Miracle-dz/UniFAR-FTUs huggingface dataset:
CfgNet: A Framework for Tracking Equality-Based Configuration Dependencies Across a Software Project

Modern software development incorporates various technologies, such as containerization, CI/CD pipelines, and build tools, which have to be jointly configured to enable building, testing, deployment, and execution of software systems. The vast configuration space spans several different configuration artifacts with their own syntax and semantics, encoding hundreds of configuration options and their values. The interplay of these technologies requires some level of coordination, which is realized by matching configurations. That is, configuration options and their according values may depend on other options and values from entirely different technologies and artifacts. This creates non-obvious configuration dependencies that are hard to track. The missing awareness and overview of such configuration dependencies across diverse configuration artifacts, tools, and frameworks can lead to dependency conflicts and severe configuration errors. We propose CfgNet, a framework that models the configuration landscape of a software project as a configuration network in an extensible and artifact-independent way. This way, we enable the early detection of possible dependency violations and proactively prevent misconfigurations during software development and maintenance. In a literature study, we found that the most common form of dependencies is the equality of values of different options. Based on this result, we developed an equality-based linker to determine dependent options across different artifacts. To demonstrate the extensibility of our framework, we also implemented nine plugins for popular technologies, such as Maven and Docker. To evaluate our approach, we injected and violated five real-world configuration dependencies extracted from Stack Overflow, which we support with our technology plugins, in five subject systems. CfgNet found all injected dependency violations and four additional ones already present in these systems. Moreover, we applied CfgNet to the commit history of 50 repositories selected from GitHub and found dependency conflicts in about two thirds of these repositories. We manually inspected 883 conflicts, with about 89 % true positives, demonstrating the need to reliably track cross-technology configuration dependencies and prevent their misconfiguration.
Predictability of Astigmatism Correction by Arcuate Incisions with a Femtosecond Laser Using the Gaussian Approximation Calculation

Planning astigmatic correction is a complex task. Biomechanical simulation models are useful for predicting the effects of the physical procedure on the cornea. Algorithms based on these models allow preoperative planning and simulate the outcome of patient-specific treatment. The objective of this study was to develop a customised optimisation algorithm and determine the predictability of astigmatism correction by femtosecond laser arcuate incisions. In this study, biomechanical models and Gaussian approximation curve calculations were used for surgical planning. Thirty-four eyes with mild astigmatism were included, and corneal topographies were evaluated before and after femtosecond laser-assisted cataract surgery with arcuate incisions. The follow-up time was up to 6 weeks. Retrospective data showed a significant reduction in postoperative astigmatism. A total of 79.4% showed a postoperative astigmatic value less than 1 D. Clinical refraction was significantly reduced from −1.39 ± 0.79 D preoperatively to −0.86 ± 0.67 D postoperatively (p 0.02). A positive reduction in topographic astigmatism was also observed (p < 0.00). The best-corrected visual acuity increased postoperatively (p < 0.001). We can conclude that customised simulations based on corneal biomechanics are a valuable tool for correcting mild astigmatism with corneal incisions in cataract surgery to improve postoperative visual outcomes.
Enhancing Basketball Game Outcome Prediction through Fused Graph Convolutional Networks and Random Forest Algorithm

Basketball is a popular sport worldwide, and many researchers have utilized various machine learning models to predict the outcome of basketball games. However, prior research has primarily focused on traditional machine learning models. Furthermore, models that rely on vector inputs tend to ignore the intricate interactions between teams and the spatial structure of the league. Therefore, this study aimed to apply graph neural networks to basketball game outcome prediction, by transforming structured data into unstructured graphs, to represent the interactions between teams in the 2012–2018 NBA season dataset. Initially, the study used a homogeneous network and undirected graph to build a team representation graph. The constructed graph was fed into a graph convolutional network, which yielded an average success rate of 66.90% in predicting the outcome of games. To improve the prediction success rate, feature extraction based on the random forest algorithm was combined with the model. The fused model yielded the best results, and the prediction accuracy was improved to 71.54%. Additionally, the study compared the results of the developed model with previous studies and the baseline model. Our proposed method considers the spatial structure of teams and the interaction between teams, resulting in superior performance in basketball game outcome prediction. The results of this study provide valuable insights for basketball performance prediction research.
Taxonomy of Consumer and Industrial IoT

The proliferation of the Internet of things (IoT) technologies has opened new avenues for technological advancements in various aspects of life. At the same time, the immense collection of competing IoT solutions has created new challenges in security and standards gaps that have attracted numerous research efforts. The majority of these research efforts are centered on the challenges of IoT in general. However, there is a growing separation between the two subcategories of IoT, the Consumer IoT (CIoT) and Industrial IoT (IIoT), in terms of application, growth, scale, requirements, and challenges. The ability to distinguish between the two is critical for developing appropriate solutions, infrastructure, and standardization. This would pave the way to creating reliable, scalable, and secure CIoT and IIoT applications. This study highlights the differences between IoT types in terms of purpose, hardware needs, communication protocol requirements, and security concerns. We also investigate existing IIoT-related solutions to provide readers with clear and concise information to help them make more informed decisions about IoT solutions.
ENSO-related centennial and millennial-scale hydroclimate changes recorded from Lake Xiaolongchi in arid Central Asia over the past 8000 years

Climate changes on different timescales and their mechanisms are of great importance in arid Central Asia (ACA). However, the contribution of El Niño−Southern Oscillation (ENSO) to Holocene hydroclimate in ACA on centennial and millennial timescales is limited by records with good age control and high temporal resolution. Here, we present high-resolution X-ray fluorescence (XRF) scanning data of a sediment core from Lake Xiaolongchi in central Tianshan Mountains to reconstruct hydroclimate changes in ACA over the last 8000 years. Our construction exhibits that the climate was dry from 8000 to 5500 cal year BP and wet from 5500 to 0 cal year BP, which resembles ENSO patterns on a millennial timescale. On the centennial timescale, hydroclimate fluctuations with eight wet periods, seven dry periods, and a shift period have a significant 800-year periodicity, coincident with periodic ENSO variance. The positive relationship between 800-year band-pass filtered hydroclimate and ENSO indicates that wet (dry) climate conditions correspond to increased (reduced) ENSO activity. We propose that the spatial pattern of “wet (dry) southern China and ACA, and dry (wet) northern China” during the El Niño-like (La Niña-like) condition may exist during the Holocene on millennial to centennial timescales.
Research on incremental learning algorithm with multiple support vector machine classifiers

In order to extend common incremental learning algorithms into a parallel computation setting,an incremental learning algorithm with multiple support vector machine classifiers is proposed.According to the results of multiple classifiers,new samples were selected to be support vectors sets by computing the distance mean of the samples to the hyperplane,until all classifiers were updated and all classification accuracies met the given threshold.The experiment results on test data sets prove the feasibility and validity of the proposed algorithm.
How Can Practitioners Assess the Value of Social Work Interventions?

In social work, practitioners are often faced with situations in which they have to choose an intervention. A fundamental ethical principle of social work practice is to minimize the risk of adverse effects caused by social services. To adhere to this principle, practitioners must be aware of the possible positive and negative effects of potential services. There are hundreds of interventions currently in use in social work. Although there are a growing number of controlled trials on social work interventions, there are only a few interventions based on research that has the highest degree of certainty, making it difficult to know how and in what way these interventions can support a person in need of assistance. This article, based in part on the experience we gained training practitioners working in Swedish social services, presents a tentative model for assessing the best available evidence comparing interventions when scientific evidence is sparse.
The Adsorption Effect of Methane Gas Molecules on Monolayer PbSe with and without Vacancy Defects: A First-Principles Study

In this paper, the adsorption effect of methane (CH4) gas molecular on monolayer PbSe with and without vacancy defects is studied based on first-principles calculations. The effects of the adsorption of methane molecular on monolayer PbSe and on the Se vacancy (VSe) and Pb vacancy (VPb) of monolayer PbSe are also explored. Our results show that methane molecules exhibit a good physical adsorption effect on monolayer PbSe with and without vacancy defects. Moreover, our simulations indicate that the adsorption capacity of CH4 molecules on monolayer PbSe can be enhanced by applying strain. However, for the monolayer PbSe with Vse, the adsorption capacity of CH4 molecules on the strained system decreases sharply. This indicates that applying strain can promote the dissociation of CH4 from VSe. Our results show that the strain can be used as an effective means to regulate the interaction between the substrate material and the methane gas molecules.
High Sensitivity Detection Method for Liquid Concentrations Based on Coupled Microwave Resonators

This article presents a new measurement method for liquid concentrations based on two inductively coupled resonators with enhanced sensitivity compared with traditional methods in microwave frequency ranges. The equivalent circuit of the coupled resonators is proposed with its eigenequation having three eigenfrequencies derived. We demonstrate that, compared to a single resonator, the sensitivity of the center eigenfrequency shift to the changed capacitance increases by an order of magnitude if the product of the coupling coefficient and $Q$ -factor of the second resonator is close to unity. In addition, that the reflection magnitude changes with the varied capacitance is also giant under impedance matching condition at the down and up eigenfrequencies. Based on the proposed method, a sensor composed of an open quarter-wavelength microstrip-line resonator (OQMR) and a split-ring resonator (SRR) for detecting concentrations of water–glucose solutions is designed. By adjusting the sample volume and the distance between two resonators, different coupling coefficients and $Q$ -factors can be attained, enabling marked increases in the sensitivity. Experimental and simulated results show that the sensitivity of the center eigenfrequency shift and of the reflection magnitude change to the concentration are as high as 9.4 MHz/(mg/mL) and 5.41 dB/(mg/mL), respectively, both of which are excellent compared to other sensors.
A Unified Framework for Evaluating Algebraic Queries over Annotated Relations

We study concepts and techniques for modeling and processing uncertain relations. Intuitively a piece of data is uncertain if its truth is not established definitely. Similarly, a relation is uncertain when its true state is not ascertained. A major source of difficulties is the semantics of uncertain relations defined based on the notion of possible worlds, which is a set of standard relations one of which represents the true state of the real world data but we don't know which one. This has posed serious challenges for over two decades in database and AI research, however, the topic has gained revived attention in database community again due to some emerging applications such as sensor networks, surveys and imputation techniques, and privacy-preserving data mining applications that require storing and processing such data effectively. Our work is motivated by and concerned with practical issues affected by the exponential number of the possible worlds. We study existing models and techniques and consider the semiring model, a representation model of annotated relations, to represent uncertain relations in our work. Our choice of model is justified for being equipped with an algebra for evaluating queries over annotated relations. We illustrate how the model lends itself to a framework to study models and algorithms for both uncertain relations and probabilistic relations in a unified manner. The ideas and solutions provide a basis for development of a system for annotated data management.
A Comparative Study of Continual, Lifelong, and Online Supervised Learning Libraries

Machine learning has shown to be a crucial part of big data analytics; however, it lacks when the data is continuously streaming in from the system and changing too much from the original training data. Online learning is machine learning for streaming data that arrives in a sequential order where the model updates after every data point. While machine learning relies on well-established libraries such as PyTorch and Keras, the libraries for online learning are less well known, but they are here to serve similar purposes of reproducibility and reducing the time from research to production. Here, we compare different libraries for online learning research, specifically supervised learning. We compare them on the axes of developmental experience and benchmark testing as researchers. Our comparison as developers takes maintenance, documentation, and offerings of state-of-the-art algorithms into account. As this is not necessarily free of bias, we also use benchmarks known to online learning to gather power usage, RAM usage, speed, and accuracy of these libraries to get an objective view. Our findings show that Avalanche and River, including River-torch, are among the best libraries in terms of performance and applicability to the research in supervised online learning.
Boosting With Multiple Clustering Memberships For Hyperspectral Image Classification

A novel hyperspectral image classification algorithm is proposed and demonstrated on benchmark hyperspectral images. We also introduce a hyperspectral sky imaging dataset that we are collecting for detecting the amount and type of cloudiness. The algorithm is designed to be applied to the Such systems could improve the spatial and temporal resolution of cloud information vital to understanding Earth’s climate. We discuss the nature of our HSI-Cloud dataset being collected and an algorithm we propose for processing the dataset using a categorical-boosting method. The proposed method utilizes multiple clusterings to augment the dataset and achieves higher pixel classification accuracy. Creating categorical features via clustering enriches the data representation and improves boosting ensembles. For the experimental datasets used in this paper, gradient boosting methods performed favorably to the benchmark algorithms.
Conversion Coefficients for Calibrating Individual Photon Dosemeters in Terms of Dose Equivalents Defined in an ICRU Tissue Cube and PMMA Slabs

The ratio of the collision kerma for ICRU tissue and the air collision kerma in free air was calculated for various phantom depths for broad parallel photon beams of perpendicular incidence on one face of a 30 cm ICRU tissue cube and on the front face of a PMMA slab using the Monte Carlo method. The calculations were performed for monoenergetic photons in the energy range between 2 keV and 1 MeV. Backscatter factors and the conversion coefficients for semi-infinite slab phantoms of the same materials are also given.
Survey of the Adequacy of Brazilian Children and Adolescents to the 24-Hour Movement Guidelines before and during the COVID-19 Pandemic

The 24-Hour Movement Guidelines provide specific recommendations on movement behaviors for children and adolescents. The objective of this study was to verify the adequacy of children and adolescents to the guidelines for moderate to vigorous physical activity, recreational screen time, and sleep duration, and the overall adequacy to the guidelines, before and during the COVID-19 pandemic. A cross-sectional study was conducted with parents or guardians of children or adolescents from different regions of Brazil using a digital interview form including sociodemographic characteristics of families, moderate to vigorous physical activity, recreational screen time, and sleep duration before and during the pandemic. Statistically significant variation was observed in both groups in relation to moderate to vigorous physical activity and recreational screen time between the two periods evaluated. Overall adequacy to the guidelines before the pandemic was 19.28% for children from Group 1 (0–5 years old) and 39.50% for those from Group 2 (6 to 17 years old). During the pandemic, it corresponded to 3.58% in Group 1 and 4.94% in Group 2 (p-value between periods ≤0.001). This study showed the significant impact of pandemic restrictions on reducing overall compliance and physical activity, and increasing screen time among Brazilian children and adolescents.
A Novel Localization of METTL7A in Bergmann Glial Cells in Human Cerebellum

Methyltransferase-like protein 7A (METTL7A) is a member of the METTL family of methyltransferases.Little information is available regarding the cellular expression of METTL7A in the brain. METTL7A is commonly located in the endoplasmic reticulum and to a lesser extent, in the lipid droplets of some cells. Several studies have reported altered protein and RNA levels in different brain areas in schizophrenia. One of these studies found reduced protein levels of METTL7A in the cerebellar cortex in schizophrenia and stress murine models. Since there is limited information in the literature about METTL7A, we characterized its cellular and subcellular localizations in the human cerebellum using immunohistochemical analysis with laser confocal microscopy. Our study reveals a novel METTL7A localization in GFAP-positive cells, with higher expression in the end-feet of the Bergmann glia, which participate in the cerebrospinal fluid–brain parenchyma barrier. Further 3D reconstruction image analysis showed that METTL7A was expressed in the contacts between the Bergmann glia and Purkinje neurons. METTL7A was also detected in lipid droplets in some cells in the white matter. The localization of METTL7A in the human cerebellar glia limitans could suggest a putative role in maintaining the cerebellar parenchyma homeostasis and in the regulation of internal cerebellar circuits by modulating the synaptic activity of Purkinje neurons.
Strong and ductile medium-Mn steel achieved via controlled martensite tempering

The present work simplifies the previous room-temperature quenching and partitioning (RT Q&P) process down to a single furnace cooling process, delivering a medium Mn steel with an ultimate tensile strength (UTS) of 1.7 GPa and a uniform elongation of 9.5%. The martensite tempering during furnace cooling is featured with slightly reduced dislocation density and is free of cementite precipitation, which is beneficial for ultrahigh UTS. Although the carbon partitioning is limited, the banded large austenite grains have proper mechanical stability owing to pre-existing high carbon content and the strong martensite matrix, allowing the gradual transformation-induced plasticity (TRIP) effect to enhance the work hardening behaviour and thus the good uniform elongation.
Drive-By-Wire Conversion of an Electric Golf-Cart for Self-Driving Vehicles Research

In this paper, we present the drive-by-conversion of an electric golf-cart and summarize how it is used for self-driving vehicles research. This work is basically a major progress compared to our earlier work presented in [1]. The drive-by-wire system has a touch-screen control panel driven by a Raspberry Pi device, but all of the timing critical tasks are executed on microcontrollers. We used industry standard DIN rails and panels to organize the control hardware and the electrical wiring. The developed system have three different modes of operation (1) Fully manual, (2) X-box wireless controller based remote controlled operation, and (3) Drive-by-wire mode where all control commands are expected to be sent from an "external" computer over a USB/serial connection. The drive-by-wire system is designed to be used to together with an x86-64 or ARM processor based device equipped by GPU hardware. This on-board computer, is called the vehicle computer, or the "external" computer, and it is mainly for more heavy-weight AI algorithms and decision making. Once steering decisions are completed, they are all sent to the Pi and/or microcontrollers to operate the actuators. The electric golf-cart also has a GPS/GNSS sensor, and solar panels with an MPPT device which are used to charge the batteries while the vehicle is being used or driven. A short demo video of the developed system is available at [2].
Antioxidant Capacity and NF-kB-Mediated Anti-Inflammatory Activity of Six Red Uruguayan Grape Pomaces

Grape pomaces have a wide and diverse antioxidant phenolics composition. Six Uruguayan red grape pomaces were evaluated in their phenolics composition, antioxidant capacity, and anti-inflammatory properties. Not only radical scavenging methods as DPPH· and ABTS·+ were employed but also ORAC and FRAP analyses were applied to assess the antioxidant potency of the extracts. The antioxidant reactivity of all extracts against hydroxyl radicals was assessed with ESR. The phenol profile of the most bioactive extract was analyzed by HPLC-MS, and a set of 57 structures were determined. To investigate the potential anti-inflammatory activity of the extracts, Nuclear Factor kappa-B (NF-κB) modulation was evaluated in the human colon cancer reporter cell line (HT-29-NF-κB-hrGFP). Our results suggest that Tannat grapes pomaces have higher phenolic content and antioxidant capacity compared to Cabernet Franc. These extracts inhibited TNF-alpha mediated NF-κB activation and IL-8 production when added to reporter cells. A molecular docking study was carried out to rationalize the experimental results allowing us to propose the proactive interaction between the NF-κB, the grape extracts phenols, and their putative anti-inflammatory bioactivity. The present findings show that red grape pomace constitutes a sustainable source of phenolic compounds, which may be valuable for pharmaceutical, cosmetic, and food industry applications.