Miracle-dz/UniFAR-FTUs huggingface dataset:
The consequences of media multitasking for adolescent development

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Plant biomass and soil moisture dynamics: analytical results

In semi-arid ecosystems, successful use of the limited water resources is of central importance in determining the evolutionary trends of the vegetation. The competition between different species and individuals for this resource is driven by variations in physiology and metabolic regulation strategies, expressed by such parameters as rooting depth, wilting point or stomatal opening, among others. It is typically not practical to simulate the full evolutionary dynamics of every plant individual in the landscape because of the difficulties introduced by the spatial heterogeneity, as well as the many timescales involved, ranging from hourly up to intergenerational. Instead, the amount of biomass of a given species assimilated per unit area of the landscape may serve as a proxy for its competitiveness and evolutionary success. It is the behaviour of the biomass, which must be described probabilistically due to the stochasticity of the rainfall, which is the subject here. This paper develops a new analytical description of the stationary and transient joint behaviour of plant biomass and soil moisture. Additionally, the effects of climatic fluctuations are considered, including the important case of a bi-seasonal climate regime consisting of alternating wet and dry seasons, which is characteristic of many ecosystems of interest.
Word-Level Embedding to Improve Performance of Representative Spatio-temporal Document Classification

Tokenization is the process of segmenting the input text into smaller units of text, and it is a preprocessing task that is mainly performed to improve the efficiency of the machine learning process. Various tokenization methods have been proposed for application in the field of natural language processing, but studies have primarily focused on efficiently segmenting text. Few studies have been conducted on the Korean language to explore what tokenization methods are suitable for document classification task. In this paper, an exploratory study was performed to find the most suitable tokenization method to improve the performance of a representative spatio-temporal document classifier in Korean. For the experiment, a convolutional neural network model was used, and for the final performance comparison, tasks were selected for document classification where performance largely depends on the tokenization method. As a tokenization method for comparative experiments, commonly used Jamo, Character, and Word units were adopted. As a result of the experiment, it was confirmed that the tokenization of word units showed excellent performance in the case of representative spatio-temporal document classification task where the semantic embedding ability of the token itself is important.
protects bovine hepatocytes against free fatty acid–induced oxidative damage by activating the transcription factor EB/peroxisome proliferator-activated receptor γ coactivator 1 alpha pathway

Excessive free fatty acid (FFA) oxidation and related metabolism are the major cause of oxidative stress and liver injury in dairy cows during the early postpartum period. In nonruminants, activation of transcription factor EB (TFEB) can improve cell damage and reduce the overproduction of mitochondrial reactive oxygen species. As a downstream target of TFEB, peroxisome proliferator-activated receptor γ coactivator 1 α (PGC-1α, gene name PPARGC1A ) is a critical regulator of oxidative metabolism. Nuciferine (Nuc), a major bioactive compound isolated from the lotus leaf, has been re-ported to possess hepatoprotective activity. Therefore, the objective of this study was to investigate whether Nuc could protect bovine hepatocytes from FFA-induced lipotoxicity and the underlying mechanisms. A mixture of FFA was diluted in RPMI-1640 basic medium containing 2% low fatty acid bovine serum albumin to treat hepatocytes. Bovine hepatocytes were isolated from newborn calves and treated with various concentrations of FFA mixture (0, 0.3, 0.6, or 1.2 m M ) or Nuc (0, 25, 50, or 100 μ M ), as well as co-treated with 1.2 m M FFA and different concentrations of Nuc. For the experiments of gene silencing, bovine hepatocytes were transfected with small interfering RNA targeted against TFEB or PPARGC1A for 36 h followed by treatment with 1.2 m M FFA for 12 h in presence or absence of 100 μ Μ Nuc. The results revealed that FFA treatment decreased protein abundance of nuclear TFEB, cytosolic TFEB, total (t)-TFEB, lysosome-associated membrane protein 1 (LAMP1) and PGC-1α and mRNA abundance of LAMP1 , but increased phosphorylated (p)-TFEB. In addition, FFA treatment increased the content of
Recollection-dependent memory for event duration in large-scale Recollection-dependent memory for event duration in large-scale spatial navigation spatial navigation

Time and space represent two key aspects of episodic memories, forming the spatiotemporal context of events in a sequence. Little is known, however, about how temporal information, such as the duration and the order of particular events, are encoded into memory, and if it matters whether the memory representation is based on recollection or familiarity. To investigate this issue, we used a real world virtual reality navigation paradigm where periods of navigation were interspersed with pauses of different durations. Crucially, participants were able to reliably distinguish the durations of events that were subjectively “reexperienced” (i.e., recollected), but not of those that were familiar. This effect was not found in temporal order (ordinal) judgments. We also show that the active experience of the passage of time (holding down a key while waiting) moderately enhanced duration memory accuracy. Memory for event duration, therefore, appears to rely on the hippocampally supported ability to recollect or reexperience an event enabling the reinstatement of both its duration and its spatial context, to distinguish it from other events in a sequence. In contrast, ordinal memory appears to rely on familiarity and recollection to a similar extent.
The Effectiveness of Arabic Stemmers Using Arabized Word Removal

Other languages have influenced Arabic because of several factors, such as geographical nearness, trade communication, past Islamic conquests, science and technology, new devices, brand names, models, and fashion. As a result of these factors, foreign words are used in Arabic text and are known as Arabised words. Arabised words affect the Arabic natural language processing (NLP) task because identifying a correct stem or root from an Arabic word becomes more difficult. Therefore, a more efficient Arabic NLP can be developed if Arabised word removal is part of a pre-processing task. In this paper, we propose an algorithm for detecting and extracting Arabised words as a pre-processing task for an Arabic stemming task. This algorithm is a combination of lexicon-based and rule-based approaches. The lexicon list has been developed based on various sources of Arabic text resources, and the rule-based algorithm has been designed to cater to Arabised words with definite articles and use pattern matching on prefixes and suffixes. To evaluate the effectiveness of the proposed Arabised word removal algorithm on the Arabic NLP task, we use Arabised word removal as part of pre-processing in Arabic stemmers. Three Arabic stemmers are used in our evaluation, namely, light stemming, condition light and ARLS, on three types of Arabic standard datasets. Comparisons were made by measuring the performance of precision, recall and IFC on the stemmers with or without our Arabised word removal pre-processing. Results show that the performance on all the stemmers improves if Arabised word removal is
Hot Spot Detection of Thermal Infrared Image of Photovoltaic Power Station Based on Multi-Task Fusion

The manual inspection of photovoltaic (PV) panels to meet the requirements of inspection work for large-scale PV power plants is challenging. We present a hot spot detection and positioning method to detect hot spots in batches and locate their latitudes and longitudes. First, a network based on the YOLOv3 architecture was utilized to identify hot spots. The innovation is to modify the RU_1 unit in the YOLOv3 model for hot spot detection in the far field of view and add a neural network residual unit for fusion. In addition, because of the misidentification problem in the infrared images of the solar PV panels, the DeepLab v3+ model was adopted to segment the PV panels to filter out the misidentification caused by bright spots on the ground. Finally, the latitude and longitude of the hot spot are calculated according to the geometric positioning method utilizing known information such as the drone's yaw angle, shooting height, and lens field-of-view. The experimental results indicate that the hot spot recognition rate accuracy is above 98%. When keeping the drone 25 m off the ground, the hot spot positioning error is at the decimeter level.
The influence of land use and management on the behaviour and persistence of soil organic carbon in a subtropical Ferralsol

A substantial carbon (C) debt has been accrued due to long-term cropping for global food production emitting carbon dioxide from soil. However, the factors regulating the persistence of soil organic C (SOC) remain unclear, with this hindering our ability to develop effective land management strategies to sequester C in soil. Using a Ferralsol from semi-arid subtropical Australia, alteration of bulk C contents and fractions due to long-term land use change (up to 72 y) were examined with a focus on understanding whether SOC lost due to cropping could be restored by subsequent conversion back 10 to pasture or plantation. It was found that use of soil from cropping for 72 y resulted in the loss of > 70% of both C and N contents. Although conversion of cropped soil to pasture or plantation for up to 39 y resulted in an increase in both C and N, the C content of all soil fractions were not restored to the original values observed under remnant vegetation. The loss of C with cropping was most pronounced from the particulate organic matter fraction, whilst in contrast, the portion of the C that bound strongly to the soil mineral particles (i.e., the mineral-associated fraction) was most resilient. Indeed, microbial-15 derived aliphatic C was enriched in the fine fraction of mineral-associated organic matter (< 53 µm). Our findings were further confirmed using synchrotron-based micro-spectroscopic analyses of intact microaggregates which highlighted that binding of C to soil mineral particles is critical to SOC persistence in disturbed soil. The results of the present study extend our conceptual understanding of C dynamics and behaviour at the fine scale where C is stabilised and accrues, but it is clear that restoring C in soils in semi-arid landscapes of subtropical regions poses a challenge.
The consequences of media multitasking for adolescent development

The rise in media multitasking among adolescents has led to serious concerns regarding its possible negative impact on sleep. Although cross-sectional studies have shown a link between media multitasking and sleep problems, knowledge about the causal direction is lacking. In a first step to understand causality, we investigated the longitudinal association between media multitasking and sleep problems among 1,443 adolescents (7 th and 8 th graders, 11-15 years, 51% boys), who completed a questionnaire three times at three-to-four month intervals. We employed random intercept cross-lagged panel models, which specifically examine cross-lagged correlations within (rather than between) individuals. The findings showed no cross-lagged correlations for the overall sample. However, the results indicated that, for 7 th graders and girls, media multitasking was (marginally) related to more subsequent sleep problems. Our findings provide first evidence that media multitasking may affect the sleep of 7 th graders and girls.
Digitalization: A Game Changer for Local Governments & Communities Enhancing Capacities to Deploy Transformative Solutions

Executive Summary Local authorities stand on the brink of a significant transformation, partly driven by harnessing digital tools and processes to enhance their capacities. There is immense potential for digitalization driven and enabled by local governments. It can spur economic growth, transparency, improved governance efficiency, and environmental sustainability. Technologies like the Internet of Things (IoT), Artificial Intelligence (AI), Blockchain, and Digital Twins are at the forefront of this revolution. They offer smart and sustainable urban systems, enhance governance, and facilitate inclusive service delivery. In combination, the transformative impact of digitalization reshapes local governance, bolsters community resilience, and paves the way for sustainable, equitable societies. This policy brief highlights the game-changing power of digitalization and how local governments and communities can harness its full potential. Despite clear benefits, adopting digitalization is challenging, especially for cities in the Global South, where local governments face increased skills and technology gaps, infrastructure deficits, socio-economic barriers, and governance issues. This digital divide, which is more pronounced in developing regions, hampers the effective deployment of digital solutions. A multilevel governance approach is essential to realize digitalization’s full potential. This approach involves a comprehensive assessment of the current operational state, strategic planning that aligns with regional and national policies, and extensive stakeholder engagement. Essential to this process are capacity building, resource allocation, and collaborative strategies such as public-private partnerships and intergovernmental cooperation. Local governments need to establish stakeholder inclusion strategies, promote innovation and trust in IT infrastructure, and implement systematic monitoring and evaluation to assess the impacts of digital initiatives. To support the digital
Research on Economic Performance of Mining Enterprises Based on Stakeholders

Conventional mining enterprises, particularly coal-related ones, exhibit substantial environmental pollution and high energy consumption, while those involved in new energy resources, such as lithium and cobalt, face severe resource shortages. Consequently, the economic efficiency of China’s mining enterprises is significantly constrained. This study examines data from nine representative listed enterprises in China spanning 2016 to 2021. Employing the DEA model—i.e., BCC (VRS) model, we analyze the economic efficiency of mining enterprises with a focus on stakeholders. The paper provides static and dynamic analyses, offering insights and recommendations for enhancing technology
Cubic bipolar fuzzy aggregation operator with priority degree with multi-criteria decision-making

Cubic bipolar fuzzy numbers ( CBFN ) are useful for real-world ambiguous data. Prioritised MCDM s use priority degrees. Aggregation operators ( AOs ) result from tight priority levels and priority degrees. Thus, "cubic bipolar fuzzy prioritised averaging operator with priority degrees ( CBFPDA )" and "cubic bipolar fuzzy geometric operator ( CBFPGD )" are CBFNs prioritised operators. Comparative studies are made. Comparison analysis veriﬁes the proposed method. The comparison study shows the approach works. Comparing the current method to others emphasises its superiority over current operators. Priorities affect object ranking and information fusion. Discussing a 3PRLP optimisation problem’s practical implementation is a secondary goal. The recommended 3PRLP reference is evaluated numerically. The best strategy is selected and compared.
An improved ORB-GMS image feature extraction and matching algorithm*

Feature point extraction and matching is the key technology in object detection and simultaneous localization and mapping (SLAM). Aiming at the problems such as easy redundancy of feature points extracted by traditional ORB algorithm, low matching accuracy of mainstream robust estimation algorithms and low real-time performance, an improved ORB-GMS image feature extraction and matching algorithm is proposed. Firstly, the algorithm uses the gray value of the image to calculate the adaptive extraction threshold of the feature points. Then the image pyramid is constructed according to the image size. The set number of total feature points to be extracted is evenly distributed to each layer image according to the area ratio; Extract feature points from each layer of the image pyramid, and count the extracted feature points from each layer. If the number of feature points extracted from each layer meets the set number of images from each layer, the extraction ends. Then the quadtree algorithm is used to homogenize the feature points. Finally, the network scoring model is optimized from 8 neighborhood to 4 neighborhood, which reduces the computing time. Experimental results show that the matching accuracy of the proposed algorithm is 14% higher than that of the original algorithm, and the running time is 12% lower.
Unlocking the Potential of German Smart Cities: Strategy Analysis through Online Content Examination

This paper presents a comprehensive analysis of smart city strategies in Germany through the examination of online content. Smart city initiatives have gained significant attention worldwide as urban areas seek innovative solutions to address various challenges. In Germany, renowned for its technological achievement and commitment to sustainability, has witnessed the emergence of numerous Smart City projects. The aim of this study is to investigate the current status of Smart Cities strategy initiatives in Germany based on a sample of 82 cities. To achieve this, an online content analysis methodology is employed, using the official web presences of the selected cities and information published on them. The content analysis focuses on identifying empirical evidence related to digitalisation, data and governance, and obtaining their transparency and progress in a sample of 82 major German cities. The findings of this study reveal the diversity and richness of German smart city approaches, but also a varying degree of sophistication and transparency of strategic initiatives. As an expression of Germany's federal structure, the cities focus on different aspects, such as mobility, energy efficiency or data-driven administration. The insights gained from this online content analysis provide valuable guidance for policymakers, urban planners and stakeholders involved in the design of smart city strategies. By understanding the strengths and weaknesses of existing initiatives, policymakers can refine their approaches, address challenges and promote sustainable and inclusive urban development. Furthermore, this study contributes to the academic discourse on smart cities by providing empirical evidence on the implementation of strategies in a German context. Overall, this study contributes to a deeper understanding of the German smart city landscape and serves as a basis for future research, policy formulation and the pursuit of smarter and more liveable cities.
Fear-Avoidance Beliefs Associated with Non-Specific Chronic Low Back Pain in College Athletes

Purpose This study aimed to determine the psychosocial factors associated with non-specific chronic low back pain (NS-CLBP) among college athletes. Material and Methods A cross-sectional study was performed at one university. A non-anonymous, self-administered online questionnaire was collected from each athlete. Participants with a history of orthopaedic spine disease or surgery were excluded. Online responses from 368 college athletes belonging to 18 clubs were collected, among which 263 were included in the analysis. In the 263 responses, 41 individuals were identified as having NS-CLBP. Multivariate logistic regression analyses were performed to determine factors associated with presence of NS-CLBP. Independent variables included the Fear-Avoidance Beliefs Questionnaire physical activity subscale (FABQ-PA) score, Tampa Scale for Kinesiophobia-11 (TSK-11) score, Roland-Morris Disability Questionnaire (RDQ) score, and body mass index (BMI). Additionally, the Mann–Whitney U-test was utilized to compare FABQ-PA, TSK-11, RDQ scores, and BMI between the NS-CLBP and non-NS-CLBP groups. Results The FABQ-PA (odd ratio = 1.096, P = 0.003) was significantly associated with NS-CLBP. No significant association was observed between NS-CLBP and TSK-11 (P = 0.776), RDQ (P = 0.074), and BMI (P = 0.296). The scores for FABQ-PA, TSK-11, RDQ, and BMI in the group with NS-CLBP were found to be significantly higher compared to the group without NS-CLBP (P < 0.001, P = 0.034, P < 0.001, and P = 0.022, respectively). Conclusion The present study revealed a significant relationship between higher FABQ-PA scores and NS-CLBP among college athletes. Conversely, TSK-11 and BMI values showed no significant association with NS-CLBP presence. The findings suggest that addressing fear-avoidance beliefs may be crucial in managing NS-CLBP among college athletes.
Fracture-Cross-Flow Equilibrium in Compositional Two-Phase Reservoir Simulation

Summary Compositional two-phase ﬂow in fractured media has wide applications, including carbon dioxide (CO 2 ) injection in the subsurface for improved oil recovery and for CO 2 sequestration. In a recent work, we used the fracture-crossﬂow-equilibrium (FCFE) approach in single-phase compressible ﬂow to simulate fractured reservoirs. In this work, we apply the same concept in compositional two-phase ﬂow and show that we can compute all details of two-phase ﬂow in fractured media with a central-processing-unit (CPU) time comparable with that of homogeneous media. Such a high computational efﬁciency is dependent on the concept of FCFE, and the implicit solution of the transport equations in the fractures to avoid the Courant-Freidricks-Levy (CFL) condition in the small fracture elements. The implicit solution of two-phase compositional ﬂow in fractures has some challenges that do not appear in single-phase ﬂow. The complexities arise from the upstreaming of the derivatives of the molar concentration of component i in phase a ð c a ; i Þ with respect to the total molar concentration ð c i Þ when several fractures intersect at one interface. In addition, because of gravity, countercurrent ﬂow may develop, which adds complexity when using the FCFE concept. We overcome these complexities by providing an upstreaming technique at the fracture/fracture interface and the matrix/fracture interface. We calculate various derivatives at constant volume V and temperature T by performing ﬂash calculations in the fracture elements and the matrix domain to capture the discontinuity at the matrix/fracture interface. We demonstrate in various examples the efﬁciency and accuracy of the proposed algorithm in problems of various degrees of complexity in eight-component mixtures. In one example with 4,300 elements (1,100 fracture elements), the CPU time to 1 pore volume injection (PVI) is approximately 3 hours. Without the fractures, the CPU time is 2 hours and 28 minutes.