Pandemics can result in large morbidity and mortality rates that can cause significant adverse effects on the social and economic situations of communities. Monitoring and predicting the spread of pandemics helps the concerned authorities manage the required resources, formulate preventive measures, and control the spread effectively. In the specific case of COVID-19, the UAE (United Arab Emirates) has undertaken many initiatives, such as surveillance and contact tracing by introducing mobile apps such as Al Hosn, containment of spread by limiting the gathering of people, online schooling and remote work, sanitation drives, and closure of public places. The aim of this paper is to predict the trends occurring in pandemic outbreak, with COVID-19 in the UAE being a specific case study to investigate. In this paper, a predictive modeling approach is proposed to predict the future number of cases based on the recorded history, taking into consideration the enforced policies and provided vaccinations. Machine learning models such as LASSO Regression and Exponential Smoothing, and deep learning models such as LSTM, LSTM-AE, and bi-directional LSTM-AE, are utilized. The dataset used is publicly available from the UAE government, Federal Competitiveness and Statistics Centre (FCSC) and consists of several attributes, such as the numbers of confirmed cases, recovered cases, deaths, tests, and vaccinations. An additional categorical attribute is manually added to the dataset describing whether an event has taken place, such as a national holiday or a sanitization drive, to study the effect of such events on the pandemic trends. Experimental results showed that the Univariate LSTM model with an input of a five-day history of Confirmed Cases achieved the best performance with an RMSE of 275.85, surpassing the current state of the art related to the UAE by over 30%. It was also found that the bi-directional LSTMs performed relatively well. The approach proposed in the paper can be applied to monitor similar infectious disease outbreaks and thus contribute to strengthening the authorities’ preparedness for future pandemics. Full article

The aim of the study was to determine the effect of step load in hypoxia on the effectiveness of preoperative rehabilitation (PR) and hormone levels based on a case study. Introduction: We assessed the impact of variables such as rate of movement and time under tension (TUT) in normobaric hypoxia on the levels of growth hormone (GH), insulin-like growth factor 1 (IGF-1), and erythropoietin (EPO). Additionally, the impact of step load on the hypertrophy and strength of knee extensors and flexors was assessed. Methods: The work uses a case study, the research subject of which was a 23-year-old female professional handball player. The tests included an isokinetic assessment of the peak torque of knee extensors and flexors as well as body composition analysis. Results: The results showed a more than (10.81-fold) increase in GH after the microcycle with time under tension (TUT). The deficit between the lower limbs was also reduced. Conclusions: Using a hypoxic environment based on an appropriate altitude, combined with changes such as a short rest break between sets and a controlled tempo of movement with an eccentric phase, TUT may offer an alternative to the PR process, especially among athletes who care about fast RTS. Full article

Electrokinetic (EK) microsystems, which are capable of performing separations without the need for labeling analytes, are a rapidly growing area in microfluidics. The present work demonstrated three distinct binary microbial separations, computationally modeled and experimentally performed, in an insulator-based EK (iEK) system stimulated by DC-biased AC potentials. The separations had an increasing order of difficulty. First, a separation between cells of two distinct domains (Escherichia coli and Saccharomyces cerevisiae) was demonstrated. The second separation was for cells from the same domain but different species (Bacillus subtilis and Bacillus cereus). The last separation included cells from two closely related microbial strains of the same domain and the same species (two distinct S. cerevisiae strains). For each separation, a novel computational model, employing a continuous spatial and temporal function for predicting the particle velocity, was used to predict the retention time ($t_{R,p}$) of each cell type, which aided the experimentation. All three cases resulted in separation resolution values $R_s>1.5,$ indicating complete separation between the two cell species, with good reproducibility between the experimental repetitions (deviations < 6%) and good agreement (deviations < 18%) between the predicted $t_{R,p}$ and experimental ($t_{R,e}$) retention time values. This study demonstrated the potential of DC-biased AC iEK systems for performing challenging microbial separations. Full article

As technology continues to evolve, so too do privacy concerns individuals have about technology. This is especially true when individuals share highly sensitive, personal content through technology. When individuals sext, they are sharing sexually explicit messages, photos, and videos with another person. Two theories are interrogated in how they may apply to sexting and privacy: communication privacy management theory and privacy calculus. Utilizing these theories, privacy is highlighted in this article as a negotiation process between partners and technologies. Individuals must consider who they share material with and the channels they use, and these theories can help in developing a better understanding of these processes. Sexting can be a positive influence on adults’ romantic relationships, whether serious or casual; yet, they need to be able to engage in these behaviors in ways that encourage trust both interpersonally and with their technology. Full article

Objective: To assess the association between pretreatment thrombocytosis, anemia, and leukocytosis and overall survival (OS) of advanced-stage EOC. Furthermore, to develop nomograms using established prognostic factors and pretreatment hematologic parameters to predict the OS of advanced EOC patients. Methods: Advanced-stage EOC patients treated between January 1996 and January 2010 in eastern Netherlands were included. Survival outcomes were compared between patients with and without pretreatment thrombocytosis (≥450,000 platelets/µL), anemia (hemoglobin level of <7.5 mmol/L), or leukocytosis (≥11.0 × 10⁹ leukocytes/L). Three nomograms (for ≤3-, ≥5-, and ≥10-year OS) were developed. Candidate predictors were fitted into multivariable logistic regression models. Multiple imputation was conducted. Model performance was assessed on calibration, discrimination, and Brier scores. Bootstrap validation was used to correct for model optimism. Results: A total of 773 advanced-stage (i.e., FIGO stages IIB–IV) EOC patients were included. The median [interquartile range, IQR] OS was 2.3 [1.3–4.2] and 3.0 [1.4–7.0] years for patients with and without pretreatment thrombocytosis (p < 0.01). The median OS was not notably different for patients with and without pretreatment leukocytosis (p = 0.58) or patients with and without pretreatment anemia (p = 0.07). The final nomograms comprised established predictors with either pretreatment leukocyte or platelet count. The ≥5- and ≥10-year OS models demonstrated good calibration and adequate discrimination with optimism-corrected c-indices [95%-CI] of 0.76 [0.72–0.80] and 0.78 [0.73–0.83], respectively. The ≤3-year OS model demonstrated suboptimal performance with an optimism-corrected c-index of 0.71 [0.66–0.75]. Conclusions: Pretreatment thrombocytosis is associated with poorer EOC survival. Two well-performing models predictive of ≥5-year and ≥10-year OS in advanced-stage EOC were developed and internally validated. Full article

Introduction: Solid pseudopapillary neoplasms (SPNs) are rare and mainly originate from the pancreas. SPNs originating from the ovary (SPN-O) are extremely rare, and only 13 cases have been reported in the English literature since 2010. Case: We report a 31-year-old woman with SPN-O accompanied by multiple metastases in the abdominal cavity. The patient underwent staging surgery and cytoreduction. Furthermore, the multidisciplinary board decided on adjuvant chemotherapy with an FP regimen (fluorouracil plus cisplatin) because a microscopic metastasis was discovered in the peritoneum near the appendix. Next-generation sequencing showed some pathologic mutations of oncogenes/cancer-associated genes, including CTNNB1 and TP53. This is the fourteenth case of SPN-O and the first one to demonstrate the TP53 pathogenic mutant variant in SPN-O. The patient showed 8 months of disease-free survival until February 2024. Conclusion: The combination of R0 cytoreduction with FOLFIRI chemotherapy appears to be an effective and feasible treatment option. Full article

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