Search results for: metal processing

Authors: Binh T. T. Nguyen, Zhenyu Li, Eric Yap, Yi Zhang, Ai-Qun Liu

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Silicon-photonic-sensor system is an emerging class of analytical technologies that use evanescent field wave to sensitively measure the slight difference in the surrounding environment. The wavelength shift induced by local refractive index change is used as an indicator in the system. These devices can be served as sensors for a wide variety of chemical or biomolecular detection in clinical and environmental fields. In our study, a system including a silicon-based micro-ring resonator, microfluidic channel, and optical processing is designed, fabricated for biomolecule detection. The system is demonstrated to detect Clostridium botulinum type A neurotoxin (BoNT) in different water sources. BoNT is one of the most toxic substances known and relatively easily obtained from a cultured bacteria source. The toxin is extremely lethal with LD50 of about 0.1µg/70kg intravenously, 1µg/ 70 kg by inhalation, and 70µg/kg orally. These factors make botulinum neurotoxins primary candidates as bioterrorism or biothreat agents. It is required to have a sensing system which can detect BoNT in a short time, high sensitive and automatic. For BoNT detection, silicon-based micro-ring resonator is modified with a linker for the immobilization of the anti-botulinum capture antibody. The enzymatic reaction is employed to increase the signal hence gains sensitivity. As a result, a detection limit to 30 pg/mL is achieved by our silicon-photonic sensor within a short period of 80 min. The sensor also shows high specificity versus the other type of botulinum. In the future, by designing the multifunctional waveguide array with fully automatic control system, it is simple to simultaneously detect multi-biomaterials at a low concentration within a short period. The system has a great potential to apply for online, real-time and high sensitivity for the label-free bimolecular rapid detection.

Keywords: biotoxin, photonic, ring resonator, sensor

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Authors: Diana Karina Baigts Allende, Mariana Gonzalez Diaz, Luis Antonio Chel Guerrero, Mukthar Sandoval Peraza

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Poverty and food insecurity are still incident problems in the developing countries, where population´s diet is based on cereals which are lack in protein content. Nevertheless, during last years the use of native plants has been studied as an alternative source of protein in order to improve the nutritional intake. Chaya crop also called Spinach tree, is a prehispanic plant native from Central America and South of Mexico (Mayan culture), which has been especially valued due to its high nutritional content particularly protein and some medicinal properties. The aim of this work was to study the effect of protein isolation processing from Chaya leaf harvest in Yucatan, Mexico on its structure quality in order: i) to valorize the Chaya crop and ii) to produce low-cost and high-quality protein. Chaya leaf was extruded, clarified and recovered using: a) acid precipitation by decreasing the pH value until reach the isoelectric point (3.5) and b) thermal coagulation, by heating the protein solution at 80 °C during 30 min. Solubilized protein was re-dissolved in water and spray dried. The presence of Fraction I protein, known as RuBisCO (Rubilose-1,5-biphosfate carboxylase/oxygenase) was confirmed by gel electrophoresis (SDS-PAGE) where molecular weight bands of 55 KDa and 12 KDa were observed. The infrared spectrum showed changes in protein structure due to the isolation method. The use of high temperatures (thermal coagulation) highly decreased protein solubility in comparison to isoelectric precipitated protein, the nutritional properties according to amino acid profile was also disturbed, showing minor amounts of overall essential amino acids from 435.9 to 367.8 mg/g. Chaya protein isolate obtained by acid precipitation showed higher protein quality according to essential amino acid score compared to FAO recommendations, which could represent an important sustainable source of protein for human consumption.

Keywords: chaya leaf, nutritional properties, protein isolate, protein structure

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Authors: Olaniyi Fawole, Umezuruike Opara

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Co-products obtained from pomegranate juice processing contain high levels of polyphenols with potential high added values. From value-addition viewpoint, the aim of this study was to evaluate the stability of polyphenolic concentrations in pomegranate fruit co-products in different solvent extracts and assess the effect on the total antioxidant capacity using the FRAP, DPPH˙ and ABTS˙+ assays during simulated in vitro digestion. Pomegranate juice, marc and peel were extracted in water, 50% ethanol (50%EtOH) and absolute ethanol (100%EtOH) and analysed for total phenolic concentration (TPC), total flavonoids concentration (TFC) and total antioxidant capacity in DPPH˙, ABST˙+ and FRAP assays before and after in vitro digestion. Total phenolic concentration (TPC) and total flavonoid concentration (TFC) were in the order of peel > marc > juice throughout the in vitro digestion irrespective of the extraction solvents used. However, 50% ethanol extracted 1.1 to 12-fold more polyphenols than water and ethanol solvents depending on co-products. TPC and TFC increased significantly in gastric digests. In contrast, after the duodenal, polyphenolic concentrations decreased significantly (p < 0.05) compared to those obtained in gastric digests. Undigested samples and gastric digests showed strong and positive relationships between polyphenols and the antioxidant activities measured in DPPH, ABTS and FRAP assays, with correlation coefficients (r2) ranging between 0.930 – 0.990 whereas, the correlation between polyphenols (TPC and TFC) and radical cation scavenging activity (in ABTS) were moderately positive in duodenal digests. Findings from this study also showed that the concentration of pomegranate polyphenols and antioxidant thereof during in vitro gastro-intestinal digestion may not reflect the pre-digested phenolic concentration. Thus, this study highlights the need to provide biologically relevant information on antioxidants by providing data reflecting their stability and activity after in vitro digestion.

Keywords: by-product, DPPH, polyphenols, value addition

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Authors: Rajashekar Badam, Vedarajan Raman, Noriyoshi Matsumi

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Efficiency of energy converting and storage systems like fuel cells and Li-Air battery principally depended on oxygen reduction reaction (ORR) which occurs at cathode. As the kinetics of the ORR is very slow, it becomes the rate determining step. Exploring carbon substrates for enhancing the dispersion and activity of the metal catalyst and commercially viable simple preparation method is a very crucial area of research in the field of energy materials. Hence, many researchers made large number of carbon-based ORR materials today. But, there are hardly few studies on the effect of interaction between Pt-carbon and carbon-electrolyte on activity. In this work, we have prepared functionalized carbon-based Pt catalyst (Pt-FAB) with enhanced interfacial properties that lead to efficient ORR catalysis. The present work deals with a single-pot method to exfoliate and functionalized acetylene black with enhanced interaction with Pt as well as electrolyte. Acetylene black was functionalized and exfoliated using a facile single pot acid treatment method. The resulted FAB was further decorated with Pt-nano particles (Pt-np). The TEM images of Pt-FAB with uniformly decorated Pt-np of ~3 nm. Further, XPS studies of Pt 4f peak revealed that Pt0 peak was shifted by 0.4 eV in Pt-FAB compared to binding energy of typical Pt⁰ found in Pt/C. The shift can be ascribed to the modulation of electronic state and strong electronic interaction of Pt with carbon. Modulated electronic structure of Pt and strong electronic interaction of Pt with FAB enhances the catalytic activity and durability respectively. To understand the electrode electrolyte interface, electrochemical impedance spectroscopy was carried out. These measurements revealed that the charge transfer resistance of electrode to electrolyte for Pt-FAB is 10 times smaller than that of conventional Pt/C. The interaction with electrolyte helps reduce the interface boundaries, which in turn affects the overall catalytic performance of the electrode. Cyclic voltammetric measurements in 0.1M HClO₄ aq. at a potential scan rate of 50 mVs-1 was employed to evaluate electrochemical surface area (ECSA) of Pt. ECSA of Pt-FAB was found to be as high as 67.2 m²g⁻¹. The three-electrode system showed very high ORR catalytic activity. Mass activity at 0.9 V vs. RHE showed 460 A/g which is much higher than the DOE target values for the year 2020. Further, it showed enhanced performance by showing 723 mW/cm² of highest power density and 1006 mA/cm² of current density at 0.6 V in fuel cell single cell type configuration and 1030 mAhg⁻¹ of rechargeable capacity in Li air battery application. The higher catalytic activity can be ascribed to the improved interaction of FAB with Pt and electrolyte. The aforementioned results evince that Pt-FAB will be a promising cathode material for efficient ORR with significant cyclability for its application in fuel cells and Li-Air batteries. In conclusion, a disordered material was prepared from AB and was systematically characterized. The extremely high ORR activity and ease of preparation make it competent for replacing commercially available ORR materials.

Keywords: functionalized acetylene black, oxygen reduction reaction, fuel cells, Functionalized battery

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Authors: Virginia Martin Torrejon, Song Hang

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Gelatine foams are widely explored materials due to their biodegradability, biocompatibility, and availability. They exhibit outstanding properties and are currently subject to increasing scientific research due to their potential use in different applications, such as biocompatible cellular materials for biomedical products or biofoams as an alternative to fossil-fuel-derived packaging. Gelatine is a highly surface-active polymer, and its concentrated solutions usually do not require surfactants to achieve low surface tension. Still, anionic surfactants like sodium dodecyl sulfate (SDS) strongly interact with gelatine, impacting its viscosity and rheological properties and, in turn, their foaming behaviour. Foaming behaviour is a key parameter for cellular solids produced by mechanical foaming as it has a significant effect on the processing and properties of cellular materials. Foamability mainly impacts the density and the mechanical properties of the foams, while foam stability is crucial to achieving foams with low shrinkage and desirable pore morphology. This work aimed to investigate the influence of SDS on the foaming behaviour of concentrated gelatine foams by using a dynamic foam analyser. The study of maximum foam height created, foam formation behaviour, drainage behaviour, and foam structure with regard to bubble size and distribution were carried out in 10 wt% gelatine solutions prepared at different SDS/gelatine concentration ratios. Comparative rheological and viscometry measurements provided a good correlation with the data from the dynamic foam analyser measurements. SDS incorporation at optimum dosages and gelatine gelation led to highly stable foams at high expansion ratios. The viscosity increase of the hydrogel solution at SDS content increased was a key parameter for foam stabilization. In addition, the impact of SDS content on gelling time and gel strength also considerably impacted the foams' stability and pore structure.

Keywords: dynamic foam analyser, gelatine foams stability and foamability, gelatine-surfactant foams, gelatine-SDS rheology, gelatine-SDS viscosity

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Authors: Velimir "monty" Vesselinov, Trais Kliplhuis, Hope Jasperson

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Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

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Authors: Husain Rajib, T. H. Sheikh, D. G. Jewel

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Background: Amblyopia is a frequent cause of monocular blindness in children. It can be unilateral or bilateral reduction of best corrected visual acuity associated with decrement in visual processing, accomodation, motility, spatial perception or spatial projection. Anisometropia is an important risk factor for amblyopia that develops when unequal refractive error causes the image to be blurred in the critical developmental period and central inhibition of the visual signal originating from the affected eye associated with significant visual problems including anisokonia, strabismus, and reduced stereopsis. Methods: It is a prospective hospital based study of newly diagnosed of amblyopia seen at the pediatric clinic of Chittagong Eye Infirmary & Training Complex. There were 50 anisometropic amblyopia subjects were examined & questionnaire was piloted. Included were all patients diagnosed with refractive amblyopia between 3 to 13 years, without previous amblyopia treatment, and whose parents were interested to participate in the study. Patients diagnosed with strabismic amblyopia were excluded. Patients were first corrected with the best correction for a month. When the VA in the amblyopic eye did not improve over month, then occlusion treatment was started. Occlusion was done daily for 6-8 hours (full time) together with vision therapy. The occlusion was carried out for 3 months. Results: In this study about 8% subjects had anisometropia from myopia, 18% from hyperopia, 74% from astigmatism. The initial mean visual acuity was 0.74 ± 0.39 Log MAR and after intervention of amblyopia therapy with active vision therapy mean visual acuity was 0.34 ± 0.26 Log MAR. About 94% of subjects were improving at least two lines. The depth of amblyopia associated with type of anisometropic refractive error and magnitude of Anisometropia (p<0.005). By doing this study 10% mild amblyopia, 64% moderate and 26% severe amblyopia were found. Binocular function also decreases with magnitude of Anisometropia. Conclusion: Anisometropic amblyopia is a most important factor in pediatric age group because it can lead to visual impairment. Occlusion therapy with at least one instructed hour of active visual activity practiced out of school hours was effective in anisometropic amblyopes who were diagnosed at the age of 8 years and older, and the patients complied well with the treatment.

Keywords: refractive error, anisometropia, amblyopia, strabismic amblyopia

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Authors: Javier Perez-Vaquero, Katryn Junker, Volker Lammers, Petra Foerst

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There is increasing importance to accelerate the transition to sustainable food systems by including environmentally friendly technologies. Our work focuses on protein enrichment and fractionation of agricultural side streams by dry triboelectrostatic separation technology. Materials are fed in particulate form into a system dispersed in a highly turbulent gas stream, whereby the high collision rate of particles against surfaces and other particles greatly enhances the electrostatic charge build-up over the particle surface. A subsequent step takes the charged particles to a delimited zone in the system where there is a highly uniform, intense electric field applied. Because the charge polarity acquired by a particle is influenced by its chemical composition, morphology, and structure, the protein-rich and fiber-rich particles of the starting material get opposite charge polarities, thus following different paths as they move through the region where the electric field is present. The output is two material fractions, which differ in their respective protein content. One is a fiber-rich, low-protein fraction, while the other is a high-protein, low-fiber composition. Prior to testing, materials undergo a milling process, and some samples are stored under controlled humidity conditions. In this way, the influence of both particle size and humidity content was established. We used two oilseed meals: lupine and rapeseed. In addition to a lab-scale separator to perform the experiments, the triboelectric separation process could be successfully scaled up to a mid-scale belt separator, increasing the mass feed from g/sec to kg/hour. The triboelectrostatic separation technology opens a huge potential for the exploitation of so far underutilized alternative protein sources. Agricultural side-streams from cereal and oil production, which are generated in high volumes by the industries, can further be valorized by this process.

Keywords: bench-scale processing, dry separation, protein-enrichment, triboelectrostatic separation

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Authors: Manish Saha, Manish Kumar Niranjan, Saket Asthana

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The K₀.₅Na₀.₅NbO₃ (KNN) system has emerged as one of the most promising lead-free piezoelectric over the years. In this work, we perform a comprehensive investigation of electronic structure, lattice dynamics and dielectric/ferroelectric properties of the room temperature phase of KNN by combining ab-initio DFT-based theoretical analysis and experimental characterization. We assign the symmetry labels to KNN vibrational modes and obtain ab-initio polarized Raman spectra, Infrared (IR) reflectivity, Born-effective charge tensors, oscillator strengths etc. The computed Raman spectrum is found to agree well with the experimental spectrum. In particular, the results suggest that the mode in the range ~840-870 cm-¹ reported in the experimental studies is longitudinal optical (LO) with A_1 symmetry. The Raman mode intensities are calculated for different light polarization set-ups, which suggests the observation of different symmetry modes in different polarization set-ups. The electronic structure of KNN is investigated, and an optical absorption spectrum is obtained. Further, the performances of DFT semi-local, metal-GGA and hybrid exchange-correlations (XC) functionals, in the estimation of KNN band gaps are investigated. The KNN bandgap computed using GGA-1/2 and HSE06 hybrid functional schemes are found to be in excellant agreement with the experimental value. The COHP, electron localization function and Bader charge analysis is also performed to deduce the nature of chemical bonding in the KNN. The solid-state reaction and hydrothermal methods are used to prepare the KNN ceramics, and the effects of grain size on the physical characteristics these ceramics are examined. A comprehensive study on the impact of different synthesis techniques on the structural, electrical, and photocatalytic properties of ferroelectric ceramics KNN. The KNN-S prepared by solid-state method have significantly larger grain size as compared to that for KNN-H prepared by hydrothermal method. Furthermore, the KNN-S is found to exhibit higher dielectric, piezoelectric and ferroelectric properties as compared to KNN-H. On the other hand, the increased photocatalytic activity is observed in KNN-H as compared to KNN-S. As compared to the hydrothermal synthesis, the solid-state synthesis causes an increase in the relative dielectric permittivity (ε^') from 2394 to 3286, remnant polarization (P_r) from 15.38 to 20.41 μC/cm^², planer electromechanical coupling factor (k_p) from 0.19 to 0.28 and piezoelectric coefficient (d_33) from 88 to 125 pC/N. The KNN-S ceramics are also found to have a lower leakage current density, and higher grain resistance than KNN-H ceramic. The enhanced photocatalytic activity of KNN-H is attributed to relatively smaller particle sizes. The KNN-S and KNN-H samples are found to have degradation efficiencies of RhB solution of 20% and 65%, respectively. The experimental study highlights the importance of synthesis methods and how these can be exploited to tailor the dielectric, piezoelectric and photocatalytic properties of KNN. Overall, our study provides several bench-mark important results on KNN that have not been reported so far.

Keywords: lead-free piezoelectric, Raman intensity spectrum, electronic structure, first-principles calculations, solid state synthesis, photocatalysis, hydrothermal synthesis

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Authors: Camellia Al-Ibrahim, Jenny Yiend, Sukhwinder S. Shergill

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Background: Attention bias to threat play a role in the development, maintenance, and exacerbation of delusional beliefs in schizophrenia in which patients emphasize the threatening characteristics of stimuli and prioritise them for processing. Cognitive control deficits arise when task-irrelevant emotional information elicits attentional bias and obstruct optimal performance. This study is investigating neural correlates of interference effect of linguistic threat and whether these effects are independent of delusional severity. Methods: Using an event-related functional magnetic resonance imaging (fMRI), neural correlates of interference effect of linguistic threat during the emotional Stroop task were investigated and compared patients with schizophrenia with high (N=17) and low (N=16) paranoid symptoms and healthy controls (N=20). Participants were instructed to identify the font colour of each word presented on the screen as quickly and accurately as possible. Stimuli types vary between threat-relevant, positive and neutral words. Results: Group differences in whole brain effects indicate decreased amygdala activity in patients with high paranoid symptoms compared with low paranoid patients and healthy controls. Regions of interest analysis (ROI) validated our results within the amygdala and investigated changes within the striatum showing a pattern of reduced activation within the clinical group compared to healthy controls. Delusional severity was associated with significant decreased neural activity in the striatum within the clinical group. Conclusion: Our findings suggest that the emotional interference mediated by the amygdala and striatum may reduce responsiveness to threat-related stimuli in schizophrenia and that attenuation of fMRI Blood-oxygen-level dependent (BOLD) signal within these areas might be influenced by the severity of delusional symptoms.

Keywords: attention bias, fMRI, Schizophrenia, Stroop

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Authors: Salim H. Al Salmani, Kevin Tansey, Mohammed S. Ozigis

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The study of the growth of built-up areas and settlement expansion is a major exercise that city managers seek to undertake to establish previous and current developmental trends. This is to ensure that there is an equal match of settlement expansion needs to the appropriate levels of services and infrastructure required. This research aims at demonstrating the potential of satellite image processing technique, harnessing the utility of single feature probability-object based image analysis technique in assessing the urban growth dynamics of the Muscat Governorate in Oman for the period 1990, 2002 and 2013. This need is fueled by the continuous expansion of the Muscat Governorate beyond predicted levels of infrastructural provision. Landsat Images of the years 1990, 2002 and 2013 were downloaded and preprocessed to forestall appropriate radiometric and geometric standards. A novel approach of probability filtering of the target feature segment was implemented to derive the spatial extent of the final Built-Up Area of the Muscat governorate for the three years period. This however proved to be a useful technique as high accuracy assessment results of 55%, 70%, and 71% were recorded for the Urban Landcover of 1990, 2002 and 2013 respectively. Furthermore, the Normalized Differential Built – Up Index for the various images were derived and used to consolidate the results of the SFP-OBIA through a linear regression model and visual comparison. The result obtained showed various hotspots where urbanization have sporadically taken place. Specifically, settlement in the districts (Wilayat) of AL-Amarat, Muscat, and Qurayyat experienced tremendous change between 1990 and 2002, while the districts (Wilayat) of AL-Seeb, Bawshar, and Muttrah experienced more sporadic changes between 2002 and 2013.

Keywords: urban growth, single feature probability, object based image analysis, landcover change

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Authors: Giang Ngo Tinh Nguyen

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Agriculture plays an important role in the economy of Vietnam and coffee is one of most crucial agricultural commodities for exporting but the current farming methods and processing infrastructure could not keep up with the development of the sector. There are many catastrophic impacts on the environment such as deforestation; soil degradation that leads to a decrease in the quality of coffee beans. Therefore, improving supply chain to develop the cultivation of sustainable coffee is one of the most important strategies to boost the coffee industry and create a competitive advantage for Vietnamese coffee in the worldwide market. If all stakeholders in the supply chain network unite together; the sustainable production of coffee will be scaled up and the future of coffee industry will be firmly secured. Buon Ma Thuot city, Dak Lak province is the principal growing region for Vietnamese coffee which accounted for a third of total coffee area in Vietnam. It plays a strategically crucial role in the development of sustainable Vietnamese coffee. Thus, the research is to improve the supply chain of sustainable Vietnamese coffee production in Buon Ma Thuot city, Dak Lak province, Vietnam for the purpose of increasing the yields and export availability as well as helping coffee farmers to be more flexible in an ever-changing market situation. It will help to affirm Vietnamese coffee brand when entering international market; improve the livelihood of farmers and conserve the environment of this area. Besides, after analyzing the data, a logistic regression model is established to explain the relationship between the dependent variable and independent variables to help sustainable coffee organizations forecast the probability of farmer will be having a sustainable certificate with their current situation and help them choose promising candidates to develop sustainable programs. It investigates opinions of local farmers through quantitative surveys. Qualitative interviews are also used to interview local collectors and staff of Trung Nguyen manufacturing company to have an overview of the situation.

Keywords: supply chain management, sustainable agricultural development, sustainable coffee, Vietnamese coffee

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

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Authors: Magdalena Rost-Roszkowska, Izabela Poprawa, Alina Chachulska-Zymelka, Lukasz Chajec, Grazyna Wilczek, Piotr Wilczek, Malgorzata Lesniewska

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Lithobius forficatus, commonly known as the brown centipede, is a widespread European species, which lives in the upper layers of soil, under stones, litter, rocks, and leaves. As the soil organism, it is exposed to numerous stressors such as xenobiotics, including heavy metals, temperature, starvation, pathogens, etc. Heavy metals are treated as the environmental pollutants of the soil because of their toxic effects on plants, animals and human being. One of the heavy metals which is xenobiotic and can be taken up by plants or animals from the soil is cadmium. The digestive system of centipedes is composed of three distinct regions: fore-, mid- and hindgut. The salivary glands of centipedes are the organs which belong to the anterior region of the digestive system and take part in the synthesis, accumulation, and secretion of many substances. The middle region having contact with the food masses is treated as one of the barriers which protect the organism against any stressors which originate from the external environment, e.g., toxic metals. As the material for our studies, we chose two organs of the digestive system in brown centipede, the organs which take part in homeostasis maintenance: the salivary glands and the midgut. The main purpose of the project was to investigate the relationship between the percentage of depolarized mitochondria, mitophagy and ATP level in cells of mentioned above organs. The animals were divided into experimental groups: K – the control group, the animals cultured in a laboratory conditions in a horticultural soil and fed with Acheta domesticus larvae; Cd1 – the animals cultured in a horticultural soil supplemented with 80 mg/kg (dry weight) of CdCl2, fed with A. domesticus larvae maintained in tap water, 12 days – short-term exposure; Cd2 – the animals cultured in a horticultural soil supplemented with 80 mg/kg (dry weight) of CdCl2, fed with A. domesticus larvae maintained in tap water, 45 days – long-term exposure. The studies were conducted using transmission electron microscopy (TEM), flow cytometry and confocal microscopy. Quantitative analysis revealed that regardless of the organ, a progressive increase in the percentage of cells with depolarized mitochondria was registered, but only in the salivary glands. These were statistically significant changes from the control. In both organs, there were no differences in the level of the analyzed parameter depending on the duration of exposure of individuals to cadmium. Changes in the ultrastructure of mitochondria have been observed. With the extension of the body's exposure time to metal, an increase in the ADP/ATP index was recorded. However, changes statistically significant to the control were demonstrated in the intestine and salivary glands. The size of this intestinal index and salivary glands in the Cd2 group was about thirty and twenty times higher, respectively than in control. Acknowledgment: The study has been financed by the National Science Centre, Poland, grant no 2017/25/B/NZ4/00420.

Keywords: cadmium, digestive system, ultrastructure, centipede

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Authors: Suveen Kumar

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Oral cancer (OC) is the sixth most death causing cancer in world which includes tumour of lips, floor of the mouth, tongue, palate, cheeks, sinuses, throat, etc. Conventionally, the techniques used for OC detection are toluidine blue staining, biopsy, liquid-based cytology, visual attachments, etc., however these are limited by their highly invasive nature, low sensitivity, time consumption, sophisticated instrument handling, sample processing and high cost. Therefore, we developed biosensing chips for non-invasive detection of OC via CYFRA-21-1 biomarker. CYFRA-21-1 (molecular weight: 40 kDa) is secreted in saliva of OC patients which is a non-invasive biological fluid with a cut-off value of 3.8 ng mL-1, above which the subjects will be suffering from oral cancer. Therefore, in first work, 3-aminopropyl triethoxy silane (APTES) functionalized zirconia (ZrO2) nanoparticles (APTES/nZrO2) were used to successfully detect CYFRA-21-1 in a linear detection range (LDR) of 2-16 ng mL-1 with sensitivity of 2.2 µA mL ng-1. Successively, APTES/nZrO2-RGO was employed to prevent agglomeration of ZrO2 by providing high surface area reduced graphene oxide (RGO) support and much wider LDR (2-22 ng mL-1) was obtained with remarkable limit of detection (LOD) as 0.12 ng mL-1. Further, APTES/nY2O3/ITO platform was used for oral cancer bioseneor development. The developed biosensor (BSA/anti-CYFRA-21-1/APTES/nY2O3/ITO) have wider LDR (0.01-50 ng mL-1) with remarkable limit of detection (LOD) as 0.01 ng mL-1. To improve the sensitivity of the biosensing platform, nanocomposite of yattria stabilized nanostructured zirconia-reduced graphene oxide (nYZR) based biosensor has been developed. The developed biosensing chip having ability to detect CYFRA-21-1 biomolecules in the range of 0.01-50 ng mL-1, LOD of 7.2 pg mL-1 with sensitivity of 200 µA mL ng-1. Further, the applicability of the fabricated biosensing chips were also checked through real sample (saliva) analysis of OC patients and the obtained results showed good correlation with the standard protein detection enzyme linked immunosorbent assay (ELISA) technique.

Keywords: non-invasive, oral cancer, nanomaterials, biosensor, biochip

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Authors: Sunantha Teyarachakul

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We consider the worker-learning Newsvendor Model, under the case of lost-sales for unmet demand, with the research objective of proposing the cost-minimization order policy and lot size, scheduled to arrive at the beginning of the selling-period. In general, the New Vendor Model is used to find the optimal order quantity for the perishable items such as fashionable products or those with seasonal demand or short-life cycles. Technically, it is used when the product demand is stochastic and available for the single selling-season, and when there is only a one time opportunity for the vendor to purchase, with possibly of long ordering lead-times. Our work differs from the classical Newsvendor Model in that we incorporate the human factor (specifically worker learning) and its influence over the costs of processing units into the model. We describe this by using the well-known Wright’s Learning Curve. Most of the assumptions of the classical New Vendor Model are still maintained in our work, such as the constant per-unit cost of leftover and shortage, the zero initial inventory, as well as the continuous time. Our problem is challenging in the way that the best order quantity in the classical model, which is balancing the over-stocking and under-stocking costs, is no longer optimal. Specifically, when adding the cost-saving from worker learning to such expected total cost, the convexity of the cost function will likely not be maintained. This has called for a new way in determining the optimal order policy. In response to such challenges, we found a number of characteristics related to the expected cost function and its derivatives, which we then used in formulating the optimal ordering policy. Examples of such characteristics are; the optimal order quantity exists and is unique if the demand follows a Uniform Distribution; if the demand follows the Beta Distribution with some specific properties of its parameters, the second derivative of the expected cost function has at most two roots; and there exists the specific level of lot size that satisfies the first order condition. Our research results could be helpful for analysis of supply chain coordination and of the periodic review system for similar problems.

Keywords: inventory management, Newsvendor model, order policy, worker learning

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Authors: Debabrata Biswas

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The current trend in the development of antibiotic resistance by multiple bacterial pathogens has resulted in a troubling loss of effective antibiotic options for human. The emergence of multi-drug-resistant pathogens has necessitated higher dosages and combinations of multiple antibiotics, further exacerbating the problem of antibiotic resistance. Zoonotic bacterial pathogens, such as Salmonella, Campylobacter, Shiga toxin-producing Escherichia coli (such as enterohaemorrhagic E. coli or EHEC), and Listeria are the most common and predominant foodborne enteric infectious agents. It was observed that these pathogens gained/developed their ability to survive in the presence of antibiotics either in farm animal gut or farm environment and researchers believe that therapeutic and sub-therapeutic antibiotic use in farm animal production might play an important role in it. The mechanism of action of antimicrobial components used in farm animal production in genomic interplay in the gut and farm environment, has not been fully characterized. Even the risk of promoting the exchange of mobile genetic elements between microbes specifically pathogens needs to be evaluated in depth, to ensure sustainable farm animal production, safety of our food and to mitigate/limit the enteric infection with multiple antibiotic resistant bacterial pathogens. Due to the consumer’s demand and considering the current emerging situation, many countries are in process to withdraw antibiotic use in farm animal production. Before withdrawing use of the sub-therapeutic antibiotic or restricting the use of therapeutic antibiotics in farm animal production, it is essential to find alternative natural antimicrobials for promoting the growth of farm animal and/or treating animal diseases. Further, it is also necessary to consider whether that compound(s) has the potential to trigger the acquisition or loss of genetic materials in zoonotic and any other bacterial pathogens. Development of alternative therapeutic and sub-therapeutic antimicrobials for farm animal production and food processing and preservation and their effective implementation for sustainable strategies for farm animal production as well as the possible risk for horizontal gene transfer in major enteric pathogens will be focus in the study.

Keywords: food safety, natural antimicrobial, sustainable farming, antibiotic resistance

Procedia PDF Downloads 259

Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

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This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

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Authors: Ozan Kahraman, Hao Feng

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Manothermosonication (MTS), which consists of the simultaneous application of heat and ultrasound under moderate pressure (100-700 kPa), is one of the technologies which destroy microorganisms and inactivates enzymes. Transmission electron microscopy (TEM) is a microscopy technique in which a beam of electrons is transmitted through an ultra-thin specimen, interacting with the specimen as it passes through it. The environmental scanning electron microscope or ESEM is a scanning electron microscope (SEM) that allows for the option of collecting electron micrographs of specimens that are "wet," uncoated. These microscopy techniques allow us to observe the processing effects on the samples. This study was conducted to investigate the effects of MTS and HTST treatments on the morphology of apple-carrot juices by using TEM and ESEM microscopy. Apple-carrot juices treated with HTST (72 0C, 15 s), MTS 50 °C (60 s, 200 kPa), and MTS 60 °C (30 s, 200 kPa) were observed in both ESEM and TEM microscopy. For TEM analysis, a drop of the solution dispersed in fixative solution was put onto a Parafilm ® sheet. The copper coated side of the TEM sample holder grid was gently laid on top of the droplet and incubated for 15 min. A drop of a 7% uranyl acetate solution was added and held for 2 min. The grid was then removed from the droplet and allowed to dry at room temperature and presented into the TEM. For ESEM analysis, a critical point drying of the filters was performed using a critical point dryer (CPD) (Samdri PVT- 3D, Tousimis Research Corp., Rockville, MD, USA). After the CPD, each filter was mounted onto a stub and coated with gold/palladium with a sputter coater (Desk II TSC Denton Vacuum, Moorestown, NJ, USA). E.Coli O157:H7 cells on the filters were observed with an ESEM (Philips XL30 ESEM-FEG, FEI Co., Eindhoven, The Netherland). ESEM (Environmental Scanning Electron Microscopy) and TEM (Transmission Electron Microscopy) images showed extensive damage for the samples treated with MTS at 50 and 60 °C such as ruptured cells and breakage on cell membranes. The damage was increasing with increasing exposure time.

Keywords: MTS, HTST, ESEM, TEM, E.COLI O157:H7

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Authors: Mayra Pilamunga, Edwin Vera

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The consumption of dark chocolate is beneficial due to its high content of flavonoids, catechins, and procyanidins. To improve its properties, fortification of chocolate with polyphenols, anthocyanins, soy milk powder and other compounds has been evaluated in several studies. However, to our best knowledge, the addition of carotenes to chocolate has not been tested. Carotenoids, especially ß-carotene and lutein, are widely distributed in fruits and vegetables so that they could be extracted from agro-industrial waste, such as fruit processing. On the other hand, limonene produces crystalline changes of cocoa butter and improves its consistency and viscosity. This study aimed to evaluate the production of dark chocolate with the addition of carotenes extracted from an agro industrial waste and to improve its rheological properties and crystallization, with orange essential oil. The dried and fermented cocoa beans were purchased in Puerto Quito, Ecuador, and had a fat content of 51%. Six types of chocolates were formulated, and two formulations were chosen, one at 65% cocoa and other at 70% cocoa, both with a solid: fat ratio of 1.4:1. With the formulations selected, the influence of the addition of 0.75% and 1.5% orange essential oil was evaluated, and analysis to measure the viscosity, crystallization and sensory analysis were done. It was found that essential oil does not generate significant changes in the properties of chocolate, but has an important effect on aroma and coloration, which changed from auburn to brown. The best scores on sensory analysis were obtained for the samples formulated with 0.75% essential oil. Prior to the formulation with carotenes, the extraction of these compounds from pineapple peels were performed. The process was done with and without a previous enzymatic treatment, with three solid-solvent ratios. The best treatment was using enzymes in a solids-solvent ratio of 1:12.5; the extract obtained under these conditions had 4.503 ± 0.214 μg Eq. β-carotene/mL. This extract was encapsulated with gum arabic and maltodextrin, and the solution was dried using a freeze dryer. The encapsulated carotenes were added to the chocolate in an amount of 1.7% however 60,8 % of them were lost in the final product.

Keywords: cocoa, fat crystallization, limonene, carotenoids, pineapple peels

Procedia PDF Downloads 151

Authors: Timine Suoware, Sylvester Edelugo, Charles Amgbari

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Natural fibre composites (NFC) are becoming very attractive especially for automotive interior and non-structural building applications because they are biodegradable, low cost, lightweight and environmentally friendly. NFC are known to release high combustible products during exposure to heat atmosphere and this behaviour has raised concerns to end users. To improve on their fire response, flame retardants (FR) such as aluminium tri-hydroxide (ATH) and ammonium polyphosphate (APP) are incorporated during processing to delay the start and spread of fire. In this paper, APP was modified with Gum Arabic powder (GAP) and synergized with carbon black (CB) to form new FR species. Four FR species at 0, 12, 15 and 18% loading ratio were added to oil palm fibre polyester composite (OPFC) panels as follows; OPFC12%APP-GAP, OPFC15%APP-GAP/CB, OPFC18%ATH/APP-GAP and OPFC18%ATH/APPGAP/CB. The panels were produced using hand lay-up compression moulding and cured at room temperature. Specimens were cut from the panels and these were tested for ignition time (Tig), peak heat released rate (HRRp), average heat release rate (HRRavg), peak mass loss rate (MLRp), residual mass (Rm) and average smoke production rate (SPRavg) using cone calorimeter apparatus as well as the available flame energy (ɸ) in driving the flame using radiant panel flame spread apparatus. From the ignitability data obtained at 50 kW/m2 heat flux (HF), it shows that the hybrid FR modified with APP that is OPFC18%ATH/APP-GAP exhibited superior flame retardancy and the improvement was based on comparison with those without FR which stood at Tig = 20 s, HRRp = 86.6 kW/m2, HRRavg = 55.8 kW/m2, MLRp =0.131 g/s, Rm = 54.6% and SPRavg = 0.05 m2/s representing respectively 17.6%, 67.4%, 62.8%, 50.9%, 565% and 62.5% improvements less than those without FR (OPFC0%). In terms of flame spread, the least flame energy (ɸ) of 0.49 kW2/s3 for OPFC18%ATH/APP-GAP caused early flame regression. This was less than 39.6 kW2/s3 compared to those without FR (OPFC0%). It can be concluded that hybrid FR modified with APP could be useful in the automotive and building industries to delay the start and spread of fire.

Keywords: flame retardant, flame regression, oil palm fibre, composite panel

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Authors: Sthanu Mahadev, Wen Chan, Melanie Lim

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Anisotropy dominated continuous-fiber composite materials have garnered attention in numerous mechanical and aerospace structural applications. Tailored mechanical properties in advanced composites can exhibit superiority in terms of stiffness-to-weight ratio, strength-to-weight ratio, low-density characteristics, coupled with significant improvements in fatigue resistance as opposed to metal structure counterparts. Extensive research has demonstrated their core potential as more than just mere lightweight substitutes to conventional materials. Prior work done by Mahadev and Chan focused on formulating a modified composite shell theory based prognosis methodology for investigating the structural response of thin-walled circular cylindrical shell type composite configurations under in-plane mechanical loads respectively. The prime motivation to develop this theory stemmed from its capability to generate simple yet accurate closed-form analytical results that can efficiently characterize circular composite shell construction. It showcased the development of a novel mathematical framework to analytically identify the location of the centroid for thin-walled, open cross-section, curved composite shells that were characterized by circumferential arc angle, thickness-to-mean radius ratio, and total laminate thickness. Ply stress variations for curved cylindrical shells were analytically examined under the application of centric tensile and bending loading. This work presents a cost-effective, small-platform experimental methodology by taking advantage of the full-field measurement capability of digital image correlation (DIC) for an accurate assessment of key mechanical parameters such as in-plane mechanical stresses and strains, centroid location etc. Mechanical property measurement of advanced composite materials can become challenging due to their anisotropy and complex failure mechanisms. Full-field displacement measurements are well suited for characterizing the mechanical properties of composite materials because of the complexity of their deformation. This work encompasses the fabrication of a set of curved cylindrical shell coupons, the design and development of a novel test-fixture design and an innovative experimental methodology that demonstrates the capability to very accurately predict the location of centroid in such curved composite cylindrical strips via employing a DIC based strain measurement technique. Error percentage difference between experimental centroid measurements and previously estimated analytical centroid results are observed to be in good agreement. The developed analytical modified-shell theory provides the capability to understand the fundamental behavior of thin-walled cylindrical shells and offers the potential to generate novel avenues to understand the physics of such structures at a laminate level.

Keywords: anisotropy, composites, curved cylindrical shells, digital image correlation

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Authors: Rodlyn Remina Hines

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Males and Females respond differently to life situations, including transnational living. Being in a transnational marriage relationship may put a strain on the relationship requiring partners to adjust their behaviors and expectancies of the other partner to accommodate the disruptions in the relationship. More so, when one partner is an immigrant to a new geographic location with the other in the native country, these disruptions may be intensive. This qualitative study examined gender differences in how married Ghanaian couples respond to making a life together as a couple while living across international borders. The study asked two questions: (1) What are the perceptions of males and females on transnational living? and (2) how do married males and females respond to transnational living situations? To answer these questions, semi-structured interviews were conducted with 24 married couples- with one partner living in the United States (U.S.) and the other spouse in Ghana via purposive and snowball sampling techniques. Participants were aged 26 to 59 years with an average age of 40; the average age of relationship: 10.41; and average years of living apart: 6.7. Induction and deduction hybrid analysis strategies were used to derive emerging themes. The results highlight significant gender differences in response to transnational living status and practices. The data indicate that transnational couples with the male spouse residing in the U.S. experience more relationship strains than is the case when the female partner is the immigrant. Three couples who were in divorce proceedings at the time of the interview had the male partner residing in the U.S. and the female spouse in Ghana. These gender differences also reflected spousal visitation frequency, duration of spousal reunification, amount of and frequency of spousal remittance(s), and immigration processing procedures. Finally, the data show female immigrant partners as better managers of transnational living stresses and strains than their male counterparts. Findings from this study have implications for marriage and family practitioners and immigration policy makers.

Keywords: gender differences, , ghanaian couples, ghanaian immigrants, transnational living

Procedia PDF Downloads 74

Authors: Zahid Ullah, Atlas Khan

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The rapid advancements in data collection, storage, and processing capabilities have led to an explosion of data in various domains. In this era of big data, mathematical sciences play a crucial role in uncovering valuable insights and driving informed decision-making through data analytics. The purpose of this abstract is to present the latest advances in mathematical sciences and their application in harnessing the power of data analytics. This abstract highlights the interdisciplinary nature of data analytics, showcasing how mathematics intersects with statistics, computer science, and other related fields to develop cutting-edge methodologies. It explores key mathematical techniques such as optimization, mathematical modeling, network analysis, and computational algorithms that underpin effective data analysis and interpretation. The abstract emphasizes the role of mathematical sciences in addressing real-world challenges across different sectors, including finance, healthcare, engineering, social sciences, and beyond. It showcases how mathematical models and statistical methods extract meaningful insights from complex datasets, facilitating evidence-based decision-making and driving innovation. Furthermore, the abstract emphasizes the importance of collaboration and knowledge exchange among researchers, practitioners, and industry professionals. It recognizes the value of interdisciplinary collaborations and the need to bridge the gap between academia and industry to ensure the practical application of mathematical advancements in data analytics. The abstract highlights the significance of ongoing research in mathematical sciences and its impact on data analytics. It emphasizes the need for continued exploration and innovation in mathematical methodologies to tackle emerging challenges in the era of big data and digital transformation. In summary, this abstract sheds light on the advances in mathematical sciences and their pivotal role in unveiling the power of data analytics. It calls for interdisciplinary collaboration, knowledge exchange, and ongoing research to further unlock the potential of mathematical methodologies in addressing complex problems and driving data-driven decision-making in various domains.

Keywords: mathematical sciences, data analytics, advances, unveiling

Procedia PDF Downloads 79

Authors: Vasantha Gouri Prathapagiri

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Innovations in ICT sector facilitate qualitative life style for citizens across the globe. Countries that facilitate usage of new techniques in ICT, i.e., big data analytics find it easier to fulfil the needs of their citizens. Big data is characterised by its volume, variety, and speed. Analytics involves its processing in a cost effective way in order to draw conclusion for their useful application. Big data also involves into the field of machine learning, artificial intelligence all leading to accuracy in data presentation useful for public policy making. Hence using data analytics in public policy making is a proper way to march towards all round development of any country. The data driven insights can help the government to take important strategic decisions with regard to socio-economic development of her country. Developed nations like UK and USA are already far ahead on the path of digitization with the support of Big Data analytics. India is a huge country and is currently on the path of massive digitization being realised through Digital India Mission. Internet connection per household is on the rise every year. This transforms into a massive data set that has the potential to improvise the public services delivery system into an effective service mechanism for Indian citizens. In fact, when compared to developed nations, this capacity is being underutilized in India. This is particularly true for administrative system in rural areas. The present paper focuses on the need for big data analytics adaptation in Indian rural administration and its contribution towards development of the country on a faster pace. Results of the research focussed on the need for increasing awareness and serious capacity building of the government personnel working for rural development with regard to big data analytics and its utility for development of the country. Multiple public policies are framed and implemented for rural development yet the results are not as effective as they should be. Big data has a major role to play in this context as can assist in improving both policy making and implementation aiming at all round development of the country.

Keywords: Digital India Mission, public service delivery system, public policy, Indian administration

Procedia PDF Downloads 151

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

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

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

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Authors: H. Khomri, A. Bentellis

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Since century of the Development Activities extraction and a dispersed mineral processing Toxic metals and much more contaminated vast areas occupied by what they natural outcrops. New types of metalliferous habitats are so appeared. A species that is Lygeum spartum attracted our curiosity because apart from its valuable role in desertification, it is apparently able to exclude antimony and other metals can be. This species, green leaf blades which are provided as cattle feed, would be a good subject for phytoremediation of mineral soils. The study of absorption and translocation of chromium by the Lygeum spartum in the mining region of Djebel Hamimat and the interaction soil-plant, revealed that soils of this species living in this region are alkaline, calcareous majority in their fine texture medium and saline in their minority. They have normal levels of organic matter. They are moderately rich in nitrogen. They contain total chromium content reaches a maximum of 66,80 mg Kg^(-1) and a total absence of soluble chromium. The results of the analysis of variance of the difference between bare soils and soils appear Lygeum spartum made a significant difference only for the silt and organic matter. But for the other variables analyzed this difference is not significant. Thus, this plant has only one action on the amendment, only the levels of silt and organic matter in soils. The results of the multiple regression of the chromium content of the roots according to all soil variables studied did appear that among the studied variables included in the model, only the electrical conductivity and clay occur in the explanation of contents chromium in roots. The chromium content of the aerial parts analyzed by regression based on all studied soil variables allows us to see only the variables: electrical conductivity and content of chromium in the root portion involved in the explanation of the content chromium in the aerial part.

Keywords: absorption, translocation, analysis of variance, chrome, Lygeum spartum, multiple regression, the soil variables

Procedia PDF Downloads 253

Authors: Ali Moharrek, Hosein Mobli, Ali Jafari, Ahmad Tabataee Far

Abstract:

Biomass is a material generally produced by plants living on soil or water and their derivatives. The remains of agricultural and forest products contain biomass which is changeable into fuel. Besides, you can obtain biogas and ethanol from the charcoal produced from biomass through specific actions. this technology was designed for as a useful Native Fuel and Technology in Energy disasters Management Due to the sudden interruption of the flow of heat energy One of the problems confronted by mankind in the future is the limitations of fossil energy which necessitates production of new energies such as biomass. In order to produce biomass from the remains of the plants, different methods shall be applied considering factors like cost of production, production technology, area of requirement, speed of work easy utilization, ect. In this article we are focusing on designing a biomass briquetting portable machine. The speed of installation of the machine on a tractor is estimated as 80 MF 258. Screw press is used in designing this machine. The needed power for running this machine which is estimated as 17.4 kW is provided by the power axis of tractor. The pressing speed of the machine is considered to be 375 RPM Finally the physical and mechanical properties of the product were compared with utilized material which resulted in appropriate outcomes. This machine is designed for Gathering Raw materials of the ground by Head Section. During delivering the raw materials to Briquetting section, they Crushed, Milled & Pre Heated in Transmission section. This machine is a Combine Portable Tractor unit machine and can use all type of Agriculture, Forest & Livestock Animals Resides as Raw material to make Bio fuel. The Briquetting Section was manufactured and it successfully made bio fuel of Sawdust. Also this machine made a biofuel with Ethanol of sugarcane Wastes. This Machine is using P.T.O power source for Briquetting and Hydraulic Power Source for Pre Processing of Row Materials.

Keywords: biomass, briquette, screw press, sawdust, animal wastes, portable, tractors

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Authors: Waqas Ashraf, Yasir Nawab, Haritham Khan, Habib Awais, Shahbaz Ahmad

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Seersucker is a popular lightweight fabric widely used in men’s and women’s suiting, casual wear, children’s clothing, house robes, bed spreads and for spring and summer wear. The puckered effect generates air spaces between body and the fabric, keeping the wearer cool in hot conditions. The aim of this work was to develop knitted seersucker fabric on single cylinder weft knitting machine using plain jersey structure. Core spun cotton yarn and cotton spun yarn of same linear density were used. Core spun cotton yarn, contains cotton fiber in the sheath and elastase filament in the core. The both yarn were fed at regular interval to feeders on the machine. The loop length and yarn tension were kept constant at each feeder. The samples were then scoured and bleached. After wet processing, the fabric samples were washed and tumble dried. Parameters like loop length, stitch density and areal density were measured after conditioning these samples for 24 hours in Standard atmospheric condition. Produced sample has a regular puckering stripe along the width of the fabric with same height. The stitch density of both the flat and puckered area of relaxed fabric was found to be different .Air permeability and moisture management tests were performed. The results indicated that the knitted seersucker fabric has better wicking and moisture management properties as the flat area contact, whereas puckered area held away from the skin. Seersucker effect in knitted fabric was achieved by the difference of contraction of both sets of courses produced from different types of yarns. The seer sucker fabric produce by knitting technique is less expensive as compared to woven seer sucker fabric as there is no need of yarn preparation. The knitted seersucker fabric is more practicable for summer dresses, skirts, blouses, shirts, trousers and shorts.

Keywords: air permeability, knitted structure, moisture management, seersucker

Procedia PDF Downloads 315

Authors: Adha Bayu Wijaya, A. Zainal Abidin, Naufal Baihaqi, Joko Suprayitno, Astika Titistiti, Muslim Adi Wijaya, Endah Tri Lestari, Agung Wibowo

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Sulfur is a non-metallic chemical element in the form of a yellow crystalline solid with the chemical formula, and is formed from several types of natural and artificial chemical reactions. Commercial applications of sulfur processed products can be found in various aspects of life, for example in the use of processed sulfur as paving blocks. The Gundih Central Processing Plant (CPP) is capable of producing 14 tons/day of sulfur pellets. This amount comes from the high H2S content of the wells with a total concentration of 20,000 ppm and a volume accumulation of 14 MMSCFD acid gas. H2S is converted to sulfur using the thiobacillus microbe in the Biological Sulfur Recovery Unit (BSRU) with a sulfur product purity level greater than 95%. In 2018 sulfur production at Gundih CPP was recorded at 4044 tons which could potentially trigger serious problems from an environmental aspect. The use of sulfur as material for making paving blocks is an alternative solution in addressing the potential impact on the environment, as regulated by Government Regulation No.22 of Year 2021 concerning the Waste Management of Non-Hazardous and Toxic Substances (B3), and the high cost of handling sulfur by third parties. The design mix of ratio sulfur paving blocks is 22% cements, rock ash 67%, and 11% of sulfur pellets. The sulfur used in making the paving mixture is pure sulfur, namely the side product category without any contaminants, thereby eliminating the potential for environmental pollution when implementing sulfur paving. Strength tests of sulfur paving materials have also been confirmed by external laboratories. The standard used in making sulfur paving blocks refers to the SNI 03-0691-1996 standard. With the results of sulfur paving blocks made according to quality B. Currently, sulfur paving blocks are used in building access to wells locations and in public roads in the Cepu Field area as a contribution from Corporate Social Responsibility (CSR).

Keywords: sulphur, innovation, paving block, CSR, sulphur paving

Procedia PDF Downloads 60