Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Speed Flexible Near-Infrared Organic Photodiode Beyond Silicon for Optical Communication

28 Optical communication is a particularly compelling technology to tackle the speed 29 and capacity bottlenecks in data communication in modern society. Currently, silicon 30 photodetector plays a dominant role in the high-speed optical communication across 31 the visible-near infrared spectrum. However, its intrinsic rigid structure, high working 32 bias, and low responsivity largely limit its applications in the next-generation flexible 33 optoelectronic devices. Herein, we report a solution-processed flexible organic 34 photodetector (OPD) based on a narrow-bandgap nonfullerene acceptors, which 35 exhibits a remarkable response time of 91 ns and peaking responsivity of 0.53 A W -1 (λ 36 = 830 nm) at zero bias, exceeding the values obtained in the state-of-the-art high-speed 37 commercial Si photodetector (326 ns; 0.26 A W -1 ). This exceptional performance 38 benefits from the low parasitic capacitance and high charge mobility due to low trap 39 states and energetic disorder in the organic photoactive film. More significantly, the 40 flexible OPD exhibits negligible performance attenuation (<1%) after bending for 500 41 cycles, and maintain 96% of its initial performance even after 550 h of indoor exposure. 42 Furthermore, it also demonstrates a high data transmission rate of 80 MHz with a with 43 a bit-error-rate of 3.5×10 -4 , offering great potential in next-generation high-speed 44 flexible optical communication system. 45
Habitat suitability modelling of five Mediterranean gorgonian species

Engineer species, such as gorgonians, provide several ecosystem services and play a significant role in the maintenance of biodiversity, which is why it is important to identify optimal strategies for their conservation. We aimed to test an ecological niche modelling approach in the marine shallow environment and identify the ecological niche of various species of gorgonians in order to lay the scientific foundation for future conservation actions. We analyzed a unique dataset of spatialized inventories on a regular grid (< 800 m) along 450 km of coastline of five gorgonian species commonly found in the Mediterranean shallow habitats (10-50 m deep). We replicated data collection in 2013 and 2020. Fourteen environmental predictors derived from the most advanced geomorphological and hydrological data were tested to assess the ecological niche of the five species using maximum entropy (MaxENT). We tested the sensitivity of the model fit to sampling bias by reducing the number of occurrences and their geographic extent. Our results showed that there was a difference in the spatial distribution of the five gorgonian species in the Gulf of Lion: Eunicella singularis and Leptogorgia sarmentosa were widely distributed while the occurrence of Paramuricea clavata, Corallium rubrum and Eunicella cavolini was limited. The model confirmed that depth, rugosity, slope, sea surface temperature, current and turbidity can be significant drivers for gorgonians distribution, but in different associations, enabling one to differentiate the niche of four of the five species. Moreover, the model did not always identify the species specific drivers suggested in previous studies. Despite the model identifying a similar niche for E. singularis and E. cavolini (based on the environmental predictors tested), the two species, in fact, displayed very different spatial distributions in the area. For all the species, except E. cavolini, the predicted suitable habitat distribution from our model matched the observed spatial distribution. The reduction of the number of presence observations did not alter the quality of the ecological niche models as long as the observation points were spread over the entire variability range of predictors. The latter can be achieved by only including presence observations from highly protected zones of the marine protected areas of the region. Our results provide a greater understanding of the factors shaping the distribution of five gorgonian species commonly found in Mediterranean shallow areas. This is an essential step in the development of spatial planning for marine biodiversity conservation aimed at these key engineering species and their resilience to climate change.
M5 model tree for land cover classification

Tree based regression models like a M5 algorithm represent a promising development in machine learning research. A recent study suggests that a M5 model tree algorithm can be used for classification problems after some modification. This letter explores the usefulness of a M5 model tree for classification problems using multispectral (Landsat‐7 Enhanced Thematic Mapper Plus (ETM+)) for a test area in eastern England. Classification accuracy achieved by using a M5 model tree is compared with a univariate decision tree with and without using boosting. Results show that the M5 model tree achieves a significantly higher level of classification accuracy than a decision tree and works equally well to a boosted decision tree. Further, a model tree based classification algorithm works well with small as well as noisy datasets.
Improving fNIRS-BCI accuracy using GAN-based data augmentation

Functional near-infrared spectroscopy (fNIRS) is expected to be applied to the brain-computer interface (BCI). Since a lengthy fNIRS measurement is uncomfortable for the participant, it is difficult to obtain a sufficient amount of data to train classification models; hence, the fNIRS-BCI accuracy decreases. In this study, to improve the fNIRS-BCI accuracy, we examined an fNIRS data-augmentation method using generative adversarial networks (GANs). Using fNIRS simulation data, we evaluated whether the proposed data-augmentation method could generate artificial fNIRS data. Comparing the cerebral blood flow (CBF) and the data generated by the GANs based on the CBF, it appeared that the proposed method could generate artificial fNIRS data. Although the data generated by the proposed method generally reproduced the fNIRS-data time series, they contained an extra noise component. We also evaluated a deep neural network (DNN) trained using fNIRS simulation data; its classification accuracy doubled after the data augmentation. This result suggests that the artificial fNIRS data generated by the proposed data-augmentation method is useful for improving BCI performance.
Enegy Resilience Assessment Model Based on Risk Matrix Analysis and Monte Carlo Simulatation for Large-Scale Solar Photovoltoic Plant

Energy resilience has emerged as one of the most critical components in ensuring the world's energy supply stability and security, particularly a large-scale solar (LSS) photovoltaic plant. However, there is a missing standard and proper approach for comprehensively assessing the resilience of any energy system for large-scale solar. The main objective of this paper is to develop an energy resilience assessment model and to analyze the assessment of the energy resilience for large-scale solar using risk matrix analysis and the Monte Carlo simulation method. At first, the types of risk to be considered in assessing the impact of a LSS photovoltaic (PV) plant in Malaysia were identified through an interview session with the operator of the plant, and then the risk probability and risk impact for each risk type was assessed by providing a score. Next, the energy resilience index was analysed by calculating the risk rating to indicate the resilience of the energy system is either sustainable, moderate, severe or critical. Then, the base model of the energy resilience was expanded to perform a Monte Carlo simulation for considering uncertainty in the input of the risk matrix. After comparing those two models, it is observed that the results for the overall risk rating for the solar farm are in the “Moderate” level when risk matrix analysis was performed deterministically, meanwhile “Severe” level when Monte Carlo simulation was performed. The proposed risk matrix analysis in this paper help the developer and operator of the LSS photovoltaic plant in making decisions and counter measures to improve the energy resilience of the solar PV plant.
Predictive value of first-trimester GPR120 levels in gestational diabetes mellitus

Background Early diagnosis of gestational diabetes mellitus (GDM) reduces the risk of unfavorable perinatal and maternal consequences. Currently, there are no recognized biomarkers or clinical prediction models for use in clinical practice to diagnosing GDM during early pregnancy. The purpose of this research is to detect the serum G-protein coupled receptor 120 (GPR120) levels during early pregnancy and construct a model for predicting GDM. Methods This prospective cohort study was implemented at the Women’s Hospital of Jiangnan University between November 2019 and November 2022. All clinical indicators were assessed at the Hospital Laboratory. GPR120 expression was measured in white blood cells through quantitative PCR. Thereafter, the least absolute shrinkage and selection operator (LASSO) regression analysis technique was employed for optimizing the selection of the variables, while the multivariate logistic regression technique was implemented for constructing the nomogram model to anticipate the risk of GDM. The calibration curve analysis, area under the receiver operating characteristic curve (AUC) analysis, and the decision curve analysis (DCA) were conducted for assessing the performance of the constructed nomogram. Results Herein, we included a total of 250 pregnant women (125 with GDM). The results showed that the GDM group showed significantly higher GPR120 expression levels in their first trimester compared to the normal pregnancy group (p < 0.05). LASSO and multivariate regression analyses were carried out to construct a GDM nomogram during the first trimester. The indicators used in the nomogram included fasting plasma glucose, total cholesterol, lipoproteins, and GPR120 levels. The nomogram exhibited good performance in the training (AUC 0.996, 95% confidence interval [CI] = 0.989-0.999) and validation sets (AUC=0.992) for predicting GDM. The Akaike Information Criterion of the nomogram was 37.961. The nomogram showed a cutoff value of 0.714 (sensitivity = 0.989; specificity = 0.977). The nomogram displayed good calibration and discrimination, while the DCA was conducted for validating the clinical applicability of the nomogram. Conclusions The patients in the GDM group showed a high GPR120 expression level during the first trimester. Therefore, GPR120 expression could be used as an effective biomarker for predicting the onset of GDM. The nomogram incorporating GPR120 levels in early pregnancy showed good predictive ability for the onset of GDM.
Meridional distributions of NOx, NOy, and other species in the lower stratosphere and upper troposphere during AASE II

The meridional distribution of NOx in the lower stratosphere and upper troposphere is inferred from 10 flights of the NASA DC-8 in the northern winter of 1992, along with like distributions of NOy, NOx/NOy, CO, and C2Cl4. In the lowest few km of the stratosphere there is little vertical gradient in NOx over the range of latitudes measured (40°–90°N). There is a substantial latitudinal gradient, with 50 pptv above the pole and 120 pptv near 40°N. In the uppermost few km of the troposphere, background values range from 30 pptv over the pole to 90 pptv near 40°N. On two occasions higher values, up to 140 pptv in the mean, were seen 2–3 km below the tropopause in association with frontal systems. The meridional distributions of CO and C2Cl4 show the same feature, suggesting that the source of the elevated NOx is near the earth's surface.
Towards an E�cient, Privacy-aware Federated Learning Scheme

With the rapid evolution of Internet-of-Things (IoT) devices and the emergence of Autonomous Vehicles (AVs), machine learning processes pose a growing privacy issue. Federated learning (FL) and current cryptography can mitigate this problem; however, these solutions might not be eﬃcient enough during the decades-long lifespans of such gadgets. In this paper, a generalization of FL schemes, incorporating sharing a part of raw data, is presented with a proof-of-concept experiment. Besides anonymization, the exchanged data portion can also directly support real-time decision-making. In contrast with cryptographical approaches, the proposed FL scheme can guarantee a certain level of privacy during the whole lifetime of IoT devices or AVs.
VERSION Gonadotropin Regulation of Glutamate Cysteine Ligase Catalytic and Modifier Subunit Expression in the Rat Ovary is Subunit and Follicle Stage-Specific Running Head : Gonadotropin Regulation of Ovarian Glutathione

We have observed that levels of the antioxidant glutathione (GSH) and protein levels of the catalytic and modifier subunits of the rate-limiting enzyme in GSH synthesis, GCLc and GCLm, increase in immature rat ovaries after treatment with gonadotropin. The goals of the present studies were to delineate the time course and intraovarian localization of changes in GSH and GCL after PMSG and after an ovulatory gonadotropin stimulus. 24h after PMSG there was a shift from predominantly granulosa cell expression of gclm mRNA, and to a lesser extent gclc, to predominantly theca cell expression. GCLc immunostaining increased in granulosa and theca cells and in interstitial cells. Next, prepubertal female rats were primed with PMSG, followed 48h later by 10 IU hCG. GCLm protein and mRNA levels increased dramatically from 0h to 4h after hCG, then declined rapidly. There was minimal change in GCLc. The increase in gclm mRNA expression was localized mainly to granulosa and theca cells of preovulatory follicles. To verify that GCL responds similarly to an endogenous preovulatory gonadotropin surge, we quantified ovarian GCL mRNA levels during the periovulatory period in adult rats. Gclm mRNA levels increased after the gonadotropin surge on proestrus and then declined rapidly. Finally, we assessed the effects of gonadotropin on ovarian GCL enzymatic activity. GCL enzymatic activity increased significantly at 48h after PMSG injection and did not increase further after hCG. These results demonstrate that gonadotropins regulate follicular GCL expression in a follicle stage-dependent manner and in a GCL subunit-dependent manner.
ANALYSIS OF COMMUNITY COMPLIANCE WITH THE COVID-19 HEALTH PROTOCOLS: A QUANTITATIVE

This study identifies the factors used in factor analysis that influence the compliance of the people of Bali Province to the COVID-19 health protocol. Based on the factor analysis results, the elimination of four variables from the initial 20 variables in the study because the low communality value was below 0.5. Thus, reducing the remaining 16 variables to six factors with a total variance of 74.8%. Six factors influenced Community compliance in Bali Province, namely someone who knows about the importance of health protocols (19,1%), trust in protocols (15,9%), someone infected with COVID-19 (12,2%), a sense of concern for themselves and their families (10,1%), government supervision and sanctions (9,3%), and the affordable price of masks (8,2%). Observing this study, the first factor that played a role in increasing adherence was knowledge about the importance of health care. There was a sense of trust in health protocols, and a less influential factor was the affordability of masks
Directional ocean wave spectrum estimation based on the joint measurement from synthetic aperture radar and wave spectrometer

Synthetic aperture radar (SAR) can measure directional wave spectrum based on the closed nonlinear SAR-ocean mapping mechanism. The according wave spectrum retrieval algorithm has been developing for decades, but some limitations remain, like high wave number cut off in azimuthal direction and the need for the first guess spectrum. Wave spectrometer is a kind of new satellite-based real aperture radar (RAR) operating at low incidence, which has a narrow beam and scans complete 360° by antenna rotation. It derives wave spectrum by the simple linear relation between the wave spectrum and the modulation spectrum from the wave spectrometer. The linear coefficient can be estimated by the nadir beam or external wind speed information. This paper proposes a method on the wave spectrum estimation based on the joint measurement from synchronous SAR and wave spectrometer. Firstly, the modulation spectrum is derived from the signal spectrum of the wave spectrometer, from which the relative wave spectrum can be constructed. Then the relative wave spectrum is seen as the first guess spectrum for the wave spectrum retrieval of SAR image. Because the relative wave spectrum has the same pattern with the real wave spectrum but has different absolute energy, we can retrieve the directional wave spectrum by the iteration way based on the relative wave spectrum from the SAR image. This paper makes use of simulation technology to validate the joint measurement. The simulation compares the input spectrum and the retrieved one in terms of peak direction, peak wavelength and significant wave height, which has a deviation of 6°, 4m and 0.3m, respectively. Simulation results show the joint measurement has the feasibility for the retrieval of directional ocean wave spectrum.
Net CO2 and H2O fluxes of terrestrial ecosystems

Using 139 flux studies, we addressed the variability of net ecosystem surface assimilation (Asmax), net ecosystem surface respiration (Rsmax), as well as net surface evapotranspiration (Esmax) among and within vegetation types. While forests and C3 crops, particularly in the northern hemisphere, have been preferentially investigated, information on tropical forests, C4 grasslands or wetlands is rather limited. Almost no data are available for disturbed sites. Despite large variations within a vegetation type, enclosure studies tended to give highest Asmax rates compared to micrometeorological techniques. Excluding enclosure studies, we tested the effect of stand age and leaf area index (LAI) on net ecosystem gas exchange. For grasslands, Asmax increased by 7 μmol m−2 s−1 per unit LAI, for C4 crops by 11 μmol m−2 s−1, and for coniferous forests by 0.9 μmol m−2 s−1 per unit LAI. In contrast, Asmax of broad‐leaved forests and C3 crops as well as Rsmax stayed constant over a wide range of LAI. Asmax and Rsmax of forests were lowest in young stands (< 20 years old) and highest in stands of age 30–80 years. Asmax of old forests (> 160 years) was within the same range as those of 30‐ to 80‐year‐old forests, and always higher than those of regenerating stands. Rsmax seemed to decrease with age. Asmax increased linearly with ecosystem surface conductance for all vegetation types (r2 = 0.65). Asmax of forests and grasslands was closely related to Esmax (r2 = 0.87), with a slope of 0.082 μmol CO2 m−2 s−1/mmol H2O m−2 s−1. The results clearly illustrated where gaps in our knowledge exist and how ecosystem properties affect the capacity of net ecosystem gas exchange.
Fault estimation based fault tolerant control design for Linear parameter varying system

A fault estimation (FE) and fault-tolerant control (FTC) conception is suggested for LPV systems with actuator defect and disturbance. To estimate simultaneously the actuator fault and system states, a Sliding Mode Observer (SMO)is conceived. Using the estimates, an FTC controller is developed to guarantee robust stability of the closed-loop system. Via $H_{\infty}$ optimization, the parameters of SMO and controller are obtained with linear matrix inequality (LMI) formulation. The FTC performance is illustrated using an example with actuator faults as well as disturbance.
Multi-Robot Task Planning to Secure Human Group Progress

Recent years have seen an increasing number of deployment of fleets of autonomous vehicles. As the problem scales up, in terms of autonomous vehicles number and complexity of their objectives, there is a growing need for decision-support tooling to help the operators in controlling the fleet. In this paper, we present an automated planning system developed to assist the operators in the CoHoMa II challenge, where a fleet of robots, remotely controlled by a handful of operators, must explore and progress through a potential hostile area. In this context, we use planning to provide the operators with suggestions about the actions to consider and their allocation to the robots. This paper especially focus on the modelling of the problem as a hierarchical planning problem for which we use a state-of-the-art automated solver.
Strain-selective in vitro and in silico structure activity relationship (SAR) of N-acyl β-lactam broad spectrum antibiotics

Different bacterial species have developed resistance to antibiotics over the years. Penicillin analogs have been developed to counter the bacteria’s acquired resistance to standard antibiotics; in this study, we investigate the antibacterial efficacy of penicillin G and its analogs amoxicillin, carbenicillin, piperacillin, cloxacillin, and ampicillin, against four species of bacteria. Penicillin antibiotics mimic the D-Alanyl-D-Alanine active site region of penicillin-binding proteins (PBP). PBPs are enzymes used in the synthesis of bacterial cell walls. Through a Kirby-Bauer assay, we found that the inhibition performance of these penicillin-type antibiotics varies greatly among different bacterial species. Moreover, to investigate the structural basis for the in vivo Structure Activity Relationship observed, we also conducted in silico virtual creening via homology modeling, molecular docking, and density functional theory (DFT) calculations. We concluded that all six penicillin-type antibiotics inhibit Staphylococcus epidermidis, Escherichia coli, and Neisseria sicca with varying degrees of efficacy but exhibited no inhibition against Bacillus cereus. Our computational results suggest that the distance between the β-lactam ring and the active serine is more influential than the thermodynamic binding affinity for these antibiotics to interact with the PBPs. Penicillin G had the greatest broad-spectrum antibacterial activity with a high radius of inhibition against S. epidermidis, E. coli, and N. sicca.
Contextualising the factors influencing human resource development in municipalities

Background: Human resource development implementation faces several challenges that prevent organisational optimisation as defined in the Local Government Sector Education and Training Authority Skills Development Handbook and related regulatory framework. The assumption is that skills development facilitators (SDFs) do not have the functional competencies to effectively implement human resource development projects and programmes.Aim: The aim of the research was to present and reflect on the results of a survey conducted in selected South African municipalities to ascertain the factors influencing human resource development implementation and to assess the competence levels of SDFs to determine whether there are lessons to be learned. It also aimed to recommend how the identified challenges faced by municipalities can be resolved.Methods: The mixed-methods research approach involved the analysis of primary data obtained from a qualitative perspective, including semi-structured interviews and questionnaires. Questionnaires were completed by 239 line managers, 66 human resource development staff , 357 other municipal employees and 34 SDFs.Results: The study concluded that SDFs are experienced and qualified but that human resource development is poorly managed in municipalities on six levels (ethical values, policy, practice, organisation, internal democracy and stakeholders), which affects the overall human resource development performance of municipalities.Conclusion: Municipalities must follow an integrated management approach to manage HRD that includes a collaborative approach involving line managers, HRD professionals and employees.Contribution: Specific implementation recommendations are made to ensure that municipalities are equipped to manage HRD and thus ensure effective service delivery in municipalities.