Miracle-dz/UniFAR-FTUs huggingface dataset:
Study algae and fungi interaction in some artificial open sand mine ponds in Kalak sub district- Duhok, Iraq.

Phycology, mycology and physicochemical parameters of some artificial open sand mine ponds in Kalak sub district-DuhokIraq were investigated, for a period of six months from July to December-2015. Water samples were collected monthly basis and analyzed for measuring of water temperature, turbidity, pH, electrical conductivity, nitrate, nitrite, ammonium, orthophosphate, dissolved oxygen and biochemical oxygen demand. Concerning algal communities, the results showed that the sixty taxa were identified in samples collected during the study period. They belong to classes Cyanophyceae (18 taxa) with percentage composition of 30%, Chlorophyceae (20 taxa) with percentage composition of 33.3%, Euglenophyceae (2 taxa) with percentage composition of 3.4%and Bacillariophyceae (20 taxa) with percentage composition of 33.3%. According to Shannon-Weiner index, species diversity varied from (2.053to 3.895bits/ind), in pond 3 and in July had the highest diversity were recorded. Concerning to Jaccard similarity index, the highest percentage of similarity to algal community observed between site2 and site 4 reached to 32%. The diatoms were the most noticeable algae in all ponds which represented by Cyclotella, Navicula, Syndra and Cymbella with respect to the frequency of occurrence and number of individuals in all ponds. The diatoms showed their best growths through this study. The dominant of Chlorophyta and Cyanophyta followed by Study algae and fungi interaction in some artificial open sand mine Ponds in kalak sub districtduhok, iraq. Yahya A. Shekha, Janan J. Toma and Hero M. Ismael 110 Vol: 13 No:2 , April 2017 DOI: http://dx.doi. org/10.24237/djps.1302. 239C P-ISSN: 2222-8373 E-ISSN: 2518-9255 diatoms. The dominate species among non-diatoms were, Oscillatoria, Pedistrum, Oedogonium, Cladophoraand Cosmarium. Out of 23 fungal species belongs to 17 fungal genera were isolated. Trichoderma sp. was most frequent species, Aspergillus spp. followed by Penicillium spp. and Eurotium spp.
Digital Commons@Georgia Southern Digital Commons@Georgia Southern

Phishing is a growing threat to Internet users and causes billions of dollars in damage every year. While there are a number of research articles that study the tactics, techniques and procedures employed by phishers in the literature, in this paper, we present a theoretical yet practical model to study this menacing threat in a formal manner. While it is common folklore knowledge that a successful phishing attack entails creating messages that are indistinguishable from the natural, expected messages by the intended victim, this concept has not been formalized. Our model attempts to capture a phishing attack in terms of this indistinguishability between the natural and phishing message probability distributions. We view the actions performed by a phisher as an attempt to create messages that are indistinguishable to the victim from that of “ normal ” messages. To the best of our knowledge, this is the first study that places phishing on a concrete theoretical framework and offers a new perspective to analyze this threat. We propose metrics to analyze the success probability of a phishing attack taking into account the input used by a phisher and the work involved in creating deceptive email messages. Finally, we study and apply our model to a new class of phishing attacks called collaborative spear phishing that is gaining momentum. Recent examples include Operation Woolen-Goldfish in 2015, Rocket Kitten in 2014 and Epsilon email breach in 2011. We point out fundamental flaws in the current email-based marketing business model which enables such targeted spear phishing collaborative attacks. In this sense, our study is very timely and presents new and emerging trends in phishing.
The Effect of Maternal Diet and Physical Activity on the Epigenome of the Offspring

The aim of this review was to examine the current literature regarding the effect of maternal lifestyle interventions (i.e., diet and physical activity) on the epigenome of the offspring. PubMed, Scopus and Cochrane-CENTRAL were screened until 8 July 2023. Only randomized controlled trials (RCTs) where a lifestyle intervention was compared to no intervention (standard care) were included. Outcome variables included DNA methylation, miRNA expression, and histone modifications. A qualitative approach was used for the consideration of the studies’ results. Seven studies and 1765 mother–child pairs were assessed. The most common types of intervention were dietary advice, physical activity, and following a specific diet (olive oil). The included studies correlated the lifestyle and physical activity intervention in pregnancy to genome-wide or gene-specific differential methylation and miRNA expression in the cord blood or the placenta. An intervention of diet and physical activity in pregnancy was found to be associated with slight changes in the epigenome (DNA methylation and miRNA expression) in fetal tissues. The regions involved were related to adiposity, metabolic processes, type 2 diabetes, birth weight, or growth. However, not all studies showed significant differences in DNA methylation. Further studies with similar parameters are needed to have robust and comparable results and determine the biological role of such modifications.
Wetland soil organic carbon balance is reversed by old carbon and iron oxide additions

Iron (Fe) oxides can stabilize organic carbon (OC) through adsorption and co-precipitation, while microbial Fe reduction can disrupt Fe-bound OC (Fe-OC) and further increase OC mineralization. The net effects of OC preservation and mineralization mediated by Fe oxides are still unclear, especially for old carbon (formed from plant litters over millions of years) and crystalline Fe oxides. Accelerating the recovery of wetland carbon sinks is critical for mitigating climate change and achieving carbon neutrality. Quantifying the net effect of Fe-mediated OC mineralization and preservation is vital for understanding the role of crystalline Fe oxides in carbon cycling and promoting the recovery of soil carbon sinks. Here, we explored the OC balances mediated by hematite (Hem) and lignite addition (Lig) to freshwater wetland (FW, rich in C and Fe) and saline-alkaline wetland (SW, poor in C and Fe) soil slurries, incubated under anaerobic conditions. Results showed that Lig caused net OC accumulation (FW: 5.9 ± 3.6 mg g−1; SW: 8.3 ± 3.2 mg g−1), while Hem caused dramatic OC loss, particularly in the FW soils. Hem inhibited microbial Fe(III) reduction by decreasing the relative abundance of Fe respiration reducers, while substantially enhancing OC mineralization through the shift in the microbial community structure of FW soils. Lig resulted in carbon emission, but its contribution to preservation by the formation of Fe-OC was far higher than that which caused OC loss. We concluded that crystalline Fe oxide addition solely favored the increase of OC mineralization by adjusting the microbial community structure, while old carbon enriched with an aromatic and alkyl promoted Fe-OC formation and further increased OC persistence. Our findings could be employed for wetland restoration, particularly for the recovery of soil carbon sinks.
First Evidence of Mineralocorticoid Receptor Gene and Protein Expression in Rat and Human Thyroid Tissues and Cell Cultures

Aldosterone (Aldo) exerts its action through binding with the mineralocorticoid receptor (MR). Clinically, a link between primary aldosteronism (PA) and thyroid diseases has been hypothesised. However, the presence and activity of MR on the thyroid have not yet been demonstrated. We investigated the gene/protein expression and activation of MR in primary thyroid cell cultures (normal rat thyroid [FRTL-5] and human papillary thyroid cancer [PTC] cell lines, BCPAP and K1) through qRT-PCR analysis, immunofluorescence, and confocal microscopy. We also studied the effects of Aldo on thyroid-specific and inflammation genes in vitro. Paired human normal and neoplastic thyroid tissues were also studied. We demonstrated both gene and protein expression and activation of MR in normal rat thyroid and human PTC lines. Incubation with Aldo induced an acute increase in IL-6 expression in both the FRTL-5 and BCPAP lines, which was antagonised by spironolactone, and an acute and late upregulation of thyroid-specific genes in FRTL-5. MR was also expressed at both gene and protein levels in normal human thyroid tissues and in PTC, with a progressive decline during neoplastic tumourigenesis, particularly in more aggressive histotypes. We present the first evidence of MR gene and protein expression in both normal and pathological thyroid cells and tissues. We have shown that MR is present and functionally activated in thyroid tissue. Binding of Aldo to MR induces the expression of inflammatory and thyroid-specific genes, and the thyroid may thus be considered a novel mineralocorticoid target tissue.
The mechanical properties of (NbMoTaW)Si2 from a first-principles calculations

The realm of ceramic materials has seen a surge in interest directed towards high entropy disilicides due to their exceptional properties. Using the methodologies of density functional theory (DFT) and the special quasi-random structure (SQS), we have delved into the examination of structural stability and the inherent elastic properties of (NbMoTaW)Si2. The available experimental data and the optimized lattice parameters coincide very well. The thermodynamic stability observed in all high entropy ceramics, specifically (NbMoTaW)Si2 disilicides, can be attributed to their negative formation enthalpies. The present results of elastic parameters show that the strength/hardness of (NbMoTaW)Si2 are larger due to the higher bulk and shear moduli. The Vickers hardness of (NbMoTaW)Si2 is higher than the average value of component binary metal silicide, indicating solid solution strengthening effect. The mechanical anisotropy of the (NbMoTaW)Si2 reveals the significant difference in various directions on different crystalline planes. Conducting theoretical research holds significance in facilitating the synthesis of enhanced high entropy disilicides ceramics. Furthermore, such studies are crucial in advancing the evolution and practical utilization of high entropy materials.
A Preliminary Methane Emission Study of Typical Coalbed Methane Production Areas Based on Multi-satellites Remote Sensing Data

The monitoring of methane emission is helpful for reducing the concentration of atmospheric greenhouse gases. A preliminary methane emission study of typical coalbed methane production areas based on multi-satellites remote sensing data was proposed. Methane monitoring for typical coalbed methane production areas in Shanxi Province, methane anomaly analysis, associated gas flaring identification as well as emission estimation were carried out, using Sentinel-5P TROPOMI (TROPOspheric Monitoring Instrument) CH4 data, Sentinel-2 false color images and VIIRS (Visible Infrared Imaging Radiometer Suite) active fire data. The research showed that typical coalbed methane production areas in Shanxi Province were featured with high CH4 concentration in the southeast and low concentration in the northwest. The maximum methane anomaly of about 80 ppb was discovered and the emission rate was estimated to be approximately 20 t/h-23 t/h for the identified associated gas flaring location. The estimation was consistent with the results from PRISMA data for the Shanxi coal mining region. The research demonstrated the capability of satellite observation to support the coalbed methane production emission source identification from space and indicated the method provided was favorable for methane anomaly analysis, emission identification as well as emission rate estimation for coalbed methane production areas.
Generalized additive model with embedded variable selection for bankruptcy prediction: Prediction versus interpretation

Abstract This paper explores the properties of using a generalized additive model with embedded variable selection for the prediction of bankruptcy. The main purpose is to explore an innovative way to close the gap between interpretation and prediction that has prevented widespread use of methods based on machine learning. An additive model enables the incorporation of nonlinear effects for each predictor, thereby enhancing the predictive power over classical linear models, while simultaneously keeping the marginal effects for interpretation separated. In addition, we propose a penalization likelihood approach that automatically selects important financial ratios and classifies them under linear and nonlinear effects, thereby improving the interpretation of the estimations. We implemented the proposed model on data from the retail industry in Colombia. The results demonstrate a good generalization performance of the algorithm and a prediction accuracy not far below typical black box algorithms such as random forest and support vector machines.
Indoor Positioning and Guiding for Drivers

In this paper, we present an indoor positioning and guiding system for drivers, which is able to operate robustly in indoor car parks. Our proposal is able to estimate with a reasonable accuracy, the position of a vehicle as well as determining the best route and providing guiding instructions (visual, textual, and auditory) to the driver. To approximate the position of the vehicle, it uses Bayesian estimation able to combine three sources of information: 1) the inertial sensors of a mobile phone placed inside the vehicle; 2) Bluetooth low energy beacons, radiofrequency fingerprinting; and 3) an occupancy map. Our system was successfully tested in a real two-floor underground car park, where the system exhibits a good performance, and a robust and reliable behavior, being able to locate the vehicle and provide instructions to different drivers.
The Importance of Parameters in Database Queries

We propose and study a framework for quantifying the importance of the choices of parameter values to the result of a query over a database. These parameters occur as constants in logical queries, such as conjunctive queries. In our framework, the importance of a parameter is its SHAP score. This score is a popular instantiation of the game-theoretic Shapley value to measuring the importance of feature values in machine learning models. We make the case for the rationale of using this score by explaining the intuition behind SHAP, and by showing that we arrive at this score in two different, apparently opposing, approaches to quantifying the contribution of a parameter. The application of the SHAP score requires two components in addition to the query and the database: (a) a probability distribution over the combinations of parameter values, and (b) a utility function that measures the similarity between the result for the original parameters and the result for hypothetical parameters. The main question addressed in the paper is the complexity of calculating the SHAP score for different distributions and similarity measures. We first address the case of probabilistically independent parameters. The problem is hard if we consider a fragment of queries that is hard to evaluate (as one would expect), and even for the fragment of acyclic conjunctive queries. In some cases, though, one can efficiently list all relevant parameter combinations, and then the SHAP score can be computed in polynomial time under reasonable general conditions. Also tractable is the case of full acyclic conjunctive queries for certain (natural) similarity functions. We extend our results to conjunctive queries with inequalities between variables and parameters. Finally, we discuss a simple approximation technique for the case of correlated parameters.
Diverse Molecular Gas Excitations in Quasar Host Galaxies at z ∼ 6

We present observations using the Northern Extended Millimetre Array (NOEMA) of CO and H2O emission lines and the underlying dust continuum in two quasars at z ∼ 6, i.e., P215−16 at z = 5.78 and J1429+5447 at z = 6.18. Notably, among all published CO spectral line energy distributions (SLEDs) of quasars at z ∼ 6, the two systems reveal the highest and the lowest CO levels of excitation, respectively. Our radiative transfer modeling of the CO SLED of P215−16 suggests that the molecular gas heated by active galactic nuclei could be a plausible origin for the high CO excitation. For J1429+5447, we obtain the first well-sampled CO SLED (from transitions from 2−1 to 10−9) of a radio-loud quasar at z ≳ 6. Analysis of the CO SLED suggests that a component from a single photodissociation region could explain the CO excitation in the radio-loud quasar J1429+5447. This work highlights the utility of the CO SLED in uncovering the properties of the interstellar medium in these young quasar–starburst systems at the highest redshift. The diversity of the CO SLEDs reveals the complexities in gas conditions and excitation mechanisms at their early evolutionary stage.
Robust Imitation Learning for Automated Game Testing

Game development is a long process that involves many stages before a product is ready for the market. Human play testing is among the most time consuming, as testers are required to repeatedly perform tasks in the search for errors in the code. Therefore, automated testing is seen as a key technology for the gaming industry, as it would dramatically improve development costs and efficiency. Toward this end, we propose EVOLUTE, a novel imitation learning-based architecture that combines behavioural cloning (BC) with energy based models (EBMs). EVOLUTE is a two-stream ensemble model that splits the action space of autonomous agents into continuous and discrete tasks. The EBM stream handles the continuous tasks, to have a more refined and adaptive control, while the BC stream handles discrete actions, to ease training. We evaluate the performance of EVOLUTE in a shooting-and-driving game, where the agent is required to navigate and continuously identify targets to attack. The proposed model has higher generalisation capabilities than standard BC approaches, showing a wider range of behaviours and higher performances. Also, EVOLUTE is easier to train than a pure end-to-end EBM model, as discrete tasks can be quite sparse in the dataset and cause model training to explore a much wider set of possible actions while training.
Caustic and hydrostatic mass bias: Implications for modified gravity

We propose and perform a joint analysis of the two different mass estimates of galaxy clusters, namely the hydrostatic and caustic techniques. Firstly, we show comprehensively that the mass bias between these two techniques can be possibly alleviated when cluster-specific assumptions constrained using the hydrostatic technique are utilized within the caustic technique. While at face value this demotes the caustic technique from a completely independent method, this allows one to further tighten the constraints on the cluster mass and subsequently, allow us to test modifications to gravity. Implementing the aforementioned formalism for two well-observed massive galaxy clusters, A2029 and A2142, we highlight the proof of concept. In the current implementation, we use this method to constrain the Chameleon screening and Vainshtein screening. As anticipated, we show that the joint analysis can help improve the constraints on these modified gravity scenarios.
Weighted degrees and truncated derived bibliographic networks

Large bibliographic networks are sparse -- the average node degree is small. This is not necessarily true for their product -- in some cases, it can ``explode'' (it is not sparse, increases in time and space complexity). An approach in such cases is to reduce the complexity of the problem by limiting our attention to a selected subset of important nodes and computing with corresponding truncated networks. The nodes can be selected by different criteria. An option is to consider the most important nodes in the derived network -- nodes with the largest weighted degree. It turns out that the weighted degrees in the derived network can be computed efficiently without computing the derived network itself.
Comparative Evaluation of Animated Scatter Plot Transitions

Scatter plots are popular for displaying 2D data, but in practice, many data sets have more than two dimensions. For the analysis of such multivariate data, it is often necessary to switch between scatter plots of different dimension pairs, e.g., in a scatter plot matrix (SPLOM). Alternative approaches include a"grand tour"for an overview of the entire data set or creating artificial axes from dimensionality reduction (DR). A cross-cutting concern in all techniques is the ability of viewers to find correspondence between data points in different views. Previous work proposed animations to preserve the mental map between view changes and to trace points as well as clusters between scatter plots of the same underlying data set. In this paper, we evaluate a variety of spline- and rotation-based view transitions in a crowdsourced user study focusing on ecological validity. Using the study results, we assess each animation's suitability for tracing points and clusters across view changes. We evaluate whether the order of horizontal and vertical rotation is relevant for task accuracy. The results show that rotations with an orthographic camera or staged expansion of a depth axis significantly outperform all other animation techniques for the traceability of individual points. Further, we provide a ranking of the animated transition techniques for traceability of individual points. However, we could not find any significant differences for the traceability of clusters. Furthermore, we identified differences by animation direction that could guide further studies to determine potential confounds for these differences. We publish the study data for reuse and provide the animation framework as a D3.js plug-in.
Global-Aware Enhanced Spatial-Temporal Graph Recurrent Networks: A New Framework For Traffic Flow Prediction

Traffic flow prediction plays a crucial role in alleviating traffic congestion and enhancing transport efficiency. While combining graph convolution networks with recurrent neural networks for spatial-temporal modeling is a common strategy in this realm, the restricted structure of recurrent neural networks limits their ability to capture global information. For spatial modeling, many prior studies learn a graph structure that is assumed to be fixed and uniform at all time steps, which may not be true. This paper introduces a novel traffic prediction framework, Global-Aware Enhanced Spatial-Temporal Graph Recurrent Network (GA-STGRN), comprising two core components: a spatial-temporal graph recurrent neural network and a global awareness layer. Within this framework, three innovative prediction models are formulated. A sequence-aware graph neural network is proposed and integrated into the Gated Recurrent Unit (GRU) to learn non-fixed graphs at different time steps and capture local temporal relationships. To enhance the model's global perception, three distinct global spatial-temporal transformer-like architectures (GST^2) are devised for the global awareness layer. We conduct extensive experiments on four real traffic datasets and the results demonstrate the superiority of our framework and the three concrete models.