Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relationship between Emotion Regulation Strategies and Life Expectancy and Resilience in the Mothers of Children with Intellectual Disability

Background & Objectives: Intellectual Disability (ID) is defined by defects in mental functioning and adaptive behavior in conceptual, social, and practical contexts. The parents of children with IDs experience decreased happiness, self–esteem, and self–efficacy. One of the personality traits, i.e., closely related to life challenges is resilience. Life expectancy is a cognitive complex based on a sense of success, i.e., influenced by various sources. Life expectancy is a powerful source and a form of rebirth, i.e., vital to individuals. Emotion Regulation (ER) is among the essential features in the mothers of children with IDs. ER can be defined as the cognitive emotion control method. Thus, the current study aimed to investigate the effects of ER strategies on life expectancy and resilience in mothers of children with IDs. Methods: This was a descriptive–analytical and correlational study. The statistical population of the study consisted of the mothers of children with IDs whose children were educated in exceptional primary schools in Tehran City, Iran, in the academic year of 2018–2019. The total sample size, based on Krejcie and Morgan’s (1970) Table was measured to be at least 123 individuals; for higher certainty and considering the odds of samples dropout, and predicting non–cooperation, 180 mothers of children with IDs who were referred to learning disability centers for their child's problem were selected by convenience sampling method. The inclusion criteria of the study included having a child with ID and a minimum diploma level of education. The exclusion criterion of the study was an unwillingness to cooperate in this study. The necessary data were collected using the Cognitive Emotion Regulation Questionnaire (Garnefski & Kraaij, 2006), Life Expectancy Scale (Snyder et al., 1991), and Ahvaz Psychological Hardiness Scale (Kiamarsi et al., 1999). The obtained data were analyzed using Pearson correlation coefficient and multiple regression analysis approaches at the significance level of 0.05 in SPSS. Results: There was a positive and significant relationship between adaptive ER strategies and life expectancy (r=0.65, p<0.001) and resilience (r=0.67, p<0.001). There was also a negative and significant relationship between maladaptive ER strategies and life expectancy (r=–0.60, p<0.001) and resilience (r=–0.56, p<0.001). According to multiple regression results, 42.3% of life expectancy variance was explained by adaptive ER strategies. Furthermore, 36% of the variance in life expectancy was explained by maladaptive ER strategies. Additionally, 44.9% of the variance of resilience was explained by adaptive ER strategies. Eventually, 30.3% of the variance of resilience was explained by maladaptive ER strategies. Conclusion: Given the importance of ER strategies in predicting life expectancy and resilience in the mothers of children with IDs, special attention to its components is suggested to be paid by the relevant specialists.
Learning Top-Down Generative Models by Short-Run Markov Chain Monte Carlo Inference with Optimal Transport Correction

—Learning top-down generative models with latent variables via maximum likelihood typically requires to infer latent variables for each training example based on the posterior distribution of latent variables. The inference step relies on either running a time-consuming long-run Markov chain Monte Carlo (MCMC) sampling from the posterior distribution or constructing a separate inference model for variational learning. In this paper, we propose to use an efﬁcient short-run MCMC, such as Langevin dynamics, as an approximate inference engine. The bias in the aggregated posterior distribution of the inferred latent variables obtained by the non-convergent short-run MCMC is corrected by optimal transport (OT), which aims at transforming the biased distribution to the prior distribution with minimum transport cost. The proposed algorithm alternates the short-run MCMC inference step, the OT correction step, and the parameter learning step. In each iteration, with more reliable latent variables obtained from the inference step and the correction step, the parameter learning step can be more accurate. Our experiments not only verify the effectiveness of the OT correction for the short-run MCMC inference, but also demonstrate that the generative models trained by the proposed strategy outperform the variational auto-encoder and the MCMC inference without using OT correction in the tasks of image reconstruction, image generation, image inpainting, anomaly detection and unsupervised image recovery.
Comparison of Riser Fatigue Methodologies Based on Measured Motion Data

In-situ riser structural monitoring has been used by a number of operators and drilling contractors to support drilling operations. The measurements have been used to understand the level of accuracy present in the up-front fatigue analysis and to confirm the integrity of the drilling systems, especially during extreme environmental events. However, the field data analysis requires detailed understanding of drilling riser modeling, loading conditions, operations, sensor characteristics and signal processing. Several methods have been developed to calculate stresses and fatigue damages in the riser using measured motion data. Careful selection of the analysis approach will determine the system configuration and validity of the measured results. Mode shape matching has been widely used for the data analysis where riser response is dominated by Vortex Induced Vibration (VIV). Another riser fatigue method has been recently developed and is based on analytical transfer functions used to convert measured accelerations into curvature. Another method considers FEA-based transfer functions. Some of these methods perform fatigue calculations in time domain while some others in frequency domain. Each method exhibits benefits and limitations depending on the characteristics of the measured riser response. Selecting the most appropriate fatigue methodology depends on the riser response and instrumentation system design. In this paper, three fundamentally different riser fatigue methods utilizing measured motion data are described and compared with each other. The advantages and disadvantages of each approach are evaluated and recommendations are provided for when each method should be considered. Introduction A number of operators and drilling contractors have installed in-situ drilling riser monitoring systems in order to support drilling operations and confirm the integrity of their equipment. The riser monitoring data facilitates decision making during extreme or unusual events in the field and can help prevent costly delays. The fatigue integrity concern of the deep water riser systems is one of the most important drivers for the use of monitoring systems. In comparison to the up-front riser analysis methods, the monitoring data presents the fatigue results with a higher level of accuracy. Learn more at www.2hoffshore. com
Lead-Free LiNbO3 Thick Film MEMS Kinetic Cantilever Beam Sensor/Energy Harvester

In this paper, we present integrated lead-free energy converters based on a suitable MEMS fabrication process with an embedded layer of LiNbO3. The fabrication technology has been developed to realize micromachined self-generating transducers to convert kinetic energy into electrical energy. The process proposed presents several interesting features with the possibility of realizing smaller scale devices, integrated systems, miniaturized mechanical and electromechanical sensors, and transducers with an active layer used as the main conversion element. When the system is fabricated in the typical cantilever configuration, it can produce a peak-to-peak open-circuit output voltage of 0.208 V, due to flexural deformation, and a power density of 1.9 nW·mm−3·g−2 at resonance, with values of acceleration and frequency of 2.4 g and 4096 Hz, respectively. The electromechanical transduction capability is exploited for sensing and power generation/energy harvesting applications. Theoretical considerations, simulations, numerical analyses, and experiments are presented to show the proposed LiNbO3-based MEMS fabrication process suitability. This paper presents substantial contributions to the state-of-the-art, proposing an integral solution regarding the design, modelling, simulation, realization, and characterization of a novel transducer.
Aeroelastic Analysis and Optimization of a Rigid/Inatable Wing

This research describes a design optimization method for combining rigid and inatable wing design. The multidisciplinary design optimization will determine minimum structural weight based on stress, divergence, and liftto-drag ratio constraints. Because the goal of this design is to create an inatable wing extension that can be packed into the rigid section, packing constraints are also applied to the design. The aeroelastic analysis of the wing is critical in determining a feasible design, and the approach must handle the discontinuities between sections of the wing. The natural modes are determined using the Rayleigh-Ritz method, while continuity between each section of the wing is handled using a penalty approach. The natural modes are compared to predictions made using other methods. The aeroelastic model is incorporated into the full multidisciplinary design optimization that determines the parameters of each section of the wing for minimum structural weight.
SEISMIC EVALUATION OF STEEL SPECIAL MOMENTRESISTING FRAMES AFFECTED BYPULSE TYPE GROUND MOTIONS

The main parameters of the dynamic behavior of steel Special Moment Resisting Frames (SMRF) which is used in steel structures as lateral load resistant system, under influencing pulse type ground motion have been evaluated in this research. The response parameters have been resulted by conducting a number of non-linear dynamic time history analyses. The structural models consist of 10, 20 and 30-story steel SMRF swhich have been designed according to the Iranian seismic code 2800 (3 edition). The criterion for selecting the strong earthquake records has been the appearance of pulse type features in the velocity time histories concerning with high amplitude factors and long period do mains. The illustrated result sindicate that the structural response parameters are obviously affected by those group of earthquake records which contain pule type features and also the height of the analytical models. As a general conclusion, it is important to note that the lateral displacement and drift of all stories must be controlled during design process. The aforementioned subject can be considered in the design process of steel SMRFs in near zones of active faults.
Bayesian Optimization Meets Hybrid Zero Dynamics: Safe Parameter Learning for Bipedal Locomotion Control

In this paper, we propose a multi-domain control parameter learning framework that combines Bayesian Optimization (BO) and Hybrid Zero Dynamics (HZD) for locomotion control of bipedal robots. We leverage BO to learn the control parameters used in the HZD-based controller. The learning process is firstly deployed in simulation to optimize different control parameters for a large repertoire of gaits. Next, to tackle the discrepancy between the simulation and the real world, the learning process is applied on the physical robot to learn for corrections to the control parameters learned in simulation while also respecting a safety constraint for gait stability. This method empowers an efficient sim-to-real transition with a small number of samples in the real world, and does not require a valid controller to initialize the training in simulation. Our proposed learning framework is experimentally deployed and validated on a bipedal robot Cassie to perform versatile locomotion skills with improved performance on smoothness of walking gaits and reduction of steady-state tracking errors.
At the Interface of Photonics and Neuroscience

―Neuromorphic Photonics,‖ by Paul R. Prucnal and Bhavin J. Shastri (CRC Press/Taylor & Francis, 2017), is a book about photonic neural networks, which enjoy a number of unusual and salutary features. They can be highly interconnected and robust in the presence of noise. The number of interconnections among spike-processing elements can be scaled indefinitely, with large fan-out, by using wavelength multiplexing, a technique similar to that used for the fiber-optic networks that serve the internet. Spike processing benefits from both the bandwidth efficiency of analog pulse encoding and from the on/off nature of the spikes themselves. Neural systems are largely immune to amplitude noise because information, rather than being encoded in amplitude, is encoded in the form of fresh, unitary action potentials (neural spikes) that are repeatedly regenerated at each synapse along the transmission pathway. As with cascaded digital transistors, amplitude noise in photonic neural networks does not propagate, enabling them to scale in size nearly indefinitely. The intriguing features of these new photonic neural networks, such as bandwidth efficiency, large interconnectivity and fan-out, and cascadability, offer promise as a platform for the next generation of signal-processing and computing systems. The manner in which the story unfolds in ―Neuromorphic Photonics‖ is, to me, a tale of déjà vu, as will become clear from my own personal chronicle at the interface of photonics and neuroscience.
The Influence of Final Inoculation on the Metallurgical Quality of Nodular Cast Iron

The article presents the results of research on the physicochemical and mechanical properties, microstructure, and the tendency to form shrinkage of nodular cast iron depending on the type of inoculant used for secondary inoculation. Six different inoculants containing different active elements in their chemical composition were used for the research. Step castings and Y2 wedges were made on the vertical forming line using an automatic pouring machine. The inoculation in the amount of 0.2% was made using a pneumatic dispenser equipped with a vision system controlling the effectiveness of the inoculation. The results of the thermal analysis were determined and compared, and the potential of each of the inoculants was assessed.
Effective tax enforcement and demand for cash 1

Cash holdings have increased worldwide in recent years. It is well known that a higher tax burden is typically an important factor for explaining a raise in cash holdings. However, the role of the quality of tax enforcement is less known. To explore the effect of tax enforcement quality on cash holdings, we study a fiscal reform carried out in a large, developing country. In mid-2013, Mexico tried to raise tax revenues by enrolling more taxpayers and introducing electronic invoices to counter a secondary market in illegal invoices. We use a Vector Error Correction (VEC) model to address this issue. The results suggest that a tax enforcement policy implemented under a weak institutional framework can exacerbate cash holdings. JEL: E40, E41, E62, H26
The Impact of Intellectual Capital on Knowledge Management Processes in Thailand

This research explores the effects of intellectual capital on knowledge management processes (creation of information, knowledge sharing, and use of knowledge) and innovation performance. The data was gathered from 224 fashion accessories in Thailand using a questionnaire. The population was collected on December 16, 2019 (https:/www.ditp.go.th) from a list database of the Department of International Trade Promotion, Ministry of Commerce of Thailand (2019). A questionnaire mail survey technique was used to collect data. Executives or managers were the key participants in this study. The statistical technique used to analyze is the ordinary least square regression. The findings show that intellectual capital has a significant positive effect on information management systems. Likewise, information management techniques have a significant positive effect on innovation performance. Moreover, increasing the use of information tends to make staff willing to use the knowledge to improve the quality and quantity of the service. This tendency contributes to productivity gain. Through the interaction of processes of information use, self-reflection, and input from customers, employees tend to increase the production of knowledge that eventually develops the results of the innovation. Hence, organizations should value knowledge management process elements to improve efficiency, and thus innovation.
Administratie si Management Public

This study examined the relationship between Industry 4.0 technologies and logistic enterprises’ performance through interlinked mediating predictors creating challenges for effective and efficient implementation of Industry 4.0 concepts in order to examine how the public services for business environment could be effectively employed. For the purpose of fair and equal representation, we combined purposive, referral, convenience, and area-cluster sampling strategies to reach total 320 (160 each) logistic enterprises in Poland and Canada. Using SPSS 23.0, we employed multiple regression test to examine the impact of various interlinked predictors affecting the performance of logistic enterprises. Results revealed that related to Industry 4.0 “limited knowledge”, “implementation barriers”, “recognition of potential changes”, “benefit appearance time period”, “preparing staff for challenges”, and “other priorities than introduction of Industry 4.0” statistically significantly mediate the relationship between Industry 4.0 technologies and performance of logistic enterprises in Canada and Poland. The comparative analysis revealed that apart from ‘preparing staff for challenges’, all other predictors are higher in Canada than Poland. The findings also revealed that the Industry 4.0 technologies have statistically significant impact on the Polish and Canadian logistic enterprises’ performance. Based on the findings and role of public services for business environment, recommendations are given to logistic companies for effective and efficient implementation of Industry 4.0 concepts.
Remote PPG for heart rate monitoring: lighting conditions and camera shutter time

— We investigate the effect of lighting conditions and camera shutter time on heart rate measurement using remote photoplethysmography (remote-PPG) on healthy volunteers in a simulated ICU setting. In the used range of lighting conditions (132-548 lux), there exists a range of shutter times (5-32 ms), in which a mean 3 beats-per-minute (BPM) agreement of 90-100% with the contact PPG sensor is achieved. This range is limited by the saturation of the skin pixels. These results indicate that shutter time control is unnecessary beyond avoiding saturation. Clinical Relevance — Replacing spot checks by continuous monitoring in a hospital will lead to timely detection of adverse events. Technologies for remote-PPG will allow continuous monitoring in a clinical setting.
Capsule Network Regression Using Information Measures: An Application in Bitcoin Market

Short-Term Memory (LSTM) as well as common used loss functions such as Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Using appropriate accuracy metrics, it is shown that the capsule network using triangular information measure is well able to predict the price of bitcoin for the medium and long term period including 10, 90 and 180 days.So its forecast accuracy is 94% in the long term and 64% in the medium term.
A 2.5D Approach for Breast Lesion detection in 3D Ultrasound

— Lesion detection on 3D Breast Ultrasound volumes is computationally expensive when performed in 3D. Alternatively, 2D slice-by-slice lesion detection loses the context provided by 3D acquisition. We propose an intermediate 2.5D approach by consolidating multi-slice information using a Minimum Intensity Projection (MinIP) onto a single 2D image. We demonstrate the superiority of 2.5D detection over slice-by-slice 2D detection while offering the same computational and memory footprint advantage. Clinical Relevance — CAD on 3D breast Ultrasound significantly reduces the throughput burden on the clinician. Efficient deployment of Deep Learning based CAD models can reduce memory and computation requirements.
STRATA OBJECTS BASED ON MALAYSIAN LADM COUNTRY PROFILE VIA WEB 3D VISUALIZATION

Abstract. Three-dimensional visualization of 3D parcels have been investigated in many viewpoints in order to fulfil the demands of expanding cities and the increasing complexity of building design. In Malaysia, with the growing strata developed area, particularly residential buildings (i.e., apartments), land administration agencies will need to improve their application in sharing and disseminating cadastral data. Hence, web-based solutions have been recognized as an important visualization requirement among end-users. It acts as a distributed information platform that allows 3D data sharing with the minimal architecture of client-server connects by the internet. There are two key topics addressed in this paper, which is the depiction of 3D strata objects on the web and the linkage to their legal data. This paper also describes the implementation of conceptual model of strata object based on Malaysian Land Administration Domain Model (LADM) Country Profile for the representation of spatial and non-spatial data. Tools such as PostgreSQL with PostGIS extension is used for storing data, CesiumJS was used to handle and visualise 3D strata objects in a 3D browser that was customised as a web application. The results demonstrate a viewer of a multi-storey buildings using web visualization to display both physical and legal information of 3D Strata objects based on LADM country profile (Malaysia). This particular paper also attempts to address 3D visualization of spatial and non-spatial data query using a web application, which also suggests further directions for 3D cadastral visualization development.
A FACTUAL STUDY OF CODE READABILITY AND SOFTWARE COMPLEXITY

: Important software quality metrics such that Code readability and software complexity that impact other software metrics such as maintainability, reusability, portability and reliability. This paper presents a Factual study of the relationships between code readability and program complexity. This analysis includes many readability and complexity metrics. Our study factually confirms the existing wisdom that readability and complexity are negatively correlated.
Metaphors about Balinese Women: From Semantic Analysis to Cultural Pragmatic Interpretations

Metaphor is practised in all languages worldwide. Over the last two decades, investigation on metaphor has relied on some perspectives, such as lexical-constructional, critical, conceptual, axiological, cognitive theory or underlined aspects, as well as semantic and cultural pragmatic as the least of all. This qualitative study focused on how semantic components and cultural pragmatic were used to study the meaning and function of Balinese women metaphor. The purposive sampling method was to collect data from various sources and triangulated it into key informants. The results showed two classifications of Balinese women's metaphors, namely physical perspective as a concrete metaphor that depicts their body parts and personality perspective as an abstract, which emphasizes their ideal role. The pragmatic cultural interpretation involved the cognitive process of declarative analogy that referred to the similarity of values. Meanwhile, the personality perspective metaphor produced higher emotive values than the physical one. Hence, this type of metaphor is used by the Balinese people to advise women.