Miracle-dz/UniFAR-FTUs huggingface dataset:
Living at the Water’s Edge: A World-Wide Econometric Panel Estimation of Arable Water Footprint Drivers

As part of the Sustainable Development Goal (SDG) for ensuring clean water and sanitation worldwide by 2030, SDG target 6.4 seeks to attain sustainable withdrawals of freshwater through efficiency gains with a view to relieving water stress in vulnerable populated areas. The water footprint (WF) is a key metric to measure this concept, although the dynamics of the drivers of the WF through space and time remain relatively under-researched, whilst in foresight studies, the WF is often subject to simplistic assumptions. Thus, constructing a panel dataset of 130 countries and 156 crops for the period 2002–2016, this paper empirically assesses the sign and magnitude of WF drivers of agricultural crop activities, employing a careful selection of demographic, economic and climatic drivers. The study uncovers evidence of significant deviations in WF drivers across regions segmented by relative wealth, relating specifically to the stage of economic development and the presence (absence) of economies of scale, whilst we confirm that geographical coordinates have a major bearing on the climatic WF driver. Moreover, examining the temporal dimension, there is compelling evidence supporting a structural break in the role that technical progress exerted on the WF prior to, and in the wake of, the 2008 financial crisis.
CONTRIBUTIONS TO THE SOLUTION OF PHYLOGENETIC PROBLEM IN FABALES

Fabales is a cosmopolitan angiosperm order which consists of four families, Leguminosae (Fabaceae), Polygalaceae, Surianaceae and Quillajaceae. The monophyly of the order is supported strongly by several studies, although interfamilial relationships are still poorly resolved and vary between studies ; a situation common in higher level phylogenetic studies of ancient, rapid radiations. In this study, we carried out simulation analyses with previously published mat K and rbc L regions. The results of our simulation analyses have shown that Fabales phylogeny can be solved and the 5,000 bp fast-evolving data type may be sufficient to resolve the Fabales phylogeny question. In our simulation analyses, while support increased as the sequence length did (up until a certain point), resolution showed mixed results. Interestingly, the accuracy of the phylogenetic trees did not improve with the increase in sequence length. Therefore, this study sounds a note of caution, with respect to interpreting the results of the “more data” approach, because the results have shown that large datasets can easily support an arbitrary root of Fabales.
Dynamic Scheduling Algorithm for Delay-Sensitive Vehicular Safety Applications in Cellular Network

The vehicular safety applications disseminate the burst messages during an emergency scenario, but effort to reduce delay of communication are hampered by wireless access technology. As conventional VANET (Vehicular ad-hoc network) connected intermittently, the LTE-based framework has been established for the vehicular communication environment. However, resource allocation which affected by many factors, such as power, PRB (physical resource block), channel quality, are challenging to guarantee the safety services QoS in LTE downlink for OFDM. In order to solve the problem of safety message dissemination in LTE vehicular network, we proposed a delay-aware control policy by leveraging cross-layer approach to maximize the system throughput. First, we model the resource allocation problem using the queuing theory based on the MISO. Second, the method casts the problem of throughput and latency for dynamic communication system into a stochastic network optimization problem, and then makes tradeoffs between them by Lyapunov optimization technique. Finally, we use the improved the branch and bound algorithm to search optimal solution in system capacity region for these decomposed subproblems. The simulation results show that our algorithm can guarantee the delay while maximum system throughput
Optimal Asset Planning for Prosumers Considering Energy Storage and Photovoltaic (PV) Units: A Stochastic Approach

In the distribution system, customers have increasingly use renewable energy sources and battery energy storage systems (BESS), transforming traditional loads into active prosumers. Therefore, methodologies are needed to provide prosumers with tools to optimize their investments and increase business opportunities. In this paper, a stochastic mixed integer linear programming (MILP) formulation is proposed to solve for optimal sizes of prosumer assets, considering the use of a BESS and photovoltaic (PV) units. The objective is to minimize the total cost of the system, which is defined as the combination of a solar PV system investment, BESS investment, maintenance costs of assets, and the cost of electricity supplied by the grid. The developed method defines the optimal size of PV units, the power/energy capacities of the BESS, and the optimal value for initial energy stored in the BESS. Both deterministic and stochastic approaches were explored. For each approach, the proposed model was tested for three cases, providing a varying combination of the use of grid power, PV units, and BESS. The optimal values from each case were compared, showing that there is potential to achieve more economic plans for prosumers when PV and BESS technologies are taken into account.
Learning A Common Granger Causality Network Using A Non-Convex Regularization

This paper proposes an estimation for learning a common Granger network of panel data. When vector time series are collected from several subjects belonging to a homogeneous group, a relationship structure of variables can be assumed to share the same topology, while the model parameters of individual subjects could be varied. The formulation is a regularized least-squares estimation of multiple vector autoregressive (VAR) models with a non-convex ℓ2,1/2-norm penalty. The common sparsity pattern of VAR coefficients from all models can reveal a consistent network in a group level. Simulation results show that the proposed formulation achieves better accuracy of learning Granger networks than the convex group lasso formulation. Preliminary results of discovering brain connectivities from ABIDE fMRI data sets showed that sparse common networks among subjects from normal and autism groups are distinctive in some brain regions.
Management of analytics-as-a-service - results from an action design research project

ABSTRACT The ability to generate business-relevant information from data and to exploit it to improve business processes, decision-making, products, and services (business analytics) is a key success factor for businesses today. Answering the call for further research on success-relevant practices and instruments for managing business analytics, we report on the results of a three-year action design research (ADR) project at a global car manufacturer. Drawing on the socio-technical systems theory, we identify seven meta-requirements and specify four principles for the design of an instrument to manage Analytics-as-a-Service (AaaS) portfolios. Our results reinforce the importance of coordinating different socio-technical components in business analytics initiatives and demonstrate how concrete management instruments, such as a proposed portfolio management tool, contribute to socio-technical alignment. For practitioners, the documented design components provide guidance on how to design and implement similar instruments that support the systematic management of AaaS portfolios.
Parameters Uncertainty Immunization of Global Synchronous Pulsewidth Modulated VSIs With Round P&O Algorithm

This letter proposes a closed-loop scheme for global synchronous pulsewidth modulated voltage source inverters with round perturbation and observation (R-P&O) algorithm to immunize the parameters uncertainty, e.g., line impedance and filter impedance variation. In specific, the rms value of measured total current harmonics at point of common coupling will be periodically sent to each inverter as feedback signal using the low-cost narrowband communication system. Then, the inverters will assume the R-P&O scheme to correct the phase shift angles among pulsewidth modulation carriers for intentionally minimizing the total current harmonics. Doing so, the closed-loop scheme can achieve the expected performance even under the severe parameters’ variation. The experimental results verified the performance of the proposed method.
How People React to Pension Risk

We show that people exposed to greater pension risk are less likely to invest in risky assets. We exploit a reform that links people’s future pension benefits to their pension funds’ funding ratio—a measure of the fund’s financial health—making funding ratios a fund-specific measure of pension risk. The effect of pension risk is stronger for people who are better informed about their pensions, for retirees and pension-age non-retirees, and for wealthier people. The funding ratio does not affect investments in a pre-reform period, nor does it affect bequest intentions, (expected) retirement, or the motivations for saving.
Contributions of physical exercise to motor symptoms and the balance of people with Parkinson's Disease: a systematic review

Objective: The aim of the study was to conduct a systematic review evaluating the effects of physical exercise on balance and motor symptoms of Parkinson’s Disease. Method: The search was carried out on the PubMed, Scielo and PEDro databases. The articles were evaluated by two independent researchers, following the criteria: presenting a control group, having more than eight weeks of intervention, assessing motor symptoms (Unified Parkinson’s Disease Rating Scale and Parkinson’s Disease Questionnaire -39) and balance (Timed Up and Go and Berg Scale). Results: The search resulted in 1377 articles, of which 11 were included in the study. The total number of participants was 925, in which 545 were allocated in the intervention groups and 380 in the control groups. The strength training and balance, performed alone or in combination, are highlighted in improvements related to balance. Exercise, regardless of type, improves motor symptoms. Conclusions: Thus, physical exercise has a positive effect on both balance and motor symptoms of Parkinson’s Disease, strength and balance modalities stand out.
Motion Dynamics Improve Speaker-Independent Lipreading

We present a novel lipreading system that improves on the task of speaker-independent word recognition by decoupling motion and content dynamics. We achieve this by implementing a deep learning architecture that uses two distinct pipelines to process motion and content and subsequently merges them, implementing an end-to-end trainable system that performs fusion of independently learned representations. We obtain a average relative word accuracy improvement of ≈6.8% on unseen speakers and of ≈3.3% on known speakers, with respect to a baseline which uses a standard architecture.
Event-Driven Signal Processing with Neuromorphic Computing Systems

Neuromorphic hardware has long promised to provide power advantages by leveraging the kind of event-driven, temporally sparse computation observed in biological neural systems. Only recently, however, has this hardware been developed to a point that allows for general purpose AI programming. In this paper, we provide an overview of tools and methods for building applications that run on neuromorphic computing devices. We then discuss reasons for observed efficiency gains in neuromorphic systems, and provide a concrete illustration of these gains by comparing conventional and neuromorphic implementations of a keyword spotting system trained on the widely used Speech Commands dataset. We show that replacing floating point operations in a conventional neural network with synaptic operations in a spiking neural network results in a roughly 4x energy reduction, with minimal performance loss.
Data Reuse for Accelerated Approximate Warps

Many data-driven applications, including computer vision, machine learning, speech recognition, and medical diagnostics show tolerance to computation error. These applications are often accelerated on GPUs, but the performance improvements require high energy usage. In this article, we present DRAAW, an approximate computing technique capable of accelerating GPGPU applications at a warp level. In GPUs, warps are groups of threads which issued together across multiple cores. The slowest thread dictates the pace of the warp, so DRAAW identifies these bottlenecks and avoids them during approximation. We alleviate computation costs by using an approximate lookup table which tracks recent operations and reuses them to exploit temporal locality within applications. To improve neural network performance, we propose neuron aware approximation, a technique which profiles operations within network layers and automatically configures DRAAW to ensure computations with more impact on the output accuracy are subject to less approximation. We evaluate our design by placing DRAAW within each core of an Nvidia Kepler Architecture Titan. DRAAW improves throughput by up to <inline-formula> <tex-math notation="LaTeX">$2.8\times $ </tex-math></inline-formula> and improves energy-delay product (EDP) by <inline-formula> <tex-math notation="LaTeX">$5.6\times $ </tex-math></inline-formula> for six GPGPU applications while maintaining less than 5% output error. We show neuron aware approximation accelerates the inference of six neutral networks by <inline-formula> <tex-math notation="LaTeX">$2.9\times $ </tex-math></inline-formula> and improves EDP by <inline-formula> <tex-math notation="LaTeX">$6.2\times $ </tex-math></inline-formula> with less than 1% impact on prediction accuracy.
A dual supramolecular crosslinked polyurethane with superior mechanical properties and autonomous self-healing ability

To prepare a robust material with high self-healing capability under ambient conditions is still a great challenge. Herein, a novel self-healing polyurethane is developed via a dual-dynamic network design, in which multiple H-bonding and metal-coordinated bonds (zinc–imidazole interaction) were incorporated into a polyurethane matrix. Benefiting from the dual-dynamic network, the synthesized metallopolymers exhibit excellent mechanical properties, such as high tensile strength (from 6.68 MPa to 14.38 MPa), excellent stretchability (about 2000%) and superior toughness (from 55.61 MJ m−3 to 96.21 MJ m−3). Moreover, these metallopolymers show good self-healing capability. After healing under ambient conditions for 48 hours, the tensile strength and strain at break of PU-IZ/Zn-0.50 can reach up to 7.13 MPa and 1800%, respectively. The dual-dynamic crosslinked metallopolymers might provide a novel approach to design tough and self-healable materials for various industrial applications.
Carbohydrate and metabonomic studies by NMR spectroscopy

NMR spectroscopy can be used to investigate organic and biomolecular structures, both as pure compounds and in complex mixtures. In this thesis NMR spectroscopy was used for carbohydrate and metabonomic studies. Lipopolysaccharides are major components of the outer cell membrane of Gram-negative bacteria and are involved in pathogenic interactions. The structure of a lipopolysaccharide from the human pathogen Plesiomonas shigelloides was analyzed by NMR spectroscopy and mass spectrometry. The structure contained an O-specific polysaccharide with a tetrasaccharide repeating unit. The core oligosaccharide was an undecasaccharide with previously not reported heterogeneity within the carbohydrate backbone. Hyaluronan is a glycosaminoglycan with high viscosity and water-retaining ability. Hydroxy protons of hyaluronan oligosaccharides were studied by NMR spectroscopy in aqueous solution to investigate hydrogen bonding interactions and hydration. Weak hydrogen bonding was observed between hydroxy protons and the ring oxygens over the β(1→3) and β(1→4) glycosidic linkages. A chemical exchange interaction was also identified between O(4)H of N-acetylglucosamine and O(3)H of glucuronic acid across the β(1→3) linkage. The interaction could be mediated through water bridges and thus contribute to the water-retaining ability of hyaluronan. Polyunsaturated fatty acids are essential nutritional components of marine lipid sources. A ¹H HR-MAS NMR method was developed to determine the amount of essential polyunsaturated fatty acids in fish muscle without any pretreatment. Additionally, the small metabolite profile of the fish muscle was obtained, which can be used to determine fish quality. Caloric restriction is known to increase the lifespan of rats. In order to evaluate the metabolic responses to graded caloric restriction in rats, blood serum was analyzed by NMR spectroscopy. Multivariate analysis showed a decrease in blood lipids and alanine, and an increase in creatine and 3-hydroxybutyrate as a response to caloric restriction.
Resistance of apple cultivars to two-spotted spider mite, Tetranychus urticae Koch [Acarina, Tetranychidae] Part II. Influence of leaf pubescence of selected apple cultivars on fecundity of two-spotted spider mite

Number of hairs on abaxial leaf surface of 13 apple cultivars differed significantly. The most hairs per 1 cm on abaxial surface were on cvs. Lodel (2,578.3) and Jonafree (2,462.2). Leaves of cultivars Antonowka and Novamac had the least number of hairs (1,054.4; 1,285.7; respectively). Correlation between number of hairs on abaxial leaf surface of investigated apple cultivars and Tetranychus urticae (Koch) female fecundity decreased during 10 first days of their lives. Along with increased number of hairs, the fecundity of this species declined.
Optimal Mode Selection in D2D Communication With Deadline Constraint: A CMDP Perspective

Device to device (D2D) communication is a potential technology in the next generation communication system. We study the mode selection problem in a scenario where the D2D communication has a deadline constraint while the cellular user requires a minimum rate. We formulate the mode selection problem as a Constrained Markov Decision Process (CMDP). By adopting the Lagrangian multiplier technique, we first transform the CMDP into a non-constrained MDP problem. Then, we propose an optimal stationary mode selection algorithm combining the value iteration algorithm and the sub-gradient updating of the Lagrangian multiplier. Further, we propose an optimal randomized mode selection algorithm. Moreover, we contribute on deriving the closed-form policy selection probability. Simulation results have verified the optimality of the proposed algorithm.
E2E-SINCNET: Toward Fully End-To-End Speech Recognition

Modern end-to-end (E2E) Automatic Speech Recognition (ASR) systems rely on Deep Neural Networks (DNN) that are mostly trained on handcrafted and pre-computed acoustic features such as Mel-filter-banks or Mel-frequency cepstral coefficients. Nonetheless, and despite worse performances, E2E ASR models processing raw waveforms are an active research field due to the lossless nature of the input signal. In this paper, we propose the E2E-SincNet, a novel fully E2E ASR model that goes from the raw waveform to the text transcripts by merging two recent and powerful paradigms: SincNet and the joint CTC-attention training scheme. The conducted experiments on two different speech recognition tasks show that our approach outperforms previously investigated E2E systems relying either on the raw waveform or pre-computed acoustic features, with a reported top-of-the-line Word Error Rate (WER) of 4.7% on the Wall Street Journal (WSJ) dataset.
Prediction oof Vessel Trajectories From AIS Data Via Sequence-To-Sequence Recurrent Neural Networks

In this paper, we address the problem of predicting vessel trajectories based on Automatic Identification System (AIS) data. The goal is to learn the predictive distribution of maritime traffic patterns using historical data during the training phase, in order to be able to forecast future target trajectory samples online on the basis of both the extracted knowledge and the available observation sequence. We explore neural sequence-to-sequence models based on the Long Short-Term Memory (LSTM) encoder-decoder architecture to effectively capture long-term temporal dependencies of sequential AIS data and increase the overall predictive power. The experimental evaluation on a real-world AIS dataset demonstrates the effectiveness of sequence-to-sequence recurrent neural networks (RNNs) for vessel trajectory prediction and shows their potential benefits compared to model-based methods.
THE CAUSES AND PREVENTION OF INJURIES IN HANDBALL

The aim of this work is to present and analyse the handball literature on the causes of injuries, types of injuries and their anatomic locations, as well as the prevention measurements. Based on the results of the analysed studies, it can be concluded that there is a significant number of injuries, of different body parts and of different severity in top-level handball players. Overuse injuries (to certain body parts, such as the more dominant shoulder, the ankle and the knee) are considered to be most prominent. In order to prevent the injuries to the mouth and teeth, the use of mouthguard is recommended. When it comes to the prevention and rehabilitation of shoulder injuries, the research results have shown positive effects that the training aiming at the increase of the inner rotation of the glenohumeral strength, the outer rotation strength and the scapular muscles strength has along with the exercises for improving the kinetic chain and the chest flexibility. The use of elastic bands and the medicine ball has proven efficient in increasing the shoulder area strength, and the whole swing and throw movement. The ankle and the knee are the most frequently injured joints among female handball players. The results of the application of the neuromuscular training, the proprioception and the balance exercises, combined with some specific players’ activities (running, jumping and planting) show that such training process is efficient in the prevention of injuries to ankle and knee joints.
Exploiting Vocal Tract Coordination Using Dilated CNNS For Depression Detection In Naturalistic Environments

Depression detection from speech continues to attract significant research attention but remains a major challenge, particularly when the speech is acquired from diverse smartphones in natural environments. Analysis methods based on vocal tract coordination have shown great promise in depression and cognitive impairment detection for quantifying relationships between features over time through eigenvalues of multi-scale cross-correlations. Motivated by the success of these methods, this paper proposes a novel way to extract full vocal tract coordination (FVTC) features by use of convolutional neural networks (CNNs), overcoming earlier shortcomings. Evaluations of the proposed FVTC-CNN structure on depressed speech data show improvements in mean F1 scores of at least 16.4% under clean conditions and comparable results under noisy conditions relative to existing VTC baseline systems.