Search results for: machine capacity

Authors: Sirus Mohammadi, Sara Ansari, Darush dehghan, Habib Hoshyari

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Batteries for storage of electricity from solar and wind generation farms are a key element in the success of sustainability. In recent years, due to large integration of Renewable Energy Sources (RESs) like wind turbine and photovoltaic unit into the Micro-Grid (MG), the necessity of Battery Energy Storage (BES) has increased dramatically. The BES has several benefits and advantages in the MG-based applications such as short term power supply, power quality improvement, facilitating integration of RES, ancillary service and arbitrage. This paper presents the cost-based formulation to determine the optimal size of the BES in the operation management of MG. Also, some restrictions, i.e. power capacity of Distributed Generators (DGs), power and energy capacity of BES, charge/discharge efficiency of BES, operating reserve and load demand satisfaction should be considered as well. In this paper, a methodology is proposed for the optimal allocation and economic analysis of ESS in MGs on the basis of net present value (NPV). As the optimal operation of an MG strongly depends on the arrangement and allocation of its ESS, economic operation strategies and optimal allocation methods of the ESS devices are required for the MG.

Keywords: microgrid, energy storage system, optimal sizing, net present value

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Authors: Akshay Dirisala

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Humans had developed a highly organized and efficient civilization to live in by improving the basic needs of humans like housing, transportation, and utilities. These developments have made a huge impact on major environmental factors. Air pollution is one prominent environmental factor that needs to be addressed to maintain a sustainable and healthier lifestyle. Textiles have always been at the forefront of helping humans shield from environmental conditions. With the growth in the field of electronic textiles, we now have the capability of monitoring the atmosphere in real time to understand and analyze the environment that a particular person is mostly spending their time at. Integrating textiles with the particulate matter sensors that measure air quality and pollutants that have a direct impact on human health will help to understand what type of air we are breathing. This research idea aims to develop a textile product and a process of collecting the pollutants through particulate matter sensors, which are equipped inside a smart textile product and store the data to develop a machine learning model to analyze the health conditions of the person wearing the garment and periodically notifying them not only will help to be cautious of airborne diseases but will help to regulate the diseases and could also help to take care of skin conditions.

Keywords: air pollution, e-textiles, particulate matter sensors, environment, machine learning models

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Authors: Supaporn Yamuangmorn, Chanakan Prom-U-Thai

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Wetland and upland purple rice are the two major types classified by its original ecotypes in Northern Thailand. Wetland rice is grown under flooded condition from transplanting until the mutuality, while upland rice is naturally grown under well-drained soil known as aerobic cultivations. Both ecotypes can be grown and adapted to the reverse systems but little is known on its responses of grain yield and qualities between the 2 ecotypes. This study evaluated responses of grain yield as well as anthocyanin and antioxidant capacity between the wetland and upland purple rice genotypes grown in the submerged and aerobic conditions. A factorial arrangement in a randomized complete block design (RCBD) with two factors of rice genotype and water condition were carried out in three replications. The two wetland genotypes (Kum Doi Saket: KDK and Kum Phayao: KPY) and two upland genotypes (Kum Hom CMU: KHCMU and Pieisu1: PES1) were used in this study by growing under submerged and aerobic conditions. Grain yield was affected by the interaction between water condition and rice genotype. The wetland genotypes, KDK and KPY grown in the submerged condition produced about 2.7 and 0.8 times higher yield than in the aerobic condition, respectively. The 0.4 times higher grain yield of upland genotype (PES1) was found in the submerged condition than in the aerobic condition, but no significant differences in KHCMU. In the submerged condition, all genotypes produced higher yield components of tiller number, panicle number and percent filled grain than in the aerobic condition by 24% and 32% and 11%, respectively. The thousand grain weight and spikelet number were affected by water condition differently among genotypes. The wetland genotypes, KDK and KPY, and upland genotype, PES1, grown in the submerged condition produced about 19-22% higher grain weight than in the aerobic condition. The similar effect was found in spikelet number which the submerged condition of wetland genotypes, KDK and KPY, and the upland genotype, KHCMU, had about 28-30% higher than the aerobic condition. In contrast, the anthocyanin concentration and antioxidant capacity were affected by both the water condition and genotype. Rice grain grown in the aerobic condition had about 0.9 and 2.6 times higher anthocyanin concentration than in the submerged condition was found in the wetland rice, KDK and upland rice, KHCMU, respectively. Similarly, the antioxidant capacity of wetland rice, KDK and upland rice, KHCMU were 0.5 and 0.6 times higher in aerobic condition than in the submerged condition. There was a negative correlation between grain yield and anthocyanin concentration in wetland genotype KDK and upland genotype KHCMU, but it was not found in the other genotypes. This study indicating that some rice genotype can be adapted in the reverse ecosystem in both grain yield and quality, especially in the wetland genotype KPY and upland genotype PES1. To maximize grain yield and quality of purple rice, proper water management condition is require with a key consideration on difference responses among genotypes. Increasing number of rice genotypes in both ecotypes is needed to confirm their responses on water management.

Keywords: purple rice, water condition, anthocyanin, grain yield

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Authors: Fang Gong

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Distance measures are an indispensable part of many instance-based learning (IBL) and machine learning (ML) algorithms. The value difference metrics (VDM) and inverted specific-class distance measure (ISCDM) are among the top-performing distance measures that address nominal attributes. VDM performs well in some domains owing to its simplicity and poorly in others that exist missing value and non-class attribute noise. ISCDM, however, typically works better than VDM on such domains. To maximize their advantages and avoid disadvantages, in this paper, a selection approach: a discriminative model for nominal attributes-based distance measures is proposed. More concretely, VDM and ISCDM are built independently on a training dataset at the training stage, and the most credible one is recorded for each training instance. At the test stage, its nearest neighbor for each test instance is primarily found by any of VDM and ISCDM and then chooses the most reliable model of its nearest neighbor to predict its class label. It is simply denoted as a discriminative distance measure (DDM). Experiments are conducted on the 34 University of California at Irvine (UCI) machine learning repository datasets, and it shows DDM retains the interpretability and simplicity of VDM and ISCDM but significantly outperforms the original VDM and ISCDM and other state-of-the-art competitors in terms of accuracy.

Keywords: distance measure, discriminative model, nominal attributes, nearest neighbor

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Authors: Alev Atak

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In this paper, we aim to create a financial sentiment index by investigating the company’s voluntary information disclosures. We retrieve structured content from BIST 100 companies’ financial reports for the period 1998-2018 and extract relevant financial information for sentiment analysis through Natural Language Processing. We measure strategy-related disclosures and their cross-sectional variation and classify report content into generic sections using synonym lists divided into four main categories according to their liquidity risk profile, risk positions, intra-annual information, and exposure to risk. We use Word Error Rate and Cosin Similarity for comparing and measuring text similarity and derivation in sets of texts. In addition to performing text extraction, we will provide a range of text analysis options, such as the readability metrics, word counts using pre-determined lists (e.g., forward-looking, uncertainty, tone, etc.), and comparison with reference corpus (word, parts of speech and semantic level). Therefore, we create an adequate analytical tool and a financial dictionary to depict the importance of granular financial disclosure for investors to identify correctly the risk-taking behavior and hence make the aggregated effects traceable.

Keywords: financial sentiment, machine learning, information disclosure, risk

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Authors: Amarachi Delight Onyemachi, Gregory Ikechukwu Onwuka

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The growing consumer trend for natural ingredients, functional foods with health benefits and the perceived risk of carcinogenesis associated with synthetic antioxidants have forced food manufacturers to look for alternatives for producing healthy and safe food. Herbs and spices are cheap, natural and harmless sources of antioxidants which can delay and prevent lipid oxidation of food products and also confer its unique organoleptic properties and health benefits to food products. The Nigerian climate has been proven to be conducive for the production of spices and herbs and is blessed bountifully with a wide range of them. Five selected Nigerian herbs and spices Piper guieense, Xylopia aethopica, Gongronema latifolium and Ocimum gratissimum were evaluated for their ability to act as radical scavengers. The spices were extracted with 80% ethanol and evaluated using total phenolic capacity (TPC), DPPH (1,1-diph diphenyl-2-picrylhydrazyl radical) ABTS (2,2’azinobis-(3-ethylbenzthiazoline-6-sulfonic acid)), total antioxidant capacity (TAC), reducing power (RP) assays. The TPC ranged from 5.33 µg GAE/mg (in Gongronema latifolium) to 15.55 µg GAE/mg (in Ocimum gratissimum). The DPPH and ABTS scavenging activity of the extracts ranged from 0.23-0.36 IC50 mg/ml and 2.32-7.25 Trolox equivalent % respectively. The TAC and RP of the extract ranged from 6.73-10.64 µg AAE/mg and 3.52-10.19 µg AAE/mg. The result of percentage yield of the extract ranged from as low as 9.94% in Gongronema latifolium and to as high as 23.85% in Xylopia aethopica. A very strong positive relationship existed between the total antioxidant capacity and total phenolic content of the tested herbs and spices (R2=0.96). All of the extracts exhibited different extent of strong antioxidant activity, high antioxidant activity was found in Ocimum gratissimum and Gongronema latifolium with the least. However, Gongronema latifolium possessed the highest total antioxidant capacity. These data confirm the appreciable antioxidant potentials and high phenolic content of Nigerian herbs and spices, thereby providing justification for their use in dishes and functional foods, prevention of cellular damage caused by free radicals and use as natural antioxidants in the food industry for prevention of lipid oxidation in food products. However, to utilize these natural antioxidants in food products, further analysis and studies of their behaviour in food systems at varying temperature, pH conditions and ionic concentrations should be carried out to displace the use of synthetic antioxidants like BHT and BHA.

Keywords: Antioxidant, free radicals, herbs, phenolic, spices

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Authors: Nivedha L. K., Dhinesh Kumar Murugaiah, Ganapathi Rao Kandregula, Raja Murugan, Kothandaraman R.

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ZnMn₂O₄, a non-precious metal catalyst for oxygen reduction reaction (ORR), was recycled from the spent primary Zn-C battery and utilized in the zinc-air battery. Catalysts exhibiting facile ORR kinetics are a requirement for building efficient Zinc-air batteries. ZnMn₂O₄ demonstrated excellent catalytic activity towards ORR in an aqueous alkaline medium, with an onset potential of 0. 90 V vs. RHE. The recycled ZnMn₂O₄ manifested a similar performance (at ~ 1.0 V) as the chemically synthesized one with a specific capacity of 210 mAh gzn-¹ at a constant current discharge of 15 mA cm-². A single electrode potential study was done to comprehend the losses at the electrodes and to identify the limiting electrode. Interestingly, the cathode was improving during discharge, which is in contrast to the expectation due to the accumulation of peroxide around the catalytic layer. Although the anode has exhibited minimal polarization, beyond a capacity of 210 mAh g-¹, the supersaturation of electrolyte occurs with zincate ion causing precipitation of ZnO on the cell components, thereby leading to sudden polarization of the cell and hence zinc electrode act as a limiting electrode in this system.

Keywords: battery recycling, oxygen reduction reaction, single electrode measurement, Zn-air battery, ZnMn₂O₄ recovery

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Authors: Umamaheswari Shanmugam, Silvia Ronchi

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that can use the large amount and variety of data generated during healthcare services every day; one of the significant advantages of these new technologies is the ability to get experience and knowledge from real-world use and to improve their performance continuously. Healthcare systems and institutions can significantly benefit because the use of advanced technologies improves the efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and protect patients' safety. The evolution and the continuous improvement of software used in healthcare must consider the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device's approval. Still, they are necessary to ensure performance, quality, and safety. At the same time, they can be a business opportunity if the manufacturer can define the appropriate regulatory strategy in advance. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems

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Authors: Mahshid Davoodpoor, Zahra Shamohammadi Ghahsareh, Saeid Razfar, Alaleh Dabbaghi

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Nowadays, air pollution has become a topic of great concern all over the world. One of the main sources of air pollution is automobile exhaust gas, which introduces a large number of toxic gases, including CO, unburned hydrocarbons (HCs), NOx, and non-methane hydrocarbons (NMHCs), into the air. The application of three-way catalysts (TWCs) is still the most effective strategy to mitigate the emission of these pollutants. Due to the stringent environmental regulations which continuously become stricter, studies on the TWCs are ongoing despite several years of research and development. This arises from the washcoat complexity and the several numbers of parameters involved in the redox reactions. The main objectives of these studies are the optimization of washcoat formulation and the investigation of different coating modes. Perovskite (ABO₃), as a promising class of materials, has unique features that make it versatile to use as an alternative to commonly mixed oxides in washcoats. High catalytic activity for oxidation reactions and its relatively high oxygen storage capacity are important properties of perovskites in catalytic applications. Herein, La₀.₈Ba₀.₂FeO₃ perovskite material was synthesized using the co-precipitation method and characterized by XRD, ICP, and BET analysis. The effect of synthesis conditions, including B site metal (Fe and Co), metal precursor concentration, and dopant (Ba), were examined on the phase purity of the products. The selected perovskite sample was used as one of the components in the TWC formulation to evaluate its catalytic performance through Light-off, oxygen storage capacity, and emission analysis. Results showed a remarkable increment in oxygen storage capacity and also revealed that T50 and emission of CO, HC, and NOx reduced in the presence of perovskite structure which approves the enhancement of catalytic performance for the new washcoat formulation. This study shows the brilliant future of advanced oxide structures in the TWCs.

Keywords: Perovskite, three-way catalyst, PGMs, PGMs reduction

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Authors: Ye Ling, Jiang Yuting, Ruan Haihui

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Zinc ion capacitors (ZICs) have garnered tremendous interest recently from researchers due to the perfect integration of batteries and supercapacitors (SC). However, ZICs are currently still facing two major challenges, one is low specific capacitance because of the limited capacity of capacitive cathode materials. In this work, TiN/CNC composites were obtained by a creative method composed of simple mixing and calcination treatment of tetrabutyl titanate (TBOT) and ZIF-8. The formed TiN particles are of ultra-small size and distributed uniformly on the nanoporous carbon matrix, which enhances the conductivity of the composites and the micropores caused by the evaporation of zinc during the calcination process and can serve as the reservoir of electrolytes; both are beneficial to zinc ion storage. When it was used as a cathode with zinc metal and 2M ZnSO₄ as the anode and electrolyte, respectively, in a ZIC device, the assembled device delivered a maximum energy density as high as 153 Wh kg-¹ at a power density of 269.4 W kg-¹, which is superior to many ZICs as reported. Also, it can maintain an energy density of 83.7 Wh kg-¹ at a peak power density of 8.6 kW kg-¹, exhibiting good rate performance. Moreover, when it was charged/discharged for 5000 cycles at a current density of 5 A g-¹, it remained at 85.8% of the initial capacity with a Coulombic efficiency (CE) of nearly 100%.

Keywords: zinc ion capacitor, metal nitride, zif-8, supercapacitor

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Authors: Thi Huong Vu, Haelee Fenton, Thi Huong Tra Nguyen, Gary Dykes

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In the present study, a submerged fermentation of carob kibble with Saccharomyces cerevisiae (S. cerevisiae) was performed. The total phenolic content and antioxidant activity in fermented carob kibble were determined by Folin–Ciocalteu method and scavenging capacity using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid (ABTS). The study showed that S. cerevisiae improved total phenolic content by 45 % and 50 % in acetone and water extracts respectively. Similarly, the antioxidant capacity of water extracts increased by 25 % and 41%, while acetone extracts indicated by 70% and 80% in DPPH and ABTS respectively. It is also found that initial pH 7.0 was more effective in improvement of total phenolic content and antioxidant activity. The efficiency of treatment was recorded at 15 h. This report suggested that submerged fermentation with S. cerevisiae is a potential and cost effective manner to further increase bioactive compounds in carob kibble, which are in use for food, cosmetic and pharmaceutical industries.

Keywords: antioxidant activity, carob kibble, saccharomyces cerevisiae, submerged fermentation, total phenolics

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Authors: Ahmet Kayabasi, Ali Akdagli

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In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results.

Keywords: a-shaped compact microstrip antenna, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM)

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Authors: Toufic Sarieddine

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The current debate on the hegemonic impact of China’s Belt and Road Initiative (BRI) is of two opposing strands: Resilient and absolute US hegemony on the one hand and various models of multipolar hegemony such as bifurcation on the other. Bifurcation theories illustrate an unprecedented division of hegemonic functions between China and the US, whereby Beijing becomes the world’s economic hegemon, leaving Washington the world’s military hegemon and security guarantor. While consensus points to China being the main driver of unipolarity’s rupturing, the debate among bifurcationists is on the location of the first rupture. In this regard, the Middle East and North Africa (MENA) region has seen increasing Chinese foreign direct investment in recent years while that to other regions has declined, ranking it second in 2018 as part of the financing for the Maritime Silk Road Initiative (MSRI). China has also become the top trade partner of 11 states in the MENA region, as well as its top source of machine imports, surpassing the US and achieving an overall trade surplus almost double that of Washington’s. These are among other features outlined in world-systems analysis (WSA) literature which correspond with the emergence of a new hegemon. WSA is further utilized to gauge other facets of China’s increasing involvement in MENA and assess whether bifurcation is unfolding therein. These features of hegemony include the adoption of China’s modi operandi, economic dominance in production, trade, and finance, military capacity, cultural hegemony in ideology, education, and language, and the promotion of a general interest around which to rally potential peripheries (MENA states in this case). China’s modi operandi has seen some adoption with regards to support against the United Nations Convention on the Law of the Sea, oil bonds denominated in the yuan, and financial institutions such as the Shanghai Gold Exchange enjoying increasing Arab patronage. However, recent elections in Qatar, as well as liberal reforms in Saudi Arabia, demonstrate Washington’s stronger normative influence. Meanwhile, Washington’s economic dominance is challenged by China’s sizable machine exports, increasing overall imports, and widening trade surplus, but retains some clout via dominant arms and transport exports, as well as free-trade deals across the region. Militarily, Washington bests Beijing’s arms exports, has a dominant and well-established presence in the region, and successfully blocked Beijing’s attempt to penetrate through the UAE. Culturally, Beijing enjoys higher favorability in Arab public opinion, and its broadcast networks have found some resonance with Arab audiences. In education, the West remains MENA students’ preferred destination. Further, while Mandarin has become increasingly available in schools across MENA, its usage and availability still lag far behind English. Finally, Beijing’s general interest in infrastructure provision and prioritizing economic development over social justice and democracy provides an avenue for increased incorporation between Beijing and the MENA region. The overall analysis shows solid progress towards bifurcation in MENA.

Keywords: belt and road initiative, hegemony, Middle East and North Africa, world-systems analysis

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Authors: Khalida Douibi, Rodrigo Balp, Solène Le Bars

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In the medical field, applications related to human experiments are frequently linked to reduced samples size, which makes the training of machine learning models quite sensitive and therefore not very robust nor generalizable. This is notably the case in Brain-Computer Interface (BCI) studies, where the sample size rarely exceeds 20 subjects or a few number of trials. To address this problem, several resampling approaches are often used during the data preparation phase, which is an overly critical step in a data science analysis process. One of the naive approaches that is usually applied by data scientists consists in the transformation of the entire database before the resampling phase. However, this can cause model’ s performance to be incorrectly estimated when making predictions on unseen data. In this paper, we explored the effect of data leakage observed during our BCI experiments for device control through the real-time classification of SSVEPs (Steady State Visually Evoked Potentials). We also studied potential ways to ensure optimal validation of the classifiers during the calibration phase to avoid overfitting. The results show that the scaling step is crucial for some algorithms, and it should be applied after the resampling phase to avoid data leackage and improve results.

Keywords: data leackage, data science, machine learning, SSVEP, BCI, overfitting

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Authors: Mirza Mujtaba Baig

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Artificial Intelligence (AI) is evolving rapidly, and one of the areas in which this field has influenced is automation. The automobile, healthcare, education, and robotic industries deploy AI technologies constantly, and the automation of tasks is beneficial to allow time for knowledge-based tasks and also introduce convenience to everyday human endeavors. The paper reviews the challenges faced with the current implementations of autonomous self-driving cars by exploring the machine learning, robotics, and artificial intelligence techniques employed for the development of this innovation. The controversy surrounding the development and deployment of autonomous machines, e.g., vehicles, begs the need for the exploration of the configuration of the programming modules. This paper seeks to add to the body of knowledge of research assisting researchers in decreasing the inconsistencies in current programming modules. Blockchain is a technology of which applications are mostly found within the domains of financial, pharmaceutical, manufacturing, and artificial intelligence. The registering of events in a secured manner as well as applying external algorithms required for the data analytics are especially helpful for integrating, adapting, maintaining, and extending to new domains, especially predictive analytics applications.

Keywords: artificial intelligence, automation, big data, self-driving cars, machine learning, neural networking algorithm, blockchain, business intelligence

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Authors: Selim M. Khan

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Indoor air quality is strongly influenced by the presence of radioactive radon (222Rn) gas. Indeed, exposure to high 222Rn concentrations is unequivocally linked to DNA damage and lung cancer and is a worsening issue in North American and European built environments, having increased over time within newer housing stocks as a function of as yet unclear variables. Indoor air radon concentration can be influenced by a wide range of environmental, structural, and behavioral factors. As some of these factors are quantitative while others are qualitative, no single statistical model can determine indoor radon level precisely while simultaneously considering all these variables across a complex and highly diverse dataset. The ability of AI- machine (deep) learning to simultaneously analyze multiple quantitative and qualitative features makes it suitable to predict radon with a high degree of precision. Using Canadian and Swedish long-term indoor air radon exposure data, we are using artificial deep neural network models with random weights and polynomial statistical models in MATLAB to assess and predict radon health risk to human as a function of geospatial, human behavioral, and built environmental metrics. Our initial artificial neural network with random weights model run by sigmoid activation tested different combinations of variables and showed the highest prediction accuracy (>96%) within the reasonable iterations. Here, we present details of these emerging methods and discuss strengths and weaknesses compared to the traditional artificial neural network and statistical methods commonly used to predict indoor air quality in different countries. We propose an artificial deep neural network with random weights as a highly effective method for assessing and predicting indoor radon.

Keywords: radon, radiation protection, lung cancer, aI-machine deep learnng, risk assessment, risk prediction, Europe, North America

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Authors: Fu-Pei Hsiao, Fung-Chung Tu, Chien-Kuo Chiu

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In this work, to develop a method for detailed assesses of post-earthquake seismic performance for RC buildings in Taiwan, experimental data for several column specimens with various failure modes (flexural failure, flexural-shear failure, and shear failure) are used to derive reduction factors of seismic capacity for specified damage states. According to the damage states of RC columns and their corresponding seismic reduction factors suggested by experimental data, this work applies the detailed seismic performance assessment method to identify the seismic capacity of earthquake-damaged RC buildings. Additionally, a post-earthquake emergent assessment procedure is proposed that can provide the data needed for decision about earthquake-damaged buildings in a region with high seismic hazard. Finally, three actual earthquake-damaged school buildings in Taiwan are used as a case study to demonstrate application of the proposed assessment method.

Keywords: seismic assessment, seismic reduction factor, residual seismic ratio, post-earthquake, reinforced concrete, building

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Authors: Abir Mohamed Mohamed Ibrahim, Amina Titouche, Mohamed Hazzit

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Phytotherapy is based on use of plant natural products holding the main sources of drugs with healing properties for the treatment of human, animal or vegetable diseases. With these aims, and to replace chemical preservatives in natural products, we are interested to use essential oils from Algerian endemic plants belonging to the Asteraceae family: Artemisia herba alba Asso, which was undergoes a hydro-distillation after its irradiation by Gamma rays at frequencies: 10, 20, and 30 KGray which gave respectively the following essential oil yields: 1.087%, 1.087%, 1.085%, compared with that of the untreated sample giving a yield of 1.27 %. Evaluation of the antioxidant activity in vitro of essential oil for A. herba alba has been assessed by two different methods: inhibition of DPPH radical and measurement of reducing power. The first method has not revealed a very big difference regardless of the dose of irradiation, the IC50 is about 4000 mg/l, the maximum of inhibition was around 49.4%, likewise, the test of reducing power awarded us a maximum reducing capacity was of 0.76%; both of results were registered by the specimen irradiated at 20 KGy, it has a more better antioxidant power than no irradiated sample but slightly. To combat Fusarium culmorum, causing the wilts and rots, we are focused on the antifungal screening of this aromatic plant. The results obtained, followed by measurements of Minimal Inhibitory Concentrations (MIC); showed promising inhibitory effect against pathogen tested. With a yield superior to l%, the essential oil has shown a remarkable efficiency on the stump, mainly for sample irradiate at 30KGray (MICs= 625 µg/ml; MICc= 1250 µg/ml) with MIC of 2%. These results demonstrate a good antifungal activity, to limit and even to stop the development of the pathogenic microorganism and also the positive effect of dose of irradiation to upgrade this capacity as well, to uphold the antioxidant capacity.

Keywords: artemisia herba alba Asso, essential oil yield, gamma ray, antioxidant activity, antifungal activity

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Authors: Michal Gregor, Jiri Polcar, Petr Horejsi, Michal Simon

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Machine visualization is an area of interest with fast and progressive development. We present a method of machine visualization which will be applicable in real industrial conditions according to current needs and demands. Real factory data were obtained in a newly built research plant. Methods described in this paper were validated on a case study. Input data were processed and the virtual environment was created. The environment contains information about dimensions, structure, disposition, and function. Hardware was enhanced by modular machines, prototypes, and accessories. We added new functionalities and machines into the virtual environment. The user is able to interact with objects such as testing and cutting machines, he/she can operate and move them. Proposed design consists of an environment with two degrees of freedom of movement. Users are in touch with items in the virtual world which are embedded into the real surroundings. This paper describes the development of the virtual environment. We compared and tested various options of factory layout virtualization and visualization. We analyzed possibilities of using a 3D scanner in the layout obtaining process and we also analyzed various virtual reality hardware visualization methods such as Stereoscopic (CAVE) projection, Head Mounted Display (HMD), and augmented reality (AR) projection provided by see-through glasses.

Keywords: augmented reality, spatial scanner, virtual environment, virtual reality

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Authors: Melissa Lubitz

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Water scarcity is one of the crucial challenges of our time. There needs to be a focus on creating a society where people and nature flourish, regardless of climatic conditions. One of the solutions we can look to is decentralized greywater recycling. The vision is simple. Every building has its own water source being greywater from the bath, shower, sink and washing machine. By treating this in the home, you can save 25-45% of potable water use and wastewater production, a reduction in energy consumption and CO2 emissions. This reusable water is clean, and safe to be used for toilet flushing, washing machine, and outdoor irrigation. Companies like Hydraloop have been committed to the greywater recycle-ready building concept for years. This means that drinking water conservation and water reuse are included as standards in the design of all new buildings. Sustainability and renewal go hand in hand. This vision includes not only optimizing water savings and waste reduction but also forging strong partnerships that bring this ambition to life. Together with regulators, municipalities and builders, a sustainable and water-conscious future is pursued. This is an opportunity to be part of a movement that is making a difference. By pushing this initiative forward, we become part of a growing community that resists dehydration, believes in sustainability, and is committed to a living environment at the forefront of change: sustainable living, where saving water is the norm and where we shape the future together.

Keywords: greywater, wastewater treatment, water conservation, circular water society

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Authors: Neritan Shkodrani

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On November 26, 2019, an earthquake hit the central western part of Albania. It was assessed as Mw 6.4. Its epicenter was located offshore north western Durrës, about 7 km north of the city. In this paper, the consequences of settlements of very soft soils have been discussed for the case of a residential building, mentioned as “K Building”, which was suffering a significant tilting after the earthquake. “KBuilding” is an RC framed building having 12+1 (basement) storiesand a floor area of 21000 m2. The construction of the building was completed in 2012. “KBuilding”, located in Durres city, suffered severe non-structural damage during November 26, 2019, Durrës Earthquake sequences. During the in-site inspections immediately after the earthquake, the general condition of the buildings, the presence of observable settlements on the ground, and the crack situation in the structure were determined, and damage inspection were performed. It was significant to note that the “K Building” presented tilting that might be attributed, as it was believed at the beginning, partially to the failure of the columns of the ground floor and partially to liquefaction phenomena, but it did not collapse. At the first moment was not clear if the foundation had a bearing capacity failure or the foundation failed because of the soil liquefaction. Geotechnical soil investigations by using CPTU test were executed, and their data are usedto evaluatebearing capacity, consolidation settlement of the mat foundation, and soil liquefaction since they were believed to be the main reasons of this building tilting.Geotechnical soil investigation consist in 5 (five) Static Cone Penetration tests with pore pressure measurement (piezocone test). They reached a penetration depth of 20.0 m to 30.0 mand, clearly shown the presence of very soft and organic soils in the soil profile of the site. Geotechnical CPT based analysis of bearing capacity, consolidation, and secondary settlement are applied, and results are reported for each test. These results shown very small values of allowable bearing capacity and very high values of consolidation and secondary settlements. Liquefaction analysis based on the data of CPTU tests and the characteristics of ground shaking of the mentioned earthquake has shown the possibility of liquefaction for some layers of the considered soil profile, but the estimated vertical settlements are at a small range and clearly shown that the main reason of the building tilting was not related to the consequences of liquefaction, but was an existing settlement caused from the applied bearing pressure of this building. All the CPTU tests were carried out on August 2021, almost two years after the November 26, 2019, Durrës Earthquake and when the building itself was demolished. After removing the mat foundation on September 2021, it was possible to carry out CPTU tests even on the footprint of the existing building, which made possible to observe the effects of long time applied of foundation bearing pressure to the consolidation on the considered soil profile.

Keywords: bearing capacity, cone penetration test, consolidation settlement, secondary settlement, soil liquefaction, etc

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Authors: Mohammad Besharatloo

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Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the user's access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these sub-populations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context.

Keywords: intrusion detection system, differential evolution, firefly algorithm, support vector machine, decision tree

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Authors: Joshna Rani S., Ahmadi Banu

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Artificial Intelligence (AI) has a significant task to carry out in the medical care contributions of things to come. As AI, it is the essential capacity behind the advancement of accuracy medication, generally consented to be a painfully required development in care. Albeit early endeavors at giving analysis and treatment proposals have demonstrated testing, we anticipate that AI will at last dominate that area too. Given the quick propels in AI for imaging examination, it appears to be likely that most radiology, what's more, pathology pictures will be inspected eventually by a machine. Discourse and text acknowledgment are now utilized for assignments like patient correspondence and catch of clinical notes, and their utilization will increment. The best test to AI in these medical services areas isn't regardless of whether the innovations will be sufficiently skilled to be valuable, but instead guaranteeing their appropriation in day by day clinical practice. For far reaching selection to happen, AI frameworks should be affirmed by controllers, coordinated with EHR frameworks, normalized to an adequate degree that comparative items work likewise, instructed to clinicians, paid for by open or private payer associations, and refreshed over the long haul in the field. These difficulties will, at last, be survived, yet they will take any longer to do as such than it will take for the actual innovations to develop. Therefore, we hope to see restricted utilization of AI in clinical practice inside 5 years and more broad use inside 10 years. It likewise appears to be progressively evident that AI frameworks won't supplant human clinicians for a huge scope, yet rather will increase their endeavors to really focus on patients. Over the long haul, human clinicians may advance toward errands and work plans that draw on remarkably human abilities like sympathy, influence, and higher perspective mix. Maybe the lone medical services suppliers who will chance their professions over the long run might be the individuals who will not work close by AI

Keywords: artificial intellogence, health care, breast cancer, AI applications

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Authors: Robin Molinier

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Industrial symbiosis (I.S) rely on two modes of cooperation that are infrastructure sharing and resource substitution to obtain economic and environmental benefits. The former consists in the intensification of use of an asset while the latter is based on the use of waste, fatal energy (and utilities) as alternatives to standard inputs. Both modes, in fact, rely on the shift from a business-as-usual functioning towards an alternative production system structure so that in a business point of view the distinction is not clear. In order to investigate the way those cooperation modes can be distinguished, we consider the stakeholders' interplay in the business model structure regarding their resources and requirements. For infrastructure sharing (following economic engineering literature) the cost function of capacity induces economies of scale so that demand pooling reduces global expanses. Grassroot investment sizing decision and the ex-post pricing strongly depends on the design optimization phase for capacity sizing whereas ex-post operational cost sharing minimizing budgets are less dependent upon production rates. Value is then mainly design driven. For resource substitution, synergies value stems from availability and is at risk regarding both supplier and user load profiles and market prices of the standard input. Baseline input purchasing cost reduction is thus more driven by the operational phase of the symbiosis and must be analyzed within the whole sourcing policy (including diversification strategies and expensive back-up replacement). Moreover, while resource substitution involves a chain of intermediate processors to match quality requirements, the infrastructure model relies on a single operator whose competencies allow to produce non-rival goods. Transaction costs appear higher in resource substitution synergies due to the high level of customization which induces asset specificity, and non-homogeneity following transaction costs economics arguments.

Keywords: business model, capacity, sourcing, synergies

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Authors: Ruchi Makani, B. V. R. Reddy

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Mobile Adhoc Networks (MANETs) are gaining popularity due to their potential of providing low-cost mobile connectivity solutions to real-world communication problems. Integrating Intrusion Detection Systems (IDS) in MANETs is a tedious task by reason of its distinctive features such as dynamic topology, de-centralized authority and highly controlled/limited resource environment. IDS primarily use automated soft-computing techniques to monitor the inflow/outflow of traffic packets in a given network to detect intrusion. Use of machine learning techniques in IDS enables system to make decisions on intrusion while continuous keep learning about their dynamic environment. An appropriate IDS model is essential to be selected to expedite this application challenges. Thus, this paper focused on fuzzy-logic based machine learning IDS technique for MANETs and presented their applicability for achieving effectiveness in identifying the intrusions. Further, the selection of appropriate protocol attributes and fuzzy rules generation plays significant role for accuracy of the fuzzy-logic based IDS, have been discussed. This paper also presents the critical attributes of MANET’s routing protocol and its applicability in fuzzy logic based IDS.

Keywords: AODV, mobile adhoc networks, intrusion detection, anomaly detection, fuzzy logic, fuzzy membership function, fuzzy inference system

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Authors: Leonie Bradfield, Stephen Fityus, John Simmons

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The shearing behavior of current and planned coal mine spoil dumps up to 400m in height is studied using large-sample-high-stress direct shear tests performed on a range of spoils common to the coalfields of Eastern Australia. The motivation for the study is to address industry concerns that some constructed spoil dump heights ( > 350m) are exceeding the scale ( ≤ 120m) for which reliable design information exists, and because modern geotechnical laboratories are not equipped to test representative spoil specimens at field-scale stresses. For more than two decades, shear strength estimation for spoil dumps has been based on either infrequent, very small-scale tests where oversize particles are scalped to comply with device specimen size capacity such that the influence of prototype-sized particles on shear strength is not captured; or on published guidelines that provide linear shear strength envelopes derived from small-scale test data and verified in practice by slope performance of dumps up to 120m in height. To date, these published guidelines appear to have been reliable. However, in the field of rockfill dam design there is a broad acceptance of a curvilinear shear strength envelope, and if this is applicable to coal mine spoils, then these industry-accepted guidelines may overestimate the strength and stability of dumps at higher stress levels. The pressing need to rationally define the shearing behavior of more representative spoil specimens at field-scale stresses led to the successful design, construction and operation of a large direct shear machine (LDSM) and its subsequent application to provide reliable design information for current and planned very-high dumps. The LDSM can test at a much larger scale, in terms of combined specimen size (720mm x 720mm x 600mm) and stress (σn up to 4.6MPa), than has ever previously been achieved using a direct shear machine for geotechnical testing of rockfill. The results of an extensive LDSM testing program on a wide range of coal-mine spoils are compared to a published framework that widely accepted by the Australian coal mining industry as the standard for shear strength characterization of mine spoil. A critical outcome is that the LDSM data highlights several non-compliant spoils, and stress-dependent shearing behavior, for which the correct application of the published framework will not provide reliable shear strength parameters for design. Shear strength envelopes developed from the LDSM data are also compared with dam engineering knowledge, where failure envelopes of rockfills are curved in a concave-down manner. The LDSM data indicates that shear strength envelopes for coal-mine spoils abundant with rock fragments are not in fact curved and that the shape of the failure envelope is ultimately determined by the strength of rock fragments. Curvilinear failure envelopes were found to be appropriate for soil-like spoils containing minor or no rock fragments, or hard-soil aggregates.

Keywords: coal mine, direct shear test, high dump, large scale, mine spoil, shear strength, spoil dump

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Authors: O. Moussous, T. Medjadj

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The suitability of a Fricke dosimeter for the measurement of a relative depth dose profile and the peak scatter factors was studied. The measurements were carried out in the secondary standard dosimetry laboratory at CRNA Algiers using a collimated 60Co gamma source teletherapy machine. The measurements were performed for different field sizes at the phantom front face, at a fixed source-to-phantom distance of 80 cm. The dose measurements were performed by first placing the dosimeters free-in-air at the distance-source-detector (DSD) of 80.5 cm from the source. Additional measurements were made with the phantom in place. The water phantom type Med-Tec 40x40x40 cm for vertical beam was used in this work as scattering martial. The phantom was placed on the irradiation bench of the cobalt unit at the SSD of 80 cm from the beam focus and the centre of the field coincided with the geometric centre of the dosimeters placed at the depth in water of 5 mm Relative depth dose profile and Peak scatter factors measurements were carried out using our Fricke system. This was intercompared with similar measurements by ionization chamber under identical conditions. There is a good agreement between the relative percentage depth–dose profiles and the PSF values measured by both systems using a water phantom.

Keywords: Fricke dosimeter, depth–dose profiles, peak scatter factors, DSD

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Authors: Gul Rahim Rahimi

Abstract:

The IoT-based soil moisture monitoring system for indoor plants is designed to address the challenges of maintaining optimal moisture levels in soil for plant growth and health. The system utilizes sensor technology to collect real-time data on soil moisture levels, which is then processed and analyzed using machine learning algorithms. This allows for accurate and timely monitoring of soil moisture levels, ensuring plants receive the appropriate amount of water to thrive. The main objectives of the system are twofold: to keep plants fresh and healthy by preventing water deficiency and to provide users with comprehensive insights into the water content of the soil on a daily and hourly basis. By monitoring soil moisture levels, users can identify patterns and trends in water consumption, allowing for more informed decision-making regarding watering schedules and plant care. The scope of the system extends to the agriculture industry, where it can be utilized to minimize the efforts required by farmers to monitor soil moisture levels manually. By automating the process of soil moisture monitoring, farmers can optimize water usage, improve crop yields, and reduce the risk of plant diseases associated with over or under-watering. Key technologies employed in the system include the Capacitive Soil Moisture Sensor V1.2 for accurate soil moisture measurement, the Node MCU ESP8266-12E Board for data transmission and communication, and the Arduino framework for programming and development. Additionally, machine learning algorithms are utilized to analyze the collected data and provide actionable insights. Cloud storage is utilized to store and manage the data collected from multiple sensors, allowing for easy access and retrieval of information. Overall, the IoT-based soil moisture monitoring system offers a scalable and efficient solution for indoor plant care, with potential applications in agriculture and beyond. By harnessing the power of IoT and machine learning, the system empowers users to make informed decisions about plant watering, leading to healthier and more vibrant indoor environments.

Keywords: IoT-based, soil moisture monitoring, indoor plants, water management

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Authors: Eyibo Ebengeobong Eddie, Halil Zafer Alibaba

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The neglect of classroom acoustics is dominant in most educational facilities, meanwhile, hearing and listening is the learning process in this kind of facilities. A classroom should therefore be an environment that encourages listening, without an obstacles to understanding what is being taught. Although different studies have shown teachers to complain that noise is the everyday factor that causes stress in classroom, the capacity of individuals to understand speech is further affected by Echoes, Reverberation, and room modes. It is therefore necessary for classrooms to have an ideal acoustics to aid the intelligibility of students in the learning process. The influence of these acoustical parameters on learning and teaching in schools needs to be further researched upon to enhance the teaching and learning capacity of both teacher and student. For this reason, there is a strong need to provide and collect data to analyse and define the suitable quality of classrooms needed for a learning environment. Research has shown that acoustical problems are still experienced in both newer and older schools. However, recently, principle of acoustics has been analysed and room acoustics can now be measured with various technologies and sound systems to improve and solve the problem of acoustics in classrooms. These acoustic solutions, materials, construction methods and integration processes would be discussed in this paper.

Keywords: classroom, acoustics, materials, integration, speech intelligibility

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Authors: Ingrid Van Der Heijden, Naeemah Abrahams, Jane Harries

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Background: Women with disabilities are largely missing from global research on violence prevention, yet research shows that women with disabilities are a particularly marginalised group who experience heightened levels and unique forms of violence than men with disabilities, and women without disabilities. They face heightened stigma, discrimination, and violence due to their gender and their disability. Including women with disabilities in violence, research helps inform policy and prevention interventions that are relevant and inclusive. To ensure their inclusion in violence research, we need ethical guidelines that are sensitive to their heightened risk and vulnerability, that recognize the diversity in the disabled population, but that also promote disabled people’s agency in defining their own violence prevention needs and agendas. Objective: To highlight pertinent ethical issues around women with disabilities’ inclusion and participation in violence research. Methodology: Considering the lack of formalized guidelines for research of people with disabilities, we draw from the literature on international ethics guidelines for researching violence against women, and the Emancipatory Disability Research paradigm, as well as drawing from our own experiences from the field in applying the guidelines when doing research with disabled women. Findings: Following the guiding ethical principles of respect, benefit, justice, and do no harm, we argue that reasonable accommodation, capacity, and equal participation need to be considered in conceptualizing and conducting ethical violence research with women with disabilities. We conclude that disability research in the area of violence is highly politicized and must be carefully scrutinized to ensure justice and the contribution of women with disabilities to their own welfare. Implications: We suggest that these issues are practically applied in the field and tested and critiqued to enhance best practice for undertaking ethical research with this particular group. It is important that not only researchers and ethics committees, but also disabled women and disabled organizations, are involved in enhancing and formalizing ethical research guidelines for marginalized populations.

Keywords: capacity, emancipatory disability research paradigm equal participation, reasonable accommodation, research ethics, violence against women with disabilities

Procedia PDF Downloads 331