Miracle-dz/UniFAR-FTUs huggingface dataset:
A CNN-Based Framework for Detection and Recognition of Objects Using Transfer Learning: A Case Study on Flower Recognition

The vast variety of floral species present on Earth poses a considerable challenge in memorizing their individual names. Acquiring knowledge about specific flower types inhabiting diverse environments like forests, mountains, and dense regions becomes crucial for their recognition and differentiation. Consequently, the development of a technique for accurately determining flower categories becomes imperative. The task of distinguishing and categorizing a particular blossom amidst the extensive array of floral species presents a formidable task. Hence, the automation of flower species identification has been a subject of extensive research interest. This research domain is characterized by the utilization of features extracted from floral images in conjunction with specialized algorithms aimed at classifying flower species. A prominent aspect of these endeavors is the employment of three widely recognized flower image datasets. The present study constitutes a comprehensive survey of various methodologies proposed by diverse researchers with the objective of accomplishing precise identification of flower species.
Delivery robots are not just small autonomous cars! How to close the gap in environmental data for planning their operation?

The configuration of autonomous cars (maximum dimension, features, behavior rules) and the operation area are well-defined. Hence, for a dedicated road, the mission planner can assume that the system can move on it, at least in principle. For smaller robots operating on footpaths and cycle tracks this prior knowledge does not exist. From an infrastructure perspective, for example, the widths of pedestrian and bicycle vary widely and integrate different types of obstacles. On the other hand, the robot itself (size, kinematics, sensory equipment, etc.) is not standardized. For mapping infrastructure and robot’s properties, a time-consuming manual analysis and experiments with the actual robot in the intended area of operation is necessary. This approach is unrealistic for the real-world, wide spread application of mobile robots - the data for the navigability analysis must be collected using a different strategy.In order to automate this process, the paper examines the state of the art in the collection of environmental data. It extracts the parameters relevant for the operation of a robotic system from previous work, compares them with established data collections/services and identifies their limits when analyzing possible application areas. Building on this, the paper evaluates 150 projects that aim to close these gaps with different concepts. The article structures and classifies the projects and analyses their focal points. We can conclude that only a multi-model approach is able to address the complexity of acquiring all necessary environmental data with a high coverage. Additionally, with that analysis, we provide methodological support to plan future robotic applications.
Multi-Domain Simulation of Integrated Power Electronics Modules for EMC and EMI Analysis

This work presents a complete electromagnetic and circuital simulation flow able to determine and analyze the EMC and EMI phenomena, allowing the designer to detect potential concerns before the prototyping, thus reducing costs and time-to-market. An LLC resonant power converter application, featuring a STMicroelectronics SiC-based AcePack module, is adopted for verification purposes. The proposed simulation flow could be an essential tool to support the optimal design that will be addressed in future works.
Convolutional Neural Network Based Algorithm for Intrusion Detection in Internet of Things Databases

The Internet of Things (IoT) is generally isolated from external networks using firewall technology[1]. However, with the continuous development of IoT technology, the interconnection is becoming closer and the frequency of intrusions is increasing[2]. This poses greater challenges for security measures. Therefore, it is crucial to efficiently detect intrusions in the IoT. This article proposes a convolutional neural network-based algorithm for intrusion detection in IoT databases[3]. IoT data has fixed formats, real-time characteristics, and short fields. Therefore, training with a convolutional neural network improves efficiency[4]. The algorithm monitors and analyzes network traffic in the IoT system, uses a convolutional neural network to classify and determine traffic data[5], and thus achieves attack detection in the IoT system. The algorithm designed in this article achieves an accuracy rate of 99.4%, which not only improves the accuracy of intrusion detection compared to SVM and KNN algorithms but also reduces false positive rates.
Neuromorphic Dendritic Synapse Integrating Gated-RRAM

Artificial neural networks (ANNs) are becoming increasingly widespread yet the current hardware implementations exceed the power, area, and time budget of many applications. Additionally, the current ANN neurons are a highly simplified model of their biological counterparts. In this work, we propose a neuromorphic dendritic synapse that improves the computational complexity of individual neurons as a foundation for more efficient implementation of complex ANNs.
Detection of Vocal Cord Disease Based on Local Contour Features of Laparoscopy Images

Vocal cord disease is usually manifested as throat discomfort, dysphonia and other symptoms. If not treated in time, it may lead to dyspnea, canceration and other serious consequences. Laryngoscope is a common examination method for detecting vocal cord diseases. It is urgent to design an intelligent method to automatically detect vocal cord diseases from laryngoscope images using computer technology, so as to reduce the workload of laryngologists and the dependence on a large number of professional laryngologists. In laryngoscopic images, the symptoms of vocal cord diseases are concentrated on the internal contour of the vocal cord, usually characterized by vegetations, sausage-like masses and small protrusions. The existing research on laryngoscope image classification has paid little attention to the role of vocal cord contour in the diagnosis of vocal cord diseases, and failed to effectively focus on the local fine-grained features contained in the internal vocal cord contour. In this paper, we study the detection of vocal cord diseases based on laryngoscopic images, and propose a new classification method of laryngoscopic images, that is, local contour feature (LCF) based on local vocal cord contour feature. LCF includes four stages: image segmentation to obtain the overall vocal cord contour, internal vocal cord contour segmentation to obtain the internal contour curve by comparing the changes of adjacent pixel values, extracting the potential features in the vocal cord internal contour, taking the tangent slope angle of each point on the contour as the potential features, and classification module. We carried out experiments on data sets constructed from real laryngoscope images. The experimental results show that the proposed method LCF can effectively improve the detection performance of vocal cord diseases, with an accuracy of 97.2%.
Extended Intra Block Copy with Adaptive Filtering and Overlapped Block Averaging

Next-generation video coding standards are attempting to improve coding performance compared to conventional standards such as VVC by extending technologies such as intra-block copy (IBC). While IBC in VVC has proven effective for screen content, its adaptation to camera-captured content presents challenges regarding sample fluctuations and the continuity of block boundaries. This paper proposes a novel approach to improve IBC performance for camera-captured content by combining adaptive filtering (F-IBC) and overlapped block averaging (OB-IBC). The F-IBC filters IBC prediction samples using filter coefficients derived from adjacent samples to predict sample fluctuations accurately. The OB-IBC is a weighted average of IBC prediction samples of the current block with adjacent samples of the adjacent block’s reference to connect block boundaries smoothly. Following common test conditions in the joint video experts team, experimental results show improved coding performance with a bitrate saving of 0.1 % over the reference software (ECM 7.0) which investigates the enhanced compression beyond VVC capability.
Robust Operation of Energy Communities in the Italian Incentive System

In this paper, we address the optimal operation of energy communities, under energy production and consumption uncertainties. In the nominal case, the operational problem is formulated as the maximization of the profit of the community over a given time horizon. Inspired by the regulation adopted in Italy since 2020, the profit includes an incentive for the self-consumption realized at the community level in each time period. In the presence of uncertainties, we use a robust formulation aiming at maximizing the worst profit achievable when energy production and consumption deviate from their nominal values. The model results in a robust scheduling policy of battery charging/discharging, guaranteeing feasible operation of the community in all scenarios of the uncertainty set. On the other hand, numerical results show that the nominal scheduling policy may suffer from a high constraint violation probability.
Flexible Voltage Support Control of Three-Phase Four-Leg Inverter with Active and Reactive Power Oscillation Optimization Under Typical Double-Line-to-Ground Faults

Owing to the existence of zero-and negative-sequence voltage during grid faults, supporting the power grid and restraining active and reactive power oscillation (RPO) have become two main fault-tolerant requirements of distributed generation (DG). This paper presents a flexible voltage support control strategy without RPO in the four-leg inverter system relies on appropriate value of defined coefficient E, which can support the power grid, optimize the active power oscillation (APO) flexibly and limit the injected current to the allowed maximum by calculating the appropriate reference currents according to the equivalent impedance and the voltage sag characteristics under typical double-line-to-ground (DLG) faults. In this paper, it is assumed that the grid impedance is known, the proposed fault-tolerant control scheme requires calculating the grid impedance angle. The availability of the proposed strategy is verified by the simulation and experimental result.
Urban fringes and their renewal for local livability and network reconnections

Urban fringes are strategic resources for contemporary cities. They contain uncertain spatialities which could host signiﬁcant open space enhancements through design actions aimed at climate adaptation and mitigation, to improve local livability and reconnect cross-scalar networks. They are places of multiple relations where different conditions – natural and agricultural green spaces and corridors, infrastructural networks with nodes and connections, decentralized historical and rural fabrics, slow mobility networks, marginal areas, abandoned spaces and leftovers – come together. This section collects papers and design experiments which investigate fringe renewal in relation to various spatial elements: architecture, abandoned spaces, open spaces, green areas and forestation
The Subprime Asset-Backed Securities Market and the Equity Prices of Large Complex Financial Institutions

In this paper, we investigate the relationship between the subprime asset-backed collateralized debt obligations (CDO) market and Large Complex Financial Institutions (LCFIs). We attempt to account for the dynamics between the ABX index returns and the banks' equity returns through conditioning our analysis on the historical correlation between the variables. Three key results emerge from the analysis. First, we find a positive correlation between movements of the ABX index and the equity returns for all the LCFIs. Second, the volatility of ABX index returns tend to be transmitted to the volatilities of the equity returns of the financial institutions. Third, ABX prices changes lead equity returns changes of the European-based LCFIs. For the US LCFIs a two-way linkage emerges.
UvA-DARE (Digital Academic Repository) Using Bayes factor hypothesis testing in neuroscience to establish evidence of absence

Most neuroscientists would agree that for brain research to progress, we have to know which experimental manipulations have no effect as much as we must identify those that do have an effect. The dominant statistical approaches used in neuroscience rely on P values and can establish the latter but not the former. This makes non-significant findings difficult to interpret: do they support the null hypothesis or are they simply not informative? Here we show how Bayesian hypothesis testing can be used in neuroscience studies to establish both whether there is evidence of absence and whether there is absence of evidence. Through simple tutorial-style examples of Bayesian t -tests and ANOVA using the open-source project JASP, this article aims to empower neuroscientists to use this approach to provide compelling and rigorous evidence for the absence of an effect.
Predicting Cardiac Arrest Recovery with Deep and Shallow Machine Learning Models Using Electroencephalography Features

Cardiac arrest affects between 1% to 10% of the world based on geographical area. Most resuscitated cardiac arrest patients are comatose and often die due to severe brain injury. With the uncertainty of which patient will survive or not, it is important for the right prognosis to be given on which patient would benefit from life support. This would help decide which patients intensive care should be focused on. Years of research has presented patterns of neurological activity of comatose patients that are commonly used for making prognosis. Machine learning, which is a revolutionary computer program that can work without explicit instructions, could be used to study these patterns, and make better prognosis. Our team, Leicester Fox, focused on the comparison of the effectiveness of shallow and deep learning machine learning models in giving the right prognosis on chances of survival of cardiac arrest comatose patients. Features extracted from the electroencephalography (EEG) of 607 patients are used for this analysis. Three groups of features (18 features) were extracted including the patient information, category, and complexity of EEG signals. The results showed that the shallow model worked better in the prognosis using feature-based analysis. The shallow model had an accuracy of 76% and a challenge score of 51% while the deep learning model had an accuracy of 65%. In conclusion, when dealing with smaller number of patients and using features for analysis, shallow classifiers would usually give a better result.
Expert Systems With Applications

Cross-modal hashing has gained popularity in similarity search due to its excellent query efficiency and economical storage costs. However, current models frequently overlook the distinctive property of each modality, resulting in reduced accuracy due to inadequate utilization of these attributes. Moreover, there is a weak semantic relevance between modality attributes and multiple supervision knowledge (the labels and similarity constraints constructed by labels), accompanied by a cumulative quantization of the models. To address these issues, we propose an Individual Mapping and Asymmetric Dual Supervision method (IMADS). It merges specific and shared information to effectively learn a cross-modal representation space. Furthermore, we present an asymmetric dual supervision learning framework to produce discriminative hash codes. This framework achieves two primary goals: (1) Combing cross-modal representation and multiple supervision information to enhance the consistent relation of distinct modalities, and (2) developing a discrete optimization algorithm to mitigate the information loss caused by the hash code. Comprehensive experimental results illustrate that the introduced IMADS outperforms other stat-of-the-art hashing methods.
Polyphenolic Profile and Biological Study of Salvia Fruticosa

Two new flavone glycosides, scutellarin 3,8-di-C-β-D-4C1-glucopyranoside (1) and luteolin 7-O-β-D-4C1-glucopyranoside-6′‘ sulfate (2) together with sixteen known metabolites viz diosmin (3), apigenin 7-O-β-D-rutinoside (4), luteolin 7-O-β-D-rutinoside (5) luteolin 7-O-(6′‘-O-acetyl)-β-D-4C1-glucopyranoside (6), luteolin 7-O-β-D-4C1-galacturonopyranoside (7), luteolin 7-O-β-D-4C1-glucuronopyranoside (8), apigenin 7-O-β-D-4C1-glucouronopyranoside (9), apigenin 7-O-β-D-4C1-glucouronopyranoside methyl ester (10), luteolin 7-O-β-D-4C1-glucuronopyranoside methyl ester (11), cosmosiin (12), cinaroside (13), homoplontagenin (14), apigenin (15), luteolin (16), caffeic (17) and chlorogenic acids (18) were isolated from the aerial parts of Salvia. fruticosa Mill. Compounds 3, 6, 7, 10 and 11 were isolated for the first time from this genus, while 4, 5 and 14 isolated for the first time from S. fruticosa. Their chemical structures have been established on the basis of chemical and spectroscopic evidence. It was found that both 80% methanol and chloroform extracts of S. fruticosa are non-toxic to mice (LD50 up to 4 g. Kg−1 b. wt.). Their administration at 100 and 200 mg Kg−1 b. wt. improved liver functions, reduced the hepatotoxicity and the 80% methanol extract showed significant hypoglycemic effect and antispasmodic activity, as well.
Pan‐Arctic river discharge: Prioritizing monitoring of future climate change hot spots

The Arctic freshwater cycle is changing rapidly, which will require adequate monitoring of river flows to detect, observe, and understand changes and provide adaptation information. There has, however, been little detail about where the greatest flow changes are projected, and where monitoring therefore may need to be strengthened. In this study, we used a set of recent climate model runs and an advanced macro‐scale hydrological model to analyze how flows across the continental pan‐Arctic are projected to change and where the climate models agree on significant changes. We also developed a method to identify where monitoring stations should be placed to observe these significant changes, and compared this set of suggested locations with the existing network of monitoring stations. Overall, our results reinforce earlier indications of large increases in flow over much of the Arctic, but we also identify some areas where projections agree on significant changes but disagree on the sign of change. For monitoring, central and eastern Siberia, Alaska, and central Canada are hot spots for the highest changes. To take advantage of existing networks, a number of stations across central Canada and western and central Siberia could form a prioritized set. Further development of model representation of high‐latitude hydrology would improve confidence in the areas we identify here. Nevertheless, ongoing observation programs may consider these suggested locations in efforts to improve monitoring of the rapidly changing Arctic freshwater cycle.
Soil nitrogen dynamics in maize field trials after slurry injection compared to broadcast application using a new sampling strategy

Slurry injection below the maize seeds is a new application technique developed to improve the nitrogen use efficiency. However, better understanding of the soil mineral nitrogen (SMN) dynamics is essential. We developed a soil sampling strategy, in which the soil profile is sampled grid-like to a depth of 90 cm. Around the injection zone soil monoliths (SM) are sampled using a purpose build shovel, while below the SMs and in the interrow space an auger procedure is performed. This sampling strategy was used in a maize field trial on a sandy soil in lower Saxony (Germany) on several dates in 2014 to compare broadcast application to slurry injection below the maize row. In the slurry broadcast treatment most of the applied nitrogen leached out of the 0 – 30 cm layer until six leaf stage due to heavy rainfall occurring after planting. Especially nitrogen in the interrow space was lost for maize nutrition. Also in the slurry injection treatment nitrogen leaching was determined, but the displacement out of the injection zone was delayed und thus nitrogen was available for maize over a longer period. Based on the new soil sampling strategy it was revealed that reduced leaching is one reason why slurry injection enhances nitrogen use efficiency in maize.
Effects of Dietary Sodium Propionate on Growth Performance, Fillet Texture, Hematologic and Plasma Biochemical Parameter, Immune Responses, and Intestine Histology of Juvenile Trachinotus ovatus

Seven graded levels of sodium propionate (SP) diets with 0 (SP1), 0.2% (SP2), 0.4% (SP3), 0.6% (SP4), 0.8% (SP5), 1.0% (SP6), and 1.2% (SP7) were prepared to feed Trachinotus ovatus (initial body weight: 8.64 ± 0.08 g) for 56 days. The results showed that increasing dietary SP levels quadratically increased significantly final body weight (FBW), weight gain rate (WGR), and specific growth rate (SGR) of T. ovatus but linearly and quadratically decreased significantly viscerosomatic index (VSI) and hepatosomatic index (HSI) of T. ovatus (P<0.05). In the SP4 treatment, FBW, WGR, and SGR presented the highest values. Both positive linear and quadratic trends were detected between crude lipid content of whole fish, adhesiveness of dorsal muscle, white blood cell (WBC), red blood cell (RBC), hemoglobin (HGB), blood performance, high-density lipoprotein cholesterol (HDL-c), intestinal villus height, and dietary SP level, while negative linear and quadratic trends were found between firmness of dorsal muscle, triglyceride (TG), glucose (GLU), and dietary SP level (P<0.05). The increasing SP led to quadratic increases in lymphocyte (Lym), complement 3 (C3), chymotrypsin, villus number, and muscle layer thickness, and a quadratic decrease in hepatic malondialdehyde (MDAP<0.05). A significant negative linear trend was found between the content of glutamic-pyruvic transaminase (GPT) and dietary SP level, while significant positive linear trends were presented between C4, immunoglobulin M (IgM), α-amylase and dietary SP level (P<0.05). The increasing SP resulted in linear and quadratic increases in superoxide dismutase (SOD), total antioxidant capacity (T-AOC) of livers and C3, C4, IgM of head kidney (P<0.05). The expression levels of tumor necrosis factor alpha (TNF-α) and interleukin-8 (IL-8) were linearly and quadratically decreased, while the mRNA levels of growth factor beta (TGF-β) were linearly and quadratically increased with the increasing SP level (P<0.05). In conclusion, SP could be considered as a beneficial feed additive for enhancing growth and immunity of fish. And dietary SP level at 0.6% is optimal for the growth of Trachinotus ovatus based on a quadratic regression model of WGR.