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Conceptualizing sexual wellbeing: a qualitative investigation to inform development of a measure (Natsal-SW)
Get PDFDespite increasing scientific and policy interest in sexual wellbeing, it remains poorly conceptualized. Many studies purporting to measure it instead measure related but distinct concepts, such as sexual satisfaction. This lack of conceptual clarity impedes understanding, measuring, and improving sexual wellbeing. We present qualitative research from multi-stage, mixed-methods work to develop a new measure of sexual wellbeing (Natsal-SW) for the fourth British National Survey of Sexual Attitudes & Lifestyles. Literature review and discussion generated a conceptual framework with seven proposed domains: respect, self-esteem, comfort, self-determination, safety and security, forgiveness, and resilience. Semi-structured interviews with 40 adults aged 18–64 then explored whether and how these domains aligned with participants’ own understandings, experiences, and language of sexual wellbeing. Data were analyzed thematically. Participants conceptualized sexual wellbeing as distinct from sexual satisfaction and sexual health and as multidimensional, dynamic, and socially and structurally influenced. All seven proposed domains resonated with accounts of sexual wellbeing as a general construct. The personal salience of different domains and their dimensions varied between individuals (especially by gender and sexual orientation) and fluctuated individually over time. This study clarifies dimensions of domains that participants considered important, providing an empirical basis to inform development of a new measure of sexual wellbeing
A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks
Get PDFWithin the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in real-time applications
Running, eyes open, into segregation: the absence of an integration strategy for Britain’s elites
Get PDFThere is an assumption in UK policy-making that there is a problem with the integration of migrant communities. This has led to political and media attention that suggests a major societal issue. This article rejects that claim but not by showing that migrants are integrated, but by changing the analytical gaze. It begins with the premise that the least integrated group in British society is not those at whom political attention is aimed. Rather than migrant communities, it is elites that live largely separate lives, and their separation is not one resulting from discrimination and/or lack of material resources, but is one actively pursued across multiple domains and transmitted inter-generationally. Using UK Government refugee integration strategies as the framework, the article examines what we know about elite integration across a multitude of domains and shows that elites live largely self-segregated lives
Advancing social sustainability in BREEAM New Construction certification standards
Get PDFAbstract
BREEAM (Building Research Establishment Environmental Assessment Method) is widely recognized for promoting environmental sustainability in the built environment, with a strong focus on energy efficiency, resource management, and ecological impact. However, as sustainability entails environmental and economic dimensions but also social dimensions, the current BREEAM New Construction standards do not fully address social sustainability targets. This article explores the potential for expanding BREEAM New Construction standards to more comprehensively incorporate social sustainability, ensuring that certified projects contribute to the well-being of their occupants and surrounding communities. Through a review of existing BREEAM categories, technical manuals, standards, and an analysis of gaps related to social sustainability, this paper identifies key areas for potential improvement, including user satisfaction, protecting workers’ and human rights, legacy planning, education and skills, and emergency response planning. These gaps are mapped against existing BREEAM categories and credits, with recommendations to introduce additional credits across the categories of management, materials, energy, waste, land use and ecology, health and well-being, and water. Additionally, this paper highlights the importance of transdisciplinary collaboration—bringing together architects, urban planners, social scientists, and public health experts—to effectively address the complexity of social sustainability in building design and certification. The proposed additions to BREEAM New Construction standards, alongside recommendations for industry and policymakers, offer guidelines for the evolution of green building certifications toward a more holistic approach to sustainability. This shift ensures that future certified buildings reduce environmental impact and promote social equity, health, and community well-being simultaneously
Systems in the Making: The Role of Companies in Implementing Sustainability Policy and Reporting
Get PDFThis paper focuses on the implementation of corporate sustainability, or
Environment, Social and Governance, reporting. The introduction from 2023 of mandatory
reporting is a key milestone in sustainability. Adopting a comparative case method, we
identify as related case studies Materiality (in reporting), Transition (in corporate strategy),
and Stewardship (in fund management). We compare these by applying the theory-led
themes of system openness, the agency or power of coalitions in producing and acting upon
reports, contests in the qualification of key data, and through business exchanges related to
or enabled by sustainability reports. Drawing on a two-year applied project, we elaborate
upon policy, regulation, business and industrial markets, and business relationships. We find
that Materiality is the most stable and well-framed system. It produces key outcomes in
depicting a reporting company’s sustainability risks and opportunities. Transition is the most
open, influenced by global and jurisdiction task forces, for example tasked with achieving net
zero policy obligations. Stewardship in the UK articulates a set of principles, which guide
fund managers in engaging with investee companies. We conclude that sustainability policy
is at the same time setting in progress the forming of three systems, corresponding to this
paper’s three case studies. Each has its own development, function and sets of facts, though
each is beginning to achieve its function through interactions and exchanges with the other
two
Systematic review and individual participant data meta-analysis: reducing self-harm in adolescents: pooled treatment effects, study, treatment and participant moderators
Get PDFObjective:
Self-harm is common in adolescents and a major public health concern. Evidence for effective interventions that stop repetition is lacking. This individual-participant-data (IPD) meta-analysis of randomised controlled trials (RCTs) aimed to provide robust estimates of therapeutic intervention effects and explore which treatments are best suited to different subgroups.
Method:
We searched databases and trial registers, to January-2022. RCTs compared therapeutic intervention to control, targeted adolescents aged 11-18 with a history of self-harm and receiving clinical care and reported on outcomes related to self-harm or suicide attempt. Primary outcome was repetition of self-harm at 12 months post-randomization . Two-stage random-effects IPD meta-analyses were conducted overall and by intervention. Secondary analyses incorporated aggregate data (AD) from RCTs without IPD. PROSPERO registration: CRD42019152119.
Results:
We identified 39 eligible studies; 26 provided IPD (3,448 participants), 7 provided AD (698 participants). There was no evidence that intervention/s were more or less effective than controls at preventing repeat self-harm by 12 months in IPD (odds ratio (OR)=1.06 [95% CI 0.86, 1.31], studies=20, n=2,949) or IPD+AD (OR=1.02 [95% CI 0.82, 1.27], studies=22, n=3,117) meta-analyses and no evidence of heterogeneity of treatment effects on study and treatment factors. Across all interventions, participants with multiple prior self-harm episodes showed evidence of improved treatment effect on self-harm repetition 6-12 months after randomization (OR=0.33 [95% CI 0.12, 0.94], studies=9, n=1,771).
Conclusion:
This large-scale meta-analysis of RCTs provided no evidence that therapeutic intervention was more, or less, effective than control for reducing repeat self-harm. We observed evidence indicating more effective interventions within youth with two or more self-harm incidents. Funders and researchers need to agree on a core set of outcome measures to include in subsequent studies
User experience of a 3D augmented reality human anatomy creative-based learning application
No full textSelf-awareness of human anatomy varies widely among the general public, leading to challenges in comprehending the intricate functions and spatial relationships of internal organs. To bridge this gap, the integration of active learning methodologies into science, technology, engineering, and mathematics (STEM) education has become imperative. These methodologies empower students to actively engage in their learning process through interactive discussions and hands-on activities, fostering a deeper understanding of complex anatomical concepts.
In anatomical education, creative learning interventions, such as body painting and crafting, have proven effective in enhancing active learning skills including motor skills, observational abilities, and visuospatial aptitude. Furthermore, the accessibility of emerging technologies such as augmented reality (AR) has presented promising opportunities to revolutionize science curricula, offering innovative and engaging methods to facilitate a more comprehensive understanding of human anatomy.
This chapter discusses the variability in human anatomy awareness and associated challenges. Emphasizing the significance of active learning, the chapter underscores its role in aiding students to grasp complex scientific concepts. This research focuses on a creative learning approach to teaching the complexities of the brain, lungs, and heart, utilizing a combination of innovative techniques, including anatomical baking, photogrammetry, 3D modeling, and AR application development. Preliminary findings underscore the usability of the AR application and its efficacy in fostering increased motivation for learning while recognizing the need for further comprehensive user testing. The approach aims to enhance public understanding of these vital organs, leveraging the popularity of baking programs to ensure widespread accessibility and engagement
Species distribution modeling approach for biased citizen science data
No full textEcological research emphasizes the criticality of comprehending species distribution for the formulation of efficient conservation strategies. The advent of Citizen Science initiatives has transformed the landscape of species occurrence data collection, offering a cost-efficient avenue for monitoring wildlife across diverse spatiotemporal scales. Nevertheless, the absence of standardized sampling protocols within these initiatives poses analytical hurdles, leading to skewed sampling efforts biased towards easily accessible or extensively studied regions. This study analyzes the national butterfly monitoring program in the UK. It uses a point process framework that combines spatial and temporal covariates to examine spatial and temporal trends of butterfly species occurrences. The methodology merges two critical elements: (i) the geographical locations visited by participating volunteers and (ii) the presence records of the species under investigation, utilizing the INLA/inlabru framework. The study highlights the need to reduce sampling bias to improve the accuracy and reliability of species distribution modeling. By combining the benefits of Citizen Science data and robust modeling methodologies, this approach provides a more nuanced understanding of species distribution dynamics. It can thus help strengthen the basis for more effective conservation efforts
Advanced AI techniques for landslide susceptibility mapping and spatial prediction: A case study in Medellín, Colombia
Get PDFLandslides, a global phenomenon, significantly impact economies and societies, especially in densely populated areas. Effective mitigation requires awareness of landslide risks, yet temporal links between occurrences are often neglected, challenging model performance due to non-stationary triggering and predisposing factors. This study presents a novel landslide susceptibility model that incorporates spatial and temporal dependencies, including landslide recurrence. We applied AI models—Naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Decision Trees, Random Forest, and Support Vector Machine (SVM)—to a case study in Medellín, a mountainous city in northwest Colombia. Using heuristic methods, we evaluated geological and geomorphological characteristics to identify high-risk areas. Integrating temporal data from four consecutive periods allowed us to enhance estimation robustness by incorporating random effects. Our findings identify slope, stream distance, geology, geomorphology, and mean annual precipitation as key factors influencing landslide susceptibility in Medellín. The SVM model demonstrated superior performance with an accuracy of 85%, closely aligning with previous studies. This research underscores the importance of temporal dynamics in landslide susceptibility assessments, improving prediction accuracy and supporting more effective risk management
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