bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of pre‐examination video education in Gd‐EOB‐DTPA‐enhanced liver MRI: A comparative study
Authors: Huang Hongfang , Li Chenhui , Zeng Zisan , Liang Junli
Published: November 2024

Introduction

Hepatocellular carcinoma (HCC) is a leading cause of cancer‐related mortality, and early diagnosis via gadolinium ethoxybenzyl‐diethylenetriamine pentaacetic acid (Gd‐EOB‐DTPA)‐enhanced magnetic resonance imaging (MRI) significantly impacts patient outcomes. However, patient anxiety during MRI can affect image quality. This study investigates the impact of pre‐examination video education on anxiety, satisfaction and image quality in Gd‐EOB‐DTPA‐enhanced liver MRI.

Methods

We prospectively enrolled 480 patients who underwent Gd‐EOB‐DTPA‐enhanced liver MRI from January 2022 to May 2023 at our hospital. Patients were divided into study and control groups in order of odd and even days, with 240 cases in each group. Before the examination, the radiology staff provided routine verbal guidance and breathing training to the patients in the control group, while the study group was given additional video education. The state anxiety scores, satisfaction scores of the provided information and motion artefact scores of the images before and after the examination were compared between the two groups.

Results

The state anxiety scores of both groups of patients were lower than before the examination (all P < 0.05), but the change value of the study group was significantly greater than that of the control group (P = 0.004). The satisfaction rate of the information provided before the scan in the study group was significantly higher (P < 0.001). The image quality scores of the arterial phase were similar between the two groups (P = 0.403), but the image quality of the study group in the pre‐contrast, portal phase, transitional phase and hepatobiliary phase was significantly better than that of the control group (all P < 0.05).

Conclusion

Supplementing routine pre‐scan care with video guidance for Gd‐EOB‐DTPA‐enhanced liver MRI offers several benefits, including reduced patient anxiety, increased satisfaction and improved image quality. These results suggest the potential for widespread application of video‐based interventions to enhance the MRI experience for patients.
Advancing the Commercialization of Perovskite‐Based Radiation Detectors for High‐Resolution Imaging
Authors: Zhang Wenqing , Wang Hu , Chen Zhilong , Shao Yuchuan
Published: November 2024

Radiation detectors play an indispensable role in medical diagnostics, industrial non‐destructive inspection and national security. Recently, halide perovskites are considered as the new generation of radiation active materials due to excellent optoelectronic properties such as adjustable bandgap, high absorption coefficient, high carrier mobility and low cost. The radiation detectors based on perovskite show high sensitivity and low detection limit, contributing to excellent spatial resolution for imaging. However, the commercialization of perovskite radiation detectors for high quality imaging still faces many challenges, including ion migration in perovskite, fermi level pinning and electrochemical reaction at the interface of perovskite/electrode, and difficulties of integration with readout circuit. All the issues hinder the further improvement of device performance. This review summarizes the material forms and the optimized growth methods of perovskite for radiation imaging detectors. Further, this work focuses on challenges and improvements of the interface between perovskites and electrodes. Meanwhile, this work outlines the technical routes used to realize array detectors for radiation imaging. The comprehensive review would guide the commercialization of perovskite radiation detectors for high‐quality imaging.
Suppressing the Dark Current Under Forward Bias for Dual‐Mode Organic Photodiodes
Authors: Xiao Jianhua , Wang Yang , Wu Jiaao , Yuan Liu , Tai Huiling , Jiang Yadong
Published: November 2024

Tremendous research efforts are developed to suppress the reverse dark current (Jd) and enhance the responsivity of organic photodiodes (OPDs). The functional layers of traditional OPDs usually follow the principle of energy level alignment to make unobstructed photo‐carriers transport under reverse bias, but this inevitably leads to a large forward Jd. Herein, a universal strategy is proposed to manipulate the carrier dynamics and effectively suppress the forward Jd of OPDs, that is, tuning the energy level and electron traps of the anode interface layers (AILs). The bandgap and electron traps of typical organometallic chelate AIL (PEIE‐Co) can be well controlled by adjusting the component ratio of PEIE and metal ions. The wide bandgap increases the carrier injection barrier under reverse and forward bias, endowing OPD with a much lower Jd; the electron traps induce hole tunneling injection by capturing photo‐generated electrons under forward bias, thereby enabling the photomultiplication effect. The obtained OPD exhibits photoconductive/photomultiplication working mode at reverse/forward bias and the specific detectivity approaches ≈1013/1012 Jones, showing promise for adaptively detecting faint and strong light. This study presents an intelligent strategy to achieve dual‐mode OPDs, paving the way for the multifunctional development of photodetectors.
Hyper‐Sampling Imaging by Measurement of Intra‐Pixel Quantum Efficiency Using Steady Wave Field
Authors: Xue Hemeng , Shang Mingtao , Zhang Ze , Yu Hongfei , Liang Jinchao , Guan Meiling , Sun Chengming , Wang Huahua , Wang Shufeng , Ye Zhengyu , Gao Feng , Gao Lu
Published: November 2024

The transition from optical film to digital image sensors (DIS) in imaging systems has brought great convenience to human life. However, the sampling resolution of DIS is considerably lower than that of optical film due to the limitation that the pixels are significantly larger than the silver halide molecules. How to break DIS's sampling limit and achieve high‐resolution imaging is highly desired for imaging applications. In the research, a novel mechanism that allows for a significant reduction in the smallest sampling unit of DIS to as small as 1/16th of a pixel, or even smaller, through measuring the intra‐pixel quantum efficiency for the first time and recomputing the image — a technique referred to as hyper‐sampling imaging (HSI) is developed. Employing the HSI method, the physical sampling resolution of regular DIS can be enhanced by 4 × 4 times or potentially higher, and detailed object information can be acquired. The HSI method has undergone rigorous testing in real‐world imaging scenarios, demonstrating its robustness and efficiency in overcoming the sampling constraints of conventional DIS. This advancement is particularly beneficial for applications such as remote sensing, long‐range reconnaissance, and astronomical observations, where the ability to capture fine details is paramount.
Highly Efficient Flexible Antimony Halide Scintillator Films with In Situ Preparation for High‐Resolution X‐Ray Imaging
Authors: Meng Haixing , Li Ying , Wang Ying , Zhu Minqi , Xiao Jiawen , Shen Guozhen
Published: November 2024

Flexible scintillators with high light yield, spatial resolution and low light scattering are ideal for X‐ray imaging application. However, conventional scintillators are always prepared by crystallization of functional layer, grinding and mixing with polymers, resulting in serious light scattering. Herein, an in situ fabrication strategy is proposed to prepare a low light scattering flexible scintillator film based on 0D antimony halide C38H36P2SbCl5 (MTP2SbCl5). The prepared scintillator film exhibits bright yellow emission with an outstanding photoluminescence quantum yield (PLQY) of 99.69%, and it demonstrates linear responsiveness to X‐ray dose, achieving an impressive light yield of 39800 photons MeV−1 and a low detection limitation of 78.4 nGyair s−1. The scintillator film possesses strong radiation hardness and stability. In addition, low light scattering greatly inhibits optical crosstalk during X‐ray detection, effectively improving the spatial resolution of MTP2SbCl5 film from 4.5 to 10.2 lp mm−1. On account of the simple preparation method and high performance, this work provides guidance for the preparation of high‐efficiency, large‐area, low‐scattering and high‐resolution flexible scintillator in the future.
A Hyperbranched Phosphorus/Nitrogen/Silicon‐Containing Polymer as a Multifunctional Additive for Epoxy Resins
Authors: Zhong Qian , Huo Siqi , Wang Cheng , Ye Guofeng , Zhang Qi , Wang Hao , Liu Zhitian
Published: November 2024

High‐performance, versatile epoxy resins (EPs) are used in a variety of fields, but the manufacture of transparent, fireproof, and strong EPs remains a major challenge. The hyperbranched, multifunctional flame retardant (DSi) is prepared by using diethanolamine, polyformaldehyde, diphenylphosphine oxide, and phenyltrimethoxysilane as raw materials in this work. When the additional amount of DSi is only 2 wt.%, the EP‐DSi2 sample reaches a vertical burning (UL‐94) V‐0, and its limiting oxygen index (LOI) is 32.8%. When the content of DSi is 3 wt.%, the peak heat release rate (PHRR) and total smoke production (TSP) of EP‐DSi samples are 43.8% and 21.4% lower than those of EP. The good compatibility of DSi and EP endows EP‐DSi with high transparency, and the hyperbranched structure of DSi makes EP‐DSi have obviously enhanced mechanical strength and toughness. The enhanced fire safety of EP‐DSi is mainly due to the promoting carbonization and radical quenching effects of DSi. This paper offers a comprehensive design concept aimed at creating high‐performance epoxy resins with good optical, mechanical, and flame‐retardant properties, which have broad application prospects.
Recent advances in the application of functional hydrogels in skin wound healing
Authors: Bei Zhongwu , Zheng Jing
Published: November 2024

The effective treatment of skin wounds has long posed a significant challenge in the medical field, impacting patient comfort, quality of life, and the rate and outcome of wound healing. With continuous advancements in science and technology, novel materials have emerged, providing new possibilities for skin wound treatment. Among these, multifunctional hydrogels have shown considerable potential in promoting skin wound healing as a type of wound dressing material. This review systematically examines the progress in the application of multifunctional hydrogels in skin wound healing. Initially, the structure and composition of the skin are introduced. Subsequently, skin wounds are classified, and the wound‐healing process is discussed in detail. Traditional and modern dressings are then categorized, with a particular emphasis on the characteristics and applications of hydrogel dressings. The various functions of hydrogels in skin wound healing, including antibacterial, antioxidant, hemostatic, adhesive, stimulus‐responsive, and wound status monitoring, are reviewed. The paper concludes with a summary of the existing research gaps and provides insights into the future development directions of multifunctional hydrogels. This review aims to guide the preparation of hydrogel wound dressings and offer theoretical references for the exploration of next‐generation functional hydrogels.
Multi‐scale contextual learning for medical image segmentation via dual distillation
Authors: Cui Ruize , Liu Lanqing , Song Youyi , Ren Ge , Hu Xiaowei , Qin Jing
Published: November 2024

Background

Recently, many studies have explored fusing features extracted from Convolutional neural networks (CNNs) and transformers to integrate multi‐scale representations for better performance in medical image segmentation tasks. Although these hybrid models have achieved better results than previous CNN‐based and transformer‐based methods, they suffer from high computation and space complexities.

Purpose

The purpose of this research is to address the prohibitive computation and space complexities of hybrid models, which limit their application in clinical practice where computational resources are usually constrained.

Methods

We propose a novel model equipped with a dual distillation scheme to sufficiently harness the complementary advantages of CNNs and transformers without compromising model efficiency. We further propose a multi‐scale prior‐knowledge distillation (MPD) module to effectively distill multi‐scale knowledge from features extracted from transformers. In addition, to cooperate with the knowledge distillation scheme, we also propose an efficient and robust Selective Fusion module in the student network.

Results

We extensively evaluate the proposed model against fourteen different network frameworks on two representative datasets: SipakMed and ISIC 2017. In the SipakMed dataset, 3037 Pap smear images are used for training and 1012 for testing. In the ISIC 2017 dataset, 2000 dermoscopic images are used for training, 150 for validation, and 600 for testing. Experimental results demonstrate that our method not only surpasses existing methods by a considerable margin with respect to the evaluation metrics of mean Intersection over Union, mean Dice coefficient, mean average symmetric surface distance, but also requires fewer computational resources in terms of model parameters and floating‐point operations per second.

Conclusions

Comprehensive comparisons in terms of segmentation accuracy and computational complexity unequivocally confirm that our method effectively and efficiently integrates the advantages of both CNNs and transformers, showing its suitability and significance for clinical applications.
Quantum‐Dot Technology: A Decade of Innovation and Future Prospects
Authors: Devenney Martin , Yurek Jeff , Luo ZhongSheng
Published: November 2024

Emerging applications in automotive displays, environmental considerations around cadmium‐free QDs, and QDEL technology highlight promise across industries.
Jenny Donelan
Authors: Peruvemba Sri
Published: November 2024

Former Editor of ID, Writer, Journalist, Problem Solver, and Kind Leader
MRI denoising with a non‐blind deep complex‐valued convolutional neural network
Authors: Dou Quan , Wang Zhixing , Feng Xue , Campbell‐Washburn Adrienne E. , Mugler John P. , Meyer Craig H.
Published: November 2024

MR images with high signal‐to‐noise ratio (SNR) provide more diagnostic information. Various methods for MRI denoising have been developed, but the majority of them operate on the magnitude image and neglect the phase information. Therefore, the goal of this work is to design and implement a complex‐valued convolutional neural network (CNN) for MRI denoising. A complex‐valued CNN incorporating the noise level map (non‐blind DnCNN) was trained with ground truth and simulated noise‐corrupted image pairs. The proposed method was validated using both simulated and in vivo data collected from low‐field scanners. Its denoising performance was quantitively and qualitatively evaluated, and it was compared with the real‐valued CNN and several other algorithms. For the simulated noise‐corrupted testing dataset, the complex‐valued models had superior normalized root‐mean‐square error, peak SNR, structural similarity index, and phase ABSD. By incorporating the noise level map, the non‐blind DnCNN showed better performance in dealing with spatially varying parallel imaging noise. For in vivo low‐field data, the non‐blind DnCNN significantly improved the SNR and visual quality of the image. The proposed non‐blind DnCNN provides an efficient and effective approach for MRI denoising. This is the first application of non‐blind DnCNN to medical imaging. The method holds the potential to enable improved low‐field MRI, facilitating enhanced diagnostic imaging in under‐resourced areas.
Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review
Authors: Aghaei M. , Kolahi M. , Nedaei A. , Venkatesh N. S. , Esmailifar S. M. , Moradi Sizkouhi A. M. , Aghamohammadi A. , Oliveira A. K. V. , Eskandari A. , Parvin P. , Milimonfared J. , Sugumaran V. , Rüther R.
Published: November 2024

This study presents a comprehensive multidisciplinary review of autonomous monitoring and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies, namely artificial intelligence (AI), machine learning (ML), deep learning (DL), internet of things (IoT), unmanned aerial vehicle (UAV), and big data analytics (BDA), aiming to automate the entire condition monitoring procedures of PV systems. Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to further enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems. This review article covers current trends, recent research paths and developments, and future perspectives of autonomous monitoring and analysis for PV power plants. Additionally, this study identifies the main barriers and research routes for the autonomous and smart condition monitoring of PV systems, to address the current and future challenges of enabling the PV terawatt (TW) transition. The holistic review of the literature shows that the field of autonomous monitoring and analysis of PV plants is rapidly growing and is capable to significantly improve the efficiency and reliability of PV systems. It can also have significant benefits for PV plant operators and maintenance staff, such as reducing the downtime and the need for human operators in maintenance tasks, as well as increasing the generated energy.
A Bayesian Dynamic Model‐Based Adaptive Design for Oncology Dose Optimization in Phase I/II Clinical Trials
Authors: Qiu Yingjie , Li Mingyue
Published: November 2024

With the development of targeted therapy, immunotherapy, and antibody‐drug conjugates (ADCs), there is growing concern over the “more is better” paradigm developed decades ago for chemotherapy, prompting the US Food and Drug Administration (FDA) to initiate Project Optimus to reform dose optimization and selection in oncology drug development. For early‐phase oncology trials, given the high variability from sparse data and the rigidity of parametric model specifications, we use Bayesian dynamic models to borrow information across doses with only vague order constraints. Our proposed adaptive design simultaneously incorporates toxicity and efficacy outcomes to select the optimal dose (OD) in Phase I/II clinical trials, utilizing Bayesian model averaging to address the uncertainty of dose–response relationships and enhance the robustness of the design. Additionally, we extend the proposed design to handle delayed toxicity and efficacy outcomes. We conduct extensive simulation studies to evaluate the operating characteristics of the proposed method under various practical scenarios. The results demonstrate that the proposed designs have desirable operating characteristics. A trial example is presented to demonstrate the practical implementation of the proposed designs.
Temporal cross‐validation in forecasting: A case study of COVID‐19 incidence using wastewater data
Authors: Lai Mallory , Wulff Shaun S. , Cao Yongtao , Robinson Timothy J. , Rajapaksha Rasika
Published: November 2024

Two predominant methodologies in forecasting temporal processes include traditional time series models and machine learning methods. This paper investigates the impact of time series cross‐validation (TSCV) on both approaches in the context of a case study predicting the incidence of COVID‐19 based on wastewater data. The TSCV framework outlined in the paper begins by engineering interpretable features hypothesized as potential predictors of COVID‐19 incidence. Feature selection and hyperparameter tuning are then utilized with TSCV to identify the best features and hyperparameters for optimal model performance given a specific forecast horizon. While evidence supporting the utility of TSCV for auto‐regressive integrated moving average model with exogenous variables (TS‐ARIMAX) forecasts is lacking in this study, such an approach proves advantageous for gradient boosting machine forecasts (TS‐GBM). In Wyoming, for instance, TS‐GBM had a 34.9% improvement compared to naïve predictions, whereas GBM without TSCV only had a 15.6% improvement. However, TSCV also enhances interpretability for both TS‐ARIMAX and TS‐GBM models as this approach selects specific features, such as lagged values of COVID‐19 cases, based on forecast performance and forecast length. Future research should work to explore the influence of stationarity and model averaging on the performance of TSCV in forecasting applications.
Assessment of Fermented Kiwifruit on Morpho‐Physiological and Productive Performances of Fragaria spp Plants, Grown Under Hydroponic Conditions
Authors: Nazeer Samreen , Agosti Anna , Del Vecchio Lorenzo , Leto Leandra , Di Fazio Andrea , Saadoun Jasmine H. , Levante Alessia , Lazzi Camilla , Cirlini Martina , Chiancone Benedetta
Published: November 2024

Climate change poses a significant threat to global agriculture by altering weather patterns, increasing the frequency of extreme events, and reducing the availability of arable land. Hydroponic systems offer a sustainable solution allowing efficient resource use, including water and nutrients, and enabling cultivation in areas with poor soil quality or limited space. The incorporation of biostimulants derived from plant byproducts further enhances sustainability by improving plant growth and resilience, reducing the use of synthetic fertilizers and the environmental footprint of agriculture, promoting, at the same time, healtier crop production. This study investigates the effect of biostimulants, derived from fermented kiwifruit byproducts, on the morpho‐physiological and productive performances of Fragaria vesca (L.), cv. Malga, and of Fragaria x ananassa (Duch.), cv. Annabelle, grown in a column hydroponic system. Plants of both species, when treated with the biostimulant, demonstrated significant improvements for all the parameters evaluated, with healthier plants and improved quality features in fruits. These findings suggest that fermented kiwi byproduct could be an effective, sustainable integration to synthetic fertilizers, promoting better growth and fruit quality in strawberry cultivation under hydroponic systems.
Learning and Distributed Expertise in Community‐Based Science
Authors: Jadallah Christopher C. , Ballard Heidi L.
Published: November 2024

In the face of growing social‐ecological challenges, multiple forms of expertise must be brought to bear in environmental problem‐solving. As such, community‐based science has been touted as a potential way to “democratize” scientific knowledge production, allowing for multiple sources of expertise to be harnessed and for learning across stakeholders to occur in new ways. The extent to which this occurs, however, is mediated by a variety of factors. Focusing on the context of a dam removal and river restoration initiative in the Western United States, we leverage community science literacy as a conceptual lens to critically interrogate if and how multiple forms of expertise are taken up through community‐based science toward the goal of watershed revitalization. Based on an analysis of empirical evidence collected from interviews, ethnographic observations, and project artifacts, we found that local residents in the watershed demonstrated robust forms of situated local knowledge that they often leveraged toward the work of science. This occurred through processes of coordination work, which were mediated by opportunities for individuals to shift between roles, the contributions of brokers and boundary spanners, and issues of power, status, and rank. While individuals demonstrated and shared their nuanced local knowledge, dominant science still structured if, when, and how this was taken up in the project. Ultimately, we suggest that leveling hierarchies in community‐based science—and affording broader publics greater epistemic agency in shaping the work of science—is key to fostering social‐ecological transformation through science learning in informal settings.
Aldehyde Directed In Situ Loading of Ag Nanodots Around the Open Metal Sites of MOFs for the Tandem Catalysis of Nitrate to Ammonia
Authors: Zhang Yuting , Ma Jiahao , Wang Biwen , Lv Anqi , Zhang Qiuyu , Zhuo Sifei
Published: November 2024

Both spatial arrangement and intrinsic activity of electrocatalysts with dual‐active sites are widely designed to match the coupling reaction between nitrate and water, in which most of the reactive intermediates can be optimized to achieve a high yield rate of ammonia. Herein, by introducing the aldehyde group inside metal‐organic frameworks (MOFs) in advance, an aldehyde‐induced method is achieved to direct the in situ nucleation of Ag nanodots depending on the mesopores of MOFs via a simple silver mirror reaction. The key point here is that the spatial arrangement between the aldehyde group and open metal sites is fixed end to end, which makes the aldehyde group a built‐in redox‐active site to drive the in situ nucleation of Ag nanodots next to the open metal sites of MOFs. Accordingly, by varying the metal sites of MOFs, a group of M‐MOFs@Ag (M = Fe, Co, Ni, Cu, etc.) hybrids with dual active sites are acquired. Taking Ni‐MOFs@Ag as an example, the interaction between Ni2+ and Ag sites makes it available for the tandem catalysis of nitrate‐to‐ammonia, in which the H· and NO2− generated on the open Ni2+ sites and Ag nanodots, respectively, can migrate to each other to evolve into ammonia.
Advancing Topoisomerase Research Using DNA Nanotechnology
Authors: Yesodi Doron , Katz Adi , Weizmann Yossi
Published: November 2024

In this Perspective, the use of DNA nanotechnology is explored as a powerful tool for studying a family of enzymes known as topoisomerases. These enzymes regulate DNA topology within a living cell and play a major role in the pharmaceutical field, serving as anti‐cancer and anti‐bacterial targets. This Perspective will provide a short historical overview of the methods employed in studying these enzymes and emphasizing recent advancements in assays using DNA nanotechnology. These innovations have substantially improved accuracy and expanded the understanding of enzyme activity. This perspective will showcase the versatile utility of DNA nanotechnology in advancing scientific knowledge and its application in exploring new drug candidates, particularly in the study of topoisomerase enzymes.
Bayesian Learning Aided Theoretical Optimization of IrPdPtRhRu High Entropy Alloy Catalysts for the Hydrogen Evolution Reaction
Authors: Huang Linke , Gariepy Zachary , Halpren Ethan , Du Li , Shan Chung Hsuan , Yang Chuncheng , Chen Zhi Wen , Singh Chandra Veer
Published: November 2024

The complex compositional space of high entropy alloys (HEAs) has shown a great potential to reduce the cost and further increase the catalytic activity for hydrogen evolution reaction (HER) by compositional optimization. Without uncovering the specifics of the HER mechanism on a given HEA surface, it is unfeasible to apply compositional modifications to enhance the performance and save costs. In this work, a combination of density functional theory and Bayesian machine learning is used to demonstrate the unique catalytic mechanism of IrPdPtRhRu HEA catalysts for HER. At high coverage of underpotential‐deposited hydrogen, a d‐band investigation of the active sites of the HEA surface is conducted to elucidate the superior catalytic performance through electronic interactions between elements. At low coverage, a novel Bayesian learning with oversampling approach is then outlined to optimize the HEA composition for performance improvement and cost reduction. This approach proves more efficacious and efficient and yields higher‐quality structures with less training set bias compared with neural‐network optimization. The proposed HEA optimization theoretically outperforms benchmark Pt catalysts’ overpotential by ≈40% at a 15% reduced synthesis cost comparing to the equiatomic ratio HEA.
Voiding sonography: Technical description of a novel ultrasound method for assessment of micturition in women
Authors: Dellar Bernadette , Chung Eric , Stafford Ryan , Hodges Paul W.
Published: November 2024

Voiding sonography (VS) is a novel non‐invasive method that can be used to assess micturition in real time. When performed in a functional seated position and combined with free uroflowmetry, detailed evaluation of voiding can be conducted. VS presents advantages over current invasive assessment methods such as urodynamics. This methodological paper describes the rationale; advantages and disadvantages of VS; participant preparation; technique; a practical approach to obtain the video/image (including transducer placement to achieve an assessment of voiding with minimal obstruction of urine flow); recommended machine and transducer specifications; and the potential usefulness of ultrasound measurements. These measurements might provide information regarding the function of the pelvic floor muscle and urethral dynamics during voiding. Measures assessed with VS can also provide information regarding urine storage. In summary, VS is a new method to assess female voiding. Future work should evaluate the potential utility of the method to evaluate characteristics and mechanisms for voiding dysfunction in females with a variety of relevant clinical conditions. Use of VS as a screening tool before and after surgical or non‐surgical treatment intervention may also be a future consideration to assess progress and provide feedback to clinicians and patients.
Template Attack Based on the Weighted Quadruple Residual Network
Authors: Zhu Lei , Chen Anjie , Dong Liang
Published: November 2024

Template attack, as a classic side‐channel attack method, has attracted extensive attention due to its ease of deployment and small number of hyperparameters. However, traditional template attacks (TA) suffer from high preprocessing complexity and poor performance in certain masked applications. To address these issues, this paper proposes a novel template attack method leveraging a weighted quadruple residual network. Specifically, we improve the quadruple network model by assigning different weights to the negative samples selected by the model, which balances the influence of the samples on the model's feedback. Aiming at the fact that the quadruple network ignores the label characteristics of samples in the side‐channel, the distance metric in the quadruple network model is improved, which can help the model select more suitable samples. The residual network designed in this paper enables the quadruplet network model to reduce feature dimensionality, thereby facilitating more effective analysis attacks in TA. Experimental results demonstrate a notable reduction in leaked traces required by the optimized template attack on the synchronous ASCAD dataset, achieving a 62.5% decrease compared to an advanced template attack scheme. On the AES_HD dataset, the energy trace needed for a successful attack diminishes by 46.5% in comparison to the advanced template attack scheme.
The Promise of Resistance: A New Lens for Climate Change Adaptation Research and Practice
Authors: Mills‐Novoa Megan , Mikulewicz Michael
Published: November 2024

Over the years, thousands of climate change adaptation projects have been implemented globally. While there has been substantial scholarship on the extent and nature of adaptation efforts, fewer studies have examined why and how adaptation projects are being resisted. An analysis of resistance to adaptation offers critical insights to scholars and practitioners by recognizing the contentious nature of adaptation pathways and highlighting alternative visions for adaptation. Drawing on 20 case studies identified through a systematic review of the literature, we identify nine insights for research and practice. First, there is little literature on resistance to adaptation, suggesting that it is being underreported. Second, resistance to adaptation is not an anomaly, spanning sectors, actors, and geographies. Third, resistance to adaptation initiatives often has little to do with climate change skepticism. Fourth, the modes and outcomes of resistance are creative and diverse. Fifth, the state plays a critical, but not uniform, role in adaptation and thus resistance. Sixth, formal participation mechanisms can accommodate, neutralize, or facilitate resistance to adaptation. Seventh, the diverse goals of resistance reflect the multiplicity of imagined adaptation futures. Eighth, resistance is generative of material outcomes and novel subjectivities. Ninth, resistance can be costly, fracturing, and ineffective. This review highlights the need to further investigate resistance to climate change adaptation, which should be seen as a site of political struggle between different conceptions of what adaptation is and should be. It also provides insights for identifying maladaptation and pathways toward more emancipative forms of adaptation.
Presence of trailers reduces culvert‐style trap success in American black bears (Ursus americanus)
Authors: Paton Rebecca A. , Frame Paul F. , Nielsen Scott E. , Miller Erin N. , Edwards Mark A.
Published: November 2024

Live capture is an important tool for wildlife research, conservation, and management as it allows for insight into movement, behavior, demographics, diet, and health of wildlife populations. Live capture is also used for conservation translocations or to remove animals from human‐wildlife conflict situations. Improving trapping efficiency is beneficial for ethical considerations of wildlife handling, human safety, and the best use of limited resources. American black bears (Ursus americanus) are often captured using traps. Culvert‐ and box‐style traps are frequently transported using a trailer, and some are affixed to trailers, elevating the trap from the ground. Traps can be set on the ground or on trailers to reduce the set‐up and take‐down times. Although trap elements such as length, height, and visibility can influence trapping efficiency, it is unknown whether trailer presence influences capture success. We assessed the influence of single‐axle trailer presence on capture success of trapping black bears in northeastern Alberta, Canada, during spring 2022 and 2023. We compared capture success between the 2 methods and the influence of trailer presence on age and sex class. Sixty traps were set for a total of 472 trap‐nights resulting in 133 captures of 80 individual bears. Traps set with trailers present required, on average, 1.6 times more trap‐nights per capture (4.9) than those set without trailers (3.1). When only considering new captures, traps with trailers present required 1.9 times more trap‐nights to capture a bear, at 8.5 trap‐nights/capture for traps on trailers and 4.5 trap‐nights/capture when trailers were absent. Trailer presence reduced the success of overall and initial bear captures but did not influence recapture success or the proportion of captured bear age and sex classes. We conclude that wildlife researchers and managers should consider improving trap efficiency of black bears by removing traps from trailers.
‘The Past Is Unforgettable’: Civil Procedural Law’s Response to the Challenges of Pseudo-Modern Society and Economy in Hungary After 2010
Authors: Timár Balázs
Published: November 2024

In this chapter I want to look at civil procedural law from a legal history perspective. My aim is to present one of the flagships of the post-2010 procedural reform, the Code of Civil Procedure, in a legal-historical context. In this context, I will describe the characteristics of pseudo-conservative legislation, with a focus on the merging of substantive and procedural law, and on dogmatic anomalies that not only theory but also practice have to face. In my view, knowledge of legal history can also bring us closer to understanding the presumed intentions of the legislator and to interpreting the resulting legislation. In this context, I will examine the process of what I call procedural law reform, after reviewing the legal and taxonomic antecedents. In the context of the latter, I will present the problems of pseudo-conservative legislation and the contradictions of legal historicism through the fundamental provisions of the Code. Also in this chapter, I intend to illustrate the consequences of centralisation and the abolition of special courts through the way in which labour courts were abolished. The chapter will conclude with a comparative and introspective evaluation of the new law.
Resilience Thinking: Emergence, Conceptualisations, and Applicability in Social Systems and Law
Authors: Goto Erik , Lőrincz Viktor Olivér
Published: November 2024

This chapter delves into the origin of ‘resilience thinking’ and how it evolved from ecology to be used in social sciences. While widely used, resilience lacks a unified definition, manifesting differently within disciplines. Resilience theory has evolved into a tool for analysing complex interactions in socio-ecological systems, and such an interdisciplinary approach calls for complex systems thinking. However, its application in social sciences faces challenges due to conceptual ambiguities, normative assumptions, and difficulties defining system boundaries. Despite these challenges, the paper suggests that resilience thinking could serve as a bridging concept, fostering interdisciplinary research and prompting critical reflections on societal dynamics. We also discuss the applicability of resilience theory and resilience models to the law as a complex system, presenting its notable limitations, analysing the possible advantages of the use of resilience as a scientific research programme, and emphasising the dangers of using it as a metaphor. Using the transfer of property as an example, we propose an alternative model of system response by the law in relation to external stimuli based on Jean Piaget’s theory of adaptation.
‘Eliminating Conservation’? The Re-Regulation of Hungarian Administrative Procedure in the Act on General Administrative Procedure
Authors: Balázs István , Gajduschek György , Hoffman István
Published: November 2024

The chapter analyses the resilience of the general rules of administrative procedural law in Hungary. Traditional doctrinal analysis, historical comparison and the sociology of legislation are applied to show the extent to which legislative transformation reflects the challenges of the socioeconomic environment after 2010. The chapter argues that the objectives declared by the legislator are not reflected properly in the legislative text. In the case of most of the legislative changes, neither the jurisprudential nor the sociological analysis can identify the expected positive impact. Legislative declarations concerning elevated public access or simplifications of the administrative procedure are in sharp contrast with the fact that, due to the reform of administrative procedure, review is inaccessible to the majority of clients. While the reform-related arguments that are officially put forward advertise the advantage of judicial legal protection, access to this and other forms of review is minimal; the advantage is not supported by statistical data.
Reflections on the Responsiveness of the Legal System in Hungary: From a Criminal Law Perspective
Authors: Tóth Mihály
Published: November 2024

In this chapter, which deals mainly with criminal law responses to the social and economic changes after 2010 in Hungary, I address three questions. After outlining the general situation of the Hungarian legal system and the role that criminal law should play, I will first examine the actual responses of criminal legislation in substantive provisions of the law to the criminal phenomena of the last decade; in the second part, I will look at the legislative responses in the field of procedural law; and finally, I will make some comments on a related issue, the relationship between the criminal law framework and responsiveness. I argue that criminal law has, in recent years, completely lost its rightly perceived ultima ratio character and has become an almost primary, but at the same time rather arbitrary, regulatory instrument in many areas.
Risks and Resilience in the European Union’s Regulation of Online Platforms and Artificial Intelligence: Hungary in Digital Europe
Authors: Mezei Kitti , Träger Anikó
Published: November 2024

The European Union is taking ambitious steps to strengthen regulation in the digital space as part of the Digital Agenda for Europe. As part of this, the digital services regulatory package (Digital Services Act and Digital Markets Act) has been adopted to define the responsibilities of digital platforms, control online content, protect users’ fundamental rights and promote competitiveness in the online space. In addition, an EU regulatory framework for artificial intelligence has been developed (AI Act), which supports a risk-based approach to regulate the technology. One common intersection of these regulations is to maintain transparency. The Digital Services Act aims to fight against filter bubbles and disinformation in social media, where online platforms also use algorithms to provide services. A new challenge in adapting to the accelerating technological development has been the COVID-19 pandemic, which has further increased the penetration of online service providers through mandatory distance and opened up a whole new platform for spreading fake news. The application of the new Digital Europe Regulations raises several questions. Still, these are complemented by application-specific areas for Hungary, such as how Hungarian authorities will act as national authorities or interpret ‘illegal content’ under the European legislation.
Study on Carbon Emission Data of Urban Buildings
Authors: Yang Chen , Yao Tong
Published: November 2024

In pursuit of global carbon peak and neutrality, precise carbon emission measurement using advanced management tools and technologies is crucial for effective urban carbon reduction and sustainable growth. We propose classifying building emissions into three distinct stages—construction, operation, and maintenance—by utilizing mathematical methods and Building Information Modeling (BIM) for separate calculation. Moreover, we recommend implementing an annual carbon emission management system tailored to each building.
Thermal and Optical Performance of Semi-Transparent BIPV Windows in High-Rise Office Buildings: A Case Study in the UK
Authors: Scibetta Joseph , Shi Shaohang , Du Hu , He Bao-Jie , Yang Siliang
Published: November 2024

Semi-transparent building-integrated photovoltaic (BIPV) windows replace the external building glazing, allowing power to be generated as part of the existing building envelope. Due to the replacing of the BIPV windows, both thermal and optical performance of a building are worth exploring. This paper presents the results of a simulation study investigating the effects of applying BIPV windows to an existing office building within the UK, with a view to assessing the thermal performance, daylighting condition and BIPV electrical production capacity. It was considered on a typical floor of the office building, consisting of an open plan office space, two meeting rooms and a kitchen suite. A baseline scenario without BIPV window was modelled for comparison, giving recommendations to the viability of the BIPV window in terms of the overall performance. Basically, the use of the BIPV window resulted an annual cooling load reduction of 16.3% compared to the baseline. Heating loads were also reduced but only by 1.89%, while lighting energy usage was slightly increased due to a significantly worse performance in daylighting condition in the open plan office when applying the BIPV window. Specifically, daylight factor dropped over 50% in the office area, which, however, was already poor to begin with. The annual electricity generation of the BIPV window was little with only 902 kWh. This study can be a reference for future research on thermal and optical performance of high-rise office buildings using semi-transparent BIPV windows.
Analysis of Urban Planning and Construction for Regional Climate Change
Authors: Zhang Yue , Yang Yifeng , Ma Bingxia
Published: November 2024

In recent years, a series of regional extreme weather events has emerged due to global climate change, resulting in various adverse impacts on regional ecological environments, human activities, and urban development. Addressing regional climate change has attracted widespread attention. Enhancing a city's ability to adapt to regional climate change is a pressing topic that requires both research and practical solutions. This study is grounded in the trends of regional climate change and takes a macroscopic perspective to explore the strategies and methods for addressing regional climate change in Mianyang City. Drawing upon the urban planning practices in Mianyang City, it presents measures and strategies for tackling regional climate change from the perspectives of urban spatial development, land use, transportation systems, and the construction of green spaces. These recommendations serve as a scientific foundation for creating a living environment well-suited to regional climate change.
Open-Pit Image Detection Based on Improved Faster – RCNN
Authors: Huang Rujin , Wang Genhou , Tian Jiahao , Zhang Quanping
Published: November 2024

Remote sensing image open-pit mine monitoring is usually affected by speckle noise, multi-scale and other factors due to the limitations of landform and other conditions, and faces the problem of low availability of monitoring area effect. Therefore, this paper introduces an improved regional convolution neural network method. The network thinning process and the improved conditional random field are proposed respectively as a circular neural network, and the accurate classification and coordinate positioning of the target are completed by establishing the network thinning process in the output part to increase the classification and regression thinning of the target features; Remove the influence of color vector for the fully connected conditional random field and improve it and construct a recurrent neural network for the conditional random field. The experiment shows that the target detection accuracy of the improved Faster-RCNN network has achieved a breakthrough in the mine image detection details, with the overall recognition accuracy of 94.67% and the detection speed of 24.03 fps. Compared with Faster-RCNN, SPP-NET and YOLOV7, it effectively improves the accuracy and provides technical support for ecological restoration monitoring of open pit.
Assessing Urban Tourism Resource Ecological Carrying Capacity: A “Desired-Undesired” Dual Perspective Approach
Authors: Yang Rui , Xiong Yuan , He Hongman , Liu Yu
Published: November 2024

This study introduces a new approach to evaluate the ecological carrying capacity of urban tourism resources (UTRECC), emphasizing the perspectives of “desired” and “undesired”. Based on urban sustainability principles, this study categorizes the load of urban tourism resources into “desired load” and “undesired load”. Using tourism system theory, the study examines the carriers of four key subsystems related to urban tourism resource carrying capacity, including tourism attraction carrier, atmospheric environmental support carrier, waterbody environmental support carrier, and land environmental support carrier. A calculation model for UTRECC is constructed based on 11 carrier indicators and 5 load indicators. This method elucidates the performance of ecological carriers in urban tourism, suggesting that, within certain parameters, a higher carrying capacity index is preferable. Empirical research was conducted in four case cities: Beijing, Shanghai, Chengdu, and Guangzhou. The results reveal significant variations in the tourism resource carrying capacities of these cities, with Beijing and Shanghai exhibiting relatively higher capacities compared to Chengdu and Guangzhou. The findings offer valuable insights for the planning, development, and management of urban tourism, providing a robust framework for future assessments and adjustments of urban resources.