Search results for: multi-agent double deep Q-network (MADDQN)

Authors: Surajit Chakrabarty, Piyush Chauhan, Subhasis Panda, Sujoy Bhattacharya

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A large proportion of roads in India are not well maintained as per the laid down public safety guidelines leading to loss of direction control and fatal accidents. We propose a technique to detect speed breakers and potholes using mobile sensor data captured from multiple vehicles and provide a profile of the road. This would, in turn, help in monitoring roads and revolutionize digital maps. Incorporating randomness in the model formulation for detection of speed breakers and potholes is crucial due to substantial heterogeneity observed in data obtained using a mobile application from multiple vehicles driven by different drivers. This is accomplished with Hidden Markov Models, whose hidden state sequence is found for each time step given the observables sequence, and are then fed as input to LSTM network with peephole connections. A precision score of 0.96 and 0.63 is obtained for classifying bumps and potholes, respectively, a significant improvement from the machine learning based models. Further visualization of bumps/potholes is done by converting time series to images using Markov Transition Fields where a significant demarcation among bump/potholes is observed.

Keywords: deep learning, hidden Markov model, pothole, speed breaker

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Authors: M. Mamor

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GaN-based compounds have attracted much interest in the fabrication of high-power, high speed and high-frequency electronic devices. Other examples of GaN-based applications are blue and ultraviolet (UV) light-emitting diodes (LEDs). All these devices require high-quality ohmic and Schottky contacts. Gaining an understanding of the electrical characteristics of metal/GaN contacts is of fundamental and technological importance for developing GaN-based devices. In this work, the barrier characteristics of Pt and Pd Schottky contacts on n-type GaN were studied using temperature-dependent forward current-voltage (I-V) measurements over a wide temperature range 80–400 K. Our results show that the barrier height and ideality factor, extracted from the forward I-V characteristics based on thermionic emission (TE) model, exhibit an abnormal dependence with temperature; i.e., by increasing temperature, the barrier height increases whereas the ideality factor decreases. This abnormal behavior has been explained based on the TE model by considering the presence of double Gaussian distribution (GD) of nonhomogeneous barrier height at the metal/GaN interface. However, in the high-temperature range (160-400 K), the extracted value for the effective Richardson constant A* based on the barrier inhomogeneity (BHi) model is found in fair agreement with the theoretically predicted value of about 26.9 A.cm-2 K-2 for n-type GaN. This result indicates that in this temperature range, the conduction current transport is dominated by the thermionic emission mode. On the other hand, in the lower temperature range (80-160 K), the corresponding effective Richardson constant value according to the BHi model is lower than the theoretical value, suggesting the presence of other current transport, such as tunneling-assisted mode at lower temperatures.

Keywords: Schottky diodes, inhomogeneous barrier height, GaN semiconductors, Schottky barrier heights

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Authors: Morteza Karami, Soheila Dayani

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Deep Soil Mixing (DSM) is a soil improvement technique that involves mechanically mixing the soil with a binder material to improve its strength, stiffness, and durability. This technique is typically used in geotechnical engineering applications where weak or unstable soil conditions exist, such as in building foundations, embankment support, or ground improvement projects. In this study, the settlement of the foundation on the improved soil using the wet DSM technique has been analyzed for a case study. Before DSM production, the initial soil mixture has been determined based on the laboratory tests and then, the proper mix designs have been optimized based on the pilot scale tests. The results show that the spacing and depth of the DSM columns depend on the soil properties, the intended loading conditions, and other factors such as the available space and equipment limitations. Moreover, monitoring instruments installed in the pilot area verify that the settlement of the foundation has been placed in an acceptable range to ensure that the soil mixture is providing the required strength and stiffness to support the structure or load. As an important result, if the DSM columns touch or penetrate into the stiff soil layer, the settlement of the foundation can be significantly decreased. Furthermore, the DSM columns should be allowed to cure sufficiently before placing any significant loads on the structure to prevent excessive deformation or settlement.

Keywords: deep soil mixing, soil mixture, settlement, instrumentation, curing age

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Authors: D. Bhowmik, Y. Tian, Q. Yin, F. Zhu

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Cyclic GMP-AMP synthase (cGAS), recognises cytoplasmic double-stranded DNA (dsDNA), indicative of bacterial and viral infections, as well as the leakage of self DNA by cellular dysfunction and stresses, to elicit the host's immune responses. Viruses also have developed numerous strategies to antagonize the cGAS-STING pathway. Kaposi's sarcoma-associated herpesvirus (KSHV) is a human DNA tumor virus that is the causative agent of Kaposi’s sarcoma and several other malignancies. To persist in the host, consequently causing diseases, KSHV must overcome the host innate immune responses, including the cGAS-STING DNA sensing pathway. We already found that ORF52 or KicGAS (KSHV inhibitor of cGAS), an abundant and basic gamma herpesvirus-conserved tegument protein, directly inhibits cGAS enzymatic activity. To better understand the mechanism, we have performed the biochemical and structural characterization of full-length KicGAS and various mutants in regarding binding to DNA. We observed that KicGAS is capable of self-association and identified the critical residues involved in the oligomerization process. We also characterized the DNA-binding of KicGAS and found that KicGAS cooperatively oligomerizes along the length of the double stranded DNA, the highly conserved basic residues at the c-terminal disordered region are crucial for DNA recognition. Deficiency in oligomerization also affects DNA binding. Thus DNA binding by KicGAS sequesters DNA and prevents it from being detected by cGAS, consequently inhibiting cGAS activation. KicGAS homologues also inhibit cGAS efficiently, suggesting inhibition of cGAS is evolutionarily conserved mechanism among gamma herpesvirus. These results highlight the important viral strategy to evade this innate immune sensor.

Keywords: Kaposi's sarcoma-associated herpesvirus, KSHV, cGAS, DNA binding, inhibition

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Authors: Opara Monica, Suriani Ismail, Ahmad Iqmer Nashriq Mohd Nazan

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The true magnitude of the mortality and morbidity attributable to malaria worldwide is, at best, a scientific guess, although it is not disputable that the greatest burden is in sub-Saharan Africa. Those at highest risk are children younger than 5 years and pregnant women, particularly primigravidae. Nationally, malaria remains the third leading cause of death and is still considered a major public health problem. Therefore, this study is aimed to assess the effectiveness of health education intervention on insecticide-treated net use and its practices among pregnant women attending antenatal clinics. Materials and Methods: This study will be an intervention study with two arms double parallel randomized controlled trial (blinded) to be conducted in 3 stages. The first stage will develop health belief model (HBM) program, while in the second stage, pregnant women will be recruited, assessed (baseline data), randomized into two arms of the study, and follow-up for six months. The third stage will evaluate the impact of the intervention on HBM and disseminate the findings. Data will be collected with the use of a structured questionnaire which will contain validated tools. The main outcome measurement will be the treatment effect using HBM, while data will be analysed using SPSS, version 22. Discussion: The study will contribute to the existing knowledge on hospital-based care programs for pregnant women in developing countries where the literature is scanty. It will generally give insight into the importance of HBM measurement in interventional studies on malaria and other related infectious diseases in this setting.

Keywords: malaria, health education, insecticide-treated nets, sub-Saharan Africa

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Authors: René A. Sørensen, Søren Skovsen, Peter Christiansen, Henrik Karstoft

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Autonomous agricultural machines act in stochastic surroundings and therefore, must be able to perceive the surroundings in real time. This perception can be achieved using image sensors combined with advanced machine learning, in particular Deep Learning. Deep convolutional neural networks excel in labeling and perceiving color images and since the cost of high-quality RGB-cameras is low, the hardware cost of good perception depends heavily on memory and computation power. This paper investigates the possibility of designing lightweight convolutional neural networks for semantic segmentation (pixel wise classification) with reduced hardware requirements, to allow for embedded usage in autonomous agricultural machines. Using compression techniques, a lightweight convolutional neural network is designed to perform real-time semantic segmentation on an embedded platform. The network is trained on two large datasets, ImageNet and Pascal Context, to recognize up to 400 individual classes. The 400 classes are remapped into agricultural superclasses (e.g. human, animal, sky, road, field, shelterbelt and obstacle) and the ability to provide accurate real-time perception of agricultural surroundings is studied. The network is applied to the case of autonomous grass mowing using the NVIDIA Tegra X1 embedded platform. Feeding case-specific images to the network results in a fully segmented map of the superclasses in the image. As the network is still being designed and optimized, only a qualitative analysis of the method is complete at the abstract submission deadline. Proceeding this deadline, the finalized design is quantitatively evaluated on 20 annotated grass mowing images. Lightweight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show competitive performance with regards to accuracy and speed. It is feasible to provide cost-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: autonomous agricultural machines, deep learning, safety, visual perception

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Authors: Rita Phillips, Mark Burgess, Maga Berlinski

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Although the positive impact of religiosity on well-being, health, and life-coping abilities is well known, up to date research has failed to provide scientific evidence for the relationship reasons. Therefore the present study took a qualitative approach by examining how religiosity interacts in coping with emotionally distressful situations, for which wedding preparations are an example. Wedding preparations, related to the experience of ambiguous emotions, can be the reason for phases of high distress. Although being per-se religious ceremonies, they are also socially-scripted and characterized by people’s striving for personally meaningful celebrations. The negotiation of these many influences can evoke conflicts. To reveal components of religiosity which contribute to stress-resolution, eight biographic-narrative interviews with recently married spouses were conducted. Participants were from different nationalities and Catholic deep-belief communities in order to determine factors independent from national-culture and social-subgroup. The audio-tape recorded, transcribed and translated interviews were analyzed by Interpretative Phenomenological Analysis. Opposing previous research on wedding-related conflicts but in-line with the quantitative account on the relation between stress-resilience and religiosity, the present study found participants reporting very low levels of distress and ambiguity. Although similar areas of potential conflicts were revealed, deep-belief Christians seemed to handle them in a different way. Participants freed themselves from own and others’ rigor mundane expectations by their spiritual preparation and the focus on a divine instance. This evoked a feeling of perceived closeness to God and of unconditional love, resulting in acceptance of oneself and others. Through relativizing mundane goods, participants perceived absolute freedom. Thus belief did not supplement coping strategies, previously defined in the literature, but substituted them. The paper implies that in explaining the connection between stress-resilience and religiosity, one’s perception and experience of unconditional love might outweigh other social or personal factors. However, further qualitative investigations are needed to fully explain the phenomenon.

Keywords: deep-belief, religiosity, resilience, wedding

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Authors: Mojtaba Heydarizad

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Andarokh basin is one of the main karstic regions in Khorasan Razavi province NE Iran. This basin is part of Kopeh-Dagh mega zone extending from Caspian Sea in the east to northern Afghanistan in the west. This basin is covered by Mozdooran Formation, Ngr evaporative formation and quaternary alluvium deposits in descending order of age. Mozdooran carbonate formation is notably karstified. The main surface karstic features in Mozdooran formation are Groove karren, Cleft karren, Rain pit, Rill karren, Tritt karren, Kamintza, Domes, and Table karren. In addition to surface features, deep karstic feature Andarokh Cave also exists in the region. Studying Ca, Mg, Mn, Sr, Fe concentration and Sr/Mn ratio in Mozdooran formation samples with distance to main faults and joints system using PCA analyses demonstrates intense meteoric digenesis role in controlling carbonate rock geochemistry. The karst evaluation in Andarokh basin varies from early stages 'deep seated karst' in Mesozoic to mature karstic system 'Exhumed karst' in quaternary period. Andarokh cave (the main cave in Andarokh basin) is rudimentary branch work consists of three passages of A, B and C and two entrances Andarokh and Sky.

Keywords: Andarokh basin, Andarokh cave, geochemical analyses, karst evaluation

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Authors: F. Pal-Fam, S. Keszthelyi

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Phytosanitary condition of sunflower (Helianthus annuus L.) was endangered by several phytopathogenic agents, mainly microfungi, such as Sclerotinia sclerotiorum, Diaporthe helianthi, Plasmopara halstedtii, Macrophomina phaseolina and so on. There are more agrotechnical and chemical technologies against them, for instance, tolerant hybrids, crop rotations and eventually several in-crop chemical treatments. There are different fungicide treatment methods in sunflower in Hungarian agricultural practice in the quest of obtaining healthy and economic plant products. Besides, there are many choices of useable active ingredients in Hungarian sunflower protection. This study carried out into the examination of the effect of five different fungicide active substances (found on the market) and three different application modes (early; late; and early and late treatments) in a total number of 9 sample plots, 0.1 ha each other. Five successive vegetation periods have been investigated in long term, between 2013 and 2017. The treatments were: 1)untreated control; 2) boscalid and dimoxystrobin late treatment (July); 3) boscalid and dimoxystrobin early treatment (June); 4) picoxystrobin and cyproconazole early treatment; 5) picoxystrobin and cymoxanil and famoxadone early treatment; 6) picoxystrobin and cyproconazole early; cymoxanil and famoxadone late treatments; 7) picoxystrobin and cyproconazole early; picoxystrobin and cymoxanil and famoxadone late treatments; 8) trifloxystrobin and cyproconazole early treatment; and 9) trifloxystrobin and cyproconazole both early and late treatments. Due to the very different yearly weather conditions different phytopathogenic fungi were dominant in the particular years: Diaporthe and Alternaria in 2013; Alternaria and Sclerotinia in 2014 and 2015; Alternaria, Sclerotinia and Diaporthe in 2016; and Alternaria in 2017. As a result of treatments ‘infection frequency’ and ‘infestation rate’ showed a significant decrease compared to the control plot. There were no significant differences between the efficacies of the different fungicide mixes; all were almost the same effective against the phytopathogenic fungi. The most dangerous Sclerotinia infection was practically eliminated in all of the treatments. Among the single treatments, the late treatment realised in July was the less efficient, followed by the early treatments effectuated in June. The most efficient was the double treatments realised in both June and July, resulting 70-80% decrease of the infection frequency, respectively 75-90% decrease of the infestation rate, comparing with the control plot in the particular years. The lowest yield quantity was observed in the control plot, followed by the late single treatment. The yield of the early single treatments was higher, while the double treatments showed the highest yield quantities (18.3-22.5% higher than the control plot in particular years). In total, according to our five years investigation, the most effective application mode is the double in-crop treatment per vegetation time, which is reflected by the yield surplus.

Keywords: fungicides, treatments, phytopathogens, sunflower

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Authors: Kakali Bhadra

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Binding of small molecules to DNA and recently to RNA, continues to attract considerable attention for developing effective therapeutic agents for control of gene expression. This work focuses towards understanding interaction of harmalol, a dihydro beta-carboline alkaloid, with different nucleic acid motifs viz. double stranded CT DNA, single stranded A-form poly(A), double-stranded A-form of poly(C)·poly(G) and clover leaf tRNAphe by different spectroscopic, calorimetric and molecular modeling techniques. Results of this study converge to suggest that (i) binding constant varied in the order of CT DNA > poly(C)·poly(G) > tRNAphe > poly(A), (ii) non-cooperative binding of harmalol to poly(C)·poly(G) and poly(A) and cooperative binding with CT DNA and tRNAphe, (iii) significant structural changes of CT DNA, poly(C)·poly(G) and tRNAphe with concomitant induction of optical activity in the bound achiral alkaloid molecules, while with poly(A) no intrinsic CD perturbation was observed, (iv) the binding was predominantly exothermic, enthalpy driven, entropy favoured with CT DNA and poly(C)·poly(G) while it was entropy driven with tRNAphe and poly(A), (v) a hydrophobic contribution and comparatively large role of non-polyelectrolytic forces to Gibbs energy changes with CT DNA, poly(C)·poly(G) and tRNAphe, and (vi) intercalated state of harmalol with CT DNA and poly(C)·poly(G) structure as revealed from molecular docking and supported by the viscometric data. Furthermore, with competition dialysis assay it was shown that harmalol prefers hetero GC sequences. All these findings unequivocally pointed out that harmalol prefers binding with ds CT DNA followed by ds poly(C)·poly(G), clover leaf tRNAphe and least with ss poly(A). The results highlight the importance of structural elements in these natural beta-carboline alkaloids in stabilizing different DNA and RNA of various motifs for developing nucleic acid based better therapeutic agents.

Keywords: calorimetry, docking, DNA/RNA-alkaloid interaction, harmalol, spectroscopy

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Authors: Tanmay Bisen, Aastha Shayla

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This study introduces an advanced approach to tree canopy detection in urban environments and a regional analysis of Northern and Central Kolkata that delves into the intricate relationship between urban development and environmental conservation. Leveraging high-resolution drone imagery from diverse urban green spaces in Kolkata, we fine-tuned the deep forest model to enhance its precision and accuracy. Our results, characterized by an impressive Intersection over Union (IoU) score of 0.90 and a mean average precision (mAP) of 0.87, underscore the model's robustness in detecting and classifying tree crowns amidst the complexities of aerial imagery. This research not only emphasizes the importance of model customization for specific datasets but also highlights the potential of drone-based remote sensing in urban forestry studies. The study investigates the spatial distribution, density, and environmental impact of trees in Northern and Central Kolkata. The findings underscore the significance of urban green spaces in met-ropolitan cities, emphasizing the need for sustainable urban planning that integrates green infrastructure for ecological balance and human well-being.

Keywords: urban greenery, advanced spatial distribution analysis, drone imagery, deep learning, tree detection

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Authors: Kirti Tewari, Rahul Dev

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Solar water heating is a thermodynamic process of heating water using sunlight with the help of solar water heater. Thus, solar water heater is a device used to harness solar energy. In this paper, a modified solar water heating system (MSWHS) has been proposed over flat plate collector (FPC) and Evacuated tube collector (ETC). The modifications include selection of materials other than glass, and glass wool which are conventionally used for fabricating FPC and ETC. Some modifications in design have also been proposed. Its collector is made of double layer of semi-cylindrical acrylic tubes and fibre reinforced plastic (FRP) insulation base. Water tank is made of double layer of acrylic sheet except base and north wall. FRP is used in base and north wall of the water tank. A concept of equivalent thickness has been utilised for calculating the dimensions of collector plate, acrylic tube and tank. A thermal model for the proposed design of MSWHS is developed and simulation is carried out on MATLAB for the capacity of 200L MSWHS having collector area of 1.6 m2, length of acrylic tubes of 2m at an inclination angle 25° which is taken nearly equal to the latitude of the given location. Latitude of Allahabad is 24.45° N. The results show that the maximum temperature of water in tank and tube has been found to be 71.2°C and 73.3°C at 17:00hr and 16:00hr respectively in March for the climatic data of Allahabad. Theoretical performance analysis has been carried out by varying number of tubes of collector, the tank capacity and climatic data for given months of winter and summer.

Keywords: acrylic, fibre reinforced plastic, solar water heating, thermal model, conventional water heaters

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Authors: Yao-Hong Tsai

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Due to the sensor technology, video surveillance has become the main way for security control in every big city in the world. Surveillance is usually used by governments for intelligence gathering, the prevention of crime, the protection of a process, person, group or object, or the investigation of crime. Many surveillance systems based on computer vision technology have been developed in recent years. Moving target tracking is the most common task for Unmanned Aerial Vehicle (UAV) to find and track objects of interest in mobile aerial surveillance for civilian applications. The paper is focused on vision-based collision avoidance for UAVs by recurrent neural networks. First, images from cameras on UAV were fused based on deep convolutional neural network. Then, a recurrent neural network was constructed to obtain high-level image features for object tracking and extracting low-level image features for noise reducing. The system distributed the calculation of the whole system to local and cloud platform to efficiently perform object detection, tracking and collision avoidance based on multiple UAVs. The experiments on several challenging datasets showed that the proposed algorithm outperforms the state-of-the-art methods.

Keywords: unmanned aerial vehicle, object tracking, deep learning, collision avoidance

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Authors: K. Shaji Mon, P. R. John Sreenidhi

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In deep-submicrometer CMOS processes, time-domain resolution of a digital signal is becoming higher than voltage resolution of analog signals. This claim is nowadays pushing toward a new circuit design paradigm in which the traditional analog signal processing is expected to be progressively substituted by the processing of times in the digital domain. Within this novel paradigm, digitally controlled delay lines (DCDL) should play the role of digital-to-analog converters in traditional, analog-intensive, circuits. Digital delay locked loops are highly prevalent in integrated systems.The proposed paper addresses the glitches present in delay circuits along with area,power dissipation and signal integrity.The digitally controlled delay lines(DCDL) under study have been designed in a 90 nm CMOS technology 6 layer metal Copper Strained SiGe Low K Dielectric. Simulation and synthesis results show that the novel circuits exhibit no glitches for dual output coarse DCDL with less power dissipation and consumes less area compared to the glitch free NAND based DCDL.

Keywords: glitch free, NAND-based DCDL, CMOS, deep-submicrometer

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Authors: Majid Habib Khan, Jinwei Zhao, Xinhong Hei, Liu Jiedong, Rana Shahzad Noor, Muhammad Imran

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Deep learning is increasingly employed in image classification, yet the scarcity and high cost of labeled data for training remain a challenge. Limited samples often lead to overfitting due to biased sample distribution. This paper introduces a dynamic distribution calibration method for few-shot learning. Initially, base and new class samples undergo normalization to mitigate disparate feature magnitudes. A pre-trained model then extracts feature vectors from both classes. The method dynamically selects distribution characteristics from base classes (both adjacent and remote) in the embedding space, using a threshold value approach for new class samples. Given the propensity of similar classes to share feature distributions like mean and variance, this research assumes a Gaussian distribution for feature vectors. Subsequently, distributional features of new class samples are calibrated using a corrected hyperparameter, derived from the distribution features of both adjacent and distant base classes. This calibration augments the new class sample set. The technique demonstrates significant improvements, with up to 4% accuracy gains in few-shot classification challenges, as evidenced by tests on miniImagenet and CUB datasets.

Keywords: deep learning, computer vision, image classification, few-shot learning, threshold

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Authors: Ciro Moreno-Ramirez, Maria Tomas-Rodriguez, Simos A. Evangelou

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A motorcycle's front end links the front wheel to the motorcycle's chassis and has two main functions: the front wheel suspension and the vehicle steering. Up to this date, several suspension systems have been developed in order to achieve the best possible front end behavior, being the telescopic fork the most common one and already subjected to several years of study in terms of its kinematics, dynamics, stability and control. A motorcycle telescopic fork suspension model consists of a couple of outer tubes which contain the suspension components (coil springs and dampers) internally and two inner tubes which slide into the outer ones allowing the suspension travel. The outer tubes are attached to the frame through two triple trees which connect the front end to the main frame through the steering bearings and allow the front wheel to turn about the steering axis. This system keeps the front wheel's displacement in a straight line parallel to the steering axis. However, there exist alternative suspension designs that allow different trajectories of the front wheel with the suspension travel. In this contribution, the authors investigate an alternative front suspension system (Hossack suspension) and its influence on the motorcycle nonlinear dynamics to identify and reduce stability risks that a new suspension systems may introduce in the motorcycle dynamics. Based on an existing high-fidelity motorcycle mathematical model, the front end geometry is modified to accommodate a Hossack suspension system. It is characterized by a double wishbone design that varies the front end geometry on certain maneuverings and, consequently, the machine's behavior/response. It consists of a double wishbone structure directly attached to the chassis. In here, the kinematics of this system and its impact on the motorcycle performance/stability are analyzed and compared to the well known telescopic fork suspension system. The framework of this research is the mathematical modelling and numerical simulation. Full stability analyses are performed in order to understand how the motorcycle dynamics may be affected by the newly introduced front end design. This study is carried out by a combination of nonlinear dynamical simulation and root-loci methods. A modal analysis is performed in order to get a deeper understanding of the different modes of oscillation and how the Hossack suspension system affects them. The results show that different kinematic designs of a double wishbone suspension systems do not modify the general motorcycle's stability. The normal modes properties remain unaffected by the new geometrical configurations. However, these normal modes differ from one suspension system to the other. It is seen that the normal modes behaviour depends on various important dynamic parameters, such as the front frame flexibility, the steering damping coefficient and the centre of mass location.

Keywords: nonlinear mechanical systems, motorcycle dynamics, suspension systems, stability

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Authors: J. I. Corcoles, J. D. Moya-Rico, A. Molina, J. F. Belmonte, J. A. Almendros-Ibanez

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One of the most common heat exchangers technology in engineering processes is the use of double-pipe heat exchangers (DPHx), mainly in the food industry. To improve the heat transfer performance, several passive geometrical devices can be used, such as the wall corrugation of tubes, which increases the wet perimeter maintaining a constant cross-section area, increasing consequently the convective surface area. It contributes to enhance heat transfer in forced convection, promoting secondary recirculating flows. One of the most extended tools to analyse heat exchangers' efficiency is the use of computational fluid dynamic techniques (CFD), a complementary activity to the experimental studies as well as a previous step for the design of heat exchangers. In this study, a double pipe heat exchanger behaviour with two different inner tubes, smooth and spirally corrugated tube, have been analysed. Hence, experimental analysis and steady 3-D numerical simulations using the commercial code ANSYS Workbench v. 17.0 are carried out to analyse the influence of geometrical parameters for spirally corrugated tubes at turbulent flow. To validate the numerical results, an experimental setup has been used. To heat up or cool down the cold fluid as it passes through the heat exchanger, the installation includes heating and cooling loops served by an electric boiler with a heating capacity of 72 kW and a chiller, with a cooling capacity of 48 kW. Two tests have been carried out for the smooth tube and for the corrugated one. In all the tests, the hot fluid has a constant flowrate of 50 l/min and inlet temperature of 59.5°C. For the cold fluid, the flowrate range from 25 l/min (Test 1) and 30 l/min (Test 2) with an inlet temperature of 22.1°C. The heat exchanger is made of stainless steel, with an external diameter of 35 mm and wall thickness of 1.5 mm. Both inner tubes have an external diameter of 24 mm and 1 mm thickness of stainless steel with a length of 2.8 m. The corrugated tube has a corrugation height (H) of 1.1 mm and helical pitch (P) of 25 mm. It is characterized using three non-dimensional parameters, the ratio of the corrugation shape and the diameter (H/D), the helical pitch (P/D) and the severity index (SI = H²/P x D). The results showed good agreement between the numerical and the experimental results. Hence, the lowest differences were shown for the fluid temperatures. In all the analysed tests and for both analysed tubes, the temperature obtained numerically was slightly higher than the experimental results, with values ranged between 0.1% and 0.7%. Regarding the pressure drop, the maximum differences between the values obtained numerically, and the experimental values were close to 16%. Based on the experimental and the numerical results, for the corrugated tube, it can be highlighted that the temperature difference between the inlet and the outlet of the cold fluid is 42%, higher than the smooth tube.

Keywords: corrugated tube, heat exchanger, heat transfer, numerical simulation

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Authors: Skyler Kim

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An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.

Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning

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Authors: M. Draman, S. Z. Muhamad Yassin, M. S. Alias, Z. Lambak, M. I. Zulkifli, S. N. Padhi, K. N. Baharim, F. Maskuriy, A. I. A. Rahim

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The performance of Malay automatic speech recognition (ASR) system for the call centre environment is presented. The system utilizes Kaldi toolkit as the platform to the entire library and algorithm used in performing the ASR task. The acoustic model implemented in this system uses a deep neural network (DNN) method to model the acoustic signal and the standard (n-gram) model for language modelling. With 80 hours of training data from the call centre recordings, the ASR system can achieve 72% of accuracy that corresponds to 28% of word error rate (WER). The testing was done using 20 hours of audio data. Despite the implementation of DNN, the system shows a low accuracy owing to the varieties of noises, accent and dialect that typically occurs in Malaysian call centre environment. This significant variation of speakers is reflected by the large standard deviation of the average word error rate (WERav) (i.e., ~ 10%). It is observed that the lowest WER (13.8%) was obtained from recording sample with a standard Malay dialect (central Malaysia) of native speaker as compared to 49% of the sample with the highest WER that contains conversation of the speaker that uses non-standard Malay dialect.

Keywords: conversational speech recognition, deep neural network, Malay language, speech recognition

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Authors: Naima Guenifi, Shiromani Balmukund Rahi, Amina Bechka

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Tunnel FET is one of the most suitable alternatives FET devices for conventional CMOS technology for low-power electronics and applications. Due to its lower subthreshold swing (SS) value, it is a strong follower of low power applications. It is a quantum FET device that follows the band to band (B2B) tunneling transport phenomena of charge carriers. Due to band to band tunneling, tunnel FET is suffering from a lower switching current than conventional metal-oxide-semiconductor field-effect transistor (MOSFET). For improvement of device features and limitations, the newly invented negative capacitance concept of ferroelectric material is implemented in conventional Tunnel FET structure popularly known as NC TFET. The present research work has implemented the idea of high-k gate dielectric added with ferroelectric material on double gate Tunnel FET for implementation of negative capacitance. It has been observed that the idea of negative capacitance further improves device features like SS value. It helps to reduce power dissipation and switching energy. An extensive investigation for circularity uses for digital, analog/RF and linearity features of double gate NCTFET have been adopted here for research work. Several essential designs paraments for analog/RF and linearity parameters like transconductance(gm), transconductance generation factor (gm/IDS), its high-order derivatives (gm2, gm3), cut-off frequency (fT), gain-bandwidth product (GBW), transconductance generation factor (gm/IDS) has been investigated for low power RF applications. The VIP₂, VIP₃, IMD₃, IIP₃, distortion characteristics (HD2, HD3), 1-dB, the compression point, delay and power delay product performance have also been thoroughly studied.

Keywords: analog/digital, ferroelectric, linearity, negative capacitance, Tunnel FET, transconductance

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Authors: Khansa Hayat

Abstract:

Organizational stress is one of the vital phenomena which is having its roots deep down in has deep roots in management, psychology, and organizational behavior research. In the meanwhile, keeping its focus on the positive strength of humans rather than the traditional negativity oriented research, positive psychology has emerged as a separate branch of organizational behavior. The current study investigates the interactive effects of Challenge and hindrance stressors and core Self Evaluations (CSE’s) of the individual on job performances including the in-role performance and extra role performances. The study also aims to investigate the supporting/buffering role of the human dispositions (i.e., self esteem, self efficacy, locus of control and emotional stability). The results show that Challenge stressors have a significant positive effect on in role performance and extra role performance of the individual. The findings of the study indicate that Core Self evaluations strengthen the relationship between challenge stressors and in role performance of the individual. In case of Hindrance Stressors the Core self Evaluations lessen the negative impact of Hindrance stressors and they let the individual perform at a better and normal position even when the Hindrance stressors are high. The relationship and implication of conservation of resource theory are also discussed. The limitations, future research directions and implications of the study are also discussed.

Keywords: challenge-hindrance stressors, core self evaluations, in-role performance, extra-role performance

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Authors: Fang Liu, Jia Jia Yao, Guan Lin Wu, Ren Jie Liu, Zhuang Guo

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Leveraging plasma-assisted molecular beam epitaxy (PA-MBE) on c-plane free-standing GaN substrates, this work demonstrates high-performance AlN/GaN double-barrier resonant tunneling diodes (RTDs) featuring stable and repeatable negative differential resistance (NDR) characteristics at room temperature. By scaling down the barrier thickness of AlN and the lateral mesa size of collector, a record peak current density of 1551 kA/cm2 is achieved, accompanied by a peak-to-valley current ratio (PVCR) of 1.24. This can be attributed to the reduced resonant tunneling time under thinner AlN barrier and the suppressed external incoherent valley current by reducing the dislocation number contained in the RTD device with the smaller size of collector. Statistical analysis of the NDR performance of RTD devices with different AlN barrier thicknesses reveals that, as the AlN barrier thickness decreases from 1.5 nm to 1.25 nm, the average peak current density increases from 145.7 kA/cm2 to 1215.1 kA/cm2, while the average PVCR decreases from 1.45 to 1.1, and the peak voltage drops from 6.89 V to 5.49 V. The peak current density obtained in this work represents the highest value reported for nitride-based RTDs to date, while maintaining a high PVCR value simultaneously. This illustrates that an ultra-scaled RTD based on a vertical quantum-well structure and lateral collector size is a valuable approach for the development of nitride-based RTDs with excellent NDR characteristics, revealing their great potential applications in high-frequency oscillation sources and high-speed switch circuits.

Keywords: GaN resonant tunneling diode, peak current density, peak-to-valley current ratio, negative differential resistance

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

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Authors: Fatima Zohra Ibn Majdoub Hassani, Ivan Lavandera, Joseph Kreit

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This study explored the effects of different organic solvents, temperature, and the amount of glycerol on the alcohol dehydrogenase (ADH)-catalysed stereoselective reduction of different ketones. These conversions were then analyzed by gas chromatography. It was found that when the amount of deep eutectic solvents (DES) increases, it can improve the stereoselectivity of the enzyme although reducing its ability to convert the substrate into the corresponding alcohol. Moreover, glycerol was found to have a strong stabilizing effect on the ADH from Ralstonia sp. (E. coli/ RasADH). In the case of organic solvents, it was observed that the best conversions into the alcohols were achieved with DMSO and hexane. It was also observed that temperature decreased the ability of the enzyme to convert the substrates into the products and also affected the selectivity. In addition to that, the recycling of DES up to three times gave good conversions and enantiomeric excess results and glycerol showed a positive effect in the stability of various ADHs. Using RasADH, a good conversion and enantiomeric excess into the S-alcohol were obtained. It was found that an enhancement of the temperature disabled the stabilizing effect of glycerol and decreased the stereoselectivity of the enzyme. However, for other ADHs a temperature increase had an opposite positive effect, especially with ADH-T from Thermoanaerobium sp. One of the objectives of this study was to see the effect of cofactors such as NAD(P) on the biocatlysis activities of ADHs.

Keywords: alcohol dehydrogenases, DES, gas chromatography, RasADH

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Authors: Aragorn Joaquin Pineda Dela Cruz

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Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

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Authors: Todd H. Buxton, Yong G. Lai

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Temperature management in regulated rivers can involve significant expenditures of water to meet the cold-water requirements of species in summer. For this purpose, flows released from Lewiston Dam on the Trinity River in Northern California are 12.7 cms with temperatures around 11oC in July through September to provide adult spring Chinook cold water to hold in deep pools and mature until spawning in fall. The releases are more than double the flow and 10oC colder temperatures than the natural conditions before the dam was built. The high, cold releases provide springers the habitat they require but may suppress the stream food base and limit future populations of salmon by reducing the juvenile fish size and survival to adults via the positive relationship between the two. Field and modeling research was undertaken to explore whether lowering summer releases from Lewiston Dam may promote thermal stratification in river pools so that both the cold-water needs of adult salmon and warmer water requirements of other organisms in the stream biome may be met. For this investigation, a three-dimensional (3D) computational fluid dynamics (CFD) model was developed and validated with field measurements in two deep pools on the Trinity River. Modeling and field observations were then used to identify the flows and temperatures that may form and maintain thermal stratification under different meteorologic conditions. Under low flows, a pool was found to be well mixed and thermally homogenous until temperatures began to stratify shortly after sunrise. Stratification then strengthened through the day until shading from trees and mountains cooled the inlet flow and decayed the thermal gradient, which collapsed shortly before sunset and returned the pool to a well-mixed state. This diurnal process of stratification formation and destruction was closely predicted by the 3D CFD model. Both the model and field observations indicate that thermal stratification maintained the coldest temperatures of the day at ≥2m depth in a pool and provided water that was around 8oC warmer in the upper 2m of the pool. Results further indicate that the stratified pool under low flows provided almost the same daily average temperatures as when flows were an order of magnitude higher and stratification was prevented, indicating significant water savings may be realized in regulated streams while also providing a diversity in water temperatures the ecosystem requires. With confidence in the 3D CFD model, the model is now being applied to a dozen pools in the Trinity River to understand how pool bathymetry influences thermal stratification under variable flows and diurnal temperature variations. This knowledge will be used to expand the results to 52 pools in a 64 km reach below Lewiston Dam that meet the depth criteria (≥2 m) for spring Chinook holding. From this, rating curves will be developed to relate discharge to the volume of pool habitat that provides springers the temperature (<15.6oC daily average), velocity (0.15 to 0.4 m/s) and depths that accommodate the escapement target for spring Chinook (6,000 adults) under maximum fish densities measured in other streams (3.1 m3/fish) during the holding time of year (May through August). Flow releases that meet these goals will be evaluated for water savings relative to the current flow regime and their influence on indicator species, including the Foothill Yellow-Legged Frog, and aspects of the stream biome that support salmon populations, including macroinvertebrate production and juvenile Chinook growth rates.

Keywords: 3D CFD modeling, flow regulation, thermal stratification, chinook salmon, foothill yellow-legged frogs, water managment

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Authors: Mehrdad Shafiei Dizaji, Hoda Azari

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The research explores the pioneering integration of Physics-Informed Neural Networks (PINNs) into the domain of Ground-Penetrating Radar (GPR) data prediction, akin to advancements in medical imaging for tracking tumor progression in the human body. This research presents a detailed development framework for a specialized PINN model proficient at interpreting and forecasting GPR data, much like how medical imaging models predict tumor behavior. By harnessing the synergy between deep learning algorithms and the physical laws governing subsurface structures—or, in medical terms, human tissues—the model effectively embeds the physics of electromagnetic wave propagation into its architecture. This ensures that predictions not only align with fundamental physical principles but also mirror the precision needed in medical diagnostics for detecting and monitoring tumors. The suggested deep learning structure comprises three components: a CNN, a spatial feature channel attention (SFCA) mechanism, and ConvLSTM, along with temporal feature frame attention (TFFA) modules. The attention mechanism computes channel attention and temporal attention weights using self-adaptation, thereby fine-tuning the visual and temporal feature responses to extract the most pertinent and significant visual and temporal features. By integrating physics directly into the neural network, our model has shown enhanced accuracy in forecasting GPR data. This improvement is vital for conducting effective assessments of bridge deck conditions and other evaluations related to civil infrastructure. The use of Physics-Informed Neural Networks (PINNs) has demonstrated the potential to transform the field of Non-Destructive Evaluation (NDE) by enhancing the precision of infrastructure deterioration predictions. Moreover, it offers a deeper insight into the fundamental mechanisms of deterioration, viewed through the prism of physics-based models.

Keywords: physics-informed neural networks, deep learning, ground-penetrating radar (GPR), NDE, ConvLSTM, physics, data driven

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Authors: Madhawa Basnayaka, Jouni Paltakari

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Fully passive backscattering chipless RFID tags are an emerging wireless technology with low cost, higher reading distance, and fast automatic identification without human interference, unlike already available technologies like optical barcodes. The design optimization of chipless RFID tags is crucial as it requires replacing integrated chips found in conventional RFID tags with printed geometric designs. These designs enable data encoding and decoding through backscattered electromagnetic (EM) signatures. The applications of chipless RFID tags have been limited due to the constraints of data encoding capacity and the ability to design accurate yet efficient configurations. The traditional approach to accomplishing design parameters for a desired EM response involves iterative adjustment of design parameters and simulating until the desired EM spectrum is achieved. However, traditional numerical simulation methods encounter limitations in optimizing design parameters efficiently due to the speed and resource consumption. In this work, a deep learning neural network (DNN) is utilized to establish a correlation between the EM spectrum and the dimensional parameters of nested centric rings, specifically square and octagonal. The proposed bi-directional DNN has two simultaneously running neural networks, namely spectrum prediction and design parameters prediction. First, spectrum prediction DNN was trained to minimize mean square error (MSE). After the training process was completed, the spectrum prediction DNN was able to accurately predict the EM spectrum according to the input design parameters within a few seconds. Then, the trained spectrum prediction DNN was connected to the design parameters prediction DNN and trained two networks simultaneously. For the first time in chipless tag design, design parameters were predicted accurately after training bi-directional DNN for a desired EM spectrum. The model was evaluated using a randomly generated spectrum and the tag was manufactured using the predicted geometrical parameters. The manufactured tags were successfully tested in the laboratory. The amount of iterative computer simulations has been significantly decreased by this approach. Therefore, highly efficient but ultrafast bi-directional DNN models allow rapid and complicated chipless RFID tag designs.

Keywords: artificial intelligence, chipless RFID, deep learning, machine learning

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Authors: M. Solairaju, Nithin J. Thomas, S. Ganesan

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Each and every rotating part of a machine element consists of bearings within its structure. In particular, the rolling element bearings such as cylindrical roller bearing and deep groove ball bearings are frequently used. Improper handling, excessive loading, improper lubrication and sealing cause bearing damage. Hence health monitoring of bearings is an important aspect for radiation pattern of bearing vibration is computed using the dipole model. Sound pressure level for defect-free and race defect the prolonged life of machinery and auto motives. This paper presents modeling and analysis of Acoustic response of deep groove ball bearing with localized race defects. Most of the ball bearings, especially in machine tool spindles and high-speed applications are pre-loaded along an axial direction. The present study is carried out with axial preload. Based on the vibration response, the orbit motion of the inner race is studied, and it was found that the oscillation takes place predominantly in the axial direction. Simplified acoustic is estimated. Acoustic response shows a better indication in identifying the defective bearing. The computed sound signal is visualized in diagrammatic representation using Symmetrised Dot Pattern (SDP). SDP gives better visual distinction between the defective and defect-free bearing

Keywords: bearing, dipole, noise, sound

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Authors: Rujia Chen, Ajit Narayanan

Abstract:

Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

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