Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceptions of Young Adults on the Critical Success Factors of the Build-to-Rent Housing Model in Sydney, Australia

Amid Australia’s surging house prices, rental housing has become the safety net for young adults experiencing housing stress. The attention of policymakers is drawn towards the build-to-rent (BTR) housing model, a growing housing option for many young Australian adults. Although BTR’s integration into Australia’s housing and planning landscape is unfolding, the perception of young adults is lacking in policy reforms for this growing industry. This study examines the perspectives of young adults aged 18 to 34 years on the critical success factors (CSFs) of the BTR housing provision in Australia. The responses are analysed using an explanatory factor analysis, relative importance index, and ANOVA. Our findings show a critically low awareness of BTR among young adults in Australia. Respondents, between 18 and 24 years, are most concerned with public awareness, an indication of their desire for transparency and engagement in decision making at the policy level. Young adults rank regulation and government policy interventions as the most important CSF of BTR housing. This shows that they are more concerned about the affordability of BTR houses. This study concludes that young adults’ viewpoints on BTR are important in ongoing policy reforms. There is a need to ensure that this asset class is accessible and affordable to this target group.
Multi-Agent Deep Reinforcement Learning for UAVs Navigation in Unknown Complex Environment

As unmanned aerial vehicles (UAVs) play an increasingly significant role in modern society, using reinforcement learning to build safe multi-UAV navigation algorithms has become a hot topic. One of the major issues is coordinating multi-UAV to navigate safely in complicated and unknown 3D environments. The current decentralized navigation systems suffer from the challenges of partially observable properties in unknown environments and unstable environments, resulting in poor learning effects. In this article, we proposed a new multi-agent recurrent deterministic policy gradient (MARDPG) algorithm based on the depth deterministic policy gradient algorithm for controlling the navigation action of multi-UAV. Specifically, each critic network learns centrally so that each UAV can estimate the policies of other UAVs, thereby resolving the problem of slow convergence caused by the unstable environment. Decentralized execution eliminates the need for communication resources between UAVs. Sharing parameters in critics network accelerates training. The added LSTM network enables UAVs to use historical exploration information to improve the prediction of action values without being trapped by local traps. Finally, thorough simulation results in a realistic simulation environment were provided to demonstrate the superiority in terms of convergence and efficacy over the state-of-the-art DRL approach.
Effect of buckling on the thermal response of microelectromechanical beam resonators

We have investigated the thermal response of the doubly clamped microelectromechanical (MEMS) beam resonators as a function of an input heating power. When a small amount of heat (<1 mW) is applied to the MEMS beam, reduction in resonance frequency is observed. As the heating power is further increased, however, the resonance frequency levels off and starts to increase, which originates from the buckling of the beam. We find that the slope of the frequency increase at large input powers is 2–3 times steeper than that of the frequency decrease at small input powers and that the buckled device can take larger heating powers without degrading the linearity. These observations suggest that both the thermal sensitivity and the dynamic range can be improved by introducing a proper amount of buckling. Using this insight, we have fabricated a buckled MEMS beam resonator by using a strained InxGa1-xAs (x = 0.001) beam structure on a GaAs substrate. We have found that the frequency responsivity of the InxGa1-xAs be.
Generating metainferences in mixed methods research: A worked example in convergent mixed methods designs

Metainferences, or the insights derived from integrating quantitative and qualitative inferences at the end of a study, are crucial for achieving added value and synergy in mixed methods research. There is an ongoing need to understand how researchers generate metainferences, especially considering their pivotal role in helping researchers achieve full quantitative and qualitative integration. While some examples of metainferences generation are available in the mixed methods literature, more explicit guidance is required. Approaches to developing metainferences must also be contextual, as inferences of this type are contingent on the nature and purpose of the mixed methods study, the type of mixed methods design, and the quality of the research data. This paper describes a seven-step process for generating metainferences using a convergent mixed methods study as an exemplar. These steps consist of identifying knowledge, experience, and data-driven inferences from the quantitative and qualitative data; developing inference association maps to draw metainferences; and assessing the validity of metainferences using backward working heuristics. This paper contributes to mixed methods research by shedding light on the development of metainferences in convergent designs and by providing practical and tangible tools for making sense of the complexity of the analysis and interpretation tasks involved in the process of generating metainferences.
Representation of Prime Powers by Binary Quadratic Forms

In this article, we consider the representation of prime powers by binary quadratic forms of discriminant D = −2 8 q1 . qt where the product of primes q1 . qt ≡ 3 (mod 4), for instance if it is of special RichaudDegert type n 2 ± 2 for odd n’s, n 2 − 1 for even n’s. We consider all the ambiguous classes and Q( |D0|), where D0 is the fundamental discriminant and we obtain a general criterion for representation of prime powers by using the residue character of the fundamental unit.
Systematic Review of Lung Cancer Screening: Advancements and Strategies for Implementation

Lung cancer is the leading cause of cancer-related deaths in Europe, with low survival rates primarily due to late-stage diagnosis. Early detection can significantly improve survival rates, but lung cancer screening is not currently implemented in Italy. Many countries have implemented lung cancer screening programs for high-risk populations, with studies showing a reduction in mortality. This review aimed to identify key areas for establishing a lung cancer screening program in Italy. A literature search was conducted in October 2022, using the PubMed and Scopus databases. Items of interest included updated evidence, approaches used in other countries, enrollment and eligibility criteria, models, cost-effectiveness studies, and smoking cessation programs. A literature search yielded 61 scientific papers, highlighting the effectiveness of low-dose computed tomography (LDCT) screening in reducing mortality among high-risk populations. The National Lung Screening Trial (NLST) in the United States demonstrated a 20% reduction in lung cancer mortality with LDCT, and other trials confirmed its potential to reduce mortality by up to 39% and detect early-stage cancers. However, false-positive results and associated harm were concerns. Economic evaluations generally supported the cost-effectiveness of LDCT screening, especially when combined with smoking cessation interventions for individuals aged 55 to 75 with a significant smoking history. Implementing a screening program in Italy requires the careful consideration of optimal strategies, population selection, result management, and the integration of smoking cessation. Resource limitations and tailored interventions for subpopulations with low-risk perception and non-adherence rates should be addressed with multidisciplinary expertise.
Value of GCET1, HGAL (GCET2), and LMO2 in the Determination of Germinal Center Phenotype in Diffuse Large B-cell Lymphoma

Objective: Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous disease that is classified into germinal center B-cell (GCB) and non-GCB subtypes, which are prognostically different. The Hans algorithm is the most widely used tool based on CD10, BCL6, and MUM1 expression, but some cases with the non-GCB phenotype are still known to be misclassified. In this study, we investigate the extent to which GCET1, HGAL, and LMO2 protein expressions reflect GCB phenotype together with their roles in determining the GCB phenotype of DLBCL and their contributions to the performance of the Hans algorithm. Materials and Methods: Sixty-five cases of DLBCL-not otherwise specified, 40 cases of follicular lymphoma (FL), and 19 non-GC-derived lymphoma cases were included in this study. The DLBCL cases were grouped as CD10+ (Group A) or only MUM1+ (Group B), and the remaining cases constituted the intermediate group (Group C). GCET1, HGAL, and LMO2 expressions were evaluated. Results: In the FL group, GCET1, HGAL, and LMO2 were positive in 85%, 77.5%, and 100% of the cases, respectively. Among the non-GC-derived lymphoma cases, all three markers were negative in cases of small lymphocytic lymphoma, plasmablastic lymphoma, peripheral T-cell lymphoma, and anaplastic large cell lymphoma. GCET1 and HGAL were negative in cases of marginal zone lymphoma (MZL) and mantle cell lymphoma (MCL). Two of the 3 MZL and 2 of the 4 MCL cases were positive for LMO2. In the DLBCL group, the number of cases with GCET1, HGAL, and LMO2 positivity was 18 (90%), 17 (85%), and 20 (100%), respectively, in Group A and 0 (0%), 2 (13.3%), and 2 (13.3%), respectively, in Group B. Considering these rates, when the cases in the intermediate group were evaluated, it was concluded that 13 cases typed as non-GCB according to the Hans algorithm may have the GCB phenotype. Conclusion: GCET1, HGAL, and LMO2 are highly sensitive markers for determining the germinal center cell phenotype and can increase the accuracy of the subclassification of DLBCL cases, especially for cases that are negative for CD10.
Sleeve leakage gas impact on fuel assembly temperature distribution

ABSTRACT The flow in a large section (80%) of an Advanced Gas-cooled Reactor fuel assembly is investigated in two configurations, the former dealing with the fuel assembly operating in normal plant conditions and the latter with a cold gas injected sideways at the top of Element 2 of the fuel assembly. Results show that injecting the cold gas decreases the temperature at the top of the assembly and that mixing is almost complete there.
Enhanced Hybrid Algorithm of Secure and Fast Chaos-based, AES, RSA and ElGamal Cryptosystems

Objectives: The principal objective of this paper is to develop an enhanced algorithm of cryptography through the hybridization of existing symmetric and asymmetric encryption. Methods/Analysis: The strength of several prevailing symmetric and asymmetric encryption were utilized in the proposed methodology. ElGamal’s key security based on difficulty of solving discrete logarithm, Chaos-based’s avalanche effect of using multiple keys, RSA’s security based on the difficulty of factorization of large numbers and AES’s S-Box mapping, which makes the encryption complex and unpredictable were adapted. Findings: The strength of security of the proposed hybrid algorithm is tested by the provided examples. It was found out that with the use of different keys in encryption, various ciphertexts were generated. The published public keys using such algorithm also make it infeasible to intrude because of the difficulty of solving discrete logarithm with large prime numbers. Furthermore, the value of the ciphertext of the proposed method is significantly different and, in some cases, larger than that of RSA’s. Applications: With this newly enhanced hybrid algorithm of data encryption, transferring and conveying of data will be much more secured.
Construction of Built-In Electric Field in TiO2@Ti2O3 Core-Shell Heterojunctions toward Optimized Photocatalytic Performance

A series of Ti2O3@TiO2 core-shell heterojunction composite photocatalysts with different internal electric fields were synthesized using simple heat treatment methods. The synthesized Ti2O3@TiO2 core-shell heterojunction composites were characterized by means of SEM, XRD, PL, UV–Vis, BET, SPV, TEM and other related analytical techniques. Tetracycline (TC) was used as the degradation target to evaluate the photocatalytic performance of the synthesized Ti2O3@TiO2 core-shell heterojunction composites. The relevant test results show that the photocatalytic performance of the optimized materials has been significantly enhanced compared to Ti2O3, while the photocatalytic degradation rate has increased from 28% to 70.1%. After verification via several different testing and characterization techniques, the excellent catalytic performance is attributed to the efficient separation efficiency of the photogenerated charge carriers derived from the built-in electric field formed between Ti2O3 and TiO2. When the recombination of electrons and holes is occupied, more charges are generated to reach the surface of the photocatalyst, thereby improving the photocatalytic degradation efficiency. Thus, this work provides a universal strategy to enhance the photocatalytic performance of Ti2O3 by coupling it with TiO2 to build an internal electric field.
Induced subgraph searching for geometric model fitting

In this paper, we propose a novel model fitting method based on graphs to fit and segment multiple-structure data. In the graph constructed on data, each model instance is represented as an induced subgraph. Following the idea of pursuing the maximum consensus, the multiple geometric model fitting problem is formulated as searching for a set of induced subgraphs including the maximum union set of vertices. After the generation and refinement of the induced subgraphs that represent the model hypotheses, the searching process is conducted on the “qualified” subgraphs. Multiple model instances can be simultaneously estimated by solving a converted problem. Then, we introduce the energy evaluation function to determine the number of model instances in data. The proposed method is able to effectively estimate the number and the parameters of model instances in data severely corrupted by outliers and noises. Experimental results on synthetic data and real images validate the favorable performance of the proposed method compared with several state-of-the-art fitting methods.
Neutral Forces and Balancing Selection Interplay to Shape the Major Histocompatibility Complex Spatial Patterns in the Striped Hamster in Inner Mongolia: Suggestive of Broad-Scale Local Adaptation

Background: The major histocompatibility complex (MHC) plays a key role in the adaptive immune response to pathogens due to its extraordinary polymorphism. However, the spatial patterns of MHC variation in the striped hamster remain unclear, particularly regarding the relative contribution of the balancing selection in shaping MHC spatial variation and diversity compared to neutral forces. Methods: In this study, we investigated the immunogenic variation of the striped hamster in four wild populations in Inner Mongolia which experience a heterogeneous parasitic burden. Our goal was to identify local adaptation by comparing the genetic structure at the MHC with that at seven microsatellite loci, taking into account neutral processes. Results: We observed significant variation in parasite pressure among sites, with parasite burden showing a correlation with temperature and precipitation. Molecular analysis revealed a similar co-structure between MHC and microsatellite loci. We observed lower genetic differentiation at MHC loci compared to microsatellite loci, and no correlation was found between the two. Conclusions: Overall, these results suggest a complex interplay between neutral evolutionary forces and balancing selection in shaping the spatial patterns of MHC variation. Local adaptation was not detected on a small scale but may be applicable on a larger scale.
Adaptive robust consensus tracking for nonlinear second‐order multi‐agent systems with heterogeneous uncertainties

This paper addresses the consensus tracking problem for a class of heterogeneous nonlinear second‐order multi‐agent systems with parametric uncertainties, unmodeled dynamics, and bounded external disturbances. By linearly parameterizing the control input of the leader, two distributed adaptive robust consensus tracking control protocols with dynamic and fixed coupling gains are constructed based on the relative information from neighboring agents. The global tracking errors are shown to be guaranteed to exponentially converge to a ball with a constant radius at a prescribed rate of convergence under external disturbances. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2017 John Wiley & Sons, Ltd.
“I Only Tell Them the Good Parts:” How Relational Others Influence Paid Careworkers’ Descriptions of Their Work as Meaningful

The occupational images associated with paid care work for older adults range from a job carried out by earthly angels to a form of stigmatized dirty work: This ambiguity makes maintaining a committed long-term care workforce challenging. Encouraging careworkers to view their work as meaningful has been touted as a potential solution. Moving beyond a purely subjective approach to meaningfulness, we explore how careworkers construe their work as meaningful and how relational others influence careworkers’ ability to speak about meaningfulness. Others’ messages matter, although their importance depends on relational others’ knowledge of care tasks and involvement in the care relationship. By documenting how others’ accounts both enhance and compromise careworkers’ ability to speak about meaningfulness and moments of meaninglessness, our study identifies sources of meaningfulness for careworkers, a socially essential workforce under-examined by meaningful work research, and extends meaningful work research in contexts where relationships are central to occupational identity.
Vortex detection and tracking in massively separated and turbulent flows

The vortex produced at the leading edge of the wing, known as the leading edge vortex (LEV), plays an important role in enhancing or destroying aerodynamic force, especially lift, upon its formation or shedding during the flapping flight of birds and insects. In this thesis, we integrate multiple new and traditional vortex identification approaches to visualize and track the LEV dynamics during its shedding process. The study is carried out using a 2D simulation of a flat plate undergoing a 45 pitch-up maneuver. The Eulerian Γ1 function and Q criterion are used along with the Lagrangian coherent structures (LCS) analyses including the finite-time Lyapunov exponent (FTLE), the geodesic LCS, and the Lagrangian-Averaged Vorticity Deviation (LAVD). Each of these Lagrangian methods is applied at the centers and boundaries of the vortices to detect the vortex dynamics. The techniques enable the tracking of identifiable features in the flow organization using the FTLE-saddles and λ-saddles. The FTLE-saddle traces have shown potential to identify the timing and location of vortex shedding, more precisely than by only studying the vortex cores as identified by Eulerian techniques. The traces and the shedding times of the FTLE-saddles on the LEV boundary matches well with the plate lift fluctuation, and indicates a consistent timing of LEV formation, growth, shedding. The formation number and vortex shedding mechanisms are compared in the thesis with the shedding time and location by the FTLE-saddle, which validates the result of the FTLE-saddles and provide explanations of vortex shedding in different aspects (vortex strength and flow dynamics). The techniques are applied to more cases involving vortex dominated flows to explore and expand their application in providing insight of flow physics. For a set of experimental two-component PIV data in the wake of a purely pitching trapezoidal panel, the Lagrangian analysis of FTLE-saddle tracking identifies and tracks the vortex breakdown location with relatively less user interaction and provide a more direct and consistent analysis. For a simulation of wall-bounded turbulence in a channel flow, tracking FTLEsaddles shows that the average structure convection speed exhibits a similar trend as a previously published result based on velocity and pressure correlations, giving validity to the method. When these Lagrangian techniques are applied in a study of the evolution of an isolated hairpin vortex, it shows the connection between primary and secondary hairpin heads of their circulation and position, and the contribution to the generation of the secondary hairpin by the flow characteristics at the channel wall. The current method of tracking vortices yields insight into the behavior of the vortices in all of the diverse flows presented, highlighting the breadth of its potential application. Vortex detection and tracking in massively separated and turbulent flows
A central-point-enhanced convolutional neural network for high-resolution remote-sensing image classification

ABSTRACT As one of the most important algorithms in the field of deep learning technology, the convolutional neural network (CNN) has been successfully applied in many fields. CNNs can recognize objects in an image by considering morphology and structure rather than simply individual pixels. One advantage of CNNs is that they exhibit translational invariance; when an image contains a certain degree of distortion or shift, a CNN can still recognize the object in the image. However, this advantage becomes a disadvantage when CNNs are applied to pixel-based classification of remote-sensing images, because their translational invariance characteristics causes distortions in land-cover boundaries and outlines in the classification result image. This problem severely limits the application of CNNs in remote-sensing classification. To solve this problem, we propose a central-point-enhanced convolutional neural network (CE-CNN) to classify high-resolution remote-sensing images. By introducing the central-point-enhanced layer when classifying a sample, the CE-CNN increases the weight of the central point in feather maps while preserving the original textures and characteristics. In our experiment, we selected four representative positions on a high-resolution remote-sensing image to test the classification ability of the proposed method and compared the CE-CNN with the traditional multi-layer perceptron (MLP) and a traditional CNN. The results show that the proposed method can not only achieves a higher classification accuracy but also less distortion and fewer incorrect results at the boundaries of land covers. We further compared the CE-CNN with six state-of-the-art methods: k-NN, maximum likelihood, classification and regression tree (CART), MLP, support vector machine, and CNN. The results show that the CE-CNN’s classification accuracy is better than the other methods.