Miracle-dz/UniFAR-FTUs huggingface dataset:
simulating carbon dynamics along the Boreal Forest Transect Case Study (BFTCS) in central Canada: 2. Sensitivity to climate change

The effects of climate change and doubling atmospheric CO2 on carbon dynamics of the boreal forest in the area of the Boreal Forest Transect Case Study in central Canada were investigated using the process‐based plant‐soil model CENTURY 4.0. The results presented here suggest that (1) across the transect climate change would result in increased total carbon in vegetation biomass but decreased overall carbon in soil; (2) increased atmospheric CO2 concentration under current climatic patterns would result in increased total carbon in vegetation and in soil organic matter; and (3) combined climate change and elevated CO2 would increase both net primary productivity and decomposition rates relative to the current climate condition, but their combined action would be a reduction of soil carbon losses relative to those due to climate change alone. The interactive effects of climate change and elevated CO2, however, are not a simple additive combination of the individual responses. The responses to climate change and elevated CO2 vary across the climate gradient from southern to northern sites on the transect. The present simulations indicate that the northern sites are more sensitive to climate change than the southern sites are, but these simulations do not consider likely changes in the disturbance regime or changes in forest species distribution.
An Accurate Path Tracking Algorithm for Autonomous Vehicles Based on Pure Pursuit with Systematically Designed Look-Ahead Distance and Sideslip Compensation

Pure pursuit is still being utilized as a path tracking algorithm for autonomous vehicles due to its advantages, such as robustness and easy implementation. However, using an inappropriate look-ahead distance and ignoring the effects of dynamics deteriorate the path tracking performance of pure pursuit. To improve the tracking performance, we propose a pure pursuit-based path tracking algorithm that quickly reduces tracking error and considers the sideslip by dynamics. For fast tracking error reduction, a dynamic look-ahead distance is designed as a function of speed and path curvature by analyzing the tracking error change according to the look-ahead distance. In addition, to compensate for the sideslip effect, the desired steering angle is calculated using the sideslip integrated-pure pursuit geometry. Simulation results show that the proposed algorithm significantly reduced the tracking error compared to the classical pure pursuit at a maximum 70 km/h speed. Also, the proposed method outperformed existing improved pure pursuits.
Student learning performance prediction based on online behavior: an empirical study during the COVID-19 pandemic

In the context of the COVID-19 global pandemic, highly intense and frequent online teaching has leapt to be one of the dominant learning patterns and become an ordinary situation in university teaching practices. In recent years, progress in feature engineering and machine learning has made it possible for more effective educational data mining, which in turn has enhanced the performance of intelligent learning models. However, the potential impact of increasing and varying features on online instruction in this new situation makes it unclear whether the existing related findings and results are practical for teachers. In this article, we use various state-of-the-art machine learning techniques to predict students’ performance. Based on the validation of the rationality of the built models, the importance of features under different feature selection techniques are calculated separately for the datasets of two groups and compared with the features before and at the beginning of the pandemic. The results show that in the current new state of highly intense online learning, without considering student information such as demographic information, campus attributes (administrative class and teaching class) and learning behavior (completion of online learning tasks and stage tests) these dynamic features are more likely to discriminate students’ academic performances, which deserves more attention than demographics for teachers in the guidance of students’ learning. In addition, it is suggested that further improvements and refinements should be made to the existing features, such as classifying features more precisely and expanding in these feature categories, and taking into account the statistics about students’ in-class performances as well as their subjective understanding of what they have learned. Our findings are in line with the new situation under the pandemic and provide more implications to teachers’ teaching guidance.
SCOR and ANP Methods for Measuring Supplier Performance with Sustainability Principle of Green Supply Chain Management in Furniture Company PT. XYZ

Sustainable Green Supply Chain Management (SGSCM) is a development of supply chain management activities with meet 3 main factors in its realization namely economic, social, and environmental. Supply Chain Management is an important strategy that supports in improving the overall performance and key of determinants of competitive advantages for companies. Adds the concept of "Green" to emphasize the importance of environmental issues and the need for environmentally friendly systems. To find out more about SGSCM, an evaluation or measurement must be carried out on it. Moreover, the measurement of supply chain performance in companies becomes very basic as a basis for determining the direction of company improvement. The study was conducted at PT. XYZ, one of the furniture company in Semarang, which so far has never measured supply chain performance. The measurement of supply chain performance is carried out by the SCOR (Supply Chain Operation Reference) method by first looking for the weight value of SCOR performance attributes with ANP (Analytical Network Process) methods.
The Case of Acoustic Cleaning of Industrial Boilers at Sasol Synfuels Power Station in Secunda

This paper is a review of boiler maintenance technologies currently used in the main power generation plants in South Africa, Sasol (coal fired plants). The poor maintenance of the boilers at these plants has been the main cause of shortage of power due the boiler breakdowns, while the demand of energy usage remains a necessity for households and businesses. At Sasol Synfuels power station in Secunda, the use of soot blower cleaning technology has contributed a lot to failure resulting in unplanned outages, with high cost of maintenance. A recently published paper discusses the design of an acoustic horn and its beam as an alternative technology that may be a better solution either to be used along with the current soot blower technology or independently for new design of boilers. In this paper the regulations for maintenance of pressure vessels in South Africa governing the maintenance of pressure equipment are reviewed for the case of the boilers at these power plants. South African National Standards (SANS), American Standards of Mechanical Engineering (ASME) and American Petroleum Institute (API) are the used international standards in supporting pressure equipment regulations (PER) and Occupational Health and Safety Act (OHSACT).
The Use and Efficacy of Oral Phenylephrine Versus Placebo Treating Nasal Congestion Over the Years on Adults: A Systematic Review

Nasal congestion is a common issue stemming from various factors such as allergies and anatomical variations. Allergic rhinitis frequently leads to nasal congestion. The pathophysiology involves inflammation, swelling, and mucus production in the nasal mucosa. Multiple treatments are available, including oral phenylephrine, an over-the-counter or prescription option. However, the effectiveness and safety of phenylephrine have been subjects of debate. This systematic review aims to provide an updated perspective on the efficacy of oral phenylephrine versus placebo in addressing nasal congestion in adults. We conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a systematic review involving searches on PubMed, Cochrane, and Scopus databases. Inclusion/exclusion criteria were defined to identify high-quality studies. The focus was on randomized controlled trials (RCTs) and case-control studies published in English between 1998 and 2023, involving adult populations. The interventions compared oral phenylephrine with placebo or standard care, with outcomes centering on changes in nasal congestion symptoms and nasal airway resistance. We identified four articles that met the criteria. These studies exhibited varied designs and populations. The findings consistently indicated that phenylephrine was not more effective than a placebo in relieving nasal congestion. This systematic review demonstrates that oral phenylephrine did not offer substantial relief from nasal congestion compared to a placebo in adults. The studies featured diverse designs, yet the prevailing conclusion was that phenylephrine's efficacy was limited. Safety assessments showed no life-threatening adverse events, with common side effects including headaches and mild discomfort. In summary, this systematic review indicates that oral phenylephrine is not significantly more effective than a placebo in alleviating nasal congestion in adults. Clinicians should explore alternative treatment options, considering the review's limitations. Additional research may be needed to clarify the role of oral phenylephrine in managing nasal congestion.
Adsorption of 4-(N,N-Dimethylamino)-4′-nitrostilbene on an Amorphous Silica Glass Surface

Stilbenes are a compelling class of organic photoswitches with a high degree of tunability that sensitively depend on their environment. In this study, we investigate the adsorption properties of 4-(N,N-dimethylamino)-4′-nitrostilbene (DANS), a push–pull stilbene, on amorphous silica glass. Plane-wave density functional theory (DFT) calculations are used to understand how the trans and cis isomers of DANS interact with the amorphous surface and which are the most preferred modes of adsorption. Our calculations revealed that the O–H···O hydrogen bonds between the nitro group and hydroxyl groups of the silica surface dominate the intramolecular interaction. In addition to hydrogen bonding, O–H···π interactions with the aromatic ring and double bond play a critical role in adsorption, whereas C–H···O interactions are present, but contribute little. Therefore, both isomers of DANS favor parallel orientations such that not only the functional groups but also the aromatic parts can strongly interact with the glass surface.
Ruminal pH sensing for monitoring volatile fatty acid concentrations in response to short-term dietary disruption

Graphical Abstract Summary The synthesis of volatile fatty acids (VFA), a primary product of microbial fermentation that provides a crucial source of energy for the animal, is driven primarily by diet characteristics. Exploration of commercially available sensors to estimate ruminal VFA concentrations across diets could drastically change how ration-formulation decisions are made. This study investigated the opportunity to leverage ruminal pH to track VFA concentrations in the rumen in response to short-term dietary disruption. Although significant, pH measurements have poor accuracy in predicting VFA, and prediction errors pattern with dietary forage to concentrate ratio.
Androctonus sumericus sp. nov., a new scorpion from Dhi Qar Province, Iraq (Scorpiones: Buthidae)

A new species Androctonus sumericus sp. nov. is described and illustrated from the Dhi Qar Province of Iraq, based on the material previously misidentified as A. crassicauda (Olivier, 1807). Therefore, the new species were compared particularly with that species, as well as with all species of Androctonus distributed in the Middle East, using their published descriptions. http://www.zoobank.org/urn:lsid:zoobank.org:pub:40E85452-7450-446D-8306-B2B07F99CF0E
Exploring Rich Semantics for Open-Set Action Recognition

Open-set action recognition (OSAR) aims to learn a recognition framework capable of both classifying known classes and identifying unknown actions in open-set scenarios. Existing OSAR methods typically reside in a data-driven paradigm, which ignore the rich semantics in both known and unknown categories. In fact, we humans have the capability of leveraging the captured semantic information, i.e., knowledge and experience, to incisively distinguish samples from known and unknown classes. Motivated by this observation, in this paper, we propose a Unified Semantic Exploration (USE) framework for recognizing actions in open-set scenarios. Specifically, we explore the explicit knowledge semantics by simulating the unknown classes with knowledge-guided virtual classes based on an external knowledge graph, which enables the model to simulate open-set perception during model training. Besides, we propose to learn the implicit data semantics by transferring the knowledge structure of action categories to the visual prototype space for semantic structure preservation. Extensive experiments on several action recognition benchmarks validate the effectiveness of our proposed method.
Development of a recombinase-aided amplification method combined with lateral flow dipstick assay to detect Porcine circovirus type 2

Background and Aim: Porcine circovirus type 2 (PCV2) is a pathogenic virus that suppresses the immune system of pigs, impacting their health and causing economic losses. Rapid diagnostic tools for early detection of PCV2 are critical to disease prevention and control. Several molecular techniques have been established for detecting PCV2 but costly equipment and time-consuming methods are unsuitable for field inspection. In this study, we developed a recombinase-aided amplification combined with lateral flow dipstick (RAA-LFD) assay to compare with polymerase chain reaction (PCR) and quantitative PCR (qPCR) in detecting PCV2 in suspected field samples. Materials and Methods: To amplify RAA products, 15 primer pairs were designed from the conserved region of the open reading frame (ORF) 1 gene based on multiple alignments of eight PCV2 genotypes. The most efficient primer pair and conditions for the RAA-LFD assay were tested and selected. Limit of detection, repeatability, and reproducibility were determined using the constructed plasmid. DNA was extracted from positive samples for specificity testing as well as from 100 field samples to compare the detection of the RAA-LFD assay with PCR and qPCR. Results: The F1/R1 primer pair was chosen and labeled with fluorescein isothiocyanate at the 5’ end of the forward primer and with biotin at the 5’ end of the reverse primer. The limit of detection of the RAA-LFD assay was 10 copies/μL at 38°C for 30 min. The RAA-LFD assay was repeatable and reproducible, with no cross-reaction with PCV3, Actinobacillus pleuropneumoniae, Porcine epidemic diarrhea virus, Classical swine fever virus, Porcine reproductive and respiratory syndrome virus - North America strain (PRRSV-US) and Porcine reproductive and respiratory syndrome virus - European strain (PRRSV-EU). Based on testing with 100 samples, the developed RAA showed 100% specificity and 90.56% and 85.71% sensitivity when compared to PCR and qPCR, respectively Cohen’s kappa coefficients showed a good agreement with the established techniques. Conclusion: The RAA-LFD assay targeting the ORF1 gene was highly sensitive, specific, quick, and simple to perform in the field.
Unveiling the Connection between Microbiota and Depressive Disorder through Machine Learning

In the last few years, investigation of the gut–brain axis and the connection between the gut microbiota and the human nervous system and mental health has become one of the most popular topics. Correlations between the taxonomic and functional changes in gut microbiota and major depressive disorder have been shown in several studies. Machine learning provides a promising approach to analyze large-scale metagenomic data and identify biomarkers associated with depression. In this work, machine learning algorithms, such as random forest, elastic net, and You Only Look Once (YOLO), were utilized to detect significant features in microbiome samples and classify individuals based on their disorder status. The analysis was conducted on metagenomic data obtained during the study of gut microbiota of healthy people and patients with major depressive disorder. The YOLO method showed the greatest effectiveness in the analysis of the metagenomic samples and confirmed the experimental results on the critical importance of a reduction in the amount of Faecalibacterium prausnitzii for the manifestation of depression. These findings could contribute to a better understanding of the role of the gut microbiota in major depressive disorder and potentially lead the way for novel diagnostic and therapeutic strategies.
“Why Don’t We Learn About the Black Social Work Pioneers?” The Erasure of Black Social Workers’ Histories and Contributions—Implications for Social Work Education

ABSTRACT While there is mounting research on the well-known white founders of social work, documentation of Black social work pioneers and their contributions is scarce – in both social work education and the broader telling of the profession’s history. Given the systemic exclusion of Black social workers in the dominant narrative of social work history, there is a critical need to understand how social work education perpetuates and centers whiteness in the teaching of historical and contemporary social work. This article uses Critical Race Theory to interrogate the role of racism and white supremacy in maintaining the Eurocentric hegemony undergirding the pedagogical and epistemological canons of social work. Moreover, the authors call for a radical shift from social work’s white-centered discourse and curricula to an equitable praxis, centering Black social work pioneers and their contributions to the profession. Implications for decolonizing pedagogy and anti-racist practice in social work education are also discussed.
Simulation and Analysis of Urban Traffic Congestions by Using Cell Transmission Model

This paper explores the application of the cell transmission model in assessing and mitigating incident-related traffic congestion within urban traffic networks. The authors introduce an extended traffic control approach that leverages the cell transmission model to emulate and analyze urban traffic gridlock scenarios triggered by accidents. They have created a versatile traffic simulation tool grounded in the cell transmission model, compatible across different platforms, to effectively replicate the spread and alleviation of traffic congestion resulting from accidents. The study delves into the use of the cell transmission model to formulate strategies for dissipating traffic jams caused by accidents and evaluates the efficacy of these strategies. In sum, this research showcases the potential of the cell transmission model as a valuable instrument for orchestrating traffic flow within urban environments.
A Generic High-Availability Solution to Next-Generation Mobile Core Networks

In addition to functional considerations, commercial core networks must also consider scalability and availability. If the core network encounters a single node or network function failure during the serving period, it must overcome the software or hardware problems and maintain a stable service. For fifth-generation (5G) and beyond core networks, network function virtualization enables scalability and flexibility for deployment, and 5G and beyond core network design follows the concept of service-oriented architecture, similar to cloud-native microservice architecture; hence, the deployment of core networks using a cloud-native solution (e.g., Kubernetes) is possible. To achieve high availability (HA) for cloud-native-based core networks, we design GO-CPSV, a generic HA solution based on the open-source OpenSAF project. GO-CPSV introduces new elements to enhance the affinity with cloud-native deployment, including life-cycle hooks, concurrent enhancement, and multiple storage providers. We then implement HA-UDR based on the Unified Data Repository (UDR) network function of the open-source free5GC core network to validate our design.
Development of a Displacement Prediction System for Deep Excavation Using AI Technology

This manuscript delineates an innovative artificial intelligence-based methodology for forecasting the displacement of retaining walls due to extensive deep excavation processes. In our selection of 17 training cases, we strategically chose a wall configuration that was not influenced by the corner effects. This careful selection was conducted with the intention of ensuring that each deep excavation instance included in our study was supported symmetrically, thereby streamlining the analysis in the ensuing phases. Our proposed multilayer functional-link network demonstrates superior performance over the traditional backpropagation neural network (BPNN), excelling in the precise prediction of displacements at predetermined observation points, peak wall displacements, and their respective locations. Notably, the predictive accuracy of our advanced model surpassed that of the conventional BPNN and RIDO assessment tools by a substantial 5%. The network process model formulated through this research offers a valuable reference for future implementations in diverse geographical settings. Furthermore, by utilizing local datasets for the training, testing, and validation phases, our system ensures the effective and accurate execution of displacement predictions.
A Carbon Nanocomposite Material Used in the Physical Modelling of the Overburden Subsidence Process

Carbon nanomaterial is widely used in structural health monitoring due to the advantage of sensitivity and good mechanical properties. This study presents a novel approach employing carbon nanocomposite materials (CNMs) to characterize deformation and damage evolution in physical modelling. As the primary measurement method, the CNM is used to investigate the deformation characteristics of a 200–400 m thick sandstone bed at a 1 km deep longwall mine. The sandstone unit is identified as an ultra-thick key stratum (UTKS), with its thicknesses varying across different mining panels of the UTKS. The results of CNM monitoring show that the UTKS remains stable even after a consecutive excavation of 900 m in width. This stability impedes the upward propagation of overlying strata failure, leading to minimal surface subsidence. The study demonstrates the huge potential of CNM in the mining area, which can be useful for investigating material damage in physical modelling studies. The findings suggest that the cumulative extraction width in individual mining areas of the mine should be controlled to avoid a sudden collapse of the UTKS, and that special attention should be paid to where the UTKS’s thickness changes substantially. The substantial variation in UTKS thickness significantly impacts the pattern of overburden subsidence.