Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Multiscale Spatial Models to Assess Potential Surrogate Habitat for an Imperiled Reptile

In evaluating conservation and management options for species, practitioners might consider surrogate habitats at multiple scales when estimating available habitat or modeling species’ potential distributions based on suitable habitats, especially when native environments are rare. Species’ dependence on surrogates likely increases as optimal habitat is degraded and lost due to anthropogenic landscape change, and thus surrogate habitats may be vital for an imperiled species’ survival in highly modified landscapes. We used spatial habitat models to examine a potential surrogate habitat for an imperiled ambush predator (eastern diamondback rattlesnake, Crotalus adamanteus; EDB) at two scales. The EDB is an apex predator indigenous to imperiled longleaf pine ecosystems (Pinus palustris) of the southeastern United States. Loss of native open-canopy pine savannas and woodlands has been suggested as the principal cause of the species’ extensive decline. We examined EDB habitat selection in the Coastal Plain tidewater region to evaluate the role of marsh as a potential surrogate habitat and to further quantify the species’ habitat requirements at two scales: home range (HR) and within the home range (WHR). We studied EDBs using radiotelemetry and employed an information-theoretic approach and logistic regression to model habitat selection as use vs. availability. We failed to detect a positive association with marsh as a surrogate habitat at the HR scale; rather, EDBs exhibited significantly negative associations with all landscape patches except pine savanna. Within home range selection was characterized by a negative association with forest and a positive association with ground cover, which suggests that EDBs may use surrogate habitats of similar structure, including marsh, within their home ranges. While our HR analysis did not support tidal marsh as a surrogate habitat, marsh may still provide resources for EDBs at smaller scales.
Tailoring Superconducting Nanowire Single-Photon Detectors for Quantum Technologies

Superconducting nanowire single-photon detectors (SNSPDs) are a key building block for photonic quantum technologies, as they provide a means for efficient readout of optical circuits with low timing jitter, dark counts and dead time. In this contribution we give a general overview of the working principle of these devices and look into several engineering aspects. We discuss the influence of the stoichiometry of NbTiN thin-films on detector performance and find that a ratio of 65:35 Nb:Ti results in the highest saturation of SNSPDs fabricated from these films. Furthermore, we study the impact of helium ion irradiation on the efficiency of SNSPDs. We show that with this technique the efficiency of an SNSPD can be improved from below 1% to 42%. Finally, we discuss the impact of embedding the SNSPDs into an optical cavity on the system detection efficiency. By employing a half-cavity under the detector we are able to boost the efficiency from 30% to 50% for a detector with a fill factor of 0.33.
GROWTH OF LUJA PLANTS (PERISTRHOPHE BIVALVIS MERRILL) AT DIFFERENT LIGHT INTENSITIES AND MATERIAL TYPES OF CUTTINGS

Light intensities and planting materials of cuttings are two critical factors affecting plant growth. This research was aimed to find out the effects of light intensities and planting material types of cuttings affecting the growth of Luja plants. The light intensity treatments consisted of 100%, 65%, 35%, 15% and the planting material types covered both basal and tip cuttings. The design used was Split Plot Design and continued with Duncan's Test. The results showed that there was no interaction between light intensities and cuttings, but the effect of light intensities and cuttings was singularly found in the SLA, RGR, LAD, LAR, and NAR variables. Moreover, Luja plants could grow well at the light intensity of 15% 65% with the optimum light intensity of 35%. Both basal and tip cuttings could be used as the planting materials. However, the material type of cuttings that could accelerate the vegetative phasegrowth of Luja plants the most was tip cuttings. Luja is categorized as a plant that can grow in full light conditions.
Solid Lipid Nanoparticles (SLN): Method, Characterization and Applications

Solid lipid nanoparticles (SLN) have emerged as a next-generation drug delivery system with potential applications in pharmaceutical field, cosmetics, research, clinical medicine and other allied sciences. Recently, increasing attention has been focused on these SLN as colloidal drug carriers for incorporating hydrophilic or lipophilic drugs. Proteins and antigens intended for therapeutic purposes may be incorporated or adsorbed onto SLN, and further administered by parenteral routes or be alternative routes such as oral, nasal and pulmonary. The obstacles associated with conventional chemotherapy may be partially overcome by encapsulating them as SLN. The present review focuses on the utility of SLN in terms of their advantages, production methodology, characterization and applications. If properly investigated, SLNs may open new vistas in therapy of complex diseases. DOI: http://dx.doi. org/10.3329/icpj.v1i11. 12065 International Current Pharmaceutical Journal 2012, 1(11): 384-393
End-to-End Defect Detection in Insulators using Detection Transformer Model

Significant advancements have been witnessed in the global development and deployment of smart grid systems in recent years. The strategic integration of advanced artificial intelligence (AI) technologies holds the potential to propel these smart grids toward a future characterized by enhanced intelligence, efficiency, and sustainability. Within the realm of smart grids, insulators assume a critical role. However, they pose a persistent challenge in terms of effective defect monitoring. To alleviate this dilemma, This article presents the application of the Detection Transformer (DETR) model for detecting insulator defects, showcasing its effectiveness in improving the safety and reliability of power transmission systems. A dataset of real-world insulator images was collected and utilized to train both the DETR and "You only look once" (YOLOv7) models. The DETR model outperformed the YOLOv7 model, accurately identifying and localizing various types of defects, including cracks, damages and contaminations. The evaluation metrics, including mean Average Precision (mAP) and average recall rate (AR), revealed the robustness of the DETR model, with an mAP score of 0.938 and a recall rate of 0.838. These findings emphasize the importance of leveraging advanced deep learning models, such as DETR, for automated inspection and defect detection tasks in the power industry. By promptly identifying and rectifying potential issues, the successful implementation of the DETR model offers new possibilities for enhancing the safety and reliability of power transmission systems. Future research directions could focus on further enhancing the model’s performance and extending its applicability to other domains within the power industry.
M-Net: A Lightweight Network Based on Multilayer Perceptron for Massive MIMO CSI Feedback

In FDD massive multiple-input multiple-output (MIMO) systems, user equipments (UEs) are required to send back the downlink channel state information (CSI) to base stations (BSs). However, the high-dimentional CSI souring proportional to antennas leads to unacceptable feedback overhead. Recently, deep learning (DL) has shown its unique powerful ability to compress and reconstruct CSI. In this paper, a novel Multilayer Perceptron (MLP) based feedback network, named M-Net, is proposed, where the specific spatial correlation of channels is emphasized by tailored feature processing modules. To meet the practical requirements of lightweight networks and ensure low inference latency, M-Net adopts parameter sharing to reduce network complexity and eliminates complex operations. Additionally, to leverage the computational resources at the BSs, we design a scalable decoder to further improve the performance of the network. Simulation results show that the proposed M-Net achieves almost the state-of-the-art (SOTA) performance with the lowest computational complexity, while exhibiting a remarkable 49.63% reduction in the inference delay compared to TransNet. Finally, we demonstrate that the M-Net variants achieve the SOTA performance by only deep enning the M-Net decoder. The open source codes are available at https://github.com/Password-YYX/M-Net.
A dynamic quality of service middleware agent for mediating application resource usage

High bandwidth applications with time-dependent resource requirements demand certain resource level assurances in order to operate correctly. Quality of service resource management techniques are being successfully developed that allow network systems to provide such assurances. These solutions generally assume that the operating system at either end of the network is capable of handling the throughput requirements of the applications. However, real operating systems have to manage many concurrent applications with varying resource requirements. Without specialized support, the operating system cannot guarantee the resources needed for any particular application. In support of these kinds of applications we have developed a middleware agent called a dynamic QoS manager (DQM) that mediates application resource usage so as to ensure that applications get the resources they need in order to provide adequate performance. The DQM employs a variety of algorithms to determine application resource allocations. Using application QoS levels, it provides for resource availability based algorithmic variation within applications and varying application periods. It also allows for inaccurate application resource usage estimates through a technique we have developed called dynamic estimate refinement. The paper discusses new developments in the design of the DQM and presents results showing DQM performance with both real and synthetic applications.
Ultra-Flexible Graphene Power Divider with Bending Resistance based on Spoof Surface Plasmon Polariton

a ultra-flexible, bend-resistant and high-isolation graphene power divider based on spoof surface plasmon polaritons (SSPPs) is proposed. This SSPPs power divider operates within 5.5-9.8 GHz, which offers ultra flexibility and bending resistance, maintaining good performance in bending and twisted conditions. The proposed SSPPs power divider is fabricated and measured, the experimental results are in good agreement with simulation.
Edge Artificial Intelligence in Large-Scale IoT Systems, Applications, and Big Data Infrastructures

As the Internet of Things (IoT) landscape grows, with estimates exceeding 75 billion devices by 2025, effective data management and processing become primary challenges. Traditional cloud-centric models may struggle under this large data volume. This research presents Edge AI as an innovative solution, integrating artificial intelligence directly at data sources like sensors and cameras. This ensures real-time analytics and decision-making, promoting responsive and tailored actions. Our literature review details Edge AI's distinct characteristics and applications. The interaction of Edge AI with large-scale IoT domains is critically examined, emphasizing their combined potential. Within big data infrastructures, a comparative study contrasts Edge AI and cloud-based AI, investigating processing speeds, optimization techniques, and essential metrics. The in-herent limitations of Edge AI and current challenges are also discussed. In summary, Edge AI offers notable improvements in operational efficiency, data privacy, and bandwidth use. As IoT continues its rapid expansion, the strategic deployment of Edge AI becomes crucial, leading to a future where data is not just collected but smartly utilized.
Adaptive Solution Space in Particle Swarm Optimization

Particle swarm optimization (PSO) is a well-known optimization technique in electro magnetics. To ensure particles remain within the designated solution space throughout the optimization procedure, various boundary conditions are employed in PSO algorithms. However, there are instances when the global optimum is not present within the specified solution space. Consequently, regardless of how extensively the particles search, they cannot achieve the desired fitness value. This paper introduces an adaptive solution space method that efficiently expands the solution space to attain the desired fitness goals. The performance of the adaptive solution space method is tested using both mathematical benchmark functions and a real-world electromagnetic problem. Simulation results confirm the efficiency and effectiveness of the proposed adaptive solution space method.
A Unified Approach for Weed Detection in Arable Acreage Using RetinaNet Architecture

One of the main elements that could reduce agricultural productivity is weeds. With the evolution in Computer Vision technologies, Deep Learning integrated with image processing techniques has proven to be an effective tool for the detection of weed. This study analyzes the benefits of accurate weed detection, which can help farmers to reduce the use of pesticides and increase agricultural output. Also, this study reviews various techniques used in recent years and discusses about the datasets that are used to train these techniques and presents certain challenges faced by the existing weed detection techniques in various situations. Moreover, this research study emphasizes the possible application of drones or unmanned aircraft systems for capturing high-quality images of weeds in fields for enabling precise treatment of weed affected areas. This study employs the VGG-16 network based RetinaNet architecture to analyze RGB photos of weeds in agricultural fields, which can aid farmers in reducing pesticide usage and enhancing crop yields.
Spatial Data Mining Based on Urban Underground Pipe Networks

As the Chinese economy experiences rapid growth, the establishment of a comprehensive 3DGIS for urban underground pipeline networks has become increasingly imperative. This paper addresses the pressing market demand for the extraction of user-relevant, unknown, and potentially valuable rules from spatial databases, thereby furnishing a technical foundation for spatial decision-making processes. The study introduces two algorithms dedicated to generating spatial association rules, leveraging data from road and pipeline tables.The first algorithm presented is the Apriori algorithm, a widely recognized method in data mining. The second algorithm is the Godin algorithm, integrated into an incremental construction approach for the concept lattice. This paper places particular emphasis on the design, implementation, and practical applications of an enhanced version of the Godin algorithm within the industrial context.Performance evaluations of the two algorithms, conducted using examples from underground pipeline networks, reveal that the concept lattice algorithm surpasses the Apriori algorithm in terms of flexibility and scalability. Additionally, the concept lattice algorithm demonstrates a notable reduction in both time and space complexity, highlighting its practical advantages for the efficient analysis of urban underground infrastructure.
Ensemble Learning for Offline Signature Verification using Fused Deep Features

Signature verification plays an important role in document authentication. The efficient and robust system is required for forgery detection in documents in the presence of distortion such as rotation, scaling and noise. This paper proposes a writer-dependent signature verification method, where hand-crafted features are fused with one-dimensional convolutional neural network features to cater different variance in the whole verification process. Proposed feature extraction methodology is validated by bagged ensemble learning technique in which machine learning classifier’s decision is finalized based on majority voting. The effectiveness of the proposed signature verification approach has been evaluated across three distinct publicly accessible datasets. The outcomes highlight the versatility of this method. Moreover, the proposed system surpasses the performance of other pre-existing systems documented in the literature.
A greedy, graph-based algorithm for the alignment of multiple homologous gene lists

MOTIVATION Many comparative genomics studies rely on the correct identification of homologous genomic regions using accurate alignment tools. In such case, the alphabet of the input sequences consists of complete genes, rather than nucleotides or amino acids. As optimal multiple sequence alignment is computationally impractical, a progressive alignment strategy is often employed. However, such an approach is susceptible to the propagation of alignment errors in early pairwise alignment steps, especially when dealing with strongly diverged genomic regions. In this article, we present a novel accurate and efficient greedy, graph-based algorithm for the alignment of multiple homologous genomic segments, represented as ordered gene lists. RESULTS Based on provable properties of the graph structure, several heuristics are developed to resolve local alignment conflicts that occur due to gene duplication and/or rearrangement events on the different genomic segments. The performance of the algorithm is assessed by comparing the alignment results of homologous genomic segments in Arabidopsis thaliana to those obtained by using both a progressive alignment method and an earlier graph-based implementation. Especially for datasets that contain strongly diverged segments, the proposed method achieves a substantially higher alignment accuracy, and proves to be sufficiently fast for large datasets including a few dozens of eukaryotic genomes. AVAILABILITY http://bioinformatics.psb. ugent.be/software. The algorithm is implemented as a part of the i-ADHoRe 3.0 package.
Characterization of Propagation in an Industrial Scenario from Sub-6 GHz to 300 GHz

We perform simultaneous multi-band ultra-wideband dual-polarized double-directional measurements at sub-6 GHz (center frequency, 6.75 GHz), mmWave (74.25 GHz), and sub-THz (305.27 GHz) in line of sight (LOS) and non-LOS in a small industrial scenario (machine room). The aim is to characterize the propagation at THz taking as a reference the lower bands and identifying shared and distinguishing features. The spatial/temporal analysis of the measurements shows strong similarities in multi-path components (MPCs) between the different bands. Moreover, high order reflections have been identified at THz. Overall, the results indicate that THz channels exhibit significant multipath, with some specular MPCs unique to the band and with lower contribution by the diffuse components. Finally, path-loss has also been computed and compared with existing multi-band models.
3MVGAT: Multi-Modal and Multi-View Knowledge Graph Attention Network for Recommendation Algorithm

The importance of recommendation systems in our lives is increasingly growing, helping people quickly access valuable information from massive datasets. Previous recommendation systems have achieved significant success by incorporating techniques such as multimodal data and deep learning. However, current multimodal data recommendation algorithms primarily rely on official content, often overlooking the multimodal data provided by users. The inclusion of user-generated multimodal data from their perspective can further enhance the accuracy of recommendations for users.This paper introduces a novel recommender algorithm, the Multi-Modal and Multi-View Knowledge Graph Attention Network (3MVGAT), which utilizes multi-modal and multi-view data. In the item encoder, we unify and fuse images and text from different perspectives, to learn item representations. Furthermore, we apply an attention mechanism to this encoder to select essential modalities for item representation learning. In the user encoder, we learn user representations based on their historical interactions with items and apply an attention mechanism to capture user preferences. Extensive experiments on real-world datasets demonstrate that our approach effectively enhances the performance of recommender algorithms.
Intelligent Modular Controller for Implementing the Digital Protection of Transformers as AI Algorithms Techniques

Accurate temperature control of power transformer is the challenging task to every power system engineers. Nowadays Mercury based temperature sensing and actuating systems are installed for controlling the power transformer temperature and tripping the breakers whenever the temperature is exceeded the desired preset value. But the drawback of these control methods is lack of accuracy in sensing of temperature and sluggish control or slow response. A new approach or method for controlling and monitoring the temperature of the power transformer is prescribed and the associated control components are discussed. The intelligent control system uses the Programmable Logic Controller (PLC) and Graphical Operating Terminal (GOT) Display unit for the control purpose. The ON command to cooling fan and TRIP signal to circuit breaker are given from the controller. The algorithm is written into the PLC in the form of ladder program. The reference values and actual values are displayed in the GOT. In this study, the required control modules and programming part are discussed to implement the intelligent control of temperature of power transformer. The controller is trained for the past history and necessary scripts are written to take the action in its own for the specific problem using AI based techniques.
Design of a Dartboard Metasurface for Broadband RCS Reduction

A dartboard metasurface (MS) is presented for broadband radar cross-section (RCS) reduction. Two reflection units are designed to achieve a wide bandwidth at 4.1-16.8 GHz with a phase difference of $(180 \pm 37)^{\circ}$ after optimization. The designed units are arranged in a dartboard-shaped layout, which has more scattered beams and is easier to conform on the conical surface than the conventional checkerboard layout. Simulation results show that the MS realizes a 10-dB RCS reduction within a continuous bandwidth from 4.3 to 16.9 GHz (118.9 %) under normal incidence. For the condition of oblique incidence, the specular RCS reduction is also investigated for both TE and TM polarizations.
Power and frequency analysis for data and control independence in embedded processors

In this work we study control independence in embedded processors. We classify control independent instructions to data dependent and data independent and measure each group's frequency and behavior. Moreover, we study how control independent instructions impact power dissipation and resource utilization. We also investigate control independent instructions' behavior for different processors and branch predictors. Our study shows that data independent instructions account for 34% of the control independent instructions in the applications studied here. We also show that control independent instructions account for upto 12% of the processor energy and 15.6%, 11.2% and 8.6% of the instructions fetched, decoded and executed respectively. We also show that control independent instruction frequency increases with register update unit (RUU) size and issue width but shows little sensitivity to branch predictor size. In addition, we illustrate that control independent data independent instructions account for upto 6% of the processor energy. We also show that control independent data independent instruction frequency increases with RUU size and issue width.