Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting rainfed rice yield with biomass of early phenophases, peak intercepted PAR and ground based remotely sensed vegetation indices

Rice is the main staple food of the country but crop productivity in some years declines due to erratic monsoon and non-uniformity in spatial and temporal distribution of rainfall. Hence, assessing productivity of the rice crop in advance using meteorological and plant physiological attributes will be helpful for planners to take decision on contingency measures. In this investigation, dry biomass of early phenophases (active tillering, panicle initiation, boot leaf stages, flowering),peak intercepted photosynthetically active radiation (IPAR), peak spectral reflectance based vegetation indices of 3 rice varieties under 3 nitrogen levels (50, 100 and 130 kg ha -1 ) were made correlated with grain yield.Based on interrelationship it was found that biomass of flowering period was better correlated with grain yield with the R 2 value of 0.75. Inter-relationship between peak IPAR(%), remotely sensed peak simple ratio index (IR/R) and normalized difference vegetation index (NDVI)with the rice yield were also established. Multiple regression modelwas developed by interrelating yield as dependant variable with dry biomass of flowering stage, peak IPAR(%) and peak IR/R and NDVI as independent variables which may be used as an effective tool for early prediction of rice yield, at least 30-40 days in advance. The grain yield was also estimated through developed algorithm using MODIS satellite derived NDVI and compared with that of actual yield.
Improved Fault Ride through Capability of SCIG-based Wind Turbine based on Photovoltaic System

This paper investigates the operation of a hybrid system including a two-stage PV system and a SCIG based wind turbine. The wind turbine is connected directly to the AC side of the PV system’s inverter and they are connected to the external grid through a step up transformer. With this configuration, the free land around the turbine tower can be utilized to install the PV system. A more important thing is that the inverter of the PV system can play the role of a STATCOM to support reactive power to the SCIG –wind turbine. This allows reactive power compensating equipments such as capacitor bank, SVC and so on to be absent at the wind turbine. The simulation results show that thank to the support of the PV system’s inverter, the SCIG-turbine can be stable in steady state and it can ride through fault easily. In addition, this investigation demonstrates a locus of critical fault-clearing time among different PV capacities and the minimum capacity of the PV system helping the wind power to ride through fault is identified.
A review of mass-transfer models and mechanistic studies of semi-volatile organic compounds in indoor environments

Semi-volatile organic compounds from artificial products can persist for long periods in indoor environments, and several semi-volatile organic compounds can lead to human diseases via inhalation, ingestion and dermal pathways. Mass-transfer models are an excellent alternative to experimental testing for predicting the transportation of semi-volatile organic compounds in indoor environments. This report presents a comprehensive review of mass-transfer models for semi-volatile organic compound transportation in indoor environments, assuming that semi-volatile organic compounds are well mixed and exhibit uniform properties in the indoor space. The mass-transfer models, including emission and surface/particle sorption models, and methods for determining material emission and mass flux on sorptive and airborne surfaces are discussed. Factors affecting the emission rate and gaseous concentration are analysed, including the airflow rate, surface sorption, particles, temperature and humidity. Finally, a few research needs are highlighted in this review.
Multidirectional hemispherical dielectric elastomer proximity sensor for collision avoidance in human-robot interaction applications

Nowadays, several industrial manufacturing processes imply direct cooperation between human operators and robots. This increases production and quality while improving the working conditions. However, the possible presence of physical contact between humans and robots asks for the study and introduction of new technical solutions that aim at guaranteeing a safe Human-Robot Interaction (HRI). Specifically, in recent years, different sensing devices have been developed for collision avoidance monitoring in HRI applications. Generally, common solutions consist of distributed resistive or capacitive sensors networks connected to a central electronic reading board, resulting in a cumbersome layout covering the whole parts of the collaborative robots. In this context, this paper presents an innovative tactile and proximity sensing strategy based on a soft-sensor module that can be installed on the collaborative robot parts or surrounding workspace. The developed module consists of a capacitive sensor based on a silicone elastomer membrane with compliant electrodes attached to the surface, disposed homogeneously on a deformable hemisphere-shape made of silicone. Thanks to the geometrical layout, such a sensor allows multidirectional objects detection resulting in a promising non-invasive solution for collisions avoidance in HRI applications. This work reports the design, manufacturing, and preliminary experimental investigation of such a sensor module, evaluating the electrodes geometry and the most relevant features that optimize objects detection distance and directivity sensing performance.
The Effect of Hydroalcoholic Seed Extract of Securigera Securidaca on the Hepatic Renin-Angiotensin System in the Streptozotocin-Induced Diabetic Animal Model

Background: The aim of the study was to investigate the effect of hydroalcoholic seed extract of Securigera securidaca ( S. securidaca ) (HESS) on liver local renin-angiotensin system (RAS) in streptozotocin-induced diabetic rats. Methods: Three groups of diabetic male Wistar rats were treated with different doses of HESS (100, 200, 400 mg/Kg-BW), and the results were compared with diabetic and healthy control groups. To test the effects of HESS on liver local RAS as well as its alternative pathway, the tissue levels of renin, angiotensin-converting enzyme (ACE), ACE2, angiotensin-II (Ang-II), and Ang-(1-7) were measured. The oxidative state of liver tissue was evaluated by biomarkers of malondialdehyde (MDA), total oxidant status (TOS), and total antioxidant status (TAS). Due to the association between local RAS activity and tissue inflammation, the production of interleukins (IL) IL-1, IL-6, tumor necrosis factor alfa (TNF-α), and IL-10 in the liver was assayed in the experimental group. Results: Dose-dependent effects of HESS showed that the highest dose of the extract had a reducing effect on the hepatic levels of local RAS components including angiotensinogen, ACE, and Ang-II. Surprisingly, despite the decrease in tissue level of ACE2, an increase in Ang-(1-7) tissue concentration was observed. Decreased local RAS activity through treatment with the highest dose of HESS was associated with decreased tissue levels of proinflammatory cytokines (IL-1, IL-6, TNF-α), and increased anti-inflammatory cytokine (IL-10). Most of the effects of the extract are attributed to its antioxidant properties. Conclusions: S. securidaca seed can be suggested as a suitable drug supplement to prevent hepatic complications of diabetes.
Identification of modal parametrs of transmission towers

The paper presents theoretical basis for identification of nonlinear systems using the method based on Hilbert transform. Discussed method was verified by analyzing the synthesized signals of known parameters. Developed and verified mathematical tools were used for analysis of dynamic responses of the real object: a laboratory (physical) model of the transmission tower. For the considered laboratory model it was possible to simulate damages of individual truss elements and observe changes in system dynamic characteristics. Results of the carried out research proved that the first natural frequency and damping factor change in the observable extent as a result of damage presence.
Synthesis of Tetrapeptides Containing Dehydroalanine, Dehydrophenylalanine and Oxazole as Building Blocks for Construction of Foldamers and Bioinspired Catalysts

The incorporation of dehydroamino acid or fragments of oxazole into peptide chain is accompanied by a distorted three-dimensional structure and additionally enables the introduction of non-typical side-chain substituents. Thus, such compounds could be building blocks for obtaining novel foldamers and/or artificial enzymes (artzymes). In this paper, effective synthetic procedures leading to such building blocks—tetrapeptides containing glycyldehydroalanine, glycyldehydrophenylalanine, and glycyloxazole subunits—are described. Peptides containing serine were used as substrates for their conversion into peptides containing dehydroalanine and aminomethyloxazole-4-carboxylic acid while considering possible requirements for the introduction of these fragments into long-chain peptides at the last steps of synthesis.
Privacy and security in genomic information

Today, whole human genome sequencing is a reality affordable for many individuals. Technology advances in this arena are going fast, in fact it is possible that we are living the beginning of a “genomic revolution”. However, human genome contains highly sensitive information about individuals. Concerns regarding privacy and security are getting wider as technology advances ever more rapidly. The present work focuses on contributing and support the development of new security and privacy mechanisms in genomic information formats. An intended software prototype has been developed and it is presented in the current report. The tool permits reading and handling genomic information after evaluating the access requirements according to defined rules.
Impact of COVID-19 Cases, Deaths, Stringency and Vaccinations on the US Stock Market

The socio-economic environment of a country may significantly influence the size and working of the country’s financial markets in the long run. Keeping this in mind, this study aims to analyse the long-run and short-run impact of COVID-19 cases, deaths, stringency index, and vaccinations on the US stock market. Daily time series data ranging from 22 January 2020, to 30 April 2021, was considered in this study. The ARDL bounds test approach was employed to examine long-run and short-run relationships. Our statistical evidence suggests that, in the long run, confirmed cases and stringency have a negative and significant impact on stock markets, whereas vaccinations have a positive and significant effect on the stock markets. This indicates that any public health emergency adversely affects the stock markets, such as a pandemic outbreak. The government should ramp up the efforts towards vaccinating their citizens in the earliest possible timeline. Such actions of resurgence from the pandemic instil confidence in the market. Policymakers should be thoughtful about formulating contingency measures to effectively safeguard the population while preventing the deterioration in investor confidence.
Construction and Validation of Angiogenesis-Related Prognostic Risk Signature to Facilitate Survival Prediction and Biomarker Excavation of Breast Cancer Patients

This study is aimed at exploring the potential mechanism of angiogenesis, a biological process-related gene in breast cancer (BRCA), and constructing a risk model related to the prognosis of BRCA patients. We used multiple bioinformatics databases and multiple bioinformatics analysis methods to complete our exploration in this research. First, we use the RNA-seq transcriptome data in the TCGA database to conduct a preliminary screening of angiogenesis-related genes through univariate Cox curve analysis and then use LASSO regression curve analysis for secondary screening. We successfully established a risk model consisting of seven angiogenesis-related genes in BRCA. The results of ROC curve analysis show that the risk model has good prediction accuracy. We can successfully divide BRCA patients into the high-risk and low-risk groups with significant prognostic differences based on this risk model. In addition, we used angiogenesis-related genes to perform cluster analysis in BRCA patients and successfully divided BRCA patients into three clusters with significant prognostic differences, namely, cluster 1, cluster 2, and cluster 3. Subsequently, we combined the clinical-pathological data for correlation analysis, and there is a significant correlation between the risk model and the patient's T and stage. Multivariate Cox regression curve analysis showed that the age of BRCA patients and the risk score of the risk model could be used as independent risk factors in the progression of BRCA. In particular, based on this angiogenesis-related risk model, we have drawn a matching nomogram that can predict the 5-, 7-, and 10-year overall survival rates of BRCA patients. Subsequently, we performed a series of pan-cancer analyses of CNV, SNV, OS, methylation, and immune infiltration for this risk model gene and used GDSC data to explore drug sensitivity. Subsequently, to gain insight into the protein expression of these risk model genes in BRCA, we used the immunohistochemical data in the THPA database for verification. The results showed that the protein expressions of IL18, RUNX1, SCG2, and THY1 molecules in BRCA tissues were significantly higher than those in normal breast tissues, while the protein expressions of PF4 and TNFSF12 molecules in BRCA tissues were significantly lower than those in normal breast tissues. Finally, we conducted multiple GSEA analyses to explore the biological pathways these risk model genes can cross in cancer progression. In summary, we believe that this study can provide valuable data and clues for future studies on angiogenesis in BRCA.
Household Food Waste Quantification and Cross-Examining the Official Figures: A Study on Household Wheat Bread Waste in Shiraz, Iran

The global consumer food waste (FW) estimates are mainly based on modeling data obtained from governments. However, a major data gap exists in FW at the household level, especially in developing countries. Meanwhile, the reliability of the existing data is questionable. This study aimed to quantify wheat bread waste (HBW) in Shiraz, Iran, and cross-examine the governmental HBW data. Face-to-face waste recall questionnaire interviews were conducted in 419 households from December 2018 to August 2019. A multistage sampling strategy consisting of stratification, clustering, and systematic sampling was employed. Moreover, we carried out a comprehensive document review to extract and analyze the official HBW data. The results revealed that the HBW in Shiraz is 1.80%—the waste amounts for traditional bread and non-traditional bread were 1.70% and 2.50%, respectively. The survey results were compared with the previous official data, revealing a substantial contradiction with the 30% HBW reported between 1991 and 2015. Possible reasons for this disparity are explored in this paper. Although our results cannot be generalized to other food commodities and locations, our findings suggest that considering the substantial likelihood of bias in the official data, policymakers should conduct more FW measurements and re-evaluate the accuracy of the existing data.
Design and Development of the eBear: A Socially Assistive Robot for Elderly People with Depression

There has been tremendous progress in the field of robotics in the past decade and especially developing humanoid robots with social abilities that can assist human at a socio-emotional level. The objective of this thesis is to develop and study a perceptive and expressive animal-like robot equipped with artificial intelligence in assisting the elderly people with depression. We investigated how social robots can become companions of elderly individuals with depression and improve their mood and increase their happiness and well-being. The robotic platform built in this thesis is a bear-like robot called the eBear. The eBear can show facial expression and head gesture, can understand users emotion using audio-video sensory inputs and machine learning, can speak and show relatively accurate visual speech, and make dialog with users. the eBear can respond to their questions by querying the Internet, and even encourage them to physically be more active and even perform simple physical exercises. Besides building the robot, the eBear was used in running a pilot study in which seven elderly people with mild to severe depression interacted with the eBear for about 45 minutes three times a week over one month. The results of the study show that interacting with the eBear can increase happiness and mood of these human users as measured by Face Scale, and Geriatric Depression Scale (GDS) score systems. In addition, using Almere Model, it was concluded that the acceptance of the social agent increased over the study period. Videos of the users interaction with the eBear was analyzed and eye gaze, and facial expressions were manually annotated to better understand the behavior changes of users with the eBear. Results of these analyses as well
Quality and Rumen Fermentation Profile of Indigenous Forage on Karst Mountain

The purpose of this study was to evaluate the quality and rumen fermentation kinetics of indigenous forages that were common to fed the goats by farmers. The method used was a survey by identifying the forages for the goat fed by the farmers surrounding South Gombong, Central Java, karst hills. The soil chemical compound consists of C-organic: 2.6-4.3%; Ca-total 0.108–0.924%; N-total 0.267–0.427%; P 2 O 5- total 0.095–0.184%; and K 2 O total 0.122– 0.237%. The identified forages were then analyzed for the nutrients content by proximate method and followed with in vitro method for analyzed the rumen fermentation profile. The results of this study showed that there were 20 species of forage that were fed to goat, which were dominated by 5 species of grasses with the nutrients content were crude fiber (CF): 32.69-39.50%, crude protein (CP): 5.73-10.96%, total digestible nutrient (TDN): 50.66-54.71%, and the rumen fermentation profiles were dry matter digestibility (DMD): 34.36-40.35%, organic matter digestibility (OMD): 48.32–52.37%, volatile fatty acids (VFA): 40-73 mM, ammonia (NH 3 ): 3.6-8.0%. There were 3 species of legumes with nutrients content and fermentation profile were CF: 21.20-39.98%, CP: 13.80-22.64%, TDN: 50.14-69.16%, DMD: 41.26-47.94%, OMD: 53.16-57.66%, VFA: 52-125 mM, NH 3 : 7.30-15.6 mM. Ten species of schrubs and two trees were also identified. The nutrients content and fermentation profile of the scrubs were CF: 22.74-36.72%, CP: 12.15-16.94%, TDN: 52.30-64.79%, DMD: 33.66-51.54%, OMD: 47.36-63.28%, VFA: 20-130 mM, NH 3 : 2.70-10.80 mM, and for the trees were CF: 23.72-33.88%, CP :12.50-13.34%, TDN: 56.06-62.99%, DMD: 37.98-38.62%, OMD: 49.62-49.64%, VFA: 17-42 mM, NH 3 : 1.90-4.80mM. This study concludes that Cynodon dactilon , Calliandra calothyrsus , Commelina difusa and Artocarpus heterophylus are the potential forage to develop in karst mountain.
SECE-based piezoelectric energy sensor for the diagnostics of timing belt

The article presents a novel idea for the fault diagnostics of the timing belt based on developing a piezoelectric energy sensor attached to the synchronized electric charge extraction (SECE) interface. The device is composed of a piezoelectric cantilever beam with a tip magnet impulsively excited by another small magnet attached on the surface of a moving timing belt. As a result, energy is harvested by frequency up-conversion mechanism operated under magnetic plucking. It is observed that the extent to which power harvested from the failing belt is higher than that from the healthy belt, giving rise to the basis for condition monitoring of the timing belt by detecting the output power. In addition, the analysis shows that the power sensitivity to the magnetic distance can be enhanced to 145% by the SECE interface circuit in comparison with the standard (STD) interface circuit. The consequence of it is that the SECE case exhibits a more accurate decision boundary determined by the logistic regression to classify the healthy and flawed states, as confirmed by the experiment.
Exploration and exploitation in Bayes sequential decision problems

Bayes sequential decision problems are an extensive problem class with wide application. They involve taking actions in sequence in a system which has characteristics which are unknown or only partially known. These characteristics can be learnt over time as a result of our actions. Therefore we are faced with a trade-off between choosing actions that give desirable short term outcomes (exploitation) and actions that yield useful information about the system which can be used to improve longer term outcomes (exploration). Gittins indices provide an optimal method for a small but important subclass of these problems. Unfortunately the optimality of index methods does not hold generally and Gittins indices can be impractical to calculate for many problems. This has motivated the search for easy-to-calculate heuristics with general application. One such non-index method is the knowledge gradient heuristic. A thorough investigation of the method is made which identifies crucial weaknesses. Index and non-index variants are developed which avoid these weaknesses. The problem of choosing multiple website elements to present to user is an important problem relevant to many major web-based businesses. A Bayesian multi-armed bandit model is developed which captures the interactions between elements and the dual uncertainties of both user preferences and element quality. The problem has many challenging features but solution methods are proposed that are both easy to implement and which can be adapted to particular applications. Finally, easy-to-use software to calculate Gittins indices for Bernoulli and normal rewards has been developed as part of this thesis and has been made publicly available. The methodology used is presented together with a study of accuracy and speed.
Autonomy of the elderly after a hip fracture surgery in a subsaharan country

Purpose: The aim of this study was to assess the impact of surgery on autonomy in the elderly after a fracture of the proximal femur; and the factors associated with its alteration. Materials and methods: A prospective longitudinal study was performed on 66 patients aged at least 90 years treated for a fracture of the proximal femur between 2008 and 2017. Physical autonomy was assessed using the Katz scale Results: After the surgical management, the previous level of autonomy was found progressively increasing in 9.1% at 1 month, 17.5% in the 3rd month, 23.5% in the 6th month and 22% at 1 year (P value <0.001). The factors associated with the deterioration in patient autonomy one month postoperatively were as follows: late admission (P = 0.02), a period of verticalization greater than 15 days (P = 0.02), at least one complication of decubitus (P = 0.004), anemia (P = 0.02) and an ASA score ≥ 2 (P = 0.05). The comparison of the autonomy between hip fixation and hip prosthetic replacement did not show a significant difference in the first postoperative month (P = 0.83). Conclusion: Even at over 90 years of age, surgery can significantly restore previous autonomy. The early admission and management of patients, the prevention of complications in the decubitus position, the early rise after surgery and the management of anemia are key points in restoring physical autonomy.