Miracle-dz/UniFAR-FTUs huggingface dataset:
Methods for Analysis of Turbulence Integral Length in Atmospheric Boundary-layer

Integral length scales of turbulence simulated in boundary-layer wind tunnel and field measured are analyzed in this paper.The Taylor's hypothesis is verified to be reasonable through experimental analysis.The results calculated with 5 different methods are compared with each other in order to find the most suitable one.Integral length scales of atmospheric boundary-layer turbulence are obtained from field measured data with direct integration of auto-correlation function and auto-regression model (AR model) methods.The results show that the method of direct integration of auto-correlation function is the most convenient and most reasonable.
Once-Daily Subcutaneous Irisin Administration Mitigates Depression- and Anxiety-like Behavior in Young Mice

Major depression is one of the most common psychiatric disorders worldwide, usually associated with anxiety. The multi-etiological nature of depression has increased the search for new antidepressant molecules, including irisin, for which, in a previous study, we tested its effect in young mice when administered intraperitoneally in a long-term intermittent manner. Here, we evaluated the effect of subcutaneous short-term irisin administration (100 µg/Kg/day/5 days) in male and female mice subjected to behavioral paradigms: Tail Suspension Test (TST), Forced Swim Test (FST), Elevated Plus Maze (EPM), and Y Maze (YM). Moreover, a qRT-PCR assay was performed to analyze the impact of irisin treatment on Pgc-1α/FNDC5 expression in the brain. A significant reduction in immobility time in TST and FST was observed in irisin-treated mice. Furthermore, irisin treatment significantly increased the number of entries and time spent in open arms, demonstrating its anxiolytic effect. Memory-enhancing effects were not reported in YM. Interestingly, no gender differences were observed in all behavioral tests. Overall, these results suggest that short-term subcutaneous irisin administration can exert an antidepressant and anxiolytic role, probably due to the activation of the Pgc-1α/FNDC5 system in the brain. Further investigation could lead to the identification of irisin as a new agent for the treatment of psychiatric disorders.
Contrast Enhanced Magnetic Resonance Imaging of Amyloid-β Plaques in a Murine Alzheimer’s Disease Model

Background: Early detection of amyloid-β (Aβ) aggregates is a critical step to improve the treatment of Alzheimer’s disease (AD) because neuronal damage by the Aβ aggregates occurs before clinical symptoms are apparent. We have previously shown that luminescent conjugated oligothiophenes (LCOs), which are highly specific towards protein aggregates of Aβ, can be used to fluorescently label amyloid plaque in living rodents. Objective: We hypothesize that the LCO can be used to target gadolinium to the amyloid plaque and hence make the plaque detectable by T1-weighted magnetic resonance imaging (MRI). Methods: A novel LCO-gadolinium construct was synthesized to selectively bind to Aβ plaques and give contrast in conventional T1-weighted MR images after intravenous injection in Tg-APPSwe mice. Results: We found that mice with high plaque-burden could be identified using the LCO-Gd constructs by conventional MRI. Conclusion: Our study shows that MR imaging of amyloid plaques is challenging but feasible, and hence contrast-mediated MR imaging could be a valuable tool for early AD detection.
Maintaining Social Connections through Direct Link Placement in Wireless Networks

Mobile Social Network (MSN), built on interconnected mobile devices, enables the flexibility of information exchanges of individuals within a virtual community. MSN may be self-organized and/or infrastructure-less, and the communication links may frequently fluctuate. When link qualities degrade, it remains critical to maintain the connections of important social pairs when supporting all social pairs is impossible. To achieve this goal, we propose to proactively place some reliable links (e.g., satellite links or UAV links) into the underlying communication network, so as to improve the service quality of the important social pairs, referred to as the Maintaining Social Connections (MSC) problem. We formulate the MSC problem and prove it is NP-hard. As such, we first study a special case of MSC, which is submodular and solvable with high approximation-ratio. Since the general MSC problem is not submodular, we propose an efficient approximation algorithm. Specially, by carefully choosing two submodular functions to lower/upper bound the MSC problem, we are able to prove the high approximation ratio of the proposed algorithm using these selected submodular functions. We further develop evolutionary algorithms to iteratively adjust the link placement via different exploration strategies, which also yield guaranteed approximation-ratio. Extensive evaluations based on both synthetic and real-world social network traces demonstrate the effectiveness of our proposed algorithms.
Cartography & geovisual analytics in personal contexts: designing for the data creator

ABSTRACT With the rise of the Quantified-Self movement and widespread adoption of self tracking technologies, 'personal' has become a new dimension in the map and geovisualization design process. Despite a rich cartographic literature on how to map movement data, and an equally extensive geovisual analytics literature on how to make sense of complex visual representations of movement data at scale, opportunities exist to adapt and create new map and interaction design paradigms to meet the disparate needs of an ever-growing number of data creators. This paper presents a cross-disciplinary review of literature that reports on the design and evaluation of personal geovisualizations. Specifically, this review paper: contextualizes individual movement data types and provides a short synthesis of cartographic and geovisual analytics approaches that have been employed to explore such data in non-personal contexts; surveys a growing yet disconnected body of foundational work beyond the GIScience discipline; presents a detailed discussion on a collection of works that exemplifies the potential of applying a geographic perspective to personal visualization; and outlines key challenges and opportunities for advancing the state-of-the-art & science in the design of maps and geovisual analytics applications constructed from personal data and that benefit the data creator.
Identification and pathogenicity of Nigrospora sphaerica and Curvularia penniseti associated with leaf spot disease of cowpea (Vigna unguiculata (L.) Walp.) in Nigeria

Abstract Pathogenic fungi are one of the main causes of plant diseases due to their ability to colonise plants and rapidly spread disease. Cowpea is one of the most highly consumed grain legumes in Nigeria, but its yearly production is usually affected by fungi attacks. Disease presenting browning of the aerial parts of the cowpea plant was observed during the dry season between 2021 and 2022 in the country. This disease caused stunted growth in the affected plant stand. The fungi were isolated from the symptomatic aerial parts. The fungi were viewed under the microscope and identified by molecular and phylogenetic tools using the Internal Transcribed Spacer (ITS) gene region. The fungi isolated from the symptomatic aerial part of cowpea were identified as Nigrospora sphaerica and Culvularia penniseti. The pathogenicity of the isolated fungi was tested on detached leaves and stems and on intact cowpea seedlings. The test gave a positive result, which confirms the pathogenicity of the isolated fungi on the cowpea plant. This is the first report of N. sphaerica and C. penniseti infecting cowpea cultivated on farmland in Nigeria. Early detection of foreign fungi causing disease in the country’s cowpea crop will aid researchers in addressing the negative effects on crop yield.
Cortical visual impairment at birth can be improved rapidly by vision training in adulthood: A case study.

BACKGROUND Cortical visual impairment (CVI) is a severe loss of visual function caused by damage to the visual cortex or its afferents, usually as a consequence of hypoxic insults during birth. It is one of the leading causes of vision loss in children, and it is most often permanent. OBJECTIVE Several studies have demonstrated limited vision restoration in adults who trained on well-controlled psychophysical tasks, after acquiring CVI late in life. Other studies have shown improvements in children who underwent vision training. However, little is known about the prospects for the large number of patients who acquired CVI at birth but received no formal therapy as children. METHODS We, therefore, conducted a proof-of-principle study in one CVI patient long after the onset of cortical damage (age 18), to test the training speed, efficacy and generalizability of vision rehabilitation using protocols that had previously proven successful in adults. The patient trained at home and in the laboratory, on a psychophysical task that required discrimination of complex motion stimuli presented in the blind field. Visual function was assessed before and after training, using perimetric measures, as well as a battery of psychophysical tests. RESULTS The patient showed remarkably rapid improvements on the training task, with performance going from chance to 80% correct over the span of 11 sessions. With further training, improved vision was found for untrained stimuli and for perimetric measures of visual sensitivity. Some, but not all, of these performance gains were retained upon retesting after one year. CONCLUSIONS These results suggest that existing vision rehabilitation programs can be highly effective in adult patients who acquired CVI at a young age. Validation with a large sample size is critical, and future work should also focus on improving the usability and accessibility of these programs for younger patients.
A Study of R-R Interval Transition Matrix Features for Machine Learning Algorithms in AFib Detection

Atrial Fibrillation (AFib) is a heart condition that occurs when electrophysiological malformations within heart tissues cause the atria to lose coordination with the ventricles, resulting in “irregularly irregular” heartbeats. Because symptoms are subtle and unpredictable, AFib diagnosis is often difficult or delayed. One possible solution is to build a system which predicts AFib based on the variability of R-R intervals (the distances between two R-peaks). This research aims to incorporate the transition matrix as a novel measure of R-R variability, while combining three segmentation schemes and two feature importance measures to systematically analyze the significance of individual features. The MIT-BIH dataset was first divided into three segmentation schemes, consisting of 5-s, 10-s, and 25-s subsets. In total, 21 various features, including the transition matrix features, were extracted from these subsets and used for the training of 11 machine learning classifiers. Next, permutation importance and tree-based feature importance calculations determined the most predictive features for each model. In summary, with Leave-One-Person-Out Cross Validation, classifiers under the 25-s segmentation scheme produced the best accuracies; specifically, Gradient Boosting (96.08%), Light Gradient Boosting (96.11%), and Extreme Gradient Boosting (96.30%). Among eleven classifiers, the three gradient boosting models and Random Forest exhibited the highest overall performance across all segmentation schemes. Moreover, the permutation and tree-based importance results demonstrated that the transition matrix features were most significant with longer subset lengths.
Overexpression of Ultrabithorax Changes the Development of Silk Gland and the Expression of Fibroin Genes in Bombyx mori

Ultrabithorax (Ubx) is a member of the Hox gene group involved in cell fate decisions, cell proliferation and organ identity. Its function has been extensively researched in Drosophila melanogaster but little is known about it in Lepidoptera. To uncover the function of Ubx in the development of lepidopterans, we constructed the Ubx overexpression (UbxOE) strain based on the Nistari strain of Bombyx mori. The UbxOE strain showed a small body size, transparent intersegmental membrane and abnormal posterior silk gland (PSG). In the current study, we focused on the effect of Ubx overexpression on the posterior silk gland. As the major protein product of PSG, the mRNA expression of fibroin heavy chain (Fib-H) and fibroin light chain (Fib-L) was upregulated three times in UbxOE, but the protein expression of Fib-H and Fib-L was not significantly different. We speculated that the overexpression of Ubx downregulated the expression of Myc and further caused abnormal synthesis of the spliceosome and ribosome. Abnormalities of the spliceosome and ribosome affected the synthesis of protein in the PSG and changed its morphology.
DEVELOPMENT OF A BELLOWS ASSEMBLY WITH RF-SHIELD FOR KEKB

A bellows assembly with RF-shield has been designed and developed for the KEK B-factory (KEKB). The RFshield is a usual finger-type but has a special spring-finger to press contact-fingers onto the beam tube. The minimum contact force is studied experimentally utilizing microwaves. A contact force of 50 g/finger is found to be necessary. The higher order mode (HOM) power leaked from beam tube into inside of bellows is estimated using measured coupling coefficients of the RF-shield and a computer simulation. The leaked power is expected to be 6 18 W and about 16 W, respectively, for the slit length of 20 mm, which are in the allowable range.
The Invasive Plant Impatiens glandulifera Manipulates Microbial Associates of Competing Native Species

Impatiens glandulifera or Himalayan balsam is one of the most invasive weeds across Europe and can seriously reduce native plant diversity. It often forms continuous monocultures along river banks, but the mechanisms of this arrested succession are largely unknown. Here, we investigated the effect of arbuscular mycorrhizal (AM) fungi on balsam competitive ability with two native plant species, Plantago lanceolata and Holcus lanatus. We also studied how competition with Impatiens affects colonisation by foliar endophytes and mycorrhizas of two other co-occurring native species, Urtica dioica and Cirsium arvense. Mycorrhizal colonisation reduced balsam growth when the plants were grown singly, but appeared to have little effect when balsam experienced intra- or interspecific competition. Competition with balsam together with the addition of mycorrhizas had no effect on P. lanceolata biomass, suggesting that the fungi were beneficial to the latter, enabling it to compete effectively with balsam. However, this was not so with H. lanatus. Meanwhile, competition with Impatiens reduced endophyte numbers and mycorrhizal colonisation in U. dioica and C. arvense, leading to enhanced susceptibility of these plants to insect attack. Himalayan balsam is known to degrade soil fungal populations and can also reduce foliar beneficial fungi in neighbouring plants. This allows the plant to compete effectively with itself and other native species, thereby leading to the continuous monocultures.
Correlation between CD4 T-Cell Counts and Seroconversion among COVID-19 Vaccinated Patients with HIV: A Meta-Analysis

Objective: To evaluate the potential factors for predicting seroconversion due to the coronavirus disease 2019 (COVID-19) vaccine in people living with HIV (PLWH). Method: We searched the PubMed, Embase and Cochrane databases for eligible studies published from inception to 13th September 2022 on the predictors of serologic response to the COVID-19 vaccine among PLWH. This meta-analysis was registered with PROSPERO (CRD42022359603). Results: A total of 23 studies comprising 4428 PLWH were included in the meta-analysis. Pooled data demonstrated that seroconversion was about 4.6 times in patients with high CD4 T-cell counts (odds ratio (OR) = 4.64, 95% CI 2.63 to 8.19) compared with those with low CD4 T-cell counts. Seroconversion was about 17.5 times in patients receiving mRNA COVID-19 vaccines (OR = 17.48, 95% CI 6.16 to 49.55) compared with those receiving other types of COVID-19 vaccines. There were no differences in seroconversion among patients with different ages, gender, HIV viral load, comorbidities, days after complete vaccination, and mRNA type. Subgroup analyses further validated our findings about the predictive value of CD4 T-cell counts for seroconversion due to COVID-19 vaccines in PLWH (OR range, 2.30 to 9.59). Conclusions: The CD4 T-cell counts were associated with seroconversion in COVID-19 vaccinated PLWH. Precautions should be emphasized in these patients with low CD4 T-cell counts, even after a complete course of vaccination.
A METHOD FOR GROWTH CURVE COMPARISONS

Suppose one has a sample of pairs of age and length measurements from each of two or more populations offish. The mathematical forms of the growth curves associated with the populations are assumed to be specified but each form contains at least one unknownparameter. Presentedin this paper is a data analytic approach to the problem of deciding which, ifany, of the populations have essentially the same growth curve and which have different ones.
Combined Reinforcement Learning and CPG Algorithm to Generate Terrain-Adaptive Gait of Hexapod Robots

Terrain adaptation research can significantly improve the motion performance of hexapod robots. In this paper, we propose a method that combines reinforcement learning with a central pattern generator (CPG) to enhance the terrain adaptation of hexapod robots in terms of gait planning. The hexapod robot’s complex task presents a high-dimensional observation and action space, which makes it challenging to directly apply reinforcement learning to robot control. Therefore, we utilize the CPG algorithm to generate the rhythmic gait while compressing the action space dimension of the agent. Additionally, the proposed method requires less internal sensor information, which exhibits strong applicability. Finally, we conduct experiments and deploy the proposed framework in the simulation environment. The results show that the terrain adaptation policy trained in our framework enables the hexapod robot to move more smoothly and efficiently on rugged terrain compared to the traditional CPG method.
Full Adder Cells Evaluation for Subthreshold Operation on 32nm CMOS Technology

The migration towards deep submicron technologies has drastically changed the face of low-power design. Leakage currents turn the static power an important part in the total system power consumption. Subthreshold operation is proposed to reduce static and dynamic power consumption. This paper focus on performance and power consumption characteristics of different full adders (FA) cells designed to work at subthreshold voltages. The selected cells are the most widespread according to literature: CMOS, CPL, Hybrid, TFA, TGA and 14T. A 32nm predictive CMOS technology was used to describe the circuits. Electrical simulations were performed and the delay, dynamic and static power were measured. Results show that the TFA and TGA have better values for dynamic power consumption and delay, following for CMOS. It also show a trade-off among power and delay, when the circuits work on a subthreshold voltage operation. When the circuit will be a long time in standby mode, i.e., a long time of static consumption, the CMOS cell presents better results in subthreshold operation, and the TFA at nominal voltage.
Quantification de la robustesse de la super-résolution par minimisation L1

Cet article etudie la robustesse aux bruits de type outlier dans le cadre de la super-resolution. On utilise l'equivalence entre reconstruction parcimonieuse et robustesse aux outliers pointee par Candes et Tao. Elle permet de deduire des bornes sur le nombre d'images acquises en fonction de la quantite d'outliers qui garantissent une reconstruction parfaite par minimisation L1.
Feature Guided Training and Rotational Standardisation for the Morphological Classification of Radio Galaxies

State-of-the-art radio observatories produce large amounts of data which can be used to study the properties of radio galaxies. However, with this rapid increase in data volume, it has become unrealistic to manually process all of the incoming data, which in turn led to the development of automated approaches for data processing tasks, such as morphological classification. Deep learning plays a crucial role in this automation process and it has been shown that convolutional neural networks (CNNs) can deliver good performance in the morphological classification of radio galaxies. This paper investigates two adaptations to the application of these CNNs for radio galaxy classification. The first adaptation consists of using principal component analysis (PCA) during preprocessing to align the galaxies’ principal components with the axes of the coordinate system, which will normalize the orientation of the galaxies. This adaptation led to a significant improvement in the classification accuracy of the CNNs and decreased the average time required to train the models. The second adaptation consists of guiding the CNN to look for specific features within the samples in an attempt to utilize domain knowledge to improve the training process. It was found that this adaptation generally leads to a stabler training process and in certain instances reduced overfitting within the network, as well as the number of epochs required for training.
Adaptive filtering algorithm based on observable degree analysis of state parameters in carrier-aircraft transfer alignment

In view of the characteristics of limited maneuver and poor observable degree in carrier-aircraft INS's transfer alignment, an adaptive filtering algorithm based on observable degree analysis of state parameters is proposed. The state parameters' observable degree is quantized, and the adaptive factors are constructed based on grouping mechanism. Filtering gain attenuation technique is used to process the variables channels with poor observable degree in order to improve the accuracy and stability of filtering estimation. Simulation results show that the adaptive filtering algorithm has higher precision and faster convergence speed compared to classic methods, which could improve alignment precision to 2? from 3? and shorten filtering time to 8 min from 15 min.