Search results for: logit regression model and food security

Authors: Atchara Sankom, Warapa Mahakarnchanakul, Ronnarit Rittiron, Tanaboon Sajjaanantakul, Thammasak Thongket

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Vegetables are frequently contaminated with pesticides residues resulting in the most food safety concern among agricultural products. The objective of this work was to develop a method to detect the organophosphate (OP) pesticides residues in vegetables using Near Infrared (NIR) spectroscopy technique. Low concentration (ppm) of OP pesticides in vegetables were investigated. The experiment was divided into 2 sections. In the first section, Chinese kale spiked with different concentrations of chlorpyrifos pesticide residues (0.5-100 ppm) was chosen as the sample model to demonstrate the appropriate conditions of sample preparation, both for a solution or solid sample. The spiked samples were extracted with acetone. The sample extracts were applied as solution samples, while the solid samples were prepared by the dry-extract system for infrared (DESIR) technique. The DESIR technique was performed by embedding the solution sample on filter paper (GF/A) and then drying. The NIR spectra were measured with the transflectance mode over wavenumber regions of 12,500-4000 cm⁻¹. The QuEChERS method followed by gas chromatography-mass spectrometry (GC-MS) was performed as the standard method. The results from the first section showed that the DESIR technique with NIR spectroscopy demonstrated good accurate calibration result with R² of 0.93 and RMSEP of 8.23 ppm. However, in the case of solution samples, the prediction regarding the NIR-PLSR (partial least squares regression) equation showed poor performance (R² = 0.16 and RMSEP = 23.70 ppm). In the second section, the DESIR technique coupled with NIR spectroscopy was applied to the detection of OP pesticides in vegetables. Vegetables (Chinese kale, cabbage and hot chili) were spiked with OP pesticides (chlorpyrifos ethion and profenofos) at different concentrations ranging from 0.5 to 100 ppm. Solid samples were prepared (based on the DESIR technique), then samples were scanned by NIR spectrophotometer at ambient temperature (25+2°C). The NIR spectra were measured as in the first section. The NIR- PLSR showed the best calibration equation for detecting low concentrations of chlorpyrifos residues in vegetables (Chinese kale, cabbage and hot chili) according to the prediction set of R2 and RMSEP of 0.85-0.93 and 8.23-11.20 ppm, respectively. For ethion residues, the best calibration equation of NIR-PLSR showed good indexes of R² and RMSEP of 0.88-0.94 and 7.68-11.20 ppm, respectively. As well as the results for profenofos pesticide, the NIR-PLSR also showed the best calibration equation for detecting the profenofos residues in vegetables according to the good index of R² and RMSEP of 0.88-0.97 and 5.25-11.00 ppm, respectively. Moreover, the calibration equation developed in this work could rapidly predict the concentrations of OP pesticides residues (0.5-100 ppm) in vegetables, and there was no significant difference between NIR-predicted values and actual values (data from GC-MS) at a confidence interval of 95%. In this work, the proposed method using NIR spectroscopy involving the DESIR technique has proved to be an efficient method for the screening detection of OP pesticides residues at low concentrations, and thus increases the food safety potential of vegetables for domestic and export markets.

Keywords: NIR spectroscopy, organophosphate pesticide, vegetable, food safety

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Authors: N. Yang, R. Linforth, I. Fisk

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The influence of food processing and choice of flavour solvent was investigated using biscuits prepared with vanillin flavour as an example. Powder vanillin either was added directly into the dough or dissolved into flavour solvent then mixed into the dough. The impact of two commonly used flavour solvents on food quality was compared: propylene glycol (PG) or triacetin (TA). The analytical approach for vanillin detection was developed by chromatography (HPLC-PDA), and the standard extraction method for vanillin was also established. The results indicated the impact of solvent choice on vanillin level during biscuit preparation. After baking, TA as a more heat resistant solvent retained more vanillin than PG, so TA is a better solvent for products that undergo a heating process. The results also illustrated the impact of mixing and baking on vanillin stability in the matrices. The average loss of vanillin was 33% during mixing and 13% during baking, which indicated that the binding of vanillin to fat or flour before baking might cause larger loss than evaporation loss during baking.

Keywords: biscuit, flavour stability, food quality, vanillin

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Authors: B. Hossen, Y. Helmut

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Soil salinity is a major problem for the coastal region of Bangladesh, which has been increasing for the last four decades. Determination of soil salinity is essential for proper land use planning for agricultural crop production. The aim of the research is to estimate and monitor the soil salinity in the study area. Remote sensing can be an effective tool for detecting soil salinity in data-scarce conditions. In the research, Landsat 8 is used, which required atmospheric and radiometric correction, and nine soil salinity indices are applied to develop a soil salinity map. Ground soil salinity data, i.e., EC value, is collected as a printed map which is then scanned and digitized to develop a point shapefile. Linear regression is made between satellite-based generated map and ground soil salinity data, i.e., EC value. The results show that maximum R² value is found for salinity index SI 7 = G*R/B representing 0.022. This minimal R² value refers that there is a negligible relationship between ground EC value and salinity index generated value. Hence, these indices are not appropriate to assess soil salinity though many studies used those soil salinity indices successfully. Therefore, further research is necessary to formulate a model for determining the soil salinity in the coastal of Bangladesh.

Keywords: soil salinity, EC, Landsat 8, salinity indices, linear regression, remote sensing

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Authors: Raana Babadi Fathipour

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Food packaging plays a crucial role in protecting food from unwanted external factors. Antibacterial edible films are a promising option for food packaging due to their biodegradability, environmental friendliness, and safety. This paper reviews recent research progress on antimicrobial edible films, focusing on those made from polysaccharides, proteins, and lipids. Polysaccharides and proteins are the primary components of antimicrobial edible films, while lipids primarily serve as plasticizers and carriers for active substances in composite films. For instance, second-generation liposomes have shown great potential as carriers for antimicrobial substances and other bioactive compounds due to their exceptional stability. Furthermore, this paper analyzes recent advancements and future trends in antimicrobial edible films. One promising direction is the integration of antimicrobial edible film materials with delivery systems, such as nanoemulsion and microencapsulation technologies, to ensure stable loading of bioactive substances. Another emerging area of interest is the development of smart and active packaging that allows consumers to assess the freshness of food products without opening the package. pH-sensitive films and smart fluorescent "on-off" sensors for humidity are currently being explored as materials for smart and active packaging to monitor food product freshness, with further exploration anticipated in the future.

Keywords: antimicrobial edible film, biopolymer, antimicrobial agent, encapsulation, antimicrobial assay

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Authors: Ali A. Ukasha

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Data security needed in data transmission, storage, and communication to ensure the security. The single step parallel contour extraction (SSPCE) method is used to create the edge map as a key image from the different Gray level/Binary image. Performing the X-OR operation between the key image and each bit plane of the original image for image pixel values change purpose. The Arnold transform used to changes the locations of image pixels as image scrambling process. Experiments have demonstrated that proposed algorithm can fully encrypt 2D Gary level image and completely reconstructed without any distortion. Also shown that the analyzed algorithm have extremely large security against some attacks like salt & pepper and JPEG compression. Its proof that the Gray level image can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services.

Keywords: SSPCE method, image compression, salt and peppers attacks, bitplanes decomposition, Arnold transform, lossless image encryption

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Navid Kaboudi, Ali Shayanfar

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Introduction: Feeding infants with safe milk from the beginning of their life is an important issue. Drugs which are used by mothers can affect the composition of milk in a way that is not only unsuitable, but also toxic for infants. Consuming permeable drugs during that sensitive period by mother could lead to serious side effects to the infant. Due to the ethical restrictions of drug testing on humans, especially women, during their lactation period, computational approaches based on structural parameters could be useful. The aim of this study is to develop mechanistic models to predict the M/P ratio of drugs during breastfeeding period based on their structural descriptors. Methods: Two hundred and nine different chemicals with their M/P ratio were used in this study. All drugs were categorized into two groups based on their M/P value as Malone classification: 1: Drugs with M/P>1, which are considered as high risk 2: Drugs with M/P>1, which are considered as low risk Thirty eight chemical descriptors were calculated by ACD/labs 6.00 and Data warrior software in order to assess the penetration during breastfeeding period. Later on, four specific models based on the number of hydrogen bond acceptors, polar surface area, total surface area, and number of acidic oxygen were established for the prediction. The mentioned descriptors can predict the penetration with an acceptable accuracy. For the remaining compounds (N= 147, 158, 160, and 174 for models 1 to 4, respectively) of each model binary regression with SPSS 21 was done in order to give us a model to predict the penetration ratio of compounds. Only structural descriptors with p-value<0.1 remained in the final model. Results and discussion: Four different models based on the number of hydrogen bond acceptors, polar surface area, and total surface area were obtained in order to predict the penetration of drugs into human milk during breastfeeding period About 3-4% of milk consists of lipids, and the amount of lipid after parturition increases. Lipid soluble drugs diffuse alongside with fats from plasma to mammary glands. lipophilicity plays a vital role in predicting the penetration class of drugs during lactation period. It was shown in the logistic regression models that compounds with number of hydrogen bond acceptors, PSA and TSA above 5, 90 and 25 respectively, are less permeable to milk because they are less soluble in the amount of fats in milk. The pH of milk is acidic and due to that, basic compounds tend to be concentrated in milk than plasma while acidic compounds may consist lower concentrations in milk than plasma. Conclusion: In this study, we developed four regression-based models to predict the penetration class of drugs during the lactation period. The obtained models can lead to a higher speed in drug development process, saving energy, and costs. Milk/plasma ratio assessment of drugs requires multiple steps of animal testing, which has its own ethical issues. QSAR modeling could help scientist to reduce the amount of animal testing, and our models are also eligible to do that.

Keywords: logistic regression, breastfeeding, descriptors, penetration

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Authors: Remon Karam Zakry Kelada

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This survey research ambitions to examine the usual of carrier quality of meals and beverage provider staffs in lodge business by way of studying the carrier fashionable of 3 pattern inns, Siam Kempinski lodge Bangkok, four Seasons lodge Chiang Mai, and Banyan Tree Phuket. as a way to locate the international provider general of food and beverage provider, triangular research, i.e. quantitative, qualitative, and survey were hired. on this research, questionnaires and in-depth interview have been used for getting the statistics on the sequences and method of services. There had been three components of modified questionnaires to degree carrier pleasant and visitor’s satisfaction inclusive of carrier facilities, attentiveness, obligation, reliability, and circumspection. This observe used pattern random sampling to derive topics with the go back fee of the questionnaires changed into 70% or 280. information have been analyzed via SPSS to find mathematics mean, SD, percent, and comparison by using t-take a look at and One-manner ANOVA. The outcomes revealed that the service first-rate of the three lodges have been in the worldwide stage that could create excessive pride to the international clients. hints for studies implementations have been to hold the area of precise carrier satisfactory, and to enhance some dimensions of service fine together with reliability. training in service fashionable, product expertise, and new generation for employees must be provided. furthermore, for you to develop the provider pleasant of the enterprise, training collaboration among inn corporation and academic institutions in food and beverage carrier should be considered.

Keywords: food and beverage staff, food poisoning, food production, hygiene knowledge BPA, health, regulations, toxicity service standard, food and beverage department, sequence of service, service method

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Authors: Mina Adel Shokry Fahim, Jūratė Sužiedelytė Visockienė

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With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realisation often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing.

Keywords: air pollution, anthropogenic and natural sources, machine learning, Gaussian process regression, tree ensemble, forecasting models, particulate matter

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Authors: Haoming Ma, Guo Yu, Peiru Zhou

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Background: Previous studies show that dysglycemia, mostly hyperglycemia, hypoglycemia and glycemic variability(GV), are associated with excess mortality in critically ill patients, especially those without diabetes. Glycemic variability is an increasingly important measure of glucose control in the intensive care unit (ICU) due to this association. However, there is limited data pertaining to the relationship between different clinical factors and glycemic variability and clinical outcomes categorized by their DM status. This retrospective study of 958 intensive care unit(ICU) patients was conducted to investigate the relationship between GV and outcome in critically ill patients and further to determine the significant factors that contribute to the glycemic variability. Aim: We hypothesize that the factors contributing to mortality and the glycemic variability are different from critically ill patients with or without diabetes. And the primary aim of this study was to determine which dysglycemia (hyperglycemia\hypoglycemia\glycemic variability) is independently associated with an increase in mortality among critically ill patients in different groups (DM/Non-DM). Secondary objectives were to further investigate any factors affecting the glycemic variability in two groups. Method: A total of 958 diabetic and non-diabetic patients with severe diseases in the ICU were selected for this retrospective analysis. The glycemic variability was defined as the coefficient of variation (CV) of blood glucose. The main outcome was death during hospitalization. The secondary outcome was GV. The logistic regression model was used to identify factors associated with mortality. The relationships between GV and other variables were investigated using linear regression analysis. Results: Information on age, APACHE II score, GV, gender, in-ICU treatment and nutrition was available for 958 subjects. Predictors remaining in the final logistic regression model for mortality were significantly different in DM/Non-DM groups. Glycemic variability was associated with an increase in mortality in both DM(odds ratio 1.05; 95%CI:1.03-1.08,p＜0.001) or Non-DM group(odds ratio 1.07; 95%CI:1.03-1.11,p=0.002). For critically ill patients without diabetes, factors associated with glycemic variability included APACHE II score(regression coefficient, 95%CI:0.29,0.22-0.36,p＜0.001), Mean BG(0.73,0.46-1.01,p＜0.001), total parenteral nutrition(2.87,1.57-4.17,p＜0.001), serum albumin(-0.18,-0.271 to -0.082,p＜0.001), insulin treatment(2.18,0.81-3.55,p=0.002) and duration of ventilation(0.006,0.002-1.010,p=0.003).However, for diabetes patients, APACHE II score(0.203,0.096-0.310,p＜0.001), mean BG(0.503,0.138-0.869,p=0.007) and duration of diabetes(0.167,0.033-0.301,p=0.015) remained as independent risk factors of GV. Conclusion: We found that the relation between dysglycemia and mortality is different in the diabetes and non-diabetes groups. And we confirm that GV was associated with excess mortality in DM or Non-DM patients. Furthermore, APACHE II score, Mean BG, total parenteral nutrition, serum albumin, insulin treatment and duration of ventilation were significantly associated with an increase in GV in Non-DM patients. While APACHE II score, mean BG and duration of diabetes (years) remained as independent risk factors of increased GV in DM patients. These findings provide important context for further prospective trials investigating the effect of different clinical factors in critically ill patients with or without diabetes.

Keywords: diabetes, glycemic variability, predictors, severe disease

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Authors: Guilan Huang

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In 2013 and 2014, the U.S. Food and Drug Administration (FDA) collected data from selected fast food restaurants and full service restaurants for tracking changes in the occurrence of foodborne illness risk factors. This paper discussed how we customized spatial random sampling method by considering financial position and availability of FDA resources, and how we enriched restaurants data with location. Location information of restaurants provides opportunity for quantitatively determining random sampling within non-government units (e.g.: 240 kilometers around each data-collector). Spatial analysis also could optimize data-collectors’ work plans and resource allocation. Spatial analytic and processing platform helped us handling the spatial random sampling challenges. Our method fits in FDA’s ability to pinpoint features of foodservice establishments, and reduced both time and expense on data collection.

Keywords: geospatial technology, restaurant, retail food risk factor study, spatially random sampling

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Authors: Leo Carlo B. Rondina

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Self-image is an important factor to improve the self-esteem of the personnel. The purpose of the study is to determine the self-image of the police. The respondents were the 503 policemen assigned in different Police Station in Davao City, and they were chosen with the used of random sampling. With the used of Exploratory Factor Analysis (EFA), latent construct variables of police image were identified as follows; professionalism, obedience, morality and justice and fairness. Further, ordinal regression indicates statistical characteristics on ages 21-40 which means the age of the respondent statistically improves self-image.

Keywords: police image, exploratory factor analysis, ordinal regression, Galatea effect

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Authors: Wajid Ali Khan

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Extensive experimentation and numerical investigation are performed to predict the machining-induced residual stresses in the end milling of AISI 1045 steel, and an optimization code has been developed using the particle swarm optimization technique. Experiments were conducted using a single factor at a time and design of experiments approach. Regression analysis was done, and a mathematical model of the cutting process was developed, thus predicting the machining-induced residual stress with reasonable accuracy. The mathematical model served as the objective function to be optimized using particle swarm optimization. The relationship between the different cutting parameters and the output variables, force, and residual stresses has been studied. The combined effect of the process parameters, speed, feed, and depth of cut was examined, and it is understood that 85% of the variation of these variables can be attributed to these machining parameters under research. A 3D finite element model is developed to predict the cutting forces and the machining-induced residual stresses in end milling operation. The results were validated experimentally and against the Johnson-cook model available in the literature.

Keywords: residual stresses, end milling, 1045 steel, optimization

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Authors: Aissa Belmeguenai, Khaled Mansouri

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In this work, an efficient implementation of Achterbahn-128 for images encryption and decryption was introduced. The implementation for this simulated project is written by MATLAB.7.5. At first two different original images are used for validate the proposed design. Then our developed program was used to transform the original images data into image digits file. Finally, we used our implemented program to encrypt and decrypt images data. Several tests are done for proving the design performance including visual tests and security analysis; we discuss the security analysis of the proposed image encryption scheme including some important ones like key sensitivity analysis, key space analysis, and statistical attacks.

Keywords: Achterbahn-128, stream cipher, image encryption, security analysis

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Authors: Minttu Laukkanen, Janne Huiskonen

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This study investigates the sharing economy business models as examples of the sustainable business models. The aim is to contribute to the limited literature on sharing economy in connection with sustainable business models by explaining sharing economy business models value capturing. Specifically, this research answers the following question: How business model choices affect captured sustainable value? A multiple case study approach is applied in this study. Twenty different successful sharing economy business models focusing on consumer business and covering four main areas, accommodation, mobility, food, and consumer goods, are selected for analysis. The secondary data available on companies’ websites, previous research, reports, and other public documents are used. All twenty cases are analyzed through the sharing economy business model framework and sustainable value analysis framework using qualitative data analysis. This study represents general sharing economy business model value attributes and their specifications, i.e. sustainable value propositions for different stakeholders, and further explains the sustainability impacts of different sharing economy business models through captured and uncaptured value. In conclusion, this study represents how business model choices affect sustainable value capturing through eight business model attributes identified in this study. This paper contributes to the research on sustainable business models and sharing economy by examining how business model choices affect captured sustainable value. This study highlights the importance of careful business model and sustainability impacts analyses including the triple bottom line, multiple stakeholders and value captured and uncaptured perspectives as well as sustainability trade-offs. It is not self-evident that sharing economy business models advance sustainability, and business model choices does matter.

Keywords: sharing economy, sustainable business model innovation, sustainable value, value capturing

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Authors: Samuel Mwangi, Josephine K. Mule

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Access control as a security technique regulates who or what can access resources. It is a fundamental concept in security that minimizes risks to the institutions that use access control. Regulating access to institutions of higher learning is key to ensure only authorized personnel and students are allowed into the institutions. The use of biometrics has been criticized due to the setup and maintenance costs, hygiene concerns, and trepidations regarding data privacy, among other apprehensions. Facial recognition is arguably a fast and accurate way of validating identity in order to guard protected areas. It guarantees that only authorized individuals gain access to secure locations while requiring far less personal information whilst providing an additional layer of security beyond keys, fobs, or identity cards. This exploratory study sought to investigate the use of facial recognition in controlling access in institutions of higher learning in Kenya. The sample population was drawn from both private and public higher learning institutions. The data is based on responses from staff and students. Questionnaires were used for data collection and follow up interviews conducted to understand responses from the questionnaires. 80% of the sampled population indicated that there were many security breaches by unauthorized people, with some resulting in terror attacks. These security breaches were attributed to stolen identity cases, where staff or student identity cards were stolen and used by criminals to access the institutions. These unauthorized accesses have resulted in losses to the institutions, including reputational damages. The findings indicate that security breaches are a major problem in institutions of higher learning in Kenya. Consequently, access control would be beneficial if employed to curb security breaches. We suggest the use of facial recognition technology, given its uniqueness in identifying users and its non-repudiation capabilities.

Keywords: facial recognition, access control, technology, learning

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Authors: Mehdi Moeinaddini, Zohreh Asadi-Shekari, Muhammad Zaly Shah, Amran Hamzah

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Currently, planners try to have more green travel options to decrease economic, social and environmental problems. Therefore, this study tries to find significant urban travel factors to be used to increase the usage of alternative urban travel modes. This paper attempts to identify the relationship between prominent urban mobility indicators and daily trips by public transport in 30 cities from various parts of the world. Different travel modes, infrastructures and cost indicators were evaluated in this research as mobility indicators. The results of multi-linear regression analysis indicate that there is a significant relationship between mobility indicators and the daily usage of public transport.

Keywords: green travel modes, urban travel indicators, daily trips by public transport, multi-linear regression analysis

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Authors: Sharareh Hekmat, Michelle Lane

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Pregnancy during adolescence affects both the growth and development of mother and baby, particularly in low socioeconomic and food insecure areas. This mixed methods study is aimed at discovering a need for a community-based probiotic yogurt program to assist pregnant women in the Mukuru slum Nairobi, Kenya. Surveys were conducted with pregnant women (14-25 years old, n=43), which included questionnaires on dietary intake, food access, and health/quality of life perception. The frequency and means procedure was used to analyze maternal characteristics, Women’s Dietary Diversity Score (WDDS) and Household Hunger Scale. 24-hour recalls were analyzed via ESHA Food Processor, and median nutrient intakes were reported as a percent of recommendations. An environmental scan was conducted to assess food availability, accessibility, and quality. WDDS reflected a low-moderate diet variation (3.86 food groups out of 9, SD ± 1.3) among the women. The 24-hour recall suggested an inadequate intake of many nutrients, most significantly B12, potassium and calcium. 86% of women reported little to no household hunger. However, the environmental scan revealed low quality and poor sanitation of food. This study provides evidence that a probiotic program would be desirable, and contribute to the nutritional status of women in the Mukuru community.

Keywords: dietary diversity, pregnant women, probiotics, urban slum, Kenya

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Ana Gabriela Diaz, Luis Fermín Córdova, Jr., Roberto Lamazares

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The island of Cuba, the largest of the Greater Antilles, is located in the tropical North Atlantic. It is annually affected by numerous weather events, which have caused severe damage to our coastal areas. In the same way that many other coastlines around the world, the beautiful beaches of the Hicacos Peninsula also suffer from erosion. This leads to a structural regression of the coastline. If measures are not taken, the hotels will be exposed to the advance of the sea, and it will be a serious problem for the economy. With the aim of studying the intensity of this type of activity, specialists of group of coastal and marine engineering from CIH, in the framework of the research conducted within the project MEGACOSTAS 2, provide their research to simulate extreme events and assess their impact in coastal areas, mainly regarding the definition of flood volumes and morphodynamic changes in sandy beaches. The main objective of this work is the evaluation of the process of Varadero beach erosion (the coastal sector has an important impact in the country's economy) on the Hicacos Peninsula for different paths of hurricanes. The mathematical model XBeach, which was integrated into the Coastal engineering system introduced by the project of MEGACOSTA 2 to determine the area and the more critical profiles for the path of hurricanes under study, was applied. The results of this project have shown that Center area is the greatest dynamic area in the simulation of the three paths of hurricanes under study, showing high erosion volumes and the greatest average length of regression of the coastline, from 15- 22 m.

Keywords: beach, erosion, mathematical model, coastal areas

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Authors: Vangelis Papadimitropoulos

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This paper is part of the research program “Techno-Social Innovation in the Collaborative Economy”, funded by the Hellenic Foundation for Research and Innovation (H.F.R.I.) for the years 2022-2024. The paper showcases the Open Food Network (OFN) as an open-sourced digital platform supporting short food supply chains in local agricultural production and consumption. The paper outlines the research hypothesis, the theoretical framework, and the methodology of research as well as the findings and conclusions. Research hypothesis: The model of open cooperativism as a vehicle for systemic change in the agricultural sector. Theoretical framework: The research reviews the OFN as an illustrative case study of the three-zoned model of open cooperativism. The OFN is considered a paradigmatic case of the model of open cooperativism inasmuch as it produces commons, it consists of multiple stakeholders including ethical market entities, and it is variously supported by local authorities across the globe, the latter prefiguring the mini role of a partner state. Methodology: Research employs Ernesto Laclau and Chantal Mouffe’s discourse analysis -elements, floating signifiers, nodal points, discourses, logics of equivalence and difference- to analyse the breadth of empirical data gathered through literature review, digital ethnography, a survey, and in-depth interviews with core OFN members. Discourse analysis classifies OFN floating signifiers, nodal points, and discourses into four themes: value proposition, governance, economic policy, and legal policy. Findings: OFN floating signifiers align around the following nodal points and discourses: “digital commons”, “short food supply chains”, “sustainability”, “local”, “the elimination of intermediaries” and “systemic change”. The current research identifies a lack of common ground of what the discourse of “systemic change” signifies on the premises of the OFN’s value proposition. The lack of a common mission may be detrimental to the formation of a common strategy that would be perhaps deemed necessary to bring about systemic change in agriculture. Conclusions: Drawing on Laclau and Mouffe’s discourse theory of hegemony, research introduces a chain of equivalence by aligning discourses such as “agro-ecology”, “commons-based peer production”, “partner state” and “ethical market entities” under the model of open cooperativism, juxtaposed against the current hegemony of neoliberalism, which articulates discourses such as “market fundamentalism”, “privatization”, “green growth” and “the capitalist state” to promote corporatism and entrepreneurship. Research makes the case that for OFN to further agroecology and challenge the current hegemony of industrial agriculture, it is vital that it opens up its supply chains into equivalent sectors of the economy, civil society, and politics to form a chain of equivalence linking together ethical market entities, the commons and a partner state around the model of open cooperativism.

Keywords: sustainability, the digital commons, open cooperativism, innovation

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Authors: Zheni S. Stefanova, Kandethody M. Ramachandran

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With the increasing dependency on our computer devices, we face the necessity of adequate, efficient and effective mechanisms, for protecting our network. There are two main problems that Intrusion Detection Systems (IDS) attempt to solve. 1) To detect the attack, by analyzing the incoming traffic and inspect the network (intrusion detection). 2) To produce a prompt response when the attack occurs (intrusion prevention). It is critical creating an Intrusion detection model that will detect a breach in the system on time and also challenging making it provide an automatic and with an acceptable delay response at every single stage of the monitoring process. We cannot afford to adopt security measures with a high exploiting computational power, and we are not able to accept a mechanism that will react with a delay. In this paper, we will propose an intrusion response mechanism that is based on artificial intelligence, and more precisely, reinforcement learning techniques (RLT). The RLT will help us to create a decision agent, who will control the process of interacting with the undetermined environment. The goal is to find an optimal policy, which will represent the intrusion response, therefore, to solve the Reinforcement learning problem, using a Q-learning approach. Our agent will produce an optimal immediate response, in the process of evaluating the network traffic.This Q-learning approach will establish the balance between exploration and exploitation and provide a unique, self-learning and strategic artificial intelligence response mechanism for IDS.

Keywords: cyber security, intrusion prevention, optimal policy, Q-learning

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Authors: Lesa A. Thompson, Yoshinori Ikenaka, Victor Wepener, Mayumi Ishizuka

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One method for controlling malaria in endemic regions is the killing of vector mosquitoes using pesticides such as DDT in indoor residual spraying (IRS). This study was carried out to investigate the presence of and human health risk due to DDT and its metabolites (collectively, DDTs) contaminating human food sources in areas where DDT is used for IRS. Free-range chicken products (meat and eggs) were collected from homesteads in KwaZulu-Natal Province in the northeast of South Africa, and fish meat samples from Maputo Bay in neighbouring Mozambique. Samples were analysed for DDTs (o,p’-DDT, p,p’-DDT, o,p’-DDD, p,p’-DDD, o,p’-DDE and p,p’-DDE) using a gas chromatograph with electron capture detector (GC-ECD). DDTs were detected in all food types, with the predominant congener being p,p’-DDE. The presence of p,p’-DDT confirmed recent release of DDT into the environment. By using concentration levels detected in foods and national consumption levels, the risk to human health through consumption of such food products was calculated. In order of risk level, these were: chicken eggs > chicken meat > fish meat. Human risk (carcinogenic) values greater than one suggest there is an increased health risk through consumption of these foods.

Keywords: DDT, food contamination, human health risk, Mozambique, South Africa

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Authors: Mobarok Hossain Bhuyain

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Video surveillance processing has attracted various security fields transforming it into one of the leading research fields. Today's demand for detection and tracking of human mobility for security is very useful for human security, such as in crowded areas. Accordingly, video surveillance technology has seen a rapid advancement in recent years, with algorithms analyzing the behavior of people under surveillance automatically. The main motivation of this research focuses on the detection and tracking of the elderly and socially vulnerable people in crowded areas. Degenerate people are a major health concern, especially for elderly people and socially vulnerable people. One major disadvantage of video surveillance is the need for continuous monitoring, especially in crowded areas. To assist the security monitoring live surveillance video, image processing, and artificial intelligence methods can be used to automatically send warning signals to the monitoring officers about elderly people and socially vulnerable people.

Keywords: human detection, target tracking, neural network, particle filter

Procedia PDF Downloads 164

Authors: Kerem Ok, Serafettin Senturk, Serdar Aktas, Cem Cevikbas

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Mobile technology is very popular nowadays and it provides a digital world where users can experience many value-added services. Service Providers are also eager to offer diverse value-added services to users such as digital identity, mobile financial services and so on. In this context, the security of data storage in smartphones and the security of communication between the smartphone and service provider are critical for the success of these services. In order to provide the required security functions, the SIM card is one acceptable alternative. Since SIM cards include a Secure Element, they are able to store sensitive data, create cryptographically secure keys, encrypt and decrypt data. In this paper, we design and implement a SIM and a smartphone framework that uses a SIM card for secure key generation, key storage, data encryption, data decryption and digital signing for mobile financial services. Our frameworks show that the SIM card can be used as a controlled Secure Element to provide required security functions for popular e-services such as mobile financial services.

Keywords: SIM card, mobile financial services, cryptography, secure data storage

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Authors: Francesco Pinci

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This research investigates the significance of product packaging as an effective marketing tool. By using commercially available pasta as an example, the study specifically examines the visual components of packaging, including color, shape, packaging material, and logo. The insights gained from studies like this are particularly valuable to food and beverage companies as they provide marketers with a deeper understanding of the factors influencing consumer purchasing decisions. The research analyzes data collected through surveys conducted via Google Forms and visual data obtained using iMotions eye-tracker software. The results affirm the importance of packaging design elements, such as color and product information, in shaping consumer buying behavior.

Keywords: consumer behaviour, eyetracker, food marketing, neuromarketing

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Authors: Shan-Hong Ying, Ying-Fang Wang

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A surrogate approach was deployed for assessing exposures of multiple chemicals at the selected working area of coating processes and applied to assess the exposure concentration of similar exposed groups using the same chemicals but different formula ratios. For the selected area, 6 to 12 portable photoionization detector (PID) were placed uniformly in its workplace to measure its total VOCs concentrations (CT-VOCs) for 6 randomly selected workshifts. Simultaneously, one sampling strain was placed beside one of these portable PIDs, and the collected air sample was analyzed for individual concentration (CVOCi) of 5 VOCs (xylene, butanone, toluene, butyl acetate, and dimethylformamide). Predictive models were established by relating the CT-VOCs to CVOCi of each individual compound via simple regression analysis. The established predictive models were employed to predict each CVOCi based on the measured CT-VOC for each the similar working area using the same portable PID. Results show that predictive models obtained from simple linear regression analyses were found with an R2 = 0.83~0.99 indicating that CT-VOCs were adequate for predicting CVOCi. In order to verify the validity of the exposure prediction model, the sampling analysis of the above chemical substances was further carried out and the correlation between the measured value (Cm) and the predicted value (Cp) was analyzed. It was found that there is a good correction between the predicted value and measured value of each measured chemical substance (R2=0.83~0.98). Therefore, the surrogate approach could be assessed the exposure concentration of similar exposed groups using the same chemicals but different formula ratios. However, it is recommended to establish the prediction model between the chemical substances belonging to each coater and the direct-reading PID, which is more representative of reality exposure situation and more accurately to estimate the long-term exposure concentration of operators.

Keywords: exposure assessment, exposure prediction model, surrogate approach, TVOC

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Authors: Suzan Arslanturk, Mohammad-Reza Siadat, Theophilus Ogunyemi, Ananias Diokno

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Missing data is a common challenge in statistical analyses of most clinical survey datasets. A variety of methods have been developed to enable analysis of survey data to deal with missing values. Imputation is the most commonly used among the above methods. However, in order to minimize the bias introduced due to imputation, one must choose the right imputation technique and apply it to the correct type of missing data. In this paper, we have identified different types of missing values: missing data due to skip pattern (SPMD), undetermined missing data (UMD), and genuine missing data (GMD) and applied rough set imputation on only the GMD portion of the missing data. We have used rough set imputation to evaluate the effect of such imputation on prediction by generating several simulation datasets based on an existing epidemiological dataset (MESA). To measure how well each dataset lends itself to the prediction model (logistic regression), we have used p-values from the Wald test. To evaluate the accuracy of the prediction, we have considered the width of 95% confidence interval for the probability of incontinence. Both imputed and non-imputed simulation datasets were fit to the prediction model, and they both turned out to be significant (p-value < 0.05). However, the Wald score shows a better fit for the imputed compared to non-imputed datasets (28.7 vs. 23.4). The average confidence interval width was decreased by 10.4% when the imputed dataset was used, meaning higher precision. The results show that using the rough set method for missing data imputation on GMD data improve the predictive capability of the logistic regression. Further studies are required to generalize this conclusion to other clinical survey datasets.

Keywords: rough set, imputation, clinical survey data simulation, genuine missing data, predictive index

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Authors: Amir Farouk Ahmed Ali Hussin

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Social media, such as Facebook, LinkedIn and Ex-Twitter have experienced exponential growth and a remarkable adoption rate in recent years. They give fantastic means of online social interactions and communications with family, friends, and colleagues from around the corner or across the globe, and they have become an important part of daily digital interactions for more than one and a half billion users around the world. The personal information sharing practices that social network providers encourage have led to their success as innovative social interaction platforms. Moreover, these practices have outcome in concerns with respect to privacy and security from different stakeholders. Guiding these privacy and security concerns in social networks is a must for these networks to be sustainable. Real security and privacy tools may not be enough to address existing concerns. Some points should be followed to protect users from the existing risks. In this research, we have checked the various privacy and security issues and concerns pertaining to social media. However, we have classified these privacy and security issues and presented a thorough discussion of the effects of these issues and concerns on the future of the social networks. In addition, we have presented a set of points as precaution measures that users can consider to address these issues.

Keywords: international legal, consultation mix, legal research, small and medium-sized enterprises, strategic International law, strategy alignment, house of laws, deployment, production strategy, legal strategy, business strategy

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Authors: Tatiana V. Melnikova, Lyudmila P. Polyakova, Alla A. Oudalova

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As a result of environmental pollution, the production of agriculture and foodstuffs inevitably contain residual amounts of Persistent Organic Pollutants (POP). The special attention must be given to organic pollutants, including various organochlorinated pesticides (OCP). Among priorities, OCP is DDT (and its metabolite DDE), alfa-HCH, gamma-HCH (lindane). The control of these substances spends proceeding from requirements of sanitary norms and rules. During too time often is lost sight of that the primary product can pass technological processing (in particular irradiation treatment) as a result of which transformation of physicochemical forms of initial polluting substances is possible. The goal of the present work was to study the OCP radiation degradation at a various gamma-radiation dosimetric characteristics. The problems posed for goal achievement: to evaluate the content of the priority of OCPs in food; study the character the degradation of OCP in model solutions (with micro concentrations commensurate with the real content of their agricultural and food products) depending upon dosimetric characteristics of gamma-radiation. Qualitative and quantitative analysis of OCP in food and model solutions by gas chromatograph Varian 3400 (Varian, Inc. (USA)); chromatography-mass spectrometer Varian Saturn 4D (Varian, Inc. (USA)) was carried out. The solutions of DDT, DDE, alpha- and gamma- isomer HCH (0.01, 0.1, 1 ppm) were irradiated on "Issledovatel" (60Co) and "Luch - 1" (60Co) installations at a dose 10 kGy with a variation of dose rate from 0.0083 up to 2.33 kGy/sec. It was established experimentally that OCP residual concentration in individual samples of food products (fish, milk, cereal crops, meat, butter) are evaluated as 10-1-10-4 mg/kg, the value of which depends on the factor-sensations territory and natural migration processes. The results were used in the preparation of model solutions OCP. The dependence of a degradation extent of OCP from a dose rate gamma-irradiation has complex nature. According to our data at a dose 10 kGy, the degradation extent of OCP at first increase passes through a maximum (over the range 0.23 – 0.43 Gy/sec), and then decrease with the magnification of a dose rate. The character of the dependence of a degradation extent of OCP from a dose rate is kept for various OCP, in polar and nonpolar solvents and does not vary at the change of concentration of the initial substance. Also in work conditions of the maximal radiochemical yield of OCP which were observed at having been certain: influence of gamma radiation with a dose 10 kGy, in a range of doses rate 0.23 – 0.43 Gy/sec; concentration initial OCP 1 ppm; use of solvent - 2-propanol after preliminary removal of oxygen. Based on, that at studying model solutions of OCP has been established that the degradation extent of pesticides and qualitative structure of OCP radiolysis products depend on a dose rate, has been decided to continue researches radiochemical transformations OCP into foodstuffs at various of doses rate.

Keywords: degradation extent, dosimetric characteristics, gamma-radiation, organochlorinated pesticides, persistent organic pollutants

Procedia PDF Downloads 248