bluuebunny/crossref_metadata_2025 huggingface dataset:
Clustering Brain Connectomes Through a Density-Peak Approach
Authors: Giubilei Riccardo
Published: December 2023

The density-peak (DP) algorithm is a mode-based clustering method that identifies cluster centers as data points being surrounded by neighbors with lower density and far away from points with higher density. Since its introduction in 2014, DP has reaped considerable success for its favorable properties. A striking advantage is that it does not require data to be embedded in vector spaces, potentially enabling applications to arbitrary data types. In this work, we propose improvements to overcome two main limitations of the original DP approach, i.e., the unstable density estimation and the absence of an automatic procedure for selecting cluster centers. Then, we apply the resulting method to the increasingly important task of graph clustering, here intended as gathering together similar graphs. Potential implications include grouping similar brain networks for ability assessment or disease prevention, as well as clustering different snapshots of the same network evolving over time to identify similar patterns or abrupt changes. We test our method in an empirical analysis whose goal is clustering brain connectomes to distinguish between patients affected by schizophrenia and healthy controls. Results show that, in the specific analysis, our method outperforms many existing competitors for graph clustering.
Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Networks
Authors: Meng Rui , Lee Herbert K. H. , Bouchard Kristofer
Published: December 2023

This paper presents an efficient variational inference framework for a family of structured Gaussian process regression network (SGPRN) models. We incorporate auxiliary inducing variables in latent functions and jointly treat both the distributions of the inducing variables and hyper-parameters as variational parameters. Then we take advantage of the collapsed representation of the model and propose structured variational distributions, which enables the decomposability of a tractable variational lower bound and leads to stochastic optimization. Our inference approach is able to model data in which outputs do not share a common input set, and with a computational complexity independent of the size of the inputs and outputs to easily handle datasets with missing values. Finally, we illustrate our approach on both synthetic and real data.
An Online Minorization-Maximization Algorithm
Authors: Nguyen Hien Duy , Forbes Florence , Fort Gersende , Cappé Olivier
Published: December 2023

Modern statistical and machine learning settings often involve high data volume and data streaming, which require the development of online estimation algorithms. The online Expectation–Maximization (EM) algorithm extends the popular EM algorithm to this setting, via a stochastic approximation approach.We show that an online version of the Minorization–Maximization (MM) algorithm, which includes the online EM algorithm as a special case, can also be constructed in a similar manner. We demonstrate our approach via an application to the logistic regression problem and compare it to existing methods.
Parsimonious Mixtures of Seemingly Unrelated Contaminated Normal Regression Models
Authors: Perrone Gabriele , Soffritti Gabriele
Published: December 2023

In recent years, the research into linear multivariate regression based on finite mixture models has been intense. With such an approach, it is possible to perform regression analysis for a multivariate response by taking account of the possible presence of several unknown latent homogeneous groups, each of which is characterised by a different linear regression model. For a continuous multivariate response, mixtures of normal regression models are usually employed. However, in real data, it is not unusual to observe mildly atypical observations that can negatively affect the estimation of the regression parameters under a normal distribution in each mixture component. Furthermore, in some fields of research, a multivariate regression model with a different vector of covariates for each response should be specified, based on some prior information to be conveyed in the analysis. To take account of all these aspects, mixtures of contaminated seemingly unrelated normal regression models have been recently developed. A further extension of such an approach is presented here so as to ensure parsimony, which is obtained by imposing constraints on the group-covariance matrices of the responses. A description of the resulting parsimonious mixtures of seemingly unrelated contaminated regression models is provided together with the results of a numerical study based on the analysis of a real dataset, which illustrates their practical usefulness.
Evolution of Media Coverage on Climate Change and Environmental Awareness: An Analysis of Tweets from UK and US Newspapers
Authors: Zammarchi Gianpaolo , Romano Maurizio , Conversano Claudio
Published: December 2023

Climate change represents one of the biggest challenges of our time. Newspapers might play an important role in raising awareness on this problem and its consequences. We collected all tweets posted by six UK and US newspapers in the last decade to assess whether 1) the space given to this topic has grown, 2) any breakpoint can be identified in the time series of tweets on climate change, and 3) any main topic can be identified in these tweets. Overall, the number of tweets posted on climate change increased for all newspapers during the last decade. Although a sharp decrease in 2020 was observed due to the pandemic, for most newspapers climate change coverage started to rise again in 2021. While different breakpoints were observed, for most newspapers 2019 was identified as a key year, which is plausible based on the coverage received by activities organized by the Fridays for Future movement. Finally, using different topic modeling approaches, we observed that, while unsupervised models partly capture relevant topics for climate change, such as the ones related to politics, consequences for health or pollution, semi-supervised models might be of help to reach higher informativeness of words assigned to the topics.
A New Regression Model for the Analysis of Microbiome Data
Authors: Ascari Roberto , Migliorati Sonia
Published: December 2023

Human microbiome data are becoming extremely common in biomedical research due to the relevant connections with different types of diseases. A widespread discrete distribution to analyze this kind of data is the Dirichletmultinomial. Despite its popularity, this distribution often fails in modeling microbiome data due to the strict parameterization imposed on its covariance matrix. The aim of this work is to propose a new distribution for analyzing microbiome data and to define a regression model based on it. The new distribution can be expressed as a structured finite mixture model with Dirichlet-multinomial components. We illustrate how this mixture structure can improve a microbiome data analysis to cluster patients into “enterotypes”, which are a classification based on the bacteriological composition of gut microbiota. The comparison between the two models is performed through an application to a real gut microbiome dataset.
Cleantech, Telehealth, and Other Emerging Technologies for Improving African Women’s Adaptation to Climate Change
Authors: Adeola Ogechi , Evans Olaniyi , Ngare Innocent
Published: December 2023

Digital technologies hold great potential for empowering women in Africa and advancing their social and economic development. This chapter examines the impact of technologies such as cleantech, telehealth, edtech, blockchain, and artificial intelligence on women’s empowerment. Improving access to information and communication technologies (ICT) and increasing women’s participation in STEM fields is crucial for empowering women and promoting sustainable development in African nations. These technologies can be leveraged to empower women and enhance their lives, but it is important to ensure that their development and deployment take into consideration the impacts of climate change, which represents one of the greatest existential threats to modern society. The chapter concludes by highlighting the importance of utilising digital technologies in addressing the challenges posed by climate change and contributing to a more sustainable future for all, with a focus on empowering women.
Green Reskilling of African Women for Climate Action
Authors: Adeola Ogechi , Evans Olaniyi , Ngare Innocent
Published: December 2023

The concept of “green growth” is gaining global momentum as a means of mitigating the environmental impact of human activities. However, transitioning to a green economy requires new green skills and a shift in mindset. Empowering women intentionally and strategically, particularly in Africa, can unlock the continent’s untapped potential and drive economic growth due to their significant labour force. Green reskilling is a crucial aspect of the green economy as it promotes gender equality and empowers women, leading to poverty alleviation and economic growth. This chapter underscores the importance of green reskilling for African women and the need to create gender-sensitive policies and strategies for climate change mitigation at all levels—local, regional, and national. By doing so, women can play a more active role in driving the transition to a greener and more sustainable future for Africa.
Support Planning with People with Disabilities
Authors: Araten-Bergman Tal
Published: December 2023

Person-centred planning is often seen as a panacea, but without the addition of thoughtful processes and person-centred actions, it too often becomes a meaningless paper exercise. Planning has many purposes: from determining funding and setting broad life goals to creating detailed blueprints for the delivery of a specific type of support. Whatever its purpose, planning with people with disabilities requires collaboration, time, skilled facilitation and knowledge about the person and their support networks. This chapter discusses the nature of support planning, outlines its core principles and processes and considers these in the context of service systems and disability support organisations.
Building Quality and Safeguarding into Disability Service Provision
Authors: Hough Alan , McEwen Jade
Published: December 2023

The processes of building quality and safeguarding are important in ensuring that people who use disability services have high-quality support that meets their needs and helps them achieve their goals. High-quality support and services are key safeguards against abuse and neglect. What constitutes good quality varies by each individual, stakeholder group, and type of service. However, compliance with regulatory obligations is the minimum that disability service providers and workers must achieve. This chapter attempts to demystify what workers and providers should do to deliver high-quality and safe supports, while still acknowledging the complexity involved. It argues that strategies for achieving high-quality and safe supports should be integrated into standard ways of working, rather than approached as a box-ticking exercise disconnected from day-to-day practice. It explains the key terms and core concepts, the levels for action, and the reactive and proactive strategies which can be implemented by managers.
Supporting Community Participation
Authors: Bigby Christine
Published: December 2023

Many people with intellectual disabilities remain excluded from communities and socially isolated despite decades of policies aiming at furthering their participation. The meaning of community participation is often vague, and outcomes expected from support service poorly defined. Evidence shows that carefully designed programs and skilled practice can support individuals with intellectual disabilities to be actively engaged in communities of their choice. This chapter reviews approaches to supporting community participation. It provides exemplars of different program designs, support worker skills and organisational features necessary to provide quality and individually tailored support for participation by people with intellectual disabilities. Finally, the chapter considers the types of community development strategies that create the foundations for social inclusion.
Relationship-Based Practice with People with a Mild Intellectual Disability Who Have Been Socially Marginalised and Excluded
Authors: Ellem Kathy , Venables Jemma
Published: December 2023

Quality of life is greatly enhanced by supportive relationships in one’s life. People with a mild intellectual disability who have been socially marginalised face significant difficulties in building positive, life-affirming relationships. This chapter discusses the importance of relationship-based practice with this group of people and illustrates how disability support workers, organisations, and systems can support authentic and caring relationships with this group. Relationship-based practice can provide the context in which people with a mild intellectual disability can build individual capacity, knowledge, and resources to navigate the challenges of everyday life.
Tool condition monitoring of diamond-coated burrs with acoustic emission utilising machine learning methods
Authors: Jessel Thomas , Byrne Carl , Eaton Mark , Merrifield Ben , Harris Stuart , Pullin Rhys
Published: December 2023

Within manufacturing there is a growing need for autonomous Tool Condition Monitoring (TCM) systems, with the ability to predict tool wear and failure. This need is increased, when using specialised tools such as Diamond-Coated Burrs (DCBs), in which the random nature of the tool and inconsistent manufacturing methods create large variance in tool life. This unpredictable nature leads to a significant fraction of a DCB tool’s life being underutilised due to premature replacement. Acoustic Emission (AE) in conjunction with Machine Learning (ML) models presents a possible on-machine monitoring technique which could be used as a prediction method for DCB wear. Four wear life tests were conducted with a $$\varnothing $$
∅
1.3 mm #1000 DCB until failure, in which AE was continuously acquired during grinding passes, followed by surface measurements of the DCB. Three ML model architectures were trained on AE features to predict DCB mean radius, an indicator of overall tool wear. All architectures showed potential of learning from the dataset, with Long Short-Term Memory (LSTM) models performing the best, resulting in prediction error of MSE = 0.559 $$\mu $$
μ
m$$^{2}$$

2

after optimisation. Additionally, links between AE kurtosis and the tool’s run-out/form error were identified during an initial review of the data, showing potential for future work to focus on grinding effectiveness as well as overall wear. This paper has shown that AE contains sufficient information to enable on-machine monitoring of DCBs during the grinding process. ML models have been shown to be sufficiently precise in predicting overall DCB wear and have the potential of interpreting grinding condition.
Imaging of flame propagation and temperature distribution in an all-metal gasoline engine with endoscopic access via anisole fluorescence
Authors: Shahbaz Muhammad Ali , Jahangir Saad , Kaiser Sebastian A.
Published: December 2023

Laser-based optical diagnostic tools are widely used to investigate in-cylinder processes in internal combustion engines. For laser-induced fluorescence (LIF), many tracers have been used in the past. Recently, anisole has been characterized spectroscopically for engine-relevant pressures and temperatures and emerged as a potentially advantageous alternative to more commonly used tracers in the past due to its photo-physical properties. Its high fluorescence quantum yield and large absorption cross section result in high signal intensity. This is particularly beneficial for endoscopic imaging systems, which typically have worse light collection efficiency than traditional imaging systems in fully optically accessible engines with transparent liners. In this work, we exploited the strong anisole LIF signal in two single-shot experiments: to image flame propagation, and the instantaneous gas-phase temperature during compression stroke and gas exchange process. Measurements were performed in a production gasoline engine modified for endoscopic optical access via an advanced UV endoscope system. First, LIF of anisole was compared to that of toluene during compression stroke. Anisole LIF yields a much higher signal-to-noise ratio and better image quality with lower tracer concentrations. Due to the higher signal of anisole LIF, small structures of the turbulent flame burning into the anisole/isooctane mixture were well visible after ignition. Second, the red-shift of the anisole fluorescence spectrum with increasing temperature and oxygen partial pressure was exploited for ratiometric temperature measurements based on single-shot images. The available spectroscopic data were used to develop several signal ratio models, which were calibrated in situ using a heated tracer/bath gas mixture introduced inside the combustion chamber. The calibrated signal ratio models were then extrapolated to the engine-relevant ranges. Models with two-step exponential interpolation show better agreement with the adiabatic temperature than linear or 3D surface exponential interpolation. The gas-phase temperature images based on single shots were obtained using one selected model, showing a near uniform and a stratified temperature distribution during the compression stroke and gas exchange process, respectively.
Hypoxic and hypercapnic burrow conditions lead to downregulation of free triiodothyronine and hematocrit in Ansell’s mole-rats (Fukomys anselli)
Authors: Henning Yoshiyuki , Adam Kamilla , Gerhardt Patricia , Begall Sabine
Published: December 2023

African mole-rats live in self-dug burrow systems under hypoxic and hypercapnic conditions. Adaptations to hypoxia include suppression of resting metabolic rate (RMR) and core body temperature (Tb). Because the thyroid hormones (THs) thyroxine (T4) and triiodothyronine (T3) are positive regulators of RMR and Tb, we hypothesized that serum TH concentrations would also be downregulated under hypoxic conditions. To test this hypothesis, we kept Ansell’s mole-rats (Fukomys anselli) in terraria filled with soil in which they were allowed to construct underground burrows to achieve chronic intermittent hypoxia and hypercapnia. The animals stayed in these hypoxic and hypercapnic burrows voluntarily, although given the choice to stay aboveground. We collected blood samples before and after treatment to measure serum T4 and T3 concentrations as well as hematological parameters. The free fraction of the transcriptionally-active T3 was significantly decreased after treatment, indicating that cellular TH signaling was downregulated via peripheral mechanisms, consistent with the assumption that aerobic metabolism is downregulated under hypoxic conditions. Furthermore, we found that hematocrit and hemoglobin concentrations were also downregulated after treatment, suggesting that oxygen demand decreases under hypoxia, presumably due to the metabolic shift towards anaerobic metabolism. Taken together, we have identified a potential upstream regulator of physiological adaptations to hypoxia in these highly hypoxia-tolerant animals.
Evaluation of insemination, blood feeding, and Plasmodium vivax infection effects on locomotor activity patterns of the malaria vector Anopheles darlingi (Diptera: Culicidae)
Authors: da Silva Bastos Alessandra , dos Santos Najara Akira Costa , Andrade Alice Oliveira , Pontual José Daniel Costa , Araújo Jéssica Evangelista , Medeiros Jansen Fernandes , da Silva Araújo Maisa
Published: December 2023

Circadian behavioral patterns in mosquitoes can be observed through their locomotor activity, which includes fundamental behaviors such as foraging, mating, and oviposition. These habits, which are fundamental to the life cycle of Anopheles mosquitoes, are closely related to pathogen transmission to humans. While rhythmic cycles of locomotor activity have been described in Anopheles species, no studies have been conducted on Anopheles darlingi species, the main malaria vector in the Amazon region. The aim of this study was to investigate how insemination status, blood meal, and Plasmodium vivax infection affect the locomotor activity of An. darlingi. The experiments were performed with 3- to 10-day-old An. darlingi females, which had been fed with 15% honey solution. These mosquitoes were obtained from the Malaria Vector Production and Infection Platform (PIVEM)/FIOCRUZ–RO. The experimental groups were divided into four categories: virgin vs. inseminated, unfed virgin vs. blood-fed virgin, unfed inseminated vs. blood-fed inseminated, and infected blood vs. uninfected blood. Locomotor activity was monitored using the Flybox equipment, capturing images that were subsequently converted into video to measure the insect activity, using PySoLo software. The periodicity and rhythmicity of mosquito locomotor activity were analyzed using MatLab® software. The locomotor activity of An. darlingi females showed a nocturnal and bimodal pattern under LD conditions. When comparing the insemination states and blood meal, there was a reduction in the locomotor activity in inseminated and blood-fed females. However, the P. vivax+ infection did not increase locomotor activity of An. darlingi species.
A Pluridisciplinary Tracheostomy Weaning Protocol for Brain-Injured Patients, Outside of the Intensive Care Unit and Without Instrumental Assessment: Results of Pilot Study
Authors: Gallice Thomas , Cugy Emmanuelle , Germain Christine , Barthélemy Clément , Laimay Julie , Gaube Julie , Engelhardt Mélanie , Branchard Olivier , Maloizel Elodie , Frison Eric , Dehail Patrick , Cuny Emmanuel
Published: December 2023

Concurrently to the recent development of percutaneous tracheostomy techniques in the intensive care unit (ICU), the amount of tracheostomized brain-injured patients has increased. Despites its advantages, tracheostomy may represent an obstacle to their orientation towards conventional hospitalization or rehabilitation services. To date, there is no recommendation for tracheostomy weaning outside of the ICU. We created a pluridisciplinary tracheostomy weaning protocol relying on standardized criteria but adapted to each patient’s characteristics and that does not require instrumental assessment. It was tested in a prospective, single-centre, non-randomized cohort study. Inclusion criteria were age > 18 years, hospitalized for an acquired brain injury (ABI), tracheostomized during an ICU stay, and weaned from mechanical ventilation. The exclusion criterion was severe malnutrition. Decannulation failure was defined as recannulation within 96 h after decannulation. Thirty tracheostomized ABI patients from our neurosurgery department were successively and exhaustively included after ICU discharge. Twenty-six patients were decannulated (decannulation rate, 90%). None of them were recannulated (success rate, 100%). Two patients never reached the decannulation stage. Two patients died during the procedure. Mean tracheostomy weaning duration (inclusion to decannulation) was 7.6 (standard deviation [SD]: 4.6) days and mean total tracheostomy time (insertion to decannulation) was 42.5 (SD: 24.8) days. Our results demonstrate that our protocol might be able to determine without instrumental assessment which patient can be successfully decannulated. Therefore, it may be used safely outside ICU or a specialized unit. Moreover, our tracheostomy weaning duration is very short as compared to the current literature.
Robot-assisted laparoscopy does not have demonstrable advantages over conventional laparoscopy in endometriosis surgery: a systematic review and meta-analysis
Authors: Csirzó Ádám , Kovács Dénes Péter , Szabó Anett , Fehérvári Péter , Jankó Árpád , Hegyi Péter , Nyirády Péter , Sipos Zoltán , Sára Levente , Ács Nándor , Szabó István , Valent Sándor
Published: December 2023

Background

Endometriosis is a chronic condition affecting 6–10% of women of reproductive age, with endometriosis-related pain and infertility being the leading symptoms. Currently, the gold standard treatment approach to surgery is conventional laparoscopy (CL); however, the increasing availability of robot-assisted surgery is projected as a competitor of CL. This study aimed to compare the perioperative outcomes of robot-assisted laparoscopy (RAL) and CL in endometriosis surgery.

Objectives

We aimed to compare the effectiveness and safety of these two procedures.

Methods

A systematic search was conducted in three medical databases. Studies investigating different perioperative outcomes of endometriosis-related surgeries were included. Results are presented as odds ratios (OR) or mean differences (MD) with 95% confidence intervals (CI).

Results

Our search yielded 2,014 records, of which 13 were eligible for data extraction. No significant differences were detected between the CL and RAL groups in terms of intraoperative complications (OR = 1.07, CI 0.43–2.63), postoperative complications (OR = 1.3, CI 0.73–2.32), number of conversions to open surgery (OR = 1.34, CI 0.76–2.37), length of hospital stays (MD = 0.12, CI 0.33–0.57), blood loss (MD = 16.73, CI 4.18–37.63) or number of rehospitalizations (OR = 0.95, CI 0.13–6.75). In terms of operative times (MD = 28.09 min, CI 11.59–44.59) and operating room times (MD = 51.39 min, CI 15.07–87.72;), the RAL technique remained inferior.

Conclusion

RAL does not have statistically demonstrable advantages over CL in terms of perioperative outcomes for endometriosis-related surgery.

Graphical abstract
Interruptions in HIV and Behavioral Health Care for Criminal-Legal Involved People Living with HIV Following Implementation of Decarceration and Shelter in Place in San Francisco, California
Authors: Clemenzi-Allen A. Asa , Hebert Jillian , Reid Michael Alistair , Mains Tyler , Hammer Hali , Gandhi Monica , Pratt Lisa , Wesson Paul
Published: December 2023

Decarceration policies, enacted for SARS-CoV-2 mitigation in carceral settings, potentially exacerbated barriers to care for people living with HIV (PWH) with criminal legal involvement (CLI) during Shelter-in-Place (SIP) by limiting opportunities for engagement in provisions of HIV and behavioral health care. We compared health care engagement for PWH with CLI in San Francisco, California before and after decarceration and SIP using interrupted time series analyses. Administrative data identified PWH booked at the San Francisco County Jail with at least one clinic encounter from 01/01/2018-03/31/2020 within the municipal health care network. Monthly proportions of HIV, substance use, psychiatric and acute care encounters before (05/01/2019-02/29/2020) and after (03/01/2020-12/31/2020) SIP and decarceration were compared using Generalized Estimating Equation (GEE) log-binomial and logistic regression models, clustering on the patient-level. Of 436 patients, mean age was 43 years (standard-deviation 11); 88% cisgender-male; 39% white, 66% homeless; 67% had trimorbidity by Elixhauser score (medical comorbidity, psychotic disorder or depression, and substance use disorder). Clinical encounters immediately dropped following SIP for HIV (aOR = 0.77; 95% CI: 0.67, 0.90) and substance use visits (aRR = 0.83; 95% CI: 0.70, 0.99) and declined in subsequent months. Differential reductions in clinical encounters were seen among Black/African Americans (aRR = 0.93; 95% CI: 0.88, 0.99) and people experiencing homelessness (aRR = 0.92; 95% CI: 0.87, 0.98). Significant reductions in care were observed for PWH with CLI during the COVID-19 pandemic, particularly among Black/African Americans and people experiencing homelessness. Strategies to End the HIV Epidemic must improve engagement across diverse care settings to improve outcomes for this key population.
Artificial intelligence in compulsory level of education: perspectives from Namibian in-service teachers
Authors: Jatileni Cloneria Nyambali , Sanusi Ismaila Temitayo , Olaleye Sunday Adewale , Ayanwale Musa Adekunle , Agbo Friday Joseph , Oyelere Peter B.
Published: December 2023

With the push to integrate Artificial Intelligence (AI) from kindergarten to twelfth-grade levels comes a need for equipped teachers. However, there needs to be more initiative in professional learning opportunities, which demands the required effort to ensure teachers learn the AI content they will be teaching. To design an effective professional development program, understanding teachers' existing knowledge, interest and disposition towards AI is crucial to devise strategies that could best support the teachers. As a result, this study aims to investigate in-service teachers' perspectives of teaching AI in schools from the perspective of planned behavior theory. Using a 7-factor scale of AI Anxiety, AI Readiness, AI Relevance, Attitude towards using AI, AI for Social Good, Confidence in AI and Behavioral Intention. This study sampled 159 in-service teachers in Namibia. The teachers' responses were analyzed with SmartPLS using Structural Equation Modelling and various Multigroup Analysis techniques. This study found that teachers' behavioral intention to teach AI depends on a combination of factors, including the relevance of AI, attitude towards using AI, the use of AI for social good and confidence. Meanwhile, AI Anxiety and readiness could not be linked to the intention to teach AI. We discussed our findings, highlighted the study implication, and suggested future directions.
Modelling STEM learners’ academic performance in advanced level secondary schools with smart classrooms in Rwanda
Authors: Ngendahayo Emmanuel , Habarurema Jean Baptiste , Limone Pierpaolo , Zhang Wenlan
Published: December 2023

Smart classroom setting is an effective technology-enhanced pedagogy when an instructor capitalises on the key drivers underlying its success. The objectives of the study are twofold. First, to investigate the factors that impact learners’ academic performance in the smart classroom setting. Second, to determine whether learners’ performance varies based on their gender and prior experience in the smart classroom. Three schools were selected and sampled from a pool of public advanced level (A’ Level) STEM secondary schools with smart classrooms in Rwanda because they were among the first to set up smart classrooms. Data were randomly obtained from 389 learners, majoring in six different combinations. Exploratory factor analysis (EFA) was used to assess the validity and reliability of the measurement scale. A valid and thought-provoking learners’ academic performance model, to enhance their learning outcomes, is presented. The model was validated using Structural equation modelling (SEM). The results indicated that technology self-efficacy, perceived ease of use, and learning satisfaction were all predictors of learners’ performance. Learners’ perceived ease of use and satisfaction were direct predictors of their performance. Besides, learners’ perceived ease of use and usefulness were directly affected by technology self-efficacy. On the other hand, technology self-efficacy indirectly impacts learners’ performance. The invariance analysis across gender groups revealed that male learners perceive smart classroom technology to be easier to use than females. Meanwhile, no evidence of metric invariance estimation of experience was found. We argue that effective pedagogical approaches can mitigate gender disparities in perceived usability of smart classroom technologies.
Seed treatment with clothianidin induces changes in plant metabolism and alters pollinator foraging preferences
Authors: Klatt Björn K. , Wurz Annemarie , Herbertsson Lina , Rundlöf Maj , Svensson Glenn P. , Kuhn Jürgen , Vessling Sofie , de La Vega Bernardo , Tscharntke Teja , Clough Yann , Smith Henrik G.
Published: December 2023

Neonicotinoids, systemic insecticides that are distributed into all plant tissues and protect against pests, have become a common part of crop production, but can unintentionally also affect non-target organisms, including pollinators. Such effects can be direct effects from insecticide exposure, but neonicotinoids can affect plant physiology, and effects could therefore also be indirectly mediated by changes in plant phenology, attractiveness and nutritional value. Under controlled greenhouse conditions, we tested if seed treatment with the neonicotinoid clothianidin affected oilseed rape’s production of flower resources for bees and the content of the secondary plant products glucosinolates that provide defense against herbivores. Additionally, we tested if seed treatment affected the attractiveness of oilseed rape to flower visiting bumblebees, using outdoor mesocosms. Flowers and leaves of clothianidin-treated plants had different profiles of glucosinolates compared with untreated plants. Bumblebees in mesocosms foraged slightly more on untreated plants. Neither flower timing, flower size nor the production of pollen and nectar differed between treatments, and therefore cannot explain any preference for untreated oilseed rape. We instead propose that this small but significant preference for untreated plants was related to the altered glucosinolate profile caused by clothianidin. Thereby, this study contributes to the understanding of the complex relationships between neonicotinoid-treated crops and pollinator foraging choices, by suggesting a potential mechanistic link by which insecticide treatment can affect insect behavior.
Transmission probability of gas molecules through porous layers at Knudsen diffusion
Authors: Macher Wolfgang , Skorov Yuri , Kargl Günter , Laddha Sunny , Zivithal Stephan
Published: December 2023

Gas flow through layers of porous materials plays a crucial role in technical applications, geology, petrochemistry, and space sciences (e.g., fuel cells, catalysis, shale gas production, and outgassing of volatiles from comets). In many applications the Knudsen regime is predominant, where the pore size is small compared to the mean free path between intermolecular collisions. In this context common parameters to describe the gas percolation through layers of porous media are the probability of gas molecule transmission and the Knudsen diffusion coefficient of the medium. We show how probabilistic considerations on layer partitions lead to the analytical description of the permeability of a porous medium to gas flow as a function of layer thickness. The derivations are made on the preconditions that the molecule reflection at pore surfaces is diffuse and that the pore structure is homogenous on a scale much larger than the pore size. By applying a bi-hemispherical Maxwell distribution, relations between the layer transmission probability, the half-transmission thickness, and the Knudsen diffusion coefficient are obtained. For packings of spheres, expressions of these parameters in terms of porosity and grain size are derived and compared with former standard models. A verification of the derived equations is given by means of numerical simulations, also providing evidence that our analytical model for sphere packing is more accurate than the former classical models.
Agrobiodiversity, Social Institutions, and Indigenous Farming Practices: A Case Study of the Rukai in Wutai, Taiwan
Authors: Ba Qing-Xiong , Wang Hong-Zen , Wang Mei-Hsiang
Published: December 2023

Agrobiodiversity plays a crucial role in maintaining sustainable agricultural systems and supporting local livelihoods, but its integration into indigenous social institutions and cultures remains under-researched. We used a qualitative approach entailing in-depth interviews and participant observation to conduct research among the Rukai indigenous farmers in Taiwan focusing on: (1) how Rukai farmers practice agrobiodiversity, and (2) how these agricultural practices integrated into social institutions and cultural norms. Our findings reveal that food security and risk minimization are the prime considerations to preserve agrobiodiversity. Rukai farmers practice swidden cultivation utilizing five mixed cropping systems in seven different ecosystems that vary by elevation, length of exposure to sunlight, and temperature, intercropping, and regular fallows to maintain soil fertility. Importantly, Rukai agricultural practices are integrated into four social institutions that regulate the distribution and management of land and resources, the timing and nature of agricultural activities, and social recognition and status to ensure maximum production throughout the year and reducing risks posed by climate change.
Motor Signature Differences Between Autism Spectrum Disorder and Developmental Coordination Disorder, and Their Neural Mechanisms
Authors: Butera Christiana , Delafield-Butt Jonathan , Lu Szu-Ching , Sobota Krzysztof , McGowan Timothy , Harrison Laura , Kilroy Emily , Jayashankar Aditya , Aziz-Zadeh Lisa
Published: December 2023

Autism spectrum disorder (ASD) and Developmental Coordination Disorder (DCD) are distinct clinical groups with overlapping motor features. We attempted to (1) differentiate children with ASD from those with DCD, and from those typically developing (TD) (ages 8–17; 18 ASD, 16 DCD, 20 TD) using a 5-min coloring game on a smart tablet and (2) identify neural correlates of these differences. We utilized standardized behavioral motor assessments (e.g. fine motor, gross motor, and balance skills) and video recordings of a smart tablet task to capture any visible motor, behavioral, posture, or engagement differences. We employed machine learning analytics of motor kinematics during a 5-min coloring game on a smart tablet. Imaging data was captured using functional magnetic resonance imaging (fMRI) during action production tasks. While subject-rated motor assessments could not differentiate the two clinical groups, machine learning computational analysis provided good predictive discrimination: between TD and ASD (76% accuracy), TD and DCD (78% accuracy), and ASD and DCD (71% accuracy). Two kinematic markers which strongly drove categorization were significantly correlated with cerebellar activity. Findings demonstrate unique neuromotor patterns between ASD and DCD relate to cerebellar function and present a promising route for computational techniques in early identification. These are promising preliminary results that warrant replication with larger samples.
The syntax of individuating and measuring pseudo-partitives in Alasha Mongolian
Authors: Toquero-Pérez Luis Miguel
Published: December 2023

Pseudo-partitive constructions give rise to multiple interpretive ambiguities including a container interpretation (i.e. individuating) and a contents (i.e. measuring) one. There are two competing analyses: one based on structural ambiguities (Landman in Indefinites and the types of sets, Oxford University Press, Oxford, 2004; Rothstein in Brill’s J Afroasiat Lang Ling 1:106–145, 2009. https://doi.org/10.1163/187666309X12491131130783, a.o.) and one based on a uniform syntax (Lehrer in Lingua 68:109–148, 1986; Matushansky and Zwarts in Lamont and Tetzloff (eds) North East Linguistic Society (NELS) 47, Volume 2, pp 261–274, GLSA, Amherst, 2016, a.o.). I contribute to this debate with data from Alasha Mongolian (Mongolic), which differentiates each interpretation via case marking on the quantizing noun: glass-comitative = individuating vs. glass-genitive/Ø = measuring. I argue that there is no large-scale structural ambiguity: the numeral and the quantizing noun always form a constituent introduced in the specifier position of a null functional head (Schwarzschild in Syntax 9(1):67–110, 2006. https://doi.org/10.1111/j.1467-9612.2006.00083.x; Svenonius in McNally and Kennedy (eds) Adjectives and adverbs: syntax, semantics and discourse, Oxford Studies in Theoretical Linguistics, pp 16–42, Oxford University Press, Oxford, 2008; Ott in J Comp Ger Ling 4:1–46, 2011. https://doi.org/10.1007/s10828-010-9040-x). I propose that (i) case differences on the quantizing constituent boil down to the presence or absence of a case probe on a higher Agr head; (ii) and, the interpretive differences between the individuating and measuring pseudo-partitives are the result of a more subtle syntactic distinction in the feature content of the quantizing noun, i.e. an interpretable [±Container] feature.
Are School-Based Interventions Effective in Preventing Internet Misuse? A Systematic Review
Authors: Martínez-Hernández Nieves , Lloret-Irles Daniel
Published: December 2023

Problems related to the use of the internet have triggered the appearance of internet addiction. With this problem comes the need for prevention. The objective of this study is to update the knowledge about the effectiveness of internet addiction prevention programs in schools. The specific aims are: (1) to identify the objectives of school’s prevention programs, (2) to analyse the components and instruments used, (3) to evaluate the quality of the relevant studies and (4) to compare the results of the publications. A search with the terms "Internet", "Internet Use Disorder", "addiction", "prevention", "school", "effectiveness", and "prevention program" was carried out in 10 bibliographic databases in August 2021. For the selection of articles, the 25 quality criteria of the CONSORT 2010 list were used. Only articles that evaluated school prevention programs and had a quality score greater than 10 were selected. The results are shown in three tables. Eleven publications that met the inclusion criteria were identified. The measurement scales used are very heterogeneous. Ten of the 11 programs were effective in preventing adolescent internet misuse, abuse, or addiction. Increasing students' self-control reduces the problem of internet abuse. The validity of the results is discussed considering the methodological quality of the studies.
The study on distribution coefficient of polonium between tri-octylphosphine oxide (TOPO) and tri-butylphosphate (TBP) in toluene and cyclohexane and selected inorganic acids
Authors: Kaczyńska Grażyna , Szaciłowski Grzegorz
Published: December 2023

In this work the distribution coefficients (D) of polonium between 0.1 mol dm−3 TOPO/toluene; TOPO/cyclohexane; TBP/toluene and TBP/cyclohexane and aqueous solutions of inorganic acids (HCl, HNO3, H2SO4 and H3PO4) were determinated. The molarities of inorganic acids were between 0.5 and 12.0 mol dm−3. The activity of extracted Po-209 was measured by means of liquid scintillation counting. The best result was obtained for TOPO/toluene and 2 mol dm−3 H2SO4 system (D = 62 ± 9). The most stable conditions of extraction were found for TOPO/toluene-HCl system.
Solving imbalanced learning with outlier detection and features reduction
Authors: Lusito Salvatore , Pugnana Andrea , Guidotti Riccardo
Published: December 2023

A critical problem for several real world applications is class imbalance. Indeed, in contexts like fraud detection or medical diagnostics, standard machine learning models fail because they are designed to handle balanced class distributions. Existing solutions typically increase the rare class instances by generating synthetic records to achieve a balanced class distribution. However, these procedures generate not plausible data and tend to create unnecessary noise. We propose a change of perspective where instead of relying on resampling techniques, we depend on unsupervised features engineering approaches to represent records with a combination of features that will help the classifier capturing the differences among classes, even in presence of imbalanced data. Thus, we combine a large array of outlier detection, features projection, and features selection approaches to augment the expressiveness of the dataset population. We show the effectiveness of our proposal in a deep and wide set of benchmarking experiments as well as in real case studies.
Holistic deep learning
Authors: Bertsimas Dimitris , Villalobos Carballo Kimberly , Boussioux Léonard , Li Michael Lingzhi , Paskov Alex , Paskov Ivan
Published: December 2023

This paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability from different train-validation splits. The proposed framework holistically improves accuracy, robustness, sparsity, and stability over standard deep learning models, as demonstrated by extensive experiments on both tabular and image data sets. The results are further validated by ablation experiments and SHAP value analysis, which reveal the interactions and trade-offs between the different evaluation metrics. To support practitioners applying our framework, we provide a prescriptive approach that offers recommendations for selecting an appropriate training loss function based on their specific objectives. All the code to reproduce the results can be found at https://github.com/kimvc7/HDL.
Looking for a Novel Vegan Protein Supplement from Faba Bean, Lupine, and Soybean: a Dietary and Industrial Standpoint
Authors: Hamed Hend A. , Kobacy Walaa , Mahmoud Elsayed A. , El-Geddawy Mennatallah M. A.
Published: December 2023

Global population growth poses a threat to sustainable development. Meanwhile, the use of plant proteins as healthy and sustainable alternatives to animal proteins needs further research. Therefore, this investigation was designed to study the nutritive, structural, and thermal properties of isolated protein fractions from different legumes, i.e., faba bean (FPI), soybean (SPI), and lupine (LPI). As a prospective plant-based protein powder, an equal mixture (MPI) of the three prior legume samples was formulated to study its properties compared to each sole sample. The alkaline extraction and isoelectric precipitation (AE-IP) technique was used for protein isolation. Results showed that all protein isolates had reasonable levels of protein with maximum protein content in SPI (96.15%). The MPI sample, however, came out on top in terms of amino acid profile followed by FBI. Compared to SPI and LPI, it had the highest isoleucine content and higher methionine, valine, leucine, phenylalanine, and lysine. Moreover, MPI showed a median particle charge (−37.1 mV) compared to FPI, SPI, and LPI samples. MPI sample peak showed resistance to heat denaturation at a temperature greater than 200 °C when the DSC test was conducted. With respect to its rheological characteristics, it outperformed the other three protein isolates and exhibited the highest values of storage modulus G’ and loss modulus G”. Consequently, our study suggests that pulse-derived protein isolate mixture can be used as a unique type of nutritious dietary protein supplement. It could be a good nutritional alternative to proteins derived from animals.