Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting Membrane Fouling in Filtration Processes using Univariate Data-Driven Models

Filtration processes play a crucial role in modern food production. Filtration techniques are, for example, used to purify wine or to produce protein powder out of high viscose suspensions. Characteristically, the filtration membrane becomes increasingly clogged during filtration by deposits of non-permeable particles on the surface. The filtration quality deteriorates caused by this membrane fouling until the process comes to a standstill due to a wholly blocked membrane. A forecast of the membrane fouling process allows appropriate actions to be taken early during filtration to maintain the process quality and reduce membrane blocking. This paper investigates if and how membrane fouling can be forecasted over time in filtration processes using non-phenomenological data-driven univariate stochastic or machine learning models. Prophet, ARIMA and LSTM models for time series forecasts are selected and evaluated on industrial data sets collected from Dynamic Crossflow Filtration plants with different observation lengths and forecast horizons. ARIMA and LSTM models show suitable results on the used data set and are evaluated with varying combinations of past observations and forecast horizons.
Towards Flexible Manufacturing: Motion Generation Concept for Coupled Multi-Robot Systems

This paper presents a motion generation concept with stiffness and path constraints for multi-robot systems in a flexible manufacturing context. An optimization approach using closed-form inverse kinematics solutions is proposed to maximize the Cartesian stiffness, simultaneously preserving the continuity of the joints' movement. We also present an efficient trajectory generation approach combining Particle Swarm Optimization (PSO) and Online Trajectory Generation (OTG) methods. For the sake of synchronization, we assume a 14 Degrees of Freedom (DOF) closed-chain kinematics for the trajectory generation of the dual-robot system. We evaluate the proposed approach on a mock-up of the sheet metal bending task using two coupled Franka Emika robots. The results show that the proposed method increases the stiffness of the robot, as it can apply three times the force compared to benchmark methods before reaching the joint torque limit in the single robot experiments. Furthermore, two physically coupled Panda robots can generate a force of up to 732 N with joint torque limits set to 10%.
Sequential Transfer Learning Models with Additional Layers for Pneumonia Diagnosis

Pneumonia is a disease caused by numerous microorganisms affecting the alveoli sac of the lungs, filling them with pleural fluid. It affects many children and adults and can be fatal to them, hence early identification is essential. X-ray is a non-invasive and cheapest method available for the diagnosis of pneumonia. For this study, the impact of custom sequential models with additional layers for pneumonia diagnosis was investigated. The research used a dataset of 5856 X-ray images from the Kermany dataset to train deep learning models for computer-aided diagnosis of pneumonia. Transfer learning was employed with pre-trained classifiers, and the results were compared between a basic approach of using only a classification layer on extracted features and a second approach that involved adding additional layers after the pre-trained classifiers. The performance of the deep learning models was evaluated using accuracy, precision, specificity, recall, F1 score, and ROC curve. The results showed that the second approach, which included additional layers, led to an increase in the AUC of all classifiers. The Vgg16 model performed particularly well, displaying an F1 score of 89.61%.
An interaction-aware approach for social influence maximization

Microblogging networks are considered a great source of social influence. One of its characteristics is their high dynamism. This fact produces that influential users continuously change according with time and topic. Several social networks metrics have been defined to rank influential users. However, these metrics fail to capture the dynamism of microblogging networks. For this reason, we propose an approach based on Credit Distribution model to identify the influential users of a microblogging social network by performing an online analysis of the users interactions. Moreover, we present a comparison of our approach with well-known metrics used for influencers ranking. The experiments were carried out in Twitter during sport events (football matches) and new product (video games) launchings. The results showed that our approach outperforms the metric-based rankings in terms of the influence spread. This confirms the importance of being updated for identifying influential users.
Estimating Software and Hardware Video Decoder Energy Using Software Decoder Profiling

In this paper, we develop models for the energy demand of hardware video decoders based on highly accurate energy models for software video decoding. While the standardization of video coding specifications has improved compression efficiency by permitting coding tools that are by the encoder, these tools also result in increased complexity. However, during the development of a video codec, the area and energy demand of a hardware implementation are unknown. To address this issue, we propose new models for estimating the hardware decoder energy demand. Our approach involves the profiling of a software decoder, which is available during standardization, to estimate the hardware energy demand using linear regression modeling. Thereby, the complexity of coding tools for future hardware implementations can be estimated already during the standardization process. We evaluate and discuss three types of models in terms of estimation accuracy. Our results show that pure software decoder modeling has an average estimation error of 1.63%. This concept is then applied with a software decoder that is profiled to estimate the complexity of a hardware decoder. Thereby, it is possible to estimate the energy demand of the hardware decoder with an average error of 13.14%.
A Decision Support Tool for District Level Planning of Agricultural Crops for Maximizing Profits of Farmers

A mismatch between the crops produced by farmers and the respective market demands leads to large-scale crop dumping. Year on year, this leads to wastage as well as huge financial losses for the farmers. To alleviate this problem, we address the macro-level problem of district level agricultural crop planning. Our interest is in how the Government or any state administration could make an informed recommendation on which crop acreages (number of acres cultivated under each crop in each district) to grow in which districts or geospatial regions in a given state or country, so as to match the demand for the crops and maximize the profits of the farmers. In this paper, we develop a tool CROP-S (CROp Planning System) to determine an assignment of crop acreages to districts so as to maximize the profits of the farmers while simultaneously ensuring required crop security levels for each district. The tool uses data about predicted demands, transportation costs, compliance ratios, and historical data about yields and prices to arrive at a nearly optimal assignment of crop acreages to districts. CROP-S uses a methodology based on genetic algorithms. We believe CROP-S will provide an effective decision support tool for the Government to issue crop recommendations to the district administrations, who in turn issue advisories to farmers. To demonstrate the effectiveness of CROP-S, we have considered the problem of allocating crop acreages to the 30 districts of Karnataka state in India. We have used real-world data on the top 7 crops grown in Karnataka. We show that assigning crop acreages among the various districts, as recommended by CROP-S, leads to a significant increase in the overall profits of the farmers.
SLAMM (SEA LEVEL AFFECTING MARSHES MODEL) MODELING OF THE EFFECTS OF SEA LEVEL RISE ON COASTAL WETLAND HABITATS OF SAN DIEGO COUNTY

OF THE THESIS SLAMM (Sea Level Affecting Marshes Model) Modeling of the Effects of Sea Level Rise on Coastal Wetland Habitats of San Diego County by Michael Patrick Naughton Master of Public Health with a Concentration in Environmental Health San Diego State University, 2013 In 2006, the Intergovernmental Panel on Climate Change noted that 11 of the last 12 years ranked among the 12 warmest years in the record of global surface temperature. The rising global temperature has increased water output from glaciers and ice sheets and caused thermal expansion of the ocean. These changes have ultimately raised the mean tide level globally, and new available data on the ice dynamics in Greenland and Antarctica have led some to suggest the sea level rise will be as much as to 1.5 or 2 meters above current levels. Natural coastal areas such as wetlands will be vulnerable to sea level rise of 1.5 or 2 meters. In this study, a high resolution digital elevation model (which was created by combining available Light Detecting and Ranging (LIDAR) data and the National Elevation Dataset) and the National Wetlands Database were used to run the Sea Level Affecting Marshes Model (SLAMM) in the coastal areas of San Diego County, San Diego Bay, and the Tijuana Estuary for a 1, 1.5, and 2 meters sea level rise. In this study, San Diego County salt marsh areas including transitional salt marshes make considerable gains countywide due to inundation of brackish marsh areas by 2100. In San Diego Bay, beach areas, brackish marshes, and tidal flat areas are reduced and salt marsh increase as a response to sea level rise. In Tijuana Estuary, ocean beaches, brackish marshes, swamps, and undeveloped dry land show significant reductions, while estuarine beaches and estuarine open water increase their areal cover. The study showed that sea level rise driven growth of salt marshes will expand the nesting grounds for the Belding’s Savannah Sparrow and Light-footed Clapper Rail countywide. The inundation of estuarine beaches in San Diego Bay make it less suitable for California Least Tern and Western Snowy Plover to nest, while the formation of estuarine beaches (because of the inundation of undeveloped lands) on the south side of the Tijuana Estuary make it more suitable for these same two species of birds. Tidal flats in the southern part of San Diego Bay become inundated by SLR, making the Bay less suitable for foraging by shorebirds such as the Long-billed Curlew. Due to the implications of this research, it will be beneficial to designate and protect areas where critical habitats that support threatened and endangered species will migrate to as current protected areas such as the San Diego Bay Wildlife Refuge become inundated as the sea level rises.
Multi-Modal Transformer with Multi-Head Attention for Emotion Recognition

Multimodal emotion recognition (MER) is a technology aimed at identifying and analyzing emotions from multiple sensory data. In modern life, people usually use not only one modality of information but also multiple modalities of data, such as images, audio, and text, at the same time during their interactions with the outside world. To address this issue, this paper proposes a sentiment recognition model called MTMAER based on Transformer architecture and a multi-head attention mechanism. Unlike existing models that only consider the unimodal feature of the text, MTMAER combines text, image, and audio data. However, existing methods use LSTM to extract semantic information in text features, which has poor long-distance modeling ability. Therefore, the paper uses Transformer to capture sequential contextual information, which can extract deeper semantic information. To achieve cross-modal feature fusion and capture complementary semantic information, the paper also employs a multi-head attention mechanism to effectively capture feature information of different modalities of data, achieving discrete emotion recognition. Experimental results show that MTMAER performs well in the task of multimodal emotion recognition, with an accuracy of 60.89% and a F1 value of 61.55% on the IEMOCAP dataset, demonstrating strong practicality and scalability and can be applied in various practical scenarios.
Perinatal Outcomes Associated With Management of Stage 1 Hypertension

In patients with mild chronic hypertension, blood pressure lower than 130/80 mm Hg was associated with improved pregnancy outcomes, without an increased risk of having a small-for-gestational-age neonate. OBJECTIVE: To evaluate the association between maternal blood pressure (BP) below 130/80 mm Hg compared with 130–139/80–89 mm Hg and pregnancy outcomes. METHODS: We conducted a planned secondary analysis of CHAP (Chronic Hypertension and Pregnancy), an open label, multicenter, randomized controlled trial. Participants with mean BP below 140/90 mm Hg were grouped as below 130/80 mm Hg compared with 130–139/80–89 mm Hg by averaging postrandomization clinic BP throughout pregnancy. The primary composite outcome was preeclampsia with severe features, indicated preterm birth before 35 weeks of gestation, placental abruption, or fetal or neonatal death. The secondary outcome was small for gestational age (SGA). RESULTS: Of 2,408 patients in CHAP, 2,096 met study criteria; 1,328 had mean BP 130–139/80–89 mm Hg and 768 had mean BP below 130/80 mm Hg. Participants with mean BP below 130/80 mm Hg were more likely to be older, on antihypertensive medication, in the active treatment arm, and to have lower BP at enrollment. Mean clinic BP below 130/80 mm Hg was associated with lower frequency of the primary outcome (16.0% vs 35.8%, adjusted relative risk 0.45; 95% CI 0.38–0.54) as well as lower risk of severe preeclampsia and indicated birth before 35 weeks of gestation. There was no association with SGA. CONCLUSION: In pregnant patients with mild chronic hypertension, mean BP below 130/80 mm Hg was associated with improved pregnancy outcomes without increased risk of SGA. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, NCT02299414.
The Role of Genetically Distinct Central Amygdala Neurons in Appetitive and Aversive Responding Assayed with a Novel Dual Valence Operant Conditioning Paradigm

Abstract To survive, animals must meet their biological needs while simultaneously avoiding danger. However, the neurobiological basis of appetitive and aversive survival behaviors has historically been studied using separate behavioral tasks. While recent studies in mice have quantified appetitive and aversive conditioned responses simultaneously (Jikomes et al., 2016; Heinz et al., 2017), these tasks required different behavioral responses to each stimulus. As many brain regions involved in survival behavior process stimuli of opposite valence, we developed a paradigm in which mice perform the same response (nose poke) to distinct auditory cues to obtain a rewarding outcome (palatable food) or avoid an aversive outcome (mild footshoock). This design allows for both within-subject and between-subject comparisons as animals respond to appetitive and aversive cues. The central nucleus of the amygdala (CeA) is implicated in the regulation of responses to stimuli of either valence. Considering its role in threat processing (Wilensky et al., 2006; Haubensak et al., 2010) and regulation of incentive salience (Warlow and Berridge, 2021), it is important to examine the contribution of the CeA to mechanisms potentially underlying comorbid dysregulation of avoidance and reward (Sinha, 2008; Bolton et al., 2009). Using this paradigm, we tested the role of two molecularly defined CeA subtypes previously linked to consummatory and defensive behaviors. Significant strain differences in the acquisition and performance of the task were observed. Bidirectional chemogenetic manipulation of CeA somatostatin (SOM) neurons altered motivation for reward and perseveration of reward-seeking responses on avoidance trials. Manipulation of corticotropin-releasing factor neurons (CRF) had no significant effect on food reward consumption, motivation, or task performance. This paradigm will facilitate investigations into the neuronal mechanisms controlling motivated behavior across valences.
A Data-Driven Status Division Scheme for Automated Container Terminal Production Process based on Graph Information

Automated Container Ports (ACTs) provide crucial linkages in the global container shipping network, the production improvements of ACTs have attracted much attention from academia. The consonant and complete production datasets of ACT can be effortlessly obtained from the Terminal Operation System (TOS), which provides a practical path to describe, analyze, and improve the ACT by data-driven approach in a comprehensive way. Better comprehending and analysis of the data will promote data-driven application of improvement. Therefore, we present a Data-Driven Status Division Scheme for ACT production process. First, a graph representation method is proposed to describe the ACT instantaneous production status. Then, a two-stage feature extraction algorithm is proposed to extract status graph features considering the global information. And then, the production status division scheme is proposed to divide the statuses on the time-continuous production process data. Finally, we illustrate and validate the proposed status division method by the datasets of world's largest ACT and give insights in the status transition of ACT. This study will provide a promising reference for explanatory data-driven decision-making in ACT.
Oscillation Damping Using Reinforcement Learning Controlled HVDC Transmission

This paper presents a novel deep reinforcement learning based control method for damping power system inter-area oscillations. A guided surrogate-gradient-based evolutionary strategy (GSES) is used to control the transferred power on high voltage DC (HVDC) transmissions, which could help improve the damping effect when inter-area oscillations occur. The GSES algorithm trains a reinforcement learning agent to learn the best parameters of the HVDC controller, with an objective of reducing the magnitude and duration of inter-area oscillations. Unlike many existing reinforcement learning methods, the GSES algorithm does not require a traditional back-propagation process to update the policy parameters, enabling an easier, more robust and efficient training procedure with less required hyper-parameters. In addition, as an evolutionary strategy, the proposed GSES-based HVDC oscillation damping control approach can engage with multiple individual workers during the training process through parallel computation techniques, which significantly accelerates the computational speed and improves the training efficiency. The proposed GSES-based HVDC controller is compared with a conventional oscillation damping method on the IEEE 39-Bus New England system. Results indicate that the proposed GSES-based HVDC control approach performs better and it can effectively damp power system inter-area oscillations.
Fault Estimation and Fault-Tolerant Control of Nonlinear Systems with Polynomial Faults

In this paper, the issue of fault estimation and fault tolerant control(FTC) of non-linear systems on the basis of event-triggered strategies is studied. The fault dynamics is designed as a case of a known polynomial function that can be represented as a function of time. The use of an event-triggered communication mechanism allows the sampled output to be transported across the network only when the given trigger condition is satisfied, thus greatly saving network communication resources. A fault estimation observer is devised to simultaneously evaluate the system state and fault signals without considering time-varying network communication delays, providing a basis for designing fault-tolerant controllers. Build a fault-tolerant controller based on estimated signals of state and fault from observers to maintain the stability and performance of the system under fault conditions. Finally, the feasibility of the presented approach is illustrated by a simulation experiment.
Multi-View Crop Detection Using an Occupancy Grid for Agricultural Applications

To progress towards sustainable agriculture, crop production processes have to be optimized. Avoiding the use of herbicides is a major issue and a way to go on the agro-ecological transition; hence the interest in robustly differentiating crops from weeds and suppressing the latter. We present in this paper a multi-view crop detection methodology to locate and track crops; allowing a mobile platform to remove weeds with high precision. The mobile platform is endowed with two perception systems, fixed at two different positions and orientations with respect to its center of mass, providing two different points of view of the crops. The proposed methodology merges both fields of view in a single occupancy grid to precisely locate crops in front and below the mobile platform. Each perception system is an edge-computing device capable of preprocessing concurrently raw imagery, facilitating the task of merging both points of view. To validate the proposed approach, multiple simulations were carried out using the Gazebo simulator. In the same manner, we also tested this methodology in a real agricultural field during the evaluation campaigns of the ROSE challenge.
CDD-YOLOv8: A Small Defect Detection and Classification Algorithm for Cigarette Packages

In order to ensure the quality of cigarette products, it is significant to conduct real-time detection and classification for defects on cigarette packages in high-speed assembly line. Most industrial anomaly detection methods can only detect anomalies by modeling normal data distribution, lacking the ability to realize the fine-grained classification of anomalies, which is indispensable for reducing defective products and thus lower production costs. To solve the problem of detecting and classifying small defects in cigarette packaging, we introduced a Cigarette Defect Detection YOLOv8 algorithm (CDD-YOLOv8). By adding a small object detection head and Convolutional Block Attention Module(CBAM), our proposed model achieved better utilization of multi-level features to assist in small defect detection, contributing to better detection performance in our cigarette dataset.
Sensors in Battery Management System for Electric Vehicles

The vehicle is a popular topic since almost every family needs one for their daily travels. With the invention of electric vehicles, the need for Battery Management Systems (BMS) has become a major challenge for researchers and companies. The development of Lithium, Lead, and other types of batteries has made it possible to store high-energy in limited space. However, if the BMS cannot detect battery problems or the state of the battery on time, it can result in battery damage, fire, or even explosion. To obtain data, different sensors such as temperature and charge sensors are used in the BMS system. The data obtained from the battery is crucial as it helps to identify problems, prevent disasters, and predict further parameters that may be used in the calculation of battery operation and maintenance. This paper provides a comprehensive review of sensors used in BMS, with a focus on temperature and charge sensors.