Miracle-dz/UniFAR-FTUs huggingface dataset:
BaCoD: An AI-Based Coconut Rhinoceros Beetle (Oryctes Rhinoceros) Detection And Monitoring Through Geographic Information System

The research focuses on the detection of the most destructive coconut pest- Rhinoceros Beetle (Oryctes rhinoceros) - using image processing, deep learning neural networks, and geotagging technologies in the Philippines. The coconut industry contributes 3.6 percent of the country’s annual gross value. This could be managed using pest control and monitoring technologies based on artificial intelligence and Geographic Information Systems. The proposed system, running on Android 10 or higher, used image processing using a deep learning neural network for pest detection and Geographic Information System for visualization. The model used 15 epochs and approximately 3000 image datasets with a 7:3 ratio to reach at least 94% validation accuracy to be considered ready for deployment. During deployment, the 48 MP resolution recorded the highest accuracy with 91% and works best during sunny weather compared to 12 MP and 25 MP resolutions. Meanwhile, the gathered Euclidean distance of the proposed system from the actual coordinate ranges from 6 meters to 272 meters and has a significant weak positive correlation with the upload delay at a 0.361 correlation coefficient. Overall, the proposed solution has a satisfactory rating for functional suitability, performance efficiency, usability, reliability, maintainability, and portability based on the design specification and addressing the objectives aligned with the key stakeholders who will be using the system.
Fast Ambiguity Resolution Using an Integer Nonlinear Programming Method

This paper presents a new and efficient strategy for ambiguity resolution on the fly. The new approach is based on the method of Integer Nonlinear Programming (INLP). After discussing some typical methods for INLP, ambiguity resolution of GPS phase measurements based on the Integer Least-Squares Method (ILSM) is formulated as a problem of INLP. A new ambiguity search method is then presented based on a combination of different INLP methods. The new ambiguity search method performs the search for the optimal integer ambiguities of 7 to 8 satellites within 0.1 - 0.2 seconds. It thus, can be used for real-time applications. Based on this search method, ambiguity resolution on the fly is carried out using a sequential approach which estimates the optimal integer ambiguities at each epoch by using all GPS observations available at that epoch. This approach is very robust because it avoids the critical issue of erroneously rejecting the optimal ambiguities. To validate the estimated integer ambiguities at each epoch, a number of criteria are discussed and tested to ensure the correctness of the estimated integer ambiguities. The method has been successfully tested and has shown robustness as well as reliability.
Characterizing Early-Onset Alzheimer Disease Using Multiprobe PET/MRI

Purpose Early-onset Alzheimer disease (EOAD) is rare, highly heterogeneous, and associated with poor prognosis. This AT(N) Framework–based study aimed to compare multiprobe PET/MRI findings between EOAD and late-onset Alzheimer disease (LOAD) patients and explore potential imaging biomarkers for characterizing EOAD. Methods Patients with AD who underwent PET/MRI in our PET center were retrospectively reviewed and grouped according to the age at disease onset: EOAD, younger than 60 years; and LOAD, 60 years or older. Clinical characteristics were recorded. All study patients had positive β-amyloid PET imaging; some patients also underwent 18F-FDG and 18F-florzolotau PET. Imaging of the EOAD and LOAD groups was compared using region-of-interest and voxel-based analysis. Correlation of onset age and regional SUV ratios were also evaluated. Results One hundred thirty-three patients were analyzed (75 EOAD and 58 LOAD patients). Sex (P = 0.515) and education (P = 0.412) did not significantly differ between groups. Mini-Mental State Examination score was significantly lower in the EOAD group (14.32 ± 6.74 vs 18.67 ± 7.20, P = 0.004). β-Amyloid deposition did not significantly differ between groups. Glucose metabolism in the frontal, parietal, precuneus, temporal, occipital lobe, and supramarginal and angular gyri was significantly lower in the EOAD group (n = 49) than in the LOAD group (n = 44). In voxel-based morphometry analysis, right posterior cingulate/precuneus atrophy was more obvious in the EOAD (P < 0.001), although no voxel survived family-wise error correction. Tau deposition in the precuneus, parietal lobe, and angular, supramarginal, and right middle frontal gyri was significantly higher in the EOAD group (n = 18) than in the LOAD group (n = 13). Conclusions Multiprobe PET/MRI showed that tau burden and neuronal damage are more severe in EOAD than in LOAD. Multiprobe PET/MRI may be useful to assess the pathologic characteristics of EOAD.
Antibacterial potential of leaf and fruit of citrus extract (Citrus aurantifolia) against klebsiella oxytoca

Antimicrobial agents play a crucial role in decreasing the impact of infectious diseases worldwide. Many medicinal plants have been identified as rich sources of natural antimicrobial compounds that can be used as an alternative to traditional treatments for bacterial infections. Indonesia, particularly in the Aceh region, has a warm and humid climate that supports a diverse range of plants that have medicinal properties. One example is the lime (Citrus aurantifolia) which can be used for medicinal purposes.This study aimed to determine the inhibitory effects of leaves and fruit extracts of lime on the growth of Klebsiella oxytoca.The method used was an experimental method with a post-test only control group design. The results showed that both types of extracts contain flavonoid, alkaloid, and saponin compounds. However, triterpenoids were only found in the fruit extract, and steroids were only found in the leaf extract. The antimicrobial test of lime fruit and leaf extracts against Klebsiellaoxytoca bacteria showed that both types of extracts were able to inhibit the growth of the test bacteria at all concentrations.
Cyclic Tableaux and Symmetric Functions

We introduce the notion of cyclic tableaux and develop involutions for Waring's formulas expressing the power sum symmetric function pn in terms of the elementary symmetric function en and the homogeneous symmetric function hn. The coefficients appearing in Waring's formulas are shown to be a cyclic analog of the multinomial coefficients, a fact that seems to have been neglected before. Our involutions also spell out the duality between these two forms of Waring's formulas, which turns out to be exactly the “duality between sets and multisets. ” We also present an involution for permutations in cycle notation, leading to probably the simplest combinatorial interpretation of the Mobius function of the partition lattice and a purely combinatorial treatment of the fundamental theorem on symmetric functions. This paper is motivated by Chebyshev polynomials in connection with Waring's formula in two variables.
Sulphur tales from the early Archean world

Abstract Sedimentary and magmatic rocks and their distinct sulphur isotopic signatures indicate the sources and processes of sulphur cycling, in particular through the analysis of all four stable sulphur isotopes (32S, 33S, 34S and 36S). Research over the past 15 years has substantially advanced our understanding of sulphur cycling on the early Earth, most notably through the discovery of mass-independently fractionated sulphur isotopic signatures. A strong atmospheric influence on the early Archean global sulphur cycle is apparent, much in contrast to the modern world. Diverse microbially driven sulphur cycling is clearly discernible, but its importance for Earth surface environments remains to be quantified.
Printed Devices for Wearable Biosensors: Laboratory to Emerging Markets

Advances in flexible electrochemical sensors have opened up opportunities for various applications, such as biomedical and fitness monitoring. This brief review explores the key advancements in printed wearable electrochemical sensing systems, including an analysis of literature trends, printing strategies (flexographic, screen, and inkjet printing techniques), recent applications of printed wearable biosensors (epidermal sensing films and patches, textile platforms, and biofuel cell (BFC) energy devices), their relative merits, and perspectives on this emerging technology. This article also provides an overview of sensing systems and concludes with a synopsis of the market drivers for printed and flexible sensors.
Pointwise Stabilization of Discrete-time Stationary Matrix-valued Markovian Chains

We study the pointwise stabilizability of a discrete-time, time-homogeneous, and stationary Markovian jump linear system. By using measure theory, ergodic theory and a splitting theorem of state space we show in a relatively simple way that if the system is essentially product-bounded, then it is pointwise convergent if and only if it is pointwise exponentially convergent.
Imbalanced Chemical Process Fault Diagnosis Using Balancing GAN With Active Sample Selection

Current deep-learning-based fault diagnosis methods, though proven to be successful with sufficient fault data, cannot well address the challenges of sample availability in real-world industrial scenarios. To address this challenge, this article proposes an effective approach by exploiting data generation and sample selection techniques. Specifically, we first develop a balancing generative adversarial network (BAGAN)-based data generation technique to generate more discriminative fault samples by utilizing not only the fault samples, but also the normal samples. Second, a strategy is devised to select the samples generated by BAGAN, and on this basis, active learning (AL) is utilized to select the most informative samples. The stacked autoencoder (SAE)-based deep neural network (DNN) is used to classify the faults. The proposed method is evaluated by conducting computational experiments on the Tennessee Eastman (TE) dataset. The evaluation results demonstrate that the proposed BAGAN-based method with an active sample selection strategy achieves improved performance in imbalanced chemical fault diagnosis tasks.
Iris Recognition Through Edge Detection Methods: Application in Flight Simulator User Identification

— To meet the increasing security requirement of authorized users of flight simulators, personal identification is becoming more and more important. Iris recognition stands out as one of the most accurate biometric methods in use today. Iris recognition is done through different edge detection methods. Therefore, it is important to have an understanding of the different edge detection methods that are in use these days. Specifically, the biomedical research shows that irises are as different as fingerprints or the other patterns of the recognition. Furthermore, because the iris is a visible organism, its exterior look can be examined remotely using a machine vision system. The main part of this paper delves into concerns concerning the selection of the best results giving method of the recognition. In this paper, three edge detection methods, namely Canny, Sobel and Prewitt, are applied to the image of eye (iris) and their comparative analysis is discussed. These methods are applied using the Software MATLAB. The datasets used for this purpose are CASIA and MMU. The results indicate that the performance of Canny edge detection method is best as compared to Sobel and Prewitt. Image quality is a key requirement in image-based object recognition. This paper provides the quality evaluation of the images using different metrics like PSNR, SNR, MSE and SSIM. However, SSIM is considered best image quality metric as compared to PSNR, SNR and MSE.
Three new species of Trichoderma (Hypocreales, Hypocreaceae) from soils in China

﻿Abstract Trichoderma spp. are diverse fungi with wide distribution. In this study, we report on three new species of Trichoderma, namely T.nigricans, T.densissimum and T.paradensissimum, collected from soils in China. Their phylogenetic position of these novel species was determined by analyzing the concatenated sequences of the second largest nuclear RNA polymerase subunit encoding gene (rpb2) and the translation elongation factor 1– alpha encoding gene (tef1). The results of the phylogenetic analysis showed that each new species formed a distinct clade: T.nigricans is a new member of the Atroviride Clade, and T.densissimum and T.paradensissimum belong to the Harzianum Clade. A detailed description of the morphology and cultural characteristics of the newly discovered Trichoderma species is provided, and these characteristics were compared with those of closely related species to better understand the taxonomic relationships within the Trichoderma.
Extent of anthropogenic influence on groundwater quality and human health-related risks: an integrated assessment based on selected physicochemical characteristics

Abstract The majority of people living on earth rely on groundwater as their primary supply of water for daily needs. However, human activities continuously threaten this natural resource. In an attempt to unravel the extent of the impact of human-related activities on groundwater physicochemical characteristics in Nnewi and Awka urban clusters (Nigeria), several techniques were integrated in this study. Groundwater samples were warm and acidic in nature. Concentrations of SO42-, NO3-, PO43-, Cl-, HCO3-, Ca2+, Mg2+, Na+ and K+ were within set benchmarks. The water nutrient pollution index (ranging from 0.060 to 0.745), nitrate pollution index (varying between −0.999 and −0.790) and water pollution index (ranging from 0.057 to 0.630) estimated the extent of anthropogenic contamination and showed low anthropogenic impact on the groundwater physicochemical characteristics. The health risks due to the ingestion and skin absorption of the nitrate-contaminated water computed for six age groups (6–12 months, 5–10 years, 10–15 years, 15–20 years, 20–60 years and >60 years) showed health risk values that were < 1, implying low chronic health risks to humans. The cumulative total health hazard index ranged between 0.006 and 0.787 with a mean value of 0.167. Chemometric analyses and geochemical plots revealed the relationships between the variables and contamination sources. Chadha’s plot showed that 55% of the samples were Ca2+-Mg2+-Cl- waters, predominating over Na+-Cl- and Ca2+-Mg2+-HCO3- waters. Bivariate and multivariate geochemical plots also indicated low anthropogenic impact. Furthermore, principal component analysis and R-type hierarchical clustering confirmed that the groundwater chemistry and quality were mostly influenced by geogenic processes than human-related acts. Conclusively, the extent of anthropogenic influence on groundwater physicochemical characteristics is low. These findings would be useful in future monitoring of groundwater in both urban clusters.
Distributed sliding-mode tracking control for multiple mechanical systems

Based on the sliding-mode and adaptive control theories, the distributed consensus tracking problem under directed topology is investigated for multiple mechanical systems in the presence of general disturbances. We consider that only a subset of the follower mechanical systems can get the information from the dynamic leader mechanical system. First, with the upper bounds of the general disturbances and the leader’s acceleration, a distributed sliding-mode tracking scheme is proposed. Then, when considering the high conservation of sliding-mode control, an improved algorithm with adaptation laws for the upper bounds is developed. Both the proposed algorithms can make tracking errors for each follower mechanical system asymptotically stable. Numerical examples and comparisons are provided to show the effectiveness and the performance of the proposed control strategies.
Heat generation and stability of a plasmonic nanogold system

The surface plasmon resonance (SPR) of Au nanostructures can be precisely tuned in the visible to near-infrared (vis–NIR) region with the size and morphology. The photothermal effect induced by the SPR can raise the temperature of Au nanostructures and the surrounding matrix under external illumination. In this work, hollow Au nanostructures such as nanoboxes and nanorings with a tunable SPR in the region of 650–1100 nm were obtained by a replacement reaction between HAuCl4 and the as-prepared Ag nanostructures as the sacrificed templates. Compared with the solid Au nanorods, studies on the photothermal conversion and stability of hollow Au nanostructures were systematically carried out with the assistance of the near-infrared (NIR) lasers available. Under NIR laser irradiation, the temperatures of the colloidal Au nanostructures increased rapidly from ~30 °C to ~65 °C. Combining the experimental results with a finite-different time-domain (FDTD) numerical simulation, the heat generation of different Au nanostructures was investigated. With the consideration of the concentration of the Au nanostructures, it is indicated that hollow Au nanostructures are superior to solid Au nanorods in photothermal conversion. On increasing the NIR laser power (3 W), Au nanorods undergo a shape deformation from nanorods to spherical nanoparticles, while the SPR and morphology of hollow Au nanoboxes and nanorings maintain high stability, promising to be candidates for nanoheaters. This work provides a standard to design optimized plasmonic nanoheaters.
Density Weighted Pruning Method for Sparse Least Squares Support Vector Machines

For lack of sparseness characteristic in support vector machines(SVM) by least squares solution(LSSVM),a density weighted pruning algorithm to improve the sparseness of the LSSVM regression model is investigated.The estimation error of training sample is weighted by corresponding density value and the potential contribution of training sample is obtained.Then the sample with the greatest value in the sorted spectrum of the potential contribution is selected as the support vector.The density and the potential contribution of training sample are updated and the potential contribution of the neighborhood sample of support vectors is significantly reduced.Thus the rechoosing of similar sample as support vector is properly avoided.More support vectors in the training sample set are iteratively selected,until the user-defined performance is achieved,thus the sparse LSSVM model is obtained.The simulation and practical applications indicate that the proposed method performs more effectively than Suykens standard sparse method for removing the redundant support vector with better sparseness and robustness.
The Property of a Special Type of Exponential Spline Function

Approximation theory experienced a long term history. Since 50’ last century, the rise of spline function as well as the advance of calculation promotes the growth of classical approximation theory and makes them develop a profound theory in maths, and application values have shown among the field of scientific calculation and engineering technology and etc. At present, the study of spline function had made a great progress and had a lot of fruits, as for that, the reader could look up the book [1] or [2]. Nevertheless, the research staff pays less attention to exponential spline function, since polynomial spline function is a special case of that, so it is much essential and meaningful for one to explore the nature of exponential spline function.
Analog Multiply-Accumulate Cell With Multi-Bit Resolution for All-Analog AI Inference Accelerators

Mixed-signal AI accelerators offer the possibility of higher energy efficiency for moderate resolution computations compared to their digital counterparts. All-analog implementations, where all operations are performed in the analog domain, can further improve this energy advantage. An energy efficient multiply-accumulate cell for all-analog neural layer processing macros is presented. The proposed analog two-quadrant multiplier circuit consists of two complementary MOSFETs where the pulse width modulated input activation is applied to the gates and the weight signal to the isolated back-gate. The analog multi-bit resolution weight is dynamically stored on a memory capacitor. The multiply-accumulate operation result is represented by charge accumulated on a summation line and drawn from or put onto a computation capacitance. Simulation results based on a 22 nm FD-SOI CMOS technology show that the cell consumes about 0.67 fJ for a circuit-level multiply-accumulate operation. An area efficiency of $166\times 10^{12}$ MAC/s/mm2 is achieved.
A 6–12 GHz Wideband Low-Noise Amplifier With 0.8–1.5 dB NF and ±0.75 dB Ripple Enabled by the Capacitor Assisting Triple-Winding Transformer

This paper presents a wideband 6-12GHz two-stage low noise amplifier (LNA) that utilizes capacitor assisting triple-winding transformer in the inter-stage matching network. The electric coupling introduced by the assisting capacitor expands the design space by allowing independent control of each effective coupling strength of the three coupled inductors. The equivalent negative resistor enhanced by this capacitor improves the quality factor of the inter-stage network at high frequencies, which further improves the overall design trade-off among gain, bandwidth and noise figure. Fabricated in a 130nm SOI CMOS process, the LNA achieves a remarkable 0.8-1.5dB NF, <inline-formula> <tex-math notation="LaTeX">$< $ </tex-math></inline-formula>-14dB S11, <inline-formula> <tex-math notation="LaTeX">$< $ </tex-math></inline-formula>-8dB S22, -11.8dBm IP<inline-formula> <tex-math notation="LaTeX">$_{\mathbf{1dB}}$ </tex-math></inline-formula> and 21.8-23.3dB power gain across the 6-12GHz passband with ±0.75dB ripple while drawing 20mA from a 3.3V supply.
Finding roots of complex polynomials with newton's method

Newton's method is one of the most widely known numerical algorithms for finding the roots of smooth functions of one variable. However, it is also considered to be "unpredictable" in that many initial values for the methods do not converge to a root of the function. We will show that, for polynomial functions, if the initial values are chosen properly convergence to the roots can be guaranteed. More specifically, Newton's method for a polynomial yields a rational function of the Riemann sphere to itself, and we can apply techniques of complex dynamics to analyze its behavior. One of the results is that the immediate basins of the roots are large and have a definite "width" which we can bound from below. For a simple root of a polynomial of degree d, this width is of order 1/d. We present an algorithm for choosing starting values for Newton's method which will find all of the roots (to within $\epsilon$) in presumably no more than order $d\sp 3$log($1\over\epsilon$) evaluations. We also present some experimental results concerning the relationship between the "Newton flow" for a polynomial, the relaxed Newton's method, and Newton's method. Some conjectures are made about how one might find polynomials which cause problems for Newton's method and related methods.