Quantitative Results Research Articles

An adaptive hexagonal deployment model for resilient wireless sensor networks in precision agriculture

This study presents an innovative hexagonal deployment model designed specifically for wireless sensor networks (WSNs) with a primary application in precision agriculture. The proposed protocol integrates advanced features, notably an adaptive frequency-hopping spread spectrum (AFHSS) mechanism and a decentralized real-time adaptation strategy to optimize data transmission in dynamic agricultural environments. The simulation study, conducted in diverse terrains with realistic sensor node distributions, meticulously evaluates the protocol’s performance using comprehensive Quality of Service (QoS) metrics. The hexagonal deployment model operates by strategically positioning sensor nodes in a hexagonal grid pattern, ensuring uniform coverage of the agricultural field. The AFHSS mechanism dynamically adjusts frequency channels, mitigating interference and fortifying the network’s robustness against external disruptions. Complementing this, the decentralized real-time adaptation empowers individual nodes to autonomously respond to the ever-changing environmental conditions, optimizing data transmission efficiency. Quantitative results from the simulations exhibit outstanding performance metrics. The protocol achieves an average latency of 50 milliseconds, a packet loss rate below 2%, a success rate exceeding 95%, and highly efficient obstacle management, with adjusted nodes accounting for less than 5%. These compelling outcomes underscore the protocol’s exceptional ability to deliver responsive and reliable data transmission, positioning it as a promising solution for enhancing environmental monitoring in precision agriculture. This study provides quantitative evidence of the protocol’s prowess and delves into the nuanced working mechanisms, offering a deeper understanding of its potential impact. The findings contribute significant insights to the field, serving as a robust foundation for researchers and practitioners engaged in designing and implementing resilient WSNs tailored for precision agriculture applications.

Faulted karst reservoir spaces in Middle-Lower Triassic carbonates, Qingjiang Region, Yangtze block, China

The complexity and heterogeneity of ultra-deep faulted karst reservoirs pose significant challenges for hydrocarbon exploration. In this study, we integrated remote sensing image analysis, field measurements, and core sample testing to evaluate the characteristics and controlling factors of carbonate reservoir spaces within the Middle and Lower Triassic formations along a regional strike-slip fault in the Qingjiang region of China. The strike-slip fault zone is composed of multiple fault cores and damage zones. Based on differences in damage zone width and linear fault density, the fault zone was subdivided into transtensional and transpressional segmentations. The carbonate reservoirs, primarily developed within the damage zones, consist of fractures, fracture clusters, and cavities. Detailed measurements of the carbonate outcrops were conducted to obtain geometric parameters of the reservoir spaces. Quantitative results indicate that in the transtensional segmentation, the reservoir is dominated by tensile fracture-cavity systems, characterized by larger fracture apertures (0.13–1.25 m), higher linear fracture density (0.38–8.37 m⁻1), and well-developed cavities (0.03–4.84 m2), which contribute to better fluid connectivity and storage capacity. In contrast, the transpressional segmentation is dominated by compressional fracture-fracture cluster systems, with longer fractures (0.11–12.52 m), smaller fracture apertures (0.01–0.94 m), and extensive fracture clusters development (0.18–17.87 m2), but with lower fluid connectivity and limited storage capacity. Mechanical testing results show that the average compressive strength in the transtensional segmentation (133.95 MPa) is significantly higher than that in the transpressional segmentation (70.28 MPa). In terms of mineral composition, the transtensional segmentation has a higher calcite content, whereas the transpressional segmentation is richer in dolomite and quartz. Based on the observed differences in reservoir space characteristics across the strike-slip fault zone, we discussed the combined effects of structural segmentation, formation thickness, rock mechanics, and brittle mineral content on reservoir space development. The study emphasizes that stress conditions (primary factor) and material properties (secondary factor) jointly control fluid migration and storage efficiency in the reservoirs. Additionally, we suggest that the outcrop studies in the Qingjiang region provide valuable geological analogs for faulted karst reservoirs, offering critical insights for improving the precision of carbonate reservoir exploration and optimizing production efficiency.

Assessing and mitigating building fire risk based on the scenario clusters in Bangladesh

The increasing occurrence of fire hazards in residential buildings in Bangladesh has led to severe property damage, physical injuries, and fatalities, underscoring a critical research gap in effective fire risk management. This study aims to address this gap by identifying key fire risks and proposing mitigation strategies using BIM technology. The research employs a comprehensive survey and scenario clustering methodology to identify these risks and assess the efficacy of various fire-resistant materials. Data collection involved questionnaires distributed to 200 respondents, including building owners, occupants, engineers, and firefighters. These clusters are based on the metrics of fatality numbers and property damage to quantify fire risk. Findings highlight the significant role of wall and ceiling lining materials (RII = 0.919) in contributing to fire risk. Further analysis was conducted using a residential building model developed in Autodesk Revit and simulated in Pyrosim software, assessing various lining materials. Quantitative results demonstrated that fire brick is the most effective material, exhibiting the lowest heat release rate (968 kW/m2), minimum adiabatic surface temperature (28°C), minimum heat transfer coefficient (1.75 W/m2/K), and lowest surface burning rate (7.5e−08 Kg/m2/s). These results suggest that fire brick is the optimal choice for enhancing fire resistance in residential buildings.

Numerical investigation of thermal influence on debonding behavior in composite structures through a friction and cohesive coupled model

AbstractThis study conducts an in-depth numerical analysis of debonding behavior in composite structures, with a particular focus on the critical role of thermal effects. Utilizing a frictional contact cohesive zone model, the research characterizes the debonding process while sequentially integrating thermal analysis to assess the impact of thermal loading. A thermomechanical coupled model is developed and implemented using the finite element software ABAQUS. The model's accuracy is validated through the Double Cantilever Beam (DCB) test, ensuring reliable results. The methodology involves a detailed finite element analysis, where thermal loads are applied to composite specimens, followed by mechanical loading to simulate debonding. The frictional contact cohesive zone model accurately captures the interface behavior under varying thermal conditions. Quantitative results indicate that thermal loading significantly affects the debonding process, with a noticeable increase in debonding initiation and propagation rates, highlighting the critical influence of thermal effects on structural integrity.

Real-time ultrasound AR 3D visualization toward better topological structure perception for hepatobiliary surgery.

Ultrasound serves as a crucial intraoperative imaging tool for hepatobiliary surgeons, enabling the identification of complex anatomical structures like blood vessels, bile ducts, and lesions. However, the reliance on manual mental reconstruction of 3D topologies from 2D ultrasound images presents significant challenges, leading to a pressing need for tools to assist surgeons with real-time identification of 3D topological anatomy. We propose a real-time ultrasound AR 3D visualization method for intraoperative 2D ultrasound imaging. Our system leverages backward alpha blending to integrate multi-planar ultrasound data effectively. To ensure continuity between 2D ultrasound planes, we employ spatial smoothing techniques to interpolate the widely spaced ultrasound planes. A dynamic 3D transfer function is also developed to enhance spatial representation through color differentiation. Comparative experiments involving our AR visualization of 3D ultrasound, alongside AR visualization of 2D ultrasound and 2D visualization of 3D ultrasound, demonstrated that the proposed method significantly reduced operational time(110.25 ± 27.83s compared to 292 ± 146.63s and 365.25 ± 131.62s), improved depth perception and comprehension of complex topologies, contributing to reduced pressure and increased personal satisfaction among users. Quantitative experimental results and feedback from both novice and experienced physicians highlight our system's exceptional ability to enhance the understanding of complex topological anatomy. This improvement is crucial for accurate ultrasound diagnosis and informed surgical decision-making, underscoring the system's clinical applicability.

Typology of Muslim Audience in Responding to Media Contents Consist of Religious Symbols

Audience is one of central terms that related with media according to the historical of the terms and the mass communication discourse. On the other sides, audiences referring to several authors who have given their concerns to it, audience related to economic, business, technology, politic, and etc. interest. Audience came after the media invented. However, the audience dynamic from the previous (ancestor) periods to our current time still relevant to be explored, described, and explained particularly that related to the Muslim community as the increasing community in world and as major community in Indonesia, in this research look through the religious symbols on media contents to be based of category of Muslim audience as the actor related with the media. Therefore, this research tries to find the typology of Muslim audience in responding to media contents consisting of religious symbols. This research using quantitative and qualitative data which was collected through cross-sectional survey. The UG approach and Active audience approach promoted by authors as Biocca being the theoretical based in collecting and analysis data of this research. The descriptive statistic is the quantitative result to be based the typology and the qualitative data to find the enrich and deeper description and explanation about how Muslim use media until researcher be able to make the categorization of Muslim audience regarding how they have responded to media contents consisting religious symbols. Finally, this research got seven big or rough categories of Muslim audiences in responding to media content containing religious symbols. There are clear enough that online platform media, primarily media social as the dominant media looking over the Muslim audience in this research as instagram. Almost 80% the audiences looking for the media content consisting of religious symbols when they are using media. Also the major of the audiences in this research are young people, which have educational background senior high school, and when they are asked through questionnaire they are students the particular sub-topics show us they are hesitated to give their judgment on them. However, even there are a number of interesting findings, two typical result that religious symbols could be used to educational purposes even they think the content usually bring to the controversy in society, at least what we find frequently on media social.

Highly sensitive time-resolved fluorescent microspheres lateral flow immunoassay for the quantitative detection of triadimefon and its metabolite residues in fruits and vegetables.

Ageneral one-step lateral flow immunochromatographic assay (LFIA) for the quantitative detection of triadimefon (TDF) and triadimenol(TDN) in fruit and vegetable samples was developedusing time-resolved fluorescence microspheres (TRFM) as labels. A specific anti-triadimefon monoclonal antibody (mAb) was conjugated with TRFM to fabricate LFIA test strips. A time-resolved fluorometer as an LFIA reader was applied to obtain quantitative results and assess risk ranges for the LFIA test strips. Under the optimized experimental conditions, the limits of detection (LODs) in buffer/cucumbers/tomatoes/oranges were 0.046ng/mL, 0.135µg/kg, 1.047µg/kg, and 5.811µg/kg, respectively, which are ca. 1000 times lower than that of colloidal gold-labeled strips. The recovery in cucumber/tomato/orange samples was 109.4-116.7%, 87.7-110.9%, and 88.0-111.9%, respectively, indicating that the test strips had good reliability. Coupled with the easily customizable pretreatment procedures for various samples, the LFIA results were obtained within 18min without the need for professional personnel or complicated equipment. TRFM-LFIA for TDF and TDN also shows remarkable specificity and precision. The test strips were also low-cost, portable, and convenientto use. These results indicate the test strips could be utilized as a novel strategy for on-site detection of TDF and TDN, which has the potential to expand and detect other pesticide or insecticide residues in food.

The Effectiveness of an Intervention Based on the Information, Motivation, and Behavioral Skills Model on Treatment Adherence and Self-Efficacy in Patients with Coronary Artery Diseases: An Explanatory Mixed Method Study

Background: Promoting adherence to treatment and self-efficacy in patients with coronary artery diseases helps to improve patient outcomes. The present study was conducted to explain the effectiveness of interventions based on information, motivation, and behavioral skills model on treatment adherence and self-efficacy in patients with coronary artery diseases. Methods: This explanatory-sequential study was conducted on patients with coronary artery diseases in Shiraz, Iran. First, a quasi-experimental study was conducted on 112 patients who were randomly assigned to two intervention and control groups. The intervention consisted of ten motivational training sessions that were designed based on Information, Motivation, and Behavioral Skills (IMBS) model and continued with six months of telephone follow-up and counseling. Data were collected before, three months, and six months after the end of the intervention. Then, in the second phase, a qualitative study including 20 in-depth interviews with 18 participants from the intervention group was conducted. The data were analyzed by deductive and inductive qualitative content analysis. Finally, quantitative findings were explained with the help of qualitative findings. Results: The findings showed that the mean score of treatment adherence, medication adherence, and self-efficacy in the intervention group was significantly higher than the control group 3 and 6 months after the intervention (p<0.001). The results of the qualitative phase of the study also showed that the four main categories include receiving targeted information, encouragement to care, gained self-efficacy/doubt in care, and improvement/non-improvement of treatment adherence behaviors can explain quantitative phase results. Conclusion: The results showed that receiving training with effective methods, strengthening the support system, self-confidence and positive attitudes about care, and promoting self-management and proper management of emotional distress contributed to the effectiveness of the interventions. However, receiving conflicting information, limited financial resources, and the inability to face challenges were among the obstacles to strengthening self-efficacy and adherence to treatment.

Nonlinear Dynamics for the Ising Model

We introduce and analyze a natural class of nonlinear dynamics for spin systems such as the Ising model. This class of dynamics is based on the framework of mass action kinetics, which models the evolution of systems of entities under pairwise interactions, and captures a number of important nonlinear models from various fields, including chemical reaction networks, Boltzmann’s model of an ideal gas, recombination in population genetics and genetic algorithms. In the context of spin systems, it is a natural generalization of linear dynamics based on Markov chains, such as Glauber dynamics and block dynamics, which are by now well understood. However, the inherent nonlinearity makes the dynamics much harder to analyze, and rigorous quantitative results so far are limited to processes which converge to essentially trivial stationary distributions that are product measures. In this paper we provide the first quantitative convergence analysis for natural nonlinear dynamics in a combinatorial setting where the stationary distribution contains non-trivial correlations, namely spin systems at high temperatures. We prove that nonlinear versions of both the Glauber dynamics and the block dynamics converge to the Gibbs distribution of the Ising model (with given external fields) in times O(nlogn)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n\\log n)$$\\end{document} and O(logn)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(\\log n)$$\\end{document} respectively, where n is the size of the underlying graph (number of spins). Given the lack of general analytical methods for such nonlinear systems, our analysis is unconventional, and combines tools such as information percolation (due in the linear setting to Lubetzky and Sly), a novel coupling of the Ising model with Erdős-Rényi random graphs, and non-traditional branching processes augmented by a “fragmentation” process. Our results extend immediately to any spin system with a finite number of spins and bounded interactions.

Y/N and OOMFs: Development, Maintenance, and Consequences of Parasocial Relationships on Stan Twitter

The study examines Stan Twitter users’ sense of belonging and their likelihood of creating PSRs as a substitute for real-life connections lost due to the COVID-19 pandemic. A convergent mixed-method design: specifically, the parallel databases variant was used to test the hypothesis and explore research questions. Conducted in January 2022, the study involved online interviews via Zoom with participants from the Philippines. Participants included Stan Twitter users (N = 126) and non-Stan Twitter users (N = 111), all at least 18 years old. Additionally, qualitative interviews were conducted with Stan Twitter users (N = 12) to explore PSRs in depth. Quantitative results (t = 0.548; p = 0.584) indicate no significant difference in the need to belong between the samples; Pandemic (i.e., period) and generational (i.e., Gen Z) effects are seen as contributors. Qualitative results show that PSRs develop due to fulfillment of belongingness needs and other factors, are maintained through parasocial interactions and fandom activities, and have both negative and positive implications for stans’ relationships, fandom behavior, personal growth, and well-being. The findings suggest reinforcing cybersecurity measures and potentially redefining PSRs due to their perceived reciprocation.

Coal-Mine Water-Hazard Risk Evaluation Based on the Combination of Extension Theory, Game Theory, and Dempster–Shafer Evidence Theory

Due to the complex hydrogeological conditions and water hazards in coal mines, there are multiple indexes, complexities, incompatibilities, and uncertainty issues in the risk evaluation process of coal-mine water hazards. To accurately evaluate the risk of coal-mine water hazards, a comprehensive evaluation method based on extension theory, game theory, and Dempster–Shafer (DS) evidence theory is proposed. Firstly, a hierarchical water-hazard risk-evaluation index system is established, and then matter-element theory in extension theory is used to establish a matter-element model for coal-mine water-hazard risk. The membership relationship between various evaluation indexes and risk grades of coal-mine water-hazard risk is quantified using correlation functions of extension set theory, and the quantitative results are normalized to obtain basic belief assignments (BBAs) of risk grades for each index. Then, the subjective weights of evaluation indexes are calculated using the order relation analysis (G1) method, and the objective weights of evaluation indexes are calculated using the entropy weight (EW) method. The improved combination weighting method of game theory (ICWMGT) is introduced to determine the combination weight of each evaluation index, which is used to correct the BBAs of risk grades for each index. Finally, the fusion of DS evidence theory based on matrix analysis is used to fuse BBAs, and the rating with the highest belief fusion result is taken as the final evaluation result. The evaluation model was applied to the water-hazard risk evaluation of Sangbei Coal Mine, the evaluation result was of II grade water-hazard risk, and it was in line with the actual engineering situation. The evaluation result was compared with the evaluation results of three methods, namely the expert scoring method, the fuzzy comprehensive evaluation method, and the extension method. The scientificity and reliability of the method adopted in this paper were verified through this method. At the same time, based on the evaluation results, in-depth data mining was conducted on the risk indexes of coal-mine water hazards, and it was mainly found that 11 secondary indexes are the focus of coal-mine water-hazard risk prevention and control, among which seven indexes are the primary starting point for coal-mine water-hazard risk prevention and control. The groundwater index in particular has the most prominent impact. These results can provide a theoretical basis and scientific guidance for the specific water-hazard prevention and control work of coal mines.

MSDA-HLGCformer-based context-aware fusion network for underwater organism detection

Underwater organism detection is crucial for marine ecology research and resource management. However, underwater images often suffer from issues like blurring, noise, and color distortion, making it challenging to distinguish targets from complex backgrounds. Existing detection methods struggle to separate targets in such conditions. To address these problems, we propose a Cross-Context Fusion Transformer YOLO network (CCFT-YOLO) for underwater organism detection. Specifically, we propose an MSDA-HLGCformer backbone network that captures and integrates local features at different depths and distances through the Multi-Scale Dilated Attention (MSDA) block at the low level, while at the high level, a hierarchical local–global context former (HLGCformer) is developed to contextualize the information at each scale, allowing the model to focus on the global context of the underwater environment. A context-aware cross-level fusion module (CACLFM) is designed to balance the retention of salient target and background texture information by integrating local and global information. Quantitative and qualitative experimental results show that CCFT-YOLO is competitive and robust in underwater organism detection compared to other state-of-the-art detectors.

EHNet: Efficient Hybrid Network with Dual Attention for Image Deblurring

The motion of an object or camera platform makes the acquired image blurred. This degradation is a major reason to obtain a poor-quality image from an imaging sensor. Therefore, developing an efficient deep-learning-based image processing method to remove the blur artifact is desirable. Deep learning has recently demonstrated significant efficacy in image deblurring, primarily through convolutional neural networks (CNNs) and Transformers. However, the limited receptive fields of CNNs restrict their ability to capture long-range structural dependencies. In contrast, Transformers excel at modeling these dependencies, but they are computationally expensive for high-resolution inputs and lack the appropriate inductive bias. To overcome these challenges, we propose an Efficient Hybrid Network (EHNet) that employs CNN encoders for local feature extraction and Transformer decoders with a dual-attention module to capture spatial and channel-wise dependencies. This synergy facilitates the acquisition of rich contextual information for high-quality image deblurring. Additionally, we introduce the Simple Feature-Embedding Module (SFEM) to replace the pointwise and depthwise convolutions to generate simplified embedding features in the self-attention mechanism. This innovation substantially reduces computational complexity and memory usage while maintaining overall performance. Finally, through comprehensive experiments, our compact model yields promising quantitative and qualitative results for image deblurring on various benchmark datasets.

A conceptual framework for the path of digital preservation of intangible cultural heritage: A thematic review

Abstract The digital preservation strategy could be concerned from the perspective of the life cycle of a digital resource, which includes creation, use, management, and preservation, which has been implemented widely for intangible cultural heritage (ICH) safeguarding. Nonetheless, the depth of development of the metaverse, digital humanities, etc., raises the question of which direction the most up-to-date strategic and theoretical developments are headed in a wider context. As a result, this study will evaluate articles from 2018 to 2024 to determine the trends in digital preservation strategies at ICH. With a thematic review (TR) instrument, a total of 24 articles out of 129 peer-reviewed database articles from the WoS and Scopus databases were extracted and screened, which cover the application of digital preservation strategies in ICH conservation. ATLAS.ti 9, the analysis software, produced quantitative results showing trends in this study. The thematic analysis reveals four key themes: computer technology, cultural transmission, strategy implementation, and metadata preservation. The authors propose a conceptual framework that establishes the logical connection between the four themes and outlines the path for ICH digital preservation. The findings provide valuable insights for researchers and practitioners involved in the digital preservation strategy of ICH, as well as mapping the scope, challenges, and practices of these initiatives.

High-precision optical modeling method for galvanometer-driven dual-camera systems.

To build a galvanometer-driven dual-camera sensing system, it is important to accurately correlate the wide-view image coordinates with the pan-tilt mirror angles for adjusting the incident light path of the zoom-in camera. Existing optical modeling methods assume sufficient target distance and simplify dual-camera optical centers as coincident. However, this simplification is not valid in many practical cases and might cause severe system malfunctions, such as complete loss tracking of important targets. To address this problem, we propose a novel approach, to the best of our knowledge, to facilitate high-precision optical modeling and calibration of galvanometer-driven dual-camera systems. The proposed method takes into consideration the dual-camera optical center misalignment issue and builds a model for accurate estimation and rectification of target localization errors under various optical configurations. Qualitative and quantitative experimental results demonstrate the superiority of our method, improving the performance of galvanometer-driven dual-camera systems for high-precision optical sensing applications.

A large-scale, international cross-sectional survey of published pediatrics authors: Perceptions of complementary, alternative, and integrative medicine

BackgroundThe use of complementary, alternative, and integrative medicine (CAIM) is commonly used among pediatric patients for various conditions. Pediatrics clinicians’ and researchers’ perceptions towards the incorporation of CAIM therapies have varied. This study aims to investigate the perceptions of both pediatrics researchers and clinicians regarding CAIM. MethodsWe conducted a large-scale, international cross-sectional online survey with published pediatrics authors who have published their work in pediatrics medical journals that are indexed in MEDLINE. In total, 34 494 researchers and clinicians were invited to complete the survey. The survey recorded respondents’ perceptions on various CAIM therapies. Descriptive statistics were generated from the quantitative survey results. A thematic analysis was conducted for responses to open ended questions. ResultsIn total, 731 pediatrics clinicians and/or researchers responded to the survey, with about half of the respondents being faculty members/principal investigators (56.10 %) and/or clinicians (43.45 %) and from the Americas (46.56 %) or Europe (30.53 %). Over half of the respondents viewed mind-body therapies favourably (62.01 %) and the fewest respondents held favourable perceptions of biofield therapies (6.98 %). Respondents agreed or strongly agreed that there is value in conducting further research on CAIM therapies (85.52 %) and disagreed or strongly disagreed that they felt comfortable recommending most CAIM therapies to patients (64.83 %). A thematic analysis of our findings demonstrates that many pediatrics clinicians and/or researchers support further research on CAIM. ConclusionThe findings from this study demonstrate that pediatrics clinicians and researchers have varying perceptions towards CAIM therapies. Respondents had the most positive perceptions of mind-body therapies and felt they did not have adequate training on CAIM. Further research is needed to establish more evidence-based educational resources on CAIM.

Instructional Method Shifts: Instructors’ Plans Post Emergency Remote Teaching

The coronavirus pandemic, beginning in 2020, caused significant disruption to higher education around the globe, forcing instructors into emergency remote teaching. This emergency response created unprecedented adaptation to online and distance education processes and activities. Yet, the ways in which those teaching changes influenced or continue to influence teaching in higher education are unknown. This study explores the changes to faculty teaching practices, and associated learning, as a result of emergency remote teaching and faculty plans to continue or discontinue those practices post-pandemic. Quantitative results indicated practices were largely intended to impact cognitive, emotional, behavioral, and social dimensions, social interactions, and learning activities. A significant majority of the practices achieved their intended results and almost two-thirds of the practices will be continued. Qualitative results illustrated a variety of benefits for both students and instructors. These shifts were largely student-centered with noted improvements in areas like flexibility, agency, access, inclusivity, engagement, and connection. Results apply to workplace training and professional development for faculty in higher education and beyond, including ways to engage faculty in valuable teaching practices and support student learning at a distance.

Group-Based Distinctive Image Captioning with Memory Difference Encoding and Attention

AbstractRecent advances in image captioning have focused on enhancing accuracy by substantially increasing the dataset and model size. While conventional captioning models exhibit high performance on established metrics such as BLEU, CIDEr, and SPICE, the capability of captions to distinguish the target image from other similar images is under-explored. To generate distinctive captions, a few pioneers employed contrastive learning or re-weighted the ground-truth captions. However, these approaches often overlook the relationships among objects in a similar image group (e.g., items or properties within the same album or fine-grained events). In this paper, we introduce a novel approach to enhance the distinctiveness of image captions, namely Group-based Differential Distinctive Captioning Method, which visually compares each image with other images in one similar group and highlights the uniqueness of each image. In particular, we introduce a Group-based Differential Memory Attention (GDMA) module, designed to identify and emphasize object features in an image that are uniquely distinguishable within its image group, i.e., those exhibiting low similarity with objects in other images. This mechanism ensures that such unique object features are prioritized during caption generation for the image, thereby enhancing the distinctiveness of the resulting captions. To further refine this process, we select distinctive words from the ground-truth captions to guide both the language decoder and the GDMA module. Additionally, we propose a new evaluation metric, the Distinctive Word Rate (DisWordRate), to quantitatively assess caption distinctiveness. Quantitative results indicate that the proposed method significantly improves the distinctiveness of several baseline models, and achieves state-of-the-art performance on distinctiveness while not excessively sacrificing accuracy. Moreover, the results of our user study are consistent with the quantitative evaluation and demonstrate the rationality of the new metric DisWordRate.

A Composite Approach for Evaluating Operational Cloud Seeding Effect in Stratus Clouds

Robust water management is in intense demand in many water scarcity areas, such as arid and semi-arid regions in the world. As part of the regional water management strategy, rain enhancement is vital to replenish groundwater reservoirs, and the key challenge is how to assess its effectiveness. Some recent weather modification experiments attained cloud seeding effect through advanced in situ measurement coupled with accurate numerical simulation. However, there is still a lack of an objective and scientific approach to quantitatively evaluate the rain enhancement effect, especially for many non-randomized operational cloud seeding activities in China. In this study, we proposed a composite evaluation approach by analyzing two operational aircraft cloud seeding cases in stratus clouds in Shaanxi, China. By calculating the aircraft cloud seeding agent plumes, the target areas (as well as the control areas) of cloud seeding were dynamically and roughly determined. Physical properties, such as radar reflectivity and precipitation, were individually quantified in these areas. The cloud seeding effect was then evaluated by calculating the difference in parameter variation between target and control areas. This approach can be applied to qualitative analysis in a single aircraft cloud seeding operation and can also provide quantitative statistical results from multiple cloud seeding cases. We found that the average precipitation enhancement percentage of 18 operational aircraft cloud seeding cases is ~4.84%. Note that the homogeneity hypothesis of the seeding cloud, the error in the calculation of the target area, and the selection of control areas are the major uncertainties likely in the evaluation of the cloud seeding effect by this approach.

Tumoral Pharmacokinetic-Pharmacodynamic Simulation of Doxorubicin-Encapsulated Polymeric Micelles Using a Tissue-Isolated Tumor Perfusion System.

Pharmacokinetic (PK) elucidation of polymeric micelles delivering anticancer drugs is crucial for accurate antitumor PK-pharmacodynamic (PK-PD) simulations. Particularly, establishing a methodology to quantify the tumor inflow and outflow of anticancer drugs encapsulated in polymeric micelles is an essential challenge. General tumor biodistribution experiments are disadvantageous in that inflow quantification is easy, but outflow quantification is challenging. We addressed this issue by proposing a quantification method that combines a tissue-isolated tumor perfusion system with microdialysis. This method aims to determine tumoral drug inflow and outflow by quantifying the drugs released from the polymeric micelles via a tumor-embedded microdialysis probe and perfusate, respectively. Furthermore, we evaluated the feasibility of this method by perfusing pH-sensitive polyethylene glycol-poly(aspartate-hydrazone-doxorubicin/phenylalanine)n (PPDF-Hyd-DOX) in a tissue-isolated tumor perfusion system, and we quantified tumor inflow and outflow released DOX. Based on the quantitative results, we performed compartmental analyses by incorporating the gamma-distributed delay function and calculated the PK rate constants. These parameters were input into a tumor-bearing rat compartment model for ex vivo-in vivo extrapolation (EVIVE) of the rat plasma PPDF-Hyd-DOX concentrations and simulated intratumorally released DOX concentrations. The simulation profiles demonstrated a good fit with the Walker 256 intratumoral released DOX concentration profiles previously reported. This EVIVE-PK model was coupled with the threshold natural-growth tumor PD model, and PK-PD analysis was performed. This model exhibited a better fit to the tumor weight profile of Walker 256-bearing rats treated with PPDF-Hyd-DOX than that of our previously reported PK-PD model. Thus, EVIVE, based on a tissue-isolated tumor perfusion system with microdialysis, is a promising approach for the PK-PD simulation of polymeric micelle anticancer therapy.