Miracle-dz/UniFAR-FTUs huggingface dataset:
UTILIZATION OF SOME NON-FLINT LITHIC RAW MATERIALS IN THE PALAEOLITHIC IN THE CSERHÁT MOUNTAINS AND THE GALGA VALLEY (NORTHERN HUNGARY)

The systematic field surveys in the area of the Cserhát Mountains and the Galga Valley had begun after the excavations of the year 1992. The primary target was to localize new Palaeolithic sites; the secondary one was to find new possible raw material sources. The first results regarding the raw material sources were published in a paper dealing with the utilization of nummulitic chert in the Middle Palaeolithic (Markó & Kázmér 2004). This paper was followed by a detailed review of András Markó in the Hungarian language on the limnic quartzite occurrences in the Cserhát Mountains (Markó 2005). In the last one and a half-decade, primarily from point of some non-flint raw materials, such as andesite, nummulitic chert, petrified wood, quartzite and siliceous pebble, several new results have been achieved. In the following summary, besides the geological occurrences, the archaeological utilization of these raw materials will be discussed as well. The results cannot be regarded as complete, neither concerning County, nor Pest County. Implicitly the field surveys could not have extended those parts of the Cserhát Mountains, which are either or agriculturally not cultivated. The approximate of the studied is
Leishmania major T Cells, Is Required for Immunity to STAT1 Expression in Dendritic Cells, but Not

The generation of Th1 responses is important for resistance to intracellular pathogens, including the parasite, Leishmania major. Although IFN- (cid:1) R/STAT1 signaling promotes a Th1 response via the up-regulation of T-bet, the requirement for STAT1 in Th1 cell differentiation remains controversial. Although in some cases Th1 cells develop independently of STAT1, STAT1 (cid:2) / (cid:2) mice fail to develop a Th1 response during L. major infection. However, the interpretation of this result is complicated by the role STAT1 plays in Ag presentation and, more importantly, in elimination of parasites by macrophages, because both defective Ag presentation and increased parasite burden can inﬂuence Th cell development. To resolve this issue, we assessed the ability of STAT1 (cid:2) / (cid:2) T cells to become Th1 cells and protect mice against L. major following adoptive transfer into STAT1-sufﬁcient mice. We found that whereas T-bet is critical for the differentiation of protective Th1 cells during L. major infection, IFN- (cid:1) R and STAT1 are dispensable. Given that a STAT1-independent Th1 cell response was generated by STAT1-sufﬁcient APCs, but not by STAT1 (cid:2) / (cid:2) cells, we next addressed whether dendritic cells (DCs) require STAT1 signaling to effectively present Ag. We found that STAT1 (cid:2) / (cid:2) DCs had impaired up-regulation of MHC and costimulatory molecules, and, as a consequence, the absence of STAT1 resulted in reduced Th1 cell priming. Taken together, these results demonstrate that T cell expression of STAT1 is not required for the development of Th1 cells protective against L. major and instead stress the importance of STAT1 signaling in DCs for the optimal induction of Th1 responses. The Journal of Immunology, 2007, 178: 7259–7266.
Unraveling the Basis of Neonicotinoid Resistance in Whitefly Species Complex: Role of Endosymbiotic Bacteria and Insecticide Resistance Genes

Bemisia tabaci (whitefly) is one of the most detrimental agricultural insect pests and vectors of many plant viruses distributed worldwide. Knowledge of the distribution patterns and insecticide resistance of this cryptic species is crucial for its management. In this study, genetic variation of mitochondrial cytochrome oxidase subunit 1 (MtCoI) gene of B. tabaci was analyzed followed by a study of the infection profile of various endosymbionts in 26 whitefly populations collected from West Bengal, India. Phylogenetic analysis revealed Asia I as the major cryptic species (65.38%), followed by Asia II 5, China 3, and Asia II 7, which were diversified into 20 different haplotypes. In addition to the primary endosymbiont (C. poriera), each of the four whitefly species showed a variable population of three secondary endosymbionts, majorly Arsenophonus with the highest infection rate (73.07%), followed by Wolbachia and Rickettsia. Further phylogenetic analyses revealed the presence of two subgroups of Arsenophonus, viz., A1 and A2, and one each in Wolbachia (W1) and Rickettsia (R3). Resistance to thiamethoxam, imidacloprid, and acetamiprid insecticides was analyzed for a clear picture of pesticide resistance status. The highest susceptibility was noted toward thiamethoxam (LC50 = 5.36 mg/L), followed by imidacloprid and acetamiprid. The whitefly population from Purulia and Hooghly districts bearing Asia II 7 and Asia II 5 cryptic species, respectively, shows maximum resistance. The differences in mean relative titer of four symbiotic bacteria among field populations varied considerably; however, a significant positive linear correlation was observed between the resistance level and relative titer of Arsenophonus and Wolbachia in the case of imidacloprid and thiamethoxam, while only Wolbachia was found in case of acetamiprid. Expression analysis demonstrated differential upregulation of insecticide resistance genes with Purulia and Hooghly populations showing maximally upregulated P450 genes. Moreover, thiamethoxam and imidacloprid resistance ratio (RR) showed a significant correlation with CYP6CM1, CYP6DZ7, and CYP4C64 genes, while acetamiprid RR correlated with CYP6CX1, CYP6DW2, CYP6DZ7, and CYP4C64 genes. Taken together, these findings suggested that P450 mono-oxygenase and symbiotic bacteria together affected whitefly resistance to neonicotinoids. Hence, a symbiont-oriented management programme could be a better alternative to control or delay resistance development in whitefly and can be used for pesticide clean-up in an agricultural field.
A comparative approach for classifying retinal OCT images based on deep learning framework

Abstract Convolutional Networks are category of deep optimizing networks used to interpret images in Deep Learning concepts. Image recognition and medical image analysis are two areas where they are useful. The increasing scale of clinical feature spaces is raising a significant obstacle, creating issues with extensive database management, and afterward compiling those repositories for retrieval and storage, that could only be addressed using content based medical image retrieval systems. The objective of this paper is to demonstrate a deep CNN architecture for retrieving research and clinical images quickly and efficiently for identifying multi-class retinal disease objects. To train the network, the datasets used are inter-modal and divided into 4 groups. The transfer learning method is used for the multi-classification of retinal images. Another augmentation technique is used for comparing the accuracy, precision, and evaluation metrics with the transfer learning method. The accuracy of 97.1%, with a recall of 97.2%, and a precision of 97.0% was achieved in research that is higher when compared with the previous methods that were deployed. With the augmentation technique, it achieved an accuracy of 94.0% with a 94.6% precision and a recall of 95.1% for the testing data which suggests that decreasing the size of data did not impact the accuracy of the model. The proposed model helps diagnose various categories of medical images for the development of a comprehensive system that can work better than the human experts and help to detect and diagnose various diseases in the medical and clinical fields.
Classification of Amyloid PET Images using novel features for Early Diagnosis of Alzheimer’s disease and MCI conversion

New positron emission tomography (PET) tracers could have a substantial impact on early diagnosis of Alzheimer’s disease (AD), particularly if they are accompanied by optimised image analysis and machine learning methods. Fractal dimension (FD) analysis, a measure of shape complexity, has been proven useful in MR imaging but its application to fluorine-18 amyloid PET has not yet been demonstrated. Shannon entropy (SE) has also been proposed as a measure of image complexity in DTI imaging, but it is not yet widely used in radiology. In this study, one volumetric FD method and one volumetric SE method were applied to 18 F-flutemetamol and 18 F-florbetapir 3D amyloid images from 65 and 281 subjects respectively, including Healthy Volunteers (HV), and patients with probable Alzheimer’s Disease (pAD) or Mild Cognitive Impairment (MCI). The group average fractal dimension of white matter surface and Shannon entropy of white matter volume for HV subjects were higher than for pAD subjects. Both FD and SE are effective in the identification of MCI patients who progress to pAD during the two-year follow-up (ground truth). Finally, we developed a support vector machine (SVM) multi-modal classification framework using both PET and MRI features, which showed higher accuracy compared to traditional SUVr or using PET alone. The classification accuracy for flutemetamol and florbetapir is 88.9% and 83.3% respectively for MCI progression, which is competitive with existing literature. The results presented in this study demonstrate the potential of FD and SE methods for the analysis of brain PET scans in early AD diagnosis and in the prediction of MCI-AD conversion.
Roadside HD Map Object Reconstruction Using Monocular Camera

The traditional HD map production based on mapping vehicles equipped with expensive sensors (e.g. LiDAR) struggles to keep the frequently changing HD maps up-to-date, thus calling for great research efforts in the HD map reconstruction using low-cost vision sensors. However, most existing works in this area limit their focus on reconstructing the landmarks on the road surface. This paper widens this limited scope by focusing on the roadside object reconstruction with a monocular camera. We propose a novel object-level reconstruction framework that boasts two advantageous features: 1) a novel 3D model estimation method to directly reconstruct the specific roadside objects in a vectorized format using their prior geometric knowledge, and 2) a novel data association method to solve the complex tracking problem of roadside objects that often yield thin and coherent observations. The proposed framework is evaluated on both typical highway and urban scenarios in public KAIST Urban dataset. The results demonstrate that our algorithm can reconstruct the roadside objects with high accuracy and recall, and outperforms classic Structure from Motion (SfM) and deep-learning-based methods by a large margin.
Autonomous state-based flipper control for articulated tracked robots in urban environments

We demonstrate a hybrid approach to autonomous flipper control, focusing on a fusion of hard-coded and learned knowledge. The result is a sample-efficient and modifiable control structure that can be used in conjunction with a mapping/navigation stack. The backbone of the control policy is formulated as a state machine whose states define various flipper action templates and local control behaviors. It is also used as an interface that facilitates the gathering of demonstrations to train the transitions of the state machine. We propose a soft-differentiable state machine neural network that mitigates the shortcomings of its naively implemented counterpart and improves over a multi-layer perceptron baseline in the task of state-transition classification. We show that by training on several minutes of user-gathered demonstrations in simulation, our approach is capable of a zero-shot domain transfer to a wide range of obstacles on a similar real robotic platform. Our results show a considerable increase in performance over a previous competing approach in several essential criteria. A subset of this work was successfully used in the Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge to alleviate the operator of manual flipper control. We autonomously traversed stairs and other obstacles, improving map coverage.
Designing a Smart Car Parking System (PoC) Prototype Utilizing CCTV Nodes: A vision of an IoT parking system via UCD process

Using smart parking systems has become very important, and particularly so for metropolitan areas, because of the benefits for drivers in many aspects, such as time, frustration, stress, and anger, in addition to the increased consumption of fuel while searching for a vacant parking space. This paper proposes a review of recent advances in sensing and communication technology concerning smart parking systems. It includes a brief study of the selected topics and provides an implementation process of those selected systems. Moreover, this work proposes a design approach for a smart car parking system prototype based on utilizing CCTVs (nodes), it is also illustrates the algorithms used for computer vision detection through simulation and real environments, as the system has been deployed in both these environments. Furthermore, the system has been tested and evaluated by stakeholders via a user-centred design process by applying a qualitative research; the promising results demonstrate the effectiveness of our prototype. Finally, this paper discusses the benefits of engaging the stakeholders to develop the prototype.
The Impact of Online Community on Farmer Empowerment: A Strategical Analysis for Development The Technology-to-Performance

This research provides empirical evidence for the benefit of social media in the online farmer community. The benefit that focus on empowerment value. The case study is used to explore the ICT usage in the community of Laying Hens Farmer in East and Central Java implementing the multi-feed and additive supplement. The research develops The Technology-to-Performance Chain (TPC) approach to analysis the strategy of farmer empowerment. The performance that was addressed by empowerment values. The study results that the farmer’s empowerment are influenced significantly by task-technology fit with a path coefficient of 0,704. It means that utilization of social media as the online media of community will encourage empowerment if the information and knowledge sharing activities inside are related to the task or farmer's livelihood. The farmer's characteristics and the facilitating condition affect the empowerment insignificantly. However, the facilitating condition determines the task-technology fit with the path value 0,868. This study discusses 3 important layers in farmer empowerment strategies through online communities. The first is the underlying factors which includes internet access, product innovation and agents of change (facilitators/agricultural extensionists), the second is the adoption process (both internet adoption and product knowledge adoption) while the third is the impact of empowerment which is direct (output) and indirect (outcome).
Fast Simulation-Based Order Sequence Optimization Assisted by Pre-Trained Bayesian Recurrent Neural Network

This paper presents a fast optimization method for the picking order sequence of automated order picking systems in logistics warehouses. In this order sequencing problem (OSP), the fulfillment sequence of the given picking order set is determined to optimize the performance measures such as makespan and deadlock occurrence. Simulation is generally necessary to evaluate these measures for complex automated systems. However, their order sequence cannot be optimized quickly due to the long calculation time. It may make the system productivity and flexibility lower than expected because its picking schedules cannot be updated frequently. We, therefore, propose a fast optimization method to solve these simulation-based OSPs by taking a pretrained surrogate-assisted optimization approach. Firstly, we utilized a Bayesian recurrent neural network (BRNN) as a surrogate model to accurately learn the relationship between picking order sequence and performances. Secondly, we developed the surrogate-assisted optimization method based on simulated annealing (SA) and BRNN. Numerical experiments show that the surrogate model can evaluate about 10000 times faster than the simulation. The proposed method also obtains an optimized solution 8.9 times faster than simulation-based optimization by the original SA.
Electroencephalography‐based imagined speech recognition using deep long short‐term memory network

This article proposes a subject‐independent application of brain–computer interfacing (BCI). A 32‐channel Electroencephalography (EEG) device is used to measure imagined speech (SI) of four words (sos, stop, medicine, washroom) and one phrase (come‐here) across 13 subjects. A deep long short‐term memory (LSTM) network has been adopted to recognize the above signals in seven EEG frequency bands individually in nine major regions of the brain. The results show a maximum accuracy of 73.56% and a network prediction time (NPT) of 0.14 s which are superior to other state‐of‐the‐art techniques in the literature. Our analysis reveals that the alpha band can recognize SI better than other EEG frequencies. To reinforce our findings, the above work has been compared by models based on the gated recurrent unit (GRU), convolutional neural network (CNN), and six conventional classifiers. The results show that the LSTM model has 46.86% more average accuracy in the alpha band and 74.54% less average NPT than CNN. The maximum accuracy of GRU was 8.34% less than the LSTM network. Deep networks performed better than traditional classifiers.
An assessment of the impact of the fit among computer self-efficacy, task characteristics and systems characteristics on performance and information systems utilization

Businesses and organizations spend large proportions of their annual budget on implementation of Information Technologies (IT) in the workplace, and train their employees to use them. Yet, there is no sufficient empirical evidence that approves that IT training and education make a difference in the workplace. This research is designed to examine the impact of computer skills on individual performance and Information System (IS) utilization. Computer Self-Efficacy (CSE) construct has been carefully selected to measure computer skills and knowledge and the ability to apply them in performing work tasks. The research model integrates elements from different theories and research models including computer self-efficacy model, task-technology fit model, social cognitive theory, and technology acceptance model. Constructing the research framework this way allows a researcher to draw conclusions and implications about impact of IT skills on performance and IS utilization as well as determining the characteristics of best fit among task, system and computer skills that associated with optimal performance and utilization. This study revealed that CSE is significantly and positively associated with individual performance and IS utilization. It is also significantly and positively associated with task analyzability, IS functionality, system information quality, and system ease of use. The results of the interaction effect tests revealed that the fit among CSE, task complexity, and IS functionality is positively and significantly associated with performance and utilization. Individuals who possess high CSE, deal with more difficult tasks, and use advanced IS are more likely to exert high level of performance and high IS utilization. This study has proved that IT knowledge and skills make a difference in a workplace. Individual with high level of CSE are more likely to excel on their work, use information technology more extensively, accept more challenging tasks, and have a positive impression of their IS. However, for the optimum positive effects of IT skills, managers should consider the alignment among level of computer skills, level of IS functionality, and level of task difficulty. Organization and businesses should consider taking the appropriate measures to enhance the level of their employees' computer self-efficacy through providing them with the appropriate information technology training, education and support.
Humans Are More Influential Than Coyotes on Mammalian Mesopredator Spatiotemporal Activity Across an Urban Gradient

For mammalian mesopredators, human-dominated landscapes offer a mosaic of risk and reward. While the reward of anthropogenic food resources may attract mesopredators to human-dominated areas, increased mesopredator activity and abundance in these areas may cause interspecific conflict. For smaller-bodied mesopredators, the perceived risk of intraguild predation by larger mesopredators may be enough to drive spatial and temporal avoidance strategies to reduce risk while still benefiting from anthropogenic resources. We evaluated how the spatiotemporal activity of four non-canid mammalian mesopredators – raccoon (Procyon lotor), striped skunk (Mephitis mephitis), opossum (Didelphis virginiana), and domestic cat (Felis catus) – might change in the presence of an intraguild predator, the coyote (Canis latrans), and a “super predator,” humans. We quantified mesopredator activity by deploying camera traps at 110 sites across an urban gradient in the Sacramento Metropolitan Area in central California, USA. We hypothesized that mesopredators would likely change their spatiotemporal activity in response to urban intensity (H1), coyotes (H2), human presence (H3), and if urban intensity might mediate the response to humans and coyotes (H4). We used single-species occupancy models to test how mesopredators responded to different spatial scales of urbanization, as well as the temporal presence of coyotes and humans. Top single-species models then informed two-species conditional occupancy models to evaluate how mesopredators responded to “dominant” coyotes. Finally, we used temporal overlap analyzes to evaluate whether activity patterns of mesopredators changed in response to humans and coyotes across three levels of urban intensity. Mesopredators did not change their spatial or temporal activity across the urban gradient when coyotes were present. Additionally, coyotes did not influence mesopredator temporal activity at 1-3-day scales; however, raccoons and cats may avoid coyotes at finer scales. Humans influenced mesopredator detectability, as cats and coyotes avoided humans after 1 and 2 days respectively, and opossums avoided urban areas when humans were present within 3 days. Coyotes may play a limited role in altering smaller-bodied mesopredator activity, especially when humans are present. While the impacts of human presence and urban features are often linked, mesopredators may perceive the risk each poses differently, and adjust their activity accordingly.