Informing for the sake of it: legal intricacies, acceleration and suspicion in the German and Swiss migration regimes

In migration law, being informed about legal and administrative procedures constitutes an essential procedural safeguard. Yet, in practice, the transparency of legal practices is often structurally undermined, resulting in the curtailment of procedural safeguards and potentially affecting perceptions of procedural justice. Building on our multi-sited ethnographic research in Germany and Switzerland, we first argue that migrants find it often difficult to anticipate how laws work, contradicting the key procedural law principle of legal certainty. Second, a general trend towards acceleration in migration administration allows limited time for information to reach migrants on the ground, leaving them uninformed about legal procedures. Third, migration law is implemented in an atmosphere of suspicion, which has a negative impact on trust between migrants and state officials – and on transparency. We thus demonstrate how procedural safeguards become empty and routinised, aggravating the structural violence at the heart of the distinction between citizens and non-citizens in interactions with the state

Machine Learning and Lean Six Sigma to Assess How COVID-19 Has Changed the Patient Management of the Complex Operative Unit of Neurology and Stroke Unit: A Single Center Study

Background: In health, it is important to promote the effectiveness, efficiency and adequacy of the services provided; these concepts become even more important in the era of the COVID-19 pandemic, where efforts to manage the disease have absorbed all hospital resources. The COVID-19 emergency led to a profound restructuring-in a very short time-of the Italian hospital system. Some factors that impose higher costs on hospitals are inappropriate hospitalization and length of stay (LOS). The length of stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. Methods: This study analyzed how COVID-19 changed the activity of the Complex Operative Unit (COU) of the Neurology and Stroke Unit of the San Giovanni di Dio e Ruggi d'Aragona University Hospital of Salerno (Italy). The methodology used in this study was Lean Six Sigma. Problem solving in Lean Six Sigma is the DMAIC roadmap, characterized by five operational phases. To add even more value to the processing, a single clinical case, represented by stroke patients, was investigated to verify the specific impact of the pandemic. Results: The results obtained show a reduction in LOS for stroke patients and an increase in the value of the diagnosis related group relative weight. Conclusions: This work has shown how, thanks to the implementation of protocols for the management of the COU of the Neurology and Stroke Unit, the work of doctors has improved, and this is evident from the values of the parameters taken into consideration

Is It Possible to Predict the Length of Stay of Patients Undergoing Hip-Replacement Surgery?

The proximal fracture of the femur and hip is the most common reason for hospitalization in orthopedic departments. In Italy, 115,989 hip-replacement surgeries were performed in 2019, showing the economic relevance of studying this type of procedure. This study analyzed the data relating to patients who underwent hip-replacement surgery in the years 2010-2020 at the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno. The multiple linear regression (MLR) model and regression and classification algorithms were implemented in order to predict the total length of stay (LOS). Lastly, using a statistical analysis, the impact of COVID-19 was evaluated. The results obtained from the regression analysis showed that the best model was MLR, with an R2 value of 0.616, compared with XGBoost, Gradient-Boosted Tree, and Random Forest, with R2 values of 0.552, 0.543, and 0.448, respectively. The t-test showed that the variables that most influenced the LOS, with the exception of pre-operative LOS, were gender, age, anemia, fracture/dislocation, and urinary disorders. Among the classification algorithms, the best result was obtained with Random Forest, with a sensitivity of the longest LOS of over 89%. In terms of the overall accuracy, Random Forest and Gradient-Boosted Tree achieved a value of 71.76% and an error of 28.24%, followed by Decision Tree, with an accuracy of 71.13% and an error of 28.87%, and, finally, Support Vector Machine, with an accuracy of 65.06% and an error of 34.94%. A significant difference in cardiovascular disease, fracture/dislocation, and post-operative LOS variables was shown by the chi-squared test and Mann-Whitney test in the comparison between 2019 (before COVID-19) and 2020 (in full pandemic emergency conditions)

Modelling the length of hospital stay after knee replacement surgery through Machine Learning and Multiple Linear Regression at San Giovanni di Dio e Ruggi daAragonaa University Hospital

Knee arthroplasty is one of the most commonly performed procedures within a hospital. The progressive aging of the population and the spread of clinical conditions such as obesity will lead to an increasing use of this procedure. Therefore, being able to make the process related to this procedure more effective and efficient becomes strategic within hospitals, subject to increasingly stringent clinical and financial pressures. A useful parameter for this purpose is the length of stay (LOS), whose early prediction allows for better bed management and resource allocation, models patient expectations and facilitates discharge planning. In this work, the data of 124 patients who underwent knee surgery in the two-year period 2019-2020 at the San Giovanni di Dio and Ruggi d’Aragona university hospital were studied using multiple linear regression and machine learning algorithms in order to evaluate and predict how patient data affect LOS

Overcrowding analysis in emergency department through indexes: a single center study

Overcrowding in the Emergency Department (ED) is one of the major issues that must be addressed in order to improve the services provided in emergency circumstances and to optimize their quality. As a result, in order to help the patients and professionals engaged, hospital organizations must implement remedial and preventative measures. Overcrowding has a number of consequences, including inadequate treatment and longer hospital stays; as a result, mortality and the average duration of stay in critical care units both rise. In the literature, a number of indicators have been used to measure ED congestion. EDWIN, NEDOCS and READI scales are considered the most efficient ones, each of which is based on different parameters regarding the patient management in the ED

Phosphorus plant removal from European agricultural land

AbstractPhosphorus (P) is an important nutrient for all plant growth and it has become a critical and often imbalanced element in modern agriculture. A proper crop fertilization is crucial for production, farmer profits, and also for ensuring sustainable agriculture. The European Commission has published the Farm to Fork (F2F) Strategy in May 2020, in which the reduction of the use of fertilizers by at least 20% is among one of the main objectives. Therefore, it is important to look for the optimal use of P in order to reduce its pollution effects but also ensure future agricultural production and food security. It is essential to estimate the P budget with the best available data at the highest possible spatial resolution. In this study, we focused on estimating the P removal from soils by crop harvest and removal of crop residues. Specifically, we attempted to estimate the P removal by taking into account the production area and productivity rates of 37 crops for 220 regions in the European Union (EU) and the UK. To estimate the P removal by crops, we included the P concentrations in plant tissues (%), the crop humidity rates, the crop residues production, and the removal rates of the crop residues. The total P removal was about 2.55 million tonnes (Mt) (± 0.23 Mt), with crop harvesting having the larger contribution (ca. 94%) compared to the crop residues removal. A Monte-Carlo analysis estimated a ± 9% uncertainty. In addition, we performed a projection of P removal from agricultural fields in 2030. By providing this picture, we aim to improve the current P balances in the EU and explore the feasibility of F2F objectives

Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy

The rapid growth in the complexity of services and stringent quality requirements present a challenge to all healthcare facilities, especially from an economic perspective. The goal is to implement different strategies that allows to enhance and obtain health processes closer to standards. The Length Of Stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. In fact, a patient's LOS can be affected by a number of factors, including their particular condition, medical history, or medical needs. To reduce and better manage the LOS it is necessary to be able to predict this value

Older Adolescents Who Did or Did Not Experience COVID-19 Symptoms: Associations with Mental Health, Risk Perception and Social Connection

After a decrease in detected cases in the summer, Europe faced the emergence of a second wave of coronavirus disease 19 (COVID-19). Few studies have investigated adolescents, who may constitute a target group with possible lower compliance to public health measures, particularly the social distancing measures. A total sample of 492 participants was included in the study, and the ages of the participants ranged from 18–24 years. According to the hypothesis of our study, the sample was divided into two groups: those who experienced COVID-19 symptoms and those who did not experience COVID-19 symptoms. Demographic characteristics, knowledge, perceptions, and behaviors related to COVID-19 were investigated with ad hoc items; in addition, mood disorders, self-efficacy, and social connectedness were explored. Our results showed significant differences in the variables of risk perception, self-efficacy, and measures of belongingness among older adolescents who did or did not experience COVID-19 symptoms. In this period, adolescents experienced unprecedented disruptions in their daily lives, leading them to isolation and loneliness. Compliance with restrictive measures is considered both a proactive behavior and a social responsibility, especially if supported by prosocial reasons to prevent others from getting sick; therefore, this must be the focus of raising awareness of anti-COVID-19 compliance among adolescents

Dati demografici degli studenti e selezione dei MOOC su Eduopen. Uno studio esplorativo sui MOOC erogati da UniFg

Today's generation of Massive Open Online Courses (MOOC) based on Open EducationalResources (OER) is able to offer high quality education to all those who decide touse this new type of online lifelong learning. Students who decide to enroll in thesecourses represent an increasingly diverse audience in terms of age and place of origin.The literature is being enriched with research studies that study the demographic dataof the students in relation to different variables, including the type of course MOOCchosen.17 universities join the Eduopen project, including the University of Foggia, which in thefirst three years has contributed to the growth and development of the platform by designingand delivering various MOOCs, involving numerous teachers and stimulatingan increasing number of students from non-geographical areas. necessarily surrounding.Specifically, in April 2019 Unifg counts:– 34 Mooc Courses;– 4 Pathway;– 45 Lecturers and Tutors.– more than 8000 students.This contribution presents an exploratory study carried out on the demographic data ofthe students enrolled in the courses offered by the University of Foggia. Specifically, thedemographic data of the students were analyzed based on age, educational qualificationsand city of origin and studied the correlations between these data and the choiceof available courses.La generazione odierna di Massive Open Online Courses (MOOC) basati su Open Educational Resources (OER) è in grado di offrire un’istruzione di qualità a tutti coloro che, per varie ragioni, utilizzano questa nuova metodologia di formazione online. Gli studenti che decidono di iscriversi a questi corsi costituiscono un pubblico sempre più diversificato per quanto riguarda età e luogo di provenienza.I ricercatori, educatori, e il pubblico in generale recentemente si è interessato molto su come differisce la provenienza dei corsisti e sulla relazione tra questa variabile e la scelta di un corso MOOC. Al progetto Eduopen aderiscono 17 Atenei, tra cui l’Università di Foggia, che nel primo triennio ha contribuito alla crescita e sviluppo della piattaforma progettando ed erogando diversi MOOC, coinvolgendo numerosi docenti e stimolando un numero sempre più crescente di studenti provenienti da zone geografiche non necessariamente circostanti. Nello specifico, ad aprile 2019 UniFg conta:– 34 Corsi MOOC;– 4 Pathway;– 45 Docenti e Tutor.– più di 8000 studenti.Questo contributo presenta uno studio esplorativo effettuato sui dati demografici degli studenti iscritti ai corsi offerti dall’ateneo foggiano. Nello specifico, sono stati analizzati i dati demografici degli studenti in base a età, titolo di studio e città di provenienza e studiate le correlazioni tra questi dati e la scelta dei corsi disponibili