Set Of Projects Research Articles

DESI z ≳ 5 Quasar Survey. I. A First Sample of 400 New Quasars at z ∼ 4.7–6.6

We report the first results of a high-redshift (z ≳ 5) quasar survey using the Dark Energy Spectroscopic Instrument (DESI). As a DESI secondary target program, this survey is designed to carry out a systematic search and investigation of quasars at 4.8 < z < 6.8. The target selection is based on the DESI Legacy Imaging Surveys (the Legacy Surveys) DR9 photometry, combined with the Pan-STARRS1 data and J-band photometry from public surveys. A first quasar sample has been constructed from the DESI Survey Validation 3 (SV3) and first-year observations until 2022 May. This sample includes more than 400 new quasars at redshift 4.7 ≤ z < 6.6, down to 21.5 magnitude (AB) in the z band, discovered from 35% of the entire target sample. Remarkably, there are 220 new quasars identified at z ≥ 5, more than one-third of existing quasars previously published at this redshift. The observations so far result in an average success rate of 23% at z > 4.7. The current spectral data set has already allowed analysis of interesting individual objects (e.g., quasars with damped Lyα absorbers and broad absorption line features), and statistical analysis will follow the survey’s completion. A set of science projects will be carried out leveraging this program, including quasar luminosity function, quasar clustering, intergalactic medium, quasar spectral properties, intervening absorbers, and properties of early supermassive black holes. Additionally, a sample of 38 new quasars at z ∼ 3.8–5.7 discovered from a pilot survey in the DESI SV1 is also published in this paper.

The use of mobile electronic prescribing to drive quality of oncology care delivery.

586 Background: This pilot technology enhancement project set out to improve the quality of care for oncology patients by developing a mobile application for writing prescriptions (non-chemotherapy) and narcotics. We describe usage of this new mobile application and associated timeliness of care delivery, provider workflow efficiencies and patient safety metrics. Methods: Agile methodology was used to develop the workflow and software for the mobile application. Internal Patient Safety Usability Testing (PSUT) was used to assess high-risk tasks amongst potential users (n = 7). High-risk tasks were defined as those which, based on human error, could lead to potential patient harm or unsafe conditions. High-risk tasks included the ability for users to understand/acknowledge allergy and drug interactions, edit a selection, select and send to the appropriate pharmacy, and locate the record of the completed prescription within the patient’s chart through the mobile application. A pilot study was also conducted among oncologists (n = 14). A quantitative metric was measured to understand the adoption and usability of the mobile application. Qualitative feedback was also obtained. Results: 1. PSUT identified a need for improvement with recognition of interaction severity and differentiating an allergy versus a drug interaction. A design modification was also made to introduce a three-second hold to acknowledge an allergy. This was done to encourage users to take appropriate time to visualize and comprehend allergy and interaction information before advancing the workflow. 2. During the pilot study, 400 mobile prescriptions were written. After-hours writing of prescriptions in the mobile application increased (11.8%) compared to the EMR (3.5%). The average amount of time from initiation of writing a prescription to completion was 62 seconds in the mobile application. There were no high-risk patient safety incidents observed during the pilot study. Qualitative data from participants included themes around convenience, feasibility, improved access to care, and safety. Conclusions: Usage of the pilot mobile application demonstrated a safe and efficient approach to increase the quality of care delivery for oncology patients. Usage also suggests an increase in patient access to care after hours.

Effect of vitamin D supplementation on inflammatory markers and total antioxidant capacity in breast cancer women using a machine learning technique.

This study aimed to establish a learning system using an artificial neural network (ANN) to predict the effects of vitamin D supplementation on the serum levels of vitamin D, inflammatory factors, and total antioxidant capacity (TAC) in women with breast cancer. The data set of the current project was created from women with breast cancer who were referred to the Shafa State Hospital of Patients with Cancers in Ahvaz city, Iran. Modeling was implemented using the data set at the serum levels of vitamin D, tumor necrosis factor-α (TNF-α), transforming growth factor β (TGF-β), and TAC, before and after vitamin D3 supplement therapy. A prediction ANN model was designed to detect the effects of vitamin D3 supplementation on the serum level changes of vitamin D, inflammatory factors and TAC. The results showed that the ANN model could predict the effect of vitamin D3 supplementation on the serum level changes of vitamin D, TNF-α, TGF-β1, and TAC with an accuracy average of 85%, 40%, 89.5%, and 88.1%, respectively. According to the findings of the study, the ANN method could accurately predict the effect of vitamin D3 supplementation on the serum levels of vitamin D, TNF-α, TGF-β1, and TAC. The results showed that the proposed ANN method can help specialists to improve the treatment process more confidently in terms of time and accuracy of predicting the influence of vitamin D supplementation on the factors affecting the progression of breast cancer (https://www.irct.ir/ identifier: IRCT2015090623924N1).

Utopian constitutionalism in Chile

AbstractIn this article, we argue that the 2022 Chilean draft Constitution helps to articulate the distinction between a transformative constitutional project and a utopian one. Whereas a transformative project lays down markers for social change that will take time to achieve, a utopian project sets out goals that are unlikely to be achieved within any reasonable timeframe. Utopianism is a product of two relationships. The first is the internal relationship between the transformative goals laid out in a constitution and the institutional pathways through which changes will occur. The second is the external relationship between the goals in the text and the views and support of key groups. In Chile, both relationships were problematic. First, the Convention adopted a draft that was heavy on ambitious programmatic content but lacked a clear vision of how to implement it. Second, the Convention produced a draft that was supported by the ephemeral civil society groups galvanized by the 2019 protests but divorced from the vision of Chile’s parties and public opinion. Some of this was a product of the peculiar electoral context in which the Convention acted, which has already been corrected. But some of it reflects deeper tensions within transformative constitutionalism.

Assessing adverse childhood experiences in young refugees – development of the BRACE questionnaire

Abstract Background With the persistence of ongoing crises and subsequent displacement of individuals, including children, knowledge about adversities experienced in this context is crucial, given the harmful effects of adverse childhood experiences (ACE) on mental and physical health. With the tools currently available, a comprehensive assessment of adversity exposure faced by refugee children is not possible. The BRACE (beyond refugee ACE) project set out to develop a core set of screening items that are refugee children specific and measure exposure and severity at all stages of migration. Here, the development of the Arabic (one of the majority languages of asylum seekers in Europe) parent report version is described. Methods The BRACE questionnaire was developed based on results of 1) a qualitative study involving semi-structured interviews with 47 refugee parents and 11 children (Germany, 2018-2020), and 2) a systematic review of relevant measures. Categories and associated adverse/protective experiences derived in 1) built the foundation of the BRACE. Whenever possible, items of validated ACE tools identified in 2) were used to assess exposure. The item set was finalized after translation into Arabic and cognitive pre-testing with 12 participants from the target population. Results The developed BRACE consisted of 52 items for 48 categories covering 8 adverse (e.g. disruption, violence) and 2 protective themes (e.g., connections). 19 items were developed by the study team (e.g., police brutality), 33 items were adopted from existing ACE tools (e.g. family member arrest). A binary response format expanding into a 4-point Likert scale was employed. Minor difficulties regarding respondent interpretation and answering ability could be resolved resulting in a final 50 item set. Conclusions This item set provides a starting point for field trials of a measure needed to facilitate the identification of at-risk individuals for targeted interventions. Key messages • Adverse childhood experiences (ACE) can harm mental/physical health. Adequate tools assessing ACE exposure in flight contexts are needed in order to develop mitigation strategies for those at risk. • The BRACE project has developed a core set of screening items specific to refugee children's experiences to measure adversity exposure and severity at all stages of migration.

EUREST-RISE: An innovative networking and training project on European Tobacco Control

Abstract Aim and objective The world's leading contributor to avoidable illness and premature death is thought to be smoking and other tobacco use. An integrated set of research projects are suggested as part of EUREST-RISE to aid in the training of researchers in the analysis of tobacco product systems, their processes and functions, and the relationships between important design elements and their evaluation in user populations. Methods Currently in its first phase of execution, EUREST-RISE is a Marie Sklodowska Curie Action (MSCA) funded under Grant Agreement (Number:101008139). In the framework of particular research objectives addressed within tasks of each WP, interdisciplinary knowledge transfer will be achieved through the intersectoral secondment of researchers and their reintegration to their home institution. Results By offering a special framework for collaboration on joint research and innovation projects in three areas, EUREST-RISE expects to achieve its goal of developing a cohort of scholars with cutting-edge practical training in European tobacco product regulatory science. A) Exchange of information about the use of big data bases to address tobacco control concerns from a product-level or population-level viewpoint, including EU Common Entry Gate product-level data and European secondary population-based databases B) offer research and training on the application of sensory and chemical analyses for the evaluation of flavors in heated tobacco products, e-cigarettes, and tobacco products C) train researchers in advocacy activities and policy integration, facilitate training in peer to peer communication, and training on communication to media and the general public. Conclusions A network of excellence in European tobacco product regulation science is anticipated to be created via the EUREST-RISE initiative. Key messages • Conflicts of Interest: None. • Funding: The EUREST-RISE Project takes place with the financial support of the European Union, under a Grant Agreement (Number:101008139, PI: Dr Vardavas).

Parametric Reduced Order Model of a Gas Bearings Supported Rotor

Abstract Gas bearings use pressurized gas as a lubricant to support and guide rotating machinery. These bearings have a number of advantages over traditional lubricated bearings, including higher efficiency in a variety of applications and reduced maintenance requirements. However, they are more complex to operate and exhibit nonlinear behaviors. This paper presents a parametric hyper reduced order model (h-ROM) of a gas bearings supported rotor enabling to speed up the computations up to a factor 100 while preserving satisfactory accuracy. A Galerkin projection setting is employed to reduce the dimension of the governing equations and the nonlinear terms are efficiently tackled with a sparse sampling technique. The performances of the h-ROM are compared to a high fidelity model both in terms of accuracy and computation time, demonstrating the potential for future anomaly detection applications.

The effect of top‐down knowledge hiding on subordinates' task performance in Chinese R&amp;D projects: the mediating role of leader‐member ‘guanxi’

Although a growing number of studies on project knowledge hiding have identified the consequences of peer knowledge hiding in research and development (R&amp;D) project settings, the effects of project managers' knowledge hiding on subordinates' task performance remain underexplored. Drawing on leader–member exchange theory, this study explores how different dimensions of project managers' knowledge hiding affect subordinates' task performance through leader‐member guanxi. Data were collected from a sample of 143 manager‐subordinate dyads in Chinese R&amp;D teams. The results indicate that project managers' evasive hiding and playing dumb had negative influences on subordinates' task performance. Leader–member guanxi partially mediates the relationship between project managers' evasive hiding and subordinates' task performance. Finally, the theoretical and practical implications and limitations are discussed.

Numerical Simulation and Design of Ensemble Learning Based Improved Software Development Effort Estimation System

This research paper proposes a novel approach to improving software development effort estimation by integrating ensemble learning algorithms with numerical simulation techniques. The objective of this study is to design an ensemble learning-based software development effort estimation system that leverages the strengths of multiple algorithms to enhance accuracy and reliability. The proposed system combines the power of ensemble learning, which involves aggregating predictions from multiple models, with numerical simulation techniques that enable the modelling and analysis of complex software development processes. A diverse set of software development projects is collected, encompassing various domains, sizes, and complexities. Ensemble learning algorithms such as Random Forest, Gradient Boosting, Bagging, and AdaBoost are selected for their ability to capture different aspects of the data and produce robust predictions. The proposed system architecture is presented, illustrating the flow of data and components. A model training and evaluation pipeline is developed, enabling the integration of ensemble learning and numerical simulation modules. The system combines the predictions generated by the ensemble models with the simulation results to produce more accurate and reliable effort estimates. The experimental setup involves a comprehensive evaluation of the proposed system. A real-world dataset comprising historical project data is utilized, and various performance metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), are employed to assess the effectiveness of the system. The results and analysis demonstrate that the ensemble learning-based effort estimation system outperforms traditional techniques, showcasing its potential to enhance project planning and resource allocation.

Fault diagnosis based on federated learning driven by dynamic expansion for model layers of imbalanced client

Federated Learning is a promising tool for fault diagnosis of critical components for electrical driving systems. However, the performance of existing method is limited by negative transferring owing to inter-client inhomogeneity related to the sample size, sample quality, and fault type. How to make balanced client benefit from imbalanced client who involves unique fault information in its poor data is crucial to encourage the balanced clients to take an active part in federation. This study aims to design an imbalanced federated learning framework based on dynamic expansion for model layers of an imbalanced client to make it powerful enough to make it powerful in extracting the unique fault feature involved. The proposed method can ensure that balanced client be benefited from imbalanced client by designing a collaboration mechanism of inter-client layer-by-layer federation and intra-client cross-scale recursive fusion. The effectiveness of the proposed method was validated using both the benchmark dataset and the data set of an actual project of Nio Inc. When there is significant inhomogeneity between clients, the proposed method can achieve an accuracy increase of 13.97% compared to existing methods.

Punishment, re-education and agriculture: Portuguese internal and imperial penal colonisation in the nineteenth century

This paper considers a set of Portuguese state-funded projects for agricultural colonisation using coerced subjects. In the second half of the nineteenth century, mounting urbanisation, joblessness, and deprivation, resulting in depopulated countryside and a fractious urban milieu, drove experts to seek international examples for institutionalisation and rehabilitation through agricultural labour. Placing those classified as ‘criminals’ but also new outlaw categories such as ‘beggars’ and ‘vagrants’, as well as ‘undisciplined’ and ‘unimputable’ minors, at the forefront of colonising initiatives in areas characterised as empty or unproductive gave rhetorical impetus to settlement plans and to claims of territorial sovereignty and self-sufficiency in imperial and metropolitan contexts. The article examines a range of experiments: in Alentejo in southern Portugal, where the youth re-education colony of Vila Fernando was the country's counterpart to the French colonie of Mettray, and in Angola, where penal colonies for exiled convicts supported the effective occupation of the hinterland. We argue that, despite their differences, metropolitan and imperial projects can be addressed using the same analytical framework as they share an allied set of practices, cultures, technologies, and agents, mobilised to achieve common goals: economic exploitation, population and territory management, confinement, discipline, punishment, and re-education.

Artificial Intelligence: When Technology is the Smallest of the Paradigms

The article analyzes, deepens, and reflects on the development and impact of artificial intelligence (AI) in various areas of society. The meaning of AI is the development of a scientific discipline that encompasses a set of cognitive and intellectual simulation capabilities expressed by computer systems through algorithms. The author distinguishes between weak artificial intelligence, which is concerned with performing specific tasks, and strong artificial intelligence, which forms an AI that will exceed human capabilities. AI is a disruptive force that radically changes traditional models and paradigms and requires constant adaptation and multidisciplinary training on the part of human beings. The proposal arises that the new generations are not only trained in technological careers, but also in humanistic disciplines such as philosophy, sociology, linguistics, and political science, to instruct AI with values and principles, even if they constitute complex filters. In their algorithms or part of their training. We must take advantage of the potential of AI to improve the quality of life and the general management of our actions in community. Some of the risks that already threaten and affect different sectors, industries, markets, etc. are described and predicted. The causes and effects of hallucinations of artificial intelligence (AI) are observed, as phenomena in which the AI produces or perceives information that does not correspond to reality. Coding errors, training biases or omissions, social engineering applied to AI, lack of information or data, or absence of strong algorithmic responses. We face ethical, social, and legal challenges. Governments, public administrations and obviously the private sector carry the responsibility of contributing to the control and regulation of AI on their backs. A new social contract between humans and the non-human autonomous entities that make up AI will soon become necessary. What role we will play in the future is a complex set of speculations and projections, not all these projections are the result of our will, they are not completely under our control. Today the strategic destiny of the society of the future no longer depends solely on our vision.

A Galaxy Morphology Classification Model Based on Momentum Contrastive Learning

The taxonomy of galaxy morphology plays an important role in astrophysics and provides great help for the study of galaxy evolution. To integrate the advantages of unsupervised learning without labels and supervised learning with high classification accuracy, this paper proposes a galaxy morphology classification model based on a momentum contrastive learning algorithm named Momentum Contrastive Learning Galaxy (MCL-Galaxy), which mainly includes two parts (i) pre-training of the model, where the ResNet_50 backbone network acts as an encoder to learn the galaxy morphology image features, which are stored in the queue and their consistency is ensured by using the momentum contrastive learning algorithm; and (ii) transfer learning, where Mahalanobis distance can assist in improving classification accuracy in downstream tasks where both encoder and queue are transferred. To evaluate the performance of MCL-Galaxy, we use the data set of the Galaxy Zoo challenge project on Kaggle for comparative testing. The experimental results show that the classification accuracy of MCL-Galaxy can reach 90.12%, which is 8.12% higher than the unsupervised state-of-the-art results. Although it is 3.1% lower than the advanced supervised method, it has the advantage of no label and can achieve a higher accuracy rate at the first epoch of classification iteration. This suggests that the gap between unsupervised and supervised representation learning in the field of Galaxy Morphologies classification tasks is well bridged.

Thermal and structural response of a pavement solar collector prototype

Thermal and structural response of a pavement solar collector prototype

Driving Factors of Concession Period in Healthcare Public Private Partnerships

Public Private Partnerships (PPPs) play a vital role in infrastructure development and public service delivery, with government entities collaborating with private sector organizations to achieve shared goals in the long term. An essential aspect of PPP contracts is the concession period, during which Special Purpose Vehicles (SPVs) are responsible for financing, building, operating, and maintaining public assets. The concession period has significant implications, associated with project risk, revenue, operations expenses, profitability, and bankability. While numerous studies focus on determining “optimal” concession periods, fewer explore the factors driving concession periods in national PPP programs, especially for social infrastructure projects relying primarily on availability payments. To fill this gap, this research aims to identify and analyze the effect of various potential factors on the length of the concession period, using linear regression analysis on a data set of healthcare PPP projects in Italy. This research theoretically contributes to understanding the driving factors of concession periods in PPP projects and provides insights for a balanced approach to PPP project planning and regulation and related strategies. On the practical side, decision makers can negotiate optimized concession periods, ensuring successful and sustainable public infrastructure projects.

Can healthy diets be achieved worldwide in 2050 without farmland expansion?

This paper analyses to what extent it would be possible to ensure food availability to the world population by 2050 with two objectives: healthy diets and no farmland expansion. Assumptions were made to project exogenous demand and supply variables. Climate change impacts on crop yields, grazing use intensities and maximum cultivable areas were taken into account. Cropland and pastureland needs were then estimated for 21 regions using a global biomass balance model. Simulation results established for two sets of crop yield projections (‘moderate’ versus ‘high’ growth) show that several regions (India, Rest of Asia, Near- and Middle-East countries and North Africa, as well as West Africa in the case of ‘moderate’ yield growth) would be constrained by their maximum cultivable areas with no deforestation. Our scenarios would be technically infeasible because of additional pastureland needs notably in sub-Saharan Africa. As a consequence, we analyse to what extent additional levers could reduce pastureland needs in sub-Saharan Africa.

Improving Mental Health Outcomes in Adolescent Patients With Universal Anxiety Screening in an Outpatient Pediatric Primary Care Office: A Quality Improvement Project

In the wake of the cultural and mental health consequences that arose since the COVID-19 pandemic, primary care providers must be able to assess and initiate treatment of anxiety disorders in the pediatric population. Only 59% of children with a diagnosable anxiety disorder are identified and receive treatment. This quality improvement project aimed to evaluate the implementation of universal anxiety screening using the General Anxiety Disorder-7 tool in pediatric primary care. Screening was performed on adolescents (aged 12-18 years) to evaluate the frequency and acceptability of tool administration and identification of anxiety and referrals to mental health care for treatment. Employing a pretest-posttest design, the effect of universal anxiety screening for adolescents was measured at well-child visits. Anxiety screening and anxiety diagnosis rates were significantly increased post-implementation. Universal screening for anxiety was efficacious in the project setting for identifying cases of anxiety.

Using participatory action research to empower district hospital staff to deliver quality-assured essential surgery to rural populations in Malawi, Zambia, and Tanzania.

In 2017 the SURG-Africa project set out to institute a surgical, obstetric, trauma and anesthesia (SOTA) care capacity-building intervention focused on non-specialist providers at district hospitals in Zambia, Malawi and Tanzania. The aim was to scale up quality-assured SOTA care for rural populations. This paper reports the process of developing the intervention and our experience of initial implementation, using a participatory approach. Participatory Action Research workshops were held in the 3 countries in July-October 2017 and in October 2018-July 2019, involving representatives of key local stakeholder groups: district hospital (DH) surgical teams and administrators, referral hospital SOTA specialists, professional associations and local authorities. Through semi-structured discussions, qualitative data were collected on participants' perceptions and experiences of barriers to the provision of SOTA care at district level, and on the training and supervision needs of district surgical teams. Data were compared for themes across countries and across surgical team cadres. All groups reported a lack of in-service training to develop essential skills to manage common SOTA cases; use and care of equipment; essential anesthesia care including resuscitation skills; and infection prevention and control. Very few district surgical teams had access to supervision. SOTA providers at DHs reported a demand for more feedback on referrals. Participants prioritized training needs that could be addressed through regular in-service training and supervision visits from referral hospital specialists to DHs. These data were used by participants in an action-planning cycle to develop site-specific training plans for each research site. The inclusive, participatory approach to stakeholder involvement in SOTA system strengthening employed by this study supported the design of a locally relevant and contextualized intervention. This study provides lessons on how to rebalance power dynamics in Global Surgery, through giving a voice to district surgical teams.

S3-4 Social prescription: a meaning for general medicine in Belgium? A qualitative exploration with patients and healthcare providers about the physical literacy counselling program

Abstract Purpose In a society where the population is aging and chronic diseases are more and more present, social determinants of health occupy a growing place in primary care medicine. As the demand for holistic care is growing, the bio-psycho-social model takes on its full meaning within primary health care.(1) The “social prescribing” model, introduced in many countries in recent years, allows the integration of non-medical care into the primary health care system without overburdening the health professional. (2) Social prescribing enables general practitioners and others primary care professionals to refer people to a range of non clinical services to support their health such as the intervention of physical activity specialists. Methods This study defines social prescribing and discusses successful aspects and challenges of the model in targeted prescription of physical activity. It also evaluates a pilot project set up in Belgium in 2022 to test the model. The pilot project aimed at promoting, through doctors and physiotherapists of the medical center, the orientation of patients to physical literacy counselling meetings leaded by a specialized physical educator. The purpose of this study was then to explore, qualitatively, patients and staff of a medical center about their opinion over a physical literacy counselling program. Interviews of patients and healthcare providers were undertaken during this project. Results The strengths of the pilot project developed include successful implementation of holistic patient-centered care, strengthening of patients’ mental and physical well-being alongside empowering their health. In addition, the project successfully foster preventive care and demonstrates how primary care may transform into primary health care. However, many challenges remain including the sustainability of the model itself, the availability of the services offered, the financial support and the low commitment of doctors and patients. Conclusions this study highlights first guidelines to introduce the model into the practice of primary healthcare in Belgium. Although the project shows limitations, the theoretical model remains promising and opens up new potential horizons for the practice of general medicine in Belgium. Funding Source This study received no external funding.

Groundwork for AI: Enforcing a benchmark for neoantigen prediction in personalized cancer immunotherapy.

This article expands on recent studies of machine learning or artificial intelligence (AI) algorithms that crucially depend on benchmark datasets, often called 'ground truths.' These ground-truth datasets gather input-data and output-targets, thereby establishing what can be retrieved computationally and evaluated statistically. I explore the case of the Tumor nEoantigen SeLection Alliance (TESLA), a consortium-based ground-truthing project in personalized cancer immunotherapy, where the 'truth' of the targets-immunogenic neoantigens-to be retrieved by the would-be AI algorithms depended on a broad technoscientific network whose setting up implied important organizational and material infrastructures. The study shows that instead of grounding an undisputable 'truth', the TESLA endeavor ended up establishing a contestable reference, the biology of neoantigens and how to measure their immunogenicity having slightly evolved alongside this four-year project. However, even if this controversy played down the scope of the TESLA ground truth, it did not discredit the whole undertaking. The magnitude of the technoscientific efforts that the TESLA project set into motion and the needs it ultimately succeeded in filling for the scientific and industrial community counterbalanced its metrological uncertainties, effectively instituting its contestable representation of 'true' neoantigens within the field of personalized cancer immunotherapy (at least temporarily). More generally, this case study indicates that the enforcement of ground truths, and what it leaves out, is a necessary condition to enable AI technologies in personalized medicine.