Miracle-dz/UniFAR-FTUs huggingface dataset:
Landscape visualisation software as a forest management decision support system

Sustainable management should take into account the visual impact on the landscape, as it can offer many opportunities for enhancing the aesthetic value of forests or woodlands. The effect of management options is, however, not easy to convey to decision makers and to the general public. Landscape visualisation software can help bring together the elements of a large-scale landscape project. A case study showing the effect of silvopastoral management on environmental changes in a forested area is presented. Various management options have been tested, providing a basis for discussion between partners concerning spatial organisation, such as the layout of forest and agricultural plots. (Resume d'auteur)
The I-TAP-PD project and the Greek participation Contribution to local practices of equal collaboration between teachers and artists and to international research

This paper presents the International Teacher-Artist Partnership – Professional Development (I-TAP-PD) programme, framing the definition and methodology of Teacher-Artist Partnership (TAP) and presenting the structure and content of this programme as implemented by organisations working in the field of education and the arts in 4 countries (Ireland, Serbia, the Netherlands, Greece) in the period between 2020 and 2023. The main characteristics and general research findings from the implementation of the methodology by 6 pairs of artists and teachers in four regions in Greece are then described. The authors outline the steps followed to design, coordinate and implement the international research conducted in the four countries as well as the actions taken so far to disseminate its results
Frequency evaluation of UTC(NPL) by NPL-Sr1 for the period MJD 58454 to MJD 58459

The secondary frequency standard NPL-Sr1 and an optical frequency comb were used to evaluate the frequency of UTC(NPL) over a period of 5 days from MJD 58454 to MJD 58459 (2 nd December 2018 – 7 th December 2018). The Sr optical lattice clock operation covers 49.2% of the total measurement period. The result of the evaluation is reported in table 1 and is made using the CCTF 2021 recommended frequency value for the 5s 2 1 S 0 – 5s5p 3 P 0 unperturbed optical transition in 87 Sr: 429 228 004 229 872 . 99 Hz with a relative standard uncertainty of u Srep = 1 . 9 × 10 − 16 [1].
A Framework for Simple and Efficient Bootstrap Validation in SAS®, with Examples

Validation is essential for assessing a predictive model's performance with respect to optimism or overfitting. While traditional sample-splitting techniques like cross validation require us to divide our data between model building and model assessment, bootstrap validation enables us to use the full sample for both. This paper demonstrates a simple method for efficiently calculating bootstrap-corrected measures of predictive model performance using SAS/STAT® procedures. While several SAS® procedures have options for automatic cross validation, bootstrap validation requires a more manual process. Examples focus on logistic regression using the LOGISTIC procedure, but these techniques can be readily extended to other procedures and statistical models. The running example can be downloaded in . sas file format from https://github.com/saspy-bffs/pharmasug-2022-bootstrap-validation
Real World Evidence in Distributed Data Networks Lessons from a Post-Marketing Safety Study

As a case study to illustrate both opportunities and challenges in distributed data networks, this paper will focus on the implementation of a post-marketing safety study in the Healthcare Systems Research Network (HCSRN) via the associated Virtual Data Warehouse (VDW) common data model. In response to an FDA post-marketing requirement, this study establishes the incidence of angioedema in chronic heart failure patients treated with sacubitril/valsartan and incorporates data from multiple distributed data networks, including HCSRN. Distributed data networks present exciting opportunities for gathering real-world evidence by pooling standardized datasets across institutions. Common data models facilitate the efficient allocation of programming work to conduct analysis while allowing participating sites to retain control of their own data. However, large-scale and high-quality data collection combining data from disparate health data systems presents technical, administrative, and scientific challenges. In addition to describing programming, data management, and validation techniques used by HCSRN analysts in this study, we will compare design choices made by the various data networks involved in the project and explore their practical consequences.
Influence of disturbance regime on liana species composition, density and basal area in the tropical montane evergreen forests (sholas) of the Western Ghats, India

Lianas have been shown to respond to natural and anthropogenic disturbances in many tropical forests worldwide. However, little is known about how disturbance affects liana composition, density and basal area in the tropical montane evergreen forests (sholas) of the Western Ghats, India. We used a large dataset from permanent monitoring plots across four sites to examine whether liana composition, stem density and basal area were influenced by moderate level disturbance. Vegetation data were collected from four sampling sites that cover 13.58 ha in the southern montane evergreen forests (sholas) of the Western Ghats. All liana individuals with diameter at breast height (dbh) g= 1 cm. were tagged, counted, mapped and identified to species level. A total of 1658 individuals belonging to 18 families, 24 genera and 33 species were recorded. Species richness did not differ significantly between disturbed and undisturbed sholas in Amaggal (AG), Kukkal (KK) and Upper Bhavani (UB) sites, but it differed significantly in Korakundah (KR) site (P l 0.05). Species composition of lianas responded to disturbance at lower percentage (9 %) among four sites. Density of lianas was significantly higher in disturbed than in undisturbed sholas across all four sites. Mean densities were 10.19 +/- 6.10 and 5.69 +/- 3.35 in disturbed and undisturbed sholas, respectively, while the proportions of stem densities were 66 % and 34 % in disturbed and undisturbed sholas, respectively. Surprisingly, basal area showed contrasting trends in disturbed and undisturbed sholas. In KR and UB sites, mean basal area was significantly higher in undisturbed than in disturbed sholas, while in AG and KK sites, mean basal area was significantly higher in disturbed than undisturbed sholas. The findings of the study indicate that moderate level human disturbance influenced liana composition, abundance and basal area within the studied region.
LibMultiLabel: a Library for Multi-label Classification LibMultiLabel Project

LibMultiLabel is an open source software for binary, multi-class and multi-label classification, supporting various neural network architectures and linear classifiers. LibMultiLabel can be found at https://www.csie. ntu.edu. tw/ ˜ cjlin/libmultilabel/ This paper provides the mathematical formulations and implementation details of LibMultiLabel.
The nuclei of comets 7P/Pons-Winnecke, 14P/Wolf and 92P/Sanguin

Jupiter Family comets (JFCs) are short period comets which have recently entered the inner solar system, having previously orbited in the Kuiper Belt since the formation of the planets. We used two nights on the 3.6 m New Technology Telescope (NTT) at the European Southern Observatory, to obtain VRI photometry of three JFCs; 7P/Pons-Winnecke, 14P/Wolf and 92P/Sanguin. These were observed to be stellar in appearance. We find mean effective radii of 2.24 ± 0.02 km for 7P, 3.16 ± 0.01 km for 14P and 2.08 ± 0.01 km for 92P, assuming a geometric albedo of 0.04. From light-curves for each comet we find rotation periods of 7.53 ± 0.10 and 6.22 ± 0.05 h for 14P and 92P respectively. 7P exhibits brightness variations which imply a rotation period of 6.8 ≤ Prot ≤ 9.5 h. Assuming the nuclei to be ellipsoidal the measured brightness variations imply minimum axial ratios a/b of 1.3 ± 0.1 for 7P and 1.7 ± 0.1 for both 14P and 92P. This in turn implies minimum densities of 0.23 ± 0.08 g cm-3 for 7P, 0.32 ± 0.02 g cm-3 for 14P and 0.49 ± 0.06 g cm-3 for 92P. Finally, we measure colour indices of (V-R) = 0.40 ± 0.05 and (R-I) = 0.41 ± 0.06 for 7P/Pons-Winnecke, (V-R) = 0.57 ± 0.07 and (R-I) = 0.51 ± 0.06 for 14P/Wolf, and (V-R) = 0.54 ± 0.04 and (R-I) = 0.54 ± 0.04 for 92P/Sanguin.
Bayesian predictive distribution for a Poisson model with a parametric restriction

Abstract Predictive probability estimation for a Poisson distribution is addressed when the parameter space is restricted. The Bayesian predictive probability against the prior on the restricted space is compared with the non-restricted Bayes predictive probability. It is shown that the former predictive probability dominates the latter under some conditions when the predictive probabilities are evaluated by the risk function relative to the Kullback-Leibler divergence. This result is proved by first showing the corresponding dominance result for estimating the restricted parameter and then translating it into the framework of predictive probability estimation.
Power or Frequency Optimization in Long-term Operation

– This paper proposes a power or frequency optimization methodology, for long-term operation, using global and local performance sensors. The methodology allows circuits to be optimized dynamically, during their life-time, according with one of the two possible needs: (1) restrict power consumption, by reducing power-supply voltage to the minimum value that prevents errors from happening; or (2) optimize performance, by increasing operating frequency to the maximum limit that prevents errors’ occurrence. The use of both global and local sensors allows an on-line performance monitoring with information from the critical elements in the circuit and being conscious with the dynamic evolution of different variables that affect performance (e.g., aging degradation, temperature and power-supply variations). Global sensors are used to perform periodic on-line monitoring. Local sensors are used to keep global sensors tuned with circuit’s performance. Both are sensitive to PVTA (Process, power supply Voltage, Temperature and Aging) variations, or to any other parameter that may affect performance, and both can trigger the supply Voltage or Frequency on-line tuning, along product lifetime, preventing functional errors. Global sensor provides a coarse performance evaluation, while local sensors provide a fine grain performance evaluation on the real functional circuit. Methodology was demonstrated with HSPICE simulations.
Simple empirical models of classifying patients from microarray data

There have been tremendous advances in bioinformatics in recent years. One of these is the use of microarrays for collecting Big Data. This paper reports on the work carried out by the author in devising models to classify patients by conducting microarray data analyses. The problem is to determine, for each patient, which class he/she belongs to. For example, one class may be ‘has the disease’ whilst the other class is ‘does not have the disease. ’ Membership of a class can aid in giving a patient a prognosis. Often only a small number of genes are significantly affected by the presence of a disease and so it is possible to classify a patient by looking at this small number of genes. Two models for classifying patients from gene expression microarray data were developed. One model involves an existing algorithm whilst the other involves a new algorithm. The models involve some simple mathematical techniques – the two sample student’s t -test, Diagonal Linear Discriminant Analysis – and a newly developed technique which shall be called Multiplicative Probabilistic Discriminant Analysis. Each model has been implemented as a computer program. The research restricted itself to one dataset. Prior to using the models, the raw data must be pre-processed.
Oceanic residual depth measurements, the plate cooling model, and global dynamic topography

Convective circulation of the mantle causes deflections of the Earth's surface that vary as a function of space and time. Accurate measurements of this dynamic topography are complicated by the need to isolate and remove other sources of elevation, arising from flexure and lithospheric isostasy. The complex architecture of continental lithosphere means that measurement of present‐day dynamic topography is more straightforward in the oceanic realm. Here we present an updated methodology for calculating oceanic residual bathymetry, which is a proxy for dynamic topography. Corrections are applied that account for the effects of sedimentary loading and compaction, for anomalous crustal thickness variations, for subsidence of oceanic lithosphere as a function of age and for non‐hydrostatic geoid height variations. Errors are formally propagated to estimate measurement uncertainties. We apply this methodology to a global database of 1936 seismic surveys located on oceanic crust and generate 2297 spot measurements of residual topography, including 1161 with crustal corrections. The resultant anomalies have amplitudes of ±1 km and wavelengths of ∼1000 km. Spectral analysis of our database using cross‐validation demonstrates that spherical harmonics up to and including degree 30 (i.e., wavelengths down to 1300 km) are required to accurately represent these observations. Truncation of the expansion at a lower maximum degree erroneously increases the amplitude of inferred long‐wavelength dynamic topography. There is a strong correlation between our observations and free‐air gravity anomalies, magmatism, ridge seismicity, vertical motions of adjacent rifted margins, and global tomographic models. We infer that shorter wavelength components of the observed pattern of dynamic topography may be attributable to the presence of thermal anomalies within the shallow asthenospheric mantle.
Glucose-responsive mesoporous silica nanoparticles to generation of hydrogen peroxide for synergistic cancer starvation and chemistry therapy

Background: The combination of novel starving therapy with chemotherapy is one of the most promising strategies to achieve an effective antitumor activity. Methods: Herein, we developed a multifunctional mesoporous silica nanoparticle (MSNs-GOx/PLL/HA) coated with poly (L-lysine) (PLL) and hyaluronic acid (HA) for co-delivery of glucose oxidase (GOx) and anticancer drug paclitaxel (PTX) for cancer treatment for the first time. Compared to single chemotherapy, introduction of GOx would not only selectively trigger the consumption of intracellular glucose, leading to the interruption of energy supply, but also elevat the endogenous H2O2 level, inducing stronger therapeutic effects. Results: The novel drug delivery system possessed desirable particle diameter of 40 nm and exhibited a pH-sensitive drug release behavior. An in vitro cellular uptake study indicated that MSNs-GOx/PLL/HA nanoparticles effectively enhanced the cellular uptake of drug in an apparently CD44 receptor-dependent manner, and delivered more cargo into cytoplasm via endolysosomal escape effect in presence of PLL. The nanoplatform has also demonstrated amplified synergistic therapeutic effects for remarkable tumor inhibition in a xenograft animal tumor model. Conclusion: Consequently, the developed synergistic starving-like/chemotherapy may provide a potential platform for next generation cancer therapy.
A Proposed Technique Using Machine Learning for the Prediction of Diabetes Disease through a Mobile App

With the increasing prevalence of diabetes in Saudi Arabia, there is a critical need for early detection and prediction of the disease to prevent long-term health complications. This study addresses this need by using machine learning (ML) techniques applied to the Pima Indians dataset and private diabetes datasets through the implementation of a computerized system for predicting diabetes. In contrast to prior research, this study employs a semisupervised model combined with strong gradient boosting, effectively predicting diabetes-related features of the dataset. Additionally, the researchers employ the SMOTE technique to deal with the problem of imbalanced classes. Ten ML classification techniques, including logistic regression, random forest, KNN, decision tree, bagging, AdaBoost, XGBoost, voting, SVM, and Naive Bayes, are evaluated to determine the algorithm that produces the most accurate diabetes prediction. The proposed approach has achieved impressive performance. For the private dataset, the XGBoost algorithm with SMOTE achieved an accuracy of 97.4%, an F1 coefficient of 0.95, and an AUC of 0.87. For the combined datasets, it achieved an accuracy of 83.1%, an F1 coefficient of 0.76, and an AUC of 0.85. To understand how the model predicts the final results, an explainable AI technique using SHAP methods is implemented. Furthermore, the study demonstrates the adaptability of the proposed system by applying a domain adaptation method. To further enhance accessibility, a mobile app has been developed for instant diabetes prediction based on user-entered features. This study contributes novel insights and techniques to the field of ML-based diabetic prediction, potentially aiding in the early detection and management of diabetes in Saudi Arabia.
Effects of a Comprehensive Dietary Intervention Program, Promoting Nutrition Literacy, Eating Behavior, Dietary Quality, and Gestational Weight Gain in Chinese Urban Women with Normal Body Mass Index during Pregnancy

In urban Chinese women with normal body weight during pregnancy, we implemented a comprehensive dietary intervention program aimed at enhancing nutrition literacy, dietary quality, and gestational weight gain. The methods included both online and offline health education on prenatal nutrition, weekly weight monitoring, family back education practices, and real-time dietary guidance. The intervention was delivered to randomly assigned control and intervention group participants from gestational week 12 to week 24. The intervention group (n = 44; 100% complete data) showed significant differences (mean (SD)) compared to the control group (n = 42; 95.5% complete data) in nutrition literacy (53.39 ± 6.60 vs. 43.55 ± 9.58, p < 0.001), restrained eating (31.61 ± 7.28 vs. 28.79 ± 7.96, p < 0.001), Diet Quality Distance (29.11 ± 8.52 vs. 40.71 ± 7.39, p < 0.001), and weight gain within the first 12 weeks of intervention (4.97 ± 1.33 vs. 5.98 ± 2.78, p = 0.029). However, there was no significant difference in the incidence of gestational diabetes (2 (4.5%) vs. 4 (9.5%), p = 0.629). Participants in the intervention group reported an overall satisfaction score of 4.70 ± 0.46 for the intervention strategy. These results emphasize the positive role of comprehensive dietary intervention in promoting a healthy diet during pregnancy.
Dual isolation of primary neurons and oligodendrocytes from guinea pig frontal cortex

Primary cell culture is a technique that is widely used in neuroscience research to investigate mechanisms that underlie pathologies at a cellular level. Typically, mouse or rat tissue is used for this process; however, altricial rodent species have markedly different neurodevelopmental trajectories comparatively to humans. The use of guinea pig brain tissue presents a novel aspect to this routinely used cell culture method whilst also allowing for dual isolation of two major cell types from a physiologically relevant animal model for studying perinatal neurodevelopment. Primary neuronal and oligodendrocyte cell cultures were derived from fetal guinea pig's frontal cortex brain tissue collected at a gestational age of 62 days (GA62), which is a key time in the neuronal and oligodendrocyte development. The major advantage of this protocol is the ability to acquire both neuronal and oligodendrocyte cellular cultures from the frontal cortex of one fetal brain. Briefly, neuronal cells were grown in 12-well plates initially in a 24-h serum-rich medium to enhance neuronal survival before switching to a serum-free media formulation. Oligodendrocytes were first grown in cell culture flasks using a serum-rich medium that enabled the growth of oligodendrocyte progenitor cells (OPCs) on an astrocyte bed. Following confluency, the shake method of differential adhesion and separation was utilized via horizontally shaking the OPCs off the astrocyte bed overnight. Therefore, OPCs were plated in 12-well plates and were initially expanded in media supplemented with growth hormones, before switching to maturation media to progress the lineage to a mature phenotype. Reverse transcription-polymerase chain reaction (RT-PCR) was performed on both cell culture types to analyze key population markers, and the results were further validated using immunocytochemistry. Primary neurons displayed the mRNA expression of multiple neuronal markers, including those specific to GABAergic populations. These cells also positively stained for microtubule-associated protein 2 (MAP2; a dendritic marker specific to neurons) and NeuN (a marker of neuronal cell bodies). Primary oligodendrocytes expressed all investigated markers of the oligodendrocyte lineage, with a majority of the cells displaying an immature oligodendrocyte phenotype. This finding was further confirmed with positive oligodendrocyte transcription factor (OLIG2) staining, which serves as a marker for the overall oligodendrocyte population. This study demonstrates a novel method for isolating both neurons and oligodendrocytes from the guinea pig brain tissue. These isolated cells display key markers and gene expression that will allow for functional experiments to occur and may be particularly useful in studying neurodevelopmental conditions with perinatal origins.
Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates

Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic modeling, chemical process design, and high-throughput solvent screening. Despite the recent advance in machine learning, a scarcity of reliable data has hindered the development of predictive models that are generalizable for diverse reactions and solvents. In this work, we generate a large set of data with the COSMO-RS method for over 28 000 neutral reactions and 295 solvents and train a machine learning model to predict the solvation free energy and solvation enthalpy of activation (ΔΔG‡solv, ΔΔH‡solv) for a solution phase reaction. On unseen reactions, the model achieves mean absolute errors of 0.71 and 1.03 kcal mol−1 for ΔΔG‡solv and ΔΔH‡solv, respectively, relative to the COSMO-RS calculations. The model also provides reliable predictions of relative rate constants within a factor of 4 when tested on experimental data. The presented model can provide nearly instantaneous predictions of kinetic solvent effects or relative rate constants for a broad range of neutral closed-shell or free radical reactions and solvents only based on atom-mapped reaction SMILES and solvent SMILES strings.