Miracle-dz/UniFAR-FTUs huggingface dataset:
DenseNet201 for Animal detection and repellent system

Animals entering the agricultural land near the forest areas destroy crops or even attack people residing nearby villages. For humans, agriculture crops, and animals to survive it is essential to utilize technology in a useful way. Therefore, there is a need for a system which detects the presence of animals and produces sound to repel the animals. Our study develops a model utilizing deep convolutional neural networks that detects animals through video analytics. The different features like color, Gabor and LBP are extracted using the segmented images of the animal. Possibilities of combining these features for improving the performance of the detection and classification have also been explored. Detection of animals is accomplished using CNN and symbolic classifiers. For validating the performance of the proposed algorithmic models and also due to non-availability of a large benchmarking related dataset, successful attempts to create an animal image dataset and an animal video dataset. Experimental results show that better detection accuracy is obtained for even partial images of animals. The system will give better accuracy in terms of f1 score precision, recall and it will support the society to safeguard agriculture, farmland, wildlife and human beings.
Cognitive Radar Tracking with Adaptation of Update Interval and Integration Time

ESA (Electronically Scanned Array) radars can simultaneously carry out many tasks. Since the radar scheduler has limited time resources, the radar time usage of each task must be limited while still achieving acceptable performance. In this paper we have further developed and by simulations studied a method from the literature for adapting the update interval and the integration time, which together determine the radar time usage, for tracking a single target. The method has properties common in “cognitive radar”. The adaptation works and gives a better performance than fix update interval and integration time but it has some problems.
Machine Learning Using Support Vector Regression in Radar Remote Sensing for Oil-Spill Thickness Estimation

In this paper, we demonstrated how machine learning can be integrated in the oil spill monitoring field for thickness estimation. A support vector regression model was trained on reflectivities calculated for different radio-wave frequencies to predict oil thicknesses ranging from 0 to 10 mm. Training data was generated using Monte-Carlo Simulations, the model was first validated using the simulated data, then, it was tested on experimental data. We then analyzed the simulated and experimental estimations where the majority of the estimated thicknesses were accurate to the millimeter. The primary goal of this paper is to present a new approach to solve the oil spill thickness estimation problem.
Development of a High-Speed Implant Communication Module Deep Into the Human Body: Realized in the 10-60-MHz band with small antennas [Bioelectromagnetics]

Implant sensors and robots are expected to sense biological information or images. They stay in the human body for a long time to make a diagnosis or move freely inside the body to perform remote treatment, such as excision of the affected area or drug injection. In this study, toward the aforementioned purpose, we developed a communication module with small antennas that use the 10-60-MHz band to enable high-speed implant communication to the deep part of the human body. A waveform equalization technique was also used to increase the transmission bandwidth limited by the small antennas and reduce the sizes of their implementation. As a result, an implant communication distance of 33 cm or more that can cover the entire human torso was achieved at a data rate of 20 Mb/s. Experimental results on a living swine also showed that the developed module is able to communicate deeper in the human body than all others reported in the literature at sufficiently high data rates. This article describes the development of the implant communication module and shows the current status of research by comparing its performance with others reported in the literature.
Indoor Positioning with a Six-Beam Planar Antenna Suitable for 2.45 GHz Wireless Communications

In this paper, we propose a novel wireless anchor node architecture for positioning estimations in indoor scenarios. The node is equipped with a planar switched beam circular polarized antenna (SBA) optimized for operation at 2.45 GHz and operates with a System-on-Chip compliant with IEEE 802.15. 4 communications radio technology. The antenna consists in two concentric patches printed on a low cost and low profile single-layer via-less substrate. The SBA is inherently capable of radiating six uncorrelated beams spanning over a semi-spherical range, by using a digitally controlled beamformer consisting of a pair of switches and a phase shifter. The use of the proposed device in conjunction with a Maximum-Likelihood algorithm leads to fine grained indoor positioning and tracking, reporting a mean error respectively of 10.9 degree and 10.2 degree for the θ and ϕ anale.
A data-driven approach to studying changing vocabularies in historical newspaper collections

Nation andnationhoodare amongthe mostfrequentlystudiedconcepts in the ﬁeldof intellectualhistory.Atthesametime,theword‘nation’anditshistoricalusageareveryvague.Theaiminthisarticlewastodevelopadata-drivenmethodusingdependency parsing and neural word embeddings to clarify some of the vagueness in the evolution
An Improved Sliding Mode Control for Maximum Power Point Tracking in Photovoltaic Systems

Tracking the maximum power point (MPP) has a great interest in the study of photovoltaic (PV) systems. This task is very difficult due to the non linearity of PV current-voltage characteristics which are dependent on the temperature and irradiation conditions. The sliding mode control (SMC) based MPPT with two different step sizes designed for Boost-type DC/DC converter method is investigated in this paper. The robustness of the proposed controller is tested under rapidly changing solar radiations. The SMC based MPPT is compared to perturb and observe (P&O) method and to incremental conductance (IncCond) method. The PV-MPPT system is simulated by Matlab/Simulink environment and verified by practical implementation within DS1104 R&D controller board. The simulation and experimental results are satisfactory and demonstrate that the new SMC can follow the PV peak power at different operating conditions with best performance.
Skin Cancer Disease Detection Using Transfer Learning Technique

Melanoma is a fatal type of skin cancer; the fury spread results in a high fatality rate when the malignancy is not treated at an initial stage. The patients’ lives can be saved by accurately detecting skin cancer at an initial stage. A quick and precise diagnosis might help increase the patient’s survival rate. It necessitates the development of a computer-assisted diagnostic support system. This research proposes a novel deep transfer learning model for melanoma classification using MobileNetV2. The MobileNetV2 is a deep convolutional neural network that classifies the sample skin lesions as malignant or benign. The performance of the proposed deep learning model is evaluated using the ISIC 2020 dataset. The dataset contains less than 2% malignant samples, raising the class imbalance. Various data augmentation techniques were applied to tackle the class imbalance issue and add diversity to the dataset. The experimental results demonstrate that the proposed deep learning technique outperforms state-of-the-art deep learning techniques in terms of accuracy and computational cost.
Identifying the Design Inhibitors of In-store Shopping Apps

In-store mobile shopping apps can reduce queues in supermarkets and improve the consumer's shopping experience, yet their usage rates remain low. Despite the advances in mobile commerce literature, only scant research discusses the design inhibitors of in-store mobile shopping apps. This paper uses the Theory of Technology Usage Inhibitors to investigate inhibitors in designing in-store mobile shopping apps. We adopt a topic modelling approach to analyse 1800 Apple store reviews to identify the most significant inhibitors in a UK supermarket in-store mobile shopping app. Our findings identify redundancy, malfunctioning, overcharging, fiddliness, poor support, difficulty registering and objecting benefits as the critical design inhibitors. Our study expands the theory of technology usage inhibitors (Cenfetelli 2004) in the context of in-store shopping apps by identifying and conceptualising design-based inhibitors that discourage use. This paper contributes important design insights and recommendations to support app designers and retailers in effectively designing in-store shopping apps.
Synthetic 18F-FDG PET Image Generation Using a Combination of Biomathematical Modeling and Machine Learning

Simple Summary Training computer-assisted algorithms on medical images, particularly 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) due to its excellent diagnostic accuracy, is difficult, considering small/fragmented samples or privacy concerns. In computer-vision, deep learning-based models, unlike the conventional data augmentation methods, are highly sought after for creating massive medical samples. For this reason, we developed a multi-scale computational framework to generate synthetic 18F-FDG PET images similar to the real ones in different stages of solid tumor growth and angiogenesis. The framework is developed based on the bio-physiological phenomena of FDG radiotracer uptake in solid tumors using a biomathematical model and a generative adversarial network (GAN)-based architecture. The non-invasive augmented 18F-FDG PET images can be used in clinical practice without the need to manage the patient data. In addition, our spatiotemporal mathematical model can calculate the distribution of various radiopharmaceuticals in different tumor-associated vasculatures. Abstract No previous works have attempted to combine generative adversarial network (GAN) architectures and the biomathematical modeling of positron emission tomography (PET) radiotracer uptake in tumors to generate extra training samples. Here, we developed a novel computational model to produce synthetic 18F-fluorodeoxyglucose (18F-FDG) PET images of solid tumors in different stages of progression and angiogenesis. First, a comprehensive biomathematical model is employed for creating tumor-induced angiogenesis, intravascular and extravascular fluid flow, as well as modeling of the transport phenomena and reaction processes of 18F-FDG in a tumor microenvironment. Then, a deep convolutional GAN (DCGAN) model is employed for producing synthetic PET images using 170 input images of 18F-FDG uptake in each of 10 different tumor microvascular networks. The interstitial fluid parameters and spatiotemporal distribution of 18F-FDG uptake in tumor and healthy tissues have been compared against previously published numerical and experimental studies, indicating the accuracy of the model. The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) of the generated PET sample and the experimental one are 0.72 and 28.53, respectively. Our results demonstrate that a combination of biomathematical modeling and GAN-based augmentation models provides a robust framework for the non-invasive and accurate generation of synthetic PET images of solid tumors in different stages.
hin T O Nano x-1 Zn x ernary Be T of Synthesis and Characterization Films prepared by Pulsed Laser Deposition Technique

Beryllium Zinc Oxide (Be x Zn 1-x O) ternary nano thin films were deposited using the pulsed laser deposition (PLD) technique under a vacuum condition of 10 -3 torr at room temperature on glass substrates with different films thicknesses, (300, 600 and 900 nm). UV-Vis spectra study found the optical band gap for Be 0.2 Zn 0.8 O to be (3.42, 3.51 and 3.65 eV) for the (300, 600 and 900nm) film thicknesses, respectively which is larger than the value of zinc oxide ZnO (3.36eV) and smaller than that of beryllium oxide BeO (10.6eV). While the X-ray diffraction (XRD) pattern analysis of ZnO, BeO and Be 0.2 Zn 0.8 O powder and nano-thin films indicated a hexagonal polycrystalline wurtzite structure. The crystal structure showed a preferential orientation line at (101). Besides the nano thin film Be 0.5 Zn 0.5 O has all orientations of ZnO and BeO. Moreover, the calculated average crystallite size for nano thin film was 16.48 nm. The surface morphology of the nano thin films investigated by atomic force microscope (AFM) showed a decrease in the average grain sizes (94.8, 79.2 and 59.4 nm) with the increase of films thickness due to quantum confinement effect.
How do we get there? Evaluating transformer neural networks as cognitive models for English past tense inflection

There is an ongoing debate of whether neural network can grasp the quasi-regularities in languages like humans. In a typical quasi-regularity task, English past tense inflections, the neural network model has long been criticized that it learns only to generalize the most frequent pattern, but not the regular pattern, thus can not learn the abstract categories of regular and irregular and is dissimilar to human performance. In this work, we train a set of transformer models with different settings to examine their behavior on this task. The models achieved high accuracy on unseen regular verbs and some accuracy on unseen irregular verbs. The models’ performance on the regulars are heavily affected by type frequency and ratio but not token frequency and ratio, and vice versa for the irregulars. The different behaviors on the regulars and irregulars suggest that the models have some degree of symbolic learning on the regularity of the verbs. In addition, the models are weakly correlated with human behavior on nonce verbs. Although the transformer model exhibits some level of learning on the abstract category of verb regularity, its performance does not fit human data well suggesting that it might not be a good cognitive model.
Physiological Responses to the Menstrual Cycle Implications for the Development of Heat Illness in Female Athletes

Fluctuations in estrogen and progesterone during the menstrual cycle can cause changes in body systems other than the reproductive system. For example, progesterone is involved in the regulation of fluid balance in the renal tubules and innervation of the diaphragm via the phrenic nerve. However, few significant changes in the responses of the cardiovascular and respiratory systems, blood lactate, bodyweight, performance and ratings of perceived exertion are evident across the cycle. Nevertheless, substantial evidence exists to suggest that increased progesterone levels during the luteal phase cause increases in both core and skin temperatures and alter the temperature at which sweating begins during exposure to both ambient and hot environments. As heat illness is characterised by a significant increase in body temperature, it is feasible that an additional increase in core temperature during the luteal phase could place females at an increased risk of developing heat illness during this time. In addition, it is often argued that physiological gender differences such as oxygen consumption, percentage body fat and surface area-to-mass ratio place females at a higher risk of heat illness than males. This review examines various physiological responses to heat exposure during the menstrual cycle at rest and during exercise, and consid-ers whether such changes increase the risk of heat illness in female athletes during a particular phase of the menstrual cycle.
Effects of OH radical and SO2 concentrations on photochemical reactions of mixed anthropogenic organic gases

Abstract. Vehicle exhaust, as a major source of air pollutants in urban areas, contains a complex mixture of organic vapours including long-chain alkanes and aromatic hydrocarbons. The atmospheric oxidation of vehicle emissions is a highly complex system as inorganic gases (e.g. NOx and SO2) from other urban sources coexist and therefore remains poorly understood. In this work, the photooxidation of n-dodecane, 1,3,5-trimethylbenzene, and their mixture is studied in the presence of NOx and SO2 to mimic the atmospheric oxidation of urban vehicle emissions (including diesel and gasoline vehicles), and the formation of ozone and secondary aerosols is investigated. It is found that ozone formation is enhanced by higher OH concentration and higher temperature, but is influenced little by SO2 concentration. However, SO2 can largely enhance the particle formation in both number and mass concentrations, likely due to the promoted new particle formation and acid-catalysed heterogeneous reactions from the formation of sulfuric acid. In addition, organo-sulfates and organo-nitrates are detected in the formed particles, and the presence of SO2 can promote the formation of organo-sulfates. These results provide a scientific basis for systematically evaluating the effects of SO2, OH concentration, and temperature on the oxidation of mixed organic gases in the atmosphere that produce ozone and secondary particles.
Inhaled siRNA Formulations for Respiratory Diseases: From Basic Research to Clinical Application

The development of siRNA technology has provided new opportunities for gene-specific inhibition and knockdown, as well as new ideas for the treatment of disease. Four siRNA drugs have already been approved for marketing. However, the instability of siRNA in vivo makes systemic delivery ineffective. Inhaled siRNA formulations can deliver drugs directly to the lung, showing great potential for treating respiratory diseases. The clinical applications of inhaled siRNA formulations still face challenges because effective delivery of siRNA to the lung requires overcoming the pulmonary and cellular barriers. This paper reviews the research progress for siRNA inhalation formulations for the treatment of various respiratory diseases and summarizes the chemical structural modifications and the various delivery systems for siRNA. Finally, we conclude the latest clinical application research for inhaled siRNA formulations and discuss the potential difficulty in efficient clinical application.
Relationship between Neuroticism, Spiritual Well-Being, and Subjective Well-Being in Korean University Students

Previous studies on mental health and quality of life have revealed that religiosity/spirituality was positively associated with indicators of well-being and personality factors. However, limited research has examined the relationship between spiritual well-being, the subfactors of the personality factor Neuroticism (i.e., anxiety, hostility, depression, self-consciousness, impulsiveness, and vulnerability), and subjective well-being in a non-Western sample. The present findings revealed that the five subfactors of neuroticism did not have an equally negative or positive effect on spiritual and subjective well-being among Korean undergraduate University students. Regarding its subdimensions, vulnerability was strongly associated with spiritual well-being, while depression was closely linked to subjective well-being. Moreover, we found that spiritual well-being exerted significant effects on subjective well-being above personality factors. The significance of the findings and directions for further research have been discussed.
A Three-Dimensional Orchestral Music Recording Technique, Optimized for 22.2 Multichannel Sound

Based on results from previous research, as well as a new series of experimental recordings, a technique for three-dimensional orchestral music recording is introduced. This technique has been optimized for 22.2 Multichannel Sound, a playback format ideal for orchestral music reproduction. A novel component of the recording technique is the use of dedicated microphones for the bottom channels, which vertically extend and anchor the sonic image of the orchestra. Within the context of highly dynamic orchestral music, an ABX listening test confirmed that subjects could successfully differentiate between playback conditions with and without bottom channels.
Document Expansions and Learned Sparse Lexical Representations for MS MARCO V1 and V2

With doc2query, we train a neural sequence-to-sequence model that, given an input span of text, predicts a natural language query that the text might answer. These predictions can be viewed as document expansions that feed standard bag-of-words term weighting models such as BM25 or neural retrieval models based on learned sparse lexical representations such as uniCOIL. Previous experiments on the MS MARCO datasets have demonstrated the effectiveness of these methods, and they serve as baselines that are widely used by the community today. Following the recent release of the MS MARCO V2 passage and document ranking test collections, we have refreshed our doc2query and uniCOIL models. This work describes a number of resources that support competitive, reproducible base-lines for both the MS MARCO V1 and V2 test collections using our Anserini and Pyserini IR toolkits. Together, they provide a solid foundation for future research on neural retrieval models using the MS MARCO datasets and beyond.
Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques

Simple Summary The ability to identify individual animals has gained great interest in beef feedlots to allow for animal tracking and all applications for precision management of individuals. This study assessed the feasibility and performance of a total of 59 deep learning models in identifying individual cattle with muzzle images. The best identification accuracy was 98.7%, and the fastest processing speed was 28.3 ms/image. A dataset containing 268 US feedlot cattle and 4923 muzzle images was published along with this article. This study demonstrates the great potential of using deep learning techniques to identify individual cattle using muzzle images and to support precision beef cattle management. Abstract Individual feedlot beef cattle identification represents a critical component in cattle traceability in the supply food chain. It also provides insights into tracking disease trajectories, ascertaining ownership, and managing cattle production and distribution. Animal biometric solutions, e.g., identifying cattle muzzle patterns (unique features comparable to human fingerprints), may offer noninvasive and unique methods for cattle identification and tracking, but need validation with advancement in machine learning modeling. The objectives of this research were to (1) collect and publish a high-quality dataset for beef cattle muzzle images, and (2) evaluate and benchmark the performance of recognizing individual beef cattle with a variety of deep learning models. A total of 4923 muzzle images for 268 US feedlot finishing cattle (>12 images per animal on average) were taken with a mirrorless digital camera and processed to form the dataset. A total of 59 deep learning image classification models were comparatively evaluated for identifying individual cattle. The best accuracy for identifying the 268 cattle was 98.7%, and the fastest processing speed was 28.3 ms/image. Weighted cross-entropy loss function and data augmentation can increase the identification accuracy of individual cattle with fewer muzzle images for model development. In conclusion, this study demonstrates the great potential of deep learning applications for individual cattle identification and is favorable for precision livestock management. Scholars are encouraged to utilize the published dataset to develop better models tailored for the beef cattle industry.