Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Intrinsic Capacity with Frailty, Physical Fitness and Adverse Health Outcomes in Community-Dwelling Older Adults

Background Intrinsic capacity (IC) and frailty are complementary in advancing disability prevention through maintaining functionality. Objectives We examined the relationship between IC and frailty status at baseline and 1-year, and evaluated if IC decline predicts frailty onset among robust older adults. The secondary objectives investigated associations between IC, physical fitness and health-related outcomes. Design Prospective cohort study. Setting Community-based assessments. Participants Older adults aged>55 years, who were independent in ambulation (walking aids permitted). Measurements 5 domains of IC were assessed at baseline: locomotion (Short Physical Performance Battery, 6-minute walk test), vitality (nutritional status, muscle mass), sensory (self-reported hearing and vision), cognition (self-reported memory, age- and education adjusted cognitive performance), psychological (Geriatric Depression Scale-15, self-reported anxiety/depression). Composite IC (0–10) was calculated, with higher scores representing greater IC. Frailty status was based on modified Fried criteria, with frailty progression defined as incremental Fried score at 1-year. Results 809 participants (67.6+6.8 years) had complete data for all 5 IC domains. 489 (60.4%) participants were robust but only 213 (26.3%) had no decline in any IC domain. Pre-frail and frail participants were more likely to exhibit decline in all 5 IC domains (p<0.05), with decremental composite IC [9 (8–9), 8 (6–9), 5.5 (4–7.5), p<0.001] across robust, prefrail and frail. IC was significantly associated with fitness performance, independent of age and gender. Higher composite IC reduced risk for frailty progression (OR=0.62, 95% CI 0.48–0.80), and reduced frailty onset among robust older adults (OR=0.53, 95% CI 0.37–0.77), independent of age, comorbidities and social vulnerability. Participants with higher IC were less likely to experience health deterioration (OR=0.70, 95% CI 0.58–0.83), falls (OR=0.76, 95% CI 0.65–0.90) and functional decline (OR=0.64, 95% CI 0.50–0.83) at 1-year. Conclusion Declining IC may present before frailty becomes clinically manifest, increasing risk for poor outcomes. Monitoring of IC domains potentially facilitates personalized interventions to avoid progressive frailty.
Research Competencies of Social Work Students during Remote Learning

Context: Acquiring research competencies by students is becoming more and more relevant in an evidence-based social work approach. Moving from traditional face-to-face pedagogical approach to a fully remote educational paradigm has greatly impacted universities in developing appropriate solutions for teaching and learning. Research skills and knowledge have been considered an important competence in social work training as best practice requires using available research data and best research requires understanding the context of practice. Method: Using a quantitative approach the current paper analyses how perception of remote learning and type and level of digital skills influence the development of research skills for social work students. 242 Romanian students enrolled in bachelor program filled in a questionnaire in June 2021. Results: The experience of a fully remote learning of research method did not have any significant impact on research competencies. However, the high level of operational skills influences students’ competency in research inquiry and quantitative/qualitative methodology, information-navigation skills positively influence dissemination of research and creative skills influences their level in quantitative/qualitative process, ethics and dissemination. Conclusion: In an evidence-based social work approach, research competences need to remain an important component of learning plans. However, considering the digitalisation trend higher education, it is expected that the content and format of research classes to adapt in order to assure the transferability of skills into the field of practice.
A Review of Modeling and Applications of Energy Storage Systems in Power Grids

As the penetration of variable renewable generation increases in power systems, issues, such as grid stiffness, larger frequency deviations, and grid stability, are becoming more relevant, particularly in view of 100% renewable energy networks, which is the future of smart grids. In this context, energy storage systems (ESSs) are proving to be indispensable for facilitating the integration of renewable energy sources (RESs), are being widely deployed in both microgrids and bulk power systems, and thus will be the hallmark of the clean electrical grids of the future. Hence, this article reviews several energy storage technologies that are rapidly evolving to address the RES integration challenge, particularly compressed air energy storage (CAES), flywheels, batteries, and thermal ESSs, and their modeling and applications in power grids. An overview of these ESSs is provided, focusing on new models and applications in microgrids and distribution and transmission grids for grid operation, markets, stability, and control.
Fuzzy control and dynamic simulation of a quadruped galloping machine

Motivated by the need for legged machines to use dynamically-stable running gaits in order to attain high speeds, this dissertation addresses issues in the simulation and control of a galloping quadruped machine. The gallop is studied because it is the preferred high-speed gait used by animals. A direct adaptive fuzzy control approach to the control of planar galloping and bounding is presented. The fuzzy controller is capable of learning the necessary leg touchdown angles and leg thrusts required for deadbeat control of the height and velocity of the quadruped at the top of flight. This is accomplished through an adaptation mechanism which is based on heuristics similar to those used in the controller designs of Raibert. Empirical relations concerning the galloping speeds of animals are interpreted in terms of the energy requirements of the galloping machine. The same fuzzy control approach is applied to starting and stopping the galloping machine in a single stride. Efficient, general algorithms for the simulation of robotic systems containing kinematic loops are developed for use in evaluating prototype leg designs and in designing control systems. Two approaches, one based on acceleration constraints and the other on compliant loop-closure joints, are implemented and compared for a set of dynamic systems. Both approaches use Articulated-Body Inertia methods for simulation of the spanning tree of the system. Two methods for constraint stabilization of the acceleration constraints, Baumgarte and a spring and damper approach, are also implemented and compared. Finally, simulation and control results for a prototype leg, with a kinematic loop and in a one-legged hopper configuration, are presented.
Impacts of the COVID‐19 pandemic on livelihoods and wild meat use in communities surrounding the Dja Faunal Reserve, South‐East Cameroon

Abstract The COVID‐19 outbreak has had considerable negative impacts on the livelihoods and living conditions of communities around the world. Although the source of COVID‐19 is still unknown, a widely spread hypothesis is that the virus could be of animal origin. Wild meat is used by rural communities as a source of income and food, and it has been hypothesised that the pandemic might alter their perceptions and use of wild meat. McNamara et al. (2020) developed a causal model hypothesising how the impacts of the pandemic could lead to a change in local incentives for wild meat hunting in sub‐Saharan African countries. From February 27 to March 19, 2021, we carried out a survey around the Dja Faunal Reserve, Southeast Cameroon, to test McNamara et al.’s model in practice, using semi‐structured questionnaires to investigate the impacts of the COVID‐19 outbreak on wild meat hunting and consumption. Our results generally agree with the causal pathways suggested by McNamara et al. However, our study highlights additional impact pathways not identified in the model. We provide revisions to McNamara's model to incorporate these pathways and inform strategies to mitigate the impacts of the pandemic.
How Much a Model be Trained by Passive Learning Before Active Learning?

Most pool-based active learning studies have focused on query strategy for active learning. In this paper, via empirical analysis on the effect of passive learning before starting active learning, we reveal that the amount of data acquired by passive learning significantly affects the performance of active learning algorithms. In addition, we confirm that the best amount of data that should be acquired by passive learning depends on the given settings: network complexity, query strategy, and datasets. Inspired by these observations, we propose a method to automatically determine the starting point of active learning for the given settings. To this end, we suggest entropy of sample-uncertainty to measure the training degree of a target model and develop three empirical formulas to determine an appropriate entropy of sample-uncertainty that should be obtained by passive learning before starting active learning. The effectiveness of the proposed method is validated by extensive experiments on popular image classification benchmarks and query strategies.
MATHEMATICAL MODEL AND SIMULATION OF FREE BALLOON LIFTOFF IN THE PRESENCE OF SURFACE WINDS

A mathematical model of free balloon launches in windy conditions is based on the conservation of the linear momentum in horizontal and vertical axes. Linear momentum conservation equations are represented by a set of four nonlinear first-order ODEs. Some ODEs were solved analytically, while the nonlinear Riccati ODE with variable coefficients for the vertical acceleration was solved using numerical ODE solvers. Transient aerodynamic lift and horizontal drag are caused by the slip flow over the balloon envelope. It takes free balloon ten half times to reach 90.9% of the wind velocity in a step function response. A launch condition was developed in terms of the minimum required envelope temperature for which the net aerostatic lift overcomes inert weight of a balloon. Perturbation analysis was used to explore changes in the net aerostatic lift. Simulations were performed to cases with and without envelope distortion and enhanced cooling due to forced convection. Since all balloon takeoffs are performed downwind, obstacle clearance becomes an issue due to rapid loss of aerodynamic lift. Balloons may stop climbing and even start descending shortly after liftoff despite intense heating representing real hazard.
Stiffness Modulus of Aged Asphalt Mixtures

Asphalt mixtures have a dominant position in road construction in most countries. The choice of asphalt mixture type and surface depends mainly on the traffic loading and climatic conditions. The stiffness of asphalt mixture is one of important parameters that determines how road pavement performs and what pavement response to traffic loading. The stiffness modulus of the asphalt mixture changes during its service life, it tends to increase. During this process the mixtures in pavement age and degrade. Aging of asphalt mixtures has a significant effect on changes in the properties of individual mixtures. Presented research was focused on monitoring the change in the stiffness modulus of two asphalt mixtures AC11 50/70 and AC11 PMB 45/80-75 by the effect of aging. The asphalt mixtures have been exposed to the effects of short-term and long-term ageing by the conditioning of loose mixture method. After aging of the asphalt mixtures, the stiffness modulus of compacted asphalt mixture samples was measured. The stiffness modulus was determined using the IT-CY method at temperature of 10 °C, 20 °C and 30 °C and showed an increase after exposure of the mixture to short-term and long-term aging.
The payoffs and tradeoffs of hygienic behavior: a five year field study on a local population of honey bees

Abstract Honey bees are exposed to a variety of risk factors, among which the ectoparasitic mite Varroa destructor and its associated viruses are considered to be the most significant problem worldwide. It has been widely recognized that honey bee stocks resistant to mites are an essential part of any sustainable long-term management of Varroa. The aim of this study was to evaluate the efficacy of hygienic behavior in a local population of honey bees in reducing Varroa infestation. A bi-directional selection for high and low rates of hygienic behavior was carried out in Israel using either artificially inseminated or naturally mated queens. Colonies were screened for performance including population size, honey production, levels of Varroa infestation, and the level of hygienic behavior. Furthermore, we examined the costs and benefits of the selection, and possible trade-offs, using the above measurements of colony performance to ensure their productivity. The selection process revealed that the trait is heritable. The maternal phenotype has a significant effect on Varroa load, as colonies founded by hygienic daughter queens showed a significantly lower parasite load. No major trade-offs were found between the rate of hygienic behavior, honey yield, and population size. Measuring the direct benefits of hygienic behavior through colony performance suggests that breeding for this trait makes bees more resistant to Varroa destructor. These results are promising for our successful local bee breeding programs in a Mediterranean climate.
Design, modeling and control of an index finger exoskeleton for rehabilitation

Abstract With diverse areas of applications, wearable robotic exoskeleton devices have gained attention in the past decade. These devices cover one or more human limbs/joints and have been presented for rehabilitation, strength augmentation and interaction with virtual reality. This research is focused towards design, modeling and control of a novel series elastic actuation (SEA) based index finger exoskeleton with a targeted torque rendering capability of 0.3 Nm and a force control bandwidth of 3 Hz. The proposed design preserves the natural range of motion of the finger by incorporating five passive and two actively actuated joints and provides active control of metacarpophalangeal and proximal interphalangeal joints. Forward and inverse kinematics for both position and velocity have been solved using closed loop vector analysis by including human finger as an integral part of the system. For accurate force control, a cascaded control structure has been presented. Force controlled trajectories have been proposed to guide the finger along preprogrammed virtual paths. Such trajectories serve to gently guide the finger towards the correct rehabilitation protocol, thus acting as an effective replacement of intervention by a human therapist. Extensive computer simulations have been performed before fabricating a prototype and performing experimental validation. Results show accurate modeling and control of the proposed design.
Spatial Shifts in Species Richness in Response to Climate and Environmental Change: An Adaption of the EUROMOVE Model in the Czech Republic

Climate change has greatly altered plant habitats, resulting in greater biodiversity loss at different scales. Therefore, it is important to quantify such changes for better monitoring and conservation. In this study, we adapt the EUROMOVE model and its mean stable area indicator (MSAi) to the conditions in the Czech Republic. Our objective was to predict change in species richness from a representative pool of 687 species from 1990 to 2100 under the RCP 8.5 climate scenario, focusing on the current period (2018). Another objective was to assess the effectiveness of the MSAi as a tool for quantifying landscape vulnerability. Our result shows that species habitat expanded between 1990 and 2018, although about 2 per cent of species were lost. The average MSAi of the most favourable highland habitats may decrease from 0.85 to 0.65 by 2100 as >20% of baseline species may be lost. Indicator species of Alnus (alder) and Festuca (fescue), typical of lowland habitats, are among the most vulnerable, already showing a net loss of their current habitat extent. The MSAi can be applied as a comprehensive tool to quantify the impact of climate change on landscape vulnerability as more survey data becomes available.
Lignin as a Bio-Sourced Secondary Template for ZSM-5 Zeolite Synthesis

The aim of this study was to investigate the effect of the addition of lignin as a sacrificial agent in ZSM-5 zeolite synthesis. Peculiar growths of ZSM-5 crystals leading to various textural properties were observed. Hence, the behavior in acid-catalyzed conversion of methanol into hydrocarbons (MTH) shifted from high selectivity toward olefins (>55%) to the sole formation of dimethyl ether (DME). Lignin acted as a bio-sourced secondary template (BSST), impacting the zeolite crystals’ shape and, thus, their physicochemical properties.
Multiple Musical Instrument Signal Recognition Based on Convolutional Neural Network

To improve the accuracy of multi-instrument recognition, based on the basic principles and structure of CNN, a multipitch instrument recognition method based on the convolutional neural network (CNN) is proposed. First of all, the pitch feature detection technology and constant Q transform (CQT) are adopted to extract the signal characteristics of multiple instruments, which are used as the input of the CNN network. Moreover, in order to improve the accuracy of multi-instrument signal recognition, the benchmark recognition model and two-level recognition model are constructed. Finally, the above models are verified by experiments. The results show that the two-level classification model established in this article can accurately identify and classify various musical instruments, and the recognition accuracy is improved most obviously in xylophone. Compared with the benchmark model, the constructed two-level recognition has the highest accuracy and precision, which shows that this model has superior performance and can improve the accuracy of multi-instrument recognition.
Radiomic Detection of Malignancy within Thyroid Nodules Using Ultrasonography—A Systematic Review and Meta-Analysis

Background: Despite investigation, 95% of thyroid nodules are ultimately benign. Radiomics is a field that uses radiological features to inform individualized patient care. We aimed to evaluate the diagnostic utility of radiomics in classifying undetermined thyroid nodules into benign and malignant using ultrasonography (US). Methods: A diagnostic test accuracy systematic review and meta-analysis was performed in accordance with PRISMA guidelines. Sensitivity, specificity, and area under curve (AUC) delineating benign and malignant lesions were recorded. Results: Seventy-five studies including 26,373 patients and 46,175 thyroid nodules met inclusion criteria. Males accounted for 24.6% of patients, while 75.4% of patients were female. Radiomics provided a pooled sensitivity of 0.87 (95% CI: 0.86–0.87) and a pooled specificity of 0.84 (95% CI: 0.84–0.85) for characterizing benign and malignant lesions. Using convolutional neural network (CNN) methods, pooled sensitivity was 0.85 (95% CI: 0.84–0.86) and pooled specificity was 0.82 (95% CI: 0.82–0.83); significantly lower than studies using non-CNN: sensitivity 0.90 (95% CI: 0.89–0.90) and specificity 0.88 (95% CI: 0.87–0.89) (p < 0.05). The diagnostic ability of radiologists and radiomics were comparable for both sensitivity (OR 0.98) and specificity (OR 0.95). Conclusions: Radiomic analysis using US provides a reproducible, reliable evaluation of undetermined thyroid nodules when compared to current best practice.
“Eastern Guanxi” and “Western Relationship Marketing” Cultures Meet in Tanzania Business Environment

Relationship marketing (RM) is a widely applicable concept in the corporate world. Recently, the Chinese version of RM called guanxi has also gained considerable attention from practitioners and business stakeholders. However, research on the interaction of the two concepts is quite under-researched, particularly in developing countries. This article intends to fill this gap by exploring the application of relationship marketing and guanxi concepts in the Tanzanian business environment and how the concepts have changed the Tanzanian business environment. Findings indicate that China is utilizing guanxi and is gaining the upper hand over the counterparts who are using relationship marketing. China through guanxi has managed to penetrate the Tanzanian market to become the dominant trading partner surpassing the western superpowers.
Optical neural network quantum state tomography

Abstract. Quantum state tomography (QST) is a crucial ingredient for almost all aspects of experimental quantum information processing. As an analog of the “imaging” technique in quantum settings, QST is born to be a data science problem, where machine learning techniques, noticeably neural networks, have been applied extensively. We build and demonstrate an optical neural network (ONN) for photonic polarization qubit QST. The ONN is equipped with built-in optical nonlinear activation functions based on electromagnetically induced transparency. The experimental results show that our ONN can determine the phase parameter of the qubit state accurately. As optics are highly desired for quantum interconnections, our ONN-QST may contribute to the realization of optical quantum networks and inspire the ideas combining artificial optical intelligence with quantum information studies.
Removing climbers more than doubles tree growth and biomass in degraded tropical forests

Abstract Huge areas of tropical forests are degraded, reducing their biodiversity, carbon, and timber value. The recovery of these degraded forests can be significantly inhibited by climbing plants such as lianas. Removal of super‐abundant climbers thus represents a restoration action with huge potential for application across the tropics. While experimental studies largely report positive impacts of climber removal on tree growth and biomass accumulation, the efficacy of climber removal varies widely, with high uncertainty as to where and how to apply the technique. Using meta‐analytic techniques, we synthesize results from 26 studies to quantify the efficacy of climber removal for promoting tree growth and biomass accumulation. We find that climber removal increases tree growth by 156% and biomass accumulation by 209% compared to untreated forest, and that efficacy remains for at least 19 years. Extrapolating from these results, climber removal could sequester an additional 32 Gigatons of CO2 over 10 years, at low cost, across regrowth, and production forests. Our analysis also revealed that climber removal studies are concentrated in the Neotropics (N = 22), relative to Africa (N = 2) and Asia (N = 2), preventing our study from assessing the influence of region on removal efficacy. While we found some evidence that enhancement of tree growth and AGB accumulation varies across disturbance context and removal method, but not across climate, the number and geographical distribution of studies limits the strength of these conclusions. Climber removal could contribute significantly to reducing global carbon emissions and enhancing the timber and biomass stocks of degraded forests, ultimately protecting them from conversion. However, we urgently need to assess the efficacy of removal outside the Neotropics, and consider the potential negative consequences of climber removal under drought conditions and for biodiversity.