Search results for: COVID-19 identification

Authors: Adjebli Ahmed, Messis Abdelaziz, Ayeche Riad, Tighilet Karim, Talbi Melissa, Smaili Yanis, Lehri Mokrane, Louardiane Mustapha

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Colorectal cancer is one of the most common types of cancer worldwide, and its incidence has been increasing in recent years. Data and fecal samples from colorectal cancer patients were collected at the Amizour Public Hospital's oncology department (Bejaia, Algeria). Microbiological and cohort study were conducted at the Biological Engineering of Cancers laboratory at the Faculty of Medicine of the University of Bejaia. All the data showed that patients aged between 50 and 70 years were the most affected by colorectal cancer, while the age categories of [30-40] and [40-50] were the least affected. Males were more likely to be at risk of contracting colorectal cancer than females. The most common types of colorectal cancer among the studied population were sigmoid cancer, rectal cancer, transverse colon cancer, and ascending colon cancer. The hereditary factor was found to be more dominant than other risk factors. Bacterial identification revealed the presence of certain pathogenic and opportunistic bacterial genera, such as E. coli, K. pneumoniae, Shigella sp, and Streptococcus group D. These results led us to conclude that dysbiosis of the intestinal microbiome is strongly present in colorectal cancer patients at the EPH of Amizour.

Keywords: microbiome, colorectal cancer, risk factors, bacterial identification

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Authors: Ming-Yen Chang, Sheng-Hung Ke

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This research focuses on the development of a speed bump identification system for real-time control of adjustable shock absorbers in vehicular suspension systems. The study initially involved the collection of images of various speed bumps, and rubber speed bump profiles found on roadways. These images were utilized for training and recognition purposes through the deep learning object detection algorithm YOLOv5. Subsequently, the trained speed bump identification program was integrated with an in-vehicle camera system for live image capture during driving. These images were instantly transmitted to a computer for processing. Using the principles of monocular vision ranging, the distance between the vehicle and an approaching speed bump was determined. The appropriate control distance was established through both practical vehicle measurements and theoretical calculations. Collaboratively, with the electronically adjustable shock absorbers equipped in the vehicle, a shock absorber control system was devised to dynamically adapt the damping force just prior to encountering a speed bump. This system effectively mitigates passenger discomfort and enhances ride quality.

Keywords: adjustable shock absorbers, image recognition, monocular vision ranging, ride

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Authors: Srinaath Anbu Durai, Wang Zhaoxia

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Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data.

Keywords: sentiment analysis, Covid-19, housing price prediction, tweets, social media, Singapore HDB, behavioral economics, neural networks

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Authors: Jingyuan Hu, Zhandong Liu

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CRISPR genome editing technology has transformed molecular biology by accurately targeting and altering an organism’s DNA. Despite the state-of-art precision of CRISPR genome editing, the imprecise mutation outcome and off-target effects present considerable risk, potentially leading to unintended genetic changes. Targeted deep sequencing, combined with bioinformatics sequence alignment, can detect such unwanted mutations. Nevertheless, the classical method, Needleman-Wunsch (NW) algorithm may produce false alignment outcomes, resulting in inaccurate mutation identification. The key to precisely identifying CRISPR-induced mutations lies in determining optimal parameters for the sequence alignment algorithm. Hidden Markov models (HMM) are ideally suited for this task, offering flexibility across CRISPR systems by leveraging forward-backward algorithms for parameter estimation. In this study, we introduce CRISPR-HMM, a statistical software to precisely call CRISPR-induced mutations. We demonstrate that the software significantly improves precision in identifying CRISPR-induced mutations compared to NW-based alignment, thereby enhancing the overall understanding of the CRISPR gene-editing process.

Keywords: CRISPR, HMM, sequence alignment, gene editing

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Authors: Rimeyah H. S. Almutairi, Alaa Eldin A. Ayoub

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The aim of this study was to identify empirical and theoretical studies that explored giftedness theories and identification. In order to assess and synthesize the mechanisms, outcomes, and impacts of gifted identification models. Thus, we sought to provide an evidence-informed answer to how does current giftedness theories work and effectiveness. In order to develop a model that incorporates the advantages of existing models and avoids their disadvantages as much as possible. We conducted a systematic literature review (SLR). The disciplined analysis resulted in a final sample consisting of 30 appropriate searches. The results indicated that: (a) there is no uniform and consistent definition of Giftedness; (b) researchers are using several non-consistent criteria to detect gifted, and (d) The detection of talent is largely limited to early ages, and there is obvious neglect of adults. This study contributes to the development of Giftedness Cloud Model (GCM) which defined as a model that attempts to interpretation giftedness within an interactive psychological and ecological framework. GCM aims to help a talented to reach giftedness core and manifestation talent in creative productivity or invention. Besides that, GCM suggests classifying giftedness into four levels of mastery, excellence, creative productivity, and manifestation. In addition, GCM presents an idea to distinguish between talent and giftedness.

Keywords: giftedness cloud model, talent, systematic literature review, giftedness concept

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Authors: Ebenezer O. Olaniyi, Oyebade K. Oyedotun, Khashman Adnan

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In the food industry high quality production is required within a limited time to meet up with the demand in the society. In this research work, we have developed a model which can be used to replace the human operator due to their low output in production and slow in making decisions as a result of an individual differences in deciding the defective and healthy banana. This model can perform the vision attributes of human operators in deciding if the banana is defective or healthy for food production based. This research work is divided into two phase, the first phase is the image processing where several image processing techniques such as colour conversion, edge detection, thresholding and morphological operation were employed to extract features for training and testing the network in the second phase. These features extracted in the first phase were used in the second phase; the classification system phase where the multilayer perceptron using backpropagation neural network was employed to train the network. After the network has learned and converges, the network was tested with feedforward neural network to determine the performance of the network. From this experiment, a recognition rate of 97% was obtained and the time taken for this experiment was limited which makes the system accurate for use in the food industry.

Keywords: banana, food processing, identification system, neural network

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Authors: Sara Seyfi

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DDT and petroleum contamination in coastal sand alters the physical and mechanical properties of contaminated soils. This article aims to understand the effects of DDT pollution on the geotechnical characteristics of sand groups, including sand, silty sand, and clay sand. First, the studies conducted on the topic of the article will be reviewed. In the initial stage of the tests, this article deals with the identification of the used sands (sand, silty sand, clay sand) by FTIR, µ-XRF and SEM methods. Then, the geotechnical characteristics of these sand groups, including density, permeability, shear strength, compaction, and plasticity, are investigated using a sand cone, head permeability test, Vane shear test, strain gauge penetrometer, and plastic limit test. Sand groups are artificially contaminated with petroleum substances with 1, 2, 4, 8, 10, 12% by weight. In a separate experiment, amounts of 2, 4, 8, 12, 16, 20 mg/liter of DDT were added to the sand groups. Geotechnical characteristics and identification analysis are performed on the contaminated samples. In the final tests, the mentioned amounts of oil pollution and DDT are simultaneously added to the sand groups, and identification and measurement processes are carried out. The results of the tests showed that petroleum contamination had reduced the optimal moisture content, permeability, and plasticity of all samples. Except silty sand’s plasticity, which petroleum increased it by 1-4% and decreased it by 8-12%. The dry density of sand and clay sand increased, but that of silty sand decreased. Also, the shear strength of sand and silty sand increased, but that of clay sand decreased. DDT contamination increased the maximum dry density and decreased the permeability of all samples. It also reduced the optimum moisture content of the sand. The shear resistance of silty sand and clayey sand decreased, and plasticity of clayey sand increased, and silty sand decreased. The simultaneous effect of petroleum and DDT pollution on the maximum dry density of sand and clayey sand has been synergistic, on the plasticity of clayey sand and silty sand, there has been antagonism. This process has caused antagonism of optimal sand content, shear strength of silty sand and clay sand. In other cases, the effect of synergy or antagonism is not observed.

Keywords: DDT contamination, geotechnical characteristics, petroleum contamination, sand

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Authors: Haixu Yu, Wenhui Cao

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Tailoring Large Language Models (LLMs) to perform legal reasoning has been a popular trend in the study of AI and law. Researchers have mainly employed two methods to unlock the potential of LLMs, namely by finetuning the LLMs to expand their knowledge of law and by restructuring the prompts (In-Context Learning) to optimize the LLMs’ understanding of the legal questions. Although claiming the finetuning and renovated prompting can make LLMs more competent in legal reasoning, most state-of-the-art studies show quite limited improvements of practicability. In this paper, drawing on the study of the complexity and low interpretability of legal texts, we propose a prompting strategy based on the Chain of Thought (CoT) method. Instead of merely instructing the LLM to reason “step by step”, the prompting strategy requires the tested LLM to identify the ambiguity in the questions as the first step and then allows the LLM to generate corresponding answers in line with different understandings of the identified terms as the following step. The proposed prompting strategy attempts to encourage LLMs to "interpret" the given text from various aspects. Experiments that require the LLMs to answer “case analysis” questions of bar examination with general LLMs such as GPT 4 and legal LLMs such as LawGPT show that the prompting strategy can improve LLMs’ ability to better understand complex legal texts.

Keywords: ambiguity-identification, prompt, large language model, legal text understanding

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Authors: Balaji Ganesh Rajagopal, Subramanian Appavu alias Balamurugan, Ayyalraj Midhun Kumar, Krishnan Nallaperumal

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Intelligent Transportation System is an Emerging area to solve multiple transportation problems. Several forms of inputs are needed in order to solve ITS problems. Advanced Traveler Information System (ATIS) is a core and important ITS area of this modern era. This involves travel time forecasting, efficient road map analysis and cost based path selection, Detection of the vehicle in the dynamic conditions and Traffic congestion state forecasting. This Article designs and provides an algorithm for traffic data generation which can be used for the above said ATIS application. By inputting the real world traffic situation in the form of video sequences, the algorithm determines the Traffic density in terms of congestion, number of vehicles in a given path which can be fed for various ATIS applications. The Algorithm deduces the key frame from the video sequences and follows the Blob detection, Identification and Tracking using connected components algorithm to determine the correlation between the vehicles moving in the real road scene.

Keywords: traffic transportation, traffic density estimation, blob identification and tracking, relative velocity of vehicles, correlation between vehicles

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Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: Muneeba Butt

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One of the most important physical evidence which leaves suspects at the crime scene is footprints. Analysis of footprints, which can provide useful information for personal identification, is helpful in crime scene investigation. For the current study, 200 samples collected (144 male and 56 female) from Pakistani population with a consent form. The footprints were collected by using black ink with an ink pad. The entire samples were photographed, and then the magnifying glass was used for visualization of individual characteristics including detail of toes, humps, phalange mark, and flat foot cracks in footprint patterns. The descriptive results of individualizing characteristics features were presented in tabular form with respective frequency and percentage. In the result in the male population, the prevalence of tibialis type (T-type) is highest. In the female population, the prevalence of midularis type (M-type) is highest. Humps on the first toe are more found in the male population rather than other humps. In the female population, humps on the third toe are more found rather than other humps. In the male population, the prevalence of phalange mark by toe 1 is highest followed by toe 3, toe 5, toe 2, toe 4 and in female population the prevalence of phalange mark by toe 1 is highest followed by toe 5, 4, 3 and 2. Creases marks are found highest in male population as compared to the female population.

Keywords: foot prints, toes, humps, cracks

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Authors: Niki K. Burns, James R. Pearson, Paul G. Stevenson, Xavier A. Conlan

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In Australian state Victoria, synthetic cannabinoids have recently been made illegal under an amendment to the drugs, poisons and controlled substances act 1981. Identification of synthetic cannabinoids in popular brands of ‘incense’ and ‘potpourri’ has been a difficult and challenging task due to the sample complexity and changes observed in the chemical composition of the cannabinoids of interest. This study has developed analytical methodology for the targeted extraction and determination of synthetic cannabinoids available pre-ban. A simple solvent extraction and solid phase extraction methodology was developed that selectively extracted the cannabinoid of interest. High performance liquid chromatography coupled with UV‐visible and chemiluminescence detection (acidic potassium permanganate and tris (2,2‐bipyridine) ruthenium(III)) were used to interrogate the synthetic cannabinoid products. Mass spectrometry and nuclear magnetic resonance spectroscopy were used for structural elucidation of the synthetic cannabinoids. The tris(2,2‐bipyridine)ruthenium(III) detection was found to offer better sensitivity than the permanganate based reagents. In twelve different brands of herbal incense, cannabinoids were extracted and identified including UR‐144, XLR 11, AM2201, 5‐F‐AKB48 and A796‐260.

Keywords: electrospray mass spectrometry, high performance liquid chromatography, solid phase extraction, synthetic cannabinoids

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Authors: Farid Benkaci-Alia, Abdelhamid Neggada, Sophie Laurentb

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A new extraction method of polyphenols from honey using a biodegradable resin was developed and compared with the common commercial resin amberlite XAD2. For this purpose, three honey samples of Algerian origin were selected for the different physico-chemical and biochemical parameters study. After extraction of the target compounds by both resins, the polyphenol content was determined, the antioxidant activity was tested, and LC-MS analyses were performed for identification and quantification. The results showed that physico-chemical and biochemical parameters meet the norms of the International Honey commission, and the H1 sample seemed to be of high quality. The optimal conditions of extraction by biodegradable resin were a pH of 3, an adsorption dose of 40 g/L, a contact time of 50 min, an extraction temperature of 60°C and no stirring. The regeneration and reuse number of both resins was three cycles. The polyphenol contents demonstrated a higher extraction efficiency of biosorbent than of XAD2, especially in H1. LC-MS analyses allowed for the identification and quantification of fifteen compounds in the different honey samples extracted using both resins and the most abundant compound was 3,4,5-trimethoxybenzoic acid. In addition, the biosorbent extracts showed stronger antioxidant activities than the XAD2 extracts.

Keywords: extraction, polyphénols, biosorbent, resin amberlite, HPLC-MS

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

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Floods have huge environmental and economic impact. Therefore, flood prediction is given a lot of attention due to its importance. This study analysed the annual maximum streamflow (discharge) (AMS or AMD) of Karkheh River in Karkheh River Basin for flood predicting using ARIMA model. For this purpose, we use the Box-Jenkins approach, which contains four-stage method model identification, parameter estimation, diagnostic checking and forecasting (predicting). The main tool used in ARIMA modelling was the SAS and SPSS software. Model identification was done by visual inspection on the ACF and PACF. SAS software computed the model parameters using the ML, CLS and ULS methods. The diagnostic checking tests, AIC criterion, RACF graph and RPACF graphs, were used for selected model verification. In this study, the best ARIMA models for Annual Maximum Discharge (AMD) time series was (4,1,1) with their AIC value of 88.87. The RACF and RPACF showed residuals’ independence. To forecast AMD for 10 future years, this model showed the ability of the model to predict floods of the river under study in the Karkheh River Basin. Model accuracy was checked by comparing the predicted and observation series by using coefficient of determination (R²).

Keywords: time series modelling, stochastic processes, ARIMA model, Karkheh river

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Authors: Zuliani Mahmood, Thirumulu Ponnuraj Kannan, Yean Yean Chan, Salahddin A. Al-Hudhairy

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Caries is the most common childhood disease which develops due to disturbances in the physiological equilibrium in the dental plaque resulting in demineralization of tooth structures. Plaque and dentine samples were collected from three different tooth surfaces representing caries progression (intact, over carious lesion and dentine) in children with early childhood caries (ECC, n=36). In caries free (CF) children, plaque samples were collected from sound tooth surfaces at baseline and after one year (n=12). The genomic DNA was extracted from all samples and subjected to 16S rRNA PCR amplification. The end products were cloned into pCR®2.1-TOPO® Vector. Five randomly selected positive clones collected from each surface were sent for sequencing. Identification of the bacterial clones was performed using BLAST against GenBank database. In the ECC group, the frequency of Lactobacillus sp. detected was significantly higher in the dentine surface (p = 0.031) than over the cavitated lesion. The highest frequency of bacteria detected in the intact surfaces was Fusobacterium nucleatum subsp. polymorphum (33.3%) while Streptococcus mutans was detected over the carious lesions and dentine surfaces at a frequency of 33.3% and 52.7% respectively. Fusobacterium nucleatum subsp. polymorphum was also found to be highest in the CF group (41.6%). Follow up at the end of one year showed that the frequency of Corynebacterium matruchotii detected was highest in those who remained caries free (16.6%), while Porphyromonas catoniae was highest in those who developed caries (25%). In conclusion, Streptococcus mutans and Porphyromonas catoniae are strongly associated with caries progression, while Lactobacillus sp. is restricted to deep carious lesions. Fusobacterium nucleatum subsp. polymorphum and Corynebacterium matruchotii may play a role in sustaining the healthy equilibrium in the dental plaque. These identified bacteria show promise as potential biomarkers in diagnosis which could help in the management of dental caries in children.

Keywords: early childhood caries, genotypic identification, oral bacteria, 16S rRNA

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Authors: Tuhina-khatun, Mohamed Hanafi Musa, Mohd Rafii Yosup, Wong Mui Yun, Aktar-uz-Zaman, Mahbod Sahebi

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Suppression subtractive hybridization (SSH) method is valuable tool for identifying differentially regulated genes in disease specific or tissue specific genes important for cellular growth and differentiation. It is a widely used method for separating DNA molecules that distinguish two closely related DNA samples. SSH is one of the most powerful and popular methods for generating subtracted cDNA or genomic DNA libraries. It is based primarily on a suppression polymerase chain reaction (PCR) technique and combines normalization and subtraction in a solitary procedure. The normalization step equalizes the abundance of DNA fragments within the target population, and the subtraction step excludes sequences that are common to the populations being compared. This dramatically increases the probability of obtaining low-abundance differentially expressed cDNAs or genomic DNA fragments and simplifies analysis of the subtracted library. SSH technique is applicable to many comparative and functional genetic studies for the identification of disease, developmental, tissue specific, or other differentially expressed genes, as well as for the recovery of genomic DNA fragments distinguishing the samples under comparison.

Keywords: suppression subtractive hybridization, differentially expressed genes, disease specific genes, tissue specific genes

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Authors: Mohammed Bakhit Kashoob, Mohammed Salim Al-Maashani, Ahmed Abdullah Al-Marhoon

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The use of Geographic Information System (GIS) tools in the analysis of traffic crash data can help to identify locations or hotspots with high instances or risk of traffic crashes. The identification of traffic crash hotspots can effectively improve road safety measures. Mapping of road traffic crash hotspots can help the concerned authorities to give priority and take targeted measures and improvements to the road structure at these locations to reduce traffic crashes and fatalities. In Oman, there are countless rural roads that have more risks for traveling vehicles compared to urban roads. The likelihood of traffic crashes as well as fatality rate may increase with the presence of risks that are associated with the rural type of community. In this paper, the traffic crash hotspots on rural roads in Oman are specified using spatial analysis methods in GIS and traffic crash data. These hotspots are ranked based on the frequency of traffic crash occurrence (i.e., number of traffic crashes) and the rate of fatalities. The result of this study presents a map visualization of locations on rural roads with high traffic crashes and high fatalities rates.

Keywords: road safety, rural roads, traffic crash, GIS tools

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Authors: Ferdinando Montemari, Antonio Vitale, Nicola Genito, Giovanni Cuciniello

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The introduction of tilt-rotor aircraft into the existing civilian air transportation system will provide beneficial effects due to tilt-rotor capability to combine the characteristics of a helicopter and a fixed-wing aircraft into one vehicle. The disposability of reliable tilt-rotor simulation models supports the development of such vehicle. Indeed, simulation models are required to design automatic control systems that increase safety, reduce pilot's workload and stress, and ensure the optimal aircraft configuration with respect to flight envelope limits, especially during the most critical flight phases such as conversion from helicopter to aircraft mode and vice versa. This article presents a process to build a simplified tilt-rotor simulation model, derived from the analysis of flight data. The model aims to reproduce the complex dynamics of tilt-rotor during the in-flight conversion phase. It uses a set of scheduled linear transfer functions to relate the autopilot reference inputs to the most relevant rigid body state variables. The model also computes information about the rotor flapping dynamics, which are useful to evaluate the aircraft control margin in terms of rotor collective and cyclic commands. The rotor flapping model is derived through a mixed theoretical-empirical approach, which includes physical analytical equations (applicable to helicopter configuration) and parametric corrective functions. The latter are introduced to best fit the actual rotor behavior and balance the differences existing between helicopter and tilt-rotor during flight. Time-domain system identification from flight data is exploited to optimize the model structure and to estimate the model parameters. The presented model-building process was applied to simulated flight data of the ERICA Tilt-Rotor, generated by using a high fidelity simulation model implemented in FlightLab environment. The validation of the obtained model was very satisfying, confirming the validity of the proposed approach.

Keywords: flapping dynamics, flight dynamics, system identification, tilt-rotor modeling and simulation

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Authors: Laura G. Caixeta, Maico R. Severino

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One of the biggest challenges that the industries find nowadays is to incorporate sustainability practices into its operations. The Green Supply Chain Management (GSCM) concept assists industries in such incorporation. For the full application of this concept is important that enterprises of a same supply chain have the GSCM practices coordinated among themselves. Note that this type of analyses occurs on the context of developed countries and sectors considered big impactors (as automotive, mineral, among others). The propose of this paper is to analyze as the GSCM concepts are applied on the Brazilian animal nutrition industries. The method used was the Case Study. For this, it was selected a supply chain relationship composed by animal nutrition products manufacturer (Enterprise A) and its supplier of animal waste, such as blood, viscera, among others (Enterprise B). First, a literature review was carried out to identify the main GSCM practices. Second, it was done an individual analysis of each one selected enterprise of the application of GSCM concept. For the observed practices, the coordination of each practice in this supply chain was studied. And, it was developed propose of GSCM applications for the practices no observed. The findings of this research were: a) the systematization of main GSCM practices, as: Internal Environment Management, Green Consumption, Green Design, Green Manufacturing, Green Marketing, Green Packaging, Green Procurement, Green Recycling, Life Cycle Analysis, Consultation Selection Method, Environmental Risk Sharing, Investment Recovery, and Reduced Transportation Time; b) the identification of GSCM practices on Enterprise A (7 full application, 3 partial application and 3 no application); c) the identification of GSCM practices on Enterprise B (2 full application, 2 partial application and 9 no application); d) the identification of how is the incentive and the coordination of the GSCM practices on this relationship by Enterprise A; e) proposals of application and coordination of the others GSCM practices on this supply chain relationship. Based on the study, it can be concluded that its possible apply GSCM on animal nutrition industries, and when occurs the motivation on the application of GSCM concepts by a supply chain echelon, these concepts are deployed for the others supply chain echelons by the coordination (orchestration) of the first echelon.

Keywords: animal nutrition industries, coordination, green supply chain management, supply chain management, sustainability

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Authors: Marisol Rodriguez-Duarte, Aide Saenz-Galindo, Carolina Flores-Gallegos, Raul Rodriguez-Herrera, Juan Ascacio-Valdes

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The plant known as tarbush (Fluorensia cernua) is a plant originating in northern Mexico, mainly in the states of Coahuila, Durango, San Luis Potosí, Zacatecas and Chihuahua. It is a branched shrub that belongs to the family Asteraceae, has oval leaves of 6 to 11 cm in length and also has small yellow flowers. In Mexico, the tarbush is a very appreciated plant because it has been used as a traditional medicinal agent, for the treatment of gastrointestinal diseases, skin infections and as a healing agent. This plant has been used mainly as an infusion. Due to its traditional use, the content and type of phytochemicals present in the plant are currently unknown and are responsible for its biological properties, so its recovery and identification is very important because the compounds that it contains have relevant applications in the field of food, pharmaceuticals and medicine. The objective of this work was to determine the best extraction condition of phytochemical compounds (mainly polyphenolic compounds) from the leaf using ultrasound/microwave assisted extraction (U/M-AE). To reach the objective, U/M-AE extractions were performed evaluating three mass/volume ratios (1:8, 1:12, 1:16), three ethanol/water solvent concentrations (0%, 30% and 70%), ultrasound extraction time of 20 min and 5 min at 70°C of microwave treatment. All experiments were performed using a fractional factorial experimental design. Once the best extraction condition was defined, the compounds were recovered by liquid column chromatography using Amberlite XAD-16, the polyphenolic fraction was recovered with ethanol and then evaporated. The recovered polyphenolic compounds were quantified by spectrophotometric techniques and identified by HPLC/ESI/MS. The results obtained showed that the best extraction condition of the compounds was using a mass/volume ratio of 1:8 and solvent ethanol/water concentration of 70%. The concentration obtained from polyphenolic compounds using this condition was 22.74 mg/g and finally, 16 compounds of polyphenolic origin were identified. The results obtained in this work allow us to postulate the Mexican plant known as tarbush as a relevant source of bioactive polyphenolic compounds of food, pharmaceutical and medicinal interest.

Keywords: U/M-AE, tarbush, polyphenols, identification

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Authors: Anna Rinaldi, Pierfrancesco Dellino

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COVID-19 vaccine hesitancy can be observed at different rates in different countries. In June 2021, 1,068 people were surveyed in France and Italy to inquire about individual potential acceptance, focusing on time preferences in a risk-return framework: having the vaccination today, in a month, and in 3 months; perceived risks of vaccination and COVID-19; and expected benefit of the vaccine. A randomized controlled trial was conducted to understand how everyday stimuli like fact-based news about vaccines impact an audience's acceptance of vaccination. The main experiment involved two groups of participants and two different articles about vaccine-related thrombosis taken from two Italian newspapers. One article used a more abstract description and language, and the other used a more anecdotal description and concrete language; each group read only one of these articles. Two other groups were assigned categorization tasks; one was asked to complete a concrete categorization task, and the other an abstract categorization task. Individual preferences for vaccination were found to be variable and unstable over time, and individual choices of accepting, refusing, or delaying could be affected by the way news is written. In order to understand these dynamic preferences, the present work proposes a new model based on seven categories of human behaviors that were validated by a neural network. A treatment effect was observed: participants who read the articles shifted to vaccine hesitancy categories more than participants assigned to other treatments and control. Furthermore, there was a significant gender effect, showing that the type of language leading to a lower hesitancy rate for men is correlated with a higher hesitancy rate for women and vice versa. This outcome should be taken into consideration for an appropriate gender-based communication campaign aimed at achieving herd immunity. The trial was registered at ClinicalTrials.gov NCT05582564 (17/10/2022).

Keywords: vaccine hesitancy, risk elicitation, neural network, covid19

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Authors: Ajibade T., Omotesho O. A., Ayinde O. E, Ajibade E. T., Muhammad-Lawal A.

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This study was carried out to examine the extent and magnitude of food security amongst farming rural households in Nigeria. Data used for this study was collected from a total of two hundred and forty rural farming households using a two-stage random sampling technique. The main tools of analysis for this study include descriptive statistics and a constructed food security index using the identification and aggregation procedure. The headcount ratio in this study reveals that 71% of individuals in the study area were food secure with an average per capita calorie and protein availability of 4,213.92kcal and 99.98g respectively. The aggregated household daily calorie availability and daily protein availability per capita were 3,634.57kcal and 84.08g respectively which happens to be above the food security line of 2,470kcal and 65g used in this study. The food insecure households fell short of the minimum daily per capita calorie and protein requirement by 2.1% and 24.9%. The study revealed that the area is food insecure due to unequal distribution of the available food amongst the sampled population. The study recommends that the households should empower themselves financially in order to enhance their ability to afford the food during both on and off seasons. Also, processing and storage of farm produce should be enhanced in order to improve on availability throughout the year.

Keywords: farming household, food security, identification and aggregation, food security index

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Authors: S. M. Dankaka, N. Abdullahi

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Context: This research was conducted to isolate and identify phenol-tolerant bacteria from petroleum-contaminated sites in the northwestern part of Nigeria. Research Aim: The aim of this study was to identify bacteria with the ability to tolerate different phenol concentrations. Methodology: Samples were obtained from different petroleum-contaminated sites, and bacteria were cultured, followed by morphological, microscopic, and molecular identification. Isolates were grown on phenol-tolerant nutrient agar. The tolerant ability of the isolates was observed at 500 mg/L, 1000 mg/L, and 1500 mg/L concentrations of phenol. Findings: Two bacteria species (NWPK and NWPKD) were obtained. The total viable counts of phenol-utilizing bacteria from NWPK and NWPKD were 2.71x10⁷ and 4.0x10⁶ cfu/g, respectively. The NWPK showed its capacity to tolerate phenol at 2.3x10⁷, 2.5x10⁷, and 1.0x10⁷ cfu/g of 500, 1000, and 1500 mg/L of phenol concentration, respectively, while NWPKD tolerance ability was 1.5x10⁷, 3.8x10⁷ and 1.0x10⁷ cfu/g of 500, 1000 and 1500 mg/L of phenol respectively. The isolates were identified as Citrobacter and Acinetobacter species, respectively, based on 16S rRNA gene sequence analysis. Conclusion: The study found that these isolates showed the ability to withstand and survive high phenol concentrations in the environment.

Keywords: phenol tolerance, bacteria, petroleum contaminated sites, 16S rRNA

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Authors: Shirin Jalili, Hadi Shirzad, Samaneh Nabavi, Somayeh Khanjani

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Forensic entomology is the science of study and analysis of insects evidences to aid in criminal investigation. Being aware of the distribution, biology, ecology and behavior of insects, which are founded at crime scene can provide information about when, where and how the crime has been committed. It has many application in criminal investigations. Its main use is estimation of the minimum time after death in suspicious death. The close association between insects and corpses and the use of insects in criminal investigations is the subject of forensic entomology. Because insects attack to the decomposing corpse and spawning on it from the initial stages. Forensic scientists can estimate the postmortem index by studying the insects population and the developing larval stages.In addition, toxicological and molecular studies of these insects can reveal the cause of death or even the identity of a victim. It also be used to detect drugs and poisons, and determination of incident location. Gathering robust entomological evidences is made possible for experts by recent Techniques. They can provide vital information about death, corpse movement or burial, submersion interval, time of decapitation, identification of specific sites of trauma, post-mortem artefacts on the body, use of drugs, linking a suspect to the scene of a crime, sexual molestations and the identification of suspects.

Keywords: Forensic entomology, post mortem interval, insects, larvae

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Authors: Sara Sousa, Veronique Gomes, Nélio Veiga, Maria José Correia

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Objectives: To assess the presence or absence of Streptococcus mutans, Streptococcus gordonii and Streptococcus salivarius in the oral biofilm of children in an elementary school of Viseu, Portugal, and verify the relationship between Streptococcus gordonii and Streptococcus salivarius and the absence of dental caries. Methods: A cross-sectional study was designed with a final sample of 40 children aged 6-11 years old. Oral examination was accomplished with the identification of their oral health status and oral biofilm collection. Analysis of biological samples by molecular techniques of DNA isolation and identification of three Streptococci bacteria by Polimerase Chain Reaction (PCR) was made. Results: We identified Streptococcus salivarius and Streptococcus gordoni only in the lower interincisal region. These species were also present mainly in the first permanent non-decayed molars. On the contrary, Streptococcus mutans was found mostly in decayed first permanent molars. Conclusion: This preliminary study establishes a possible association between the absence of dental caries and the presence of Streptococcus gordonii and Streptococcus salivarius. Since these two species are described as alkali producers, it is suggested that their presence somehow confers protection against caries. These results support new dental caries prevention strategies based on oral biofilm modulation by enrichment with alkalinogenic species.

Keywords: dental caries, oral biofilm, Streptococcus gordonii, Streptococcus salivarius

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Authors: Masoud Sheidaei, Melica Tabasi, Fahimeh Koohdar, Mona Sheidaei

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Juglans regia L. is an economically important species of edible nuts. Iran is known as a center of origin of genetically rich walnut germplasm and expected to be found a large diversity within Iranian walnut populations. A detailed population genetic of local populations is useful for developing an optimal strategy for in situ conservation and can assist the breeders in crop improvement programs. Different phylogenetic studies have been carried out in this genus, but none has been concerned with genetic changes associated with geographical divergence and the identification of adaptive SNPs. Therefore, we carried out the present study to identify discriminating ITS nucleotides among Juglans species and also reveal association between ITS SNPs and geographical variables. We used different computations approaches like DAPC, CCA, and RDA analyses for the above-mentioned tasks. We also performed population genetics analyses for population effective size changes associated with the species expansion. The results obtained suggest that latitudinal distribution has a more profound effect on the species genetic changes. Similarly, multiple analytical approaches utilized for the identification of both discriminating DNA nucleotides/ SNPs almost produced congruent results. The SNPs with different phylogenetic importance were also identified by using a parsimony approach.

Keywords: Persian walnut, adaptive SNPs, data analyses, genetic diversity

Procedia PDF Downloads 124

Authors: Raymon P. Española

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Hearing impaired students are admitted to regular high schools in the context of inclusive education. In this setting, several academic difficulties and social struggles are disregarded by many educators. The study aimed to describe the aspirations and lived experiences in mainstream classrooms of hearing impaired students. In the research process, the participants were interviewed using sign language. Thematic analysis of interview responses was done, supplemented by interviews with teachers and classroom observations. The study revealed four patterns of experiences: academic difficulties, coping mechanisms, identification with hearing peers, and impression management. This means that these learners were struggling in inclusive classrooms, where identification with and modeling the positive qualities of hearing peers were done to cope with academic difficulties and alter negative impressions about them. By implication, these learners tended to socially immerse themselves rather than resort to isolation. Along with this tendency was the aspiration for achievement as they were eager to finish post-secondary technical-vocational education. This means aspiring for continuing social immersion into the mainstream. All these findings provide insights to K-12 educators to increase the use of collaborative techniques and experiential learning strategies, as well as to adequately address the special educational needs of these students.

Keywords: descriptive, experiences and aspirations of hearing impaired learners, inclusive classrooms, Surigao City Philippines

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Authors: Ilir Rexhepi, Naim Ismajli

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Quality and quality management are key factors of success nowadays. Public sector and quality management in this sector contains many challenges and difficulties, most notably in a new country like Kosovo. This study analyses the process of implementation of quality management system in public administration institutions in this country. The main objective is to show how to set up a quality management system and how does the quality management system setup affect the overall public administration services in Kosovo. This study shows how the efficiency and effectiveness of public institution services/performance is rapidly improving through the establishment and functionalization of Quality Management System. The specific impact of established QMC within the organization has resulted with the identification of mission related processes within the entire system including input identification, the person in charge and the way of conversion to the output of each activity though the interference with other service processes within the system. By giving detailed analyses of all steps of implementation of the Quality Management System, its effect and consequences towards the overall public institution service performance, we try to go one step further, by showing it as a very good example or tool of other public institutions for improving their service performance. Interviews with employees, middle and high level managers including the quality manager and general secretaries are also part of analyses in this paper.

Keywords: quality, quality management system, efficiency, public administration institutions

Procedia PDF Downloads 282

Authors: Hamidreza Saberkari, Mousa Shamsi, Hossein Ahmadi, Saeed Vaali, , MohammadHossein Sedaaghi

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In the recent years, using signal processing tools for accurate identification of the protein coding regions has become a challenge in bioinformatics. Most of the genomic signal processing methods is based on the period-3 characteristics of the nucleoids in DNA strands and consequently, spectral analysis is applied to the numerical sequences of DNA to find the location of periodical components. In this paper, a novel ensemble algorithm for gene selection in DNA sequences has been presented which is based on the combination of Goertzel algorithm and anti-notch filter (ANF). The proposed algorithm has many advantages when compared to other conventional methods. Firstly, it leads to identify the coding protein regions more accurate due to using the Goertzel algorithm which is tuned at the desired frequency. Secondly, faster detection time is achieved. The proposed algorithm is applied on several genes, including genes available in databases BG570 and HMR195 and their results are compared to other methods based on the nucleotide level evaluation criteria. Implementation results show the excellent performance of the proposed algorithm in identifying protein coding regions, specifically in identification of small-scale gene areas.

Keywords: protein coding regions, period-3, anti-notch filter, Goertzel algorithm

Procedia PDF Downloads 385

Authors: Raees Khan, Sheikh Z. Ul Abidin, Abdul S. Mumtaz, Jie Liu

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The present study is intended to assess gymnosperms pollen flora of Pakistan using Light Microscope (LM) and Scanning Electron Microscopy (SEM) for its taxonomic significance in identification of gymnosperms. Pollens of 35 gymnosperm species (12 genera and five families) were collected from its various distributional sites of gymnosperms in Pakistan. LM and SEM were used to investigate different palyno-morphological characteristics. Five pollen types (i.e., Inaperturate, Monolete, Monoporate, Vesiculate-bisaccate, and Polyplicate) were observed. In equatorial view seven types of pollens were observed, in which ten species were sub-angular, nine species were triangular, six species were perprolate, three species were rhomboidal, three species were semi-angular, two species were rectangular and two species were prolate. While five types of pollen were observed in polar view, in which ten species were spheroidal, nine species were angular, eight were interlobate, six species were circular, and two species were elliptic. Eighteen species have rugulate and 17 species has faveolate ornamentation. Eighteen species have verrucate and 17 have gemmate type sculpturing. The data was analysed through cluster analysis. The study showed that these palyno-morphological features have significance value in classification and identification of gymnosperms. Based on these different palyno-morphological features, a taxonomic key was proposed for the accurate and fast identifications of gymnosperms from Pakistan.

Keywords: gymnosperms, palynology, Pakistan, taxonomy

Procedia PDF Downloads 219