Malignant melanoma represents a form of skin cancer characterized by a bleak prognosis and heightened resistance to traditional therapies. Quercetin has demonstrated notable anti-carcinogenic, anti-inflammatory, anti-oxidant, and pharmacological effects across various cancer types. However, the intricate relationship between quercetin’s anti-cancer properties and ganglioside expression in melanoma remains incompletely understood. In this study, quercetin manifests specific anti-proliferative, anti-migratory, and cell-cycle arrest effects, inducing mitochondrial dysfunction and apoptosis in two melanoma cancer cell lines. This positions quercetin as a promising candidate for treating malignant melanoma. Moreover, our investigation indicates that quercetin significantly reduces the expression levels of ganglioside GD3 and its synthetic enzyme. Notably, this reduction is achieved through the inhibition of the FAK/paxillin/Akt signaling pathway, which plays a crucial role in cancer development. Taken together, our findings suggest that quercetin may be a potent anti-cancer drug candidate for the treatment of malignant melanoma. Full article

The study of dynamic singularity formation in spacetime, focusing on scalar field collapse models, is analyzed. We revisit key findings regarding open spatial topologies, concentrating on minimal conditions necessary for singularity and apparent horizon formation. Moreover, we examine the stability of initial data in the dynamical system governed by Einstein’s equations, considering variations in parameters that influence naked singularity formation. We illustrate how these results apply to a family of scalar field models, concluding with a discussion on the concept of genericity in singularity studies. Full article

Power quality improvement and Power quality disturbance (PQD) detection are two significant concerns that must be addressed to ensure an efficient power distribution within the utility grid. When the process to analyze PQD is migrated to real-time platforms, the possible occurrence of a phase mismatch can affect the algorithm’s accuracy; this paper evaluates phase shifting as an additional stage in signal acquisition for detecting and classifying eight types of single power quality disturbances. According to their mathematical models, a set of disturbances was generated using an arbitrary waveform generator BK Precision 4064. The acquisition, detection, and classification stages were embedded into a BeagleBone Black. The detection stage was performed using multiresolution analysis. The feature vectors of the acquired signals were obtained from the combination of Shannon entropy and log-energy entropy. For classification purposes, four types of classifiers were trained: multilayer perceptron, K-nearest neighbors, probabilistic neural network, and decision tree. The results show that incorporating a phase-shifting stage as a preprocessing stage significantly improves the classification accuracy in all cases. Full article

Previous studies on the clap–fling mechanism have predominantly focused on the initial downward and forward phases of flight in miniature insects, either during hovering or forward flight. However, this study presents the first comprehensive kinematic data of Coccinella septempunctata during climbing flight. It reveals, for the first time, that a clap-and-fling mechanism occurs during the initial upward and backward phase of the hind wings’ motion. This discovery addresses the previously limited understanding of the clap-and-fling mechanism by demonstrating that, during the clap motion, the leading edges of beetle’s wings come into proximity to form a figure-eight shape before rotating around their trailing edge to open into a “V” shape. By employing numerical solutions to solve Navier–Stokes (N-S) equations, we simulated both single hind wings’ and double hind wings’ aerodynamic conditions. Our findings demonstrate that this fling mechanism not only significantly enhances the lift coefficient by approximately 9.65% but also reduces the drag coefficient by about 1.7%, indicating an extension of the applicability range of this clap-and-fling mechanism beyond minute insect flight. Consequently, these insights into insect flight mechanics deepen our understanding of their biological characteristics and inspire advancements in robotics and biomimetics. Full article

This study introduces an innovative version of a recently studied converter. A Double Dual Ćuk Converter was recently studied with advantages like the possibility of designing it for achieving a low-input current ripple. The proposed converter, called the Improved Double Dual Ćuk Converter, maintains the advantages of the former one, and it is characterized by requiring one less capacitor and inductor than its predecessor. This allows addressing the challenge of optimizing the topology to reduce component count without compromising the operation; this work proposes an efficient design methodology based on theoretical analysis and experimental validation. Results demonstrate that the improved topology not only retains the advantages of the previous version, including high efficiency and robustness, but also enhances power density by reducing the number of components. These advancements open new possibilities for applications requiring compact and efficient power converters, such as renewable energy systems, electric vehicles, and portable power supply systems. This work underscores the importance of continuous innovation in power converter design and lays the groundwork for future research aimed at optimizing converter topologies. A detailed discussion of the operating principles and modeling of the converter is provided. Furthermore, simulation outcomes highlighting differences in steady-state duration, output voltage, input current ripple, and operational efficiency are shared. The results from an experimental test bench are also presented to corroborate the efficacy of the improved converter. Full article

The performance of the no-till seeder is one of the most important factors that affect the success of the no-tillage. Striking the right balance between furrow opener design and residue cover is essential for optimizing seeding conditions and ensuring sustainable agricultural practices that promote both soil conservation and high-yield crop production. This study investigates the impact of residue cover on no-tillage maize seeding after wheat harvest, focusing on plant spacing, seeding depth, mean emergence time, and percent emergence. Trials with hoe-type and double-disc-type furrow openers, accompanied by plain- or ripple-disc-type coulters, were conducted in Antalya, Turkey. The results indicate that residue cover had no significant effect on mean plant spacing, but a higher residue cover increased spacing variation. The seeding depth in hoe-type furrow opener trials remained consistent, while double-disc-type furrow openers showed lower depths with 80% and 90% residue covers. The percentage of plant emergence and mean emergence time decreased as the residue cover increased in double-disc-type furrow opener trials. At 90% residue cover, PE decreased to 60%. The impact of disc coulters on hoe-type furrow openers was limited, but they increased seeding depth and MET in double-disc-type furrow openers. These findings can help optimize residue management for improved efficiency in no-till farming systems. Full article

The imperative of sustainability in healthcare is becoming more pressing due to global health crises, climate change, and the rising burden of chronic diseases. Sustainability in healthcare involves social, economic, and environmental dimensions, each important for equitable and robust healthcare delivery. Health literacy may play a central role in embracing all three dimensions, bridging the gap between complex health information and individuals’ capability to understand and use it effectively: In fact, as a factor influencing the link between adverse social and economic conditions and subsequent health issues, health literacy could represent a practical target for mitigating health disparities within various demographic groups, thereby enhancing social sustainability. Furthermore, when people possess a solid understanding of their health conditions and the necessary steps for health management, they can contribute to a reduction in generated healthcare costs. Finally, health literacy and environmental health literacy equips individuals and communities with the knowledge and skills to understand how environmental factors affect health and empowers them to take proactive measures to protect the environment, potentially reducing the environmental impact of healthcare services. Therefore, integrating health literacy into education curricula and healthcare professional training is crucial for promoting sustainability. Despite some known barriers to the advancement and improvement of health literacy, such as limited awareness of its importance, the collaboration between academia, healthcare institutions, and community organizations is a path to follow to overcome these challenges. Full article

Vehicular Edge Computing (VEC) is a crucial application of Mobile Edge Computing (MEC) in vehicular networks. In VEC networks, the computation tasks of vehicle terminals (VTs) can be offloaded to nearby MEC servers, overcoming the limitations of VTs’ processing power and reducing latency caused by distant cloud communication. However, a mismatch between VTs’ demanding tasks and MEC servers’ limited resources can overload MEC servers, impacting Quality of Service (QoS) for computationally intensive tasks. Additionally, vehicle mobility can disrupt communication with static MEC servers, further affecting VTs’ QoS. To address these challenges, this paper proposes a vehicle collaborative partial computation offloading model. This model allows VTs to offload tasks to two types of service nodes: collaborative vehicles and MEC servers. Factors like a vehicle’s mobility, remaining battery power, and available computational power are also considered when evaluating its suitability for collaborative offloading. Furthermore, we design a deep reinforcement learning-based strategy for collaborative partial computation offloading that minimizes overall task delay while meeting individual latency constraints. Experimental results demonstrate that compared to traditional approaches without vehicle collaboration, this scheme significantly reduces latency and achieves a significant reduction (around 2%) in the failure rate under tighter latency constraints. Full article

Multi-Camera Multi-Vehicle Tracking (MCMVT) is a critical task in Intelligent Transportation Systems (ITS). Differently to in urban environments, challenges in highway tunnel MCMVT arise from the changing target scales as vehicles traverse the narrow tunnels, intense light exposure within the tunnels, high similarity in vehicle appearances, and overlapping camera fields of view, making highway MCMVT more challenging. This paper presents an MCMVT system tailored for highway tunnel roads incorporating road topology structures and the overlapping camera fields of view. The system integrates a Cascade Multi-Level Multi-Target Tracking strategy (CMLM), a trajectory refinement method (HTCF) based on road topology structures, and a spatio-temporal constraint module (HSTC) considering highway entry–exit flow in overlapping fields of view. The CMLM strategy exploits phased vehicle movements within the camera’s fields of view, addressing such challenges as those presented by fast-moving vehicles and appearance variations in long tunnels. The HTCF method filters static traffic signs in the tunnel, compensating for detector imperfections and mitigating the strong lighting effects caused by the tunnel lighting. The HSTC module incorporates spatio-temporal constraints designed for accurate inter-camera trajectory matching within overlapping fields of view. Experiments on the proposed Highway Surveillance Traffic (HST) dataset and CityFlow dataset validate the system’s effectiveness and robustness, achieving an IDF1 score of 81.20% for the HST dataset. Full article

The level of yield variation of primary crops has a considerable effect on the vulnerability of agriculture. The main factor that makes the agriculture of Hungary so vulnerable is climate change, and technological development cannot compensate for its unfavourable effects. We examined the yield failures of the two major field crops grown in Hungary that occurred during the last 100 years. The goals of our study were to determine how often yield losses at 15% and 30% occur, what their probability is and whether the probability has changed in recent decades. The Wald–Wolfowitz runs test was used to determine the randomness of yield failures. A series of yield failures for maize and winter wheat were found to be random. Based on the data for 1985–2023, failure by 15% and 30% can be expected approximately every 8th and 19th year for winter wheat and 3rd and 5th year for maize. Winter wheat yield failure at 15% shows a decreasing trend in occurrence, while at 30% it increases. On the other hand, the frequency of maize yield failure increased at both levels. The consideration of historical yield data can help to determine the extent of crop loss to be expected in the long term to maintain sustainable winter wheat and maize production in our changing climate. Full article

Freeze-drying (FD) processing preserves foods by combining the most effective traditional technologies. FD conserves the structure, shape, freshness, nutritional/bioactive value, color, and aroma at levels similar to or better than those of refrigerated and frozen foods while delivering the shelf-stable convenience of canned/hot-air-dehydrated foods. The mass transfer rate is the essential factor that can slow down the FD process, resulting in an excessive primary drying time and high energy consumption. The objective of this study was to reduce the FD processing time using CO₂ laser technology to improve product competitiveness in the preservation of whole strawberries. The research process consisted of the selection and characterization of fresh strawberries, followed by preparation, pre-treatment, freeze-drying, a primary drying time assessment, and a quality comparison. Experiments were carried out using strawberries without micro-perforation and with five and eight micro-perforations. Quality parameters were determined for fresh, frozen/thawed, and freeze-dried/rehydrated strawberries. It was found that the primary drying time can be significantly reduced by 20% (95% CI) from 26.7 h for non-perforated fruits to 22.3 h when five micro-perforations are made on each strawberry. The quality parameters used to evaluate the strawberries did not show significant differences when comparing frozen/thawed fruits with freeze-dried/rehydrated fruits. The experiments conducted in this study showed that freeze-drying may efficiently compete with freezing technology when processing whole strawberries. Full article

Image quality assessment of magnetic resonance imaging (MRI) data is an important factor not only for conventional diagnosis and protocol optimization but also for fairness, trustworthiness, and robustness of artificial intelligence (AI) applications, especially on large heterogeneous datasets. Information on image quality in multi-centric studies is important to complement the contribution profile from each data node along with quantity information, especially when large variability is expected, and certain acceptance criteria apply. The main goal of this work was to present a tool enabling users to assess image quality based on both subjective criteria as well as objective image quality metrics used to support the decision on image quality based on evidence. The evaluation can be performed on both conventional and dynamic MRI acquisition protocols, while the latter is also checked longitudinally across dynamic series. The assessment provides an overall image quality score and information on the types of artifacts and degrading factors as well as a number of objective metrics for automated evaluation across series (BRISQUE score, Total Variation, PSNR, SSIM, FSIM, MS-SSIM). Moreover, the user can define specific regions of interest (ROIs) to calculate the regional signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thus individualizing the quality output to specific use cases, such as tissue-specific contrast or regional noise quantification. Full article

The surge in opioid use disorder (OUD) over the past decade escalated opioid overdoses to a leading cause of death in the United States. With adverse effects on cognition, risk-taking, and decision-making, OUD may negatively influence financial well-being. This study examined the financial health of individuals diagnosed with OUD by reviewing financial beliefs and financial behaviors. We evaluated quality of life, perceptions of financial condition during active use and recovery, and total debt. We distributed a 20-item survey to 150 individuals in an outpatient treatment program for OUD in a large metropolitan area, yielding a 56% response rate. The results revealed low overall financial health, with a median debt of USD 12,961 and a quality-of-life score of 72.80, 9.4% lower than the U.S. average (82.10). Most participants (65.75%) reported improved financial health during recovery, while a higher majority (79.45%) worsened during active use. Unemployment affected 42% of respondents, and 9.52% were employed only part-time. Regression analysis highlighted a strong association between lack of full-time employment and a lack of financial advising with total debt. High financial anxiety and active use were associated with lower quality of life. Individuals with OUD may benefit from financial interventions, resources, and counseling to improve their financial health. Full article

Glioblastoma (GBM) is the most common malignancy of the central nervous system in adults. GBM has high levels of therapy failure and its prognosis is usually dismal. The phenotypic heterogeneity of the tumor cells, dynamic complexity of non-tumor cell populations within the GBM tumor microenvironment (TME), and their bi-directional cross-talk contribute to the challenges of current therapeutic approaches. Herein, we discuss the etiology of GBM, and describe several major types of non-tumor cells within its TME, their impact on GBM pathogenesis, and molecular mechanisms of such an impact. We also discuss their value as potential therapeutic targets or prognostic biomarkers, with reference to the most recent works on this subject. We conclude that unless all “key player” populations of non-tumor cells within the TME are considered, no breakthrough in developing treatment for GBM can be achieved. Full article

Access to veterinary care for animal owners is an important part of keeping animals healthy and keeping pets and people together whenever that is appropriate. Insufficient financial and other resources to allocate to veterinary care are major barriers for pet owners to receiving preventative, sick, and emergency services. The veterinary community has begun to incorporate offering a range of diagnostic and treatment options more intentionally in response to clients’ inability to pay and to a lesser extent to mitigate other barriers to care. Many veterinarians are nonetheless oriented toward providing specialized and more sophisticated care based on their training. This study sought to identify the self-reported behaviors, knowledge, attitudes, and beliefs of veterinarians about offering a spectrum of care options (SoC) to clients. The finding that many reported offering SoC is encouraging. However, veterinarians who report comfort and confidence in a variety of aspects of clinical care were most likely to offer SoC. Practitioners in the field for 20 or more years were less likely to offer SoC to clients with financial limitations. Rural veterinarians were more likely to offer SoC to any client compared to urban veterinarians. These results provide a point of reference and potential focus for veterinarians who are not currently offering SoC as well as an exploration of veterinarians’ reported knowledge, behaviors, attitudes, perceptions, and concerns about SoC. Full article

Photovoltaic (PV) power forecasting plays a crucial role in optimizing renewable energy integration into the grid, necessitating accurate predictions to mitigate the inherent variability of solar energy generation. We propose a novel forecasting model that combines improved variational mode decomposition (IVMD) with the temporal convolutional network-gated recurrent unit (TCN-GRU) architecture, enriched with a multi-head attention mechanism. By focusing on four key environmental factors influencing PV output, the proposed IVMD-TCN-GRU framework targets a significant research gap in renewable energy forecasting methodologies. Initially, leveraging the sparrow search algorithm (SSA), we optimize the parameters of VMD, including the mode component K-value and penalty factor, based on the minimum envelope entropy principle. The optimized VMD then decomposes PV power, while the TCN-GRU model harnesses TCN’s proficiency in learning local temporal features and GRU’s capability in rapidly modeling sequence data, while leveraging multi-head attention to better utilize the global correlation information within sequence data. Through this design, the model adeptly captures the correlations within time series data, demonstrating superior performance in prediction tasks. Subsequently, the SSA is employed to optimize GRU parameters, and the decomposed PV power mode components and environmental feature attributes are inputted into the TCN-GRU neural network. This facilitates dynamic temporal modeling of multivariate feature sequences. Finally, the predicted values of each component are summed to realize PV power forecasting. Validation using real data from a PV station corroborates that the novel model demonstrates a substantial reduction in RMSE and MAE of up to 55.1% and 54.5%, respectively, particularly evident in instances of pronounced photovoltaic power fluctuations during inclement weather conditions. The proposed method exhibits marked improvements in accuracy compared to traditional PV power prediction methods, underscoring its significance in enhancing forecasting precision and ensuring the secure scheduling and stable operation of power systems. Full article

Efficient resource allocation is crucial in clusters with frugal Single-Board Computers (SBCs) possessing limited computational resources. These clusters are increasingly being deployed in edge computing environments in resource-constrained settings where energy efficiency and cost-effectiveness are paramount. A major challenge in Hadoop scheduling is load balancing, as frugal nodes within the cluster can become overwhelmed, resulting in degraded performance and frequent occurrences of out-of-memory errors, ultimately leading to job failures. In this study, we introduce an Adaptive Multi-criteria Selection for Efficient Resource Allocation (AMS-ERA) in Frugal Heterogeneous Hadoop Clusters. Our criterion considers CPU, memory, and disk requirements for jobs and aligns the requirements with available resources in the cluster for optimal resource allocation. To validate our approach, we deploy a heterogeneous SBC-based cluster consisting of 11 SBC nodes and conduct several experiments to evaluate the performance using Hadoop wordcount and terasort benchmark for various workload settings. The results are compared to the Hadoop-Fair, FOG, and IDaPS scheduling strategies. Our results demonstrate a significant improvement in performance with the proposed AMS-ERA, reducing execution time by 27.2%, 17.4%, and 7.6%, respectively, using terasort and wordcount benchmarks. Full article

This study offers, by an empirical analysis, another perspective on post-socialist development, highlighting the role of the urban–rural interface in regional dynamics. The current literature on the relationships between both issues is not too rich and our paper analyzes the relationships between core cities, their peri-urban areas, and their regions, through a comparative overview of their growth over the last three decades. Romania, as a special case study for a contradictory transition, due to the great step from a drastic dictatorial regime to a democracy and a market economy, is a good example to test these complex relationships. Considering the new development trend at the urban–rural interfaces, our key idea was to depict their contribution to regional development (NUTS 3) compared to city cores. The second question was how this differentiated contribution can be measured, using the simplest tool. The starting point was the fact that population dynamics reflect all changes in the city core and at the urban–rural interface, and less so at a regional level. Consequently, we selected the dynamics of the number of inhabitants for the first two, as well as the dynamics of GDP per capita at the regional level. We found higher and significant correlations between GDP per capita and urban–rural interfaces, but no significant correlations in the case of city cores. Our conclusion is that, in the transition period, the dynamics of urban–rural interfaces influenced more regional development dynamics, than those of city cores. This means that urban–rural interfaces amplify the development coming from cities, adding their own contribution and then dissipating it regionally. Future research should identify what the urban–rural interface offers to regions, in addition to the city core. Full article

Different factors, not limited to the lung, influence the progression of ILDs. A “treatable trait” strategy was recently proposed for ILD patients as a precision model of care to improve outcomes. However, no data have been published so far on the prevalence of TTs in ILD. A prospective, observational, cohort study was conducted within the ILD Program at the IRCCS Humanitas Research Hospital (Milan, Italy) between November 2021 and November 2023. TTs were selected according to recent literature and assigned during multidisciplinary discussion (MDD) to one of the following categories: pulmonary, etiological, comorbidities, and lifestyle. Patients were further divided into four groups according to their post-MDD diagnosis: idiopathic ILD, sarcoidosis, connective tissue disease–ILD, and other ILD. The primary study outcome was the prevalence of each TT in the study population. A total of 116 patients with ILD [63.9% male; median (IQR) age: 69 (54–78) years] were included in the study. All the TTs identified in the literature were found in our cohort, except for intractable chronic cough. We also recognized differences in TTs across the ILD groups, with less TTs in patients with sarcoidosis. This analysis provides the first ancillary characterization of TTs in ILD patients in a real setting to date. Full article

A breakthrough “Green Revolution” in rice enhanced lodging resistance by using gibberellin-deficient semi-dwarf varieties. However, the gibberellic acid (GA) signaling regulation on rice disease resistance remains unclear. The resistance test showed that a positive GA signaling regulator DWARF1 mutant d1 was more susceptible while a negative GA signaling regulator Slender rice 1 (SLR1) mutant was less susceptible to sheath blight (ShB), one of the major rice diseases, suggesting that GA signaling positively regulates ShB resistance. To isolate the regulator, which simultaneously regulates rice lodging and ShB resistance, SLR1 interactors were isolated. Yeast two-hybrid (Y2H), bimolecular fluorescence complementation (BiFC), and Co-IP assay results indicate that SLR1 interacts with Calcineurin B-like-interacting protein kinase 31 (CIPK31). cipk31 mutants exhibited normal plant height, but CIPK31 OXs showed semi-dwarfism. In addition, the SLR1 level was much higher in CIPK31 OXs than in the wild-type, suggesting that CIPK31 OX might accumulate SLR1 to inhibit GA signaling and thus regulate its semi-dwarfism. Recently, we demonstrated that CIPK31 interacts and inhibits Catalase C (CatC) to accumulate ROS, which promotes rice disease resistance. Interestingly, CIPK31 interacts with Vascular Plant One Zinc Finger 2 (VOZ2) in the nucleus, and expression of CIPK31 accumulated VOZ2. Inoculation of Rhizoctonia solani AG1-IA revealed that the voz2 mutant was more susceptible to ShB. Thus, these data prove that CIPK31 promotes lodging and ShB resistance by regulating GA signaling and VOZ2 in rice. This study provides a valuable reference for rice ShB-resistant breeding. Full article

The purpose of this study was to explore the effects of the integration of machine learning into daily radiological diagnostics, using the example of the machine learning software mdbrain^® (Mediaire GmbH, Germany) in the diagnostic MRI workflow of patients with multiple sclerosis at the University Medicine Greifswald. The data were assessed through expert interviews, a comparison of analysis times with and without the machine learning software, as well as a process analysis of MRI workflows. Our results indicate a reduction in the screen-reading workload, improved decision-making regarding contrast administration, an optimized workflow, reduced examination times, and facilitated report communication with colleagues and patients. Our results call for a broader and quantitative analysis. Full article

Bearings are crucial components of machinery and equipment, and it is essential to inspect them thoroughly to ensure a high pass rate. Currently, bearing scratch detection is primarily carried out manually, which cannot meet industrial demands. This study presents research on the detection of bearing surface scratches. An improved YOLOV5 network, named YOLOV5-CDG, is proposed for detecting bearing surface defects using scratch images as targets. The YOLOV5-CDG model is based on the YOLOV5 network model with the addition of a Coordinate Attention (CA) mechanism module, fusion of Deformable Convolutional Networks (DCNs), and a combination with the GhostNet lightweight network. To achieve bearing surface scratch detection, a machine vision-based bearing surface scratch sensor system is established, and a self-made bearing surface scratch dataset is produced as the basis. The scratch detection final Average Precision (AP) value is 97%, which is 3.4% higher than that of YOLOV5. Additionally, the model has an accuracy of 99.46% for detecting defective and qualified products. The average detection time per image is 263.4 ms on the CPU device and 12.2 ms on the GPU device, demonstrating excellent performance in terms of both speed and accuracy. Furthermore, this study analyzes and compares the detection results of various models, demonstrating that the proposed method satisfies the requirements for detecting scratches on bearing surfaces in industrial settings. Full article