Search results for: deep ecology

Authors: Hyeongbok Kim, Lingling Zhao, Xiaohong Su

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Deep reinforcement learning has been applied to address various problems in robotics, such as autonomous driving and unmanned aerial vehicle. However, because of the sparse reward penalty for a collision with obstacles during the navigation mission, the agent fails to learn the optimal policy or requires a long time for convergence. Therefore, using obstacles and enemy agents, in this paper, we present a curriculum-based boost learning method to effectively train compound skills during multi-agent reinforcement learning. First, to enable the agents to solve challenging tasks, we gradually increased learning difficulties by adjusting reward shaping instead of constructing different learning environments. Then, in a benchmark environment with static obstacles and moving enemy agents, the experimental results showed that the proposed curriculum learning strategy enhanced cooperative navigation and compound collision avoidance skills in uncertain environments while improving learning efficiency.

Keywords: curriculum learning, hard exploration, multi-agent reinforcement learning, robotic navigation, sparse reward

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Authors: Thanakrit Taweesoontorn, Sarucha Yanyong, Poom Konghuayrob

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Robotic arms have gained popularity in various industries due to their accuracy and efficiency. This research proposes a method for bin-picking tasks using the Cobot, combining the YOLOv5 CNNs model for object detection and pose estimation with traditional feature detection (FAST), feature description (BRISK), and matching algorithms. By integrating these algorithms and utilizing a small-scale depth sensor camera for capturing depth and color images, the system achieves real-time object detection and accurate pose estimation, enabling the robotic arm to pick objects correctly in both position and orientation. Furthermore, the proposed method is implemented within the ROS framework to provide a seamless platform for robotic control and integration. This integration of robotics, cameras, and AI technology contributes to the development of industrial robotics, opening up new possibilities for automating challenging tasks and improving overall operational efficiency.

Keywords: robotic vision, image processing, applications of robotics, artificial intelligent

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Authors: Ilan Tamir

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Fans’ chants in sports stadium have, over the years, become an integral part of the spectator experience. While chants add color, atmosphere, and a demonstration of fans’ support for their team, chants also play a significant role in defining fans’ perceptions of their team’s identity and its differentiation from other teams. An analysis of football chants may therefore shed light on fans’ deep-seated worldviews of their own role, their team, the sport in general, and even life itself. This study, based on an analysis of Israeli football chants over years, identifies key changing and stable perceptions of football fans. Overall 94 chants collected, over a period of five decades. After a pilot study, the chants organized in two groups (one covering 1970-1999 and the other 2000-2016). The chants analyzed through qualitative content analysis in order to understand fans values as a reflection of the society. Findings point to several values that have remained stable over years, including fans’ attitudes toward their team and its rivals, and their attitude toward God. On the other hand, recently emerging phenomena such as radicalization of hatred toward the commercialization of sport reflect social and cultural changes, both in and outside the world of sport.

Keywords: sport, fans, chants, soccer

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Authors: L. Kutateldze, T. Urushadze, R. Khvedelidze, N. Zakariashvili, I. Khokhashvili, T. Sadunishvili

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Microbial cellulase/xylanase have shown their potential application in various industries including pulp and paper, textile, laundry, biofuel production, food and feed industry, brewing, and agriculture. Extremophilic micromycetes and their enzymes that are resistant to critical values of temperature and pH, and retaining enzyme activity for a long time are of great industrial interest. Among strains of microscopic fungi from the collection of S. Durmishidze Institute of Biochemistry and Biotechnology, strains isolated from different ecological niches of Southern Caucasus-active producers of cellulase/xylanase have been selected by means of screening under deep cultivation conditions. Extremophilic micromycetes and their enzymes that are resistant to critical values of temperature and pH, and retaining enzyme activity for a long time are of great industrial interest. Among strains of microscopic fungi from the collection of S. Durmishidze Institute of Biochemistry and Biotechnology, strains isolated from different ecological niches of Southern Caucasus-active producers of cellulase/xylanase have been selected by means of screening under deep cultivation conditions. Representatives of the genera Aspergillus, Penicillium and Trichoderma are outstanding by relatively high activities of these enzymes. Among the producers were revealed thermophilic strains, representatives of the genus Aspergillus-Aspergillus terreus, Aspergillus versicolor, Aspergillus wentii, also strains of Sporotrichum pulverulentum and Chaetomium thermophile. As a result of optimization of cultivation media and conditions, activities of enzymes produced by the strains have been increased by 4 -189 %. Two strains, active producers of cellulase/xylanase – Penicillium canescence E2 (mesophile) and Aspergillus versicolor Z17 (thermophile) were chosen for further studies. Cellulase/xylanase enzyme preparations from two different genera of microscopic fungi Penicillium canescence E2 and Aspergillus versicolor Z 17 were obtained with activities 220 U/g /1200 U/g and 125 U/g /940 U/g, correspondingly. Main technical characteristics were as follows: the highest enzyme activities were obtained for mesophilic strain Penicillium canescence E2 at 45-500C, while almost the same enzyme activities were fixed for the thermophilic strain Aspergillus versicolor Z 17 at temperature 60-65°C, exceeding the temperature optimum of the mesophile by 150C. Optimum pH of action of the studied cellulase/xylanases from mesophileic and thermophilic strains were similar and equaled to 4.5-5.0 It has been shown that cellulase/xylanase technical preparations from selected strains of Penicillium canescence E2 and Aspergillus versicolor Z17 hydrolyzed cellulose of untreated wheat straw to reducible sugars by 46-52%, and to glucose by 22-27%. However the thermophilic enzyme preparations from the thermophilic A.versicolor strains conducted the process at 600C higher by 100C as compared to mesophlic analogue. Rate of hydrolyses of the pretreated substrate by the same enzyme preparations to reducible sugars and glucose conducted at optimum for their action 60 and 500C was 52-61% and 29-33%, correspondingly. Thus, maximum yield of glucose and reducible sugars form untreated and pretreated wheat straw was achieved at higher temperature (600C) by enzyme preparations from thermophilic strain, which gives advantage for their industrial application.

Keywords: cellulase/xylanase, cellulose hydrolysis, microscopic fungi, thermophilic strain

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Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

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Authors: Silke Bustamante, Andrea Pelzeter, Andreas Deckmann, Rudi Ehlscheidt, Franziska Freudenberger

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Against the background of demographic changes and the need for motivated and talented employees, companies have a growing interest in being perceived as an attractive employer and gain a competitive edge in the market for talents. Research indicates that corporate social responsibility plays an important role employer choice. This is assumed to be particularly true for the cohorts of the so-called generations y younger applicants born later. Several studies identified cultural, educational and generational differences in CSR preferences. However, until now literature that links preferences for employers’ responsibility with the individual value orientation of the job applicant is scarce. This paper seeks to close this gap and analyzes the preference structure of 500 German university graduates for CSR-related and non-CSR-related employer attributes contingent on their value orientation. In a first step, we identified 17 CSR related attributes in five CSR dimensions (ecology, community, workplace, governance, and market) as well as 11 traditional attributes such as salary, reputation, personal development etc. based on literature review and focus groups. We then used an adapted Conjoint Analysis framework in order to understand the relative preference of each attribute for each participant. Additionally, we surveyed the value orientation of participants based on the European Social Survey (ESS) 21-item human values scale that allow a differentiation of participants into clusters of value orientation. The results allow us not only to operationalize preferences for CSR and other important employer attributes, but – more importantly – to answer the question how different values structures play together with CSR preferences of potential employees. They hereby allow companies to customize employee directed messages to their respective target group and design their employer brand accordingly.

Keywords: corporate social responsibility, employer attractiveness, employer brand, recruiting, values

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Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Patricia P. M. C. Lourenco, Javier Bringué Sala, Anaisa D. A. de Sena

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Almost everyone in Dramaland knows the names of big Korean stars that grace their computer screens on a roll through social media and video streaming platforms that enable awareness of Korean dramas and lifestyle at a click. A surface culture instilled with notions of belonging has redefined the meaning of friendship and challenged deep inner values. Not everyone, however, knows Chinese Dramas or their stars, which is a consequence of Dramaland's focus on Korean dramas and promoting the Korean experience. Despite a parity in terms of production quality, star power, scripts and compelling visual settings, Chinese Dramas have been playing catch up to their famous counterparts. While they might have a strong competitive soft power for international drama fans, the soft power of Korean dramas is imbued with substantial societal values that they want to share with others. Those values are portrayed in an artistic way that connects with audiences who experience loneliness in the non-virtual world contrary to the way Chinese Dramas are perceived.

Keywords: Chinese dramas, fandom, Korean dramas, participatory culture, value perception, soft power, surface culture

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Authors: Ma Lei-Lei, Zhou You

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Aiming at the problems of low detection accuracy of deep learning algorithm in traffic sign detection, this paper proposes improved EfficientDet based traffic sign detection algorithm. Multi-head self-attention is introduced in the minimum resolution layer of the backbone of EfficientDet to achieve effective aggregation of local and global depth information, and this study proposes an improved feature fusion pyramid with increased vertical cross-layer connections, which improves the performance of the model while introducing a small amount of complexity, the Balanced L1 Loss is introduced to replace the original regression loss function Smooth L1 Loss, which solves the problem of balance in the loss function. Experimental results show, the algorithm proposed in this study is suitable for the task of traffic sign detection. Compared with other models, the improved EfficientDet has the best detection accuracy. Although the test speed is not completely dominant, it still meets the real-time requirement.

Keywords: convolutional neural network, transformer, feature pyramid networks, loss function

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Authors: Cheng Fang Wang, Fu Li Gao, Jian Ying Zhou

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The '1+4' integration pilot area in the border area of Guangzhou-Foshan is an important platform for Guangzhou-Foshan strategic cooperation, as well as a typical semi-urbanized area with mixed urban and rural landscapes, of which the Baiyun-Nanhai pilot area is one of them. Baiyun district and Nanhai district are only separated by the Pearl River. In this paper, the three dimensions, which include production, living, and ecology, have been put forward, as well as cross-regional multi-agency negotiation mechanism has been discussed. Taking 'Baiyun-Nanhai' pilot area as a case study, POI (Point of Interest) data to analyze the distribution characteristics of 'production-living-ecological space' from the spatial dimension has been introduced in this paper, as well as the land-use change of 'production-living-ecological space' in western region of Baiyun district in 2007 and 2017 from the temporal dimension has been analyzed. Based on the above analysis, the integration development strategy and rethinking of cross-administrative region based on 'production-living-ecological integration' mechanism have been discussed later. It will explore the mechanism of industrial collaborative innovation, infrastructure co-construction, and ecological co-protection in semi-urban areas across borders. And it is expected to provide a reference for the integrated construction of the Guangdong-Hong Kong-Macao Greater Bay Area.

Keywords: semi-urbanization, production-living-ecological integration, multi-agency negotiation, Guangzhou-Foshan integration, synergetic development

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Authors: Ghusen al-Kafri, Mohammed Ali Abdallah Elsageer, Ahmed Mohamed Hadya Alsdaai, Abdeimanam Salhien Salih Khalifa

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In this paper, external reinforcement to enhance a reinforced concrete structure performance has been done using externally bonded steel plate. This technique has been reported effective in enhancing the strength of reinforced concrete beam, a study to determine the effectiveness of steel plate as an external reinforcement was carried out. A total of two groups of beams and one group content five beams, each 750 mm long, 150 mm wide, and 150 mm deep were cast, strengthened and tested till failure under two point loads. One beam was act as a control beam without strengthening and other four beams were strengthened with steel plate at a different arrangement. Other group beams were strengthened with steel plate in shear zone and also strengthened at bottom as first group. The behaviours of the strengthened beams were studied through their load-deflection characteristic upon bending, cracking and mode of failure. The results confirmed that all steel plate arrangements enhanced the strength of the reinforced concrete beam, the positioning of the steel plate affect the moment carrying capacity of the beam.

Keywords: beams, bending, beflection, steel plates

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Authors: M. Khajezadeh, M. Saied Fallah Niasar, S. Ali Asli, D. Davani Davari, M. Godarzi, Y. Asgari

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This study aims to explore the joint application of both Boston and Ansoff matrices in the operational development of the product. We conduct deep analysis, by utilizing the Artificial Neural Network, to predict the position of the product in the market while the company is interested in increasing its share. The data are gathered from two industries, called hygiene and detergent. In doing so, the effort is being made by investigating the behavior of top player companies and, recommend strategic orientations. In conclusion, this combination analysis is appropriate for operational development; as well, it plays an important role in providing the position of the product in the market for both hygiene and detergent industries. More importantly, it will elaborate on the company’s strategies to increase its market share related to a combination of the Boston Consulting Group (BCG) Matrix and Ansoff Matrix.

Keywords: artificial neural network, portfolio analysis, BCG matrix, Ansoff matrix

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Authors: Tegan H. Emerson, Colin C. Olson, George Stantchev, Jason A. Edelberg, Michael Wilson

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Detecting small targets at range is difficult because there is not enough spatial information present in an image sub-region containing the target to use correlation-based methods to differentiate it from dynamic confusers present in the scene. Moreover, this lack of spatial information also disqualifies the use of most state-of-the-art deep learning image-based classifiers. Here, we use characteristics of target tracks extracted from video sequences as data from which to derive distinguishing topological features that help robustly differentiate targets of interest from confusers. In particular, we calculate persistent homology from time-delayed embeddings of dynamic statistics calculated from motion tracks extracted from a wide field-of-view video stream. In short, we use topological methods to extract features related to target motion dynamics that are useful for classification and disambiguation and show that small targets can be detected at range with high probability.

Keywords: motion tracks, persistence images, time-delay embedding, topological data analysis

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Authors: Alexander Hudson, Shaogang Gong

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Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate (≥80%) predictions of protein class and architecture from structures determined at low (>3A) resolution, using a deep convolutional neural network trained on high-resolution (≤3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution.

Keywords: transfer learning, protein distance maps, protein structure classification, neural networks

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Authors: Clarissa Delariarte

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With the aim of helping the poorest of the poor achieve quality education, Psychology students supported and served as teacher assistants to its Early Childhood Education Center in two barangays since the program began in 1999. Making use of qualitative approach, the impact of the program to 29 alumni who served as teacher assistants between 2000-2014 was assessed. Results show that the impact to the alumni is in cognitive as well as social-emotional in terms of feelings of deep satisfaction and sense of volunteerism which is being carried out in their respective workspaces. They also expressed positive feelings of inspiration, gratefulness and happiness. A wider perspective in life, being confident, creative and resourceful was also articulated as concrete impacts. It is concluded that the program had an impact on helping the helper and is a concrete manifestation of the academe being successful in its commitment of forming individuals into becoming integrated and compassionate in the service of the Church and Society. It implies that more opportunities of helping others be provided to students since, in the final analysis, is actually an opportunity of helping the helper be of better service to others.

Keywords: applied psychology, life skill, qualitative research, quality education

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Authors: Stamataki Erofili Nellie, Benardos Andreas

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Winemaking and aging in Greece has been performed so far in special facilities, designed either as above ground or shallow underground buildings. The latter are well-known in Santorini as “canaves,” dating back to the 1700s. Canaves were mainly used for wine storage and aging, although occasionally, they included a winepress to complete there the whole wine production. On the other hand, wine caves are subterranean caves of the same use as canaves in the wine manufacturing industry, but they are excavated at a much greater depth of more than 53 meters or 175 feet. Whereas canaves or a typical wine cellar is around 10 feet deep, with is equivalent to almost 3 meters. This paper discusses the advantages and the disadvantages of creating a wine cave for the vinification of a winery in Greece and the financial investment or risk that has to be taken. The data presented and analysed are given from wineries in Greece and especially from those located in Santorini island. The estimation of the cost for the excavation of the model selected as a wine cave will be compared with the financial budget of the existing premises and facilities above ground in Greek wineries. In order to show whether it is viable for a greek winery to invest in a wine cave.

Keywords: underground space use, subterranean winery, wine cave, underground winery, greece

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Authors: Arun Sanjel, Greg Speegle

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Every second, massive amounts of data are generated, and Data Intensive Scalable Computing (DISC) frameworks have evolved into effective tools for analyzing such massive amounts of data. Since the underlying architecture of these distributed computing platforms is often new to users, building a DISC application can often be time-consuming and prone to errors. The automated conversion of a sequential program to a DISC program will consequently significantly improve productivity. However, synthesizing a user’s intended program from an input specification is complex, with several important applications, such as distributed program synthesizing and code refactoring. Existing works such as Tyro and Casper rely entirely on deductive synthesis techniques or similar program synthesis approaches. Our approach is to develop a data-driven synthesis technique to identify sequential components and translate them to equivalent distributed operations. We emphasize using reinforcement learning and unit testing as feedback mechanisms to achieve our objectives.

Keywords: program synthesis, distributed computing, reinforcement learning, unit testing, DISC

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Authors: Rudi Kurniawan Arief

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Manufacturing cost and setup time are the hot topics to improve in Metal Stamping industry because material and components price are always rising up while costumer requires to cut down the component price year by year. The Single Minute Exchange of Die (SMED) is one of many methods to reduce waste in stamping industry. The Japanese Quick Die Change (QDC) dies system is one of SMED systems that could reduce both of setup time and manufacturing cost. However, this system is rarely used in stamping industries. This paper will analyze how deep the QDC dies system could reduce setup time and the manufacturing cost. The research is conducted by direct observation, simulating and comparing of QDC dies system with conventional dies system. In this research, we found that the QDC dies system could save up to 35% of manufacturing cost and reduce 70% of setup times. This simulation proved that the QDC die system is effective for cost reduction but must be applied in several parallel production processes.

Keywords: press die, metal stamping, QDC system, single minute exchange die, manufacturing cost saving, SMED

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Authors: Po-Chun Wang, Yan-Ru Lai, Sophia I. C. Lin, Alvin W. Y. Su

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The Generative Theory of Tonal Music (GTTM) provides systematic approaches to recognizing local boundaries of music. The rules have been implemented in some automated melody segmentation algorithms. Besides, there are also deep learning methods with GTTM features applied to boundary detection tasks. However, these studies might face constraints such as a lack of or inconsistent label data. The GTTM database is currently the most widely used GTTM database, which includes manually labeled GTTM rules and local boundaries. Even so, we found some problems with these labels. They are sometimes discrepancies with GTTM rules. In addition, since it is labeled at different times by multiple musicians, they are not within the same scope in some cases. Therefore, in this paper, we examine this database with musicians from the aspect of classical music and relabel the scores. The relabeled database - GTTM Database v2.0 - will be released for academic research usage. Despite the experimental and statistical results showing that the relabeled database is more consistent, the improvement in boundary detection is not substantial. It seems that we need more clues than GTTM rules for boundary detection in the future.

Keywords: dataset, GTTM, local boundary, neural network

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Authors: Somayeh Bahrami, Seyed Jalal Dehghani Firoozabadi

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Environmental governance is an interdisciplinary concept in political ecology and environmental policy focusing on the necessity of embedding the environmental issues in all levels of decision-making and act of states. Similar to sustainable development the concept of environmental governance believes that economic and political life of societies and countries need to be considered as a subset of the environment. This concept has been accepted by North Countries, those that have done the most irreparable environmental damage since the Industrial Revolution. Although North Countries are more responsible for damage to the environment, considering the global fluidity logic of environmental challenges, such an impression doesn’t cause developing countries to disavow responsibility for regional and international cooperation to protect the environment. Establishing an environmental governance at all levels of local, national, regional and global is one of the most significant ways to improve sustainable development. Given to the various political and economic difficulties developing countries including the Middle East face, building environmental governance in these countries is difficult but feasible, as these difficulties have not impeded their mutual partnership for confronting joint environmental issues. However, the environmental issues wouldn’t be solved only by mutual partnership but by establishing environmental governance, establishing regional environmental institutions (an introduction to building Regional Environmental Governance) and delegation of some environmental authorities to the mentioned institutions. The research is aimed at examining necessities, opportunities, and barriers to establishing Regional Environmental Governance in the Middle East. Therefore, this research seeks to answer the question of whether establishing Regional Environmental Governance is possible in the Middle East and if so then why. This study used descriptive-analytical methods and the inferential methodology has been used to reach the goals. Data has been collected by using library and internet sources as well as news sources on the basis of objective-historical data.

Keywords: environmental democracy (ED), environmental governance (EG), middle east (ME), regional environmental governance (REG)

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Authors: Weena Siangphro, Orawon Chailapakul, Kriangsak Songsrirote

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A simple, rapid, sensitive, and economical method based on colorimetry for the determination of paraquat, a widely used herbicide, was developed. Citrate-coated silver nanoparticles (AgNPs) were synthesized as colorimetric probe. The mechanism of the assay is related to aggregation of negatively charged AgNPs induced by positively-charged paraquat resulting from coulombic attraction which causes the color change from deep greenish yellow to pale yellow upon the concentrations of paraquat. Silica gel was exploited as paraquat adsorbent for purification and pre-concentration prior to the direct determination with negatively charged AgNPs without elution step required. The validity of the proposed approach was evaluated by spiking standard paraquat in water and plant samples. Recoveries of paraquat in water samples were 93.6-95.4%, while those in plant samples were 86.6-89.5% by using the optimized extraction procedure. The absorbance of AgNPs at 400 nm was linearly related to the concentration of paraquat over the range of 0.05-50 mg/L with detection limits of 0.05 ppm for water samples, and 0.10 ppm for plant samples.

Keywords: colorimetric assay, paraquat, silica gel, silver nanoparticles

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Authors: Ziyue Zeng

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Bacteriophages are viruses which infect bacteria specifically. Over the past decades, phage λ has been extensively studied, especially its interaction with the Escherichia coli LamB (EcLamB) protein receptor. Nonetheless, despite the enormous numbers and near-ubiquity of environmental phages, aside from phage λ, there is a paucity of information on other phages which target EcLamB as a receptor. In this study, to answer the question of whether there are other EcLamB-targeting phages in the natural environment, a simple and convenient method was developed and used for isolating environmental phages which target a particular surface structure of a particular bacterium; in this case, the EcLamB outer membrane protein. From the enrichments with the engineered bacterial hosts, a collection of EcLamB-targeting phages (ΦZZ phages) were easily isolated. Intriguingly, unlike phage λ, an obligate EcLamB-dependent phage in the Siphoviridae family, the newly isolated ΦZZ phages alternatively recognised EcLamB or E. coli OmpC (EcOmpC) as a receptor when infecting E. coli. Furthermore, ΦZZ phages were suggested to represent new species in the Tequatrovirus genus in the Myoviridae family, based on phage morphology and genomic sequences. Most phages are thought to have a narrow host range due to their exquisite specificity in receptor recognition. With the ability to optionally recognise two receptors, ΦZZ phages were considered relatively promiscuous. Via the heterologous expression of EcLamB on the bacterial cell surface, the host range of ΦZZ phages was further extended to three different enterobacterial genera. Besides, an interesting selection of evolved phage mutants with a broader host range was isolated, and the key mutations involved in their evolution to adapt to new hosts were investigated by genomic analysis. Finally, and importantly, two ΦZZ phages were found to be putative generalised transducers, which could be exploited as tools for DNA manipulations.

Keywords: environmental microbiology, phage, microbe-host interactions, microbial ecology

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Authors: H. N. Anjanayappa, S. Benakappa, A. T. Ramachandra Naik, P. Nayana, D. P. Rajesh

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Fishes of the family Priacanthidae, popularly called big eye or bulls eye. Priacanthus hamrur is an important deep-water inhabitant of great commercial value. High percentage of landings of Priancanthids used as raw material for surimi, sausage and other fishery by-products. Presently, it has great demand in Singapore Thailand, Taiwan, Hong Kong and other countries. For the maturation studies, samples were collected from commercial landing centre, Mangalore. Studies on reproductive biology showed that Priacanthus hamrur spawns twice in a year, the spawning season extending from March to May and October to November. Based on the percentage occurrence of mature fishes in various size group it was inferred that male attained maturity at smaller size than female. This study will enable us to understand the spawning periodicity, cyclic morphological changes in male, female gonads and also it helps to improve stock size by enforcing fishing ban in particular season by assessing spawning periodicity.

Keywords: breeding biology, Mangalore, morphological changes, Priacanthus hamrur

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Authors: Getaneh Berie Tarekegn

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Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. With reconfigurable intelligent surfaces (RIS), this problem can be effectively addressed. To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communication system. According to simulation results, LSTM can improve the channel estimation performance of RIS-assisted UAV-enabled wireless communication.

Keywords: channel estimation, reconfigurable intelligent surfaces, long short-term memory, unmanned aerial vehicles

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Authors: Dmitriy Chernov, Maria Karavassilis, Suhyoun Youn, Amna Izhar, Devasenan Devendra

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Introduction and Background: A well-structured and comprehensive departmental induction improves patient safety and job satisfaction amongst doctors. The aims of our Project were as follows: 1. Assess the perceived preparedness of junior doctors starting their rotation in Acute Medicine at Watford General Hospital. 2. Develop a supplemental Induction Guide and Pocket reference in the form of an iOS mobile application. 3. To collect feedback after implementing the mobile application following a trial period of 8 weeks with a small cohort of junior doctors. Materials and Methods: A questionnaire was distributed to all new junior trainees starting in the department of Acute Medicine to assess their experience of current induction. A mobile Induction application was developed and trialled over a period of 8 weeks, distributed in addition to the existing didactic induction session. After the trial period, the same questionnaire was distributed to assess improvement in induction experience. Analytics data were collected with users’ consent to gauge user engagement and identify areas of improvement of the application. A feedback survey about the app was also distributed. Results: A total of 32 doctors used the application during the 8-week trial period. The application was accessed 7259 times in total, with the average user spending a cumulative of 37 minutes 22 seconds on the app. The most used section was Clinical Guidelines, accessed 1490 times. The App Feedback survey revealed positive reviews: 100% of participants (n=15/15) responded that the app improved their overall induction experience compared to other placements; 93% (n=14/15) responded that the app improved overall efficiency in completing daily ward jobs compared to previous rotations; and 93% (n=14/15) responded that the app improved patient safety overall. In the Pre-App and Post-App Induction Surveys, participants reported: a 48% improvement in awareness of practical aspects of the job; a 26% improvement of awareness on locating pathways and clinical guidelines; a 40% reduction of feelings of overwhelmingness. Conclusions and recommendations: This study demonstrates the importance of technology in Medical Education and Clinical Induction. The mobile application average engagement time equates to over 20 cumulative hours of on-the-job training delivered to each user, within an 8-week period. The most used and referred to section was clinical guidelines. This shows that there is high demand for an accessible pocket guide for this type of material. This simple mobile application resulted in a significant improvement in feedback about induction in our Department of Acute Medicine, and will likely impact workplace satisfaction. Limitations of the application include: post-app surveys had a small number of participants; the app is currently only available for iPhone users; some useful sections are nested deep within the app, lacks deep search functionality across all sections; lacks real time user feedback; and requires regular review and updates. Future steps for the app include: developing a web app, with an admin dashboard to simplify uploading and editing content; a comprehensive search functionality; and a user feedback and peer ratings system.

Keywords: mobile app, doctor induction, medical education, acute medicine

Procedia PDF Downloads 86

Authors: Go Woon Kwak, Siyoung Jun, Soyun Maeng, Haeyoung Lee

Abstract:

Entry is an education platform used in South Korea, created to help students learn to program, in which they can learn to code while playing. Using the online version of the entry, teachers can easily assign programming homework to the student and the students can make programs simply by linking programming blocks. However, the programs may be made by others, so that the authors of the programs should be identified. In this paper, as the first step toward author identification of entry programs, we present an artificial neural network based classification approach to identify genders of authors of a program written in an entry. A neural network has been trained from labeled training data that we have collected. Our result in progress, although preliminary, shows that the proposed approach could be feasible to be applied to the online version of entry for gender identification of authors. As future work, we will first use a machine learning technique for age identification of entry programs, which would be the second step toward the author identification.

Keywords: artificial intelligence, author identification, deep neural network, gender identification, machine learning

Procedia PDF Downloads 324

Authors: Yasser Shabana, Khaled Ghoneem, Nehal Arafat, Younes Rashad, Dalia Aseel, Bruce Fitt, Aiming Qi, Benjamine Richard

Abstract:

Providing food for more than 100 million people is one of Egypt's main challenges facing development. The overall goal is to formulate strategies to enhance food security in light of population growth. Two hundred samples of maize grains from 25 governates were collected. For the detection of seed-borne fungi, the deep-freezing blotter method (DFB) and washing method (ISTA 1999) were used. A total of 41 fungal species was recovered from maize seed samples. Weather data from 30 stations scattered all over Egypt and covering the major maize growing areas were obtained. Canonical correspondence analysis of data for the obtained fungal genera with temperature, relative humidity, precipitation, wind speed, or solar radiation revealed that relative humidity, temperature and wind speed were the most influential weather variables.

Keywords: biodiversity, climate change, maize, seed-borne fungi

Procedia PDF Downloads 162

Authors: Leila Ramezani, Mohammad Saeid Mossadegh

Abstract:

Different uses of land by humans alter the physico chemical characteristics of the soil and affect the soil microhabitat. The objective of this study was to evaluate the influence of tillage in three different human land uses on microarthropods biodiversity in Khuzestan province, southwest of Iran. Three microhabitats including a permanent grassland with old Date-Palms around and no till system, and two wheat fields, one with conservative agricultural practices and low till system and the other with conventional agricultural practices (deep tillage), were compared for the biodiversity of the two main groups of soil microarthropods (Oribatida and Collembola). Soil samples were collected from the top to a depth of 15 cm bimonthly during a period of two years. Significant differences in the biodiversity index of microarthropods were observed between the different tillage systems (F = 36.748, P =0.000). Indeed, analysis of species diversity showed that the diversity index at the conservative field with low till (2.58 ± 0.01) was higher (p < 0.05) than the conventional tilled field (2.45 ± 0.08) and the diversity of natural grassland was the highest (2.79 ± 0.19, p < 0.05). Indeed, the index of biodiversity and population abundance differed significantly in different seasons (p < 0.00).

Keywords: biodiversity, Collembola, microarthropods, Oribatida

Procedia PDF Downloads 175

Authors: Ana Luísa Mouro

Abstract:

When the financial crisis exploded in 2008 in the United States, unleashed by the collapse of Lehman Brothers, and contaminated the economies of the European periphery, Germany appeared as the anchor of the stability of all European institutions and countries in difficulty. The solutions provided by the German government have triggered a deep political debate about the key position Germany has conquered at the heart of Europe - a new “German question” has been created. Some say Germany has achieved by peaceful means what was not able to get through military conquest - the domination of Europe – and many fear Germany’s economic power. This debate about the new role of Germany in Europe has received special attention in the European media and Portugal has not been the exception. The present study has been based on the survey, selection and critical analysis of news reporting, opinion articles, interviews and editorials, published in the weekly Expresso and in the daily Público, between 2008 and 2015 (year of the 25th anniversary of Germany’s unification). The findings of this study will show the paradox of German power and its relevance for Europe’s future.

Keywords: Euro crises, German Europe, intercultural hermeneutics, Portuguese quality press

Procedia PDF Downloads 238

Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

Abstract:

The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection

Procedia PDF Downloads 169