bluuebunny/crossref_metadata_2025 huggingface dataset:
Ageing well with diabetes: the role of technology
Authors: Maltese Giuseppe , McAuley Sybil A. , Trawley Steven , Sinclair Alan J.
Published: August 2024

Over the past two decades there has been a substantial rise in the adoption of diabetes therapeutic technology among children, adolescents and younger adults with type 1 diabetes, and its use is now also advocated for older individuals. Older people with diabetes are more prone to experience hypoglycaemia because of numerous predisposing factors and are at higher risk of hypoglycaemic events requiring third-party assistance as well as other adverse sequelae. Hypoglycaemia may also have long-term consequences, including cognitive impairment, frailty and disability. Diabetes in older people is often characterised by marked glucose variability related to age-associated changes such as variable appetite and levels of physical activity, comorbidities and polypharmacotherapy. Preventing hypoglycaemia and mitigating glucose excursions may have considerable positive impacts on physical and cognitive function and general well-being and may even prevent or improve frailty. Technology for older people includes continuous glucose monitoring systems, insulin pumps, automated insulin delivery systems and smart insulin pens. Clinical trials and real-world studies have shown that older people with diabetes benefit from technology in terms of glucose management, reductions in hypoglycaemic events, emergency department attendance and hospital admissions, and improvement in quality of life. However, ageing may bring physical impairments and other challenges that hinder the use of technology. Healthcare professionals should identify older adults with diabetes who may benefit from therapeutic technology and then adopt an individualised approach to education and follow-up for individuals and their caregivers. Future research should explore the impact of diabetes technology on outcomes relevant to older people with diabetes.

Graphical Abstract
Elevations in plasma glucagon are associated with reduced insulin clearance after ingestion of a mixed-macronutrient meal in people with and without type 2 diabetes
Authors: Smith Kieran , Taylor Guy S. , Peeters Wouter , Walker Mark , Perazzolo Simone , Atabaki-Pasdar Naeimeh , Bowden Davies Kelly A. , Karpe Fredrik , Hodson Leanne , Stevenson Emma J. , West Daniel J.
Published: August 2024

Aims/hypothesis

The temporal suppression of insulin clearance after glucose ingestion is a key determinant of glucose tolerance for people without type 2 diabetes. Whether similar adaptations are observed after the ingestion of a mixed-macronutrient meal is unclear.

Methods

In a secondary analysis of data derived from two randomised, controlled trials, we studied the temporal responses of insulin clearance after the ingestion of a standardised breakfast meal consisting of cereal and milk in lean normoglycaemic individuals (n=12; Lean-NGT), normoglycaemic individuals with central obesity (n=11; Obese-NGT) and in people with type 2 diabetes (n=19). Pre-hepatic insulin secretion rates were determined by the deconvolution of C-peptide, and insulin clearance was calculated using a single-pool model. Insulin sensitivity was measured by an oral minimal model.

Results

There were divergent time course changes in insulin clearance between groups. In the Lean-NGT group, there was an immediate post-meal increase in insulin clearance compared with pre-meal values (p<0.05), whereas insulin clearance remained stable at baseline values in Obese-NGT or declined slightly in the type 2 diabetes group (p<0.05). The mean AUC for insulin clearance during the test was ~40% lower in the Obese-NGT (1.3 ± 0.4 l min−1 m−2) and type 2 diabetes (1.4 ± 0.7 l min−1 m−2) groups compared with Lean-NGT (1.9 ± 0.5 l min−1 m−2; p<0.01), with no difference between the Obese-NGT and type 2 diabetes groups. HOMA-IR and glucagon AUC emerged as predictors of insulin clearance AUC, independent of BMI, age or insulin sensitivity (adjusted R2=0.670). Individuals with increased glucagon AUC had a 40% reduction in insulin clearance AUC (~ −0.75 l min−1 m−2; p<0.001).

Conclusions/interpretation

The ingestion of a mixed-macronutrient meal augments differing temporal profiles in insulin clearance among individuals without type 2 diabetes, which is associated with HOMA-IR and the secretion of glucagon. Further research investigating the role of hepatic glucagon signalling in postprandial insulin kinetics is warranted.

Trial registration

ISRCTN17563146 and ISRCTN95281775

Graphical Abstract
Surrogate gradient methods for data-driven foundry energy consumption optimization
Authors: Chen Shikun , Kaufmann Tim , Martin Robert J.
Published: August 2024

In many industrial applications, data-driven models are more and more commonly employed as an alternative to classical analytical descriptions or simulations. In particular, such models are often used to predict the outcome of an industrial process with respect to specific quality characteristics from both observed process parameters and control variables. A major step in proceeding from purely predictive to prescriptive analytics, i.e., towards leveraging data-driven models for process optimization, consists of, for given process parameters, determining control variable values such that the output quality improves according to the process model. This task naturally leads to a constrained optimization problem for data-driven prediction algorithms. In many cases, however, the best available models suffer from a lack of regularity: methods such as gradient boosting or random forests are generally non-differentiable and might even exhibit discontinuities. The optimization of these models would therefore require the use of derivative-free techniques. Here, we discuss the use of alternative, independently trained differentiable machine learning models as a surrogate during the optimization procedure. While these alternatives are generally less accurate representations of the actual process, the possibility of employing derivative-based optimization methods provides major advantages in terms of computational performance. Using classical benchmarks as well as a real-world dataset obtained from an industrial environment, we demonstrate that these advantages can outweigh the additional model error, especially in real-time applications.
Long-term co-occurrence and gregariousness in the migratory common stingray using network analysis
Authors: Kraft S. , Winkler A. C. , Abecasis D. , Mourier J.
Published: August 2024

Aggregations and social interactions play an important role in the movement ecology of many animals, including elasmobranchs. Several of these species have shown the capability of carrying out complex social behaviours, and the importance of sociality in this taxon is being realized. Although it is a growing field of study in the case of these organisms, these processes still need to be better understood, especially to support management and conservation policies. In this study, a long-term acoustic telemetry data set collected on Dasyatis pastinaca in a coastal marine protected area was analysed. A co-occurrence network analysis was done to investigate preferential associations among individuals, revealing non-random associations among them. The analysis revealed a few strong and consistent associations that were maintained across inter-migratory periods, as this species performs seasonal migrations to a nearby estuary, suggesting temporal stability of the observed associations. Moreover, individuals had similar average positions and a generally high overlap of space use in both periods, indicating some level of site fidelity to the fully protected area. Groups of up to 64% of tagged individuals were co-detected at a same receiver, particularly in the western side of the array. Despite our limited sample size, likely underestimating associations, these results show that in addition to their large-scale movement pattern, D. pastinaca is likely to also present active partner preference and spatial structure at a finer spatial scale. The nature of such results is relevant to support the protection of these species.
The relationship between MRI-detected hip abnormalities and hip pain in hip osteoarthritis: a systematic review
Authors: Fang Haonan , Zhang Xiaoyue , Wang Junjie , Xing Xing , Shen Ziyuan , Cai Guoqi
Published: August 2024

Magnetic resonance imaging (MRI) is increasingly used in the classification and evaluation of osteoarthritis (OA). Many studies have focused on knee OA, investigating the association between MRI-detected knee structural abnormalities and knee pain. Hip OA differs from knee OA in many aspects, but little is known about the role of hip structural abnormalities in hip pain. This study aimed to systematically evaluate the association of hip abnormalities on MRI, such as cartilage defects, bone marrow lesions (BMLs), osteophytes, paralabral cysts, effusion-synovitis, and subchondral cysts, with hip pain. We searched electronic databases from inception to February 2024, to identify publications that reported data on the association between MRI features in the hip joint and hip pain. The quality of the included studies was scored using the Newcastle-Ottawa Scale (NOS). The levels of evidence were evaluated according to the Cochrane Back Review Group Method Guidelines and classified into five levels: strong, moderate, limited, conflicting, and no evidence. A total of nine studies were included, comprising five cohort studies, three cross-sectional studies, and one case-control study. Moderate level of evidence suggested a positive association of the presence and change of BMLs with the severity and progress of hip pain, and evidence for the associations between other MRI features and hip pain were limited or even conflicting. Only a few studies with small to modest sample sizes evaluated the association between hip structural changes on MRI and hip pain. BMLs may contribute to the severity and progression of hip pain. Further studies are warranted to uncover the role of hip MRI abnormalities in hip pain. The protocol for the systematic review was registered with PROSPERO (https://www.crd.york.ac.uk/PROSPERO/, CRD42023401233).
ESR Essentials: basic physics of MR safety—practice recommendations by the European Society for Magnetic Resonance in Medicine and Biology
Authors: Nordin Love Engström , Åberg Karin , Kihlberg Johan , Owman Titti , Hansson Boel , Björkman-Burtscher Isabella M. , Petersen Cecilia , Lundberg Peter
Published: August 2024

Objectives

The use of magnetic resonance imaging (MRI) is safe from a long-term perspective since there are no known cumulative risks for patients or personnel. However, the technique comes with several acute risks associated with the powerful electromagnetic fields that are necessary to produce medical images. These risks include, among other things, a projectile hazard, loud noise, and the risk of heating. Safe use of MRI requires knowledge about the different hazards related to MRI and organizational structured work including the implementation of routines describing a safe workflow from the referral of a patient to the signed report. In this article, the risks associated with MRI are described along with suggestions for how each risk can be minimized or eliminated.

Conclusion

The aim of this article is to provide support for the development of, and compliance with, MRI safety routines, and to work with the technique in a safe way. The scope of this treatise does not cover specific details of implant safety, however, the physical principles described can be applied to the risk assessment of implants.

Key Points

Establish whether any MR contraindications apply to the patient.

Evaluate means to deal with identified risks for both patients and personnel.

It is imperative to always perform and document a risk-benefit assessment.
Runtime Analysis of Quality Diversity Algorithms
Authors: Bossek Jakob , Sudholt Dirk
Published: August 2024

Quality diversity (QD) is a branch of evolutionary computation that gained increasing interest in recent years. The Map-Elites QD approach defines a feature space, i.e., a partition of the search space, and stores the best solution for each cell of this space. We study a simple QD algorithm in the context of pseudo-Boolean optimisation on the “number of ones” feature space, where the ith cell stores the best solution amongst those with a number of ones in $$[(i-1)k, ik-1]$$

[
(
i
-
1
)
k
,
i
k
-
1
]

. Here k is a granularity parameter $$1 \le k \le n+1$$

1
≤
k
≤
n
+
1

. We give a tight bound on the expected time until all cells are covered for arbitrary fitness functions and for all k and analyse the expected optimisation time of QD on OneMax and other problems whose structure aligns favourably with the feature space. On combinatorial problems we show that QD finds a $${(1-1/e)}$$

(
1
-
1
/
e
)

-approximation when maximising any monotone sub-modular function with a single uniform cardinality constraint efficiently. Defining the feature space as the number of connected components of an edge-weighted graph, we show that QD finds a minimum spanning forest in expected polynomial time. We further consider QD’s performance on classes of transformed functions in which the feature space is not well aligned with the problem. The asymptotic performance is unaffected by transformations on easy functions like OneMax. Applying a worst-case transformation to a deceptive problem increases the expected optimisation time from $$O(n^2 \log n)$$

O
(

n
2

log
n
)

to an exponential time. However, QD is still faster than a (1+1) EA by an exponential factor.
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

Passive 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

Shunt 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

IgA 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

As 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

Hypernetworks, 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

The 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

In 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

In 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

Deadwood 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

Label 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

The 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

The 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

The 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

We 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

The 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.
Learning recommendations from educational event data in higher education
Authors: Park Gyunam , Liss Lukas , van der Aalst Wil M. P.
Published: August 2024

This paper presents a novel approach for generating actionable recommendations from educational event data collected by Campus Management Systems (CMS) to enhance study planning in higher education. The approach unfolds in three phases: feature identification tailored to the educational context, predictive modeling employing the RuleFit algorithm, and extracting actionable recommendations. We utilize diverse features, encompassing academic histories and course sequences, to capture the multi-dimensional nature of student academic behaviors. The effectiveness of our approach is empirically validated using data from the computer science bachelor’s program at RWTH Aachen University, with the goal of predicting overall GPA and formulating recommendations to enhance academic performance. Our contributions lie in the novel adaptation of behavioral features for the educational domain and the strategic use of the RuleFit algorithm for both predictive modeling and the generation of practical recommendations, offering a data-driven foundation for informed study planning and academic decision-making.
Aluminum alloys for electrical engineering: a review
Authors: Czerwinski Frank
Published: August 2024

High-performance conductors are essential for economically and environmentally sustainable ways of electricity transfer in modern infrastructure, manufacturing and transportation, including electric vehicles. This report reviews the aluminum conductors, their fundamentals, classification and utilization markets, focusing on metallurgical characteristics of present commercial solutions and the strategy of future development directions. The inherent features of aluminum, both beneficial and detrimental, for electrical engineering are emphasized along with alloying concepts that provide the accelerated decomposition of matrix solid solution to minimize the electron scattering. Development activities are assessed of new generation of aluminum conductors that in addition to alloying utilize novel processing techniques such as ultra-fast crystallization, severe plastic deformation and complex thermomechanical treatments aiming at grain reduction to nanometer scale, crystallographic texture control and grain boundary engineering. Transition metals and rare earths are considered as the promising alloying candidates for high-strength conductors having superior thermal stability with extra importance given to immiscible systems of Al–Ce, Al–La and Al–Y along with multiply additions, combined to generate the synergy effects. The composites with cladding configuration and particulate reinforcement including via carbon-type strengtheners are discussed as the effective solutions of advanced conductors. A variety of strategies that aim at overcoming the strength–conductivity trade-off in conductor materials are presented throughout the report.

Graphical abstract
Feasibility of using diamond-like carbon films in total joint replacements: a review
Authors: Roy Anurag , Bennett Annette , Pruitt Lisa
Published: August 2024

Diamond-like Carbon (DLC) has been used as a coating material of choice for a variety of technological applications owing to its favorable bio-tribo-thermo-mechanical characteristics. Here, the possibility of bringing DLC into orthopedic joint implants is examined. With ever increasing number of patients suffering from osteoarthritis as well as with the ingress of the osteoarthritic joints’ malaise into younger and more active demographics, there is a pressing need to augment the performance and integrity of conventional total joint replacements (TJRs). Contemporary joint replacement devices use metal-on-polymer articulations to restore function to worn, damaged or diseased cartilage. The wear of polymeric components has been addressed using crosslinking and antioxidants; however, in the context of the metallic components, complications pertaining to corrosion and metal ion release inside the body still persist. Through this review article, we explore the use of DLC coatings on metallic bearing surfaces and elucidate why this technology might be a viable solution for ongoing electrochemical challenges in orthopedics. The different characteristics of DLC coatings and their feasibility in TJRs are examined through assessment of tribo-material characterization methods. A holistic characterization of the coating-substrate interface and the wear performance of such systems are discussed. As with all biomaterials used in TJRs, we need mindful consideration of potential in-vivo challenges. We present a few caveats for DLC coatings including delamination, hydrophobicity, and other conflicting as well as outdating findings in the literature. We recommend prudently exploring DLC films as potential coatings on metallic TJR components to solve the problems pertaining to wear, metal ion release, and corrosion. Ultimately, we advise bringing DLC into clinical use only after addressing all challenges and concerns outlined in this article.

Graphical Abstract
Small-Molecule:Polymer Composites for Transparent Films with Visible Emission
Authors: Pérez-Gutiérrez Enrique , Calvo Franciso D. , Beristain Miriam F. , Venkatesan Perumal , Thamotharan Subbiah , Percino M. Judith
Published: August 2024

The analysis of the shift in photoluminescence emission for a blend of polyvinylcarbazole and acrylonitrile derivative compounds is reported. The small-molecule compounds have different functional groups, phenyl, pyridine, or methyl phenyl, attached to an acrylonitrile group. According to the functional group, the blue emission for pure dye shifts to green or yellowish in the blend film. Several PVK:dye ratios from 0:100 to 20:80 were used for film deposition. The film morphology was analyzed by atomic force microscopy; for low dye content, homogeneous films were achieved. However, aggregates of several micrometers are formed on the surface of films with higher dye concentrations. The shift in emission occurs only with PVK, and for a non-conjugated matrix such as polystyrene, the emission remains unchanged. The interaction of dyes with PVK leading to change in emission was also achieved by grinding dye and polymer. Results showed that shifts in emission could come from exciplex formation along with changes in dye intermolecular interactions. The blend films were highly transparent in the visible spectra due to the absorption in the UV region for dye and matrix. The films with ratio PVK: dye ratio 80:20 was used as active layer in OLEDs.
Impact of technology transfer offices on university spin-off bankruptcy
Authors: Kirihata Tetsuya
Published: August 2024

University technology transfer offices (TTOs) support the creation and subsequent growth of university spin-offs (USOs) as a part of their “third mission.” This study empirically analyzed the impact of TTO involvement on USO bankruptcy from a social capital perspective. Using Cox proportional hazards model survival analysis, it revealed that close business relationships, customer introduction, market/customer knowledge sharing, and technology knowledge sharing with USOs significantly negatively affected their bankruptcy. None of the models promoted USO bankruptcy at any of the stages from relationship building to resource provision, aligning with the assumptions from the social capital perspective. The results revealed three vectors for effective TTO involvement in preventing USO bankruptcy: (1) professionalism in relationship building, (2) knowledge-based involvement, and (3) customer-oriented resource provision. The findings highlight the relationship between TTO involvement and USO bankruptcy from the social capital perspective, thus bridging a gap in the literature. TTOs must recruit and promote specialists who have a professional mindset, consistently maintain professional business relationships with USOs, have experience and knowledge, and are customer oriented. USOs must seek other meaningful relationship building opportunities and stimulate self-help efforts if TTOs lack supporting capabilities and remain unwilling to help.
Private entrepreneurial support organizations in European fintech entrepreneurial ecosystems
Authors: Micol Federico , Battaglia Daniele , Ughetto Elisa
Published: August 2024

The importance of Entrepreneurial Support Organizations in promoting the development of Entrepreneurial Ecosystems is well recognized in the literature. Surprisingly, no research has addressed how governments can promote the emergence of Private Entrepreneurial Support Organizations in parallel with Public ones. Using the European FinTech Entrepreneurial Ecosystem as an empirical setting, we investigate through a Qualitative Comparative Analysis which enabling factors the government can intervene on to foster the development of Private Entrepreneurial Support Organizations. We then determine how much the same factors can influence the availability of such programs through a Poisson fixed effects model. The results suggest that Private Entrepreneurial Support Organizations are consistently present in regions with a combination of enabling factors that are conducive to market competition. Policies aimed at simultaneously increasing private R&D spending, reducing subsidies to industry, developing talent, strengthening the knowledge economy and increasing demand could not only encourage the emergence of more startups but also have a positive impact on the overall availability of Private Entrepreneurial Support Organizations.
On generalization of some theorems with absolute summability factors of infinite series
Authors: Łenski Włodzimierz , Szal Bogdan
Published: August 2024

The generalizations of some results of H. Bor, L. Leindler, and H. Sevli pertaining to absolute summability are examined.