Search results for: microbial detection

Authors: Obsi Terfasa, Bhanupriya Das, Mithilish Passawan

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The large-scale application of microbial fuel cell (MFC) technology is significantly hindered by the high cost of the commonly used proton exchange membrane, Nafion. This has led to the recent development of ceramic membranes using various clay minerals. This study evaluates the characteristics and potential use of a new ceramic membrane made from potter’s clay © mixed with different proportions (0, 5, 10 wt%) of fly ash (FA), labeled as CFA0, CFA5, CFA10, for cost-effective and sustainable MFC use. Among these, the CFA10 membrane demonstrated superior quality with a fine pore size distribution (average 0.41 μm), which supports higher water uptake and reduced oxygen diffusion. Its oxygen mass transfer coefficient was 4.13 ± 0.13 × 10⁻⁴ cm/s, about 40% lower than the control. X-ray diffraction analysis revealed that the CFA membrane is rich in quartz, which enhances proton conductance and water retention. Electrochemical kinetics studies, including cyclic voltammetry and electrochemical impedance spectroscopy (EIS), also confirmed the effectiveness of the CFA10 membrane in MFC, showing a peak current output of 15.35 mA and low ohmic resistance (78.2 Ω). The novel CFA10 ceramic membrane, incorporating coal fly ash, a waste material, shows promise for high MFC performance at a significantly reduced cost (96%), making it suitable for sustainable scaling up of the technology.

Keywords: ceramic membrane, Coulombic efficiency, electro-chemical kinetics, fly ash, proton conductivity, microbial fuel cell

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Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

Procedia PDF Downloads 188

Authors: H. Abu-Ali, A. Nabok, T. Smith

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Aptamers are single-strand of DNA or RNA nucleotides sequence which is designed in vitro using selection process known as SELEX (systematic evolution of ligands by exponential enrichment) were developed for the selective detection of many toxic materials. In this work, we have developed an electrochemical biosensor for highly selective and sensitive detection of Hg2+ and Pb2+ using a specific aptamer probe (SAP) labelled with ferrocene (or methylene blue) in (5′) end and the thiol group at its (3′) termini, respectively. The SAP has a specific coil structure that matching with G-G for Pb2+ and T-T for Hg2+ interaction binding nucleotides ions, respectively. Aptamers were immobilized onto surface of screen-printed gold electrodes via SH groups; then the cyclic voltammograms were recorded in binding buffer with the addition of the above metal salts in different concentrations. The resulted values of anode current increase upon binding heavy metal ions to aptamers and analyte due to the presence of electrochemically active probe, i.e. ferrocene or methylene blue group. The correlation between the anodic current values and the concentrations of Hg2+ and Pb2+ ions has been established in this work. To the best of our knowledge, this is the first example of using a specific DNA aptamers for electrochemical detection of heavy metals. Each increase in concentration of 0.1 μM results in an increase in the anode current value by simple DC electrochemical test i.e (Cyclic Voltammetry), thus providing an easy way of determining Hg2+ and Pb2+concentration.

Keywords: aptamer, based, biosensor, DNA, electrochemical, highly, specific

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Authors: Sujay Kotwale, Ramasubba Reddy M.

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Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques.

Keywords: cardiac arrhythmia, electrocardiogram, principal component analysis, XGBoost

Procedia PDF Downloads 119

Authors: McClain Thiel

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Object detection and localization has historically required two or more sensors due to the loss of information from 3D to 2D space, however, most surveillance systems currently in use in the real world only have one sensor per location. Generally, this consists of a single low-resolution camera positioned above the area under observation (mall, jewelry store, traffic camera). This is not sufficient for robust 3D tracking for applications such as security or more recent relevance, contract tracing. This paper proposes a lightweight system for 3D person tracking that requires no additional hardware, based on compressed object detection convolutional-nets, facial landmark detection, and projective geometry. This approach involves classifying the target into a demographic category and then making assumptions about the relative locations of facial landmarks from the demographic information, and from there using simple projective geometry and known constants to find the target's location in 3D space. Preliminary testing, although severely lacking, suggests reasonable success in 3D tracking under ideal conditions.

Keywords: monocular distancing, computer vision, facial analysis, 3D localization

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Authors: Faozia A. S. Abuhtana, Yahia S. Abujnah, Said O. Gnann

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Ultra High Temperature (UHT) processed milk is widely distributed and preferred in numerous countries all over the world due its relatively high quality and long shelf life. Because of the notable high consumption rate of UHT in Libya in addition to negligible studies related to such product on the local level, this study was designed to assess the shelf life of locally produced as well as imported reconstituted sterilized whole milk samples marketed in Tripoli, Libya . Four locally produced vs. three imported brands were used in this study. All samples were stored at room temperature (25± 2C ) for 8 month long period, and subjected to physical, chemical, microbiological and sensory tests. These tests included : measurement of pH, specific gravity, percent acidity, and determination of fat, protein and melamine content. Microbiological tests included total aerobic count, total psychotropic bacteria, total spore forming bacteria and total coliform counts. Results indicated no detection of microbial growth of any type during the study period, in addition to no detection of melamine in all samples. On the other hand, a gradual decline in pH accompanied with gradual increase in % acidity of both locally produced and imported samples was observed. Such changes in both pH and % acidity reached their lowest and highest values respectively during the 24th week of storage. For instance pH values were (6.40, 6.55, 6.55, 6.15) and (6.30, 6.50, 6.20) for local and imported brands respectively. On the other hand, % acidity reached (0.185, 0181, 0170, 0183) and (0180, 0.180, 0.171) at the 24th week for local and imported brands respectively. Similar pattern of decline was also observed in specific gravity, fat and protein content in some local and imported samples especially at later stages of the study. In both cases, some of the recorded pH values, % acidity, sp. gravity and fat content were in violation of the accepted limits set by Libyan standard no. 356 for sterilized milk. Such changes in pH, % acidity and other UHT sterilized milk constituents during storage were coincided with a gradual decrease in the degree of acceptance of the stored milk samples of both types as shown by sensory scores recorded by the panelists. In either case degree of acceptance was significantly low at late stages of storage and most milk samples became relatively unacceptable after the 18th and 20th week for both untrained and trained panelists respectively.

Keywords: UHT milk, shelf life, quality, gravity, bacteria

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Authors: Ghulam Abbas Shah, Maqsood Sadiq, Ahsan Yasin

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Massive global plastic production, combined with poor degradation and recycling, leads to significant environmental pollution from microplastics, whose effects on plants in the soil remain understudied. Besides, effective mitigation strategies and their impact on ammonia (NH₃) emissions under varying fertilizer management practices remains sketchy. Therefore, the objectives of the study were (i) to determine the impact of organic fertilizers on the toxicity of microplastics in sorghum and physicochemical characteristics of microplastics-contaminated soil and (ii) to assess the impacts of these fertilizers on NH₃ emissions from this soil. A field experiment was conducted using sorghum as a test crop. Treatments were: (i) Control (C), (ii) Microplastics (MP), (iii) Inorganic fertilizer (IF), (iv) MPIF, (v) Farmyard manure (FM), (vi) MPFM, (vii) Biochar (BC), and (viii) MPBC, arranged in a randomized complete block design (RCBD) with three replicates. Microplastics of polyvinyl chloride (PVC) were applied at a rate of 1.5 tons ha-¹, and all fertilizers were applied at the recommended dose of 90 kg N ha-¹. Soil sampling was done before sowing and after harvesting the sorghum, with samples analyzed for chemical properties and microbial biomass. Crop growth and yield attributes were measured. In a parallel pot experiment, NH₃ emissions were measured using passive flux samplers over 72 hours following the application of treatments similar to those used in the field experiment. Application of MPFM, MPBC and MPIF reduced soil mineral nitrogen by 8, 20 and 38% compared to their sole treatments, respectively. Microbial biomass carbon (MBC) was reduced by 19, 25 and 59% in MPIF, MPBC and MPFM as compared to their sole application, respectively. Similarly, the respective reduction in microbial biomass nitrogen (MBN) was 10, 27 and 66%. The toxicity of microplastics was mitigated by MPFM and MPBC, each with only a 5% reduction in grain yield of sorghum relative to their sole treatments. The differences in nitrogen uptake between BC vs. MPBC, FM vs. MPFM, and IF vs. MPIF were 8, 10, and 12 kg N ha-¹, respectively, indicating that organic fertilizers mitigate microplastic toxicity in the soil. NH₃ emission was reduced by 5, 11 and 20% after application of MPFM, MPBC and MPIF than their sole treatments, respectively. The study concludes that organic fertilizers such as FM and BC can effectively mitigate the toxicity of microplastics in soil, leading to improved crop growth and yield.

Keywords: microplastics, soil characteristics, crop n uptake, biochar, NH₃ emissions

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Authors: M. A. Alavianmehr, A. Tashk, A. Sodagaran

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Modeling background and moving objects are significant techniques for video surveillance and other video processing applications. This paper presents a foreground detection algorithm that is robust against illumination changes and noise based on adaptive mixture Gaussian model (GMM), and provides a novel and practical choice for intelligent video surveillance systems using static cameras. In the previous methods, the image of still objects (background image) is not significant. On the contrary, this method is based on forming a meticulous background image and exploiting it for separating moving objects from their background. The background image is specified either manually, by taking an image without vehicles, or is detected in real-time by forming a mathematical or exponential average of successive images. The proposed scheme can offer low image degradation. The simulation results demonstrate high degree of performance for the proposed method.

Keywords: image processing, background models, video surveillance, foreground detection, Gaussian mixture model

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Authors: Francis Gyapong, Denis Yar

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The use of cell phones has become an indispensable tool in the hospital's settings. Cell phones are used in hospitals without restrictions regardless of their unknown microbial load. However, the indiscriminate use of mobile devices, especially at health facilities, can act as a vehicle for transmitting pathogenic bacteria and other microorganisms. These potential pathogens become exogenous sources of infection for the patients and are also a potential health hazard for self and as well as family members. These are a growing problem in many health care institutions. Innovations in mobile communication have led to better patient care in diabetes, asthma, and increased in vaccine uptake via SMS. Notwithstanding, the use of cell phones can be a great potential source for nosocomial infections. Many studies reported heavy microbial contamination of cell phones among healthcare workers and communities. However, limited studies have been reported in our region on bacterial contamination on cell phones among healthcare workers. This study assessed microbial contamination of cell phones of health care workers (HCWs) at the Mampong Municipal Government Hospital (MMGH), Ghana. A cross-sectional design was used to characterize bacterial microflora on cell phones of HCWs at the MMGH. A total of thirty-five (35) swab samples of cell phones of HCWs at the Laboratory, Dental Unit, Children’s Ward, Theater and Male ward were randomly collected for laboratory examinations. A suspension of the swab samples was each streak on blood and MacConkey agar and incubated at 37℃ for 48 hours. Bacterial isolates were identified using appropriate laboratory and biochemical tests. Kirby-Bauer disc diffusion method was used to determine the antimicrobial sensitivity tests of the isolates. Data analysis was performed using SPSS version 16. All mobile phones sampled were contaminated with one or more bacterial isolates. Cell phones from the Male ward, Dental Unit, Laboratory, Theatre and Children’s ward had at least three different bacterial isolates; 85.7%, 71.4%, 57.1% and 28.6% for both Theater and Children’s ward respectively. Bacterial contaminants identified were Staphylococcus epidermidis (37%), Staphylococcus aureus (26%), E. coli (20%), Bacillus spp. (11%) and Klebsiella spp. (6 %). Except for the Children ward, E. coli was isolated at all study sites and predominant (42.9%) at the Dental Unit while Klebsiella spp. (28.6%) was only isolated at the Children’s ward. Antibiotic sensitivity testing of Staphylococcus aureus indicated that they were highly sensitive to cephalexin (89%) tetracycline (80%), gentamycin (75%), lincomycin (70%), ciprofloxacin (67%) and highly resistant to ampicillin (75%). Some of these bacteria isolated are potential pathogens and their presence on cell phones of HCWs could be transmitted to patients and their families. Hence strict hand washing before and after every contact with patient and phone be enforced to reduce the risk of nosocomial infections.

Keywords: mobile phones, bacterial contamination, patients, MMGH

Procedia PDF Downloads 103

Authors: Jia Li, Xing Wei, Yuchen Hong, Yang Lu

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Detecting vehicle behavior has always been the focus of intelligent transportation, but with the explosive growth of the number of vehicles and the complexity of the road environment, the vehicle behavior videos captured by traditional surveillance have been unable to satisfy the study of vehicle behavior. The traditional method of manually labeling vehicle behavior is too time-consuming and labor-intensive, but the existing object detection and tracking algorithms have poor practicability and low behavioral location detection rate. This paper proposes a vehicle behavior detection algorithm based on the dual-stream convolution network and the multi-dimensional video dynamic detection network. In the videos, the straight-line behavior of the vehicle will default to the background behavior. The Changing lanes, turning and turning around are set as target behaviors. The purpose of this model is to automatically mark the target behavior of the vehicle from the untrimmed videos. First, the target behavior proposals in the long video are extracted through the dual-stream convolution network. The model uses a dual-stream convolutional network to generate a one-dimensional action score waveform, and then extract segments with scores above a given threshold M into preliminary vehicle behavior proposals. Second, the preliminary proposals are pruned and identified using the multi-dimensional video dynamic detection network. Referring to the hierarchical reinforcement learning, the multi-dimensional network includes a Timer module and a Spacer module, where the Timer module mines time information in the video stream and the Spacer module extracts spatial information in the video frame. The Timer and Spacer module are implemented by Long Short-Term Memory (LSTM) and start from an all-zero hidden state. The Timer module uses the Transformer mechanism to extract timing information from the video stream and extract features by linear mapping and other methods. Finally, the model fuses time information and spatial information and obtains the location and category of the behavior through the softmax layer. This paper uses recall and precision to measure the performance of the model. Extensive experiments show that based on the dataset of this paper, the proposed model has obvious advantages compared with the existing state-of-the-art behavior detection algorithms. When the Time Intersection over Union (TIoU) threshold is 0.5, the Average-Precision (MP) reaches 36.3% (the MP of baselines is 21.5%). In summary, this paper proposes a vehicle behavior detection model based on multi-dimensional dynamic detection network. This paper introduces spatial information and temporal information to extract vehicle behaviors in long videos. Experiments show that the proposed algorithm is advanced and accurate in-vehicle timing behavior detection. In the future, the focus will be on simultaneously detecting the timing behavior of multiple vehicles in complex traffic scenes (such as a busy street) while ensuring accuracy.

Keywords: vehicle behavior detection, convolutional neural network, long short-term memory, deep learning

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Authors: Zeynep R. Ege, Aydin Akan, Faik N. Oktar, Betul Karademir, Oguzhan Gunduz

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Early detection of cancer could save human life and quality in insidious cases by advanced biomedical imaging techniques. Designing targeted detection system is necessary in order to protect of healthy cells. Electrospun nanofibers are efficient and targetable nanocarriers which have important properties such as nanometric diameter, mechanical properties, elasticity, porosity and surface area to volume ratio. In the present study, indocyanine green (ICG) organic dye was stabilized and encapsulated in polymer matrix which polyethylene oxide (PEO) and chitosan (CHI) multilayer nanofibers via co-axial electrospinning method at one step. The co-axial electrospun nanofibers were characterized as morphological (SEM), molecular (FT-IR), and entrapment efficiency of Indocyanine Green (ICG) (confocal imaging). Controlled release profile of PEO/CHI/ICG nanofiber was also evaluated up to 40 hours.

Keywords: chitosan, coaxial electrospinning, controlled releasing, drug delivery, indocyanine green, polyethylene oxide

Procedia PDF Downloads 169

Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Sakshi Gupta, Seema Joshi

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Thiosemicarbazones (TSCs) have garnered significant attention in coordination chemistry due to their versatile coordination modes and pharmacological properties. Mixed ligand complexes of TSCs represent a promising area of research, offering enhanced antimicrobial activities compared to their parent compounds. This review provides an overview of the synthesis, characterization, and antimicrobial properties of mixed ligand complexes incorporating thiosemicarbazones. The synthesis of mixed ligand complexes typically involves the reaction of a metal salt with TSC ligands and additional ligands, such as nitrogen- or oxygen-based ligands. Various transition metals, including copper, nickel, and cobalt, have been employed to form mixed ligand complexes with TSCs. Characterization techniques such as spectroscopy, X-ray crystallography, and elemental analysis are commonly utilized to confirm the structures of these complexes. One of the key advantages of mixed ligand complexes is their enhanced antimicrobial activity compared to pure TSC compounds. The synergistic effect between the TSC ligands and additional ligands contributes to increased efficacy, possibly through improved metal-ligand interactions or enhanced membrane permeability. Furthermore, mixed ligand complexes offer the potential for selective targeting of microbial species while minimizing toxicity to mammalian cells. This selectivity arises from the specific interactions between the metal center, TSC ligands, and biological targets within microbial cells. Such targeted antimicrobial activity is crucial for developing effective treatments with minimal side effects. Moreover, the versatility of mixed ligand complexes allows for the design of tailored antimicrobial agents with optimized properties. By varying the metal ion, TSC ligands, and additional ligands, researchers can fine-tune the physicochemical properties and biological activities of these complexes. This tunability opens avenues for the development of novel antimicrobial agents with improved efficacy and reduced resistance. In conclusion, mixed ligand complexes of thiosemicarbazones represent a promising class of compounds with potent antimicrobial activities. Further research in this field holds great potential for the development of novel therapeutic agents to combat microbial infections effectively.

Keywords: metal complex, thiosemicarbazones, mixed ligand, selective targeting, antimicrobial activity

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Authors: Mulanga Luscious Mulaudzi, Ignatious Ncube

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In nature, enzymatic degradation of lignocellulose is more efficient compared to in vivo bioprocessing. Thus, a co-culture should enable production of more efficient enzyme preparations that would mimic the natural decomposition of lignocellulose. The aim of the study was to establish a microbial co-culture for the production of highly active cellulase preparations. The objectives were to determine the use of a variety of culture media to isolate cellulose degrading microorganisms from decomposing banana pseudo stem and to optimize production of cellulase by co-cultures of microorganisms producing high levels of cellulose. Screening of fungal isolates was done on carboxylmethylcellulose agar plates which were stained with Congo red to show hydrolytic activity of the isolates. Co-culture and mixed culture of these microorganisms were cultured using Mendel salts with Avicel as the carbon source. Cultures were incubated at 30 °C with shaking at 200 rpm for 240 hrs. Enzyme activity assays were performed to determine endoglycosidase and β-glucosidase. Mixed culture of fungi-dead bacterial cells showed to be the best co-culture/ mixed culture to produce higher levels of cellulase activity in submerged fermentations (SmF) using Avicel™ as a carbon source. The study concludes use microorganism 5A in co-cultures is highly recommended in order to produce high amounts of β-glucosidases, no matter the combination used.

Keywords: avicel, co-culture, submerged fermentation, pseudostem

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Authors: Elmahdi Khalil, Saad Enniari, Mostapha Zbakh

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Cloud computing promises to increase innovation and the velocity with witch applications are deployed, all while helping any enterprise meet most IT service needs at a lower total cost of ownership and higher return investment. As the march of cloud continues, it brings both new opportunities and new security challenges. To take advantages of those opportunities while minimizing risks, we think that Intrusion Detection Systems (IDS) integrated in the cloud is one of the best existing solutions nowadays in the field. The concept of intrusion detection was known since past and was first proposed by a well-known researcher named Anderson in 1980's. Since that time IDS's are evolving. Although, several efforts has been made in the area of Intrusion Detection systems for cloud computing environment, many attacks still prevail. Therefore, the work presented in this paper proposes a multi criteria analysis and a comparative study between several IDS architectures designated to work in a cloud computing environments. To achieve this objective, in the first place we will search in the state of the art of several consistent IDS architectures designed to work in a cloud environment. Whereas, in a second step we will establish the criteria that will be useful for the evaluation of architectures. Later, using the approach of multi criteria decision analysis Mac Beth (Measuring Attractiveness by a Categorical Based Evaluation Technique we will evaluate the criteria and assign to each one the appropriate weight according to their importance in the field of IDS architectures in cloud computing. The last step is to evaluate architectures against the criteria and collecting results of the model constructed in the previous steps.

Keywords: cloud computing, cloud security, intrusion detection/prevention system, multi-criteria decision analysis

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Authors: Odi Fawwaz Alrebei, Rim Ismail

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Sustainable aviation fuel (SAF) has emerged as a critical solution for reducing the aviation sector's carbon footprint. Biological feedstocks, such as lignocellulosic biomass, microalgae, used cooking oil, and municipal solid waste, offer significant potential to replace fossil-based jet fuels with renewable alternatives. This review paper aims to critically examine the current landscape of biological feedstocks for SAF production, focusing on feedstock availability, conversion technologies, and environmental impacts. The paper evaluates the biochemical pathways employed in transforming these feedstocks into SAF, such as hydrothermal liquefaction, Fischer-Tropsch synthesis, and microbial fermentation, highlighting the advancements and challenges in each method. Additionally, the sustainability of biological feedstocks is analyzed with respect to lifecycle emissions, land use, and water consumption, emphasizing the need for region-specific strategies to maximize benefits. Special attention is given to the role of microbial consortia in optimizing feedstock degradation and conversion processes. The review concludes by discussing the scalability and economic viability of biological feedstock-based SAF, with a focus on policy frameworks and technological innovations that can facilitate widespread adoption. This comprehensive review underscores the pivotal role of biological feedstocks in achieving a decarbonized aviation sector and identifies future research directions for improving SAF production efficiency and sustainability.

Keywords: fuel diversity, biological feedstocks, SAF, aviation

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Authors: Afroza Khatun, Masuma, Md. Younus Mia, Kamal Kanta Das

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Analysis of the microbial status and physicochemical parameters of some branded and street vended ice cream showed that total viable bacteria in branded ice cream ranged from 4.8×10³ to 1.10×10⁵ cfu/ml, and in street vended ice-cream ranged from 7.5×10⁴ to 1.6×10⁸ cfu/ml. Total coliform bacteria present up to 9.20×10³ cfu/ml in branded ice cream and 5.3×10³ to 9.6×10⁶ cfu/ml observed in street vended ice cream. Total E. coli were found to be present within a range from 0 to 4.5×10³ cfu/ml in branded and 4.1×10² to 7.5×10⁴ cfu/ml in street ice cream. The ranges of Staphylococcus aureus count were 1.8×10² to 2.9×10⁴ cfu/ml (branded) and 3.9×10⁴ to 7.9×10⁶ cfu/ml (street). The pH of both types of ice cream showed acidic to neutral conditions where the concentration of pH for branded ice cream was 5.5 to 6.9, as well as the value of pH in street ice cream, was 6.2 to 7.0. The range of Total soluble solids in several branded ice creams was 26 to 29%, and the value of TSS obtained in street-vended ice-creams ranged from 5 to 10%. The overall results of this research demonstrated that the microbial quality in all street ice creams exceeded the BSTI standard and exhibited lower quality than the industrially produced branded ice creams due to comparatively faulty manufacturing processes and poor hygiene practices. The presence of pathogenic microbes was also observed in branded ice creams which was quite alarming for public health. So it is suggested that the government authorized organization should conduct the proper monitoring system to ensure that both branded and street vended ice-creams are microbiologically safe to prevent public health hazards.

Keywords: food safety, microbiological analysis, physicochemical, ice-cream, E. coli, Staphylococcus aureus

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Authors: B. Lokuketagoda, M. Weerakoon, U. Madushan, A. N. Senaratne, K. Y. Abeywardena

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Ransomware has become a common threat in past few years and the recent threat reports show an increase of growth in Ransomware infections. Researchers have identified different variants of Ransomware families since 2015. Lack of knowledge of the user about the threat is a major concern. Ransomware detection methodologies are still growing through the industry. Email is the easiest method to send Ransomware to its victims. Uninformed users tend to click on links and attachments without much consideration assuming the emails are genuine. As a solution to this in this paper R-Killer Ransomware detection tool is introduced. Tool can be integrated with existing email services. The core detection Engine (CDE) discussed in the paper focuses on separating suspicious samples from emails and handling them until a decision is made regarding the suspicious mail. It has the capability of preventing execution of identified ransomware processes. On the other hand, Sandboxing and URL analyzing system has the capability of communication with public threat intelligence services to gather known threat intelligence. The R-Killer has its own mechanism developed in its Proactive Monitoring System (PMS) which can monitor the processes created by downloaded email attachments and identify potential Ransomware activities. R-killer is capable of gathering threat intelligence without exposing the user’s data to public threat intelligence services, hence protecting the confidentiality of user data.

Keywords: ransomware, deep learning, recurrent neural networks, email, core detection engine

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Authors: Mohamad Kassab, Amal El Fallah Seghrouchni, Frederic Barbaresco, Raed Abu Zitar

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Detecting objects such as drones is a challenging task as their relative size and maneuvering capabilities deceive machine learning models and cause them to misclassify drones as birds or other objects. In this work, we investigate applying several deep learning techniques to benchmark real data sets of flying drones. A deep learning paradigm is proposed for the purpose of mitigating the complexity of those systems. The proposed paradigm consists of a hybrid between the AdderNet deep learning paradigm and the Single Shot Detector (SSD) paradigm. The goal was to minimize multiplication operations numbers in the filtering layers within the proposed system and, hence, reduce complexity. Some standard machine learning technique, such as SVM, is also tested and compared to other deep learning systems. The data sets used for training and testing were either complete or filtered in order to remove the images with mall objects. The types of data were RGB or IR data. Comparisons were made between all these types, and conclusions were presented.

Keywords: drones detection, deep learning, birds versus drones, precision of detection, AdderNet

Procedia PDF Downloads 182

Authors: Kelemewerk Destalem, Joongjae Cho, Jaeseong Lee, Ju H. Park, Joonhyuk Yoo

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Background subtraction and temporal difference are often used for moving object detection in video. Both approaches are computationally simple and easy to be deployed in real-time image processing. However, while the background subtraction is highly sensitive to dynamic background and illumination changes, the temporal difference approach is poor at extracting relevant pixels of the moving object and at detecting the stopped or slowly moving objects in the scene. In this paper, we propose a moving object detection scheme based on adaptive background subtraction and temporal difference exploiting dynamic background updates. The proposed technique consists of a histogram equalization, a linear combination of background and temporal difference, followed by the novel frame-based and pixel-based background updating techniques. Finally, morphological operations are applied to the output images. Experimental results show that the proposed algorithm can solve the drawbacks of both background subtraction and temporal difference methods and can provide better performance than that of each method.

Keywords: background subtraction, background updating, real time, light weight algorithm, temporal difference

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Authors: S. Sehar, S. Khan, N. Ali, S. Ahmed

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In the last decade, the hunt for cost-effective, eco-friendly and energy sustainable technologies for waste water treatment are gaining much attention due to emerging water crisis and rapidly depleting existing water reservoirs all over the world. In this scenario, constructed wetland being a “green technology” could be a reliable mean for waste water treatment especially in small communities due to cost-effectiveness, ease in management, less energy consumption and sludge production. Therefore, a low cost, lab-scale sub-surface flow hybrid constructed wetland (SS-HCW) was established for domestic waste water treatment.It was observed that not only the presence but also choice of suitable vegetation along with hydraulic retention time (HRT) are key intervening ingredients which directly influence pollutant removals in constructed wetlands. Another important aspect of vegetation is that it may facilitate microbial attachment in rhizosphere, thus promote biofilm formation via microbial interactions. The major factors that influence initial aggregation and subsequent biofilm formation i.e. divalent cations (Ca2+) and extra cellular DNA (eDNA) were also studied in detail. The presence of Ca2+ in constructed wetland demonstrate superior performances in terms of effluent quality, i.e BOD5, COD, TDS, TSS, and PO4- than in absence of Ca2+. Finally, light and scanning electron microscopies coupled with EDS were carried out to get more insights into the mechanics of biofilm formation with or without Ca addition. Therefore, the same strategy can be implemented in other waste water treatment technologies.

Keywords: hybrid constructed wetland, biofilm formation, waste water treatment, waste water

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Authors: Bengi Hakguder Taze, Sevcan Unluturk

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Şalak apricot (Prunus armeniaca L., cv. Şalak) is a specific variety grown in Igdir, Turkey. The fruit has distinctive properties distinguish it from other cultivars, such as its unique size, color, taste and higher water content. Drying is the widely used method for preservation of apricots. However, fresh consumption is preferred for Şalak apricot instead of drying due to its low dry matter content. Higher amounts of water in the structure and climacteric nature make the fruit sensitive against rapid quality loss during storage. Hence, alternative processing methods need to be introduced to extend the shelf life of the fresh produce. Mild heat (MH) treatment is of great interest as it can reduce the microbial load and inhibit enzymatic activities. Therefore, the aim of this study was to evaluate the impact of mild heat treatment on the natural microflora found on Şalak apricot surfaces and some physical quality parameters of the fruit, such as color and firmness. For this purpose, apricot samples were treated at different temperatures between 40 and 60 ℃ for different periods ranging between 10 to 60 min using a temperature controlled water bath. Natural flora on the fruit surfaces was examined using standard plating technique both before and after the treatment. Moreover, any changes in color and firmness of the fruit samples were also monitored. It was found that control samples were initially containing 7.5 ± 0.32 log CFU/g of total aerobic plate count (TAPC), 5.8±0.31 log CFU/g of yeast and mold count (YMC), and 5.17 ± 0.22 log CFU/g of coliforms. The highest log reductions in TAPC and YMC were observed as 3.87-log and 5.8-log after the treatments at 60 ℃ and 50 ℃, respectively. Nevertheless, the fruit lost its characteristic aroma at temperatures above 50 ℃. Furthermore, great color changes (ΔE ˃ 6) were observed and firmness of the apricot samples was reduced at these conditions. On the other hand, MH treatment at 41 ℃ for 10 min resulted in 1.6-log and 0.91-log reductions in TAPC and YMC, respectively, with slightly noticeable changes in color (ΔE ˂ 3). In conclusion, application of temperatures higher than 50 ℃ caused undesirable changes in physical quality of Şalak apricots. Although higher microbial reductions were achieved at those temperatures, temperatures between 40 and 50°C should be further investigated considering the fruit quality parameters. Another strategy may be the use of high temperatures for short time periods not exceeding 1-5 min. Besides all, MH treatment with UV-C light irradiation can be also considered as a hurdle strategy for better inactivation results.

Keywords: color, firmness, mild heat, natural flora, physical quality, şalak apricot

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Authors: Ifedapo Francis Awolowo

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The unrelenting series of embarrassing audit failures should stimulate a paradigm shift in accounting. And in this age of information revolution, there is need for a constant improvement on the products or services one offers to the market in order to be relevant. This study explores the perceptions of external auditors, forensic accountants and accounting academics on whether a paradigm shift to forensic accounting can reduce financial statement frauds. Through Neo-empiricism/inductive analytical approach, findings reveal that a paradigm shift to forensic accounting might be the right step in the right direction in order to increase the chances of fraud prevention and detection in the financial statement. This research has implication on accounting education on the need to incorporate forensic accounting into present day accounting curriculum. Accounting professional bodies, accounting standard setters and accounting firms all have roles to play in incorporating forensic accounting education into accounting curriculum. Particularly, there is need to alter the ISA 240 to make the prevention and detection of frauds the responsibilities of bot those charged with the management and governance of companies and statutory auditors.

Keywords: financial statement fraud, forensic accounting, fraud prevention and detection, auditing, audit expectation gap, corporate governance

Procedia PDF Downloads 366

Authors: Anoop T. R., Otman Basir, Robert F. Hess, Eileen E. Birch, Brooke A. Koritala, Reed M. Jost, Becky Luu, David Stager, Ben Thompson

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Strabismus refers to a misalignment of the eyes. Early detection and treatment of strabismus in childhood can prevent the development of permanent vision loss due to abnormal development of visual brain areas. We developed a two-stage method for strabismus detection and classification based on photographs of the face. The first stage detects the presence or absence of strabismus, and the second stage classifies the type of strabismus. The first stage comprises face detection using Haar cascade, facial landmark estimation, face alignment, aligned face landmark detection, segmentation of the eye region, and detection of strabismus using VGG 16 convolution neural networks. Face alignment transforms the face to a canonical pose to ensure consistency in subsequent analysis. Using facial landmarks, the eye region is segmented from the aligned face and fed into a VGG 16 CNN model, which has been trained to classify strabismus. The CNN determines whether strabismus is present and classifies the type of strabismus (exotropia, esotropia, and vertical deviation). If stage 1 detects strabismus, the eye region image is fed into stage 2, which starts with the estimation of pupil center coordinates using mask R-CNN deep neural networks. Then, the distance between the pupil coordinates and eye landmarks is calculated along with the angle that the pupil coordinates make with the horizontal and vertical axis. The distance and angle information is used to characterize the degree and direction of the strabismic eye misalignment. This model was tested on 100 clinically labeled images of children with (n = 50) and without (n = 50) strabismus. The True Positive Rate (TPR) and False Positive Rate (FPR) of the first stage were 94% and 6% respectively. The classification stage has produced a TPR of 94.73%, 94.44%, and 100% for esotropia, exotropia, and vertical deviations, respectively. This method also had an FPR of 5.26%, 5.55%, and 0% for esotropia, exotropia, and vertical deviation, respectively. The addition of one more feature related to the location of corneal light reflections may reduce the FPR, which was primarily due to children with pseudo-strabismus (the appearance of strabismus due to a wide nasal bridge or skin folds on the nasal side of the eyes).

Keywords: strabismus, deep neural networks, face detection, facial landmarks, face alignment, segmentation, VGG 16, mask R-CNN, pupil coordinates, angle deviation, horizontal and vertical deviation

Procedia PDF Downloads 93

Authors: Siti Aishah Hasbullah

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Studies on identification of pork content in food have grown rapidly to meet the Halal food standard in Malaysia. The used mitochondria DNA (mtDNA) approaches for the identification of pig species is thought to be the most precise marker due to the mtDNA genes are present in thousands of copies per cell, the large variability of mtDNA. The standard method commonly used for DNA detection is based on polymerase chain reaction (PCR) method combined with gel electrophoresis but has major drawback. Its major drawbacks are laborious, need longer time and toxic to handle. Therefore, the need for simplicity and fast assay of DNA is vital and has triggered us to develop DNA biosensors for porcine DNA detection. Therefore, the aim of this project is to develop electrochemical DNA biosensor based on ruthenium (II) complex, [Ru(bpy)2(p-PIP)]2+ as DNA hybridization label. The interaction of DNA and [Ru(bpy)2(p-HPIP)]2+ will be studied by electrochemical transduction using Gold Screen-Printed Electrode (GSPE) modified with gold nanoparticles (AuNPs) and succinimide acrylic microspheres. The electrochemical detection by redox active ruthenium (II) complex was measured by cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The results indicate that the interaction of [Ru(bpy)2(PIP)]2+ with hybridization complementary DNA has higher response compared to single-stranded and mismatch complementary DNA. Under optimized condition, this porcine DNA biosensor incorporated modified GSPE shows good linear range towards porcine DNA.

Keywords: gold, screen printed electrode, ruthenium, porcine DNA

Procedia PDF Downloads 309

Authors: M. Zafar, S. Rasheed, Imran Hashmi

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Very little is concerned about the bacterial contamination in drinking water biofilm which provide a potential source for bacteria to grow and increase rapidly. So as to understand the microbial density in DWDs, a three-month study was carried out. The aim of this study was to examine biofilm in three different pipe materials including PVC, PPR and GI. A set of all these pipe materials was installed in DWDs at nine different locations and assessed on monthly basis. Drinking water quality was evaluated by different parameters and characterization of biofilm. Among various parameters are Temperature, pH, turbidity, TDS, electrical conductivity, BOD, COD, total phosphates, total nitrates, total organic carbon (TOC) free chlorine and total chlorine, coliforms and spread plate counts (SPC) according to standard methods. Predominant species were Bacillus thuringiensis, Pseudomonas fluorescens , Staphylococcus haemolyticus, Bacillus safensis and significant increase in bacterial population was observed in PVC pipes while least in cement pipes. The quantity of DWDs bacteria was directly depended on biofilm bacteria and its increase was correlated with growth and detachment of bacteria from biofilms. Pipe material also affected the microbial community in drinking water distribution network biofilm while Similarity in bacterial species was observed between systems due to same disinfectant dose, time period and plumbing pipes.

Keywords: biofilm, DWDs, pipe material, bacterial population

Procedia PDF Downloads 347

Authors: Renata Gerhardt, Detlev Belder

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Raman spectroscopy has attracted much attention as a structurally descriptive and label-free detection method. It is particularly suited for chemical analysis given as it is non-destructive and molecules can be identified via the fingerprint region of the spectra. In this work possibilities are investigated how to integrate Raman spectroscopy as a detection method for chip-based chromatography, making use of a droplet interface. A demanding task in lab-on-a-chip applications is the specific and sensitive detection of low concentrated analytes in small volumes. Fluorescence detection is frequently utilized but restricted to fluorescent molecules. Furthermore, no structural information is provided. Another often applied technique is mass spectrometry which enables the identification of molecules based on their mass to charge ratio. Additionally, the obtained fragmentation pattern gives insight into the chemical structure. However, it is only applicable as an end-of-the-line detection because analytes are destroyed during measurements. In contrast to mass spectrometry, Raman spectroscopy can be applied on-chip and substances can be processed further downstream after detection. A major drawback of Raman spectroscopy is the inherent weakness of the Raman signal, which is due to the small cross-sections associated with the scattering process. Enhancement techniques, such as surface enhanced Raman spectroscopy (SERS), are employed to overcome the poor sensitivity even allowing detection on a single molecule level. In SERS measurements, Raman signal intensity is improved by several orders of magnitude if the analyte is in close proximity to nanostructured metal surfaces or nanoparticles. The main gain of lab-on-a-chip technology is the building block-like ability to seamlessly integrate different functionalities, such as synthesis, separation, derivatization and detection on a single device. We intend to utilize this powerful toolbox to realize Raman detection in chip-based chromatography. By interfacing on-chip separations with a droplet generator, the separated analytes are encapsulated into numerous discrete containers. These droplets can then be injected with a silver nanoparticle solution and investigated via Raman spectroscopy. Droplet microfluidics is a sub-discipline of microfluidics which instead of a continuous flow operates with the segmented flow. Segmented flow is created by merging two immiscible phases (usually an aqueous phase and oil) thus forming small discrete volumes of one phase in the carrier phase. The study surveys different chip designs to realize coupling of chip-based chromatography with droplet microfluidics. With regards to maintaining a sufficient flow rate for chromatographic separation and ensuring stable eluent flow over the column different flow rates of eluent and oil phase are tested. Furthermore, the detection of analytes in droplets with surface enhanced Raman spectroscopy is examined. The compartmentalization of separated compounds preserves the analytical resolution since the continuous phase restricts dispersion between the droplets. The droplets are ideal vessels for the insertion of silver colloids thus making use of the surface enhancement effect and improving the sensitivity of the detection. The long-term goal of this work is the first realization of coupling chip based chromatography with droplets microfluidics to employ surface enhanced Raman spectroscopy as means of detection.

Keywords: chip-based separation, chip LC, droplets, Raman spectroscopy, SERS

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Authors: Sylwia Baluta, Joanna Cabaj, Karol Malecha

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The evolution of biosensors was driven by the need for faster and more versatile analytical methods for application in important areas including clinical, diagnostics, food analysis or environmental monitoring, with minimum sample pretreatment. Rapid and sensitive neurotransmitters detection is extremely important in modern medicine. These compounds mainly occur in the brain and central nervous system of mammals. Any changes in the neurotransmitters concentration may lead to many diseases, such as Parkinson’s or schizophrenia. Classical techniques of chemical analysis, despite many advantages, do not permit to obtain immediate results or automatization of measurements.

Keywords: adrenaline, biosensor, dopamine, laccase, tyrosinase

Procedia PDF Downloads 142

Authors: Alejandro Molina-Villegas, Silvia Fernández-Sabido, Eduardo Mendoza-Vargas, Fátima Miranda-Pestaña

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The automatic detection of indigenous languages ​​in digital texts is essential to promote their inclusion in digital media. Underrepresented languages, such as Maya, are often excluded from language detection tools like Google’s language-detection library, LANGDETECT. This study addresses these limitations by developing a hybrid language detection solution that accurately distinguishes Maya (YUA) from Spanish (ES). Two strategies are employed: the first focuses on creating a profile for the Maya language within the LANGDETECT library, while the second involves training a Naive Bayes classification model with two categories, YUA and ES. The process includes comprehensive data preprocessing steps, such as cleaning, normalization, tokenization, and n-gram counting, applied to text samples collected from various sources, including articles from La Jornada Maya, a major newspaper in Mexico and the only media outlet that includes a Maya section. After the training phase, a portion of the data is used to create the YUA profile within LANGDETECT, which achieves an accuracy rate above 95% in identifying the Maya language during testing. Additionally, the Naive Bayes classifier, trained and tested on the same database, achieves an accuracy close to 98% in distinguishing between Maya and Spanish, with further validation through F1 score, recall, and logarithmic scoring, without signs of overfitting. This strategy, which combines the LANGDETECT profile with a Naive Bayes model, highlights an adaptable framework that can be extended to other underrepresented languages in future research. This fills a gap in Natural Language Processing and supports the preservation and revitalization of these languages.

Keywords: indigenous languages, language detection, Maya language, Naive Bayes classifier, natural language processing, low-resource languages

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Authors: Ali Raza, Shafiq Ur Rehman Khan, Raja Sher Afgun Usmani, Asif Raza, Basit Umair

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Detection of Fake news and missing information is gaining popularity, especially after the advancement in social media and online news platforms. Social media platforms are the main and speediest source of fake news propagation, whereas online news websites contribute to fake news dissipation. In this study, we propose a framework to detect fake news using the temporal features of text and consider user feedback to identify whether the news is fake or not. In recent studies, the temporal features in text documents gain valuable consideration from Natural Language Processing and user feedback and only try to classify the textual data as fake or true. This research article indicates the impact of recurring and non-recurring events on fake and true news. We use two models BERT and Bi-LSTM to investigate, and it is concluded from BERT we get better results and 70% of true news are recurring and rest of 30% are non-recurring.

Keywords: natural language processing, fake news detection, machine learning, Bi-LSTM

Procedia PDF Downloads 22