Search Results (13)

Laboratory magnetoplasmas can become an intriguing experimental environment for fundamental studies relevant to nuclear astrophysics processes. Theoretical predictions indicate that the ionization state of isotopes within the plasma can significantly alter their lifetimes, potentially due to nuclear and atomic mechanisms such as bound-state β-decay. However, only limited experimental evidence on this phenomenon has been collected. PANDORA (Plasmas for Astrophysics, Nuclear Decay Observations, and Radiation for Archaeometry) is a novel facility which proposes to investigate nuclear decays in high-energy-density plasmas mimicking some properties of stellar nucleosynthesis sites (Big Bang Nucleosynthesis, s-process nucleosynthesis, role of CosmoChronometers, etc.). This paper focuses on the case of ⁷Be electron capture (EC) decay into ⁷Li, since its in-plasma decay rate has garnered considerable attention, particularly concerning the unresolved Cosmological Lithium Problem and solar neutrino physics. Numerical simulations were conducted to assess the feasibility of this possible lifetime measurement in the plasma of PANDORA. Both the ionization and atomic excitation of the ⁷Be isotopes in a He buffer Electron Cyclotron Resonance (ECR) plasma within PANDORA were explored via numerical modelling in a kind of “virtual experiment” providing the expected in-plasma EC decay rate. Since the decay of ⁷Be provides γ-rays at 477.6 keV from the ⁷Li excited state, Monte-Carlo GEANT4 simulations were performed to determine the γ-detection efficiency by the HPGe detectors array of the PANDORA setup. Finally, the sensitivity of the measurement was evaluated through a virtual experimental run, starting from the simulated plasma-dependent γ-rate maps. These results indicate that laboratory ECR plasmas in compact traps provide suitable environments for β-decay studies of ⁷Be, with the estimated duration of experimental runs required to reach 3σ significance level being few hours, which prospectively makes PANDORA a powerful tool to investigate the decay rate under different thermodynamic conditions and related charge state distributions. Full article

The study of personalities is a major component of human psychology, and with an understanding of personality traits, practical applications can be used in various domains, such as mental health care, predicting job performance, and optimising marketing strategies. This study explores the prediction of Big Five personality trait scores from online comments using transformer-based language models, focusing on improving the model performance with a larger dataset and investigating the role of intercorrelations among traits. Using the PANDORA dataset from Reddit, the RoBERTa and BERT models, including both the base and large variants, were fine-tuned and evaluated to determine their effectiveness in personality trait prediction. Compared to previous work, our study utilises a significantly larger dataset to enhance the model’s generalisation and robustness. The results indicate that RoBERTa outperforms BERT across most metrics, with RoBERTa large achieving the best overall performance. In addition to evaluating the overall predictive accuracy, this study investigates the impact of intercorrelations among personality traits. A comparative analysis is conducted between a single-model approach, which predicts all five traits simultaneously, and a multiple-model approach, fine-tuning the models independently and each predicting a single trait. The findings reveal that the single-model approach achieves a lower RMSE and higher $R^2$ values, highlighting the importance of incorporating trait intercorrelations in improving the prediction accuracy. Furthermore, RoBERTa large demonstrated a stronger ability to capture these intercorrelations compared to previous studies. These findings emphasise the potential of transformer-based models in personality computing and underscore the importance of leveraging both larger datasets and intercorrelations to enhance predictive performance. Full article

This study investigates the age, growth, and mortality of the common pandora (Pagellus erythrinus) in the Central Aegean Sea, providing critical insights into its population dynamics and sustainability. A total of 589 specimens were analyzed, identifying nine age cohorts with mean total lengths ranging from 13.18 cm to 32.94 cm. Growth parameters, estimated using the von Bertalanffy growth model, yielded an asymptotic length (L∞) of 39.53 cm and a growth coefficient (k) of 0.16 year⁻¹, indicating moderate growth rates. The population exhibited non-isomorphic growth (b = 2.49, R² = 98.4), suggesting slower weight gain relative to length. Mortality estimates indicated natural mortality (M) at 0.321 year⁻¹, total mortality (Z) at 0.52 year⁻¹, and fishing mortality (F) at 0.2 year⁻¹, resulting in an exploitation rate (E) of 0.38. The fishing mortality at maximum sustainable yield (FMSY) was estimated at 0.33, with an exploitation rate at MSY (EMSY) of 0.51, suggesting that the population is currently harvested sustainably but close to the threshold of overexploitation. These findings provide essential reference points for fisheries management and highlight the need for continuous monitoring to ensure the long-term sustainability of P. erythrinus in Greek waters. Full article

Emerging small non-coding RNAs (sncRNAs), including tRNA-derived small RNAs (tsRNAs) and rRNA-derived small RNAs (rsRNAs), are critical in various biological processes, such as neurological diseases. Traditional sncRNA-sequencing (seq) protocols often miss these sncRNAs due to their modifications, such as internal and terminal modifications, that can interfere with sequencing. We recently developed panoramic RNA display by overcoming RNA modification aborted sequencing (PANDORA-seq), a method enabling comprehensive detection of modified sncRNAs by overcoming the RNA modifications. Using PANDORA-seq, we revealed a previously unrecognized sncRNA profile enriched by tsRNAs/rsRNAs in the mouse prefrontal cortex and found a significant downregulation of mitochondrial tsRNAs and rsRNAs in an Alzheimer’s disease (AD) mouse model compared to wild-type controls, while this pattern is not present in the genomic tsRNAs and rsRNAs. Moreover, our integrated analysis of gene expression and sncRNA profiles reveals that those downregulated mitochondrial sncRNAs negatively correlate with enhanced lysosomal activity, suggesting a crucial interplay between mitochondrial RNA dynamics and lysosomal function in AD. Given the versatile tsRNA/tsRNA molecular actions in cellular regulation, our data provide insights for future mechanistic study of AD with potential therapeutic strategies. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. The MoveONParkinson project aims to enhance exercise engagement among people with Parkinson’s Disease (PwPD) in the Portuguese context through the ONParkinson digital platform, which provides mobile and web interfaces. While the broader MoveONParkinson project has been previously described from a health-focused perspective, this study specifically focuses on the development and integration of an AI-driven conversational agent (CA) for the Portuguese language, called PANDORA, within the mobile interface of the solution to assist and motivate PwPD in their exercise routines. PANDORA (Parkinson Assistant in Natural Dialogue and Oriented by Rules and Assessments), designed based on Self-Determination Theory (SDT), addresses the psychological needs of autonomy, competence, and relatedness. A preliminary study involving 20 PwPD, 10 caregivers, and 5 healthcare professionals informed the design requirements for PANDORA. The development process involved four main phases: (1) Design of the Chatbot’s Motivation Model, (2) Design and implementation of the conversational agent, (3) Technical Performance Evaluation, and (4) User Experience Evaluation. Technical Performance Evaluation, conducted with three physiotherapists, assessed domain coverage, coherence response capacity, and dialog management capacity, achieving 100% accuracy in domain coverage and coherence response capacity and 89% in dialog management capacity. The User Experience Study involved eight PwPD users recruited from Portuguese healthcare units performing predefined tasks, with user satisfaction scores ranging from 4.2 to 4.9 on a five-point Likert scale. The findings indicate that integrating a conversational agent with motivational cues tends to increase patient engagement. However, further studies are required to determine PANDORA’s impact on exercise engagement in PwPD. Full article

PANDORA (Plasmas for Astrophysics Nuclear Decays Observation and Radiation for Archaeometry) is an INFN project aiming at measuring, for the first time, possible variations in in-plasma $\beta$-decay lifetimes in isotopes of astrophysical interest as a function of thermodynamical conditions of the in-laboratory controlled plasma environment. Theoretical predictions indicate that the ionization state can dramatically modify the $\beta$-decay lifetime (even of several orders of magnitude). The PANDORA experimental approach consists of confining a plasma able to mimic specific stellar-like conditions and measuring the nuclear decay lifetime as a function of plasma parameters. The $\beta$-decay events will be measured by detecting the $\gamma$-ray emitted by the daughter nuclei, using an array of 12 HPGe detectors placed around the magnetic trap. In this frame, plasma parameters have to be continuously monitored online. For this purpose, an innovative, non-invasive multi-diagnostic system, including high-resolution time- and space-resolved X-ray analysis, was developed, which will work synergically with the $\gamma$-rays detection system. In this contribution, we will describe this multi-diagnostics system with a focus on spatially resolved high-resolution X-ray spectroscopy. The latter is performed by a pin-hole X-ray camera setup operating in the 0.5–20 keV energy domain. The achieved spatial and energy resolutions are 450 µm and 230 eV at 8.1 keV, respectively. An analysis algorithm was specifically developed to obtain SPhC (Single Photon-Counted) images and local plasma emission spectrum in High-Dynamic-Range (HDR) mode. Thus, investigations of image regions where the emissivity can change by even orders of magnitude are now possible. Post-processing analysis is also able to remove readout noise, which is often observable and dominant at very low exposure times (ms). Several measurements have already been used in compact magnetic plasma traps, e.g., the ATOMKI ECRIS in Debrecen and the Flexible Plasma Trap at LNS. The main outcomes will be shortly presented. The collected data allowed for a quantitative and absolute evaluation of local emissivity, the elemental analysis, and the local evaluation of plasma density and temperature. This paper also discusses the new plasma emission models, implemented on PIC-ParticleInCell codes, which were developed to obtain powerful 3D maps of the X-rays emitted by the magnetically confined plasma. These data also support the evaluation procedure of spatially resolved plasma parameters from the experimental spectra as well as, in the near future, the development of appropriate algorithms for the tomographic reconstruction of plasma parameters in the X-ray domain. The described setups also include the most recent upgrade, consisting of the use of fast X-ray shutters with special triggering systems that will be routinely implemented to perform both space- and time-resolved spectroscopy during transient, stable, and turbulent plasma regimes (in the ms timescale). Full article

This study’s main goal is to create a useful software application for finding and classifying fine art photos in museums and art galleries. There is an increasing need for tools to swiftly analyze and arrange art collections based on their artistic styles as a result of the digitization of art collections. To increase the accuracy of the style categorization, the suggested technique involves two parts. The input image is split into five sub-patches in the first stage. A DCNN that has been particularly trained for this task is then used to classify each patch individually. A decision-making module using a shallow neural network is part of the second phase. Probability vectors acquired from the first-phase classifier are used to train this network. The results from each of the five patches are combined in this phase to deduce the final style classification for the input image. One key advantage of this approach is employing probability vectors rather than images, and the second phase is trained separately from the first. This helps compensate for any potential errors made during the first phase, improving accuracy in the final classification. To evaluate the proposed method, six various already-trained CNN models, namely AlexNet, VGG-16, VGG-19, GoogLeNet, ResNet-50, and InceptionV3, were employed as the first-phase classifiers. The second-phase classifier was implemented as a shallow neural network. By using four representative art datasets, experimental trials were conducted using the Australian Native Art dataset, the WikiArt dataset, ILSVRC, and Pandora 18k. The findings showed that the recommended strategy greatly surpassed existing methods in terms of style categorization accuracy and precision. Overall, the study assists in creating efficient software systems for analyzing and categorizing fine art images, making them more accessible to the general public through digital platforms. Using pre-trained models, we were able to attain an accuracy of 90.7. Our model performed better with a higher accuracy of 96.5 as a result of fine-tuning and transfer learning. Full article

Artificial intelligence (AI) is one of the ground-breaking innovations of the 21st century that has accelerated the digitalization of societies. ChatGPT is a newer form of AI-based large language model that can generate complex texts that are almost indistinguishable from human-generated text. It has already garnered substantial interest from people due to its potential utility in a variety of contexts. The current study was conducted to evaluate the utility of ChatGPT in generating accurate, clear, concise, and unbiased information that could support a scientific research process. To achieve this purpose, we initiated queries on both versions of ChatGPT regarding digital school leadership and teachers’ technology integration, two significant topics currently discussed in educational literature, under four categories: (1) the definition of digital leadership, (2) the digital leadership skills of school principals, (3) the factors affecting teachers’ technology integration, and (4) the impact of digital leadership on teachers’ technology integration. Next, we performed a comparative analysis of the responses generated by ChatGPT-3.5 and ChatGPT-4. The results showed that both versions were capable of providing satisfactory information compatible with the relevant literature. However, ChatGPT-4 provided more comprehensive and categorical information as compared to ChatGPT-3.5, which produced responses that were more superficial and short-cut. Although the results are promising in aiding the research process with AI-based technologies, we should also caution that, in their current form, these tools are still in their infancy, and there is a long way to go before they become fully capable of supporting scientific work. Meanwhile, it is significant that researchers continue to develop the relevant knowledge base to support the responsible, safe, and ethical integration of these technologies into the process of scientific knowledge creation, as Pandora’s box has already been opened, releasing newer opportunities and risks to be tackled. Full article

Optimizing the layout of green infrastructure (GI) is an effective way to alleviate the fragmentation of urban landscapes, coordinate the relationship between urban development and urban ecosystem services, and ensure the sustainable development of cities. This study provides a new technical framework for optimizing GI networks based on a case study of Kaifeng, an exemplar of many ancient cities along the Yellow River in China. To do this, we used a morphological spatial pattern analysis (MSPA) methodology and combined it with Procedure for mAthematical aNalysis of lanDscape evOlution and equilibRium scenarios Assessment (PANDORA) model to determine the hubs of the GI network. Then we employed a least-cost path approach to simulate potential corridors linking the hubs. We further identify the key ‘pinch points’ of the GI network that need priority protection based on circuit theory. Altogether, this framework takes patches that have a high level of ecosystem services and are more important in maintaining overall connectivity as hubs, thereby improving the accuracy of hub identification. Moreover, it establishes a direct connection between GI construction and ecosystem services, and improves biodiversity conservation by optimizing the network structure of GI. The results of the case study show that this framework is suitable for GI planning and construction, and can provide effective technical support for the formulation of urban sustainable development strategies. Full article

Land use/land cover (LULC) maps are a key input in environmental evaluations for the sustainable planning and management of socio-ecological systems. While the impact of map spatial resolution on environmental assessments has been evaluated by several studies, the effect of thematic resolution (the level of detail of LU/LC typologies) is discordant and still poorly investigated. In this paper, four scenarios of thematic resolutions, corresponding to the four levels of the CORINE classification scheme, have been compared in a real case study of landscape connectivity assessment, a major aspect for the biodiversity conservation and ecosystem service provision. The PANDORA model has been employed to investigate the effects of LULC thematic resolution on Bio-Energy Landscape Connectivity (BELC) at the scale of the whole system, landscape units, and single land cover patches, also in terms of ecosystem services. The results show different types of impacts on landscape connectivity due to the changed spatial pattern of the LULC classes across the four thematic resolution scenarios. Moreover, the main priority areas for conservation objectives and future sustainable urban expansion have been identified. Finally, several indications are given for supporting practitioners and researchers faced with thematic resolution issues in environmental assessment and land use planning. Full article

Mainstream methods treat head pose estimation as a supervised classification/regression problem, whose performance heavily depends on the accuracy of ground-truth labels of training data. However, it is rather difficult to obtain accurate head pose labels in practice, due to the lack of effective equipment and reasonable approaches for head pose labeling. In this paper, we propose a method which does not need to be trained with head pose labels, but matches the keypoints between a reconstructed 3D face model and the 2D input image, for head pose estimation. The proposed head pose estimation method consists of two components: the 3D face reconstruction and the 3D–2D matching keypoints. At the 3D face reconstruction phase, a personalized 3D face model is reconstructed from the input head image using convolutional neural networks, which are jointly optimized by an asymmetric Euclidean loss and a keypoint loss. At the 3D–2D keypoints matching phase, an iterative optimization algorithm is proposed to match the keypoints between the reconstructed 3D face model and the 2D input image efficiently under the constraint of perspective transformation. The proposed method is extensively evaluated on five widely used head pose estimation datasets, including Pointing’04, BIWI, AFLW2000, Multi-PIE, and Pandora. The experimental results demonstrate that the proposed method achieves excellent cross-dataset performance and surpasses most of the existing state-of-the-art approaches, with average MAEs of ${4.78}^{\circ }$ on Pointing’04, ${6.83}^{\circ }$ on BIWI, ${7.05}^{\circ }$ on AFLW2000, ${5.47}^{\circ }$ on Multi-PIE, and ${5.06}^{\circ }$ on Pandora, although the model of the proposed method is not trained on any of these five datasets. Full article

Green infrastructure is one of the key components that provides critical ecosystems services in urban areas, such as regulating services (temperature regulation, noise reduction, air purification), and cultural services (recreation, aesthetic benefits), but due to rapid urbanization, many environmental impacts associated with the decline of green space have emerged and are rarely been evaluated integrally and promptly. The Chinese government is building a new city as the sub-center of the capital in Tongzhou District, Beijing, China. A series of policies have been implemented to increase the size of green urban areas. To support this land-use decision-making process and achieve a sustainable development strategy, accurate assessments of green space are required. In the current study, using land-use data and environmental parameters, we assessed the urban green space in the case study area. The bio-energy and its fluxes, landscape connectivity, as well as related ecosystem services were estimated using a novel approach, the PANDORA model. These results show that (1) in the highly urbanized area, green space is decreasing in reaction to urbanization, and landscape fragmentation is ubiquitous; (2) the river ecology network is a critical part for ecosystem services and landscape connectivity; and (3) the alternative non-green patches to be changed to urban, urban patches which can improve landscape quality the most by being changed to green, and conservation priority patches for biodiversity purposes of urban green were explicitly identified. Conclusively, our results depict the spatial distribution, fluxes, and evolution of bio-energy, as well as the conservation prioritization of green space. Our methods can be applied by urban planners and ecologists, which can help decision-makers achieve a sustainable development strategy in these rapidly urbanizing areas worldwide. Full article

Pandora neoaphidis and Entomophthora planchoniana (phylum Entomophthoromycota) are important fungal pathogens on cereal aphids, Sitobion avenae and Rhopalosiphum padi. Here, we evaluated and compared for the first time the virulence of these two fungi, both produced in S. avenae cadavers, against the two aphid species subjected to the same exposure. Two laboratory bioassays were carried out using a method imitating entomophthoralean transmission in the field. Healthy colonies of the two aphid species were exposed to the same conidial shower of P. neoaphidis or E. planchoniana, in both cases from a cadaver of S. avenae. The experiments were performed under LD 18:6 h at 21 °C and a successful transmission was monitored for a period of nine days after initial exposure. Susceptibility of both S. avenae and R. padi to fungal infection showed a sigmoid trend. The fitted nonlinear model showed that the conspecific host, S. avenae, was more susceptible to E. planchoniana infection than the heterospecific host R. padi, was. In the case of P. neoaphidis, LT₅₀ for S. avenae was 5.0 days compared to 5.9 days for R. padi. For E. planchoniana, the LT₅₀ for S. avenae was 4.9 days, while the measured infection level in R. padi was always below 50 percent. Our results suggest that transmission from conspecific aphid host to heterospecific aphid host can occur in the field, but with expected highest transmission success to the conspecific host. Full article