Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Model for Segmentation of Okra Plants with Unconstrained Backgrounds

In this research, it is inclined to extract the Okra plant region from an unconstrained background using deep semantic segmentation network. A static thresholding model based on Hue, Saturation, and Value (HSV) space is used to create annotated dataset of images. A deep semantic segmentation module, U-Net, is trained and tested with camera-captured images of Okra plants with complex backgrounds to extract plant regions. The datasets of low to high-resolution images captured using smartphones which comprise a variety of early-stage bacterial wilt diseases of Okra plants are considered for experimentation The effectiveness of the segmentation module is assessed using metrics such as Intersection over Union (IOU), Dice coefficient, and Jaccard similarity index. Additionally, the segmented samples of images from U-Net are classified into healthy and disease-infected samples using the Inception V3 network. An IoU of 0.89, Jaccard index of 0.99, and Dice coefficient of more than 0.74 is obtained concerning segmentation efficiency.
The proportion of components in field pea and spring triticale mixtures and harvest stage affect crude fiber content and forage digestibility

The work presents results of a study conducted in 2016–2018 to determine the effect of component share in the mixture and harvest stage on concentration of crude fibre and its fractions as well as digestibility of field pea/spring triticale mixtures. The following two factors were examined in a field experiment: factor I – component share in a mixture: field pea in pure stand 100%, spring triticale in pure stand 100%, field pea 75% + spring triticale 25%, field pea 50% + spring triticale 50%, field pea 25% + spring triticale 75%; factor II – harvest stage: field pea flowering stage (BBCH 65), field pea flat green pod stage (BBCH 79). The concentration of crude fibre and its fractions (NDF, ADF, ADL) were determined in the dry matter in addition to dry matter digestibility and organic matter digestibility. The lowest content of crude fiber and its fractions, among the mixtures, was revealed in the mixture with the share of components of pea and spring triticale 75% + 25% and 50% + 50%, respectively. Harvesting mixtures at a later stage caused an increase in crude fiber content and its fraction in dry matter. The superior dry matter digestibility and organic matter digestibility were found for field pea and field pea/spring triticale mixtures containing 75% + 25% and 50% + 50% of the respective components and harvested at the stage of field pea flowering.
ANN-based optimized robust nonlinear MPPT algorithm for bidirectional quadratic converter in a PV system

As the demand for energy continues to rise, traditional fossil fuel reserves are depleting rapidly. Solar energy has garnered significant attention in sustainable energy systems due to its widespread appeal and potential. The output of photovoltaic systems fluctuates due to changes in factors like load requirements and atmospheric conditions hence the efficiency of photovoltaic energy is often hindered. To address this challenge, the pursuit of achieving optimal electricity production through the technique of maximum power point tracking (MPPT) is used. Due to the inherently nonlinear nature of PV systems, non-linear controllers are better suited for MPPT. The effectiveness of a PV system is intricately linked to the efficiency of the DC converters employed. The quadratic buck-boost converter boasts a broader voltage conversion range in contrast to the conventional buck-boost converter. This expanded range enhances its ability to adjust to fluctuating sunlight conditions, resulting in more effective extraction of power from PV panels. Artificial intelligence have emerged more valuable MPPT approaches and have gained widespread prominence. A robust controller is essential to guarantee effective maximum power point tracking, because the system’s nonlinearities are adapted. The aim of this paper is to employ the MPPT method utilizing Artificial Neural Network (ANN) and the non-linear condition-based super twisting sliding mode (CST-SMC) control to supervise the PV voltage reference up to a desired threshold value. A crucial application involves employing a pre-trained artificial neural network offline to establish the reference for peak power voltage. The experimental outcomes conducted through a hardware-in-the-loop (HIL) setup authenticate the real-time efficacy of the suggested controller.
Efficient Time Series Classification Based on Learning Similar Trend Features in the Same Class Sequences

Time series shapelets have been widely studied for their optimal interpretability and accuracy in solving time series classification (TSC) tasks. This study determine that similar trends of the same type of time series can be used to reduce the size of the candidate solution set and improve the efficiency of shapelets discovery. However, only a few studies have been conducted on this. Therefore, this study proposes a shapelets discovery algorithm that learns the common trend of the same type of time series to reconstruct the sequence. First, by constructing an autoencoder to fit time series of the same type with similar trends, we reconstruct a sequence that integrates the features of this type, thereby reducing the size of the candidate set. Thereafter, to compress the regenerated sequence and reduce its dimensionality, it is represented by a Symbolic Aggregate approXimation. Furthermore, filtering out impossible and low-quality candidate solutions further reduces the size of the candidate solution set. Finally, this study proposes a candidate solution evaluation method based on the Matrix Profile structure to find the discriminative shapelets that can best represent each type of time series and uses them to construct a classifier to complete the classification task. Extensive experiments demonstrate that the proposed algorithm effectively reduces the candidate solution set size and has advantages in efficiency, accuracy, and interpretability compared to traditional methods.
Potential role for vitamin D vs. intermittent fasting in controlling aquaporin-1 and aquaporin-3 expression in HFD-induced urinary bladder alterations in rats

Background: High-fat diet-induced obesity is linked to suppression of aquaporins (AQPs) expression in different tissues. Both vitamin D and intermittent fasting were identified to enhance AQPs expression. In the urinary bladder, AQP-1 and AQP-3 mRNA transcripts were identified. Vitamin D has an impact on a variety of genes that encode proteins that control cell proliferation, differentiation, and death. Aim: To assess potential benefits of vitamin D and intermittent fasting (IF) and to explore alterations to the urinary bladder triggered by high-fat diet (HFD) in a rat model of obesity. Methods: Each of the 4 groups contained six adult male albino rats; control: a standard rodent chew for 12 weeks, HFD: HFD and fructose were administered orally via gastric gavage for 12 weeks, and vitamin D: HFD and fructose were administered orally for 8 weeks, then 4 weeks of intraperitoneal injection of vitamin D (5 microns/Kg/2 days) and IF group: Received intraperitoneal injections of vitamin D (5 microns/Kg/2 days) for 4 weeks after consumption of HFD and fructose orally for 8 weeks. The serum lipid profile was conducted at end of the experiment. In the bladder homogenates, the levels of oxidative stress indicators were assessed. Quantitative real-time PCR was performed on recently collected bladder samples. AQP-1 and AQP-3 immunohistochemistry was done. Results: When compared to the HFD group, the vitamin D and IF groups both demonstrated a substantial improvement in histopathological, immunohistochemical, biochemical, and molecular markers. Conclusion: In all examined parameters, IF exceeded vitamin D as a preventive factor for the urinary bladder deterioration.
Pharmacological effects of monoterpene carveol on the neuromuscular system of nematodes and mammals

The control of parasitic nematode infections relies mostly on anthelmintics. The potential pharmacotherapeutic application of phytochemicals, in order to overcome parasite resistance and enhance the effect of existing drugs, is becoming increasingly important. The antinematodal effects of carveol was tested on the free-living nematode Caenorhabditis elegans and the neuromuscular preparation of the parasitic nematode Ascaris suum. Carveol caused spastic paralysis in C. elegans. In A. suum carveol potentiated contractions induced by acetylcholine (ACh) and this effect was confirmed with two-electrode voltage-clamp electrophysiology on the A. suum nicotinic ACh receptor expressed in Xenopus oocytes. However, potentiating effect of carveol on ACh-induced contractions was partially sensitive to atropine, indicates a dominant nicotine effect but also the involvement of some muscarinic structures. The effects of carveol on the neuromuscular system of mammals are also specific. In micromolar concentrations, carveol acts as a non-competitive ACh antagonist on ileum contractions. Unlike atropine, it does not change the EC50 of ACh, but reduces the amplitude of contractions. Carveol caused an increase in Electrical Field Stimulation-evoked contractions of the isolated rat diaphragm, but at higher concentrations it caused an inhibition. Also, carveol neutralized the mecamylamine-induced tetanic fade, indicating a possibly different pre- and post-synaptic action at the neuromuscular junction.
Patterns of Animal Rabies Prevalence in Northern South Africa between 1998 and 2022

Rabies is endemic in South Africa and rabies cycles are maintained in both domestic and wildlife species. The significant number of canine rabies cases reported by the World Organization for Animal Health Reference Laboratory for Rabies at Onderstepoort suggests the need for increased research and mass dog vaccinations on specific targeted foci in the country. This study aimed to investigate the spatiotemporal distribution of animal rabies cases from 1998 to 2017 in northern South Africa and environmental factors associated with highly enzootic municipalities. A descriptive analysis was used to investigate temporal patterns. The Getis-Ord Gi statistical tool was used to exhibit low and high clusters. Logistic regression was used to examine the association between the predictor variables and highly enzootic municipalities. A total of 9580 specimens were submitted for rabies diagnosis between 1998 and 2022. The highest positive case rates were from companion animals (1733 cases, 59.71%), followed by livestock (635 cases, 21.88%) and wildlife (621 cases, 21.39%). Rabies cases were reported throughout the year, with the majority occurring in the mid-dry season. Hot spots were frequently in the northern and eastern parts of Limpopo and Mpumalanga. Thicket bush and grassland were associated with rabies between 1998 and 2002. However, between 2008 and 2012, cultivated commercial crops and waterbodies were associated with rabies occurrence. In the last period, plantations and woodlands were associated with animal rabies. Of the total number of municipalities, five consistently and repeatedly had the highest rabies prevalence rates. These findings suggest that authorities should prioritize resources for those municipalities for rabies elimination and management.
The NLPR Speech Synthesis entry for Blizzard Challenge 2019

The paper describes the CASIA speech synthesis system entry for Blizzard Challenge 2019. About 8 hours of speech data from online talkshow is adopted as the training data for the construction this year. Our synthesis system is built based on the multi-speaker end-to-end speech synthesis system. And LPCNet based neural vovoder is adpted to improve the quality . Different from our previous system, some improvements about data pruing and speaker adaptation strategies were made to improve the robustness of our system. In this paper, the whole system structure, data pruing method, and duration control will be introduced and discussed. Finally, the results of the listening test will be presented.
Method of two-factor authentication of electronic documents using enhanced encrypted non-certified digital signature with the use of security token with biometric data

The purpose of this study is to develop a method for two-factor authentication of electronic documents using an enhanced encrypted non-certified digital signature with the use of a security token with biometric data (fingerprint image). In order to achieve the goal, the method of comparative analysis was used. Existing algorithms for the electronic signature operation were studied. The method of multi-factor authentication of an electronic signature using biometric data has been studied in detail. Biometric data included: a handwritten password, an autograph, typing biometrics when typing a pass phrase, a facial image, typing biometrics when typing a free text. An external storage medium with a biometric authentication method based on a fingerprint image was also studied. Information on this media was accessed by scanning a fingerprint image. After conducting a comparative analysis and studying in detail these methods, a method for two-factor authentication of an electronic signature using a security token with biometric data was developed.
Prediction of Happiness Score by Adopting Simple Linear Regression Model

Happiness is a topic that experts are studying more and more in today’s culture. Investigating pleasure from the standpoint of data mining is intriguing. The use of mining data, which operates from a fresh knowledge-driven perspective, is steadily becoming more ubiquitous and well-liked in the psychology field. Research on machine learning in particular, which is now being studied, brings fresh concepts and novel approaches to research to the field of psychiatry. This study examines the accuracy of learning-based happiness prediction methods using linear regression. We discuss the happiness variables that influence it and present the experimental findings of happiness prediction. This study examines the factors influencing happiness and examine the effects of various ensembles for pleasure prediction using the four stages of factor analysis, feature engineering, model establishment, and evaluation. According to the findings of the investigation, certain individual factors—such as mental health, socioeconomic level, and social rank—have a stronger impact on happiness than others. These factors include societal attitudes, income of the person and family capital. Furthermore, boosting performs the best in modelling fusion among the happiness prediction models created by these five characteristics.
Multiobjective Spatiotemporal Subpixel Mapping for Remote Sensing Imagery

Subpixel mapping (SPM) aims to reconstruct a subpixel-level class distribution map from the pixel-level abundance maps, which is an under-determined problem that has nonunique solutions. To address this, the spatiotemporal SPM uses the abundance, spatial, and temporal constraints to reduce the uncertainty of the mapping solutions, so the spatiotemporal SPM is essentially a constrained optimization problem. However, it is hard to find the optimal weighting parameters to combine the three joint constraints. In addition, the existing spatiotemporal SPM methods mainly use the temporal information either for the unchanged subpixels detection or for the subpixel classification, which is insufficient in the utilization of the temporal information. In this article, a novel spatiotemporal SPM algorithm based on multiobjective optimization (STSPM_MO) is proposed. STSPM_MO is composed of an unchanged subpixels detection stage and a multiobjective spatiotemporal mapping stage. In the former stage, the historical thematic map is used for identifying the unchanged subpixels. In the latter stage, the historical thematic map is further used for providing the temporal dependence, so that the temporal information can be more fully utilized. Moreover, to solve the constrained optimization problem of the spatiotemporal SPM, the abundance, spatial, and temporal constraints are modeled as three objectives and are dynamically fused through the subfitness-based multiobjective evolution, to generate the optimal subpixel classification map. Both synthetic and real-data experiments have been conducted, and the results show the proposed method, and its two variants are superior, stable, and effective.
Detecting Semantic Errors in Tables using Textual Evidence

Tables can contain various types of errors, including both syntactic and semantic errors. Semantic errors relate to the meaning of the data and can be detrimental for downstream applications. The existing approaches for semantic error detection use structured knowledge sources such as Wikidata and DBpedia, but the coverage of such sources is quite limited. There is much more information available in free text to validate the contents of tables. In this paper, we present a novel semantic-error-detection approach that exploits open-domain textual data to verify the semantic correctness of tables. Our approach leverages contrastive learning, table linearization, and pre-trained language models to implement the error detection process. We implement our approach in a system called SEED and show in the evaluation that it significantly outperforms the other competing approaches.
Unraveling the impact of nitric oxide, almitrine, and their combination in COVID-19 (at the edge of sepsis) patients: a systematic review

Introduction: During the coronavirus disease 2019 (COVID-19) pandemic, a large number of critically ill and severe COVID-19 patients meet the diagnostic criteria for sepsis and even septic shock. The treatments for COVID-19 patients with sepsis are still very limited. For sepsis, improving ventilation is one of the main treatments. Nitric oxide (NO) and almitrine have been reported to improve oxygenation in patients with “classical” sepsis. Here, we conducted a systematic review and meta-analysis to evaluate the efficacy and safety of NO, almitrine, and the combination of both for COVID-19 (at the edge of sepsis) patients. Method: A systematic search was performed on Embase, PubMed, the Cochrane Library, the Web of Science, Wanfang Data, and China National Knowledge Infrastructure. Randomized clinical trials, cohort studies, cross-sectional studies, case-control studies, case series, and case reports in COVID-19 patients with suspected or confirmed sepsis were performed. Study characteristics, patient demographics, interventions, and outcomes were extracted from eligible articles. Results: A total of 35 studies representing 1,701 patients met eligibility criteria. Inhaled NO did not affect the mortality (OR 0.96, 95% CI 0.33–2.8, I2 = 81%, very low certainty), hospital length of stay (SMD 0.62, 95% CI 0.04–1.17, I2 = 83%, very low certainty), and intubation needs (OR 0.82, 95% CI 0.34–1.93, I2 = 56%, very low certainty) of patients with COVID-19 (at the edge of sepsis). Meanwhile, almitrine did not affect the mortality (OR 0.44, 95% CI 0.17–1.13, low certainty), hospital length of stay (SMD 0.00, 95% CI -0.29–0.29, low certainty), intubation needs (OR 0.94, 95% CI 0.5–1.79, low certainty), and SAEs (OR 1.16, 95% CI 0.63–2.15, low certainty). Compared with pre-administration, the PaO2/FiO2 of patients with NO (SMD-0.87, 95% CI -1.08–0.66, I2 = 0%, very low certainty), almitrine (SMD-0.73, 95% CI-1.06–0.4, I2 = 1%, very low certainty), and the combination of both (SMD-0.94, 95% CI-1.71–0.16, I2 = 47%, very low certainty) increased significantly. Conclusion: Inhaled NO, almitrine, and the combination of the two drugs improved oxygenation significantly, but did not affect the patients’ mortality, hospitalization duration, and intubation needs. Almitrine did not significantly increase the patients’ SAEs. Well-designed high-quality studies are needed for establishing a stronger quality of evidence. Systematic Review Registration: https://www.crd. york.ac. uk/PROSPERO/display_record. php? RecordID=367667, identifier CRD42022367667.
Type 1 Diabetes Mellitus, Psychopathology, Uncertainty and Alexithymia: A Clinical and Differential Exploratory Study

Type 1 diabetes mellitus (T1DM) represents a complex pathology affecting a large number of people. Research suggests that psychological factors influence coping with T1DM. This study aimed to investigate the presence and role of psychopathology, alexithymia and uncertainty in people affected by T1DM. The sample consisted of 137 patients (88 females, 49 males) affected by T1DM aged from 11 to 19 years old (Mean: 13.87; SD: 2.40). The diagnostic protocol consisted of a sociodemographic questionnaire, Self-administration Psychiatric Scales for Children and Adolescents (SAFA), Toronto Alexithymia Scale-20 (TAS-20) and Intolerance to Uncertainty Scale-12 (IUS-12). Descriptive, differential, correlational and regression analyses were performed in order to examine the relationships between these variables. The results suggested the sample had high levels of psychopathological indexes, alexithymia and intolerance of uncertainty. Also, there were significant differences between TAS-20 and IUS-12 distributions with respect to psychopathology. Correlations and multivariate linear regressions indicated age, gender and education significantly predicted alexithymia and intolerance of uncertainty. This data suggest the presence of elevated psychopathology, alexithymia and uncertainty in people with diabetes.
Crop Loss Estimation in Maize Agriculture: A Deep Learning Perspective

The growing disparity between maize crop demand and actual production is concerning for both the food industry and farmers. Worldwide production of 1147.7 million MT of maize is insufficient to meet the demand of approximately 1149.96 million MT. Diseases like Turcicum Leaf Blight and Rust significantly hamper maize production. Manual disease detection, classification, severity calculation, and estimating crop loss are time-consuming and demand specific expertise. Hence, there’s a pressing need for automatic disease detection, severity prediction, and crop loss estimation. Machine learning and deep learning techniques, known for their success in pattern recognition and data analysis, have encouraged researchers to apply them in detecting diseases and estimating crop losses in maize. While existing literature showcases potential in disease detection, there’s a lack of reliable, real-world labeled datasets for training these models. Also, the focus on severity prediction and crop loss estimation is lacking in previous works. The paper provides a comprehensive overview of deep-learning approaches for Crop Loss Estimation in Maize Agriculture.