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We aimed to use the willingness to pay (WTP) method to calculate the cost of traffic injuries in Iran in 2013. We conducted a cross-sectional questionnaire-based study of 846 randomly selected road users. WTP data was collected for four scenarios for vehicle occupants, pedestrians, vehicle drivers, and motorcyclists. Final analysis was carried out using Weibull and maximum likelihood method. Mean WTP was 2,612,050 Iranian rials (IRR). Statistical value of life was estimated according to 20,408 fatalities 402,314,106,073,648 IRR (US$13,410,470,202 based on purchasing power parity at (February 27th, 2014). Injury cost was US$25,637,870,872 (based on 318,802 injured people in 2013, multiple daily traffic volume of 311, and multiple daily payment of 31,030 IRR for 250 working days). The total estimated cost of injury and death cases was 39,048,341,074$. Gross national income of Iran was, US$604,300,000,000 in 2013 and the costs of traffic injuries constituted 6·46% of gross national income. WTP was significantly associated with age, gender, monthly income, daily payment, more payment for time reduction, trip mileage, drivers and occupants from road users. The costs of traffic injuries in Iran in 2013 accounted for 6.64% of gross national income, much higher than the global average. Policymaking and resource allocation to reduce traffic-related death and injury rates have the potential to deliver a huge economic benefit.

The eight member countries of the Central American Integration System (SICA)—Belize, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, Panama, and the Dominican Republic—are signatories to the Convention for the Protection of World Heritage and the Convention on Biological Diversity. Both binding international instruments use the terms ‘cultural landscape’ and ‘protected landscape’, respectively. For this reason, the environmental/natural and cultural legislation of the SICA region has been analyzed to identify the legal frameworks that allow for the declaration of the categories mentioned above for the protection of landscapes. In five of the eight SICA member states, 38 ‘protected landscapes’ were found to exist under environmental law. No designation has been reported for cultural legislation in this region. In addition, the designations and management plans for ‘protected landscapes’ were reviewed, and it was noted that most of them were similar in denomination, but their protection objectives were not aligned with the binding instruments from which they were derived. Thus, we conclude that, given the particular natural and cultural wealth of the SICA region, it is necessary to identify and map landscapes and establish common guidelines for managing them to foster harmony between nature and mankind and according to the international conventions’ objectives.

Abstract Background Pulmonary hypertension due to interstitial lung disease (PH-ILD) is associated with high rates of respiratory failure and death. Healthcare resource utilization (HCRU) and cost data are needed to characterize PH-ILD disease burden. Methods A retrospective cohort analysis of the Truven Health MarketScan® Commercial Claims and Encounters Database and Medicare Supplemental Database between June 2015 to June 2019 was conducted. Patients with ILD were identified and indexed based on their first claim with a PH diagnosis. Patients were required to be 18 years of age on the index date and continuously enrolled for 12-months pre- and post-index. Patients were excluded for having a PH diagnosis prior to ILD diagnosis or the presence of other non-ILD, PH-associated conditions. Treatment patterns, HCRU, and healthcare costs were compared between the 12 months pre- versus 12 months post-index date. Results In total, 122 patients with PH-ILD were included (mean [SD] age, 63.7 [16.6] years; female, 64.8%). The same medication classes were most frequently used both pre- and post-index (corticosteroids: pre-index 43.4%, post-index 53.5%; calcium channel blockers: 25.4%, 36.9%; oxygen: 12.3%, 25.4%). All-cause hospitalizations increased 2-fold, with 29.5% of patients hospitalized pre-index vs. 59.0% post-index (P

Abstract Wire arc additive manufacturing (WAAM) is an efficient technique for producing medium to large‐size components, due to its accessibility and sustainability in fabricating large‐scale parts with high deposition rates, employing low‐cost and simple equipment, and achieving high material efficiency. Consequently, WAAM has garnered attention across various industrial sectors and experienced significant growth, particularly over the last decade, as it addresses and mitigates challenges within production markets. One of the primary limitations of WAAM is its thermal history during the process, which directly influences grain formation and microstructure heterogeneity in the resulting part. Understanding the thermal cycle of the WAAM process is thus crucial for process improvement. Typically, fabricating a part using WAAM results in a microstructure with three distinct zones along the build direction: an upper zone (thin surface layer) with fine grains, a middle zone dominated by undesirably long and large columnar grains covering more than 90% of the produced part, and a lower zone with smaller to intermediate columnar grains closer to the substrate material. These zones arise from variations in cooling rates, with the middle zone exhibiting the lowest cooling rate due to 2D conduction heat transfer. Consequently, producing a component with a microstructure comprising three different zones, with a high fraction of large and long columnar grains, significantly impacts the final mechanical properties. Therefore, controlling the size and formation of these grain zones plays a key role in improving WAAM. The aim of this work is to investigate the formation of undesired columnar grains in austenitic stainless steel 316L during WAAM and propose a simple hybrid technique by combining WAAM with a hot forging process (with or without interlayer cooling time). This approach targets the disruption of the solidification pattern of columnar grain growth during deposition progression and aims to enhance the microstructure of WAAM components.

Abstract Cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs) are powerful in vitro models to study the mechanisms underlying cardiomyopathies and cardiotoxicity. Quantification of the contractile function in single hiPSC-CMs at high-throughput and over time is essential to disentangle how cellular mechanisms affect heart function. Here, we present CONTRAX, an open-access, versatile, and streamlined pipeline for quantitative tracking of the contractile dynamics of single hiPSC-CMs over time. Three software modules enable: parameter-based identification of single hiPSC-CMs; automated video acquisition of >200 cells/hour; and contractility measurements via traction force microscopy. We analyze >4,500 hiPSC-CMs over time in the same cells under orthogonal conditions of culture media and substrate stiffnesses; +/− drug treatment; +/− cardiac mutations. Using undirected clustering, we reveal converging maturation patterns, quantifiable drug response to Mavacamten and significant deficiencies in hiPSC-CMs with disease mutations. CONTRAX empowers researchers with a potent quantitative approach to develop cardiac therapies.

Kinetic inductance traveling-wave parametric amplifiers (KI-TWPAs) have a wide instantaneous bandwidth with a near quantum-limited noise performance and a relatively high dynamic range. Because of this, they are suitable readout devices for cryogenic detectors and superconducting qubits and have a variety of applications in quantum sensing. This work discusses the design, fabrication, and performance of a KI-TWPA based on four-wave mixing in a NbTiN microstrip transmission line. This device amplifies a signal band from 4 to 8 GHz without contamination from image tones, which are produced in a separate higher frequency band. The 4–8 GHz band is commonly used to read out cryogenic detectors, such as microwave kinetic inductance detectors and Josephson junction-based qubits. We report a measured maximum gain of over 20 dB using four-wave mixing with a 1 dB gain compression point of −58 dBm at 15 dB of gain over that band. The bandwidth and peak gain are tunable by adjusting the pump-tone frequency and power. Using a Y-factor method, we measure an amplifier-added noise of 0.5 ≤ Nadded ≤ 1.5 photons from 4.5 to 8 GHz.

PURPOSETo guide clinicians and policymakers in three global resource-constrained settings on treating patients with metastatic breast cancer (MBC) when Maximal setting–guideline recommended treatment is unavailable.METHODSA multidisciplinary, multinational panel reviewed existing ASCO guidelines and conducted modified ADAPTE and formal consensus processes.RESULTSFour published resource-agnostic guidelines were adapted for resource-constrained settings; informing two rounds of formal consensus; recommendations received ≥75% agreement.RECOMMENDATIONSClinicians should recommend treatment according to menopausal status, pathological and biomarker features when quality results are available. In first-line, for hormone receptor (HR)–positive MBC, when a non-steroidal aromatase inhibitor and CDK 4/6 inhibitor combination is unavailable, use hormonal therapy alone. For life-threatening disease, use single-agent chemotherapy or surgery for local control. For premenopausal patients, use ovarian suppression or ablation plus hormone therapy in Basic settings. For human epidermal growth factor receptor 2 (HER2)–positive MBC, if trastuzumab, pertuzumab, and chemotherapy are unavailable, use trastuzumab and chemotherapy; if unavailable, use chemotherapy. For HER2-positive, HR-positive MBC, use standard first-line therapy, or endocrine therapy if contraindications. For triple-negative MBC with unknown PD-L1 status, or if PD-L1–positive and immunotherapy unavailable, use single-agent chemotherapy. For germline BRCA1/2 mutation–positive MBC, if poly(ADP-ribose) polymerase inhibitor is unavailable, use hormonal therapy (HR-positive MBC) and chemotherapy (HR-negative MBC). In second-line, for HR-positive MBC, Enhanced setting recommendations depend on prior treatment; for Limited, use tamoxifen or chemotherapy. For HER2-positive MBC, if trastuzumab deruxtecan is unavailable, use trastuzumab emtansine; if unavailable, capecitabine and lapatinib; if unavailable, trastuzumab and/or chemotherapy (hormonal therapy alone for HR-positive MBC).Additional information is available at www.asco.org/resource-stratified-guidelines. It is ASCO's view that healthcare providers and system decision-makers should be guided by the recommendations for the highest stratum of resources available. The guideline is intended to complement but not replace local guidelines.

With the increasing demand for high temporal and spatial resolution multispectral image sequences, many studies have been carried out on fusion on Landsat-8 and Sentinel-2 images to obtain image sequences with a revisit cycle of 2 and 3 days and a spatial resolution of 10 m. However, current fusion methods suffer from complex computation and loss of spectral and spatial information. To address these issues, a Landsat-8 and Sentinel-2 image fusion based on multiscale smoothing-sharpening filter (MSSF) method is proposed. MSSF combines well the initial spatial prediction obtained from the Landsat-8 image at the target date and the detailed image extracted from the Sentinel-2 image at the reference date. Thin plate spline interpolation with morphological opening-closing algorithm is implemented on the Landsat-8 image at the target date, and the Laplacian of Gaussian enhancement algorithm is applied to the Sentinel-2 image at the reference date in the preprocessing stage. Smoothing-sharpening filter (SSIF) is employed to separate the high and low frequency components of the two preprocessed images. The multiscale SSIF is then utilized to migrate the details from the preprocessed Sentinel-2 image to the preprocessed Landsat-8 image. The performance of MSSF and five compared methods was evaluated qualitatively and quantitatively. Experiments on three remote sensing data sets gathered from different experimental sites confirm that the proposed MSSF method could efficiently generate Sentinel-2-like images with high spatial and spectral resolution.

Compared with large-scale ships, small-scale ships occupy few pixels and have low contrast, so it poses a great challenge to detect multiscale ships in synthetic aperture radar (SAR) images. In order to improve the accuracy of multiscale ship detection in SAR images, this article designs a general multiscale pyramid attention module (MPAM), which is a plug-and-play lightweight module that can adapt to many ship detection networks. In the MPAM, a deep feature extraction submodule is first designed to use the multiscale pyramid structure to divide the feature map into different levels, extracting rich features with resolution and semantic information for multiscale ship detection. The channel multilayer attention fusion submodule and spatial multilayer attention fusion submodule are then designed to fuse the channel and spatial attention blocks on different level feature maps, which could better learn the dependent features from the channel and spatial dimensions, to enhance the feature representation. Finally, the fused feature map is input into the existing ship detection networks to obtain the detection result. Experiments on SAR datasets containing multiscale ships show that the effectiveness of the MPAM in improving the accuracy of the existing ship detection networks.

Object detection architectures struggle to detect small objects across applications including remote sensing and autonomous vehicles. Specifically, for unmanned aerial vehicles, poor detection of small objects directly limits this technology’s applicability. Objects both appear smaller than they are in large-scale images captured in aerial imagery and are represented by reduced information in high-altitude imagery. This paper presents a new architecture, CR-CNN, which predicts independent regions of interest from two unique prediction branches within the first stage of the network: a conventional R-CNN convolutional backbone and an hourglass backbone. Utilizing two independent sources within the first stage, our approach leads to an increase in successful predictions of regions that contain smaller objects. Anchor-based methods such as R-CNNs also utilize less than half the number of small objects compared to larger ones during training due to the poor intersection over union (IoU) scores between the generated anchors and the groundtruth—further reducing their performance on small objects. Therefore, we also propose artificially inflating the IoU of smaller objects during training using a simple, size-based Gaussian multiplier—leading to an increase in the quantity of small objects seen per training cycle based on an increase in the number of anchor–object pairs during training. This architecture and training strategy led to improved detection overall on two challenging aerial-based datasets heavily composed of small objects while predicting fewer false positives compared to Mask R-CNN. These results suggest that while new and unique architectures will continue to play a part in advancing the field of object detection, the training methodologies and strategies used will also play a valuable role.

Ship detection in synthetic aperture radar (SAR) imagery faces significant challenges due to the limitations of traditional methods, such as convolutional neural network (CNN) and anchor-based matching approaches, which struggle with accurately detecting smaller targets as well as adapting to varying environmental conditions. These methods, relying on either intensity values or single-target characteristics, often fail to enhance the signal-to-clutter ratio (SCR) and are prone to false detections due to environmental factors. To address these issues, a novel framework is introduced that leverages the detection transformer (DETR) model along with advanced feature fusion techniques to enhance ship detection. This feature enhancement DETR (FEDETR) module manages clutter and improves feature extraction through preprocessing techniques such as filtering, denoising, and applying maximum and median pooling with various kernel sizes. Furthermore, it combines metrics like the line spread function (LSF), peak signal-to-noise ratio (PSNR), and F1 score to predict optimal pooling configurations and thus enhance edge sharpness, image fidelity, and detection accuracy. Complementing this, the weighted feature fusion (WFF) module integrates polarimetric SAR (PolSAR) methods such as Pauli decomposition, coherence matrix analysis, and feature volume and helix scattering (Fvh) components decomposition, along with FEDETR attention maps, to provide detailed radar scattering insights that enhance ship response characterization. Finally, by integrating wave polarization properties, the ability to distinguish and characterize targets is augmented, thereby improving SCR and facilitating the detection of weakly scattered targets in SAR imagery. Overall, this new framework significantly boosts DETR’s performance, offering a robust solution for maritime surveillance and security.