Search results for: subtle change detection and quantification

Authors: Sethu Lekshmi, Shahanas, Prettha P.

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All existing protocols in wireless networks are mainly based on Carrier Sense Multiple Access with Collision avoidance. By applying collision detection in wireless networks, the time spent on collision can be reduced and thus improves system throughput. However in a real WLAN scenario due to the use of nonlinear modulation techniques only receiver can decided whether a packet loss take place, even there are multiple transmissions. In this proposed method, the receiver or Access Point detects the collision when multiple data packets are transmitted from different wireless stations. Whenever the receiver detects a collision, it transmits a jamming signal to all the transmitting stations so that they can immediately stop their on-going transmissions. We also provide preferential access to all collided packet to reduce unfairness and to increase system throughput by reducing contention. However, this preferential access will not block the channel for the long time. Here, an in-band transmission is considered in which both the data frames and control frames are transmitted in the same channel. We also provide a simple mathematical model for the proposed protocol and give the simulation result of WLAN scenario under various capture thresholds.

Keywords: 802.11, WLAN, capture effect, collision detection, collision resolution, receiver initiated

Procedia PDF Downloads 351

Authors: Achim Kampker, Heiner Hans Heimes, Mathias Ordung, Nemanja Sarovic

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A key component of the drive train of electric vehicles is the lithium-ion battery system. Among various other components, such as the battery management system or the thermal management system, the battery system mostly consists of several cells which are integrated mechanically as well as electrically. Due to different vehicle concepts with regards to space, energy and power specifications, there is a variety of different battery systems. The corresponding assembly lines are specially designed for each battery concept. Minor changes to certain characteristics of the battery have a disproportionally high effect on the set-up effort in the form of high change-related costs. This paper will focus on battery systems which are made out of battery cells with a prismatic format. The product architecture and the assembly process will be analyzed in detail based on battery concepts of existing electric cars and key variety-causing drivers will be identified. On this basis, several measures will be presented and discussed on how to change the product architecture and the assembly process in order to reduce change-related costs.

Keywords: assembly, automotive industry, battery system, battery concept

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Authors: Giancarlo La Marca, Engy Shokry, Fabio Villanelli

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Epilepsy is one of the most common neurological disorders, with an estimated prevalence of 50 million people worldwide. Twenty five percent of the epilepsy population is represented in children under the age of 15 years. For antiepileptic drugs (AED), there is a poor correlation between plasma concentration and dose especially in children. This was attributed to greater pharmacokinetic variability than adults. Hence, therapeutic drug monitoring (TDM) is recommended in controlling toxicity while drug exposure is maintained. Carbamazepine (CBZ) is a first-line AED and the drug of first choice in trigeminal neuralgia. CBZ is metabolised in the liver into carbamazepine-10,11-epoxide (CBZE), its major metabolite which is equipotent. This develops the need for an assay able to monitor the levels of both CBZ and CBZE. The aim of the present study was to develop and validate a LC-MS/MS method for simultaneous quantification of CBZ and CBZE in dried blood spots (DBS). DBS technique overcomes many logistical problems, ethical issues and technical challenges faced by classical plasma sampling. LC-MS/MS has been regarded as superior technique over immunoassays and HPLC/UV methods owing to its better specificity and sensitivity, lack of interference or matrix effects. Our method combines advantages of DBS technique and LC-MS/MS in clinical practice. The extraction process was done using methanol-water-formic acid (80:20:0.1, v/v/v). The chromatographic elution was achieved by using a linear gradient with a mobile phase consisting of acetonitrile-water-0.1% formic acid at a flow rate of 0.50 mL/min. The method was linear over the range 1-40 mg/L and 0.25-20 mg/L for CBZ and CBZE respectively. The limit of quantification was 1.00 mg/L and 0.25 mg/L for CBZ and CBZE, respectively. Intra-day and inter-day assay precisions were found to be less than 6.5% and 11.8%. An evaluation of DBS technique was performed, including effect of extraction solvent, spot homogeneity and stability in DBS. Results from a comparison with the plasma assay are also presented. The novelty of the present work lies in being the first to quantify CBZ and its metabolite from only one 3.2 mm DBS disc finger-prick sample (3.3-3.4 µl blood) by LC-MS/MS in a 10 min. chromatographic run.

Keywords: carbamazepine, carbamazepine-10, 11-epoxide, dried blood spots, LC-MS/MS, therapeutic drug monitoring

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Authors: Olomon Wu, Hung Lu, Nick Wilkins, Daniel Kerr, Zekeriya Aliyazicioglu, H. K. Hwang

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Multiple Input Multiple Output (MIMO) radar has received increasing attention in recent years. MIMO radar has many advantages over conventional phased array radar such as target detection, resolution enhancement, and interference suppression. In this paper, the results are presented from a simulation study of MIMO Uniformly-Spaced Linear Array (ULA) antennas. The performance is investigated under varied parameters, including varied array size, Pseudo Random (PN) sequence length, number of snapshots, and Signal to Noise Ratio (SNR). The results of MIMO are compared to a traditional array antenna.

Keywords: MIMO radar, phased array antenna, target detection, radar signal processing

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Authors: Janika Vien K. Valsorable, Filma C. Calalo

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Climate change is one of the most damaging and serious environmental threats worldwide being faced today. While almost everyone highly depends and puts their trust on what technology, innovations, and initiatives from hard-core science can do to cope with the changing climate, there are still people who find hope on indigenous knowledge systems. The study aimed to analyze the traditional farming practices of the Dumagats in Tanay, Rizal and how these relate to their adaptation and mitigation of climate change. The analysis is based on interviews with 17 members of the Dumagat tribe specifically residing in Barangay Cuyambay, San Andres, and Mamuyao, and supported by Key Informant Interview and Focus Group Discussion as well as document reviews. Results of the study showed that the Dumagats adopt indigenous knowledge systems and their high sensitivity and resilience to climate change aid them in their farming system and activities. These traditional farming practices are exemplified from land preparation to planting, fertilizer application, weed and pest management, harvesting and post-harvest activities. Owing to their dependence upon, and close relationship with the environment and its resources, the Dumagats have learned to interpret and react to the impacts of climate change in creative ways, drawing on their traditional knowledge to cope with the impending changes. With the increasing trend at all levels of government to service the needs of rural communities, there is the need for the extension to contextualize advisory service delivery for indigenous communities.

Keywords: climate change, Dumagat tribe, indigenous knowledge systems, traditional farming practices

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Authors: Vimala Rani P, Narasimha Malikarjunan, Mercy Shalinie S

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Data Networking was brought forth as an instantiation of information-centric networking. The attackers can send a colossal number of spoofs to take hold of the Pending Interest Table (PIT) named an Interest Flooding attack (IFA) since the in- interests are recorded in the PITs of the intermediate routers until they receive corresponding Data Packets are go beyond the time limit. These attacks can be detrimental to network performance. PIT expiration rate or the Interest satisfaction rate, which cannot differentiate the IFA from attacks, is the criterion Traditional IFA detection techniques are concerned with. Threshold values can casually affect Threshold-based traditional methods. This article proposes an accurate IFA detection mechanism based on a Multiple Feature-based Extreme Learning Machine (MF-ELM). Accuracy of the attack detection can be increased by presenting the entropy of Internet names, Interest satisfaction rate and PIT usage as features extracted in the MF-ELM classifier. Furthermore, we deploy a queue-based hostile Interest prefix mitigation mechanism. The inference of this real-time test bed is that the mechanism can help the network to resist IFA with higher accuracy and efficiency.

Keywords: information-centric network, pending interest table, interest flooding attack, MF-ELM classifier, queue-based mitigation strategy

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Authors: Abdulsallam A. Bakdash, Aiyshah M. Alshehri, Hind M. Alenzi

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Benzatropine (benztropine) is used to treat symptoms of Parkinson's disease or involuntary movements due to the side effects of certain psychiatric drugs. We report in this study, results of a procedure for the determination of benzatropine in hair using LLE, once with methanol and second with phosphate buffer (pH 6.0), followed by filtration and then re-extraction with dichloromethane. A GC/MS method was developed and validated for this determination using selected ion monitoring (SIM) detection without derivatization. Linearity established over the concentration range 0.1-20.0 ng/mg hair, and the correlation coefficients were greater than 0.99. Recoveries were 52.2% and 21.1% using methanol and phosphate buffer extraction, respectively. Detection limits of benzatropine in hair were between 0.65 and 3.0 ng/mg hair, while the accuracy were 10.4% and 18.5% (RSD), respectively. We also applied this method to the analysis of soaked hair samples and demonstrated that the LLE using methanol meets the requirement for the analysis of benzatropine in hair.

Keywords: hair analysis, benzatropine, liquid-liquid extraction, GC/MS

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Authors: Muhammad Solikhudin Nafi, Wahyu Afif Mufida, Mita Erna Wati, Fitri Setyani Rokim, M. Al-Rizqi Dharma Fauzi

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High dose activity without any pre-exercise will impact Delayed Onset Muscle Soreness (DOMS). DOMS known as delayed pain post-exercise and induce skeletal injury which will decrease athletes’ performances. From now on, post-exercise muscle damage can be detected by measuring skeletal troponin I (sTnI) concentration in serum using ELISA but this method needs more time and cost. To prevent decreased athletes performances, screening need to be done rapidly. We want to introduce our new prototype to detect DOMS acutely. Rapid detection tests are based on immunological reaction between skeletal troponin I antibodies and sTnI in human serum or whole blood. Chemical methods that are used in the manufacture of diagnostic test is lateral flow immunoassay. The material used is rat monoclonal antibody sTnI, colloidal gold, anti-mouse IgG, nitrocellulose membrane, conjugate pad, sample pad, wick and backing card. The procedure are made conjugate (colloidal gold and mAb sTnI) and insert into the conjugate pad, gives spray sTnI mAb and anti-mouse IgG into nitrocellulose membrane, and assemble RDT. RDT had been evaluated by measuring the sensitivity of positive human serum (n = 30) and negative human serum (n = 30). Overall sensitivity value was 93% and specificity value was 90%. ADCOR as the first rapid detection test qualitatively showed antigen-antibody reaction and showed good overall performances for screening of muscle damage. Furthermore, these finding still need more improvements to get best results.

Keywords: DOMS, sTnI, rapid detection test, ELISA

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Authors: Mi-Ju Kim, Hae-Yeong Kim

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Product mislabeling and adulteration have been increasing the concerns in processed meat products. Relatively inexpensive pork meat compared to meat such as beef was adulterated for economic benefit. These food fraud incidents related to pork were concerned due to economic, religious and health reasons. In this study, a rapid on-site detection method using loop-mediated isothermal amplification (LAMP) was developed for the simultaneous identification of beef and pork. Each specific LAMP primer for beef and pork was designed targeting on mitochondrial D-loop region. The LAMP assay reaction was performed at 65 ℃ for 40 min. The specificity of each primer for beef and pork was evaluated using DNAs extracted from 13 animal species including beef and pork. The sensitivity of duplex LAMP assay was examined by serial dilution of beef and pork DNAs, and reference binary mixtures. This assay was applied to processed meat products including beef and pork meat for monitoring. Each set of primers amplified only the targeted species with no cross-reactivity with animal species. The limit of detection of duplex real-time LAMP was 1 pg for each DNA of beef and pork and 1% pork in a beef-meat mixture. Commercial meat products that declared the presence of beef and/or pork meat on the label showed positive results for those species. This method was successfully applied to detect simultaneous beef and pork meats in processed meat products. The optimized duplex LAMP assay can identify simultaneously beef and pork meat within less than 40 min. A portable real-time fluorescence device used in this study is applicable for on-site detection of beef and pork in processed meat products. Thus, this developed assay was considered to be an efficient tool for monitoring meat products.

Keywords: beef, duplex real-time LAMP, meat identification, pork

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Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

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Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

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Authors: Christos Chaschatzis, Chrysoula Karaiskou, Pantelis Angelidis, Sotirios K. Goudos, Igor Kotsiuba, Panagiotis Sarigiannidis

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Over the past few decades, the rapid growth of the global population has led to the need to increase agricultural production and improve the quality of agricultural goods. There is a growing focus on environmentally eco-friendly solutions, sustainable production, and biologically minimally fertilized products in contemporary society. Precision agriculture has the potential to incorporate a wide range of innovative solutions with the development of machine learning algorithms. YOLOv5 and YOLOv8 are two of the most advanced object detection algorithms capable of accurately recognizing objects in real time. Detecting tree diseases is crucial for improving the food production rate and ensuring sustainability. This research aims to evaluate the efficacy of YOLOv5 and YOLOv8 in detecting the symptoms of Armillaria spp. in sweet cherry trees and determining their health status, with the goal of enhancing the robustness of precision agriculture. Additionally, this study will explore Computer Vision (CV) techniques with machine learning algorithms to improve the detection process’s efficiency.

Keywords: Armillaria spp., machine learning, precision agriculture, smart farming, sweet cherries trees, YOLOv5, YOLOv8

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Authors: Asma Omri, Iheb Sifaoui, Sofiane Sayahi, Hichem Besbes

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Future vehicle systems demand advanced capabilities, notably in-cabin life detection and driver monitoring systems, with a particular emphasis on drowsiness detection. To meet these requirements, several techniques employ artificial intelligence methods based on real-time vital sign measurements. In parallel, Frequency-Modulated Continuous-Wave (FMCW) radar technology has garnered considerable attention in the domains of healthcare and biomedical engineering for non-invasive vital sign monitoring. FMCW radar offers a multitude of advantages, including its non-intrusive nature, continuous monitoring capacity, and its ability to penetrate through clothing. In this paper, we propose a system utilizing the AWR6843AOP radar from Texas Instruments (TI) to extract precise vital sign information. The radar allows us to estimate Ballistocardiogram (BCG) signals, which capture the mechanical movements of the body, particularly the ballistic forces generated by heartbeats and respiration. These signals are rich sources of information about the cardiac cycle, rendering them suitable for heart rate estimation. The process begins with real-time subject positioning, followed by clutter removal, computation of Doppler phase differences, and the use of various filtering methods to accurately capture subtle physiological movements. To address the challenges associated with FMCW radar-based vital sign monitoring, including motion artifacts due to subjects' movement or radar micro-vibrations, Long Short-Term Memory (LSTM) networks are implemented. LSTM's adaptability to different heart rate patterns and ability to handle real-time data make it suitable for continuous monitoring applications. Several crucial steps were taken, including feature extraction (involving amplitude, time intervals, and signal morphology), sequence modeling, heart rate estimation through the analysis of detected cardiac cycles and their temporal relationships, and performance evaluation using metrics such as Root Mean Square Error (RMSE) and correlation with reference heart rate measurements. For dataset construction and LSTM training, a comprehensive data collection system was established, integrating the AWR6843AOP radar, a Heart Rate Belt, and a smart watch for ground truth measurements. Rigorous synchronization of these devices ensured data accuracy. Twenty participants engaged in various scenarios, encompassing indoor and real-world conditions within a moving vehicle equipped with the radar system. Static and dynamic subject’s conditions were considered. The heart rate estimation through LSTM outperforms traditional signal processing techniques that rely on filtering, Fast Fourier Transform (FFT), and thresholding. It delivers an average accuracy of approximately 91% with an RMSE of 1.01 beat per minute (bpm). In conclusion, this paper underscores the promising potential of FMCW radar technology integrated with artificial intelligence algorithms in the context of automotive applications. This innovation not only enhances road safety but also paves the way for its integration into the automotive ecosystem to improve driver well-being and overall vehicular safety.

Keywords: ballistocardiogram, FMCW Radar, vital sign monitoring, LSTM

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Authors: Mohamad R. Moshtagh, Ahmad Bagheri

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Accurate fault detection in rotating machinery is of utmost importance to ensure optimal performance and prevent costly downtime in industrial applications. This study presents a robust fault detection system based on vibration data collected from rotating gears under various operating conditions. The considered scenarios include: (1) both gears being healthy, (2) one healthy gear and one faulty gear, and (3) introducing an imbalanced condition to a healthy gear. Vibration data was acquired using a Hentek 1008 device and stored in a CSV file. Python code implemented in the Spider environment was used for data preprocessing and analysis. Winner features were extracted using the Wiener feature selection method. These features were then employed in multiple machine learning algorithms, including Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), and Random Forest, to evaluate their performance in detecting and classifying faults in both the training and validation datasets. The comparative analysis of the methods revealed the superior performance of the Wiener-CNN approach. The Wiener-CNN method achieved a remarkable accuracy of 100% for both the two-class (healthy gear and faulty gear) and three-class (healthy gear, faulty gear, and imbalanced) scenarios in the training and validation datasets. In contrast, the other methods exhibited varying levels of accuracy. The Wiener-MLP method attained 100% accuracy for the two-class training dataset and 100% for the validation dataset. For the three-class scenario, the Wiener-MLP method demonstrated 100% accuracy in the training dataset and 95.3% accuracy in the validation dataset. The Wiener-KNN method yielded 96.3% accuracy for the two-class training dataset and 94.5% for the validation dataset. In the three-class scenario, it achieved 85.3% accuracy in the training dataset and 77.2% in the validation dataset. The Wiener-Random Forest method achieved 100% accuracy for the two-class training dataset and 85% for the validation dataset, while in the three-class training dataset, it attained 100% accuracy and 90.8% accuracy for the validation dataset. The exceptional accuracy demonstrated by the Wiener-CNN method underscores its effectiveness in accurately identifying and classifying fault conditions in rotating machinery. The proposed fault detection system utilizes vibration data analysis and advanced machine learning techniques to improve operational reliability and productivity. By adopting the Wiener-CNN method, industrial systems can benefit from enhanced fault detection capabilities, facilitating proactive maintenance and reducing equipment downtime.

Keywords: fault detection, gearbox, machine learning, wiener method

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Authors: Helmut Geiblinger

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Dismounts performed by elite gymnasts during competition require great courage and virtuoso displays of precisely organized movements and skills. The dismount and landing leave the final impression in a routine and are often the key to a successful evaluation by the judges. Landings require precise body control and the skillful dissipation of substantial body momentum. The aim of this research study was to investigate landing techniques and strategies used by elite male gymnasts through the eyes of gymnastics experts. It drew from the accrued knowledge and experience of 21 male expert participants who were elite coaches, elite gymnasts, international judges or combinations of these. The experts made a number of subtle points, many of which are not in the extant literature. The experts highlighted concerns about safety and the study concluded that on-going monitoring of the rules on competition landings within the Code of Points would be beneficial to the sport.

Keywords: controlled competition landings, landing technique, landing strategies, optimal body segment coordination

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Authors: Ayse Arslan

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One of the important elements underpinning a conscious design is the degree of toxicity in communication. This study explores the mechanisms and strategies for identifying toxic content by avoiding dark patterns. Given the breadth of hate and harassment attacks, this study explores a threat model and taxonomy to assist in reasoning about strategies for detection, prevention, mitigation, and recovery. In addition to identifying some relevant techniques such as nudges, automatic detection, or human-ranking, the study suggests the use of major metrics such as the overhead and friction of solutions on platforms and users or balancing false positives (e.g., incorrectly penalizing legitimate users) against false negatives (e.g., users exposed to hate and harassment) to maintain a conscious design towards fairness.

Keywords: AI, ML, algorithms, policy, system design

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Authors: Jamal A. Radaideh, A. J. Radaideh

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Being a problem threatening the planet and its ecosystems, the climate change has been considered for a long time as a disturbing topic impacting water resources in Jordan. Jordan is expected for instance to be highly vulnerable to climate change consequences given its unbalanced distribution between water resources availability and existing demands. Thus, action on adaptation to climate impacts is urgently needed to cope with the negative consequences of climate change. Adaptation to global change must include prudent management of treated wastewater as a renewable resource, especially in regions lacking groundwater or where groundwater is already over exploited. This paper highlights the expected negative effects of climate change on the already scarce water sources and to motivate researchers and decision makers to take precautionary measures and find alternatives to keep the level of water supplies at the limits required for different consumption sectors in terms of quantity and quality. The paper will focus on assessing the potential for wastewater recycling as an adaptation measure to cope with water scarcity in Jordan and to consider wastewater as integral part of the national water budget to solve environmental problems. The paper also identified a research topic designed to help the nation progress in making the most appropriate use of the resource, namely for agricultural irrigation. Wastewater is a promising alternative to fill the shortage in water resources, especially due to climate changes, and to preserve the valuable fresh water to give priority to securing drinking water for the population from these resources and at the same time raise the efficiency of the use of available resources. Jordan has more than 36 wastewater treatment plants distributed throughout the country and producing about 386,000 CM/day of reclaimed water. According to the reports of water quality control programs, more than 85 percent of this water is of a quality that is completely identical to the quality suitable for irrigation of field crops and forest trees according to the requirements of Jordanian Standard No. 893/2006.

Keywords: climate change effects on water resources, adaptation on climate change, treated wastewater recycling, arid and semi-arid regions, Jordan

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Authors: Leila Rashidian

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Temperature rise on Earth has had harmful effects on the Earth's surface and has led to change in precipitation patterns all around the world. The present research was aimed to study the process of climate change according to the data simulation in future and compare these parameters with current situation in the studied stations in Semnan province including Garmsar, Shahrood and Semnan. In this regard, LARS-WG software, HADCM₃ model and A₂ scenario were used for the 2010-2030 period. In this model, climatic parameters such as maximum and minimum temperature, precipitation and radiation were used daily. The obtained results indicated that there will be a 4.4% increase in precipitation in Semnan province compared with the observed data, and in general, there will be a 1.9% increase in temperature. This temperature rise has significant impact on precipitation patterns. Most of precipitation will be raining (torrential rains in some cases). According to the results, from west to east, the country will experience more temperature rise and will be warmer.

Keywords: climate change, Semnan province, Lars.WG model, climate parameters, HADCM₃ model

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Authors: Shiliang Wu, Huanxin Zhang, Aditya Kumar

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There have been increasing concerns on atmospheric mercury as a toxic and bioaccumulative pollutant in the global environment. Global change, including changes in climate change, land use, land cover and wildfires activities can all have significant impacts on atmospheric mercury. In this study, we use a global chemical transport model (GEOS-Chem) to examine the potential impacts from global change on atmospheric mercury. All of these factors in the context of global change are found to have significant impacts on the long-term evolution of atmospheric mercury and can substantially alter the global source-receptor relationships for mercury. We also estimate the global Hg emissions from wildfires for present-day and the potential impacts from the 2000-2050 changes in climate, land use and land cover and Hg anthropogenic emissions by combining statistical analysis with global data on vegetation type and coverage as well as fire activities. Present global Hg wildfire emissions are estimated to be 612 Mg year-1. Africa is the dominant source region (43.8% of global emissions), followed by Eurasia (31%) and South America (16.6%). We find significant perturbations to wildfire emissions of Hg in the context of global change, driven by the projected changes in climate, land use and land cover and Hg anthropogenic emissions. 2000-2050 climate change could increase Hg emissions by 14% globally. Projected changes in land use by 2050 could decrease the global Hg emissions from wildfires by 13% mainly driven by a decline in African emissions due to significant agricultural land expansion. Future land cover changes could lead to significant increases in Hg emissions over some regions (+32% North America, +14% Africa, +13% Eurasia). Potential enrichment of terrestrial ecosystems in 2050 in response to changes in Hg anthropogenic emissions could increase Hg wildfire emissions both globally (+28%) and regionally. Our results indicate that the future evolution of climate, land use and land cover and Hg anthropogenic emissions are all important factors affecting Hg wildfire emissions in the coming decades.

Keywords: climate change, land use, land cover, wildfires

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Authors: J. Yang, M. Monnot, T. Eljaddi, L. Simonian, L. Ercolei, P. Moulin

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The application of membrane technology to wastewater treatment has expanded rapidly under increasing stringent legislation and environmental protection requirements. At the same time, the water resource is becoming precious, and water reuse has gained popularity. Particularly, ultrafiltration (UF) is a very promising technology for water reuse as it can retain organic matters, suspended solids, colloids, and microorganisms. Nevertheless, few studies dealing with operating optimization of UF as a tertiary treatment for water reuse on a semi-industrial scale appear in the literature. Therefore, this study aims to explore the permeate water quality and to optimize operating parameters (maximizing productivity and minimizing irreversible fouling) through the operation of a UF pilot plant under real conditions. The fully automatic semi-industrial UF pilot plant with periodic classic backwashes (CB) and air backwashes (AB) was set up to filtrate the secondary effluent of an urban wastewater treatment plant (WWTP) in France. In this plant, the secondary treatment consists of a conventional activated sludge process followed by a sedimentation tank. The UF process was thus defined as a tertiary treatment and was operated under constant flux. It is important to note that a combination of CB and chlorinated AB was used for better fouling management. The 200 kDa hollow fiber membrane was used in the UF module, with an initial permeability (for WWTP outlet water) of 600 L·m-2·h⁻¹·bar⁻¹ and a total filtration surface of 9 m². Fifteen filtration conditions with different fluxes, filtration times, and air backwash frequencies were operated for more than 40 hours of each to observe their hydraulic filtration performances. Through comparison, the best sustainable condition was flux at 60 L·h⁻¹·m⁻², filtration time at 60 min, and backwash frequency of 1 AB every 3 CBs. The optimized condition stands out from the others with > 92% water recovery rates, better irreversible fouling control, stable permeability variation, efficient backwash reversibility (80% for CB and 150% for AB), and no chemical washing occurrence in 40h’s filtration. For all tested conditions, the permeate water quality met the water reuse guidelines of the World Health Organization (WHO), French standards, and the regulation of the European Parliament adopted in May 2020, setting minimum requirements for water reuse in agriculture. In permeate: the total suspended solids, biochemical oxygen demand, and turbidity were decreased to < 2 mg·L-1, ≤ 10 mg·L⁻¹, < 0.5 NTU respectively; the Escherichia coli and Enterococci were > 5 log removal reduction, the other required microorganisms’ analysis were below the detection limits. Additionally, because of the COVID-19 pandemic, coronavirus SARS-CoV-2 was measured in raw wastewater of WWTP, UF feed, and UF permeate in November 2020. As a result, the raw wastewater was tested positive above the detection limit but below the quantification limit. Interestingly, the UF feed and UF permeate were tested negative to SARS-CoV-2 by these PCR assays. In summary, this work confirms the great interest in UF as intensified tertiary treatment for water reuse and gives operational indications for future industrial-scale production of reclaimed water.

Keywords: semi-industrial UF pilot plant, water reuse, fouling management, coronavirus

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Authors: Srushti R. Joshi, Ujjwal Rakesh, Spoorthi Sripad

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The rapid advancement of remote sensing technologies has revolutionized plant health monitoring, offering valuable insights for precision agriculture and environmental management. This paper presents a comprehensive comparative analysis between the widely employed normalized difference vegetation index (NDVI) and state-of-the-art hyperspectral sensors in the context of plant health assessment. The study aims to elucidate the weigh ups of spectral resolution. Employing a diverse range of vegetative environments, the research utilizes simulated datasets to evaluate the performance of NDVI and hyperspectral sensors in detecting subtle variations indicative of plant stress, disease, and overall vitality. Through meticulous data analysis and statistical validation, this study highlights the superior performance of hyperspectral sensors across the parameters used.

Keywords: normalized difference vegetation index, hyperspectral sensor, spectral resolution, infrared

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Authors: Nougrara Zineb

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The unique structure of the blood vessels in the retina has been used for biometric identification. The retina blood vessel pattern is a unique pattern in each individual and it is almost impossible to forge that pattern in a false individual. The retina biometrics’ advantages include high distinctiveness, universality, and stability overtime of the blood vessel pattern. Once the creases have been extracted from the images, a registration stage is necessary, since the position of the retinal vessel structure could change between acquisitions due to the movements of the eye. Image registration consists of following steps: Feature detection, feature matching, transform model estimation and image resembling and transformation. In this paper, we present an algorithm of registration; it is based on the characterization of retinal feature points. For experiments, retinal images from the DRIVE database have been tested. The proposed methodology achieves good results for registration in general.

Keywords: fovea, optic disc, registration, retinal images

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Authors: They Shao Peng

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Machine vision system for inspection is an automated technology that is normally utilized to analyze items on the production line for quality control purposes, it also can be known as an automated visual inspection (AVI) system. By applying automated visual inspection, the existence of items, defects, contaminants, flaws, and other irregularities in manufactured products can be easily detected in a short time and accurately. However, AVI systems are still inflexible and expensive due to their uniqueness for a specific task and consuming a lot of set-up time and space. With the rapid development of mobile devices, smartphones can be an alternative device for the visual system to solve the existing problems of AVI. Since the smartphone-based AVI system is still at a nascent stage, this led to the motivation to investigate the smartphone-based AVI system. This study is aimed to provide a low-cost AVI system with high efficiency and flexibility. In this project, the object detection models, which are You Only Look Once (YOLO) model and Single Shot MultiBox Detector (SSD) model, are trained, evaluated, and integrated with the smartphone and webcam devices. The performance of the smartphone-based AVI is compared with the webcam-based AVI according to the precision and inference time in this study. Additionally, a mobile application is developed which allows users to implement real-time object detection and object detection from image storage.

Keywords: automated visual inspection, deep learning, machine vision, mobile application

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Authors: Angela Meyer

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The operation and maintenance cost for wind parks make up a major fraction of the park’s overall lifetime cost. To minimize the cost and risk involved, an optimal operation and maintenance strategy requires continuous monitoring and analysis. In order to facilitate this, we present a decision support system that automatically scans the stream of telemetry sensor data generated from the turbines. By learning decision boundaries and normal reference operating states using machine learning algorithms, the decision support system can detect anomalous operating behavior in individual wind turbines and diagnose the involved turbine sub-systems. Operating personal can be alerted if a normal operating state boundary is exceeded. The presented decision support system and method are applicable for any turbine type and manufacturer providing telemetry data of the turbine operating state. We demonstrate the successful detection and diagnosis of anomalous operating states in a case study at a German onshore wind park comprised of Vestas V112 turbines.

Keywords: anomaly detection, decision support, machine learning, monitoring, performance optimization, wind turbines

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Authors: Hazem Munawer Al-Otum

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In this work, an efficient watermarking technique is proposed and can be used for detecting intentional attacks in RGB color images. The proposed technique can be implemented for image authentication and exhibits high robustness against unintentional common image processing attacks. It deploys two measures to discern between intentional and unintentional attacks based on using a quantization-based technique in a modified 2D multi-pyramidal DWT transform. Simulations have shown high accuracy in detecting intentionally attacked regions while exhibiting high robustness under moderate to severe common image processing attacks.

Keywords: image authentication, copyright protection, semi-fragile watermarking, tamper detection

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Authors: Navpreet Kaur, Himkusha Thakur, Nirmal Prabhakar

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The most controversial issue in crop production is the use of Organophosphate insecticides. This is evident in many reports that Organophosphate (OP) insecticides, among the broad range of pesticides are mainly involved in acute and chronic poisoning cases. OPs detection is of crucial importance for health protection, food and environmental safety. In our study, a nanocomposite of poly (3,4 ethylenedioxythiophene) (PEDOT) and multi-walled carbon nanotubes (MWCNTs) has been deposited electrochemically onto the surface of fluorine doped tin oxide sheets (FTO) for the analysis of malathion OP. The -COOH functionalization of MWCNTs has been done for the covalent binding with amino groups of AChE enzyme. The use of PEDOT-MWCNT films exhibited an excellent conductivity, enables fast transfer kinetics and provided a favourable biocompatible microenvironment for AChE, for the significant malathion OP detection. The prepared biosensors were characterized by Fourier transform infrared spectrometry (FTIR), Field emission-scanning electron microscopy (FE-SEM) and electrochemical studies. Various optimization studies were done for different parameters including pH (7.5), AChE concentration (50 mU), substrate concentration (0.3 mM) and inhibition time (10 min). Substrate kinetics has been performed and studied for the determination of Michaelis Menten constant. The detection limit for malathion OP was calculated to be 1 fM within the linear range 1 fM to 1 µM. The activity of inhibited AChE enzyme was restored to 98% of its original value by 2-pyridine aldoxime methiodide (2-PAM) (5 mM) treatment for 11 min. The oxime 2-PAM is able to remove malathion from the active site of AChE by means of trans-esterification reaction. The storage stability and reusability of the prepared biosensor is observed to be 30 days and seven times, respectively. The application of the developed biosensor has also been evaluated for spiked lettuce sample. Recoveries of malathion from the spiked lettuce sample ranged between 96-98%. The low detection limit obtained by the developed biosensor made them reliable, sensitive and a low cost process.

Keywords: PEDOT-MWCNT, malathion, organophosphates, acetylcholinesterase, biosensor, oxime (2-PAM)

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Authors: Jozef Mindas, Jaroslav Skvarenina, Jana Skvareninova

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Study of the effects of climate change on Norway Spruce (Picea abies) forests has mainly focused on the diversity of tree species diversity of tree species as a result of the ability of species to tolerate temperature and moisture changes as well as some effects of disturbance regime changes. The tree species’ diversity changes in spruce forests due to climate change have been analyzed via gap model. Forest gap model is a dynamic model for calculation basic characteristics of individual forest trees. Input ecological data for model calculations have been taken from the permanent research plots located in primeval forests in mountainous regions in Slovakia. The results of regional scenarios of the climatic change for the territory of Slovakia have been used, from which the values are according to the CGCM3.1 (global) model, KNMI and MPI (regional) models. Model results for conditions of the climate change scenarios suggest a shift of the upper forest limit to the region of the present subalpine zone, in supramontane zone. N. spruce representation will decrease at the expense of beech and precious broadleaved species (Acer sp., Sorbus sp., Fraxinus sp.). The most significant tree species diversity changes have been identified for the upper tree line and current belt of dwarf pine (Pinus mugo) occurrence. The results have been also discussed in relation to most important disturbances (wind storms, snow and ice storms) and phenological changes which consequences are little known. Special discussion is focused on biomass production changes in relation to carbon storage diversity in different carbon pools.

Keywords: biodiversity, climate change, Norway spruce forests, gap model

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Authors: Saeid Gharechelou, Ryutaro Tateishi

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Earthquake is an inevitable catastrophic natural disaster. The damages of buildings and man-made structures, where most of the human activities occur are the major cause of casualties from earthquakes. A comparison of optical and SAR data is presented in the case of Kathmandu valley which was hardly shaken by 2015-Nepal Earthquake. Though many existing researchers have conducted optical data based estimated or suggested combined use of optical and SAR data for improved accuracy, however finding cloud-free optical images when urgently needed are not assured. Therefore, this research is specializd in developing SAR based technique with the target of rapid and accurate geospatial reporting. Should considers that limited time available in post-disaster situation offering quick computation exclusively based on two pairs of pre-seismic and co-seismic single look complex (SLC) images. The InSAR coherence pre-seismic, co-seismic and post-seismic was used to detect the change in damaged area. In addition, the ground truth data from field applied to optical data by random forest classification for detection of damaged area. The ground truth data collected in the field were used to assess the accuracy of supervised classification approach. Though a higher accuracy obtained from the optical data then integration by optical-SAR data. Limitation of cloud-free images when urgently needed for earthquak evevent are and is not assured, thus further research on improving the SAR based damage detection is suggested. Availability of very accurate damage information is expected for channelling the rescue and emergency operations. It is expected that the quick reporting of the post-disaster damage situation quantified by the rapid earthquake assessment should assist in channeling the rescue and emergency operations, and in informing the public about the scale of damage.

Keywords: Sentinel-1A data, Landsat-8, earthquake damage, InSAR, rapid damage monitoring, 2015-Nepal earthquake

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Authors: M. Hemnath, S. Kannan, R. Kiran, K. Thanigaivelu

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This paper is based on exploring the possible opportunities and applications using Power Line Communication (PLC) for security and surveillance operations. Various research works are done for introducing PLC into onboard vehicle communication and networking (CAN, LIN etc.) and various international standards have been developed. Wireless power transfer (WPT) is also an emerging technology which is studied and tested for recharging purposes. In this work we present a system which embeds the detection and the response into one which eliminates the need for dedicated network for data transmission. Also we check the feasibility for integrating wireless power transfer system into this proposed security system for transmission of power to detection unit wirelessly from the response unit.

Keywords: power line communication, wireless power transfer, surveillance

Procedia PDF Downloads 523

Authors: K. M. Kim, S. J. Lee, D. S. Oh, Sadohara Satoru

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The climate change poses a serious challenge to urban sustainability. To alleviate the environmental risk, urban planning has been concentrated on climate adaptation and mitigation, and the sustainable urban model, smart city, has been suggested. However, with regard to sustainable smart city development, a majority of researchers have focused mainly on the aspect of adaptation, which causes the lack of the approaches for mitigation. Therefore, the objective was to identify the planning elements of smart city with integrative reviews about mitigation and adaptation. Moreover, the concepts of smart cities in Sweden, Japan, and Korea were analyzed to find out the country-specific characteristics and strategies for achieving smart city.

Keywords: sustainable urban planning, climate change, mitigating and adaptation, smart city

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Authors: Adriana Radan Ungureanu

Abstract:

The industrial landscape is changing due to the financial crises, poor availability of raw materials, new discoveries and interdisciplinary collaborations. New ideas shape the change through technologies and bring responses for a better life. The process of change is leaded by big players like states and companies, but they cannot keep their places on the market without the help of the small ones. The main tool of change is technology and the entire developed world dedicated efforts for decades in this direction. Even the expectations are not yet met, the research for finding adequate solutions is far from to be stopped. A relevant example is nanotechnology where most of discoveries still remain into laboratory and could not succeed to find the right way to the market. In front of this situation the right question could be: ”Is it worth investing in nanotechnology in the name of an uncertain future but with very little impact on present?” This paper tries to find a positive answer from a three-dimensional approach using a descriptive analyse based on available database supplied by the European case studies, reports, and literature.

Keywords: Europe, KET’s, nanotechnology, technology

Procedia PDF Downloads 402