Growth performance, carcass traits, and blood biochemistry of broiler chicks fed with low-fibre sunflower meal and phytase

This study was designed to evaluate the impact of graded replacements of soybean meal (SBM) with low-fibre sunflower meal (LF-SFM), with and without phytase supplementation, on growth performance, carcass traits, and plasma profile of broilers. A total of 800 mixed sex one-day-old broiler chicks (Cobb 500) were randomly assigned to eight dietary treatment groups (five replicates each) in a 4 × 2 factorial arrangement, including four levels of LF-SFM (0%, 25%, 50%, and 75% to replace SBM) and two levels of microbial phytase (0 or 0.2 g/kg diet). Feed conversion ratio (FCR), and bodyweight gain (BWG) were decreased significantly if LF-SFM replaced more than 25% SBM. There was no significant effect between the interaction of the main factors (LF-SFM × phytase) on growth performance during the starter, grower, finisher and the overall period. The addition of phytase had no beneficial effects on performance traits. Carcass traits were not influenced by feeding LF-SFM or enzyme addition. However, increasing the level of LF-SFM (50% and 75%, respectively) in diets reduced the abdominal fat, whereas the weight of the small intestine was increased. Plasma metabolic profile was not affected by LF-SFM levels in broiler diets, except for high-density lipoproteins cholesterol (HDL-C) and total cholesterol (T-Cho), which were not affected by the dietary enzyme. It is concluded that the diets with LF-SFM levels up to 25% generated growth performance and carcass traits comparable with the diet that contained SBM.Keywords: Cobb 500, digestive organs, enzyme, growth traits, low-fibre sunflower meal, plasma profil

Effect of the Topology on Wetting and Drying of Hydrophobic Porous Materials

Establishing molecular mechanisms of wetting and drying of hydrophobic porous materials is a general problem for science and technology within the subcategories of the theory of liquids, chromatography, nanofluidics, energy storage, recuperation, and dissipation. In this article, we demonstrate a new way to tackle this problem by exploring the effect of the topology of pure silica nanoparticles, nanotubes, and zeolites. Using molecular dynamics simulations, we show how secondary porosity promotes the intrusion of water into micropores and affects the hydrophobicity of materials. It is demonstrated herein that for nano-objects, the hydrophobicity can be controlled by changing the ratio of open to closed nanometer-sized lateral pores. This effect can be exploited to produce new materials for practical applications when the hydrophobicity needs to be regulated without significantly changing the chemistry or structure of the materials. Based on these simulations and theoretical considerations, for pure silica zeolites, we examined and then classified the experimental database of intrusion pressures, thus leading to the prediction of any zeolite’s intrusion pressure. We show a correlation between the intrusion pressure and the ratio of the accessible pore surface area to total pore volume. The correlation is valid for some zeolites and mesoporous materials. It can facilitate choosing prospective candidates for further investigation and possible exploitation, especially for energy storage, recuperation, and dissipation

Subnanometer Topological Tuning of the Liquid Intrusion/Extrusion Characteristics of Hydrophobic Micropores

Intrusion (wetting)/extrusion (drying) of liquids in/from lyophobic nanoporous systems is key in many fields, including chromatography, nanofluidics, biology, and energy materials. Here we demonstrate that secondary topological features decorating main channels of porous systems dramatically affect the intrusion/extrusion cycle. These secondary features, allowing an unexpected bridging with liquid in the surrounding domains, stabilize the water stream intruding a micropore. This reduces the intrusion/extrusion barrier and the corresponding pressures without altering other properties of the system. Tuning the intrusion/extrusion pressures via subnanometric topological features represents a yet unexplored strategy for designing hydrophobic micropores. Though energy is not the only field of application, here we show that the proposed tuning approach may bring 20–75 MPa of intrusion/extrusion pressure increase, expanding the applicability of hydrophobic microporous materials

Enhancing the Sustainability of the Aviation Industry: Airlines’ Commitment to “Green” Practices

The aviation industry represents an important polluter, being responsible for increasing environmental impacts on global scale. Aiming to approach the adoption of suitable policies in the aviation industry towards the achievement of the national and international sustainability goals, the present research tackles airlines’ commitment to aviation-related environmental issues, as well as their willingness to adopt sustainable aviation fuel (i.e., bio jet fuel) and sustainable development strategies, focusing on those companies operating flights in the Karol Wojtyła Airport (Bari, Italy). The paper adopts the χ2 test and the logistic regression to investigate three different hypotheses related to airlines’ headquarters, carriers’ typology (i.e., low-cost or not, flag carriers or not) and years of service. Results outline that traditional airlines, either flag carriers or not, as well as South and North American companies, are more likely to be aware of aviation environmental consequences, publishing environmental reports and offering to passengers the chance to participate to climate change reduction (e.g., through online carbon offset programs or more expensive ticket to produce bio jet fuels). In addition, airlines transiting in Karol Wojtyła Airport show a small willingness to share information through environmental reports and are scarcely intentioned to make use of bio jet fuels, confirming that low-cost companies are still less attentive towards aviation environmental issues. The present research contributes to the empirical studies on sustainable aviation and carriers’ commitment to environmental strategies, highlighting the need to enhance carbon offsets programs and digital technologies as the online compensation of CO2 emissions

Lumpability for Uncertain Continuous-Time Markov Chains

The assumption of perfect knowledge of rate parameters in continuous-time Markov chains (CTMCs) is undermined when confronted with reality, where they may be uncertain due to lack of information or because of measurement noise. In this paper we consider uncertain CTMCs, where rates are assumed to vary non-deterministically with time from bounded continuous intervals. This leads to a semantics which associates each state with the reachable set of its probability under all possible choices of the uncertain rates. We develop a notion of lumpability which identifies a partition of states where each block preserves the reachable set of the sum of its probabilities, essentially lifting the well-known CTMC ordinary lumpability to the uncertain setting. We proceed with this analogy with two further contributions: a logical characterization of uncertain CTMC lumping in terms of continuous stochastic logic; and a polynomial time and space algorithm for the minimization of uncertain CTMCs by partition refinement, using the CTMC lumping algorithm as an inner step. As a case study, we show that the minimizations in a substantial number of CTMC models reported in the literature are robust with respect to uncertainties around their original, fixed, rate values

Approximate probabilistic verification of hybrid systems

Hybrid systems whose mode dynamics are governed by non-linear ordinary differential equations (ODEs) are often a natural model for biological processes. However such models are difficult to analyze. To address this, we develop a probabilistic analysis method by approximating the mode transitions as stochastic events. We assume that the probability of making a mode transition is proportional to the measure of the set of pairs of time points and value states at which the mode transition is enabled. To ensure a sound mathematical basis, we impose a natural continuity property on the non-linear ODEs. We also assume that the states of the system are observed at discrete time points but that the mode transitions may take place at any time between two successive discrete time points. This leads to a discrete time Markov chain as a probabilistic approximation of the hybrid system. We then show that for BLTL (bounded linear time temporal logic) specifications the hybrid system meets a specification iff its Markov chain approximation meets the same specification with probability $1$. Based on this, we formulate a sequential hypothesis testing procedure for verifying -approximately- that the Markov chain meets a BLTL specification with high probability. Our case studies on cardiac cell dynamics and the circadian rhythm indicate that our scheme can be applied in a number of realistic settings

Amino-acid PET versus MRI guided re-irradiation in patients with recurrent glioblastoma multiforme (GLIAA) – protocol of a randomized phase II trial (NOA 10/ARO 2013-1)

Background: The higher specificity of amino-acid positron emission tomography (AA-PET) in the diagnosis of gliomas, as well as in the differentiation between recurrence and treatment-related alterations, in comparison to contrast enhancement in T1-weighted MRI was demonstrated in many studies and is the rationale for their implementation into radiation oncology treatment planning. Several clinical trials have demonstrated the significant differences between AA-PET and standard MRI concerning the definition of the gross tumor volume (GTV). A small single-center non-randomized prospective study in patients with recurrent high grade gliomas treated with stereotactic fractionated radiotherapy (SFRT) showed a significant improvement in survival when AA-PET was integrated in target volume delineation, in comparison to patients treated based on CT/MRI alone. Methods: This protocol describes a prospective, open label, randomized, multi-center phase II trial designed to test if radiotherapy target volume delineation based on FET-PET leads to improvement in progression free survival (PFS) in patients with recurrent glioblastoma (GBM) treated with re-irradiation, compared to target volume delineation based on T1Gd-MRI. The target sample size is 200 randomized patients with a 1:1 allocation ratio to both arms. The primary endpoint (PFS) is determined by serial MRI scans, supplemented by AA-PET-scans and/or biopsy/surgery if suspicious of progression. Secondary endpoints include overall survival (OS), locally controlled survival (time to local progression or death), volumetric assessment of GTV delineated by either method, topography of progression in relation to MRIor PET-derived target volumes, rate of long term survivors (> 1 year), localization of necrosis after re-irradiation, quality of life (QoL) assessed by the EORTC QLQ-C15 PAL questionnaire, evaluation of safety of FET-application in AA-PET imaging and toxicity of re-irradiation. Discussion: This is a protocol of a randomized phase II trial designed to test a new strategy of radiotherapy target volume delineation for improving the outcome of patients with recurrent GBM. Moreover, the trial will help to develop a standardized methodology for the integration of AA-PET and other imaging biomarkers in radiation treatment planning. Trial registration: The GLIAA trial is registered with ClinicalTrials.gov (NCT01252459, registration date 02.12.2010), German Clinical Trials Registry (DRKS00000634, registration date 10.10.2014), and European Clinical Trials Database (EudraCT-No. 2012-001121-27, registration date 27.02.2012)

A generative neural network model for the quality prediction of work in progress products

© 2019 Elsevier B.V. One of the key challenges in manufacturing processes is improving the accuracy of quality monitoring and prediction. This paper proposes a generative neural network model for automatically predicting work-in-progress product quality. Our approach combines an unsupervised feature-extraction step with a supervised learning method. An autoencoding neural network is trained using raw manufacturing process data to extract rich information from production line recordings. Then, the extracted features are reformed as time-series and are fed into a multi-layer perceptron for predicting product quality. Finally, the outputs are decoded into a forecast quality measure. We evaluate the performance of the generative model on a case study from a powder metallurgy process. Our experimental results suggest that our method can precisely capture the defective products