Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors affecting the use of weather station data in predicting surface soil moisture for agricultural applications

Abstract: Weather stations often provide key information related to soil moisture; temperature and evaporation are used by farmers to decide farm operations of nearby agricultural fields. However, the site conditions at the weather stations where data are recorded may not be similar with these nearby fields. The objective of this study was to determine the level of discrepancies in surface soil moisture between weather stations and nearby agricultural fields based on (i) the soil texture, crop residue cover, crop type, growth stages and (ii) temporal dependency of soil moisture to recent rainfall and evaporation rates. Soil moisture from 25 weather stations in the North Dakota Agricultural Weather Network (NDAWN) and 75 nearby fields were measured biweekly during the 2019 growing season in Red River Valley. Field characteristics including soil texture, crop residue cover, crop type, and growth stages along with rainfall and PET were collected during the study period. The regression analysis between surface soil moisture at weather station and nearby field showed higher values for corn at V10 stage (r2 = 0.92) and for wheat at flowering stage (r2 = 0.68) and opposite was observed with soybean. We found the regression coefficient of soil moisture with 4-d cumulative rainfall slightly increased to 0.51 with an increase in percent residue cover resulting in a decreased root mean square error (RMSE) to 0.063 m3·m−3. In general, we observed that surface soil moisture at weather stations could reasonably predict moisture in nearby agricultural fields considering crop type, soil type, weather, and distance from weather station.
Does Caste-based Social Stratification Moderate the Relationship Between Social Capital and Life Satisfaction? Evidence from India

Abstract This study examined if India’s caste system, a form of social stratification that divides different social groups into ranked categories, moderates the relationship between social capital and life satisfaction. Using data extracted from the International Social Survey Programme’s module on Social Network and Social Resources (N = 909), we conducted Principal Component Analysis and identified two distinct components of social capital – formal and informal. Using Multiple Hierarchical Regression, we examined the relationship between social capital (formal and informal) and life satisfaction, and it was found to be significant. We also found that the relationship is different for different castes. Average life satisfaction was lowest among the middle castes (Other Backward Classes), and not the lower castes (Scheduled Castes and Scheduled Tribes). Formal social capital was found to positively contribute to life satisfaction across all the caste categories, but heavy informal social capital was observed to reduce life satisfaction among upper castes.
Analysis of the torque characteristic of a magnetorheological clutch using neural networks

In this paper the dependence of the torque characteristic of a magnetorheological clutch on several working parameters is analyzed by means of a feedforward neural network. The clutch was envisaged to have the possibility to disengage the vacuum pump in diesel engine vehicles, in order to increase the overall vehicle efficiency. A large set of test was carried out following different protocols in order to obtain a detailed characterization of the clutch. Results showed that, due to the characteristics of MR fluids and to the complex mechanism of torque transmission, the torque characteristics (both the yield torque and the torque-slip behavior) are a function of the relative speed between the input and output shafts, of the energy dissipated during the clutch slip and of the rest time between consecutive slippages. The data acquired during all tests were used to train a neural network with five input elements and one output element. The developed neural network proved to satisfactorily reproduce the actual clutch properties and could be used in the future in an engine-vehicle simulator in order to model the torque characteristic of the clutch subjected to different operating cycles.
State tomography for magnetization dynamics

State tomography is an essential tool for analyzing physical states in quantum science. In magnets, elementary excitation called a magnon, or a spin wave, dominates magnetization dynamics and various magnetic properties. Here, we propose and demonstrate state tomography for magnetization dynamics, enabling us to obtain a density matrix and Wigner function of the magnetization dynamics. Using the technique, we found that parametrically excited magnons can form a mixed state composed of two coherent states. Magnetization state tomography will pave a way to explore a wide range of states of magnons, such as a squeezed state in condensed matter.
A SIMPLIFIED METHOD FOR ELASTIC SEISMIC ANALYSIS OF HILL BUILDINGS

Buildings on hill slope are highly irregular and asymmetric in plan and elevation. They are subjected to severe torsion in addition to lateral forces under the action of earthquakes. In the present study, simplified 3D dynamic analysis of hill buildings based on transformation of stiffness and mass of various components about a common arbitrarily-chosen reference axis is presented. Few actual hill building problems have been analysed with the simplifed method and the rigorous method of analysis. The results of the two methods of analysis advocate the use of the simplified method in the Code of Practices of different countries. With the use of the simplified method, the configurations for buildings on hill slope can be decided by taking various trial configurations so as to obtain the most economical and safe design from the seismic point of view.
Public transport path choice estimation based on trip data from dedicated smartphone app survey

Having access to realistic and empirically grounded passenger valuations of public transport trip components facilitate the undertaking of necessary trade-offs during planning of transport networks. Discrete choice estimation of path choice preferences is a practical way to obtain such preferences. This paper proposes a new take on the empirical foundation of path choice estimation based on revealed choices by introducing trip data for full activity-based ‘door-to-door’ public transport trips collected from a dedicated survey application for smartphones. Choice probabilities were modelled based on an explicitly generated choice set, where the public transport trip parts were generated using a branch-and-bound approach. Results in terms of estimated preferences are comparable to those based on conventional surveying methods and suggest significant premiums for paths involving public transport stops with an elevated level of passenger service as well as differences in preferences across population groups.
Diminished Relational Energy: How and When Co-Worker Incompetence Accusations Hinder Employee Creativity

ABSTRACT Drawing upon social support resource theory, this study examines the negative influences of co-worker incompetence accusations on a focal employee’s relational energy and subsequent creativity and the moderating role of servant leadership in mitigating these negative influences. The results of a two-wave, multi-source survey reveal that co-worker incompetence accusations are negatively related to a focal employee’s creativity through decreased relational energy. In addition, servant leadership moderates the negative relationship between co-worker incompetence accusations and creativity through decreased relational energy. Specifically, the indirect relationship is weaker when supervisors demonstrate higher servant leadership. Implications for theory, practice, and future research are discussed.
Analisis Penerapan Corporate Governance, Kualitas Audit dan Pengungkapan CSR Serta Ukuran Perusahaan terhadap Integritas Laporan Keuangan

The information presented in the financial statements (LK) is expected to have a high integrity value. To present financial statements with integrity, it is influenced by many factors, both external and internal to the company. The purpose of this study was to determine the effect of internal control (audit committee) and external supervision (institutional ownership and external audit quality), disclosure of corporate social responsibility (CRS), firm size on the integrity of financial statements. The sample of this research is companies listed on the Stock Exchange for the manufacturing sector for 3 years from 2016 to 2018 with a purpose-based sampling method in data collection. This type of research is quantitative using secondary data and data analysis is carried out by multiple regression with SPSS 25 in data processing. The results of this study provide empirical evidence that institutional ownership, audit quality, and CSR disclosure have a positive and significant effect on the integrity of financial statements. While the size of the company and the audit committee have no significant effect on the integrity of the financial statements.
Estrutura probabilística de uma série anual de precipitação pluvial extrema de uma região do litoral do estado de São Paulo

This study aimed to describe the probabilistic structure of the annual series of extreme daily rainfall (Preabs), available from the weather station of Ubatuba, State of Sao Paulo, Brazil (1935-2009), by using the general distribution of extreme value (GEV). The autocorrelation function, the Mann-Kendall test, and the wavelet analysis were used in order to evaluate the presence of serial correlations, trends, and periodical components. Considering the results obtained using these three statistical methods, it was possible to assume the hypothesis that this temporal series is free from persistence, trends, and periodicals components. Based on quantitative and qualitative adhesion tests, it was found that the GEV may be used in order to quantify the probabilities of the Preabs data. The best results of GEV were obtained when the parameters of this function were estimated using the method of maximum likelihood. The method of L-moments has also shown satisfactory results.
EFFECT OF BUSINESS INTELLIGENCE SYSTEM ON ORGANIZATIONAL AGILITY: EVIDENCE FROM SYRIA

This study aims to identify the effect of the business intelligence (BI) system on organizational agility. A questionnaire has been used with Twenty-Three (23) questions to collect the primary data from Seventy-Five (75) people to achieve the objectives of this study. The study depends on SPSS software to analyze data collected by the questionnaire. The results prove a significant effect directly correlates between the business intelligence system, sensor agility, and application agility. The results also prove a significant effect with a strong direct correlation between the business intelligence system and the decision-making and organizational agility.
Managing the identity paradox in inter‐organisational collaborations

A persistent contradiction in inter-organisational collaborations (IOCs) is between individual actors' IOC identity and their other multiple identities, and the subsequent need to maintain a delicate balance between these opposing but mutually dependent identities. Using a paradox lens and individual actors as the unit of analysis, this ethnographic study defines the “identity paradox” often present in IOCs as the interaction between actors' professional, personal, team and organisational identities with an overall IOC identity. The case demonstrates that the identity paradox allows actors to maintain a balance between adhering to IOC, which offers stability, and adjusting to emergent needs, which enables innovative behaviour. The theorisation of the identity paradox reveals that the conditions that trigger each identity tension relate to the IOC tensions of cooperation and competition, rigidity and flexibility, trust and mistrust, and confrontation and dialogue. The findings offer contribution to IOC research and to management practice.
Exploiting information sharing, stock management and capacity oversizing in the management of lumpy demand

The aim of this paper is to present a new approach to the management of uncertain lumpy demand. After providing a comprehensive review of the related literature, a new model is introduced, with the objective of improving the firm's ability to cope with lumpy orders. Its innovative principle relies on the exploitation of the information collected by the Sales Department during the negotiation process; such information helps to minimize the impact of the lumpy orders, thus reducing the need for oversized production capacity and/or working capital. An analytical model is presented to solve the trade-off between anticipating the production as soon as the order negotiation starts (thus risking production with no final order) and waiting until negotiation ends (thus risking a low service level due to the shorter time available to respond to the lumpy order): the right balance is found by keeping the right amount of stocks and of overcapacity. The model described turns out to be simple and easy to implement, since its informative needs consist of data usually available within the company, although often not exploited.
Erratum to "Complete Characterization of Trees with Maximal Augmented Zagreb Index"

The augmented Zagreb index (AZI) has attracted more and more attentions in the past years. Some significant mathematical properties of AZI were obtained. In particular, Lin et al. [MATCH Commun. Math. Comput. Chem. 83 (2020) 167] recently claimed a complete solution to the problem of characterizing n-vertex tree(s) with maximal AZI. In this note we correct some errors in the paper.
A Retrospective Evaluation of Operative and Postoperative Outcomes in Patients with Spinal Metastases from a Single Center to Compare Vertebrectomy with Combined Vertebrectomy and Radiofrequency Ablation

Background This retrospective study was conducted at a single center and aimed to evaluate operative and postoperative outcomes in patients with spinal metastases using vertebrectomy and combined vertebrectomy and radiofrequency ablation (RFA). Material/Methods Patients diagnosed with spinal metastases between April 2009 and March 2016 (n=49) included patients who underwent vertebrectomy (n=26) and patients who underwent combined vertebrectomy and RFA (n=23). The characteristics of the 2 groups were similar in primary tumor types, comorbidities, Tomita score, vertebral involvement, preoperative bone pain, and neurologic deficit. Results The results showed for the both groups that the visual analog scale (VAS) pain score was significantly decreased (P<0.05) and the neurological status was improved after treatment. Compared with the control group (vertebrectomy only), the combination group (combined vertebrectomy and RFA) had less intraoperative blood loss (P=0.002) and shorter operation time (P<0.001). The recurrence rate was lower (P=0.003) in the patients who received combined treatment, and the period of local recurrence was prolonged (P=0.030) in the combination group. Conclusions This retrospective study showed that the selective use of combined vertebrectomy and RFA significantly reduced surgical time and blood loss, improved recovery of neurologic deficit, and reduced the tumor recurrence rate in patients with spinal metastases.
Junk Stocks vs Quality Stocks, A Perspective from Mood Seasonality

ABSTRACT Based on their own quality measure, Asness et al. (2019) document that quality stocks significantly outperform junk stocks. In this article, we attempt to explore the quality-based return patterns from behavioural perspective. The empirical findings demonstrate that the relative performance of quality stocks exhibits significant mood seasonality, i.e. the return spreads of quality and junk stocks decrease (increase) during high (low)-mood months. Furthermore, we also find that the return spreads exhibit significant mood recurrence and reversal effects proposed by Hirshleifer et al. (2020). All of these findings support the argument that mood would be an important source for the outperformance of quality stocks.
Axicon-Based Concentrator for Cherenkov Radiation

We propose and discuss a new type of axisymmetric dielectric target – an “axicon-based concentrator” – which effectively concentrates generated Cherenkov radiation (CR) into a small vicinity of a focus point. It consists of two “glued” bodies of revolution: a hollow axicon and a hollow “lens. ” A theoretical investigation of the radiation field produced by a charge moving through the discussed radiator is performed for the general case where a charge trajectory is shifted with respect to the structure axis. The idea of a dielectric target with a specific profile of the outer surface and suitable analytical methods were presented and developed in our preceding papers. An essential advantage of the current version of the device is that it allows the efficient concentration of CR energy from relativistic particles, making this device extremely prospective for various applications such as beam-driven THz sources and bunch diagnostic systems.
Meiotic Studies, Pollen Fertility and Seed Set of Trillium govanianum, an Endangered Endemic Plant Species of the Himalaya

Summary Detailed knowledge about the meiotic behavior of threatened plant species is crucial to develop successful conservation strategies. Here we provide a detailed account of the male meiotic behavior of Trillium govanianum Wall ex. D. Don ̶ an endangered endemic medicinal plant species of the Himalaya. We collected the plant material from the Bangus valley of the Kashmir Himalaya. The species showed chromosome number n =2 x =10, which is the ﬁrst gametophytic report for this species. The pollen fertility was found to be in the range of 88–90%. The reduced fertility was due to the meiotic abnormalities such as chromatin stickiness, a late disjunction of bivalents, chromatin bridges, formation of laggards, and polysporads with micronuclei. The seed set was found to be 65.85%. The reduced seed set might be attributed to the presence of various meiotic abnormalities
Prognostic Significance of Trophoblastic Cell Surface Protein 2 (Trop2) Expression in Breast Cancer and Relation to Microvascular Density

Background: The expression of trophoblastic cell surface protein 2 (Trop2) has been correlated with aggressive behavior in multiple malignant neoplasms. Moreover, it is a promising therapeutic target for several malignancies. However, data on the prognostic significance of Trop2 in breast cancer is insufficient. The current work aimed to study the prognostic significance of Trop2 expression in breast cancer and its correlation with microvascular density (MVD). Patients and Methods: Immunohistochemical expression of Trop2 was assessed in 70 cases of breast cancer and its relation to clinicopathological parameters, MVD (as assessed by CD34 immunostaining) and patient’s survival was evaluated. Results: High Trop2 expression was detected in 44 cases (62.9%) of the studied cases. The expression of Trop2 was significantly related to tumor size, lymph node stage, lymphovascular invasion, tumor grade and high Ki67 index (P<0.05). Moreover, the expression of Trop2 was significantly higher in triple-negative breast carcinoma (TNBC) than other molecular subtypes (P=0.015). Additionally, Trop2 expression was significantly correlated to MVD (r= 0.845). Both overall survival and disease-free survival were significantly shorter in the high Trop2 group than low Trop2 group (P=0.008, 0.021), respectively. Conclusion: Trop2 can be considered as a poor prognostic marker and potential therapeutic target for breast cancer, mainly the triple-negative subtype. Since Trop2 expression was significantly correlated to MVD, we speculate that enhancement of angiogenesis could be one of the mechanisms involved in Trop2 induced tumor invasion and metastasis.
TEACHING SPOKEN ENGLISH IN SPAIN: CLASSROOM PRACTICES AND TEACHERS´' PERCEPTIONS

Aim. This article presents the results obtained in a qualitative research related to classroom practices and perceptions of teachers of English as a foreign language in Spain. The aim of this study is to present examples of good teaching practices, including the types of resources, materials and assessment used in spoken English teaching. Methods. A qualitative investigation that used ethnographic (non-participant) observation in primary and secondary English classes was implemented in thirty-two schools. Additionally, twenty semi-structured interviews with primary and secondary teachers were conducted. Results and conclusion. The results show significant differences between both applied methods Non-participant observation indicates that spoken communication in English is not practised sufficiently. However, based on interview results, teachers do apply appropriate language learning strategies that could allow them to successfully teach speaking skills in their students. The results imply that in Spain, there are still many teachers and educational institutions that follow the Grammar Translation Method and other traditional methodologies, which still focus primarily on writing skills. However, several examples of good practices and inspiring methodological and motivational strategies have been found throughout this research, which might be considered as a precedent for those that focus on writing approaches. Cognitive value. This article displays an original research supported by University of Oviedo, through which, the reader can approach to the teaching of spoken English in Spain by means of some teachers´ perceptions and examples of good practices.
Autonomous Penetration Testing Based on Improved Deep Q-Network

Penetration testing is an effective way to test and evaluate cybersecurity by simulating a cyberattack. However, the traditional methods deeply rely on domain expert knowledge, which requires prohibitive labor and time costs. Autonomous penetration testing is a more efficient and intelligent way to solve this problem. In this paper, we model penetration testing as a Markov decision process problem and use reinforcement learning technology for autonomous penetration testing in large scale networks. We propose an improved deep Q-network (DQN) named NDSPI-DQN to address the sparse reward problem and large action space problem in large-scale scenarios. First, we reasonably integrate five extensions to DQN, including noisy nets, soft Q-learning, dueling architectures, prioritized experience replay, and intrinsic curiosity model to improve the exploration efficiency. Second, we decouple the action and split the estimators of the neural network to calculate two elements of action separately, so as to decrease the action space. Finally, the performance of algorithms is investigated in a range of scenarios. The experiment results demonstrate that our methods have better convergence and scaling performance.