Miracle-dz/UniFAR-FTUs huggingface dataset:
Storage Optimal Control under Net Metering Policies

Electricity prices and the end user net load vary with time. Electricity consumers equipped with energy storage devices can perform energy arbitrage, i.e., buy when energy is cheap or when there is a deficit of energy, and sell it when it is expensive or in excess, taking into account future variations in price and net load. Net metering policies indicate that many of the utilities apply a {customer selling} rate lower than or equal to the retail {customer buying rate} in order to compensate excess energy generated by end users. In this paper, we formulate the optimal control problem for an end user energy storage device in presence of net metering. We propose a computationally efficient algorithm, with worst case run time complexity of quadratic in terms of number of samples in lookahead horizon, that computes the optimal energy ramping rates in a time horizon. The proposed algorithm exploits the problem's piecewise linear structure and convexity properties for the \textit{discretization} of optimal Lagrange multipliers. The solution has a \textit{threshold-based structure} in which optimal control decisions are independent of past or future price as well as of net load values beyond a certain time horizon, defined as a \textit{sub-horizon}. Numerical results show the effectiveness of the proposed model and algorithm. Furthermore, we investigate the impact of forecasting errors on the proposed technique. We consider an Auto-Regressive Moving Average (ARMA) based forecasting of net load together with the Model Predictive Control (MPC). We numerically show that adaptive forecasting and MPC significantly mitigate the effects of forecast error on energy arbitrage gains.
Thermodynamics of Gauss–Bonnet–de Sitter black holes

We investigate the thermodynamics of Gauss-Bonnet black holes in asymptotically de Sitter spacetimes embedded in an isothermal cavity, via a Euclidean action approach. We consider both charged and uncharged black holes, working in the extended phase space where the cosmological constant is treated as a thermodynamic pressure. We examine the phase structure of these black holes through their free energy. In the uncharged case, we find both Hawking-Page and small-to-large black hole phase transitions, whose character depends on the sign of the Gauss-Bonnet coupling. In the charged case, we demonstrate the presence of a swallowtube, signaling a compact region in phase space where a small-to-large black hole transition occurs.
Analysis of D2D Communication with RF Energy Harvesting and Interference Management

Device-to-device (D2D) underlaid cellular network, enabled with radio frequency energy harvesting (RFEH), and enhanced interference management schemes is a promising candidate to improve spectral and energy efficiency of next generation wireless networks. In this paper, we propose a time division duplexing (TDD)-based protocol, in which allows the devices to harvest energy from the downlink transmissions of the base station, while controlling the interference among D2D and cellular communication in the uplink. We propose two schemes for transmission coordination, based on fixed transmission probability (FTP) and adaptive transmission probability (ATP), respectively. In FTP, the D2D transmitters that have harvested enough energy can initiate data transmission with a fixed probability. Differently from this, in ATP a device utilizes its sensing capability to get improved coordination and interference control among the transmitting devices. We evaluate the network performance by presenting an accurate energy model and leveraging tools from stochastic geometry. The results on outage probability and D2D sum-rate reveal the importance of transmission coordination on network performance. These observations led to a solution for choosing the parameters of the ATP scheme that achieves an optimal tradeoff between the D2D outage probability and number of transmitting users.
Prediction of head motion from speech waveforms with a canonical-correlation-constrained autoencoder

This study investigates the direct use of speech waveforms to predict head motion for speech-driven head-motion synthesis, whereas the use of spectral features such as MFCC as basic input features together with additional features such as energy and F0 is common in the literature. We claim that, rather than combining different features that originate from waveforms, it is more effective to use waveforms directly predicting corresponding head motion. The challenge with the waveform-based approach is that waveforms contain a large amount of information irrelevant to predict head motion, which hinders the training of neural networks. To overcome the problem, we propose a canonical-correlation-constrained autoencoder (CCCAE), where hidden layers are trained to not only minimise the error but also maximise the canonical correlation with head motion. Compared with an MFCC-based system, the proposed system shows comparable performance in objective evaluation, and better performance in subject evaluation.
Using Explainable Artificial Intelligence to Increase Trust in Computer Vision

Computer Vision, and hence Artificial Intelligence-based extraction of information from images, has increasingly received attention over the last years, for instance in medical diagnostics. While the algorithms' complexity is a reason for their increased performance, it also leads to the "black box" problem, consequently decreasing trust towards AI. In this regard, "Explainable Artificial Intelligence" (XAI) allows to open that black box and to improve the degree of AI transparency. In this paper, we first discuss the theoretical impact of explainability on trust towards AI, followed by showcasing how the usage of XAI in a health-related setting can look like. More specifically, we show how XAI can be applied to understand why Computer Vision, based on deep learning, did or did not detect a disease (malaria) on image data (thin blood smear slide images). Furthermore, we investigate, how XAI can be used to compare the detection strategy of two different deep learning models often used for Computer Vision: Convolutional Neural Network and Multi-Layer Perceptron. Our empirical results show that i) the AI sometimes used questionable or irrelevant data features of an image to detect malaria (even if correctly predicted), and ii) that there may be significant discrepancies in how different deep learning models explain the same prediction. Our theoretical discussion highlights that XAI can support trust in Computer Vision systems, and AI systems in general, especially through an increased understandability and predictability.
Time–energy uncertainty principle for irreversible heat engines

Even though irreversibility is one of the major hallmarks of any real-life process, an actual understanding of irreversible processes remains still mostly semi-empirical. In this paper, we formulate a thermodynamic uncertainty principle for irreversible heat engines operating with an ideal gas as a working medium. In particular, we show that the time needed to run through such an irreversible cycle multiplied by the irreversible work lost in the cycle is bounded from below by an irreducible and process-dependent constant that has the dimension of an action. The constant in question depends on a typical scale of the process and becomes comparable to Planck’s constant at the length scale of the order Bohr radius, i.e. the scale that corresponds to the smallest distance on which the ideal gas paradigm realistically applies. This article is part of the theme issue ‘Fundamental aspects of nonequilibrium thermodynamics’.
Toward a Curriculum Proposal for CSCW Education and Training in Latin America

Computer-Supported Cooperative Work, or simply CSCW, is the research area that studies the design and use of socio-technical technology for supporting group work. CSCW has a long tradition in interdisciplinary work exploring technical, social, and theoretical challenges for the design of technologies to support cooperative and collaborative work and life activities. However, most of the research tradition, methods, and theories in the field follow a strong trend grounded in social and cultural aspects from North America and Western Europe. Therefore, it is inevitable that some of the underlying—and established—knowledge in the field will not be directly transferrable or applicable to other populations. This paper presents the results of an interview study conducted with Latin American faculty on the feasability, viability, and prospect of a curriculum proposal for CSCW Education in Latin America. To this end, we conducted nine interviews with faculty currently based in six countries of the region, aiming to understand how a CSCW course targeted to undergraduate and/or graduate students in Latin America might be deployed. Our findings suggest that there are specific traits that need to be addressed in such a course, such as: tailoring foundational CSCW concepts to the diversity of local cultures, motivating the involvement of students by tackling relevant problems to their local communities, and revitalizing CSCW research and practice in the continent.
Multi-objective path optimization for arc welding robot based on discrete DN multi-objective particle swarm optimization

Rational weld seams sequence is important for improving welding productivity and quality. Hence, intelligent path optimization strategy is introduced to obtain optimized weld seams sequence in this article. The path length and total welding deformation are considered for multi-objective path planning. First, the optimization problem description is presented. At the same time, the path length and the total welding deformation of some sequences are calculated. Then, improved multi-objective particle swarm optimization algorithm is studied. In addition, the agent model is obtained based on the sample data and experiment design. At last, the proposed algorithm is applied to optimize the welding path length and total welding deformation. The simulation results show the effectiveness of the optimization strategy.
Structural damage diagnosis under varying environmental conditions with very limited measurements

In this article, we present a damage diagnostic scheme with very limited measurements. The influence of environmental and operational variabilities has been considered in the present investigations apart from measurement noise. We employ null subspace analysis in the first stage to confirm the presence of damage using ambient vibration data through online monitoring. Once the presence of damage is established, in the second stage, through offline monitoring, we use the zero strain energy node concept to localize damage through inference from the set of excitation frequencies at which the structure with localized damage behaves like a healthy structure. The proposed technique requires the numerical model of the healthy structure to establish the relationship between zero strain energy nodes and excitation frequencies. In order to evaluate the proposed technique, numerical simulation studies are carried out initially on a simple beam-like structure and later considering a more practical example of the bridge existing across the Amaravathi River in Tamil Nadu, India. These investigations are subsequently complemented with an experimental study on steel I beam girder. It can be concluded from the investigations that the proposed approach is capable of detecting and localizing multiple and also subtle damages under varying environmental conditions with very limited noise-contaminated measurements.
Modeling and verification of contingency resolution strategies for multi-robot missions using temporal logic

This article presents an approach for assessing contingency resolution strategies using temporal logic. We present a framework for nominal mission modeling, then specifying contingency resolution strategies and evaluating their effectiveness for the mission. Our approach focuses on leveraging the use of model checkers to the domain of multi-robot missions to assess the adequacy of contingency resolution strategies that minimize the adverse effects of contingencies on the mission execution. We consider missions with deterministic as well as probabilistic transitions. We demonstrate our approach using two case studies. We consider the escorting of a ship in a port where multiple contingencies may occur concurrently and assess the adequacy of the proposed contingency resolution strategies. We also consider a manufacturing scenario where multiple assembly stations collaborate to create a product. In this case, assembly operations may fail, and human intervention is needed to complete the assembly process. We investigate several different strategies and assess their effectiveness based on mission characteristics.
Exploiting null space potentials to control arm robots compliantly performing nonlinear tactile tasks

In this article, two new compliant control architectures are introduced that utilize null space solutions to decouple force and position control. They are capable to interact with uncertain surfaces and environments with varying materials and require fewer parameters to be tuned than the common architectures – hybrid or impedance control. The general concept behind these approaches allows to consider manipulators with six degrees of freedom as redundant by creating a virtual redundancy with a reduced work space. It will be demonstrated that the introduced approaches are superior regarding orthogonal separation of the Cartesian degrees of freedom and avoid inner singularities. To demonstrate their performance, the controllers are tested on a standard industrial robot (Stäubli, RX90B, six degrees of freedom) that actuates two different biomechanically inspired models of the human knee joint.
Unmanned aerial vehicle-aided stereo camera calibration for outdoor applications

Abstract. Monitoring the static and dynamic displacements of large engineering structures, such as buildings and bridges, can provide quantitative information for evaluating structural safety and maintenance purposes. Camera calibration is a key process in vision-based sensor systems for remote displacement measurement. Due to the large field of view of engineering structures, conventional camera calibration methods using precise calibration boards are difficult to apply. A modified calibration method for a binocular stereo vision system based on the epipolar constraint relationship is proposed to simplify the calibration process. Due to the absence of reference points in outdoor applications, an unmanned aerial vehicle that carries a reference marker is adopted. During its flight in the field, sequential images are captured simultaneously with the left and right imaging stations. An alternative to determine the scale factor is also proposed, which provides adequate precision for camera calibration. Two important issues are discussed, including the number of reference points and their selection with regards to the depth of view. The experimental results show that the proposed method is convenient to apply in outdoor situations and can achieve high accuracy in displacement measurement.
Thermal Intravesical Chemotherapy Reduce Recurrence Rate for Non-muscle Invasive Bladder Cancer Patients: A Meta-Analysis

Background: Non-muscle invasive bladder cancer accounts for nearly 80% of newly diagnosed bladder cancer cases, which often recur and progress. This meta-analysis was evaluated by the adverse events and recurrence rate of thermal intravesical chemotherapy vs. normal temperature intravesical chemotherapy in the treatment of non-muscle invasive bladder cancer. Methods: A systematic review and cumulative analysis of studies reporting adverse events and recurrence rate of thermal intravesical chemotherapy vs. normal temperature intravesical chemotherapy was performed through a comprehensive search of Pubmed, Embase, Cochranelibrary.com, CNKI, Wanfang Med Online database and VIP database. All analyses were performed using the Revman manager 5. Result: Twelve studies (11 randomized controlled trials and 1 retrospective study) including 888 patients, 445 in the thermal intravesical chemotherapy group, and 443 in the normal temperature intravesical chemotherapy group, met the eligibility criteria. Patients in the thermal intravesical chemotherapy group had a lower risk of disease recurrence than those who had normal temperature intravesical chemotherapy (24 months follow-up group: RR = 0.30, 95% CI: 0.21–0.43, P < 0.00001, I2 = 0%; 36 months follow-up group: RR = 0.27, 95% CI: 0.14–0.54, P = 0.0002, I2 = 0%) while no significant difference in adverse events rate (RR = 0.89, 95% CI = 0.53–1.52; P = 0.67, I2 = 78%). Conclusions: When compared with normal temperature intravesical chemotherapy, thermal intravesical chemotherapy can reduce the recurrence rate without increasing incidence of adverse events in patients with non-muscle invasive bladder cancer.
The Evolutionarily Conserved Serine Residues in BRI1 LRR Motifs Are Critical for Protein Secretion

As a well-studied leucine-rich-repeat receptor-like kinases (LRR-RLKs) in Arabidopsis (Arabidopsis thaliana), BRI1 functions as a cell surface receptor for sensing the smallest ligand molecule identified thus far. The weak allele bri1-9 (S662F) harbors a mutation at the conserved serine (Ser*) residue among 25 LRRs, which leads to the protein retention in the ER. However, very little is known about the importance of these residues. Through site-directed mutagenesis and a phenotypic complementation test, we examined the effects of these conserved serine residues (S*-chain) on protein secretion and functions. The results showed that the replacements of these serine residues significantly changed the sub-localization of BRI1-GFPs to the ER and that rigid space constraints, as well as the requirement of successive inner polar contacts, affect these sites. In addition, the continuous presence of Ser* is mainly disrupted at the LRR-island domain interface, and the changes of these four nonserine residues to serine greatly decreased the protein ability to complement bri1-301 compact phenotype and the BR signaling activation. The sequence alignment revealed that other known LRR-RLK also harbors the S*-chain and the non-Ser* residues at the ligand-binding region along the S*-chain, which confirms the evolutionary significance of residues at these sites in plant LRR-RLKs.
Formation of a Mixed-Species Biofilm Is a Survival Strategy for Unculturable Lactic Acid Bacteria and Saccharomyces cerevisiae in Daqu, a Chinese Traditional Fermentation Starter

The existence and function of unculturable microorganisms are necessary to explain patterns of microbial diversity and investigate the assembly and succession of the complex microbial community. Chinese traditional alcoholic fermentation starter contains a complex microbial community harboring unculturable species that control the microbial diversity and have distinct functions. In this study, we revealed the presence, functions, and interactions of these unculturable species. Results of microbial diversity revealed by culture-dependent and metagenomic sequencing methods identified unculturable species and the potential functional species. Unculturable Saccharomyces cerevisiae and Lactobacillus sp. had a strong ability to form biofilms and co-existed as a mixed-species biofilm in the starter community. Using a hydrolase activity assay and fortified fermentation, we determined that the function of S. cerevisiae and Lactobacillus sp. to produce ethanol and flavor compounds. Widespread microbial interactions were identified among the biofilm isolates. S. cerevisiae was the main component of the biofilm and dominated the metabolic activities in the mixed-species biofilm. The environmental adaptability and biomass of Lactobacillus sp. were increased through its interaction with S. cerevisiae. The mixed biofilm of S. cerevisiae and Lactobacillus sp. also provides a tool for correlating microbial diversity patterns with their function in the alcoholic fermentation starter, and may provide a new understanding of fermentation mechanisms. Formation of a mixed-species biofilm represents a strategy for unculturable species to survive in competition with other microbes in a complex community.
In-vivo Activity of IFN-λ and IFN-α Against Bovine-Viral-Diarrhea Virus in a Mouse Model

Bovine-viral-diarrhea virus (BVDV) can cause significant economic losses in livestock. The disease is controlled with vaccination and bovines are susceptible until vaccine immunity develops and may remain vulnerable if a persistently infected animal is left on the farm; therefore, an antiviral agent that reduces virus infectivity can be a useful tool in control programs. Although many compounds with promising in-vitro efficacy have been identified, the lack of laboratory-animal models limited their potential for further clinical development. Recently, we described the activity of type I and III interferons, IFN-α and IFN-λ respectively, against several BVDV strains in-vitro. In this study, we analyzed the in-vivo efficacy of both IFNs using a BALB/c-mouse model. Mice infected with two type-2 BVDV field strains developed a viremia with different kinetics, depending on the infecting strain's virulence, that persisted for 56 days post-infection (dpi). Mice infected with the low-virulence strain elicited high systemic TNF-α levels at 2 dpi. IFNs were first applied subcutaneously 1 day before or after infection. The two IFNs reduced viremia with different kinetics, depending on whether either one was applied before or after infection. In a second experiment, we increased the number of applications of both IFNs. All the treatments reduced viremia compared to untreated mice. The application of IFN-λ pre- and post-infection reduced viremia over time. This study is the first proof of the concept of the antiviral potency of IFN-λ against BVDV in-vivo, thus encouraging further trails for a potential use of this cytokine in cattle.
Learning to Predict Perceptual Distributions of Haptic Adjectives

When humans touch an object with their fingertips, they can immediately describe its tactile properties using haptic adjectives, such as hardness and roughness; however, human perception is subjective and noisy, with significant variation across individuals and interactions. Recent research has worked to provide robots with similar haptic intelligence but was focused on identifying binary haptic adjectives, ignoring both attribute intensity and perceptual variability. Combining ordinal haptic adjective labels gathered from human subjects for a set of 60 objects with features automatically extracted from raw multi-modal tactile data collected by a robot repeatedly touching the same objects, we designed a machine-learning method that incorporates partial knowledge of the distribution of object labels into training; then, from a single interaction, it predicts a probability distribution over the set of ordinal labels. In addition to analyzing the collected labels (10 basic haptic adjectives) and demonstrating the quality of our method's predictions, we hold out specific features to determine the influence of individual sensor modalities on the predictive performance for each adjective. Our results demonstrate the feasibility of modeling both the intensity and the variation of haptic perception, two crucial yet previously neglected components of human haptic perception.