Glycosylation is a complex post-translational modification that conveys functional diversity to glycoconjugates. Cell surface glycosylation mediates several biological activities such as induction of intracellular signaling pathway and pathogen recognition. Red blood cell (RBC) membrane N-glycans determine blood type and influence cell lifespan. Although several proteomic studies were carried out, glycosylation of RBC membrane proteins has not been systematically investigated. This work aims at exploring the human RBC N-glycome by high-sensitivity MALDI-MS techniques to outline a fingerprint of RBC N-glycans. To this purpose, MALDI-TOF spectra of healthy subjects harboring different blood groups were acquired. Results showed the predominant occurrence of neutral and sialylated complex N-glycans with bisected N-acetylglucosamine, core- and/or an-tennary fucosylation. In the higher mass region these species presented with multiple N-acetyllactosamine repeating units. Amongst the detected glycoforms, the presence of glycans bearing ABO(H) antigens allowed us to define a distinctive spectrum for each blood group. For the first time, advanced glycomic techniques have been applied to a comprehensive exploration of human RBC N-glycosylation, providing a new tool for the early detection of distinct glycome changes associated with disease conditions as well as to understand pathogens molecular recognition.

The increasing regulatory pressure to monitor and reduce GHG emissions and air pollutants requires cost-effective methods for their surveillance. The most common technique used for scientific investigations on gas concentration monitoring in barns are accurate but expensive and with a complex maintenance. This research study analysed the potential use of a low-cost portable measurement devices for the measurement of ammonia (NH3) and carbon dioxide (CO2) concentrations in an open dairy barn. A comparison between gas concentrations acquired at different heights from the floor by using portable devices and those acquired by a photoacoustic infrared multigas spectroscope (i.e., reference measurement) in the same sampling locations was carried out to determine the precision of the low-cost portable devices. The performance of low-cost portable devices was statistically analysed by the application of the one-way analysis of variance, correlation analysis and regression analysis. The results showed a significant difference between gas concentration values at various heights from the floor for both NH3 and CO2. The correlations between concentrations acquired by the low-cost portable device and the INNOVA were statistically significant (r=0.83; P<0.001) for gas concentrations monitored at 0.4 m from the floor. Compared with the reference measurement device, the low-cost devices were effective in the monitoring of NH3 concentrations at 0.40 m from the floor though it underestimated them in the barn at increasing of the height from the floor, whereas the device was not adequate for CO2 concentrations. In detail, the relative measurement error of the low-cost devices compared to INNOVA was reduced close to the floor during NH3 concentration measurements. Within these limitations, this device could be useful for monitoring NH3 concentration in the barn and to assess variation in NH3 concentrations mainly related to the animal occupied zone. Further efforts are needed in this field of research to identify low-cost device that could simplify emission estimation from open dairy barns.

The boreal Hemisphere has been experiencing increasing extreme hot and dry conditions over the past few decades, consistent with anthropogenic climate change expectations. The continental extension of the phenomenon calls for tools and techniques capable of monitoring the global to regional scales. In this context, the satellite is the only system that can satisfy the need for global coverage. The main objective we have addressed in the present paper is the capability of infrared satellite observations helping to monitor the vegetation stress due to increasing drought and heatwaves in summer. We have designed and implemented a new water deficit index (wdi) that exploits satellite observations in the infrared to retrieve humidity, air temperature, and surface temperature simultaneously. These three parameters are combined to provide the water deficit index. The index has been developed based on the Infrared Atmospheric Sounder Interferometer or IASI, which covers the infrared spectral range 645 to 2760 cm-1 with a sampling of 0.25 cm-1. The index has been used to study the 2017 heatwave, which hit Continental Europe from May to October. In particular, we have examined Southern Italy, where Mediterranean forests suffer from climate change. We have computed the index's time series and show that it can be used to indicate the atmospheric background conditions associated with meteorological drought. We have also found a good agreement with soil moisture, which suggests that the persistence of anomalously high water deficit index was an essential driver of the rapid development and evolution of the exceptionally severe 2017 droughts.

This article addresses the problem of formation control of a quadrotor and one (or more) marine vehicles operating at the surface of the water with the end goal of encircling the boundary of a chemical spill, enabling such vehicles to carry and release chemical dispersants used during ocean cleanup missions to break-up oil molecules. Firstly, the mathematical models of the Medusa class of marine robots and quadrotor aircrafts are introduced, followed by the design of single vehicle motion controllers that allow these vehicles to follow a parameterized path individually using Lyapunov based techniques. At a second stage, a distributed controller using event triggered communications is introduced, enabling the vehicles to perform cooperative path following missions according to a pre-defined geometric formation. In the next step, a real time path planning algorithm is developed that makes use of a camera sensor, installed on-board the quadrotor. This sensor enables the detection in the image of which pixels encode parts of a chemical spill boundary and use them to generate and update in real time a set of smooth B-spline based paths for all the vehicles to follow cooperatively. The performance of the complete system is evaluated by resorting to 3-D simulation software, making it possible to simulate visually a chemical spill. Results from real water trials are also provided for parts of the system, where two Medusa vehicles are required to perform a static lawn-mowing path following mission cooperatively at the surface of the water.

Reggio Calabria province (South Italy) is known for being almost the only area of cultivation of the bergamot fruit, grown principally for its essential oil, but today much studied for the health benefits of its juice. The biometrics and physico-chemical properties of the three (Citrus bergamia Risso) existing genotypes namely Castagnaro, Fantastico and Femminello were studied during fruit ripening from October to March. Castagnaro cv had the biggest and heaviest fruit during this harvest period. °Brix (7.9-10.0), pH (2.2-2.8) and Formol number (1.47-2.37 mL NaOH 0.1N/100mL) were shown to be influenced by both the genotype and by the harvest date. Titratable acidity (34.98-59.50 g/L) and Vitamin C (Ascorbic acid) (341-867 g/L) decreased during fruit ripening. The evolution of flavonoids such as neoeriocitrin, naringin, neohesperidin brutieridin and melitidin was studied both in bergamot juice and in the bergamot cloudy which is the aqueous extract of bergamot during fruit processing. Bergamot cloudy contained a higher quantity of flavonoids compared to the juice. This study gives important information regarding the cultivar and the harvest date for producers who want to obtain the highest juice quantity or the highest juice quality from the bergamot fruit.

Understanding that organizational communication is crucial for the consistent functioning of any establishment, this research assesses its influence on work-life balance, organizational climate, and job satisfaction among Portuguese firefighters, a rarely studied group. Effective organizational communication is identified as a potential pathway for nurturing a favorable work setting and bolstering employee contentment, essential elements of work-life balance. Data compiled from a group of 102 firefighters, using a sociodemographic questionnaire, the Work Social Climate Scale, and Portuguese versions of the ICA Communication Audit Survey Instrument and the Labor Satisfaction Questionnaire S20/23, unveil multiple significant correlations among various dimensions of the ICA, the Work Social Climate Scale, and communication metrics. Explicitly, correlations were discerned between received and sent communications and aspects of implication, support, and cohesion. Links were also evident between desired communications and the control dimension. Positive and statistically significant relationships were further observed between received and sent communications, all five satisfaction dimensions, necessary outgoing communications and the physical environment, and required outgoing communications and benefits, highlighting the pivotal role of adept communication in augmenting work-life balance, organizational climate, and job satisfaction among firefighters.

Myasthenia gravis (MG) is a disease characterized by typical clinical and immunopathogenic pe-culiarities, which correlate with age and sex/gender. Accordingly, MG patients show thymic al-terations and auto-antibodies productions, that can be the expression of a different pathogenetic mechanism related to age and gender. This determines, in relation to age at and sex/gender, a dif-ferent MG onset, prognosis, and outcome. Therefore, diverse MG forms can be identified, classi-fied, and characterized in relation to their different features, and precisely there are early onset and late-onset forms of MG. It has been hypothesized that the cause of appearance the two diverse forms of MG can be ascribed mainly to the diverse genetic and epigenetic factors. In this narrative review, we will analyze and discuss about the interplay of age- and sex-related factors (i.e. genet-ic and epigenetic factors) related to two different MG forms and how they contribute to their di-verse pathogenesis.

A large share of the reinforced concrete (RC) building stock in Mediterranean countries faces a dual challenge of seismic vulnerability and energy inefficiency, calling for urgent renovation efforts. While energy upgrades have been the focus of previous renovation policies, recent research highlights the critical need for integrated retrofitting solutions that address both structural integrity and energy performance. Multi-criteria decision-making (MCDM) approaches are a promising tool for optimizing the combined choice of these integrated interventions, considering various decision variables (DVs) of economic, social, environmental, and technical nature. To understand the impact of climate and seismic hazard conditions on multi-criteria-based retrofitting assessment, a case-study RC school building is selected and assumed to be located in three distinct climate conditions, cold, mild, and warm, and three seismic hazard levels, low, medium and high. Moreover, given the complexity and challenges of quantifying seismic performance metrics for practitioners, an available simplified (practice-oriented) approach is compared herein with a more thorough research-based one for quantifying the seismic performance of RC buildings within the MCDM framework. Both approaches are applied to the case-study building, considering twelve possible combinations of energy and seismic interventions. The accuracy of the practice-oriented approach and its impact on the retrofitting rankings is evaluated, emphasizing the importance of accessible and efficient evaluation methods in facilitating informed decision-making for building renovation

In 1998, the Lisbon Universal Exhibition – Expo'98 – led to an urban regeneration process on Lisbon’s waterfront. Following other cities, this event was a pretext to replace a depressed area and to re-connect it with the river, through the creation of a set of new spaces for common use along the water. For them, it was promoted a public art program, which can be considered quite innovative in the Portuguese context, and that resulted in a monumentalisation of Lisbon’s eastern riverfront, later extended to other areas. Behind this framework, this article aims to debate the relations between public art and the dynamics of urban regeneration at the end of the 20th century. For that, it will analyse: 1) the Expo'98’s public art program, comparing its initial assumptions with the final results; 2) the impact of this program, through the identification of public art’s placements before (1974-1998) and after (1999-2009) the event. As a result, it is possible to find that the placement of public art reveals the spaces that were "conquered" to the port system, and a dialectic between functional/economic and leisure/symbolic values. It is concluded that public art had a significant role in the urban processes of the late 20th century, which is quite evident in a discourse that considers it as a qualifying factor of urban space and a mean of economic and social development.

in the following article I analyze the trend of cultural and creative employment in the Italian regions between 2004 and 2022 through the use of ISTAT-BES data. After presenting a static analysis, I also present the results of the clustering analysis aimed at identifying groupings between Italian regions. Subsequently, an econometric model is proposed for estimating the value of cultural and creative employment in the Italian regions. Finally, I compare various machine learning models for predicting the value of cultural and creative employment. The results are critically discussed through an economic policy analysis.

The main aim of this work is to present a step-by-step procedure to model and analyze the power loss distribution of three-level Gallium Nitride (GaN) inverters. It has been applied to three distinct three-phase three-levels voltage source inverters utilized in electric traction drives: Active Neutral Point Clamped, Neutral Point Clamped and T-Type Neutral Point Clamped. The proposed analytical power loss modelling, combined to an equivalent representation of the electrical machine demonstrated to be a viable solution to achieve a time-saving and low computational burden simulation platform, leading to satisfying accuracy results. This has been confirmed by comparing the results carried out from the simulations of a 110kW permanent magnet synchronous motor drive and those determined by considering a simplified circuital representation based on the proposed analytical power loss modelling.

In this article, we analyse the determinants of Regulatory Quality-RQ for 193 countries in the period 2011-2020. We use a database from ESG-Environment Social Governance of the World Bank. We apply OLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that the variables that have the most positive impact on RQ, among others, are “GHG Net Emission”, “Mean Drought Index”, and “Heat Index”. We also found that the variables that have the most negative impact on RQ are among others “Renewable Energy Consumption”, “Voice and Accountability” and “Rule of Law”. Furthermore, we have applied the k-Means algorithm optimized with the Elbow Method and we find the presence of five clusters. In adjunct, we confront eight machine learning algorithms to predict the value of RQ and we found that the best predictor is Polynomial Regression. The predictive level of RQ for the analysed countries is expected to diminish of -1,29%. In the end, we present a network analysis with the Euclidean distance and we found the presence of a structure of seven networks using augmented data.

We show that the van der Waals gas does not exhibit just the celebrated liquid-gas 1 transition but also a quantum-classical one. We study the issue with reference to the LMC statistical 2 complexity and in relation to the order-disorder contrast.

In this article, I estimate the value of “Individual Using Internet”-IUI in the context of Environmental, Social and Governance-ESG database of the World Bank. I use data from 193 countries for the period 2011-2020. I found that among others the value of IUI is positively associated to “Methane Emissions” and “People Using Safely Managed Sanitation Services” and negatively associated among others to “Fossil Fuel Energy Consumption” and “Renewable Energy Consumption”. I apply the k-Means algorithm for the clusterization optimized with the Elbow Method and we find the presence of three clusters. Finally, I confront eight machine-learning algorithms to predict the future value of IUI. I found that the best predictive algorithm is Linear Regression and that the value of IUI is expected to decrease on average of 0.30% for the analysed countries.

There is an increasing interest in biomarkers of nitric oxide dysregulation and oxidative stress to guide management and identify new therapeutic targets in patients with chronic obstructive pulmonary disease (COPD). We conducted a systematic review and meta-analysis of the association between circulating metabolites within the arginine (arginine, citrulline, ornithine, asymmetric, ADMA, and symmetric, SDMA dimethylarginine), transsulfuration (methionine, homocysteine, and cysteine) and folic acid (folic acid, vitamin B6, and vitamin B12) metabolic pathways and COPD. We searched electronic databases from inception to 30 June 2023 and assessed the risk of bias and the certainty of evidence. In 21 eligible studies, compared to healthy controls, patients with stable COPD had significantly lower methionine (standardized mean difference, SMD=-0.50, 95% CI -0.95 to -0.05, p=0.029) and folic acid (SMD=-0.37, 95% CI -0.65 to -0.09, p=0.009), and higher homocysteine (SMD=0.78, 95% CI 0.48 to 1.07, p˂0.001) and cysteine concentrations (SMD=0.34, 95% CI 0.02 to 0.66, p=0.038). Additionally, COPD was associated with significantly higher ADMA (SMD=1.27, 95% CI 0.08 to 2.46, p=0.037), SDMA (SMD=3.94, 95% CI 0.79 to 7.08, p=0.014), and ornithine concentrations (SMD=0.67, 95% CI 0.13 to 1.22, p=0.015). In subgroup analysis, the SMD of homocysteine was significantly associated with the biological matrix assessed and the forced expiratory volume in the first second to forced vital capacity ratio, but not with age, study location, or analytical method used. Our study suggests the presence of significant alterations in metabolites within the arginine, transsulfuration, and folic acid pathways which can be useful for assessing nitric oxide dysregulation and oxidative stress and identifying novel treatment targets in COPD. (PROSPERO registration number: CRD42023448036)

Hypercholesterolemia is a major risk for the development of cardiovascular diseases (CVDs), the main cause of mortality worldwide, and it is characterized by high levels of circulating cholesterol. The drugs currently available for hypercholesterolemia control have several side effects, so it is necessary to develop new effective and safer therapies. Seaweeds serve as sources of several bio-active compounds with claimed beneficial effects. Eisenia bicyclis (Aramé) and Porphyra tenera (Nori) are edible seaweeds which were previously recognized as rich in bioactive compounds. In the present study, we aim to evaluate the anti-hypercholesterolemia effect of these two seaweeds ex-tracts and their health potential. Both extracts, but more efficiently Aramé extract demonstrated to have liver 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) inhibitory activity and to reduce approximately 30 % cholesterol permeation through human Caco-2 cells simulating the intestinal lining, targets for hypercholesterolemia treatments. An untargeted metabolomic assay on human intestinal Caco-2 and liver Hep-G2 cells lines exposed to Aramé and Nori extracts revealed changes in the cell’s metabolism as indicators of the extract’s health beneficial effects. The metabolic pathways affected by exposure to both extracts were associated to lipid metabolism, such as phospholipids, and fatty acid metabolism, pathways of amino acid, cofactors and vitamins and cellular respiration metabolism. More profound for Aramé treated cells, but also seen for Nori ex-posed cells, the metabolites modifications were associated with the protection against CVDs and other diseases and, to the improvement of cells oxidative stress tolerance. The results obtained for the anti-hypercholesterolemia properties, in addition to the revelation of the positive impact on cells metabolism, are an important contribution for further evaluation of these seaweeds extracts as functional foods or for CVDs prevention.

We estimate the value of Research and Development Expenditures as a percentage of GDP-RDE in the context of Environmental, Social and Governance-ESG model. We use the ESG World Bank database. We analyze data from193 countries in the period 2011-2020. We apply a set of econometric techniques i.e. Pooled Ordinary Least Squares-OLS, Panel Data with Random Effects, Panel Data with Fixed Effects, Weighted Least Squares-WLS. We found that the level of RDE is positively associated, among others, to “Nitrous Oxide Emissions” and “Scientific and Technical Journal Articles”, and negatively associated, among others to “Heat Index 35”, “Maximum 5-day Rainfall”. Furthermore, we perform a cluster analysis with the application of the k-Means algorithm optimized with the Elbow Method. The results show the presence of four clusters. Finally, we confront eight different machine-learning algorithms to predict the future value of RDE. We find that Linear Regression is the best predictive algorithms. RDE is expected to growth on average of 0.07% for the analysed countries.

With the term neuroinflammation has defined the typical inflammatory response of the brain closely related to the onset of many neurological diseases. Neuroinflammation is well known, but its mechanisms and pathways are not entirely comprehended. Currently, some progress has been achieved through many efforts and research. Consequently, new cellular and molecular mecha-nisms, diverse from conventional ones, are emerging. In listing some of those that will be the sub-ject of our description and discussion, essential are the important role of peripheral and infiltrated monocytes and clonotypic cells, alterations in gut/brain axis, dysregulation of the apelinergic sys-tem, as well as changes in the endothelial glycocalyx of blood-brain barrier, variation in expres-sion of some genes and levels of the encoding molecules by microRNAs (miRNAs), or other epige-netic factors and distinctive transcriptional factors, as well as the role of autophagy, ferroptosis, sex differences and modifications in the circadian cycle. Such mentioned mechanisms can add significant pieces in understanding the complex etiological puzzle of neuroinflammation. In addi-tion, they could represent biomarkers and targets of neurodegenerative diseases, in increase in our old populations.

Vulvovaginitis is a common and potentially serious gynecological condition that can have a significant impact on the physical, emotional, and social well-being of young women and adolescents. The condition is caused by a variety of factors, including bacterial, viral, and fungal infections, as well as hormonal changes and other underlying conditions. The symptoms of vulvovaginitis can include itching, burning, pain, discharge, and bleeding, which can impact the quality of life of affected individuals. This article examines the effect of Vulvovaginitis on Nigerian students residing in dormitories, highlighting the causes, symptoms, complications, and treatment options. The study emphasizes the need for improved sanitation, education on personal hygiene, and easy access to effective treatment to mitigate the spread of this infection.

This paper reviews the current knowledge of pepper anthracnose in the Philippines. We present research outputs on pepper anthracnose from the last three years. Then, we present evidence of the widespread occurrence of C. acutatum sensu lato in the Philippines. Finally, we highlight some research prospects that would contribute towards developing an integrated anthracnose management program.

Energy and information are fundamental concepts that play crucial roles in the foundations of science, each contributing to different aspects of our understanding of the natural world. Here we encounter that Fisher' measure is able to link them. We also investigate some interesting relations between Fisher's information, Poisson's distribution, and thermal quantifiers in a grand canonical ensemble. These connections permit one to visualize interesting novel artifacts in statistical mechanics. We clearly see them emerge in discussing the ideal gas. For example, we encounter that the Poisson distribution is intrinsically involved in the physics of the ideal gas, and we are able to give a Fisher-estimation of the difference in informational content between the grand canonical and canonical ensembles.

In this article we investigate the determinants of marketing or organizational innovators in Europe for 36 countries in the period 2010-2019. We have used data from the European Innovation Scoreboard-EIS of the European Commission. We perform different econometric models i.e. Dynamic Panel, Pooled OLS, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. Results show that the level of marketing or organizational innovators in positively associated, among others variables to “Innovation Index”, “Innovators” and “Knowledge Intensive Service Exports”, while is negatively associated with “Sales Impacts”, “Foreign Controlled Enterprises Share of Value Added” and “Government procurement of advanced technology products”.

The following article analyzes Italian companies with more than 10 employees that use online sales tools. The data used were acquired from the ISTAT-BES database. The article first presents a static analysis of the data aimed at framing the phenomenon in the context of Italian regional disparities. Subsequently, a clustering with k-Means algorithm is proposed by comparing the Silhouette coefficient and the Elbow method. The investigation of the innovative and technological determinants of the observed variable is carried out through the application of a panel econometric model. Finally, different machine learning algorithms for prediction are compared. The results are critically discussed with economic policy suggestions.

In the following article I analyze the determinants of regular internet users in the Italian regions. The data is analyzed both in terms of static analysis and also through the application of the k-Means algorithm optimized with the Elbow method. Subsequently, an econometric model is presented for estimating regular internet users in the Italian regions based on variables that reflect the state of technological innovation and digital culture. The results are analyzed and discussed in light of the implications that digitalisation has for triggering economic growth.

In the following article I take into consideration the role of knowledge workers in the Italian regions. The analysed data refers to the ISTAT-BES database. The metric analysis consists of an in-depth analysis of the trends of the regions and macro-regions, followed by clustering with the k-Means algorithm, the application of machine learning algorithms for prediction, and the presentation of an econometric model with panel methods date. The results are also critically discussed in light of the North-South divide and the economic policy implications.

In this article we analyze the determinants and the export trend of European countries of medium and high technology products. The data were analyzed using various econometric models, namely WLS, Pooled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects. The results show that exports of medium and high-tech products are positively associated, among other variables, with the value of “Average Annual GDP Growth”, “Total Entrepreneurial Activity” and “Sales Impacts”, and negatively associated with, among other variables, “Human Resources”, “Government and Procurement of Advanced Technology Products” and “Buyer Sophistication”. A cluster analysis was realized with the k-Means algorithm optimized with the Silhouette coefficient. The result showed the presence of only two clusters. Since this result was considered poorly representative of the industrial complexity of the European Union countries, a further analysis was carried out with the Elbow method. The result showed the presence of 6 clusters with the dominance of Germany and the economies connected to the German economy. In addition, a network analysis was carried out using the distance to Manhattan. Four complex network structures and two simplified network structures were detected. A comparison was then made between 10 machine learning algorithms for predicting the value of exports of medium and high-tech products. The result shows that the best performing algorithm is the SGD. An analysis with Augmented Data-AD was implemented with a comparison between 10 machine learning algorithms for prediction and the result shows that the Linear Regression algorithm is the best predictor. The prediction with the Augmented Data-AD allows to reduce the MAE by about 0.0022131 compared to the prediction with the Original Data-OD.

In this article we investigate the determinants of the European “Most Cited Publications”. We use data from the European Innovation Scoreboard-EIS of the European Commission for the period 2010-2019. Data are analyzed with Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, and Pooled OLS. Results show that the level of “Most Cited Publications” is positively associated, among others, to “Innovation Index” and “Enterprise Birth” and negatively associated, among others, to “Government Procurement of Advanced Technology Products” and “Human Resources”. Furthermore, we perform a cluster analysis with the k-Means algorithm either with the Silhouette Coefficient and the Elbow Method. We find that the Elbow Method shows better results than the Silhouette Coefficient with a number of clusters equal to 3. In adjunct we perform a network analysis with the Manhattan distance, and we find the presence of 4 complex and 2 simplified network structures. Finally, we present a confrontation among 10 machine learning algorithms to predict the level of “Most Cited Publication” either with Original Data-OD either with Augmented Data-AD. Results show that the best machine learning algorithm to predict the level of “Most Cited Publication” with Original Data-OD is SGD, while Linear Regression is the best machine learning algorithm for the prediction of “Most Cited Publications” with Augmented Data-AD.

We consider an $N$ fermion system at low temperature $T$ in which we encounter special particle number values $N_m$ exhibiting special traits. These values arise in focusing attention upon the degree of mixture (DM) of the pertinent quantum states. Given the coupling constant of the Hamiltonian, the DMs stay constant for all $N$-values, but experience sudden jumps at the $N_m$. For a quantum state described by the matrix $\rho$, its purity is expressed by $Tr \rho^2$ and then the degree of mixture is given by $1 - Tr \rho^2$, a quantity that coincides with the entropy $S_q$ for $q=2$. Thus, Tsallis entropy of index two faithfully represents the degree of mixing of a state, that is, it measures the extent to which the state departs from maximal purity . Macroscopic manifestations of the degree of mixing can be observed through various physical quantities. Our present study is closely related to properties of many-fermion systemsn that are usually manipulated at zero temperature. Here we wish to study the subject at finite temperature. Gibbs' ensemble is appealed to. Some interesting insights are thereby gained.

Solid state sensors having timing capabilities are becoming an absolute need in particle tracking techniques of future experiments at colliders. In this sense, silicon sensors having 3D structure are becoming an interesting solution, due to their intrinsic speed and radiation resistance. A characteristics of such devices is the strict dependence of their performance on their geometric structure, which can be widely optimised by design, thus requiring suitable tools for an accurate modeling of their behaviour. This paper illustrates the development, performance and use of the TCoDe simulator, specifically dedicated to the fast simulation of carrier transportation phenomena in solid state sensors. Some examples of its effectiveness in the design and analysis of 3D sensors is also given.

The new IoT/IoE (internet of things/everythings) paradigm and architecture permits to rethink about the way the Smart City infrastructures are designed and managed, on the other hand a number of problems have to be solved. In terms of mobility the cities that embrace the sensoring era can take advantage of this disruptive technology to improve the quality of life of their citizen, also thanks the rationalization in the use of their resources. In Sii-Mobility, a national smart city project on mobility and transportation, a flexible platform has been designed and here, in this paper, is presented. It permits to setup heterogeneous and complex scenarios that integrate sensors/actuators as IoT/IoE in an overall scenario of Big Data, Machine Learning and Data Analytics. A detailed and complex case-study has been presented to validate the solution in the context of a system that dynamically reverse the traveling direction of a road segment, with all the safety conditions in place. This case study composes several building blocks of the IoT platform, which demonstrate that a flexible and dynamic set-up is possible, supporting off-grid, security, safety, cloud and mixed solutions.

In this article we investigate the role of “Renewable Energy Consumption” in the context of Circular Economy. We use data from the World Bank for 193 countries in the period 2011-2020. We perform several econometric techniques i.e., Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS. Our results show that “Renewable Energy Consumption” is positively associated among others to “Cooling Degree Days” and “Adjusted savings: net forest depletion” and negatively associated among others to “GHG net emissions/removals by LUCF” and “Mean Drought Index”. Furthermore, we perform a cluster analysis with the application of the k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of two clusters. Finally, we compare eight different machine learning algorithms to predict the value of Renewable Energy Consumption. Our results show that the Polynomial Regression is the best algorithm in the sense of prediction and that on average the renewable energy consumption is expected to growth of 2.61%.

We study different aspects of an exactly solvable master equation (ME). Its simplicity allows one direct access to the details of its workings, highlighting significant features and peculiarities. The existence of phase transitions is unveiled.

In this article, we estimate the value of Government Expenditure on Education-GEE in the context of Environmental, Social and Governance-ESG dataset of the World Bank. We use data from 193 countries in the period 2011-2020. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS, and Weighted Least Squares-WLS. Our results show that the value of GEE is positively associated among others to “Case of Death, by communicable disease and maternal, prenatal and nutrition conditions”, and “Unemployment”, and negatively associated among others to “Hospital Beds” and “Government Effectiveness”. Furthermore, we apply the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of the future value of GEE. We found that the Polynomial Regression is the best predictive algorithm. The Polynomial Regression predicts an increase in GEE of 7.09% on average for the analysed countries.

We analyze the question of GDP Growth-GDPG rate in the context of Environmental, Social and Governance-ESG framework. We use World Bank data for 193 countries in the period 2011-2020 using different econometric techniques i.e., Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled Ordinary Least Squares-OLS. We found that GDPG rate is positively associated, among others, to “Government Effectiveness” and “Prevalence of Undernourishment” and negatively associated among others to “Unemployment” and “Research and Development Expenditure”. Furthermore, we have applied the k-Means algorithm optimized with the Elbow method and we found the presence of four clusters in the sense of GDPG rate. Finally, we confront eight machine learning algorithms to predict the value of GDPG rate and we found that the Polynomial Regression is the best predictor. The Polynomial Regression predicts an increase of GDPG rate equal to 2.88% on average for the analysed countries.

We estimate the determinants of Emissions-COE in the context of Environmental, Social and Governance-ESG model at world level. We use data of the World Bank for 193 countries in the period 2011-2020. We found that the level of COE is positively associated, among others to “Methane Emissions”, “Research and Development Expenditures”, and negatively associated among others to “Renewable Energy Consumption” and “Mean Drought Index”. Furthermore, we have applied a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four cluster. Finally, we apply eight machine-learning algorithms for the prediction of the future value of COE and we find that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predicts a reduction in the level of COE equal to 5.69% on average for the analysed countries.

In this article, we estimate the role of Political Stability and Absence of Violence and Terrorism-PS in the context of Environmental, Social and Governance-ESG data at world level. We analyse data from 193 countries in the period 2011-2020. We apply Panel Data with Fixed Effects, Panel Data with Random Effects and Pooled Ordinary Least Square-OLS. We found that PS is positively associated, among others, to Population Density and Government Effectiveness, and negatively associated, among others, to Research and Development Expenditure and Maximum 5-day Rainfall. Furthermore, we apply the k-Means algorithm optimized with the application of the Elbow Method and we find the presence of four clusters. Finally, we propose a confrontation among eight different machine-learning algorithms for the prediction of PS and we find that the Polynomial Regression shows the higher performance. The Polynomial Regression predicts an increase in the level of PS of 0.25% on average for the analysed countries.

: Greenhouse plants are usually grown in containers filled with growing media. Peat is the main component of the growing media used in greenhouses. However, peat substrate is made from peat from drained peatlands, which is a limited resource. Wood fiber can be an alternative to use as a growing media for controlled-climate crops. The chemical properties of the growing media interact and change continuously due to the small volume of the growing media, which is limited by the vegetative container. Therefore, this study aims to gain new knowledge on the impact of nutrient changes in the microbial degradation of carbon compounds in wood fiber and mixtures with peat substrate on the content and uptake of nutrients required by plants. Cucumbers (Cucumis sativus L.) were cultivated in growing media of peat substrate and wood fiber: (1) peat substrate (PS); (2) wood fiber (WF); (3) wood fiber and peat substrate 50/50 v/v (WF/PS 50/50); (4) wood fiber and peat substrate 25/75 v/v (WF/PS 25/75). The rates of fertilization were the following: (1) conventional fertilization (CF); (2) N13; (3) N23; (4) N30. Fertilization of cucumbers with different rates of nitrogen influenced the nutrient uptake. Plants grown in WF/PS 50/50 and WF/PS 25/75 growing media had the best Cu uptake when fertilized with N23. When the plants grown in the wood fiber media and the 50/50 media were fertilized with N13, N23, and N30, the Mn content in the growing media at the end of the growing season was significantly lower than the Mn content in the media with conventional fertilization. Thus, nitrogen improved the uptake of Mn by plants grown not only in the wood fiber but also in combinations with peat substrate. Growing plants in wood fiber and fertilizing them with N13 can result in optimum uptake of micronutrients. The number and mass of cucumber fruits per plant were influenced by the amount of wood fiber in the growing media and the application of nitrogen fertilizer. The highest number of fruits and mass of fruits per plant was obtained significantly higher when cucumbers were grown on WF/PS 50/50 growing media with additional N13 fertilization.

In this study, using information theory tools, we investigate the quantum degree of mixedness, particle number fluctuations, and temperature variations in the paradigmatic Lipkin model, focusing on the interplay between these factors in a system of many fermions. Our analysis reveals intriguing dependencies of quantum mixedness on the total fermion number, showcasing distinct behaviors at different temperatures. Notably, we find that the degree of quantum mixedness exhibits a strong dependence on the total fermion number, with varying trends observed across different temperature regimes. Remarkably, this dependence remains unaffected by the strength of the fermion-fermion interaction (if it does not vanish, of course), underscoring the robustness of the observed phenomena. Through comprehensive numerical simulations, we provide illustrative graphs that depict these dependencies, offering valuable insights into the fundamental characteristics of many-fermion systems. Our findings shed light on the intricate dynamics of quantum degree of mixedness and particle number fluctuations in fermionic systems, with potential implications for diverse fields ranging from condensed matter physics to quantum information science.

Background: Cervical myelopathy (CM) is a common cause of morbidity and disability in patients with mucopolysaccharidosis (MPS) and, therefore, early detection is crucial for best surgical intervention and follow-up. Transcranial Magnetic Stimulation (TMS) non-invasively evaluates the conductivity along the cortico-spinal tract, also allowing preclinical diagnosis and monitoring. Methods: motor evoked potentials (MEPs) to TMS were recorded in a group of 8 patients with MPS-related CM. Responses were obtained during mild tonic contraction through a circular coil applied over the “hot spot” of the first dorsal interosseous and tibialis anterior muscles, bilaterally. Central motor conduction time was estimated as the difference between MEP cortical latency and the peripheral motor conduction time by cervical or lumbar magnetic stimulation. Peak-to-peak MEP amplitude to cortical stimulation and right-to-left difference of each parameter were also measured. Results: TMS revealed abnormal findings from both upper and lower limbs compatible with axonal damage and demyelination in 6 of them. Notably, a subclinical cervical spinal disease was detected before the occurrence of an overt CM in two patients, whereas TMS signs compatible with a CM of variable degree persisted despite surgery in all treated subjects. Conclusions: TMS screening should be performed in MPS patients, before and after surgery.

Abstract: The high entropy alloy (HEA) of equiatomic composition CrNiFeCoMn and with FCC 11 crystal structure was prepared by either induction melting and additive manufacturing with a se- 12 lective laser melting (SLM) process, starting from mechanically alloyed powders. Unlike induction 13 melting, the as-produced SLM alloy showed fine nitride and Cr-rich s phase precipitates. Mechan- 14 ical spectroscopy measurements were performed on specimens cold worked and/or recrystallized. 15 Young’s modulus values of (140±10) GPa and (90±10) GPa were measured at 300 K for induction 16 melted and SLM samples, respectively. The values increased to (160±10) GPa and (170±10) GPa for 17 recrystallized samples. Damping measurements as a function of temperature showed two peaks 18 which were attributed to dislocations bending and grain boundaries sliding. The peaks were super- 19 posed on a temperature increasing background.

The high entropy alloy (HEA) of equiatomic composition CrNiFeCoMn and with FCC crystal structure was additively manufactured with a selective laser melting (SLM) process starting from mechanically alloyed powders. The as produced alloy shows finenitride and  phase pre-cipitates, which are Cr-rich and stable up to about 900 K. The precipitates increase in number and dimensions after long-period annealing at 900-1300 K, with a change in the HEA mechanical properties. Higher aging temperatures in furnace, above 1300 K, turn the alloy in a single FCC structure, with disappearance of the nitrate and  phase precipitates inside the grains and at the grain boundaries, but with still a presence of a finer Cr-rich nitride precipitation phase. These re-sults suggest that the as-produced HEA is a supersatured solid solution at low and intermediate temperature with nitrides and  nanostructures.

The entropic gravity conception proposes that what has been traditionally interpreted as unobserved dark matter might be merely the product of quantum effects. These effects would produce a novel sort of positive energy that translates into dark matter via $E=mc^2$. In the case of axions, this perspective as been shown to yield quite sensible, encouraging results [DOI:10.13140/RG.2.2.17894.88641] . There, a simple Schrodinger mechanism was utilized, in which his celebrated equation is solved with a potential function based on the microscopic Verlinde's entropic force advanced in [Physica A {\bf 511} (2018) 139]. In this effort we revisit such technique with regards to fermions' behavior (specifically, baryons).

The problem addressed in this article consists of the motion control of a quadrotor, comprising model disturbances and uncertainties. In order to tackle model uncertainty, adaptive control based on reinforcement learning is used. The distinctive feature of this article, in comparison with other works on quadrotor control using reinforcement learning is the exploration of the underlying optimal control problem in which a quadratic cost and a linear dynamics allow an algorithm that runs in real time. Instead of identifying a plant model, adaptation is obtained by approximating the performance index given by the Q-function using directional forgetting recursive least squares that rely on a linear regressor build from quadratic functions of input/output data. The adaptive algorithm proposed is tested in simulation in a cascade control structure that drives a quadrotor. Simulations show the improvement in performance that results when the proposed algorithm is turned on.

Background: Aging impacts semantic abilities, therefore semantic exercises might be a valuable tool for cognitive stimulation in healthy older adults. However, programs in this area are limited. Methods: In the current paper, we adapted the BOX linguistic semantic program for online use via the CerUp platform. Our goal was to prepare the optimization future experience of CerUp users, including potential changes in task structure, level of difficulty and instructions, and participants’ adherence, engagement, performance accuracy, perceived difficulty, and adequacy ratings assigned to the instructions we provided that might guide future changes. Additionally, we created a map of hypothesized task-specific features that may guide tailor-made uses of the program. Results: To that end, we present preliminary results on the usability of CerUp. By analyzing task-specific usability data alongside the hypothesized map of task-specific features, we also tested the validity of the latter. Conclusions: These preliminary results on the usability of the CerUp platform highlight the potential of programs for semantic stimulation. Such findings can inform future optimizations of the program, tailored to users’ needs and effectively supporting overall language skills and general cognition.

Consuming fish sustainably means consuming less and better, considering environmental, social, and economic impacts. This demands more knowledge to make informed decisions. In this study, the main goal was to understand how sustainability education can be used as a tool to raise awareness and promote new habits regarding the sustainable consumption of fish in 4th-grade students at primary school. During the COVID-19 pandemic, face-to-face and online learning methodologies were used in two different groups of students to understand whether the teaching method impacted learning. A sequential explanatory mixed methodology was applied, with a pre-test/post-test design, combined with focus group sessions. Between the pre and post-tests three learning modules were performed. The results showed that the students were unaware of many aspects related to fish sustainable consumption, but the developed activities significantly increased their knowledge of the subject and impacted students to change their consumption behaviours. It was also found that the construction of knowledge by the students after the activities was achieved either by face-to-face or online learning. Interviews from focus group sessions showed that students got to know new fish species, learned about the concept of minimum landing size and closure season and that fish are labelled like other types of food.

The port environments have some structures that still need regular control. However, the nature of these environments presents a number of challenges in determining an accurate vehicle position and consequently performing successful image similarity detection. These scenarios, however, can have poor visibility conditions and are highly dynamic, making place recognition difficult because the appearance of a place can change over time. Under these operational conditions, imaging sonars are a promising solution. There are some factors that affect the quality of the captured images, but the fact that they do not suffer from haze is a plus. Therefore, in this paper, a purely acoustic similarity detection method based on forward-looking sonar (FLS) data is proposed to overcome the inherent perceptual problems in a port scenario. Considering the lack of available data in this context and to facilitate the variation of environmental parameters and sensor configurations, a port scenario was simulated using the Stonefish simulator. Experiments were conducted on predefined trajectories near structures. The place recognition approach performs better compared to the results obtained with optical imagery. In general, it provides a good compromise in terms of distinctiveness and achieves a performance of 87.5% when appropriate constraints and assumptions are made.

In recent years, soil addition with organic amendments, such as biochar and compost, has gained attention as an effective agronomic practice to sustain soil fertility, enhance plant growth and crop yield. Well known are the positive effects of compost on yield of a wide crop varieties, while both positive and negative responses are reported for biochar Therefore, the aim of the study was to verify the effect of biochar mixed with three types of compost on quanti-qualitative response of Swiss chard (Beta vulgaris L. cycla), a leafy green vegetable rich in dietary antioxidants, largely consumed worldwide. A factorial experiment in pots with two factors, including biochar (without biochar and with biochar from vine pruning residues) and compost (without compost, with compost from olive pomace, with vermicompost from cattle manure and with compost from cattle anaerobic digestate), was setup. Two growth cycles were considered and a set of quantitative (height of plants, number, area and fresh weight of leaves) and qualitative parameters (carotenoid, chlorophyll, total N and NO3-content of leaves) were analyzed. Biochar decreased plant growth and NO3- leaf content; on the contrary, it increased total N leaf content, while compost improved all the considered parameters. The interactive effect of biochar and compost was evident only on total N and NO3- leaf content. In our experimental conditions, the compost showed to be the best option to improve Swiss chard growth and increase the phytopigments content, while the biochar-compost mixtures didn’t produce the expected effect.

Mycobacterium tuberculosis (Mtb) is the etiological agent of tuberculosis (TB), a disease that alt-hough preventable and curable, remains a global epidemic due to the emergence of resistance and a latent form responsible for a long period of treatment. Drug discovery in TB is a challenging task due to the heterogeneity of the disease, the emergence of resistance and an uncomplete knowledge of the pathophysiology of the disease. The limited permeability of the cell wall and the presence of multiple efflux pumps remain a major barrier to achieve effective intracellular drug accumulation. While the complete genome sequence of Mtb has been determined and several potential protein targets have been validated, the lack of adequate models for in vitro and in vivo studies is a limit-ing factor in TB drug discovery programs. In current therapeutic regimens, less than 0.5% of bac-terial proteins are targeted being the biosynthesis of the cell wall and the energetic metabolism two of the most important processes exploited for TB chemotherapeutics. This review provides an overview on the current challenges in TB drug discovery and emerging Mtb druggable proteins, and how chemical probes for protein profiling enabled the identification of new targets and bi-omarkers, paving the way to disruptive therapeutic regimens and diagnostic tools.

In this article, we analyze the relationships that connect graduates from high school, the training system and employment rates and conditions in the Italian regions between 2004 and 2022. The data used refer to the Istat Bes database. The results show that the growth in the number of high school graduates is positively associated with higher university education and employment with the exception of job satisfaction. Subsequently we also present a clusterization with k-Means algorithm confronting the Silhouette Coefficient with the Elbow Method. Finally, we confront seven different machine-learning algorithms for the prediction of the level of graduated from high school. We also present economic policy suggestions to increase schooling in the Italian regions. The results are critically discussed.

Furthermore, the nasal congestion typical of rhinitis can contribute to upper airway obstruction and sleep-disordered breathing. A vicious circle is created between asthma and sleep-disordered breathing: uncontrolled asthma and sleep-disordered breathing can worsen each other. In children with genetic obesity, despite alterations in the immune system, fewer allergies are found compared to the general population. The causes of this reduced allergenicity are unclear but probably involve genetic, immunological, and environmental factors. Further studies are needed to understand the mechanisms. The study emphasizes the importance of jointly evaluating and managing allergies, obesity, and sleep-disordered breathing in children, considering their close interconnection.

The use of several light-related variables, such as the Secchi disc depth, the euphotic depth, and in particular, the diffuse attenuation coefficient (Kd), is deeply rooted in phytoplankton research, but these are not the most appropriate indicators of the amount of light available for photosynthesis. We argue that the variable of interest for phytoplankton is the mean light intensity in the mixed layer (Im), which represents the mean light to which phytoplankton cells are exposed throughout their life cycle, while being continuously mixed in the mixed layer. We use empirical data collected in different coastal ecosystems in southern Portugal to demonstrate why Im should be the preferred metric instead of the deeply rooted Kd. We show that, although the relationship between Im and Kd is inversely proportional, it is not always strong or even significant. Different Im values can be associated to the same Kd, but distinct Im have different physiological effects of phytoplankton. Therefore, Kd does not capture the amount of light available for photosynthesis, given that, unlike Im, Kd calculation does not consider the depth of the mixed layer. Therefore, we urge phytoplankton researchers to consider the measurement and calculation of Im when evaluating light-related processes in phytoplankton ecology.

Underwater communication for Underwater Wireless Sensor Networks (UWSNs) faces challenges due to the unique underwater environment. This review explores these challenges (attenuation, multipath propagation, limited bandwidth) across acoustic, optical, and radio frequency (RF) communication channels. It highlights how innovative antenna designs are crucial to mitigate these limitations and enable efficient data transmission. The review analyzes monopole, dipole, and helical antennas, discussing their trade-offs in radiation pattern, efficiency, and mitigating multipath effects. Advancements in materials, metamaterials, and reconfigurable antennas offer promising solutions for improved signal propagation, reduced attenuation, and better environmental adaptability. Research gaps include material exploration for low-conductivity antennas and optimizing antenna designs. Rigorous performance evaluation remains essential. Overall, advancements in underwater antenna technology are key to unlocking the full potential of UWSNs and enabling breakthroughs in marine applications.

Structural deformations are one of the most significant factor that affects machine tool (MT) positioning accuracy. These induced errors are complex to be represented by a model, nevertheless they need to be evaluated and predicted in order to increase the machining performance. This paper presents a novel approach to calibrate a machine tool in real-time, analyzing the thermo-mechanical errors through Fibre Bragg Grating (FBG) sensors embedded in the MT frame. The proposed configuration consists of an adaptronic structure of passive materials, Carbon Fibre Reinforced Polymers (CFRP), equipped by FBG sensors that are able to measure in real-time the deformed conditions of the frame. By using a proper thermo-mechanical kinematic model, the displacement of the end effector may be predicted and corrected when it is subjected to external undesired factors. By starting from a set of FE simulations to develop a model able to describe the MT structure stresses, a prototype has been fabricated and tested. The scope was to compare the numerical model with the experimental tests using FBG sensors. The experimental campaign has been performed varying the structure temperature over time and measuring the tool tip point (TTP) positions. The obtained results showed a substantial matching between the real and the predicted position of TTP confirming the effectiveness of the proposed calibration system.

In the last decades there has been a progressive aging of the population, known as “demographic revolution” or “demographic transition”. As a consequence of the worldwide progressive aging of population and of the increasing of general life expectancy, the relationship between aging and disability became a very important one and received a huge interest in research for its consequences on participation, inclusion and quality of life of ageing people and for its consequences on socio-sanitary organizations. The aim of this paper is to analyze this relationship and to discuss consequences on participation, inclusion and quality of life of ageing people, according to recent conceptual models of disability and active ageing. According to previous papers this relationship could be considered in two ways: ageing with disability (which refers to people living with long-term effects of disabling conditions acquired from birth to middle age) and disability with ageing (which refers to people which disabling conditions acquired later or age-related conditions), but newer papers proposed a convergence of these two approaches, taking into account the similarities and the differences between the two ways.

This article examines the evolution of bariatric surgery, with a focus on emerging technologies such as robotics and laparoscopy. In the case of gastric bypass, no significant differences have emerged between the two techniques in terms of hospitalization duration, weight loss, weight regain, or 30-day mortality. Robotic surgery, while requiring more time in the operating room, has been associated with lower rates of bleeding, mortality, transfusions, and infections. In revisional bariatric surgery, the robotic approach has shown fewer complications, shorter hospital stays, and a reduced need for conversion to open surgery. In the case of sleeve gastrectomy, robotic procedures have required more time and longer post-operative stays but have recorded lower rates of transfusions and bleeding compared to laparoscopy. However, robotic surgeries have proven to be more costly and potentially more complex in terms of post-operative complications. The review has also addressed the topic of the single anastomosis duodeno-ileal switch (SADIS), finding comparable results between robotic and laparoscopic techniques, although robotic procedures have required more time in the operating room. Robotic technology has proven to be safe and effective, albeit with slightly longer operating times in some cases.

Bio-separation of natural molecules as well as clinical compounds has been constantly developed in last decades. Several techniques are available but the majority of them presents drawbacks such us impossibility to be applied for industrial purposes. The main limitations for the scaling up are high costs and the fact that the devices work with microfluid dynamics. Nevertheless, magnetic bio-separation is considered the most prone to be used for large scale applications. Herein, we propose a simple magnetic separation method that is not based on microfluid dynamics, can work in a continuous- and high-flow rate and can be easily automated in order to be used for standard separation purposes. It is based on the use of an anisotropic flexible ferric magnetic strip, Teflon hoses and a pumping device. We show the modelling of the separation process along with an experimental test on iron oxide magnetic particles. The results showed that it is possible to remove, and separately collect, more than 92% of magnetic particles from a liquid solution of 100 ml in roughly 15 minutes.

In this paper we establish information theoretical bridges between 1) Thermal Heisenberg uncertainties $\Delta x \Delta p$ (at temperature $T$), and 2) LMC structural quantifiers. After having achieved such purpose, we determine to what an extent our bridges can be extended to both the semi classical and classical realms. Also, we find a strict bound relating a special LMC structural quantifier to quantum uncertainties.

Intermittent fasting (IF) has been indicated as a valuable alternative to the classical caloric restriction dietary regimen for lowering body weight and preventing obesity-related complications such as metabolic syndrome and type II diabetes. However, is it effective? In this review article, we analyzed over 50 clinical studies in which IF, conducted by alternate day fasting (ADF) or time-restricted feeding (TRF), was compared with the caloric restriction approach. We evaluated the different roles of IF in treating and preventing human disorders such as metabolic syndrome, type II diabetes, and some types of cancer, as well as the usefulness of IF in reducing body weight and cardiovascular risk factors such as hypertension. Furthermore, we explored the cellular pathway targeted by IF to exert their beneficial effects by activating effector proteins that modulate cell functions and resistance to oxidative stress. In contrast, we investigated the concerns for human health related to the adoption of IF dietary regimen, highlighting the profound debate on weight loss regimens. We have examined and compared several clinical trials to formulate an updated conception regarding IF and its therapeutic potential.

The postpartum period is marked by profound changes in women at a physical, psychological, and physiological level. Many of these changes persist after four to six weeks postpartum, and most women do not resume their levels of physical activity, which increases the risk of remaining inactive for many years. It is crucial to implement effective programs that promote exercise during the postpartum period. The objective of this study was to test the feasibility and analyze the effects of a structured and supervised postpartum exercise program on the maternal health and fitness parameters. To analyze the potential effects of the intervention, the level of physical activity, quality of life, pelvic girdle and low back pain, fatigue, depression, and the level of functional and physical fitness were assessed at baseline, after 8 weeks, and after 16 weeks of intervention. Feedback on the exercise program was collected after the final assessment. The results showed that a structured and supervised postpartum exercise program was feasible and safe, and produced positive effects on selected maternal health and fitness parameters. These results will encourage a study protocol with a larger sample in order to prove its effectiveness, improve the guidelines for postpartum exercise, and incorporate this program into a routine health care setting.

In the following article, it is presented an investigation of the determinants of Beds for High-Care Specialties-BHCS in the Italian regions in the context of Environmental, Social and Governance-ESG approach. Data from ISTAT-BES for 20 countries in the period 2004-2021 are been used. Different econometric techniques have been applied i.e.: Pooled Ordinary Least Squares, Panel Data with Fixed Effects, Panel Data with Random Effects, Dynamic Panel at 1 stage. Furthermore, a cluster analysis performed with a k-Means algorithm optimized with the Silhouette Coefficient indicated the presence of three clusters. Finally, eight different machine-learning algorithms are analysed to predict the future value of BHCS. The results show that the Artificial Neural Network-ANN algorithm is the best algorithm. The future value of BHSC is expected to growth on average of 4.88% for the analysed regions.

The dynamic behaviour of a machine tool (MT) directly influences the machining performances. The adoption of lightweight structures may reduce the effects of undesired vibrations and increasing the workpiece quality. This paper aims to present and compare a set of hybrid materials that may be excellent candidate to fabricate the MT moving parts. The selected materials have high dynamic characteristics and capacity to damp mechanical vibrations. In this way, starting by the kinematic model of a milling machine that highlights the main critical factors, this study paper evaluates a number of prototypes made of Al Foam sandwiches (AFS), Al Corrugated sandwiches (ACS) and materials reinforced by carbon fibres (CFRP). A set of prototypes has been fabricated, represented the Z-axis ram of a commercial milling machine. The static and dynamical properties have been evaluated by using both FE simulations and experimental tests. The obtained results show that the proposed structures may be a valid alternative to the conventional materials of MT moving parts, increasing machining performances. In particular, the AFS prototype highlighted a damping coefficient that is 20 times greater than a conventional ram (e.g. steel). The CFRP structure is able to satisfy the machining requirements with a reduced weight of 48.5%, while the ACS prototype showed a good trade-off between stiffness and damping.

This paper proposes an innovative mechatronic piezo-actuated module to control vibrations in modern machine tools. Vibrations represent one of the main issues that compromise seriously the quality of the workpiece. The active vibration control (AVC) device is composed by a host part integrated with sensors and actuators synchronized by a regulator, able to make a self-assessment and adjust to the environmental alteration. This study presents the mechatronic model based on the kinematic and dynamic analysis of the AVC device. To ensure a real time performance, a H2-LQG controller has been developed and validated by simulations involving machine tool, PZT actuator and controller models. The Hardware-in-the-loop (HIL) architecture is adopted to control and attenuate the vibrations. A set of experimental tests has been performed to validate the AVC module on a commercial machine tool. The feasibility of the real time vibration damping is demonstrated and the simulation accuracy is evaluated.

Background and Objectives Depression in childhood often co-occurs with anxiety disorders and a range of somatic symptoms. Recent studies have identified physical activity as a target for preventing the onset of depression. Idiopathic ventricular extrasystoles (VE) in children are sometimes associated with somatic symptoms and limitations in physical activity. The problem of arrhythmia existence can also be distressing for children and their parents. The study was conducted to determine the relationship between symptoms of depression, physical activity and somatic symptoms in children with idiopathic VE. Materials and Methods The study of children with structurally normal hearts and VE was approved by the Ethics Committee No. 2021/10-1383˗859(1). The authors developed Questionnaire to assess symptoms, physical activity, and general well-being. Symptoms of depression evaluated with modified pediatric PHQ-9 (MP-PHQ-9) questionnaire with the interpretation of 0-4 scores for none; 5-9 mild; 10-14 moderate; >15 severe depression. Children aged ≥12 years and parents who assessed their children's condition completed Questionnaires. All children underwent 24-hour electrocardiography and echocardiography to evaluate arrhythmia frequency and cardiac condition. Results Questionnaires were completed by 60 children’s parents and 39 children (≥12 years old). Median children’s age was 13 (5-17) years. Palpitations experienced 26 (43.3%), chest pain 13 (21.7%), exercise intolerance 15 (25%) children. All patients had normal ventricular function and hemodynamically normal hearts. The median score of the MP-PHQ-9 completed by parents was 2 (0-16); by children - 4 (0-18). The median VE frequency was 4,77 (0,1-32,77) % per 24 hours. Extra-sports participation had 31 (51.7%) children with a median time of 3.75 (0.75-25) hours/ week. Eleven-patients were suspended from sports. There was no significant difference between VE frequency, and MP-PHQ-9 scores. Higher MP-PHQ-9 scores showed <5 hours/week active symptomatic children. Conclusions Idiopathic VE, limitations in physical activity and depression remain the actual theme for clinicians and need further research. Higher depression scores showed patients with somatic symptoms.

The following article presents an analysis of the impact of the Environmental, Social and Governance-ESG determinants on Hospital Emigration to Another Region-HEAR in the Italian regions in the period 2004-2021. The data are analysed using Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Squares-WLS, and Dynamic Panel at 1 Stage. Results show that HEAR is negatively associated to E, positively to S and negatively associated to the G within the ESG model. The data were subjected to clustering with a k-Means algorithm optimized with the Silhouette coefficient. The optimal clustering with k=2 is compared to the sub-optimal cluster with k=3. The results suggest a negative relationship between the resident population and hospital emigration at regional level. Finally, a prediction is proposed with machine learning algorithms classified based on statistical performance. The results show that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predictions are critically analyzed in light of health economic policy directions.

Security in the Internet of Things (IoT) is a critical concern due to the growing number of connected devices and the limited resources to implement robust security mechanisms in most of them. Adopting standard assessment frameworks, such as CVSS (Common Vulnerability Score System), allows for systematic vulnerability assessment. At the same time, CVE (Common Vulnerabilities and Exposures) records provide unique identifiers for known security issues, making it easier to share information. However, comparing security solutions is still challenging due to the diverse nature of IoT devices and the ever-evolving threat landscape, requiring continual innovation and collaboration between stakeholders. Furthermore, there still needs to be a simple way to compare technologies and solutions, taking into account the security provided by them. In this sense, in this article, we propose a tool for calculating a security score based on the CVSS index of each existing vulnerability in a given technology. We explain how the tool performs the calculation, discuss some challenges, and propose improvements for future work.

The complexity of RNA cannot be fully expressed with the canonical A, C, G, and U alphabet. To date, over 140 distinct chemical modifications to RNA have been discovered. RNA modifications can profoundly impact the cellular outcomes of messenger RNAs (mRNAs), transfer and ribosomal RNAs, and noncoding RNAs. Additionally, aberrant RNA modifications are associated with human disease. RNA modifications are a rising topic within the fields of biochemistry and molecular biology. This review aims to provide budding scientists with an appreciation for the significance of RNA modifications, alongside the skills required to identify and fluently discuss fundamental RNA-protein interactions. The Pumilio RNA-binding protein and YT521-B homology (YTH) family of modified RNA-binding proteins serve as examples to highlight the fundamental biochemical interactions that underlie the specific recognition of both unmodified and modified ribonucleotides, respectively.

In the following article, we analyse the determinants of the number of physicians in the context of ISTAT BES-Benessere Equo Sostenibile data among twenty Italian regions in the period 2004-2022. We apply Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS-Ordinary Least Squares. We found that the number of Physicians among Italian regions is positively associated, among others, to “Trust in the Police and Firefighters”, “Net Income Inequality”, and negatively associated, among others, to “Research and Development Intensity” and “Soil waterproofing by artificial cover”. Furthermore, we apply the k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of two clusters. Finally, we confront eight different machine-learning algorithms to predict the future value of physicians and we find that the PNN-Probabilistic Neural Network is the best predictive algorithm.

In this article we estimate the level of Government Effectiveness-GE in 193 countries in the period 2011-2020 using data of the ESG World Bank Database. Different econometric techniques are used i.e. Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS. Results show that GE is positively related among others to “Control of Corruption”, “Political Stability and Absence of Violence/Terrorism”, and negatively associated with “Percentage Annual GDP Growth”. We perform a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of GE. Results show that the Polynomial Regression is the best predictive algorithm. The value of GE is expected to growth on average by 15.97%.

In this article we estimate the level of Control of Corruption for 193 countries in the period 2011-2020 using data from the ESG World Bank Database. Various econometric techniques are applied i.e.: Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled OLS, WLS. Results show that “Control of Corruption” is positively associated, among others, to “Government Effectiveness” and “Political Stability and Absence of Violence/Terrorism”, while it is negatively associated among others to “Agriculture, Forestry, and Fishing Value Added as Percentage of GDP” and “GHG Net Emissions/Removals by LUCF”. A cluster analysis implemented with the k-Means algorithm optimized with the Elbow Method shows four clusters. A confrontation among eight Machine Learning algorithms is proposed for the prediction of Control of Corruption. Polynomial Regression is the best predictor for the training data. The level of Control of Corruption is expected to growth by 10.36% on average.

In this article we investigate the impact of “Renewable Electricity Output” on green economy in the context of circular economy for 193 countries in the period 2011-2020. We use data from World Bank ESG framework. We perform Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, and Pooled OLS. Our results show that Renewable Electricity Output is positively associated, among others, to “Adjusted Savings-Net Forest Depletion” and “Renewable Energy Consumption” and negatively associated, among others, to “CO2 Emission” and “Cooling Degree Days”. Furthermore, we perform a cluster analysis implementing the k-Means algorithm optimized with the Elbow Method and we find the presence of 4 clusters. Finally, we confront seven different machine learning algorithms to predict the future level of “Renewable Electricity Output”. Our results show that Linear Regression is the best algorithm and that the future value of renewable electricity output is predicted to growth on average at a rate of 0.83% for the selected countries.

We investigate the innovational determinants of “Patent Applications” in Europe. We use data from the European Innovation Scoreboard-EIS of the European Commission for 36 countries in the period 2010-2019. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS and Dynamic Panel. We found that the variables that have a deeper positive association with “Patent Applications” are “Human Resources” and “Intellectual Assets”, while the variables that show a more intense negative relation with Patent Applications are “Employment Share in Manufacturing” and “Total Entrepreneurial Activity”. A cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient has been realized. The results show the presence of two clusters. A network analysis with the distance of Manhattan has been performed and we find three different complex network structures. Finally, a comparison is made among eight machine learning algorithms for the prediction of the future value of the “Patent Applications”. We found that PNN-Probabilistic Neural Network is the best performing algorithm. Using PNN the results show that the mean future value of “Patent Applications” in the estimated countries is expected to decrease of -0.1%.

In this article we have estimated the value of “Opportunity Driven Entrepreneurship” in Europe. We use data from European Innovation Scoreboard-EIS of the European Commission for 36 countries in the period 2010-2019. We use Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, Pooled OLS, and Dynamic Panel. Our results show that “Opportunity Driven Entrepreneurship” is positively associated, among others, to “Innovation Friendly Environment” and “Turnover Share Large Enterprises”, while it is negatively associated, among others, to “Sales Impacts” and “R&D Expenditure Business Sectors”.

In this article we estimate the value of “Non-R&D Innovation Expenditures” in Europe. We use data from the European Innovation Scoreboard-EIS of the European Commission from the period 2010-2019. We test data with the following econometric models i.e.: Pooled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. We found that “Non-R&D Innovation Expenditures” is positively associated among others to “Innovation Index” and “Firm Investments” and negatively associated among others to “Human Resources” and “Government Procurement of Advanced Technology Products”. We use the k-Means algorithm with either the Silhouette Coefficient and the Elbow Method in a confrontation with the network analysis optimized with the Distance of Manhattan and we find that the optimal number of clusters is four. Furthermore, we propose a confrontation among eight machine learning algorithms to predict the level of “Non-R&D Innovation Expenditures” either with Original Data-OD either with Augmented Data-AD. We found that Gradient Boost Trees Regression is the best predictor for OD while Tree Ensemble Regression is the best Predictor for AD. Finally, we verify that the prediction with AD is more efficient of that with OD with a reduction in the average value of statistical errors equal to 40,50%.

In this article we investigate the determinants of “New Doctorate Graduates” in Europe. We use data from the EIS-European Innovation Scoreboard of the European Commission for 36 countries in the period 2010-2019 with Pooled OLS, Dynamic Panel, WLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that “New Doctorate Graduates” is positively associated, among others, with “Human Resources” and “Government Procurement of Advanced Technology Products” and negatively, associated among others, with “Total Entrepreneurial Activity” and “Innovation Index”. We apply a clusterization with k-Means algorithm either with the Silhouette Coefficient either with the Elbow Method and we found that in both cases the optimal number of clusters is three. Furthermore, we use the Network Analysis with the Distance of Manhattan, and we find the presence of seven network structures. Finally, we propose a confrontation among ten machine learning algorithms to predict the value of “New Doctorate Graduates” either with Original Data-OD either with Augmented Data-AD. Results show that SGD-Stochastic Gradient Descendent is the best predictor for OD while Linear Regression performs better for AD.

The article affords the question of lifelong learning in Europe using data from the European Innovation Scoreboard-EIS in the period 2010-2019 for 36 countries. The econometric analysis is realized using WLS, Dynamic Panel, Pooled OLS, Panel Data with Fixed Effects and Random Effects. The results show that lifelong learning is, among other variables, positively associated to “Human Resources” and “Government procurement of advanced technology products” and is negatively associated, among others, to “Average annual GDP growth” and “Innovation Index”. A clusterization is realized using the k-Means algorithm with a confrontation between the Elbow Method and the Silhouette Coefficient. Subsequently, a Network Analysis was applied with the distance of Manhattan. The results show the presence of 4 complex and 2 simplified network structures. Finally, a comparison was made among eight machine learning algorithms for the prediction of the value of lifelong learning. The results show that the linear regression is the best predictor algorithm and that the level of lifelong learning is expected to growth on average by 1.12%.

In the following article, the value of the "Knowledge Intensive Services Exports in Europe" in 36 European countries is estimated. The data were analyzed through a set of econometric models or: Poled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. The results show that “Knowledge Intensive Services Exports” is negatively associated, among others, with "Buyer Sophistication", "Government Procurement of Advanced Technology Products", and positively associated with the following variables i.e. "Innovation Index", "Sales Impacts" and "Total Entrepreneurial Activity". Then a clusterization with k-Means algorithm was made with the Elbow method. The results show the presence of 3 clusters. A network analysis was later built and 4 complex network structures and three structures with simplified networks were detected. To predict the future trend of the variable, a comparison was made with eight different machine learning algorithms. The results show that prediction with Augmented Data-AD is more efficient than prediction with Original Data-AD with a reduction of the mean of statistical errors equal to 55,94%.

The following article analyzes the determinants of the innovation index in Europe. The data refer to the European Innovation Scoreboard-EIS of the European Commission for the period between 2010 and 2019 for 36 countries. The data are analyzed using the following econometric techniques: Panel Data with Random Effects, Panel Data with Fixed Effects, Dynamic Panel Data, Pooled OLS, WLS. The results show that the Innovation Index is negatively connected to some variables, among which the most significant are "GDP per capita", "R&D expenditure public sector", "Venture capital", "Tertiary education", and positively connected to some variables among which the most relevant are: "Government procurement of advanced technology products", "Average annual population growth", "Finance and support", "Human resources", "Marketing or organisational innovators", "Linkages". A clustering was then carried out using the unsupervised k-Means algorithm optimized with the Silhouette coefficient which shows the presence of 2 clusters per value of the Innovation Index. Eight machine learning algorithms has been used for prediction with real data. The Tree Ensemble Regression algorithm has been chosen as best performer. A further prediction has been made with the augmented data. The result shows that the best performing algorithm is Linear Regression with an innovation index value predicted to grow by approximately 3.38%.

Abstract: Supplementation of infant formula with ingredients potentially able to manage, in a way positive for the host, the gut microbiota of infants, is nowadays widely used by infant food producers. The impact of this class of compounds, named prebiotics, on gut microbiota composition, was initially measured on the enrichment of Bifidobacterium and Lactobacillus spp., but the use of culture-independent analytical techniques has allowed to establish that a larger number of intestinal microorganisms are affected by the ingestion of prebiotics. However, despite a relevant number of scientific publications, actually there is no consensus on the amount of prebiotics to be administered daily, the potentially different actions exerted by prebiotics which differ in biochemical structures, as well as the impact of different percentages of the same prebiotics when used in mixtures and/or combination. This paper is aimed to critically review the available data on the use of prebiotics in infant formula, with special attention paid to identify a link between, the dosage used, the mixture composition and the observed outcomes, keeping in mind the influences in nutrition quality and growth influence.

In this article we derive a closed form expression for the symmetric logarithmic derivative of Fermionic Gaussian states. This provides a direct way of computing the quantum Fisher Information for Fermionic Gaussian states. Applications range from quantum Metrology with thermal states to non-equilibrium steady states with Fermionic many-body systems.

The embryonic epicardium originates from the proepicardium, an extracardiac primordium constituted by a cluster of mesothelial cells. In early embryos, the embryonic epicardium is characterized by a squamous cell epithelium resting on the myocardium surface. Subsequently, it invades the subepicardial space and thereafter the embryonic myocardium by means of epithelial-mesenchymal transition. Within the myocardium, epicardial-derived cells present multilineage potential, later differentiating into smooth muscle cells and contributing both to coronary vasculature and cardiac fibroblasts in the mature heart. Over the last decades, we have progressively increased our understanding of those cellular and molecular mechanisms driving proepicardial/embryonic epicardium formation. This study provides a state-of-the-art review of the transcriptional and emerging post-transcriptional mechanisms involved in the formation and differentiation of the embryonic epicardium.

We work with reference to a well-known semiclassical model, in which quantum degrees of freedom interact with classical ones. We show that, in the classical limit, it is possible to represent classical results (e.g., classical chaos) by means a pure-state density matrix.

We investigate the physics of a medium-size quantum particle' gases, of the order of hundreds or thousands of fermions/bosons (far below the thermodynamic limit $N \, \rightarrow \, 10^{24}$). Appeal to microcanonical ensemble becomes then unavoidable. The so-called statistical complexity is our protagonist. Interesting facts are uncovered, both for fermions and bosons, yielded by statistical quantifiers.

The concrete sector is known for its significant contribution to the CO2 emissions. There are two main contributing factors for this situation: the large amount of concrete consumed per year on the planet and the high CO2 released from Portland cement manufacture, the key binding agent in concrete. To face the consequent sustainability issues, diverse strategies have been explored on the carbon capture and storage potential of cementitious materials. This paper addresses the potential of storing CO2 in concrete during the curing stage, applied to the precast Portuguese industry. To this purpose, it was assumed that CO2 will become a waste that will require an outlet in the future, considering that carbon capture will become mandatory in many industries. This work concluded that the net benefit in terms of carbon retention is positive for the process of storing carbon in concrete during the curing stage. More specifically, it was demonstrated that the additional emissions from the introduction of this new operation are only 10% of the stored amount, returning a storage potential of 76 000 tonnes of CO2 yearly. Moreover, the overall net reduction in the concrete life cycle averages 9.4% and 8.8% for precast elements and only non-structural elements, respectively. When a low cement dosage strategy is coupled with carbonation curing technology, the overall carbon net reduction is estimated to be 45%.

The conditions for safe Internet access and the development of skills enabling full participation in online environments are recognized in the Council of Europe’s strategy for child rights, from 2022. The guarantee of this right has implications for experiences inside and outside the school context. Therefore, this article aims to compare the perceptions of students from different educational levels, who participated in a digital storytelling workshop, regarding online safety, searching habits, and digital competences. Data were collected through a questionnaire survey completed by 84 Portuguese students from elementary and secondary schools. A non-parametric multivariate analysis of variance was used to identify differences as children advance across educational stages. The results revealed that secondary students tend to spend more time online, and demonstrated more advanced search skills. Interestingly, the youngest children exhibited higher competences in creating games and practicing safety measures regarding online postings. These findings emphasize the importance of schools, in a joint action with the educational community, including parents, teachers and students, developing a coordinated and vertically integrated approach to digital education that considers the children's current knowledge, attitudes, and skills as a starting point for pedagogical intervention.

Objective: Urinary catheters are known cause of urinary morbidities. The longer the catheter is retained, the greater the risk for contamination and infection. An increasing body of literature suggests routine practice of catheterization and retaining it for 24 hours does not add any procedural advantage. Thus, we sought to study outcomes in relation to early vs. delayed removal of urinary catheters following cesarean section. Methods: We randomly assigned 116 patients into early and delayed removal of urinary catheter groups. In the early group, catheter was removed immediately after the procedure and in the delayed removal group, catheter was removed 24 hours later. Clinical outcomes were measured in terms of significant bacteriuria 72 hours postop, voiding difficulties, urinary retention, mobilization time, length of hospital stay, and patient satisfaction. Results: Study revealed higher incidence of bacteriuria in the delayed removal group (32.8% vs. 15.5%, P = 0.030). Urinary frequency was also higher (34.6% Vs. 8.6%, P=0.001). However, there were no difference between the two groups in other urinary complaints including dysuria and urgency (P = 0.103 & P = 0.087). Urinary retention was more frequent in the early group, but difference was not significant (P = 0.080). Patients with immediate removal of the urinary catheter had early ambulation and early discharge from hospital (P = 0.001 and P = 0.040) and were generally satisfied with the procedure (P= 0.010). Conclusion: Our study showed that immediate removal of urinary catheter was associated with lower urinary complications, shorter length of hospitalization and associated cost.

Background: Mental synthesis is the conscious purposeful process of synthesizing a novel mental image from objects stored in memory. In our everyday use of language, we rely on mental synthesis to communicate an infinite number of images with a finite number of words. In typical children, the timeline of mental synthesis acquisition is highly correlated with an increasing vocabulary. Children with ASD, on the other hand, may learn hundreds of words but never acquire mental synthesis. In these individuals, tests assessing vocabulary comprehension may fail to demonstrate the profound deficit in mental synthesis and the resulting inability to understand flexible syntax and spatial prepositions. Objective: We developed a 20-question parent-reported evaluation tool designed to quantitatively assess mental synthesis ability and to serve as a complimentary scale for Autism Treatment Evaluation Checklist (ATEC). Results: Internal reliability was good (Cronbach’s alpha > .9), and the MSEC exhibited adequate test-retest reliability after a three- and nine-months follow up period. The MSEC results positively correlated with the ATEC communication subscale, providing support for construct validity. Moreover, MSEC scores were significantly different for children of different ASD severity levels confirming the known groups validity. Conclusions: This study represents the first step toward the development of an instrument to measure mental synthesis in children with ASD. Although the current empirical evaluation demonstrated strong evidence of excellent psychometric properties, such as validity and reliability, additional studies should be performed to replicate these findings.

South Italy is characterised by a semi-arid climate with scarce rain and high evaporative demand, therefore the need to optimise water resources in this area is crucial, and climate change could worsen this condition. In citrus cultivation, which is one of the most important crops bred in Southern Italy, and more generally in Mediterranean regions, deficit irrigation strategies are im-plemented in order to cope with limited resource availability. On this basis, knowledge on how the territorial distribution of citrus would change in relation to these strategies constitutes valuable information for the stakeholders. Therefore, the objective of this study was to determine the prob-ability of presence of citrus in Sicily at changing of the percentage of water deficit application, in order to analyse change in the surface area and localisation of the crop. The methodology was based on the application of Species Distribution Models and Geographic Information Systems to the case study of the Province of Syracuse in Sicily. Different geostatistical and machine learning models were applied, based on 3-decades bioclimatic variables, DTM and irrigation; assessment of the outcomes was carried by using classification evaluation metrics. The analysis of the outcomes showed that uncorrelated predictor layers mainly included water input that affected most the probability of presence. Moreover, GIS analyses showed that deficit irrigation strategies would generate an overall reduction of cultivation surfaces in the territory and a decrease of citrus pres-ence in southern areas of the considered territory, where climate conditions are less favourable in terms of temperature and precipitations, thus providing useful information for decision support tools in agriculture and land use policy.

The onset of neurodegenerative diseases involves a complex interplay of pathological mecha-nisms, including protein aggregation, oxidative stress, impaired autophagy. This review focuses on the intricate connection between oxidative stress and autophagy in neurodegenerative disor-ders, highlighting autophagy as pivotal in disease pathogenesis. Reactive oxygen species (ROS) play dual roles in cellular homeostasis and autophagy regulation, with disruptions of redox sig-naling contributing to neurodegeneration. The activation of the Nrf2 pathway represents a criti-cal antioxidant mechanism, while autophagy maintains cellular homeostasis by degrading al-tered cell components. The interaction between p62/SQSTM1, Nrf2 and Keap1 forms a regulatory pathway essential for cellular stress response, whose dysregulation leads to impaired autophagy and aggregate accumulation. Targeting the Nrf2-p62/SQSTM1 pathway holds promise for thera-peutic intervention, mitigating oxidative stress and preserving cellular functions. Additionally, the review explores the potential synergy between the endocannabinoid system and Nrf2 signal-ing for neuroprotection. Further research is needed to elucidate the involved molecular mecha-nisms and develop effective therapeutic strategies against neurodegeneration.

Despite the efforts made to stop the tuberculosis (TB) epidemic, it still remains one of the leading causes of death from an infectious disease. Previous work in the group uncovered a new family of amides which showed promising activities against Mycobacterium tuberculosis. A closer look at the literature showed that these compounds are structurally related to the DNB family of DprE1 inhibitors, so, we decided to study a wide range of substituted amides and determine their activity, focusing on unexplored structures related to the dinitrobenzamides (DNB) found in the literature. To synthesize our library of compounds we started from 3,5-dinitrobenzoic acid to form the nitroaromatic core that is characteristic of the DNB’s, to which we then added linear or cyclic amine moieties. Additionally, the impact of terminal aromatic moieties was also assessed for some derivatives, via an ether, ester, or amide bond. In order to obtain the desired derivatives, multiple synthetic approaches were used, mainly focused on nucleophilic addition/elimination reactions, SN2 reactions and Mitsunobu reactions. The activity was impacted mainly by two structural features, the addition of an aromatic moiety as a terminal group and the type of the linker. The most interesting compounds exhibited activities comparable to isoniazid and DNB1. Computational studies were also performed aimed at understanding their possible interactions with DprE1. The most active compounds are a good starting point for further development, and we plan to study an extended family of compounds based in those structures.

Coronavirus disease-2019 (COVID-19) was seen as a respiratory disease, however, an increasing number of reports indicated that the spike protein could also be the cause of the long-term post-infectious conditions known as Long-COVID characterized by a group of unresponsive idiopathic severe neuro, cardio-vascular disorders, including strokes, cardiopathies, neuralgias, fibromyalgia, and Parkinson's like-disease. Different lines of pieces of evidence confirmed that the spike protein that can be found on the surface of the SARS-CoV-2 virus latches onto angiotensin-converting enzyme 2 (ACE2) receptors located on target cells. The RNA genome of coronaviruses, which, has a median length of 29 kb and is the longest among all RNA viruses, is comprised of six to ten open reading frames (ORFs) that are responsible for encoding both the replicase and structural proteins for the virus. Each of the components of the viral genome is packaged into a helical nucleocapsid that is surrounded by a lipid bilayer. The viral envelope of coronaviruses is typically made up of three proteins that include the membrane protein (M), the envelope protein (E), and the spike protein (S). The spike protein not only facilitates the virus entry into healthy cells, which is the first step in infection but also promote profound damage to different organs and tissues leading to severe impairments and long-term disabilities. Here, we discussed the pervasive mechanism that spikes mRNA adopted to alter multipotent and pluripotent stem cell (SCs) genomes and the acquired disability of generating an infinite number of affected clonal cells. This stance is based on the molecular and evolutionary aspects obtained from retrotransposons-retrotransposition in mammalians and humans that documented the frequent integration of mRNA molecules into genomes and thus into DNA. Retrotransposition is the molecular process in which transcribed and spliced mRNAs are accidentally reverse-transcribed and inserted into new genomic positions to form a retrogene. Sequence-specific traits of mRNA clearly showed long interspersed element-1 (LINE-1 or L1) to confirm the retrotransposition, considered the most abundant autonomously active retrotransposons in the human genome. In mammals, L1 retrotransposons drive retrotransposition and are composed of long terminal repeats (LTRs) and non-LTR retrotransposons (mainly long interspersed nuclear elements or LINEs); specifically, the LTR-mediated retrocopies are immediately cotranscribed with their flanking LTR retrotransposons. In response to retrotransposons transposition, stem cells (SCs) employ a number of silencing mechanisms, such as DNA methylation and histone modification. This manuscript theorizes the expression patterns, functions, and regulation of mRNA Spike protein imprinted by SCs retrotransposons which generate unlimited lines of affected cell progenies and tissues as the main condition of untreatable Spike-related inflammatory conditions.

It is widely known that vitamin D receptors have been found in neurons and glial cells and their highest expression is in the hippocampus, hypothalamus, thalamus and subcortical grey nuclei, and substantia nigra. The vitamin D helps the regulation of neurotrophin, neural differentiation and maturation, through the control operation of growing factors synthesis (ie NGF and GDNF), the trafficking of the septo-hyppocampal pathway, and the control of the synthesis process of different neuromodulators (such as Ach, DA, and GABA). Based on these assumptions, we have written this review in order to summarize the potential role of vitamin D in neurological pathologies. The work could be titanic, and might result very fuzzy and even incoherent, if we would not have conjectured to taper our first intentions and devoted our interests towards three mainstreams: demyelinating pathologies, vascular syndromes and neurodegeneration. Due to the lack of effective therapeutic options, a part from the disease modifying strategies, the role of different risk factors should be investigated in neurology, as far as their correction may lead to the improvement of the cerebral conditions. We have explored the relationships between the gene-environmental influence and long term vitamin D deficiency, as a risk factor for the development of different types of neurological disorders, along with the role and the rationale of therapeutic trials with vitamin D implementation.

Forest community connections are crucial to ensure forest stewardship and sustainability. We explored the potential of mushrooming to enable such connections in contexts where these connections have been historically broken, alienating local people from forests. Taking the case of the recent devolution of a community forest (baldios) in central Portugal to the local population, we present a five-year pilot project to rework mycology from a mushroom-centered approach to a mushroom-in-baldios approach. Mushrooms were used as an entry-point to connect the forest ecology with the challenges of governance and community building. The devised activities provided an opportunity for people inside and outside the local community to adventure into the woods and find out more about their socio-ecological history, develop communal and convivial relationships and engage in the responsible gathering of wild mushrooms. However, the hosting of mushroomers to know, value and engage with the community forest recovery has constantly working against the enclosure of mushrooms to provide marketable forms of leisure. The outcome of these activities depends on the relationships established between mushrooms, mycologists, local administrators, commoners and poachers, all operating within a framework that favors the eradication of resources instead of long-term relationships that sustain places.

MicroRNAs are small ribonucleotides which act as key gene regulators. Their altered expression is often associated with onset and progression of several human diseases, including cancer. Given their potential use as biomarkers, there is a need to find detection methods of microRNAs suitable for use in clinical setting. Field Effect Transistor based biosensors (bioFETs) appear to be valid tools to detect microRNAs, since they may reliably quantitate the specific binding between the immobilized probe and free target in solution through an easily detectable electrical signal. We have investigated the detection of human microRNA 155 (miR-155) using an innovative cap-turing probe constituted by a synthetic peptide nucleic acid (PNA), which has the advantage to form a duplex even at ionic strengths approaching the physiological conditions. With the aim to develop an optimized BioFET setup, the interaction kinetics between miR-155 and the chosen PNA was preliminary investigated by Surface Plasmon Resonance (SPR). By exploiting both these results and our custom-made bioFET system, we were able to attain a low-cost, real-time, label-free, highly specific detection of miR-155 in the nano-molar range.

Introduction: Due to the great development of digital technology, through CAD (Computer - Aided Design) and CAM (Computer - Aided Manufacturing) systems, digital models could be used in orthodontic treatment planning decision-making, as there are numerous studies in the literature that support the validity of measurements of digital models of anterior teeth and the total coefficient of Bolton analysis. The aim of the study was to compare the average length value of the current upper and lower arches with that of a hypothetical nickel-titanium wire and the accuracy of the Bolton index measurement. In the present study, 138 dental casts were analyzed using Ortho3Shape software. Using the Ortho3Shape software, it was possible to measure the real and ideal lower arch lengths and, with regard to Bolton analysis, the values of the anterior and total Bolton coefficients. By comparing the values obtained with those of the study by A. Anand Kumar et al. using CBCT and plaster casts, the reliability of the measurements obtained with such CAD/CAM orthodontic software was evaluated. Considering the validity of digital measurements demonstrated by the studies and systematic reviews in the literature, it can be stated that CAD/CAM digital models can be a viable alternative to plaster models in the coming years, as they can facilitate model preservation and retrieval. For future studies and research, it would be preferable to use intra-oral scanners (IOS) in order to ensure greater accuracy, requiring only one step and ensuring a better outcome for the patient.

Chitosan is a biopolymer derived from chitin. It is a non-toxic, biocompatible, bioactive, and biodegradable polymer. Due to its properties, chitosan has found applications in several and different fields such as agriculture, food industry, medicine, paper fabrication, textile industry, and water treatment. In addition to these properties, chitosan has a good film-forming ability which allows it to be widely used for the development of sensors and biosensors. This review is focused on the use of chitosan for the formulation of electrochemical chemosensors. It also aims to provide an overview of the advantages of using chitosan as an immobilization platform for biomolecules by highlighting its applications in electrochemical biosensors. Finally, applications of electro-chemical chitosan-based chemosensors and biosensors in food safety are illustrated

Understanding the dynamics of Organic Carbon mineralization is fundamental in forecasting biosphere to atmosphere Net Carbon Ecosystem Exchange (NEE). With this perspective, we developed 3D-CMCC-PSM, a new version of the hybrid Process Based Model 3D‐CMCC FEM where also heterotrophic respiration (R_h) is explicitly simulated. The aim was to quantify NEE as a forward problem, by subtracting Ecosystem Respiration (R_eco) to Gross Primary Productivity (GPP). To do so, we developed a simplification of the Soil Carbon dynamics routine proposed in DNDC [1]. The method calculates decomposition as a function of soil moisture, temperature, state of the organic compartments, and relative abundance of microbial pools. Given the pulse dynamics of soil respiration, we introduced modifications in some of the principal constitutive relations involved in phenology and littering sub-routines. We quantified the model structure related uncertainty in NEE, by running our training simulations over 1000 random parameter-sets extracted from parameters distributions expected from literature. 3D-CMCC-PSM predictability was tested on independent time series for 6 Fluxnet sites. The model resulted in daily and monthly estimations highly consistent with the observed time series. It showed lower predictability in Mediterranean ecosystems, suggesting that it may need further improvements in addressing evapotranspiration and water dynamics.

In this work we explored how solvents can affect olefin oxidation reactions catalyzed by MCM-bpy-Mo catalysts and whether their control can be made with those players. The results of this study evidenced that polar and apolar aprotic solvents modulated the reactions in different ways. Experimental data showed that acetonitrile (aprotic polar) could hinder largely the reaction rate whereas toluene (aprotic apolar) did not. In both cases product selectivity at isoconversion was not affected. Further insights were obtained by means of neutron diffraction experiments, which confirmed the kinetic data allowing to propose a model based on substrate-solvent crosstalk by means of hydrogen bonding. In addition, the model was also validated in the ring-opening reaction (overoxidation) of styrene oxide towards benzaldehyde, which progressed when toluene was the solvent (reaching 31% styrene oxide conversion) but was strongly hindered when acetonitrile was used instead (reaching only 7% conversion), due to the establishment of H-bonds in the latter. Although this model was confirmed and validated for olefin oxidation reactions, it can be envisaged that it may also be applied to other catalytic reaction systems where reaction control is critical, while widening its use.

In recent years, the discharge of various emerging pollutants, chemicals and dyes in water and wastewater has represented one of the prominent human problems. Since water pollution is directly related to human health, highly resistant and emerging compounds in aquatic environments will pose many potential risks to the health of all living. Therefore, water pollution is a very acute problem that has constantly increased in recent years with the expansion of various industries. Consequently, choosing efficient and innovative wastewater treatment methods to remove contaminants is crucial. Among advanced oxidation processes, electrochemical oxidation (EO) is the most common and effective method for removing persistent pollutants from municipal and industrial wastewater. However, there are still many gaps despite the great progress in using EO to treat real wastewater. This is due to the lack of comprehensive information on the operating parameters which affect the process and its operating costs. In this paper, among various scientific articles, the impact of operational parameters on the EO performances, a comparison between different electrochemical reactor configurations, and a report on general mechanisms of electrochemical oxidation of organic pollutants have been reported. Moreover, an evaluation of cost analysis and energy consumption requirements have also been discussed. Finally, the combination process between EO and another important advanced oxidation technology, PEC, called photoelectrocatalysis (PEC), has shortly been discussed and reviewed. This article showed that there is a direct relationship between important operating parameters with the amount of costs and the final removal efficiency of emerging pollutants. Optimal operating conditions can be achieved by paying special attention to reactor design, which can lead to higher efficiency and more efficient treatment. The rapid development of EO for removing emerging pollutants from impacted water and its combination with other green methods can result in more efficient approaches to face the pressing water pollution challenge.

Mountains can be described as socio-ecological systems (SESs), a complex set of relations that involve natural aspects and human communities. Mountains provide indispensable ecosystem services, but they are affected by climate change. For this, stakeholders’ initiatives to face global warming, particularly farmers, are essential and participatory strategy are largely considered as a best solution to involve them in adopting resilience actions. Despite that, farmers are considered passive actors and their epistemology weak. Investigating mountain actors' vulnerability to climate change in the North of Molise (an Italian region), called «Alto Molise», we suggest considering farmers agency as a result of interconnections among heterogeneous elements present in the SES and their “lay” knowledge and epistemology equally relevant that experts one. Farmers’ perception of climate change impact is appropriate, although they lack a clear understanding of it. Their resilience actions or suggestions are coherent with their resources endowment (financial and knowledge) and their position in the economic system but not necessarily effective. The work contributes to the debate on climate resilience in mountain areas stressing the significance of local actors' agency, the presence of different epistemologies (lay and expert one) and the need to actively involve them in designed effective and suitable initiatives to face climate change.

Mediterranean climate is marked by arid climate conditions in summer, therefore, crop irrigation is crucial to sustain plant growth and productivity in this season. If groundwater is utilized for irrigation, an impressive water pumping is needed to satisfy crop water requirements at catchment scale. Consequently, irrigation water quality gets worse, specifically considering groundwater salinization near the coastal areas due to seawater intrusion, also triggering soil salinization. With reference to an agricultural coastal area in the Mediterranean basin (Southern Italy), close to the Adriatic sea, an assessment of soil salinization risk due to processing tomato cultivation was carried out. A simulation model was arranged to perform, on daily basis, a water and salt balance along the soil profile. Long-term weather data and soil physical parameters representative of the considered area were utilized in applying the model, also considering three salinity levels of irrigation water. Based on the climatic analysis performed and the model outputs, the probability of soil salinity came out very high, such as to seriously threaten tomato yield. Autumn-winter rainfall resulted frequently insufficient to leach excess salts away from the soil profile and reach sustainable conditions of tomato cultivation. Therefore, alternative cropping strategies were prospected.