Miracle-dz/UniFAR-FTUs huggingface dataset:
Algılanan Örgütsel Destek ve Duygusal Bağlılık Arasındaki İlişkinin İncelenmesi: Mersin İlinde Banka Çalışanları Üzerinde Bir Araştırma (Investigation of the Relationship Between Perceived Organizational Support and Affective Commitment: A Research on Bank Employees in Mersin Province)

Makale Kategorisi: Araştırma Makalesi Amaç – Bu araştırmanın amacı, algılanan örgütsel destek ve duygusal bağlılığın demografik faktörlere göre farklılıklarını incelemek ve algılanan örgütsel destek ile duygusal bağlılık arasındaki ilişkiyi tespit etmektir. Yöntem: Araştırmada nicel araştırma yöntemlerinden bağıntısal ve nedensel karşılaştırma yöntemleri kullanılmıştır. Araştırmanın evrenini, Mersin ilindeki banka çalışanları oluşturmaktadır. Araştırma verileri 2019 yılında, 19 banka şubesinde çalışanlardan anket formu kullanılarak kolayda örnekleme yöntemi ile elde edilmiştir (n=344). Araştırmada, Eisenberger vd. (1986) tarafından geliştirilen ve Akkoç vd. (2012) tarafından Türkçe’ye uyarlanan “Algılanan Örgütsel Destek Ölçeği” ile Meyer ve Allen (2001) tarafından geliştirilen ve Wasti (2000) tarafından Türkçe’ye uyarlanan “Örgütsel Bağlılık Ölçeği”nin duygusal bağlılık boyutu kullanılmıştır. Veriler SPSS 23 istatistik paket programı ile tanımlayıcı istatistikler, bağımsız gruplarda t testi, tek yönlü varyans analizi, regresyon analizi ve korelasyon analizi yapılarak incelenmiştir. Bulgular – Araştırma sonucunda, algılanan örgütsel desteğin medeni durum, yaş, eğitim durumu ve gelire göre duygusal bağlılığın ise medeni durum, yaş ve eğitim durumuna göre farklılaştığı tespit edilmiştir. Korelasyon ve regresyon analizleri sonucunda duygusal bağlılık ile algılanan örgütsel destek arasında pozitif yönlü ilişki olduğu ve duygusal bağlılığın %23 oranında algılanan örgütsel destek tarafından açıklandığı bulgulanmıştır. Tartışma – Örgütsel bağlılık ve alt boyutları ile algılanan örgütsel desteğin birey ve örgüt düzeyindeki davranış değişkenleriyle ilişkili olduğu çok sayıda araştırma tarafından desteklenmektedir. Duygusal bağlılık ile algılanan örgütsel destek kavramları algıya dayanan, bireye özgü, düşünsel ve duygusal boyutta oluşan kavramlardır. Bu nedenle yapılan çalışmaların bireysel ve örgüt ortamdaki öncülleri ile farklı örgüt ortamlarında tekrarlanmasının alanyazına ve uygulayıcılara katkıda bulunacağı öngörülmektedir.
BEDS-Bench: Behavior of EHR-models under Distributional Shift-A Benchmark

Machine learning has recently demonstrated impressive progress in predictive accuracy across a wide array of tasks. Most ML approaches focus on generalization performance on unseen data that are similar to the training data (In-Distribution, or IND). However, real world applications and deployments of ML rarely enjoy the comfort of encountering examples that are always IND. In such situations, most ML models commonly display erratic behavior on Out-of-Distribution (OOD) examples, such as assigning high confidence to wrong predictions, or vice-versa. Implications of such unusual model behavior are further exacerbated in the healthcare setting, where patient health can potentially be put at risk. It is crucial to study the behavior and robustness properties of models under distributional shift, understand common failure modes, and take mitigation steps before the model is deployed. Having a benchmark that shines light upon these aspects of a model is a first and necessary step in addressing the issue. Recent work and interest in increasing model robustness in OOD settings have focused more on image modality, while the Electronic Health Record (EHR) modality is still largely under-explored. We aim to bridge this gap by releasing BEDS-Bench, a benchmark for quantifying the behavior of ML models over EHR data under OOD settings. We use two open access, de-identified EHR datasets to construct several OOD data settings to run tests on, and measure relevant metrics that characterize crucial aspects of a model's OOD behavior. We evaluate several learning algorithms under BEDS-Bench and find that all of them show poor generalization performance under distributional shift in general. Our results highlight the need and the potential to improve robustness of EHR models under distributional shift, and BEDS-Bench provides one way to measure progress towards that goal.
Comparison of Three Deoxidation Agents for Ozonated Broths Used in Anaerobic Biotechnological Processes

Anaerobic fermentation of organic compounds is used in many biotechnological processes and has been the subject of much research. A variety of process conditions and different growth media can be used to obtain microbial metabolites. The media must be free from contamination before fermentation. Sterilization is most often achieved by applying heat or other treatments, such as ozonation. Sterilization of liquid media using ozone can be very beneficial, but this method introduces high concentrations of residual oxygen, which inhibit anaerobic processes. Deoxidation is therefore necessary to remove the oxygen from ozonated broths. This study evaluates the effectiveness of three deoxidation agents for two kinds of fermentation media based on malt or molasses: ultrasound, iron(II) sulfate, and Metschnikowia sp. yeast. The time needed for deoxidation varied, depending on the kind of broth and the deoxidation agent. In general, the dynamics of oxygen removal were faster in malt broth. A comparative analysis showed that yeast biomass was the most effective agent, achieving deoxidation in the shortest time. Moreover, the fully deoxidated broth was supplemented with yeast biomass, which is rich in biogenic substrates, expressed as a protein content of 0.13–0.73 g/L. Application of Metschnikowia sp. may therefore be considered as an effective strategy for simultaneous deoxidation and nutrient supplementation of broths used in anaerobic biotechnological processes.
Robot Shaping: An Experiment in Behavior Engineering

From the Publisher: foreword by Lashon Booker "[This] book gives a clear and comprehensive exposition of [the authors] extensive experience in integrating reinforcement learning and autonomous robotics. Their continuing contribution is to the development of a distinct engineering discipline (`Behavior engineering') through which such robots can be created. I am excited because their efforts combine some of the best theoretical ideas with a strong eye for the practical - for what will actually work. " -- Stewart W. Wilson, The Rowland Institute for Science To program an autonomous robot to act reliably in a dynamic environment is a complex task. The dynamics of the environment are unpredictable, and the robots' sensors provide noisy input. A learning autonomous robot, one that can acquire knowledge through interaction with its environment and then adapt its behavior, greatly simplifies the designer's work. A learning robot need not be given all of the details of its environment, and its sensors and actuators need not be finely tuned. Robot Shaping is about designing and building learning autonomous robots. The term "shaping" comes from experimental psychology, where it describes the incremental training of animals. The authors propose a new engineering discipline, "behavior engineering," to provide the methodologies and tools for creating autonomous robots. Their techniques are based on classifier systems, a reinforcement learning architecture originated by John Holland, to which they have added several new ideas, such as "mutespec," classifier system "energy," and dynamic population size. In the book they present Behavior Analysis and Training (BAT) as an example of a behavior engineering methodology.
Stress, Dependence, and COVID-19–related Changes in Past 30-day Marijuana, Electronic Cigarette, and Cigarette Use among Youth and Young Adults

Background Studies show smoking and vaping behaviors increase risk of contracting and worse symptoms of COVID-19. This study examines whether past 30-day youth and young adult users of marijuana, e-cigarettes, and cigarettes self-reported changes in their use of these substances due to the COVID-19 pandemic; and cross-sectional associations between perceived stress, nicotine or marijuana dependence, and COVID-19–related changes in use. Methods Participants were 709 past 30-day self-reported substance users from the Texas Adolescent Tobacco and Marketing Surveillance study (TATAMS; mean age = 19; 58% female; 38% Hispanic, 35% white). Multiple logistic regression models assessed cross-sectional associations between perceived stress and dependence and increased, decreased, or sustained past 30-day use of marijuana, e-cigarettes, and cigarettes due to COVID-19 (e.g., “Has your marijuana use changed due to the COVID-19 outbreak? ”). Covariates included age, race/ethnicity, socio-economic status (SES), dependence (exposure: stress), and stress (exposure: dependence). Results Most participants reported sustained (41%, 43%, 49%) or increased (37%, 34%, 25%) use of marijuana, e-cigarettes, and cigarettes due to COVID-19, respectively. Participants who reported symptoms of dependence were significantly more likely than their non-dependent peers to report increasing their marijuana (AOR: 1.66; 95% CI: 1.15–2.39) and e-cigarette (AOR: 2.57; 95% CI: 1.38–4.77) use. Those who reported higher perceived stress were significantly more likely to report increasing their marijuana use (AOR: 1.55; 95% CI: 1.01–2.42). Conclusions Most youth and young adults did not decrease their substance use amid a global, respiratory disease pandemic. Health messaging and interventions that address the health effects of smoking and vaping as well as factors like stress and dependence that may be barriers to decreasing use are vital in curbing the COVID-19 pandemic.
Multimodal Fusion Transformer for Remote Sensing Image Classification

Vision transformers (ViTs) have been trending in image classification tasks due to their promising performance when compared with convolutional neural networks (CNNs). As a result, many researchers have tried to incorporate ViTs in hyperspectral image (HSI) classification tasks. To achieve satisfactory performance, close to that of CNNs, transformers need fewer parameters. ViTs and other similar transformers use an external classification (CLS) token, which is randomly initialized and often fails to generalize well, whereas other sources of multimodal datasets, such as light detection and ranging (LiDAR), offer the potential to improve these models by means of a CLS. In this article, we introduce a new multimodal fusion transformer (MFT) network, which comprises a multihead cross-patch attention (mCrossPA) for HSI land-cover classification. Our mCrossPA utilizes other sources of complementary information in addition to the HSI in the transformer encoder to achieve better generalization. The concept of tokenization is used to generate CLS and HSI patch tokens, helping to learn a distinctive representation in a reduced and hierarchical feature space. Extensive experiments are carried out on widely used benchmark datasets, i.e., the University of Houston (UH), Trento, University of Southern Mississippi Gulfpark (MUUFL), and Augsburg. We compare the results of the proposed MFT model with other state-of-the-art transformers, classical CNNs, and conventional classifiers models. The superior performance achieved by the proposed model is due to the use of mCrossPA. The source code will be made available publicly at https://github.com/AnkurDeria/MFT.
A Machine Learning Method for Modeling Wind Farm Fatigue Load

Wake steering control can significantly improve the overall power production of wind farms. However, it also increases fatigue damage on downstream wind turbines. Therefore, optimizing fatigue loads in wake steering control has become a hot research topic. Accurately predicting farm fatigue loads has always been challenging. The current interpolation method for farm-level fatigue loads estimation is also known as the look-up table (LUT) method. However, the LUT method is less accurate because it is challenging to map the highly nonlinear characteristics of fatigue load. This paper proposes a machine-learning algorithm based on the Gaussian process (GP) to predict the farmlevel fatigue load under yaw misalignment. Firstly, a series of simulations with yaw misalignment were designed to obtain the original load data, which considered the wake interaction between turbines. Secondly, the rainflow counting and Palmgren miner rules were introduced to transfer the original load to damage equivalent load. Finally, the GP model trained by inputs and outputs predicts the fatigue load. GP has more accurate predictions because it is suitable for mapping the nonlinear between fatigue load and yaw misalignment. The case study shows that compared to LUT, the accuracy of GP improves by 17% (RMSE) and 0.6% (MAE) at the blade root edgewise moment and 51.87% (RMSE) and 1.78% (MAE) at the blade root flapwise moment.
Flowering in Crabapple Seedlings: Methods of Shortening the Juvenile Phase1

The change from juvenile to adult phase of seedlings of tea crabapple [(Malus hupenhensis (Pamp.) Rehd. )] is influenced by 2 factors, the height and the age of the plants. Seedlings grown continuously in the greenhouse are unbranched and undergo phase change from juvenile to adult in 7 to 10 months at a height of approximately 2 m. Trees can be in bloom within 1 year from the time of seed germination. In contrast, seedlings grown periodically in the greenhouse or in the field are branched and produce more shoot growth but are shorter and take longer to flower. Greenhouse-grown seedlings can be brought into flower by chilling for 6 weeks or more, by withholding water until the leaves drop, or by treating the buds with cytokinins and gibberellins. Young seedlings cannot be stimulated to flower by grafting onto older seedlings although older seedlings will continue to form flower buds after grafting onto young seedlings still in the juvenile phase.
High-Dimensional Partial Differential Equations in Science and Engineering

Singularity-free methods for the time-dependent Schrodinger equation for nonlinear molecules in intense laser fields--A non-perturbative approach by A. D. Bandrauk and H. Lu Feasibility and competitiveness of a reduced basis approach for rapid electronic structure calculations in quantum chemistry by E. Cances, C. Le Bris, Y. Maday, N. C. Nguyen, A. T. Patera, and G. S. H. Pau Some fundamental mathematical properties in atomic and molecular quantum mechanics by G. Chen, Z. Ding, A. Perronnet, M. O. Scully, R. Xie, and Z. Zhang Sparse tensor-product Fokker-Planck-based methods for nonlinear bead-spring chain models of dilute polymer solutions by P. Delaunay, A. Lozinski, and R. G. Owens A partial differential equation for credit derivatives pricing by M. Escobar and L. Seco A short review on computational issues arising in relativistic atomic and molecular physics by M. J. Esteban Model Hamiltonians in density functional theory by P. Gori-Giorgi, J. Toulouse, and A. Savin Simulation of quantum-classical dynamics by surface-hopping trajectories by H. Kim and R. Kapral Simulating realistic and nonadiabatic chemical dynamics: Application to photochemistry and electron transfer reactions by D. M. Koch, Q. K. Timerghazin, and G. H. Peslherbe A Maxwell-Schrodinger model for non-perturbative laser-molecule interaction and some methods of numerical computation by E. Lorin, S. Chelkowski, and A. Bandrauk Parareal in time algorithm for kinetic systems based on model reduction by Y. Maday.
PartitionVAE - a human-interpretable VAE

VAEs, or variational autoencoders, are autoencoders that explicitly learn the distribution of the input image space rather than assuming no prior information about the distribution. This allows it to classify similar samples close to each other in the latent space's distribution. VAEs classically assume the latent space is normally distributed, though many distribution priors work, and they encode this assumption through a K-L divergence term in the loss function. While VAEs learn the distribution of the latent space and naturally make each dimension in the latent space as disjoint from the others as possible, they do not group together similar features -- the image space feature represented by one unit of the representation layer does not necessarily have high correlation with the feature represented by a neighboring unit of the representation layer. This makes it difficult to interpret VAEs since the representation layer is not structured in a way that is easy for humans to parse. We aim to make a more interpretable VAE by partitioning the representation layer into disjoint sets of units. Partitioning the representation layer into disjoint sets of interconnected units yields a prior that features of the input space to this new VAE, which we call a partition VAE or PVAE, are grouped together by correlation -- for example, if our image space were the space of all ping ping game images (a somewhat complex image space we use to test our architecture) then we would hope the partitions in the representation layer each learned some large feature of the image like the characteristics of the ping pong table or the characteristics and position of the players or the ball. We also add to the PVAE a cost-saving measure: subresolution. Because we do not have access to GPU training environments for long periods of time and Google Colab Pro costs money, we attempt to decrease the complexity of the PVAE by outputting an image with dimensions scaled down from the input image by a constant factor, thus forcing the model to output a smaller version of the image. We then increase the resolution to calculate loss and train by interpolating through neighboring pixels. We train a tuned PVAE on MNIST and Sports10 to test its effectiveness.
Introduction to environmental management

Preface. 1. Introduction (S.E. Jorgensen). 2. Soil Pollution (S. S. Jorgensen and K. Rasmussen). 3. Air pollution (H. Saxe). 4. Water Pollution (L.K. Nielsen). 5. Water Management and Water Resources (S.E. Jorgensen). 6. Pesticides (B. Mogensen, A. Helweg, J. Eilenberg, H. Philipsen, L. Ogaard, and J. Streibig). 7. Gene-technology (E. Lund). 8. Countryside Planning (J. Primdahl). 9. Environmental Economics (S. Kjeldsen-Kragh). 10. Danish environmental Law (H. Wulff). 11. Ecological Aspects (S.E. Jorgenson). 12. Environmental Impact Assessment (S.E. Jorgensen). 13. Environmental Management Modelling (S.E. Jorgensen). Index.
Assessing Different Chronic Wasting Disease Training Aids for Use with Detection Dogs

Simple Summary Chronic wasting disease (CWD) is a fatal prion illness that affects deer, elk, moose, and other animals in the cervid family. One way to identify areas contaminated by CWD is to use detection dog–handler teams trained to find fecal matter from CWD-positive cervids. In this case, dogs are trained on white-tailed deer fecal matter. However, fecal matter from CWD-positive deer contains the infectious proteins (prions) that can spread CWD. Using this fecal matter as a training aid for these dogs can be risky, as it can contaminate soil in the training area and potentially infect other deer that come into contact with the prions. One solution is to use training aids that can replicate the odor of fecal matter from CWD-positive and CWD-negative deer but that do not contain prions; these would be safe to use in the environment. This study examined different materials, incubation times, and incubation temperatures for training aids to identify the aid that both maximizes the dogs’ detection performance and is practical to use. Based on these criteria, the best-performing aid was made of cotton and was incubated with fecal matter from CWD-positive deer for 24 h at 21 °C. Abstract Chronic wasting disease (CWD) is a highly infectious, fatal prion disease that affects cervid species. One promising method for CWD surveillance is the use of detection dog–handler teams wherein dogs are trained on the volatile organic compound signature of CWD fecal matter. However, using fecal matter from CWD-positive deer poses a biohazard risk; CWD prions can bind to soil particles and remain infectious in contaminated areas for extended periods of time, and it is very difficult to decontaminate the affected areas. One solution is to use noninfectious training aids that can replicate the odor of fecal matter from CWD-positive and CWD-negative deer and are safe to use in the environment. Trained CWD detection dogs’ sensitivity and specificity for different training aid materials (cotton, GetXent tubes, and polydimethylsiloxane, or PDMS) incubated with fecal matter from CWD-positive and CWD-negative deer at two different temperatures (21 °C and 37 °C) for three different lengths of time (6 h, 24 h, and 48 h) were evaluated. Cotton incubated at 21 °C for 24 h was identified as the best aid for CWD based on the dogs’ performance and practical needs for training aid creation. Implications for CWD detection training and for training aid selection in general are discussed.
Environment for the evaluation and certification of data products quality

Data is the most important asset of any IT organization. The most successful companies of the world are data-driven businesses. As any other raw material or asset for creating goods and services, data must be good enough for those companies to obtain benefits. When making choices based on data, it is vital that this raw material has the necessary levels of quality. Otherwise, created goods and services using the data might be useless or not appropriate for the intended purposes, and data-based decisions might be worthless or even harmful for the companies. Considering that both the industry and public entities have interest in Data Quality, several solutions on Data Quality Assessment are present in the literature. Unfortunately, none of them focus on the certification and the assurance of the levels of quality of this precious asset. Consequently, this research digs deeply in the evaluation and certification of data in terms of its quality. The main contribution of this thesis is the creation of an environment called I25K, for the evaluation and certification of Data Quality. I25K is composed of a Data Quality Model, an evaluation process, and a certification process. This environment has been defined to be easily implementable and deployable, including details on the necessary resources, the roles that participate in the evaluation and certification processes, and their responsibilities. I25K was validated through several case studies and it has been implemented and deployed alongside two important Spanish companies. Future lines of research have been drawn as well, including the improvement of Data Quality and the extension of the results.