Miracle-dz/UniFAR-FTUs huggingface dataset:
Transitions towards sustainable agriculture and food chains in peri-urban areas

Agriculture is changing rapidly. The greatest current challenge to the agricultural sector is for it to become sustainable in all three of the dimensions profit, people and planet. This is certainly the case in highly urbanized countries like the Netherlands, where agriculture is confronted with high land prices, rising consumer concerns for issues like animal welfare and negative environmental effects but also with new demands from the city for recreation, health care and local food products. These are some of the developments in our society that are forcing agriculture to change. The government, farmers, the agri-food industry and the retail sector struggle to meet this challenge and find new forms of governance. In the Netherlands, the government has called for a 'transition towards sustainable agriculture' and it is investing in this programme with its research and education policy. Similar trends have been observed in other countries. This book presents the expertise that has been accrued from at least five years of Dutch research in this area. The aim is to collate the results of the experiments, to learn from them, to confront them with existing theory and to share them with a larger audience in order to foster learning about transition. Given the leading position of the Netherlands in global agriculture, in a highly urbanized setting, and its leading position in the study of transition theory this should be of significant interest to students and researchers of the transitions in agriculture.
Investigation on the Thermal Stability of Silicon-carbide MOSFETs operating in Controlled Shoot-through mode

The Controlled Shoot-Through technique is an emerging and promising one for thermal control and monitoring of power MOSFETs. In this work we investigate the thermal stability of the power device in current-controlled mode at low gate voltages, to identify the effects of long pulses, as needed by in-circuit thermal analysis.
Detection of Brain Tumor from MRI Images Using VGG-16 CNN Model with Transfer Learning

Brain tumors are a serious health concern caused by the abnormal growth of cells in or around the brain. These tumors can either be benign or malignant. Few of the causes for Brain tumors are genetic mutations, exposure to radiation, or environmental factors. Early detection of brain tumors is essential for effective treatment and improved patient outcomes. To address this, a novel approach has been proposed for brain tumor detection using transfer learning with the VGG-16 convolutional neural network (CNN) architecture. By leveraging the pre-trained VGG-16 model on the ImageNet dataset, which provides a broad range of data for learning and representing complex features of various objects, meaningful features can be extracted from MRI scans and differentiate between tumor and normal tissue accurately. This refined VGG-16 model can provide a more precise and reliable diagnosis, which is crucial for successful treatment. This research study demonstrates that the utilization of VGG-16 model for brain tumor diagnosis can provide an efficient and accurate method for identifying brain tumors. Moreover, this approach can be applied to develop automated systems to assist medical professionals in diagnosing and treating brain tumors more effectively.
Sporadic E ionization layers observed with radar imaging and ionospheric modification

Sporadic E ionization layers have been observed in the daytime subauroral ionospheric E layer by a 30 MHz radar in Alaska. The radar detects coherent backscatter from meter‐scale field‐aligned plasma density irregularities. The irregularities were generated by ionospheric modification—by the emission of strong HF electromagnetic waves directly beneath the layers—making the layers visible to the radar. Aperture‐synthesis methods are used to generate imagery of the layers from the radar data. The layers are patchy, with patches organized along fronts spaced by tens of kilometers and propagating slowly toward the southwest. Similar, naturally occurring layers are commonly observed at middle latitudes at night in the absence of ionospheric modification. That the patchy layers can be found at high magnetic latitudes during the day argues that they are most likely produced through the interaction of the ionospheric layer with neutral atmospheric waves and instabilities. Attenuation of the radar echoes when the HF emission frequency exceeded the third harmonic of the electron gyrofrequency was observed and is discussed.
Time-of-Day-Aware Slice Admission Control

Network slicing is expected to be a core feature of modern telecommunication networks as it enables the creation of multiple virtual networks sharing a common infrastructure. An efficient Slice Admission Control (SAC) mechanism is critical for the massive deployment of network slices. This paper presents a novel, Deep Reinforcement Learning-based SAC algorithm that considers network users’ activity patterns and resulting daily resource demand changes to make admission decisions. Compared to conventional approaches, the proposed solution enables a higher slice admission rate with limited customer Service Level Agreement (SLA) violations due to more efficient usage of available resources, thus facilitating the increase of operator revenue.
Convolutional Neural Network Algorithm for Lung Cancer Identification from X-Ray Images

There are many distinct ailments that have caused different difficulties to the society. Out of those diseases, cancer has shown the foremost devastating results. Researchers have proposed that cancer may be identified in its early stages and may be prevented by conducting research and looking into the disease's pattern of growth. In this research, a hybrid method of detection, extracting further to classify the various lung cancers using deep learning technique has been proposed. First, datasets of various carcinomas are acquired, and then the dataset images are labelled using feature extraction. Deep learning, a latest branch of AI which can facilitate to boost the performance of Convolutional Neural Network-based systems has been employed.
Crop Prediction using Machine Learning Techniques and IoT

Agriculture being the backbone of the Indian economy faces multiple challenges in the production of quality food and good yield due to the unnatural climatic changes, which reduces crop yield adversely. Farmers lack the knowledge to forecast the future crop yield and take the necessary precautions. Therefore, this research aims to assist farmers in crop selection by predicting suitable crops for a given region using supervised machine learning algorithms. The work focusses on achieving the improved accuracy of crop prediction when compared to the existing models. Seed data with the appropriate parameters such as humidity, soil pH, temperature, rainfall, nitrogen, potassium, and phosphorus content are collected to support its maximum growth. Based on the obtained data, the crop prediction is performed. Performance of the Naïve Bayes Classifier, Ada Boost Classifier, Decision Tree Classification, and Voting Classifier algorithms are compared based on the accuracy, confusion matrix, and other performance metrics like precision, recall, and F1 score. The Decision Tree classifier algorithm is chosen as the best algorithm for crop prediction and the model is hence deployed with it. The finest accuracy of around 99.4 % is achieved by the proposed model.
Advanced cooling technique to improve efficiency of Solar Panel

Increased energy demand due to demand from industry, home use, etc. Not satisfied with just that traditional resource. Hence, alternative energy resources will also increase; to satisfy this demand solar energy is a critical energy. This paper look into different techniques used to improve the thermal efficiency of a solar module to improve the efficiency. Solar panels are one of the renewable energy sources. Green energy (sunlight) is converted to electrical energy without the presence of mechanical energy. Energy created by Photovoltaic panels varies with the variation in the intensity of solar radiation received by the panel and the temperature of the photovoltaic surface. The module has a nonlinear voltage-current characteristic with a single point at which the source product is the maximum. Temperature and radiation levels of the module affect the maximum power point. Different geographical areas and seasons or time of year have different effect on these conditions. Many studies have been carried out on enhancing the efficiency of solar panels by cooling photovoltaic cells using different cooling techniques like- running water through tube on surface of panel at different rates, submerging the panel in fluids, using wet sponges or water holding things and keep it in contact with the panel's surface, etc. All these techniques have helped in improving the efficiency of panel. Here the real time mentoring can be done by the use of IoT with Raspberry Pi cheap which can be used to inculcate multiple sensors which can be used to track the temperature of solar panel and hence command the respective actuators to perform cooling actions. Such as turning on pumps or fans.
Simulating Magnetized Plasma using an Accelerated Finite Element-Boundary Integral Code

This paper describes an accelerated finite element-boundary integral code with support for complex media. The code is implemented in Python using several open source packages, and uses Numba just-in-time compilation for improved performance. To accelerate the boundary integral part of the code, a multilevel implementation of the adaptive cross approximation is used. As a numerical example, scattering against a magnetized plasma is demonstrated. For this example, compression of boundary integral matrices is achieved to 9 % of the memory required for full matrices.
Potato Plant Leaf Disease Detection and Recognition Using R-CNN Model

A smart farming system that makes use of the appropriate infrastructure is an example of a new technology that aids in the improvement of the quality and quantity of the country's agricultural goods, such as potatoes. Diseases cannot be prevented since developing potato plants requires consideration of factors such as the environment, soil, and quantity of sunlight. Deep learning has resulted in recent advances in computer vision. These advancements have enabled Potato to utilize a camera to diagnose disease. This study developed an effective new method for detecting infections in potato plants. Yukon Gold, a kind of potato, served as the trial subject. Specifically, Alternaria, Blackleg, and Target Spot were targeted for detection using this method. Using 5,615 images of diseased and healthy Potato plant leaves taken in controlled environments, deep CNN is trained to identify between the presence and absence of three illnesses. To identify the Potato illnesses affecting the tracked plants, the system used a CNN. Anomaly detection using the F-RCNN-trained model achieved a confidence score of 80%, while illness identification using the Transfer Learning model achieved an accuracy of 97.85%. The effectiveness of the automated image-capturing system for diagnosing diseases in Potato plant leaves was determined to be 93.33%.
The Measurement of Knowledge in Knowledge Graphs

Knowledge graphs (KG) have emerged as comprehensive tools to enable knowledge discovery. Knowledge representation learning learns knowledge graph embeddings, which are extremely useful feature inputs for a wide variety of prediction and graph analysis tasks. Due to the diverse and subjective nature of KG, KG evaluation becomes an important and open problem. Existing evaluation methods are often based on case studies or downstream tasks like information retrieval and question answering systems, which is either lack generalizability or hard to implement. To address the challenges above, we propose three direct metrics for the knowledge graph, the K Score, I Score, and C Score, derived from the science of science, information theory, and causality perspectives, respectively. We propose a human-centered approach to evaluate the effectiveness of our metrics. Through a pilot study, we share insights on the complications of human-centered evaluation and motivate future work.
Integration of SWOT-AHP Approach for Measuring the Critical Factors Affecting Rural Development

This study uses the combined method of SWOT and Analytic Hierarchy Process (AHP) to assess the factors affecting the process of new rural development (NRD) in An Giang province Vietnam. Objectives of the study is assessing the current situation of NRD and factors affecting the NRD process in An Giang province, Vietnam, on the basis of research to propose and select strategic options and key solutions to improve efﬁciency program implementation in the next phase.
An FFT Accelerator Using Deeply-coupled RISC-V Instruction Set Extension for Arbitrary Number of Points

Fast Fourier Transform (FFT) is one of the most widely used algorithms in digital signal processing (DSP). Many Internet of Things (IoT) devices include dedicated FFT hardware accelerators at the price of both extra hardware resources and power consumption. Limited by constrained resources for IoT applications at the edge, it is favorable as well as crucial to reduce such overhead as much as possible without sacrificing FFT performance. Meanwhile, open-source RISC-V microprocessors have become serious contenders to ARM or x86 alternatives, especially in IoT applications. Recent studies have shown that Application-specific Instruction-set Processors (ASIP) can achieve considerable acceleration performance with low overhead. This paper proposes a fine-grained RISC-V Instruction Set Architecture (ISA) extension scheme for efficient FFT operations with 12 new instructions, using four 16-bit multipliers and adders as computing units to minimize overhead. Our extended instructions are deeply coupled with the CPU pipeline, thus significantly reducing the hardware overhead of decoding and executing logic, and theoretically can accelerate the calculation of FFT with an arbitrary number of points. We demonstrate the proposed scheme on a Xilinx PYNQ-Z2 FPGA using the open-source RISC-V NutShell soft IP core, with data loading from either specially designed vector registers (V-mode) or RAM off-the-core (R-mode). The evaluation shows the proposed FFT acceleration scheme achieves a performance gain of 118 times in V-mode and 6.5 times in R-mode respectively, with only 16% power consumption required as compared to the vanilla NutShell RISC-V microprocessor.
Sedimentary evolution of the eastern Bay of Biscay since the Upper Eocene

Analysis of sedimentary evolution from upper Eocene to Quaternary in the easternmost Cantabrian continental margin has been carried out by seismic profiles recovered during the MARCONI cruise. The stratigraphic division has been established based on the correlation with published seismic data. Six seismic units (1 to 6, from older to younger) and four main seismic sequences have been defined. The integration of seismic facies, stratal architecture and geological history analysis has allowed the reconstruction of its evolution. During Upper Eocene a syn-tectonic deposition of gravitative sediments and small turbidite fans occur. This scenario maintains during the Lower Oligocene until the interplay between the consecutive Mid Oligocene sea level falls and denudation of the emerging Cantabrian Mountains provokes an increase in sediment transference towards deep basin with formation of a thick stacking of slope aprons and sandy turbidity systems. During the Miocene predominates hemipelagic settling and the formation of proximal, small and starved turbidite systems in continental rise. Towards Upper Miocene a new period of high sediment transfer occurs that ended in the recent development of the large turbidite system created by the confluence of CapFerret, Santander and Torrelavega systems.
Deep Learning-Based Networks to Detect Leaf Disease in Maize and Corn

Corn or maize possesses an exceptionally high level of genetic diversity as well as a very high capacity for productivity. It is classified with the other grains as a food group and is onto important crop grown for both people and animals. Corn, despite its crucial role, is particularly vulnerable to both pests and diseases due to its susceptibility to both. Both productionand quality are experiencing a decline as a result. The plant's tissue becomes infected, which causes the plant's growth to become stunted and its colour to change from green to yellow. The consequences of such issues are not just emotionally and physically taxing but also financially costly. Taking that part into consideration, we have worked with deep neural networks and performed experiments using ResNet50, VGG-16, EfficientNetB0, and EfficientNet B3. The best results were obtained on ResNet50 with an accuracy of 97.02 percent and a loss of 8.56 percent, as the model has been fine-tuned by utilizing a gridsearch algorithm, which searches for the best parameters to be utilized in order to achieve best possible train and test accuracy.
A Frequency Selective Surface based Polarization-Independent Band Notched Electromagnetic (EM) Wave Absorber

This work presents an EM wave absorber with a band notch between the absorption band. The proposed work consists of two vertically arranged FSS-based resonators over a common metal on the bottom side. The SMD resistor loaded top FSS pattern offers wide absorption bandwidth, and the bottom FSS pattern provides the band notch between the absorption band. The simulation results show that the proposed structure offers more than 90% absorption in 5.8-12.9 GHz and 14.48-20.8 GHz frequency bands with a reflection notch at 13.5 GHz with a 3dB bandwidth of 500 MHz. The designed structure is also polarization-independent due to the four-fold symmetry inside the resonator. Analysis of the proposed structure's angular stability shows that the presented design is angularly stable up to 50° and 40° for TE and TM polarization, respectively. The proposed structure is compact in nature with the unit cell dimension of $0.19 \lambda_{L} \times 0.19 \lambda_{L} \times 0.12 \lambda_{L}$, where $\lambda_{L}$ is the wavelength's lowest frequency. Due to presence of the notch band along with absorption, this structure can be used for RCS reduction application of antenna where the notch band can be used as reflector of the antenna.
Developing framework to constrain the geometry of the seismic rupture plane on subduction interfaces a priori– a probabilistic approach

SUMMARY A key step in many earthquake source inversions requires knowledge of the geometry of the fault surface on which the earthquake occurred. Our knowledge of this surface is often uncertain, however, and as a result fault geometry misinterpretation can map into significant error in the final temporal and spatial slip patterns of these inversions. Relying solely on an initial hypocentre and CMT mechanism can be problematic when establishing rupture characteristics needed for rapid tsunami and ground shaking estimates. Here, we attempt to improve the quality of fast finite-fault inversion results by combining several independent and complementary data sets to more accurately constrain the geometry of the seismic rupture plane of subducting slabs. Unlike previous analyses aimed at defining the general form of the plate interface, we require mechanisms and locations of the seismicity considered in our inversions to be consistent with their occurrence on the plate interface, by limiting events to those with well-constrained depths and with CMT solutions indicative of shallow-dip thrust faulting. We construct probability density functions about each location based on formal assumptions of their depth uncertainty and use these constraints to solve for the ‘most-likely’ fault plane. Examples are shown for the trench in the source region of the Mw 8.6 Southern Sumatra earthquake of March 2005, and for the Northern Chile Trench in the source region of the November 2007 Antofagasta earthquake. We also show examples using only the historic catalogues in regions without recent great earthquakes, such as the Japan and Kamchatka Trenches. In most cases, this method produces a fault plane that is more consistent with all of the data available than is the plane implied by the initial hypocentre and CMT mechanism. Using the aggregated data sets, we have developed an algorithm to rapidly determine more accurate initial fault plane geometries for source inversions of future earthquakes.
A Review on Chatbots: Advancements and its Downsides

Nowadays, using chatbots in the corporate and technology worlds is necessary. The recent development of chatbots like ChatGPT has raised various issues for technological titans and academic researchers, particularly with regard to the education sector, the health area, and business enterprises. The world was astonished by the exceptional proficiency of the generative AI tool, ChatGPT in handling remarkably complex tasks. Natural language generation (NLG), machine learning (ML), and natural language processing (NLP) are popular technologies utilized in Chabot development. In this paper, we have attempted to explain how chatbots are used in everyday life as well as the rapid advances in their design methodologies. This study includes a survey of numerous works that were based on research on the use of chatbots like ChatGPT and their effects in the real world. These recent articles have been analyzed with a focus on the type of knowledge provided to these systems and the domains for which these systems have been created, among other variables. This analysis aims to understand the most recent developments in the evolution of chatbot systems.
Knowledge-enhanced Pre-training large language model for depression diagnosis and treatment

Depression, a pervasive psychiatric disorder characterized by concealment, dependence on expert judgment, and a notable rate of misdiagnosis, poses a substantial burden on society. To enhance the diagnosis and treatment of depression, this study puts forth a proposition of employing knowledge-enhanced pre-training technology leveraging large language models. By integrating domain knowledge and depression knowledge graph directives, the pre-trained model undergoes optimization. Expert involvement in depression diagnosis and treatment fosters a guided learning process facilitated by expert feedback. Through the application of dialogue therapy, the efficacy of treatment is augmented. This technical approach aims to ameliorate the societal burden by improving the diagnosis and treatment of depressed individuals.
Improvement of Field Uniformity in Microwave Heating Cavity Using beam-splitting Metasurface

A one-bit coding metasurface is proposed and employed in a microwave heating cavity to improve the electric field uniformity. The proposed metasurface can split the incident beam into multiple ones, resulting in a more uniform field distribution. Simulation results show that the relative standard deviation (RSD) of the electric field distribution in the cavity decreases from 0.63 to 0.44 when the proposed metasurface is put in front of one cavity wall.