Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive Modeling of Run Time for Model and Data Distributed Inferencing Using Gradient Boosting Regression

In the realm of Internet of Things (IoT) systems, accurately forecasting the runtime of Inferencing models on heterogeneous devices is paramount for optimizing resource allocation, particularly in the contexts of Model Distributed Inferencing (MDI) and Data Distributed Inferencing (DDI). This paper delves into the application of Gradient Boosting Regression (GBR) as a predictive modeling technique for estimating runtime in both MDI and DDI scenarios. GBR presents an equitable trade-off between interpretability, robustness against noise, and suitability for moderately sized datasets. The study reviews previous research on IoT inference optimization. It accentuates the multifaceted intricacies of device diversity and the significance of model interpretability within MDI and DDI setups. The primary contribution of this research is the novel application of the GBR model to predict machine learning inferencing runtime in MDI and DDI contexts. This approach is invaluable when empirical data is limited and characterizing the behavior of newly introduced devices is imperative. The paper elaborates on the GBR algorithm’s utilization, hyperparameters, and custom loss functions tailored explicitly for MDI and DDI. The results section exemplifies GBR’s performance across various computational regimes, including MDI and DDI. It offers insights into the model’s balance between accuracy and complexity. A performance comparison with prior models underscores GBR’s efficacy in predicting runtime in MDI and DDI. This work contributes to the ongoing discourse on IoT optimization and predictive modeling.
The Influence of Photovoltaic Grid Connection on Power Quality of Power Grid and Control Technology Based on Big Data

With the rapid growth of social economy, the contradiction between energy demand and supply has become increasingly prominent. This paper takes photovoltaic grid connected inverter as the research object, through analyzing the current situation and future growth trend of solar power generation in China, combining relevant domestic and foreign literature, national policies and regulations and other factors to elaborate. According to the existing technical conditions and application environment, determine the feasibility scheme and measures based on GIS platform visualization, build a corresponding monitoring system against the background of big data(BD), use intelligent meter devices to achieve real-time monitoring of power quality, analyze the solar grid connected inverter as the research object, and conduct simulation verification by establishing appropriate mathematical models and calculation methods. The verification results show that, The impact of BD based photovoltaic grid connection on power quality of the grid and the control technology have excellent performance in controlling power stability and security.
Schwarz Information Criterion Based Thompson Sampling for Dynamic Spectrum Access in Non-Stationary Environment

The listen before talk (LBT) mechanism is often used in dynamic spectrum access (DSA) schemes, which requires secondary users (SUs) to perform spectrum sensing before accessing a channel so as to avoid transmission collisions with primary users (PUs). In the scenario of DSA with multiple PU channels, channel sensing order according to the idle probabilities of PU channels is important for SUs to improve the spectrum efficiency. However, conventional DSA schemes are sluggish in updating the estimates of idle probabilities sequentially, which hinders their application in highly dynamic channels (with time-varying idle probabilities). To overcome this issue, we propose a change detection algorithm with a binary hypothesis testing of Schwarz Information Criterion (SIC), and present an SIC based Thompson Sampling Algorithm (SIC-TSA) to promptly update the estimates of idle probabilities. Moreover, the collision probabilities among SUs are analyzed. Numerical results are provided to show that SIC-TSA outperforms state-of-the-art methods, especially when channel traffic is high.
Classical and Statistical Optimization by Response Surface Methodology For Enhancing Biomass and Bacteriocin Production by Lactobacillus Plantarum

Response surface methodology (RSM) based on central composite design was successfully applied to redesign MRS media for maximizing both biomass and bacteriocin production from Lactobacillus plantarum NH40. First, glucose and yeast extract were chosen as the best carbon and nitrogen sources based on classical optimization results of one factor at time which also revealed the possibility of eliminating peptone and meat extract from the original composition of medium without affecting the growth and bacteriocin production. Statistical experimental design based on a regression model generated using the Design expert 7 software showed that the optimum concentrations of glucose, yeast extract, tween80, NH 4 Cr, CH 3 COONa and K 2 PO 4 were 40, 19.9, 1, 3.06, 7, 1.25 g/L respectively for maximum production of biomass (15.87 mg/mL) and bacteriocin (634.74 U/mL). In addition, from the analysis of variance, yeast extract with F-value 77.2 and glucose with 185.4 were the most effective factors on biomass and bacteriocin production. Formulation of empirical model explained that the interaction among factors showed that the determination coefficient R 2 of biomass and bacteriocin production were 0.8777 and 0.8539 respectively. Furthermore, the accuracy of model of the optimized MRS medium suggested by design expert 7 for both biomass and bacteriocin was verified and results showed that concentrations of biomass and bacteriocin were 15 mg/mL and 640AU/mL respectively, which were approximately closed to predicted values.
Analysis of Petrogenic Hydrocarbons in Plant Tissues: A Simple GC-MS-Based Protocol to Distinguish Biogenic Hydrocarbons from Diesel-Derived Compounds

Diesel contamination of farming soils is of great concern because hydrocarbons are toxic to all forms of life and can potentially enter the food web through crops or plants used for remediation. Data on plant ability to uptake, translocate and accumulate diesel-derived compounds are controversial not only due to the probable diverse attitude of plant species but also because of the lack of a reliable method with which to distinguish petrogenic from biogenic compounds in plant tissues. The purpose of this study was to set up a GC-MS-based protocol enabling the determination of diesel-derived hydrocarbons in plants grown in contaminated soil for assessing human and ecological risks, predicting phytoremediation effectiveness and biomass disposal. To this end, two plant species, Vicia sativa L. and Secale cereale L. , belonging to two diverse vascular plant families, were used as plant models. They were grown in soil spiked with increasing concentrations of diesel fuel, and the produced biomass was used to set up the hydrocarbon extraction and GC-MSD analysis. The developed protocol was also applied to the analysis of Typha latifolia L. plants, belonging to a different botanical family and grown in a long-time and highly contaminated natural soil. Results showed the possibility of distinguishing diesel-derived compounds from biogenic hydrocarbons in most terrestrial vascular plants, just considering the total diesel compounds in the n-alkanes carbon range C10–C26, where the interference of biogenic compounds is negligible. Diesel hydrocarbons quantification in plant tissues was strongly correlated (0.92 < r2 < 0.99) to the concentration of diesel in spiked soils, suggesting a general ability of the considered plant species to adsorb and translocate relatively low amounts of diesel hydrocarbons and the reliability of the developed protocol.
Estimation of three‐phase relative permeability by simulating fluid dynamics directly on rock‐microstructure images

Given the world's growing demand for energy, a combination of geological CO2 sequestration and enhanced oil recovery (EOR) technologies is currently regarded as a promising solution, as it would provide a means of reducing carbon emissions into the atmosphere while also leading to the economic benefit of simultaneously recovering oil. The optimization of injection strategies to maximize CO2 storage and increase the oil recovery factors requires complicated pore‐scale flow information within a reservoir system consisting of coexisting oil, water, and CO2 phases. In this study, an immiscible three‐phase lattice‐Boltzmann (LB) model was developed to investigate the complicated flow state with interaction between water, oil, and CO2 systems in porous media. The two main mechanisms of oil remobilization, namely, double‐drainage and film flow, can be captured by our model. The estimation of three‐phase relative permeability is proposed using the digital rock physics (DRP) simulations. The results indicate that the relative permeability of CO2 as calculated using our steady state method is not sensitive to the initial oil fraction if the oil distribution is originally uniform. Baker's (1988) empirical model was tested and found to be able to provide a good prediction of the three‐phase relative permeability data. Our numerical method provides a new tool for accurately predicting three‐phase relative permeability data directly based on micro‐CT rock images.
Melanoma Skin Cancer Detection Through Feature Extraction Using Deep Convolutional Neural Network Models

In the complicated domain of early melanoma skin cancer detection, this scholarly exposition conducts an in-depth exploration into using pre-trained DCNN models-specifically, DenseNet121, VGG19, Xception, and ResNet50. The research unfolds an avant-garde methodology that harnesses the potency of feature extraction strategies and fine-tuning procedures, thereby substantially augmenting the detection capabilities of Machine Learning (ML) classifiers. A salient finding from the investigation is the remarkable performance metrics attained by the DenseNet121 model when integrated with the Random Forest classifier and an Ensemble of Classifiers. Notwithstanding the relatively lower Specificity, which could lead to a certain degree of false positives, the accuracy, precision, recall, and sensitivity metrics are laudable, underscoring the model's understanding in accurately classifying positive instances and eliminating negatives. Parallel trends were discernible when the VGG19 and Xception architectures were engaged. Despite minor variations, the Random Forest models consistently manifested a slight edge regarding overall accuracy, Specificity, and precision. While the performance of the ResNet50 model was somewhat lower, it nevertheless demonstrated competent proficiency. The research underscores the potency of Deep Learning-based feature extraction strategies in enhancing ML classifiers' performance, thereby signifying a promising approach toward facilitating early melanoma detection. The judicious selection amongst various DCNN models and classifiers should be contingent upon specific project requirements, carefully balancing the trade-offs between precision, recall, sensitivity, and Specificity. As a necessary result, this study encourages further academic scrutiny to enhance these models, expand their scope, and explore their viability within diverse medical detection scenarios. By delineating these critical insights, the study contributes significantly to the growing scholarly discourse on ML applications within medical diagnostics.
AGV Quality of Service Throughput Prediction via Neural Networks

The recent development of Autonomous Guided Vehicles (AGV) use in industry has resulted in the need to model new solutions based on the latest technological achievements. One of the areas worth attention and development is Quality of Service (QoS) in relation to communication between vehicles. QoS makes it possible to divide the bandwidth in such a way that tasks performed by devices are completed with a certain priority. However, in order to manage these resources effectively, it is necessary to anticipate available network throughput. Therefore, this paper presents a neural-based model to ensure throughput prediction for AGV. The proposed solution assumes the use of information on both historical throughput values and data obtained from other sensors that AGV are equipped with. Therefore, the idea is to integrate two neural networks with another network, which is supposed to predict the result based on these two previously obtained predictions. Ultimately, prediction results with a Root Mean Squared Error (RMSE) of 0.1 for the downlink and 1.6 for the uplink were obtained.
Promoting healthy eating in early pregnancy in individuals at risk of gestational diabetes mellitus: does it improve glucose homeostasis? A study protocol for a randomized control trial

Background Healthy eating during pregnancy has favorable effects on glycemic control and is associated with a lower risk of gestational diabetes mellitus (GDM). According to Diabetes Canada, there is a need for an effective and acceptable intervention that could improve glucose homeostasis and support pregnant individuals at risk for GDM. Aims This unicentric randomized controlled trial (RCT) aims to evaluate the effects of a nutritional intervention initiated early in pregnancy, on glucose homeostasis in 150 pregnant individuals at risk for GDM, compared to usual care. Methods Population: 150 pregnant individuals ≥18 years old, at ≤14 weeks of pregnancy, and presenting ≥1 risk factor for GDM according to Diabetes Canada guidelines. Intervention: The nutritional intervention initiated in the first trimester is based on the health behavior change theory during pregnancy and on Canada’s Food Guide recommendations. It includes (1) four individual counseling sessions with a registered dietitian using motivational interviewing (12, 18, 24, and 30 weeks), with post-interview phone call follow-ups, aiming to develop and achieve S.M.A.R. T. nutritional objectives (specific, measurable, attainable, relevant, and time-bound); (2) 10 informative video clips on healthy eating during pregnancy developed by our team and based on national guidelines, and (3) a virtual support community via a Facebook group. Control: Usual prenatal care. Protocol: This RCT includes three on-site visits (10–14, 24–26, and 34–36 weeks) during which a 2-h oral glucose tolerance test is done and blood samples are taken. At each trimester and 3 months postpartum, participants complete web-based questionnaires, including three validated 24-h dietary recalls to assess their diet quality using the Healthy Eating Food Index 2019. Primary outcome: Difference in the change in fasting blood glucose (from the first to the third trimester) between groups. This study has been approved by the Ethics Committee of the Centre de recherche du CHU de Québec-Université Laval. Discussion This RCT will determine whether a nutritional intervention initiated early in pregnancy can improve glucose homeostasis in individuals at risk for GDM and inform Canadian stakeholders on improving care trajectories and policies for pregnant individuals at risk for GDM. Clinical trial registration https://clinicaltrials.gov/study/NCT05299502, NCT05299502
Engineering Graduates' Skill Acquisition and Employers Skill Need as applied to Science Education in Ethiopia

Demand for relevant skills and competences have increased with changing science and technology, globalization, and the intensity and complexity of the business environment across the world. This study investigated levels of engineering graduate skill acquisition and employers' skill needs as applied to science education. Using a mixed-methods approach, the study employed both primary and secondary sources of data. The study involved 275 participants recruited using simple random and purposive sampling techniques. Data were analyzed using the mean, standard deviation, and one-way ANOVA. The study found a wider mismatch between levels of higher education supply and labor market needs for indicators of academic, technical, interpersonal, and generic skills. While higher education moderately equipped engineering graduates with the majority of indicators of these skill sets, labor market needs for the same skills remain high. The mismatches between the demand and supply of skills have several implications, it compromises firms' productivity, result in market losses, hamper economic growth, competitiveness, and innovative capacity at the macro-economic level, decrease quality human capital by discouraging investment in education and training, and challenge graduates in finding jobs suitable for their field of study. These require universities and employers to conduct real labor market skills' needs assessments before designing training curriculum; shifting curriculum delivery from theory-focused to practical-oriented modes of teaching and ensuring learners' acquisition of skills demanded by employers; and establishing strong and sustainable linkages between industries and training institutes. It is also good if future research focuses on skills employees acquire at work through experience and factors attributed to mismatches between higher education skill supply and employers' skill needs.
Cancer-Associated Fibroblast Heterogeneity in Malignancy with Focus on Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) remains an understudied and significant global cancer killer and dismal survival rates have not changed in decades. A better understanding of the molecular basis of OSCC progression and metastasis is needed to develop new approaches for treating this disease. The supportive network surrounding cancer tumor cells known as the tumor microenvironment (TME) has gained increasing interest lately since it performs essential protumorigenic functions. Cancer-associated fibroblasts (CAFs) are one of the main cell types in the TME and are known to play a key role in influencing the biological behavior of tumors. CAFs present a heterogeneity both in phenotype as well as functions, leading to the suggestion of different CAF subtypes in several cancer forms. The task to subtype CAFs in OSCC has, however, just begun, and there is today no united way of subtyping CAFs in this disease. This review aims to define the features of CAFs and to summarize CAF subtype research in malignancy with focus on OSCC including aspects as disease prognosis and therapeutic opportunities.
Adaptive Intrusion Detection Systems: Class Incremental Learning for IoT Emerging Threats

In the evolving landscape of Internet of Things (IoT) security, the need for continuous adaptation of defenses is critical. Class Incremental Learning (CIL) can provide a viable solution by enabling Machine Learning (ML) and Deep Learning (DL) models to $( i)$ learn and adapt to new attack types (0-day attacks), $( ii)$ retain their ability to detect known threats, (iii) safeguard computational efficiency (i.e. no full re-training). In IoT security, where novel attacks frequently emerge, CIL offers an effective tool to enhance Intrusion Detection Systems (IDS) and secure network environments. In this study, we explore how CIL approaches empower DL-based IDS in IoT networks, using the publicly-available IoT-23 dataset. Our evaluation focuses on two essential aspects of an IDS: $( a)$ attack classification and $( b)$ misuse detection. A thorough comparison against a fully-retrained IDS, namely starting from scratch, is carried out. Finally, we place emphasis on interpreting the predictions made by incremental IDS models through eXplainable AI (XAI) tools, offering insights into potential avenues for improvement.
A Statistical Analysis of Experimental Data on Filling Temperature of Fluid Inclusion in Fluorite from Fluorite Deposits of Patagonia, Argentina

peratures) of fluid inclusions in fluorite have been measured with the heating stage by numerous investigators in order to obtain the information on their formation temperatures, for example, GROGAN and SHRODE (1952), FREAS (1961), IMAI and TSUTSUMI (1965), ROEDDER et al. (1968) and MILLER (1969). For the last few years, the writers have studied several fluorite deposits in Rio Negro Province, Argentina. In the present paper, the results of statistical study dealing with data on the filling temperatures of fluid inclusions in fluorites from these deposits are
Modified interferometric imaging condition for reverse-time migration

For reverse-time migration, high-resolution imaging mainly depends on the accuracy of the velocity model and the imaging condition. In practice, however, the small-scale components of the velocity model cannot be estimated by tomographical methods; therefore, the wavefields are not accurately reconstructed from the background velocity, and the imaging process will generate artefacts. Some of the noise is due to cross-correlation of unrelated seismic events. Interferometric imaging condition suppresses imaging noise very effectively, especially the unknown random disturbance of the small-scale part. The conventional interferometric imaging condition is extended in this study to obtain a new imaging condition based on the pseudo-Wigner distribution function (WDF). Numerical examples show that the modified interferometric imaging condition improves imaging precision. The artefacts caused by random fluctuations in the background velocity model will contaminate the final image, which makes the work of interpretation difficult to implement in reverse-time migration. The imaging condition proposed in this paper, which exploits the pseudo-Wigner distribution function, can reduce this noise, and also help improve the continuity of complex layers.
Engineering and Characterization of 3-Aminotyrosine-Derived Red Fluorescent Variants of Circularly Permutated Green Fluorescent Protein

Introducing 3-aminotyrosine (aY), a noncanonical amino acid (ncAA), into green fluorescent protein (GFP)-like chromophores shows promise for achieving red-shifted fluorescence. However, inconsistent results, including undesired green fluorescent species, hinder the effectiveness of this approach. In this study, we optimized expression conditions for an aY-derived cpGFP (aY-cpGFP). Key factors like rich culture media and oxygen restriction pre- and post-induction enabled high-yield, high-purity production of the red-shifted protein. We also engineered two variants of aY-cpGFP with enhanced brightness by mutating a few amino acid residues surrounding the chromophore. We further investigated the sensitivity of the aY-derived protein to metal ions, reactive oxygen species (ROS), and reactive nitrogen species (RNS). Incorporating aY into cpGFP had minimal impact on metal ion reactivity but increased the response to RNS. Expanding on these findings, we examined aY-cpGFP expression in mammalian cells and found that reductants in the culture media significantly increased the red-emitting product. Our study indicates that optimizing expression conditions to promote a reduced cellular state proved effective in producing the desired red-emitting product in both E. coli and mammalian cells, while targeted mutagenesis-based protein engineering can further enhance brightness and increase method robustness.
Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar

Automatic fall detection plays a significant role in monitoring the health of senior citizens. In particular, millimeter-wave radar sensors are relevant for human pose recognition in an indoor environment due to their advantages of privacy protection, low hardware cost, and wide range of working conditions. However, low-quality point clouds from 4D radar diminish the reliability of fall detection. To improve the detection accuracy, conventional methods utilize more costly hardware. In this study, we propose a model that can provide high-quality three-dimensional point cloud images of the human body at a low cost. To improve the accuracy and effectiveness of fall detection, a system that extracts distribution features through small radar antenna arrays is developed. The proposed system achieved 99.1% and 98.9% accuracy on test datasets pertaining to new subjects and new environments, respectively.