Miracle-dz/UniFAR-FTUs huggingface dataset:
Similar successional development of functional community structure in glacier forelands despite contrasting bedrocks

Questions: Primary plant succession is expected to be driven by habitat filtering and competitive exclusion. However, such findings typically come from experimental or single- site case studies. As a result, we lack field studies that investigate the functional community structures across successional series with differing site conditions. Here, we address the following question: how do plant trait patterns and functional diversity change along two chronosequences with distinct bedrocks? Methods: We established two soil chronosequences with contrasting bedrock types (siliceous vs calcareous) in the Swiss Alps spanning a terrain age gradient of 13,500 years. We analysed plant ecological strategies at 40 plots per glacier foreland relating six functional traits to terrain age using RLQ analysis. We used the variation in plant ecological strategies revealed by RLQ analysis to calculate indices of functional diversity and analysed their temporal development with terrain age. Results: The RLQ analysis revealed that canopy height and dispersal type were significantly associated with terrain age. In both glacier forelands, functional richness (FRic) increased with terrain age, suggesting similar development of niche differentiation along the chronosequences, irrespective of bedrock types. In addition, we observed a decrease of functional evenness (FEve) and functional divergence (FDiv) in both sites, indicating an overall trend to habitat filtering. Conclusions: The results support the idea of a similar development of functional com munity structure along the two chronosequences, underlining the deterministic model of functional structure during succession. The functional approach of this study improves knowledge of the adaptive strategies of plant communities colonising glacier forefields and highlights the potential of comparing successional series with differing site condi tions to gain a deeper understanding of successional drivers and underlying mechanisms.
Agglomeration is in the eye of the beholder: the changing governance of polycentrism

ABSTRACT With a focus on evolving governance configurations, this paper traces attempts at promoting polycentrism. The paper steers attention to the policy approaches that seek to develop and promote a polycentric urban region (PUR), whether that polycentric economic system is actual now or something only faintly sketched out now but aspired to in the future. Tracking policy shifts concerning a perceived Glasgow–Edinburgh economy over a period of two decades, the paper explores why different projects, strategies and initiatives have come and gone. In doing this, the paper operationalizes the territory, place, scale, network (TPSN) framework, showing how polycentrism inserts through structuring principles to try to shape existing fields of socio-spatial relations and organization (notably concerns for city-regionalism). In this framing, agglomeration is presented as a malleable and seductive notion that helps to secure views on why certain forms of subnational development should take priority.
Tokamak ITG-KBM transition benchmarking with the mixed variables/pullback transformation electromagnetic gyrokinetic scheme

Electromagnetic gyrokinetic simulation of high temperature plasma is required to predict confinement in magnetic fusion devices and has posed challenges for existing codes. In this paper, we demonstrate successful global gyrokinetic simulation of the ion temperature gradient-driven mode-kinetic ballooning mode transition in a toroidal fusion plasma test case using the mixed variables/pullback transformation (MV/PT) scheme with the particle-in-cell codes XGC and ORB5, and compare to results from a conventional continuum code from the literature. The MV/PT scheme combines explicit time integration with mitigation of the well-known electromagnetic gyrokinetic “cancelation problem. ” We calculate eigenmodes in the electrostatic and parallel vector potentials, and find good agreement in growth rate, real frequency, and the normalized plasma pressure of mode transition.
Teaching Students to Learn Better in Introductory Psychology: A Replication and Extension Study

Background: Psychology courses provide a good opportunity for instructors to teach students effective learning strategies integrated with content. Objective: This replication and extension study explored changes in students’ self-reported use of learning strategies before and after a term paper assignment and examined the relationships between learning strategy use and academic performance. Method: Three hundred eighty-five introductory psychology students completed surveys on their use of 11 learning strategies at the beginning and end of the semester, read an empirical article and wrote a term paper about the learning strategy of practice testing, and completed four exams among other assessments. Results: Replicating prior work, students generally reported improvements in their use of learning strategies over the course of the semester, though improvements were largely attributable to grade point average (GPA). Two learning strategies—self-explanation and practice testing—were positively correlated with course performance in the second half of the semester, over and above the effects associated with GPA. Conclusion: Teaching students about beneficial learning strategies may increase their adoption of those strategies as well as their course performance, replicating prior research. Teaching Implications: In order to improve course performance, instructors may consider adapting assignments in ways that increase students’ knowledge and use of effective learning strategies.
Mid-infrared free electron laser induced periodic surface structures on semiconductors

It is known that the several-times irradiation of a short pulse laser with fluence near the ablation threshold on material induces self-organization of fine periodic surface structures (so called LIPSS: laser induced periodic surface structure) with periodic interspaces Ʌ smaller than the laser wavelength ʎ. In particular, LIPSS such as Ʌ ≪ ʎ are called “fine LIPSS”. And they are attracting much attention as a method of processing for structures finer than the diffraction limit of laser light. However, their formation mechanism has not been clarified yet. To clarify the formation mechanism of LIPSS, in-situ observation is an effective way. However, the measurement of the small periodic interspaces with fast time scale (comparable to the laser pulse duration) are the difficulties. On a way to in-situ observation, we have reported the LIPSS formation on semiconductors irradiated by mid-infrared Free-Electron Laser (MIR-FEL). Their periodic interspaces are several micrometers and therefore can be measured by visible light. In this presentation, we compared the features of LIPSSs by MIR-FEL to the several models applied to LIPSSs by near-infrared or visible light lasers. Through this discussion, we estimated the temporal resolution needed for in-situ observation.
Multiplexing FRET by PIE-FastFLIM and the phasor plots

Förster resonance energy transfer (FRET) microscopy is a technique that provides the spatial and temporal resolution necessary to investigate protein-protein interactions in live specimens. One of the most robust ways of quantifying FRET is to measure the fluorescence lifetime of the donor fluorophore. Fluorescence lifetime imaging microscopy (FLIM) is routinely used in the biological sciences to monitor dynamic signaling events inside living cells. However, the demands of studying complex signaling networks in living cells using FRET-based biosensor probes requires the ability to track more than one donor fluorophore at the same time. Here, we demonstrate a novel (Pulsed Interleave Excitation) PIE-FastFLIM technique that can be used to simultaneously measure two FRET-based biosensor probes at the same pixel. The PIE-FastFLIM configuration uses tunable wavelengths and synchronized gating detection, which can be tailored and optimized for each biosensor probe. When combined with the model-free phasor plots analysis, the PIEFastFLIM approach enables quantitative multiplexed FRET measurements for tracking multiple signaling events simultaneously in live cells.
Evaluating participants' perceptions of a functional physical fitness assessment for those aged 60 years and over conducted in the community by student physiotherapists

Physical fitness is crucial in preserving independence and quality of life for older adults. A functional physical fitness assessment has been designed specifically for those aged 60 years and over. This study explored older people's perceptions of this assessment, conducted by student physiotherapists, and the feasibility of a larger study to evaluate the long-term effects of its use. A total of five scheduled drop-in sessions were arranged. Participants aged 60 years and over were recruited. Physiotherapy students conducted the assessment and participants completed follow-up physical activity status and quality of life questionnaires as well as an evaluation of the session. Focus groups were conducted to explore participant's perceptions and experiences. Data was analysed using descriptive statistics and thematic analysis. Overall, 91 adults (aged 60–93 years) participated, 75 (46 (61%) women and 10 (13%) men) completed questionnaires, and seven attended focus groups. In total, 100% of the 91 participants suggested that those aged 60 years and over would benefit from functional fitness assessment, and 79% perceived this would lead to increased physical activity. Focus group participants reported they enjoyed working with students and suggested that regular assessment would provide a yardstick for their level of fitness, that they would like to repeat their tests next year and that ‘they were mentally a positive thing’. Functional fitness assessments were perceived as useful and encouraged older adults to increase their activity levels. Students successfully completed the tests in non-healthcare settings. This study suggests that a large trial designed to assess the benefit of regular functional fitness assessment for the over 60s in community settings is feasible.
Aluminum oxide as a dielectric and passivation layer for (flexible) metal-oxide and 2D semiconductor devices

We discuss the role of aluminum oxide (i.e. Al2O3 when stoichiometric) for transistors and sensors based on oxide semiconductors such as InGaZnO (IGZO) and two-dimensional (2D) semiconductors, such as monolayer MoS2. Aluminum oxide is a well-known capping and dielectric layer in semiconductor technology typically deposited by atomic-layer deposition (ALD), which offers a dense and high-quality film with low gas permeability even when deposited on flexible substrates. However, when deposited at low temperature (< 200°C), aluminum oxide can include a significant amount of fixed charges and defects, which lead to unusual charge trapping and doping effects in semiconductor devices. For example, such charge trapping can cause (apparent) sub-60 mV/decade subthreshold swing at room temperature in IGZO transistors, but can also lead to potential applications in neuromorphic computing. We also discuss effective doping (~1013 cm-2) of 2D semiconductors by thin ALD-grown non-stoichiometric AlOx capping layers. This is achieved with an aluminum seed layer, which enables uniform growth of the subsequently deposited ALD film. This approach leads to a negative shift in threshold voltage, record on-state current (~700 μA/μm) in a monolayer semiconductor, and drastic reduction in contact resistance. Finally, we investigate the passivation effects of Al2O3 capping, which limits the interaction of the underlying semiconductors with ambient air and moisture. We demonstrate improved response in MoS2 temperature sensors and long-term stability in flexible MoS2 transistors (8 months). Further, we evaluate the effects of Al2O3 passivation on IGZO transistors after aging for 80 months.
A Novel Framework for Mobile-Edge Computing by Optimizing Task Offloading

With the emergence of mobile computing offloading paradigms, such as mobile-edge computing (MEC), many Internet of Things applications can take advantage of the computing powers of end devices to perform local tasks without the need to rely on a centralized server. Computation offloading is becoming a promising technique that helps to prolong the device’s battery life and reduces the computing tasks’ execution time. Many previous works have discussed task offloading to the cloud. However, these schemes do not differentiate between types of application tasks. It is not reasonable to offload all application tasks into the cloud. Some application tasks with low computing and high communication cost are more suitable to be executed on the end devices. On the other hand, most resources on the end devices are idle and can be used to process tasks with low computing and high communication cost. In this article, a three-layer task offloading framework named DCC is proposed, which consists of the device layer, cloudlet layer and cloud layer. In DCC, the tasks with high computing requirement are offloaded to the cloudlet layer and cloud layer. Whereas tasks with low computing and high communication cost are executed on the device layer, hence DCC avoids transmitting large amount of data to the cloud, and can effectively reduce the processing delay. We have introduced a greedy task graph partition offloading algorithm, where the tasks scheduling process is assisted according to the device computing capabilities following a greedy optimization approach to minimize the tasks communication cost. To show the effectiveness of the proposed framework, We have implemented a facial recognition system as usecase scenario. Furthermore, experiment and simulation results show that DCC can achieve high performance when compared to state-of-the-art computational offloading techniques.
How does policy alienation develop? Exploring street-level bureaucrats’ agency in policy context shift in UK telehealthcare

Policies can fail when frontline staff feel they have limited influence on policy implementation (powerlessness) or that policy has little or no personal meaning (meaninglessness) – they become alienated from the policy – but how does this alienation develop? In this article we ask whether policy alienation might be viewed as a process that develops over time: a process that ebbs and flows, interacting with the policy landscape as it shifts, rather than a psychological state. Feelings of alienation can be shared across groups of actors, as they collectively shift and initiate change. This study uses participant observation and interviews with front-line employees as they navigate a UK government policy introducing telehealthcare to improve health management of patients with chronic conditions. We find that: (i) cumulative misalignment between different policy implementation contexts allows policy alienation to develop over time; (ii) the shared experience of alienation in co-worker groups contributes to further misalignment; and (iii) front-line staff use their discretion to respond to policy alienation, which has the power to enhance or destroy policy implementation. We offer an alternative perspective for understanding how policy alienation can be prevented and policy implementation can be enhanced.
Environmental context alters the magnitude of conspecific negative density dependence in a temperate forest

Conspecific negative density dependence (CNDD) is an important driver of stand-level tree diversity in temperate forests. CNDD can occur via two main processes. First, soil pathogens and herbivores form a halo of high enemy density surrounding established trees at local spatial scales. As a result, seedlings and saplings residing within this halo experience reduced growth and survival. Second, intraspecific competition can lead to local depletion of resources, reducing the performance of conspecific seedlings and saplings, and encouraging spatial mixing of tree species. While seedlings and saplings respond to nearby conspecific adults, they are simultaneously responding to local environmental conditions. Solar radiation, soil moisture, and soil nutrients can all influence tree recruitment patterns. Furthermore, these environmental factors can influence densities and activities of soil pathogens, as well as seedling and sapling carbon balance. As a result, we expect small-scale variation in the abiotic environment to influence the magnitude of CNDD, although this expectation has thus far received little attention. We combined tree stem and environmental data from a mapped oak-hickory forest plot to evaluate whether the local environment modulates the direction or magnitude of negative density dependence. We tested for the interaction between conspecific and heterospecific basal area within a sapling’s neighborhood and each of five environmental covariates: index of canopy gaps, soil organic matter, soil cations, topographic curvature, and topographic slope/aspect. We found that increasing local conspecific and heterospecific basal areas reduced sapling growth and that the conspecific basal area reduced sapling growth more so than did heterospecific basal area. Furthermore, we observed significant interactions between adult tree density and all environmental covariates that differed depending on the category of nearby adults (i.e., conspecific vs. heterospecific), and this effect was most important with variations in soil organic matter, soil cations, and topographic slope/aspect. We conclude that tree basal area not only interacts with environmental conditions to influence sapling growth, but more importantly, that small-scale environmental conditions interact with the density of nearby conspecifics to modulate sapling growth. Thus, if this result proves general, a central mechanism underlying the maintenance of diversity can be modified by small-scale environmental
Impact of Industry 4.0 Platform on the Formation of Construction 4.0 Concept: A Literature Review

In recent years, due to the rapid development of the fourth industrial revolution and new platforms of information technologies, intelligent systems have received widespread attention in many industries and have brought the potential to improve the efficiency of the construction industry. These facts led to the appearance of a new concept in construction industry called Construction 4.0. Therefore, this article seeks to explore the state of implementation of Industry 4.0 technologies in the construction industry and analyze their impact on the formation of the Construction 4.0 concept. In order to achieve the aim of this article, a literature review was conducted using the most relevant publication in this field. Moreover, authors carried out a bibliometric analysis among 195 selected research articles related to the Industry 4.0 and Construction 4.0 to identify interconnections between these concepts. The results show that Industry 4.0 has the greatest impact on productivity growth in construction and that interest in digital technologies is growing every year, but their penetration into the construction industry is currently slow and limited. The authors suggest that further research needs to be focused on future ethical issues that may arise and on synergies between Construction 4.0 technologies.
Modelling and comparison of image-analysis algorithms to measure blood velocity with laser scanning microscopy

Laser scanning microscopy is widely used to measure blood hemodynamics with line-scans. Analysis of red blood cell (RBC) velocity involves two types of algorithms. The algorithms of the first kind are the image-processing algorithms such as the shear [1], the LSPIV [2], the radon-transform [3] and the Fourier transform [4, 5] algorithms. Each of them provides the apparent velocity (VRBCapp) of RBCs in the image, which does not consider the scanning conditions. The algorithms of the second kind give the real RBC velocity from VRBC app and the scanning conditions [6]. Here we developed a predictive model that provide the accuracy of one image-processing algorithms. We find that the accuracy of the image-processing algorithm depends on several parameters such as the design of the scanned line, the scanning velocity and the velocity of RBCs. Last we validate our model by testing various types of artificial linescan images. The same strategy will be followed for various image-processing algorithms. Overall, our analysis will allow accurate comparisons of blood velocity from all vessel types in control and pathological animal models.
Prediction effective capacity battery of cellular BTS for an effective maintenance

A cellular base transceiver station (BTS) systems must be operated continuously in transmitting signals to the mobile stations. Reliability operation must be performed by this BTS to serve users without any interruptions. In several cases the BTS are found failed due to several disturbances that occurred during operation. One of the disturbances is caused by electrical energy supply interruptions. Although the cellular BTS power supply system is usually equipped by battery as a secondary power supply, the reliability can be influenced by the health of the battery. The health of battery is inclined by instability of main electricity supply, temperature fluctuation of the battery environment, increment of battery aging, the number of charging and discharging cycles (CDC), and the number depth of discharging conditions (DOD) that occurred. The impact of these factors will affect to the strength of battery. This paper proposes an investigation method to the battery health to ensure the performance of BTS. The method is applied by considering the charging and discharging characteristics of the battery. Since the characteristic signals are not linear, the calculation is performed using fuzzy method. Early results show that this prediction method can be applied to support maintenance work. The proposed method gives an overview of the battery health that can be followed up by technician to ensure the reliability of the BTS.
Design of workpiece recognition and sorting system based on deep learning

Aiming at the actual needs of industrial robots in the handling process of sorting and picking according to the types of objects, a design scheme of visual inspection and workpiece recognition based on deep learning is proposed. The solution uses deep learning to model the object recognition system, builds the mapping relationship between the image and the object category, and realizes the effective recognition of the object image information. After the analysis is completed, the industrial robot classifies the object according to the given information. Experiments show that the use of deep learning image detection mode can realize the classification and picking operation of industrial robots, which can meet the needs of some actual scene operations.