Raw Input Research Articles

Firm size and enterprise productivity in Nigeria: Evidence from firm-level data

Anchored on the economies of scale of production theory, this study utilized Nigeria’s firm-level enterprise survey data of the World Bank collected through stratified random sampling of 2 676 firms and face-to-face interviews with the application of the multinomial logit model to examine how enterprise productivity influences the size of firms in Nigeria. It was found that raising enterprise productivity relates to about 0.0009261 insignificant fall in the relative log odds of running micro-sized firms, about 0.010299 significant rise in relative log odds of having medium-sized firms, and about 0.0201428 significant encouragement in relative log odds of running large-sized enterprises/firms when related with small-sized enterprises/firms. It is recommended that governments at all levels (state, federal, and local), should encourage micro-sized firms in a bid to make them increase their productivity level. This encouragement can come in the form of providing increased access to credit, the provision of raw material inputs, and constant electricity supplies. The original contribution of this research work is hinged on its empirical contribution in the study area since there is dearth of literature in the study area as no study has looked at firm size and enterprise productivity in Nigeria using evidence from firm-level data.

Sustainable Production of Biomass-Derived Graphite and Graphene Conductive Inks from Biochar.

Graphite is a commonly used raw material across many industries and the demand for high-quality graphite has been increasing in recent years, especially as a primary component for lithium-ion batteries. However, graphite production is currently limited by production shortages, uneven geographical distribution, and significant environmental impacts incurred from conventional processing. Here, an efficient method of synthesizing biomass-derived graphite from biochar is presented as a sustainable alternative to natural and synthetic graphite. The resulting bio-graphite equals or exceeds quantitative quality metrics of spheroidized natural graphite, achieving a Raman ID/IG ratio of 0.051 and crystallite size parallel to the graphene layers (La) of 2.08 µm. This bio-graphite is directly applied as a raw input to liquid-phase exfoliation of graphene for the scalable production of conductive inks. The spin-coated films from the bio-graphene ink exhibit the highest conductivity among all biomass-derived graphene or carbon materials, reaching 3.58 ± 0.16 × 104 Sm-1. Life cycle assessment demonstrates that this bio-graphite requires less fossil fuel and produces reduced greenhouse gas emissions compared to incumbent methods for natural, synthesized, and other bio-derived graphitic materials. This work thus offers a sustainable, locally adaptable solution for producing state-of-the-art graphite that is suitable for bio-graphene and other high-value products.

Knowledge abstraction and filtering based federated learning over heterogeneous data views in healthcare

Robust data privacy regulations hinder the exchange of healthcare data among institutions, crucial for global insights and developing generalised clinical models. Federated learning (FL) is ideal for training global models using datasets from different institutions without compromising privacy. However, disparities in electronic healthcare records (EHRs) lead to inconsistencies in ML-ready data views, making FL challenging without extensive preprocessing and information loss. These differences arise from variations in services, care standards, and record-keeping practices. This paper addresses data view heterogeneity by introducing a knowledge abstraction and filtering-based FL framework that allows FL over heterogeneous data views without manual alignment or information loss. The knowledge abstraction and filtering mechanism maps raw input representations to a unified, semantically rich shared space for effective global model training. Experiments on three healthcare datasets demonstrate the framework’s effectiveness in overcoming data view heterogeneity and facilitating information sharing in a federated setup.

Highfield imaging of the subgenual anterior cingulate cortex in uni- and bipolar depression.

The subgenual Anterior Cingulate Cortex (sgACC), as a part of the Anterior Cingulate Cortex and the limbic system plays a crucial role in mood regulation. Previous structural and functional brain imaging studies of the sgACC have revealed alterations of Gray Matter (GM) volumes and Blood Oxygenation Level Dependent signals (BOLD) in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD), suggesting potential biomarker traits for affective disorders. In this study we investigated the gray matter volume of the sgACC in 3 different patient groups: 40 MDD patients, of which 20 were medicated (MDDm) and 20 were unmedicated (MDDu), and 21 medicated BD patients, and compared them with 23 healthy volunteers. We examined GM volume alteration using high-resolution 7T Magnetic Resonance Imaging (MRI) which produced quantitative maps of the spin-lattice relaxation time (T1). T1 maps provide high contrast between gray and white matter, and at 7 Tesla voxels with submillimeter resolution can be acquired in a reasonable scan time. We developed a semi-automatic segmentation protocol based on refined landmarks derived from previous volumetric studies using quantitative T1 maps as raw input data for automatic tissue segmentation of GM, WM and CSF (cerebrospinal fluid) tissue. The sgACC ROI was then superimposed on these tissue probability maps and traced manually by two independent raters (F.B., M.M.) following our semi-automatic segmentation protocol. Interrater reliability was calculated on a subset of 10 brain scans for each hemisphere, showing an Intra-Class Correlation coefficient (ICC) r = 0.96 for left sgACC and r = 0.84 for right sgACC respectively. In summary, we have developed a reproducible and reliable semi-automatic segmentation protocol to measure gray matter volume in the sgACC. Based on previous findings from meta-analyses on morphometric studies of the sgACC, we hypothesized that patients with MDD would have lower gray matter sgACC volumes compared to healthy subjects. Post-hoc analysis revealed smaller subgenual volumes for the left hemisphere in both the medicated (MDDm) and non-medicated (MDDu) group versus healthy controls (p = .001, p = .008) respectively. For the right hemisphere, the (MDDu) and BD group exhibited significantly lower subgenual volumes than healthy controls (p < .001, p = .004) respectively. To our knowledge, this is the first morphometric MRI study using T1 maps obtained in high-resolution 7 Tesla MRI to compare MDD and BD patients with healthy controls.

A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop.

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information on transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. We also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

Classification of the L-, H-mode, and plasma-free state: Convolutional neural networks and variational autoencoders on the edge reflectometer for KSTAR.

Classifying and monitoring the L-, H-mode, and plasma-free state are essential for the stable operational control of tokamaks. Edge reflectometry measures plasma density profiles, but the large volume of data and complexity in reconstruction pose significant challenges. There is a need for efficient methods to analyze complex reflectometer data in real-time, which can be addressed using advanced computational techniques. Here, we show that machine learning (ML) techniques can classify discharge states using raw signal data from an edge reflectometer installed on the Korea Superconducting Tokamak Advanced Research. The deep convolutional neural network models achieved classification accuracy of up to 99% when using 2D spectrogram inputs, demonstrating a significant improvement over 1D raw signal inputs. Additionally, the variational autoencoder model effectively clustered the discharge states in the latent space without any label information, further validating the model's capability to classify discharge states. These results suggest that the ML model can effectively handle the complexity of reflectometer data and accurately classify plasma discharge states. This approach not only facilitates real-time diagnosis but also reduces the need for manual data processing.

Automated Provenance-Based Screening of ML Data Preparation Pipelines

SummarySoftware systems that learn from data via machine learning (ML) are being deployed in increasing numbers in real world application scenarios. These ML applications contain complex data preparation pipelines, which take several raw inputs, integrate, filter and encode them to produce the input data for model training. This is in stark contrast to academic studies and benchmarks, which typically work with static, already prepared datasets. It is a difficult and tedious task to ensure at development time that the data preparation pipelines for such ML applications adhere to sound experimentation practices and compliance requirements. Identifying potential correctness issues currently requires a high degree of discipline, knowledge, and time from data scientists, and they often only implement one-off solutions, based on specialised frameworks that are incompatible with the rest of the data science ecosystem.We discuss how to model data preparation pipelines as dataflow computations from relational inputs to matrix outputs, and propose techniques that use record-level provenance to automatically screen these pipelines for many common correctness issues (e.g., data leakage between train and test data). We design a prototypical system to screen such data preparation pipelines and furthermore enable the automatic computation of important metadata such as group fairness metrics. We discuss how to extract the semantics and the data provenance of common artifacts in supervised learning tasks and evaluate our system on several example pipelines with real-world data.

Automatically learning linguistic structures for entity relation extraction

A sentence is composed of linguistically linked units, such as words or phrases. The dependencies between them compose the linguistic structures of a sentence, which indicates the meanings of linguistic units and encodes the syntactic or semantic relationships between them. Therefore, it is important to learn the linguistic structures of a sentence for entity relation extraction or other natural language processing (NLP) tasks. In related works, manual rules or dependency trees are usually adopted to capture the linguistic structures. These methods heavily depend on prior knowledge or external toolkits. In this paper, we introduce a Supervised Graph Autoencoder Network (SGAN) model to automatically learn the linguistic structures of a sentence. Unlike traditional graph neural networks that use a fixed adjacency matrix initialized with prior knowledge, the SGAN model contains a learnable adjacency matrix that is dynamically tuned by a task-relevant learning objective. It can automatically learn linguistic structures from raw input sentences. After being evaluated on seven public datasets, the SGAN achieves state-of-the-art (SOTA) performance, outperforming all compared models. The results show that automatically learned linguistic structures have better performance than manually designed linguistic patterns. It exhibits great potential for supporting entity relation extraction and other NLP tasks.

Optimization and characterization of boric acid powder mixed electrical discharge machining of Ti-6Al-4V ELI

The powder mixed electrical discharge machining (PMEDM) process is used for enhancing machining efficiency and improving the surface characteristics of the workpiece compared to normal EDM. The present study evaluates the effect of boric acid powder mixed in deionized water in PMEDM of Ti-6Al-4V ELI (extra low interstitial). The PMEDM process is assessed by evaluating material removal rate (MRR) and surface roughness (SR) at varying powder concentration (PC), current and pulse duration ( Ton). Boric acid PMEDM exhibits advantageous for both MRR and SR, where MRR increases and SR decreases with an increase in PC. To simultaneously achieve higher MRR and lower SR, the grey-fuzzy logic method is employed. Initially, the raw input data is normalized into grey numbers and a single index is obtained through grey relational analysis. Subsequently, triangular membership functions are assigned to the inputs and output. Fuzzy logic rules are then formulated, and grey fuzzy relational grade (GFRG) is calculated for each condition. The optimum condition (GFRG = 0.787) is attained at 15 g/l PC, 9 A current and 106 µs Ton, resulting in a 63% improvement compared to the initial condition. At the optimum condition, MRR increased by 145% and SR decreased by 29% compared to the initial condition. The effect of boric acid in PMEDM can be visualized through the formation of smooth surfaces, exhibiting reduced SR, smaller and shallower craters, and fewer debris and globules. Additionally, the boric acid PMEDM experiments resulted in the formation of a hard TiB phase on Ti-6Al-4V, thus improving its wear resistance.

Machine cross-domain remaining useful life prediction via contrastive adversarial variational recurrent method

The performance degradation process of the machine is non-stationary which varies with the operating environment and is reflected as a temporal shift phenomenon. Models or methods that assume the test and training sets have the same distribution will no longer be suitable for solving problems where domain shifts exist. This brings new challenges for accurately evaluating the machine’s remaining useful life (RUL). This paper studies a contrastive adversarial variational recurrent method for machine RUL prediction under different service conditions. In this new approach, the variational recurrent networks are developed to extract distribution features and latent spaces of raw data, meanwhile, the adversarial strategies are applied to enable the learning of domain-invariant features to make the model achieve cross-domain task processing. To supervise the learning process of the model to learn more mutual information between the extracted features and the raw input data, a contrastive loss is also introduced in the proposed method. Sufficient experiments were conducted to verify the feasibility and superiority of the suggested approach, including 12 sets of cross-scenario tests on the turbofan engine dataset C-MAPSS from NASA. Experimental findings confirm that the proposed method performs satisfactorily and competitively even compared to current state-of-the-art works.

Timely ICU Outcome Prediction Utilizing Stochastic Signal Analysis and Machine Learning Techniques with Readily Available Vital Sign Data.

The ICU is a specialized hospital department that offers critical care to patients at high risk. The massive burden of ICU-requiring care requires accurate and timely ICU outcome predictions for alleviating the economic and healthcare burdens imposed by critical care needs. Existing research faces challenges such as feature extraction difficulties, low accuracy, and resource-intensive features. Some studies have explored deep learning models that utilize raw clinical inputs. However, these models are considered non-interpretable black boxes, which prevents their wide application. The objective of the study is to develop a new method using stochastic signal analysis and machine learning techniques to effectively extract features with strong predictive power from ICU patients' real-time time series of vital signs for accurate and timely ICU outcome prediction. The results show the proposed method extracted meaningful features and outperforms baseline methods, including APACHE IV (AUC = 0.750), deep learning-based models (AUC = 0.732, 0.712, 0.698, 0.722), and statistical feature classification methods (AUC = 0.765) by a large margin (AUC = 0.869). The proposed method has clinical, management, and administrative implications since it enables healthcare professionals to identify deviations from prognostications timely and accurately and, therefore, to conduct proper interventions.

Drawback And Its Contributions To Exports: Analysis Of The Special Regime In Brazilian Companies In The Piracicaba/Sp Region

Background: The drawback is a special customs regime designed to allow companies to exempt or defer taxes and fees on imported raw materials, inputs, packaging materials, and intermediate products that are used in the production of goods intended for export. This mechanism helps lower the cost of finished products, thereby enhancing their competitiveness in the global market. As an export incentive, the drawback plays a significant role in alleviating the tax burden on manufacturers. This study sought to explore the concept of drawback, its different modalities, and the key benefits it offers. Materials and Methods: The research methodology included a literature review and a case study involving two companies located in the Piracicaba/SP region that utilize this customs regime. Data was gathered through a questionnaire with open-ended questions directed at the Foreign Trade managers of these companies. Results: The findings reveal that the companies studied leverage the drawback regime to significantly reduce the prices of their export products, taking advantage of the tax benefits to lower costs and enhance product competitiveness Conclusion: Both theory and practice show that this special customs regime serves as a stimulus to imports and exports, thus enabling the growth of companies in the international market. It can be said that it is an important mechanism for promoting the internationalization process of companies.

Learning the feature distribution similarities for online time series anomaly detection

Identifying anomalies in multi-dimensional sequential data is crucial for ensuring optimal performance across various domains and in large-scale systems. Traditional contrastive methods utilize feature similarity between different features extracted from multidimensional raw inputs as an indicator of anomaly severity. However, the complex objective functions and meticulously designed modules of these methods often lead to efficiency issues and a lack of interpretability. Our study introduces a structural framework called SimDetector, which is a Local–Global Multi-Scale Similarity Contrast network. Specifically, the restructured and enhanced GRU module extracts more generalized local features, including long-term cyclical trends. The multi-scale sparse attention module efficiently extracts multi-scale global features with pattern information. Additionally, we modified the KL divergence to suit the characteristics of time series anomaly detection, proposing a symmetric absolute KL divergence that focuses more on overall distribution differences. The proposed method achieves results that surpass or approach the State-of-the-Art (SOTA) on multiple real-world datasets and synthetic datasets, while also significantly reducing Multiply-Accumulate Operations (MACs) and memory usage.

Human hand gesture recognition using fast Fourier transform with coot optimization based on deep neural network

ABSTRACT Hand motion detection is particularly important for managing the movement of individuals who have limbs amputated. The existing algorithm is complex, time-consuming and difficult to achieve better accuracy. A DNN is suggested to recognize human hand movements in order to get over these problems.Initially, the raw input EMG signal is captured then the signal is pre-processed using high-pass Butterworth filter and low-pass filter which is utilized to eliminate the noise present in the signal. After that pre-processed EMG signal is segmented using sliding window which is used for solving the issue of overlapping. Then the features are extracted from the segmented signal using Fast Fourier Transform. Then selected the appropriate and optimal number of features from the feature subset using coot optimization algorithm. After that selected features are given as input for deep neural network classifier for recognizing the hand movements of human. The simulation analysis shows that the proposed method obtain 95% accuracy, 0.05% error, precision is 94%, and specificity is 92%.The simulation analysis shows that the developed approach attain better performance compared to other existing approaches. This prediction model helps in controlling the movement of amputee patients suffering from disable hand motion and improve their living standard.

Evaluating the Influence of Incorrect Reassurances on Trust in Imperfect Automated Decision Aid

Prior research on trust in automation has primarily focused on instances where decision aids form predictions based on the same raw input information as humans. This study extends the paradigms into the human-processed input framework, where machine predictions are based on human-processed data, instead of the raw input. The human-processed input framework introduces a unique error pattern, “incorrect reassurance,” where faulty automation prediction erroneously validates users’ initial errors. This study examined the impact of “incorrect reassurance” on human trust in automation in the human-processed input framework. Thirty-five participants completed a Mental Rotation Task (MRT), using an imperfect automated decision aid. Results show that incorrect reassurance patterns led to larger trust decrement, lower final performance, and quicker final reaction time compared to other performance patterns. The findings emphasize the importance of understanding trust in automation in the context of the human-processed input framework.

Machine learning-based in-process monitoring for laser deep penetration welding: A survey

In-process monitoring (IPM) of laser deep penetration welding (LDPW) has witnessed a rapid growth in approaches that embrace machine learning algorithms, utilizing raw sensor input to generate various weld quality evaluations, instead of concentrating on thermomechanical modeling that is hypotheses-driven and hence biased by it. Benefitting from the capability to unravel hidden interactions in the complex laser welding process, numerous data-driven IPM methods have been proposed to address different problems in this area. In this survey, we present a comprehensive analysis of both classical and recent studies, covering the unique physical mechanisms, sensing techniques, methodologies, strengths, and limitations of machine learning-based IPM-LDPW systems. We delve into several critical tasks, including mechanical performance prediction, weld penetration estimation, and weld defects detection. Meanwhile, we explore the latest developments in deep learning and how to incorporate these techniques into IPM systems for LDPW. To inspire future research, we outline unresolved challenges and explore potential opportunities and new perspectives for addressing these challenges.

Deep quality-related stacked isomorphic autoencoder for batch process quality prediction

Abstract Batch processes play an important role in modern chemical industrial and manufacturing production, while the control of product quality relies largely on online quality prediction. However, the complex nonlinearities of batch process and the dispersion of quality-related features may affect the quality prediction performance. In this paper, a deep quality-related stacked isomorphic autoencoder for batch process quality prediction is proposed. Firstly, the same raw input data is reconstructed layer-by-layer by isomorphic autoencoder and the raw data features are obtained. Secondly, the correlation between the isomorphic representations of each layer and the output is analyzed by maximum information coefficient to construct the relevant loss function and enhance the quality-related information. Thirdly, deep quality-related prediction model is constructed to predict the batch process quality variables. Finally, the effectiveness of the proposed method is verified by applying on penicillin fermentation process.&amp;#xD;

A Deep Transfer Learning Approach for Sleep Stage Classification and Sleep Apnea Detection Using Wrist-Worn Consumer Sleep Technologies.

Obstructive sleep apnea (OSA) is a common, underdiagnosed sleep-related breathing disorder with serious health implications Objective - We propose a deep transfer learning approach for sleep stage classification and sleep apnea (SA) detection using wrist-worn consumer sleep technologies (CST). Methods - Our model is based on a deep convolutional neural network (DNN) utilizing accelerometers and photo-plethysmography signals from nocturnal recordings. The DNN was trained and tested on internal datasets that include raw data from clinical and wrist-worn devices; external validation was performed on a hold-out test dataset containing raw data from a wrist-worn CST. Results - Training on clinical data improves performance significantly, and feature enrichment through a sleep stage stream gives only minor improvements. Raw data input outperforms feature-based input in CST datasets. The system generalizes well but performs slightly worse on wearable device data compared to clinical data. However, it excels in detecting events during REM sleep and is associated with arousal and oxygen desaturation. We found; cases that were significantly underestimated were characterized by fewer of such event associations. Conclusion - This study showcases the potential of using CSTs as alternate screening solution for undiagnosed cases of OSA. Significance - This work is significant for its development of a deep transfer learning approach using wrist-worn consumer sleep technologies, offering comprehensive validation for data utilization, and learning techniques, ultimately improving sleep apnea detection across diverse devices.

Very short-term wind power forecasting for real-time operation using hybrid deep learning model with optimization algorithm

This paper proposes a new hybrid deep learning model to enhance the accuracy of forecasting very short-term wind power generation. The proposed model comprises a convolutional layer, a long-short-term memory (LSTM) unit, and fully connected neural network. Convolution layer can automatically learn complicated features from the raw input, whereas the LSTM layers can retain useful information through which gradient information may flow over extended periods. To obtain the best performance from the forecasting model, a random search optimization technique has been developed for tuning hyper-parameters of the model developed. The 5 min datasets from the White Rock wind farm, Australia are used to investigate the effectiveness of the proposed model as wind farms are participating in spot electricity market. To compare the effectiveness, the proposed model is compared with the existing models, such as convolution neural network (CNN), LSTM, gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), artificial neural network (ANN), and support vector machine (SVM). The root-mean-square error (RMSE), mean absolute error (MAE), and Theil’s inequality coefficient (TIC) are used to analyze and compare the performances of the predictive models. Based on RMSE and MAE, the proposed model exhibits a higher accuracy of approximately 23.79% and 28.63% compared to other forecasting methods, respectively.

Exposure difference network for low-light image enhancement

Low-light image enhancement aims to simultaneously improve the brightness and contrast of low-light images and recover the details of the visual content. This is a challenging task that makes typical data-driven methods suffer, especially when faced with severe information loss in extreme low-light conditions. In this work, we approach this task by proposing a novel exposure difference network. The proposed network generates a set of possible exposure corrections derived from the differences between synthesized images under different exposure levels, which are fused and adaptively combined with the raw input for light compensation. By modeling the intermediate exposure differences, our model effectively eliminates the redundancy existing in the synthesized data and offers the flexibility to handle image quality degradation resulting from varying levels of inadequate illumination. To further enhance the naturalness of the output image, we propose a global-aware color calibration module to derive low-frequency global information from inputs, which is further converted into a projection matrix to calibrate the RGB output. Extensive experiments show that our method can achieve competitive light enhancement performance both quantitatively and qualitatively.