Miracle-dz/UniFAR-FTUs huggingface dataset:
Compact Power Front-end Management for Wireless Supply of Multi-voltages to Neuromodulators

Herein, we present a CMOS power management module (PMM) implemented to deliver three DC supply voltages for powering various building blocks of an implantable medical device. This PMM includes an active rectifier, bandgap reference (BGR) circuit, and three independent low-dropout (LDO) voltage regulators. The input voltage of this PMM is a sinusoidal wave form that reaches a maximum of 2.6 V; this is derived from a power source placed outside the body through a wireless power transfer link, which is connected to the rectifier. The three delivered DC output voltages are 1.24 V, 1.86 V, and 3.08 V. Post-layout output simulation results indicate values of 1.24 V, 1.82 V, and 3.09 V, thus meeting the expected circuit simulation results. Corners, including process and Monte Carlo, were analyzed. Compared with the replica-biased loop, which has a small output voltage range with a main output to generate the other three outputs, this integrated PMM utilizing three independent LDOs has a greater output voltage range. The design also meets the small-size and low-power consumption requirements and can, therefore, be used to prevent obstructive sleep apnea.
Experimental investigation of rotor rubbing faults

The nonlinear dynamics of a rubbing rotor in a multi-disk rotor system were investigated with a device designed to simulate the full rubbing. The vibration of the rubbing rotor was observed for different conditions. The vibration waveforms, spectrums, orbits and Poincare's maps were used to analyze the nonlinear responses and bifurcation characteristics of the system during rubbing. The rubbing process of the rotor system was studied by changing the rotor speed. The results show that higher harmonic components and fractional harmonic components are excited when rubbing occurs with chaotic motion observed with severe rubbing.
Human-Robot Contact Detection in Assembly Tasks

Industry 5.0 complements the existing Industry 4.0 paradigm by, amongst others, focusing on flexible and human-centered manufacturing. However, there are some challenges when humans and robots share a workspace, such as human safety and limited robot perception. Addressing the above-mentioned challenges, we present in this paper a modular contact detection architecture for physical human-robot collaborations, especially for assembly tasks. This architecture consists of two sub-modules: a torque regressor using Gaussian Process Regressor (GPR) and a contact classifier using a Convolutional Neural Network (CNN). In fact, the GPR calculates deviations from expected joint torque values and the CNN detects object/human contacts with the robot, based on the estimated torque values. The result of the real-time implementation on an actual robot shows that the model can achieve a balanced accuracy of over 99% when the robot speed is over 27% and below 45% of its maximum speed even though the dataset was gathered with a speed of only 25%. This indicates the model's generalization capability to higher robot speeds. In addition, the generalization capability of the modular architecture for new movements was tested. The results show, that retraining the GPR can handle the data distribution shift and consequently, the contact classifier is not required to be retrained. Solving the impact of data distribution shift on the contact classifier makes the proposed modular architecture superior to the current state-of-the-art models.
Search theory and duration data: a theory of sorts

I. An introduction to sampling from length-biased populations, 39. —II. The pure sorting model implied by job-search theory, 44. —III. Empirical estimation of the pure sorting model, 48. —IV. Determining the underlying process—a possible experiment, 53. —Appendix, 56. (This abstract was borrowed from another version of this item.)
Analysis of Robot Skill Learning Methods and Secondary Approaches to Enhance Learning

Robot skill learning is a long-existing problem that has gained attention due to the success of deep learning applications. When child learning is analyzed, they use a combination of methods for learning manipulation tasks. This situation inspired us to analyze different learning approaches. We present a comprehensive analysis of robot skill learning. The paper's primary focus is on exploring the effect of robot skill learning based on various learning methods, such as representation learning, predictive learning, and multi-modal learning. This paper also analyzes the advantages and disadvantages of these algorithms.
Bio-Hydrogen Production Through Gasification of Agro-residues

Hydrogen is a clean energy carrier and is considered energy for the future. Biomass gasification is thermochemical technology that can be used for the production of bio-hydrogen or green hydrogen. The produced hydrogen can be used in various applications such as power generation, liquid fuel and chemical production. Using waste biomass or agroresidues from the agricultural farm can be a good resource for hydrogen. Thus waste management and utilization along with sustainable renewable energy production can be achieved through technological intervention.
Enhancing Forest Attribute Prediction by Considering Terrain and Scan Angles From Lidar Point Clouds: A Neural Network Approach

Sensitivity of lidar metrics to scan angle can affect the robustness of area-based approach (ABA) models, and modelling the interplay of scan geometry and terrain properties can be complex. The study hypothesizes that neural networks can manage the interplay of lidar acquisition parameters, terrain properties, and vegetation characteristics to improve ABA models. The study area is in Massif des Bauges Natural Regional Park, eastern France, comprising 291 field plots in a mountainous environment with broadleaf, coniferous, and mixed forest types. Field plots were scanned with a high overlap from multiple flight lines and the corresponding point clouds were considered independently to expand the standard ABA dataset (291 observations) to create a dataset containing 1095 independent observations. Computation of lidar, terrain, and scan angle metrics for each point cloud associated each observation in the expanded dataset with the scan information in addition to the lidar and terrain information. A multilayer perceptron (MLP) was used to model basal area and total volume to compare the predictions resulting from standard and expanded ABA datasets. With expanded datasets containing lidar, terrain, and scan information, the R2 for the median predictions per plot were higher (R2 of 0.83 and 0.85 for BA and Vtot) than predictions with standard datasets (R2 of 0.66(BA) and 0.71(Vtot)) containing only lidar metrics. It also outperformed an MLP model for a dataset with lidar and terrain information [R2 of 0.77(BA and Vtot)]. The MLP performed better than Random forest regression, which could not sufficiently exploit additional terrain and scan information.
Children and adolescents as respondents. Experiments on question order, response order, scale effects and the effect of numeric values associated with response options

The results presented in this article are relevant to the general question to what extent the available methodological knowledge regarding the question–answer process applies also to children and adolescents. Based on the assumption that children lack full cognitive functioning due to their cognitive-developmental stage, we predicted a less sophisticated question–answer process for these respondents. In order to assess this hypothesis we have focused on age and educational achievement as explanatory factors, while the size of response effects was treated as an indicator for the cognitive processes in the respondents’ minds while preparing to answer a survey question. In sum, the size of response order effects as well as that of scale effects and that of the effects of numeric values associated with response categories decreases with age. In general, in each age-group comparison the response effects reach the highest level among the youngest group – that is assumed to be cognitively less developed. At the same time, younger respondents are more likely to ignore contextual information when decoding the question than older respondents. With some restrictions this holds also for the differences between the size of the response effects for respondents with good school achievement on the one hand and respondents with intermediate or poor school achievement on the other.
The Effects of Linguistic Coordination on Perceived Quality of Consumer Reviews: A Dual Process Perspective

Online consumer reviews, as a major source of information and influence, are of great interest to marketing researchers and practitioners. This study investigates the effects of linguistic coordination on perceived review quality. Drawing on the elaboration likelihood model, the authors theorize that two types of linguistic coordination—topic matching (semantic component) and language style matching (lexical component)—have profound effects on perceived review quality. Utilizing natural language processing tools and a novel clustering technique to measure matching, empirical analyses based on an IMDb data set support the positive direct effects of both types of matching. Moreover, the authors find that there is a negative interaction between topic matching and language style matching in affecting perceived review quality. The findings contribute to the understanding of online review quality, and the application of natural language processing enriches the methodological tool kit available to researchers.
The capacity of stem cells in treatment of diabetes

Diabetes, a chronic metabolic disease, is recognized as the most frequent disorder in the endocrine system with hyperglycemia dealing with either insulin resistance or insufficiency or both. This disease is usually associated with numerous acute and chronic complications. Also, the treatment of diabetes complications has imposed a heavy financial burden on most societies. During the last decade, pancreatic islet transplantation has been widely studied as a potential therapy for diabetes. Of course, due to its limitations removing pancreatic cells from the corpse is very difficult. Stem cells are renewable cellular sources that are proposed as a substitute for organ transplantation. These cells which can be found in almost all multicellular organisms are capable of division and transforming into highly specialized cells, they can also replace injured and lost cells. The possibility of using stem cells in diabetes therapy and building insulin-producing islets has long been considered by most scientists and can be a future hope for controlling diabetes. Interestingly, human stem cells derived from hematopoietic organs, liver, pancreas, and embryonic human stem cells are among these factors. In this article, a series of studies carried out on this field is briefly reviewed.
Designing an acceptance model and using public health apps by Iranian users

BACKGROUND: Nowadays, there are hundreds of mobile applications related to sports, health and fitness. The benefits of using mobile phones in physical activity can be seen in the increasing use of mobile health applications. The purpose of this study was to design a behavioral model of acceptance and use of public health apps by Iranian users. MATERIALS AND METHODS: The present study was a qualitative and exploratory approach that was conducted based on the theme analysis method (team). The statistical population included programmers and designers of sports programs and academic specialists in the field of sports and computers. Data collection was performed through review of documents, backgrounds and semi-structured interviews. The interviews were conducted in person or by telephone and each interview lasted about 20 to 40 minutes. RESULTS: In total, 249 key points with marker codes were extracted from 14 interviews which were classified into 21 sub-themes and 6 main themes (app quality, digital literacy, social influences, facilitating conditions, intention to use, trust and accept the app. Finally, the pattern of acceptance and use of health apps by Iranian users were presented in accordance with UTAUT theory. CONCLUSION: The results of this study can help the officials of the federation, public sports boards and clubs to use information and communication technology as a media in their strategies and programs to develop sports and health at the community level. It also contributes to social vitality and improves the quality of life of individuals.
A Product Diffusion Model for Single-Function and Fusion Products

The prosperity of multifunction products (also referred to as fusion products) has changed the landscape of the marketplace for several electronics products. To illustrate, as fusion products gain popularity in cellular phones and office machines, we observe that single-function products (e.g., stand-alone PDAs and stand-alone scanners) gradually disappear from the market as they are supplanted by fusion products. This paper presents a product diffusion model that captures the diffusion transition from two distinct single-function products into one fusion product. We investigate the optimal launch time of the fusion product in various conditions and conduct a numerical analysis to demonstrate the dynamics among the three products. Similar to previous multi-generation single product diffusion models, we find that the planning horizon, the products’ relative profit margin, and substitution effects are important to the launch time decision. However, there are several unique factors that warrant special consideration when a firm introduces a fusion product to the market: the firm’s competitive role, the buyer overlapping of two single-function products, the fusion technology and the age of current single-function products. Keyword: multifunction products, fusion products, product diffusion
Community Attitudes Toward Local Foods and Producers: The Role of Warmth Versus Competence Across Demographics for Social Media Engagement.

Abstract Connecting local food producers with consumers is useful for ensuring individuals have access to healthy, fresh, foods. Small farmers, however, lack the resources to effectively connecting with consumers through traditional forms of marketing. Marketing to consumers through social media is a low-cost method that local food producers can use to promote their products. Creating engaging content on social media can be challenging, thus there is a need for guidance on how to effectively engage with local consumers through social media. Studies in advertising have shown dimensions of warmth and competence can be useful frames for engaging consumers across a variety of consumer goods. In this study we ran a nationally representative survey (n = 966) with an experimental component to determine if videos were an effective method of connecting with consumers, and if so, how this varies across demographics. We further investigated whether aspects of warmth of competence influence consumer willingness to purchase locally produced foods. We find warmth does have a small effect on consumer perceptions of local foods and videos help reduce the lack of familiarity of a farm, a barrier to purchasing local foods. Consumers across demographics demonstrate high levels of interest in purchasing local foods yet access to continues to be a barrier to purchasing. Additionally, we find consumers are willing to purchase locally produced foods at a higher price if available in more convenient locations, such as grocery stores, provided they were clearly labeled and from a farm that is familiar.
Exploring Multivariate Data using the Minor Principal Components

The minor principal components of a random vector x are the orthonormal linear combinations of the variables in x which have minimum variance. This fact may be used to detect interrelations amongst the variables as well as to identify those variables which can be predicted with high accuracy from the others. Application of these ideas to the variable selection problem in multiple regression and to the identification of outliers in multivariate data are discussed.
Low-order sliding mode repetitive control for uncertain linear systems perturbed by band-limited periodic disturbances

This article presents a systematic method for designing discrete-time low-order sliding mode repetitive controller for a class of uncertain linear systems. A linear system considered in this class is perturbed by band-limited periodic disturbances and parametric uncertainties. The digital controller design method we propose combines two control strategies, namely the repetitive control and the sliding mode control techniques. The proposed controller is required to be low order and is intended to reject the periodic disturbances with specific dominant frequencies and to establish robustness against plant parametric uncertainties. Moreover, the proposed method yields a repetitive controller with only a small number of delay terms and can perform fast transient responses. We show through mathematical analyses that the linear system applying the low-order sliding mode repetitive controller satisfies sliding mode stability and robustness criteria. In addition, we demonstrate via a numerical example that a servomotor controlled by the low-order sliding mode repetitive controller can track a frequency-shifted triangular signal with a short transient period and minor errors. Other examples are also presented and show that the low-order sliding mode repetitive controller exhibits better performance than other relevant control methods.
Effect of reclamation years on soil physical, chemical, bacterial, and fungal community compositions in an open‐pit coal mine dump in grassland area of Inner Mongolia, China

Understanding the process of changes in substrate physical, chemical, and biological properties is critical for vegetation ecosystem reconstruction of open‐pit coal mining dumps. However, the relationships of soil chemical, biological, bacterial, and fungal characteristics in open‐pit coal mining dumps in grassland area are still unclear. The purpose of this study is to understand the changes and relationships of soil physic‐chemical, bacterial, and fungal properties under the reclamation time series of open‐pit coal mining dumps. Soil samples were collected from the waste dump reclaimed after 2, 5, 10, and 15 years in Shengli West No. 2 open pit coal mine in Inner Mongolia, China, and the surrounding natural grassland was set as a control at 0–10 cm and 10–20 cm. Soil pH, electrical conductivity (EC), soil organic matter (SOM), nutrient properties, bacterial, and fungal community structures were measured in the waste dump reclaimed after 2, 5, 10, and 15 years. The results showed that soil pH was decreased at 0–20 cm in the waste dump while SOM was increased by 1.2 times in 0–10 cm reclaimed after 15 years than that after 2 years. Soil EC was decreased by 55.5% and 58.3%, respectively, in 0–10 cm and 10–20 cm, respectively, after 10 years than that after 2 years. Soil available nitrogen, available phosphorus, and available potassium were 97.8%, 13.2%, and 60.9% lower after 15 years' reclamation than those in control. The acid phosphatase in 0–10 cm and 10–20 cm was increased by 2.2 and 2.1 times, respectively, after 15 years compared with that after 2 years. Alkaline phosphatase was decreased by 16.8% reclaimed after 15 years than that after 2 years in 10–20 cm soil. The Shannon diversity index of fungi was increased by 42.0% and 43.8%, respectively, in 0–10 cm and 10–20 cm, in the waste dump reclaimed after 15 years than that after 2 years. Soil organic matter was positively correlated with soil urease activity. Soil pH, EC, and texture had different effects on bacterial and fungal communities. Therefore, appropriate measurements should be applied to the reclamation dumps to help the soil properties of reclamation areas to recover to the natural grassland.
International labour migration and people’s views towards economic and political systems in transition countries

ABSTRACT During the 30 years of transition from communist regimes, the people’s views towards transition have varied from support to opposition. We empirically examine the influences of international labour migration on these views, using the survey data covering 28 transition countries in Europe and the former Soviet Union area and three points of time, 2006, 2010 and 2016, based on multivariate regression models. Our main findings are twofold. (I) Labour emigration to Western Europe increased people’s support for a market economy and democracy over a planned economy and authoritarian government, albeit a less robust result for democracy. This suggests the role of social remittances – migrants transmit their experiences and promote the support for transition among people remaining in the home countries. (II) However, immigrant inflows reduced these support, suggesting that the economic and cultural conflicts with immigrants made people sceptic towards liberal policies or nostalgic towards the communist era.
Leading Economic Indicators--New Approaches and Forecasting Records.

Developed fifty years ago by the National Bureau of Economic Research, the analytic methods of business cycles and economic indicators enable economists to forecast economic trends by examining the repetitive sequences that occur in business cycles. The methodology has proven to be an inexpensive and useful tool that is now used extensively throughout the world. In recent years, however, significant new developments have emerged in the field of business cycles and economic indicators. This volume contains twenty-two articles by international experts who are working with new and innovative approaches to indicator research. They cover advances in three broad areas of research: the use of new developments in economic theory and time-series analysis to rationalise existing systems of indicators; more appropriate methods to evaluate the forecasting records of leading indicators, particularly of turning point probability; and the development of new indicators.
A Comprehensive Pan-Cancer Analysis of the Regulation and Prognostic Effect of Coat Complex Subunit Zeta 1

The Coatomer protein complex Zeta 1 (COPZ1) has been reported to play an essential role in maintaining the survival of some types of tumors. In this study, we sought to explore the molecular characteristics of COPZ1 and its clinical prognostic value through a pan-cancers bioinformatic analysis. We found that COPZ1 was extremely prevalent in a variety of cancer types, and high expression of COPZ1 was linked to poor overall survival in many cancers, while low expression in LAML and PADC was correlated with tumorigenesis. Besides, the CRISPR Achilles’ knockout analysis revealed that COPZ1 was vital for many tumor cells’ survival. We further demonstrated that the high expression level of COPZ1 in tumors was regulated in multi-aspects, including abnormal CNV, DNA-methylation, transcription factor and microRNAs. As for the functional exploration of COPZ1, we found a positive relationship between COPZ1’s expression and stemness and hypoxia signature, especially the contribution of COPZ1 on EMT ability in SARC. GSEA analysis revealed that COPZ1 was associated with many immune response pathways. Further investigation demonstrated that COPZ expression was negatively correlated with immune score and stromal score, and low expression of COPZ1 has been associated to more antitumor immune cell infiltration and pro-inflammatory cytokines. The further analysis of COPZ1 expression and anti-inflammatory M2 cells showed a consistent result. Finally, we verified the expression of COPZ1 in HCC cells, and proved its ability of sustaining tumor growth and invasion with biological experiments. Our study provides a multi-dimensional pan-cancer analysis of COPZ and demonstrates that COPZ1 can serve as both a prospective target for the treatment of cancer and a prognostic marker for a variety of cancer types.