Key Performance Metrics Research Articles

Hybrid PSO with tree-based models for predicting uniaxial compressive strength and elastic modulus of rock samples

The mechanical characteristics of rocks, specifically uniaxial compressive strength (UCS) and elastic modulus (E), serve as crucial factors in ensuring the integrity and stability of relevant projects in mining and civil engineering. This study proposes a novel hybrid PSO (particle swarm optimization) with tree-based models, such as gradient boosting regressor (GBR), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost) for predicting UCS and E of rock samples from Block IX of the Thar Coalfield in Pakistan. A total of 122 datasets were divided into training and testing sets, with an 80:20 ratio, respectively, to develop the predictive models. Key performance metrics, including the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), were employed to assess the model’s predictive performance. The results indicate that the PSO-XGBoost model demonstrated the highest accuracy in predicting UCS and E, outperforming the other models, which exhibited inferior predictive performance. Furthermore, this study utilized the SHAP (Shapley Additive exPlanations) machine learning method to enhance our understanding of how each input feature variable influences the output values of UCS and E. In conclusion, the proposed framework offers significant advantages in evaluating the strength and deformation of rocks at Thar Coalfield, with promising applications in the field of mining and rock engineering.

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions.

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

A Comparative Study of Vehicle Velocity Prediction for Hybrid Electric Vehicles Based on a Neural Network

Vehicle velocity prediction (VVP) plays a pivotal role in determining the power demand of hybrid electric vehicles, which is crucial for establishing effective energy management strategies and, subsequently, improving the fuel economy. Neural networks (NNs) have emerged as a powerful tool for VVP, due to their robustness and non-linear mapping capabilities. This paper describes a comprehensive exploration of NN-based VVP methods employing both qualitative theory analysis and quantitative numerical simulations. The used methodology involved the extraction of key feature parameters for model inputs through the utilization of Pearson correlation coefficients and the random forest (RF) method. Subsequently, three distinct NN-based VVP models were constructed comprising the following: a backpropagation neural network (BPNN) model, a long short-term memory (LSTM) model, and a generative pre-training (GPT) model. Simulation experiments were conducted to investigate various factors, such as the feature parameters, sliding window length, and prediction horizon, and the prediction accuracy and computation time were identified as key performance metrics for VVP. Finally, the relationship between the model inputs and velocity prediction performance was revealed through various comparative analyses. This study not only facilitated the identification of an optimal NN model configuration to balance prediction accuracy and computation time, but also serves as a foundational step toward enhancing the energy efficiency of hybrid electric vehicles.

Advancing Global Health Surveillance of Mycotoxin Exposures using Minimally Invasive Sampling Techniques: A State-of-the-Science Review.

Mycotoxins are a heterogeneous group of toxins produced by fungi that can grow in staple crops (e.g., maize, cereals), resulting in health risks due to widespread exposure from human consumption and inhalation. Dried blood spot (DBS), dried serum spot (DSS), and volumetric tip microsampling (VTS) assays were developed and validated for several important mycotoxins. This review summarizes studies that have developed these assays to monitor mycotoxin exposures in human biological samples and highlights future directions to facilitate minimally invasive sampling techniques as global public health tools. A systematic search of PubMed (MEDLINE), Embase (Elsevier), and CINAHL (EBSCO) was conducted. Key assay performance metrics were extracted to provide a critical review of the available methods. This search identified 11 published reports related to measuring mycotoxins (ochratoxins, aflatoxins, and fumonisins) using DBS/DSS and VTS assays. Multimycotoxin assays adapted for DBS/DSS and VTS have undergone sufficient laboratory validation for applications in large-scale population health and human biomonitoring studies. Future work should expand the number of mycotoxins that can be measured in multimycotoxin assays, continue to improve multimycotoxin assay sensitivities of several biomarkers with low detection rates, and validate multimycotoxin assays across diverse populations with varying exposure levels. Validated low-cost and ultrasensitive minimally invasive sampling methods should be deployed in human biomonitoring and public health surveillance studies to guide policy interventions to reduce inequities in global mycotoxin exposures.

Nonreciprocity in cavity magnonics at millikelvin temperature

Incorporating cavity magnonics has opened up a new avenue in controlling non-reciprocity. This work examines a yttrium iron garnet sphere coupled to a planar microwave cavity at millikelvin temperature. Non-reciprocal device behavior results from the cooperation of coherent and dissipative coupling between the Kittel mode and a microwave cavity mode. The device’s bi-directional transmission was measured and compared to the theory derived previously in the room temperature experiment. Investigations are also conducted into key performance metrics such as isolation, bandwidth, and insertion loss. The findings point to the coexistence of coherent and dissipative interactions at cryogenic conditions, and one can leverage their cooperation to achieve directional isolation. This work foreshadows the application of a cavity magnonic isolator for on-chip readout and signal processing in superconducting circuitry.

Comparative analysis of machine learning algorithms for predicting Dubai property prices

IntroductionPredicting property prices is a crucial task in the real estate market, and machine learning algorithms offer valuable tools for accurate predictions. In this study, we introduce a comprehensive comparison of eight well-known machine learning algorithms, namely, ensemble empirical mode decomposition (EEMD)–stochastic (S) + deterministic (D)–support vector machine (EEMD-SD-SVM), support vector machine (SVM), gradient boosting, random forest, K-nearest neighbors (KNN), linear regression, artificial neural networks (ANN), and decision trees. The focus is on predicting property prices in Dubai, with the primary objective of assessing the predictive performance of these algorithms within this specific market context.MethodsThe evaluation is based on four key performance metrics: R-squared (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). These metrics provide insights into prediction errors, accuracy in percentage terms, and the proportion of variance in property prices explained by independent variables. The study compares the strengths and limitations of each algorithm for predicting property prices in Dubai, highlighting scenarios where certain algorithms excel based on the nature of decision boundaries, handling complex data, capturing localized patterns, and offering interpretability.ResultsFindings from the comparative analysis shed light on the performance of each algorithm in predicting property prices in Dubai. EEMD-SD-SVM and SVM excel in scenarios requiring precise decision boundaries, while gradient boosting and random forests demonstrate robust performance with complex and noisy property price data. KNN captures localized patterns effectively, linear regression is suitable for straightforward regression tasks, ANN excels with extensive datasets, and decision trees offer interpretability in understanding factors influencing property prices.DiscussionThe study emphasizes the significance of model tuning, feature selection, and data pre-processing to enhance predictive power. Additionally, practical aspects such as computational efficiency, model interpretability, and scalability in real-world applications are discussed. The comparative analysis provides valuable guidance for stakeholders, including real estate professionals, data scientists, and stakeholders interested in selecting the most suitable machine learning algorithm for predicting property prices in Dubai, with a focus on the essential evaluation metrics of MSE, RMSE, MAPE, and R2. This study offers insights into the applicability and performance of different machine learning algorithms for predicting property prices in Dubai. Stakeholders such as real estate agents, buyers, sellers, or investors can leverage these insights to make informed decisions in the Dubai real estate market.

Meta-lasso: new insight on infection prediction after minimally invasive surgery.

Surgical site infection (SSI) after minimally invasive lung cancer surgery constitutes an important factor influencing the direct and indirect economic implications, patient prognosis, and the 5-year survival rate for early-stage lung cancer patients. In the realm of predictive healthcare, machine learning algorithms have been instrumental in anticipating various surgical outcomes, including SSI. However, accurately predicting infection after minimally invasive surgery remains a clinical challenge due to the multitude of physiological and surgical factors associated with it. Furthermore, clinical patient data, in addition to being high-dimensional, often exists the long-tail problem, posing difficulties for traditional machine learning algorithms in effectively processing such data. Based on this insight, we propose a novel approach called meta-lasso for infection prediction following minimally invasive surgery. Our approach leverages the sparse learning algorithm lasso regression to select informative features and introduces a meta-learning framework to mitigate bias towards the dominant class. We conducted a retrospective cohort study on patients who had undergone minimally invasive surgery for lung cancer at Shanghai Chest Hospital between 2018 and 2020. The evaluation encompassed key performance metrics, including sensitivity, specificity, precision (PPV), negative predictive value (NPV), and accuracy. Our approach has surpassed the performance of logistic regression, random forest, Naive Bayes classifier, gradient boosting decision tree, ANN, and lasso regression, with sensitivity at 0.798, specificity at 0.779, precision at 0.789, NPV at 0.798, and accuracy at 0.788 and has greatly improved the classification performance of the inferior class.

Enhancing efficiency in photo chemical machining: a multivariate decision-making approach

Non-Traditional Machining (NTM) outperforms traditional processes by offering superior geometric and dimensional accuracy, along with a better surface finish. Photo Chemical Machining (PCM) represents one such NTM process, using chemical etching for material removal. PCM finds substantial application in the creation of microchannels in pharmaceutical, chemical and energy industries. Several input parameters—such as etchant concentration, etching time and etchant temperature—profoundly influence the machining’s quality and efficiency. Therefore, the optimization of these parameters is crucial. This study presents a comparative analysis of five Multiple Criteria Decision Making (MCDM) techniques—Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Multi-Objective Optimization on the basis of Ratio Analysis (MOORA), Additive Ratio Assessment (ARAS), Weighted aggregated sum product assessment method (WASPAS) and Multi-Attributive Border Approximation Area Comparison Method (MABAC)—for the optimization of the PCM process. Key performance metrics considered are Material Removal Rate (MRR), Surface Roughness (SR), Undercut (Uc) and etch factor (EF). The weights of these criteria were calculated using the Criterion-Induced Aggregation Technique (CRITIC) and was compared with other popular methods like MEREC, Entropy and equal weights. MRR and EF are seen as beneficial criteria, while SR and Uc are perceived as cost criteria. Optimum process parameters were identified as 850 g/L etchant concentration, 40 min etching time and 70°C etchant temperature. Two of the three employed MCDM techniques agreed on these optimal parameters, reinforcing the findings. Furthermore, a strong correlation was observed amongst the employed MCDM techniques, further validating the results.

LiFi-enabled UAV swarm networks.

Unmanned aerial vehicle (UAV) swarm communication is a powerful component of aerial relays; however, conventional radio frequency (RF)-based UAV swarm networks struggle to ensure timely and reliable communication. To this end, a light spectrum-based wireless system, LiFi, is presented to supplement in this work thanks to its distinctive benefits. We present the analytical derivation of the average block error probability (ABEP) as Chebyshev approximation, lower and upper bounds. Then, the key performance metrics of reliability, throughput, and latency expressions are provided as a function of the ABEP. The results show that the severe requirements of ultra-reliable (99.99%) and low-latency (sub-millisecond) communication (URLLC) are satisfied at even low signal-to-noise ratio (SNR) values. Additionally, in the numerical results, the impact of the blocklength, packet size, different distances among UAVs, SNR value, and light-emitting diode (LED) semi-angle are explored.

DeepVAQ : an adaptive deep learning for prediction of vascular access quality in hemodialysis patients

BackgroundChronic kidney disease is a prevalent global health issue, particularly in advanced stages requiring dialysis. Vascular access (VA) quality is crucial for the well-being of hemodialysis (HD) patients, ensuring optimal blood transfer through a dialyzer machine. The ultrasound dilution technique (UDT) is used as the gold standard for assessing VA quality; however, its limited availability due to high costs impedes its widespread adoption. We aimed to develop a novel deep learning model specifically designed to predict VA quality from Photoplethysmography (PPG) sensors.MethodsClinical data were retrospectively gathered from 398 HD patients, spanning from February 2021 to February 2022. The DeepVAQ model leverages a convolutional neural network (CNN) to process PPG sensor data, pinpointing specific frequencies and patterns that are indicative of VA quality. Meticulous training and fine-tuning were applied to ensure the model’s accuracy and reliability. Validation of the DeepVAQ model was carried out against established diagnostic standards using key performance metrics, including accuracy, specificity, precision, F-score, and area under the receiver operating characteristic curve (AUC).ResultDeepVAQ demonstrated superior performance, achieving an accuracy of 0.9213 and a specificity of 0.9614. Its precision and F-score stood at 0.8762 and 0.8364, respectively, with an AUC of 0.8605. In contrast, traditional models like Decision Tree, Naive Bayes, and kNN demonstrated significantly lower performance across these metrics. This comparison underscores DeepVAQ's enhanced capability in accurately predicting VA quality compared to existing methodologies.ConclusionExemplifying the potential of artificial intelligence in healthcare, particularly in the realm of deep learning, DeepVAQ represents a significant advancement in non-invasive diagnostics. Its precise multi-class classification ability for VA quality in hemodialysis patients holds substantial promise for improving patient outcomes, potentially leading to a reduction in mortality rates.

An efficient modeling workflow for high-performance nanowire single-photon avalanche detector

Single-photon detector (SPD), an essential building block of the quantum communication system, plays a fundamental role in developing next-generation quantum technologies. In this work, we propose an efficient modeling workflow of nanowire SPDs utilizing avalanche breakdown at reverse-biased conditions. The proposed workflow is explored to maximize computational efficiency and balance time-consuming drift-diffusion simulation with fast script-based post-processing. Without excessive computational effort, we could predict a suite of key device performance metrics, including breakdown voltage, dark/light avalanche built-up time, photon detection efficiency, dark count rate, and the deterministic part of timing jitter due to device structures. Implementing the proposed workflow onto a single InP nanowire and comparing it to the extensively studied planar devices and superconducting nanowire SPDs, we showed the great potential of nanowire avalanche SPD to outperform their planar counterparts and obtain as superior performance as superconducting nanowires, i.e. achieve a high photon detection efficiency of 70% with a dark count rate less than 20 Hz at non-cryogenic temperature. The proposed workflow is not limited to single-nanowire or nanowire-based device modeling and can be readily extended to more complicated two-/three dimensional structures.

Leveraging Positive-Unlabeled Learning for Enhanced Black Spot Accident Identification on Greek Road Networks

Identifying accidents in road black spots is crucial for improving road safety. Traditional methodologies, although insightful, often struggle with the complexities of imbalanced datasets, while machine learning (ML) techniques have shown promise, our previous work revealed that supervised learning (SL) methods face challenges in effectively distinguishing accidents that occur in black spots from those that do not. This paper introduces a novel approach that leverages positive-unlabeled (PU) learning, a technique we previously applied successfully in the domain of defect detection. The results of this work demonstrate a statistically significant improvement in key performance metrics, including accuracy, precision, recall, F1-score, and AUC, compared to SL methods. This study thus establishes PU learning as a more effective and robust approach for accident classification in black spots, particularly in scenarios with highly imbalanced datasets.

A Novel Piecewise Cubic Hermite Interpolating Polynomial-Enhanced Convolutional Gated Recurrent Method under Multiple Sensor Feature Fusion for Tool Wear Prediction.

The monitoring of the lifetime of cutting tools often faces problems such as life data loss, drift, and distortion. The prediction of the lifetime in this situation is greatly compromised with respect to the accuracy. The recent rise of deep learning, such as Gated Recurrent Unit Units (GRUs), Hidden Markov Models (HMMs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention networks, and Transformers, has dramatically improved the data problems in tool lifetime prediction, substantially enhancing the accuracy of tool wear prediction. In this paper, we introduce a novel approach known as PCHIP-Enhanced ConvGRU (PECG), which leverages multiple-feature fusion for tool wear prediction. When compared to traditional models such as CNNs, the CNN Block, and GRUs, our method consistently outperformed them across all key performance metrics, with a primary focus on the accuracy. PECG addresses the challenge of missing tool wear measurement data in relation to sensor data. By employing PCHIP interpolation to fill in the gaps in the wear values, we have developed a model that combines the strengths of both CNNs and GRUs with data augmentation. The experimental results demonstrate that our proposed method achieved an exceptional relative accuracy of 0.8522, while also exhibiting a Pearson's Correlation Coefficient (PCC) exceeding 0.95. This innovative approach not only predicts tool wear with remarkable precision, but also offers enhanced stability.

Adaptive auditory assistance for stride length cadence modification in older adults and people with Parkinson's.

Gait rehabilitation using auditory cues can help older adults and people with Parkinson's improve walking performance. While auditory cues are convenient and can reliably modify gait cadence, it is not clear if auditory cues can reliably modify stride length (SL), another key gait performance metric. Existing algorithms also do not address habituation or fluctuation in motor capability, and have not been evaluated with target populations or under dual-task conditions. In this study, we develop an adaptive auditory cueing framework that aims to modulate SL and cadence. The framework monitors the gait parameters and learns a personalized cue-response model to relate the gait parameters to the input cues. The cue-response model is represented using a multi-output Gaussian Process (MOGP) and is used during optimization to select the cue to provide. The adaptive cueing approach is benchmarked against the fixed approach, where cues are provided at a fixed cadence. The two approaches are tested under single and dual-task conditions with 13 older adults (OA) and 8 people with Parkinson's (PwP). The results show that more than half of the OA and PwP in the study can change both SL and cadence using auditory cues. The fixed approach is best at changing people's gait without secondary task, however, the addition of the secondary task significantly degrades effectiveness at changing SL. The adaptive approach can maintain the same level of SL change regardless of the presence of the secondary task. A separate analysis is conducted to identify factors that influence the performance of the adaptive framework. Gait information from the previous time step, along with the previous input cue, can improve its prediction accuracy. More diversity in the initialization data can also improve the GP model. Finally, we did not find a strong correlation between stride length and cadence when the parameters are contingent upon input cues.

DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing

AbstractIn the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric refraction, equipment malfunctions, and other factors, resulting in diminished data quality. Traditional radar blockage correction methods, such as employing approximate radial information interpolation and supplementing missing data, often fail to effectively exploit potential patterns in massive radar data, for the large volume of data precludes a thorough analysis and understanding of the inherent complex patterns and dependencies through simple interpolation or supplementation techniques. Fortunately, edge computing possesses certain data processing capabilities and cloud center boasts substantial computational power, which together can collaboratively offer timely computation and storage for the correction of radar beam blockage. To this end, an edge-cloud collaborative driven deep learning model named DenMerD is proposed in this paper, which includes dense connection module and merge distribution (MD) unit. Compared to existing models such as RC-FCN, DenseNet, and VGG, this model greatly improves key performance metrics, with 30.7$$\%$$ % improvement in Critical Success Index (CSI), 30.1$$\%$$ % improvement in Probability of Detection (POD), and 3.1$$\%$$ % improvement in False Alarm Rate (FAR). It also performs well in the Structure Similarity Index Measure (SSIM) metrics compared to its counterparts. These findings underscore the efficacy of the design in improving feature propagation and beam blockage accuracy, and also highlights the potential and value of mobile edge computing in processing large-scale meteorological data.

AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis

(1) Background: Lung cancer’s high mortality due to late diagnosis highlights a need for early detection strategies. Artificial intelligence (AI) in healthcare, particularly for lung cancer, offers promise by analyzing medical data for early identification and personalized treatment. This systematic review evaluates AI’s performance in early lung cancer detection, analyzing its techniques, strengths, limitations, and comparative edge over traditional methods. (2) Methods: This systematic review and meta-analysis followed the PRISMA guidelines rigorously, outlining a comprehensive protocol and employing tailored search strategies across diverse databases. Two reviewers independently screened studies based on predefined criteria, ensuring the selection of high-quality data relevant to AI’s role in lung cancer detection. The extraction of key study details and performance metrics, followed by quality assessment, facilitated a robust analysis using R software (Version 4.3.0). The process, depicted via a PRISMA flow diagram, allowed for the meticulous evaluation and synthesis of the findings in this review. (3) Results: From 1024 records, 39 studies met the inclusion criteria, showcasing diverse AI model applications for lung cancer detection, emphasizing varying strengths among the studies. These findings underscore AI’s potential for early lung cancer diagnosis but highlight the need for standardization amidst study variations. The results demonstrate promising pooled sensitivity and specificity of 0.87, signifying AI’s accuracy in identifying true positives and negatives, despite the observed heterogeneity attributed to diverse study parameters. (4) Conclusions: AI demonstrates promise in early lung cancer detection, showing high accuracy levels in this systematic review. However, study variations underline the need for standardized protocols to fully leverage AI’s potential in revolutionizing early diagnosis, ultimately benefiting patients and healthcare professionals. As the field progresses, validated AI models from large-scale perspective studies will greatly benefit clinical practice and patient care in the future.

Harnessing Conformational Disorder of Organic Cations for Efficient Blue Quasi‐2D Perovskite LEDs

AbstractDespite quasi‐2D perovskite offering great control over the optoelectronic properties, disordered organic cations are often perceived as detrimental to device performance, primarily affecting charge carrier mobility. However, it is proposed that such disordered organic cations‐enabled excellent excitonic properties can be beneficial for fabricating high‐efficiency perovskite light‐emitting diodes (PeLEDs) facilitated by reduced dielectric screening effect. Here, by incorporating acetamidinium bromide additives, the conformational disorder of organic cation is precisely manipulated and meticulously probed using sum frequency generation vibrational spectroscopy. Finally, a distinctive inverse relationship is elucidated between the degree of conformational order, characterized as relative structure ordering , and key performance metrics such as photoluminescence quantum yield and external quantum efficiency (EQE). By optimizing the configurational disorder, sky‐blue (485 nm) PeLEDs achieve a noteworthy EQE of 14.42% and exhibit significantly prolonged operational stability in open‐air conditions. This finding underscores the potential advantages of disordered organic cations in enhancing exciton properties and radiative recombination efficiency.

Abstract WP74: Organized System of Care Improves Door to Needle Times in Rural Montana

Early reperfusion is critical to improved outcomes from acute stroke, with door-to-CT times < 25 min and door-to-needle times < 60 min as key performance metrics. These may be difficult to achieve in critical access hospitals (CAHs) due to low volumes, longer EMS transport, delays to provider response, challenges in obtaining rapid CT, and lack of access to neurology consultation. Each of these represent appropriate targets for performance improvement efforts. Methods: In 2022, The Montana Stroke Initiative began a quality improvement project to improve door-to-CT and door-to-needle times for acute stroke in rural Montana (MT) with the following objectives: 1. Engage more CAHs in the stroke registry. 2. Initiate monthly clinical learning collaboratives to share stroke data and best practices in reducing door-to-CT times. 3. Create statewide stroke order-sets. 4. Involve stroke coordinators and physicians from MT’s certified stroke centers in providing education on the order-sets, NIHSS, and the importance of data abstraction for quality improvement. The Montana Department of Health and Human Services collects statewide stroke data using the Get With The Guidelines®-Stroke registry. For this analysis, all CAH acute stroke patients evaluated for thrombolytic therapy in 2021 and 2022-23 were included. These cohorts were compared to total number of patients and the primary outcome measures of door-to-CT time <25 min and door-to-needle time < 60 min. Registry participation doubled from 12 hospitals in 2021 to 24 in 2022-23. The total number of suspected stroke patients increased from 126 patients in 2021 to 269 patients in 2022-23. Mean Door-to-CT time decreased from 56 min to 39 min (p=0.004), with Door-to-CT times 25 min increasing from 31.7% to 50.6% (p<0.001). Thrombolytic therapy was given to a total of 10 patients in 2021 and 36 patients in 2022-23. Mean Door-to-Needle time decreased from 118 min to 77 min (p=0.026), with Door-to-Needle times 60 min increasing from 10.0% to 33.3% (p=0.24).In conclusion, an organized effort to improve door-to-CT processes for suspected stroke patients, including sharing data and best practices, and providing focused education, has resulted in more patients receiving lytic therapy in the recommended time of < 60 minutes.

Evaluating diagnostic accuracy of the Prostate Imaging after Focal Ablation (PI-FAB) scoring system in detecting clinically significant prostate cancer after primary focal therapy.

277 Background: Focal therapy (FT) has emerged as a promising, organ-sparing alternative to radiotherapy and radical prostatectomy for the management of localized prostate cancer (PCa). Multi-parametric magnetic resonance imaging (mpMRI) is often used to monitor for post-FT tumor recurrence. However, post-treatment mpMRI assessment can be quite challenging due to treatment-related changes and distortion of the prostate gland anatomy. The Prostate Imaging after Focal Ablation (PI-FAB) score was proposed by Giganti et al. in May 2023 as a standardized three-point scale for rating mpMRI sequences to aid in decision for follow-up imaging or biopsy. The purpose of this study is to investigate the performance of this new scoring system in predicting recurrent clinically significant prostate cancer (csPCa), defined as Gleason grade > 1 disease. Methods: Patients with localized PCa who underwent focal laser ablation (FLA), high-intensity focused ultrasound (HIFU), or cryoablation who had post-treatment mpMRI imaging and prostate biopsy were included in the study. The mpMRIs were evaluated and assigned a PI-FAB score (range, 1-3) by one genitourinary radiologist with 16 years of experience in PCa imaging. A total of 39 patients were included in the study, of which 30 patients underwent FLA, 6 patients underwent HIFU, and 3 patients underwent cryoablation. PI-FAB scores were correlated with MRI/TRUS fusion-guided and systematic prostate biopsies. A confusion matrix was constructed using confirmed csPCa predicted by PI-FAB scores of 3. Key performance metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated to assess the diagnostic accuracy of the PI-FAB score. Results: Among the 39 patients included in this study, the distribution of PI-FAB scores was as follows: 16 (41.0%) scored as PI-FAB 1, 1 (2.6%) as PI-FAB 2, and 22 (56.4%) as PI-FAB 3. Eighteen (46.2%) patients had pathologically confirmed csPCa. An additional 5 patients had Gleason grade group 1 disease, bringing the total number of PCa cases to 23 (59%). Utilizing a cutoff score of PI-FAB 3 for the identification of csPCa yielded a sensitivity of 77.8% (14/18), specificity of 61.9% (13/21), PPV of 63.6% (14/22), NPV of 76.5% (13/17), and accuracy of 69.2% (27/39). The PI-FAB scoring system missed four cases of csPCa in our cohort, equating to a false negative rate of 22.2% (4/18). Conclusions: The new PI-FAB scoring system demonstrates potential as a standardized evaluation of mpMRI. Its moderately high sensitivity can be useful for stratifying patients who may benefit from subsequent prostate biopsy, but PI-FAB should be further validated in larger cohorts to better understand its utility and potential.

Transformational Leadership: A Comparative Exploration of the Leadership Prowess of Jeff Bezos and Steve Jobs

This exploratory study delves into the intricacies of transformational leadership within the technology sector, drawing insights from the leadership styles of Steve Jobs of Apple and Jeff Bezos of Amazon. The research aimed to understand how transformational leadership traits influence organizational outcomes like innovation, employee motivation, engagement, and overall performance. To achieve this, the study employed a mixed-method approach, incorporating both quantitative and qualitative data collection techniques. Primary data was gathered using a structured questionnaire, which was administered to a purposively sampled group of 567 respondents, all holding leadership or managerial roles in various industries. The questionnaire design integrated both closed-ended and open-ended questions to facilitate a comprehensive analysis. The data analysis involved rigorous regression analysis to quantitatively assess the relationships between specific transformational leadership traits and organizational outcomes. Key findings revealed a strong positive correlation between the level of inspirational motivation demonstrated by leaders and the degree of organizational innovation. Additionally, significant relationships were observed between the extent of individualized consideration by leaders and employee motivation and engagement, as well as between strategic vision and risk-taking by leaders and key organizational performance metrics. Drawing from these insights, the study recommends cultivating a leadership style that emphasizes inspirational motivation, individualized consideration, and strategic risk-taking. This approach is crucial for fostering innovation, enhancing employee motivation and engagement, and improving overall organizational performance in the rapidly evolving technology sector.