bluuebunny/crossref_metadata_2025 huggingface dataset:
Seismic risk assessment of a large metropolitan area by means of simulated earthquakes
Authors: Fischer Eliana , Barreca Giovanni , Greco Annalisa , Martinico Francesco , Pluchino Alessandro , Rapisarda Andrea
Published: May 2023

The determination of seismic risk in urban settlements has received increasing attention in the scientific community during the last decades since it allows to identify the most vulnerable portions of urban areas and therefore to plan appropriate strategies for seismic risk reduction. In order to accurately evaluate the seismic risk of urban settlements it should be necessary to estimate in detail the seismic vulnerability of all the existing buildings in the considered area. This task could be very cumbersome due to both the great number of information needed to accurately characterize each building and the huge related computational effort. Several simplified methods for the assessment of the seismic vulnerability of existing buildings have been therefore presented in the literature. In order to estimate the occurrence of damage in buildings due to possible seismic phenomena, the published studies usually refer to response spectra evaluated according to seismic events expected in the territory with assumed probabilities. In the present paper seismic events are instead simulated using a modified Olami–Feder–Christensen (OFC) model, within the framework of self-organized criticality. The proposed methodology takes into account some geological parameters in the evaluation of the seismic intensities perceived by each single building, extending the approach presented in a previous study of some of the authors. Here, a large territory in the Sicilian oriental coast, the metropolitan area of Catania, which includes several urbanized zones with different features, has been considered as a new case study. Applications of the procedure are presented first with reference to seismic sequences of variable intensity, whose occurrence is rather frequent in seismic territories, showing that the damage can be progressively accumulated in the buildings and may lead to their collapse even when the intensities of each single event are moderate. Moreover, statistically significant simulations of single major seismic events, equivalent to a given sequence in terms of produced damages on buildings, are also performed. The latter match well with a novel a-priori risk index, introduced with the aim of characterizing the seismic risk of each single municipality in the considered metropolitan area. The proposed procedure can be applied to any large urbanized territory and, allowing to identify the most vulnerable areas, can represent a useful tool to prioritize the allocation of funds. This could be a novelty for risk policies in many countries in which public subsidies are currently assigned on a case-by-case basis, taking into account only hazard and vulnerability. The use of an a-priori risk index in the allocation process will allow to take into due account the relevant role of exposure.
Perceptions and practices of aged care pharmacists regarding osteoporosis management: a qualitative study
Authors: Laird Catherine , Williams Kylie A. , Benson Helen
Published: May 2023

Background

Osteoporosis is a common but sub-optimally managed disease among aged care residents. Although pharmacists are one of the key healthcare providers responsible for osteoporosis medication management there is limited research on their involvement.

Aim

This study explored the perceptions and practices of Australian pharmacists regarding osteoporosis management for aged care residents.

Method

Semi-structured interviews were conducted with aged care pharmacists. Interviews were recorded, transcribed, and analysed using a constructivist grounded theory approach.

Results

Twenty-one aged care pharmacists were interviewed. Three main themes were identified: osteoporosis is highly prevalent but given low priority, factors affecting pharmacists’ management of osteoporosis, and optimism for the future role of pharmacists in osteoporosis management. The complexity of aged care residents’ healthcare needs and the silent, insidious nature of osteoporosis contribute to the low priority it is afforded. Barriers identified by pharmacists included their current practice model, limited access to residents’ medical histories and difficulties accessing bone mineral density (BMD) testing. Interdisciplinary collaboration and education regarding osteoporosis management were seen as facilitators. Pharmacists were optimistic that an embedded practice model would improve their capacity to influence osteoporosis management.

Conclusion

The high prevalence and low priority of osteoporosis in the aged care setting presents pharmacists with an opportunity to improve medication management and reduce fracture risk. Barriers to osteoporosis management identified by pharmacists can be addressed by interdisciplinary collaboration and education. Pharmacists being embedded in aged care could enable more opportunities to contribute to the interdisciplinary team and become champions of osteoporosis management.
Exploring the root-associated microbiome of the resurrection plant Myrothamnus flabellifolia
Authors: Tebele Shandry M. , Marks Rose A. , Farrant Jill M.
Published: May 2023

Aims and background

The resurrection plant Myrothamnus flabellifolia tolerates complete desiccation and is a great model for studying how plants cope with extreme drought. Root-associated microbes play a major role in stress tolerance and are an attractive target for enhancing drought tolerance in staple crops. However, how these dynamics play out under the most extreme water limitation remains underexplored. This study aimed to identify bacterial and fungal communities that tolerate extreme drought stress in the bulk soil, rhizosphere, and endosphere of M. flabellifolia.

Methods

High-throughput amplicon sequencing was used to characterise the microbial communities associated with M. flabellifolia.

Results

The bacterial phyla that were most abundant across all compartments were Acidobacteriota, Actinobacteriota, Chloroflexota, Planctomycetota, and Pseudomonadota, while the most abundant fungal phyla were Ascomycota and Basidiomycota. Although the bulk soil hosted multiple beneficial root-associated microbes, the rhizosphere compartment showed the highest functional diversity of bacteria and fungi. In contrast, the endosphere exhibited a low abundance and diversity of microbes. These findings share consistent with the theory that M. flabellifolia recruits soil microbes from the bulk to the rhizosphere and finally to the endosphere. It is possible that these microbes could promote drought tolerance in associated plant tissues.

Conclusion

We find that compartments act as the major driver of microbial diversity, but the soil physicochemical factors also influence microbial composition. These results suggest that the root-associated microbiome of M. flabellifolia is highly structured and may aid in plant function.
Exploring tomato phenotypic variability under combined nitrogen and water deficit
Authors: Machado Joana , Heuvelink Ep , Vasconcelos Marta W. , Cunha Luís M. , Finkers Richard , Carvalho Susana M. P.
Published: May 2023

Background

Despite drought and nitrogen (N) deficit being two of the most important crops’ growth limiting factors, only few studies have explored tomato phenotypic variability in response to both abiotic stresses.

Aims

In this study, we aimed to perform a phenotypic evaluation and an analysis of the growth traits of 40 tomato genotypes (mostly focusing on old cultivars, but also including modern hybrids and wild tomato relatives’ accessions as anchors) grown in pots that were subjected to combined N and water deficit.

Methods

Each genotype was divided into two groups: control (100% N, 100% field capacity) and combined deficit (20% N, 50% field capacity). A total of 14 morpho-physiological traits were evaluated and further analyzed using multivariate statistical methods.

Results

The Principal Component Analyses revealed considerable phenotypical diversity among tomato genotypes, with four principal components explaining 82% of the variability. Data integration on a cluster analysis separated the studied genotypes into three distinct clusters based on their ability to handle the combined deficit. Tolerance was associated with traits such as lower specific leaf area, lower leaf area ratio and higher water use efficiency, comparing to the sensitive genotypes.

Conclusions

This study shows that tomato tolerance to combined N and water deficit largely varies between genotypes and that old cultivars represent a valuable gene pool towards more sustainable food production systems.
A W-shaped convolutional network for robust crop and weed classification in agriculture
Authors: Moazzam Syed Imran , Nawaz Tahir , Qureshi Waqar S. , Khan Umar S. , Tiwana Mohsin Islam
Published: May 2023

Agricultural image and vision computing are significantly different from other object classification-based methods because two base classes in agriculture, crops and weeds, have many common traits. Efficient crop, weeds, and soil classification are required to perform autonomous (spraying, harvesting, etc.) activities in agricultural fields. In a three-class (crop–weed–background) agricultural classification scenario, it is usually easier to accurately classify the background class than the crop and weed classes because the background class appears significantly different feature-wise than the crop and weed classes. However, robustly distinguishing between the crop and weed classes is challenging because their appearance features generally look very similar. To address this problem, we propose a framework based on a convolutional W-shaped network with two encoder–decoder structures of different sizes. The first encoder–decoder structure differentiates between background and vegetation (crop and weed), and the second encoder–decoder structure learns discriminating features to classify crop and weed classes efficiently. The proposed W network is generalizable for different crop types. The effectiveness of the proposed network is demonstrated on two crop datasets—a tobacco dataset and a sesame dataset, both collected in this study and made available publicly online for use by the community—by evaluating and comparing the performance with existing related methods. The proposed method consistently outperforms existing related methods on both datasets.
Determinants of Subjective Social Status in South Africa
Authors: Kirsten Frederich , Botha Ilse , Biyase Mduduzi , Pretorius Marinda
Published: May 2023

Recent studies suggest that South Africa has experienced increased income polarization and is dealing with a struggling middle class. However, with some studies reporting a strong middling tendency, there seems to be a large discrepancy between how people perceive their social position and their actual economic status. Surprisingly, even with the most unequal society label, South Africa has received little attention on the dynamics behind how people perceive their social status. This study uses an ordered probit regression to analyze the determinants of individuals’ subjective social status in South Africa. Results show that objective factors, education, and occupation status positively influence subjective social status. However, subjective social mobility and class imagery are as crucial, confirming the multidimensionality behind subjective social status. Given the high-income polarization and racial inequality in South Africa, the study also showed that factors driving subjective social status are heterogeneous for different race and income groups. The results confirm this and find that discrepant objective and subjective factors influence different populations’ and income groups’ subjective social status.
Amino acid substitutions in grapevine (Vitis vinifera) acetolactate synthase conferring herbicide resistance
Authors: Wuddineh Wegi A. , Xu Xia , Zhong Gan-Yuan
Published: May 2023

Acetolactate synthase genes (ALS) have successfully been modified for providing resistance to ALS-inhibiting herbicides in many plant species. Based on sequence and expression analyses, we confirmed VvALS1 as the best functional ALS candidate in grapevine. To develop an ALS-based herbicide selection system for facilitating grape transformation, we firstly evaluated the responses of Vitis vinifera cv Chardonnay callus and young in vitro shoots of Vitis vinifera cv Thompson Seedless to several representative ALS-inhibiting herbicides and found a typical linear response curves to some of the herbicides, including chlorsulfuron and imazapyr belonging to the sulfonylurea or imidazolinone families, respectively. Secondly, we created constructs containing amino acid substitutions in the domains which are known to be critical to herbicide resistance and generated transgenic plants for 3 amino acid substitutions using Agrobacterium-mediated transformation of meristematic bulk tissues of Thompson Seedless. Finally, we showed that ectopic expression of two amino acid substitutions (P191S and P191T) at the N-terminal region and another (W568L) at C-terminal region in VvALS resulted in high resistance to chlorsulfuron or imazapyr herbicides in transgenic in vitro shoots. Our work highlighted the potential use of VvALS mutations imparting herbicide resistance as a selectable marker in grapevine transformation research and as a means in fostering grapevine improvement via cisgenesis, paving the way for developing a selectable co-editing system to facilitate transgene-free gene-editing.
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Authors: Hoyer Lukas , Dai Dengxin , Wang Qin , Chen Yuhua , Van Gool Luc
Published: May 2023

Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process. To address this issue, we present a framework for semi-supervised and domain-adaptive semantic segmentation, which is enhanced by self-supervised monocular depth estimation (SDE) trained only on unlabeled image sequences. In particular, we utilize SDE as an auxiliary task comprehensively across the entire learning framework: First, we automatically select the most useful samples to be annotated for semantic segmentation based on the correlation of sample diversity and difficulty between SDE and semantic segmentation. Second, we implement a strong data augmentation by mixing images and labels using the geometry of the scene. Third, we transfer knowledge from features learned during SDE to semantic segmentation by means of transfer and multi-task learning. And fourth, we exploit additional labeled synthetic data with Cross-Domain DepthMix and Matching Geometry Sampling to align synthetic and real data. We validate the proposed model on the Cityscapes dataset, where all four contributions demonstrate significant performance gains, and achieve state-of-the-art results for semi-supervised semantic segmentation as well as for semi-supervised domain adaptation. In particular, with only 1/30 of the Cityscapes labels, our method achieves 92% of the fully-supervised baseline performance and even 97% when exploiting additional data from GTA. The source code is available at https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth.
Implementing a Social Needs Screening and Referral Program Among Veterans: Assessing Circumstances & Offering Resources for Needs (ACORN)
Authors: Russell Lauren E. , Cohen Alicia J. , Chrzas Steven , Halladay Christopher W. , Kennedy Meaghan A. , Mitchell Kathleen , Moy Ernest , Lehmann Lisa Soleymani
Published: May 2023

Background

The Department of Veterans Affairs (VA) healthcare system routinely screens Veterans for food insecurity, housing instability, and intimate partner violence, but does not systematically screen for other health-related social needs (HRSNs).

Objectives

To (1) develop a process for systematically identifying and addressing Veterans’ HRSNs, (2) determine reported prevalence of HRSNs, and (3) assess the acceptability of HRSN screening among Veterans.

Design

“Assessing Circumstances and Offering Resources for Needs” (ACORN) is a Veteran-tailored HRSN screening and referral quality improvement initiative. Veterans were screened via electronic tablet for nine HRSNs (food, housing, utilities, transportation, legal needs, social isolation, interpersonal violence, employment, and education) and provided geographically tailored resource guides for identified needs. Two-week follow-up interviews with a purposive sample of Veterans explored screening experiences.

Participants

Convenience sample of Veterans presenting for primary care at a VA urban women’s health clinic and suburban community-based outpatient clinic (October 2019–May 2020).

Main Measures

Primary outcomes included prevalence of HRSNs, Veteran-reported acceptability of screening, and use of resources guides. Data were analyzed using descriptive statistics, chi-square tests, and rapid qualitative analysis.

Key Results

Of 268 Veterans screened, 50% reported one or more HRSNs. Social isolation was endorsed most frequently (29%), followed by educational needs (19%), interpersonal violence (12%), housing instability (9%), and utility concerns (7%). One in five Veterans reported at least one form of material hardship. In follow-up interviews (n = 15), Veterans found screening acceptable and felt VA should continue screening. No Veterans interviewed had contacted recommended resources at two-week follow-up, although several planned to use resource guides in the future.

Conclusion

In a VA HRSN screening and referral program, Veterans frequently reported HRSNs, felt screening was important, and thought VA should continue to screen for these needs. Screening for HRSNs is a critical step towards connecting patients with services, identifying gaps in service delivery, and informing future resource allocation.
Lead Recovery From PbO Using Hydrogen as a Reducing Agent
Authors: Rukini A. , Rhamdhani M. A. , Brooks G. A. , Van den Bulck A.
Published: May 2023

A systematic isothermal reduction study was carried out on pure PbO pellets using 15 pct H2/85 pct N2 gas. The reduction was carried out at 350 °C to 800 °C at different reaction times (30 minutes to 4 hours) with gas flowrate of 500 mL/min. The kinetics of the reaction were evaluated by measuring the mass change and applying kinetic models to the data. The results from microstructure observation showed that globular and non-wetting lead droplets form on the surface of PbO samples. The droplet's diameter was observed to increase with increasing temperature and reduction time. It was observed that this lead droplets layer, once covers the whole surface, appears to reduce the overall reduction rate. The kinetics analysis showed that PbO pellet reduction is a diffusion-controlled process as supported by the SEM (scanning electron microscope) micrographs, samples’ cross-section observation, and phase analyses using EDS (energy-dispersive spectrometry) and XRD (X-ray diffraction). The energy activation, Ea, was calculated for two temperature ranges, i.e., 61 kJ/mol (for 350 °C to 450 °C) and 224 kJ/mol (for 600 °C to 800 °C), respectively. These results suggest that, in an industrial context, continued reduction process will require constant removal of lead product from the surface of the lead monoxide.
Free-fall drop test with interchangeable surfaces to recreate concussive ice hockey head impacts
Authors: Haid D. , Duncan O. , Hart J. , Foster L.
Published: May 2023

Ice hockey has one of the highest concussion rates in sport. During collisions with other players, helmets offer limited protection. Various test protocols exist often requiring various types of laboratory equipment. A simplified test protocol was developed to facilitate testing by more researchers, and modifications to certification standards. Measured kinematics (acceleration vs. time trace shape, peak accelerations, and impact duration) of a Hybrid III headform dropped onto different surfaces were compared to published laboratory representations of concussive impacts. An exemplary comparison of five different helmets, ranging from low (US$50) to high cost (US$300), covering a range of helmet and liner designs, was also undertaken. Different impact conditions were created by changing the impact surface (Modular Elastomer Programmer pad, or 24 to 96 mm of EVAZOTE-50 foam with a Young's modulus of ~ 1 MPa), surface orientation (0 or 45°), impact site, and helmet make/model. With increasing impact surface compliance, peak accelerations decreased and impact duration increased. Impacts onto a 45° anvil covered with 48 mm of foam produced a similar response to reference concussive collisions in ice hockey. Specifically, these impacts gave similar acceleration vs. time trace shapes, while normalized pairwise differences between reference and measured peak acceleration and impact duration, were less than 10% (difference/maximum value), and mean (± SD) of accelerations and duration fell within the interquartile range of the reference data. These results suggest that by modifying the impact surface, a free-fall drop test can produce a kinematic response in a helmeted headform similar to the method currently used to replicate ice hockey collisions. A wider range of impact scenarios, i.e., fall onto different surfaces, can also be replicated. This test protocol for ice hockey helmets could facilitate simplified testing in certification standards and research.
Codifying Wildness: Wild Behaviour for Improving Human-Robot Interaction
Authors: Aguado Esther , Rossi Claudio , Gil Pablo
Published: May 2023

Animals, even when domesticated, maintain a series of innate characteristics that humans perceive as wild. Human responses to animal behavior vary with many factors such as historical time and culture. Even after their domestication, humans perceive animals’ distinctive behavior patterns that are considered as being wild, even in pets. It is common understanding that these wild patterns are linked to the specific animal species but also that there is a common characteristic to wild behavior in general. In this paper, we present our work on coding such a quality, exemplified in an application to robot pets. We consider that an application of wild character in the behavior of the pet robot can improve its realism and make the human/robot social interaction more captivating. To this aim, we have designed and implemented a behavioral architecture to reproduce well-known behavioral patterns of domestic cats that might be considered wild and applied it to a commercial pet robot that then was used to assess human reaction to its behavior. We demonstrate the effectiveness of the proposed approach with experiments carried out with humans that found the robot cat “independent” and “unpredictable” to a significant degree, two distinctive features of the wild side of animals.
Iron Age combustion structures in the north-eastern Iberian Peninsula: an interdisciplinary experimental study
Authors: Belarte Maria Carme , Pastor Quiles María , Mateu Marta , Portillo Marta , Saorin Carme , Pecci Alessandra , Vila Sílvia , Gomar Ada
Published: May 2023

The aim of this research is to investigate the efficiency of combustion structures, the fuels used, the structure maintenance and the range of related domestic activities. An experimental programme was carried out in which replicas of archaeological Iron Age combustion structures were put to work. Based on the available archaeobotanical records, various fuel types (wood, grasses, palm leaves and animal dung) were used. Variables such as increased heating and times were measured in order to determine whether variations occurred depending on the fuels used, the type of structure, the location, or the weather conditions. The possibility of their use for cooking was also tested. A combination of methodologies was applied for integrated analyses: anthracology, phytoliths, calcitic microfossils, Fourier transform infrared spectrometry, micromorphology and chemical analysis of hearth surfaces. Observations and data recorded during the experimental tasks, together with the results of the interdisciplinary analyses, contribute to a better understanding of the Iron Age archaeological combustion structures.
Forecasting of residential unit’s heat demands: a comparison of machine learning techniques in a real-world case study
Authors: Kemper Neele , Heider Michael , Pietruschka Dirk , Hähner Jörg
Published: May 2023

A large proportion of the energy consumed by private households is used for space heating and domestic hot water. In the context of the energy transition, the predominant aim is to reduce this consumption. In addition to implementing better energy standards in new buildings and refurbishing old buildings, intelligent energy management concepts can also contribute by operating heat generators according to demand based on an expected heat requirement. This requires forecasting models for heat demand to be as accurate and reliable as possible. In this paper, we present a case study of a newly built medium-sized living quarter in central Europe made up of 66 residential units from which we gathered consumption data for almost two years. Based on this data, we investigate the possibility of forecasting heat demand using a variety of time series models and offline and online machine learning (ML) techniques in a standard data science approach. We chose to analyze different modeling techniques as they can be used in different settings, where time series models require no additional data, offline ML needs a lot of data gathered up front, and online ML could be deployed from day one. A special focus lies on peak demand and outlier forecasting, as well as investigations into seasonal expert models. We also highlight the computational expense and explainability characteristics of the used models. We compare the used methods with naive models as well as each other, finding that time series models, as well as online ML, do not yield promising results. Accordingly, we will deploy one of the offline ML models in our real-world energy management system in the near future.
Mindfulness-Based and Mindfulness-Informed Interventions at the Workplace: A Systematic Review and Meta-Regression Analysis of RCTs
Authors: Michaelsen Maren M. , Graser Johannes , Onescheit Miriam , Tuma Matthias P. , Werdecker Lena , Pieper Dawid , Esch Tobias
Published: May 2023

Objectives

Positive effects of mindfulness-based interventions (MBIs) on occupational health have been demonstrated by several systematic review studies during the last two decades. So far, existing reviews excluded mindfulness-informed interventions (MIIs) that build on informal approaches or mixed techniques aiming at improving mindfulness indirectly. To address this research gap, the present comprehensive meta-analysis synthesizes the results of RCTs of MBIs and MIIs conducted in various workplace settings.

Method

A systematic literature search was conducted in five electronic databases complemented by manual search. Random-effects models were used to synthesize standardized mean differences (SMDs) for 25 outcomes and seven overarching categories of outcomes, and to detect various temporal effects. Meta-regressions were run to elucidate average SMDs between mindfulness intervention types and intervention and population characteristics, with the goal of detecting sources of heterogeneity and help guide the selection of the most appropriate mindfulness intervention type.

Results

Based on 91 eligible studies (from 92 publications), including 4927 participants and 4448 controls, the synthesis shows that MBIs and MIIs significantly improve mindfulness (SMD = 0.43; 95%-CI [0.33;0.52]), well-being (SMD = 0.63; 95%-CI [0.34;0.93]), mental health (SMD = 0.67; 95%-CI [0.48;0.86]), stress (SMD = 0.72; 95%-CI [0.54;0.90]), resilience (SMD = 1.06; 95%-CI [−0.22;2.34]), physical health (SMD = 0.45; 95%-CI [0.32;0.59]), and work-related factors (SMD = 0.62; 95%-CI [0.14;1.10]). Sensitivity analyses demonstrate a tendency towards smaller effect sizes due to extreme outliers. Effect sizes are stable in short-term follow-up assessments (1-12 weeks) for most outcomes, but not for long-term follow-up assessments (13-52 weeks). Meta-regressions suggest that observable intervention characteristics (e.g., online delivery) and population characteristics (e.g., age of participants), as well as study quality, do not explain the prevalence of heterogeneity in effect sizes.

Conclusions

Generally effective, mindfulness interventions are a useful tool to enhance aspects of employee health. However, because of heterogeneity and risk of bias, studies aiming at high-quality data collection and thorough reporting are necessary to draw firm conclusions.

Preregistration

A protocol of this systematic review was registered with PROSPERO (Registration-No. CRD42020159927).
Advances of exosomal miRNAs in the diagnosis and treatment of ovarian cancer
Authors: Xiong Jun , Fu Fen , Yu Feng , He Xiaoju
Published: May 2023

Ovarian cancer is a tumor with the highest fatalities among female malignant tumors. This disease has no typical symptoms in its early stage, and most of the patients are in an advanced stage when being treated. The treatment effect is poor and it is easy to develop chemotherapy resistance. Therefore, it is particularly urgent to clarify the pathogenesis of ovarian cancer, explore its early diagnosis of biomarkers, and discover new treatment methods. As a carrier of intercellular information and genetic material transfer, exosomes are widely distributed in body fluids (e.g. blood and urine), which are regarded as latent tumor markers and take effects on tumor occurrence and invasion. Several articles have recently signified that exosomal miRNAs are widely implicated in the formation of the ovarian cancer tumor microenvironment, disease initiation and progression, and the generation of chemotherapy resistance. This article reviews the research on exosomal miRNAs in ovarian cancer.
A data-driven model to estimate the pore volume to breakthrough for carbonate acidizing
Authors: Alkathim Murtadha , Aljawad Murtada Saleh , Hassan Amjed , Alarifi Suliman A. , Mahmoud Mohamed
Published: May 2023

This research investigates the impact of different rock, acid, and reaction dynamic properties on the pore volume to breakthough (PVBT) at different acid injection rates using in-house developed two-scale continuum simulation model. We analyzed the parameters relation and developed a reliable machine learning model to accurately predict the PVBT at similar range of investigated parameters. In the simulation, it was found that different acid concentrations result in the same optimum injection velocity but at large contrast in PVBT between low and high acid concentration. However, other parameters such as diffusion coefficient and reaction rate exhibited an inverse PVBT behavior across optimum injection velocity due to change in acid transport and reaction behavior. After that, different reliable machine learning algorithms were employed to predict the optimum PVBT for carbonates matrix acidizing. The utilized machine learning models undergone multiple optimizations and comparison to obtain the most accurate prediction performance. The artificial neural network model with 2 hidden layers outperforms the other optimizations with 11.27% estimation error, 0.96 R2and 0.98 correlation coefficient for the testing data set. Finally, an empirical correlation was developed to accurately estimate PVBT at a low cost and very short time compared to lab experiments and numerical simulation models. The novelty of this research stems from examining PVBT curves behavior by varying five matrix acidizing parameters independently, analyzing the correlation between these parameters and developing machine learning model for handy and reliable optimum PVBT estimation.
Game-theoretical problems for fractional-order nonstationary systems
Authors: Matychyn Ivan , Onyshchenko Viktoriia
Published: May 2023

Nonstationary fractional-order systems represent a new class of dynamic systems characterized by time-varying parameters as well as memory effect and hereditary properties. Differential game described by system of linear nonstationary differential equations of fractional order is treated in the paper. The game involves two players, one of which tries to bring the system’s trajectory to a terminal set, whereas the other strives to prevent it. Using the technique of set-valued maps and their selections, sufficient conditions for reaching the terminal set in a finite time are derived. Theoretical results are supported by a model example.
Rational Design of Cellulosic Triboelectric Materials for Self-Powered Wearable Electronics
Authors: Meng Xiangjiang , Cai Chenchen , Luo Bin , Liu Tao , Shao Yuzheng , Wang Shuangfei , Nie Shuangxi
Published: May 2023

With the rapid development of the Internet of Things and flexible electronic technologies, there is a growing demand for wireless, sustainable, multifunctional, and independently operating self-powered wearable devices. Nevertheless, structural flexibility, long operating time, and wearing comfort have become key requirements for the widespread adoption of wearable electronics. Triboelectric nanogenerators as a distributed energy harvesting technology have great potential for application development in wearable sensing. Compared with rigid electronics, cellulosic self-powered wearable electronics have significant advantages in terms of flexibility, breathability, and functionality. In this paper, the research progress of advanced cellulosic triboelectric materials for self-powered wearable electronics is reviewed. The interfacial characteristics of cellulose are introduced from the top-down, bottom-up, and interfacial characteristics of the composite material preparation process. Meanwhile, the modulation strategies of triboelectric properties of cellulosic triboelectric materials are presented. Furthermore, the design strategies of triboelectric materials such as surface functionalization, interfacial structure design, and vacuum-assisted self-assembly are systematically discussed. In particular, cellulosic self-powered wearable electronics in the fields of human energy harvesting, tactile sensing, health monitoring, human–machine interaction, and intelligent fire warning are outlined in detail. Finally, the current challenges and future development directions of cellulosic triboelectric materials for self-powered wearable electronics are discussed.
Towards transforming teaching and learning in higher education: interrogating poverty through a decolonial perspective
Authors: Chiramba Otilia , Motala Shireen
Published: May 2023

This paper aims to address three questions relating to the imperatives of knowledge transformation, the impact of poverty broadly and material poverty in particular, and questions of pedagogy or approaches to delivering knowledge in higher education institutions. Transformation of teaching and learning in higher education is at a crossroads in South Africa. A major driver of this is the recently emerged student-led protests on decolonisation. At these crossroads are a number of factors with the potential to facilitate or inhibit the decolonisation process. First is a conceptual paralysis regarding understanding of the idea in the country’s higher education. Second are competing ideological imperatives which confound universities’ strategic choices. Third is the fact that the academy has a high proportion of staff who themselves are products of the system they hope to transform. The paper utilises conceptual and theoretical arguments to explore the state of teaching and learning in South Africa and to learn how the protests have enhanced or/and diminished educational gains among students who are poverty stricken and facing deprivation. The paper argues for a theory of decoloniality to shape thinking and practices which enable higher education institutions to transform teaching and learning and specially to drive the type of transformation which enhances poor students’ epistemological access. The paper recommends investment in further research to explore the impact of material poverty in higher education and how the transformation of knowledge and pedagogy could be better understood for the majority of students who live with poverty in higher education.
Harnessing self-management to tackle grand challenges: the points-based participation architecture of São Paulo’s housing movement
Authors: Gil Nuno A. , Sousa Maria C. , Massa Felipe G.
Published: May 2023

We propose that hierarchical organizations can engender and sustain the collaboration of large numbers of autonomous actors by establishing self-managed, mission-aligned collectives. Informing our claim are preliminary findings from an ongoing study of the housing movement in São Paulo, Brazil. Unexpectedly, we find that hierarchical Social Movement Organizations (SMOs) have, for more than three decades, incentivized broad-based voluntary engagement in protest actions aimed at formulating new housing policy by educating and encouraging low-income families to join collectives tasked with developing and self-managing new housing projects. We trace the sustainability of this participation architecture to an SMO-designed, points-based system, which functions as an integrating mechanism affording: (1)goal alignmentbetween large numbers of autonomous actors and the leadership of a hierarchical organization; (2)voluntary engagementby autonomous actors in activities that simultaneously address local and higher order goals; (3)role and task allocationwithout legal control or close oversight; and (4)retentionwithin the participation architecture by equipping autonomous actors with structure and measurable progress towards local goals and fairly distributing benefits of collective work. We discuss implications to our understanding of how to achieve concerted action at scale towards a grand challenge.
Differential Fiscal Performances of Plausible Disaster Events: A Storyline Approach for the Caribbean and Central American Governments under CCRIF
Authors: Hochrainer-Stigler Stefan , Zhu Qinhan , Ciullo Alessio , Peisker Jonas , Van den Hurk Bart
Published: May 2023

Fiscal resilience against disasters is vital for the recovery in the aftermath of climate hazards. Without swift access to available funds for disaster relief, damages to human and the economy would be further exacerbated. How insurance may influence fiscal performance over time and can increase fiscal resilience for today and under a future climate has not been looked at yet in detail. Focusing on the Caribbean region and on the fiscal performance of governments after disaster events, we empirically analyze the effectiveness of the Caribbean Catastrophe Risk Insurance Facility (CCRIF) regarding the reduction of short-term fiscal effects. We embed this analysis within a novel climate impact storyline approach where we produce past plausible events and investigate the usefulness of insurance under such events. The storylines were modified according to global and climate change related boundary conditions to address the issue whether the CCRIF is fit for purpose or will need to be adapted in the future. We found that both hurricane strikes and the CCRIF affect fiscal outcomes of Caribbean countries. Furthermore, there are indications that CCRIF can counteract the negative fiscal consequences over the short term period induced by the disaster. Our analysis should shed some light on the current discussions on how development related assistance can be structured to enhance climate resilience in highly exposed countries for both direct and fiscal impacts of disasters.
Predicting STEM Major Choice: a Machine Learning Classification and Regression Tree Approach
Authors: Chang Chi-Ning , Lin Shuqiong , Kwok Oi-Man , Saw Guan Kung
Published: May 2023

Despite the increasing demand for professionals in science, technology, engineering, and mathematics (STEM), only a small portion of young people in the USA pursue a postsecondary degree in STEM. To identify the major predictors of STEM participation, this study uses a machine learning approach, a Classification and Regression Tree (CART), to analyze a wide range of individual, family, and school factors obtained from national survey data of US high school freshmen in fall 2009 who eventually enrolled in STEM college majors by 2016. The analytic results indicate that calculus credits, science identity, total STEM credits, and math achievement are the most predictive factors during the high school years of college STEM major selection. The CART-based tree also shows how these four variables interactively predict the likelihood of students enrolling in STEM college majors.
Plant Phosphorus Efficiency from Raw and Composted Agro- and Bio-Waste Anaerobic Digestates
Authors: Grigatti Marco , Petroli Alessandra , Ciavatta Claudio
Published: May 2023

The rising generation of organic waste (OW) can be a concern, representing at the same time a valuable opportunity for the phosphorous (P) recycling; however, yet little is known about plant-available P release from this source. In this work, two anaerobic digestates, from agro- (AWD) and bio-waste (BWD), and their respective composts (AWC and BWC), were selected to assess their P-release via sequential chemical extraction (SCE) and P species via solution 31P-NMR in NaOH+EDTA extracts. These products were also tested for the relative-P efficiency (RPEsoil) in a soil incubation (30 mg P kg−1), in comparison with a chemical-P source and a reference compost. The organic products were also compared for the ryegrass relative-P efficiency (RPEtissue) at the same P-rate (30 mg P kg−1), in a 112-day pot experiment in an Olsen-P poor soil (<3 mg kg−1), under a non-limiting N environment. The NaOH+EDTA extractions showed that inorganic P prevailed in all samples as proven by solution 31P-NMR. SCE showed very different labile-P (H2O+NaHCO3) and AW doubling BW products (84 vs. 48%); this was also confirmed by the soil incubation test in which AWD and AWC attained the best RPEsoil. Pot tests generally confirmed laboratory outcomes showing that AWD and AWC attained the best RPEtissue, from 3 to 4-folds of the BWD and BWC performance. The results showed that the feedstock mainly affects plant P availability from recycled OW and that their SCE are very informative in the description of plant-available P from this type of products to be used in rational fertilization plan.
Super-elastic Scintillating Fibers and Fabrics for Efficient and Visual Radiation Detection
Authors: Du Minghui , Wang Jin , Xu Sijia , Li Hongwei , Zhang Zhihao , Qi Yankun , Lv Shichao , Qiu Jianrong , Yan Yurong , Zhou Shifeng
Published: May 2023

The fabrication of advanced radiation detectors is an important subject due to the wide use of radiation sources in scientific instruments, medical services, security check, non-destructive inspection, and nuclear industries. However, the manufacture of flexible and stretchable radiation detectors remains a challenge. Here, we report the scalable fabrication of super-elastic scintillating fibers and fabrics for visual radiation detection by thermal drawing and melt-spinning methods using styrene-b-(ethylene-co-butylene)-b-styrene, and scintillating Gd2O2S: Tb (GOS). Microstructure evolution, rheological properties, and radiation–composite interaction are studied to reveal the excellent processability, elasticity, and radiation detection ability of the fabricated fibers. Benefiting from the physical crosslinking structural features of the polymer matrix and the excellent radiation absorption capacities of GOS, the resulting fiber can sustain high strains of 765% with a high content of GOS dopants (2 wt.%) and has excellent X-ray detection performance with the limit down to 53 nGyair s−1. Furthermore, stretchable fabrics are constructed, and their applications in various fields, such as radiation warning, and X-ray imaging, are demonstrated. Our work not only provides a new type of super-elastic scintillating fibers and fabrics for smart textiles but also demonstrates their potential applications in the nuclear field.

Graphical Abstract
Exploring Scalability from a Triple Bottom Line Perspective: Challenges and Strategic Resources for Fashion Resale
Authors: Hultberg Emelie , Pal Rudrajeet
Published: May 2023

Resale-based circular business models (CBMs) hold immense potential to contribute towards sustainability in fashion retail. To significantly impact triple bottom line (TBL) sustainability, these fashion CBMs need to be scalable. This paper investigates the challenges for scaling resale-based fashion from a TBL perspective, as well as the strategic resources required to solve these. Based on 15 semi-structured interviews, six key scalability challenges and six main strategic resources are identified. Findings show that even though challenges are mostly referred to in economic terms, environmental and social challenges are also present. The difference is that those challenges are less visible, partly due to the lack of clear and standardised tools and definitions to interpret scale. Lessons from the study can be insightful for practitioners in reflecting on the scalability potential of their CBMs from a TBL perspective and understanding what resources to make use of or develop.
Candida diagnostic techniques: a review
Authors: Arafa Sara H. , Elbanna Khaled , Osman Gamal E. H. , Abulreesh Hussein H.
Published: May 2023

Fungal infections (mycoses) represent a major health issue in humans. They have emerged as a global concern for medical professionals by causing high morbidity and mortality. Fungal infections approximately impact one billion individuals per annum and account for 1.6 million deaths. The diagnosis of Candida infections is a challenging task. Laboratory-based Candida species identification techniques (molecular, commercial, and conventional) have been reviewed and summarized. This review aims to discuss the mycoses history, taxonomy, pathogenicity, and virulence characteristics.