Search results for: early detection

Authors: Chunhua Liao, Jinfei Wang, Bo Shan, Yang Song, Yongjun He, Taifeng Dong

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Winter wheat is one of the main crops in Canada. Forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. It was found that the four-band reflectance (blue, green, red, near-infrared) performed better than their vegetation indices (NDVI, EVI, WDRVI and OSAVI) in wheat yield prediction. The optimum phenological stage for wheat yield prediction with highest accuracy was at the growing stages from the end of the flowering to the beginning of the filling stage. The best MLR model was therefore built to predict wheat yield before harvest using Sentinel-2 data acquired at the end of the flowering stage. Further, to improve the ability of the yield prediction at the early stages, three simple unsupervised domain adaptation (DA) methods were adopted to transform the reflectance data at the early stages to the optimum phenological stage. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. The average testing RMSE of the MLR model at the end of the flowering stage was 604.48 kg/ha. Near the booting stage, the average testing RMSE of yield prediction using the best MLR was reduced to 799.18 kg/ha when applying the mean matching domain adaptation approach to transform the data to the target domain (at the end of the flowering) compared to that using the original data based on the models developed at the booting stage directly (“MLR at the early stage”) (RMSE =1140.64 kg/ha). This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that “DA then MLR at the optimum stage” performed better than “MLR directly at the early stages” for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages. This study indicated that the simple domain adaptation methods had a great potential in crop yield prediction at the early stages using remote sensing data.

Keywords: wheat yield prediction, domain adaptation, Sentinel-2, within-field scale

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Authors: Sonja Giese, Tess N. Peacock

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South African based Innovation Edge is experimenting with technology to drive positive behavior change, enable data-driven decision making, and scale quality early years services. This paper uses five case studies to illustrate how technology can be used in resource-constrained environments to first, encourage parenting practices that build early language development (using a stage-based mobile messaging pilot, ChildConnect), secondly, to improve the quality of ECD programs (using a mobile application, CareUp), thirdly, how to affordably scale services for the early detection of visual and hearing impairments (using a mobile tool, HearX), fourthly, how to build a transparent and accountable system for the registration and funding of ECD (using a blockchain enabled platform, Amply), and finally enable rapid data collection and feedback to facilitate quality enhancement of programs at scale (the Early Learning Outcomes Measure). ChildConnect and CareUp were both developed using a design based iterative research approach. The usage and uptake of ChildConnect and CareUp was evaluated with qualitative and quantitative methods. Actual child outcomes were not measured in the initial pilots. Although parents who used and engaged on either platform felt more supported and informed, parent engagement and usage remains a challenge. This is contrast to ECD practitioners whose usage and knowledge with CareUp showed both sustained engagement and knowledge improvement. HearX is an easy-to-use tool to identify hearing loss and visual impairment. The tool was tested with 10000 children in an informal settlement. The feasibility of cost-effectively decentralising screening services was demonstrated. Practical and financial barriers remain with respect to parental consent and for successful referrals. Amply uses mobile and blockchain technology to increase impact and accountability of public services. In the pilot project, Amply is being used to replace an existing paper-based system to register children for a government-funded pre-school subsidy in South Africa. Early Learning Outcomes Measure defines what it means for a child to be developmentally ‘on track’ at aged 50-69 months. ELOM administration is enabled via a tablet which allows for easy and accurate data collection, transfer, analysis, and feedback. ELOM is being used extensively to drive quality enhancement of ECD programs across multiple modalities. The nature of ECD services in South Africa is that they are in large part provided by disconnected private individuals or Non-Governmental Organizations (in contrast to basic education which is publicly provided by the government). It is a disparate sector which means that scaling successful interventions is that much harder. All five interventions show the potential of technology to support and enhance a range of ECD services, but pathways to scale are still being tested.

Keywords: assessment, behavior change, communication, data, disabilities, mobile, scale, technology, quality

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Authors: Levente Kovács, Fruzsina Luca Kézér, Ottó Szenci

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Prediction of the onset of calving and recognizing difficulties at calving has great importance in decreasing neonatal losses and reducing the risk of health problems in the early postpartum period. In this study, changes of rumination time, reticuloruminal pH and temperature were investigated in eutocic (EUT, n = 10) and dystocic (DYS, n = 8) dairy cows around parturition. Rumination time was continuously recorded using an acoustic biotelemetry system, whereas reticuloruminal pH and temperature were recorded using an indwelling and wireless data transmitting system. The recording period lasted from 3 d before calving until 7 days in milk. For the comparison of rumination time and reticuloruminal characteristics between groups, time to return to baseline (the time interval required to return to baseline from the delivery of the calf) and area under the curve (AUC, both for prepartum and postpartum periods) were calculated for each parameter. Rumination time decreased from baseline 28 h before calving both for EUT and DYS cows (P = 0.023 and P = 0.017, respectively). After 20 h before calving, it decreased onwards to reach 32.4 ± 2.3 and 13.2 ± 2.0 min/4 h between 8 and 4 h before delivery in EUT and DYS cows, respectively, and then it decreased below 10 and 5 min during the last 4 h before calving (P = 0.003 and P = 0.008, respectively). Until 12 h after delivery rumination time reached 42.6 ± 2.7 and 51.0 ± 3.1 min/4 h in DYS and EUT dams, respectively, however, AUC and time to return to baseline suggested lower rumination activity in DYS cows than in EUT dams for the 168-h postpartum observational period (P = 0.012 and P = 0.002, respectively). Reticuloruminal pH decreased from baseline 56 h before calving both for EUT and DYS cows (P = 0.012 and P = 0.016, respectively), but did not differ between groups before delivery. In DYS cows, reticuloruminal temperature decreased from baseline 32 h before calving by 0.23 ± 0.02 °C (P = 0.012), whereas in EUT cows such a decrease was found only 20 h before delivery (0.48 ± 0.05 °C, P < 0.01). AUC of reticuloruminal temperature calculated for the prepartum period was greater in EUT cows than in DYS cows (P = 0.042). During the first 4 h after calving, it decreased from 39.7 ± 0.1 to 39.00 ± 0.1 °C and from 39.8 ± 0.1 to 38.8 ± 0.1 °C in EUT and DYS cows, respectively (P < 0.01 for both groups) and reached baseline levels after 35.4 ± 3.4 and 37.8 ± 4.2 h after calving in EUT and DYS cows, respectively. Based on our results, continuous monitoring of changes in rumination time and reticuloruminal temperature seems to be promising in the early detection of cows with a higher risk of dystocia. Depressed postpartum rumination time of DYS cows highlights the importance of the monitoring of cows experiencing difficulties at calving.

Keywords: reticuloruminal pH, reticuloruminal temperature, rumination time, dairy cows, dystocia

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Authors: Mahdi Alshamasin, Riad Al-Kasasbeh, Nikolay Korenevskiy

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We apply mathematical models for the interaction of the internal and biologically active points of meridian structures. Amongst the diseases for which reflex diagnostics are effective are those of the stomach disease. It is shown that use of fuzzy logic decision-making yields good results for the prediction and early diagnosis of gastrointestinal tract diseases, depending on the reaction energy of biologically active points (acupuncture points). It is shown that good results for the prediction and early diagnosis of diseases from the reaction energy of biologically active points (acupuncture points) are obtained by using fuzzy logic decision-making.

Keywords: acupuncture points, fuzzy logic, diagnostically important points (DIP), confidence factors, membership functions, stomach diseases

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Authors: Theresa Robinson Harris

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Small businesses are important to the United States economy, yet the majority struggle to remain relevant and close before their fifth year. This qualitative study explored small business failure by comparing the experiences of small-business owners to understand their involvement with leadership during the early stages of the business, and the impact of this on the firms’ ability to survive. Participants’ experiences from two groups were compared to glean an understanding of the leadership process, how leadership differs between the groups, and to see what themes or constructs emerged that could help to explain the high failure rate. Leadership was perceived to be important when envisioning a path for the future and when providing a platform for employees to succeed. Those who embraced leadership as a skillset were more likely to get through the challenges of the early developmental years while those ignoring the importance of leadership were more likely to close prematurely. These findings suggest a disconnect with regards to the understanding, role, and benefits of leadership in small organizations, particularly young organizations in the early stages of development.

Keywords: leadership, small business, entrepreneurship, success, failure

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Authors: Barbra Bhebhe, Melvyn Quan

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A serological survey was done to detect antibodies against Shuni virus (SHUV) from cattle in Western Kenya. In Kenya the disease status of SHUV in cattle has never been established. It is a zoonotic virus and even though studies have been carried out as early as the 1960s, little research has been published and SHUV is still not a well-recognised Orthobunyavirus. One hundred serum samples were collected from healthy cattle in Kenya and tested for antibodies against SHUV by a serum neutralization assay. All antibody titre values were greater than 1:160, with most of the samples greater than 1:320. Of the samples tested, 87 % had titres greater than 1:320, 12% had a titre of 1:320 and 2% had a titre of 1:160. Samples were classified as positive if the antibody titre was ≥ 1:10 and negative if < 1:10. This study suggests that cattle are exposed commonly to SHUV, which may be endemic in Kenya.

Keywords: Shuni virus, Orthobunyavuruses, serum neutralization test, cell-culture

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Authors: Kevin Zhao, Norman J. Horing

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A methodology for cells sorting and circulating tumor cell detection and discrimination is presented in this paper. The technique is based on Dielectrophoresis and microfluidic device theory. Specifically, the sorting of the cells is realized by adjusting the relation among the sedimentation forces, the drag force provided by the fluid, and the Dielectrophortic force that is relevant to the bias voltage applied on the device. The relation leads to manipulation of the elevation of the cells of the same kind to a height by controlling the bias voltage. Once the cells have been lifted to a position next to the bottom of the cell collection channel, the buffer fluid flashes them into the cell collection channel. Repeated elevation of the cells leads to a complete sorting of the cells in the sample chamber. A proof-of-principle example is presented which verifies the feasibility of the methodology.

Keywords: cell sorter, CTC cell, detection and discrimination, dielectrophoresisords, simulation

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Authors: Lan Zhang, Cailing Zhang

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In recent years, many face anti-spoofing algorithms have high detection accuracy when detecting 2D face anti-spoofing or 3D mask face anti-spoofing alone in the field of face anti-spoofing, but their detection performance is greatly reduced in multidimensional and cross-datasets tests. The rPPG method used for face anti-spoofing uses the unique vital information of real face to judge real faces and face anti-spoofing, so rPPG method has strong stability compared with other methods, but its detection rate of 2D face anti-spoofing needs to be improved. Therefore, in this paper, we improve an rPPG(Remote Photoplethysmography) method(MrPPG) for face anti-spoofing which through color space fusion, using the correlation of pulse signals between real face regions and background regions, and introducing the cyclic neural network (LSTM) method to improve accuracy in 2D face anti-spoofing. Meanwhile, the MrPPG also has high accuracy and good stability in face anti-spoofing of multi-dimensional and cross-data datasets. The improved method was validated on Replay-Attack, CASIA-FASD, Siw and HKBU_MARs_V2 datasets, the experimental results show that the performance and stability of the improved algorithm proposed in this paper is superior to many advanced algorithms.

Keywords: face anti-spoofing, face presentation attack detection, remote photoplethysmography, MrPPG

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Authors: Masoud Safarishaal

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Detecting the location of pole-to-earth faults is essential for the safe operation of the electrical system of the railroad. This paper aims to use a combination of evolutionary algorithms and neural networks to increase the accuracy of single pole-to-earth fault detection and location on the Tehran railroad power supply system. As a result, the Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) are used to train the neural network to improve the accuracy and convergence of the learning process. Due to the system's nonlinearity, fault detection is an ideal application for the proposed method, where the 600 Hz harmonic ripple method is used in this paper for fault detection. The substations were simulated by considering various situations in feeding the circuit, the transformer, and typical Tehran metro parameters that have developed the silicon rectifier. Required data for the network learning process has been gathered from simulation results. The 600Hz component value will change with the change of the location of a single pole to the earth's fault. Therefore, 600Hz components are used as inputs of the neural network when fault location is the output of the network system. The simulation results show that the proposed methods can accurately predict the fault location.

Keywords: single pole-to-pole fault, Tehran railway, ICA, PSO, artificial neural network

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Authors: Fulya Gokalp Yavuz, Mark Daniel Ward

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This study focuses on a contemporary and inevitable topic of Data Science and its exemplary application for early career building: Big Data and Leaving Learning Community (LLC). ‘Academia’ and ‘Industry’ have a common sense on the importance of Big Data. However, both of them are in a threat of missing the training on this interdisciplinary area. Some traditional teaching doctrines are far away being effective on Data Science. Practitioners needs some intuition and real-life examples how to apply new methods to data in size of terabytes. We simply explain the scope of Data Science training and exemplified its early stage application with LLC, which is a National Science Foundation (NSF) founded project under the supervision of Prof. Ward since 2014. Essentially, we aim to give some intuition for professors, researchers and practitioners to combine data science tools for comprehensive real-life examples with the guides of mentees’ feedback. As a result of discussing mentoring methods and computational challenges of Big Data, we intend to underline its potential with some more realization.

Keywords: Big Data, computation, mentoring, training

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Authors: Ahmad Manbohi, Seyyed Hamid Ahmadi

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

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

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Authors: Lokeswara Rao P., Naga Venkata Rakesh N., V. Anantha Subramanian

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It is important to estimate, predict, and avoid the dynamic instability of high speed planing crafts. It is known that design parameters like relative location of center of gravity with respect to the dynamic lift centre and length to beam ratio of the craft have influence on the tendency to porpoise. This paper analyzes the hydrodynamic performance on the basis of the semi-empirical Savitsky method and also estimates the same by numerical simulations based on Reynolds Averaged Navier Stokes (RANS) equations using a commercial code namely, STAR- CCM+. The paper examines through the same numerical simulation considering dynamic equilibrium, the changing running trim, which results in porpoising. Some interesting results emerge from the study and this leads to early detection of the instability.

Keywords: CFD, planing hull, porpoising, Savitsky method

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Authors: Kidane workelul, Li xu, Xiaoyang Pang, Jiaping Lv

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Dairy products are exceptionally ideal media for the growth of microorganisms because of their high nutritional content. There are several ways that milk might get contaminated throughout the milking process, including how the raw milk is transported and stored, as well as how long it is kept before being processed. Psychrotrophic bacteria are among the one which can deteriorate the quality of milk mainly their heat resistance proteas and lipase enzyme. For this research purpose 8 selected strains of Psychrotrophic bacteria (Entrococcus hirae, Pseudomonas fluorescens, Pseudomonas azotoformans, Pseudomonas putida, Exiguobacterium indicum, Pseudomonas paralactice, Acinetobacter indicum, Serratia liquefacients)are chosen and try to determine their characteristics based on the research methodology protocol. Thus, the 8 selected strains are cultured, plated incubate, extracted their genomic DNA and genome DNA was amplified, the purpose of the study was to identify their Psychrotrophic properties, lipase hydrolysis positive test, their optimal incubation temperature, designed primer using the noble strain P,flourescens conserved region area in target with lipA gene, optimized primer specificity as well as sensitivity and PCR detection for lipase positive strains using the design primers. Based on the findings both the selected 8 strains isolated from stored raw milk are Psychrotrophic bacteria, 6 of the selected strains except the 2 strains are positive for lipase hydrolysis, their optimal temperature is 20 to 30 OC, the designed primer specificity is very accurate and amplifies for those strains only with lipase positive but could not amplify for the others. Thus, the result is promising and could help in detecting the Psychrotrophic bacteria producing heat resistance enzymes (lipase) at early stage before the milk is processed and this will safe production loss for the dairy industry.

Keywords: dairy industry, heat-resistant, lipA, milk, primer and psychrotrophic

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Authors: Soo Ho Lim, Minju Lee, Dong In Kim, Gi Youn Han, Seunghun Hong, Hyung Wook Kwon

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We report a floating electrode-based bioelectronic tongue mimicking honeybee taste systems for the detection and discrimination of various nutrients. Here, carbon nanotube field effect transistors with floating electrodes (CNT-FET) were hybridized with nanovesicles containing honeybee nutrient receptors, gustatory receptors of Apis mellifera. This strategy enables us to detect nutrient substance with a high sensitivity and selectivity. It could also be utilized for the detection of nutrients in liquid food. This floating electrode-based bioelectronic tongue mimicking insect taste systems can be a simple, but highly effective strategy in many different basic research areas about sensory systems. Moreover, our research provides opportunities to develop various applications such as food screening, and it also can provide valuable insights on insect taste systems.

Keywords: taste system, CNT-FET, insect gustatory receptor, biolelectronic tongue

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Authors: Sizhu Wang, Cuisong Tang, Lin Zhang, Guangyu Tang

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Objective: We aimed to detect the composition of bone marrow fatty acids early after ovariectomized (OVX) surgery and explore the potential mechanism. Methods: Thirty-two female Sprague-Dawley (SD) rats (12 weeks) were randomly divided into OVX group and Sham group (N=16/group), and received ovariectomy or sham surgery respectively. After 3 and 28 days, eight rats in each group were sacrificed to detect the composition of bone marrow fatty acids by gas chromatography–mass spectrometry (GC–MS) and evaluate the trabecular bone microarchitecture by micro-CT. Significant different fatty acids in the early stage of post-menopausal osteoporosis were selected by OPLS-DA and t test. Then selected fatty acids were further studied in the process of osteogenic differentiation through RT-PCR and Alizarin Red S staining. Results: An apparent sample clustering and group separation were observed between OVX group and sham group three days after surgery, which suggested the role of bone marrow fatty acids in the early stage of postmenopausal osteoporosis. Specifically, myristate, palmitoleate and arachidonate were found to play an important role in classification between OVX group and sham group. We further investigated the effect of palmitoleate and arachidonate on osteogenic differentiation and found that palmitoleate promoted the osteogenic differentiation of MC3T3-E1 cells while arachidonate inhibited this process. Conclusion: Profound bone marrow fatty acids changes have taken place in the early stage of post-menopausal osteoporosis. Bone marrow fatty acids may begin to affect osteogenic differentiation shortly after deficiency of estrogen.

Keywords: bone marrow fatty acids, GC-MS, osteoblast, osteoporosis, post-menopausal

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Authors: Abu Salim Mustafa

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Polymerase chain reaction (PCR) assays targeting genomic DNA segments have been established for the detection of Helicobacter pylori in clinical specimens. However, the data on comparative evaluations of various targets in detection of H. pylori are limited. Furthermore, the frequencies of vacA (s1 and s2) and cagA genotypes, which are suggested to be involved in the pathogenesis of H. pylori in other parts of the world, are not well studied in Kuwait. The aim of this study was to evaluate PCR assays for the detection and genotyping of H. pylori by targeting the amplification of DNA targets from four genomic segments. The genomic DNA were isolated from 72 clinical isolates of H. pylori and tested in PCR with four pairs of oligonucleotides primers, i.e. ECH-U/ECH-L, ET-5U/ET-5L, CagAF/CagAR and Vac1F/Vac1XR, which were expected to amplify targets of various sizes (471 bp, 230 bp, 183 bp and 176/203 bp, respectively) from the genomic DNA of H. pylori. The PCR-amplified DNA were analyzed by agarose gel electrophoresis. PCR products of expected size were obtained with all primer pairs by using genomic DNA isolated from H. pylori. DNA dilution experiments showed that the most sensitive PCR target was 471 bp DNA amplified by the primers ECH-U/ECH-L, followed by the targets of Vac1F/Vac1XR (176 bp/203 DNA), CagAF/CagAR (183 bp DNA) and ET-5U/ET-5L (230 bp DNA). However, when tested with undiluted genomic DNA isolated from single colonies of all isolates, the Vac1F/Vac1XR target provided the maximum positive results (71/72 (99% positives)), followed by ECH-U/ECH-L (69/72 (93% positives)), ET-5U/ET-5L (51/72 (71% positives)) and CagAF/CagAR (26/72 (46% positives)). The results of genotyping experiments showed that vacA s1 (46% positive) and vacA s2 (54% positive) genotypes were almost equally associated with VaCA+/CagA- isolates (P > 0.05), but with VacA+/CagA+ isolates, S1 genotype (92% positive) was more frequently detected than S2 genotype (8% positive) (P< 0.0001). In conclusion, among the primer pairs tested, Vac1F/Vac1XR provided the best results for detection of H. pylori. The genotyping experiments showed that vacA s1 and vacA s2 genotypes were almost equally associated with vaCA⁺/cagA^- isolates, but vacA s1 genotype had a significantly increased association with vacA⁺/cagA⁺ isolates.

Keywords: H. pylori, PCR, detection, genotyping

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Authors: Neda Navidi, Rene Jr. Landry

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Vehicle tracking and accident recognizing are considered by many industries like insurance and vehicle rental companies. The main goal of this paper is to detect the location of a car accident by combining different methods. The methods, which are considered in this paper, are Global Navigation Satellite Systems/Inertial Measurement Units (GNSS/IMU)-based navigation and vehicle accident detection algorithms. They are expressed by a set of raw measurements, which are obtained from a designed integrator black box using GNSS and inertial sensors. Another concern of this paper is the definition of accident detection algorithm based on its jerk to identify the position of that accident. In fact, the results convinced us that, even in GNSS blockage areas, the position of the accident could be detected by GNSS/INS integration with 50% improvement compared to GNSS stand alone.

Keywords: driver behavior monitoring, integration, IMU, GNSS, monitoring, tracking

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Authors: Deepa Das, Susmita Das

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Cognitive Radio (CR) is an emerging technology to combat the spectrum scarcity issues. This is achieved by consistently sensing the spectrum, and detecting the under-utilized frequency bands without causing undue interference to the primary user (PU). In soft decision fusion (SDF) based cooperative spectrum sensing, various evolutionary algorithms have been discussed, which optimize the weight coefficient vector for maximizing the detection performance. In this paper, we propose the hybrid invasive weed optimization and particle swarm optimization (IWO/PSO) algorithm as a fast and global optimization method, which improves the detection probability with a lesser sensing time. Then, the efficiency of this algorithm is compared with the standard invasive weed optimization (IWO), particle swarm optimization (PSO), genetic algorithm (GA) and other conventional SDF based methods on the basis of convergence and detection probability.

Keywords: cognitive radio, spectrum sensing, soft decision fusion, GA, PSO, IWO, hybrid IWO/PSO

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Authors: Michaela Porubanova

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In this exploratory study to examine the effects of emotional significance on change detection using the flicker paradigm, three different categories of scenes were randomly presented (neutral, positive and negative) in three different blocks. We hypothesized that because of the different effects on attention, performance in change detection tasks differs for scenes with different effective values. We found the greatest accuracy of change detection was for changes occurring in positive and negative scenes (compared with neutral scenes). Secondly and most importantly, changes in negative scenes (and also positive scenes, though not with statistical significance) were detected faster than changes in neutral scenes. Interestingly, women were less accurate than men in detecting changes in emotionally significant scenes (both negative and positive), i.e., women detected fewer changes in emotional scenes in the time limit of 40s. But on the other hand, women were quicker to detect changes in positive and negative images than men. The study makes important contributions to the area of the role of emotions on information processing. The role of emotion in attention will be discussed.

Keywords: attention, emotion, flicker task, IAPS

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Authors: Asit K. Sikary, O. P. Murty

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Adipocere formation is a modification of the process of putrefaction. It consists mainly of saturated fatty acids, formed by the post-mortem hydrolysis and hydrogenation of body fats with the help of bacterial enzymes in the presence of warmth, moisture and anaerobic bacteria. In temperate climate, it takes weeks to develop while in India it starts to begin within 4-5 days. In this study, we have collected cases with adipocere formation, which were from the South Delhi region (average room temperature 27-390C) and autopsied at our centre. Details of the circumstances of the death, cause and time of death, surrounding environment and demographic profile of the deceased were taken into account. Total 16 cases were included in this study. Adipocere formation was predominantly present over cheeks, shoulder, breast, flanks, buttocks, and thighs. Out of 16, 11 cases were found in a dry atmosphere, 5 cases were brought from the water. There were 5 cases in which adipocere formation was seen in less than 2 days, and among them, in 1 case, as early as one day. This study showed that adipocere formation can be seen as early as 1 day in a hot and humid environment.

Keywords: adipocere, drowning, hanging, humid environment, strangulation, subtropical climate

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Authors: Longfei Wang, Selçuk Köse

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Hardware Trojan becomes great concern as integrated circuit (IC) technology advances and not all manufacturing steps of an IC are accomplished within one company. Real-time hardware Trojan detection is proven to be a feasible way to detect randomly activated Trojans that cannot be detected at testing stage. On-chip sensors serve as a great candidate to implement real-time hardware Trojan detection, however, the optimization of on-chip sensors has not been thoroughly investigated and the location of Trojan has not been carefully explored. On-chip sensor ellipse distribution method and equivalent mapping technique are proposed based on the characteristics of on-chip power delivery network in this paper to address the optimization and distribution of on-chip sensors for real-time hardware Trojan detection as well as to estimate the location and current consumption of hardware Trojan. Simulation results verify that hardware Trojan activation can be effectively detected and the location of a hardware Trojan can be efficiently estimated with less than 5% error for a realistic power grid using our proposed methods. The proposed techniques therefore lay a solid foundation for isolation and even deactivation of hardware Trojans through accurate location of Trojans.

Keywords: hardware trojan, on-chip sensor, power distribution network, power/ground noise

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Authors: Suharni Mohamad Suharni Mohamad, Nurul Izzati Hamzan Nurul Izzati Hamzan, Norhayu Abdul Rahman Norhayu Abdul Rahman, Siti Suraiya Md Noor Siti Suraiya Md Noor

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Human papillomavirus (HPV) is an important risk factor for development of oral cancer. HPV16 is the most common type found in HPV-positive squamous cell carcinoma. In the present study, we established a real-time loop-mediated isothermal amplification (real-time LAMP) for detection of HPV16. A set of six primers was specially designed to recognize eight distinct sequences of HPV16-E6. Detection and quantification was achieved by real-time monitoring using a real-time turbidimeter based on threshold time required for turbidity in the LAMP reaction. LAMP reagents (MgSO4, dNTPs, Bst polymerase concentrations) and various incubation times and temperatures were optimized. The sensitivity was determined using 10-fold serial dilutions of HPV16 standard strain. The specificity of was evaluated using other HPV genotypes. The optimized method was established with specifically designed primers by real-time detection in approximately 30 min at 65°C. The limit of detection of HPV16 using the LAMP assay was 10 pg/ml that could be detected in 30 min. The LAMP assay was 10 times more sensitive than the conventional PCR in detecting HPV16. No cross-reactivity with other HPV genotypes was observed. This quantitative real-time LAMP assay may improve diagnostic potential for the detection and quantification of HPV16 in clinical samples and epidemiological studies due to its rapidity, simplicity, high sensitivity and specificity. This assay will be further evaluated with HPV DNAs of saliva from patients with oral squamous cell carcinoma. Acknowledgement: This study was financially supported by the ScienceFund Grant, Ministry of Science, Technology and Innovation (305/PPSG/6113219).

Keywords: Oral Squamous Cell Carcinoma (OSCC), Human Papillomavirus 16 (HPV16), Loop-Mediated Isothermal Amplification (LAMP), rapid detection

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Authors: Derlis Gregor, Kevin Cikel, Mario Arzamendia, Raúl Gregor

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This paper presents a self-sustaining mobile system for counting and classification of vehicles through processing video. It proposes a counting and classification algorithm divided in four steps that can be executed multiple times in parallel in a SBC (Single Board Computer), like the Raspberry Pi 2, in such a way that it can be implemented in real time. The first step of the proposed algorithm limits the zone of the image that it will be processed. The second step performs the detection of the mobile objects using a BGS (Background Subtraction) algorithm based on the GMM (Gaussian Mixture Model), as well as a shadow removal algorithm using physical-based features, followed by morphological operations. In the first step the vehicle detection will be performed by using edge detection algorithms and the vehicle following through Kalman filters. The last step of the proposed algorithm registers the vehicle passing and performs their classification according to their areas. An auto-sustainable system is proposed, powered by batteries and photovoltaic solar panels, and the data transmission is done through GPRS (General Packet Radio Service)eliminating the need of using external cable, which will facilitate it deployment and translation to any location where it could operate. The self-sustaining trailer will allow the counting and classification of vehicles in specific zones with difficult access.

Keywords: intelligent transportation system, object detection, vehicle couting, vehicle classification, video processing

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Authors: Akimitsu Kugimiya, Kouhei Iwato, Toru Saito, Jiro Kohda, Yasuhisa Nakano, Yu Takano

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The measurement of amino acid content is reported to be useful for the diagnosis of several types of diseases, including lung cancer, gastric cancer, colorectal cancer, breast cancer, prostate cancer, and diabetes. The conventional detection methods for amino acid are high-performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC-MS), but they have several drawbacks as the equipment is cumbersome and the techniques are costly in terms of time and costs. In contrast, biosensors and biosensing methods provide more rapid and facile detection strategies that use simple equipment. The authors have reported a novel approach for the detection of each amino acid that involved the use of aminoacyl-tRNA synthetase (aaRS) as a molecular recognition element because aaRS is expected to a selective binding ability for corresponding amino acid. The consecutive enzymatic reactions used in this study are as follows: aaRS binds to its cognate amino acid and releases inorganic pyrophosphate. Hydrogen peroxide (H₂O₂) was produced by the enzyme reactions of inorganic pyrophosphatase and pyruvate oxidase. The Trinder’s reagent was added into the reaction mixture, and the absorbance change at 556 nm was measured using a microplate reader. In this study, an amino acid-sensing method using histidyl-tRNA synthetase (HisRS; histidine-specific aaRS) as molecular recognition element in combination with the Trinder’s reagent spectrophotometric method was developed. The quantitative performance and selectivity of the method were evaluated, and the optimal enzyme reaction and detection conditions were determined. The authors developed a simple and rapid method for detecting histidine with a combination of enzymatic reaction and spectrophotometric detection. In this study, HisRS was used to detect histidine, and the reaction and detection conditions were optimized for quantitation of these amino acids in the ranges of 1–100 µM histidine. The detection limits are sufficient to analyze these amino acids in biological fluids. This work was partly supported by Hiroshima City University Grant for Special Academic Research (General Studies).

Keywords: amino acid, aminoacyl-tRNA synthetase, biosensing, enzyme reaction

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Authors: Tarek Kandil, Ameen Ali

Abstract:

Distributed generation (DG) has recently gained a lot of momentum in power industry due to market deregulation and environmental concerns. One of the most technical challenges facing DGs is islanding of distributed generators. The current industry practice is to disconnect all distributed generators immediately after the occurrence of islands within 200 to 350 ms after loss of main supply. To achieve such goal, each DG must be equipped with an islanding detection device. Frequency relays are one of the most commonly used loss of mains detection method. However, distribution utilities may be faced with concerns related to false operation of these frequency relays due to improper settings. The commercially available frequency relays are considering standard tight setting. This paper investigates some factors related to relays internal algorithm that contribute to their different operating responses. Further, the relay operation in the presence of multiple distributed at the same network is analyzed. Finally, the relay setting can be accurately determined based on these investigation and analysis.

Keywords: frequency relay, distributed generation, islanding detection, relay setting

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Authors: F. Ghazalnaz Sharifonnasabi, Iman Makhdoom

Abstract:

Breast cancer is a serious health concern that affects many people around the world. According to a study published in the Breast journal, the global burden of breast cancer is expected to increase significantly over the next few decades. The number of deaths from breast cancer has been increasing over the years, but the age-standardized mortality rate has decreased in some countries. It’s important to be aware of the risk factors for breast cancer and to get regular check- ups to catch it early if it does occur. Machin learning techniques have been used to aid in the early detection and diagnosis of breast cancer. These techniques, that have been shown to be effective in predicting and diagnosing the disease, have become a research hotspot. In this study, we consider two deep learning approaches including: Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). We also considered the five-machine learning algorithm titled: Decision Tree (C4.5), Naïve Bayesian (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Algorithm and XGBoost (eXtreme Gradient Boosting) on the Breast Cancer Wisconsin Diagnostic dataset. We have carried out the process of evaluating and comparing classifiers involving selecting appropriate metrics to evaluate classifier performance and selecting an appropriate tool to quantify this performance. The main purpose of the study is predicting and diagnosis breast cancer, applying the mentioned algorithms and also discovering of the most effective with respect to confusion matrix, accuracy and precision. It is realized that CNN outperformed all other classifiers and achieved the highest accuracy (0.982456). The work is implemented in the Anaconda environment based on Python programing language.

Keywords: breast cancer, multi-layer perceptron, Naïve Bayesian, SVM, decision tree, convolutional neural network, XGBoost, KNN

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Authors: Syamala Krishnannair

Abstract:

A multivariate statistical process monitoring methodology using dissimilarity scale-based singular spectrum analysis (SSA) is proposed for the detection and diagnosis of process faults in the base metal flotation plant. Process faults are detected based on the multi-level decomposition of process signals by SSA using the dissimilarity structure of the process data and the subsequent monitoring of the multiscale signals using the unified monitoring index which combines T² with SPE. Contribution plots are used to identify the root causes of the process faults. The overall results indicated that the proposed technique outperformed the conventional multivariate techniques in the detection and diagnosis of the process faults in the flotation plant.

Keywords: fault detection, fault diagnosis, process monitoring, dissimilarity scale

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Authors: Ruchi Makani, B. V. R. Reddy

Abstract:

Mobile Adhoc Networks (MANETs) are gaining popularity due to their potential of providing low-cost mobile connectivity solutions to real-world communication problems. Integrating Intrusion Detection Systems (IDS) in MANETs is a tedious task by reason of its distinctive features such as dynamic topology, de-centralized authority and highly controlled/limited resource environment. IDS primarily use automated soft-computing techniques to monitor the inflow/outflow of traffic packets in a given network to detect intrusion. Use of machine learning techniques in IDS enables system to make decisions on intrusion while continuous keep learning about their dynamic environment. An appropriate IDS model is essential to be selected to expedite this application challenges. Thus, this paper focused on fuzzy-logic based machine learning IDS technique for MANETs and presented their applicability for achieving effectiveness in identifying the intrusions. Further, the selection of appropriate protocol attributes and fuzzy rules generation plays significant role for accuracy of the fuzzy-logic based IDS, have been discussed. This paper also presents the critical attributes of MANET’s routing protocol and its applicability in fuzzy logic based IDS.

Keywords: AODV, mobile adhoc networks, intrusion detection, anomaly detection, fuzzy logic, fuzzy membership function, fuzzy inference system

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Authors: Amit Kumar, Amanpreet Chander, Ashish Sahani

Abstract:

This paper presents our recent work on real-time human activity detection based on the media pipe pipeline and machine learning algorithms. The proposed system can detect human activities, including running, jumping, squatting, bending to the left or right, and standing still. This is a robust solution for developing a yoga, dance, metaverse, and fitness application that checks for the correction of the pose without having any additional monitor like a personal trainer. MediaPipe solution offers an open-source cross-platform which utilizes a two-step detector-tracker ML pipeline for live detection of key landmarks on our body which can be used for motion data collection. The prediction of real-time poses uses a variety of machine learning techniques and different types of analysis. Without primarily relying on powerful desktop environments for inference, our method achieves real-time performance on the majority of contemporary mobile phones, desktops/laptops, Python, or even the web. Experimental results show that our method outperforms the existing method in terms of accuracy and real-time capability, achieving an accuracy of 99.92% on testing datasets.

Keywords: human activity detection, media pipe, machine learning, metaverse applications

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Authors: Halimatu S. Abdullahi, Ray E. Sheriff, Fatima Mahieddine

Abstract:

Agriculture is essential to the continuous existence of human life as they directly depend on it for the production of food. The exponential rise in population calls for a rapid increase in food with the application of technology to reduce the laborious work and maximize production. Technology can aid/improve agriculture in several ways through pre-planning and post-harvest by the use of computer vision technology through image processing to determine the soil nutrient composition, right amount, right time, right place application of farm input resources like fertilizers, herbicides, water, weed detection, early detection of pest and diseases etc. This is precision agriculture which is thought to be solution required to achieve our goals. There has been significant improvement in the area of image processing and data processing which has being a major challenge. A database of images is collected through remote sensing, analyzed and a model is developed to determine the right treatment plans for different crop types and different regions. Features of images from vegetations need to be extracted, classified, segmented and finally fed into the model. Different techniques have been applied to the processes from the use of neural network, support vector machine, fuzzy logic approach and recently, the most effective approach generating excellent results using the deep learning approach of convolution neural network for image classifications. Deep Convolution neural network is used to determine soil nutrients required in a plantation for maximum production. The experimental results on the developed model yielded results with an average accuracy of 99.58%.

Keywords: convolution, feature extraction, image analysis, validation, precision agriculture

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