Search results for: artificial ground freezing

Authors: Valeria Corinaldesi, Nicola Generosi, Jacopo Donnini

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Ultra high performance concrete (UHPC) is an innovative material which tends to exhibit superior properties such as incredible mechanical and durability performance and non-brittleness behavior. Over the last twenty years, phenomenal advances have taken place in the research and application of UHPC. Recently, the approach is to improve UHPC sustainability by reducing its embodied energy. First of all, this goal can be achieved by reducing Portland cement dosage. In this work, an experimental investigation was carried out to characterize the mechanical behavior and durability of UHPCs prepared by reducing the cement amount by 30% in order to verify the impact of lower cement content and higher water-to-cement ratio on both mechanical performance and durability, if any. Eight different UHPC mixtures were compared, with two different cement dosages (either 1000 or 700 kg) and four different brass-coated steel fibres dosages (0 - 50 - 100 - 150 kg), in terms of 28-day compressive and flexural strengths. Then, the mixtures prepared with the lower cement content were further investigated in terms of abrasion resistance, water absorption, freezing and thawing cycles, and resistance to sulphate attack. Results obtained showed the feasibility of reducing cement dosage without compromising mechanical performance and UHPC's extraordinary durability.

Keywords: abrasion resistance, durability, eco-efficiency, freeze-thawing cycles, steel fibres, sulphate exposure, sustainability, UHPC

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Authors: Rajkamal Kumar, Sudhir Mishra

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The connections between various elements of concrete structures play a vital role in determining the durability of structures. These connections produce discontinuities and to ensure the monolithic behavior of structures, these connections should be carefully designed. The connections between concrete layers may occur in various situations such as structure repairing and rehabilitation or construction of huge structures with cast-in-situ or pre-cast elements, etc. Bond strength at the interface of these concrete layers should be able to prevent the progressive slip from taking place and it should also ensure satisfactory performance of the structure. Different approaches to enhance the bond strength at interface have been a major area of research. Nowadays, micro-concrete is getting popular as a repair material. Under this ambit, this paper aims to present the experimental results of connections between concrete layers of different age with artificial indentation at interface with two types of repair material: Concrete with same parent concrete composition and ready-mix mortar (micro-concrete), artificial indentations (grooves and holes) were made on the old layer of concrete to increase the bond strength. Curing plays an important role in determining the bond strength. Optimum duration for curing have also been discussed for each type of repair material. Different types of failure patterns have also been mentioned.

Keywords: adhesion, cohesion, compressive stress, micro-concrete, shear stress, slant shear test

Procedia PDF Downloads 316

Authors: S. Sharan Sundar, Nithish B. Moudhgalya, Nidhi Bhandari, Vineeth Vijayaraghavan

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Automatic Short Answer Grading is one of the prime applications of artificial intelligence in education. Several approaches involving the utilization of selective handcrafted features, graphical matching techniques, concept identification and mapping, complex deep frameworks, sentence embeddings, etc. have been explored over the years. However, keeping in mind the real-world application of the task, these solutions present a slight overhead in terms of computations and resources in achieving high performances. In this work, a simple and effective solution making use of elemental features based on statistical, linguistic properties, and word-based similarity measures in conjunction with tree-based classifiers and regressors is proposed. The results for classification tasks show improvements ranging from 1%-30%, while the regression task shows a stark improvement of 35%. The authors attribute these improvements to the addition of multiple similarity scores to provide ensemble of scoring criteria to the models. The authors also believe the work could reinstate that classical natural language processing techniques and simple machine learning models can be used to achieve high results for short answer grading.

Keywords: artificial intelligence, intelligent systems, natural language processing, text mining

Procedia PDF Downloads 117

Authors: Mauricio Quintero Angel, Alexander Pereira, Selene Alarcón

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One of the elements of greatest importance in agricultural production is the harvesting; an activity associated to different occupational health risks such as harvesting in high altitudes, the transport of heavy materials and the application of excessive muscle strain that leads to muscular-bone disorders. Therefore, there is an urgent necessity to improve and validate interventions to reduce exposition and risk to harvesters. This article has the objective of describing the co-development under the ergonomic analysis framework of an assisted manual harvesting tool for peach palm oriented to reduce the risk of death and accidents as it avoid the harvest in heights. The peach palm is a palm tree that is cultivated in Colombia, Perú, Brasil, Costa Rica, among others and that reaches heights of over 20 m, with stipes covered with spines. The fruits are drupes of variable size. For the harvesting of peach palm, in Colombia farmers use the “Marota” or “Climber”, a tool in a closed X shape built in wood, that has two supports adjusted at the stipe, that elevate alternately until reaching a point high enough to grab the bunch that is brought down using a rope. An activity of high risk since it is done at a high altitude without any type of protection and safety measures. The Marota is alternated with a rod, which as variable height between 5 and 12 Meters with a harness system at one end to hold the bunch that is lowered with the whole system (bamboo bunch). The rod is used from the ground or from the Marota in height. As an alternative to traditional tools, the Bajachonta was co-developed with farmers, a tool that employs a traditional bamboo hook system with modifications, to be able to hold it with a rope that passes through a pulley. Once the bunch is hitched, the hook system is detached and this stays attached to the peduncle of the palm tree, afterwards through a pulling force being exerted towards the ground by tensioning the rope, the bunch comes loose to be taken down using a rope and the pulley system to the ground, reducing the risk and efforts in the operation. The bajachonta was evaluated in tree productive zones of Colombia, with innovative farmers, were the adoption is highly probable, with some modifications to improve its efficiency and effectiveness, keeping in mind that the farmers perceive in it an advantage in the reduction of death and accidents by not having to harvest in heights.

Keywords: assisted harvesting, ergonomics, harvesting in high altitudes, participative design, peach palm

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Authors: Waleed A. Basuliman, Khalid S. AlSaleh, Mohamed Z. Ramadan

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Applying the anthropometric dimensions is considered one of the important factors when designing any human-machine system. In this study, the estimation of anthropometric dimensions has been improved by developing artificial neural network that aims to predict the anthropometric measurements of the male in Saudi Arabia. A total of 1427 Saudi males from age 6 to 60 participated in measuring twenty anthropometric dimensions. These anthropometric measurements are important for designing the majority of work and life applications in Saudi Arabia. The data were collected during 8 months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining fifteen dimensions were set to be the measured variables (outcomes). The hidden layers have been varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was significantly able to predict the body dimensions for the population of Saudi Arabia. The network mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found 0.0348 and 3.225 respectively. The accuracy of the developed neural network was evaluated by compare the predicted outcomes with a multiple regression model. The ANN model performed better and resulted excellent correlation coefficients between the predicted and actual dimensions.

Keywords: artificial neural network, anthropometric measurements, backpropagation, real anthropometric database

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Authors: Anurakti Shukla, Sudhakar Srivastava

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Cyanobacteria are known for rapid growth rates, high biomass, and the ability to accumulate potentially toxic elements and contaminants. The present work was planned to develop a low-cost, feasible prototype, CyanoClean, for the growth of a cyanobacterial consortium for the removal of arsenic (As) from water. The cyanobacterial consortium consisting of Oscillatoria, Phormidiumand Gloeotrichiawas used, and the conditions for optimal growth of the consortium were standardized. A pH of 7.6, initial cyanobacterial biomass of 10 g/L, and arsenite [As(III)] and arsenate [As(V)] concentration of 400 μΜand 600 μM, respectively, were found to be suitable. The CyanoClean prototype was designed with acrylic sheet and had arrangements for optimal cyanobacterial growth in natural sunlight and also in artificial light. The As removal experiments in concentration- and duration-dependent manner demonstrated removal of up to 39-69% and 9-33% As respectively from As(III) and As(V)-contaminated water. In field testing of CyanoClean, natural As-contaminated groundwater was used, and As reduction was monitored when a flow rate of 3 L/h was maintained. In a field experiment, As concentration in groundwater was found to reduce from 102.43 μg L⁻¹ to <10 μg L⁻¹ after 6 h in natural sunlight. However, in shaded conditions under artificial light, the same result was achieved after 9 h. The CyanoClean prototype is of simple design and can be easily up-scaled for application at a small- to medium-size land and shall be affordable even for a low- to middle-income group farmer.

Keywords: cyanoclean, gloeotrichia, oscillatoria, phormidium, phycoremediation

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Authors: Damian Czubak

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The Białowieża Forest is a very valuable natural area, included in the World Natural Heritage at UNESCO, where, due to infestation by the bark beetle (Ips typographus), norway spruce (Picea abies) have deteriorated. This catastrophic scenario led to an increase in fire danger. This was due to the occurrence of large amounts of dead wood and grass cover, as light penetrated to the bottom of the stands. These factors in a dry state are materials that favour the possibility of fire and the rapid spread of fire. One of the objectives of the study was to monitor the quantitative and qualitative changes of combustible material on the permanent decay plots of spruce stands from 2012-2022. In addition, the size of the area with highly flammable vegetation was monitored and a classification of the stands of the Białowieża Forest by flammability classes was made. The key factor that determines the potential fire hazard of a forest is combustible material. Primarily its type, quantity, moisture content, size and spatial structure. Based on the inventory data on the areas of forest districts in the Białowieża Forest, the average fire load and its changes over the years were calculated. The analysis was carried out taking into account the changes in the health status of the stands and sanitary operations. The quantitative and qualitative assessment of fallen timber and fire load of ground cover used the results of the 2019 and 2021 inventories. Approximately 9,000 circular plots were used for the study. An assessment was made of the amount of potential fuel, understood as ground cover vegetation and dead wood debris. In addition, monitoring of areas with vegetation that poses a high fire risk was conducted using data from 2019 and 2021. All sub-areas were inventoried where vegetation posing a specific fire hazard represented at least 10% of the area with species characteristic of that cover. In addition to the size of the area with fire-prone vegetation, a very important element is the size of the fire load on the indicated plots. On representative plots, the biomass of the land cover was measured on an area of 10 m2 and then the amount of biomass of each component was determined. The resulting element of variability of ground covers in stands was their flammability classification. The classification developed made it possible to track changes in the flammability classes of stands over the period covered by the measurements.

Keywords: classification, combustible material, flammable vegetation, Norway spruce

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Authors: Keisuke Takahata, Hiroshi Suetsugu

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Forest height is an important parameter for forest biomass estimation, and precise elevation data is essential for accurate forest height estimation. There are several globally or nationally available digital elevation models (DEMs) like SRTM and ASTER. However, its accuracy is reported to be low particularly in mountainous areas where there are closed canopy or steep slope. Recently, space-borne LiDAR, such as the Global Ecosystem Dynamics Investigation (GEDI), have started to provide sparse but accurate ground elevation and canopy height estimates. Several studies have reported the high degree of accuracy in their elevation products on their exact footprints, while it is not clear how this sparse information can be used for wider area. In this study, we developed a digital terrain model correction algorithm by spatially interpolating the difference between existing DEMs and GEDI elevation products by using Gaussian Process (GP) regression model. The result shows that our GP-based methodology can reduce the mean bias of the elevation data from 3.7m to 0.3m when we use airborne LiDAR-derived elevation information as ground truth. Our algorithm is also capable of quantifying the elevation data uncertainty, which is critical requirement for biomass inventory. Upcoming satellite-LiDAR missions, like MOLI (Multi-footprint Observation Lidar and Imager), are expected to contribute to the more accurate digital terrain model generation.

Keywords: digital terrain model, satellite LiDAR, gaussian processes, uncertainty quantification

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Authors: Zulfajri Basri Hasanuddin

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There is an increasing demand for broadband services in Indonesia. Therefore, the answer is the use of Ka-Band which has some advantages such as wider bandwidth, the higher transmission speeds, and smaller size of antenna in the ground. However, rain attenuation is the primary factor in the degradation of signal at the Kaband. In this paper, the author will determine whether the Ka-band frequency can be implemented in Indonesia which has high intensity of rainfall.

Keywords: Ka-band, link budget, link availability, BER, Eb/No, C/N

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Authors: Xingyu Gao, Qiang Wu

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Patents play a crucial role in protecting innovation and intellectual property. Early prediction of the impact of artificial intelligence (AI) patents helps researchers and companies allocate resources and make better decisions. Understanding the key factors that influence patent impact can assist researchers in gaining a better understanding of the evolution of AI technology and innovation trends. Therefore, identifying highly impactful patents early and providing support for them holds immeasurable value in accelerating technological progress, reducing research and development costs, and mitigating market positioning risks. Despite the extensive research on AI patents, accurately predicting their early impact remains a challenge. Traditional methods often consider only single factors or simple combinations, failing to comprehensively and accurately reflect the actual impact of patents. This paper utilized the artificial intelligence patent database from the United States Patent and Trademark Office and the Len.org patent retrieval platform to obtain specific information on 35,708 AI patents. Using six machine learning models, namely Multiple Linear Regression, Random Forest Regression, XGBoost Regression, LightGBM Regression, Support Vector Machine Regression, and K-Nearest Neighbors Regression, and using early indicators of patents as features, the paper comprehensively predicted the impact of patents from three aspects: technical, social, and economic. These aspects include the technical leadership of patents, the number of citations they receive, and their shared value. The SHAP (Shapley Additive exPlanations) metric was used to explain the predictions of the best model, quantifying the contribution of each feature to the model's predictions. The experimental results on the AI patent dataset indicate that, for all three target variables, LightGBM regression shows the best predictive performance. Specifically, patent novelty has the greatest impact on predicting the technical impact of patents and has a positive effect. Additionally, the number of owners, the number of backward citations, and the number of independent claims are all crucial and have a positive influence on predicting technical impact. In predicting the social impact of patents, the number of applicants is considered the most critical input variable, but it has a negative impact on social impact. At the same time, the number of independent claims, the number of owners, and the number of backward citations are also important predictive factors, and they have a positive effect on social impact. For predicting the economic impact of patents, the number of independent claims is considered the most important factor and has a positive impact on economic impact. The number of owners, the number of sibling countries or regions, and the size of the extended patent family also have a positive influence on economic impact. The study primarily relies on data from the United States Patent and Trademark Office for artificial intelligence patents. Future research could consider more comprehensive data sources, including artificial intelligence patent data, from a global perspective. While the study takes into account various factors, there may still be other important features not considered. In the future, factors such as patent implementation and market applications may be considered as they could have an impact on the influence of patents.

Keywords: patent influence, interpretable machine learning, predictive models, SHAP

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Authors: Raphael Tuor, Denis Lalanne

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The ATM research community is missing suitable tools to design, test, and validate new UI prototypes. Important stakes underline the implementation of both DSS and XAI methods into current systems. ML-based DSS are gaining in relevance as ATFM becomes increasingly complex. However, these systems only prove useful if a human can understand them, and thus new XAI methods are needed. The human-machine dyad should work as a team and should understand each other. We present xSky, a configurable benchmark tool that allows us to compare different versions of an ATC interface in conflict detection tasks. Our main contributions to the ATC research community are (1) a conflict detection task simulator (xSky) that allows to test the applicability of visual prototypes on scenarios of varying difficulty and outputting relevant operational metrics (2) a theoretical approach to the explanations of AI-driven trajectory predictions. xSky addresses several issues that were identified within available research tools. Researchers can configure the dimensions affecting scenario difficulty with a simple CSV file. Both the content and appearance of the XAI elements can be customized in a few steps. As a proof-of-concept, we implemented an XAI prototype inspired by the maritime field.

Keywords: air traffic control, air traffic simulation, conflict detection, explainable artificial intelligence, explainability, human-automation collaboration, human factors, information visualization, interpretability, trajectory prediction

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Authors: Apichaya Aneksampant, Xuefei Tu, Masami Fukushima, Mitsuo Yamamoto

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The restoration of seaweed beds recovery has been developed using a fertilization technique for supplying dissolved iron to barren coastal areas. The fertilizer is composed of iron oxides as a source of iron and compost as humic substance (HS) source, which can serve as chelator of iron to stabilize the dissolved species under oxic seawater condition. However, elution mechanisms of iron from iron oxide surfaces have not sufficiently elucidated. In particular, roles of microbial activities in the elution of iron from the fertilizer are not sufficiently understood. In the present study, a fertilizer (iron oxide/compost = 1/1, v/v) was incubated in a water tank at Mashike coast, Hokkaido Japan. Microorganisms in the 6-month fertilizer were isolated and identified as Exiguobacterium oxidotolerans sp. (T-2-2). The identified bacteria were inoculated to perform iron elution test in a postgate B medium, prepared in artificial seawater. Hematite was used as a model iron oxide and anthraquinone-2,7-disolfonate (AQDS) as a model for HSs. The elution test performed in presence and absence of bacteria inoculation. ICP-AES was used to analyze total iron and a colorimetric technique using ferrozine employed for the determination of ferrous ion. During the incubation period, sample contained hematite and T-2-2 in both presence and absence of AQDS continuously showed the iron elution and reached at the highest concentration after 9 days of incubation and then slightly decrease to stabilize within 20 days. Comparison to the sample without T-2-2, trace amount of iron was observed, suggesting that iron elution to seawater can be attributed to bacterial activities. The levels of total organic carbon (TOC) in the culture solution with hematite decreased. This may be to the adsorption of organic compound, AQDS, to hematite surfaces. The decrease in UV-vis absorption of AQDS in the culture solution also support the results of TOC that AQDS was adsorbed to hematite surfaces. AQDS can enhance the iron elution, while the adsorption of organic matter suppresses the iron elution from hematite.

Keywords: anthraquinone-2, 7-disolfonate, barren ground, E.oxidotolerans sp., hematite, humic substances, iron elution

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Authors: Shizra Waris, Bilal Shoaib, Munib Ahmad

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Background; The using of clinical decision support systems (CDSSs) may recover unceasing disease organization, which requires regular visits to multiple health professionals, treatment monitoring, disease control, and patient behavior modification. The objective of this survey is to determine if these CDSSs improve the processes of unceasing care including diagnosis, treatment, and monitoring of diseases. Though artificial intelligence is not a new idea it has been widely documented as a new technology in computer science. Numerous areas such as education business, medical and developed have made use of artificial intelligence Methods: The survey covers articles extracted from relevant databases. It uses search terms related to information technology and viral hepatitis which are published between 2000 and 2016. Results: Overall, 80% of studies asserted the profit provided by information technology (IT); 75% of learning asserted the benefits concerned with medical domain;25% of studies do not clearly define the added benefits due IT. The CDSS current state requires many improvements to hold up the management of liver diseases such as HCV, liver fibrosis, and cirrhosis. Conclusion: We concluded that the planned model gives earlier and more correct calculation of hepatitis B and it works as promising tool for calculating of custom hepatitis B from the clinical laboratory data.

Keywords: detection, hapataties, observation, disesese

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Authors: Muhammad Zaheer Awan, Adnan Qadir, Zeeshan Anjum

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Housefly, Musca domestica Linnaeus is ubiquitous and hazardous for Homo sapiens and livestock in sundry venerations. Musca domestica cart 100 different pathogens, such as typhoid, salmonella, bacillary dysentery, tuberculosis, anthrax and parasitic worms. The flies in rural areas usually carry more pathogens. Houseflies feed on liquid or semi-liquid substances besides solid materials which are softened by saliva. Neem botanically known as Azadirachta indica belongs to the family Meliaceae and is an indigenous tree to Pakistan. The neem tree is also one such tree which has been revered by the Pakistanis and Kashmiris for its medicinal properties. Present study showed neem seed extract has potentially toxic ability that affect Total Haemocyte Count (THC) and Differential Haemocytes Count (DHC) in insect’s blood cells, of the housefly. A significant variation in haemolymph density was observed just after application, 30 minutes and 60 minutes post treatment in term of THC and DHC in comparison with novastar. The study strappingly acclaim use of neem seed extract as insecticide as compare to artificial insecticides.

Keywords: neem, Azadirachta indica, Musca domestica, differential haemocyte count (DHC), total haemocytes count (DHC), novastar

Procedia PDF Downloads 188

Authors: Sultan Al Shafian, Mozaher Ul Kabir, Khondoker Istiak Ahmad, Masnun Abrar, Mahfuza Khanum, Hossain M. Shahin

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For structural evaluation of shallow foundation, the modulus of subgrade reaction is one of the most widely used and accepted parameter for its ease of calculations. To determine this parameter, one of the most common field method is Plate Load test method. In this field test method, the subgrade modulus is considered for a specific location and according to its application, it is assumed that the displacement occurred in one place does not affect other adjacent locations. For this kind of assumptions, the modulus of subgrade reaction sometimes forced the engineers to overdesign the underground structure, which eventually results in increasing the cost of the construction and sometimes failure of the structure. In the present study, the settlement of a shallow foundation has been analyzed using both conventional and numerical analysis. Around 25 plate load tests were conducted on a sand fill site in Bangladesh to determine the Modulus of Subgrade reaction of ground which is later used to design a shallow foundation considering different depth. After the collection of the field data, the field condition was appropriately simulated in a finite element software. Finally results obtained from both the conventional and numerical approach has been compared. A significant difference has been observed in the case of settlement while comparing the results. A proper correlation has also been proposed at the end of this research work between the two methods of in order to provide the most efficient way to calculate the subgrade modulus of the ground for designing the shallow foundation.

Keywords: modulus of subgrade reaction, shallow foundation, finite element analysis, settlement, plate load test

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Authors: Mylena McCoggle, Shyra Wilson, Andrea Rivera, Rocio Alba-Flores

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This work introduces the use of EMGs (electromyography) from muscle sensors to develop an Artificial Neural Network (ANN) for pattern recognition to control a small unmanned aerial vehicle. The objective of this endeavor exhibits interfacing drone applications beyond manual control directly. MyoWare Muscle sensor contains three EMG electrodes (dual and single type) used to collect signals from the posterior (extensor) and anterior (flexor) forearm and the bicep. Collection of raw voltages from each sensor were connected to an Arduino Uno and a data processing algorithm was developed with the purpose of interpreting the voltage signals given when performing flexing, resting, and motion of the arm. Each sensor collected eight values over a two-second period for the duration of one minute, per assessment. During each two-second interval, the movements were alternating between a resting reference class and an active motion class, resulting in controlling the motion of the drone with left and right movements. This paper further investigated adding up to three sensors to differentiate between hand gestures to control the principal motions of the drone (left, right, up, and land). The hand gestures chosen to execute these movements were: a resting position, a thumbs up, a hand swipe right motion, and a flexing position. The MATLAB software was utilized to collect, process, and analyze the signals from the sensors. The protocol (machine learning tool) was used to classify the hand gestures. To generate the input vector to the ANN, the mean, root means squared, and standard deviation was processed for every two-second interval of the hand gestures. The neuromuscular information was then trained using an artificial neural network with one hidden layer of 10 neurons to categorize the four targets, one for each hand gesture. Once the machine learning training was completed, the resulting network interpreted the processed inputs and returned the probabilities of each class. Based on the resultant probability of the application process, once an output was greater or equal to 80% of matching a specific target class, the drone would perform the motion expected. Afterward, each movement was sent from the computer to the drone through a Wi-Fi network connection. These procedures have been successfully tested and integrated into trial flights, where the drone has responded successfully in real-time to predefined command inputs with the machine learning algorithm through the MyoWare sensor interface. The full paper will describe in detail the database of the hand gestures, the details of the ANN architecture, and confusion matrices results.

Keywords: artificial neural network, biosensors, electromyography, machine learning, MyoWare muscle sensors, Arduino

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Authors: Makhlouf Sandy, Adem Ziad, Taher Fadia, Magnier Sylvie

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The electronic structure of 90ZrS has been investigated using Ab-initio methods based on Complete Active Space Self Consistent Field and Multi-reference Configuration Interaction (CASSCF/MRCI). The number of predicted states has been extended to 14 singlet and 12 triplet lowest-lying states situated below 36000cm-1. The equilibrium energies of these 26 lowest-lying electronic states have been calculated in the 2S+1Λ(±) representation. The potential energy curves have been plotted in function of the inter-nuclear distances in a range of 1.5 to 4.5Å. Spectroscopic constants, permanent electric dipole moments and transition dipole moments between the different electronic states have also been determined. A discrepancy error of utmost 5% for the majority of values shows a good agreement with available experimental data. The ground state is found to be of symmetry X1Σ+ with an equilibrium inter-nuclear distance Re= 2.16Å. However, the (1)3Δ is the closest state to X1Σ+ and is situated at 514 cm-1. To the best of our knowledge, this is the first time that the spin-orbit coupling has been investigated for all the predicted states of ZrS. 52 electronic components in the Ω(±) representation have been predicted. The energies of these components, the spectroscopic constants ωe, ωeχe, βe and the equilibrium inter-nuclear distances have been also obtained. The percentage composition of the Ω state wave-functions in terms of S-Λ states was calculated to identify their corresponding main parents. These (SOC) calculations have determined the shift between (1)3Δ1 and X1Σ+ states and confirmed the ground state type being 1Σ+.

Keywords: CASSCF/MRCI, electronic structure, spin-orbit effect, zirconium monosulfide

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Authors: Jaruwan Chutrtong

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Yoghurt is a fermented milk product. The process of making yogurt involves fermenting milk with live and active bacterial cultures by adding bacteria directly to the dairy product. It is usually made with a culture of Lactobacillus sp. (L. acidophilus or L. bulgaricus) and Streptococcus thermophilus. Many people like to eat it plain or flavored and it's also use as ingredient in many dishes. Yogurt is rich in nutrients including the microorganism which have important role in balancing the digestion and absorption of the boy.Consumers will benefit from lactic acid bacteria more or less depending on the amount of bacteria that lives in yogurt while eating. When purchasing yogurt, consumers should always check the label for live cultures. Yoghurt must keep in refrigerator at 4°C for up to ten days. After this amount of time, the cultures often become weak. This research studied freezing dry yogurt storage by monitoring on the survival of microorganisms when stored at different temperatures. At 300°C, representative room temperature of country in equator zone, number of lactic acid bacteria reduced 4 log cycles in 10 week. At 400°C, representative temperature in summer of country in equator zone, number of lactic acid bacteria also dropped 4 log cycle in 10 week, similar as storage at 300°C. But drying yogurt storage at 400°C couldn’t reformed to be good character yogurt as good as storage at 400°C only 4 week storage too. After 1 month, it couldn’t bring back the yogurt form. So if it is inevitable to keep yogurt powder at a temperature of 40°C, yoghurt is maintained only up to 4 weeks.

Keywords: dynamic, dry yoghurt, storage, temperature

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Authors: Yinghui Shang, Ziqing Zhou, Rong Han, Hang Wang, Xiaodi Liu, Yong Yang

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The design of advanced amorphous solids, such as metallic glasses, with targeted properties through artificial intelligence signifies a paradigmatic shift in physical metallurgy and materials technology. Here, we developed a machine-learning architecture that facilitates the generation of metallic glasses with targeted multifunctional properties. Our architecture integrates the state-of-the-art unsupervised generative adversarial network model with supervised models, allowing the incorporation of general prior knowledge derived from thousands of data points across a vast range of alloy compositions, into the creation of data points for a specific type of composition, which overcame the common issue of data scarcity typically encountered in the design of a given type of metallic glasses. Using our generative model, we have successfully designed copper-based metallic glasses, which display exceptionally high hardness or a remarkably low modulus. Notably, our architecture can not only explore uncharted regions in the targeted compositional space but also permits self-improvement after experimentally validated data points are added to the initial dataset for subsequent cycles of data generation, hence paving the way for the customized design of amorphous solids without human intervention.

Keywords: metallic glass, artificial intelligence, mechanical property, automated generation

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Authors: Dike H. Ogbuagu, Etsede J. Oritsematosan

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This work investigated possible inductions of CaC₂, often misused by fruit vendors to stimulate artificial ripening, on mammalian sperm morphology and viability. Thirty isogenic strains of male albino mice, Mus musculus (age≈ 8weeks; weight= 32.5±2.0g) were acclimatized (ambient temperature 28.0±1.0°C) for 2 weeks and fed standard growers mash and water ad libutum. They were later exposed to graded toxicant concentrations (w/w) of 2.5000, 1.2500, 0.6250, and 0.3125% in 4 cages. A control cage was also established. After 5 weeks, 3 animals from each cage were sacrificed by cervical dislocation and the cauda epididymis excised. Sperm morphology and viability were determined by microscopic procedures. The ANOVA, means plots, Student’s t-test and variation plots were used to analyze data. The common abnormalities observed included Double Head, Pin Head, Knobbed Head, No Tail and With Hook. The higher toxicant concentrations induced significantly lower body weights [F(829.899) ˃ Fcrit(4.19)] and more abnormalities [F(26.52) ˃ Fcrit(4.00)] at P˂0.05. Sperm cells in the control setup were significantly more viable than those in the 0.625% (t=0.005) and 2.500% toxicant doses (t=0.018) at the 95% confidence limit. CaC₂ appeared to induced morphological abnormalities and reduced viability in sperm cells of M. musculus.

Keywords: artificial ripening, calcium carbide, fruit vendors, sperm morphology, sperm viability

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Authors: Chung-Yuan Huang, Yu-Hsiang Fu, Chuen-Tsai Sun

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Community detection represents an important data-mining tool for analyzing and understanding real-world complex network structures and functions. We believe that at least four criteria determine the appropriateness of a community detection algorithm: (a) it produces useable normalized mutual information (NMI) and modularity results for social networks, (b) it overcomes resolution limitation problems associated with synthetic networks, (c) it produces good NMI results and performance efficiency for Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks, and (d) it produces good modularity and performance efficiency for large-scale real-world complex networks. To our knowledge, no existing community detection algorithm meets all four criteria. In this paper, we describe a simple hierarchical arc-merging (HAM) algorithm that uses network topologies and rule-based arc-merging strategies to identify community structures that satisfy the criteria. We used five well-studied social network datasets and eight sets of LFR benchmark networks to validate the ground-truth community correctness of HAM, eight large-scale real-world complex networks to measure its performance efficiency, and two synthetic networks to determine its susceptibility to resolution limitation problems. Our results indicate that the proposed HAM algorithm is capable of providing satisfactory performance efficiency and that HAM-identified communities were close to ground-truth communities in social and LFR benchmark networks while overcoming resolution limitation problems.

Keywords: complex network, social network, community detection, network hierarchy

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Authors: Mohamed Ahmed Khalil

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The number of deep well anode ground beds (GBs) have been retrieved due to unoperated anode chains. New identical magnetite anode chains (MAC) have been installed at Raslanuf complex impressed current Cathodic protection (ICCP) system, distributed at different plants (Utility, ethylene and polyethylene). All problems associated with retrieving and installation of MACs have been discussed, rectified and presented. All GB-associated severely corroded wellhead casings were well maintained and/or replaced by new fabricated and modified ones. The main cause of the wellhead casing's severe internal corrosion was discussed and the conducted remedy action to overcome future corrosion problems is presented. All GB-connected anode junction boxes (AJBs) and shunts were closely inspected, maintained and necessary replacement and/or modifications were carried out on shunts. All damaged GB concrete foundations (CF) have been inspected and completely replaced. All GB-associated Transformer-Rectifiers Units (TRU) were subjected to thorough inspection and necessary maintenance was performed on each individual TRU. After completion of all MACs and TRU maintenance activities, each cathodic protection station (CPS) has been re-operated, alternative current (AC), direct current (DC), voltage and structure to soil potential (S/P) measurements have been conducted, recorded and all obtained test results are presented. DC current outputs have been adjusted and DC current outputs of each MAC have been recorded for each GB AJB.

Keywords: magnetite anodes, deep well, ground beds, cathodic protection, transformer rectifier, impressed current, junction boxes

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Authors: Cheh-Shyh Ting, Jiin-Liang Lin

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This study was conducted at the Wanglung Farm in Pingtung County to test the groundwater seepage influences on the implemented project for artificial groundwater recharge. The study was divided into three phases. The first phase, conducted on natural groundwater that was recharged through the local climate and growing conditions, observed the natural form of vegetation species. The original plants were flooded, and after 60 days it was observed that of the original plants only Goosegrass (Eleusine indica) and Black heart (Polygonum lapathifolium Linn.) remained. Direct infiltration tests were carried out, and calculations for the effect of vegetation on infiltration velocity of the recharge pool were noted. The second phase was an indoor test. Bahia grass and wild amaranth were selected as vegetation roots. After growth, the distribution of different grassroots was observed in order to facilitate a comparison permeability coefficient calculated by the amount of penetration and to explore the relationship between density and the efficiency to groundwater recharge. The third phase was the root tomography analysis, further observation of the development of plant roots using computed tomography technology. Computed Tomography, also known as (CT), is a diagnostic imaging examination, normally used in the medical field. In the first phase of the feasibility study, most non-aquatic plants wilted and died within seven days. In seven days, the remaining plants were used for experimental infiltration analysis. Results showed that in eight hours of infiltration test, Eleusine indica stems averaged 0.466 m/day and wild amaranth averaged 0.014 m/day. The second phase of the experiment was conducted on the remains of the plant a week in it had died and rotted, and the infiltration experiment was performed under these conditions. The results showed eight hours in end of the infiltration test, Eleusine indica stems averaged 0.033 m/day, and wild amaranth averaged 0.098 m/day. Non-aquatic plants died within two weeks, and their rotted remains clogged the pores of bottom soil particles, causing obstruction of recharge pool infiltration. Experiment results showed that eight hours in the test the average infiltration velocity for Eleusine indica stems was 0.0229 m/day and wild amaranth averaged 0.0117 m/day. Since the rotted roots of the plants blocked the pores of the soil in the recharge pool, which resulted in the obstruction of the artificial infiltration pond and showed an immediate impact on recharge efficiency. In order to observe the development of plant roots, the third phase used computed tomography imaging. Iodine developer was injected into the Black heart, allowing its cross-sectional images to be shown on CT and to be used to observe root development.

Keywords: artificial recharge of groundwater, computed tomography, infiltration velocity, vegetation root system

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Authors: Adelina Vrancianu

Abstract:

The problem of artificial real accession will be analyzed in this study both in terms of old and current Civil Code provisions and in terms of comparative law, European legal and Canadian systems. The current Civil Code from 2009 has brought new changes about the application and solutions regarding artificial real accession. The hypothesis in which a person is making works with his own materials on the real estate belonging to another person is developed and analyzed in detail from national and international point of view in relation with the good faith. The scope of this analysis is to point out what are the changes issued from case-law and which ones are new, inspired from other law systems in regard to the good/bad faith. The new civil code has promoted a definition for this notion. Is this definition a new one inspired from the comparative law or is it inspired from the case-law? Is it explained for every case scenario of accession or is a general notion? The study tries to respond to these questions and to present the new aspects in the area. has reserved a special place for the situation of execution of works with own materials exceeding the border with violation of another’s right of property, where the variety of solutions brings into discussion the case of expropriation for private interest. The new Civil Code is greatly influenced by the Civil Code from Quebec in comparison with the old code of French influence. The civil reform was needed and has brought into attention new solutions inspired from the Canadian system which has mitigated the permanent conflict between the constructor and the immovable owner.

Keywords: accession, good faith, new civil code, comparative law

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Authors: Mohamed Korany, Azza Gazy, Essam Khamis, Marwa Adel, Miranda Fawzy

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Background: Two new, simple and specific methods; First, a Zero-crossing first-derivative technique and second, a chemometric-assisted spectrophotometric artificial neural network (ANN) were developed and validated in accordance with ICH guidelines. Both methods were used for the simultaneous estimation of the two closely related antioxidant nutraceuticals ; Coenzyme Q10 (Q) ; also known as Ubidecarenone or Ubiquinone-10, and Vitamin E (E); alpha-tocopherol acetate, in their pharmaceutical binary mixture. Results: For first method: By applying the first derivative, both Q and E were alternatively determined; each at the zero-crossing of the other. The D1 amplitudes of Q and E, at 285 nm and 235 nm respectively, were recorded and correlated to their concentrations. The calibration curve is linear over the concentration range of 10-60 and 5.6-70 μg mL-1 for Q and E, respectively. For second method: ANN (as a multivariate calibration method) was developed and applied for the simultaneous determination of both analytes. A training set (or a concentration set) of 90 different synthetic mixtures containing Q and E, in wide concentration ranges between 0-100 µg/mL and 0-556 µg/mL respectively, were prepared in ethanol. The absorption spectra of the training sets were recorded in the spectral region of 230–300 nm. A Gradient Descend Back Propagation ANN chemometric calibration was computed by relating the concentration sets (x-block) to their corresponding absorption data (y-block). Another set of 45 synthetic mixtures of the two drugs, in defined range, was used to validate the proposed network. Neither chemical separation, preparation stage nor mathematical graphical treatment were required. Conclusions: The proposed methods were successfully applied for the assay of Q and E in laboratory prepared mixtures and combined pharmaceutical tablet with excellent recoveries. The ANN method was superior over the derivative technique as the former determined both drugs in the non-linear experimental conditions. It also offers rapidity, high accuracy, effort and money saving. Moreover, no need for an analyst for its application. Although the ANN technique needed a large training set, it is the method of choice in the routine analysis of Q and E tablet. No interference was observed from common pharmaceutical additives. The results of the two methods were compared together

Keywords: coenzyme Q10, vitamin E, chemometry, quantitative analysis, first derivative spectrophotometry, artificial neural network

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Authors: Ahmad Haidar

Abstract:

Modern economic systems are characterized by growing complexity, and addressing their challenges requires innovative approaches. This study examines the implications of artificial intelligence (AI) on unemployment in Europe from a macroeconomic perspective, employing data modeling techniques to understand the relationship between AI integration and labor market dynamics. To understand the AI-unemployment nexus comprehensively, this research considers factors such as sector-specific AI adoption, skill requirements, workforce demographics, and geographical disparities. The study utilizes a panel data model, incorporating data from European countries over the last two decades, to explore the potential short-term and long-term effects of AI implementation on unemployment rates. In addition to investigating the direct impact of AI on unemployment, the study also delves into the potential indirect effects and spillover consequences. It considers how AI-driven productivity improvements and cost reductions might influence economic growth and, in turn, labor market outcomes. Furthermore, it assesses the potential for AI-induced changes in industrial structures to affect job displacement and creation. The research also highlights the importance of policy responses in mitigating potential negative consequences of AI adoption on unemployment. It emphasizes the need for targeted interventions such as skill development programs, labor market regulations, and social safety nets to enable a smooth transition for workers affected by AI-related job displacement. Additionally, the study explores the potential role of AI in informing and transforming policy-making to ensure more effective and agile responses to labor market challenges. In conclusion, this study provides a comprehensive analysis of the macroeconomic implications of AI on unemployment in Europe, highlighting the importance of understanding the nuanced relationships between AI adoption, economic growth, and labor market outcomes. By shedding light on these relationships, the study contributes valuable insights for policymakers, educators, and researchers, enabling them to make informed decisions in navigating the complex landscape of AI-driven economic transformation.

Keywords: artificial intelligence, unemployment, macroeconomic analysis, european labor market

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Authors: Ishita Singh

Abstract:

Social-dimension of Climate Change is no longer peripheral to Science, Technology and Innovation (STI). Indeed, STI is being mobilized to address small farmers’ vulnerability and adaptation to Climate Change. The experiences from the cold desert of Leh (Ladakh) in North-West Himalaya illustrate the potential of STI to address the challenges of Climate Change and the needs of small farmers through the use of Artificial Glacier Techniques. Small farmers have a unique technique of water harvesting to augment irrigation, called “Artificial Glaciers” - an intricate network of water channels and dams along the upper slope of a valley that are located closer to villages and at lower altitudes than natural glaciers. It starts to melt much earlier and supplements additional irrigation to small farmers’ improving their livelihoods. Therefore, the issue of vulnerability, adaptive capacity and adaptation strategy needs to be analyzed in a local context and the communities as well as regions where people live. Leh (Ladakh) in North-West Himalaya provides a Case Study for exploring the ways in which adaptation to Climate Change is taking place at a community scale using Artificial Glacier Technology. With the above backdrop, an attempt has been made to analyze the rural poor households' vulnerability and adaptation practices to Climate Change using this technology, thereby drawing lessons on vulnerability-livelihood interactions in the cold desert of Leh (Ladakh) in North-West Himalaya, India. The study is based on primary data and information collected from 675 households confined to 27 villages of Leh (Ladakh) in North-West Himalaya, India. It reveals that 61.18% of the population is driving livelihoods from agriculture and allied activities. With increased irrigation potential due to the use of Artificial Glaciers, food security has been assured to 77.56% of households and health vulnerability has been reduced in 31% of households. Seasonal migration as a livelihood diversification mechanism has declined in nearly two-thirds of households, thereby improving livelihood strategies. Use of tactical adaptations by small farmers in response to persistent droughts, such as selling livestock, expanding agriculture lands, and use of relief cash and foods, have declined to 20.44%, 24.74% and 63% of households. However, these measures are unsustainable on a long-term basis. The role of policymakers and societal stakeholders becomes important in this context. To address livelihood challenges, the role of technology is critical in a multidisciplinary approach involving multilateral collaboration among different stakeholders. The presence of social entrepreneurs and new actors on the adaptation scene is necessary to bring forth adaptation measures. Better linkage between Science and Technology policies, together with other policies, should be encouraged. Better health care, access to safe drinking water, better sanitary conditions, and improved standards of education and infrastructure are effective measures to enhance a community’s adaptive capacity. However, social transfers for supporting climate adaptive capacity require significant amounts of additional investment. Developing institutional mechanisms for specific adaptation interventions can be one of the most effective ways of implementing a plan to enhance adaptation and build resilience.

Keywords: climate change, adaptation, livelihood, stakeholders

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Authors: Zerroug Abdelhamid, Danielle Chassoux

Abstract:

Various models are given to simulate homogeneous or heterogeneous cancerous tumors and extract in each case the boundary. The fractal dimension is then estimated by least squares method and compared to some previous methods.

Keywords: simulation, cancerous tumor, Markov fields, fractal dimension, extraction, recovering

Procedia PDF Downloads 351

Authors: Sinem Yıldırım, Çimen Özburak, Özge Özden

Abstract:

Cities are facing a growing environmental issue as a result of the combined effect of urbanization and climate change. Climate change is the most conspicuousimpact on environmental issues. Nowadays, energy conservation is a very important subject for planners. It is known that green roofs can provide environmental benefits, which include building insulation and mitigating urban heat island effect within the cities. Some of the studies shown that green roofs regulate roof temperature and they have an effect on indoor temperatures of buildings. This research looks at the experimental investigation of different type green roof vegetation with control of no vegetation and their effect on indoor temperatures. The research has been carried out at Near East University Campus with the duration of four months in Nicosia, Cyprus. The experiment was consisting of four green roof types; three of them covered with vegetation, and one of them was not vegetated for control of the experiment. Each hut had 2.7 m2 roof areas, and the soil depth was 8 cm. Mediterranean climate drought resistant ground covers and shrubs were planted on the roof of the three huts. Three different vegetation type was used: 1-Low growing ground cover succulents 2-Mixture of low growing succulents and low shrubs 3-Mixture of low growing succulents, low shrubs, and high growing foliage plantsElitech RC-5 temperature data loggers were used in order to measure indoor temperatures of the huts. Research results were shown that the hut with a highly vegetated roof had the lowest temperatures during hot summer period in Cyprus.

Keywords: green roofs, indoor temperature, vegetation, mediterranean, cyprus

Procedia PDF Downloads 186

Authors: Hao Yan, Xiaobing Zhang

Abstract:

The working mechanism of miniaturized solid rocket motors (SRMs) is not yet fully understood. It is imperative to explore its unique features. However, there are many disadvantages to using common multi-objective evolutionary algorithms (MOEAs) in predicting the parameters of the miniaturized SRM during its conceptual design phase. Initially, the design variables and objectives are constrained in a lumped parameter model (LPM) of this SRM, which leads to local optima in MOEAs. In addition, MOEAs require a large number of calculations due to their population strategy. Although the calculation time for simulating an LPM just once is usually less than that of a CFD simulation, the number of function evaluations (NFEs) is usually large in MOEAs, which makes the total time cost unacceptably long. Moreover, the accuracy of the LPM is relatively low compared to that of a CFD model due to its assumptions. CFD simulations or experiments are required for comparison and verification of the optimal results obtained by MOEAs with an LPM. The conceptual design phase based on MOEAs is a lengthy process, and its results are not precise enough due to the above shortcomings. An artificial neural network (ANN) based parameter prediction is proposed as a way to reduce time costs and improve prediction accuracy. In this method, an ANN is used to build a surrogate model that is trained with a 3D numerical simulation. In design, the original LPM is replaced by a surrogate model. Each case uses the same MOEAs, in which the calculation time of the two models is compared, and their optimization results are compared with 3D simulation results. Using the surrogate model for the parameter prediction process of the miniaturized SRMs results in a significant increase in computational efficiency and an improvement in prediction accuracy. Thus, the ANN-based surrogate model does provide faster and more accurate parameter prediction for an initial design scheme. Moreover, even when the MOEAs converge to local optima, the time cost of the ANN-based surrogate model is much lower than that of the simplified physical model LPM. This means that designers can save a lot of time during code debugging and parameter tuning in a complex design process. Designers can reduce repeated calculation costs and obtain accurate optimal solutions by combining an ANN-based surrogate model with MOEAs.

Keywords: artificial neural network, solid rocket motor, multi-objective evolutionary algorithm, surrogate model

Procedia PDF Downloads 71