Miracle-dz/UniFAR-FTUs huggingface dataset:
COMPARATIVE ANALYSIS OF GENETIC ALGORITHM - SUPPORT VECTOR MACHINE AND DEEP LEARNING WITH CONVOLUTIONAL NEURAL NETWORK FOR BRAIN TUMOR DETECTION AND CLASSIFICATION

A brain tumor occurs when abnormal cells form within the brain. Many people suffer from brain tumor, and it is a serious and dangerous disease. The detection and classification of brain tumor is an important and difficult task in the medical field. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. But, as the data in MRI images are of high-quality, tumor detection and classification are very hard in this process. Medical imaging plays a major role in properly diagnosing the disease, wherein an essential part in detecting the tumor is image segmentation. The segmentation provides an automatic brain tumor detection technique in order to increase the precision of the yields while also decreasing the diagnosis time. The goal is to detect the tumor from the MRI images and extract the features from the segmented tumor and finally classify it. The image detection and classification process includes image acquisition, image pre-processing, denoising, image segmentation, feature extraction and classification. The input image is pre-processed using Weiner and median filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it is used for segmentation process, where Mean Shift Clustering is used. The segmented tumor undergoes features extraction stage, where Gray Level Co-occurrence Matrix (GLCM) features are used. In the last stage, images are classified either as tumorous or non-tumorous. Classification is done using Genetic Algorithm Support Vector Machine (GA-SVM), Deep Learning with Convolutional Neural Network (CNN). Early detection of the tumor region can be achieved without much time lapse in the calculation by using this efficient classifier model. This system presents a prototype for detecting objects based on GA-SVM that classifies images and assesses whether the image is cancerous. While comparing the accuracy and computational time of these classifiers, CNN would provide high accuracy and GA-SVM with lesser simulation time. The simulation results obtained for brain tumor detection and analysis are done with minimum computational time and with reasonable accuracy. This proposed system is tested using SPL dataset, which consists of 20 cases with 40 image samples of T2 FLAIR weighted MRI image and implemented using MATLAB software.
Applications of Distinct Element Method to the numerical simulation of sand molding

In the casting process, molten metal is poured into the mold and then solidified by cooling. As a result, we are able to get complex shaped metal products. The quality of the mold is one of the important factors for quality of resultant casting product. The molds called sand mold, which are widely used in the cast iron casting because of their inexpensiveness and high heat resistance compared to metal die, are manufactured by molding sands in order to obtain sufficient strength not to collapse when molten metal is poured. In addition, by the squeezing compacting, we are able to obtain the sand mold with high strength and uniform granularity. Therefore, in order to manufacture high quality sand mold, it is important to clarify the behaviour of sands in the squeezing process. Numerical simulation of squeezing process using Distinct Element Method (DEM) are expected to be a powerful tool for optimizing the molding conditions. In the previous studies of the numerical simulation of squeeze molding process [1], they demonstrated that pressure of the sand when compacting on the squeezing plate can be represented by the DEM simulation, despite the diameter of DEM element is larger than that of actual sand. However, recent result [2] reported that the pressure on the squeezing plate obtained by DEM is affected by the initial condition of the particle size distribution. This result suggests that we need to research the effect of the diameter of DEM element more detail. Additionally, these simulations of squeezing were performed by using only the simple cylindrical shape, although actual sand molds often have complex shape. In this study, we calculate the same process of [2] with smaller element size. We have parallelized the numerical method of [2] to deal with the increase in computational cost associated with decreasing particle size. And we investigate the dependency of the simulation results on element size. Moreover, in order to calculate squeezing process with complex shape, we introduce the representation method of shape using signed distance function [3] [4]. We will show some numerical results of our method.
Microbial Community Composition during a Bloom of Purple Bacteria in Intertidal Sediments in Vigo (Northwest Spain)

Urban beaches are valuable recreational areas particularly vulnerable to human disturbance. In these areas, the intertidal sediments harbor a diverse community of microorganisms, including virus, bacteria, fungi, and protozoa. ABSTRACT In summer 2019, a large, bright pink microbial mat was visible on top of macroalgal deposits in muddy sediments of an urban beach (Playa do Adro, Vigo). In order to characterize the dominant organisms in these colored mats, results from microscopic observations, photosynthetic pigments, and molecular analysis were gathered. Light microscopy examination revealed pinkish microbial aggregates with minor contributions of larger protists and cyanobacteria. High-performance liquid chromatography (HPLC) pigment analysis documented the dominance of bacteriochlorophyll a and carotenoids whose spectra were compatible with those described in photosynthetic purple bacteria. 16S rRNA gene amplicon sequencing confirmed that the vast majority of reads belonged to Proteobacteria (73.5%), and among them, nearly 88% of those reads belonged to purple sulfur bacteria (Gammaproteobacteria). A single family, Chromatiaceae, constituted the bulk of this assemblage, including the genera Thiohalocapsa (32%), Marichromatium (12.5%), Phaeochromatium (5%), and Halocromatium (2%) as main contributors. Nonetheless, a considerable number of sequences could not be assigned to a particular genus, stressing the large biological diversity in these microbial mats and the potential presence of novel taxa of purple sulfur bacteria. IMPORTANCE Urban beaches are valuable recreational areas particularly vulnerable to human disturbance. In these areas, the intertidal sediments harbor a diverse community of microorganisms, including virus, bacteria, fungi, and protozoa. In this sense, pollution events can introduce pathogenic allochthonous microbes which may constitute a human health risk. Visual and sensory observations, such as a weird color or bad smell, are usually appreciated as a warning by beachgoers and authorities, as indeed was the case at do Adro beach in 2019. The observed proliferation seems to be common in summertime, but its dimension alerted beachgoers and media. The obtained results allowed for the identification of purple sulfur bacteria as responsible for the pink-violet top layer staining the intertidal zone. These blooms have never been associated with public health risks. Beyond solving the sanitary concern, other important findings were its diversity and large proportion of novel taxa, illustrating the complexity of these ecosystems.
Crowdfunding for a Sustainable Future: A Systematic Literature Review

A transition toward a more sustainable economy requires substantial innovations. To this end, sustainability innovators need not only ideas but also financial resources. Yet, especially nascent sustainability innovators suffer from trust issues and other barriers to funding through traditional financing. In this regard, crowdfunding (CF) offers a promising approach but also creates additional challenges, such as how to phrase campaign descriptions in order to credibly communicate sustainability intentions. As CF involves complex choices, it is far from obvious how sustainability innovators can best utilize it. To address this gap, in this article, we describe a systematic literature review (SLR) that analyzed 78 relevant publications. The results of the SLR show that innovation's sustainability orientation influences the dynamics in the components of the CF system through the two key mechanisms of information asymmetries and motivational effects. Building upon this, the article presents and analyzes critical choice parameters for sustainability innovators who tap into CF, which in particular concerns the choice of CF platform, CF type, and campaign communication. Furthermore, related propositions and a research agenda on CF and sustainability are provided.
Deep-Confidentiality: An IoT-Enabled Privacy-Preserving Framework for Unstructured Big Biomedical Data

Due to the Internet of Things evolution, the clinical data is exponentially growing and using smart technologies. The generated big biomedical data is confidential, as it contains a patient’s personal information and findings. Usually, big biomedical data is stored over the cloud, making it convenient to be accessed and shared. In this view, the data shared for research purposes helps to reveal useful and unexposed aspects. Unfortunately, sharing of such sensitive data also leads to certain privacy threats. Generally, the clinical data is available in textual format (e.g., perception reports). Under the domain of natural language processing, many research studies have been published to mitigate the privacy breaches in textual clinical data. However, there are still limitations and shortcomings in the current studies that are inevitable to be addressed. In this article, a novel framework for textual medical data privacy has been proposed as Deep-Confidentiality. The proposed framework improves Medical Entity Recognition (MER) using deep neural networks and sanitization compared to the current state-of-the-art techniques. Moreover, the new and generic utility metric is also proposed, which overcomes the shortcomings of the existing utility metric. It provides the true representation of sanitized documents as compared to the original documents. To check our proposed framework’s effectiveness, it is evaluated on the i2b2-2010 NLP challenge dataset, which is considered one of the complex medical data for MER. The proposed framework improves the MER with 7.8% recall, 7% precision, and 3.8% F1-score compared to the existing deep learning models. It also improved the data utility of sanitized documents up to 13.79%, where the value of the k is 3.
A CRITICAL REVIEW OF RISK MANAGEMENT SUPPORT TOOLS

It has been claimed by many researchers that “a risk driven approach” to project management is necessary to increase the success of construction projects. Literature is very rich in conceptual frameworks to overcome the informality of risk management efforts. However, risk management paradigms exist as methodologies rather than systems which can fully support the risk management process. The existing risk management support tools are usually based on quantitative risk analysis whereas the other phases are carried out external to the software. Risk registers and risk assessment tools are proposed as decision support systems which can only be used at specific stages of a construction project for specific purposes such as time/cost estimation at the bidding stage, country risk assessment during international market selection etc. Moreover, the proposed risk management support tools usually do not foster integration of risk management activities between the parties involved in the construction supply chain, do not consider impact of risks on all of the project success criteria, and can not handle subjectivity. In the recent years, the research has shifted to information and process models in which risks and response strategies may be identified, analysed and managed in a formal way by the use of database and model management systems. The major objective of this paper is to make a critical review of existing risk management support tools and propose development of a risk management corporate memory coupled with a decision support tool for successful management of risk.
Minimax Learning for Distributed Inference

The classical problem of supervised learning is to infer an accurate estimate of a target variable <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> from a measured variable <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> using a set of labeled training samples. Motivated by the increasingly distributed nature of data and decision making, this paper considers a variation of this classical problem in which the inference is distributed between two nodes, e.g., a mobile device and a cloud, with a rate constraint on the communication between them. The mobile device observes <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> and sends a description <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> to the cloud, which computes an estimate <inline-formula> <tex-math notation="LaTeX">$\hat {Y}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula>. We follow the recent minimax learning approach to study this inference problem and show that it corresponds to a one-shot minimax noisy lossy source coding problem. We then establish information theoretic bounds on the risk-rate Lagrangian cost, leading to a general method for designing a near-optimal descriptor-estimator pair. A key ingredient in the proof of our result is a refined version of the strong functional representation lemma previously used to establish several one-shot source coding theorems. Our results show that a naive estimate-compress scheme for rate-constrained inference is not optimal in general. When the distribution of <inline-formula> <tex-math notation="LaTeX">$(X,Y)$ </tex-math></inline-formula> is known and the error is measured by the logarithmic loss, our bounds on the risk-rate Lagrangian cost provide a new one-shot operational interpretation of the information bottleneck. We also demonstrate a way to bound the excess risk of the descriptor-estimator pair obtained by our method.
Inverse Identification of Single-Crystal Plasticity Parameters of HCP Zinc from Nanoindentation Curves and Residual Topographies

This paper investigates the orientation-dependent characteristics of pure zinc under localized loading using nanoindentation experiments and crystal plasticity finite element (CPFEM) simulations. Nanoindentation experiments on different grain orientations exhibited distinct load–depth responses. Atomic force microscopy revealed two-fold unsymmetrical material pile-up patterns. Obtaining crystal plasticity model parameters usually requires time-consuming micromechanical tests. Inverse analysis using experimental and simulated loading–unloading nanoindentation curves of individual grains is commonly used, however the solution to the inverse identification problem is not necessarily unique. In this study, an approach is presented allowing the identification of CPFEM constitutive parameters from nanoindentation curves and residual topographies. The proposed approach combines the response surface methodology together with a genetic algorithm to determine an optimal set of parameters. The CPFEM simulations corroborate with measured nanoindentation curves and residual profiles and reveal the evolution of deformation activity underneath the indenter.
Coupling Apollo with the CommonRoad Motion Planning Framework

The development of autonomous vehicles requires extensive testing of software modules. Developing a reliable software platform which allows testing on a real vehicle is yet a challenging task. Therefore, open-source software platforms are becoming more important for researchers in the field of autonomous driving. For example, Baidu provides the open-source autonomous driving platform Apollo which aims to accelerate testing and deployment of autonomous vehicles. However, the complex software structure hinders an easy integration of developed software modules, especially the motion planning module. Moreover, Baidu's Apollo provides only one possibility to test one's own algorithms in simulation, namely to upload the algorithm in Baidu's cloud platform, which is unacceptable for most autonomous driving companies. In contrast, the open-source CommonRoad benchmark suites contain diverse testing scenarios, e.g., highway, urban, dense traffic, and interaction with bicyclists and pedestrians. In addition, CommonRoad provides a motion planning framework in Python which enables rapid planner prototyping, along with additional tools, e.g., efficient collision checker, map format converter, and interface with the traffic simulator SUMO. In this work, we introduce a Python API between the planning module of the Baidu Apollo platform and the CommonRoad software framework. The developed interface aims to bridge the gap between rapid prototyping for safe planning algorithms and real-time test drives. The API transfers perception and map information to the planner and then returns the planned trajectory. The users can either replace the Apollo planner with their own planner or integrate their planner as a fail-safe planner if the planned trajectory by Apollo is unsafe. With our interface, developers can first test their planners in diverse scenarios from the CommonRoad benchmark, and directly on a real vehicle afterwards using the Apollo platform. The latter can be performed without adapting their algorithms to Apollo software structures. Moreover, developers can record their test drives in CommonRoad format for offline analyses. We demonstrate our interface in several scenarios with increasing complexity.
The relation between a generalized Fibonacci sequence and the length of Cunningham chains

Let $p$ be a prime number. A chain $\{p,2p+1,4p+3,\cdots,(p+1)2^{l(p)-1}-1\}$ is called the Cunningham chain generated by $p$ if all elements are prime number and $(p+1)2^{l(p)}-1$ is composite. Then $l(p)$ is called the length of the Cunningham chain. It is conjectured by Bateman and Horn in 1962 that the number of prime $p\leq N$ such that $l(p)\geq k$ is asymptotically equal to $B_k N/(\log N)^k$ with a real $B_k>0$ for all natural number $k$. This suggests that $l(p)=\Omega(\log p/\log\log p)$. However, so far no good estimation is known. It has not even been proven whether $\limsup_{p\to\infty} l(p)$ is infinite or not. All we know is that $l(p)=5$ if $p=2$ and $l(p)<p$ for odd $p$ by Fermat's little theorem. Let $\alpha\geq3$ be an integer. In this article, a generalized Fibonacci sequence $\mathcal{F}_\alpha=\{F_n\}_{n=0}^\infty$ is defined as $F_0=0,F_1=1, F_{n+2}=\alpha F_{n+1}+F_n (n\geq0)$, and ${}_{\mathcal{F}_\alpha}\sigma(n)=\sum_{d\mid n, 0<d\in\mathcal{F}_\alpha}d$ is called a divisor function on $\mathcal{F}_\alpha$. Then we obtain an interesting relation between the iteration of ${}_{\mathcal{F}_\alpha}\sigma$ and the length of Cunningham chains. For two primes $p$ and $q$, the fact $p=2q+1$ or $2q-1$ is equivalent to ${}_{\mathcal{F}_\alpha}\sigma({}_{\mathcal{F}_\alpha}\sigma(F_p))={}_{\mathcal{F}_\alpha}\sigma(F_q)$ for some $\alpha$. By this relation, we get $l(p)\ll\log p$ under a certain condition. It seems that this sufficient condition is plausible by numerical test. Furthermore, the condition, written in terms of prime numbers, can be replaced by the condition written in terms of natural numbers. This implies that the problem of upper estimation of $l(p)$ is reduced to that on natural numbers.
Step-Down Partial Power DC-DC Converters for Two-Stage Photovoltaic String Inverters

Photovoltaic (PV) systems composed by two energy conversion stages are attractive from an operation point of view. This is because the maximum power point tracking (MPPT) range is extended, due to the voltage decoupling between the PV system and the dc-link. Nevertheless, the additional dc-dc conversion stage increases the volume, cost and power converter losses. Therefore, central inverters based on a single-stage converter, have been a mainstream solution to interface large-scale PV arrays composed of several strings connected in parallel made by the series connections of PV modules. The concept of partial power converters (PPC), previously reported as a voltage step-up stage, has not addressed in depth for all types of PV applications. In this work, a PPC performing voltage step-down operation is proposed and analyzed. This concept is interesting from the industry point of view, since with the new isolation standards of PV modules are reaching 1500 V, increasing both the size of the string and dc-link voltage for single-stage inverters. Since grid connection remains typically at 690 V, larger strings impose more demanding operation for single-stage central inverters (required to operate at lower modulation indexes and demand higher blocking voltage devices), making the proposed step-down PPC an attractive solution. Theoretical analysis and an experimental test-bench was built in order to validate the PPC concept, the control performance and the improvement of the conversion efficiency. The experimental results corroborate the benefits of using a PPC, in terms of increasing the system efficiency by reducing the processed power of the converter, while not affecting the system performance.
Non-Intrusive Load Disaggregation Based on a Feature Reused Long Short-Term Memory Multiple Output Network

Load decomposition technology is an important aspect of power intelligence. At present, there are mainly machine learning methods based on artificial features and deep learning methods for load decomposition. The method based on artificial features has a difficult time obtaining effective load features, leading to low accuracy. The method based on deep learning can automatically extract load characteristics, which improves the accuracy of load decomposition. However, with the deepening of the model structure, the number of parameters becomes too large, the training speed is slow, and the computing cost is high, which leads to the reduction of redundant features and the learning ability in some shallow networks, and the traditional deep learning model has a difficult time obtaining effective features on the time scale. To address these problems, a feature reused long short-term memory multiple output network (M-LSTM) is proposed and used for non-invasive load decomposition tasks. The network proposes an improved multiscale fusion residual module to extract basic load features and proposes the use of LSTM cyclic units to extract time series information. Feature reuse is achieved by combining it with the reorganization of the input data into multiple branches. The proposed structure reduces the difficulty of network optimization, and multi-scale fusion can obtain features on multiple time scales, which improves the ability of model feature extraction. Compared with common network models that tend to train network models for a single target load, the structure can simultaneously decompose the target load power while ensuring the accuracy of load decomposition, thus reducing computational costs, avoiding repetitive model training, and improving training efficiency.