bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Image Enhancement Algorithm of Low Light Tea Disease Based on FPAGAN Model
Authors: Ye Rong , Li Tong , Shao Guoqi , Gao Quan
Published: July 2024

Disease image analysis is vital in various real-world applications, particularly in the field of smart agriculture, where it plays a crucial role in crop disease diagnosis. Given the uncertainties associated with environmental conditions, there is a pressing need to improve the visual quality of crop images. In this paper, we propose an algorithm based on an improved FPAGAN algorithm to address the problem of color distortion and fuzzy texture details of tea disease images caused by low-light environments. Firstly, we combine the feature pyramid module, the attention module and the multi-scale aggregation module to improve the generative network, which effectively reduces the noise effect while increasing the attention to the feature information of the disease region. In order to distinguish between the generated image and the real image, the PatchGAN in the discriminative network is improved and the local discriminative strategy is adopted. Then, the Nash equilibrium is achieved through the continuous interaction between the generator and the discriminator. To guide the network training process, a joint loss function is introduced to suppress the image noise and make the enhanced image color more natural and balanced. Finally, ablation experiments are conducted on the FPANet architecture to verify the advantages of the proposed FPANet. The results show that the FPAGAN algorithm can reach an average value of 19.065, 0.735, and 7.435 on the three evaluation indexes of PSNR, SSIM, and information entropy, and the processed disease images outperform other image enhancement techniques in terms of color preservation, contrast improvement, and enhancement of image details. This holds a certain significance in the prompt detection of tea diseases and minimizing growth expenses.
A Review of Behavior Recognition Research Based on Mobile Vision Devices
Authors: Wang Yaqian , Chao Yuan , Cao Zhen , Zhu Huaiyang , Du Shuaishuai , Lu Hengyu , Zhang Yijun
Published: July 2024

In view of the behavior recognition technology of mobile vision devices in security scenarios, this paper first describes the research and application progress of behavior recognition technology in security scenarios, and expounds the difficulties in its actual detection tasks, such as camera movement, behavior occlusion, illumination variation, background interference, multi-view variation, and interclass similarity. Then, according to the different network structures, the architecture composition and recognition characteristics of the model based on the two-stream convolutional architecture, the 3D convolutional architecture and the behavior recognition method based on the self-attention mechanism are detailed analyzed and elaborated. Then, the deployment and application of relevant behavior recognition algorithms are carried out on high-performance GPU and embedded microcomputer platforms. The accuracy, detection rate, parameter quantity and actual detection effect are compared and analyzed. Finally, based on the theoretical analysis and comparative experimental results, the limitations of the current behavior recognition algorithm are summarized, and the development direction of algorithm lightweight and adaptability improvement is pointed out.
A Review of Low Altitude Aircraft Detection
Authors: Wang Yue , Wang Yiming , Zhang Yao , Zhang Xu , Liu Shaonan
Published: July 2024

Low altitude flying objects have been widely used in many fields, bringing a lot of convenience to society, and have important strategic significance in both military and civilian fields. However, low altitude flying objects often pose a significant threat to the safety of low altitude areas in low altitude environments. In recent years, the number of accidents caused by drones has been increasing. In recent years, low altitude target detection technology has received increasing attention from researchers and has made significant progress in various research methods. It is urgent to conduct a systematic review of effective detection technologies for low altitude targets. This article provides a comprehensive overview and analysis of the latest progress in low altitude target detection. Initially, it outlined the contemporary research landscape, including various detection technologies such as radar, radio, and vision. Subsequently, a thorough review and analysis of these methods were conducted, and the main challenges in this field were explored. Finally, the discussion integrates key insights and insights drawn from the discussion.
Anime Avatar Generation Based on Generative Adversarial Networks
Authors: Sun Wanxin , Jia Shijie
Published: July 2024

This study addresses the challenges of facial detail loss and discriminator bias in image style transfer tasks. Building upon the DualStyleGAN network, the proposed the MixStyleGAN network, which improves upon the existing method by introducing a de-bias discriminator. The de-bias discriminator aims to remove style bias by generating mixed features of the original and reference images in the discriminator’s feature space, ensuring consistency between the prediction of mixed features and the original image. The AdaIN network is employed for feature map fusion across different resolution layers to achieve de-biasing effects. Additionally, localized discriminators are introduced to preserve facial details. Separate discriminators are employed for specific facial attributes such as eyes and mouth, enhancing the representation of local details through adversarial training. Experimental results on the FFHQ dataset demonstrate that MixStyleGAN achieves a 29.03% improvement in Arcface metric and a 12% improvement in LPIPS metric.
Detection of Image Tampering Using Multiscale Fusion and Anomalousness Assessment
Authors: Wang Yichen , Liu Lijuan , Huang Tingting
Published: July 2024

In response to the growing importance of detecting maliciously altered images to mitigate their harmful effects, we propose a deep learning-based image tampering detection method that incorporates multiscale fusion and anomaly assessment. This approach addresses the limitations of existing methods that often struggle to detect diverse tampering types and exhibit suboptimal precision and localization performance. The proposed method employs a Channel-Spatial Attention module to enhance feature representations extracted from multiscale input images, thereby capitalizing on both spatial and channel-wise dependencies within the data. Furthermore, it uses a Z-score scoring mechanism and an LSTM-based mechanism to effectively capture and evaluate anomalous regions within the image. These components collectively contribute to a more robust identification of the manipulated content. For training supervision, we introduce a binary cross entropy loss, which jointly optimizes pixel-level classification and regression tasks, ensuring accurate tampering detection and localization. Experimental evaluations demonstrate that our method significantly outperforms prevailing tampering detection techniques, exhibiting an increase in AUC values ranging from 21% to 62% and achieving up to a 99.8% improvement in the best F1 score. Specifically, on benchmark datasets CASIA1.0, Coverage, and NIST16, our method attains F1 scores of 0.673, 0.714, and 0.981, respectively, underscoring its superior performance across diverse scenarios and tampering types.
Design and Research on Visual Recognition System for Fruit Sorting Collaborative Robot
Authors: Ma Yingchen , Zhu Hanan , Jia Hongli , Guo Chunlai , Li Jiaqi , Li Zhe , Kalaf Omer
Published: July 2024

This study aimed to enhance the efficiency and accuracy of fruit sorting through the development of a collaborative robotic vision recognition system based on neural networks. Leveraging convolutional neural networks (CNNs), a substantial dataset of fruit images was collected, preprocessed, and utilized for training. The core algorithm employed was the neural network, particularly effective in pattern recognition and feature extraction. Integration with the control system of collaborative robots allowed real-time fruit identification and analysis using visual sensors during sorting. The system’s design considered optimization and acceleration of neural networks, incorporating advanced deep learning techniques like CNNs and recurrent neural networks. This fusion of collaborative robots, neural networks, and visual recognition technologies significantly improved fruit sorting efficiency and accuracy. The automated sorting process achieved through thoughtful system design and optimization reduces labor costs and enhances productivity. Despite notable progress in the design and research of collaborative robotic and neural network-based vision recognition systems, challenges and areas for improvement persist. Future work may involve further optimizing neural network models for increased precision and robustness and refining collaborative robot control strategies for more complex task collaboration. The collaborative robotic and neural network-based vision recognition system opens new possibilities in the field of fruit sorting, propelling the industry towards intelligent and efficient practices, thereby creating greater economic and societal value.
Fault Diagnosis and Static Analysis of Micro-Short Circuit in Lithium-Ion Batteries
Authors: Yang Chao , Ye Zhihao , Xiong Xin , Wang Zerun , Su Chang
Published: July 2024

This paper systematically discusses the causes of micro-short circuit fault of lithium ion batteries, diagnosis methods, static condition analysis and diagnosis method verification. Firstly, the basic principle and structure of lithium-ion battery are introduced, as well as the potential safety risks caused by micro-short circuit fault. Then the diagnosis methods of micro-short circuit fault are described in detail, including electrochemical analysis, X-ray detection, infrared thermal imaging, acoustic detection and internal inspection of the battery. On this basis, a quantitative diagnosis method of micro-short circuit based on residual charge estimation is proposed, and the related principles of charging voltage curve transformation and residual charge estimation are introduced. Finally, the effectiveness of the proposed method is verified by simulation, and the micro-short circuit fault is effectively identified. It provides a reference for the diagnosis of micro-short circuit fault and static condition analysis of lithium ion battery.
Quantitative Analysis and Simulation of Electricity Bill Recovery Risk Project Performance Based on Machine Learning Algorithm
Authors: Liao Weiting , Yang Xiaoyan , Su Hongyu , Yao Qiongrong , Xie Xiaohong
Published: July 2024

Electricity bill recovery project involves a lot of data, including user electricity data, arrearage situation, recovery situation, etc., the data is easy to exist incomplete and inaccurate, resulting in the analysis of electricity bill recovery risk project is more difficult. Therefore, a simulation method based on machine learning algorithm for quantitative analysis of project performance of electricity bill recovery risk is proposed. Subspace identification method is used to create the performance distribution model of electricity bill recovery project and collect electricity bill recovery data. The classification of electricity rate recovery data is realized by using machine learning algorithm. The collected performance data of electric charge recovery risk projects are normalized and the local density of electric charge recovery data is calculated. The density peak clustering algorithm is used to determine the clustering center of electricity and electricity bill recovery data, construct the characteristic curve of electricity and electricity bill recovery data, and complete the quantitative processing of electricity and electricity bill recovery risk project performance. The simulation results show that: The F1 score of the research method is higher, and the false detection rate is always lower than 0.1, which indicates that the research method has reliable application performance.
A Comparative Study on Deep Learning Models for Stock Price Prediction
Authors: He Zhexin , Zhang Huan , Por Lip Yee
Published: July 2024

With the advancement of deep learning technology, researchers have begun employing deep neural network models such as LSTM, MLP, CNN, and GRU to tackle nonlinear prediction problems in stock markets. This study harnesses these deep learning models, including LSTM, MLP, CNN, and GRU, to investigate the nonlinear forecasting of stock market price indices. The experiment selects globally representative stock indices and, through analyzing short-term, mid-term, and long-term data, discovers that the GRU model demonstrates exceptional performance in both prediction accuracy and generalization capabilities. To further enhance precision, the research constructs a filtering ensemble decision-making system based on multiple GRU models. This strategy reduces the error by at least 0.3% in medium to long-term predictions, significantly outperforming a single standalone GRU model. Consequently, the GRU-filtered ensemble decision model has been empirically proven as an effective tool for predicting stock indices, not only broadening the methods available for stock market prediction but also deepening the understanding and application of the generalization abilities of deep learning models within financial markets.
Adaptive Navigation Message Authentication Scheme for BeiDou-III Navigation System
Authors: Dong Wenhao , Liu Chunpeng
Published: July 2024

With the wide application of BeiDou navigation system (BDS), the openness of its navigation signal makes it vulnerable to spoofing attacks. In this paper, an Adaptive NMA Scheme for BeiDou-III Navigation System (BD-ANMA) is proposed to enhance the anti-spoofing performance of BeiDou navigation system. The scheme combines Navigation Message Authentication (NMA) and Time Effect Stream Loss Tolerant Authentication (TESLA), utilizes the SM2 algorithm to generate signatures, and employs the SHA256 algorithm to generate the TESLA key chain and Message Authentication Code (MAC). In order to adapt to the characteristic differences between D1 navigation messages and D2 navigation messages in BDS, two authentication schemes are designed in this paper to meet the authentication requirements of different receivers in terms of synchronization performance. Through experimental tests, the results show that the scheme achieves good performance and provides an effective solution for the safety and security of the BDS.
Vehicle Detection Algorithm for Foggy Roads at Night Based on Improved Gated Context Attention Network
Authors: Zhang Liyan , Zhao Jianing , Fang Liu , Lang Zhengang
Published: July 2024

The dim lighting at night increases the possibility of vehicle detection omissions and objects on foggy roads at night. To improve the accuracy of vehicle detection in foggy nighttime environments and strengthen road safety monitoring, this paper proposes an improved GCA Net algorithm for vehicle detection in foggy nighttime environments. Firstly, this article introduces the SE attention module after the convolutional block of GCA Net, which focuses more on the channels related to the defogging task to capture more critical information and improve the accuracy of vehicle detection. Secondly, this article adopts the MS-SSIM+L1 mixed loss function to further improve the model’s attention to brightness, thereby better handling situations with darker nighttime lighting. Compared with the original GCA Net, the improved algorithm achieves a peak signal-to-noise ratio increase of 1.063dB. Once again, in order to further enhance the information content and visual effect of nighttime images, ACE nighttime image color correction and GCA Net fusion network were introduced for nighttime image dehazing to highlight the contours and details of targets such as vehicles and roads. Finally, YOLOv7 was used for vehicle target detection and experimentally validated on a real foggy dataset. The experimental results show that compared with the previous method, this method has better recognition performance in vehicle detection and recognition on foggy roads at night, successfully improving the accuracy and reliability of vehicle detection.
Personalized Privacy Protection Mechanism Integrating Spatiotemporal Correlation
Authors: Shen Yanmei , Hua Rui , Wang Hui , Shen Zihao , Liu Peiqian
Published: July 2024

In view of the insufficient data availability in traditional trajectory privacy protection schemes, in order to achieve a balance between privacy security and data usage efficiency. This paper achieves personalization of privacy protection and optimization of data availability by comprehensively considering sensitive locations and their correlations in user trajectories. It can realize personalized protection of sensitive locations with different privacy levels based on the user’s preset privacy needs and sensitivity assessment. At the same time, by introducing trajectory candidate sets and cross-correlation functions, a new trajectory publishing mechanism is constructed, which can maintain the spatiotemporal characteristics of trajectory data while ensuring privacy. Through simulation experiments, the effectiveness of the proposed scheme was verified on real data sets. Experimental results show that the method proposed in this article has good results in terms of privacy protection strength and data availability.
From transformations to the notion of time
Authors: Hadad Cacier
Published: July 2024

In this paper I expose an ontological-existential interpretation of time via its unveiling and analysis in an example of scientific measurement in a chemical transformation. I begin with the search for time in the transformation, and little by little I explain its characteristics and structures. I finally interpret the time involved in the measurement and the usual concept of linear time by means of the Heidegger's concept of temporality (originary time). The paper is an attempt to explain Heidegger's existential ontology to non-specialist thinkers interested in understanding the subject of time from approaches different from those of their field, as may be the case of many contemporary scientists. For this reason, it develops the ideas step by step and in an expository way, but gradually entering the precise and rigorous Heideggerian terminology necessary for the temporal analysis. Finally, it should be noted that this work contains novel interpretations of several of the ideas and expressions of Heidegger's thought.
Growing Confidence and Remaining Uncertainty About Animal Consciousness
Authors: Irwin Louis
Published: July 2024

While the study of consciousness has had a controversial history and, until recently, a pessimistic prognosis, recent views within the neuroscientific community suggest that a maturing if incomplete scientific understanding of consciousness is close at hand. The purpose of this essay is to specify the major points on which in my opinion there is widespread though not universal agreement, as proposed benchmarks for the current state of consciousness research across the phylogenetic spectrum. Most published definitions of consciousness boil down to a focus on it as a process arising in a nervous system engaged with a body and its environment, giving rise to subjective (personal) experience. A broad consensus on the phenomenology of consciousness sees it consisting at a minimum of (1) awareness_ _and focused attention, (2) unity of perception, (3) qualitative variations in content, (4) mental causation, and (5) a sense of self. There is also broad agreement that the substrate of consciousness requires sizeable, complex nervous systems organized into several hierarchical levels of processing. Further insight is gained by reconstructing the evolution of subjective phenomenological experience ― most likely from multiple origins, hosted by a diversity of body and brain architectures, and diverging into markedly different forms across the animal kingdom. However, three major mysteries about phenomenological experience that remain unresolved are (1) the neurological correlates of consciousness, (2) the apparent gap between phenomenology and mechanism, and (3) the process that monitors the brain activity admitted into consciousness.
4-H Swine Record Book 2
Authors: Glauer Debbie
Published: July 2024

Swine Record Book II (4H SWR 11) is an animal sciences publication suggested for 4-H members ages 13 and up. This book can be used after the Swine Record Book I (4H SWR 10). This record book includes a form to take into account the amount of money borrowed to raise your pig. The companion to this record book is Swine Project Manual Book II (4H SWM 11).
Florida 4-H Livestock Market Animal Record Book
Authors: Johnson Nancy
Published: July 2024

4-H Livestock Market Animal Record Book (4H ASM 50) is an animal sciences record book for all members in this project area to keep record of their market animals. In EDIS this publication is DLN 4H 108. Visit the 4-H Youth Development Curriculum website for more information on related project materials.
Florida 4-H Livestock Breeding Project Record Book
Authors: Johnson Nancy
Published: July 2024

4-H Livestock Breeding Project Record Book (4H ASM 51) is an animal sciences record book for all members in this project area to keep record of their breeding project. In EDIS this publication is DLN 4H 109. Visit the 4-H Youth Development Curriculum website for more information on related project materials. First published in June 1997.
Farm Ponds in Florida Irrigation Systems
Authors: Clark Gary A. , Stanley C. D. , Zazueta F. S. , Albregts E.E.
Published: July 2024

Many areas of Florida annually receive between 50 and 60 inches of rainfall (Butson and Prine 1968). However, while the levels of rainfall meet or exceed the annual potential evapotranspiration (ETp) levels of 46 to 56 inches (Smajstrla et al. 1984), the distribution in rainfall and the low water holding characteristics of sandy soils necessitate irrigation of most crops. Most of the rainfall occurs during the summer months when many seasonal crops are not in production. Also, rainfall events which occur during the growing season often exceed the infiltration capacities of the fields, resulting in surface runoff. Both of these circumstances produce free water which is not utilized for crop production. Ponds can capitalize on this free resource for many benefits, This bulletin discusses the uses of ponds, types, and some procedures for designing and sizing excavated ponds. An appendix contains specific examples to help with the understanding of this material.
Safe Tractor Operations: Foundations of Tractor Safety
Authors: Gorucu Serap , Lehtola Carol J. , Brown Charles M.
Published: July 2024

The Safe Tractor Operations series contains six articles devoted to tractor safety. This publication aims to familiarize new and beginning farmers, Extension agents and specialists, and the public with safe tractor operations. It gives guidance about how everyone in an agricultural operation can work together to make using tractors safer. Written by Serap Gorucu, Carol J. Lehtola, and Charles M. Brown and published by the UF/IFAS Department of Agricultural and Biological Engineering, June 2024.
Sugarcane Cultivar CP 80-1743 Descriptive Fact Sheet
Authors: Sandhu H.S. , Singh M.P. , Gilbert R.A.
Published: July 2024

The selection of a sugarcane cultivar that eventually becomes a production success is probably due in equal parts to the cultivar's attributes, word of mouth, gut feeling, and luck. This fact sheet provides many of the attributes cultivar identification is based upon. Some of these you will use as the basis for cultivar selection. First published Dec. 2001.
Sugarcane Cultivar CP 88-1762 Descriptive Fact Sheet
Authors: Singh M.P. , Sandhu H.S. , Gilbert R.A.
Published: July 2024

The selection of a sugarcane cultivar that eventually becomes a production success is probably due in equal parts to the cultivar’s attributes, word of mouth, gut feeling, and luck. This fact sheet provides many of the attributes a cultivar identification is based upon. First published Dec. 2001.
Managing South Florida Range for Cattle
Authors: Vendramini J. , Kalmbacher R.S. , Boughton R.
Published: July 2024

This document states that South Florida's range management for cattle involves understanding the unique characteristics and historical context of the region's range types. The southeastern pine forest region, particularly Florida, has been utilized for cattle production since 1521 and is classified as the longleaf/slash-pine/wiregrass range type. The state has approximately 3 million acres of range, primarily forested in the northern half, while two-thirds of Florida's cattle are found in the south.
The Natural Resource Conservation Service (NRCS) recognizes 28 range sites in Florida, but only four are significant for cattle production in South Florida. The document highlights the importance of native grasses, distinguishing between "decreaser" grasses, which are preferred and decline with overgrazing, and "increaser" grasses, which are less desirable and increase with overgrazing.
Range condition class, a traditional method of assessing range health based on the prevalence of decreaser grasses, is discussed alongside the more modern concept of rangeland health, which evaluates the integrity of soil, vegetation, water, and air. The document emphasizes the importance of available forage and its nutritive value, noting that Florida ranges typically support low cattle carrying capacities due to poor to fair range conditions.
This document was first published in June, 2002.
Production of Biofuel Crops in Florida: Sugarcane/Energycane
Authors: Sandhu Hardev S. , Guilbert Robert
Published: July 2024

Sugarcane, a complex hybrid of Saccharum spp., is a perennial grass most often grown in the United States for the production of sugar and molasses; however, the sugars extracted from sugarcane can be easily fermented to produce ethanol that is known as first-generation ethanol. Sugarcane byproducts (i.e., bagasse, which is biomass remaining after the juice is extracted from the stalks) and other energy grasses (such as energy cane, giant reed, elephant grass, and erianthus) can be used to produce cellulosic ethanol, known as second-generation ethanol. The term energy cane is used to describe hybrids of Saccharum sp. that have been selected for high biomass, high fiber, and low sucrose concentration. Due to high biomass and non-invasiveness, energy cane has high potential to be considered as feedstock for cellulosic ethanol production in Florida.
Ambersweet Orange
Authors: Jackson L.K. , Futch Stephen H.
Published: July 2024

The Ambersweet, while classified as an orange for both fresh and processing purposes, is actually a rather complex citrus hybrid. Developed by Dr. C. J. Hearn of the United States Department of Agriculture Horticultural Research Station in Orlando, Ambersweet is a hybrid of Clementine tangerine by Orlando tangelo crossed with a seedling mid-season sweet orange. Since one of the parents was part tangelo (a tangerine-grapefruit hybrid) Ambersweet would be 3/8 tangerine, 1/8 grapefruit and 1/2 sweet orange.
Osceola Citrus Hybrid
Authors: Jackson L.K. , Futch Stephen H.
Published: July 2024

This document is about  Osceola, one of the released citrus hybrids arising from a cross of Clementine mandarin and Orlando tangelo made by Gardner and Bellows in 1942 at the United States Department of Agriculture Laboratory in Orlando. It was officially released as a variety in 1959 but has never attained important commercial status primarily because of a less than outstanding flavor. The fruit is 3/4 tangerine and 1/4 grapefruit and most closely resembles a tangerine or tangelo. First published in 1993.
Nova Tangelo
Authors: Futch Stephen H. , Jackson L.K.
Published: July 2024

This document is about the Nova Tangelo. This is a hybrid of Clementine tangerine crossed with Orlando tangelo that was released in 1964 by the U.S. Department of Agriculture. This is the same cross that produced the Robinson, Osceola and Lee tangerines.
Guide to Citrus Nutritional Deficiency and Toxicity Identification
Authors: Futch Stephen H. , Tucker P.H.
Published: July 2024

This document explains the importance of accurately identifying symptoms of various disorders in citrus trees, which can affect their health, vigor, and productivity. It emphasizes the challenges in distinguishing nutritional deficiencies or toxicities from other issues, such as herbicide or fungicide damage and physiological stress. Nutritional disorders, particularly those involving micronutrients like zinc, manganese, and iron, can manifest differently on different parts of the tree and may not always match descriptions in publications. The text advises seeking expert advice when symptoms are unclear to avoid costly and potentially harmful remedial actions. First published Oct. 2000.
Guide to Citrus Mite Identification
Authors: Futch Stephen H. , Childers C.C. , McCoy C.W.
Published: July 2024

This document provides insights into the Citrus Rust Mite (CRM), detailing its morphology, behavior, and impact on citrus crops. CRM, characterized by its piercing-sucking mouthparts, targets epidermal cells of citrus leaves, fruit, and twigs, causing surface blemishes and potential reductions in fruit size and quality. The text also discusses other mites affecting citrus, such as the Pink Citrus Rust Mite (PCRM), Citrus Red Mite (RM), Texas Citrus Mite (TCM), and Six-Spotted Mite (SSM), outlining their distinctive features and damage patterns. Understanding these mites and their effects is crucial for effective pest management in citrus cultivation.
Container Production of Palms
Authors: Meerow Alan W. , Broschat Timothy K.
Published: July 2024

This document summarizes all the good practices for growing palms in containers. Container-grown palms are raised for essentially three markets: liners for field production wholesale or mass market retail sales for landscapes, and interior specimens, both mass market houseplants and interiorscape use.
The largest market for container grown palms is the interior. Palms are outstanding plants for the interior environment. When properly acclimated, a large number of subtropical and tropical palm species are capable of residing under low light conditions for a relatively long period of time.
Summary of N, P, and K Research with Squash in Florida
Authors: Hochmuth George , Hanlon Ed
Published: July 2024

The purpose of this publication is to summarize squash fertilization research results from studies conducted in Florida. These research results have been used to develop the current University of Florida nitrogen (N) recommendations for squash fertilization. Production of squash in Florida represents almost 3% of the total value of the state's vegetable industry. The value of the squash crop was $52,788,000 in the 2007-08 season (Fla. Agri. Stat. Bulletin 2009). Planted squash acreage has decreased steadily from 9500 acres planted in 1996 to 8600 acres in 2008. Major squash production areas are in southeastern Florida, where 60% of the squash is grown, followed by 16% in the southwest, with lesser production areas in the east, west, and central regions of the state. Peak harvest and out-of-state shipping months occur in December and May, though harvest of squash continues from October to June each year. Various types of squash are grown in Florida, including acorn, butternut, yellow straight and crookneck, white (scallop), and zucchini squash. Florida-grown squash represents 21% of the squash shipped to cities throughout the U.S.
Greenhouse Cucumber Production - Florida Greenhouse Vegetable Production Handbook, Vol 3
Authors: Hochmuth Robert
Published: July 2024

This document outlines all the considerations to take into account when growing cucumbers in greenhouses. Greenhouse cucumber production is very popular in many areas of the world. The cucumber is a warm season crop with required growing conditions of 80°F to 85°F and plenty of sunlight. Traditionally the primary type of cucumber grown in Florida greenhouses is the standard European seedless type. The fruits are mild in flavor, seedless, and have a thin edible skin that requires no peeling. Harvested fruit generally range from 12 to 14 inches in length and weigh about 1 pound each. This text is one of the chapters of the Florida Greenhouse Vegetable Production Handbook. First published Dec. 1990.
Grass Tetany in Cattle
Authors: Newman Y.C. , Hersom M.J. , Kunkle W.E.
Published: July 2024

This document is about grass tetany, sometimes called grass staggers or hypomagnesemia, that can be a serious problem in Florida with cattle grazing small grain or ryegrass pastures. The problem is usually confined to lactating cows. It is always associated with an imbalance in the mineral components of blood serum, especially reduced magnesium levels. In Florida, grass tetany is more severe when cattle are grazing young forage, particularly the first flush of growth during December and January. Once the forage becomes more mature, the likelihood of problems is reduced. Grass tetany is apt to appear under conditions of nutritional stress. Placing cattle on winter pasture directly after being on frosted or other low quality pasture may cause such a nutritional stress. First published in April 1997.
Enviroscaping to Conserve Energy: Trees for South Florida
Authors: Broschat Timothy K. , Meerow Alan W. , Black R.J.
Published: July 2024

Landscape plants can improve the appearance of our surroundings and modify the extremes of local climate (microclimate modification). Plants provide shade, insulate the home from heat loss or gain, and cool the air that surrounds their leaves through transpiration (release of water from plant pores). Trees are the main types of landscape plants used around the home for passive energy conservation. They provide shade, influence air movement around the house and, once established, require little maintenance. The energy-conserving impact of a particular tree species depends on 1) whether it keeps its leaves during the winter and 2) the shape of a tree and density of its foliage. This document is about choosing the right species of trees to plant in Florida, the sites to plant them, and how to properly maintain them. First published Jul. 1993.
Common Native Wildflowers of North Florida
Authors: Norcini Jeffrey G.
Published: July 2024

The species in this publication are plants that are native to the US and occur in Florida; most of them are considered native to Florida. You might observe these species along the roadside in North Florida, or while you're taking a hike in a natural area. Information shown in the following tables is based on personal observations and on information obtained from the references listed in the next section. Plant type, flowering, native habitat, and light requirement refer to North Florida conditions. Some of these species may be available at local garden centers or retail nurseries, especially those that specialize in native plants. First published Feb. 2000.
Potential of Collected Storm Water and Irrigation Runoff for Foliage and Bedding Plant Production
Authors: Chen J. , Beeson R.C. , Yeager T.H. , Stamps R.H. , Felter L.A.
Published: July 2024

This document explores the potential of using collected stormwater and irrigation runoff for greenhouse foliage and bedding plant production. Conducted over two years at the UF/IFAS Mid-Florida Research and Education Center, the study involved collecting and filtering runoff and stormwater to irrigate plants using overhead sprinkler and ebb-and-flow systems. Results showed that plants irrigated with pond water had equal or better growth and quality compared to those irrigated with well water, despite higher pH and turbidity in pond water due to algae. This indicates that collected stormwater and irrigation runoff can be effective alternatives to well water in greenhouse crop production, providing a sustainable solution for water use in nursery operations.
Redlegged Ham Beetle, Copra Beetle Necrobia rufipes (De Geer, 1775) (Insecta: Coleoptera: Cleridae: Korynetinae)
Authors: Williams Ian , Scharf Michael
Published: July 2024

The redlegged ham beetle (Necrobia rufipes) is a predaceous beetle that feeds on a variety of decaying organic matter, especially matter with high protein and fat content such as dead vertebrate animals, and some of plant origin, such as copra, nuts, and palm kernels. The redlegged ham beetle is of economic importance as a pest of stored products and has relevance to forensic entomology. Other common names include the ham beetle, the copra beetle or bug, and the “paper worm.”
Best Practices for Grant Development: Starting With the Idea
Authors: Stofer Kathryn A.
Published: July 2024

Grant writing has become an expectation for Extension faculty at the state and county level. However, aside from a few workshops provided by the university, new faculty may feel like they have few resources to help them sharpen their practices at applying for and securing grant funding. This publication is meant to support Extension professionals, especially at the county level, in building good grant development habits.
What Matters in Irony Detection: An Extended Feature Engineering for Irony Detection in English Tweets.
Authors: Zhang Linrui , Pang Qixiang , Copus Belinda
Published: July 2024

In recent years, large-scale language models (LLMs) have nearly become the dominant force in almost every natural language processing (NLP) task. The primary research approach has focused on selecting the most appropriate language model for specific NLP tasks and then incorporating linguistic features to enhance the model’s performance. With swift progress in this field, new features and models are evolving rapidly, and outdated systems require timely updates. In this paper, we extended the accomplishments of SemEval-2018 Task 3, enhancing its irony detection systems with novel features and more sophisticated language models. Subsequently, we conducted an ablation study to showcase the contributions of these enhancements to the LLM-based system. Furthermore, we compared our leading system with the top performers in the SemEval-2018 competition, and our best model exhibited superior performance when compared to the leading performers applied to the same corpus.
Using Earley Parser for Verification of Totally Ordered Hierarchical Plans
Authors: Pantůčková Kristýna , Ondrčková Simona , Barták Roman
Published: July 2024

Hierarchical planning extends classical planning by capturing the hierarchical structure of tasks via decomposition of tasks to subtasks. Hierarchical plan verification is the problem of determining whether a given plan is valid according to that structure. As decomposition trees in totally ordered hierarchical planning domains resemble parsing trees of context-free grammars, one may exploit the techniques for context-free grammars also for hierarchical plans. In this paper, we study how the Earley parser, proposed for checking whether a given word belongs to a language defined by context-free grammar, can be extended to verification of totally ordered hierarchical plans.
AI Tutor: Student's Perceptions and Expectations of AI-Driven Tutoring Systems: A Survey-Based Investigation
Authors: Keshtkar Fazel , Rastogi Neelesh , Chalarca Sebastian , Bukhari Syed Ahmad Chan
Published: July 2024

Generative AI (GenAI) and LLMs have started to influence how teachers teach and studentslearn, including the ones in programming languages and tutoring. However, there have been debates onwhether AI could be beneficial to students’ learning or not. One way to see this issue is from the perspectives of thestudents. Therefore, this study explored how students perceive the use of AI in their education. The data wasgathered through interviews with 62 students and other stakeholders, such as instructors and IT specialists. The results showed that the students positively perceived using AI as a tutor. Moreover, this study suggests several things to consider when integrating AI tutors for programming. The findings reveal positive student perceptions regarding AI's potential within the teaching-learning process. Students envision AI tutors offering personalized assistance, adapting to individual learning styles, and providing immediate feedback, potentially augmenting their grasp of programming concepts. We applied Statistical analysis, machine learning, and natural language processing techniques such as PCA, t-SNE, LDA, and sentiment analysis.
The Impact of PDDL+ Language Features on Planning Performance: An Empirical Analysis on a Real-world Case Study
Authors: El Kouaiti Anas , Percassi Francesco , Saetti Alessandro , Vallati Mauro
Published: July 2024

PDDL+ is an expressive formalism that allows for the use of planning in complex real-world applications. It includes a number of features designed to improve the readability and conciseness of the resulting knowledge models, but that are commonly doubted to have detrimental impact on the performance of domain-independent searches and heuristics. In this paper we empirically assess the impact of such features in a challenging real-world case study.
A Machine Learning Pipeline for Emotion Recognition based on Brain Topographic Maps Derived from Electroencephalogram Signals
Authors: Cascaes Alves Bruno , Pereira Melo Marla , Melchiori Cerri Artur , Astrogildo de Freitas Larissa , Francisca Adamatti Diana , Sanchotene de Aguiar Marilton
Published: July 2024

Emotion recognition is an increasingly relevant field due to its direct implications for various sectors of society. The area aims to enhance the understanding of how emotions influence human behavior. Exploring brain activity analysis through electroencephalogram signals becomes possible when considering that emotions can manifest non-verbally. In this scenario, machine learning applications prove promising due to the complexity of recognizing emotions from electrical signal data from the brain. The case study focuses on DEAP, a recognized dataset constructed through experiments in electroencephalography, exposing subjects to musical and visual stimuli. The main objective of this work is to present a pipeline for the classification of emotions based on images of topographic maps generated from the EEGLAB tool and electroencephalogram signals. Additionally, the contributions of this work include the presentation of a structured dataset created through the mapping of temporal, spatial, and frequency data derived from topographic images and models for predicting dimensional emotions of arousal and valence based on the new dataset. Results demonstrate accuracies of 85.46% and 85.05% for the classification of low/high arousal and valence emotions, respectively.
Multiclass Classification of Solar Flares in Imbalanced Data Using Ensemble Learning and Sampling Methods
Authors: Jiang Haodi , Matsuura Ryoma , Wang Jason T. L.
Published: July 2024

Solar flares are intense bursts of radiation across the electromagnetic spectrum on the surface of the Sun. They are categorized into four classes: B, C, M, and X, depending on their intensity, with X-class flares being the strongest. Being able to predict a flare’s class before its occurrence is critical for anticipating the severity of its impact on Earth. We used the Space-weather HMI Active Region Patches (SHARP) parameters available from Stanford’s Joint Science Operations Center (JSOC) to train machine learning models to classify these flares. However, predicting the flare class is a challenging task, as it is a multiclass classification probleminvolving imbalanced data due to the small number of X-class flares in a solar cycle. We propose a new method that uses a combination of random undersampling and the synthetic minority oversampling technique (SMOTE) to combat the imbalanced data problem. Furthermore, we develop an ensemble algorithm that uses nine classifiers as base learners and logistic regression as meta-learner. Experimental results show that the proposed method is effective in predicting solar flares, especially the most intense X-class flares, within the next 24 hours.
DeepGray: Malware Classification Using Grayscale Images with Deep Learning
Authors: Jiang Haodi , Polsani Harshitha , Liu Yuexin
Published: July 2024

In the ever-evolving landscape of cybersecurity, the threat posed by malware continues to loom large, necessitating innovative and robust approaches for its effective detection and classification. In this paper, we introduce a novel method, DeepGray, for multi-class malware classification utilizing malware images and the power of deep learning. Our dataset combines the malware sample from the BODMAS dataset and the benign sample from the DikeDataset. The methodology involves transforming executable files into a deep learning-friendly format by converting them into grayscale images while preserving essential data characteristics. Subsequently, Principal Component Analysis (PCA) is applied to distill the most significant features. The study harnesses the power of deep learning and transfer learning, utilizing established neural network architectures such as VGG16, InceptionV3, Efficientnetv2b0, and Vision Transformers (ViT) for malware classification. Experimental results demonstrate the effectiveness of the proposed method in accurately classifying malware.
Dementia Detection with Phonetic and Phonological Features
Authors: Kurdi M. Zakaria
Published: July 2024

In this paper the ADDReSS challenge dataset was used for training and testing a binary classifier designed to diagnose AD. This dataset consists of transcripts of descriptions of the Cookie Theft picture, produced by 54 subjects in the training part and 24 subjects in the test part. Two machine learning experiments were conducted on the task of classifying transcribed speech samples with text samples that were produced by people with AD from those produced by normal subjects. The first experiment showed that, among all the subtypes of phonetic and phonological features covered in this paper, vowels provided the best classification performance. The second experiment that used four feature selection techniques showed with the adopted phonetic and phonological features provided about 0.87 F1 score, that is close to the best performance reported in the address challenge by systems using multiple linguistic levels and machine learning techniques. This result confirms the importance of the covered features as indicators of dementia.
Mobile Fog AI for Internet of BattleField Things (IoBT)
Authors: Paul Sheuli , Silaghi Marius , Kepuska Veton , Alghanmi Akram , Liu Steven
Published: July 2024

New mechanisms provide soldiers automation capabilities that include interactive, mobile, fast, scalable, secured, powerful computational communication in contested environments such as battle fields. Internet of BattleField Things (IoBT) is a technology that develops integrated Internet of Things (IoT) solutions for the war of the future. The IoBT connects soldiers with smart technology in armour, radios, weapons, munitions, other objects such as health sensors, GPS, and various IoT devices. These ``things'', objects, or constrained devices can communicate with each other or across existing network infrastructure such as the Internet or network meshes. They most of the time come with limited capacity in terms of memory storage, computational power and energy. IoT, networking (Device Communication (D2D), Peer to Peer Communication (P2P)), machine intelligence, multimodal sensing and fusion are some areas involved in developing interdisciplinary IoBT networks. Our aim is to equip soldiers with AI powered IoBT devices. Existing D2D/P2P/LPWAN based IoBT technology focuses on edge computing devices which are very limited, being slow, insecure, and lack sufficient scalability. We present a novel concept, i.e., Mobile Fog AI for IoBT to overcome existing IoBT limitations.