Critical Context Research Articles

Shattered innocence: uncovering the epidemic of sexual violence against women workers in Dhaka, Bangladesh

PurposeThis study aims to explore the prevalence and patterns of workplace sexual violence against women workers in Dhaka, Bangladesh.Design/methodology/approachThis study used a mixed-methods approach to investigate workplace sexual violence against women workers in Dhaka, Bangladesh. Data collection spanned four months, using structured interviews, self-administered questionnaires and qualitative methods such as in-depth interviews and focus group discussions. A stratified random sampling technique ensured a diverse representation across sectors like garment manufacturing, domestic work and construction. Quantitative data from 384 respondents were analyzed using descriptive statistics and Pearson correlation analysis, while qualitative data provided practical insights into participants' experiences. Limitations included sample specificity to Dhaka, underreporting bias and potential biases in self-reported data, impacting generalizability and data validity.FindingsThe findings reveal significant demographic trends, with younger age groups (16–35 years) experiencing higher rates of harassment, particularly within the garment industry. Occupation-wise, garment workers report more incidents of harassment, while housemaids and child domestic workers face comparatively lower risks. The length of time in current employment shows mixed correlations with harassment prevalence. Fears of job loss, social stigma and reliance on internal resolution mechanisms within workplaces influence reporting behaviors.Practical implicationsThis research highlights the urgent need for tailored interventions to protect young women in the garment sector and vulnerable occupations from sexual harassment. Policy reforms should enhance reporting mechanisms and address cultural barriers to reporting, ensuring safer workplaces and fair treatment for all female workers in Bangladesh.Originality/valueThis research contributes original insights by examining demographic correlates of sexual harassment across diverse occupations in Dhaka, Bangladesh. It uniquely identifies age-related vulnerabilities, occupational disparities and reporting behaviors, offering a comprehensive framework for understanding and addressing sexual violence in a critical socio-economic context.

Sensuality and Spirituality in the Novel Crossroads Jasmine Musabegović

This essay follows Fatima (Fata), the protagonist of the novel Crossroads within the context of contemporary criticism, which essentially identifies all types of novelistic prose with a certain note of dialogism. In the role of mother and wife, the main character's communicative position directs the idea pattern of the novel from the external to the internal dialogue with herself. Fatima, wrapped in the mantle of an absolute subject, in the general chaos of war and existential paradox, transcends the role of victim and passive witness of the pogrom and through spiritual healing becomes an actor in the struggle. In the cognitive structure of the novel, the author breathes into her protagonist the knowledge that human destiny is almost always subject to the discursive power of intuition. And this undoubted power is given to people as a means and a rare possibility to overcome chaos.

High-Specificity Imaging Mass Spectrometry.

Imaging mass spectrometry (IMS) enables highly multiplexed, untargeted tissue mapping for a broad range of molecular classes, facilitating in situ biological discovery. Yet, challenges persist in molecular specificity, which is the ability to discern one molecule from another, and spatial specificity, which is the ability to link untargeted imaging data to specific tissue features. Instrumental developments have dramatically improved IMS spatial resolution, allowing molecular observations to be more readily associated with distinct tissue features across spatial scales, ranging from larger anatomical regions to single cells. High-performance mass analyzers and systems integrating ion mobility technologies are also becoming more prevalent, further improving molecular coverage and the ability to discern chemical identity. This review provides an overview of recent advancements in high-specificity IMS that are providing critical biological context to untargeted molecular imaging, enabling integrated analyses, and addressing advanced biomedical research applications.

A Framework for Selecting and Assessing Wearable Sensors Deployed in Safety Critical Scenarios.

Wearable sensors for psychophysiological monitoring are becoming increasingly mainstream in safety critical contexts. They offer a novel solution to capturing sub-optimal states and can help identify when workers in safety critical environments are suffering from states such as fatigue and stress. However, sensors can differ widely in their application, design, usability, and measurement and there is a lack of guidance on what should be prioritized or considered when selecting a sensor. The paper aims to highlight which concepts are important when creating or selecting a device regarding the optimization of both measurement and usability. Additionally, the paper discusses how design choices can enhance both the usability and measurement capabilities of wearable sensors. The hopes are that this paper will provide researchers and practitioners in human factors and related fields with a framework to help guide them in building and selecting wearable sensors that are well suited for deployment in safety critical contexts.

Life Cycle Analysis of the Production of Neutral Alcohol Obtained from Winemaking Byproducts for Use in Perfumery

As is the case in many other industries, major fragrance brands are now incorporating circular-economy in their product-development strategies. This is in response to growing consumer demand for more environmentally friendly products. In light of this objective, perfumery raw materials must be evaluated against environmental and societal criteria. This more critical context, coupled with the fact that alcohol can constitute up to 95% of a fragrance, means greater attention is being given the origin of this fragrance solvent. Industries are now becoming keenly interested in seeking out more environmentally friendly alternatives to alcohols derived from field crops (sugar beet, wheat, corn, sugarcane) so as to use a sustainable alcohol with excellent olfactory performance. To this end, this study entailed evaluating the environmental impact of an extra-neutral grape alcohol produced in France and obtained from winemaking residues. Its olfactory performance has been proven to meet the expectations and industrial needs of perfumers. The environmental impact was assessed by a life cycle analysis (LCA) carried out per the current NF EN ISO 14040 standard. The results show that extra-neutral grape alcohol is positively comparable to sugar beet alcohol, particularly as concerns greenhouse gas emissions. Upcycled grape alcohol is also produced through sustainable management of winemaking waste and does not conflict with land needs for food production. Lastly, unlike field crops, grapevines in the form of vineyards are a perennial crop and therefore serve as major carbon sinks, much the same way forests do.

Planning development in a critical context

Planning development in a critical context

AI-powered trustable and explainable fall detection system using transfer learning

Accidental falls pose a significant public health challenge, especially among vulnerable populations. To address this issue, comprehensive research on fall detection and rescue systems is essential. Vision-based technologies, with their promising potential, offer an effective means to detect falls. This research paper presents a cutting-edge fall detection methodology aimed at enhancing individual safety and well-being. The proposed methodology utilizes deep neural networks, leveraging their capabilities to drive advancements in fall detection. To overcome data limitations and computational efficiency concerns, this study employ transfer learning by fine-tuning pre-trained models on large-scale image datasets for fall detection. This approach significantly enhances model performance, enabling better generalization and accuracy, especially in real-time applications with constrained resources. Notably, the methodology achieved an impressive test accuracy of 98.15%. Additionally, the incorporation of Explainable Artificial Intelligence (XAI) techniques is used to ensure transparent and trustworthy decision-making in fall detection using deep learning models, especially in critical healthcare contexts for vulnerable individuals. XAI provides valuable insights into complex model architectures and parameters, enabling a deeper understanding of fall identification patterns. To evaluate the effectiveness of this approach, a rigorous experimentation was conducted using a diverse dataset containing real-world fall and non-fall scenarios. The results demonstrate substantial improvements in both accuracy and interpretability, confirming the superiority of this method over conventional fall detection approaches.

Implementing a new Research Data Alliance recommendation, the I-ADOPT framework, for the naming of environmental variables of continental surfaces

To improve data usage in an interdisciplinary context, a clear understanding of the variables being measured is required for both humans and machines. In this paper, the I-ADOPT framework, which decomposes variable names into atomic elements, was tested within the context of continental surfaces and critical zone science, characterized by a large number and variety of observed environmental variables. We showed that the I-ADOPT framework can be used effectively to describe environmental variables with precision and that it was flexible enough to be used in the critical zone science context. Variable names can be documented in detail while allowing alignment with other ontologies or thesauri. We have identified difficulties in modeling complex variables, such as those monitoring fluxes between different environmental compartments and for variables monitoring ratios of physical quantities. We also showed that, for some variables, different decompositions were possible, which could make alignments with other ontologies and thesauri more difficult. The precision of variable names proved inadequate for data discovery services and a non-standard label (SimplifiedLabel) had to be defined for this purpose. In the context of open science and interdisciplinary research, the I-ADOPT framework has the potential to improve the interoperability of information systems and the use of data from various sources and disciplines.

Special dossier introduction: horror fans and audiences

ABSTRACT This introduction to the New Review of Film and Television’s special dossier on horror fans and audiences discusses how the study of horror viewership has frequently been limited to certain US cultural contexts and twentieth-century historical periods that, in turn, reduce the genre within essentialised cult discourses. Concurrently, the introduction highlights how audiences’ affective responses and relationships to the genre have been routinely inferred, imagined, and, therefore, surmised from textual analysis methodologies that cannot account for myriad audiences’ heterogeneous engagement with horror media. Addressing these shortcomings, the introduction sets up the key critical contexts that inform the dossier’s four articles and how they provide contemporary case studies of horror audiences and fandom.

Understanding patient and family experiences of critical care in Bangladesh and India: What are the priority actions to promote person-centred care?

Patients' experiences in the intensive care unit (ICU) can enhance or impair their subsequent recovery. Improving patient and family experiences on the ICU is an important part of providing high quality care. There is little evidence to guide how to do this in a South Asian critical care context. This study addresses this gap by exploring the experiences of critically ill patients and their families in ICUs in Bangladesh and India. We elicit suggestions for improvements from patients, families and staff and highlight examples of practices that support person-centred care. This multi-site hospital ethnography was carried out in five ICUs in government hospitals in Bangladesh and India, selected using purposive sampling. Qualitative data were collected using non-participant observation and semi-structured interviews and analysed using reflexive thematic analysis. A total of 108 interviews were conducted with patients, families, and ICU staff. Over 1000 hours of observation were carried out across the five study sites. We identified important mediators of patient and family experience that span many different aspects of care. Factors that promote person-centred care include access to ICU for families, support for family involvement in care delivery, clear communication with patients and families, good symptom management for patients, support for rehabilitation, and measures to address the physical, environmental and financial needs of the family. This study has generated a list of recommendations that can be used by policy makers and practitioners who wish to implement person-centred principles in the ICU.

Clinicians' Use of Choice Framing in ICU Family Meetings.

To quantify the frequency and patterns of clinicians' use of choice frames when discussing preference-sensitive care with surrogate decision-makers in the ICU. Secondary sequential content analysis. One hundred one audio-recorded and transcribed conferences between surrogates and clinicians of incapacitated, critically ill adults from a prospective, multicenter cohort study. Surrogate decision-makers and clinicians. None. Four coders identified preference-sensitive decision episodes addressed in the meetings, including topics such as mechanical ventilation, renal replacement, and overall goals of care. Prior critical care literature provided specific topics identified as preference-sensitive specific to the critical care context. Coders then examined each decision episode for the types of choice frames used by clinicians. The choice frames were selected a priori based on decision science literature. In total, there were 202 decision episodes across the 101 transcripts, with 20.3% of the decision episodes discussing mechanical ventilation, 19.3% overall goals of care, 14.4% renal replacement therapy, 14.4% post-discharge care (i.e., discharge location such as a skilled nursing facility), and the remaining 32.1% other topics. Clinicians used default framing, in which an option is presented that will be carried out if another option is not actively chosen, more frequently than any other choice frame (127 or 62.9% of decision episodes). Clinicians presented a polar interrogative, or a "yes or no question" to accept or reject a specific care choice, in 43 (21.3%) decision episodes. Clinicians more frequently presented options emphasizing both potential losses and gains rather than either in isolation. Clinicians frequently use default framing and polar questions when discussing preference-sensitive choices with surrogate decision-makers, which are known to be powerful nudges. Future work should focus on designing interventions promoting the informed use of these and the other most common choice frames used by practicing clinicians.

Induction and antiviral activity of ferret myxovirus resistance (Mx) protein 1 against influenza A viruses

Myxovirus resistance (Mx) proteins are products of interferon stimulated genes (ISGs) and Mx proteins of different species have been reported to mediate antiviral activity against a number of viruses, including influenza A viruses (IAV). Ferrets are widely considered to represent the ‘gold standard’ small animal model for studying pathogenesis and immunity to human IAV infections, however little is known regarding the antiviral activity of ferret Mx proteins. Herein, we report induction of ferret (f)Mx1/2 in a ferret lung cell line and in airway tissues from IAV-infected ferrets, noting that fMx1 was induced to higher levels that fMx2 both in vitro and in vivo. Overexpression confirmed cytoplasmic expression of fMx1 as well as its ability to inhibit infection and replication of IAV, noting that this antiviral effect of fMx1was modest when compared to cells overexpressing either human MxA or mouse Mx1. Together, these studies provide the first insights regarding the role of fMx1 in cell innate antiviral immunity to influenza viruses. Understanding similarities and differences in the antiviral activities of human and ferret ISGs provides critical context for evaluating results when studying human IAV infections in the ferret model.

An Exploration of Police Corruption at Durban Harbour Port of Entry in South Africa

This study delves into the pervasive issue of police corruption within a critical context of the Durban Harbour port of entry in South Africa. Police corruption poses a significant challenge to the integrity of law enforcement functioning of ports. This research highlights the role of inadequate training, resource constraints, bureaucratic inefficiencies, and lack of oversight as a significant contributing factor. It emphasises the necessity of enhancing organisational accountability, improving training programmes, and fostering a culture of ethical conduct to mitigate corruption. Lastly, this article recommends that addressing police corruption at the Durban Harbour port of entry demands a holistic and interdisciplinary approach involving law enforcement agencies, government bodies, civil society, and the private sector. The insights gained from this investigation can inform targeted interventions and policy reforms to fortify the integrity of law enforcement and enhance the security and efficiency of harbour port operations.

Explanation, semantics, and ontology

The terms ‘semantics’ and ‘ontology’ are increasingly appearing together with ‘explanation’, not only in the scientific literature, but also in everyday social interactions, in particular, within organizations. Ontologies have been shown to play a key role in supporting the semantic interoperability of data and knowledge representation structures used by information systems. With the proliferation of applications of Artificial Intelligence (AI) in different settings and the increasing need to guarantee their explainability (but also their interoperability) in critical contexts, the term ‘explanation’ has also become part of the scientific and technical jargon of modern information systems engineering. However, all of these terms are also significantly overloaded. In this paper, we address several interpretations of these notions, with an emphasis on their strong connection. Specifically, we discuss a notion of explanation termed ontological unpacking, which aims at explaining symbolic domain descriptions (e.g., conceptual models, knowledge graphs, logical specifications) by revealing their ontological commitment in terms of their so-called truthmakers, i.e., the entities in one’s ontology that are responsible for the truth of a description. To illustrate this methodology, we employ an ontological theory of relations to explain a symbolic model encoded in the de facto standard modeling language UML. We also discuss the essential role played by ontology-driven conceptual models (resulting from this form of explanation processes) in supporting semantic interoperability tasks. Furthermore, we revisit a proposal for quality criteria for explanations from philosophy of science to assess our approach. Finally, we discuss the relation between ontological unpacking and other forms of explanation in philosophy and science, as well as in the subarea of Artificial Intelligence known as Explainable AI (XAI).

FATHOM OUT THE NUANCES OF SOCIAL MEDIA LITERACY AND ITS PRACTICAL ALLUSIONS TO MISINFORMATION – A REVIEW

Digital media has become an integral part of our daily lives, connecting people from all around the world in a matter of seconds. In recent years, social media has revolutionized the flow of information and helped people to connect, share, and discuss things at their fingertips. However, this rapid exchange of information has also given rise to a concerning phenomenon – the spread of misinformation. "Misinformation" has led to a wide range of negative consequences, such as political polarization, social unrest, and erosion of trust in traditional media. This review explores the importance of social media literacy, its advantages, impacts, and effects, along with allusions to misinformation. The methodology includes a systematic search of literature in the databases from Internationally double-blinded peer-reviewed journals between 2016 and 2022, in English that also includes scientific articles. A total of 64 articles were obtained. A selection process of articles took place, applying certain inclusion and exclusion criteria, resulting in a total of 10 articles being selected. The findings signify that the conception of social media literacy has just started and more research is needed to identify its practical and theoretical implications. This is linked to the content and competencies, critical thinking, content, and context of the information, and the time and literacy level and fear of missing out on the user. Socio-emotional competencies, literacy level, and time spent stand out since social media is a frequent place of interaction between people. Thus, it is crucial to develop social media literacy skills to combat the dissemination of fake news and ensure a more responsible citizen and informed society.

An explainable & prescriptive solution for space-based energy consumption optimization using BIM data & genetic algorithm

Creating energy-efficient buildings is a multifaceted challenge that involves carefully considering architectural, mechanical, and electrical parameters. With the increasing emphasis on low-energy building mandates and sustainable construction practices, integrating energy performance simulation into the design process has become imperative. However, existing methods often struggle to handle the vast amount of design information and to explore optimal alternatives effectively. While recent approaches such as Building Information Models (BIM) and data-driven solutions show promise for energy analysis, they frequently lack transparency and robust design optimization capabilities, limiting their applicability in critical contexts within the Architecture, Engineering, Construction, and Operation (AECO) industry. In response to these challenges, this research proposes a novel prescriptive model aimed at addressing these limitations. The proposed methodology integrates building energy simulation with optimization techniques, utilizing BIM data and a Genetic Algorithm (GA). The Genetic algorithm provides some optimized solutions, including different alternatives of geometry, material, electrical, and mechanical elements to help engineering for data-driven decision-making. This integration aims to develop a model that is both prescriptive and explainable for indoor building design. Leveraging the value engineering method, the proposed approach seeks to strike a balance between energy consumption, functionality, and cost. By doing so, it aims to enhance energy efficiency while providing tangible design optimization solutions.

Double-Edged Sword? The Impact of Online Misogyny on Female College Students' Digital Activism in the Post-COVID-19 Era.

Using original surveys to investigate how online gender-based harassment marginalizes or empowers female college students, we found increased exposure to misogyny polarizes digital participation. Women aware of gender inequality in Korea encountering online hate speech were more inclined to engage in online movements, unlike women who did not recognize such inequality. This highlights distinct opportunities in relation to digital activism, showing online hate speech can sometimes be a catalyst for online political participation. This study expands current research on digital protest, highlighting the political ramifications of empowering women in the critical context of their unequal democratic rights outside the West.

A prediction framework for pharmaceutical drug consumption using short time-series

The increasing pharmaceutical expenditure in many countries has raised concerns regarding the sustainability of healthcare services. To address this issue, accurate forecasting of pharmaceutical demand is crucial for healthcare planning and policy development. This paper proposes a novel prediction framework that integrates different types of historical data and simulates a part of the generative process that produces pharmaceutical consumption, considering both exogenous and endogenous factors, such as per capita consumption trends and population dynamics. The output of the framework is a distribution of likely values, enabling the use not only of the central value for making a prediction but also of the explicitly stated uncertainty, which is crucial for decision-makers in such a critical and complex context. The reliability and consistency of the framework are ensured through backtesting and comparing the predicted results with actual data.

How power shapes behavior: Evidence from physicians.

Power-the asymmetric control of valued resources-affects most human interactions. Although power is challenging to study with real-world data, a distinctive dataset allowed us to do so within the critical context of doctor-patient relationships. Using 1.5 million quasi-random assignments in US military emergency departments, we examined how power differentials between doctor and patient (measured by using differences in military ranks) affect physician behavior. Our findings indicate that power confers nontrivial advantages: "High-power" patients (who outrank their physician) receive more resources and have better outcomes than equivalently ranked "low-power" patients. Patient promotions even increase physician effort. Furthermore, low-power patients suffer if their physician concurrently cares for a high-power patient. Doctor-patient concordance on race and sex also matters. Overall, power-driven variation in behavior can harm the most vulnerable populations in health care settings.

Dinamismo e identificación de participantes en la construcción discursiva de la nación estadounidense

This article analyzes the linguistic choices used in political discourse to represent social actors and their role in the construction of national storylines. We focus on Donald Trump’s and Joseph Biden’s inaugural speeches as a critical exploratory context. The analytical approach involves contrasting their choices for assigning dynamism to socialactors through transitivity roles and for identifying participants through direct, indirect, and pronominal strategies. Our findings show that Trump assigns Actor + Goal roles that construe citizens as agents impinging on material reality, while Biden assigns them Actor – Goal roles that represent citizens as participants in happenings. Trump’s identification of participants shows a higher proportion of indirect strategies linked with possessivequalification, whereas Biden stands out for his frequent use of the singular first person. The findings suggest connections between the strategies identified and the construction of national identities and storylines, both permeated by political leaders’ ideological orientations.