Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural networks for Anatomical Therapeutic Chemical (ATC)

PurposeAutomatic anatomical therapeutic chemical (ATC) classification is progressing at a rapid pace because of its potential in drug development. Predicting an unknown compound's therapeutic and chemical characteristics in terms of how it affects multiple organs and physiological systems makes automatic ATC classification a vital yet challenging multilabel problem. The aim of this paper is to experimentally derive an ensemble of different feature descriptors and classifiers for ATC classification that outperforms the state-of-the-art.Design/methodology/approachThe proposed method is an ensemble generated by the fusion of neural networks (i.e. a tabular model and long short-term memory networks (LSTM)) and multilabel classifiers based on multiple linear regression (hMuLab). All classifiers are trained on three sets of descriptors. Features extracted from the trained LSTMs are also fed into hMuLab. Evaluations of ensembles are compared on a benchmark data set of 3883 ATC-coded pharmaceuticals taken from KEGG, a publicly available drug databank.FindingsExperiments demonstrate the power of the authors’ best ensemble, EnsATC, which is shown to outperform the best methods reported in the literature, including the state-of-the-art developed by the fast.ai research group. The MATLAB source code of the authors’ system is freely available to the public at https://github.com/LorisNanni/Neural-networks-for-anatomical-therapeutic-chemical-ATC-classification.Originality/valueThis study demonstrates the power of extracting LSTM features and combining them with ATC descriptors in ensembles for ATC classification.
Failure Detection Methods for Pipeline Networks: From Acoustic Sensing to Cyber-Physical Systems

Pipeline networks have been widely utilised in the transportation of water, natural gases, oil and waste materials efficiently and safely over varying distances with minimal human intervention. In order to optimise the spatial use of the pipeline infrastructure, pipelines are either buried underground, or located in submarine environments. Due to the continuous expansion of pipeline networks in locations that are inaccessible to maintenance personnel, research efforts have been ongoing to introduce and develop reliable detection methods for pipeline failures, such as blockages, leakages, cracks, corrosion and weld defects. In this paper, a taxonomy of existing pipeline failure detection techniques and technologies was created to comparatively analyse their respective advantages, drawbacks and limitations. This effort has effectively illuminated various unaddressed research challenges that are still present among a wide array of the state-of-the-art detection methods that have been employed in various pipeline domains. These challenges include the extension of the lifetime of a pipeline network for the reduction of maintenance costs, and the prevention of disruptive pipeline failures for the minimisation of downtime. Our taxonomy of various pipeline failure detection methods is also presented in the form of a look-up table to illustrate the suitability, key aspects and data or signal processing techniques of each individual method. We have also quantitatively evaluated the industrial relevance and practicality of each of the methods in the taxonomy in terms of their respective deployability, generality and computational cost. The outcome of the evaluation made in the taxonomy will contribute to our future works involving the utilisation of sensor fusion and data-centric frameworks to develop efficient, accurate and reliable failure detection solutions.
Development of a Smart Leg Splint by Using New Sensor Technologies and New Therapy Possibilities

Nowadays, after suffering a fracture in an upper or lower limb, a plaster cast is placed on the affected limb. It is a very old and efficient technique for recovery from an injury that has not had significant changes since its origin. This project aims to develop a new low-cost smart 3D printed splint concept by using new sensing techniques. Two rapidly evolving Advanced Manufacturing (AM) technologies will be used: 3D scanning and 3D printing, thus combining engineering, medicine and materials evolution. The splint will include new small and lightweight sensors to detect any problem during the treatment process. Previous studies have already incorporated this kind of sensor for medical purposes. However, in this study it is implemented with a new concept: the possibility of applying treatments during the immobilization process and obtaining information from the sensors to modify the treatment. Due to this, rehabilitation treatments like infrared, ultrasounds or electroshock may be applied during the treatment, and the sensors (as it is showed in the study) will be able to detect changes during the rehabilitation process. Data of the pressure, temperature, humidity and colour of the skin will be collected in real time and sent to a mobile device so that they can be consulted remotely by a specialist. Moreover, it would be possible to include these data into the Internet of Things movement. This way, all the collected data might be compared and studied in order to find the best treatment for each kind of injury. It will be necessary to use a biocompatible material, submersible and suitable for contact with skin. These materials make it necessary to control the conditions in which the splint is produced, to assure that the properties are maintained. This development, makes it possible to design a new methodology that will help to provide faster and easier treatment.
Neural Dynamics of Target Detection via Wireless EEG in Embodied Cognition

Embodied cognitive attention detection is important for many real-world applications, such as monitoring attention in daily driving and studying. Exploring how the brain and behavior are influenced by visual sensory inputs becomes a major challenge in the real world. The neural activity of embodied mind cognitive states can be understood through simple symbol experimental design. However, searching for a particular target in the real world is more complicated than during a simple symbol experiment in the laboratory setting. Hence, the development of realistic situations for investigating the neural dynamics of subjects during real-world environments is critical. This study designed a novel military-inspired target detection task for investigating the neural activities of performing embodied cognition tasks in the real-world setting. We adopted independent component analysis (ICA) and electroencephalogram (EEG) dipole source localization methods to study the participant’s event-related potentials (ERPs), event-related spectral perturbation (ERSP), and power spectral density (PSD) during the target detection task using a wireless EEG system, which is more convenient for real-life use. Behavioral results showed that the response time in the congruent condition (582 ms) was shorter than those in the incongruent (666 ms) and nontarget (863 ms) conditions. Regarding the EEG observation, we observed N200-P300 wave activation in the middle occipital lobe and P300-N500 wave activation in the right frontal lobe and left motor cortex, which are associated with attention ERPs. Furthermore, delta (1–4 Hz) and theta (4–7 Hz) band powers in the right frontal lobe, as well as alpha (8–12 Hz) and beta (13–30 Hz) band powers in the left motor cortex were suppressed, whereas the theta (4–7 Hz) band powers in the middle occipital lobe were increased considerably in the attention task. Experimental results showed that the embodied body function influences human mental states and psychological performance under cognition attention tasks. These neural markers will be also feasible to implement in the real-time brain computer interface. Novel findings in this study can be helpful for humans to further understand the interaction between the brain and behavior in multiple target detection conditions in real life.
EVA Films Loaded with Layered Double Hydroxide (LDH) Modified with Methacrylic Anion: Effect of the Nanohybrid Filler on the Photodegradation Phenomena

The photo-oxidative studies of ethylene vinyl acetate copolymer (EVA) matrix, filled with Layered Double Hydroxide (LDH) modified with methacrylic anion (MA), were herein reported, together with gas permeation tests. The formulation of nano-hybrid LDHs was characterized using X-ray diffractometry (XRD) and thermogravimetric analysis (TGA), demonstrating the partial intercalation of the 30% of MA anion between the LDH’s galleries. The as-modified filler was introduced into an EVA matrix by mechanical milling, producing free-standing films subjected to accelerated aging. Fourier transform infrared spectroscopy (FT-IR) results suggested that the nanohybrid presence determined a stabilizing effect up to 45 days of UV irradiation, especially if compared to the EVA/LDH references for all formulated EVA hybrid nanocomposites. Conversely, the presence of nanohybrid in the matrix did not significantly change the thermal stability of EVA samples. The dispersion of modified MA-LDH in the EVA matrix produces defect-free samples in the whole range of investigated loadings. The samples show a slight decrease in gas permeability, coupled with a substantial stabilization of the original CO2/O2 selectivity, which also proves the integrity of the films after 30 days of UV irradiation.
Using Plasma Etching to Access the Polymer Density Distribution and Diffusivity of Gel Particles

In this paper we examine the polymer density distribution of gel particles and its effect on solvent diffusivity through the polymer network. In order to access the inner particle regions, external polymer layers were removed by plasma etching, thus reducing them from the outside. Higher polymer densities after erosion showed internal heterogeneity, with the density increasing towards the center of the particles. An exponential decay polymer density model is proposed, and the spatial relaxation length measured. The diffusion of solvent through the particles, before and after the plasma oxidation, revealed a correlation between the diffusion coefficient and the internal density.
Cardiorespiratory Responses to 10 Weeks of Exoskeleton-Assisted Overground Walking Training in Chronic Nonambulatory Patients with Spinal Cord Injury

Exercise intensity of exoskeleton-assisted walking in patients with spinal cord injury (SCI) has been reported as moderate. However, the cardiorespiratory responses to long-term exoskeleton-assisted walking have not been sufficiently investigated. We investigated the cardiorespiratory responses to 10 weeks of exoskeleton-assisted walking training in patients with SCI. Chronic nonambulatory patients with SCI were recruited from an outpatient clinic. Walking training with an exoskeleton was conducted three times per week for 10 weeks. Oxygen consumption and heart rate (HR) were measured during a 6-min walking test at pre-, mid-, and post-training. Exercise intensity was determined according to the metabolic equivalent of tasks (METs) for SCI and HR relative to the HR reserve (%HRR). Walking efficiency was calculated as oxygen consumption divided by walking speed. The exercise intensity according to the METs (both peak and average) corresponded to moderate physical activity and did not change after training. The %HRR demonstrated a moderate (peak %HRR) and light (average %HRR) exercise intensity level, and the average %HRR significantly decreased at post-training compared with mid-training (31.6 ± 8.9% to 24.3 ± 7.3%, p = 0.013). Walking efficiency progressively improved after training. Walking with an exoskeleton for 10 weeks may affect the cardiorespiratory system in chronic patients with SCI.
A Review on Mental Stress Assessment Methods Using EEG Signals

Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.
Combining Sensors Information to Enhance Pneumatic Grippers Performance

The gripper is the far end of a robotic arm. It is responsible for the contacts between the robot itself and all the items present in a work space, or even in a social space. Therefore, to provide grippers with intelligent behaviors is fundamental, especially when the robot has to interact with human beings. As shown in this article, we built an instrumented pneumatic gripper prototype that relies on different sensors’ information. Thanks to such information, the gripper prototype was able to detect the position of a given object in order to grasp it, to safely keep it between its fingers and to avoid slipping in the case of any object movement, even very small. The gripper performance was evaluated by means of a generic grasping algorithm for robotic grippers, implemented in the form of a state machine. Several slip tests were carried out on the pneumatic gripper, which showed a very fast response time and high reliability. Objects of various size, shape and hardness were employed to reproduce different grasping scenarios. We demonstrate that, through the use of force, torque, center of pressure and proximity information, the behavior of the developed pneumatic gripper prototype outperforms the one of the traditional pneumatic gripping devices.
Improved Mechanical, Thermal, and Hydrophobic Properties of PLA Modified with Alkoxysilanes by Reactive Extrusion Process

An eco-friendly strategy for the modification of polylactic acid (PLA) surface properties, using a solvent-free process, is reported. Reactive extrusion (REX) allowed the formation of new covalent bonds between functional molecules and the PLA polymeric matrix, enhancing its mechanical properties and modifying surface hydrophobicity. To this end, the PLA backbone was modified using two alkoxysilanes, phenyltriethoxysilane and N-octyltriethoxysilane. The reactive extrusion process was carried out under mild conditions, using melting temperatures between 150 and 180 °C, 300 rpm as screw speed, and a feeding rate of 3 kg·h−1. To complete the study, flat tapes of neat and functionalized PLA were obtained through monofilament melt extrusion to quantify the enhancement of mechanical properties and hydrophobicity. The results verified that PLA modified with 3 wt% of N-octyltriethoxysilane improves mechanical and thermal properties, reaching Young’s modulus values of 4.8 GPa, and PLA hydrophobic behavior, with values of water contact angle shifting from 68.6° to 82.2°.
Investigating Physio-Thermo-Mechanical Properties of Polyurethane and Thermoplastics Nanocomposite in Various Applications

The effect of the soft and hard polyurethane (PU) segments caused by the hydrogen link in phase-separation kinetics was studied to investigate the morphological annealing of PU and thermoplastic polyurethane (TPU). The significance of the segmented PUs is to achieve enough stability for further applications in biomedical and environmental fields. In addition, other research focuses on widening the plastic features and adjusting the PU–polyimide ratio to create elastomer of the poly(urethane-imide). Regarding TPU- and PU-nanocomposite, numerous studies investigated the incorporation of inorganic nanofillers such as carbon or clay to incorporating TPU-nanocomposite in several applications. Additionally, the complete exfoliation was observed up to 5% and 3% of TPU–clay modified with 12 amino lauric acid and benzidine, respectively. PU-nanocomposite of 5 wt. % Cloisite®30B showed an increase in modulus and tensile strength by 110% and 160%, respectively. However, the nanocomposite PU-0.5 wt. % Carbone Nanotubes (CNTs) show an increase in the tensile modulus by 30% to 90% for blown and flat films, respectively. Coating PU influences stress-strain behavior because of the interaction between the soft segment and physical crosslinkers. The thermophysical properties of the TPU matrix have shown two glass transition temperatures (Tg’s) corresponding to the soft and the hard segment. Adding a small amount of tethered clay shifts Tg for both segments by 44 °C and 13 °C, respectively, while adding clay from 1 to 5 wt. % results in increasing the thermal stability of TPU composite from 12 to 34 °C, respectively. The differential scanning calorimetry (DSC) was used to investigate the phase structure of PU dispersion, showing an increase in thermal stability, solubility, and flexibility. Regarding the electrical properties, the maximum piezoresistivity (10 S/m) of 7.4 wt. % MWCNT was enhanced by 92.92%. The chemical structure of the PU–CNT composite has shown a degree of agglomeration under disruption of the sp2 carbon structure. However, with extended graphene loading to 5.7 wt. %, piezoresistivity could hit 10−1 S/m, less than 100 times that of PU. In addition to electrical properties, the acoustic behavior of MWCNT (0.35 wt.%)/SiO2 (0.2 wt.%)/PU has shown sound absorption of 80 dB compared to the PU foam sample. Other nanofillers, such as SiO2, TiO2, ZnO, Al2O3, were studied showing an improvement in the thermal stability of the polymer and enhancing scratch and abrasion resistance.
Hemostatic Patches Based on Crosslinked Chitosan Films Applied in Interventional Procedures

In this study, we manufactured biocompatible hemostatic crosslinked chitosan (CS) patches and analyzed their physicochemical and biological properties for femoral arterial puncture applications. CS is a representative hemostatic material but has some drawbacks, such as swelling, shrinkage, and brittleness. Thus, it was crosslinked via a 1-ethyl-3-(3-dimethyl aminopropyl) carbodiimide (EDC)/N-hydroxysuccinimide (NHS) coupling reaction and a nucleophilic addition reaction with citric acid (CA), glutaraldehyde (GTA), and genipin (GP) to remedy its shortcomings. The CSCA (crosslinked CS with CA/EDC), CSGTA (crosslinked CS with GTA), and CSG (crosslinked CS with GP) films showed low swelling degrees and good mechanical properties (excluding CSCA) compared with those of neat CS films. Additionally, every crosslinked CS film coated with thrombin (TB-CS) showed enhanced hemostatic ability in the whole blood clotting and activated partial thromboplastin time tests. Furthermore, the CSCA, CSGTA, and CSGP were nontoxic in an in vitro cell cytotoxicity test (3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide assay) using L-929 mouse fibroblasts cells.
Biomimetic Hierarchical Structuring of PLA by Ultra-Short Laser Pulses for Processing of Tissue Engineered Matrices: Study of Cellular and Antibacterial Behavior

The influence of ultra-short laser modification on the surface morphology and possible chemical alteration of poly-lactic acid (PLA) matrix in respect to the optimization of cellular and antibacterial behavior were investigated in this study. Scanning electron microscopy (SEM) morphological examination of the processed PLA surface showed the formation of diverse hierarchical surface microstructures, generated by irradiation with a range of laser fluences (F) and scanning velocities (V) values. By controlling the laser parameters, diverse surface roughness can be achieved, thus influencing cellular dynamics. This surface feedback can be applied to finely tune and control diverse biomaterial surface properties like wettability, reflectivity, and biomimetics. The triggering of thermal effects, leading to the ejection of material with subsequent solidification and formation of raised rims and 3D-like hollow structures along the processed zones, demonstrated a direct correlation to the wettability of the PLA. A transition from superhydrophobic (θ > 150°) to super hydrophilic (θ < 20°) surfaces can be achieved by the creation of grooves with V = 0.6 mm/s, F = 1.7 J/cm2. The achieved hierarchical architecture affected morphology and thickness of the processed samples which were linked to the nature of ultra-short laser-material interaction effects, namely the precipitation of temperature distribution during material processing can be strongly minimized with ultrashort pulses leading to non-thermal and spatially localized effects that can facilitate volume ablation without collateral thermal damage The obtained modification zones were analyzed employing Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS), Energy dispersive X-ray analysis (EDX), and optical profilometer. The modification of the PLA surface resulted in an increased roughness value for treatment with lower velocities (V = 0.6 mm/s). Thus, the substrate gains a 3D-like architecture and forms a natural matrix by microprocessing with V = 0.6 mm/s, F = 1.7 J/cm2, and V = 3.8 mm/s, F = 0.8 J/cm2. The tests performed with Mesenchymal stem cells (MSCs) demonstrated that the ultra-short laser surface modification altered the cell orientation and promoted cell growth. The topographical design was tested also for the effectiveness of bacterial attachment concerning chosen parameters for the creation of an array with defined geometrical patterns.
Phase Transitions in Poly(vinylidene fluoride)/Polymethylene-Based Diblock Copolymers and Blends

The crystallization and morphology of two linear diblock copolymers based on polymethylene (PM) and poly(vinylidene fluoride) (PVDF) with compositions PM23-b-PVDF77 and PM38-b-PVDF62 (where the subscripts indicate the relative compositions in wt%) were compared with blends of neat components with identical compositions. The samples were studied by SAXS (Small Angle X-ray Scattering), WAXS (Wide Angle X-ray Scattering), PLOM (Polarized Light Optical Microscopy), TEM (Transmission Electron Microscopy), DSC (Differential Scanning Calorimetry), BDS (broadband dielectric spectroscopy), and FTIR (Fourier Transform Infrared Spectroscopy). The results showed that the blends are immiscible, while the diblock copolymers are miscible in the melt state (or very weakly segregated). The PVDF component crystallization was studied in detail. It was found that the polymorphic structure of PVDF was a strong function of its environment. The number of polymorphs and their amount depended on whether it was on its own as a homopolymer, as a block component in the diblock copolymers or as an immiscible phase in the blends. The cooling rate in non-isothermal crystallization or the crystallization temperature in isothermal tests also induced different polymorphic compositions in the PVDF crystals. As a result, we were able to produce samples with exclusive ferroelectric phases at specific preparation conditions, while others with mixtures of paraelectric and ferroelectric phases.