Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between Measured Aerobic Capacity and Total Energy Expenditure Obtained by Doubly Labeled Water in Community-Dwelling Healthy Adults Aged 81-94y: A Preliminary Study

Adequate energy intake is critical for the healthy longevity of older adults, and the estimated energy requirement is determined by total energy expenditure (TEE). We aimed to identify the relationship between measured aerobic capacity and TEE, activity energy expenditure (AEE) or physical activity level (PAL) with the doubly labeled water (DLW) methods in the advanced older adults. A total of 12 physically independent older adults (10 males and 2 females) aged between 81 to 94 years participated in this study. Aerobic capacity was evaluated according to the lactate threshold (LT). TEE under free-living conditions was assessed using the DLW method, and self-reported physical activity was obtained through the Japanese version of the International Physical Activity Questionnaire (IPAQ). LT was significantly positively correlated with TEE, AEE, and PAL after adjustment for age and sex (ρ= 0.77 (P<0.01), 0.86 (p<0.01), and 0.86 (p<0.01), respectively). We found the LT as an aerobic capacity is positively and independently correlated with TEE, AEE or PAL. The present results suggest that maintaining aerobic capacity is an important factor for preventing frailty, although further research is needed to multisite studies and many samples.
Optical Measurement of Stem Xylem Vulnerability

The vulnerability of plant water transport tissues to a loss of function by cavitation during 5 water stress is a key indicator of the survival capabilities of plant species during drought. Quantifying 6 this important metric has been greatly advanced by non-invasive techniques that allow embolisms to 7 be directly viewed in the vascular system. Here we present a new method for evaluating the spatial 8 and temporal propagation of embolising bubbles in the stem xylem during imposed water stress. We 9 demonstrate how the “optical method”, previously used in leaves, can be adapted to measure the 10 xylem vulnerability of stems. Validation of the technique is carried out by measuring the xylem 11 vulnerability of 13 conifers and two short vesselled angiosperms and comparing results with 12 measurements made using the “cavitron” centrifuge method. Very close agreement between the 13 two methods confirms the reliability of the new optical technique, and opens the way to simple, 14 efficient and reliable assessment of stem vulnerability using standard flatbed scanners, cameras or 15 microscopes. 16
Public Acceptability of Low Emission Zones: The Case of “Madrid Central”

Cities have intensified the adoption of Low Emission Zones (LEZs) to improve urban livability. Despite the high social controversy caused by LEZs in many cities, the scientific literature has paid little attention to study their public acceptability. This paper conducts a modelling approach exploring the impact of four groups of variables on the public acceptability of LEZs: (i) socio-economic and demographic characteristics; (ii) personal attitudes; (iii) travel-related variables; and (iv) perceptions and mobility habits linked to LEZs. The city of Madrid, Spain, is a case study of great interest because a LEZ called “Madrid Central” has been recently implemented. A total of 799 individual questionnaires were used to calibrate an ordered logit model. Results indicate that socio-economic and demographic variables are weakly related to the level of public acceptability towards the LEZ. On the contrary, the political ideology of individuals, their environmental awareness, their primary transport mode, the use of shared mobility systems, and the frequency of access to “Madrid Central” have a higher explanatory power. The results may be useful for policy-makers to understand the factors that increase the public acceptability of LEZs.
Deep Learning Based Code Generation from Requirements Text: Are We There Yet?

—To release developers from time consuming software development, many approaches have been proposed to generate source code automatically according to software requirements. With signiﬁcant advances in deep learning and natural language processing, deep learning based approaches are proposed to generate source code from natural language descriptions. The key insight is that given a large corpus of software requirements and their corresponding implementations, advanced deep learning techniques may learn how to translate software requirements into source code that fulﬁll such requirements. Although such approaches are reported to be highly accurate, they are evaluated on datasets that are rather small, lack of diversity, and signiﬁcantly different from real-world software requirements. To this end, we build a large scale dataset that is composed of longer requirements as well as validated implementations. We evaluate the state-of-the-art approaches on this new dataset, and the results suggest that their performance on our dataset is signiﬁcantly lower than that on existing datasets. Evaluation results also suggest that the generated programs often contain syntactic and semantical errors, and none of them can pass even a single predeﬁned test case. Further analysis reveals that the state-of-the-art approaches learn little from software requirements, and most of the successfully generated statements are popular statements in the training programs. Based on this ﬁnding, we propose a popularity based approach that always generates the most popular statements in training programs regardless of the input (software requirements). Evaluation results suggest that none of the state-of-the-art approaches can outperform this simple statistics-based approach. As a conclusion, deep learning based code generation requires signiﬁcant improvement in future, and our dataset may serve as a basis for future research in this direction.
Parallel Graph Mining on Shared Memory Architectures (cid:0)

Mining graph based data sets has practical applications in many areas including molecular substructure discovery, web link analysis, fraud detection, and social network analysis. The discovery challenge in graph mining is to ﬁnd all subgraphs which occur in at least (cid:2) graphs of a graph database, where (cid:2) is user speciﬁed parameter. Subgraph isomorphism, the enormous search space of candidate graph patterns, and the importance of interactive response times make addressing this challenge particularly daunting. In this work, building on the existing state-of-the-art, we propose a novel approach for parallelizing such algorithms on shared memory multiprocessor systems. We present several novel task partitioning and load balancing schemes, and evaluate their efﬁcacy. We also propose several queuing models which afford dynamic task sharing. We show that dynamic partitioning and dynamic task allocation provide a synergy which greatly improves scalability. Our parallelization algorithm achieves excellent speedup of 27 times on 32 nodes for several real world datasets, as compared to a naive approach which affords only 5-fold speedup. We also discuss implications of this work in light of recent trends in micro-architecture design, particularly multiple core and multithreaded systems.
Business counterintelligence as a protection strategy for SMEs

The objective of this research was to analyze the processes of business counterintelligence as a strategy to shield the SMEs from competitive intelligence services. It was framed in the convergence of research approaches given in 2 phases: quantitative phase (Likert type questionnaire applied to 385 Colombian businessmen), qualitative phase (semi-structured interview to 4 police officers with more than 25 years of experience in police counter-intelligence) and the conclusions are formulated taking into account the convergence of approaches by dimension/category. The significant findings allude to the existence of a perception on the part of the entrepreneurs about the counterintelligence systems implemented in the SMEs they manage. However, experts on the subject agree on the lack of knowledge of risk factors regarding information vulnerability and the lack of strategies to shield SMEs from competitive intelligence services.
EFFECTS OF DIFFERENT CONCENTRATIONS OF BAP AND NAA ON MICROPROPAGATION OF Crambe orientalis L. var. orientalis L

Crambe L. is a species of the Brassicaceae family. It is an important industrial oil plant with high erusic acid content. The objective of this study was to develop an efficient micropropagation protocol for Crambe orientalis L. var. orientalis L. species using different concentrations of BAP (6 benzylaminopurine)–NAA (α naphthalene acetic acid) hormone combinations. Seeds of Crambe orientalis var. orientalis were germinated in vitro. Cotyledon node and hypocotyl parts from fourteen-days seedlings were used as explant sources. The explants were cultured on MS media containing 0.25, 0.50, 1.00 and 2.00 mg/L BAP and 0, 0.25 and 0.50 mg/L NAA. Effects of hormone concentrations on mean callus regeneration percentage (%), mean shoot regeneration percentage (%), mean number of shoots per explant and mean length of shoots were determined. Callus formation and shoot regeneration resulted in all hormone concentrations used for the hypocotyl and cotyledon explants in our study. All explants were rooted on MS media containing 1.00 and 2.00 (mg/L) NAA.
NFS/RDMA over In(cid:2)niBand: Is It Bene(cid:2)cial? (cid:3)

Network File System (NFS) is a ubiquitous component of most modern clusters. It allows users to transparently share (cid:2)le and IO services on a variety of different platforms. Traditionally NFS has used TCP or UDP as the underlying transport for communication. However, these protocols add considerable overhead to communication and may not allow applications to extract performance from high-speed networks. The emergence of modern high-performance networking technologies like In(cid:2)niBand in conjunction with technologies like PCI-Express have dramatically enhanced the achievable network bandwidth and reduced message latency. These networks also allow the user to use operations like Remote Direct Memory Access (RDMA) to achieve zero-copy communication. Recent NFS protocol extensions can communicate directly over RDMA-capable networks. This may potentially help improve performance of these protocols. However, given the dependency of NFS performance on the application and underlying system characteristics, it is not clear whether there can be an improvement in performance at the application level. In this paper, we evaluate the performance of NFS over RDMA in OpenSolaris on In(cid:2)niBand using a variety of benchmarks, applications and system utilities. These experiments show that NFS/RDMA can increase MPI Tile I/O write bandwidth by 126% compared to NFS/IPoIB, as well as reduce kernel compile time by 73%. In addition, PCI-Express can help reduce the bottlenecks imposed by PCI-X and help improve IOzone aggregate Write bandwidth in NFS/RDMA up to 24%. Finally, we show that NFS critically depends on the back end (cid:2)lesystem being used. With design changes to the back end memory based (cid:2)lesystem, the performance of NFS over the same transport show an order of magnitude improvement of up to 11 times.
Augmenting the Rooﬂine Model via Lower Bounds on Data Movement

The rooﬂine model is a popular approach to bottleneck-bounds performance analysis. It models upper bounds on performance as a function of operational intensity, ratio of arithmetic operations per byte of data moved from/to memory. While operational intensity can be measured for a given implementation, it is of interest to characterize the upper limits of operational intensity for different algorithms. Currently there is no systematic systematic methodology to do so. In this paper, we address the problem of developing upper bounds on the operational intensity of computations as a function of cache capacity. We demonstrate the utility of the approach in comparative analysis of algorithms, as well as in assessing fundamental performance limits for different algorithms as architectural parameters are varied
Interactive comment on “Global root zone storage capacity from satellite-based evaporation” by L. Wang-Erlandsson et al

1) MAJOR: The description of the method should be improved. Is the method the same as in previous studies (e.g., Gao et al. 2014 GRL)? If yes, it should be clearly acknowledged. Is it different from the paper (under review, not available to reviewers) by Boer-Euser et al.? It should be clear to the reader if the novelty of the papers is on the method or in the satellite dataset used as input. Please clarify.
Relationships between Urbanization, Economic Growth, Energy Consumption, and CO 2 Emissions: Empirical Evidence from Indonesia

This study aims to investigate the relationship between urbanization, economic growth, energy consumption, and CO2 emissions in Indonesia. The data used in the study are time-series data for the period 1985–2017; the data utilized are sourced from World Development Indicators obtained on the World Bank database. The method uses a quantitative approach that applies the vector error correction model based on the Granger causality test. The empirical results reveal that, in the short run, there is evidence that urbanization and energy consumption can causes CO2 emissions, and they also prove that urbanization can cause energy consumption. Also, other findings prove the existence of longrun relationships flowing from energy consumption, economic growth, and CO2 emissions toward urbanization, as well as the existence of the relationship flowing from urbanization, economic growth, and CO2 emissions towards energy consumption. The results of testing the relationship between economic growth and CO2 emissions reveal that the environmental Kuznets curve hypothesis is proven in Indonesia. Thus, policies are needed to limit the impact of urbanization through high awareness-raising to maintain environmental quality and greater use of energy. Also, energy conservation policies are needed in all sectors, especially the electricity, industry, and transportation sectors.
A review of ecosystems services trade-offs, synergies and scenarios modelling for policy development support

Information about the effect of land management on ecosystem services is essential to make balanced decisions, develop sustainable political strategies, and determine future scenarios. Previous methods and tools have been developed to analyze the effects of land use/land cover on ecosystem services. Nevertheless, being able to model the uncertainties, complexities, interconnections, and different interactions between multiple drivers of change in future scenario analysis are still a challenge in ecosystem service research. Modelling ecosystem service trade-offs and synergies and evaluating their use in scenario analysis are important issues that require more understanding. Therefore, this study explores the link and relationships among scenarios, models, and ecosystem services that support the decision-making processes. Based on electronic database publications, a conceptual framework illustrating the key components of this approach is presented. Further implications in terms of innovative tools that aim to identify pathways towards sustainable and balanced land use are also presented. It was concluded that spatial modelling of ecosystem services relationships associated with scenario building allows decision makers to better understand the complex interactions that occur in social-ecological systems. This approach brings important elements to set decisions, strategies, regulations, and policies for holistic land-use planning and management at different scales, notably in Brazil, a large and environmentally diversified country.
How can socioducts contribute to urban social sustainability?

Since traditional methods of bridging physical barriers, such as roads and railways, have not fully solved the problem of creating safe and attractive environments, new solutions have emerged. Socioducts aims to bridge barriers, provide safe and inviting environments for pedestrians and cyclists, create social cohesion and connectivity. The construction is mentioned in several comprehensiveand detailed development plans around Sweden, but there are few completed examples that can be compared to its planned design. This study aims to investigate how socioducts can contribute to urban social sustainability, which is analyzed through qualitative interviews. The study seeks answers to which needs are guiding the planning of the construction and how users perceive socioducts. In order to understand how urban residents perceive socioducts, a case study has been conducted in Hammarby Sjöstad where two combined eco/socioducts have been built. Through qualitative interviews, users were asked questions related to the social aspects of the passages in order to understand what values of urban social sustainability the users perceives in socioducts. The findings show that the purpose of socioducts in creating social cohesion, providing an inviting environment and increasing accessibility, can be related to urban social sustainability in regard to several factors. From a planning perspective the need to create accessibility for vulnerable groups is highlighted where the socioduct may fulfill an important function. The studied cases can be attributed to aspects of urban social sustainability, as it appears that users of the passages in Hammarby Sjöstad perceive the constructions as safe, accessible and inviting, which in addition can be interpreted to have created cohesion and connectivity. It also becomes apparent that there is a need to separate the socioduct from its relation and banding to ecoducts in order to optimize its social effects.
Modeling of Shear Crack Propagation in Rock Masses Using Mesh-Free LRPIM

The modeling of shear cracks in materials is critical in various engineering applications, such as the safety analysis of concrete structures and stability analysis of rock slopes. Based on the idea of Goodman element, the elastic-plastic constitutive model of the shear cracks is derived, and the elastic-plastic analysis of shear crack propagation is realized in the local radial basis point interpolation method (LRPIM). This method avoids the loss of accuracy caused by the mesh in the analysis of fracture propagation, and the crack propagation of rock brittle material is simulated. The investigation indicates that ( 1 ) the LRPIM results are close to the FDM results, which demonstrates that it is feasible to analyze shear cracks in rock masses. (2) Compared with the results of the built-in oblique crack model, when the LRPIM is used to analyze crack propagation, the results are close to the experimental results, showing that the LRPIM can model shear crack propagation in a rock mass. (3) The propagation path using the LRPIM is not sufficiently smooth, which can be explained as the crack tip stress and strain not being sufficiently accurate and still requiring further improvement.
Determinants of Farmers’ Adoption of Drought Resistant Crops in Adapting to Drought Impacts on Crop Production in Same District, Kilimanjaro Region

Droughts’ intensity, duration, spatial coverage and frequency have been increasing with current climate change. Th is has been especially the case with arid and semi-arid tropical areas. Yet, farmers in such areas have persistently growing water intensive crops like maize, leading to poor production. Consequently, food insecurity and unsustainable livelihoods have been rampant. Th ere is a broad scientifi c and political consensus that growing drought resistant crops (DRCs) is a sustainable drought adaptation strategy. Th e crops have anatomical and physiological characteristics enabling drought toleration, hence, improve water productivity (increase crop output per water input). Besides, adoption of DRCs is simple, cheap, easy and feasible for farmers to adopt compared to other strategies. Studies have shown that adoption of DRCs, even in drought prone areas, is very low. However, causes of low adoption of these crops are not adequately understood. Th is study aimed at analyzing determinants of farmers’ adoption of DRCs as an adaptation strategy to drought impacts. Data were collected from 281 farmers from four villages in Same district (a semi-arid and drought prone district in Tanzania). Th e study deployed logistic regression analysis and descriptive statistics to analyze such determinants. It was observed that adoption of DRCs is determined by a number of factors, including, access to climate information, access to agricultural extension services, food preference, location, sex of head of the household and farm size. Besides, adoption of DRCs is hampered by various barriers, including, cultural constraints, lack of knowledge, unpredictable rainfall, lack of access to climate information and lack of market. Other barriers were lack of seeds, lack of capital, destructive birds and forceful motivation approaches.
Urban Spider Diversity in Los Angeles Assessed Using a Community Science Approach

Spiders are ubiquitous organisms in all but the most hostile terrestrial environments and are frequently encountered in urban areas. Assessing spider diversity in cities can require a significant investment of time and resources because the majority of the land is privately owned and fragmented into thousands of difficult-to-access properties. To overcome these challenges, the Natural History Museum of Los Angeles County began the Los Angeles Spider Survey in May 2002. This project, using a community (= citizen) science collecting approach, attempts to inventory the fauna and assess the prevalence of introduced spiders in the greater Los Angeles area. Through 2016, a total of 6,896 spiders have been collected, representing 241 species or morphospecies in 39 families. Nine species, including some of medical importance, represented new records for the area at the time of collection. A comparison to a survey performed in 1904 shows larger changes in the spider fauna.
Calibrating Trip Distribution Neural Network Models with Different Scenarios of Transfer Functions Used in Hidden and Output Layers

— The transfer function is used to process the summation outputs in the hidden and output nodes. It can generally be categorized as either a non-linear or linear function. Examples are Sigmoid and Purelin functions representing non-linear and linear transfer functions. It is often mentioned that there is no standard guideline in the transfer function selection, and the Sigmoid or Logsig is widely used. However, the transfer function and training algorithm have a procedural relationship in training Multilayer Feedforward Neural Network (MLFFNN), a famous Artificial Neural Network model structure. In the feedforward stage, this function transforms the linear summation output to either linear (Purelin) or non-linear form (Sigmoid). In the backpropagation stage, this function is used in calculating the magnitude of change in the connection weights involving its derivative. Nine scenarios of MLFFNN were developed based on different transfer functions used in both hidden and output layers. In order to make fair comparisons, each scenario has the same initial connection weight. The modelling is conducted at the calibration level only; however, it involves different levels of complexity. It was calibrated by using the Levenberg-Marquard training algorithm. The results suggest that some calibrations failed and negative estimations occurred once non-linear transfer functions were used in hidden and output layers. It was found that Purelin was superior to other transfer functions. However, it has a weakness which is its negative estimations.