Search results for: stone processing

Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

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Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: brain computer interface, electroencephalogram, EEGLab, BCILab, emotive, emotions, interval features, spectral features, artificial neural network, control applications

Procedia PDF Downloads 317

Authors: Basave Gowda, Lokesh K., Prasanth S. M., Bellad S. B., Radha J., Lokesh G. Y., Patil S. B., Vijayakumar D. K., Ganigar B. S., Rakesh C. Mathad

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Quality seed plays an important role in enhancing the crop productivity. It was reported and confirmed by large scale verification research trials that by use of quality seeds alone, the crop yield can be enhanced by 15 to 20 per cent. At present, the quality seed production and distribution through organised sectors comprising both public and private seed sector was only 20-25% of the requirement and the remaining quantity is met through unorganised sector which include the farmer to farmers saved seeds. With an objective of developing an alternative seed system, the University of Agricultural Sciences, Raichur in Karnataka state has implemented Seed Village Programme in more than 100 villages covering around 5000 farmers every year since 2009-10 and in the selected seed villages, a group of 50-150 farmers were supplied the foundation seeds of new varieties to an extent of 0.4 ha at 50 % subsidy. And two to three training programmes were conducted in the targeted villages for quality seed production and the seed produced in the target group was processed locally in the university seed processing units and arranged for distribution in the local villages by the seed growers themselves. By this new innovative and modified seed system, the university can able to replace old varieties of pigeon pea and green gram by producing 1482, 2978, 2729, 2560, and 4581 tonnes of seeds of new varieties on large scale under farmers and scientists participatory seed village programmes respectively during 2009-10, 2010-11, 2011-12, 2012-13 and 2013-14. From this new alternate model of seed system, there should be large scale promotion of regional seed system involving farmers, NGO and voluntary organisation for quick and effective replacement of old, low yielding, disease susceptible varieties with new high yielding, disease resistant for enhanced food production and food security.

Keywords: seed system, seed village, seed replacement, varietal replacement

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Authors: Ganeshkumar, Ashika, Valavan, Ramesh, Thangam, Chirayu

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MSCs are found in much higher concentration in the Wharton’s jelly compared to the umbilical cord blood, which is a rich source of hematopoietic stem cells. Umbilical cord tissue is collected at the time of birth; it is processed and stored in liquid nitrogen for future therapeutical purpose. The source of contamination might be either from vaginal tract of mother or from hospital environment or from personal handling during cord tissue sample collection. If the sample were contaminated, decontamination procedure will be done with 70% ethanol (1 minute) in order to avoid sample rejection. Ethanol is effective against a wide range of bacteria, protozoa and fungi and has low toxicity to humans. Among the 1954 samples taken for the study, 24 samples were found to be contaminated with microorganism. The organisms isolated from the positive samples were found to be E. coli, Stenotrophomonas maltophilia, Pseudomonas aueroginosa, Enterococcus fecalis, Acinetobacter bowmani, Staphylococcus epidermidis, Enterobacter cloacae, and Proteus mirabilis. Among these organisms 70% ethanol successfully eliminated E. coli, Enterococcus fecalis, Acinetobacter bowmani, Staphylococcus epidermidis, and Proteus mirabilis. 70% ethanol was unsuccessful in eliminating Stenotrophomonas maltophilia, Pseudomonas aueroginosa, and Enterobacter cloacae. Stenotrophomonas maltophilia and Pseudomonas aueroginosa have the ability to form biofilm that make them resistant to alcohol. Biofilm act as protective layer for bacteria and which protects them from host defense and antibiotic wash. Finally it was found 70% ethanol wash saved 58.3% cord tissue samples from rejection and it is ineffective against 41% of the samples. The contamination rate can be reduced by maintaining proper aseptic techniques during sample collection and processing.

Keywords: umblical cord tissue, decontamination, 70% ethanol effectiveness, contamination

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Authors: Rajashree Naik, Laxmi Kant Sharma

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Sambhar lake is the largest inland Salt Lake of India, declared as a Ramsar site on 23 March 1990. Due to high salinity and alkalinity condition its biodiversity richness is contributed by haloalkaliphilic flora and fauna along with the diverse land cover including waterbody, wetland, salt crust, saline soil, vegetation, scrub land and barren land which welcome large number of flamingos and other migratory birds for winter harboring. But with the gradual increase in the irrational salt extraction activities, the ecological diversity is at stake. There is an urgent need to assess the ecosystem. Advanced technology like remote sensing and GIS has enabled to look into the past, compare with the present for the future planning and management of the natural resources in a judicious way. This paper is a research work intended to present a vegetation in typical inland lake environment of Sambhar wetland using satellite data of NASA’s EO-1 Hyperion sensor launched in November 2000. With the spectral range of 0.4 to 2.5 micrometer at approximately 10nm spectral resolution with 242 bands 30m spatial resolution and 705km orbit was used to produce a vegetation map for a portion of the wetland. The vegetation map was tested for classification accuracy with a pre-existing detailed GIS wetland vegetation database. Though the accuracy varied greatly for different classes the algal communities were successfully identified which are the major sources of food for flamingo. The results from this study have practical implications for uses of spaceborne hyperspectral image data that are now becoming available. Practical limitations of using these satellite data for wetland vegetation mapping include inadequate spatial resolution, complexity of image processing procedures, and lack of stereo viewing.

Keywords: Algal community, NASA’s EO-1 Hyperion, salt-tolerant species, wetland vegetation mapping

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Authors: Meriem Abid, Erika Oliveria-Jardim, Andres Fullana, Joaquin Silvestre-Albero

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The rapid industrial development associated with the increase of volatile organic compounds (VOCs) has seriously impacted the environment. Among VOCs, hydrogen sulfide (H₂S) is known as a highly toxic, malodorous, flammable, and corrosive gas, which is emitted from diverse chemical processes, including industrial waste-gas streams, natural gas processing, and biogas purification. The high toxicity, corrosively, and very characteristic odor threshold of H2S call for urgent development of efficient desulfurization processes from the viewpoint of environmental protection and resource regeneration. In order to reduce H₂S emissions, effective technologies for have been performed. The general method of H₂S removal included amine aqueous solution, adsorption process, biological methods, and fixed-bed solid catalytic oxidation processes. Ecologically and economically, low-temperature direct oxidation of H₂S to elemental sulfur using catalytic oxidation is the preferred approach for removing H₂S-containing gas streams. A large number of catalysts made from carbon, metal oxides, clay, and others, have been studied extensively for this application. In this sense, activated carbon (AC) is an attractive catalyst for H₂S removal because it features a high specific surface area, diverse functional groups, low cost, durability, and high efficiency. It is interesting to stand out that AC is modified using metal oxides to promote the efficiency of H₂S removal and to enhance the catalytic performance. Based on these premises, the main goal of the present study is the evaluation of the H₂S adsorption performance in carbon-encapsulated iron nanoparticles obtained from an olive mill, thermally treated at 600, 800 and 1000 ºC temperatures under anaerobic conditions. These results anticipate that carbon-encapsulated iron nanoparticles exhibit a promising performance for the H₂S removal up to 360 mg/g.

Keywords: H₂S removal, catalytic oxidation, carbon encapsulated iron, olive mill wastewater

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Authors: Sahil Kumar, Rajaram Ghadge, Ramesh Bhujade

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Historically, lipid extraction from dried algal biomass remained a focus area of the algal research. It has been realized over the past few years that the lipid-centric approach and conversion technologies that require dry algal biomass have several challenges. Algal culture in cultivation systems contains more than 99% water, with algal concentrations of just a few hundred milligrams per liter ( < 0.05 wt%), which makes harvesting and drying energy intensive. Drying the algal biomass followed by extraction also entails the loss of water and nutrients. In view of these challenges, focus has shifted toward developing processes that will enable oil production from wet algal biomass without drying. Hydrothermal liquefaction (HTL), an emerging technology, is a thermo-chemical conversion process that converts wet biomass to oil and gas using water as a solvent at high temperature and high pressure. HTL processes wet algal slurry containing more than 80% water and significantly reduces the adverse cost impact owing to drying the algal biomass. HTL, being inherently feedstock agnostic, i.e., can convert carbohydrates and proteins also to fuels and recovers water and nutrients. It is most effective with low-lipid (10--30%) algal biomass, and bio-crude yield is two to four times higher than the lipid content in the feedstock. In the early 2010s, research remained focused on increasing the oil yield by optimizing the process conditions of HTL. However, various techno-economic studies showed that simply converting algal biomass to only oil does not make economic sense, particularly in view of low crude oil prices. Making the best use of every component of algae is a key for economic viability of algal to oil process. On investigation of HTL reactions at the molecular level, it has been observed that sequential HTL has the potential to recover value-added products along with biocrude and improve the overall economics of the process. This potential of sequential HTL makes it a most promising technology for converting wet waste to wealth. In this presentation, we will share our experience on the techno-economic and engineering aspects of sequential HTL for conversion of algal biomass to algal bio-oil and co-products.

Keywords: algae, biomass, lipid, protein

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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: Chih-Jer Lin, Jian-Hong Hou

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Industry 4.0 is a vision of integrated industry implemented by artificial intelligent computing, software, and Internet technologies. The main goal of industry 4.0 is to deal with the difficulty owing to competitive pressures in the marketplace. For today’s manufacturing factories, the type of production is changed from mass production (high quantity production with low product variety) to medium quantity-high variety production. To offer flexibility, better quality control, and improved productivity, robot manipulators are used to combine material processing, material handling, and part positioning systems into an integrated manufacturing system. To implement the automated system for sheet metal bending operations, motion planning of a 7-degrees of freedom (DOF) robot is studied in this paper. A virtual reality (VR) environment of a bending cell, which consists of the robot and a bending machine, is established using the virtual robot experimentation platform (V-REP) simulator. For sheet metal bending operations, the robot only needs six DOFs for the pick-and-place or tracking tasks. Therefore, this 7 DOF robot has more DOFs than the required to execute a specified task; it can be called a redundant robot. Therefore, this robot has kinematic redundancies to deal with the task-priority problems. For redundant robots, Pseudo-inverse of the Jacobian is the most popular motion planning method, but the pseudo-inverse methods usually lead to a kind of chaotic motion with unpredictable arm configurations as the Jacobian matrix lose ranks. To overcome the above problem, we proposed a method to formulate the motion planning problems as optimization problem. Moreover, a genetic algorithm (GA) based method is proposed to deal with motion planning of the redundant robot. Simulation results validate the proposed method feasible for motion planning of the redundant robot in an automated sheet-metal bending operations.

Keywords: redundant robot, motion planning, genetic algorithm, obstacle avoidance

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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: 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

Procedia PDF Downloads 53

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: E. E. Tereshatov, M. Yu. Boltoeva, V. Mazan, M. F. Volia, C. M. Folden III

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Room temperature ionic liquids (RTILs) are a class of liquid organic salts with melting points below 20 °C that are considered to be environmentally friendly ‘designers’ solvents. Pure hydrophobic ILs are known to extract metallic species from aqueous solutions. The closest analogues of ionic liquids are deep eutectic solvents (DESs), which are a eutectic mixture of at least two compounds with a melting point lower than that of each individual component. DESs are acknowledged to be attractive for organic synthesis and metal processing. Thus, these non-volatile and less toxic compounds are of interest for critical metal extraction. The US Department of Energy and the European Commission consider indium as a key metal. Its chemical homologue, thallium, is also an important material for some applications and environmental safety. The aim of this work is to systematically investigate In and Tl extraction from aqueous solutions into pure fluorinated ILs and hydrophobic DESs. The dependence of the Tl extraction efficiency on the structure and composition of the ionic liquid ions, metal oxidation state, and initial metal and aqueous acid concentrations have been studied. The extraction efficiency of the TlXz3–z anionic species (where X = Cl– and/or Br–) is greater for ionic liquids with more hydrophobic cations. Unexpectedly high distribution ratios (> 103) of Tl(III) were determined even by applying a pure ionic liquid as receiving phase. An improved mathematical model based on ion exchange and ion pair formation mechanisms has been developed to describe the co-extraction of two different anionic species, and the relative contributions of each mechanism have been determined. The first evidence of indium extraction into new quaternary ammonium- and menthol-based hydrophobic DESs from hydrochloric and oxalic acid solutions with distribution ratios up to 103 will be provided. Data obtained allow us to interpret the mechanism of thallium and indium extraction into ILs and DESs media. The understanding of Tl and In chemical behavior in these new media is imperative for the further improvement of separation and purification of these elements.

Keywords: deep eutectic solvents, indium, ionic liquids, thallium

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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: Leila Baghaarabani, Parvin Razzaghi, Mennatolla Magdy Mostafa, Ahmad Albaqsami, Al Warith Al Rushaidi, Masoud Al Rawahi

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Predicting the interaction between drugs and their molecular targets is pivotal for advancing drug development processes. Due to the time and cost limitations, computational approaches have emerged as an effective approach to drug-target interaction (DTI) prediction. Most of the introduced computational based approaches utilize the drug molecule and protein sequence as input. This study does not only utilize these inputs, it also introduces a protein representation developed using a masked protein language model. In this representation, for every individual amino acid residue within the protein sequence, there exists a corresponding probability distribution that indicates the likelihood of each amino acid being present at that particular position. Then, the similarity between each pair of amino acids is computed to create a similarity matrix. To encode the knowledge of the similarity matrix, Bi-Level Routing Attention (BiFormer) is utilized, which combines aspects of transformer-based models with protein sequence analysis and represents a significant advancement in the field of drug-protein interaction prediction. BiFormer has the ability to pinpoint the most effective regions of the protein sequence that are responsible for facilitating interactions between the protein and drugs, thereby enhancing the understanding of these critical interactions. Thus, it appears promising in its ability to capture the local structural relationship of the proteins by enhancing the understanding of how it contributes to drugprotein interactions, thereby facilitating more accurate predictions. To evaluate the proposed method, it was tested on two widely recognized datasets: Davis and KIBA. A comprehensive series of experiments was conducted to illustrate its effectiveness in comparison to cutting edge techniques.

Keywords: BiFormer, transformer, protein language processing, self-attention mechanism, binding affinity, drug target interaction, similarity matrix, protein masked representation, protein language model

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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: 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

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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

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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: 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

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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

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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

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