Miracle-dz/UniFAR-FTUs huggingface dataset:
A path planning model based on nested regular hexagons using weighted centroid localization

In many wireless sensor network (WSN) applications, the location of a sensor node is crucial for determining where the event or situation of interest occurred. Therefore, localization is one of the critical challenges in WSNs. Mobile anchor node assisted localization (MANAL) is one of the promising solutions for the localization of statically deployed sensors. The main problem in MANAL localization is that the path planning of the mobile anchor (MA) node should be done so that the localization error in the network will be minimal and that all unknown nodes in the network are covered. This paper proposes a new path planning approach called nested hexagons curves (NHexCurves) for MANAL. NHexCurves guarantees that it will receive messages from at least three non‐collinear anchors to locate all unknown nodes in the network. The proposed model has compared six different path planning schemes in the literature using weighted centroid localization (WCL). In these comparisons, first of all, localization errors of the models are compared using some statistical concepts. Second, the variation of the localization error according to parameters such as resolution (R) and the standard deviation of noise (σ) is observed. Then, with similar approaches, the standard deviation of errors, localization ratio, scalability performances, and finally, path lengths of the models are examined. The simulation results show that the NHexCurves static path planning model proposed in this study stands out compared to other models with high localization error and localization ratio performance, especially at low resolutions, due to its path design. At the same time, the lowest error values according to σ are obtained with the proposed model among all models considered.
Post-Neoliberalism and External Financial Liberalization: Comparing Left-Wing and Right-Wing Populism

Abstract This article aims to discuss to what extent populist parties with opposite ideological backgrounds have differed in their policies towards inherited external financial liberalization (EFL). Building upon a comparative case study centred on Argentina under Kirchnerism (2003–15) and Hungary under Viktor Orbán (since 2010), I conclude that both experiences led to a partial EFL reversal. However, reflecting their opposite ideological underpinnings, each subtype of populism opted to restrict a different dimension of EFL. Argentina's left-wing populism re-regulated cross-border capital flows, harming financial operators, foreign investors and primary exporters through capital controls and export surrenders. These interventionist capital account regulations were needed to shield expansionary macroeconomic policies that attended the interests of subordinate socioeconomic strata, fuelling the tension with financial markets and domestic economic elites. Conversely, Hungary's right-wing populism focused on the ownership structure of the banking sector, aiming to redistribute assets from foreign to domestic private banks and improve the credit conditions for native capitalists. In this case, even when resorting to macroeconomic heterodoxy, the maintenance of fiscal balance and price stability retained support from both foreign investors and domestic business groups, mitigating tensions derived from financial nationalism.
Different Polyoxometalate Structures Obtained from the
Na11H[H(2-x)Bi2W20O70(HWO3)x]·46H2O(x=1.4).

In the supramolecular chemistry world, Polyoxometalates (POMs) are considered as a new family of inorganic molecular containers, construct itself by self-assembly reaction from very small units, to form a cluster with unique structural and properties. three structures reported in this paper - differ in their unit cell parameters and also differ from the well-known - as the result of reaction of the Na11H[H(2- x)Bi2W20O70(HWO3)x]·46H2O (x=1.4) with Ca ion at different pH conditions. These structures are [Ca(H2O)7]2[Na(H2O)2]2[HBi2W20O70(HWO3)]. 14H2O(1), H2[NH4]10[HBi2W20O70(HWO3)] (2), and [NH4]6[Na(H2O)4]2[Ca(H2O)4]2[W12O42]. 2H2O (3). The last one is bismuth-free and it is formed through reassembly of the precursor. Full structural characterization was made by multiple testing techniques such as single-crystal X-ray diffraction, UV-visible spectroscopy, FT-IR, thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS). The single-crystal X-ray diffraction results for the three compounds are as follows:(1), Triclinic, space group P-1, while (2) and (3) crystallize in monoclinic space groups C2/m and P21/n respectively.
Tripodal tris-disulfides as capping agents for a controlled mixed functionalization of gold nanoparticles

The design and synthesis of tripodal 1,3,5-R-2,4,6-R′-benzene tris-disulfides (R = Et, R′ = phenyl or alkyl) is reported. These multi-armed systems were employed in the stable capping of colloidal AuNPs, allowing control of the ratio between the benzene core and peripheral moieties. These systems were shown to act as sensing nanomaterials for nitro aromatic explosives.
Synthesis and Characterization of the Chitosan Silver Nanoparticle-Reinforced Borassus flabellifer Trichome- and Prosopis juliflora Wood-Based Nanocomposite for Environmental Application

Wood is a wide flexible material appreciated extremely for its cost-effectiveness, great quantity, and biocompatibility. In addition, naturally existing materials possess prominent biomedical applications, and they can withstand efficiently when compared to other materials like glass, steel, and plastics. The present study revealed the prepared chitosan, silver nanoparticles incorporated with Borassus flabellifer trichome, and fabrication of Prosopis juliflora wood-based biomaterial. A characterization study was done by UV-visible spectroscopic analysis, FTIR analysis, and SEM analysis expressing and confirming a significant characteristic and morphological property of the prepared biomaterial.
FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings

Data often have a relational nature that is most easily expressed in a network form, with its main components consisting of nodes that represent real objects and links that signify the relations between these objects. Modeling networks is useful for many purposes, but the efficacy of downstream tasks is often hampered by data quality issues related to their construction. In many constructed networks, ambiguity may arise when a node corresponds to multiple concepts. Similarly, a single entity can be mistakenly represented by several different nodes. In this paper, we formalize both the node disambiguation (NDA) and node deduplication (NDD) tasks to resolve these data quality issues. We then introduce FONDUE, a framework for utilizing network embedding methods for data-driven disambiguation and deduplication of nodes. Given an undirected and unweighted network, FONDUE-NDA identifies nodes that appear to correspond to multiple entities for subsequent splitting and suggests how to split them (node disambiguation), whereas FONDUE-NDD identifies nodes that appear to correspond to same entity for merging (node deduplication), using only the network topology. From controlled experiments on benchmark networks, we find that FONDUE-NDA is substantially and consistently more accurate with lower computational cost in identifying ambiguous nodes, and that FONDUE-NDD is a competitive alternative for node deduplication, when compared to state-of-the-art alternatives.
Organic Acids Improve Growth Performance with Potential Regulation of Redox Homeostasis, Immunity, and Microflora in Intestines of Weaned Piglets

The objective of this study is to evaluate the effects of organic acids on piglet growth performance and health status. A total of 360 weanling pigs (5.3 ± 0.6 kg) were randomly allotted to 3 treatment groups with 12 replicates of 10 pigs/pen. Piglets were fed the same basal diet and given either water (control) or water plus 2.0 L/Ton organic acid (OA) blends, such as OA1 or OA2, respectively, for 7 weeks. Compared to the control, OA1 and OA2 improved growth performance and/or reduced the piglets’ diarrhea rate during the various periods and improved small intestinal morphology at days 14 and/or 49. OA1 and OA2 also increased serum CAT and SOD activities and/or T-AOC and, as expected, decreased MDA concentration. Moreover, at day 14 and/or day 49, OA1 and OA2 increased the jejunal mRNA levels of host defense peptides (PBD1, PBD2, NPG1, and NPG3) and tight junction genes (claudin-1) and decreased that of cytokines (IL-1β and IL-2). Additionally, the two acidifiers regulated the abundance of several cecum bacterial genera, including Blautia, Bulleidia, Coprococcus, Dorea, Eubacterium, Subdoligranulum, and YRC2. In conclusion, both of the organic acid blends improved piglet growth performance and health status, potentially by regulating intestinal redox homeostasis, immunity, and microflora.
Abundance of NO3 Derived Organo-Nitrates and Their Importance in the Atmosphere

The chemistry of the nitrate radical and its contribution to organo-nitrate formation in the troposphere has been investigated using a mesoscale 3-D chemistry and transport model, WRF-Chem-CRI. The model-measurement comparisons of NO2, ozone and night-time N2O5 mixing ratios show good agreement supporting the model’s ability to represent nitrate (NO3) chemistry reasonably. Thirty-nine organo-nitrates in the model are formed exclusively either from the reaction of RO2 with NO or by the reaction of NO3 with alkenes. Temporal analysis highlighted a significant contribution of NO3-derived organo-nitrates, even during daylight hours. Night-time NO3-derived organo-nitrates were found to be 3-fold higher than that in the daytime. The reactivity of daytime NO3 could be more competitive than previously thought, with losses due to reaction with VOCs (and subsequent organo-nitrate formation) likely to be just as important as photolysis. This has highlighted the significance of NO3 in daytime organo-nitrate formation, with potential implications for air quality, climate and human health. Estimated atmospheric lifetimes of organo-nitrates showed that the organo-nitrates act as NOx reservoirs, with particularly short-lived species impacting on air quality as contributors to downwind ozone formation.
Escherichia coli ribosomal protein S1 enhances the kinetics of ribosome biogenesis and RNA decay

Escherichia coli ribosomal protein S1 is essential for translation initiation of mRNAs and for cellular viability. Two oligonucleotide binding (OB)-fold domains located in the C-terminus of S1 are dispensable for growth, but their deletion causes a cold-shock phenotype, loss of motility and deregulation of RNA mediated stress responses. Surprisingly, the expression of the small regulatory RNA RyhB and one of its repressed target mRNA, sodB, are enhanced in the mutant strain lacking the two OB domains. Using in vivo and in vitro approaches, we show that RyhB retains its capacity to repress translation of target mRNAs in the mutant strain but becomes deficient in triggering rapid turnover of those transcripts. In addition, the mutant is defective in of the final step of the RNase E-dependent maturation of the 16S rRNA. This work unveils an unexpected function of S1 in facilitating ribosome biogenesis and RyhB-dependent mRNA decay mediated by the RNA degradosome. Through its RNA chaperone activity, S1 participates to the coupling between ribosome biogenesis, translation, and RNA decay.
A Remote Raman System and Its Applications for Planetary Material Studies

A remote Raman prototype with a function of excitation energy adjusting for the purpose of obtaining a Raman signal with good signal-to-noise ratio (SNR), saving power consumption, and possibly avoiding destroying a target by high energy pulses, which may have applications for Chinese planetary explorations, has been setup and demonstrated for detecting different minerals. The system consists of a spectrograph equipped with a thermoelectrically cooled charge-coupled device (CCD) detector, a telescope with 150 mm diameter and 1500 mm focus length, and a compact 1064 nm Nd:YAG Q-switched laser with an electrical adjusted pulse energy from 0 to 200 mJ/pulse. A KTP crystal was used for second harmonic generation in a 1064 nm laser to generate a 532 nm laser, which is the source of Raman scatting. Different laser pulse energies and integration time were used to obtain distinguishable remote Raman spectra of various samples. Results show that observed remote Raman spectra at a distance of 4 m enable us to identify silicates, carbonates, sulfates, perchlorates, water/water ice, and organics that have been found or may exist on extraterrestrial planets. Detailed Raman spectral assignments of the measured planetary materials and the feasible applications of remote Raman system for planetary explorations are discussed.
Validating Poly(3,4-ethylene dioxythiophene) Polystyrene Sulfonate-Based Textile Electroencephalography Electrodes by a Textile-Based Head Phantom

It is important to go through a validation process when developing new electroencephalography (EEG) electrodes, but it is impossible to keep the human mind constant, making the process difficult. It is also very difficult to identify noise and signals as the input signal is unknown. In this work, we have validated textile-based EEG electrodes constructed from a poly(3,4-ethylene dioxythiophene) polystyrene sulfonate:/polydimethylsiloxane coated cotton fabric using a textile-based head phantom. The performance of the textile-based electrode has also been compared against a commercial dry electrode. The textile electrodes collected a signal to a smaller skin-to-electrode impedance (−18.9%) and a higher signal-to-noise ratio (+3.45%) than Ag/AgCl dry electrodes. From an EEGLAB, it was observed that the inter-trial coherence and event-related spectral perturbation graphs of the textile-based electrodes were identical to the Ag/AgCl electrodes. Thus, these textile-based electrodes can be a potential alternative to monitor brain activity.
Intelligent Perception-Based Cattle Lameness Detection and Behaviour Recognition: A Review

Simple Summary Cattle lameness detection as well as behaviour recognition are the two main objectives in the applications of precision livestock farming (PLF). Over the last five years, the development of smart sensors, big data, and artificial intelligence has offered more automatic tools. In this review, we discuss over 100 papers that used automated techniques to detect cattle lameness and to recognise animal behaviours. To assist researchers and policy-makers in promoting various livestock technologies for monitoring cattle welfare and productivity, we conducted a comprehensive investigation of intelligent perception for cattle lameness detection and behaviour analysis in the PLF domain. Based on the literature review, we anticipate that PLF will develop in an objective, autonomous, and real-time direction. Additionally, we suggest that further research should be dedicated to improving the data quality, modeling accuracy, and commercial availability. Abstract The growing world population has increased the demand for animal-sourced protein. However, animal farming productivity is faced with challenges from traditional farming practices, socioeconomic status, and climate change. In recent years, smart sensors, big data, and deep learning have been applied to animal welfare measurement and livestock farming applications, including behaviour recognition and health monitoring. In order to facilitate research in this area, this review summarises and analyses some main techniques used in smart livestock farming, focusing on those related to cattle lameness detection and behaviour recognition. In this study, more than 100 relevant papers on cattle lameness detection and behaviour recognition have been evaluated and discussed. Based on a review and a comparison of recent technologies and methods, we anticipate that intelligent perception for cattle behaviour and welfare monitoring will develop towards standardisation, a larger scale, and intelligence, combined with Internet of things (IoT) and deep learning technologies. In addition, the key challenges and opportunities of future research are also highlighted and discussed.
Residents' perceptions of the role and management of green spaces to provide cultural ecosystem services in Dhaka, Bangladesh

Ongoing rapid urbanization and socio-environmental changes are posing challenges to proper urban management. Cultural ecosystem services (CES) offered by urban green spaces play a crucial role in the well-being of urban residents through ecosystems’ non-material benefits. However, these services, especially from the residents’ perspective, are often neglected in urban management strategies because of their intangible nature, particularly in the Global South. In this study, conducted in Dhaka, Bangladesh, we investigated residents’ perceptions of the importance of CES offered by green spaces and how green space should be managed. As a methodological tool, we used a questionnaire-based stated preference method where non-monetary statements were the basis. Questions in the survey included respondents’ social background and visiting frequency to green space, perceived importance of CES and green space, and perceptions about the management of green space. We analyzed 405 questionnaires using descriptive and inferential statistics. Our results indicate that rooftop gardens and green parks are considered the most important areas that provide CES, and among different types of CES (i.e., recreation, aesthetic, mental satisfaction, social relations), recreation is considered the most important. The high importance respondents place on the CES categories of aesthetics, sense of place, and social cohesion was also revealed. CES categories tended to be positively linked with each other. The valuation of CES was diverse depending on gender, income, educational background, and visiting frequency. Respondents prioritized government and community roles over the role of individuals and international and national non-government organizations in managing CES. Collaboration between individual and organizations was deemed a high priority. Our results highlight that a better understanding of CES and how different people perceive their benefits can be used to foster public participation and increase support for green space management initiatives.
Discrimination of Different Breast Cell Lines on Glass Substrate by Means of Fourier Transform Infrared Spectroscopy

Fourier transform infrared (FTIR) micro-spectroscopy has been attracting the interest of many cytologists and histopathologists for several years. This is related to the possibility of FTIR translation in the clinical diagnostic field. In fact, FTIR spectra are able to detect changes in biochemical cellular components occurring when the cells pass to a pathological state. Recently, this interest has increased because it has been shown that FTIR spectra carried out just in the high wavenumber spectral range (2500–4000 cm−1), where information mainly relating to lipids and proteins can be obtained, are able to discriminate cell lines related to different tissues. This possibility allows to perform IR absorption measurements of cellular samples deposited onto microscopy glass slides (widely used in the medical environment) which are transparent to IR radiation only for wavenumber values larger than 2000 cm−1. For these reasons, we show that FTIR spectra in the 2800–3000 cm−1 spectral range can discriminate three different cell lines from breast tissue: a non-malignant cell line (MCF10A), a non-metastatic adenocarcinoma cell line (MCF7) and a metastatic adenocarcinoma cell line (MDA). All the cells were grown onto glass slides. The spectra were discriminated by means of a principal component analysis, according to the PC1 component, whose values have the opposite sign in the pairwise score plots. This result supports the wide studies that are being carried out to promote the translation of the FTIR technique in medical practice, as a complementary diagnostic tool.
Comprehensive Analysis of the Implication of PGRMC1 in Triple-Negative Breast Cancer

TNBC represents the most malignant subtype of breast cancer with heterogenicity and poor prognosis. PGRMC1 has been reported to predict worse prognosis and correlate with MHT mediated signal transduction in breast cancer, whereas its involvement in TNBC remains poorly explored. The purpose of the study was to explore the roles of PGRMC1 in TNBC. Bioinformatic approaches were performed to analyzed the expression of PGRMC1 among different subtypes of breast cancers using RNA-seq data from the TCGA, METABRIC and GEO databases. PGRMC1 mRNA expression and survival in breast cancer were analyzed. Furthermore, we analyzed the expression of PGRMC1 in TNBC by single cell RNA-seq data and immunohistochemistry. The expression of PGRMC1 in TNBC group was significantly higher compared with that of Luminal subtypes, especially in the epithelia cells, which was further proved by IHC at protein level. Better overall survival (p = 0.027) was observed in the patients with lower expression of PGRMC1. Different states of hormone and Her2 receptors contributed to the distinct functions of PGRMC1. In TNBC, PGRMC1 might play an important role in mitochondrial functions. In summary, this study revealed the correlation between PGRMC1 expression and its clinical significance in TNBC, probably through mitochondria-associated pathway, which may provide new ideas for prognosis and therapy of TNBC.
Optimal control strategy for COVID-19 concerning both life and economy based on deep reinforcement learning

At present, the global COVID-19 is still severe. More and more countries have experienced second or even third outbreaks. The epidemic is far from over until the vaccine is successfully developed and put on the market on a large scale. Inappropriate epidemic control strategies may bring catastrophic consequences. It is essential to maximize the epidemic restraining and to mitigate economic damage. However, the study on the optimal control strategy concerning both sides is rare, and no optimal model has been built. In this paper, the Susceptible-Infectious-Hospitalized-Recovered (SIHR) compartment model is expanded to simulate the epidemic’s spread concerning isolation rate. An economic model affected by epidemic isolation measures is established. The effective reproduction number and the eigenvalues at the equilibrium point are introduced as the indicators of controllability and stability of the model and verified the effectiveness of the SIHR model. Based on the Deep Q Network (DQN), one of the deep reinforcement learning (RL) methods, the blocking policy is studied to maximize the economic output under the premise of controlling the number of infections in different stages. The epidemic control strategies given by deep RL under different learning strategies are compared for different reward coefficients. The study demonstrates that optimal policies may differ in various countries depending on disease spread and anti-economic risk ability. The results show that the more economical strategy, the less economic loss in the short term, which can save economically fragile countries from economic crises. In the second or third outbreak stage, the earlier the government adopts the control strategy, the smaller the economic loss. We recommend the method of deep RL to specify a policy which can control the epidemic while making quarantine economically viable.