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This paper aims to analyze the impact of sustainability measures taken during the design and construction phases, by examining two categories of sustainability: energy efficiency and reducing carbon emissions and material selection and waste management. These aspects are examined from the perspectives of long-term building performance and maintenance practices, as well as user/tenant satisfaction.

This study includes a literature review related to the two topics under consideration, followed by a comparative case study analysis of four projects to determine practical validity. All case studies in this paper used a semi-structured survey with various project stakeholders, which helped the authors identify measures taken as well as obstacles and challenges during the process.

According to the four case studies, adequate attention should be paid to the two areas of interest during a project’s design and construction phases. Including case studies from around the world (four case studies from three different countries) offers insights into effective sustainability practices in building design and construction, providing instances of successful implementation and emphasizing the obstacles and potential when incorporating sustainability into the design and construction phases.

The findings also show that design and construction participants and companies should reduce waste generation and carbon emissions. In addition, they should make decisions on material selection to enhance projects’ sustainability and to contribute to creating a habitable planet for the future.

The influence of the design and construction phases on long-term project sustainability is of major importance and concern to users, owners, designers, contractors and facility managers. This study illustrates the necessity of including sustainability measures in the design and construction phases, highlighting the importance of sustainability in building design and construction through effective implementation techniques and interdisciplinary teamwork to realize sustainable goals.

This paper is based on the personal connections of the four authors to Shivaji Park, the largest public space in Mumbai. Three of the authors are childhood friends and were once long-term residents of that area. The focus of this article is Shivaji Park, anecdotally the largest park in the island city of Mumbai, with its historical connotations and its ongoing role as a relational and cultural artefact in the lives of these authors. The ongoing member status of all four authors in connection with the public space is explored despite all of them now ceasing to be locals.

This article uses a qualitative approach utilising informal conversations between the four authors recorded on zoom as the research method. Supported by belonging and emotional reflexivity as conceptual frames, it investigates how the spatial context fosters a binding relationality, which is ongoing despite the now disparate locations of the authors.

Based on a critical analysis of the recorded conversations between the authors the findings highlight that belonging/unbelonging centres around emotionally tinged representations of place.

The core of this paper rests in the emotional connections between the authors based on their collective memories with a public space and its surrounding areas as a focus. The use of informal conversations is crucial in teasing out nuanced aspects of data collected based on human relationalities. The paper emphasises the repercussions of ongoing changes stemming from urban progress. They incur emotional and human costs through a “culling” of connections and belongings.

Collaborative filtering (CF), one of the most popular recommendation techniques, is based on the principle of word-of-mouth communication between other like-minded users. The process of identifying these like-minded or similar users remains crucial for a CF framework. Conventionally, a neighbor is the one among the similar users who has rated the item under consideration. To select neighbors by the existing practices, their similarity deteriorates as many similar users might not have rated the item under consideration. This paper aims to address the drawback in the existing CF method where “not-so-similar” or “weak” neighbors are selected.

The new approach proposed here selects neighbors only on the basis of highest similarity coefficient, irrespective of rating the item under consideration. Further, to predict missing ratings by some neighbors for the item under consideration, ordinal logistic regression based on item–item similarity is used here.

Experiments using the MovieLens (ml-100) data set prove the efficacy of the proposed approach on different performance evaluation metrics such as accuracy and classification metrics. Apart from higher prediction quality, coverage values are also at par with the literature.

This new approach gets its motivation from the principle of the CF method to rely on the opinion of the closest neighbors, which seems more meaningful than trusting “not-so-similar” or “weak” neighbors. The static nature of the neighborhood addresses the scalability issue of CF. Use of ordinal logistic regression as a prediction technique addresses the statistical inappropriateness of other linear models to make predictions for ordinal scale ratings data.

This chapter focuses on a case study of Kareem Exchange Company and its use of big data analysis to detect and prevent fraud and suspicious financial transactions. The chapter describes the various phases of the big data analysis cycle, including discovery, data preparation, model planning, model building, operationalization, and communicating results, and how the Kareem Exchange Company team implemented each phase. This chapter emphasizes the importance of identifying the business problem, understanding the resources and stakeholders involved, and developing an initial hypothesis to guide the analysis. The case study results demonstrate the potential of big data analysis to improve fraud detection capabilities in financial institutions, leading to informed decision making and action.

Quality management among multiple business units of a large organization is often difficult if each unit is run independently in terms on their quality standards. In this case, participants will discuss how Bukhari Group of Companies should establish a common brand image through standardized quality. Participants should also understand that common brand image for diverse products does not mean identical level of rejection or customer complaints. It should be understood that different markets have different tolerance for product failures. The participants can chalk out the measures the protagonist of the case should be able to take to effectively steer the Bhukari Group to achieve profits and excellence.

Since 1991 with the advent of globalization and economic liberalisation, basic conceptual and discursive changes are taking place in housing sector in India. The new changes suggest how housing affordability, quality and lifestyles reality is shifting for various segments of the population. Such shift not only reflects structural patterns but also stimulates an ongoing transition process. The paper highlights a twin impetus that continue to shape the ongoing transition: expanding middle class and their wealth - a category with distinctive lifestyles, desires and habits and corresponding ‘market defining’ of affordable housing standards - to articulate function of housing as a conceptualization of social reality in modern India. The paper highlights the contradictions and paradoxes, and the manner in which the concept of affordability, quality and lifestyles are embedded in both discourse and practice in India. The housing ‘dream’ currently being packaged and fed through to the middle class population has an upper middle class bias and is set to alienate those at the lower end of the middle-and low-income population. In the context of growing agreement and inevitability of market provision of ‘affordable housing’, the unbridled ‘market-defining’ of housing quality and lifestyles must be checked.

The growing incidence of precarious employment across many sectors is a serious challenge for a developing country like India. Neo-liberal arguments justify precarity as essential for the development of the free market economy and advocate realigning human resource practices with an ever-changing business environment and labor cost conditions. This chapter seeks to identify the determinants and dynamics surrounding precarity of workers engaged in temporary employment in India. It uses the unique Employment and Unemployment Survey data set published by the National Sample Survey Organisation of Government of India for two time periods 2009–2010 (66th Round) and 2011–2012 (68th Round) to bring out the dimensions of precarity and identify the determinants (both micro- and macro-levels) of participation in temporary employment. We find that precarious employment is most likely to affect the young, women, non-union members, those belonging to minority and socially deprived communities with low land holding and low educational status. Precarious employment is also most pronounced in states where labor-intensive industries are exposed to global import competition and where labor laws are rigid. The chapter concludes by discussing the implications of these findings for the economic and social policies that Indian governments have adopted in recent years.

This paper aims to clarify the relationship between mechanical behaviors and the underlying geometry of periodic cellular structures. Particularly, the answer to the following research question is investigated: Can seemingly different geometries of the repeating unit cells of periodic cellular structure result in similar functional behaviors? The study aims to cluster the geometry-functional behavior relationship into different categories.

Specifically, the effects of the geometry on the compressive deformation (mechanical behavior) responses of multiple standardized cubic periodic cellular structures (CPCS) at macro scales are investigated through both physical tests and finite element simulations of three-dimensional (3D) printed samples. Additionally, these multiple CPCS can be further nested into the shell of 3D models of various mechanical domain parts to demonstrate the influence of their geometries in practical applications.

The paper provides insights into how different CPCS (geometrically different unit cells) influence their compressive deformation behaviors. It suggests a standardized strategy for comparing mechanical behaviors of different CPCS.

This paper is the first work in the research domain to investigate if seemingly different geometries of the underlying unit cell can result in similar mechanical behaviors. It also fulfills the need to infill and lattify real functional parts with geometrically complex unit cells. Existing work mainly focused on simple shapes such as basic trusses or cubes with spherical holes.

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.