Search results for: light detection and ranging

Authors: Rizka Ardhiyana, Liesbetini Haditjaroko, Sri Mulijani, Reki Ashadi Wicaksono, Raafqi Ranasasmita

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Designing a sensitive, specific and easy to use method to detect pork contamination in the food industry remains a major challenge. In the current study, we developed a sensitive thiol-bond AuNP-Probe biosensor that will change color when detecting pork DNA in the Cytochrome B region. The interaction between the biosensors and DNA sample is measured by spectrophotometer at 540 nm. The biosensor is made by reducing gold with trisodium citrate to produce gold nanoparticle with 39.05 nm diameter. The AuNP-Probe biosensor (gold nanoprobe) achieved 16.04 ng DNA/µl limit of detection and 53.48 ng DNA/µl limit of quantification. The linearity (R2) between color absorbance changes and DNA concentration is 0.9916. The biosensor has a good specificty as it does not cross-react with DNA of chicken and beef. To verify specificity towards the target sequence, PCR was tested to the target sequence and reacted to the PCR product with the biosensor. The PCR DNA isolate resulted in a 2.7 fold higher absorbance compared to pork-DNA isolate alone (without PCR). The sensitivity and specificity of the method show the promising application of the thiol-bond AuNP biosensor in pork-detection.

Keywords: biosensor, DNA probe, gold nanoparticle (AuNP), pork meat, qPCR

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Authors: Nino G. Vepkhvadze, Tea Enukidze

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Every year the number of countries around the world face the risk of the spread of infectious diseases that bring significant ecological and social-economic damage. Hence, the importance of food product safety is emphasized that is the issue of interest for many countries. To solve them, it’s necessary to conduct preventive measures against the diseases, have accurate diagnostic results, leadership, and management. The Peste des petits ruminants (PPR) disease is caused by a morbillivirus closely related to the rinderpest virus. PPR is a transboundary disease as it emerges and evolves, considered as one of the top most damaging animal diseases. The disease imposed a serious threat to sheep-breeding when the farms of sheep, goats are significantly growing within the country. In January 2016, PPR was detected in Georgia. Up to present the origin of the virus, the age relationship of affected ruminants and the distribution of PPRV in Georgia remains unclear. Due to the nature of PPR, and breeding practices in the country, reemerging of the disease in Georgia is highly likely. The purpose of the studies is to provide laboratories with efficient tools allowing the early detection of PPR emergence and re-emergences. This study is being accomplished under the Biological Threat Reduction Program project with the support of the Defense Threat Reduction Agency (DTRA). The purpose of the studies is to investigate the samples and identify areas at high risk of the disease. Georgia has a high density of small ruminant herds bred as free-ranging, close to international borders. Kakheti region, Eastern Georgia, will be considered as area of high priority for PPR surveillance. For this reason, in 2019, in Kakheti region investigated n=484 sheep and goat serum and blood samples from the same animals, utilized serology and molecular biology methods. All samples were negative by RT-PCR, and n=6 sheep samples were seropositive by ELISA-Ab. Future efforts will be concentrated in areas where the risk of PPR might be high such as international bordering regions of Georgia. For diagnostics, it is important to integrate the PPRV knowledge with epidemiological data. Based on these diagnostics, the relevant agencies will be able to control the disease surveillance.

Keywords: animal disease, especially dangerous pathogen, laboratory diagnostics, virus

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Authors: Mohammad Ali Al-Deeb, Hamda Ateeq Al Dhaheri

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Insects cause major damage to crops and fruit trees worldwide. In the United Arab Emirates, the date palm tree is the most economically important tree which is used for date production as well as an ornamental tree. In 2002, the number of date palm trees in UAE was 40,700,000 and it is increasing over time. The longhorn stem borer (Jebusaea hammerschmidtii) and the lesser date month (Batrachedra amydraula) are important insect pests causing damage to date palm trees in UAE. Population dynamics of the Jebusaea hammerschmidtii and Batrachedra amydraula were studied by using light and pheromons traps, respectively in Al-Ain, UAE. The first trap catch of B. amydraula adults occurred on 19 April and the insect population peaked up on 26 April 2014. The first trap catch of J. hammerschmidtii occurred in April 2014. The numbers increased over time and the population peak occurred in June. The trapping was also done in 2015. The changes in insect numbers in relation to weather parameters are discussed. Also, the importance of the results on the management of these two pests is highlighted.

Keywords: date palm, integrated pest management, UAE, light trap, pheromone trap

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Authors: Ekaterina Zvorykina

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Nowadays, Arctic and Antarctic regions are distinguished to be one of the most important strategic resources for global development. Nonetheless, living conditions in Arctic regions still demand certain improvements. As soon as the region is rarely populated, one of the main points of interest is health accommodation of the people, who migrate to Arctic region for permanent and shift work. At Arctic and Antarctic latitudes, personnel face polar day and polar night conditions during the time of the year. It means that they are deprived of natural sunlight in winter season and have continuous daylight in summer. Firstly, the change in light intensity during 24-hours period due to migration affects circadian rhythms. Moreover, the controlled artificial light in winter is also an issue. The results of the recent studies on night shift medical professionals, who were exposed to permanent artificial light, have already demonstrated higher risks in cancer, depression, Alzheimer disease. Moreover, people exposed to frequent time zones change are also subjected to higher risks of heart attack and cancer. Thus, our main goals are to understand how high latitude work and living conditions can affect human health and how it can be prevented. In our study, we analyze molecular and cellular factors, which play important role in circadian rhythm change and distinguish main risk groups in people, migrating to high latitudes. The main well-studied index of circadian timing is melatonin or its metabolite 6-sulfatoxymelatonin. In low light intensity melatonin synthesis is disturbed and as a result human organism requires more time for sleep, which is still disregarded when it comes to working time organization. Lack of melatonin also causes shortage in serotonin production, which leads to higher depression risk. Melatonin is also known to inhibit oncogenes and increase apoptosis level in cells, the main factors for tumor growth, as well as circadian clock genes (for example Per2). Thus, people who work in high latitudes can be distinguished as a risk group for cancer diseases and demand more attention. Clock/Clock genes, known to be one of the main circadian clock regulators, decrease sensitivity of hypothalamus to estrogen and decrease glucose sensibility, which leads to premature aging and oestrous cycle disruption. Permanent light exposure also leads to accumulation superoxide dismutase and oxidative stress, which is one of the main factors for early dementia and Alzheimer disease. We propose a new screening system adjusted for people, migrating from middle to high latitudes and accommodation therapy. Screening is focused on melatonin and estrogen levels, sleep deprivation and neural disorders, depression level, cancer risks and heart and vascular disorders. Accommodation therapy includes different types artificial light exposure, additional melatonin and neuroprotectors. Preventive procedures can lead to increase of migration intensity to high latitudes and, as a result, the prosperity of Arctic region.

Keywords: circadian rhythm, high latitudes, melatonin, neuroprotectors

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Authors: Said Abdul Ahad Ahadi, Elyas Baray, Nitin Rakesh, Sudeep Varshney

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Wi-Fi is a widely used internet source that is used to provide internet access in many areas such as Stores, Cafes, University campuses, Restaurants and so on. This technology brought more facilities in communication and networking. On the other hand, due to the transmission of data over the air, which makes the network vulnerable, so it becomes prone to various threats such as Evil Twin and etc. The Evil Twin is a kind of adversary which impersonates a legitimate access point (LAP) as it can happen by spoofing the name (SSID) and MAC address (BSSID) of a legitimate access point (LAP). And this attack can cause many threats such as MITM, Service Interruption, Access point service blocking. Various Evil Twin Attack Detection Techniques are proposed, but they require additional hardware, or they require protocol modification. In this paper, we proposed a new technique based on Access Point’s two fingerprints, Received Signal Strength Indicator (RSSI) and Hop Count, that is hard to copy by an adversary. And we implemented the technique in a system called “ETDetector,” which can detect and prevent the attack.

Keywords: evil twin, LAP, SSID, Wi-Fi security, signal variation, ETAD, kali linux, scapy, python

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Authors: Amina Adala, Nadra Debbache, Tahar Sehili

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Coordination polymers and uniformly {[Zn(II)(BIPY)(Pht)]n} (1), {[Zn (HYD)(Pht)]n} (2) (BIPY = 4,4’ bipyridine, Pht = terephtalic acid, HYD = 8-hydroxyquinoline) have been successfully synthesized by a hydrothermal process using aqueous zinc solution. The as-prepared compounds phases were characterized by X-ray diffraction (XRD), Fourier Transform Infrared spectroscopy, UV-visible spectroscopy, thermogravimetric analysis (TGA), and the electrochemistry study by the voltammetry cyclic. The results showed a crystalline phase for CP1 however, CP2 requires recrystallization; the FTIR showed the presence of characteristic bands of all ligands; besides that, TGA shows thermal stability up to 300°C. The electrochemistry study showed a good charge transfer between the ligands and Zn metal for the two components. UV-Vis measurement showed strong absorption in a wide range from UV to visible light with a band gap of 2.69 eV for CP1 and 2.56 eV for CP2, smaller than that of ZnO. This represents an alternative to using ZnO. The Ibuprofen IBP decomposition kinetics of 5.10⁻⁵ mol.L⁻¹ under solar and artificial light were studied for different irradiation conditions. Good photocatalytic properties were observed due to their high surface area.

Keywords: metal-organic frameworks, photocatalysis, photodegradation, organic pollutant, ibuprofen

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Authors: R. Karmouch

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A thin gold metal layer was deposited on the top of silicon oxide films containing embedded Si nanocrystals (Si-nc). The sample was annealed in gas containing nitrogen, and subsequently characterized by photoluminescence. We obtained 3-fold enhancement of photon emission from the Si-nc embedded in silicon dioxide covered with a Gold layer as compared with an uncovered sample. We attribute this enhancement to the increase of the spontaneous emission rate caused by the coupling of the Si-nc emitters with the surface plasmons (SP). The evolution of PL emission with laser irradiated time was also collected from covered samples, and compared to that from uncovered samples. In an uncovered sample, the PL intensity decreases with time, approximately with two decay constants. Although the decrease of the initial PL intensity associated with the increase of sample temperature under CW pumping is still observed in samples covered with a gold layer, this film significantly contributes to reduce the permanent deterioration of the PL intensity. The resistance to degradation of light-emitting silicon nanocrystals can be increased by SP coupling to suppress the permanent deterioration. Controlling the permanent photodeterioration can allow to perform a reliable optical gain measurement.

Keywords: photodeterioration, silicon nanocrystals, ion implantation, photoluminescence, surface plasmons

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Authors: Houria Hamitouche, Abdellah Khelifa

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Quaternary ammonium salts (QACs) are the most common cationic surfactants of natural or synthetic origin usually. They possess one or more hydrophobic hydrocarbon chains and hydrophilic cationic group. In fact, the hydrophobic groups are derived from three main sources: petrochemicals, vegetable oils, and animal fats. These QACs have attracted the attention of chemists for a long time, due to their general simple synthesis and their broad application in several fields. They are important as ingredients of cosmetic products and are also used as corrosion inhibitors, in emulsion polymerization and textile processing. Within biological applications, QACs show a good antimicrobial activity and can be used as medicines, gene delivery agents or in DNA extraction methods. The 2004 worldwide annual consumption of QACs was reported as 500,000 tons. The petroleum product is considered a true reservoir of a variety of chemical species, which can be used in the synthesis of quaternary ammonium salts. The purpose of the present contribution is to synthesize the quaternary ammonium salts by Menschutkin reaction, via chloromethylation/quaternization sequences, from Algerian petroleum products namely: reformate, light naphtha and kerosene and characterize.

Keywords: quaternary ammonium salts, reformate, light naphtha, kerosene

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Authors: Dylan Elliott, Katya Pertsova

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Preposition errors are some of the most common errors created by L2 speakers. In addition, improving error correction and detection methods remains an open issue in the realm of Natural Language Processing (NLP). This research investigates whether the bidirectional encoder representations from the transformers model (BERT) have the potential to correct preposition errors accurately enough to be useful in error correction software. This research finds that BERT performs strongly when the scope of its error correction is limited to preposition choice. The researchers used an open-source BERT model and over three hundred thousand edited sentences from Wikipedia, tagged for part of speech, where only a preposition edit had occurred. To test BERT’s ability to detect errors, a technique known as multi-level masking was used to generate suggestions based on sentence context for every prepositional environment in the test data. These suggestions were compared with the original errors in the data and their known corrections to evaluate BERT’s performance. The suggestions were further analyzed to determine if BERT more often agreed with the judgements of the Wikipedia editors. Both the untrained and fined-tuned models were compared. Finetuning led to a greater rate of error-detection which significantly improved recall, but lowered precision due to an increase in false positives or falsely flagged errors. However, in most cases, these false positives were not errors in preposition usage but merely cases where more than one preposition was possible. Furthermore, when BERT correctly identified an error, the model largely agreed with the Wikipedia editors, suggesting that BERT’s ability to detect misused prepositions is better than previously believed. To evaluate to what extent BERT’s false positives were grammatical suggestions, we plan to do a further crowd-sourcing study to test the grammaticality of BERT’s suggested sentence corrections against native speakers’ judgments.

Keywords: BERT, grammatical error correction, preposition error detection, prepositions

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Authors: B. Tayebani, N. Hosseinibalam, D. Mostofinejad

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Interest in using bacteria in cement materials due to its positive influences has been increased. Cement materials such as mortar and concrete basically suffer from higher porosity and water absorption compared to other building materials such as steel materials. Because of the negative side-effects of certain chemical techniques, biological methods have been proposed as a desired and environmentally friendly strategy for reducing concrete porosity and diminishing water absorption. This paper presents the results of an experimental investigation carried out to evaluate the influence of Sporosarcina pasteurii bacteria on the behaviour of two types of concretes (light weight aggregate concrete and normal weight concrete). The resistance of specimens to water penetration by testing water absorption and evaluating the electrical resistance of those concretes was examined and compared. As a conclusion, 20% increase in electrical resistance and 10% reduction in water absorption of lightweight aggregate concrete (LWAC) and for normal concrete the results show 7% decrease in water absorption and almost 10% increase in electrical resistance.

Keywords: bacteria, biological method, normal weight concrete, lightweight aggregate concrete, water absorption, electrical resistance

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Authors: C. Gopi Jinimole, A. Harsha

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For detecting strokes, Computed Tomography (CT) scan is preferred for imaging the abnormalities or infarction in the brain. Because of the problems in the window settings used to evaluate brain CT images, they are very poor in the early stage infarction detection. This paper presents an automatic estimation method for the window settings of the CT images for proper contrast of the hyper infarction present in the brain. In the proposed work the window width is estimated automatically for each slice and the window centre is changed to a new value of 31HU, which is the average of the HU values of the grey matter and white matter in the brain. The automatic window width estimation is based on the average of median of statistical central moments. Thus with the new suggested window centre and estimated window width, the hyper infarction or post-stroke regions in CT brain images are properly detected. The proposed approach assists the radiologists in CT evaluation for early quantitative signs of delayed stroke, which leads to severe hemorrhage in the future can be prevented by providing timely medication to the patients.

Keywords: computed tomography (CT), hyper infarction or post stroke region, Hounsefield Unit (HU), window centre (WC), window width (WW)

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Authors: ChoLiang Chung, YuMin Chen

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C-doped TiO2 nanofibers were prepared by electrospinning successfully. Different amounts of carbon were added into the nanofibers by using chitosan, aiming to shift the wave length that is required to excite the photocatalyst from ultraviolet light to visible light. Different amounts of carbon and different atmosphere fibers were calcined at 500oC, and the optical characteristic of C-doped TiO2 nanofibers had been changed. characterizes of nanofibers were identified by X-Ray Diffraction (XRD), Field Emission Scanning Electron Microscope (FE-SEM), UV-vis, Atomic Force Microscope (AFM), and Fourier Transform Infrared Spectroscopy (FTIR). The XRD is used to identify the phase composition of nanofibers. The morphology of nanofibers were explored by FE-SEM and AFM. Optical characteristics of absorption were measured by UV-Vis. Three dimension surface images of C-doped TiO2 nanofibers revealed different effects of processing. The results of XRD showed that the phase of C-doped TiO2 nanofibers transformed to rutile phase and anatase phase successfully. The results of AFM showed that the surface morphology of nanofibers became smooth after high temperature treatment. Images from FE-SEM revealed the average size of nanofibers. UV-vis results showed that the band-gap of TiO2 were reduced. Finally, we found out C-doped TiO2 nanofibers can change countenance of nanofiber and make it smoother.

Keywords: carbon, TiO2, chitosan, electrospinning

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Authors: S. Madhusudhan, G. V. Vaniprabha

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Worldwide statistics have shown that smoking contributes significantly to mortality, with nicotine, being more addictive. Smoking causes more than 7,00,000 deaths/year in India. Compared to the general population, the prevalence of smoking is found to be much higher among people with psychotic disorders and, more so in schizophrenia. Schizophrenic patients who smoke tend to have higher frequency of heavy smoking, with rates ranging from 60% to as high as 80%. Hence, smokers with psychiatric disorders suffer higher rates of morbidity and mortality secondary to smoking related illnesses.

Keywords: brief intervention, nicotine dependence, schizophrenia

Procedia PDF Downloads 388

Authors: Rubén Valcarce-Álvarez, Francisco Jáñez-Martino, Rocío Alaiz-Rodríguez

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Cybersecurity challenges, including scams, drug sales, the distribution of child sexual abuse material, fake news, and hate speech on both the surface and deep web, have significantly increased over the past decade. Users who post such content often employ strategies to evade detection by automated filters. Among these tactics, text obfuscation plays an essential role in deceiving detection systems. This approach involves modifying words to make them more difficult for automated systems to interpret while remaining sufficiently readable for human users. In this work, we aim at spotting obfuscated words and the employed techniques, such as leetspeak, word inversion, punctuation changes, and mixed techniques. We benchmark Named Entity Recognition (NER) using models from the BERT family as well as two large language models (LLMs), Llama and Mistral, on XX_NER_WordCamouflage dataset. Our experiments evaluate these models by comparing their precision, recall, F1 scores, and accuracy, both overall and for each individual class.

Keywords: natural language processing (NLP), text obfuscation, named entity recognition (NER), deep learning

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Authors: Gopi Kandaswamy, P. Balamuralidhar

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Fully autonomous small Unmanned Aerial Vehicles (UAVs) are increasingly being used in many commercial applications. Although a lot of research has been done to develop safe, reliable and durable UAVs, accidents due to electronic and structural failures are not uncommon and pose a huge safety risk to the UAV operators and the public. Hence there is a strong need for an automated health monitoring system for UAVs with a view to minimizing mission failures thereby increasing safety. This paper describes our approach to monitoring the electronic and structural components in a small UAV without the need for additional sensors to do the monitoring. Our system monitors data from four sources; sensors, navigation algorithms, control inputs from the operator and flight controller outputs. It then does statistical analysis on the data and applies a rule based engine to detect failures. This information can then be fed back into the UAV and a decision to continue or abort the mission can be taken automatically by the UAV and independent of the operator. Our system has been verified using data obtained from real flights over the past year from UAVs of various sizes that have been designed and deployed by us for various applications.

Keywords: fault detection, health monitoring, unmanned aerial vehicles, vibration analysis

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Authors: K. Jahani, J. Razavi

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Nowadays, to decrease the number of downtimes in the industries such as metal mining, petroleum and chemical industries, predictive maintenance is crucial. In order to have efficient predictive maintenance, knowing the performance of critical equipment of production line such as pumps and hydro-cyclones under variable operating parameters, selecting best indicators of this equipment health situations, best locations for instrumentation, and also measuring of these indicators are very important. In this paper, computer aided engineering (CAE) tools are implemented to study some important elements of copper process line, namely slurry pumps and cyclone to predict the performance of these components under different working conditions. These modeling and simulations can be used in predicting, for example, the damage tolerance of the main shaft of the slurry pump or wear rate and location of cyclone wall or pump case and impeller. Also, the simulations can suggest best-measuring parameters, measuring intervals, and their locations.

Keywords: computer aided engineering, predictive maintenance, fault detection, mining process line, slurry pump, hydrocyclone

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Authors: Elmo Thiago Lins Cöuras Ford, Valentina Alessandra Carvalho do Vale

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It is presented a model of two subsystems of Central Solar Tower to produce steam in applications to help in energy consumption. The first subsystem consists of 24 heliostats constructed of adaptive and mobile metal structures to track the apparent movement of the sun on its focus and covered by 96 layers of mirror of 150 mm at width and 220 mm at length, totaling an area of concentration of 3.2 m². Thereby obtaining optical parameters essential to reflection of sunlight by the reflector surface and absorption of this light by focus located in the light receiver, which is inserted in the second subsystem, which is at the top of a tower. The tower was built in galvanized iron able to support the absorber, and a gas cylinder to cool the equipment. The area illuminated by the sun was 9 x 10-2m2, yielding a concentration factor of 35.22. It will be shown the processes of manufacture and assembly of the Mini-Central Tower proposal, which has as main characteristics the construction and assembly facilities, in addition to reduced cost. Data of tests to produce water vapor parameters are presented and determined to diagnose the efficiency of the mini-solar central tower. It will be demonstrated the thermal, economic and material viability of the proposed system.

Keywords: solar oven, solar cooker, composite material, low cost, sustainable development

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Authors: Simiao Ren, En Wei

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Drowning is a significant safety issue worldwide, and a robust computer vision-based alert system can easily prevent such tragedies in swimming pools. However, due to domain shift caused by the visual gap (potentially due to lighting, indoor scene change, pool floor color etc.) between the training swimming pool and the test swimming pool, the robustness of such algorithms has been questionable. The annotation cost for labeling each new swimming pool is too expensive for mass adoption of such a technique. To address this issue, we propose a domain-aware data augmentation pipeline based on Gaussian Poisson Generative Adversarial Network (GP-GAN). Combined with YOLOv8, we demonstrate that such a domain adaptation technique can significantly improve the model performance (from 0.24 mAP to 0.82 mAP) on new test scenes. As the augmentation method only require background imagery from the new domain (no annotation needed), we believe this is a promising, practical route for preventing swimming pool drowning.

Keywords: computer vision, deep learning, YOLOv8, detection, swimming pool, drowning, domain adaptation, generative adversarial network, GAN, GP-GAN

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Authors: Mojtaba Valinataj

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Reversible logic as a new favorable design domain can be used for various fields especially creating quantum computers because of its speed and intangible power consumption. However, its susceptibility to a variety of environmental effects may lead to yield the incorrect results. In this paper, because of the importance of multiplication operation in various computing systems, some novel reversible logic array multipliers are proposed with error detection capability by incorporating the parity-preserving gates. The new designs are presented for two main parts of array multipliers, partial product generation and multi-operand addition, by exploiting the new arrangements of existing gates, which results in two signed parity-preserving array multipliers. The experimental results reveal that the best proposed 4×4 multiplier in this paper reaches 12%, 24%, and 26% enhancements in the number of constant inputs, number of required gates, and quantum cost, respectively, compared to previous design. Moreover, the best proposed design is generalized for n×n multipliers with general formulations to estimate the main reversible logic criteria as the functions of the multiplier size.

Keywords: array multipliers, Baugh-Wooley method, error detection, parity-preserving gates, quantum computers, reversible logic

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Authors: Alireza Karimi

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Artificial Intelligence (AI) is reshaping various industries, and accounting is no exception. With the ability to process vast amounts of data quickly and accurately, AI is revolutionizing how financial professionals manage, analyze, and report financial information. In this article, we will explore the diverse applications of AI in accounting and its profound impact on the field. Automation of Repetitive Tasks: One of the most significant contributions of AI in accounting is automating repetitive tasks. AI-powered software can handle data entry, invoice processing, and reconciliation with minimal human intervention. This not only saves time but also reduces the risk of errors, leading to more accurate financial records. Pattern Recognition and Anomaly Detection: AI algorithms excel at pattern recognition. In accounting, this capability is leveraged to identify unusual patterns in financial data that might indicate fraud or errors. AI can swiftly detect discrepancies, enabling auditors and accountants to focus on resolving issues rather than hunting for them. Real-Time Financial Insights: AI-driven tools, using natural language processing and computer vision, can process documents faster than ever. This enables organizations to have real-time insights into their financial status, empowering decision-makers with up-to-date information for strategic planning. Fraud Detection and Prevention: AI is a powerful tool in the fight against financial fraud. It can analyze vast transaction datasets, flagging suspicious activities and reducing the likelihood of financial misconduct going unnoticed. This proactive approach safeguards a company's financial integrity. Enhanced Data Analysis and Forecasting: Machine learning, a subset of AI, is used for data analysis and forecasting. By examining historical financial data, AI models can provide forecasts and insights, aiding businesses in making informed financial decisions and optimizing their financial strategies. Artificial Intelligence is fundamentally transforming the accounting profession. From automating mundane tasks to enhancing data analysis and fraud detection, AI is making financial processes more efficient, accurate, and insightful. As AI continues to evolve, its role in accounting will only become more significant, offering accountants and finance professionals powerful tools to navigate the complexities of modern finance. Embracing AI in accounting is not just a trend; it's a necessity for staying competitive in the evolving financial landscape.

Keywords: artificial intelligence, accounting automation, financial analysis, fraud detection, machine learning in finance

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Authors: Marta Skwarecka, Patrycja Bloch, Rafal Walkusz, Oliwia Urbanowicz, Grzegorz Zielinski, Sabina Zoledowska, Dawid Nidzworski

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Upper respiratory tract infections are one of the most common reasons for visiting a general doctor. Streptococci are the most common bacterial etiological factors in these infections. There are many different types of Streptococci and infections vary in severity from mild throat infections to pneumonia. For example, S. pyogenes mainly contributes to acute pharyngitis, palatine tonsils and scarlet fever, whereas S. Streptococcus pneumoniae is responsible for several invasive diseases like sepsis, meningitis or pneumonia with high mortality and dangerous complications. There are only a few diagnostic tests designed for detection Streptococci from the infected throat of patients. However, they are mostly based on lateral flow techniques, and they are not used as a standard due to their low sensitivity. The diagnostic standard is to culture patients throat swab on semi selective media in order to multiply pure etiological agent of infection and subsequently to perform antibiogram, which takes several days from the patients visit in the clinic. Therefore, the aim of our studies is to develop and implement to the market a Point of Care device for the rapid identification of Streptococcus pyogenes and Streptococcus pneumoniae with simultaneous identification of antibiotic resistance genes. In the course of our research, we successfully selected genes for to-species identification of Streptococci and genes encoding antibiotic resistance proteins. We have developed a reaction to amplify these genes, which allows detecting the presence of S. pyogenes or S. pneumoniae followed by testing their resistance to erythromycin, chloramphenicol and tetracycline. What is more, the detection of β-lactamase-encoding genes that could protect Streptococci against antibiotics from the ampicillin group, which are widely used in the treatment of this type of infection is also developed. The test is carried out directly from the patients' swab, and the results are available after 20 to 30 minutes after sample subjection, which could be performed during the medical visit.

Keywords: antibiotic resistance, Streptococci, respiratory infections, diagnostic test

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Authors: Varsha Gupta, Priya Datta, Gursimran Mohi, Jagdish Chander

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Fluoroquinolones form the mainstay of therapy for the treatment of infections due to Salmonella enterica subsp. enterica. There is a complex interplay between several resistance mechanisms for quinolones and various fluoroquinolones discs, giving varying results, making detection and interpretation of fluoroquinolone resistance difficult. For detection of fluoroquinolone resistance in Salmonella ssp., we compared the use of pefloxacin and nalidixic acid discs as surrogate marker. Using MIC for ciprofloxacin as the gold standard, 43.5% of strains showed MIC as ≥1 μg/ml and were thus resistant to fluoroquinoloes. Based on the performance of nalidixic acid and pefloxacin discs as surrogate marker for ciprofloxacin resistance, both the discs could correctly detect all the resistant phenotypes; however, use of nalidixic acid disc showed false resistance in the majority of the sensitive phenotypes. We have also tested newer antimicrobial agents like cefixime, imipenem, tigecycline and azithromycin against Salmonella spp. Moreover, there was a comeback of susceptibility to older antimicrobials like ampicillin, chloramphenicol, and cotrimoxazole. We can also use cefixime, imipenem, tigecycline and azithromycin in the treatment of multidrug resistant S. typhi due to their high susceptibility.

Keywords: salmonella, pefloxacin, surrogate marker, chloramphenicol

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Authors: T. S. Ijimdiya, K. J. Osinubi

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This study examines the influence of compactive efforts on hydraulic conductivity behaviour of compacted black cotton soil treated with bagasse ash which is necessary in assessing the performance of the soil - bagasse ash mixture for use as a suitable barrier material in waste containment application. Black cotton soil treated with up to 12% bagasse ash (obtained from burning the fibrous residue from the extraction of sugar juice from sugarcane) by dry weight of soil for use in waste containment application. The natural soil classifies as A-7-6 or CH in accordance with the AASHTO and the Unified Soil Classification System, respectively. The treated soil samples were prepared at molding water contents of -2, 0, +2, and +4 % of optimum moisture contents and compacted using four compactive efforts of Reduced British Standard Light (RBSL), British Standard light (BSL), West African Standard (WAS) and British Standard Heavy (BSH). The results obtained show that hydraulic conductivity decreased with increase in bagasse ash content, moulding water content and compaction energy.

Keywords: bagasse ash treatment, black cotton soil, hydraulic conductivity, moulding water contents, compactive efforts

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: Ayşe Nihan Avcı, İpek Memikoğlu

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Lighting systems in interior architecture need to be designed according to the function of the space, the type of task within the space, user comfort and needs. Desired and comfortable lighting levels increase task efficiency. When natural lighting is inadequate in a space, artificial lighting is additionally used to support the level of light. With the technological developments, the characteristics of light are being researched comprehensively and several business segments have focused on its qualitative and quantitative characteristics. These studies have increased awareness and usage of artificial lighting systems and researchers have investigated the effects of lighting on physical and psychological aspects of human in various ways. The aim of this study is to research the effects of illuminance levels of LED lighting on user visual comfort. Eighty participants from the Department of Interior Architecture of Çankaya University participated in three lighting scenarios consisting of 200 lux, 500 lux and 800 lux that are created with LED lighting. Each lighting scenario is evaluated according to six visual comfort criteria in which a reading task is performed. The results of the study indicated that LED lighting with three different illuminance levels affect visual comfort in different ways. The results are limited to the participants and questions that are attended and used in this study.

Keywords: illuminance levels, LED lighting, reading task, visual comfort criteria

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Authors: Gholba Niranjan Dilip, Anil Kumar

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Conduct of accurate reliable mapping of oil palm plantations and census of individual palm trees is a huge challenge. This study addresses this challenge and developed an optimized solution implemented deep learning techniques on remote sensing data. The oil palm is a very important tropical crop. To improve its productivity and land management, it is imperative to have accurate census over large areas. Since, manual census is costly and prone to approximations, a methodology for automated census using panchromatic images from Cartosat-2, SkySat and World View-3 satellites is demonstrated. It is selected two different study sites in Indonesia. The customized set of training data and ground-truth data are created for this study from Cartosat-2 images. The pre-trained model of Single Shot MultiBox Detector (SSD) Lite MobileNet V2 Convolutional Neural Network (CNN) from the TensorFlow Object Detection API is subjected to transfer learning on this customized dataset. The SSD model is able to generate the bounding boxes for each oil palm and also do the counting of palms with good accuracy on the panchromatic images. The detection yielded an F-Score of 83.16 % on seven different images. The detections are buffered and dissolved to generate polygons demarcating the boundaries of the oil palm plantations. This provided the area under the plantations and also gave maps of their location, thereby completing the automated census, with a fairly high accuracy (≈100%). The trained CNN was found competent enough to detect oil palm crowns from images obtained from multiple satellite sensors and of varying temporal vintage. It helped to estimate the increase in oil palm plantations from 2014 to 2021 in the study area. The study proved that high-resolution panchromatic satellite image can successfully be used to undertake census of oil palm plantations using CNNs.

Keywords: object detection, oil palm tree census, panchromatic images, single shot multibox detector

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Authors: Syed Beenish Rufai, Sarman Singh

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Background: Performance of Genotype MTBDRsl (Hain Life science GmbH Germany) for detection of mutations associated with second-line drug resistance is well known. However, less evidence regarding the association of mutations and genetic background of strains is known which, in the future, is essential for clinical management of anti-tuberculosis drugs in those settings where the probability of particular genotype is predominant. Material and Methods: During this retrospective study, a total of 259 MDR-TB isolates obtained from pulmonary TB patients were tested for second-line drug susceptibility testing (DST) using Genotype MTBDRsl VER 1.0 and compared with BACTEC MGIT-960 as a reference standard. All isolates were further characterized using spoligotyping. The spoligo patterns obtained were compared and analyzed using SITVIT_WEB. Results: Of total 259 MDR-TB isolates which were screened for second-line DST by Genotype MTBDRsl, mutations were found to be associated with gyrA, rrs and emb genes in 82 (31.6%), 2 (0.8%) and 90 (34.7%) isolates respectively. 16 (6.1%) isolates detected mutations associated with both FQ as well as to AG/CP drugs (XDR-TB). No mutations were detected in 159 (61.4%) isolates for corresponding gyrA and rrs genes. Genotype MTBDRsl showed a concordance of 96.4% for detection of sensitive isolates in comparison with second-line DST by BACTEC MGIT-960 and 94.1%, 93.5%, 60.5% and 50% for detection of XDR-TB, FQ, EMB, and AMK/CAP respectively. D94G was the most prevalent mutation found among (38 (46.4%)) OFXR isolates (37 FQ mono-resistant and 1 XDR-TB) followed by A90V (23 (28.1%)) (17 FQ mono-resistant and 6 XDR-TB). Among AG/CP resistant isolates A1401G was the most frequent mutation observed among (11 (61.1%)) isolates (2 AG/CP mono-resistant isolates and 9 XDR-TB isolates) followed by WT+A1401G (6 (33.3%)) and G1484T (1 (5.5%)) respectively. On spoligotyping analysis, Beijing strain (46%) was found to be the most predominant strain among pre-XDR and XDR TB isolates followed by CAS (30%), X (6%), Unique (5%), EAI and T each of 4%, Manu (3%) and Ural (2%) respectively. Beijing strain was found to be strongly associated with D94G (47.3%) and A90V mutations by (47.3%) and 34.8% followed by CAS strain by (31.6%) and 30.4% respectively. However, among AG/CP resistant isolates, only Beijing strain was found to be strongly associated with A1401G and WT+A1401G mutations by 54.5% and 50% respectively. Conclusion: Beijing strain was found to be strongly associated with the most prevalent mutations among pre-XDR and XDR TB isolates. Acknowledgments: Study was supported with Grant by All India Institute of Medical Sciences, New Delhi reference No. P-2012/12452.

Keywords: tuberculosis, line probe assay, XDR TB, drug susceptibility

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Authors: Manal Kamel, Sara Maher

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Development of sensitive non-invasive tests for detection of SARS-CoV-2 antigens is imperative to manage the extent of infection throughout the population, yet, it is still challenging. Here, we designed and optimized a sandwich enzyme-linked immunosorbent assay (ELISA) for SARS-CoV-2 S1 antigen detection in saliva. Both saliva samples and nasopharyngeal swapswere collected from 170 PCR-confirmed positive and negative cases. Gold nanoparticles (AuNPs) were conjugated with S1protein receptor binding domain (RBD) nanobodies. Recombinant S1 monoclonal antibodies (S1mAb) as primery antibody and gold conjugated nanobodies as secondary antibody were employed in sandwich ELISA. Our developed system were optimized to achieve 87.5 % sensitivity and 100% specificity for saliva samples compared to 89 % and 100% for nasopharyngeal swaps, respectively. This means that saliva could be a suitable replacement for nasopharyngeal swaps No cross reaction was detected with other corona virus antigens. These results revealed that our developed ELISAcould be establishedas a new, reliable, sensitive, and non-invasive test for diagnosis of SARS-CoV-2 infection, using the easily collected saliva samples.

Keywords: COVID 19, diagnosis, ELISA, nanobodies

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Authors: Kamila Weissova, Jitka Skrabalova, Katerina Skalova, Jana Koprivova, Zdenka Bendova

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Any Arctic visitor has to deal with extreme conditions, including constant light during the summer season or constant darkness during winter time. Light/dark cycle is the most powerful synchronizing signal for biological clock and the absence of daily dark period during the polar day can significantly alter the functional state of the internal clock. However, the inner clock can be synchronized by other zeitgebers such as physical activity, food intake or social interactions. Here, we investigated the effect of polar day on circadian clock of 10 researchers attending the polar base station in the Svalbard region during July. The data obtained on Svalbard were compared with the data obtained before the researchers left for the expedition (in the Czech Republic). To determine the state of circadian clock we used wrist actigraphy followed by sleep diaries, saliva, and buccal mucosa samples, both collected every 4 hours during 24h-interval to detect melatonin by radioimmunoassay and clock gene (PER1, BMAL1, NR1D1, DBP) mRNA levels by RT-qPCR. The clock gene expression was analyzed using cosinor analysis. From our results, it is apparent that the constant sunlight delayed melatonin onset and postponed the physical activity in the same order. Nevertheless, the clock gene expression displayed higher amplitude on Svalbard compared to the amplitude detected in the Czech Republic. These results have suggested that the common daily schedule at the Svalbard expedition can strengthen circadian rhythm in the environment that is lacking light/dark cycle. In conclusion, the constant sunlight delays melatonin onset, but it still maintains its rhythmic secretion. The effect of constant sunlight on circadian clock can be minimalized by common daily scheduled activity.

Keywords: actighraph, clock genes, human, melatonin, polar day

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Authors: Shih-Yu Lo

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Stereotypes exist in almost every society, affecting how people interact with each other. However, to our knowledge, the influence of stereotypes was rarely explored in the context of basic perceptual processes. This study aims to explore how the gender stereotype affects object recognition. Participants were presented with a series of scene pictures, followed by a target display with a man or a woman, holding a weapon or a non-weapon object. The task was to identify whether the object in the target display was a weapon or not. Although the gender of the object holder could not predict whether he or she held a weapon, and was irrelevant to the task goal, the participant nevertheless tended to identify the object as a weapon when the object holder was a man than a woman. The analysis based on the signal detection theory showed that the stereotype effect on object recognition mainly resulted from the participant’s bias to make a 'weapon' response when a man was in the scene instead of a woman in the scene. In addition, there was a trend that the participant’s sensitivity to differentiate a weapon from a non-threating object was higher when a woman was in the scene than a man was in the scene. The results of this study suggest that the irrelevant social cues implied in the visual scene can be very powerful that they can modulate the basic object recognition process.

Keywords: gender stereotype, object recognition, signal detection theory, weapon

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