Search results for: food frequency and biomedical data

Authors: Maria Fernanda Ordoñez Martinez, Alvaro Mauricio Montenegro

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This work presents a statistical methodology for measuring and founding constructs in Latent Semantic Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations present on Item Response Theory. More precisely, we propose initially reducing dimensionality with specific use of Principal Component Analysis for the linguistic data and then, producing axes of groups made from a clustering analysis of the semantic data. This approach allows the user to give meaning to previous clusters and found the real latent structure presented by data. The methodology is applied in a set of real semantic data presenting impressive results for the coherence, speed and precision.

Keywords: semantic analysis, factorial analysis, dimension reduction, penalized logistic regression

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Authors: Khosro Ghazvinian, Touba Khodaeian

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To improve the oxidative stability of meat products, the use of dietary form of antioxidants can extend the shelf life and acceptability of muscle food during exposition or storage condition. As shown, this method is more effective than adding direct preservatives due to uniform incorporation of dietary additives into sub cellular membrane and therefore, they can properly inhibit the oxidative reaction at their localized sites. Furthermore, postmortem addition of antioxidants to meat cannot directly inhibit the oxidation in membrane phospholipids. Therefore, this study was designed to evaluate the effects of feed supplementation with Melissa officinalis leaves on lipid peroxidation of chicken breast fillets during refrigerated storage. In this study, 72 one-day old Ross 308 broilers distributed in four groups with six replicates (3 chickens each) were fed a basal diet (CONT) or basal diet supplemented with 5, 10, and 15 gr/Kg M.officinalis, for 6 weeks. Following slaughter, fillets from breast were stored at 4 °C in the dark for 12 days, and lipid oxidation was assessed on the basis of thiobarbituric acid reactive substances (TBARS) formed. Results showed that incorporation of M.officinalis in broiler diets delayed lipid oxidation in raw breast meat during refrigerated storage comparative with CONT(p<0.05). In this regard, TBARS levels of breast samples containing higher concentrations (10 and 15 gr/Kg) of M. officinalis (625.43 and 504.32 µg/kg MDA equivalents, respectively )were significantly lower than those of control and 5g/kg samples (872.75 and 841.32 µg/kg MDA equivalents, respectively) (p<0.05). Therefore, M. officinalis might be utilized in novel applications as a nutritional supplement or a functional food component.

Keywords: breast fillet, lipid oxidation, Melissa officinalis, TBARS assay

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Authors: Dongkwon Han, Sangho Kim, Sunil Kwon

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Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that applies to advanced approaches for production forecasting due to uncertainty and complexity of fluid flow. In this study, artificial neural network (ANN) model which integrates machine learning and data-driven approach was developed to predict productivity in shale gas. The database of 129 wells of Eagle Ford shale basin used for testing and training of the ANN model. The Input data related to hydraulic fracturing, well completion and productivity of shale gas were selected and the output data is a cumulative production. The performance of the ANN using all data sets, clustering and variables importance (VI) models were compared in the mean absolute percentage error (MAPE). ANN model using all data sets, clustering, and VI were obtained as 44.22%, 10.08% (cluster 1), 5.26% (cluster 2), 6.35%(cluster 3), and 32.23% (ANN VI), 23.19% (SVM VI), respectively. The results showed that the pre-trained ANN model provides more accurate results than the ANN model using all data sets.

Keywords: unconventional gas, artificial neural network, machine learning, clustering, variables importance

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Authors: Farideh Tabatabei-Yazdi, Fereshteh Falah, Alireza Vasiee

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Nowadays, production of functional foods has become very essential. Inulin is one of the most functional hydrocolloid compounds used in such products. In the present study, the production of a synbiotic yogurt containing 1, 2.5, and 5% (w/v) inulin has been investigated. The yogurt was fermented with Lactobacillus brevis PML1 derived from Tarkhineh, an Iranian cereal-dairy fermented food. Furthermore, the physicochemical properties, antioxidant activity, sensory attributes, and microbial viability properties were investigated on the 0th, 7th, and 14th days of storage after fermentation. The viable cells of L. brevis PML1 reached 108 CFU/g, and the product resisted to simulated digestive juices. Moreover, the synbiotic yogurt impressively increased the production of antimicrobial compounds and had the most profound antimicrobial effect on S. typhimurium. The physiochemical properties were in the normal range, and the fat content of the synbiotic yogurt was reduced remarkably. The antioxidant capacity of the fermented yogurt was significantly increased (p<0:05), which was equal to those of DPPH (69:18±1:00%) and BHA (89:16±2:00%). The viability of L. brevis PML1 was increased during storage. Sensory analysis showed that there were significant differences in terms of the impressive parameters between the samples and the control (p<0:05). Addition of 2.5% inulin not only improved the physical properties but also retained the viability of the probiotic after 14 days of storage, in addition to the viability of L. brevis with a viability count above 6 log CFU/g in the yogurt. Therefore, a novel synbiotic product containing L. brevis PML1, which can exert the desired properties, can be used as a suitable carrier for the delivery of the probiotic strain, exerting its beneficial health effects.

Keywords: functional food, lactobacillus brevis, symbiotic yogurt, physiochemical properties

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Authors: Ruyuf Y. Alnafisah, Nouf S. Alammari, Amani S. Alqahtani

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Background: Beverage choice can have implications for the risk of non-communicable diseases. However, there is a lack of knowledge in assessing the nutritional content of these beverages. This study aims to describe the nutrient content of pre-packaged beverages available in the Saudi market. Design: Data were collected from the Saudi Branded Food Data-base (SBFD). Nutrient content was standardized in terms of units and reference volumes to ensure consistency in analysis. Results: A total of 1490 beverages were analyzed. The highest median levels of the majority of nutrients were found among dairy products; energy (68.4(43-188]kcal/100 ml in a milkshake); protein (8.2(0.5-8.2]g/100 ml in yogurt drinks); total fat (2.1(1.3-3.5]g/100 ml in milk); saturated fat (1.4(0-1.4]g/100 ml in yogurt drinks); cholesterol (30(0-30]mg/100 ml in yogurt drinks); sodium (65(65-65].4mg/100 ml in yogurt drinks); and total sugars (12.9(7.5-27]g/100 ml in milkshake). Carbohydrate level was the highest in nectar (13(11.8-14.2] g/100ml]; fruits drinks (12.9(11.9-13.9] g/100ml), and sparkling juices (12.9(8.8-14] g/100ml). The highest added sugar level was observed among regular soft drinks (12(10.8-14] g/100ml). The average rate of nutrient declaration was 60.95%. Carbo-hydrate had the highest declaration rate among nutrients (99.1%), and yogurt drinks had the highest declaration rate among beverage categories (92.7%). The median content of vitamins A and D in dairy products met the mandatory addition levels. Conclusion: This study provides valuable insights into the nutrient content of pre-packaged beverages in the Saudi market. It serves as a foundation for future research and monitoring. The findings of the study support the idea of taxing sugary beverages and raise concerns about the health effects of high sugar in fruit juices. Despite the inclusion of vitamins D and A in dairy products, the study highlights the need for alternative strategies to address these deficiencies.

Keywords: pre-packaged beverages, nutrients content, nutrients declaration, daily percentage value, mandatory addition of vitamins

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Authors: Deepika, Kashika Kapoor, Nistha Khanna, Lakshmi, Archna Kumar

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Plant-parasitic nematodes cause major loss in global food production and destroying at least 21.3% of food annually. About 4100 species of plant-parasitic nematodes are reported, out of this, Meloidogyne species is prominent and worldwide in distribution. Observing the harmful effects of chemical based nematicides, there is a great need for an eco-friendly, highly efficient, sustainable control measure for Meloidogyne. Therefore, In vitro study was carried out to observe the impact of volatile cues obtained from the Tagetes erecta leaves on plant parasitic nematodes. Volatile cues were collected from marigold leaves. For chemical characterization, GCMS (Gas Chromatography Mass Spectrometry) profiling was conducted. VOCs (Volatile Organic Compounds) profile of marigold indicated the presence of several types of alkanes, alkenes varying in number and quantity. Status of nematodes population by counting the live and dead individuals after applying a definite volume (100µl) of extract was recorded at different concentrations (100%, 50%, 25%) with contrast of control (hexane) during different time durations i.e.,24hr, 48hr and 72hr. Result indicated that mortality increases with increasing time (72hr) and concentration (100%) i.e., 50%. Thus, application of prominent compound present in Marigold in pure form may be tested individually or in combination to find out the most efficient active compound/s, which may be highly useful in eco-friendly management of targeted plant parasitic nematode.

Keywords: plant-parasitic nematode, meloidogyne, tagetes erecta, volatile organic compounds

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Authors: Tania Viju, Bimal P., Naseer M. A.

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This study aims to develop a conceptual framework for the decision support system (DSS), that helps the decision-makers to dynamically choose appropriate safety measures for each identified accident blackspot. An accident blackspot is a segment of road where the frequency of accident occurrence is disproportionately greater than other sections on roadways. According to a report by the World Bank, India accounts for the highest, that is, eleven percent of the global death in road accidents with just one percent of the world’s vehicles. Hence in 2015, the Ministry of Road Transport and Highways of India gave prime importance to the rectification of accident blackspots. To enhance road traffic safety and reduce the traffic accident rate, effectively identifying and rectifying accident blackspots is of great importance. This study helps to understand and evaluate the existing methods in accident blackspot identification and prediction that are used around the world and their application in Indian roadways. The decision support system, with the help of IoT, ICT and smart systems, acts as a management and planning tool for the government for employing efficient and cost-effective rectification strategies. In order to develop a decision criterion, several factors in terms of quantitative as well as qualitative data that influence the safety conditions of the road are analyzed. Factors include past accident severity data, occurrence time, light, weather and road conditions, visibility, driver conditions, junction type, land use, road markings and signs, road geometry, etc. The framework conceptualizes decision-making by classifying blackspot stretches based on factors like accident occurrence time, different climatic and road conditions and suggesting mitigation measures based on these identified factors. The decision support system will help the public administration dynamically manage and plan the necessary safety interventions required to enhance the safety of the road network.

Keywords: decision support system, dynamic management, road accident blackspots, road safety

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Authors: Mariia V. Efremova, Iana O. Tcareva, Anastasia D. Blokhina, Ivan S. Grebennikov, Anastasia S. Garanina, Maxim A. Abakumov, Yury I. Golovin, Alexander G. Savchenko, Alexander G. Majouga, Natalya L. Klyachko

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During last decades magnetite nanoparticles (NPs) attract a deep interest of scientists due to their potential application in therapy and diagnostics. However, magnetite nanoparticles are toxic and non-stable in physiological conditions. To solve these problems, we decided to create two types of hybrid systems based on magnetite and gold which is inert and biocompatible: gold as a shell material (first type) and gold as separate NPs interfacially bond to magnetite NPs (second type). The synthesis of the first type hybrid nanoparticles was carried out as follows: Magnetite nanoparticles with an average diameter of 9±2 nm were obtained by co-precipitation of iron (II, III) chlorides then they were covered with gold shell by iterative reduction of hydrogen tetrachloroaurate with hydroxylamine hydrochloride. According to the TEM, ICP MS and EDX data, final nanoparticles had an average diameter of 31±4 nm and contained iron even after hydrochloric acid treatment. However, iron signals (K-line, 7,1 keV) were not localized so we can’t speak about one single magnetic core. Described nanoparticles covered with mercapto-PEG acid were non-toxic for human prostate cancer PC-3/ LNCaP cell lines (more than 90% survived cells as compared to control) and had high R2-relaxivity rates (>190 mМ-1s-1) that exceed the transverse relaxation rate of commercial MRI-contrasting agents. These nanoparticles were also used for chymotrypsin enzyme immobilization. The effect of alternating magnetic field on catalytic properties of chymotrypsin immobilized on magnetite nanoparticles, notably the slowdown of catalyzed reaction at the level of 35-40 % was found. The synthesis of the second type hybrid nanoparticles also involved two steps. Firstly, spherical gold nanoparticles with an average diameter of 9±2 nm were synthesized by the reduction of hydrogen tetrachloroaurate with oleylamine; secondly, they were used as seeds during magnetite synthesis by thermal decomposition of iron pentacarbonyl in octadecene. As a result, so-called dumbbell-like structures were obtained where magnetite (cubes with 25±6 nm diagonal) and gold nanoparticles were connected together pairwise. By HRTEM method (first time for this type of structure) an epitaxial growth of magnetite nanoparticles on gold surface with co-orientation of (111) planes was discovered. These nanoparticles were transferred into water by means of block-copolymer Pluronic F127 then loaded with anti-cancer drug doxorubicin and also PSMA-vector specific for LNCaP cell line. Obtained nanoparticles were found to have moderate toxicity for human prostate cancer cells and got into the intracellular space after 45 minutes of incubation (according to fluorescence microscopy data). These materials are also perspective from MRI point of view (R2-relaxivity rates >70 mМ-1s-1). Thereby, in this work magnetite-gold hybrid nanoparticles, which have a strong potential for biomedical application, particularly in targeted drug delivery and magnetic resonance imaging, were synthesized and characterized. That paves the way to the development of special medicine types – theranostics. The authors knowledge financial support from Ministry of Education and Science of the Russian Federation (14.607.21.0132, RFMEFI60715X0132). This work was also supported by Grant of Ministry of Education and Science of the Russian Federation К1-2014-022, Grant of Russian Scientific Foundation 14-13-00731 and MSU development program 5.13.

Keywords: drug delivery, magnetite-gold, MRI contrast agents, nanoparticles, toxicity

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Authors: Prima Luna, Afroditi Chatzifragkou, Dimitris Charalampopoulos

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Sorghum byproducts, namely bran, stalk, and panicle are examples of lignocellulosic biomass. These raw materials contain large amounts of polysaccharides, in particular hemicelluloses, celluloses, and lignins, which if efficiently extracted, can be utilised for the development of a range of added value products with potential applications in agriculture and food packaging sectors. The aim of this study was to characterise fractions extracted from sorghum bran and stalk with regards to their physicochemical properties that could determine their applicability as food-packaging materials. A sequential alkaline extraction was applied for the isolation of cellulosic, hemicellulosic and lignin fractions from sorghum stalk and bran. Lignin content, phenolic content and antioxidant capacity were also investigated in the case of the lignin fraction. Thermal analysis using differential scanning calorimetry (DSC) and X-Ray Diffraction (XRD) revealed that the glass transition temperature (T_g) of cellulose fraction of the stalk was ~78.33 ^oC at amorphous state (~65%) and water content of ~5%. In terms of hemicellulose, the T_g value of stalk was slightly lower compared to bran at amorphous state (~54%) and had less water content (~2%). It is evident that hemicelluloses generally showed a lower thermal stability compared to cellulose, probably due to their lack of crystallinity. Additionally, bran had higher arabinose-to-xylose ratio (0.82) than the stalk, a fact that indicated its low crystallinity. Furthermore, lignin fraction had T_g value of ~93 ^oC at amorphous state (~11%). Stalk-derived lignin fraction contained more phenolic compounds (mainly consisting of p-coumaric and ferulic acid) and had higher lignin content and antioxidant capacity compared to bran-derived lignin fraction.

Keywords: alkaline extraction, bran, cellulose, hemicellulose, lignin, stalk

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Authors: Rajendra Singh, Krishna Mondal, Chandranath Chatterjee

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The water, energy, and food (WEF) nexus describes the interconnectedness of these three essential elements of human life. Each of these three sectors depends on the others. India's expanding population, urbanization, and industrialization make WEF nexus management difficult. Coupling and coordination degrees can be used as indicators of a complex system's level of sustainable development. Thus, coupling and coordination of WEF sectors in India are essential for achieving Sustainable Development Goals (SDGs) 2 (zero hunger), 6 (clean water and sanitation), and 7 (affordable and clean energy). This study used a newly developed WEF nexus model and the concept of coupling coordination degree model to examine the coupling and coordination degrees of the WEF nexus at India's sub-national scale (States/Union Territories (UTs)) for the years 2011 and 2021. Results indicate that the WEF nexus coupling degree was reasonably stable among the Indian States/UTs in both years, with all having a coupling degree above 0.90, indicating high-quality coupling. However, the degree of coordination varied spatially and temporally from ‘primary development’ to ‘quality development’ for the Indian States/UTs. In 2021, it went from 53% to 14% intermediate development and 44% to 83% good development compared to 2011. Most Indian States/UTs developed SDG2 more than SDG6 and SDG7. This study also suggests that most States/UTs must implement WEF-related policies and programmes effectively to achieve quality coordinated WEF nexus development. This study may help administrators and policymakers identify States/UTs that need more attention to implement existing or new policies for achieving SDGs 2, 6, and 7.

Keywords: WEF nexus model, Pardee-RAND WEF nexus, sustainable development, policy

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Authors: M. Graus, K. Westhoff, X. Xu

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The climate change causes a change in all aspects of society. While the expansion of renewable energies proceeds, industry could not be convinced based on general studies about the potential of demand side management to reinforce smart grid considerations in their operational business. In this article, a procedure model for a case-specific data-driven decision support for industrial energy management based on a holistic data analytics approach is presented. The model is executed on the example of the strategic decision problem, to integrate the aspect of renewable energies into industrial energy management. This question is induced due to considerations of changing the electricity contract model from a standard rate to volatile energy prices corresponding to the energy spot market which is increasingly more affected by renewable energies. The procedure model corresponds to a data analytics process consisting on a data model, analysis, simulation and optimization step. This procedure will help to quantify the potentials of sustainable production concepts based on the data from a factory. The model is validated with data from a printer in analogy to a simple production machine. The overall goal is to establish smart grid principles for industry via the transformation from knowledge-driven to data-driven decisions within manufacturing companies.

Keywords: data analytics, green production, industrial energy management, optimization, renewable energies, simulation

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Authors: K. Umbleja, M. Ichino, H. Yaguchi

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In this paper, we propose a coloring method for multivariate data visualization by using parallel coordinates based on dissimilarity and tree structure information gathered during hierarchical clustering. The proposed method is an extension for proximity-based coloring that suffers from a few undesired side effects if hierarchical tree structure is not balanced tree. We describe the algorithm by assigning colors based on dissimilarity information, show the application of proposed method on three commonly used datasets, and compare the results with proximity-based coloring. We found our proposed method to be especially beneficial for symbolic data visualization where many individual objects have already been aggregated into a single symbolic object.

Keywords: data visualization, dissimilarity-based coloring, proximity-based coloring, symbolic data

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Authors: Adenike O. Olatunji-Akioye, Emmanual B Farayola

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In order to improve the animal protein recommended daily intake of Nigerians, there is an upsurge in breeding of hitherto shunned food animals one of which is the rabbit. Radiography and ultrasonography are tools for diagnosing disease and evaluating the anatomical architecture of parts of the body non-invasively. As the rabbit is becoming a more important food animal, to achieve improved breeding of these animals, the best of the species form a breeding stock and will usually depend on breeding soundness which may be evaluated by assessment of the male reproductive organs by these tools. Four male intact rabbits weighing between 1.2 to 1.5 kg were acquired and acclimatized for 2 weeks. Dorsoventral views of the testes were acquired using a digital radiographic machine and a 5 MHz portable ultrasound scanner was used to acquire images of the testes in longitudinal, sagittal and transverse planes. Radiographic images acquired revealed soft tissue images of the testes in all rabbits. The testes lie in individual scrotal sacs sides on both sides of the midline at the level of the caudal vertebrae and thus are superimposed by caudal vertebrae and the caudal limits of the pelvic girdle. The ultrasonographic images revealed mostly homogenously hypoechogenic testes and a hyperechogenic mediastinum testis. The dorsal and ventral poles of the testes were heterogeneously hypoechogenic and correspond to the epididymis and spermatic cord. The rabbit is unique in the ability to retract the testes particularly when stressed and so careful and stressless handling during the procedures is of paramount importance. The imaging of rabbit testes can be safely done using both imaging methods but ultrasonography is a better method of assessment and evaluation of soundness for breeding.

Keywords: breeding soundness, rabbit, radiography, ultrasonography

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Authors: Roberto Machado

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We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, analyzing 2,996 studies and synthesizing 41 of them to explore the evolution of data science and its integration into geography. By employing optimization algorithms, we accelerated the review process, significantly enhancing the efficiency and precision of literature selection. Our findings indicate that data science has developed over five decades, facing challenges such as the diversified integration of data and the need for advanced statistical and computational skills. In geography, the integration of data science underscores the importance of interdisciplinary collaboration and methodological innovation. Techniques like large-scale spatial data analysis and predictive algorithms show promise in natural disaster management and transportation route optimization, enabling faster and more effective responses. These advancements highlight the transformative potential of data science in geography, providing tools and methodologies to address complex spatial problems. The relevance of this study lies in the use of optimization algorithms in systematic reviews and the demonstrated need for deeper integration of data science into geography. Key contributions include identifying specific challenges in combining diverse spatial data and the necessity for advanced computational skills. Examples of connections between these two fields encompass significant improvements in natural disaster management and transportation efficiency, promoting more effective and sustainable environmental solutions with a positive societal impact.

Keywords: data science, geography, systematic review, optimization algorithms, supervised learning

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Authors: Hina Kausher, Sangita Srivastava

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In India, there is a lack of standard, systematic sizing approach for producing readymade garments. Garments manufacturing companies use their own created size tables by modifying international sizing charts of ready-made garments. The purpose of this study is to tabulate the anthropometric data which covers the variety of figure proportions in both height and girth. 3,000 data has been collected by an anthropometric survey undertaken over females between the ages of 16 to 80 years from some states of India to produce the sizing system suitable for clothing manufacture and retailing. This data is used for the statistical analysis of body measurements, the formulation of sizing systems and body measurements tables. Factor analysis technique is used to filter the control body dimensions from a large number of variables. Decision tree-based data mining is used to cluster the data. The standard and structured sizing system can facilitate pattern grading and garment production. Moreover, it can exceed buying ratios and upgrade size allocations to retail segments.

Keywords: anthropometric data, data mining, decision tree, garments manufacturing, sizing systems, ready-made garments

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Authors: Levylee Bautista, Dawn Grace Santos, Aishwarya Veluchamy, Jesusa Santos, Ghafoor, Jr. I Haque, Rodolfo Rafael

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The booming interest in using natural compounds as an alternative to conventional medications has paved way to focus the attention on plants that provide rich sources of bioactive phytochemicals. For regulatory purposes, evaluation of the genotoxic effects of such alternatives is therefore empirical as part of the plant’s hazard assessment. Sesbania grandiflora is among the plants used as a traditional remedy in folk medicine and a subject of research for its medicinal benefits. This study aimed to evaluate the genotoxic potential induced by S. grandiflora flower methanol extract (SGFME) in terms of the frequency of micronucleus (MN) in polychromatic erythrocyte (PCE) (MNPCE) and PCE ratio employing the micronucleus assay. The frequency of MN was examined in bone marrow cells (BMCs) obtained from male Swiss albino mice exposed in vivo to four different concentrations (11.25, 22.5, 40, and 90 mg/kg) of SGFME and MMC (70 mg/kg; positive control) and sacrificed 24 hours post-intraperitoneal injection. Results showed a significant (p < 0.01) rate of MNPCEs for 11.25 and 22.5 tested concentrations of SGFME and is comparable with the MMC-treated mice. Although PCE ratio values in all doses of SGFME-treated mice were over 0.20, it is worth noting that 40 and 90 tested concentrations of SGFME-treated mice exhibited the lowest value, i.e., 0.22 and 0.28, respectively. The present study has demonstrated that S. grandiflora possesses genotoxic potential for murine BMCs. Such activity could be ascribed from the bioactive compounds present in S. grandiflora that require further isolation and characterization of the active molecules. Likewise, findings of this study warrant a caution of the use of S. grandiflora insomuch as further investigations do not demonstrate their safety.

Keywords: genotoxicity, micronucleus, phytochemicals, Sesbania grandiflora

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Authors: Vishnu Nagendra, Marten Van Der Veen, Stefania Giodini

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Effective humanitarian logistics operations are a cornerstone in the success of disaster relief operations. However, for effectiveness, they need to be demand driven and supported by adequate data for prioritization. Without this data operations are carried out in an ad hoc manner and eventually become chaotic. The current availability of geospatial data helps in creating models for predictive damage and vulnerability assessment, which can be of great advantage to logisticians to gain an understanding on the nature and extent of the disaster damage. This translates into actionable information on the demand for relief goods, the state of the transport infrastructure and subsequently the priority areas for relief delivery. However, due to the unpredictable nature of disasters, the accuracy in the models need improvement which can be done using remote sensing data from UAVs (Unmanned Aerial Vehicles) or satellite imagery, which again come with certain limitations. This research addresses the need for a framework to combine data from different sources to support humanitarian logistic operations and prediction models. The focus is on developing a workflow to combine data from satellites and UAVs post a disaster strike. A three-step approach is followed: first, the data requirements for logistics activities are made explicit, which is done by carrying out semi-structured interviews with on field logistics workers. Second, the limitations in current data collection tools are analyzed to develop workaround solutions by following a systems design approach. Third, the data requirements and the developed workaround solutions are fit together towards a coherent workflow. The outcome of this research will provide a new method for logisticians to have immediately accurate and reliable data to support data-driven decision making.

Keywords: unmanned aerial vehicles, damage prediction models, remote sensing, data driven decision making

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Authors: Said Yousif Khairi

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Being a developing economy, imports of capital, raw materials and manufactories goods are vital for sustainable economic growth. In 2006, Libya imported LD 8 billion (US$ 6.25 billion) which composed of mainly machinery and transport equipment (49.3%), raw material (18%), and food products and live animals (13%). This represented about 10% of GDP. Thus, it is pertinent to investigate factors affecting the amount of Libyan imports. An econometric model representing the aggregate import demand for Libya was developed and estimated using the bounds test procedure, which based on an unrestricted error correction model (UECM). The data employed for the estimation was from 1970–2010. The results of the bounds test revealed that the volume of imports and its determinants namely real income, consumer price index and exchange rate are co-integrated. The findings indicate that the demand for imports is inelastic with respect to income, index price level and The exchange rate variable in the short run is statistically significant. In the long run, the income elasticity is elastic while the price elasticity and the exchange rate remains inelastic. This indicates that imports are important elements for Libyan economic growth in the long run.

Keywords: import demand, UECM, bounds test, Libya

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Authors: Shruti Sengupta

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According to the nationally representative data, about 73% of India's rural workforce is engaged in agriculture. While women make significant contributions to total agriculture production, they contribute to about one-third in India. In terms of gender composition, about 80% of the female and 69% of the male workforce is engaged in agriculture in rural India. Still, it is common to find gender differences in plot management within the household. In the last two and half years, India's agri-food system has undergone several changes due to this pandemic, both the demand and supply side, making agriculture more information and knowledge-intensive. Therefore, this paper investigates, using a nationally representative sample, how information asymmetry affects the net returns per hectare of land between female and male farm managers. Empirical results show that information intensity has a significant positive effect on net farm returns per hectare. Results suggest that if females have the same access to technical information as their male counterparts, their farm income can go up by .96 pp compared to male-headed farms. Results also indicate that literate females have higher farm incomes than non-literate females. The study contributes to the literature by employing gender differentials in farm income due to the information gap.

Keywords: agriculture, gender, information asymmetry, farm income, social bias

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Authors: Nikola Tomasevic, Marko Batic, Sanja Vranes

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Emerging Semantic Web technologies can be seen as the next step in evolution of the intelligent facility management systems. Particularly, this considers increased usage of open source and/or standardized concepts for data classification and semantic interpretation. To deliver such facility management systems, providing the comprehensive integration and interoperability platform in from of the facility data model is a prerequisite. In this paper, one of the possible modelling approaches to provide such integrative facility data model which was based on the ontology modelling concept was presented. Complete ontology development process, starting from the input data acquisition, ontology concepts definition and finally ontology concepts population, was described. At the beginning, the core facility ontology was developed representing the generic facility infrastructure comprised of the common facility concepts relevant from the facility management perspective. To develop the data model of a specific facility infrastructure, first extension and then population of the core facility ontology was performed. For the development of the full-blown facility data models, Malpensa and Fiumicino airports in Italy, two major European air-traffic hubs, were chosen as a test-bed platform. Furthermore, the way how these ontology models supported the integration and interoperability of the overall airport energy management system was analyzed as well.

Keywords: airport ontology, energy management, facility data model, ontology modeling

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Authors: Amita Rana

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From the ancient to modern times, the nature of fitness activities has varied. We can choose any form of exercise that is suitable for our particular need. Watchers of fitness trends say that the road to better health is paved with new possibilities along with some old ones that are poised to make a comeback. Educated, certified and experienced fitness professionals; strength training; fitness programmes for older adults; exercise and weight loss; children and obesity; personal training; core training; group personal training; Zumba and other dance workouts; functional fitness; yoga; comprehensive health promotion programmes at worksite; boot-camp; outdoor activities; reaching new markets; spinning; sport-specific training; worker incentive programmes; wellness coaching; and physician referrals are among the fitness trends included in worldwide surveys. However, trends related to fitness in India could be the same or different. Hence, the present paper makes an attempt to analyze the latest fitness trends in India. A total of eighteen (18) surveys were shortlisted on the basis of their relevance to the present topic of study and were arranged in descending order of their chronology. Content analysis was done after the preliminary set of data collection, which formed the basis of a group of data. Further, frequency and percentage were used to statistically represent the data. It can be concluded from the analysis of data regarding recent fitness trends in India that yoga dominates the fitness activity list, followed by numerous other activities including running, Zumba and sh’bam, boot camp, boxing, kickboxing, cycling, swimming, TRX, ass-pocalypse, ballet, biking, bokwa fitness, dance-iso-bic, masala bhangra, outdoor activities, pilates, planks, push-ups, sofa workouts, stairs Workouts, tabata training, and twerking. The body weight/ gym-specified/ strength training as well as high intensity interval training dominate the preferred workouts; followed by mixed work-outs, cross training work-outs, express work-outs, functional fitness, natural body movements, personalized training, and stay-at-home workouts. General areas that featured in the latest fitness trends in India demonstrates that the fitness is making an impact on all sections of the society be it children, women, older adults, senior citizens, worksite fitness. Fitness is becoming the lifestyle of the masses. People are doing exercise for weight-loss, combining diet with exercising; prefer sweating, making groups participate in fitness activities and wellness programmes. Technology is another area which has a high impact on the lives of people. They are using wearable technology for workout tracking and following numerous mobile friendly apps.

Keywords: fitness, India, survey, trend

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Authors: Abrar Abuzaid, Maha Alaaeddine, Haya Alesayi

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This project is centered around understanding the usage and impact of Alexa, Amazon's popular virtual assistant, in everyday life. Alexa, known for its integration into devices like Amazon Echo, offers functionalities such as voice interaction, media control, providing real-time information, and managing smart home devices. Our primary focus is to conduct a straightforward survey aimed at uncovering how people use Alexa in their daily routines. We plan to reach out to a wide range of individuals to get a diverse perspective on how Alexa is being utilized for various tasks, the frequency and context of its use, and the overall user experience. The survey will explore the most common uses of Alexa, its impact on daily life, features that users find most beneficial, and improvements they are looking for. This project is not just about collecting data but also about understanding the real-world applications of a technology like Alexa and how it fits into different lifestyles. By examining the responses, we aim to gain a practical understanding of Alexa's role in homes and possibly in workplaces. This project will provide insights into user satisfaction and areas where Alexa could be enhanced to meet the evolving needs of its users. It’s a step towards connecting technology with everyday life, making it more accessible and user-friendly

Keywords: Amazon Alexa, artificial intelligence, smart speaker, natural language processing

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Authors: Andreas Aceranti, Riccardo Dossena, Marco Colorato, Simonetta Vernocchi

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By the term “limbic resonance,” we usually refer to a state of deep connection, both emotional and physiological, between people who, when in resonance, find their limbic systems in tune with one another. Limbic resonance is not only about sharing emotions but also physiological states. In fact, people in such resonance can influence each other’s heart rate, blood pressure, and breathing. Limbic resonance is fundamental for human beings to connect and create deep bonds among a certain group. It is fundamental for our social skills. A relationship between gifted and resonant subjects is perceived as feeling safe, living the relation like an isle of serenity where it is possible to recharge, to communicate without words, to understand each others without giving explanations, to strengthen the balance of each member of the group. Within the circle, self-esteem is consolidated and makes it stronger to face what is outside, others, and reality. The idea that gifted people who are together may be unfit for the world does not correspond to the truth. The circle made up of people with high cognitive potential characterized by a limbic resonance is, in general, experienced as a solid platform from which you can safely move away and where you can return to recover strength. We studied 8 adults (between 21 and 47 years old). All of them with IQ higher than 130. We monitored their brain waves frequency (alpha, beta, theta, gamma, delta) by means of biosensing tracker along with their physiological states (heart beat frequency, blood pressure, breathing frequency, pO2, pCO2) and some blood works only (5-HT, dopamine, catecholamines, cortisol). The subjects of the study were asked to adhere to a protocol involving bonding activities (such as team building activities), role plays, meditation sessions, and group therapy. All these activities were carried out together. We observed that after about 4 months of activities, their brain waves frequencies tended to tune quicker and quicker. After 9 months, the bond among them was so important that they could “sense” each other inner states and sometimes also guess each others’ thoughts. According to our findings, it may be hypothesized that large synchronized outbursts of cortex neurons produces not only brain waves but also electromagnetic fields that may be able to influence the cortical neurons’ activity of other people’s brain by inducing action potentials in large groups of neurons and this is reasonably conceivable to be able to transmit information such as different emotions and cognition cues to the other’s brain. We also believe that upcoming research should focus on clarifying the role of brain magnetic particles in brain-to-brain communication. We also believe that further investigations should be carried out on the presence and role of cryptochromes to evaluate their potential roles in direct brain-to-brain communication.

Keywords: limbic resonance, psychotherapy, brain waves, emotion regulation, giftedness

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Authors: Wen Zhang

Abstract:

The smartphone plays a significant role in media convergence, and smartphone photography is reconstructing the way we communicate and think. This article aims to explore the smartphone photography practices of urban Chinese smartphone users and images produced by smartphones from a techno-cultural perspective. The analysis consists of two types of data: One is a semi-structured interview of 21 participants, and the other consists of the images created by the participants. The findings are organised in two parts. The first part summarises the current tendencies of capturing, editing, sharing and archiving digital images via smartphones. The second part shows that food and selfie/anti-selfie are the preferred subjects of smartphone photographic images from a technical and multi-purpose perspective and demonstrates that screenshots and image texts are new genres of non-photographic images that are frequently made by smartphones, which contributes to improving operational efficiency, disseminating information and sharing knowledge. The analyses illustrate the positive impacts between smartphones and photography enthusiasm and practices based on the diffusion of innovation theory, which also makes us rethink the value of photographs and the practice of ‘photographic seeing’ from the screen itself.

Keywords: digital photography, image-text, media convergence, photographic- seeing, selfie/anti-selfie, smartphone, technological innovation

Procedia PDF Downloads 346

Authors: Nani Mchedlishvili, Nino Omiadze, Marine Abutidze, Jose Neptuno Rodriguez-Lopez, Tinatin Sadunishvili, Nikoloz Pruidze, Giorgi Kvesitadze

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Recently, there is an increased interest in the development of food natural colorants as alternatives to synthetic dyes because of both legislative action and consumer concern. Betalains are widely used in the food industry as an alternative of synthetic colorants. The interest of betalains are caused not only by their coloring effect but also by their beneficial properties. The aim of the work was to study of stability of crude and purified red pigments of pokeberry (Phytolacca america L.). The pokeberry fruit juice was filtrated and concentrated by rotary vacuum evaporator up to 25% and the concentrated juice was passed through the Sepadex-25(fine) column (20×1.1 cm). From the column the pigment elution rate was 18 ml/hr. 1.5ml fractions of pigment were collected. In the fractions the coloring substances were determined using CuS04 x 7 H2O as a standard. From the Sephadex G-25 column only one fraction of the betalain red pigment was eluted with the absorption maximum at 538 nm. The degree of pigment purification was 1.6 and pigment yield from the column was 15 %. It was shown that thermostability of pokeberry fruit red pigment was significantly decreased after the purification. For example, during incubation at 100C for 10 min crude pigment retained 98 % of its color while under the same conditions only 72% of the color of purified pigment was retained. The purified pigment was found to be characterized by less storage stability too. The storage of the initial crude juice and the pigment fraction obtained after the gelfiltration for 10 days at 4°C showed the lost of color by 29 and 74 % respectively. From the results obtained, it can be concluded that during the gelfiltration the pokeberry fruit red pigment gets separated from such substances that cause its stabilization in the crude juice.

Keywords: betalains, gelfiltration, pokeberry fruit, stability

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Authors: Amadou Wurry Jallow, Adama N. S. Bah, Karamo Bah, Shih-Ye Wang, Kuo-Chung Chu, Chien-Yeh Hsu

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Chronic kidney disease (CKD) is a major public health challenge with high prevalence, rising incidence, and serious adverse consequences. Developing effective risk prediction models is a cost-effective approach to predicting and preventing complications of chronic kidney disease (CKD). This study aimed to develop an accurate machine learning model that can dynamically identify individuals at risk of CKD using various kinds of diagnostic data, with or without laboratory data, at different follow-up points. Creatinine is a key component used to predict CKD. These models will enable affordable and effective screening for CKD even with incomplete patient data, such as the absence of creatinine testing. This retrospective cohort study included data on 19,429 adults provided by a private research institute and screening laboratory in Taiwan, gathered between 2001 and 2015. Univariate Cox proportional hazard regression analyses were performed to determine the variables with high prognostic values for predicting CKD. We then identified interacting variables and grouped them according to diagnostic data categories. Our models used three types of data gathered at three points in time: non-laboratory, laboratory, and metabolic indices data. Next, we used subgroups of variables within each category to train two machine learning models (Random Forest and XGBoost). Our machine learning models can dynamically discriminate individuals at risk for developing CKD. All the models performed well using all three kinds of data, with or without laboratory data. Using only non-laboratory-based data (such as age, sex, body mass index (BMI), and waist circumference), both models predict chronic kidney disease as accurately as models using laboratory and metabolic indices data. Our machine learning models have demonstrated the use of different categories of diagnostic data for CKD prediction, with or without laboratory data. The machine learning models are simple to use and flexible because they work even with incomplete data and can be applied in any clinical setting, including settings where laboratory data is difficult to obtain.

Keywords: chronic kidney disease, glomerular filtration rate, creatinine, novel metabolic indices, machine learning, risk prediction

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Authors: Jai Bakliya, Palak Dhamecha

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Today customer satisfaction is given utmost priority be it any industry. But when it comes to hospitality industry this applies even more as they come in direct contact with customers while providing them services. Employee engagement is new concept adopted by Human Resource Department which impacts customer satisfactions. To satisfy your customers, it is necessary to see that the employees in the organisation are satisfied and engaged enough in their work that they meet the company’s expectations and contribute in the process of achieving company’s goals and objectives. After all employees is human capital of the organisation. Employee engagement has become a top business priority for every organisation. In this fast moving economy, business leaders know that having a potential and high-performing human resource is important for growth and survival. They recognize that a highly engaged manpower can increase innovation, productivity, and performance, while reducing costs related to retention and hiring in highly competitive talent markets. But while most executives see a clear need to improve employee engagement, many have yet to develop tangible ways to measure and tackle this goal. Employee Engagement is an approach which is applied to establish an emotional connection between an employee and the organisation which ensures the employee’s commitment towards his work which affects the productivity and overall performance of the organisation. The study was conducted in hospitality industry. A popular branded hotel was chosen as a sample unit. Data were collected, both qualitative and quantitative from respondents. It is found that employee engagement level of the organisation (Hotel) is quite low. This means that employees are not emotionally connected with the organisation which may in turn, affect performance of the employees it is important to note that in hospitality industry individual employee’s performance specifically in terms of emotional engagement is critical and, therefore, a low engagement level may contribute to low organisation performance. An attempt to this study was made to identify employee engagement level. Another objective to take this study was to explore the factors impeding employee engagement and to explore employee engagement facilitation. While in the hospitality industry where people tend to work for as long as 16 to 18 hours concepts like employee engagement is essential. Because employees get tired of their routine job and in case where job rotation cannot be done employee engagement acts as a solution. The study was conducted at Trident Hotel, Udaipur. It was conducted on the sample size of 30 in-house employees from 6 different departments. The various departments were: Accounts and General, Front Office, Food & Beverage Service, Housekeeping, Food & Beverage Production and Engineering. It was conducted with the help of research instrument. The research instrument was Questionnaire. Data collection source was primary source. Trident Udaipur is one of the busiest hotels in Udaipur. The occupancy rate of the guest over there is nearly 80%. Due the high occupancy rate employees or staff of the hotel used to remain very busy and occupied all the time in their work. They worked for their remuneration only. As a result, they do not have any encouragement for their work nor they are interested in going an extra mile for the organisation. The study result shows working environment factors including recognition and appreciation, opinions of the employee, counselling, feedback from superiors, treatment of managers and respect from the organisation are capable of increasing employee engagement level in the hotel. The above study result encouraged us to explore the factors contributed to low employee engagement. It is being found that factors such as recognition and appreciation, feedback from supervisors, opinion of the employee, counselling, feedback from supervisors, treatment from managers has contributed negatively to employee engagement level. Probable reasons for the low contribution are number of employees gave the negative feedback in accordance to the factors stated above of the organisation. It seems that the structure of organisation itself is responsible for the low contribution of employee engagement. The scope of this study is limited to trident hotel situated in the Udaipur. The limitation of the study was that that the results or findings were only based on the responses of respondents of Trident, Udaipur. And so the recommendations were also applicable in Trident, Udaipur and not to all the like organisations across the country. Through the data collected was further analysed, interpreted and concluded. On the basis of the findings, suggestions were provided to the hotel for improvisation.

Keywords: human resource, employee engagement, research, study

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Authors: Mulata Haile Nega, Derebew Fikadu Berhe, Vera Ribeiro Marques

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There is no epidemiologic data on this gene polymorphism in several countries. Therefore, this study aimed to assess the genotype and allele frequencies of the gene variant in three countries. This study involved healthy individuals from Colombia, Mozambique, and Portugal. Genomic DNA was isolated from blood samples using the Qiamp DNA Extraction Kit (Qiagen). The isolated DNA was genotyped using Polymerase Chain Reaction (PCR) - Restriction Fragment Length Polymorphism. Microstat and GraphPad quick cal software were used for the Chi-square test and evaluation of Hardy-Weinberg equilibrium, respectively. A total of 181 individuals’ blood sample was analyzed. Overall, TT (74.0%) genotype was the highest, and CC (7.8%) was the lowest. Country wise genotypic frequencies were Colombia 47(70.2%) TT, 12(17.9%) TC and 8(11.9%) CC; Mozambique 47(88.7%) TT, 5(9.4%) TC, and 1(1.9%) CC; and Portugal 40(65.6%) TT, 16(26.2%) TC, and 5(8.2%) CC. The reference (T) allele was highest among Mozambicans (93.4%) compared to Colombians (79.1%) and Portuguese (78.7%). Mozambicans showed statistically significant genotypic and allelic frequency differences compared to Colombians (p<0.01) and Portuguese (p <0.01). Overall and country-wise, the CC genotype was less frequent and relatively high for Colombians and Portuguese populations. This finding may imply statins risk-benefit variability associated with CC genotype among these populations that needs further understanding.

Keywords: c.521T>C, polymorphism, SLCO1B1, SNP, statins

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Authors: Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

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Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work.

Keywords: support vector mechanism (SVM), machine learning (ML), support vector machines (SVM), department of transportation (DFT)

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Authors: Raleigh Blasdell, Amanda M. Sharp Parker, Lauren Waldrop, Brigid Toney

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It has been demonstrated that fictional media sources have persuasive effects on public beliefs; this study contributes to the social constructionist literature by conducting a content analysis of U.S. major motion pictures involving terrorism. Using the Unified Film Population Sampling Methodology, the top-grossing films were identified to examine the frequency and context of several constructs of terrorism, including terrorist demographics, type of terrorism, country of origin, organizational affiliation, crime typology, and victim demographics. Comparisons of these constructs, as depicted in the films, were then made with the extant academic literature on terrorism. The data provide notable information regarding the representation of terrorism by the film industry, as well the discrepancies between the scholarly literature and depictions in popular films. The results indicate vast differences between fiction and reality, emphasizing a 'Middle Eastern Islamic male' terrorist stereotype. Using the theoretical foundation of social constructionism, the findings provide insight into how inaccurate depictions in film can influence society’s beliefs about terrorism and terrorists, which subsequently can translate into public support for legislation and policies that are often fueled by misinformation.

Keywords: film, media, social constructionism, terrorism

Procedia PDF Downloads 164