Miracle-dz/UniFAR-FTUs huggingface dataset:
Medicinal Plant Species Identification Using AI

Medicinal plants are useful in various disease conditions. The Medicinal plant contains many bioactive vital components that are used to prepare different varieties of Ayurvedic formulations. The Medicinal plants, with their medici-nal value, preparation methodology, and identification techniques are mentioned in ancient foundational texts like Charak Samhita. However, most of the identification techniques a re subjective. Also with a global surge in Ayurveda remedies, the common man is also interested in remedies using Ayurvedic plants. Hence we used AI for the identification o f medicinal plants. Using a customized dataset of 95 medicinal plant species, we trained SSD MobileNet v2 for medicinal plant species detection (with 87.22% Precision and 89.79% Recall) and VGG16 for medicinal plant species classification (with 9 5 % accuracy). Furthermore, primary leaf morphological features - venation of the leaves (with 98.23% accuracy), and margin or edges of the leaves (with 98.83% accuracy), are also extracted and used to make an accurate recommendation.
Smart Building Demand Side Management Using Multi-Objective Archimedes Optimization Algorithms

This study presents an intelligent house energy management system that uses demand-side management load shifting to improve energy usage. This system optimizes home appliance management to moderate load demand, reducing power costs and peak-to-average ratio while prioritizing user comfort. Day-ahead and real-time load scheduling meet power customers’ demands. Multi-objective Archimedes optimization techniques help distribute workload evenly across ON-peak and Off-peak hours and provide a fitness criterion. Additionally, home appliance coordination for real-time rescheduling is being proposed. Due to its similarities, the real-time rescheduling problem is called a knapsack problem and is solved using dynamic programming. This study also tests the proposed methodology in real-time pricing. Simulation results with a 95% confidence interval demonstrate the significance of the proposed optimization technique.
Transformers Effectiveness in Medical Image Segmentation: A Comparative Analysis of UNet-Based Architectures

Medical image segmentation is a crucial task in healthcare as it helps in the accurate diagnosis and treatment of various medical conditions. UNet-based architectures have been widely used for medical image segmentation due to their ability to produce high-quality segmentations. However, there is a need to improve the performance of these architectures to enhance their effectiveness in medical image segmentation further. One promising approach is using transformers, which have shown great potential in improving the performance of various deep learning models. This research compares four UNet-based architectures (UNet, UNetR, TransUNet, and Swin-UNet) with and without transformers to evaluate their effectiveness in medical imaging using four independent datasets. The findings of this study will be valuable in advancing the field of medical image segmentation and contributing to the optimization of Unet-based architectures.
Spontaneous bacterial empyema in cirrhotic patients: A prospective study

Spontaneous bacterial empyema (SBEM) is an infection of a preexisting hydrothorax in cirrhotic patients and has seldom been reported. To determine its incidence and primary characteristics, all cirrhotic patients with pleural effusion underwent thoracentesis at our hospital either on admission or when an infection was suspected. Pleural fluid (PF) study included biochemical analysis, polymorphonuclear (PMN) leukocyte count, and culture by two methods: conventional and modified (inoculation of 10 mL of PF into a blood culture bottle at the bedside). SBEM was defined according to previously reported criteria: PF culture positive or PMN count greater than 500 cells/μL, and exclusion of parapneumonic effusions. Sixteen of the 120 (13%) cirrhotic patients admitted with hydrothorax had 24 episodes of SBEM. In 10 of the 24 episodes (43%), SBEM was not associated with spontaneous bacterial peritonitis (SBP). PF culture was positive by the conventional method in 8 episodes (33%) and by the modified method (blood culture inoculation) in 18 (75%) (P = .004, McNemar). The microorganisms identified in PF were Escherichia coli in 8 episodes, Streptococcus species in 4, Enterococcus species in 3, Klebsiella pneumoniae in 2, and Pseudomonas stutzeri in 1. All episodes were treated with antibiotics without inserting a chest tube in any case. Mortality during treatment was 20%. We conclude that SBEM is a common complication of cirrhotic patients with hydrothorax. Almost half of the episodes were not associated with SBP; thus, thoracentesis should be performed in patients with cirrhosis, pleural effusion, and suspected infection. Culture of PF should be performed by inoculating 10 mL into a blood culture bottle at the bedside.
Generic Mental Workload Measurement Using a Shared Spatial Map Network With Different EEG Channel Layouts

Passive brain-computer interfaces with electroencephalographs (EEGs) could measure human mental workload levels. However, the use of EEG headsets with varying electrode configurations presents challenges in creating an interpretable mental workload recognizer that can be applied across different individuals and databases. To address this issue, we propose a novel shared spatial map network (SSMN), which abstracts EEG data representations from different individuals and electrode layouts. The SSMN utilizes a shared map encoder (SME) to generate EEG feature maps with consistent spatial locations on the scalp. Then, an instance augmentation encoder (IAE) is employed to increase the sample size and filter out individual-specific components. Finally, a workload recognition committee (WRC) is established by combining shallow and deep architectures of convolutional neural networks (ConvNets), allowing for adaptation to different mental workload measurement scenarios. We evaluate SSMN’s performance by combining two feature types and four validation approaches on three databases. The classification accuracy of the mental workload level measured by the EEG samples is comparable with the state-of-the-art models. The shared feature maps also clearly interpret the contribution of the cortical regions to the mental workload variations.
Anchored desynchronization

Distributed algorithms based on pulse-coupled oscillators have been recently proposed in [4], [14] for achieving desynchronization of a system of identical nodes. Though these algorithms are shown to work properly by various computer simulations, they are still lack of rigorous theoretical proofs for both the convergence of the algorithms and the rates of convergence for these algorithms. On the other hand, all the nodes are not likely to be identical in many practical applications. In particular, there might be a node that needs to interact with the “outside” world and thus may not have the freedom to adjust its local clock. Motivated by all these, in this paper we consider the desynchronization problem in a system where there exists an anchored node that never adjusts the phase of its oscillator. For such a system, we propose a generic anchored desynchronization algorithm that achieves ∈-desynchrony (defined in [4]) in O(n2ln(n/∈)) rounds of firings. We also prove that our algorithm converges even for the generalized processor sharing (GPS) scheme, where every node is assigned a weight and the amount of resource received by a node is proportional to its weight. In comparison with the original algorithm in [4], we show that the rate of convergence of the original algorithm in [4] is not always better than ours and it is only better in the asymptotic regime.
Distinct regulation of MHC molecule expression on astrocytes and microglia during viral encephalomyelitis

The potential interplay of glial cells with T cells during viral induced inflammation was assessed by comparing major histocompatibility complex molecule upregulation and retention on astrocytes and microglia. Transgenic mice expressing green fluorescent protein under control of the astrocyte‐specific glial fibrillary acidic protein promoter were infected with a neurotropic coronavirus to facilitate phenotypic characterization of astrocytes and microglia using flow cytometry. Astrocytes in the adult central nervous system up‐regulated class I surface expression, albeit delayed compared with microglia. Class II was barely detectable on astrocytes, in contrast to potent up‐regulation on microglia. Maximal MHC expression in both glial cell types correlated with IFN‐γ levels and lymphocyte accumulation. Despite a decline of IFN‐γ concomitant to virus clearance, MHC molecule expression on glia was sustained. These data demonstrate distinct regulation of both class I and class II expression by microglia and astrocytes in vivo following viral induced inflammation. Furthermore, prolonged MHC expression subsequent to viral clearance implies a potential for ongoing presentation. © 2007 Wiley‐Liss, Inc.
Three classes of glucocerebrosidase inhibitors identified by quantitative high-throughput screening are chaperone leads for Gaucher disease

Gaucher disease is an autosomal recessive lysosomal storage disorder caused by mutations in the glucocerebrosidase gene. Missense mutations result in reduced enzyme activity that may be due to misfolding, raising the possibility of small-molecule chaperone correction of the defect. Screening large compound libraries by quantitative high-throughput screening (qHTS) provides comprehensive information on the potency, efficacy, and structure–activity relationships (SAR) of active compounds directly from the primary screen, facilitating identification of leads for medicinal chemistry optimization. We used qHTS to rapidly identify three structural series of potent, selective, nonsugar glucocerebrosidase inhibitors. The three structural classes had excellent potencies and efficacies and, importantly, high selectivity against closely related hydrolases. Preliminary SAR data were used to select compounds with high activity in both enzyme and cell-based assays. Compounds from two of these structural series increased N370S mutant glucocerebrosidase activity by 40–90% in patient cell lines and enhanced lysosomal colocalization, indicating chaperone activity. These small molecules have potential as leads for chaperone therapy for Gaucher disease, and this paradigm promises to accelerate the development of leads for other rare genetic disorders.
The Top ten astronomical "Breakthroughs" of the 20th century

Introduction The progress of astronomy leapt forward when astrophysics was added to its sub-disciplines. The science of astrophysics essentially started in the early 19 th century and has advanced at a great pace, especially so in the last century. In fact we might suggest that the 20 th century was an epoch of enlightenment, in which our understanding of the Universe was revolutionized. As with many of today's sciences, we might wonder whether this rate of progress will continue. Science advances in two ways. On the one hand we have the gradual accumulation of knowledge and data. There are many examples of this in astronomy. Just think of the slow and painstaking accumulation of accurate stellar distances, masses, luminosities, temperatures and spectra. On the other hand, we have " breakthroughs ". These are major paradigm shifts, the realization that we have actually been 'barking up the wrong tree. ' Here, our concept of the astronomical Universe changes dramatically over a relatively short period of time. The Earth's cosmic position is a good example. In the 15 th century the vast majority of thinkers placed the Earth at the centre of the Universe. By the 17 th century our understanding of the cosmos had changed dramatically and Earth was demoted to being a mere planet. The Sun then became the centre of the Universe, but even this view did not last long. In this paper we aimed to recognize the major astronomical breakthroughs that occurred in the 20 th century. These stand out as landmarks in the progress of astronomical history. Our subtext is the implicit suggestion that the breakthroughs of the twentieth century might have been better and more numerous than the breakthroughs of previous centuries. We are also asking the reader to consider whether it is it possible that a similar number of major changes and impressive breakthroughs might also occur in the next century. Perhaps the rate of astronomical advance is slowing down. Let us start by being pedantic, and define the word " breakthrough ". In the context of astronomy this can be thought of in terms of parameters , processes, or objects. To illustrate this we will provide examples in each category. (i) Parameters. A typical astronomical parameter would be " the distance between the Earth and nearby stars ". Here, we stray away from the 20 th century. In the early part of the 19th century …
Carbon Starved Anther Encodes a MYB Domain Protein That Regulates Sugar Partitioning Required for Rice Pollen Development[W][OA]

The authors identified a rice R2R3 MYB transcription factor, Carbon Starved Anther (CSA), that regulates sugar partitioning from leaves to anthers and is required for the production of functional pollen. CSA directly regulates the expression of the monosaccharide transporter gene MST8, which encodes a key component of the anther sugar unloading pathway. In flowering plants, sink tissues rely on transport of carbohydrates from photosynthetic tissues (sources) for nutrition and energy. However, how sugar partitioning in plants is regulated at the molecular level during development remains unknown. We have isolated and characterized a rice (Oryza sativa) mutant, carbon starved anther (csa), that showed increased sugar contents in leaves and stems and reduced levels of sugars and starch in floral organs. In particular, the csa mutant had reduced levels of carbohydrates in later anthers and was male sterile. The csa mutant had reduced accumulation of 14C-labeled sugars in anther sink tissue. CSA was isolated by map-based cloning and was shown to encode an R2R3 MYB transcription factor that was expressed preferentially in the anther tapetal cells and in the sugar-transporting vascular tissues. In addition, the expression of MST8, encoding a monosaccharide transporter, was greatly reduced in csa anthers. Furthermore, CSA was found to be associated in vivo and in vitro with the promoter of MST8. Our findings suggest that CSA is a key transcriptional regulator for sugar partitioning in rice during male reproductive development. This study also establishes a molecular model system for further elucidation of the genetic control of carbon partitioning in plants.
MILPdM: A Predictive Maintenance Architecture for the Military Domain

Equipment monitoring for failure prediction is receiving attention from different sectors of society, such as industry, healthcare, and defense. In the defense domain, assets like military vehicles generate data that one can use to identify behavior changes and anticipate possible real-time failures, avoiding unnecessary maintenance interventions. Failure anticipation is crucial, as assets operated in the military domain perform critical tasks in which unexpected equipment failures result in high material and human costs. Approaches found in the literature typically deal with failure generation, aiming at analyzing the equipment's behavior. This paper proposes a broader approach called MILPdM. This proposal is a failure prediction architecture covering the whole failure prediction and predictive maintenance procedures. We evaluate MILPdM architecture by analyzing an engine-failure scenario where we train models to predict time series by collecting vibration data that describes the degradation of the vehicle's health. Considering the implementation of an LSTM-based neural network and Random Forest, the acquired results lead to a root mean square error of 0.15015 in the best case, which allows to predict the failure status two minutes in advance with only 3 hours of data history. This result shows that MILPdM is capable of anticipating failures with high assertiveness.
Study of the Tilted Mounting Angle of Automotive Radar Antenna Installed Behind a Painted Bumper

The paper studies the effect of the tilted mounting angle of an automotive radar antenna installed behind a painted bumper. First, a series-fed radar antenna array is designed and installed behind a painted bumper. Afterwards, the radar antenna is rotated about the x-, y-, and z-axis to investigate the impact of the tilted mounting angle. Further, the gain performance of the antenna in the yz and xz planes is studied and compared with the no rotation case. The performed study aid in understanding the rotation limit that can be tolerated for the application of Long Range Radar.
Expression of apoptotic and antiapoptotic markers in epithelial cells in idiopathic pulmonary fibrosis.

STUDY OBJECTIVE Idiopathic pulmonary fibrosis (IPF) is a chronic, usually fatal lung disease of unknown etiology. A common feature is the presence of microscopic areas of epithelial cell dropout. Increased apoptosis of these cells could elucidate the speculative pathogenesis of the disease. Therefore, the aim of our study was to examine the expression of p53, p21, bcl-2, bax, and caspase-3 in association with DNA strand breaks in bronchial and alveolar epithelial cells in lung specimens from IPF patients and control subjects. PATIENTS AND METHODS We examined by immunohistochemistry the expression of p53, p21, bax, bcl-2, and caspase-3 in association with DNA strand breaks detected by terminal deoxynucleotide transferase-mediated deoxyuridine triphosphate-biotin nick end-labeling (TUNEL) in bronchial and alveolar epithelial cells in lung specimens taken by biopsy in 12 IPF patients and 10 control subjects. An independent tissue evaluation by two pathologists graded semiquantatively the degree of staining present. RESULTS TUNEL was positive in epithelial cells in all IPF patients and only in one control subject. The expression of p53, p21, bax, and caspase-3 was up-regulated in IPF patients compared to control subjects. Bcl-2 was expressed less in IPF patients than in control subjects. CONCLUSIONS These results confirm that apoptotic hyperplastic epithelial cells are present in patients with IPF and that the expression of p53, p21, bax, and caspase-3 appears to be up-regulated and that of bcl-2 down-regulated in these cells. The increased expression of proapoptotic molecules in epithelial cells in IPF may be involved in the inadequate and delayed reepithelialization, which in turn contributes to fibroblast proliferation.
ALLFA: Active Learning Through Label and Feature Augmentation

We introduce a novel feature-based active learning approach that leverages leading feature selection methods from explainable AI (XAI) to elicit user corrections on both the classification label and the leading features contributing to the classification decision. Uniquely, our approach can handle such user corrections, which identify missing and superfluous features in a model-agnostic manner that can be applied to any supervised classification model. Our approach generates augmented samples in a fine-grained manner according to user corrections, and then adds them to the training data to teach relevant and irrelevant features. Our results show that our approach outperforms traditional active learning and a leading baseline in two domains, which includes improvement in classification performance from 0.572 to 0.637 given the same number of queries.
Document Segmentation for Topic Modelling with Sentence Embeddings

Topic modeling, a powerful technique to extract hidden structures from voluminous documents, encounters challenges with large documents encompassing myriad topics. Traditional segmentation methods struggle to identify precise points of topic shifts. This paper presents an innovative approach to document segmentation that leverages embeddings and vector distances to discern topic transitions. Building upon the TextTiling method, which primarily relies on patterns of lexical distribution, our method enriches the segmentation process by incorporating syntactic structures and rhetorical cues.Our method utilizes Sentence-BERT (SBERT) to generate embeddings for individual sentences. Through this, our approach compares embeddings of consecutive sentences to identify coherence and determine segmentation points governed by a tunable parameter, n. The computed similarities, termed 'gap scores,' undergo a smoothing process to counteract noise, improving the accuracy of segment identification.Furthermore, recognizing that longer documents may revisit similar topics at different intervals, our method introduces a clustering mechanism. This groups segments of analogous content, ensuring each topic's unique representation in the document, mitigating redundancy, and enhancing topic modeling results. Our approach delivers a robust, comprehensive, and efficient method for document segmentation tailored for advanced topic modeling applications.
Optimizing Garbage Collection Scheduling and Reporting with Truck Location Tracking Towards an Efficient Waste Management System

The annual global waste output hits a substantial 2.12 billion tons, posing a significant challenge for effective management. In the Philippines, the daily accrual of household waste stands at an imposing 35,580 tons, necessitating inventive solutions. This study introduces a sophisticated automated waste management system tailored for Nasugbu, Batangas, utilizing the Agile process model. Employing an iterative approach encompassing brainstorming, design, development, quality assurance, and deployment, the system optimizes garbage collection schedules and enhances user experience. The core objective of the study is to streamline garbage collection and enable real-time garbage truck tracking in the locality. The research delves into the challenges faced by Nasugbu's current waste management system, proposing an automated solution to revolutionize the process. Specific evaluation results substantiate high satisfaction levels among residents, barangay staff, and IT experts. Residents benefit from an intuitive interface offering features such as scheduling, waste calendar management, garbage truck tracking, and proactive SMS notifications. The system not only meets but surpasses expectations, bringing tangible improvements to the waste collection process.
Exploring Highway Overtaking and Lane Changing Based on Soft Actor Critic for Discrete Algorithm

Highway is one of the important scenarios faced by autonomous driving. However, in this scenario, smart cars need to meet the requirements of high efficiency (i.e. high speed) and high safety, and provide passengers with high comfort (i.e. fewer lane changes), which poses a serious challenge to autonomous driving. This work uses highway-env as the simulation platform and proposes a discrete action based SACD (soft actor critical for discrete) algorithm that combines CNN(convolutional neural networks) and LSTM(long short term memory) to solve the safety, efficiency, and comfort issues of smart cars during highway driving. The simulation results indicate that although smart cars have high driving efficiency, they perform poorly in terms of safety and comfort. Based on this, this work adopts the idea of distributed sampling and proposes a dual buffers structure combined with safety module. The final simulation results show that the introduction of dual buffers combined with safety module architecture can significantly improve the safety and comfort of smart cars driving on highway while maintaining high efficiency.