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Federated learning enables model training on multiple clients locally, without the need to transfer their data to a central server, thus ensuring data privacy. In this paper, we investigate the impact of Non-Independent and Identically Distributed (non-IID) data on the performance of federated training, where we find a reduction in accuracy of up to 29% for neural networks trained in environments with skewed non-IID data. Two optimization strategies are presented to address this issue. The first strategy focuses on applying a cyclical learning rate to determine the learning rate during federated training, while the second strategy develops a sharing and pre-training method on augmented data in order to improve the efficiency of the algorithm in the case of non-IID data. By combining these two methods, experiments show that the accuracy on the CIFAR-10 dataset increased by about 36% while achieving faster convergence by reducing the number of required communication rounds by 5.33 times. The proposed techniques lead to improved accuracy and faster model convergence, thus representing a significant advance in the field of federated learning and facilitating its application to real-world scenarios. [ABSTRACT FROM AUTHOR]

The photochemical dynamics of the acetic acid and trifluoro-acetic acid dimers in hexane are studied using time-resolved infrared absorption spectroscopy and ab initio electronic structure calculations. The different hydrogen bond strengths of the two systems lead to changes in the character of the accessed excited states and in the timescales of the initial structural rearrangement that define the early time dynamics following UV excitation. The much stronger hydrogen bonding in the acetic acid dimer stabilizes the system against dissociation. Ground state recovery is mediated by a structural buckling around the hydrogen bond itself with no evidence for excited state proton transfer processes that are usually considered to drive ultrafast relaxation processes in hydrogen bonded systems. The buckling of the ring leads to relaxation through two conical intersections and the eventual reformation of the electronic and vibrational ground states on a few picosecond timescale. In trifluoro-acetic acid, the weaker hydrogen bonding interaction means that the dimer dissociates under similar irradiation conditions. The surrounding solvent cage restricts the full separation of the monomer components, meaning that the dimer is reformed and returns to the ground state structure via a similar buckled structure but over a much longer, ∼100 ps, timescale. [ABSTRACT FROM AUTHOR]

The mode is a fundamental descriptive statistic in data analysis, signifying the most frequent element within a dataset. The range mode query (RMQ) problem expands upon this concept by preprocessing an array A containing n natural numbers. This allows for the swift determination of the mode within any subarray A[a..b], thus optimizing the computation of the mode for a multitude of range queries. The efficacy of this process bears considerable importance in data analytics and retrieval across diverse platforms, including but not limited to online shopping experiences and financial auditing systems. This study is dedicated to exploring and benchmarking different algorithms and data structures designed to tackle the RMQ problem. The goal is to not only address the theoretical aspects of RMQ but also to provide practical solutions that can be applied in real-world scenarios, such as the optimization of an online shopping platform's understanding of customer preferences, enhancing the efficiency and effectiveness of data retrieval in large datasets. [ABSTRACT FROM AUTHOR]

This article presents a Visual Servoing Nonlinear Model Predictive Control (NMPC) scheme for autonomously tracking a moving target using multirotor Unmanned Aerial Vehicles (UAVs). The scheme is developed for surveillance and tracking of contour-based areas with evolving features. NMPC is used to manage input and state constraints, while additional barrier functions are incorporated in order to ensure system safety and optimal performance. The proposed control scheme is designed based on the extraction and implementation of the full dynamic model of the features describing the target and the state variables. Real-time simulations and experiments using a quadrotor UAV equipped with a camera demonstrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Systems for graph processing are a key enabler for insights from large-scale graphs that are critical to many new advanced technologies such as Artificial Intelligence, Internet of Things, and blockchain. In this study, we benchmark another two widely utilized graph processing systems, Apache Spark GraphX and Apache Fink, concerning the key performance criterion by means of response time, scalability, and computational complexity. We demonstrate our results which show the capability of each system for real-world graph applications, and hence, providing a quantitative understanding to select the system for our purpose. GraphX's strength was in processing batch in-memory workloads typical of blockchain and machine learning model optimization, while Flink excelled in processing stream data, which is timely and important to the IoT world. These performance characteristics emphasize how the capabilities of graph processing systems can match the requirements for the performance of different emerging technology applications. Our findings ultimately inform practitioners about system efficiencies and limitations, but also the recent advances in hardware accelerators and algorithmic improvements aimed at shaping the new graph processing frontier in diverse technology domains. [ABSTRACT FROM AUTHOR]

Simple Summary: The current study focused on understanding the longevity and frailty of sterile, non-sterile, and wild male mosquitoes subjected to different treatments, using the novel captive cohort method. Key findings include that marking mosquitoes, following IAEA protocols, had an insignificant effect on longevity under controlled conditions, and that sterilization had no negative effect on male longevity. Moreover, we recorded that exposure to the wild increased post-capture longevity, particularly for sterile males, with longer time in the wild correlating with extended lifespan. Interestingly, the wild experience seems to benefit sterile males more than non-sterile ones, possibly due to demographic selection or hormetic effects. This suggests a possible advantage in field performance for sterile males, which is a key consideration for SIT programs. These findings underscore the importance of ongoing research to optimize rearing, sterilization, and transportation methods for sterile males, ultimately enhancing their performance and longevity in field applications. Invasive mosquito species, such as Aedes albopictus, pose significant threats to both ecosystems and public health due to their role in transmitting diseases, such as dengue, Zika, and chikungunya. The Sterile Insect Technique (SIT) is a promising vector control strategy aimed at reducing mosquito populations by releasing sterile males to mate with wild females and reduce their reproduction rates. In this study, we employed the captive cohort method, which assesses the remaining longevity of randomly caught released individuals, to assess the longevity and frailty dynamics of sterile and non-sterile Ae. albopictus males. Using a mark–release–recapture approach (MRR), we compared the residual lifespan of sterile and non-sterile released males with that of wild, non-sterile males, aiming to understand the frailty dynamics of released males and, therefore, their quality and field performance. Contrary to expectations, our results revealed that released sterile males showed increased longevity compared to non-sterile males. Further, the marking process did not impact the longevity between lab-kept and marked males, suggesting that the marking process does not adversely affect survival under controlled conditions. These findings underscore the importance of optimizing pre-release and mass-rearing practices to enhance the effectiveness of SIT programs. Our study also demonstrates for the first time the use of the captive cohort method for understanding the biological dynamics of sterile mosquito populations in SIT programs, providing valuable insights for improving vector control strategies. [ABSTRACT FROM AUTHOR]

Background/Objectives: The Mediterranean diet has been widely suggested to exert significant beneficial effects on endothelial oxidative status and cardiometabolic health. Greek Orthodox monasteries, due to their specific nutritional and sartorial habits, comprise a population thatstrictly adheres to nutritional patterns with restricted eating and a plant-based subset of the Mediterranean diet, often accompanied by profound hypovitaminosis D. Time-restricted eating (TRE) is also adopted bya large part of the general lay Greek population for health-promoting reasons, without restrictions on animal product consumption, as imposed by Orthodox religious fasting. However, the comparative effects of these nutritional patterns on oxidative stress markers remain scarce. Methods: The present study attempted to evaluate the effects of Christian Orthodox fasting (COF) in a group of vitamin D-deficient and overweight Orthodox nuns from Central and Northern Greece compared to the implementation of TRE, a 16:8 dietary regimen (16 h of food abstinence and 8 h of feeding) in a cohort of adult women from the general population from the same region with regard to markers of endothelial oxidative status. A group of 50 women from two Orthodox monasteries in Northern Greece and one group of 50 healthy lay women were included. During the enrollment, a detailed recording of their dietary habits was performed, along with a scientific registry of their demographic and anthropometric characteristics (via bioimpedance). The Orthodox nuns followed a typical Orthodox fasting regimen [daily feeding window (8 a.m.–4 p.m.)], whereas the lay women followed a TRE 16:8 regimen with the same feeding time-window with a recommendation to follow a low-fat diet, without characteristics of the Mediterranean diet. We included a complete biochemical analysis, as well as calciotropic profiles [calcium—Ca, albumin, parathyroid hormone—PTH, and 25-hydroxyvitamin D—25(OH)D] and markers of TAC (total antioxidant capacity), GSH (glutathione),and thiobarbituric acid reactive substances (TBARSs) concentrations as markers of oxidative status. Results: All the groups were compared at the baseline regarding their calcium, PTH, and 25(OH)D concentrations, with no statistically significant differences between the groups apart from higher PTH levels in the nuns due to lower 25(OH)D levels. The Orthodox nuns manifested a lower median GSH compared to the controls (6.0 vs. 7.2, p 0.04) and a higher median TAC (0.92 vs. 0.77, p < 0.001). The TBARS comparisons showed no significant difference between the two groups. No significant associations of oxidative status with 25(OH)D, PTH, and the markers of glucose homeostasis were evident. Conclusions: The results of this small pilot study indicate that both dietary regimens have advantages over the oxidative markers compared to each other, with increased TAC in the group of Orthodox nuns after a 16-week period of COF compared to a 16:8 TRE and increased GSH concentrations in the lay women group. Future randomized trials are required to investigate the superiority or non-inferiority between these dietary patterns in the daily clinical setting. [ABSTRACT FROM AUTHOR]

Objective: To characterize neurocognitive response to cerebrospinal fluid (CSF) diversion during a multiday external lumbar drainage (ELD) trial in patients with suspected normal pressure hydrocephalus (NPH). Methods: Inpatients (N = 70) undergoing an ELD trial as part of NPH evaluation participated. Cognition and balance were assessed using standardized measures before and after a three-day ELD trial. Cognitive change pre- to post-ELD trial was assessed in relation to change in balance, baseline neuroimaging findings, NPH symptoms, demographics, and other disease-relevant clinical parameters. Results: Multiday ELD resulted in significant cognitive improvement (particularly on measures of memory and language). This improvement was independent of demographics, test-retest interval, number of medical and psychiatric comorbidities, NPH symptom duration, estimated premorbid intelligence, baseline level of cognitive impairment, cerebrovascular disease burden, degree of ventriculomegaly, or other NPH-related morphological brain alterations. Balance scores evidenced a greater magnitude of improvement than cognitive scores and were weakly, but positively correlated with cognitive change scores. Conclusions: Findings suggest that cognitive improvement associated with a multiday ELD trial can be sufficiently captured with bedside neurocognitive testing. These findings support the utility of neuropsychological consultation, along with balance assessment, in informing clinical decision-making regarding responsiveness to temporary CSF diversion for patients undergoing elective NPH evaluation. Implications for the understanding of neuroanatomical and cognitive underpinnings of NPH are discussed. [ABSTRACT FROM AUTHOR]

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Though culturally responsive practices (CRP) are widely lauded as important for educational practice, the field to date does not have a systematic video-based strategy to observe CRP across classrooms. Through a qualitative review of 68 classroom videos from the Measures of Effective Teaching dataset, we examined the presence, absence, and antithesis of CRP. Strikingly, our analyses revealed that CRP were largely absent from the observations. Building upon our meticulous and reflective iterative process, we discuss measurement challenges and link findings with practice-focused recommendations that can serve to bolster teacher education in a crucial domain for an increasingly complex, multicultural society. [ABSTRACT FROM AUTHOR]

Background: The bioeconomy relies strongly on the availability of biomass, including biogenic waste, residues and by-products. The cost of supply often represents a significant proportion of the total value of the resource. However, there is limited insight into the current supply costs of wastes, residues and by-products. This includes straw, which is the most important agricultural by-product in Germany. Despite its importance, standardised information on supply costs or market prices, as well as their temporal and spatial variation, is missing. Aim: Therefore, there is an urgent need for the temporal and spatial monitoring of individual cost components within total supply costs. This is essential to identify the most cost-effective options for the utilisation of agricultural by-products. Therefore, this study focuses on the case of straw to develop a model capable of visualising and mapping regional supply costs over time. Method: We use an activity-based costing approach to calculate and monitor regional supply costs, defined as the monetary expenditure required to make straw available at the farm level. Our methodology combines typical technical and operational aspects of straw collection and transport with regional wage statistics, yield data, farm sizes, fuel prices and labour costs. We also consider storage costs and opportunity costs associated with nutrient replacement and conduct sensitivity analyses to measure their impact. To validate our calculations, we compare them with actual straw prices. To establish a reliable cost monitoring system, we propose an approach to assess the quality of input data. Result: In 2011, the regional supply costs for straw varied from 45.72 EUR/Mg[FM] to 92.92 EUR/Mg[FM], showing a wide range. Over the years, the German average supply cost for straw increased from 56.78 EUR/Mg[FM] in 2010 to 58.79 EUR/Mg[FM] in 2020, with a peak of 61.24 EUR/Mg[FM] in 2018. This suggests that the temporal impact on mass-specific costs is relatively moderate compared to the spatial distribution of supply costs. The sensitivity analysis highlights storage time and costs, straw yield and wage levels as the main drivers of supply costs. Doubling the storage period from 3 to 6 months increases total costs by 20%. On average, the costs explain 75% of the straw price across all federal states, depending on annual price and cost levels. The quality assessment of input data shows that currently 68% of the data cannot be automatically extracted for continuous monitoring. Detailed results are available in a corresponding data publication: https://doi.org/10.5281/zenodo.8145082. Conclusion: In the absence of standardised market prices, the model presented provides an approach to estimating the supply costs of straw, expressed in terms of the monetary cost to farmers of mobilising straw. This cost information could be a valid database for further techno-economic assessments or models to evaluate the economic feasibility of straw valorisation. Due to the modular structure of the model, the future development of supply costs can be considered if the input data are adapted to future scenarios. [ABSTRACT FROM AUTHOR]

Despite the unfavourable labour market and work environment conditions that have emerged in the last decade, this study identifies the factors that positively influence employee motivation. The study was conducted in Greece's banking sector, which was at the epicentre of the economic crisis. A new research model is proposed exploring the relationships between employee motivating factors, employee performance and organizational effectiveness. This model is empirically tested using structural equation modelling on data from 328 bank employees. The results are controversial, showing that non‐financial incentives motivate Greek bank employees most, emphasizing their need to stay in their jobs. Highly motivated workers may boost corporate effectiveness by improving employee performance. Even though this study found that non‐financial incentives motivate employees most, management should not overlook financial and job‐related reward programmes. Managers must provide financial compensation, job security, and supportive leadership to reduce employee dissatisfaction and keep employees motivated. Managers should view recognition as an essential component of motivation because it contributes to the creation of an environment that is both productive and efficient for the organization. The conclusions drawn from studying Greece's 10‐year‐long economic crisis are significant because many other countries around the world are experiencing (or may experience) a similar crisis. [ABSTRACT FROM AUTHOR]

In open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting the detection of a range of dynamic human movements into specific robot actions. Various machine learning models were evaluated to classify these movements, with Long Short-Term Memory (LSTM) demonstrating the highest performance. Furthermore, the Robot Operating System (ROS) software (Melodic Version) capabilities were employed to interpret the movements into certain actions to be performed by the unmanned ground vehicle (UGV). The novel interaction framework exploiting vision-based human activity recognition was successfully tested through three scenarios taking place in an orchard, including (a) a UGV following the authorized participant; (b) GPS-based navigation to a specified site of the orchard; and (c) a combined harvesting scenario with the UGV following participants and aid by transporting crates from the harvest site to designated sites. The main challenge was the precise detection of the dynamic hand gesture "come" alongside navigating through intricate environments with complexities in background surroundings and obstacle avoidance. Overall, this study lays a foundation for future advancements in human–robot collaboration in agriculture, offering insights into how integrating dynamic human movements can enhance natural communication, trust, and safety. [ABSTRACT FROM AUTHOR]

Background: Epstein–Barr virus (EBV) is a herpesvirus linked to nine different human tumors and lymphoproliferative disorders. Immunosuppression promotes EBV‐driven malignancies. The most frequent EBV‐induced malignancies are lymphomas and nasopharyngeal carcinoma. By promoting smooth muscle proliferation, EBV can induce EBV‐associated smooth muscle tumors (EBV‐SMT). EBV‐SMT is a rare oncological entity for which no current guideline for diagnosis or management exists. Data on posttransplant EBV‐SMT (PT‐SMT) are scarce in kidney transplant recipients. Methods: We conducted a national multicentric retrospective study and collected cases among transplantation centers in France. Kidney transplant recipients experiencing histologically proven PT‐SMT were included. We collected data on demographic characteristics of patient, history of kidney transplantation, history of PT‐SMT, evolution of graft function, and patient survival. Results: Eight patients were included. The median age at PT‐SMT diagnosis was 31 years (range 6.5–40). PT‐SMT occurred after a median delay of 37.8 months after transplantation (range 6–175). PT‐SMT management consisted in immunosuppressive regimen minimization in all patients. Introduction of mTOR inhibitors was performed in two patients. Four patients (50%) needed chemotherapy. Surgical resection was performed in four patients. At last follow‐up after PT‐SMT diagnosis (median 33 months (range 17–132)), five patients were considered in complete remission, and two patients had died. Two patients experienced graft rejection; two resumed dialysis (25%). All patients with available data presented with impaired graft function at last follow‐up. Conclusion: PT‐SMT is a subacute and progressive disease during kidney transplantation. Even if the risk of developing PT‐SMT is low in kidney transplant recipients (0.07% in our cohort), PT‐SMT is associated with significant graft loss, possibly due to reduced immunosuppression. Developing guidelines could help transplantation teams better manage these patients. [ABSTRACT FROM AUTHOR]

Introduction The objective of this study was to describe kidney involvement in patients with myelodysplastic syndromes (MDS), their treatments, and outcomes. Methods We conducted a multicenter retrospective study in seven centers, identifying MDS patients with acute kidney injury (AKI), chronic kidney disease (CKD), and urine abnormalities. Results Fifteen patients developed a kidney disease 3 months after MDS diagnosis. Median urine protein-to-creatinine ratio was 1.9 g/g, and median serum creatinine was 3.2 mg/dL. Ten patients had AKI at presentation, and 12 had extra-renal symptoms. The renal diagnoses included anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), ANCA negative vasculitis, C3 glomerulonephritis, immune complex-mediated glomerulonephritis, polyarteritis nodosa, and IgA vasculitis. All patients but one received a specific treatment for the MDS-associated kidney injury. The effect of MDS treatment on kidney injury could be assessed in six patients treated with azacitidine, and renal function evolution was heterogenous. After a median follow-up of 14 months, four patients had CKD stage 3, five had CKD stage 4, and three had end stage kidney disease. On the other hand, three evolved to an acute myeloid leukemia and three died. Compared to 84 MDS controls, patients who had kidney involvement were younger, had a higher number of dysplasia lineages, and were more eligible to receive hypomethylating agents, but no survival difference was seen between the two groups. Compared to 265 AAV without MDS, the ten with MDS-associated pauci-immune vasculitis were older, ANCA serology was more frequently negative, and more cutaneous lesions were seen. Conclusion The spectrum of kidney injuries associated with MDS is mostly represented by vasculitis with glomerular involvement, and especially AAV. [ABSTRACT FROM AUTHOR]

Objective: Crisis line callers experience reductions in distress and suicidal ideation and utilize more health care following calls. The purpose of this study was to determine whether changes in distress and suicidal ideation during a call are associated with later healthcare contact and utilization. Method: Veterans Crisis Line calls from 599 veterans were extracted with call dates between 12/1/2018 and 11/30/2019. Calls were coded for changes in distress and suicidal ideation and linked with VA medical records to obtain healthcare data. Generalized Linear Mixed Modeling was used to examine the associations of changes in distress and suicidal ideation with healthcare contact (yes/no) and utilization (days of treatment) in the month (30 days) following the call. Results: Reductions in distress were associated with behavioral (i.e., mental and substance use) healthcare utilization, F(1, 596) = 4.52, p = 0.03, and reductions in suicidal ideation were associated with any healthcare utilization, F(1, 596) = 6.45, p = 0.01. Changes in distress and suicidal ideation were not associated with healthcare contact. Conclusion: Responders need to help resolve distress and suicidal ideation and link callers with treatment. Unresolved distress and suicidal thoughts may signify later problems with treatment utilization. Research is needed to determine causality. [ABSTRACT FROM AUTHOR]

Objectives Neutrophils play a key role in ANCA-associated vasculitis, both as targets of autoimmunity and as facilitators of vascular damage. In granulomatosis with polyangiitis (GPA), the data regarding the production of reactive oxygen species (ROS) in neutrophils are unclear. Further, recent data suggests that ROS production could have an anti-inflammatory effect through the regulation of inflammasomes and IL-1–related cytokines. We aimed to analyse ROS production in neutrophils from patients with GPA and investigate its association with IL-1–related cytokines and the autoantigen PR3. Methods Seventy-two GPA patients with disease flare were included in the NEUTROVASC prospective cohort study. ROS production in whole blood of patients with active GPA was evaluated and compared with that in the same patients in remission or healthy controls. Associations between ROS production, PR3 membrane expression on neutrophils, serum levels of IL-1–related cytokines as well as inflammasome-related proteins were analysed. Results We observed a robust defect in ROS production by neutrophils from patients with active GPA compared with healthy controls, independent of glucocorticoid treatment. Serum levels of IL-1–related cytokines were significantly increased in GPA patients, particularly in patients with kidney involvement, and levels of these cytokines returned to normal after patients achieved remission. Further, inflammasome-related proteins were significantly dysregulated in the cytosol of neutrophils as well as the serum from GPA patients. Conclusion Our data suggests that ROS production and regulation of inflammasomes in neutrophils from patients with GPA are disturbed and may be a potential therapeutic target. Trial registration ClinicalTrials.gov, https://www.clinicaltrials.gov , NCT01862068. [ABSTRACT FROM AUTHOR]

Glycol sidechains are often used to enhance the performance of organic photoconversion and electrochemical devices. Herein, we study their effects on electronic states and electronic properties. We find that polymer glycolation not only induces more disordered packing, but also results in a higher reorganisation energy due to more localised π-electron density. Transient absorption spectroscopy and femtosecond stimulated Raman spectroscopy are utilised to monitor the structural relaxation dynamics coupled to the excited state formation upon photoexcitation. Singlet excitons are initially formed, followed by polaron pair formation. The associated structural relaxation slows down in glycolated polymers (5 ps vs. 1.25 ps for alkylated), consistent with larger reorganisation energy. This slower vibrational relaxation is found to drive ultrafast formation of the polaron pair state (5 ps vs. 10 ps for alkylated). These results provide key experimental evidence demonstrating the impact of molecular structure on electronic state formation driven by strong vibrational coupling. Glycol sidechains are often used to enhance the performance of organic photoconversion and electrochemical devices. Here, the authors provide photophysical insight into the role of glycol sidechains for the formation of polaron pairs induced by strong vibrational coupling. [ABSTRACT FROM AUTHOR]

Background: Apart from the well-established skeletal effects, vitamin D has been explored as a secretagogue influencing various adipokines, including adiponectin and irisin. Recent evidence suggests that specific forms of 25-Hydroxyvitamin D (25(OHD), such as free and bioavailable 25(OH)D, may provide more accurate measurements of vitamin D status. The relationship between vitamin D status and serum irisin and adiponectin concentrations remains largely unexplored, particularly during pregnancy. Methods: We analyzed data from 67 healthy maternal-neonatal pairs from Northern Greece at birth. Biochemical and hormonal tests were conducted on each maternal-neonatal pair. The vitamin D forms were estimated using validated mathematical models. Subsequently, regression analyses were conducted to determine the association between the vitamin D forms and adipokine levels. Results: Bioavailable maternal 25(OH)D was inversely associated with neonatal irisin concentrations [b=-73.46 (-140.573 to -6.341), p=0.034]. No other associations were observed between maternal vitamin D status and neonatal adipokine concentrations. Conclusion: In conclusion, maternal bioavailable vitamin D concentrations are inversely associated with neonatal serum irisin concentrations, warranting further studies to evaluate the underlying mechanisms for this finding. [ABSTRACT FROM AUTHOR]

The flavivirus West Nile Virus (WNV), which is transmitted by mosquitoes, poses a significant threat to both humans and animals, and its outbreaks often challenge public health in Europe and other continents. In recent years, there is an increasing trend of WNV incidence rates across several European countries. However, whether there is a year-round circulation or seasonal introduction has yet to be elucidated. Real-time polymerase chain reaction (PCR) identified WNV-positive Culex pipiens mosquitos in 6 out of 146 pools examined in winter 2022 that correspond to three out of the 24 study areas, located in two coastal regions units in Attica, Greece. Spatial dispersion of the six positive pools in the same region suggests a clustered circulation of WNV during the winter of 2022. This is the first study that documents the identification of WNV in Cx. pipiens populations, captured in adult traps during winter period. Our findings underscore the need to extend entomological surveillance programs to include the winter period, specifically in temperate climates and historically affected areas by WNV. [ABSTRACT FROM AUTHOR]

How can we adapt the composition of a post over a series of rounds to make it more appealing in a social network? Techniques that progressively learn how to make a fixed post more influential over rounds have been studied in the context of the Influence Maximization (IM) problem, which seeks a set of seed users that maximize a post's influence. However, there is no work on progressively learning how a post's features affect its influence. In this article, we propose and study the problem of Adaptive Content-Aware Influence Maximization (ACAIM), which calls to find k features to form a post in each round so as to maximize the cumulative influence of those posts over all rounds. We solve ACAIM by applying, for the first time, an Online Learning to Rank (OLR) framework for IM purposes. We introduce the CATRID propagation model, which expresses how posts disseminate in a social network using click probabilities and post visibility criteria and develop a simulator that runs CATRID via a training-testing scheme based on real posts of the VK social network, so as to realistically represent the learning environment. We deploy three learners that solve ACAIM in an online (real-time) manner. We experimentally prove the practical suitability of our solutions via exhaustive experiments on multiple brands (operating as different case studies) and several VK datasets; the best learner is evaluated on 45 separate case studies yielding convincing results. [ABSTRACT FROM AUTHOR]

Increased fetal lung heterogeneity has been associated with term fetal lungs in singleton gestations. The objective of this study was to determine if fetal lung heterogeneity index (HI) differs between twin and singleton fetuses in the late second and third trimesters. Prospective cohort study of women with singleton and twin gestations with medically-indicated ultrasound examinations at 24 weeks of gestation onward. Grayscale transverse fetal lung images were obtained at the level of the four-chamber heart. A region of interest was selected in each fetal lung image. Fetal lung HI was determined with MATLAB software using a dithering technique with ultrasound image pixels transformed into a binary map form from which a dynamic range value was determined. HI averages and standard deviations were generated for twin and singleton fetuses from 24 weeks gestation onward. Two sample t-tests were used to compare the mean HI at each gestational week between singleton and twin fetuses. In total, 388 singleton and 478 twin images were analyzed. From 35 through 38 weeks of gestation a statistically significant divergence in mean HI was observed with higher means in singleton compared to twin fetuses. At 24 weeks of gestation there was a significantly higher HI in twin fetuses compared to singletons. No differences in fetal lung HI were observed between 25 and 34 weeks gestational age. Differences in fetal lung HI were observed when comparing twin and singleton fetuses. Further investigation is required to determine the potential clinical significance of these findings. [ABSTRACT FROM AUTHOR]

In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ, operate together to enhance data processing, storage, and quality control in IoT networks, utilizing the capabilities of Edge AI. In particular, TinyCleanEDF applies federated learning for Edge-based data cleaning and anomaly detection. EdgeClusterML combines reinforcement learning with self-organizing maps for effective data clustering. CompressEdgeML uses neural networks for adaptive data compression. CacheEdgeML employs predictive analytics for smart data caching, and TinyHybridSenseQ concentrates on data quality evaluation and hybrid storage strategies. Our experimental evaluation of the proposed techniques includes executing all the algorithms in various numbers of Raspberry Pi devices ranging from one to ten. The experimental results are promising as we outperform similar methods across various evaluation metrics. Ultimately, we anticipate that the proposed algorithms offer a comprehensive and efficient approach to managing the complexities of IoT, Big Data, and Edge AI. [ABSTRACT FROM AUTHOR]

In this work, we present a Distributed Bayesian Inference Classifier for Large-Scale Systems, where we assess its performance and scalability on distributed environments such as PySpark. The presented classifier consistently showcases efficient inference time, irrespective of the variations in the size of the test set, implying a robust ability to handle escalating data sizes without a proportional increase in computational demands. Notably, throughout the experiments, there is an observed increase in memory usage with growing test set sizes, this increment is sublinear, demonstrating the proficiency of the classifier in memory resource management. This behavior is consistent with the typical tendencies of PySpark tasks, which witness increasing memory consumption due to data partitioning and various data operations as datasets expand. CPU resource utilization, which is another crucial factor, also remains stable, emphasizing the capability of the classifier to manage larger computational workloads without significant resource strain. From a classification perspective, the Bayesian Logistic Regression Spark Classifier consistently achieves reliable performance metrics, with a particular focus on high specificity, indicating its aptness for applications where pinpointing true negatives is crucial. In summary, based on all experiments conducted under various data sizes, our classifier emerges as a top contender for scalability-driven applications in IoT systems, highlighting its dependable performance, adept resource management, and consistent prediction accuracy. [ABSTRACT FROM AUTHOR]

Public stigma is a significant deterrent to mental health service use for U.S. veterans. Media campaigns are often used to dispel stigmatizing beliefs and actions. Segmentation is an evidence-based practice for their effective use; however, little data has been published on veteran segments to target with anti-stigma messages. This article aims to identify and describe initial typologies of stigmatizing attitudes within a group of U.S. military veterans. Telephone-based cross-sectional surveys were conducted with a national random sample of veterans from 2014 to 2016 (N = 2142). Stigma outcomes were measured using a brief, validated instrument used in population-based surveys of public perceptions toward people with mental illness. Cluster analysis was conducted to identify specific groupings along multiple dimensions. A final four-cluster solution was identified among veterans with distinct patterns of attitudes toward mental illness and include: 1) the undecided, 2) the influencer, 3) the ambivalent, and 4) the potential ally. Several strategies were also identified for designing anti-stigma messaging toward these segments. This research demonstrates veterans can be segmented by attitudes to target with anti-stigma campaign messages. [ABSTRACT FROM AUTHOR]

Nesfatin concentrations are positively correlated with beta cell function. However, it is unclear whether diet composition mediates this relationship. We recruited 27 overweight individuals who practiced Orthodox fasting (OF), a subset of the Mediterranean diet (MedDiet), for 7 weeks. Fourteen overweight people who practiced 16:8 time-restricted eating served as control group. Anthropometric parameters, biochemical data and adipokine levels were evaluated at baseline and after the end of the diet period (7 weeks from baseline). Subsequently, participants were asked to return to their usual eating plans, and an additional evaluation was performed 5 weeks after the end of the research diets (12 weeks from baseline). We observed a significant and negative correlation between HOMA-B and nesfatin values at 12 weeks, only in the OF group (r = −0.455, p = 0.01). In conclusion, returning to normal eating habits after 7 weeks of strict adherence to MedDiet affects the homeostatic balance between insulin secretion and nesfatin. [ABSTRACT FROM AUTHOR]

Metastasis is a multistep process by which cancer cells break away from their original location and spread to distant organs, and is responsible for the vast majority of cancer-related deaths. Preventing early metastatic dissemination would revolutionize the ability to fight cancer. Unfortunately, the relatively poor understanding of the molecular underpinnings of metastasis has hampered the development of effective anti-metastatic drugs. Although it is now accepted that disseminating tumour cells need to acquire multiple competencies to face the many obstacles they encounter before reaching their metastatic site(s), whether these competencies are acquired through an accumulation of metastasis-specific genetic alterations and/or non-genetic events is often debated. Here we review a growing body of literature highlighting the importance of both genetic and non-genetic reprogramming events during the metastatic cascade, and discuss how genetic and non-genetic processes act in concert to confer metastatic competencies. We also describe how recent technological advances, and in particular the advent of single-cell multi-omics and barcoding approaches, will help to better elucidate the cross-talk between genetic and non-genetic mechanisms of metastasis and ultimately inform innovative paths for the early detection and interception of this lethal process.This Review discusses the importance of genetic and non-genetic reprogramming events during the metastatic cascade. [ABSTRACT FROM AUTHOR]

Angle-resolved photoemission spectroscopy (ARPES) is a technique used to map the occupied electronic structure of solids. Recent progress in x-ray focusing optics has led to the development of ARPES into a microscopic tool, permitting the electronic structure to be spatially mapped across the surface of a sample. This comes at the expense of a time-consuming scanning process to cover not only a three-dimensional energy-momentum (E, kx, ky) space but also the two-dimensional surface area. Here, we implement a protocol to autonomously search both k- and real-space in order to find positions of particular interest, either because of their high photoemission intensity or because of sharp spectral features. The search is based on the use of Gaussian process regression and can easily be expanded to include additional parameters or optimization criteria. This autonomous experimental control is implemented on the SGM4 micro-focus beamline of the synchrotron radiation source ASTRID2. [ABSTRACT FROM AUTHOR]

Simple Summary: Meningiomas are the most common intracranial tumors, and significant advances have been made in our understanding of the biology that leads to meningioma growth and aggressiveness. This review summarizes molecular advances and the historical contexts that led to them. Meningioma classification and treatment have evolved over the past eight decades. Since Bailey, Cushing, and Eisenhart's description of meningiomas in the 1920s and 1930s, there have been continual advances in clinical stratification by histopathology, radiography and, most recently, molecular profiling, to improve prognostication and predict response to therapy. Precise and accurate classification is essential to optimizing management for patients with meningioma, which involves surveillance imaging, surgery, primary or adjuvant radiotherapy, and consideration for clinical trials. Currently, the World Health Organization (WHO) grade, extent of resection (EOR), and patient characteristics are used to guide management. While these have demonstrated reliability, a substantial number of seemingly benign lesions recur, suggesting opportunities for improvement of risk stratification. Furthermore, the role of adjuvant radiotherapy for grade 1 and 2 meningioma remains controversial. Over the last decade, numerous studies investigating the molecular drivers of clinical aggressiveness have been reported, with the identification of molecular markers that carry clinical implications as well as biomarkers of radiotherapy response. Here, we review the historical context of current practices, highlight recent molecular discoveries, and discuss the challenges of translating these findings into clinical practice. [ABSTRACT FROM AUTHOR]

This work introduces an identification scheme capable of obtaining the unknown dynamics of a nonlinear plant process. The proposed method employs an iterative algorithm that prevents confinement to a sole trajectory by fitting a neural network over a series of trajectories that span the desired subset of the state space. At the core of our contributions lie the applicability of our method to open-loop unstable systems and a novel way of generating the system's reference trajectories, which aim at sufficiently stimulating the underlying dynamics. Following this, the prescribed performance control (PPC) technique is utilized to ensure accurate tracking of the aforementioned trajectories. The effectiveness of our approach is showcased through successful identification of the dynamics of a two-degree of freedom (DOF) robotic manipulator in both a simulation study and a real-life experiment. [ABSTRACT FROM AUTHOR]

In the evolving landscape of Industry 4.0, the convergence of peer-to-peer (P2P) systems, LoRa-enabled wireless sensor networks (WSNs), and distributed hash tables (DHTs) represents a major advancement that enhances sustainability in the modern agriculture framework and its applications. In this study, we propose a P2P Chord-based ecosystem for sustainable and smart agriculture applications, inspired by the inner workings of the Chord protocol. The node-centric approach of WiCHORD+ is a standout feature, streamlining operations in WSNs and leading to more energy-efficient and straightforward system interactions. Instead of traditional key-centric methods, WiCHORD+ is a node-centric protocol that is compatible with the inherent characteristics of WSNs. This unique design integrates seamlessly with distributed hash tables (DHTs), providing an efficient mechanism to locate nodes and ensure robust data retrieval while reducing energy consumption. Additionally, by utilizing the MAC address of each node in data routing, WiCHORD+ offers a more direct and efficient data lookup mechanism, essential for the timely and energy-efficient operation of WSNs. While the increasing dependence of smart agriculture on cloud computing environments for data storage and machine learning techniques for real-time prediction and analytics continues, frameworks like the proposed WiCHORD+ appear promising for future IoT applications due to their compatibility with modern devices and peripherals. Ultimately, the proposed approach aims to effectively incorporate LoRa, WSNs, DHTs, cloud computing, and machine learning, by providing practical solutions to the ongoing challenges in the current smart agriculture landscape and IoT applications. [ABSTRACT FROM AUTHOR]

In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks. By utilising Federated Learning (FL), Apache Spark, and Federated AI Technology Enabler (FATE), we skilfully investigated the complicated area of IoT data management while simultaneously reinforcing privacy across broad network configurations. Our FLIBD architecture was thoughtfully designed to safeguard data and model privacy through a synergistic integration of distributed model training and secure model consolidation. Notably, we delved into an in-depth examination of adversarial activities within federated learning contexts. The Federated Adversarial Attack for Multi-Task Learning (FAAMT) was thoroughly assessed, unmasking its proficiency in showcasing and exploiting vulnerabilities across various federated learning approaches. Moreover, we offer an incisive evaluation of numerous federated learning defence mechanisms, including Romoa and RFA, in the scope of the FAAMT. Utilising well-defined evaluation metrics and analytical processes, our study demonstrated a resilient framework suitable for managing IoT Big Data across widespread deployments, while concurrently presenting a solid contribution to the progression and discussion surrounding defensive methodologies within the federated learning and IoT areas. [ABSTRACT FROM AUTHOR]

This study explores the design and capabilities of a Geographic Information System (GIS) incorporated with an expert knowledge system, tailored for tracking and monitoring the spread of dangerous diseases across a collection of fish farms. Specifically targeting the aquacultural regions of Greece, the system captures geographical and climatic data pertinent to these farms. A feature of this system is its ability to calculate disease transmission intervals between individual cages and broader fish farm entities, providing crucial insights into the spread dynamics. These data then act as an entry point to our expert system. To enhance the predictive precision, we employed various machine learning strategies, ultimately focusing on a reinforcement learning (RL) environment. This RL framework, enhanced by the Multi-Armed Bandit (MAB) technique, stands out as a powerful mechanism for effectively managing the flow of virus transmissions within farms. Empirical tests highlight the efficiency of the MAB approach, which, in direct comparisons, consistently outperformed other algorithmic options, achieving an impressive accuracy rate of 96%. Looking ahead to future work, we plan to integrate buffer techniques and delve deeper into advanced RL models to enhance our current system. The results set the stage for future research in predictive modeling within aquaculture health management, and we aim to extend our research even further. [ABSTRACT FROM AUTHOR]

The vitamin D status diagnosticator (VDSD), a 16-item tool, effectively identifies hypovitaminosis D in healthy older adults and can assist in determining the need for blood tests in this population. Assessing vitamin D levels is particularly crucial in the context of COVID-19. This study aimed to evaluate the VDSD's effectiveness in pinpointing hypovitaminosis D in older adults affected by COVID-19. The research involved 102 unsupplemented geriatric inpatients consecutively admitted to the acute geriatric division of Angers University Hospital, France, with an average age of 85.0 ± 5.9 years (47.1% women). The physician-administered VDSD was conducted simultaneously with the measurement of serum 25-hydroxyvitamin D (25(OH)D). Hypovitaminosis D was defined as a serum 25(OH)D concentration of ≤75 nmol/L for vitamin D insufficiency and ≤50 nmol/L for vitamin D deficiency. Results revealed that 87 participants (85.3%) had vitamin D insufficiency and 63 (61.8%) had vitamin D deficiency. The VDSD accurately identified vitamin D deficiency with an area under the curve (AUC) of 0.81 and an odds ratio (OR) of 40. However, its accuracy in identifying vitamin D insufficiency was lower (AUC = 0.57). In conclusion, the 16-item VDSD, a concise questionnaire, effectively identifies vitamin D deficiency in geriatric patients with COVID-19. This tool can be valuable in guiding the decision to administer vitamin D supplementation during the early stages of COVID-19. [ABSTRACT FROM AUTHOR]

The success of software projects depends on developing a system that satisfies the stakeholders' wishes and needs according to their mental models of the intended system. However, stakeholders may have misaligned mental models of the same system, resulting in conflicting requirements. For this reason, a shared understanding of the project vision is essential for the success of software projects. While it is already challenging to achieve shared understanding in synchronous contexts, such as meetings, it is even more challenging when only asynchronous contexts, like messaging services, are possible. When multiple stakeholders are involved from different locations and time zones, primarily asynchronous communication occurs. The use of asynchronous communication tools for the development of a shared understanding has hardly been analyzed. In this paper, we look to turn the potential detriment of having to discuss a project vision asynchronously into an opportunity for stakeholders to achieve a shared understanding. For this purpose, we give an overview of common challenges of asynchronous communication. We also propose five concepts designed to minimize the impact of these challenges. We examine categories of asynchronous communication tools and assess their adaptability to our concepts. In a workshop, we chose three most suited representatives to include in our main experiment. In this experiment, we evaluate the adapted representatives and a prototype of our own with 30 participants. Our results show the suitability of our concepts. Participants using our concepts were able to achieve a higher level of shared understanding. [ABSTRACT FROM AUTHOR]

Inspired by a "whole child" framing, the current study takes a "whole classroom" perspective to consider classroom practice. Study aims included: (1) presenting a systematic video-based observational coding strategy to concurrently consider practice domains that have implications for learning—cognitive instruction, classroom management, and teacher–student relational interactions; (2) identifying distinct and interrelated classroom typologies based upon this coding strategy. The framework was developed through coding and analysis of 58 purposively sampled urban 4th–9th grade classrooms from the Measures of Effective Teaching study. Analyses revealed three overarching typologies: task-focused (52%), low stimulation (43%), and optimal (5%). We conclude by discussing implications for urban education. [ABSTRACT FROM AUTHOR]

Vehicle exterior inspection is a critical operation for identifying defects and ensuring the overall safety and integrity of vehicles. Visual-based inspection of moving objects, such as vehicles within dynamic environments abounding with reflections, presents significant challenges, especially when time and accuracy are of paramount importance. Conventional exterior inspections of vehicles require substantial labor, which is both costly and prone to errors. Recent advancements in deep learning have reduced labor work by enabling the use of segmentation algorithms for defect detection and description based on simple RGB camera acquisitions. Nonetheless, these processes struggle with issues of image orientation leading to difficulties in accurately differentiating between detected defects. This results in numerous false positives and additional labor effort. Estimating image poses enables precise localization of vehicle damages within a unified 3D reference system, following initial detections in the 2D imagery. A primary challenge in this field is the extraction of distinctive features and the establishment of accurate correspondences between them, a task that typical image matching techniques struggle to address for highly reflective moving objects. In this study, we introduce an innovative end-to-end pipeline tailored for efficient image matching and stitching, specifically addressing the challenges posed by moving objects in static uncalibrated camera setups. Extracting features from moving objects with strong reflections presents significant difficulties, beyond the capabilities of current image matching algorithms. To tackle this, we introduce a novel filtering scheme that can be applied to every image matching process, provided that the input features are sufficient. A critical aspect of this module involves the exclusion of points located in the background, effectively distinguishing them from points that pertain to the vehicle itself. This is essential for accurate feature extraction and subsequent analysis. Finally, we generate a high-quality image mosaic by employing a series of sequential stereo-rectified pairs. [ABSTRACT FROM AUTHOR]

Vitamin D deficiency is considered a public health problem due to its worldwide high prevalence and adverse clinical consequences regarding musculoskeletal health. In addition, vitamin D may also be crucial for the prevention of certain extraskeletal diseases. Despite decades of intensive scientific research, several knowledge gaps remain regarding the precise definition of vitamin D deficiency and sufficiency, the health benefits of improving vitamin D status, and the required vitamin D intakes. Consequently, various societies and expert groups have released heterogeneous recommendations on the dosages for vitamin D supplementation. In this brief narrative review, we outline and discuss recent advances regarding the scientific evidence arguing for a daily vitamin D supplementation with 2000 international units (IU) (50 µg) of vitamin D3 to prevent and treat vitamin D deficiency. According to data from randomized controlled trials (RCTs), such a dose may improve some health outcomes and is sufficient to raise and maintain serum 25(OH)D concentrations above 50 nmol/L (20 ng/mL) and above 75 nmol/L (30 ng/mL) in >99% and >90% of the general adult population, respectively. According to large vitamin D RCTs, there are no significant safety concerns in supplementing such a dose for several years, even in individuals with an already sufficient vitamin D status at baseline. A daily vitamin D supplementation with 2000 IU (50 µg) may be considered a simple, effective, and safe dosage to prevent and treat vitamin D deficiency in the adult general population. [ABSTRACT FROM AUTHOR]

This article, titled "Surgical Resection for Trigeminal Schwannomas: An Institutional Experience," discusses the clinical characteristics and outcomes of patients who underwent surgical resection for trigeminal schwannoma (TS) in an institutional cohort. The study included 20 patients, with ages ranging from 21 to 68 years. The majority of tumors were Samii type C, and the most common presenting symptom was trigeminal hypesthesia. The study found that surgical resection of TS resulted in good progression-free survival and symptom improvement, although cranial nerve injuries were a common perioperative complication. [Extracted from the article]

With the increasing challenge to shift our economic system from carbon to renewable energy carriers, the demand for biogenic resources is growing. Biogenic municipal waste, agricultural by-products and industrial residues are under-utilised but are increasingly gaining in value. To date, there is no continuous database for these resources in the EU-27 countries. Existing datasets that estimate resource potentials for a single point in time often lack validation. A reliable and continuous database is thus needed to support the growing bioeconomy. Spatial and temporal high-resolution data of biogenic residues serve as an invaluable resource for identifying areas with significant theoretical biomass potential and allows an in-depth understanding of dynamic patterns over time. This study elucidates the theoretical biomass potentials of 13 distinct biomasses from municipal waste, agricultural by-products and industrial residues quantified annually from 2010–2020. The spatial scope of the research covers the EU-27 Member States incorporating all entities represented at various levels within the Nomenclature of Territorial Units for Statistics (NUTS) as delineated by Eurostat, where possible. The regionalised data are subsequently validated against regional statistics from different countries. The findings demonstrate the feasibility of creating a time series of theoretical biomass potentials for the 13 selected waste types, by-products, and residues, and underscore the critical role of data validation when regionalising national or sub-national data to smaller NUTS entities. It could be shown that the values of small regions (NUTS 3) correlated well on average. When looking at individual regions in detail, regional characteristics such as the location of cultivation, waste management or reporting methods could lead to over- or underestimates of up to 100 %. Therefore, data at the regional level provide only limited reliability. In the case of industrial residues, regionalisation gave good results localising preference regions of high theoretical biomass potential, but more data on industrial production are needed to also estimate residual quantities at sub-national and local levels. The biomass potentials modelled in this study have been published in an open-access database, which is designed as an extensible tool, enabling the understanding of national and regional trends of theoretical biomass potentials in the European Union and of the reliability of the regionalised data. The estimated theoretical potential dataset can be downloaded free of charge from: 10.48480/g53t-ks72 (Günther et al., 2023). [ABSTRACT FROM AUTHOR]

The Open Research Knowledge Graph (ORKG) is a digital library for machine-actionable scholarly knowledge, with a focus on structured research comparisons obtained through expert crowdsourcing. While the ORKG has attracted a community of more than 1,000 users, the curated data has not been subject to an in-depth quality assessment so far. Here, proposed as a first exemplary step, within a team of domain experts, we evaluate the quality of six selected ORKG Comparisons based on three criteria, namely: 1) the quality of semantic modelling, 2) the maturity of the Comparisons in terms of their completeness, syntactic representation, identifier stability, and their linkability mechanisms ensuring the interoperability and discoverability. Finally, 3) the informative usefulness of the Comparisons to expert and lay users. We have found that each criterion addresses a unique and independent aspect of quality. Backed by the observations of our quality evaluations presented in this paper, a fitting model of knowledge graph quality appears one that is indeed multidimensional as ours. [ABSTRACT FROM AUTHOR]

Today, we readily understand that medieval marriage was often preceded by complex economic negotiations about dowers, dowries, inheritances, and even the costs of weddings. This article argues that medieval courtship—whether aimed at marriage or not—was also an occasion for negotiation and benefit. Focusing on evidence from late medieval England, it shows that courtship was not solely (or even predominantly) a path to marriage: it was also a pastime, an amusement, and—for women—a transaction of its own. Medieval people expected that any man might give any woman material benefits in return for the pleasures of her company and, possibly, the pleasures of sexual relations with her, too. This article also shifts well-recognized medieval discourses about female greed to new modern understandings about female need. The reliance of poor singlewomen on the material benefits of courtship speaks to us today about their profound vulnerability and neediness. [ABSTRACT FROM AUTHOR]