Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Bias in AI using Simulation

Machine learned models exhibit bias, often because the datasets used to train them are biased. This presents a serious problem for the deployment of such technology, as the resulting models might perform poorly on populations that are minorities within the training set and ultimately present higher risks to them. We propose to use high-fidelity computer simulations to interrogate and diagnose biases within ML classifiers. We present a framework that leverages Bayesian parameter search to efficiently characterize the high dimensional feature space and more quickly identify weakness in performance. We apply our approach to an example domain, face detection, and show that it can be used to help identify demographic biases in commercial face application programming interfaces (APIs).
Relationships Between Fungal and Plant Communities Differ Between Desert and Grassland in a Typical Dryland Region of Northwest China

The relationships between soil fungal and plant communities in the dryland have been well documented, yet the associated difference in relationships between soil fungal and plant communities among different habitats remains unclear. Here, we explored the relationships between plant and fungal functional communities, and the dominant factors of these fungal communities in the desert and grassland. Soil fungal functional communities were assessed based on fungal ITS sequence data which were obtained from our previous study. The results showed that the total, saprotrophic and pathotrophic fungal richness were predominantly determined by plant species richness and/or soil nutrients in the desert, but by MAP or soil CN in the grassland. AM fungal richness was only significantly related to soil nutrients in two habitats. The total and saprotrophic fungal species compositions were mainly shaped by abiotic and spatial factors in the desert, but by plant and abiotic factors in the grassland. Pathotrophic fungal species composition was more strongly correlated with plant and spatial factors in the desert, but with spatial and abiotic factors in the grassland. AM fungal species composition was more strongly correlated with MAP in the grassland, but with no factors in the desert. These results provide robust evidence that the relationships between soil fungal and plant communities, and the drivers of soil fungal communities differ between the desert and grassland. Furthermore, we highlight that the linkages between soil fungal and plant communities, and the drivers of soil fungal communities may also be affected by fungal traits (e.g., functional groups).
Neuroprotection against CCl4 induced brain damage with crocin in Wistar rats

ABSTRACT Owing to its lipophilic property, carbon tetrachloride (CCl4) is rapidly absorbed by both the liver and brain. We investigated the protective effects of crocin against brain damage caused by CCl4. Fifty rats were divided into five groups of ten: control, corn oil, crocin, CCl4 and CCl4 + crocin. CCl4 administration decreased glutathione (GSH) and total antioxidant status (TAS) levels, and catalase (CAT) activity, while significant increases were observed in malondialdehyde (MDA) and total oxidant status (TOS) levels and superoxide dismutase (SOD) activity. The cerebral cortex nuclear lamina developed a spongy appearance, neuronal degeneration was observed in the hippocampus, and heterochromatic and pyknotic neurons with increased cytoplasmic eosinophilia were observed in the hippocampus after CCl4 treatment. Because crocin exhibits strong antioxidant properties, crocin treatment increased GSH and TAS levels and CAT activities, and decreased MDA and TOS levels and SOD activity; significant improvements also were observed in histologic architecture. We found that crocin administration nearly eliminated CCl4 induced brain damage by preventing oxidative stress.
Estimating Visual Focus of Attention in Multiparty Meetings using Deep Convolutional Neural Networks

Convolutional neural networks (CNNs) are employed to estimate the visual focus of attention (VFoA), also called gaze direction , in multiparty face-to-face meetings on the basis of multimodal nonverbal behaviors including head pose, direction of the eyeball, and presence/absence of utterance. To reveal the potential of CNNs, we focus on aspects of multimodal and multiparty fusion including individual/group models, early/late fusion, and robustness when using inputs from image-based trackers. In contrast to the individual model that separately targets each person specific to one's seat, the group model aims to jointly estimate the gaze directions of all participants. Experiments confirmed that the group model outperformed the individual model especially in predicting listeners' VFoA when the inputs did not include eyeball directions. This result indicates that the group CNN model can implicitly learn underlying conversation structures, e.g., the listeners' gazes converge on the speaker. When the eyeball direction feature is available, both models outperformed the Bayes models used for comparison. In this case, the individual model was superior to the group model, particularly in estimating the speaker's VFoA. Moreover, it was revealed that in group models, two-stage late fusion, which integrates an individual features first, and multiparty features second, outperformed other structures. Furthermore, our experiment confirmed that image-based tracking can provide a comparable level of performance to that of sensor-based measurements. Overall, the results suggest that the CNN is a promising approach for VFoA estimation.
EMBA: An Efficient Anonymous Mutual and Batch Authentication Schemes for Vanets

Nowadays, Vehicular ad-hoc networks (VANETs) have enormous research attention due to the challenges in safe driving and the demand for infotainment services during the travel. To ensure the proper operations of VANETs, it is important to provide an efficient user authentication scheme with privacy preserving. In this paper, we present a novel anonymous mutual and batch authentication schemes for improving VANET security. The proposed scheme makes use of some well-known cryptographic operations to authenticate vehicles. In our scheme, the vehicle users communicate with Road Side Units (RSUs) to get location based information (LBI) to enrich their driving comfort. The security strength of our proposed scheme is analyzed against the various security attacks to aid a better performance than the previously reported schemes. Extensive experimental simulations are demonstrated to validate the computational complexity of our proposed scheme.
Microwave-Assisted Extraction of Phenolic Compounds from Melastoma sanguineum Fruit: Optimization and Identification

A microwave-assisted extraction (MAE) technology optimized by response surface methodology (RSM) was established to extract phenolic compounds from the fruit of Melastoma sanguineum. The effects of solvent composition, ratio of solvent to material, temperature, time and microwave power on phenol yield were evaluated in single-factor tests. The three parameters exerting main impacts on phenol yield were further optimized by RSM. Under optimal extraction conditions (31.33% ethanol, solvent/material ratio of 32.21 mL/g, 52.24 °C, 45 min and 500 W), the total phenolic content was 39.02 ± 0.73 mg gallic acid equivalent (GAE)/g dry weight (DW). This MAE method performed better in comparison with two conventional methods, those being maceration (25.79 ± 1.03 mg GAE/g DW) and Soxhlet extraction (18.40 ± 1.34 mg GAE/g DW), using lower process temperature, shorter irradiation time, and lower organic solvent consumption. In addition, five flavonoids (epicatechin gallate, epicatechin, rutin, pigallocatechin and quercetin) and two phenolic acids (protocatechuic acid and chlorogenic acid) in the extract were identified and quantified using UPLC-MS/MS.
Estimation-Based Model Predictive Control for Automatic Crosswind Stabilization of Hybrid Aerial Vehicles

In this paper, we study the control design of an automatic crosswind stabilization system for a novel, buoyantly-assisted aerial transportation vehicle. This vehicle has several advantages over other aircraft including the ability to take-off and land in very short distances and without the need for roads or runways. Despite these advantages, the large surface area of the vehicle's wing makes it more susceptible to wind, which introduces undesirable roll angle motions. The role of the automatic crosswind stabilization system is to detect the roll angle deviation, and then use motors at the wingtips to counteract the wind effect. However, due to the relatively large inertia of the wing compared to small-size unmanned aerial vehicles and additional input time delays, an automatic crosswind stabilization system based on traditional control algorithms such as the proportional-integral-derivative (PID) controller results in a response time that is too slow. Another challenge is the lack of high-accuracy wind sensors that can be mounted on the vehicle's wing. Therefore, we first design a wind torque estimator that relies on inertial measurements, and then use feed-forward compensation to directly correct for the wind torque, resulting in a significantly faster response. We second combine the proposed estimator with a model predictive controller (MPC), and compare constrained MPC with unconstrained MPC for the considered application. Experimental results show that our proposed estimation-based MPC strategy reduces the response time of the system by around 80-90% compared to a standard PID controller, without the need for adding wind sensors or changing the hardware of the stabilization system.
Implementation of a Multi-Agent System to Support ZDM Strategies in Multi-Stage Environments

This paper describes the development of a multiagent system (MAS) to support the implementation of zero-defect manufacturing strategies in multi-stage production systems. The MAS infrastructure, combined with on-line inspection tools, data analytics and knowledge generation, constitutes a suitable approach to integrate process and quality control in multi-stage environments. This will allow the early detection of product defects, the adaptation to operating condition changes and the optimisation of manufacturing processes. This type of integrated management structure is aligned with a zero-defect manufacturing production model which is of paramount importance in the actual state-of-the-art manufacturing paradigms. As a proof of concept, the devised manufacturing supervision model was deployed into an experimental multi-stage system that run a set of several tests on electrical motors. The agent-based solution was implemented using the JADE framework and the exchange of information structured by proper data models and industrial based Internet-of-Things and Machine-to-Machine technologies, such as OPC-UA, REST and JSON. The obtained results demonstrate the suitability of the devised integrated management model as a vehicle to achieve dynamic and continuous system improvement in multi-stage manufacturing environments.
This Is the End: Regulation of Rab7 Nucleotide Binding in Endolysosomal Trafficking and Autophagy

Rab7 – or in yeast, Ypt7p – governs membrane trafficking in the late endocytic and autophagic pathways. Rab7 also regulates mitochondrion-lysosome contacts, the sites of mitochondrial fission. Like all Rab GTPases, Rab7 cycles between an “active” GTP-bound form that binds downstream effectors – e.g., the HOPS and retromer complexes and the dynactin-binding Rab-interacting lysosomal protein (RILP) – and an “inactive” GDP-bound form that cannot bind effectors. Accessory proteins regulate the nucleotide binding state of Rab7: guanine nucleotide exchange factors (GEFs) stimulate exchange of bound GDP for GTP, resulting in Rab7 activation, whereas GTPase activating proteins (GAPs) boost Rab7’s GTP hydrolysis activity, thereby inactivating Rab7. This review will discuss the GEF and GAPs that control Rab7 nucleotide binding, and thus regulate Rab7’s activity in endolysosomal trafficking and autophagy. It will also consider how bacterial pathogens manipulate Rab7 nucleotide binding to support intracellular invasion and immune evasion.
Longitudinal effects of acculturation and enculturation on mental health: Does the measure of matter?

Abstract A great deal of research has focused on acculturation and enculturation, which represent the processes of adapting to a new culture. Despite this growing literature, results have produced inconsistent findings that may be attributable to differences in terms of the instruments used to assess acculturation and enculturation. Utilizing a 3-year longitudinal data set (with 1-year lags between assessments), the present study explored the psychometric properties of the Bicultural Involvement Questionnaire—Short Version (BIQ-S) and the Acculturation Rating Scale for Mexican Americans II (ARSMA-II) and examined the overlap between changes in these measures as they relate to internalizing and externalizing problem behavior. The present sample consisted of 216 immigrant Latino youth (43% boys; mean age 13.6 years at baseline; SD = 1.44 years, range 10 to 17). Exploratory structural equation modeling identified factor structures for the BIQ-S and ARSMA-II that diverged from their hypothesized structure. Growth curve models also indicate divergence between the BIQ-S and ARSMA-II in terms of change in acculturation and enculturation processes. Finally, the present findings emphasized that measures of acculturation and enculturation are not equivalent in terms of their effects on internalizing and externalizing problems.
DSM-5 Criteria and Depression Severity: Implications for Clinical Practice

Background: Depression diagnosis requires five or more symptoms (Diagnostic and Statistical Manual of Mental Disorders-DSM-5). One of them must be either Depressed mood or Anhedonia, named main criteria. Although the secondary symptoms can be divided into somatic and non-somatic clusters, the DSM-5 identify depression in all or none fashion. In contrast, depression severity is a continuous variable. Therefore, it is commonly assessed with scales such as the Hamilton Depression Rating Scale (HAMD). Previously, we reported that patients with moderate depression (MD) exhibit greater impairments in cardiac-autonomic modulation than severely depressed (SD) patients. However, clinicians usually do not use scales. Objective: To verify whether the DSM-5 symptoms would be able to discriminate SD from MD and MD from non-depressed (ND) subjects. Material and Methods: Depression was diagnosed based on the Structured Clinical Interview for DSM-5® Disorders. The HAMD evaluated depression severity. In depressed subjects, MD and SD were defined considering the HAMD scores. ND was defined considering both the absence of DSM-5 criteria for depression and the HAMD score. Among 782 outpatients, 46 SD were found. MD and ND subjects were randomly sampled to match the demographic variables of the SD group. Results: Discriminant analysis showed that Depressed Mood was the most reliable symptom to discriminate ND from MD. Anhedonia discriminated SD from MD. Among the secondary DSM-5 criteria, the somatic cluster discriminated ND from MD and the non-somatic cluster SD from MD patients. Discussion: The presence of the somatic cluster in MD may indicate decreased vagal tone and/or increased sympathetic tone, leading to higher cardiovascular risk. As SD is associated with the non-somatic cluster, these patients are at risk of committing suicide. The DSM-5 symptoms exhibited by the patient may help the choice of adequate pharmacological treatment. This would avoid the use of antidepressants that unnecessarily increase cardiac risk in MD. When the symptom cluster suggests SD, the treatment must focus on the prevention of suicide. Conclusions: Depression severity may be inferred based on the DSM-5 criteria. The presence of the Anhedonia main criterium accompanied by non-somatic criteria indicate SD. The Depressive Mood criterium followed by somatic criteria suggest MD.
High Throughput Manufacturing of Bacteriophages Using Continuous Stirred Tank Bioreactors Connected in Series to Ensure Optimum Host Bacteria Physiology for Phage Production

Future industrial demand for large quantities of bacteriophages e.g., for phage therapy, necessitates the development of scalable Good Manufacturing Practice compliant (cGMP) production platforms. The continuous production of high titres of E coli T3 phages (1011 PFU mL−1) was achieved using two continuous stirred tank bioreactors connected in series, and a third bioreactor was used as a final holding tank operated in semi-batch mode to finish the infection process. The first bioreactor allowed the steady-state propagation of host bacteria using a fully synthetic medium with glucose as the limiting substrate. Host bacterial growth was decoupled from the phage production reactor downstream of it to suppress the production of phage-resistant mutants, thereby allowing stable operation over a period of several days. The novelty of this process is that the manipulation of the host reactor dilution rates (range 0.1–0.6 hr−1) allows control over the physiological state of the bacterial population. This results in bacteria with considerably higher intracellular phage production capability whilst operating at high dilution rates yielding significantly higher overall phage process productivity. Using a pilot-scale chemostat system allowed optimisation of the upstream phage amplification conditions conducive for high intracellular phage production in the host bacteria. The effect of the host reactor dilution rates on the phage burst size, lag time, and adsorption rate were evaluated. The host bacterium physiology was found to influence phage burst size, thereby affecting the productivity of the overall process. Mathematical modelling of the dynamics of the process allowed parameter sensitivity evaluation and provided valuable insights into the factors affecting the phage production process. The approach presented here may be used at an industrial scale to significantly improve process control, increase productivity via process intensification, and reduce process manufacturing costs through process footprint reduction.
Contribution of structural recalcitrance to the formation of the deep oceanic dissolved organic carbon reservoir

The origin of the recalcitrant dissolved organic carbon (RDOC) reservoir in the deep ocean remains enigmatic. The structural recalcitrance hypothesis suggests that RDOC is formed by molecules that are chemically resistant to bacterial degradation. The dilution hypothesis claims that RDOC is formed from a large diversity of labile molecules that escape bacterial utilization due to their low concentrations, termed as RDOCc . To evaluate the relative contributions of these two mechanisms in determining the long-term persistence of RDOC, we model the dynamics of both structurally recalcitrant DOC and RDOCc based on previously published data that describes deep oceanic DOC degradation experiments. Our results demonstrate that the majority of DOC (84.5 ± 2.2%) in the deep ocean is structurally recalcitrant. The intrinsically labile DOC (i.e., labile DOC that rapidly consumed and RDOCc ) accounts for a relatively small proportion and is consumed rapidly in the incubation experiments, in which 47.8 ± 3.2% of labile DOC and 21.9 ± 4.6% of RDOCc are consumed in 40 days. Our results suggest that the recalcitrance of RDOC is largely related to its chemical properties, whereas dilution plays a minor role in determining the persistence of deep-ocean DOC.
Comparative Study between Traditional Machine Learning and Deep Learning Approaches for Text Classification

In this contemporaneous world, it is an obligation for any organization working with documents to end up with the insipid task of classifying truckload of documents, which is the nascent stage of venturing into the realm of information retrieval and data mining. But classification of such humongous documents into multiple classes, calls for a lot of time and labor. Hence a system which could classify these documents with acceptable accuracy would be of an unfathomable help in document engineering. We have created multiple classifiers for document classification and compared their accuracy on raw and processed data. We have garnered data used in a corporate organization as well as publicly available data for comparison. Data is processed by removing the stop-words and stemming is implemented to produce root words. Multiple traditional machine learning techniques like Naive Bayes, Logistic Regression, Support Vector Machine, Random forest Classifier and Multi-Layer Perceptron are used for classification of documents. Classifiers are applied on raw and processed data separately and their accuracy is noted. Along with this, Deep learning technique such as Convolution Neural Network is also used to classify the data and its accuracy is compared with that of traditional machine learning techniques. We are also exploring hierarchical classifiers for classification of classes and subclasses. The system classifies the data faster and with better accuracy than if done manually. The results are discussed in the results and evaluation section.
The Effects of Carbon Content on the Anisotropic Deformation Mechanism of Boron Carbide

The effects of carbon content on the mechanical properties and deformation mechanisms of boron carbides were investigated by first-principles calculations, based on the density functional theory. The B12–CBC (13.33 at % C) and B10C2P–CC (28.75 at % C) were studied and then compared with the deformation of regular B11CP–CBC (20.0 at % C). The results show the B10C2P–CC, which has the lowest carbon content, has the highest strength and hardness as well as the lowest toughness. With the increase of carbon content, the rhombohedral symmetry will be broken and the three-atoms chains will be replaced by diatomic carbon chains. These changes may have an influence on their anisotropic deformation mechanisms. For the B12–CBC, the destruction of icosahedra without bending three-atom chains causes structural failure for compression along the c axis; while for compression along the a axis, new B–B bonds are formed, causing an unrecoverable deformation; then it is gradually destroyed until full destruction. For the B10C2P–CC, the anisotropic deformation mechanism is not obvious. For both loading directions, the breakage of B–CP bonds causes the stress to drop, suggesting that the structure is beginning to be destroyed. Finally, the icosahedra are fully destroyed, resulting in structural failure.
Similarity of Data from Bee Bread with the Same Taxa Collected in India and Romania

Bee Bread samples from Romania and India were analysed by microscopy and High Performance Liquid Chromatography with Diode Array Detection (HPLC/DAD) and compared with pollen from the correspondent taxa. The quantification of sugars, fructose/glucose ratio, total phenolics and flavonoids was also carried out. From the results was possible to identify Brassica and Eucalyptus samples that present similar HPLC/DAD profiles with the respective ultraviolet (UV) identification of the main compounds as Kaempferol-3-O-glycosides and Hydrocinnamic acid derivatives. The Fructose/Glucose (F/G) ratio and the total amounts of phenolics and flavonoids was in line with the prevalence of the specie identified. These coincident fingerprints gave the identification of the samples, as was previously proposed for bee pollens. This paper relates for the first time the achievement on the taxon carried out previously only for bee pollens. It was reported for the first time that this phenolic profile remains unchanged in the case of floral pollen (hand collected), bee pollen and bee bread. Despite the biochemical transformation that occurs during the fermentation of bee bread, it seems that these phenolic compounds are not affected and remain unchanged. Also, variables such as soil and climate do not seem to influence these compounds for the kind of samples under study.