Miracle-dz/UniFAR-FTUs huggingface dataset:
An Integrated Conceptual Framework Linking Attachment Insecurity to Increased Risk for Both Enacting and Experiencing Objectification

Sexual objectification (i.e., reducing a person to their appearance, body, or sex appeal and functions) is a significant risk factor for negative health outcomes. In the present investigation, we examined multiple manifestations of objectification (i.e., objectification of others, objectification of self, and objectification by others) in an interpersonal context. We merged objectification theory with attachment theory, one of the most prominent theories of close relationships, and propose that sexual objectification can shed light on attachment processes (and vice versa). To bolster this conceptual overlap, we tested this novel, integrated framework across two independent samples of women and men including (a) a sample of 813 undergraduate students—both partnered and single—who completed self-report questionnaires of attachment security and multiple forms of objectification and (b) a sample of 159 committed couples navigating pregnancy who were observed during naturalistic interactions to assess attachment security and completed self-report questionnaires of attachment security and objectification (including partner objectification). Results from both studies demonstrate the utility of our proposed conceptual framework linking attachment insecurity to increased risk for both enacting and experiencing objectification. The most compelling evidence emerged for (a) a link between attachment anxiety and self-objectification with moderate effect sizes across both samples, and (b) an association between a less secure base within the couple relationship during pregnancy and feeling more objectified by one's partner as well as less humanized (i.e., feeling that your partner values you more for your physical attributes and less for your non-physical attributes). Additional online materials for this article are available on PWQ's website at https://journals.sagepub. com/doi/suppl/10.1177/03616843231165476.
On State Transition Probability and Performance of Direct Acyclic Graph Based Ledgers

Direct acyclic graph (DAG) based ledgers with multi-chain structures aim to solve the technical bottlenecks associated with classical blockchain technologies in the Internet of Things (IoT). The basic working principle of DAG-based ledgers is to validate new transactions by previous transactions in order to be added to the system. During the tip selection process in the unsteady regime, the state transition probability refers to the probability of a transaction changing from the initial state to an arbitrary state. The state transition probability plays an indispensable role in the performance and security analysis of the IoT relying on DAG-based ledgers. In this paper, we derive the exact expression and an approximate expression of the state transition probability, which both are in closed form. In addition, we propose and analyze three performance metrics, i.e., the expected cumulative weight, the expected number of steps, and the confirmation failure probability, which are derived from the state transition probability and greatly enrich the performance analysis and evaluation of the IoT. Markov chain Monte Carlo (MCMC) simulations are carried out to verify the derived analytical results and provide insight into the IoT using DAG-based ledgers.
Real-Time Map Matching Algorithm Based on Low-Sampling-Rate Probe Vehicle Data

By considering GPS errors,candidate links were selected for every GPS point which was partitioned into different time windows according to 5-minute time interval.Then,map-matching for low sampling-rate probe vehicle data was developed via fuzzy logic inference to search for a final path from candidate paths set obtained by the shortest path computation.Finally,the proposed algorithm was evaluated in terms of matching accuracy and calculating time with different sampling-rate probe vehicle data collected in Beijing.
An extended TPB model of waste-sorting intention: a case study of Indonesia

PurposeThis research investigates factors affecting the intention to sort waste. Specifically, this research aims to develop a waste-sorting intention model by extending the theory of planned behavior (TPB) model and to test the model to ensure the model's goodness-of-fit, validity and reliability.Design/methodology/approachThis research used a quantitative research methodology. Data were collected from 460 respondents using an online questionnaire. Some statistical analyses were performed to analyze the data: descriptive statistics, factor analysis, confirmatory factor analysis-structural equation modeling (CFA-SEM), SEM and Cronbach's alpha analysis.FindingsThe result shows that the intention to sort waste was directly affected by attitude, subjective norms, perceived behavioral control (PBC), moral obligation and facility support. Environmental concerns, waste-sorting knowledge and time availability indirectly influenced the intention to sort waste. The testing indicated that the proposed model was fit, valid and reliable.Practical implicationsThe model provides a more comprehensive understanding of waste-sorting intention. The central and local governments can use the results to encourage waste-sorting intention in the community.Originality/valueThis research is believed to be the first study to develop and test the waste-sorting intention model that extends the TPB model by incorporating moral obligation, facility support, policy and regulation support, environmental concerns, waste-sorting knowledge and time availability into the traditional TPB model.
A Random Forest in the Great Lakes: Stream Nutrient Concentrations Across the Transboundary Great Lakes Basin

Excess nutrient inputs from agricultural and urban sources have accelerated eutrophication and increased the incidence of algal blooms in the Great Lakes Basin (GLB). Lake basin management to address these threats relies on understanding the key drivers of pollution. Here, we use a random forest machine learning model to leverage information from 202 monitored streams in the GLB to predict seasonal and annual flow‐weighted concentrations of nitrogen and phosphorus, as well as nutrient ratios across the GLB. Land use (agricultural and urban land) and land management (tile drainage and wetland density) emerge as the two most important predictors for dissolved inorganic nitrogen (DIN; NO3− + NO2−) and soluble reactive phosphorus (SRP; PO43), while soil type and wetland density are more important for particulate P (PP). Partial dependence plots demonstrate increasing nutrient concentrations with increasing tile density and decreasing wetland density. In addition, increasing tile and livestock densities and decreasing forest cover correspond to higher SRP:Total Phosphorus (TP) ratios. Seasonally, the highest proportions of SRP occur in summer and fall. Higher livestock densities are also correlated with increasing N:P (DIN:TP) ratios. Livestock operations can contribute to the buildup of soil nutrients from excess manure application, while increasing subsurface drainage can provide transport pathways for dissolved nutrients. Given that both SRP:TP and the N:P ratios are strong predictors of harmful algal blooms, our study highlights the importance of livestock management, drainage management, and wetland restoration in efforts to address eutrophication in intensively managed landscapes.
Electric Vehicle Trip Information Inference Based on Time-Series Residential Electricity Consumption

Pure electric vehicles (EVs) become more and more popular in current automotive markets. With the wide application of EVs in the market, more EVs will be charged in residential homes. An advanced metering infrastructure measures real-time electricity consumption of individual residential homes. However, the advanced metering infrastructure is exposed to attacks given the fact that its components (e.g., smart meters) are located at public places. Under this situation, electricity consumption information of an individual residential home with an EV can be easily stolen. In this article, we propose an EV trip information inference system (TIIS) to infer EV trip information (origin-destination of each driving trip in a day) of a residential home based on its real-time electricity consumption measurements in a day. To the best of our knowledge, this is the first driving privacy relevant work caused only by residential electricity consumption data. For a residential home with an EV, TIIS derives the relationship between trip numbers in a day and charging-starting time, and the relationship between total driving distance and total charging energy. To derive the EV trip information of a residential home, TIIS first extracts an EV charging profile from its electricity consumption data and identifies EV model type and trip numbers as well as total driving distance based on EV charging profiles. Then, TIIS infers EV trip information based on the identified information. We used electricity consumption data and EV driving data to evaluate inference performance of TIIS. The experimental results demonstrate TIIS has as high as 81% trip inference accuracy and works on residential homes with different EV model types.
Preparation of Mesoporous Si Nanoparticles by Magnesiothermic Reduction for the Enhanced Reactivity

In this study, mesoporous silicon nanoparticles (M-Si) were successfully prepared by a magnesiothermic reduction of mesoporous silica nanoparticles, which were synthesized by a templated sol-gel method and used as the precursors. M-Si exhibited a uniform size distribution with an average diameter of about 160 nm. The measured BET surface area was 93.0 m2 g−1, and the average pore size calculated by the BJH method was 16 nm. The large internal surface area provides rich reaction sites, resulting in unique interfacial properties and reduced mass diffusion limitations. The mechanism of the magnesiothermic reduction process was discussed. The reactivity of prepared M-Si was compared with that of commercially available non-porous Si nanopowder (with the average diameter of about 30 nm) by performing simultaneous thermogravimetry and differential scanning calorimetry in the air. The results showed that the reaction onset temperature indicated by weight gain was advanced from 772 °C to 468 °C, indicating the promising potential of M-Si as fuel for metastable intermolecular composites.
Identifying 3D expression domains by graph clustering

In this work we explore 3D expression domains by using Hi-C contact information to construct an interaction network in combination with TRIP expression and position data. After clustering this network using a community finding algorithm, resulting clusters are analysed for enrichment of Gene Ontology terms. Our work demonstrates a link between the presence of expression measuring reporters and GO enrichments. Furthermore we find that the presence of an insignificant number of a single type of these reporters is generally not linked to these enrichments. In clusters containing a large number of these reporters, enrichment of GO terms is more prevalent. By performing analyses on presence of 3D expression domains we hope to gain more insight into permissive / non-permissive chromatin domains and how these domains interact over longer distances.
Air Quality Prediction using Ensemble Voting based Deep Learning with Mud Ring Algorithm for Intelligent Transportation Systems

In recent times, advanced technologies in transportation are developing like connected and automated vehicles and shared mobility services. A rapidly increasing number of vehicles in intelligent transportation system (ITS) and smart cities causes pollution and degrade the quality of air. Owing to the incredible effect of air quality on individual lives, it is indispensable to design a system by which air pollutants (PM2.5, NOx, COx, SOx) are predicted. But predicting air quality and its pollutants was complex since air quality relies on various elements like power plants, weather, and vehicular emissions. Deep learning (DL) and Machine learning (ML) techniques are leveraged for developing an air quality predictive method. This study develops an Air Quality Prediction utilizing Ensemble Voting based Deep Learning with Mud Ring Algorithm (AQP-EDLMRA) technique. The presented AQP-EDLMRA technique follows the ensemble voting model, which exploits three DL classification methods like long short-term memory (LSTM), deep belief network (DBN), and stacked autoencoder (SAE). Then, the new data can be classified by the weighted vote of their prediction outcomes. To adjust the hyperparameter values of the DL methods, the MRA was exploited, showing the novelty of the work. The simulation values of the AQP-EDLMRA approach are tested using a series of air quality data and the comprehensive comparative results demonstrated that the AQP-EDLMRA technique has reached improved forecasting performance.
Porcine-derived soft block bone substitutes for the treatment of severe class II furcation-involved mandibular molars: a prospective controlled follow-up study

Purpose No evidence exists regarding the advantages of periodontal regeneration treatment for furcation defects using soft block bone substitutes. Therefore, this randomized controlled trial aimed to assess the clinical and radiographic outcomes of regenerative therapy using porcine-derived soft block bone substitutes (DPBM-C, test group) compared with porcine-derived particulate bone substitutes (DPBM, control group) for the treatment of severe class II furcation defects in the mandibular molar regions. Methods Thirty-five enrolled patients (test group, n=17; control group, n=18) were available for a 12-month follow-up assessment. Clinical (probing pocket depth [PPD] and clinical attachment level [CAL]) and radiographic (vertical furcation defect; VFD) parameters were evaluated at baseline and 6 and 12 months after regenerative treatment. Early postoperative discomfort (severity and duration of pain and swelling) and wound healing outcomes (dehiscence, suppuration, abscess formation, and swelling) were also assessed 2 weeks after surgery. Results For both treatment modalities, significant improvements in PPD, CAL, and VFD were found in the test group (PPD reduction of 4.1±3.0 mm, CAL gain of 4.4±2.9 mm, and VFD reduction of 4.1±2.5 mm) and control group (PPD reduction of 2.7±2.0 mm, CAL gain of 2.0±2.8 mm, and VFD reduction of 2.4±2.5 mm) 12 months after the regenerative treatment of furcation defects (P<0.05). However, no statistically significant differences were found in any of the measured clinical and radiographic parameters, and no significant differences were observed in any early postoperative discomfort and wound healing outcomes between the 2 groups. Conclusions Similar to DPBM, DPBM-C showed favorable clinical and radiographic outcomes for periodontal regeneration of severe class II furcation defects in a 12-month follow-up period. Trial Registration Clinical Research Information Service Identifier: KCT0007305
Separated shear layers of rectangular prisms in turbulent flows

Abstract Rectangular prisms with different depth‐to‐width ratios (B/D) were placed in flow fields with various turbulence intensities (T.I.) and length scales to study the unsteady structures of separated shear layers. Velocity profiles, turbulence intensity profiles, velocity gradient profiles, and wake spectra of the prisms were measured. When B/D=0.4 to 1.0, differences between the acceleration and deceleration phases of these profiles were prominent, indicating strong vorticities especially when B/D=0.6. These differences, also supported by the spectra peaks, became less obvious when B/D=2.0 and 3.0. The approaching field T.I. had a larger effect than the length scale. Higher T.I. led to increased intensity, flow mixing, and thickness of the boundary layer, but weaker wake vorticity.
Prediction of structure and function of proteins and ligands by means of neural and kernel methods for structured data

In the post-genomics era the new challenge posed by proteomics is the translation of the millions of already sequenced genes into high-resolution protein three-dimensional structures. The study of these protein structures would then eventually lead to a better understanding of the molecular mechanisms that define life. Machine learning can play an important role in this process. Using the evidence coming from known native structures, the structural and functional features of the other proteins can be rapidly predicted without the need of costly equipment. For this purpose, the use of neural and kernel methods for structured data is explored here on several predictive tasks in computational biology and biochemistry. A two-stages SVM-BRNN architecture for sequence translation tasks is applied to the problem of secondary structure (SS) prediction. The proposed approach, coupled with a Viterbi decoder that enforce constraints in the output sequences, is tested on a significant dataset and demonstrates state-of-the-art performances. The same architecture is then applied to the identification of protein metal binding sites (MBS), an important problem that has so far received scarce attention by the machine learning community. The proposed approach greatly improves the coverage of predicted MBS compared to pattern search methods, and additionally provide accurate prediction of disulfide bonded cysteines. An improved bidirectional recursive neural network architecture is introduced to solve sequence learning tasks in the presence of explicit knowledge about interacting positions. This architecture, tested on the SS prediction task using predicted contacts between residues, provides interesting directions of future investigation, while additional work is needed to advance the current state-of-the-art. An efficient structure reconstruction procedure is presented that can be used to reconstruct native structures when incomplete (coarse) information is available. While the proposed approach is able
Detecting overlapping communities in networks via dominant label propagation

Community detection is an important methodology for understanding the intrinsic structure and function of a real-world network. In this paper, we propose an effective and efficient algorithm, called Dominant Label Propagation Algorithm (Abbreviated as DLPA), to detect communities in complex networks. The algorithm simulates a special voting process to detect overlapping and non-overlapping community structure in complex networks simultaneously. Our algorithm is very efficient, since its computational complexity is almost linear to the number of edges in the network. Experimental results on both real-world and synthetic networks show that our algorithm also possesses high accuracies on detecting community structure in networks.
Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board , Washington , D . C . Property Taxes and Elderly Labor Supply

The recent housing market boom in the U.S. has caused sharp increases in residential property taxes. Anecdotal evidence suggests that rising property taxes have induced elderly homeowners to increase their labor supply. This paper uses 1992-2004 panel data from the Health and Retirement Study (HRS) as well as a newly collected dataset on state-provided property tax relief programs to investigate the effect of property taxes on the labor supply of elderly homeowners. It is the first rigorous study on the link between property taxes and elderly labor supply. I examine both the extensive margin whether elderly homeowners delay retirement or reenter the labor market in the face of rising property taxes, and the intensive margin whether elderly homeowners work longer hours when property taxes increase. A simulated IV approach is used to address the potential endogeneity problem associated with property taxes. I find little evidence that property taxes have a significant impact on elderly homeowners’ decisions to retire, to re-enter the labor force, or to increase working hours.
Across‐Layer Sliding Ferroelectricity in 2D Heterolayers

Although the monolayers of most 2D materials are non‐ferroelectric with highly symmetric lattices, symmetry breaking may take place in their bilayers upon some stacking configuration, giving rise to so‐called sliding ferroelectricity where the vertical polarizations can be electrically reversed via interlayer translation. However, it is not supposed to appear in systems like graphene bilayer with centro‐symmetry at any stacking configuration, and the origin of the recently reported ferroelectricity (Nature 2020, 588, 71) in graphene bilayer intercalated between h‐BN remains mysterious. Here, a type of across‐layer sliding ferroelectricity that arises from the asymmetry of next‐neighbor interlayer couplings is proposed. The first‐principles evidence is shown that the vertical polarizations in intercalated centro‐symmetric 2D materials like graphene bilayer can be switched via multilayer sliding, which is likely to be the origin of the observed ferroelectric hysteresis. Moreover, such ferroelectricity may exist in a series of other heterolayers with quasi‐degenerate polar states, like graphene bilayer or trilayer on BN substrate, or even with a molecule layer on surface where each molecule can store 1‐bit data independently, resolving the bottleneck issue of sliding ferroelectricity for high‐density data storage.
Interlayer transport of an electron in bilayer graphene with phonon-induced lattice distortion in the presence of biased potential

The interlayer transport of an electron in bilayer graphene influenced by a phonon in the presence of a biased potential is investigated using the tight-binding approach. The in-plane optical mode E2g and out-of-plane optical mode B1g associated with the applied biased potential are considered to compute and discuss the interlayer transport probability of an electron initially localized on the bottom layer at the Dirac point in the Brillouin zone. Without the biased potential, the interlayer transport probability is equal to 0.5 regardless of the phonon displacement except for a few special cases. Applying a biased potential to the layers, we find that in different phonon modes the function of the transport probability with respect to the applied biased potential and phonon displacement is complex and various, but on the whole the transport probability decreases with the increase in the absolute value of the applied biased potential. These phenomena are discussed in detail in this paper.