Search results for: degradation gradient

Authors: Amanda Kavner, Richard Lamb

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Faster growth in science and technology of other nations may make staying globally competitive more difficult without shifting focus on how science is taught in US classes. An integral part of learning science involves visual and spatial thinking since complex, and real-world phenomena are often expressed in visual, symbolic, and concrete modes. The primary barrier to spatial thinking and visual literacy in Science, Technology, Engineering, and Math (STEM) fields is representational competence, which includes the ability to generate, transform, analyze and explain representations, as opposed to generic spatial ability. Although the relationship is known between the foundational visual literacy and the domain-specific science literacy, science literacy as a function of science learning is still not well understood. Moreover, the need for a more reliable measure is necessary to design resources which enhance the fundamental visuospatial cognitive processes behind scientific literacy. To support the improvement of students’ representational competence, first visualization skills necessary to process these science representations needed to be identified, which necessitates the development of an instrument to quantitatively measure visual literacy. With such a measure, schools, teachers, and curriculum designers can target the individual skills necessary to improve students’ visual literacy, thereby increasing science achievement. This project details the development of an artificial neural network capable of measuring science literacy using functional Near-Infrared Spectroscopy (fNIR) data. This data was previously collected by Project LENS standing for Leveraging Expertise in Neurotechnologies, a Science of Learning Collaborative Network (SL-CN) of scholars of STEM Education from three US universities (NSF award 1540888), utilizing mental rotation tasks, to assess student visual literacy. Hemodynamic response data from fNIRsoft was exported as an Excel file, with 80 of both 2D Wedge and Dash models (dash) and 3D Stick and Ball models (BL). Complexity data were in an Excel workbook separated by the participant (ID), containing information for both types of tasks. After changing strings to numbers for analysis, spreadsheets with measurement data and complexity data were uploaded to RapidMiner’s TurboPrep and merged. Using RapidMiner Studio, a Gradient Boosted Trees artificial neural network (ANN) consisting of 140 trees with a maximum depth of 7 branches was developed, and 99.7% of the ANN predictions are accurate. The ANN determined the biggest predictors to a successful mental rotation are the individual problem number, the response time and fNIR optode #16, located along the right prefrontal cortex important in processing visuospatial working memory and episodic memory retrieval; both vital for science literacy. With an unbiased measurement of science literacy provided by psychophysiological measurements with an ANN for analysis, educators and curriculum designers will be able to create targeted classroom resources to help improve student visuospatial literacy, therefore improving science literacy.

Keywords: artificial intelligence, artificial neural network, machine learning, science literacy, neuroscience

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Authors: Walid Wahballah, Taher Bazan, Fawzy Eltohamy

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One of the important functions of the satellite attitude control system is to provide the required pointing accuracy and attitude stability for optical remote sensing satellites to achieve good image quality. Although offering noise reduction and increased sensitivity, time delay and integration (TDI) charge coupled devices (CCDs) utilized in high-resolution satellites (HRS) are prone to introduce large amounts of pixel smear due to the instability of the line of sight. During on-orbit imaging, as a result of the Earth’s rotation and the satellite platform instability, the moving direction of the TDI-CCD linear array and the imaging direction of the camera become different. The speed of the image moving on the image plane (focal plane) represents the image motion velocity whereas the angle between the two directions is known as the drift angle (β). The drift angle occurs due to the rotation of the earth around its axis during satellite imaging; affecting the geometric accuracy and, consequently, causing image quality degradation. Therefore, the image motion velocity vector and the drift angle are two important factors used in the assessment of the image quality of TDI-CCD based optical remote sensing satellites. A model for estimating the image motion velocity and the drift angle in HRS is derived. The six satellite attitude control parameters represented in the derived model are the (roll angle φ, pitch angle θ, yaw angle ψ, roll angular velocity φ֗, pitch angular velocity θ֗ and yaw angular velocity ψ֗ ). The influence of these attitude parameters on the image quality is analyzed by establishing a relationship between the image motion velocity vector, drift angle and the six satellite attitude parameters. The influence of the satellite attitude parameters on the image quality is assessed by the presented model in terms of modulation transfer function (MTF) in both cross- and along-track directions. Three different cases representing the effect of pointing accuracy (φ, θ, ψ) bias are considered using four different sets of pointing accuracy typical values, while the satellite attitude stability parameters are ideal. In the same manner, the influence of satellite attitude stability (φ֗, θ֗, ψ֗) on image quality is also analysed for ideal pointing accuracy parameters. The results reveal that cross-track image quality is influenced seriously by the yaw angle bias and the roll angular velocity bias, while along-track image quality is influenced only by the pitch angular velocity bias.

Keywords: high-resolution satellites, pointing accuracy, attitude stability, TDI-CCD, smear, MTF

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Authors: E. Mosiniewicz-Szablewska, P. Suchocki, P. C. Morais

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Despite the significant progress in cancer treatment, there is a need to search for new therapeutic methods in order to minimize side effects. Chemotherapy, the main current method of treating cancer, is non-selective and has a number of limitations. Toxicity to healthy cells is undoubtedly the biggest problem limiting the use of many anticancer drugs. The problem of how to kill cancer without harming a patient can be solved by using organic selenium (IV) compounds. Organic selenium (IV) compounds are a new class of materials showing a strong anticancer activity. They are first organic compounds containing selenium at the +4 oxidation level and therefore they eliminate the multidrug-resistance for all tumor cell lines tested so far. These materials are capable of selectively killing cancer cells without damaging the healthy ones. They are obtained by the incorporation of selenous acid (H2SeO3) into molecules of fatty acids of sunflower oil and therefore, they are inexpensive to manufacture. Attaching these compounds to magnetic carriers enables their precise delivery directly to the tumor area and the simultaneous application of the magnetic hyperthermia, thus creating a huge opportunity to effectively get rid of the tumor without any side effects. Polylactic-co-glicolic acid (PLGA) nanocapsules loaded with maghemite (-Fe2O3) nanoparticles and organic selenium (IV) compounds are successfully prepared by nanoprecipitation method. In vitro antitumor activity of the nanocapsules were evidenced using murine melanoma (B16-F10), oral squamos carcinoma (OSCC) and murine (4T1) and human (MCF-7) breast lines. Further exposure of these cells to an alternating magnetic field increased the antitumor effect of nanocapsules. Moreover, the nanocapsules presented antitumor effect while not affecting normal cells. Magnetic properties of the nanocapsules were investigated by means of dc magnetization, ac susceptibility and electron spin resonance (ESR) measurements. The nanocapsules presented a typical superparamagnetic behavior around room temperature manifested itself by the split between zero field-cooled/field-cooled (ZFC/FC) magnetization curves and the absence of hysteresis on the field-dependent magnetization curve above the blocking temperature. Moreover, the blocking temperature decreased with increasing applied magnetic field. The superparamagnetic character of the nanocapsules was also confirmed by the occurrence of a maximum in temperature dependences of both real ′(T) and imaginary ′′ (T) components of the ac magnetic susceptibility, which shifted towards higher temperatures with increasing frequency. Additionally, upon decreasing the temperature the ESR signal shifted to lower fields and gradually broadened following closely the predictions for the ESR of superparamagnetoc nanoparticles. The observed superparamagnetic properties of nanocapsules enable their simple manipulation by means of magnetic field gradient, after introduction into the blood stream, which is a necessary condition for their use as magnetic drug carriers. The observed anticancer and superparamgnetic properties show that the magnetic nanocapsules loaded with organic selenium (IV) compounds should be considered as an effective material system for magnetic drug delivery and magnetohyperthermia inductor in antitumor therapy.

Keywords: cancer treatment, magnetic drug delivery system, nanomaterials, nanotechnology

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Authors: Masood Otarod, Ronald M. Supkowski

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This abstract discusses a method that reduces the general dispersion model in cylindrical coordinates to a second order linear ordinary differential equation with constant coefficients so that it can be utilized to conduct kinetic studies in packed bed tubular catalytic reactors at a broad range of Reynolds numbers. The model was tested by 13CO isotope transient tracing of the CO adsorption of Boudouard reaction in a differential reactor at an average Reynolds number of 0.2 over Pd-Al2O3 catalyst. Detailed experimental results have provided evidence for the validity of the theoretical framing of the model and the estimated parameters are consistent with the literature. The solution of the general dispersion model requires the knowledge of the radial distribution of axial velocity. This is not always known. Hence, up until now, the implementation of the dispersion model has been largely restricted to the plug-flow regime. But, ideal plug-flow is impossible to achieve and flow regimes approximating plug-flow leave much room for debate as to the validity of the results. The reduction of the general dispersion model transpires as a result of the application of a factorization theorem. Factorization theorem is derived from the observation that a cross section of a catalytic bed consists of a solid phase across which the reaction takes place and a void or porous phase across which no significant measure of reaction occurs. The disparity in flow and the heterogeneity of the catalytic bed cause the concentration of reacting compounds to fluctuate radially. These variabilities signify the existence of radial positions at which the radial gradient of concentration is zero. Succinctly, factorization theorem states that a concentration function of axial and radial coordinates in a catalytic bed is factorable as the product of the mean radial cup-mixing function and a contingent dimensionless function. The concentration of adsorbed compounds are also factorable since they are piecewise continuous functions and suffer the same variability but in the reverse order of the concentration of mobile phase compounds. Factorability is a property of packed beds which transforms the general dispersion model to an equation in terms of the measurable mean radial cup-mixing concentration of the mobile phase compounds and mean cross-sectional concentration of adsorbed species. The reduced model does not require the knowledge of the radial distribution of the axial velocity. Instead, it is characterized by new transport parameters so denoted by Ωc, Ωa, Ωc, and which are respectively denominated convection coefficient cofactor, axial dispersion coefficient cofactor, and radial dispersion coefficient cofactor. These cofactors adjust the dispersion equation as compensation for the unavailability of the radial distribution of the axial velocity. Together with the rest of the kinetic parameters they can be determined from experimental data via an optimization procedure. Our data showed that the estimated parameters Ωc, Ωa Ωr, are monotonically correlated with the Reynolds number. This is expected to be the case based on the theoretical construct of the model. Computer generated simulations of methanation reaction on nickel provide additional support for the utility of the newly conceptualized dispersion model.

Keywords: factorization, general dispersion model, isotope transient kinetic, partial differential equations

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Authors: Kulin Dave, Kapil Mohan

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Earthquakes are considered to be the most destructive rapid-onset disasters human beings are exposed to. The amount of loss it brings in is sufficient to take careful considerations for designing of structures and facilities. Seismic Hazard Analysis is one such tool which can be used for earthquake resistant design. Ground Response Analysis is one of the most crucial and decisive steps for seismic hazard analysis. Rapar district of Kutch, Gujarat falls in Zone 5 of earthquake zone map of India and thus has high seismicity because of which it is selected for analysis. In total 8 bore-log data were studied at different locations in and around Rapar district. Different soil engineering properties were analyzed and relevant empirical correlations were used to calculate maximum shear modulus (Gmax) and shear wave velocity (Vs) for the soil layers. The soil was modeled using Pressure-Dependent Modified Kodner Zelasko (MKZ) model and the reference curve used for fitting was Seed and Idriss (1970) for sand and Darendeli (2001) for clay. Both Equivalent linear (EL), as well as Non-linear (NL) ground response analysis, has been carried out with Masing Hysteretic Re/Unloading formulation for comparison. Commercially available DEEPSOIL v. 7.0 software is used for this analysis. In this study an attempt is made to quantify ground response regarding generated acceleration time-history at top of the soil column, Response spectra calculation at 5 % damping and Fourier amplitude spectrum calculation. Moreover, the variation of Peak Ground Acceleration (PGA), Maximum Displacement, Maximum Strain (in %), Maximum Stress Ratio, Mobilized Shear Stress with depth is also calculated. From the study, PGA values estimated in rocky strata are nearly same as bedrock motion and marginal amplification is observed in sandy silt and silty clays by both analyses. The NL analysis gives conservative results of maximum displacement as compared to EL analysis. Maximum strain predicted by both studies is very close to each other. And overall NL analysis is more efficient and realistic because it follows the actual hyperbolic stress-strain relationship, considers stiffness degradation and mobilizes stresses generated due to pore water pressure.

Keywords: DEEPSOIL v 7.0, ground response analysis, pressure-dependent modified Kodner Zelasko model, MKZ model, response spectra, shear wave velocity

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Authors: Belachew Gebrewold

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The Horn of Africa is marred by complex and systematic internal and external political, economic and social-cultural causes of conflict that result in internal displacement and migration. This paper engages with them and shows how such a study can help us to understand migration, both in this region and more generally. The conflict has occurred within states, between states, among proxies, between armies. Human insecurities as a result of the state collapse of Somalia, the rise of Islamic fundamentalism in the whole region, recurrent drought affecting the livelihoods of subsistence farmers as well as nomads, exposure to hunger, environmental degradation, youth unemployment, rapid growth of slums around big cities, and political repression (especially in Eritrea) have been driving various segments of the regional population into regional and international migration. Eritrea has been going through a brutal dictatorship which pushes many Eritreans to flee their country and be exposed to human trafficking, torture, detention, and agony on their way to Europe mainly through Egypt, Libya and Israel. Similarly, Somalia has been devastated since 1991 by unending civil war, state collapse, and radical Islamists. There are some important aspects to highlight in the conflict-migration nexus in the Horn of Africa: first, the main push factor for the Somalis and Eritreans to leave their countries and risk their lives is the physical insecurity they have been facing in their countries. Secondly, as a result of the conflict the economic infrastructure is massively destroyed. Investment is rare; job opportunities are out of sight. Thirdly, in such a grim situation the politically and economically induced decision to migrate is a household decision, not only an individual decision. Based on this third point this research study took place in the Horn of Africa between 2014 and 2016 during different occasions. The main objective of the research was to understanding how the increasing migration is affecting the socio-economic and socio-political environment, and conversely how the socio-economic and socio-political environments are increasing migration decisions; and whether and how these decisions are individual or family decisions. The main finding is the higher the human insecurity, the higher the family decision; the lower the human insecurity, the higher the individual decision. These findings apply not only to the Eritrean, Somali migrants but also to Ethiopian migrants. But the general impacts of migration on sending countries’ human security is quite mixed and complex.

Keywords: Eritrea, Ethiopia, Horn of Africa, insecurity, migration, Somalia

Procedia PDF Downloads 277

Authors: David Taillis, Oussama Becissa, Julien Gabaston, Jean-Michel Merillon, Tristan Richard, Stephanie Cluzet

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Nowadays, there is a strong pressure to reduce the phytosanitary inputs of synthetic chemistry in vineyards. It is, therefore, necessary to find viable alternatives in order to protect the vine against its major diseases. For this purpose, we suggest the use of a plant extract enriched in antimicrobial compounds. Being produced from vine trunks and roots, which are co-products of wine production, the extract produced is part of a circular economy. The antimicrobial molecules present in this plant material are polyphenols and, more particularly, stilbenes, which are derived from a common base, the resveratrol unit, and that are well known vine phytoalexins. The stilbenoids were extracted from trunks and roots (30/70, w/w) by a double extraction with ethyl acetate followed by enrichment by liquid-liquid extraction. The produced extract was characterized by UHPLC-MS, then its antimicrobial activities were tested on Plasmopara viticola and Botrytis cinerea in the laboratory and/or in greenhouse and in vineyard. The major compounds were purified, and their antimicrobial activity was evaluated on B. cinerea. Moreover, after its spraying, the effect of the stilbene extract on the plant defence status was evaluated by analysis of defence gene expression. UHPLC-MS analysis revealed that the extract contains 50% stilbenes with resveratrol, ε-viniferin and r-viniferin as major compounds. The extract showed antimicrobial activities on P. viticola with IC₅₀ and IC₁₀₀ respectively of 90 and 300 mg/L in the laboratory. In addition, it inhibited 40% of downy mildew development in greenhouse. However, probably because of the sensitivity of stilbenes to the environment, such as UV degradation, no activity has been observed in vineyard towards P. viticola development. For B. cinerea, the extract IC50 was 123 mg/L, with resveratrol and ε-viniferin being the most active stilbenes (IC₅₀ of 88 and 142 mg/L, respectively). The analysis of the expression of defence genes revealed that the extract can induce the expression of some defence genes 24, 48, and 72 hours after treatment, meaning that the extract has a defence-stimulating effect at least for the first three days after treatment. In conclusion, we produced a plant extract enriched in stilbenes with antimicrobial properties against two major grapevine pathogenic agents P. viticola and B. cinerea. In addition, we showed that this extract displayed eliciting activity of plant defences. This extract can therefore represent, after formulation development, a viable eco-friendly alternative for vineyard protection. Subsequently, the effect of the stilbenoid extract on primary metabolism will be evaluated by quantitative NMR.

Keywords: antimicrobial, bioprotection, grapevine, Plasmopara viticola, stilbene

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Authors: Jung-En Kuan, Whei-Fen Wu

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In this study, a symbiotic bacteria from yellow mealworm's (Tenebrio molitor) mid-gut was isolated with characteristics of growth on minimal-tributyrin medium. After a PCR-amplification of its 16s rDNA, the resultant nucleotide sequences were then analyzed by schemes of the phylogeny trees. Accordingly, it was designated as Pseudomonas nitroreducens D-01. Next, by searching the lipolytic enzymes in its protein data bank, one of those potential lipolytic α/β hydrolases was identified, again using PCR-amplification and nucleotide-sequencing methods. To construct an expression of this lipolytic gene in plasmids, the target-gene primers were then designed, carrying the C-terminal his-tag sequences. Using the vector pET21a, a recombinant lipolytic hydrolase D gene with his-tag nucleotides was successfully cloned into it, of which the lipolytic D gene is under a control of the T7 promoter. After transformation of the resultant plasmids into Eescherichia coli BL21 (DE3), an IPTG inducer was used for the induction of the recombinant proteins. The protein products were then purified by metal-ion affinity column, and the purified proteins were found capable of forming a clear zone on tributyrin agar plate. Shortly, its enzyme activities were determined by degradation of p-nitrophenyl ester(s), and the substantial yellow end-product, p-nitrophenol, was measured at O.D.405 nm. Specifically, this lipolytic enzyme efficiently targets p-nitrophenyl butyrate. As well, it shows the most reactive activities at 40°C, pH 8 in potassium phosphate buffer. In thermal stability assays, the activities of this enzyme dramatically drop when the temperature is above 50°C. In metal ion assays, MgCl₂ and NH₄Cl induce the enzyme activities while MnSO₄, NiSO₄, CaCl₂, ZnSO₄, CoCl₂, CuSO₄, FeSO₄, and FeCl₃ reduce its activities. Besides, NaCl has no effects on its enzyme activities. Most organic solvents decrease the activities of this enzyme, such as hexane, methanol, ethanol, acetone, isopropanol, chloroform, and ethyl acetate. However, its enzyme activities increase when DMSO exists. All the surfactants like Triton X-100, Tween 80, Tween 20, and Brij35 decrease its lipolytic activities. Using Lineweaver-Burk double reciprocal methods, the function of the enzyme kinetics were determined such as Km = 0.488 (mM), Vmax = 0.0644 (mM/min), and kcat = 3.01x10³ (s⁻¹), as well the total efficiency of kcat/Km is 6.17 x10³ (mM⁻¹/s⁻¹). Afterwards, based on the phylogenetic analyses, this lipolytic protein is classified to type IV lipase by its homologous conserved region in this lipase family.

Keywords: enzyme, esterase, lipotic hydrolase, type IV

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Authors: N. Balaban, A. Buernstein, F. Gelman, Z. Ronen

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Brominated flame retardants are widespread pollutants, and are known to be toxic, carcinogenic, endocrinic disrupting as well as recalcitrant. The industrial complex Neot Hovav, in the Northern Negev, Israel, is situated above a fractured chalk aquitard, which is polluted by a wide variety of halogenated organic compounds. Two of the abundant pollutants found in the site are Dibromoneopentyl-glycol (DBNPG) and tribromoneopentyl-alcohol (TBNPA). Due to the elusive nature of the groundwater flow, it is difficult to connect between the spatial changes in contaminant concentrations to degradation. In this study, we attempt to determine whether these compounds are biodegraded in the groundwater, and to gain a better understanding concerning the bacterial community in the groundwater. This was achieved through the application of compound-specific isotope analysis (CSIA) of carbon (13^C/12^C) and bromine (81^Br/79^Br), and new-generation MiSeq pyrosequencing. The sampled boreholes were distributed among three main areas of the industrial complex: around the production plant of TBNPA and DBNPG; along the Hovav Wadi (small ephemeral stream) which crosses and drains the industrial complex; and downstream to the industrial area. TBNPA and DBNPG are found in all three areas, with no clear connection to the proximity of the borehole to the production plant. Initial isotopic data of TBNPA from boreholes in the area surrounding the production plant, reveal no changes in the carbon and bromine isotopic values. When observing the microbial groundwater community, the dominant phylum is Proteobacteria. Known anaerobic dehalogenating bacteria such as Dehalococcoides from the Chloroflexi phylum have also been detected. A statistical comparison of the groundwater microbial diversity using a multi-variant ordination of non-metric multidimensional scaling (NMDS) reveals three main clusters in accordance to spatial location in the industrial complex: all the boreholes sampled adjacent to the production plant cluster together and separately from the Wadi Hovav boreholes cluster and the downstream to the industrial area borehole cluster. This work provides the basis for the development and implication of an isotopic fractionation based tool for assessing the biodegradation of brominated organic compounds in contaminated environments, and a novel attempt to characterize the spatial microbial diversity in the contaminated site.

Keywords: biodegradation, brominated flame retardants, groundwater, isotopic fractionation, microbial diversity

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Authors: Jian Yu

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Carbo dioxide (CO2) from fossil fuel combustion is a prime green-house gas emission. It can be mitigated by microalgae through conventional photosynthesis. The algal oil is a feedstock of biodiesel, a carbon neutral liquid fuel for transportation. The conventional CO2 fixation, however, is quite slow and affected by the intermittent solar irradiation. It is also a technical challenge to reform the bio-oil into a drop-in liquid fuel that can be directly used in the modern combustion engines with expected performance. Here, an artificial photosynthesis system is presented to produce a biopolyester and liquid fuels from CO2, water, and solar power. In this green process, solar energy is captured using photovoltaic modules and converted into hydrogen as a stable energy source via water electrolysis. The solar hydrogen is then used to fix CO2 by Cupriavidus necator, a hydrogen-oxidizing bacterium. Under the autotrophic conditions, CO2 was reduced to glyceraldehyde-3-phosphate (G3P) that is further utilized for cell growth and biosynthesis of polyhydroxybutyrate (PHB). The maximum cell growth rate reached 10.1 g L-1 day-1, about 25 times faster than that of a typical bio-oil-producing microalga (Neochloris Oleoabundans) under stable indoor conditions. With nitrogen nutrient limitation, a large portion of the reduced carbon is stored in PHB (C4H6O2)n, accounting for 50-60% of dry cell mass. PHB is a biodegradable thermoplastic that can find a variety of environmentally friendly applications. It is also a platform material from which small chemicals can be derived. At a high temperature (240 - 290 oC), the biopolyester is degraded into crotonic acid (C4H6O2). On a solid phosphoric acid catalyst, PHB is deoxygenated via decarboxylation into a hydrocarbon oil (C6-C18) at 240 oC or so. Aromatics and alkenes are the major compounds, depending on the reaction conditions. A gasoline-grade liquid fuel (77 wt% oil) and a biodiesel-grade fuel (23 wt% oil) were obtained from the hydrocarbon oil via distillation. The formation routes of hydrocarbon oil from crotonic acid, the major PHB degradation intermediate, are revealed and discussed. This work shows a novel green process from which biodegradable plastics and high-grade liquid fuels can be directly produced from carbon dioxide, water and solar power. The productivity of the green polyester (5.3 g L-1 d-1) is much higher than that of microalgal oil (0.13 g L-1 d-1). Other technical merits of the new green process may include continuous operation under intermittent solar irradiation and convenient scale up in outdoor.

Keywords: bioplastics, carbon dioxide fixation, drop-in liquid fuels, green process

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Authors: N. Hadhoum, B. Guerfi, T. M. Sider, Z. Yassa, T. Djerboua, M. Boursouti, M. Mamou, F. Z. Hadjadj Aoul, L. R. Mekacher

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Introduction and objectives: Chlorobutanol is a raw material, mainly used as an antiseptic and antimicrobial preservative in injectable and ophthalmic preparations. The main objective of our study was the synthesis and evaluation of the antimicrobial activity of chlorobutanol hemihydrates. Material and methods: Chlorobutanol was synthesized according to the nucleophilic addition reaction of chloroform to acetone, identified by an infrared absorption using Spectrum One FTIR spectrometer, melting point, Scanning electron microscopy and colorimetric reactions. The dosage of carvedilol active substance was carried out by assaying the degradation products of chlorobutanol in a basic solution. The chlorobutanol obtained was subjected to bacteriological tests in order to study its antimicrobial activity. The antibacterial activity was evaluated against strains such as Escherichia coli (ATCC 25 922), Staphylococcus aureus (ATCC 25 923) and Pseudomonas aeroginosa (ATCC = American type culture collection). The antifungal activity was evaluated against human pathogenic fungal strains, such as Candida albicans and Aspergillus niger provided by the parasitology laboratory of the Hospital of Tizi-Ouzou, Algeria. Results and discussion: Chlorobutanol was obtained in an acceptable yield. The characterization tests of the product obtained showed a white and crystalline appearance (confirmed by scanning electron microscopy), solubilities (in water, ethanol and glycerol), and a melting temperature in accordance with the requirements of the European pharmacopoeia. The colorimetric reactions were directed towards the presence of a trihalogenated carbon and an alcohol function. The spectral identification (IR) showed the presence of characteristic chlorobutanol peaks and confirmed the structure of the latter. The microbiological study revealed an antimicrobial effect on all strains tested (Sataphylococcus aureus (MIC = 1250 µg/ml), E. coli (MIC = 1250 µg/ml), Pseudomonas aeroginosa (MIC = 1250 µg/ml), Candida albicans (MIC =2500 µg/ml), Aspergillus niger (MIC =2500 µg/ml)) with MIC values close to literature data. Conclusion: Thus, on the whole, the synthesized chlorobutanol satisfied the requirements of the European Pharmacopoeia, and possesses antibacterial and antifungal activity; nevertheless, it is necessary to insist on the purification step of the product in order to eliminate the maximum impurities.

Keywords: antimicrobial agent, bacterial and fungal strains, chlorobutanol, MIC, minimum inhibitory concentration

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Authors: Mohammad Abu-Shaira, Weishi Shi

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Adaptive learning, a commonly employed solution to drift, involves updating predictive models online during their operation to react to concept drifts, thereby serving as a critical component and natural extension for online learning systems that learn incrementally from each example. This paper introduces LSTM-SCCM “Long Short-Term Memory Stream Cruise Control Method”, a drift adaptation-as-a-service framework for online learning. LSTM-SCCM automates drift adaptation through prompt detection, drift magnitude quantification, dynamic hyperparameter tuning, performing shortterm optimization and model recalibration for immediate adjustments, and, when necessary, conducting long-term model recalibration to ensure deeper enhancements in model performance. LSTM-SCCM is incorporated into a suite of cutting-edge online regression models, assessing their performance across various types of concept drift using diverse datasets with varying characteristics. The findings demonstrate that LSTM-SCCM represents a notable advancement in both model performance and efficacy in handling concept drift occurrences. LSTM-SCCM stands out as the sole framework adept at effectively tackling concept drifts within regression scenarios. Its proactive approach to drift adaptation distinguishes it from conventional reactive methods, which typically rely on retraining after significant degradation to model performance caused by drifts. Additionally, LSTM-SCCM employs an in-memory approach combined with the Self-Adjusting Memory (SAM) architecture to enhance real-time processing and adaptability. The framework incorporates variable thresholding techniques and does not assume any particular data distribution, making it an ideal choice for managing high-dimensional datasets and efficiently handling large-scale data. Our experiments, which include abrupt, incremental, and gradual drifts across both low- and high-dimensional datasets with varying noise levels, and applied to four state-of-the-art online regression models, demonstrate that LSTM-SCCM is versatile and effective, rendering it a valuable solution for online regression models to address concept drift.

Keywords: automated drift detection and adaptation, concept drift, hyperparameters optimization, online and adaptive learning, regression

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Alok Madan, Ashok Gupta, Arshad K. Hashmi

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Non-linear dynamic time history analysis is considered as the most advanced and comprehensive analytical method for evaluating the seismic response and performance of multi-degree-of-freedom building structures under the influence of earthquake ground motions. However, effective and accurate application of the method requires the implementation of advanced hysteretic constitutive models of the various structural components including masonry infill panels. Sophisticated computational research tools that incorporate realistic hysteresis models for non-linear dynamic time-history analysis are not popular among the professional engineers as they are not only difficult to access but also complex and time-consuming to use. And, commercial computer programs for structural analysis and design that are acceptable to practicing engineers do not generally integrate advanced hysteretic models which can accurately simulate the hysteresis behavior of structural elements with a realistic representation of strength degradation, stiffness deterioration, energy dissipation and ‘pinching’ under cyclic load reversals in the inelastic range of behavior. In this scenario, push-over or non-linear static analysis methods have gained significant popularity, as they can be employed to assess the seismic performance of building structures while avoiding the complexities and difficulties associated with non-linear dynamic time-history analysis. “Push-over” or non-linear static analysis offers a practical and efficient alternative to non-linear dynamic time-history analysis for rationally evaluating the seismic demands. The present paper is based on the analytical investigation of the effect of distribution of masonry infill panels over the elevation of planar masonry infilled reinforced concrete (R/C) frames on the seismic demands using the capacity spectrum procedures implementing nonlinear static analysis (pushover analysis) in conjunction with the response spectrum concept. An important objective of the present study is to numerically evaluate the adequacy of the capacity spectrum method using pushover analysis for performance based design of masonry infilled R/C frames for near-field earthquake ground motions.

Keywords: nonlinear analysis, capacity spectrum method, response spectrum, seismic demand, near-field earthquakes

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Authors: Aurela Jahja, Katarzyna Wątor, Arjan Beqiraj, Piotr Rusiniak, Nevton Kodhelaj

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Albanian geological terrains represent an important node of the Alpine – Mediterranean mountain belt and are divided into several predominantly NNW - SSE striking geotectonic units, which, based on the presence or lack of Cretaceous transgression and magmatic rocks, belong to Internal or External Albanides. The internal (Korabi, Mirdita and Gashi) units are characterized by the Lower Cretaceous discordance and the presence of abundant magmatic rocks whereas in the external (Alps, Krasta-Cukali, Kruja, Ionian, Sazani and Peri Adriatic Depression) units an almost continuous sedimentation from Triassic to Paleogene is evidenced. The internal and external units show relevant differences in both geothermal and heat flow density values. The gradient values vary from 15-21.3 to 36 mK/m, while the heat flow density ranges from 42 to 60 mW/m2, in the external (Preadriatic Depression) and internal (ophiolitic belt) units, respectively. The geothermal fluids, which are found in natural springs and deep oil wells of Albania, are located in four thermo-mineral provinces: a) Peshkopi (Korabi) province; b) Kruja province; c) Preadriatic basin province, and d) South Ionian province. Thirteen geothermal waters were sampled from 11 natural springs and 2 deep wells, of which 6 springs and 2 wells from Kruja, 1 spring from Peshkopia, 2 springs from Preadriatic basin and 2 springs South Ionian province. Temperature, pH and Electrical Conductivity were measured in situ, while in laboratory were analyzed by ICP method major anions and cations and several trace elements (B, Li, Sr, Rb, I, Br, etc.). The measured values of temperature, pH and electrical conductivity range within 17-63°C, 6.26-7.92 and 724- 26856µS/cm intervals, respectively. The chemical type of the Albania thermal waters is variable. In the Kruja province prevail the Cl-SO4-NaCa and Cl-Na-Ca water types; while SO4-Ca, HCO3-Ca and Cl-HCO3-Na-Ca, and Cl-Na are found in the provinces of Peshkopi, Ionian and Preadriatic basin, respectively. In the Cl-SO4-HCO3 triangular diagram most of the geothermal waters are close to the chloride corner that belong to “mature waters”, typical of geothermal deep and hot fluids. Only samples from the Ionian province are located within the region of high bicarbonate concentration and they can be classified as peripheral waters that may have mixed with cold groundwater. In the Na-Ca-Mg and Na-K-Mg triangular diagram the majority of waters fall in the corner of sodium, suggesting that their cation ratios are controlled by mineral-solution equilibrium. There is a linear relationship between Cl and B which indicates the mixing of geothermal water with cold water, where the low-chlorine thermal waters from Ionian basin and Preadriatic depression provinces are distinguished by high-chlorine thermal waters from Kruja province. The Cl/Br molar ration of the thermal waters from Kruja province ranges from 1000 to 2660 and separates them from the thermal waters of Ionian basin and Preadriatic depression provinces having Cl/Br molar ratio lower than 650. The apparent increase of Cl/Br molar ratio that correlates with the increasing of the chloride, is probably related with dissolution of the Halite.

Keywords: geothermal fluids, geotectonic units, natural springs, deep wells, mature waters, peripheral waters

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Authors: Mashad Maramaei, Behrooz Chogan, Reza Ahmadi

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Human health and the health of his watershed are inseparable. This is because a watershed is an interconnected system of "land", "water", "air" and "life". Nowadays, most of the world's watersheds show symptoms of unhealthiness and require a prompt solution. It is believed that suitable solution is a participatory and Integrated Watershed Management (IWM). In recent decades the Hyrcanian forests in the north of Iran, which belongs to the end of the third geological era, are suffering from many environmental challenges such as land degradation, increasing trends of flood, drought and accelerated soil erosion. These challenges in the main forested area of the country impose many tangible and intangible damages and human losses. This is despite the fact that in the past decades, forestry programs, watershed management and other activities in the region have been implemented in a parallel and uncoordinated manner. Therefore, recently; the Natural Resources and Watershed Management Organization has resorted to the concept of IWM planning the Hyrcanian watersheds. The Chelchai watershed as mostly degraded watershed in the eastern part of the Hyrcanian forests has been selected as a pilot watershed for implementation of the IWM. It has a drainage area of 25680 hectares and receives an average annual precipitation of 650 mm. In this mountainous region, the average temperature is 17.3 degrees Celsius. About 34% of the watershed is under cultivation, 64% under forest cover, 2% under built up areas and etc. In this research, the effectiveness or ineffectiveness of the IWM model implementation of the Natural Resources and Watershed Management Organization has been evaluated based on questionnaire method and field studies. The results indicated that IWM activities in the study area should be reconsidered and revived. Based on this research and the lessons learned during five years' experience in the Chelchai watershed; authors believe that seven important tasks are necessary for socially acceptable and successful implementation of IWM projects. These are: 1) Establishment of Local Coordination Committee (LCC) at the watershed level 2) working for development of a IWM law among government organizations to organize watershed management and eliminate parallel and contradictory activities 3) More investment on education of local communities, especially women and children 4) Development of trust builder and pattern projects that showing best agricultural and livestock management activities at each of 26 villages 5) Assigning forest protection to local communities. 6) Capacity building of government stakeholders. 7) Helping in the marketing of watershed products.

Keywords: integrated watershed management, Chelchai, Hyrcanian forests, Iran

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Authors: Huan Peng, Chenye Zeng, Zhao Wang

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Alzheimer’s disease (AD) is an age-related neurodegenerative disease that is a leading cause of dementia syndromes and has become a huge burden on society and families. The main pathological features of AD involve excessive deposition of β-amyloid (Aβ) and Tau proteins in the brain, resulting in loss of neurons, expansion of neuroinflammation, and cognitive dysfunction in patients. Researchers have found effective drugs to clear the brain of error-accumulating proteins or to slow the loss of neurons, but their direct administration has key bottlenecks such as single-drug limitation, rapid blood clearance rate, impenetrable blood-brain barrier (BBB), and poor ability to target tissues and cells. Therefore, we are committed to seeking a suitable and efficient delivery system. Inspired by the possibility that exosomes may be involved in the secretion and transport mechanism of many signaling molecules or proteins in the brain, exosomes have attracted extensive attention as natural nanoscale drug carriers. We selected exosomes derived from bone marrow mesenchymal stem cells (MSC-EXO) with low immunogenicity and exosomes derived from hippocampal neurons (HT22-EXO) that may have excellent homing ability to overcome the deficiencies of oral or injectable pathways and bypass the BBB through nasal administration and evaluated their delivery ability and effect on AD. First, MSC-EXO and HT22 cells were isolated and cultured, and MSCs were identified by microimaging and flow cytometry. Then MSC-EXO and HT22-EXO were obtained by gradient centrifugation and qEV SEC separation column, and a series of physicochemical characterization were performed by transmission electron microscope, western blot, nanoparticle tracking analysis and dynamic light scattering. Next, exosomes labeled with lipophilic fluorescent dye were administered to WT mice and APP/PS1 mice to obtain fluorescence images of various organs at different times. Finally, APP/PS1 mice were administered intranasally with two exosomes 20 times over 40 days and 20 μL each time. Behavioral analysis and pathological section analysis of the hippocampus were performed after the experiment. The results showed that MSC-EXO and HT22-EXO were successfully isolated and characterized, and they had good biocompatibility. MSC-EXO showed excellent brain enrichment in APP/PS1 mice after intranasal administration, could improve the neuronal damage and reduce inflammation levels in the hippocampus of APP/PS1 mice, and the improvement effect was significantly better than HT22-EXO. However, intranasal administration of the two exosomes did not cause depression and anxious-like phenotypes in APP/PS1 mice, nor significantly improved the short-term or spatial learning and memory ability of APP/PS1 mice, and had no significant effect on the content of Aβ plaques in the hippocampus, which also meant that MSC-EXO could use their own advantages in combination with other drugs to clear Aβ plaques. The possibility of realizing highly effective non-invasive synergistic treatment for AD provides new strategies and ideas for clinical research.

Keywords: Alzheimer’s disease, exosomes derived from mesenchymal stem cell, intranasal administration, therapy-carrier dual roles

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Authors: Mehmood Ali, Attaullah Khan, Md. Abul Kalam

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Petroleum crude oil reserves are going to deplete in future due to the consumption of fossil fuels in transportation and energy generating sector. Thus, increasing the fossil fuel prices and also causing environmental degradation issues such as climate change and global warming due to air pollution. Therefore, to tackle these issues the environmentally friendly fuels are the potential substitute with lower emissions of toxic gases. A non-edible vegetable oilseed crop, Jatropha curcas, from different origins such as Malaysia, Thailand and India were cultivated in Pakistan. The harvested seeds physical, chemical and mechanical properties were measured, having an influence on the post-harvesting machines design parameters for dehulling, storing bins, drying, oil extraction from seeds with a screw expeller and in-situ transesterification reaction to produce biodiesel fuel. The seed variety from Thailand was found better in comparison of its properties with other varieties from Malaysia and India. The seed yield from these three varieties i.e. Malaysia, Thailand and India were 829, 943 and 735 kg/ acre/ year respectively. While the oil extraction yield from Thailand variety seed was found higher (i.e. 32.61 % by wt.) as compared to other two varieties from Malaysia and India were 27.96 and 24.96 % by wt respectively. The physical properties investigated showed the geometric mean diameter of seeds from three varieties Malaysia, Thailand and India were 11.350, 10.505 and 11.324 mm, while the sphericity of seeds were found 0.656, 0.664 and 0.655. The bulk densities of the powdered seeds from three varieties Malaysia, Thailand and India, were found as 0.9697, 0.9932 and 0.9601 g/cm³ and % passing was obtained with sieve test were 78.7, 87.1 and 79.3 respectively. The densities of the extracted oil from three varieties Malaysia, Thailand and India were found 0.902, 0.898 and 0.902 g/ mL with corresponding kinematic viscosities 54.50, 49.18 and 48.16 mm2/sec respectively. The higher heating values (HHV) of extracted oil from Malaysia, Thailand and India seed varieties were measured as 40.29, 36.41 and 34.27 MJ/ kg, while the HHV of de-oiled cake from these varieties were 21.23, 20.78 and 17.31 MJ/kg respectively. The de-oiled cake can be used as compost with nutrients and carbon content to enhance soil fertility to grow future Jatropha curcas oil seed crops and also can be used as a fuel for heating and cooking purpose. Moreover, the mechanical parameter micro Vickers hardness of Malaysia seed was found lowest 16.30 HV measured with seed in a horizontal position to the loading in comparison to other two varieties as 25.2 and 18.7 HV from Thailand and India respectively. The fatty acid composition of three varieties of seed oil showed the presence of C8-C22, required to produce good quality biodiesel fuel. In terms of physicochemical properties of seeds and its extracted oil, the variety from Thailand was found better as compared to the other two varieties.

Keywords: biodiesel, Jatropha curcas, mechanical property, physico-chemical properties

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Authors: A. Matiushkina, A. Bazhenova, I. Litvinov, E. Kornilova, A. Dubavik, A. Orlova

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Magnetic-luminescent complexes based on superparamagnetic iron oxide nanoparticles (SPIONs) and semiconductor quantum dots (QDs) have been recognized as a new class of materials that have high potential in modern medicine. These materials can serve for theranostics of oncological diseases, and also as a target agent for drug delivery. They combine the qualities characteristic of magnetic nanoparticles, that is, magneto-controllability and the ability to local heating under the influence of an external magnetic field, as well as phosphors, due to luminescence of which, for example, early tumor imaging is possible. The complexity of creating complexes is the energy transfer between particles, which quenches the luminescence of QDs in complexes with SPIONs. In this regard, a relatively new type of alloyed (CdₓZn₁₋ₓSeᵧS₁₋ᵧ)-ZnS QDs is used in our work. The presence of a sufficiently thick gradient semiconductor shell in alloyed QDs makes it possible to reduce the probability of energy transfer from QDs to SPIONs in complexes. At the same time, Forster Resonance Energy Transfer (FRET) is a perfect instrument to confirm the formation of complexes based on QDs and different-type energy acceptors. The formation of complexes in the aprotic bipolar solvent dimethyl sulfoxide is ensured by the coordination of the carboxyl group of the stabilizing QD molecule (L-cysteine) on the surface iron atoms of the SPIONs. An analysis of the photoluminescence (PL) spectra has shown that a sequential increase in the SPIONs concentration in the samples is accompanied by effective quenching of the luminescence of QDs. However, it has not confirmed the formation of complexes yet, because of a decrease in the PL intensity of QDs due to reabsorption of light by SPIONs. Therefore, a study of the PL kinetics of QDs at different SPIONs concentrations was made, which demonstrates that an increase in the SPIONs concentration is accompanied by a symbatic reduction in all characteristic PL decay times. It confirms the FRET from QDs to SPIONs, which indicates the QDs/SPIONs complex formation, rather than a spontaneous aggregation of QDs, which is usually accompanied by a sharp increase in the percentage of the QD fraction with the shortest characteristic PL decay time. The complexes have been studied by the magnetic circular dichroism (MCD) spectroscopy that allows one to estimate the response of magnetic material to the applied magnetic field and also can be useful to check SPIONs aggregation. An analysis of the MCD spectra has shown that the complexes have zero residual magnetization, which is an important factor for using in biomedical applications, and don't contain SPIONs aggregates. Cell penetration, biocompatibility, and stability of QDs/SPIONs complexes in cancer cells have been studied using HeLa cell line. We have found that the complexes penetrate in HeLa cell and don't demonstrate cytotoxic effect up to 25 nM concentration. Our results clearly demonstrate that alloyed (CdₓZn₁₋ₓSeᵧS₁₋ᵧ)-ZnS QDs can be successfully used in complexes with SPIONs reached new hybrid nanostructures, which combine bright luminescence for tumor imaging and magnetic properties for targeted drug delivery and magnetic hyperthermia of tumors. Acknowledgements: This work was supported by the Ministry of Science and Higher Education of Russian Federation, goszadanie no. 2019-1080 and was financially supported by Government of Russian Federation, Grant 08-08.

Keywords: alloyed quantum dots, magnetic circular dichroism, magneto-luminescent complexes, superparamagnetic iron oxide nanoparticles

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Authors: Magdalena Diaka, Paweł Mazierski, Joanna Żebrowska, Michał Winiarski, Tomasz Klimczuk, Adriana Zaleska-Medynska

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During the last years, TiO2 nanotubes have been widely studied due to their unique highly ordered array structure, unidirectional charge transfer and higher specific surface area compared to conventional TiO2 powder. These photoactive materials, in the form of thin layer, can be activated by low powered and low cost irradiation sources (such as LEDs) to remove VOCs, microorganism and to deodorize air streams. This is possible due to their directly growth on a support material and high surface area, which guarantee enhanced photon absorption together with an extensive adsorption of reactant molecules on the photocatalyst surface. TiO2 nanotubes exhibit also lots of other attractive properties, such as potential enhancement of electron percolation pathways, light conversion, and ion diffusion at the semiconductor-electrolyte interface. Pure TiO2 nanotubes were previously used to remove organic compounds from the gas phase as well as in water splitting reaction. The major factors limiting the use of TiO2 nanotubes, which have not been fully overcome, are their relatively large band gap (3-3,2 eV) and high recombination rate of photogenerated electron–hole pairs. Many different strategies were proposed to solve this problem, however titania nanostructures containing incorporated metal oxides like Ag2O shows very promising, new optical and photocatalytic properties. Unfortunately, there is still very limited number of reports regarding application of TiO2/MxOy nanostructures. In the present work, we prepared TiO2/Ag2O nanotubes obtained by anodization of Ti-Ag alloys containing 5, 10 and 15 wt. % Ag. Photocatalysts prepared in this way were characterized by X-ray diffraction spectroscopy (XRD), scanning electron microscopy (SEM), luminescence spectroscopy and UV-Vis spectroscopy. The activities of new TiO2/Ag2O were examined by photocatalytic degradation of toluene in gas phase reaction and phenol in aqueous phase using 1000 W Xenon lamp (Oriel) and light emitting diodes (LED) as a irradiation sources. Additionally efficiency of bacteria (Pseudomonas aeruginosa) removal from the gas phase was estimated. The number of surviving bacteria was determined by the serial twofold dilution microtiter plate method, in Tryptic Soy Broth medium (TSB, GibcoBRL).

Keywords: photocatalysis, antibacterial properties, titania nanotubes, new TiO2/MxOy nanostructures

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: I. Irska, S. Paszkiewicz, D. Pawlikowska, E. Piesowicz, A. Linares, T. A. Ezquerra

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Due to the number of advantages such as high tensile strength, sensitivity to hydrolytic degradation, and biocompatibility poly(lactic acid) (PLA) is one of the most common polyesters for biomedical and pharmaceutical applications. However, PLA is a rigid, brittle polymer with low heat distortion temperature and slow crystallization rate. In order to broaden the range of PLA applications, it is necessary to improve these properties. In recent years a number of new strategies have been evolved to obtain PLA-based materials with improved characteristics, including manipulation of crystallinity, plasticization, blending, and incorporation into block copolymers. Among the other methods, synthesis of aliphatic-aromatic copolyesters has been attracting considerable attention as they may combine the mechanical performance of aromatic polyesters with biodegradability known from aliphatic ones. Given the need for highly flexible biodegradable polymers, in this contribution, a series of aromatic-aliphatic based on poly(butylene terephthalate) and poly(lactic acid) (PBT-b-PLA) copolyesters exhibiting superior mechanical properties were copolymerized with an additional poly(tetramethylene oxide) (PTMO) soft block. The structure and properties of both series were characterized by means of attenuated total reflectance – Fourier transform infrared spectroscopy (ATR-FTIR), nuclear magnetic resonance spectroscopy (¹H NMR), differential scanning calorimetry (DSC), wide-angle X-ray scattering (WAXS) and dynamic mechanical, thermal analysis (DMTA). Moreover, the related changes in tensile properties have been evaluated and discussed. Lastly, the viscoelastic properties of synthesized poly(ester-ether) copolymers were investigated in detail by step cycle tensile tests. The block lengths decreased with the advance of treatment, and the block-random diblock terpolymers of (PBT-ran-PLA)-b-PTMO were obtained. DSC and DMTA analysis confirmed unambiguously that synthesized poly(ester-ether) copolymers are microphase-separated systems. The introduction of polyether co-units resulted in a decrease in crystallinity degree and melting temperature. X-ray diffraction patterns revealed that only PBT blocks are able to crystallize. The mechanical properties of (PBT-ran-PLA)-b-PTMO copolymers are a result of a unique arrangement of immiscible hard and soft blocks, providing both strength and elasticity.

Keywords: aliphatic-aromatic copolymers, multiblock copolymers, phase behavior, thermoplastic elastomers

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Authors: Belhaj Mohamed, M. Saidi, N. Boucherab, N. Ouertani, I. Bouazizi, M. Ben Jrad

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Natural hydrocarbon seepage has helped petroleum exploration as a direct indicator of gas and/or oil subsurface accumulations. Surface macro-seeps are generally an indication of a fault in an active Petroleum Seepage System belonging to a Total Petroleum System. This paper describes a case study in which multiple analytical techniques were used to identify and characterize trace petroleum-related hydrocarbons and other volatile organic compounds in groundwater samples collected from Sousse aquifer (Central Tunisia). The analytical techniques used for analyses of water samples included gas chromatography-mass spectrometry (GC-MS), capillary GC with flame-ionization detection, Compund Specific Isotope Analysis, Rock Eval Pyrolysis. The objective of the study was to confirm the presence of gasoline and other petroleum products or other volatile organic pollutants in those samples in order to assess the respective implication of each of the potentially responsible parties to the contamination of the aquifer. In addition, the degree of contamination at different depths in the aquifer was also of interest. The oil and gas seeps have been investigated using biomarker and stable carbon isotope analyses to perform oil-oil and oil-source rock correlations. The seepage gases are characterized by high CH4 content, very low δ13CCH4 values (-71,9 ‰) and high C1/C1–5 ratios (0.95–1.0), light deuterium–hydrogen isotope ratios (-198 ‰) and light δ13CC2 and δ13CCO2 values (-23,8‰ and-23,8‰ respectively) indicating a thermogenic origin with the contribution of the biogenic gas. An organic geochemistry study was carried out on the more ten oil seep samples. This study includes light hydrocarbon and biomarkers analyses (hopanes, steranes, n-alkanes, acyclic isoprenoids, and aromatic steroids) using GC and GC-MS. The studied samples show at least two distinct families, suggesting two different types of crude oil origins: the first oil seeps appears to be highly mature, showing evidence of chemical and/or biological degradation and was derived from a clay-rich source rock deposited in suboxic conditions. It has been sourced mainly by the lower Fahdene (Albian) source rocks. The second oil seeps was derived from a carbonate-rich source rock deposited in anoxic conditions, well correlated with the Bahloul (Cenomanian-Turonian) source rock.

Keywords: biomarkers, oil and gas seeps, organic geochemistry, source rock

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Authors: Hanwei Gao, Louis Jezequel, Eric Cabrol, Bernard Vitry

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The chassis system is composed of complex elements that take up all the loads from the tire-ground contact area and thus it plays an important role in numerous specifications such as durability, comfort, crash, etc. During the development of new vehicle projects in Renault, durability validation is always the main focus while deployment of comfort comes later in the project. Therefore, sometimes design choices have to be reconsidered because of the natural incompatibility between these two specifications. Besides, robustness is also an important point of concern as it is related to manufacturing costs as well as the performance after the ageing of components like shock absorbers. In this paper an approach is proposed aiming to realize a multi-objective optimization between chassis endurance and comfort while taking the random factors into consideration. The adaptive-sparse polynomial chaos expansion method (PCE) with Chebyshev polynomial series has been applied to predict responses’ uncertainty intervals of a system according to its uncertain-but-bounded parameters. The approach can be divided into three steps. First an initial design of experiments is realized to build the response surfaces which represent statistically a black-box system. Secondly within several iterations an optimum set is proposed and validated which will form a Pareto front. At the same time the robustness of each response, served as additional objectives, is calculated from the pre-defined parameter intervals and the response surfaces obtained in the first step. Finally an inverse strategy is carried out to determine the parameters’ tolerance combination with a maximally acceptable degradation of the responses in terms of manufacturing costs. A quarter car model has been tested as an example by applying the road excitations from the actual road measurements for both endurance and comfort calculations. One indicator based on the Basquin’s law is defined to compare the global chassis durability of different parameter settings. Another indicator related to comfort is obtained from the vertical acceleration of the sprung mass. An optimum set with best robustness has been finally obtained and the reference tests prove a good robustness prediction of Chebyshev PCE method. This example demonstrates the effectiveness and reliability of the approach, in particular its ability to save computational costs for a complex system.

Keywords: chassis durability, Chebyshev polynomials, multi-objective optimization, polynomial chaos expansion, ride comfort, robust design

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Authors: Karolina Porada

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Over the last 40 years, there has been a trend in landscape architecture which supporters do not perceive the role of pro-ecological or postmodern solutions in the design of public green spaces as an essential goal, shifting their attention to the 'sculptural' shaping of areas with the use of slopes, hills, embankments, and other forms of terrain. This group of designers can be considered avant-garde, which in its activities refers to land art. Initial research shows that such applications are particularly frequent in places of former post-industrial sites and landfills, utilizing materials such as debris and post-mining waste in their construction. Due to the high degradation of the environment surrounding modern man, the brownfields are a challenge and a field of interest for the representatives of landscape architecture avant-garde, who through their projects try to recover lost lands by means of transformations supported by engineering and ecological knowledge to create places where nature can develop again. The analysis of a dozen or so facilities made it possible to come up with an important conclusion: apart from the cultural aspects (including artistic activities), the green areas formally referring to the land are important in the process of remediation of post-industrial sites and waste recycling (e. g. from construction sites). In these processes, there is also a potential for applying the concept of Natural Based Solutions, i.e. solutions allowing for the natural development of the site in such a way as to use it to cope with environmental problems, such as e.g.  air pollution, soil phytoremediation and climate change. The paper presents examples of modern parks, whose compositions are based on shaping the surface of the terrain in a way referring to the land art, at the same time providing an example of brownfields reuse and application of waste recycling.  For the purposes of object analysis, research methods such as historical-interpretation studies, case studies, qualitative research or the method of logical argumentation were used. The obtained results provide information about the role that landscape architecture can have in the process of remediation of degraded areas, at the same time guaranteeing the benefits, such as the shaping of landscapes attractive in terms of visual appearance, low costs of implementation, and improvement of the natural environment quality.

Keywords: brownfields, contemporary parks, landscape architecture, remediation

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Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

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Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

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Authors: Priya Kulkarni, Soumya Koppikar, Narendrakumar Wagh, Dhanshri Ingle, Onkar Lande, Abhay Harsulkar

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Cartilage is an extracellular matrix composed of aggrecan, which imparts it with a great tensile strength, stiffness and resilience. Disruption in cartilage metabolism leading to progressive degeneration is a characteristic feature of Osteoarthritis (OA). The process involves enzymatic depolymerisation of cartilage specific proteoglycan, releasing free glycosaminoglycan (GAG). This released GAG in synovial fluid (SF) of knee joint serves as a direct measure of cartilage loss, however, limited due to its invasive nature. Western Ontario and McMaster Universities Arthritis Index (WOMAC) is widely used for assessing pain, stiffness and physical-functions in OA patients. The scale is comprised of three subscales namely, pain, stiffness and physical-function, intends to measure patient’s perspective of disease severity as well as efficacy of prescribed treatment. Twenty SF samples obtained from OA patients were analysed for their GAG values in SF using DMMB based assay. LK 1.0 vernacular version was used to attain WOMAC scale. The results were evaluated using SAS University software (Edition 1.0) for statistical significance. All OA patients revealed higher GAG values compared to the control value of 78.4±30.1µg/ml (obtained from our non-OA patients). Average WOMAC calculated was 51.3 while pain, stiffness and function estimated were 9.7, 3.9 and 37.7, respectively. Interestingly, a strong statistical correlation was established between WOMAC function subscale and GAG (p = 0.0102). This subscale is based on day-to-day activities like stair-use, bending, walking, getting in/out of car, rising from bed. However, pain and stiffness subscale did not show correlation with any of the studied markers and endorsed the atypical inflammation in OA pathology. On one side, where knee pain showed poor correlation with GAG, it is often noted that radiography is insensitive to cartilage degenerative changes; thus OA remains undiagnosed for long. Moreover, active cartilage degradation phase remains elusive to both, patient and clinician. Through analysis of large number of OA patients we have established a close association of Kellgren-Lawrence grades and increased cartilage loss. A direct attempt to correlate WOMAC and radiographic progression of OA with various biomarkers has not been attempted so far. We found a good correlation in GAG levels in SF and the function subscale.

Keywords: cartilage, Glycosaminoglycan, synovial fluid, western ontario and McMaster Universities Arthritis Index

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Authors: U. Isyaku, C. Upton, J. Dickinson

Abstract:

Critical institutional approach for understanding pathways to shared prosperity among forest communities enabled questioning the underlying rational choice assumptions that have dominated traditional institutional thinking in natural resources management. Common pool resources framing assumes that communities as social groups share collective interests and values towards achieving greater development. Hence, policies related to natural resources management in the global South prioritise economic prosperity by focusing on how to maximise material benefits and improve the livelihood options of resource dependent communities. Recent trends in commodification and marketization of ecosystem goods and services into tradable natural capital and incentivising conservation are structured in this paradigm. Several researchers however, have problematized this emerging market-based model because it undermines cultural basis for protecting natural ecosystems. By exploring how forest people’s motivations for conservation differ within the context of reducing emissions from deforestation and forest degradation (REDD+) project in Nigeria, we aim to provide an alternative approach to conceptualising prosperity beyond the traditional economic thinking. Through in depth empirical work over seven months with five communities in Nigeria’s Cross River State, Q methodology was used to uncover communities’ perspectives and meanings of forest values that underpin contemporary and historic conservation practices, expected benefits, and willingness to participate in the REDD+ process. Our study finds six discourses about forest and conservation values that transcend wealth creation, poverty reduction and livelihoods. We argue that communities’ decisions about forest conservation consist of a complex mixture of economic, emotional, moral, and ecological justice concerns that constitute new meanings and dimensions of prosperity. Prosperity is thus reconfigured as having socio-cultural and psychological pathways that could be derived through place identity and attachment, connectedness to nature, family ties, and ability to participate in everyday social life. We therefore suggest that natural resources policy making and development interventions should consider institutional arrangements that also include the psycho-cultural dimensions of prosperity among diverse community groups.

Keywords: critical institutionalism, Q methodology, REDD+, shared prosperity

Procedia PDF Downloads 345

Authors: Satyen Mondal, Frederickson Entila, Shalabh Dixit, Pompe C. Sta. Cruz, Abdelbagi M. Ismail

Abstract:

Anaerobic condition caused by flooding during germination in direct seeded rice systems, known as anaerobic germination (AG), severely reduces crop establishment in both rainfed and irrigated areas. Seeds germinating in flooded soils could encounter hypoxia or even anoxia in severe cases, and this hinders germination and seedling growth. This study was conducted to quantify the effects of incorporating two major QTLs, AG1 and AG2, associated with tolerance of flooding during germination and to assess their interactive effects on enhancing crop establishment. A greenhouse experiment was conducted at the International Rice Research Institute (IRRI), Los Baňos, Philippines, using elite lines incorporating AG1, AG2 and AG1+AG2 in the background of the popular varieties PSBRc82 (PSBRc82-AG1, PSBRc82-AG2, PSBRc82-AG1+AG2) and Ciherang-Sub1 (Ciherang-Sub1-AG1, Ciherang-Sub1-AG2, Ciherang-Sub1-AG1+AG2), along with the donors Kho Hlan On (for AG1) and Ma-Zhan Red (AG2) and the recipients PSBRc82 and Ciherang-Sub1. The experiment was conducted using concrete tanks in an RCBD with three replications. Dry seeds were sown in seedling trays then flooded with 10 cm water depth. Seedling survival, root and shoot growth and relative growth rate were measured. The germinating seedlings were used for assaying nonstructural carbohydrate (NSC) and ascorbate concentrations, lipid peroxidation, total phenolic concentration, reactive oxygen species and total amylase enzyme activity. Flooding reduced overall survival, though survival of AG1+AG2 introgression lines was greater than other genotypes. Soluble sugars increased, while starch concentration decreased gradually under flooding especially in the tolerant checks and AG1+AG2 introgression lines. Less lipid peroxidation and higher amylase activity, reduced-ascorbate (RAsA) and total phenolic contents (TPC) were observed in the tolerant checks and in AG1+AG2 introgression lines. Lipid peroxidation correlated negatively with ascorbate and total phenolic concentrations and with reactive oxygen species (ROS). Introgression of AG1+AG2 QTLs upregulated total amylase activity causing rapid starch degradation and increase in ascorbate and total phenolic concentrations resulting in higher germination and seedling growth in flooded soils.

Keywords: amylase, anaerobic germination, ascorbate, direct-seeded rice, flooding, lipid peroxidation

Procedia PDF Downloads 274

Authors: Umer Farooq

Abstract:

The present work is being presented with a general aim of highlighting the role of protection/propagation of indigenous breeds of livestock in an area as a sustainable tool for poverty alleviation. Specifically, the aim is to introduce a formerly neglected Cholistani breed of cattle being reared by the Cholistani desert nomads of Pakistan. The said work will present a detaile account of research work conducted during the last five years by the author. Furthermore, it will present the performance (productive and reproductive traits) of this breed as being reared under various nomadic systems of the desert. Results will be deducted on the basis of the research work conducted on Cholistani cattle and keeping abreast the latest reforms being provided by the Food and Agriculture Organization (FAO) and World Initiative to Support Pastoralism (WISP) of the UN. The timely attention towards the protection and propagation of this neglected breed of cattle will pave a smoother way towards poverty alleviation of rural/suburban areas and a successful sustainable agriculture in low input production systems such as Pakistan. The 15 recognized indigenous breeds of cattle constitute 43% of the total livestock population in Pakistan and belong to Zebu cattle. These precious breeds are currently under threat and might disappear even before proper documentation until and unless streamlined efforts are diverted towards them. This horrific state is due to many factors such as epidemic diseases, urbanization, indiscriminate crossing with native stock, misdirected cross breeding with exotic stock/semen, inclined livestock systems from extensive (subsistence) to intensive (commercial), lack of valuation of local breeds, decreasing natural resources, environmental degradation and global warming. Hefty work has been documented on many aspects of Sahiwal and Red Sindhi breeds of cattle in their respective local climates which have rightly gained them an international fame as being the vital tropical milk breeds of Pakistan. However, many other indigenous livestock breeds such as Cholistani cattle being reared under pastoral systems of Cholistan are yet unexplored. The productive and reproductive traits under their local climatic conditions need to be studied and the future researches may be streamlined to manipulate their indigenous potential. The timely attention will pave a smoother way towards poverty alleviation of rural/suburban areas and a successful sustainable agriculture in low input production systems.

Keywords: Cholistan desert, Pakistan, indigenous cattle, Sahiwal cattle, pastoralism

Procedia PDF Downloads 556