Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiphysics Analysis and Optimal Design of Compressible Micro-Interconnect for 2.5D/3D Heterogeneous Integration

Compressible Micro-Interconnect (CMI) shows tremendous potential in 2.5D/3D heterogeneous integration due to its outstanding performance in integration, electrical isolation, and thermal management. In this work, an optimal design approach for CMIs is developed based on a coupling framework of multiphysics simulation and particle swarm optimization (PSO). In the framework, the mechanical simulation was conducted firstly to obtain the stress distributions as CMI switched from the initial state to the working position. The contact resistance between CMI and the top pad was modeled and quantitively analyzed. Subsequently, the PSO method was utilized to implement the structural optimization of CMI to improve the performance. Multiphysics simulations of both the original and optimized CMIs were carried out and compared. With the implementation of the optimized CMIs, the contact resistance dropped from 155.3 mΩ to 108.8 mΩ, which brought significant improvement in both DC voltage drop and self-heating effect. The influence of the self-heating effect on the electrical performance of CMI is also discussed qualitatively.
Functional Impact Analysis for Complex Critical Infrastructure Systems

The well-being of the population depends to a large extent on services of critical infrastructures (CIs). It is a well-known fact that the dependencies between CIs lead to even greater impacts on society in the case of malfunctions. To assess these impacts, understand and mitigate them, it is essential to determine and possibly quantify the dependencies. The approach described here consists of three steps. First, the disruptive event for which the impacts should be analyzed is deﬁned (e.g. a blackout). In a second step, the infrastructure system is modeled. For this purpose, the system is divided into its entities according to common CI deﬁnition. Then, the possible service levels of each entity are assessed and quantiﬁed, such as standard operation mode, emergency operation mode or breakdown. Logically linked requirements for reaching those levels are deﬁned, either autonomously or as an external dependency (e.g. a power generator), forming a system of dependent services. In a third step, impacts of events are analyzed by degrading selected services and calculating the effects on other services according to the logically linked requirements. Uncertainties can be described by specifying probabilities for service levels. The development over time can be examined by evaluating the impacts of sequential time intervals, e.g. for a regional blackout, where more CIs will face difﬁculties over time.
Factors that Impact Consumers’ Expenditure on Electric Vehicles: A Case Study from China

Development of electric vehicles is dynamic all around the world, and so does in China. This is because of the characteristics of electric vehicles, such as powered by green energy, no pollution in functioning, and more convenient in driving. However, the stock of electric vehicles is still relatively small, which is only about one percent of total vehicles in the world. Therefore, it is necessary to continue to stimulate the development of electric vehicles and improve their ownership. The purpose of this paper is to study the factors that will influence consumers’ expenditure on electric vehicles in China. This paper implies the model based on Theory of Planned Behavior, Technology Acceptance Model, and Innovation Diffusion Theory, and further develops the model by adding environmental impact as a new factor. The paper utilizes Cronbach alpha score and regression analyze to analyze and validate data. This study concludes that there are eight dimensions that impact consumers’ expenditure on electric vehicles in China: Consumer’s attitude towards electronic cars, consumer’s subjective norm on electronic cars, consumer’s self-control ability, consumer’s perceived usefulness of electronic cars, consumer’s perceived convenience of electronic cars, consumer’s adaptability on electronic cars, external information, and perceived environmental effect of electronic cars. The implication of this paper is to help firms and government understand what is required to improve the popularity of electric vehicles in consumers’ perspective and increase number of stocks of electric vehicles.
Changes in Lutein Status Markers (Serum and Faecal Concentrations, Macular Pigment) in Response to a Lutein-Rich Fruit or Vegetable (Three Pieces/Day) Dietary Intervention in Normolipemic Subjects

Lutein is mainly supplied by dietary fruit and vegetables, and they are commonly jointly assessed in observational and interventional studies. Lutein bioavailability and health benefits depend on the food matrix. This study aimed to assess the effect of dietary intervention with lutein-rich fruit or vegetables on lutein status markers, including serum and faecal concentrations (by high pressure liquid chromatography), dietary intake (24 h recalls ×3), and macular pigment optical density (MPOD) and contrast threshold (CT) as visual outcomes. Twenty-nine healthy normolipemic subjects, aged 45–65 y, consumed 1.8 mg lutein/day supplied from fruits (14 subjects, 500 g/day of oranges, kiwi and avocados) or vegetables (15 subjects, 180 g/day of green beans, pumpkin, and sweet corn) for four weeks. Serum lutein concentration increased by 37%. The effect of the food group intervention was statistically significant for serum lutein+zeaxanthin concentration (p = 0.049). Serum α- and β-carotene were influenced by food type (p = 0.008 and p = 0.005, respectively), but not by time. Serum lutein/HDL-cholesterol level increased by 29% (total sample, p = 0.008). Lutein+zeaxanthin/HDL-cholesterol increased, and the intervention time and food group eaten had an effect (p = 0.024 and p = 0.010, respectively) which was higher in the vegetable group. The MPOD did not show variations, nor did it correlate with CT. According to correlation matrixes, serum lutein was mainly related to lutein+zeaxanthin expressed in relation to lipids, and MPOD with the vegetable group. In faecal samples, only lutein levels increased (p = 0.012). This study shows that a relatively low amount of lutein, supplied by fruit or vegetables, can have different responses in correlated status markers, and that a longer intervention period is needed to increase the MPOD. Therefore, further study with larger sample sizes is needed on the different responses in the lutein status markers and on food types and consumption patterns in the diet, and when lutein in a “pharmacological dose” is not taken to reduce a specific risk.
The impact of positioning on click-through-rates in hotel metasearch engines

The recent arrival of the metasearch as a distribution channel within the hotel industry has the potential to provide hotels less dependency on online travel agencies. Metasearch engines display different reservation options for the same hotel in the same interface, which might include online travel agencies and the official hotel website, establishing an apparent level playing field. In this context, it remains unclear if there will be a presentation-order effect of the different options when price and cancellation policy are the same due to two reasons. First, hotels compete against popular and trustworthy online travel agencies. Second, the minimalist and functional design of metasearch platforms might help consumers differentiate among the options, possibly impacting the decision-making process. Accordingly, the present study aims at investigating the relationship between a hotel's site position and a higher click-through rate in a metasearch whilst competing directly and on the same page with a well-known online travel agency. To this end, an experiment was conducted in which participants had to finish the booking process for a given hotel on a major metasearch engine, where they could choose between the hotel's official site and a leading online travel agency. The main finding implies that occupying the top position increases the click-through rate for unknown hotels, whilst hotels associated with well-known chains are not subject to this effect.
Spatial variability of snow density and its estimation in different periods of snow season in the middle Tianshan Mountains, China

Snow density is an essential property of snowpack. To obtain the spatial variability of snow density and estimate it in different periods of the snow season remain challenging, particularly in the mountainous area. This study analysed the spatial variability of snow density with in‐situ measurements in three different periods (i.e., accumulation, stable and melt periods) of the snow seasons of 2017/2018 and 2018/2019 in the middle Tianshan Mountains, China. The simulation performances of the multiple linear regression (MLR) model and three machine learning (random forest [RF], extreme gradient boosting [XGB] and light gradient boosting machine [LGBM]) models were evaluated. Results showed that snow density in the melt period (0.27 g cm−3) was generally greater than that in the stable (0.20 g cm−3) and accumulation periods (0.18 g cm−3), and the spatial variability of snow density in the melt period was slightly smaller compared to that in other two periods. The snow density in the mountainous areas was generally higher than that in the plain or oasis areas. It increased significantly (p < 0.05) with elevation during the accumulation and stable periods. In addition to elevation, latitude and ground surface temperature also had critically impacted the spatial variability of snow density in the study area. In the current study, the machine learning models, especially RF, performed better than MLR for simulating snow density in the three periods. Based on the key environmental variables identified by the machine learning model and correlation analysis, this study also provides practical MLR equations to estimate the spatial variance of snow density during different snow periods in the middle Tianshan Mountains. This method can be used for regional snow mass and snow water equivalent prediction, leading to a better understanding of local snow resources.
Prevalence and predictors of post-liver transplantation metabolic bone diseases

Introduction Post-liver transplantation (LTx) bone diseases have been poorly investigated. The frequency of bone diseases (osteopenia and osteoporosis) after LTx is unknown. Aim of the study To define prevalence and risk factors of bone disorders following LTx. Material and methods This prospective study was conducted on 100 consecutive adult patients who underwent living donor liver transplantation (LDLT) at the National Liver Institute (NLI) and survived longer than a year. Bone mineral density (BMD) was evaluated by dual-energy X-ray absorption (DEXA), as well as other pre- and postoperative risk factors. Results The frequencies of osteopenia and osteoporosis were found to be 14% and 8% among post-LTx patients. Seven recipients of the osteoporotic group were males, with mean age, and body mass index (BMI) before and after LTx 49.5 ±7.4 years, 24.1 ±4.7 kg/m2 and 22.8 ±1.5 kg/m2, respectively. A significant association between hepatitis C virus (HCV)-related cirrhosis, liver disease severity according to Child-Turcotte-Pugh (CTP) score, and alcoholism with decreased post-LTx BMD was substantiated (p < 0.05, 0.006). Post-LTx development of diabetes mellitus (DM), weight gain, use of corticosteroids and basiliximab all significantly affected decreased post-LTx BMD (p < 0.05). However, binary regression revealed that post-LTx occurrence of DM (p = 0.012, odds ratio [OR] = 0.099), the severity of liver disease (p = 0.023, OR = 0.217), and HCV (p = 0.011, OR = 0.173) are the main independent predictors of metabolic bone disease (MBD) occurrence one year after LTx. Conclusions Post-LTx bone disorders are not infrequent complications and should be more considered in those with HCV-related severe liver disease or developed DM after LTx.
Local scour protection using geocell for downstream of spillway

Local scour is an important problem for hydraulic structures. The local scour in the downstream of dams causes problems such as the damage of the dam body stabilization, erosion of the slopes, and the submergence of the turbines. There are many studies investigating the local scour prediction of the downstream of the hydraulic structures, but in recent years, these studies have been replaced by studies of local scour reduction. The new idea of confining the bed materials using the geocell is becoming a popular solution. This solution can be especially used for the reinforcement of the soils. In this study, the preventability of the local scour downstream of chute channel by cellular confinement system, also known as geocell, was investigated. As a result, in case of using geocell, percentage reduction of the maximum scour depth up to 40.63% was observed.
Orientação a Práticas Parentais: Descrição de um Programa de Intervenção Individual Breve

Resumo A orientação a práticas parentais visa modificar o contexto no qual as crianças estão inseridas como forma de potencializar mudanças em seu comportamento. Esse artigo descreve uma intervenção breve de sete encontros em modalidade individual. O programa teve como finalidades principais auxiliar os cuidadores a identificar e estimular comportamentos adequados em seus filhos; ensinar novos comportamentos; incentivar a autonomia das crianças; encontrar abordagens não-agressivas para lidar com maus comportamentos; e auxiliar na organização da rotina. Como metodologia, realizou-se a descrição de três casos, exemplificando as estratégias adotadas em cada um. A análise dos casos permitiu o delineamento de características comuns entre as práticas parentais dos pais ou cuidadores. Os casos relatados neste estudo evidenciam que as famílias utilizam punição corporal, apresentam dificuldade de empatizar com a criança, possuem um estilo parental autoritário e expectativas incompatíveis com a idade das crianças. Além disso, a infância dos pais ou cuidadores apresentou forte influência no desenvolvimento de suas práticas parentais. A sobrecarga na figura materna também ficou evidente nos casos de famílias intactas. Por fim, a intervenção pode estabelecer um contato inicial da família com um serviço de psicologia, incentivando contato posterior com serviços que possam atender às demais dificuldades. A partir dos resultados examinados, conclui-se que a intervenção breve apresentou resultados positivos na mudança das relações familiares. Porém, o estudo precisa estabelecer uma medição quantitativa antes e depois da intervenção, além de uma sessão de seguimento para verificar se as mudanças são de longo prazo.
Reduction Sequences During the Hoabinhian Technocomplex in Cambodia and Thailand: A New Knapping Strategy in Southeast Asia from the Terminal Upper Pleistocene to mid Holocene

ABSTRACT The Hoabinhian is a cultural marker of the late Paleolithic in Mainland Southeast Asia. And it is one of the most debated topics in prehistoric research in Southeast Asia. However, today there is a lack of reliable information on the diversity of production strategies and tool structures in the Hoabinhian, due to poor scientific methods and few well-excavated sites in the twentieth century. Over the last two decades, a technological approach that aims to identify reduction sequences (chaîne opératoires) has been applied to Hoabinhian lithic assemblages. Results have been generated that broaden our understanding of the technological characteristics and nature of the Hoabinhian industry. In this study, we present two Hoabinhian case studies from Cambodia (Laang Spean Cave) and Thailand (Moh Khiew Cave) to reveal the reduction sequences and end products of the Hoabinhian industry. We also compare and discuss the unity and variability between the two sites.
Effect of COVID-19 on US TV Stocks Based on Fama-French Model Analysis

The COVID-19 has become a global crisis since it broke out at the beginning of 2020, which has led to a global economic recession and a deep influence on the stock market. This research adopted the Fama-French model to analyze the impact of the epidemic on the listed TV companies' stock in the US stock market. The research data was consulted from the CRSP database with the factors of the Fama-French model and the value & equal-weighted returns for 38 industry portfolios in the US market. The research selected the data above from April 1, 2019, to January 29, 2021 and chose March 1 as the date of the outbreak of the epidemic in the US, which represented the timeline before and after COVID19. After multiple linear regression, it was found that COVID-19 has had a significant effect on the TV industry. The analysis of five factors concluded that the market preference shows the following characteristics: value stock, high profitability, and aggressive investment style. It is recommended that the investors who want to invest in the TV industry in the US stock market can take the stocks with the above features as the investment targets in the post-epidemic period, and this theory can also be applied to future investment whenever an analogous event happens.
Electricity Demand Time Series Forecasting Based on Empirical Mode Decomposition and Long Short-Term Memory

Load forecasting is critical for a variety of applications in modern energy systems. Nonetheless, forecasting is a difficult task because electricity load profiles are tied with uncertain, non-linear, and non-stationary signals. To address these issues, long short-term memory (LSTM), a machine learning algorithm capable of learning temporal dependencies, has been extensively integrated into load forecasting in recent years. To further increase the effectiveness of using LSTM for demand forecasting, this paper proposes a hybrid prediction model that incorporates LSTM with empirical mode decomposition (EMD). EMD algorithm breaks down a load time-series data into several sub-series called intrinsic mode functions (IMFs). For each of the derived IMFs, a different LSTM model is trained. Finally, the outputs of all the individual LSTM learners are fed to a meta-learner to provide an aggregated output for the energy demand prediction. The suggested methodology is applied to the California ISO dataset to demonstrate its applicability. Additionally, we compare the output of the proposed algorithm to a single LSTM and two state-of-the-art data-driven models, specifically XGBoost, and logistic regression (LR). The proposed hybrid model outperforms single LSTM, LR, and XGBoost by, 35.19%, 54%, and 49.25% for short-term, and 36.3%, 34.04%, 32% for long-term prediction in mean absolute percentage error, respectively.
Teacher Leadership for Love, Solidarity, and Justice: The Invisibilized and Contested Practices of Teacher Leaders of Color

Despite widespread acknowledgment of teachers of Color as critical agents of change, white supremacist, colonial, and cis-heteropatriarchal ontologies of “teacher leadership” marginalize the counterhegemonic leadership they embody. Guided by critical leadership and feminist of Color scholarship, I develop and employ an embodied raciolinguistic analysis to examine how a Latina teacher leader of Color facilitated organization-wide action in the educational interests of Black students. My analysis demonstrates that her discursive and embodied practices as a non-Black woman of Color and “official” teacher leader were simultaneously (re)constructed as catalysts and hindrance for racial progress within and across social spaces. Grappling with these possibilities and tensions at interpersonal, institutional, and societal scales, she reflexively adapted her practices to recenter Black leadership while facing professional consequences. Arguing for radical social change by amplifying the multi-faceted and contested nature of counterhegemonic teacher leadership, I offer implications to foster the critical ingenuity needed to lead in love, solidarity, and justice for and among communities of Color.
Virtual sounding of solar-wind effects on the AU and AL indices based on an echo state network model

Abstract. The properties of the auroral electrojets are examined on the basis of a trained machine learning model. The relationships between solar-wind parameters and the AU and AL indices are modeled with an echo state network (ESN), a kind of recurrent neural network. We can consider this trained ESN model to represent nonlinear effects of the solar-wind inputs on the auroral electrojets. To identify the properties of auroral electrojets, we obtain various synthetic AU and AL data by using various artificial inputs with the trained ESN. The analyses of various synthetic data show that the AU and AL indices are mainly controlled by the solar-wind speed in addition to Bz of the interplanetary magnetic field (IMF) as suggested by the literature. The results also indicate that the solar-wind density effect is emphasized when solar-wind speed is high and when IMF Bz is near zero. This suggests some nonlinear effects of the solar-wind density.
Phospholipids in Salt Stress Response

High salinity threatens crop production by harming plants and interfering with their development. Plant cells respond to salt stress in various ways, all of which involve multiple components such as proteins, peptides, lipids, sugars, and phytohormones. Phospholipids, important components of bio-membranes, are small amphoteric molecular compounds. These have attracted significant attention in recent years due to the regulatory effect they have on cellular activity. Over the past few decades, genetic and biochemical analyses have partly revealed that phospholipids regulate salt stress response by participating in salt stress signal transduction. In this review, we summarize the generation and metabolism of phospholipid phosphatidic acid (PA), phosphoinositides (PIs), phosphatidylserine (PS), phosphatidylcholine (PC), phosphatidylethanolamine (PE) and phosphatidylglycerol (PG), as well as the regulatory role each phospholipid plays in the salt stress response. We also discuss the possible regulatory role based on how they act during other cellular activities.
Advances in Understanding Landscape Influences on Freshwater Habitats and Biological Assemblages

Abstract. —Over the past decade, numerous studies have identified correlative relationships between aquatic biota and human activities at landscape scales. In addition to demonstrating the pervasive effects of these activities on aquatic biota, these findings have encouraged researchers to suggest that predictive relationships between human activities and aquatic biota could be used to enhance diagnostic power of biological assessments, predict future changes in species distributions, and inform land-use planning. However, to achieve these important goals, descriptions of human activities will need to become more detailed than the simple land use/land cover classifications frequently used. Our purpose is to highlight four sources of human activity data (existing geographic information system layers, census data, remotely sensed images, and visual landscape surveys) that can be used to increase the level of detail with which the human environment is described. Strengths and weaknesses of each data source are discussed and methods for adapting those data to aquatic studies are described by drawing on experiences from studies in the agricultural landscapes of southern Manitoba and southwestern Ontario, Canada. Based on the observations and lessons learned from our previous experiences, we make recommendations for how researchers can identify and apply the data sources that best meet their needs. We also discuss challenges and possible solutions for applying the described data sources as well as for improving data availability in the future. Moreover, we encourage aquatic researchers to allot more time to detailed description of human activities because we believe this to be an effective approach to improving our ability to predict the effects of human activity and thus better assist decision makers in protecting aquatic ecosystems.