Miracle-dz/UniFAR-FTUs huggingface dataset:
Informal Caregiving Networks of Older Adults With Dementia Superimposed on Multimorbidity: A Social Network Analysis Study

Abstract Background and Objectives Caregiving is a social process and commonly involves more than a single caregiver, especially for older adults with multimorbidity, including dementia. This study was to characterize informal caregiving networks of older adults with dementia superimposed on multimorbidity (e.g., end-stage kidney disease) and to examine the relationships of network properties to outcomes of caregivers and older adults. Research Design and Methods An egocentric social network survey was conducted. Up to 3 family caregivers of older adults on dialysis who had moderate-to-severe irreversible cognitive impairment with or without a documented diagnosis of dementia were recruited from 11 dialysis centers in 2 states. Caregivers completed a social network survey about individuals providing caregiving to the older adult and measures of caregiving burden and rewards, depression, and financial hardship. Older adults’ emergency department visits and hospital admissions during the past 12 months were abstracted from the medical records. Results A total of 76 caregiver informants of 46 older adults (78% Black) participated in the study. Of the 46 older adults, 65% had a multimember network (median size of 4). As the network density (the proportion of ties between members among all possible ties) increased, primary caregivers’ financial hardship decreased whereas nonprimary caregivers’ financial hardship increased. Further, for every 1-unit increase in mean degree (the average number of connections among members), there was a nearly fourfold increase in the odds of no hospital admission during the prior year for the older adult. Discussion and Implications The network dynamics of informal caregiving networks may have an impact on the well-being of caregivers and older adults with dementia, but confirmatory longitudinal studies are needed.
On first-hitting time of a linear boundary by perturbed brownian motion

We consider the first-hitting time, \tau_ Y , of the linear boundary S(t) = a + bt by the process X_Y (t) = x + B_t +Y, with a >x, b> 0, where B_t is Brownian motion and Y is a random variable independent of B_t and such that P(x +Y a) = 1. For a given distribution function F, we find the distribution of \tau_ Y in such a way P( \tau_ Y < t) = F(t).
Normed Right Alternative Algebras Over the Reals

One of the most interesting results on real normed division algebras says that every real normed associative division algebra is finite dimensional [6, Theorem 1.7. 6], and hence by a classical theorem of Frobenius either isomorphic to the real field, the complex field, or the algebra of quaternions. Thus the dimension of the algebra can only be either 1, 2 or 4.
Relativistic Jets in Active Galactic Nuclei

Relativistic jets are a well-established feature of the influence of supermassive black holes in active galactic nuclei. There are two candidate processes for the launching of a jet from the vicinity of a black hole – a centrifugally driven outflow from the surface of the accretion disk or an outflow that relies on the physics of processes occurring in the ergosphere. The emission from jets spans the entire electromagnetic spectrum – form radio waves through to the highest energy gamma rays. Emission in gamma rays and the rapid variability of this emission pose interesting questions concerning the velocities of jets with some estimates of the Lorentz factor as high as 50 within a 100 gravitational radii of the black hole.
An Analog Linearizing Circuit for TMR Angle Sensor with Flexible Measurement Range

In this paper, an Analog Linearizing Circuit (ALC) is presented for Tunneling Magneto-Resistance (TMR) angle sensor. The ALC is designed to fetch a linear analog output from sine-cosine natured TMR sensor. The ALC utilizes a quadratureoscillator (QO) to excite the sensor. The ALC output is minimally affected by the phase-imbalance between the excitation signals and the parasitic capacitances of the TMR sensor. The linearizing circuit part of the ALC is designed for the applications that require 360° measurement range. However, a simplified equivalent, suitable for applications with 180° range, is also proposed. The ALC performance is evaluated in simulation studies for several values of QO phase-imbalance. The final output is minimally affected by the QO phase-imbalance. Later, additional simulation tests are carried out with the help of AAT003-10E TMR angle sensor-based evaluation kit. The ALC output non-linearity, in this case, lies close to the inherent non-linearity of the angle sensing kit.
Spectral Characteristics of Dissolved Organic Matter in Farmland Soils around Urumqi, China

The dissolved organic matter (DOM) is one of the most sensitive indicators of changes in the soil environment, and it is the most mobile and active soil component that serves as an easily available source of nutrients and energy for microbes and other living organisms. In this paper, DOM structural characteristics and main properties were investigated by three-dimensional fluorescence spectroscopy (EEM) and UV–visible spectrum technology in the farmland soils around Urumqi of China, and its possible sources and pathways were analyzed by spectroscopic indices. The results showed that humic-like substances were the main composition of the soil DOM, and its autogenesis characteristics were not obvious. Main DOM properties such as aromatability, hydrophobicity, molecular weight, molecular size, and humification degree in the southern region of Urumqi were higher than those of the northern region of Urumqi and Fukang in China, and higher on the upper layers of the soil (0–0.1 and 0.2 m) than in the deeper layer (0.2–0.3 m). This may be because the tilled layer is more subjected to fertilization and conducive to microbial activities. The spectroscopic analysis showed that the source of DOM of these regions is mainly from microbial metabolites. These results provide basic scientific data for the further research on the environmental chemical behavior of pollutants and pollution control in this region.
A Battery-less NFC Sensor Transponder for Cattle Health Monitoring

This paper presents the design and assessment of a battery-less NFC sensor transponder to measure biomarkers in the tear fluid of cattle eyes. A battery-less NFC temperature sensor prototype with a diameter of 22 mm is developed for the feasibility analysis of cattle health monitoring. With a measured wireless communication range of 44 mm, the developed NFC sensor prototype is shown to be a potential solution for wireless power and data transfer. In addition, the design method to develop a screen-printed, NFC-enabled smart contact lens for next-generation cattle health monitoring is also presented.
An Integrated Substrate Gap Waveguide Electromagnetic Coupled Filter

In this paper, a novel integrated substrate gap waveguide (ISGW) electromagnetically coupled 3-order filter is proposed. The filter includes three layers of substrates. The bottom substrate realizes the substrate integrated waveguide (SIW) electromagnetic coupling filter, the electric coupling is generated by the etched slots, and the magnetic coupling is generated by the metal vias. The top substrate and the middle substrate form an integrated substrate gap waveguide (ISGW), which packages the SIW filters on the bottom substrate, effectively preventing internal electromagnetic leakage and external electromagnetic interference. The simulation results show that the -3dB bandwidth of the designed ISGW filter is 15.47-17.49Ghz, and two transmission zeros are generated at 14.96 and 18.69GHz.
Computational Microscopy: Breaking the Limit of Conventional Optics

Computational imaging is flourishing thanks to the recent advancement in array photodetectors and image processing algorithms. This thesis presents Fourier ptychography, which is a computational imaging technique implemented in microscopy to break the limit of conventional optics. With the implementation of Fourier ptychography, the resolution of the imaging system can surpass the diffraction limit of the objective lens's numerical aperture; the quantitative phase information of a sample can be reconstructed from intensity-only measurements; and the aberration of a microscope system can be characterized and computationally corrected. This computational microscopy technique enhances the performance of conventional optical systems and expands the scope of their applications.
Reflections on a 50 Year Journey in Electrical Safety

For more than 50 years, I have explored advancements in technology, safe work practices, management systems, and human and organizational performance as they relate to electrical safety. During this journey, I have seen annual electrocution fatalities decrease by more than 80% in the U.S. However, other industrialized countries have demonstrated significantly better results in reducing electrical fatalities, which suggests that a significant reduction in the U.S. is possible. This paper discusses the limitations of compliance-based safety management systems that have evolved in the U.S. over the past 50 years and how systems safety concepts can complement and improve the effectiveness of compliance-based electrical safety programs.
Introdução ao grupo SO(4) com aplicações à Física: transformação de Galilei e átomo de hidrogênio

Este trabalho tem como objetivo apresentar uma introdução ao grupo S O ( 4 ) e duas aplicações na Física: uma na Mecânica Clássica e outra na Mecânica Quântica. Os geradores do grupo S O ( 4 ) serão determinados, assim como sua álgebra de Lie. A aplicação na Mecânica Clássica será na obtenção da transformação de Galilei homogênea e na Mecânica Quântica será na obtenção do espectro de energia do átomo de hidrogênio, no regime não-relativístico. A versão quântica do vetor de Laplace-Runge-Lenz será fundamental para a construção da fórmula de Bohr para os níveis de energia do respectivo átomo.
Deep Network-based Slow Feature Analysis for Human Fall Detection

One of the most concerning safety hazards for elderly people is abnormal falls in public places. Vision-based fall detection using ambient cameras is a popular non-intrusive solution. Recent research uses Slow Feature Analysis (SFA), which can learn the slow invariant varying shape features obtained from input signals and is efficient. Another recent famous approach in motion detection is deep learning. However, the fall event in actual cases is diverse, resulting in complications in the detection task. Additionally, it is difficult to acquire fall-related data; hence, simulation is done on fall events to generate a training dataset, resulting in smaller data. Considering these complications, we have presented a novel method by combining SFA, deep learning models, namely Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), and rule-base. CNN is used to extract the object region, thereby reducing the region of interest (RoI). Two shape features, such as aspect ratio and area of RoI are considered as input to the LSTM for retrieving the temporal information which is further used for rule generation, thereby increasing the detection accuracy. The efficacy of the proposed method for various features, such as aspect ratio, area, and aspect r$a$ tio+area is demonstrated over the UR Fall data with an accuracy of 95.2%, 93.8%, and 96.36%, respectively.
Design an Adaptive Trajectory to Support UAV Assisted VANET Networks

Establishing the connection between unmanned aerial vehicles and ground vehicles is essential to pave the way for networks of UAV_assisted VANET. Due to the dynamic environment VANETs (Vehicular ad hoc networks) face the challenge of frequent connection outages, which significantly degrade network performance. To address this challenge, UAVs can be considered as suitable candidates for improving the interconnection in VANET، an effective itinerary will try to maintain contact as long as possible. It is proposed a pathway diagram for effective UAV communication with VANETs, servicing the largest number of ground vehicles. The track was designed under the constraints of the UAV's limited power and link capabilities. Therefore, a simplified- path diagram has proposed in which the model of known traffic routes and vehicular traffic and flows was exploited. The simulation results demonstrate that the proposed scheme improves the network performance of VANET in terms of end-to-end delay, and throughput compared to its counterpart.
Age-related reduction of amplitude modulation frequency selectivity.

The perception of amplitude modulations (AMs) has been characterized by a frequency-selective process in the temporal envelope domain and simulated in computational auditory processing and perception models using a modulation filterbank. Such AM frequency-selective processing has been argued to be critical for the perception of complex sounds, including speech. This study aimed at investigating the effects of age on behavioral AM frequency selectivity in young (n = 11, 22-29 years) versus older (n = 10, 57-77 years) listeners with normal hearing, using a simultaneous AM masking paradigm with a sinusoidal carrier (2.8 kHz), target modulation frequencies of 4, 16, 64, and 128 Hz, and narrowband-noise modulation maskers. A reduction of AM frequency selectivity by a factor of up to 2 was found in the older listeners. While the observed AM selectivity co-varied with the unmasked AM detection sensitivity, the age-related broadening of the masked threshold patterns remained stable even when AM sensitivity was similar across groups for an extended stimulus duration. The results from the present study might provide a valuable basis for further investigations exploring the effects of age and reduced AM frequency selectivity on complex sound perception as well as the interaction of age and hearing impairment on AM processing and perception.
Multi-Step Solar Power Forecasting using Deep Learning Methods

For the effective use and integration of solar energy into the power system, precise forecasting of photovoltaic power generation is required. Currently, forecasting methods can accurately predict PV power with different step horizons, but they fail to maintain a good resolution of accurate forecasting when we increase the number of steps ahead. This paper addresses the forecasting of PV performance of a system with multi-step ahead by considering 1, 2 and 3 steps ahead of 15-minute scenarios using Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) which are kinds of Deep Learning method . The originality of this study is that the forecasted value of solar power is put against the real data issued from the PV system. All the obtained results are evaluated using different statistical indicators such as Mean Absolute Percentage Error (MAPE), Normalized Mean Square Error (NMSE), Coefficient of determination (R2) and Forecast skill (FS). The low values of the different statistical indicators justify the goodness of the proposed approaches.
Impact of COVID-19 pandemic on chronic pain and opioid use in marginalized populations: A scoping review

Introduction The COVID-19 pandemic has had a variable effect on vulnerable populations, including patients with chronic pain who rely on opioid treatment or have comorbid opioid use disorder. Limited access to care due to isolation measures may lead to increased pain severity, worse mental health symptoms, and adverse opioid-related outcomes. This scoping review aimed to understand the impact of the COVID-19 pandemic on the dual epidemics of chronic pain and opioids in marginalized communities worldwide. Methods Searches of primary databases including PubMed, Web of Science, Scopus, and PsycINFO were performed in March 2022, restricting the publication date to December 1, 2019. The search yielded 685 articles. After title and abstract screening, 526 records were screened by title and abstract, 87 through full-text review, of which 25 articles were included in the final analysis. Results Our findings illuminate the differential distribution of pain burden across marginalized groups and how it serves to heighten existing disparities. Service disruptions due to social distancing orders and infrastructural limitations prevented patients from receiving the care they needed, resulting in adverse psychological and physical health outcomes. Efforts to adapt to COVID-19 circumstances included modifications to opioid prescribing regulations and workflows and expanded telemedicine services. Conclusion Results have implications for the prevention and management of chronic pain and opioid use disorder, such as challenges in adopting telemedicine in low-resource settings and opportunities to strengthen public health and social care systems with a multidisciplinary and multidimensional approach.
The Convergence Speed of Pricing European-Style Derivatives under Lattice Models

It is known that the Black-Scholes model can be well approximated by lattice models such as the Cox-Ross-Rubinstein (CRR) model in Cox, Ross, and Rubinstein (1979). The orders of convergence of several lattice models have been shown in Leisen and Reimer (1996). In this study, we consider a derivative pricing problem where the value of the underlying asset follows a more general stochastic process instead of a geometric Brownian motion (GBM). In addition, we do not focus on only European call options. Instead, a more general European-style derivative is allowed. We provide a different proof from the one in Leisen and Reimer (1996) and show that the order of convergence of lattice approximation used to price European-style derivatives is 1.
Substrate‐Free Transfer of Large‐Area Ultra‐Thin Electronics

Innovation in materials and technologies has promoted the fabrication of thin‐film electronics on substrates previously considered incompatible because of their chemical or mechanical properties. Indeed, conventional fabrication processes, typically based on photolithography, involve solvents and acids that might harm fragile or exotic substrates. In this context, transfer techniques define a route to overcome the issues related to the nature of the substrate by using supportive carriers in the electronics stack that mitigate or avoid any damages during the fabrication process. Here, a substrate‐free approach is presented for the transfer of ultra‐thin electronics (<150nm‐thick) where no additional layer besides the electronics one remains on the final substrate. Devices are transferred on several surfaces showing good adhesion and an average performance variation of 27%. Furthermore, a sensor bent to a radius of 15. 25µm, shows variation in performance of 5%. The technique can also be sequentially repeated for the fabrication of stacked electronics, enabling the development of ultra‐thin devices, compliant on unconventional surfaces.
Transfer learning approach for Alzheimer’s disease diagnosis using MRI images

Alzheimer's disease is the most prevalent type of dementia and is defined as a slow-progressing neurological disorder. As a first step, early diagnosis of Alzheimer's disease is crucial, then classification is required as a second step for patients to be offered the most effective treatment available. For testing and analyzing this research, the Alzheimer's Disease Neuroimaging Initiative (ADNI) Baseline dataset is used. In this study, we suggested utilizing a convolutional neural network (CNN) algorithm to diagnose Alzheimer's disease from MRI images using a supervised deep learning approach based on transfer learning. The implemented system examines two different CNN architectures, including VGG-16 and MobileNet-V2. According to our results, this study achieved the highest accuracy and F1-score with 99.71% and 100%, respectively.
A Broadband Multifunctional Reconfigurable Polarization Conversion Metasurface

In this work, a reflective polarization conversion metasurface (PCM) with symmetrical L-shaped patches is proposed which enables to switch between different polarization modes by PIN diodes, i.e., linear-to-linear (LTL) cross-polarization and linear-to-circular (LTC) polarization. Because of multimodes activated in the structure, the PCM shows an ultrawide bandwidth both for LTL and LTC polarization conversion. A microstrip line-based dc biasing network composed of radial stubs is introduced to isolate RF currents, which can resolve problems raised by RF-lumped choke inductors in the ultrawide bandwidth. A prototype with $15\times15$ units is fabricated. The simulated and measured results show that when the PIN diodes are forward biased, the PCM achieves LTC polarization conversion in the range of 5.96–15.34 GHz (relative bandwidth of 88.4%) with the axial ratio (AR) less than 3 dB; when the PIN diodes are reverse biased, the PCM achieves LTL polarization conversion in the range of 6.05–14.76 GHz (relative bandwidth of 83.75%) with the polarization conversion ratio (PCR) higher than 90%. The proposed PCM is expected to be potentially applied to a broad scanning range array, intelligent reflecting surfaces (IRSs), and remote sensing.
Holistic Enlightening of Blackspots With Passive Tailorable Reflecting Surfaces for Efficient Urban mmWave Networks

Mitigating outage regions is of particular interest for emerging millimeter-wave (mmWave) and future sub-terahertz frequency cellular networks. Whereas smart radio environments based on metasurfaces constitute a key concept for 6G research, current networks cannot be served as new control procedures are required. Although small prototypes are already emerging, costs will be higher for the dense deployment of reconfigurable intelligent surfaces (RISs) compared to passive reflectors. It is argued that passive reflectors can be used to effectively, cost-efficiently, and permanently boost connectivity in well-defined service areas. Leveraging the advantages of 3D printing and spray-painted conductive varnish, we introduce the Holistic Enlightening of bLackspots with passIve reflectOr moduleS (HELIOS) approach which is characterized by its scalability and parametrizability to meet the reflection requirements derived from sophisticated network planning. These slim reflectors meet the core criteria of ease of installation and minimal visual impact on the cityscape, which are imperative for market success. Our measurement-based comparison of prototypes against typical metal/aluminum reflectors shows at least equal reflectivity at a higher practicality of the proposed manufacturing process. The conducted simulation study validates the modular reflector pattern design process and finds a trade-off between the reflector efficiency and the minimum protrusion depth, which relates to the number of modules in the designated mounting area. An urban ray-tracing simulation-based case study further underlines the high applicability of the proposed approach, with the growth of the beyond line-of-sight (LOS) connectivity region being nearly twice as large for a site-tailored heterogeneous HELIOS configuration than that for a simple reflector plate.
Unmanned Aerial Vehicle Aided Hybrid Two-Tier Maritime Edge Computing Networks

The rapid development of maritime networks has led to the emergence of various maritime applications, especially those computing-intensive and delay-sensitive services. Due to the limited computing and communication resource in maritime networks, how to improve the quality of service (QoS) of mar-itime applications is challenging issue. To this end, we propose unmanned aerial vehicle (UAV)-aided hybrid two-tier maritime edge computing networks. In underwater tier, underwater wireless sensors (UWSs) collect oceanic data and upload data to unmanned surface vehicle (USV) via acoutic transmission. Each USV is responsible for receiving data from UWSs and performing local computing. In sea surface tier, each USV can offload partial workloads to multi-UAV via radio channel for aerial computing. We formulate a joint optimization problem to optimize the offloading decision and transmission duration, with the objective of minimizing the energy consumption of USV and UWSs. Simulation results demonstrate advantages of our proposed scheme.