Miracle-dz/UniFAR-FTUs huggingface dataset:
IntelliStore: IoT And AI‐based intelligent storage monitoring for perishable food

Among all the food produced annually, the loss rate for fruits and vegetables is the highest. This is due to the inability to detect critical ambient environmental parameters in cold storage. The self‐life of food plays an important characteristic in minimizing loss. We present IntelliStore in this work, an intelligent Machine Learning (ML) and Internet of Things (IoT) powered storage monitoring system that enables real‐time monitoring of temperature, humidity & CO2$$ {CO}_2 $$ concentration, and pest detection using a Passive Infrared (PIR) sensor and microphone. We collected a dataset of 10 different fruit and vegetables from the Food Quality and Analysis lab. We have experimented with SVM, Decision Tree, AdaBoost, and Gradient Boosting machine learning algorithms and have achieved the highest accuracy of 88% with SVM. Moreover, the proposed system has additional functionality to update the dataset with actual observations in the future and retraining models that would allow improvement of ML performance over time.
Beta limits for the N=1 mode in rotating-toroidal-resistive plasmas surrounded by a resistive wall

The stability analysis of a high-β toroidal tokamak plasma is carried out in the presence of toroidal flow, finite plasma resistivity, and a surrounding shell of finite electrical resistivity. The beta limits for the n=1 mode are set by the resistive-wall-tearing mode (RWTM), the ideal-wall-tearing mode (IWTM), and the ideal-plasma-resistive-wall mode (IPRWM). Slow plasma rotation suppresses the RWTM while the IPRWM is not directly affected by slow plasma flow. For small plasma resistivity, the IPRWM is stabilized by fast flow only. For large plasma resistivity, the IPRWM only exists in a plasma rotating faster than the typical tearing mode growth rate, and its instability threshold is a complicated function of the wall position and rotation frequency. Very fast rotation can destabilize the ideal kink through centrifugal effects. Furthermore, for b/a (wall radius/plasma radius) below a critical value, a stationary plasma is stable to the n=1 ideal kink and tearing mode for large values of β.
ECG noise filtering using wavelets with soft-thresholding methods

We analyze algorithms for filtering white gaussian noise and 50 Hz power line noise from ECG signals. We used several wavelets to study their effect and efficiency in the filtering process. To deal with these different kinds of noises we used two distinct soft-thresholding techniques: the Donoho's statistical threshold estimator and a method developed by us. This last method exploits one of the wavelet processing main features: time-frequency relation. The de-noising methods led to good filtering results: noise reduction with only minor change of the ECG waveforms as confirmed by the high correlation values between the processed signal and the original one. These good results are due to a wavelet advantage over classical filtering-time-frequency relation-enabling the possibility of filtering noise in the same frequency band of the ECG signal with minimal interference.
The universal constant for the Lagrangian structure function

The Langevin equation has been used for many years to model the dispersion of passive scalars in turbulent flow. It is a stochastic differential equation for the incremental change of Lagrangian particle velocity as a function of the sum of a deterministic term and a stochastic term. The stochastic term is the product of a coefficient and an incremental Wiener process. The coefficient can be written as (C0e)1/2, where C0 is a universal constant associated with the Lagrangian structure function and e is the mean rate of turbulent kinetic energy dissipation. There is considerable uncertainty about the value of C0. The values obtained by different investigators are reviewed. A value of C0=5.7 is calculated for the constant‐stress region in the neutral boundary layer.
Describing the Ecology of Parenting Based on Preschool Mothers’ Social Relationships in Korea: An Ecological Theory Approach

Mothers’ social networks are important to their children’s health but still remains poorly understood in Korea. The purpose of the study was to explore the elements of social relationships in Korean preschool mothers to describe their parenting ecology. Data were collected from interviews with 32 mothers according to the Social Network Interview questionnaire and from their seven-day diaries describing their daily social relationships. Eleven ecological elements were identified. The social relationships of these mothers involved seven types of people: relatives, mothers of their children’s friends, kindergarten and learning center teachers, domestic workers providing parenting support, husbands, friends, and children’s friends. The mothers’ social relationships were also associated with four location types: kindergartens, private learning centers, health services centers, and private entertainment centers. The strongest element of the ecology of parenting in terms of frequency was relatives, followed by the mothers of their children’s friends and kindergarten teachers; for the location, the strongest was kindergartens, followed by private learning centers and health services centers. In conclusion, the strongest elements in parenting ecology should be evaluated during the assessment of children’s growth and development, and incorporated in the assessment tools used.
Diversity and Epidemiological Study of Hard Ticks Infesting Goats and Sheep of Hazara Division, Khyber Pakhtunkhwa, Pakistan

Ticks are the haematophagous arthropods; their prevalence in various areas of Pakistan is associated with huge economic and health losses. In this perspective 6469 tick specimens were collected from goats and sheep breeds of the Hazara Division and identified into seven species on morphometrics basis, in which four species were recorded new to this area. Out of identified seven species, four tick species Rhipicephalus microplus , Hyalomma rufipes , Rhipicephalus haemaphysaloides , Haemaphysalis bispinosa were identified from goats and three species Hyalomma marginatum , Hyalomma excavatum , Rhipicephalus microplus from sheep. Goat breed wise infestation rate was recorded in Kaghani, Kamori, Sindhi, Lehri, Barbari, Beetal and Dera Din Panah. In sheep breeds infestation rate was recorded in Gauder, Balkhi Afghani and Rambouillet. On basis of ITS2 gene and 16SrRNA phylogenetic analysis of Rhipicephalus haemaphysaloides and Rhipicephalus microplus showed similarity with already reported species in neighbor countries of study area. Number of tick specimens and infestation rate shows direct correlation with temperature but it shows fluctuation with humidity. Current study will be helpful in tick’s controlling strategies, goats and sheep breed selection and future research in this area.
Supervised Learning Algorithms for Predicting Customer Churn with Hyperparameter Optimization

Abstract Churn risk is one of the most worrying issues in the telecommunications industry. The methods for predicting churn have been improved to a great extent by the remarkable developments in the word of artificial intelligence and machine learning. In this context, a comparative study of four machine learning models was conducted. The first phase consists of data preprocessing, followed by feature analysis. In the third phase, feature selection. Then, the data is split into the training set and the test set. During the prediction phase, some of the commonly used predictive models were adopted, namely k-nearest neighbor, logistic regression, random forest, and support vector machine. Furthermore, we used cross-validation on the training set for hyperparameter adjustment and for avoiding model overfitting. Next, the hyperparameters were adjusted to increase the models' performance. The results obtained on the test set were evaluated using the feature weights, confusion matrix, accuracy score, precision, recall, error rate, and f1 score. Finally, it was found that the support vector machine model outperformed the other prediction models with an accuracy equal to 96.92%. Keywords: Churn Prediction, Classification Algorithms, Hyperparameter Optimization, Machine Learning, Telecommunications.
AMIS-MU: Edge Computing Based Adaptive Video Streaming for Multiple Mobile Users

The increasing demand for online high-quality video streaming has brought huge challenges to the traditional client-server video streaming systems due to the high feedback delay, rigorous bandwidth requirement, and the lack of a mechanism of centralized resource management between users. In this work, we propose AMIS-MU, an edge computing-based mobile video streaming system that optimizes the watching experience of users via playback adaptation and channel resource allocation. AMIS-MU fully explores the power of edge servers from three perspectives. First, by pre-caching videos from the cloud, AMIS-MU analyzes video contents at the edge, and achieves a nearly imperceptible content-based playback speed adaptation. Second, as the edge server controls the channel resources of users in a centralized fashion, AMIS-MU adaptively updates the channel configuration to optimize the overall watching experience. Last, the plenty of computational power available at the edge enables a more intelligent playback control by using deep reinforcement learning (DRL). We propose a novel usage of DRL which significantly reduces the complexity of the cross-layer joint optimization problem and solve the non-convex channel resource allocation problem by Lyapunov optimization. Experiments show that AMIS-MU outperforms other existing algorithms in terms of average QoE and fairness.
Nuclear magnetic resonance magnetic dipolar spectral density functions for two-dimensional lattice diffusion: hexagonal systems

Previous calculations of spectral density functions for like-spin magnetic dipolar interactions between spins undergoing two-dimensional diffusion are extended to the hexagonal lattice and the honeycomb structure. The diffusion models used are the random-walk and mean-field models. Analytic approximations to the functions are obtained. It is shown that the simple function proposed by Richards cannot provide good accuracy over the whole range of frequency and temperature, and a modified function is suggested.
Efficient counter-propagating wave acoustic micro-particle manipulation

A simple acoustic system consisting of a pair of parallel singe layered piezoelectric transducers submerged in a fluid used to form standing waves by a superposition of two counter-propagating waves is reported. The nodal positions of the standing wave are controlled by applying a variable phase difference to the transducers. This system was used to manipulate polystyrene micro-beads trapped at the nodal positions of the standing wave. The demonstrated good manipulation capability of the system is based on a lowering of the reflection coefficient in a narrow frequency band near the through-thickness resonance of the transducer plates.
A VLA radio-continuum survey of a sample of confirmed and marginal barium stars

Results are reported from a 6-cm VLA survey of five confirmed Ba II stars and eight mild Ba II stars, undertaken to search for evidence of gyrosynchrotron emission or thermal emission from the primary star's wind that is enhanced or photoionized by a white dwarf companion. Of these 13 stars, only Beta UMi was detected as a possible radio source at a flux level of 0.11 mJy (3sigma). The 6-cm radio luminosities (L6) of the other stars are as small as log L6 less than or equal to 14.0 and are an order of magnitude or more lower than the average levels found in RS CVn systems, but are consistent with the L6 upper limits previously found for stars of spectral type similar to the Ba II stars and normal elemental abundances. The upper limit to the radio luminosity for the possible mild Ba II star 56 Peg, when combined with its previously known X-ray luminosity, may provide useful constraints on the various models that have been proposed for this interesting object, once its orbital period is known. 28 references.
Novel architecture of multi-granularity OXC combining static and dynamic photonic switching

In this paper, we proposed a novel architecture of MG-OXC (Multi-Granularity Optical Cross Connect) that can support both the static and dynamic optical switching. It can obviously reduce the complexity of the switching matrix and the number of the switching ports without increasing the network blocking probability. We gave the ILP model to describe the multi-granularity switching optical network. This model is applicable not only to the novel MG-OXC, but also to the traditional MG-OXC network. We also proposed a novel routing and wavelength assignment algorithm for dealing with multi-granularity traffic in optical network that was set up by this kind of novel MG-OXC. It is shown that when the network load is low, the network blocking probability of the novel MG-OXC will be quite lower than the ordinary multi-layer and single-layer MG-OXC with the strategy of the shortest wavelength path and random choice of the wavelength. While when the network load is comparatively higher, the network blocking probabilities of these structures are more or less the same.
Democratizing the Visual Stylometry of Art : Analysis of Artistic Style through Computational Workflows

Visual stylometry is a new interdisciplinary research field that sits at the junction of digital humanities, empirical aesthetics, and computer science. Research in this field employs image analysis algorithms to study key aspects of artistic style. The nature of artistic style is the subject of ongoing debate within art history and philosophy of art. Computational and statistical methods in visual stylometry allow researchers to quantify and compare aspects of artistic style over the course of the career of an individual artist, among artists who share in a common artistic style, and across different schools of art. The results of these studies provide clues to the image features that enable viewers to categorize works as belonging to different artistic styles. We are developing a digital image analysis framework for studying paintings called WAIVS (Workflows for Analysis of Images and Visual Stylometry). WAIVS will be powerful enough to support advanced academic research in computer science, cognitive science, art history, and the philosophy of art while at the same time providing a user friendly interface accessible to students and researchers with little or no computer science background. WAIVS is designed around the idea of scientific workflows. Scientific workflows allow users to build programs in the same way they would draw a flowchart, dragging objects representing data sets and image analysis procedures onto the workspace and drawing links between them. WAIVS will be used to promote computational literacy and data analytic skills among humanities students, to introduce science students to research in art and the humanities, to explore the nature of artistic style and its role in our understanding of artwork, and to help researchers in cognitive science understand how viewers perceptually categorize, recognize, and otherwise engage with artworks. ACM ISBN 978-1-4503-2138-9. DOI: 10.1145/1235
Technologies Driving the Shift to Smart Farming: A Review

As today’s agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment, and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgrading farming practices by shifting toward smart farming (SF)—utilizing advanced information and communication technologies to improve the quantity and quality of the crop with minimal labor interference. SF has gained lots of interest in recent years utilizing a variety of technological innovations in the field, which imposes a challenge on farmers and technology integrators to identify suitable technologies and best practices for a particular application. This article provides a survey of the most recent SF scientific literature to identify common practices toward technology integration, challenges, and solutions. The survey was conducted on 588 papers published on the IEEE database following Cochrane methods to ensure appropriate analysis and interpretation of results. The papers’ contributions were analyzed to identify necessary technologies that constitute SF, and consequently, research themes were identified. The identified themes are sensors, communication, big data, actuators and machines, and data analysis. Besides presenting an in-depth analysis of each identified theme, this article discusses integrating more than one technology in systems to achieve independency. The most common SF systems are remote monitoring, autonomous, and intelligent decision-making systems.
Specimen size effect in discrete element simulations of granular assemblies

The paper addresses the question of whether the number of particles in a noncemented granular assembly will affect the mechanical characteristics of the assembly: its strength and stiffness. The question is answered by applying the discrete element method to assemblies of different sizes. To isolate the effect of assembly size, apart from the scatter that usually accompanies such simulations, multiple assemblies were tested. The two-dimensional assemblies had nearly identical initial porosities and fabrics, and they were all loaded in biaxial compression. Two different boundaries were tested: periodic and wall boundaries. We find that the peak compressive strength decreases with assembly size for both types of boundaries and over a range of assembly sizes that contain 256 particles to over 66,000 particles. Stiffness is only slightly reduced and only with wall boundaries. Deformation is less uniform in the larger assemblies, with deformation concentrated in a smaller fraction of the assembly area. An analysis of deformation patterning shows that at least a few thousand particles are required for realistic microband patterning.
Stream health index for the Puget Sound Lowland

Stream health is often measured by the multimetric benthic index of biotic integrity (B‐IBI). For Puget Sound Lowland (PSL) streams, the B‐IBI comprises ten metrics which quantify the well‐being of benthic inhabitants of the stream. Each metric is converted to a score of 1, 3 or 5, where a higher value indicates a healthier stream with respect to the metric. Summing the metric scores yields the B‐IBI. Stream health is then rated as very poor, poor, fair, good or excellent according to the index value.
The Evolution of Raindrop Spectra. Part II: Collisional Collection/Breakup and Evaporation in a Rainshaft

Abstract The evolution of raindrop spectra with altitude through collisional collection/breakup sedimentation and evaporation is presented. Two-moment treatment of sedimentation and evaporation is developed to complement Part I (Feingold et al.) of this series. We have obtained an accurate, stable numerical scheme for evaporation that enables the investigation of the effect of evaporation on spectra subject to entrainment of strongly subsaturated air (including ventilation). The method includes provision for treatment of the variation of the sub/supersaturation within a time step in a dynamical framework. Results confirm that steady-state raindrop spectra are characterized by a bimodal or trimodal structure that becomes evident shortly after evolution commences. After sufficient evolution, peaks become clearly defined at 0.25 mm and 0.8 mm and further evolution with altitude affects only the relative magnitude of these peaks. It is shown that the evaporation process is not only dependent on the subsaturat.
A review of the Mediterranean diet and nutritional genomics in relation to cancer in women

Summary Cancer is the leading cause of death among women all over the world. Female tissue-specific cancers are the most commonly diagnosed among women and account for most cancer-related deaths. The main risk factors for women’s cancer are hereditary factors, specific exposure to dangerous chemicals, disorders such as hormone imbalance, and lifestyle. High body mass index, low physical activity, low intake of fruit and vegetables, smoking, excessive alcohol consumption, lack of cancer screening and treatment are the most common risk factors. Nutrigenetics and nutrigenomics are both part of nutritional genomics. Nutrigenetics is how a person’s body reacts to nutrients based on his/her genotype. It can be used to create a personalized diet, maintain a person’s health, avoid disease, and if necessary to sustain therapy. Nutrigenomics studies the impact of nutrition on gene expression and the epigenomic, proteomic, transcriptomic and metabolomic effects of dietary intake. There is evidence that diet matters for different women’s cancers, and is related to cancer progression, survival and treatment. The optimum combination for cancer prevention is a diet rich in vitamins and fibre, with low meat consumption, low milk intake and moderate use of alcohol. The Mediterranean diet looks to be an optimal diet with a good nutrition pattern, qualifying it as a therapy to prescribe.
Drone exploration of bat echolocation: A UAV‐borne multimicrophone array to study bat echolocation

Abstract Multimicrophone array techniques offer crucial insight into bat echolocation, yet they severely undersample the environments bats operate in as they are limited in geographic placement and mobility. UAVs are excellent candidates to greatly increase the environments in which such arrays can be deployed, but the impact of UAV noise on recording quality and the UAV's behavioral impact on the bats may affect usability. We developed a UAV‐borne multimicrophone setup capable of recording bat echolocation across diverse environments. We quantify and mitigate the impact of UAV noise on the recording setup and test the recording capability of the array by recording four common Danish bat species: Pipistrellus pygmaeus, Myotis daubentonii, Eptesicus serotinus, and Nyctalus noctula. The UAV produces substantial noise at ultrasonic frequencies relevant to many bat species. However, suspending the array 30 m below the UAV attenuates the noise to levels below the self‐noise of our recording system at 20 kHz and above, and we successfully record and acoustically localize all four bat species. The behavioral impact of the UAV is minimal as all four species approached the array to within 1 m and all emitted recordable feeding buzzes. UAV‐borne multimicrophone arrays will allow us to quantify bat echolocation in hitherto unexplored habitats and provide crucial insight into how bats operate their sonar across their entire natural habitat.
ControlVC: Zero-Shot Voice Conversion with Time-Varying Controls on Pitch and Speed

Recent developments in neural speech synthesis and vocoding have sparked a renewed interest in voice conversion (VC). Beyond timbre transfer, achieving controllability on para-linguistic parameters such as pitch and Speed is critical in deploying VC systems in many application scenarios. Existing studies, however, either only provide utterance-level global control or lack interpretability on the controls. In this paper, we propose ControlVC, the first neural voice conversion system that achieves time-varying controls on pitch and speed. ControlVC uses pre-trained encoders to compute pitch and linguistic embeddings from the source utterance and speaker embeddings from the target utterance. These embeddings are then concatenated and converted to speech using a vocoder. It achieves speed control through TD-PSOLA pre-processing on the source utterance, and achieves pitch control by manipulating the pitch contour before feeding it to the pitch encoder. Systematic subjective and objective evaluations are conducted to assess the speech quality and controllability. Results show that, on non-parallel and zero-shot conversion tasks, ControlVC significantly outperforms two other self-constructed baselines on speech quality, and it can successfully achieve time-varying pitch and speed control.