Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting cellular damages in freezing plants: are acoustic emissions useful?

Ultrasonic acoustic emission analysis has often been used to detect hydraulic failures in xylem conducting elements under drought and/or frost stress. However, other sources can generate acoustic signals. In this study, we hypothesized that frost stress generates acoustic signals in living cells in relation to frost-induced dehydration. Living cells are able to transiently increase their frost resistance through the process of frost acclimation / deacclimation. The temperature at which freeze-induced acoustic signals would be recorded should therefore show seasonal changes. Well-hydrated lavender plants were frozen in three different seasons: winter (January and February), spring (April) and summer (June and July). The acoustic activity as a function of temperature showed seasonal changes. The temperature inducing 50% acoustic emissions was recorded at -25°C in winter and spring while it occurred at -15°C in summer. This temperature was very similar and strongly correlated to the temperature inducing 50% damages, detected by the electrolyte leakage method. Although further studies are needed to identify the exact acoustic source, these results represent a promising avenue for monitoring frost stress in a constrained environment and an alternative technique for measuring plant frost hardiness.
Characterizing Groundwater Potential Using GIS-Based Machine Learning Model in Chihe River Basin, China

Mapping of groundwater potential over space, built by synergizing environmental variables and machine learning models, was of great significance for regional water resources management. Taking the Chihe River basin in Anhui province as an example, thirteen influence factors were used to predict the spatial distribution of groundwater, including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), drainage density, distance to rivers, distance to faults, lithology, soil type, land use, and normalized difference vegetation index (NDVI). The potential of groundwater resource in this region was predicted using GIS-based machine learning models, including logistic regression (LR), deep neural networks (DNN), and random forest (RF) model. Then, the accuracy of prediction results was evaluated by calculating the RMSE, MAE and R evaluation index. The results show that there is no collinearity among the 13 environmental impact factors, which can provide corresponding environmental variables for the evaluation of regional groundwater potential. Machine learning models show that groundwater potential is concentrated in moderate to high potential areas. Among them, the moderate to the high potential of this area accounted for 81.14% in the LR model, 90.36% and 87.55% in the DNN model and the RF model, respectively. According to the result of these evaluation indexes, the three models all have high prediction accuracy, among which the LR model performs more prominently. The good prediction capabilities of these machine learning technologies can provide a reliable scientific basis for spatial prediction of groundwater potential and management of water resources.
Cooperative Behaviors BetweenTwo Teaming RTS Bots in StarCraft

Context. Video games are a big entertainment industry. Many video games let players play against or together. Some video games also make it possible for players to play against or together with computer controlled players, called bots. Artificial Intelligence (AI) is used to create bots.Objectives. This thesis aims to implement cooperative behaviors between two bots and determine if the behaviors lead to an increase in win ratio. This means that the bots should be able to cooperate in certain situations, such as when they are attacked or when they are attacking.Methods. The bots win ratio will be tested with a series of quantitative experiments where in each experiment two teaming bots with cooperative behavior will play against two teaming bots without any cooperative behavior. The data will be analyzed with a t-test to determine if the data are statistical significant.Results and Conclusions. The results show that cooperative behavior can increase performance of two teaming Real Time Strategy bots against a non-cooperative team with two bots. However, the performance could either be increased or decreased depending on the situation. In three cases there were an increase in performance and in one the performance was decreased. In three cases there was no difference in performance. This suggests that more research is needed for these cases.
Air Quality Assessment in the Central Mediterranean Sea (Tyrrhenian Sea): Anthropic Impact and Miscellaneous Natural Sources, including Volcanic Contribution, on the Budget of Volatile Organic Compounds (VOCs)

The results of air pollution assessment during a 2017 cruise of the research ship “Minerva Uno” in the Tyrrhenian Sea are reported. Volatile Organic Compounds (VOCs), Oxygenated Volatile Organic Compounds (OVOCs), and pollutants such as nitrogen oxides, ozone, and sulphur dioxide were monitored throughout the cruise. The shallow waters at ten sites of the investigated area were also analyzed. Organic compounds such as n-alkanes showed a bimodal distribution with a maximum at C5–C6 and C10–C11 at sites the most affected by anthropic impact, whereas remote sites showed a unimodal distribution with maximum at C10–C11. The most abundant atmospheric OVOC was acetone (3.66 μg/m3), accounting for 38%; formaldehyde (1.23 μg/m3) and acetaldehyde (0.99 μg/m3) made up about 22–29% of the total. The influence of some natural sources as volcanoes, in the southern part of the Tyrrhenian Sea near the Aeolian arc was studied. This source did not induce any noticeable effect on the total amount of hydrocarbons nor on the levels of trace gases such as CFCs, whereas the trends of sulphur dioxide seemed to confirm a possible contribution. The impact of underwater emissions was observed near the Panarea and Vulcano islands, where lower pHs, high levels of Fe and Mn, and diagnostic of vent activity, were measured.
Electrochemical determination of tramadol using modified screen printed electrode

The detection of tramadol using a screen printed electrode modified with La3+/ZnO nano-flowers and multi-walled carbon nanotubes (La3+/ZnO NFs-MWCNTs/SPE) is reported in this work. In order to examine tramadol electrochemical oxidation, the modified electrode was implemented with the utilization of differential pulse voltammetry, chronoamperometry and cyclic voltammetry as diagnostic techniques. The proposed electrode displays favorable electrocatalytic behavior concerning tramadol oxidation with an approximately 330 mV potential shift to a lesser positive potential. In the 0.5 to 800.0 μM range for tramadol, differential pulse voltammetry displays linear dynamic activity. Tramadol detection limit of 0.08 μM was derived within optimized testing conditions for this simple construction sensor. Lastly, this fabricated sensor was utilized with desirable results to determine tramadol in tramadol samples and urine samples.
Elementary Subgraph Features for Link Prediction With Neural Networks

The enclosing subgraph of a target link has been proved to be effective for prediction of potential links. However, it is still unclear what topological features of the subgraph play the key role in determining the existence of links. To give a possible answer to this question, in this paper, we propose a neural network based learning method for link prediction with only 1-hop neighborhood information. In detail, we extract the one-hop neighborhood of a target link as the enclosing subgraph, then encode the subgraph into different types of topological features, and lastly feed these features to train a fully connected neural network for link prediction. The experimental results show that our proposed learning method with the 1-hop neighborhood features could outperform those heuristic-based methods and achieve nearly equal performance to the state-of-the-art learning-based method WLNM and SEAL. Furthermore, it is observed that these features can be concatenated with attribute vectors to greatly promote the link prediction performance in attributed graphs. This indicates that the topological pattern within an enclosing subgraph, which determines the existence of a possible link, can be aggregated by some elementary subgraph features.
Issues and advances in high-frequency magnetics for switching power supplies

Magnetic components have been and will continue to be an essential element in power conversion and management circuits. Due to this pivotal role, magnetic components have seen continuous efforts to develop materials, winding structures, and manufacturing and assembly techniques toward the never ending goals of high efficiency, smaller size, and automated manufacturing. The authors present an up-to-date view as well as an outlook for the future covering the technologies that have enabled the advanced in magnetic components for power electronics in telecommunications. Finally, they also provide their view on emerging technologies in this field in line with broader developments in the telecommunications, electronics and semiconductor industries.
Sellar, suprasellar, and parasellar masses: Imaging features and neurosurgical approaches

The sellar, suprasellar, and parasellar space contain a vast array of pathologies, including neoplastic, congenital, vascular, inflammatory, and infectious etiologies. Symptoms, if present, include a combination of headache, eye pain, ophthalmoplegia, visual field deficits, cranial neuropathy, and endocrine manifestations. A special focus is paid to key features on CT and MRI that can help in differentiating different pathologies. While most lesions ultimately require histopathologic evaluation, expert knowledge of skull base anatomy in combination with awareness of key imaging features can be useful in limiting the differential diagnosis and guiding management. Surgical techniques, including endoscopic endonasal and transcranial neurosurgical approaches are described in detail.
Estimation of Tropical Cyclone Intensity using Synthetic Satellite Microwave Temperature Anomaly Structure and A Multi-Feature Distribution Learning Network

Accurate intensity estimation of tropical cyclone (TC) is important and challenging. Currently, no standardized method is available for estimating the TC intensity, usually in terms of the maximum surface wind speed, using satellite remote sensing data. This paper proposes a multi-feature distribution learning (MFDL) model that utilizes satellite microwave brightness temperatures to estimate the TC intensity. The MFDL model uses the brightness temperatures of Advanced Technology Microwave Sounder to generate the synthetic temperature anomaly fields of TCs at different pressure levels. These 3D temperature anomaly data are trained by MFDL to establish the relationship between the anomaly fields and the TC intensity. MFDL treats the TC intensity estimation as a probability distribution of the wind speed, instead of as a regression problem as in the conventional TC intensity estimation methods. In this study, 964 TCs occurred in the North Atlantic (NA) and North Pacific (NP) from 2012 to 2019 are used for training and testing. Simulation results suggest that estimation accuracy is greatly improved using the pre-processed 3D TC anomaly data. MFDL network achieves the average mean absolute errors (MAE) of 4.37 m/s for NA TCs and 4.92 m/s for NP TCs in the years of 2018 and 2019. Results show that the MFDL network and ATMS temperature based anomaly can be a promising means for TC intensity estimation in the operational weather forecast.
Finding Central Vertices and Community Structure via Extended Density Peaks-Based Clustering

Community detection is a significant research field of social networks, and modularity is a common method to measure the division of communities in social networks. Many classical algorithms obtain community partition by improving the modularity of the whole network. However, there is still a challenge in community division, which is that the traditional modularity optimization is difficult to avoid resolution limits. To a certain extent, the simple pursuit of improving modularity will cause the division to deviate from the real community structure. To overcome these defects, with the help of clustering ideas, we proposed a method to filter community centers by the relative connection coefficient between vertices, and we analyzed the community structure accordingly. We discuss how to define the relative connection coefficient between vertices, how to select the community centers, and how to divide the remaining vertices. Experiments on both real and synthetic networks demonstrated that our algorithm is effective compared with the state-of-the-art methods.
Root cause analysis of TCP throughput : methodology, techniques and applications

The interest for the research community to measure the Internet has grown tremendously during the last couple of years. Thisncrease of interest is largely due to the growth and expansion of the Internet that has been overwhelming. We have experienced exponential growth in terms of traffic volumes and number of devices connected to the Internet. In addition, the heterogeneity of the Internet is constantly increasing: we observe more and more different devices with different communication needs residing in or moving between different types of networks. This evolution has brought up many needs - commercial, social, and technical needs - to know more about the users, traffic, and devices connected to the Internet. Unfortunately, little such knowledge is available today and more is required every day. That is why Internet measurements has grown to become a substantial research domain today. This thesis is concerned with TCP traffic. TCP is estimated to carry over 90% of the Internet's traffic, which is why it plays a crucial role in the functioning of the entire Internet. The most important performance metrics for applications is typically throughput, i. E. The amount of data transmitted over a period of time. Our definition of the root cause analysis of TCP throughput is the analysis and inference of the reasons that prevent a given TCP connection from achieving a higher throughput. These reasons can be many: application, network, or even the TCP protocol itself. This thesis comprises three parts: methodology, techniques, and applications. The first part introduces our database management system-based methodology for passive traffic analysis. In that part we explain our approach, the InTraBase, which is based on an object-relational database management system. We also describe our prototype of this approach, which is implemented on PostgreSQL, and evaluate and optimize its performance. In the second part, we present the primary ontributions of this thesis: the techniques for root cause analysis of TCP throughput. We introduce the different potential causes that can prevent a given TCP connection to achieve a higher throughput and explain in detail the algorithms we developed and used to detect such causes. Given the large heterogeneity and potentially large impact of applications that operate on top of TCP, we emphasize their analysis. The core of the third part of this thesis is a case study of traffic originating from clients of a commercial ADSL access network. The study focuses on performance analysis of data transfers from a point of view of the client. We discover some surprising results, such as poor overall performance of P2P applications for file distribution due to upload rate limits enforced by client applications. The third part essentially binds the two first ones together: we give an idea of the capabilities of a system combining the methodology of the first part with the techniques of the second part to produce meaningful results in a real world case study.
Agrobiodiversity Documentation in Puranchaur, Pokhara Through Four Cell Analysis: Biodiversity of Rice

Introduction Agrobiodiversity is the combination of environment, biological diversity, and monitoring systems employed by a varying group of people and thereby exploitation of water and land resources to maintain the architecture of agro-ecosystem for food security and agricultural production (FAO, 1999). Nepal‘s geographical variety is immense, ranging from tropical flatlands on the southern side to the rocky, delicate, and snow-covered Himalayas on the northern side (MOFSC, 2014). Moving from the terai to the mountainous area of Nepal revealed a total of 118 ecosystems. A broad range of climate and topography has benefitted agricultural methods, their wild counterparts, and biological types (Khanal and Dangol, 2016). Nepal ranks tenth in Asia and thirty-first worldwide in terms of flowering plant diversity. There are 790 plant species important from the nutritional point of view and 577 cultivated plant species, including pasture species. There are 484 indigenous species and 93 exotic species among the Agrobiodiversity documentation in Puranchaur, Pokhara through four cell analysis: biodiversity of rice
Echtzeit-Identifikation nichtlinearer instationärer Systeme mit RBF-Netzwerken (Real-time Identification of Non-linear Non-stationary Systems by RBF-Networks)

Abstract Der Beitrag behandelt den Einsatz Künstlicher Neuronaler Netze (KNN) zur Modellbildung für nichtlineare dynamische, stationäre und instationäre Systeme. Es wird gezeigt, dass Radiale Basisfunktionsnetze (RBF) in Zusammenhang mit dem neu entwickelten schnellen Mehrschritt-Algorithmus die besten Identifikationsergebnisse liefern. Die Struktur des Netzes wird während des Lernprozesses automatisch so lange erweitert, bis für alle Trainingsdaten eine definierte Fehlerschranke unterschritten wird. Am Beispiel einer nichtlinearen Differenzengleichung erster Ordnung mit einem zeitvarianten Faktor werden die Eigenschaften des neuen Identifikationsverfahrens demonstriert und mit anderen bekannten Verfahren verglichen.
Security threats and risks of Intelligent Building Systems: Protecting facilities from current and emerging vulnerabilities

Intelligent Buildings (IB) are facility-wide systems that connect, control, and monitor the plant and equipment of a facility. The aim of IB is to ensure a facility is more efficient, productive, and safe, at a reduced cost. A typical IB integrates diverse subsystems into a common and open data communication network, using both software and hardware; however, IBs suffer from diverse generic vulnerabilities. Identified vulnerabilities may include limited awareness of security threats and system vulnerabilities, physical access to parts of the system, compromise of various networks, insertion of foreign devices, lack of physical security, and reliance on utility power. IB risks are contextual and aligned with the threat exposure of the facility. Nevertheless, there are generic mitigation strategies that can be put in place to protect IB systems. Strategies include threat-driven security risk management, an understanding of system criticality, greater integration of departments, network isolation, layered protection measures, and increased security awareness.
Genetic Algorithm-Based Parameters Optimization for the PID Controller Applied in Heave Compensation System

To reduce the negative impact of the unexpected motion in the heave direction when hoisting on the sea, a heave compensation system is indispensable for a floating crane. In this paper, a genetic algorithm-based PID controller is presented to improve the dynamic performance of the heave compensation system. A simulation model of electro-mechanical and hydraulic integrated system of the heave compensation system is built. The simulation results show that the genetic algorithm-based PID control system improves the dynamic characteristics, strengthens the stability and rapidity of the system and even ensures the control effect of the heave compensation system.
Study of Sintering Blending Based on Swarm Intelligence Optimization Algorithm

The sintering blending is a complex nonlinear optimization problem. The traditional single algorithm can not meet the requirement of good quality of sinter and lowest costs well. So, a hybrid optimization method of particle swarm and ant colony algorithm was proposed. The method gives full play to the global convergence of particle swarm optimization algorithm, takes it as a preliminary search, then use the positive feedback mechanism of ant colony algorithm for the exact solution, to make these two algorithms to reach a complementary, in order to get a rapid exact solution. The simulation results show that the proposed hybrid algorithm has fast convergence and high accuracy, which can effectively reduce the sintering cost.
Electrical insulation study using partial discharge model

For partial discharge (PD) processes of electrical insulation internal defaults ignition and their parameters measurement, different electrical models were developed. These models present the combination of electrical insulation and its defaults. All these models use the "a-b-c" diagram. Using these kind of equivalent diagrams, the paper presents the methods of apparent charge (q) and PD energy evaluation. The elaborated diagrams are used for apparent charge, electrical insulation, geometrical parameters and fault dimension dependency analysis. The obtained experimental results give very important information for the electrical insulation producer and also for the users.
A HEXAPOD ROBOT BASED ON SHAPE MEMORY ALLOY ACTUATORS

Abstract The purpose of this paper is to develop a hexapod robot that uses shape memory alloy (SMA) actuators. The design of the robot follows a bio-mimetic approach. This paper first discusses various gaits for six-legged motion. The paper then introduces the characteristics of the SMA actuator and the corresponding driver design. The robot motion controller is implemented with an ARM9 based embedded system. Finally, the test results are presented and the robot performance is discussed.
Optimising hearing aid output to paediatric prescriptive targets improves outcomes in children

Abstract Objective This study examined the accuracy of hearing aid output to DSL v5.0 Child targets in a group of children who wear hearing aids, and the impact on their auditory outcomes. Design For each participant, the output of the initial hearing aid fitting was compared to DSL v5.0 Child prescriptive targets and again after the fitting was adjusted using coupler-based verification and RECD measures. Outcomes for initial and adjusted fittings were examined using the Speech Intelligibility Index (SII), Parent’s Evaluation of Aural/Oral Performance of Children (PEACH) rating scale, and speech perception tests in quiet and noise. Study sample Sixty-eight children aged 3 months to 17 years with moderate to profound hearing loss participated in the study. Results Fit-to-targets improved significantly after hearing aids were adjusted to match targets to within 5 dB RMSE. Adjusted hearing aids provided increased aided audibility compared to initial fittings and resulted in improved speech perception scores and parent-reported hearing performance. Fifty percent of the children aged 6 to 17 years preferred their adjusted fitting compared to 10% who preferred their initial fitting. Conclusions Improvement in fit-to-target to a validated paediatric prescriptive formula using best practice procedures can result in improved auditory outcomes and possible self-reported satisfaction.