Potential of hydrogen addition to natural gas or ammonia as a solution towards low- or zero-carbon fuel for the supply of a small turbocharged SI engine

Nowadays there is an increasing interest in carbon-free fuels such as ammonia and hydrogen. Those fuels, on one hand, allow to drastically reduce CO2 emissions, helping to comply with the increasingly stringent emission regulations, and, on the other hand, could lead to possible advantages in performances if blended with conventional fuels. In this regard, this work focuses on the 1D numerical study of an internal combustion engine supplied with different fuels: pure gasoline, and blends of methane-hydrogen and ammonia-hydrogen. The analyses are carried out with reference to a downsized turbocharged two-cylinder engine working in an operating point representative of engine operations along WLTC, namely 1800 rpm and 9.4 bar of BMEP. To evaluate the potential of methane-hydrogen and ammonia-hydrogen blends, a parametric study is performed. The varied parameters are air/fuel proportions (from 1 up to 2) and the hydrogen fraction over the total fuel. Hydrogen volume percentages up to 60% are considered both in the case of methane-hydrogen and ammonia-hydrogen blends. Model predictive capabilities are enhanced through a refined treatment of the laminar flame speed and chemistry of the end gas to improve the description of the combustion process and knock phenomenon, respectively. After the model validation under pure gasoline supply, numerical analyses allowed to estimate the benefits and drawbacks of considered alternative fuels in terms of efficiency, carbon monoxide, and pollutant emissions

Effects of Pre-Lift Intake Valve Strategies on the Performance of a DISI VVA Turbocharged Engine at Part and Full Load Operation☆

Abstract In the paper, the potentialities offered by an advanced valve lift design are numerically analyzed. In particular, the study is carried out by a 1D approach and regards the characterization of a VVA strategy named " pre-lift" applied to a downsized turbocharged four-cylinder engine. The pre-lift consists of a small, almost constant lift of the intake valve during the exhaust stroke, so to increase the valves overlapping. The results show a benefit on the fuel economy and on the gas-dynamic noise at part load and a substantial increase in the delivered torque at full load, while preserving the fuel consumption

Homozygous mutation in the prokineticin-receptor2 gene (Val274Asp) presenting as reversible Kallmann syndrome and persistent oligozoospermia: case report.

Prokineticin 2 (Prok2) or prokineticin-receptor2 (Prok-R2) gene mutations are associated with Kallmann syndrome (KS). We describe a new homozygous mutation of Prok-R2 gene in a man displaying KS with an apparent reversal of hypogonadism. The proband, offspring of consanguineous parents, presented at age 19 years with absent puberty, no sense of smell, low testosterone and gonadotrophin levels. Magnetic resonance imaging showed olfactory bulb absence. The patient achieved virilization and spermatogenesis with gonadotrophin administration. Two years after discontinuing hormonal therapy, he maintained moderate oligozoospermia and normal testosterone levels. Prok2 and Prok- R2 gene sequence analyses were performed. The proband had a homozygous mutation in Prok-R2 exon 2 that harbours the c.T820>A base substitution, causing the introduction of an aspartic acid in place of valine at position 274 (Val274Asp). His mother had the same mutation in heterozygous state. This report describes a novel homozygous mutation of Prok-R2 gene in a man with variant KS, underlying the role of Prok-R2 gene in the olfactory and reproductive system development in humans. Present findings indicate that markedly delayed activation of gonadotrophin secretion may occur in some KS cases with definite gene defects, and that oligozoospermia might result from a variant form of reversible hypogonadotrophic hypogonadism

A numerical model of the human cornea accounting for the fiber-distributed collagen microstructure

We present a fiber-distributed model of the reinforcing collagen of the human cornea. The model describes the basic connections between the components of the tissue by defining an elementary block (cell) and upscaling it to the physical size of the cornea. The cell is defined by two sets of collagen fibrils running in approximately orthogonal directions, characterized by a random distribution of the spatial orientation and connected by chemical bonds of two kinds. The bonds of the first kind describe the lamellar crosslinks, forming the ribbon-like lamellae; while the bonds of the second kind describe the stacking crosslinks, piling up the lamellae to form the structure of the stroma. The spatial replication of the cell produces a truss structure with a considerable number of degrees of freedom. The statistical characterization of the collagen fibrils leads to a mechanical model that reacts to the action of the deterministic intraocular pressure with a stochastic distribution of the displacements, here characterized by their mean value and variance. The strategy to address the solution of the heavy resulting numerical problem is to use the so-called stochastic finite element improved perturbation method combined with a fully explicit solver. Results demonstrate that the variability of the mechanical properties affects in a non-negligible manner the expected response of the structure to the physiological action

Molecular responses to cadmium exposure in two contrasting durum wheat genotypes

Cadmium is a heavy metal that can be easily accumulated in durum wheat kernels and enter the human food chain. Two near-isogenic lines (NILs) with contrasting cadmium accumulation in grains, High-Cd or Low-Cd (H-Cd NIL and L-Cd NIL, respectively), were used to understand the Cd accumulation and transport mechanisms in durum wheat roots. Plants were cultivated in hydroponic solution, and cadmium concentrations in roots, shoots and grains were quantified. To evaluate the molecular mechanism activated in the two NILs, the transcriptomes of roots were analyzed. The observed response is complex and involves many genes and molecular mechanisms. We found that the gene sequences of two basic helix–loop–helix (bHLH) transcription factors (bHLH29 and bHLH38) differ between the two genotypes. In addition, the transporter Heavy Metal Tolerance 1 (HMT-1) is expressed only in the low-Cd genotype and many peroxidase genes are up-regulated only in the L-Cd NIL, suggesting ROS scavenging and root lignification as active responses to cadmium presence. Finally, we hypothesize that some aquaporins could enhance the Cd translocation from roots to shoots. The response to cadmium in durum wheat is therefore extremely complex and involves transcription factors, chelators, heavy metal transporters, peroxidases and aquaporins. All these new findings could help to elucidate the cadmium tolerance in wheat and address future breeding programs

Cerebrovascular complications and infective endocarditis. impact of available evidence on clinical outcome

Infective endocarditis (IE) is a life-threatening disease. Its epidemiological profile has substantially changed in recent years although 1-year mortality is still high. Despite advances in medical therapy and surgical technique, there is still uncertainty on the best management and on the timing of surgical intervention. The objective of this review is to produce further insight intothe short- and long-term outcomes of patients with IE, with a focus on those presenting cerebrovascular complications

Secondary metabolites in xylella fastidiosa-plant interaction

During their evolutionary history, plants have evolved the ability to synthesize and accumulate small molecules known as secondary metabolites. These compounds are not essential in the primary cell functions but play a significant role in the plants’ adaptation to environmental changes and in overcoming stress. Their high concentrations may contribute to the resistance of the plants to the bacterium Xylella fastidiosa, which has recently re‐emerged as a plant pathogen of global importance. Although it is established in several areas globally and is considered one of the most dangerous plant pathogens, no cure has been developed due to the lack of effective bactericides and the difficulties in accessing the xylem vessels where the pathogen grows and produces cell aggregates and biofilm. This review highlights the role of secondary metabolites in the defense of the main economic hosts of X. fastidiosa and identifies how knowledge about biosynthetic pathways could improve our understanding of disease resistance. In addition, current developments in non-invasive techniques and strategies of combining molecular and physiological techniques are examined, in an attempt to identify new metabolic engineering options for plant defense

Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence

Grapevine yellows (GY) are a significant threat to grapes due to the severe symptoms and lack of treatments. Conventional diagnosis of the phytoplasmas associated to GYs relies on symptom identification, due to sensitivity limits of diagnostic tools (e.g. real time PCR) in asymptomatic vines, where the low concentration of the pathogen or its erratic distribution can lead to a high rate of false-negatives. GY's primary symptoms are leaf discoloration and irregular wood ripening, which can be easily confused for symptoms of other diseases making recognition a difficult task. Herein, we present a novel system, utilizing convolutional neural networks, for end-to-end detection of GY in red grape vine (cv. Sangiovese), using color images of leaf clippings. The diagnostic test detailed in this work does not require the user to be an expert at identifying GY. Data augmentation strategies make the system robust to alignment errors during data capture. When applied to the task of recognizing GY from digital images of leaf clippings—amongst many other diseases and a healthy control—the system has a sensitivity of 98.96% and a specificity of 99.40%. Deep learning has 35.97% and 9.88% better predictive value (PPV) when recognizing GY from sight, than a baseline system without deep learning and trained humans respectively. We evaluate six neural network architectures: AlexNet, GoogLeNet, Inception v3, ResNet-50, ResNet-101 and SqueezeNet. We find ResNet-50 to be the best compromise of accuracy and training cost. The trained neural networks, code to reproduce the experiments, and data of leaf clipping images are available on the internet. This work will advance the frontier of GY detection by improving detection speed, enabling a more effective response to the disease

Manufacture and electromechanical characterization of highly conductive multilayer-graphene/polydimethylsiloxane flexible paper

Multilayer graphene (MLG) micro- and nanosheets have been investigated for use as nanofiller in polymer composite in order to obtain multifunctional materials with enhanced electrical conductivity and mechanical properties. In order to take advantage of the conductivity properties of MLG sheets, a large amount of nanofiller should be used. Although, increasing filler loading alters the mechanical properties of the composite because of serious filler agglomeration. It has been shown that a promising approach to realize electrically conductive light-weight composite is to incorporate an electrically conductive graphene paper (GP), obtained by vacuum filtration of a nanofillers suspension, into the polymer matrix. One advantage of infiltrating the GP with polymer is that the tensile modulus of the composite can be greatly improved as compared with either GP or neat polymer, without weakening the electrical properties of the highly continuous nanofillers network formed in the paper making process. In this work we present experimental results related to the fabrication process and the electromechanical behaviour of a free standing, highly-conductive MLG paper impregnated with polydimethylsiloxane (PDMS)

Research Trends on Greenhouse Engineering Using a Science Mapping Approach

Horticultural protected cultivation has spread throughout the world as it has proven to be extremely effective. In recent years, the greenhouse engineering research field has become one of the main research topics within greenhouse farming. The main objectives of the current study were to identify the major research topics and their trends during the last four decades by analyzing the co-occurrence network of keywords associated with greenhouse engineering publications. A total of 3804 pertinent documents published, in 1981-2021, were analyzed and discussed. China, the United States, Spain, Italy and the Netherlands have been the most active countries with more than 36% of the relevant literature. The keyword cluster analysis suggested the presence of five principal research topics: energy management and storage; monitoring and control of greenhouse climate parameters; automation of greenhouse operations through the internet of things (IoT) and wireless sensor network (WSN) applications; greenhouse covering materials and microclimate optimization in relation to plant growth; structural and functional design for improving greenhouse stability, ventilation and microclimate. Recent research trends are focused on real-time monitoring and automatic control systems based on the IoT and WSN technologies, multi-objective optimization approaches for greenhouse climate control, efficient artificial lighting and sustainable greenhouse crop cultivation using renewable energy