Miracle-dz/UniFAR-FTUs huggingface dataset:
Hydrological response of a volcanic medium as a potential substrate for green roofs

This study aimed at evaluating the hydrological response of a volcanic medium (VM) obtained from ash deposition that followed the recent eruptions of Mount Etna (Italy). Due to its lightness and sustainability, this material appears appropriate for preparing “engineered soils” suitable as substrates for green roofs. Simulated rainfall experiments were conducted on microcosms to assess both detention and retention capacities of VM under dynamic conditions. Furthermore, to improve the physical and hydraulic properties of VM, the particle distribution was graded by artificially increasing or decreasing the coarse fraction. Starting from initial dry conditions, the green roof was able to detain from 19.3% to 46.0% of the total rain volume in line with the results of the literature. When initial wet conditions were considered, detention was reduced but anyway in the order of 10% of the total rainfall. Decreasing the VM coarse fraction reduced the distribution of macroporosity and increased that of mesoporosity thus enhancing the drainable water capacity with positive effects on the substrate’s detention capacity.
Exploring the Representation of Cyber-Risk Data Through Sketching

Dealing with complex information regarding cyber security risks is increasingly important as attacks rise in frequency. Visualisation techniques are used to support decision-making and insight. However, the use of visualisations across different stakeholders and cyber security risk data is not well explored. This work presents an exploratory study in which participants use sketching to represent cyber-risk data. We critically discuss the method and our results demonstrate the usefulness of the method to identify new, diverse visualisation approaches, as well as the richness of stakeholder visualisation conceptualisation.
A Lightweight BiSeNet Embedded with YOLOv5 Feature Fusion Network

The semantic segmentation task is to classify the object category for each pixel in the image, so its complexity is higher than that of general object detection task. Currently, the challenge of semantic segmentation lies in how to perform image segmentation at a high frame rate and with high accuracy, as well as how to deploy it on low-computing-power platforms. Therefore, this paper researches a lightweight BiSeNet semantic segmentation network that combines YOLOv5 feature fusion network. This network is based on BiSeNet (bilateral segmentation network) and introduces MobileNet v2 as the backbone to reduce model parameters. Additionally, the YOLOv5 feature fusion layer is embedded to obtain enhanced multi-scale feature maps and improve the model's segmentation accuracy. Experiments show that the Mean Intersection over Union (MIoU) of the prosed method on the CamVid dataset is 1.83% higher than the one before improvement, and the model parameters are reduced by 71.6%.
Personalizing the Treatment of Women with Ductal Carcinoma In Situ (DCIS) Using the DCIS Score: A Qualitative Study on Score Use

Background: A twelve-gene molecular expression assay (DCIS score) may help guide radiation oncology treatment under specific circumstances. We undertook a study to examine radiation oncologist (RO), surgeon, and decision maker views on implementing the DCIS score in practice for women with low-risk DCIS. Methods: We conducted a qualitative study involving telephone interviews that were audio-recorded and transcribed. Two researchers conducted a thematic analysis of transcripts. Results: Twenty-eight individuals (ROs, breast cancer surgeons, and cancer policy decision makers) were invited to participate; 22 out of the 28 people (79%) agreed. The final sample included 20 participants: 11 of 13 (85%) ROs, 5 of 7 (71%) surgeons, and 4 of 8 (50%) decision makers. Most ROs expressed concerns about overtreatment but could not predict with certainty which low-risk patients could safely avoid radiation. The DCIS score was viewed as contributing valuable personalized risk information as part of treatment decision making that included clinicopathological factors and women’s preferences. Future implementation would require guidelines with input from the oncology team. Conclusions: ROs had concerns about the overtreatment of women with DCIS, but lacked the tools to reliably predict which women could safely avoid radiation. By providing oncologists and women with personalized tumor information, the DCIS score was an important component of treatment decision making.
Quantitative models in asset management: A review of efficacy and limitations

This review provides a comprehensive overview of the efficacy and limitations associated with quantitative models in the field of asset management. Over the past few decades, the financial industry has witnessed a significant shift towards algorithmic and data-driven approaches in investment decision-making. Quantitative models, ranging from traditional risk-return frameworks to sophisticated machine learning algorithms, play a crucial role in shaping investment strategies and portfolio management. The review begins by examining the strengths of quantitative models, highlighting their ability to process vast amounts of financial data efficiently and objectively. These models enable investors to make data-driven decisions, manage risk, and optimize portfolio allocations. Furthermore, quantitative models facilitate the identification of patterns and trends in market behavior, allowing for timely adjustments to investment strategies. However, the efficacy of quantitative models is not without its limitations. The review explores challenges such as model risk, data quality issues, and the inherent complexity of financial markets. Model risk refers to the possibility of errors or inaccuracies in the mathematical models used, leading to suboptimal investment decisions. Additionally, the reliance on historical data assumes that future market conditions will resemble the past, a presumption that may be invalidated during unforeseen events or structural shifts. The review also delves into the ongoing debate surrounding the balance between human expertise and algorithmic decision-making. While quantitative models offer objectivity and systematic processes, the human element remains crucial for interpreting results, adapting to dynamic market conditions, and exercising judgment in situations where models may fall short. In conclusion, this review provides valuable insights into the evolving landscape of asset management through the lens of quantitative models. Recognizing their efficacy in processing vast datasets and identifying patterns, it also underscores the importance of acknowledging and addressing the limitations inherent in these models. Achieving a harmonious integration of human judgment and quantitative methodologies is crucial for enhancing the overall effectiveness of asset management strategies in a rapidly changing financial environment.
Species Richness and Composition of Forest Birds in Urban Parks and Reserves of Buenos Aires City, Argentina

Simple Summary Urban green spaces provide multiple services and are important for biodiversity and human well-being. Nevertheless, studies analyzing the relationship between green spaces and forest birds in the Global South are still scarce. Using citizen science data, we evaluated the variation in the richness and specific composition of forest birds in two types of urban green spaces in the Metropolitan Area of Buenos Aires, Argentina: parks and reserves. Sampling effort was considered as the number of checklists for each site. Forest bird species richness was higher in reserves and was positively related to sampling effort. Forest bird species composition varied as a function of green space type and sampling effort. Moreover, the species present in sites with lower richness were a subset of the species present in richer sites. Reserves and sites with the highest sampling effort concentrated all species. The results obtained show the importance of urban reserves and citizen science platforms in the conservation of forest birds. Abstract Urbanization is among the main factors of ecosystem transformation and threats to global biodiversity. Urban green spaces provide multiple services, being important for biodiversity and human well-being. However, the relationship between green spaces and forest birds has been scarcely studied in the Global South. In this work, we used citizen science data (eBird) to assess the variation in the species richness and composition of forest birds in two types of public urban green spaces characterized by different vegetation composition and management: parks and reserves. In general, reserves had more native and unmanaged vegetation than parks. We selected parks and reserves located in the coastal area of the Metropolitan Area of Buenos Aires, Argentina. Sampling effort was considered as the number of checklists for each site. The database allowed information to be extracted from 12 sites and 33 species. The most common species were the Green-barred Woodpecker (Colaptes melanochloros), the Narrow-billed Woodcreeper (Lepidocolaptes angustirostris), and the White-crested Tyrannulet (Serpophaga subcristata). Bird species richness was higher in reserves than in parks and was positively related to sampling effort. The forest bird species composition varied according to the type of green area and sampling effort. Species composition showed a significant nestedness, with the least rich sites being a subset of species from the richest sites. Reserves and sites with the highest sampling effort concentrated all species. The results obtained show the importance of urban reserves in the conservation of forest birds.
Influence of Backpack Carriage and Walking Speed on Muscle Synergies in Healthy Children

Four to five muscle synergies account for children’s locomotion and appear to be consistent across alterations in speed and slopes. Backpack carriage induces alterations in gait kinematics in healthy children, raising questions regarding the clinical consequences related to orthopedic and neurological diseases and ergonomics. However, to support clinical decisions and characterize backpack carriage, muscle synergies can help with understanding the alterations induced in this condition at the motor control level. In this study, we investigated how children adjust the recruitment of motor patterns during locomotion, when greater muscular demands are required (backpack carriage). Twenty healthy male children underwent an instrumental gait analysis and muscle synergies extraction during three walking conditions: self-selected, fast and load conditions. In the fast condition, a reduction in the number of synergies (three to four) was needed for reconstructing the EMG signal with the same accuracy as in the other conditions (three to five). Synergies were grouped in only four clusters in the fast condition, while five clusters were needed for the self-selected condition. The right number of clusters was not clearly identified in the load condition. Speed and backpack carriage altered nearly every spatial–temporal parameter of gait, whereas kinematic alterations reflected mainly hip and pelvis adaptations. Although the synergistic patterns were consistent across conditions, indicating a similar motor pattern in different conditions, the fast condition required fewer synergies for reconstructing the EMG signal with the same level of accuracy.
Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review

Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases’ symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.
Retrieval Delay Minimization for Reconfigurable Intelligent Surface Empowered Edge Caching

Edge caching can reduce system retrieval latency with effect, while reconfigurable intelligent surface (RIS) can repair losses caused by wireless propagation environments. In this paper, a RIS-empowered edge caching is proposed to minimize the content retrieval latency by optimizing the content placement vectors, transmit beamforming, and RIS's phase shift. To overcome the difficulties in coupled optimization variables and binary content placement, we decouple the underlying problem into two subproblems. Specifically, the mixed integer subproblem of caching strategy is relaxed as the binary constraints of content placement vectors and is solved via Karush-Kuhn-Tucker conditions. The minimum latency can be equivalently transformed as maximization of average sum rate, where the sum-of-logarithms is reformulated as a multiple ratio fractional programming problem, where the transmit beamforming is obtained by using quadratic transformation approach, and the passive beamforming is achieved via alternating direction method of multipliers alogrithm. Simulation results indicate the effectiveness of the RIS-aided edge caching network in reducing system retrieval delay.
Regulation of the Activity of the Dual Leucine Zipper Kinase by Distinct Mechanisms

The dual leucine zipper kinase (DLK) alias mitogen-activated protein 3 kinase 12 (MAP3K12) has gained much attention in recent years. DLK belongs to the mixed lineage kinases, characterized by homology to serine/threonine and tyrosine kinase, but exerts serine/threonine kinase activity. DLK has been implicated in many diseases, including several neurodegenerative diseases, glaucoma, and diabetes mellitus. As a MAP3K, it is generally assumed that DLK becomes phosphorylated and activated by upstream signals and phosphorylates and activates itself, the downstream serine/threonine MAP2K, and, ultimately, MAPK. In addition, other mechanisms such as protein–protein interactions, proteasomal degradation, dephosphorylation by various phosphatases, palmitoylation, and subcellular localization have been shown to be involved in the regulation of DLK activity or its fine-tuning. In the present review, the diverse mechanisms regulating DLK activity will be summarized to provide better insights into DLK action and, possibly, new targets to modulate DLK function.
Machine Learning Based Approach to Reduce Food Wastage

Food wastage is a major issue that affects the economy, society, and environment on a global scale. Over one-third of the food produced worldwide is lost or wasted each year, underscoring the necessity for sustainable food systems. This research proposes an innovative solution that leverages technology to address food wastage reduction. An application is developed with a focus on four key categories: food wastage prediction and analysis, demand prediction and ingredient optimization, forecasting of near-expiry food prices, and accurate recycling method prediction. By incorporating machine learning and predictive modeling, the application provides restaurants and food administrators with comprehensive organizational statistics to effectively mitigate food wastage. The solution offers a data-driven approach to better plan events, reduce waste in the food industry, accurately forecast both demand and ingredient requirements, strategically focus on prices based on shelf life and predict optimal recycling methods. Through this multi-pronged approach, we aim to tackle the complex issue of food wastage and help to establish sustainable food systems.
Image-to-image translation for satellite and UAV remote sensing: a use case for Cercospora Leaf Spot monitoring on sugar beet.

In the field of vegetation remote sensing, the concept and conversion across scales has been addressed—but not concluded-in recent years. A large array of different sensors is deployed using various platforms such as uncrewed aerial vehicles (UAVs) and satellites. In this context, while multiple concepts of scale exist, the influence of spatial scale plays a major role when comparing and validating optical data derived from either of these platforms. Until to date, no satisfying method to allow cross sensor and scale studies exists. The presented work proposes a custom U-Net architecture to convert multispectral UAV imagery to multispectral satellite imagery. U-Nets are convolutional networks that have shown to be capable of learning mapping patterns between an input image and an output image. Therefore, artificially created images resembles original images at a high level of local detail and global semantic coherence. By using a multi-year, multispectral image UAV dataset, capturing a biologically and structural complex pathosystem (Cercosopora beticola + Beta vulgaris L.), it is shown that the developed custom U-Net can convert original UAV images with low structural and spectral error (MSE of 0.0001, SSIM test score of 0.0003, DWT test score of 0.0104, and a PSNR value of 40.3551) into artificial satellite images. Also, it is compared whether band center and bandwidth are accurately converted. To showcase an application, artificial images have been used to classify levels of Cercospora Leaf Spot in sugar beet. Classification accuracies are similar to those derived from the classification using original images. Therefore, it can be concluded that image to image translation using U-Nets can be a powerful tool for scaling studies in remote sensing. But they also offer to address the issue of data sparsity in machine learning as the conversion allows to generate images across different scales, therefore potentially adding valuable data to time-series studies.
In Vivo Cellular Phosphatidylcholine Kinetics of CD15+ Leucocytes and CD3+ T-Lymphocytes in Adults with Acute Respiratory Distress Syndrome

Mammalian cell membranes composed of a mixture of glycerophospholipids, the relative composition of individual phospholipids and the dynamic flux vary between cells. In addition to their structural role, membrane phospholipids are involved in cellular signalling and immunomodulatory functions. In this study, we investigate the molecular membrane composition and dynamic flux of phosphatidylcholines in CD15+ leucocytes and CD3+ lymphocytes extracted from patients with acute respiratory distress syndrome (ARDS). We identified compositional variations between these cell types, where CD15+ cells had relatively higher quantities of alkyl-acyl PC species and CD3+ cells contained more arachidonoyl-PC species. There was a significant loss of arachidonoyl-PC in CD3+ cells in ARDS patients. Moreover, there were significant changes in PC composition and the methyl-D9 enrichment of individual molecular species in CD15+ cells from ARDS patients. This is the first study to perform an in vivo assessment of membrane composition and dynamic changes in immunological cells from ARDS patients.
Effect of CB1 Receptor Deficiency on Mitochondrial Quality Control Pathways in Gastrocnemius Muscle

Simple Summary The cannabinoid type 1 receptor (CB1) is a primary cannabinoid receptor prominently found in the central nervous system and peripheral tissues, notably skeletal muscle. Muscle tissue CB1 is located on the cell and mitochondrial membrane. This study investigates how this receptor contributes to mitochondrial homeostasis in gastrocnemius muscle. The results indicate that CB1 absence induces a shift from fast-twitch to slow-twitch fibers, coupled with increased oxidative capacity and alterations in the antioxidant defense systems. Analysis of mitochondrial quality control (MQC) shows enhanced biogenesis, fusion, mitophagy, and UPRmt. Our study reveals the multifaceted impact of CB1 absence on the mitochondrial homeostasis of gastrocnemius muscle. Abstract This study aims to explore the complex role of cannabinoid type 1 receptor (CB1) signaling in the gastrocnemius muscle, assessing physiological processes in both CB1+/+ and CB1−/− mice. The primary focus is to enhance our understanding of how CB1 contributes to mitochondrial homeostasis. At the tissue level, CB1−/− mice exhibit a substantial miRNA-related alteration in muscle fiber composition, characterized by an enrichment of oxidative fibers. CB1 absence induces a significant increase in the oxidative capacity of muscle, supported by elevated in-gel activity of Complex I and Complex IV of the mitochondrial respiratory chain. The increased oxidative capacity is associated with elevated oxidative stress and impaired antioxidant defense systems. Analysis of mitochondrial biogenesis markers indicates an enhanced capacity for new mitochondria production in CB1−/− mice, possibly adapting to altered muscle fiber composition. Changes in mitochondrial dynamics, mitophagy response, and unfolded protein response (UPR) pathways reveal a dynamic interplay in response to CB1 absence. The interconnected mitochondrial network, influenced by increased fusion and mitochondrial UPR components, underlines the dual role of CB1 in regulating both protein quality control and the generation of new mitochondria. These findings deepen our comprehension of the CB1 impact on muscle physiology, oxidative stress, and MQC processes, highlighting cellular adaptability to CB1−/−. This study paves the way for further exploration of intricate signaling cascades and cross-talk between cellular compartments in the context of CB1 and mitochondrial homeostasis.
Mitigating Co-Channel Interference to SOQPSK

Shaped offset quadrature phase-shift keying (SOQPSK) has been widely used in aviation telemetry and deep space communication. However, co-channel interference (CCI) and signal of interest (SOI) partially overlap in the time and frequency domains, resulting in an increase in the bit error rate of SOQPSK signal demodulation. To address this issue, this paper proposes a frequency-shift (FRESH) filter-based decoder for the SOQPSK signals with interference. Specifically, we first prove the cyclostationary of SOQPSK signals and derive its cyclic frequency and conjugate cyclic frequency. Then, we decode bit in a sequential manner based on the standard FRESH filter and selected cyclic and conjugate cyclic frequencies. Finally, we analyze the outage probability and obtain its closed-form expression under Rayleigh fading channel. Numerical results demonstrate the notable gains of the proposed method over the existing methods.