Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation analysis between indoor/outdoor environment and occupants’ window adjustment behavior using explainable AI (XAI)

Window adjustment behavior (WAB) is one of the most popular ways for occupants to control the indoor environment. In recent decades, many studies have attempted to develop a reliable WAB model and treated it in an ‘average occupants’ fashion. The average WAB model is prone to ignore the variability of individual preferences. This study aims to develop a reliable WAB model taking into account individual diversities. For this purpose, the authors collected occupant data and indoor/outdoor environmental data from 12 households for one year. Three XAI approaches were introduced to analyze the correlation between the environment data and occupants’ behavioural patterns. As a result, it was found that (1) occupants’ personal preferences on WAB significantly vary according to each household, (2) occupants’ personal preferences on WAB cannot be defined only with a single environmental parameter, (3) there are unmeasurable unknown factors, (4) current black box models can
Research progress on ferroptosis in the pathogenesis and treatment of neurodegenerative diseases

Globally, millions of individuals are impacted by neurodegenerative disorders including Huntington's disease (HD), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Alzheimer's disease (AD). Although a great deal of energy and financial resources have been invested in disease-related research, breakthroughs in therapeutic approaches remain elusive. The breakdown of cells usually happens together with the onset of neurodegenerative diseases. However, the mechanism that triggers neuronal loss is unknown. Lipid peroxidation, which is iron-dependent, causes a specific type of cell death called ferroptosis, and there is evidence its involvement in the pathogenic cascade of neurodegenerative diseases. However, the specific mechanisms are still not well known. The present article highlights the basic processes that underlie ferroptosis and the corresponding signaling networks. Furthermore, it provides an overview and discussion of current research on the role of ferroptosis across a variety of neurodegenerative conditions.
The development of population research institutions in Asia

In this chapter, the terminology, population research institutions, is used broadly to include any institution in which population research is conducted including universities, statistical and other government agencies and, rarely in the case of Asia, private organizations. The fi rst formal associations and institutions in the fi eld of population studies date from the 1920s. While the history of the discipline itself lies in academic studies in Europe, the history of the discipline’s institutions lies initially in the United States and the main driving forces were philanthropists and advocates rather than scientists. The Scripps Foundation for Research in Population Problems which can claim to be the fi rst institution in the fi eld of demography was founded in 1922, interestingly with research on the demography of East Asia as its rationale. Warren Thompson was appointed as its director and it was located at Miami University in Ohio. Thompson was soon joined by Pascal Whelpton and the two worked together for over 30 years. The philanthropist Edward Scripps was a successful media magnate who was concerned about the eff ects that population pressure might have upon the countries of the Far East (Zunz 2012 ). Consistent with Scripps’s view, in his 1929 book, Danger Spots in World Population , Thompson argued that the eff ects of population pressures in East Asia, the Indian Ocean and Central Europe (including Italy and Germany) would be ‘certain to make trouble in the not distant future’ (Thompson 1929 ; Notestein 1981 ). Thompson took the neoMalthusian position that vigorous family planning programmes were the solution to these population pressures. Mainly at the instigation of American philanthropists and the family planning advocate, Margaret Sanger, a World Population Conference was convened in Geneva in September 1927. The conference resolved ‘that a permanent international organization be created for the object of studying population problems in a strictly scientifi c spirit’. Professor Kiyo Sue Inui, as Japan’s representative at the conference, was coopted to serve on the committee set up to defi ne the constitution as its only Asian member. At the time, he was lecturing at Waseda and Tokyo universities but he was a graduate of the University of Michigan and had had a previous appointment at the University of Southern California. The constitutional committee met on
Novel quantum spin liquid ground state in the trimer rhodate Ba$_4$NbRh$_3$O$_{12}$

Frustrated magnets offer a plethora of exotic magnetic ground states, including quantum spin liquids (QSLs), in which enhanced quantum fluctuations prevent a long-range magnetic ordering of the strongly correlated spins down to lowest temperature. Here we have investigated the trimer based mixed valence hexagonal rhodate Ba$_4$NbRh$_3$O$_{12}$ using a combination of dc and ac magnetization, electrical resistivity, specific heat, and muon spin rotation/relaxation ($\mu$SR) measurements. Despite the substantial antiferromagnetic exchange interactions, as evident from the Weiss temperature ($\theta_{\mathrm{W}}\sim -35$ to -45 K), among the Rh-local moments, neither long-range magnetic ordering nor spin-freezing is observed down to at least 50 mK, in ac-susceptibility, specific heat and ZF-$\mu$SR measurements (down to 0.26 K). We ascribe the absence of any magnetic transition to enhanced quantum fluctuations as a result of geometrical frustration arising out of the edge-sharing equilateral Rh-triangular network in the structure. Our longitudinal-field $\mu$SR result evidences persistent spin fluctuations down to 0.26~K, thus stabilizing a dynamic QSL ground state in Ba$_4$NbRh$_3$O$_{12}$. Furthermore, the magnetic specific heat ($C_{\mathrm{m}}$) data at low-$T$ reveal a significant $T$-linear contribution plus a quadratic $T$-dependence. A $T$-linear behavior is evocative of gapless spin excitations, while the $T^2$-term of $C_{\mathrm{m}}$ may indicate the Dirac QSL phenomenology of the spinon excitations with a linear dispersion.
Public perception on active aging after COVID-19: an unsupervised machine learning analysis of 44,343 posts

Introduction To analyze public perceptions of active aging in China on mainstream social media platforms to determine whether the “14th Five Year Plan for the Development of the Aging Career and Older Adult Care System” issued by the CPC in 2022 has fully addressed public needs. Methods The original tweets posted on Weibo between January 1, 2020, and June 30, 2022, containing the words “aging” or “old age” were extracted. A bidirectional encoder representation from transformers (BERT)-based model was used to generate themes related to this perception. A qualitative thematic analysis and an independent review of the theme labels were conducted by the researchers. Results The findings indicate that public perceptions revolved around four themes: (1) health prevention and protection, (2) convenient living environments, (3) cognitive health and social integration, and (4) protecting the rights and interests of the older adult. Discussion Our study found that although the Plan aligns with most of these themes, it lacks clear planning for financial security and marital life.
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction

In this study, we investigate stochastic optimization on Riemannian manifolds, focusing on the crucial variance reduction mechanism used in both Euclidean and Riemannian settings. Riemannian variance-reduced methods usually involve a double-loop structure, computing a full gradient at the start of each loop. Determining the optimal inner loop length is challenging in practice, as it depends on strong convexity or smoothness constants, which are often unknown or hard to estimate. Motivated by Euclidean methods, we introduce the Riemannian Loopless SVRG (R-LSVRG) and PAGE (R-PAGE) methods. These methods replace the outer loop with probabilistic gradient computation triggered by a coin flip in each iteration, ensuring simpler proofs, efficient hyperparameter selection, and sharp convergence guarantees. Using R-PAGE as a framework for non-convex Riemannian optimization, we demonstrate its applicability to various important settings. For example, we derive Riemannian MARINA (R-MARINA) for distributed settings with communication compression, providing the best theoretical communication complexity guarantees for non-convex distributed optimization over Riemannian manifolds. Experimental results support our theoretical findings.
UDCR: Unsupervised Aortic DSA/CTA Rigid Registration Using Deep Reinforcement Learning and Overlap Degree Calculation

The rigid registration of aortic Digital Subtraction Angiography (DSA) and Computed Tomography Angiography (CTA) can provide 3D anatomical details of the vasculature for the interventional surgical treatment of conditions such as aortic dissection and aortic aneurysms, holding significant value for clinical research. However, the current methods for 2D/3D image registration are dependent on manual annotations or synthetic data, as well as the extraction of landmarks, which is not suitable for cross-modal registration of aortic DSA/CTA. In this paper, we propose an unsupervised method, UDCR, for aortic DSA/CTA rigid registration based on deep reinforcement learning. Leveraging the imaging principles and characteristics of DSA and CTA, we have constructed a cross-dimensional registration environment based on spatial transformations. Specifically, we propose an overlap degree calculation reward function that measures the intensity difference between the foreground and background, aimed at assessing the accuracy of registration between segmentation maps and DSA images. This method is highly flexible, allowing for the loading of pre-trained models to perform registration directly or to seek the optimal spatial transformation parameters through online learning. We manually annotated 61 pairs of aortic DSA/CTA for algorithm evaluation. The results indicate that the proposed UDCR achieved a Mean Absolute Error (MAE) of 2.85 mm in translation and 4.35{\deg} in rotation, showing significant potential for clinical applications.
Research progress on the PEGylation of therapeutic proteins and peptides (TPPs)

With the rapid advancement of genetic and protein engineering, proteins and peptides have emerged as promising drug molecules for therapeutic applications. Consequently, there has been a growing interest in the field of chemical modification technology to address challenges associated with their clinical use, including rapid clearance from circulation, immunogenicity, physical and chemical instabilities (such as aggregation, adsorption, deamination, clipping, oxidation, etc.), and enzymatic degradation. Polyethylene glycol (PEG) modification offers an effective solution to these issues due to its favorable properties. This review presents recent progress in the development and application of PEGylated therapeutic proteins and peptides (TPPs). For this purpose, firstly, the physical and chemical properties as well as classification of PEG and its derivatives are described. Subsequently, a detailed summary is provided on the main sites of PEGylated TPPs and the factors that influence their PEGylation. Furthermore, notable instances of PEG-modified TPPs (including antimicrobial peptides (AMPs), interferon, asparaginase and antibodies) are highlighted. Finally, we propose the chemical modification of TPPs with PEG, followed by an analysis of the current development status and future prospects of PEGylated TPPs. This work provides a comprehensive literature review in this promising field while facilitating researchers in utilizing PEG polymers to modify TPPs for disease treatment.
A Comparative Analysis of Camera Rig Parameters in Photogrammetric Software for Small-Format Oblique Camera System on Unmanned Aerial Vehicle

Utilizing small-format oblique camera systems to capture simultaneous nadir and oblique photographs from unmanned aerial vehicles (UAVs) is a common practice in modern photogrammetry. Oblique photographs provide enhanced geometric insights into building side views, terrain morphology, and vegetation, thereby enriching interpretation and classification. However, the design of camera rig parameters and their precise mathematical modeling for small-format oblique camera systems in multi-view processing is essential to ensure accurate representation of the physical camera geometry and results. This study investigates the camera rig parameters of the ‘3DM-V3’ small-format oblique camera system, focusing specifically on the relative relationship between nadir and oblique cameras, within two prominent photogrammetric software: PIX4Dmapper and Agisoft Metashape. The research concludes that optimal parameterization involves fully constrained relative translation parameters (T X , T Y , T Z ) rel for the four oblique cameras, while setting approximate initial estimates as free constrained for relative rotation parameters (R X , R Y , R Z ) rel . This approach aligns with the physical geometry of the camera system and yields a precise camera model, as confirmed through bundle block adjustment (BBA) computations. PIX4Dmapper yields horizontal and vertical root mean square errors (RMSE) of 0.023 m and 0.019 m, respectively, while Agisoft Metashape results in RMSE of 0.018 m and 0.046 m. These RMSE values, considering the ground sample distance and ground control point accuracy, reflect the robustness of the approach. The insights from this research offer valuable guidance for industries, facilitating informed decisions regarding the selection of appropriate software and parameters for small-format oblique camera systems mounted on UAVs, thus ensuring consistency between theoretical models and real-world applications.
Environmental Transport and Fate Process Descriptors for Propellant Compounds

Abstract : Sustainable management of Army training ranges requires quantification of the distribution, transport, and fate of munitions constituents (propellants and explosives) in soil, surface and groundwater. Propellant formulations are mixtures consisting of energetic compounds, binders, stabilizers, and burning-rate modifiers. Factors that affect the transport and fate of these diverse compounds include dissolution, sorption, biotransformation, volatilization, and photochemical transformation. This report summarizes the current understanding of these processes, and provides process descriptors for propellant compounds. Results of leaching experiments on representative single-base, double-base, and triple-base propellant mixtures also are presented.
I/O Transit Caching for PMem-based Block Device

Byte-addressable non-volatile memory (NVM) sitting on the memory bus is employed to make persistent memory (PMem) in general-purpose computing systems and embedded systems for data storage. Researchers develop software drivers such as the block translation table (BTT) to build block devices on PMem, so programmers can keep using mature and reliable conventional storage stack while expecting high performance by exploiting fast PMem. However, our quantitative study shows that BTT underutilizes PMem and yields inferior performance, due to the absence of the imperative in-device cache. We add a conventional I/O staging cache made of DRAM space to BTT. As DRAM and PMem have comparable access latency, I/O staging cache is likely to be fully filled over time. Continual cache evictions and fsyncs thus cause on-demand flushes with severe stalls, such that the I/O staging cache is concretely unappealing for PMem-based block devices. We accordingly propose an algorithm named Caiti with novel I/O transit caching. Caiti eagerly evicts buffered data to PMem through CPU's multi-cores. It also conditionally bypasses a full cache and directly writes data into PMem to further alleviate I/O stalls. Experiments confirm that Caiti significantly boosts the performance with BTT by up to 3.6x, without loss of block-level write atomicity.
A Study on Domain Generalization for Failure Detection through Human Reactions in HRI

Machine learning models are commonly tested in-distribution (same dataset); performance almost always drops in out-of-distribution settings. For HRI research, the goal is often to develop generalized models. This makes domain generalization - retaining performance in different settings - a critical issue. In this study, we present a concise analysis of domain generalization in failure detection models trained on human facial expressions. Using two distinct datasets of humans reacting to videos where error occurs, one from a controlled lab setting and another collected online, we trained deep learning models on each dataset. When testing these models on the alternate dataset, we observed a significant performance drop. We reflect on the causes for the observed model behavior and leave recommendations. This work emphasizes the need for HRI research focusing on improving model robustness and real-life applicability.
A Novel Chain Formation Scheme for Balanced Energy Consumption in WSN-based IoT Network

: In the Internet of Things (IoT) technologies, wireless sensor networks (WSNs) are one essential part. The IoT network commonly consists of WSNs, where hundreds or even thousands of small sensors are capable of sensing, processing, and sending environmental phenomena in the targeted region. The energy consumption imbalance of sensors becomes the cause of the network performance decrement, as sensor nodes have limited energy available for operation after being randomly deployed. Therefore, more research is necessary for the design of energy-efficient routing algorithms in energy-constrained WSNs. This paper focuses on the chain-based routing algorithm, which is a popular algorithm for achieving energy efficiency in WSN-based IoT network. Chain-based routing algorithms offer numerous advantages for WSNs, such as energy conservation and extended lifetime of WSNs. However, they face challenges due to the issue of internal communication imbalance. The objective of our study is to design a novel chain formation scheme that improves the energy consumption imbalance caused by internal communication in WSN-based IoT network. The proposed scheme is categorized in three phases (initial communication phase, chain formation phase, and data collection phase). In the first phase, the sink acquires their location information from sensors deployed in the sensing region. Then the sensing region is separated into sub-regions and with the number of sensor nodes is balanced employing the concept of the k-dimensional binary tree (K-D-B-tree). The sub-regions are organized into a binary tree structure, which is then formed into a chain. Lastly, data is collected along the chain, and the selected representative sensor transmits the collected data to the sink. We utilized the OMNET++ simulator and demonstrated effective simulation results in terms of network lifetime and average residual energy. In the simulation results, a novel chain formation scheme outperforms the power-efficient gathering in sensor information systems (PEGASIS) and the concentric clustering scheme for efficient energy consumption in the PEGASIS (CCS)
Machine Learning-Based Online MPC for Large-Scale Charging Infrastructure Management

The rapid expansion of Electric Vehicle (EV) charging capacities, driven by the surge in transportation electrification, has a direct impact on the electric power system, particularly the Low Voltage (LV) grid. Consequently, there is a pressing need to redesign energy management systems, placing a greater emphasis on mitigating the effects of EV charging. In this article, we crafted an online grid-level Model Predictive Control (MPC) incorporating Machine Learning (ML) prediction enhanced with Singular Spectrum Analysis (SSA). ML models predicts the future EV charging demands, which are the key factors for the MPC for managing efficiently a large-scale charging infrastructure, aiming to mitigate EV charging impacts, specifically phase unbalance and overloading. The MPC results, especially on phase unbalance metrics, mirrored in a predictive manner those of offline/no forecast optimization from our previous work. A week-long MPC simulation comparison with uncontrolled case showcased a 63% reduction in peak demand duration, a 68% decrease in Phasing Unbalance Index (PUI), and a 45% reduction in Voltage Unbalance Factor (VUF). The minimum voltage unbalance improved from 0.897 pu to 0.913 pu, reducing out-of-standard drops duration by 21%. Lastly, we observed a 20% cut in lines’ losses, equating to a 50 kgCO2 reduction within a week.
A new specimen of Orthosuchus stormbergi (Nash 1968) and a review of the distribution of Southern African Lower Jurassic crocodylomorphs

Abstract Orthosuchus stormbergi (Nash 1968) is a rare member of the Lower Jurassic crocodylomorph fauna of Southern Africa. Here we report on a new specimen, consisting of a nearly complete dentary bone, and a previously collected specimen that can confidently be referred to this taxon. The dentary presents several features of utility for generic-level identification of Southern African crocodylomorphs, and furthermore the anatomy of this specimen allows for a modest revision of the generic diagnosis. Furthermore, it provides an opportunity to reassess differences in dentary morphology between basal South African crocodylomorphs with the intention of refining taxonomic identification. Finally, we review the stratigraphic provenance of all of Southern Africa’s known crocodylomorph taxa, and show that most are confined to the uppermost Elliot Formation. Protosuchus, Litargosuchus, Notochampsa, and Orthosuchus have first appearance data in uppermost Elliot Formation. Only Notochampsa has a confirmed last appearance datum in the Lower Clarens Formation. These findings have implications with regards to the true position of the Triassic-Jurassic Boundary, and paleoenvironmental pressures acting on the Crocodylomorpha over the boundary of the Upper Elliot and Lower Clarens Formations.