Green synthesis of vanillin: Pervaporation and dialysis for process intensification in a membrane reactor

In the present work, two different membrane processes (pervaporation and dialysis) are compared in view of their utilization in a membrane reactor, where vanillin, which is probably the most important aroma of the food industry, is synthesized in a green and sustainable way. The utilized precursor (ferulic acid, which is possibly a natural product from agricultural wastes) is partially oxidized (photocatalytically or biologically) and the product is continuously recovered from the reacting solution by the membrane process to avoid its degradation. It is observed that pervaporation is much more selective towards vanillin than dialysis, but the permeate flux of dialysis is much higher. Furthermore, dialysis can work also at lower temperatures and can be used to continuously restore the consumed substrate into the reacting mixture. A mathematical model of the integrated process (reaction combined with membrane separation) reproduces quite satisfactorily the experimental results and can be used for the analysis and the design of the process

Sequential biological and photocatalysis based treatments for shipboard slop purification: A pilot plant investigation

This study investigated the treatment of a shipboard slop containing commercial gasoline in a pilot plant scale consisting of a membrane biological reactor (MBR) and photocatalytic reactor (PCR) acting in series. The MBR contributed for approximately 70% to the overall slop purification. More precisely, the biological process was able to remove approximately 40%, on average, of the organic pollution in the slop. Nevertheless, the membrane was capable to retain a large amount of organic molecules within the system, amounting for a further 30% of the influent total organic content removal. However, this affected the membrane fouling, thus resulting in the increase of the pore blocking mechanism that accounted for approximately 20% to the total resistance to filtration (2.85∙10 13 m −1 ), even if a significant restoration of the original membrane permeability was obtained after chemical cleanings. On the other hand, the biological treatment produced a clear solution for the photocatalytic system, thereby optimizing the light penetration and generation of highly oxidizing active oxygen species that enabled the degradation of bio-recalcitrant compounds. Indeed, low total organic carbon (TOC) values (<10 mg L −1 ) were achieved in the output of the photocatalytic reactor by means of only 60 Einstein (E) of cumulative impinging energy after the addition of K 2 S 2 O 8 . Overall, coupling the two processes enabled very high TOC removal (ca. 95%)

A Shallow Learning Investigation for COVID-19 Classification

COVID-19, an infectious coronavirus disease, triggered a pandemic that resulted in countless deaths. Since its inception, clinical institutions have used computed tomography as a supplemental screening method to reverse transcription-polymerase chain reaction. Deep learning approaches have shown promising results in addressing the problem; however, less computationally expensive techniques, such as those based on handcrafted descriptors and shallow classifiers, may be equally capable of detecting COVID-19 based on medical images of patients. This work proposes an initial investigation of several handcrafted descriptors well known in the computer vision literature already been exploited for similar tasks. The goal is to discriminate tomographic images belonging to three classes, COVID-19, pneumonia, and normal conditions, and present in a large public dataset. The results show that kNN and ensembles trained with texture descriptors achieve outstanding accuracy in this task, reaching accuracy and F-measure of 93.05% and 89.63%, respectively. Although it did not exceed state of the art, it achieved satisfactory performance with only 36 features, enabling the potential to achieve remarkable improvements from a computational complexity perspective

Controlling the Er content of porous silicon using the doping current intensity

The results of an investigation on the Er doping of porous silicon are presented. Electrochemical impedance spectroscopy, optical reflectivity, and spatially resolved energy dispersive spectroscopy (EDS) coupled to scanning electron microscopy measurements were used to investigate on the transient during the first stages of constant current Er doping. Depending on the applied current intensity, the voltage transient displays two very different behaviors, signature of two different chemical processes. The measurements show that, for equal transferred charge and identical porous silicon (PSi) layers, the applied current intensity also influences the final Er content. An interpretative model is proposed in order to describe the two distinct chemical processes. The results can be useful for a better control over the doping process

On The Potential of Image Moments for Medical Diagnosis

Medical imaging is widely used for diagnosis and postoperative or post-therapy monitoring. The ever-increasing number of images produced has encouraged the introduction of automated methods to assist doctors or pathologists. In recent years, especially after the advent of convolutional neural networks, many researchers have focused on this approach, considering it to be the only method for diagnosis since it can perform a direct classification of images. However, many diagnostic systems still rely on handcrafted features to improve interpretability and limit resource consumption. In this work, we focused our efforts on orthogonal moments, first by providing an overview and taxonomy of their macrocategories and then by analysing their classification performance on very different medical tasks represented by four public benchmark data sets. The results confirmed that convolutional neural networks achieved excellent performance on all tasks. Despite being composed of much fewer features than those extracted by the networks, orthogonal moments proved to be competitive with them, showing comparable and, in some cases, better performance. In addition, Cartesian and harmonic categories provided a very low standard deviation, proving their robustness in medical diagnostic tasks. We strongly believe that the integration of the studied orthogonal moments can lead to more robust and reliable diagnostic systems, considering the performance obtained and the low variation of the results. Finally, since they have been shown to be effective on both magnetic resonance and computed tomography images, they can be easily extended to other imaging techniques

Cryptocurrency scams: analysis and perspectives

Since the inception of Bitcoin in 2009, the market of cryptocurrencies has grown beyond the initial expectations, as witnessed by the thousands of tokenised assets available on the market, whose daily trades amount to dozens of USD billions. The pseudonymity features of these cryptocurrencies have attracted the attention of cybercriminals, who exploit them to carry out potentially untraceable scams. The wide range of cryptocurrency-based scams observed over the last ten years has fostered the research on the analysis of their effects, and the development of techniques to counter them. However, doing research in this field requires addressing several challenges: for instance, although a few data sources about cryptocurrency scams are publicly available, they often contain incomplete or misclassified data. Further, there is no standard taxonomy of scams, which leads to ambiguous and incoherent interpretations of their nature. Indeed, the unavailability of reliable datasets makes it difficult to train effective automatic classifiers that can detect and analyse cryptocurrency scams. In this paper, we perform an extensive review of the scientific literature on cryptocurrency scams, which we systematise according to a novel taxonomy. By collecting and homogenising data from different public sources, we build a uniform dataset of thousands of cryptocurrency scams.We devise an automatic tool that recognises scams and classifies them according to our taxonomy.We assess the effectiveness of our tool through standard performance metrics.We also give an in-depth analysis of the classification results, offering several insights into threat types, from their features to their connection with other types. Finally, we provide a set of guidelines that policymakers could follow to improve user protection against cryptocurrency scams

Photocatalytic degradation of dyes by using a membrane reactor

Dyes are organic compounds used in textile, food and drug industries, and their abatement represents one of the main problems in the treatment processes because generally they are very stable toxic compounds. In this work, two commercial azo-dyes, i.e. Congo Red (C32H22N6Na2O6S2) and Patent Blue (C27H31N2NaO6S2), in aqueous solution were degraded in a photocatalytic membrane reactor by using TiO2 Degussa P25 as the catalyst. Different system con\ufb01gurations and irradiating sources were studied, and the in\ufb02uence of some operational parameters such as the pressure in the membrane cell and the initial concentration of the substrates was determined. A comparison between suspended and entrapped TiO2 was also done. The experimental results showed a satisfactory degradation ef\ufb01ciency of the photocatalytic membrane process. The in\ufb02uence of various parameters (e.g. feed concentration, recirculation rate) has been discussed to obtain high reaction rates, operating stability and high membrane rejection, both for substrates and by-products. Congo Red was photodegraded with higher rate under the same experimental conditions probably due to its higher adsorption onto the catalyst surface. It was possible to treat successfully highly concentrated solutions (500 mg/L) of both dyes by means of a continuous process obtaining good values of permeate \ufb02uxes (30\u201370 L/m2 h); this could be interesting for industrial applications. The reactor containing the suspended photocatalyst was signi\ufb01cantly more ef\ufb01cient than the reactor containing the catalyst entrapped into the membrane

Overview on oxidation mechanisms of organic compounds by TiO2 in heterogeneous photocatalysis

This review provides the reader with a general overview on heterogeneous photocatalytic oxidation mechanisms in the presence of TiO2, with a special address to conversion of aliphatic and aromatic organic species. The aim was to clarify the steps of the photo-oxidation of the various classes of compounds and to relate them with the properties of the catalysts and the experimental conditions used. Reactions carried out to perform complete degradation and photocatalytic partial oxidations have been deeply discussed. Recent isotopic studies highlighted new reaction pathways concerning partial oxidation of alcohols to aldehyde and oxidation of benzene while EPR investigations confirmed that not only the photogenerated hole but also the OH radicals are involved in the oxidation of the substrates

Alcohol-Selective Oxidation in Water under Mild Conditions via a Novel Approach to Hybrid Composite Photocatalysts

The oxidation of alcohols to carbonyl compounds in a clean fashion (i.e., with water as a solvent or under solvent-free conditions, and using O2 or H2O2 as the primary oxidant) is the subject of considerable research efforts. A new approach for the selective oxidation of soluble aromatic alcohols in water under mild conditions via a novel composite photocatalyst has been developed. The catalyst is synthesized by grafting 4-(4-(4-hydroxyphenylimino)cyclohexa-2,5dienylideneamino)phenol and silver nanoparticles onto the surface of moderately crystalline titanium dioxide. The titanium dioxide-based composite was first extensively characterized and then employed in the catalytic oxidation of 4-methoxybenzyl alcohol to 4-methoxybenzaldehyde under UV irradiation in water at room temperature. The corresponding aldehyde was obtained with unprecedented high selectivity (up to 86 %). The method is general and opens the route to fabrication of photocatalytic composites based on titanium dioxide functionalized with shuttle organic molecules and metal nanoparticles for a variety of oxidative conversions

Violent and Complex Behaviors and Non-Restorative Sleep Are the Main Features of Disorders of Arousal in Adulthood: Real Picture or a More Severe Phenotype?

Disorders of arousal (DoA) are NREM parasomnias characterized by motor and emotional behaviors emerging from incomplete arousals from deep sleep. DoA are largely present in pediatric populations, a period during which they are labeled as self-limited manifestations. However, an extensive literature has shown that DoA can persist in adulthood, with different characteristics from childhood DoA. Adult DoA patients usually report excessive daily sleepiness, sleep-related violence during DoA episodes or potentially harmful behaviors, which are rare in childhood. The semeiological features of DoA episodes in adulthood may complicate differential diagnoses with other motor manifestations during sleep, in particular sleep-related hypermotor epilepsy. However, it cannot be excluded that adults with DoA attending sleep centers constitute a more severe phenotype, thus not being representative of adult DoA in the general population. Video-polysomnographic studies of DoA document a spectrum of motor patterns of different complexities, the simplest of which may often go unnoticed. Despite the different complexities of the episodes, neurophysiologic studies showed the co-existence of deep sleep and wakefulness during DoA episodes or even before their onset. These aspects make DoA an ideal model to investigate the mechanisms regulating local sleep, sleep arousal and cognitive functions including spatial and temporal orientation, attention or memory