bluuebunny/crossref_metadata_2025 huggingface dataset:
Substitution Estimates for Wood Products in the United States, 1990 to 2020
Authors: Taylor Adam , Hurmekoski Elias , Brandeis Consuelo , Domke Grant
Published: January 2024

Forest carbon stock changes are routinely reported at the national level. Such accounting usually includes carbon sequestered by live trees and stored in living and dead trees, litter, and soil. The carbon stored in harvested wood products (HWPs), while in use and after disposal, may also be reported. However, wood products may provide a further indirect carbon benefit by providing an alternative to materials that require relatively large inputs of fossil-derived carbon. This “substitution impact” of HWPs is not explicitly considered in national reporting for forest carbon accounts because it is not directly comparable with absolute emissions and removals. However, the substitution impact is avoided fossil emissions, which do affect the net climate impact of wood use. Here, we estimate the total substitution impact for wood products harvested recently from forests in the United States. The total substitution impact is based on reported volumes of national wood production and the substitution factors for individual products. The substitution factors are obtained from comparative life-cycle assessments of specific wood products and their nonwood alternatives. The total substitution impact for wood products in the United States in 2020 was 188 TgCO2 equivalents. Calculation of substitution factors is challenged by assumptions about the use of wood products and their alternatives and by limited availability of current and specific life-cycle assessment data; however, our findings indicate that the substitution impact of forest products are a relatively important component of the carbon benefits of wood-based products.
A Specialized Data Crawler for Urban Wood Information
Authors: Thomas R. Edward , Espinoza Omar
Published: January 2024

The Internet is composed of >50 billion Web pages and grows larger every day. As the number of links and specialty subject areas increases, it becomes ever more difficult to find pertinent information. For some subject areas, special purpose data crawlers continually search the Internet for specific information; examples include real estate, air travel, auto sales, and others. The use of such special purpose data crawlers (i.e., targeted crawlers and knowledge databases), also allows the collection and analysis of agricultural and forestry data. Such single-purpose crawlers can search for hundreds of keywords and use machine learning to determine whether or not what is found is relevant. In this paper, we examine the design and data return of such a specialty knowledge database and crawler system developed to find information related to urban wood utilization—products made from timber harvested in cities and municipalities. Our search engine uses intelligent software to locate and update pertinent references related to urban wood as well as to categorize information with respect to common application and interest areas. At the time of this publication, the urban wood knowledge database has cataloged >700 publications regarding various aspects of urban wood.
Housing and Life Course Dynamics
Authors: Coulter Rory
Published: January 2024

This book argues that housing plays a crucial but surprisingly poorly understood role within the ongoing restructuring of 21st-century lives. To address this lacuna, the first two chapters of the book develop a new life course framework which is designed to equip researchers, policymakers and practitioners with the conceptual tools to understand how housing processes interact with the dynamics of other areas of people’s lives. This life course conceptual toolbox is then applied in the subsequent chapters to explore how changes in people’s housing careers are interwoven with contemporary trends in four other life course domains: (1) household structures and family lives, (2) education and training, (3) employment and economic prosperity, and (4) health, well-being and care. Throughout the book, particular attention is paid to the ways that housing and life course dynamics interact to reshape patterns of social and spatial inequality in Global North societies. The role that housing and life course dynamics play in helping to drive local patterns of population change is also examined in detail.
Territorial policy communities: scale, style and scope
Authors: Needham Catherine , Hall Patrick
Published: January 2024

This chapter considers the institutional design and approaches to policy-making in the four nations to explain some of the similarities and differences in their approaches to care. It looks at how the scale, style and scope of policy has made it easier for Scotland to be the most active on care policy reform, making the most of a relatively consensual policy environment, with limited veto players. England has been hampered by its larger and more siloed institutions, influential veto players and an overloaded agenda. Wales and Northern Ireland have had constitutional factors to deal with throughout the period, which have delayed (Wales) or inhibited (Northern Ireland) their capacity to be active policy makers on social care. In Scotland and Wales there has been a discursive rejection of the market and consumerist approaches to care, with a preference for seeking to pursue care reform through community and co-production. In Northern Ireland, the crisis of the state has had a very distinctive hue, and some of the work of reforming social care has been about detoxifying the state. In England, the market continues to be dominant, although a role for community in prevention is recognised.
Managing Risk during the COVID-19 Pandemic
Authors: Alaszewski Andy
Published: January 2024

In modern society, risk plays a key role in individual and collective decision making. It provides a way of predicting the future and accounting for mistakes in the past. In early 2020, the rapid spread of a highly infectious virus (SARS-CoV-2) that caused illness (COVID-19) especially among the elderly and other vulnerable individuals threatened to overwhelm health and social care services and cause widespread social and economic disruption. Policy makers sought to make sense of and manage the uncertainties of this new virus. Risk provided one way of doing this. It was a way of identifying the potential danger, identifying who was most exposed to it and what actions could be taken to mitigate it. As a technical tool to assist decision making, risk depends on using evidence from past events to predict future incidents. In the early stages of the pandemic, such evidence was lacking, so policy makers had to frame the new disease, and the ways they did this had fateful consequences. Risk is attractive to policy makers because it appears to provide a technical framework for decision making. However, it addresses the probability of different outcomes, and such outcomes not only have different values but can also impact in a range of ways on different groups in society. Risk assessments and the decisions based on them are underpinned by value judgements. In the pandemic, policy makers were reluctantly forced to make such judgements.
Risk work to maintain services during the pandemic
Authors: Alaszewski Andy
Published: January 2024

Illness is usually an unexpected event, and one that removes the ill person from their familiar environment – transferring them to the unfamiliar setting of the hospital and to the care of strangers. For the patient, the hospital is essentially an abstract system, one in which experts use technical knowledge to identify and treat them. For the doctor, treating patients is part of the routine of their everyday job and forms part of risk work, in which they manage the uncertainties of individual cases and present their actions in a good or moral light. While hospitals can be dangerous environments, routines and rituals enable professionals to disregard or bracket out this personal danger. The COVID-19 pandemic presented a serious challenge to risk work: normal activities and routines were disrupted, while hospital staff were at risk. To manage these new uncertainties, professionals developed new knowledge and innovative routines to provide new certainties and security. This security was always fragile, and professionals often had to rely on default strategies such as trust, hope, intuition and fatalism. These strategies can be used when action has to be taken and there is not enough time or knowledge to rationally evaluate alternatives.
Conclusion: risk and the pandemic
Authors: Alaszewski Andy
Published: January 2024

The COVID-19 pandemic was inextricably linked to risk. The emergence of a new highly infectious and lethal virus from Wuhan in late 2019 created uncertainty, and policy makers sought to assess the risk it presented. The concept of risk shaped the experience of the pandemic in different ways. With initially limited evidence available, the ways in which policy makers framed the new disease shaped the responses they adopted, whether zero-COVID versus wait and see. Once community transmission was established, measures to limit harm were shaped by risk categorisation and risk work in hospitals. While policy makers claimed to be following the science in their decision making, risk issues were contested within scientific communities and outside, given the lack of consensus and transparency. Policy makers have argued that a proper inquiry cannot take place until the pandemic is over. This has not stopped inquiries being set up. In countries with high infection and death rates, such as the US, UK and Brazil, inquiries have blamed policy makers for failing to recognise the risks of SARS-CoV-2 and for failing to take and communicate timely action.
Coloniality and Meritocracy in Unequal EU Migrations
Authors: Varriale Simone
Published: January 2024

This book rethinks meritocracy as a form of coloniality, namely, a social imaginary that reproduces narratives of ethnic and racial difference between European centres and peripheries, and between Europe and its others. Drawing on interviews with working and middle class, white and Black Italians who moved to Britain after the 2008 economic crisis, the book explores the narratives of Northern meritocracy and Southern backwardness that inform migrants’ motivations for moving abroad; how these narratives are experienced within classed, racialized and gendered migrations; and how they are negotiated vis-à-vis unequal experiences of work, education, belonging and Brexit. Connecting decolonial theory with the sociology of Pierre Bourdieu, this book provides innovative insights into the relationships between meritocracy, coloniality and European whiteness, and into the social stratification of EU migrations.
Making Information Matter
Authors: Kaufmann Mareile
Published: January 2024

Mareile Kaufmann is a Professor at the Institute for Criminology and Sociology of Law, University of Oslo. Her research focuses on digital criminology, in particular data technologies and practices in the context of surveillance and crime control. She works with qualitative research designs that combine theory with innovative angles and strong empirical components. Mareile published broadly on profiling and data analysis, Internet politics, resilience, critique, and method. She edited a number of special issues and is a member of several editorial boards. Her work was awarded prizes and she received funding under diverse excellence schemes. True to the spirit of Making Information Matter I need to thank the many individuals that made sure this book would materialize. My former and current employers played a big role, because they accepted my proposal of studying surveillance cultures and those who respond to them actively. As a result of that, it was critical to get access to sites and spaces for empirical work. I thank those who approved research ethics applications as well as those who granted me access. It was both a relief and an inspiration to be allowed to venture into some relatively guarded environments. I am thankful for anyone who agreed to explain, converse, provide insight, let me in on secrets, and allowed me to learn. These include commercial software providers, developers, police and intelligence officers, hackers, children, their parents and teachers, as well as artists.
Penguin Parables: Picturebooks, Interspecies Companionship, and Ecoliteracy
Authors: Smulders Sharon
Published: January 2024

Abstract: The erasures and evasions that characterize picturebook retellings of the true story of Dindim and João Pereira de Souza are instructive of the ways in which some children’s literature understates, distorts, and displaces human impacts on the environment. By reframing anthropogenic disaster as animal rescue, the story of interspecies companionship effectively mutes and disarms the story of environmental catastrophe. In the process, it forestalls eco-critical awareness of and solutions to problems such as chronic pollution, habitat destruction, species extinction, and climate change. By decontextualizing complex origin stories, erasing uncomfortable facts, and romanticizing interspecies relationships, the picturebooks discussed reveal the challenges to environmental consciousness imposed by the imperative to moral or emotional uplift in children’s literature.
Adaptive locating foveated ghost imaging based on affine transformation
Authors: Zhou Chang , Cao Jie , Hao Qun , Cui Huan , Yao Haifeng , Ning Yaqian , Zhang Haoyu , Shi Moudan
Published: January 2024

Ghost imaging (GI) has been widely used in the applications including spectral imaging, 3D imaging, and other fields due to its advantages of broad spectrum and anti-interference. Nevertheless, the restricted sampling efficiency of ghost imaging has impeded its extensive application. In this work, we propose a novel foveated pattern affine transformer method based on deep learning for efficient GI. This method enables adaptive selection of the region of interest (ROI) by combining the proposed retina affine transformer (RAT) network with minimal computational and parametric quantities with the foveated speckle pattern. For single-target and multi-target scenarios, we propose RAT and RNN-RAT (recurrent neural network), respectively. The RAT network enables an adaptive alteration of the fovea of the variable foveated patterns spot to different sizes and positions of the target by predicting the affine matrix with a minor number of parameters for efficient GI. In addition, we integrate a recurrent neural network into the proposed RAT to form an RNN-RAT model, which is capable of performing multi-target ROI detection. Simulations and experimental results show that the method can achieve ROI localization and pattern generation in 0.358 ms, which is a 1 × 105 efficiency improvement compared with the previous methods and improving the image quality of ROI by more than 4 dB. This approach not only improves its overall applicability but also enhances the reconstruction quality of ROI. This creates additional opportunities for real-time GI.
Formation of E-band luminescence-active centers in bismuth-doped silica fiber via atomic layer deposition
Authors: Wang Weiqi , Yang Yuanyuan , Wen Jianxiang , Dong Yanhua , Huang Caihong , Luo Yanhua , Shang Yana , Pang Fufei , Wang Tingyun
Published: January 2024

In this study, a Si defect structure was added into the silica network in order to activate the bismuth and silica structure active center. TD-DFT theoretical simulations show that the Bi and Si ODC(I) models can excite the active center of the E-band at 1408 nm. Additionally, the Bi-doped silica fiber (BDSF) with improved fluorescence was fabricated using atomic layer deposition (ALD) combined with the modified chemical vapor deposition (MCVD) technique. Some tests were used to investigate the structural and optical properties of BDSF. The UV-VIS spectral peak of the BDSF preform is 424 cm−1, and the binding energy of XPS is 439.3 eV, indicating the presence of Bi° atom in BDSF. The Raman peak near 811 cm−1 corresponds to the Bi-O bond. The Si POL defect lacks a Bi-O structure, and the reason for the absence of simulated active center from the E-band is explained. A fluorescence spectrometer was used to analyze the emission peak of a BDSF at 1420 nm. The gain of the BDSF based optical amplifier was measured 28.8 dB at 1420 nm and confirmed the effective stimulation of the bismuth active center in the E-band.
Ultracompact polarization multiplexing meta-combiner for augmented reality display
Authors: Li Yuzhao , Yang Jingyu , Zhao Ruizhe , Zhao Yidan , Tian Chenyi , Li Xin , Li Yao , Li Junjie , Wang Yongtian , Huang Lingling
Published: January 2024

Augmented reality (AR) display, as a next-generation innovative technology, is revolutionizing the ways of perceiving and communicating by overlaying virtual images onto real-world scenes. However, the current AR devices are often bulky and cumbersome, posing challenges for long-term wearability. Metasurfaces have flexible capabilities of manipulating light waves at subwavelength scales, making them as ideal candidates for replacing traditional optical elements in AR display devices. In this work, we propose and fabricate what we believe is a novel reflective polarization multiplexing gradient metasurface based on propagation phase principle to replace the optical combiner element in traditional AR display devices. Our designed metasurface exhibits different polarization modulations for reflected and transmitted light, enabling efficient deflection of reflected light while minimizing the impact on transmitted light. This work reveals the significant potential of metasurfaces in next-generation optical display systems and provides a reliable theoretical foundation for future integrated waveguide schemes, driving the development of next-generation optical display products towards lightweight and comfortable.
Chip-level mass detection for micro-LED displays based on regression analysis and deep learning
Authors: Chiang Hung-Yi , Chen Szu-An , Chou Jyun-Jhe , Lin Kuan-Heng , Chen Yi-Hong , Shih Chi-Sheng , Huang Jian-Jang
Published: January 2024

Though micro-light-emitting diode (micro-LED) displays are regarded as the next-generation emerging display technology, challenges such as defects in LED's light output power and radiation patterns are critical to the commercialization success. Here we propose an electroluminescence mass detection method to examine the light output quality from the on-wafer LED arrays before they are transferred to the display substrate. The mass detection method consists of two stages. In the first stage, the luminescent image is captured by a camera by mounting an ITO (indium-tin oxide) transparent conducting glass on the LED wafer. Due to the resistance of the ITO contact pads and on-wafer n-type electrodes, we develop a calibration method based on the circuit model to predict the current flow on each LED. The light output power of each device is thus calibrated back by multi-variable regression analysis. The analysis results in an average variation as low as 6.89% for devices predicted from luminescent image capturing and actual optical power measurement. We also examine the defective or non-uniform micro-LED radiation profiles by constructing a 2-D convolutional neural network (CNN) model. The optimized model is determined among three different approaches. The CNN model can recognize 99.45% functioning LEDs, and show a precision of 96.29% for correctly predicting good devices.
Asymptotic Multilayer Pooled Transformer Strategy in Medical Assisted Decision-Making Systems
Authors: WU Jia , HE Keke , Zhu Jun , Li Limiao , Gou Fangfang
Published: January 2024

Medical image analysis is one of the most important tools used by medical professionals in clinical diagnosis to diagnose patients' conditions. Digital pathology images contain rich information that is more conducive to identifying tissue structure. However, the boundaries between tissues in pathology images are sometimes too blurred, resulting in a high rate of misdiagnosis. Medical decision-making systems are able to assist in the recognition of medical images, thereby improving diagnostic accuracy. Transformer-based models have made excellent progress in the field of segmentation. However, the computational complexity of self-focused modules is high. Single-layer pooling is often used to reduce computational expenditure, but this strategy is prone to feature loss. Even worse, a large number of self-attention operations may exacerbate distraction and negatively impact segmentation performance. Therefore, this study proposes an asymptotic multilayer pooling transformer strategy in medical decision-making systems. First, we denoise and contrast-enhance the pathology images, and then segment them using the asymptotic pyramid structure based on multilayer pooling transformer (PMPSNet). In this, multilayer pooling is applied to the self-attention module to simplify the sequence while capturing the contextual aspects of the image sequence more efficiently. Meanwhile, the pyramid encoder is used to come to acquire multi-scale feature maps and the novel MLP structure is used to limit the distraction. Experimental results show that our proposed method not only obtains high DSC and IOU, but also has fewer model parameters and computational complexity.
Conversion of spikers to bursters in pituitary cell networks: Is it better to disperse for maximum exposure or circle the wagons?
Authors: Fazli Mehran , Bertram Richard
Published: January 2024

The endocrine cells of the pituitary gland are electrically active, and in vivo they form small networks where the bidirectional cell-cell coupling is through gap junctions. Numerous studies of dispersed pituitary cells have shown that typical behaviors are tonic spiking and bursting, the latter being more effective at evoking secretion. In this article, we use mathematical modeling to examine the dynamics of small networks of spiking and bursting pituitary cells. We demonstrate that intrinsic bursting cells are capable of converting intrinsic spikers into bursters, and perform a fast/slow analysis to show why this occurs. We then demonstrate the sensitivity of network dynamics to the placement of bursting cells within the network, and demonstrate strategies that are most effective at maximizing secretion from the population of cells. This study provides insights into the in vivo behavior of cells such as the stress-hormone-secreting pituitary corticotrophs that are switched from spiking to bursting by hypothalamic neurohormones. While much is known about the electrical properties of these cells when isolated from the pituitary, how they behave when part of an electrically coupled network has been largely unstudied.
AutoEdge-CCP: A novel approach for predicting cancer-associated circRNAs and drugs based on automated edge embedding
Authors: Chen Yaojia , Wang Jiacheng , Wang Chunyu , Zou Quan
Published: January 2024

The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool.
Are households with under-five children in Nigeria socioeconomically disadvantaged?
Authors: Deshpande Ashwini Sunil , Ogbuoji Osondu
Published: January 2024

Although the sociodemographic and economic contributors to under-five mortality are well established, very little research has been done to assess the levels of disadvantage under-five children in Nigeria face along these dimensions. Nigeria has the second-highest under-five mortality rate (U5MR) in the world (111 deaths per 1000 live births) and contributed to the highest number of annual under-five deaths globally in 2020 (844,321 deaths). The country has also implemented several decades of policy interventions to reduce under-five mortality by improving sociodemographic and economic conditions at the household level. In this paper, we assess the sociodemographic and economic disadvantages that households with children under-five face compared to other households and discuss the implications for health policy. Using the Nigeria Living Standard Survey 2018–19, we conducted a bivariate analysis to compare the sociodemographic and economic characteristics of households with and without under-five children. We performed independent samples t-test and proportions test to assess whether these sociodemographic and economic factors were significantly different for both groups. We found that households with under-five children typically had larger sizes (6.6 vs. 3.6), lower mean adult age (36.5 vs. 45.3), and male household heads (91.3% vs. 71.5%) than households without under-five children. Furthermore, households with under-five children were less likely to have access to improved drinking water (77.2% vs. 86.0%) and sanitation sources (54.0% vs. 61.9%) than those without under-five children. Despite having more adult working members, 71.2% of households with under-five children lived below the poverty line compared to 37.7% of other households. Although their total consumption expenditure was lower than households without under-five children, they spent a higher proportion of their expenditure on health care and were at a higher risk of experiencing catastrophic health expenditure. Our study has shown that households with children under five are disproportionately disadvantaged than other households in Nigeria. The households with under-five children are larger, younger, and poorer than those without children. We also show a wide variation in the proportion of households with children under five by state. Any efforts to reduce under-five mortality and morbidity in Nigeria should recognize these sociodemographic and economic differences.
How development and survival combine to determine the thermal sensitivity of insects
Authors: Abarca Mariana , Parker Anna L. , Larsen Elise A. , Umbanhowar James , Earl Chandra , Guralnick Robert , Kingsolver Joel , Ries Leslie
Published: January 2024

Thermal performance curves (TPCs) depict variation in vital rates in response to temperature and have been an important tool to understand ecological and evolutionary constraints on the thermal sensitivity of ectotherms. TPCs allow for the calculation of indicators of thermal tolerance, such as minimum, optimum, and maximum temperatures that allow for a given metabolic function. However, these indicators are computed using only responses from surviving individuals, which can lead to underestimation of deleterious effects of thermal stress, particularly at high temperatures. Here, we advocate for an integrative framework for assessing thermal sensitivity, which combines both vital rates and survival probabilities, and focuses on the temperature interval that allows for population persistence. Using a collated data set of Lepidopteran development rate and survival measured on the same individuals, we show that development rate is generally limiting at low temperatures, while survival is limiting at high temperatures. We also uncover differences between life stages and across latitudes, with extended survival at lower temperatures in temperate regions. Our combined performance metric demonstrates similar thermal breadth in temperate and tropical individuals, an effect that only emerges from integration of both development and survival trends. We discuss the benefits of using this framework in future predictive and management contexts.
An interpretable machine learning framework for opioid overdose surveillance from emergency medical services records
Authors: Graham S. Scott , Shifflet Savannah , Amjad Maaz , Claborn Kasey
Published: January 2024

The goal of this study is to develop and validate a lightweight, interpretable machine learning (ML) classifier to identify opioid overdoses in emergency medical services (EMS) records. We conducted a comparative assessment of three feature engineering approaches designed for use with unstructured narrative data. Opioid overdose annotations were provided by two harm reduction paramedics and two supporting annotators trained to reliably match expert annotations. Candidate feature engineering techniques included term frequency-inverse document frequency (TF-IDF), a highly performant approach to concept vectorization, and a custom approach based on the count of empirically-identified keywords. Each feature set was trained using four model architectures: generalized linear model (GLM), Naïve Bayes, neural network, and Extreme Gradient Boost (XGBoost). Ensembles of trained models were also evaluated. The custom feature models were also assessed for variable importance to aid interpretation. Models trained using TF-IDF feature engineering ranged from AUROC = 0.59 (95% CI: 0.53–0.66) for the Naïve Bayes to AUROC = 0.76 (95% CI: 0.71–0.81) for the neural network. Models trained using concept vectorization features ranged from AUROC = 0.83 (95% 0.78–0.88)for the Naïve Bayes to AUROC = 0.89 (95% CI: 0.85–0.94) for the ensemble. Models trained using custom features were the most performant, with benchmarks ranging from AUROC = 0.92 (95% CI: 0.88–0.95) with the GLM to 0.93 (95% CI: 0.90–0.96) for the ensemble. The custom features model achieved positive predictive values (PPV) ranging for 80 to 100%, which represent substantial improvements over previously published EMS encounter opioid overdose classifiers. The application of this approach to county EMS data can productively inform local and targeted harm reduction initiatives.
A lightweight and robust authentication scheme for the healthcare system using public cloud server
Authors: Abbasi Irshad Ahmed , Jan Saeed Ullah , Alqahtani Abdulrahman Saad , Khan Adnan Shahid , Algarni Fahad
Published: January 2024

Cloud computing is vital in various applications, such as healthcare, transportation, governance, and mobile computing. When using a public cloud server, it is mandatory to be secured from all known threats because a minor attacker’s disturbance severely threatens the whole system. A public cloud server is posed with numerous threats; an adversary can easily enter the server to access sensitive information, especially for the healthcare industry, which offers services to patients, researchers, labs, and hospitals in a flexible way with minimal operational costs. It is challenging to make it a reliable system and ensure the privacy and security of a cloud-enabled healthcare system. In this regard, numerous security mechanisms have been proposed in past decades. These protocols either suffer from replay attacks, are completed in three to four round trips or have maximum computation, which means the security doesn’t balance with performance. Thus, this work uses a fuzzy extractor method to propose a robust security method for a cloud-enabled healthcare system based on Elliptic Curve Cryptography (ECC). The proposed scheme’s security analysis has been examined formally with BAN logic, ROM and ProVerif and informally using pragmatic illustration and different attacks’ discussions. The proposed security mechanism is analyzed in terms of communication and computation costs. Upon comparing the proposed protocol with prior work, it has been demonstrated that our scheme is 33.91% better in communication costs and 35.39% superior to its competitors in computation costs.
The balance between traffic control and economic development in tourist cities under the context of COVID-19: A case study of Xi’an, China
Authors: Xiang Wang , Wang Zezhi , Pan Xin , Liu Xiaobing , Yan Xuedong , Chen Li
Published: January 2024

Selecting an appropriate intensity of epidemic prevention and control measures is of vital significance to promoting the two-way dynamic coordination of epidemic prevention and control and economic development. In order to balance epidemic control and economic development and suggest scientific and reasonable traffic control measures, this paper proposes a SEIQR model considering population migration and the propagation characteristics of the exposed and the asymptomatic, based on the data of COVID-19 cases, Baidu Migration, and the tourist economy. Further, the factor traffic control intensity is included in the model. After determining the functional relationship between the control intensity and the number of tourists and the cumulative number of confirmed cases, the NSGA-II algorithm is employed to perform multi-objective optimization with consideration of the requirements for epidemic prevention and control and for economic development to get an appropriate traffic control intensity and suggest scientific traffic control measures. With Xi’an City as an example. The results show that the Pearson correlation coefficient between the predicted data of this improved model and the actual data is 0.996, the R-square in the regression analysis is 0.993, with a significance level of below 0.001, suggesting that the predicted data of the model are more accurate. With the continuous rise of traffic control intensity in different simulation scenarios, the cumulative number of cases decreases by a significant amplitude. While balancing the requirements for epidemic prevention and control and for tourist economy development, the model works out the control intensity to be 0.68, under which some traffic control measures are suggested. The model presented in this paper can be used to analyze the impacts of different traffic control intensities on epidemic transmission. The research results in this paper reveal the traffic control measures balancing the requirements for epidemic prevention and control and for economic development.
A novel genetic model provides a unique perspective on the relationship between postexercise glycogen concentration and increases in the abundance of key metabolic proteins after acute exercise
Authors: Kwak Seong Eun , Zheng Amy , Arias Edward B. , Wang Haiyan , Pan Xiufang , Yue Yongping , Duan Dongsheng , Cartee Gregory D.
Published: January 2024

Some acute exercise effects are influenced by postexercise (PEX) diet, and these diet-effects are attributed to differential glycogen resynthesis. However, this idea is challenging to test rigorously. Therefore, we devised a novel genetic model to modify muscle glycogen synthase 1 (GS1) expression in rat skeletal muscle with an adeno-associated virus (AAV) short hairpin RNA knockdown vector targeting GS1 (shRNA-GS1). Contralateral muscles were injected with scrambled shRNA (shRNA-Scr). Muscles from exercised (2-hour-swim) and time-matched sedentary (Sed) rats were collected immediately postexercise (IPEX), 5-hours-PEX (5hPEX), or 9-hours-PEX (9hPEX). Rats in 5hPEX and 9hPEX experiments were refed (RF) or not-refed (NRF) chow. Muscles were analyzed for glycogen, abundance of metabolic proteins (pyruvate dehydrogenase kinase 4, PDK4; peroxisome proliferator-activated receptor γ coactivator-1α, PGC1α; hexokinase II, HKII; glucose transporter 4, GLUT4), AMP-activated protein kinase phosphorylation (pAMPK), and glycogen metabolism-related enzymes (glycogen phosphorylase, PYGM; glycogen debranching enzyme, AGL; glycogen branching enzyme, GBE1). shRNA-GS1 versus paired shRNA-Scr muscles had markedly lower GS1 abundance. IPEX versus Sed rats had lower glycogen and greater pAMPK, and neither of these IPEX-values differed for shRNA-GS1 versus paired shRNA-Scr muscles. IPEX versus Sed groups did not differ for abundance of metabolic proteins, regardless of GS1 knockdown. Glycogen in RF-rats was lower for shRNA-GS1 versus paired shRNA-Scr muscles at both 5hPEX and 9hPEX. HKII protein abundance was greater for 5hPEX versus Sed groups, regardless of GS1 knockdown or diet, and despite differing glycogen levels. At 9hPEX, shRNA-GS1 versus paired shRNA-Scr muscles had greater PDK4 and PGC1α abundance within each diet group. However, the magnitude of PDK4 or PGC1α changes was similar in each diet group regardless of GS1 knockdown although glycogen differed between paired muscles only in RF-rats. In summary, we established a novel genetic approach to investigate the relationship between muscle glycogen and other exercise effects. Our results suggest that exercise-effects on abundance of several metabolic proteins did not uniformly correspond to differences in postexercise glycogen.
Survival and adaptation of Streptococcus phocae in host environments
Authors: Numberger Daniela , Siebert Ursula , Valentin Weigand Peter
Published: January 2024

Marine mammals are sentinel species representing the “health” of our oceans on which we are dependent. There are many threats to marine mammals including infectious diseases that increase with climate change and pollution of the marine environment. Streptococcus phocae has frequently been isolated from diseased or dead marine mammals. However, its pathogenicity and contribution to disease in marine mammals is still unknown. As bacteria including (potential) pathogens has to deal with different host environments during colonization or infection, we investigated the survival of S. phocae in fresh porcine and phocid blood, in seawater and in the presence of macrophages and (epithelial) cells from harbor seals and pigs. Furthermore, we tested adherence on and invasion of different (marine) mammalian cells by S. phocae. Our results showed that S. phocae can survive in seawater for at least 11 and 28 days at 16°C and 4°C, respectively. It is able to grow in blood of harbor and grey seals, but not in porcine blood. Furthermore, S. phocae is adherent and invasive to cells from seals and pigs, while the portion of invasive cells was higher in seal derived cells. Macrophages of harbor seals were more efficient in killing S. phocae than porcine macrophages. Our results indicate that S. phocae has strategies enabling it to adapt to the marine environment and seal hosts.
Reservoir parameters prediction based on spatially transferred long short-term memory network
Authors: Huang Wancheng , Tian Yuan
Published: January 2024

Reservoir reconstruction, where parameter prediction plays a key role, constitutes an extremely important part in oil and gas reservoir exploration. With the mature development of artificial intelligence, parameter prediction methods are gradually shifting from previous petrophysical models to deep learning models, which bring about obvious improvements in terms of accuracy and efficiency. However, it is difficult to achieve large amount of data acquisition required for deep learning due to the cost of detection, technical difficulties, and the limitations of complex geological parameters. To address the data shortage problem, a transfer learning prediction model based on long short-term memory neural networks has been proposed, and the model structure has been determined by parameter search and optimization methods in this paper. The proposed approach transfers knowledge from historical data to enhance new well prediction by sharing some parameters in the neural network structure. Moreover, the practicality and effectiveness of this method was tested by comparison based on two block datasets. The results showed that this method could significantly improve the prediction accuracy of the reservoir parameters in the event of data shortage.
Characterizing differences in retinal and choroidal microvasculature and structure in individuals with Huntington’s Disease compared to healthy controls: A cross-sectional prospective study
Authors: Joseph Suzanna , Robbins Cason B. , Haystead Alice , Hemesath Angela , Allen Ariana , Kundu Anita , Ma Justin P. , Scott Burton L. , Moore Kathryn P. L. , Agrawal Rupesh , Gunasan Vithiya , Stinnett Sandra S. , Grewal Dilraj S. , Fekrat Sharon
Published: January 2024

Objective

To characterize retinal and choroidal microvascular and structural changes in patients who are gene positive for mutant huntingtin protein (mHtt) with symptoms of Huntington’s Disease (HD).

Methods

This study is a cross-sectional comparison of patients who are gene positive for mHtt and exhibit symptoms of HD, either motor manifest or prodromal (HD group), and cognitively normal individuals without a family history of HD (control group). HD patients were diagnosed by Duke movement disorder neurologists based on the Unified Huntington’s Disease Rating Scale (UHDRS). Fovea and optic nerve centered OCT and OCTA images were captured using Zeiss Cirrus HD-5000 with AngioPlex. Outcome metrics included central subfield thickness (CST), peripapillary retinal nerve fiber layer (pRNFL) thickness, ganglion cell-inner plexiform layer (GCIPL) thickness, and choroidal vascularity index (CVI) on OCT, and foveal avascular zone (FAZ) area, vessel density (VD), perfusion density (PD), capillary perfusion density (CPD), and capillary flux index (CFI) on OCTA. Generalized estimating equation (GEE) models were used to account for inter-eye correlation.

Results

Forty-four eyes of 23 patients in the HD group and 77 eyes of 39 patients in the control group were analyzed. Average GCIPL thickness and FAZ area were decreased in the HD group compared to controls (p = 0.001, p < 0.001). No other imaging metrics were significantly different between groups.

Conclusions

Patients in the HD group had decreased GCIPL thickness and smaller FAZ area, highlighting the potential use of retinal biomarkers in detecting neurodegenerative changes in HD.
Application of connectivity index of cubic fuzzy graphs for identification of danger zones of tsunami threat
Authors: Shi Xiaolong , Kosari Saeed , Hameed Saira , Shah Abdul Ghafar , Ullah Samee
Published: January 2024

Fuzzy graphs are very important when we are trying to understand and study complex systems with uncertain and not exact information. Among different types of fuzzy graphs, cubic fuzzy graphs are special due to their ability to represent the membership degree of both vertices and edges using intervals and fuzzy numbers, respectively. To figure out how things are connected in cubic fuzzy graphs, we need to know about cubic α−strong, cubic β−strong and cubic δ−weak edges. These concepts better help in making decisions, solving problems and analyzing things like transportation, social networks and communication systems. The applicability of connectivity and comprehension of cubic fuzzy graphs have urged us to discuss connectivity in the domain of cubic fuzzy graphs. In this paper, the terms partial cubic α−strong and partial cubic δ−weak edges are introduced for cubic fuzzy graphs. The bounds and exact expression of connectivity index for several cubic fuzzy graphs are estimated. The average connectivity index for cubic fuzzy graphs is also defined and some results pertaining to these concepts are proved in this paper. The results demonstrate that removing some vertices or edges may cause a change in the value of connectivity index or average connectivity index, but the change will not necessarily be related to both values. This paper also defines the concepts of partial cubic connectivity enhancing node and partial cubic connectivity reducing node and some related results are proved. Furthermore, the concepts of cubic α−strong, cubic β− strong, cubic δ−weak edge, partial cubic α−strong and partial cubic δ−weak edges are utilized to identify areas most affected by a tsunami resulting from an earthquake. Finally, the research findings are compared with the existing methods to demonstrate their suitability and creativity.
Evaluation of microbiome and physico-chemical profiles of fresh fruits of Musa paradisiaca, Citrus sinensis and Carica papaya at different ripening stages: Implication to quality and safety management
Authors: Kifle Dawit Raga , Bacha Ketema Bedanie , Hora Reda Nemo , Likasa Lata Lachisa
Published: January 2024

Introduction

The ripening of fleshy fruits is a developmental process that involves changes in color, texture, aroma, nutrients, and diversity of microbiomes. Some microorganisms, specially, bacteria and molds are responsible for postharvest spoilage of fruits. Thus, this study is aimed at evaluating the alterations in microbiome and physico-chemical properties of selected fruits at different ripening stages.

Methods

Totally, 108 fresh fruit samples of Musa paradisiaca, Citrus sinensis and Carica papaya at three ripening stages were collected and processed in this study. The biochemical methods and MALD-TOF MS were used in identification. The physico-chemical properties of all samples were analyzed using standard methods.

Results

The minimum counts (6.74± 0.48–6.76± 0.42 log CFU/mL) and the maximum count (7.51± 0.43–7.96± 0.34 log CFU/mL) of AMB in all fruit samples was observed at mature green and overripe stages of the fruits, respectively. The ripening stage has significantly affected the microbial counts (P < 0.05) in all fruits, except counts of Enterobacteriaceae in banana and orange, and fungal counts in orange. The bacterial community of all fruits was predominated by B. cereus (33.7%), A. faecalis (17.3%), P. putida (15.2%), M. morganii (11.1%), S. sciuri (6.6%) and S. epidermidis (4.9%); while the fungal microbiome was constituted by Candida spp. (33.9%) followed by Saccharomyces spp. (18.1%) and Aspergillus spp. (16.3%). The ripening stages have also significantly affected the physico-chemical property in all samples. Accordingly, the lowest pH (3.53) and highest content of ascorbic acid (69.87 mg/100g) were observed in mature green oranges and overripe papaya, respectively, while the maximum concentration of total sugar (17.87%) and reducing sugar (14.20%) were recorded in overripe bananas.

Conclusion

The presence of some potential human pathogens and spoilage microorganisms in fruit samples could contribute to post-harvest product losses besides the potential health risk associated with consumption of the tainted fruits. Hence, proper safety management practices and preservation mechanisms should be developed and put in place to ensure consumers safety against pathogens besides minimizing product losses through microbial spoilage.