Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of a Voice Assistant to be used in an IoT Home Automation Environment

The development of the Internet of Things and smart cities also brings the design of home automation systems, i.e., intelligent buildings that respond to changes in the environment and wishes of the tenant. To decide on different aspects of this type of environment, a control is necessary, preferably wireless, that allows interaction between the building and the user. There are many types of controls, including voice controls. This paper describes a voice assistant development, which is used to control different home automation building electronic devices. To design this system, different hardware and software tools have been used, such as Arduino UNO, NodeMCU, Visual Studio Code, Google Firebase, MAMP, jquery, PHP, etc. A web platform with which it has been possible to control a Temperature and Humidity Sensor (DHT11), a relay to turn lights on and off, a presence sensor and a gas sensor has been successfully developed. The evolution from the manual connection to voice control connection is described.
A Causal Discovery Approach to Streamline Ionic Currents Selection to Improve Drug-Induced TdP Risk Assessment

Causality is of paramount importance in bio-medical data analysis: assessing the causal relationships between the observed variables allows to improve our understanding of the tackled condition and better support decisionmaking. Torsade de Pointes (TdP) is a serious druginduced cardiac side effect which can lead to sudden death. TdP is related to abnormal repolarizations in single cells, and the minimum set of ion channels needed to correctly assess TdP risk is still an open question. In this work, we propose to apply the causal discovery method ICA-Linear Non-Gaussian Acyclic Model (ICA-LiNGAM) to uncover the relations across the 7 ion channels identified by the Comprehensive in vitro Pro-arrhythmia Assay (CiPA) initiative as potentially related to the induction of TdP: IKr, INa, INa, Ical, Ikl, IKs and Ito. We consider 109 drugs of known torsadogenic risk listed by CredibleMeds. We identify INaL and.ICaL as the ones that directly affect TdPrisk assessment, and suggest that INa perturbations could potentially have a high impact on pro-arrhythmic risk induction. Our causality-based results were further confirmed by independently performing binary drug risk classification, which shows that the combination of the 3 selected ionic currents maximizes the classification accuracy and specificity, outperforming state-of-the-art approaches based on alternative ion channel combinations.
A Comparative Study between Artificial Neural Networks and Optical Neural Networks

Artificial intelligence has gained widespread interest from people from all walks of life since the big data era arrived, so artificial neural networks (ANNs) are one of the most important areas of artificial intelligence that have a close relationship and an effective role in diverse artificial intelligence applications, data is loaded between memory and processor during the calculation, which reduces the calculation rate and increases the power consumption. Training artificial neural network parameters take a long time and consume a lot of energy. Therefore, it is necessary to use new neural networks that differ from artificial neural networks, called optical neural networks. Optical neural networks can overcome the above shortcomings. Compared with artificial neural networks, they have obvious advantages such as high speed, high bandwidth, high balanced processing, low latency, and low power consumption. The optical neural network can significantly improve the performance of required tasks by taking advantage of the high-speed characteristics. The study compares artificial neural networks and optical neural networks to train networks by nonlinear activation functions to perform image classification tasks. The Neural networks are trained and tested with the help of the KMNIST dataset. The backward propagation occurs using the backpropagation algorithm.
BIER-Like Multicast for Mobile Ad Hoc Networks

Tactical Mobile Ad hoc Networks (MANETs) often rely on multicast to carry their traffic, e.g., time-sensitive and critical mission data and situational awareness information. Implementing efficient and scalable multicast forwarding in MANETs can be challenging. Traditional protocols require per-flow state at intermediate nodes, leading to high overhead and vulnerability to nodes’ mobility. BIER is a recent multicast proposal (for static networks) that eliminates the need for explicit distribution trees or group membership information by addressing multiple destinations via a bit-string in each packet’s header. This paper identifies and addresses several challenges in designing a BIER-like multicast scheme for MANETs, including using hashing to map bit indices (from the bit-string) to IP addresses (slots in a routing table implemented as a hash-table), proposing header constructions to preserve bandwidth, and supporting forwarding using longest prefix matching.
An Algorithm for Non-Invasive Mapping Based on Cardiac Anatomy and 12-Lead Electrocardiogram Data

Cardiac mapping provides detailed insights into a patient's unique heart anatomy and electrical patterns. In this paper, we propose an algorithm that integrates cardiac anatomical information with 12-lead electrocardiogram (ECG) data to create activation maps without invasive procedures. To find the activation map of the corresponding ECG of the patient, we solve the multiple forward ECG problems. We use the anisotropic eikonal equation in conjunction with the Lead-Field approach to compute the ECG. By varying the activation area parameters and conductivity parameters in the eikonal equation we generate a subset of ECGs. We use a variational autoencoder to parameterise the 12-channel ECG. Then we train a surrogate model on the generated dataset and apply it to find the closest solutions to the clinical ECG. To calculate the activation map, we use the average solution obtained from the found range of parameters that give close solutions to the patient's ECG. Electroanatomical mapping (EAM) data from the left ventricular endocardium of five patients were used to validate the algorithm. Comparing local activation times, the mean absolute error was 16 ms and a mean correlation of 0.82 was obtained between EAM endocardial activation times and the proposed algorithm.
Counter-propagating scalar and vector beams for subwavelength shaping and particle manipulation

This article discusses using two counterpropagating scalar and/or vector Bessel-Gaussian Beams to generate a complex light field with three-dimensionally varying amplitude, phase, and polarization. The interaction creates oscillating polarization patterns, altering ellipticity and handedness. This technique facilitates subwavelength customization of light, thereby enabling advanced optical manipulation.
Enhancing Fault Localization by incorporating Statement Frequency and Test Case Contribution

Fault Localization (FL) is the key activity while debugging a program. Any improvement to this activity leads reduction in total software development cost. Spectrum-based fault localization (SBFL) techniques are considered to be the most prominent for FL because of their scalability and efficiency. However, these techniques suffer from the problem of ties, focus only on the binary coverage (0/1) information of program elements, and have a huge scope for improvement in their effectiveness. Also, the test suites available for FL have a biased number of pass and failed test cases. In order to solve these issues, we proposed a novel fault localization technique in this paper. Our proposed technique uses statement frequency information and considers individual test case contributions to assign different suspiciousness scores to the statements and generates an effective ranked list of test cases by balancing the test cases. Experimental results show that the proposed method performs on average 42.48% better than other contemporary FL methods such as $\mathrm{D}^{\ast}$, Tarantula, Ochiai, Ample, Barinel, BPNN, RBFNN, and Crosstab.
Measurement Data Complement of Power Distribution System Based on Clustering-BayesLSTM Combined Model

To address the problems of missing measurement data and low efficiency of large-scale measurement data complement in power distribution system, a measurement data complement method based on Clustering-BayesLSTM combined model is proposed. Firstly, K-mediods clustering is utilized to categorize all the quantity measurements, select the main/standby crucial measurements in each category, and calculate their average deviation from other measurements in order to effectively reduce the solution size of the long short-term memory (LSTM) neural network model. Then, through the LSTM prediction model based on Bayesian optimization, the missing main/standby crucial measurements are complemented. In addition, the average deviations from the primary/backup crucial quantity measurements are used to supplement the other missing measurements. Finally, the effectiveness of the proposed method is verified based on the real measurement data of a local power distribution system.
Coverage of PPG-Based Wearable Devices in Office Tasks

This study aims to evaluate the coverage (percentage of usable data) in heart rate (HR) monitoring terms provided by two photoplethysmography-based wearable devices during usual office tasks. These two devices are Polar OH1 (armband device) and Maxim MAXREFDES103 (wrist-worn device). A conventional Holter-type monitor (Medicom MTD) was used as reference. 19 healthy patients were monitored with the three devices mentioned above while performing some office tasks following an adhoc protocol during 34 minutes. HR was estimated from both wearable devices separately, and the estimates were compared to the HR estimated with the reference ECG device. Obtained results show that the best average coverage across stages (70%) was given by the Polar OH1 device, while a 50% was obtained by the MAXREFDES103.
Power Hardware-in-the-Loop Interface Method for Grid Forming Inverters Using a Voltage-Controlled Power Amplifier

Incorporating grid-forming inverters (GFMI) into a power hardware-in-the-loop (PHIL) setup presents critical stability challenges if the GFMI operates in isochronous mode. Attempting to use the ideal transformer method (ITM) to interface a GFMI may pose the risk of catastrophic damage to either: the GFMI, or the power amplifier. This is caused by the lack of a synchronization mechanism in some commercially available GFMI designed to operate only in isochronous mode. One possible solution is to use a power amplifier that operates in current-control mode. However, most of the power amplifiers available in the market operate only under voltage-control mode. This paper presents a novel yet simple method to convert a voltage-controlled power amplifier into a current-controlled device by using the computational capabilities of a real-time simulator to embed the current control. This way, a stable synchronization between the GFMI and the power amplifier can be achieved, and the PHIL setup stability can be maintained.
LibDI: A Direction Identification Framework for Detecting Complex Reuse Relationships in Binaries

With the continuous evolution of software development, the increasing reuse and complexity of programs have become prominent. Detecting and analyzing reuse relationships in binary programs are crucial for software maintenance, performance optimization, and security enhancement. However, existing methods only identify whether binaries have reuse relationships without distinguishing the source binary of the reused code. This limitation hinders the detection of complex reuse relationships, such as nested reuse and pseudo-propagation reuse, and adversely affects the detection of 1-day vulnerability propagation relationships. To address this challenge, we propose LibDI, a Direction Identification framework specifically designed for detecting complex reuse relationships in C/C++ binaries. LibDI utilizes the Multi-level direction identification technique to accurately determine the direction of reuse between binaries, enabling the identification of complex reuse relationships. Experimental results demonstrate that LibDI achieves a recognition accuracy of 0.976 on public datasets, significantly outperforming existing methods. Moreover, LibDI successfully identifies complex reuse relationships in large-scale IoT firmware and effectively detects the propagation paths of vulnerabilities.
Foundation Models for Mining 5.0: Challenges, Frameworks, and Opportunities

In recent years, there has been a widespread interest in autonomous driving, with researchers primarily focusing on issues such as edge computing, constrained scenarios, and model generalization. ChatGPT is a large-scale natural language model based on the Generative Pre-trained Transformer algorithm. It has gained popularity worldwide and is commonly referred to as a "large model" or "foundation model" due to its high parameter count (over 100M). These models possess human-like understanding capabilities, enabling them to address complex challenges through self-learning and unifying multiple subtasks. While these models are primarily designed for natural language processing (NLP) tasks, their effectiveness has also been proven in the visual and multi-modal domains. Therefore, foundation models hold promise for contributing to the development of autonomous driving technology. Autonomous driving in mining areas is considered an edge scenario, and the reliable capabilities of foundation models make them highly beneficial in such complex and harsh working environments. This article aims to explore the characteristics of mining areas, the features of foundation models, as well as the framework and key technologies related to large models in mining. It provides technical guidance for the implementation of next-generation (mining 5.0) foundation models in unmanned mines.
Tropical precipitation anomalies and d‐excess evolution during El Niño 2014‐16

The last 2014‐16 El Niño event was among the three strongest episodes on record. El Niño considerably changes annual and seasonal precipitation across the tropics. Here, we present a unique stable isotope data set of daily precipitation collected in Costa Rica prior to, during, and after El Niño 2014‐16, in combination with Lagrangian moisture source and precipitation anomaly diagnostics. δ2H composition ranged from ‐129.4 to +18.1 (‰) while δ18O ranged from ‐17.3 to +1.0 (‰). No significant difference was observed among δ18O (P=0.186) and δ2H (P=0.664) mean annual compositions. However, mean annual d‐excess showed a significant decreasing trend (from +13.3 to +8.7 ‰) (P<0.001) with values ranging from +26.6 to ‐13.9 ‰ prior to and during the El Niño evolution. The latter decrease in d‐excess can be partly explained by an enhanced moisture flux convergence across the southeastern Caribbean Sea coupled with moisture transport from northern South America by means of an increased Caribbean Low Level Jet regime. During 2014‐15, precipitation deficit across the Pacific domain averaged 46% resulting in a very severe drought; while a 94% precipitation surplus was observed in the Caribbean domain. Understanding these regional moisture transport mechanisms during a strong El Niño event may contribute to a) better understanding of precipitation anomalies in the tropics and b) re‐evaluate past stable isotope interpretations of ENSO events in paleoclimatic archives within the Central America region.
A Dual-Core Digitally Controlled Oscillator with a Switched Capacitor

This paper proposes a digitally controlled oscillator (DCO) using novel switched capacitor units. Compared with the traditional switched capacitor unit, the new unit reduces the device stress and improves the quality factor. To further reduce phase noise, the DCO uses a dual-core structure and replaces the traditional tail-current source transistor with a resistor. The proposed DCO is designed in 2Snm process, realizes a phase noise of-110.2dBc/Hz@1MHz at 28GHz, and the frequency modulation range is 27.1GHz-29.2GHz. It consumes 25mW under 0. 9V supply voltage for a figure of merit (FOM) of l85.2dB.
A Study of the Inﬂuence of Environmental Factors on Water–Heat Exchange Process in Alpine Wetlands

: Wetlands, which are composed of soil, vegetation and water, have sufﬁcient water supply and are sensitive to climate change. This study analyzes the coupling degree between wetlands and atmosphere and discusses the inﬂuence of environmental factors (solar radiation and water vapor pressure deﬁcit) on latent heat ﬂux by using the experimental data from the Maduo Observatory of Climate and Environment of the Northwest Institute of Eco-Environment and Resource, CAS and WRF models. The results showed that, during the vegetation growing season, the average value of Ω (decoupling factor) is 0.38 in alpine wetlands, indicating that the coupling between wetlands and atmosphere is poor. Solar radiation is the main factor inﬂuencing the latent heat ﬂux in the results of both observation data analysis and model simulation, and solar radiation and water vapor pressure deﬁcit still have the opposite reaction to latent heat ﬂux; when solar radiation increased by 30%, the average daily amount of latent heat ﬂux increased from 5.57 MJ · m − 2 to 7.50 MJ · m − 2 ; when water vapor pressure deﬁcit increased by 30%, the average daily amount of latent heat ﬂux decreased to 5.17 MJ · m − 2 . This study provides a new research approach for the study of the parameterization of latent heat ﬂux and evapotranspiration in the context of global climate change
Forage Yield and Nutritive Values of Phacelia (Phacelia tanacetifolia Benth.) as affected by different nitrogen doses under warm temperate type climate in Turkey

The aim of the study was to examine the changes in the yield, forage quality and nutritive value of the phacelia due to different nitrogen doses for potential use in Köpen Geiger Csa type hot and temperate The investigation evaluated nutritive values of the three phacelia genotypes (Saglamtimur, Stala and Enton) after application of five nitrogen doses (0 kg ha -1 , 25 kg ha -1 , 50 kg ha -1 , 75 kg ha -1 , 100 kg ha -1 ). The fresh matter yield and dry matter yield increased in with increasing nitrogen doses (0 to 100 kg ha -1 ). The maximum fresh matter and dry matter yield was determined in Stala genotype with 20.01 t ha -1 and 18.48 t ha -1 , while minimum fresh matter and dry matter yield was determined in Saglamtimur with 17.64 t ha -1 15.79 t ha -1 , respectively. The results confirmed that crude protein percentage increased using 100 kg ha -1 nitrogenous fertilizer with the highest crude protein percentage of 19.68%. A significant decrease in acid detergent fiber and neutral detergent fiber values was also noted. Total Digestible Nutrient values increased with increased nitrogen doses. Digestible dry matter (65.87 %), dry matter intake (2.95 %), relative feed value (150.68 %) values gave the highest values using 75 kg ha -1 N. Whereas, 50, 75 and 100 kg ha -1 application of nitrogen gave the maximum net energy lactation value (1.526), and availability of potassium (4.063 %), calcium (1.972 %), magnesium (0.696 %) and phosphorus (0.296 %) contents from the dried matter of the plant. Comparing analyzed parameters for genotypes and nitrogen doses, the genotype Enton using 75 kg ha -1 nitrogen dose appeared as the optimum genotype. In light of these results, it is concluded that phacelia is suitable for Csa climate type conditions. Due to crude protein percentage (19.68%) and high digestion percentage, it should be used more in terms of animal feeding rotation.
IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study

OBJECTIVES: Elevated intracranial pressure (ICP) is a potentially devastating complication of neurologic injury. Developing an ICP prediction algorithm to help the clinician adjust treatments and potentially prevent elevated ICP episodes. DESIGN: Retrospective study. SETTING: Three hundred thirty-five ICUs at 208 hospitals in the United States. SUBJECTS: Adults patients from the electronic ICU (eICU) Collaborative Research Database was used to train an ensemble machine learning model to predict the ICP 30 minutes in the future. Predictive performance was evaluated using a left-out test dataset and externally evaluated on the Medical Information Mart for Intensive Care-III (MIMIC-III) Matched Waveform Database. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Predictors included age, assigned sex, laboratories, medications and infusions, input/output, Glasgow Coma Scale (GCS) components, and time-series vitals (heart rate, ICP, mean arterial pressure, respiratory rate, and temperature). Each patient ICU stay was divided into successive 95-minute timeblocks. For each timeblock, the model was trained on nontime-varying covariates as well as on 12 observations of time-varying covariates at 5-minute intervals and asked to predict the 5-minute median ICP 30 minutes after the last observed ICP value. Data from 931 patients with ICP monitoring in the eICU dataset were extracted (46,207 timeblocks). The root mean squared error was 4.51 mm Hg in the eICU test set and 3.56 mm Hg in the MIMIC-III dataset. The most important variables driving ICP prediction were previous ICP history, patients’ temperature, weight, serum creatinine, age, GCS, and hemodynamic parameters. CONCLUSIONS: IntraCranial pressure prediction AlgoRithm using machinE learning, an ensemble machine learning model, trained to predict the ICP of a patient 30 minutes in the future based on baseline characteristics and vitals data from the past hour showed promising predictive performance including in an external validation dataset.
UvA-DARE ( Digital Academic Repository ) Thyroid disease and haemostasis : a relationship with clinical implications ?

Introduction: Various changes in the coagulation-fibrinolytic system have been described in patients with an excess or deficiency of thyroid hormones. The purpose of this systematic review is to summarize the effects of hyperthyroidism and hypothyroidism on these systems. Methods: All published case-control or interventional cohort studies that evaluated the effects of hyperthyroidism and hypothyroidism on the coagulationfibrinolytic system in vivo were identified by a computer-assisted search of the MEDLINE and EMBASE electronic databases. A scoring system was used to divide studies into three quality categories: high, medium, and low quality. Results: A total of 36 papers were included. Because in several papers more than one case-control study or both a case-control and intervention study were described, a total of 39 case-control studies and 24 interventional cohort studies were analyzed. No high quality study was identified. Three (7.7%) casecontrol and eight (33.3%) cohort studies were of medium quality. A total of 19 tests were investigated in the medium-quality studies. These tests revealed a hypocoagulable state for overt hypothyroidism and a hypercoagulable state for overt hyperthyroidism. Conclusion: This analysis confirmed that clinically overt hyperthyroidism and hypothyroidism modify the coagulation-fibrinolytic balance, indicating that thyroid hormone excess or deficit is the probable main pathophysiological mechanism. Patients with overt hypothyroidism and overt hyperthyroidism appear to have an increased risk of bleeding and of thrombosis, respectively. 32