The extensive collection of paper documents and books stored in the archives of universities worldwide is a hidden cultural heritage that is frequently inaccessible. To overcome this problem, the University of Genoa, Italy, seeks to collect, store, and digitize a wide variety of items, encompassing books, manuscripts, archival materials, and documents related to museum artifacts, which together form a cultural heritage of great importance and historical significance. To make such a cultural heritage accessible to both humans and machines, images and videos must be provided with alternate descriptions, metadata, and speech-to-text transcriptions while ancient texts, for which OCR techniques are often not effective, must be accompanied by word-for-word transcripts. This work presents the design of a transcription system for the “University Museum System” at the University of Genoa, Italy (SMA-UniGe), including user interface elements and users’ engagement techniques. The goal is to create an accessible digital heritage that can be enjoyed by all, facilitated by a community of digital volunteers who are eager to dedicate their time, have a great experience, socialize, and interact on the proposed transcription system. The system exploits gamification theory to transform the typically monotonous task of transcription into a captivating experience. This activity is in line with the so-called University third mission, i.e., the activity of public engagement that aims at generating knowledge outside the academic environment to the benefit of the social, cultural, and economic development.

Virtual tours with 360 photos present a quick, easy and accessible way to create immersive experiences. The visualization in diverse devices, provide a user experience that can be different. A virtual tour with 360 photos of the Chapel of Dolores (Tenerife), has been created. Whose access is limited to specific days, so the creation of a virtual tour allows you to visit it at any time, in addition, a pilot test has been carried out with students of the degree of restoration and conservation of cultural heritage in order to measure the user experience of the virtual tour depending on the device used. The participants took a virtual tour of the Dolores Chapel, first with a cell phone and then with the virtual reality headset, and then completed a user experience questionnaire. The results obtained comparing both visualization technologies are detailed, highlighting that when using the virtual reality headset no better results are obtained in the immersion subscale, in addition, the visualization on a Smartphone is more comfortable and obtains better results in consequences such as dizziness and vertigo. Therefore, it may be an interesting strategy to have simple versions that work quickly on the cell phones.

This study innovatively assesses the Mun River flow components in Thailand, integrating Artificial Neural Networks (ANN) and Isotopic (18O) End-Member Mixing Analysis (IEMMA). It quantifies the contributions of the Upper Mun River (UMR) and Chi River (CR) to the overall flow, revealing a discrepancy in their estimated contributions. ANN predicts the UMR and CR contribute approximately 70.1% and 29.5% respectively, while IEMMA indicates a more pronounced disparity with 84% from UMR and 16% from CR. This divergence highlights the distinct perspectives of ANN, focusing on hydrological data patterns, and IEMMA, emphasizing isotopic signatures. Despite discrepancies, both methods validate UMR as a significant contributor to the overall flow, highlighting their utility in hydrological research. The findings underscore the complexity of river systems and advocate for an integrated approach of river flow analysis for a comprehensive understanding, crucial for effective water resource management and planning.

The aim of this study was to identify factors, that influence satisfaction and dissatisfaction in the use of public e-procurement. The use of IT solutions promotes sustainable development. Hence, there is a need to conduct research that will allow for the best possible adjustment of e-procurement solutions in order to implement them as widely as possible in business practice. This study used a quantitative exploratory survey-based approach. The obtained research results were analyzed using statistical methods, including factor analysis PCA. Factor analysis identified three main satisfaction factors and two dissatisfaction factors in e-procurement use, highlighting the importance of support, intuitiveness, and security in user satisfaction. This study contributes to the existing literature by providing a nuanced understanding of the factors influencing user satisfaction and dissatisfaction within e-procurement systems, specifically highlighting the role of intuitive design and technical support. It offers a unique perspective by integrating these insights with the specific requirements of public procurement, thereby filling a gap in research on enhancing user experience in the context of sustainable purchasing practices.

Health comprises physical, mental and social dimensions whose interactions influence users’ experiences. Health experiences are within the context of environments. These contexts are definitive contributors to experiences – primarily because of the link to social dimensions as human beings - psychological and economic. Therefore, the study aims to investigate health resort users’ psycho-social and socio-economic characteristics to improve their access to quality healthcare. The objectives are identifying the socio-economic characteristics of an inclusive health resort, analysing the psycho-social characteristics of the inclusive health resort user, and analysing the effects of users’ psycho-social and socio-economic characteristics on their access to healthcare. The study employs the mixed research method (qualitative and quantitative). The researchers engaged in a pragmatic research philosophy that underscores positivist and interpretivist paradigms. The data collection instruments include survey questionnaires, an interview guide with a digital recorder, a direct participant observation schedule, field notes and photographs. The data analysis process involves the clustering and categorisation of data and examining concepts and themes. The research findings show a correlation between the users’ socio-economic status and access to quality healthcare in an inclusive health resort. The result further reveals that psycho-social and socio-economic characteristics of health resort users are quality healthcare access predictors. Therefore, the users’ social, psychological, and economic status should play a vital role in the design of an inclusive health resort by all stakeholders.

Objective: To examine variations in end-of-life care for breast cancer, heart failure, and dementia patients. Data and methods: Data from four Norwegian health registries were linked using a personal identification number. Longitudinal trends over 365 days and the type of care on the final day of life were analyzed using descriptive techniques and logistic regression analysis. Results: Patients with dementia were most commonly placed in nursing homes, while patients with heart failure and breast cancer were more frequently hospitalized. Breast cancer and heart failure patients had a higher likelihood of dying at home. The presence of general practitioners increased the probability of home-based end-of-life care for cancer patients, while non-physician healthcare workers increased the likelihood of home-based care for the other patient groups. Conclusion: Diagnoses, individual characteristics, and service availability are all associated with the place of death in end-of-life care.

This study explores the Experience Economy dimensions within fishing tourism, adopting a data science perspective by employing web crawling for extensive data gathering on reviewer profiles from TripAdvisor. Utilizing Natural Language Processing (NLP) techniques, it scrutinizes the relationship between Experience Economy dimensions and user profiling aspects. Findings reveal "Entertainment" as the predominant dimension in tourist reviews, followed by "Aesthetic," "Educational," and "Escapist." Particularly notable are the frequent co-occurrences of "Entertainment"-"Aesthetic" and "Educational"-"Entertainment" pairs. The practical implications underscore the potential socioeconomic benefits of fishing tourism for local communities and fishermen, emphasizing the necessity for governmental support in terms of infrastructure, leadership, legislation, and financial backing to foster sustainable development in this sector. Notably, this research stands as a pioneering effort in its focus on Experience Economy dimensions and user profiling within the context of fishing tourism, drawing data from business pages and user profiles.

This paper presents a study of the 2D roughness profiles on a flat surface generated on a steel work piece, by ball nose end milling, with linear equidistant tool paths (pick-intervals). The milled surface exploration with a surface roughness tester (on pick and feed directions) produces usually 2D roughness profiles having periodical evolutions. These evolutions can be seen as time-dependent signals, describable as a sum of sinusoidal components (the wavelength of each component being regarded as a period). In order to detect a well approximated description of these components, two appropriate signal processing techniques are used in this work: first technique provides a direct mathematical (analytical) description and is based on computer aided curve (signal) fitting (more accurate), the second technique (providing an indirect and incomplete description) is based on the spectrum generated by fast Fourier transform (less accurate). This study can be seen as a way for a better understanding the interaction between the tool and the work piece or to achieve a mathematical description of the machined surface microgeometry in terms of roughness (e. g. its description being a collection of closely spaced 2D roughness profiles).

Recent literature has seen a rapid expansion in the reference to front-end forensics as an indication of the untapped value of forensic science. While some of these contributions have centered on development of forensic intelligence from a single area of investigation, others call for a more fundamental change in the relationship between crime laboratories and policing, particularly relating early laboratory analysis with big datasets to provide leads to investigators. We highlight several recently implemented tactical strategies of crime laboratories that contribute to the body of forensic intelligence. Beyond the scientific gains from these tactical applications, the corresponding details on associated efficiencies, costs, time savings, and quality improvements offer insights towards patterns of success for the community of crime laboratories. Further details expand an interpretation of what constitutes success with an eye on the contributions of the crime laboratory towards public health, safety, and protection of the innocent in addition to societal gains from conviction of the guilty. The economic interpretation of the value provided by the forensic laboratory assists in the cost-benefit review of strategic and tactical decisions and supports the justification for laboratory public funding with measures of the return on investment from public support of the forensic laboratory

Speech recognition, or automatic speech recognition (ASR), is a technology designed to convert spoken language into text using software. However, conventional ASR methods involve several distinct components, including language, acoustic, and pronunciation models with dictionaries. This modular approach can be time-consuming and may influence performance. In this study, we propose a method that streamlines the speech recognition process by incorporating a unified recurrent neural network (RNN) architecture. Our architecture integrates a convolutional neural network (CNN) with an RNN and employs a connectionist temporal classification (CTC) loss function. Key experiments were carried out using a dataset comprising 576,656 valid sentences, using erosion techniques. Evaluation of the model performance, measured by the word error rate (WER) metric, demonstrated remarkable results, achieving a WER of 2%. This approach has significant implications for the realm of speech recognition, as it alleviates the need for labor-intensive dictionary creation, enhancing the efficiency and accuracy of ASR systems, and making them more applicable to real-world scenarios. For future enhancements, we recommend the inclusion of dialectal and spontaneous data in the dataset to broaden the model's adaptability. Additionally, fine-tuning the model for specific tasks can optimize its performance for targeted objectives or domains, further enhancing its effectiveness in those areas.

Speech recognition, also known as automatic speech recognition (ASR), is a technology that enables software to transcribe spoken language into text. However, existing Amharic ASR methods require multiple separate blocks, such as language, acoustic, and pronunciation models with dictionaries, which can be time-consuming and influence performance. This study proposes an approach that replaces much of the speech pipeline with a single recurrent neural network (RNN) architecture. Our proposed architecture is based on a hybrid approach that combines a convolutional neural network (CNN) with a recurrent neural network (RNN) and a connectionist temporal classification (CTC) loss function. We conducted several experiments with noisy audio data that contain 20,000 valid sentences. The model was evaluated using the word error rate (WER) metric, achieving impressive results of 7% WER on noisy data. This approach has significant implications for the field of speech recognition, as it reduces the human effort required to create dictionaries and improves the efficiency and accuracy of ASR systems, making them more practical for real-world applications.

Biotechnological potential of Thermoanaerobacter strain AK15 to produce ethanol and valuable alcohols from carbohydrates and amino acids were evaluated in present study. The strain is highly ethanologenic, producing a maximum of 1.57 mol ethanol per mol of glucose degraded at high liquid-gas phase ratios. The strain degrades most of the sugars tested as well as starch and pretreated hydrolysates of biopolymers (Whatman paper, newspaper, Timothy grass, Rhubarb leaves, and several macroalgae species) to ethanol mainly. The strains degraded serine and threonine when used as a single substrate, producing mainly acetate and ethanol as end products, and the branched-chain amino acids (leucine, isoleucine, valine) when cultivated in the presence of thiosulfate. The main end products from branched-chain amino acids were a mixture of their corresponding branched-chain fatty acids and alcohols. Finally, the strain was also shown to use butyrate as an electron sink during glucose degradation resulting in the reduced product butanol in addition to end products produced from glucose. Thus, strain AK15 is a promising candidate for ethanol and fine chemical production from a range of lignocellulosic and algal biomass.

IgA nephropathy (IgAN) is the most common primary glomerulonephritis worldwide. IgAN causes end-stage kidney disease (ESKD) in 20–40% of all cases. The activation of the complement system by pathological circulating IgAs, which is often associated with low serum C3 levels (LowC3), seems to play a crucial role. Previous studies have shown an association between histological evidence of TMA, which is the result of alternative complement activation, and poor outcome. However, it is not known to what extent the decrease in serum C3 levels reflects ongoing TMA injury. Our study aimed at assessing the association of LowC3 and ESKD and whether this association reflects ongoing TMA. We enrolled all patients with biopsy-proven IgAN and followed-up patients until last visit, ESKD or death. Of the 56 patients included in the study, 12 (21%) presented low serum C3 (LowC3) at the time of renal biopsy. TMA was significantly more frequent in the LowC3 group [7/12 (58%) vs 9/44 (20%), p=0.02]. After adjusting for potential confounders, LowC3 was strongly associated with an increased hazard of ESKD (hazard ratio [HR]: 5.84 [95%CI: 1.69,20.15; P=0.005). The association was not affected by adjusting for TMA. The estimated overall proportion of the relation between C3 and ESKD mediated by TMA was low and not statistically significant. Our study provides evidence that C3 hypocomplementemia is associated with an increased risk of ESKD with mechanisms that are largely independent from TMA.

Autonomous driving navigation relies on diverse approaches, each with advantages and limitations depending on various factors. For HD maps, modular systems excel, while end-to-end methods dominate mapless scenarios. However, few leverage the strengths of both. This paper innovates by proposing a hybrid architecture that seamlessly integrates modular perception and control modules with data-driven path planning. This innovative design leverages the strengths of both approaches, enabling a clear understanding and debugging of individual components while simultaneously harnessing the learning power of end-to-end approaches. Our proposed architecture achieved 1st and 2nd place in the 2023 CARLA Autonomous Driving Challenge’s SENSORS and MAP tracks, respectively. These results demonstrate the architecture’s effectiveness in both map-based and mapless navigation.

This study investigates the application of regression neural networks, in particular the fitrnet model, in predicting the hardness of steels. The experiments involve extensive tuning of hyperparameters using Bayesian optimization and employ 5-fold and 10-fold cross-validation schemes. The trained models are rigorously evaluated, and their performances are compared using various metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results provide valuable insights into the effectiveness of the models and their ability to generalize to unseen data. In particular, Model 4208 (8-85-141-1) emerges as the top performer with an impressive RMSE of 1.0790 and an R² of 0.9900. The research paper contains an illustrative example that demonstrates the practical application of the developed model in determining the hardenability band for a specific steel grade and shows the effectiveness of the model in predicting and optimizing heat treatment results.

In the domain of financial trading, where accuracy and swift response to data discrep- ancies are paramount for maintaining clienttrust, our research paper delves into the development of a real-time alert system utilizing SpringBoot, a framework renowned for its robustness and adaptability in enterprise applications. We embark on a comprehensive journey, beginning with addressing thefoundational challenges inherent in monitoring trade inquiries - the necessity for real-time processing, unwavering data accuracy,and ensuring unwavering system reliability. The study then methodically transitions to the architectural design, where we explore howSpringBoot’s capabilities are optimally utilized to meet these challenges. Following this, we delve into the im- -plementationphase, detailing the strategic selection of compatible technologies and methods for effective data management and discrepancydetection. The paper culminates in a thorough evalu- ation of the system, where we assess performance across various operationalscenarios, scrutinize the accuracy of the discrepancy detection mechanisms, and integrate real-world feedback from users in thetrading sector. This holistic approach not only highlights the versatility and efficiency of SpringBoot in creating reliable alert systems in the high-stakes world of financial trading but also serves as a detailed guide for similar endeavors in this field

Introduction: Advanced glycation end-products (AGEs) may play a relevant role as inducers in the chronic inflammatory pathway present in immune-mediated diseases, such as systemic lupus erythematosus (SLE). AGEs concentrations have been associated, with discrepant results to date, with some parameters such as disease activity or accrual damage, suggesting their potential usefulness as biomarkers of the disease. Objectives: To confirm differences in AGEs levels measured by cutaneous autofluorescence between SLE patients and healthy controls (HC) and study their correlation with various disease parameters. Methods: Cross-sectional study, where AGEs levels were measured by skin autofluorescence, and SLE patients’ data were compared with those of sex- and age-matched HC in a 1:3 proportion through a multiple linear regression model. Correlations of AGEs levels with demographic and clinical data were analyzed through ANOVA tests. Both analyses were adjusted for confounders. Results: AGEs levels in SLE patients were significantly higher than in HC (p<0.001). We found statistically significant positive correlation with SLE disease activity index (SLEDAI) and damage index (SDI), physician and patient global assessment, C-reactive protein, leukocyturia, complement C4, IL6 and oral ulcers. We also found a negative statistically significant correlation with current positivity of anti-nuclear and anti-Ro60 antibodies. Conclusions: AGEs seem to have a role in LES pathophysiology, becoming a new management and prognosis biomarker in this disease. The association with specific antibodies and disease manifestations may indicate a specific clinical phenotype related to higher AGEs levels.

Incorporating AI tools like ChatGPT into higher educational settings has been beneficial for education, yet the extent of user satisfaction with the information provided by these tools, known as user information satisfaction (UIS), remains underexplored. Our study introduces a UIS model specifically designed for ChatGPT's application in the educational sector. Drawing from established UIS theory, we crafted a model centered around seven essential factors that influence the effective use of ChatGPT, aiming to guide both educators and learners in overcoming common challenges such as plagiarism and ensuring the ethical use of AI. We gathered data from Indonesian university participants and applied Structural Equation Modeling using Smart-PLS 4.0 for our data analysis. The results reveal that completeness, precision, timeliness, convenience, and the format of information as the most influential factors driving user satisfaction with ChatGPT. Interestingly, our research indicated that the accuracy and reliability of the information, typically deemed paramount, were not the primary concerns in the academic use of ChatGPT. Our findings lead to a recommendation for a cautious approach to the integration of ChatGPT in higher education. We advocate for a strategic use that recognizes its innovative potential while also acknowledging its limitations, thus ensuring a responsible and effective application in educational contexts. This balanced perspective is crucial for integrating AI tools into the academic fabric without compromising educational integrity or quality.

To study the coupling mechanism and dynamic response of the end-face movable gear trans-mission system under complex excitation, a specific configuration of end-meshing movable gear reduction mechanism was used to achieve predetermined rigid thrust transmission and mis-matched gear meshing functions, which solved the inherent defects of traditional harmonic gear mechanism thin-wall flexible wheel easy to be damaged by fatigue. Considering the phenomenon of elastic deformation of live teeth accompanied by significant changes in meshing characteristics in the transmission process of end-meshing harmonic reducer, the influence of dynamic meshing parameters, live tooth deformation, time-varying stiffness of tooth meshing, and time-varying backlash on nonlinear dynamic performance was explored, as well as the mechanism of mul-ti-parameter coupling on transmission performance. The nonlinear dynamics model of the end-meshing harmonic reducer is established to solve the chattering prediction problem. Finally, a comprehensive test bed for the transmission system of harmonic reducer with meshing type with adjustable characteristic end was built to verify the correctness of the theoretical model and provide the theoretical and technical basis for exploring the optimal parameter selection of pas-sive vibration suppression problem.

A significant body of research has examined the accuracy and diversity of several types of recommendation systems. However, little is known about the psychological mechanisms underlying the effects of the different types of recommendation systems on user satisfaction. The findings of our experiment showed that user satisfaction is influenced by shopping goal. Specifically, when a user’s shopping goal aligns with the type of recommendation system, the user satisfaction is enhanced. Our study evaluated the mediating role of feeling right and psychological reactance for a better understanding of this relationship. We further tested the moderated mediation effect of feeling right and psychological reactance moderated by the user shopping goal. For goal-directed users, accurate recommendations trigger the activation of feeling right, consequently increasing the user satisfaction. Conversely, when exploratory users face accurate recommendations, they activate psychological reactance, which leads to a reduction in user satisfaction.

Researchers are always looking to find ways to use energy more efficiently while keeping people comfortable at home. They've tried different methods like fuzzy logic and genetic algorithms to do this. In this paper, we introduce a novel model that combines different techniques like data smoothing, optimization, machine learning, and control algorithms to reach this goal in smart homes. The proposed model is made up of three main parts: smoothing, optimization, and control. The smoothing part uses the Kalman filter to get rid of any unnecessary noise and make sure the data used to predict energy use is clean and smooth. The optimization part uses both genetic with firefly algorithms and artificial neural networks to minimize the gap between the actual environmental conditions and the conditions users find comfortable. It adjusts the comfort settings in real-time using machine learning. The control part uses Mamdani fuzzy logic to make sure energy is delivered in the best way possible to the systems that control temperature, lighting, etc., making the home comfortable and saving energy at the same time. Comparative analyses demonstrate the superiority of the proposed model over existing algorithms, including the Particle Swarm Optimization (PSO) algorithm. Additionally, the paper discusses the importance of adaptive controllers in addressing issues associated with incorrect PID controller selection and highlights the benefits of using the FA-GA in energy optimization and comfort improvement. Overall, the proposed model offers an effective solution for efficient energy consumption optimization and enhanced user comfort in diverse settings through the integration of advanced algorithms and optimization modules.

This study introduces a fruit harvesting mechanism powered by a single motor, designed for integration with unmanned aerial vehicles (UAVs). The mechanism performs reciprocating motion by converting linear motion into rotational motion. Consequently, the end-effector can execute multi-dimensional kinematic trajectories, including biaxial and rotational movements, synchronized with the motor's position. These axial and rotational motions facilitate the gripper's ability to reach, retrieve, and detach fruit from branches during the harvesting process. Notably, a critical consideration in designing this fruit harvesting mechanism is to generate the necessary torque at the end-effector while minimizing reaction forces and torque that could destabilize the UAV during flight. With these considerations in mind, this preliminary study aimed to harvest a Fuji apple and conducted dynamic analysis. We constructed a prototype of the single motor-driven fruit harvesting mechanism using a suitable servo motor. To assess its mechanical performance and evaluate its impact on the hexacopter, we developed both a specific test platform featuring a six-spherical-prismatic-spherical parallel structure and a virtual environmental flight simulator. Overall, the results demonstrate the successful harvesting of a Fuji apple weighing approximately 300 g by the single motor-driven fruit harvesting mechanism, with no adverse effects observed on the hexacopter's operation.

Breeding for low hydrogen cyanide (HCN) varieties is a major objective of programmes targeting boiled cassava food product. To enhance breeding for low HCN, knowledge on genetic variation and trait heritability is essential. In this study, 64 cassava clones established across four locations and evaluated for HCN using three HCN assessment methods; 1 to 9 scale, 0 ppm to 800 ppm scale and a quantitative assay based on spectrophotometer readings (HCN_Spec). Data were also collected on the weather variables precipitation, relative humidity, and temperature. Significant differences were observed among clones (P &lt; 0.001) and locations (p &lt; 0.001). There were also significant (p &lt; 0. 001) clone by environment interactions. Locations Arua and Serere posted higher HCN scores among clones and were associated with significantly higher (p &lt; 0.001) mean daily temperature (K) and lower relative humidity (%) across 12-hour and 18-hour intervals. Within locations HCN broad sense heritability estimates ranged 0.22 – 0.64 while combined location heritability estimates ranged 0.14 to 0.32. Relationships between the methods were positive and strong (R2 = 0.75 – 0.92). It is expected that the information herein will accelerate efforts towards breeding for low HCN cassava varieties.

This research explores the transformation of learning methodologies from traditional to modern approaches, focusing on the development of a gamified educational application with an Artificial Intelligence (AI) mentor for secondary school graduates preparing for Croatian state graduation exams. Traditionally, education has been marked by a passive student role and teacher-centric transmission of knowledge. In contrast, modern learning tools, such as gamified applications, introduce interactive and engaging elements. The purpose of this study is to identify user-centric learning approaches through a comprehensive survey of graduating students, assessing their readiness for exams, learning styles, and preferences. The research explores issues such as financial burdens on parents for preparatory courses and copyright concerns with external educational materials. In summary, the learning application should prioritise sustained knowledge retention, motivation, and stress reduction, offering an alternative to external preparatory courses. Conclusions underscore the importance of fostering an engaging learning environment, incorporating teacher-like elements, and aligning with user expectations. As digital technology positively impacts the learning experience, understanding user preferences is crucial in designing educational apps that cater to learners' needs. The study provides insights for developing a gamified learning application that not only prepares students for exams but also enhances long-term knowledge acquisition and engagement.

The Odunpazarı Modern Museum (OMM), conceptualized by Architect Kengo Kuma and inaugurated in 2019 in Eskişehir, stands as a testament to the historical significance of the timber trade in the region. The deliberate choice of wood as the primary construction material serves as a tribute to this historical narrative. The conspicuous incorporation of wooden lath materials into both the external and internal spaces signifies a conscious reference to the region's historical heritage and aligns with sustainability principles in design. The timber used to construct OMM was sourced from exhausted industrial pine forests. The creation of exhibition spaces at diverse scales involved the assembly of boxes of varying dimensions, illustrating OMM's inclination towards a formative structure characterized by “Addition and Integration.” This architectural methodology involves juxtaposing two or more primary forms to achieve a partial amalgamation. Factors such as form characteristics, dimensional distinctions, spatial arrangements, and extent of surface interactions collectively contribute to the compelling effect of this integrative approach. Within the confines of the museum, unconventional partitioning is implemented, and the strategic arrangement of masses results in multiple façades, even in the absence of overt wooden surfaces. The discernible visibility of the building's façade from an internal vantage point underscores the pivotal role played by wood in the architectural design of the OMM. The exterior impact of the wooden shell complements its interior application. The voluminous void at the heart of the structure allows for the infiltration of natural light into the internal spaces, and the wooden facade facilitates the transmission of light. The use of glass to segregate the cafeteria area from the museum entrance preserves a visual continuity between these spaces. The fundamental research inquiry in this study revolves around the user experience of wood, a material that actively influences diverse facets, including structural elements, contextual considerations, interior arrangements, and external aesthetics. To address this inquiry, a comprehensive case study and survey were conducted with the objective of elucidating the functional, sensory, perceptual, and psychological impacts of wood, the primary material in the museum's spatial configuration, on the overarching user experience.

Active front-end (AFE) rectifiers have been becoming widely employed in power systems to achieve unity power factor and harmonic mitigations. The typical modeling approaches applied for AFE rectifiers in the literature mostly relied on two baselines named the detailed model and the time average model. The former approach deals with the switching element model (SEM) leads to significant harmonics in currents with distorted waveforms. The latter approach uses the average-value model (AVM) to overcome the currents’ harmonics as well as provide fast responses. However, even the AVM baseline has contained problems during the starting stage (lack of control signals) and over the dead-time periods which causes serious issues in the implementation process. This paper presents an improved dynamic AVM for AFE rectifiers by precisely considering the issues mentioned above along with the practical starting procedure and desirable initialization. The studied AFE rectifier is developed using the voltage-oriented control (VOC) technique based on the different modeling methodologies including SEM, Conventional AVM, and the proposed AVM. The performance of all models is analyzed and compared using simulation results with MATLAB/Simulink R2023a Function blocks for all the algorithm parts and SimScape elements for the electrical circuit model. The simulation results illustrate that the performance of the proposed AVM approach can closely resemble the behavior of the SEM baseline with low harmonic distortion.

With young adults (18-30 years) exhibiting the lowest vegetable intake, primarily due to in-adequate cooking skills, this study aims to explore the usability and desirability of educational tools such as written recipes and cooking videos to foster sustainable eating habits. Employing a descriptive, qualitative, and user-centered design, three focus groups were held at a major urban university campus in New Zealand, engaging students, and staff within the target age group. Discussions with 15 participants identified key facilitators for vegetable-based cooking, including access to ingredients, ease of recipe visualization, and cost-effectiveness. Conse-quently, 13 vegetable-centric recipes and instructional videos were created, incorporating nu-tritional value, affordability, and preparation efficiency. These resources, tailored to young adults' preferences, were designed to mitigate the identified barriers to vegetable use, con-tributing to sustainable food practices. The application of a user-centered approach in devel-oping educational content yielded a set of recipes and videos that not only address the gaps in cooking skills among young adults but also promote the broader goals of sustainability in food consumption patterns. This approach offers actionable insights for practitioners and food marketers to enhance vegetable utilization and sustainable eating behaviors in this demo-graphic.

Pedestrians, bicyclists, scooterists are Vulnerable Road Users (VRU) in traffic accidents. The number of fatalities and injuries in traffic accidents involving vulnerable road users has been steadily increasing in the last two decades in the U.S. even though road vehicles now have perception sensors like camera to detect risk and issue collision warnings or apply emergency braking. Perception sensors like camera are highly affected by lighting and weather conditions. Camera, radar and lidar cannot detect vulnerable road users in partially occluded and occluded situations. This paper proposes the use of Vehicle-to-VRU communication to inform nearby vehicles of VRUs having trajectories with potential collision risk. An Android smartphone app with low energy Bluetooth (BLE) advertising is developed and used for this communication. The same app is also used to collect motion data of VRUs for training. VRU motion data is smoothed using a Kalman filter and an LSTM neural network is used for future motion prediction. This information is used in an algorithm comparing Time-To-collision-Zone (TTZ) for the vehicle and VRU and issue a driver warning with different severity levels. The warning severity level is based on analysis of real data from a smart intersection for close vehicle and VRU interactions. The resulting driver warning system is demonstrated using proof-of-concept experiments. The method can easily be extended to a VRU collision mitigation system.

The recycling of wastes to obtain value-added products is a great challenge in the sustainable economy. In our studies, a mixed precursor containing aluminium scrap from end – of – life vehicles and TiO2 Degussa P25 was used to prepare TiO2 modified Al photocatalyst through solid state reaction. Al scrap and TiO2 Degussa P25 in the ration of n(Ti) / n(Al) = x (x = 0.0 - 2.5%), and acetylacetonate and Tween 80 was mixed under magnetic stirring and sintered using a con-ventional furnance at 550°C. DTA – TG – DTG, X – ray diffraction, Fourier – transform infrared, UV – Vis and photoluminescence spectroscopies were assed to investigate the thermal, structural, optical properties and photocatalytic activity. The effect of aluminium concentration on the characteristics of TiO2 Degussa P25 depending on the above instruments are studied. XRD revealed that the materials have crystal structure of anatase TiO2, rutile TiO2 and aluminium. The opto-electronic properties show that Al addition shifted the absorption spectra toward the visible range. The photocatalytic performance of Al – TiO2 composites was quantified by the degradation of Allura Red solution under a visible condition. The highest photocatalytic activity (79.99%) was achieved for the sample containing 0.5% Al and a higher concentration of Al particles shields the active centres of the semiconductor, thus reducing its photocatalytic activity. Aluminium scrap from end-of-life vehicles can be a source of raw material to be considered in semiconductor production. The spin-trapping technique evidenced that •O2- was the main species generated responsible for the Allura Red degradation.

: Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America, and to enhance the utility of drought monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impacts information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and the colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and the warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and indicators are changing over time as the climate warms, and by recommending ways to strengthen the use of indices and indicators in drought decision-making.

A sustainable smart tourism ecosystem relies on building digital networks that link tourists to destinations. This study explores how web and immersive technologies, such as the VRRO platform, can improve tourism by offering interactive experiences to augment traditional travel. The study underscores the role of VRRO's design in enhancing user interaction and engagement, thereby encouraging the generation of user content and the development of a virtual tour network. An empirical analysis investigates user engagement, revealing that the platform is accessible to users regardless of their technological familiarity. It suggests a high likelihood of sustained use and recommendations. In conclusion, VRRO demonstrates how accessible and innovative technologies in tourism modernize travel experiences and significantly contribute to the evolution of the broader tourism ecosystem.

User experience-related challenges play a significant part in user-technology interactions. However, design processes have not always kept up with the evolving needs of users in terms of technology interactions. This paper aims to present the design, structure and evaluation of an integrated mental health intervention platform (AMGR-I), emphasizing the importance of user-centred design and engagement in addressing the evolving needs of users in technology interactions, specifically focusing on individuals with mental health disorders. The AMGR-I pilot platform integrates community, e-learning, and care sub-platforms to provide a holistic approach to mental health intervention. The study utilizes participatory design research with targeted users to gather insights into user preferences and expectations. By involving the targeted audience in the design process, the platform’s user-centered design can be improved effectively. The platform recognizes the changing view of users, who now seek products that deliver pleasure, enjoyment, engagement, and embodiment. This research contributes to the field by highlighting the importance of user-centered design in the development of a comprehensive platform for mental health support.

This study examines the impact of GenAI tools on the daily tasks of designers within corporations. It investigates both the operational changes and employee anxieties regarding job security. The research employs a qualitative approach: one without the use of ChatGPT and one with its use. The findings indicate significant improvements in operational experience and subjective perceptions across various tasks, as demonstrated through a user experience map. Moreover, the study highlights the potential of AI for enhancing managerial efficiency, streamlining workflows, and improving collaboration. However, it also addresses challenges concerning information authenticity, copyright protection, and professional identity. The goal of this study is to comprehend AI's current role in businesses, evaluate its effects on designers, and offer balanced recommendations that emphasize the integration of AI thinking into future corporate workflows from a human-centric perspective.

Chronic hemodialysis (HD) patients have very high cardiovascular risk. Acute vascular changes during dialysis mediated by factors of the endothelium may have a crucial role in this. Aim: To study acute vascular changes during HD. In 29 consecutive chronic HD patients (age: 65.6±10.4 years) pre-, mid-, and post-HD plasma syndecan-1 (SDC-1) and endothelin-1 (ET-1) levels were measured. Applanation tonometry was performed before HD. SDC-1 levels increased during HD (p=0.004). Males had higher ET-1 levels. Patients were divided into 2 groups based on pulse wave velocity (PWV): PWV≥12 m/s and PWV<12 m/s. Pre-HD and mid-HD SDC-1 levels were higher in the group with PWV≥12 m/s (10.174±2.568 vs. 7.928±1.794 ng/ml, p=0.013, and 10.319±3.482 vs. 8.248±1.793 ng/ml, p=0.044, respectively). Post-HD ET-1 levels were higher in the patient group having PWV≥12 m/s (10.88±3.00 vs. 8.05±3.48 pg/l, p=0.027). Patients having PWV≥12 m/s had higher pre-HD peripheral and aortic systolic blood pressure (p<0.05). Total cholesterol correlated with the SDC-1 decrease during HD (r=0.539; p=0.008). Pre-, mid-, and post-HD SDC-1 correlated with the ultrafiltration (r=0.432, p=0.019; r=0.377, p=0.044; and r=0.401, p=0.012, respectively). SDC-1 and ET-1 contribute to vascular changes observed during HD, and they have correlations with some cardiovascular risk factors.

The LED light source is an important light source for indoor visible light communication. It has the characteristics of a large divergence angle and a high transmission rate. Therefore, using LED as the light source for visible light full-duplex communication can not only satisfy the lighting requirements but also transmit information at high speed. To analyze the factors that affect the optical power of the indoor single-light source visible light communication uplink receiving end, an indoor visible light communication model was established, and mathematical calculation formulas were derived based on the model establishment, and the maximum movable range formula of the retroreflective end under specific conditions was derived; The reliability of the formula was verified with the help of Zemax simulation software. Theoretical analysis and experimental results show that increasing the movement range of the retroreflective end can be achieved by increasing the lens diameter, reducing the focal length, and increasing the link distance.

Cryo electron microscopy (cryo-EM) instrumentation allows obtaining 3D reconstruction of the structure of biomolecular complexes in vitro (purified complexes studied by single particle analysis) and in situ (complexes studied in cells by cryo electron tomography). Standard cryo-EM approaches allow high-resolution reconstruction of only a few conformational states of a molecular complex, as they rely on data classification into a given number of classes to increase the resolution of the reconstruction from a few, most populated classes and discard all other classes. Such discrete-classification approaches result in a partial picture of the full conformational variability of the complex, due to continuous conformational transitions with many, uncountable intermediate states. In this article, we present the software with user-friendly graphical interface for running two recently introduced methods, namely MDSPACE and MDTOMO, to obtain continuous conformational landscapes of biomolecules by analyzing in vitro and in situ cryo-EM data (single particle images and subtomograms) based on molecular dynamics simulations of an available atomic model of one of the conformations. The MDSPACE and MDTOMO software is part of the open-source ContinuousFlex package, which can be run as a plugin of the Scipion software package, broadly used in the cryo-EM field.

Background: The UNHLM pledged to enroll 30 million in TB preventive treatment (TPT) by 2022, necessitating TPT expansion to all at TB risk. We assessed uptake and completion of a 12-dose, weekly isoniazid-rifapentine (3HP) TPT regimen. Methods: Between February 2018 - March 2019 in Dhaka, community-based TPT using 3HP targeted household contacts of 883 confirmed drug-sensitive pulmonary TB patients. Adhering to WHO guidelines, contacts underwent active TB screening before TPT initiation. Results: Of 3,193 contacts who advised health facility visits for screening, 67% (n=2,149) complied. Among these, 1,804 (84%) received chest X-rays. Active TB was diagnosed in 39 (2%) contacts; they commenced TB treatment. Over 97% of 1,216 contacts began 3HP TPT, with completion rates higher among females, those with more education and income, non-slum residents, and those without 3HP-related adverse events. Adverse events, mainly mild, occurred in 5% of participants. Conclusions: The 3HP weekly regimen, with its short duration, self-administered option, and minimal side effects, achieved satisfactory completion rates. A community-focused 3HP TPT approach is feasible, scalable nationally, and aligns with UNHLM targets.

Urban mobility and sustainable transportation are fundamental for the European Union's goal of achieving climate neutrality by 2050. The EU encourages national governments to prioritize zero-emission urban transport systems that emphasize safety, accessibility, and inclusiveness. Promoting walking plays a fundamental role in sustainable urban mobility, offering advantages such as emission reduction, better air quality, and enhanced public health. Recent research underscores the importance of creating appealing and safe pedestrian environments to encourage walking. These efforts align with the United Nations' Agenda 2030 sustainability goals, particularly Objective 11, which aims to build inclusive, safe, and sustainable cities and communities. This paper explores the factors influencing pedestrians' willingness to walk and categorizes them into four main groups: Physical Characteristics, Comfort, Safety, and Attractiveness. Significantly, the study reveals that the importance of these factors varies based on demographics, mainly the age of the users. Understanding these factors and their relative significance for pedestrian satisfaction is crucial for shaping effective policies and urban planning strategies aimed at promoting sustainable mobility. By prioritizing pedestrian satisfaction and addressing the specific needs and preferences of diverse groups, cities can create more walkable and environmentally friendly urban environments. These findings offer valuable insights for policymakers and urban planners working toward EU climate-neutral objectives and enhancing the well-being of citizens.

Abstract: In recent years, the construction engineering sector has undergone a transformative shift towards the integration of machine learning (ML) techniques, particularly in predicting the properties of steel fiber-reinforced concrete (SFRC). Despite the theoretical sophistication of existing models, a persistent challenge remains – their opacity, lacking transparency and real-world relevance for practi-tioners. To address this gap and advance our current understanding, this study employs the extra gradient (XG) Boosting algorithm, crafting a comprehensive ap-proach. Grounded in a meticulously curated database drawn from 43 seminal pub-lications, encompassing 420 distinct records, this research focuses predominantly on three primary fiber types: crimped, hooked, and mil-cut. Complemented by hands-on experimentation involving 20 diverse SFRC mixtures, this empirical campaign is further illuminated through the strategic use of Partial Dependence Plots (PDPs), revealing intricate relationships between input parameters and con-sequent compressive strength. A pivotal revelation of this research lies in the identification of optimal SFRC formulations, offering tangible insights for real-world applications. The developed ML model stands out not only for its sophistication but also its tangible accuracy, evidenced by exemplary performance against inde-pendent datasets, boasting a commendable mean target-prediction ratio of 99%. To bridge the theory-practice gap, we introduce a user-friendly digital interface, thri-ughly designed to guide professionals in optimizing and accurately predicting the compressive strength of SFRC. This research thus contributes to the construction and civil engineering sectors by enhancing predictive capabilities and refining mix designs, fostering innovation, and addressing the evolving needs of the industry.

Philips Visual Patient Avatar represents an alternative method of patient monitoring that, according to computer-based simulation studies, enhances diagnostic accuracy, confidence and reduces workload. After its clinical integration, we assessed pediatric anesthesia providers' perspectives on this technology. This is a single-center qualitative study, conducted at the University Hospital Zurich using in-depth individual interviews. We aimed to identify the advantages and limitations of Visual Patient Avatar in pediatric anesthesia and to assess children and parents' reactions from caregivers' perspective. Thematic analysis was used to identify the dominant themes. Fourteen members of the institution's pediatric anesthesia team were interviewed. The most prevalent themes were: children's positive reactions towards the Visual Patient Avatar (92.9%) and enhanced speed in problem identification (71.4%). Additionally, 50% of participants reported finding the Visual Patient Avatar useful for diverting children's attention during anesthesia induction, and 50% suggested that its vital sign thresholds should be adaptable for different age groups. The study revealed that the Visual Patient Avatar was recognized as a valuable tool in pediatric anesthesia, enabling prompt identification of underlying issues and receiving positive feedback from both children and parents. The most commonly voiced wish for improvement in the study was the ability to customize the Visual Patient Avatar's thresholds for different age groups.

Methylglyoxal (MGO) is the major compound belonging to reactive carbonyl species (RCS) responsible for the generation of advanced glycation end products (AGEs). Its upregulation followed by deleterious effects at the cellular and systemic level is associated with metabolic disturbances (hyperglycemia/hyperinsulinemia/insulin resistance/hyperlipidemia/inflammatory processes/carbonyl stress/oxidative stress/hypoxia). Therefore, it is implicated in a variety of disorders including metabolic syndrome, diabetes mellitus and cardiovascular diseases. In this review an interplay between pathways leading to MGO generation and scavenging is addressed, in regard to this system’s impairment in pathology. The issues associated with mechanistic MGO involvement in pathological processes, as well as the discussion on its possible causative role in cardiometabolic diseases are enclosed. Finally, the main strategies aimed at MGO and its AGEs downregulation with respect to cardiometabolic disorders treatment are addressed. Potential glycation inhibitors and MGO scavengers are discussed, as well as the mechanisms of their action.

This study explored drivers’ emotion-based impressions of car-borne central control platforms (CBCCPs) personal-use vehicles. Thus, this preference-based study examined experts’ and drivers’ opinions regarding CBCCPs’ appeal from the perspective of Miryoku engineering. To this end, this study analyzed data via the EGM (evaluation grid method (EGM) and quantification theory type I. Results: Drivers’ preferences for specific CBCCP design characteristics were categorized into the factors “legible,” convenient,” and “tasteful,” which comprised the core of the EGM semantic hierarchical diagram. In addition, the importance of CBCCPs’ appeal factors and characteristics was assessed through quantification theory type I.The findings of this study provide valuable insights for designers, manufacturers, and researchers interested in the design of CBCCPs. Additionally, the results of this study can contribute to research on applied psychology, human-computer interactions, and car interface design.

Virtual reality software might be challenging to utilize for beginners and unskilled professionals who do not have a programming or 3D modeling background. Concurrently, there is a knowledge gap in software project design for intuitive virtual reality authoring tools, which were supposed to be easier to use. These tools are frequently insufficient due to a lack of support and standard operating procedures. Adopting the Design Science Research paradigm, this study aims to evaluate the validity of fourteen design guidelines for the development of intuitive virtual reality authoring tools as an artifact. While a previous study completed the first steps of the Design Science Research, by identifying problems, defining solution objectives, and developing and demonstrating the design guidelines, this work seeks to qualitatively evaluate their application in a practical experiment. A group of engineering students with no prior experience in creating virtual worlds were tasked with examining the design guidelines while using the NVIDIA Omniverse Enterprise as an exemplary use case and responding to a questionnaire and a focus group interview about how they perceived these guidelines. A correlation analysis confirmed that most guidelines scores behaved as expected and were ranked according to the use-case functionality. The participants understood the guidelines' definition and could decide if they agreed or disagreed with their presence during the experiment. We conclude that, in accordance with the Design Science Research, the proposed artifact is useful, i.e., the design guidelines for virtual reality authoring tools perform what they are designed to do and are operationally reliable in accomplishing their goals.

Machine learning offers advanced tools for efficient management of radio resources in modern wireless networks. In this study, we leverage a multi-agent deep reinforcement learning (DRL) approach, specifically the Parameterized Deep Q-Network (DQN), to address the challenging problem of power allocation and user association in massive multiple-input multiple-output (M-MIMO) communication networks. Our approach tackles a multi-objective optimization problem aiming to maximize network utility while meeting stringent quality of service requirements in M-MIMO networks. To address the non-convex and nonlinear nature of this problem, we introduce a novel multi-agent DQN framework. This framework defines a large action space, state space, and reward functions, enabling us to learn a near-optimal policy. Simulation results demonstrate the superiority of our Parameterized Deep DQN (PD-DQN) approach when compared to traditional DQN and RL methods. Specifically, we show that our approach outperforms traditional DQN methods in terms of convergence speed and final performance. Additionally, our approach shows 72.2 % and 108.5 % improvement over DQN methods and RL method respectively in handling large-scale multi-agent problems in M-MIMO networks.

Falls are extremely damaging to the elderly. The number of elderly who have experienced falls has increased over the years, several of the elderly stay alone or in in badly maintained elderly homes. This makes a low-cost fall detection system a necessity. There has been huge improvements in terms of IoT systems, ML algorithms. Varied data sets have been collected across the world for fall detection. These data sets have a very little in common among them, in terms of user demographics, sensors used, the ADL and Fall activities Hence in this paper we present a data set that has wide user demographics, we used various sensors – such as accelerometer, gyroscope, magnetometer and hear rate. We used wrist worn sensors to collect data. In this paper we present a detailed analysis of the data set we collected using common ML algorithms such as – Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF) and Support Vector Machines (SVM). We analyzed the performance of these algorithms for variations in accuracy with respect to age, gender, height, weight and health issues and we have identified outliers by analyzing each incorrect prediction. This paper provides the complete details of the data collection methodology, The methods used for analysis and presents the results of analysis in complete detail.

Dielectric elastomer actuators (DEAs) offer robust, high energy density solutions for soft robotics. The proposed end effector consists of three spring roll configuration DEAs, each acting as a robotic finger, using a 3M VHB-F9473PC adhesive membrane. The lateral force, bending angle and response time of the actuator are measured experimentally and compared with the predictions of an analytical model. The cylindrical actuator measures 70 mm in length and 15 mm in diameter and achieved a lateral force of 30 mN, a bending angle of 6.8 ° and a response time of ≈ 1 s. Spring roll configuration DEAs are shown to reduce the effects of viscoelasticity seen in the membrane, making the actuator more controllable at higher voltages. The dielectric constant of the membrane is shown to be a limiting factor of actuation, with a decrease in dielectric constant resulting in larger actuation. The end effector successfully gripped numerous light objects for extended periods, showing the applicability of spring roll DEAs for soft end effectors.

Hundreds of thousands of kilometers of communication and power (umbilical) cables and flowlines lie undersea worldwide. Most of these offshore cables and flowlines have reached or will soon be nearing the end of their service life, necessitating the need for a viable recycling approach to recover some valuable material, e.g., copper. However, separation into constituent materials has proven very challenging due to the highly robust design of the composite cables (and flowlines) to withstand service conditions and the tough external plastic sheaths that protect against seawater corrosion. Here, we summarize the findings of the cryogenic comminution of subsea cables and flowlines for an effective separation and recovery of component materials. Heat transfer analyses of complex multilayer flowlines and umbilicals were conducted to evaluate the time required for these structures to reach their respective critical brittle-transition temperatures. Subsequently, the time was used as a guide to crush the flowline and umbilical cables under cryogenic conditions. The results show that the flowlines and umbilical cables will reach the brittle-transition temperature after approximately 1000s (i.e., 17 min) of submergence in liquid nitrogen (LN). Comminution of the materials at temperatures near the brittle-transition temperature was proven relatively efficient compared to room-temperature processing. The present evaluation of heat transfer and lab-scale crushing will afford accurate process modelling and design of a pilot cryogenic comminution of decommissioned flowlines in an LN-doped atmosphere.

Abstract: Like other plants, every PV power plant will one day reach the end of its service life. Calculations show that 20,400 tons of PV waste will be generated worldwide by 2030 and 60.2 million tons by 2050, not including the mass of the support structure. Such large amounts of waste pose a potential threat to the environment and people if not properly managed. The paper analysed the options for dealing with PV waste, namely reuse, recycling and landfilling. For recycling as the best option in terms of environmental protection and circular economy, an overview of recycling technologies and the percentage of achievable recycling for the materials contained in each PV system component is given. In addition, the current situation of legislation and recycling of PV modules in Europe was examined with special reference to the Balkan countries. There are a small number of factories for recycling PV modules in Europe, but none in the Balkan countries. The main reason for this is the small amount of PV waste in these countries and consequently the economic unprofitability. For this reason, PV modules (after dismantling the aluminum frame and cables) are mostly disposed of as non-hazardous waste in landfills in these countries. Finally, the main barriers to faster implementation of PV module reuse and recycling are listed, along with guidelines for their removal. The cost of recycling Si modules is about ten times higher than the cost of disposal. To change this ratio in favor of recycling, cheaper recycling methods need to be developed and taxes on landfill disposal need to be increased.

Visual Patient Avatar ICU is an innovative approach to patient monitoring enhancing the user's situation awareness in intensive care settings. It dynamically displays the patient's current vital signs through changes in color, shape and animation. The technology can also indicate patient-inserted devices, such as arterial lines, central lines and urinary catheters, along with their insertion locations. We conducted an international, multi-center study using a sequential qualitative-quantitative design to evaluate users' perception of Visual Patient Avatar ICU among physicians and nurses. Twenty-five nurses and twenty-five physicians from the ICU participated in the structured interviews. Forty of them completed the online survey. Overall, ICU professionals expressed a positive outlook on Visual Patient Avatar ICU. They described Visual Patient Avatar ICU as a simple and intuitive tool that improved information retention and facilitated problem identification. However, a subset of participants expressed concerns about potential information overload and a sense of incompleteness due to missing exact numerical values. These findings provide valuable insights into user perceptions of Visual Patient Avatar ICU and encourage further technology development before clinical implementation.

There is a pressing need for more precise biomarkers of chronic kidney disease (CKD). Plasma samples from 820 subjects [231 with CKD, 325 with end-stage kidney disease (ESKD) and 264 controls] were analyzed by LC-MS/MS to determine a metabolic profile of 28 aminoacids (AA) and biogenic amines to test their value as markers of CKD risk and progression. The Kynurenine/Tryptophan ratio showed the strongest correlation with estimated glomerular filtration rate values (coefficient=-0.731, P&lt;0.0001). Models created with Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) containing the metabolic signature showed high goodness of fit and predictability for controls/CKD (R2X:0.73;R2Y:0.92;Q2:0.92) and lower for CDK/ESKD (R2X:0.56;R2Y:0.59;Q2:0.55). Based on generated VIP scores, the most relevant markers for segregating samples into control/CKD or CKD/ESKD groups were citrulline (1.67) and tryptophan (1.59), respectively. ROC analysis showed that the addition of the metabolic profile to a model including CKD classic risk factors improved AUC from 86.7% (83.6-89.9) to 100% (100-100) for CKD risk (P&lt;0.0001), and from 63.0% (58.2-67.8) to 96.5% (95.3-97.8) for the risk of progression from CKD to ESKD (P&lt;0.0001). Plasma concentrations of AA and related amines may be useful as diagnostic biomarkers of kidney disease, both for CKD risk and for progression of CKD patients to ESKD.

In contrast to some low molar-mass per- and polyfluoroalkyl substances (PFASs), well-established to be toxic, persistent, bioaccumulative and mobile, fluoropolymers (FPs) are water insoluble, safe, bio-inert, durable niche high performance polymers which fulfil the 13 polymer of low concern (PLC) criteria in their recommended conditions of use. In addition, more recent innovations (e.g., the use of non-fluorinated surfactants in aqueous radical (co)polymerization of fluoroalkenes) from industrial manufacturers of FPs are highlighted. This review also aims at showing how these specialty polymers endowed with outstanding properties are essential (even irreplaceable since hydrocarbon polymer alternatives used in similar conditions fail) for our daily life (electronics, Energy, optics, internet of things, transportation, etc) and constitute a special family a part from other “conventional” C1-C10 PFASs found everywhere on the Earth and Oceans. Furthermore, some information reports the recycling (e.g. the unzipping depolymerization of polytetrafluoroethylene, PTFE, into TFE), end of life of FPs, their risk assessment, circular economy and regulations. Various researches are devoted to Environment involving FPs, though they represent a niche volume (with a yearly production of 330,300 tonnes) compared to all plastics (with 460 million tonnes). Complementary to other reviews on PFASs, which lack of such above data, this review presents both fundamental and applied strategies as evidenced by major FP producers.

Recently, there has been growing interest in using smart eHealth systems to manage asthma. However, limitations still exist in providing smart services and accurate predictions tailored to individual patients’ needs. This study aims to develop an adaptive ubiquitous computing framework that leverages different bio-signals and spatial data to provide personalized asthma attack prediction and safe route recommendations. We proposed a smart eHealth framework consisting of multiple layers that employ telemonitoring application, environmental sensors, and advanced machine-learning algorithms to deliver smart services to the user. The proposed smart eHealth system predicts asthma attacks and uses spatial data to provide a safe route that drives the patient away from any asthma trigger. Additionally, the framework incorporates an adaptation layer that continuously updates the system based on real-time environmental data and daily bio-signals reported by the user. The developed telemonitoring application collected a dataset containing 665 records used to train the prediction models. The testing result demonstrates a remarkable 98% accuracy in predicting asthma attacks with a recall of 96%. The eHealth system was tested online by ten asthma patients, and its accuracy achieved 94% of accuracy and a recall of 95.2% in generating safe routes for asthma patients, ensuring a safer and asthma-trigger-free experience. The test shows that 89% of patients were satisfied with the safer recommended route than their usual one. This research contributes to enhancing the capabilities of smart healthcare systems in managing asthma and improving patient outcomes. The adaptive feature of the proposed eHealth system ensures that the predictions and recommendations remain relevant and personalized to the current conditions and needs of the individual.

Heat using behavior has a large impact on in heating energy in heat metering system, and therefore a better understanding can assist in behavior guidance to reduce energy. To understand the current situation of indoor thermal environment and heat using behavior for heat metering households in northern China, including adjusting heating end valves and operating windows, 30 households were measured and surveyed. The factors influencing heat using behavior, including outdoor and indoor environmental parameters and time of the day, were analyzed. The results are :1) Thermal neutral temperature for heat metering households is relatively high, up to 24.5℃; 2) The heat using behavior of households is lack of rationality: low proportion of active households; high indoor temperature setting; more frequency of window opening. Improving indoor comfort is the main reason for households to adjust the heating end valve, and only 7.15% of households have considered the economic benefits brought by adjusting the valve. " Thermostat control valve does not work" is the main reason for households without adjustment; 3) Time of the day and indoor temperature affect active households’ willingness to adjusting heating end valve. Time of the day, indoor temperature and outdoor temperature have impacts on opening window during heating period.

Blood gas analysis plays a central role in modern medicine. Advances in technology have expanded the range of available parameters and increased the complexity of their interpretation. By applying user-centered design principles, it is possible to reduce the cognitive load associated with interpreting blood gas analysis. In this international, multicenter study, we explored anesthesiologists' perspectives on Visual Blood, a novel visualization technique for presenting blood gas analysis results. We conducted interviews with participants following two computer-based simulation studies: the first utilizing Virtual Reality (VR) (50 participants) and the second without VR (70 participants). Employing the template approach, we identified key themes in the interview responses and formulated six statements, which were rated using Likert scales: from 1 (strongly disagree) to 5 (strongly agree) in an online questionnaire. The most frequently mentioned theme was the positive usability features of Visual Blood. The online survey revealed that participants found Visual Blood to be an intuitive method for interpreting blood gas analysis (median 4, IQR 4-4, p<0.001). Participants noted that minimal training was required to effectively learn how to interpret Visual Blood (median 4, IQR 4-4, p<0.001). However, adjustments are necessary to reduce visual overload (median 4, IQR 2-4, p<0.001). Overall, Visual Blood received a favorable response. The strengths and weaknesses derived from these data will help optimize future versions of Visual Blood to improve the presentation of blood gas analysis results.

Recent digital transformations have significantly impacted the current society, economy, and culture. This includes adoption of rapidly emerging technologies, such as artificial intelligence (AI), immersive technology (e.g., virtual, augmented, mixed and extended reality (VR/AR/MR/XR)), cloud services, and internet of things, in our day-to-day life. `Metaverse’ has emerged as a technology that incorporates many of these transforming technologies to deliver personalised immersive experience related to specific application areas. A potential major beneficiary of Metaverse is the educational ecosystem, where highly immersive learning experiences can be delivered to students, catering to their personal learning needs and train them on life skills such as empathy, ethical qualities, and communication skills. The present-day Metaverse allows physical engagement, enabling users to utilise limb movements to interact with the presented materials. However, ethical concerns and technical limitations impede the widespread implementation of the Metaverse in the real-world. This paper provides a comprehensive survey of the existing literature covering the architecture, type and components of Metaverse, followed by practical aspects of metaverse implementation. The paper is concluded by discussing on the open challenges and issues along with their mitigation strategies and future research directions after the detailed usecase of Metaverse in Education.

With the view toward enlivening the Chinese automobile market, this work seeks to develop a framework to reveal the tastes of Chinese car users from online user-generated content (UGC) and identify future product improvement orientation. We construct an importance-satisfaction gap analysis (ISGA) model based on sentiment analysis technique. Specifically, a novel unsupervised word-boundary-identified algorithm for the Chinese language is used to extract domain professional feature words and a set of sentiment scoring rules is constructed. By matching feature-sentiment word pairs, we calculate car users' satisfaction with different attributes based on the rules, and weigh the importance of attributes using TF-IDF method, which lay the statistic foundation for our ISGA model. We also take time into account and further analyze the ISGA model, aiming to provide new insight into the changing tastes of Chinese car users so as to suggest correct design positioning and resource allocation. The final evaluation results show that the method proposed in this paper, which integrates Chinese word-boundary-identified algorithm and sentiment lexicon matching rules, is well supported.

Medical Self-service Terminals (MST) offer potential for process optimization and enhanced patient convenience in hospitals, including the elderly. However, the behavioral inclination (BI) of elderly Chinese individuals towards MST adoption remains suboptimal. This study aims to elucidate BI determinants for MST among the aging demographic, focusing on User Interface (UI) attributes within the extended Unified Technology Acceptance and Use Theory (UTAUT) Model. The empirical inquiry examines seven factors, comprising three key UI attributes and four fundamental UTAUT factors. Results reveal significant positive associations of Performance Expectancy (β=0.40, p&lt;0.001), Effort Expectancy (β=0.50, p&lt;0.001), and Social Influence (β=0.25, p&lt;0.05) with BI. Notably, User Interface Design positively correlates with Performance Expectancy (β=0.89, p&lt;0.001) and Effort Expectancy (β=0.81, p&lt;0.001). These findings emphasize the interplay between objective UI attributes and subjective UTAUT determinants. This research enhances understanding of UI's impact on MST BI and acceptance, particularly within the Chinese elderly population, urging MST researchers to prioritize elderly attitudes for effective technological integration in healthcare.

People with Intellectual Disability (ID) encounter several problems in their daily living regarding their needs, activities, interrelationships, and communication. On this concept, an interactive platform is proposed, aiming to provide personalized recommendations for information and entertainment, including creative and educational activities, tailored to the special users’ needs of this population. Furthermore, the proposed platform integrates capabilities of automatic recognition of health related emergencies, such as fever, oxygen saturation decline and tachycardia, as well as location tracking and detection of wandering behavior based on smartwatch/smartphone sensors, while providing automated assistance and appropriate alerts and notifications to the caregivers.

The right to live with dignity during the final stages of existence, enshrined in national and supranational Charters of Rights, represents a significant step towards humanizing medicine and is integral to the right to health. Palliative Care, rooted in health, dignity, and therapeutic self-determination, has emerged as a fundamental human right and a moral imperative within health systems. It seeks to alleviate suffering, emphasizing the holistic well-being of patients with life-limiting illnesses. This paper provides a comprehensive global perspective on Palliative Care, with special attention to its implementation in Italy. Palliative Care is a crucial dimension of care, designed to enhance the quality of life for patients suffering from progressive and incurable diseases. Despite its growing need due to an aging population and increased prevalence of chronic illnesses, still remains a significant disparity in access to these essential services worldwide. Insufficient resources, legislative constraints, and limited training for healthcare providers pose challenges to the widespread adoption of Palliative Care. In Italy, although laws have been enacted to ensure the provision of Palliative Care, its availability remains inconsistent across different regions. Financial constraints and insufficient support hinder the comprehensive dissemination of these services. Recognizing the significance of Palliative Care, the Catholic Church also endorses its implementation as a response to human suffering and an approach to end-of-life care. Efforts to strengthen Palliative Care are critical to meeting the rising demand and ensuring access to compassionate and dignified care for all individuals in need. Through legislative advancements and adequate resources, Italy can make significant strides in advancing the provision of Palliative Care.

The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit consists of (i) a sensor and hardware layer that can be configured in a modular manner per research needs, (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study for photo-plethysmography (PPG) sensor with 15 participants, PhysioKit shows strong agreement with research-grade PPG sensors. Furthermore, we report usability survey results from 10 micro-project teams (44 individual members in total) who use PhysioKit for 4-6 weeks, providing insights into its use cases and research benefits. We conclude by discussing extensibility and potential impact of the toolkit on the research community.

Traditional end-effector robots for arm rehabilitation are usually attached a­t the hand, primarily focusing on coordinated multi-joint training. Therapy at an individual joint level of the arm for severely impaired stroke survivors is not always possible with existing end-effector robots. The Arm Rehabilitation Robot (AREBO)—an end-effector robot—was designed to provide both single and multi-joint assisted training while retaining the advantages of traditional end-effector robots, such as ease of use and cost-effectiveness (compared to exoskeletons). This work presents the design, optimization, and characterization of AREBO for training single-joint movements of the arm. The AREBO has three actuated and three unactuated degrees of freedom, allowing it to apply forces in any arbitrary direction at its endpoint and self-align to arbitrary orientations within its workspace. The AREBO’s link lengths were optimized to maximize its workspace and manipulability. The AREBO provides single-joint training in both unassisted and adaptive weight support modes using a human arm model to estimate the human arm's kinematics and dynamics without using additional sensors. The characterization of the robot’s controller and the algorithm for estimating the human arm parameters were performed using a two degrees of freedom mechatronic model of the human shoulder joint. The results demonstrate that: (a) the movements of the human arm can be estimated using a model of the human arm and robot’s kinematics, (b) the AREBO has similar transparency to that of existing arm therapy robots in the literature, and (c) the adaptive weight support mode control can adapt to different levels of impairment in the arm. This work demonstrates how an appropriately designed end-effector robot can be used for single-joint training, which can be easily extended to multi-joint training. Future work will focus on the evaluation of the system on patients with any neurological condition requiring arm training.

Introduction: Obstructive sleep apnea (OSA) is the most common sleep-breathing disorder. OSA is associated with cardiovascular diseases such as coronary artery disease (CAD) and arrhythmias with an increased risk of sudden cardiac death. Some changes in ECG markers are seen as an indicator of arrhythmogenicity, especially ventricular arrhythmias. The goal of this study was to investigate if OSA patients with CAD have QT dispersion and T peak to T end (TpTe) changes as a surrogate of arrhythmogenicity. Methods: We studied 75 patients with CAD who underwent polysomnographic studies. Patients were categorized into mild, moderate, and severe OSA according to the Apnea hypopnea index (AHI). Baseline ECG and echocardiography for assessing LV function and valvular heart disease were done for all of them. Results: Results showed that in patients with moderate and severe sleep apnea QT dispersion was higher in a group with more involvement of coronary artery disease, reduced ejection fraction, and a history of old MI. However, we didn’t find any significant statistical relationship between OSA and TpTe marker. Conclusion: Our results showed a positive relation between ECG arrhythmic indices such as QT dispersion and OSA severity in patients with CAD.

Doppler findings of persistent reverse end-diastolic flow (PREDF) in a fetal middle cerebral artery (MCA) are a very rare sonographic finding and are a marker of poor fetal condition. This finding often leads to intrauterine fetal death or early neonatal death. The reverse end-diastolic flow in the middle cerebral artery is the terminal hemodynamic event. Fetal cerebral circulation normally has a high impedance; in the event of fetal hypoxemia, impedance decreases, resulting in the central redistribution of blood flow to vital organs, which maintains the oxygen delivery to the brain. Reverse flow in the middle cerebral arteries describes the loss of this autoregulatory process. PREDF is a sequence happening due to increased extracranial or intracranial pressure. Previous case reports mentioned intracranial hemorrhage, intrauterine growth restriction (IUGR), fetal anemia, and fetal hepatic abnormalities as leading problems to PREDF. This condition presumably arises due to cerebral edema associated with severe hypoxemia. We reported Doppler findings of PREDF MCA in a 33-year-old female at 30 weeks gestation who was referred to the hospital with severe preeclampsia accompanied by IUGR and oligohydramnios. A cesarean section was performed due to severe preeclampsia and a low bishop score. Hypotheses on various etiologies and their association with intrauterine/neonatal death as well as the best management still require further investigation.

To achieve user retention through multifactor synergy, Internet enterprises must reduce costs and increase efficiency and sustainable development. In response to the dilemma that Internet compa-nies are experiencing increasingly high user acquisition costs and serious user churn, this paper uses a sample of 46,695 user reviews of nine product series from Xiaomi Ecological Chain. Case studies and qualitative comparative analysis are used to explore the influence mechanisms of quality of experience, brand trust, and brand attachment on users' retention intentions. Our find-ings are as follows. (1) Brand attachment alone is not necessary for high user retention intention, but user perception, cognition, and brand trust are necessary. (2) Quality of experience positively impacts brand trust, attachment, and user retention intention. Therefore, improving user perception and cognition is critical in generating high user retention intention. (3) Five configuration paths can achieve high user retention intention, three configuration paths lead to low user retention intention, and there is an asymmetric relationship between these paths. Among them, the role of quality of experience-driven configuration paths in generating user retention intention is the most prominent. (4) In the substitution relationship between configuration paths, user perception, and cognition can substitute with brand trust and attachment. Our findings have important theoretical and practical implications for revealing the realization paths of high user retention intention in Internet companies and provide a new perspective for future research.

This article presents the graphical user interface of the ACUX-R mobile recommendation system, tailored for the cultural tourism domain. ACUX-R offers personalized recommendations based on visiting preferences, augmenting the overall user experience. Building upon a comprehensive methodology and recommendation algorithms from previous work, this contribution focuses on the user interface aspects of the ACUX-R by highlighting key design considerations, user interface elements and functionalities that contribute to an effective and engaging user experience. In an effort to evaluate the proposed interface, an initial case study with a dataset consisting of points of interest from the City of Athens, Greece has been performed. In the framework of the aforementioned study, the proposed user interface attained high ratings with respect to inspiring, exciting, interesting, and enthusiastic user experiences.

Integrating voice assistant technology can potentially enhance accessibility and user interaction in web-based platforms for geospatial data visualisation and processing. This paper presents an open-source development incorporating a voice virtual assistant into the BStreams web application platform, designed specifically for visualising geospatial data. The study aimed to mitigate the observed limitations of virtual assistants in understanding the semantics and context of user commands in the geospatial domain. A discourse framework specialised for geospatial data was developed using a structured prototype methodology. In addition, a survey assisted in identifying commonly used English terminologies for geospatial data interaction. The questionnaire results were subsequently assessed using the ChatGPT natural language processing model. The application used open-source frameworks, including Web Speech API, Leaflet, Mapbox geocoding, and JavaScript. This investigation indicates that voice virtual assistants can potentially enhance human-computer interaction for geospatial data visualisation and processing. However, challenges related to natural language understanding, accents, and domain knowledge were raised. The paper advocates for further research to refine the integration of voice technology in this domain.

Artificial Intelligence (AI) has many advantages over humans in that it can detect incredibly subtle patterns within large quantities of data. This study suggests using AI algorithms in user research tasks for mining variables ranging from the tone of voice, image banks, historical records, and product use to determine where brands sit and where there could be competitive advantages. This study reviews the current design processes of User Experience (UX) designers and the advancements in Deep Learning (DL). It identifies potential areas of automation in the Preparation, Incubation and Illumination stages of the design process. It recognises the possibility of using Long Short-Term Memory (LSTM) models to automate design feedback and the creation of alternate designs with Generative Adversarial Networks (GANs).

Background. There is limited evidence on the relationship between chronic kidney disease (CKD) and frailty in older people in Vietnam. This study aimed to examine the prevalence of frailty and its impact on mortality in older patients with end-stage renal disease. Method. This is a prospective, observational study at two large Dialysis Centres in Vietnam from November 2020 to June 2021. Consecutive older patients diagnosed with end-stage renal disease and on dialysis were recruited. Participants’ frailty status was defined by the Clinical Frailty Scale (CFS). The study outcome was all-cause mortality at 6th month. Results. A total of 175 participants were recruited (mean age 72.4 years, 58.9% female). Using the cut-point of CFS ≥4, 87.4% of the participants were frail. Mortality at 6th month was 14.9%, 31.9% in participants with CFS ≥7, 12.8% in participants with CFS=6, 7.5% in participants with CFS from 4 to 5, and 4.5% in participants with CFS ≤3 (p=0.001). Cox regression analysis showed that compared to the non-frail participants, the probability of death over 6 months was nearly 2-fold higher in the mildly frail, 3-fold higher in the moderately frail, and 9-fold higher in the severely frail participants. Conclusion. This study demonstrated a very high prevalence of frailty in older patients with end-stage renal disease and dialysis and the significant impact of frailty severity on mortality. Healthcare providers should consider incorporating frailty screening into routine care for older patients with end-stage renal disease and dialysis.

The phenomenon of seat occupancy in university libraries is a prevalent issue. However, existing solutions, such as software-based seat reservations and sensors-based occupancy detection, have proven to be inadequate in effectively addressing this problem. In this study, we propose a novel approach: a serial dual-channel object detection model based on Faster RCNN. Furthermore, we develop a user-friendly web interface and mobile APP to create a computer vision-based platform for library seat occupancy detection. To construct our dataset, we combine real-world data collection with UE5 virtual reality. The results of our tests also demonstrate that the utilization of personalized virtual dataset significantly enhances the performance of the convolutional neural network (CNN) in dedicated scenarios. The serial dual-channel detection model comprises three essential steps. Firstly, we employ Faster RCNN algorithm to determine whether a seat is occupied by an individual. Subsequently, we utilize an object classification algorithm based on transfer learning, to classify and identify images of unoccupied seats. This eliminates the need for manual judgment regarding whether a person is suspected of occupying a seat. Lastly, the web interface and APP provide seat information to librarians and students respectively, enabling comprehensive services. By leveraging deep learning methodologies, this research effectively addresses the issue of seat occupancy in library systems. It significantly enhances the accuracy of seat occupancy recognition, reduces the computational resources required for training CNNs, and greatly improves the efficiency of library seat management.

The rising importance of Big Data in modern information analysis is supported by vast quantities of user data, but it is only possible to collect sufficient data for all tasks within certain data-gathering contexts. There are many cases where a domain is too novel, too niche, or too sparsely collected to adequately support Big Data tasks. To remedy this, we have created ADG Engine that allows for the generation of additional data that follows the trends and patterns of the data that’s already been collected. Using a database structure that tracks users across different activity types, ADG Engine can use all available information to maximize the authenticity of the generated data. Our efforts are particularly geared towards data analytics by identifying abnormalities in the data and allowing the user to generate normal and abnormal data at custom ratios. In situations where it would be impractical or impossible to expand the available dataset by collecting more data, it can still be possible to move forward with algorithmically expanded data datasets.

(1) Background: This article reviews the available scientific literature on drug-related problems and negative outcomes associated with medications identified by medication review with follow-up for end-stage renal disease and discussed with the physicians; (2) Methods: A systematic review was conducted of the scientific literature retrieved from the following databases: MEDLINE (via PubMed), Web of Science, SCOPUS, Cochrane Library: The Cochrane Central Register and Control Trials (CENTRAL) and Literatura Latinoamericana y del Caribe (LILACS), Medicina en Español (MEDES), and the SciELO bibliographic database (collection of scientific journals). The following terms were used as descriptors and searched in free text: "end-stage renal disease", "medication review", and "drug-related problems” and “negative outcomes associated with medication". The following limits were applied: "humans" and "adults (more than 18 years)”; (3) Results: A total of 59 references were recovered and after applying inclusion/exclusion criteria, 16 articles were selected. Of these selected articles, 15 provided information on drug-related problems and only 1 on negative outcomes associated with medications; (4) Conclusions: It can be concluded that drug-related problems and negative outcomes associated with medication affect patients with end-stage renal disease, mainly those receiving renal replacement therapy. More evidence is needed, especially on negative outcomes associated with medication.

This article focuses on developing a continuous authentication system using behavioral biometrics to recognize users accessing computing devices. The user’s distinct behavioral biometric is captured through keystroke dynamics, and reward-based reinforcement learning (RL) ideas are applied to recognize them throughout the session. The suggested system adds an extra layer of security to traditional authentication methods, forming a robust continuous authentication system that can be added to static authentication systems. The methodology involves training a RL model to detect unusual user typing patterns and flag suspicious activity. Each user has an agent trained on their historical data, which is preprocessed and used to create episodes for the agent to learn from. The environment involves fetching observations and randomly corrupting them to learn out-of-order behavior. The observation vector includes both running features and summary features. The re-ward function is binary and minimalistic. The Principal Component Analysis (PCA) model is used to encode the running features, and the Double Deep Q-Network (DDQN) algorithm with a fully connected neural network is used as the policy net. The evaluation achieved an average training accuracy and EER (equal error rate) of 94.7% and 0.0126 and test accuracy and ERR of 81.06% and 0.0323 for all users when the number of encoder features was increased. Therefore, it is concluded that by continuously learning and adapting to changing behavior patterns, this approach can provide more secure and personalized authentication, lowering the possibility of unauthorized access and cyberattacks. Overall, the use of reinforcement learning and behavioral biometrics for continuous authentication has the potential to significantly enhance security in the digital age and are effective in identifying each user.

In recent years, with the rapid development of computer vision technology and the popularization of intelligent hardware, as well as people’s increasing demand for intelligent products for human-computer interaction, visual grounding technology can help machines and humans identify and locate objects, thereby promoting human-computer interaction and intelligent manufacturing. At the same time, human-computer interaction is constantly evolving and improving, becoming increasingly intelligent, humane, and efficient. This paper proposes a new VG model and designs a language verification module that uses language information as the main information to increase the model’s interactivity. Additionally, we propose the combination of visual grounding and human-computer interaction, aiming to explore the research status and development trends of visual grounding and human-computer interaction technology, as well as their application in practical scenarios and optimization directions, to provide references and guidance for relevant researchers and promote the development and application of visual grounding and human-computer interaction technology.

The use of smart grids has enabled a number of planning methods to be developed to optimize energy costs, Peak to Average Ratios (PARs), and consumer satisfaction for load management in industrial, commercial, and domestic sectors. From a technical point of view, achieving optimal outcomes requires Demand Side Management (DSM). In smart grids, utility companies and electric users communicate two-way using digital technology to make a sustainable and economic system. This paper proposes a novel framework within which an Energy Management Controller (EMC) keeps track of each appliance, its operational time, and the costs associated with them. Customers of smart grids are motivated to shift their Off-Peak Hours (OPH) from Peak Hours by presenting incentives in OPH. The metering devices would also save customers costs by preventing load shifting between high- and low-cost periods. In addition, the study proposes the bacterial foraging algorithm and grasshopper optimization algorithm for lessening power price and PAR without compromising user comfort (UC) through appliance planning. The simulation results on a practical test system advocate the high effectiveness and reliable performance of the proposed model.

Diabetic nephropathy (DN), one of the primary consequences of diabetes mellitus, affecting many people worldwide. Diabetes affects yearly 463 million adults globally between the ages of 20 and 79, and is the main cause of death under the age of 60. ROS production rises during hyperglycemia and is crucial to the development of diabetic complications. Advanced glycation end products (AGEs) are produced excessively in a diabetic state and accumulate in the kidney, where they change renal architecture and impair renal function. Another important targeted pathway for the formation of DN includes NF-B, Nrf2, NLRP3, Akt/mTOR, and autophagy. About 40% of individuals with diabetes eventually acquire diabetic kidney disease and end stage renal disease that needs hemodialysis, peritoneal dialysis, or kidney transplantation, to survive. The current state of acceptable therapy for this kidney ailment is limited. The studies revealed that some naturally occurring bioactive substances might shield the kidney by controlling oxidative stress, renal fibrosis, inflammation, and autophagy. In order to provide new potential therapeutic lead bioactive compounds for contemporary drug discovery and clinical management of DN, this review was designed to examine the various mechanistic pathways by which current conventional plants derive phytocompounds that are effective for the control and treatment of DN.

Toxic advanced glycation end-products (TAGE), formed by glyceraldehyde (GA) as an intermediate in the non-enzymatic reaction with intracellular proteins, are highly cytotoxic and have been implicated in the pathogenesis of various diseases. However, the mechanisms underlying the degradation and removal of TAGE remain largely unknown. In the present study, we identified the checkpoint kinase-1 (CHK1) mutant, d270KD, which was rapidly degraded intracellularly by GA, and showed that its degradation was mainly mediated by the ubiquitin-proteasome pathway. The high-molecular-weight complexes formed by the GA stimulation of d270KD were abundant in the RIPA-insoluble fraction, which also contained high levels of TAGE. The knockdown of p62/SQSTM1 reduced the amount of high-molecular-weight complexes in the RIPA-insoluble fraction, indicating its involvement in the formation of TAGE aggregates. The present results suggest that the ubiquitin-proteasome pathway and p62 play a role in the degradation and aggregation of intracellular TAGE formed by GA. This study provides new insights into the mechanisms underlying TAGE metabolism and may lead to the development of novel therapeutic strategies for diseases associated with TAGE accumulation.

Mental Health Care Users (MHCUs) are admitted in general wards with medical and surgical conditions where non-psychiatric nurses are allocated to render services for medical and surgical conditions. Non-psychiatric nurses are expected to care for MHCUs in general wards regardless of their knowledge and skills which is against the Mental Health Care Act. The purpose of the study was to explore and describe the experiences of non-psychiatric nurses in car-ing for MHCUs admitted in general wards in Limpopo Province. Qualitative approach using explorative, descriptive, and contextual design was adopted for the study. Methodology included the setting, population, sampling, ethical con-siderations, and the measures to ensure trustworthiness. The study was conducted in four general hospitals of Limpopo Province, South Africa with mental health care wards, medical ward and surgical ward which were selected through simple random sampling. Semi structured Individual interviews were used to collect data from twenty professional nurses until data saturation. Data was analysed using Tesch’ s method. Ethical considerations were adhered to throughout the study. Themes that emerged after data analysis were: Managing MHCUs, Types of patient’s behaviour. The study concluded that professional nurses without psychiatry caring for MHCUs in general wards need training to provide holistic care.

The worldwide population is undergoing a fundamental change in its age structure, which challenges the health and social services system. The need to migrate towards a more person-centered and coordinated model of care that supports the optimization of abilities and capacities for older people has to be matched. In this sense, eHealth technologies can play a fundamental role. In this paper, through a questionnaire-based data collection on 30 primary (older people) and 32 secondary (informal caregivers) end-users, we share the sustainable way to develop a product optimizing the user experience and ensuring adoption. Both older adults and caregivers consider the use of smartphone and smartwatch to be very important, but also the use of digital devices for healthcare can be helpful. Seniors care about self-monitoring of health parameters using wearable devices, regardless of their health status, and would like to be included in the process of making good health decisions, because they need to feel in control of their healthcare process. Digital solutions in health and care can support the wellbeing of older adults in many areas of their daily lives at home and in their communities, but only if such innovation is designed around the natural voice of the intended target.

Children with Autism Spectrum Disorder have deficits in social interaction and expressing and understanding emotions. Many robots for children with ASD have been proposed. However, few studies were found about how to design a social robot for children with ASD. Studies reviewed employed non-experimental studies to evaluate a social robot, but the methodology to design a social robot is rare. This study proposes a design path for a social robot for emotional communi-cation for children with ASD, following User-Centered Design approach. This design path is ap-plied with a study case, evaluated with an expert group on psychologist, human-robot interaction, human-computer interaction, and parents of children with ASD. Results show than following the design path proposed to design a social robot to communication emotions for children ASD is favorable.

Augmented reality (AR) offers an accessible, inexpensive, and rich user experience that has the potential to engage end-users in an immersive environment. Along with vivid visualizations coupled with virtual agents, this technology further develops learning interest in end-users, guides them in various tasks, and boosts motivation and productivity. In this study, we leverage the deep penetration of mobile phones in daily lives and their advanced features to design, develop and demonstrate an AR application (CirculAR) that offers unique user-environment interaction in a gamified way. CirculAR combines learning with enjoyment to help end-users understand fundamental sustainability and circular economy principles. The application has been showcased in a controlled environment to heterogenous audiences and has been shown to improve end-user engagement and motivation. First, participants older than 18 were recruited to showcase the technology acceptance and engagement towards circular economy principles through AR. Then, students aged 5 to 15 years old, along with their parents and educators, were invited to a treasure hunt game where our virtual agent ARis guided them through a map full of virtual experiences. Assessment and evaluation were performed through a survey and a questionnaire. The outcome of their analysis showcased an increase in the dedication and enjoyment of the performed activities, engagement and learning attributes given the AR virtual agent supporting functionalities. Observations during showcasing reported a need for more commitment from the younger audience compared to the older one. This application contributes to the discourse on mobile AR as a tool for the education of novel concepts with a high impact on our daily lives and decisions and aims to shed light on the design principles of educative tools.

Artificial intelligence (AI) applications in different fields are developing rapidly, among which AI painting technology, as an emerging technology, has received wide attention from users for its creativity and efficiency. This study aimed to investigate the factors that influence user acceptance of the use of AIBPS by proposing an extended model that combines the Extended Technology Acceptance Model (ETAM) with the AI-based Painting System (AIBPS).A questionnaire was administered to 528 Chinese participants, using validated factor analysis data and Structural Equation Modeling (SEM) was used to test the hypotheses. The findings showed that hedonic motivation (HM) and perceived trust (PE) had a positive effect (+) on users' perceived usefulness (PU) and perceived ease of use (PEOU), while previous experience (PE) and technical features (TF) had no effect (-) on users' perceived usefulness (PU). This study provides an important contribution to the literature on AIBPS and the evaluation of systems of the same type, which helps to promote the sustainable development of AI in different domains and provides a possible space for further extension of TAM, thus helping to improve the user experience of AIBPS. The results of the study provide insights for system developers and enterprises to better motivate users to use AIBPS.

Wellness is a multidimensional concept that touches upon the various physical, mental, emotional, spiritual, social and environmental facets of health. Interest towards and importance of wellness have been growing constantly for the past two decades and thus makes it crucial to understand which factors affect public engagement with wellness information for multiple stakeholders. The Instagram account of New York Times (NYT) specifically for sharing wellness content with the handle nyt_well was selected as the object of study. 773 posts from this account between March of 2019 and December of 2022 were collected and analyzed to answer the research question of which factors are most influential to public engagement with wellness content. Two negative binomial regressions were run on features including the type of post, length, word count, sentiment score and topic with number of likes and comments as the dependent variables for each of those regression models. Results indicated that the type of post and its sentiment score were the two most influential determinants of public engagement with p-values smaller than 0.05. While the effects of some of these factors aligned with findings from previous studies conducted on social media content not related to wellness (e.g., marketing), some others affected the two separate public engagement metrics in opposite directions, warranting future studies to investigate further on the cause of this phenomenon.

One of the key issues in sustainable tourism research is the gap between tourists' expressed friendly attitudes towards sustainable behaviors and their actual behaviors. Although many "low-carbon" themed restaurants have emerged during the low-carbon transformation of the Chinese tourism industry, low-carbon food services have not been significantly improved. This study takes food as the entry point to explore tourists' behavior and attitudes towards low-carbon tourism in relation to food. We conducted two interviews. The first interview was a semi-structured contextual interview with 120 tourists who had experiences in food streets, aim-ing to identify the core user group: low-carbon attitude-friendly tourists with high-carbon food behaviors. The second interviews was an in-depth interview based on grounded theory with 29 core users, analyzing the four main reasons for their high-carbon food behaviors and their re-quirements for low-carbon food services in tourism. Based on this, we extracted four design ele-ments for low-carbon tourism food services: low-carbon information show service, low-carbon service product attractiveness improvement, low-carbon food environment atmosphere creation, and service providers' low-carbon behaviors. Through these four service elements, we constructed a low-carbon tourism food service design framework based on the core users' needs, discussed the mechanism of service elements, and provided service design suggestions accordingly. The re-search results serve tourism providers, low-carbon tourism researchers, and designers.

Industrial data scarcity is one of the largest factors holding back the widespread use of machine learning in manufacturing. To overcome this problem, the concept of transfer learning was developed and it achieved high attention in recent industrial research. Our paper focuses on the problem of time series segmentation and presents the first in-depth research about transfer learning for deep-learning based time series segmentation on the example of industrial end-of-line pump testing. In particularly, we investigate if the performance of deep learning models can be increased by pretraining the network with data from other domains. Three different scenarios are analyzed: source and target data being closely related, source and target data being distantly related, and source and target data being non-related. The results demonstrate that transfer learning can enhance the performance of time series segmentation models in respect to accuracy and training speed. The benefit is most clearly seen in scenarios where source and training data are closely related and the number of target training data samples is lowest. However, in the scenario of non-related datasets, cases of negative Transfer Learning were observed as well. Thus, the research emphasizes the potential, but also the challenges of industrial Transfer Learning.

Poor social skills in autism spectrum disorder (ASD) are associated with reduced independence in daily life. Current interventions for improving the social skills of individuals with ASD fail to represent the complexity of real-life social settings and situations. Virtual reality (VR) may facilitate social skills training in social environments and situations proximal to real life, however, more research is needed for elucidating aspects such as the acceptability, usability, and user experience of VR systems in ASD. Twenty-five participants with ASD attended a neuropsychological evaluation and three sessions of VR social skills training, incorporating 5 social scenarios with three difficulty levels for each. Participants reported high acceptability, system usability, and user experience. Significant correlations were observed between performance in social scenarios, self-reports, and executive functions. Working memory and planning ability were significant predictors of functionality level in ASD and the VR system’s perceived usability respectively. Yet, performance in social scenarios was the best predictor of usability, acceptability, and functionality level in ASD. Planning ability substantially predicted performance in social scenarios, postulating an implication in social skills. Immersive VR social skills training appears effective in individuals with ASD, yet an error-less approach, which is adaptive to the individual’s needs, should be preferred.

Virtual reality experiences are frequently created using game engines, yet they are not simple for novices and unskilled professionals who don’t have programming and 3D modeling skills. Concurrently, there is a knowledge gap in software project design for intuitive virtual reality authoring tools, which were supposed to be easier to use. This work compiles design guidelines derived from a systematic literature review to contribute in the development of more intuitive virtual reality authoring tools. We searched the Scopus and Web of Science databases for works published between 2018 and 2021 and discovered 14 papers. We compiled 14 requirement and feature design guidelines, such as Visual Programming, Immersive Authoring, Reutilization, Sharing and Collaboration, Metaphors, and Movement Freedom, among others. The collected guidelines have the potential to either guide the development of new authoring tools or to evaluate the intuitiveness of existing tools. Furthermore, they can also support the development of the metaverse, since virtual content creation is one of its bases.

Background: Mental health difficulties during teenage years e are common and are a risk factor for later mental and physical health problems. Rural young people are at greater risk for mental health difficulties and have less access to services than their urban counterparts. The purpose of this study was to explore young people and their carers’ experiences of mental health support provided by a rural mobile service, and to identify access enablers from the service users’ perspective. Methods: A qualitative descriptive approach was used to analyse twelve interviews with current service users and eight interviews with family members of young people who had accessed the service.Results: Three main themes were identified: (a) Access and flexibility, (b) Clinicians’ qualities and strategies, and (c) Experiences of change. The mobile service was perceived to be effective in producing positive change in mental health, relationships and attainment of life goals. Key enablers to access included the flexibility of the mobile service, the variety of service delivery modes and therapeutic methods offered, the ease of access facilitated by location in schools, and young people’s autonomy in how they chose to utilise the service. Conclusion: This study provides information about what is important to rural young people and their families in mental health service provision. The findings have implications for changing the way services are organized and operated. Healthcare policy and services could support user-led model design that incorporates the access and use enablers and removes the barriers to rural mental health support.

User experience and user requirements are two independent approaches. User requirements address the customer's requirements and expectations for the solution, whereas user experience encompasses all aspects of how the user interacts with and uses the software. The software product should be easy to use and has usable features. Moreover, the additional value for the software is if the product has an attractive design or working environment that is in line with user behaviors, it can occur if integrate software requirements and user experience. Integration escalates developer productivity by focusing on features that meet the user's needs and expectations. That integration improves efficiency in software development by identifying and addressing problems that may arise during the development process, saving developer time and effort in developing software. The usage context of integration of user experience and user requirements in UX Journey contributes increase developer productivity and self-efficacy in developing software by focusing development on features that match the user needs, as well as increasing efficiency in overcoming problems that arise during the development process. UX Journey makes developers feel more confident in their ability to develop quality software.

The rapid penetration of social media has been redefining every facet of the old marketing and customer engagement tactics, not only for the low-end and mass products but also for luxury brands. In this context, brands are dealing with the challenge of keeping the balance between using mass marketing strategies concurrent with accentuating the exclusivity of their offerings. Social media can be considered a boon if brands employ them to reach the right audience and use the right platform by incorporating the right content. In this work, we propose a sector-specific, integrated, and holistic investigation of the social media strategies of luxury brands, together with the impact they generate in terms of the engagement level of the users as an indicator of their success. We provide empirical validation of the method in the sector of luxury fashion brands in the Italian market, providing qualitative and quantitative analysis of the content shared on social media, considering the type, timing, and modality of the sharing. We evaluate consumer-brand engagement in different contexts, including important live events in the field.

Chronic oxidative stress in long-distance runners adversely affects conditioning. Therefore, it is important to objectively assess and monitor oxidative stress but measuring oxidative stress can be invasive or require skill to measure. Therefore, this study aimed to verify whether skin autofluorescence (SAF), a noninvasive, rapid, and easily calculable metric for calculating advanced glycation end products (AGEs), is useful as an oxidative stress biomarker. The subjects were 50 young Japanese male long-distance runners (aged 20.2 ± 1.2 years); 35 average male university students (aged 19.8 ± 1.1 years) served as controls. The interactions and relationships between SAF and plasma pentosidine and oxidative stress markers (reactive oxygen metabolite-derived compounds [d-ROMs], biological antioxidant potential [BAP], and the BAP/d-ROMs ratio) in runners were examined, and SAF in the runners and controls was compared. The results suggest that plasma pentosidine in runners is associated with oxidative stress markers and that plasma pentosidine can assess oxidative stress. However, SAF was not validated as an oxidative stress marker because it was not associated with oxidative stress marker. In future, clarifying the factors affecting SAF may clarify the relationship between SAF, plasma pentosidine, and oxidative stress markers.

When designing rule-based dialogue systems, the need for elaborate design by the designer is a challenge for dialogue systems. One way to reduce the cost of creating content is to generate utterances from data collected in an objective and reproducible manner. This study focuses on rule-based dialogue systems using survey data, and more specifically on opinion dialogue where the system models the user. In opinion dialogue, there has not been much study of topic transition methods for modeling users while maintaining their motivation for dialogue. To model them, we adopted information content. Our contribution includes the design of a rule-based dialogue system that does not require elaborate design. We also reported an appropriate topic transition method based on information content. This is confirmed by the influence of the user's personality characteristics. The content of questions can give the user a sense of the system's intention to understand them. We also reported the possibility that the system's rational intention contributes to the user's motivation for dialogue with the system.

We report the first case of Daratumumab interference of allogeneic crossmatch tests repeatedly causing aberrant false-positive results, which inadvertently delayed transplant for a waitlisted renal patient with multiple myeloma. Daratumumab is an IgG1κ human monoclonal antibody commonly used to treat multiple myeloma, characterized by cancerous plasma cells and often leads to renal failure requiring kidney transplant, by depleting CD38-expressing plasma cells. In this case study, the patient had end-stage renal disease secondary to multiple myeloma and was continuously receiving Daratumumab infusions. The patient did not have any detectable antibodies to human leukocyte antigens but repeatedly had unexpected positive crossmatch by the flow cytometry-based method with 26 of the 27 potential deceased organ donors, implying donor-recipient immunological incompatibility. However, further review and analysis suggested that the positive crossmatches were likely false-positive as a result of interference from Daratumumab binding to donor cell surface CD38 as opposed to the presence of donor-specific antibodies. The observed intensity of the false-positive crossmatches was also highly variable, potentially due to donor- and/or cell-dependent expression of CD38. The variability of CD38 expression was, therefore, for the first time, characterized on the T and B cells isolated from various tissues and peripheral blood of 78 individuals. Overall, T cells were found to have a lower CD38 expression profile than the B cells, and no significant difference was observed between deceased and living individuals. Finally, we show that a simple cell treatment by dithiothreitol can effectively mitigate Daratumumab interference thus preserving the utility of pre-transplant crossmatch in multiple myeloma patients awaiting kidney transplant.

This study's main target was to analyze and design rectangular edges supported two ways solid slabs by using ES EN 1992-1-1:2015. Slab design is often carried out either manually or with the use of design and analytic software. The researcher sees that some software cannot accept some countries' standard codes. For example, currently in Ethiopia analysis and design of two-way solid slab is done using readily available Excel sheet template. But working with this might have many problems, firstly the structure that is already analyzed by SAP or SAFE or any other international software application that uses international codes but which cannot design structure using ES EN 1992; for instant euro codes and designed by excel sheet can create failure and uneconomical analysis and design result. In this paper, the slab is designed and analyzed based on the chosen concrete grade, chosen reinforcement bar diameter, chosen steel grade for design and analysis of slabs calculations like load, moment, shear, and deflection checking using the moment coefficient method for analysis and design and Microsoft Visual Basic 2010 for coding. All input values are given by the International Standard units and are also used to represent output values. Using manual calculations delays time and mostly the result is not correct. But using this Computer program can increase computation accuracy and save time. The procedure the researcher followed is, first the manual calculation has been done and then SADSE2021 has been done. The result is that both are 99.9% identical, and the disadvantage of this method is that it cannot be used to determine the detailed drawing.

Excessive intracellular glucose in insulin independent tissues including nerve, nephron, lens and retina invites mishandling of metabolism of glucose resulting in a background of increased oxidative stress, advanced glycation end products (AGE) formation, lipid peroxidation and failure of antioxidant defense systems in type 2 diabetes mellitus (T2DM). All these detrimental biochemical anomalies ultimately attack biological membranes and especially capillary beds of retina and glomerulus of kidney, resulting in break-down of inner blood-retinal i.e. initiation of diabetic retinopathy (DR). If these disarrays are corrected to a large extent, development of DR can be avoided or delayed. In this prospective clinical trial, 185 patients with T2DM who received B-vitamins, vitamin-C, and E along with anti-diabetic medication for five years, demonstrated a slower rate of the development of DR and reduced abnormal biochemical mediators like reactive oxygen species (ROS), malondialdehyde (MDA), AGE, and vascular endothelial growth factor (VEGF) compared to 175 T2DM individuals who were treated with only anti-hyperglycemic drugs.

In the last years, interactive exhibitions based on digital technologies are becoming widely common, thanks to their flexibility and effectiveness in engaging visitors and creating memorable experiences. One of the topics in which digital technologies can be particularly effective is the communication of abstract concepts that are difficult for the human mind to imagine. An emblematic example is the astronomy discipline, which requires us to imagine and understand phenomena far away from our everyday life. In this paper, the authors present a research project, MARSS, in which digital technologies are used effectively to enhance the Users’ Experience of the Museo Astronomico di Brera located in Milan. Specifically, the MARSS project aims at designing and developing a new digital journey inside the museum to allow different categories of visitors to enjoy the exhibition in an engaging and interactive way. The paper presents the design and development phases of the experience and its evaluation with users. The results of the evaluation indicate that the digital interactive experience has been appreciated by users and is successful in translating the content of high scientific value into more engaging and easily understandable elements.

Digital health research is an emerging discipline that requires easy-to-understand theoretical frameworks and implementation models for digital health providers in health and social care settings. The COVID-19 pandemic has heightened demand for digital health discipline-specific instruction on ‘how to’ manage evidence-based digital health transformation. Access to the use of these models guarantees that digital health providers can investigate phenomena using safe and suitable approaches and methods to conduct research and identify answers to challenges and problems that arise in health and social care settings. The COMPASS theory is designed to aid transformation of health and social care environments. A navigational rose of primary quadrants is divided by four main compass points, with person-centred care being central to the philosophy. Two axes produce Cartesian planes that intersect to form a box plot, which can be used to discover human and physical resource weightings to augment digital health research design and implementation. A third continuum highlights stakeholders' capabilities, critical for any multidisciplinary study. The COMPASS mnemonic guides end-users through the process of design, development, implementation, evaluation, and communication of digital health transformations. The theory's foundations are presented and explained in context of the 'new normal' of health and social care delivery.

This study investigates the cultural dimension in sustainable lighting design to create inclusive environments. India being one of the most culturally and ethnically diverse nations in the world, with a population of almost 18% of the world population, requires attention to include cultural dimension in the design of sustainable environments. With the changing lifestyle and growth in organized retailing, the Indian retail market needs an upgrade to create inclusive environments for shared retail experiences. Lighting is among most influencing atmospheric attribute to create simulating environment for a holistic shopping experience. Preference of lighting conditions vary across the store profiles and users’ cultural background. Very little research has been carried out to understand the lighting preferences of retail customers in India. This study investigated the effects of correlated colour temperature and illuminance levels on spatial impressions and user preferences in mid-range store profile. This study involved ninety-three participants in evaluating high definition visualisations of the sixteen lighting conditions. The observations from this study emphasizes the necessity of similar studies across various states of India to identify the lighting preferences for other functional spaces and cultural backgrounds within the country. The findings may contribute towards providing recommended guidelines in lighting design and include a cultural dimension in the design of sustainable store environments.

Background: Membranous glomerulonephritis (MGN) is one of the most common causes of nephrotic syndrome in non-diabetic adults, accounting for up to one-third of biopsy diagnostics in some regions. A watchful waiting strategy has been adopted to accurately assess the success of MGN. In this study, we predict prognostic risk factors for primary MGN patients for effective intervention and prognosis. Materials and Methods: This is a retrospective study design involving 125 biopsy-proven MGN subjects on follow-up from January 2012 to October 2019 in Hospital Serdang and Hospital Kuala Lumpur, Malaysia. Result and Conclusion: After the median follow-up of 36 (15.0-57.0) months, 26 (20.8%) consisting of 17 (13.6%) primary MGN and 9 (7.2%) secondary MGN subjects achieved complete remission while 17 (13.6%) and 10 (8.0%) of primary and secondary MGN subjects respectively achieved spontaneous remission. Subjects with nephrotic range proteinuria have 2.917 odds more at risk of primary MGN while subjects with hypertension have 2.417 odds risk of primary MGN compared to those with no hypertension at presentation. Multiple logistic regression models retained only factors directly associated with MGN with only nephrotic range proteinuria associated with primary MGN. Those with nephrotic range proteinuria were 3.04 times the odds of primary MGN when compared with those without nephrotic syndrome (95% C. I = 1.089, 8.472). The nephrotic syndrome could be used to determine the prognosis in patients with primary MGN especially those at risk of end-stage kidney disease.

(1) Background: Existing research has demonstrated the potential of mHealth apps in improving the caregiving outcomes of stroke. Since several apps were published in commercially available app stores without explaining their design and evaluation processes, it is necessary to identify the usability and user experience issues to promote long-term adherence and usage; (2) Methods: User reviews were extracted from the 47 previously identified apps that support stroke caregiving needs using a python-scraper. The reviews were pre-processed and filtered using python scripts. The final corpus was classified based on usability and user experience dimensions to highlight issues within the app; (3) Results: A total of 162,095 were extracted from the two app stores. After filtration, 15,818 reviews were included and classified based on the usability and user experience dimensions. Findings highlight critical issues related to the errors/effectiveness, efficiency and support that contribute to decreased satisfaction, emotion and frustration in using the app; (4) Conclusion: The study identified several usability and user experience issues due to the inability of the app developers to understand the needs of the user. Further, the study describes the inclusion of a participatory design approach to promote an improved understanding of user needs; therefore, limiting any issues and ensuring continued use.

Introduction: The treatment of peritoneal dialysis in end-stage renal disease is increasing in clinical practice. The main purpose of this study was to evaluate the effect of far-infrared radiation therapy on inflammation and the cellular immunity of patients undergoing peritoneal dialysis. Materials and Methods: We recruited 56 patients undergoing peritoneal dialysis, and we included 32 patients from the experimental group and 24 patients from the control group in the final analysis. The experimental evaluation in our study was as follows: (1) We used abdominal computed tomography to measure the diameter and degree of hardening of the abdominal veins and large blood vessels to explore the changes in abdominal blood vessels. (2) The study compared the effects of peritoneal dialysis using albumin, blood urea nitrogen, creatinine, white blood cell, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, erythrocyte sedimentation rate, Hs-CRP clearance rate, and HBAlC. (3) We compared the cytokine concentration of blood between the two groups while controlling for GM-CSF, IL-2, IFN-γ, IL-6, IL-18, IL-4, IL-5, IL-13, IL-12p70, TNF-α, and IL-1β. Results and Discussion: (1) There was no significant difference in the abdominal blood vessels of the experimental group relative to the control group according to abdominal CT measured over 6 months. (2) Our study demonstrates the statistically significant effect of FIR therapy on the following parameters: albumin (p = 0.048*), creatinine (p = 0.039*), and Hs-CRP (p < 0.001**) decreased significantly, and glomerular filtration rate (eGFR, p = 0.043*) and glucose (p < 0.001**) increased significantly. Our study found that, in the experimental group, albumin and creatinine decreased significantly due to FIR therapy for 6 months. However, our study also found that, in the experimental group, glucose (p < 0.001**) increased significantly due to FIR therapy for 6 months. Peritoneal dialysis combined with FIR can reduce the side effects of glucose in dialysis buffer, which interferes with peritoneal inflammation and peritoneal mesothelial cell fibrosis. In addition, we also found that, in the experimental group, Hs-CRP (p < 0.001**) decreased significantly due to FIR therapy for 6 months. (3) No statistical significance in the inflammatory cytokines related to FIR therapy differences was observed in our study. IFN-γ (p = 0.124), IL-12p70 (p = 0.093), IL-18 (p = 0.213), and TNF-α (p = 0.254) did not exhibit significant improvements in peritoneal dialysis with FIR treatment over 6 months. IFN-γ and IL-18 in the plasma of patients in the experimental group and the control group were higher in the third month than in the first month. Conclusion: We found that the effect of peritoneal dialysis improved significantly with FIR therapy, and significant improvements in the peritoneal permeability and inflammatory response were observed. end-stage renal disease; peritoneal dialysis; far-infrared therapy (FIR)