Miracle-dz/UniFAR-FTUs huggingface dataset:
Permeability and Groundwater Flow Dynamics in Deep‐Reaching Orogenic Faults Estimated From Regional‐Scale Hydraulic Simulations

Numerical modeling is used to understand the regional scale flow dynamics of the fault‐hosted orogenic geothermal system at the Grimsel Mountain Pass in the Swiss Alps. The model is calibrated against observations from thermal springs discharging in a tunnel some 250 m underneath Grimsel Pass to derive estimates for the bulk permeability of the fault. Simulations confirm that without the fault as a hydraulic conductor the thermal springs would not exist. Regional topography alone drives meteoric water in a single pass through the fault plane where it penetrates to depths exceeding 10 km and acquires temperatures in excess of 250°C. Thermal constraints from the thermal springs at Grimsel Pass suggest bulk fault permeabilities in the range of 2e−15 m2–4.8e−15 m2. Reported residence times of >30,000 and 7 years for the deep geothermal and shallow groundwater components in the thermal spring water, respectively, suggest fault permeabilities of around 2.5e−15 m2. We show that the long residence time of the deep geothermal water is likely a consequence of low recharge rates during the last glaciation event in the Swiss Alps, which started some 30,000 years ago. Deep groundwater discharging at Grimsel Pass today thus infiltrated the Grimsel fault prior to the last glaciation event. The range of permeabilities estimated from observational constraints is fully consistent with a subcritical single‐pass flow system in the fault plane.
Teachers’ Perceptions of and Concerns About Integrating Computational Thinking into Science Teaching After a Professional Development Activity

Although there is a lot of interest in the development of computational thinking (CT) and the benefits it could have for every student, integrating it into science classrooms may be more difficult than traditional teaching. This can be very challenging for South African science teachers. Thus, there is an increasing need to prepare teachers and systematically engage them in professional development activities to help them learn, unlearn, relearn and restructure their thoughts, perceptions and attitudes towards integrating CT in science classrooms. This study explored teachers’ perceptions and concerns about integrating CT in science teaching. Twenty-two science teachers from innercity secondary schools in South Africa were chosen to be part of this exploratory case study. Data were collected through take-home assignments and focus group discussions containing a series of open-ended questions. The concern-based adoption model was used as a lens to understand teachers’ stages of concern. Data were analyzed using thematic analysis. It was found that sampled science teachers viewed CT as a valuable tool that can help promote learners’ data management practices and problem-solving practices, especially in reducing complex problems to researchable problems. The findings also showed that teachers intend to integrate CT into their science classrooms by refining their existing practices. However, teachers showed high informational, personal and management-related concerns regarding integrating CT in South African science classrooms: they needed more practical and contextual ideas on strategically integrating CT in their daily lessons. Conversely, teachers showed limited concern at the unrelated, consequence, collaboration and refocusing stage. This suggests a need for teacher education programmes in South Africa to focus more acutely on training pre-service teachers on how to plan and design science instruction using computational thinking concepts and practices.
Immune Enhancement of Nanoparticle-Encapsulated Ginseng Stem-Leaf Saponins on Porcine Epidemic Diarrhea Virus Vaccine in Mice

Porcine epidemic diarrhea virus (PEDV) causes severe enteric disease in pigs, particularly neonatal piglets. Current vaccines do not provide complete protection against PEDV. Ginseng stem-leaf saponins (GSLS), a promising oral adjuvant candidate, can improve intestinal immune responses in poultry and mice. However, its low stability limits further use. Poly lactic-co-glycolic acid (PLGA), a biocompatible and biodegradable nanoparticle, has been widely used in biomedicine for stable and targeted drug delivery. In this study, we developed GSLS-PLGA nanoparticles (GSLS-NPs) and evaluated the mucosal adjuvant efficacy in vitro and in vivo. GSLS-NPs significantly enhanced antigen internalization and pro-inflammatory cytokine secretion by DC2.4 cells. Mice orally administered GSLS-NPs before intramuscular inoculation generated CD11b+CD8α− and CD11b−CD103+ dendritic cells in the spleen and draining mesenteric lymph nodes, respectively, which are the types mainly responsible for antigen presentation. Additionally, enhanced neutralizing and non-neutralizing antibody responses and expanded activities of specific effector and memory CD4+ and CD8+ T cells were also observed in mice immunized with PEDV vaccines plus GSLS-NPs compared to mice receiving the vaccines alone. Furthermore, GSLS-NPs showed a good safety profile and presented great advantages over GSLS aqueous solution. Collectively, our results highlight the potential of GSLS-NPs as a mucosal adjuvant and provide an attractive vaccination strategy for combatting PEDV. Further study is required to evaluate the efficacy of this mucosal adjuvant in swine.
Microlensing towards the Magellanic Clouds and M31: is the quest for MACHOs still open?

Microlensing is the tool of choice for the search and the analysis of compact halo objects ("MACHOs"), a still viable class of dark matter candidates at the galactic scale. Different analyses point towards an agreement in excluding dark matter MACHOs of less than about 0.1 solar mass; it remains however an ongoing debate for values in the mass range (0.1-1) solar mass. The more robust constraints, though not all in agreement, come from the observational campaigns towards the Magellanic Clouds (the LMC and the SMC). The analyses towards the nearby galaxy of M31, in the so called "pixel lensing" regime, have expanded the perspectives in this field of research. In this contribution first we draw a critical view on recent results and then we focus on the pixel lensing analysis towards M31 of the PLAN collaboration.
A multi-user task offloading strategy in edge computing based on Lyapunov optimization

Based on three tier heterogeneous network, we examine the problem of energy consumption optimization with latency constraints, establishing a hierarchical computing model with multiple layers under heterogeneous network, each layer representing its individual computing model. An offloading strategy will be optimized to reduce energy consumption while ensuring latency constraints. Lyapunov optimization theory will be utilized along with a heuristic algorithm called Coral Reef Optimization (CRO) to analyze the current workload and available computing capacity of edge and cloud servers, making an efficient compute offloading choice reduces the energy used by cloud servers while still adhering to latency restrictions. Simulation result shows that compared to cloud-only and edge-ward strategies, the proposed strategy shows improvements in term of energy reduction as well as execution time.
A Comprehensive Dynamic Life Cycle Assessment Model: Considering Temporally and Spatially Dependent Variations

Life cycle assessment (LCA) is a widely-used international environmental evaluation and management method. However, the conventional LCA is in a static context without temporal and spatial variations considered, which fails to bring accurate evaluation values and hinders practical applications. Dynamic LCA research has developed vigorously in the past decade and become a hot topic. However, systematical analysis of spatiotemporal dynamic variations and comprehensive operable dynamic models are still lacking. This study follows LCA paradigm and incorporates time- and space-dependent variations to establish a spatiotemporal dynamic LCA model. The dynamic changes are classified into four types: dynamic foreground elementary flows, dynamic background system, dynamic characterization factors, and dynamic weighting factors. Their potential dynamics and possible quantification methods are analyzed. The dynamic LCA model is applied to a residential building, and significant differences can be observed between dynamic and static assessment results from both temporal and spatial perspectives. This study makes a theoretical contribution by establishing a comprehensive dynamic model with both temporal and spatial variations involved. It is expected to provide practical values for LCA practitioners and help with decision-making and environmental management.
An evidence-based study on the current status of Chinese secondary school mathematics teachers’ autonomous learning capacity across demographic and contextual factors

Autonomous learning capacity is a key competency that supports teachers’ professional development. In this study, a stratified sampling method was used to recruit 396 junior and senior high school mathematics teachers in T city, one of the provincial city in China. A questionnaire with high reliability and validity developed prior to the study by the researchers was employed to measure their autonomous learning capacity and differences across groups. Twelve teachers were then selected for interviews. The results showed satisfactory overall performance. By subdimension, teachers’ performance was the best in the development of study plans, followed by evaluation of learning outcomes, while they needed improvement in learning habit formation and proficiency in using learning methods. Furthermore, the analysis of differences across groups indicated that for autonomous learning capacity, female teachers were significantly better than their male peers; junior high school teachers were better compared to those in senior high school; teachers aged 41–50 underperformed those aged 51 and above; teachers who work in rural areas and townships did not perform worse than urban teachers; and those with doctoral degrees did not demonstrate considerable advantage over others. There were no significant differences in the overall autonomous learning capacity across years of teaching and job title groups. However, in the subdimensions, those with 21–30 years of teaching experience had lower proficiency in using learning methods and evaluation of learning outcomes, and teachers with senior titles did not demonstrate expected advantages in learning habit formation.
EEMDS: Efficient and Effective Malware Detection System with Hybrid Model based on XceptionCNN and LightGBM Algorithm

The security threats posed by malware make it imperative to build a model for efficient and effective classification of malware based on its family, irrespective of the variant. Preliminary experiments carried out demonstrate the suitability of the generic LightGBM algorithm for Windows malware as well as its effectiveness and efficiency in terms of detection accuracy, training accuracy, prediction time and training time. The prediction time of the generic LightGBM is 0.08s for binary class and 0.40s for multi-class on the Malimg dataset. The classification accuracy of the generic LightGBM is 99% True Positive Rate (TPR). Its training accuracy is 99.80% for binary class and 96.87% for multi-class, while the training time is 179.51s and 2224.77s for binary and multi classification respectively. The performance of the generic LightGBM leaves room for improvement, hence, the need to improve the classification accuracy and training accuracy of the model for effective decision making and to reduce the prediction time and training time for efficiency. It is also imperative to improve the performance and accuracy for effectiveness on larger samples. The goal is to enhance the detection accuracy and reduce the prediction time. The reduction in prediction time provides early detection of malware before it damages files stored in computer systems. Performance evaluation based on Malimg dataset demonstrates the effectiveness and efficiency of the hybrid model. The proposed model is a hybrid model which integrates XceptionCNN with LightGBM algorithm for Windows Malware classification on google colab environment. It uses the Malimg malware dataset which is a benchmark dataset for Windows malware image classification. It contains 9,339 Malware samples, structured as grayscale images, consisting of 25 families and 1,042 Windows benign executable files extracted from Windows environments. The proposed XceptionCNN-LightGBM technique provides improved classification accuracy of 100% TPR, with an overall reduction in the prediction time of 0.08s and 0.37s for binary and multi-class respectively. These are lower than the prediction time for the generic LightGBM which is 0.08s for binary class and 0.40s for multi-class, with an improved 100% classification accuracy. The training accuracy increased to 99.85% for binary classification and 97.40% for multi classification, with reduction in the training time of 29.97s for binary classification and 447.75s for multi classification. These are also lower than the training times for the generic LightGBM model, which are 179.51s and 2224.77s for the binary and multi classification respectively. This significant reduction in the training time makes it possible for the model to converge quickly and train a large sum of data within a relatively short period of time. Overall, the reduction in detection time and improvement in detection accuracy will minimize damages to files stored in computer systems in the event of malware attack.
Elastic Buckling of Laterally Continuous I-Beams

An approximate method for determining the elastic flexural torsional buckling loads of laterally continuous structures is developed. The method can be applied to structures loaded at braced points. The braces and supports are assumed to prevent lateral deflection and twisting. The procedure locates a critical segment and adjacent restraining segments which together form a subassemblage. The elastic critical load is obtained by determining the effective length of the critical segment. Charts of effective length factors are presented and the factors are shown to depend on the amount of end restraint and on the critical segment beam parameter and end moment ratio. The proposed method is applied to a worked example. The result, along with other results presented in the paper, is in close agreement with accurate numerical solutions (a).
Between the Buttons: Stress Assessment in Video Games using Players' Behavioural Data

: Flow lies at the heart of the interaction between players and video games. It is usually regarded as the optimal experience blooming in the fragile equilibrium that floats between boredom and anxiety. Under such circumstances, stress assessment can be a crucial experiential marker. In this preliminary study, we propose a computational approach to characterise the stress level of video game players, suitable to be exploited in the development of adaptive video games while enhancing players’ experience. To such purpose, a Virtual Reality (VR)-based video game has been created to gather data from participants. The information collected includes both physiological data and motion behavioural data (from game controllers), as well as the subjects’ self-reports of perceived stress. Behavioural data are specifically considered in the work presented here. We characterize the stress level evolution in terms of state-space dynamics, which is suitable for either discrete (classification) and continuous stress level assessment. Different experiments have been performed and results so far obtained are encouraging. In particular, along the stress vs. no-stress classification test, an accuracy of up to 84.4% is achieved by using VR-based data.
The changing association between political ideology and closed-mindedness: Left and right have become more alike

Evidence suggests that politically right-leaning individuals are more likely to be closed-minded. Whether this association is inherent or subject to change has been the subject of debate, yet has not been formally tested. Through a meta-analysis, we find evidence of a changing association between conservatism and facets of closed-mindedness in the U.S. and international context using 341 unique samples, over 200,000 participants, and 920 estimates over 71 years. In the U.S., data ranging from 1948 to 2019 revealed a linear decline in the association between social conservatism (SC) and closed-mindedness, though economic conservatism (EC) did not vary in its association with closed-mindedness over time. Internationally across 18 countries, excluding the U.S., we observed a curvilinear decline in the association between SC and closed-mindedness over that same time, but no change in ECs association. We also tested variation over time for attitudinal measures of conservatism ranging between 1987 to 2018. In the U.S., we observed a linear increase in the association between right-wing authoritarianism (RWA) and closed-mindedness, with a similar linear increase in the association between social dominance orientation (SDO) and closed-mindedness. Internationally, there was a curvilinear increase in the association between RWA and closed-mindedness, but no change in the association with SDO. We discuss the changes to the political landscape that might explain our findings.
Generation of continuous-wave broadband entangled beams using periodically poled lithium niobate waveguides

Continuous-wave light beams with broadband quantum entanglement are created with two independent squeezed light beams generated by two periodically poled lithium niobate waveguides and a symmentric beam splitter. The quantum entanglement is confirmed with a sufficient criterion ΔA,B2=⟨[Δ(xA−xB)]2⟩+⟨[Δ(pA+pB)]2⟩<1 and the observed ΔA,B2 is 0.75 over the bandwidth of 30MHz. Although the bandwidth is limited by that of the detector so far, it would be broadened up to 10THz which would be only limited by the bandwidth of phase matching for the second-order nonlinear process.
Transcriptome Analysis Reveals Putative Induction of Floral Initiation by Old Leaves in Tea-Oil Tree (Camellia oleifera ‘changlin53’)

Floral initiation is a major phase change in the spermatophyte, where developmental programs switch from vegetative growth to reproductive growth. It is a key phase of flowering in tea-oil trees that can affect flowering time and yield, but very little is known about the molecular mechanism of floral initiation in tea-oil trees. A 12-year-old Camellia oleifera (cultivar ‘changlin53’) was the source of experimental materials in the current study. Scanning electron microscopy was used to identify the key stage of floral initiation, and transcriptome analysis was used to reveal the transcriptional regulatory network in old leaves involved in floral initiation. We mined 5 DEGs related to energy and 55 DEGs related to plant hormone signal transduction, and we found floral initiation induction required a high level of energy metabolism, and the phytohormones signals in the old leaves regulate floral initiation, which occurred at stage I and II. Twenty-seven rhythm-related DEGs and 107 genes associated with flowering were also identified, and the circadian rhythm interacted with photoperiod pathways to induce floral initiation. Unigene0017292 (PSEUDO-RESPONSE REGULATOR), Unigene0046809 (LATE ELONGATED HYPOCOTYL), Unigene0009932 (GIGANTEA), Unigene0001842 (CONSTANS), and Unigene0084708 (FLOWER LOCUS T) were the key genes in the circadian rhythm-photoperiod regulatory network. In conjunction with morphological observations and transcriptomic analysis, we concluded that the induction of floral initiation by old leaves in C. oleifera ‘changlin53’ mainly occurred during stages I and II, floral initiation was completed during stage III, and rhythm–photoperiod interactions may be the source of the main signals in floral initiation induced by old leaves.
Characterization of Glossy Spike Mutants and Identification of Candidate Genes Regulating Cuticular Wax Synthesis in Barley (Hordeum vulgare L.)

Cuticular waxes comprise the hydrophobic layer that protects crops against nonstomatal water loss and biotic and abiotic stresses. Expanding on our current knowledge of the genes that are involved in cuticular wax biosynthesis and regulation plays an important role in dissecting the processes of cuticular wax metabolism. In this study, we identified the Cer-GN1 barley (Hordeum vulgare L.) mutant that is generated by ethyl methanesulfonate mutagenesis with a glossy spike phenotype that is controlled by a single recessive nuclear gene. A physiological analysis showed that the total cuticular wax loads of Cer-GN1 were one-third that of the progenitor wild-type (WT), and its water loss rate was significantly accelerated (p < 0.05). In addition, Cer-GN1 was defective in the glume’s cuticle according to the toluidine blue dye test, and it was deficient in the tubule-shaped crystals which were observed on the glume surfaces by scanning electron microscopy. Using metabolomics and transcriptomics, we investigated the impacts of cuticular wax composition and waxy regulatory genes on the loss of the glaucous wax in the spikes of Cer-GN1. Among the differential metabolites, we found that 16-hydroxyhexadecanoic acid, which is one of the predominant C16 and C18 fatty acid-derived cutin monomers, was significantly downregulated in Cer-GN1 when it was compared to that of WT. We identified two novel genes that are located on chromosome 4H and are downregulated in Cer-GN1 (HvMSTRG.29184 and HvMSTRG.29185) that encode long-chain fatty acid omega-monooxygenase CYP704B1, which regulates the conversion of C16 palmitic acid to 16-hydroxyhexadecanoic acid. A quantitative real-time PCR revealed that the expression levels of HvMSTRG.29184 and HvMSTRG.29185 were downregulated at 1, 4, 8, 12, and 16 days after the heading stage in Cer-GN1 when it was compared to those of WT. These results suggested that HvMSTRG.29184 and HvMSTRG.29185 have CYP704B1 activity, which could regulate the conversion of C16 palmitic acid to 16-hydroxyhexadecanoic acid in barley. Their downregulation in Cer-GN1 reduced the synthesis of the cuticular wax components and ultimately caused the loss of the glaucous wax in the spikes. It is necessary to verify whether HvMSTRG.29184 and HvMSTRG.29185 truly encode a CYP704B1 that regulates the conversion of C16 palmitic acid to 16-hydroxyhexadecanoic acid in barley.
Classical Simulation of Intermediate-Size Quantum Circuits

We introduce a distributed classical simulation algorithm for general quantum circuits, and present numerical results for calculating the output probabilities of universal random circuits. We find that we can simulate more qubits to greater depth than previously reported using the cluster supported by the Data Infrastructure and Search Technology Division of the Alibaba Group. For example, computing a single amplitude of an $8\times 8$ qubit circuit with depth $40$ was previously beyond the reach of supercomputers. Our algorithm can compute this within $2$ minutes using a small portion ($\approx$ 14% of the nodes) of the cluster. Furthermore, by successfully simulating quantum supremacy circuits of size $9\times 9\times 40$, $10\times 10\times 35 $, $11\times 11\times 31$, and $12\times 12\times 27 $, we give evidence that noisy random circuits with realistic physical parameters may be simulated classically. This suggests that either harder circuits or error-correction may be vital for achieving quantum supremacy from random circuit sampling.