Birmingham City University

Rate Splitting Multiple Access (RSMA) precoder design with the practical finite-alphabet constellations instead of Gaussian inputs has been addressed in this paper. Considering a multiuser (MU) multiple-input single-output (MISO) broadcast channel (BC) system, we derive a generalized expression of the achievable rate for each user, in a way that the derived expression is generically applicable, e.g., for both underloaded and overloaded cases. Building upon the achievable rate expression, we formulate a multi-objective problem that maximizes the weighted sum rate (WSR) of the considered system, which incorporates with the optimization of the RS precoder for both common and private symbol streams in RSMA. The emphasis here is that our derivation of the achievable rate expression, the problem formulation of the WSR and the optimization of the RSMA precoder all involve the finite alphabet constellation constraint. An iterative gradient descent algorithm with alternative optimization and line search methods is applied to solve the optimization problem. Numerical results show that RSMA can reach the maximum achievable WSR, under both underloaded and overloaded scenarios, with less transmit power compared to the traditional schemes, e.g., space division multiple access (SDMA) and power-domain non-orthogonal multiple access (NOMA). Moreover, thanks to its flexibility, RSMA subsumes both SDMA and NOMA as its subset to fit into different scenarios such as underloaded and overloaded cases with different constellation sizes.

Businesses with owners from ethnic minorities function differently. Ethnocultural differences shape business decision-making and are impacted by discriminatory practices across the marketplace. While research has centered on the way ethnicity shapes business development and on the impact barriers have on commercial practices within ethnic minority populations, this chapter provides an instrumental case study of 20 immigrant student entrepreneurs at a Canadian university, investigating their perceptions of the capabilities needed to overcome barriers and rapidly pursue foreign market opportunities. It builds on a body of knowledge that features immigrant entrepreneurs’ ability to overcome barriers and identify and exploit opportunities. This chapter focuses on the experiences of immigrant student entrepreneurs in Canada and observes that this population of ethnic minority business owners possess certain generic, and unique capabilities that are further developed from within the university space in order to allow this population of entrepreneurs to overcome barriers and excel in the marketplace. These observations help transform how we understand ethnic minority entrepreneurship and offer important recommendations to facilitate effective support mechanisms to develop this potential beyond this university-centered instrumental case study.

One of the longest-serving Masters of the Revels, Charles Killigrew oversaw censorship of the English stage between 1677 and his death in 1725. He survived changes in political leadership and a high turnover in the office that regulated his own, that of the Lord Chamberlain. Son of one of the original Restoration theatre patentees, Killigrew also maintained a financial interest in the King’s Company and others that followed it. Some modern scholars have taken a dim view of his achievements as a censor, deeming him slack and detached from his duties, while others have focused on his investments in the theatre. This chapter examines how he juggled spheres of activity that often came into conflict with each other. Killigrew’s record as censor is assessed against those of his Restoration predecessors, and placed in the context of a network of shifting obligations and circumstances. The result is a nuanced view of a shadowy yet pivotal figure in theatre history which explores the adjacencies and collisions of politics, money, and opinion that made Restoration stage censorship an increasingly fragile project.

Adsorption using covalent organic frameworks (COFs) is very effective and favoured for removing per- and polyfluoroalkyl substances (PFAS) from various matrices. The prominent classes of COF, their synthesis methods, and their application in COF-based technologies for PFAS adsorption in myriad environments are discussed. Furthermore, the influencing PFAS adsorption characteristics of the distinct COF classes are also examined. COFs have large specific surface areas and porosity, offering PFASs a host of adsorption sites and thus high adsorption capabilities. β-Cyclodextrin-based COFs (β-CD-COFs), ionic COFs (iCOFs), amine-functionalised COFs, porphyrin-based COFs and hydrophobic COFs are some of the most notable examples of COFs and as such have been employed for large-scale PFAS remediation. Direct and post-synthetic modification are the two main COF design methodologies. The general approach in constructing various frameworks involves the reaction of ion monomers with other neutral monomers. For COFs, solvothermal synthesis is currently the main direct synthetic method. The process used to synthesise COFs tremendously impacts how effectively they adsorb PFAS. High-performance materials for PFAS remediation are created by researchers by customising COF characteristics and using suitable synthesis techniques. The authors’ objective is to give readers and researchers alike a broad overview of the current status of COF research and development, including numerous challenges and prospects associated with the adsorption of PFASs by COFs. Graphical abstract Application of Cement Stabilized OB dump, BOF slag, Fly Ash Mixes as Sustainable Pavement Material

Ransomware is a type of malware that locks access to or encrypts its victim’s files for a ransom to be paid to get back locked or encrypted data. With the invention of obfuscation techniques, it became difficult to detect its new variants. Identifying the exact malware category and family can help to prepare for possible attacks. Traditional machine learning-based approaches failed to detect and classify advanced obfuscated ransomware variants using existing pattern-matching and signature-based detection techniques. Deep learning-based approaches have proven helpful in both detection and classification by analyzing obfuscated ransomware deeply. Researchers have contributed mainly to detection and minimaly to family attribution. This research aims to address all these multi-class classification problems by leveraging the power of deep learning. We have proposed a novel group normalization-based bidirectional long short-term memory (GN-BiLSTM) method to detect and classify ransomware variants with high accuracy. To validate the technique, five other deep learning models are also trained on the CIC-MalMem-2022, an obfuscated malware dataset. The proposed approach outperformed with an accuracy of 99.99% in detection, 85.48% in category-wise classification, and 74.65% in the identification of ransomware families. To verify its effectiveness, models are also trained on 10,876 self-collected latest samples of 26 malware families and the proposed model has achieved 99.20% accuracy in detecting malware, 97.44% in classifying its category, and 96.23% in identifying its family. Our proposed approach has proven the best for detecting new variants of ransomware with high accuracy and can be implemented in real-world applications of ransomware detection.

The chapter summarizes the best studies published in the Proceedings of the International Scientific Conference “Digital Transformation in Industry: Trends, Management, Strategies” (DTI2023). The conference was organized by the Institute of Economics of the Ural Branch of the Russian Academy of Sciences and held on October 25–27, 2023. ‘From Industry 4.0 to Industry 5.0: Challenges and Perspectives’ was chosen as the central topic for discussion. The editors delve into the context and background that underlie research in this area. The chapter analyzes the major results and conclusions presented at the conference with a special focus on the human factor playing a strategic role in industrial digitalization. The key challenges and risks enterprises face amid global technological changes are identified, and opportunities for sustainable innovative development within the new industrial paradigm of Industry 5.0 are discussed. In conclusion, we provide a brief overview of the chapters’ contributions to the book and emphasize their significance for exploring the conference’s topic. This overview allows the readers to appreciate the diversity of research approaches presented. The editors highlight the importance of the authors’ managerial recommendations for adapting industrial enterprises to new values in the context of Industry 5.0.

Venture capital in emerging economies is part of shadow economy and consequently under-explored. The paper attempts to disentangle the hidden operations of informal venture capital (IVCs) in emerging economy such as Pakistan. Moreover, the study examines the impact and consequences of the un-documented economy, the role of IVCs and networks that give rise to the shadow economic systems. Using 21 semi-structured interviews, (1) we developed a conceptual framework to study the presence of IVCs; (2) the analysis suggests that IVCs fil the finance gap where formal financial institutions and government funding schemes failed to meet the needs of high-growth entrepreneurs; (3) IVCs have a significant presence in case of Pakistan and serve a vital role in promoting economic well-being; (4) the findings suggest that government-sponsored schemes give rise to favouritism and malpractices in the distribution of funds give rise to “Hybrid” IVCs operation; (5) finally, our results suggest rationalisation of IVCs promote sustainability and agenda for entrepreneurial growth. The findings have implications for policymakers to develop venture capital market and facilitate the transition of IVCs to formal capital market. Keywords: Informal venture capital, sustainability, shadow economy, emerging market

Housing provision is an integrated network of stakeholders, resources, institutions, and regulations. This study explores the stakeholder analysis approach through grounded theory to rationalize the stakeholder influence and role in developing collaborative frameworks for the sustainable provision of low-income housing in Pakistan. This study aims to theorize the Institutional Stakeholder Collaborations (ISCs) conceptual framework derived from institutional, stakeholder, collaboration, and resource dependence theories. The research also presents an interesting feature, i.e. stakeholder-resource cross-tabulation in achieving the research objective to develop the ISCs theory by placing core categories against stakeholder categories and resource domains. Stakeholder mapping plotted the influence against attributes of power, legitimacy, and interest (PLI) within the context of low-income housing in Punjab, Pakistan. The new theory was generated from the grounded data as a collaborative model for the sustainable provision of low-income housing, i.e., the Malik ISCs Model. This informed discovery of collaboration through the Malik ISCs Model for effectively providing low-income housing projects suggests some key points for the broader global policy discourse of housing development, emphasizing the low-income segment.

Poisoning attacks represent one of the most common and practical adversarial attempts on machine learning systems. In this paper, we have conducted a deep behavioural analysis of six machine learning algorithms, analyzing poisoning impact and correlation between poisoning levels and classification accuracy. Adopting an empirical approach, we highlight practical feasibility of data poisoning, comprehensively analyzing factors of individual algorithms affected by poisoning. We used public datasets (UNSW-NB15, BotDroid, CTU13, and CIC-IDS-2017) and varying poisoning levels (5–25%) to conduct rigorous analysis across different settings. In particular, we analyzed the accuracy, precision, recall, f1-score, false positive rate and ROC of the chosen algorithms. Further, we conducted a sensitivity analysis of each algorithm to understand the impact of poisoning on its performance and characteristics underpinning its susceptibility against data poisoning attacks. Our analysis shows that, for 15% poisoning of UNSW-NB15 dataset, the accuracy of Decision Tree decreases by 15.04% with an increase of 14.85% in false positive rate. Further, with 25% poisoning of BotDroid dataset, accuracy of K-nearest neighbours (KNN) decreases by 15.48%. On the other hand, Random Forest is comparatively more resilient against poisoned training data with a decrease of 8.5% in accuracy with 15% poisoning of UNSW-NB15 dataset and 5.2% for BotDroid dataset. Our results highlight that 10–15% of dataset poisoning is the most effective poisoning rate, significantly disrupting classifiers without introducing overfitting, whereas 25% is detectable because of high performance degradation and overfitting algorithms. Our analysis also helps understand how asymmetric features and noise affect the impact of data poisoning on machine learning classifiers. Our experimentation and analysis are publicly available at: https://github.com/AnumAtique/Behavioural-Analaysis-of-Poisoned-ML/.

Discussions of space, place and intersectionality have been present in rural studies since the early 2010s. Drawing upon the ‘relational turn’ in rural sociology and geography, this research has tended to focus on the ways in which the materiality of rural space interlocks with the connective lines of the various identity markers (e.g., ‘race’, gender, classed, able‐bodied, sexuality and so on) of the body to produce criss‐crossing and rhizomic assemblages and networks of rurality that has ability to produce inclusionary and/or exclusionary experiences of the rural based on the social locatedness of the individual. This article argues such theorising of rural intersectionality does not foreground rurality enough. Instead, it has the tendency to reproduce intersectional thinking in ‘additive’ ways within the rural literature. The purpose of this article is to provide a philosophical intervention to the debates in rural sociology and geography on intersectionality. Merleau‐Ponty's concept of embodied perception will be deployed to theorise rural as an identity category, which is always already inscribed with ‘raced’, gendered, heteronormative, abled‐bodied and classed configurations because of the historicity of the motor intentionality of the body. Here, I argue that the rural is an extension of the body in which it gets to know itself as an included ( being‐towards‐the‐rural ) or excluded ( being‐away‐from‐the‐rural ) being due to its pre‐reflexive bodily habituation and orientation. Such theorising sees rural as an impregnated reversibility with the identification demarcations of the body—opposed to being ontologically criss‐cross and rhizomatic as understood in the current relational literature on rural and rural intersectionality—and thus repositioning rurality as an inherently intersectional category/concept.

Objectives Violence against older adults is a prevalent global harm and there is evidence that perceptions of violence toward older adults may impact reporting and intervention. The present study examines the perception of violence against older adults in contrast to violence against other age groups and investigates the role of ageism in those perceptions. Method 290 participants were surveyed and asked to indicate whether they perceived 15 abusive behaviours reflecting physical, psychological, sexual, and financial abuse and neglect to be abuse toward either an older adult (age 60+), adult or child. Ageism was measured using the Ambivalent Ageism Scale. Results On average, 25% of participants did not consider the abusive behaviours to be abuse. Perceptions of abuse were relatively stable across the five types of abuse examined (range: 25-27%). Perceptions of the 15 abusive behaviours only varied due to older age in three instances, differences were sometimes between older adults and adults and sometimes children. Regression analyses showed increased ageism to be predictive of disagreement that behaviours were abusive in the older age group, explaining 8-14% of the variance in perception. Regression models were not significant in the adult or child groups. Discussion Results raise serious concern about the perception of abuse toward older adults. Future studies should investigate the reasons for such perceptions and other contributing factors in order to identify effective mechanisms for change.

How do you train an artificial intelligence (AI), or automated image processing model, to classify and recognize images? This question is central to Trevor Paglen’s Adversarially Evolved Hallucination series (2017–ongoing), a project that employs a generative adversarial network (GAN) to classify, identify and crucially, produce unique images. Paglen’s series demonstrates how images produced by AI image processing platforms—in this instance, a GAN—are, despite claims, never predictable or, indeed, accurate in their classifications. A significant indicator of this unreliability is evident in the potential for GANs, alongside other generative AI (GenAI) models, to hallucinate and erroneously classify images. Notwithstanding this systemic failing, automated image processing platforms remain central to classification tasks, including those associated with facial recognition and surveillance. They remain, for that reason, central to defining, if not pre-defining, how we perceive and look at the world through automated models of machine vision. Encouraged to see like machines, or at least take their classifications seriously and act upon them accordingly, we now inhabit a realm of perception defined by “machine realism”, if not algorithmic delusion. Enquiring into how we can better understand the degree to which AI encodes our perception of the world, it is this regimen of “machine realism” that Paglen and Downey explore throughout the following conversation: If AI models of image perception replace ocular-centric ways of seeing, they ask, do these apparatuses have the capacity to not only (pre)define but, in time, further estrange and alienate us from the world?