Search results for: soil crusting processing

Authors: Ehsan Moradi, Parisa Paikhaste, Amir Alam Beigi, Seyedeh Somayeh Bathaei

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The need for entrepreneurial sustainability is as important as the entrepreneurship category itself. By transferring competencies in a sustainable entrepreneurship framework, entrepreneurship education can make a significant contribution to the effectiveness of businesses, especially for start-up entrepreneurs. This study analyzes the essential competencies of students in the development of sustainable entrepreneurship. It is an applied causal study in terms of nature and field in terms of data collection. The main purpose of this research project is to study and explain the dimensions of sustainability entrepreneurship competencies among agricultural students. The statistical population consists of 730 junior and senior undergraduate students of the Campus of Agriculture and Natural Resources, University of Tehran. The sample size was determined to be 120 using the Cochran's formula, and the convenience sampling method was used. Face validity, structure validity, and diagnostic methods were used to evaluate the validity of the research tool and Cronbach's alpha and composite reliability to evaluate its reliability. Structural equation modeling (SEM) was used by the confirmatory factor analysis (CFA) method to prepare a measurement model for data processing. The results showed that seven key dimensions play a role in shaping sustainable entrepreneurial development competencies: systems thinking competence (STC), embracing diversity and interdisciplinary (EDI), foresighted thinking (FTC), normative competence (NC), action competence (AC), interpersonal competence (IC), and strategic management competence (SMC). It was found that acquiring skills in SMC by creating the ability to plan to achieve sustainable entrepreneurship in students through the relevant mechanisms can improve entrepreneurship in students by adopting a sustainability attitude. While increasing students' analytical ability in the field of social and environmental needs and challenges and emphasizing curriculum updates, AC should pay more attention to the relationship between the curriculum and its content in the form of entrepreneurship culture promotion programs. In the field of EDI, it was found that the success of entrepreneurs in terms of sustainability and business sustainability of start-up entrepreneurs depends on their interdisciplinary thinking. It was also found that STC plays an important role in explaining the relationship between sustainability and entrepreneurship. Therefore, focusing on these competencies in agricultural education to train start-up entrepreneurs can lead to sustainable entrepreneurship in the agricultural higher education system.

Keywords: sustainable entrepreneurship, entrepreneurship education, competency, agricultural higher education

Procedia PDF Downloads 144

Authors: Attila Kiss, Erzsébet Némedi, Zoltán Naár

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As natural prebiotic (non-digestible) carbohydrates stimulate the growth of colon microflora and contribute to maintain the health of the host, analytical studies aiming at revealing the chemical behavior of these beneficial food components came to the forefront of interest. Food processing (especially baking) may lead to a significant conversion of the parent compounds, hence it is of utmost importance to characterize the transformation patterns and the plausible decomposition products formed by thermal degradation. The relevance of this work is confirmed by the wide-spread use of these carbohydrates (fructo-oligosaccharides, cyclodextrins, raffinose and resistant starch) in the food industry. More and more functional foodstuffs are being developed based on prebiotics as bioactive components. 12 different types of oligosaccharides have been investigated in order to reveal their thermal degradation characteristics. Different carbohydrate derivatives (D-fructose and D-glucose oligomers and polymers) have been exposed to elevated temperatures (150 °C 170 °C, 190 °C, 210 °C, and 220 °C) for 10 min. An advanced HPLC method was developed and used to identify the decomposition products of carbohydrates formed as a consequence of thermal treatment. Gradient elution was applied with binary solvent elution (acetonitrile, water) through amine based carbohydrate column. Evaporative light scattering (ELS) proved to be suitable for the reliable detection of the UV/VIS inactive carbohydrate degradation products. These experimental conditions and applied advanced techniques made it possible to survey all the formed intermediers. Change in oligomer distribution was established in cases of all studied prebiotics throughout the thermal treatments. The obtained results indicate increased extent of chain degradation of the carbohydrate moiety at elevated temperatures. Prevalence of oligomers with shorter chain length and even the formation of monomer sugars (D-glucose and D-fructose) might be observed at higher temperatures. Unique oligomer distributions, which have not been described previously are revealed in the case of each studied, specific carbohydrate, which might result in various prebiotic activities. Resistant starches exhibited high stability when being thermal treated. The degradation process has been modeled by a plausible reaction mechanism, in which proton catalyzed degradation and chain cleavage take place.

Keywords: prebiotics, thermal degradation, fructo-oligosaccharide, HPLC, ELS detection

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Authors: Haresh Sharma, Jaimini Luharia

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The ecology in the Western Himalayan region transforms with the change in altitude. This change is observed in terms of topography, species of flora and fauna and the quality of the soil. The current study focuses on women of indigenous communities of Pangi Valley, which is located in the state of Himachal Pradesh, India. The valley is bifurcated into three different areas –Saichu, Hudan Bhatori, and Sural Bhatori valleys. It is one of the most remote, rugged and difficult to access tribal regions of Chamba district. The altitude of the valley ranges from 2,000 m to 6,000 m above sea level. The Pangi valley is inhabited by ‘Pangwals’ and ‘Bhots’ tribes of the Himalayas who speak their local tribal language called’ Pangwali’. The valley is cut-off from the mainland due to heavy snow and lack of proper roads during peak winters. Due to difficult geographical location, the daily lives of the people are constantly challenged, and they are most of the times deprived of benefits targeted through government programs. However, the indigenous communities earn their livelihood through livestock and forest-based produce while some of them migrate to nearby places for better work. The current study involves snowball sampling methodology for data collection along with in-depth interviews of women members of Self-Help Groups and women farmers. The findings reveal that the lives of these indigenous communities largely depend on forest-based products. So, it creates all the more significance of enhancing, maintaining, and consuming natural resources sustainably. Under such circumstances, the women of the community play a significant role of guardians in conservation and protection of the forests. They are the custodians of traditional knowledge of environment conservation practices that have been followed for many years in the region. The present study also sought to establish a relationship between some of the development initiatives undertaken by the women in the valley that stimulate sustainable mountain economy and conservation practices. These initiatives include cultivation of products like hazelnut, ‘Gucchi’ rare quality mushroom, medicinal plants exclusively found in the region, thereby promoting long term sustainable conservation of agro-biodiversity of the Western Himalayan region. The measures taken by the community women are commendable as they ensure access and distribution of natural resources as well as manage them for future generations. Apart from this, the tribal women have actively formed Self-Help Groups promoting financial inclusion through various activities that augment ownership and accountability towards the overall development of the communities. But, the results also suggest that there’s not enough recognition given to women’s role in forests conservation practices due to several local socio-political reasons. There are not enough research studies done on communities of Pangi Valley due to inaccessibility created out of lack of proper roads and other resources. Also, there emerged a need to concretize indigenous and traditional knowledge of conservation practices followed by women in the community.

Keywords: forest conservation, indigenous community women, sustainable livelihoods, sustainable development, poverty alleviation, Western Himalayas

Procedia PDF Downloads 122

Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

Procedia PDF Downloads 87

Authors: Daniel Bülz, Franziska Lüttich, Sreetama Banerjee, Georgeta Salvan, Dietrich R. T. Zahn

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For the development of electronics, organic semiconductors are of great interest due to their adjustable optical and electrical properties. Especially for spintronic applications they are interesting because of their weak spin scattering, which leads to longer spin life times compared to inorganic semiconductors. It was shown that some organic materials change their resistance if an external magnetic field is applied. Pentacene is one of the materials which exhibit the so called photoinduced magnetoresistance which results in a modulation of photocurrent when varying the external magnetic field. Also the soluble derivate of pentacene, the 6,13-bis (triisopropylsilylethynyl)-pentacene (TIPS-pentacene) exhibits the same negative magnetoresistance. Aiming for simpler fabrication processes, in this work, we compare TIPS-pentacene organic field effect transistors (OFETs) made from solution with those fabricated by thermal evaporation. Because of the different processing, the TIPS-pentacene thin films exhibit different morphologies in terms of crystal size and homogeneity of the substrate coverage. On the other hand, the interface treatment is known to have a high influence on the threshold voltage, eliminating trap states of silicon oxide at the gate electrode and thereby changing the electrical switching response of the transistors. Therefore, we investigate the influence of interface treatment using octadecyltrichlorosilane (OTS) or using a simple cleaning procedure with acetone, ethanol, and deionized water. The transistors consist of a prestructured OFET substrates including gate, source, and drain electrodes, on top of which TIPS-pentacene dissolved in a mixture of tetralin and toluene is deposited by drop-, spray-, and spin-coating. Thereafter we keep the sample for one hour at a temperature of 60 °C. For the transistor fabrication by thermal evaporation the prestructured OFET substrates are also kept at a temperature of 60 °C during deposition with a rate of 0.3 nm/min and at a pressure below 10-6 mbar. The OFETs are characterized by means of optical microscopy in order to determine the overall quality of the sample, i.e. crystal size and coverage of the channel region. The output and transfer characteristics are measured in the dark and under illumination provided by a white light LED in the spectral range from 450 nm to 650 nm with a power density of (8±2) mW/cm2.

Keywords: organic field effect transistors, solution processed, surface treatment, TIPS-pentacene

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Authors: N. Pérez-Rodríguez, D. García-Bernet, A. Torrado-Agrasar, J. M. Cruz, A. B. Moldes, J. M. Domínguez

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World economy is based on non-renewable, fossil fuels such as petroleum and natural gas, which entails its rapid depletion and environmental problems. In EU countries, the objective is that at least 20% of the total energy supplies in 2020 should be derived from renewable resources. Biogas, a product of anaerobic degradation of organic substrates, represents an attractive green alternative for meeting partial energy needs. Nowadays, trend to circular economy model involves efficiently use of residues by its transformation from waste to a new resource. In this sense, characteristics of agricultural residues (that are available in plenty, renewable, as well as eco-friendly) propitiate their valorisation as substrates for biogas production. Corn cob is a by-product obtained from maize processing representing 18 % of total maize mass. Corn cob importance lies in the high production of this cereal (more than 1 x 109 tons in 2014). Due to its lignocellulosic nature, corn cob contains three main polymers: cellulose, hemicellulose and lignin. Crystalline, highly ordered structures of cellulose and lignin hinders microbial attack and subsequent biogas production. For the optimal lignocellulose utilization and to enhance gas production in anaerobic digestion, materials are usually submitted to different pretreatment technologies. In the present work, enzymatic hydrolysis, ultrasounds and combination of both technologies were assayed as pretreatments of corn cob for biogas production. Enzymatic hydrolysis pretreatment was started by adding 0.044 U of Ultraflo® L feruloyl esterase per gram of dry corncob. Hydrolyses were carried out in 50 mM sodium-phosphate buffer pH 6.0 with a solid:liquid proportion of 1:10 (w/v), at 150 rpm, 40 ºC and darkness for 3 hours. Ultrasounds pretreatment was performed subjecting corn cob, in 50 mM sodium-phosphate buffer pH 6.0 with a solid: liquid proportion of 1:10 (w/v), at a power of 750W for 1 minute. In order to observe the effect of the combination of both pretreatments, some samples were initially sonicated and then they were enzymatically hydrolysed. In terms of methane production, anaerobic digestion of the corn cob pretreated by enzymatic hydrolysis was positive achieving 290 L CH4 kg MV-1 (compared with 267 L CH4 kg MV-1 obtained with untreated corn cob). Although the use of ultrasound as the only pretreatment resulted detrimentally (since gas production decreased to 244 L CH4 kg MV-1 after 44 days of anaerobic digestion), its combination with enzymatic hydrolysis was beneficial, reaching the highest value (300.9 L CH4 kg MV-1). Consequently, the combination of both pretreatments improved biogas production from corn cob.

Keywords: biogas, corn cob, enzymatic hydrolysis, ultrasound

Procedia PDF Downloads 267

Authors: Prudhvinath Reddy Ankireddy, Narasimha Mangadoddy

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Spiral concentrators are commonly used in various industries, including mineral and coal processing, to efficiently separate materials based on their density and size. In these concentrators, a mixture of solid particles and fluid (usually water) is introduced as feed at the top of a spiral channel. As the mixture flows down the spiral, centrifugal and gravitational forces act on the particles, causing them to stratify based on their density and size. Spiral flows exhibit complex fluid dynamics, and interactions involve multiple phases and components in the process. Understanding the behavior of these phases within the spiral concentrator is crucial for achieving efficient separation. An experimental bi-component particle interaction study is conducted in this work utilizing magnetite (heavier density) and silica (lighter density) with different proportions processed in the spiral concentrator. The observation separation reveals that denser particles accumulate towards the inner region of the spiral trough, while a significant concentration of lighter particles are found close to the outer edge. The 5th turn of the spiral trough is partitioned into five zones to achieve a comprehensive distribution analysis of bicomponent particle segregation. Samples are then gathered from these individual streams using an in-house sample collector, and subsequent analysis is conducted to assess component segregation. Along the trough, there was a decline in the concentration of coarser particles, accompanied by an increase in the concentration of lighter particles. The segregation pattern indicates that the heavier coarse component accumulates in the inner zone, whereas the lighter fine component collects in the outer zone. The middle zone primarily consists of heavier fine particles and lighter coarse particles. The zone-wise results reveal that there is a significant fraction of segregation occurs in inner and middle zones. Finer magnetite and silica particles predominantly accumulate in outer zones with the smallest fraction of segregation. Additionally, numerical simulations are also carried out using the computational fluid dynamics (CFD) model based on the volume of fluid (VOF) approach incorporating the RSM turbulence model. The discrete phase model (DPM) is employed for particle tracking, thereby understanding the particle segregation of magnetite and silica along the spiral trough.

Keywords: spiral concentrator, bi-component particle segregation, computational fluid dynamics, discrete phase model

Procedia PDF Downloads 68

Authors: Hui Li, Xiongqi Pang

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In recent years, tight oil has become a hot spot for unconventional oil and gas exploration and development in the world. Dongpu Depression is a typical hydrocarbon-rich basin in the southwest of Bohai Bay Basin, in which tight sandstone oil and gas have been discovered in deep reservoirs, most of which are buried more than 3500m. The distribution and development characteristics of deep tight sandstone reservoirs need to be studied. The main source rocks in study area are dark mudstone and shale of the middle and lower third sub-member of Shahejie Formation. Total Organic Carbon (TOC) content of source rock is between 0.08-11.54%, generally higher than 0.6% and the value of S1+S2 is between 0.04–72.93 mg/g, generally higher than 2 mg/g. It can be evaluated as middle to fine level overall. The kerogen type of organic matter is predominantly typeⅡ1 andⅡ2. Vitrinite reflectance (Ro) is mostly greater than 0.6% indicating that the source rock entered the hydrocarbon generation threshold. The physical property of reservoir was poor, the most reservoir has a porosity lower than 12% and a permeability of less than 1×10⁻³μm. The rocks in this area showed great heterogeneity, some areas developed desserts with high porosity and permeability. According to SEM, thin section image, inclusion test and so on, the reservoir was affected by compaction and cementation during early diagenesis stage (44-31Ma). The diagenesis caused the tight reservoir in Huzhuangji, Pucheng, Weicheng Area while the porosity in Machang, Qiaokou, Wenliu Area was still over 12%. In the process of middle diagenesis phase stage A (31-17Ma), the reservoir porosity in Machang, Pucheng, Huzhuangji Area increased due to dissolution; after that the oil generation window of source rock was achieved for the first phase hydrocarbon charging (31-23Ma), formed the conventional oil deposition in Machang, Qiaokou, Wenliu, Huzhuangji Area and unconventional tight reservoir in Pucheng, Weicheng Area. Then came to stage B of middle diagenesis phase (17-7Ma), in this stage, the porosity of reservoir continued to decrease after the dissolution and led to a situation that the reservoirs were generally compacted. And since then, the second hydrocarbon filling has been processing since 7Ma. Most of the pools charged and formed in this procedure are tight sandstone oil reservoir. In conclusion, tight sandstone oil was formed in two patterns in Dongpu Depression, which could be concluded as ‘density fist then accumulation’ pattern and ‘accumulation fist next density’ pattern.

Keywords: accumulation process, diagenesis, dongpu depression, tight sandstone oil

Procedia PDF Downloads 119

Authors: Simone Cinquemani, Lorenzo Mantovani, Aleksander Dabek

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New technologies in agriculture are opening new challenges and new opportunities. Among these, certainly, robotics, vision, and artificial intelligence are the ones that will make a significant leap, compared to traditional agricultural techniques, possible. In particular, the indoor farming sector will be the one that will benefit the most from these solutions. Vertical farming is a new field of research where mechanical engineering can bring knowledge and know-how to transform a highly labor-based business into a fully autonomous system. The aim of the research is to develop a multi-purpose, modular, and perfectly integrated platform for crop production in indoor vertical farming. Activities will be based both on hardware development such as automatic tools to perform different activities on soil and plants, as well as research to introduce an extensive use of monitoring techniques based on machine learning algorithms. This paper presents the preliminary results of a research project of a vertical farm living lab designed to (i) develop and test vertical farming cultivation practices, (ii) introduce a very high degree of mechanization and automation that makes all processes replicable, fully measurable, standardized and automated, (iii) develop a coordinated control and management environment for autonomous multiplatform or tele-operated robots in environments with the aim of carrying out complex tasks in the presence of environmental and cultivation constraints, (iv) integrate AI-based algorithms as decision support system to improve quality production. The coordinated management of multiplatform systems still presents innumerable challenges that require a strongly multidisciplinary approach right from the design, development, and implementation phases. The methodology is based on (i) the development of models capable of describing the dynamics of the various platforms and their interactions, (ii) the integrated design of mechatronic systems able to respond to the needs of the context and to exploit the strength characteristics highlighted by the models, (iii) implementation and experimental tests performed to test the real effectiveness of the systems created, evaluate any weaknesses so as to proceed with a targeted development. To these aims, a fully automated laboratory for growing plants in vertical farming has been developed and tested. The living lab makes extensive use of sensors to determine the overall state of the structure, crops, and systems used. The possibility of having specific measurements for each element involved in the cultivation process makes it possible to evaluate the effects of each variable of interest and allows for the creation of a robust model of the system as a whole. The automation of the laboratory is completed with the use of robots to carry out all the necessary operations, from sowing to handling to harvesting. These systems work synergistically thanks to the knowledge of detailed models developed based on the information collected, which allows for deepening the knowledge of these types of crops and guarantees the possibility of tracing every action performed on each single plant. To this end, artificial intelligence algorithms have been developed to allow synergistic operation of all systems.

Keywords: automation, vertical farming, robot, artificial intelligence, vision, control

Procedia PDF Downloads 45

Authors: Xin Jiao, Xin Zhang, Jian Fan, Zhenwei Cai, Yiming Xu

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Tower cranes play a crucial role in the construction industry, facilitating the vertical and horizontal movement of materials and aiding in building construction, especially for high-rise structures. However, tower crane accidents can lead to severe consequences, highlighting the importance of effective safety management and inspection. This paper presents an innovative approach to tower crane inspection utilizing Unmanned Aerial Vehicles (UAVs) and an Intelligent Inspection APP System. The system leverages UAVs equipped with high-definition cameras to conduct efficient and comprehensive inspections, reducing manual labor, inspection time, and risk. By integrating advanced technologies such as Real-Time Kinematic (RTK) positioning and digital image processing, the system enables precise route planning and collection of safety hazards images. A case study conducted on a construction site demonstrates the practicality and effectiveness of the proposed method, showcasing its potential to enhance tower crane safety. On-site testing of UAV intelligent inspections reveals key findings: efficient tower crane hazard inspection within 30 minutes, with a full-identification capability coverage rates of 76.3%, 64.8%, and 76.2% for major, significant, and general hazards respectively and a preliminary-identification capability coverage rates of 18.5%, 27.2%, and 19%, respectively. Notably, UAVs effectively identify various tower crane hazards, except for those requiring auditory detection. The limitations of this study primarily involve two aspects: Firstly, during the initial inspection, manual drone piloting is required for marking tower crane points, followed by automated flight inspections and reuse based on the marked route. Secondly, images captured by the drone necessitate manual identification and review, which can be time-consuming for equipment management personnel, particularly when dealing with a large volume of images. Subsequent research efforts will focus on AI training and recognition of safety hazard images, as well as the automatic generation of inspection reports and corrective management based on recognition results. The ongoing development in this area is currently in progress, and outcomes will be released at an appropriate time.

Keywords: tower crane, inspection, unmanned aerial vehicle (UAV), intelligent inspection app system, safety management

Procedia PDF Downloads 43

Authors: Vinicius G. Goecks, Gregory M. Gremillion, William D. Nothwang

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Modern approaches to train intelligent agents rely on prolonged training sessions, high amounts of input data, and multiple interactions with the environment. This restricts the application of these learning algorithms in robotics and real-world applications, in which there is low tolerance to inadequate actions, interactions are expensive, and real-time processing and action are required. This paper addresses this issue introducing CyberSteer, a novel approach to efficiently design intrinsic reward functions based on human intention to guide deep reinforcement learning agents with no environment-dependent rewards. CyberSteer uses non-expert human operators for initial demonstration of a given task or desired behavior. The trajectories collected are used to train a behavior cloning deep neural network that asynchronously runs in the background and suggests actions to the deep reinforcement learning module. An intrinsic reward is computed based on the similarity between actions suggested and taken by the deep reinforcement learning algorithm commanding the agent. This intrinsic reward can also be reshaped through additional human demonstration or critique. This approach removes the need for environment-dependent or hand-engineered rewards while still being able to safely shape the behavior of autonomous robotic agents, in this case, based on human intention. CyberSteer is tested in a high-fidelity unmanned aerial vehicle simulation environment, the Microsoft AirSim. The simulated aerial robot performs collision avoidance through a clustered forest environment using forward-looking depth sensing and roll, pitch, and yaw references angle commands to the flight controller. This approach shows that the behavior of robotic systems can be shaped in a reduced amount of time when guided by a non-expert human, who is only aware of the high-level goals of the task. Decreasing the amount of training time required and increasing safety during training maneuvers will allow for faster deployment of intelligent robotic agents in dynamic real-world applications.

Keywords: human-robot interaction, intelligent robots, robot learning, semisupervised learning, unmanned aerial vehicles

Procedia PDF Downloads 259

Authors: Lucas M. Furlan, Cesar A. Moreira, Jepherson F. Sales, Guilherme T. Bueno, Manuel E. Ferreira, Carla V. S. Coelho, Vania Rosolen

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Located in the western part of the State of Minas Gerais, Brazil, the study area consists of a savanna environment, represented by sedimentary plateau and a soil cover composed by lateritic and hydromorphic soils - in the latter, occurring the deferruginization and concentration of high-alumina clays, exploited as refractory material. In the hydromorphic topographic depressions (wetlands) the hydropedogical relationships are little known, but it is observed that in times of rainfall, the depressed region behaves like a natural seasonal reservoir - which suggests that the wetlands on the surface of the plateau are places of recharge of the aquifer. The aquifer recharge areas are extremely important for the sustainable social, economic and environmental development of societies. The understanding of hydrodynamics in relation to the functioning of the ferruginous and hydromorphic lateritic soils system in the savanna environment is a subject rarely explored in the literature, especially its understanding through the joint application of geoprocessing by UAV (Unmanned Aerial Vehicle) and electrical tomography. The objective of this work is to understand the hydrogeological dynamics in a wetland (with an area of 426.064 m²), in the Brazilian savanna,as well as the understanding of the subsurface architecture of hydromorphic depressions in relation to the recharge of aquifers. The wetland was compartmentalized in three different regions, according to the geoprocessing. Hydraulic conductivity studies were performed in each of these three portions. Electrical tomography was performed on 9 lines of 80 meters in length and spaced 10 meters apart (direction N45), and a line with 80 meters perpendicular to all others. With the data, it was possible to generate a 3D cube. The integrated analysis showed that the area behaves like a natural seasonal reservoir in the months of greater precipitation (December – 289mm; January – 277,9mm; February – 213,2mm), because the hydraulic conductivity is very low in all areas. In the aerial images, geotag correction of the images was performed, that is, the correction of the coordinates of the images by means of the corrected coordinates of the Positioning by Precision Point of the Brazilian Institute of Geography and Statistics (IBGE-PPP). Later, the orthomosaic and the digital surface model (DSM) were generated, which with specific geoprocessing generated the volume of water that the wetland can contain - 780,922m³ in total, 265,205m³ in the region with intermediate flooding and 49,140m³ in the central region, where a greater accumulation of water was observed. Through the electrical tomography it was possible to identify that up to the depth of 6 meters the water infiltrates vertically in the central region. From the 8 meters depth, the water encounters a more resistive layer and the infiltration begins to occur horizontally - tending to concentrate the recharge of the aquifer to the northeast and southwest of the wetland. The hydrodynamics of the area is complex and has many challenges in its understanding. The next step is to relate hydrodynamics to the evolution of the landscape, with the enrichment of high-alumina clays, and to propose a management model for the seasonal reservoir.

Keywords: electrical tomography, hydropedology, unmanned aerial vehicle, water resources management

Procedia PDF Downloads 148

Authors: Basil Nkosilathi Dube, Wilson R. Nyemba, Panashe Mandevu

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In highway driving, over 50 percent of the power produced by the engine is used to overcome aerodynamic drag, which is a force that opposes a body’s motion through the air. Aerodynamic drag and thus fuel consumption increase rapidly at speeds above 90kph. It is desirable to minimize fuel consumption. Aerodynamic drag reduction in highway driving is the best approach to minimize fuel consumption and to reduce the negative impacts of greenhouse gas emissions on the natural environment. Fuel economy is the ultimate concern of automotive development. This study aims to design and analyze drag-reducing devices for a Mazda T3500 truck, namely, the cab roof and rear (trailer tail) fairings. The aerodynamic effects of adding these append devices were subsequently investigated. To accomplish this, two 3D CAD models of the Mazda truck were designed using the Design Modeler. One, with these, append devices and the other without. The models were exported to ANSYS Fluent for computational fluid dynamics analysis, no wind tunnel tests were performed. A fine mesh with more than 10 million cells was applied in the discretization of the models. The realizable k-ε turbulence model with enhanced wall treatment was used to solve the Reynold’s Averaged Navier-Stokes (RANS) equation. In order to simulate the highway driving conditions, the tests were simulated with a speed of 100 km/h. The effects of these devices were also investigated for low-speed driving. The drag coefficients for both models were obtained from the numerical calculations. By adding the cab roof and rear (trailer tail) fairings, the simulations show a significant reduction in aerodynamic drag at a higher speed. The results show that the greatest drag reduction is obtained when both devices are used. Visuals from post-processing show that the rear fairing minimized the low-pressure region at the rear of the trailer when moving at highway speed. The rear fairing achieved this by streamlining the turbulent airflow, thereby delaying airflow separation. For lower speeds, there were no significant differences in drag coefficients for both models (original and modified). The results show that these devices can be adopted for improving the aerodynamic efficiency of the Mazda T3500 truck at highway speeds.

Keywords: aerodynamic drag, computation fluid dynamics, fluent, fuel consumption

Procedia PDF Downloads 140

Authors: Tatyane Lopes de Freitas, Antonio Diogo S. Vieira, Susana Marta Isay Saad, Maria Ines Genovese

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The fruit processing industries generate a large volume of residues to produce juices, pulps, and jams. These residues, or by-products, consisting of peels, seeds, and pulps, are routinely discarded. Fruits are rich in bioactive compounds, including polyphenols, which have positive effects on health. Dry residues from two fruits, acerola (M. emarginata D. C.) and orange (C. sinensis), were characterized in relation to contents of ascorbic acid, minerals, total dietary fibers, moisture, ash, lipids, proteins, and carbohydrates, and also high performance liquid chromatographic profile of flavonoids, total polyphenols and proanthocyanidins contents, and antioxidant capacity by three different methods (Ferric reducing antioxidant power assay-FRAP, Oxygen Radical Absorbance Capacity-ORAC, 1,1-diphenyl-2-picrylhydrazil (DPPH) radical scavenging activity). Acerola by-products presented the highest acid ascorbic content (605 mg/100 g), and better antioxidant capacity than orange by-products. The dry residues from acerola demonstrated high contents of proanthocyanidins (617 µg CE/g) and total polyphenols (2525 mg gallic acid equivalents - GAE/100 g). Both presented high total dietary fiber (above 60%) and protein contents (acerola: 10.4%; orange: 9.9%), and reduced fat content (acerola: 1.6%; orange: 2.6%). Both residues showed high levels of potassium, calcium, and magnesium, and were considered sources of these minerals. With acerola by-product, four formulations of probiotics fermented milks were produced: F0 (without the addition of acerola residue (AR)), F2 (2% AR), F5 (5% AR) and F10 (10% AR). The physicochemical characteristics of the fermented milks throughout of storage were investigated, as well as the impact of in vitro simulated gastrointestinal conditions on flavonoids and probiotics. The microorganisms analyzed maintained their populations around 8 log CFU/g during storage. After the gastric phase of the simulated digestion, the populations decreased, and after the enteric phase, no colonies were detected. On the other hand, the flavonoids increased after the gastric phase, maintaining or suffering small decrease after enteric phase. Acerola by-products powder is a valuable ingredient to be used in functional foods because is rich in vitamin C, fibers and flavonoids. These flavonoids appear to be highly resistant to the acids and salts of digestion.

Keywords: acerola, orange, by-products, fermented milk

Procedia PDF Downloads 132

Authors: Mohammad Ghahramani, Fahimeh Saei Manesh

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Winning a soccer game is based on thorough and deep analysis of the ongoing match. On the other hand, giant gambling companies are in vital need of such analysis to reduce their loss against their customers. In this research work, we perform deep, real-time analysis on every soccer match around the world that distinguishes our work from others by focusing on particular seasons, teams and partial analytics. Our contributions are presented in the platform called “Analyst Masters.” First, we introduce various sources of information available for soccer analysis for teams around the world that helped us record live statistical data and information from more than 50,000 soccer matches a year. Our second and main contribution is to introduce our proposed in-play performance evaluation. The third contribution is developing new features from stable soccer matches. The statistics of soccer matches and their odds before and in-play are considered in the image format versus time including the halftime. Local Binary patterns, (LBP) is then employed to extract features from the image. Our analyses reveal incredibly interesting features and rules if a soccer match has reached enough stability. For example, our “8-minute rule” implies if 'Team A' scores a goal and can maintain the result for at least 8 minutes then the match would end in their favor in a stable match. We could also make accurate predictions before the match of scoring less/more than 2.5 goals. We benefit from the Gradient Boosting Trees, GBT, to extract highly related features. Once the features are selected from this pool of data, the Decision trees decide if the match is stable. A stable match is then passed to a post-processing stage to check its properties such as betters’ and punters’ behavior and its statistical data to issue the prediction. The proposed method was trained using 140,000 soccer matches and tested on more than 100,000 samples achieving 98% accuracy to select stable matches. Our database from 240,000 matches shows that one can get over 20% betting profit per month using Analyst Masters. Such consistent profit outperforms human experts and shows the inefficiency of the betting market. Top soccer tipsters achieve 50% accuracy and 8% monthly profit in average only on regional matches. Both our collected database of more than 240,000 soccer matches from 2012 and our algorithm would greatly benefit coaches and punters to get accurate analysis.

Keywords: soccer, analytics, machine learning, database

Procedia PDF Downloads 239

Authors: Sonika Sharma, Mohan G. Vairale, Sibnarayan Datta, Soumya Chatterjee, Dharmendra Dubey, Rajesh Prasad, Raghvendra Budhauliya, Bidisha Das, Vijay Veer

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Bacteriophages are viruses that parasitize specific bacteria and multiply in metabolising host bacteria. Bacteriophages hunt for a single or a subset of bacterial species, making them potential antibacterial agents. Utilizing the ability of phages to control bacterial populations has several applications from medical to the fields of agriculture, aquaculture and the food industry. However, harnessing phage based techniques in wastewater treatments to improve quality of effluent and sludge release into the environment is a potential area for R&D application. Phage mediated bactericidal effect in any wastewater treatment process has many controlling factors that lead to treatment performance. In laboratory conditions, titer of bacteriophages (coliphages) isolated from effluent water of a specially designed anaerobic digester of human night soil (DRDO Biotoilet) was successfully increased with a modified protocol of the classical double layer agar technique. Enrichment of the same was carried out and efficacy of the phage enriched medium was evaluated at different conditions (specific media, temperature, storage conditions). Growth optimization study was carried out on different media like soybean casein digest medium (Tryptone soya medium), Luria-Bertani medium, phage deca broth medium and MNA medium (Modified nutrient medium). Further, temperature-phage yield relationship was also observed at three different temperatures 27˚C, 37˚C and 44˚C at laboratory condition. Results showed the higher activity of coliphage 27˚C and at 37˚C. Further, addition of divalent ions (10mM MgCl2, 5mM CaCl2) and 5% glycerol resulted in a significant increase in phage titer. Besides this, effect of antibiotics addition like ampicillin and kanamycin at different concentration on plaque formation was analysed and reported that ampicillin at a concentration of 1mg/ml ampicillin stimulates phage infection and results in more number of plaques. Experiments to test viability of phage showed that it can remain active for 6 months at 4˚C in fresh tryptone soya broth supplemented with fresh culture of coliforms (early log phase). The application of bacteriophages (especially coliphages) for treatment of effluent of human faecal matter contaminated effluent water is unique. This environment-friendly treatment system not only reduces the pathogenic coliforms, but also decreases the competition between nuisance bacteria and functionally important microbial populations. Therefore, the phage based cocktail to treat fecal pathogenic bacteria present in black water has many implication in wastewater treatment processes including ‘DRDO Biotoilet’, which is an ecofriendly appropriate and affordable human faecal matter treatment technology for different climates and situations.

Keywords: wastewater, microbes, virus, biotoilet, phage viability

Procedia PDF Downloads 436

Authors: Carmel Sofer, Dan Vilenchik, Ron Dotsch, Galia Avidan

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Studies investigating emotional facial expressions typically reveal consensus among observes regarding the meaning of basic expressions, whose number ranges between 6 to 15 emotional states. Given this limited number of discrete expressions, how is it that the human vocabulary of emotional states is so rich? The present study argues that perceivers use sequences of these discrete expressions as the basis for a much richer vocabulary of emotional states. Such mechanisms, in which a relatively small number of basic components is expanded to a much larger number of possible combinations of meanings, exist in other human communications modalities, such as spoken language and music. In these modalities, letters and notes, which serve as basic components of spoken language and music respectively, are temporally linked, resulting in the richness of expressions. In the current study, in each trial participants were presented with sequences of two images containing facial expression in different combinations sampled out of the eight static basic expressions (total 64; 8X8). In each trial, using single word participants were required to judge the 'state of mind' portrayed by the person whose face was presented. Utilizing word embedding methods (Global Vectors for Word Representation), employed in the field of Natural Language Processing, and relying on machine learning computational methods, it was found that the perceived meanings of the sequences of facial expressions were a weighted average of the single expressions comprising them, resulting in 22 new emotional states, in addition to the eight, classic basic expressions. An interaction between the first and the second expression in each sequence indicated that every single facial expression modulated the effect of the other facial expression thus leading to a different interpretation ascribed to the sequence as a whole. These findings suggest that the vocabulary of emotional states conveyed by facial expressions is not restricted to the (small) number of discrete facial expressions. Rather, the vocabulary is rich, as it results from combinations of these expressions. In addition, present research suggests that using word embedding in social perception studies, can be a powerful, accurate and efficient tool, to capture explicit and implicit perceptions and intentions. Acknowledgment: The study was supported by a grant from the Ministry of Defense in Israel to GA and CS. CS is also supported by the ABC initiative in Ben-Gurion University of the Negev.

Keywords: Glove, face perception, facial expression perception. , facial expression production, machine learning, word embedding, word2vec

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Authors: Dipak Subedi, Anil Giri, Christine Whitt, Tia McDonald

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This study aims to understand the impact of the pandemic on the overall economic well-being of the agricultural sector of the United States. The two key metrics used to examine the economic well-being are the bankruptcy rate of the U.S. farm operations and the operating profit margin. One of the primary reasons for farm operations (in the U.S.) to file for bankruptcy is continuous negative profit or a significant decrease in profit. The pandemic caused significant supply and demand shocks in the domestic market. Furthermore, the ongoing trade disruptions, especially with China, also impacted the prices of agricultural commodities. The significantly reduced demand for ethanol and closure of meat processing plants affected both livestock and crop producers. This study uses data from courts to examine the bankruptcy rate over time of U.S. farm operations. Preliminary results suggest there wasn’t an increase in farm operations filing for bankruptcy in 2020. This was most likely because of record high Government payments to producers in 2020. The Federal Government made direct payments of more than $45 billion in 2020. One commonly used economic metric to measure farm profitability is the operating profit margin (OPM). Operating profit margin measures profitability as a share of the total value of production and government payments. The Economic Research Service of the United States Department of Agriculture defines a farm operation to be in a) a high-risk zone if the OPM is less than 10 percent and b) a low-risk zone if the OPM is higher than 25 percent. For this study, OPM was calculated for small, medium, and large-scale farm operations using the data from the Agriculture Resource Management Survey (OPM). Results show that except for small family farms, the share of farms in high-risk zone decreased in 2020 compared to the most recent non-pandemic year, 2019. This was most likely due to higher commodity prices at the end of 2020 and record-high government payments. Further investigation suggests a lower share of smaller farm operations receiving lower average government payments resulting in a large share (over 70 percent) being in the critical zone. This study should be of interest to multiple stakeholders, including policymakers across the globe, as it shows the resilience of the U.S. agricultural system as well as (some) impact of government payments.

Keywords: U.S. farm sector, COVID-19, operating profit margin, farm bankruptcy, ag finance, government payments to the farm sector

Procedia PDF Downloads 90

Authors: Yi Li, Rui Lu, Lianjun Wang

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With the rapid development of chemical industry, the consumption of volatile organic compounds (VOCs) has increased extensively. In the process of VOCs production and application, plenty of them have been transferred to environment. As a result, it has led to pollution problems not only in soil and ground water but also to human beings. Thus, it is important to develop a sensitive and cost-effective analytical method for trace VOCs detection in environment. Surface-enhanced Raman Spectroscopy (SERS), as one of the most sensitive optical analytical technique with rapid response, pinpoint accuracy and noninvasive detection, has been widely used for ultratrace analysis. Based on the plasmon resonance on the nanoscale metallic surface, SERS technology can even detect single molecule due to abundant nanogaps (i.e. 'hot spots') on the nanosubstrate. In this work, a self-supported flexible silver nitrate (AgNO3)/ethylene-propylene copolymer (EPM) hybrid nanofibers was fabricated by electrospinning. After an in-situ chemical reduction using ice-cold sodium borohydride as reduction agent, numerous silver nanoparticles were formed on the nanofiber surface. By adjusting the reduction time and AgNO3 content, the morphology and dimension of silver nanoparticles could be controlled. According to the principles of solid-phase extraction, the hydrophobic substance is more likely to partition into the hydrophobic EPM membrane in an aqueous environment while water and other polar components are excluded from the analytes. By the enrichment of EPM fibers, the number of hydrophobic molecules located on the 'hot spots' generated from criss-crossed nanofibers is greatly increased, which further enhances SERS signal intensity. The as-prepared Ag/EPM hybrid nanofibers were first employed to detect common SERS probe molecule (p-aminothiophenol) with the detection limit down to 10-12 M, which demonstrated an excellent SERS performance. To further study the application of the fabricated substrate for monitoring hydrophobic substance in water, several typical VOCs, such as benzene, toluene and p-xylene, were selected as model compounds. The results showed that the characteristic peaks of these target analytes in the mixed aqueous solution could be distinguished even at a concentration of 10-6 M after multi-peaks gaussian fitting process, including C-H bending (850 cm-1), C-C ring stretching (1581 cm-1, 1600 cm-1) of benzene, C-H bending (844 cm-1 ,1151 cm-1), C-C ring stretching (1001 cm-1), CH3 bending vibration (1377 cm-1) of toluene, C-H bending (829 cm-1), C-C stretching (1614 cm-1) of p-xylene. The SERS substrate has remarkable advantages which combine the enrichment capacity from EPM and the Raman enhancement of Ag nanoparticles. Meanwhile, the huge specific surface area resulted from electrospinning is benificial to increase the number of adsoption sites and promotes 'hot spots' formation. In summary, this work provides powerful potential in rapid, on-site and accurate detection of trace VOCs using a portable Raman.

Keywords: electrospinning, ethylene-propylene copolymer, silver nanoparticles, SERS, VOCs

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Authors: Maria Fonseca, Ana Branco, Joao Graca, Rui Mendes, Pedro Mimoso

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The present work is focused on the production of a carbon composite element for cycling through different techniques, namely, blow-molding and high-pressure resin transfer injection (HP-RTM). The main objective of this work is to compare both processes to produce carbon composite elements for the cycling industry. It is well known that the carbon composite components for cycling are produced mainly through blow-molding; however, this technique depends strongly on manual labour, resulting in a time-consuming production process. Comparatively, HP-RTM offers a more automated process which should lead to higher production rates. Nevertheless, a comparison of the elements produced through both techniques must be done, in order to assess if the final products comply with the required standards of the industry. The main difference between said techniques lies in the used material. Blow-moulding uses carbon prepreg (carbon fibres pre-impregnated with a resin system), and the material is laid up by hand, piece by piece, on a mould or on a hard male. After that, the material is cured at a high temperature. On the other hand, in the HP-RTM technique, dry carbon fibres are placed on a mould, and then resin is injected at high pressure. After some research regarding the best material systems (prepregs and braids) and suppliers, an element was designed (similar to a handlebar) to be constructed. The next step was to perform FEM simulations in order to determine what the best layup of the composite material was. The simulations were done for the prepreg material, and the obtained layup was transposed to the braids. The selected material was a prepreg with T700 carbon fibre (24K) and an epoxy resin system, for the blow-molding technique. For HP-RTM, carbon fibre elastic UD tubes and ± 45º braids were used, with both 3K and 6K filaments per tow, and the resin system was an epoxy as well. After the simulations for the prepreg material, the optimized layup was: [45°, -45°,45°, -45°,0°,0°]. For HP-RTM, the transposed layup was [ ± 45° (6k); 0° (6k); partial ± 45° (6k); partial ± 45° (6k); ± 45° (3k); ± 45° (3k)]. The mechanical tests showed that both elements can withstand the maximum load (in this case, 1000 N); however, the one produced through blow-molding can support higher loads (≈1300N against 1100N from HP-RTM). In what concerns to the fibre volume fraction (FVF), the HP-RTM element has a slightly higher value ( > 61% compared to 59% of the blow-molding technique). The optical microscopy has shown that both elements have a low void content. In conclusion, the elements produced using HP-RTM can compare to the ones produced through blow-molding, both in mechanical testing and in the visual aspect. Nevertheless, there is still space for improvement in the HP-RTM elements since the layup of the braids, and UD tubes could be optimized.

Keywords: HP-RTM, carbon composites, cycling, FEM

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Authors: Fikret Yalcinkaya, Hamza Unsal

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To understand how neurons work, it is required to combine experimental studies on neural science with numerical simulations of neuron models in a computer environment. In this regard, the simplicity and applicability of spiking neuron modeling functions have been of great interest in computational neuron science and numerical neuroscience in recent years. Spiking neuron models can be classified by exhibiting various neuronal behaviors, such as spiking and bursting. These classifications are important for researchers working on theoretical neuroscience. In this paper, three different spiking neuron models; Izhikevich, Adaptive Exponential Integrate Fire (AEIF) and Hindmarsh Rose (HR), which are based on first order differential equations, are discussed and compared. First, the physical meanings, derivatives, and differential equations of each model are provided and simulated in the Matlab environment. Then, by selecting appropriate parameters, the models were visually examined in the Matlab environment and it was aimed to demonstrate which model can simulate well-known biological neuron behaviours such as Tonic Spiking, Tonic Bursting, Mixed Mode Firing, Spike Frequency Adaptation, Resonator and Integrator. As a result, the Izhikevich model has been shown to perform Regular Spiking, Continuous Explosion, Intrinsically Bursting, Thalmo Cortical, Low-Threshold Spiking and Resonator. The Adaptive Exponential Integrate Fire model has been able to produce firing patterns such as Regular Ignition, Adaptive Ignition, Initially Explosive Ignition, Regular Explosive Ignition, Delayed Ignition, Delayed Regular Explosive Ignition, Temporary Ignition and Irregular Ignition. The Hindmarsh Rose model showed three different dynamic neuron behaviours; Spike, Burst and Chaotic. From these results, the Izhikevich cell model may be preferred due to its ability to reflect the true behavior of the nerve cell, the ability to produce different types of spikes, and the suitability for use in larger scale brain models. The most important reason for choosing the Adaptive Exponential Integrate Fire model is that it can create rich ignition patterns with fewer parameters. The chaotic behaviours of the Hindmarsh Rose neuron model, like some chaotic systems, is thought to be used in many scientific and engineering applications such as physics, secure communication and signal processing.

Keywords: Izhikevich, adaptive exponential integrate fire, Hindmarsh Rose, biological neuron behaviours, spiking neuron models

Procedia PDF Downloads 183

Authors: Yi-Tung Lin

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Researchers commonly assess implicit self-esteem with the Implicit Association Test (IAT). The IAT’s measure, often referred to as the IAT effect, indicates the strengths of automatic preferences for the self relative to others, which is often considered an index of implicit self-esteem. However, based on the Dual-process theory, the IAT does not rely entirely on the automatic process; it is also influenced by a controlled process. The present study, therefore, analyzed the IAT data with the Quad model, separating four processes on the IAT performance: the likelihood that automatic association is activated by the stimulus in the trial (AC); that a correct response is discriminated in the trial (D); that the automatic bias is overcome in favor of a deliberate response (OB); and that when the association is not activated, and the individual fails to discriminate a correct answer, there is a guessing or response bias drives the response (G). The AC and G processes are automatic, while the D and OB processes are controlled. The AC parameter is considered as the strength of the association activated by the stimulus, which reflects what implicit measures of social cognition aim to assess. The stronger the automatic association between self and positive valence, the more likely it will be activated by a relevant stimulus. Therefore, the AC parameter was used as the index of implicit self-esteem in the present study. Meanwhile, the relationship between implicit self-esteem and depression is not fully investigated. In the cognitive theory of depression, it is assumed that the negative self-schema is crucial in depression. Based on this point of view, implicit self-esteem would be negatively associated with depression. However, the results among empirical studies are inconsistent. The aims of the present study were to examine the psychometric properties of the AC (i.e., test-retest reliability and its correlations with explicit self-esteem and depression) and compare it with that of the IAT effect. The present study had 105 patients with depressive disorders completing the Rosenberg Self-Esteem Scale, Beck Depression Inventory-II and the IAT on the pretest. After at least 3 weeks, the participants completed the second IAT. The data were analyzed by the latent-trait multinomial processing tree model (latent-trait MPT) with the TreeBUGS package in R. The result showed that the latent-trait MPT had a satisfactory model fit. The effect size of test-retest reliability of the AC and the IAT effect were medium (r = .43, p < .0001) and small (r = .29, p < .01) respectively. Only the AC showed a significant correlation with explicit self-esteem (r = .19, p < .05). Neither of the two indexes was correlated with depression. Collectively, the AC parameter was a satisfactory index of implicit self-esteem compared with the IAT effect. Also, the present study supported the results that implicit self-esteem was not correlated with depression.

Keywords: cognitive modeling, implicit association test, implicit self-esteem, quad model

Procedia PDF Downloads 128

Authors: Imran Aziz Tunio

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Islamkot is an underdeveloped sub-district (Taluka) in the Tharparkar district Sindh province of Pakistan located between latitude 24°25'19.79"N to 24°47'59.92"N and longitude 70° 1'13.95"E to 70°32'15.11"E. The Islamkot has an arid desert climate and the region is generally devoid of perennial rivers, canals, and streams. It is highly dependent on rainfall which is not considered a reliable surface water source and groundwater is the only key source of water for many centuries. To assess groundwater’s potential, an electrical resistivity survey (ERS) was conducted in Islamkot Taluka. Groundwater investigations for 128 Vertical Electrical Sounding (VES) were collected to determine the groundwater potential and obtain qualitatively and quantitatively layered resistivity parameters. The PASI Model 16 GL-N Resistivity Meter was used by employing a Schlumberger electrode configuration, with half current electrode spacing (AB/2) ranging from 1.5 to 100 m and the potential electrode spacing (MN/2) from 0.5 to 10 m. The data was acquired with a maximum current electrode spacing of 200 m. The data processing for the delineation of dune sand aquifers involved the technique of data inversion, and the interpretation of the inversion results was aided by the use of forward modeling. The measured geo-electrical parameters were examined by Interpex IX1D software, and apparent resistivity curves and synthetic model layered parameters were mapped in the ArcGIS environment using the inverse Distance Weighting (IDW) interpolation technique. Qualitative interpretation of vertical electrical sounding (VES) data shows the number of geo-electrical layers in the area varies from three to four with different resistivity values detected. Out of 128 VES model curves, 42 nos. are 3 layered, and 86 nos. are 4 layered. The resistivity of the first subsurface layers (Loose surface sand) varied from 16.13 Ωm to 3353.3 Ωm and thickness varied from 0.046 m to 17.52m. The resistivity of the second subsurface layer (Semi-consolidated sand) varied from 1.10 Ωm to 7442.8 Ωm and thickness varied from 0.30 m to 56.27 m. The resistivity of the third subsurface layer (Consolidated sand) varied from 0.00001 Ωm to 3190.8 Ωm and thickness varied from 3.26 m to 86.66 m. The resistivity of the fourth subsurface layer (Silt and Clay) varied from 0.0013 Ωm to 16264 Ωm and thickness varied from 13.50 m to 87.68 m. The Dar Zarrouk parameters, i.e. longitudinal unit conductance S is from 0.00024 to 19.91 mho; transverse unit resistance T from 7.34 to 40080.63 Ωm2; longitudinal resistance RS is from 1.22 to 3137.10 Ωm and transverse resistivity RT from 5.84 to 3138.54 Ωm. ERS data and Dar Zarrouk parameters were mapped which revealed that the study area has groundwater potential in the subsurface.

Keywords: electrical resistivity survey, GIS & RS, groundwater potential, environmental assessment, VES

Procedia PDF Downloads 110

Authors: Devon Simpson

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Recycled Manure Solids (RMS), attained via centrifugation from Canadian dairy farms, were synthesized into a non-polluting fertilizer by bonding micronutrients (Fe, Zn, and Mn) to cellulose fibers and then assessed for the effectiveness of nutrient recovery in tomatoes. Manure management technology is critical for improving the sustainability of agroecosystems and has the capacity to offer a truly circular economy. The ability to add value to manure byproducts offers an opportunity for economic benefits while generating tenable solutions to livestock waste. The dairy industry is under increasing pressure from new environmental protections such as government restrictions on manure applications, limitations on herd size as well as increased product demand from a growing population. Current systems use RMS as bedding, so there is a lack of data pertaining to RMS use as a fertilizer. This is because of nutrient distribution, where most nutrients are retained in the liquid effluent of the solid-liquid separation. A literature review on the physical and chemical properties of dairy manure further revealed more data for raw manure than centrifuged solids. This research offers an innovative perspective and a new avenue of exploration in the use of RMS. Manure solids in this study were obtained directly from dairy farms in Salmon Arm and Abbotsford, British Columbia, and underwent physical, chemical, and biological characterizations pre- and post-synthesis processing. Samples were sent to A&L labs Canada for analysis. Once characterized and bonded to micronutrients, the effect of synthesized RMS on nutrient recovery in tomatoes was studied in a greenhouse environment. The agricultural research package ‘agricolae’ for R was used for experimental design and data analysis. The growth trials consisted of a randomized complete block design (RCBD) that allowed for analysis of variance (ANOVA). The primary outcome was to measure nutrient uptake, and this was done using an Inductively Coupled Plasma Mass Spectrometer (IC-PMS) to analyze the micronutrient content of both the tissue and fruit of the tomatoes. It was found that treatments containing bonded dairy manure solids had an increased micronutrient concentration. Treatments with bonded dairy manure solids also saw an increase in yield, and a brix analysis showed higher sugar content than the untreated control and a grower standard.

Keywords: aoecosystems, dairy manure, micronutrient fertilizer, manure management, nutrient recovery, nutrient recycling, recycled manure solids, regenerative agricugrlture, sustainable farming

Procedia PDF Downloads 194

Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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Diabetic nephropathy (DN) is a major complication of diabetes, with fibrosis and propionate metabolism playing critical roles in its progression. Identifying biomarkers linked to these pathways may provide novel insights into DN diagnosis and treatment. This study aims to identify biomarkers associated with fibrosis and propionate metabolism in DN. Analyze the biological pathways and regulatory mechanisms of these biomarkers. Develop a machine learning model to predict DN-related biomarkers and validate their functional roles. Publicly available transcriptome datasets related to DN (GSE96804 and GSE104948) were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/gds), and 924 propionate metabolism-related genes (PMRGs) and 656 fibrosis-related genes (FRGs) were identified. The analysis began with the extraction of DN-differentially expressed genes (DN-DEGs) and propionate metabolism-related DEGs (PM-DEGs), followed by the intersection of these with fibrosis-related genes to identify key intersected genes. Instead of relying on traditional models, we employed a combination of deep neural networks (DNNs) and ensemble methods such as Gradient Boosting Machines (GBM) and XGBoost to enhance feature selection and biomarker discovery. Recursive feature elimination (RFE) was coupled with these advanced algorithms to refine the selection of the most critical biomarkers. Functional validation was conducted using convolutional neural networks (CNN) for gene set enrichment and immunoinfiltration analysis, revealing seven significant biomarkers—SLC37A4, ACOX2, GPD1, ACE2, SLC9A3, AGT, and PLG. These biomarkers are involved in critical biological processes such as fatty acid metabolism and glomerular development, providing a mechanistic link to DN progression. Furthermore, a TF–miRNA–mRNA regulatory network was constructed using natural language processing models to identify 8 transcription factors and 60 miRNAs that regulate these biomarkers, while a drug–gene interaction network revealed potential therapeutic targets such as UROKINASE–PLG and ATENOLOL–AGT. This integrative approach, leveraging deep learning and ensemble models, not only enhances the accuracy of biomarker discovery but also offers new perspectives on DN diagnosis and treatment, specifically targeting fibrosis and propionate metabolism pathways.

Keywords: diabetic nephropathy, deep neural networks, gradient boosting machines (GBM), XGBoost

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Authors: Ahmad Abu-Akel, Emilie De Montpellier, Sophie Von Bentivegni, Lyn Luechinger, Alessandro Ishii, Christine Mohr

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Convergent and divergent thinking are two main components of creativity that have been viewed as complementary. While divergent thinking refers to the fluency and flexibility of generating new ideas, convergent thinking refers to the ability to systematically apply rules and knowledge to arrive at the optimal solution or idea. These creativity components have been shown to be susceptible to variation in subclinical expressions of autistic and schizotypal traits within the general population. Research, albeit inconclusively, mainly linked positive schizotypal traits with divergent thinking and autistic traits with convergent thinking. However, cumulative evidence suggests that these trait dimensions can co-occur in the same individual more than would be expected by chance and that their concurrent effect can be diametric and even interactive. The current study aimed at investigating the concurrent effect of these trait dimensions on tasks assessing convergent and divergent thinking abilities. We predicted that individuals with high positive schizotypal traits alone would perform particularly well on the divergent thinking task, whilst those with high autistic traits alone would perform particularly well on the convergent thinking task. Crucially, we also predicted that individuals who are high on both autistic and positive schizotypal traits would perform particularly well on both the divergent and convergent thinking tasks. This was investigated in a non-clinical sample of 142 individuals (Males = 45%; Mean age = 21.45, SD = 2.30), sufficient to minimally observe an effect size f² ≥ .10. Divergent thinking was evaluated using the Alternative Uses Task, and convergent thinking with the Anagrams Task. Autistic and schizotypal traits were respectively assessed with the Autism Quotient Questionnaire (AQ) and the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE). Regression analyses revealed that the positive association of autistic traits with convergent thinking scores was qualified with an interaction with positive schizotypal traits. Specifically, positive schizotypal traits were negatively associated with convergent thinking scores when AQ scores were relatively low, but this trend was reversed when AQ scores were high. Conversely, the positive effect of AQ scores on convergent thinking progressively increased with increasing positive schizotypal traits. The results of divergent thinking task are currently being analyzed and will be reported at the conference. The association of elevated autistic and positive schizotypal traits with convergent thinking may represent a unique profile of creative thinkers who are able to simultaneously draw on trait-specific advantages conferred by autistic and positively schizotypal traits such as local and global processing. This suggests that main-effect models can tell an incomplete story regarding the effect of autistic and positive schizotypal traits on creativity-related processes. Future creativity research should consider their interaction and the benefits conferred by their co-presence.

Keywords: autism, schizotypy, convergent thinking, divergent thinking, comorbidity

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Authors: Yaser S. Kishawi, Sadi R. Ali

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Part of Palestine, Gaza Strip (365 km2 and 1.8 million inhabitants) is considered a semi-arid zone relies solely on the Coastal Aquifer. The coastal aquifer is only source of water with only 5-10% suitable for human use. This barely cover the domestic and agricultural needs of Gaza Strip. Palestinian Water Authority Strategy is finding non-conventional water resource from treated wastewater to cover agricultural requirements and serve the population. A new WWTP project is to replace the old-overloaded Biet Lahia WWTP. The project consists of three parts; phase A (pressure line & infiltration basins - IBs), phase B (a new WWTP) and phase C (Recovery and Reuse Scheme – RRS – to capture the spreading plume). Currently, only phase A is functioning. Nearly 23 Mm3 of partially treated wastewater were infiltrated into the aquifer. Phase B and phase C witnessed many delays and this forced a reassessment of the RRS original design. An Environmental Management Plan was conducted from Jul 2013 to Jun 2014 on 13 existing monitoring wells surrounding the project location. This is to measure the efficiency of the SAT system and the spread of the contamination plume with relation to the efficiency of the proposed RRS. Along with the proposed location of the 27 recovery wells as part of the proposed RRS. The results of monitored wells were assessed compared with PWA baseline data. This was put into a groundwater model to simulate the plume to propose the best suitable solution to the delays. The redesign mainly manipulated the pumping rate of wells, proposed locations and functioning schedules (including wells groupings). The proposed simulations were examined using visual MODFLOW V4.2 to simulate the results. The results of monitored wells were assessed based on the location of the monitoring wells related to the proposed recovery wells locations (200m, 500m and 750m away from the IBs). Near the 500m line (the first row of proposed recovery wells), an increase of nitrate (from 30 to 70mg/L) compare to a decrease in Chloride (1500 to below 900mg/L) was found during the monitoring period which indicated an expansion of plume to this distance. On this rate with the required time to construct the recovery scheme, keeping the original design the RRS will fail to capture the plume. Based on that many simulations were conducted leading into three main scenarios. The scenarios manipulated the starting dates, the pumping rate and the locations of recovery wells. A simulation of plume expansion and path-lines were extracted from the model monitoring how to prevent the expansion towards the nearby municipal wells. It was concluded that the location is the most important factor in determining the RRS efficiency. Scenario III was adopted and showed an effective results even with a reduced pumping rates. This scenario proposed adding two additional recovery wells in a location beyond the 750m line to compensate the delays and effectively capture the plume. A continuous monitoring program for current and future monitoring wells should be in place to support the proposed scenario and ensure maximum protection.

Keywords: soil aquifer treatment, recovery and reuse scheme, infiltration basins, north gaza

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Authors: Nawras Kurzom, Avi Mendelsohn

Abstract:

The effects of background music on learning and memory are inconsistent, partly due to the intrinsic complexity and variety of music and partly to individual differences in music perception and preference. A prominent musical feature that is known to elicit strong emotional responses is musical tension. Musical tension can be brought about by building anticipation of rhythm, harmony, melody, and dynamics. Delaying the resolution of dominant-to-tonic chord progressions, as well as using dissonant harmonics, can elicit feelings of tension, which can, in turn, affect memory formation of concomitant information. The aim of the presented studies was to explore how forming declarative memory is influenced by musical tension, brought about within continuous music as well as in the form of isolated chords with varying degrees of dissonance/consonance. The effects of musical tension on long-term memory of declarative information were studied in two ways: 1) by evoking tension within continuous music pieces by delaying the release of harmonic progressions from dominant to tonic chords, and 2) by using isolated single complex chords with various degrees of dissonance/roughness. Musical tension was validated through subjective reports of tension, as well as physiological measurements of skin conductance response (SCR) and pupil dilation responses to the chords. In addition, music information retrieval (MIR) was used to quantify musical properties associated with tension and its release. Each experiment included an encoding phase, wherein individuals studied stimuli (words or images) with different musical conditions. Memory for the studied stimuli was tested 24 hours later via recognition tasks. In three separate experiments, we found positive relationships between tension perception and physiological measurements of SCR and pupil dilation. As for memory performance, we found that background music, in general, led to superior memory performance as compared to silence. We detected a trade-off effect between tension perception and memory, such that individuals who perceived musical tension as such displayed reduced memory performance for images encoded during musical tension, whereas tense music benefited memory for those who were less sensitive to the perception of musical tension. Musical tension exerts complex interactions with perception, emotional responses, and cognitive performance on individuals with and without musical training. Delineating the conditions and mechanisms that underlie the interactions between musical tension and memory can benefit our understanding of musical perception at large and the diverse effects that music has on ongoing processing of declarative information.

Keywords: musical tension, declarative memory, learning and memory, musical perception

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Authors: Mohammad Abu-Shaira, Weishi Shi

Abstract:

Adaptive learning, a commonly employed solution to drift, involves updating predictive models online during their operation to react to concept drifts, thereby serving as a critical component and natural extension for online learning systems that learn incrementally from each example. This paper introduces LSTM-SCCM “Long Short-Term Memory Stream Cruise Control Method”, a drift adaptation-as-a-service framework for online learning. LSTM-SCCM automates drift adaptation through prompt detection, drift magnitude quantification, dynamic hyperparameter tuning, performing shortterm optimization and model recalibration for immediate adjustments, and, when necessary, conducting long-term model recalibration to ensure deeper enhancements in model performance. LSTM-SCCM is incorporated into a suite of cutting-edge online regression models, assessing their performance across various types of concept drift using diverse datasets with varying characteristics. The findings demonstrate that LSTM-SCCM represents a notable advancement in both model performance and efficacy in handling concept drift occurrences. LSTM-SCCM stands out as the sole framework adept at effectively tackling concept drifts within regression scenarios. Its proactive approach to drift adaptation distinguishes it from conventional reactive methods, which typically rely on retraining after significant degradation to model performance caused by drifts. Additionally, LSTM-SCCM employs an in-memory approach combined with the Self-Adjusting Memory (SAM) architecture to enhance real-time processing and adaptability. The framework incorporates variable thresholding techniques and does not assume any particular data distribution, making it an ideal choice for managing high-dimensional datasets and efficiently handling large-scale data. Our experiments, which include abrupt, incremental, and gradual drifts across both low- and high-dimensional datasets with varying noise levels, and applied to four state-of-the-art online regression models, demonstrate that LSTM-SCCM is versatile and effective, rendering it a valuable solution for online regression models to address concept drift.

Keywords: automated drift detection and adaptation, concept drift, hyperparameters optimization, online and adaptive learning, regression

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Authors: Justo Rodriguez, Daming Chen, Amador M. Guzman

Abstract:

The detection, treatment, and control of rapidly propagating, deadly viruses such as COVID-19, require the development of inexpensive, fast, and accurate devices to address the urgent needs of the population. Microfluidics-based sensors are amongst the different methods and techniques for detection that are easy to use. A micro analyzer is defined as a microfluidics-based sensor, composed of a network of microchannels with varying functions. Given their size, portability, and accuracy, they are proving to be more effective and convenient than other solutions. A micro analyzer based on the concept of “Lab on a Chip” presents advantages concerning other non-micro devices due to its smaller size, and it is having a better ratio between useful area and volume. The integration of multiple processes in a single microdevice reduces both the number of necessary samples and the analysis time, leading the next generation of analyzers for the health-sciences. In some applications, the flow of solution within the microchannels is originated by a pressure gradient, which can produce adverse effects on biological samples. A more efficient and less dangerous way of controlling the flow in a microchannel-based analyzer is applying an electric field to induce the fluid motion and either enhance or suppress the mixing process. Electrokinetic flows are characterized by no less than two non-dimensional parameters: the electric Rayleigh number and its geometrical aspect ratio. In this research, stable and unstable flows have been studied numerically (and when possible, will be experimental) in a T-shaped microchannel. Additionally, unstable electrokinetic flows for Rayleigh numbers higher than critical have been characterized. The flow mixing enhancement was quantified in relation to the stretching and folding that fluid particles undergo when they are subjected to supercritical electrokinetic flows. Computational simulations were carried out using a finite element-based program while working with the flow mixing concepts developed by Gollub and collaborators. Hundreds of seeded massless particles were tracked along the microchannel from the entrance to exit for both stable and unstable flows. After post-processing, their trajectories, the folding and stretching values for the different flows were found. Numerical results show that for supercritical electrokinetic flows, the enhancement effects of the folding and stretching processes become more apparent. Consequently, there is an improvement in the mixing process, ultimately leading to a more homogenous mixture.

Keywords: microchannel, stretching and folding, electro kinetic flow mixing, micro-analyzer

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