Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning for Strawberry Canopy Delineation and Biomass Prediction from High-Resolution Images

Modeling plant canopy biophysical parameters at the individual plant level remains a major challenge. This study presents a workflow for automatic strawberry canopy delineation and biomass prediction from high-resolution images using deep neural networks. High-resolution (5 mm) RGB orthoimages, near-infrared (NIR) orthoimages, and Digital Surface Models (DSM), which were generated by Structure from Motion (SfM), were utilized in this study. Mask R-CNN was applied to the orthoimages of two band combinations (RGB and RGB-NIR) to identify and delineate strawberry plant canopies. The average detection precision rate and recall rate were 97.28% and 99.71% for RGB images and 99.13% and 99.54% for RGB-NIR images, and the mean intersection over union (mIoU) rates for instance segmentation were 98.32% and 98.45% for RGB and RGB-NIR images, respectively. Based on the center of the canopy mask, we imported the cropped RGB, NIR, DSM, and mask images of individual plants to vanilla deep regression models to model canopy leaf area and dry biomass. Two networks (VGG-16 and ResNet-50) were used as the backbone architecture for feature map extraction. The R2 values of dry biomass models were about 0.76 and 0.79 for the VGG-16 and ResNet-50 networks, respectively. Similarly, the R2 values of leaf area were 0.82 and 0.84, respectively. The RMSE values were approximately 8.31 and 8.73 g for dry biomass analyzed using the VGG-16 and ResNet-50 networks, respectively. Leaf area RMSE was 0.05 m2 for both networks. This work demonstrates the feasibility of deep learning networks in individual strawberry plant extraction and biomass estimation.
Development of On-Demand Antiviral Electrostatic Precipitators with Electrothermal-Based Antiviral Surfaces against Airborne Virus Particles

Particulate matter, including airborne pathogens, is of particular concern because it can cause the spread of diseases through aerosol transmission. In this study, a new concept is proposed: on-demand antiviral electrostatic precipitators (ESPs) with electrothermal-based antiviral surfaces. We applied electrothermal-based antiviral surfaces to air-purifying applications and demonstrated that the proposed method is effective with regard to collecting airborne virus particles on collection plates in a two-stage ESP. With alternating current power, MS2 bacteriophage and H1N1 viruses were completely deactivated after exposure to 50 °C for 30 min. This remarkable antiviral performance via electrothermal effects indicates that on-demand platforms for self-antiviral surfaces can perform sterilization immediately without generating secondary pollutants, thus effectively preventing the spread of infectious microorganisms in public places. We believe that the results of this study can provide useful guidelines for the design and realization of practical and wearable devices for antiviral air-purifying applications.
Impact of Illegible Prescriptions on Dispensing Practice: A Pilot Study of South African Pharmacy Personnel

Illegible prescriptions are an illegal, frequent, and longstanding problem for pharmacy personnel engaged in dispensing. These contribute to patient safety issues and negatively impact safe dispensing in pharmaceutical delivery. To date, little is documented on measures taken to assess the negative impact posed by illegible prescriptions on South African pharmacy dispensing personnel. Therefore, this pilot study was performed to evaluate the ability of pharmacy personnel to read and interpret illegible prescriptions correctly; and to report on their perceived challenges, views and concerns when presented with an illegible prescription to dispense. A cross-sectional, three-tiered self-administered survey was conducted among pharmacy personnel. A total of 885 measurements were recorded. The ability to read an illegible prescription is not an indicator of competency, as all (100%) participants (novice and experienced) made errors and experienced difficulty evaluating and deciphering the illegible prescription. The medication names and dosages were correctly identified by only 20% and 18% of all participants. The use of digital prescriptions was indicated by 70% of the participants as a probable solution to the problem. Overall, improving the quality of written prescriptions and instructions can potentially assist dispensing pharmacy personnel in reducing illegible prescription-related patient safety issues and dispensing errors.
A Comparative Study on Steady-State Water Inflow into a Circular Underwater Tunnel with an Excavation Damage Zone

Excavation damage zones that occur around tunnels usually lead to a change in rock permeability, which has an impact on the water inflow into tunnels and even induces water inrush disasters. For a better understanding of the effect of the excavation damage zone, analytical solutions that consider the excavation damage zone are developed based on the review and modification of the solutions that consider linings and grouting circles. Then, both analytical solutions and the finite element method are applied to estimate the water inflow, and the results are in good agreement. The effect of the excavation damage zone on water inflow is analyzed based on an impact factor of the excavation damage zone and evaluated in a real engineering case, and the seepage-preventing effects of grouting are discussed. The results reveal that the water inflow increases with increasing permeability and thickness of the excavation damage zone and that there is a limit for the effects of the excavation damage zone. In addition, the effect is stronger for underwater tunnels with small water inflows and stabilizes gradually as the magnitude of water inflow increases. The increase in the impermeability and thickness of the lining and grouting circle can reduce the effect.
Scalable fabrication of nanostructured p-Si/n-ZnO heterojunctions by femtosecond-laser processing

We present a versatile, large-scale fabrication method for nanostructured semiconducting junctions. Silicon substrates were processed by femtosecond laser pulses in methanol and a quasi-ordered distribution of columnar nanospikes was formed on the surface of the substrates. A thin (80 nm) layer of ZnO was deposited on the laser-processed silicon surface by pulsed laser deposition, forming a nanostructured p-Si/n-ZnO heterojunction. We characterized the structural, optical, and electrical properties of the heterojunction. Electrical I–V measurements on the nanostructured p-Si/n-ZnO device show non-linear electric characteristics with a diode-like behavior. Electrical I–V measurements on a flat p-Si/n-ZnO reference sample show similar characteristics, however the forward current and rectification ratio are improved by orders of magnitude in the nanostructured device owing to its increased surface area. The fabrication method employed in this work can be extended to other homojunctions or heterojunctions for electronic and optoelectronic devices with large surface area.
Optimization of N-PERT Solar Cell under Atacama Desert Solar Spectrum

In the Atacama Desert, the spectral distribution of solar radiation differs from the global standard, showing very high levels of irradiation with a particularly high ultraviolet content. Additionally, the response of photovoltaic (PV) technologies is spectrally dependent, so it is necessary to consider local conditions and type of technology to optimize PV devices since solar cells are usually designed for maximum performance under standard testing conditions (STC). In this work, we determined geometrical and doping parameters to optimize the power of an n-type bifacial passivated emitter and rear totally diffused solar cell (n-PERT). Six parameters (the thicknesses of cell, emitter, and back surface field, as well as doping concentration of emitter, base, and back surface field) were used to optimize the cell under the Atacama Desert spectrum (AM 1.08) and under standard conditions (AM 1.5) through a genetic algorithm. To validate the model, the calculated performance of the n-PERT cell was compared with experimental measurements. Computed and experimental efficiencies showed a relative difference below 1% under STC conditions. Through the optimization process, we found that different geometry and doping concentrations are necessary for cells to be used in the Atacama Desert. Reducing the thickness of all layers and increasing doping can lead to a relative increment of 5.4% in the cell efficiency under AM 1.08. Finally, we show the potential effect of metallization and the viability of reducing the thicknesses of the emitter and the back surface field.
Collagen Alignment via Electro-Compaction for Biofabrication Applications: A Review

As the most prevalent structural protein in the extracellular matrix, collagen has been extensively investigated for biofabrication-based applications. However, its utilisation has been impeded due to a lack of sufficient mechanical toughness and the inability of the scaffold to mimic complex natural tissues. The anisotropic alignment of collagen fibres has been proven to be an effective method to enhance its overall mechanical properties and produce biomimetic scaffolds. This review introduces the complicated scenario of collagen structure, fibril arrangement, type, function, and in addition, distribution within the body for the enhancement of collagen-based scaffolds. We describe and compare existing approaches for the alignment of collagen with a sharper focus on electro-compaction. Additionally, various effective processes to further enhance electro-compacted collagen, such as crosslinking, the addition of filler materials, and post-alignment fabrication techniques, are discussed. Finally, current challenges and future directions for the electro-compaction of collagen are presented, providing guidance for the further development of collagenous scaffolds for bioengineering and nanotechnology.
Pets and a Pandemic: An Exploratory Mixed Method Analysis of How the COVID-19 Pandemic Affected Dogs, Cats, and Owners

The purpose of the present study was to explore how the COVID-19 pandemic impacted dogs, cats, and interactions between these pets and their owners. Participants included 102 dog and/or cat owners residing primarily in the United States. Participants completed an online questionnaire between late April to late May 2020. Analyses included t -tests comparing retrospective estimates of pre-pandemic functioning and functioning during the pandemic, and qualitative thematic analysis was used to analyze participants’ responses to three open-ended questions. Quantitative analyses found that pets and owners spent significantly more time together, with increases in physical contact between owners and pets, exercise with dogs, and engagement in dog-related activities. No significant changes were found regarding owners’ percentage of pet care responsibility, attachment to their pets, pleasantness derived from pet-related activities, or upset feelings toward their pets when comparing pre-pandemic and during pandemic scores. Five themes arose from the thematic analysis: Social/Attachment (i.e., changes to owner-pet, pet-pet, and owner-owner relationships), Physical (i.e., increases in owners’ physical contact and proximity with pets, physical benefits to pets and owners), Psychological (i.e., changes in owners’ and pets’ behavior and emotionality), Safety/Well-Being (i.e., health concerns regarding owners and pets), and Responsibilities/Routines (i.e., changes in owners’ and pets’ daily routines, changes in owners’ responsibilities and productivity). These findings provide valuable insight into how dogs, cats, owners, and interactions between these pets and owners were affected by the COVID-19 pandemic as well as considerations for animal welfare in the wake of the pandemic. Additionally, the study generated many hypotheses pertaining to how and why these changes occurred, providing a foundation for additional research in this area.
Moderate Boron Concentration Beneficial for Flue-Cured Tobacco (Nicotiana tabacum L.) Seedlings Growth and Development

Boron (B) deficiency is a common phenomenon in most tobacco-planting areas in Yunnan, China. In 2020 and 2022, hydroponic experiments that contained B in a concentration gradient of 0.000, 0.125, 0.250, 0.750, 5.000, 10.000, 20.000, and 40.000 mmol L−1 were conducted to investigate tobacco cultivar K326′s agronomic traits, photosynthetic performance, antioxidant enzymes, and boron and nicotine concentration. As B concentration increased, indices including leaves biomass and net photosynthetic rate (Pn) generally increased first and then decreased, which was in contrast to antioxidant enzymes, including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). With increasing B concentration, boron content in tobacco seedlings increased significantly by 24.00~96.44%, while decreased nicotine content by 21.60~82.03%. The highest biomass and photosynthetic performance were obtained within 0.75 and 5.00 mmol L−1 treatments. The results of the sandy soil pot verification experiment were similar to the hydroponic experiment obtained. The beneficial mechanism of moderate B on tobacco seedlings is to maintain cell structure integrity, enhance photosynthetic capability, and promote root growth. Consequently, the optimum B concentration for tobacco seedlings is 0.75~5.00 mmol L−1, and applying 0.25~0.50 B kg hm−1 in soil under available B insufficiency could meet the needs of the growth of flue-cured tobacco.
Willingness to participate in a cocoa pension scheme in Ghana: An application of discrete choice experiment

Abstract Studies on involuntary pension participation in general and among farmers, in particular, are scanty. Therefore, this paper aims at investigating cocoa farmers’ awareness of pension schemes as well as assessing their willingness to participate in a cocoa pension scheme. A sample of 450 cocoa farmers were interviewed using a structured questionnaire. The discrete choice experiment (DCE) comprising conditional logit (CL) and random parameter logit (RPL) models were used to analyse farmers’ willingness to pay for pension schemes. From the study, only 43 percent of farmers were aware of the cocoa pension scheme. The results of DCE analysis revealed that farmers’ decision and willingness to pay for pension scheme were influenced by the scheme attributes such as premium, payment period, payment method and pension pay. For pension institutions, the revelation that farmers are less likely to participate in a pension scheme that takes longer years to realise the benefits should be crucial in developing schemes for cocoa farmers. Therefore, it is suggested that actuarial calculations should relax the cap for being declared due for pension and reduce the premium payment period for pension benefits.
NOMA-based Power Control for Machine-Type Communications: A Mean Field Game Approach

Attracted by the advantages of Non-Orthogonal Multiple Access (NOMA) in accommodating multiple users within the same resource, this paper investigates the power allocation problem for Machine Type Devices (MTDs) in a Hybrid NOMA system. Particularly, we consider a densely deployed network in which the devices are divided into orthogonal coalitions. Firstly, the power control problem is modeled as a differential game. Then, we formulate the proposed game as a Mean Field Game (MFG) in order to handle massive Internet of Things (IoT) access scenarios. Furthermore, we design an iterative algorithm that paves the way for distributed control in which the devices can appropriately regulate their transmit power in response to brief information received from the Base Station (BS). The analysis of the proposed approach is conducted through coupled equations, namely the Hamilton-Jacobi-Bellman (HJB) and the Fokker-Planck-Kolmogorov (FPK). Our simulation results prove the convergence of the proposed power control strategy and spotlight the robustness of our formulated MFG.
Countering Physical Eavesdropper Evasion with Adversarial Training

Signal classification is a universal problem in adversarial wireless scenarios, especially when an eavesdropping radio receiver attempts to glean information about a target transmitter’s patterns, attributes, and contents over a wireless channel. In recent years, research surrounding the idea of Machine Learning (ML)-based signal classification has focused on modulation classification, with the downstream objective of demodulation. However, while the computer vision data domain has made significant progress in ensuring robust classification of images despite crafted perturbations, this success has not been translated to secure modulation classification. In this work, we perform the first-ever physical test of an eavesdropping ML-based modulation classifier radio, which we trained offline using a ensemble of i.i. d. models. Each model is trained with a weighted mixture of data perturbed by iterative, “least likely” white box attacks and non-attacked data. We then tested the ensemble online using coaxial-connected Software Defined Radios (SDRs). We conducted a case study comparing our results to the state-of-the-art computer vision approaches to investigate the presence of “label leaking”, model capacity sensitivity, understand the viability of parallel and sequential variations on perturbation training, and assess the effectiveness of iterative attack training. Our results show that perturbations can result in guessing-level classification performance from eavesdroppers, and that varying levels of robustness can be achieved against all presented attacks. These findings confirm that any receiver presents a new attack vector by utilizing ML techniques for classification tasks, and can be vulnerable to evasion attacks at little-to-no cost to transmitters. Consequently, we argue for the use of our training scheme in all ML-based classifying radios where security is a concern.
Benchmarks of KIPP: Vlasov‐Fokker‐Planck Code for Parallel Plasma Transport in the SOL and Divertor

Benchmarks of the new 1D2V kinetic code KIPP (KInetic code for Plasma Periphery) for parallel plasma transport in the scrape‐off layer (SOL) and divertor of magnetic confinement fusion devices are presented. The code is based on the continuum discretisation scheme for the Vlasov‐Fokker‐Planck equation. It assumes plasma quasi‐neutrality, maintained by the electron parallel momentum balance, and uses a logical sheath boundary condition for the Debye potential drop at material surfaces. The code is parallelized under the MPI environment, with the most time consuming part, Coulomb collisions, being calculated simultaneously for all spatial locations by separate processors using a sparse matrix solver for the full non‐linear collision operator in two velocity variables: parallel and gyro‐averaged perpendicular velocities. KIPP is being developed with the aim of integrating it into SOLPS (B2‐Eirene), to replace fluid treatment of parallel plasma transport with the kinetic one. Benchmark results for parallel electron heat conduction and electron‐ion thermoforce for highly collisional plasmas are presented, as well as some results for less collisional plasmas. This paper focuses on the 1d2v features of the code, with the 0d2v features presented earlier in [1, 2]. (© 2014 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Improving Estuarine Flood Risk Knowledge through Documentary Data Using Multiple Correspondence Analysis

Estuarine margins are usually heavily occupied areas that are commonly affected by compound flooding triggers originating from different sources (e.g., coastal, fluvial, and pluvial). Therefore, estuarine flood management remains a challenge due to the need to combine the distinct dimensions of flood triggers and damages. Past flood data are critical for improve our understanding of flood risks in these areas, while providing the basis for a preliminary flood risk assessment, as required by European Floods Directive. This paper presents a spin-off database of estuarine flood events built upon previously existing databases and a framework for working with qualitative past flood information using multiple correspondence analysis. The methodology is presented, with steps ranging from a spin-off database building process to information extraction techniques, and the statistical method used was further explored through the study of information acquired from the categories and their relation to the dimensions. This work enabled the extraction of the most relevant estuarine flood risk indicators and demonstrates the transversal importance of triggers, since they are of utmost importance for the characterization of estuarine flood risks. The results showed a relation between sets of triggers and damages that are related to estuarine margin land use, demonstrating their ability to inform flood risk management options. This work provides a consistent and coherent approach to use qualitative information on past floods, as a useful contribution in the context of scarce data, where measured and documentary data are not simultaneously available.
Planning Curricular Proposals on Sound and Music with Prospective Secondary-School Teachers

Sound is a preferred context to build foundations on wave phenomena, one of the most important disciplinary referents in physics. It is also one of the best-set frameworks to achieve transversality, overcoming scholastic level and activating emotional aspects which are naturally connected with every day life, as well as with music and perception. Looking at sound and music by a transversal perspective - a border-line approach between science and art, is the adopted statement for a teaching proposal using meta-cognition as a strategy in scientific education. This work analyzes curricular proposals on musical acoustics, planned by prospective secondary-school teachers in the framework of a Formative Intervention Module answering the expectation of making more effective teaching scientific subjects by improving creative capabilities, as well as leading to build logical and scientific categorizations able to consciously discipline artistic activity in music students. With this aim, a particular emphasis is given to those concepts - like sound parameters and structural elements of a musical piece, which are best fitted to be addressed on a transversal perspective, involving simultaneously physics, psychophysics and music.
Prognostic Value of Late Gadolinium Enhancement in Left Ventricular Noncompaction: A Multicenter Study

Current diagnostic criteria for left ventricular noncompaction (LVNC) may be poorly related to adverse prognosis. Late gadolinium enhancement (LGE) is a predictor of major adverse cardiovascular events (MACE), but risk stratification of LGE in patients with LVNC remains unclear. We retrospectively analyzed the clinical and cardiovascular magnetic resonance (CMR) data of 75 patients from three institutes and examined the correlation between different LGE types and MACE based on the extent, pattern (including a specific ring-like pattern), and locations of LGE in LVNC. A total of 51 patients (68%) presented LGE. A specific ring-like pattern was observed in 9 (12%). MACE occurred in 29 (38.7%) at 4.3 years of follow-up (interquartile range: 2.1–5.7 years). The adjusted hazard ratio (HR) for patients with ring-like LGE were 6.10 (95% CI, 1.39–26.75, p < 0.05). Free-wall or mid-wall LGE was associated with an increased risk of MACE after adjustment (HR 2.85, 95% CI, 1.31–6.21; HR 4.35, 95% CI, 1.23–15.37, respectively, p < 0.05). The risk of MACE in LVNC significantly increased when the LGE extent was greater than 7.5% and ring-like, multiple segments, and free-wall LGE were associated with MACE. These results suggest the value of LGE risk stratification in patients with LVNC.