bluuebunny/crossref_metadata_2025_split huggingface dataset:
Leveraging time-based acoustic patterns for ecosystem analysis
Authors: Castro-Ospina Andrés E. , Rodríguez-Marín Paula , López José D. , Martínez-Vargas Juan D. 
Published: August 2024

AbstractPassive acoustic monitoring (PAM) is an effective, non-intrusive method for studying ecosystems, but obtaining meaningful ecological information from its large number of audio files is challenging. In this study, we take advantage of the expected animal behavior at different times of the day (e.g., higher acoustic animal activity at dawn) and develop a novel approach to use these time-based patterns. We organize PAM data into 24-hour temporal blocks formed with sound features from a pretrained VGGish network. These features feed a 1D convolutional neural network with a class activation mapping technique that gives interpretability to its outcomes. As a result, these diel-cycle blocks offer more accurate and robust hour-by-hour information than using traditional ecological acoustic indices as features, effectively recognizing key ecosystem patterns.
Risk factors of shunt-dependent hydrocephalus after subarachnoid hemorrhage: a systematic review and meta-analysis based on observational cohort studies
Authors: Chen Lingzhuo , Meng Yichen , Xue Qiang , Zhao Yuanyu , Zhou Xuhui , Hu Kejia , He Hua 
Published: August 2024

AbstractShunt dependent hydrocephalus (SDHC) is a common sequel after aneurysmal subarachnoid hemorrhage (aSAH) and factors contributing to the development of SDHC remain obscure. The aim of this study was to identify predictors of SDHC following aSAH. We conducted a systematic review based on the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. We searched electronic databases including Pubmed, Embase, and Cochrane databases from 1980 through August 2019 for studies on the risk factors of SDHC after aSAH. Inclusion criteria were: (1) SAH and hydrocephalus confirmed by CT or magnetic resonance imaging findings; (2) the odds ratios (ORs) or the relative risk (RR) with 95% confidence interval (95%CI; or crude data that allowed their calculation) were reported; and (3) languages were restricted to English and Chinese. Two independent authors collected the data including study design, characteristics of patients and potential risk factors. Random-effects models were used to estimate weighted mean differences (WMD), relative risks (RR) with corresponding 95% confidence intervals (CI). For analysis with significant heterogeneity, subgroup analyses stratified by study design and geographic area were performed. In all, 37 cohort studies met inclusion criteria. Several factors were associated with SDHC. Infection, acute hydrocephalus, placement of external ventricular drainage, older age, higher Hunt and Hess grade, intraventricular hemorrhage, rebleeding, and mechanical ventilation were associated with greater 2-fold increased risk of SDHC. Vasospasm, female gender, high Fisher grade, preexisting hypertension, aneurysm in posterior location and intracerebral hemorrhage were associated with less than 2-fold increased risk. Treatment modality and diabetes mellitus were not associated with SDHC. SDHC is a multi-factorial disease that is associated with patient and treatment factors. Acknowledgement of these potential factors could help prevent SDHC.
Exploring the crosstalk molecular mechanisms between IgA nephropathy and Sjögren’s syndrome based on comprehensive bioinformatics and immunohistochemical analyses
Authors: He Peng , Wei Lei , Zhang Ruijing , Zhao Jin , Zhang Yuzhan , Huang Liuyifei , Bai Xiao , Ning Xiaoxuan , Sun Shiren 
Published: August 2024

AbstractIgA nephropathy (IgAN) and Sjogren's syndrome (SS) are two autoimmune diseases with undetermined etiology and related to abnormal activation of lymphocytes. This study aims to explore the crucial genes, pathways and immune cells between IgAN and SS. Gene expression profiles of IgAN and SS were obtained from the Gene Expression Omnibus and Nephroseq data. Differentially expressed gene (DEG) and weighted gene co-expression network analyses (WGCNA) were done to identify common genes. Enrichment analysis and protein–protein interaction network were used to explore potential molecular pathways and crosstalk genes between IgAN and SS. The results were further verified by external validation and immunohistochemistry (IHC) analysis. Additionally, immune cell analysis and transcription factor prediction were also conducted. The DEG analysis revealed 28 commonly up-regulated genes, while WGCNA identified 98 interactively positive-correlated module genes between IgAN and SS. The enrichment analysis suggested that these genes were mainly involved in the biological processes of response to virus and antigen processing and presentation. The external validation and IHC analysis identified 5 hub genes (PSMB8, PSMB9, IFI44, ISG15, and CD53). In the immune cell analysis, the effector memory CD8 T and T follicular helper cells were significantly activated, and the corresponding proportions showed positively correlations with the expressions of the 5 hub genes in the two autoimmune diseases. Together, our data identified the crosstalk genes, molecular pathways, and immune cells underlying the IgAN and SS, which provides valuable insights into the intricate mechanisms of these diseases and offers potential intervention targets.
Transitions between care networks: a prospective study among older adults in the Netherlands
Authors: Gardeniers Maura K. M. , Huisman Martijn , Meijboom Erik Jan , Hoogendijk Emiel O. , Broese van Groenou Marjolein I. 
Published: August 2024

AbstractAs health impairment increases, older adults utilize care from different types of caregivers, but little is known about changes in the composition of care networks. We mapped the transitions between different care networks to gain insight into which people develop care networks that include informal, privately and publicly paid care. We used three waves (2012–2015–2018) of the Longitudinal Aging Study Amsterdam with 1413 Dutch community-dwelling adults, aged 64–100. Network types were identified using six types of caregivers: (1) co-resident, (2) non-co-resident children, (3) other kin, (4) neighbours/friends/acquaintances, (5) publicly paid, (6) privately paid, in a latent transition analysis with mortality and moving to a care facility as missing states. Five types of care networks were identified: (1) no care, (2) privately paid, (3) mixed informal, (4) mixed publicly paid, (5) co-resident. The co-resident network was the most unstable and had a high transition rate to nursing homes. Participants from the privately paid care network often transitioned to a mixed informal network and rarely transitioned to a mixed publicly paid network. Transitions out of the no-care network were mostly to the privately paid network. The two mixed care networks were the most stable. Transitions appeared to be most triggered by deteriorating health. Transitions to institutional care were most likely in the mixed informal, mixed publicly paid and the co-resident network. Thus, these networks appear to require additional support to facilitate ageing in place.
A brief review of hypernetworks in deep learning
Authors: Chauhan Vinod Kumar , Zhou Jiandong , Lu Ping , Molaei Soheila , Clifton David A. 
Published: August 2024

AbstractHypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and model compression. Hypernets have shown promising results in a variety of deep learning problems, including continual learning, causal inference, transfer learning, weight pruning, uncertainty quantification, zero-shot learning, natural language processing, and reinforcement learning. Despite their success across different problem settings, there is currently no comprehensive review available to inform researchers about the latest developments and to assist in utilizing hypernets. To fill this gap, we review the progress in hypernets. We present an illustrative example of training deep neural networks using hypernets and propose categorizing hypernets based on five design criteria: inputs, outputs, variability of inputs and outputs, and the architecture of hypernets. We also review applications of hypernets across different deep learning problem settings, followed by a discussion of general scenarios where hypernets can be effectively employed. Finally, we discuss the challenges and future directions that remain underexplored in the field of hypernets. We believe that hypernetworks have the potential to revolutionize the field of deep learning. They offer a new way to design and train neural networks, and they have the potential to improve the performance of deep learning models on a variety of tasks. Through this review, we aim to inspire further advancements in deep learning through hypernetworks.
A novel robust decomposition algorithm for a profit-oriented production routing problem with backordering, uncertain prices, and service level constraints
Authors: Zouadi Tarik , Chargui Kaoutar , Zhani Najlae , Charles Vincent , V Raja Sreedharan 
Published: August 2024

AbstractThe Production Routing Problem (PRP) seeks optimal production and distribution planning that minimises costs and fulfils customer orders. Yet, existing literature often overlooks the potential impact on profitability. Achieving optimal profit does not necessarily imply meeting all customer orders. The cost-to-profit ratio should be considered when serving customer orders, as there are circumstances where it might be more profitable to cancel or backorder certain orders. Thus, this paper proposes, for the first time, a novel extension of PRP that maximises profit where demand is price-sensitive and allows order cancellation and backorders under service level targets. From on-field observations, price is inherently subject to uncertainty; thus, we propose a robust mathematical model for the problem that optimises the worst-case profit. To solve the problem, the paper proposes a decomposition algorithm that splits the problem into a master problem and a set of subproblems, enhanced by valid inequalities and warming up lower bounds to alleviate the model complexity. Through a series of computational tests, we prove the ability of the proposed algorithm to tighten the optimality gaps and alleviate computational time. An additional economic study is conducted to investigate how parameter variation affects profit and how sensitive it is to service level targets.
Semiseparable Functors and Conditions up to Retracts
Authors: Ardizzoni Alessandro , Bottegoni Lucrezia 
Published: August 2024

AbstractIn a previous paper we introduced the concept of semiseparable functor. Here we continue our study of these functors in connection with idempotent (Cauchy) completion. To this aim, we introduce and investigate the notions of (co)reflection and bireflection up to retracts. We show that the (co)comparison functor attached to an adjunction whose associated (co)monad is separable is a coreflection (reflection) up to retracts. This fact allows us to prove that a right (left) adjoint functor is semiseparable if and only if the associated (co)monad is separable and the (co)comparison functor is a bireflection up to retracts, extending a characterization pursued by X.-W. Chen in the separable case. Finally, we provide a semi-analogue of a result obtained by P. Balmer in the framework of pre-triangulated categories.
Engaging bait retailers as part of the live bait pathway: bait retailer characteristics and aquatic invasive species prevention
Authors: Johnson Rachel K. , Mulligan Hannah R. , Masterson Cheyanne , Hayden Sarah , Feuerbach Logan , Nester Peter , Shea Shaylee , Lucchesi David O. , Schall Benjamin J. , Coulter Alison A. 
Published: August 2024

AbstractIn the Great Plains region, the live bait industry relies on approved species of cultured and wild-harvested baitfish. The release of baitfish by anglers is prohibited in most locations, but angler compliance with these regulations can be low. Bait retailers can increase compliance with regulations by communicating with anglers about aquatic invasive species (AIS) risk and through their business practices. We conducted 66 telephone surveys with bait retailers in southeastern South Dakota from June to July 2023 to (1) characterize bait retailer practices that may influence AIS risk and (2) determine the extent and willingness of bait retailers to display educational materials to reduce AIS risk. Fathead minnows (Pimephales promelas) were the most sold species (96% of respondents), and most bait came from wholesale retailers (74%). However, 24% of bait retailers also occasionally bought from local bait harvesters. Non-advertised species were observed at least once in shipments by 35% of bait retailers, but retailers only encountered species they did not intend to order in 1% of their inspections. Regular inspections for non-advertised species in baitfish shipments were conducted by 74% of retailers. Most inspections occurred at the time of receiving a baitfish shipment (36%) or daily (36%). Bait retailers were interested in displaying provided AIS educational materials (80%), including posters and pamphlets (59%), stickers (41%), bait bags (39%), and keychains/boat key floats (36%). Displaying educational materials at bait retail locations may increase angler knowledge of AIS and compliance with bait disposal regulations.
From deadwood to forest soils: quantifying a key carbon flux in boreal ecosystems
Authors: Stokland Jogeir , Alfredsen Gry 
Published: August 2024

AbstractDeadwood represents a dynamic carbon pool in forest ecosystems where microbial decomposition causes fluxes of CO2 to the atmosphere through respiration and organic carbon to the soil through leakage and fragmentation. This study characterises different stages of deadwood of Norway spruce (Picea abies). 35 Norway spruce trees were sampled and categorized on a 0–5 decay scale. For the 14 trees in classes 0–3, two stem discs were collected from two heights. For the 21 trees in classes 4 and 5, a single sample per tree was taken, because decay was relatively uniform throughout the stem. The relative amount of hemicellulose and cellulose declined moderately from decay class 1 to 3 and substantially from decay class 3 to class 4 but small amounts were still present in decay class 5. The relative lignin proportion increased substantially from decay class 3 to 4 and dominated in decay class 5. Relative carbon content increased from 50 to 56% during the decomposition process due to the increasing accumulation of lignin residuals being a typical signature of brown rot decay. A laboratory experiment including three species of brown rot fungi verified decomposition close to 70% of Norway spruce biomass and resulted in 55% carbon content. This was similar to the carbon content in decay class 4 and 5. A novel approach is presented to quantify the carbon flux from deadwood to the soil. First, we calculated the residual proportion of carbon in decayed wood compared to the initial carbon content of live trees. Subsequently, we extended the calculation to determine the amount of remaining carbon from non-decayed wood that was transferred to the soil during each decay class. The approach showed that Norway spruce wood decomposition under field conditions transfers at least 39–47% of the initial wood carbon to the soil carbon pool, depending on soil type. This strengthens the previously under-communicated fact that the carbon flux from deadwood to soil is higher from brown rot decomposition in boreal forests than the corresponding carbon flux in temperate and tropical forests where deadwood is more influenced by white rot fungi.
Random walks with variable restarts for negative-example-informed label propagation
Authors: Maxwell Sean , Koyutürk Mehmet 
Published: August 2024

AbstractLabel propagation is frequently encountered in machine learning and data mining applications on graphs, either as a standalone problem or as part of node classification. Many label propagation algorithms utilize random walks (or network propagation), which provide limited ability to take into account negatively-labeled nodes (i.e., nodes that are known to be not associated with the label of interest). Specialized algorithms to incorporate negatively-labeled nodes generally focus on learning or readjusting the edge weights to drive walks away from negatively-labeled nodes and toward positively-labeled nodes. This approach has several disadvantages, as it increases the number of parameters to be learned, and does not necessarily drive the walk away from regions of the network that are rich in negatively-labeled nodes. We reformulate random walk with restarts and network propagation to enable “variable restarts", that is the increased likelihood of restarting at a positively-labeled node when a negatively-labeled node is encountered. Based on this reformulation, we develop CusTaRd, an algorithm that effectively combines variable restart probabilities and edge re-weighting to avoid negatively-labeled nodes. To assess the performance of CusTaRd, we perform comprehensive experiments on network datasets commonly used in benchmarking label propagation and node classification algorithms. Our results show that CusTaRd consistently outperforms competing algorithms that learn edge weights or restart profiles, and that negatives close to positive examples are generally more informative than more distant negatives.
Effects of weather on productivity growth of South African table grape industry: a comparison between index approaches
Authors: Myeki Lindikaya W. , Temoso Omphile , Mkhabela Thulasizwe 
Published: August 2024

AbstractThe current literature on productivity growth analysis in South Africa often overlooks two key factors, namely the weather effects and pollution-related issues. This study aims to address the first of these gaps by utilising a decade’s worth of panel data from the Table grape industry in the country. We conduct a comparison between Färe-Primont Index that ignores environmental change and statistical noise, and; Proper Index. The latter offers a measure of sustainable productivity, decomposing into technical change, technical efficiency, scale efficiency, mix efficiency, environmental change (weather effects), and statistical noise. In simpler terms, the Färe-Primont Index results in conventional growth (TFP), while the Proper Index leads to sustainable TFPI growth (TFPI). Our findings reveal that ignoring weather effects and statistical noise leads to an increase in TFP. However, accounting for these factors results in a decline in TFPI. Despite this decline, weather effects had an average 0.11% positive impact on TFPI growth. We conclude that weather effects posed a lesser challenge to TFPI growth during the observed period compared to technical change (−4.80%) and scale-mix inefficiency (−0.76%). Therefore, we recommend that the table grape industry prioritise investment in research and innovation as a strategy to improve technical change and improve infrastructure such as irrigation systems, roads, and storage facilities to reduce costs, increase the scale of production, and thus improve scale efficiency. Government policies, such as funding for technology adoption and encouraging diversification, can improve mix efficiency. Future studies should strive to address the second gap in the literature by focusing on pollution-related issues.
On Logical and Scientific Strength
Authors: Incurvati Luca , Nicolai Carlo 
Published: August 2024

AbstractThe notion of strength has featured prominently in recent debates about abductivism in the epistemology of logic. Following Timothy Williamson and Gillian Russell, we distinguish between logical and scientific strength and discuss the limits of the characterizations they employ. We then suggest understanding logical strength in terms of interpretability strength and scientific strength as a special case of logical strength. We present applications of the resulting notions to comparisons between logics in the traditional sense and mathematical theories.
The Distributive Demands of Relational Egalitarianism
Authors: Heilinger Jan-Christoph 
Published: August 2024

AbstractThe article outlines the distributive demands of relational equality in the form of a dynamic corridor of legitimate distributive inequality. It does so by complementing the already widely accepted sufficientarian floor with a limitarian ceiling, leading, in a first step, to a "corridor" of limited distributive inequality as a necessary condition for relational equality. This corridor alone, however, only provides necessary distributive conditions for relational equality and still allows for degrees of distributive inequality that would risk undermining egalitarian relations. Thus, in a second step, intra-corridor distributive inequalities must be regulated by two further constraints: a (context- dependent) ratio between the best- and worst-off, and a demand for equality of opportunity so that inequalities result from people’s responsible choices. This set of demands spells out the distributive conditions that are not only necessary, but sufficient to provide a sound distributive basis for relational equality. After presenting this view, the article defends it against several objections.
Misreporting in the Norwegian business cash support scheme
Authors: Alpysbayeva Dinara , Alstadsæter Annette , Kopczuk Wojciech , Markussen Simen , Raaum Oddbjørn 
Published: August 2024

AbstractWe analyze the reporting response to an ambitiously targeted government support scheme for Norwegian businesses at the very start of the Coronavirus crisis in 2020. Our empirical design is based on cross-checking self-reported data in the applications for support with administratively reported data used for VAT. We find strong evidence that strategic misreporting was present but conclude that its remaining quantitative extent after enforcement actions already taken by the tax authorities was relatively small. Firms tend to misreport 4% more often than expected, and the actual support paid out was 5% higher than it should have been. We discuss possible reasons for the relatively limited extent of non-compliance and more general lessons for the design of transfer programs.
Effect of Parental Severe Mental Disorders on the Timing of Autism Diagnosis: A Family Linkage Study
Authors: Tuan Yu , Chen Li-Chi , Chen I.-Chun , Tsai Shih-Jen , Chen Tzeng-Ji , Chen Mu-Hong 
Published: August 2024

AbstractThe mean diagnosis age of autism was about 5 years in Taiwan. Whether the delayed diagnosis of autism (≥ 6 years) was associated with parental severe mental disorders remained unknown. The parents of 22,859 autistic individuals and 228,590 age- and sex-matched nonautistic individuals were assessed for the presence of severe mental disorders (schizophrenia, bipolar disorder, major depressive disorder, alcohol use disorder, and substance use disorder). The timing of autism diagnosis was classified into three age categories: < 6 years, 6–11 years, and ≥ 12 years. Logistic regression models were used to examine associations between parental severe mental disorders and these age categories of autism diagnosis. Parental schizophrenia and substance use disorders were associated with the delayed diagnosis of autism, both diagnosis at ≥ 12 years (odds ratio [OR]: 2.14; 1.57) and at 6–11 years (1.87; 1.38). Parental bipolar disorder and major depressive disorder were also associated with the delayed diagnosis of autism, especially diagnosis at 6–11 years (OR 1.98; 1.86). Our findings underscore the need for clinicians to monitor the neurodevelopmental conditions of offspring born to parents with severe mental disorders during the early stages of their life.