evidence.

This paper aims to investigate the unsteady mixed convection along an exponentially stretching surface in presence of transverse magnetic field applied at the wall and the opposing buoyancy flow.

The dimensional partial differential equations governing the flow field are transformed to non-dimensional coupled partial differential equations with the aid of suitable non-similar transformations. The resulting equations are then solved by the coalition of quasilinearization technique and the finite difference method.

Effects of volumetric heat source/sink, suction/blowing and other dimensionless parameters on velocity and temperature profiles are examined numerically. This investigation reveals that in presence of opposing buoyancy flow, the suction and volumetric heat source enhances the skin-friction coefficient, while the rise in the MHD increases the momentum boundary layer.

To the best of the authors’ knowledge, no such investigation has been carried out in the literature.

The purpose of this paper, is to predict the various phases and crystal structure from multi-component alloys. Nowadays, the concept and strategies of the development of multi-principal element alloys (MPEAs) significantly increase the count of the potential candidate of alloy systems, which demand proper screening of large number of alloy systems based on the nature of their phase and structure. Experimentally obtained data linking elemental properties and their resulting phases for MPEAs is profused; hence, there is a strong scope for categorization/classification of MPEAs based on structural features of the resultant phase along with distinctive connections between elemental properties and phases.

In this paper, several machine-learning algorithms have been used to recognize the underlying data pattern using data sets to design MPEAs and classify them based on structural features of their resultant phase such as single-phase solid solution, amorphous and intermetallic compounds. Further classification of MPEAs having single-phase solid solution is performed based on crystal structure using an ensemble-based machine-learning algorithm known as random-forest algorithm.

The model developed by implementing random-forest algorithm has resulted in an accuracy of 91 per cent for phase prediction and 93 per cent for crystal structure prediction for single-phase solid solution class of MPEAs. Five input parameters are used in the prediction model namely, valence electron concentration, difference in the pauling negativeness, atomic size difference, mixing enthalpy and mixing entropy. It has been found that the valence electron concentration is the most important feature with respect to prediction of phases. To avoid overfitting problem, fivefold cross-validation has been performed. To understand the comparative performance, different algorithms such as K-nearest Neighbor, support vector machine, logistic regression, naïve-based approach, decision tree and neural network have been used in the data set.

In this paper, the authors described the phase selection and crystal structure prediction mechanism in MPEA data set and have achieved better accuracy using machine learning.