Miracle-dz/UniFAR-FTUs huggingface dataset:
Meaningful Test and Evaluation of Indoor Localization Systems in Semi-Controlled Environments

Despite their enormous potential, the use of indoor localization systems (ILS) remains seldom. One reason is the lack of market transparency and stakeholders’ trust in the systems’ performance as a consequence of insufficient use of test and evaluation (T&E) methodologies. The heterogeneous nature of ILS, their influences, and their applications pose various challenges for the design of a methodology that provides meaningful results. Methodologies for building-wide testing exist, but their use is mostly limited to associated indoor localization competitions. In this work, the T&E 4iLoc Framework is proposed—a methodology for T&E of indoor localization systems in semi-controlled environments based on a system-level and black-box approach. In contrast to building-wide testing, T&E in semi-controlled environments, such as test halls, is characterized by lower costs, higher reproducibility, and better comparability of the results. The limitation of low transferability to real-world applications is addressed by an application-driven design approach. The empirical validation of the T&E 4iLoc Framework, based on the examination of a contour-based light detection and ranging (LiDAR) ILS, an ultra wideband ILS, and a camera-based ILS for the application of automated guided vehicles in warehouse operation, demonstrates the benefits of T&E with the T&E 4iLoc Framework.
Registro Sedimentar de Ciclones Extratropicais no Início do Século XX na Região dos Lagos Fluminenses, Sudeste do Brasil: Análise de Dados Multi-Proxies

Ciclones são agentes meteorológicos-oceanográficos, responsáveis por mudanças rápidas nos padrões de erosão e deposição de sedimentos nas zonas costeiras. O setor oeste do Oceano Atlântico Sul, próximo à costa leste da América do Sul é favorável à gênese de ciclones extratropicais. No litoral do Estado do Rio de Janeiro, a passagem de ciclones, ocorre de forma episódica, acarretando inundações costeiras e impactos ambientais sobre a infraestrutura urbana local. O presente artigo, objetiva investigar o registro sedimentar ciclogênico em camadas de lamas orgânicas superficiais de pântanos costeiros desse segmento de litoral brasileiro. Procedimentos metodológicos incluem análises de dados multi-proxies, envolvendo, testemunhagem, padrões granulométricos, perfis faciológicos, tratamento palinológico, interpretação de lâminas delgadas de palinomorfos, complementadas por técnicas geocronológicas de percentual de carbono moderno - pMC e isótopos estáveis de carbono. Resultados obtidos em testemunhos posicionados a 0,5 m acima do nível médio do mar, à 1,0 km da linha de praia, sugerem inundação marinha de grande proporção no início do século XX na área ocupada pela Marina Porto Búzios, Armação dos Búzios - Rio de Janeiro.
Establishing an Expert Consensus on Key Indicators of the Quality of Life among Breast Cancer Survivors: A Modified Delphi Study

(1) Background: The needs of cancer survivors are often not reflected in practice. One of the main barriers of the use of patient-reported outcomes is associated with data collection and the interpretation of patient-reported outcomes (PROs) due to a multitude of instruments and measuring approaches. The aim of the study was to establish an expert consensus on the relevance and key indicators of quality of life in the clinical practice of breast cancer survivors. (2) Methods: Potential indicators of the quality of life of breast cancer survivors were extracted from the established quality of life models, depicting survivors’ perspectives. The specific domains and subdomains of quality of life were evaluated in a two-stage online Delphi process, including an international and multidisciplinary panel of experts. (3) Results: The first round of the Delphi process was completed by 57 and the second by 37 participants. A consensus was reached for the Physical and Psychological domains, and on eleven subdomains of quality of life. The results were further supported by the additional ranking of importance of the subdomains in the second round. (4) Conclusions: The current findings can serve to optimize the use of instruments and address the challenges related to data collection and interpretation as the facilitators of the adaption in routine practice.
Extinction learning as pretrauma vulnerability factor of posttraumatic stress: a replication study

ABSTRACT Background Learning tasks have been used to predict why some, and not others, develop posttraumatic stress disorder (PTSD) after exposure to a traumatic event. There is some evidence from prospective studies in high risk profession samples that reduced extinction learning might represent a marker or even a vulnerability factor for PTSD development. Objective Since the evidence is scarce, the aim of this study was to perform a conceptual replication of an earlier prospective study, testing whether pretrauma extinction learning predicts later PTSD symptom severity. Method A sample of 529 fire fighters performed a conditioning task at baseline and filled out questionnaires to assess PTSD symptom severity and neuroticism. At six and 12 months follow-up, exposure to stressful events and PTSD symptom severity were measured. Results Results indicate that previous findings were not replicated: although reduced extinction learning was associated with higher PTSD symptom severity at baseline, extinction learning did not predict PTSD symptom severity at follow-up. Only PTSD symptom severity at baseline and stressor severity predicted PTSD symptom severity at follow-up. Conclusions Since earlier findings on the predictive value of pre-trauma extinction learning on PTSD symptom severity were not replicated, extinction learning might not be a general risk factor PTSD for all individuals. More prospective studies including multiple factors seem needed to unravel the complex relationships of these factors influencing PTSD development. HIGHLIGHTS Reduced extinction learning correlated with higher PTSD symptom severity at baseline. Reduced extinction learning did not predict PTSD symptom severity at follow-up. The predictive effect of pre-trauma extinction learning on PTSD was not replicated
Rutaecarpine ameliorates lipopolysaccharide-induced BEAS-2B cell injury through inhibition of endoplasmic reticulum stress via activation of the AMPK/SIRT1 signaling pathway

Rutaecarpine (RUT) is an alkaloid isolated from Tetradium ruticarpum, which has been reported to protect against several inflammatory diseases. However, to the best of our knowledge, the role of RUT in acute lung injury (ALI) and the specific molecular mechanism remain unknown. In the present study, an in vitro model of ALI was established in BEAS-2B cells by lipopolysaccharide (LPS) administration. Cell viability following RUT treatment with or without LPS stimulation was evaluated using a Cell Counting Kit-8 assay. The inflammatory response and oxidative stress were detected using ELISA kits and commercially available kits, respectively. TUNEL assay and western blotting were performed to assess cell apoptosis. The expression levels of endoplasmic reticulum (ER) stress-related proteins and AMP-activated protein kinase (AMPK)/sirtuin 1 (SIRT1) signaling pathway-related proteins were measured by western blotting. The results revealed that RUT markedly improved cell viability after LPS treatment in a dose-dependent manner. In addition, RUT inhibited the LPS-induced inflammatory response and oxidative stress in BEAS-2B cells, and suppressed the LPS-induced apoptosis of BEAS-2B cells. Mechanistically, RUT alleviated ER stress by inhibiting the production of CHOP, glucose-regulated protein-78, caspase-12 and activating transcription factor 6. Additionally, western blotting demonstrated that RUT activated the phosphorylation of AMPK and SIRT1, which indicated the involvement of the AMPK/SIRT1 signaling pathway in the protective effect of RUT against LPS-induced lung injury. In conclusion, these results demonstrated that RUT mitigated LPS-induced lung cell injury by inhibiting ER stress via the activation of the AMPK/SIRT1 signaling pathway.
New species, redescriptions and new records of deep-sea brittle stars (Echinodermata: Ophiuroidea) from the South China Sea, an integrated morphological and molecular approach

Deep-sea ophiuroids were collected by the manned submersible ‘Shenhaiyongshi’ from the South China Sea at depths of 500–3550 m, in 2017 to 2020. A total of 18 species were identified, including three new species and eight new records, increasing the total number of species known from the South China Sea to 304. Most of the ophiuroids recorded from the South China Sea were found in shallow waters (77.9%) and a few of them occurred only in deep water (20.4%). Three new species are described as Ophiacantha aster sp. nov., Ophiomoeris petalis sp. nov. and Ophiopristis shenhaiyongshii sp. nov. We provide comprehensive descriptions of morphological features, including characteristics of the arm skeletons, and a phylogenetic analysis based on COI and 16S sequences. Overall intraspecific and interspecific genetic distance variations among the families found in this study were 0.5% to 2.47% and 1.16% to 44.16%, respectively, along the South Pacific region to the South China Sea. Our phylogenetic analysis suggested that COI partial genes resolved the interspecies complexity in the class Ophiuroidea better than 16S partial genes. The order Euryalida had low interspecies genetic distance variation within the class Ophiuroidea. The present study suggests a high probability that species of Asteroschema and Gorgonocephalus are more widely spread around the Indo-Pacific region than previously expected.
Learning Real-world Autonomous Navigation by Self-Supervised Environment Synthesis

Machine learning approaches have recently enabled autonomous navigation for mobile robots in a data-driven manner. Since most existing learning-based navigation systems are trained with data generated in artificially created training environments, during real-world deployment at scale, it is inevitable that robots will encounter unseen scenarios, which are out of the training distribution and therefore lead to poor real-world performance. On the other hand, directly training in the real world is generally unsafe and inefficient. To address this issue, we introduce Self-supervised Environment Synthesis (SES), in which, after real-world deployment with safety and efficiency requirements, autonomous mobile robots can utilize experience from the real-world deployment, reconstruct navigation scenarios, and synthesize representative training environments in simulation. Training in these synthesized environments leads to improved future performance in the real world. The effectiveness of SES at synthesizing representative simulation environments and improving real-world navigation performance is evaluated via a large-scale deployment in a high-fidelity, realistic simulator and a small-scale deployment on a physical robot.
Extracted Pomace Olive Oil Use for the Preparation of Starch Graft Copolymer

In this work, the relevant parameters of a pomace olive oil consecutive solvent extraction method using ethanol, and petroleum ether are investigated from dry and wet pomace samples. It is found that, oil extraction from dry samples with petroleum ether showed a high yield (11.72±0.30%) with solvent recovery of 89%, while extraction yield with ethanol is (11.1±0.60%) with solvent recovery of 90%. Moreover, it is found that the oil extraction from wet samples with ethanol is possible but the economic feasibility is not proven as the solvent recovery is of 62%. On the other hand, the possibility of hydrolysis of the crude extracted pomace oil in alkaline medium is demonstrated in this work. In this context, the starch grafting of the obtained long chain mono-fatty acids was accomplished in the presence of Fenton's reagent in a Dimethyl formamide (DMFA)/Water solution. Sonication is used for reaction mixture homogeny and the biopolymer was obtained using domestic microwave heating. After characterizing the obtained grafting polymer, it was employed to prepare a composite material with polyvinyl alcohol. The prepared PVA composite film of grafted starch (PVA/gSt) has tensile strength of (5.84 MPa) while its elongation modulus increased by 123.6%. Moreover, it was observed, in this work, that (PVA/gSt) copolymerization increases both the crystalline structure and the morphological order. This result is contrary to the available literature related to copolymerization of starch with different mono acids. It is found that, the consecutive solvent extraction method is of promising aspect from technical and economic point of view and the grafted starch compatibility with other polymers may be improved. The PVA/gSt films could be applied as packaging films.
A Review on Nanoparticles - Classifications, Synthesis and Applications

: This review paper focused on detailed overview of the properties, synthesis and application of the nanoparticles (NP) subsist in different forms. Nanoparticles are in very small size which ranges 1 to 100nm scale. They are classifieds into various classes based on their characteristic, size and shapes. The dissimilar group included metal NP, ceramic NP, and polymeric NP and fullerenes. NP own unique chemical characteristics and physical characteristics due to their nano-scale size and high surface area. Their optical characteristics are informed to be reliant on the size, which imparts dissimilar colors due to absorption in the noticeable region. Their toughness, reactivity and other characteristics are also reliant on on their unique shape, structure and sizes. Due to these features, they are appropriate candidates for numerous commercial and domestic applications, which comprise medical applications, imaging, environmental applications, energy-based research, and catalysis. Heavy metal NP of mercury, tin and lead are reported to be so rigid and steady that their degradation is not simply attainable, which can lead to numerous environmental toxicities.
Alpha and Omega? Gauging the influence of Answer-Pointer Frameworks in Question Answering Models

Answer-Pointer RNNs have factored into successful machine comprehension models, especially those designed for the SQUaD challenge. However, whether or not these RNNs may be applied, generally, to boost model performance is unclear. In our project, we perform an ablation study to isolate the precise performance benefits of these Answer-Pointer RNN components in different models. We observe the improvements of an Answer-Pointer RNN on a baseline BiDAF model, a BiDAF model with character embeddings, and a BiDAF model with a self-attention encoding layer, after the R-net model (1). We introduce a novel output layer that combines succesful elements of the BiDAF and R-net models (2). The Answer-Pointer output contributed to improved performance in some models, but worse performance in others. We conclude that an Answer-Pointer RNN must be applied with careful regularization, and propose that they may be less effective in models that leverage gratuitous self attention.
Squadobots and Decepticonvs

We consider the problem of building a model capable of performing Question Answering (QA) on the Stanford Question Answering Dataset (SQuAD) 2.0. The QA problem refers to the problem of producing a response to some query given some context information (e.g., a document database, the web, etc.) and is relevant to many modern day services ranging from digital assistants like Siri and Alexa to Google search. In this work, we build upon the BiDAF architecture and investigate the effect of introducing Transformer blocks and convolution layers on the model’s QA performance. Our best model yields respective F1 and EM scores of 64.39 and 61.55 on the development data set and 63.936 and 60.372 on the test data set. We show that pretraining the model was responsible for a respective increase of 0.58 and 0.89 in these scores evaluated on the development set.
New Robust Tensor PCA via Affine Transformations and L 2 , 1 Norms for Exact Tubal Low-Rank Recovery from Highly Corrupted and Correlated Images in Signal Processing

In this latest work, the Newly Modiﬁed Robust Tensor Principal Component Analysis (New RTPCA) using aﬃne transformation and L 2 , 1 norms is proposed to remove the outliers and heavy sparse noises in signal processing. This process is done by decomposing the original data matrix as the low-rank heavy sparse noises. The determination of the potential variables is casted as constrained convex optimization problem, and the Alternating Direction Method of Multipliers (ADMM) method is considered to reduce the computational loads in an iterative manner. The simulation results validate the eﬀectiveness of the new method as compared with that of the state-of-the-art methods.
MemoryBank: A Flexible Framework for Systemic Beliefs

While large language models have achieved success in the Question-Answering domain, one significant limitation is their inconsistencies across answers, such as providing contradictory answers to questions referring to the same fact. The BeliefBank [1] framework tackles this issue by augmenting a QA model with external storage for previous questions and answers ( beliefs ) and by periodically performing constraint solving on these beliefs. This approach results in increasing accuracy and high consistency on the question answering task
Digital Commons@Georgia Southern Disease Prevalence and the Population Genetic Structure of Crassostrea virginica Along the Georgia Coast

The Eastern Oyster, Crassostrea virginica , is an ecologically important species that plays a role in protecting shorelines, water filtration, and providing habitat and food for various fish species. Perkinsus marinus and Haplosporidium nesloni are two pathogens currently affecting oyster populations. These pathogens have both been documented as causes of mass mortality events along the East coast. They greatly diminish the health of oysters, resulting in the degradation of oyster tissue, reduction in growth and reproductive ability, and may cause death. In this study, wild oysters were collected from eleven locations along the Georgia coast to investigate the presence, intensity, and impact on the health of local populations. The condition of each oyster was calculated during processing, and qPCR was used to determine the presence and intensity of both P. marinus and H. nelsoni . Additionally, population genetic structure and kinship within and among populations was examined using 20 microsatellite loci. Results showed that >90% of oysters tested were infected by one or both pathogens, and 48.8% of these were cases of co-infection . Intensity of infection varied significantly across sites for both P. marinus and H. nelsoni (p = <0.0001, p = <.0001, respectively). Across sites, variation in oyster condition was driven by a single study site exhibiting higher oyster condition (p = <0.0001), however, being infected had no effect on oyster condition (p = 0.071), and neither did infection intensity of either pathogen (p = 0.346, p = 0.819). There was evidence supporting the theory of sweepstakes reproductive success among Georgia oyster populations; fine-scale population structure was detected, however, there was no link to geographic location. Additionally, oyster kin were found to be more likely to settle near each other (p = <0.0001).
A Study on the Use of Cognitive Radio Networks in the Military Operation Environment

The needs in terms of wireless communications are growing up both for civil and military applications. Therefore a constant improvement of this technology is required to meet customer wishes. One of its main shortcomings is the inefficient use of the spectrum in which a large part of the allocated bands of frequencies is unused. Since communication is crucial, spectrum shortage problems can lead a multi-national and coalition operation to failure. Cognitive Radio Networks (CRNs) are a promising technology which continuously analyses the spectrum searching for available frequencies. It can solve this spectrum issue by avoiding interferences, improving system-wide spectral efficiency, robustness to dynamic conditions and allowing more spectrum flexibility This paper specifically analyzed and presented the application of the CRNs in the military operational environment
Comparative verification of ciprofloxacin HCI by HPLC chromatogram, UV-Vis absorption spectra, and FT-IR spectra was tested using a variety of procedures to finds its efficacy towards antibiotics: A DFT study

Ciprofloxacin is a fluoroquinolone antibiotic that is widely used in anti-infective therapy nowadays because of its broad spectrum of activity and potential therapeutic applications. One of the reasons for its widespread use is the presence of multiresistant pathogens that are solely vulnerable to ciprofloxacin. This drug's potential improved efficacy in the treatment of different community-acquired and nosocomial infections, including as respiratory tract, urinary tract, and skin infections, as well as sexually transmitted diseases, is supported by the existing clinical evidence. Ciprofloxacin's pharmacokinetic profile, when compared to that of other medicines in its class, reveals similar or better bioavailability, higher plasma concentrations, and improved tissue penetration, all of which are reflected in the greater volume of distribution. Different molecular modifications of this medicine have been produced in order to improve its features even further. In this paper, several approaches for the analytical determination of ciprofloxacin and its metabolites in biological fluids have been described that make use of a variety of methodologies. In order to validate the experimental findings connected to the ciprofloxacin medicine, a theoretical inquiry is also carried out. The stock ml for final The stock ,100 ppm (0.1 ,200 ppm (0.2 ,250 ppm (0.25mg/ml) ,300 ppm (0.3 and 400 ppm (0.4 mg/ml) respectively. The calibration graph linear relationship observed between the concentration and absorbance of of calibration data This test procedure obeys Beer’s law.
Constant-roll inﬂation driven by holographic dark energy

The scenario of constant-roll inﬂation is investigated assuming that it is caused by holographic dark energy with energy density 3 M 2 p c 2 /L 2 . The energy density with varying c parameter and Hubble radius as infrared cutoﬀ provides a proper description for the late time acceleration expansion. This motivates us to study the role of the same holographic dark energy in the scenario of constant-roll inﬂation. One of the main features of constant-roll inﬂation is that the second slow-roll parameter is constant which is not necessarily small. This feature brings a diﬀerential equation for the parameter c . After ﬁnding an analytical solution for the parameter, the observable parameters are obtained at the time of inﬂation, and by comparing the model with data, the constants of the model are determined. The energy scale of inﬂation is estimated which is about 10 16 GeV. Finally, a correspondence between the HDE and canonical scalar ﬁeld is considered and the behavior of the constructed potential and its magnitude is discussed.
Building a QA system (IID SQuAD track)

Our final project aims to implement a question answering system that works well on SQuAD 2.0 by capturing long-term dependency. We implemented a Transformer-based model on QA task, QANet to improve the performance compared to the baseline model, BiDAF. In addition, we add character embedding and applied the Transformer-XL to the original QANet to model long-term dependency. We modified the starter code and trained our QANet according to the original paper, and yielded better results than the baseline