Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of nitrogen status and yield of rice crop using unmanned aerial vehicle equipped with multispectral camera

Abstract. Nitrogen is one of the essential nutrients required for crop growth, and hence should be applied efficiently for attaining optimum yield. To fulfil nitrogen need, absorbed nitrogen in the plant is required to be estimated. Various methods are available to estimate crop nitrogen such as tissue analysis using the methods of Kjeldahl and Dumas, which are accurate, but time-consuming and destructive. Satellite imagery provides a more extensive field view. However, they are limited to their spatial and temporal resolution. Unmanned aerial vehicle (UAV) is emerging as a promising tool that can provide the status of crop nitrogen rapidly with high spatial and temporal resolution. The objective of the study was to evaluate UAV-based imageries to show nitrogen status and predict rice yield at different growth stages. The experiments were conducted using two rice cultivars, six nitrogen applications, two water management practices, and with three replications. Soil plant analysis development (SPAD) meter readings were collected at various growth stages. First, aerial imageries of experimental site were collected using an octocopter UAV equipped with a multispectral sensor that provides reflectance values in four different bands (red, green, red edge, and near-infrared) along with SPAD values for respective flight. Second, aerial images were processed in pix4D software, to identify the most appropriate vegetation index that shows nitrogen status variation in the field and to predict yield using different vegetation indices. Nine vegetation indices were considered: ratio vegetation index, normalized difference vegetation index, normalized green red difference index, red edge difference vegetation index, green ratio vegetation index, green normalized difference vegetation index (GNDVI), wide dynamic range vegetation index, transformed normalized difference vegetation index (TNDVI), and normalized difference red edge. After that, a linear regression model was developed between the representative index and SPAD values. Finally, linear regression models developed by using VI and SPAD values were evaluated and results revealed that GNDVI-based model simulates SPAD values with R2 of 0.49, 0.49, and 0.74 at panicle, milky, and booting stages, respectively. It is also found that TNDVI-based linear regression model predicts yield with R2 of 0.71 at milky stage.
Fabrication of P3HT/PCBM inverted solar cells with ZnO electron transport layer

Organic solar cells (OSCs) have gained much popularity among researchers as possible candidate for fulfilment of future energy requirements. Poly(3-hexylthiophene) (P3HT): Phenyl-C61-Butyric Acid Methyl Ester (PC61BM) based bulk heterojunction OSC is one of the most popular types. In this study, inverted P3HT:PCBM OSCs were fabricated on Stainless Steel (SS) substrate with and without ZnO layer in between the SS and active material. In fabrication of the device, ZnO and P3HT:PCBM layers were deposited using spin coating technique while poly-(4,3-ethylene dioxythiophene) (PEDOT): poly(styrenesulfonate) (PSS) layer was deposited using doctor blade method. Finally, gold (Au) front contact was sputter coated. In comparison with the best SS/P3HT:PCBM/PEDOT:PSS/Au device, we could fabricate a SS/ZnO/P3HT:PCBM/PEDOT:PSS/Au device with a 280% increase in power conversion efficiency (PCE). This PCE enhancement is due to the improvement of short wavelength response with the introduction of ZnO to the device.
Microbiological Study of Periodontal Disease in Populations with HIV: a Systematic Review and Meta-Analysis.

BACKGROUND No systematic review/meta-analysis has been conducted on the microbiological profile associated with the occurrence of periodontitis in patients with HIV. The aim of this study was to evaluate the prevalence of identified bacteria in HIV-infected patients with periodontal disease. METHODS Three English electronic databases (MEDLINE (via PubMed), SCOPUS, and Web of Science) were searched systematically from the beginning to February 13, 2021. The frequency of each identified bacteria in HIV-infected patients with periodontal disease was extracted. All meta-analysis methods were performed using STATA software. RESULTS Twenty-two articles met inclusion criteria and were enrolled into the systematic review. This review analyzed a total of 965 HIV-infected patients with periodontitis. The prevalence of periodontitis was higher in HIV-infected male patients (83% (CI 95%: 76 - 88%)) compared to females (28% (CI 95%: 17 - 39%)). In our study, the pooled prevalence of necrotizing ulcerative periodontitis and necrotizing ulcerative gingivitis in patients with HIV infection was 67% (CI 95%: 52 - 82%) and 60% (CI 95%: 45 - 74%), while a lower prevalence of linear gingivitis erythema was reported (11% (CI 95%: 5 - 18%)). More than 140 bacterial species were identified from HIV-infected patients with periodontal disease. High prevalence of Tannerella forsythia (51% (CI 95%: 5 - 96%)), Fusobacterium nucleatum (50% (CI 95%: 21 - 78%)), Prevotella intermedia (50% (CI 95%: 32 - 68%)), Peptostreptococcus micros (44% (CI 95%: 25 - 65%)), Campylobacter rectus (35% (CI 95%: 25 - 45%)), and Fusobacterium spp. (35% (CI 95%: 3 - 78%)) in HIV-infected patients with periodontal disease was found. CONCLUSIONS Our study demonstrated that the prevalence of the red and orange complex of bacteria in HIV patients with periodontal disease is relatively high.
A Data-Driven Methodology for the Simulation of Turbulent Flame Speed across Engine-Relevant Combustion Regimes

Turbulent combustion modelling in internal combustion engines (ICEs) is a challenging task. It is commonly synthetized by incorporating the interaction between chemical reactions and turbulent eddies into a unique term, namely turbulent flame speed sT. The task is very complex considering the variety of turbulent and chemical scales resulting from engine load/speed variations. In this scenario, advanced turbulent combustion models are asked to predict accurate burn rates under a wide range of turbulence–flame interaction regimes. The framework is further complicated by the difficulty in unambiguously evaluating in-cylinder turbulence and by the poor coherence of turbulent flame speed (sT) measurements in the literature. Finally, the simulated sT from combustion models is found to be rarely assessed in a rigorous manner. A methodology is presented to objectively measure the simulated sT by a generic combustion model over a range of engine-relevant combustion regimes, from Da = 0.5 to Da = 75 (i.e., from the thin reaction regime to wrinkled flamelets). A test case is proposed to assess steady-state burn rates under specified turbulence in a RANS modelling framework. The methodology is applied to a widely adopted combustion model (ECFM-3Z) and the comparison of the simulated sT with experimental datasets allows to identify modelling improvement areas. Dynamic functions are proposed based on turbulence intensity and Damköhler number. Finally, simulations using the improved flame speed are carried out and a satisfactory agreement of the simulation results with the experimental/theoretical correlations is found. This confirms the effectiveness and the general applicability of the methodology to any model. The use of grid/time resolution typical of ICE combustion simulations strengthens the relevance of the proposed dynamic functions. The presented analysis allows to improve the adherence of the simulated burn rate to that of literature turbulent flames, and it unfolds the innovative possibility to objectively test combustion models under any prescribed turbulence/flame interaction regime. The solid data-driven representation of turbulent combustion physics is expected to reduce the tuning effort in ICE combustion simulations, providing modelling robustness in a very critical area for virtual design of innovative combustion systems.
Biotransformations in Microflow Systems: Bridging the Gap between Academia and Industry

Recent remarkable progress in understanding and engineering enzymes and whole cells as highly selective and environment-friendly catalysts enabling novel routes for the production of pharmaceuticals, fine and platform chemicals, and biofuels has spurred the quest for fast biocatalyst screening and development of efficient processes with long-term biocatalyst use. Besides this, current efforts towards more sustainable production systems and bio-based products have triggered an intense research on chemo-enzymatic cascades and establishment of continuous end-to-end processing. Microreaction technology, which has in the last two decades changed the paradigm in the laboratory and production scale organic synthesis, is recently gaining attention also in the field of applied biocatalysis. Based on the trends highlighted within this article, microfluidic systems linked with appropriate monitoring and feedback control can greatly contribute to successful implementation of biocatalysis in industrial production. Microflow-based droplets facilitate ultrahigh-throughput biocatalyst engineering, screening at various operational conditions, and very fast collection of data on reaction kinetics using minute amounts of time and reagents. Harnessing the benefits of microflow devices results in faster and cheaper selection of substrate(s) and media, and development of suitable immobilization methods for continuous biocatalyst use. Furthermore, the use of highly efficient reactor designs integrated with downstream processing enabling also faster and more reliable scale-up can bridge the gap between the academic research and industrial use of biocatalysts.
Lipocalin-2 Mediated Polarization of Neutrophil in Cerebral Ischemia-Reperfusion Injury

Cerebral ischemia-reperfusion (I/R) injury is a difficult point in the treatment of ischemic stroke. Lipocalin-2 (LCN2) and neutrophils play an important role in I/R injury. We explored the effect of anti-LCN2 antibody (LCN2mAb) and to further clarify the relationship between LCN2mAb and neutrophil polarization (N1/N2 neutrophils) in I/R injury. A mouse middle cerebral artery occlusion (MCAO) model was used to induce transient cerebral ischemia. LCN2mAb was administered 1h before MCAO; Anti-Ly6G was administered for 3d before MCAO. The expression of LCN2 and Ly6G was Measured by western blot. Infarct size,behavior and the blood-brain barrier (BBB) damage were assessed. The polarized N2 neutrophils were measured by western blot and Flow Cytometry. Using HL-60 as a cell model explores the role of LCN2mAb in the polarity transition of neutrophils. The expression of LCN2 and Ly6G in the brain at different time points reached a peak after I/R 24h and then gradually decreased. As demonstrated previously, LCN2mAb-treated mice had neuroprotective effects in cerebral infarction volume, behavior, and blood-brain barrier damage. The expression of Ly6G was not significantly different, but the expression of N2 neutrophils was increased. The expression of anti-inflammatory factor CD206 was significantly increased, and pro-inflammatory factor TNF-α was significantly reduced. Compared with mice treated with Anti-Ly6G, Anti-Ly6G-LCN2mAb combined treatment of I/R, there was no further improvement in behavior and pathology. Based on HL-60 as a cell model, N1-HL-60 cells were pretreated with LCN2mAb, and N2-HL-60 cells were significantly increased. LCN2 may affect the prognosis of ischemic stroke by mediating neutrophils polarization.
Power dynamics in software platform ecosystems

In software platform ecosystems, the technological and structural peculiarities vest the platform owner with an extremely powerful position that puts any complementor at the mercy of the platform owner's actions. Paradoxically, it is the self‐determination and proactivity of the complementors that determine the ecosystem's success through their surprising outside innovations. This study addresses this power paradox by unpacking the power dynamics between platform owners and complementors. Based on an exploratory multiple‐case study of six platform partnerships, we find that power in platform ecosystems unfolds as a reciprocal process of three interlocking cycles, in which both the platform owner and the complementors take an active role. The modus operandi of power in platform ecosystems is a “central power cycle” in which the complementors repeatedly evaluate whether to accept or reject the platform owner's domination power. Thriving partnerships sustain this central power cycle over time, which requires that the platform owner and the complementors dynamically adapt their wielding of power to the changing needs of the partnership (partnership adaptation cycle) or the ecosystem (ecosystem redefinition cycle). For the platform owner, this entails the occasional use of manipulation to favour a particular partnership or redefining the ecosystem's framework and sporadically wielding coercion in favour of the broader ecosystem. For the complementor, this entails over‐subjectification to entice the platform owner to wield its power in favour of their partnership. Our findings have important implications for platform ecosystem and power theory, as well as managerial practice.
Liquid-Liquid Equilibrium of Deep Eutectic Solvent-Aromatic-Aliphatic Ternary Systems: Experimental Study with COSMO Model Predictions

Common solvents used for aromatic extraction from aliphatics typically degrade into toxic compounds, while green alternatives perform poorly compared to the state-of-the-art solvents. Deep eutectic solvents (DES) are a novel solvent type made of hydrogen bond donors (HBD) and hydrogen bond acceptors (HBA). DES have been applied in various applications, including advanced separations. In this study, DES were studied experimentally and using the Conductor-like Screening Model (COSMO) to separate benzene from cyclohexane as model compounds for an aromatic:aliphatic system. Both equilibrium and kinetic studies were performed to determine the liquid liquid equilibrium (LLE) and mass transfer rate for the DES-based separation. Selected HBAs including tetrabutylammonium bromide (N4444Br), tetrahexylammonium bromide (N6666Br), choline chloride (ChCl), and methyltriphenylphosphonium bromide (METPB) were paired with HBDs including ethylene glycol (EG) and glycerol (Gly). COSMO was used, with adjustments to reflect DES specific interactions, to predict the liquid-liquid equilibrium (LLE). COSMO results showed that ChCl and N6666Br-based DES extracted too little benzene or too much cyclohexane, respectively, to be considered for experimental evaluation. Overall, the COSMO model predictions for LLE of EG-based DES were very accurate, with root-mean-square deviations (RMSD) below 1% for both N4444Br:EG and METPB:EG. The glycerol systems were less accurately modeled, with RMSD’s of 4% for N4444Br:Gly and 6% for METPB:Gly. The lower accuracy of glycerol system predictions fmay be due to limitations in COSMO for handling glycerol’s influence on polarizability in the DES that is not seen in EG-based DES. Mass transfer kinetics were determined experimentally for DES and the results were fit to a first order kinetics model. METPB:Gly had the highest mass transfer coefficient at 0.180 min−1, followed by N4444Br:EG at 0.143 min−1. N4444Br:Gly and METPB:EG had the lowest mass transfer coefficients at 0.096 min−1 and 0.084 min−1, respectively. It was found that mass transfer rate was not directly related to maximum benzene solubility, as N4444Br:EG and METPB:Gly had the highest and lowest benzene removal, respectively, but had similar mass transfer coefficients.
Spin polarized vertical transport in stacked TMDCs hetero-junctions

Electronic and transport properties of stacked metal/semiconductor and metal/metal heterojunctions of transition metal dichalcogenides (TMDCs) are studied in this paper. MoS2, MoSe2, WS2, and WSe2 are considered for the semiconducting material, while NbS2, NbSe2, CoS2, and CoSe2 are considered for the metallic contact. Our density functional theory-based calculations reveal a strongly spin-polarized conductance for the studied systems. Furthermore, our results predict that the band gap of the semiconducting TMDC shifts above the Fermi energy of the connected system, leading to a diode-like semiconductor/metal contact. Moreover, the quantum conductance calculations for these junctions, carried out within the Landauer–Büttiker formalism for ballistic transport, shows that even the metal/metal heterojunctions have a diode-like character near the Fermi level. This interesting behaviour is discussed in terms of the misalignment between electronic states on each of the materials comprising the junction.
Digital mapping as feminist method: critical reflections

Digital maps have been taken up as a productive tool in both activism and academic research. However, there has been less consideration of their use as a research method in qualitative social sciences research. This paper aims to contribute towards scholarship on qualitative research by providing a critical reflection on the use of digital mapping as a research method in a feminist research project on street-based harassment in Australia. Drawing on practices of reflexivity, as well as comments made by participants across 46 qualitative interviews, I consider how digital mapping can be used to facilitate feminist research, arguing that it represents a generative instrument which lends itself to the development of in-depth insights from participants. Yet, mapping also delimits the epistemological possibilities of qualitative research, and I consider how this method simultaneously constrains what can be known about street harassment.
The effect of servant leadership on innovative work behavior and employee knowledge sharing in the Latvian ICT sector

PurposeThis study aims to peruse the underlying effect of servant leadership (SL) on innovative work behavior (IWB) and employee knowledge sharing behavior (KSB), directly and through the mediating effect of job autonomy (JA), by using autonomous psychological needs of self-determination theory and embracing Hofstede's framework in information and telecommunication technology (ICT) companies in Latvia.Design/methodology/approachA quantitative analysis of data from 271 employees and managers in Latvian ICT companies was used by applying structural equation modeling.FindingsThe result discloses that SL can promote IWB and KSB directly and through mediating effects of JA.Research limitations/implicationsThe research is constricted by geography and sample data representation from a specific sector. Hence, future studies can determine the gender effects, carry out more preventive measures to avoid common method bias between constructs, measure antecedents and the mediator before outcomes and examine JA as a moderator.Practical implicationsThe findings demonstrate that the Latvian ICT sector should recruit managers with SL potentials, train and equip managers with the required resources to implement SL practices properly and integrate JA across the organization to increase the manifestation of IWB and KSB.Originality/valueThis paper is the first to examine JA as the underlying process through which SL’s effects on IWB and KSB are explained in an individualistic country.
Approaching Optimal Nonlinear Dimensionality Reduction by a Spiking Neural Network

This work deals with the presentation of a spiking neural network as a means for efficiently solving the reduction of dimensionality of data in a nonlinear manner. The underneath neural model, which can be integrated as neuromorphic hardware, becomes suitable for intelligent processing in edge computing within Internet of Things systems. In this sense, to achieve a meaningful performance with a low complexity one-layer spiking neural network, the training phase uses the metaheuristic Artificial Bee Colony algorithm with an objective function from the principals in the machine learning science, namely, the modified Stochastic Neighbor Embedding algorithm. To demonstrate this fact, complex benchmark data were used and the results were compared with those generated by a reference network with continuous-sigmoid neurons. The goal of this work is to demonstrate via numerical experiments another method for training spiking neural networks, where the used optimizer comes from metaheuristics. Therefore, the key issue is defining the objective function, which can relate optimally the information at both sides of the spiking neural network. Certainly, machine learning techniques have advanced in defining efficient loss functions that can become suitable objective function candidates in the metaheuristic training phase. The practicality of these ideas is shown in this article. We use MSE values for evaluating the relative quality of the results and also co-ranking matrices.
Synthesis of Polyurethane Foams Loaded with TiO2 Nanoparticles and Their Modification for Enhanced Performance in Oil Spill Cleanup

Sponge-like superhydrophobic and superoleophilic materials are attracting significant attention for effective cleanup and recovery of spilled oils from water. We report on the synthesis of TiO2 nanoparticle/polyurethane (TPU) composite foam substrate and its surface modification with tetradecylamine (TDA)-amidated graphene oxide (GO-TDA), thereby forming superhydrophobic and superoleophilic TPU-GO-TDA sorbent for oil spill clean-ups. Spectroscopic analyses confirmed that TDA was successfully grafted onto GO and the reduced GO by TDA was successfully grafted onto the TPU foam. Sorption experiments with engine oil, crude oil, silicone oil, and chloroform demonstrated that the TPU-GO-TDA foam was an effective sorbent with sorption capacity of 20.2–62.4 times its own weight. The absorbed oils in the sorbent could be recovered simply by squeezing the oil-laden foam. The much higher selectivity to oils and better reusability of the TPU-GO-TDA foam than commercial sorbents make the TPU-GO-TDA foam promising for .
Precisely Equal Group Size and Allocation Bias in Nursing Randomized Controlled Trials: A Scientiometric Study

The manipulation of participant allocation in randomized controlled trials to achieve equal groups sizes may introduce allocation bias potentially leading to larger treatment effect estimates. This study aimed to estimate the proportion of nursing trials that have precisely equal group sizes and examine if there was an association with trial outcome. Data were extracted from a sample of 148 randomized controlled trials published in nursing science journals in 2017. One hundred trials (68%) had precisely equal group sizes. Respectively, a positive outcome was reported in 70% and 58% of trials with equal/unequal groups. Trials from Asia were more likely to have equal group sizes than those from the rest of the world. Most trials reported a sample size calculation (n=105, 71%). In a third of trials (n=36, 34%), the number of participants recruited precisely matched the requirement of the sample size calculation; this was significantly more common in studies with equal group sizes. The high number of nursing trials with equal groups may suggest nurses con-ducting clinical trials are manipulating participant allocation to ensure equal group size increasing the risk of bias.