Miracle-dz/UniFAR-FTUs huggingface dataset:
Using the “Footprint” Approach to Examine the Potentials and Impacts of Renewable Energy Sources in the European Alps

The expansion of renewable energies is regarded as a key way to mitigate global climate change and to ensure the provision of energy in the long term. However, conflicts between these goals and local nature conservation goals are likely to increase because of the additional space required for renewable energies. This is particularly true for mountainous areas with biodiversity-rich ecosystems. Little effort has been undertaken to systematically compare different renewable energy sources and to examine their environmental impacts using an interdisciplinary approach. This study adapted the concept of the “ecological footprint” to examine the impact on ecosystem services of land use changes involved in exploiting renewable energy sources. This innovative approach made it possible to assess and communicate the potentials of those energy sources in light of both space consumption and sustainability. The European Alps are an ideal test area because of their high energy potentials and biodiversity-rich ecosystems and the high demand for multiple ecosystem services. Our results demonstrate that energy consumption in the Alps could not be covered with the available renewable energy potentials, despite the utilization of large parts of the Alpine land area and the majority of larger rivers. Therefore, considerable effort must be invested in resolving conflicting priorities between expanding renewable energies and nature conservation, but also in realizing energy-saving measures. To this end, the approach presented here can support decision-making by revealing the energy potentials, space requirements, and environmental impacts of different renewable energy sources.
Enhanced Subseasonal Variability of Spring Temperature Over Eastern China in 2022: Initial Role of Extremely Heavy Arctic Sea Ice in Previous Winter

In spring 2022, strong temperature whiplash events occurred in eastern China (EC), characterized by enhanced subseasonal variability in surface air temperature (SAT). Our results show that extreme temperature fluctuations are dominated by the amplified Eurasian wave train on a biweekly timescale as a possible response to increased sea ice concentrations (SICs) over the Barents‐Kara Seas (BK) in previous winter. Increased winter SICs weaken the upward planetary waves and enhance the stratospheric polar vortex. In spring, the downward propagation of enhanced polar vortex intensifies the polar front jet, increasing kinetic energy gain of the North Atlantic wave train and guiding it to travel farther eastward. The Eurasian wave train strengthens through an upstream development and enhanced local baroclinicity and enhances subseasonal SAT variability over EC. Therefore, the state of BK SICs in previous winter could be a useful signal for the enhanced likelihood of spring extreme temperature whiplash events.
Paternal-effect genes revealed through semen cryopreservation in Perca fluviatilis

Knowledge about paternal-effect genes (the expression of which in progeny is controlled by the paternal genome) in fish is very limited. To explore this issue, we used semen cryopreservation as a specific challenge test for sperm cells, thus enabling selection amidst cryo-sensitivity. We created two groups of Eurasian perch (Perca fluviatilis) as a model – eggs fertilized either with fresh (Fresh group) or cryopreserved (Cryo group) semen from the same male followed by zootechnical-transcriptomic examination of consequences of cryopreservation in obtained progeny (at larval stages). Most of the zootechnical observations were similar in both groups, except the final weight was higher in the Cryo group. Semen cryopreservation appeared to act as a “positive selection” factor, upregulating most paternal-effect genes in the Cryo group. Transcriptomics profile of freshly hatched larvae sourced genes involved in the development of visual perception as paternal-effect genes. Consequently, larvae from the Cryo group exhibited enhanced eyesight, potentially contributing to more efficient foraging and weight gain compared to the Fresh group. This study unveils, for the first time, the significant influence of the paternal genome on the development of the visual system in fish, highlighting pde6g, opn1lw1, and rbp4l as novel paternal-effect genes.
Evaluation of Mechanical Strength Characteristics of Double Ducted Unmanned Amphibious Aerial Vehicle using Finite Element Analysis

Unmanned Aerial Vehicles are extensively exploited for diverse applications importantly surveillance, defence and military, photography. Development of unmanned amphibious vehicle with integrating features of hovercraft principles and multirotor to navigate along and above the water surface, land surface and flying in the air is challenging demand. This article presents conceptual design of amphibious vehicle for the payload capacity of 7 kg with an endurance of 20 minutes and provision for mounting water sampler to collect water samples in remote water bodies. Structural strength characteristics of each part of the amphibious vehicle and integrity of same are analysed by Finite Element Analysis. FEA results indicated that the designed amphibious vehicle structure is well within the stress limit and minimal displacement is obtained. Based on structural analysis materials for various parts of the amphibious vehicle are determined and integrated structure is analysed.
TileFlow: A Framework for Modeling Fusion Dataflow via Tree-based Analysis

With the increasing size of DNN models and the growing discrepancy between compute performance and memory bandwidth, fusing multiple layers together to reduce off-chip memory access has become a popular approach in dataflow design. However, designing such dataflows requires flexible and accurate performance models to facilitate evaluation, architecture analysis, and design space exploration. Unfortunately, current state-of-the-art performance models are limited to the dataflows of single operator acceleration, making them inapplicable to operator fusion dataflows.In this paper, we propose a framework called TileFlow that models dataflows for operator fusion. We first characterize the design space of fusion dataflows as a 3D space encompassing compute ordering, resource binding, and loop tiling. We then introduce a tile-centric notation to express dataflow designs within this space. Inspired by the tiling structure of fusion dataflows, we present a tree-based approach to analyze two critical performance metrics: data movement volume within the accelerator memory hierarchy and accelerator compute/memory resource usage. Finally, we leverage these metrics to calculate latency and energy consumption. Our evaluation validates TileFlow’s modeling accuracy against both real hardware and state-of-the-art performance models. We use TileFlow to aid in fusion dataflow design and analysis, and it helps us discover fusion dataflows that achieve an average runtime speedup of 1.85× for self-attention and 1.28× for convolution chains compared to the state-of-the-art dataflow.CCS CONCEPTS• Computing methodologies → Machine learning algorithms; • Hardware → Power estimation and optimization; Analysis and design of emerging devices and systems.
Polymer-induced liquid precursors: How are they being used and what is the effect on dentin remineralization? A scoping review

To identify the effectiveness of polymer-induced liquid precursors (PILP) on dentin remineralization and the assessment approaches used to evaluate remineralization. The analyses were done into six stages: (1) Identifying the research question; (2) Identifying the studies; (3) Selecting relevant studies; (4) Plotting the data; (5) Reporting results; and (6) Risk of bias. The searches were carried out in the following databases: PubMed, Web of Science, and Scopus. A total of 247 articles were identified in the electronic database search. After applying the eligibility criteria, only 12 articles were included for data extraction. The outcomes of dentin remineralization patterns were plotted in association to PILP agent type and application mode. The intrafibrillar mineralization occurred in 78% of studies where polyaspartic acid (pAsp) was used, and 50% when the polyacrylic acid (PAA) was used. All the studies where PILP was used in a restorative material (resin composite, glass ionomer, and adhesive system)—extra and intrafibrillar mineralization pattern was found. The association of PILP agents with other materials with potential for mineralization showed beneficial results in remineralization, since they provide a medium to high concentration of calcium and phosphate. The pAsp showed better results for interfibrillar remineralization when compared to PAA. The association with Ca/P release materials or solution Ca P are essentials for mineralization via PILP agent. Further studies are needed to assess the effectiveness of remineralization through PILP agents because the level of evidence of the studies was low.
Path Forward Beyond Simulators: Fast and Accurate GPU Execution Time Prediction for DNN Workloads

Today, DNNs’ high computational complexity and sub-optimal device utilization present a major roadblock to democratizing DNNs. To reduce the execution time and improve device utilization, researchers have been proposing new system design solutions, which require performance models (especially GPU models) to help them with pre-product concept validation. Currently, researchers have been utilizing simulators to predict execution time, which provides high flexibility and acceptable accuracy, but at the cost of a long simulation time. Simulators are becoming increasingly impractical to model today’s large-scale systems and DNNs, urging us to find alternative lightweight solutions.To solve this problem, we propose using a data-driven method for modeling DNNs system performance. We first build a dataset that includes the execution time of numerous networks/layers/kernels. After identifying the relationships of directly known information (e.g., network structure, hardware theoretical computing capabilities), we discuss how to build a simple, yet accurate, performance model for DNNs execution time. Our observations on the dataset demonstrate prevalent linear relationships between the GPU kernel execution times, operation counts, and input/output parameters of DNNs layers. Guided by our observations, we develop a fast, linear-regression-based DNNs execution time predictor. Our evaluation using various image classification models suggests our method can predict new DNNs performance with a 7% error and new GPU performance with a 15.2% error. Our case studies also demonstrate how the performance model can facilitate future DNNs system research.CCS CONCEPTS• Computing methodologies → Modeling methodologies.
The neural mechanisms tone recognition in deaf children: an fMRI study

Vocal tone is an important component of language and it plays a key role in language comprehension and communication. However, children with hearing loss face challenges in vocal tone recognition due to hearing impairment. In this study, five deaf children and two children with normal hearing were recruited to compare the differences in third and fourth tone recognition tasks between deaf and normal children. The results revealed that (1) some of the deaf children's brain regions that process vocal tones did not work properly due to hearing loss; (2) deaf children may rely on different neural networks when processing vocal tone information. (3) Deaf children process vocal tone information with hemispheric characteristics.
Road damage detection method based on improved YOLOv8n

An improved YOLOv8n network model is proposed to cope with key challenges in road damage detection, including feature extraction, multi-scale feature processing, fusion, and efficiency. By integrating the feature extraction structure RepVGG-SSE and the multi-branch downsampling into the backbone, the receptive field of our model is broadened so that it is capable of dealing with the diverse road damage scales. As part of our model, the Efficient-GFPN feature pyramid structure makes effective fusion of multi-scale features possible, and the performance for detecting objects of different sizes and complexities is enhanced greatly. Additionally, the lightweight convolution model GPConv is proposed to replace the 3x3 Conv in the C2f structure in the neck layer, so that both the parameters and computational complexity of the network model can be reduced greatly without compromising accuracy, so as to achieve the balance of efficiency and performance of the detection model in a reasonable way. The Improved YOLOv8n network was trained and validated on the RDD-2020 and UAPD datasets, and both the ablation and comparison experimental results demonstrate that the improved YOLOv8n model is both effective and efficient, and outperforms the state-of-the-art methods, suggesting it a promising solution to the real-world road damage detection tasks.
Lacrimal Gland Abscess: A Case Report

Introduction: The aim of the study was to report a rare case of lacrimal gland abscess. Case Presentation: A 47-year-old woman noticed upper eyelid swelling on the right side 1.5 months before referral to our service. Oral antibiotics were administered, based on the diagnosis of acute dacryoadenitis at another clinic. The symptom had once subsided 20 days later but recurred. On the first examination, the right upper eyelid was swollen with tenderness. The right lacrimal gland was palpable. Blood tests revealed positive proteinase 3-anti-neutrophil cytoplasmic antibody. T2-weighted magnetic resonance and diffusion-weighted images showed a high signal intensity lesion in an enlarged right lacrimal gland, while apparent diffusion coefficient map demonstrated the lesion with a low signal intensity. We started administration of intravenous antibiotics. Abscess drainage and lacrimal gland biopsy were performed 4 days after the first examination. Culture test of the abscess showed only 1 colony growth of Cutibacterium acnes. The specimen harvested from the lacrimal gland showed proliferation of fibrous connective tissue and infiltration of inflammatory cells without vasculitis. After the drainage, the swelling gradually subsided. Administration of antibiotics discontinued at 22 days of follow-up. At 4-month follow-up, the patient did not have any symptom related to the lacrimal gland abscess. Conclusion: The diffusion-weighted images and apparent diffusion coefficient map are helpful for the diagnosis of lacrimal gland abscess when the culture tests provide poor results.
Multi-station Water Quality Prediction Considering Temporal and Spatial Correlation: Based On T-GCN

The water quality monitoring data reflects the ecological environment of the river and its changing trend. The accurate analysis and prediction of water quality is of great significance to improve the efficiency of river management and maintenance, ensure the safety and stability of the ecological environment of the river basin. The traditional water quality prediction model combines hydrodynamic theory and water quality change process mechanism, which can represent the overall change trend of water quality. However, this method is mostly aimed at a fixed watershed, with poor portability ability and difficult to accurately predict key areas. Although some new mathematical statistical methods and machine learning methods can achieve high accuracy in the prediction of water quality data from a single station, the spatial correlation characteristics between multiple stations are ignored in the comprehensive water quality prediction of the basin, resulting in a decline in accuracy. In this paper, based on the spatial distribution and graph structure characteristics of multiple water quality monitoring stations in the basin, the temporal graph convolutional neural network is used to analyse the time series characteristics of water quality, and realize the prediction of water quality data from multiple monitoring stations. The analysis of water quality monitoring data of a river located in the Guangdong Province of China proves that, compared with the commonly used model LSTM, the T-GCN model can better fit the spatial correlation characteristics and time series characteristics of water quality monitoring data, and achieve a more accurate water quality predictive analysis. Based on the water quality monitoring data of multiple stations, T-GCN model can reflect the changing trend of water quality in the entire basin, and accurately predict the water quality changing in specific locations. The research results can provide a more accurate and effective reference for river operation and management.
Uncovering the diversity of heterotrophic culturable bacteria within the gut of bay scallop Argopecten purpuratus (Lamarck, 1819) from two geographically distant natural banks

We study the bacterial diversity within the gut of the peruvian bay scallop Argopecten purpuratus (Lamarck, 1819) (Mollusca: Pectinidae) collected from two shellfish growing areas in Independence Bay (IB) and Sechura Bay (SB) in Peru. This work aimed to identify isolates of culturable gut-associated microbiota of A. purpuratus through repetitive sequence-based PCR (rep-PCR) and 16S rRNA gene sequences comparison. A total of 20 healthy adult scallops were collected from each location twice, in spring and autumn, 2015 and 2016, respectively. Fecal pellets from the distal intestinal tract of each individual were extracted, and after pooling, samples were cultivated on general and selective media following standard culture conditions. In order to prevent duplicate sequencing of the ribosomal 16S rRNA gene sequences of the isolates, a pre-screened step by (rep-PCR) amplification utilizing BOXA1R primer was performed, then isolates showing different banding profiles were selected for further sequencing. Taxonomic distribution based on the 16S rRNA gene comparison of selected strains placed isolates from IB and SB into 29 and 27 different genera, representing 50 and 42 species, respectively. The major proportions of bacterial species found in IB corresponded to the division Firmicutes (46%), whereas the Proteobacteria and Firmicutes were almost equally distributed in (SB) with 38 and 43% respectively. Remarkable, (IB) showed a high number (28%) and diversity of species within the division Actinobacteria compared to (SB); while the species Salinimicrobium sp. of the division Flavobacteria was isolated only in (SB). Common species in both areas are mainly related to the genus Bacillus, while strains of species Vibrio alginolyticus and V. parahaemoliticus were common representatives of the Proteobacteria. The intestinal tract of A. purpuratus may represent an uninvestigated niche for analyzing the structure of new complex microbial communities. This study showed that the diversity of culturable microbiota within the gut of two populations of A. purpuratus is significantly different at each location.
Diagnostic Accuracy of PET with 18F-Fluciclovine ([18F]FACBC) in Detecting High-Grade Gliomas: A Systematic Review and Meta-Analysis

Background: 18F-Fluciclovine ([18F]FACBC) has been recently proposed as a synthetic radiolabeled amino acid for positron emission tomography (PET) imaging in patients with brain neoplasms. Our aim is to evaluate the diagnostic performance of [18F]FACBC PET in high-grade glioma (HGG) patients, taking into account the literature data. Methods: A comprehensive literature search was performed. We included original articles evaluating [18F]FACBC PET in the detection of HGG before therapy and for the suspicion of tumor recurrence. Pooled sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR−), and diagnostic odds ratios (DOR), including 95% confidence intervals (95% CI), were measured. Statistical heterogeneity and publication bias were also assessed. Results: ten studies were included in the review and eight in the meta-analysis (113 patients). Regarding the identification of HGG, the sensitivity of [18F]FACBC PET ranged between 85.7% and 100%, with a pooled estimate of 92.9% (95% CI: 84.4–96.9%), while the specificity ranged from 50% to 100%, with a pooled estimate of 70.7% (95% CI: 47.5–86.5%). The pooled LR+, LR−, and DOR of [18F]FACBC PET were 2.5, 0.14, and 37, respectively. No significant statistical heterogeneity or publication bias were found. Conclusions: evidence-based data demonstrate the good diagnostic accuracy of [18F]FACBC PET for HGG detection. Due to the still limited data, further studies are warranted to confirm the promising role of [18F]FACBC PET in this context.
Assessment of different methods for the prediction of marine propellers induced pressures

: This paper addresses the problem of the prediction of propellers induced pressures; to this aim numerical and experimental results (both in model and full scale) in correspondence to different functioning conditions for a fast twin screw ship are reported. Numerical results were obtained by means of two different BEM codes and, only for specific cases, of a RANSE solver. Experiments were carried out both in model scale at cavitation tunnel and in full scale.
A high-quality genome assembly of the ghost moth Druceiella hillmani provides new evidence of genome size augmentation in Hepialidae

Ghost moths are an unusual family of primitive moths (Lepidoptera: Hepialidae) known for their large body size and crepuscular adult activity. These moths represent an ancient lineage, frequently have soil dwelling larvae, and are adapted to high elevations, deserts, and other extreme environments. Despite being rather speciose with more than 700 species, there is a dearth of genomic resources for the family. Here, we present the first high quality, publicly available hepialid genome, generated from an Andean species of ghost moth, Druceiella hillmani. Our genome assembly has a length of 2,586 Mbp with contig N50 of 28.1 Mb and N50 of 29, and BUSCO completeness of 97.1%, making it one of the largest genomes in the order Lepidoptera. Our assembly is a vital resource for future research on ghost moth genomics.
How to Kill the Second Bird with One ECC: The Pursuit of Row Hammer Resilient DRAM

Error-correcting code (ECC) has been widely used in DRAM-based memory systems to address the exacerbating random errors following the fabrication process scaling. However, ECCs including the strong form of Chipkill have not been so effective against Row Hammer (RH), which incurs bursts of errors discretely corrupting the whole row beyond the ECC correction capability.We propose Cube, a novel chip-wise physical to DRAM address randomization scheme that leverages the abundant detection capability of on-die-ECC (OECC) and correction capability of Chipkill against RH. Cube allows for synergistic cooperation between ECC, probabilistic RH-protection schemes, and the system with minimal to no modification for each. First, Cube scrambles the rows of each chip using a boot-time key in a way that distributes RH victims to multiple Chipkill codewords. Second, Cube utilizes the newly observed distinct RH error characteristics from real DRAM chips, to swiftly diagnose the RH victim rows using the error profile from OECC scrubbing, and even correct it leveraging Chipkill. When combined, Cube decreases the failure probability of PARA and a state-of-the-art RH protection scheme SRS by up to 10−25. At a target failure probability of 10−10 per year on a DDR5 rank under the RH threshold of 2K, Cube reduces the performance and table size overhead of SRS by up to 24.3% and 39.9%, respectively.CCS CONCEPTS• Security and privacy → Security in hardware.