Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Potential Fishing Zones for Skipjack Tuna During the Northwest Monsoon Using Remotely Sensed Satellite Data

One of economically important fish in the Bay of Bone is Skipjack tuna which their distribution and migration are influenced by surrounding environment. This study aims to investigate the relationship between skipjack tuna and their environments, and to predict potential fishing zones (PFZs) for the fish in the Bone Bay-Flores Sea using satellite-based oceanography and catch data. Generalized additive models (GAMs) were used to assess the relationship. A generalized linear model(GLM) constructed from GAMs was used for prediction. Monthly mean sea surface temperature (SST) and chlorophyll-a during the northwest monsoon (December-January) together with catch data were used for the year 2012-2013. We used the GAMs to assess the effect of the environment variables on skipjack tuna CPUE (catch per unit effort). The best GLM was selected to predict skipjack tuna abundance. Results indicated that the highest CPUEs (fish/trip) occurred in areas where SST and chlorophyll-a ranged from 29.5°-31.5°C and 0.15 - 0.25 mg m-3, respectively. The PFZs for skipjack were closely related to the spatial distribution of the optimum oceanographic conditions and these mainly developed in three locations, northern area of Bone Bay in December, in the middle area of the bay (4°-5.5°S and 120.5°-121.5°E) during January and moved to the Flores Sea in February. The movement of skipjack concentration was consistent with the fishery data. This suggests that the dynamics of the optimum oceanographic signatures provided a good indicator for predicting feeding grounds as hotspot areas for skipjack tuna in Bone Bay-Flores Sea during northwest monsoon. Keywords: skipjack tuna, potential fishing zones, satellite based-oceanographic data, Northwest monsoon
Selective Harmonic Elimination In 5-Level MLI Using Particle Swarm Optimization

Multilevel inverters are highly capable to obtaining higher power ratings and higher output voltage waveforms by their modular multilevel structure. Currently, multilevel inverter applications is increasing in the various industries and renewable energy sources. High performance, modular design, and low harmonic distortion in voltage waveform are the most remarkable characteristics of multilevel inverters. The paper proposes a PSO algorithm for selective harmonic elimination in 5-level multilevel inverters. The algorithm consists of particle swarm optimization (PSO) to obtain optimized firing angles, resulting Elimination of 3rd harmonics from the system witch is shown by FFT analysis, it also provides lower total harmonic distortion (THD).
A Review of Mobile Robot Path-planning Research

Path-planning algorithms are crucial for enabling mobile robots to navigate autonomously and develop automation and artificial intelligence (AI). Thus, we analyzed mainstream path-planning algorithms and categorized them into conventional, smart search, sampling-based, and AI path-planning solutions. The model principle of the relevant algorithms was introduced, and their advantages and disadvantages were analyzed in this study. We also summarised the existing problems in path-planning for mobile robots and analyzed the development trends of future path-planning algorithms for mobile robots. We made suggestions to promote the progress of related research.
Review on the Applications of AI in Laboratory Experiments in Supply Chain Management Education

This paper comprehensively reviews the profound impact of Artificial Intelligence (AI) on laboratory sessions within supply chain management education. The primary objective is to elucidate the diverse applications of AI in educational settings and assess how AI has reshaped traditional laboratory sessions. This paper uses a systematic review methodology to analyse literature sources, identifying key trends, methodologies, and outcomes. The analysis categorises and scrutinises supply chain education laboratory sessions, including inventory management, demand forecasting, logistics optimisation, and procurement strategies. The paper delves into how AI technologies, such as machine learning algorithms, optimisation models, and simulation tools, have revolutionised these laboratory sessions. Findings reveal that AI integration has substantially improved the quality and effectiveness of laboratory experiments. AI-powered sessions empower students with enhanced problem-solving skills and a deeper understanding of real-world supply chain challenges. Moreover, AI-driven experiments create dynamic and adaptive learning environments, fostering student engagement and critical thinking. The implications extend beyond the classroom, as AI in laboratory sessions enhances supply chain management education and prepares future professionals for an AI-driven industry. This paper underscores the need for continued research and innovation in AI applications for supply chain management education and emphasises the importance of educators staying updated with evolving AI technologies.
Cross-layer Age-aware Scheme for Highly Reliable Memristor Crossbar Accelerator Design

Memristor crossbars (MC) are being widely adopted in research for energy-efficient deep learning (DL) due to their low power and fast switching characteristics, and nonvolatile nature. MCs have been demonstrated as an excellent vector matrix multiplication engine. However, MCs encounter severe reliability challenges due to aging of their memory cells. The major contributor to MC's aging in deep learning applications is the frequent weight updates. During a weight update operation, target cells in an MC is written with new conductance values. Over time, cells degrade (called aging) and hence, their written conductance values differ from the target value in each weight-update cycle. As a result, aging drastically impacts the overall performance of MC-based deep learning thereby becoming a major obstacle to commercialadop-tionof MCs. Limited attempts have been made to address this challenge. In this paper, we introduce a centralized online test framework to locate aged cell. In addition, the proposed test framework is complimented by a novel aging-aware mapping scheme to demonstrate reliable MC-based deep learning. Experiments with standard CAD tools in conjunction with an established deep learning framework show an average of 84% success rate in identifying aged cells in the crossbar. Complemented by our aging aware mapping scheme, the percentage of erroneous cells in the crossbars was found to be significantly less compared to the crossbars without the mapping scheme.
Ridge Structure Enhancement for Optical Coherence Tomography Latent Fingerprint Images

This paper proposes a method to improve the ridge structure in latent fingerprint images captured through optical coherence tomography (OCT). Friction ridges, which compose fingerprints, play a crucial role in forensic investigations as they enable individual identification. Unfortunately, latent fingerprint images frequently possess low quality, leading to subpar performance in automatic fingerprint identification systems (AFIS). To address this issue, a technique that combines wavelet-based denoising, morphological segmentation and ridge structure enhancement is presented. The experimental results, measured by the orientation certainty level (OCL), demonstrate the effectiveness of the proposed enhancement method in noise reduction while preserving accurate values in areas with high curvature.
A Transfer Learning-Based Method for Context-Aware Recommender Systems

Context-aware recommender systems (CARS) are specially designed to consider contextual conditions in which users interact with items, aiming to generate enhanced recommendations. A well-recognized challenge when constructing recommender systems is how to resolve sparse data problem. Although the utilization of contextual information offers detailed signals for the recommendation process, it further exacerbates data sparsity and introduces increased computational complexity. In this paper, we present a method for making context-aware recommendations, which is less sensitive to data sparseness and allows full integration of context information. In which, context integration is done by splitting users based on contextual conditions and the sparse data problem is solved through a transfer learning-based method. We experimentally evaluated the proposed method on some contextually-tagged data sets. The results show that our method outperforms several baselines and state-of-the-art context-aware recommendation methods.
On binary and ratio time-frequency masks for robust speech recognition

A time-varying Wiener ﬁlter extracts the speech signal from a noisy mixture using the a priori signal-to-noise ratio in a lo-cal time-frequency unit. We estimate this ratio using a binaural processor and derive a ratio time-frequency mask. This mask is used to extract the speech signal, which is then fed to a conventional speech recognizer operating in the cepstral domain. We compare the performance of this system with a missing data recognizer that operates in the spectral domain using the time-frequency units dominated by speech. For use by the missing data recognizer, the same processor is used to estimate an ideal time-frequency binary mask, which selects the speech signal if it is stronger than the interference in a local time-frequency unit. We ﬁnd that the performance of the missing data recognizer is better on a small vocabulary recognition task but the performance of the conventional recognizer is substantially better when the vocabulary size is larger.
Fake News Detection Using Boosting Ensemble Method

Around the globe, Fake news is a raising concern and has several negative repercussions on people, societies, and countries. It has several detrimental effects on society, including disseminating false information and causing confusion, inciting social unrest and division, eroding credibility and confidence, endangering public health, and causing political instability. To protect people from damage, preserve informational accuracy, stop the spread of hate speech, support a vibrant democracy, and maintain journalism's reputation, fake news detection is crucial. In this work, we attempt to tackle this problem by putting forth a method that uses machine learning classification to distinguish between authentic and fraudulent content. Natural language processing and machine learning ensemble approach are taken into consideration for this. In natural language processing, TF-IDF and Word2vec features are used and optimized using wrapper method (via RFE algorithm). Training and testing are done on “Fake News Detection” dataset from Kaggle. Exploratory examination upon the boosting ensemble method tells that our learners perform better than other options.
Implications of Tn5-associated adjacent deletions

The prokaryotic transposable element Tn5 has been found to promote the formation of adjacent deletions. The frequency of adjacent deletion formation is much lower than that of normal transposition events. Like normal transposition, however, adjacent deletion formation requires the activity of the transposase protein. The deletions can be divided into two classes, as distinguished by their endpoints. The occurrence of one of the two deletion classes is increased when the frequency of normal transposition is reduced by the introduction of a deletion or a certain base substitution at one of the two outside ends (OEs). The nature of the base substitution at the mutant OE influences the class of deletion found adjacent to the wild-type OE, even though these two ends are about 12 kbp apart. By studying the formation of these deletions, we have gained some insight into the way in which the transposase interacts with the OEs. Our observations suggest that there is a protein-mediated interaction between the two ends, that different end base pairs are involved in different transposition-related processes, and that the adjacent deletions are the result of nonproductive attempts at transposition.
Monotone Submodularity in Opinion Summaries

Opinion summarization is the task of producing the summary of a text, such that the summary also preserves the sentiment of the text. Opinion Summarization is thus a trade-off between summarization and sentiment analysis. The demand of compression may drop sentiment bearing sentences, and the demand of sentiment detection may bring in redundant sentences. We harness the power of submodularity to strike a balance between two conflicting requirements. We investigate an incipient class of submodular functions for the problem, and a partial enumeration based greedy algorithm that has performance guarantee of 63%. Our functions generate summaries such that there is good correlation between document sentiment and summary sentiment along with good ROUGE score, which outperforms thestate-of-the-art algorithms.
Determining the Reliability of the Graf Classification for Hip Dysplasia

We sought to establish the levels of interrater reliability and intrarater reliability of the Graf classification among orthopaedic surgeons in their final training year and who learned the method by instructed teaching or self study. Using standard teaching material developed by Graf, two groups of senior orthopaedic residents at the same training level received structured teaching sessions (Group A, n = 2) or performed self study (Group B, n = 2). Interrater reliability and intrarater reliability were determined (Cohen's weighted kappa). Proportions of correctly rated sonograms were compared between groups, implications of misclassifications were analyzed, and sensitivity analyses were performed. Interrater reliability was 0.59 (95% CI = 0.32-0.85) for Group A, and 0.47 (95% CI = 0.14-0.79) for Group B. Intrarater reliability showed an overall kappa of 0.57 (95% CI = 0.35-0.78) in Group A, and 0.47 (95% CI = 0.19-0.75) in Group B. The proportion of correctly rated sonograms between groups was similar in the original dataset and in the sensitivity analysis. Misclassifications influencing treatment were infrequent; one patient would have received unwarranted treatment and three patients would not have received warranted treatment. The Graf classification showed moderate reliability. Using self study, it can be learned almost, but not quite as effectively as by a structured program.Level of Evidence: Diagnostic Study, Level I (testing of previously developed diagnostic criteria on consecutive patients [with universally applied reference “gold” standard]). See the Guidelines for Authors for a complete description of levels of evidence.
Improving Machine Learning Techniques for Personalized Cardiac Arrest Risk Estimation

This study researches the efficacy of gadget learning algorithms for predicting private cardiac arrest chances. Precise evaluation of existing methods and datasets for predicting cardiac arrest hazards could be performed to identify areas of improvement. Ultimately, unique system mastering algorithms and techniques may be tested and compared to modern-day fashions to identify the one that could offer the first-rate performance. Moreover, this research will provide insights into the finer info of version personalization to create custom fashions tailored for people consistent with their applicable chance profile. It will be carried out using information from numerous assets, including the affected person's clinical records, demographics, lifestyle, and environmental elements. Further, accuracy, sensitivity, and specificity parameters can be additionally evaluated to decide the most appropriate predictive fashions. The results of this research challenge will offer treasured insights into the utility of device studying techniques for customized chance estimation of cardiac arrest, which can be used to improve the accuracy and efficacy of outcomes. It technical summary introduces how to improve modern-day machine getting-to-know strategies for customized cardiac arrest threat estimation. The proposed method uses a convolutional neural community with a unique structure optimized for gaining knowledge of functions from medical information of cardiac arrest sufferers. In particular, the proposed structure utilizes a shared convolutional-recurrent layer for statistics extraction, accompanied by multiple fully connected layers, a dropout layer for noise regularization, and a softmax output layer with sigmoid-go entropy loss characteristic. The evolved model was examined on a cardiac arrest-affected person facts dataset and showed statistically considerable overall performance development compared to modern-day system learning procedures. These results illustrate the capability of the proposed technique to improve threat estimation for personalized cardiac arrest remedies.
High Speed Mode Division Multiplexing based Visible Light Communication for IoT Applications

Today, high bandwidth and high capacity optical wireless networks for internet of things application are growing. The economical light emitting diode (LED) basics visible light communication (VLC) is a fast growing optical wireless scenario. Thus, a high speed 12×20Gbps hybrid VLC and mode division multiplexing system considering laguerre-gaussian (LG)[00] and LG [01] modes is proposed. Two sets of six LEDs are used for signal transmission where each set operates at different LG modes. OptiSystem is incorporated to design and investigate the system performance at 10−3 bit error rate. The results indicate that faithful VLC range of 30-50m is obtained successfully for LG[00] as well as LG [01] operating modes for red LED, with supportable 60 internet of things devices. The comparative analysis of the proposed design indicates its superiority over other existing ones. This system can be incorporated for future based high bandwidth next generation optical access networks.
A Novel Design for Geometric Constellation Shaping

In this paper, a novel design is proposed for geometric constellation shaping (GCS) based on a uniform quadrature-amplitude modulation (QAM) constellations. The basic idea is to select a subset of constellation points from a higher order QAM constellation to form a GCS constellation pattern which achieves the highest bit-interleaved coded modulation (BICM) capacity. Since the selected constellation points are from the larger uniform QAM constellation, it is easy to implement a low-complexity demodulation scheme by leveraging existing schemes for uniform constellations. To reduce the complexity of an exhaustive search, a novel search scheme based on the simulated annealing (SA) algorithm is proposed to find the optimal constellation pattern. According to simulation results, the performance of the proposed GCS constellation is 0.2dB and 0.5dB better than uniform 16-QAM and uniform 64-QAM in AWGN channel, respectively, when the same BICM capacity is achieved. Compared to existing GCS schemes, the proposed scheme has lower demodulation complexity and comparable or better performance.
PLC Hardware-in-the-Loop Simulation of Wind Turbine Master Controller Based on RT-LAB

This paper introduces the new achievements of wind turbine modeling and master controller hardware-in-the-loop simulation based on the panoramic co-simulation architecture. The panoramic co-simulation technology is based on multidimensional and multi-body dynamic modeling computation, and builds a digital wind turbine model in a real-time simulation platform. As the representative application of multi-time scale simulation, the panoramic co-simulation architecture connects the slow subsystem Programmable Logic Controller hardware and the fast subsystem RT-LAB simulator with both Modbus and I/O communication protocol, enabling it to achieve control logic debugging of the wind turbine controller through HIL testing, providing an effective test and verification platform for the development and experiments of new-type wind turbines.
Smart Home Automation with Smart Metering using ZigBee Technology and Deep Belief Network

The smart metering in the smart home application is vital one to ensure the reduction of energy usage and become a hot topic in the research field with the increasing usage of energy in both industrial and residential. With the advance technologies the energy saving techniques are used for the smart homes. The Internet of Things (IoT) devices are used for monitoring, controlling, and detecting the devices for the smart home applications. In this study, a novel Deep Belief Neural Network (DBN) approach for analyzing the data that are gathered from smart home applications with IoT devices, and big data technologies which promises the smart metering in smart home applications is proposed. The energy consumption patterns and classifications are effectuated with the proposed DBN approach and ensures the safety of the home. Experimental study was made and analyzed the performance of the proposed wo rk in terms energy consumption of different devices of smart home applications. Our proposed approach surpasses all the other approaches for the smart metering based smart applications.
A Generic Framework for the Cost Analysis of Java Bytecode

Cost analysis of Java bytecode is complicated by its unstructured control flow, the use of an operand stack and its object-oriented programming features (like dynamic dispatching). This paper addresses these problems and develops a generic framework for the automatic cost analysis of sequential Java bytecode. Our method generates cost relations which define at compile-time the cost of programs as a function of their input data size. To the best of our knowledge, this is the first approach to the automatic cost analysis of Java bytecode.
Large Shift in Symbiont Assemblage in the Invasive Red Turpentine Beetle

Changes in symbiont assemblages can affect the success and impact of invasive species, and may provide knowledge regarding the invasion histories of their vectors. Bark beetle symbioses are ideal systems to study changes in symbiont assemblages resulting from invasions. The red turpentine beetle (Dendroctonus valens) is a bark beetle species that recently invaded China from its native range in North America. It is associated with ophiostomatalean fungi in both locations, although the fungi have previously been well-surveyed only in China. We surveyed the ophiostomatalean fungi associated with D. valens in eastern and western North America, and identified the fungal species using multi-gene phylogenies. From the 307 collected isolates (147 in eastern North America and 160 in western North America), we identified 20 species: 11 in eastern North America and 13 in western North America. Four species were shared between eastern North America and western North America, one species (Ophiostoma floccosum) was shared between western North America and China, and three species (Grosmannia koreana, Leptographium procerum, and Ophiostoma abietinum) were shared between eastern North America and China. Ophiostoma floccosum and O. abietinum have worldwide distributions, and were rarely isolated from D. valens. However, G. koreana and L. procerum are primarily limited to Asia and North America respectively. Leptographium procerum, which is thought to be native to North America, represented >45% of the symbionts of D. valens in eastern North America and China, suggesting D. valens may have been introduced to China from eastern North America. These results are surprising, as previous population genetics studies on D. valens based on the cytochrome oxidase I gene have suggested that the insect was introduced into China from western North America.