Search results for: heterogeneous Fenton

Authors: Modi Alsubaie, Chris Dickens, Barnaby Dunn, Andy Gibson, Obioha Ukoumunned, Alison Evans, Rachael Vicary, Manish Gandhi, Willem Kuyken

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Background: Depression co-occurs in 20% of people with cardiovascular disorders, can persist for years and predicts worse physical health outcomes. While psychosocial treatments have been shown to effectively treat acute depression in those with comorbid cardiovascular disorders, to date there has been no evaluation of approaches aiming to prevent relapse and treat residual depression symptoms in this group. Therefore, the current study aimed to examine the feasibility and acceptability of a randomised controlled trial design evaluating an adapted version of mindfulness-based cognitive therapy (MBCT) designed specifically for people with co-morbid depression and cardiovascular disorders. Methods: A 3-arm feasibility randomised controlled trial was conducted, comparing MBCT adapted for people with cardiovascular disorders plus treatment as usual (TAU), mindfulness-based stress reduction (MBSR) plus TAU, and TAU alone. Participants completed a set of self-report measures of depression severity, anxiety, quality of life, illness perceptions, mindfulness, self-compassion and affect and had their blood pressure taken immediately before, immediately after, and three months following the intervention. Those in the adapted-MBCT arm additionally underwent a qualitative interview to gather their views about the adapted intervention. Results: 3400 potentially eligible participants were approached when attending an outpatient appointment at a cardiology clinic or via a GP letter following a case note search. 242 (7.1%) were interested in taking part, 59 (1.7%) were screened as being suitable, and 33 (<1%) were eventually randomised to the three groups. The sample was heterogeneous in terms of whether they reported current depression or had a history of depression and the time since the onset of cardiovascular disease (one to 25 years). Of 11 participants randomised to adapted MBCT seven completed the full course, levels of home mindfulness practice were high, and positive qualitative feedback about the intervention was given. Twenty-nine out of 33 participants randomised completed all the assessment measures at all three-time points. With regards to the primary outcome (depression), five out of the seven people who completed the adapted MBCT and three out of five under MBSR showed significant clinical change, while in TAU no one showed any clinical change at the three-month follow-up. Conclusions: The adapted MBCT intervention was feasible and acceptable to participants. However, aspects of the trial design were not feasible. In particular, low recruitment rates were achieved, and there was a high withdrawal rate between screening and randomisation. Moreover, the heterogeneity in the sample was high meaning the adapted intervention was unlikely to be well tailored to all participants needs. This suggests that if the decision is made to move to a definitive trial, study recruitment procedures will need to be revised to more successfully recruit a target sample that optimally matches the adapted intervention.

Keywords: mindfulness-based cognitive therapy (MBCT), depression, cardiovascular disorders, feasibility, acceptability

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Authors: Kanchan Mondal, Adam Sims, Madhav Soti, Jitendra Gautam, David Carron

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Fischer-Tropsch (FT) synthesis is a complex series of heterogeneous reactions between CO and H2 molecules (present in the syngas) on the surface of an active catalyst (Co, Fe, Ru, Ni, etc.) to produce gaseous, liquid, and waxy hydrocarbons. This product is composed of paraffins, olefins, and oxygenated compounds. The key challenge in applying the Fischer-Tropsch process to produce transportation fuels is to make the capital and production costs economically feasible relative to the comparative cost of existing petroleum resources. To meet this challenge, it is imperative to enhance the CO conversion while maximizing carbon selectivity towards the desired liquid hydrocarbon ranges (i.e. reduction in CH4 and CO2 selectivities) at high throughputs. At the same time, it is equally essential to increase the catalyst robustness and longevity without sacrificing catalyst activity. This paper focuses on process development to achieve the above. The paper describes the influence of operating parameters on Fischer Tropsch synthesis (FTS) from coal derived syngas in supercritical carbon dioxide (ScCO2). In addition, the unreacted gas and solvent recycle was incorporated and the effect of unreacted feed recycle was evaluated. It was expected that with the recycle, the feed rate can be increased. The increase in conversion and liquid selectivity accompanied by the production of narrower carbon number distribution in the product suggest that higher flow rates can and should be used when incorporating exit gas recycle. It was observed that this process was capable of enhancing the hydrocarbon selectivity (nearly 98 % CO conversion), reducing improving the carbon efficiency from 17 % to 51 % in a once through process and further converting 16 % CO2 to liquid with integrated recycle of the product gas stream and increasing the life of the catalyst. Catalyst robustness enhancement has been attributed to the absorption of heat of reaction by the compressed CO2 which reduced the formation of hotspots and the dissolution of waxes by the CO2 solvent which reduced the blinding of active sites. In addition, the recycling the product gas stream reduced the reactor footprint to one-fourth of the once through size and product fractionation utilizing the solvent effects of supercritical CO2 were realized. In addition to the negative CO2 selectivities, methane production was also inhibited and was limited to less than 1.5%. The effect of the process conditions on the life of the catalysts will also be presented. Fe based catalysts are known to have a high proclivity for producing CO2 during FTS. The data of the product spectrum and selectivity on Co and Fe-Co based catalysts as well as those obtained from commercial sources will also be presented. The measurable decision criteria were the increase in CO conversion at H2:CO ratio of 1:1 (as commonly found in coal gasification product stream) in supercritical phase as compared to gas phase reaction, decrease in CO2 and CH4 selectivity, overall liquid product distribution, and finally an increase in the life of the catalysts.

Keywords: carbon efficiency, Fischer Tropsch synthesis, low GHG, pressure tunable fractionation

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Authors: Saiyed M Ishtiaque, Priyal Dixit

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Objectives: An approach is being suggested to design the filter media to maximize the filtration efficiency with minimum possible pressure drop of composite nonwoven by incorporating the layers of different packing densities induced by fibre of different deniers and punching parameters by using the concept of sequential punching technique in specific order in layered composite nonwoven structure. X-ray computed tomography technique is used to measure the packing density along the thickness of layered nonwoven structure composed by placing the layer of differently oriented fibres influenced by fibres of different deniers and punching parameters in various combinations to minimize the pressure drop at maximum possible filtration efficiency. Methodology Used: This work involves preparation of needle punched layered structure with batts 100g/m2 basis weight having fibre denier, punch density and needle penetration depth as variables to produce 300 g/m2 basis weight nonwoven composite. X-ray computed tomography technique is used to measure the packing density along the thickness of layered nonwoven structure composed by placing the layers of differently oriented fibres influenced by considered variables in various combinations. to minimize the pressure drop at maximum possible filtration efficiencyFor developing layered nonwoven fabrics, batts made of fibre of different deniers having 100g/m2 each basis weight were placed in various combinations. For second set of experiment, the composite nonwoven fabrics were prepared by using 3 denier circular cross section polyester fibre having 64 mm length on needle punched nonwoven machine by using the sequential punching technique to prepare the composite nonwoven fabrics. In this technique, three semi punched fabrics of 100 g/m2 each having either different punch densities or needle penetration depths were prepared for first phase of fabric preparation. These fabrics were later punched altogether to obtain the overall basis weight of 300 g/m2. The total punch density of the composite nonwoven fabric was kept at 200 punches/ cm2 with a needle penetration depth of 10 mm. The layered structures so formed were subcategorised into two groups- homogeneous layered structure in which all the three batts comprising the nonwoven fabric were made from same denier of fibre, punch density and needle penetration depth and were placed in different positions in respective fabric and heterogeneous layered structure in which batts were made from fibres of different deniers, punch densities and needle penetration depths and were placed in different positions. Contributions: The results concluded that reduction in pressure drop is not derived by the overall packing density of the layered nonwoven fabric rather sequencing of layers of specific packing density in layered structure decides the pressure drop. Accordingly, creation of inverse gradient of packing density in layered structure provided maximum filtration efficiency with least pressure drop. This study paves the way for the possibility of customising the composite nonwoven fabrics by the incorporation of differently oriented fibres in constituent layers induced by considered variablres for desired filtration properties.

Keywords: filtration efficiency, layered nonwoven structure, packing density, pressure drop

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Authors: Eszter Repasi, Akos Koller

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Our genetic information determines our look, physiology, sports performance and all our features. Maximizing the performances of athletes have adopted a science-based approach to the nutritional support. Nowadays genetics studies have blended with nutritional sciences, and a dynamically evolving, new research field have appeared. Nutritional genomics is needed to be used by nutritional experts. This is a recent field of nutritional science, which can provide a solution to reach the best sport performance using correlations between the athlete’s genome, nutritions, molecules, included human microbiome (links between food, microbiome and epigenetics), nutrigenomics and nutrigenetics. Nutritional genomics has a tremendous potential to change the future of dietary guidelines and personal recommendations. Experts need to use new technology to get information about the athletes, like nutritional genomics profile (included the determination of the oral and gut microbiome and DNA coded reaction for food components), which can modify the preparation term and sports performance. The influence of nutrients on the genes expression is called Nutrigenomics. The heterogeneous response of gene variants to nutrients, dietary components is called Nutrigenetics. The human microbiome plays a critical role in the state of health and well-being, and there are more links between food or nutrition and the human microbiome composition, which can develop diseases and epigenetic changes as well. A nutritional genomics-based profile of athletes can be the best technic for a dietitian to make a unique sports nutrition diet plan. Using functional food and the right food components can be effected on health state, thus sports performance. Scientists need to determine the best response, due to the effect of nutrients on health, through altering genome promote metabolites and result changes in physiology. Nutritional biochemistry explains why polymorphisms in genes for the absorption, circulation, or metabolism of essential nutrients (such as n-3 polyunsaturated fatty acids or epigallocatechin-3-gallate), would affect the efficacy of that nutrient. Controlled nutritional deficiencies and failures, prevented the change of health state or a newly discovered food intolerance are observed by a proper medical team, can support better sports performance. It is important that the dietetics profession informed on gene-diet interactions, that may be leading to optimal health, reduced risk of injury or disease. A special medical application for documentation and monitoring of data of health state and risk factors can uphold and warn the medical team for an early action and help to be able to do a proper health service in time. This model can set up a personalized nutrition advice from the status control, through the recovery, to the monitoring. But more studies are needed to understand the mechanisms and to be able to change the composition of the microbiome, environmental and genetic risk factors in cases of athletes.

Keywords: gene-diet interaction, multidisciplinary team, microbiome, diet plan

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Authors: Anupalli Roja Rani, Pavithra Dasari

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Background: Among several, breast cancer has emerged as the most common female cancer in developing countries. It is the most common cause of cancer-related deaths worldwide among women. It is a molecularly and clinically heterogeneous disease. Moreover, it is a hormone–dependent tumor in which estrogens can regulate the growth of breast cells by binding with estrogen receptors (ERs). Moreover, the use of natural products in cancer therapeutics is due to their properties of biocompatibility and less toxicity. Plants are the vast reservoirs for various bioactive compounds. Coleus barbatus (Lamiaceae) contains anticancer properties against several cancer cell lines. Method: In the present study, an attempt is being made to enrich the knowledge of the anticancer activity of pure compounds extracted from Coleus barbatus (Andrew). On human breast cancer cell lines MCF-7. Here in, we are assessing the antiproliferative activity of Coleus barbatus (Andrew) plant extracts against MCF 7 and also evaluating their toxicity in normal human mammary cell lines such as Human Mammary Epithelial Cells (HMEC). The active fraction of plant extract was further purified with the help of Flash chromatography, Medium Pressure Liquid Chromatography (MPLC) and preparative High-Performance Liquid Chromatography (HPLC). The structure of pure compounds will be elucidated by using modern spectroscopic methods like Nuclear magnetic resonance (NMR), Electrospray Ionisation Mass Spectrometry (ESI-MS) methods. Later, the growth inhibition morphological assessment of cancer cells and cell cycle analysis of purified compounds were assessed using FACS. The growth and progression of signaling molecules HER2, GRP78 was studied by secretion assay using ELISA and expression analysis by flow cytometry. Result: Cytotoxic effect against MCF-7 with IC50 values were derived from dose response curves, using six concentrations of twofold serially diluted samples, by SOFTMax Pro software (Molecular device) and respectively Ellipticine and 0.5% DMSO were used as a positive and negative control. Conclusion: The present study shows the significance of various bioactive compounds extracted from Coleus barbatus (Andrew) root material. It acts as an anti-proliferative and shows cytotoxic effects on human breast cancer cell lines MCF7. The plant extracts play an important role pharmacologically. The whole plant has been used in traditional medicine for decades and the studies done have authenticated the practice. Earlier, as described, the plant has been used in the ayurveda and homeopathy medicine. However, more clinical and pathological studies must be conducted to investigate the unexploited potential of the plant. These studies will be very useful for drug designing in the future.

Keywords: coleus barbatus, HPLC, MPLC, NMR, MCF7, flash chromatograph, ESI-MS, FACS, ELISA.

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Authors: Eric Pla Erra, Mariana Jimenez Martinez

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While aggregated loads at a community level tend to be easier to predict, individual household load forecasting present more challenges with higher volatility and uncertainty. Furthermore, the drastic changes that our behavior patterns have suffered due to the COVID-19 pandemic have modified our daily electrical consumption curves and, therefore, further complicated the forecasting methods used to predict short-term electric load. Load forecasting is vital for the smooth and optimized planning and operation of our electric grids, but it also plays a crucial role for individual domestic consumers that rely on a HEMS (Home Energy Management Systems) to optimize their energy usage through self-generation, storage, or smart appliances management. An accurate forecasting leads to higher energy savings and overall energy efficiency of the household when paired with a proper HEMS. In order to study how COVID-19 has affected the accuracy of forecasting methods, an evaluation of the performance of a state-of-the-art LGBM (Light Gradient Boosting Model) will be conducted during the transition between pre-pandemic and lockdowns periods, considering day-ahead electric load forecasting. LGBM improves the capabilities of standard Decision Tree models in both speed and reduction of memory consumption, but it still offers a high accuracy. Even though LGBM has complex non-linear modelling capabilities, it has proven to be a competitive method under challenging forecasting scenarios such as short series, heterogeneous series, or data patterns with minimal prior knowledge. An adaptation of the LGBM model – called “resilient LGBM” – will be also tested, incorporating a concept drift detection technique for time series analysis, with the purpose to evaluate its capabilities to improve the model’s accuracy during extreme events such as COVID-19 lockdowns. The results for the LGBM and resilient LGBM will be compared using standard RMSE (Root Mean Squared Error) as the main performance metric. The models’ performance will be evaluated over a set of real households’ hourly electricity consumption data measured before and during the COVID-19 pandemic. All households are located in the city of Barcelona, Spain, and present different consumption profiles. This study is carried out under the ComMit-20 project, financed by AGAUR (Agència de Gestiód’AjutsUniversitaris), which aims to determine the short and long-term impacts of the COVID-19 pandemic on building energy consumption, incrementing the resilience of electrical systems through the use of tools such as HEMS and artificial intelligence.

Keywords: concept drift, forecasting, home energy management system (HEMS), light gradient boosting model (LGBM)

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Authors: Lia Marchi, Ernesto Antonini

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The impact of industrial sites on people’s living environment both involves detrimental effects on the ecosystem and perceptual-aesthetic interferences with the scenery. These, in turn, affect the economic and social value of the landscape, as well as the wellbeing of workers and local communities. Given the diffusion of the phenomenon and the relevance of its effects, it emerges the need for a joint approach to assess and thus mitigate the impact of factories on the landscape –being this latest assumed as the result of the action and interaction of natural and human factors. However, the impact assessment tools suitable for the purpose are quite heterogeneous and mostly monodisciplinary. On the one hand, green building rating systems (GBRSs) are increasingly used to evaluate the performance of manufacturing sites, mainly by quantitative indicators focused on environmental issues. On the other hand, methods to detect the visual and social impact of factories on the landscape are gradually emerging in the literature, but they generally adopt only qualitative gauges. The research addresses the integration of the environmental impact assessment and the perceptual-aesthetic interferences of factories on the landscape. The GBRSs model is assumed as a reference since it is adequate to simultaneously investigate different topics which affect sustainability, returning a global score. A critical analysis of GBRSs relevant to industrial facilities has led to select the U.S. GBC LEED protocol as the most suitable to the scope. A revision of LEED v4 Building Design+Construction has then been provided by including specific indicators to measure the interferences of manufacturing sites with the perceptual-aesthetic and social aspects of the territory. To this end, a new impact category was defined, namely ‘PA - Perceptual-aesthetic aspects’, comprising eight new credits which are specifically designed to assess how much the buildings are in harmony with their surroundings: these investigate, for example the morphological and chromatic harmonization of the facility with the scenery or the site receptiveness and attractiveness. The credits weighting table was consequently revised, according to the LEED points allocation system. As all LEED credits, each new PA credit is thoroughly described in a sheet setting its aim, requirements, and the available options to gauge the interference and get a score. Lastly, each credit is related to mitigation tactics, which are drawn from a catalogue of exemplary case studies, it also developed by the research. The result is a modified LEED scheme which includes compatibility with the landscape within the sustainability assessment of the industrial sites. The whole system consists of 10 evaluation categories, which contain in total 62 credits. Lastly, a test of the tool on an Italian factory was performed, allowing the comparison of three mitigation scenarios with increasing compatibility level. The study proposes a holistic and viable approach to the environmental impact assessment of factories by a tool which integrates the multiple involved aspects within a worldwide recognized rating protocol.

Keywords: environmental impact, GBRS, landscape, LEED, sustainable factory

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Authors: Anna Kolanowska, Anna Kuziel, Sławomir Boncel

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Carbon nanotubes (CNTs) represent a combination of lightness and nanoscopic size with high tensile strength, excellent thermal and electrical conductivity. By now, CNTs have been used as a support in heterogeneous catalysis (CuCl anchored to pre-functionalized CNTs) in the Ullmann-type coupling with aryl halides toward formation of C-N and C-O bonds. The results indicated that the stability of the catalyst was much improved and the elaborated catalytic system was efficient and recyclable. However, CNTs have not been considered as the substrate itself in the Ullmann-type reactions. But if successful, this functionalization would open new areas of CNT chemistry leading to enhanced in-solvent/matrix nanotube individualization. The copper-catalyzed Ullmann-type reaction is an attractive method for the formation of carbon-heteroatom and carbon-carbon bonds in organic synthesis. This condensation reaction is usually conducted at temperature as high as 200 oC, often in the presence of stoichiometric amounts of copper reagent and with activated aryl halides. However, a small amount of organic additive (e.g. diamines, amino acids, diols, 1,10-phenanthroline) can be applied in order to increase the solubility and stability of copper catalyst, and at the same time to allow performing the reaction under mild conditions. The copper (pre-)catalyst is prepared by in situ mixing of copper salt and the appropriate chelator. Our research is focused on the application of Ullmann-type reaction for the covalent functionalization of CNTs. Firstly, CNTs were chlorinated by using iodine trichloride (ICl3) in carbon tetrachloride (CCl4). This method involves formation of several chemical species (ICl, Cl2 and I2Cl6), but the most reactive is the dimer. The fact (that the dimer is the main individual in CCl4) is the reason for high reactivity and possibly high functionalization levels of CNTs. This method, indeed, yielded a notable amount of chlorine onto the MWCNT surface. The next step was the reaction of CNT-Cl with three substrates: aniline, iodobenzene and phenol for the formation C-N, C-C and C-O bonds, respectively, in the presence of 1,10-phenanthroline and cesium carbonate (Cs2CO3) as a base. As the CNT substrates, two multi-wall CNT (MWCNT) types were used: commercially available Nanocyl NC7000™ (9.6 nm diameter, 1.5 µm length, 90% purity) and thicker MWCNTs (in-house) synthesized in our laboratory using catalytic chemical vapour deposition (c-CVD). In-house CNTs had diameter ranging between 60-70 nm and length up to 300 µm. Since classical Ullmann reaction was found as suffering from poor yields, we have investigated the effect of various solvents (toluene, acetonitrile, dimethyl sulfoxide and N,N-dimethylformamide) on the coupling of substrates. Owing to the fact that the aryl halides show the reactivity order of I>Br>Cl>F, we have also investigated the effect of iodine presence on CNT surface on reaction yield. In this case, in first step we have used iodine monochloride instead of iodine trichloride. Finally, we have used the optimized reaction conditions with p-bromophenol and 1,2,4-trihydroxybenzene for the control of CNT dispersion.

Keywords: carbon nanotubes, coupling reaction, functionalization, Ullmann reaction

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Authors: Muhammad Ahnaf Zahin, Yaw Adu-Gyamfi

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Bus transit is a crucial component of transportation networks, especially in urban areas. Any intelligent transportation system must have accurate real-time information on bus travel times since it minimizes waiting times for passengers at different stations along a route, improves service reliability, and significantly optimizes travel patterns. Bus agencies must enhance the quality of their information service to serve their passengers better and draw in more travelers since people waiting at bus stops are frequently anxious about when the bus will arrive at their starting point and when it will reach their destination. For solving this issue, different models have been developed for predicting bus travel times recently, but most of them are focused on smaller road networks due to their relatively subpar performance in high-density urban areas on a vast network. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. In this study, Gated Recurrent Unit (GRU) neural network was followed to predict the mean vehicle travel times for different hours of the day for multiple stations along multiple routes. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. The spatial and temporal information and the historical average travel times were captured from the dataset for model input parameters. As adjacency matrices for the spatial input parameters, the station distances and sequence numbers were used, and the time of day (hour) was considered for the temporal inputs. Other inputs, including volatility information such as standard deviation and variance of journey durations, were also included in the model to make it more robust. The model's performance was evaluated based on a metric called mean absolute percentage error (MAPE). The observed prediction errors for various routes, trips, and stations remained consistent throughout the day. The results showed that the developed model could predict travel times more accurately during peak traffic hours, having a MAPE of around 14%, and performed less accurately during the latter part of the day. In the context of a complicated transportation network in high-density urban areas, the model showed its applicability for real-time travel time prediction of public transportation and ensured the high quality of the predictions generated by the model.

Keywords: gated recurrent unit, mean absolute percentage error, single-step forecasting, travel time prediction.

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Authors: Brayden Mi

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Reverse time migration has been widely used in the Petroleum exploration industry to reveal subsurface images and to detect rock and fluid properties since the early 1980s. The seismic technology involves the construction of a velocity model through interpretive model construction, seismic tomography, or full waveform inversion, and the application of the reverse-time propagation of acquired seismic data and the original wavelet used in the acquisition. The methodology has matured from 2D, simple media to present-day to handle full 3D imaging challenges in extremely complex geological conditions. Conventional Ultrasound computed tomography (USCT) utilize travel-time-inversion to reconstruct the velocity structure of an organ. With the velocity structure, USCT data can be migrated with the “bend-ray” method, also known as migration. Its seismic application counterpart is called Kirchhoff depth migration, in which the source of reflective energy is traced by ray-tracing and summed to produce a subsurface image. It is well known that ray-tracing-based migration has severe limitations in strongly heterogeneous media and irregular acquisition geometries. Reverse time migration (RTM), on the other hand, fully accounts for the wave phenomena, including multiple arrives and turning rays due to complex velocity structure. It has the capability to fully reconstruct the image detectable in its acquisition aperture. The RTM algorithms typically require a rather accurate velocity model and demand high computing powers, and may not be applicable to real-time imaging as normally required in day-to-day medical operations. However, with the improvement of computing technology, such a computational bottleneck may not present a challenge in the near future. The present-day (RTM) algorithms are typically implemented from a flat datum for the seismic industry. It can be modified to accommodate any acquisition geometry and aperture, as long as sufficient illumination is provided. Such flexibility of RTM can be conveniently implemented for the application in USCT imaging if the spatial coordinates of the transmitters and receivers are known and enough data is collected to provide full illumination. This paper proposes an implementation of a full 3D RTM algorithm for USCT imaging to produce an accurate 3D acoustic image based on the Phase-shift-plus-interpolation (PSPI) method for wavefield extrapolation. In this method, each acquired data set (shot) is propagated back in time, and a known ultrasound wavelet is propagated forward in time, with PSPI wavefield extrapolation and a piece-wise constant velocity model of the organ (breast). The imaging condition is then applied to produce a partial image. Although each image is subject to the limitation of its own illumination aperture, the stack of multiple partial images will produce a full image of the organ, with a much-reduced noise level if compared with individual partial images.

Keywords: illumination, reverse time migration (RTM), ultrasound computed tomography (USCT), wavefield extrapolation

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Authors: Sharmin Sultana, Nicolas Nuns, Pardis Simon, Jean-Marc Giraudon, Jean-Francois Lamonior, Nathalie D. Geyter, Rino Morent

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Environmental issues, especially air pollution, have become a huge concern of environmental legislation as a consequence of growing awareness in our global world. In this regard, control of volatile organic compounds (VOCs) emission has become an important issue due to their potential toxicity, carcinogenicity, and mutagenicity. The research of innovative technologies for VOC abatement is stimulated to accommodate the new stringent standards in terms of VOC emission. One emerging strategy is the coupling of 2 existing complementary technologies, namely here non-thermal plasma (NTP) and heterogeneous catalysis, to get a more efficient process for VOC removal in air. The objective of this current work is to investigate the abatement of trichloroethylene (TCE-highly toxic chlorinated VOC) from moist air (RH=15%) as a function of time by combined use of multi-pin-to-plate negative DC corona/glow discharge with Fe-doped cryptomelanes catalyst downstream i.e. post plasma-catalysis (PPC) process. For catalyst alone case, experiments reveal that, initially, Fe doped cryptomelane (regardless the mode of Fe incorporation by co-precipitation (Fe-K-OMS-2)/ impregnation (Fe/K-OMS-2)) exhibits excellent activity to decompose TCE compared to cryptomelane (K-OMS-2) itself. A maximum obtained value of TCE abatement after 6 min is as follows: Fe-KOMS-2 (73.3%) > Fe/KOMS-2 (48.5) > KOMS-2 (22.6%). However, with prolonged operation time, whatever the catalyst under concern, the abatement of TCE decreases. After 111 min time of exposure, the catalysts can be ranked as follows: Fe/KOMS-2 (11%) < K-OMS-2 (12.3%) < Fe-KOMS-2 (14.5%). Clearly, this phenomenon indicates catalyst deactivation either by chlorination or by blocking the active sites. Remarkably, in PPC configuration (energy density = 60 J/L, catalyst temperature = 150°C), experiments reveal an enhanced performance towards TCE removal regardless the type of catalyst. After 6 min time on stream, the TCE removal efficiency amount as follows: K-OMS-2 (60%) < Fe/K-OMS-2 (79%) < Fe-K-OMS-2 (99.3%). The enhanced performances over Fe-K-OMS-2 catalyst are attributed to its high surface oxygen mobility and structural defects leading to high O₃ decomposition efficiency to give active species able to oxidize the plasma processed hazardous\by-products and the possibly remaining VOC into CO₂. Moreover, both undoped and doped catalysts remain strongly capable to abate TCE with time on stream. The TCE removal efficiencies of the PPC processes with Fe/KOMS-2 and KOMS-2 catalysts are not affected by time on stream indicating an excellent catalyst stability. When using the Fe-K-OMS-2 as catalyst, TCE abatement slightly reduces with time on stream. However, it is noteworthy to stress that still a constant abatement of 83% is observed during at least 30 minutes. These results prove that the combination of NTP with catalysts not only increases the catalytic activity but also allows to avoid, to some extent, the poisoning of catalytic sites resulting in an enhanced catalyst stability. In order to better understand the different surface processes occurring in the course of the total TCE oxidation in PPC experiments, a detailed X-ray Photoelectron Spectroscopy (XPS) and Time of Flight-Secondary Ion Mass Spectrometry (ToF-SIMS) study on the fresh and used catalysts is in progress.

Keywords: Fe doped cryptomelane, non-thermal plasma, plasma-catalysis, stability, trichloroethylene

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Authors: N. Šarlija, S. Pfeifer, M. Jeger, A. Bilandžić

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Enterprise growth is generally considered as a key driver of competitiveness, employment, economic development and social inclusion. As such, it is perceived to be a highly desirable outcome of entrepreneurship for scholars and decision makers. The huge academic debate resulted in the multitude of theoretical frameworks focused on explaining growth stages, determinants and future prospects. It has been widely accepted that enterprise growth is most likely nonlinear, temporal and related to the variety of factors which reflect the individual, firm, organizational, industry or environmental determinants of growth. However, factors that affect growth are not easily captured, instruments to measure those factors are often arbitrary, causality between variables and growth is elusive, indicating that growth is not easily modeled. Furthermore, in line with heterogeneous nature of the growth phenomenon, there is a vast number of measurement constructs assessing growth which are used interchangeably. Differences among various growth measures, at conceptual as well as at operationalization level, can hinder theory development which emphasizes the need for more empirically robust studies. In line with these highlights, the main purpose of this paper is twofold. Firstly, to compare structure and performance of three growth prediction models based on the main growth measures: Revenues, employment and assets growth. Secondly, to explore the prospects of financial indicators, set as exact, visible, standardized and accessible variables, to serve as determinants of enterprise growth. Finally, to contribute to the understanding of the implications on research results and recommendations for growth caused by different growth measures. The models include a range of financial indicators as lag determinants of the enterprises’ performances during the 2008-2013, extracted from the national register of the financial statements of SMEs in Croatia. The design and testing stage of the modeling used the logistic regression procedures. Findings confirm that growth prediction models based on different measures of growth have different set of predictors. Moreover, the relationship between particular predictors and growth measure is inconsistent, namely the same predictor positively related to one growth measure may exert negative effect on a different growth measure. Overall, financial indicators alone can serve as good proxy of growth and yield adequate predictive power of the models. The paper sheds light on both methodology and conceptual framework of enterprise growth by using a range of variables which serve as a proxy for the multitude of internal and external determinants, but are unlike them, accessible, available, exact and free of perceptual nuances in building up the model. Selection of the growth measure seems to have significant impact on the implications and recommendations related to growth. Furthermore, the paper points out to potential pitfalls of measuring and predicting growth. Overall, the results and the implications of the study are relevant for advancing academic debates on growth-related methodology, and can contribute to evidence-based decisions of policy makers.

Keywords: growth measurement constructs, logistic regression, prediction of growth potential, small and medium-sized enterprises

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Authors: A. Falenski, A. Kaesbohrer, M. Filter

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Introduction: Risk assessments can be performed in various ways and in different degrees of complexity. In order to assess risks associated with imported foods additional information needs to be taken into account compared to a risk assessment on regional products. The present review is an overview on currently available best practise approaches and data sources used for food import risk assessments (IRAs). Methods: A literature review has been performed. PubMed was searched for articles about food IRAs published in the years 2004 to 2014 (English and German texts only, search string “(English [la] OR German [la]) (2004:2014 [dp]) import [ti] risk”). Titles and abstracts were screened for import risks in the context of IRAs. The finally selected publications were analysed according to a predefined questionnaire extracting the following information: risk assessment guidelines followed, modelling methods used, data and software applied, existence of an analysis of uncertainty and variability. IRAs cited in these publications were also included in the analysis. Results: The PubMed search resulted in 49 publications, 17 of which contained information about import risks and risk assessments. Within these 19 cross references were identified to be of interest for the present study. These included original articles, reviews and guidelines. At least one of the guidelines of the World Organisation for Animal Health (OIE) and the Codex Alimentarius Commission were referenced in any of the IRAs, either for import of animals or for imports concerning foods, respectively. Interestingly, also a combination of both was used to assess the risk associated with the import of live animals serving as the source of food. Methods ranged from full quantitative IRAs using probabilistic models and dose-response models to qualitative IRA in which decision trees or severity tables were set up using parameter estimations based on expert opinions. Calculations were done using @Risk, R or Excel. Most heterogeneous was the type of data used, ranging from general information on imported goods (food, live animals) to pathogen prevalence in the country of origin. These data were either publicly available in databases or lists (e.g., OIE WAHID and Handystatus II, FAOSTAT, Eurostat, TRACES), accessible on a national level (e.g., herd information) or only open to a small group of people (flight passenger import data at national airport customs office). In the IRAs, an uncertainty analysis has been mentioned in some cases, but calculations have been performed only in a few cases. Conclusion: The current state-of-the-art in the assessment of risks of imported foods is characterized by a great heterogeneity in relation to general methodology and data used. Often information is gathered on a case-by-case basis and reformatted by hand in order to perform the IRA. This analysis therefore illustrates the need for a flexible, modular framework supporting the connection of existing data sources with data analysis and modelling tools. Such an infrastructure could pave the way to IRA workflows applicable ad-hoc, e.g. in case of a crisis situation.

Keywords: import risk assessment, review, tools, food import

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Authors: Binder Hans

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Cancer is no longer seen as solely a genetic disease where genetic defects such as mutations and copy number variations affect gene regulation and eventually lead to aberrant cell functioning which can be monitored by transcriptome analysis. It has become obvious that epigenetic alterations represent a further important layer of (de-)regulation of gene activity. For example, aberrant DNA methylation is a hallmark of many cancer types, and methylation patterns were successfully used to subtype cancer heterogeneity. Hence, unraveling the interplay between different omics levels such as genome, transcriptome and epigenome is inevitable for a mechanistic understanding of molecular deregulation causing complex diseases such as cancer. This objective requires powerful downstream integrative bioinformatics methods as an essential prerequisite to discover the whole genome mutational, transcriptome and epigenome landscapes of cancer specimen and to discover cancer genesis, progression and heterogeneity. Basic challenges and tasks arise ‘beyond sequencing’ because of the big size of the data, their complexity, the need to search for hidden structures in the data, for knowledge mining to discover biological function and also systems biology conceptual models to deduce developmental interrelations between different cancer states. These tasks are tightly related to cancer biology as an (epi-)genetic disease giving rise to aberrant genomic regulation under micro-environmental control and clonal evolution which leads to heterogeneous cellular states. Machine learning algorithms such as self organizing maps (SOM) represent one interesting option to tackle these bioinformatics tasks. The SOMmethod enables recognizing complex patterns in large-scale data generated by highthroughput omics technologies. It portrays molecular phenotypes by generating individualized, easy to interpret images of the data landscape in combination with comprehensive analysis options. Our image-based, reductionist machine learning methods provide one interesting perspective how to deal with massive data in the discovery of complex diseases, gliomas, melanomas and colon cancer on molecular level. As an important new challenge, we address the combined portrayal of different omics data such as genome-wide genomic, transcriptomic and methylomic ones. The integrative-omics portrayal approach is based on the joint training of the data and it provides separate personalized data portraits for each patient and data type which can be analyzed by visual inspection as one option. The new method enables an integrative genome-wide view on the omics data types and the underlying regulatory modes. It is applied to high and low-grade gliomas and to melanomas where it disentangles transversal and longitudinal molecular heterogeneity in terms of distinct molecular subtypes and progression paths with prognostic impact.

Keywords: integrative bioinformatics, machine learning, molecular mechanisms of cancer, gliomas and melanomas

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Authors: Claudio Di Benedetto, Irene Bengo

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The objective of the paper is to study how large European companies develop social business (SB) at the base of the economic pyramid (BoP). BoP markets are defined as the four billions people living with an annual income below $3,260 in local purchasing power. Despite they are heterogeneous in terms of geographic range they present some common characteristics: the presence of significant unmet (social) needs, high level of informal economy and the so-called ‘poverty penalty’. As a result, most people living at BoP are excluded from the value created by the global market economy. But it is worth noting, that BoP population with an aggregate purchasing power of around $5 trillion a year, represent a huge opportunity for companies that want to enhance their long-term profitability perspective. We suggest that in this context, the development of SB is, for companies, an innovative and promising way to satisfy unmet social needs and to experience new forms of value creation. Indeed, SB can be considered a strategic model to develop CSR programs that fully integrate the social dimension into the business to create economic and social value simultaneously. Despite in literature many studies have been conducted on social business, only few have explicitly analyzed such phenomenon from a company perspective and their role in the development of such initiatives remains understudied with fragmented results. To fill this gap the paper analyzes the key characteristics of the social business initiatives developed by European companies at BoP. The study was performed analyzing 1475 European companies participating in the United Nation Global Compact, the world’s leading corporate social responsibility program. Through the analysis of the corporate websites the study identifies companies that actually do SB at BoP. For SB initiatives identified, information were collected according to a framework adapted from the SB model developed by preliminary results show that more than one hundred European companies have already implemented social businesses at BoP accounting for the 6,5% of the total. This percentage increases to 15% if the focus is on companies with more than 10.440 employees. In terms of geographic distribution 80% of companies doing SB at BoP are located in western and southern Europe. The companies more active in promoting SB belong to financial sector (20%), energy sector (17%) and food and beverage sector (12%). In terms of social needs addressed almost 30% of the companies develop SB to provide access to energy and WASH, 25% of companies develop SB to reduce local unemployment or to promote local entrepreneurship and 21% of companies develop SB to promote financial inclusion of poor. In developing SB companies implement different social business configurations ranging from forms of outsourcing to internal development models. The study identifies seven main configurations through which company develops social business and each configuration present distinguishing characteristics respect to the involvement of the company in the management, the resources provided and the benefits achieved. By performing different analysis on data collected the paper provides detailed insights on how European companies develop SB at BoP.

Keywords: base of the economic pyramid, corporate social responsibility, social business, social enterprise

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Authors: M. F. Abreu, A. I. Aguiar, R. Gaio, R. Duarte

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Background: Prison establishments constitute high-risk environments for the transmission and spread of tuberculosis (TB), given their epidemiological context and the difficulty of implementing preventive and control measures. Guidelines for control and prevention of tuberculosis in prisons have been described as incomplete and heterogeneous internationally, due to several identified obstacles, for example scarcity of human resources and funding of prisoner health services. In Portugal, a protocol was created in 2014 with the aim to define and standardize procedures of detection and prevention of tuberculosis within prisons. Objective: The main objective of this study was to identify and describe barriers to tuberculosis detection in prisons of Porto and Lisbon districts in Portugal. Methods: A cross-sectional study was conducted from 2ⁿᵈ January 2018 till 30ᵗʰ June 2018. Semi-structured questionnaires were applied to health care professionals working in the prisons of the districts of Porto (n=6) and Lisbon (n=8). As inclusion criteria we considered having work experience in the area of tuberculosis (either in diagnosis, treatment, or follow up). The questionnaires were self-administered, in paper format. Descriptive analyses of the questionnaire variables were made using frequencies and median. Afterwards, a hierarchical agglomerative clusters analysis was performed. After obtaining the clusters, the chi-square test was applied to study the association between the variables collected and the clusters. The level of significance considered was 0.05. Results: From the total of 186 health professionals, 139 met the criteria of inclusion and 82 health professionals were interviewed (62,2% of participation). Most were female, nurses, with a median age of 34 years, with term employment contract. From the cluster analysis, two groups were identified with different characteristics and behaviors for the procedures of this protocol. Statistically significant results were found in: elements of cluster 1 (78% of the total participants) work in prisons for a longer time (p=0.003), 45,3% work > 4 years while 50% of the elements of cluster 2 work for less than a year, and more frequently answered they know and apply the procedures of the protocol (p=0.000). Both clusters answered frequently the need of having theoretical-practical training for TB (p=0.000), especially in the areas of diagnosis, treatment and prevention and that there is scarcity of funding to prisoner health services (p=0.000). Regarding procedures for TB screening (periodic and contact screening) and procedures for transferring a prisoner with this disease, cluster 1 also answered more frequently to perform them (p=0.000). They also referred that the material/equipment for TB screening is accessible and available (p=0.000). From this clusters we identified as barriers scarcity of human resources, the need to theoretical-practical training for tuberculosis, inexperience in working in health services prisons and limited knowledge of protocol procedures. Conclusions: The barriers found in this study are the same described internationally. This protocol is mostly being applied in portuguese prisons. The study also showed the need to invest in human and material resources. This investigation bridged gaps in knowledge that could help prison health services optimize the care provided for early detection and adherence of prisoners to treatment of tuberculosis.

Keywords: barriers, health care professionals, prisons, protocol, tuberculosis

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Authors: Shatakshi Garg

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As the world continues to grapple with the COVID-19 pandemic, India, one of the most populous democratic federal developing nation, continues to report the highest active cases and deaths, as well as struggle to let its health infrastructure not succumb to the exponentially growing requirements of hospital beds, ventilators, oxygen to save thousands of lives daily at risk. In this context, the paper outlines the handling of the COVID-19 pandemic since it first hit India in January 2020 – the policy decisions taken by the Union and the State governments from the larger perspective of its federal structure. The Constitution of India adopted in 1950 enshrined the federal relations between the Union and the State governments by way of the constitutional division of revenue-raising and expenditure responsibilities. By way of the 72nd and 73rd Amendments in the Constitution, powers and functions were devolved further to the third tier, namely the local governments, with the intention of further strengthening the federal structure of the country. However, with time, several constitutional amendments have shifted the scales in favour of the union government. The paper briefly traces some of these major amendments as well as some policy decisions which made the federal relations asymmetrical. As a result, data on key fiscal parameters helps establish how the union government gained upper hand at the expense of weak state governments, reducing the local governments to mere constitutional bodies without adequate funds and fiscal autonomy to carry out the assigned functions. This quasi-federal structure of India with the union government amassing the majority of power in terms of ‘funds, functions and functionaries’ exposed the perils of weakening sub-national governments post COVID-19 pandemic. With a complex quasi-federal structure and a heterogeneous population of over 1.3 billion, the announcement of a sudden nationwide lockdown by the union government was followed by a plight of migrants struggling to reach homes safely in the absence of adequate arrangements for travel and safety-net made by the union government. With limited autonomy enjoyed by the states, they were mostly dictated by the union government on most aspects of handling the pandemic, including protocols for lockdown, re-opening post lockdown, and vaccination drive. The paper suggests that certain policy decisions like demonetization, the introduction of GST, etc., taken by the incumbent government since 2014 when they first came to power, have further weakened the states and local governments, which have amounted to catastrophic losses, both economic and human. The role of the executive, legislature and judiciary are explored to establish how all these three arms of the government have worked simultaneously to further weaken and expose the fault-lines of the federal structure of India, which has lent the nation incapacitated to handle this pandemic. The paper then suggests the urgency of re-looking at the federal structure of the country and undertaking measures that strengthen the sub-national governments and restore the federal spirit as was enshrined in the constitution to avoid mammoth human and economic losses from a pandemic of this sort.

Keywords: COVID-19 pandemic, India, federal structure, economic losses

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Authors: J. C. Colmenares, M. Paszkiewicz-Gawron, D. Lomot, S. R. Pradhan, A. Qayyum

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This work is a report of recent selected experiments of photocatalysis intensification using flow microphotoreactors (fabricated by an ultrasound-based technique) for photocatalytic selective oxidation of benzyl alcohol (BnOH) to benzaldehyde (PhCHO) (in the frame of the concept of lignin valorization), and the proof of concept of intensifying a flow selective photocatalytic oxidation process by acoustic cavitation. The synthesized photocatalysts were characterized by using different techniques such as UV-Vis diffuse reflectance spectroscopy, X-ray diffraction, nitrogen sorption, thermal gravimetric analysis, and transmission electron microscopy. More specifically, the work will be on: a Design and development of metal-containing TiO₂ coated microflow reactor for photocatalytic partial oxidation of benzyl alcohol: The current work introduces an efficient ultrasound-based metal (Fe, Cu, Co)-containing TiO₂ deposition on the inner walls of a perfluoroalkoxy alkanes (PFA) microtube under mild conditions. The experiments were carried out using commercial TiO₂ and sol-gel synthesized TiO₂. The rough surface formed during sonication is the site for the deposition of these nanoparticles in the inner walls of the microtube. The photocatalytic activities of these semiconductor coated fluoropolymer based microreactors were evaluated for the selective oxidation of BnOH to PhCHO in the liquid flow phase. The analysis of the results showed that various features/parameters are crucial, and by tuning them, it is feasible to improve the conversion of benzyl alcohol and benzaldehyde selectivity. Among all the metal-containing TiO₂ samples, the 0.5 at% Fe/TiO₂ (both, iron and titanium, as cheap, safe, and abundant metals) photocatalyst exhibited the highest BnOH conversion under visible light (515 nm) in a microflow system. This could be explained by the higher crystallite size, high porosity, and flake-like morphology. b. Designing/fabricating photocatalysts by a sonochemical approach and testing them in the appropriate flow sonophotoreactor towards sustainable selective oxidation of key organic model compounds of lignin: Ultrasonication (US)-assitedprecipitaion and US-assitedhydrosolvothermal methods were used for the synthesis of metal-oxide-based and metal-free-carbon-based photocatalysts, respectively. Additionally, we report selected experiments of intensification of a flow photocatalytic selective oxidation through the use of ultrasonic waves. The effort of our research is focused on the utilization of flow sonophotocatalysis for the selective transformation of lignin-based model molecules by nanostructured metal oxides (e.g., TiO₂), and metal-free carbocatalysts. A plethora of parameters that affects the acoustic cavitation phenomena, and as a result the potential of sonication were investigated (e.g. ultrasound frequency and power). Various important photocatalytic parameters such as the wavelength and intensity of the irradiated light, photocatalyst loading, type of solvent, mixture of solvents, and solution pH were also optimized.

Keywords: heterogeneous photo-catalysis, metal-free carbonaceous materials, selective redox flow sonophotocatalysis, titanium dioxide

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Authors: Justyna P. Majewska, Szymon M. Truskolaski

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The growing importance of digitalized services in the so-called new economy, including the e-sports industry, can be observed recently. Various demographic or technological changes lead consumers to modify their needs, not regarding the services themselves but the method of their application (attracting customers, forms of payment, new content, etc.). In the case of leisure-related to competitive spectating activities, there is a growing need to participate in events whose content is not sports competitions but computer games challenge – e-sport. The literature in this area so far focuses on determining the number of e-sport fans with elements of a simple statistical description (mainly concerning demographic characteristics such as age, gender, place of residence). Meanwhile, the development of the industry is influenced by a combination of many different, intertwined demographic, personality and psychosocial characteristics of customers, as well as the characteristics of their environment. Therefore, there is a need for a deeper recognition of the determinants of the behavioral patterns upon selecting digitalized services by customers, which, in the absence of available large data sets, can be achieved by using econometric simulations – multi-agent modeling. The cognitive aim of the study is to reveal internal and external determinants of behavioral patterns of customers taking into account various variants of economic development (the pace of digitization and technological development, socio-demographic changes, etc.). In the paper, an agent-based model with heterogeneous agents (characteristics of customers themselves and their environment) was developed, which allowed identifying a three-stage development scenario: i) initial interest, ii) standardization, and iii) full professionalization. The probabilities regarding the transition process were estimated using the Method of Simulated Moments. The estimation of the agent-based model parameters and sensitivity analysis reveals crucial factors that have driven a rising trend in e-sport spectating and, in a wider perspective, the development of digitalized services. Among the psychosocial characteristics of customers, they are the level of familiarization with the rules of games as well as sports disciplines, active and passive participation history and individual perception of challenging activities. Environmental factors include general reception of games, number and level of recognition of community builders and the level of technological development of streaming as well as community building platforms. However, the crucial factor underlying the good predictive power of the model is the level of professionalization. While in the initial interest phase, the entry barriers for new customers are high. They decrease during the phase of standardization and increase again in the phase of full professionalization when new customers perceive participation history inaccessible. In this case, they are prone to switch to new methods of service application – in the case of e-sport vs. sports to new content and more modern methods of its delivery. In a wider context, the findings in the paper support the idea of a life cycle of services regarding methods of their application from “traditional” to digitalized.

Keywords: agent-based modeling, digitalized services, e-sport, spectators motives

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Authors: Leo Nnamdi Ozurumba-Dwight

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Messenger ribooxynucleic acid (mRNA) molecules are compositional, protein-based. These proteins, encoding mRNA molecules (which collectively connote the transcriptome), when analyzed by RNA sequencing (RNAseq), unveils the nature of gene expression in the RNA. The obtained gene expression provides clues of cellular traits and their dynamics in presentations. These can be studied in relation to function and responses. RNAseq is a practical concept in Genomics as it enables detection and quantitative analysis of mRNA molecules. Single cell and spatial transcriptomics both present varying avenues for expositions in genomic characteristics of single cells and pooled cells in disease conditions such as cancer, auto-immune diseases, hematopoietic based diseases, among others, from investigated biological tissue samples. Single cell transcriptomics helps conduct a direct assessment of each building unit of tissues (the cell) during diagnosis and molecular gene expressional studies. A typical technique to achieve this is through the use of a single-cell RNA sequencer (scRNAseq), which helps in conducting high throughput genomic expressional studies. However, this technique generates expressional gene data for several cells which lack presentations on the cells’ positional coordinates within the tissue. As science is developmental, the use of complimentary pre-established tissue reference maps using molecular and bioinformatics techniques has innovatively sprung-forth and is now used to resolve this set back to produce both levels of data in one shot of scRNAseq analysis. This is an emerging conceptual approach in methodology for integrative and progressively dependable transcriptomics analysis. This can support in-situ fashioned analysis for better understanding of tissue functional organization, unveil new biomarkers for early-stage detection of diseases, biomarkers for therapeutic targets in drug development, and exposit nature of cell-to-cell interactions. Also, these are vital genomic signatures and characterizations of clinical applications. Over the past decades, RNAseq has generated a wide array of information that is igniting bespoke breakthroughs and innovations in Biomedicine. On the other side, spatial transcriptomics is tissue level based and utilized to study biological specimens having heterogeneous features. It exposits the gross identity of investigated mammalian tissues, which can then be used to study cell differentiation, track cell line trajectory patterns and behavior, and regulatory homeostasis in disease states. Also, it requires referenced positional analysis to make up of genomic signatures that will be sassed from the single cells in the tissue sample. Given these two presented approaches to RNA transcriptomics study in varying quantities of cell lines, with avenues for appropriate resolutions, both approaches have made the study of gene expression from mRNA molecules interesting, progressive, developmental, and helping to tackle health challenges head-on.

Keywords: transcriptomics, RNA sequencing, single cell, spatial, gene expression.

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Authors: A. Odoom, A. Salama, H. Ibrahim

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Hydrogen is a clean source of energy for power production and transportation. The main source of hydrogen in this research is biodiesel. Glycerol also called glycerine is a by-product of biodiesel production by transesterification of vegetable oils and methanol. This is a reliable and environmentally-friendly source of hydrogen production than fossil fuels. A typical composition of crude glycerol comprises of glycerol, water, organic and inorganic salts, soap, methanol and small amounts of glycerides. Crude glycerol has limited industrial application due to its low purity thus, the usage of crude glycerol can significantly enhance the sustainability and production of biodiesel. Reforming techniques is an approach for hydrogen production mainly Steam Reforming (SR), Autothermal Reforming (ATR) and Partial Oxidation Reforming (POR). SR produces high hydrogen conversions and yield but is highly endothermic whereas POR is exothermic. On the downside, PO yields lower hydrogen as well as large amount of side reactions. ATR which is a fusion of partial oxidation reforming and steam reforming is thermally neutral because net reactor heat duty is zero. It has relatively high hydrogen yield, selectivity as well as limits coke formation. The complex chemical processes that take place during the production phases makes it relatively difficult to construct a reliable and robust numerical model. Numerical model is a tool to mimic reality and provide insight into the influence of the parameters. In this work, we introduce a finite volume numerical study for an 'in-house' lab-scale experiment of ATR. Previous numerical studies on this process have considered either using Comsol or nodal finite difference analysis. Since Comsol is a commercial package which is not readily available everywhere and lab-scale experiment can be considered well mixed in the radial direction. One spatial dimension suffices to capture the essential feature of ATR, in this work, we consider developing our own numerical approach using MATLAB. A continuum fixed bed reactor is modelled using MATLAB with both pseudo homogeneous and heterogeneous models. The drawback of nodal finite difference formulation is that it is not locally conservative which means that materials and momenta can be generated inside the domain as an artifact of the discretization. Control volume, on the other hand, is locally conservative and suites very well problems where materials are generated and consumed inside the domain. In this work, species mass balance, Darcy’s equation and energy equations are solved using operator splitting technique. Therefore, diffusion-like terms are discretized implicitly while advection-like terms are discretized explicitly. An upwind scheme is adapted for the advection term to ensure accuracy and positivity. Comparisons with the experimental data show very good agreements which build confidence in our modeling approach. The models obtained were validated and optimized for better results.

Keywords: autothermal reforming, crude glycerol, hydrogen, numerical model

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Authors: Sascha Kraus, Wolfgang Hora, Philipp Stieg, Thomas Niemand, Ferdinand Thies, Matthias Filser

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In most countries, small and medium-sized enterprises (SME) are the backbone of the economy due to their impact on job creation, innovation and wealth creation. Moreover, the ongoing globalization makes it inevitable – even for SME that traditionally focused on their domestic markets – to internationalize their business activities to realize further growth and survive in international markets. Thus, internationalization has become one of the most common growth strategies for SME and has received increasing scholarly attention over the last two decades. One the downside internationalization can be also regarded as the most complex strategy that a firm can undertake. Particularly for family firms, that are often characterized by limited financial capital, a risk-averse nature and limited growth aspirations, it could be argued that family firms are more likely to face greater challenges when taking the pathway to internationalization. Especially the triangulation of family, ownership, and management (so-called ‘familiness’) manifests in a unique behavior and decision-making process which is often characterized by the importance given to noneconomic goals and distinguishes a family firm from other businesses. Taking this into account, the concept of socio-emotional wealth (SEW) has been evolved to describe the behavior of family firms. In order to investigate how different internal and external firm characteristics shape internationalization success of family firms, we drew on a sample consisting of 297 small and medium-sized family firms from Germany, Austria, Switzerland, and Liechtenstein. Thus, we include SEW as essential family firm characteristic and added the two major intra-organizational characteristics, entrepreneurial orientation (EO), absorptive capacity (AC) as well as collaboration intensity (CI) and relational knowledge (RK) as two major external network characteristics. Based on previous research we assume that these characteristics are important to explain internationalization success of family firm SME. Regarding the data analysis, we applied a Fuzzy Set Qualitative Comparative Analysis (fsQCA), an approach that allows identifying configurations of firm characteristics, specifically used to study complex causal relationships where traditional regression techniques reach their limits. Results indicate that several combinations of these family firm characteristics can lead to international success, with no permanently required key characteristic. Instead, there are many roads to walk down for family firms to achieve internationalization success. Consequently, our data states that family owned SME are heterogeneous and internationalization is a complex and dynamic process. Results further show that network related characteristics occur in all sets, thus represent an essential element in the internationalization process of family owned SME. The contribution of our study is twofold, as we investigate different forms of international expansion for family firms and how to improve them. First, we are able to broaden the understanding of the intersection between family firm and SME internationalization with respect to major intra-organizational and network-related variables. Second, from a practical perspective, we offer family firm owners a basis for setting up internal capabilities to achieve international success.

Keywords: entrepreneurial orientation, family firm, fsQCA, internationalization, socio-emotional wealth

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Authors: Izabela P. Vatanabe, Rafaela Peron, Patricia Manzine, Marcia R. Cominetti

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Introduction: Considering the increase in life expectancy of world population, there is an emerging concern in health services to allocate better care and care to elderly, through promotion, prevention and treatment of health. It has been observed that frailty syndrome is prevalent in elderly people worldwide and this complex and heterogeneous clinical syndrome consist of the presence of physical frailty associated with cognitive dysfunction, though in absence of dementia. This can be characterized by exhaustion, unintentional weight loss, decreased walking speed, weakness and low level of physical activity, in addition, each of these symptoms may be a predictor of adverse outcomes such as hospitalization, falls, functional decline, institutionalization, and death. Cognitive frailty is a recent concept in literature, which is defined as the presence of physical frailty associated with mild cognitive impairment (MCI) however in absence of dementia. This new concept has been considered as a subtype of frailty, which along with aging process and its interaction with physical frailty, accelerates functional declines and can result in poor quality of life of the elderly. MCI represents a risk factor for Alzheimer's disease (AD) in view of high conversion rate for this disease. Comorbidities and physical frailty are frequently found in AD patients and are closely related to heterogeneity and clinical manifestations of the disease. The decreased platelets ADAM10 levels in AD patients, compared to cognitively healthy subjects, matched by sex, age and education. Objective: Based on these previous results, this study aims to evaluate whether ADAM10 platelet levels of could act as a biomarker of cognitive frailty. Methods: The study was approved by Ethics Committee of Federal University of São Carlos (UFSCar) and conducted in the municipality of São Carlos, headquarters of Federal University of São Carlos (UFSCar). Biological samples of subjects were collected, analyzed and then stored in a biorepository. ADAM10 platelet levels were analyzed by western blotting technique in subjects with MCI and compared to subjects without cognitive impairment, both with and without presence of frailty. Statistical tests of association, regression and diagnostic accuracy were performed. Results: The results have shown that ADAM10/β-actin ratio is decreased in elderly individuals with cognitive frailty compared to non-frail and cognitively healthy controls. Previous studies performed by this research group, already mentioned above, demonstrated that this reduction is still higher in AD patients. Therefore, the ADAM10/β-actin ratio appears to be a potential biomarker for cognitive frailty. The results bring important contributions to an accurate diagnosis of cognitive frailty from the perspective of ADAM10 as a biomarker for this condition, however, more experiments are being conducted, using a high number of subjects, and will help to understand the role of ADAM10 as biomarker of cognitive frailty and contribute to the implementation of tools that work in the diagnosis of cognitive frailty. Such tools can be used in public policies for the diagnosis of cognitive frailty in the elderly, resulting in a more adequate planning for health teams and better quality of life for the elderly.

Keywords: ADAM10, biomarkers, cognitive frailty, elderly

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Authors: Pedro Llanos, Diego García

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This paper is addressed to expanding our understanding of the effects of hypoxia training on our bodies to better model its dynamics and leverage some of its implications and effects on human health. Hypoxia training is a recommended practice for military and civilian pilots that allow them to recognize their early hypoxia signs and symptoms, and Scientist Astronaut Candidates (SACs) who underwent hypobaric hypoxia (HH) exposure as part of a training activity for prospective suborbital flight applications. This observational-analytical study describes physiologic responses and symptoms experienced by a SAC group before, during and after HH exposure and proposes a model for assessing predicted versus observed physiological responses. A group of individuals with diverse Science Technology Engineering Mathematics (STEM) backgrounds conducted a hypobaric training session to an altitude up to 22,000 ft (FL220) or 6,705 meters, where heart rate (HR), breathing rate (BR) and core temperature (Tc) were monitored with the use of a chest strap sensor pre and post HH exposure. A pulse oximeter registered levels of saturation of oxygen (SpO₂), number and duration of desaturations during the HH chamber flight. Hypoxia symptoms as described by the SACs during the HH training session were also registered. This data allowed to generate a preliminary predictive model of the oxygen desaturation and O₂ pressure curve for each subject, which consists of a sixth-order polynomial fit during exposure, and a fifth or fourth-order polynomial fit during recovery. Data analysis showed that HR and BR showed no significant differences between pre and post HH exposure in most of the SACs, while Tc measures showed slight but consistent decrement changes. All subjects registered SpO₂ greater than 94% for the majority of their individual HH exposures, but all of them presented at least one clinically significant desaturation (SpO₂ < 85% for more than 5 seconds) and half of the individuals showed SpO₂ below 87% for at least 30% of their HH exposure time. Finally, real time collection of HH symptoms presented temperature somatosensory perceptions (SP) for 65% of individuals, and task-focus issues for 52.5% of individuals as the most common HH indications. 95% of the subjects experienced HH onset symptoms below FL180; all participants achieved full recovery of HH symptoms within 1 minute of donning their O₂ mask. The current HH study performed on this group of individuals suggests a rapid and fully reversible physiologic response after HH exposure as expected and obtained in previous studies. Our data showed consistent results between predicted versus observed SpO₂ curves during HH suggesting a mathematical function that may be used to model HH performance deficiencies. During the HH study, real-time HH symptoms were registered providing evidenced SP and task focusing as the earliest and most common indicators. Finally, an assessment of HH signs of symptoms in a group of heterogeneous, non-pilot individuals showed similar results to previous studies in homogeneous populations of pilots.

Keywords: slow onset hypoxia, hypobaric chamber training, altitude sickness, symptoms and altitude, pressure cabin

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Authors: Gaurav Kumar Sinha

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In an era defined by the proliferation of data and the utilization of cloud computing environments, the efficient transfer and processing of big data workloads across multi-cloud platforms have emerged as critical challenges. This research paper embarks on a comprehensive exploration of the complexities associated with managing and optimizing big data in a multi-cloud ecosystem.The foundation of this study is rooted in the recognition that modern enterprises increasingly rely on multiple cloud providers to meet diverse business needs, enhance redundancy, and reduce vendor lock-in. As a consequence, managing data across these heterogeneous cloud environments has become intricate, necessitating innovative approaches to ensure data integrity, security, and performance.The primary objective of this research is to investigate strategies and techniques for enhancing the efficiency of data transfer and processing in multi-cloud scenarios. It recognizes that big data workloads are characterized by their sheer volume, variety, velocity, and complexity, making traditional data management solutions insufficient for harnessing the full potential of multi-cloud architectures.The study commences by elucidating the challenges posed by multi-cloud environments in the context of big data. These challenges encompass data fragmentation, latency, security concerns, and cost optimization. To address these challenges, the research explores a range of methodologies and solutions. One of the key areas of focus is data transfer optimization. The paper delves into techniques for minimizing data movement latency, optimizing bandwidth utilization, and ensuring secure data transmission between different cloud providers. It evaluates the applicability of dedicated data transfer protocols, intelligent data routing algorithms, and edge computing approaches in reducing transfer times.Furthermore, the study examines strategies for efficient data processing across multi-cloud environments. It acknowledges that big data processing requires distributed and parallel computing capabilities that span across cloud boundaries. The research investigates containerization and orchestration technologies, serverless computing models, and interoperability standards that facilitate seamless data processing workflows.Security and data governance are paramount concerns in multi-cloud environments. The paper explores methods for ensuring data security, access control, and compliance with regulatory frameworks. It considers encryption techniques, identity and access management, and auditing mechanisms as essential components of a robust multi-cloud data security strategy.The research also evaluates cost optimization strategies, recognizing that the dynamic nature of multi-cloud pricing models can impact the overall cost of data transfer and processing. It examines approaches for workload placement, resource allocation, and predictive cost modeling to minimize operational expenses while maximizing performance.Moreover, this study provides insights into real-world case studies and best practices adopted by organizations that have successfully navigated the challenges of multi-cloud big data management. It presents a comparative analysis of various multi-cloud management platforms and tools available in the market.

Keywords: multi-cloud environments, big data workloads, data transfer optimization, data processing strategies

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Authors: Bensaid A., Mostephaoui T., Nedjai R.

Abstract:

A considerable area of Algerian lands are threatened by the phenomenon of wind erosion. For a long time, wind erosion and its associated harmful effects on the natural environment have posed a serious threat, especially in the arid regions of the country. In recent years, as a result of increases in the irrational exploitation of natural resources (fodder) and extensive land clearing, wind erosion has particularly accentuated. The extent of degradation in the arid region of the Algerian Mécheriadepartment generated a new situation characterized by the reduction of vegetation cover, the decrease of land productivity, as well as sand encroachment on urban development zones. In this study, we attempt to investigate the potential of remote sensing and geographic information systems for detecting the spatial dynamics of the ancient dune cords based on the numerical processing of PlanetScope PSB.SB sensors images by September 29, 2021. As a second step, we prospect the use of a multi-scale geographic object-based image analysis (GEOBIA) approach to segment the high spatial resolution images acquired on heterogeneous surfaces that vary according to human influence on the environment. We have used the fractal net evolution approach (FNEA) algorithm to segment images (Baatz&Schäpe, 2000). Multispectral data, a digital terrain model layer, ground truth data, a normalized difference vegetation index (NDVI) layer, and a first-order texture (entropy) layer were used to segment the multispectral images at three segmentation scales, with an emphasis on accurately delineating the boundaries and components of the sand accumulation areas (Dune, dunes fields, nebka, and barkhane). It is important to note that each auxiliary data contributed to improve the segmentation at different scales. The silted areas were classified using a nearest neighbor approach over the Naâma area using imagery. The classification of silted areas was successfully achieved over all study areas with an accuracy greater than 85%, although the results suggest that, overall, a higher degree of landscape heterogeneity may have a negative effect on segmentation and classification. Some areas suffered from the greatest over-segmentation and lowest mapping accuracy (Kappa: 0.79), which was partially attributed to confounding a greater proportion of mixed siltation classes from both sandy areas and bare ground patches. This research has demonstrated a technique based on very high-resolution images for mapping sanded and degraded areas using GEOBIA, which can be applied to the study of other lands in the steppe areas of the northern countries of the African continent.

Keywords: land development, GIS, sand dunes, segmentation, remote sensing

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Authors: Duncan Wallace, M-Tahar Kechadi

Abstract:

In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: A. Chávez, A. Rodríguez, F. Pinzón

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Society is concerned about measures of environmental, economic and social impacts generated in the solid waste disposal. These places of confinement, also known as landfills, are locations where problems of pollution and damage to human health are reduced. They are technically designed and operated, using engineering principles, storing the residue in a small area, compact it to reduce volume and covering them with soil layers. Problems preventing liquid (leachate) and gases produced by the decomposition of organic matter. Despite planning and site selection for disposal, monitoring and control of selected processes, remains the dilemma of the leachate as extreme concentration of pollutants, devastating soil, flora and fauna; aggressive processes requiring priority attention. A biological technology is the activated sludge system, used for tributaries with high pollutant loads. Since transforms biodegradable dissolved and particulate matter into CO2, H2O and sludge; transform suspended and no Settleable solids; change nutrients as nitrogen and phosphorous; and degrades heavy metals. The microorganisms that remove organic matter in the processes are in generally facultative heterotrophic bacteria, forming heterogeneous populations. Is possible to find unicellular fungi, algae, protozoa and rotifers, that process the organic carbon source and oxygen, as well as the nitrogen and phosphorus because are vital for cell synthesis. The mixture of the substrate, in this case sludge leachate, molasses and wastewater is maintained ventilated by mechanical aeration diffusers. Considering as the biological processes work to remove dissolved material (< 45 microns), generating biomass, easily obtained by decantation processes. The design consists of an artificial support and aeration pumps, favoring develop microorganisms (denitrifying) using oxygen (O) with nitrate, resulting in nitrogen (N) in the gas phase. Thus, avoiding negative effects of the presence of ammonia or phosphorus. Overall the activated sludge system includes about 8 hours of hydraulic retention time, which does not prevent the demand for nitrification, which occurs on average in a value of MLSS 3,000 mg/L. The extended aeration works with times greater than 24 hours detention; with ratio of organic load/biomass inventory under 0.1; and average stay time (sludge age) more than 8 days. This project developed a pilot system with sludge leachate from Doña Juana landfill - RSDJ –, located in Bogota, Colombia, where they will be subjected to a process of activated sludge and extended aeration through a sequential Bach reactor - SBR, to be dump in hydric sources, avoiding ecological collapse. The system worked with a dwell time of 8 days, 30 L capacity, mainly by removing values of BOD and COD above 90%, with initial data of 1720 mg/L and 6500 mg/L respectively. Motivating the deliberate nitrification is expected to be possible commercial use diffused aeration systems for sludge leachate from landfills.

Keywords: sludge, landfill, leachate, SBR

Procedia PDF Downloads 243

Authors: Ayman Hijazi, Witold Kwapinski, J. J. Leahy

Abstract:

Finding a sustainable alternative energy to fossil fuel is an urgent need as various environmental challenges in the world arise. Therefore, formic acid (FA) decomposition has been an attractive field that lies at the center of biomass platform, comprising a potential pool of hydrogen energy that stands as a new energy vector. Liquid FA features considerable volumetric energy density of 6.4 MJ/L and a specific energy density of 5.3 MJ/Kg that qualifies it in the prime seat as an energy source for transportation infrastructure. Additionally, the increasing research interest in FA decomposition is driven by the need of in-situ H2 production, which plays a key role in the hydrogenation reactions of biomass into higher value components. It is reported elsewhere in literature that catalytic decomposition of FA is usually performed in poorly designed setup using simple glassware under magnetic stirring, thus demanding further energy investment to retain the used catalyst. it work suggests an approach that integrates designing a novel catalyst featuring magnetic property with a robust setup that minimizes experimental & measurement discrepancies. One of the most prominent active species for dehydrogenation/hydrogenation of biomass compounds is palladium. Accordingly, we investigate the potential of engrafting palladium metal onto functionalized magnetic nanoparticles as a heterogeneous catalyst to favor the production of CO-free H2 gas from FA. Using ordinary magnet to collect the spent catalyst renders core-shell magnetic nanoparticles as the backbone of the process. Catalytic experiments were performed in a jacketed batch reactor equipped with an overhead stirrer under inert medium. Through a novel approach, FA is charged into the reactor via high-pressure positive displacement pump at steady state conditions. The produced gas (H2+CO2) was measured by connecting the gas outlet to a measuring system based on the amount of the displaced water. The novelty of this work lies in designing a very responsive catalyst, pumping consistent amount of FA into a sealed reactor running at steady state mild temperatures, and continuous gas measurement, along with collecting the used catalyst without the need for centrifugation. Catalyst characterization using TEM, XRD, SEM, and CHN elemental analyzer provided us with details of catalyst preparation and facilitated new venues to alter the nanostructure of the catalyst framework. Consequently, the introduction of amine groups has led to appreciable improvements in terms of dispersion of the doped metals and eventually attaining nearly complete conversion (100%) of FA after 7 hours. The relative importance of the process parameters such as temperature (35-85°C), stirring speed (150-450rpm), catalyst loading (50-200mgr.), and Pd doping ratio (0.75-1.80wt.%) on gas yield was assessed by a Taguchi design-of-experiment based model. Experimental results showed that operating at lower temperature range (35-50°C) yielded more gas while the catalyst loading and Pd doping wt.% were found to be the most significant factors with a P-values 0.026 & 0.031, respectively.

Keywords: formic acid decomposition, green catalysis, hydrogen, mesoporous silica, process optimization, nanoparticles

Procedia PDF Downloads 15

Authors: Ayman Hijazi, Witold Kwapinski, J. J. Leahy

Abstract:

Finding a sustainable alternative energy to fossil fuel is an urgent need as various environmental challenges in the world arise. Therefore, formic acid (FA) decomposition has been an attractive field that lies at the center of the biomass platform, comprising a potential pool of hydrogen energy that stands as a distinct energy vector. Liquid FA features considerable volumetric energy density of 6.4 MJ/L and a specific energy density of 5.3 MJ/Kg that qualifies it in the prime seat as an energy source for transportation infrastructure. Additionally, the increasing research interest in FA decomposition is driven by the need for in-situ H₂ production, which plays a key role in the hydrogenation reactions of biomass into higher-value components. It is reported elsewhere in the literature that catalytic decomposition of FA is usually performed in poorly designed setups using simple glassware under magnetic stirring, thus demanding further energy investment to retain the used catalyst. Our work suggests an approach that integrates designing a distinct catalyst featuring magnetic properties with a robust setup that minimizes experimental & measurement discrepancies. One of the most prominent active species for the dehydrogenation/hydrogenation of biomass compounds is palladium. Accordingly, we investigate the potential of engrafting palladium metal onto functionalized magnetic nanoparticles as a heterogeneous catalyst to favor the production of CO-free H₂ gas from FA. Using an ordinary magnet to collect the spent catalyst renders core-shell magnetic nanoparticles as the backbone of the process. Catalytic experiments were performed in a jacketed batch reactor equipped with an overhead stirrer under an inert medium. Through a distinct approach, FA is charged into the reactor via a high-pressure positive displacement pump at steady-state conditions. The produced gas (H₂+CO₂) was measured by connecting the gas outlet to a measuring system based on the amount of the displaced water. The uniqueness of this work lies in designing a very responsive catalyst, pumping a consistent amount of FA into a sealed reactor running at steady-state mild temperatures, and continuous gas measurement, along with collecting the used catalyst without the need for centrifugation. Catalyst characterization using TEM, XRD, SEM, and CHN elemental analyzer provided us with details of catalyst preparation and facilitated new venues to alter the nanostructure of the catalyst framework. Consequently, the introduction of amine groups has led to appreciable improvements in terms of dispersion of the doped metals and eventually attaining nearly complete conversion (100%) of FA after 7 hours. The relative importance of the process parameters such as temperature (35-85°C), stirring speed (150-450rpm), catalyst loading (50-200mgr.), and Pd doping ratio (0.75-1.80wt.%) on gas yield was assessed by a Taguchi design-of-experiment based model. Experimental results showed that operating at a lower temperature range (35-50°C) yielded more gas, while the catalyst loading and Pd doping wt.% were found to be the most significant factors with P-values 0.026 & 0.031, respectively.

Keywords: formic acid decomposition, green catalysis, hydrogen, mesoporous silica, process optimization, nanoparticles

Procedia PDF Downloads 12