Related Approaches Research Articles

Promoting social and emotional learning (SEL) in settings through emotion coaching practice: a model of engagement for practitioner use

ABSTRACT Emotion Coaching (EC) is a universal, relational approach focussed on promoting emotional wellbeing through attuned relationships. Adults coregulate children to understand different emotions they experience, why they occur and how to manage them. EC was introduced into UK educational settings in 2010 to promote children’s social and emotional learning (SEL) and build resiliency. With evidence of child and practitioner benefit, it is an adaptable ‘technique’ to manage dysregulated children and emotional situations. This paper, reports on educational practitioners’ EC experience. An inductive study, using semi-structured interviews, captured experiences and perceptions of 21, EC trained practitioners. Using constructivist grounded theory, analysis identified the fundamental role of the practitioner’s emotional identity and emotional awareness; recognised practice integration was influenced by practice opportunity and reflection with colleagues and acknowledged the need for managerial support to normalise and sustain EC in settings. With use, practitioner emotional awareness increased, suggesting modification in emotional identity, creating the opportunity to reaffirm, reconsider and re-engage in practice. Integration of EC practice was viewed as an ongoing journey, being described as ‘a way of being and a way of becoming’. The model of EC engagement conceptualised practice integration as a recursive cycle, starting and returning to the practitioner, with sequential, incremental stages. Through attuned child and adult interactions, EC confers, normalises and sustains symbiotic advantage. The model proposed is a practical tool, to guide planning, delivery and monitoring of practice integration.

Relational visioning and the emerging future: Transforming towards a sustainable local society

The Earth is currently facing unprecedented anthropogenic impacts. In this context of widespread environmental degradation and climate anxiety, there is an urgent need to reimagine our approaches to sustainability. This paper explores how a relational framing of sustainability can be applied to visioning exercises to foster transformative actions in local governance. While relational approaches are increasingly recognized as important to spark transformative change, their benefits and practical applicability to visioning are still under-explored. Drawing on findings from a multi-stakeholder workshop in a Norwegian municipality, we make two main contributions to the existing literature. First, we demonstrate how a relational visioning process can be carried out, identifying three key elements for establishing a relational foundation that can catalyse effective sustainability transformations: 1) the act of listening deeply, 2) integrating values, and 3) defining direction. Second, we contribute with new insights concerning the benefits and practical application in local sustainability governance contexts, highlighting the need for facilitating incremental (yet radical) changes in existing governance frameworks that are sensitive to available bureaucratic resources.

Practical Probabilistic Model-Based Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling.

This article addresses the prediction stability, prediction accuracy, and control capability of the current probabilistic model-based reinforcement learning (MBRL) built on neural networks. A novel approach to dropout-based probabilistic ensembles with trajectory sampling (DPETS) is proposed, where the system uncertainty is stably predicted by combining the Monte Carlo dropout (MC Dropout) and trajectory sampling in one framework. Its loss function is designed to correct the fitting error of neural networks for more accurate prediction of probabilistic models. The state propagation in its policy is extended to filter the aleatoric uncertainty for superior control capability. Evaluated by several Mujoco benchmark control tasks under additional disturbances and one practical robot arm manipulation task, DPETS outperforms related MBRL approaches in both average return and convergence velocity while achieving superior performance than well-known model-free baselines with significant sample efficiency. The open-source code of DPETS is available at https://github.com/mrjun123/DPETS.

Lived experiences of youth justice detention in Australia: reframing the institution in a decarcerated state

ABSTRACT This article reconsiders youth justice detention in the Australian context by exploring the lived experiences of youth detention in New South Wales (NSW) and the Northern Territory (NT). In drawing upon narratives from children and young people who have experienced detention, this article argues for shifting youth detention away from highly securitised environments, instead presenting an alternative and provisional relational rights-based framework and approach. This reimagined youth justice detaining environment builds upon the current efforts to raise the minimum age of criminal responsibility across Australia to 14, and other significant measures of decarceration. This dramatically limited and transformed carceral environment would actively seek to support these young people to reach their potential through nurturing their capabilities in a trauma-informed, therapeutic and relational rights-based environment, and then linking them in with highly resourced long-term transition pathways away from the justice system.

Research partnerships as transformational learning experiences

A common partnership between faculty and students in post-secondary education is that of researcher as a primary investigator (PI) and research assistant (RA). In this reflective essay, we examine an unconventional research partnership between Allison, a PI, and Bailey, an RA. Our experiences as PI and RA prompted us to reflect upon our learning partnership. We asked: In what ways might the role of RA be transformative (Mezirow, 1994) in teacher education for both researcher and student when it is approached through partnership? This partnership is unique because of its relational approach, where the PI and the RA collaborated in making decisions about the direction of the RA work. The RA’s perspective offered a different viewpoint on the research focus through which the researcher aimed to gain insights. This approach altered the dynamics of the research partnership, which evolved along the lines of both partners acting as equal contributors and co-learners through the research process. In this paper, we reflect on our consequential learning. Specifically, we focus on the learning that occurred when Bailey self-directed RA work on the topics of radical imagination, restorative justice, and appreciative inquiry, as well as the insights Allison gained through Bailey’s work. We use “I” in our respective sections and “we” and “our” in our collective writing, and we also refer to ourselves in the third person for clarity when needed.

Chemoinformatics for corrosion science: Data-driven modeling of corrosion inhibition by organic molecules.

This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure-property relationships (QSPR) and related data-driven approaches. The characteristic features of their key components are considered as applied to corrosion inhibition, including datasets, response properties, molecular descriptors, machine learning methods, and structure-property models. It is shown that the most important factors determining their choice and application features are: (1) the small or very small size of datasets, (2) the mechanism of corrosion inhibition associated with the adsorption of inhibitor molecules on the metal surface, and (3) multifactorial conditioning and noisiness of response property. On this basis, the application of machine learning to the inhibition of corrosion of materials based on iron, aluminum, and magnesium is considered. The main trends in the development of QSPR and related data-driven modeling of corrosion inhibition are discussed, the shortcomings and common errors are considered, and the prospects for their further development are outlined.

Slum upgrading and participation: Insights from a marginalized neighbourhood in Buenos Aires

This paper rethinks participation in slum upgrading beyond a well-established dichotomy of “top-down” and “bottom-up” processes and instead proposes a relational and strategic approach to urban transformation. Based on qualitative research with a small informal settlement, Barrio Saldías, in Buenos Aires, that fell off the city government's agenda, this paper explores the role of participation in promoting upgrading from the margins. It argues that Saldías differs from instances of community-led upgrading due to the central role of party brokers and political representatives (especially from the local state) alongside residents, all of whom participate together in a political climate dominated by the discourses of citizen participation and slum upgrading. Rather than collapsing these experiences into the logic of clientelism, participation is centred as the articulatory logic based on a shared desire and commitment to transforming urban space, towards multiple political ends. Participatory upgrading in Saldías exceeds the narrow focus of project-centric approaches and draws on a wider trajectory of participation in Buenos Aires. It demonstrates this through an analysis of three moments of participatory upgrading in Saldías.

Wasserstein task embedding for measuring task similarities

Measuring similarities between different tasks is critical in a broad spectrum of machine learning problems, including transfer, multi-task, continual, and meta-learning. Most current approaches to measuring task similarities are architecture-dependent: (1) relying on pre-trained models, or (2) training networks on tasks and using forward transfer as a proxy for task similarity. In this paper, we leverage the optimal transport theory and define a novel task embedding for supervised classification that is model-agnostic, training-free, and capable of handling (partially) disjoint label sets. In short, given a dataset with ground-truth labels, we perform a label embedding through multi-dimensional scaling and concatenate dataset samples with their corresponding label embeddings. Then, we define the distance between two datasets as the 2-Wasserstein distance between their updated samples. Lastly, we leverage the 2-Wasserstein embedding framework to embed tasks into a vector space in which the Euclidean distance between the embedded points approximates the proposed 2-Wasserstein distance between tasks. We show that the proposed embedding leads to a significantly faster comparison of tasks compared to related approaches like the Optimal Transport Dataset Distance (OTDD). Furthermore, we demonstrate the effectiveness of our embedding through various numerical experiments and show statistically significant correlations between our proposed distance and the forward and backward transfer among tasks on a wide variety of image recognition datasets.

Party politics across levels of governance: relational dynamics of immigrant integration policy making in Belgian cities

ABSTRACT Whereas authors have increasingly studied immigrant integration policies across governance levels, little has been done to explore how relations between actors at different levels of governance shape immigrant integration policies. This paper examines how a relational approach can shed light on the role of party politics in shaping immigrant integration policies. We explore whether party (in)congruence between the regional and local levels can explain the convergence or divergence of policy frames in four Belgian towns. Our findings indicate that in Flanders – where migration is highly politicised – party (in)congruence plays a role in explaining the convergence and divergence of immigrant integration policy frames, while in Wallonia – where migration is little politicised – party (in)congruence cannot explain divergent policy frames. We argue that the degree of politicisation of migration might impact the extent to which party (in)congruence can explain the consistency of immigrant integration policy frames across governance levels.

Emergency Detection in Smart Homes Using Inactivity Score for Handling Uncertain Sensor Data

In an aging society, the need for efficient emergency detection systems in smart homes is becoming increasingly important. For elderly people living alone, technical solutions for detecting emergencies are essential to receiving help quickly when needed. Numerous solutions already exist based on wearable or ambient sensors. However, existing methods for emergency detection typically assume that sensor data are error-free and contain no false positives, which cannot always be guaranteed in practice. Therefore, we present a novel method for detecting emergencies in private households that detects unusually long inactivity periods and can process erroneous or uncertain activity information. We introduce the Inactivity Score, which provides a probabilistic weighting of inactivity periods based on the reliability of sensor measurements. By analyzing historical Inactivity Scores, anomalies that potentially represent an emergency can be identified. The proposed method is compared with four related approaches on seven different datasets. Our method surpasses existing approaches when considering the number of false positives and the mean time to detect emergencies. It achieves an average detection time of approximately 05:23:28 h with only 0.09 false alarms per day under noise-free conditions. Moreover, unlike related approaches, the proposed method remains effective with noisy data.

Segmenting hotspots from medical thermal images using Density-based modified FC-Pc FS with spatial information

ABSTRACT Thermal cameras complemented with robust and automated computational intelligence-aided diagnostic systems can be deployed at public gathering points and on smartphones to raise alarms about thriving diseases of their body due to inflammation in a timely manner, thereby strengthening human health systems. In this work, we have proposed two robust Density-based modified Picture Fuzzy Clustering methods with spatial information to segment hotspots from human’s abnormal thermal images. We formulated and solved the objective function by combining P c FS-based clustering method with modified Renyi’s Entropy. We demonstrated the robustness of our methods, DSIFC-Pc FS and DSIMFC-Pc FS, on three publicly available datasets and an artificially created noisy dataset of thermal images. We have obtained a ZC score of 0.933, 0.947 and 0.933 for DB-DMR-IR, DB-FOOT-IR and DB-THY-IR datasets, respectively. Also, our method converged to an optimal partition matrix in minimum iterations. The experimental performance shows that the proposed approaches, DSIFC- P c FS and DSIMFC- P c FS, outperformed other ten related approaches in segmenting the medical thermal images with and without noise, indicating their robustness and efficacy, in terms of various unsupervised evaluation metrics. Also, we statistically analysed their performance in contrast to other methods using the Friedman Test, which advocate their superiority.

Promoting social competence and social inclusion in the preschool: a peer-based relational intervention

ABSTRACT Reviews concerning the enhancement of preschool children’s social competencies reveal areas within which teacher approach, definitions of social competence and methods of study can be enhanced. In response, teachers and researchers co-developed and introduced a peer-based relational intervention to support children’s communication, empathic response and non-homophilic choice of friendship/work mates. Within this design-based approach, a three-phase comparison was made in 14 English preschool classrooms. Results identified changes attributed to the intervention regarding: teacher behaviour (movement from giving directions to observation and support); child behaviour (enhanced reciprocal communication, empathic response to others and engagement in cognitively challenging activities) and an expansion of socially inclusive play/work mates. Not all teachers/classes responded to the relational approach in the same manner: those that engaged actively in the approach encouraged more social competence enhancements amongst children; those with lower engagement identified support/implementation problems and children showed less social competence advances.

Correction: Conducting sustainability research in the anthropocene: toward a relational approach

Correction: Conducting sustainability research in the anthropocene: toward a relational approach

Raising relational legal consciousness through co‐production research? Making law more accessible

AbstractThis article offers co‐production as a new methodology for relational approaches to legal consciousness studies that allows for deeper analysis of and engagement with the everyday experience of law. We argue that the inherent relationality of co‐production has the potential to both expose and change legal consciousness. As an approach that equalizes status in the co‐production of knowledge, social structures and hierarchies are reproduced in real time, allowing the relational networks through which legal consciousness is formed to emerge. We demonstrate both the possibility and the value of this approach through a discussion of early findings from a co‐produced project focused on accessible legal information for disabled people with cognitive impairments. Our emerging data show that disabled people's experience as ‘outsiders’ in their communities, and the barriers to justice that they encounter through being not believed or information being given in inaccessible formats, creates uncertainty and distrust of the utility of legal professionals as routes for resolution – even as they express a desire for formal legal process. These data also show that engaging with co‐production work can increase the legal confidence of people from marginalized groups.

Intensity- and time-based strategies for micro/nano-sizing via single-particle ICP-mass spectrometry: A comparative assessment using Au and SiO2 as model particles

BackgroundSingle-particle ICP-mass spectrometry (SP-ICP-MS) is a powerful method for micro/nano-particle (MNP) sizing. Despite the outstanding evolution of the technique in the last decade, most studies still rely on traditional approaches based on (1) the use of integrated intensity as the analytical signal and (2) the calculation of the transport efficiency (TE). However, the increasing availability of MNP standards and advancements in hardware and software have unveiled new venues for MNP sizing, including TE-independent and time-based approaches. This work systematically examines these different methodologies to identify and summarize their strengths and weaknesses, thus helping to determine their preferred application areas. ResultsDifferent SP-ICP-MS methods for MNP sizing were assessed using AuNPs (20–70 nm) and SiO2MNPs (100–1000 nm). Among TE-dependent approaches, the particle frequency method was characterized by larger uncertainties than the particle size method. The results of the latter were dependent on the appropriate selection of the reference MNP, making the use of multiple reference MNPs recommended. TE-independent methods were based on external (linear and polynomial) calibrations and a relative approach. These methods exhibited the lowest uncertainties of all the strategies evaluated. External calibrations benefited from simpler calculations, but their application could be hindered by a lack of reference MNPs within the desired size range or by the need for interpolations outside the calibration range. Finally, transit time signals are directly proportional to the MNP size rather than its mass. The time-based method demonstrated adequate performance for sizing AuNPs but failed when sizing the largest SiO2MNPs (1000 nm). Significance and noveltyThis work provides further insights into the application of different SP-ICP-MS methodologies for MNP sizing. Both TE-independent approaches and the monitoring of the transit time as the analytical signal are underused strategies; in this context, a Python script was developed for accurate transit time measurement. After 20 years of development, a quantitative comparison of the different methodologies, including the most novel approaches, is deemed necessary for further growth on solid theoretical ground.

Perception of Public Relations Officers on Government Public Relations Approaches Towards the Acceptability of COVID 19 Vaccination in Taraba State Nigeria

Perception of Public Relations Officers on Government Public Relations Approaches Towards the Acceptability of COVID 19 Vaccination in Taraba State Nigeria

Physiology-based pharmacokinetic model with relative transcriptomics to evaluate tissue distribution and receptor occupancy of anifrolumab.

Type I interferons contribute to the pathogenesis of several autoimmune disorders, including systemic lupus erythematosus (SLE), systemic sclerosis, cutaneous lupus erythematosus, and myositis. Anifrolumab is a monoclonal antibody that binds to subunit 1 of the type I interferon receptor (IFNAR1). Results of phase IIb and phase III trials led to the approval of intravenous anifrolumab 300 mg every 4 weeks (Q4W) alongside standard therapy in patients with moderate-to-severe SLE. Here, we built a population physiology-based pharmacokinetic (PBPK) model of anifrolumab by utilizing the physiochemical properties of anifrolumab, binding kinetics to the Fc gamma neonatal receptor, and target-mediated drug disposition properties. A novel relative transcriptomics approach was employed to determine IFNAR1 expression in tissues (blood, skin, gastrointestinal tract, lungs, and muscle) using mRNA abundances from bioinformatic databases. The IFNAR1 expression and PBPK model were validated by testing their ability to predict clinical pharmacokinetics over a large dose range from different clinical scenarios after subcutaneous and intravenous anifrolumab dosing. The validated PBPK model predicted high unbound local concentrations of anifrolumab in blood, skin, gastrointestinal tract, lungs, and muscle, which exceeded its IFNAR1 dissociation equilibrium constant values. The model also predicted high IFNAR1 occupancy with subcutaneous and intravenous anifrolumab dosing. The model predicted more sustained IFNAR1 occupancy ≥90% with subcutaneous anifrolumab 120 mg once-weekly dosing vs. intravenous 300 mg Q4W dosing. The results informed the dosing of phase III studies of anifrolumab in new indications and present a novel approach to PBPK modeling coupled with relative transcriptomics in simulating pharmacokinetics of therapeutic monoclonal antibodies.

Technology, capital, and sustainability: Frontiers in just transitions for African mining

Transitions towards sustainability are crucial in the natural resource sector in Africa, particularly in artisanal and small-scale gold mining (ASGM). This sector is notably characterized by the extensive use of mercury for gold extraction, which poses significant risks to both human health and ecosystems. This study focuses on Tanzania, where international incentives to reduce mercury have led the state to promote alternative methods and scale up production primarily through cyanide leaching—a more mechanized gold extraction technique.This article examines the implementation of these strategies, identifies the barriers hindering this transition, and explores the resulting inequalities. We employ a mixed methods approach, combining surveys, semi-structured interviews, and participant observation. We adopt the Sustainability and Just Transitions frameworks for the Global South, arguing that applying an in-depth relational approach to the interactions between actors and technologies can illuminate the persistence of environmentally unsustainable practices within the local context. In conclusion, we advocate for international strategies aimed at the Global South to consider the how promoting the eradication of environmentally unsustainable practices can affect livelihood creation mechanisms.

Methodological approach to quantitative assessment of the security of open operating systems AS of the Internal Affairs Bodies

Objective. The article considers the provisions of the fuzzy logic approach in relation to the method of quantifying the security of open operating systems (OS) of automated systems of the internal affairs bodies of the Russian Federation (AS of the Russian Federation), taking into account possible security threats and the requirements of the standard GOST R ISO/IEC 15408 for leveling possible consequences. Information Message № 240/24/4893 dated October 18, 2016 «On Approval of Information Security Requirements for Operating Systems» of the FSTEC of Russia defines 6 OS security classes. Operating systems corresponding to protection classes 1, 2 and 3 are used in information (automated) systems in which information containing information constituting a state secret is processed, operating systems corresponding to protection classes 4, 5 and 6 are not intended for processing such information. In the presented study, the open operating systems of the AS of the Russian Federation are understood as OS AS, in which information containing information constituting a state secret is not processed. Method. The study was conducted based on the method of analyzing possible security threats to open operating systems, as well as the requirements of GOST R ISO/IEC 15408 standard, using the provisions of fuzzy logic. Result. The result of the automated system for calculating the security index of the analyzed open OS is one of the specified criteria for the degree of security of the OS, based on the provisions of fuzzy logic. Conclusion. The authors propose a method for assessing the security of open OS of the AS of the Russian Federation, based on the provisions of fuzzy logic.

Consensus Control of Multiagent Systems Subject to Biasing Faults: Relative Output Information-Based Approach

Consensus Control of Multiagent Systems Subject to Biasing Faults: Relative Output Information-Based Approach