Testing Phase Research Articles

Comparative analysis of advanced deep learning models for predicting evapotranspiration based on meteorological data in bangladesh.

Evapotranspiration is one of the crucial elements in water balance equations and plays a pivotal role in the water and energy cycle of an area. An accurate and precise estimation and prediction of reference evapotranspiration (ETo) is necessary for regional management of water resources and irrigation scheduling. The challenge of predicting daily evapotranspiration with limited meteorological data in Bangladesh. This study aims to predict daily evapotranspiration using limited meteorological data of Bangladesh by three deep learning (CNN, GRU, LSTM) and one hybrid (CNN-GRU) model. The novel method of hybrid CNN-GRU models, which have not been commonly used for this purpose. The performance of models was evaluated by five accuracy matrices R2, RMSE, MAE, MAPE, and CE and comparison is visualized by radar graphs. The study's novelty lies in the use of hybrid CNN-GRU models to estimate reference evapotranspiration, as this algorithm has not been commonly used for this purpose. In the case of the Rangpur station, the hybrid CNN-GRU algorithm outperformed other models, achieving the best values across various statistical metrics during both the training and testing phases. The highest correlation coefficient values of approximately 0.994 and 0.995. Moreover, during training and testing stages, the hybrid model had the lowest MAE (0.076, 0.068) and RMSE (0.138, 0.106) at the Rangpur station. Additionally, in the Sreemangal station, it can be notable that the statistical parameter RSME found superior results in the hybrid model around 0.225 and 0.174, respectively. In addition, the highest R2 and CE values were noted as 0.986, 0.987 and 0.985, 0.986 during the training and testing phases, respectively. The comparison suggests that the hybrid model will be best suited for prediction with the limited meteorological data. The outcome of the present research signifies the ability of deep learning methods in the prediction of evapotranspiration and the dominant variables affecting the changes the in context of Bangladesh.

Acoustics and vibration based milling diagnostics using supervised machine learning approaches

In industrial machining processes, tool failures may result in losses in surface and dimensional accuracy of a finished part, or possible damage to both the work piece and the machine. Consequently, tool condition monitoring has become essential to achieve high-quality machining as well as cost-effective production. Moreover, cutting tool degradation may vary considerably under different operation conditions and materials behaviour. Therefore, real time identification of the tool state during machining is critical. In this study the acoustic and vibration responses of the cutting tool and of the workpiece material are measured with the help of a microphone and a piezoelectric accelerometer. Many milling experiments were conducted for different conditions and tool failures, from which features were calculated in time and frequency domains and then split into two groups: 80% for the machine learning model training, 20% for the test phase. Then several supervised machine learning approaches have been applied and compared. Results of our study are implemented in a real tool wear predictive maintenance framework.

Emotion Recognition Using EEG Signals and Audiovisual Features with Contrastive Learning.

Multimodal emotion recognition has emerged as a promising approach to capture the complex nature of human emotions by integrating information from various sources such as physiological signals, visual behavioral cues, and audio-visual content. However, current methods often struggle with effectively processing redundant or conflicting information across modalities and may overlook implicit inter-modal correlations. To address these challenges, this paper presents a novel multimodal emotion recognition framework which integrates audio-visual features with viewers' EEG data to enhance emotion classification accuracy. The proposed approach employs modality-specific encoders to extract spatiotemporal features, which are then aligned through contrastive learning to capture inter-modal relationships. Additionally, cross-modal attention mechanisms are incorporated for effective feature fusion across modalities. The framework, comprising pre-training, fine-tuning, and testing phases, is evaluated on multiple datasets of emotional responses. The experimental results demonstrate that the proposed multimodal approach, which combines audio-visual features with EEG data, is highly effective in recognizing emotions, highlighting its potential for advancing emotion recognition systems.

Development of an application for training management of the maintenance team in a metallurgical industry in the suape industrial complex – cabo de Santo Agostinho

This work presents the development of an application designed to complement traditional training management methods within an organization. The application, developed on the PowerApps platform using a Low-Code approach, aims to facilitate the monitoring and management of employee training in the maintenance department of a company located in the Suape industrial complex, Cabo de Santo Agostinho. Its main objective is to provide a digital platform that allows for the verification of the training status of maintenance technicians, identifying which are in "Attention," "Expired," and/or "OK" status. Traditionally, this data is managed manually, in a time-consuming and error-prone process. The transition to a digital system ensures data accuracy, reduces the time required to access and update information, and improves the overall efficiency of the department's activity scheduling process. During the testing phase, conducted between January and August 2024, comparative data was collected on the training consultation process, before and after the implementation of the application. Preliminary results indicated a significant improvement in the efficiency and productivity of the maintenance department, comparable to other works that used PowerApps as a quick and low-cost solution.

Time Series Installed Capacity Forecasting with Deep Learning Approach for Türkiye

Deep learning methods have been developed to solve different problems due to the complex nature of real-world problems. Accurate future forecasting of a country's installed capacity is also crucial for developing a good energy sustainability strategy for the country. In this paper, three different time series forecasting methods are used for forward forecasting of installed capacity: Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Installed power values for the years 1923-2021 were used in the study. Then, future forecasts are made until 2030. The GRU model achieved the best RMSE in the testing phase compared to the LSTM and CNN models. Although CNN is successful during training, it has a higher RMSE during testing compared to GRU. While all models predict a potential increase in electricity capacity by 2030, GRU and LSTM predict a more significant increase up to this point compared to CNN.

A-209 Preanalytical Phase Errors Constitute the Vast Majority of Errors in Clinical Laboratory Testing

Abstract Background Laboratory errors can occur during the preanalytical, analytic, or post-analytic phase of testing and may pose a considerable threat to patient safety. Studies have estimated that pre-analytical error encapsulates ∼45-68% of laboratory errors and occurs in up to 1.52% of specimens. However, limitations of previously published studies include the use of manual error detection, reliance on manual transcription, small study sizes, and exclusion of common errors. Herein, we aim to identify the types and frequency of errors occurring in each phase of clinical laboratory testing using a large retrospective dataset across a broad range of errors detected in clinical laboratories. Methods This study was performed in the Barnes-Jewish Hospital core laboratory which provides testing for a 1,400-bed tertiary care hospital and included all tests performed for clinical chemistry, special chemistry, toxicology, automated hematology, blood gas testing, coagulation, non-infectious serology, infectious disease serology, urinalysis, and send out testing between January 2022 and May 2023. Errors were compiled from two separate sources. The first source of errors was captured from the laboratory information system (LIS, Cerner). These errors were flagged manually by laboratory staff in real time. The second source of error was a daily report manually curated by a trained medial laboratory scientist as part of routine, laboratory operating procedures and standard workflow. Results There were ∼40,000,000 individual results from ∼11,000,000 specimens during the study period and 87,317 errors observed (∼0.8% of specimens). Of these errors, specimens that were hemolyzed sufficiently to impact result but still reportable were the majority (41,047, 47.2%). When excluding minimal hemolysis, there were 46,270 errors documented. Among these, 44,847 (97.0%) were in the preanalytical phase, 451 (1.0%) were in the analytical phase, and 972 (2.0%) occurred in the postanalytical phase. Subcategorization of preanalytical errors revealed that most of the errors were related to hemolyzed samples requiring results to be suppressed (n=19,701), quantity not sufficient samples (n=8,068), clotted specimens (n=5,840), collection errors (e.g., wrong tube type, n=5,780), not on ice (n=1,369) and IV fluid contamination (n=1,122). In the analytical phase, the causes of error were interfering substances (n=279), QC out of range (n=65), instrument problems (n=58) and reagent issues (n=49). Among the postanalytical errors, 581 were comment errors, 216 were results entry errors, 131 were comments errors with potential impact on patient care, and 44 were dilution errors. Conclusions This study demonstrates that the vast majority of lab errors (∼97.0%) occurring during the preanalytical phase. The high incidence of preanalytical errors, especially errors related to sample integrity implies a need for enhanced tools and training to minimizing errors from specimen collections, maintenance of sample stability, transportation, and processing.

Forecasting Lake Nokoué Water Levels Using Long Short-Term Memory Network

The forecasting of hydrological flows (rainfall depth or rainfall discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Lake Nokoué in Benin. This paper aims to provide an effective and reliable method to enable the reproduction of the future daily water level of Lake Nokoué, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Lake Nokoué up to a forecast horizon of t + 10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R2), Nash–Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t + 3 days. The values of these metrics remain stable for forecast horizons of t + 1 day, t + 2 days, and t + 3 days. The values of R2 and NSE are greater than 0.97 during the training and testing phases in the Lake Nokoué basin. Based on the evaluation indices used to assess the model’s performance for the appropriate forecast horizon of water level in the Lake Nokoué basin, the forecast horizon of t + 3 days is chosen for predicting future daily water levels.

A-346 Evaluating 14-3-3η Detection in Finger Prick Blood Using Lateral Flow Technology: Advancing Clinical Laboratory Diagnostics

Abstract Background The protein 14-3-3η is a critical biomarker in the diagnosis and monitoring of autoimmune rheumatologic disorders, and its early detection is pivotal for patient outcomes. Previous research has established the significance of 14-3-3η as a biomarker, particularly in inflammatory polyarthritis, as well as patients’ and physicians’ interest in near-patient testing. While 14-3-3η is traditionally measured in serum via ELISA, this study explores the innovative use of lateral flow technology (LFT) for its detection in finger prick blood, addressing the urgent need for near-patient testing.This research aims to evaluate the technical and clinical performance of 14-3-3η measurement using LFT in comparison to standard ELISA methods, with an emphasis on its detectability in finger prick blood. Methods This study included two phases of testing involving patient serum and spiked whole blood. In the serum phase, 53 samples (19 NEG, ≤0.19 ng/mL, and 34 POS, 0.27 to 28 ng/mL 14-3-3η) were tested using LFT. LFT results were compared to JOINTstat® ELISA measurements for concordance. For whole blood, recombinant 14-3-3η and ELISA-positive serum samples were spiked into whole blood at increasing concentrations (0.4 to 3.2 ng/mL) and tested with LFT. Concordance criteria included visual inspection of test and control lines, with results captured and analyzed using a reference key card. The study was extended to assess 14-3-3η levels in consented patients with 22 matched finger prick and venous serum samples​​. Results In serum testing, LFT demonstrated 100% positivity in detecting 14-3-3η for POS samples. Among NEG samples, 85% tested negative, with 15% positive, indicating a strong concordance between LFT and ELISA. The LFT's ability to semi-quantitatively measure 14-3-3η was confirmed by a significant Kruskal-Wallis test (p<0.0001) across four intensity groups. In whole blood testing, LFT correctly identified all samples spiked at concentrations ≥0.5 ng/mL. The Spearman correlation showed a strong and significant association (r=0.96 to 0.98, p=0.001) between LFT test line intensity and spiked 14-3-3η levels. Finally, the equivalence testing for finger prick and venous serum samples affirmed the LFT’s reliability, with a strong correlation (p<0.0001), underscoring its potential for near-patient testing​​. Conclusions The study validates LFT as a reliable and semi-quantitative method for detecting 14-3-3η in finger prick blood, demonstrating strong concordance with traditional ELISA measurements. The findings support the feasibility of LFT in near-patient testing for early detection, prognosis, and monitoring of autoimmune diseases, paving the way for enhanced patient accessibility. Future research will focus on integrating this technology into routine clinical care and examining its impact on patient management and outcomes.

Pembangunan permainan papan untuk pengajaran dan pembelajaran kewarganegaraan digital [The development of a board game for teaching and learning digital citizenship

This study aims to examine the need for using board games in teaching and learning (PdP) sessions related to the theme of digital citizenship. This study employed a qualitative approach using the Design and Development Research (DDR) methodology. Data was collected through interviews involving five Moral Education trainee teachers who had taught topics related to Digital Citizenship. The data was analyzed by adapting the three phases in the DDR Design and Development Model, where the first phase is needs analysis, the second phase is development, and the third phase is evaluation. The study results showed that the study participants had a high awareness of providing early exposure to the elements of citizenship. A teaching aid, namely a board game that incorporates three elements of digital citizenship, is needed. In the usability testing phase, the board game produced is attractive, capable of achieving three moral dimensions, namely moral reasoning, moral emotion, and moral behavior; the board game can also strengthen the relationship between students and teachers. The study's implications provide exposure to teachers to create a game-based teaching for the topic of moral and citizenship education, which can increase students' interest.

Testing the validity and reliability of the Korean nursing surveillance scale: a methodological study

BackgroundBecause of the importance of nursing surveillance, there is a need to develop a scale to measure nursing surveillance that reflects the roles of nurses in South Korea. This study aimed to develop a scale to measure surveillance by Korean nurses and to test its reliability and validity.MethodsIn the development phase, a literature review was conducted to verify the attributes of nursing surveillance, and preliminary items were developed based on the surveillance activities in the Nursing Intervention Classification (NIC) and the interviews of Korean nurses and modified through content validation and a pilot study. In the psychometric testing phase, two surveys were conducted with Korean nurses working in acute hospitals, using the preliminary scale in exploratory factor analysis (EFA, n = 220) and confirmatory factor analysis (CFA, n = 219). Data were analyzed through EFA, CFA, correlation, and reliability analyses to verify convergent validity, discriminant validity, criterion validity, and reliability. To verify the validity of the preliminary scale, the exploratory factor analysis and confirmatory factor analysis convergent validity, discriminant validity, criterion validity, and reliability were performed.ResultsIn the EFA, 16 items were grouped into four factors, accounting for 70.1% of the cumulative variance. In the CFA, the model exceeded the criteria for all fit indices (χ2 = 155.62 [df = 94, p < .001], CMIN = 1.65, SRMR = .048, RMSEA = .055, GFI = .921, NFI = .916, TLI = .955, CFI = .964) and was acceptable. The convergent validity, discriminant validity, criterion validity, and reliability were verified. The final Korean nursing surveillance scale consists of four factors: ‘anticipation of problems and decision-making’ with six items; ‘systematic assessment’ with five items; ‘recognition of patterns’ with three items; and ‘identification of the patient’s self-care and coping strategies’ with two items.ConclusionThe Korean nursing surveillance scale developed in this study comprised questions that included NIC’s surveillance activities and empirical data from Korean nurses; based on the attributes of nursing monitoring derived from concept analysis, its validity and reliability were verified. This study can provide precedent to motivate the development of nursing surveillance scales in other countries, and ultimately stimulate studies on nursing surveillance, which is essential for patient safety.

Exponential-trigonometric optimization algorithm for solving complicated engineering problems

This article presents an innovative optimization algorithm called the Exponential-Trigonometric Optimization (ETO) algorithm, which is based on a sophisticated combination of exponential and trigonometric functions. The ETO algorithm is structured to strike a balance between two important stages: exploration and exploitation, a challenge that many other optimization algorithms have also sought to address. ETO incorporates several additional random and adaptive variables, resulting in better performance compared to existing algorithms. The algorithm's performance and potential were validated through three phases of testing. First, the ETO algorithm is evaluated against seven other meta-heuristic algorithms, including SCHO, SCA, AOA, GWO, HHO, HGS, and GJO, using 23 classical benchmark functions of varying sizes. These benchmark functions are categorized into three main groups: "unimodal" (F1 to F7), "multi-modal" (F8 to F13), and "fixed-dimension" (F14 to F23). Next, the ETO algorithm was tested on CEC2019 and CEC2020 functions and compared with 7 algorithms mentioned above to evaluate performance. In addition, the ETO algorithm is tested on the CEC2017 benchmark functions with dimensions of 10-D, 30-D, and 50-D. It is then compared and evaluated against other potential algorithms, such as SHADE, LSHADE, and JADE, using the Wilcoxon rank-sum test. Finally, the ETO algorithm is utilized to tackle five intricate engineering problems to show its robustness, including optimizing the shape of aircraft wings to increase lift. These results have demonstrated the effectiveness and potential of the ETO algorithm, yielding outstanding results and ranking first among other algorithms. Notably, the optimization of the improved aircraft wing increased the lift force from 0.797055 to 0.951817, signaling promising prospects for advancing optimization problems in the future. Source codes of ETO is publicly available at https://ceats.ou.edu.vn/us/exponential-trigonometric-optimization-algorithm-.html.

Design and Operation of IoT System and Power Communication with Digital Twin Technology

Abstract This paper explores how to integrate digital twin technology into IoT system design to achieve more efficient monitoring and maintenance of power communication networks. By introducing digital twin technology, the real-time performance and accuracy of embedded systems in power communication are enhanced, providing strong data support and simulation environments for predictive maintenance and fault diagnosis. This paper combines theoretical research with practical applications to propose an embedded IoT system design based on the ARM9 platform and verifies its effectiveness in power communication networks. In the testing phase, the ARM9-based IoT system demonstrated a processing speed 15% faster than previous designs, with a latency reduction of 18% in communication processes. These enhancements contribute to the overall efficiency and reliability of power communication networks.

Speaker Demographics Modulate Listeners' Neural Correlates of Spoken Word Processing.

In language comprehension, listeners expect a speaker to be consistent in their word choice for labeling the same object. For instance, if a speaker previously refers to a piece of furniture as a "couch," in subsequent references, listeners would expect the speaker to repeat this label instead of switching to an alternative label such as "sofa." Moreover, it has been found that speakers' demographic backgrounds, often inferred from their voice, influence how listeners process their language. The question in focus, therefore, is whether speaker demographics influence how listeners expect the speaker to repeat or switch labels. In this study, we used ERPs to investigate whether listeners expect a child speaker to be less likely to switch labels compared to an adult speaker, given the common belief that children are less flexible in language use. In the experiment, we used 80 pictures with alternative labels in Mandarin Chinese (e.g., yi1sheng1 vs. dai4fu, "doctor"). Each picture was presented twice over two experimental phases: In the establishment phase, participants listened to an adult or a child naming a picture with one of the labels and decided whether the label matched the picture they saw; in the test phase, participants listened to the same speaker naming the same picture by either repeating the original label or switching to an alternative label and, again, decided whether the label matched the picture they saw. ERP results in the test phase revealed that, compared to repeated labels, switched labels elicited an N400 effect (300-600 msec after label onset) and a P600 effect (600-1000 msec after label onset). Critically, the N400 effect was larger when listeners were exposed to the child speaker than to the adult speaker, suggesting that listeners found a switched label harder to comprehend when it was produced by a child speaker than an adult speaker. Our study shows that the perceived speaker demographic backgrounds influence listeners' neural responses to spoken words, particularly in relation to their expectations regarding the speaker's label switching behavior. This finding contributes to a broader understanding of the relationship between social cognition and language processing.

Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining

The vibrations generated by rock blasting are a serious and hazardous outcome of these activities, causing harmful effects on the surrounding environment as well as the nearby residents. Both the local ecology and human communities suffer from the consequences of these vibrations. Assessing the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity (PPV) and frequency, which are essential parameters for measuring vibration velocity. Accurate prediction of vibration occurrence is critically important. Therefore, this study employs five machine learning models for predicting the PPV and frequency resulting from quarry blasting. This work compares five machine learning models (XGBoost, Catboost, Bagging, Gradient Boosting, and Random Forest Regression) to choose the most efficient performance model. The performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase, there was a strong correlation observed between the actual and the predicted ones. The analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R2 = 0.983, MSE = 0.000078, RMSE = 0.008, NRMSE = 0.019, MAD = 0.004, MAPE= 35.197 in the PPV prediction, and R2 = 0.975, MSE = 0.000243, RMSE = 0.015, NRMSE = 0.031, MAD = 0.008, MAPE = 37.281 for the frequency prediction. This study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration, and frequency. In the context of the mining and civil industry, the application of this study offers significant potential for enhancing safety protocols and optimizing operational efficiency. By employing machine learning models, this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting.

The Development of Pusara Finder: Muslim Grave Finder Mobile Application

In Islam, visiting graves reflects and reminds us of death and life in the Barzakh. The cemeteries must be adequately cared for and maintained since they play a significant role in the community as an important location for remembering and lamenting. However, remembering or finding the specific grave can sometimes be challenging. Thus, this project aims to develop a mobile application in which users can track their loved one’s grave to help them locate, save, and share the grave’s location by utilising the GPS sensors on the mobile phone. It is easier for them to track their loved ones and build a community for the Muslim cemetery community. The objectives of this project are to identify user requirements and to design and develop Pusara Finder – Muslim Grave Finder Mobile Application. This project uses the Android platform, and the mobile application has been developed using Visual Studio or Android Studio. There is a novelty of this project in which Pusara Finder is anl app designed to simplify the process of locating and navigating to Muslim graves in Shah Alam. This apps offers a seamless and intuitive experience, making it easier to pay tribute to the loved ones. The user also can access the complete database of Muslim graves with ease. The target user of this project was Muslim Public Users or people who want to find their specific grave. The methodology used for this project will be the Mobile Application Development Life Cycle (MADLC) Model. This project, however, has been developed up until the testing phase only. The project has been developed, and it’s in a prototype. In order to develop the mobile application, Flutter and Firebase has been implemented in this project. There were 11 users who had been involved in testing the usability of the mobile application by using System Usability Scale method with the score of 76.59 which in adjective rating is good.

Out-of-Distribution Detection by Cross-Class Vicinity Distribution of In-Distribution Data.

Deep neural networks for image classification only learn to map in-distribution inputs to their corresponding ground-truth labels in training without differentiating out-of-distribution samples from in-distribution ones. This results from the assumption that all samples are independent and identically distributed (IID) without distributional distinction. Therefore, a pretrained network learned from in-distribution samples treats out-of-distribution samples as in-distribution and makes high-confidence predictions on them in the test phase. To address this issue, we draw out-of-distribution samples from the vicinity distribution of training in-distribution samples for learning to reject the prediction on out-of-distribution inputs. A cross-class vicinity distribution is introduced by assuming that an out-of-distribution sample generated by mixing multiple in-distribution samples does not share the same classes of its constituents. We, thus, improve the discriminability of a pretrained network by finetuning it with out-of-distribution samples drawn from the cross-class vicinity distribution, where each out-of-distribution input corresponds to a complementary label. Experiments on various in-/out-of-distribution datasets show that the proposed method significantly outperforms the existing methods in improving the capacity of discriminating between in- and out-of-distribution samples.

Sintering enhancement process based on fluidity and bonding phase strength

The sintering process is influenced by complex low-grade ores. This study used seven types of imported iron ore, A–F and domestically produced iron ore, G, as raw materials to study their chemical composition, as well as the liquid fluidity and bonding strength of the mixed ore after single and optimised ore blending. The high-temperature characteristic test values of mixed ores are positively correlated with the weighted calculated values of individual ores. Under the sintering cup conditions, the optimal ore mixing includes a 9% increase in liquid fluidity and a 6% increase in bonding strength. The latter significantly improved the sintering yield and drum strength, reaching 75.89% and 65.67%, respectively. In addition, the consumption of solid fuel has slightly decreased by 0.06 kg·t−1, and the utilisation coefficient has slightly increased. In addition, the sintering yield and quality indicators of the scheme have been comprehensively improved. Compared with the benchmark scheme, through liquid fluidity optimisation, an increase in magnetite phase, a decrease in haematite, and a significant decrease in acicular calcium ferrite were observed, interwoven with magnetite. The silicate-bounding iron oxide also showed an increase in this optimisation. In the optimisation of bonding strength, the corrosive structure interwoven with magnetite and needle shaped ferrite significantly increases, while the iron oxide bound with silicate decreases. The improvement effect of liquid phase fluidity and bonding phase strength optimisation on sintering process and mineralisation behaviour was verified through sintering cup test and mineral phase analysis.

Optimization of surgical evaluation algorithms for living donor liver transplantation

BackgroundLiving donor liver transplantation (LDLT) is an established and endorsed alternative for deceased donor liver transplantation with better recipient outcomes. Nevertheless, while extensive evaluation of potential donors is crucial, evaluation algorithms differ between transplant centres and guidelines. MethodsWe included 317 individuals evaluated for LDLT between 07/2007–07/2022 in a retrospective analysis. The evaluation process was analysed to identify the key reasons for declining 77 potential donors. Additionally, 146 donors that underwent LDLT were analysed regarding risk factors for complications. ResultsThe main reasons for donor refusal were liver volumetry (40.3 %) and metabolic factors including obesity or steatotic liver disease (20.8 %). Contrast-enhanced computed tomography (CECT) identified 63.6 % of all declined donors; CECT combined with assessment of medical history, physical examination, blood testing and ultrasonography, identified 87.0 % of declined potential donors. Associated with this selection, complication rates in donors were low (≥II in 17.1 %; none with ≥IVb). Notably, higher age was a risk factor for developing a complication ≥II after hemi-hepatectomy (p = 0.0373). ConclusionsWe propose a progressive 4-step evaluation algorithm that begins with a very basic assessment combined with up-front CECT. This early phase of testing is expected to identify nearly 90 % of ineligible donors, thereby conserving critical resources, time and money, as well as minimising burden for potential donors. FundingJ.M.W. received funding by grant We-4675/6–1 from the Deutsche Forschungsgemeinschaft (DFG) in Bonn, Germany.

The Tests and Calibrations of the Hard X-ray Imager Aboard ASO-S

The Hard X-ray Imager (HXI) aboard the Advanced Space-based Solar Observatory (ASO-S) is an instrument designed to observe hard X-ray (HXR) spectra and images of solar flares. Having 91 subcollimators to modulate incident X-rays, HXI can obtain 91 modulation data and 45 visibilities to reconstruct images with a spatial resolution as high as ∼3.1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\sim 3.1$\\end{document} arcsec. HXI was launched on 9 October 2022 and powered up on 17 October 2022. After the on-orbit testing phase lasting for three months, HXI was ready for regular observations on 18 January 2023. With fine-tuning of the detectors and electronics, we were able to expand the energy range from ∼30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\sim 30$\\end{document} – 200 keV to ∼10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\sim 10$\\end{document} keV – 300 keV, which significantly raised the scientific values of the data and the number of detected flare events. This paper presents the changes and improvements of HXI instrument since 2019, the important ground tests, on-orbit tests, and calibration works. We also present the light curves, spectra, and reconstructed images of one flare observed by HXI on 6 January 2023.

Redefining Hospital Accessibility: A Service Design Framework for Inclusive Healthcare

Abstract In the framework of becoming a carbon neutral city by 2030, Bologna faces challenges related to enhancing the quality and accessibility of its built environment to improve the quality of life and well-being of its residents. The IRCCS Azienda Ospedaliero-Universitaria Sant’Orsola Polyclinic is a key agent of change within this initiative. The hospital has initiated a comprehensive renovation project aligned with its commitment to the United Nations Sustainable Development Goals (SDGs), particularly SDG 3, which addresses Good Health and Well-being, and SDG 10, which addresses Reduce Inequalities. To address these goals, the hospital has engaged a research team from the University of Bologna to apply Service Design methodologies. The research aims to reshape accessibility and user experience systematically and sustainably, adhering to the principles of Design for All. The study maps diverse user groups, analyses circulation paths, and identifies physical and procedural barriers affecting vulnerable individuals, especially those with disabilities. The research introduces a novel approach by developing a comprehensive strategy rather than isolated solutions, focusing on interconnected accessibility services across pavilions. Dedicated hubs within each pavilion facilitate resource and support device exchange, improving users’ experiences. A pilot project, initially targeting wheelchair accessibility hubs, employs innovative technologies to enhance independent tool usage and monitor distribution and maintenance efficiently. The project is now entering a testing phase, during which the solution will be refined based on user feedback.