Miracle-dz/UniFAR-FTUs huggingface dataset:
Oil Spill Detection with Dual-Polarimetric Sentinel-1 SAR Using Superpixel-Level Image Stretching and Deep Convolutional Neural Network

Synthetic aperture radar (SAR) has been widely applied in oil spill detection on the sea surface due to the advantages of wide area coverage, all-weather operation, and multi-polarization characteristics. Sentinel-1 satellites can provide dual-polarized SAR data, and they have high potential for successful application to oil spill detection. However, the characteristics of the sea surface and oil film on different images are not the same when imaging at different locations and in different conditions, which leads to the inconsistent accuracy of these images with the application of the current oil spill detection methods. In order to avoid the above limitation, we propose an oil spill detection method using image stretching based on superpixels and a convolutional neural network. Experiments were carried out on eight Sentinel-1 dual-pol data, and the optimal superpixel number and image stretching parameters are discussed. Mean intersection over union (MIoU) was used to evaluate classification accuracy. The proposed method could effectively improve the classification accuracy; when the expansion and inhibition coefficients of image stretching were set to 1.6 and 1.2 respectively, the experiments achieved a maximum MIoU of 85.4%, 7.3% higher than that without image stretching.
Commercial Building Sensors and Controls Systems - Barriers, Drivers, and Costs

be grid-interactive, and help accelerate decarbonization of the U.S. electric grid. Nearly 60% of commercial buildings over 50,000 square feet in the United States have a building automation system (BAS) to control heating, ventilating, and air conditioning (HVAC), lighting, and more. But only 13% of smallto medium-size (<50,000 square feet) buildings have adopted the technology, leaving over 75% of all commercial buildings in the U.S. primed for opportunity. To support widespread adoption of commercial building sensors and controls systems, the National Renewable Energy Laboratory (NREL) and Joint Institute for Strategic Energy Analysis (JISEA), under the Clean Energy Manufacturing Analysis Center, assessed barriers and drivers for adoption and created a breakdown of costs, including hardware and various installation labor costs. NREL/JISEA collected qualitative data from 21 interviews with 28 experts representing five key industry roles: building owners, contractors, controls vendors, analytics vendors, and researchers. NREL/JISEA also gathered quantitative cost data from original sources, including project invoices and estimates, the RSMeans (RSMeans 2021), available U.S. General Services Administration schedule cost information from various manufacturers, and other cost data provided by interviewees.
COVID‐19 and pre‐tenure counseling faculty: A collaborative autoethnographic investigation

Abstract Higher education faculty worldwide experienced heightened stressors due to the COVID‐19 pandemic, from completing their professional roles and responsibilities virtually to balancing personal and professional stressors. Consequently, the pandemic created many adjustments for pre‐tenure counseling faculty across research, teaching, and service. In response to this pandemic, we explored the impact of the COVID‐19 pandemic on the personal and professional lives of four pre‐tenure counseling faculty members. Accordingly, we used collaborative autoethnography to investigate our experiences and narratives as pre‐tenure counseling faculty, which resulted in seven overarching themes. We discussed the implications for practice, advocacy, education, and faculty training.
Test-retest Reliability of Unilateral and Bilateral Carrying Protocol Based on Joule’s Functional Capacity Evaluation

Introduction: Load carriage is one of the most frequent manual material handlings in industrial settings. However, due to the biomechanical risk exposures, carrying could lead to work-related musculoskeletal disorders. Joule’s Functional Capacity Evaluation is an FCE system used widely in Malaysia in return-to-work programs to ensure work readiness and prevent re-injury. However, the literature regarding the test-retest reliability of the carrying protocols is minimal, leading to a questionable level of consistency. Therefore, this study aimed to investigate the test-retest reliability of the carrying protocols in Joule’s FCE. Methods: A cross-sectional study was conducted to evaluate test-retest reliability where the carrying protocol of Joule’s Functional Capacity Evaluation was used among healthy university students (N=30). The participants were asked to perform a retest of the carrying protocol after one week.Results: The ICC values for the carrying protocols were good for dominant unilateral carrying was (ICC: 0.82; 95% CI: -6.00 to 5.81), non-dominant carrying was (ICC: 0.74; 95% CI: -6.78 to 6.02) and bilateral carrying was (ICC: 0.85; 95% CI: -6.26 to 5.51). Bland and Altman’s plot indicated no visible distribution patterns of the differences without exceeding the 95% limits of agreement. The standard error of measurements (SEMs) was relatively small for all carrying protocols. Conclusion: The test-retest reliability of carrying protocols in Joule’s FCE was good. Future studies are needed to replicate this study in real patients to further verify the reliability for clinical settings.
Velocity Prediction Based on Map Data for Optimal Control of Electrified Vehicles Using Recurrent Neural Networks (LSTM)

In order to improve the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. This article presents the extension of a generic prediction approach already proposed in a previous paper, which allows a robust forecasting of all traction torque-relevant variables for such strategies. The extension primarily includes the proper utilization of map data in the case of an a priori known route. Approaches from Artificial Intelligence (AI) have proven to be effective for such proposals. With regard to this, Recurrent Neural Networks (RNN) are to be preferred over Feed-Forward Neural Networks (FNN). First, preprocessing is described in detail including a wide overview of both calculating the relevant quantities from global navigation satellite system (GNSS) data in several steps and matching these with data from the chosen map provider. Next, an RNN including Long Short-Term Memory (LSTM) cells in an Encoder–Decoder configuration and a regular FNN are trained and applied. The models are used to forecast real driving profiles over different time horizons, both including and excluding map data in the model. Afterwards, a comparison is presented, including a quantitative and a qualitative analysis. The accuracy of the predictions is therefore assessed using Root Mean Square Error (RMSE) computations and analyses in the time domain. The results show a significant improvement in velocity prediction with LSTMs including map data.
Bioinspired Ultrasensitive and Flexible Airflow Sensor based on Short Carbon Fiber Network

Electronic skin or wearable electronics have attached great attention due to their huge potential applications, and flexible airflow sensor, as an important part, reveals extensive applications. However, finding an efficient, low‐cost, and room‐temperature operation condition method to prepare flexible airflow sensors with high sensing performance still remains a challenge. Herein, inspired by filiform hairs of insects, a highly sensitive, stable, and flexible airflow sensor based on short carbon fiber network (SCFN) structure via electrostatic flocking is developed. The fabricated SCFN is endowed with an ultralow detection limit (0.053 m s−1), wide detection range (0.053–2.66 m s−1), multiangle response (0°–90°) with a fast response (1.7 s) and recovery time (3.4 s), together with excellent stability. Besides, its sensing mechanism is elaborated and demonstrated from microscale. Furthermore, it is applied to detect the velocity and direction of airflow through a 3D layout decoration of sensors and integrated into monitoring the leak location in a sealed reaction device. The combination of mild high‐throughput preparation and high sensing performance of SCFN airflow sensor shows significant potentials in electronic skin or wearable electronics filed.
The Dementia Assessment of Service Needs (DEMAND): Development and Validation of a Standardized Needs Assessment Instrument

Background: Dementia is one of the main triggers for care dependency among older adults who are predominantly cared for at home by relatives. To provide support in the care situation, health systems need valid information about the central needs of the affected people. Objective: The present study aimed to develop a research instrument to assess the most important needs of people with dementia and their family caregivers. Methods: The development of the ‘Dementia Assessment of Service Needs (DEMAND)’ took place within the project ‘Digital Dementia Registry Bavaria (digiDEM Bayern)’. A focus group and an online survey with dementia experts were conducted to identify the most relevant support services and to develop the design of the instrument. The questionnaire was deployed in the digiDEM baseline data collection. Participants were asked to evaluate the comprehensibility of the questionnaire. Readability was assessed using the Flesch reading ease score. Results: Seventeen experts participated in the focus group and 59 people in the online survey. The final questionnaire included 13 support services. One hundred eighty-three participants (50 people with dementia and 133 family caregivers) completed the questionnaire at baseline. The mean comprehensibility score was 3.6 (SD = 2.3). The Flesch reading ease score result was 76. Conclusion: A research instrument could be developed, enabling people with dementia and family caregivers to directly express their individual needs for specific support services. Results show that the DEMAND is easy to understand and short in execution. Therefore, supply gaps can be identified and transformed into a specific health care plan.
Diagnostic and Prognostic Nomograms for Lung Metastasis in Triple-Negative Breast Cancer

Background The lungs are one of the common sites of metastasis of triple-negative breast cancer (TNBC). Patients with lung metastases (LM) have a shorter duration of survival. This study is aimed at determining the prognostic factors of patients with TNBC with LM and constructing two nomograms to assess the risk of LM and the prognosis of patients with TNBC with LM. Methods Clinicopathological and follow-up data of patients with TNBC between 2010 and 2015 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analyses were used to screen for independent predictors of LM in patients with TNBC and identify the independent prognostic factors of patients with TNBC with LM. The two nomograms were appraised using calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Results A total of 27,048 patients with TNBC were included in this study. Age, tumour size, T stage, and N stage were identified as independent risk factors for LM in patients with TNBC. Histological type, marital status, prior surgery, chemotherapy, bone metastases, brain metastases, and LM were confirmed as independent prognostic factors for patients with TNBC with LM. The area under the ROC curve (AUC) of the diagnostic nomogram was 0.838 (95% confidence interval 0.817-0.860) in the training cohort and 0.894 (95% confidence interval 0.875-0.917) in the verification cohort. The AUC values of the 6-, 12-, and 18-month prognostic nomograms in the training cohort were 0.809 (95% confidence interval 0.771-0.868), 0.779 (95% confidence interval 0.737-0.834), and 0.735 (95% confidence interval 0.699-0.811), respectively, and the corresponding AUC values in the validation cohort were 0.735(95% confidence interval 0.642-0.820), 0.672 (95% confidence interval 0.575-0.758), and 0.705 (95% confidence interval 0.598-0.782), respectively. According to the calibration curves and data analysis, both nomograms exhibited good performance. Conclusion We successfully constructed and verified two valuable nomograms for predicting the incidence of LM and prognosis of patients TNBC with LM.
The Effect of Managerial Adverse Experience on Financial Reporting

We identify executives who have experienced significant accounting-related adverse events during their careers as a powerful setting to examine the extent to which prior professional experience can influence subsequent financial reporting policies. We find that firms led by senior financial executives who have experienced accounting-related adverse events during their careers exhibit greater unconditional accounting conservatism, a lower likelihood of experiencing future accounting-related adverse events, and less positive abnormal discretionary accruals. This effect tends to be stronger when the experience is more frequent, recent, severe, or proximate. Overall, our results reveal a meaningful relationship between managers’ professional experience and accounting policies.
High-Purity Fucoxanthin Can Be Efficiently Prepared from Isochrysis zhangjiangensis by Ethanol-Based Green Method Coupled with Octadecylsilyl (ODS) Column Chromatography

The exploitation of new economically valuable microalgae as a sustainable source of minor high-value products can effectively promote the full utilization of microalgae. The efficient preparation of minor products from microalgae remains the challenge, owing to the coexistence of various components with a similar polarity in the microalgae biomass. In this study, a novel approach based on the sustainable-oriented strategy for fucoxanthin (FX) production was proposed, which consisted of four steps, including the culture of microalga, ethanol extraction, ODS column chromatography, and ethanol precipitation. The high-purity FX (around 95%) was efficiently obtained in a total recovery efficiency of 84.28 ± 2.56%. This study reveals that I. zhangjiangensis is a potentially promising feedstock for FX production and firstly provides a potentially eco-friendly method for the scale-up preparation of FX from the microalga I. zhangjiangensis.
Effects of Levodopa-Carbidopa Intestinal Gel on Dyskinesia and Non-Motor Symptoms Including Sleep: Results from a Meta-Analysis with 24-Month Follow-Up

Background: In advanced Parkinson’s disease (PD), dyskinesias and non-motor symptoms such as sleep dysfunction can significantly impair quality of life, and high-quality management is an unmet need. Objective: To analyze changes in dyskinesia and non-motor symptoms (including sleep) among studies with levodopa-carbidopa intestinal gel (LCIG) in patients with advanced PD. Methods: A comprehensive literature review identified relevant studies examining LCIG efficacy. Outcomes of interest were dyskinesia (UDysRS, UPDRS IV item 32), overall non-motor symptoms (NMSS), mentation/behavior/mood (UPDRS I), and sleep/daytime sleepiness (PDSS-2, ESS). The pooled mean (95% confidence interval) change from baseline per outcome was estimated for each 3-month interval with sufficient data (i.e., reported by≥3 studies) up to 24 months using a random-effects model. Results: Seventeen open-label studies evaluating 1243 patients with advanced PD were included. All outcomes of interest with sufficient data for meta-analysis showed statistically significant improvement within 6 months of starting LCIG. There were statistically significant improvements in dyskinesia duration as measured by UPDRS IV item 32 at 6 months (–1.10 [–1.69, –0.51] h/day) and 12 months (–1.35 [–2.07, –0.62] h/day). There were statistically and clinically significant improvements in non-motor symptoms as measured by NMSS scores at 3 months (–28.71 [–40.26, –17.15] points). Significant reduction of NMSS burden was maintained through 24 months (–17.61 [–21.52, –13.70] points). UPDRS I scores significantly improved at 3 months (–0.39 [–0.55, –0.22] points). Clinically significant improvements in PDSS-2 and ESS scores were observed at 6 and 12 months in individual studies. Conclusion: Patients with advanced PD receiving LCIG showed significant sustained improvements in the burden of dyskinesia and non-motor symptoms up to 24 months after initiation.
SARS-CoV-2: Pathophysiology, Prophylaxis and Treatment Opportunities – A Current Review

and ABSTRACT: Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) was emerged in 2003, Middle East Respiratory Syndrome Coronavirus (MERS-CoV) got reported in the year 2012. Following, SARS-CoV-2 was observed as pandemic around the globe in the end of 2019. COVID-19 has produced significant rate of morbidity and mortality rate causing severe loss of life. An immediate attention is required to find out active therapeutic methods and appropriate vaccines to control this epidemic condition. This is the crucial time to find out the lead compounds for the inhibition of SARS-CoV-2 by targeting its enzymes. The present review will make it possible to better understand the pathogenesis caused by the virus through their various proteins and finding path for the development of effective new drug targets and vaccines for this newly raised pathogen. In order to achieve these objectives, the current review will focus on the biology of the virus and describe the chemistry of a cellular targeted drug delivery pattern of two novel drug conjugates: HA-2-Deoxy-D-Glucose
Modeling and Forecasting Selected Meteorological Parameters for the Environmental Awareness in Sub-Sahel West Africa Stations

The monthly air temperature, rainfall, air pressure, and wind speed direction for the environmental time series recorded between January 1, 1980 and December 31, 2020 in six African stations from different climatic zones were modeled and forecasted. In the forecasting, Augmented Dickey Fuller test, ARIMA models, Auto correlation Function (ACF) and Partial Autocorrelation Function (PACF) were used. Result showed that in most of the fitted models, the Moving Average terms both seasonal and non- seasonal were also significant (p<0.05) indicating that the previous day value of the stochastic term also had a significant effect on the present value of meteorological parameters in the environment. It was observed that in all the fitted models except for wind direction in Conakry and rainfall in Abidjan have all their Autoregressive term of order 1 significant (p<0.05) which implies that previous day value of these meteorological parameter had a significant effect on the present day value of the parameters. Therefore, the forecast model indicates that maximum temperature are expected in February, March, April, and June while minimum temperatures in January, August, December. Although, the selected models cannot forecast the precise air temperature, this can also provide information that can be of help to create tactics for appropriate preparation of farming which can be used as tools for effective envi­ronmental preparation and policymaking.
Interaction of the Emerging Mycotoxins Beauvericin, Cyclopiazonic Acid, and Sterigmatocystin with Human Serum Albumin

Beauvericin (BEA), cyclopiazonic acid (CPA), and sterigmatocystin (STC) are emerging mycotoxins. They appear as contaminants in food and animal feed, leading to economic losses and health risks. Human serum albumin (HSA) forms stable complexes with certain mycotoxins, including ochratoxins, alternariol, citrinin, and zearalenone. HSA binding can influence the toxicokinetics of xenobiotics, and albumin can also be considered and applied as a relatively cheap affinity protein. Therefore, we examined the potential interactions of BEA, CPA, and STC with HSA employing fluorescence spectroscopy, ultracentrifugation, ultrafiltration, and molecular modeling. Spectroscopic and ultracentrifugation studies demonstrated the formation of low-affinity BEA–HSA (Ka ≈ 103 L/mol) and moderately strong CPA–HSA and STC–HSA complexes (Ka ≈ 104 L/mol). In ultrafiltration experiments, CPA slightly displaced each site marker (warfarin, naproxen, and camptothecin) tested, while BEA and STC did not affect significantly the albumin binding of these drugs. Modeling studies suggest that CPA occupies Sudlow’s site I, while STC binds to the Heme site (FA1) on HSA. Considering the interactions of CPA with the site markers, the CPA–HSA interaction may have toxicological importance.
Characterization of coagulase-negative staphylococci and macrococci isolated from cheese in Germany.

Cheese, especially ripened varieties, harbor a very complex and heterogeneous microbiota. In addition to the desired microorganisms (starter cultures) added during cheese production, potentially harmful bacteria may also enter the production chain. Regarding the latter, the focus of this study was on coagulase-negative staphylococci (CNS) and Macrococcus caseolyticus. Both are known to harbor a variety of genes coding for antibiotic resistance, including mecA, mecB, mecC, and mecD. Coagulase-negative staphylococci or macrococci carrying such genes or other virulence factors should not be present in cheese. Cheese samples (101 in total) were collected from retail sources. Coagulase-negative staphylococci and M. caseolyticus were isolated utilizing selective agars, and species were identified by phenotypical tests and partial sequencing of the sodA gene. The results allowed identification of 53 CNS strains and 19 M. caseolyticus strains. Among the CNS, 11 isolates of Staphylococcus saprophyticus and one Staphylococcus epidermidis isolate were obtained. Both species are potential human pathogens and may thus adversely affect the safety of these food products. Screening for antimicrobial resistance was performed by application of disc diffusion tests, a gradient strip-test, and 14 different PCR tests. Evidence for methicillin resistance (by either positive disc diffusion assay for cefoxitin or by mec PCR) was found in CNS isolates and M. caseolyticus (9 isolates each). Regarding other virulence factors, no genetic determinants for coagulase or the most common staphylococcal enterotoxins sea, seb, sec, sed, and see were detected in any of the CNS or M. caseolyticus isolates by PCR testing. In conclusion, the presence of facultatively pathogenic CNS and carriers of genes for antibiotic resistance in both groups of microorganisms, especially mec genes, and the respective food safety issues need further evaluation and surveillance.
Recent Advances of Optical Sensors for Copper Ion Detection

A trace element copper (Cu2+) ion is the third most plentiful metal ion that necessary for all living organisms and playing a critical role in several processes. Nonetheless, according to cellular needs, deficient or excess Cu2+ ion cause various diseases. For all these reasons, optical sensors have been focused rapid Cu2+ ion detection in real-time with high selectivity and sensitivity. Optical sensors can measure fluorescence in the refractive index—adsorption from the relationships between light and matter. They have gained great attention in recent years due to the excellent advantages of simple and naked eye recognition, real-time detection, low cost, high specificity against analytes, a quick response, and the need for less complex equipment in analysis. This review aims to show the significance of Cu2+ ion detection and electively current trends in optical sensors. The integration of optical sensors with different systems, such as microfluidic systems, is mentioned, and their latest studies in medical and environmental applications also are depicted. Conclusions and future perspectives on these advances is added at the end of the review.