Miracle-dz/UniFAR-FTUs huggingface dataset:
What would most help improve the quality of life of older family carers of people with dementia? A qualitative study of carers’ views

Older family carers of people with dementia provide a substantial amount of care for people with dementia in the UK. Caregiving can be stressful and burdensome for these individuals, who are also experiencing psychological and physical changes resulting from their own ageing process. However, little is known about what impacts their quality of life, how this can be improved and what we should prioritise. This brief report asks one simple question to older family carers of people living with dementia – “What would most help improve your quality of life as a carer? ” Qualitative data were collected from 150 carers who completed an anonymous paper survey during the development and validation of a quality of life tool for use with this population (DQoL-OC). Participants were individuals aged 60 and over and were providing care for a family member with dementia at home in the UK. Carers were recruited from a variety of voluntary organizations, community-based carers’ groups, health services and via online forums. A thematic approach was used to analyse the carers’ comments and three main overarching themes were identified. The quality of life of older family carers can be enhanced by having more time away from caregiving, accessing health and social services that are dementia friendly and by having economic support. Future care, policies and research should aim to address these key areas in order to promote better quality of life for older carers of people with dementia. Further implications for practice, policy and research are discussed.
Endothelialization mechanisms in vascular grafts

Despite the wide variety of tissue‐engineered vascular grafts that are currently being developed, autologous vessels, such as the saphenous vein, are still the gold standard grafts for surgical treatment of vascular disease. Recently developed technologies have shown promising results in preclinical studies, but they still do not overcome the issues that native vessels present, and only a few have made the transition into clinical use. The endothelial lining is a key aspect for the success or failure of the grafts, especially on smaller diameter grafts (<5 mm). However, during the design and evaluation of the grafts, the mechanisms for the formation of this layer are not commonly examined. Therefore, a significant amount of established research might not be relevant to the clinical context, due to important differences that exist between the vascular regeneration mechanisms found in animal models and humans. This article reviews current knowledge about endothelialization mechanisms that have been so far identified: in vitro seeding, transanastomotic growth, transmural infiltration, and fallout endothelialization. Emphasis is placed on the models used for study of theses mechanisms and their effects on the development of tissue‐engineering vascular conduits.
A Novel Compact Superwideband Bowtie Antenna for 420 MHz to 5.5 GHz Operation

Bowtie antennas are ubiquitous in practical ground-penetrating radars. In spite of having a wide impedance bandwidth, boresight gain of these antennas is small and the main beam gets tilted beyond a 4:1 bandwidth. In this paper, a bowtie antenna design is presented, which has a return loss of at least 10 dB from 420 MHz to 5.5 GHz (13:1 bandwidth) and a stable radiation pattern and improved gain throughout the bandwidth. A superwide bandwidth is obtained without any kind of dissipative loading. The peak gain and average gain are 7.96 and 5.89 dBi, respectively. The gain of the antenna is improved using parasitic loops as directors. The size of the antenna is only $0.322\lambda $ at 420 MHz.
Reduced Interference Vertex-Frequency Distributions

Vertex-frequency analysis of graph signals is a challenging topic for research and applications. Counterparts of the short-time Fourier transform, the wavelet transform, and the Rihaczek distribution have recently been introduced to the graph-signal analysis. In this letter, we have extended the energy distributions to a general reduced interference distributions class. It can improve the vertex-frequency representation of a graph signal while preserving the marginal properties. This class is related to the spectrogram of graph signals as well. Efficiency of the proposed representations is illustrated in examples.
Universal Metadata Repository: Integrating Data Profiles Across an Organization

Managing ever-growing content from heterogeneous data sources is a significant challenge in enterprise environments. Many data analysis tools work in isolation to capture various statistical, quality, and provenance information within the enterprise. Yielding meaningful and consistent information from a landscape of different vendor tools requires a holistic and transparent view over all existing extracted metadata. In this paper, we present our Universal Metadata Repository (UMR) applied to three in-flight use cases which combine the power of a technical and business view using knowledge graphs for: searching, inferencing, traceability, administration, enforcing accessibility standards, and providing consistent organizational architecture.
QoS Support for Scientific Workflows Using Software-Defined Storage Resource Enclaves

Data-intensive knowledge discovery requires scientific applications to run concurrently with analytics and visualization codes, executing in situ for timely output inspection and knowledge extraction. Consequently, I/O pipelines of scientific workflows can be long and complex because they comprise many "stages" of analytics across different layers of the I/O stack of high-performance computing systems. Performance limitations at any I/O layer or stage can cause an I/O bottleneck resulting in longer than expected end-to-end I/O latency. The causes of such performance issues are missing a performance guarantee (e.g., lower bounds of I/O throughput) across stages of I/O pipelines and across layers of the I/O stacks. In this paper, we present the design and implementation of a novel data management infrastructure called Software-defined Storage Resource Enclaves (SIREN) at system levels to enforce end-to-end policies that dictate an I/O pipeline's performance. Our results demonstrate that SIREN provides performance isolation among scientific workflows sharing multiple storage servers across two I/O layers while maintaining high system scalability and resource utilization.
Derailment: Conceptualization, measurement, and adjustment correlates of perceived change in self and direction.

Developmental perspectives on self and identity view a sense of personal sameness and continuity as critical for positive adjustment. Thus, the degree to which individuals perceive change over time in self and direction constitutes an important individual difference. Here, we offer an empirically sound instrument for assessing the extent to which people feel temporally discrepant and off course-a sense we term derailment. First, we develop and empirically validate a self-report measure that is sensitive to our conceptualization of derailment (Studies 1-3). Employing the new measure with adult samples, Study 3 demonstrates its predictive ability above and beyond other widely used measures of subjective change and identity distress. Study 4 shows the negative effects of derailment persist independent of whether individuals perceive changing for the better or worse, or actually experience status-changing life events. Study 5 demonstrates the prospective utility of this measure by predicting depressive symptoms 18 months later. Finally, levels of derailment are shown to be reduced by a daily writing experiment that emphasizes goal continuity (Study 6). The discussion situates derailment at the intersection of developmental, clinical, and social psychological literatures as a unique and measurable source of psychological vulnerability, and strategies for attenuating its potentially deleterious impact are suggested. (PsycINFO Database Record
Distributed Optimization of Multiagent Systems With Preserved Network Connectivity

This paper deals with the problem of distributed optimization of a multiagent system with network connectivity preservation. In order to realize cooperative interactions, a connected network is the prerequisite for high-quality information exchange among agents. However, sensing or communication capability is range-limited, so it is impractical to simply make an assumption that network connectivity is preserved by default. To address this concern, a class of generalized potentials including discontinuities caused by unexpected obstacles or noises are designed. For a class of quadratic cost functions, based on the potentials, a new distributed protocol is proposed to formally guarantee the network connectivity over time and to realize the state agreement in finite time while the sum of local functions known to individual agents is optimized. Since the right-hand side of the proposed protocol is discontinuous, some nonsmooth analysis tools are applied to analyze system performance. In some practical scenarios, where initial states are unavailable, a distributed protocol is further developed to realize the consensus in a prescribed finite time while solving the distributed optimization problem and maintaining network connectivity. Illustrative examples are provided to demonstrate the effectiveness of the proposed protocols.
Hardware Implementation of an Event-Based Message Passing Graphical Model Network

This paper presents a hardware system that implements a factor graph, where messages are sent using an event-based belief propagation algorithm. The system, comprising an FPGA and an application specific integrated circuit (ASIC) chip, can be used to construct a graph with upto 16 output message channels. The ASIC chip with 16 channels is fabricated in a 0.35 um 2P4M CMOS process and occupies <inline-formula> <tex-math notation="LaTeX">$2.16 \times 2.74$ </tex-math></inline-formula> mm<sup>2</sup>. Each channel dissipates 46 uW. The output analog messages of the channels are encoded through the interspike intervals of the output spike streams or events. The system can be used to implement graphs with arbitrary variable distributions for its inputs and using constraint functions, such as “plus” and “equality”. Using Kullback–Leibler divergence, we show that the measured distributions from the implemented graphs on the hardware show close similarity to the theoretical distributions.
PHI Scrubber: A Deep Learning Approach

Confidentiality of patient information is an essential part of Electronic Health Record System. Patient information, if exposed, can cause a serious damage to the privacy of individuals receiving healthcare. Hence it is important to remove such details from physician notes. A system is proposed which consists of a deep learning model where a de-convolutional neural network and bi-directional LSTM-CNN is used along with regular expressions to recognize and eliminate the individually identifiable information. This information is then removed from a medical practitioner's data which further allows the fair usage of such information among researchers and in clinical trials.
Dual-Phase-Shift Control Scheme With Current-Stress and Efficiency Optimization for Wireless Power Transfer Systems

Traditional wireless power transfer (WPT) systems usually adopt the single-phase-shift (SPS) control method to maintain a constant output current or a constant output voltage for various applications. However, the current-stress on one side is much higher than that of the other side especially under operating conditions with high voltage conversion ratio and light-load. This phenomenon actually can be proven by the established mathematical model of the current-stress of WPT systems using SPS control. This large current-stress will damage power devices and lower the efficiency of WPT systems. In addition, it will result in an increase of system total cost because of the need for the devices with the higher volt-ampere rating. A control strategy based on dual-phase-shift control is developed for WPT systems with an active rectifier to reduce the maximum value of current-stresses on both sides as well as to maintain a constant output voltage or current. Consequently, the system efficiency can be improved compared to traditional WPT systems with SPS control. A scaled-down experimental prototype is constructed to verify the effectiveness of the proposed WPT topology and the control method. Experimental results show that the proposed method can reduce the maximum current-stress significantly and improve the system efficiency compared to traditional WPT systems under SPS control.
Systematic Testing of Genetic Algorithms: A Metamorphic Testing based Approach

Genetic Algorithms are a popular set of optimization algorithms often used to aid software testing. However, no work has been done to apply systematic software testing techniques to genetic algorithms because of the stochasticity and the lack of known outputs for genetic algorithms. Statistical metamorphic testing is a useful technique for testing programs when the output is unknown or when the program has random elements. In this paper, we identify 17 metamorphic relations for testing a genetic algorithm and show, through mutation testing, that these relations are more effective at finding defects than traditional unit tests based on known outputs. We examined the failure rates of the system-level relations when initialized with various fitness functions. We found three relations failed excessively and we then modified these relations so that they failed less often. We also identified some metamorphic relations for genetic algorithms that are generalizable across different types of evolutionary algorithms and showed that our relations had similar mutation scores between two implementations. This is the first time statistical metamorphic testing has been applied for testing genetic algorithms.
An Analytical Model for Permeability of Underfill Flow in Flip-Chip Packaging With Consideration of the Actual Specific Surface and Tortuosity

Permeability is an important concept in understanding and modeling the flow or dynamic behavior of fluids in a porous medium (e.g., flip chip in electronics packaging). There are two factors (specific surface and tortuosity) that affect the permeability of a porous medium, but they are ignored by analytical models for permeability in the literature. In this paper, we present an analytical model for permeability of the underfill porous medium in flip-chip packaging by consideration of the actual specific surface and tortuosity. The proposed model and other models without consideration of the actual specific surface and tortuosity are compared with reference to a well-proven numerical model. The result of comparison has shown a significant improvement in accuracy with our model. This paper contributes not only to the underfill flip-chip package, but also to the field of porous mediums with regular arrangements of pores.
Cost and Energy Aware Scheduling Algorithm for Scientific Workflows with Deadline Constraint in Clouds

Cloud computing is a suitable platform to execute the deadline-constrained scientific workflows which are typical big data applications and often require many hours to finish. Moreover, the problem of energy consumption has become one of the major concerns in clouds. In this paper, we present a cost and energy aware scheduling (CEAS) algorithm for cloud scheduler to minimize the execution cost of workflow and reduce the energy consumption while meeting the deadline constraint. The CEAS algorithm consists of five sub-algorithms. First, we use the VM selection algorithm which applies the concept of cost utility to map tasks to their optimal virtual machine (VM) types by the sub-makespan constraint. Then, two tasks merging methods are employed to reduce execution cost and energy consumption of workflow. Further, In order to reuse the idle VM instances which have been leased, the VM reuse policy is also proposed. Finally, the scheme of slack time reclamation is utilized to save energy of leased VM instances. According to the time complexity analysis, we conclude that the time complexity of each sub-algorithm is polynomial. The CEAS algorithm is evaluated using Cloudsim and four real-world scientific workflow applications, which demonstrates that it outperforms the related well-known approaches.
GreenSprint: Effective Computational Sprinting in Green Data Centers

Computational Sprinting has proven to be an effective way to boost the computing performance for bursty workloads, which allows a chip to exceed its power and thermal limits temporarily by turning on all processor cores and absorbing the extra heat dissipation with certain phase-changing materials. However, extra power available for sprinting is constrained by existing power distribution infrastructures. Using batteries alone to provide the additional power to achieve performance target not only limits the effectiveness of sprinting, but also negatively impacts the lifetime of the batteries. Leveraging renewable power supply in a green data center provides an opportunity to exploit the maximal potential of Computational Sprinting. However, the intermittent nature of renewable energy makes it very challenging. In this paper, we propose GreenSprint, a renewable energy driven approach that enables a data center to boost its computing performance efficiently by conducting computational sprinting. We present four sprinting strategies to address the challenge imposed by the intermittent and time-varying nature of renewable energy supply. We build an experimental prototype to evaluate GreenSprint on a cluster of 10 servers with a simulated solar power generator. The results show that renewable energy by itself can sustain different duration lengths of sprinting when its supply is sufficient and can improve performance by up to 4.8x for representative interactive applications. We also show the effectiveness of core-count and frequency scaling in the presence of varied renewable power and limited battery energy.
A Hybrid Variational Autoencoder for Collaborative Filtering

In today's day and age when almost every industry has an online presence with users interacting in online marketplaces, personalized recommendations have become quite important. Traditionally, the problem of collaborative filtering has been tackled using Matrix Factorization which is linear in nature. We extend the work of [11] on using variational autoencoders (VAEs) for collaborative filtering with implicit feedback by proposing a hybrid, multi-modal approach. Our approach combines movie embeddings (learned from a sibling VAE network) with user ratings from the Movielens 20M dataset and applies it to the task of movie recommendation. We empirically show how the VAE network is empowered by incorporating movie embeddings. We also visualize movie and user embeddings by clustering their latent representations obtained from a VAE.
Thermal and Optical Performance of LEDs Mounted on Substrates With Converging–Diverging and Diverging–Converging Channels

Though light-emitting diodes (LEDs) are gaining attention for implementation in various applications, thermal management remains a major concern in solid-state lighting devices. Thermal resistance and junction temperature are the critical parameters of LEDs, which should be maintained at nominal levels for its reliable and robust operation. In this paper, novel substrates were proposed to enhance the thermal performance of LEDs. Aluminum substrates with converging–diverging (CD) and diverging–converging (DC) geometries were fabricated using high-precision milling machine. Three different converge–diverge angles such as 45°, 60°, and 90° were used in CD and DC substrates. Numerical simulations were done using computational fluid dynamics software to gain more insights on heat distribution and flow dynamics in the proposed substrates. Thermal characterization was done using thermal transient method by mounting the LED on CD and DC substrates. The thermal performances of the proposed substrates were compared with bare aluminum substrate by measuring the thermal resistance and junction temperature of LED. Optical analysis on LED was done by utilizing still air chamber and spectrometer. From the analysis, DC substrates showed lower thermal resistance and junction temperature compared to the other two substrates due to the conjugate heat transfer effect. Precisely, DC substrate with 90° showed higher thermal and optical performance than any other substrates considered.
Filtering Antenna With High Selectivity Using Multiple Coupling Paths From Source/Load to Resonators

This communication presents a filtering antenna with high selectivity using multiple coupling path from source/load to resonators. The high selectivity of the filtering antenna’s boresight gain response is achieved by a novel coupling scheme, which induces additional coupling between source/load and nonadjacent resonators to construct cross coupling paths. Thanks to the extra cross-coupling paths, the filtering antenna can generate a maximum of N radiation nulls with N resonators. Besides, a resonator using a combination of open-ended stub and short-ended stub is adopted to construct this novel coupling scheme more easily. For demonstration, a three-order filtering stacked patch antenna with a center frequency of 2.3 GHz and a fractional bandwidth of 15.2% is fabricated. Two independently controllable radiation nulls are located at 1.97 and 2.61 GHz. Unidirectional radiation patterns have been achieved with cross polarization lower than −20 dB and back lobes lower than −18 dB.
Trusted Environments for Volunteer Computing

Technology becomes more and more advanced everyday, both from the software and from the hardware perspective. Brand new devices, more powerful and capable of the generation preceding them, are steadily released. Everybody owns laptops, smartphones and many other devices with great compute capabilities, able to easily solve problems a few years ago considered almost impossible. These devices are, however, most of the time underused, resulting in an incredible waste of computational resources. The needs of professional people and scientists are evolving too, alongside the advances of technology. In many fields, from financial and biomedical simulation to insurance predictions, from 3D rendering to mathematical computations etc huge compute capabilities are still required. Despite the always growing presence of powerful devices and their increasingly cheap prices, in many situations using only our own devices is not enough. The contrast between underused devices and needs of computational resources led to the birth of a new approach: volunteer computing. This approach brings a user to be part of a network, sharing his idle CPU (or GPU) cycles in order to solve a subtask of a problem thus contributing, along with many other users, to the solution of a bigger task. In a very large number of cases, however, volunteer computing needs to tackle some serious security concerns. Many tasks, in fact, deal with sensitive information whose disclosure, even in a minimal part, must be avoided. For this reason still many problems are usually not handled by collaborative networks, since it is not possible, in many cases, to hide confidential data from inputs of every subtask. In this paper we present a way to solve the problem described. Through the use of trusted computing we are able to manage complex problems in a distributed network of volunteer computing devices without the risks related to the spread of sensitive data.