Miracle-dz/UniFAR-FTUs huggingface dataset:
Agency costs, board structure and institutional investors: case of India

The author examines the role of board structure and institutional investors in dealing with the agency issues for the Indian firms by taking the data of NSE-500 nonfinancial firms for the period 2010–2019. ,The author applies dynamic panel data methodology to deal with endogeneity concerns prevalent in corporate finance variables. ,The agency view is consistent with the board size in the context of India. The author observed that the board size has a harmful effect on agency cost. A larger board size may create a coordination problem, or CEO may find it easy to thrust his or her decisions on board. The author also noticed that firms should have sizeable institutional ownership, particularly pressure-insensitive investors, in equity as they can reduce agency-related issues. ,This study focuses on one of the largest emerging economies, i.e. India.
Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry

Deep neural networks have achieved state of the art accuracy at classifying molecules with respect to whether they bind to speciﬁc protein targets. A key breakthrough would occur if these models could reveal the fragment pharma-cophores that are causally involved in binding. Extracting chemical details of binding from the networks could potentially lead to scientiﬁc discoveries about the mechanisms of drug actions. But doing so requires shining light into the black box that is the trained neural network model, a task that has proved difﬁcult across many domains. Here we show how the binding mechanism learned by deep neural network models can be interrogated, using a recently described attribution method. We ﬁrst work with carefully constructed synthetic datasets, in which the ’fragment logic’ of binding is fully known. We ﬁnd that networks that achieve perfect accuracy on held out test datasets still learn spurious correlations due to biases in the datasets, and we are able to exploit this non-robustness to construct adversarial examples that fool the model. The dataset bias makes these models unreliable for accurately revealing information about the mechanisms of protein-ligand binding. In light of our ﬁndings, we prescribe a test that checks for dataset bias given a hypothesis. If the test fails, it indicates that either the model must be simpliﬁed or regularized and/or that the
Fiber Optic Sensor Design and Prototyping for Humidity Detection in Biogas Reactors

Biyogaz reaktorlerinde, artan nem ile su buharinin yogunlasmasinin izlenmesi hayati onem tasir. Bu kontrolun cevrimici olarak hizli ve guvenilir bir sekilde yapilmasina izin veren fiber optik tabanli sensorlerin kullanilmasi, sureci kolaylastirir. Buna gore bu calismada, biyogaz reaktorleri icin fiber optik su buhari ve yogunlasma sensoru tasarlanmis ve uygulamasi yapilmistir. Fiber yansima ve boyuna hizalama kayiplarina bagli olarak nem seviyesinin artmasi ile olusacak su buhari ve yogunlasmasi algilanabilmistir. Hizli tepki verebilen ve tespit edilecek parametre disindaki cevresel degiskenlerden etkilenmeyen cok dusuk maliyetli bir sensor mekanizmasi olusturularak, artan su buhari ve yogunlasmanin baslangic ani yuksek hassasiyet ve hizla tespit edilebilecegi gosterilmistir.
Running title: METAZOAN PARASITES OF Pimelodus maculatus AND Rhamdia quelen Academy Section: ECOSYSTEMS

In the freshwater ecosystems of Brazil can be found high biodiversity of fi sh, about 5160 species. However, the Jacaré-Pepira River, located in the state of São Paulo, Brazil, presents a diversity of fi sh still to be explored in ichthyological studies. Metazoan parasites of Pimelodus maculatus and Rhamdia quelen were qualitatively and quantitatively diagnosed. Ten species of parasites (Demidospermus sp., D. majusculus, D. bidiverticulatum, D. paravalenciennesi, Ameloblastella paranaensis, Scleroductus sp., Riggia sp., Austrodiplostomum compactum, Helobdella sp. and Neochinorhynchus pimelodi) were collected in P. maculatus and eight species of parasites (Aphanoblastella robustus, A. mastigatus, Phyllodistomum rhamdiae, Crocodilicola pseudostoma, Henneguya jundiai, Contracaecum sp., Rhabdochona sp. and Capillariidae gen. sp.) were collected in R. quelen. All parasites presented aggregate distribution . A signifi cant correlation was observed in P. maculatus concerning the weight with the prevalence of ectoparasite D. majusculus; however, R. quelen showed a relation to the length and weight with the abundance of ectoparasite A. mastigatus and endoparasites. The parasitic community of P. maculatus and R. quelen was characterized by high diversity, high richness, and low uniformity.
Finding the optimal set of parking locations for maintenance trains in the Dutch railway network

The Dutch railway system is subject to maintenance, which is carried out by a group of rail contractors who need parking space to park and prepare trains in between projects. The objective of this study is to minimise the total costs as a result of the distance travelled by maintenance trains and the preservation of the included parking locations in the Dutch railway network. According to the dynamic nature and the NP-hardness of the Facility Location Problem (FLP), a Simulation-Optimisation (Sim-Opt) approach is proposed. This Sim-Opt consists of a Discrete Event Simulation (DES) and Simulated Annealing (SA) optimisation. Two neighbourhood functions are evaluated: a Random Search Algorithm (RSA) and a Utility Level Search Algorithm (ULSA), which takes the utility level of parking locations into consideration. This research shows that DES is a feasible evaluation method for finding possible solutions to the FLP in a Sim-Opt approach. The results of this evaluation show that the ULSA converges sooner than the RSA. Furthermore, the spread in best solutions across all instances is tighter when applying the ULSA. The findings indicate that the ULSA gives more robust solutions when compared to the RSA and makes the SA process more efficient. This research found that the best solutions in terms of overall cost includes on average 22 parking locations, which can reduce the maintenance cost by ̃30%. The average increase in travel costs of ̃9% does not justify the use of more capacity than the absolute minimum to house all trains.
Investigation of Nonlinear Behavior of the Reinforced Concrete Columns for Different Confined Concrete Models

Stress-strain and moment-curvature behavior of the reinforced concrete (RC) square columns have been analytically investigated according to different confined concrete models. The effect of transverse reinforcement diameter, transverse reinforcement spacing and concrete grade on the behavior of RC column models were investigated. For different confined concrete models, the confinement effectiveness coefficient, effective lateral confining stress, confined concrete compressive strength, strain at maximum concrete stress and ultimate concrete compressive strain values were calculated. In the second part, a parametric investigation was carried out for examining the effects of different design parameters on the moment-curvature relationships. Analytical momentcurvature relationships were obtained for RC cross-sections by using the TBEC (2018), Mander model (1988), Saatcioglu and Ravzi (1992) confined concrete models. The effects of the design parameters on the RC square column behavior were evaluated in terms of moment capacity and the curvature of the cross-section. In RC column models, stress-strain and moment-curvature relationships are obtained and compared according to different parameters. Confined concrete strength and the ultimate moment values obtained from the Mander model were higher than the Saatcioglu and Ravzi model when the transverse reinforcement close to the minimum spacing values. The results obtained from the Mander model and TBEC (2018) are close to each other.
Implementation of a Hybrid Educational Program between the Model of Personal and Social Responsibility (TPSR) and the Teaching Games for Understanding (TGfU) in Physical Education and Its Effects on Health: An Approach Based on Mixed Methods

The present study investigates the effect of an educational program hybridized between the Model of Personal and Social Responsibility (TPSR) and the Teaching Games for Understanding (TGfU) in physical education classes on the health and psychosocial variables of students, as well as knowing the advantages and disadvantages of its implementation by teachers. The applied program lasted 11 weeks in two Secondary Education centers with a total of four teachers (two in the experimental group and two in the control group) and 99 students (55 from the control group and 44 from the experimental group). We use research methodology Mixed Methods with a quasi-experimental design, where students completed a questionnaire before and after the educational program and teachers were interviewed at the end of the intervention. The results of the questionnaires indicate significant improvements in the experimental group over time in terms of the intention to be physically active, as well as in autonomous motivation, the self-determination index, the index of psychological mediators, personal and social responsibility, and enjoyment. Moreover, the interviews show positive opinions regarding the organizational capacity of the session using this methodology and the interest of teachers in continuing to apply it in the future, as well as the need for initial and ongoing training for proper implementation. In conclusion, the hybridization between the TPSR and TGfU model is presented as an effective alternative to be applied in the educational context with the aim of improving young peoples’ intention to be physically active and psychological variables, such as motivation, responsibility, and enjoyment, in physical education classes.
MIP-10-2019-0527_proof 670..686

Purpose – The aim of this study is to develop and evaluate a market-oriented business model (MOBM) and analyze how it contributes to internationalization of SMEs’ disruptive innovation. Design/methodology/approach – Based on market orientation literature, an MOBM is developed and assessed through collaboration among companies, researchers and networking partners. For the evaluation of themodel, qualitative datawas collected throughworkshops, interviews and participatory observations at four case SMEs. Methodologically, the implementation of the MOBM consists of a systematic knowledge development process by following four work packages to support the companies’ market-oriented internationalization. Findings – The results show that SMEs face internal barriers to developing innovativeness that hinder them from creating effective disruptive innovation for the international buyer chain. The study finds that SMEs need to work with an MOBM for developing market intelligence within the organization and seek external support for entering the international market. Practical implications –Themethodological strength allows application, evaluation andmodification of the MOBM in close collaboration with the SMEs that directly benefit from its implementation. Modifying the principles of market orientation by practical application, SMEs can apply the MOBM to analyze their internationalization capacity for high-tech disruptive innovations. Originality/value – This article contributes to new thinking by introducing market orientation to SMEs’ internationalization of disruptive innovation. The study highlights the less researched field of disruptive innovation by developing the MOBM to deal with SMEs’ internationalization.
Investigating Methylene Blue Removal from Aqueous Solution by Cysteine-Functionalized Mesoporous Silica

In this study, mesoporous silica nanoparticles (MSNs) were synthesised using the Stober method and functionalised with cysteine (MSN-Cys) for removal of Methylene Blue (MB) from aqueous solution using the batch method. The adsorbent nanoparticles were characterised by Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), FTIR, BET, and TGA. Several influential factors on the adsorption of MB onto the surface of MSN-Cys particles were investigated, including pH, initial concentration, and contact time. The adsorption capacity of MB from aqueous solution increased from circa 70 mg/g MSN-Cys in acidic media to circa 140 mg/g MSN-Cys in basic media. Adsorption isotherms and kinetic models of adsorption were used to clarify the adsorption process. The measured adsorption isotherm was fitted with a Freundlich model for all solutions, and the kinetic model was determined to be pseudo-second-order.
BJM-08-2020-0287_proof 318..341

Purpose – The research is aimed at elaborating a model in which dynamic capabilities affect sustainable competitiveness via organisational sustainability practices and the mediating role of organisational ambidexterity. Design/methodology/approach – Emphasising the need for business sustainability in the face of technological breakthroughs, resource depletion and increasing expectations of stakeholders, it is necessary to reflect on a long-term organisational resilience that would enable sustainable competitiveness through dynamic capabilities. Hence, the paper provides insights on how an organisation can sustain its competitiveness by constantly balancing between the need for continuous improvement due to the pressure in economic, social and ecological environment, and the pursuit of continuous improvement of performance. The authors used structural equation modelling on data collected via a survey of 455 organisations from the Baltic region. Findings – The results confirm the relationships between sensing and reconfiguring capabilities and sustainability practices, but reject them for scanning capabilities. They also confirm the impact of sustainability practices on some of the pillars of sustainable competitiveness. The research disclosed that ambidexterity was a mediator between dynamic capabilities and sustainable competitiveness. Originality/value – The paper discloses the link between dynamic capabilities and sustainable competitive advantage by identifying the main characteristics of the constructs and revealing the linkage between them.
Drought Resistance and Recovery of Kentucky Bluegrass (Poa pratensis L.) Cultivars under Different Nitrogen Fertilisation Rates

Kentucky bluegrass (Poa pratensis L.) is one of the most popular cool-season turfgrass species. However, little is known about the effects of N supply on its resistance to drought stress. The objective of this study was to assess the effects of acute drought followed by a recovery period on four Kentucky bluegrass cultivars (‘Barduke’, ‘Brooklawn’, ‘NuBlue Plus’ and ‘Marauder’) and one tall fescue (‘Rhambler SRP’) under two different nitrogen fertilisation rates (100 or 200 kg N ha−1 yr−1). The study was conducted over two years in a rain-out structure to control water input in spring and summer. The cultivars were subjected to a drought stress phase (absence of irrigation) followed by a recovery phase (weekly irrigation with an intake of 80% of ET). The green cover percentage, visual quality, NDVI, and soil moisture were measured weekly. We found that Kentucky bluegrass maintains sufficient turf quality for 2 weeks without irrigation. During the first year of the experiment, slight differences were observed among the Kentucky bluegrass cultivars, with ‘Marauder’ showing a 15% of green turf cover less than ‘Brooklawn’ after 6 weeks of acute drought, while in the second year, ‘NuBlue Plus’ displayed higher green turf cover and NDVI than the other cultivars. Nitrogen treatment had limited influence on the performances of the cultivars, ‘Marauder’ being the only one benefitting from the higher rate of applications.
B O T ORCH : Bayesian Optimization in PyTorch

Bayesian optimization provides sample-efﬁcient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. We introduce B O T ORCH , a modern programming framework for Bayesian optimization that combines Monte-Carlo (MC) acquisition functions, a novel sample average approximation optimization approach, auto-differentiation, and variance reduction techniques. B O T ORCH ’s modular design facilitates ﬂexible speciﬁcation and optimization of probabilistic models written in PyTorch, simplifying implementation of new acquisition functions. Our approach is backed by novel theoretical convergence results and made practical by a distinctive algorithmic foundation that leverages fast predictive distributions, hardware acceleration, and deterministic optimization. In experiments, we demonstrate the improved sample efﬁciency of B O T ORCH relative to other popular libraries.
Mechanical design and kinematic analysis of a hand prosthesis from computerized tomography images

Advancement of technology brought along many prostheses design and developments. The main purpose of prostheses are to improve the life standard of people with limb loss. There are many types of prostheses that were developed in recent years. Prostheses can compensate many limb losses, upper body prostheses can be for not only finger losses but also full arm losses. Finger losses are the most common limb losses. Finger prostheses are imitating the functionality of human fingers. However most of the finger prostheses are only for cosmetic purposes and non-functional. On the other hand, the most of the functional prostheses are not perfectly fit to the users and are not designed personalised. Another big problem, some prostheses need a surgical operation to apply. The main purpose of this study is to design a finger prosthesis that is a perfect fit to user (personalised), easy to apply (does not required surgical operation), accessible (easy to manufacture) and sustainable. In this study, finger prosthesis was modelled based on human hand skeleton structure from computerized tomography (CT) images. Index finger distal and middle phalanges bones were removed from the hand model to simulate finger losses. Finger prosthesis was created on the modelled skeleton structure. Hand skeleton model and newly designed prosthesis were assembled by using CAD software. The designed prosthesis movement capability was examined, parts size and connections were optimised. Gripping ability of the designed prosthesis were analysed by kinematic analyses and a new mathematic model was developed. Created mathematic model can be use at other user’s prosthesis, in this way analyses are not required for new designs, user parameters and measurements are enough to future designs manufacturs. Joint rotation rates were set to grip 40 mm diameter cylinder. The gripping ability of the newly designed finger prosthesis was analysed. Mechanical structure and movement abilities of the prosthesis were confirmed by experiments and measurements. Gripping tests are show that, designed and optimised prosthesis capable to grip perfectly 38-42 mm diameter cylinder. Smaller object also can be moved using prosthesis finger tips.
PHONETIC CONVERGENCE EVALUATION BASED ON FUNDAMENTAL FREQUENCY VARIABILITY

There is a growing body of studies investigating whether interlocutors become more similar to each other during a dialogue. The present study contributes to this research line by investigating the pitch profiles, and convergence and synchrony in mean F0 values, in Polish dyadic conversations on provocative art between students and between a student and a teacher. We found different pitch profiles for the different scenarios and identities of the interlocutors. In the studentstudent and student-teacher conversations where both interlocutors agree (whether or not they accept the provocative art), the students show higher long-term distributional (LTD) F0 values for level, span and standard deviation when interacting with a teacher than with a fellow student. In the student–teacher conversations the students achieve significantly higher LTD F0 values when the two interlocutors do not share the same opinion about the provocative art. Regarding convergence we applied a variety of measures to our data (local, global convergence and synchrony). Using these measures, we found evidence of local and global convergence in the student-teacher conversations and of synchrony in both student-student and student-teacher conversations.
Future Water Budgets and Water Supply Stress under Climate Change and Urbanization in the Upper Neuse River Basin, North Carolina, USA

Urbanization and climate change are the two major threats to watersheds and the environment in the United States. It is imperative to understand the sensitivity of hydrologic responses to climate and urbanization across different physiographic regions in order to formulate sound watershed management strategies and policies. This study evaluated a watershed water balance model, Water Supply Stress Index (WaSSI), with measured historic streamflow data for the Upper Neuse River basin in central North Carolina. A user-friendly web version of the WaSSI model was applied to examine how climate change and potential urbanization over the next 50 years may affect streamflow (or water yield) of this river basin and water supply stress (water demand/water supply). Our simulation study suggested that urbanization could increase water yield (8%), while climate change could reduce (30%) water yield depending on future changes in precipitation and warming potential in the study basin. Climate change is likely to overwhelm the basin-wide impacts of urbanization in terms of its influences on water supply stress, but urbanization may aggravate the environmental problems by increasing stormflow and water quality degradation under a changing climate. Future climatic change models should reduce the uncertainty of climate projection in precipitation, a major control on watershed hydrology.
A linear time pattern based algorithm for n-queens problem N-vezir problemi için lineer zamanlı örüntü temelli algoritma

The n-queens problem is the placing of n number of queens on an n x n chessboard so that no two queens attack each other. This problem is important due to various usage fields such as VLSI testing, traffic control job scheduling, data routing, dead-lock or blockage prevention, digital image processing and parallel memory storage schemes mentioned in the literature. Besides, this problem has been used as a benchmark for developing new artificial intelligence search techniques. However, it is known that backtracking algorithms, one of the most frequently used algorithms to solve this problem, cannot produce all solutions in large n values due to the exponentially growing time complexity. Therefore, various methods have been proposed for producing one or more solutions, not all solutions for each n value. In this study, a pattern based approach that produces at least one unique solution for all n values ( n >3) was detected. By using this pattern, a new algorithm that produces at least one unique solution for even very large n values in linear time was developed. The developed algorithm with О(n) time complexity produces quite faster solution to n-queens problem and even in some values, this algorithm produces ( n -1)/2 unique solutions in linear time.
Practical Bayesian Neural Networks via Adaptive Optimization Methods

We introduce a novel framework for the estimation of the posterior distribution over the weights of a neural network, based on a new probabilistic interpretation of adaptive optimisation algorithms such as A DA G RAD and A DAM . We demonstrate the effectiveness of our Bayesian A DAM method, B ADAM , by experimentally showing that the learnt uncertainties correctly relate to the weights’ predictive capabilities by weight pruning. We also demonstrate the quality of the derived uncertainty measures by comparing the performance of B ADAM to standard methods in a Thompson sampling setting for multi-armed bandits, where good uncertainty measures are required for an agent to balance exploration and exploitation.