Miracle-dz/UniFAR-FTUs huggingface dataset:
The Optical Response of a Mediterranean Shrubland to Climate Change: Hyperspectral Reflectance Measurements during Spring

Remote sensing techniques in terms of monitoring plants’ responses to environmental constraints have gained much attention during recent decades. Among these constraints, climate change appears to be one of the major challenges in the Mediterranean region. In this study, the main goal was to determine how field spectrometry could improve remote sensing study of a Mediterranean shrubland submitted to climate aridification. We provided the spectral signature of three common plants of the Mediterranean garrigue: Cistus albidus, Quercus coccifera, and Rosmarinus officinalis. The pattern of these spectra changed depending on the presence of a neighboring plant species and water availability. Indeed, the normalized water absorption reflectance (R975/R900) tended to decrease for each species in trispecific associations (11–26%). This clearly indicates that multispecific plant communities will better resist climate aridification compared to monospecific stands. While Q. coccifera seemed to be more sensible to competition for water resources, C. albidus exhibited a facilitation effect on R. officinalis in trispecific assemblage. Among the 17 vegetation indices tested, we found that the pigment pheophytinization index (NPQI) was a relevant parameter to characterize plant–plant coexistence. This work also showed that some vegetation indices known as indicators of water and pigment contents could also discriminate plant associations, namely RGR (Red Green Ratio), WI (Water Index), Red Edge Model, NDWI1240 (Normalized Difference Water Index), and PRI (Photochemical Reflectance Index). The latter was shown to be linearly and negatively correlated to the ratio of R975/R900, an indicator of water status.
Effect of γ-Heptalactone on the Morphology and Production of Monascus Pigments and Monacolin K in Monascus purpureus

Monascus is used widely in Asian countries and produces various biologically active metabolites, such as Monascus pigments (MPs) and monacolin K (MK). In this study, the effect of γ-heptalactone on secondary metabolites and mycelial growth during Monascus purpureus M1 fermentation was investigated. After the addition of 50 μM γ-heptalactone, the yields of MPs (yellow, orange, and red) reached maxima, increased by 115.70, 141.52, and 100.88%, respectively. The 25 μM γ-heptalactone groups showed the highest yield of MK was increased by 62.38% compared with that of the control. Gene expression analysis showed that the relative expression levels of MPs synthesis genes (MpPKS5, MpFasA2, mppB, mppC, mppD, mppG, mpp7, and mppR1/R2) were significantly upregulated after γ-heptalactone treatment. The relative expression levels of MK synthesis genes (mokA, mokC, mokE, mokH, and mokI) were significantly affected. The mycelium samples treated with γ-heptalactone exhibited more folds and swelling than that in the samples of the control group. This study confirmed that the addition of γ-heptalactone has the potential to induce yields of MPs and MK, and promote the expression of biosynthesis genes, which may be related to the transformation of mycelial morphology in M. purpureus.
Services Acquisition in the Department of Defense: Analysis of Operational and Performance Data to Identify Drivers of Success

Abstract : The Department of Defense (DoD) acquires billions of dollars of supplies and services every year. In fiscal year (FY) 2013, the DoD obligated over $258 billion for military-unique weapon systems as well as commercial supplies and services. An integral part of the DoD s contract management process is the source selection phase when offerors proposals are evaluated and the contract award decision is made. A critical aspect of the source selection phase is the evaluation of contractor past performance information as part of the overall proposal evaluation process. The DoD uses the Past Performance Information Retrieval System (PPIRS), which consists of contractor report cards extracted from the Contractor Performance Assessment Reporting System (CPARS). In this research we examine the value of CPARS report card narratives for service contracts as they relate to their associated objective scores. Our primary focus in this research is to examine if the CPARS report card written narrative section provides value to the contractor performance evaluation process. Our data analysis includes sentiment and statistical analysis, as well as interviews with government agency contracting professionals. Using CPARS data, narrative analyses, and interviews, we answer the following research questions: (1) To what degree are government contracting professionals submitting to CPARS contractor performance narratives in accordance with the guidelines provided in the CPARS user s manual? (2) What is the added value of the contractor performance narratives beyond the value of the objective scores for performance? (3) What is the statistical relationship between the sentiment contained in the narratives and the objective scores for contractor evaluations? The research revealed that there are a variety of opportunities to improve the contracting process specifically related to the narrative portion of past performance assessment reports.
Techniques for Performance Improvement of Cognitive Radio

Cognitive Radio is a promising technology to resolve spectrum scarcity issue by exploiting RF spectrum in opportunistic fashion. Spectrum Sensing is a key enabling step towards successful implementation of this emerging technology. Sensing refers to the detection of unused spectrum spaces also known as "white spaces". This dissertation proposes, investigates and analyses several algorithms for spectrum sensing cognitive radio applications. Receiver Operating Characteristic (ROC) is compared among different proposed algorithms. It is one of the most important measures to classify detectors. In the first part of the dissertation, ROC is analysed for multiple-antenna assisted spectrum sensing radios under shadowing and is derived using linear test statistic. Furthermore, a highly useful cluster-driven architecture for spectrum sensing is also proposed and analysed that improves detection probability by exploiting cooperation among cognitive radios using hard decision combining strategy. Hard decision combination strategy computes the detection probability by combining one bit decisions among various cooperative cognitive radios. Detection probability is achieved 80% at PFA rate of 10% for a single user, whereas using hard decision combing approach the same detection probability is achieved at 1% PFA. For Binary Symmetric Channel with 10-3 error probability, PD results 32% (at PFA 10-2) for a single user whereas 65% for a five user case. In the second part of the dissertation, a novel channel model i.e. double exponential correlation is incorporated for spectrum sensing algorithms under suburban environments. Asymptotic probability of detection is derived, analysed and compared with classical exponential correlation model (also known as Gudmundson's model). Using proposed model missed detection probability reaches Zero for less than ten sensors whereas Gudmundson? s model results a constant 0.7 missed detection probability even when the sensing users are increased to hundred. Thus, results verify that the proposed model performs significantly better than the classical one. In the third part of the dissertation, a cooperative sensing strategy is proposed for mobility-driven cognitive radio. It is also verified through simulation results that the proposed decision-fusion based architecture performs significantly better than the independent sensing radios. Using collaboration under urban environments, missed detection results in 30% in comparison to 62% for a single user under false alarm probability of 10%.
Comparison and efficacy of two different sheep pox vaccines prepared from the Bakırköy strain against lumpy skin disease in cattle

Purpose Lumpy skin disease (LSD) is a highly contagious and economically important viral infection of cattle, which leads to financial losses in the livestock industry of affected countries. Vaccination is the most effective control measure to prevent the disease. Heterologous sheep pox (SP) vaccine was used against LSD in Turkey. In this research, it was aimed to adapt SP Bakırköy vaccine strain attenuated in lamb kidney cells to Madin-Darby bovine kidney (MDBK) cells to provide better protection than commercial SP vaccine in cattle. Materials and Methods To evaluate safety and efficacy of vaccines, while animals were immunized with 10 doses (104.75 50% tissue culture infectious dose [TCID50]) and 5 doses of SP vaccine (104 TCID50) produced in MDBK cells, others were immunized with commercial Penpox-M vaccine (103.9 TCID50). Two cattle were kept as unvaccinated. At day 31 post-vaccination, all animals were challenged with the virulent LSD virus. Blood and swab samples were taken on certain days post-inoculation. Logarithmic differences challenge virus titers between vaccinated and unvaccinated animals were calculated. Results The clinical sign was not observed in animals immunized with 10 doses of SP vaccine. The differences between the animals immunized with SP vaccine and control group was less than log 2.5 and the viremia occurred in immunized animals. The difference in titer was higher than log 2.5 in animals immunized with the Penpox-M, and viremia did not occur. Conclusion SP vaccine strain propagated in MDBK cells and can be used for immunization to prevent LSD infections. However, SP vaccine strain propagated in MDBK showed poor protection as compared to Penpox-M.
CGT: Consistency Guided Training in Semi-Supervised Learning

We propose a framework, CGT, for semi-supervised learning (SSL) that involves a unification of multiple image-based augmentation techniques. More specifically, we utilize Mixup and CutMix in addition to introducing one-sided stochastically augmented versions of those operators. Moreover, we introduce a generalization of the Mixup operator that regularizes a larger region of the input space. The objective of CGT is expressed as a linear combination of multiple constituents, each corresponding to the contribution of a different augmentation technique. CGT achieves state-of-the-art performance on the SVHN, CIFAR-10, and CIFAR-100 benchmark datasets and demonstrates that it is beneficial to heavily augment unlabeled training
Novel Evidence-Based Combination of Plant Extracts with Multitarget Mechanisms of Action for the Elimination of Hot Flashes during Menopause

Hot flashes are considered the most bothersome complaint during menopause. Although hormone therapy is an effective option to relieve hot flashes, it has been associated with significant side effects. The aim of our study is to suggest a novel combination of different plant extracts with distinct mechanisms of action against hot flashes. We selected the rhizome of Glycyrrhiza glabra L. (Fabaceae), the rhizome of Actaea racemosa L. (Ranunculaceae), the aerial parts of Hypericum perforatum L. (Hypericaceae) to produce extracts rich in bioactive phytochemicals and the seed oil of Oenothera biennis L. (Onagraceae). We investigated their estrogenic and antioxidant potential and their inhibitory effect against prostaglandin D2 receptor 1 (DP1) as a novel mechanistic pathway for vasodilation in hot flashes, alone or in combination. The phytochemical footprint of the extracts was analyzed using HPLC-PDA and UPLC-HRMS. We observed that the tested extracts possess different mechanisms of action. A. racemosa exerts a beneficial activation of the estrogen receptor, H. perforatum possesses the highest antioxidant capacity and the seed oil of O. biennis inhibits the DP1 receptor. The triple combination in the optimal doses pertains to efficacy against all three mechanisms of action, serves as a multitarget plant-based therapy and could serve as a novel strategy for the alleviation of hot flashes in postmenopausal women.
Knowledge of COVID-19 Infection Guidelines among the Dental Health Care Professionals of Jazan Region, Saudi Arabia

Background: This study aimed to assess the knowledge about guidelines related to COVID-19 infection control procedures among dental health care professionals (DHCPs) in the Jazan region. Methods: A cross-sectional study involving DHCPs (dental students, interns, and dentists) of the Jazan region between January and March 2021. A questionnaire with 35 items was developed and circulated online among the DHCPs. The dimensionality of the questionnaire was assessed using exploratory factor analysis (EFA). The level of awareness (LOA) was compared across the genders, level of professional experience, and exposure to guidelines. Participants were considered to have high LOA when they responded to 26 or more items correctly. Results: A total of 363 DHCPs participated in the survey. The questionnaire was found to be valid and reliable. EFA revealed a distinct three-factor structure. Moreover, 61.2% of the respondents had high LOA related to COVID-19 infection prevention. Among those who had high LOA, dentists (65.5%) were relatively more than the students (62.5%) and interns (46.2%). Among the six guideline statements related explicitly to operative dentistry, more than 50% of the respondents were aware of 3 guideline statements, while less than 50% of the respondents were aware of the remaining statements. Conclusions: Most DHCP had a high LOA for general COVID-19 infection prevention and control guidelines. Dentists, males, and those who read the guidelines had higher LOA than their counterparts.
The Effect of Educational Mismatch on Wages: A Comparative Study of Migrant and Native Workers

One of the important issues in the Indonesian labor market is an educational mismatch. And one of the implications caused by educational mismatch is that the wages received are unsuitable with the educational qualifications have. This study attempts to relate the educational mismatch phenomenon to the issue of internal migration. Hence, this study aimed to reveal the effect of educational mismatch on the earning of workers, especially migrant and native workers. The discussion of educational mismatch was more specific to migrant workers and native workers because these two types of workers had quite different potential earnings. The data used in this research were gained from the National Labor Force Survey (SAKERNAS) in August 2019. The unit of analysis used was workers with labor/employees’ status other than TNI/POLRI aged 15-64 years and over. The results showed that migrant workers were more likely to experience over education than native workers, and native workers were more likely to experience undereducation than migrant workers. Then, based on the results of the multiple linear regression analysis, it was found that migrant workers encountered greater wage penalties than native workers.
Parametric Variations in Aspect Ratio, Leading Edge and Planform Shapes for the Rectilinear Pitch Cases of AVT-202 (Invited)

© 2016, (publisher Name). All Rights Reserved. The Applied Vehicle Technology Panel Task Group AVT-202 “Extension of Fundamental Flow Physics to Practical MAV Aerodynamics” has introduced four canonical cases. Rectilinear pitch is probably the most studied one, especially considering its parametric variations. The canonical case of rectilinear pitch considers a rectangular flat plate wing of aspect ratio 4, with a thickness of less than 5% and round edges, undergoing a pitch up motion in a freestream. In the beginning, the wing is at condition CL = 0 and then pitches up to 45° of effective incidence either in 1 or 6 chord lengths of travel relative to fluid (known as fast and slow cases), pivoting around its leading edge. This paper focuses on parametric variations of the aforementioned canonical case, namely variations in aspect ratio, leading edge and planform shapes.
Filler Effects on H2 Diffusion Behavior in Nitrile Butadiene Rubber Blended with Carbon Black and Silica Fillers of Different Concentrations

Filler effects on H2 diffusion in nitrile butadiene rubbers (NBRs) blended with carbon black and silica fillers of different concentrations are first investigated by employing a volumetric analysis. Total uptake, solubility, and diffusivity of hydrogen for ten filled-NBR, including neat NBR, are determined in an exposed pressure range of 1.3 MPa~92.6 MPa. Filler dependence on hydrogen uptake and diffusion is distinctly observed in the NBRs blended with high abrasion furnace (HAF) carbon black (CB) fillers compared to NBRs blended with medium thermal furnace (MT) CB and silica filler, which is related to the specific surface area of carbon black and interface structure. The HAF CB filled-NBR follows dual sorption behavior combined with Henry’s law and the Langmuir model, responsible for two contributions of solubility from polymer and filler. However, a single gas sorption behavior coming from the polymer is observed satisfying Henry’s law up to 92.6 MPa for NBR blended with MT CB filled-NBR and silica filled-NBR. Diffusion demonstrates Knudsen and bulk diffusion behavior below and above, respectively, at certain pressures. With increasing pressure, the filler effect on diffusion is reduced, and diffusivity converges to a value. The correlation observed between diffusivity and filler content (or crosslink density) is discussed.
A Methodology for Developing Transit Bus Speed-Acceleration Matrices to be Used in Load-Based Mobile Source Emissions Models

A physical-activity-based transit bus emissions model estimates emissions as a function of transit bus power demand for given transit bus activities and environmental conditions. Transit bus speed and acceleration rates are key activity parameters as well as the most important parameters in the estimation of transit bus power demand, also know as the engine load. Once the transit bus engine load is calculated for a given speed and acceleration, emissions in grams or grams/vehicle-hour can be calculated using grams per brake-horsepower-hour emission rates. To quantify Atlanta regional transit bus speed and acceleration rates, the Georgia Tech research team installed a Georgia Tech Trip Data Collector (consisting of an onboard computer, GPS receiver, a wireless communication device, and data storage) in a transit bus operated by Metropolitan Atlanta Rapid Transit Authority. The team collected second-by-second speed and location data for three weeks, and created speed-acceleration matrices by roadway facility type and by time of day. In this paper, the researchers focused on a methodology development to create transit bus speed-acceleration matrices in use of load-based modal mobile source emissions models for the Atlanta metropolitan area. Once a bus service route is specified by roadway facility type and by time of day, engine power demand for each matrix speed bin can be calculated, weighted by acceleration frequency fractions on each corresponding matrix bin, and then multiplied by emissions levels that can be obtained from engine dynamometer or chassis dynamometer test results. TRB 2005 Annual Meeting CD-ROM Paper revised from original submittal. Seungju Yoon, Hainan Li, Jungwook Jun, Jennifer Ogle, Randall Guensler, and Michael Rodgers 3
Construction skills requirement issues in Zambia

The construction industry in Zambia plays a pivotal role in the socio-economic development of the country. Being a labour intensive industry, competition between firms in construction depends on the quality of the workforce that companies employ. This paper presents a study that is intended to identify essential factors in the formulation and implementation of policies, training and retraining programmes tailored to deal effectively with the industry’s labour resource requirements. The labour resource audit should serve as a first step towards the process of planning for future construction skill requirements. The study is capturing official details such as head count for trend audits as well as underlying factors obtainable from further qualitative probing to explain any quantitative trends.
Assisting Multitargeted Ligand Affinity Prediction of Receptor Tyrosine Kinases Associated Nonsmall Cell Lung Cancer Treatment with Multitasking Principal Neighborhood Aggregation

A multitargeted therapeutic approach with hybrid drugs is a promising strategy to enhance anticancer efficiency and overcome drug resistance in nonsmall cell lung cancer (NSCLC) treatment. Estimating affinities of small molecules against targets of interest typically proceeds as a preliminary action for recent drug discovery in the pharmaceutical industry. In this investigation, we employed machine learning models to provide a computationally affordable means for computer-aided screening to accelerate the discovery of potential drug compounds. In particular, we introduced a quantitative structure–activity-relationship (QSAR)-based multitask learning model to facilitate an in silico screening system of multitargeted drug development. Our method combines a recently developed graph-based neural network architecture, principal neighborhood aggregation (PNA), with a descriptor-based deep neural network supporting synergistic utilization of molecular graph and fingerprint features. The model was generated by more than ten-thousands affinity-reported ligands of seven crucial receptor tyrosine kinases in NSCLC from two public data sources. As a result, our multitask model demonstrated better performance than all other benchmark models, as well as achieving satisfying predictive ability regarding applicable QSAR criteria for most tasks within the model’s applicability. Since our model could potentially be a screening tool for practical use, we have provided a model implementation platform with a tutorial that is freely accessible hence, advising the first move in a long journey of cancer drug development.
Examining spatiotemporal trends of drought in the conterminous United States using self-organizing maps

ABSTRACT Droughts are a natural, recurrent climate extreme that can inflict long-lasting devastation on natural ecosystems and socio-economic sectors. Unlike other natural hazards, drought onset is insidious and often affects a greater spatial extent over a prolonged temporal scale. In the United States the evolution of drought and its impacts are typically region-specific and intensified precipitation variability may obscure how drought may manifest. In this study, we examine the spatiotemporal trends of drought using self-organizing maps (SOM), competitive learning subset of artificial neural networks (ANN), requiring unsupervised training of inputs. We introduced monthly Palmer Drought Severity Index (PDSI) values to the SOM to identify existing clusters of wetting and drying patterns from 1895 to 2016. After training, we created cartographic visualizations of the SOM output and conducted a subsequent time-series analysis to link with the geographic patterns of drought. Over the last 40 years, precipitation intensified in the Northeast, Midwest, and upper Great Plains across several nodes. Across the majority of SOM patterns, we identified no significant changes of drying or wetting patterns over the last century for the greater part of the CONUS.