The Fluidization Pattern of Density-Segregating Two-Solid Beds

An experimental work is presented meant to clarify the specific role played by density differences between components in the segregating fluidization process of two-solid beds. The overall behaviour of such systems is characterized by substituting the traditional concept of “minimum fluidization velocity” of the binary mixture with the “velocity interval of fluidization” of the bed, which is limited by its “initial” and “final” fluidization velocity”. The dependence of these characteristic velocities on parameters such as component densities and mixture composition is illustrated by several series of experiments. The experimental results are analysed in the light of the fundamental theory, so as to establish quantitative relationships for their prediction. The evolution of the axial profile of component concentration at varying fluidization velocity is also discussed

Genetic transformation of Vitis vinifera via organogenesis

BACKGROUND: Efficient transformation and regeneration methods are a priority for successful application of genetic engineering to vegetative propagated plants such as grape. The current methods for the production of transgenic grape plants are based on Agrobacterium-mediated transformation followed by regeneration from embryogenic callus. However, grape embryogenic calli are laborious to establish and the phenotype of the regenerated plants can be altered. RESULTS: Transgenic grape plants (V. vinifera, table-grape cultivars Silcora and Thompson Seedless) were produced using a method based on regeneration via organogenesis. In vitro proliferating shoots were cultured in the presence of increasing concentrations of N(6)-benzyl adenine. The apical dome of the shoot was removed at each transplantation which, after three months, produced meristematic bulk tissue characterized by a strong capacity to differentiate adventitious shoots. Slices prepared from the meristematic bulk were used for Agrobacterium-mediated transformation of grape plants with the gene DefH9-iaaM. After rooting on kanamycin containing media and greenhouse acclimatization, transgenic plants were transferred to the field. At the end of the first year of field cultivation, DefH9-iaaM grape plants were phenotypically homogeneous and did not show any morphological alterations in vegetative growth. The expression of DefH9-iaaM gene was detected in transgenic flower buds of both cultivars. CONCLUSIONS: The phenotypic homogeneity of the regenerated plants highlights the validity of this method for both propagation and genetic transformation of table grape cultivars. Expression of the DefH9-iaaM gene takes place in young flower buds of transgenic plants from both grape cultivars

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)

Analgesia induced by the epigenetic drug, L-acetylcarnitine, outlasts the end of treatment in mouse models of chronic inflammatory and neuropathic pain

Background: L-acetylcarnitine, a drug marketed for the treatment of chronic pain, causes analgesia by epigenetically up-regulating type-2 metabotropic glutamate (mGlu2) receptors in the spinal cord. Because the epigenetic mechanisms are typically long-lasting, we hypothesized that analgesia could outlast the duration of L-acetylcarnitine treatment in models of inflammatory and neuropathic pain. Results: A seven-day treatment with L-acetylcarnitine ( 100 mg/kg, once a day, i.p.) produced an antiallodynic effect in the complete Freund adjuvant mouse model of chronic inflammatory pain. L-Acetylcarnitine-induced analgesia persisted for at least 14 days after drug withdrawal. In contrast, the analgesic effect of pregabalin, amitryptiline, ceftriaxone, and N-acetylcysteine disappeared seven days after drug withdrawal. L-acetylcarnitine treatment enhanced mGlu2/3 receptor protein levels in the dorsal region of the spinal cord. This effect also persisted for two weeks after drug withdrawal and was associated with increased levels of acetylated histone H3 bound to the Grm2 gene promoter in the dorsal root ganglia. A long-lasting analgesic effect of L-acetylcarnitine was also observed in mice subjected to chronic constriction injury of the sciatic nerve. In these animals, a 14-day treatment with pregabalin, amitryptiline, tramadol, or L-acetylcarnitine produced a significant antiallodynic effect, with pregabalin displaying the greatest efficacy. In mice treated with pregabalin, tramadol or L-acetylcarnitine the analgesic effect was still visible 15 days after the end of drug treatment. However, only in mice treated with L-acetylcarnitine analgesia persisted 37 days after drug withdrawal. This effect was associated with an increase in mGlu2/3 receptor protein levels in the dorsal horns of the spinal cord. Conclusions: Our findings suggest that L-acetylcarnitine has the unique property to cause a long-lasting analgesic effect that might reduce relapses in patients suffering from chronic pain

Sequence-specific modification of a β-thalassemia locus by small DNA fragments in human erythroid progenitor cells

Gene therapy has been proposed as a definitive cure of beta-thalassemia. We applied a gene targeting approach, based on the introduction of small DNA fragments (SDF) into erythroid progenitor cells, to specifically modify the beta-globin gene sequence at codon 39. The strategy was first tested in normal individuals by delivering mutant SDF that were able to produce the beta-39 (C-T) mutation. Secondly, wild-type SDF were electroporated into target cells of beta-3i9/beta-39 b-thalassemic patients to correct the endogenous mutation. In both cases, gene modification was assayed by allele-specific polymerase chain reaction of DNA and mRNA, by restriction fragment length polymorphism analysis and by direct sequencing

Boosting the Performance of One-Step Solution-Processed Perovskite Solar Cells Using a Natural Monoterpene Alcohol as a Green Solvent Additive

The perovskite film is the core of a perovskite solar cell (PSC), and its quality is crucial for the performance of such devices. The morphology, crystallinity, and surface coverage of the perovskite layer greatly affect the power conversion efficiency (PCE), hysteresis, and long-term stability of PSCs. The incorporation of appropriate solvent additives in the perovskite precursor solution is an effective strategy to control the film morphology and reduce the defects and grain boundaries. However, the commonly used solvent additives are environmentally harmful and highly toxic. In this work, α-terpineol (a nontoxic, eco-friendly, and low-cost monoterpene alcohol) is employed for the first time as an alternative green solvent additive to improve the quality of one-step solution-processed CH3NH3PbI3–xClx films and to restrain nonradiative recombination in the corresponding devices. An in-depth investigation of the physicochemical effects induced by such a high-boiling-point, polar protic solvent when incorporated into a conventional perovskite solvent system is provided. The collected data demonstrate that the addition of a precise amount of α-terpineol can generate uniform and highly crystalline perovskite films with improved photovoltaic performances. Through this approach, the PCE of planar n–i–p PSCs is boosted up to 17.5% (against 16.1% of the top control device) with reduced hysteresis and enhanced ambient stability

Exciton dynamics in hybrid polymer/QD blends

Abstract The prospect of exploiting quantum dots (QDs) properties (tunable absorption spectrum, multiple exciton generation) while maintaining the flexible structure of polymer systems opens new possibilities in the photovoltaic field. Although charge transport dynamics in pristine polymer and QDs systems have been quite well established lately, a complete understanding of the charge transfer process between QDs and polymers when they are in blends is still lacking. In this work we used static and ultrafast fluorescence spectroscopy together with Atomic force Microscopy (AFM) to study the exciton dynamics in polymer/QDs films. Specifically we used poly(3-hexylthiophene) (P3HT) as the hole conducting donor material and the core shell CdSe(ZnS) QDs as the electron acceptor material. The QDs surface has been treated with two different capping ligands treatments: one based on the use of pyridine and the other one on hexanoic acid. The influence of the two different methods on the exciton dynamics and on the morphology will also be discussed. Blends containing differently treated P3HT/CdSe(ZnS) wt% ratios have been prepared producing films having uniform morphology and good intermixing, as proved by AFM measurements. Ultrafast fluorescence decays allowed us to compare the exciton dynamics in the polymer pristine respect to the treated P3HT/CdSe(ZnS) films. Efficient fluorescence quenching has been shown by both kind of blends respect to the pure polymer

Editorial: Advances in genetic engineering strategies for fruit crop breeding, volume II

Breeding of fruit crops is a challenging process that must consider the need to preserve the characteristics of elite cultivars and the urgent need to obtain new cultivars with high resilience and high productivity. Changes in climate and the progressive limits on the use of agrochemicals may require the development of new genetic stocks in relatively short periods of time. Classical and innovative genetic engineering approaches may help achieve these goals. The second volume of this Research Topics aimed to present an update on the genetic tools available for the breeding of fruits crops for new traits. The articles of this Research Topic represent well the opportunities offered by genetic engineering to future fruit crop breeding

Compensatory growth following long term multi-phase cyclic feeding in rainbow trout (Oncorhynchus mykiss)

Compensatory growth (CG) during recovery from feed deprivation is a well-known phenomenon in fish, making the practice of cyclic feed restriction-refeeding a possible tool for aquaculturists to optimize growth performance. While earlier studies in this direction focused on relatively short single feed restriction-refeeding protocols, the present trial was designed to evaluate the impact of different repeated cyclic feeding schemes on the zootechnical response of rainbow trout (O. mykiss) over a complete growing phase up to the commercial size. Three hundred trout (body weight 72\ub16 g) were randomly distributed among 12 tanks, each of 0.5 m3 capacity and supplied with 8 L min-1 of well water at a temperature of 12.7\ub10.8\ub0C. Triplicated groups of fish were subjected over 27 weeks to one of the following treatments: C, control, continuous feeding to visual satiety 6 days a week; T1, cyclic feeding regularly alternating 1 week starvation (S) and 3 weeks refeeding (F) (1S+3F); T2, cyclic feeding consisting in 3 consecutive phases: 1S+3F, 2S+6F, and 3S+12F; T3, where a feed restriction (70% of the satiety level observed in the previous week) was applied instead of starvation with the same schedule as T2. The same trout feed (45% crude protein, 28% crude lipid) was used throughout the trial. At the end of the trial the different cyclic feeding protocols resulted in the same zootechnical outcome (P>0.05). A nearly complete convergence of body mass was evident as no significant differences were found among treatments in individual weight (543\ub128g), specific growth rate (1.06\ub10.03%), feed conversion ratio (0.84\ub10.03) and protein efficiency ratio (2.64\ub10.12) despite a lower feed consumption in treatments T1, T2 and T3 relative to controls (357 vs. 390 g fish-1, P<0.05). All protocols imposing fasting or feed restriction resulted in CG at the end of each re-feeding phase. Hyperphagia was a major cause of CG. Both phenomena were emphasized with treatment T3 after recovering from the last 3-week fasting period, when they were associated to a marked improvement of feed conversion ratio relative to controls (0.75 vs. 0.85, P<0.05). The results obtained so far suggest repeated cyclic feeding as a reliable practice in trout farming, provided fasting or feed restriction periods are followed by refeeding phases of suitable length to allow recovery of body mass. This could result in improved profitability and environmental sustainability