2-[(2-Amino­phen­yl)sulfan­yl]-N-(4-meth­oxy­phen­yl)acetamide

In the title compound, C15H16N2O2S, the dihedral angle between the 4-meth­oxy­aniline and 2-amino­benzene­thiole fragments is 35.60 (9)°. A short intra­molecular N—H⋯S contact leads to an S(5) ring. In the crystal, mol­ecules are consolidated in the form of polymeric chains along [010] as a result of N—H⋯O hydrogen bonds, which generate R 3 2(18) and R 4 3(22) loops. The polymeric chains are interlinked through C—H⋯O inter­action and complete R 2 2(8) ring motifs

Ethyl (3E)-3-[2-(4-bromo­phenyl­sulfon­yl)hydrazin-1-yl­idene]butano­ate

The asymmetric unit of title compound, C12H15BrN2O4S, contains two mol­ecules (A and B), with slightly different conformations: the bromo­phenyl rings and the SO2 planes of the sulfonyl groups are oriented at dihedral angles of 50.2 (2) (mol­ecule A) and 58.24 (7)° (mol­ecule B), and the ethyl acetate groups make dihedral angles of 63.99 (19)° (A) and 65.35 (16)° (B) with their bromo­phenyl groups. In the crystal, both mol­ecules exist as inversion dimers linked by pairs of N—H⋯O hydrogen bonds, which generate R 2 2(14) loops. The dimers are linked by C—H⋯O inter­actions

A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

Knowledge-based decision support systems for financial management are an important part of investment plans. Investors are avoiding investing in traditional investment areas such as banks due to low return on investment. The stock exchange is one of the major areas for investment presently. Various non-linear and complex factors affect the stock exchange. A robust stock exchange forecasting system remains an important need. From this line of research, we evaluate the performance of a regression-based model to check the robustness over large datasets. We also evaluate the effect of top stock exchange markets on each other. We evaluate our proposed model on the top 4 stock exchanges—New York, London, NASDAQ and Karachi stock exchange. We also evaluate our model on the top 3 companies—Apple, Microsoft, and Google. A huge (Big Data) historical data is gathered from Yahoo finance consisting of 20 years. Such huge data creates a Big Data problem. The performance of our system is evaluated on a 1-step, 6-step, and 12-step forecast. The experiments show that the proposed system produces excellent results. The results are presented in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE)

Characterising the heterogeneous nature of tufa mounds by integrating petrographic, petrophysical, acoustic and electromagnetic measurements

Abstract Determination of the physical properties of subsurface geological bodies is essential for georesource management and geotechnical applications. In the absence of direct measurements, this usually passes via geophysical methods such as seismic and ground‐penetrating radar. These require conversion to physical properties, and measurements at different scales to test for consistency. This approach is non‐trivial in geobodies with heterogeneous patterns of properties. Tufa mounds—in‐situ terrestrial carbonate buildups precipitating from geothermal waters—are characterised by high contrasts in facies and petrophysical properties from microscale to macroscale, and are therefore ideally suited to test the ability of non‐invasive geophysical methods to estimate such contrasts, and to develop petrophysical models based on geophysical properties. Here, a laboratory‐based study of a Pleistocene tufa mound in Spain is presented that combines (1) petrography, (2) digital 2D pore network analysis, (3) gas porosity and permeability measurements, (4) acoustic velocity measurements and (5) electromagnetic wave velocity and porosity determination from ground‐penetrating radar, to develop empirical petrophysical models. These results show the consistency of petrophysical properties determined with different methods across various observational scales. Electromagnetically derived porosity positively correlates with gas porosity. Petrophysical properties depend on measurable rock fabric parameters and the degree of cementation, which provide predictive tools for subsurface geobodies. Strongly cemented peloidal‐thrombolitic fabrics with intergranular and intercrystalline pores, and a dominance of small complex pores best transmit acoustic waves. Weak cementation and a significant fraction of large simple pores (framework, vegetation moulds) increase porosity and permeability of shrubby fabrics, while causing lower acoustic velocity. This study demonstrates that ground‐penetrating radar models can be used in combination with direct measurements of physical subsurface properties to capture highly contrasting physical properties associated with different sedimentary facies that would not be achievable with other methods, thus improving the understanding of formational processes

Heat release rate estimation in laminar premixed flames using laser-induced fluorescence of CH2O and H-atom

The present work demonstrates the feasibility of heat release rate imaging using the laser-induced fluorescence (LIF) of atomic hydrogen (H-atom) and formaldehyde (CH2O) in laminar premixed flames. The product of H-atom LIF and CH2O LIF signals is evaluated on a pixel-by-pixel basis and is compared with that of the OH × CH2O technique. These results for equivalence ratio ranging from 0.8 to 1.1 are compared with computations of one-dimensional freely-propagating flames. The performance of these markers is studied based on the following two aspects: the spatial accuracy of the local heat release rate and the trend in the total heat release rate with equivalence ratio. The measured trend in the spatial distribution of radicals and the deduced heat release rate agree well with the computational values. The variation in the spatially integrated heat release rate as a function of equivalence ratio is also investigated. The results suggest that the trend in the variation of the integrated heat release rate and the spatial location of heat release rate can be evaluated by either of these markers. The OH-based marker showed certain sensitivity to the chemical mechanism as compared to the H-atom based marker. Both the OH-based and H-atom based techniques provide close estimates of heat release rate. The OH based technique has practical advantage when compared to the H-atom based method, primarily due to the fact that the H-atom LIF is a two-photon process

A method for translational rat ex vivo lung perfusion experimentation

The application of ex vivo lung perfusion (EVLP) has significantly increased the successful clinical use of marginal donor lungs. While large animal EVLP models exist to test new strategies to improve organ repair, there is currently no rat EVLP model capable of maintaining long-term lung viability. Here, we describe a new rat EVLP model that addresses this need, while enabling the study of lung injury due to cold ischemic time (CIT). The technique involves perfusing and ventilating male Lewis rat donor lungs for 4 h before transplanting the left lung into a recipient rat and then evaluating lung function 2 h after reperfusion. To test injury within this model, lungs were divided into groups and exposed to different CITs (i.e., 20 min, 6 h, 12 h, 18 h and 24 h). Experiments involving the 24-h-CIT group were prematurely terminated due to the development of severe edema. For the other groups, no differences in the ratio of arterial oxygen partial pressure to fractional inspired oxygen (PaO₂/FIO₂) were observed during EVLP; however, lung compliance decreased over time in the 18-h group (P = 0.012) and the PaO₂/FIO₂ of the blood from the left pulmonary vein 2 h after transplantation was lower compared with 20-min-CIT group (P = 0.0062). This new model maintained stable lung function during 4-h EVLP and after transplantation when exposed to up to 12 h of CIT

Preclinical development of a vaccine against oligomeric alpha-synuclein based on virus-like particles

Parkinson's disease (PD) is a progressive and currently incurable neurological disorder characterised by the loss of midbrain dopaminergic neurons and the accumulation of aggregated alpha-synuclein (a-syn). Oligomeric a-syn is proposed to play a central role in spreading protein aggregation in the brain with associated cellular toxicity contributing to a progressive neurological decline. For this reason, a-syn oligomers have attracted interest as therapeutic targets for neurodegenerative conditions such as PD and other alpha-synucleinopathies. In addition to strategies using small molecules, neutralisation of the toxic oligomers by antibodies represents an attractive and highly specific strategy for reducing disease progression. Emerging active immunisation approaches using vaccines are already being trialled to induce such antibodies. Here we propose a novel vaccine based on the RNA bacteriophage (Qbeta) virus-like particle conjugated with short peptides of human a-syn. High titres of antibodies were successfully and safely generated in wild-type and human a-syn over-expressing (SNCA-OVX) transgenic mice following vaccination. Antibodies from vaccine candidates targeting the C-terminal regions of a-syn were able to recognise Lewy bodies, the hallmark aggregates in human PD brains. Furthermore, antibodies specifically targeted oligomeric and aggregated a-syn as they exhibited 100 times greater affinity for oligomeric species over monomer a-syn proteins in solution. In the SNCA-OVX transgenic mice used, vaccination was, however, unable to confer significant changes to oligomeric a-syn bioburden. Similarly, there was no discernible effect of vaccine treatment on behavioural phenotype as compared to control groups. Thus, antibodies specific for oligomeric a-syn induced by vaccination were unable to treat symptoms of PD in this particular mouse model.</p

Digital transformation in the agri-food industry: recent applications and the role of the COVID-19 pandemic

Providing food has become more complex because of climate change and other environmental and societal stressors, such as political instability, the growth in the world population, and outbreaks of new diseases, especially the COVID-19 pandemic. In response to these challenges, the agri-food industry has increased its efforts to shift to using more digital tools and other advanced technologies. The transition toward digital has been part of the fourth industrial revolution (called Industry 4.0) innovations that have and are reshaping most industries. This literature review discusses the potential of implementing digital technologies in the agri-food industry, focusing heavily on the role of the COVID-19 pandemic in fostering the adoption of greater digitalization of food supply chains. Examples of the use of these digital innovations for various food applications, and the barriers and challenges will be highlighted. The trend toward digital solutions has gained momentum since the advent of Industry 4.0 and implementations of these solutions have been accelerated by the outbreak of the COVID-19 pandemic. Important digital technology enablers that have high potential for mitigating the negative effects of both the current global health pandemic and the environmental crisis on food systems include artificial intelligence, big data, the Internet of Things, blockchain, smart sensors, robotics, digital twins, and virtual and augmented reality. However, much remains to be done to fully harness the power of Industry 4.0 technologies and achieve widespread implementation of digitalization in the agriculture and food industries

The fourth industrial revolution in the food industry—part II: Emerging food trends

The food industry has recently been under unprecedented pressure due to major global challenges, such as climate change, exponential increase in world population and urbanization, and the worldwide spread of new diseases and pandemics, such as the COVID-19. The fourth industrial revolution (Industry 4.0) has been gaining momentum since 2015 and has revolutionized the way in which food is produced, transported, stored, perceived, and consumed worldwide, leading to the emergence of new food trends. After reviewing Industry 4.0 technologies (e.g. artificial intelligence, smart sensors, robotics, blockchain, and the Internet of Things) in Part I of this work (Hassoun, Aït-Kaddour, et al. 2022. The fourth industrial revolution in the food industry—Part I: Industry 4.0 technologies. Critical Reviews in Food Science and Nutrition, 1–17.), this complimentary review will focus on emerging food trends (such as fortified and functional foods, additive manufacturing technologies, cultured meat, precision fermentation, and personalized food) and their connection with Industry 4.0 innovations. Implementation of new food trends has been associated with recent advances in Industry 4.0 technologies, enabling a range of new possibilities. The results show several positive food trends that reflect increased awareness of food chain actors of the food-related health and environmental impacts of food systems. Emergence of other food trends and higher consumer interest and engagement in the transition toward sustainable food development and innovative green strategies are expected in the future.The fourth industrial revolution in the food industry—part II: Emerging food trendssubmittedVersio