Transparent Support Research Articles

Understanding the Experiences and Support Needs of Close Relatives in Psychiatric Euthanasia Trajectories: A Qualitative Exploration.

In Belgium, adults with psychiatric disorders can opt for euthanasia under strict conditions. The impact of these euthanasia trajectories on close relatives remains insufficiently studied. This research is the first in Belgium to explore the concrete experiences and support needs of relatives involved in psychiatric-based euthanasia trajectories by means of an in-depth interview study. The interviews with 18 relatives were conducted from March to May 2023 and analyzed using inductive thematic coding. The results reveal the complex and ambivalent emotional and cognitive experiences among relatives. Experiences with euthanasia trajectories varied from positive to negative, marked by shared feelings of surrealism and unreality. This included the farewell process that relatives go through, regardless of whether euthanasia was carried out. While the desired level of involvement varied, everyone sought some degree of recognition and understanding for their complex position during the euthanasia procedure, aiding in a better comprehension and contextualization of the request. The level of actual involvement and support depended on the stage of the euthanasia request, their social network, and the reasons behind the request. There was a demand for transparent communication, more emotional and practical support, and assistance in coping with the emotionally charged process. Specific attention is needed for the emotional and cognitive rollercoaster, even if euthanasia is not ultimately pursued. Future research should employ a longitudinal design to gain deeper insights into relatives' fluctuating experiences and support needs throughout euthanasia trajectories. Seeking greater context diversity and combining perspectives in cluster research can improve understanding of interconnected needs.

Predicting ICU Interventions: A Transparent Decision Support Model Based on Multivariate Time Series Graph Convolutional Neural Network.

In this study, we present a novel approach for predicting interventions for patients in the intensive care unit using a multivariate time series graph convolutional neural network. Our method addresses two critical challenges: the need for timely and accurate decisions based on changing physiological signals, drug administration information, and static characteristics; and the need for interpretability in the decision-making process. Drawing on real-world ICU records from the MIMIC-III dataset, we demonstrate that our approach significantly improves upon existing machine learning and deep learning methods for predicting two targeted interventions, mechanical ventilation and vasopressors. Our model achieved an accuracy improvement from 81.6% to 91.9% and a F1 score improvement from 0.524 to 0.606 for predicting mechanical ventilation interventions. For predicting vasopressor interventions, our model achieved an accuracy improvement from 76.3% to 82.7% and a F1 score improvement from 0.509 to 0.619. We also assessed the interpretability by performing an adjacency matrix importance analysis, which revealed that our model uses clinically meaningful and appropriate features for prediction. This critical aspect can help clinicians gain insights into the underlying mechanisms of interventions, allowing them to make more informed and precise clinical decisions. Overall, our study represents a significant step forward in the development of decision support systems for ICU patient care, providing a powerful tool for improving clinical outcomes and enhancing patient safety.

Skin Examination Analysis System Using Ayurvedic Interpretation

A human skin moisture measuring device having an interdigitated resistive sensor formed on a plate-like transparent Support is described, with a face designed to be placed into contact with the skin to be subjected to the moisture degree measurement. We can estimate/measure Skin Colour Index , Skin Texture Index, Skin Allergies Index , Skin Radical Index, Means for processing the signal coming from the interdigitated resistive sensor and from the image sensing means give as output a value of the measured moisture degree that is normalized with respect to the actual Surface of contact between skin and sensor. KeyWords: AI/MI,SKIN INDEX

H-Glass Supported Hybrid Gold Nano-Islands for Visible-Light-Driven Hydrogen Evolution.

Flat panel reactors, coated with photocatalytic materials, offer a sustainable approach for the commercial production of hydrogen (H2) with zero carbon footprint. Despite this, achieving high solar-to-hydrogen (STH) conversion efficiency with these reactors is still a significant challenge due to the low utilization efficiency of solar light and rapid charge recombination. Herein, hybrid gold nano-islands (HGNIs) are developed on transparent glass support to improve the STH efficiency. Plasmonic HGNIs are grown on an in-house developed active glass sheet composed of sodium aluminum phosphosilicate oxide glass (H-glass) using the thermal dewetting method at 550°C under an ambient atmosphere. HGNIs with various oxidation states (Au0, Au+, and Au-) and multiple interfaces are obtained due to the diffusion of the elements from the glass structure, which also facilitates the lifetime of the hot electron to be ≈2.94ps. H-glass-supported HGNIs demonstrate significant STH conversion efficiency of 0.6%, without any sacrificial agents, via water dissociation. This study unveils the specific role of H-glass-supported HGNIs in facilitating light-driven chemical conversions, offering new avenues for the development of high-performance photocatalysts in various chemical conversion reactions for large-scale commercial applications.

MSA: A Novel App Development Framework for Transparent Multiscreen Support on Android Apps

Multi-display Android systems are emerging and require new display management. The current Android design exposes and offloads the multi-screen display management to app developers. As a consequence, apps cannot utilize multi-screen without redevelopment. This paper proposes MSA, a novel Android app development framework for transparent multi-screen support. MSA cooperates with existing Android system services to map the single-screen views provided by apps to multiple screens and maps input events backward correspondingly. Following the new framework, the app development of single-screen Android systems and multi-screen Android systems are unified. Thus, both existing apps and future app development can directly utilize multiple screens with single-screen-based techniques such as multi-window and resizable-activity, instead of redevelopment through multi-screen-dedicated new methods such as presentation classes. We have implemented an MSA prototype with real hardware and released the source code for public access. Experimental results show that using MSA, without any modifications, existing apps can directly run and fully exploit multiple screens with better performance and less overhead compared with the state-of-the-art multi-screen Android system.

Transparent (Support) Structures: On Visibility and Social Reproduction in Socially Engaged Art

Transparent (Support) Structures: On Visibility and Social Reproduction in Socially Engaged Art

Microsphere photolithography using reusable microsphere array mask for low-cost infrared metasurface fabrication

Microsphere photolithography (MPL) is an alternative low-cost technique for the large-scale fabrication of periodic structures, such as metasurfaces. This technique utilizes the photonic nanojet generated in the photoresist (PR), by microspheres in near proximity, which are exposed to collimated ultraviolet (UV) flood illumination. In the basic approach, a microsphere array is self-assembled on, or transferred to, the substrate prior to exposure. After exposure, the microspheres are washed away in the development step. The process to recover and clean these microspheres for reuse is complicated. This paper investigates the use of reusable microsphere masks created by fixing the microspheres on a UV transparent support. This is then brought into contact with the photoresist with controlled pressure. There is a trade-off between the quality of the fabricated samples and the wear of the mask determined by the contact pressure. The system is demonstrated using a digital micromirror device (DMD)-based direct-write exposure system to fabricate infrared (IR) metasurfaces. These metasurfaces are characterized and compared to simulation models. Finally, a series of 50 hierarchically patterned IR metasurfaces was fabricated using a single reusable mask. These samples had a <3% coefficient of variance when viewed with a thermal camera. This work shows the potential of mask-based MPL and other contact microlens array-based photolithography techniques for low-cost large-scale fabrication.

Experimental method and application of the slurry “diffusion-bleeding-seepage” of isolated overburden grout injection

Isolated overburden grout injection (IOGI) is a green mining method to control surface subsidence. Slurry water significantly influences grouting effectiveness and mining safety. This study establishes a three-dimensional visualization experimental system for slurry “diffusion-bleeding-seepage” to investigate the seepage law of slurry water. The system is composed primarily of a transparent box (1.2 m × 0.5 m × 0.25 m) and support, with the solid–liquid coupling seepage similar material and modules for mining, grouting injection, slurry pressure and displacement monitoring, overburden saturation monitoring, and water leakage monitoring of the working face. A similar material with good permeability and non-disintegration is obtained by hydrophilic, water absorption, and permeability tests. Grouting and mining are simulated by pulling acrylic sheets and pumping slurry. With the fly ash slurry entering the injection layer, the slurry undergoes water–cement separation, and the water bleeding is formed to seep into the similar material. The volumetric water content of the similar material is obtained by arranging multiple groups of volumetric water content sensors into the similar material. The corresponding saturation is obtained by theoretical calculation. The experimental system is used to simulate the seepage of slurry water in a high initial saturation overburden, and the characteristics of injection slurry diffusion and water bleeding are obtained. The profile distribution of seepage of slurry water is found to possess a semi-elliptical shape. Under the condition of high initial saturation, slurry water appears in the working face. The system provides a convenient method for further research of IOGI slurry water seepage.

Is digital transformation the Deus ex Machina towards sustainability transition of the European SMEs?

The 2030 Digital Compass aligned with the European Green Deal prioritize transparency through the digital transformation of the European SMEs paving the way to a more sustainable production paradigm. Thus, a twin transition is in motion facilitated by the New Industrial Strategy supporting the adoption of green business strategies across industrial ecosystems. This paper investigates whether the digital transformation and the decision to participate in public procurement as a transparent external funding source support adoption of business actions fostering sustainability transition. Data over more than 20,000 SMEs in the EU-28 over the period 2015–2019 complemented by country-specific attributes towards sustainability and institutional business environment are combined. Econometric results indicate that digital transformation fosters sustainability transition. Although participation in public tenders exerts a positive and systematic effect on the adoption of a sustainability supporting strategy, the two decisions appear independent indicating that SMEs are determined to shift the production paradigm irrespectively. Business operational problems affect but not deter sustainability transition. Business corruption in countries of low levels of sustainability impedes sustainable business actions, yet evidence favors higher transparency. The latter highlights the necessity towards building a coherent although adaptable institutional framework. This study contributes to SDGs 7, 12, 13 and 16.

Towards greater neuroimaging classification transparency via the integration of explainability methods and confidence estimation approaches

The field of neuroimaging has increasingly sought to develop artificial intelligence-based models for neurological and neuropsychiatric disorder automated diagnosis and clinical decision support. However, if these models are to be implemented in a clinical setting, transparency will be vital. Two aspects of transparency are (1) confidence estimation and (2) explainability. Confidence estimation approaches indicate confidence in individual predictions. Explainability methods give insight into the importance of features to model predictions. In this study, we integrate confidence estimation and explainability approaches for the first time. We demonstrate their viability for schizophrenia diagnosis using resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data. We compare two confidence estimation approaches: Monte Carlo dropout (MCD) and MC batch normalization (MCBN). We combine them with two gradient-based explainability approaches, saliency and layer-wise relevance propagation (LRP), and examine their effects upon explanations. We find that MCD often adversely affects model gradients, making it ill-suited for integration with gradient-based explainability methods. In contrast, MCBN does not affect model gradients. Additionally, we find many participant-level differences between regular explanations and the distributions of explanations for combined explainability and confidence estimation approaches. This suggests that a similar confidence estimation approach used in a clinical context with explanations only output for the regular model would likely not yield adequate explanations. We hope that our findings will provide a starting point for the integration of the two fields, provide useful guidance for future studies, and accelerate the development of transparent neuroimaging clinical decision support systems.

ФОРМУВАННЯ СТІЙКОСТІ АГЛОМЕРОВАНИХ МІСЬКИХ ПОСЕЛЕНЬ ПРИБЕРЕЖНОЇ ОДЕСЬКОЇ АГЛОМЕРАЦІЇ

Modern cities and their agglomerations are experiencing significant threats related to climate change and its consequences, pandemics, wars, etc. It is difficult for large agglomerations to mitigate the consequences of such challenges in a coordinated manner. The small cities have even fewer resources to effectively respond to threats, which makes them more vulnerable and unstable to new challenges of development. Coastal agglomerations face significant challenges to resilience in the face of internal and external threats. The research purpose is to identify features, opportunities, as well as problems and potential risks of forming the resilience of agglomerated urban settlements of the coastal Odesa agglomeration. The authors' attention was focused on a comparison of the resilience potentials of two types of cities according to their socio-geographical location: coastal (Chornomorsk and Yuzhne) and internal (Biliaivka and Teplodar). The research results were formed on the basis of content analysis of municipal official documents and local mass media from January to May 2023 as well as developing a SWOT analysis determining the problems and prospects of implementing resilience mechanisms in the cities. The different components of resilience potential were analyzed for the selected cities, especially economic, social, and environmental ones. It was revealed that the level of economic and social development of agglomerated settlements in the Odesa agglomeration is uneven. Coastal cities have a higher level of economic and social development due to the presence of the largest seaports in the country. However, the dependence of the urban economy on the activities of few large enterprises does not guarantee economic resilience. At the beginning of the full-scale russian military invasion, these cities felt the greatest vulnerability due to the temporary shutdown of businesses and ports. Yuzhne and Chornomorsk require an increased level of climate change preparedness due to the vulnerability of the sea coast, so rewilding measures can improve their adaptation to climate change and natural events. The active work of local authorities and public involvement, positive migration growth, a high proportion of children and adolescents, transparent support for small businesses, and improved tourist attractiveness are beginning to form the high level for the economic and social resilience of Biliaivka. The potential for economic and social resilience in Teplodar is low due to the significant shortage of jobs and large flows of commuting labor migration to the core city, therefore it is necessary to create attractive conditions for small and medium-sized businesses in various spheres for economic diversification. The creation of an industrial park or business incubator can compensate for the absence of the city-forming enterprise. The deep research of all threats and possible challenges, as well as the integration of urban resilience instruments into official documents based on the cooperation of all stakeholders, should be a further step in the introduction of economic, social, and environmental resilience in urban management process for the cities of the coastal Odesa agglomeration.

Probabilistic cost-benefit analysis for mitigating hydrogeological risks in underground construction

Leakage of groundwater into underground facilities can subsequently cause groundwater drawdown, subsidence and subsidence damages to the built-up environment. In order to reduce the risk of damage, measures to mitigate the risks must often be implemented. The aim of this paper is to describe and demonstrate a probabilistic cost-benefit analysis approach to assess the economic profitability of investing in different risk mitigation alternatives. Since underground construction is always associated with uncertainties, the analysis uses probability distribution functions for uncertain parameters and Monte Carlo simulations to quantify probabilities of damage and implementation costs. The proposed approach is exemplified with a case study, the road tunnel project Bypass (Förbifart) Stockholm in eastern Sweden, for which four risk mitigation alternatives were evaluated. In conclusion, the approach helps to highlight the economic effects of different risk mitigation approaches and constitute a transparent support for decisions on implementation of risk mitigation. For the case study, the analysis indicates that the implementation costs of ∼ 7000 MSEK (700 million EUR) for risk mitigation needed to fulfil the legal requirements, from the Swedish Land- and Environmental court, in the form of ambitious sealing strategies are disproportionate relative to the benefits of ∼ 50 MSEK (5 million EUR) gained in the form of reduced damage risk for the built-up environment. In other words, billions SEK of taxpayers' money are spent on unnecessary expenses to fulfill legal requirements without societal benefits. The novelty of the paper constitutes the coupling of models and combination of established methods for management of hydrogeological risks.

Selection of a Transparent Meta-Model Algorithm for Feasibility Analysis Stage of Energy Efficient Building Design: Clustering vs. Tree

Energy Efficient Building (EEB) design decisions that have traditionally been made in the later stages of the design process now often need to be made as early as the feasibility analysis stage. However, at this very early stage, the design frame does not yet provide sufficient details for accurate simulations to be run. In addition, even if the decision-makers consider an exhaustive list of options, the selected design may not be optimal, or carefully considered decisions may later need to be rolled back. At this stage, design exploration is much more important than evaluating the performance of alternatives, thus a more transparent and interpretable design support model is more advantageous for design decision-making. In the present study, we develop an EEB design decision-support model constructed by a transparent meta-model algorithm of simulations that provides reasonable accuracy, whereas most of the literature used opaque algorithms. The conditional inference tree (CIT) algorithm exhibits superior interpretability and reasonable classification accuracy in estimating performance, when compared to other decision trees (classification and regression tree, random forest, and conditional inference forest) and clustering (hierarchical clustering, k-means, self-organizing map, and Gaussian mixture model) algorithms.

Chiral Liquid Crystalline Properties of Cellulose Nanocrystals: Fundamentals and Applications.

By using an independent self-assembly process that is occasionally controlled by evaporation, cellulose nanocrystals (CNCs) may create films (pure or in conjunction with other materials) that have iridescent structural colors. The self-forming chiral nematic structures and environmental safety of a new class of photonic liquid crystals (LCs), referred to as CNCs and CNC-embedded materials, make them simple to make and treat. The structure of the matrix interacts with light to give structural coloring, as opposed to other dye pigments, which interact with light by adsorption and reflection. Understanding how CNC self-assembly constructs structures is vital in several fields, including physics, science, and engineering. To constructure this review, the colloidal characteristics of CNC particles and their behavior during the formation of liquid crystals and gelling were studied. Then, some of the recognized applications for these naturally occurring nanoparticles were summarized. Different factors were considered, including the CNC aspect ratio, surface chemistry, concentration, the amount of time needed to produce an anisotropic phase, and the addition of additional substances to the suspension medium. The effects of alignment and the drying process conditions on structural changes are also covered. The focus of this study however is on the optical properties of the films as well as the impact of the aforementioned factors on the final transparency, iridescent colors, and versus the overall response of these bioinspired photonic materials. Control of the examined factors was found to be necessary to produce reliable materials for optoelectronics, intelligent inks and papers, transparent flexible support for electronics, and decorative coatings and films.

Using XAI Techniques to Persuade Text Classifier Results: A Case Study of Covid-19 Tweets

Background: To offer a transparent decision support system able of classifying tweets’ sentiment into positive, neutral, and negative sentiment and explains the prediction result by XAI techniques Methods: We started by data preprocessing phase. For data representation, we used TF-IDF, and we applied four machine-learning algorithms including Naive Bayes, random forest, logistic regression, and support vector machine, as well as four deep learning RNN, LSTM, GRU, and Bi-directional RNN. To raise model trust, we used LIME and SHAP to improve model explainability. Findings: The empirical findings show that the Logistic Regression model and SVM model using the TF-IDF feature extraction approach have the best performance when compared to the other models, with an average accuracy of 84% and 86% respectively. The data balancing step pushed the accuracy of the Random Forest model from 47% to 73%, other models slightly changed. The performance of deep learning models was better than traditional machine learning models, LSTM and GRU achieve approximately 78%, and Bi-directional RNN achieve 79% for dataset 2. Novelty and applications: we propose a highly accurate approach for SA which has been tested on two datasets. Also, to increase trust in model prediction, we explain the predicted sentiment. Keywords: Explainable Artificial Intelligent (XAI); Sentiment Analysis; Covid19; Deep Learning; machine learning

Solution-plasma-induced oxygen vacancy enhances MoOx/Pt electrocatalytic counter electrode for bifacial dye-sensitized solar cells

Bifacial dye-sensitized solar cells (DSCs), harvesting light from both front and rear sides, are potential high-efficiency photovoltaic devices with broad application environments. The electrocatalytic counter electrodes (CEs) of bifacial DSCs could determine the light-harvesting from the rear side and the charge collection of solar cells through electrocatalytic processes. As a result, high-activity and high-transparency CEs are essential for bifacial DSCs. Recently, novel CEs based on strong metal-support interaction (SMSI) have been proven to improve the catalysis and stability of the metal catalytic sites and induce great efficiency increase of bifacial DSCs. However, the contradiction between the transparency and conductivity of support is still a major challenge for the application of SMSI-based CEs on bifacial DSCs. Herein, we utilized a solution plasma (SP) method to introduce oxygen vacancies into a transparent MoOx support film. These SP-induced oxygen vacancies improved the conductivity of MoOx and the interaction between the metal Pt catalytic sites and support, thereby enhancing the catalytic activity and transparency of MoOx/Pt CEs. Consequently, the bifacial DSCs with MoOx/Pt CEs yielded a high efficiency of 7.56% and 6.41% with the front- and rear-side illumination, respectively. This impressive front-to-rear efficiency ratio of 85% indicates that the SP method has a positive effect in constructing high-performance CEs and other electrocatalytic materials based on the SMSI.

Design and implementation of a fully transparent partial abort support for software transactional memory

AbstractSoftware transactional memory (STM) provides synchronization support to ensure atomicity and isolation when threads access shared data in concurrent applications. With STM, shared data accesses are encapsulated within transactions automatically handled by the STM layer. Hence, programmers are not requested to use code‐synchronization mechanisms explicitly, like locking. In this article, we present our experience in designing and implementing a partial abort scheme for STM. The objective of our work is threefold: (1) enabling STM to undo only part of the transaction execution in the case of conflict, (2) designing a scheme that is fully transparent to programmers, thus also allowing to run existing STM applications without modifications, and (3) providing a scheme that can be easily integrated within existing STM runtime environments without altering their internal structure. The scheme we designed is based on automated software instrumentation, which injects into the application capabilities to undo the required portions of transaction executions. Further, it can correctly undo also non‐transactional operations executed on the stack and the heap during a transaction. This capability allows programmers to write transactional code without concerns about the side effects of aborted transactions on both shared and thread‐private data. We integrated and evaluated our partial abort scheme within the TinySTM open‐source library. We analyze the experimental results we achieved with common STM benchmark applications, focusing on the advantages and disadvantages of the proposed solutions for implementing our scheme's different components. Hence, we highlight the appropriate choices and possible solutions to improve partial abort schemes further.

Anomaly Detection in Time Series for Smart Agriculture

Purpose: Enormous amounts of data from sensors and automatic systems is available to contemporary managers of farms and agricultural enterprises. Therefore, the automated monitoring of the basic processes is becoming more and more necessary. Manager of a modern agricultural company is responsible for his decisions but must have accurate and transparent support in the decision making process. The reports based on wide-scale of mathematical and statistical approaches integrated to information system can provide a suitable hierarchy of alerts concerning important indicators. This paper provides a brief overview of methods usable for anomaly detection in time series together with a basic (and open) possibilities of their implementation.
 Study design/methodology/approach: A brief overview of the most important and sufficiently flexible modern methods for detection anomaly detection in time series with focus particularly on agriculture.
 Findings: The paper can serve as a starting point for creation of alerting in agricultural information systems as a support for managerial decision making. In connection to the extremely fast development in data and information spheres in smart agriculture within the last two decades develop also the new approaches for prediction in time series and search for unexpected fluctuations in their course. Besides the traditional mathematical and statistical approaches new methods emerge which are tailor-made to the internet environment and environment of social networks.
 Originality/value: This basic overview can be used for a primary orientation in the problem and can serve as a basis for creation of automated alerting system. It is always necessary to remember the domain of use, economic relations, structure and hierarchy of time series and their periodicity, seasonality and cycles.

A novel optimized resource management model for software defined future mobile networks

SummaryExtreme densities are an important factor in future mobile networks that can provide very high data rate to mobile users. It also increases complexity in handover decisions and resource management. However, SDNs (software‐defined networks) and mobility models can provide seamless mobility and efficient resource management in heterogeneous mobile environment, and ensure QoS (quality of service) is achieved. The paper proposes SDN based on GMM (Group Mobility Model) called MoMo, and resource management using MLOAs (mutation lion optimization algorithms) are used to alleviate handover and addressing issues in network congestion. The proposed method is based on SDN controllers working in global perspectives for achieving required network conditions and end user QoSs. SDN with GMM‐MoMo‐MLOA offer transparent and dynamic support for sessions during handoffs and thus eliminates congestion overheads and packet losses related to mobile traffic, resulting in improved QoS for mobile users. The performance analysis through experiments depicts that the proposed model gives enhanced performance when compared to that of the other conventional methods and also has proven efficiency in terms of other parameters such as handover latency, signaling cost, throughput, and packet loss. The results from the simulation show that the proposed method SDN‐GMM‐MoMo‐MLOA greatly improves the performance of network and also maintains optimum resource utilization and efficiency.

GREEN INFRASTRUCTURE PLANNING THROUGH EO AND GIS ANALYSIS: THE CANOPY PLAN OF LIÈGE, BELGIUM, TO MITIGATE ITS URBAN HEAT ISLAND

Abstract. As a place to live, work and play, urban spaces are the nerve centers of human activity. The urban spaces, which are becoming increasingly extensive and dense, are particularly sensitive to the effects of climate change. Greening cities has been identified as one of the most effective ways to mitigate one of these effects, the urban heat island. In this context, the city of Liège, Belgium, has adopted a greening plan, called “Plan canopée”, to increase the tree canopy area at city level. The aim of this study is to present the methodology used to objectivize and prioritize the selection of potential planting sites. This research proposes the production of a series of spatial indicators allowing, on the one hand, the precise mapping of urban surfaces ready for planting, and on the other hand, the objective prioritization of city districts to be vegetated. The research integrates airborne image classification, urban heat island modeling and a series of reference geodata combinations. The set of data produced provides a transparent decision support tool to the authorities for the operational planning of tree plantations. These results contribute to the development of a planting strategy that maximizes the chances of ownership by the citizens, and consequently the sustainable and effective impact of the achievements.