Combined Set Research Articles

Engineered Prompts in ChatGPT for Educational Assessment in Software Engineering and Computer Science

AI Assess, a ChatGPT-based assessment system utilizing the ChatGPT platform by OpenAI, composed of four components, is proposed herein. The components are tested on the GPT model to determine to what extent they can grade various exam questions based on learning outcomes, generate relevant practice problems to improve content retention, identify student knowledge gaps, and provide instantaneous feedback to students. The assessment system has been explored using software engineering and computer science courses and is successful through testing and evaluation. AI Assess has demonstrated the ability to generate practice problems based on syllabus information and learning outcomes. The components have been shown to identify weak areas for students. Finally, it has been shown to provide different levels of feedback. The combined set of components, if incorporated into a complete software system and implemented in classrooms with proposed transparency mechanisms, has vast potential to reduce instructor workload, improve student understanding, and enhance the learning experience. The potential for GPT-powered chatbots in educational assessments is vast and must be embraced by the education sector.

A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images

Neoadjuvant chemotherapy (NAC) is a systemic and systematic chemotherapy regimen for breast cancer patients before surgery. However, NAC is not effective for everyone, and the process is excruciating. Therefore, accurate early prediction of the efficacy of NAC is essential for the clinical diagnosis and treatment of patients. In this study, a novel convolutional neural network model with bimodal layer-wise feature fusion module (BLFFM) and temporal hybrid attention module (THAM) is proposed, which uses multistage bimodal ultrasound images as input for early prediction of the efficacy of neoadjuvant chemotherapy in locally advanced breast cancer (LABC) patients. The BLFFM can effectively mine the highly complex correlation and complementary feature information between gray-scale ultrasound (GUS) and color Doppler blood flow imaging (CDFI). The THAM is able to focus on key features of lesion progression before and after one cycle of NAC. The GUS and CDFI videos of 101 patients collected from cooperative medical institutions were preprocessed to obtain 3000 sets of multistage bimodal ultrasound image combinations for experiments. The experimental results show that the proposed model is effective and outperforms the compared models. The code will be published on the https://github.com/jinzhuwei/BLTA-CNN.

Enhancing the accuracy of XPS calculations: Exploring hybrid basis set schemes for CVS-EOMIP-CCSD calculations.

Reliable computational methodologies and basis sets for modeling x-ray spectra are essential for extracting and interpreting electronic and structural information from experimental x-ray spectra. In particular, the trade-off between numerical accuracy and computational cost due to the size of the basis set is a major challenge, since molecular orbitals undergo extreme relaxation in the core-hole state. To gain clarity on the changes in electronic structure induced by the formation of a core-hole, the use of sufficiently flexible basis for expanding the orbitals, particularly for the core region, has been shown to be essential. This work focuses on the refinement of core-hole ionized state calculations using the equation-of-motion coupled cluster family of methods through an extensive analysis on the effectiveness of "hybrid" and mixed basis sets. In this investigation, we utilize the CVS-EOMIP-CCSD method in combination and construct hybrid basis sets piecewise from readily available Dunning's correlation consistent basis sets in order to calculate x-ray ionization energies (IEs) for a set of small gas phase molecules. Our results provide insights into the impact of basis sets on the CVS-EOMIP-CCSD calculations of K-edge IEs of first-row p-block elements. These insights enable us to understand more about the basis set dependence of the core IEs computed and allow us to establish a protocol for deriving reliable and cost-effective theoretical estimates for computing IEs of small molecules containing such elements.

Effectiveness of different path guide modes: road guide signs and navigation systems

ABSTRACT The way drivers obtain road information has evolved from using only road guide signs to using both road guide signs and navigation systems. However, the effectiveness of their separate and combination settings has not yet been explored, making it difficult to determine whether their configurations should be optimized. This study explores the effectiveness of road guide signs, navigation systems, and their combination. First, a driving-simulation experiment was conducted. Subsequently, four objective indicators based on driving behavior rather than subjective or attitudinal indicators were selected. Finally, the differences in driving behaviors under different modes were examined. The results indicated that drivers performed better when using only the navigation system and the combination mode, based on the driving behaviors of the entire section. The vehicle operation efficiency under the three modes was consistent in the three subsections; however, the driving safety performance varied with the changes in the subsections.

A novel non-invasive EEG-SSVEP diagnostic tool for color vision deficiency in individuals with locked-in syndrome.

Color vision deficiency (CVD), a common visual impairment, affects individuals' ability to differentiate between various colors due to malfunctioning or absent color photoreceptors in the retina. Currently available diagnostic tests require a behavioral response, rendering them unsuitable for individuals with limited physical and communication abilities, such as those with locked-in syndrome. This study introduces a novel, non-invasive method that employs brain signals, specifically Steady-State Visually Evoked Potentials (SSVEPs), along with Ishihara plates to diagnose CVD. This method aims to provide an alternative diagnostic tool that addresses the limitations of current tests. Electroencephalography (EEG) recordings were obtained from 16 subjects, including 5 with CVD (specifically Deuteranomaly), using channels O1, O2, Pz, and Cz. The subjects were exposed to visual stimuli at frequencies of 15 Hz and 18 Hz to assess the proposed method. The subjects focused on specific visual stimuli in response to questions related to the Ishihara plates. Their responses were analyzed to determine the presence of CVD. Feature extraction was performed using Power Spectral Density (PSD), Canonical Correlation Analysis (CCA), and a combined PSD + CCA, followed by classification to categorize subjects into two classes: normal vision and CVD. The results indicate that the proposed method effectively diagnoses CVD in individuals with limited communication abilities. The classification accuracy of SSVEP exceeded 75% across the three classifiers: Decision Tree (DT), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The SVM classifier demonstrated higher accuracy compared to the other classifiers, exceeding 90%. These observations suggest that the SVM classifier, utilizing the combined feature set of PSD + CCA, may be the most effective in this classification task. These findings demonstrate that the proposed method is an accurate and reliable diagnostic tool for CVD, particularly for individuals unable to communicate.

Automated Deep Learning-Based Detection and Segmentation of Lung Tumors at CT.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans. This dataset was used to train a 3D U-Net-based, image-multiresolution ensemble model to detect and segment lung tumors on CT scans. Model performance was evaluated on internal and external test sets composed of CT simulation scans and lung tumor segmentations from two affiliated medical centers, including single primary and metastatic lung tumors. Performance metrics included sensitivity, specificity, false positive rate, and Dice similarity coefficient (DSC). Model-predicted tumor volumes were compared with physician-delineated volumes. Group comparisons were made with Wilcoxon signed-rank test or one-way ANOVA. P < 0.05 indicated statistical significance. Results The model, trained on 1,504 CT scans with clinical lung tumor segmentations, achieved 92% sensitivity (92/100) and 82% specificity (41/50) in detecting lung tumors on the combined 150-CT scan test set. For a subset of 100 CT scans with a single lung tumor each, the model achieved a median model-physician DSC of 0.77 (IQR: 0.65-0.83) and an interphysician DSC of 0.80 (IQR: 0.72-0.86). Segmentation time was shorter for the model than for physicians (mean 76.6 vs. 166.1-187.7 seconds; p<0.001). Conclusion Routinely collected radiotherapy data were useful for model training. The key strengths of the model include a 3D U-Net ensemble approach for balancing volumetric context with resolution, robust tumor detection and segmentation performance, and the ability to generalize to an external site.

Harnessing Machine Learning for High-Throughput Screening of High Thermal Conductivity Polyimides: A Multiscale Feature Engineering Approach

Polyimides are known for their exceptional thermal stability and are widely utilized in high-temperature and electronic applications. To further explore and broaden their application in electronic packaging and thermal management, developing polyimide with high thermal conductivity remains a significant challenge. In this study, we present a machine learning technique with novel multiscale feature engineering approach to predict and identify high thermal conductivity polyimide efficiently. Our workflow involves digitally representing chemical structures using physical insights and refining these representations through the Statistical – Tree – Recursive feature engineering pipeline, which includes three steps--statistical selection, tree-based cascading feature selection, and recursive feature elimination. This process results in the creation of a comprehensive combinational feature set. Multiple machine learning models were trained and validated, demonstrating high predictive accuracy and generalizability. High-throughput screening identified polyimide candidates with thermal conductivity values exceeding 0.4W/(m⋅K), and these predictions were validated using non-equilibrium molecular dynamics simulation. This workflow provides valuable insights into structure-property relationships, offering a robust framework for designing polymer materials with improved thermal properties for applications in electronics packaging, flexible sensors, and other high-performance devices.

Detection of Aspergilloma Disease Using Feature-Selection-Based Vision Transformers.

Background: Aspergilloma disease is a fungal mass found in organs such as the sinuses and lungs, caused by the fungus Aspergillus. This disease occurs due to the accumulation of mucus, inflamed cells, and altered blood elements. Various surgical methods are used in clinical settings for the treatment of aspergilloma disease. Expert opinion is crucial for the diagnosis of the disease. Recent advancements in next-generation technologies have made them crucial for disease detection. Deep-learning models, which benefit from continuous technological advancements, are already integrated into current early diagnosis systems. Methods: This study is distinguished by the use of vision transformers (ViTs) rather than traditional deep-learning models. The data used in this study were obtained from patients treated at the Department of Thoracic Surgery at Fırat University. The dataset consists of two class types: aspergilloma disease images and non-aspergilloma disease images. The proposed approach consists of pre-processing, model training, feature extraction, efficient feature selection, feature fusion, and classification processes. In the pre-processing step, unnecessary regions of the images were cropped and data augmentation techniques were applied for model training. Three types of ViT models (vit_base_patch16, vit_large_patch16, and vit_base_resnet50) were used for model training. The feature sets obtained from training the models were merged, and the combined feature set was processed using feature selection methods (Chi2, mRMR, and Relief). Efficient features selected by these methods (Chi2 and mRMR, Chi2 and Relief, and mRMR and Relief) were combined in certain proportions to obtain more effective feature sets. Machine-learning methods were used in the classification process. Results: The most successful result in the detection of aspergilloma disease was achieved using Support Vector Machines (SVMs). The SVM method achieved a 99.70% overall accuracy with the cross-validation technique in classification. Conclusions: These results highlight the benefits of the suggested method for identifying aspergilloma.

Disentangling protein metabolic costs in human cells and tissues.

While more data are becoming available on gene activity at different levels of biological organization, our understanding of the underlying biology remains incomplete. Here, we introduce a metabolic efficiency framework that considers highly expressed proteins (HEPs), their length, and biosynthetic costs in terms of the amino acids (AAs) they contain to address the observed balance of expression costs in cells, tissues, and cancer transformation. Notably, the combined set of HEPs in either cells or tissues shows an abundance of large and costly proteins, yet tissues compensate this with short HEPs comprised of economical AAs, indicating a stronger tendency toward mitigating costs. We additionally observe that short proteins are prevalent HEPs across individual cells and tissues, whereas long ones are more specific. Furthermore, the precise proportion of short, long, economical, or costly HEP classes indicates that particular cell types and tissues align more closely with the metabolic efficiency model, with some tissues displaying behavior akin to their constituent cells. Finally, tumors typically increase the production of short and low-cost HEPs compared with matched normal tissues, while genes that decrease their high expression levels in tumors often tend to be associated with high costs. Overall, the metabolic efficiency framework serves as a useful simplifying model for interpreting genome-wide expression data across scales.

Simultaneous tumors: ovarian cancer in a patient with multiple myeloma

Multiple myeloma is the second most common hematological malignancy, accounting for approximately 1% and 10–15% of malignant tumors of the hematopoietic and lymphoid systems, respectively. Until recently, the diagnosis of multiple myeloma was inherently associated with unfavorable prognosis of overall survival. On the one hand, advanced diagnosis techniques and the development of new treatment approaches have led to the improved life expectancy and significant reduction of mortality of patients with multiple myeloma. On the other hand, the medical and research communities have encountered a previously unknown issue: the combination of multiple myeloma with other types of cancer. This combination sets a task of multidisciplinary approach in the diagnosis and requires development of patient management strategy and prognostic parameters of the outcome. Some researchers adhere to the theory of independent tumor appearance in patients suffering from multiple myeloma, while other authors suppose that multiple myeloma and secondary cancer virtually represent multiple primary malignant neoplasms. This article presents a clinical case of postmortem verification of ovarian cancer in a patient with recurrent multiple myeloma.

Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches

Nowadays, the consequence of quick access to information has lead to the spread of fake news, which has a strong damaging impact on democracy, justice, and public trust. Thus, it is crucial to analyze and evaluate detection methods for fake news. This paper focuses on the detection of Romanian fake news. In this study, we made a comparative analysis of machine learning algorithms and Transformer-based models on Romanian fake news detection using three datasets—FakeRom, NEW, and both FakeRom + NEW. The NEW dataset was build using a scrapping algorithm applied on the Veridica platform. Our approach uses the following machine learning models for detection: Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM). We also used two Transformer-based models—BERT-based-multilingual-cased and RoBERTa-large. The performance of the models was evaluated using various metrics: accuracy, precision, recall, and F1 score. The results revealed that the BERT model trained on the NEW dataset consistently achieved the highest performance metrics across all test sets, with 96.5%. Also, Support Vector Machine trained on NEW was another top performer, reaching a very good accuracy of 94.6% on the combined test set.

Twistocaloric effect versus elastocaloric effect in shape memory alloys for low-force mechanocaloric design

Abstract The mechanocaloric effect refers to the reversible thermal effect under an external mechanical field and includes the elastocaloric effect (under the uniaxial stress field) and the twistocaloric effect (under the torsional stress field). In mechanocaloric designs, the elastocaloric effect has been the mainstream of the field with an emphasis on enhancing the performance of the elastocaloric materials and implementing them into elastocaloric systems. The twistocaloric effect has been recognized in materials implementation and exhibits the potential of miniaturized design. In this report, we compare the elastocaloric effect to the twistocaloric effect in the aspects of 1) superelastic behaviors including the critical transformation stress, transformation plateau, and transformation hysteresis, 2) reversible thermal behaviors, and 3) distribution of stress and temperature. We have provided the threshold of applied force for the twistocaloric effect under a combined set of strain and strain rates. Compared to the elastocaloric effect, the twistocaloric effect requires less force for the comparable temperature change and at a similar level of applied force generates a higher temperature change. To capture the distribution of the mechanical and thermal fields, we have conducted simulation and in-situ experiments to drive insights into the low-field activated transformation process in twistocaloric design compared to elastocaloric design. These results provide mechanical and thermal information on comparing the elastocaloric and twistocaloric effects and can facilitate the advanced mechanocaloric design for solid-state cooling technologies.

Supporting Patients' Use of Digital Services in Primary Health Care in England: Synthesis of Evidence From a Mixed Methods Study of "Digital Facilitation".

General medical practitioners and other staff at primary care medical practices have an important role in facilitating patient access to online services in the National Health Service in England. These services range from online ordering of repeat prescriptions to conducting online consultations with health care professionals. We have defined "digital facilitation" as that range of processes, procedures, and personnel that seeks to support patients in their uptake and use of online services. We report how we have synthesized the evidence from a mixed methods study of digital facilitation in primary care in England. The study's objectives were to identify, characterize, and explore the benefits and challenges of different models of digital facilitation in general medical practices in England and to design a framework for evaluation of the effectiveness and costs of digital facilitation interventions. Our study comprised scoping review of literature, survey of staff in general practices, survey of patients, and ethnography at case study practices plus stakeholder interviews. We compiled a triangulation matrix of the findings from individual work packages through an iterative process whereby each work package's results were first analyzed separately and were then cumulatively combined across work packages in 3 successive workshops. From the resulting matrix, we developed a program theory and an implementation theory and constructed a framework for evaluations of digital facilitation in primary care. The final step of the synthesis process was to discuss the results with national and regional National Health Service stakeholders. Triangulation yielded a combined set of findings summarized within 11 thematic groupings: 3 setting the scene within which digital facilitation takes place, and 8 related to different types of digital facilitation, their implementation, and effectiveness. Some thematic groupings were evident in the findings of all 4 of the research work packages; others were not addressed in all the work packages but were evident from those where they were addressed. Throughout the synthesis, there were no instances where findings from one work package contradicted the findings of another. Findings either reinforced each other or offered complementary or additional insights. The discussion at the stakeholder meeting held at the end of the study resulted in the research team clarifying some findings but not changing any of them. Digital facilitation can take many forms, though much of what is currently done in primary care practices in England is reactive and passive. Clear lines of responsibility, digital tools and platforms that work well for patients and practice staff, and investment in staff time and training are all needed if digital facilitation is to deliver on its promise. We propose a framework for future evaluations of the effectiveness and costs of digital facilitation interventions.

Economic Assessment of Maritime Fuel Transformation for GHG Reduction in the International Shipping Sector

This study aims to predict the economic transition pathway for alternative fuels in accordance with the 2023 IMO GHG Strategy goals. The assessment considers the impact of alternative fuel transition on fuel costs (∆COSTFuel,t), carbon emission costs (∆COSTCO2 eq,t), and ship new/retrofit costs (∆COSTship). The parameters and boundary conditions were set based on the current status and trends in the international shipping industry, as determined from previous research, to predict the economic transition pathway for alternative fuels. The results show that in 2050, with a standardized economic efficiency of 130%, profit will reach its maximum value, approximately −54,000 million USD. The study standardized fuel ΔCOSTj, normalized, and ΔNPV%j, normalized as a basis for adjusting penetration rates. At this time, considering fuel costs and NPV%, the composition of alternative fuels is as follows: bio-LNG, bio-Methanol, e-LNG, e-Methanol, e-Ammonia, BD, and Fossil-LNG, with shares of 18.56%, 4.00%, 25.64%, 6.00%, 10.00%, 28.00%, and 0%, respectively. Compared to conventional marine fuel HFO, the increase ranges from 23.54% to 69.50% in the 2030s, 0.52% to 0.55% in the 2040s, and decreases by 6.88%–14.69% in 2050. Using more LNG and BD in the 2040s and 2050 is an alternative way to achieve a better economic fuel transition. Moreover, the economic penetration rate combination set in this study can achieve sufficiently small ∆COSTT,t and sufficiently large NPVΔt under specific assumptions and boundary conditions, rather than an absolute minimum ∆COSTT,t or the absolute maximum NPVΔt. The results revealed that no single alternative fuel has a comprehensive advantage in reducing carbon intensity and economic performance at all times. Given the uncertainties in the supply chain, cost-effectiveness, and infrastructure for Methanol and Ammonia, LNG and BD play a crucial role in the transition of international shipping fuels. Our work provides a fundamental and comprehensive prediction of fuel transition based on the current status and trends in the international shipping industry.

Understanding the Effectiveness of Water, Sanitation, and Hygiene Interventions: A Counterfactual Simulation Approach to Generalizing the Outcomes of Intervention Trials.

While water, sanitation, and hygiene (WASH) interventions can reduce diarrheal disease, many large-scale trials have not found the expected health gains for young children in low-resource settings. Evidence-based guidance is needed to improve interventions and remove barriers to diarrheal disease reduction. We aimed to estimate how sensitive WASH intervention effectiveness was to underlying contextual and intervention factors in the WASH Benefits (WASH-B) Bangladesh cluster-randomized controlled trial. The investigators measured diarrheal prevalence in children enrolled in the WASH-B trial at three time points approximately 1 year apart ( observations). We developed a susceptible-infectious-susceptible model with transmission across multiple environmental pathways and evaluated each of four interventions [water (W), sanitation (S), hygiene (H), and nutrition (N) applied individually and in combination], compliance with interventions, and the impact of individuals not enrolled in the study. Leveraging a set of mechanistic parameter combinations fit to the WASH-B Bangladesh trial using a hybrid Bayesian sampling-importance resampling and maximum-likelihood estimation approach, we simulated trial outcomes under counterfactual scenarios to estimate how changes in six WASH factors (preexisting WASH conditions, disease transmission potential, intervention compliance, intervenable fraction of transmission, intervention efficacy, and community coverage) impacted intervention effectiveness. Increasing community coverage had the greatest impact on intervention effectiveness (e.g., median increases in effectiveness of 34.0 and 45.5 percentage points in the WSH and WSHN intervention arms when increasing coverage to 20%). The effect of community coverage on effectiveness depended on how much transmission was along pathways not modified by the interventions. Intervention effectiveness was reduced by lower levels of preexisting WASH conditions or increased baseline disease burden. Individual interventions had complementary but not synergistic effects when combined. To realize the expected health gains, future WASH interventions must address community coverage and transmission along pathways not traditionally covered by WASH. The effectiveness of individual-level WASH improvements is reduced more the further the community is from achieving the coverage needed for herd protection. https://doi.org/10.1289/EHP15200.

Circadian clock disruption impairs immune oscillation in chronic endogenous hypercortisolism: a multi-level analysis from a multicentre clinical trial

Glucocorticoids (GC) are potent entrainers of the circadian clock. However, their effects on biological rhythms in chronic human exposure have yet to be studied. Endogenous hypercortisolism (Cushing's Syndrome, CS) is a rare condition in which circadian disruption is sustained by a tumorous source of GC excess, offering the unique opportunity to investigate GC's chronic effects invivo. In a 12-month prospective case-control multicentre trial, the daily fluctuations in the number of circulating peripheral blood mononuclear cells (PBMCs) and the time-specific expression of clock-related genes were analysed in a cohort of 68 subjects, 34 affected by CS and 34 matched controls. Cosinor mixed effects model, rhythmicity algorithms and machine learning techniques were applied to the multi-level dataset. Multiple, 5-point daily sampling revealed profound changes in the levels, amplitude, and rhythmicity of several PBMC populations during active CS, only partially restored after remission. Clock gene analyses in isolated PBMCs showed a significant flattening of circadian oscillation of CLOCK, PER1, PER2, PER3, and TIMELESS expression. In active CS, all methods confirmed a loss of rhythmicity of those genes which were circadian in the PBMCs of controls. Most, but not all, genes regained physiological oscillation after remission. Machine learning revealed that while combined time-course sets of clock genes were highly effective in separating patients from controls, immune profiling was efficient even as single time points. In conclusion, the oscillation of circulating immune cells is profoundly altered in patients with CS, representing a convergence point of circadian rhythm disruption and metabolic and steroid hormone imbalances. Machine learning techniques proved the superiority of immune profiling over parameters such as cortisol, anthropometric and metabolic variables, and circadian gene expression analysis to identify CS activity. The research leading to these results has received funding from the European Union in the context of the National Recovery and Resilience Plan, Investment PE8 - Project Age-It: "Ageing Well in an Ageing Society". This resource was co-financed by the Next Generation EU [DM 1557 11.10.2022], the PRecisiOn Medicine to Target Frailty of Endocrine-metabolic Origin (PROMETEO) project (NET-2018-12365454) by the Italian Ministry of Health, and through internal funding to Sapienza University of Rome.

Bladder cancer detection in urine by novel methylation markers

Although cystoscopy is a reliable tool for detecting bladder cancer (BC) in patients with hematuria, it is invasive, costly and often unnecessary since most patients with hematuria do not have BC. Consequently, developing urinary biomarkers for non-invasive BC detection is a major clinical need. While DNA methylation markers hold promise, diagnostic performance can still be improved. We assessed 11 candidate methylation markers for urinary BC detection. Urine samples from 77 primary BC patients and 69 controls were used for marker selection and training, with independent validation conducted on samples from 63 primary BC patients and 71 controls. Samples were self-collected at home, mailed to the hospital and analyzed via quantitative methylation-specific polymerase chain reaction. Marker performance was evaluated through univariable and multivariable logistic regression analyses. Decision curve analysis (DCA) gauged clinical utility by potential cystoscopy reduction. Evaluation identified three most promising markers: NRN1, GALR1, and HAND2. These markers exhibited significantly elevated methylation levels in BC compared to controls in both cohorts (P < 0.001). The combined marker set demonstrated an area under the curve (AUC) of 0.94 at 84% (95% CI: 76–92%) sensitivity and 96% (95% CI: 91–100%) specificity. Validation yielded nearly equivalent accuracy (AUC 0.89, sensitivity 76% (95% CI: 65–86%), specificity 93% (95% CI: 86–99%)). DCA indicated a potential of 20 to 35% reduction in cystoscopies depending on the clinical scenario. The excellent diagnostic potential of our methylation markers for non-invasive BC detection, emphasizes their significance for future diagnostic strategies.

Cross-operating-condition fault diagnosis of a small module reactor based on CNN-LSTM transfer learning with limited data

Fault samples of Small Modular Reactor (SMR) are scarce and the data distribution cross operating conditions are different, resulting in poor generalization performance of fault diagnosis model. In this paper, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are integrated and a novel CNN-LSTM transfer learning method is proposed for fault diagnosis of SMR at different power levels, which is used to improve the deep extraction capability of spatial and temporal features of fault data. Based on small sample data and feature visualization technology, five different feature transfer strategies are compared to obtain the best generalization performance of the cross-operating condition fault diagnosis model. In addition, the effects of different network structures and hyperparameter combination settings of the CNN-LSTM model on the convergence speed and accuracy of transfer learning are analyzed to optimize the design of SMR fault diagnosis model. The results show that the CNN-LSTM method has stronger fault feature extraction capability than other deep learning methods. The simulation results also show that the proposed transfer learning strategy and the optimized network structure and hyperparameters could significantly enhance the generalization performance and accuracy of SMR fault diagnosis under different operating conditions with few labeled samples.

The Many Faces of Guilt: A Review Mapping Unique and Overlapping Expressions in OCD and Depression.

Guilt, a complex emotional experience pervading many lives, takes on an intricate form when intertwined with psychiatric conditions. As a multifaceted concept, guilt represents a key diagnostic feature in depression and is an integral part of obsessive compulsive disorder (OCD). This systematic review aimed to synthesize empirical research on the varied dimensions of guilt across these two mental illnesses, where guilt is emphasized as a pathognomonic factor. A systematic computer-based literature search was conducted, using a rigorous set of eligibility criteria and specific keyword combinations to ensure relevant and exhaustive coverage of the topic. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a comprehensive literature search spanning multiple databases identified 12 eligible studies on guilt in OCD and 8 in depression. The findings revealed 20 distinct guilt constructs measured across the two disorders. Notably, 5 unique guilt subtypes and 15 themes for OCD and 6 exclusively studied guilt for depression emerged, reflecting potential disorder-specific manifestations of guilt. Delineating such guilt profiles holds the potential to untangle the role of guilt in the onset, perpetuation, and clinical course of OCD and depression. This comprehensive mapping of guilt constructs provides an empirical foundation for elucidating disorder-specific pathways influenced by guilt, thereby informing the development of targeted psychological interventions tailored to the distinct guilt patterns underpinning these debilitating conditions.

Designing Multi-axis Compliant Mechanisms With Lockable Decoupled Inputs: A Tip–Tilt Case Study

Abstract This research is about designing multi-degree-of-freedom (multi-DOF) compliant mechanisms with decoupled inputs that can be independently locked/unlocked using bistable switches to achieve different combinations of DOFs. A case study mechanism achieving two decoupled rotational DOFs (tip and tilt) is designed, fabricated, and characterized. It can be triggered using two pairs of bistable switches, achieving drastically different states of torsional stiffness for each DOF in four sets of DOF combinations—no DOFs, a tip DOF, a tilt DOF, and both tip and tilt DOFs. Bistability and stiffness cancelation principles are exploited to achieve the desired changes in stiffness. Two flexure elements can be identified—the switch providing a negative stiffness and the cross-axis-flexural-pivot (CAFP) producing a positive stiffness. The mechanism is tuned to achieve static balancing, reaching a near-zero stiffness over much of its range. The pseudo-rigid body model and two-dimensional (2D) finite element model (FEM) are combined defining a fast method to dimension the system. The 3D FEM is simulated to validate the obtained results. For each DOF, the system is tested in two configurations (stiff and compliant) for three cycles over a ±10 deg rotation, achieving a stiffness reduction of around 99%. Comparable stiffness values were measured after triggering the switches more than once, repetitively reaching the required two states of stiffness, confirming the system's usability in practical applications. The positive stiffness provided by the CAFP is measured and compared to the device's overall stiffness, highlighting the stiffness cancelation concept.