Miracle-dz/UniFAR-FTUs huggingface dataset:
The Misallocation Problem of Subsidized Housing: A Lesson from Hong Kong

Providing affordable housing has become one of China’s key national policy agenda items. The shared-equity model in Hong Kong, implemented since the late 1970s, has assisted many families in owning a home in the public housing market. However, little attention has been paid to their welfare after acquiring their subsidized units. This study aims to examine how shared-equity homeownership distorts residential mobility through in-kind subsidies. Panel data analysis reveals that the more in-kind subsidies owners receive, the longer they would hold on to their units in spite of spatial mismatches. Private owners, on the other hand, would trade their units without such distortion. Conceptually, the lower mobility of assisted owners could be interpreted as a new source of misallocation in Glaeser and Luttmer’s welfare analysis. Practically, this throws into question the sustainability of a subsidizing homeownership policy: does the government ultimately want assisted homeowners to move from public housing to private housing in the future (for which high mobility would be intended)? If so, new thinking on how to make in-kind subsidies transferable is needed.
Mineralogical, Physico-chemical and Geochemical Characterization of Three Kaolinitic Clays (Ne Algeria): Comparative Study

Forteen clay samples collected from three kaolin deposits (Tamazert, Hadj Ali and Chekfa; NE Algeria) are characterized by several techniques in order to compares them to somes kaolins used in industry especially that used in ceramics. All the samples were investigated by X-ray diffraction, Infrared absorption spectroscopy, thermal analysis (TG), plasticity, environmental scanning electron microscopy and chemical major elements analysis. The bulk mineralogical composition of all clays samples is dominated by kaolinite (21-75%), illite/ muscovite (33-76%) and quartz (7-21%). K-feldspar and plagioclase are only present in Chekfa and Hadj Ali clays with small amounts. Clay fraction (< 2µm) dominated by kaolinite and illite (98%). Chlorite and smectite are present in some samples of Chekfa and Hadj Ali clays with insignificant amount (˂1%). The particles-size distribution of all samples showed the abundance of sandy silt fraction (28-63%) and silty sand (39-64%) with moderate clayey fraction (2-7%). The chemical composition showed variable amounts of SiO2 (59-68%), Al2O3 (18-39%), Fe2O3 (.26-1.38%) and TiO2 (0.34-0.69%) in accordance with the free quartz in all studied samples. Plasticity-index (7.5-7.9%), Specific surface (28-47m2) and Cation exchange (5-11meq/100g) values are moderate in all samples. Given these properties, these clays may be suitable in bricks and ceramic product.
Triterpenoids and fatty alcohols as indicators of mangrove derived organic matter in Northern Kerala Coast, India

Abstract Triterpenoids and fatty alcohols were identified from the sediments of five mangrove ecosystems (Kunjimangalam, Pazhayangadi, Pappinissery, Thalassery, and Kadalundi) in the northern Kerala coast, India, to illustrate the organic matter sources. Straight chain fatty alcohols in the range C12–32 were detected, and among these the long chain fatty alcohols (C20–32) were exclusively even chained compounds. Branched fatty alcohols (6,10,14-trimethyl-pentadecan-2-ol and 3,7,11,15-tetramethyl-2-hexadecanol) of bacterial origin were detected in a few observations. Twenty triterpenoids were detected from the study region in which 13 of them were sterols. Triterpenoids originated from higher plants such as stigmasterol, campesterol, β-sitosterol, taraxerol, lupeol, germanicol, β-amyrin, α-amyrin, and betulin were identified in the study. Brassicasterol, a diatom marker and dinosterol, a dinoflagellates marker were also detected in the study region. An elevated % composition of dinosterol and dissolved oxygen super-saturation were noticed in Kunjimangalam during pre-monsoon, which may suggest elevated input of algal organic matter in the mangrove system. Cholesterol transformation products such as coprostanol and epi-coprostanol were detected, which indicated the presence of fecal contamination in the sedimentary organic matter. From the ternary diagram, it was recognized that the organic matter contribution from marine algae was feeble as compared to terrestrial input.
CHARACTERIZATION AND CONCENTRATION OPTIMIZATION OF HIBISCUS ROSA-SINENSIS L. MUCILAGE POWDER AS SUPERDISINTEGRANT

Objective: The main purpose of this study is to characterize Hibiscus rosa-sinensis L. mucilage (MHR) powder as superdisintegrant and to decide the optimum concentration of Hibiscus rosa-sinensis L. Methods: Characterization was conducted in many tests such as organoleptic, swelling ratio, solubility, polysaccharide, viscosity, particle size distribution, flowability and compressibility index. Next, MHR powder was included in fast disintegrating tablet (FDT) domperidone formulation in several concentrations and compared with FDT domperidone formulation that using sodium starch glycolate as superdisintegrant. Results: The result of characterization of MHR powder were brownish powder, specific smell like traditional medicines, swelling ratio of 24, solubility of 0.426±0.034 mg/ml, positive polysaccharide, the viscosity of 491.33±119.44 cps (2% w/v), 4520.00±1224.42 cps (4% w/v), Dv(10) of 26.2 µm, Dv(50) of 157 µm, Dv(90) of 260 µm, Dv(100) of 380 µm, flowless, and average compressibility index of 26.75±1.79%. The optimum MHR powder concentration was 1% because the average disintegration time was 39.67±4.73 seconds and the average wetting time was 66.33±14.29 seconds. Those times were faster than domperidone FDT that used this superdisintegrant in other concentrations or sodium starch glycolate in the same concentration. Conclusion: Hibiscus rosa-sinensis L. mucilage powder can be used as superdisintegrant in FDT formulation with an optimum concentration of 1%.
Dynamic fracture toughness of ultra‐high‐performance fiber‐reinforced concrete under impact tensile loading

The fracture toughness and fracture energy of ultra‐high‐performance fiber‐reinforced concrete (UHPFRC) at both static and impact rates (43–92 s−1) were investigated using double‐edge‐notched tensile specimens. Two types of steel fiber, smooth and twisted fiber, were used in producing UHPFRC with different volume ratios of 0.5%, 1.0%, 1.5%, and 2%. The results indicated that UHPFRCs produced very high fracture resistance at impact rates, with first stress intensity factor (KIC) up to 3.995 MPa√m, critical stress intensity factor ( KIC* ) up to 7.778 MPa√m, and fracture energy (GF) up to 86.867 KJ/m2, which were 2.5, 5.0, and 16.9 times higher than those of ultra‐high‐performance concrete, respectively. The KIC* was clearly sensitive to the applied loading rate, whereas the KIC and GF were not. Smooth fiber specimens exhibited not only higher KIC* and GF at impact rates but also higher dynamic increase factor than twisted fiber specimens. A minimum fiber volume content of 1% should be used in UHPFRC to provide a significant enhancement in crack resistance. The maximum value of UHPFRC crack velocity at impact rates was found to be 527 m/s by using a dynamic fracture mechanic model.
Searching for alternative worldviews – how need thwarting, group characteristics and the social environment determine ideological extremism

Using data from the Profiles of Individual Radicalization in the United States (PIRUS) database (N = 884), this study shows that lack of identification with the American belief and social value system, and group grievance, are both significant predictors in explaining why individuals reach out to ideological, extremist groups, prior of showing violent behavior. This means that low levels of identification with socially shared norms, values, narratives and beliefs and the attachment to a group that is believed to be under threat, increase the chance that individuals question the established systems in the United States and search for alternative, even extremist, worldviews.
Classification and feature analysis of the Human Connectome Project dataset for differentiating between males and females

We analysed features relevant for differentiation between males and females based on the data available from the Human Connectome Project (HCP) S1200 dataset. We used 354 features containing cognitive and emotional measures as well as measures derived from task functional magnetic resonance imaging (MRI) and structural brain MRI. The paper presents a thorough analysis of this extensive set of features using a machine learning approach with a goal of identifying features that have the ability to differentiate between males and females. We used two state of the art classification algorithms with different properties: support vector machine (SVM) and random forest classifier (RFC). For each classifier the hyperparameters were obtained and classifiers were optimized using nested cross validation and grid search. This resulted in the classification performance of 91% and 89% accuracy using SVM and RFC, respectively. Using SHAP (SHapley Additive exPlanations) method we obtained relevance of features as indicators of sex differences and identified features with high discriminative power for sex classification. The majority of top features were brain morphological measures, and only a small proportion were features related to cognitive performance. Our results demonstrate the importance and advantages of using a machine learning approach when analysing sex differences.
A neural network-based prediction model in water monitoring networks

To improve the prediction accuracy of ammonia nitrogen in water monitoring networks, the combination of a bio-inspired algorithm and back propagation neural network (BPNN) has often been deployed. However, due to the limitations of the bio-inspired algorithm, it would also fall into the local optimal. In this paper, the seagull optimization algorithm (SOA) was used to optimize the structure of BPNN to obtain a better prediction model. Then, an improved SOA (ISOA) was proposed, and the common functional validation method was used to verify its optimization performance. Finally, the ISOA was applied to improve BPNN, which is known as the improved seagull optimization algorithm–back propagation (ISOA–BP) model. The simulation results showed that the prediction accuracy of ammonia nitrogen was greatly improved and the proposed model can be better applied to the prediction of complex water quality parameters in water monitoring networks.
Applying Self-Coding to the Measurement of Self-Generated Video Gaming and Gambling Memory Associations

Recently the use of implicit memory associations has expanded in the addiction literature to include the assessment of video gaming and gambling. However, the issue with memory associations lies in the open-ended nature of the answers that must be coded, which is often labour-intensive, costly, and where the ambiguity cannot always be resolved. The present study evaluates participant self-coding of memory associations versus researcher coding in the assessment of memory associations for video gaming and gambling. A sample of 3,176 Canadian adults were asked to produce responses to ten ambiguous words and ten potential behavioural associations for engagement in video gaming or gambling. Participants were subsequently asked to classify what categories their responses belonged to, including video gaming and gambling. Consistent with the literature on alcohol and marijuana memory associations, self-coded scores for video gaming and gambling were significantly higher than scores coded by the researchers, had significantly higher correlations with self-reported behaviours, and significantly improved the prediction of video gaming and gambling behaviours. Resume L’utilisation des associations de la memoire implicite comprend depuis peu l’evaluation des jeux video et des jeux de hasard dans le vocabulaire de la dependance. Le probleme des associations de la memoire reside toutefois dans le caractere ouvert des reponses qui doivent etre codees, ce qui est souvent a haute intensite de main-d’œuvre et couteux. De plus, il est parfois impossible de resoudre l’ambiguite. La presente etude evalue l’autocodage par les participants des associations de la memoire par rapport au codage effectue par le chercheur dans le cadre des jeux video et des jeux de hasard. On a demande a un echantillon de 3 176 Canadiens d’âge adulte de repondre a dix mots ambigus et dix associations comportementales potentielles en rapport avec la participation a des jeux video ou des jeux de hasard. On leur a ensuite demande de classer leurs reponses dans differentes categories, y compris les jeux video et les jeux de hasard. Tout comme dans la documentation sur les associations de memoire dans le domaine de l’alcool et de la marijuana, les pointages autocodes pour les jeux video et les jeux de hasard etaient considerablement plus eleves que ceux codes par les chercheurs, faisaient l’objet d’une correlation beaucoup plus elevee avec des comportements autodeclares, et amelioraient considerablement la prediction de comportements lies aux jeux video et aux jeux de hasard.
Challenges and Opportunities in Dock-Based Bike-Sharing Rebalancing: A Systematic Review

Bike-sharing systems (BSS) have raised in popularity in the last years due to their potential share in sustainable cities. Although the first attempts to implement a bike-sharing public service date back to 1965 (Amsterdam), their widespread use arrived with the millennium becoming a vibrant research area whose activity has increased steadily in the last decade. Several authors have attempted to summarize the current state of the art, but the literature on BSS is still scattered in different fields. Thus, there is a lack of literature that summarizes and categorizes the available research on BSS. In this paper, we perform a thorough review of the challenges behind rebalancing in bike-sharing systems. The objectives of this paper are to collect papers on the repositioning problem in dock-based bike-sharing services, classify them and point to novel research venues. We render a keyword analysis in the literature and a timeline that shows the evolution of those keywords throughout the last decade. We also include an exhaustive table that will assist researchers from different disciplines to address the open challenges in the field and to transition towards more sustainable cities.
The Optimization Model for Reducing RON Loss in Gasoline Refining Process

As gasoline is the main fuel of small vehicles, the exhaust emissions from its combustion will affect air quality. The focus of gasoline cleaning is to reduce the sulfur and olefin content in gasoline while maintaining its RON as much as possible. The reduction of RON will bring great economic losses to enterprises. Therefore, it is very important for petrochemical enterprises to construct a RON loss model in the gasoline refining process. The model construction, which reduces RON loss during gasoline refining, is the main question in this paper. By Python and SPSS software, we got two variable filtering methods: the random forest importance filtering and PCA filtering, and combined with SVR and random forest models, RON of the product and sulfur content were predicted. The filtering order of the original data by Excel and Python is maximum and minimum removal, 3σ criterion removal, deletion of too many sites in incomplete data, and filling of empty values in the mean within two hours. Several RON prediction models were established with the help of Python software, and the variables selected were compared by two filtering methods: one is the SVR model based on Gaussian, linear, polynomial, and Sigmoid kernel functions; the other is the random forest model. The sulfur content and RON prediction model was constructed, which use evaluation functions such as MSE, , and RMSE to evaluate and sulfur content as the subject condition. We convert the problem into linear and nonlinear model variable optimization problems: the linear model is the variable selected by the SVR linear kernel function model and random forest; the nonlinear model is the combination of variables selected by the random forest model and random forest. Optimizing for each sample, the optimization method is to find the optimal solution for each variable and use the optimal method for each variable as the local optimal solution for the sample. The two models are evaluated from the perspectives of optimization degree, optimization rate, model running speed, etc.
Enhanced URLLC-Enabled Edge Computing Framework for Device-Level Innovation in 6G

The upcoming beyond 5G (B5G) wireless networks target various innovative technologies, services, and interfaces such as edge computing (EC), ultra-reliable and low-latency communication (URLLC), backscatter communications, and TeraHertz (THz) technology-enabled inter-chip communications, and high capacity links. Although there are ongoing advances in the system/network level, it is crucial to introduce innovations at the device-level to efficiently support these novel technologies by addressing various practical constraints such as power limitations, computational capacity, and storage capacity. Considering the contemporary latency requirements of future consumer electronics (CE) (e.g., entertainment, gaming, etc), to enhance the commercial potential of ``edge processing as service'', it is envisioned that URLLC will further evolve as enhanced-URLLC (e-URLLC). In this regard, this paper proposes a novel EC-enabled e-URLLC framework for the next-generation CE devices, named EC for CE (ECCE), in order to enable the support of e-URLLC in the wireless 6G era. Starting with the discussion on recent trends and advances in CE, the proposed framework and its importance in the 6G era are described. Subsequently, several potential enabling technologies and tools for the implementation of the proposed ECCE framework are identified along with a discussion on some interesting open research topics.
Linking Top Management Team Potency, Corporate Social Responsibility, and Firm Performance in SMEs: The Role of Environmental Dynamism

This study empirically validates the characteristics choices performance of upper echelons theory (UET) in small and medium-sized enterprises (SMEs) context. Specifically, the current study examines how SME’s top management team potency (TMTP) affects the strategic corporate social responsibility (CSR) choice to improve corporate growth. Furthermore, the moderator role of environmental dynamism (ED) on TMTP-CSR has also been investigated. Survey data gathered from 686 SMEs in China were analyzed. The results show a robust partial mediation effect of CSR on SME’s growth. Furthermore, ED was also found to positively moderate TMTP - CSR. Implications and future research directions are provided.
Survey of symptoms following COVID-19 vaccination in India

Background: COVID-19 vaccines have been rolled out recently in several parts of the world. Although the protective efficacy is frequently discussed, little is known about the real-world post-vaccination experience outside of clinical trial conditions. Knowledge about what to expect after vaccination will help educate the public, dispel misinformation and reduce vaccine hesitancy. Aim: 1. To assess the immediate response to the first dose of COVID-19 vaccine. 2. To study the spectrum of post-vaccination symptom profile for individual vaccines. Methods: A cross-sectional online survey was done which included questions pertaining to the immediate post vaccination experience in India. Results: A total of 5396 people responded to the survey over a one-week period from 29 January to 4 February. Overall, 65.9 % of respondents reported at least one post-vaccination symptom. Tiredness (45%), myalgia (44%), fever (34%), headache (28%), local pain at injection site (27%), joint pain (12%), nausea (8%) and diarrhea (3%) were the most prevalent symptoms. The chance of having symptoms decreased with advancing age. The frequency of symptoms was 81% (3rd decade or 20-29 years), 80% (4th decade or 30-39 years), 68% (5th decade), 58% (6th decade), 45% (7th decade), 34% (8th decade) and 7% (9th decade, 80-90 years). Post-vaccination symptoms were more likely to be reported by women (74.7%) compared to men (58.6%) (p < 0.001). Among those who reported symptoms, 79% noticed them within the first 12 hours. 472 out of 5396 (8.7%) reported past history of COVID-19. Their symptom profile was not different to those who did not have a past history. Conclusions: Two-thirds of healthcare professionals who completed the survey reported mild and short-lived post-vaccination symptoms. Tiredness, myalgia and fever were most commonly reported. These symptoms were consistent with an immune response commonly associated with vaccines, and correlated with the findings from previously published phase 2/3 trials. In 90% cases, the symptoms were either milder than expected or meeting the expectation of the vaccine recipient. No serious events were reported. Symptoms were more common among younger individuals. There was no difference in symptoms among those who had a past history of COVID-19.
Deep multi-sequence multi-grained cascade forest for tobacco drying condition identification

Abstract The term, “drying condition” refers to the actual dehydration capacity of a rotary dryer in the tobacco drying process which is directly related to the drying effect. However, identifying different drying conditions relies heavily on the judgment of field engineers who have rich domain knowledge and practical experiences. In this study, we proposed an improved multi-sequence multi-grained cascade forest model, MSgcForest, to classify and identify different drying conditions. An improved multi-sequence multi-grained feature scanning mechanism is added to perform spacial and sequential feature extraction from raw production-related data, which transforms the input features into high-dimensional feature vectors and increases the discriminative power of the drying condition features. Comparison with existing models indicates that the proposed MSgcForest outperforms the other alternatives even for small-scale training data. In particular, this method successfully transforms the fuzzy artificial judgment of the drying condition into a data-driven identification with high precision, which provides a promising prospect for identifying working conditions in industrial processes.
Multibeam steered pattern synthesis in time‐modulated antenna array with controlled harmonic radiation

This article deals with the synthesis of multibeam steerable patterns in time‐modulated arrays (TMAs) for electronic scanning. The simultaneous generation of multiple beams in TMAs can be achieved through the exploitation of undesired harmonic radiations emitted at the multiples of modulating frequency due to the periodic switching. The direction‐dependent harmonic beams are capable of producing independent information channels with different spatial distributions beneficial for electronic beam steering. Multibeam patterns associated with harmonic frequencies along with the fundamental pattern can be synthesized with suitably designed switching schemes. The potentialities of linear TMAs are explored in this article to generate a simultaneous sum and difference pattern useful for monopulse tracking systems. Further assessment of multipattern operations with linear TMAs are proposed with beam steering at lower‐order harmonics. Steered double sum pattern with controlled harmonic radiations are also generated to achieve a steered multibeam with simplified beamforming network. Towards this aim, both the analytical and the optimized solutions of periodic switching sequences for different multibeam patterns are developed. Modified differential evolution with a wavelet‐based mutation strategy (DEWM) is employed for the optimal solution. Representative results are assessed and compared with other published literature results to show the effectiveness of the proposed methods.