Miracle-dz/UniFAR-FTUs huggingface dataset:
Monitoring olive tree water status by unmanned aerial vehicles (UAVs) and trunk dendrometers

In the last decade unmanned aerial vehicles (UAVs) and plant based sensors have been proposed for the monitoring of plant water status. Thermal infrared images from UAV have been widely used for monitoring plant water status in orchards and vineyards. Among plant based sensors, linear variable differential transformer (LVDT) dendrometers allow to measure changes in diameter induced by diurnal fluctuations of transpiration in different organs. We tested the use of UAV thermal imaging and dendrometers for the estimation of olive tree water status in a commercial olive orchard (Olea europaea cvs. Frantoio e Leccino) where three irrigation regimes were established: Full irrigation (FI, 100% of ETc), sustained deficit irrigation (DI, 40% of full irrigation) and rainfed (RF, only rainfall). The stem water potential ($\Psi_{stem}$) was measured throughout the growing season. Thermal images were acquired from an altitude of 50 m above ground level. Three trees for each cultivar were equipped with trunk dendrometers in order to measure continuously the daily trunk diameter fluctuations. The signal intensity (SI) values of $\Psi_{stem}$ and crop water stree index (CWSI) were consistent and reflected the positive and almost linear relationship existing between SWP and CWSI in olive. On the other hand, SI values of maximum daily shrinkage (MDS) were almost always lower than one. The relationship between $\Psi_{stem}$ and MDS showed a positive relationship as long as $\Psi_{stem}$ values were higher than -2 MPa, whereas a decrease in the amplitude of trunk diurnal fluctuations was observed at higher level of water deficit.
A Manifold Learning Based User Clustering and Beamforming Design in MIMO-SCMA Systems

In this paper, we study the user clustering and down-link beamforming problem in multiple-input multiple-output sparse code multiple access (MIMO-SCMA) systems. A K-means user clustering method based on manifold learning is proposed, which combines SCMA technology to limit the number of users within each cluster. Subsequently, a beamforming method is developed by the block diagonalization (BD) technology to mitigate inter-cluster interference, and different clusters share a common codebook set of SCMA. Within each cluster, individual users employ distinct codebooks consisting of sparse codewords to effectively minimize intra-cluster interference. Simulation results demonstrate the superior performance of the proposed method.
Buyers Collusion in Incentivized Forwarding Networks: A Multi-Agent Reinforcement Learning Study

We present the issue of monetarily incentivized forwarding in a multi-hop mesh network architecture from an economic perspective. It is anticipated that credit-incentivized forwarding and relaying will be a simple method of exchanging transmission power and spectrum for connectivity. However, gateways and forwarding nodes, like any other free market, may create an oligopolistic market for the users they serve. In this study, a coalition scheme between buyers aims to address price control by gateways or nodes closer to gateways. In a Stackelberg competition game, buyer agents (users) and sellers (gateways) make decisions using reinforcement learning (RL), with decentralized Deep Q-Networks to buy and sell forwarding resources. We allow communication links between the buyers with a limited messaging space, without defining a collusion mechanism. The idea is to demonstrate that through messaging, and RL tacit collusion can emerge between agents in a decentralized setup. The multi-agent reinforcement learning (MARL) system is presented and analyzed from a machine-learning perspective. Moreover, MARL dynamics are discussed via mean field analysis to better understand divergence causes and make implementation recommendations for such systems. Finally, the simulation results show the results of coordination among the users.
Regenerative Reconstructive Treatment of A Peri- Implantitis Case: New Perspectives for Implant Surface Decontamination

Introduction : Implantology is a therapeutic approach that has allowed the fixed prosthetic treatment of an increasing number of patients who would otherwise be destined for removable prosthetics. Peri-implant diseases are inflammatory conditions caused by biofilm that affect the tissues around dental implants; they are distinguished into peri-implant mucositis and peri-implantitis. Studies have shown a prevalence of 43-46.8% for mucositis and 19.83-22% for peri-implantitis. The primary goal of peri-implantitis treatment is to halt progressive bone loss by controlling bacterial infection. Case Presentation : In 2013, a 68-year-old patient underwent rehabilitation with a fixed prosthesis on 5
Overview of Ecology and Aspects of Antibiotic Resistance in Campylobacter spp. Isolated from Free-Grazing Chicken Tissues in Rural Households

Rural households all over the world rear backyard chicken mainly for their own consumption and, to a lesser extent, for barter trade. These chickens represent a staple dish with numerous culinary variations and a cheap source of protein. Although some Campylobacter species, and particularly Campylobacter jejuni and Campylobacter coli, have been associated with industrial poultry carcasses, studies concerning the ecology of this genus in rural households do not exist. To assess the prevalence of Campylobacter species in the tissues of backyard chickens, samples were collected from birds Gallus domesticus bred in households in the rural area of Epirus (Greece), and Campylobacter strains were isolated by quantitative methods at 37 °C and 42 °C. In total, 256 strains were identified, belonging to 17 Campylobacter species, with C. jejuni and C. coli being the most prevalent. From the four ecological parameters studied (size of the flock, presence of small ruminants in the same household, presence of other poultry species in the same household, and feeding leftovers of the household), the size of the flock and the presence of small ruminants and/or pigs in the same household mostly affected the distribution of these strains. To study the phenotypical resistance against 14 antibiotics, 215 strains were selected. The results showed a high prevalence of multidrug-resistance (MDR) strains extending to all classes of antibiotics. Further genome analysis revealed the presence of genes coding resistance (blaOxA-61, tet(O), tet(A) cmeA, cmeB, cmeC, and gyrA (Thr-86-Ile mutation)), with the efflux pump CmeABC being the most prevalent. All antimicrobial resistance-encoded genes co-circulated, except for blaOXA-61, which moved independently. The minimum inhibitory concentration (MIC) values of two out of three antibiotics (representing different classes) were reduced when the strains tested were exposed to carbonyl cyanide 3-chlorophenylhydrazone (CCCP), a known efflux pump inhibitor. The same result was obtained with the addition of CCCP to the MIC values of bile salts. These results lead to the conclusion that Campylobacter species are present in an impressive diversity in backyard chicken tissues and that they exert a significant resistance to antibiotics, raising a potential danger for public health.
Binocular stereo vision system based on phase matching

Binocular stereo vision is an efficient way for three dimensional (3D) profile measurement and has broad applications. Image acquisition, camera calibration, stereo matching, and 3D reconstruction are four main steps. Among them, stereo matching is the most important step that has a significant impact on the final result. In this paper, a new stereo matching technique is proposed to combine the absolute fringe order and the unwrapped phase of every pixel. Different from traditional phase matching method, sinusoidal fringe in two perpendicular directions are projected. It can be realized through the following three steps. Firstly, colored sinusoidal fringe in both horizontal (red fringe) and vertical (blue fringe) are projected on the object to be measured, and captured by two cameras synchronously. The absolute fringe order and the unwrapped phase of each pixel along the two directions are calculated based on the optimum three-fringe numbers selection method. Then, based on the absolute fringe order of the left and right phase maps, stereo matching method is presented. In this process, the same absolute fringe orders in both horizontal and vertical directions are searched to find the corresponding point. Based on this technique, as many as possible pairs of homologous points between two cameras are found to improve the precision of the measurement result. Finally, a 3D measuring system is set up and the 3D reconstruction results are shown. The experimental results show that the proposed method can meet the requirements of high precision for industrial measurements.
PHY Secure Hybrid Precoding with Max-Min Fair Energy Harvesting for mm Wave MU-MIMO-SWIPT Communications

Physical Layer Secure (PLS) hybrid precoding for Simultaneous Wireless Information and Power Transfer (SWIPT) in a multi-user multiple-input-multiple-output (MU-MIMO) communication system over the millimeter wave (mmWave) band is investigated. Because a limited radio frequency (RF) chain architecture is adopted at the Base Station (BS) of the system, the precoding scheme is divided into two parts, i.e. the analog precoding part and the digital precoding part. Considering a typical use case where the users are Internet-of-Things (IoT)-type devices whose proper operations rely on the harvested energy from wireless power transfer, a fairness problem for harvested energy among users is considered, and a constrained max-min optimization problem is formulated accordingly. Given specific analog precoding based on quantized phase shifter array, the formulated problem aims to maximize the minimum received power of the users by jointly optimizing the digital precoding, the additive Artificial Noise (AN) for improved secrecy, and the power allocation, subject to the secrecy rate constraint and the total transmission power constraint. Due to the non-convexity property of the original optimization problem with the design variables coupled in both the objective function and the constraints, an iterative Successive Convex Approximation (SCA) method is employed to give sub-optimal solutions. Effectiveness and convergence of the proposed design are validated through extensive simulation with different system setting parameters, and tradeoffs between the secrecy rate, the wireless energy transfer, and the hardware complexity are discussed.
Applying Hierarchical Task Analysis to Reduce Human Errors of Tower Controllers

The innovative concept of remote tower operation is where air traffic controllers provide air traffic services to airports from a geographically separated virtual tower. Compared with the traditional tower, the remote tower has not changed the content and regulations of providing the airport with air traffic service. The major changes are infrastructure and technical systems, the resulting adjustment of operational processes and methods, and human factor ergonomic adaptation. Monitoring performance is foreseen as the most promising way to increase capacity and safety. However, the nature of task properties and potential human errors in tower control are not fully understood. The research objective is to analyze the possible human errors in tower control and find out tasks determined by visual scanning. We conducted semi-structured interviews with five experienced air traffic instructors with practicing certificates working at the CAFUC. The hierarchical task analysis (HTA) is used to analyze the critical operations of remote tower controllers. The systematic human error reduction and prediction approach (SHERPA) is used to analyze the possible human errors of each task throughout the entire process. Then we make a categorized analysis to collect tasks determined by visual scanning and describe the reason and influence factors for human errors in each task. We also interviewed design experts and conduct participatory design to put forward suggestions for preventing human errors, performance improvement of tower controllers, and the optimization of human-computer interaction in the future.
CSI Feedback for Massive MIMO Communication Systems Based on Joint Convolutional-Coordinate Attention Neural Networks Algorithm

In this paper, we study the downlink channel state information (CSI) feedback problem in frequency-division duplexing (FDD) mode for massive multi-input multi-output (MIMO) systems. We proposed a joint convolution-coordinate attention neural networks algorithm based on deep learning. to address the shortcomings of insufficient performance, long operation time and high channel sparsity requirement of existing CSI feedback methods. The algorithm is based on the idea of autoencoder network, and converts the CSI feedback problem into an encoding and decoding problem. We proposed a new Joint Convolution-Coordinate Attention Mechanism Module (JCCAM), and a JCCAM-CsiNet neural network model is proposed based on CsiNet neural network model. Simulation results show that the algorithm proposed in this paper has better performance compared with existing channel estimation algorithms.
Land Use and Land Cover Classification Using Deep Learning Techniques

Large datasets of sub-meter aerial imagery represented as orthophoto mosaics are widely available today, and these data sets may hold a great deal of untapped information. This imagery has a potential to locate several types of features; for example, forests, parking lots, airports, residential areas, or freeways in the imagery. However, the appearances of these things vary based on many things including the time that the image is captured, the sensor settings, processing done to rectify the image, and the geographical and cultural context of the region captured by the image. This thesis explores the use of deep convolutional neural networks to classify land use from very high spatial resolution (VHR), orthorectified, visible band multispectral imagery. Recent technological and commercial applications have driven the collection a massive amount of VHR images in the visible red, green, blue (RGB) spectral bands, this work explores the potential for deep learning algorithms to exploit this imagery for automatic land use/ land cover (LULC) classification.
Improved Cardiovascular Effects of a Novel Pomegranate Byproduct Extract Obtained through Hydrodynamic Cavitation

The healthy properties of pomegranate fruit, a highly consumed food, have been known for a long time. However, the pomegranate supply chain is still rather inefficient, with the non-edible fraction, whose weight is roughly half the total and is endowed with plenty of valuable bioactive compounds, either disposed of or underutilized. A novel extract obtained from non-edible byproducts (called PPE), using hydrodynamic cavitation, a green, efficient, and scalable technique, was investigated for its cardiovascular effects in vivo. PPE showed efficacy in an acute phenylephrine (PE)-induced hypertensive rat model, similar to the extract of whole fruit (PFE) obtained using the same extractive technique, along with good intestinal bioaccessibility after oral administration. Finally, when chronically administered for 6 weeks to spontaneously hypertensive rats, PPE was shown to significantly contain the increase in systolic blood pressure, comparable to the reference drug Captopril, and at a dose remarkably lower than the reported effective dose of ellagic acid. The extract from the non-edible fraction of the pomegranate fruit also showed good anti-inflammation and anti-fibrotic effects. The findings of this study, along with the extraction technique, could contribute to enhancing the value of the pomegranate supply chain, relieve the related environmental burden, and potentially improve public health.
Metformin Alters mRNA Expression of FOXP3, RORC, and TBX21 and Modulates Gut Microbiota in COVID-19 Patients with Type 2 Diabetes

COVID-19 remains a significant global concern, particularly for individuals with type 2 diabetes who face an elevated risk of hospitalization and mortality. Metformin, a primary treatment for type 2 diabetes, demonstrates promising pleiotropic properties that may substantially mitigate disease severity and expedite recovery. Our study of the gut microbiota and the mRNA expression of pro-inflammatory and anti-inflammatory T-lymphocyte subpopulations showed that metformin increases bacterial diversity while modulating gene expression related to T-lymphocytes. This study found that people who did not take metformin had a downregulated expression of FOXP3 by 6.62-fold, upregulated expression of RORC by 29.0-fold, and upregulated TBX21 by 1.78-fold, compared to the control group. On the other hand, metformin patients showed a 1.96-fold upregulation in FOXP3 expression compared to the control group, along with a 1.84-fold downregulation in RORC expression and an 11.4-fold downregulation in TBX21 expression. Additionally, we found a correlation with gut microbiota (F/B ratio and alpha-diversity index) and pro-inflammatory biomarkers. This novel observation of metformin’s impact on T-cells and gut microbiota opens new horizons for further exploration through clinical trials to validate and confirm our data. The potential of metformin to modulate immune responses and enhance gut microbiota diversity suggests a promising avenue for therapeutic interventions in individuals with type 2 diabetes facing an increased risk of severe outcomes from COVID-19.
Integrating data from different taxonomic resolutions to better estimate community alpha diversity

Integrated distribution models (IDMs), in which datasets with different properties are analysed together, are becoming widely used to model species distributions and abundance in space and time. To date, the IDM literature has focused on technical and statistical issues, such as the precision of parameter estimates and mitigation of biases arising from unstructured data sources. However, IDMs have an unrealised potential to estimate ecological properties that could not be properly derived from the source datasets if analysed separately. We present a model that estimates community alpha diversity metrics by integrating one species‐level dataset of presence–absence records with a co‐located dataset of group‐level counts (i.e. lacking information about species identity). We illustrate the ability of community IDMs to capture the true alpha diversity through simulation studies and apply the model to data from the UK Pollinator Monitoring Scheme, to describe spatial variation in the diversity of solitary bees, bumblebees and hoverflies. The simulation and case studies showed that the proposed IDM produced more precise estimates of the community diversity than the single models, and the analysis of the real dataset further showed that the alpha diversity estimates from the IDM were averages of the single models. Our findings also revealed that IDMs had a higher prediction accuracy for all the insect groups in most cases, with this performance linked to the information provided by a data source into the IDM.
Adaptive Convolutional Feature Fusion Target Tracking Algorithm based on Spatio-Temporal Regularization

This paper proposes an adaptive fusion convolutional feature tracking algorithm based on the spatio-temporal regularized tracking algorithm to address the issue of instability in tracking models under complex scenarios such as lighting and scale variations. The algorithm in this paper replaces traditional handcrafted features with convolutional features from different layers of a convolutional neural network, assigning different weights to these features. It utilizes stable spatio-temporal regularized correlation filters for target estimation and continuously updates the weights to enhance the stability of the model in different scenes. The proposed algorithm is tested on the OTB-2015 dataset. Experimental results demonstrate that the algorithm in this paper achieves good accuracy and stability, with favorable performance in terms of precision and success rate across all video sequences.
Soil Water Characteristics of Cores from Low- and High-Centered Polygons, Barrow, Alaska, 2012

This dataset includes soil water characteristic curves for soil and permafrost in two representative frozen cores collected from a high-center polygon (HCP) and a low-center polygon (LCP) from the Barrow Environmental Observatory. Data include soil water content and soil water potential measured using the simple evaporation method for hydrological and biogeochemical simulations and experimental data analysis. Data can be used to generate a soil moisture characteristic curve, which can be fit to a variety of hydrological functions to infer critical parameters for soil physics. Considering the measured the soil water properties, the van Genuchten model predicted well the HCP, in contrast, the Kosugi model well fitted LCP which had more saturated condition.
A Systematic Literature Review of the Analysis of Costs and Other Dimensions in Process Mining and Related Disciplines

Process mining allows the discovery, monitoring, and improvement of processes through records of their executions. When used for discovery, models depicting the process behavior can be obtained and then analyzed through a performance perspective, focusing on performance indicators for the dimension of interest. In process mining, there has been scarce research on the analysis of some dimensions, such as the cost dimension. A systematic literature review has been conducted to identify the methods used for analyzing the cost dimension in process mining and other process-oriented disciplines, the dimensions considered jointly with costs, and how such a joint analysis is carried out. The review identified 68 publications. The results indicate ample research within distinct disciplines, like business process management and cost management, regarding the costs of performing processes, their reporting and forecasting. However, there is a need to further enrich their visualization and analysis, and to provide cost-aware operational support. Moreover, the utility of further researching cost analysis jointly with other dimensions, like time, flexibility, and quality, is also observed. These results provide a structured focal point of research that can be considered when analyzing costs from a process perspective and the identification of research gaps within this domain.
The new normal of remote work: exploring individual and organizational factors affecting work-related outcomes and well-being in academia

Background Flexible work arrangements have become increasingly popular, driven by the widespread adoption of digital technologies in the workplace because of the pandemic. However, there is a scarcity of studies concerning remote work, especially related to technical-administrative staff (TAS) in academia. Therefore, the current study, adopting the Job Demands-Resources model, aimed to investigate the relationships between remote working self-efficacy, organizational support, techno-complexity, mental well-being, and job performance among TAS during remote working. Methods A total of 373 individuals from TAS of a large Italian university participated in this study by completing a self-report questionnaire. Results The findings showed positive and significant relationships between remote self-efficacy and job satisfaction as well as between such a perceived efficacy and mental well-being. Perceived support from supervisors acted as a protective factor against techno-complexity. In contrast, perceived support from colleagues emerged as able to promote well-being and job satisfaction. In addition, the latter was positively associated with well-being. Finally, individual job performance was positively affected by job satisfaction and negatively by techno-complexity. Conclusion This study highlights the need for interventions to support TAS in remote working environments by leveraging employees’ self-efficacy as a key factor in reducing stress related to new technologies as well as enhancing well-being, job satisfaction, and, in turn, their performance.