Miracle-dz/UniFAR-FTUs huggingface dataset:
Tropical forest temperature thresholds for gross primary productivity

Tropical forests are hyper-diverse and perform critical functions that regulate global climate, yet they are also threatened by rising temperatures. Canopy temperatures depart considerably from air temperatures, sometimes by as much as air temperatures are projected to increase by the end of this century; however, canopy temperatures are rarely measured or considered in climate change analyses. Our results from near-continuous thermal imaging of a well-studied tropical forest show that canopy temperatures reached a maximum of ~34°C, and exceeded maximum air temperatures by as much as 7°C. Comparing different canopy surfaces reveals that bark was the warmest, followed by a deciduous canopy, flowers, and coolest was an evergreen canopy. Differences among canopy surfaces were largest during afternoon hours, when the evergreen canopy cooled more rapidly than other canopy surfaces, presumably due to transpiration. Gross primary productivity (GPP), estimated from eddy covariance measurements, was more strongly associated with canopy temperatures than air temperatures or vapor pressure deficit. The rate of GPP increase with canopy temperatures slowed above ~28–29°C, but GPP continued to increase until ~31–32°C. Although future warming is projected to be greater in high-latitude regions, we show that tropical forest productivity is highly sensitive to small changes in temperature. Important biophysical and physiological characteristics captured by canopy temperatures allow more accurate predictions of GPP compared to commonly used air temperatures. Results suggest that as air temperatures continue to warm with climate change, canopy temperatures will increase at a ~40% higher rate, with uncertain but potentially large impacts on tropical forest productivity.
The current nature of police officer fatalities in Australia and opportunities for prevention

ABSTRACT Policing is considered one of the most dangerous and stressful occupations. The role and duties of officers mean that they are more likely to encounter not only accidents, but also assaults. Furthermore, the physical demands and occupational stresses of the job mean that officers are at risk of medical conditions that cause strain on the body such as heart attacks. This paper examines the current nature of police officer fatalities in Australia. In turn, this paper aims to identify strategies to prevent police fatalities. To do this, the study examined all police officer deaths in Australia between 2002 and 2019 (n = 39). Results revealed three main typologies of police fatalities in Australia: accidents, assaults and health-related fatalities. Accidents accounted for the largest proportion of fatalities, followed by assaults and health fatalities. Furthermore, the results showed a significant decrease in the number of police fatalities post 2006. These results are discussed in terms of changes to policing policies and practices that may have influenced this decline. Overall, these findings provided valuable insights into the current nature of police fatalities. These findings have implications for policing authorities in implementing prevention strategies to reduce and prevent officer deaths.
Scaling-Up the Anaerobic Digestion of Pretreated Microalgal Biomass within a Water Resource Recovery Facility

Microalgae-based wastewater treatment plants are low-cost alternatives for recovering nutrients from contaminated effluents through microalgal biomass, which may be subsequently processed into valuable bioproducts and bioenergy. Anaerobic digestion for biogas and biomethane production is the most straightforward and applicable technology for bioenergy recovery. However, pretreatment techniques may be needed to enhance the anaerobic biodegradability of microalgae. To date, very few full-scale systems have been put through, due to acknowledged bottlenecks such as low biomass concentration after conventional harvesting and inefficient processing into valuable products. The aim of this study was to evaluate the anaerobic digestion of pretreated microalgal biomass in a demonstration-scale microalgae biorefinery, and to compare the results obtained with previous research conducted at lab-scale, in order to assess the scalability of this bioprocess. In the lab-scale experiments, real municipal wastewater was treated in high rate algal ponds (2 × 0.47 m3), and harvested microalgal biomass was thickened and digested to produce biogas. It was observed how the methane yield increased by 67% after implementing a thermal pretreatment step (at 75 °C for 10 h), and therefore the very same pretreatment was applied in the demonstration-scale study. In this case, agricultural runoff was treated in semi-closed tubular photobioreactors (3 × 11.7 m3), and harvested microalgal biomass was thickened and thermally pretreated before undergoing the anaerobic digestion to produce biogas. The results showed a VS removal of 70% in the reactor and a methane yield up to 0.24 L CH4/g VS, which were similar to the lab-scale results. Furthermore, photosynthetic biogas upgrading led to the production of biomethane, while the digestate was treated in a constructed wetland to obtain a biofertilizer. In this way, the demonstration-scale plant evidenced the feasibility of recovering resources (biomethane and biofertilizer) from agricultural runoff using microalgae-based systems coupled with anaerobic digestion of the microalgal biomass.
Adaptive stabilisation via passive identifier for an ODE system cascaded by coupled parabolic equations with serious uncertainties

Stabilisation via adaptive feedback is investigated for an ODE system cascaded by coupled parabolic equations. The remarkable characteristic of the system is the presence of serious uncertainties since no known or bounded intervals are needed for the unknown parameters. Besides, strong coupling exists in the PDE subsystem which brings essential difference compared with those of the related literature. For this, a passive identifier is skillfully constructed firstly, and in turn adaptive laws are given to compensate the serious parametric unknowns. Then, by constructing a backstepping transformation and its inverse one, the identifier joint with the original system is transformed into a new system. From the new system, an adaptive state-feedback controller is designed which ensures that all the states of the closed-loop system are bounded and particularly those of the original system converge to zero. Finally, a numerical example is provided to validate the proposed algorithm.
Indirect Methods for Validating Shallow Geothermal Potential Using Advanced Laboratory Measurements from a Regional to Local Scale—A Case Study from Poland

This paper presents a broad overview of laboratory methods for measuring thermal properties and petrophysical parameters of carbonate rocks, and analytical methods for interpreting the obtained data. The investigation was conducted on carbonate rock samples from the Kraków region of Poland in the context of shallow geothermal potential assessment. The measurement techniques used included standard macroscopic examinations; petrophysical investigations (porosity, density); analysis of mineral composition thermal conductivity (TC) and specific heat measurements; and advanced investigations with the use of computed tomography (CT). Various mathematical models, such as layer model, geometric mean, and spherical and non-spherical inclusion models, were applied to calculate thermal conductivity based on mineralogy and porosity. The aim of this paper was to indicate the optimal set of laboratory measurements of carbonate rock samples ensuring sufficient characterization of petrophysical and thermal rock properties. This concerns both the parameters directly characterizing the geothermal potential (thermal conductivity) and other petrophysical parameters, e.g., porosity and mineral composition. Determining the quantitative relationship between these parameters can be of key importance in the case of a shortage of archival thermal conductivity data, which, unlike other petrophysical measurements, are not commonly collected. The results clearly show that the best correlations between calculated and measured TC values exist for the subgroup of samples of porosity higher than 4%. TC evaluation based on porosity and mineral composition correlation models gives satisfactory results compared with direct TC measurements. The methods and results can be used to update the existing 3D parametric models and geothermal potential maps, and for the preliminary assessment of geothermal potential in the surrounding area.
MD&A Disclosure of Critical Accounting Policies and Financial Reporting Risk: Evidence From Restatements

In this study, I examine whether management discloses areas with higher financial reporting risk as critical accounting policies (CAPs) and how investors react to restatements of areas previously disclosed as CAPs. Through CAPs, management annually discloses the accounting policies that it views as most crucial to the portrayal of the firm’s financial condition and results and require its most difficult, subjective, and/or complex judgments. I find that CAPs covering four areas are predictive of subsequent restatements: revenue, derivatives, accruals and short-term liabilities, and capitalization of expenditures. In tests of investor reactions to restatements, I find that cumulative abnormal returns to restatements are more severe (negative) when the areas restated were previously disclosed as CAPs. Collectively, these findings support that management discloses area-specific financial reporting risk through CAPs in some, but not all, areas and that investors view restatements of CAP areas as more egregious than restatements of non-CAP areas.
Cytotoxic Activity on Brine Shrimp, MCF-7 Cell Line and Thrombolytic Potential: Seven Different Medicinal Plant Leaves Extract

The aim of our study was to find out the preliminary cytotoxic and thrombolytic effect of the seven selected medicinal plant leaves extract. In cytotoxic activity, out of the seven leaves extract three of them exhibited stronger brine shrimp lethality with LC50 122.548 (Uncaria acida), 170.861 (Leea indica) and 175.469 (Piper porphyrophyllum) μg /mL respectively, and on MCF-7 cell line, they also exhibited moderate cytotoxic activity with different concentration of the extract of the same plant leaves such as,Uncaria acida (72.31, 56.22, 38.12 and 9.24%), Leea indica (67.31, 58.22, 43.12 and 15.24%), Piper porphyrophyllum (65.88, 48.12, 40.12 and 21.34%). In thrombolytic assessments, all the leaves extract showed moderate (considering > 20% moderate; **p < 0.01; *p < 0.05) clot lysis activity, but among the extracts, Uncaria cordata (27.36 ± 0.10%) showed the highest and Stachytarpheta indica the lowest (6.14 ± 0.20%) percent clot lysis as compared with the standard streptokinase (65.15 ± 0.16%). This study was conducted to legalize the folkloric use of seven medicinal plant leaves.
Higher-Order Thinking Skills (HOTS)-based Formative Assessment: A Proposed Model for Language Learning Assessment

The integration of higher-order thinking skills (HOTS) in language learning assessments has become a crucial issue in 21st-century learning. However, not many teachers are aware of the need to incorporate HOTS in assessments due to their insufficient knowledge and the absence of good examples. Further, there is not much research and literature on HOTS-based formative assessment that can be used as references. This research aims to fill the existing gap by providing a model of higher-order thinking skills (HOTS)-based formative assessments for English learning, especially in junior high schools. By employing research and development design, this research describes the validation of the assessment model. The proposed model of assessment may be used as a prototype for assessing language learning.
Does the dietary graded levels of herbal mixture powder impact growth, carcass traits, blood indices and meat quality of the broilers?

Abstract The present study was conducted to investigate the impacts of dietary supplementation of graded levels of herbal mixture powder (HMP) on growth performance, carcass traits, blood indices and meat quality of broilers. The herbal mixture consisted of (300 g [Capsicum annuum] Hot red pepper + 300 g Thymus vulgaris + 300 g Salvia rosmarinus + 150 g Pimpinella anisum + 150 g Mentha spicata + 300 g Nigella sativa + 300 g garlic [Allium sativum]). A total of 360 chicks of Hubbard broiler one-day-old were randomly distributed into six treatment groups. The groups consisted of the basal diet as a control and a powdered mixture of HMP groups at levels of (2.0, 3.0, 4.0, 5.0 and 6.0 g HMP/kg diet). The chicks fed a basal diet plus 5 g HMP/kg diet had the highest (p < .05) values of live body weight (LBW) and body weight gain (BWG) at five weeks of age. Serum total protein, globulin, liver enzymes and kidney parameter values were reduced in groups treated with different levels of HMP compared with the control. At the same time, values of albumin, A/G ratio, IgM and lysozymes were increased in chicks treated with HMP. The quality of stored meat and antioxidative parameters were improved in groups fed HMP. Thus, it is concluded that HMP could be used as a natural additive to the broiler diet to enhance the growth rate and improve blood parameters and meat quality. Also, the oxidative rancidity was less in the meat of treated chicks during the storage period as compared to control. Highlights Phytogenic additives are used to enhance growth and productive performance. Chicks fed 5 g herbal mixture/kg diet had the best growth performance. The quality of stored meat and antioxidative parameters were improved in groups fed the herbal mixture.
Sustainable Development of Entrepreneurial Orientation through Social Drivers

Given that social dilemmas such as climate change, unemployment, and public health are growing globally, recognition of social drivers related to sustainable development while generating social impact is of particular interest to professionals and academics. This study aims to highlight the antecedents of sustainable social impact. Thus, we evaluate the association between entrepreneurial orientation and social impact through the mediation effects of social factors named social drivers, including service innovation, social innovation, and social value orientation. The data collection is from the service and non-service industries in Taiwan. A total of 270 samples consisting of employees, managers, SME owners, and CEOs is used. We employed structural equation modeling (SEM) to test the proposed hypotheses. Research findings indicate entrepreneurial orientation is a determining factor of sustainable innovation that not only renders entrepreneurially strategic dispositions of an organization but also significantly contributes to generating drastic social impact. In particular, entrepreneurs are found to be more proactive in solving problematic social issues through their entrepreneurial characteristics. Statistical findings support that all direct effects, besides partial and full mediation, are proved, which reveals the transformative potential of selected social drivers. Altogether, research findings provide exciting insights into entrepreneurship knowledge, innovation, and value assumptions in the social context. We further discuss theoretical and practical implications.
The neurocognitive architecture of fluid ability in children and adolescents

Fluid ability is the capacity to solve novel problems in the absence of task-specific knowledge, and is highly predictive of outcomes like educational attainment and psychopathology. Here, we modelled the neurocognitive architecture of fluid ability in two cohorts: CALM (N=551, aged 5-17) and NKI-RS (N=335, aged 6-17). We used multivariate Structural Equation Modelling to test a preregistered ‘watershed model’ of fluid ability. We found that the watershed model fit the data well for both samples: White matter contributed to working memory and processing speed, which, in turn, contributed to fluid ability (R2(CALM)=51.2%, R2(NKI-RS)=78.3%). The relationship between cognitive abilities and white matter differed with age and showed a dip in strength around ages 7-12 years. Speculatively, this age-effect may reflect a reorganization of the neurocognitive architecture around pre- and early puberty. Overall, these findings highlight that fluid ability is part of a complex hierarchical system of partially independent effects.
Factors that Influence the Timing of Calling and Oviposition of a Lotic Frog in Northwestern California

Abstract Species that breed in stream environments with unpredictable interannual variability in hydrological regimes may exhibit plasticity in the timing of their breeding activities. Breeding phenology should coincide with conditions and habitats that maximize a species' reproductive success. For the lotic-breeding frog Rana boylii, the timing of breeding activities of a population can vary by more than a month among years. To examine the influence of abiotic factors on the variation in the timing of onset and patterns (i.e., peaks and pauses) in breeding activity of R. boylii, we sampled seven geographically separated sites that covered an extensive portion of the species' range in northwestern California for two breeding seasons. We collected daily environmental and male vocalization data and conducted weekly egg mass surveys at breeding sites. Here, we found the timing of calling activity and oviposition varied markedly among geographically separated sites and between years. Water depth and water temperature influenced calling phenology, whereas water depth and both temperatures (water and air) were significant factors in the timing of oviposition. In general, breeding activity did not commence until water temperatures reached 10°C. Calling and oviposition occurred later at deeper sites with colder, spring water temperatures. Models that predict the timing of breeding activities can improve survey and monitoring efforts and can assist managers of regulated streams in developing flow assessments that are compatible with species' breeding requirements. This information may be particularly useful in developing individual based models to assess overall reproductive success.
Quantifying the Influence of Achievement Emotions for Student Learning in MOOCs

Massive Open Online Courses (MOOCs) have become a popular tool for worldwide learners. However, a lack of emotional interaction and support is an important reason for learners to abandon their learning and eventually results in poor learning performance. This study applied an integrative framework of achievement emotions to uncover their holistic influence on students’ learning by analyzing more than 400,000 forum posts from 13 MOOCs. Six machine-learning models were first built to automatically identify achievement emotions, including K-Nearest Neighbor, Logistic Regression, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machines. Results showed that Random Forest performed the best with a kappa of 0.83 and an ROC_AUC of 0.97. Then, multilevel modeling with the “Stepwise Build-up” strategy was used to quantify the effect of achievement emotions on students’ academic performance. Results showed that different achievement emotions influenced students’ learning differently. These findings allow MOOC platforms and instructors to provide relevant emotional feedback to students automatically or manually, thereby improving their learning in MOOCs.
Numerical Investigation of Top-Coal Migration in the First Coal-Drawing Process by an FDM–DEM Coupling Method

In Longwall Top-Coal Caving (LTCC), the shape of the loose body (LB) and top-coal boundary (TCB) formed after the first coal-drawing are the initial conditions for the common coal-drawing period. Taking the Panel 12309 in Wangjialing coal mine as the research object, the weight of the blocks of caved top coal was measured on-site, and the distribution of their equivalent diameter was calculated. By using a coupled numerical method, the “Finite difference method (FDM)–Discrete element method (DEM)” numerical model was established, and the evolutions of the drawing body (DB), LB, and TCB were obtained. The results show that, in the initial stage of first coal-drawing (0–8.09 s), the amount of DB reached its maximum of 7.18 m3 (0.89 m3/s) and then decreased to a stable value of 0.44 m3/s. The relationships between the characteristic parameters of DB, LB, and TCB and the drawing time were fitted. Taking the second derivative of each parameter with respect to time as its sensitivity (η), it was concluded that due to the large coal-drawing volume in the initial stage (0–8.09 s), the values of the above parameters increased, and the sensitivities reached 6.02 × 10−3, 3.09 × 10−3, and 6.99 × 10−3, respectively. Here, the top-coal migration rule in the first coal-drawing process was revealed from the perspectives of DB, LB, and TCB, thus providing a theoretical basis for the further study of common coal-drawing processes.
A simple nearly unbiased estimator of cross‐covariances

In this article, we propose a simple estimator of cross‐covariance matrices for a multi‐variate time series with an unknown mean based on a linear combination of the circular sample cross‐covariance estimator. Our estimator is exactly unbiased when the data generating process follows a vector moving average (VMA) model with an order less than one half of the sampling period, and is nearly unbiased if such VMA model can approximate the data generating process well. In addition, our estimator is shown to be asymptotically equivalent to the conventional sample cross‐covariance estimator. Via simulation, we show that the proposed estimator can to a large extent eliminate the finite sample bias of cross‐covariance estimates, while not necessarily increase the mean squared error.
Characterization of indeterminate breast lesions on B-mode ultrasound using automated machine learning models

Abstract. Purpose: While mammography has excellent sensitivity for the detection of breast lesions, its specificity is limited. Adjunct screening with ultrasound may partially alleviate this issue but also increases false positives, resulting in unnecessary biopsies. Our study investigated the use of Google AutoML Vision (Mountain View, California), a commercially available machine learning service, to both identify and characterize indeterminate breast lesions on ultrasound. Approach: B-mode images from 253 independent cases of indeterminate breast lesions scheduled for core biopsy were used for model creation and validation. The performances of two sub-models from AutoML Vision, the image classification model and object detection model, were evaluated, while also investigating training strategies to enhance model performances. Pathology from the patient’s biopsy was used as a reference standard. Results: The image classification models trained under different conditions demonstrated areas under the precision–recall curve (AUC) ranging from 0.85 to 0.96 during internal validation. Once deployed, the model with highest internal performance demonstrated a sensitivity of 100% [95% confidence interval (CI) of 73.5% to 100%], specificity of 83.3% (CI = 51.6 % to 97.9%), positive predictive value (PPV) of 85.7% (CI = 62.9 % to 95.5%), and negative predictive value (NPV) of 100% (CI non-evaluable) in an independent dataset. The object detection model demonstrated lower performance internally during development (AUC = 0.67) and during prediction in the independent dataset [sensitivity = 75 % (CI = 42.8 to 94.5), specificity = 80 % (CI = 51.9 to 95.7), PPV = 75 % (CI = 50.8 to 90.0), and NPV = 80 % (CI = 59.3 % to 91.7%)], but was able to demonstrate the location of the lesion within the image. Conclusions: Two models appear to be useful tools for identifying and classifying suspicious areas on B-mode images of indeterminate breast lesions.