Search results for: biological monitoring

Authors: Slimane Mokrani, Nabti El hafid

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Description of the subject: Currently, several efforts focus on the study of biodiversity, microbial biotechnology, and the use of ecological strategies. Objectives: The aim of this present work is to determine the functional diversity of bacteria in rhizospheric and non-rhizospheric soils of different plants. Methods: Bacteria were isolated from soil and identified based on physiological and biochemical characters and genotypic taxonomy performed by 16S rDNA and BOX-PCR. As well as the characterization of various PGPR traits. Then, they are tested for their effects on the stimulation of seed germination and the growth of Phaseolus vulgaris L. As well as their biological control activities with regard to the phytopathogenic bacterial isolate Xapf. Results and Discussion: The biochemical and physiological identification of 75 bacterial isolates made it possible to associate them with the two groups of fluorescent Pseudomonas (74.67%) and non-fluorescent Pseudomonas (25.33%). The identification by 16S rDNA of 27 strains made it possible to attribute the majority of the strains to the genus Pseudomonas (81.48%), Serratia (7.41%) and Bacillus (11.11%). The bacterial strains showed a high capacity to produce IAA, siderophores, HCN and to solubilize phosphate. A significant stimulation of germination and growth was observed by applying the Pseudomonas strains. Furthermore, significant reductions in the severity and intensity of the disease caused caused by Xapf were observed. Conclusion: The bacteria described in this present study endowed with different PGPR activities seem to be very promising for their uses as biological control agents and bio-fertilization.

Keywords: biofertilization, biological control, phaseolus vulgaris L, pseudomonas, Xanthomonas axonopodis pv. phaseoli var. fuscans and common blight

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Authors: Ian N. Robertson

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Alkali Silica Reaction (ASR) occurs in concrete when the alkali hydroxides (Na, K and OH) from the cement react with unstable silica, SiO2, in some types of aggregate. The gel that forms during this reaction will expand when it absorbs water, potentially leading to cracking and overall expansion of the concrete. ASR has resulted in accelerated deterioration of concrete highways, dams and other structures that are exposed to moisture during their service life. Concrete aggregates available in Hawaii have not demonstrated a history of ASR, however, accelerated laboratory tests using ASTM 1260 indicated a potential for ASR with some aggregates. Certain clients are now requiring import of aggregates from the US mainland at great expense. In order to assess the accuracy of the laboratory test results, a long-term field study of the potential for ASR in concretes made with Hawaiian aggregates was initiated in 2011 with funding from the US Federal Highway Administration and Hawaii Department of Transportation. Thirty concrete specimens were constructed of various concrete mixtures using aggregates from all Hawaiian aggregate sources, and some US mainland aggregates known to exhibit ASR expansion. The specimens are located in an open field site in Manoa valley on the Hawaiian Island of Oahu, exposed to relatively high humidity and frequent rainfall. A weather station at the site records the ambient conditions on a continual basis. After two years of monitoring, only one of the Hawaiian aggregates showed any sign of expansion. Ten additional specimens were fabricated with this aggregate to confirm the earlier observations. Admixtures known to mitigate ASR, such as fly ash and lithium, were included in some specimens to evaluate their effect on the concrete expansion. This paper describes the field evaluation program and presents the results for all forty specimens after four years of monitoring.

Keywords: aggregate, alkali silica reaction, concrete durability, field exposure

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Authors: Akuélé Radha Kondo, Kathrin Heitz-Tokpa, Bassirou Bonfoh, Francis Akindes

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Postgraduate supervision contributes significantly to a student’s academic career, a supervisor’s promotion, and a university’s reputation. Despite this, the length of graduation in the Côte d’Ivoire University system is beyond the normal duration, two years for a master's and three years for a PhD. The paper analyses supervision practices regarding the challenges and strategies mobilised by students, supervisors, and administration staff to manage various relationships. Using a qualitative research design, this study was conducted at three public universities in Côte d’Ivoire. Data were generated from thirty-two postgraduate students, seventeen supervisors, and four administration staff through semi-structured interviews. Data were analysed using content analysis and presented thematically. Findings revealed delegated supervision and co-supervision, two types of supervision relationship practices. Students pointed out that feedback is often delayed from their supervisors in delegation supervision. However, they acknowledged receiving input and scientific guidance. All students believed that their role is to be proactive, not to wait to receive everything from the supervisor, and need to be more autonomous and hardworking. They developed strategies related to these qualities. Supervisors were considered to guide, give advice, control, motivate, provide critical feedback, and validate the work. The administration was rather absent in monitoring supervision delays. Major challenges were related to the supervision relationships and access to the research funds. The study showed that more engagement of the main supervisor, administration monitoring, and secured funding would reduce the time and increase the completion rate.

Keywords: Côte d’Ivoire, postgraduate supervision, practices, strategies

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Authors: Natasha Correa Vitória Bandeira, Lais Cordeiro Soares, Claudineia Brazil, Luciane Teresa Salvi

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The intense rain event that occurred between February 16 and 18, 2018, in the city of Barcarena in Pará, located in the North region of Brazil, demonstrates the importance of analyzing this type of event. The metropolitan mesoregion of Belem was severely punished by rains much above the averages normally expected for that time of year; this phenomenon affected, in addition to the capital, the municipalities of Barcarena, Murucupi and Muruçambá. Resulting in a great flood in the rivers of the region, whose basins were affected with great intensity of precipitation, causing concern for the local population because in this region, there are located companies that accumulate ore tailings, and in this specific case, the dam of any of these companies, leaching the ore to the water bodies of the Murucupi River Basin. This article aims to characterize this phenomenon through a special analysis of the distribution of rainfall, using data from atmospheric soundings, satellite images, radar images and data from the GPCP (Global Precipitation Climatology Project), in addition to rainfall stations located in the study region. The results of the work demonstrated a dissociation between the data measured in the meteorological stations and the other forms of analysis of this extreme event. Monitoring carried out solely on the basis of data from pluviometric stations is not sufficient for monitoring and/or diagnosing extreme weather events, and investment by the competent bodies is important to install a larger network of pluviometric stations sufficient to meet the demand in a given region.

Keywords: extreme precipitation, great flood, GPCP, ore dam

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Authors: A. Chávez, A. Rodríguez, F. Pinzón

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Society is concerned about measures of environmental, economic and social impacts generated in the solid waste disposal. These places of confinement, also known as landfills, are locations where problems of pollution and damage to human health are reduced. They are technically designed and operated, using engineering principles, storing the residue in a small area, compact it to reduce volume and covering them with soil layers. Problems preventing liquid (leachate) and gases produced by the decomposition of organic matter. Despite planning and site selection for disposal, monitoring and control of selected processes, remains the dilemma of the leachate as extreme concentration of pollutants, devastating soil, flora and fauna; aggressive processes requiring priority attention. A biological technology is the activated sludge system, used for tributaries with high pollutant loads. Since transforms biodegradable dissolved and particulate matter into CO2, H2O and sludge; transform suspended and no Settleable solids; change nutrients as nitrogen and phosphorous; and degrades heavy metals. The microorganisms that remove organic matter in the processes are in generally facultative heterotrophic bacteria, forming heterogeneous populations. Is possible to find unicellular fungi, algae, protozoa and rotifers, that process the organic carbon source and oxygen, as well as the nitrogen and phosphorus because are vital for cell synthesis. The mixture of the substrate, in this case sludge leachate, molasses and wastewater is maintained ventilated by mechanical aeration diffusers. Considering as the biological processes work to remove dissolved material (< 45 microns), generating biomass, easily obtained by decantation processes. The design consists of an artificial support and aeration pumps, favoring develop microorganisms (denitrifying) using oxygen (O) with nitrate, resulting in nitrogen (N) in the gas phase. Thus, avoiding negative effects of the presence of ammonia or phosphorus. Overall the activated sludge system includes about 8 hours of hydraulic retention time, which does not prevent the demand for nitrification, which occurs on average in a value of MLSS 3,000 mg/L. The extended aeration works with times greater than 24 hours detention; with ratio of organic load/biomass inventory under 0.1; and average stay time (sludge age) more than 8 days. This project developed a pilot system with sludge leachate from Doña Juana landfill - RSDJ –, located in Bogota, Colombia, where they will be subjected to a process of activated sludge and extended aeration through a sequential Bach reactor - SBR, to be dump in hydric sources, avoiding ecological collapse. The system worked with a dwell time of 8 days, 30 L capacity, mainly by removing values of BOD and COD above 90%, with initial data of 1720 mg/L and 6500 mg/L respectively. Motivating the deliberate nitrification is expected to be possible commercial use diffused aeration systems for sludge leachate from landfills.

Keywords: sludge, landfill, leachate, SBR

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Authors: J. K Adedeji, O. H Olowomofe, C. O Alo, S.T Ijatuyi

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The issue of high blood sugar level, the effects of which might end up as diabetes mellitus, is now becoming a rampant cardiovascular disorder in our community. In recent times, a lack of awareness among most people makes this disease a silent killer. The situation calls for urgency, hence the need to design a device that serves as a monitoring tool such as a wrist watch to give an alert of the danger a head of time to those living with high blood glucose, as well as to introduce a mechanism for checks and balances. The neural network architecture assumed 8-15-10 configuration with eight neurons at the input stage including a bias, 15 neurons at the hidden layer at the processing stage, and 10 neurons at the output stage indicating likely symptoms cases. The inputs are formed using the exclusive OR (XOR), with the expectation of getting an XOR output as the threshold value for diabetic symptom cases. The neural algorithm is coded in Java language with 1000 epoch runs to bring the errors into the barest minimum. The internal circuitry of the device comprises the compatible hardware requirement that matches the nature of each of the input neurons. The light emitting diodes (LED) of red, green, and yellow colors are used as the output for the neural network to show pattern recognition for severe cases, pre-hypertensive cases and normal without the traces of diabetes mellitus. The research concluded that neural network is an efficient Accu-Chek design tool for the proper monitoring of high glucose levels than the conventional methods of carrying out blood test.

Keywords: Accu-Check, diabetes, neural network, pattern recognition

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Authors: Alberto Azevedo, Ricardo Martins, André B. Fortunato, Anabela Oliveira

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Water is under severe threat today because of the rising population, increased agricultural and industrial needs, and the intensifying effects of climate change. Due to sea-level rise, erosion, and demographic pressure, the coastal regions are of significant concern to the scientific community. The Water Monitoring Sentinel Cloud platform (WORSICA) service is focused on providing new tools for monitoring water in coastal and inland areas, taking advantage of remote sensing, in situ and tidal modeling data. WORSICA is a service that can be used to determine the coastline, coastal inundation areas, and the limits of inland water bodies using remote sensing (satellite and Unmanned Aerial Vehicles - UAVs) and in situ data (from field surveys). It applies to various purposes, from determining flooded areas (from rainfall, storms, hurricanes, or tsunamis) to detecting large water leaks in major water distribution networks. This service was built on components developed in national and European projects, integrated to provide a one-stop-shop service for remote sensing information, integrating data from the Copernicus satellite and drone/unmanned aerial vehicles, validated by existing online in-situ data. Since WORSICA is operational using the European Open Science Cloud (EOSC) computational infrastructures, the service can be accessed via a web browser and is freely available to all European public research groups without additional costs. In addition, the private sector will be able to use the service, but some usage costs may be applied, depending on the type of computational resources needed by each application/user. Although the service has three main sub-services i) coastline detection; ii) inland water detection; iii) water leak detection in irrigation networks, in the present study, an application of the service to Óbidos lagoon in Portugal is shown, where the user can monitor the evolution of the lagoon inlet and estimate the topography of the intertidal areas without any additional costs. The service has several distinct methodologies implemented based on the computations of the water indexes (e.g., NDWI, MNDWI, AWEI, and AWEIsh) retrieved from the satellite image processing. In conjunction with the tidal data obtained from the FES model, the system can estimate a coastline with the corresponding level or even topography of the inter-tidal areas based on the Flood2Topo methodology. The outcomes of the WORSICA service can be helpful for several intervention areas such as i) emergency by providing fast access to inundated areas to support emergency rescue operations; ii) support of management decisions on hydraulic infrastructures operation to minimize damage downstream; iii) climate change mitigation by minimizing water losses and reduce water mains operation costs; iv) early detection of water leakages in difficult-to-access water irrigation networks, promoting their fast repair.

Keywords: remote sensing, coastline detection, water detection, satellite data, sentinel, Copernicus, EOSC

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Authors: S. M. Ishtiaque, V. K. Yadav, S. D. Joshi, J. K. Chatterjee

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The DIAMETRIC FAULTS system has been developed that captures a bi-directional image of yarn continuously in sequentially manner and provides the detailed classification of faults. A novel mathematical framework developed on the acquired bi-directional images forms the basis of fault classification in four broad categories, namely, Thick1, Thick2, Thin and Normal Yarn. A discretised version of Radon transformation has been used to convert the bi-directional images into one-dimensional signals. Images were divided into training and test sample sets. Karhunen–Loève Transformation (KLT) basis is computed for the signals from the images in training set for each fault class taking top six highest energy eigen vectors. The fault class of the test image is identified by taking the Euclidean distance of its signal from its projection on the KLT basis for each sample realization and fault class in the training set. Euclidean distance applied using various techniques is used for classifying an unknown fault class. An accuracy of about 90% is achieved in detecting the correct fault class using the various techniques. The four broad fault classes were further sub classified in four sub groups based on the user set boundary limits for fault length and fault volume. The fault cross-sectional area and the fault length defines the total volume of fault. A distinct distribution of faults is found in terms of their volume and physical dimensions which can be used for monitoring the yarn faults. It has been shown from the configurational based characterization and classification that the spun yarn faults arising out of mass variation, exhibit distinct characteristics in terms of their contours, sizes and shapes apart from their frequency of occurrences.

Keywords: Euclidean distance, fault classification, KLT, Radon Transform

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Authors: Samy M. Shaban, Ismail Aiad, Mohamed M. El-Sukkary, E. A. Soliman, Moshira Y. El-Awady

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In situ and green synthesis of cubic and spherical silver nanoparticles were developed using sun light as reducing agent in the presence of newly prepared cationic surfactant which acting as capping agents. The morphology of prepared silver nanoparticle was estimated by transmission electron microscope (TEM) and the size distribution determined by dynamic light scattering (DLS). The hydrophobic chain length of the prepared surfactant effect on the stability of the prepared silver nanoparticles as clear from zeta-potential values. Also by increasing chain length of the used capping agent the amount of formed nanoparticle increase as indicated by increasing the absorbance. Both prepared surfactants and surfactants capping silver nanoparticles showed high antimicrobial activity against gram positive and gram-negative bacteria.

Keywords: photosynthesis, hexaonal shapes, zetapotential, biological activity

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Authors: Emanuele Bonamente, Andrea Aquino

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This study presents an innovative design for geothermal heat pumps with the goal of maximizing the system efficiency (COP - Coefficient of Performance), reducing the soil use (e.g. length/depth of geothermal boreholes) and initial investment costs. Based on experimental data obtained from a two-year monitoring of a working prototype implemented for a commercial building in the city of Perugia, Italy, an upgrade of the system is proposed and the performance is evaluated via CFD simulations. The prototype was designed to include a thermal heat storage (i.e. water), positioned between the boreholes and the heat pump, acting as a flywheel. Results from the monitoring campaign show that the system is still capable of providing the required heating and cooling energy with a reduced geothermal installation (approx. 30% of the standard length). In this paper, an optimization of the system is proposed, re-designing the heat storage to include phase change materials (PCMs). Two stacks of PCMs, characterized by melting temperatures equal to those needed to maximize the system COP for heating and cooling, are disposed within the storage. During the working cycle, the latent heat of the PCMs is used to heat (cool) the water used by the heat pump while the boreholes independently cool (heat) the storage. The new storage is approximately 10 times smaller and can be easily placed close to the heat pump in the technical room. First, a validation of the CFD simulation of the storage is performed against experimental data. The simulation is then used to test possible alternatives of the original design and it is finally exploited to evaluate the PCM-storage performance for two different configurations (i.e. single- and double-loop systems).

Keywords: geothermal heat pump, phase change materials (PCM), energy storage, renewable energies

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Authors: Eoin Kirwan, Christopher Nulty, Declan Browne

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The professional ice hockey season consists of approximately 60 regular season games with periods of fixture congestion occurring several times in the average season. These periods of congestion provide limited time for recovery, exposing the athletes to the risk of competing whilst not fully recovered. Although a body of research is growing with respect to monitoring fatigue, particularly during periods of congested fixtures in team sports such as rugby and soccer, it has received little to no attention thus far in ice hockey athletes. Consequently, there is limited knowledge on monitoring tools that might effectively detect a fatigue response and the magnitude of fatigue that can accumulate when recovery is limited by competitive fixtures. The benefit of quantifying and establishing fatigue status is the ability to optimise training and provide pertinent information on player health, injury risk, availability and readiness. Some commonly used methods to assess fatigue and recovery status of athletes include the use of perceived fatigue and wellbeing questionnaires, tests of muscular force and ratings of perceive exertion (RPE). These measures are widely used in popular team sports such as soccer and rugby and show promise as assessments of fatigue and recovery status for ice hockey athletes. As part of a larger study, this study explored the magnitude of changes in adductor muscle strength after game play and throughout a period of fixture congestion and examined the relationship between internal game load and perceived wellbeing with adductor muscle strength. Methods 8 professional ice hockey players from a British Elite League club volunteered to participate (age = 29.3 ± 2.49 years, height = 186.15 ± 6.75 cm, body mass = 90.85 ± 8.64 kg). Prior to and after competitive games each player performed trials of the adductor squeeze test at 0˚ hip flexion with the lead investigator using hand-held dynamometry. Rate of perceived exertion was recorded for each game and from data of total ice time individual session RPE was calculated. After each game players completed a 5- point questionnaire to assess perceived wellbeing. Data was collected from six competitive games, 1 practice and 36 hours post the final game, over a 10 – day period. Results Pending final data collection in February Conclusions Pending final data collection in February.

Keywords: Conjested fixtures, fatigue monitoring, ice hockey, readiness

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Authors: Victor Iorungwa Gwa, Ebenezer Jonathan Ekefan

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Potential of Trichoderma harzianum for biological control of postharvest fungal rot of white yam (Dioscorea rotundata Poir) tubers in storage was studied. Pathogenicity test revealed the susceptibility of healthy looking yam tubers to Aspergillus niger, Botryodiplodia theobromae, and Fusarium oxysporum f. sp. melonganae after fourteen days of inoculation. Treatments comprising A. niger, B. theobromae, and F. oxysporum each paired with T. harzianum and were arranged in completely randomized design and stored for five months. Experiments were conducted between December 2015 and April 2016 and December 2016 and April 2017. Results showed that tubers treated with the pathogenic fungi alone caused mean percentage rot of between 6.67 % (F. oxysporum) and 22.22 % (A. niger) while the paired treatments produced only between 2.22 % (T. harzianum by F. oxysporum) and 6.67 % (T. harzianum by A. niger). In the second year of storage, mean percentage rot was found to be between 13.33 % (F. oxysporum) and 28.89 % (A. niger) while in the paired treatment rot was only between 6.67 % (F. oxysporum) and 8.89% (A. niger). Tubers treated with antagonist alone produced 0.00 % and 2.22 % in the first and second year, respectively. Result revealed that there was a significant difference (P ≤ 0.05) in mean percentage rot between the first year and the second year except where B. theobromae was inoculated alone, A. niger and T. harzianum paired and B. theobromae and T. harzianum paired. The most antagonised fungus in paired treatment for both years was F. oxysporum f. sp. melonganae, while the least antagonised, was A. niger and B. theobromae. It is, therefore, concluded that T. harzianum has potentials to control rot causing pathogens of yam tubers in storage. This can compliment or provide better alternative ways of reducing rot in yam tubers than by the use of chemical fungicides which are not environmentally friendly.

Keywords: biological control, fungal rot, postharvest, Trichoderma harzianum, white yam

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Authors: Morné Roman, Robert van Zyl, Nishanth Parus, Nishal Mahatho

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Leakage current monitoring for high voltage transmission line insulators is of interest as a performance indicator. Presently, to the best of our knowledge, there is no commercially available, clamp-on type, non-intrusive device for measuring leakage current on energised high voltage direct current (HVDC) transmission line insulators. The South African power utility, Eskom, is investigating the development of such a hand-held sensor for two important applications; first, for continuous real-time condition monitoring of HVDC line insulators and, second, for use by live line workers to determine if it is safe to work on energised insulators. In this paper, a DC leakage current sensor based on magnetic field sensing techniques is developed. The magnetic field sensor used in the prototype can also detect alternating current up to 5 MHz. The DC leakage current prototype detects the magnetic field associated with the current flowing on the surface of the insulator. Preliminary HVDC leakage current measurements are performed on glass insulators. The results show that the prototype can accurately measure leakage current in the specified current range of 1-200 mA. The influence of external fields from the HVDC line itself on the leakage current measurements is mitigated through a differential magnetometer sensing technique. Thus, the developed sensor can perform measurements on in-service HVDC insulators. The research contributes to the body of knowledge by providing a sensor to measure leakage current on energised HVDC insulators non-intrusively. This sensor can also be used by live line workers to inform them whether or not it is safe to perform maintenance on energized insulators.

Keywords: direct current, insulator, leakage current, live line, magnetic field, sensor, transmission lines

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Authors: R. Kaleeswari

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An investigation was carried out to assess the soil biological quality parameters in rice soils under rainfed and to compare soil quality indexing methods viz., Principal component analysis, Minimum data set and Indicator scoring method and to develop soil quality indices for formulating soil and crop management strategies.Soil samples were collected and analyzed for soil biological properties by adopting standard procedure. Biological indicators were determined for soil quality assessment, viz., microbial biomass carbon and nitrogen (MBC and MBN), potentially mineralizable nitrogen (PMN) and soil respiration and dehydrogenease activity. Among the methods of rice cultivation, Organic nutrition, Integrated Nutrient Management (INM) and System of Rice Intensification (SRI ), rice cultivation registered higher values of MBC, MBN and PMN. Mechanical and conventional rice cultivation registered lower values of biological quality indicators. Organic nutrient management and INM enhanced the soil respiration rate. SRI and aerobic rice cultivation methods increased the rate of soil respiration, while conventional and mechanical rice farming lowered the soil respiration rate. Dehydrogenase activity (DHA) was registered to be higher in soils under organic nutrition and Integrated Nutrient Management INM. System of Rice Intensification SRI and aerobic rice cultivation enhanced the DHA; while conventional and mechanical rice cultivation methods reduced DHA. The microbial biomass carbon (MBC) of the rice soils varied from 65 to 244 mg kg-1. Among the nutrient management practices, INM registered the highest available microbial biomass carbon of 285 mg kg-1.Potentially mineralizable N content of the rice soils varied from 20.3 to 56.8 mg kg-1. Aerobic rice farming registered the highest potentially mineralizable N of 78.9 mg kg-1..The soil respiration rate of the rice soils varied from 60 to 125 µgCO2 g-1. Nutrient management practices ofINM practice registered the highest. soil respiration rate of 129 µgCO2 g-1.The dehydrogenase activity of the rice soils varied from 38.3 to 135.3µgTPFg-1 day-1. SRI method of rice cultivation registered the highest dehydrogenase activity of 160.2 µgTPFg-1 day-1. Soil variables from each PC were considered for minimum soil data set (MDS). Principal component analysis (PCA) was used to select the representative soil quality indicators. In intensive rice cultivating regions, soil quality indicators were selected based on factor loading value and contribution percentage value using principal component analysis (PCA).Variables having significant difference within production systems were used for the preparation of minimum data set (MDS).

Keywords: soil quality, rice, biological properties, PCA analysis

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Richard L. Summers

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The advanced computational analysis of biological systems is becoming increasingly dependent upon an understanding of the information-theoretic structure of the materials, energy and interactive processes that comprise those systems. The stability and survival of these living systems are fundamentally contingent upon their ability to acquire and process the meaning of information concerning the physical state of its biological continuum (biocontinuum). The drive for adaptive system reconciliation of a divergence from steady-state within this biocontinuum can be described by an information metric-based formulation of the process for actionable knowledge acquisition that incorporates the axiomatic inference of Kullback-Leibler information minimization driven by survival replicator dynamics. If the mathematical expression of this process is the Lagrangian integrand for any change within the biocontinuum then it can also be considered as an action functional for the living system. In the direct method of Lyapunov, such a summarizing mathematical formulation of global system behavior based on the driving forces of energy currents and constraints within the system can serve as a platform for the analysis of stability. As the system evolves in time in response to biocontinuum perturbations, the summarizing function then conveys information about its overall stability. This stability information portends survival and therefore has absolute existential meaning for the living system. The first derivative of the Lyapunov energy information function will have a negative trajectory toward a system's steady state if the driving force is dissipating. By contrast, system instability leading to system dissolution will have a positive trajectory. The direction and magnitude of the vector for the trajectory then serves as a quantifiable signature of the meaning associated with the living system’s stability information, homeostasis and survival potential.

Keywords: meaning, information, Lyapunov, living systems

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Authors: Constant Gillot, Pauline Michaux, Julien Favresse, Jean-Michel Dogné, Jonathan Douxfils

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Introduction: NETosis has emerged as a crucial yet paradoxical factor in severe COVID-19 cases. While neutrophil extracellular traps (NETs) help contain and eliminate viral particles, excessive NET formation can lead to hyperinflammation, exacerbating tissue damage and acute respiratory distress syndrome (ARDS). Aims: This study evaluates the relationship between COVID-19-infected sera and NETosis using an ex-vivo model. Methods: Sera from 8 post-admission COVID-19 patients, after receiving corticoid therapy, were used to induce NETosis in neutrophils from a healthy donor. NET formation was tracked using fluorescent markers for DNA and neutrophil elastase (NE) every 2 minutes for 8 hours. The results were expressed as a percentage of DNA/NE released over time. Key metrics, including T50 (time to 50% release) and AUC (area under the curve), representing total NETosis potential), were calculated. A 27-cytokine screening kit was used to assess the cytokine composition of the sera. Results: COVID-19 sera induced NETosis based on their cytokine profile. The AUC of NE and DNA release decreased with time following corticoid therapy, showing a significant reduction in 6 of the 8 patients (p<0.05). T50 also decreased in parallel with AUC for both markers. Cytokines concentration decrease with time after therapy administration. There is correlation between 14 cytokines concentration and NE release. Conclusion: This ex-vivo model successfully demonstrated the induction of NETosis by COVID-19 sera using two markers. A clear decrease in NETosis potential was observed over time with glucocorticoid therapy. This model can be a valuable tool for monitoring NETosis and investigating potential NETosis inducers and inhibitors.

Keywords: NETosis, COVID-19, cytokine storm, biomarkers

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Authors: John Sodeau, David O'Connor, Shane Daly, Stig Hellebust

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This study is the first to employ the online WIBS (Waveband Integrated Biosensor Sensor) technique for the monitoring of bioaerosol emissions and non-fluorescing “dust” released from a composting/green waste site. The purpose of the research was to provide a “proof of principle” for using WIBS to monitor such a location continually over days and nights in order to construct comparative “bioaerosol site profiles”. Current impaction/culturing methods take many days to achieve results available by the WIBS technique in seconds.The real-time data obtained was then used to assess variations of the bioaerosol counts as a function of size, “shape”, site location, working activity levels, time of day, relative humidity, wind speeds and wind directions. Three short campaigns were undertaken, one classified as a “light” workload period, another as a “heavy” workload period and finally a weekend when the site was closed. One main bioaerosol size regime was found to predominate: 0.5 micron to 3 micron with morphologies ranging from elongated to elipsoidal/spherical. The real-time number-concentration data were consistent with an Andersen sampling protocol that was employed at the site. The number-concentrations of fluorescent particles as a proportion of total particles counted amounted, on average, to ~1% for the “light” workday period, ~7% for the “heavy” workday period and ~18% for the weekend. The bioaerosol release profiles at the weekend were considerably different from those monitored during the working weekdays.

Keywords: bioaerosols, composting, fluorescence, particle counting in real-time

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Authors: Harish Bisht, Bahadur Singh Kotlia, Kireet Kumar

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In order to reconstruct past retreating rates, total area loss, volume change and shift in snout position were measured through multi-temporal satellite data from 1989 to 2016 and kinematic GPS survey from 2015 to 2016. The results obtained from satellite data indicate that in the last 27 years, Chaturangi glacier snout has retreated 1172.57 ± 38.3 m (average 45.07 ± 4.31 m/year) with a total area and volume loss of 0.626 ± 0.001 sq. Km and 0.139 Km³, respectively. The field measurements through differential global positioning system survey revealed that the annual retreating rate was 22.84 ± 0.05 m/year. The large variations in results derived from both the methods are probably because of higher difference in their accuracy. Snout monitoring of the Gangotri glacier during the ablation season (May to September) in the years 2005 and 2015 reveals that the retreating rate has been comparatively more declined than that shown by the earlier studies. The GPS dataset shows that the average recession rate is 10.26 ± 0.05 m/year. In order to determine the possible causes of decreased retreating rate, a relationship between debris thickness and melt rate was also established by using ablation stakes. The present study concludes that remote sensing method is suitable for large area and long term study, while kinematic GPS is more appropriate for the annual monitoring of retreating rate of glacier snout. The present study also emphasizes on mapping of all the tributary glaciers in order to assess the overall changes in the main glacier system and its health.

Keywords: Chaturangi glacier, Gangotri glacier, glacier snout, kinematic global positioning system, retreat rate

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Authors: Roy. H. A. Lindelauf

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Cyberattacks are complex processes consisting of multiple interwoven tasks conducted by a set of agents. Interdictions and defenses against such attacks often rely on cyber kill chain (CKC) models. A CKC is a framework that tries to capture the actions taken by a cyber attacker. There exists a growing body of literature on CKCs. Most of this work either a) describes the CKC with respect to one or more specific cyberattacks or b) discusses the tools and technologies used by the attacker at each stage of the CKC. Defenders, facing scarce resources, have to decide where to allocate their resources given the CKC and partial knowledge on the tools and techniques attackers use. In this presentation CKCs are analyzed through the lens of covert projects, i.e., interrelated tasks that have to be conducted by agents (human and/or computer) with the aim of going undetected. Various aspects of covert project models have been studied abundantly in the operations research and game theory domain, think of resource-limited interdiction actions that maximally delay completion times of a weapons project for instance. This presentation has investigated both cooperative and non-cooperative game theoretic covert project models and elucidated their relation to CKC modelling. To view a CKC as a covert project each step in the CKC is broken down into tasks and there are players of which each one is capable of executing a subset of the tasks. Additionally, task inter-dependencies are represented by a schedule. Using multi-glove cooperative games it is shown how a defender can optimize the allocation of his scarce resources (what, where and how to monitor) against an attacker scheduling a CKC. This study presents and compares several cooperative game theoretic solution concepts as metrics for assigning resources to the monitoring of agents.

Keywords: cyber defense, cyber kill chain, game theory, information warfare techniques

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Authors: Raj Chavan

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At present, the United States is home to more than 14,000 Water Resource Recovery Facilities (WRRFs), of which approximately 35% have implemented nutrient limits of some kind. These WRRFs contribute 10 to 15% of the total nutrient burden to surface rivers in the United States and account for approximately 1% of total power demand and 2% of total greenhouse gas emissions (GHG). There are several factors that have influenced the development of densification technologies in the direction of more compact and energy-efficient nutrient removal processes. Prior to surface water discharge, existing facilities that necessitate capacity expansion or biomass densification for greater treatability within the same footprint are being subjected to stricter nutrient removal requirements. Densification of activated sludge as a method for nutrient removal and process intensification at WRRFs has garnered considerable attention in recent times. The biological processes take place within the aerobic sediment granules, which form the basis of the technology. The possibility of generating granular sludge through continuous (or conventional) activated sludge processes (CAS) or densification of biomass through the transfer of activated sludge flocs to a denser biomass aggregate as an exceptionally efficient intensification technique has generated considerable interest. This presentation aims to furnish attendees with a foundational comprehension of densification through the illustration of practical concerns and insights. The subsequent subjects will be deliberated upon. What are some potential techniques for producing and preserving densified granules? What processes are responsible for the densification of biological flocs? How do physical selectors contribute to the process of biological flocs becoming denser? What viable strategies exist for the management of densified biological flocs, and which design parameters of physical selection influence the retention of densified biological flocs? determining operational solutions for floc and granule customization in order to meet capacity and performance objectives? The answers to these pivotal questions will be derived from existing full-scale treatment facilities, bench-scale and pilot-scale investigations, and existing literature data. By the conclusion of the presentation, the audience will possess a fundamental comprehension of the densification concept and its significance in attaining effective effluent treatment. Additionally, case studies pertaining to the design and operation of densification procedures will be incorporated into the presentation.

Keywords: densification, intensification, nutrient removal, granular sludge

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Authors: Randa Alharbi, Vladislav Vyshemirsky

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Systems biology is an important field in science which focuses on studying behaviour of biological systems. Modelling is required to produce detailed description of the elements of a biological system, their function, and their interactions. A well-designed model requires selecting a suitable mechanism which can capture the main features of the system, define the essential components of the system and represent an appropriate law that can define the interactions between its components. Complex biological systems exhibit stochastic behaviour. Thus, using probabilistic models are suitable to describe and analyse biological systems. Continuous-Time Markov Chain (CTMC) is one of the probabilistic models that describe the system as a set of discrete states with continuous time transitions between them. The system is then characterised by a set of probability distributions that describe the transition from one state to another at a given time. The evolution of these probabilities through time can be obtained by chemical master equation which is analytically intractable but it can be simulated. Uncertain parameters of such a model can be inferred using methods of Bayesian inference. Yet, inference in such a complex system is challenging as it requires the evaluation of the likelihood which is intractable in most cases. There are different statistical methods that allow simulating from the model despite intractability of the likelihood. Approximate Bayesian computation is a common approach for tackling inference which relies on simulation of the model to approximate the intractable likelihood. Particle Markov chain Monte Carlo (PMCMC) is another approach which is based on using sequential Monte Carlo to estimate intractable likelihood. However, both methods are computationally expensive. In this paper we discuss the efficiency and possible practical issues for each method, taking into account the computational time for these methods. We demonstrate likelihood-free inference by performing analysing a model of the Repressilator using both methods. Detailed investigation is performed to quantify the difference between these methods in terms of efficiency and computational cost.

Keywords: Approximate Bayesian computation(ABC), Continuous-Time Markov Chains, Sequential Monte Carlo, Particle Markov chain Monte Carlo (PMCMC)

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Authors: Makkia Ahmad Ali Al-Falahi, Abdullah Abdelaziz Muharram

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Background: Healthcare workers (HCWs) in Yemen are exposed to a myriad of occupational health hazards, including biological, physical, ergonomic, chemical and psychosocial hazards. HCWs operate in an environment that is considered to be one of the most hazardous occupational settings. Objective: To assess the prevalence of occupational health hazards among healthcare workers and associated risk factors in public hospitals in Sana'a City, Yemen. Method: Descriptive cross-sectional design was utilized; out of 5443 totals of HCWs 396 were selected by multistage sampling technique was carried out in the public hospitals in Sana'a city, Yemen. Results: More the half (60.6%) of HCWs aged between 20-30 years, (50.8%) were males, (56.3%) were married, and (45.5%) had a diploma qualification, while (65.2%) of HCWs had less than 6 years of experience. The result showed that the highest prevalence of occupational hazards was (99%), (ergonomic hazards (93.4%), biological hazards (87.6%), psychosocial (86.65%), physical hazards (83.3%), and chemical hazards (73.5%). There were no statistically significant differences between demographic characteristics and the prevalence of occupational hazards (p >0.05). Conclusion and recommendations: The study showed the highest prevalence of occupational hazards; regarding the prevalence of biological hazards exposure to sharp-related injury, the most prevalent physical hazards were slip/trip/and fall. Ergonomic hazards had back or neck pain during work. Chemical hazards were allergic to medical gloves powder. On psychosocial hazards was suffered from verbal and physical harassment. The study concluded by raising awareness among HCWs by conducting training courses to prevent occupational hazards.

Keywords: health workers, occupational hazards, risk factors, the prevalence

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Authors: Mohamed E. Khalifa, Adil A. Gobouri

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Potential synthesis of a series of 3-amino-4-arylazothiophene derivatives from reaction of 2-cyano-2-phenylthiocarbamoyl acetamide and the appropriate α-halogenated reagents, followed by coupling with different aryl diazonium salts (Japp-Klingemann reaction), and another series of 5-arylazo-thiazol-2-ylcarbamoyl-thiophene derivatives from base-catalyzed intramolecular condensation of 5-arylazo-2-(N-chloroacetyl)amino-thiazole with selected B-keto compounds (Thorpe-Ziegler reaction) was performed. The biological activity of the two series was studied in vitro. Their versatility for pharmaceutical purposes was reported, where they displayed remarkable activities against selected pathogenic microorganisms; Bacillus subtilize, Staphylococcus aureus (Gram positive bacteria), Escherichia coli, Pseudomonas aeruginosa (Gram negative bacteria) and Aspergillus flavus, Candida albicans (fungi) with various degrees related to their chemical structures.

Keywords: thiophene, 2-aminothiazole, compounds, antioxidant, antitumor, antimicrobial

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Authors: Ahmad Manbohi, Seyyed Hamid Ahmadi

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A low-cost paper-based microfluidic device (PAD) for the multiplex electrochemical determination of glucose, uric acid, and dopamine in biological fluids was developed. Using wax printing, PAD containing a central zone, six channels, and six detection zones was fabricated, and the electrodes were printed on detection zones using pre-made electrodes template. For each analyte, two detection zones were used. The carbon working electrode was coated with chitosan-BSA (and enzymes for glucose and uric acid). To detect glucose and uric acid, enzymatic reactions were employed. These reactions involve enzyme-catalyzed redox reactions of the analytes and produce free electrons for electrochemical measurement. Calibration curves were linear (R^² > 0.980) in the range of 0-80 mM for glucose, 0.09–0.9 mM for dopamine, and 0–50 mM for uric acid, respectively. Blood samples were successfully analyzed by the proposed method.

Keywords: biological fluids, biomarkers, microfluidic paper-based electrochemical biosensors, Multiplex

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Authors: A. Degale Desta, Tamirat Kebamo

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Background: Recent advances in computer vision and pattern recognition have significantly improved activity recognition through video analysis, particularly with the application of Deep Convolutional Neural Networks (CNNs). One-stage detectors now enable efficient video-based recognition by simultaneously predicting object categories and locations. Such advancements are highly relevant in educational settings where CCTV surveillance could automatically monitor academic activities, enhancing security and classroom management. However, current datasets and recognition systems lack the specific focus on campus environments necessary for practical application in these settings.Objective: This study aims to address this gap by developing a dataset and testing an automated activity recognition system specifically tailored for educational campuses. The EthioCAD dataset was created to capture various classroom activities and teacher-student interactions, facilitating reliable recognition of academic activities using deep learning models. Method: EthioCAD, a novel video-based dataset, was created with a design science research approach to encompass teacher-student interactions across three domains and 18 distinct classroom activities. Using the Roboflow AI framework, the data was processed, with 4.224 KB of frames and 33.485 MB of images managed for frame extraction, labeling, and organization. The Ultralytics YOLOv8 model was then implemented within Google Colab to evaluate the dataset’s effectiveness, achieving high mean Average Precision (mAP) scores. Results: The YOLOv8 model demonstrated robust activity recognition within campus-like settings, achieving an mAP50 of 90.2% and an mAP50-95 of 78.6%. These results highlight the potential of EthioCAD, combined with YOLOv8, to provide reliable detection and classification of classroom activities, supporting automated surveillance needs on educational campuses. Discussion: The high performance of YOLOv8 on the EthioCAD dataset suggests that automated activity recognition for surveillance is feasible within educational environments. This system addresses current limitations in campus-specific data and tools, offering a tailored solution for academic monitoring that could enhance the effectiveness of CCTV systems in these settings. Conclusion: The EthioCAD dataset, alongside the YOLOv8 model, provides a promising framework for automated campus activity recognition. This approach lays the groundwork for future advancements in CCTV-based educational surveillance systems, enabling more refined and reliable monitoring of classroom activities.

Keywords: deep CNN, EthioCAD, deep learning, YOLOv8, activity recognition

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Authors: Subhajit Das, Nirjhar Dhang

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Structural damage detection is a challenging work in the field of structural health monitoring (SHM). The damage detection methods mainly focused on the determination of the location and severity of the damage. Model updating is a well known method to locate and quantify the damage. In this method, an error function is defined in terms of difference between the signal measured from ‘experiment’ and signal obtained from undamaged finite element model. This error function is minimised with a proper algorithm, and the finite element model is updated accordingly to match the measured response. Thus, the damage location and severity can be identified from the updated model. In this paper, an error function is defined in terms of modal data viz. frequencies and modal assurance criteria (MAC). MAC is derived from Eigen vectors. This error function is minimized by teaching-learning-based optimization (TLBO) algorithm, and the finite element model is updated accordingly to locate and quantify the damage. Damage is introduced in the model by reduction of stiffness of the structural member. The ‘experimental’ data is simulated by the finite element modelling. The error due to experimental measurement is introduced in the synthetic ‘experimental’ data by adding random noise, which follows Gaussian distribution. The efficiency and robustness of this method are explained through three examples e.g., one truss, one beam and one frame problem. The result shows that TLBO algorithm is efficient to detect the damage location as well as the severity of damage using modal data.

Keywords: damage detection, finite element model updating, modal assurance criteria, structural health monitoring, teaching learning based optimization

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Authors: Rose A. Yemson, Ping Jiang, Oyedeji L. Inumoh

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The deployment of wireless sensor network has rapidly increased, however with the increased capacity and diversity of sensors, and applications ranging from biological, environmental, military etc. generates tremendous volume of data’s where more attention is placed on the distributed sensing and little on how to manage, analyze, retrieve and understand the data generated. This makes it more quite difficult to process live sensor data, run concurrent control and update because sensor data are either heavyweight, complex, and slow. This work will focus on developing a web service platform for automatic detection of sensors, acquisition of sensor data, storage of sensor data into a database, processing of sensor data using reconfigurable software components. This work will also create a web service based sensor data management system to monitor physical movement of an individual wearing wireless network sensor technology (SunSPOT). The sensor will detect movement of that individual by sensing the acceleration in the direction of X, Y and Z axes accordingly and then send the sensed reading to a database that will be interfaced with an internet platform. The collected sensed data will determine the posture of the person such as standing, sitting and lying down. The system is designed using the Unified Modeling Language (UML) and implemented using Java, JavaScript, html and MySQL. This system allows real time monitoring an individual closely and obtain their physical activity details without been physically presence for in-situ measurement which enables you to work remotely instead of the time consuming check of an individual. These details can help in evaluating an individual’s physical activity and generate feedback on medication. It can also help in keeping track of any mandatory physical activities required to be done by the individuals. These evaluations and feedback can help in maintaining a better health status of the individual and providing improved health care.

Keywords: HTML, java, javascript, MySQL, sunspot, UML, web-based, wireless network sensor

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Authors: Alejandro Rodríguez-Molinero, Carlos Pérez-López, Jorge Hernández-Vara, Àngels Bayes-Rusiñol, Juan Carlos Martínez-Castrillo, David A. Pérez-Martínez

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Background: Monitoring motor symptoms in Parkinson's patients is often a complex and time-consuming task for clinicians, as Hauser's diaries are often poorly completed by patients. Recently, new automatic devices (Parkinson's holter: STAT-ON®) have been developed capable of monitoring patients' motor fluctuations. The MoMoPa-EC clinical trial (NCT04176302) investigates which of the two methods produces better clinical results. In this sub-analysis, the concordance between both methods is analyzed. Methods: In the MoMoPa-EC clinical trial, 164 patients with moderate-severe Parkinson's disease and at least two hours a day of Off will be included. At the time of patient recruitment, all of them completed a seven-day motor fluctuation diary at home (Hauser’s diary) while wearing the Parkinson's holter. In this sub-analysis, 71 patients with complete data for the purpose of this comparison were included. The intraclass correlation coefficient was calculated between the patient diary entries and the Parkinson's holter data in terms of time On, Off, and time with dyskinesias. Results: The intra-class correlation coefficient of both methods was 0.57 (95% CI: 0.3-0.74) for daily time in Off (%), 0.48 (95% CI: 0.14-0.68) for daily time in On (%), and 0.37 (95% CI %: -0.04-0.62) for daily time with dyskinesias (%). Conclusions: Both methods have a moderate agreement with each other. We will have to wait for the results of the MoMoPa-EC project to estimate which of them has the greatest clinical benefits. Acknowledgment: This work is supported by AbbVie S.L.U, the Instituto de Salud Carlos III [DTS17/00195], and the European Fund for Regional Development, 'A way to make Europe'.

Keywords: Parkinson, sensor, motor fluctuations, dyskinesia

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Authors: Surajbhan Sevda

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Metal containing wastewater is generated in large quintiles due to rapid industrialization. Generally, the metal present in wastewater is not biodegradable and can be accumulated in living animals, humans and plant tissue, causing disorder and diseases. The conventional metal recovery methods include chemical, physical and biological methods, but these are chemical and energy intensive. The recent development in microbial fuel cell (MFC) technology provides a new approach for metal recovery; this technology offers a flexible platform for both reduction and oxidation reaction oriented process. The use of MFCs will be a new platform for more efficient and low energy approach for metal recovery from the wastewater. So far metal recover was extensively studied using chemical, physical and biological methods. The MFCs present a new and efficient approach for removing and recovering metals from different wastewater, suggesting the use of different electrode for metal recovery can be a new efficient and effective approach.

Keywords: metal recovery, microbial fuel cell, wastewater, bioelectricity

Procedia PDF Downloads 217