Search Results (60)

Background/Objectives: Proton therapy delivers highly conformal doses to the target area without producing an exit dose, minimizing cumulative doses to healthy liver tissue. This study aims to evaluate current practices, challenges, and variations in the implementation of proton stereotactic body radiation therapy (SBRT) and hypofractionated therapy for liver malignancies, with the goal of providing a technical assessment to promote broader adoption and support future clinical trials. Methods and Materials: An extensive survey was conducted by NRG Oncology across North American proton treatment centers to assess the current practices of proton liver SBRT and hypofractionated therapy. The survey focused on key aspects, including patient selection, prescription and normal tissue constraints, simulation and motion management, treatment planning, quality assurance (QA), treatment delivery, and the use of image-guided radiation therapy (IGRT). Results: This survey captures the current practice patterns and status of proton SBRT and hypofractionated therapy in liver cancer treatment.  Proton therapy is increasingly preferred for treating inoperable liver malignancies due to its ability to minimize healthy tissue exposure. However, the precision required for proton therapy presents challenges, particularly in managing uncertainties and target motion during high-dose fractions and short treatment courses. Survey findings revealed significant variability in clinical practices across centers, highlighting differences in motion management, dose fractionation schedules, and QA protocols. Conclusion: Proton SBRT and hypofractionated therapy offer significant potential for treating liver malignancies. A comprehensive approach involving precise patient selection, treatment planning, and QA is essential for ensuring safety and effectiveness. This survey provides valuable insights into current practices and challenges, offering a foundation for technical recommendations to optimize the use of proton therapy and guide future clinical trials. Full article

Advanced Synthetic Aperture Radar (SAR) has become an essential modality in remote sensing, offering all-weather capabilities and sensitivity to vegetation biophysical parameters and surface conditions, while effectively complementing optical sensor data. This study evaluates the impact of precipitation on the Normalized Polarimetric Radar Vegetation Index (NPRVI) using dual-polarization Sentinel-1 C-band SAR data from agricultural fields at the Beltsville Agricultural Research Center (BARC). Field-measured precipitation and Global Precipitation Measurement (GPM) precipitation datasets were temporally aligned with Sentinel-1 acquisition times to assess the sensitivity of radar signals to precipitation events. NPRVI exhibited a strong sensitivity to precipitation, particularly within the 1 to 7 h prior to the satellite overpass, even for small amounts of precipitation. A quality assessment (QA) framework was developed to flag and correct precipitation-affected radar observations through interpolation. The adjusted NPRVI values, based on the QA framework using precipitation within a 6 h window, showed strong agreement between field- and GPM-derived data, with an RMSE of 0.09 and a relative RMSE of 19.8%, demonstrating that GPM data can serve as a viable alternative for quality adjustment despite its coarse spatial resolution. The adjusted NPRVI for both soybean and corn fields significantly improved the temporal consistency of the time series and closely followed NDVI trends, while also capturing crop-specific seasonal variations, especially during periods of NDVI saturation or limited variability. These findings underscore the value of the proposed radar-based QA framework in enhancing the interpretability of vegetation dynamics. NPRVI, when adjusted for precipitation effects, can serve as a reliable and complementary tool to optical vegetation indices in agricultural and environmental monitoring. Full article

A vital step for any hydrochemical assessment is properly carrying out quality assurance and quality control (QA/QC) techniques to evaluate data confidence before performing the assessment. Understanding the processes governing groundwater evolution in coastal aquifers is critical for managing freshwater resources under increasing anthropogenic and climatic pressures. This study reassesses the hydrochemical and isotopic data from the Deep Confined Aquifer System (DCAS) in the Jiangsu Coastal Plain, China, by firstly applying QA/QC protocols. Anomalously high Fe and Mn concentrations in several samples were identified and excluded, yielding a refined dataset that enabled a more accurate interpretation of hydrogeochemical processes. Using hierarchical cluster analysis (HCA), principal component analysis (PCA), and stable and radioactive isotope data (δ²H, δ¹⁸O, ³H, and ¹⁴C), we identify three dominant drivers of groundwater evolution: water–rock interaction, evaporation, and seawater intrusion. In contrast to earlier interpretations, we present clear evidence of active seawater intrusion into the DCAS, supported by salinity patterns, isotopic signatures, and local hydrodynamics. Furthermore, inconsistencies between tritium- and radiocarbon-derived residence times—modern recharge indicated by ³H versus Pleistocene ages from ¹⁴C—highlight the unreliability of previous paleoclimatic reconstructions based on unvalidated datasets. These findings underscore the crucial role of robust QA/QC and integrated tracer analysis in groundwater studies. Full article

The emergence of large language models (LLMs) has introduced new possibilities for government-oriented question-answering (QA) systems. Nonetheless, limitations in retrieval accuracy and response quality assessment remain pressing challenges. This study presents ARGUS (Answer Retrieval and Governance Understanding System), a fine-tuned LLM built on a domain-adapted framework that incorporates hybrid retrieval strategies using LlamaIndex. ARGUS improves factual consistency and contextual relevance in generated answers by incorporating both graph-based entity retrieval and associated text retrieval. A comprehensive evaluation protocol combining classical metrics and RAGAS indicators is employed to assess answer quality. The experimental results show that ARGUS achieved a ROUGE-1 score of 0.68 and a semantic relevance score of 0.81. To validate the effectiveness of individual system components, a chain-of-thought mechanism inspired by human reasoning was employed to enhance interpretability. Ablation results revealed improvements in ROUGE-1 to 68.5% and S-BERT to 74.9%, over 20 percentage points higher than the baseline. Additionally, the hybrid retrieval method outperformed pure vector (0.73) and pure graph-based (0.71) strategies, achieving an F1 score of 0.75. The main contributions of this study are twofold: first, it proposes a hybrid retrieval-augmented QA framework tailored for government scenarios; second, it demonstrates the system’s reliability and practicality in addressing complex government-related queries through the integration of human-aligned metrics and traditional evaluation methods. ARGUS offers a novel paradigm for providing trustworthy, intelligent government QA systems. Full article

This study presents the results of an investigation into the potential use of the DiagBelt+ magnetic diagnostic system for assessing the quality of conveyor belt splices. Splices in conveyor belts are susceptible to damage and irregularities resulting from assembly errors, improper vulcanization parameters, or unfavorable operational conditions. Detecting geometric deviations from the reference standard after splice fabrication can serve as a component of QA/QC systems. Later deviations may indicate material or fabrication defects. To date, applications of the DiagBelt+ system have been limited to locating damage within the belt and its splices. Recently, efforts have been made to extend the system’s functionality to include splice diagnostics. This study was conducted under laboratory conditions on an ST2500 belt featuring five splices (three bias and two straight splices). Data acquisition was performed under various configurations of measurement parameters, including sensor-to-belt distance, belt travel speed, and system sensitivity threshold. For each splice, the signal width was measured and analyzed as a potential indicator of splice geometry and quality. The results indicate that the DiagBelt+ system can be effectively used for splice diagnostics. Work has commenced on automating the splice quality assessment process. Full article

This study proposes that a proactive quality assurance (QA) framework for asphalt mixes with recycled materials, i.e., reclaimed asphalt pavement and recycled asphalt shingles, should be developed. Quality control (QC) is generally concerned with the contractor’s obligation to produce mixes which meet the job mix formula (JMF) targets. However, QA considers the variability in fabrication processes and materials and offers monitoring by evaluating the contractor’s performance. Although both aggregate gradations and asphalt contents were within the JMF specifications, the recovered binders revealed significant differences from the contract binders in the JMF. Rheological tests indicated increased stiffness and elasticity but reduced capability to relax thermal stresses in binders recovered from plant–lab- and lab-fabricated mixtures, compared to field mixtures. Thermal-rheological analysis models corroborated these results by demonstrating reduced decomposition areas for more aged binders, enhancing performance prediction—especially for limited binder amounts. The creation of a QA decision matrix facilitated uniform, performance-oriented assessments. The matrix indicated only 23% of the mixtures satisfied JMF criteria and reported QC data—predominantly field mixtures—underscoring the impact of the fabrication mechanisms and the use of soft binders. This matrix integrates statistical analysis and binder performance assessments as a tool for verifying material compliance and tracking contractor efficiency. It reflects a transition from traditional QC toward a more proactive QA framework for sustainable pavements. Full article

While large language models (LLMs) are vital for retrieving relevant information from extensive knowledge bases, they always face challenges, including high costs and issues of credibility. Here, we developed a question answering system focused on human health risk using Retrieval-Augmented Generation (RAG). We first proposed a framework to generate question–answer pairs, resulting in 300 high-quality pairs across six subfields. Subsequently, we created both a Naive RAG and an Advanced RAG-based Question-Answering (Q&A) system. Performance evaluation of the 300 question–answer pairs in individual research subfields demonstrated that the Advanced RAG outperformed traditional LLMs (including ChatGPT and ChatGLM) and Naive RAG. Finally, we integrated the developed module for a single subfield to launch a multi-knowledge base question answering system. Our study represents a novel application of RAG technology and LLMs to optimize knowledge retrieval methods in human health risk assessment. Full article

The clumping index (CI) is a key structural parameter that quantifies the nonrandomness of the spatial distribution of vegetation canopy leaves. Investigating seasonal variations in the CI is crucial, especially for estimating the leaf area index (LAI) and studying global carbon and water cycles. However, accurate estimations of the seasonal CI have substantial challenges, e.g., from the need for accurate hot spot measurements, i.e., the typical feature of the bidirectional reflectance distribution function (BRDF) shape in the current CI algorithm framework. Therefore, deriving a phenologically simplified stable CI product from a high-frequency CI product (e.g., 8 days) to reduce the uncertainty of CI seasonality and simplify CI applications remains important. In this study, we applied the discrete Fourier transform and an improved dynamic threshold method to estimate the start of season (SOS) and end of season (EOS) from the CI time series and indicated that the CI exhibits significant seasonal variation characteristics that are generally consistent with the MODIS land surface phenology (LSP) product (MCD12Q2), although seasonal differences between them probably exist. Second, we divided the vegetation cycle into two phenological stages based on the MODIS LSP product, ignoring the differences mentioned above, i.e., the leaf-on season (LOS, from greenup to dormancy) and the leaf-off season (LFS, after dormancy and before greenup of the next vegetation cycle), and developed the phenologically simplified two-stage CI product for the years 2001–2020 using the MODIS 8-day CI product suite. Finally, we assessed the accuracy of this CI product (RMSE = 0.06, bias = 0.01) via 95 datasets from 14 field-measured sites globally. This study revealed that the CI exhibited an approximately inverse trend in terms of phenological variation compared with the NDVI. Globally, based on the phenologically simplified two-stage CI product, the CI_LOS is smaller than the CI_LFS across all land cover types. Compared with the LFS stage, the quality for this CI product is better in the LOS stage, where the QA is basically identified as 0 and 1, accounting for more than ~90% of the total quality flag, which is significantly higher than that in the LFS stage (~60%). This study provides relatively reliable CI datasets that capture the general trend of seasonal CI variations and simplify potential applications in modeling ecological, meteorological, and other surface processes at both global and regional scales. Therefore, this study provides both new perspectives and datasets for future research in relation to CI and other biophysical parameters, e.g., the LAI. Full article

Arbitrary translational Six Degrees of Freedom (6DoF) video represents a transitional stage towards immersive terminal videos, allowing users to freely switch viewpoints for a 3D scene experience. However, the increased freedom of movement introduces new distortions that significantly impact human visual perception quality. Therefore, it is crucial to explore quality assessment (QA) to validate its application feasibility. In this study, we conduct subjective and objective QAs of arbitrary translational 6DoF videos. Subjectively, we establish an arbitrary translational 6DoF synthesized video quality database, specifically exploring path navigation in 3D space, which has often been limited to planar navigation in previous studies. We simulate path navigation distortion, rendering distortion, and compression distortion to create a subjective QA database. Objectively, based on the spatio-temporal distribution characteristics of various distortions, we propose a no-reference video quality assessment (VQA) metric for arbitrary translational 6DoF videos. The experimental results on the established subjective dataset fully demonstrate the effectiveness and superiority of the proposed objective method. Full article

Assessing the relationship between litter characteristics and soil microbial community traits across different forest types can enhance our understanding of the synergistic interactions among litter, soil, and microorganisms. This study focused on three representative forest types in the Funiu Mountains—Larix gmelinii (LG), Quercus aliena var. acutiserrata (QA), and Quercus aliena var. acutiserrata + Pinus armandii (QAPA). The findings indicated no significant differences in Chao1 among the three forests; however, the Shannon index exhibited an initial increase followed by a decline. NMDS and ANOSIM analyses revealed significant structural differences across these forest types. Network topological metrics (nodes, edges, average degree, and average path distance) for bacterial taxa were higher in LG and QA compared with QAPA. Additionally, LG and QA demonstrated significantly greater average niche breadth than QAPA. The results from the null models (the proportion occupied by dispersal limitation is 62.2%, 82.2%, and 64.4% in LG, QA, and QAPA), modified stochasticity ratio (LG: 0.708, QA: 0.664, and QAPA: 0.801), and neutral community models (LG: R² = 0.665, QA: R² = 0.630, and QAPA: R² = 0.665) suggested that stochastic processes predominantly govern the assembly of soil bacterial communities. Random forest analysis alongside Mantel tests highlighted LTP (litter total phosphorus), STN (soil total nitrogen), MCP (carbon-to-phosphorus ratio of microbial biomass), and SCN (soil carbon-to-nitrogen ratio) as critical factors affecting bacterial niche width; conversely LCN (litter carbon-to-nitrogen ratio), RCP (ratio of dissolved carbon to phosphorus), MCP, and SCN emerged as key determinants influencing community assembly processes. Furthermore, the PLS-SEM results underscored how both litter characteristics along with soil properties—and their associated alpha diversity—impact variations in niche breadth while also shaping community assembly dynamics overall. This research provides vital insights into understanding synergistic relationships between litter quality, soil characteristics, and microbial community across diverse forest ecosystems. Full article

Background: Humans cannot avoid plastic exposure due to its ubiquitous presence in the natural environment. The waste generated is poorly biodegradable and exists in the form of MPs, which can enter the human body primarily through the digestive tract, respiratory tract, or damaged skin and accumulate in various tissues by crossing biological membrane barriers. There is an increasing amount of research on the health effects of MPs. Most literature reports focus on the impact of plastics on the respiratory, digestive, reproductive, hormonal, nervous, and immune systems, as well as the metabolic effects of MPs accumulation leading to epidemics of obesity, diabetes, hypertension, and non-alcoholic fatty liver disease. MPs, as xenobiotics, undergo ADMET processes in the body, i.e., absorption, distribution, metabolism, and excretion, which are not fully understood. Of particular concern are the carcinogenic chemicals added to plastics during manufacturing or adsorbed from the environment, such as chlorinated paraffins, phthalates, phenols, and bisphenols, which can be released when absorbed by the body. The continuous increase in NMP exposure has accelerated during the SARS-CoV-2 pandemic when there was a need to use single-use plastic products in daily life. Therefore, there is an urgent need to diagnose problems related to the health effects of MP exposure and detection. Methods: We collected eligible publications mainly from PubMed published between 2017 and 2024. Results: In this review, we summarize the current knowledge on potential sources and routes of exposure, translocation pathways, identification methods, and carcinogenic potential confirmed by in vitro and in vivo studies. Additionally, we discuss the limitations of studies such as contamination during sample preparation and instrumental limitations constraints affecting imaging quality and MPs detection sensitivity. Conclusions: The assessment of MP content in samples should be performed according to the appropriate procedure and analytical technique to ensure Quality and Control (QA/QC). It was confirmed that MPs can be absorbed and accumulated in distant tissues, leading to an inflammatory response and initiation of signaling pathways responsible for malignant transformation. Full article

Penetration testing, a critical component of cybersecurity, typically requires extensive time and effort to find vulnerabilities. Beginners in this field often benefit from collaborative approaches with the community or experts. To address this, we develop Cybersecurity Intelligent Penetration-testing Helper for Ethical Researchers (CIPHER), a large language model specifically trained to assist in penetration testing tasks as a chatbot. Unlike software development, penetration testing involves domain-specific knowledge that is not widely documented or easily accessible, necessitating a specialized training approach for AI language models. CIPHER was trained using over 300 high-quality write-ups of vulnerable machines, hacking techniques, and documentation of open-source penetration testing tools augmented in an expert response structure. Additionally, we introduced the Findings, Action, Reasoning, and Results (FARR) Flow augmentation, a novel method to augment penetration testing write-ups to establish a fully automated pentesting simulation benchmark tailored for large language models. This approach fills a significant gap in traditional cybersecurity Q&A benchmarks and provides a realistic and rigorous standard for evaluating LLM’s technical knowledge, reasoning capabilities, and practical utility in dynamic penetration testing scenarios. In our assessments, CIPHER achieved the best overall performance in providing accurate suggestion responses compared to other open-source penetration testing models of similar size and even larger state-of-the-art models like Llama 3 70B and Qwen1.5 72B Chat, particularly on insane difficulty machine setups. This demonstrates that the current capabilities of general large language models (LLMs) are insufficient for effectively guiding users through the penetration testing process. We also discuss the potential for improvement through scaling and the development of better benchmarks using FARR Flow augmentation results. Full article

Harmful algae blooms (HABs) have been reported with greater frequency in lakes across New York State (NYS) in recent years. In situ sampling is used to assess water quality, but such observations are time intensive and therefore practically limited in their spatial extent. Previous research has used remote sensing imagery to estimate phytoplankton pigments (typically chlorophyll-a or phycocyanin) as HAB indicators. The primary goal of this study was to validate a remote sensing-based method to estimate cyanobacteria concentrations at high temporal (5 days) and spatial (10–20 m) resolution, to allow identification of lakes across NYS at a significant risk of algal blooms, thereby facilitating targeted field investigations. We used Google Earth Engine (GEE) as a cloud computing platform to develop an efficient methodology to process Sentinel-2 image collections at a large spatial and temporal scale. Our research used linear regression to model the correlation between in situ observations of chlorophyll-a (Chl-a) and phycocyanin and indices derived from Sentinel-2 data to evaluate the potential of remote sensing-derived inputs for estimating cyanobacteria concentrations. We tested the performance of empirical models based on seven remote-sensing-derived indices, two in situ measurements, two cloud mitigation approaches, and three temporal sampling windows across NYS lakes for 2019 and 2020. Our best base model (R² of 0.63), using concurrent sampling data and the ESA cloud masking—i.e., the QA60 bitmask—approach, related the maximum peak height (MPH) index to phycocyanin concentrations. Expanding the temporal match using a one-day time window increased the available training dataset size and improved the fit of the linear regression model (R² of 0.71), highlighting the positive impact of increasing the training dataset on model fit. Applying the Cloud Score+ method for filtering cloud and cloud shadows further improved the fit of the phycocyanin estimation model, with an R² of 0.84, but did not result in substantial improvements in the model’s application. The fit of the Chl-a models was generally poorer, but these models still had good accuracy in detecting moderate and high Chl-a values. Future work will focus on exploring alternative algorithms that can incorporate diverse data sources and lake characteristics, contributing to a deeper understanding of the relationship between remote sensing data and water quality parameters. This research provides a valuable tool for cyanobacteria parameter estimation with confidence quantification to identify lakes at risk of algal blooms. Full article

Traditionally, for indoor radon testing, predominantly passive measurements have been used, typically applying the solid-state alpha track-etch method for long-term and the charcoal method for short-term measurements. However, increasingly, affordable consumer-grade active monitors have become available in the last few years, which can generate a concentration time series of an almost arbitrary duration. Firstly, we argue that consumer-grade monitors can well be used for quality-assured indoor radon assessment and consequent reliable decisions. Secondly, we discuss the requirements of quality assurance, which actually allow for reliable decision-making. In particular, as part of a rational strategy, we discuss how to interpret measurement results from low-cost active monitors with high and low sensitivity with respect to deciding on conformity with reference levels that are the annual average concentration of indoor radon. Rigorous analysis shows that temporal variations in radon are a major component of the uncertainty in decision-making, the reliability of which is practically independent of monitor sensitivity. Manufacturers of low-cost radon monitors already provide sufficient reliability and quality of calibration for their devices, which can be used by both professional inspectors and the general public. Therefore, within the suggested measurement strategy and metrologically assured criteria, we only propose to clarify the set and values of the key metrological characteristics of radon monitors as well as to upgrade user-friendly online tools. By implementing clear metrological requirements as well as the rational measurement strategy for the reliable conformity assessment of a room (building) with radon safety requirements, we anticipate significant reductions in testing costs, increased accessibility, and enhanced quality assurance and control (QA/QC) in indoor radon measurements. Full article

In response to novel and complex uncertainties, the present research is conducted to characterize the most significant indicators of watershed health including drought, water quality, and vegetation for the Tajan watershed, Mazandaran, Iran. The Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) are, respectively, used to quantify the meteorological and hydrological droughts in the present (1993–2020) and future (2023–2050) employing optimistic RCP2.6 and pessimistic RCP8.5 scenarios. To concoct discharge data for the future, IHACRES v1.0 software is used with a Nash–Sutcliffe coefficient (NSE) of 0.48 and a coefficient of determination (R²) of 0.58. Maps of land use and Normalized Difference Vegetation Index (NDVI) are also prepared using Landsat images. Subsequently, the surface water quality is assessed using AqQA v1.1.0 software. The results show the difference in the severity of future meteorological droughts in different stations. In addition, the predominance of non-drought (SDI ≥ 0) or mild drought (−1 ≤ SDI < 0) is indicated for future hydrology. The land use changes show a decrease in rangeland (−5.47%) and an increase in residential land (9.17%). The water quality analysis also indicates an increase in carbonate ions in the watershed outlet. Communicating the relationships between study indicators, which is a big gap in the current watershed management approach, avoids future failures and catastrophes. Full article