Miracle-dz/UniFAR-FTUs huggingface dataset:
Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach

Climate change is affecting global viticulture, increasing heatwaves and drought. Precision irrigation, supported by robust water status indicators (WSIs), is inevitable in most of the Mediterranean basin. One of the most reliable WSIs is the leaf water potential (Ψleaf), which is determined via an intrusive and time-consuming method. The aim of this work is to discern the most effective variables that are correlated with plants’ water status and identify the variables that better predict Ψleaf. Five grapevine varieties grown in the Alentejo region (Portugal) were selected and subjected to three irrigation treatments, starting in 2018: full irrigation (FI), deficit irrigation (DI), and no irrigation (NI). Plant monitoring was performed in 2023. Measurements included stomatal conductance (gs), predawn water potential Ψpd, stem water potential (Ψstem), thermal imaging, and meteorological data. The WSIs, namely Ψpd and gs, responded differently according to the irrigation treatment. Ψstem measured at mid-morning (MM) and mid-day (MD) proved unable to discern between treatments. MM measurements presented the best correlations between WSIs. gs showed the best correlations between the other WSIs, and consequently the best predictive capability to estimate Ψpd. Machine learning regression models were trained on meteorological, thermal, and gs data to predict Ψpd, with ensemble models showing a great performance (ExtraTrees: R2=0.833, MAE=0.072; Gradient Boosting: R2=0.830; MAE=0.073).
Adjusting the Quasi-Stiffness of an Ankle-Foot Prosthesis Improves Walking Stability during Locomotion over Compliant Terrain

Despite significant advances in the design of robotic lower-limb prostheses for individuals with impaired mobility, there is a need for further progress in improving the robustness, safety, and stability of these devices in a wide range of activities of daily living. Although powered prostheses have been able to adapt to different speeds, conditions, and rigid terrains, no control strategies have been proposed for addressing walking over compliant surfaces. This work proposes a continuous admittance controller that adjusts the ankle quasistiffness of a powered ankle-foot prosthesis and improves gait stability during locomotion over compliant terrain. The proposed controller is evaluated with walking experiments on an instrumented treadmill that can accurately change the walking surface stiffness. In these experiments, the proposed controller accurately changes the prosthesis ankle quasi-stiffness across a wide range of $10-20\frac{Nm}{deg}-($ while improving local dynamic stability compared to a standard phase-variable controller. The proposed controller can significantly improve the performance of lower-limb prostheses in dynamic and compliant environments frequently encountered in daily activities, resulting in improved quality of life for people with lower-limb amputation.
The insignificance of Seyfert 2 activity in driving cold-gas galactic winds

We study the interstellar Na I λλ5890, 5895 (Na D) absorption-line doublet in a large sample of ∼9900 nearby Seyfert 2 galaxies, in order to quantify the significance of optical, unobscured AGN activity in driving kpc-scale outflows that can quench star formation. Comparison to a carefully matched sample of ∼44 000 control objects indicates that the Seyfert and control population have similar Na D detection rates (∼5–6 per cent). Only 53 Seyferts (or 0.5 per cent of the population) are found to potentially display galactic-scale winds, compared to 0.8 per cent of the control galaxies. While nearly a third of the Na D outflows observed in our Seyfert 2 galaxies occur around the brightest AGN, both radio and infrared data indicate that star formation could play the dominant role in driving cold-gas outflows in an even higher fraction of the Na D-outflowing Seyfert 2s. Our results indicate that galactic-scale outflows at low redshift are no more frequent in Seyferts than they are in their control-sample counterparts, that optical AGN are not direct significant contributors to the quenching of star formation in the nearby Universe, and that star-formation may actually be the principal driver of outflows even in systems that do host an unobscured AGN.
The Effect of Computerized Cognitive Training on Body Function and Activity Participation of Patient with Schizophrenia

Schizophrenia spectrum disorder (SSD) is a neurobiological illness that causes considerable deficits in body functions and activity participation. This study examined the effects of a computerized cognitive training (CCT) on cognition, motor functions and activity participation with a quasi-experimental, pretest-posttest design. A total of 14 patients participated. All participants underwent two stages of CCT but one did not complete the posttest. The training was 3 times a week, 40 minutes each time and lasted for 12 weeks. Cognition, upper extremity motor, postural control performance, and activity participation (work behavior) were measured at three times: before and after 6 and 12 weeks of training. CCT improved participant’s cognition and work behavior significantly and have the tendency to improve motor function. The training dosage might be insufficient for motor function improvement.Clinical Relevance— SSD patients benefit from the CCT in cognition and work behavior significantly but Motor function slightly
Adaptive and Fail-Safe Magnetic Gripper with Charging Function for Drones on Power Lines

Drone grasping on power lines for recharging is challenging since it requires the gripper to be lightweight, carried by a drone, and efficient for a firm grasp. A deep understanding of the power line nature and its magnetic characteristic helps ease such challenges and bring new knowledge to gripper design. In this work, a novel adaptive, lightweight, and fail-safe magnetic gripper with a recharging feature is presented. The gripper exploits the radiated magnetic field of the lines for charging and holding the drone and can easily detach from the line. The gripper design has been validated in the lab and on a quadcopter with a real power line.
Deep-Learning Markerless Tracking of Infant General Movements using Standard Video Recordings

Monitoring spontaneous General Movements (GM) of infants 6–20 weeks post-term age is a reliable tool to assess the quality of neurodevelopment in early infancy. Abnormal or absent GMs are reliable prognostic indicators of whether an infant is at risk of developing neurological impairments and disorders such as cerebral palsy (CP). Therapeutic interventions are most effective at improving neuromuscular outcomes if administered in early infancy. Current clinical protocols require trained assessors to rate videos of infant movements, a time-intensive task. This work proposes a simple, inexpensive, and broadly applicable markerless pose-estimation approach for automatic infant movement tracking using conventional video recordings from handheld devices (e.g., tablets and mobile phones). We leverage the enhanced capabilities of deep-learning technology in image processing to identify 12 anatomical locations (3 per limb) in each video frame, tracking a baby’s natural movement throughout the recordings. We validate the capability of resnet152 and a mobile-net-v2-1 to identify body-parts in unseen frames from a full-term male infant, using a novel automatic unsupervised approach that fuses likelihood outputs of a Kalman filter and the deep-nets. Both deep-net models were found to perform very well in the identification of anatomical locations in the unseen data with high average Percentage of Correct Keypoints (aPCK) performances of >99.65% across all locations.Clinical relevance—Results of this research confirm the feasibility of a low-cost and publicly accessible technology to automatically track infants’ GMs and diagnose those at higher risk of developing neurological conditions early, when clinical interventions are most effective.
Influence of Physiological Determinants on Acoustic Parameters of Forced Expiratory Tracheal Sounds

Significant disadvantages of standard lung auscultation are subjectivity and exclusively qualitative assessment of the measurement results. Developments in digital signal processing of breath sounds have obvious potential to turn auscultation into a scientific method. Forced expiratory (FE) tracheal noise analysis has proven to be a useful tool for detecting bronchial obstruction. The developed processing method with splitting the signal into bands of 200 Hz in the range of 200-2000 Hz has the potential to increase the efficiency of acoustic diagnostics. However, the influence of physiological determinants on the spectra of respiratory noises has not been studied enough. The purpose of this work is to study the relationships between the spectral characteristics of FE wheezes and 200 Hz frequency-time parameters of FE noises with age and anthropometry. As well as assess gender differences in these parameters. On a sample of 41 healthy subjects of both sexes aged from 20 to 59 years, significant correlations of acoustic parameters with age and anthropometry were found. But the strength of these relationships was very moderate. However, the acoustic characteristics of the tracheal noises differed between men and women. Peak frequencies of mid-frequency wheezes were higher in women, and their maximum amplitudes were higher in men. Gender differences were found in both time and frequency domains. In women, the proportion of the higher frequency band (600-800 Hz) of the mid-frequency range prevailed, while in men the proportion of the band 200-400 Hz. This should be taken into account when developing approaches to normalizing acoustic parameters.
Real-Time NMPC for an Automated Valet Parking with Load-Based Safety Constraints and a Path-Parametric Model

As global living standards continue to rise and urbanization accelerates, cities worldwide face high demands for parking. To tackle this issue and foster the efficient use of limited parking capacity, automated valet parking (AVP) solutions have emerged as a resource optimization and hassle-free alternative. This paper addresses the problem of reducing retrieval time in AVP systems while ensuring the integrity of both the robot and the car through critical adherence conditions. The key novelty of our approach is that we dovetail these conditions as acceleration constraints into a multi-stage motion generation framework formulated as a real-time nonlinear model predictive control (NMPC) scheme that approximates time-optimal behavior by maximizing progress on a path. The NMPC generates trajectories based on the motion direction, allowing for convenient parameterization of controller instances and unlimited parking maneuvers. Thanks to high-performance software implementations, the resulting quadratic subprograms can be solved in the order of milliseconds. Comparative analysis against the currently implemented pure pursuit algorithm and an experimental validation on Stanley Robotics' robot have shown a 31% time improvement following a path and constraint satisfaction in the loaded scenario for the proposed controller.
Ultrasound for Venous Local Pulse Wave Velocity: Comparison of Pulse Transit Time Methods

Venous pulse wave velocity (vPWV) is a potential marker for determining the state of venous hemodynamics, venosclerosis, and vascular filling. Although there have been several studies on pulse wave velocity through blood vessels, the majority have focused on arteries, with only limited studies on veins. To our knowledge, this study is the first to compare the local vPWV estimation metrices. An in vivo study was conducted on 10 participants where the jugular venous pulses (JVP) from two proximal sites were simultaneously acquired using a dual-element high frame rate system. The local vPWV was computed using different transit time-based techniques. The study demonstrates the comparison between vPWV ranges computed using thresholding, fiduciary point (c and v) and correlation-based approaches indicated as vPWV|th, vPWV|c, vPWV|v and vPWV|Xcorr respectively. High fidelity echo frames were acquired from the jugular vein (JV) at a temporal resolution of 2 ms and an amplitude resolution of 10 µm. The study findings indicated that the vPWV computed using various transit time metrics were comparable without significant bias (p > 0.05). Among the VPWV metrices, vPWV|th had the lowest beat-to-beat variation (CoV = 18 %). The mean deviations in vPWV|c, vPWV|v and vPWV|Xcorr values from vPWV|th were 0.28, 0.17 and 0.22 m/s respectively, where the average beat-to-beat variation was minimal. The results suggested that the thresholding and cross-correlation metrices offered better performance in comparison with the fiduciary point techniques for vPWV estimation.Clinical Relevance— The study demonstrated the potential of direct transit time methods to reliably estimate the local vPWV on the internal jugular vein.
A Pilot In Vivo Study of Flexible Fully Polymeric Nerve Cuff Electrodes*

Recent trends in the field of bioelectronics have been focused on the development of electrodes that facilitate safe and efficient stimulation of nervous tissues. Novel conducting polymer (CP) based materials, such as flexible and fully polymeric conductive elastomers (CEs), constitute a promising alternative to improve on the limitations of current metallic devices. This pilot study demonstrates the performance of tripolar CE-based peripheral nerve cuffs compared to current commercial tripolar platinum-iridium (PtIr) nerve cuffs in vivo. CE and metallic cuff devices were implanted onto rodent sciatic nerves for a period of 8 weeks. Throughout the entire study, the CE device demonstrated improved charge transfer and electrochemical safety compared to the PtIr cuff, able to safely inject 2 to 3 times more charge. In comparison to the commercial control, the CE cuff was able to record in the in vivo setting with reduced noise and produced smaller voltages at all simulation levels. CE technologies provide a promising alternative to metallic devices for the development of bioelectronics with enhanced chronic device functionality.
Assessment of a Robot Design: A novel Methodology using Eye-tracking and Semantic Associations Among undergraduate students´ contexts

Robots are increasingly being used in different industries, so product design education must be familiar with such contexts. Specifically, in the medical and nursing sectors, robots perform duties to enhance patients' physiological and emotional health. This paper focuses on showing the methodology designed by professors and design students for the assessment of robot design for medical purposes. The objective of this work is to demonstrate the advantages of the methodology that integrates two main assessment tools: the semantic differential (SD) questionnaire and Eye-Tracking (ET) technology. These tools were applied to deepen the assessment of specific morphological elements of the robot body, as well as emotional descriptive elements of a previously evaluated robot. The research protocol was applied among undergraduate students from a Mexican and a Japanese university. First, a survey on robot-related aptitude and previous experience was conducted. Second, an SD tool based on the Goodspeed index, along with ET technology using high-quality renders of the robot, was implemented. The findings of the methodology application demonstrated the novelty of the procedure and its outcomes. The survey on previous experience showed a similarity between the students of the two universities, indicating a baseline experience with SD and ET regarding variables such as emotional perception and visual element observation. The students exhibited similarities in most of the outcomes, both in terms of the understanding of emotional and physical elements observed.
Exploring the Success Factors Digital Lending: An Approach from Consumer's Perspective

The rapid growth of fintech has given rise to many fintech companies and new banking services. Thanks to technological advances, digital lending is a service that is widely used by the public. However, the number of inabilities to pay is a problem with digital lending, which is why this research was conducted. By conducting this research, we can determine what factors influence money borrowers to be aware of paying their digital loan debts. This research was conducted quantitatively by distributing questionnaires to borrowers of money in digital lending at banks. Data was collected using purposive sampling using Google Forms, and SMART PLS, a statistical tool used to calculate 100 data responses from Indonesia. The result from eight hypotheses found only the five hypotheses significant. It found that perceived risk and financial inclusion are the factors most influencing the intention to pay for digital lending. It can be a concern for banking actors to pay more attention to matters that affect their customers' perceived risk and financial inclusion.
Synchronization on Sparse-Preamble for GFDM

Though the generalized frequency division multiplexing (GFDM) is a strong waveform candidate in replacing the orthogonal frequency division multiplexing (OFDM) in 5G scenario, its implementation must have proper synchronization on both symbol timing offset (STO) as well as carrier frequency offset (CFO). The multipath may deviate the estimation by altering the correlation result, leading to estimation error, and ultimately affecting the decoding result. This paper proposes and evaluates the edge and sparse preamble. The concentrated samples in edge preamble lead to poorer performance due to consecutive inactive samples. In contrast, the sparse preamble can maintain the estimation. However, too many subsymbols led to a closer configuration to edge preamble, thus leading to worse performance. Ultimately, the usage of 2 subsymbols is able to improve the CFO estimation without affecting the estimation of STO significantly.
Optimal Planning of Distribution Network with DG Units and ESS Considering Costs

This paper focuses on the application of the artificial ecosystem-based optimizer opposition-based learning (AEO-OBL) technique to optimal integration of renewable energy sources (RES) based distributed generation (DG) units, namely wind turbine (WT), photovoltaic (PV) and energy storage system (ESS) in the 33-bus distribution network (DN). This study is designed to minimize the total costs associated with ESS, PV, and WT-based DG units, installation, operation, maintenance costs, energy loss cost, cost of purchasing energy from the grid and emission cost taking into account uncertainty in generation of power, and variable load demands. According to the simulation results, it has been demonstrated that the total costs associated with the operation of the integrated system can be significantly reduced by optimally connecting DG units and ESS. Furthermore, the proposed algorithm has been demonstrated to be more effective in solving the optimization problem compared to other techniques, such as artificial ecosystem-based optimization (AEO), moth-flame optimization (MFO), and particle swarm optimization (PSO). According to the results of the test, AEO-OBL proved to be more efficient than other techniques when compared with other techniques.
New insights into fault activation and stress transfer between en echelon thrusts: The 2012 Emilia, Northern Italy, earthquake sequence

Here we present the results of the inversion of a new geodetic data set covering the 2012 Emilia seismic sequence and the following 1 year of postseismic deformation. Modeling of the geodetic data together with the use of a catalog of 3‐D relocated aftershocks allows us to constrain the rupture geometries and the coseismic and postseismic slip distributions for the two main events (Mw 6.1 and 6.0) of the sequence and to explore how these thrust events have interacted with each other. Dislocation modeling reveals that the first event ruptured a slip patch located in the center of the Middle Ferrara thrust with up to 1 m of reverse slip. The modeling of the second event, located about 15 km to the southwest, indicates a main patch with up to 60 cm of slip initiated in the deeper and flatter portion of the Mirandola thrust and progressively propagated postseismically toward the top section of the rupture plane, where most of the aftershocks and afterslip occurred. Our results also indicate that between the two main events, a third thrust segment was activated releasing a pulse of aseismic slip equivalent to a Mw 5.8 event. Coulomb stress changes suggest that the aseismic event was likely triggered by the preceding main shock and that the aseismic slip event probably brought the second fault closer to failure. Our findings show significant correlations between static stress changes and seismicity and suggest that stress interaction between earthquakes plays a significant role among continental en echelon thrusts.
Pervasive and Mobile Computing

Mobile phone users have to deal with limited battery lifetime through a reciprocal process wecallhuman–batteryinteraction.Weconductedthreeuserstudiesinordertounderstand human–battery interaction and discover the problems in existing designs that prevent users from effectively dealing with the limited battery lifetime. The studies include a large-scale international survey, two long-term field trials including quantitative battery logging and qualitative inquiries, and structured interviews with twenty additional mobile phone users. We evaluated various aspects of human–battery interaction, including charging behavior,batteryindicators,userinterfacesforpower-savingsettings,userknowledge,and userreaction.Wefindthatmobilephoneuserscanbecategorizedintotwotypesregarding human–battery interaction and often have inadequate knowledge regarding phone power characteristics. We provide qualitative and quantitative evidence that problems in state-of-the-art user interfaces have led to under-utilized power-saving settings, under-utilized battery energy, and dissatisfied users. Our findings provide insights into improving mobile phone design for users to effectively deal with the limited battery lifetime. Our work is the first to systematically address human–battery interaction on mobile phones and is complementary to the extensive research on energy-efficient design for a longer battery lifetime.
Effect of Priming and Storage on Seed Quality of Chilli

The present research was undertaken to study the effect of priming and storage of seeds of chilli freshly harvested seeds of chilli var. Anugraha were stored under ambient condition for 12 months. Seed samples were drawn after 3, 6 and 9 months of storage and subjected to priming treatments viz. Control (P1) , Water soaking (P2), NaCl (10-5M) (P3), CaCl2 (10-5M), (P4 5), PEG 6000 (-1.5MPa) (P6) and (10g/kg seed) (P7). The primed seeds were stored in two types of storage containers i.e. cloth bag (S1) and polythene bag (700 gauge) (S2). The seeds primed with PEG 6000 (-1.5MPa) and stored in polythene bag (700 gauge) (S2) retained high hand, the seed quality parameters of control seeds stored in cloth bag was declined.