Set Of Projects Research Articles

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.

Geometric perspective for explaining Hubble tension: theoretical and observational aspects

The Universe expansion rate has two different but very precise values ( and km s−1Mpc−1) that are not compatible. This problem, known as a Hubble tension, adds to other cosmological questions such as the origin of dark energy and the flatness problem. In turn, alternative models have attempted to explain similar phenomena but without dark energy. The aim of this work was to explain the Hubble tension by using a geometrical interpretation of observational viewpoints in embedded manifolds. Our technique consists of a set of parametric projections of radially inhomogeneous metrics, linking indistinguishable behaviours of accelerated flat and non-accelerated closed universes. A dark-energy-like phenomenon emerges from the distortion of matter-independent hyperconical metrics. To contrast our model, numerical solutions of dark energy/matter densities and Hubble parameter were obtained and compared to the standard model fitted to the Pantheon Supernovae Ia sample and in contrast to the SH0ES LMC Cepheid findings. Finally, Hubble tension is modelled by the different extrinsic/intrinsic viewpoints of the manifold. Compared to the Planck Legacy’s 2018 release of km s−1Mpc−1, we found ΛCDM-dependent (intrinsic) ranges between 66.38 and 68.87 km s−1Mpc−1, which were theoretically derived by setting local compatibility of metrics. The ΛCDM-independent extrinsic viewpoint resulted in a Hubble parameter between 73 and 74 km s−1Mpc−1 (compared to km s−1Mpc−1 of SH0ES). Datasets of 1048 Pantheon Type Ia supernovae () and 34 cosmic chronometers combined with 7 radial baryon acoustic oscillation size-based samples () were used to constraint the model. According to this geometrical perspective, dark parameters (energy and matter) could partially or totally be considered ‘apparent physical quantities’, a consequence of the stereographic projection of the extrinsic curvature.

A comparison of activity ranking methods for taking corrective actions during project control

Monitoring and controlling projects in progress is key to support corrective actions in case of delays and to deliver these projects timely to the client. Various project control methodologies have been proposed in literature to include activity variability in the project schedule and measure the performance of projects in progress. Much of these studies rely on a schedule risk analysis to rank activities according to their time sensitivity and expected impact on the total project duration.This paper compares two classes of activity ranking methods to improve the corrective action process of projects under uncertainty. Each method ranks activities based on certain criteria and places the highest ranked activity in a so-called action set that is then used to take certain corrective actions. The first method is the analytical based ranking method which relies on exact or approximate analytical calculations to provide a ranking of activities. This analytical ranking method will be compared with a second simulation-based ranking method that relies on Monte Carlo simulations to measure the sensitivity of each activities.Results on a set of artificial projects show that the analytical ranking method and one specific simulation-based ranking outperform all other methods, not only for predicting the contribution of actions on the expected project duration and its variability, but also in the efficiency of the project manager’s control.

Uncertainty in land-use adaptation persists despite crop model projections showing lower impacts under high warming

Climate change is expected to impact crop yields and alter resource availability. However, the understanding of the potential of agricultural land-use adaptation and its costs under climate warming is limited. Here, we use a global land system model to assess land-use-based adaptation and its cost under a set of crop model projections, including CO2 fertilization, based on climate model outputs. In our simulations of a low-emissions scenario, the land system responds through slight changes in cropland area in 2100, with costs close to zero. For a high emissions scenario and impacts uncertainty, the response tends toward cropland area changes and investments in technology, with average adaptation costs between −1.5 and +19 US$05 per ton of dry matter per year. Land-use adaptation can reduce adverse climate effects and use favorable changes, like local gains in crop yields. However, variance among high-emissions impact projections creates challenges for effective adaptation planning.

WhatsApp as an educational tool: perspectives of pre-service teachers

ABSTRACT This study explores pre-service teachers’ perspectives about the use of WhatsApp as an educational tool. This is a qualitative project set within an interpretive paradigm. Sixteen pre-service teachers at a University of Technology voluntarily agreed to be part of this study. These pre-service teachers were divided into two groups of eight participants each to take part in a focus group discussion. The data collected were analysed deductively, linking it to literature and the Community of Inquiry theoretical framework. The results show that despite challenges, pre-service teachers used WhatsApp because of its benefits, which include cost-effectiveness, quick dissemination of information, supporting learning and relieving stress among preservice teachers. The researchers concluded that lecturers and pre-service teachers should adopt and adapt WhatsApp for teaching and learning as a tool that supplements the learning management system in order to meet the contextual and diverse learning needs of students.

Constructing Estimates of Reachability Sets in Crowd Flows Modeling

Mathematical modeling of crowd flows in a building is studied. The study is based on a modification of the discrete CTM macromodel built on guaranteed estimates. Two methods for an approximate calculation of the reachability set—the number of people in each room at the next point in time—are proposed. Interval estimates and estimates in the form of sets of two-dimensional projections are constructed. The proposed algorithms are illustrated by numerical examples.

A Model in Which Well-Orderings of the Reals First Appear at a Given Projective Level, Part III—The Case of Second-Order PA

A model of set theory ZFC is defined in our recent research, in which, for a given n≥3, (An) there exists a good lightface Δn1 well-ordering of the reals, but (Bn) no well-orderings of the reals (not necessarily good) exist in the previous class Δn−11. Therefore, the conjunction (An)∧(Bn) is consistent, modulo the consistency of ZFC itself. In this paper, we significantly clarify and strengthen this result. We prove the consistency of the conjunction (An)∧(Bn) for any given n≥3 on the basis of the consistency of PA2, second-order Peano arithmetic, which is a much weaker assumption than the consistency of ZFC used in the earlier result. This is a new result that may lead to further progress in studies of the projective hierarchy.

Project Leadership Functions and the Associated Behaviour for Projects and Project Organisations

Individuals and their leadership competencies have been the sole focus of project leadership research, rather than the project leadership’s behaviour and functions in project and organisational settings. As a result, this study investigates the project leadership functions and behaviours required for various project and organisational situations in order to propose a project leadership model that will be effective in all project and organisational situations. The model that was developed and tested in this study theorised about the project leadership function that project leaders are expected to perform as a result of their positions. It also described how project leaders could tailor their leadership functions and behaviours to address issues related to positions, organisational situations, and project situations. Three hypotheses were developed and tested using structural equation modelling to validate the model. The model’s perceptive power demonstrates adequate validity. The model’s validity implies that there are appropriate project leadership functions for different leadership levels and circumstances. The model’s validity also implies that each project leadership behaviour has its own value, as conditions and necessities dictate. The study assumes that a project leader can embrace project leadership by combining a few different project leadership functions and behaviours.

Saudi Arabia’s “Vision 2030”: Structural Reforms and Their Challenges

This paper assesses the Saudi crown prince's visionary mega-project, "Vision 2030," superimposed as a new form of a social contract to diversify the country's economy and end dependency on oil revenue. The project sets to revive the long-staggering economy and propel the indicators of all sectors above the international averages. Concomitantly, the vision faces a colossal challenge as the kingdom is wheeled by authoritarian monarchical Islamic values that repudiate the concept of modernity, the final corollary of the prince's vision. This paper primarily assesses the education and healthcare system management based on the old social contract and seeks possible changes or restructuring according to "Vision 2030" objectives. The paper also looks into managing water shortages which are doomed to become more acute with the launch of mega infrastructural development projects. Further, this paper found that "Vision 2030" is steadily pushing boundaries, albeit there are impeding challenges ranging from the availability of skilled professionals to social, cultural and religious intransigence. Nevertheless, it can be convincible to argue that the cultural, social and political reformation is conceivable unless the citizenry has a strong will and vision regardless of how tumultuous the transition is, which would, in turn, help in streamlining the economic growth of the kingdom.

Solution techniques for Bi-level Knapsack Problems

Traditional funding mechanisms for healthcare projects involve ranking the projects and awarding funds based on their cost to benefit ratio. An alternative funding mechanism based on Bi-level programming was proposed in the literature. We refer to this as Donor-Recipient Bi-level Knapsack Problem (DR-BKP), which we explore further in this work. There are two participants, a leader (a donor agency) and a follower (recipient country) in this problem. Both the participants have their individual budgets. There is a set of projects, each having a certain cost and profit associated. The cost of projects are common to both the participants however the profits can be different for them. There is an external project that is of exclusive interest to the follower. The leader decides on cost subsidies to provide for the projects that is within her budget, while the follower solves a knapsack problem with the cost subsidised projects and the external project. Two enumerative algorithms were proposed in the literature for Bi-level problems with discrete upper level variables. We adapt them for DR-BKP that has continuous upper level variables having non-linear interaction with lower level variables. We first show the existence of a solution for DR-BKP and show the convergence of these algorithms. We provide evidence for Σ2P-hardness by showing that the problem is both NP-hard and Co-NP hard. Finally, we have implemented these two enumerative algorithms and shared the results and analyses of the computational experiments. A set of fifteen differing data sets each having randomly generated 10 instances have been solved to evaluate the performance of the proposed algorithms.

An undecidable extension of Morley's theorem on the number of countable models

We show that Morley's theorem on the number of countable models of a countable first-order theory becomes an undecidable statement when extended to second-order logic. More generally, we calculate the number of equivalence classes of equivalence relations obtained by countable intersections of projective sets in several models of set theory. Our methods include random and Cohen forcing, Woodin cardinals and Inner Model Theory.