Search results for: intelligent identification

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: K. R. Roopesh Bharatwaj, Avinash Maharana, Favour Tobi Aborisade, Roger Young

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Presently, there are few cases of complete automation of drones and its allied intelligence capabilities. In essence, the potential of the drone has not yet been fully utilized. This paper presents feasible methods to build an intelligent drone with smart capabilities such as self-driving, and obstacle avoidance. It does this through advanced Reinforcement Learning Techniques and performs object detection using latest advanced algorithms, which are capable of processing light weight models with fast training in real time instances. For the scope of this paper, after researching on the various algorithms and comparing them, we finally implemented the Deep-Q-Networks (DQN) algorithm in the AirSim Simulator. In future works, we plan to implement further advanced self-driving and object detection algorithms, we also plan to implement voice-based speech recognition for the entire drone operation which would provide an option of speech communication between users (People) and the drone in the time of unavoidable circumstances. Thus, making drones an interactive intelligent Robotic Voice Enabled Service Assistant. This proposed drone has a wide scope of usability and is applicable in scenarios such as Disaster management, Air Transport of essentials, Agriculture, Manufacturing, Monitoring people movements in public area, and Defense. Also discussed, is the entire drone communication based on the satellite broadband Internet technology for faster computation and seamless communication service for uninterrupted network during disasters and remote location operations. This paper will explain the feasible algorithms required to go about achieving this goal and is more of a reference paper for future researchers going down this path.

Keywords: convolution neural network, natural language processing, obstacle avoidance, satellite broadband technology, self-driving

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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: Kai Warsoenke, Maik Mackiewicz

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To achieve a high quality of assembled car body structures, tolerancing is used to ensure a geometric accuracy of the single car body parts. There are two main techniques to determine the required tolerances. The first is tolerance analysis which describes the influence of individually tolerated input values on a required target value. Second is tolerance synthesis to determine the location of individual tolerances to achieve a target value. Both techniques are based on classical statistical methods, which assume certain probability distributions. To ensure competitiveness in both saturated and dynamic markets, production processes in vehicle manufacturing must be flexible and efficient. The dimensional specifications selected for the individual body components and the resulting assemblies have a major influence of the quality of the process. For example, in the manufacturing of forming tools as operating equipment or in the higher level of car body assembly. As part of the metrological process monitoring, manufactured individual parts and assemblies are recorded and the measurement results are stored in databases. They serve as information for the temporary adjustment of the production processes and are interpreted by experts in order to derive suitable adjustments measures. In the production of forming tools, this means that time-consuming and costly changes of the tool surface have to be made, while in the body shop, uncertainties that are difficult to control result in cost-intensive rework. The stored measurement results are not used to intelligently design tolerances in future processes or to support temporary decisions based on real-world geometric data. They offer potential to extend the tolerancing methods through data analysis and machine learning models. The purpose of this paper is to examine real-world measurement data from individual car body components, as well as assemblies, in order to develop an approach for using the data in short-term actions and future projects. For this reason, the measurement data will be analyzed descriptively in the first step in order to characterize their behavior and to determine possible correlations. In the following, a database is created that is suitable for developing machine learning models. The objective is to create an intelligent way to determine the position and number of measurement points as well as the local tolerance range. For this a number of different model types are compared and evaluated. The models with the best result are used to optimize equally distributed measuring points on unknown car body part geometries and to assign tolerance ranges to them. The current results of this investigation are still in progress. However, there are areas of the car body parts which behave more sensitively compared to the overall part and indicate that intelligent tolerancing is useful here in order to design and control preceding and succeeding processes more efficiently.

Keywords: automotive production, machine learning, process optimization, smart tolerancing

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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: 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: L. Wei Sheng, M. T. Noor Syazwanee, C. J. Carolyna, M. Amiruddin, M. Pauziah

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This paper exhibits the alternatives towards active suspension systems revised based on the classical passive suspension system to improve comfort and handling performance. An active Magneto rheological (MR) suspension system is proposed as to explore the active based suspension system to enhance performance given its freedom to independently specify the characteristics of load carrying, handling, and ride quality. Malaysian quarter car with two degrees of freedom (2DOF) system is designed and constructed to simulate the actions of an active vehicle suspension system. The structure of a conventional twin-tube shock absorber is modified both internally and externally to comprehend with the active suspension system. The shock absorber peripheral structure is altered to enable the assembling and disassembling of the damper through a non-permanent joint whereby the stress analysis of the designed joint is simulated using Finite Element Analysis. Simulation on the internal part where an electrified copper coil of 24AWG is winded is done using Finite Element Method Magnetics to measure the magnetic flux density inside the MR damper. The primary purpose of this approach is to reduce the vibration transmitted from the effects of road surface irregularities while maintaining solid manoeuvrability. The aim of this research is to develop an intelligent control system of a consecutive damping automotive suspension system. The ride quality is improved by means of the reduction of the vertical body acceleration caused by the car body when it experiences disturbances from speed bump and random road roughness. Findings from this research are expected to enhance the quality of ride which in return can prevent the deteriorating effect of vibration on the vehicle condition as well as the passengers’ well-being.

Keywords: active suspension, FEA, magneto rheological damper, Malaysian quarter car model, vibration control

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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: Julia Langen

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This article describes the challenges of a wireless charging system for a cab waiting lane in a public space and presents a concept for solving them. In this concept, multiple cabs can be charged simultaneously and during stopping and rolling. Particular technical challenges are a coil topology that meets the EMF requirements and an intelligent control concept that allows the individual coil segments to be switched on and off. The charging concept explained here is currently being implemented as a pilot project, so that initial results on the operation can be presented.

Keywords: charge lane, inductive charging solution, smart city, wireless power transfer

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Authors: Berk Ecer, Ebru Akcapinar Sezer

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Traditional management models of intersections, such as no-light intersections or signalized intersection, are not the most effective way of passing the intersections if the vehicles are intelligent. To this end, Dresner and Stone proposed a new intersection control model called Autonomous Intersection Management (AIM). In the AIM simulation, they were examining the problem from a multi-agent perspective, demonstrating that intelligent intersection control can be made more efficient than existing control mechanisms. In this study, autonomous intersection management has been investigated. We extended their works and added a potential-based lane organization layer. In order to distribute vehicles evenly to each lane, this layer triggers vehicles to analyze near lanes, and they change their lane if other lanes have an advantage. We can observe this behavior in real life, such as drivers, change their lane by considering their intuitions. Basic intuition on selecting the correct lane for traffic is selecting a less crowded lane in order to reduce delay. We model that behavior without any change in the AIM workflow. Experiment results show us that intersection performance is directly connected with the vehicle distribution in lanes of roads of intersections. We see the advantage of handling lane management with a potential approach in performance metrics such as average delay of intersection and average travel time. Therefore, lane management and intersection management are problems that need to be handled together. This study shows us that the lane through which vehicles enter the intersection is an effective parameter for intersection management. Our study draws attention to this parameter and suggested a solution for it. We observed that the regulation of AIM inputs, which are vehicles in lanes, was as effective as contributing to aim intersection management. PLO-AIM model outperforms AIM in evaluation metrics such as average delay of intersection and average travel time for reasonable traffic rates, which is in between 600 vehicle/hour per lane to 1300 vehicle/hour per lane. The proposed model reduced the average travel time reduced in between %0.2 - %17.3 and reduced the average delay of intersection in between %1.6 - %17.1 for 4-lane and 6-lane scenarios.

Keywords: AIM project, autonomous intersection management, lane organization, potential-based approach

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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

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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: Gabriela Iordachescu, Mariana Cretu Stuparu, Mirela Praisler, Camelia Busila, Doina Voinescu, Camelia Vizireanu

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The aim of the study was to evaluate the consumers’ degree of confidence in food labeling, how they use and understand the label and respectively food labeling elements. The label is a bridge between producers, suppliers, and consumers. It has to offer enough information in terms of public health and food safety, statement of ingredients, nutritional information, warnings and advisory statements, producing date and shelf-life, instructions for storage and preparation (if required). The survey was conducted on 500 consumers group in Romania, aged 15+, males and females, from urban and rural areas and with different graduation levels. The questionnaire was distributed face to face and online. It had single or multiple choices questions and label images for the efficiency and best understanding of the question. The law 1169/2011 applied to food products from 13 of December 2016 improved and adapted the requirements for labeling in a clear manner. The questions were divided on following topics: interest and general trust in labeling, use and understanding of label elements, understanding of the ingredient list and safety information, nutrition information, advisory statements, serving sizes, best before/use by meanings, intelligent labeling, and demographic data. Three choice selection exercises were also included. In this case, the consumers had to choose between two similar products and evaluate which label element is most important in product choice. The data were analysed using MINITAB 17 and PCA analysis. Most of the respondents trust the food label, taking into account some elements especially when they buy the first time the product. They usually check the sugar content and type of sugar, saturated fat and use the mandatory label elements and nutrition information panel. Also, the consumers pay attention to advisory statements, especially if one of the items is relevant to them or the family. Intelligent labeling is a challenging option. In addition, the paper underlines that the consumer is more careful and selective with the food consumption and the label is the main helper for these.

Keywords: consumers, food safety information, labeling, labeling nutritional information

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Authors: Haffaf Hafid

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Hypergraphs, after being used to model the structural organization of System of Sytems (SoS) at macroscopic level, has recent trends towards generalizing this powerful representation at different stages of complex system modelling. In this paper, we first describe different applications of hypergraph theory, and step by step, introduce multilevel modeling of SoS by means of integrating Constraint Programming Langages (CSP) dealing with engineering system reconfiguration strategy. As an application, we give an A.C.T Terminal controlled by a set of Intelligent Automated Vehicle.

Keywords: hypergraph model, structural analysis, bipartite graph, monitoring, system of systems, reconfiguration analysis, hypernetwork

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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

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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 383