Analysis of postdisaster economy using high-resolution disaster and economy simulations.

We present an integrated framework that utilizes high-resolution seamless simulations of disasters and national economies for estimating the economic impacts of disasters. The framework consists of three components: a physics-based simulator to simulate the disaster and estimate the response of the infrastructure; a tool that estimates the losses suffered by the infrastructure based on its response; and an agent-based economic model (ABEM) that simulates the national economy considering the infrastructure damage and postdisaster decisions of the economic entities. The ABEM used in the framework has been implemented in a high-performance computing environment to simulate large economies at 1:1 scale. Furthermore, it has been calibrated to the Japanese economy using publicly available macroeconomic data and validated to the Japanese economy under the business-as-usual scenario. We demonstrate the integrated framework by simulating a potential Nankai-trough earthquake disaster and estimating its impacts on the Japanese economy. The seismic response of 1.8 million buildings of the Osaka-bay area has been estimated using a large-scale earthquake disaster simulator and corresponding repair costs are estimated using the Performance Assessment Calculation Tool. As per our estimates, repair costs amount to approximately 15 trillion Yen. Considering the investments made by impacted households and firms toward recovery, the postdisaster economy is simulated using the ABEM for 5 years under two recovery scenarios. Industrial production is expected to recover in three quarters whereas 10-13 quarters will be required to finish all the repair work.

Enhanced Safety in Autonomous Driving: Integrating a Latent State Diffusion Model for End-to-End Navigation.

Ensuring safety in autonomous driving is crucial for effective motion planning and navigation. However, most end-to-end planning methodologies lack sufficient safety measures. This study tackles this issue by formulating the control optimization problem in autonomous driving as Constrained Markov Decision Processes (CMDPs). We introduce an innovative, model-based approach for policy optimization, employing a conditional Value-at-Risk (VaR)-based soft actor-critic (SAC) to handle constraints in complex, high-dimensional state spaces. Our method features a worst-case actor to ensure strict compliance with safety requirements, even in unpredictable scenarios. The policy optimization leverages the augmented Lagrangian method and leverages latent diffusion models to forecast and simulate future trajectories. This dual strategy ensures safe navigation through environments and enhances policy performance by incorporating distribution modeling to address environmental uncertainties. Empirical evaluations conducted in both simulated and real environments demonstrate that our approach surpasses existing methods in terms of safety, efficiency, and decision-making capabilities.

Multi-Task Environmental Perception Methods for Autonomous Driving.

In autonomous driving, environmental perception technology often encounters challenges such as false positives, missed detections, and low accuracy, particularly in detecting small objects and complex scenarios. Existing algorithms frequently suffer from issues like feature redundancy, insufficient contextual interaction, and inadequate information fusion, making it difficult to perform multi-task detection and segmentation efficiently. To address these challenges, this paper proposes an end-to-end multi-task environmental perception model named YOLO-Mg, designed to simultaneously perform traffic object detection, lane line detection, and drivable area segmentation. First, a multi-stage gated aggregation network (MogaNet) is employed during the feature extraction process to enhance contextual interaction by improving diversity in the channel dimension, thereby compensating for the limitations of feed-forward neural networks in contextual understanding. Second, to further improve the model's accuracy in detecting objects of various scales, a restructured weighted bidirectional feature pyramid network (BiFPN) is introduced, optimizing cross-level information fusion and enabling the model to handle object detection at different scales more accurately. Finally, the model is equipped with one detection head and two segmentation heads to achieve efficient multi-task environmental perception, ensuring the simultaneous execution of multiple tasks. The experimental results on the BDD100K dataset demonstrate that the model achieves a mean average precision (mAP50) of 81.4% in object detection, an Intersection over Union (IoU) of 28.9% in lane detection, and a mean Intersection over Union (mIoU) of 92.6% in drivable area segmentation. The tests conducted in real-world scenarios show that the model performs effectively, significantly enhancing environmental perception in autonomous driving and laying a solid foundation for safer and more reliable autonomous driving systems.

Utility-Driven End-to-End Network Slicing for Diverse IoT Users in MEC: A Multi-Agent Deep Reinforcement Learning Approach.

Mobile Edge Computing (MEC) is crucial for reducing latency by bringing computational resources closer to the network edge, thereby enhancing the quality of services (QoS). However, the broad deployment of cloudlets poses challenges in efficient network slicing, particularly when traffic distribution is uneven. Therefore, these challenges include managing diverse resource requirements across widely distributed cloudlets, minimizing resource conflicts and delays, and maintaining service quality amid fluctuating request rates. Addressing this requires intelligent strategies to predict request types (common or urgent), assess resource needs, and allocate resources efficiently. Emerging technologies like edge computing and 5G with network slicing can handle delay-sensitive IoT requests rapidly, but a robust mechanism for real-time resource and utility optimization remains necessary. To address these challenges, we designed an end-to-end network slicing approach that predicts common and urgent user requests through T distribution. We formulated our problem as a multi-agent Markov decision process (MDP) and introduced a multi-agent soft actor-critic (MAgSAC) algorithm. This algorithm prevents the wastage of scarce resources by intelligently activating and deactivating virtual network function (VNF) instances, thereby balancing the allocation process. Our approach aims to optimize overall utility, balancing trade-offs between revenue, energy consumption costs, and latency. We evaluated our method, MAgSAC, through simulations, comparing it with the following six benchmark schemes: MAA3C, SACT, DDPG, S2Vec, Random, and Greedy. The results demonstrate that our approach, MAgSAC, optimizes utility by 30%, minimizes energy consumption costs by 12.4%, and reduces execution time by 21.7% compared to the closest related multi-agent approach named MAA3C.

BGAT-CCRF: A novel end-to-end model for knowledge graph noise correction.

Knowledge graph (KG) noise correction aims to select suitable candidates to correct the noises in KGs. Most of the existing studies have limited performance in repairing the noisy triple that contains more than one incorrect entity or relation, which significantly constrains their implementation in real-world KGs. To overcome this challenge, we propose a novel end-to-end model (BGAT-CCRF) that achieves better noise correction results. Specifically, we construct a balanced-based graph attention model (BGAT) to learn the features of nodes in triples' neighborhoods and capture the correlation between nodes based on their position and frequency. Additionally, we design a constrained conditional random field model (CCRF) to select suitable candidates guided by three constraints for correcting one or more noises in the triple. In this way, BGAT-CCRF can select multiple candidates from a smaller domain to repair multiple noises in triples simultaneously, rather than selecting candidates from the whole KG to repair noisy triples as traditional methods do, which can only repair one noise in the triple at a time. The effectiveness of BGAT-CCRF is validated by the KG noise correction experiment. Compared with the state-of-the-art models, BGAT-CCRF improves the fMRR metric by 3.58% on the FB15K dataset. Hence, it has the potential to facilitate the implementation of KGs in the real world.

Behavior recognition of cage-free multi-broilers based on spatiotemporal feature learning.

Poultry behavior indicates their health, welfare, and production performance. Timely access to broilers' behavioral information can improve their welfare and reduce disease spread. Most behaviors require a period of observation before they can be accurately judged. However, the existing approaches for multi-object behavior recognition were mostly developed based on a single-frame image and ignored the temporal features in videos, which led to misrecognition. This study proposed an end-to-end method for recognizing multiple simultaneous behavioral events of cage-free broilers in videos by Broiler Behavior Recognition System (BBRS) based on spatiotemporal feature learning. The BBRS consisted of 3 main components: the improved YOLOv8s detector, the Bytetrack tracker, and the 3D-ResNet50-TSAM model. The basic network YOLOv8s was improved with MPDIoU to identify multiple broilers in the same frame of videos. The Bytetrack tracker was used to track each identified broiler and acquire its image sequence of 32 continuous frames as input for the 3D-ResNet50-TSAM model. To accurately recognize behavior of each tracked broiler, the 3D-ResNet50-TSAM model integrated a temporal-spatial attention module for learning the spatiotemporal features from its image sequence and enhancing inference ability in the case of its image sequence less than 32 continuous frames due to its tracker ID switching. Each component of BBRS was trained and tested with the rearing density of 7 to 8 birds/m2. The results demonstrated that the mAP@0.5 of the improved YOLOv8s detector was 99.50%. The Bytetrack tracker achieved a mean MOTA of 93.89% at different levels of occlusion. The Accuracy, Precision, Recall, and F1score of the 3D-ResNet50-TSAM model were 97.84, 97.72, 97.65, and 97.68%, respectively. The BBRS showed satisfactory inference ability with an Accuracy of 93.98% when 26 continuous frames of the tracked broiler were received by the 3D-ResNet50-TSAM model. This study provides an efficient tool for automatically and accurately recognizing behaviors of cage-free multi-broilers in videos. The code will be released on GitHub (https://github.com/CoderYLH/BBRS) as soon as the study is published.

Beyond neural data: Cognitive biometrics and mental privacy.

Innovations in wearable technology and artificial intelligence have enabled consumer devices to process and transmit data about human mental states (cognitive, affective, and conative) through what this paper refers to as "cognitive biometrics." Devices such as brain-computer interfaces, extended reality headsets, and fitness wearables offer significant benefits in health, wellness, and entertainment through the collection and processing and cognitive biometric data. However, they also pose unique risks to mental privacy due to their ability to infer sensitive information about individuals. This paper challenges the current approach to protecting individuals through legal protections for "neural data" and advocates for a more expansive legal and industry framework, as recently reflected in the draft UNESCO Recommendation on the Ethics of Neurotechnology, to holistically address both neural and cognitive biometric data. Incorporating this broader and more inclusive approach into legislation and product design can facilitate responsible innovation while safeguarding individuals' mental privacy.

"The Jigsaw Puzzle Locking Technique" to Augment the Stabilization between the Septal Cartilage and Extension Graft in End-to-End Suture Fixation.

INTRODUCTION: Nasal tip stability is crucial for long-lasting results. Usage of the extension graft is one of the most reliable stabilization techniques. With a septum in the midline, the extension graft is fixed end to end. The "jig-saw puzzle technique" reinforces this end-to-end fixation. The specific protrusion on one piece of the puzzle is inserted in a groove on adjacent piece. This settlement provides stabilization between the pieces. Application of this philosophy between the nasal septum and extension graft may provide a long-term fixation. MATERIAL METHOD: Between April 2022 and March 2024, the "jig-saw puzzle technique" was applied in 26 female patients. Trapezoid-shaped protrusion was created at the septum. Similar indentation was created at the extension grafts. The protrusion is then settled in the indentation. The preop, immediate postoperative and one-year postoperative pictures are compared to assess the rotations and projections with Adobe Photoshop Program. RESULTS: All 26 patients were females with mean age 27.5 years (20-32). Satisfactory graft stabilization was obtained without loss of projection and rotation in all patients. Statistically significant difference was found between the preoperative, immediate postoperative and postoperative 1-year projection and rotation assessments of the patients (p = 0.001; p < 0.01). The results were evaluated at a 95% confidence interval and significance was evaluated at p < 0.05 level. These statistical analyses verify that both projection and rotation were preserved with jig-saw puzzle technique. CONCLUSION: The jigsaw puzzle technique may provide suture-independent, long-term end-to-end fixation opportunity of the septal cartilage and the extension graft. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

End-to-end [18F]PSMA-1007 PET/CT radiomics-based pipeline for predicting ISUP grade group in prostate cancer.

OBJECTIVES: To develop an end-to-end radiomics-based pipeline for the prediction of International Society of Urological Pathology grade group (ISUP GG) in prostate cancer (PCa). METHODS: This retrospective study includes 356 patients (241 in training set and 115 in independent test set) with histopathologically confirmed PCa who underwent [18F]PSMA-1007 PET/CT scan. Patients were classified into two groups according to their ISUP GG (1-3 vs. 4-5). Radiomics features were extracted from the whole, automatically segmented prostate on PET/CT images, 30 models were constructed by combining 6 feature selection algorithms and 5 machine learning classifiers. The clinical model incorporated age, total prostate-specific antigen (tPSA), maximum standardized uptake value (SUVmax), and prostate volume. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC), balanced accuracy (bAcc), and decision curve analysis (DCA). RESULTS: The best-performing radiomics model significantly outperformed clinical model (AUC 0.879 ± 0.041 vs. 0.799 ± 0.051, bAcc 0.745 ± 0.074 vs. 0.629 ± 0.045). On an external independent test set, best-performing radiomics model perform better than clinical model, with an AUC of 0.861 vs. 0.750, p = 0.002 (Delong), and bAcc of 0.764 vs. 0.582, p = 0.043 (McNemar). The learning curve, calibration curve and DCA demonstrated goodness-of-fit and improved benefits in clinical practice. CONCLUSION: The end-to-end radiomics-based pipeline is an effective non-invasive tool to predict ISUP GG in PCa.

Synchronous End-to-End Vehicle Pedestrian Detection Algorithm Based on Improved YOLOv8 in Complex Scenarios.

In modern urban traffic, vehicles and pedestrians are fundamental elements in the study of traffic dynamics. Vehicle and pedestrian detection have significant practical value in fields like autonomous driving, traffic management, and public security. However, traditional detection methods struggle in complex environments due to challenges such as varying scales, target occlusion, and high computational costs, leading to lower detection accuracy and slower performance. To address these challenges, this paper proposes an improved vehicle and pedestrian detection algorithm based on YOLOv8, with the aim of enhancing detection in complex traffic scenes. The motivation behind our design is twofold: first, to address the limitations of traditional methods in handling targets of different scales and severe occlusions, and second, to improve the efficiency and accuracy of real-time detection. The new generation of dense pedestrian detection technology requires higher accuracy, less computing overhead, faster detection speed, and more convenient deployment. Based on the above background, this paper proposes a synchronous end-to-end vehicle pedestrian detection algorithm based on improved YOLOv8, aiming to solve the detection problem in complex scenes. First of all, we have improved YOLOv8 by designing a deformable convolutional improved backbone network and attention mechanism, optimized the network structure, and improved the detection accuracy and speed. Secondly, we introduced an end-to-end target search algorithm to make the algorithm more stable and accurate in vehicle and pedestrian detection. The experimental results show that, using the algorithm designed in this paper, our model achieves an 11.76% increase in precision and a 6.27% boost in mAP. In addition, the model maintains a real-time detection speed of 41.46 FPS, ensuring robust performance even in complex scenarios. These optimizations significantly enhance both the efficiency and robustness of vehicle and pedestrian detection, particularly in crowded urban environments. We further apply our improved YOLOv8 model for real-time detection in intelligent transportation systems and achieve exceptional performance with a mAP of 95.23%, outperforming state-of-the-art models like YOLOv5, YOLOv7, and Faster R-CNN.

Short-term outcomes of delta-shaped anastomosis versus functional end-to-end anastomosis using linear staplers for colon cancer.

BACKGROUND: Several methods are used for reconstruction in colon cancer surgery, including hand-sewn or stapled anastomosis. However, few reports have compared short-term outcomes among reconstruction methods. This study compared short-term outcomes between delta-shaped anastomosis (Delta) and functional end-to-end anastomosis (FEEA). METHODS: We retrospectively reviewed 1314 consecutive patients who underwent colorectal surgery with FEEA or Delta reconstruction between January 2016 and December 2023. Patients were divided into two groups according to reconstruction by FEEA (F group; n = 1242) or Delta (D group; n = 72). Propensity score matching was applied to minimize the possibility of selection bias and to balance covariates that could affect postoperative complications. Short-term outcomes were compared between groups. RESULTS: Postoperative complications occurred in 215 patients (17.3%) in F group and 8 patients (11.1%) in D group. Before matching, transverse colon cancer was more frequent (p = 0.002), clinical N-positive status was less frequent (44.1% versus 16.7%, p < 0.001), distant metastasis was less frequent (11.7% versus 1.4%, p = 0.003), and laparoscopic approach was more frequent (87.8% versus 100%, p < 0.001) in D group. After matching, no differences in any clinical factor were evident between groups. Blood loss was significantly lower (28 mL versus 10 mL, p = 0.002) in D group, but operation time and postoperative complication rates were similar between groups. CONCLUSIONS: Delta and FEEA were both considered safe as reconstruction methods. Further studies are needed to clarify appropriate case selection for Delta and FEEA.

Use of the Staple Line Reinforcement Tool to Reduce the Rate and Completely Avoid Anastomotic Leakage in Functional End-to-End Anastomosis.

Background In our hospital, anastomotic leakage (AL) is observed in approximately 2% of functional end-to-end anastomosis (FEEA) cases annually. It is also usually observed at the staple line of the entry hole closure in several reoperation cases. This study aimed to investigate whether AL would occur in FEEA using a new staple line reinforcement tool, ECHELON ENDOPATH® Staple Line Reinforcement (SLR) (Ethicon, Raritan, NJ, USA). Methods A total of 380 patients (400 anastomoses performed from September 2021, when SLR use began, to the end of February 2024) were compared retrospectively, with a total of 459 patients (469 anastomoses performed from April 2019 to August 2021), the same period before SLR was initiated. In the SLR group, ECHELON FLEX® (Ethicon) 60 mm and GST® system (Ethicon) cartridges were used as stapling devices. A p-value of <0.05 was considered statistically significant. Results No AL was observed in the SLR group, with a significant difference between the SLR and non-SLR groups (p=0.0021). By anastomotic organ, the AL rate significantly decreased for small intestine-colon anastomosis (p=0.023), but there was no significant difference in small intestine-small intestine anastomosis (p=0.061) or colon-colon anastomosis (p=0.35) between groups. Conclusion Reinforcing the staple line using SLR in FEEA may reduce the AL rate. Although AL has not been observed, we will continue to investigate its causes should it occur in the future.

The urinary and sexual outcomes of buccal mucosal graft urethroplasty versus end-to-end anastomosis: a systematic review with meta-analysis.

Background: The urinary and sexual outcomes after urethroplasty may be a concern for patients, but there are still some controversies regarding the consequences of buccal mucosal graft urethroplasty (BMG) in terms of erectile dysfunction (ED). Aim: This meta-analysis aimed to compare urinary and sexual outcomes of BMG and end-to-end urethroplasty (EE). Methods: The PubMed, Web of Science, Cochrane, and Embase databases were searched until February 31, 2023. Data extraction and quality assessment were performed by 2 designated researchers. Dichotomous data were analyzed as odds ratios with 95% confidence intervals (CIs). Heterogeneity across studies was assessed by the I2 quantification, and publication bias using Begg's and Egger's tests. Meta-analysis was performed using RevMan software. Outcomes: Outcomes included stricture recurrence, ED, penile complications, and voiding symptoms. Results: Eighteen studies, including 1648 participants, were included in our meta-analysis. The meta-analysis revealed that there was no significant difference in stricture recurrence (OR = 0.74; 95% CI, 0.48-1.13; P = .17) and voiding symptoms (OR = 1.12; 95% CI, 0.32-3.88; P = .86) between the BMG group and the EE group. BMG was associated with lower risk of penile complications (OR = 0.40; 95% CI, 0.24-0.69; P = .001) and ED (OR = 0.53, 95% CI, 0.32-0.90, P = .02). Clinical Implications: The study may help clinicians choose procedures that achieve better recovery of the urological and sexual function in the treatment of urethral stricture. Strengths and Limitations: This meta-analysis is the first to evaluate the urinary and sexual outcomes of BMG vs EE. A limitation is that most of the included studies were retrospective cohort studies. Conclusion: BMG is as effective as EE in the treatment of bulbar urethral stricture, but BMG has fewer complications and ED than EE.

Bile Duct Injuries after Cholecystectomy: An Individual Patient Data Systematic Review.

Background: Post-cholecystectomy bile duct injuries (BDIs) represent a challenging complication, with negative impacts on clinical outcomes. Several surgical and endoscopic/interventional radiologist (IR) approaches have been proposed to manage these damages, though with high failure rates. This individual patient data (IPD) systematic review analyzes the potential risk factors for failure after treatment interventions for BDIs, both surgical and endoscopic/IR. Methods: An extensive literature search was conducted on MEDLINE and Scopus for relevant articles published in English on the management of BDIs after cholecystectomy, between 1 January 2010 and 31 December 2023. Our series of BDIs was included. BDIs were always categorized according to the Strasberg's classification. The composite primary endpoints evaluated were the failure of treatment interventions, defined as patient death or the requirement of any other procedure, whatever surgical and/or endoscopic/IR, after the primary treatment. Results: A total of 342 cases were retrieved from our literature analysis, including our series of 19 patients. Among these, three groups were identified: "upfront surgery", "upfront endoscopy and/or IR" and "no upfront treatment", consisting of 224, 109 and 9 patients, respectively. After eliminating the third group, treatment intervention failure was observed overall in 34.2% (114/333) of patients, of whom 80.7% (92/114) and 19.3% (22/114) in the "upfront surgery" and in the "upfront endoscopy/IR" groups, respectively. At multivariable analysis, injury type D and E, and repair in a non-specialized center represented independent predictors of treatment failure in both groups, whereas laparoscopic cholecystectomy (LC) converted to open and immediate attempt of surgical repair exclusively in the first group. Conclusions: Significant treatment failure rates are responsible for remarkable negative effects on immediate and longer-term clinical outcomes of post-cholecystectomy BDIs. Understanding the important risk factors for this outcome may better guide the most appropriate therapeutical approach and improve clinical decisions in case this serious complication occurs.

DNA-DISK: Automated end-to-end data storage via enzymatic single-nucleotide DNA synthesis and sequencing on digital microfluidics.

In the age of information explosion, the exponential growth of digital data far exceeds the capacity of current mainstream storage media. DNA is emerging as a promising alternative due to its higher storage density, longer retention time, and lower power consumption. To date, commercially mature DNA synthesis and sequencing technologies allow for writing and reading of information on DNA with customization and convenience at the research level. However, under the disconnected and nonspecialized mode, DNA data storage encounters practical challenges, including susceptibility to errors, long storage latency, resource-intensive requirements, and elevated information security risks. Herein, we introduce a platform named DNA-DISK that seamlessly streamlined DNA synthesis, storage, and sequencing on digital microfluidics coupled with a tabletop device for automated end-to-end information storage. The single-nucleotide enzymatic DNA synthesis with biocapping strategy is utilized, offering an ecofriendly and cost-effective approach for data writing. A DNA encapsulation using thermo-responsive agarose is developed for on-chip solidification, not only eliminating data clutter but also preventing DNA degradation. Pyrosequencing is employed for in situ and accurate data reading. As a proof of concept, DNA-DISK successfully stored and retrieved a musical sheet file (228 bits) with lower write-to-read latency (4.4 min of latency per bit) as well as superior automation compared to other platforms, demonstrating its potential to evolve into a DNA Hard Disk Drive in the future.

Two-step optimization for accelerating deep image prior-based PET image reconstruction.

Deep learning, particularly convolutional neural networks (CNNs), has advanced positron emission tomography (PET) image reconstruction. However, it requires extensive, high-quality training datasets. Unsupervised learning methods, such as deep image prior (DIP), have shown promise for PET image reconstruction. Although DIP-based PET image reconstruction methods demonstrate superior performance, they involve highly time-consuming calculations. This study proposed a two-step optimization method to accelerate end-to-end DIP-based PET image reconstruction and improve PET image quality. The proposed two-step method comprised a pre-training step using conditional DIP denoising, followed by an end-to-end reconstruction step with fine-tuning. Evaluations using Monte Carlo simulation data demonstrated that the proposed two-step method significantly reduced the computation time and improved the image quality, thereby rendering it a practical and efficient approach for end-to-end DIP-based PET image reconstruction.

Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring.

Tool condition monitoring technology is an indispensable part of intelligent manufacturing. Most current research focuses on complex signal processing techniques or advanced deep learning algorithms to improve prediction performance without fully leveraging the end-to-end advantages of deep learning. The challenge lies in transforming multi-sensor raw data into input data suitable for direct model feeding, all while minimizing data scale and preserving sufficient temporal interpretation of tool wear. However, there is no clear reference standard for this so far. In light of this, this paper innovatively explores the processing methods that transform raw data into input data for deep learning models, a process known as an input paradigm. This paper introduces three new input paradigms: the downsampling paradigm, the periodic paradigm, and the subsequence paradigm. Then an improved hybrid model that combines a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) was employed to validate the model's performance. The subsequence paradigm demonstrated considerable superiority in prediction results based on the PHM2010 dataset, as the newly generated time series maintained the integrity of the raw data. Further investigation revealed that, with 120 subsequences and the temporal indicator being the maximum value, the model's mean absolute error (MAE) and root mean square error (RMSE) were the lowest after threefold cross-validation, outperforming several classical and contemporary methods. The methods explored in this paper provide references for designing input data for deep learning models, helping to enhance the end-to-end potential of deep learning models, and promoting the industrial deployment and practical application of tool condition monitoring systems.

ASD-GANNet: A Generative Adversarial Network-Inspired Deep Learning Approach for the Classification of Autism Brain Disorder.

The classification of a pre-processed fMRI dataset using functional connectivity (FC)-based features is considered a challenging task because of the set of high-dimensional FC features and the small dataset size. To tackle this specific set of FC high-dimensional features and a small-sized dataset, we propose here a conditional Generative Adversarial Network (cGAN)-based dataset augmenter to first train the cGAN on computed connectivity features of NYU dataset and use the trained cGAN to generate synthetic connectivity features per category. After obtaining a sufficient number of connectivity features per category, a Multi-Head attention mechanism is used as a head for the classification. We name our proposed approach "ASD-GANNet", which is end-to-end and does not require hand-crafted features, as the Multi-Head attention mechanism focuses on the features that are more relevant. Moreover, we compare our results with the six available state-of-the-art techniques from the literature. Our proposed approach results using the "NYU" site as a training set for generating a cGAN-based synthetic dataset are promising. We achieve an overall 10-fold cross-validation-based accuracy of 82%, sensitivity of 82%, and specificity of 81%, outperforming available state-of-the art approaches. A sitewise comparison of our proposed approach also outperforms the available state-of-the-art, as out of the 17 sites, our proposed approach has better results in the 10 sites.

Discovery of an End-to-End Pattern for Contaminant-Oriented Advanced Oxidation Processes Catalyzed by Biochar with Explainable Machine Learning.

The utilization of biochar-catalyzed peroxymonosulfate in advanced oxidation processes (BC-PMS AOPs) is widely acknowledged as an effective and economical method for mitigating emerging contaminants (ECs). Especially, state-of-the-art machine learning (ML) technology has been employed to accurately predict the reaction rate constants of EC degradation in BC-PMS AOPs, primarily focusing on three aspects: performance prediction, operating condition optimization, and mechanism interpretation. However, its real application in specific degradation optimization targeting different ECs is seldom considered, hindering the realization of contaminant-oriented BC-PMS AOPs. Herein, we propose a hierarchical ML pipeline to achieve an end-to-end (E2E) pattern for addressing this issue. First, the overall XGB model, trained with the comprehensive data set, can perform well in predicting the reaction constants of EC degradation in BC-PMS AOPs, additionally providing the basis for further analysis of various ECs. Then, the submodels trained with different EC clusters can offer specific strategies for the selection of the optimum option for BC-PMS AOPs of specific ECs with different HOMO-LUMO gaps, thus forming an E2E operating pattern for BC-PMS AOPs. This study not only increases our understanding of contaminant-oriented optimization of AOPs but also successfully bridges the gap between ML model development and its environmental application.

Comprehensive stereotactic radiosurgery platform characterization: A novel end-to-end approach with anthropomorphic 3D dosimetry.

BACKGROUND: Stereotactic radiosurgery (SRS) is a widely employed strategy for intracranial metastases, utilizing linear accelerators and volumetric modulated arc therapy (VMAT). Ensuring precise linear accelerator performance is crucial, given the small planning target volume (PTV) margins. Rapid dose falloff is vital to minimize brain radiation necrosis. Despite advances in SRS planning, tools for end-to-end testing of SRS treatments are lacking, hindering confidence in the procedure. PURPOSE: This study introduces a novel end-to-end three-dimensional (3D) anthropomorphic dosimetry system for characterization of a radiosurgery platform, aiming to measure planning metrics, dose gradient index (DGI), brain volumes receiving at least 10 and 12 Gy (V10, V12), as well as assess delivery uncertainties in multitarget treatments. The study also compares metrics from benchmark plans to enhance understanding and confidence in SRS treatments. METHODS: The developed anthropomorphic 3D dosimetry system includes a modified Stereotactic End-to-End Verification (STEEV) phantom with a customized insert integrating 3D dosimeters and a fiber optic CT scanner. Labview and MATLAB programs handle optical scanning, image preprocessing, and dosimetric analysis. SlicerRT is used for 3D dose visualization and analysis. A film stack insert was used to validate the 3D dosimeter measurements at specific slices. Benchmark plans were developed and measured to investigate off-axis errors, dose spillage, small field dosimetry, and multi-target delivery. RESULTS: The accuracy of the developed 3D dosimetry system was rigorously assessed using radiochromic films. Two two-dimensional (2D) dose planes, extracted from the 3D dose distribution, were compared with film measurements, resulting in high passing rates of 99.9% and 99.6% in gamma tests. The mean relative dose difference between film and 3D dosimeter measurements was -1%, with a standard deviation of 2.2%, well within dosimeter uncertainties. In the first module, evaluating single-isocenter multitarget treatments, a 1.5 mm dose distribution shift was observed when targets were 7 cm off-axis. This shift was attributed to machine mechanical errors and image-guided system uncertainties, indicating potential limitations in conventional gamma tests. The second module investigated discrepancies in intermediate-to-low dose spillage, revealing higher measured doses in the connecting region between closely positioned targets. This discrepancy was linked to uncertainties in treatment planning system (TPS) modeling of out-of-field dose and multileaf collimator (MLC) characteristics, resulting in lower DGI values and higher V10 and V12 values compared to TPS calculations. In the third module, irradiating multiple targets showed consistent V10 and V12 values within 1 cm3 agreement with dose calculations. However, lower DGI values from measurements compared to calculations suggested intricacies in the treatment process. Conducting vital end-to-end testing demonstrated the anthropomorphic 3D dosimetry system's capacity to assess overall treatment uncertainty, offering a valuable tool for enhancing treatment accuracy in radiosurgery platforms. CONCLUSIONS: The study introduces a novel anthropomorphic 3D dosimetry system for end-to-end testing of a radiosurgery platform. The system effectively measures plan quality metrics, captures mechanical errors, and visualizes dose discrepancies in 3D space. The comprehensive evaluation capability enhances confidence in the commissioning and verification process, ensuring patient safety. The system is recommended for commissioning new radiosurgery platforms and remote auditing of existing programs.