Efficiency for Vector Variational Quotient Problems with Curvilinear Integrals on Riemannian Manifolds via Geodesic Quasiinvexity

In the paper, we analyze the necessary efficiency conditions for scalar, vectorial and vector fractional variational problems using curvilinear integrals as objectives and we establish sufficient conditions of efficiency to the above variational problems. The efficiency sufficient conditions use of notions of the geodesic invex set and of (strictly, monotonic) ( ρ , b)-geodesic quasiinvex functions

Artificial Intelligence for Managing the Complexity of the Socio-Economic Systems towards Horizon 2020 and Agenda 2030

The seeds of modern Artificial Intelligence (AI) were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. AI is an area of computer science that emphasises the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech Recognition, Learning, Planning, Problem Solving and Fuzzy Sets. In the past 15 years, Amazon, Google  and others leveraged machine learning to their huge commercial advantage. In this talk, we discuss Machine learning, and Fuzzy Settings theory. Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. Fuzzy Modelling helps us to deal with the phenomena including uncertain parameters and conditions. It gives us enough tools to model a real-world system and approaches our behaviour much closer. The Fuzzy Set represent a class of objects with a continuum of grades of membership. So the above framework of consideration gives us a natural way of dealing with imprecise phenomena, when classes of objects lack precise criteria of membership for their elements. The context to be considered as scientific fertile ground concerns the management of the complexity of modern anthropic socio-economic systems (Cities, Urban areas and their socio-sustainable development). Intelligenza artificiale per la gestione della complessità dei sistemi socio-economici verso Horizon 2020 e Agendo 2030I semi dell'Intelligenza Artificiale (AI) furono piantati da filosofi classici che tentarono di descrivere il processo del pensiero umano come la manipolazione meccanica dei simboli. L'AI è un'area di informatica che enfatizza la creazione di macchine intelligenti che funzionano e reagiscono come gli umani. Alcune delle attività che i computer con AI possono progettate includono: riconoscimento vocale, apprendimento, pianificazione, problem solving e set fuzzy. Negli ultimi 15 anni, Amazon, Google e altri hanno sfruttato l'apprendimento automatico per il loro enorme vantaggio commerciale. L'apprendimento automatico alla base dell'AI è la pratica dell'uso di algoritmi per analizzare i dati al fine di fare una determinazione o una previsione su qualche fenomeno. La modellazione fuzzy ci aiuta ad affrontare i fenomeni inclusi i parametri e le condizioni incerti, ci fornisce strumenti per modellare il sistema considerato nel mondo reale e avvicinarci molto più al suo comportamento. Il set fuzzy, quindi, rappresenta una classe di oggetti con un continuum di gradi di appartenenza. Il quadro sopra descritto ci dà un modo naturale di affrontare fenomeni così imprecisi, quando le classi di oggetti mancano di criteri precisi di adesione per i loro elementi. Il contesto da consideare terreno fertile scientico concerne la gestione della complessità dei moderni sistemi antropici socio-economici (città, aree urbane e il loro sviluppo socio-sostenibile).The seeds of modern Artificial Intelligence (AI) were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. AI is an area of computer science that emphasises the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech Recognition, Learning, Planning, Problem Solving and Fuzzy Sets. In the past 15 years, Amazon, Google  and others leveraged machine learning to their huge commercial advantage. In this talk, we discuss Machine learning, and Fuzzy Settings theory. Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. Fuzzy Modelling helps us to deal with the phenomena including uncertain parameters and conditions. It gives us enough tools to model a real-world system and approaches our behaviour much closer. The Fuzzy Set represent a class of objects with a continuum of grades of membership. So the above framework of consideration gives us a natural way of dealing with imprecise phenomena, when classes of objects lack precise criteria of membership for their elements. The context to be considered as scientific fertile ground concerns the management of the complexity of modern anthropic socio-economic systems (Cities, Urban areas and their socio-sustainable development). Intelligenza artificiale per la gestione della complessità dei sistemi socio-economici verso Horizon 2020 e Agenda 2030I semi dell'Intelligenza Artificiale (AI) furono piantati da filosofi classici che tentarono di descrivere il processo del pensiero umano come la manipolazione meccanica dei simboli. L'AI è un'area di informatica che enfatizza la creazione di macchine intelligenti che funzionano e reagiscono come gli umani. Alcune delle attività che i computer con AI possono progettate includono: riconoscimento vocale, apprendimento, pianificazione, problem solving e set fuzzy. Negli ultimi 15 anni, Amazon, Google e altri hanno sfruttato l'apprendimento automatico per il loro enorme vantaggio commerciale. L'apprendimento automatico alla base dell'AI è la pratica dell'uso di algoritmi per analizzare i dati al fine di fare una determinazione o una previsione su qualche fenomeno. La modellazione fuzzy ci aiuta ad affrontare i fenomeni inclusi i parametri e le condizioni incerti, ci fornisce strumenti per modellare il sistema considerato nel mondo reale e avvicinarci molto più al suo comportamento. Il set fuzzy, quindi, rappresneta una classe di oggetti con un continuum di gradi di appartenenza. Il quadro sopra descritto ci dà un modo naturale di affrontare fenomeni così imprecisi, quando le classi di oggetti mancano di criteri precisi di adesione per i loro elementi. Il contesto da consideare terreno fertile scientico concerne la gestione della complessità dei moderni sistemi antropici socio-economici (città, aree urbane e il loro sviluppo socio-sostenibile)

Neuroprotective Potential of Dendritic Cells and Sirtuins in Multiple Sclerosis

Myeloid cells, including parenchymal microglia, perivascular and meningeal macrophages, and dendritic cells (DCs), are present in the central nervous system (CNS) and establish an intricate relationship with other cells, playing a crucial role both in health and in neurological diseases. In this context, DCs are critical to orchestrating the immune response linking the innate and adaptive immune systems. Under steady-state conditions, DCs patrol the CNS, sampling their local environment and acting as sentinels. During neuroinflammation, the resulting activation of DCs is a critical step that drives the inflammatory response or the resolution of inflammation with the participation of different cell types of the immune system (macrophages, mast cells, T and B lymphocytes), resident cells of the CNS and soluble factors. Although the importance of DCs is clearly recognized, their exact function in CNS disease is still debated. In this review, we will discuss modern concepts of DC biology in steady-state and during autoimmune neuroinflammation. Here, we will also address some key aspects involving DCs in CNS patrolling, highlighting the neuroprotective nature of DCs and emphasizing their therapeutic potential for the treatment of neurological conditions. Recently, inhibition of the NAD+ -dependent deac(et)ylase sirtuin 6 was demonstrated to delay the onset of experimental autoimmune encephalomyelitis, by dampening DC trafficking towards inflamed LNs. Thus, a special focus will be dedicated to sirtuins’ role in DCs functions

An integrated mode to assess service volleyball among power glove, video analysis and testing

Technological development in recent times has had a rapid spread in various sports to have an improvement in equipment, training methods and performance of athletes through the use of tools. Although in volleyball a good use of video analysis is expected, it is not connected with other technologies. Previous studies have shown that the use of the technological tool, Power Glowe, is able to collect information on force, precision, time impact and direction of the hit when jumping and on the ground. The purpose of the study is to connect and compare the functions of video analysis with those related to the Power Glowe with the actual data on strength, speed, accuracy and tests to assess the effectiveness of the service on a sample divided into 2 groups of female athletes, one from 12 to 16 years and the other from 18 to 26 years old with the aim of comparing the data recruited in a different way. In conclusion, we want to integrate individual methods of data recruitment to optimize the analysis and consequently the evaluation

Evaluating growth and intrinsic water-use efficiency in hardwood and conifer mixed plantations

Abstract Key message Juglans, Fraxinus, Quercus and Pinus species seem to better maximize the carbon–water ratio providing useful indications on species selection for forestry plantations in areas with increasing drought risk. Abstract Maximizing carbon sequestration for a given water budget is extremely important in the contest of climate change in the Mediterranean region, which is characterized by increasing temperatures and rising water stress. This issue is fundamental for plantation stands, where limited water availability during the growing season reduces CO2 assimilation and, consequently, tree growth. In this study, the main objective was to investigate the performances in terms of carbon–water balance of conifer (Pinus halepensis and Cupressus sempervirens) and hardwood (Quercus robur, Juglans regia, Fraxinus excelsior and Populus spp.) mixed plantations. To this aim, we used carbon isotope signatures to evaluate the intrinsic water-use efficiency (iWUE) and the species-specific relationship between basal area increments (BAI) and iWUE. At the species level, the highest iWUE values corresponded to the lowest carbon accumulation in terms of BAI, for water-saving species such as Cupressus. Conversely, Populus had the lowest iWUE and the highest BAI accumulation. Juglans, Fraxinus, and Pinus showed the most balanced ratio between BAI and iWUE. Overall, no clear correlation of iWUE and BAI was evident within all species, except for Populus and Cupressus. Considering projected aridification and increased temperatures that will negatively impact the growth, our data suggest that Pinus, for conifers, and Quercus, Juglans, Fraxinus for hardwood species should be preferred when choosing species for forestry plantation, as they performed better in terms of BAI and iWUE ratio

Genotoxicity and epigenotoxicity of carbazole-derived molecules on mcf-7 breast cancer cells

The carbazole compounds PK9320 (1-(9-ethyl-7-(furan-2-yl)-9H-carbazol-3-yl)-N-methylmethanamine) and PK9323 (1-(9-ethyl-7-(thiazol-4-yl)-9H-carbazol-3-yl)-N-methylmethanamine), second-generation analogues of PK083 (1-(9-ethyl-9H-carbazol-3-yl)-N-methylmethanamine), restore p53 signaling in Y220C p53-mutated cancer cells by binding to a mutation-induced surface crevice and acting as molecular chaperones. In the present paper, these three molecules have been tested for mutant p53-independent genotoxic and epigenomic effects on wild-type p53 MCF-7 breast adenocarcinoma cells, employing a combination of Western blot for phospho-γH2AX histone, Comet assay and methylation-sensitive arbitrarily primed PCR to analyze their intrinsic DNA damageinducing and DNA methylation-changing abilities. We demonstrate that small modifications in the substitution patterns of carbazoles can have profound effects on their intrinsic genotoxic and epigenetic properties, with PK9320 and PK9323 being eligible candidates as “anticancer compounds” and “anticancer epi-compounds” and PK083 a “damage-corrective” compound on human breast adenocarcinoma cells. Such different properties may be exploited for their use as anticancer agents and chemical probes

A large scale hearing loss screen reveals an extensive unexplored genetic landscape for auditory dysfunction

The developmental and physiological complexity of the auditory system is likely reflected in the underlying set of genes involved in auditory function. In humans, over 150 non-syndromic loci have been identified, and there are more than 400 human genetic syndromes with a hearing loss component. Over 100 non-syndromic hearing loss genes have been identified in mouse and human, but we remain ignorant of the full extent of the genetic landscape involved in auditory dysfunction. As part of the International Mouse Phenotyping Consortium, we undertook a hearing loss screen in a cohort of 3006 mouse knockout strains. In total, we identify 67 candidate hearing loss genes. We detect known hearing loss genes, but the vast majority, 52, of the candidate genes were novel. Our analysis reveals a large and unexplored genetic landscape involved with auditory function

Air Quality Characterization at Three Industrial Areas in Southern Italy

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Measuring collective action intention toward gender equality across cultures

Collective action is a powerful tool for social change and is fundamental to women and girls’ empowerment on a societal level. Collective action towards gender equality could be understood as intentional and conscious civic behaviors focused on social transformation, questioning power relations, and promoting gender equality through collective efforts. Various instruments to measure collective action intentions have been developed, but to our knowledge none of the published measures were subject to invariance testing. We introduce the gender equality collective action intention (GECAI) scale and examine its psychometric isomorphism and measurement invariance, using data from 60 countries (N = 31,686). Our findings indicate that partial scalar measurement invariance of the GECAI scale permits conditional comparisons of latent mean GECAI scores across countries. Moreover, this metric psychometric isomorphism of the GECAI means we can interpret scores at the country-level (i.e., as a group attribute) conceptually similar to individual attributes. Therefore, our findings add to the growing body of literature on gender based collective action by introducing a methodologically sound tool to measure collective action intentions towards gender equality across cultures