ethicalabs/Research-Intent-Collab huggingface dataset:
The Cross‐Sectional and Longitudinal Associations Between Social Media and Mental Health Among a Large Sample of Adolescents, Inclusive of Transgender/Gender‐Diverse Adolescents

ABSTRACT                                        Introduction                     The objectives of this study were to examine: (1) the cross‐sectional and longitudinal associations between social media (SM) time and mental health outcomes among a large sample of Canadian adolescents (ages 12–18) and (2) if associations are moderated by gender.                                                           Methods                                            Data from the Cannabis, Obesity, Mental health, Physical activity, Alcohol, Smoking, and Sedentary behaviour (COMPASS) study were used to conduct two cross‐sectional analyses (2021/2022:                       n                        = 58,289; 2022/2023:                       n                        = 44,777) and a linked longitudinal analysis (2021/2022 to 2022/2023:                       n                        = 26,743). The exposure variable was time spent browsing/scrolling SM (cross‐sectionally), and the change in SM time from baseline to follow‐up (longitudinally). The outcome variables were anxiety, depression, flourishing, personal relationships, and emotional regulation (measured at follow‐up in longitudinal models). The moderator variable was gender, categorized into cisgender girls, cisgender boys, and transgender/gender‐diverse adolescents. Multilevel linear modeling was conducted.                                                                                Results                     An additional hour/day of SM (cross‐sectionally) and one additional hour/day of SM from baseline to follow‐up (longitudinally) was significantly associated with increased depression and anxiety symptoms, decreased flourishing, and poorer personal relationships and emotional regulation at follow‐up. Gender was a significant moderator of SM time and mental health outcome associations cross‐sectionally, but gender did not moderate change in SM time and mental health outcome associations longitudinally. Cisgender girls experienced significantly larger negative associations between SM time and mental health outcomes in comparison to cisgender boys and transgender/gender‐diverse adolescents cross‐sectionally.                                                           Conclusions                     Among Canadian adolescents, SM time was associated with poorer mental health outcomes both cross‐sectionally and over 1 year.
Implementing<i>the</i>COPA Model in Nursing Education and Practice Settings:<i>Promoting Competence, Quality Care, and Patient Safety</i>

The Competency Outcomes and Performance Assessment Model (COPA) prepares learners for effective role performance in the workplace by delineating comprehensive, core clinical behaviors that provide evidence of competence. This article explores the experiences of COPA model implementation in two nursing programs and a statewide nurse internship program. The article describes the challenges and benefits of implementation, discusses the transition from a traditional to an outcomes- and competency-based curriculum, and examines the process of assessment, with a focus on the faculty role. The influences of outcomes-based learning strategies and evaluation on competence, confidence, learning, and practice are discussed. In the statewide internship program, with 10 years of implementation experience in practice environments across the continuum of care, the COPA framework has provided significant benefits for preceptors, new nurse graduates, employers, and patient safety.
A Bayesian deep learning method for freeway incident detection with uncertainty quantification

Incident detection is fundamental for freeway management to reduce non-recurrent congestions and secondary incidents. Recently, machine learning technologies have made considerable progress in the incident detection field, but many still face challenges in uncertainty quantification due to the aleatoric uncertainty of traffic data and the epistemic uncertainty of model deviations. In this study, a Bayesian deep learning method was proposed for freeway incident detection with uncertainty quantification. A convolutional neural network variant was designed on a Bayesian framework, and mechanisms of Bayes by backpropagation and local reparameterization technics were used to update the weight of the proposed model. The predictive uncertainty of the proposed method was modeled jointly by integrating the aleatoric and epistemic uncertainty. The proposed model was tested on the PORTAL dataset and compared with four benchmark models: standard normal deviate, wavelet neural network, long-short term memory neural network, and convolutional neural network. The results show that the proposed model outperforms the baseline methods in terms of accuracy, detection rate and false alarm rate. Perturbation experiments were used to test the robustness of the model against noise. The results indicated that the aleatoric uncertainty of the model remained almost constant under different noise levels. The proposed method may benefit future studies on uncertainty quantification while using machine learning method in incident management and other fields in intelligent transportation systems.
New Trends in E-Science: Machine Learning and Knowledge Discovery in Databases

Data mining, or Knowledge Discovery in Databases (KDD), while being the main methodology to extract the scientific information contained in Massive Data Sets (MDS), needs to tackle crucial problems since it has to orchestrate complex challenges posed by transparent access to different computing environments, scalability of algorithms, reusability of resources. To achieve a leap forward for the progress of e-science in the data avalanche era, the community needs to implement an infrastructure capable of performing data access, processing and mining in a distributed but integrated context. The increasing complexity of modern technologies carried out a huge production of data, whose related warehouse management and the need to optimize analysis and mining procedures lead to a change in concept on modern science. Classical data exploration, based on local user own data storage and limited computing infrastructures, is no more efficient in the case of MDS, worldwide spread over inhomogeneous data centres and requiring teraflop processing power. In this context modern experimental and observational science requires a good understanding of computer science, network infrastructures, Data Mining, etc. i.e. of all those techniques which fall into the domain of the so called e-science (recently assessed also by the Fourth Paradigm of Science). Such understanding is almost completely absent in the older generations of scientists and this reflects in the inadequacy of most academic and research programs. A paradigm shift is needed: statistical pattern recognition, object oriented programming, distributed computing, parallel programming need to become an essential part of scientific background. A possible practical solution is to provide the research community with easy-to understand, easy-to-use tools, based on the Web 2.0 technologies and Machine Learning methodology. Tools where almost all the complexity is hidden to the final user, but which are still flexible and able to produce efficient and reliable scientific results. All these considerations will be described in the detail in the chapter. Moreover, examples of modern applications offering to a wide variety of e-science communities a large spectrum of computational facilities to exploit the wealth of available massive data sets and powerful machine learning and statistical algorithms will be also introduced.
Roles of RNAi in Chromatin Regulation and Epigenetic Inheritance

Rapid progress in our understanding of chromatin regulation has fueled considerable interest in epigenetic mechanisms governing the stable inheritance of chromatin states. Findings from several systems reveal small RNAs of the RNAi pathway as critical determinants of epigenetic gene silencing. Notably, recent investigations into the mechanisms of RNAi-mediated heterochromatin assembly in the fission yeast Schizosaccharomyces pombe have yielded new insights regarding the roles of RNAi in chromatin regulation and epigenetic inheritance.
Peer Review Quality and Transparency of the Peer-Review Process in Open Access and Subscription Journals

Recent controversies highlighting substandard peer review in Open Access (OA) and traditional (subscription) journals have increased the need for authors, funders, publishers, and institutions to assure quality of peer-review in academic journals. I propose that transparency of the peer-review process may be seen as an indicator of the quality of peer-review, and develop and validate a tool enabling different stakeholders to assess transparency of the peer-review process.Based on editorial guidelines and best practices, I developed a 14-item tool to rate transparency of the peer-review process on the basis of journals' websites. In Study 1, a random sample of 231 authors of papers in 92 subscription journals in different fields rated transparency of the journals that published their work. Authors' ratings of the transparency were positively associated with quality of the peer-review process but unrelated to journal's impact factors. In Study 2, 20 experts on OA publishing assessed the transparency of established (non-OA) journals, OA journals categorized as being published by potential predatory publishers, and journals from the Directory of Open Access Journals (DOAJ). Results show high reliability across items (α = .91) and sufficient reliability across raters. Ratings differentiated the three types of journals well. In Study 3, academic librarians rated a random sample of 140 DOAJ journals and another 54 journals that had received a hoax paper written by Bohannon to test peer-review quality. Journals with higher transparency ratings were less likely to accept the flawed paper and showed higher impact as measured by the h5 index from Google Scholar.The tool to assess transparency of the peer-review process at academic journals shows promising reliability and validity. The transparency of the peer-review process can be seen as an indicator of peer-review quality allowing the tool to be used to predict academic quality in new journals.
Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligence

Cancer is one of the most life-threatening diseases worldwide, and head and neck (H&amp;N) cancer is a prevalent type with hundreds of thousands of new cases recorded each year. Clinicians use medical imaging modalities such as computed tomography and positron emission tomography to detect the presence of a tumor, and they combine that information with clinical data for patient prognosis. The process is mostly challenging and time-consuming. Machine learning and deep learning can automate these tasks to help clinicians with highly promising results. This work studies two approaches for H&amp;N tumor segmentation: (i) exploration and comparison of vision transformer (ViT)-based and convolutional neural network-based models; and (ii) proposal of a novel 2D perspective to working with 3D data. Furthermore, this work proposes two new architectures for the prognosis task. An ensemble of several models predicts patient outcomes (which won the HECKTOR 2021 challenge prognosis task), and a ViT-based framework concurrently performs patient outcome prediction and tumor segmentation, which outperforms the ensemble model.
User Perception and Acceptance of Central Bank Digital Currency in Developed and Developing Countries: An Empirical Study using Technology Acceptance Model.

This study aims to investigate the factors influencing individuals' intention to use Central Bank Digital Currencies (CBDC) in the digital economy era from an individual perspective using the Technology Acceptance Model (TAM) as a framework. The study contributes academically by examining individual intention to use CBDC from the user's perspective, filling a research gap, expanding the TAM's application area and theoretical sources, testing the validity of hypotheses, and introducing technology into behavioral research in social psychology. Managerially, the study provides recommendations for the government and central bank on improving individuals' intention to use CBDC, formulating effective development paths and promotion plans, and enhancing public interest while reducing confusion and worry. The study finds that the technical characteristics and economic attributes of CBDC, such as offline trading, non-forgery, no double spending, irrevocability, and controlled anonymity, have a positive impact on individuals' intention to use CBDC. It also identifies new factors influencing human behavior patterns in the digital economy, such as perceived usefulness, perceived ease of use, perceived security, perceived trust, perceived privacy, perceived social influence, and perceived compatibility. Furthermore, the study highlights the importance of understanding individuals' intention to use CBDC in the digital economy era. The growth rate of mobile payments in China has been significant over the past decade, indicating the increasing importance of mobile payments and digital currencies in the Chinese economy and the potential for further growth in the future. The introduction of CBDCs can help facilitate transactions, enhance financial inclusion, and support the growth of the digital economy. In summary, this study provides valuable insights for governments, central banks, and other stakeholders in developing and promoting CBDCs effectively. The findings have significant implications for policy-makers, industry practitioners, and academic researchers interested in digital currencies, mobile payments, and the future of money in the digital economy era.
sj-pdf-1-smr-10.1177_00491241231155883 - Supplemental material for Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Optimal Transport and Conformal Prediction Sets

Supplemental material, sj-pdf-1-smr-10.1177_00491241231155883 for Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Optimal Transport and Conformal Prediction Sets by Richard A. Berk, Arun Kumar Kuchibhotla and Eric Tchetgen Tchetgen in Sociological Methods &amp; Research
Decay of the Greenland Ice Sheet due to surface-meltwater-induced acceleration of basal sliding

Simulations of the Greenland Ice Sheet are carried out with a high-resolution version of the ice-sheet model SICOPOLIS for several global-warming scenarios for the period 1990-2350. In particular, the impact of surface-meltwater-induced acceleration of basal sliding on the stability of the ice sheet is investigated. A parameterization for the acceleration effect is developed for which modelled and measured mass losses of the ice sheet in the early 21st century agree well. The main findings of the simulations are: (i) the ice sheet is generally very susceptible to global warming on time-scales of centuries, (ii) surface-meltwater-induced acceleration of basal sliding leads to a pronounced speed-up of ice streams and outlet glaciers, and (iii) this ice-dynamical effect accelerates the decay of the Greenland Ice Sheet as a whole significantly, but not catastrophically, in the 21st century and beyond.
Climate Resilience, Vulnerability, Differential Impacts and Adaptive Capacity in Nigeria

This research investigates and promotes climate resilience in Nigeria’s vulnerable communities, which are disproportionately affected by climate change. How climate change worsens existing problems such as food insecurity, poverty, and conflict in Nigeria serve as the rationale to this study. Hence, defining climate resilience, reviewing relevant literature, and conducting a case study to generate evidence-based recommendations give insight into the objectives of this study. Therefore, reviewed literature shows the diverse impacts of climate change on disadvantaged groups, such as small-scale farmers and urban coastal dwellers. Vulnerability assessments highlight the need for adaptive capacity, integrating local knowledge in solutions. Case studies illustrate vulnerabilities in poor urban coastal areas, rural regions, and the Niger Delta. Differential impacts are examined, such as the gendered effects of climate change on women and the challenges faced by indigenous communities and urban slums. Adaptive capacity is discussed, stressing social capital, collective action, and the interrelation of adaptation, adaptive capacity, and vulnerability. Adaptive capacity assessment in Nigeria indicates difficulties in rural farming households and urban coastal communities, requiring enhanced capacities. Indigenous knowledge plays a vital role in adaptive measures, fostering resilience. Resilience enhancement strategies emphasize social justice, inclusive governance, and community-based adaptation, acknowledging the significance of local empowerment and participation. Technology and innovation provide possibilities but need context-specific methods. These strategies can help build climate resilience in Nigeria’s vulnerable communities, offering a basis for policymakers, practitioners, and stakeholders to tackle the complex challenges of climate change.
A primal-dual neural network for online resolving constrained kinematic redundancy in robot motion control

This paper proposes a primal-dual neural network with a one-layer structure for online resolution of constrained kinematic redundancy in robot motion control. Unlike the Lagrangian network, the proposed neural network can handle physical constraints, such as joint limits and joint velocity limits. Compared with the existing primal-dual neural network, the proposed neural network has a low complexity for implementation. Compared with the existing dual neural network, the proposed neural network has no computation of matrix inversion. More importantly, the proposed neural network is theoretically proved to have not only a finite time convergence, but also an exponential convergence rate without any additional assumption. Simulation results show that the proposed neural network has a faster convergence rate than the dual neural network in effectively tracking for the motion control of kinematically redundant manipulators.
Patient safety, satisfaction, and quality of hospital care: cross sectional surveys of nurses and patients in 12 countries in Europe and the United States

OBJECTIVE: To determine whether hospitals with a good organisation of care (such as improved nurse staffing and work environments) can affect patient care and nurse workforce stability in European countries. DESIGN Cross sectional surveys of patients and nurses. SETTING: Nurses were surveyed in general acute care hospitals (488 in 12 European countries; 617 in the United States); patients were surveyed in 210 European hospitals and 430 US hospitals. PARTICIPANTS: 33 659 nurses and 11 318 patients in Europe; 27 509 nurses and more than 120 000 patients in the US. MAIN OUTCOME MEASURES: Nurse outcomes (hospital staffing, work environments, burnout, dissatisfaction, intention to leave job in the next year, patient safety, quality of care), patient outcomes (satisfaction overall and with nursing care, willingness to recommend hospitals). RESULTS: The percentage of nurses reporting poor or fair quality of patient care varied substantially by country (from 11% (Ireland) to 47% (Greece)), as did rates for nurses who gave their hospital a poor or failing safety grade (4% (Switzerland) to 18% (Poland)). We found high rates of nurse burnout (10% (Netherlands) to 78% (Greece)), job dissatisfaction (11% (Netherlands) to 56% (Greece)), and intention to leave (14% (US) to 49% (Finland, Greece)). Patients’ high ratings of their hospitals also varied considerably (35% (Spain) to 61% (Finland, Ireland)), as did rates of patients willing to recommend their hospital (53% (Greece) to 78% (Switzerland)). Improved work environments and reduced ratios of patients to nurses were associated with increased care quality and patient satisfaction. In European hospitals, after adjusting for hospital and nurse characteristics, nurses with better work environments were half as likely to report poor or fair care quality (adjusted odds ratio 0.56, 95% confidence interval 0.51 to 0.61) and give their hospitals poor or failing grades on patient safety (0.50, 0.44 to 0.56). Each additional patient per nurse increased the odds of nurses reporting poor or fair quality care (1.11, 1.07 to 1.15) and poor or failing safety grades (1.10, 1.05 to 1.16). Patients in hospitals with better work environments were more likely to rate their hospital highly (1.16, 1.03 to 1.32) and recommend their hospitals (1.20, 1.05 to 1.37), whereas those with higher ratios of patients to nurses were less likely to rate them highly (0.94, 0.91 to 0.97) or recommend them (0.95, 0.91 to 0.98). Results were similar in the US. Nurses and patients agreed on which hospitals provided good care and could be recommended. Conclusions Deficits in hospital care quality were common in all countries. Improvement of hospital work environments might be a relatively low cost strategy to improve safety and quality in hospital care and to increase patient satisfaction.
Confinement Effects on Phase Behavior of Soft Matter Systems

When systems that can undergo phase separation between two coexisting phases in the bulk are confined in thin film geometry between parallel walls, the phase behavior can be profoundly modified. These phenomena shall be described and exemplified by computer simulations of the Asakura-Oosawa model for colloid-polymer mixtures, but applications to other soft matter systems (e.g. confined polymer blends) will also be mentioned. Typically a wall will prefer one of the phases, and hence the composition of the system in the direction perpendicular to the walls will not be homogeneous. If both walls are of the same kind, this effect leads to a distortion of the phase diagram of the system in thin film geometry, in comparison with the bulk, analogous to the phenomenon of "capillary condensation" of simple fluids in thin capillaries. In the case of "competing walls", where both walls prefer different phases of the two phases coexisting in the bulk, a state with an interface parallel to the walls gets stabilized. The transition from the disordered phase to this "soft mode phase" is rounded by the finite thickness of the film and not a sharp phase transition. However, a sharp transition can occur where this interface gets localized at (one of) the walls. The relation of this interface localization transition to wetting phenomena is discussed. Finally, an outlook to related phenomena is given, such as the effects of confinement in cylindrical pores on the phase behavior, and more complicated ordering phenomena (lamellar mesophases of block copolymers or nematic phases of liquid crystals under confinement).
Time optimal control of an experimental continuous stirred tank reactor

Abstract   In this paper the methods for applying Pontryagin's Maximum Principle to determine the time-optimal control policies for both a linearized and non-linear continuous stirred tank reactor system are presented. Both the time optimal control to a desired operating steady-state and to a specified region about this steady-state are considered. The paper also describes the successful simulation and control of the experimental system of the exothermic decomposition of hydrogen peroxide. The reaction is catalyzed by iodide ion which is introduced in a potassium iodide solution. The system is controlled by varying the water flow through an internal cooling coil.