Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of the Sexting Behavior and Motives Questionnaire(SBM-Q).

BACKGROUND Sexting has garnered interest from the public and the scientific community given its ever-increasing presence in adolescents’ lives. However, analysis varies depending on the baseline study used. This calls for a standardised sexting instrument that addresses scientific evidence-based recommendations. The primary aim of this study was to develop and validate the structure of a questionnaire that includes the various sexting behaviours and motives. METHOD the sample comprised 1,362 students (51.1% female; 12-18 years old). Participants were randomly split into two halves, controlling for the gender variable (n=681), and exploratory and confirmatory factor analyses were performed. RESULTS Validity of the Sexting Behaviours and Motives Questionnaire (SBM-Q) was confirmed, along with the suitability of the factor structure, internal consistency, and divergent validity. This was also reported by gender. Six dimensions were identified: sending, reasons for sending, receiving, forwarding, victim of forwarding, and reasons for forwarding. CONCLUSIONS The SBM-Q presents good psychometric properties, providing a detailed and consolidated overview of the behaviours that adolescents might engage in when sexting as well as the context in which it occurs.
Mafia infiltrations in Italian municipalities: two statistical indicators

ABSTRACT We study the administrative behaviour of a sample of Italian municipalities dismissed by the Government because of mafia infiltrations, looking at their balance-sheet data. Using linear discriminant analysis and logistic regressions, we put forward two statistical indicators of the probability that a mafia infiltrated a city council. Our results characterize the administrative profile of infiltrated municipalities. We obtain non-obvious results concerning their i) high levels of arrears, both of tax collections and payments; ii) low levels of indebtness; and iii) low levels of capital expenditures, especially for public works.
Paraprofessional support in Irish schools: from special needs assistants to inclusion support assistants

ABSTRACT Paraprofessional support to enable access to education has developed in differing directions in a range of countries. In Ireland, the role of the special needs assistant has focused on supporting the additional care needs of children with special educational needs to ensure their participation in lessons. The role of the special needs assistant (SNA) was considered crucial as Irish mainstream schools adapted to delivering more inclusive learning environments. Support provision has been recently reconceptualised and within this model special needs assistants are now designated as Inclusion Support Assistants with a very specific care role excluding any teaching activity. This research draws on data from a longitudinal study of SEN provision with a specific focus on the role of SNAs within Irish schools. Findings indicated that the contribution of special needs assistants was highly valued by education stakeholders. It is suggested that the evolution of the SNA role in Ireland can support schools in adopting a flexible approach to create inclusive learning environments.
The importance of out-group characteristics for the own-group face memory bias

ABSTRACT The own-group bias (OGB) in face recognition refers to the finding that in-group faces are recognized with greater accuracy than out-group faces. Current literature emphasizes the importance of in-group characteristics for the OGB. Across two experiments we explored the importance of both in-group and out-group characteristics. Using an old/new task, Experiment 1 categorized faces by university membership and found no evidence of the OGB or a relationship between the OGB and attitudes towards the in-group. Experiment 2 used real-world categories of football (soccer) teams, with the out-group representing a high or low threat to the in-group. Here we found an out-group disadvantage (rather than an in-group advantage) only when the out-group was low threat, and a relationship between out-group but not in-group attitudes and the size of the OGB. These findings suggest that the characteristics of the out-group can be more important than the in-group in modulating the OGB.
Partner Violence among Female Students Attending Historically Black Colleges and Universities (HBCUs) in Emerging Adulthood

ABSTRACT Studies examining college dating violence to date have focused exclusively on university settings where White students are overrepresented and Black students are limited. Historically Black Colleges and Universities (HBCUs), which have traditionally matriculated Black American students, present a compelling case for study as empirical evidence has shown that Black females experience a higher rate of violence compared to their White counterparts. Using data from the Historically Black College and University Campus Sexual Assault (HBCU-CSA) Study, this study examined contextual and individual factors that were associated with partner violence victimization and perpetration among female undergraduate students attending HBCUs in their emerging adulthood. In sum, our study found a constellation of contextual factors (i.e., residence on campus, community engagement) and individual factors (i.e., depressive symptoms, adverse sexual experience, substance use, condom use, drug use before sex, ever been married) that were associated with partner violence among female undergraduate students attending HBCUs.
Comparing Motivations for Including Enforcement in US COVID-19 State Executive Orders

Abstract The United States’ response to COVID-19 has been predominantly led by state governments. To understand if, why, and how state governments include enforcement language in their executive order response, this article conducts an analysis based on 1,357 coded executive orders. It is found that decisions to include enforcement language are influenced by a governor’s political circumstances and perceived risks associated with the crisis. This paper offers insight into how these findings are important for future research and an explanation of the distinct ways that US state governments are choosing to address COVID-19.
Government-Led or Public-Led? Chinese Policy Agenda Setting during the COVID-19 Pandemic

Abstract This paper compares government-led agenda and public-led agenda setting during the COVID-19 pandemic in China to investigate whether or not the pandemic enhances the government’s role in agenda setting. Within-case comparison, aided by big data analysis and case study, finds an overall public-led pattern of agenda setting in China, and a mixture of government-led and public-led agenda setting during the pandemic. It is also found that Chinese government bodies pay attention to and are responsive to citizens’ emotions expressed through social media.
Constructing Ebola martyrs, warriors, and saviours: online heroisation in a context of risk and unease

The perception of epidemic risk has been associated with the production of narratives in which figures such as villains and heroes emerge. This article critically analyzes social media users’ construction of heroic figures during the 2013–2016 Ebola epidemic. We used international Twitter and Facebook comments as our raw material, collected by key word extraction. A thematic analysis resulted in a descriptive typology of three heroic figures: the martyr, the warrior, and the saviour. Our analysis showed that heroic status (highly associated with willingness to take high risks on behalf of others) was mostly conferred on common individuals and ‘insiders’ living in the Ebola-afflicted communities – often deemed ‘unsung’ – rather than the foreigners frequently heroised in previous crises. We deconstruct this heroisation dynamic, showing that it is a socially strategic move because it is embedded in a potential instrumentalization of heroes. First, this production of ‘everyday heroes’ may encourage the involvement of lay people in the epidemic response, by conveying that anyone can become a health hero. Second, we show that this heroisation process maintains the status quo by encouraging adherence to biomedical discourses, and by individualising the narrative and neglecting the structural changes needed to address the epidemic. Finally, we caution against discourses that seem socially ‘progressive’ but may be used as a smokescreen to hide discriminatory dynamics, and we recommend changes in communication strategies.
Face recognition in beginning readers: Investigating the potential relationship between reading and face recognition during the first year of school

ABSTRACT Reading acquisition has been suggested to drive the lateralization of the face processing system to the right cerebral hemisphere. To investigate whether this developmental co-dependency has a behavioural cost, at least in the short run, we tested whether learning to read reduced face recognition ability. In a longitudinal design, 82 children (5–7 years) were tested twice, at the beginning and end of their first year in school. In both sessions, visual letter recognition, word recognition, sentence reading, and immediate face recognition were tested. Reliable increases were found across all four measures, suggesting no absolute cost of literacy acquisition on face processing efficiency. In addition, we found no evidence suggesting a negative relationship between literacy acquisition and face recognition performance. Indeed, our results were most supportive of the null hypothesis suggesting independence in the development of literacy and face processing skills on the behavioural level.
Seastar: vertex-centric programming for graph neural networks

Graph neural networks (GNNs) have achieved breakthrough performance in graph analytics such as node classification, link prediction and graph clustering. Many GNN training frameworks have been developed, but they are usually designed as a set of manually written, GNN-specific operators plugged into existing deep learning systems, which incurs high memory consumption, poor data locality, and large semantic gap between algorithm design and implementation. This paper proposes the Seastar system, which presents a vertex-centric programming model for GNN training on GPU and provides idiomatic python constructs to enable easy development of novel homogeneous and heterogeneous GNN models. We also propose novel optimizations to produce highly efficient fused GPU kernels for forward and backward passes in GNN training. Compared with the state-of-the art GNN systems, DGL and PyG, Seastar achieves better usability, up to 2 and 8 times less memory consumption, and 14 and 3 times faster execution, respectively.
Memory-mapped I/O on steroids

With current technology trends for fast storage devices, the host-level I/O path is emerging as a main bottleneck for modern, data-intensive servers and applications. The need to improve I/O performance requires customizing various aspects of the I/O path, including the page cache and the method to access the storage devices. In this paper, we present Aquila, a library OS that allows applications to reduce I/O overhead by customizing the memory-mapped I/O (mmio) path for files or storage devices. Compared to Linux mmap, Aquila (a) offers full mmio compatibility and protection to minimize application modifications, (b) allows applications to customize the DRAM I/O cache, its policies, and access to storage devices, and (c) significantly reduces I/O overhead. Aquila achieves its mmio compatibility, flexibility, and performance by placing the application in a privileged domain, non-root ring 0. We show the benefits of Aquila in two cases: (a) Using mmio in key-value stores to reduce I/O overhead and (b) utilizing mmio in graph processing applications to extend the memory heap over fast storage devices. Aquila requires 2.58× fewer CPU cycles for cache management in RocksDB, compared to user-space caching and read/write system calls and results in 40% improvement in request throughput. Finally, we use Ligra, a graph processing framework, to show the efficiency of Aquila in extending the memory heap over fast storage devices. In this case, Aquila results in up to 4.14× lower execution time compared to Linux mmap.
Tesseract: distributed, general graph pattern mining on evolving graphs

Tesseract is the first distributed system for executing general graph mining algorithms on evolving graphs. Tesseract scales out by decomposing a stream of graph updates into per-update mining tasks and dynamically assigning these tasks to a set of distributed workers. We present a novel approach to change detection that efficiently determines the exact modifications to the algorithm's output for each update to the input graph. We use a disaggregated, multiversioned graph store to allow workers to process updates independently, without producing duplicates. Moreover, Tesseract provides interactive mining insights for complex applications using an incremental aggregation API. Finally, we implement and evaluate Tesseract and demonstrate that it achieves orders-of-magnitude improvements over state-of-the-art systems.
M3: end-to-end memory management in elastic system software stacks

This paper proposes M3, an end-to-end system that dynamically distributes memory resources among competing applications to maximize their overall performance. Today's data center workloads, can adapt to a wide range of memory sizes, and they are built on complex software stacks. M3 consists of a set of mechanisms and policies allowing the layers of the system stack to make coordinated decisions. Applications continuously adapt to current resource availability, and resources are distributed to competing applications according to their needs. Experiments show that compared to the best possible static configurations, M3 achieves up to 3.05x speed-up.
DZiG: sparsity-aware incremental processing of streaming graphs

State-of-the-art streaming graph processing systems that provide Bulk Synchronous Parallel (BSP) guarantees remain oblivious to the computation sparsity present in iterative graph algorithms, which severely limits their performance. In this paper we propose DZiG, a high-performance streaming graph processing system that retains efficiency in presence of sparse computations while still guaranteeing BSP semantics. At the heart of DZiG is: (1) a sparsity-aware incremental processing technique that expresses computations in a recursive manner to be able to safely identify and prune updates (hence retaining sparsity); (2) a simple change-driven programming model that naturally exposes sparsity in iterative computations; and, (3) an adaptive processing model that automatically changes the incremental computation strategy to limit its overheads when computations become very sparse. DZiG outperforms state-of-the-art streaming graph processing systems, and pushes the boundary of dependency-driven processing for streaming graphs to over 10 million simultaneous mutations, which is orders of magnitude higher compared to the state-of-the-art systems.
Fetal lung underdevelopment is rescued by administration of amniotic fluid stem cell extracellular vesicles in rodents

Administration of extracellular vesicles derived from amniotic fluid stem cells regenerates underdeveloped fetal lungs in rodents. Promoting lung development with EVs Pulmonary hypoplasia is a congenital malformation due to incomplete development of the lungs. Currently, there are no therapies that effectively promote lung development. Here, Antounians et al. used in vivo and ex vivo rodent models and a lung injury model in human alveolar epithelial cells to demonstrate that the intratracheal administration of amniotic fluid stem cell–derived extracellular vesicles (AFSC-EVs) promoted lung regeneration by releasing their RNA content. Early administration of AFSC-EVs in fetuses with pulmonary hypoplasia could be an effective therapy for promoting lung development and reducing other secondary fetal abnormalities associated with incomplete lung development. Fetal lung underdevelopment, also known as pulmonary hypoplasia, is characterized by decreased lung growth and maturation. The most common birth defect found in babies with pulmonary hypoplasia is congenital diaphragmatic hernia (CDH). Despite research and clinical advances, babies with CDH still have high morbidity and mortality rates, which are directly related to the severity of lung underdevelopment. To date, there is no effective treatment that promotes fetal lung growth and maturation. Here, we describe a stem cell–based approach in rodents that enhances fetal lung development via the administration of extracellular vesicles (EVs) derived from amniotic fluid stem cells (AFSCs). Using fetal rodent models of pulmonary hypoplasia (primary epithelial cells, organoids, explants, and in vivo), we demonstrated that AFSC-EV administration promoted branching morphogenesis and alveolarization, rescued tissue homeostasis, and stimulated epithelial cell and fibroblast differentiation. We confirmed this regenerative ability in in vitro models of lung injury using human material, where human AFSC-EVs obtained following good manufacturing practices restored pulmonary epithelial homeostasis. Investigating EV mechanism of action, we found that AFSC-EV beneficial effects were exerted via the release of RNA cargo. MicroRNAs regulating the expression of genes involved in lung development, such as the miR17–92 cluster and its paralogs, were highly enriched in AFSC-EVs and were increased in AFSC-EV–treated primary lung epithelial cells compared to untreated cells. Our findings suggest that AFSC-EVs hold regenerative ability for underdeveloped fetal lungs, demonstrating potential for therapeutic application in patients with pulmonary hypoplasia.
Freshwater Mussel Assemblages in the Black River, Missouri and Arkansas

ABSTRACT Freshwater mussel assemblages show predictable variation according to large-scale biogeographic factors and stream-size gradients, but smaller-scale assemblage patterns are less well known. The goal of this study was to classify and delineate mussel assemblages of the Black River, Missouri and Arkansas, USA, along an upstream–downstream gradient and with regard to physiography and biogeographical regions. We analyzed mussel assemblages using nonmetric multidimensional scaling and indicator-species analysis. Our results yielded three assemblage groupings distributed along the upstream–downstream gradient and thereby considered aquatic ecological systems (100–1,000 km2) in a hierarchical spatial classification scheme. These groupings also support previously proposed biogeographical differences for mussels and fishes between the Ozark Highlands and Mississippi Alluvial Plain physiographic regions. Each group was characterized by 2–13 indicator species. Our demonstration of small-scale patterns of mussel assemblage change will be useful for conservation planning and for a better understanding of mussel assemblage dynamics.
Discovery of the Blue Ridge Springsnail, Fontigens orolibas, Hubricht, 1957 (Gastropoda: Emmericiidae) in East Tennessee and Its Conservation Implications

ABSTRACT The study of spring- and subterranean-associated microsnail species in the Appalachian karst region has focused disproportionately on the northern Appalachian Valley and Ridge (AVR), leaving many areas in the southern Appalachians unexplored. Consequently, biological inventories of subterranean habitats have been initiated in the southern AVR, particularly in the state of Tennessee. In 2013 and 2018, several previously unknown populations of a microsnail species were discovered from caves in eastern Tennessee. Through both morphological and molecular analysis, we identified these populations as the Blue Ridge Springsnail, Fontigens orolibas. These newly discovered populations represent a significant range extension of F. orolibas. As such, we reassess the conservation status of F. orolibas under NatureServe criteria and emphasize the need for further sampling efforts in the southern AVR for microsnails.
Patient-derived glial enriched progenitors repair functional deficits due to white matter stroke and vascular dementia in rodents

A specific prorepair glial stem cell therapy promotes brain repair in vascular dementia in rodent models. Astro-therapy for stroke Astrocytes, axons, and myelin are subjected to major damage during subcortical white matter stroke (WMS), a debilitating disorder leading to cognitive and motor impairments. Previous studies have shown that immature astrocyte transplantation could promote remyelination in rodent models. Now, Llorente et al. used a model of WMS in mice to demonstrate that transplantation of glial enriched progenitor cells differentiated from human-induced pluripotent stem cells (hiPSC-GEPs) shortly after stroke matured into a mature astrocyte phenotype and had therapeutic effect on axonal damage, demyelination, and cognitive impairments more effectively than hiPSC-derived neuronal precursor cells. The results suggest that astrocyte precursors have therapeutic potential in stroke. Subcortical white matter stroke (WMS) accounts for up to 30% of all stroke events. WMS damages primarily astrocytes, axons, oligodendrocytes, and myelin. We hypothesized that a therapeutic intervention targeting astrocytes would be ideally suited for brain repair after WMS. We characterize the cellular properties and in vivo tissue repair activity of glial enriched progenitor (GEP) cells differentiated from human-induced pluripotent stem cells, termed hiPSC-derived GEPs (hiPSC-GEPs). hiPSC-GEPs are derived from hiPSC–neural progenitor cells via an experimental manipulation of hypoxia inducible factor activity by brief treatment with a prolyl hydroxylase inhibitor, deferoxamine. This treatment permanently biases these cells to further differentiate toward an astrocyte fate. hiPSC-GEPs transplanted into the brain in the subacute period after WMS in mice migrated widely, matured into astrocytes with a prorepair phenotype, induced endogenous oligodendrocyte precursor proliferation and remyelination, and promoted axonal sprouting. hiPSC-GEPs enhanced motor and cognitive recovery compared to other hiPSC-differentiated cell types. This approach establishes an hiPSC-derived product with easy scale-up capabilities that might be effective for treating WMS.
Robust adaptive observer-based finite control set model predictive current control for sensorless speed control of surface permanent magnet synchronous motor

The objective of the paper is to present the efficient and dynamic sensorless speed control of a surface permanent magnet synchronous motor (SPMSM) drive at a wide speed range. For high-performance speed sensorless control, a finite control set model predictive current control (FCS-MPCC) algorithm based on a model reference adaptive system (MRAS) is proposed. With the FCS-MPCC algorithm, the inner current control loop is eliminated, and the limitations of the cascaded linear controller are overcome. The proposed speed sensorless control algorithm provides an efficient speed control technique for the SPMSM drive owing to its fast dynamic response and simple principle. The adaptive mechanism is adopted to estimate the rotor shaft speed and position used in FCS-MPCC for dynamic sensorless control. FCS-MPCC uses a square cost function to determine the optimal output voltage vector (VV) from the switching states that give the low cost index. A discrete-time model of a system is used to predict future currents across all the feasible VVs produced by the voltage source inverter. The VV that reduced the cost function is adopted and utilized. Simulation results showed the efficacy of the presented scheme and the viability of the proposed sensorless speed control design under various load conditions at a wide speed operation range.