Goat milk is a nutritious food, with high levels of vitamins and minerals. In this context, there is a growing need to improve preservation techniques. Thus, the present work was developed with the aim of drying goat milk using a spray dryer. Chemical, physical, and physicochemical analyses of powdered goat milk and reconstituted goat milk were performed, as well as the characterization of raw goat milk. The statistical model used was the full factorial design 2³ + 3, observing the Pareto Diagram and Analysis of Variance to study the effect of atomization frequency (Hz), inlet temperature (°C), and peristaltic pump rotation (rpm) on the responses for the powdered and reconstituted product. The planning was executed with eleven trials, including nine different trials and three central points. For the analysis of the planning at a 95% confidence level, the Statistica 8.0 software was used. The results showed that atomization frequency and inlet temperature had the greatest effect on the powdered product and the reconstituted food, respectively. Therefore, the importance of drying this food is evident as it offers several advantages for the food industry and consumers.

This study sought to investigate the problem of losses along the milk value chain in Nandi and Kakamega Counties in Kenya. Using semi structured questionnaire, quantitative data were collected from different stages of the value chain consisting of 400 dairy farmers proportionally and randomly selected in Chesumei and Ikolomani sub counties; 15 cooperatives, 40 transporters, 20 milk bars, 17 supermarkets. Additionally Key informant interviews and focus group discussions we held with extension staff and farmers, respectively.The results showed losses occurred at all stages of the value chain in Nandi and Kakamega Counties with the highest losses at Cooperative (50.6%), collection centres (33.7%) and milk bars (7.5%). Annual milk loss was estimated to be 260,000 litres and 20,000 litres in Nandi and Kakamega counties respectively. The economic value of losses per year was estimated at KES 13 million (USD 100 000). Significant factors (P<0.001) influencing milk losses at farm level were: type of cow housing, level of training of milker, mixing morning and evening milk and Lack of cooling facilities. In cooperatives, poor roads, poor preservation of milk and mastitis influenced post harvest losses (PHL) causing milk rejection due to spoilages; while transporters attributed PHL to storage during transportation. Inadequate storage facilities and milk handling practices influenced PHL in milk bars while supermarkets attributed PHL to poor packaging materials and short shelf life. The most important causes of milk PHL along the chain were spoilages, spillages, adulteration and physical dirt. Key interventions to minimize PHL should target improvement of rural road infrastructure connecting farmers to markets; milk storage facilities and capacity building on milk handling practices for all actors. A sustainable PHL reduction should entail policy dialogue on milk payment system based on quality rather than quantity.

I want to meaningfully average a pathalogical function (i.e., an everywhere surjective function whose graph has zero Hausdorff measure in its dimension). We do this by taking the most generalized, satisfying extension of the expected value, w.r.t the Hausdorff measure in its dimension, on bounded functions which takes finite values only. As of now, I'm unable to solve this due to limited knowledge of advanced math and most people are too busy to help. Therefore, I'm wondering if anyone knows a research paper which solves my doubts.

Our article AI, cultural heritage and bias examines the challenges and potential solutions for using machine learning to interpret and classify human memory and cultural heritage artifacts. We argue that bias is inherent in cultural heritage collections (CHCs) and their digital versions, and that AI pipelines may amplify this bias. We hypothesise that effective AI methods require vast, well-annotated datasets with structured metadata, which CHCs often lack due to diverse digitisation practices and limited interconnectivity. The paper discusses the definition of bias in CHCs and other datasets, exploring how it stems from training data and insufficient humanities expertise in generative platforms. We conclude that scholarship, guidelines, and policies on AI and CHCs should address bias as both inherent and augmented by AI technologies. We recommend implementing bias mitigation techniques throughout the process, from collection to curation, to support meaningful curation, embrace diversity, and cater to future heritage audiences.

Burnout, characterized by emotional and physical exhaustion, poses a significant challenge to adolescent athletes, particularly in high-intensity sports like basketball. Emotional Intelligence (EI) is the ability to manage emotions, which is negatively associated with burnout. Emotional labor, including strategies of surface acting (SA), deep acting (DA), and genuine expression (GE), is a potential key role in emotion management between EI and burnout for athletes. This study aims to investigate the relationship between EI and burnout, as well as the mediating role of emotional labor strategies among adolescent basketball players. Our cross-sectional study, conducted in youth sports schools in four different places of China, involved 260 basketball players. Our study found EI to be negatively associated with burnout. SA and GE were identified as significant mediators in this relationship, with SA positively associated with burnout and GE negatively associated. These findings suggest that enhancing EI and optimizing emotional labor strategies could be key in mitigating burnout among young athletes, thereby improving their well-being and performance.

Wearable devices have a significant impact on society and recent advancements in modern sensor technologies are opening up new possibilities for healthcare applications. Continuous vital sign monitoring using IoT can be a crucial tool for emergency management, reducing risk in rescue operations and ensuring the safety of workers.The massive amounts of data, high network traffic, and computational demands of a typical IoT monitoring application can be challenging to manage with traditional infrastructure. Cloud computing provides a solution with its built-in resilience and elasticity capabilities.This article presents a cloud-based monitoring architecture for remote vital sign tracking of paramedics and medical workers through the use of a mobile wearable IoT device. It enables the management of ECG, breathing patterns, and other vital signs during work sessions, and sends real-time alarm events to a personnel management center. The platform was extensively used and tested in a large-scale monitoring application for tracking the activities of emergency room and ambulance workers, and the results of this testing are presented.

Various short-range radars, such as impulse-radio ultra-wideband (IR-UWB) and frequency-modulated continuous-wave (FMCW) radars, are currently employed to monitor vital signs, including respiratory and cardiac rates (RR and CR). However, these methods do not consider the motion of an individual, which can distort the phase of the reflected signal, leading to inaccurate estimation of RR and CR because of a smeared spectrum. Therefore, motion compensation (MOCOM) is crucial for accurately estimating these vital rates. This paper proposes an efficient method incorporating MOCOM to estimate RR and CR with super-resolution accuracy. The proposed method effectively models the radar signal phase and compensates for motion. Additionally, applying the super-resolution technique to RR and CR separately further increases the estimation accuracy. Experimental results from the IR-UWB and FMCW radars demonstrate that the proposed method successfully estimates RR and CR even in the presence of body movement.

The large-scale introduction of renewable energy, replacing fossil fuels, is presented as an essential part of the energy transition; this substitution is being observed in electrical systems, but it will also be necessary to introduce them in other sectors, such as transportation, either by incorporating renewable energy sources in the sector's facilities, including automotive service centers, or by electrifying transportation technology. The introduction of electromobility in a country is associated with a group of technologies that are required to make this introduction feasible. In Cuba, the introduction of electric vehicles, electric charging stations and charging points, including residential charging points, could increase the current demand by about 330 MW, based on the prospects for the introduction of electrification in the country's transportation system. Taking all of the above as a point of reference, this article addresses the basic elements of an energy transition proposal in the transportation sector, arriving at a proposed Roadmap to achieve the required synergy between energy transition and electromobility.

Adjunctive treatment of periodontitis lacks solutions allowing enough time for wound healing in the periodontal pockets by avoiding a fast re-colonization. Such a solution might be an antibiotic containing formulation with a controlled release over weeks. Here, a recently described minocycline containing approach is qualified for further clinical development by focusing on proof-of-concept, systemic burden, resistance development and degradation studies. Animal studies were done in two different (mouse-chamber, rat Porphyromonas gingivalis challenging) models, including effects on inflammation markers, bone loss and bone structure. Also, serum concentrations of minocycline after local application were determined by HPLC-MS/MS. The resistance status of bacterial clinical isolates against minocycline was investigated and the degradation of the formulation was characterized by laser scanning- and scanning electron microscopy. Animal studies clearly demonstrated the applicability of the new formulation in the investigated models. Inflammation markers decreased dosage dependent and reduced bone loss compared to non-treated group was observed. Thereby, the systemic burden of the antibiotic was neglectable. Minocycline is still effective against oral pathogens; resistance development was not seen. The biodegradable thread was first swollen and subsequently degraded over weeks. The results support the continued clinical development of this new formulation. A phase I clinical trial is planned to further evaluate its safety and efficacy.

Recent advancements in Artificial Intelligence (AI) and generative models have significantly accelerated the evolution of internet services. In data centers, where data is often distributed across multiple machines, leveraging these machines to expedite the training of machine learning applications is common. However, this approach incurs substantial communication overhead. To mitigate this, data compression techniques such as quantization, sparsification, and dimension reduction are gaining attention, especially with iterative training algorithms that frequently exchange gradients. With the advancement of FPGA technology, FPGA-based bump-in-the-wire network interface controllers (smartNICs) have demonstrated notable benefits for data centers, matching ASIC switches in port-to-port latency and capacity. However, FPGA-based switches often have spare hardware resources beyond their switching functions, which are typically wasted. we propose a novel architecture that integrates FPGA-based switches with ensemble learning methods, utilizing these spare resources on FPGA-based network switches. This architecture employs random projection as a compression method to accelerate machine learning tasks while maintaining high accuracy. Our system accelerates the training process for Multilayer Perceptron (MLP) models with a speedup of 2.1-6.7 times across four high-dimensional datasets. In addition, it leverages the Hamming distance metric for k-Nearest Neighbors (kNN) classification, combining random projection with Hamming encoding, to offload large-scale classification tasks from downstream servers. This achieves a worst-case speedup of 2.3-8.7 times and a memory reduction of 3.9-27.7 times compared to CPU-based kNN classifiers using cosine distance. Our architecture addresses the challenges of high-dimensional data processing and exemplifies the potential of AI-driven solutions in accelerating and optimizing internet services. Moreover, the programmability of FPGAs allows the system to adopt various compression methods, extending its applicability. Offloading tasks to FPGAs can significantly reduce query response times, showcasing a crucial step towards achieving greater levels of autonomic computing and the rapid evolution of internet systems.

This paper details the approach to the efficient design and optimization of reversible adder and multiplier utilizing Peres gates which is a three input, three output gate. Peres gates are recognized for their universality and energy efficient properties and present an intriguing option for constructing reversible circuits. Reversible logic are characterized by its ability to uniquely determine input states from output states. The design methodology involves a cascading arrangement of Peres gates each performing a reversible XOR operation on corresponding bits of the input numbers and the carry out from the preceding stage. The paper presents a detailed schematic representation, simulation results and analysis of the proposed designs of different adders and multipliers showcasing their potential for reversible applications. The integration of Peres gates in the adder and multiplier design signifies a step forward in the exploration of reversible logic circuits and their applications in contemporary computing paradigms. The overall hardware reduction is the main achievement of this research in terms of quantum cost.

Background/Objectives: Intracranial Epidermoid Cysts (IECs) are extremely rare intracranial tumors. The primary treatment is surgery; however, cyst adherence results in difficult removal; subtotal resection yields high recurrence rates. The mechanisms of IECs remain unknown, consequently, advances in treatment have fallen short. Tumor genetic profiling has rendered targets for drug development and for which FDA-approved options exist, reshaping cancer treatment. The genetics of IECs has not been explored. We applied Whole Exome Sequencing (WES) in IECs to gain insights into the mechanisms of oncogenesis and identify actionable targets. Methods: We performed WES in tumor tissue and matching blood, when available. Read processing, quality control and somatic variant calling followed GATK best practices. Read alignments were also used for somatic copy-number inference. Data analyses and visualizations were conducted in R. Results: Top altered genes were associated with immune system and microenvironment, suggesting an immune evasion mechanism. Recurrent and deleterious NOTCH2 and USP8 alterations were identified in 50% and 30% of the cohort, respectively. Frequent amplifications in deubiquitinases and beta-defensins, strengthened the involvement of an immune mechanism for oncogenic transformation. Drug-gene interactions highlighted NOTCH2 as clinically relevant for which FDA-approved treatments exists. Conclusions: Top altered genes may have a role in shaping an epidermoid cyst-permissive tumor microenvironment and modulating tumor immune evasion. USP8 and NOTCH2 may be clinically relevant drivers of IECs. NOTCH2 may be an interesting candidate for drug repurposing.

The hydrophobic reagent 6-hexyl-4-(2-thiazolylazo)resorcinol (HTAR) was investigated as part of a cloud point extraction (CPE) system for the spectrophotometric determination of Zn(II). In the system, complexes with different stoichiometries, including 1:1 and 2:2 (Zn:HTAR), are formed. Their ground-state equilibrium geometries were optimized at the B3LYP/6-31G level of theory. The obtained structures were then used to calculate vertical excitation energies in order to generate theoretical UV/Vis absorption spectra. The comparison between theoretical and experimental spectra demonstrated that, under optimal conditions, a binuclear complex containing oxygen bridging atoms is the dominant species. The absorbance was found to be linearly dependent on the concentration of Zn(II) within the range of 15.7 to 209 ng mL–1 (R2 = 0.9996). The fraction extracted (%E), logarithm of the conditional extraction constant (log Kex), and molar absorption coefficient (ε) at λmax = 553 nm were calculated to be 98.3%, 15.9, and 4.47 105 L mol–1 cm–1, respectively. The method developed is characterized by simplicity, convenience, profitability, sensitivity, and ecological friendliness. It has been successfully applied to the analysis of pharmaceutical and industrial samples.

With the sharp increase in the number of vehicles, the issue of parking difficulties has emerged as an urgent challenge that many cities need to address promptly. In the task of predicting large-scale urban parking data, existing research often lacks effective deep learning models and strategies. To tackle this challenge, this paper proposes an innovative framework for predicting large-scale urban parking graphs leveraging real-time service capabilities, aimed at improving the accuracy and efficiency of parking predictions. Specifically, we introduce a graph attention mechanism that assesses the real-time service capabilities of parking lots to construct a dynamic parking graph that accurately reflects real preferences in parking behavior. To effectively handle large-scale parking data, this study combines graph coarsening techniques with temporal convolutional autoencoders to achieve unified dimension reduction of the complex urban parking graph structure and features. Subsequently, we use a spatio-temporal graph convolutional model to make predictions based on the coarsened graph, and a pre-trained autoencoder-decoder module restores the predicted results to their original data dimensions, completing the task. Our methodology has been rigorously tested on a real dataset from parking lots in Shenzhen. The experimental results indicate that compared to traditional parking prediction models, our framework achieves improvements of 46.8% and 30.5% in accuracy and efficiency, respectively. Remarkably, with the expansion of the graph’s scale, our framework’s advantages become even more apparent, showcasing its substantial potential for solving complex urban parking dilemmas in practical scenarios.

Low back pain (LBP) is a significant global health issue, contributing to considerable disability and socioeconomic burdens. The degeneration of the human intervertebral disc (IVD) is a critical factor in the pathogenesis of LBP. Recent studies have emphasized the significance of its specific set of genes and extracellular matrix (ECM) in IVD health. The Noggin gene has emerged as a critical factor due to its high expression levels in healthy nucleus pulposus cells (NPCs) observed in our previous research. In this study, we investigated the hypothesis that decreased Noggin expression in NPCs is associated with IVD degeneration, contributing to LBP development. A lentivirus-mediated RNAi was applied to knockdown Noggin expression in primary NPCs from six human donors. Cell viability analysis, XTT assay, and cell apoptosis were conducted to reveal the effect after transduction. After two weeks, colony formation unit (CFU) assay was used to examine the anchor-independent growth ability of transduced cells. Additionally, qPCR was performed to quantify cellular anabolism and catabolism makers at the transcript level. The results showed lentivirus-mediated downregulation of Noggin suppressed cell growth, cell viability, and colony formation, and induced apoptosis in human NPCs in vitro. Noteworthy, it hindered cellular anabolism and enhanced catabolism activity in human NPCs after transduction. Our findings indicated that the degeneration of human IVD is possibly related to decreased Noggin expression in NPCs. This research provides valuable insights into the role of Noggin in IVD homeostasis and its implications for LBP treatment.

In Korea, substantial disparities exist in physical environments across regions, exacerbating the polarization between metropolitan and provincial areas, and urban and rural regions. This study examines the differences in physical activity and self-rated health of local communities in relation to satisfaction with the physical environment. By identifying and addressing these underlying causes of health disparities, the study provides foundational data to inform policy efforts. The study utilized data from the 2023 Community Health Survey conducted by the Korea Disease Control and Prevention Agency. It included 231,752 individuals aged 19 and older (105,754 men and 125,998 women) selected through a two-stage probability proportional systematic sampling method. One-on-one online interviews were conducted from May 16 to July 31, 2023. Physical environment was assessed based on public satisfaction with safety, natural and living environments, public transportation, and medical services. Study variables included subjective health status and physical activity, with sub-variables for physical activity encompassing the duration of vigorous and moderate physical activity, walking, and flexibility exercises. For data analysis, MANOVA and ANOVA were conducted, with Bonferroni correction for multiple comparisons. Considerable variations in physical activity were observed based on satisfaction with the local community environment. Regarding self-rated health, significant differences were found in safety levels, living environment, and medical services. Satisfaction with the physical environment positively influenced physical activity and self-rated health. Therefore, it is imperative to reduce disparities in the physical environment between regions and enhance residents' satisfaction.

This paper designs and fabricates a small-type permanent-magnet linear motor and driver for automation applications. It covers structural design, magnetic circuit analysis, control strategies, and hardware development. Magnetic circuit analysis software JMAG is used for flux density distribution, back electromotive force(back-EMF), and electromagnetic force analysis. To address end effects and cogging force, auxiliary core structures and control strategies are proposed. The motor has a three-phase, six-coil, seven-pole configuration, achieving a peak phase voltage of 3.621 V at 1.0 m/s with a total harmonic distortion(THD) of 0.49 % and the voltages of each phase are balancing a phase voltage error of -0.002 V. The average electromagnetic force is 5.46 N/A. The driver uses a three-phase inverter with voltage space vector pulse width modulation(VSVPWM), dq-axes current closed-loop control, electromagnetic force control, and S-curve motion trajectory control. Simulations with JMAG-RT models in MATLAB/Simulink verify the control strategies. A microcontroller (TMS320F280045) serves as the control core, and a magnetic encoder with a 488 nm resolution provides position feedback. Each phase winding has an equivalent resistance of approximately 3.0 Ω and an inductance of approximately 1.98 mH. The S-curve motion trajectory control achieves a maximum position error of approximately 5.0 μm and a speed ripple of approximately 0.03 m/s at steady speed. With cogging force improvement, the maximum electromagnetic force is 4.90 N, and the q-axis current compensates around 1.05 A based on mover position. S-curve motion trajectory control results in highly stable and responsive performance. Practical tests show that the designed small-type permanent-magnet linear motor and its driver provide efficient, stable, and high-precision solutions for automation applications.

The increase in greenhouse gas emissions fosters the transport sector towards new technological perspectives on personal mobility. Addressing sustainable mobility through electric micromobility requires interdisciplinary design research methods and approaches. In the context of the LEONARDO project, funded under the Horizon 2020 framework, the paper addresses a critical literature review on the design thinking, design research models, tools, and mixed methods to be undertaken for driving product mobility innovation in a cross-disciplinary context. Following the “research through design” research strategy, the authors applied the Double-Diamond design thinking model to frame the design research process in 4 phases, aligning with 3 general objectives, 12 specific research objectives, and 24 tasks, supported by a total of 71 mixed methods and tools. As a result, the transdisciplinary process provides a co-designed energy-efficient stand-alone microvehicle and a scalable interdisciplinary design model for urban transport product innovation. In conclusion, this case study suggests the value of the Double-Diamond design thinking model as a design research instrument capable of addressing sustainable mobility and guiding interdisciplinary design research, design practice, and education in the Industrial Engineering and Design disciplinary sectors.

Cartilage microfracturation is a surgical technique specifically designed to address chondral defects, which are injuries to the cartilage that covers the ends of bones in joints. These defects can result from traumatic injuries, degenerative conditions such as osteoarthritis, or congenital abnormalities. The primary objective of microfracture surgery is to promote the regeneration of functional cartilage tissue, thereby restoring joint function, alleviating pain, and enhancing mobility. The procedure involves creating small, controlled perforations, or microfractures, in the subchondral bone plate beneath the damaged cartilage. This process, performed with precision to minimize damage to surrounding healthy tissue, penetrates the subchondral bone to reach the bone marrow, which is rich in mesenchymal stem cells (MSCs). MSCs are multipotent cells capable of differentiating into various cell types, including chondrocytes, which are essential for cartilage production. The microfractures provide an access pathway for MSCs, allowing them to migrate from the bone marrow to the defect site. Once at the site, these stem cells differentiate into chondrocytes and initiate the formation of new cartilage tissue. This newly formed tissue helps fill the defect, aiming to restore the smooth surface of the cartilage and improve the joint's structural integrity. Understanding the molecular processes involved in cartilage repair is crucial for optimizing rehabilitation strategies and improving clinical outcomes. These processes include the regulation of various signaling pathways that control stem cell migration, differentiation, and extracellular matrix (ECM) synthesis. Growth factors such as transforming growth factor-beta (TGF-β), bone morphogenetic proteins (BMPs), and fibroblast growth factors (FGFs) play pivotal roles in these pathways, guiding MSCs in their transformation into functional cartilage cells. Additionally, the role of the ECM, composed primarily of collagen type II and proteoglycans like aggrecan, is paramount in providing structural support and biochemical signals. The ECM supports the newly differentiated chondrocytes and helps maintain the cartilage's mechanical properties. Matrix metalloproteinases (MMPs) and their inhibitors, tissue inhibitors of metalloproteinases (TIMPs), are crucial in ECM remodeling, ensuring the balance between synthesis and degradation of the matrix to support tissue repair and integrity. Angiogenesis and vascularization are also critical aspects of the molecular repair process. The initial phase of cartilage repair often includes transient vascularization facilitated by vascular endothelial growth factor (VEGF), ensuring that the newly formed tissue receives adequate nutrients and oxygen. However, as the repair tissue matures, it transitions back to an avascular state characteristic of healthy articular cartilage, which is essential for maintaining its mechanical properties and longevity. Rehabilitation strategies following microfracture surgery are meticulously crafted to facilitate recovery and functional restoration. These strategies include early mobilization techniques, controlled mechanical loading, biological augmentation, nutritional support, and advanced therapeutic modalities. Early mobilization with gentle passive range-of-motion exercises promotes synovial fluid circulation within the joint, preventing adhesions and joint stiffness, and delivering essential nutrients and growth factors. Controlled mechanical loading through gradual weight-bearing activities helps condition the repaired tissue, stimulating chondrocyte proliferation and ECM production while ensuring proper integration and function. Biological augmentation, including platelet-rich plasma (PRP) and hyaluronic acid (HA), supports cartilage repair by enhancing cellular activities and ECM formation. PRP, rich in growth factors, promotes cell proliferation and matrix synthesis, while HA injections improve joint lubrication, reduce pain, and provide a scaffold for new tissue growth. Nutritional support, including amino acids, vitamins, minerals, glucosamine, chondroitin sulfate, and antioxidants, plays a vital role in collagen synthesis and overall cartilage maintenance, protecting chondrocytes from oxidative stress and modulating inflammation. Advanced therapeutic modalities such as low-intensity pulsed ultrasound (LIPUS), pulsed electromagnetic fields (PEMF), photobiomodulation therapy, and cryotherapy offer additional benefits by enhancing chondrocyte activity, reducing inflammation, and promoting ECM production. These modalities work through various molecular mechanisms, such as activating signaling pathways and modulating gene expression, to improve the quality and durability of the repaired cartilage. By comprehensively understanding the molecular and practical aspects of cartilage repair, clinicians can develop more effective treatment plans that optimize cartilage healing and improve patient outcomes. The integration of molecular insights with advanced rehabilitation techniques holds the promise of revolutionizing cartilage repair, offering hope for those suffering from debilitating joint conditions. This review aims to bridge the gap between molecular biology and clinical practice, providing a roadmap for optimizing cartilage repair strategies and ensuring long-term success for patients.

Circadian rhythm (CR) influences various physiological functions, including physical and cognitive performance, which fluctuate throughout the day. The present study aimed to investigate the combined and separate effects of CR and physical fatigue on cognitive and physical performance. A sample of 18 amateur athletes was subjected to a series of tests at three different times of the day: morning, afternoon, and evening. Fatigue was induced following isokinetic concentric exercise combined with a 20-minute treadmill run, followed by assessments of selected physical and cognitive tasks. Repeated measures ANOVA did not reveal an interaction between CR and fatigue in cognitive performance (p &gt; 0.05). However, a significant main effect of fatigue was observed in visual reaction time (VisRT) across all three time points. Moreover, peak torque (PT) and peak torque fatigue index (PTFI) showed significant differences between the three times of the day, peaking in the evening. Although we found no interaction between CR and physical fatigue state on selected cognitive parameters at the three times of day, a separate effect of fatigue on cognitive performance was identified. Additionally, physical parameters exhibited peak values occurring in the evening hours. Future research should further explore underlying mechanisms that potentially influence cognitive performance at different times of the day.

HIV-associated cardiovascular diseases remain a leading cause of death in people living with HIV/AIDS (PLWHA). Although antiretroviral drugs suppress the viral load, they fail to remove the virus entirely. HIV-1 Nef protein is known to play a role in viral virulence and HIV latency. Expression of Nef protein can be detected in different organs, including cardiac tissue. Despite the established role of Nef protein in HIV-1 replication, its impact on organ function inside the human body is not clear. To understand the effect of Nef at the organ level, we created a new Nef transgenic (Nef-TG) mouse that expresses Nef protein in the heart. Our study found that Nef expression caused inhibition of cardiac function and pathological changes in the heart with increased fibrosis, leading to heart failure and early mortality. Further, we found that cellular autophagy is significantly inhibited in cardiac tissue of Nef-TG mice. Mechanistically, we found that Nef protein causes the accumulation of Bcl2 and Beclin-1 protein in the tissue, which may affect the cellular autophagy system. Additionally, we found Nef expression causes upregulation of cellular senescence marker p21, and senescence-associated -galactosidase expression. Our findings suggested that Nef-mediated inhibition of autophagy and induction of senescence markers may promote aging in the PLWHA. Our mouse model could help us to understand the effect of Nef protein on organ function during latent HIV infection.

Breast cancer remains a global health problem requiring effective diagnostic methods for early detection in order to achieve WHO’s ultimate goal of breast self-examination (BSE). A literature review indicates the urgency of improving diagnostic methods and identifies thermography as a promising, cost-effective, and non-invasive and adjunctive and complementary detection method. This research explores the potential of using machine learning (ML) techniques, specifically Bayesian Networks (BN) combined with Convolutional Neural Networks (CNN) to improve breast cancer diagnosis at early stages. Explainable artificial intelligence (XAI) aims to clarify the reasoning behind any output of artificial neural networks based models. The proposed integration adds interpretability of the diagnosis, which is particularly significant for a medical diagnosis. We have constructed two diagnostic expert models. In research model A, combining thermal images after XAI process together with medical records, an accuracy of 84.07% has been achieved, while model B, that includes also CNN prediction, achieved an accuracy of 90.93%. These results demonstrate the potential of XAI to transform breast cancer diagnosis, increasing accuracy and reducing the risk of misdiagnosis.

Within the research of pressure pipeline inspection methods there is an industry that is constantly growing and becoming more efficient. The purpose of inspection methods on pressure pipelines comes back to the issue of resolving leaks, corrosion, and inability of a pipeline to function (eventually). Evidently, these issues can hurt the environment, the public using the material being carried out by these pressure pipelines such as natural gas or petroleum and can hurt the pockets of pipeline companies that did not react quick enough to their pipeline solely for the reason that they may have not noticed there was an issue with their pressure pipeline. In-line inspection methods are continuously growing and have already made an impact to prevent the catastrophes that occur when a pipeline is not maintained. Technology such as smart pigs, MFLs, and ultrasounds are the future of pressure pipeline inspection. This research paper explains their pros and cons and how they’re overall more efficient and preferred as the primary inspection method for a pipeline. Older, more traditional methods such as aerial inspection, by-foot inspection, hydrostatic pressure testing, and Direct Assessment does not cover every base of inspection like in-line inspection technology does and this research explains why that is and the areas of where in-line inspection technology can be improved and deemed not as useful. This is where trenchless renewal and replacement methods come into play with inspection methods and determining the characteristics of a pipeline that matches with which inspection method and which trenchless method to use. The future of inspection methods for pressure pipelines is attainable and is somewhat already here, this research discusses the new emerging technologies and how designing in-line inspection technology to be cheaper and more versatile with pipeline characteristics can make everyone a winner in the future. A winner as far the environment being safe, neighborhoods near pressure pipelines being safe, companies being able to install in-line inspection technology at a fraction of the cost of today and the efficiency of pipelines by monitoring the characteristics with this new technology.

The study of the morphology, thermal, and chemical stability of the fluorinated compounds was performed using Becke’s three-parameter hybrid functional approach with the correlation provided by Lee, Yang, and Parr and the cc-pVTZ basis set aiming to design low sensitivity, toxicity, instability, and proneness to decomposition or degradation over a short time high-energy materials. The most stable conformers of the compounds under study were selected based on their total energy. Their thermal and chemical stability was evaluated based on the binding energy per atom, chemical hardness, and softness. The oxygen-fluorine balance is assessed to evaluate the sensitivity of these new materials. The density, detonation pressure, and velocity of the selected conformers were theoretically obtained to reveal the influence of -CF3, -OCF3, and cyclic -O(CF2)nO- fragments on the energetic properties of nitroaromatics as well as their stability and resistance to shock stimuli. The results allow us to predict new multipurpose energetic materials with a good balance between power and stability. Referring to the results obtained, we recommend CF3N2, OCF3N2, C2F6N2, 1CF2N2/O2CF2N2, and 2CF4N2/O2C2F4N2 for practical usage because these compounds possess greater stability compared to tetryl and better explosive properties than TNT.

Laser range-gated underwater imaging technology, by removing most of the back scattering noise, can effectively increase the image contrast and extend the detection range. The optical signal captured by a range-gated imaging system primarily comprises reflected light from the object and backscattered light from the surrounding water. Consequently, surfaces with low reflectivity or highly turbid water environments substantially constrain the applicability of the range-gated imaging system. To enhance the detection capability of underwater laser range-gated imaging, this paper proposes the incorporation of underwater polarized light imaging technology as an enhancement method. Based on polarization differences, backscattered light and reflected light from an object can be distinguished. Experimental results indicate that, compared to images obtained using a conventional range-gated laser imaging system, those captured with a polarization-enhanced system exhibit an increase of up to 47%. The proposed approach, which integrates polarization imaging with range-gated laser imaging, has the potential to broaden the applicability of underwater laser imaging scenarios.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of Wnt-X (X, a particular gene/protein) at 2nd order level after drug administration. These rank- ings reveal which Wnt-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and deter- mine the mechanism of functioning between the Wnt and X. In this research work, we cover combinations of Wnt with Achaete-scute complex homolog 2 (ASCL2), ATP- binding cassette (ABC) domain transporters, Interleukin (IL), ubiquitin conjugating enzyme E2 (UBE2) family, exosome (EXOSC), caspase (CASP), TP53 and B-cell lymphoma (BCL) family.

This paper explores the ever-changing topic of cybersecurity and provides a summary of the most recent developments. In a time of unparalleled digital revolution and changing threat environments, being aware of new trends is essential to managing risks and protecting important assets. To highlight significant advancements in fields like artificial intelligence and machine learning for threat detection, the proliferation of IoT devices and related security challenges, the emergence of quantum-resistant cryptography, the development of zero trust architectures, and the growing significance of cybersecurity awareness and education, the report synthesizes insights from industry experts, academic research, and real-world case studies. The goal of the report's analysis of these patterns is to provide stakeholders with useful information so they may modify their cybersecurity plans and strengthen defenses against new threats. Cybersecurity plays a significant role in the IT industry.

In mammals, fatty acid transport protein 1 (FATP1) is involved in cellular long-chain fatty acid (LCFA) uptake and activation, especially in processes of transportation, oxidation and triglyceride synthesis. However, the role of Fatp1 in invertebrates, especially decapod crustaceans, is still poorly understood. In this study, the cDNA of a fatp1 gene of a decapod crustacean, mud crab Scylla paramamosain, was cloned and functionally characterized. The fatp1 gene encoded a polypeptide of 643 amino acids that possessed all the typical features of the Fatp family, and shared high homology with the other Fatp orthologs of crustaceans. The relative expression levels of fatp1 were higher in metabolically active tissues such as hepatopancreas, stomach and gill than the other parts of crabs. Knockdown of the fatp1 gene in vivo reduced triglyceride and total lipid levels in the hepatopancreas, and increased the tissue expression levels of genes related to fatty acid transportation, allocation and hydrolysis including long-chain acyl-CoA synthetase 3/4 (acsl3/4) and carnitine palmitoyl transferase 1 (cpt1), and decreased expression levels of genes related to fatty acid synthesis such as acetyl-CoA carboxylase (acc) and fatty acid synthase (fas). Increased dietary n-3 LC-PUFA levels resulted in the up-regulation of fatp1 gene expression in the hepatopancreas, and the increase in LC-PUFA content, especially EPA and DHA, in both polar and nonpolar lipids in hepatopancreas and muscles of crabs. The data collectively suggested that the fatp1 gene, identified in S. paramamosain, might play important roles in regulating fatty acid absorption, utilization and allocation in crustaceans.

In this paper, a structural property of prime numbers will be introduced and analyzed. Our analysis is based on the application of the sequence of even integers 2,10,16,22,26,28… which is defined as follows: F(0) = 2, for i > 0, F(i) = F(i − 1)+ l, where l is the smallest even integer that is valid for: F(i − 1)+ 2k − 1 ∈ P, for k = 1, 2, . . . l/2-1, F(i − 1)+ 2k − 1 ∉ P, for k = 2l and P is the set of prime numbers. The sequence F answers the question, which are the natural numbers that cannot be expressed as a sum of two primes. We prove that numbers expressed as j = F(i)+ 1 cannot be written as the sum of two primes, for i = 1, 2, ··· .

Background and Objectives. The purpose of this study is to report on the results of treatment of distal and stem forms of hypospadias in boys with newly developed methods of surgical treatment compared with traditional methods of urethroplasty. Materials and Methods. A prospective non-randomized comparative study was conducted in January for the period 2014 to December 2023. All included patients were diagnosed with distal and stem hypospadias. 136 patients who met the inclusion and exclusion criteria were divided into 2 groups. Group I included 50 patients (study subgroup AI and comparison subgroup AI patients with distal hypospadias). Group II included 86 patients (study subgroup VI and comparison subgroup III patients with the stem form of hypospadias). Outcomes were assessed by the nature of postoperative wound healing, the presence of complications: neomeatus dystopia, formation of a cutaneous urethral fistula.Results. The average age of patients for the A(I) subgroup was 48 months (95%CI:45.29 – 60.64), CO=19.0. The average age of patients for subgroup A (II) was 51 months (95%CI:46.08-65.00), CO=22.4. The average age of patients for group B (I) was 56 months (95% CI:54.22 – 87.11), CO=46.3. The average age of patients for B (II) The period of the group was 60 months (95%CI:54.10 – 71.44), CO=31.4.The outcome of recovery was registered in 92.9% (13) of patients, repeated surgery in 7.1% (1) of patients. Neomeatus dystopia in group A (I) was not detected in any of the 100% (26) operated patients. Neomeatus dystopia in group A (II) was observed in 16.7% (4) of patients, at the same time in 83.3% (20) of patients. Neomeatus dystopia in subgroup B (I) was not observed in anyone–100% (33). Neomeatus dystopia in group B (II) was observed in 15.1% (8) of patients, was not observed in 84.9% (45). The differences are statistically significant (p=0.016, p=0.019). In group I, 80% (40) patients recovered, and 20% (10) patients underwent repeated surgery. In subgroup A (I), postoperative fistula was observed in 16.7% (1) cases, whereas in subgroup A(II), in patients after Snodgrass and MAGPI surgery, fistula developed in 20.8% (5) patients, neomeatus dystopia in 16.6% (4) cases (p=0.065; p=0.030;).According to the outcome in group II, 61.6% (53) of patients recovered, 38.4% (33) of patients underwent repeated surgery. Of these, in subgroup B (I), postoperative fistula was observed in 12.1% (4) cases, whereas in subgroup B (II), in patients after Snodgraft and Bracka surgery, fistula developed in 35.8% (19) patients, neomeatus dystopia in 15.1% (8) cases (p=0.016; p=0.019;). Wound healing in group (II): primary–82.1% (23), secondary–17.9% (5). Wound healing in group B (II): primary–70.1% (54), secondary–29.9% (23). Despite the fact that the duration of surgery is statistically significantly lower in the comparison group p<0.001, the frequency of primary wound healing is statistically significantly higher in the study group p<0.001. Conclusions. We came to the conclusion that after the use of traditional methods of urethroplasty, there is still a high percentage of postoperative complications that require the development of new methods of surgical treatment of hypospadias in boys.

The Internet of Things (IoT) is a disruptive technology innovation that has the potential to link billions of gadgets and alter multiple industries. The Internet of Things (IoT) represents a significant advancement in the networking of devices and systems. However, because IoT devices add new attack avenues and vulnerabilities to networks, their rapid proliferation has also caused serious cyber security concerns. The present study investigates the influence of cyber security on the progress of the Internet of Things (IoT), emphasizing the obstacles, prospects, and future paths towards safeguarding the IoT network. This study attempts to shed light on the crucial role that cyber security will play in determining the direction of the Internet of Things through an examination of recent trends, case studies, and research findings.

As urbanization continues to increase, key conservation areas like World Heritage sites are being altered. This transformation is a cause for concern as it threatens the Outstanding Universal Value of these sites. To address this issue, a research question was formulated: How urban is World Heritage? The research focused on identifying and monitoring urbanization in heritage core zones listed on the World Heritage by tracking changes in built-up areas from 1985 to 2015. The findings revealed that urban developments, including housing, transport infrastructure construction, visitor accommodation facilities, and land conversions, have significantly impacted heritage properties. More than a third (37\%, 426) of the cultural heritage properties reported urban development, with the highest occurrences in Europe, North America, Asia, and the Pacific. Furthermore, these regions also displayed the highest rate of change per year between 1985 and 2015. This indicates the urgent need to identify and monitor the urbanization of World Heritage to preserve their OUV amidst increasing urban development.

Helicobacter pylori infection constitutes a silent pandemic of global concern. In the last decades, the alarming increase of multidrug resistance evolved by this pathogen has led to a marked drop in the eradication rates of traditional therapies worldwide, encouraging scientific community to discover and develop new antimicrobial strategies. In the present work, we identified a battery of novel drug-like HsrA inhibitors with MIC values ranging from 0.031 to 4 mg/L against antibiotic-resistant strains of H. pylori. Most of these novel antimicrobials exhibited minor effects against both Gram-negative and Gram-positive species of human microbiota. The most potent anti-H. pylori candidate demonstrated a high therapeutic index, additive effect in combination with metronidazole and clarithromycin, as well as a strong antimicrobial action against Campylobacter jejuni, another clinically relevant pathogen of phylum Campylobacterota. Transcriptomic analysis suggested that in vivo inhibition of HsrA triggered lethal global disturbances in H. pylori physiology, including the arrest of protein biosynthesis, malfunction of respiratory chain, detriment in ATP generation, and oxidative stress. The novel drug-like HsrA inhibitors described here constitute valuable candidates to a new family of narrow-spectrum antibiotics that allow overcoming the current resistome, protecting from dysbiosis, and increasing therapeutic options for novel personalized treatments against H. pylori.

The amount of solar energy produced is directly proportional to the surface area of solar panels exposed to sun rays. To increase the productivity of solar panels, a number of techniques are proposed by curving the surface or protruding the panel surface to maximise the surface area, e.g. Protruding panel surfaces with shapes such as pyramidal, cone, spherical cap, sinusoidal wavy curve, etc. These techniques are analysed to determine whether the panel surface area increases. The geometrical design analysis showed that, depending on the chosen surface curving method, the panel can achieve a remarkable increase in surface area.The system advisor model was used to assess a sample of solar cells with standard measurements of 6 by 6 inches that were organised in a zigzag pattern with different angles of inclination (10°, 15°, 20°, 25°, 30°, and 35°). Overall, the maximum power pump and annual AC energy performance increase by 1.4%, 4.2%, 6.8%, 10.8%, 14.9%, and 21.6%, respectively, over a one-year period of productivity. However, by applying the concept of solar panel efficiency degradation due to tilting solar panels at an angle by Mamun et al. (2022), for each 5° increase in tilt angle beyond the ideal 10°, the efficiency decreases by approximately 0.76%. In conclusion, the overall efficiency is greater than that of standard flat solar panels, provided the tilt angle is within a reasonable range.

The demand for information technology expertise has grown rapidly in the last few decades, signaling a firm’s commitment to integrating IT into its core business strategy. Understanding the conditions under which firms appoint a chief information officer (CIO) can provide valuable insights into the evolving role of IT in corporate governance. This study addresses a crucial gap in the literature by exploring the determinants of a firm's decision to hire a CIO at the top management level. The study identifies several factors that influence a firm’s decision to appoint a CIO, including the firm's size, its level of innovation, and its prior performance. The study examines these assertions by comparing the characteristics of firms that appoint a CIO at the top management level with those of similar firms in their industries that do not have a CIO position prior to the appointment. A logistic regression model that considers CIO firms and their matched firms indicates that firms are more likely to hire a new CIO after they experience loss, have larger capital expenditures and higher market value. Our study provides empirical evidence on why certain firms prioritize IT leadership at the executive level.

New chemicals including nanomaterials are being created and spread at high speed. In the E.U., REACH compliance imposes to manufacturers some tests but they are limited. Often, they cannot detect anything if the cells or organisms are able to overcome the toxic effect at the macroscopic level, preventing the suspicion of a potential long-term effect. Concerning bacteria that are essential for ecosystems and human health (microbiota), they are often considered as a whole in a consortium and only global parameters are measured. To overcome these limitations, we performed a study on the essential soil bacterium Bacillus subtilis. Two types of biofilm that are closer to its natural growth mode (particularly around plant roots) than the usual liquid type culture have been assayed: pellicle biofilm and swarming. We then used the power of shotgun proteomics to analyze in details these two somehow similar modes of cultures. Several physiological and metabolic patterns including proteins implicated in sporulation have shown significant differences. The effect of two types of nanoparticles (silicon and cerium oxide nanoparticles) has then been studied in the same way. This allowed us to investigate in details the response of the bacteria and focus on the differences observed depending on the biofilm type. This clearly highlighted the importance of testing several growth conditions and pinpointed the power of proteomics.

Background/Objectives: Multiple sclerosis (MS) is a chronic neurological disorder characterized by various clinical presentations and manifestations that include biopsychosocial impediments. This study has three interrelated goals relevant to biopsychosocial functioning. The first is to compare cognitive, neuropsychological, and affective functioning and quality of life between patients with MS (PwMS) and a control group. The second is to examine whether cognitive and neuropsychological performance is affected by gender, and the third is to examine whether variation in QoL hinges on cognitive neuropsychological performance. Methods: This multicenter study was carried out among clinically stable PwMS under follow-up in two tertiary care units in urban Oman. Healthy controls, matched for age and sex, were also recruited as a comparison group. Data were collected using cognitive batteries sensitive to current reasoning ability and conventional neuropsychological batteries designed to measure verbal learning, visual-spatial ability, and processing speed. The affective range (anxiety and depressive symptoms) and quality of life (QoL) were also assessed. Results: The PwMS group scored lower on current reasoning ability, verbal learning, visual-spatial ability, and processing speed compared with the control group. The incidence of anxiety was higher in the PwMS group but there was no statistically significant difference in depressive symptoms. No significant differences were found in cognitive variables between the two sexes, except in visual-spatial ability, where women outperformed men. PwMS with low QoL scored lower on the attention and concentration indices than those with adequate QoL. According to QoL, no significant differences were observed in reasoning, verbal learning, and visual-spatial ability. Conclusions: The present sentinel study suggests that the Omani cohort with MS tends to have lower indices of current reasoning ability, visual and spatial memory and cognitive speed compared to control subjects. Gender differences are minimal, except for visual-spatial abilities where women outperform men. Quality of life significantly affects cognitive functioning. In general, the biopsychosocial impediment appears to be significant, indicating the need for comprehensive evaluation and care in the management of MS.

Gastrointestinal parasites (GIP) are diverse parasitic organisms known to have coevolved with bacterial populations inside the gut of mammalian hosts. These helminths produce small molecules, peptides and proteins to evade or fight their hosts' immune systems. Small molecules include polar compounds, amino acids, lipids and carbohydrates. Metabolomics is an emerging field of research recently applied to study helminth infections and host-parasite interactions. This review comprehensively discusses metabolomics studies of small molecules of GIP in animals, providing insights into the available tools and techniques, databases, and analytical software. Most metabolomics investigations employed LC-MS, MS or MS/MS, NMR, or a combination thereof. Recent advancements in metabolomics platforms, including artificial intelligence (AI)-assisted software tools and databases, have propelled helminth metabolomics forward, offering new avenues to explore host-parasite interactions, immunomodulation, and the intricacies of parasitism. As our understanding of helminth metabolomics continues to expand, it promises to unveil novel perspectives and enrich the knowledge of these complex host-parasite relationships.

This paper proposes Pierre Bourdieu's sociological concepts of social field, capital, and habitus to analyze water management in Colombia. By mapping the social dynamics of water management, this study examines the interactions and power relationships among agents, including government agencies, private companies, academic institutions, non-profits, and local communities. The analysis reveals how various forms of capital, such as economic, cultural, social, and symbolic, influence water management practices, policies, and the distribution of power. Integrating agent-based modeling with hydrological simulations provides a more nuanced understanding of how social dynamics influence water management. This interdisciplinary approach helps develop more adaptive and equitable strategies by capturing the complex interactions between human behavior and environmental factors. The research demonstrates that agent-based modeling, informed by Bourdieu's sociological framework, offers valuable insights for policy development in regions facing socio-economic and environmental challenges. The study highlights the need to localize the analysis of the social field to capture regional customs and specific social dynamics. This localized approach ensures that water management strategies are more relevant, context-sensitive, and sustainable. This paper advocates for the wider adoption of agent-based modeling in water management, proposing a methodology that combines the engineering principles of practical problem-solving and adaptive design with an understanding of the social complexities in water management.

Where mining activities cause disturbance in catchments, streams are often impacted by heavy loads of fine eroded material. Since geomorphological processes are very slow, it is expected that during rehabilitation typically hundreds of years are required for a mine landform to return back to stability. A sensitive approach to analyzing post-mining landform stability in tropical regions is to assess the quantity of fine suspended sediments (FSS = silt + clay (0.45 µm < diameter < 63 µm)) leaving the catchment where the mine resides and entering the receiving streams in response to storm events. Continuous stream discharge and FSS quantities upstream and downstream of the catchment where the mine resides were modeled using HEC-HMS (Hydrologic Engineering Centre-Hydrologic Modeling System). Once calibrated, the model was run for a thousand years to predict continuous stream discharge and FSS quantities for various predicted rainfall scenarios. Short-term erosion and deposition across the mine catchment were also evaluated using a cali-brated landform evolution model, CAESAR-Lisflood. This paper reviews watershed soil erosion measurements and modeling research. It compares the previous research to approach above that assesses mine landform erosion and stability in terms of fine-suspended sediments. This approach can be used to determine mine landform erosion dynamics, predict achieving stability equilib-rium, and as a post-mining rehabilitation assessment tool.

Background: G6PD is a rate-limiting enzyme of the pentose phosphate pathway. The loss of G6PD activity in red blood cells exposes to the risk of acute haemolytic anaemia under oxidative stress, induced by infections, some medications, or fava beans. More than 200 single missense mutations are known in the G6PD gene. This study reports a 41-year-old woman coming from Basilicata region (Italy) that carried out the G6PD status in our hospital for prenatal investiga-tions. Methods: DNA was extracted from peripheral blood sample and genotyped for common-est G6PD pathogenic variants (PVs). Positive results obtained by Restriction Fragment Length Polymorphism (RFLP), as per practice in our laboratory, are then re-confirmed in Sanger se-quencing. Results: RFLP analysis highlighted a variant compatible with G6PD Cassano variant. Confirmatory testing by Sanger unexpectedly identified a novel variant: c.1357G>A, p.(Val453Met) (NM_001360016.2). In silico models predicted a deleterious effect of this variant at the protein level. The novel G6PD variant was named "G6PD Potenza", on the basis of the pa-tient's regional origin. Conclusion: This case describes a novel G6PD variant. It also highlights how the Sanger sequencing technique still represents an indispensable confirmatory standard method for variants that could be miss-interpreted by using of “first-level” approached, as the RFLP. We stress that the evaluation of clinical manifestations in G6PD deficient patients is of primary importance for the classification of each new G6PD mutation, in agreement with the new WHO guidelines.

The agricultural industry prioritizes crop protection, particularly against threats like pink bollworm infestations in cotton crops. This paper presents a comprehensive solution using the Robot Operating System (ROS) to deploy an autonomous robot for detecting pink bollworm infestations and tracking red illnesses. Drones collect multispectral photos of cotton fields, aiding path planning via Normalized Difference Vegetation Index (NDVI) computations. A bespoke dataset of RGB and multispectral images trained a YOLOv8 model for object detection, achieving a mean Average Precision (mAP) of 67.1%, Precision of 67.9%, and Recall of 61.8%. The robot was tested in two separate cotton fields, with rows around 20 meters long and the camera at 60 cm height for optimal real-time detection. The findings, compared to hand counts and confirmed against NDVI estimates, showed an average accuracy of 76.04% for B.T. Cotton and 67.54% for Organic Cotton fields. This study demonstrates the effectiveness of the proposed system in accurately detecting and managing crop threats. Combining ROS, drones, NDVI calculations, and a robust detection model, it provides a scalable approach for autonomous crop monitoring and protection, with the potential for broader agricultural adoption.

Duchenne Muscular Dystrophy (DMD) is a pediatric disorder characterized by progressive muscle degeneration and premature death, with no cure at the moment. The currently most promising therapeutic avenue is based on gene replacement mediated by adeno-associated viruses (AAVs) using a shortened, but still functional, version of Dystrophin, known as microdystrophin (µDys), to fit AAV capacity. The limited improvements observed in clinical trials suggest a sub-optimal performance of µDys in the human context, that could be due to the lack of key domains in the protein. Therefore, expressing larger dystrophin proteins may be necessary for a more complete correction of the disease phenotype. In this study, we developed three novel Midi-Dystrophin constructs using a dual-AAV approach, leveraging split intein-based protein trans-splicing. The midi-dystrophins include additional domains compared to µDys, such as the central cytoskeleton-binding domain, nNOS and Par1b interacting domains, and a complete C-terminal region. Given the limited capacity of each AAV vector, we strategically partially reduced hinge regions while ensuring that the structural stability of the protein remains intact. We predicted the interactions between the two halves of the split Midi-Dys proteins thanks to the deep-learning algorithm AphaFold3. We observed strong associations between the N- and C-termini in Midi-Dys 1 and 2, while a weaker interaction in Midi-Dys 3 was revealed. Our subsequent experiments confirmed the efficient protein trans-splicing both in vitro and in vivo in DBA2/mdx mice of the Midi-Dys 1 and 2 and not in Midi-Dy3 as expected from the structural prediction. Additionally, we demonstrated that Midi-Dys 1 and 2 exhibit significant therapeutic efficacy in DBA2/mdx mice, highlighting their potential as therapeutic agents for DMD. Overall, these findings highlight the potential of deep learning-based structural modeling for the generation of intein-based dystrophin versions and pose the basis for further investigation of these new midi-dystrophins versions for clinical studies.

The search for more sustainable and efficient propulsion & power systems has become a focal point in aerospace engineering, driven by the necessity to reduce carbon emissions and mitigate environmental impact. In response, this paper explores the optimized fabrication of an Adams pulse motor integrated with an Arduino microcontroller, aiming to enhance performance while addressing the specific demands of aerospace applications. The Adams pulse motor, renowned for its simplicity and efficiency, serves as the basis of this study. Operating on the principles of electromagnetism, the motor comprises a stator and rotor configuration. When given electricity in pulses, the magnetic fields generated by stators interact with the rotor, inducing rotational motion. This simplicity makes the Adams pulse motor an attractive candidate for aerospace propulsion systems, where reliability and fuel efficiency are very important. Integration with an Arduino microcontroller introduces sophisticated control capabilities, enabling real-time adjustment of motor parameters such as speed, torque, and direction. Furthermore, the Arduino facilitates sensor feedback integration, allowing for autonomous operation and fault detection. This level of control not only enhances performance but also contributes to overall system reliability. One of the key highlights of this study is the application of the optimized Adams pulse motor integrated with an Arduino microcontroller in the aerospace industry. Aircraft propulsion systems are undergoing a shift towards more sustainable alternatives. The integration of the Adams pulse motor with Arduino aligns with this motive, offering a greener and more efficient propulsion/ power solution for aerospace applications. In conclusion, the optimized fabrication of Adams pulse motors integrated with Arduino microcontrollers holds tremendous promise for the aerospace industry. As the demand for sustainable propulsion solutions continues to grow, this integrated system presents a compelling option for future aircraft propulsion systems.

Objectives: Stress urinary incontinence (SUI) negatively affects the quality of life and sexual function in women. This study aimed to evaluate the efficacy of radiofrequency (RF) therapy in reducing SUI symptoms and its impact on sexual function. Methods: Thirty-four women with SUI were enrolled and underwent a single RF treatment session using the Viveve® System (Viveve Medical Inc., USA) with parameters of 90 J/cm² and 220 pulses per hour. Assessments at baseline and 6 months post-treatment included perineal ultrasound and personal interviews to evaluate lower urinary tract symptoms and sexual function. Urodynamic studies, voiding diaries, and questionnaires such as the Female Sexual Function Index (FSFI), Overactive Bladder Symp-tom Score (OABSS), Urogenital Distress Inventory-6 (UDI-6), Incontinence Impact Question-naire-7 (IIQ-7), and International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF) measured outcomes. Results: RF therapy significantly improved sexual function, with higher FSFI scores in all domains except pain at 6 months. SUI symptoms were significantly re-duced, as indicated by improved scores on OABSS, UDI-6, IIQ-7, and ICIQ-SF, alongside better voiding diary results. Anatomical changes included reduced bladder neck mobility, decreased vaginal width, and a reduced rotation angle of the proximal urethra. Conclusions: RF therapy is effective and safe for treating mild to moderate SUI and enhances sexual function, potentially due to changes in vaginal topography. These results suggest RF therapy as a viable non-surgical op-tion for managing SUI and improving sexual health.

Automation in agriculture is transforming the management of field tasks. This article focuses on identifying the needs and preliminary requirements for designing an autonomous robot for fruit harvesting. These robotic systems integrate advanced machine vision technologies and neural networks to accurately detect ripe fruits and obstacles, thereby optimizing harvesting efficiency. Additionally, the use of proximity and touch sensors is emphasized, ensuring careful and safe handling of fruits to prevent damage during harvesting. The technical specifications of these robots, including traction, speed, and their ability to operate on slopes, are evaluated to ensure adaptability to different types of crops and ground conditions. Perception systems, utilizing advanced cameras and sensors, provide essential data for crop inspection and route planning. Through a thorough analysis of operational demands, environmental conditions, and technical specifications, this article aims to establish a solid foundation for guiding the development and construction of an efficient and viable harvesting robot. The prospects and potential of fruit-harvesting robots to transform agriculture are highlighted, underscoring the importance of ongoing research and development to overcome current challenges and enhance the feasibility and effectiveness of these robotic systems.

The identification of spatiotemporal heterogeneity, its key determinants, and the interaction effects between the driving factors of urban resilience (UR) within and between subregions is fundamental for understanding its underlying mechanisms. A resilience evaluation model was applied to analyze the temporal and spatial differences in UR in Hunan Province, China. The standard deviation ellipse was used to explore the spatial and dynamic evolution and direction of UR. A hot spot analysis identified clusters of cold and hot spots. The contribution of spatiotemporal differences in UR within and between subregions was assessed using the Theil index. A geodetector analysis determined the factors influencing UR and their interactions. There was an increasing trend in UR from 0.2692 in 2014 to 0.3422 in 2022. The number of cities with high resilience gradually increased from 2014 to 2022, while there was a decreasing gradient in UR from northeast to southwest across the province. High-resilience cities had positive spillover effects on the surrounding area. Hot spots were predominantly located in the northeast, while cold spots were concentrated in the southwest. The barycenter of UR shifted from northeast to southwest by 2018, before moving southeast by 2022. The Theil index values declined over time both within and between subregions. Per capita GDP, average wages of on-the-job employees, per capita social consumption, and doctor density enhanced UR. Two-factor interactions had a greater influence on the spatiotemporal heterogeneity of UR than single factors. Two-factor and nonlinear enhancements were identified as the primary mode of influence.

Urban logistics is a critical component of smart city initiatives, aiming to enhance the efficiency and sustainability of goods delivery in densely populated areas. This paper explores the integration of artificial intelligence (AI), autonomous vehicles, and Internet of Things (IoT) technologies to optimize urban logistics. By leveraging real-time data from IoT-enabled infrastructure and AI-driven analytics, we propose a framework for dynamic route planning, traffic signal control, and predictive demand management for delivery services.

Background: Cancer-Related Fatigue (CRF) is a prevalent and debilitating symptom in breast cancer patients, significantly impacting their quality of life and potentially affecting postoperative outcomes. Despite advances in breast cancer treatment, the long-term implications of CRF on prognosis remain unclear. Methods: This retrospective cohort study included 310 postoperative breast cancer patients from two large comprehensive hospitals. We systematically collected demographic, medical history, lifestyle, psychological status, and CRF data. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors. Random forest survival analysis, propensity score matching, Kaplan-Meier survival analysis, and restricted cubic spline analysis were utilized to comprehensively evaluate the relationship between CRF and postoperative prognosis. Results: Higher CRF scores were associated with worse postoperative outcomes (HR = 1.07, 95% CI: 1.03-1.10, P < 0.001). Other independent prognostic factors included higher HADS scores (HR = 1.09, 95% CI: 1.01-1.17, P = 0.032), not undergoing breast-conserving surgery (HR = 0.43, 95% CI: 0.20-0.89, P = 0.024), and not receiving endocrine therapy (HR = 7.13, 95% CI: 2.64-19.22, P < 0.001). Random forest survival analysis highlighted CRF as the most critical variable impacting prognosis. Propensity score matching effectively balanced baseline characteristics, and Kaplan-Meier survival curves demonstrated significantly lower survival rates in high-CRF patients both before and after matching (P < 0.05). Restricted cubic spline analysis revealed a nonlinear relationship between CRF scores and disease progression risk, with significant risk increases beyond specific thresholds. Conclusions: CRF is a crucial predictor of disease progression in postoperative breast cancer patients. Higher CRF and HADS scores, non-breast-conserving surgery, and lack of endocrine therapy are associated with poorer prognosis. These findings underscore the importance of comprehensive postoperative care, including effective management of fatigue and psychological health, to improve patient outcomes. Future research should focus on prospective studies and targeted interventions for CRF to validate these findings and enhance clinical practices.

Automatic and blind parameter estimation based on the inherent features of wireless signals is a major research area due to the fact that these techniques lead to the simplification of receivers, especially in terms of coarse synchronization, more importantly reduce down the signaling load at the control channels. Thus, in the literature many techniques are proposed to estimate a vast set of parameters including modulation types and orders, data and chip rates, phase and frequency offsets and so on. In this paper, a cyclostationary feature detection based method is proposed to estimate the carrier numbers and carrier spacing of carrier-aggregated direct sequence spread spectrum cellular signals blindly. The particular chip rate of the signal is also estimated through the process jointly. In the paper, after the proposed method is formulated, the measurement setup which is developed to collect the data for the validation of the proposed method is introduced. The measurement results are post-processed for performance analysis purposes. To that end, the method is investigated in terms of SNR values, different channel conditions, and measurement durations. Furthermore, the performance of the proposed method is compared with that of energy detection. Measurement results indicate superior performance of the proposed method under significant wireless channel impairments and in low-SNR regions.

Energy generation now heavily relies on the field of photocatalysis. Many conventional research topics have been superseded by the conversion of solar energy into chemical or thermal energy for a variety of energy-related applications. The urgent necessity of moving away from non-renewable energy sources is a result of the problem of global warming. In an effort to create the best photocatalysts for effective solar conversion, scientists are investigating a variety of material systems and material combinations. By examining the influence of morphology changes on the photoelectrochemical activity of zinc oxide nanostructures, here electrodeposition was used to create the nanostructures. By using the proper capping agents, it is possible to control the growth rates of different ZnO facets and get well-defined nanostructures and orientation. In order to generate distinct ZnO morphologies, we report here the electrodeposition of ZnO nanostructured thin films in the presence of different capping agents. A zinc nitrate (Zn (NO₃)₂) bath was used to electrodeposit ZnO nanostructures on indium tin oxide glass (ITO) substrate at 70 °C with an applied potential of -1.0 V. Capping agents such as ethylenediamine (EDA) or ammonium fluoride (NH₄F) were added to this zinc nitrate bath. The photoelectrochemical capabilities of the morphology-controlled zinc oxide nanostructures were extensively evaluated and characterised. Thus various ZnO morphologies had an impact on the PEC properties.

In a V2V communication environment, the control of electric vehicle platoons faces issues such as random communication delays, packet loss, and external disturbances. To address these issues, a robust UKF-MPC-based longitudinal control algorithm for the platoon is designed. First, a longitudinal kinematic model of the vehicle platoon is constructed, and discrete state-space equations are established. The robust UKF algorithm is derived by enhancing the UKF algorithm with Huber-M estimation. This enhanced algorithm is then used to estimate the state information of the leading vehicle. Based on the vehicle state information obtained from the robust UKF estimation, feedback correction compensation is added to the MPC algorithm to design the robust UKF-MPC longitudinal controller. Finally, The effectiveness of the proposed controller is verified through CarSim/Simulink joint simulation. Simulation results show that the robust UKF-MPC longitudinal controller performs better compared to the MPC and UKF-MPC longitudinal controllers. When facing the communication delay and packet loss problems, the robust UKF-MPC longitudinal controller has higher control accuracy and less performance degradation. It has strong robustness and stability.

The development of straddle-type monorail tour-transit systems (MTTSs) has received keen attention, raising the need for revisions on related design codes. This work presents a comprehensive assessment of the lateral stiffness limit of steel substructures in MTTSs. Firstly, a wind‒vehicle‒bridge coupling model was established, integrating multibody dynamics and finite element methods. This model was then validated against field test results, considering measured track irregularities as the excitation. Afterwards, a trend analysis and a variance-based sensitivity analysis was performed to investigate the effect of various factors on the dynamic response of MTTS. Referring to existing specifications, an evaluation index for the lateral stiffness of pier columns and track beams was proposed. Results indicate that the pier height significantly impacts the lateral displacement of the pier top, accounting for 87% of the first-order sensitivity index and 75% of the total sensitivity index. In comparison, the lateral stiffness of track beams contributes over 70% to the maximum lateral acceleration and displacement at the mid-span. Finally, the lateral limited values for the displacement at the pier top and the deflection–span ratio of the track beam were determined as 8.04 mm and L/4200, respectively. These findings can serve as valuable references for future research and designs in this field.

Automated Machine Learning (AutoML) is revolutionizing how businesses utilize data, making advanced analytics accessible to a broader range of organizations. By automating complex tasks like data preprocessing, model selection, and hyperparameter tuning, AutoML reduces the time and resources needed to develop and deploy machine learning models. This accelerates decision-making and enables quicker responses to market changes. AutoML empowers businesses to build accurate predictive models using sophisticated algorithms, optimizing model performance for reliable insights and better outcomes. A key advantage of AutoML is its accessibility; even organizations without a dedicated data science team can leverage machine learning, reducing technical barriers and democratizing innovation. As businesses grow, AutoML scales to handle larger datasets and more complex problems without extensive manual intervention. AutoML enhances efficiency, accuracy, and scalability, becoming a crucial driver of business innovation and success. The systematic review will examine the bibliometric literature on how AutoML can boost business, analyzing 74 academic and scientific documents from the Scopus database.

Background and Objectives: The Ventilatory Workload Kinetic Index (VWKI) has been proposed as a clinical instrument to evaluate ventilatory balance-imbalance. On the other hand, the regulated application of scales that allow an integral evaluation of the object of study and their subsequent reliability evaluation should be a continuous. The objective was determined the reliability of the VWKI between two evaluators and its applicability in the clinical field of physiotherapy. Materials and Methods: Methodology was divided into three stages: i) induction period, ii) application of the VWKI, and iii) reliability process. Results: The VWKI total score obtained excellent inter-rater reliability (ICC=0.913, p&lt;0.05). The airway resistance (AR) showed moderate inter-rater reliability (ICC=0.528, p&lt;0.05), while the use of accessory musculature (UAM) showed poor inter-rater reliability (ICC=0.483, p&lt;0.05). The physiotherapy care prioritization system, for both evaluator A (EA) and evaluator B (EB), the total VWKI score was significantly higher in priority 1 (p=0.001; p&lt;0.05, respectively). The Total VWKI score by services, it was observed that both EA and EB rated the VWKI significantly higher in the intensive care unit (ICU) than in Medicine and the other services (p=0.001, p=0.0001 and p&lt;0.05, p&lt;0.05, respectively). Conclusions: VWKI have of excellent reliability your the total score. Also, have consistency with the system of prioritization of physiotherapy care, and the ability to determine the severity of respiratory clinical pictures.

This work aims to investigate the impact of chemical treatment of Juncus acutus fibers on the hygric and mechanical performances of an innovative bio-sourced clay-sand-Juncus acutus fibers composite. This lightweight specimen has been produced from a mixture of 60 % natural clay and 40 % sand by mass, as a matrix, and reinforced with different amounts of Juncus fibers. The fibers were used as a partial replacement of sand in mixture by volume at 0 % (Control Specimen), 5 %, 10 %, and 20 %. In order to enhance the fiber-matrix interfacial bound, the raw fibers have undergone an NaOH alkali treatment with different concentrations of 1, and 2 wt. %. Morphological and mineralogical evaluation based on SEM micrographs and EDX analyses revealed that, the alkaline concentration of 1 wt. % is the optimized one for natural fiber surface modifications (removal of impurities without any surface damage). This optimal concentration is confirmed by tensile strength tests, showing that the treated fibers with this alkali solution presents the higher tensile stress value of 81.97 MPa, compared to those of treated fibers with 2 % NaOH (74.45 MPa), and the untreated fibers (70.24 MPa). However, mechanical test-results highlighted the benefit effect of the fiber alkali treatment on both the compressive and flexural strengths, particularly for fiber contents of 5 % and 10 %, which corresponds to a strengthening rate of 25 % and 30 %, respectively. Results also showed that the sample containing treated fibers exhibited a better moisture regulating property than that produced with untreated fibers. The specimens are classified as excellent hygric regulator based on their moisture buffer performance (MBV > 2 g/(m2.%RH)), according to NORDTEST classification. Results also indicated that the capillary water absorption and the apparent moisture diffusivity of composites was lowered due to high fiber-matrix interfacial bound, after fiber treatment. Consequently, the composite with treated fibers is less diffusive compared to that with untreated fibers, and thus expected to be more durable in a humid environment. These results were confirmed by morphological analysis of fibers-matrix interfacial transition zone.

In the increasingly competitive SVOD market, platforms face high churn rates and substantial revenue losses from SVOD content piracy, all of which limit their ability to invest in acquir-ing/creating content compelling enough to win and retain subscribers. Based on social exchange theory, this study argues that platforms can improve relationships with SVOD content users by offering tiered discounts in exchange for advertising/loyalty and by promoting anti-piracy messages with a prosocial (threatening) approach that emphasizes harm to filmmakers (punishment for pi-rates). We hypothesize that these incentives enhance subscription intention when the incentive specifications (advertising levels, loyalty levels, message approach, and message credibility) match the public’s heterogeneous dispositions (advertising attitude, loyalty attitude, justice sensitivity, and fear of punishment). In a survey on the intention to subscribe to a hypothetical new platform, we confirmed the hypothesized interactions for advertising-based discounts, loyalty-based discounts, and prosocial messages, but did not find support for threatening messages. Further exploration showed that the evaluation of platform content was much more influential than any other incentive and that tiered loyalty discounts had a remarkable capacity to enhance subscription intention. This study’s findings may help shape incentives that are more satisfying to users and ultimately more profitable for platforms.

The retinoblastoma gene product (Rb1), a master regulator of the cell cycle, also plays a prominent role in cell differentiation. Previously, by analyzing the differentiation of cells transiently overexpressing the ΔS/N DN Rb1 mutant, we demonstrated that these cells fail to differentiate into mature adipocytes and that they constitutively silence Pparγ2 through CpG methylation. Here, we demonstrate that the consequences of transient expression of ΔS/N DN Rb1 are accompanied by retention of Cebpa promoter methylation near the TSS under adipogenic differentiation, thereby preventing its expression. The CGIs of the promoters of the Rb1, Ezh2, Mll4, Utx and Tet2 genes, which are essential for adipogenic differentiation, have an unmethylated status regardless of the cell differentiation state. Moreover, Dnmt3a, a de novo DNA methyltransferase, is overexpressed in undifferentiated ΔS/N cells compared with wild-type cells, and in addition to Dnmt1, Dnmt3a is significantly upregulated by adipogenic stimuli in both wild-type and ΔS/N cells. Notably, the chromatin modifier Ezh2, which is also involved in epigenetic reprogramming, is highly induced in ΔS/N cells. Overall, we demonstrate that two major genes, Pparγ2 and Cebpa, which are responsible for terminal adipocyte differentiation, are selectively epigenetically reprogrammed to constitutively silent states. We hypothesize that the activation of Dnmt3a, Rb1 and Ezh2 observed in ΔS/N cells may be a consequence of a stress response caused by the accumulation and malfunctioning of Rb1-interacting complexes for epigenetic reprogramming of Pparγ2/Cebpa and prevention of adipogenesis in an inappropriate cellular context. The failure of ΔS/N cells to differentiate and express Pparγ2 and Cebpa in culture following the expression of the DN Rb1 mutant may indicate the creation of epigenetic memory for new reprogrammed epigenetic states of genes.

Salvia miltiorrhiza (SM) contains the tanshinones, a compound with various pharmacological effects, and has been extensively studied as a pharmaceutical material. However, conventional methods for extracting tanshinones face challenges such as environmental hazards and high cost. In this study, we aimed to effectively extract tanshinones from SM using cloud point extraction (CPE) with lecithin, a natural surfactant. By optimizing various extraction conditions including the solid-to-liquid ratio, lecithin concentration, NaCl concentration, pH, and equilibrium temperature, the optimal extraction efficiency was achieved using 20 mL of solvent per 1 g of sample, 3% lecithin (w/v), 2% NaCl (w/v), pH 6, and room temperature (25 ± 2°C). The CPE method, which minimizes the use of organic solvent and is eco-friendly demonstrated improvements in extraction efficiency, with a 4.55% increased for dihydrotanshinone I, 8.32% for cryptotanshinone, 15.77% for tanshinone I, and 6.81% for tanshinone IIA compared to the conventional water extraction method. These results suggest that CPE is promising, environmentally friendly, and efficient approach for extracting hydrophobic components from pharmacologically active materials such as SM, with potential applications across various fields of natural product extraction.

The aim of this article is to discuss the transformative amalgamation of Internet of Things and Machine Learning technologies within the domain of vegetable hydroponic systems for nutrition management. Hydroponics, being an efficient cultivation method, benefits a great deal from the precision and adaptability that machine learning algorithms can offer, along with the real-time monitoring facilitated by the devices using the Internet of Things. This study summarizes the latest research and underlines how ML and IoT may work together, focusing on nutrient optimization, plant development, and resource efficiency. The use of ML algorithms, the function of IoT devices for real-time monitoring, communication protocols, scalability issues, and implementation are among some of the key subjects of the discussion. The research indicates benefits to crop output, efficiency in using resources, and sustainability based on the case studies and the analysis of results. However, ethical problems and some complications concerning data privacy do call for responsible adoptions. The conclusion of this paper provides directions for further research and calls for more investigation into state-of-the-art machine learning approaches and scalable solutions for the re-silient and sustainable future of hydroponic agriculture. The machine learning and deep learning models introduced in this research were evaluated against contemporary studies, revealing an accuracy enhancement ranging from 1.17% to 5.25%, depending on the dataset and algorithm employed. The present study conducts a comparative analysis involving machine learning algo-rithms, indicating that among all the models, the Decision Tree and Gradient Boosting Classifier achieved an accuracy of 99.42% in the dataset by making stage-wise decisions.

Abstract Background: This study evaluates the ability of six popular chatbots; ChatGPT-3.5, ChatGPT-4.0, Gemini, Copilot, Chatsonic, and Perplexity to provide reliable answers to questions concerning keratoconus. Methods: Chatbots responses were assessed using mDISCERN and Global Quality Score (GQS) metrics. Readability was evaluated using nine validated readability assessments. We also addressed the quality and accountability of websites from which the questions originated. Results: We analyzed 20 websites, 65% "Private practice or independent user" and 35% "Official patient education materials." The mean JAMA Benchmark score was 1.5±0.68, indicating low accountability. Reliability, measured using mDISCERN, ranged from 42.9±3.16 (ChatGPT-3.5) to 46.95±3.53 (Copilot). The most frequent question was "What is Keratoconus?" with 70% of websites providing relevant information. This received the highest mDISCERN score (49.33±4.96) and a relatively high GQS score (3.50±0.55), with an Automated Readability Level Calculator score of 13.17±2.13. Moderate positive correlations were determined between the website numbers and both mDISCERN (?=0.265,?=0.25) and GQS (?=0.453,?=0.05) scores. The quality of information, assessed using the GQS, ranged from 3.01±0.51 (ChatGPT-3.5) to 3.3±0.65 (Gemini) (p=0.34). The differences between the texts were statistically significant. Gemini emerged as the easiest to read, while ChatGPT-3.5 and Perplexity were the most difficult. Based on mDISCERN scores, Gemini and Copilot exhibited the highest percentage of responses in the "Good" range (51-62 points). For the GQS, the Gemini model exhibited the highest percentage of responses in the "Good" quality range, with 40% of its responses scoring 4-5. Conclusions: While all chatbots performed well, Gemini and Copilot showed better reliability and quality. However, their readability often exceeded recommended levels. Continuous improvements are essential to match information with patients' health literacy for effective use in ophthalmology.

Cervical cancer remains a significant global health challenge, ranking as the fourth most common cancer among women. The primary causative agents, Human papillomavirus (HPV) strains 16 and 18, are well-known for their role in cervical carcinogenesis, primarily through the actions of the E6 and E7 oncoproteins. This study aimed to identify a natural compound capable of inhibiting both E6 and E7 oncoprotein activities, thereby offering a potential therapeutic approach using a single agent. This study also comprises an extensive in silico analysis with molecular docking studies by AutoDock Vina and Schrodinger Maestro to analyse the binding affinity, hydrogen bonds interactions of selected compounds against HPV 16 E6 &E7 proteins. GROMACS package and iMODS server was used to perform molecular dynamics simulations of protein-ligand complexes to elucidate stability of the ligands around the receptor at different time points. SwissADME was utilized for the prediction of pharmacokinetic properties while PROTOX-II helped in predicting toxicological data Capsaicin emerged as the most promising candidate, exhibiting binding energies of -7.3 kcal/mol and -5.6 kcal/mol against the HPV 16 E6 and E7 proteins, respectively. Notably, Capsaicin formed a single hydrogen bond with each oncoprotein, indicating a strong and specific interaction. The compound also demonstrated a favourable pharmacokinetic profile, with no violations of drug-likeness rules, and was classified as Toxicity Class 4, suggesting a moderate safety profile. These results, thus branding Capsaicin as a promising lead compound for further investigations in cervical cancer therapy that deserves documentation through well-equipped wet lab experiments followed by clinical studies.

Genetically diverse influenza A viruses (IAV) circulates in wild aquatic birds with relatively frequent spillover (cases of single infections) to other mammals, but only a small proportion of outbreaks. The spillover of IAV to marine mammals such as seals have been reported throughout the last 45 years. Majority of these viruses when reached seals population cased pneumonia and death of infected animals. Until 2016, all avian influenza viruses found in seals were of low pathotype (LP) IAV. The H5Nx clade 2.4.4.4b highly pathogenic avian influenza viruses (HPAIV) were found in seals for the first time in 2016/2017. Our review highlights 45 years of AIV history in seals population, showing interesting features of different AIV subtypes and emphasis on avian-to-mammals transmission. Furthermore, we also included phylogenetic characterisation of the representative seal isolates.

Emerging evidence suggests that patients suffering from COVID-19 may experience neurocognitive symptoms. Besides, other studies indicate a probable association between leukocyte telomere length (LTL) and neurocognitive changes in subjects with post-COVID-19 condition. Our study was designed to determine the correlation between telomere length and cognitive changes in subjects post-COVID-19. The study included 256 subjects, categorized based on SARS-CoV-2 infection from 2020 to 2023. On the other hand, those subjects who have a psychiatric diagnosis are considered. Besides, the MoCA and MMSE scales were applied. Telomere length was determined using a Polymerase Chain Reaction, and statistical analysis was employed ANOVA and X2 tests. We identified a decrease in LTL in individuals with post-COVID-19 conditions compared to those without SARS-CoV-2 infection (p≤0.05). However, no association was found between LTL and cognitive impairment in subjects post-COVID-19. The findings suggest that LTL is affected by SARS-CoV-2 infection. Nonetheless, this important finding requires further research by monitoring neurological changes in subjects with post-COVID condition.

Artificial Intelligence (AI) is increasingly becoming integral to medical practice, potentially enhancing outcomes in thoracic surgery. AI-driven models have shown significant accuracy in diagnosing non-small cell lung cancer (NSCLC), predicting lymph node metastasis, and aiding in the efficient extraction of electronic medical records (EMR) data. Moreover, AI applications in robotic-assisted thoracic surgery (RATS) and perioperative management reveal potential in improving surgical precision, patient safety, and overall care efficiency. Despite these advancements, challenges such as data privacy, biases, and ethical concerns remain. This manuscript explores AI applications, particularly machine learning (ML) and natural language processing (NLP), in thoracic surgery, emphasizing their role in diagnosis and peri-operative management. It also provides a comprehensive overview of the current state, benefits, and limitations of AI in thoracic surgery, highlighting future directions in the field.

Language barriers exacerbate health disparities among minorities. This study highlights the efficacy of simple interventions in mitigating language barriers, optimizing clinic efficiency, and enhancing preparedness for surgical procedures in Spanish-speaking (SS) females diagnosed with breast cancer in a safety net community hospital. A bilingual questionnaire was given to English and SS patients during their initial clinic visit. Additionally, a bilingual handout on surgical options and postoperative care was provided to selected surgical patients. Among the 41 patients studied, those who completed the questionnaire had an average encounter time of 27.06 minutes, compared to 45.26 minutes for those who did not. For SS patients, the average encounter time with the questionnaire was 34.07 minutes versus 46.54 minutes without it. The average cost of a medical interpreter was $17.04 with the questionnaire, versus $23.27 without. All SS patients who received the educational handout expressed greater satisfaction and understanding of their treatment plans and were less likely to require additional pre-surgery visits. This study demonstrated how the simple implementation of a translated questionnaire can reduce interpretation cost, encounter time, and improve clinic flow. Furthermore, utilizing a bilingual surgical educational handout enhances patient understanding, surgery readiness and reduces unnecessary additional visits.

Questionnaires for self-report of food-elicited emotions are an established practice in consumer studies. However, most tools are originally developed in English and translated into other languages, which limits their use. The aim of this study was to develop the first Greek emotion lexicon and measurement tool for consumers, using standard methodology and extending it. The lexicon was compiled using linguistic sources and extended using the World Wide Web and a popular social media platform for the collection of authentic language and usage data. Terms were also collected from consumers using questionnaires and a variety of stimuli. Terms collected from consumers only comprise: healthy, distrustful, curious, childish, impressed, tolerant, impatient It was validated by application in various case studies, providing discrimination across and within food categories, and by comparison to the Greek translation of a standard tool in English, the EsSense Profile. The comparison was conducted by measuring emotions elicited by six food categories and demonstrated that it is preferable to use emotion measurement tools developed in the linguistic and cultural context of intended use, as they convey consumers’ emotions naturally and more accurately. This tool can be used as a Check- or Rate- All-That-Apply questionnaire to measure consumer responses to food categories and products. The methodology and validation presented can be applied for the compilation of emotion lexicons in languages less studied than English.

Background: Previous studies of the hydroethanolic extract of Virola elongata inner stem bark (HEVe) have demonstrated its antioxidant, gastroprotective, and antiulcer properties, but have not evaluate its anti-inflammatory potential. Methods: HEVe was obtained by maceration and phytochemicallly analyzed. Its systemic anti-inflammatory activity was assessed by its effect on lipopolysaccharide (LPS)-induced peritonitis in mice. HEVe gel (HEgVe) was employed to evaluate topical anti-inflammatory activity by measuring the ear edema resulted from croton oil-induced dermatitis in mice. Cell viability assay was conducted to determine non-cytotoxic concentrations of HEVe. RAW 264.7 cells were stimulated by LPS to determinate cytokine and nitric oxide production.Results: Phytochemical analysis of HEVe revealed the presence of phenolic acids, neolignans, flavonoids, and monomeric catechins. Oral treatment of acute peritonitis with HEVe reduced total leukocytes, neutrophils, TNF-α and IL-1β, and elevated IL-10 levels. The application of HEgVe reduced local edema. The HEVe on RAW 264.7 cells exhibited no cytotoxicity and cells with HEVe reduced TNF-α, IL-1β, and NO levels, and increased IL-13 levels. Conclusions: HEVe demonstrated systemic and topical multitarget anti-inflammatory activity, which is likely due to the combined effects of secondary metabolites. HEVe emerges as a promising herbal remedy for inflammation with minimal cytotoxicity, emphasizing its potential therapeutic significance.

Chemical and physical properties of nanoparticles (NPs) are strongly influenced not only by the crystal structure of the respective material including crystal structure defects, but also by the NP size and shape. Contemporary transmission electron microscopy (TEM) is capable of describing all of these NP characteristics, however typically with a different statistical relevance. While the size and shape of NPs are frequently determined on a large ensemble of NPs and thus with good statistics, the characteristics on the atomic scale are usually quantified for a small number of individual NPs and thus with a low statistical relevance. In this contribution, we present a TEM-based characterization technique, which is capable of determining relevant characteristics of NPs in a scale-bridging way – from the crystal structure and crystal structure defects up to the NP size and morphology – with a sufficient statistical relevance. This technique is based on a correlative multi-scale TEM approach that combines information on atomic scale obtained from the high-resolution imaging with the results of the low-resolution imaging assisted by a semi-automatic segmentation routine. The capability of the technique is illustrated on several examples including Au NPs having different shape, Au nanorods having different facet configurations and multi-core iron oxide nanoparticles having a hierarchical structure.

Previous studies in our lab show that that administration of OT into the fourth ventricle (4V; hindbrain) elicits weight loss and stimulates interscapular brown adipose tissue temperature (TIBAT) in male DIO rats. What remains unclear is whether chronic hindbrain (4V) OT can impact body weight in female high fat diet-fed (HFD) rodents and whether this involves activation of BAT. We hypothesized that OT-elicited stimulation of sympathetic nervous system (SNS) activation of IBAT contributes to its ability to activate BAT and reduce body weight in female high fat diet-fed rats. To test this hypothesis, we determined the effect of disrupting SNS activation of IBAT on OT-elicited stimulation of TIBAT and reduction of body weight in DIO rats. We first measured the impact of bilateral surgical SNS denervation to IBAT on the ability of acute 4V OT (0.5, 1, and 5 µg ≈ 0.5, 0.99, and 4.96 nmol) to stimulate TIBAT in female HFD-fed rats. We found that the high dose of 4V OT (5 µg ≈ 4.96 nmol) stimulated TIBAT similarly between sham rats and denervated rats (P=NS). We subsequently measured the effect of bilateral surgical denervation of IBAT on the effect of chronic 4V OT (16 nmol/day ≈ 16.1 mg/day) or vehicle infusion to reduce body weight, adiposity and energy intake in female HFD-fed rats (N=7-8/group). Chronic 4V OT reduced body weight gain (sham: -18.0±4.9 g; denervation: -15.9±3.7 g) and adiposity (sham: -13.9±3.7 g; denervation: -13.6±2.4 g) relative to vehicle treatment (P

Sleep disturbances and chronic pain are prevalent and interrelated conditions that significantly impact individuals' quality of life. Understanding the intricate dynamics between sleep and pain is crucial for developing effective treatments that enhance the well-being of affected individuals and reduce the economic burden of these debilitating conditions. This narrative review examines the complex relationship between sleep disturbances and chronic pain. We describe the prevalence and types of sleep disturbances and sleep disorders in chronic pain patients. Posteriorly we critically review the clinical and experimental evidence investigating the relationship between sleep disturbances and chronic pain aiming to clarify the impact of chronic pain on sleep and, conversely, the impact of sleep disturbances on pain perception. We conclude that a bidirectional relationship between chronic pain and sleep disturbances is almost a consensus in the literature, however, the strength of each direction of the association is less clear. As of now, the literature suggests that sleep impairment is a stronger predictor of pain than pain is a predictor of sleep impairment. Additionally, addressing sleep disturbances in chronic pain patients is crucial, as poor sleep has been linked to higher levels of disability, depression, and pain-related catastrophizing.

Recent epidemiologic studies have revealed an upward trend in young-onset colorectal cancer (YOCRC) overall, whereas specific data on young-onset colorectal neuroendocrine neoplasms (YONEN) remain limited. This study investigates demographic characteristics and survival trends in YONEN and compares these with those of young-onset colorectal adenocarcinoma (YOADC), the most common histologic subtype of YOCRC. A retrospective analysis was conducted from 2000 to 2019 using the Surveillance, Epidemiology, and End Results (SEER) database. Survival outcomes were assessed using univariate and multivariable Cox proportional models, with demographic differences evaluated via Wilcoxon Rank Sum and Chi-square tests. Out of 61,705 patients aged 20-49 with colorectal cancer, 8% had NEN, and 92% had adenocarcinoma. The YONEN cohort had a higher proportion of Black patients and a lower proportion of White patients than the YOADC cohort (21% vs. 13% and 44% vs. 57%, respectively). NEN was more commonly found in the rectum (79%), and adenocarcinoma was mostly colonic (57%) in origin. YONEN patients had better survival than YOADC patients. Multivariate analysis in YONEN patients revealed that Hispanic patients had better overall survival compared to White patients (HR 0.67, 95% CI 0.47-0.95, p=0.024). Racial disparities should be investigated further to aid in policymaking and targeted interventions.

The present study aims at investigating the potential impact of agricultural policies on GHG emissions from agriculture across the European Union. The study begins with a brief analysis of the context of implementation of the 2014-2022 Common Agricultural Policy (CAP), within which many interventions were designed to improve sustainability and increase competitiveness. It follows a description of the structural changes the European agriculture is witnessing, that are both influenced and contribute influencing policies. The method of Qualitative Comparative Analysis (QCA) is used to investigate possible causalities and to cluster observations based on similar combinations of conditions (i.e., drivers) and outcomes (i.e., positive or negative variations in GHG emissions from agriculture between the end and the begin of the CAP programming period). The results reveal that the increase in GHG emissions from agriculture over the study period is mainly attributable to the low share of agricultural land under management contracts targeting climate change mitigation and carbon sequestration through the CAP. CAP payments coupled with the production is found to contribute to further increasing GHG emission from agriculture in some eastern and northern EU countries. Livestock concentration, income support payments and the high price of agricultural land drive the increase in GHG emissions for other central and eastern EU countries. The paper concludes addressing existing shortcomings due to conflicting interventions in the current CAP Strategic Plans.

This study explores the efficiency and applicability of a HONO collection system that incorporates an ultrasonic nozzle and spray chamber for the measurement of ambient air. The system demonstrates (1) a remarkable efficiency of 97.7% across two serial stages, (2) lower detection limits of 0.1 ppbv for HONO, and (3) absence of interference from NO2 or OH radicals. Practical ambient monitoring with the HONO collection system revealed typical diurnal variations in HONO, O3, and HNO3 concentrations, aligning with photolysis dynamics. Notably, HONO concentrations peaked at 0.37 ppb during nighttime and decreased to 0.27 ppb by midday. O3 demonstrated an inverse relationship with HONO, especially during ozone depletion phases, with r2 values of 0.94, 0.81, and 0.52 across various intervals. The HONO/NOx ratio during periods of enhanced HONO suggested the presence of additional formation mechanisms beyond heterogeneous NOx reactions. Moreover, ozone levels often fell below 20 ppb, indicating a consistent inverse correlation with HONO, thereby confirming periods and highlighting further mechanisms of HONO formation beyond heterogeneous NOx reactions. The real-time atmospheric chemical reactions involving HONO, monitored concurrently with O3 and NOx, were effectively validated by the HONO collection system employed in this investigation.

Aim: This study aims to analyze the evolution of co-author networks in Gastroesophageal Reflux Disease (GERD) research from 2000 to 2023, using data from the Web of Science (WoS) Core Collection. The objective is to provide a structural overview of researcher collaborations in this specialized field, highlighting key contributors and the impact of international cooperation. Method: I conducted a co-author network analysis on 19,463 GERD -related articles indexed in the WoS Core Collection between 2000 and 2023. Using Python (Version 3.10.5) within the PyCharm development environment (Software Version 2022.1.3), I assessed macro-level indicators including network density, clustering coefficient, number of components, and average distance. Micro-level indicators such as degree centrality, closeness centrality, and betweenness centrality were also evaluated to understand the roles of individual researchers in the network.Result: The GERD co-authorship network exhibited a sparse structure with low network density, indicating limited realized collaborations compared to potential connections. The clustering coefficient was consistently high across the analyzed periods, suggesting that researchers frequently formed tight-knit groups. The network was fragmented into numerous components, with significant disconnection between groups. Degree centrality highlighted key researchers such as Dent, J., and Shaheen, N.J., who had extensive collaborative reach. In contrast, closeness and betweenness centrality identified strategic intermediaries like Bredenoord, A.J. and Kahrilas, P.J., who facilitated connections across otherwise isolated segments of the network.Conclusion: The co-authorship network analysis revealed a persistently fragmented landscape in GERD research, with considerable room for enhancing collaboration among researchers. Identifying key contributors and understanding their roles within the network can guide future efforts to foster more integrated and comprehensive research collaborations. This study underscores the importance of strategic networking and partnership development in advancing GERD research and optimizing the field's collaborative potential.

Blockchain is a shared, unchangeable, decentralized ledger that allows transactions to be recorded securely and transparently via a network of computers. In addition to its significant contributions to cryptocurrencies, blockchain technology has shown promise as a game-changing advancement in cyber security. This study offers a thorough analysis of how blockchain technology is strongly linked to defending against cyber security risks in the modern world. This research clarifies the primary mechanisms of decentralization, immutability, transparency, and cryptographic security by which blockchain improves cyber security through a review of the literature and case studies. Furthermore, the study addresses the difficulties and constraints associated with incorporating blockchain technology into cybersecurity frameworks and proposes potential directions for future research.

Under physiological conditions the blood-brain barrier (BBB) is required for regulating the passage of substances between the bloodstream and the brain. To date, disruption of the BBB has been associated with nearly all neurological diseases. Dye and tracer experiments have been used to assess BBB integrity for decades, yet quantitative assessment of these experiments has only recently been implemented. Further, interpretation of these results requires the consideration of tracer-specific properties. Here, we describe a protocol for evaluating BBB leakage using the small molecule cadaverine in quantitative way. We also cover advantages/disadvantages of using some reagents and highlight limitations in data interpretations based on assays used to assess BBB permeability.

Thermal comfort indices are risk indicators for environmental conditions in livestock production. Predictive models based on these indices of heat stress, respiratory frequency, and presence of heat waves for lactating cows, comparing the prediction and correlation between these factors, generate responses that can improve animal management regarding physiological responses to heat stress during the incidence of heat waves. Therefore, the objective was to develop predictive models for heat wave risk alerts based on thermal stress indices, physiological responses, and surface temperature of lactating cows. The region's climatic conditions were evaluated based on data on temperature, air humidity, wind speed, black globe temperature, solar radiation, dew point temperature, and precipitation compiled from a meteorological station. In the processing phase, a data set was discretized into classes to apply machine learning techniques to generate a classification model with cross-validation of the data. The performance of the classifiers was evaluated based on the metrics: accuracy, precision, sensitivity (recall), and Kappa statistics. Predicting the risk of heat waves for lactating cows can meet the need to predict the frequency of heat waves for decision-making in environmental, nutritional, and health management to minimize adverse impacts on production. The accuracy of Decision Tree models (J48), Naive Bayes, and Logistic Regression was equal to 96.72%, 96.72%, and 98.36%, respectively. The performance in the evaluation metrics of the Logistic Regression prediction model was better than that of the decision tree and Naive Bayes models.

Metabolomics studies require a deep understanding of enzyme dynamics and their responses to environmental disturbances within a complex metabolic network. This paper presents a novel approach to modeling enzyme activity as a dynamic topological manifold, where each enzyme is represented as a node, and their interactions are described by differential equations. These equations account for both compensatory interactions and external perturbations, simulating how enzymes dynamically stabilize their activities in response to varying conditions. We explore the application of this model to visualize enzyme interactions, using graph theory to represent the network structure and color gradients to illustrate interaction intensities. Multiple disturbances are introduced to analyze the network's resilience and adaptability over time. The results provide insights into the compensatory mechanisms within enzymatic networks, offering a comprehensive visualization through both static and dynamic representations. Our approach allows for a better understanding of how enzymatic systems buffer against mutations and environmental stressors, contributing to the broader field of systems biology and metabolomics.

Background/Objectives: Adolescence represents a period of heightened vulnerability to risky sexual behaviours. In Portugal, adolescents are initiating sexual activity at increasingly younger ages. Yet, there are no studies evaluating their knowledge about sexually transmitted diseases and prevention methods at an earlier stage of their development. Study how middle school ad-olescents define their sexuality and assess their level of information regarding sexuality, STI, and prevention methods. Additionally, it explores adolescents' sources of information on sexuali-ty-related issues. Methods: A cross-sectional observational study was conducted involving the application of a questionnaire to 750 9th-grade students of Vila Nova de Gaia, Porto, Portugal. The questionnaire covered topics such as perceptions of sexuality, knowledge of sexuality and STI, methods of transmission prevention, and sources of information. Results: The mean age was 14.4 years, with 19.9% reporting having already initiated sexual ac-tivity. Overall, the knowledge about sexually transmitted infections is low, pointing to 7.08 out of a maximum score of 18. Condoms and immunisation are well-recognized preventive measures, but many misconceptions persist. Better knowledge is associated with better attitudes in the likelihood of having sexual intercourse. Conclusions: This study reveals adolescents' limited sexually transmitted infections knowledge, misconceptions about prevention, and reliance on peers and the internet over trusted sources, highlighting the need for comprehensive sexual education in middle school.

With the aim of developing an innovative water treatment approach for developing countries in the Global South, we have applied the method of treating a cavitating water stream with a plasma discharge under real conditions. To this end, we have optimised the approach after investigating the effects that occur in the treated medium during such a treatment. Based on the obtained light absorption curves of treated model solutions of titanium oxysulphate and potassium bichromate, it was found that inside the reactor the main role in the destruction of chemical contaminants is played by hydroxide ions, while outside the reactor the main chemical interaction takes place with hydrogen peroxide. The plasma treatment unit was tested in the biological wastewater treatment plants of a health resort on the territory of the Russian Federation (Almetyevsk, Republic of Tatarstan). Water samples taken directly from the tertiary decantation tank were used as real wastewater samples instead of adding chemical reagents for disinfection. It was found that with different modes of operation of the plasma treatment plant, the concentration levels of coliform bacteria, coliphages and Escherichia coli decrease significantly and fall below the limit of permissible concentrations for wastewater discharge. At the same time, the possible effect of the plasma on persistent inorganic compounds was investigated. It was shown that the plasma discharge in the flow of the incoming liquid can almost completely destroy compounds that are difficult to remove, such as hydrogen sulphide and chlorides. In the course of the study, the optimum frequency of the electrical pulses of 68 kHz was selected, which ensures the lowest consumption of electrical energy while maintaining the required efficiency.

Almost one fifth of breast cancer cases express Human Epidermal Growth Factor-2 (HER2), and such expression is associated with highly proliferative tumor and poor prognosis. The introduction of anti-HER2 therapies has dramatically changed the natural course of this aggressive subtype of breast cancer. However, anti-HER2 therapy can be associated with substantial toxicities, mostly cardiac, and high cost. Over the past few years, there has been growing interest in de-escalation of anti-HER2 therapies to minimize adverse events and healthcare costs, while maintaining the efficacy of treatment. Data from clinical observations and single-arm studies have eluted to the minimal impact of anti-HER2 therapy in low-risk patients, like those with node-negative and small tumors. Though single-arm, the APT trial, in which patients with node-negative, small tumors received single-agent paclitaxel for 12 cycles plus trastuzumab for 1 year, was a practice-changing study. Several other recently published studies, like the PERSEPHONE trial, have shown more convincing data that 6 months of trastuzumab is not inferior to 12 months, in terms of disease-free survival (DFS), suggesting that de-escalating strategies with shorter treatment may be appropriate for some low-risk patients. Other de-escalating strategies involved adaptive, response-directed approach, and personalized therapy that depends on tumor genomic profiling.

The COVID-19 pandemic has not just gently nudged but forcefully thrust environmental issues into the forefront of public consciousness. This shift in awareness has been a long-time aspiration of conservation scientists, who have played a crucial role in advocating for recognizing nature’s contributions to human life and a healthy environment. We explain the advantages of using newly available tools and sources of digital data, i.e., the Absolute Search Volume in Google per flag keywords, i.e., biodiversity, climate change, and sustainability, and The GDELT Project that monitors the world's broadcast, print, and web news, as well as the Difference-in-Difference method comparing paired samples of public interest before and after the pandemic outbreak. We focus on the case of UK citizens’ public interest. Public interest in the flag keywords in the UK shows a highly significant increase during the pandemic. Results contradict hypotheses or findings presented elsewhere that the public interest is attenuated during -and because- the public health crisis. We support growing public awareness of the existential risks springing from human materialism misappropriating nature, environment, and resources. In conclusion, we advocate for a ‘new conservation narrative’ that could be fostered by the increased public interest in environmental topics during the pandemic.

This paper presents a comprehensive study on the design and aerodynamic optimization of a hybrid surveillance drone, conducted within the academic framework of Abu Dhabi University. The research primarily focuses on the comparison between flying wing and conventional aircraft layouts, with an emphasis on reducing drag coefficients and enhancing stall behavior through integrated design strategies. Utilizing SST k-omega viscous model (CFD) simulations, the study evaluates the aerodynamic performance of the drone model, analyzing lift, drag, and pitching moment coefficients against existing models. The paper further explores winglet selection and propeller dynamics, aiming to optimize the lift-to-drag ratio and achieve desired lift-to-weight ratios through careful consideration of propeller-wing interactions. Findings from the study indicate a notable improvement in aerodynamic efficiency, with the new drone model achieving a maximum lift coefficient (Cl, max) of 0.746, a minimum drag coefficient (Cd, min) of 0.039, and a peak lift-to-drag ratio (Cl/Cd) of 8.507. The research contributes significantly to the fields of Aerospace Engineering and Mechanical Engineering, providing a foundation for future advancements in drone design and application.

The Kennedy model offers a robust framework for modeling forward rates, leveraging Gaussian random fields to accommodate emerging phenomena such as negative rates. In our study, we employ maximum likelihood estimations to determine the parameters of the Kennedy field, utilizing Radon-Nikodym derivatives for enhanced accuracy. We introduce an efficient simulation method for the Kennedy field and develop a Black-Scholes-like analytical pricing formula for diverse financial assets. Additionally, we present a novel parameter estimation algorithm grounded in numerical extreme value optimization, enabling the recalibration of parameters based on observed financial product prices. To validate the efficacy of our approach, we assess its performance using real-world par swap rates in the latter part of this article.

The stress-inducible heat shock protein 70 (Hsp70), which functions as a molecular chaperone and is frequently overexpressed in different cancer cell types, is presented on the cell surface of tumor cells and is actively released into the circulation in free and extracellular lipid vesicle-associated forms. Since the exact pathomechanism of endometriosis has not yet been elucidated but has been associated with the development of endometrial and ovarian cancer, we asked whether extracellular Hsp70 and circulating endometriotic cells (CECs) reflects the presence and development of endometriosis. Therefore, circulating levels of free and lipid microvesicle-associated Hsp70 were measured using the Hsp70-exo ELISA, and the presence of circulating CECs in the peripheral blood of patients with endometriosis was determined using membrane Hsp70 (mHsp70) and EpCAM monoclonal antibody (mAb)-based bead isolation approaches. Isolated CECs were further characterized by immunofluorescence using reagents directed against cytokeratin (epithelial), CD45 (leukocyte), CD105/CD44 (mesenchymal-stemness) and by comparative RNA analysis. Similar to the situation in patients with cancer, levels of circulating Hsp70 were elevated in the blood of patients with histologically proven endometriosis compared to a healthy control cohort, with the highest Hsp70 level in a patient with lymph node endometriosis. Moreover, CECs could be isolated using the cmHsp70.1 mAb- and to a lesser extent EpCAM mAb-based bead approaches in all patients with endometriosis. The longevity in cell culture and the expression of cytokeratin, CD105 and CD44, together with differentially expressed genes related to EMT reveals similarities of mHsp70 expressing CECs with circulating tumor cells (CTCs) and suggest a mesenchymal-stemness origin. These findings may support the involvement of mHsp70 positive, stem cell-like cells in the development of endometriotic lesions. In summary, elevated levels of Hsp70 in the blood and CECs could serve as liquid biopsy markers for endometriosis and help to elucidate the underlying pathomechanism of the disease.

Installation of offshore wind farms is rising, driven by the goal of changing the global energy matrix. However, many of its possible impacts are still unknown. Increased noise levels, disruptions to food chains, pollution due to traffic, and impacts on fishing communities and tourism are all potential effects to consider. Marine habitats are essential carbon dioxide sinks. Therefore, losing marine biodiversity due to offshore wind farms can be counterproductive in mitigating climate change. Balancing biodiversity conservation, wind potential, and political interests is challenging. Today, Uruguay has practically decarbonized its electrical matrix, incorporating wind, solar, and biomass energy alongside hydroelectricity. In line with this, the country's Hydrogen Roadmap highlights green hydrogen as relevant, potentially serving as a transportation fuel for both domestic consumption and export. Combining the country's strong base of wind experience with its sustainable policy, it plans to implement offshore wind farms to produce green hydrogen, making studies of its impacts crucial. This paper reviews the current social and environmental information on the Uruguayan coastal habitat, analyzes onshore wind farms' ecological studies, and examines offshore wind farms' global environmental and social impacts. Finally, it proposes suggested studies for environmental approval for offshore wind farms.

In this paper, we investigate a class of hierarchical variational inequalities (HVIPs, i.e., strongly monotone variational inequality problems defined on the solution set of multiple-set split common fixed point problems) with quasi-pseudocontractive mappings in real Hilbert spaces, which special cases can be found in many important engineering practical applications such as image recognizing, signal processing and machine learning. In order to solve HVIPs of potential application value, inspired by the primal-dual algorithm, we propose a novel accelerated cyclic iterative algorithm that combines the inertial method with a correction term and a self-adaptive step-size technique. Our approach eliminates the need for prior knowledge of the bounded linear operator norm. Under appropriate assumptions, we establish strong convergence of the algorithm. Finally, we apply our novel iterative approximation to solve multiple-set split feasibility problems and verify the effectiveness of the proposed iterative algorithm through numerical results.

The permeability, heat conductivity and reaction rate will be varied with the change of natural gas hydrate saturation, when thermal fluid injected into the natural gas hydrate reservoirs. In order to characterize the variation of physical field parameters with hydrate saturation, DDF-LBM was applied to simulate the hydrate dissolving process by thermal fluid injection under pore scale modelling. Based on the forced conjugate heat transfer case, the relaxation frequency of the thermal lattice in the pores is corrected. Based on the P-T phase equilibrium relationship of hydrates and considering the heat absorbed by the hydrate reaction, the solid-liquid state of the hydrate lattice is judged in real time, and the dynamic simulation of the heat flow solidification multi-physics field is realized. The simulation results show that the dissolution rate of hydrates by thermal fluid injection was higher than that by heating the hydrate surface alone, and was positively correlated with the hydrate saturation. On the basis of the above results, this paper provided exponential fitting equations between different hydrate saturations and average permeability, effective thermal conductivity, and inherent reaction rate. The fitting results show that saturation has a negative correlation with relative permeability and effective thermal conductivity, and a positive correlation with the inherent reaction rate. The above results can provide a reference basis for accurately describing the heat and mass transfer of natural gas hydrate under macro-scale.

Shiga toxin-producing Escherichia coli (STEC) is a significant public health threat due to its ability to cause severe gastrointestinal diseases in humans, ranging from diarrhea to life-threatening conditions such as hemorrhagic colitis and hemolytic uremic syndrome (HUS). As the primary reservoir of STEC, cattle play a crucial role in its transmission through contaminated food and water, posing a considerable risk to human health. This comprehensive review explores host-pathogen interactions during STEC colonization of the bovine gut, focusing on the role of gut microbiota in modulating these interactions and influencing disease outcomes. We integrated findings from published transcriptomics, proteomics, and genomics studies to provide a thorough understanding of how STEC adheres to and colonizes the bovine gastrointestinal tract. The insights from this review offer potential avenues for the development of novel preventative and therapeutic strategies aimed at controlling STEC colonization in cattle threby reducing the risk of zoonotic transmission.

Background: Reliable HPV testing and genotyping are essential for quality assurance in HPV-based primary screening, disease management and for monitoring the impact of HPV vaccination. The clinical validation of HPV molecular diagnostic assays has significantly contributed to these objectives, however little emphasis has been placed in assuring sample quality. This study aims to assess the accuracy of sample cellularity assessment using C-C Motif Chemokine Receptor 5 (CCR5) gene target as a marker of sample adequacy in molecular diagnostics. Methods: Jurkat cell line samples were counted both using Thoma cell-counting chamber and Fluorescent-Activated Cell Sorting (FACS). Jurkat cell line samples at three different concentrations were subsequently evaluated using the OncoPredict HPV Quality Control (QC) real-time PCR assay, employing CCR5 for molecular cellularity quantification. Results: Cellularity values obtained were comparable across the 3 different methods for all dilutions of the cell line tested. Conclusions: The results obtained from this study show that CCR5 represents a promising molecular marker for the accurate quantification of sample cellularity, confirming its use as a reliable sample adequacy control, thus reducing the risk of “false negative” results.

Monitoring seaweed growth rates and biomass is crucial for optimizing harvest strategies in aquaculture systems. While such a task can be performed manually on a small farm, such as the Integrated Multi-Trophic Aquaculture (IMTA) system at Harbor Branch Oceanographic Institute at Florida Atlantic University (HBOI), a commercial farm will have to rely on the automated sensor to perform such a task. This study introduces an advanced LSTM-based approach for forecasting seaweed growth and biomass. Utilizing a combination of real and synthetic data, LSTM models are trained and evaluated for their predictive performance. Synthetic sensor data was generated using mathematical equations that simulate realistic aquaculture conditions, with added noise to reflect sensor variability. Building on the foundation of the Pseudorandom Encoded Light for Evaluating Biomass (PEEB) sensor deployed at the seaweed tank in the HBOI IMTA system, we refine the process of biomass estimation by introducing non-linear regression models for predicting seaweed growth and biomass. The results showed that the LSTM model trained with a loss function under physics constraint, combining MSE and physical laws, outperformed models trained with MSE alone, achieving a significantly lower error in predicting seaweed growth. The variation trend of the predicted biomass from the network matched well with the sensor measurement after moving average preprocessing, demonstrating the robustness of the proposed technique in handling noisy sensor data. This study highlights the potential of integrating machine learning with physical models to optimize seaweed cultivation and support sustainable aquaculture practices.

PET imaging with [¹⁸F]F-DOPA has demonstrated high potential for the evaluation and management of pediatric brain gliomas. Manual extraction of PET parameters is time-consuming, lacks reproducibility, and varies with operator experience. In this study, we tested whether a semi-automated image processing framework could overcome these limitations. Pediatric patients with available static and/or dynamic [¹⁸F]F-DOPA PET studies were evaluated retrospectively. We developed a Python software to automate clinical index calculations, including preprocessing to delineate tumor volumes from structural MRI, accounting for lesions with low [¹⁸F]F-DOPA uptake. 73 subjects with treatment-naïve low and high-grade gliomas, who underwent brain MRI within two weeks of [¹⁸vF]F-DOPA PET, were included and analyzed. Static analysis was conducted on all subjects, while dynamic analysis was performed on 32 patients. For 68 subjects, the Intraclass Correlation Coefficient for T/S between manual and ground truth segmentation was 0.91. Using our tool, ICC improved to 0.94. Our method demonstrated good reproducibility in extracting static tumor-to-striatum ratio (p = 0.357), though significant differences were observed in tumor slope (p < 0.05). No significant differences were found in time-to-peak (p = 0.167) and striatum slope (p = 0.36). Our framework aids in analyzing [¹⁸F]F-DOPA PET images of pediatric brain tumors by automating clinical score extraction, simplifying segmentation and Time Activity Curve extraction, reducing user variability, and enhancing reproducibility.

Aiming at the problem of underactuated unmanned surface vehicles (USVs) performing fixed-point operations in the sea but without dynamic positioning control system. This paper introduces an original approach for positioning control, and the method is named as virtual anchor control method. This method is applicable in slow-varying current environments, and this approach does not need the prior knowledge of current information or vessel motion model parameters, thus offering convenient usability. This method is comprised by four steps. Firstly, a linear concise motion model with unknown disturbances is proposed. Then, the motion planning law is designed by imitating underlying principles of ship anchoring. Next, an adaptive disturbance observer is proposed for estimating uncertainties in the motion model. Last step, based on the observer, a sliding-mode method is used for heading control law designing, and thrust control law is also conducted by applying the Lyapunov method. Finally, numerical simulation experiments with significant disturbances and tidal current variations are conducted, which demonstrate that the proposed method has good control effect and robustness.

Recent algorithmic-based research in the stock market has predominantly focused on ultra-high-frequency trading and index estimation. We developed an improved algorithm to address the need for a low-frequency, real-world trading model to build on the existing high hit-ratio and low maximum drawdown. We utilized established price indicators, Stochastic and Williams %R, and introduced a volume factor to increase the model's safety to enhance the model's performance. This improved algorithm demonstrated superior returns while maintaining high hit-ratio and low maximum drawdown. Specifically, we leveraged the existing algorithm to trade using 2X and 3X signals, incorporating volume data, the 52-week average, standard deviation, and other factors. The data set comprised SPY ETF price and volume from 2010 to 2023, covering over 13 years. Our enhanced algorithmic trading model outperformed both the benchmark and previous results, achieving a hit rate of over 90%, a maximum drawdown of less than 1%, an average of 1.5 trades per year, a total return of +519.3%, and an annualized return (AnnR) of +15.1%. The analysis demonstrates that the model's simplicity, ease of application, and comprehensibility can assist investors in making informed investment decisions albeit the past performance does not guarantee future returns.

Since surface relief transmission gratings have very strict requirements on operators and use environment, according to the semiconductor laser external cavity spectral beam combining system, this paper proposes a design scheme of semiconductor laser array spectral beam combining system based on grating-external cavity. The finite element approach was used to create a wideband, high-efficiency fill-in multilayer dielectric transmission grating structure for a high-power spectrum beam combining system. The incidence angle, ridge height, duty cycle, and sidewall inclination angle of the transmission grating were tuned and evaluated, and a link between the transmission grating's diffraction efficiency and grating characteristics was discovered. The calculated design of the high-power fused silica transmission grating has a negative first-order peak diffraction efficiency of 99.5% in the 800nm range. In the spectral region of 765-872 nm, the transmission grating's diffraction effectiveness exceeds 92%. The filled ultra-high diffraction efficiency multilayer dielectric transmission grating design addresses the issue of resistance to high-power lasers under complicated operating settings, it’s intended to maintain a high diffraction efficiency even after several cleaning cycles, and it’s an ideal component for high-power spectrum beam combining systems.

Microplastic pollution, particularly particles smaller than 5 mm, poses significant environmental and health risks due to their potential for long-range transport and inhalation. This study provides the first long-term assessment of airborne microplastics in Porto, Portugal, over 18 months (September 2022 to March 2024). Bi-weekly samples were collected using a Microplastic Collector NILU, which were size-fractionated into five categories (>125 μm, 125-63 μm, 63-25 μm, 25-12 μm, and 12-1.2 μm), and quantified via optical microscopy. Microplastics (26 to 1,484 MPs/day/m²) and fibers (14 to 646 fibers/day/m²) showed increasing pollution trends. With a focus on the 12-1.2 μm size-range due to its classification as PM10 and PM2.5, the highest microplastic concentrations were 164 MPs/day/m² (12-1.2 μm) and 534 MPs/day/m² (25-12 μm). Recovery rates of methodology varied among polymers, with PP, PE-HD, and ABS showing high accuracy (75.9%) and PES significantly lower (26.5%). The study highlights significant temporal variability in airborne microplastic pollution, increasing trends, and the need for ongoing monitoring and targeted mitigation strategies to address associated health risks.

The microfluidic industry recognizes the lack of sensitive and standardized testing methods as one of the major hurdles to overcome before fulfilling its revolutionary promises. Highly ranked upon the list of things in need of testing methods are the internal dimensions of microfluidic structures after chip assembly. This work, performed under the MFMET project, aims to address this challenge by presenting and comparing multiple protocols for measuring the dimensions of internal microfluidic channels of fully assembled transparent chips. The protocols include optical profilometry, optical microscopy and tiled digital imagery. Several standardized chip designs out of two different materials commonly used in microfluidics (D263©bio and TOPAS© COC) were measured by the different protocols. A consistency analysis through normalized error statistics revealed optical profilometry as the preferred method due to its low uncertainty compared to the other protocols and its higher consistency with nominal geometry values. All protocols showed difficulty with vertical depth measurements of internal structures. Associated possible directions of future research are considered including further study of the influence of multiple refractive surfaces on optical profilometry measurements and further investigation of confocal microscopy as a potential measurement method showing promising preliminary results. The influence of the different materials appears to be minimal on optical profilometry, but other materials and method would merit looking into before such an observation might be generalized. In conclusion, the results of this work directly address the current lack of measurement methods for internal microfluidic structures by providing a comprehensive comparison of different protocols, ultimately suggesting a preferred option for immediate application within the microfluidic industry. Additionally, this work offers valuable directions for future research, serving as an initial step in overcoming a significant challenge that impedes the microfluidic industry from realizing its revolutionary potential.

The use of cardiac implantable electronic devices (CIEDs) has increased considerably, becoming a cornerstone of management for patients with brady- or tachyarrhythmia or for the prevention of sudden cardiac death. On the other hand, tricuspid regurgitation (TR) associated with CIEDs is progressively accepted as a serious clinical issue; prognostic impact of TR is profound, as it is independently associated with increased mortality and a higher risk of heart failure hospitalization. Additionally, management of established CIED-related TR continues to be challenging, with limited options for intervention once significant TR has developed. The balance between the lifesaving benefits of CIEDs and the risk of TR underlines the necessity for cautious patient selection and innovative approaches to device implantation and management. This review highlighting the clinical importance, underlying mechanisms, and challenges associated with lead-related tricuspid regurgitation in patients with CIEDs.

This study investigates the capabilities of high-resolution Sentinel-2 observations and deep learning (DL) segmentation models, specifically convolutional neural networks (CNNs), for accurate mapping of small and fragmented agricultural burn areas in Punjab, India. Initially, the model was trained using ICNF burn area data from Portugal to capture large fire and burn area delineation, achieving moderate accuracy. Subsequent fine-tuning using annotated data from Punjab improved the model’s ability to detect small burn patches, demonstrating higher accuracy and precision than the baseline Normalised Burn Ratio (NBR) Index method. On-ground validation using buffer zone analysis and crop field images confirms the DL approach effectiveness. Cloud interference and temporal gaps in satellite data posed some challenges. Despite these limitations, the study highlights the methodological advancements and potential of using DL models for small burn area detection in agricultural settings. The model achieved overall testing accuracy of 0.64 in Dice score assessment. This report presents a promising approach for improved mapping of the burn area, which might engender better estimation of emissions and the management practices in regions with little available ground truth data.