A number of quantum-based protocols exists for distributed parties to share correlated information, including distributed entanglement, and quantum key distribution, where the shared information or bits, are determined probabilistically. Such correlated information or bits can be used for security applications such as encryption, but in this paper, we show how such correlated information or bits, with the use of a priori agreed action-tables, can be used for probabilistic coordination schemes among multiple parties, which we term abstractly as agents (e.g., agents could be processes running on different computers across the Internet, different computer nodes, robots, or vehicles). We identify the properties of such a coordination approach, including fairness and built-in quantum security. We also outline a number of applications of such a coordination approach.

In the analysis of the operational reliability of integrated electrical energy systems, traditional numerical algorithms for solving natural gas dynamics require a substantial amount of computation, making it challenging to complete the analysis within operational time scales. This paper proposes a novel method: replacing traditional numerical methods with a CNN-LSTM neural network algorithm. The algorithm first utilizes a Convolutional Neural Network (CNN) for feature extraction, followed by a Long Short-Term Memory network (LSTM) for feature sequence prediction. Through a sequence-to-sequence learning process, the model learns the mapping relationships between adjacent time steps from the data, constructing a dynamic surrogate model for the gas network. This dynamic surrogate model is further integrated with the power system load flow model, combined with Monte Carlo simulation and multi-state models, to comprehensively analyze the operational reliability of integrated electrical energy systems. In the validation phase, the proposed model was applied to a distribution network-level electric-gas integrated energy system. The validation results demonstrate that the model not only accurately simulates the complex characteristics of gas network dynamics but also significantly reduces the computation time for operational reliability.

The prevalence of Alzheimer’s disease (AD) is on the rise due to the global aging population. AD is the most common cause of dementia, accounting for 60-80% of all cases, which makes it a significant health concern. In recent years, the failure to develop targeted pathological drugs has led researchers to shift their focus. The blood-brain barrier (BBB) is a specialized structure that separates the circulating blood from the brain tissue and tightly regulates the passage of molecules, ions, and cells between the blood and the brain. Through a comprehensive review of current literature, we highlight the multifaceted role of BBB dysfunction in the pathogenesis of AD and discuss the complex mechanisms involved. Changes in the structure and function of endothelial cells, pericytes, and astrocytes, along with elevated expression of the highest-risk gene APOE4 can all lead to BBB damage, thereby promoting the onset of AD. Furthermore, we explore potential therapeutic targets aimed at preserving BBB integrity and function as a means of mitigating AD progression. This review underscores the significance of ongoing research efforts in elucidating the intricate interplay between BBB integrity and AD pathology, offering valuable insights for future investigations and therapeutic strategies.

Spinal cord injury (SCI) is a devastating pathological state causing motor, sensory, and autonomic dysfunction. To date, SCI remains without viable treatment for its patients. After the injury, molecular events centered at the lesion epicenter create a non-permissive environment for cell survival and regeneration. This newly hostile setting is characterized by necrosis, inflammation, demyelination, axotomy, apoptosis, and gliosis, among other events that limit locomotor recovery. Understanding the pathophysiological mechanisms underlying SCI is crucial for the development of effective treatment. This review provides an overview of the pathophysiology of SCI, highlighting the complex cascade of events that occur after the lesion. Moreover, the review discusses possible treatments for SCI, including pharmacological interventions to mitigate secondary injury processes. Emerging evidence suggests a potential role of the gut microbiota in modulating the inflammatory response and influencing neurological recovery following SCI. Therefore, this review also explores the interplay between the gut microbiota and SCI, emphasizing the bidirectional communication between the gut and central nervous system, known as the gut-brain axis. Lastly, the gut-brain/spinal cord axis after trauma promotes the production of pro-inflammatory metabolites that provide a non-permissive environment for cell survival and locomotor recovery. Therefore, any possible pharmacological treatment, including antibiotics and painkillers, must consider their effects on microbiome dysbiosis to promote cell survival, regeneration, and behavioral improvement. Overall, this review provides valuable insights into the pathophysiology of SCI and the evolving understanding of the role of the gut microbiota in SCI, with implications for future research and clinical practice.

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Alzheimer’s disease (AD) is one of the most severe diseases worldwide. This disease affects the cognitive abilities of a patient. Treating AD in its early stage can prolong the independence of the patient. This early intervention can considerably delay the institutionalization of the patient. In this regard, artificial intelligence-based early detection of AD and its stages has recently gained significant attention. Thus, this study proposed an approach to detect the stage of AD that involves six steps: image acquisition, preprocessing, image augmentation, segmentation, feature extraction, and classification. First, input images are obtained from a publicly available standard dataset, ADNI and curated according to need. Then, preprocessing is performed to remove unwanted non-brain tissues and noise using the skull-stripping algorithm and adaptive median-filter approach. Subsequently, image augmentation is performed by image rotation (90 °, 180 °, 270 °) and image cropping (right, left, bottom, corner, and top) to avoid class imbalance. From the augmented images, gray matter, white matter, and cerebrospinal fluid are segmented using a residual squeeze-excited U-network architecture. Moreover, the required features from three planes: axial, sagittal, and coronal, are extracted using an extended sign magnitude-based local binary pattern method to improve accuracy. The features derived from each plane are fused, and Alzheimer’s disease classification is performed by position attention-based dense capsule network models. Python and its libraries are used for model implementation. The performance measure achieves an impressive accuracy of 98.40%, proving that the proposed model provides higher accuracy than that of existing techniques.

Deep-focus earthquakes occur at 300-660 km depth. Geophysical observations and deformation experiments propose the olivine-spinel (wadsleyite/ringwoodite) phase transformation as the faulting mechanism. While geophysical observations indicate that fault geometry influences the b values in the Gutenberg-Richter law for the phase transformation faulting, deformation experiments reveal that b values are also influenced by rock properties, including structural heterogeneity. Grain sizes play a crucial role in the rate of phase transformation, impacting the occurrence of faulting. Consequently, grain sizes may also influence b values. We conducted deformation experiments on germanate olivine, an analog silicate olivine material, with various grain sizes to reveal the effect of grain size on the difference in b value during the phase transformation faulting. We used a Griggs-type deformation apparatus and measured acoustic emissions (AE) with an AE transducer, which was calibrated by laser-doppler interferometry. This calibration enabled the acquisition of AE waveforms with a unit of velocity (m/s), facilitating comparison to natural earthquakes. b values in the fine-grained aggregates (a few μm) are smaller than those in the coarse-grained aggregates (hundreds μm) at the same deformation conditions. In the coarse-grained aggregates, the heterogeneous formation of spinel aggregates contributes to high b values. Conversely, in the fine-grained aggregates, the homogeneous formation of spinel grains inside olivine at the grain boundaries results in lower b values. Therefore, the homogeneity (or heterogeneity) of spinel formation appears to be a controlling factor for b values in phase transformation faulting associated with deep-focus earthquakes.

Seismic surveys are crucial for investigating subsurface geological formations and require significant logistical and economic resources. This study explores the reuse of legacy seismic surveys from the Granada Basin conducted by the Chevron Oil Company in the mid-1980s to gain new geological insights. In a previous paper, data from two deep boreholes and 30 epochal seismic sections were recovered and reprocessed using Machine Learning, and this work interprets the results to generate three complete pseudo-three-dimensional models of the entire basin: a P-wave velocity model, a sedimentary sequences model, and a fault systems model. The sedimentary sequence model identified five distinct depocenters with varying sediment compositions throughout the basin. The study found a progressive decline in sediment accumulation rates over time, from 0.18 mm/yr in the Tortonian to 0.10 mm/yr in the Pliocene-Quaternary. This trend reflects changes in the sedimentary system, moving from transitional platforms to regression and transgression episodes and finally to a stable continental state. The differences in sediment accumulation rates suggest that greater disparities are linked to intense tectonic activity, while lower differences indicate reduced tectonic activity and a consistent sedimentary ratio since the Pliocene. Additionally, 17 new faults were detected. Using the fault model and seismic activity data from 1984 to 2023 provided by the Andalusian Institute of Geophysics (IAG), a hazard analysis was performed based on the maximum magnitude supported by each fault, demonstrating the value of reusing vintage seismic data to update geological models and improve our understanding of subsurface formations and seismic hazards

Collected from the southwestern Gulf of Mexico (GoM), the marine piston Core RC10-265 was used to reconstruct surface water masses, circulation and paleotemperature shifts at orbital scales, over the last ~180 ka. The chronology was constructed based on radiocarbon ages, planktonic foraminifera biostratigraphy and stable oxygen isotopes (δ18O). The ocean surface conditions were reconstructed based on planktonic foraminifera assemblages and the sea surface temperatures (SST) based on Mg/Ca ratios, analysed through LA-ICP-MS. Applying a Q-mode Factor Analysis, two scenarios were characterised through seven species. (1) The subtropical assemblage (Globigerinoides ruber - Globoconella inflata (positive scores) and the Globorotalia menardii group - Pulleniatina obliquiloculata (negative scores)) suggests that during the end of MIS-6, and MIS-4 to MIS-2, the surface waters were colder (~22-25 °C), with less oligotrophic and a more profound mixed layer depth (MLD) relative to interglacials. (2) The tropical assemblage (Globigerinoides ruber - Neogloboquadrina dutertrei - Globorotalia truncatulinoides (positive scores) and Globoconella inflata - Globigerina falconensis (negative scores)) suggests that during the early MIS-6, MIS-5e to 5b and MIS-1, the surface waters were warm (~28-32 °C), oligotrophic, with a shallow MLD like average modern conditions. Despite evidence of early diagenesis by clay coatings, the reconstructed paleotemperatures are consistent with palaeothermometry studied in the Caribbean and northern Gulf. The studied information shows the relevance of the Loop Current (extended vs. contracted mode) and the associated variability in mesoscale eddies as a key control of the GoM paleoecology and paleotemperatures at orbital scales.

The Moon lacks a global magnetic field and atmosphere, leaving its surface been directly exposed to high-energy cosmic radiation. Sporadic Solar Particle Events are sources of a significant radiation exposure, potentially posing serious threats to the health of astronauts exploring the Moon. Generally, Solar Energetic Particles (SEPs) have a limited penetration capabilities (value needed), and associated radiation doses diminish significantly with increasing astronauts shielding. In this paper, we use the Radiation Environment and Dose at the Moon (REDMoon) model based on GEometry And Tracking (GEANT4) Monte-Carlo method to calculate the body effective dose induced by 262 large historical SEP events on the Moon under different shielding depths which can result from the lunar regolith shielding and/or additional aluminum shielding. We calculate and compare the contributions of SEPs within different energy ranges to the total body effective dose and carry out a statistical analysis based on the results from different SEP events. Additionally, we develop empirical functions to rapidly assess SEP-induced effective dose on the Moon under different shielding scenarios.

This article is based on the doctoral research claiming that higher community involvement leads to effective conservation of urban ponds and their catchments. Amidst rapid urbanization scenario country has lost many urban ponds and those that remain, faces increasing pressure due to agricultural land drainage, pollution and urban development. Communities residing around ponds are significant stakeholders in conservation process therefore the research involves study of theoretical underpinnings related to community participation, assessment of the status of urban ponds and their catchments, understanding participation process through identified indicators that eventually led to design of a framework for conservation of ponds and their catchments. Communities were surveyed from the three selected case study urban villages having ponds with an area ranging from 1-5 acres and statistical analysis was done to establish the relationship between community participation and conservation of ponds and their catchments. The study found that community participation depends upon gender, age, caste, distance of residence from pond, accommodation ownership, education, occupation and income group at various stages of conservation. The empirical finding revealed that higher community's involvement in monitoring leads to effective conservation. Moreover it emphasizes the need to identify an inviting platform for participation where all the stakeholders can formally engage to address the issues and report monitoring results to sustain conservation efforts. From policy perspective for effective participation, it stresses on the need to strengthen feedback systems by governing institutes to overcome implementation challenges.

In its first part, this article develops a variational formulation for the incompressible Euler system in fluid mechanics. The results are based on standard tools of calculus of variations and constrained optimization. In a second step, we present a variational formulation for a compressible Euler system in fluid mechanics assuming an approximately constant scalar field of temperature. In the subsequent sections we also present a variational formulation for a relativistic fluid motion. Finally, in the last sections, we develop a duality principle applied to a Ginzburg-Landau type equation.

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This work explores technical challenges and potential methodologies for understanding electrochemical Reactive CO2 Capture (RCC) mechanisms. RCC offers potential energy cost advantages by directly converting captured CO 2 into fuels and chemicals, unlike traditional carbon capture and utilization (CCU) processes that require sequential capture, concentration, and compression. However, direct conversion of captured CO 2 introduces complexity due to additional equilibrium buffer reactions, making it challenging to identify active species for reduction in electrochemical studies. This work discusses methods to integrate transport, thermodynamics, and kinetics concepts to identify active carbon sources in RCC. Vapor-Liquid Equilibrium (VLE) and transport models are validated against experimental results obtained in a gastight rotating cylinder electrode reactor and are shown as useful tools for studying RCC in heterogeneous electrocatalysts across different capture agents, solvents, and temperatures. This work establishes an experimental framework for advancing research in electrochemical RCC.

This research delves into the utilization of smartwatch sensor data and heart rate monitoring to discern individual emotions based on body movement and heart rate. Emotions play a pivotal role in human life, influencing mental well-being, quality of life, and even physical and physiological responses. The data were sourced from prior research by Juan C. Quiroz, PhD. The study enlisted 50 participants who donned smartwatches and heart rate monitors while completing a 250-meter walk. Emotions were induced through both audio-visual and audio stimuli, with participants' emotional states evaluated using the PANAS questionnaire. The study scrutinized three scenarios: viewing a movie before walking, listening to music before walking, and listening to music while walking. Personal baselines were established using DummyClassifier with the 'most_frequent' strategy from the sklearn library, and various models, including Logistic Regression and Random Forest, were employed to gauge the impacts of these activities. Notably, a novel approach was undertaken by incorporating hyperparameter tuning to the Random Forest model using RandomizedSearchCV. The outcomes showcased substantial enhancements with hyperparameter tuning in the Random Forest model, yielding mean accuracies of 86.63% for happy vs. sad and 76.33% for happy vs. neutral vs. sad.

In recent decades, less aggressive ventilatory practices have been favored in extremely preterm newborns (EPNB), as invasive ventilation (IV) is a major risk factor for bronchopulmonary dysplasia (BPD). However, these changes have not been accompanied by consistent improvements in the incidence of BPD. The aim was to evaluate changes in ventilatory practices and their association with morbidity in EPNB. A single-center retrospective study was performed over the last 2 decades (2001-2020) on all newborns (NB) born with less than 28 weeks requiring ventilatory support. A total of 249 NB were included. There were no statistically significant differences in median gestational age and birth weight between the two decades. There was a significant decrease in IV (p=0.007) and a significant increase in exclusively non-invasive ventilation (p=0.007) in the second decade. There was a significant decrease in the use of IV in the first 24 hours of life (p=0.002). There was a higher prevalence of BPD in the second decade (p=0.042), although there was no difference in the prevalence of severe BPD (p=0.614) or when BPD was adjusted for mortality (p=0.324). Duration of IV predicts the development of BPD with good accuracy (AUC=0.911, CI95% 0.849-0.973). Only gestational age seems to be an independent factor for BPD (aOR 0.683; CI95% 0.517-0.902). Despite the use of less aggressive ventilation techniques, with an increase in exclusive non-invasive ventilation, there was not the expected improvement in the prevalence of BPD. Changing ventilation practices will probably not be a sufficient measure to improve BPD in EPNB.

Angiogenin (ANG) is a ribonuclease that plays a crucial role in various physiological processes in the brain and the development of neurological diseases. ANG is widely expressed in different brain cells, including neurons, the endothelium of cerebral blood vessels, and glial cells. It exhibits a wide range of mechanisms, such as promoting the formation of new blood vessels, reducing oxidative stress, suppressing inflammation, promoting tissue repair, stimulating cell growth, and inhibiting cell death. These findings suggest that ANG has the potential to be used as a therapeutic agent for neurological diseases. This review aims to explore the physiological functions of ANG in the brain and its pathological roles in neurological disorders, with the ultimate goal of identifying potential future neuroprotection strategies.

The Rough Set (RS) theory has clinched more popularity in input dimensionality reduction and managing impreciseness in datasets. Rough set applications in artificial intelligence have grown many folds in recent times. This heightened interest led to the covering several research domains such as artificial intelligence development thinking, inductive reasoning, decision analysis, and machine learning. Further, the rough set theory concepts show a wide scope for applications in pattern recognition, expert systems, and knowledge discovery. This paper reviews rough set theory fundamentals and highlights several research directions and applications that utilize this theory. Additionally, it probes the rough set theory concepts applications in various machine learning techniques, such as clustering, feature selection, and rule induction.

In urban regions, traffic congestion is a serious issue. Every year, there is a 25-40% percent increase in the number of vehicles, aggravating problems like traffic jams, noise and air pollution, and travel delays. The traditional approach requires organizations such as traffic police to manually maintain and set the timings for red and green lights. Modern city traffic signals are inefficient and unable to deal with the aforementioned issues because they do not have automated processes. Because of this, traffic congestion is still a problem in many cities throughout the world, with poor traffic management and broken signals being the main causes. The main objective is that cities are looking at creative ways to address this issue, such as better infrastructure, adaptive signal timing, and intelligent traffic management systems. In the existing system, traffic is often navigated using ordinary GPS-based navigation devices in emergency response circumstances. These systems provide real-time route recommendations based on traffic data, but they might not be the greatest options in emergency situations where quick response is crucial. When there is heavy traffic, they usually struggle to adjust, which delays emergency vehicles. The proposedsystem aims to propose an Internet of Things-based intelligent traffic navigation to predict and manage accidents within congested traffic which shortens wait times in emergency situations by controlling traffic signals. Using real-time data and machine learning techniques, IoT-enabled intelligent traffic guidance is a considerable improvement over existing methods and can greatly improve emergency response capabilities during periods of excessive congestion. It has the potential to save lives by accelerating response times and ensuring that emergency vehicles reach at their destinations safely and on time.

Patients with dry eye disease (DED) often exhibit neurological abnormalities and may even suffer from neuropathic pain and pain-related anxiety or depression. However, addressing nerve abnormalities in DED remains a formidable challenge, as current therapies fail to halt disease progression. Our study found that activating α-7 nicotinic acetylcholine receptor (α7nAChR), a pivotal regulator in the anti-inflammatory pathway connecting the nervous and immune systems, effectively restores corneal epithelium integrity and enhances nerve sensitivity in DED, pointing to its promising therapeutic potential. Furthermore, we have revealed that α7nAChR stimulates genes involved in immune-mediated inflammatory progression and neuroregulation, inhibits the expression of transient receptor potential vanilloid-1 (TRPV1), reinstates corneal nerve density, and alleviates anxiety-like behaviors associated with severe DED by downregulating the proportion of CD86+ M1 macrophages (pro-inflammatory phenotypes). In summary, our findings underscore the activation of α7nAChR as a pioneering therapeutic approach for preserving corneal nerves balance and controlling inflammation in DED.

This research involved designing and building an interactive generative AI application to conduct a comparative analysis of two advanced Large Language Models (LLMs), GPT-4 and Claude 2, using Langsmith evaluation tools. The project was developed to explore the potential of LLMs in facilitating postgraduate course recommendations within a simulated environment at Munster Technological University (MTU). Designed for comparative analysis, the application enables testing of GPT-4 and Claude 2 and can be hosted flexibly on either AWS (Amazon Web Services) or Azure. It utilizes advanced natural language processing and retrieval-augmented generation (RAG) techniques to process proprietary data tailored to postgraduate needs. A key component of this research was the rigorous assessment of the LLMs using the Langsmith evaluation tool against both customized and standard benchmarks. The evaluation focused on metrics such as bias, safety, accuracy, cost, robustness, and latency. Additionally, adaptability covering critical features like language translation and internet access, was independently researched since the Langsmith tool does not evaluate this metric. This ensures a holistic assessment of the LLM’s capabilities.

Objective: To explore the safety of different termination methods of hydatidiform mole coexisting with a normal fetus in the second trimester of pregnancy. Design: A retrospective cohort study. Setting: A referral center for difficult and critical diseases in Obstetrics and Gynecology in China (Beijing). Population: Patients diagnosed hydatidiform mole coexisting with a normal fetus receiving termination of pregnancy in the second trimester (12 weeks to 27 +6 weeks). Methods: Data were extracted and summarized on the complications of different termination methods. Chi square analysis was used to explore the association of factors and complications. Main outcome measures: Volume of blood loss and progression to gestational trophoblastic neoplasm. Results: Different methods of terminating pregnancy in the second trimester of hydatidiform mole coexisting with a normal fetus were feasible, including forceps curettage, combination of mifepristone and misoprostol, intra-amniotic injection of rivanol, and cesarean section. The incidence of massive blood loss (over 300ml) was 50.0%. Molar tissues closer to the lower uterine segment than the fetus (P=0.035), and presence of complications (P=0.015) were the risk factors for massive blood loss during termination of pregnancy. The incidence of progression to gestational trophoblastic neoplasm was 35.7%. Conclusion: Different termination methods might lead to complications including massive blood loss and progression to gestational trophoblastic neoplasm. More medical measures should be taken to prevent and reduce the volume of bleeding among patients with high risk factors.

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