Search results for: toxicity monitoring

Authors: Minakshi Goswami, M. V. Khire

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The urban land use is an outcome of geographical and socio economic factors over the decades. Hence, spatial information on land use and possibilities of alternate use is essential for the selection, planning and implementation to meet the increasing demands of human needs and welfare of the urban area. This information assists in monitoring the land use resulting out of charging demands of increasing urban population over the decades. So in this paper, a detailed work on urban land use pattern, with a special reference to build up land in Bangalore city is analyzed in view of the various master plans from 1975to 2011. An attempt has been made to study the status of urban land use of Bangalore city during this period to detect the changes on land utilization rate that has taken place in each master plan period, particularly in the built-up land. The set of measures taken by the city corporation to contain the problems regarding the extremely bothering existing land use in Bangalore city is analyzed.

Keywords: built up land, land use changes, master plan, population

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Authors: A. Aslihan Gokaltun, Martin L. Yarmush, Ayse Asatekin, O. Berk Usta

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In the last decade microfabrication processes including rapid prototyping techniques have advanced rapidly and achieved a fairly matured stage. These advances encouraged and enabled the use of microfluidic devices by a wider range of users with applications in biological separations, and cell and organoid cultures. Accordingly, a significant current challenge in the field is controlling biomolecular interactions at interfaces and the development of novel biomaterials to satisfy the unique needs of the biomedical applications. Poly(dimethylsiloxane) (PDMS) is by far the most preferred material in the fabrication of microfluidic devices. This can be attributed its favorable properties, including: (1) simple fabrication by replica molding, (2) good mechanical properties, (3) excellent optical transparency from 240 to 1100 nm, (4) biocompatibility and non-toxicity, and (5) high gas permeability. However, high hydrophobicity (water contact angle ~108°±7°) of PDMS often limits its applications where solutions containing biological samples are concerned. In our study, we created a simple, easy method for modifying the surface chemistry of PDMS microfluidic devices through the addition of surface-segregating additives during manufacture. In this method, a surface segregating copolymer is added to precursors for silicone and the desired device is manufactured following the usual methods. When the device surface is in contact with an aqueous solution, the copolymer self-organizes to expose its hydrophilic segments to the surface, making the surface of the silicone device more hydrophilic. This can lead to several improved performance criteria including lower fouling, lower non-specific adsorption, and better wettability. Specifically, this approach is expected to be useful for the manufacture of microfluidic devices. It is also likely to be useful for manufacturing silicone tubing and other materials, biomaterial applications, and surface coatings.

Keywords: microfluidics, non-specific protein adsorption, PDMS, PEG, copolymer

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Authors: Saleh Alkarri, Dimple Sharma, Teresa M. Bergholz, Muhammad Rabnawaz

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Aims: Develop a methodology for the fabrication of anti-microbial polypropylene (PP) surfaces with (i) leachable copper, (II) chloride dihydrate (CuCl₂·₂H₂O) and (ii) non-leachable magnesium hydroxide (Mg(OH)₂) biocides. Methods and Results: Two methodologies are used to develop anti-microbial PP surfaces. One method involves melt-blending and subsequent injection molding, where the biocide additives were compounded with PP and subsequently injection-molded. The other method involves the thermal embossing of anti-microbial agents on the surface of a PP substrate. The obtained biocide-bearing PP surfaces were evaluated against E. coli K-12 MG1655 for 0, 4, and 24 h to evaluate their anti-microbial properties. The injection-molded PP bearing 5% CuCl2·₂H₂O showed a 6-log reduction of E. coli K-12 MG1655 after 24 h, while only 1 log reduction was observed for PP bearing 5% Mg(OH)2. The thermally embossed PP surfaces bearing CuCl2·2H2O and Mg(OH)₂ particles (at a concentration of 10 mg/mL) showed 3 log and 4 log reduction, respectively, against E.coli K-12 MG1655 after 24 h. Conclusion: The results clearly demonstrate that CuCl₂·2H₂O conferred anti-microbial properties to PP surfaces that were prepared by both injection molding as well as thermal embossing approaches owing to the presence of leachable copper ions. In contrast, the non-leachable Mg(OH)₂ imparted anti-microbial properties only to the surface prepared via the thermal embossing technique. Significance and Impact of The Study: Plastics with leachable biocides are effective anti-microbial surfaces, but their toxicity is a major concern. This study provides a fabrication methodology for non-leachable PP-based anti-microbial surfaces that are potentially safer. In addition, this strategy can be extended to many other plastics substrates.

Keywords: anti-microbial activity, E. coli K-12 MG1655, copper (II) chloride dihydrate, magnesium hydroxide, leachable, non-leachable, compounding, thermal embossing

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Authors: Saleh Alkarri, Dimple Sharma, Teresa M. Bergholz, Muhammad Rabnawa

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Aims: Develop a methodology for the fabrication of anti-microbial polypropylene (PP) surfaces with (i) leachable copper (II) chloride dihydrate (CuCl2·2H2O) and (ii) non-leachable magnesium hydroxide (Mg(OH)2) biocides. Methods and Results: Two methodologies are used to develop anti-microbial PP surfaces. One method involves melt-blending and subsequent injection molding, where the biocide additives were compounded with PP and subsequently injection-molded. The other method involves the thermal embossing of anti-microbial agents on the surface of a PP substrate. The obtained biocide-bearing PP surfaces were evaluated against E. coli K-12 MG1655 for 0, 4, and 24 h to evaluate their anti-microbial properties. The injection-molded PP bearing 5% CuCl2·2H2O showed a 6-log reduction of E. coli K-12 MG1655 after 24 h, while only 1 log reduction was observed for PP bearing 5% Mg(OH)2. The thermally embossed PP surfaces bearing CuCl2·2H2O and Mg(OH)2 particles (at a concentration of 10 mg/mL) showed 3 log and 4 log reduction, respectively, against E.coli K-12 MG1655 after 24 h. Conclusion: The results clearly demonstrate that CuCl2·2H2O conferred anti-microbial properties to PP surfaces that were prepared by both injection molding as well as thermal embossing approaches owing to the presence of leachable copper ions. In contrast, the non-leachable Mg(OH)2 imparted anti-microbial properties only to the surface prepared via the thermal embossing technique. Significance and Impact of The Study: Plastics with leachable biocides are effective anti-microbial surfaces, but their toxicity is a major concern. This study provides a fabrication methodology for non-leachable PP-based anti-microbial surfaces that are potentially safer. In addition, this strategy can be extended to many other plastics substrates.

Keywords: anti-microbial activity, E. coli K-12 MG1655, copper (II) chloride dihydrate, magnesium hydroxide, leachable, non-leachable, compounding, thermal embossing

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Authors: Dian Zhang, Brendan Heery, Maria O’Neill, Ciprian Briciu-Burghina, Noel E. O’Connor, Fiona Regan

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Recent developments in sensors, wireless data communication and the cloud computing have brought the sensor web to a whole new generation. The introduction of the concept of ‘Internet of Thing (IoT)’ has brought the sensor research into a new level, which involves the developing of long lasting, low cost, environment friendly and smart sensors; new wireless data communication technologies; big data analytics algorithms and cloud based solutions that are tailored to large scale smart sensor network. The next generation of smart sensor network consists of several layers: physical layer, where all the smart sensors resident and data pre-processes occur, either on the sensor itself or field gateway; data transmission layer, where data and instructions exchanges happen; the data process layer, where meaningful information is extracted and organized from the pre-process data stream. There are many definitions of smart sensor, however, to summarize all these definitions, a smart sensor must be Intelligent and Adaptable. In future large scale sensor network, collected data are far too large for traditional applications to send, store or process. The sensor unit must be intelligent that pre-processes collected data locally on board (this process may occur on field gateway depends on the sensor network structure). In this case study, three smart sensing methods, corresponding to simple thresholding, statistical model and machine learning based MoPBAS method, are introduced and their strength and weakness are discussed as an introduction to the smart sensing concept. Data fusion, the integration of data and knowledge from multiple sources, are key components of the next generation smart sensor network. For example, in the water level monitoring system, weather forecast can be extracted from external sources and if a heavy rainfall is expected, the server can send instructions to the sensor notes to, for instance, increase the sampling rate or switch on the sleeping mode vice versa. In this paper, we describe the deployment of 11 affordable water level sensors in the Dublin catchment. The objective of this paper is to use the deployed river level sensor network at the Dodder catchment in Dublin, Ireland as a case study to give a vision of the next generation of a smart sensor network for flood monitoring to assist agencies in making decisions about deploying resources in the case of a severe flood event. Some of the deployed sensors are located alongside traditional water level sensors for validation purposes. Using the 11 deployed river level sensors in a network as a case study, a vision of the next generation of smart sensor network is proposed. Each key component of the smart sensor network is discussed, which hopefully inspires the researchers who are working in the sensor research domain.

Keywords: smart sensing, internet of things, water level sensor, flooding

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Authors: Joshefina Bengoechea Fernandez, Alex Bell

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In its 2021 guidance for policy makers, the UNESCO has proposed 4 areas where AI can be applied in educational settings: These are: 1) Education management and delivery; 2) Learning and assessment; 3) Empowering teachers and facilitating teaching, and 4) Providing lifelong learning possibilities (UNESCO, 2021). Like with wblockchain technologies, AI will automate the management of educational institutions. These include, but are not limited to admissions, timetables, attendance, and homework monitoring. Furthermore, AI will be used to select relevant learning content across learning platforms for each student, based on his or her personalized needs. A problem educators face is the “one-size-fits-all” approach that does not work with a diverse student population. The purpose of this paper is to illustrate if the implementation of Technology is the solution to the Problems faced in Higher Education. The paper builds upon a constructivist approach, combining a literature review and research on key publications and academic reports.

Keywords: artificial intelligence, learning platforms, students personalised needs, life- long learning, privacy, ethics

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Authors: Pawel Twardowski, Maciej Tabaszewski, Jakub Czyżycki

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The main goal of the work was to use machine learning to predict cutting-edge wear. The research was carried out while milling hardened steel with sintered carbide cutters at various cutting speeds. During the tests, cutting-edge wear was measured, and vibration acceleration signals were also measured. Appropriate measures were determined from the vibration signals and served as input data in the machine-learning process. Two approaches were used in this work. The first one involved a two-state classification of the cutting edge - suitable and unfit for further work. In the second approach, prediction of the cutting-edge state based on vibration signals was used. The obtained research results show that the appropriate use of machine learning algorithms gives excellent results related to monitoring cutting edge during the process.

Keywords: milling of hardened steel, tool wear, vibrations, machine learning

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Authors: AmirHossein Mohajerzadeh, Mohammad Alishahi, Saeed Aslishahi, Mohsen Zabihi

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In this paper, we consider sensor nodes with the capability of measuring the bearings (relative angle to the target). We use geometric methods to select a set of observer nodes which are responsible for collecting data from the target. Considering the characteristics of target tracking applications, it is clear that significant numbers of sensor nodes are usually inactive. Therefore, in order to minimize the total network energy consumption, a set of sensor nodes, called sentinel, is periodically selected for monitoring, controlling the environment and transmitting data through the network. The other nodes are inactive. Furthermore, the proposed algorithm provides a joint scheduling and routing algorithm to transmit data between network nodes and the fusion center (FC) in which not only provides an efficient way to estimate the target position but also provides an efficient target tracking. Performance evaluation confirms the superiority of the proposed algorithm.

Keywords: coverage, routing, scheduling, target tracking, wireless sensor networks

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Authors: Mary Allagoa, Abir Al-Tabbaa

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The study explores the use of binders and additives, such as Portland cement, pulverized fuel ash, ground granulated blast furnace slag, and MgO, to decrease the concentration and leachability of pollutants in contaminated site soils. The research investigates their effectiveness and associated risks of using the binders, with a focus on Total Heavy metals (THM) and Total Petroleum Hydrocarbon (TPH). The goal of this research is to evaluate the performance and effectiveness of binders and additives in remediating soil pollutants. The study aims to assess the suitability of the mixtures for ground improvement purposes, determine the optimal dosage, and investigate the associated risks. The research utilizes physical (unconfined compressive strength) and chemical tests (batch leachability test) to assess the efficacy of the binders and additives. A completely randomized design one-way ANOVA is used to determine the significance within mix binders of THM. The study also employs incremental lifetime cancer risk assessments (ILCR) and other indexes to evaluate the associated risks. The study finds that Ground Granulated Blast Furnace Slag (GGBS): MgO is the most effective binder for remediation, particularly when using low dosages of MgO combined with higher dosages of GGBS binders on TPH. The results indicate that binders and additives can encapsulate and immobilize pollutants, thereby reducing their leachability and toxicity. The mean unconfined compressive strength of the soil ranges from 285.0- 320.5 kPa, while THM levels are less than 10 µg/l in GGBS: MgO and CEM: PFA but below 1 µg/l in CEM I based. The ILCR ranged from 6.77E-02 - 2.65E-01 and 5.444E-01 – 3.20 E+00, with the highest values observed under extreme conditions. The hazard index (HI), Risk allowable daily dose intake (ADI), and Risk chronic daily intake (CDI) were all less than 1 for the THM. The study identifies MgO as the best additive for use in soil remediation.

Keywords: risk ADI, risk CDI, ILCR, novel binders, additives binders, hazard index

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Authors: Faisal A. Salih

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Vinpocetine (Vin) is an ethyl ester of apovincamic acid and is a semisynthetic derivative of vincamine, an alkaloid from plants of the genus Periwinkle (plant) vinca minor. It was found that this compound stimulates cerebral metabolism: it increases the uptake of glucose and oxygen, as well as the consumption of these substances by the brain tissue. Vinpocetine enhances the flow of blood in the brain and has a vasodilating, antihypertensive, and antiplatelet effect. Vinpocetine seems to improve the human ability to acquire new memories and restore memories that have been lost. This drug has been clinically used for the treatment of cerebrovascular disorders such as stroke and dementia memory disorders, as well as in ophthalmology and otorhinolaryngology. It has no side effects, and no toxicity has been reported when using vinpocetine for a long time. For the quantitative determination of Vin in dosage forms, the HPLC methods are generally used. A promising alternative is potentiometry with Vin- selective electrode, which does not require expensive equipment and materials. Another advantage of the potentiometric method is that the pills and solutions for injections can be used directly without separation from matrix components, which reduces both analysis time and cost. In this study, it was found that the choice of a good plasticizer an electrode with the following membrane composition: PVC (32.8 wt.%), ortho-nitrophenyl octyl ether (66.6 wt.%), tetrakis-4-chlorophenyl borate (0.6 wt.%) exhibits excellent analytical performance: lower detection limit (LDL) 1.2•10⁻⁷ M, linear response range (LRR) 1∙10⁻³–3.9∙10⁻⁶ M, the slope of the electrode function 56.2±0.2 mV/decade). Vin masses per average tablet weight determined by direct potentiometry (DP) and potentiometric titration (PT) methods for the two different sets of 10 tablets were (100.35±0.2–100.36±0.1) mg for two sets of blister packs. The mass fraction of Vin in individual tablets, determined using DP, was (9.87 ± 0.02–10.16 ±0.02) mg, while the RSD was (0.13–0.35%). The procedure has very good reproducibility, and excellent compliance with the declared amounts was observed.

Keywords: vinpocetine, potentiometry, ion selective electrode, pharmaceutical analysis

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Authors: Zahrul Faizi M. S., Ali A., Norhuda A. M.

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Emission of greenhouse gases (GHG) from solid waste treatment and dependency on fossil fuel to produce electricity are the major concern in Malaysia as well as global. Innovation in downdraft gasification with combined heat and power (CHP) systems has the potential to minimize solid waste and reduce the emission of anthropogenic GHG from conventional fossil fuel power plants. However, the efficiency and capability of downdraft gasification to generate electricity from various alternative fuels, for instance, agriculture residues (i.e., woodchip, coconut shell) and municipal solid waste (MSW), are still controversial, on top of the toxicity level from the produced bottom ash. Thus this study evaluates the adaptability and reliability of the 20 kW downdraft gasification system to generate electricity (while considering environmental sustainability from the bottom ash) using flexible local feedstock at 20, 40, and 60% mixed ratio of MSW: agriculture residues. Feedstock properties such as feed particle size, moisture, and ash contents are also analyzed to identify optimal characteristics for the combination of feedstock (feedstock flexibility) to obtain maximum energy generation. Results show that the gasification system is capable to flexibly accommodate different feedstock compositions subjected to specific particle size (less than 2 inches) at a moisture content between 15 to 20%. These values exhibit enhance gasifier performance and provide a significant effect to the syngas composition utilizes by the internal combustion engine, which reflects energy production. The result obtained in this study is able to provide a new perspective on the transition of the conventional gasification system to a future reliable carbon-negative energy technology. Subsequently, promoting commercial scale-up of the downdraft gasification system.

Keywords: carbon-negative energy, feedstock flexibility, gasification, renewable energy

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Authors: Gilbert Omorodion Osayemwenre, Edson Meyer, R. T. Taziwa

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The electrical analysis of single junction amorphous silicon solar modules is carried out using outdoor monitoring technique. Like crystalline silicon PV modules, the electrical characterisation and performance of single junction amorphous silicon modules are best described by its current-voltage (IV) characteristic. However, IV curve has a direct dependence on the type of PV technology and material properties used. The analysis reveals discrepancies in the modules performance parameter even though they are of similar technology. The aim of this work is to compare the electrical performance output of each module, using electrical parameters with the aid of PVPM 100040C IV tracer. These results demonstrated the relevance of standardising the performance parameter for effective degradation analysis of a-Si:H.

Keywords: PVPM 100040C IV tracer, SolarWatt part, single junction amorphous silicon module (a-Si:H), Staebler-Wronski (S-W) degradation effect

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Authors: Getaneh Berie Tarekegn, Li-Chia Tai

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With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarms, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Arlinda Memetaj

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A sound system of administrative justice represents a vital element of democratic governance. The proper control of public administration consists not only of a sound civil service framework and legislative oversight, but empowerment of the public and courts to hold public officials accountable for their decision-making through the application of fair administrative procedural rules and the use of appropriate administrative appeals processes and judicial review. The establishment of both effective public administration and administrative justice system has been for a long period of time among the most ‘important and urgent’ final strategic objectives of almost any country in the Balkans region, including Macedonia, Albania and Kosovo. Closely related to this is their common strategic goal to enter the membership in the European Union, which requires fulfilling of many criteria and standards as incorporated in EU acquis communautaire. The latter is presently done with the framework of the Stabilization and Association Agreement which each of these countries has concluded with the EU accordingly. To above aims, each of the three countries has so far adopted a huge series of legislative and strategic documents related to any aspects of their individual administrative justice system. ‘Changes and reforms’ in this field have been thus the most frequent terms being used in any of these countries. The three countries have already established their own national administrative judiciary, while permanently amending their laws on the general administrative procedure introducing thereby considerable innovations concerned. National administrative courts are expected to have crucial important role within the broader judiciary systems-related reforms of these countries; they are designed to check the legality of decisions of the state administration with the aim to guarantee an effective protection of human rights and legitimate interests of private persons through a regular, conform, fast and reasonable judicial administrative process. Further improvements in this field are presently an integral crucial part of all the relevant national strategic documents including the ones on judiciary reform and public administration reform, as adopted by each of the three countries; those strategic documents are designed among others to provide effective protection of their citizens` rights` of administrative justice. On the basis of the later, the paper finally is aimed at highlighting selective common challenges and problems of the three countries on their European road, while claiming (among others) that the current status quo situation in each of them may be overcome only if there is a proper implementation of the administrative courts decisions and a far stricter international monitoring process thereof. A new approach and strong political commitment from the highest political leadership is thus absolutely needed to ensure the principles of transparency, accountability and merit in public administration. The main methods used in this paper include the analytical and comparative ones due to the very character of the paper itself.

Keywords: administrative courts , administrative justice, administrative procedure, benefit, effective administrative justice, human rights, implementation, monitoring, reform

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Authors: Elham Koohikerade, Silvio Jose Gumiere

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In this research, irrigation is highlighted as crucial for improving both the yield and quality of ‎potatoes due to their high sensitivity to soil moisture changes. The study presents a hybrid Long ‎Short-Term Memory (LSTM) model aimed at optimizing irrigation scheduling in potato fields in ‎Quebec City, Canada. This model integrates model-based and satellite-derived datasets to simulate ‎soil moisture content, addressing the limitations of field data. Developed under the guidance of the ‎Food and Agriculture Organization (FAO), the simulation approach compensates for the lack of direct ‎soil sensor data, enhancing the LSTM model's predictions. The model was calibrated using indices ‎like Surface Soil Moisture (SSM), Normalized Vegetation Difference Index (NDVI), Enhanced ‎Vegetation Index (EVI), and Normalized Multi-band Drought Index (NMDI) to effectively forecast ‎soil moisture reductions. Understanding soil moisture and plant development is crucial for assessing ‎drought conditions and determining irrigation needs. This study validated the spectral characteristics ‎of vegetation and soil using ECMWF Reanalysis v5 (ERA5) and Moderate Resolution Imaging ‎Spectrometer (MODIS) data from 2019 to 2023, collected from agricultural areas in Dolbeau and ‎Peribonka, Quebec. Parameters such as surface volumetric soil moisture (0-7 cm), NDVI, EVI, and ‎NMDI were extracted from these images. A regional four-year dataset of soil and vegetation moisture ‎was developed using a machine learning approach combining model-based and satellite-based ‎datasets. The LSTM model predicts soil moisture dynamics hourly across different locations and ‎times, with its accuracy verified through cross-validation and comparison with existing soil moisture ‎datasets. The model effectively captures temporal dynamics, making it valuable for applications ‎requiring soil moisture monitoring over time, such as anomaly detection and memory analysis. By ‎identifying typical peak soil moisture values and observing distribution shapes, irrigation can be ‎scheduled to maintain soil moisture within Volumetric Soil Moisture (VSM) values of 0.25 to 0.30 ‎m²/m², avoiding under and over-watering. The strong correlations between parcels suggest that a ‎uniform irrigation strategy might be effective across multiple parcels, with adjustments based on ‎specific parcel characteristics and historical data trends. The application of the LSTM model to ‎predict soil moisture and vegetation indices yielded mixed results. While the model effectively ‎captures the central tendency and temporal dynamics of soil moisture, it struggles with accurately ‎predicting EVI, NDVI, and NMDI.‎

Keywords: irrigation scheduling, LSTM neural network, remotely sensed indices, soil and vegetation ‎monitoring

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Authors: Sunpetch Angkititrakul, Seree Klaengair, Dusadee Phongaran, Arunee Ritthipanun

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The objective of this study is to determine the prevalence and antimicrobial resistance pattern of Salmonella spp. isolated from pigs at slaughterhouses in the northeast of Thailand. During 2015-2016, all samples were isolated and identified by ISO 6579:2002. A total of 699 samples of rectal swab were collected and isolated for the presence of Salmonella. Salmonella was detected in 275 of 699 (39.34%) samples. 24 serovars were identified in the 275 isolates. The most prevalent serovars were rissen (36.97%), S. enterica ser.4,5,12:i: (25.35%) and typhimurium (21.33%). In this study, 76.30% of the isolates were resistant to at least one antimicrobial drug and 38.39% were multidrug resistant. The highest resistances were found in ampicillin (69.20%), tetracycline (66.35%), sulfamethoxazole/trimethoprim (35.55%) and chloramphenicol (9.00%) The results showed high prevalence of Salmonella spp. in pigs and high antimicrobial resistance among the isolates, and indicated the need for monitoring program to control Salmonella contamination and reduce the dissemination of antimicrobial resistance in pig supply chain.

Keywords: prevalence, antimicrobial resistance, Salmonella spp., pig

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Authors: Kyung-Jung Lee, Hyun-Sik Ahn

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This paper suggests a design methodology for the hardware and software of the Electronic Control Unit (ECU) of safety-critical vehicle applications such as braking and steering. The architecture of the hardware is a high integrity system such that it incorporates a high performance 32-bit CPU and a separate Peripheral Control-Processor (PCP) together with an external watchdog CPU. Communication between the main CPU and the PCP is executed via a common area of RAM and events on either processor which are invoked by interrupts. Safety-related software is also implemented to provide a reliable, self-testing computing environment for safety critical and high integrity applications. The validity of the design approach is shown by using the Hardware-in-the-Loop Simulation (HILS) for Electric Power Steering (EPS) systems which consists of the EPS mechanism, the designed ECU, and monitoring tools.

Keywords: electronic control unit, electric power steering, functional safety, hardware-in-the-loop simulation

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Authors: Roshani S. Golhar, Neerajkumar S. Sathawane, Snehal Dongre

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Background and objectives: As the, cardiovascular illnesses increasing and becoming cause of mortality worldwide, killing around lot of people each year. Arrhythmia is a type of cardiac illness characterized by a change in the linearity of the heartbeat. The goal of this study is to develop novel deep learning algorithms for successfully interpreting arrhythmia using a single second segment. Because the ECG signal indicates unique electrical heart activity across time, considerable changes between time intervals are detected. Such variances, as well as the limited number of learning data available for each arrhythmia, make standard learning methods difficult, and so impede its exaggeration. Conclusions: The proposed method was able to outperform several state-of-the-art methods. Also proposed technique is an effective and convenient approach to deep learning for heartbeat interpretation, that could be probably used in real-time healthcare monitoring systems

Keywords: electrocardiogram, ECG classification, neural networks, convolutional neural networks, portable document format

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Authors: Amaliny Yoganathan-Hasselbeck

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Based on the assumption, that strategic licensing is more valuable and sustainable for the economy than a legal dispute and action for an injunction, the strategy of licensing in patent infringement cases was studied. A theoretical framework was developed based on the transaction costs approach, describing the major variables within the process of licensing to an alleged patent infringer. An exploratory case study analysis was conducted on the basis of expert interviews with patent licensing agencies, patent attorneys, licensing departments of companies and research institutions. Key findings define the major criteria in each step of the licensing process and include the factors determining the intensity of patent tracking e.g. patent policies, the decision criteria when dealing with patent infringement cases, e.g. market position and reputation, and the transaction itself starting with the initiation of the contact with the alleged patent infringer, negotiating the licensing contract and monitoring the license agreement.

Keywords: innovation, licensing, patent, patent infringement, strategy, technology

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Authors: Gulten Yilmaz, A. Beril Tugrul, Mustafa Demir, Dogan Yasar, Bayram Demir, Bulent Buyuk

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A Positron Emission Tomography (PET) is a radioisotope imaging technique that illustrates the organs and the metabolisms of the human body. This technique is based on the simultaneous detection of 511 keV annihilation photons, annihilated as a result of electrons annihilating positrons that radiate from positron-emitting radioisotopes that enter biological active molecules in the body. This study was conducted on ten patients in an effort to conduct patient-related experimental studies. Dosage monitoring for the bladder, which was the organ that received the highest dose during PET applications, was conducted for 24 hours. Assessment based on measuring urination activities after injecting patients was also a part of this study. The MIRD method was used to conduct dosage calculations for results obtained from experimental studies. Results obtained experimentally and theoretically were assessed comparatively.

Keywords: PET/CT, TLD, MIRD, dose measurement, patient doses

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Authors: H. Mahdjoub, B. Hamaidi, B. Zerouali, S. Rouabhia

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The issue of fault detection and diagnosis (FDD) has gained widespread industrial interest in process condition monitoring applications. Accordingly, the use of neuro-fuzzy technic seems very promising. This paper treats a diagnosis modeling a strategic equipment of an industrial installation. We propose a diagnostic tool based on adaptive neuro-fuzzy inference system (ANFIS). The neuro-fuzzy network provides an abductive diagnosis. Moreover, it takes into account the uncertainties on the maintenance knowledge by giving a fuzzy characterization of each cause. This work was carried out with real data of a lubrication circuit from the gas turbine. The machine of interest is a gas turbine placed in a gas compressor station at South Industrial Centre (SIC Hassi Messaoud Ouargla, Algeria). We have defined the zones of good and bad functioning, and the results are presented to demonstrate the advantages of the proposed method.

Keywords: fault detection and diagnosis, lubrication system, turbine, ANFIS, training, pattern recognition

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Authors: Silvia Moreno, Banu Iyisan, Hannes Gumz, Brigitte Voit, Dietmar Appelhans

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Polymersomes are materials which are considered as artificial counterparts of natural vesicles. The nanotechnology of such smart nanovesicles is very useful to enhance the efficiency of many therapeutic and diagnostic drugs. Those compounds show a higher stability, flexibility, and mechanical strength to the membrane compared to natural liposomes. In addition, they can be designed in detail, the permeability of the membrane can be controlled by different stimuli, and the surface can be functionalized with different biological molecules to facilitate monitoring and target. For this purpose, this study demonstrates the formation of multifunctional and pH sensitive polymersomes and their functionalization with different reactive groups or biomolecules inside and outside of polymersomes´ membrane providing by crossing the membrane and docking/undocking processes for biomedical applications. Overall, they are highly versatile and thus present new opportunities for the design of targeted and selective recognition systems, for example, in mimicking cell functions and in synthetic biology.

Keywords: multifunctionalized, pH stimulus, controllable release, cellular uptake

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Authors: Indriyani Nur, Yusnaini

Abstract:

Lobster cultures have failed because of mortalities associated with parasitic and fungal infections. Monitoring of spawned eggs and larva of bamboo lobsters, Panulirus versicolour, and ornate lobsters, P. ornatus, in a hatchery, was conducted in order to characterize fungal and parasitic diseases of eggs and larva. One species of protozoan parasite (Vorticella sp.) was identified from larvae while two species of fungi (Lagenidium sp. and Haliphthoros sp.) were found on eggs. Furthermore, adult lobsters cultured in floating net cage had burning-like diseases on their pleopod, uropod, and telson. Histopathological samples were collected for parasite and tissue changes. There were two parasites found to infect lobsters on external body and gill which are Octolasmis sp. and Oodinium sp. Histopathology showed tissue changes which are necrosis on hepatopancreas, necrosis in the gills and around the uropods and telson.

Keywords: fungal, histopathology, lobster, parasite, infection

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Authors: Po-Chen Chen, Tsung-Han Yang, Zhi-Wei Zheng, Shih-Tsang Tang

Abstract:

Falls are the most common accidents during mountain hiking, especially in high-altitude environments with unstable terrain or adverse weather. Balance ability is a crucial factor in hiking, effectively ensuring hiking safety and reducing the risk of injuries. If balance ability can be assessed simply and effectively, hikers can identify their weaknesses and conduct targeted training to improve their balance ability, thereby reducing injury risks. With the widespread use of smartphones and their built-in inertial sensors, this project aims to develop a simple Inertial Measurement Unit (IMU) balance measurement technique based on smartphones. This will provide hikers with an easy-to-use, low-cost tool for assessing balance ability, monitoring training effects in real-time, and continuously tracking balance ability through uploading cloud data uploads, facilitating personal athletic performance.

Keywords: balance, IMU, smartphone, wearable devices

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Authors: Getaneh Berie Tarekegn, Li-Chia Tai

Abstract:

With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Byung Cheol Kim

Abstract:

This study explores a method for the signal-noise separation challenge in project control, focusing on the limitations of traditional deterministic approaches that use single-point performance metrics to predict project outcomes. We detail how traditional methods often overlook future uncertainties, resulting in tracking biases when reliance is placed solely on immediate data without adjustments for predictive accuracy. Our investigation led to the development of the Predictive Tracking Project Control (PTPC) framework, which incorporates network simulation and Bayesian control models to adapt more effectively to project dynamics. The PTPC introduces controlled disturbances to better identify and separate tracking biases from useful predictive signals. We will demonstrate the efficacy of the PTPC with examples, highlighting its potential to enhance real-time project monitoring and decision-making, marking a significant shift towards more accurate project management practices.

Keywords: predictive tracking, project control, signal-noise separation, Bayesian inference

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Authors: Baghdasaryan Marinka

Abstract:

Monitoring the distribution of electric power consumption in the technological process of ore grinding is conducted. As a result, the impacts of the mill filling rate, the productivity of the ore supply, the volumetric density of the grinding balls, the specific density of the ground ore, and the relative speed of the mill rotation on the specific consumption of electric power have been studied. The power and technological factors affecting the reactive power generated by the synchronous motors, operating within the technological scheme are studied. A block diagram for evaluating the power consumption modes of the technological process is presented, which includes the analysis of the technological scheme, the determination of the place and volumetric density of the ore-grinding mill, the evaluation of the technological and power factors affecting the energy saving process, as well as the assessment of the electric power standards.

Keywords: electric power standard, factor, ore grinding, power consumption, reactive power, technological

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Authors: Adistya Maulidya, Mujuna Abbas, Nur Assyifa, Putri Dewi Gutiyani

Abstract:

Countries are moving forward to develop databases from electronic health records for monitoring and research. Since the issuance of Information and Electonic Transaction Constitution No. 11 of 2008 as well as Minister Regulation No. 269 of 2008, there has been a gradual progress of Indonesian hospitals adopting Electonic Health Record (EHR) in its systems. This paper is the result of a literature study about the progress that has been made in Indonesia to develop national health information infrastructure through EHR within the hospitals. The purpose of this study was to describe trends in adoption of EHR systems among hospitals in Indonesia from 2008 to 2015 as well as to assess the preparedness of Indonesian national health information infrastructure facing ASEAN Economic Community.

Keywords: adoption, Indonesian hospitals, electronic health record, ASEAN economic community

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Authors: Adane Gebresilassie Hailemariam, Belete Yilma Hirpaye

Abstract:

The enhanced concentrations of heavy metals, especially in sediments, may indicate human-induced perturbations rather than natural enrichment through geological weathering. Heavy metals are non-biodegradable, persist in the environment, and are concentrated up to the food chain, leading to enhanced levels in the liver and muscle tissues of fishes, aquatic bryophytes, and aquatic biota. Marine organisms, in general fish in particular, accumulate metals to concentrations many times higher than present in water or sediment as they can take up metals in their organs and concentrate at different levels. Thus, metals acquired through the food chain due to pollution are potential chemical hazards, threatening consumers. The Nile tilapia (oreochromic niloticus), catfish (clarius garpinus), and water samples were collected from five sampling sites, namely, inlet-1, inlet-2, center, outlet-1 and outlet-2 of Lake Chamo. The concentration of major and trace metals Na, K, Mg, Ca, Cr, Co, Ni, Mn and Cu in the two fish muscles, gill and liver, was determined using an atomic absorption spectrometer (AAS) and flame photometer (FP). Metal concentrations in the water have also been evaluated within the two consecutive seasons, winter (dry) and spring (wet). The results revealed that the concentration of those metals in Tilapia’s (O. niloticus) muscle, gill, and liver were Na 44.5, 35.1, 28, Mg 2.8, 8.41, 4.61, K 43, 32, 30, Ca 1.5, 6.0, 5.5, Cr 0.91, 1.2, 3.5, Co 3.0, 2.89, 2.62, Ni 0.94, 1.99, 2.2, Mn 1.23, 1.51, 1.6 and Cu 1.1, 1.99, 3.5 mg kg-1 respectively and in catfish’s muscle, gill and liver Na 25, 39, 41.5, Mg 4.8, 2.87, 6, K 29, 38, 40, Ca 2.5, 8.10, 3.0, Cr 0.65, 3.5, 5.0, Co 2.62, 1.86, 1.73, Ni 1.10, 2.3, 3.1, Mn 1.54, 1.57, 1.59 and Cu 1.01, 1.10, 3.70 mg kg-1 respectively. The highest accumulation of Na and K were observed for tilapia muscle and catfish gill, Mg and Ca got higher in tilapia gill and catfish liver, while Co is higher in muscle of the two fish. The Cr, Ni, Mn and Cu levels were higher in the livers of the two fish species. In conculusion, metal toxicity through food chain is the current dangerous issue for human and othe animals. This needs deep focus to promot the health of living animals. The Details of the work are going to be discussed at the conference.

Keywords: bioaccumulation, catfish, essential metals, nile tilapia

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Authors: Amir Taghizadeh Vahed, Adithya Thaduri

Abstract:

Shovels are the main components in the mining transportation system. The productivity of the mines depends on the availability of shovels due to its high capital and operating costs. The unplanned failure/shutdowns of a shovel results in higher repair costs, increase in downtime, as well as increasing indirect cost (i.e. loss of production and company’s reputation). In order to mitigate these failures, predictive maintenance can be useful approach using failure prediction. The modern mining machinery or shovels collect huge datasets automatically; it consists of reliability and maintenance data. However, the gathered datasets are useless until the information and knowledge of data are extracted. Machine learning as well as data mining, which has a major role in recent studies, has been used for the knowledge discovery process. In this study, data mining and machine learning approaches are implemented to detect not only anomalies but also patterns from a dataset and further detection of failures.

Keywords: maintenance, machine learning, shovel, conditional based monitoring

Procedia PDF Downloads 212