Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrocoagulation for nutrients removal in the slaughterhouse wastewater: comparison between iron and aluminum electrodes treatment

ABSTRACT The poultry slaughterhouse wastewater has a high pollutant load, mainly organic matter, and nutrient content. The nitrogen and phosphorus discharge can cause eutrophication of the receiving water bodies. Electrocoagulation has been studied for several pollutants removal from different sources. The objective of this work was to evaluate the electrocoagulation process in the poultry slaughterhouse wastewater treatment using both iron and electrodes to remove total nitrogen and phosphorus. After the raw and polished wastewater characterisation, a 2³ Central Composite Rotatable Design was applied to evaluate the current density, initial pH, and electrocoagulation time influence on the nutrients removal and to find the optimum condition of nutrients removal. Once the optimum condition for nutrient removal was stablished, other physicochemical, microbiological, and ecotoxicological parameters, as well as the treatment cost, were investigated to determine which electrode material was the most efficient. For raw wastewater, applying the optimum treatment condition of 20 mA cm−2 current density, initial pH 6.2, and time of 20 min, the nitrogen and phosphorus removal presented similar for both electrode materials. Besides being cheaper ($ 4.13 m−3), iron electrode treatment presented better Chemical Oxygen Demand, oils and greases, solids, and ecotoxicity removal. For polished wastewater, the treatment with aluminum electrode was more efficient under the applied current density of 30 mA cm−2, initial pH 8 and time of 10 min, obtaining the lowest cost $ 3.89 m−3. In the iron electrode case, the final pH exceeds the limits established by local legislation requiring correction for release into water bodies. GRAPHICAL ABSTRACT
Spatial and temporal analysis of vegetation mosaics for conservation: poor fen communities in a Cornish valley mire

Aim Biogeographers increasingly realize the importance of seeing plant communities as spatial mosaics and understanding the spatial and temporal heterogeneity of a site is often a key to successful conservation. The aim of this paper is to examine the approaches to the description and analysis of spatial and temporal variation in sub‐communities within patch mosaics of vegetation in order to inform conservation management. The activities of the tin streaming industry in Cornwall over the last century have created a highly varied mosaic of poor fen vegetation on Goss Moor National Nature Reserve (NNR). The wetland mosaics comprise dry hummocks and different sized wet pools. The size and depth of the pools determines the rate and type of vegetation that develops, as does the nature of boundary or edge. The ergodic hypothesis is used to describe the various plant sub‐communities and their boundaries to identify pathways of hydroseral succession. A further aim was to test the use of Ellenberg Indicator (EI) values as a tool for the rapid description of spatial and temporal environmental change on wetland sites with a view to their management.
Deep Reinforcement Learning for Discrete and Continuous Massive Access Control optimization

Cellular-based networks are expected to offer connectivity for massive Internet of Things (mIoT) systems, however, their Random Access CHannel (RACH) procedure suffers from unreliability, due to the collision during the simultaneous massive. Despite that this collision problem has been treated in existing RACH schemes by organizing IoT devices’ transmission and retransmission via the central control at the Base Station (BS), these existing RACH schemes are usually fixed over time, thus can hardly adapt to time-varying traffic patterns. In order to optimize the long-term objective in the number of success devices, this paper aims to design Deep Reinforcement Learning (DRL)-based optimizers with Deep Q-Network (DQN) and Deep Deterministic Policy Gradients (DDPG) for optimizing RACH schemes, including Access Class Barring (ACB), Back-Off (BO), and Distributed Queuing (DQ). Specifically, we apply DQN to handle discrete action selection for the BO as well as the DQ schemes, and DDPG to handle continuous action selection for the ACB scheme. Both agents are integrated with Gated recurrent unit Gated Recurrent Unit (GRU) network to approximate their value function/policy, which can improve the optimization performance by capturing temporal traffic correlations. Numerical results showcase that our proposed DRL-based optimizers considerably outperform conventional heuristic solutions in terms of the number of success access devices.
Direction Shift Keying Modulation for Molecular Communication

Molecular communication is a biologically-inspired technique to communicate in very small dimension networks. Many different modulation schemes were proposed in the literature based on different properties of molecules. In this paper, we propose to use the direction of releasing molecules as a new property to convey information and we propose Direction Shift Keying (DSK) modulation scheme. Then, we obtain a solid analytical formulation for the concentration of molecules in the 3-D diffusion environment when the transmitter employs DSK modulation. We analyze the concentration of molecules by numerical results. It is observed that the concentration of molecules becomes maximum on the receiver surface in the releasing direction and at a specific time. The derived results may be useful for designing molecular communication systems and evaluating the performance of DSK scheme in the future works.
NOMA-Based Cell-Free Massive MIMO Over Spatially Correlated Rician Fading Channels

This paper considers non-orthogonal multiple access (NOMA) based cell-free massive multiple-input multiple-output (mMIMO) systems over spatially correlated Rician fading channels. Closed-form downlink achievable sum-rate expression is derived by taking into account spatial correlation among multi-antenna access points, inter-cluster interference, intra-cluster pilot contamination, and imperfect successive interference cancellation (SIC). In particular, we propose an intra-cluster power allocation design for improving the system performance. Furthermore, we investigate the downlink performance with both minimum mean-squared error (MMSE) and element-wise MMSE channel estimation. It is interesting to find out that the correlation magnitude has a negligible effect on the system sum-rate in spatially correlated Rician fading channels. The numerical results validate the correctness of the presented results and confirm the effectiveness of the proposed power allocation.
Expression patterns of auxin-responsive genes during tomato flower pedicel abscission and potential effects of calcium

This study aimed to determine the expression patterns of auxin (Aux/IAA)-responsive genes (ARG) during tomato flower pedicel abscission and the role of calcium in this auxin-mediated abscission. Most of the 19 proteins encoded by SlIAA genes showed the presence of all four conserved domains (I, II, III and IV). Expressions of some SlIAA genes decreased significantly (SlIAA 1, 3, 5, 8, 9, 10, 16, 17 and 27), while others increased (SlIAA 2, 4, 6, 7, 11, 12, 13, 26 and 29) at 0.5 h after excision. Most SlIAA genes were significantly upregulated at 1 h (except 9 and 27) then decreased to relatively low levels until 4 h after excision (except 4, 5, 8, 12, 14, 26 and 29). The SIAA genes were analysed and screened based on their expression patterns during different abscission phases. SlIAA4, 6, 9, 12 and 27 had relatively high expression levels consistent with the abscission rate, indicating potential roles in mediating abscission. SlIAA2, 3, 4, 5, 7, 9, 12, 13, 14, 16, 17, 26, 27 and 29 may have been important in delaying abscission, while SlIAA1, 9 and 12 may have been required for the completion of ethylene-induced abscission. SlIAA4, 6, 7, 8, 14, 16, 17 and 29 were important in calcium-delayed abscission. Analysis of other ARG revealed that tomato GH3 may have acted as an effective negative regulator in IAA-induced delay in abscission, while small auxin-up RNA expression patterns indicated that it may be a marker of IAA level throughout the abscission process.
Predictive Control of Building Thermal Loads for Participation in Energy and Regulation Markets

This paper presents a model-predictive control (MPC) solution for optimal thermal load scheduling in support of buildings’ participation in the wholesale energy and frequency regulation markets. The solution combines 1) a lower-level regulation capacity reset strategy that identifies the available regulation capacity for each hour, and 2) an upper-level zone temperature scheduling algorithm to find the optimal load trajectory with a minimum net electricity cost. In the supervisory scheduling strategy, piece-wise linear surrogate models, derived from offline optimization analysis of the lower-level capacity reset mechanism, are used to predict the cooling power and regulation capacity; and a mixed-integer convex program is formulated and solved to determine the optimal control actions in a receding horizon scheme. The proposed strategy was compared to a baseline energy-priority control strategy in a simulation test case and achieved clear performance improvements including a 126% regulation credit increase and a net cost reduction of 16.3%.
Sparse Code Multiple Access Codebook Design using Equiangular Tight Frames

Sparse Code Multiple Access (SCMA) is considered to be one of the potential non-orthogonal multiple access (NOMA) techniques mainly targeting uplink transmission to support massive connectivity. In this work, an efficient high dimensional uplink SCMA codebook design is proposed using the Equiangular Tight Frames. Codebook design problem is posed as construction of generalized Equiangular Tight Frames where mutual coherence among codewords are minimized toward the optimal bound. The minimization problem is handled using gradient-based alternating projection technique under average symbol energy constraint. An important feature of the proposed design is monotonically improved Bit Error Rate (BER) performance with scaling of the proposed codebook.
Capacity of flexible loads for grid support: statistical characterization for long term planning

Flexible loads are a valuable resource for the power grid of the future to help with balancing demand and generation. A balancing authority (BA) needs to know how much flexibility a load has, meaning what type of power deviation (from the baseline demand) signals are feasible for the load. In this work we present a characterization of capacity for a flexible load in terms of the power spectral density of the power deviation. We then show how this characterization can be used for resource allocation for the grid by determining what portion of the grid’s needs can be met by a collection of such loads. The key difference with prior work on flexibility characterization is that ours is posed in terms of the statistical properties grid’s net load and load’s demand deviation, not on specific instances of these signals. The proposed characterization can thus be used for long term planning.
DNA Damage-Inducing Anticancer Therapies: From Global to Precision Damage

DNA damage-inducing therapies are of tremendous value for cancer treatment and function by the direct or indirect formation of DNA lesions and subsequent inhibition of cellular proliferation. Of central importance in the cellular response to therapy-induced DNA damage is the DNA damage response (DDR), a protein network guiding both DNA damage repair and the induction of cancer-eradicating mechanisms such as apoptosis. A detailed understanding of DNA damage induction and the DDR has greatly improved our knowledge of the classical DNA damage-inducing therapies, radiotherapy and cytotoxic chemotherapy, and has paved the way for rational improvement of these treatments. Moreover, compounds targeting specific DDR proteins, selectively impairing DNA damage repair in cancer cells, form a promising novel therapy class that is now entering the clinic. In this review, we give an overview of the current state and ongoing developments, and discuss potential avenues for improvement for DNA damage-inducing therapies, with a central focus on the role of the DDR in therapy response, toxicity and resistance. Furthermore, we describe the relevance of using combination regimens containing DNA damage-inducing therapies and how they can be utilized to potentiate other anticancer strategies such as immunotherapy.
Finite-Sample Analysis of Distributed Q-learning for Multi-Agent Networks

We will provide a finite-sample analysis to the convergence of a distributed algorithm for Q-learning in multi-agent systems, in which each agent is assigned a private reward function and can only communicate with its nearby neighbors under time-varying networks. We will provide an upper bound of the average squared error, which takes into account the time evolution of a consensus error and does not rely on the assumption of independent and identically distributed (i.i.d) samples. In addition, our results indicate that the error of each agent converges to zero asymptotically, supporting empirical results in the literature.
Nutrient and temperature control of the contribution of picoplankton to phytoplankton biomass and production

The observation that the relative importance of picophytoplankton is greatest in warm and nutrient‐poor waters was tested here based on a comprehensive review of the data available in the literature from oceanic and coastal estuarine areas. Results show that picophytoplankton dominate (≥50%) the biomass and production in oligotrophic (chlorophyll a [Chl a] < 0.3 mg m−3), nutrient poor (NO3 + NO2 < 1 µM), and warm (>26°C) waters, but represent <10% of autotrophic biomass and production in rich (Chl a > 5 mg m−3) and cold <3°C) waters. There is, however, a strong covariation between temperature and nutrient concentration (r = −0.95, P < 0.001), but the number of observations where both temperature and nutrient concentrations are available is too small to allow attempts to statistically separate their effects. The results of mesocosm nutrient addition experiments during summer in the Mediterranean Sea allowed the dissociation of the effects of temperature from those of nutrients on pico phytoplankton production and biomass and validated the magnitude at which picoplankton dominates (≥50%) autotrophic biomass and production obtained in the comparative analysis. The fraction contributed by picoplankton significantly declined (r2 = 0.76 and 0.90, respectively, P < 0.001) as total autotrophic production and biomass increased. These results support the increasing importance of picophytoplankton in warm, oligotrophic waters. The reduced contribution of picophytoplankton in warm productive waters is hypothesized here to be due to increased loss rates, whereas the dominance of picophytoplankton in warm, oligotrophic waters is attributable to the differential capacity to use nutrients as a function of differences in size and capacity of intrinsic growth of picophytoplankton and larger phytoplankton cells.
Functional estimation of perturbed positive real infinite dimensional systems using adaptive compensators

This paper extends earlier results on the adaptive estimation of nonlinear terms in finite dimensional systems utilizing a reproducing kernel Hilbert space to a class of positive real infinite dimensional systems. The simplest class of strictly positive real infinite dimensional systems has collocated input and output operators with the state operator being the generator of an exponentially stable C0 semigroup on the state space X . The parametrization of the nonlinear term is considered in a reproducing kernel Hilbert space Q and together with the adaptive observer, results in an evolution system considered in X × Q. Using Lyapunov-redesign methods, the adaptive laws for the parameter estimates are derived and the well-posedness of the resulting evolution error system is summarized. The adaptive estimate of the unknown nonlinearity is subsequently used to compensate for the nonlinearity. A special case of finite dimensional systems with an embedded reproducing kernel Hilbert space to handle the nonlinear term is also considered and the convergence results are summarized. A numerical example on a one-dimensional diffusion equation is considered.
LDPC for receive antennas selection in massive MiMo

Massive Multiple-Input Multiple-Output (m-MIMO) is a promising technology for improving the capacity of fifth generation (5G) wireless networks. However, m-MiMo suffers from the cost and complexity of the radio frequency (RF) chain. One solution to solve this problem is the method of antenna selection. However, this method requires information on the channel state (CSI) in order to select the most efficient subset, which is impossible in the presence of the pilot contamination. In addition, the exhaustive search method, used in conventional MiMo for selecting a subset of antennas, is ineffective for the massive MIMO system because it adds complexity to the processing and requires high energy consumption. For this purpose, a model using a water-filling algorithm and Low-Density Parity-Check (LDPC) decoded symbols is proposed in this article. This method exploits the characteristic of the Physical Layer to address the problem. It does not require supplementary chain, and it does not use pilot symbols to estimate the channel, which makes this proposed model frugal in term of energy consumption and processing resources comparing to the exhaustive method. Simulation results show that the proposed solution attains the same optimal values when the Exhaustive search method is used.
Healthy Body, Healthy Mind: A Mindset Intervention for Obese Youth

Abstract As growth mindset intervention research continues to develop, more work is needed to understand how to most effectively implement these interventions to encourage healthy cognitions and behaviors. The present study details the initial testing of a single-session, online mindset intervention (Healthy Body, Healthy Mind) for obese children and adolescents enrolled in obesity treatment clinics. Using a pre to post-test design, results indicated that growth mindsets of health and cognitions related to health behavior (nutrition and exercise self-efficacy and perceived control) increased significantly. However, despite efforts to mitigate feelings of culpability, blame also increased from pretest to post-test. Yet, body dissatisfaction decreased significantly. Intrinsic value for health behaviors remained unchanged from pretest to post-test. Analysis of narratives suggests that youth were engaged with the intervention content. Additionally, when youth’s narratives incorporated themes related to the changeable nature of the attribute, they also self-reported stronger growth mindsets. In the discussion, we note implications of findings for the development of large-scale health-based growth mindset interventions that are developmentally-appropriate for children and adolescents.
A System for Acquisition and Modelling of Ice-Hockey Stick Shape Deformation from Player Shot Videos

In Ice-Hockey, a player shot significantly deforms the hockey-stick. Since this deformation plays a dynamic role in determining the flight of the puck, it is used in the study of hockey stick shapes, material properties, match to player style, etc. Reconstructing the deformable 3D shape of the stick during the course of a player shot has important applications. In this work we present a new, low cost, portable system to acquire videos of a player shot and to automatically reconstruct the deformation in 3D shape of the stick. The point clouds obtained are low resolution and noisy, as it is difficult to separate players hand geometry from the stick. We use the medial axis to constrain the point cloud to stick only geometry, and then use physics-based co-rotational FEM to determine the stick bend. We have tested the system with different sticks, players and shot styles, and our system yields accurate reconstructions. The results are discussed both qualitatively and where possible, quantitatively.
Photoplethysmography based stratification of blood pressure using multi information fusion artificial neural network

Regular monitoring of blood pressure (BP) is an effective way to prevent cardiovascular diseases, especially for elderly people. At present, BP measurement mainly relies on cuff-based devices which are inconvenient for users and may cause discomfort. Therefore, many new approaches have been proposed to achieve cuff-less BP detection in recent years. However, the accuracy of the existing approaches still needs to be improved. In this study, holistic-based PPG and its first and second derivative features are extracted and a new multi information fusion artificial neural network (MIF-ANN) is designed to effectively fuse and exploit multiple input data. Experimental results on a public database which contains 12000 subjects show that the proposed network can model the relation between Photo-plethysmography (PPG) and BP well, achieving averagely accuracy of 91.33% for 5-category BP stratification. Additionally, this study verified that multi information fusion based on meticulously designed network plays an important role in improving the accuracy of BP detection.
Optimization on Cooperative Communications Based on Network Coding in Multi-hop Wireless Networks

Cooperative communications (CC) based on network coding (NC) can significantly increase the capacity of wireless networks. In this paper, we investigate how to apply NC in multi-hop wireless networks with multiple relay nodes and multiple sessions to achieve more practical and general CC based on NC. Based on NC, we consider to jointly optimize relay node selecting, scheduling and flow routing in CC in a multi-hop wireless network. We not only construct a mathematical model but also formulate a maximum-minimum (Max-Min) optimization problem for it. Then we reformulate and solve it with CPLEX. Through simulation, we validate that the achieved rate gain by NC can be greatly improved than the one by CC.
CPNDD:Content Placement Approach in Content Centric Networking

Content Centric Networks (CCN) has been evolved as a promising internet architecture that focuses on content centric approach for content requests rather than host centric approach. CCN provide in-network caching and content distribution capability improves Quality-of-Service by reducing intermediatory hop count and server load, which condequently reduces bandwidth requirements. Existing work in CCN emphasis on minimizing content caching operations and maximizing network hit ratio. In this paper, we have investigated the effect of in-network caching based on content provider distance and node centrality parameters over network hit ratio. A novel content placement approach named CPNDD (Content Placement based on Normalized Node Degree and Distance), has been proposed that collectively implement both parameters to intelligently select caching location in the network to maximize gain in hit ratio. The weightage of both parameters has been computed using extensive simulation on abilene network topology. We have compared our scheme with several peer caching algorithms in CCN. Simulation results are obtained for different cache size, exponent value of zipf distribution and number of requests. The results demonstrate that CPNDD increases in-network hit ratio gain upto 40% as comparison to existing algorithms.