Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring competitive intensity in sports leagues

For managers of sports leagues, it is crucial to produce an attractive competition. For that to happen, it is vital to consider that leagues frequently have more sub-competitions than “just” the championship. In European top football leagues, for instance, four sub-competitions are common (championship, qualification for Champions- or Europa League, avoiding relegation). This paper introduces a new method for measuring competitive intensity (CI) in round-robin sports leagues considering all relevant sub-competitions and applies it to Germany's Bundesliga. ,The newly developed model calculates a CI-Index for each sub-competition and the league as a whole. The application to the Bundesliga analyzes its viability and the development of the league's CI over the past 22 seasons. ,The newly introduced CI-Indices prove to be a viable tool for evaluating a league's competitive intensity. The application to the Bundesliga shows that the seasonal CI dropped after 2009/10, which can mainly be attributed to a decline in the championship's CI. ,The results show that it is important to facilitate a high CI in each of Bundesliga's four sub-competitions. Efforts have to be made to ensure that especially the Europa League remains as attractive as possible for the participating teams and their fans because this sub-competition constantly makes the greatest contribution to the seasonal CI. ,The new method measures competitive intensity by quantifying the different sub-competitions and their contribution to the seasonal CI. This allows the organizers of sports leagues to assess the intensity of the individual sub-competitions as well as the league as a whole.
The Climatology of Significant Tornadoes in the Czech Republic

In the Czech Republic, tornadoes may reach an intensity of F2 and F3 on the Fujita scale, causing “considerable” to “severe” damage. Documentary evidence is sufficient to allow the creation of a chronology of such events, from the earliest recorded occurrence in 1119 CE (Common Era) to 2019, including a total of 108 proven or probable significant tornadoes on 90 separate days. Since only 11 significant tornadoes were documented before 1800, this basic analysis centers around the 1811–2019 period, during which 97 tornadoes were recorded. Their frequency of occurrence was at its highest in the 1921–1930, 1931–1940, and 2001–2010 decades. In terms of annual variations, they took place most frequently in July, June, and August (in order of frequency), while daily variation favored the afternoon and early evening hours. Conservative estimates of human casualties mention 8 fatalities and over 95 people injured. The most frequent types of damage were related to buildings, individual trees, and forests. Tornadoes of F2–F3 intensity were particularly associated with synoptic types characterized by airflow from the western quadrant together with troughs of low pressure extending or advancing over central Europe. Based on parameters calculated from the ERA-5 re-analysis for the period of 1979–2018, most of these tornadoes occurred over a wide range of Convective Available Potential Energy (CAPE) values and moderate-to-strong vertical wind shear. The discussion herein also addresses uncertainties in tornado selection from documentary data, the broader context of Czech significant tornadoes, and the environmental conditions surrounding their origins.
Formulation and Evaluation of Sonidegib Loaded Poly (Ethyl Methacrylate) Nanoparticles for Effective Treatment of Cancer

Aim: The present research is aimed to develop and evaluate sonidegib loaded poly(ethyl methacrylate) nanoparticles (PEM-NPs) to improve its resistance toward pH and chemical conditions in exposed cancerous lesions. Materials and Methods: The polymer PEM is prepared from ethyl methacrylate (monomer) followed by designing 17 formulations of sonidegib loaded PEM-NPs using 3-factor, 3-level Box–Behnken design, and the results analyzed using Stat-Ease Design Expert® software V8.0. 1. Three optimal batches (F1, F2, and F3) with comparable values of observed and predicted values are characterized for particle size, polydispersity index (PDI), zeta potential (ZP), entrapment efficiency, and percentage drug loading. The formulation (F3) with minimum particle size and maximum percentage conversion is further subjected to powder X-ray diffraction (PXRD), Fourier-transform infrared (FTIR), scanning electron microscopy (SEM) studies, drug release, and stability study. Results and Discussion: The particle size of sonidegib PEM-NPs (F1, F2, and F3) ranges between 191.5 ± 42.9 nm to 355 ± 39.7 nm and PDI 0.454 to 0.626. The ZPs are within the acceptable limits of −22.9 ± 2.48 mV–−24.7 ± 1.89 mV. The entrapment efficiency of the NPs ranges between 68.46 ± 0.37% and 70.24 ± 0.18% and percent drug loading between 20.62 ± 2.12% and 21.24 ± 1.72. The in vitro release study indicated an improvement in drug release of formulation F3 (95.878%) in comparison with the pure drug (2.86%). The optimized formulation F3 characterized for FTIR, PXRD, and SEM studies indicated molecular state dispersion of the drug with the polymers. The stability studies conducted for 90 days indicated that the developed formulation is stable. Conclusion: Sonidegib loaded PEM-NPs prepared using 3-factor, 3-level Box–Behnken design with increased solubility and stability.
The Role of El Niño in Driving Drought Conditions over the Last 2000 Years in Thailand

Irregular climate events frequently occur in Southeast Asia due to the numerous climate patterns combining. Thailand sits at the confluence of these interactions, and consequently experiences major hydrological events, such as droughts. Proxy data, speleothem records, lake sediment sequences and tree ring chronologies were used to reconstruct paleo drought conditions. These trends were compared with modelled and historic El Niño Southern Oscillation (ENSO) data to assess if the ENSO climate phenomena is causing droughts in Thailand. Drought periods were found to occur both during El Niño events and ENSO neutral conditions. This indicates droughts are not a product of one climate pattern, but likely the result of numerous patterns interacting. There is uncertainty regarding how climate patterns will evolve under climate change, but changes in amplitude and variability could potentially lead to more frequent and wider reaching hydrological disasters. It is vital that policies are implemented to cope with the resulting social and economic repercussions, including diversification of crops and reorganisation of water consumption behaviour in Thailand.
Franchisee advisory councils and justice: franchisees finding their voice

ABSTRACT Drawing on the four dimensions of justice: distributive, procedural, interactional, and informational justice, this research examines justice as an antecedent of franchisee performance. Empirical research tested a theoretical model using structural equation modelling techniques. Our results show significant support for two of the four dimensions: distributive and interpersonal justice. Furthermore, our model tested, and found support for the moderation of franchisee membership in the Franchisee Advisory Council (FAC). Perceptions of distributive justice is considered the most important contributor to franchisees’ perceived performance in franchise systems that endorse and actively participate with the FAC through membership. Thus, one major implication of this research is the moderation effect of membership of the FAC on the influence of justice on franchisees’ perceptions of positive performance outcomes. This speaks to the FAC’s particular importance for facilitating best practice, strong, and financially viable franchise systems.
Cyber-attack Detection Strategy Based on Distribution System State Estimation

Cyber-attacks that tamper with measurement information threaten the security of state estimation for the current distribution system. This paper proposes a cyber-attack detection strategy based on distribution system state estimation (DSSE). The uncertainty of the distribution network is represented by the interval of each state variable. A three-phase interval DSSE model is proposed to construct the interval of each state variable. An improved iterative algorithm (IIA) is developed to solve the interval DSSE model and to obtain the lower and upper bounds of the interval. A cyber-attack is detected when the value of the state variable estimated by the traditional DSSE is out of the corresponding interval determined by the interval DSSE. To validate the proposed cyber-attack detection strategy, the basic principle of the cyber-attack is studied, and its general model is formulated. The proposed cyber-attack model and detection strategy are conducted on the IEEE 33-bus and 123-bus systems. Comparative experiments of the proposed IIA, Monte Carlo simulation algorithm, and interval Gauss elimination algorithm prove the validation of the proposed method.
In vitro propagation of the endangered orchid Dendrobium chryseum Rolfefrom protocorms culture

The immature seeds of Dendrobium chryseum, a sympodial epiphytic orchid with yellow flowers, were cultured in vitro, and the resultant protocorms were used as explants for seedling development. Protocorms were cultured on 1⁄2 M.S. medium fortified with Kinetin (Kn), 6-Benzylaminopurine (BAP), and Gibberellic Acid (GA3) in three concentrations (0.5mg/L, 1.0 mg/L and 2.0 mg/L) both alone and supplemented with 5% and 10% coconut water (C.W.). The highest number of shoots of D. chryseum developed on 1⁄2 M.S. medium fortified with 2.0 mg/L of Kn and10% C.W. and the longest shoots developed on 1⁄2 M.S. media fortified with 1.0 mg/L GA3, and 10% C.W. The shoot derived from protocorms were placed in 1⁄2 M.S. medium fortified with three different rooting hormones, Indole -3acetic acid (IAA), Indole -3-butyric acid (IBA) and α-Naphthalene acetic acid (NAA) in different concentrations alone as well as with each 1.0 mg/L hormone combined with 10% C.W. The most effective of these media was 1⁄2 M.S. medium fortified with 1.5 mg/L IAA for rooting as well as for the production of longest roots. The present study could be useful for standardizing the protocol for mass propagation of the endangered orchid Dendrobium chryseum.
Conceptualizing reverse logistics to plastics recycling system

Purpose Plastics waste management is a critical agenda for the global community. Recycling is the most important strategy option for recovering plastics wastes. This study aims to review reverse logistics (RL) implementation practices and conceptualizing it to the plastic recycling system. Design/methodology/approach The paper is organized after evaluating the studies related to plastics waste recycling and analyzing the available frameworks to use RL as a strategic tool. Findings The paper has investigated that previous research on RL implementation focused on a few stages of RL activities and did not include the most important issues. However, for successful RL implementation, taking into account the whole stage and including the most important factors is very important. To elaborate on this finding a new conceptual framework is developed. Research limitations/implications The paper is fully based on literature review and international reports. The developed framework is required for further empirical validation in the plastics sector. Practical implications The paper has considered the important issues and the applications of those factors that can improve plastics recycling performances. Social implications This study can enhance the active involvement of main actors (plastics producers, users, municipal and recyclers) in the plastics recycling system. Originality/value This paper deliberates on how RL can be conceptualized and implemented in plastics recycling systems in considering the most important factors for plastics recycling.
A Hybrid Deep Learning Approach for Sentiment Analysis using CNN and Improved SVM with Multi Objective Swarm Optimization for Domain Independent Datasets

In the Big Data world, there is an exponential increase in the volume of numerous data, such like text, image, audio and video, as text is the largest among them. Sentiment analysis is a trendy application in text mining, where text data concerning the feelings or attitude of the consumer is collected using different methods or techniques. Sentiment detection of online product reviews is helpful to figure out emotions and viewpoints of customers. Many researchers have just developed the Deep Learning model for obtaining tremendous performance in NLP. This paper suggested novel deep-learning hybrid architecture that is highly effective for analyzing sentiments on domain independent datasets. We blend deep Convolutional Neural Networks (CNN) and support vector machines (SVM) for a better overall classi ﬁ cation. The reason for using CNN is not only to extract local features but also the framework for predicting sentiments and combining CNN output into SVM progress the classi ﬁcation. In addition, we have adopted MSPSO (Multi-swarm Particle Swarm Optimization) system to obtain optimized feature selection and to train the SVM for further improved in sentiment classification. To demonstrate that our proposed method is of a generic nature, we have evaluated on datasets of online product reviews from various domain. Evaluation exposes that the implementation of the proposed approach is reliable among all the datasets and often outshine the state-of-art systems.
Identification of a Heterozygous Microsatellite Marker in Pinus pumila

1 Department of Biological Sciences, Kanagawa University, Tsuchiya, Hiratsuka, Kanagawa 259–1293, Japan 2 Research Institute for Integrated Science, Kanagawa University, Tsuchiya, Hiratsuka, Kanagawa 259–1293, Japan 3 Bunkyo Study Center, Tokyo, The Open University of Japan, Otsuka, Bunkyo, Tokyo 112–0012, Japan 4 Depatment of Chemistry, Faculty of Science, Ochanomizu University, Otsuka, Bunkyo, Tokyo 112–8610, Japan 5 Department of Environmental Systems Sciences, Musashino University, Ariake, Koto, Tokyo 135–8686, Japan 6 Department of Chemical Biological Sciences, Faculty of Science, Japan Women’s University, Mejirodai, Bunkyo, Tokyo 112–8681, Japan 7 Department of Food Business, Faculty of Health and Human Life, Nagoya Bunri University, Maeda, Inazawa, Aichi 492–8520, Japan
ACCUMULATION AND HISTOLOGICAL OBSERVATION OF HEAVY METAL IN Brassica campestris L. AND Chenopodium album L. GROWING ON SLUDGE OF PULP PAPER INDUSTRY AFTER SECONDARY TREATMENT

The discharged sludge from the pulp paper industry contains several organic and inorganic pollutants even after secondary treatment, which might cause toxicity in plants. The present study was designed to investigate the accumulation and histological findings of heavy metal in Brassica campestris and Chenopodium album growing on sludge waste of pulp paper industry after secondary treatment. The physico-chemical analysis of sludge viz., pH, total solid (2678 mg L -1 ), total dissolved solid (2756 mg L -1 ), total suspended solid (189 mg L -1 ), chemical oxygen demand (43587 mg L -1 ), biological oxygen demand (1569 mg L -1 ), and electric conductivity (2067 ms cm -1 ) contents in it were found beyond the permissible limit. The result of study revealed that the heavy metal content in sludge viz., Cu (59), Ni (66), Fe (153), Mn (9.37) Zn (12.31) and Mg (11.8 mg L -1 ). Furthermore, the chlorophyll and carotenoids contents in B. campestris and C. album was (Chl-a 4.57-5.21 mg g -1 fw), (chl-b 5.29-5.89 mg g -1 fw), and (carotenoids 0.84-1.07 mg g -1 fw) also high. The effect of heavy metals on antioxidants enzyme of B. campestris and C. album growing on organometallic containing sludge waste was observed to be high. Further, the concentrations of heavy metals in B. campestris and C. album were reported in descending order as Fe (211-208)> Cu (62.37-49.87)>Zn (56.67-34.26) > Mn (36.37-42.37) > Mg (35.69-13.64)> Ni (9.36-6.51 mg kg -1 ) root> shoot> leaves respectively. Hence the result of study concluded that pulp paper industry sludge is the main source of metals accumulation in crop plants, and need to be appropriately treated before discharge in the environment for human and animal health safety.
Examining the factors affecting driver behavior in Metropolitan Kuwait

Abstract One of the important factors of road safety is drivers’ behavior. It is very useful to examine the factors of driver behavior to reduce the human factors to improve road safety. The objective of this study is to employ Manchester driver behavior questionnaire (DBQ) to measure various types of driver behavior in Kuwait. Data collection was performed by using an online questionnaire. The factor analysis was used to examine the data which compute into three factors structure. However, these three factors could not be classified into errors, lapses and violations categories as other studied due to social desirability bias. The future studies should examine this issue further.
Adsorption based realistic molecular model of amorphous kerogen

This paper reports the results of Grand Canonical Monte Carlo (GCMC)/molecular dynamics (MD) simulations of N2 and CO2 gas adsorption on three different organic geomacromolecule (kerogen) models. Molecular models of kerogen, although being continuously developed through various analytical and theoretical methods, still require further research due to the complexity and variability of the organic matter. In this joint theory and experiment study, three different kerogen models, with varying chemical compositions and structure from the Bakken, were constructed based on the acquired analytic data by Kelemen et al. in 2007: 13C nuclear magnetic resonance (13C-NMR), X-ray photoelectron spectroscopy (XPS), and X-ray absorption near-edge structure (XANES). N2 and CO2 gas adsorption isotherms obtained from GCMC/MD simulations are in very good agreement with the experimental isotherms of physical samples that had a similar geochemical composition and thermal maturity. The N2/CO2 uptake by the kerogen model at a range of pressure shows considerable similarity with our experimental data. The stronger interaction of CO2 molecules with the model leads to the penetration of CO2 molecules to the sub-surface levels in contrast to N2 molecules being concentrated on the surface of kerogen. These results suggest the important role of kerogen in the separation and transport of gas in organic-rich shale that are the target for sequestration of CO2 and/or enhanced oil recovery (EOR).
DREAMPIace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement

Placement for very-large-scale integrated (VLSI) circuits is one of the most important steps for design closure. This paper proposes a novel GPU-accelerated placement framework DREAMPlace, by casting the analytical placement problem equivalently to training a neural network. Implemented on top of a widely-adopted deep learning toolkit PyTorch, with customized key kernels for wirelength and density computations, DREAMPlace can achieve over $ 30\times $ speedup in global placement without quality degradation compared to the state-of-the-art multi-threaded placer RePlAce. We believe this work shall open up new directions for revisiting classical EDA problems with advancement in AI hardware and software.
Conceptualizing Lifestyle and Routine Activities in the Early 21st Century: A Systematic Review of Self-Report Measures in Studies on Direct-Contact Offenses in Young Populations

Criticism is commonly directed at research based on lifestyle-exposure theory and routine activity theory for the use of imprecise measures. To examine this issue further, this systematic review maps activities used as indicators of lifestyle and routine activities in studies on direct-contact crime among young individuals (mean age of 24 or younger). The results indicate that a broad range of activities are used as measures of lifestyle and routine activities in the 101 included studies. Studies on victimization often use illegal activities and substance use as measures of lifestyle or routine activities while studies on offending mainly rely on measures of unstructured and peer-oriented leisure activities. Overall, most studies seem more concerned with specific activities rather than routines or lifestyles.
Preliminary Analysis of Malaysian Corpus of Financial English (MaCFE)

This paper presents the findings of the preliminary analysis conducted on the Malaysian Corpus of Financial English (MaCFE). MaCFE is a specialised corpus consisting of written documents compiled from banks in Malaysia and the corpus is currently housing approximately 4.3 million word tokens. The aim of the analysis was to evaluate the suitability of the texts chosen to represent the financial domain. The preliminary analysis involved generating the word list and lists of co-occurrences from MaCFE. RapidMiner Studio Educational 7.5. 001 and an in-house Java programming solution was utilised to perform the analysis. The word list and lists of 50 most frequent two-word and three-word co-occurrences generated from the analysis reveal that the text compilation is representative of the financial domain in Malaysia. The study concludes by discussing the pedagogical implications of the findings. Keywords: Corpus linguistics; Co-occurrences; Financial corpus; Specialised corpus; Word list
The Application of Forecasting Sales of Services to Increase Business Competitiveness

The accurate forecasting of business variables is a key element for a company’s competitiveness which is becoming increasing necessary in this globalized and digitalized environment. Companies are responding to this need by intensifying accuracy requirements for forecasting economic variables. The objective of this article is to verify the correctness of the models predicting revenue in the service sector against 6 precision criteria to determine whether the use of certain criteria may lead to the adoption of particular models to improve competitive forecasting. This article seeks to determine the best accuracy predictors in 32 service areas broken down by NACE. Exponential smoothing models, ARIMA models, BATS models and artificial neural network models were selected for the assessment. Six criteria were chosen to measure accuracy using a group of scale-dependent errors and scaled errors. Services for which the result was ambiguous were subject to complete forecasting, both ex-post and ex-ante. Based on the analysis, the main result of the article is that only two types of services do not achieve the same accuracy results when using other measure criteria. It can therefore be said that for 93.75% of services, an assessment according to one precision parameter would suffice. Thus, a model’s competitiveness is not affected by the choice of accuracy.