Software Tools Research Articles

Impact of Artificial Intelligence-Based Autosegmentation of Organs at Risk in Low- and Middle-Income Countries

Radiation therapy (RT) processes require significant human resources and expertise, creating a barrier to rapid RT deployment in low- and middle-income countries (LMICs). Accurate segmentation of tumor targets and organs at risk (OARs) is crucial for optimal RT. This study assessed the impact of artificial intelligence (AI)-based autosegmentation of OARs in 2 LMICs. Ten patients, comprising 5 head and neck (HN) cancer patients and 5 prostate cancer patients, were randomly selected. Planning computed tomography images were subjected to autosegmentation using an Food and Drug Administration-approved AI software tool and manual segmentation by experienced radiation oncologists from 2 LMIC RT clinics. The control data, obtained from a large academic institution in the United States, consisted of contours obtained by an experienced radiation oncologist. The segmentation time, DICE similarity coefficient (DSC), Hausdorff distance, and mean surface distance were evaluated. AI significantly reduced segmentation time, averaging 2 minutes per patient, compared with 57 to 84 minutes for manual contouring in LMICs. Compared with the control data, the AI pelvic contours provided better agreement than did the LMIC manual contours (mean DSC of 0.834 vs 0.807 in LMIC1 and 0.844 vs 0.801 in LMIC2). For HN contours, AI provided better agreement for the majority of OAR contours than manual contours in LMIC1 (mean DSC: 0.823 vs 0.821) or LMIC2 (mean DSC: 0.792 vs 0.748). Neither the AI nor LMIC manual contours had good agreement with the control data (DSC < 0.600) for the optic nerves, chiasm, and cochlea. AI-based autosegmentation generates OAR contours of comparable quality to manual segmentation for both pelvic and HN cancer patients in LMICs, with substantial time savings.

Ionic Liquid-Based Green Solvents for Extraction and Purification of Natural Plant Products

Introduction: This research paper explores the environmental sustainability of ionic liquid-based green solvents in the extraction and purification of natural plant products, with a focus on their entire life cycle. The objectives of the study were to assess the environmental impact of ionic liquid synthesis, energy consumption, water usage, emissions, recycling rates, policy effects, and stakeholder perceptions. Methods: Methodologically, we conducted a comprehensive Life Cycle Assessment (LCA) that involved primary data collection through field surveys and interviews with key stakeholders in the ionic liquid production and usage industry across various regions in India. The data were analyzed using specialized LCA software tools to quantify environmental impacts. Key findings include the identification of synthesis as a major contributor to environmental impact, emphasizing the need for greener synthesis methods. Results: The study revealed the significant carbon footprint, energy consumption, and water usage during production, highlighting opportunities for improvement. Emissions data underscored the importance of emission control measures, particularly for greenhouse gases and volatile organic compounds. Recycling and reuse were identified as environmentally friendly disposal methods. Policy compliance varied among stakeholders, indicating room for stricter regulations. Stakeholder perceptions varied, with researchers having the most positive outlook. Implications of the findings extend to sustainable chemistry practices, emphasizing interdisciplinary collaboration and the importance of considering the entire life cycle of chemical processes. Conclusion: This research contributes to a deeper understanding of green solvents and provides a foundation for promoting sustainable practices in industrial processes in India and globally.

Beyond The Basics: A Detailed Survey of Advanced Python Applications and Innovations

Abstract: Python is the general purpose, high-level, scriptable, interpreted, object oriented and easy to learn language was developed by Guido van Rossum. Python is a popular programming language because of its simplicity, ease of use, open-source licensing, and accessibility. It provides various libraries and frameworks for learning and real world programming. Python’s versatility and active community make it an ideal language for Machine Learning, Data Science, Artificial Intelligence, Networking, Game Development, Web Development and various other domains. In this paper, we will provide an introduction to the main Python programming software tools used for Web development, Data science, Machine learning techniques, Educational Development, etc. In this paper will first introduce Python as a language, and give introduction about Web Development, Data science, Machine learning, Educational Development &amp; Financial Analysis and then describe packages that are popular in that sector

A standards-based software tool to support prognostic modelling: application to exemplar heart failure risk scores

Abstract Introduction Heart failure prognosis is an active research area, with the development of several new risk scores each year. While risk score derivation and evaluation have become common practices, these processes currently lack standardisation. Purpose We aimed to: i) develop a standards-based software tool to support prognostic modelling; and ii) apply the tool to derive, evaluate and compare exemplar heart failure risk models to address a hypothetical question of whether using only non-invasive measurements provides similar prognosis value compared to adding invasive measurements. Methods A software tool was developed based on a prognostic modelling workflow defined using the Common Workflow Language and containing 7 steps: univariate Cox proportional hazard (PH) regression, multivariable Cox PH regression with assessment of proportional hazards and linearity with outcome (Schoenfeld and Martingale residuals), derivation of points per risk factor, risk calculation and stratification, discrimination and calibration assessment. Using data from the PEOPLE heart failure cohort [1], two integer-based risk scores were developed to predict 2-year all-cause mortality. The first model utilised 12 variables available at recruitment informed by prior analysis of the PEOPLE cohort to provide a benchmark for comparison, while the second model is based on 9 non-invasive parameters. The analysis was conducted on cases with complete data (n=781), with no internal or external validation. Results By inputting the dataset and indicating the variables of interest for each model, our software tool automatically generated the patient-specific risk scores, risk strata, c-index, and discrimination and calibration plots. A 12-parameter model based on age, sex, ejection fraction, hypertension, diabetes, atrial fibrillation, ischaemic history, NYHA class, heart rate, systolic blood pressure, creatinine and NT-proBNP yielded reasonable discrimination (c-index=0.77). Graphical assessment of risk strata and calibration (Fig. 1.a and c) revealed good performance overall, apart from the highest risk decile where survival was overestimated. The reduced 9-parameter model excluded creatinine, NT-proBNP, and automatically dropped diabetes status for non-linearity. It showed a substantial drop in discrimination (c-index=0.67), confirmed in graphical assessment of risk strata (Fig. 1.b). Calibration was more variable per decile than seen with the 12-parameter model but was more robust to survival overestimation (Fig. 1.d). Conclusion Our software tool ensures the uniformity and comparability of the two risk models derived by a common statistical process. In this example, we showed how the addition of NT-proBNP, creatinine, and diabetes status improves discrimination for predicting heart failure survival. This tool can be leveraged to test new hypotheses in a standardised and reproducible manner to enhance research in heart failure prognosis.

Optimizing Colorectal Screening in Portugal with Process Mining

Abstract Background Efficient healthcare screening programs management influences patient outcomes. Process mining has emerged as a powerful tool to enhance these programs by refining complex workflows and pinpointing operational deviations. Objectives This study utilizes process mining techniques to analyze the colorectal cancer screening program in Northern Portugal, focusing on detecting deviations and performance bottlenecks in process models. Methods This screening program started in March 2018, but process mining was only used on data posterior to 2020. Over 1 million case observations from administrative data were included in the process model analysis, involving more than six key process activities and numerous resources. We used new software and statistical tools for process mining to rapidly compute observations and filter for different operational needs and monitor interventions. Results The discovery analysis identified deviations, such as unexpected performance issues and irregular activity sequences. Although over 100 traces were identified, fewer than five traces accounted for more than 90% of all case paths, indicating the most common traces that have a true impact in participation rates, referral rates, and treatment rates. Notable differences in resource performance during specific periods, such as COVID-19 or seasonal events, were also observed. These findings provided insights for adjusting resource allocation and improving coordination, with impact in minimizing seasonal phenomena or peak production periods. Lessons Process mining offers actionable insights for effective health program coordination, enabling the monitoring and timely response to process deviations. With key areas for action identified in a more timely and precise manner, we can enhance efficiency, and expand population coverage and treatment referral. Future initiatives will focus on further process optimization and national coverage expansion using these management tools. Key messages • Process mining revolutionizes healthcare by enabling the strategic management of complex oncological screening program workflows. • Comprehensive data analysis across the patient pathway enhances program efficiency and expands treatment access.

8.B. Scientific session: Strengthening health information systems in the European Region

Abstract Strong health information systems (HIS) are essential for robust, evidence-informed policymaking and decision making in all levels of healthcare. The COVID-19 pandemic underscored the vital importance of HIS, but also highlighted flaws and weaknesses across HIS in Europe. Common HIS challenges include limited resources and capacity; insufficient coordination and collaboration between HIS stakeholders, leading to fragmentation and problems with interoperability; lack of central governance; and limited use of health information for decision-making. Both at the level of the WHO Regional Office for Europe (WHO/Europe) and the European Commission, policies and strategies are being implemented to support Member States in strengthening their HIS, thereby improving the availability of high quality, timely data for evidence-informed decision making, both at the country and international level. Supporting the further digitalization of HIS is an important element in this. In 2023, WHO/Europe and the European Commission joined forces in the field of HIS strengthening. This workshop will inform participants about current and future capacity building tools and activities that will be implemented under the umbrella of the EU-funded action ‘Supporting Member States in the WHO European Region in strengthening HIS and boosting health data governance and interoperability’. It will explain the policy as well as situation-on-the-ground contexts in which the action will be implemented. The workshop will start with a (1) presentation by WHO/Europe on the current HIS situation in the Region and what are the main priority areas in WHO’s work for the coming years. After that, (2) the European Commission will give more information on the above-mentioned EU-funded action and the related EU policy landscape, most notably the European Health Data Space (EHDS). Next, (3) WHO/Europe will present their catalogue of capacity building tools and activities, and share experiences with implementing these. Finally, (4) HIS modernization cases with enabling factors and challenges will be presented based on different countries’ real life experiences, illustrating the impact of HIS strengthening activities at the country level. Workshop participants will get a comprehensive overview of available HIS strengthening tools and activities in the European Region. This will help them in selecting and applying tools for strengthening their own national or regional HIS, and ensuring their HIS is prepared for aligning with the EHDS and other important policies and strategies. This will contribute to sustainable and future-proof HIS across Europe. During the workshop, there will be ample time for questions and discussion. In the discussion, participants will be asked whether they have any HIS strengthening needs that are not yet covered in the current package of tools and activities. This will help WHO and the European Commission to further harmonise their efforts with what is needed on the ground. Key messages • Despite investments in HIS following the COVID-19 pandemic, many challenges remain, which are often similar across European countries. • WHO/Europe and the European Commission have joined forces to strengthen HIS and boost health data governance and interoperability, while leveraging EHDS developments.

Enacting Social Protection Laws in Jigawa and Zamfara States, Nigeria

Legal frameworks are important for institutionalising and strengthening social protection systems. Two states in Nigeria, Jigawa and Zamfara, have enacted social protection laws. This article explores how the process of enacting such a law is influenced by state-level politics and local contexts, and how the activities of a project financed by the Foreign, Commonwealth &amp; Development Office (FCDO) contributed to much greater understanding and acceptance of social protection in both states. Primary data collected through in-depth interviews with state and non-state stakeholders and project staff was analysed to tease out the political, cultural, and power dynamics involved in enacting the laws. Findings show that a range of factors aimed at building the case for, and understanding of, social protection across a range of stakeholders was critical, and that a fairly standard package of tools employed in advocating for institutionalising social protection was influenced considerably by domestic political dynamics.

Diabetes worsens the adverse association between myocardial diffuse fibrosis and left ventricular strain

Abstract Background Heart failure is a common cardiovascular complication to diabetes. Increased fibrosis of the myocardium has been suggested to be a mechanism, however, no convincing clinical evidence has so far been presented. A better understanding of how myocardial tissue characteristics influence functional aberration in diabetes could aid diagnosis, risk assessment and guide treatment. Native T1 is a CMR marker of diffuse fibrosis with diagnostic and prognostic value (1,2), and feature tracking (CMR-FT) strain is a sensitive measure of myocardial systolic function. In this study, we evaluated the association of CMR markers of fibrosis with markers of systolic function in individuals with and without diabetes. Purpose To understand the relationship between native T1 and myocardial strain and how diabetes may affect this. Methods Participants from the UK Biobank population imaging study were included. Left ventricular global longitudinal strain (GLS) was derived using a semi-automated batch processing software tool. Native T1 was derived from segmentation of the entire short axis slice using an automated quality control tool, excluding scans with predicted dice score of &amp;lt;0.7. Multivariable linear models were used with the strain metrics as the outcome variable and native T1 and diabetes as the exposure variables. These models were adjusted for age, sex, BMI, ethnicity, smoking and alcohol status, prevalent hypertension hypercholesterolaemia and coronary artery disease. The effect of interaction between diabetes and native T1 on GLS was examined by introducing an interaction term. Results In total 41,533 participants were included, of which 1957 had diabetes – see Table 1 for baseline characteristics. The average GLS measured for those with diabetes was -17% and was -18% for those without. The average native T1 in those with diabetes was 935ms and 933ms for those without on the background of greater prevalence of cardiovascular risk factors in those with diabetes. In model adjusted for all relevant co-variates, individuals with diabetes had worse GLS measured compared to those without diabetes (β-coefficient: 0.51, p value &amp;lt;0.001). Increasing fibrosis (native T1) was associated with worsening GLS (β-coefficient: 0.005, p value &amp;lt;0.001) in all participants. However, in those with diabetes, increasing native T1 was associated with a greater decline in GLS compared to those without diabetes (interaction term β-coefficient: 0.005, p value &amp;lt;0.001), with a relative impairment seen within normal ranges for native T1 - see Figure 1. Conclusion(s) This is the largest study of its kind to demonstrate a dose dependent relationship between increasing native T1, representing potential myocardial fibrosis, and myocardial strain, representing cardiac dysfunction. Our findings indicate that increased fibrosis alone does not explain the higher risk of HF in diabetes, other mechanisms, perhaps intracellular, might be at play.

DYNAMO-HIA: A health impact assessment tool fit for quantifying tobacco control policy

Abstract DYNAMO-HIA is a generic, publicly-available and easy to use software tool for health impact assessments of changes in health risk-factors such as obesity, air pollution, processed meat consumption, physical activity, alcohol taxation, or smoking. The tool uses a graphic use interface, does not require programming skills, and has comparatively modest data requirements. Health outcomes that can be investigated are mortality, life expectancy, healthy-life expectancy, disability and prevalence of various chronic disease such as chronic obstructive pulmonary disease (COPD), cancer, or coronary heart diseases. DYNAMO-HIA can in principle be used for any tobacco control policy or ordinance-e.g. price increases or pictorial health warning labels-that target changes in smoking prevalence or smoking initiation/quitting rates. Past applications of DYNAMO-HIA for tobacco control policy included country studies for Belgium, Mongolia, or Korea and several comparative country studies. In this presentation we will give a general overview into the use of DYNAMO-HIA and show-case a recently published study. This study quantified the reduction in COPD burden that would occur in Italy, England and Sweden over 40 years if tobacco prices were increased by 5%, 10% and 20% over current local prices.

A software tool for fabricating phantoms mimicking human tissues with designated dielectric properties and frequency.

Dielectric materials play a crucial role in assessing and refining the measurement performance of dielectric properties for specific tasks. The availability ofviable and standardized dielectric materials could greatly enhance medical applications related to dielectric properties. However, obtaining reliable phantoms with designated dielectric properties across a specified frequency range remains challenging. In this study, we propose software to easily determine the components of dielectric materials in the frequency range of 16 MHz to 3 GHz. A total of 184 phantoms were fabricated and measured using open-ended coaxial probe method. The relationship among dielectric properties, frequency, and thecomponents of dielectric materials was fitted through feedforward neural networks. Software was developed to quickly calculate the composition of dielectric materials. We performed validation experiments including blood, muscle, skin, and lung tissue phantoms at 128 MHz, 298 MHz, 915 MHz, and 2.45 GHz. Compared with literature values, the relative errors of dielectric properties are less than 15 %. This study establishes a reliable method for fabricating dielectric materials with designated dielectric properties and frequency through the development of the software. This research holds significant importance in enhancing medical research and applications that rely on tissue simulation using dielectric phantoms.

Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium.

Establishing suitable in vitro culture conditions for microorganisms is crucial for dissecting their biology and empowering potential applications. However, a significant number of bacterial and fungal species, including Pneumocystis jirovecii, remain unculturable, hampering research efforts. P. jirovecii is a deadly pathogen of humans that causes life-threatening pneumonia in immunocompromised individuals and transplant patients. Despite the major impact of Pneumocystis on human health, limited progress has been made in dissecting the pathobiology of this fungus. This is largely due to the fact that its experimental dissection has been constrained by the inability to culture the organism in vitro. We present a comprehensive in silico genome-scale metabolic model of Pneumocystis growth and metabolism, to identify metabolic requirements and imbalances that hinder growth in vitro. We utilise recently published genome data and available information in the literature as well as bioinformatics and software tools to develop and validate the model. In addition, we employ relaxed Flux Balance Analysis and Reinforcement Learning approaches to make predictions regarding metabolic fluxes and to identify critical components of the Pneumocystis growth medium. Our findings offer insights into the biology of Pneumocystis and provide a novel strategy to overcome the longstanding challenge of culturing this pathogen in vitro.

Using EEA data to calculate health impacts of air pollution via the AirQ+ model: a practical example

Abstract The AirQ+ software tool for health risk assessment of air pollution, developed by the World Health Organization, helps quantify the effects of exposure to air pollution. Specifically, AirQ+ can estimate the effects of short-term changes in air pollution (based on risk estimates from time-series studies) and the effects of long-term exposures (using life-tables approach and based on risk estimates from cohort studies). AirQ+ is available in English, French, German and Russian. In this presentation, AirQ+ will be loaded with air pollution data from the European Environment Agency datasets for a quick evaluation of health effects from air pollution in an European city.

Exploring Architectural Tools for Oculus Quest 2

Virtual Reality (VR) has emerged as a pivotal technology in architectural design and education, offering immersive and interactive experiences that surpass traditional methods. This article delves into the diverse applications of VR in architecture, with a particular emphasis on the various software tools utilized in architectural modeling, visualization, and education. The study conducts a thorough examination of both professional and entry-level VR tools and platforms like Blender, Rhino, Unity, and Enscape, which are increasingly integrated into architectural workflows. By providing real-time rendering, interactive design environments, and enhanced spatial understanding, these tools are transforming the way architects and designers conceive and develop their projects. The article also explores the educational implications of VR software such as Tilt Brush, SketchUp Viewer, and Arkio, which offer accessible platforms for students and educators to engage with complex spatial concepts in an intuitive manner. Through comparative analysis, the study highlights the strengths and limitations of these tools, focusing on their usability, accessibility, and relevance to architectural practice and pedagogy. Furthermore, the integration of VR with Building Information Modeling (BIM) is identified as a significant innovation that enhances collaboration and information synchronization throughout the design and construction process. This article provides a comprehensive overview of the current state of VR software in architecture, offering insights into future trends and potential advancements that could further revolutionize the field. The findings underscore VR’s critical role in fostering creativity, improving design accuracy, and enhancing educational experiences, positioning it as a cornerstone technology in the future of architecture.

Advanced waveform analysis of the electrocardiogram signals using complementary signal processing techniques to investigate the response to a QTc prolonging drug vs control

Abstract Background Thorough QTc (TQT) studies using Electrocardiogram (ECG) data, are a well-established method for testing the pro-arrhythmic propensity of drugs performed during safety studies as part of drug development. ECG analysis is currently reduced to simplified biomarkers, such as rate, intervals and amplitudes. However, ECG waveforms are complex and generally sampled with high fidelity (125-1000 Hz). Our novel mathematical method, Symmetric Projection Attractor Reconstruction (SPAR) analyses the morphology and variability of such waveforms, using every data point to generate a new, faithful 2D waveform representation termed an ‘attractor’. Moxifloxacin is a reversible blocker of the rapid component of the delayed rectifier, potassium current of the cardiac IKr potassium channel and has been shown to cause a mean increase of the QTc interval of 10–14 ms after an oral single dose of 400 mg. Separately, studies have shown that meals can alter QT interval as well as altering the pharmacokinetic profile of moxifloxacin itself with gender and ethnicity impacting individual responses. In-depth knowledge of these physiological factors could support safer medicines development and clinical use. Purpose This project investigated the feasibility of SPAR analysis of ECG signals to detect drug induced changes which a higher degree of sensitivity, to support safety evaluation during medicines development. Methods High resolution triplicate 12 lead ECG time-series data from a Phase one study of healthy human volunteers cross over of placebo, moxifloxacin or moxifloxacin + food was retrospectively analysed using SPAR. Using a bespoke software tool was used to perform SPAR analyses to transform ECG time-series into corresponding attractors. Corresponding single point measurements were previously obtained as part of the original Phase one study. Results Initial evaluation of attractors revealed high inter-individual variation, consistent with previous SPAR ECG analysis. Nevertheless, we identified changes in subjects receiving Moxifloxacin (six hour time point) when compared to those receiving placebo control. These changes were not as evident in previously obtained QT intervals. Conclusion This pilot study illustrates that SPAR is highlighting changes on the ECG that are less obvious from QT data alone. Further evaluation of the whole dataset to identify generalisable SPAR features, and their comparison with existing markers is now necessary. The individualised nature of ECGs and corresponding attractors indicates the method may have highest utility in personalised medicine, evaluating each individual’s change and hence individual risk to drug induced arrhythmia.Visualisations of SPAR analysis of ECGs

E-Brachy: New dosimetry package for electronic brachytherapy sources.

Large reported variability in the material composition and geometrical components of the Xoft electronic high-dose-rate brachytherapy Causes inter-source discrepancy in the source output. This variability is due to the manual manufacturing and assembly of thesources. This study aimed to develop a dosimetry software tool called E-Brachy to characterize the Xoft source and quantify the discrepancies in its photon spectrum and dosimetricproperties. E-Brachy is based on the Geant4 Monte Carlo toolkit and consists of two parts. In part one, the geometry and material composition for the source received in the computer-aided design format from the vendor were converted to the geometry description markup language format using the GUIMesh Python tool and integrated into the E-Brachy software. There was a large variation in material composition and thickness for some of the tube components. The simulation started from electrons and resulted in x-ray generations in the anode region. Multithreading, a track length estimation, and the uniform bremsstrahlung splitting variance reduction techniques were used to decrease the simulation time and increase the x-ray production. The photon energy, position, and momentum were saved into a phase space file as the photon exited the source, but before interacting with the external environment. The obtained x-ray energy spectrum was compared with measurements from the National Institute of Standards and Technology (NIST). In part two, by sampling from the generated photons, the dose rates and dosimetric parameters according to the TG-43 protocol were calculated for model S7500 and compared to the ones previously calculated for model S700 source, which were deemed identical by themanufacturer. The material composition that resulted in the most similar spectrum as the measured NIST spectrum with Pearson's correlation coefficient of 0.99 and a calculated Euclidean difference of keV was chosen for further dosimetric analysis of the model S7500 source. Characteristic peaks showed the presence of tungsten, yttrium, and silver in the source components. Differences in dose rates between the two source models surpassed 20% for polar angles , reaching a peak at cm and . The differences in the radial dose function values were within 5%. The relative difference in percentage between the anisotropy function values of the two models was closer to 0 for smaller values, but at higher polar angles, they increased to 300%. A software package called E-Brachy was successfully developed for the characterization and dosimetry of Xoft electronic brachytherapy sources. E-Brachy can be combined with spectral measurements to investigate the inter- and intra-source variability. The software package was tested by comparing the simulated spectra from the S7500 Xoft source model with NIST measurements and its TG-43 parameters with the S700 model. The TG-43 parameters between the two sources significantly exceed the recommendations ofTG-56.

Design of a high-power harmonic waveguide filter for radar transmitter

In this article, we present the research results on the designing and manufacturing method of a high-power harmonic filter applied in the X-band transmitter system. The harmonic filter utilizes Waffle-iron filters and quarter-wavelength converters on the waveguide. As a result, the loss and ripple within the passband are improved, while meeting the requirements of cutting off high-order harmonics. Based on the application of microwave theory and simulation software tools, the research team have calculated, designed and manufactured a harmonic filter that meets all the requested specifications for application in X-band transmitter systems: Loss ≤ 0,12dB@8,9-9,6GHz; Reflection coefficient ≤ -20dB@8,9-9,6GHz; Frequency selectivity ≤ -40dB@2nd harmonic, 3rd harmonic, 4th harmonic.

A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision

Abnormal crowd behavior recognition is one of the research hotspots in computer vision. Its goal is to use computer vision technology and abnormal behavior detection models to accurately perceive, predict, and intervene in potential abnormal behaviors of the crowd and monitor the status of the crowd system in public places in real time, to effectively prevent and deal with public security risks and ensure public life safety and social order. To this end, focusing on the abnormal crowd behavior recognition technology in the computer vision system, a systematic review study of its theory and cutting-edge technology is conducted. First, the crowd level and abnormal behaviors in public places are defined, and the challenges faced by abnormal crowd behavior recognition are expounded. Then, from the dimensions based on traditional methods and based on deep learning, the mainstream technologies of abnormal behavior recognition are discussed, and the design ideas, advantages, and limitations of various methods are analyzed. Next, the mainstream software tools are introduced to provide a comprehensive reference for the technical framework. Secondly, typical abnormal behavior datasets at home and abroad are sorted out, and the characteristics of these datasets are compared in detail from multiple perspectives such as scale, characteristics, and uses, and the performance indicators of different algorithms on the datasets are compared and analyzed. Finally, the full text is summarized and the future development direction of abnormal crowd behavior recognition technology is prospected.

Extubation Advisor: Implementation and Evaluation of A Novel Extubation Clinical Decision Support Tool.

Extubation Advisor (EA) is a novel software tool that generates a synoptic report for each Spontaneous Breathing Trial (SBT) conducted to inform extubation decision-making. To assess bedside EA implementation, perceptions of utility, and identify barriers and facilitators of use. We conducted a phase I mixed-methods interventional study in three mixed intensive care unit (ICUs) in two academic hospitals. We interviewed critical care physicians (MDs) and respiratory therapists (RTs) regarding user-centered design principles and usability. We evaluated our ability to consent participants (feasibility threshold 50%), capture complete data (threshold 90%), generate and review EA reports in real-time (thresholds 75% and 80%, respectively), and MD perception of tool usefulness (6-point Likert scale). We analyzed interview transcripts using inductive coding to identify facilitators and barriers to EA implementation and perceived benefit of tool use. We enrolled 31 patients who underwent 70 SBTs. Although consent rates [31/31 (100%], complete data capture [68/68 (100%)], and EA report generation [68/70 (97.1%)] exceeded feasibility thresholds, reports were reviewed by MDs for [55/70 (78.6%)] SBTs. Mean MD usefulness score was 4.0/6. Based on feedback obtained from 36 interviews (15 MDs, 21 RTs), we revised the EA report twice and identified facilitators (ability to track patient progress, enhance extubation decision-making, and provide support in resource-limited settings) and barriers (resource constraints, need for education) to tool implementation. Half of respondents (9 MDs, 9 RTs; combined 50%) perceived definite or potential benefit to EA tool use. This is the first study of a waveform-based variability-derived, predictive clinical decision support tool evaluated in adult ICUs. Our findings support the feasibility of integrating the EA tool into bedside workflow. Clinical trials are needed to assess the utility of the EA tool in practice and its impact on extubation decision-making and outcomes. NCT04708509.

Basic aspects of IOL calculation

IOL calculation can now be considered a solved problem. However, the problem of sharing the knowledge of the underlying physical and mathematical basics is still unsolved. Also, the necessary hard- and software tools for an IOL calculation approximating the accuracy of prescribed glasses are not yet generally available. The aim of this survey is a clarification of the basics and of some historical pitfalls that still lead to persisting misunderstandings.

The digital evolution in toxicology: pioneering computational education for emerging challenges

The educational landscape of toxicology is increasingly integrating computational methodologies due to ethical concerns about animal testing and advancements in biotechnological and data analysis tools. This paper examines the evolution and significance of the Toxicology in the 21st century (Tox21) initiative and its impact on computational toxicology education. It contrasts computational toxicology with traditional methods, highlighting the limitations of conventional approaches and the new perspectives offered by computational techniques. The study emphasizes the importance of incorporating computational toxicology into curricula, including case studies that demonstrate how this integration enhances students’ problem-solving abilities, real-time data analysis skills, and innovation capabilities. Furthermore, it outlines effective teaching content and methods, including software tools, online resources, and academic literature. The paper also addresses the challenges and limitations faced in this educational shift and explores prospects for advancing computational toxicology education. By documenting these developments, the study aims to clarify the current advancements in toxicology education and the preparedness of students to address global chemical safety challenges with innovative solutions.