Low-cost sensors are widely used for air quality monitoring due to their affordability, portability and easy maintenance. However, the performance of such sensors, such as PurpleAir Sensors (PAS), is often affected by changes in environmental (e.g., temperature and humidity) or emission conditions, and hence the resulting measurements require corrections to ensure accuracy and validity. Traditional correction methods, like those developed by the US-EPA, have limitations, particularly for applications to geographically diverse settings and sensors with no collocated referenced monitoring stations available. This study introduces BaySurcls, a Bayesian-optimised surrogate model integrating deep learning (DL) algorithms to improve the PurpleAir sensors PM2.5 (PAS2.5) measurement accuracy. The framework incorporates environmental variables such as humidity and temperature alongside aerosol characteristics, to refine sensor readings. The BaySurcls model corrects the PAS2.5 data for both collocated and non-collocated monitoring scenarios. A case study showed that BaySurcls outperforms all tested standalone models (DL or classical machine learning based) and the US-EPA correction method in terms of reducing root mean square errors in PAS2.5 data and enhancing correlations with the reference data, under both the collocation and non-collocation monitoring scenarios. This improvement is evident across multiple locations in New South Wales, Australia, demonstrating the model's adaptability. The findings confirm BaySurcls as a promising solution for improving the reliability of low-cost sensor data, thus facilitating its valid use in air quality research, impact assessment, and environmental management.

In this study, gravity data were analyzed and interpreted to assess the ground water flow of Ziway Langano corridor. Gravity survey sets out to investigate the subsurface geology on the basis of anomalies causing gravity field to result from density properties of the subsurface rocks and also used in mapping geological boundaries between contrasting lithologies including (fractures and faults).The field data was qualitatively and quantitatively interpreted using geosoft oasis montaj software. The study area is located in the Central Main Ethiopian rift (CMER). The area ranges in elevation between 2150 and 1500 m above sea level. Geographically the Ziway- Langano corridor is bounded between 7045’N-800’N longitude and 38030’E- 39000’E latitude. In the present study, gravity data have been compiled, processed and interpreted to examine the subsurface geological structures and groundwater flow in the Ziway- Langano Corridor. Further, based on the results of gravity anomaly and profiles, this paper attempts to describe the tectonic and geologic implications of the stud yarea. The major rocks suspected to be found in the study area are volcanic ash, sediment deposit, rhyolite, ignimbrite, and basalt.

This paper explores the intersection of geometry and music, investigat- ing how mathematical structures shape musical theory, composition, and perception. It begins by examining the geometric foundations of pitch organization, including the circular representation of pitch classes and the tonal space. We then explore rhythmic structures through symmetry, analysing how patterns in time are geometrically conceived. Finally, the study examines more recent developments in computational music theory, such as the use of topological data analysis to model musical transformations and voice leading.

This article explores the critical role of SIM provisioning in modern telecommunications, emphasizing its importance in ensuring secure and scalable mobile connectivity. It delves into the evolution of SIM provisioning processes, from traditional physical SIMs to eSIMs and remote provisioning. The paper discusses the technical aspects of provisioning, including unique identifier assignment and security configurations. It examines the challenges faced in scaling these systems for the growing number of connected devices. Furthermore, it explores future directions in SIM provisioning, including the integration of AI and blockchain technologies. It also considers the economic implications and relationship to emerging network technologies like 5G and IoT.

This paper examines the transformative impact of data science, machine learning (ML), and artificial intelligence (AI) on network management in telecommunications, focusing on techniques such as network monitoring, predictive maintenance, anomaly detection, automated network configuration, and self-healing mechanisms. We analyze specific methodologies, including deep learning for anomaly detection and federated learning for predictive maintenance, and address current challenges such as data quality, system integration, and model interpretability. Emerging technologies like edge computing, federated learning, and quantum computing are explored for their potential to enhance predictive maintenance and network management. The paper provides an overview of how AI-driven solutions are revolutionizing telecom networks, offering unprecedented efficiency, reliability, and performance while highlighting the need for ongoing research to tackle complex issues of explainability and privacy.

The rapid advancement of 5G networks, coupled with the increasing complexity of resource management, traffic handling, and dynamic service demands, has underscored the need for more intelligent network optimization techniques. This paper comprehensively reviews AI-driven methods applied to 5G network optimization, focusing on resource allocation, traffic management, and network slicing. Traditional models face limitations in adapting to the dynamic nature of modern telecommunications, and AI techniques-especially machine learning (ML) and deep reinforcement learning (DRL)-offer scalable, adaptive solutions. These approaches enable real-time optimization by learning from network conditions, predicting traffic patterns, and intelligently managing resources across virtual network slices. AI's integration into 5G networks enhances performance, reduces latency, and ensures efficient bandwidth utilization. It is indispensable for handling the demands of emerging applications such as IoT, autonomous systems, and augmented reality. This paper highlights key AI techniques, their application to 5G challenges, and their potential to drive future innovations in network management, laying the groundwork for autonomous network operations in 6G and beyond.

Treatment efficacy for age-related macular degeneration relies on early diagnosis and precise determination of the disease stage. It involves analyzing biomarkers in retinal images, which can be challenging when handling a large flow of patients and compromise the quality of healthcare services. Clinical decision support systems offer a solution to this issue by employing intelligent algorithms to recognize biomarkers and specify the age-related macular degeneration stage through the analysis of retinal images. However, different stages of age-related macular degeneration may exhibit similar biomarkers, complicating the application of intelligent algorithms. This paper introduces an approach to overcome these challenges using hybrid and hierarchical classification. By leveraging the hybrid structure of the classifier, we can effectively manage issues commonly encountered with medical data sets, such as class imbalance and strong correlations between variables. The modifications to the intelligent algorithm proposed in this work for staging age-related macular degeneration resulted in an increase in average accuracy, sensitivity, and specificity by 20% compared to initial values. The Cohen’s Kappa coefficient used for consistency estimation between the regression model and expert assessments of the intermediate class severity was 0.708, indicating a high level of agreement.

In recent years, the widespread use of drones in daily life and large public events has raised serious safety concerns, especially due to incidents, both intentional and accidental. One of the most important aspects to prevent these risks is the ability to detect and accurately predict the distance of UAVs (Unmanned Aerial Vehicles) from people and restricted areas. One of the pressing issues is to develop systems to monitor their flights in restricted areas and predict suspicious movements in cases where suspicious drone flights are detected, which may be launched for video reconnaissance and information theft purposes. The advancement of acoustic-based recognition systems is growing with the development of deep learning. This study explores deep learning model architectures for the task of predicting UAV distances based on their flight sounds. The objective of this study is to determine whether sound-based classification can effectively predict drone movements at different distances from acoustic sensor points as they move from one area to another. Our experimental tests tried to predict the movement of UAVs based on the classification into 3 main zones. The results showed that drone sounds could be reliably detected in the movements between zones with an average recognition accuracy of 90 % using the hybrid CNN-BiLSTM model. Moreover, the implementation of such advanced acoustic sensor systems for UAV detection can improve the accuracy of real-time prediction, especially when integrated into a multimodal system with multi-sensor fusion methods.

Abstract: Nitrogen injection technology has become an important production technology after water injection development in the karst fracture-vuggy reservoir in Tahe oilfield. However, due to the influence of reservoir heterogeneity and high mobility of gas fluid, nitrogen is easy to form a dominant channel and gas channelling occurs, and the recovery effect time is short. Based on this, a visual surface karst model is designed and made to study nitrogen foam assisted gas drive. The results show that after gas channeling occurs in the dominant channel of nitrogen flooding, foam injection assisted gas flooding can improve oil recovery. In the longitudinal direction, foam assisted gas drive mainly displaces the remaining oil because of gravity differentiation and the reduction of oil-water interfacial tension. In the horizontal direction, foam assisted gas drive is mainly used to block the large pore cracks and dominant channels, and promote the gas to turn into the large tortuous and small cracks, and expand the swept efficiency of the gas.After forming the dominant channel, injecting 0.3pv salt-sensitive foam with a gas-liquid ratio of 2:1 in the middle of the gas channel can improve the recovery rate of the model from 4% to about 25%, and the recovery rate can be increased by about 20%, which improves the effect of gas flushing and improves the development efficiency of the oil field at the same time.

Background: Codonopsis pilosula (CP) is one of the most popular Qi-invigorating herbal medicines and has been extensively used to promote health and vitality in China for a long time. C. pilosula polysaccharide (CPP) is the principal active components of CP, which is considered as the reason for CP widespread application. However, there are not revealed that CPP exert a Qi-invigoration effect by protecting mitochondria and/or improving mitochondrial function in the existing traditional Chinese medicine theories. Methods: Herein, we extracted CPP from C. pilosula and investigated the effects of CPP on energy metabolism and mitochondrial protection. Based on the mice chronic hypoxia model for imitating the “Qi” deficiency state of the human body in traditional Chinese medicine, which was administered with CPP by oral gavage daily for 10 days, mitochondrial permeability transition (MPT), lipid peroxidation product malondialdehyde (MDA) in brain, mitochondrial respiratory function, the levels of adenosine triphosphate (ATP), adenosine diphosphate (ADP) and adenosine monophosphate (AMP) in liver cells were assayed. Adenylate energy charge (AEC), total adenylate pool (TAP), ATP/ADP, and ATP/AMP ratios were calculated. Results: CPP could inhibit the formation of MDA in mice brains, decrease oxygen consuming rate and respiratory control ratio (RCR) of liver mitochondria, increase levels of ATP, TAP and AEC in liver cells under chronic hypoxia condition. Conclusion: Overall, CPP can invigorate “Qi” by improving mitochondrial energy metabolism and bioenergetic levels.

This study investigates the innovative use of natural porous clays from the Bejaad region in Morocco as a support matrix for the encapsulation and controlled release of lemon essential oil (Citrus limonum, EOCL), a natural compound with well-documented insecticidal properties. The research aims to address the inherent challenges of essential oils, particularly their high volatility and rapid degradation, by improving their stability and insecticidal efficiency against the grain pest Sitophilus granarius. By anchoring EOCL onto clay matrices, the study seeks to achieve a sustained and controlled release of the active components, thereby enhancing their practical application as biopesticides. The clays were comprehensively characterized using advanced analytical techniques, including X-ray diffraction (XRD), X-ray fluorescence (XRF), Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy with energy-dispersive X-ray analysis (SEM-EDX), and thermogravimetric analysis (TGA). These techniques revealed the mineralogical composition, thermal properties, and morphology of the clays, demonstrating their suitability for effectively adsorbing and retaining EOCL. The insecticidal performance of the clay/EOCL composites was rigorously tested under controlled conditions, revealing a marked improvement in efficacy, with significantly lower lethal doses required to achieve high mortality rates in Sitophilus granarius. The diffusion of EOCL through the clay matrix was modeled using Fick law of diffusion, and the results were further refined through statistical optimization to identify key parameters that influence the release and effectiveness of the active compounds. Complementing the experimental approach, a bioinformatics analysis was conducted to explore the molecular interactions between limonene, the primary active component of EOCL, and target proteins in insects. This theoretical investigation provided insights into the potential mechanisms of action, reinforcing the empirical findings. The study concludes that encapsulating EOCL within porous clay matrices not only enhances the stability and controlled release of the oil but also significantly boosts its insecticidal effectiveness. This approach presents a promising, environmentally sustainable strategy for crop protection, integrating material science, theoretical modeling, and bioinformatics to develop more efficient and durable biopesticides.

Optimizing faculty workload in higher education institutions is essential for enhancing faculty satisfaction, student learning, and institutional productivity. Traditional workload management approaches tend to simplify the realities of academic work, creating distortions that undermine the quality of the academic system. In this paper, we present the simplex method, a linear programming technique that can be used as a sophisticated technological solution for optimizing faculty workload. Using a comprehensive mathematical model and a Python code outline, we demonstrate the effective use of the simplex method to balance the teaching loads across the faculty. The obtained results indicate that this method is superior to traditional approaches in terms of efficiency and satisfaction. By doing this, we not only explore new horizons in scholarly discourse on faculty workload but also offer a practical tool for higher education institutions to enhance their operational efficiency and faculty functioning.

Nanoemulsions represent to cosmetic industry new options of formulations which are easier to apply, better functional benefit and potentially safer formulations. They are very interesting for cosmetic products intended for skin care and also for health products, due to the reduced size (range 20 to 500 nm) of the globules, which avoids processes of instability; they present transparency, fluidity, wettability, spreadability, increase skin penetration and good sensorial sensation. The main mechanism of instability of nanoemulsions is called Ostwald ripening, responsible to increase the average diameter of the emulsion globules, which, over time, the smaller globules have their contents enveloped by the larger ones. Recent research has investigated the effects of specific plant oils on skin hydration and barrier function, providing insights for personal care formulators. Among the so-called moisturizing agents commonly used in the cosmetic industry and which reduce Trans Epidermal Water Loss (TEWL) avoiding damage to the skin barrier, there are vegetable oils, especially sesame oil, raspberry seed oil. High-quality vegetable oils present specific compounds with common properties such as antimicrobial, antioxidant and anti-inflammatory activities and even moisturizing, emollient and sunscreen actions, making them attractive alternative and complementary treatments to soften the process of skin aging. This research proposes the development of nanoemulsions containing Sesame Oil (SO) added with Raspberry Oil (RO) by low energy method and consists and to evaluate in vitro SO and RO antioxidant activity; formulation studies; to instability process and in vitro evaluation HET CAM.

This paper explores the influence of various camera settings on the quality of 3D reconstructions, particularly in indoor crime scene investigations. Utilizing Neural Radiance Fields (NeRF) and Gaussian Splatting for 3D reconstruction, we analysed the impact of ISO, shutter speed, and aperture settings on the quality of the resulting 3D reconstructions. By conducting controlled experiments in a meeting room setup, we identified optimal settings that minimize noise and artifacts while maximizing detail and brightness. Our findings indicate that an ISO of 200, a shutter speed of 1/60 seconds, and an aperture of f/3.5 provide the best balance for high-quality 3D reconstructions. These settings are especially useful for forensic applications, architectural visualization, and cultural heritage preservation, offering practical guidelines for professionals in these fields. The study also highlights the potential for future research to expand on these findings by exploring other camera parameters and real-time adjustment techniques.

In this study the transboundary transport of air masses over the water table of Lake Baikal during the raining period of July–August 2010–2019 was investigated. For this purpose, the backward trajectories with an interval of 6 hours were constructed by using the NOAA hysplit method. These trajectories started at the center of Lake Baikal at altitudes of 250 and 2500 m. It was shown that during these years the western air mass transport passed over the marshland of Western Siberia, as well as over the steppe and forest-steppe regions of south of Western Siberia, and the angular sectors of WNW (West - North West) and WSW (West - South West) directions, accordingly, dominated. In July, at altitudes of 2500 m, in average 23.68 % percents of the total amount of trajectories have passed over the arid areas, which are located at WSW direction, and 34.42 % of trajectories passed from the WNW direction. In August, at altitudes of 2500 m, in average arid WSW part in the trajectory ensemble was equal to 24.22 %, and the wet WNW part from the swamps of Western Siberia was equal to 41.05 %. It was also shown that the air masses that passed in July and August 2010–2019 over the polluted industrial agglomerations, that are allocated in the south of Beijing, did not passed over the area of the Lake Baikal, and therefore these air masses can not participate both in the ecological situation change in the area of the Lake Baikal, and in the processes of nuclearization at the cloud formation. For the period of 2010–2019 the average value of the SSE (South-South-East) part in the trajectory ensemble, which is corresponding to the transfer from the Central regions of China, was equal at an altitude of 250 m: in July to 1.00 %, in August to 0.49 %. At an altitude of 2500 m SSE sector in July was equal to 1.77 % and in August – 2.09 % of the total number of studied trajectories.

Salivary gland tumors are relatively rare neoplasms, comprising approximately 3-6% of all head and neck tumors. Parotid gland carcinoma (PGC) represents approximately 70-80% of all salivary gland malignancies. Treatment strategies depend on tumor histology, stage, and molecular characteristics, with surgical resection and adjuvant radiotherapy being the mainstays of treatment for localized disease. Conversely, in advanced stages therapeutic approaches, including chemotherapy and targeted agents, are more challenging. We present a case report of a 60-year-old patient with hepatic and nodal metastases of parotid gland’s carcinoma, with the aim of highlighting the management and treatment outcomes.

Myo-inositol (MYO) and D-chiro-inositol (DCI) are the two most significant isomeric forms of inositol, playing a critical role in intracellular signaling. MYO is the most abundant form of inositol in nature; DCI is produced from MYO through epimerization by an insulin-dependent enzyme. Recently, it has been demonstrated that inositols may influence oocyte maturation, improve intracellular Ca2⁺ oscillation in the oocytes and have been proposed as potential interventions for restoring spontaneous ovulation. MYO concentration in human follicular fluid is considered a bioindicator of oocyte quality. In the ovary, DCI modulates the activity of aromatase thus regulating androgen synthesis. Under physiological conditions, MYO/DCI ratio is maintained at 40:1 in the plasma. In women with PCOS, MYO/DCI ratio lowers to 0:2:1, contributing to elevated androgen production. By regulating FSH signaling, MYO administration increases the number of high-quality embryos available for transfer in poor responder patients. Finally, by acting downstream to insulin signaling inositol administration during pregnancy may represent a novel strategy for counteracting gestational diabetes. These findings show that diet supplementation with inositols may be a promising strategy to address female infertility and sustain healthy pregnancy.

Flaxseed has been suggested as a viable solution to improve sex hormones. However, conflicting and uncertain evidence regarding its impact on sex hormone profiles exists. This meta-analysis assesses the effects of flaxseed on sex hormone profiles. PubMed, Scopus, Embase, and the Web of Science were searched for relevant articles in August 2021. The Cochrane Collaboration tool was used for the quality assessment, and the evidence's trustworthiness was assessed using the Grading of Assessment, Development, and Evaluation of Recommendations (GRADE). A total of ten articles revealed that flaxseed might not have a significant effect on follicle-stimulating hormone (FSH) ((WMD: 0.84; 95% CI: -5.53, 7.00, p =0.791; I²= 88.6%, p<0.001, level of confidence: low), sex hormone-binding globulin (SHBG) ((WMD: 2.17; 95% CI: -1.19, 5.54, p =0.206; I²= 66.3%, p=0.011, level of confidence: low), total testosterone (TT) (WMD: -0.00; 95% CI: -0.07, 0.07, p=0.968; I²= 41.3%, p =0.146, level of confidence: low), free androgen index (FAI) (WMD: -0.13; 95% CI: -0.68, 0.42; p=0.645; I²= 85.4%, p<0.001, level of confidence: low) and dehydroepiandrosterone sulfate (DHEAS) (WMD: 0.62; 95% CI: -3.35, 4.59, p=0.761; I²=63.6%, p =0.064, level of confidence: very low). It is demonstrated that flaxseed might not affect sex hormones in adults. Flaxseed supplementation had no significant effect on sex hormones in adults. Nevertheless, due to the limited included trials, this topic is still open and needs further studies in future RCTs.

The CH2Cl2-MeOH extract of the Mediterranean sponge Crambe crambe was investigated via UHPLC-HRMS/MS employing manual dereplication and in silico mass spectrometry tools. A deconvolution approach was implemented toward the extensive metabolic characterization of the sample, resulting in the annotation of 53 compounds. The analysis of data-dependent HRMS/MS scans was conducted to establish fragmentation patterns characteristic of each crambescin A, B and C sub-families. Among the 39 compounds identified from these groups, 22 analogues were reported for the first time including 4 new homologous series that differed by the ratio of methylene units in the upper (n+2) and lower (m+2) alkyl side chains. More specifically, crambesins presenting m= 5 or 6 and n=5 (compounds 7, 11, 22 and 24) as well as m=5 or 6 and n=4 (compounds 5, 6, 8, 9, 12 and 14) were characterized. Additionally, 4 new crambescidin analogues (compounds 13, 15, 35 and 39) were also reported. The identity of the dereplicated features was further validated by studying crambescins spectral similarities through feature-based molecular networking approach. Overall, this study suggests UHPLC-HRMS/MS—through integration of manual and computational dereplication approaches—as a valuable tool for the investigation and high-throughput characterization of C. crambe metabolome.

This study investigates the impact of AI-generated contexts on preservice teachers' computational thinking (CT) skills and their acceptance of educational robotics. This article presents a methodology for teaching robotics based on AI-generated contexts aimed at enhancing CT. An experience was conducted with 122 undergraduate students enrolled in an Early Childhood Education program, aged 18-19 years, who were training in a Computer Science and Digital Competence course. The experimental group utilized a methodology involving AI-generated practical assignments designed by their lecturers to learn educational robotics, while the control group engaged with traditional teaching methods. The research addressed five key questions: the effectiveness of AI-generated contexts in improving CT skills, the specific domains of CT that showed significant improvement, the perception of student teachers regarding their ability to teach with educational robots, the enhancement of perceived knowledge about educational robots, and the overall impact of these methodologies on teaching practices. Findings revealed that the experimental group exhibited higher engagement and understanding of CT concepts, with notable improvements in problem-solving and algorithmic thinking. Participants in the AI-generated context group reported increased confidence in their ability to teach with educational robots and a more positive attitude towards technology integration in education. The results underscore the importance of context and support in fostering both confidence and acceptance of educational technologies among future educators. This research contributes to the growing body of literature at the intersection of AI, robotics, and education, advocating for the integration of AI and robotics into teacher training curricula. Future research should explore the long-term effects of these methodologies on CT skills and teaching practices across diverse educational settings.

Dendropanax morbifera Leveille is a traditional medicine used to treat migraine headache and dysmenorrhea. In this study, a new phenolic glycoside, morbiferoside A (1), and three polyacetylenes, methyl (10E,9R,16R)-16-acetoxy-9-hydroxyoctadeca-10,17-dien-12,14-diynoate (2), methyl (10E,9R,16S)-9,16-dihydroxyoctadeca-10-en-12,14-diynoate (3), and methyl (10Z,9R,16S)-9,16-dihydroxyoctadeca-10,17-dien-12,14-diynoate (4), were isolated from the aerial parts of D. morbifera, together with seven known compounds (5–11). Importantly, the isolates, 7 and 9 were found in the family Araliaceae for the first time in this study. Compounds 1−11 were evaluated for their binding affinity to AMPK and CTSS receptors using in silico docking simulations. Only compound 8 increased the protein expression levels of PPAR-α, Sirt1, and AMPK when administered to HepG2 cells as a PPAR-α agonist. On the other hand, 8 did not produce any significant reduction in CTSS activity. This study could pave the way for the discovery of novel treatments from D. morbifera targeting PPAR-α and AMPK.

Given the pivotal role played by the microbiome in skin health it is important to understand how its composition varies with age, gender and body site, and regular use of topical products. Four studies have been carried out to determine the effects of long term (4 week) use of different dexpanthenol containing topical products on the skin microbiome of a varied population with cosmetically dry skin. Skin microbiome composition was assessed before and after product usage. No significant changes in microbiome richness or diversity were found for the individual test products, however meta-analysis of the combined dataset did show changes in microbiome composition as a function of subject sex, age and body site. The work presented here demonstrates how the use of carefully formulated topical products on skin, when used in a way which is representative of real life usage conditions, can respect the microbial diversity present on skin across a widely varied study population.

This paper examines the current state of food security in Pakistan, focusing on the factors influencing the nation's ability to ensure a stable food supply. The analysis covers food production, distribution, accessibility, and stability, while also considering socioeconomic and environmental factors. The role of government regulations and international collaborations is explored in addressing food security challenges. Despite the critical importance of agricultural governance for sustainable food production and economic development, Pakistan's agricultural sector remains under prioritized, suffering from a lack of strategic vision and inconsistent government support. This neglect undermines efforts to achieve food security and stability. The study evaluates the alignment of Pakistan's National Food Security Policy (2018) with global, national, and provincial standards for sustainable agricultural management. A framework based on principles, criteria, and indicators (PCI) was developed, utilizing both quantitative and qualitative methodologies. Forty-three indicators, eleven criteria, and one guiding principle across four dimensions were identified. Thirteen governance tools were assessed using a ratio scale (0–5), with results presented in a policy coherence matrix. The coherence index score of 2.14 indicates significant misalignment among regulatory instruments, highlighting gaps in addressing climate change, low-income food access, service management, and institutional capacity. The study reveals that many food policies in Pakistan are outdated and inconsistent, requiring revision to address local challenges and align with emerging international obligations. The findings provide a comprehensive overview of the current food security landscape in Pakistan, offering insights for more informed decision-making to build a resilient and sustainable food system.

In the domain of software engineering, the accurate and effective classification of requirements is of paramount importance. Proper classifications of these requirements enable developers to create robust and error-free software solution. Traditional methods of user requirements classification face the issue of the reliance on manual processes, which are time-consuming, labor-intensive, and prone to human error. The limitations of traditional methods underscore the need for more automated, scalable, and robust approaches to user requirements classification in order to meet the demands of modern software development practices. To improve the classification process, we employed a Deep Learning (DL) methodology termed the Bidirectional Long Short-Term Memory (Bi-LSTM) model to conduct feature extraction, after which we merged these feature vectors into ML classifiers. Our research methodology is structured around a five-step process. Initially, the textual input is tokenized and converted to lowercase. Subsequently, we eliminate all punctuation. The pre-processed text is then subjected to a Bi-LSTM (Bidirectional Long Short-Term Memory) model for feature vector extraction. After that, this feature vector is fed into different classifiers such as Medium KNN, Cubic KNN, Linear SVM, Quadratic SVM, and Cubic SVM and obtained an accuracy of around 99.60% to 99.80% on a publicly available dataset of requirements.

In the article, we define a sequence F: F(0) = 2, F(1) = 10, F(2) = 16, F(3) = 22, F(4) = 26, F(4) = 28, · · · , F(i) = F(i − 1) + l, where the number l ≤ 8 is defined by using prime numbers and matrix algebra. The sequence includes only even numbers and can be used to define a series of numbers F(i) + 1, the non-Goldbach sequence, that contains all numbers that cannot be written as the sum of two primes. The result is related to Goldbach’s conjecture. The famous Goldbach conjecture states that every even integer greater than 2 can be expressed as the sum of two primes.

Water pollution is a major global problem and the deterioration of water quality in rivers in many situations is becoming more widespread day by day, this causes a serious threat to all living beings and is eventually leading to the extinction of many aquatic species and others that depend on them. Therefore, with the advancement of Internet of Things (IoT) and Wireless Sensor Networks (WSN), monitoring the state of river water, for taking quick action to safeguard rivers becomes a pressing need, and is the main objective. An investigation was made into some existing IoT technologies and other related work to help achieve the objective of this project. In this paper we develop a low-cost solution based on Long Range (LoRa) to check in real-time, with specific sensors, some water parameters such as temperature, pH, conductivity, and turbidity. The data collected in a sensor node is sent via LoRa modulation to a gateway, where it is processed, forwarded to a local Message Queuing Telemetry Transport (MQTT) server, displayed on a local Node-RED dashboard, and alternatively it can be sent to the Internet. The developed prototype system is tested in a real scenario and proves that it can reliably monitor the water quality of a river in a reasonably sized area.

Modeling carbonate growth in fractures and pores is important for understanding carbon sequestration in the environment or when supersaturated solutions are injected into rocks. Here we study the simple but nontrivial problem of calcite growth on fractures with rough walls of the same mineral using kinetic Monte Carlo simulations of attachment and detachment of molecules and scaling approaches. First we consider wedge-shaped fracture walls whose upper terraces are in the same low energy planes and show that the valleys are slowly filled by propagation of parallel monolayer steps in the wedge sides. The growth ceases when the walls reach these low energy configurations, so that a gap between the walls may not be filled. Secondly we consider fracture walls with equally separated monolayer steps (vicinal surfaces with roughness below $1$~nm) and show that growth by step propagation will eventually clog the fracture gap. In both cases, scaling approaches predict the times to attain the final configurations as a function of the initial geometry and of the step propagation velocity in the chosen supersaturation. The same reasoning applied to a random wall geometry shows that step propagation leads to lateral filling of surface valleys until the wall reaches the low-energy crystalline plane that has the smallest initial density of molecules. Thus, the final configurations of the fracture walls are much more sensitive to the crystallography than to the roughness or to the local curvature. The framework developed here may be used to determine those configurations, the times to reach them, and the mass of deposited mineral.

Background: Human β-defensin 2 (HBD2) is a protein important in immune system activation, produced by epithelial cells, mainly as a result of contact with microorganisms or the action of various pro-inflammatory cytokines. According to previous research, it has proven to be important in chronic urticaria, leading us to research on the HBD2 in isolated chronic angioedema. Methods: This cross-sectional study involved 102 participants (three groups): 33 patients with isolated chronic non-hereditary angioedema (AE); 33 patients with angioedema associated with chronic urticaria (CU+AE); and 35 healthy controls (CTRL). They provided saliva and serum samples to determine HBD2 levels using the Enzyme-Linked Immunosorbent Assay-ELISA. Results: Salivary HBD2 levels were significantly higher in those with CU+AE than in the CTRL (p=0.019). While salivary HBD2 values differed between those with angioedema and CTRL, the serum HBD2 values did not. Also, no correlation between the levels of HBD2 in saliva and serum was found. Conclusions: Since we found that salivary HBD2 values were significantly higher in those with CU+AE than in CTRL, this points to a possible role of the HBD2 molecule in pathogenesis of AE. Therefore, more research is needed to determine how reliabile salivary HBD2 measurement is and its significance.

Coastal ecosystems are crucial in mitigating the impact of coastal hazards on society. However, the paucity of information about the purpose of these ecosystems in reducing floods in Rivers State, Nigeria. This study aims to assess the contribution of mangrove ecosystems in protecting coastal communities from flooding using the InVEST coastal vulnerability model (version 3.10.2). The model analyzes various data inputs and assigns relative numbers, ranging from 1 to 5, indicating different levels of exposure. Data on population, bathymetry, shoreline type, land use land cover, and continental shelf were obtained from relevant websites and the InVEST model package. The findings indicate that the mangrove ecosystems in River State offer minimal protection against coastal flooding due to their degraded state caused by oil spills and over-exploitation. Additionally, sandy beaches provide little to no protection, and the socio-economic conditions in the communities contribute to increased vulnerability to flooding. The study recommends awareness programs to educate the public about the importance of mangroves for coastal protection in addition to their conservation and restoration.

Bread is one of the most popular products consumed almost daily and, simultaneously, the most frequently wasted product in large quantities. The factors determining this phenomenon in a household must first be known in order to develop and implement effective programmes to combat consumers throwing away bread. Minimizing bread waste contributes to achieving the UN's Sustainable Development Goal 12 of "halting food waste by 2030". The aim of the study was to examine the influence of selected sociodemographic, economic and behavioural characteristics of consumers on the methods of managing stale bread. The study was conducted on a nationwide, random, and representative group of 1115 adult respondents. Over 15% of respondents admitted throwing stale bread into the waste container. Statistical analysis showed that the highest percentage of younger people, residents of large cities, and working people declared throwing unused bread into the waste container. More than 1/3 of respondents declared that they process stale bread into breadcrumbs. A higher percentage of the oldest people, residents of rural areas, and families with more than four adults declared that they manage bread this way. There was no significant relationship between the frequency of bread purchase and all methods of managing stale bread (except for feeding animals). The place of purchase influenced four methods of handling stale bread.

The evolution of jaws represents one of the most transformative innovations in vertebrate history, fundamentally reshaping both animal anatomy and ecological dynamics. At the core of this innovation lies the neural crest, a unique vertebrate cell population whose molecular and developmental properties enabled the formation of articulated jaws. This review explores how neural crest cells, as multipotent stem cells, contributed to jaw development through their extraordinary capacity to form cartilage, bone, and associated tissues. Beyond the developmental perspective, we examine how the emergence of jawed vertebrates dramatically altered marine ecosystems, leading to new predator-prey relationships and driving an unprecedented diversification of feeding strategies. This innovation triggered an evolutionary arms race that shaped marine biodiversity and established new trophic relationships that persist in modern oceans. Understanding this ancient innovation provides crucial insights into both developmental mechanisms and ecological dynamics that defined vertebrate evolution.

The Bridge Health Index (BHI) serves as an essential tool for assessing the structural and functional condition of bridges, calculated based on the condition of structural components and the serviceability of the bridge. Its primary purpose is to identify the most deteriorated structures in an asset inventory and prioritize those in most urgent need of repair. However, a frequently cited issue is the lack of accurate and objective data, with the determination of BHI often heavily reliant on expert opinions and engineering judgment. Furthermore, the BHI systems used in most countries are dependent on the current state of bridge components, making it challenging to use as a proactive indicator for factors such as the rate of bridge aging. To address this issue, this study introduces a novel BHI as a quantitative evaluation metric for reinforced concrete slab bridges and details the process of deriving the BHI based on deterioration models. The deterioration models are derived by preprocessing the deterioration data of reinforced concrete (RC) slab bridges, wherein the relationship between time and deterioration is directly employed for training a long short-term memory model. The BHI was validated through a case study involving six RC-slab bridges, wherein accuracies of &gt;93% were achieved, confirming that the proposed quantitative evaluation methodology can significantly contribute to maintenance decisions for bridges.

Keywords: antisense oligonucleotides; RNA therapeutics; endosomal escape; nuclear localization; drug delivery

Aligning Information Technology (IT) with business strategies is crucial for improving operational efficiency, innovation, and competitiveness, particularly in small and medium-sized enterprises (SMEs). However, SMEs face unique challenges in achieving this alignment due to limited resources, technical expertise, and a rapidly changing technological landscape. This systematic review aims to assess the achievements and challenges of IT-business alignment in SMEs from 2014 to 2024, with a focus on the impact of emerging technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and blockchain. A systematic search of Google Scholar, Web of Science, and Scopus databases identified 63 relevant papers. Studies were selected based on their relevance to IT-business alignment in SMEs, and both quantitative and qualitative data were extracted and analyzed to identify key trends, achievements, and challenges. The review found that 74% of SMEs reported improved operational efficiency and 68% experienced enhanced innovation opportunities due to effective IT-business alignment. However, 59% of SMEs identified resource limitations as a significant barrier, while 45% highlighted a lack of technical expertise. Additionally, 35% of SMEs faced organizational resistance to change. Emerging technologies such as AI and IoT contributed positively to IT alignment in 60% of cases, though 30% of SMEs noted challenges related to cybersecurity and skills gaps. IT-business alignment is essential for SMEs to enhance productivity and remain competitive in a dynamic technological environment. While the integration of emerging technologies shows promise, SMEs continue to face significant barriers, including resource constraints and technical skills shortages. Tailored frameworks, industry-specific policies, and targeted training programs are needed to help SMEs overcome these challenges. Future research should investigate the long-term impacts of IT-business alignment and explore strategies to increase its accessibility and effectiveness.

Leishmaniasis is the second deadliest parasitic disease in the world, after malaria, with an estimated 1.6 million new cases each year. While cutaneous leishmaniasis can result in permanent scars from lesions after treatment, the mucocutaneous and visceral diseases can result in life-altering and life-threatening complications. Mucocutaneous leishmaniasis can lead to permanent facial disfigurement and destruction of airways, while the visceral form is nearly 100% fatal if left untreated, as parasites spread into internal organs. Accurate species diagnosis which can be predictive of future disease is critical for treatment and follow-up. PCR-based diagnostics can provide sensitive detection of parasites and identify the species, but are slow, expensive, and not suitable for low-resource environments. In this publication, we describe our efforts to develop a simple, affordable, and instrument-free leishmania DNA diagnostic that can be used in both high-tech settings and the field. We have developed methods for parasite lysis, RPA amplification of DNA, and CRISPR crRNA detection for all leishmania species as well as specific identification of L. panamensis as a predictor of mucocutaneous disease. We have demonstrated the ability to detect single-digit parasites without compromising the specificity in identifying single species. In tube analysis of individual assays were carried out in succession culminating in un-quenched fluorescent signal quantifiable over negative control. The described work is the foundation which will be implemented into a three-track [all leishmania, mucocutaneous or visceral only, and a human positive control] assay that we plan to utilize in a Funnel Adapted Sensing Tube (FAST) single use, instrument free, and affordable diagnostic.

Low learning rates in Convolutional Neural Networks (CNNs) for image segmentation tasks can lead to convergence issues, unstable models, large oscillations, risk of divergence, sensitivity to the initial weights, and biases of the model. All these challenges make CNNs computationally expensive and require considerable training data. In contrast, achieving a high learning rate has been a significant challenge for CNNs. In response to the abovementioned challenge, this work aims to increase the learning rate of the image segmentation process. Images of any object in the universe taken from any camera can broadly be divided into spectral and geometric properties. Both these properties are prominently visible in satellite images. Therefore, if a method can classify satellite images, it can be applied to classify any image using the same technique. In this paper, Evolutionary Convergent Functions (ECF) are proposed. They convert features' spectral and geometric properties in a satellite image into mathematical equations using a decision tree and then converge with a neural network. Different high-resolution data have been chosen to extract additional features from it. This transformation process, anchored in decision tree methodology, converges with neural networks to yield unparalleled results, all while eliminating the need for computationally intensive convolutions. The proposed method extends beyond conventional boundaries by using varied high-resolution datasets. Each dataset is carefully selected with distinctive features. This research explores the untapped potential of ECF to advance the field of automated image classification, aiming to broaden the scope of current methodologies. The synergy between spectral and geometric properties emerges as a powerful combination, endowing our methods with the ability to extract nuanced and context-rich information for image analysis. The results show a high accuracy, i.e., above 90%, in almost all objects of different shapes and spectral signatures. Additionally, in terms of prediction time, ECF is faster than U-Net, a state-of-the-art method that is evidence of ECF efficiency and speed.

The interaction of pulsed lasers with matter involving nanomaterials as a pure target or thin layer deposited on a target, initiates transient plasma which shows strong enhancement in a spectral line emission. This domain of research has been explored via two well established techniques dubbed NELIBS and NELIPS. These Nano-Enhanced Laser Induced Breakdown or Plasma Spectroscopy techniques entail similarities as well as differences. Thereupon, certain confusion has arisen from various aspects of the similarities as well as differences between the two techniques. In this article, we will investigate the application of either technique to retrieve relevant data about enhanced spectral line plasma emission phenomenon. To discriminate between these two techniques, survey on the nature of target, the origin of enhancement and prevalent theoretical approaches is presented. Finally, potential achievements, challenges, and the expected prospective are highlighted.

This work is a physical review, with elements of additions and thinning, on the methods of theoretical studies of nonlinear electrophysical phenomena in crystals with ion-molecular chemical bonds (CIMB). Crystals of this class include ionic dielectrics (characterized by high ionic conductivity), layered crystals, a special case of which are hydrogen-bonded crystals (HBC), defined as proton semiconductors and dielectrics (PSD).A scientific review (comparative analysis and justification of various approximations) was carried out on the methods of constructing and solving a generalized quasi-classical kinetic equation describing the mechanism of nonlinear relaxation polarization and conductivity processes in dielectric materials with ion-molecular chemical bonds (a special case is hydrogen-bonded crystals (HBC)) in a wide temperature range (1-1550 K) and polarizing field strengths (0.1-1000 V/m) at alternating field frequencies of the order of 1 kHz - 1000 MHz. The most important variant of the equations of the kinetic theory of dielectric relaxation in this work is the generalized non-linear by polarizing field quasi-classical kinetic equation of ionic (in HBC, proton) relaxation, based on the particle number balance equation (conductivity ions) in potential wells and having (in these models) the meaning of the ion current continuity equation (in HBC, protons), solved by the method of successive approximations by decomposition into infinite power series by degrees of a small dimensionless comparison parameter. It was found that in the area of ​ ​ weak fields (0.1-1 MW/m) at temperatures T = 50 - 550 K, for a number of ionic dielectrics (including HBC and similar dielectric properties and lattice structure) the generalized quasi-classical kinetic equation transforms to the linearized Fokker – Planck equation and, in the region of low (50-100K) and higher temperatures (250-550 K) begin to manifest non-linear polarization effects due to respectively proton tunneling (in the case of HBC) and volume charge relaxation (in the case of the HBC and for a wider class of ionic dielectrics). At ultra-low (1-10 K) temperatures in the region of weak fields (0.1-1 MW/m) and ultra-high temperatures (550-1550 K) in the region of strong fields (10-1000 MW/m), the contribution of this kind of effects to polarization is significantly enhanced. The effect of nonlinearities on relaxation times for microscopic acts of proton transitions across a potential barrier (assumed to be parabolic) is investigated. Nonlinear effects at volume-charge polarization in the hydrogen-bonded crystals (HBC) in alternating electric field, in radio frequency range are investigated. From the solution of the system of nonlinear Fokker-Planck equations (macroscopic kinetic equation) and Poisson, with blocking electrodes, using Fourier series, a recurrent (convenient for use in any approximation of perturbation theory) expression is constructed for complex amplitudes of relaxation modes of volumetric charge. Complex dielectric permittivity (CDP) is calculated as a series decomposition over even frequency harmonics of a variable field. The effect of quantum proton transitions and polarizing field parameters (strength, frequency) on the nonlinear properties of proton semiconductors and dielectrics has been established.

Background/Objectives: Chronic pain syndromes pose a significant global health challenge to patients and physicians with a complex relationship of biological and psychosocial factors that are only partly understood. Emerging research suggests an association between prenatal and childhood adversity and the development of somatic syndromes, particularly in females. This study aims to explore the relationship between sexual dimorphic epigenetic changes in the connectome and prenatal and early life adversity (ELA). Methods: A review of existing literature was conducted, examining studies utilising MRI to identify critical periods of environmental influence on neural phenotypes. Results: The findings indicate a significant association between prenatal and childhood adversity and the emergence of central sensitisation syndromes, particularly among females. Notably, alterations in grey matter volume and neural connectivity patterns were observed, suggesting that early adverse experiences can influence pain signalling mechanisms. Conclusions: Understanding the role of sex differences in brain circuitry is crucial for developing personalised pain management strategies. This study highlights the importance of considering both biological and psychosocial factors in addressing chronic pain, as interventions based predominantly on male subjects may be less effective for females. Further research is warranted to explore these differences and refine therapeutic approaches.

Abstract. The volatility of global energy markets, particularly electricity prices, plays a crucial role in influencing international economic activities. In the era of big data, machine learning has revolutionized the field of cancer research, particularly in analyzing gene expression data. This study explores the application of machine learning models to the GSE45827 dataset, which contains breast cancer gene expression profiles. With over 54,000 genes and 151 samples categorized into six classes, the dataset presents a high-dimensional challenge that is addressed using dimensionality reduction techniques such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). The PCA method proved most effective in retaining the critical features of the data in lower dimensions, allowing for clearer visualization and enhanced model performance.The reduced dataset was then classified using the eXtreme Gradient Boosting (XGBoost) model, achieving promising multi-class classification results. The model demonstrated high precision, recall, and F1-scores across several classes, particularly excelling in classes 1, 2, and 5. However, certain classes, such as 0 and 4, exhibited lower recall, highlighting areas for further refinement. The integration of PCA and XGBoost not only improved the interpretability and computational efficiency of the model but also contributed to the accurate identification of breast cancer subtypes, emphasizing the importance of machine learning in cancer diagnosis and treatment.

Heart disease, including myocardial infarction (MI), remains a leading cause of morbidity and mortality worldwide, necessitating the development of more effective regenerative therapies. Direct reprogramming of cardiomyocyte-like cells from resident fibroblasts offers a promising avenue for myocardial regeneration, but its efficiency and consistency in generating functional cardiomyocytes remain limited. Alternatively, reprogramming induced cardiac progenitor cells (iCPCs) could generate essential cardiac lineages, but existing methods often involve complex procedures. These limitations underscore the need for advanced mechanistic insights and refined reprogramming strategies to improve reparative outcomes in the heart. Partial cellular fate transitions, while still a relatively less well-defined area and primarily explored in longevity and neurobiology, hold remarkable promise for cardiac repair. It enables the re-programming or rejuvenation of resident cardiac cells into a stem or progenitor-like state with enhanced cardiogenic potential, generating the reparative lineages necessary for comprehen-sive myocardial recovery while reducing safety risks. As an emerging strategy, partial cellular fate transitions play a pivotal role in reversing myocardial infarction damage and offer sub-stantial potential for therapeutic innovation. This review will summarize current advances in these areas, including recent findings involving two transcription factors that critically regulate stemness and cardiogenesis. It will also explore considerations for further refining these ap-proaches to enhance their therapeutic potential and safety.

The true parakeets (Aratinga jandaya) inhabit the northern and central-eastern regions of South America, where their survival is increasingly threatened by illegal wildlife trafficking and anthropogenic activities. These pressures force the birds to seek new habitats, increasing their exposure to heavy metal ingestion in urban environments. This study presents the first documented cases of heavy metal poisoning and its treatment in two A. jandaya specimens rescued and treated at a university hospital in Castanhal, Pará, Brazil. Clinically, bird 1 exhibited dry nostrils and diarrhea, while bird 2 showed wing weakness, slight opisthotonos, a tilted posture, and more erect feathers. Radiographs and blood tests confirmed the presence of metallic content in the ventriculus of both birds. Chelation therapy with calcium edetate at a dose of 70 mg/kg, administered twice daily via oral route for 7 and 10 days, respectively, resulted in the successful elimination of the metal. Both birds were discharged after 1 month and 6 days of treatment, and subsequently released. The chosen chelation protocol proved effective without the need for invasive procedures, marking a significant step in the management of heavy metal toxicity in A. jandaya.

The integration of advanced nuclear technologies like the System-Integrated Modular Advanced Reactor (SMART) into developing power grids presents unique opportunities for grid reliability. This network, connected to geothermal, hydro, solar, and wind power sources, comprises 220kV and 132kV transmission lines feeding 66kV, 33kV, and 11kV distribution lines to various load centers. As Kenya expands electricity access, maintaining grid stability while integrating new technologies becomes crucial for economic growth. While previous research has examined grid stability with renewable integration, the effects of advanced nuclear technologies in developing grids remain understudied. The study employs ETAP 22 simulation software to model three scenarios: a base case, compensator-only operation, and SMART-SMR integration. The analysis includes steady-state stability and reliability metrics such as SAIFI, SAIDI, CAIDI, and EENS. Results demonstrate significant improvements with SMART integration: SAIDI reduced by 2.8% to 5.0433 hours/customer-year, SAIFI improved by 5.1% to 0.0516 interruptions/customer-year, and voltage profiles stabilized between 98.21% and 98.91% of nominal. These findings highlight SMART potential to enhance grid performance in developing power systems, offering insights for policymakers towards achieving more reliable and clean energy infrastructure.

AYOLO differentiates itself with a new fusion architecture proposal that leverages the strengths of unsupervised learning and integrates the Vision Transformer approach, taking the YOLO series models as a reference. This innovation enables the model to effectively use rich, unlabeled data, providing a new example in pre-training methodology for YOLO architectures. On the benchmark COCO val2017 dataset, AYOLO demonstrates its superiority by achieving an impressive 38.7 % AP while maintaining an outstanding rendering speed of 239 FPS (Frame Per Second) on the Tesla K80 GPU. This performance outperforms the previous state-of-the-art YOLO v6-3.0 by a significant margin of +2.2 % AP while maintaining comparable FPS. AYOLO is presented as a demonstration of the potential of integrating complex information fusion techniques with supervised pretraining in improving the precision and speed of object detection models.

The aim of this study was to determine whether long-term storage of UHT milk and its heating in a microwave has an impact on the fatty acid profile. Material consisted of 4 types of UHT milk from different manufacturers with a fat content 3.2%. Analyses conducted for 13 days. Milk stored in the original container in a refrigerator at 4°C. Milk samples after opening the package was heated for 1 and 2 minutes in a microwave. Estimated absolute density, acidity, freezing point, electrolytic conductivity and solid non fat, crude fat, lactose, total protein and minerals. Physical and chemical parameters of milk were carried out using ultrasonic analyzer Lactoscan MCC. The fatty acid composition was determined using a gas chromatograph. Fatty acid methyl esters identified by comparison with the standards (Supelco 37 Component FAME). UHT milk storage after opening at 4 °C and heating in microwave for 1 or 2 minutes (microwave power for 1100 W and power heater 750 W) did not result in the period of 13 days significant changes in the fatty acid profile. Also were not stated other significant changes in the values of physical and chemical parameters of milk during the experiment. Differences that occurred in the electrolytic conductivity may result from evaporation of water during storage of milk and heating, as indicated by its freezing point. On this basis, it can be concluded that storing milk in the fridge and reheat in the microwave does not have a significant impact on its physicochemical properties and fatty acid profile.

Knowledge on river quality variations and of exceeding quality limits is essential for the allocation of water to different uses and for applying remedial measures. Thessaly plain was extremely fertile, and up until the early 20th century the area was a breadbasket for Greece. The highly important for the national agricultural production, albeit severely degraded Pinios river, has been assessed for its Chemical-Physicochemical (C-P) status, based on the results of the National Monitoring Program for the years 2018-2020, considering 218 seasonal samples. Forty percent (40%) of the total sample number, and 70% of the 30 monitoring stations revealed a lower than good C-P status, predominately as a result of elevated phosphate, total phosphorous and nitrate concentrations. Exceptionally, the Pinios river seems to be predominately affected by point sources of organic pollution, and secondarily by agricultural return flows and land flushing processes, whereas dominant mineralization and nitrification processes control the concentration and speciation of N and P compounds. The COVID lock-down seems not to have affected aquatic quality through an increase of WWT effluents or/and diminishing of atmospheric pollution, whereas the improvement of C-P status at the river outflow via dilution by local mountain springs is threatened by an ongoing dry spell affecting the country. Within the upcoming RBMPs, urgent remediation measures in Pinios basin should both target point sources of pollution and control agrochemicals, particularly focusing on adaptation strategies on extreme weather events.

A palladium-catalyzed aromatic dual C-H acylations followed with intramolecular cyclizations have been developed by the assistance of bidentate N-(quinolin-8-yl)benzamide. This tandem process involves the formation of three new chemical bonds, providing access to novel quinoline substituted hydroxyl isoindolones skeleton under simple reaction conditions. The deuterium-labeled competition reaction has revealed that C-H bond cleavage is the turnover limiting step.

Background/Objectives: Type 1 Diabetes Mellitus (T1DM) is the reduction or non-production of insulin. As T1DM increases cardiovascular risk, in addition to glycemic control, the monitoring of other risk factors, such as lipid profile, has gained attention. Vitamin D deficiency has been associated with the pathophysiology of T1DM, metabolic syndrome, and cardiovascular diseases. This study investigated the relationship between vitamin D, glycemic control, and lipid profiles in children and adolescents with T1DM. Methods: This observational study consisted of 81 patients with T1DM. Laboratory test data were collected to determine the lipid profile. Glycated hemoglobin (HbA1c) and fasting glucose levels assessed glycemic control. Results: A significant association was observed between serum vitamin D levels and a pubertal stage and a moderate negative correlation between vitamin D, age, and HbA1c. There is a negative correlation between vitamin D levels and age, as well as with HbA1c, and a positive correlation of HbA1c with glucose, total cholesterol, triglycerides, Low-density lipoprotein (LDL-c), non-HDL cholesterol (non-HDL cholesterol), apolipoprotein B (apoB), and the apolipoprotein B/apolipoprotein A ratio. Finally, the study indicated that vitamin D levels, insulin administration method, and total cholesterol, when considered together, explained approximately 43.6% of the observed variation in HbA1c, highlighting the complex interdependence of these factors in regulating glycemia. Conclusions: Our results show that Vitamin D levels are associated with glycemic control and other biochemical parameters and its control can bring benefits to the patient with T1DM.

In this study, we explored the structural and chemical modifications of cellulose fibres subjected to chemical and mechanical treatments through an innovative analytical approach. We employed photoacoustic spectroscopy (PAS) and reversed double-beam photoacoustic spectroscopy (RDB-PAS) to examine the morphological changes and the chemical integrity of the treated fibres. The methodology provided enhanced sensitivity and specificity in detecting subtle alterations in the treated cellulose structure. Additionally, we applied Coifman wavelet transformation to the PAS signals, which facilitated a refined analysis of the spectral features indicative of chemical and mechanical modifications at a molecular level. This advanced signal processing technique allowed for a detailed decomposition of the PAS signals, revealing hidden characteristics that are typically overshadowed in raw data analyses. Further, we utilized the concept of energy trap distribution to interpret the wavelet-transformed data, providing insights into the distribution and density of energy states within the fibres. Our results indicated significant differences in the energy trap spectra between untreated and treated fibres, reflecting the impact of chemical and mechanical treatments on the fibre’s physical properties. The combination of these sophisticated analytical techniques elucidated the complex interplay between mechanical and chemical treatments and their effects on the structural integrity and chemical composition of cellulose fibres.

The Internet of Things (IoT) is revolutionizing manufacturing, serving as a digital makeover tool that collects essential production data. The transition to IoT generates vast data volumes, requiring robust storage and system architecture. Cloud computing steps in here, accommodating these needs with ease. In this setup, a network of sensors across a manufacturing unit captures valuable data, which is transmitted to the cloud. Specialized software then transforms this data into operational performance insights. This digital integration can elevate a manufacturing business, coalescing with existing systems to deliver remarkable results. As the IoT gains popularity, digital manufacturing is poised to soar. However, security has become a paramount concern, especially for retrofitted IoT devices operating with limited human intervention. Researchers have proposed various solutions for IoT security issues. In this study, we conduct a literature review (LR) to identify the security requirements for retrofitting IoT devices in manufacturing. Our goal is to provide a straightforward roadmap for industry stakeholders, highlighting the most suitable security solutions for their retrofitted IoT systems. Our review focuses on scientific papers from the past four years, specifically addressing the security and simplicity of retrofitted IoT architecture in manufacturing. The overarching aim is to define the security needs for IoT retrofitted devices in manufacturing, facilitating the smooth integration of process data into cloud platforms.

A seven-year-old spayed female dog presented with multiple, small spindle-shaped masses on the abdominal skin. The excised tissue was submitted for histopathological evaluation. Routine his-tology revealed basaloid nodules with stippled chromatin and pleomorphic nuclei. Mor-pho-logically, the tumor resembled a solid papillary carcinoma of the breast or an endocrine mucin-producing sweat gland carcinoma (EMPSGC). EMPSGC is a rare neuroendo-crine-differentiated neoplasm. This study aimed to compare different immunohistochemical markers for distinguishing between canine sweat gland carcinoma (SGC) and mammary gland carcinoma (MGC). CK19 and Sox9 were useful in identifying SGC. Additionally, the tumor was positive for CK5, AE1/AE3+CK8/18, p63, vimentin, E-cadherin, and synaptophysin, while estrogen receptor staining was negative. The final diagnosis of canine EMPSGC was based on the tumor’s morphological and neuroendocrine immunohistopathological features. Our report provides the first detailed description of a canine EMPSGC, an exceedingly rare tumor in dogs.

One of the keys to understanding the universe is the proper measurement of the relation between the luminosity distance dL and the angular diameter distance dA. In 1933, Etherington deduced from general relativity the reciprocity equation dL=dA(1+z)γ, with γ=1 for a local, i.e. non-expanding, universe. This equation was adapted to an expanding universe –through the comoving distance concept– with the value γ=2. On the other hand, the feasibility of an expanding universe rest on the Friedmann-Lemaître-Robertson-Walker (FLRW) metric that meets the Einstein’s Field Equations for an homogeneous and isotropic universe. The FLRW metric includes a time dependent factor a(t) – unlike the Einstein’s static universe– that accounts for the cosmological redshift. In this work, we show that the radial coordinate (r) of FLRW metric admits two different interpretations driving to an expanding and non-expanding universes. When r is considered has a radial comoving coordinate, the integration on r produces the comoving distance, and hence the metric drives to an expanding universe. Nevertheless, when r is considered simply has a radial coordinate, the integration on r produces the luminosity distance, and hence the metric drives to a non-expanding universe. Thus, weeding out the comoving concept, a non-expanding nature of FLRW model emerges, where a(t) accounts for the cosmological redshift in the form of a time downscaling or as a magnetic permeability growth with cosmic time, rather than as space scaling. Even more, the presence of the time varying factor a(t) drives to the same Friedmann equations, which guarantees the stability of the non-expanding universe, unlike the Einstein’s static universe.

This is the Part II paper of our series of papers on a gauge-invariant perturbation theory on the Schwarzschild background spacetime. After reviewing our general framework of the gauge-invariant perturbation theory and the proposal on the gauge-invariant treatments for l=0,1 mode perturbations on the Schwarzschild background spacetime in the Part I paper [K. Nakamura, arXiv:2110.13508 [gr-qc]], we examine the linearized Einstein equations for even-mode perturbations. We discuss the strategy to solve the linearized Einstein equations for these even-mode perturbations including l=0,1 modes. Furthermore, we explicitly derive the l=0,1 mode solutions to the linearized Einstein equations in both the vacuum and the non-vacuum cases. We show that the solutions for l=0 mode perturbations includes the additional Schwarzschild mass parameter perturbation, which is physically reasonable. Then, we conclude that our proposal of the resolution of the l=0,1-mode problem is physically reasonable due to the realization of the additional Schwarzschild mass parameter perturbation and the Kerr parameter perturbation in the Part I paper.

This paper presents an advanced and highly sophisticated model for hybrid electric special-purpose machines (HE-SPMs), specifically engineered to significantly enhance sustainability and efficiency in logistics operations. The primary objective of this model is centered around the optimization of energy flow, a feature that has undergone extensive testing and validation to ensure its reliability and effectiveness. The inclusion of auxiliary systems such as cutting-edge cooling and control systems further elevates the overall performance capabilities of the HE-SPMs, resulting in a seamless integration within various industries. By implementing these enhanced systems, substantial reductions in energy consumption and operational costs have been observed and well-documented through compelling case studies in both the construction and transportation sectors. The holistic nature of this approach impeccably aligns with the ongoing global efforts aimed at combating the pressing issue of climate change. By focusing on the development and utilization of eco-friendly technologies, this model actively contributes to the mitigation of harmful environmental impacts and promotes sustainable practices within the logistics industry. To further reinforce the efficacy of this remarkable model, future research endeavors will concentrate on expanding its applications to encompass other logistics domains.

The present paper investigates the intricacies of the Chinese English slang world with a focus on the use of the term "vibe." The goal here is to provide a look at how terms change and what their metonymic uses are in the approaches of translanguaging. Through the use of a corpus approach to this issue, the goal is to bring out the meanings and their cultural dimensions expressed in this slang word. Against this backdrop, this paper uses the rich linguistic resource from a translanguaging perspective to explore how users navigate the complexities implicated in online self-presentation with the use of such slang. Therefore, the paper scrutinizes the concept of metonymy in Chinese English slang in an attempt to unearth the symbolic meanings or connotations that have been buried in the meaning of the term "vibe." The result of this analysis is a contribution toward understanding the changing landscape of language and communication in this digital era using linguistic data from the Chinese English slang ecosystem.

This paper presents shortening the payback period of greenhouse gas reduction benefits from photovoltaic rooftop system. The objective was to evaluate the amount and returns of carbon credits. Economic analysis, and comparison of economic analysis with and without consideration of carbon credits from a 149.80 kWp, and 25.68 kWp photovoltaic rooftop system. This study evaluated the amount of electrical energy produced by a photovoltaic rooftop system estimated from the PVsyst program of the factory in Pathum Thani Province, Thailand. The economic indices that the researcher analyzed include the payback period, net present value (NPV), benefit cost ratio (B/C ratio), internal rate of return (IRR). It is divided into four cases studies: Case 1 is the base case, and Cases 2, 3, and 4 are considered carbon credits for 7, 14, and 25 years, respectively. The economic indices analyzed in Case 1 include financial internal rate of return (FIRR), payback period, financial net present value (FNPV), and B/C ratio. The economic indices analyzed in Cases 2, 3, and 4 are the economic internal rate of return (EIRR), economic net present value (ENPV), B/C ratio, payback periods, respectively. This paper outlines a new economic calculation approach that considers carbon credits produced by photovoltaic rooftop system to reach break-even points more quickly, and the application of carbon credits in conjunction with renewable energy.

Abstract: Fanconi syndrome is a disorder of renal proximal tubule transport characterized by metabolic acidosis, amino aciduria, glucosuria, and phosphaturia. There are acquired and hereditary forms of this disorder. Fanconi syndrome in Basenjis was first described in 1976 and is now recognized as an inherited disease in these dogs. Linkage analysis within a large family of Basenjis that included both affected and unaffected individuals was performed to localize the causative variant within the genome. Significant linkage was identified between chromosome 3 (CFA3) makers and the disease phenotype. Fine mapping restricted the region to a 2.7 Mb section of CFA3. A whole genome sequence of a Basenji affected with Fanconi syndrome was generated, and the sequence data were examined for the presence of potentially deleterious homozygous variants within the mapped region. A homozygous 317 bp deletion was identified in the last exon of FAN1 of the proband. 78 Basenjis of known disease status were genotyped for the deletion variant. Among these dogs, there was almost complete concordance between genotype and phenotype. The only exception was one dog that was homozygous for the deletion variant but did not exhibit signs of Fanconi syndrome. These data indicate that the disorder is very likely the result of FAN1 deficiency. The mechanism by which this deficiency causes the disease signs remains to be elucidated. FAN1 has endonuclease and exonuclease activity that catalyzes incisions in regions of double-stranded DNA containing interstrand crosslinks. FAN1 inactivation may cause Fanconi syndrome in Basenjis by sensitization of kidney proximal tubule cells to toxin-mediated DNA crosslinking resulting in accumulation of genomic and mitochondrial DNA damage in the kidney. Differential exposure to environmental toxins that promote DNA crosslink formation may explain the wide age-at-onset variability for the disorder in Basenjis.

This comprehensive state-of-the-art literature review investigates the status of electric vehicle (EV) market share and the key factors that affect EV adoption. Investigating the current scenarios of EV, this study observes a rapid increase in the number of EVs and charging stations in different parts of the world. It reports that people’s socio-economic features (e.g., age, gender, income, education, vehicle ownership, home ownership, political affiliation) significantly influence EV adoption. Moreover, factors such as high driving range, fuel economy, safety technology, financial incentives, availability of free charging stations, and the capacity of EVs to contribute to decarbonization emerge as key motivators for EV purchases. The literature also indicates that EVs are predominantly used for short-distance travel and users commonly charge their vehicles at home. Most users prefer fast chargers and maintain a high state of charge (SoC) to avoid unforeseen situations. Despite the emergent trend, there is a disparity in charging infrastructure supply compared to the growing demand. Thus, there is a pressing need for more public charging stations to meet the surging charging demand. The integration of smart charging stations equipped with advanced technologies to optimize charging patterns based on energy demand, grid capacity, and people’s demand can help policymakers to leverage smart city movement. This paper makes valuable contributions to the literature by presenting a conceptual framework articulating the factors of EV adoption, outlying their role in achieving smart cities, and proposing suggestions for future research directions.

4-aminopyridine (4-AP) is a non-selective blocker of voltage-dependent K+ channels used to im-prove walking in multiple sclerosis patients, and it may be useful in the treatment of cerebellar diseases. In animal models, 4-AP is used as a convulsant agent. Intrahippocampal 4-AP induces acute local glucose hypermetabolism and significant brain damage, while i.p. administration causes less neuronal damage. This study aimed to investigate the effects of a single i.p. admin-istration of 4-AP on acute brain glucose metabolism and to assess the short-term effects on neu-ronal viability and signs of neuroinflammation. We evaluated 4-AP-induced acute changes in brain glucose metabolism by [18F]FDG PET neuroimaging evaluated by standard volumes of interest (VOIs) and voxel-based (SPM) analyses. We show that 4-AP induces acute generalized brain glucose hypometabolism, except in the cerebellum, area that was significantly more resilient to this effect. Accordingly, to the appropriate neurohistochemical assays, 4-AP induced hippocampal astrocyte reactivity 3 days after the insult, without changes in signs of neuronal integrity or micro-glia-mediated neuroinflammation. Thus, acute brain glucose metabolic and short-term neuroin-flammatory profiles in response to 4-AP i.p. clearly differ from that reported for intracerebral administration. Our results also suggest cerebellar metabolism as a potential marker of the effects of 4-AP.

Multiple engineering projects have confirmed that hydraulic machinery operating in sediment-laden rivers suffers from sediment abrasion. Guide vanes are among the most severely worn flow-passing components and have long been a key research focus in hydraulic machinery. This study conducted wear tests using asymmetric incoming flow with a 10° impact angle and an NACA0012 aluminum alloy airfoil as the research object. The development of wear was analyzed, revealing that it primarily occurs at the airfoil’s leading edge and progresses through three stages: initiation, development, and stabilization. The Finnie model was used to analyze the initial wear mechanism, with calculated results closely matching the latest wear outcomes. In the initial stage, wear rate density was influenced by particle impact velocity, angle, volume fraction, and y-direction shear stress. A low-velocity zone near the impact point, combined with rebounding particles causing secondary impacts, increases particle volume fraction and wear rate density. These secondary impacts are the primary causes of wear on both the upstream and downstream surfaces. Furthermore, flow separation downstream of the leading edge makes this region highly susceptible to wear. This study provides valuable insights for addressing wear in hydraulic machinery for practical engineering applications.

Plants of the subfamily Amaryllidoideae are a source of unique and bioactive alkaloids called Amaryllidaceae alkaloids. The study of their anticancer potential has intensified in recent years. This work aims to locate and characterize the profile of cytotoxic alkaloids biosynthesized and stored in different tissues of Phaedranassa lehmannii Regel using different histochemical methods and chromatographic analysis. The histochemical analysis in the bulbs revealed the presence of alkaloids at the basal edge of scale-like leaves and bud apical zone. The GC-MS analysis indicated that the bulbs biosynthesize alkaloids distributed in crinine- (9.80 µg/g DW), galanthamine- (8.04 µg/g DW), lycorine- (7.38 µg/g DW) and narciclasine-type (3.75 µg/g DW). The root biosynthesizes alkaloids distributed mostly in lycorine- (225.29 µg/g DW) and galanthamine-type (0.723 µg/g DW) alkaloids. The total alkaloids biosynthesized by the root (324.93 µg/g DW) exceeded eleven times the abundance of the alkaloids identified in the bulbs (28.97 µg/g DW). In addition, the total alkaloids fractions exhibited a cytotoxic effect dose‐dependent in the evaluated concentrations with IC50 values of 11.76 ± 0.99 µg/mL and 2.59 ± 0.56 µg/mL against human lung (A549) cancer cells and 8.00 ± 1.35 µg/mL and 18.74 ± 1.99 µg/mL against gastric (AGS) cancer cells. The present study provided evidence to locate and characterize the alkaloids of P. lehmannii grown under nursery conditions as a species producing potential antiproliferative alkaloids.

Nowadays, considering the urgent issue related to climate change and land consumption as well as increasing urban population, urban spaces, enhanced by appropriate design solutions, can help reduce risks and improve conditions of environmental well-being. In this frame, thanks to collaboration between the Department of Architecture of the University of Palermo and the Municipality of Naro, in the province of Agrigento (Sicily, Italy), an urban environmental design workshop was promoted to redefine an urban space without a precise identity and denied to public use, within the fabric of a minor historical center in the Sicilian hinterland. The article summarizes the adopted methodology, including the population needs’ assessment, the environmental context assessment, the historical analysis of the urban space to redefine within the context of whole historical center, possible urban and greening solutions and design references according to the urban biophilic approach. The resulting preliminary design aims to be an example of an interdisciplinary approach, respectful of the existing, which places the comfort and well-being of the user at the center of the design. Moreover, it is an interesting example of urban regeneration through the biophilic approach applied to a small urban center, which could be replicated in other similar contexts.

A new 2 parameter unit Weibull distribution is defined on the unit interval (0,1). The methodology of deducing its PDF, some of its properties and related functions are discussed. The paper is supplied by many figures illustrating the new distribution and how this can make it illegible to fit a wide range of skewed data. The new distribution holds a name (Attia) as a nickname.

This paper examines the fundamental distinctions and complementary relationships between generative and descriptive methods in research and analysis. Through a systematic review of their applications across various fields, we explore how descriptive methods excel in capturing and characterizing existing phenomena, while generative methods enable the creation of new instances based on learned patterns. The analysis reveals that while these approaches serve different primary purposes, their integration often leads to more robust and comprehensive research outcomes. Our findings suggest that understanding the strengths and limitations of both methodologies is crucial for researchers and practitioners in choosing appropriate approaches for their specific contexts.

Opioid analgesics have been used for more than 5,000 years and remain the principal pain medications prescribed today. Although morphine is considered the gold standard for pain relief, this µ-opioid receptor (MOP)-selective agonist provides only moderate relief for many chronic pain conditions and promotes several unwanted effects that can adversely affect patient quality-of-life, prevent adherence to treatment or generate addiction. In addition to the lack of progress in the development of better analgesics, there have been no significant breakthroughs to date that have addressed side effects mentioned above. Fortunately, a better understanding of opioid pharmacology has restored hope for the development of better and safer pain medications. In this review, we describe how clinically approved opioids were initially characterized as biased ligands and what impact this approach could have on clinical practice. We also looked at the preclinical and clinical development of MOP biased agonists, with an emphasis on the oliceridine story, as the first specifically designed biased painkiller. Moreover, we explore the discrepancies between ligands with low intrinsic efficacy and those with biased properties. Finally, we examine the rationale behind biased ligand development during the opioid crisis era.

The opportunistic pathogen Pseudomonas aeruginosa is responsible for numerous chronic infections and has a case fatality rate of 50%. All P. aeruginosa strains express β–lactamase, an enzyme capable of inactivating β–lactam antibiotics, including penicillins. However, β–lactams are still considered desirable therapeutics by physicians due to their low toxicity. The short-term goal of this study was to create an optimal model for ampC expression in P. aeruginosa and determine the selecting antibiotic to be used during mutagenesis. In the long term, this study aims to renew effectiveness of β–lactams by identifying potential allosteric binding sites on β–lactamase for the development of novel β–lactamase inhibitors. To create a system for ampC expression, the chromosomal P. aeruginosa ampC gene encoding for β–lactamase was cloned into Escherichia coli, then conjugated into P. aeruginosa ∆ampC. β– lactamase assays on recombinant E. coli and P. aeruginosa detected much higher production of β–lactamase in pMMB67HE, which was confirmed with antibiotic susceptibility testing (AST) to numerous β-lactams. From AST, carbenicillin was chosen as the antibiotic to select functional β–lactamase production during mutagenesis. Future work includes conducting mutagenesis on ampC to identify allosteric binding sites on the β–lactamase enzyme that may enable the discovery of new β–lactamase inhibitors. This work allows for the development of novel β–lactamase inhibitors that could renew the utility of β–lactams and drastically reduce morbidity and mortality from chronic Pseudomonas infections.

The Temperate Highland Peat Swamps on Sandstone (TPHSS), unique to the Sydney Basin of Australia, were burnt during the 2019-2020 wildfire season. This study assessed the post fire recovery of swamps and the ability of remote sensing technique to determine the post-fire recovery patterns. Specifically, the study investigated differences in fire recovery between swamps where groundwater level and soil moisture content have been impacted by underground mining and unimpacted by mining. Two mined and one non-mined under swamps were investigated. Soil moisture measurements were taken at five sites and previously conducted vegetation field surveys (from 2016 to 2022) were used. Using remote sensing information, Normalised Differenced Vegetation Index (NDVI) and Soil Moisture Index (SMI) time-series were calculated and compared with ground data to map responses over the swamps following the fire impact. This study revealed that hydrological conditions of swamps have a direct effect on post-fire recovery of swamps, with slower recovery in mined under swamps compared with non-mined under swamps. This study indicated that NDVI and SMI indices can exhibit the recovery pattern of swamps in terms of vegetation and hydrology. However, the evaluation of recovery pattern of a specific vegetation species requires a frequent field survey.

In this study we summarized our work on various aspects of colorectal adenocarcinomas, 1 focusing on tumor distribution (left- sided, right- sided, and rectal cancers), histologycal type, patient 2 age, gender and several factors affecting prognosis. These predictive and prognostic factors include 3 peritumoral budding, lymphovascular ant perineural invasion, lymph node metastasis, mismatch 4 repair status, and corellations with some frequently known mutations in colorectal adenocarcinomas, 5 detected using Next- Generation Sequencing (NGS) platform. This comprehensive approach allowed 6 us to explore how these factors interplay and influence the clinical outcome of patients with colorectal 7 adenocarcinoma. Additionaly, our findings contribute to the understanding of how genetic mutations 8 may correlate with histological features and prognostic markers, potentially guiding personalized 9 treatment strategies in the future. In our study, we do not claim to have exhausted the research on 10 the topic, and we fully recognized the need for more extensive and in-depth studies in this area of 11 tumor pathology.

Self-organization in complex systems is a process in which internal entropy is reduced and emergent structures appear that allow the system to function in a more competitive way with other states of the system or with other systems. It occurs only in the presence of energy gradients, facilitating energy transmission through the system and entropy production. Being a dynamic process, self-organization requires a dynamic measure and dynamic principles. The principles of decreasing unit action and increasing total action and the principle of decreasing unit entropy and increasing total entropy are dynamic variational principles that are viable to utilize in a self-organizing system. Based on this, average action efficiency can serve as a quantitative measure of the degree of self-organization. Positive feedback loops connect this measure with all other characteristics of a complex system, providing all of them with a mechanism for exponential growth, and indicating power law relationships between each of them as confirmed by data and simulations. In this study, we apply those principles and the model to agent-based simulations of ants traveling between two locations on a 2D grid. We find that those principles explain self-organization well and that the results confirm the model. We derive a compact model of ant-behavior based on the action of their trajectories, and then estimate a variety of metrics from the simulated behavior. By measuring action efficiency we can have a new answer to the question: "What is complexity and how complex is a system?". This work shows the explanatory and predictive power of those models, which can help understand and design better complex systems.

In aerial image object detection, small targets present significant challenges due to limited pixel information, complex backgrounds, and sensitivity to bounding box perturbations. To tackle these issues, we propose SO-RTDETR for small object detection. The model introduces a Cross-Scale Feature Fusion with S2 (S2-CCFF) module, a Parallelized Patch-Aware attention (PPA) module, and the Normalized Wasserstein Distance (NWD) loss function, leading to significant performance improvements. Specifically, the S2-CCFF module enhances small object information by incorporating an additional S2 layer, while SPDConv downsampling maintains key details and reduces computational cost. The CSPOK-Fusion mechanism integrates global, local, and large branch features, capturing multi-scale representations and effectively mitigating interference from complex backgrounds and occlusions, thereby enhancing the spatial representation of features across scales. The PPA module, embedded in the Backbone network, leverages multi-level feature fusion and attention mechanisms to retain and strengthen small object features, addressing the issue of information loss. The NWD loss function, by focusing on the relative positioning and shape differences of bounding boxes, increases robustness to minor perturbations, enhancing detection accuracy. Experimental results on the VisDrone and NWPU VHR-10 aerial datasets demonstrate that our approach outperforms state-of-the-art detectors.

Ammonium dihydrogenphosphate is the main ingredient of ABC powders used for widespread portable fire extinguishers. Rules in force today state the powder should be disposed of as a waste at the end of life (a few years depending on local regulations). However, phosphorous is nowadays recognized as one of the critical elements, in particular as regards to social balance and planet development, therefore new procedures to recycle the powder would be desirable to preserve this element. Although some examples of the reuse of ABC powders as fertilizers or flame retardants have been described, additional resources are consumed to remove powder additives. Therefore, it would be more sustainable to recycle the powder directly, once its desired properties have been verified. Herein, we describe some results that suggest the possibility to reuse the powder from a circular economy perspective.

Argentina is the current and three-time “World Cup” Champion, as well as the current and most successful team in “Copa América” history. We aimed to evaluate the cardiopulmonary fitness profile of youth soccer players from the first league of the Argentine Football Association (AFA). We evaluated ten highly trained youth soccer players from the AFA by applying an on-field cardiopulmonary exercise test (20-meter Shuttle Run Test - 20m-SRT) with wearable gas analyzer (mix-chamber mode). Mean velocity at maximal oxygen uptake (vO_2max) was 12.3±0.7 km·h^-1. The maximal oxygen uptake (O_2max) on-field was 67.1 ± 5.3 mL·kg⁻¹·min⁻¹. The O₂ at the first and second ventilatory thresholds (VT1 and VT2) were identified at 67.0±3.0% and 84.7 ± 3.7%, respectively. Argentine soccer players exhibit exceptional cardiopulmonary fitness, comparable to that of professional athletes, underscoring their potential for high performance. Support staff, fitness or health professionals, and researchers can now compare cardiopulmonary differences between Argentine youth soccer players and other populations.

Background: The aim of this study was to determine the effect of Sparstolonin B (SsnB) on cell proliferation and apoptosis in human breast cancer (MCF-7) and human ovarian epithelial cancer (OVCAR-3) cell lines in the presence and absence of estradiol hemihydrate (ES). Phosphoinositol-3 kinase (PI3K), phosphorylated protein kinase B alpha (p-AKT), phosphorylated mTOR (mechanistic target of rapamycin) signaling proteins and sphingomyelin/ceramide metabolites were also measured within the scope of the study. Methods: The anti-proliferative effects of SsnB therapy were evaluated over a range of times and concentrations. Cell proliferation was determined by measurement of Proliferating Cell Nuclear Antigen (PCNA). PCNA was quantified by ELISA and cell distribution was assessed by immunofluorescence microscopy. MTT analysis was used to test the vitality of the cells, while LC-MS/MS was used to analyze the amounts of ceramides (CERs), sphingosine-1-phosphate (S1P), and sphingomyelins (SMs). TUNEL labeling was used to assess apoptosis, while immunofluorescence staining and enzyme-linked immunosorbent assay (ELISA) were used to measure the levels of PI3K, p-AKT, and p-mTOR proteins. Results: Sparstolonin B administration significantly decreased cell viability in MCF-7 and OVCAR-3 cells both in the presence and absence of ES, while it did not cause toxicity in healthy human fibroblasts. In comparison to controls, cancer cells treated with SsnB showed a significant drop in the levels of S1P, PI3K, p-AKT, and p-mTOR. In cancer cells cultured with SsnB, a significant increase in intracellular concentrations of C16-C24 CERs and apoptosis was observed. Conclusions: SsnB downregulated the levels of S1P, PI3K, p-AKT, and p-mTOR while reducing cell proliferation and promoting ceramide buildup and apoptosis.

With the rapid development of modern industrial technology, ultra-high-speed motors have demonstrated tremendous application potential in fields such as aerospace, high-speed machining, and micro-mechanical systems due to their high efficiency, high power density, and compact structure. However, the design of ultra-high-speed motors faces numerous challenges, particularly under mechanical boundary constraints, how to balance the interactions among electromagnetic performance, thermodynamic characteristics, mechanical strength, and other multi-physical fields has become a key factor limiting performance enhancement. This study first establishes a multi-physical field coupling model for ultra-high-speed motors, comprehensively considering the mutual influences of electromagnetic fields, temperature fields, and stress fields. Through finite element analysis, precise simulations are conducted on the electromagnetic distribution, heat conduction, mechanical stress, and rotor dynamics characteristics within the motor. Based on this, the impact of various design parameters (such as rotor structure, material selection, and cooling methods) on motor performance is investigated, revealing the variation laws of motor performance under multi-physical field coupling. With mechanical boundary constraints taken into account, this paper proposes an optimization design method for high-speed motors based on a multi-physical field coupling model. Initial samples are obtained through MaxPro experimental design, and a Kriging surrogate model is used for fitting. Subsequently, the NSGA-2 algorithm is employed for optimization design. During the optimization process, RMAE and RAAE are adopted for accuracy evaluation, and the Kriging model is iteratively updated based on the evaluation results until the optimal design of the ultra-high-speed motor is completed. This optimization design method enhances the overall performance of the motor while ensuring its mechanical boundary conditions and reducing the computational load during the optimization process. Experimental results demonstrate that the proposed multi-physical field coupling optimization design method can significantly improve the efficiency and power density of ultra-high-speed motors. This study not only provides theoretical support and technical guidance for the design of ultra-high-speed motors but also offers new ideas and methods for motor research and development in related fields. In the future, more efficient and intelligent optimization design algorithms will be explored to continuously advance ultra-high-speed motor technology.

In recent years, the demand for efficient and accurate measurement techniques has surged across various industries, driving the development of non-invasive methods that leverage computer vision technology. This paper explores the principles and applications of non-invasive measurement techniques utilizing computer vision, emphasizing their ability to deliver precise measurements without the need for physical contact or specialized sensors. We delve into the underlying algorithms, including image processing, machine learning, and deep learning methods, that enable the extraction of dimensional data from visual inputs. Case studies across diverse fields such as manufacturing, healthcare, and logistics illustrate the effectiveness of these techniques in enhancing operational efficiency and reducing measurement errors. Furthermore, we discuss the challenges and limitations of implementing computer vision-based measurement systems, including the need for robust algorithms capable of functioning in varied environmental conditions. By highlighting the transformative potential of non-invasive measurement techniques, this paper aims to provide insights into future developments in computer vision and its role in advancing measurement practices across multiple sectors.

The problem is how to create a comprehensive "tool", applicable to any company, which will allow to achieve multiple effects at the same time, such as adhering to the ISO 31000 procedure, connecting risks and company goals in the manner defined by the COSO framework (by connecting the elements of the organizational structure with the company goals), assessment of all types of risks that a company may face, including technical, enable decision support and given visualization of data and effects. Solving this general problem requires solving many specific problems such as the creation of a specific risk matrixes for each company that will express its risk appetite but also to give the possibility of a comprehensive overview of all possible types of consequences, linking entities one-to-many and many-to-one in order to link the elements of the organizational structure to the goals that can be and common to several units, the acquisition of potential risks based on the knowledge of external experts, which overcomes the limitation of insight from the company and introduces elements of artificial intelligence, the application of standards for the evaluation of technical risks such as EN 16991 or API 580, 581 etc. while enabling the management of the implementation project and evergreening in the future. Our research question is can an integral model be made that will solve the above problems in an integrated way. The goal is to create software that will support this solution and that will be practically applicable. Solving such a complex problem of connecting various theoretical and practical aspects required creation of a special algorithm for connecting project activities, risks, organizational structure, goals, knowledge, data management and software. The algorithm, model and software were developed and successfully tested in two Serbian companies.

This paper proposes a novel hybrid model that integrates Failure Mode and Effects Analysis (FMEA), Rank Order Centroid (ROC) and COmbined COmpromise SOlution (COCOSO) to improve risk assessment and prioritization of failure modes. A case study in the healthcare sector will be conducted to validate the effectiveness of the proposed model. ROC is used to assign weights to the FMEA criteria (severity, occurrence and detectability). COCOSO is then applied to create a robust ranking of failure modes by considering multiple criteria simultaneously. The results of the case study show that the hybrid FMEA-ROC-COCOSO model improves the accuracy and objectivity of risk prioritization. It effectively identifies critical failure modes, outperforming traditional FMEA. The hybrid approach not only improves risk management decision making, leading to better mitigation strategies and higher system reliability, but also reduces the complexity typically found in FMEA hybrid models. This model provides a more comprehensive risk assessment tool suitable for application in different industries.

Despite the enormous efforts and funds spent to find an effective treatment for COVID-19, the results have been disappointing. In previous publications, we have demonstrated the remarkable effect of high-dose colchicine in inhibiting the cytokine storm and preventing multiorgan damage and death. However, this treatment is beneficial only after virus entry into the cell. The question of prophylaxis and entry prohibition should also be explored. We now demonstrate the prophylactic effect of bromhexine hydrochloride (BRH), an over-the-counter, non-invasive, effective, well-tolerated medicine, with proven safety, affordable, and inexpensive on 125 men and women. The effect of BRH is best when given continuously for prophylaxis during peaks in contagion in the wave of COVID-19. Then the probability of infection drops sharply, and if a disease does occur, it proceeds mildly. BRH is also effective when given by inhalation for post-exposure prophylaxis.When COVID-19 manifests itself clinically, the efficacy of BRH drops sharply because the virus is already in the cell. However, BRH inhalations are useful because they limit the spread of the virus and have an anti-inflammatory and possibly antiviral effect.

The aim of the research carried out between 2021 and 2023 was to analyse the influence of competitiveness between Festuca arundinacea Schreb. and Trifolium pratense L., cultivated in simple mixtures, on the quality of the fodder obtained, under the conditions of the northern Romanian forest steppe. In the experiment organized at the Ezăreni Student Research and Practice Station of Iasi University for Life Sciences, two factors were studied, namely the cultivation system used, with five graduations: a1 - Festuca arundinacea Schreb. (100 % - control); a2 - Festuca arundinacea Schreb. (75 %) and Trifolium pratense L. (25 %); a3 - Festuca arundinacea Schreb. (50 %) and Trifolium pratense L. (50 %); a4 - Festuca arundinacea Schreb. (25 %) and Trifolium pratense L. (75 %); a5 - Trifolium pratense L. (100 %) and mineral fertilization with 5 graduations, respectively: b1 - unfertilized (control), b2 - N50P50; b3 - N75P75; b4 - N100P100; b5 - N150P150. The obtained results showed that the process of interspecific competitiveness was greatly influenced by the percentage of participation in the sowing norm of the species in the mixture, the biological peculiarities of the species under study, as well as the climatic conditions specific to each agricultural year.

Deformable image registration plays a crucial role in medical imaging by aligning anatomical structures across multiple datasets, essential for accurate diagnosis and treatment planning. However, existing deep learning-based deformable registration models often face challenges ensuring anatomical plausibility, leading to unnatural deformations in critical brain structures. This paper proposes a novel framework integrating Bayesian optimization to address these challenges, focusing on registering 3D point clouds representing brain structures. Our method uses probabilistic modeling to optimize non-rigid transformations, providing smooth and interpretable deformations that align with anatomical constraints. The proposed framework is validated using MRI data from patients diagnosed with hypoxic-ischemic encephalopathy (HIE) due to perinatal asphyxia. These datasets include brain scans taken at multiple time points, enabling the modeling of structural changes over time. By incorporating Bayesian optimization, we enhance the accuracy of the registration process while maintaining anatomical fidelity. Our results demonstrate that the approach provides interpretable, anatomically plausible deformations, outperforming conventional methods in accuracy and reliability. This work offers an improved tool for brain MRI analysis, aiding healthcare professionals in better understanding disease progression and guiding therapeutic interventions.

As psychology evolves, the phenomenon of concept overlap becomes more pronounced, increasing participant burden and complicate data interpretation. This study introduces an Embedding-based Semantic Analysis Approach (ESAA) for detecting redundancy in psychological concepts, which are operationalized through their respective scales, using natural language processing techniques. ESAA utilizes OpenAI’s GPT-3 large model to generate high-dimensional semantic embeddings of scale items and applies hierarchical clustering to group semantically similar items, uncovering potential redundancy. In three preliminary experiments, ESAA was tested on well-known psychological scales, such as Conscientiousness, Gratitude, and Grit. The experiments assessed ESAA’s ability to (1) converge semantically similar items, (2) discriminate semantically distinct items, and (3) identify overlapping scales measuring concepts known for redundancy. Additionally, comparative analyses were conducted to assess ESAA's robustness and incremental validity against the most advanced chat bots based on GPT-4. The results demonstrated that ESAA consistently produced stable outcomes and surpassed all evaluated chatbots in performance. As a novel, objective approach for analyzing relationships between concepts operationalized as scales, ESAA has potential to facilitate future research on theory refinement and scale optimization.

The Schwarzschild metric encompasses both exterior and interior regions, with a reversal of signature upon transition from one to the other when using Schwarzschild coordinates. In the interior region, the radial spacelike coordinate transforms into a timelike radius, introducing a time-dependent scale factor on the interior metric's angular term. Though the interior metric is a Kantowski-Sachs type metric, by examining the geometry in Kruskal-Szekeres coordinates, we find that the interior geometry must be spatially isotropic and homogeneous. The azimuthal term of the metric is describing the 'spin' of a reference frame relative to the surrounding shell and it is this spin that goes to infinity at the singularity, not the density of the 2-sphere. After detailed analyses supporting these hypotheses, we investigate why the interior Schwarzschild and FRW metrics, which are both spatially homogeneous and isotropic metrics, treat space and time so differently.

Dimethyl fumarate (DMF) and diroximel fumarate (DRF) are two treatments used in multiple sclerosis (MS), that have been shown to be effective in controlling MS patients. DRF was intro-duced in 2019, with the aim of decreasing the gastrointestinal side effects caused by DMF. Few real-life studies verify the data provided in the clinical trials. Methods: A retrospective descriptive study was conducted in MS patients at the Hospital Universitario Torrecárdenas under treatment with DRF and DMF. Demographic, clinical, and analytical variables were studied and compared between groups. Results: 60 patients were recruited, 30 with each treatment, observing similar baseline characteristics. Fewer gastrointestinal (GI) effects were observed in the DRF group, while more infections were detected in the DMF group. We recorded lower levels in the DRF group, with 4 moderate-severe lymphopenia in the DRF group vs. none in the DMF group. In addition, we observed a decrease in lymphocytes after the change from DMF to DRF in patients with a change. Conclusions: Our real-life analysis of patients treated with DMF or DRF supports studies’ findings of decreased GI side effects with DRF vs. DMF, without decreasing efficacy. However, our data show greater reduction in lymphocytes in patients with DRF compared to DMF, so more studies are necessary.

Course recommendation is an important service of Intelligent tutoring systems (ITS), the rapid development of large-scale online courses has generated a substantial amount of learning process data for online learning platforms, providing valuable support for understanding learners' interests and preferences. However, despite made considerable achievements, there are still unresolved challenges:(1) Current research neglects the sequential relationship between the course learners learn. (2) It does not take advantage of the differences in learners' interest in different courses during the knowledge dissemination process. To overcome these challenges, we propose an innovative solution called PGDB (Preference-aware graph diffusion network and BiLSTM) for course recommendation. Specifically, we analyze learners' preferences for courses and relational paths using a relation-aware multi-head attention network, learn the semantic diversity of different contexts, effectively distinguish the difference of learners' interest in different courses in the process of knowledge transmission. In addition, the temporal preference modeling module employs a bi-directional long short-term memory network to mine learners' interest evolution patterns, generating learner-dependent representations, efficient use of the sequential relationship between the courses taken by learners. Experimental results on real datasets demonstrate that the proposed model greatly exceeds the performance of the most recent baseline models, thereby validating the model's effectiveness.

Background: RO and ChRCC are kidney tumours with overlapping characteristics, making differentiation between them challenging. Objectives: The objective of this research is to create a radiogenomics map by correlating radiomic features to molecular phenotypes in ChRCC and RO, using resection as the gold standard. Methods: Fourteen patients (6 RO and 8 ChRCC) were included in the study. A total of 1,875 radiomic features were extracted from CT scans, alongside 632 cytobands containing 16,303 genes from the genomic data. Results: Feature selection algorithms applied to the radiomic features resulted in 13 key features. From the genomic data, 24 cytobands highly correlated with histology were selected and cross-correlated with the radiomic features. The analysis identified four radiomic features that were strongly associated with seven genomic features. Conclusion: These findings demonstrate the potential of integrating radiomic and genomic data to enhance the differential diagnosis of RO and ChRCC, paving the way for more precise and non-invasive diagnostic tools in clinical practice.

The vast quantities of bauxite residue generated globally necessitate environmentally responsible disposal strategies. This study investigated the long-term (5-year) behavior of modified bauxite residue (MBR) with an initial pH of 8.5, using lysimeters to test various configurations: raw MBR, sand/soil capping, revegetation, and used MBR (UMBR) previously applied for acid mine drainage remediation. Throughout the 5-year experiment and across all configurations, the pH of the leachates stabilized between 7 and 8 and their salinity decreased. Their low sodium absorption ratio (SAR) indicated minimal material clogging risk and suitability for salt-tolerant plant growth. Emission of potentially toxic elements (except V) decreased rapidly after the first year to low levels. Leachate concentrations consistently remained below LD50 for Hyelella Azteca, at least an order of magnitude lower by the experiment's end (except for first-year Cr). Sand capping performed poorly, while revegetation and soil capping slightly increased emissions, though these were negligible given the low final emission levels. Revegetated MBR shows promise as a suitable and sustainable solution for managing bauxite residues, provided the pH is maintained above 6.5. This study highlights the importance of long-term assessments and appropriate management strategies for bauxite residue disposal.

Background: Bone marrow aspiration concentrate (BMAC) has garnered increasing interest due to its potential for healing musculoskeletal injuries. While the iliac crest remains a common harvest site, the aspiration technique’s efficacy in offering the highest yield and prevalence of mesen-chymal stem cells (MSCs) is controversial. This study aimed to compare two different techniques of bone marrow aspiration over the anterior iliac crest from a single level (SL) versus multiple level (ML). Methods: Anterior iliac crests were selected in 9 adult patients (aged between 31 and 59 years old). Aspiration was achieved using an 11-gauge needle (length: 100 mm; diameter: 2.3 mm) specifically manufactured for bone marrow collection (BD, Becton, US) connected to a 10-mL syringe. On one side, 4cc of bone marrow was aspirated at an SL to a depth of 7cm without changing the needle direction. On the other side, over the same portion of the iliac crest, 1 cc of bone marrow was obtained from ML of different depths during needle retrieval, maintaining a distance of 1cm and changing the tip direction. The samples were blindly sent to the laboratory without indicating whether they came from an SL or ML. Fluorescence-activated cell sorting (FACS) and osteoblast differentiation were analyzed and compared. Results: In the FACS analysis, the SL resulted in a higher population of MSCs that were positive for CD105, CD73, and CD90 and negative for CD34, compared to the ML method. In the process of osteoblast differentiation, it was observed that MSCs exhibited more advanced features of en-hanced osteoblastic abilities in the SL method rather than the ML method. Conclusion: An SL aspiration technique at the anterior iliac crest may produce a high-quality bone marrow aspirate. This technique may help obtain specific populations of MSCs with the desired characteristics for use in regenerative therapies for musculoskeletal injuries.

Background/Objectives: Alcohol consumption during pregnancy poses notable risks to maternal and foetal health, yet its prevalence and impact in Nigeria remain understudied. This commentary article aims to review the current state of alcohol intake by expectant mothers in Nigeria and provide an overview of Foetal Alcohol Spectrum Disorder (FASD) and recommendations for addressing this public health issue. Methods: Drawing on existing literature, we highlight the prevalence of alcohol intake by expectant mothers in Nigeria and explore the socio-cultural factors influencing drinking behaviours during pregnancy. Results: Despite limited empirical evidence, anecdotal reports suggest that alcohol consumption during pregnancy is not uncommon in Nigeria, driven by factors such as social norms, lack of awareness, and limited access to prenatal care and education. The implications of prenatal alcohol exposure for maternal and child health are substantial, with potential consequences including FASD, birth defects, and mortality. Conclusions: We provide recommendations for policymakers, healthcare providers, and community stakeholders to enhance awareness, prevention, and support systems for pregnant women and their families. Key recommendations include implementing public health campaigns to raise awareness concerning the dangers of alcohol use by expectant mothers, integrating alcohol screening and counselling into routine prenatal care services, strengthening regulations on alcohol advertising and availability, and providing support services for women struggling with alcohol use disorders during pregnancy. By addressing the gaps in knowledge and practice surrounding alcohol consumption during pregnancy, Nigeria can take meaningful steps toward promoting maternal and child health and preventing the lifelong consequences of FASD and related conditions.

Cooper-pair distribution function Dcp(ω,Tc) of Untwisted-Misaligned Bilayer Graphene (UMBLG) in the presence of an external electric field is calculated and analyzed for the first time, within the framework of first-principle calculations. A bilayer graphene structure is proposed using a structural geometric approximation, enabling the simulation of a structure rotated at a small angle, avoiding a supercell calculation. Dcp(ω,Tc) function of UMBLG indicates the presence of the superconducting state for specific structural configurations, which is consistency with superconductivity in Twisted Bilayer Graphene (TBLG) reported in the literature. Dcp(ω,Tc) function of UMBLG suggests that Cooper-pairs are possible in the low-frequency vibration region. Furthermore, the structural geometric approximation allowed evaluating the effect of the electric field on superconducting state of UMBLG and its superconducting critical temperature through Ncp parameter.

This work investigated the fabrication, properties, and sensing applications of TiO2 nanotubes. A purity of 99.7% and 1mm thickness of titanium metal sheet was used to elaborate titanium dioxide nanotubes for gas sensor application through electrochemical anodization in a mixture of Ethylene glycol (EG)/NH4F electrolytes. As a result, X-ray diffraction indicates the crystallization of the titanium dioxide layer. Scanning electron microscopy and Transmission electron microscopy reveal that the average diameter of the TiO₂ nanotubes is approximately 100 nm, with tube length ranging between 3 and 9 microns and a thickness of the nanotube walls of about 25 nm. This type of TiO2 nanotube is suitable for NO2 gas sensor applications. As a result, at an oxidation time of 15 min, the detection of TiO2 nanotube gas toward NO2 gas shows a good result at 250 °C, when exposed to a NO2 gas flow of 100 ppm, where a maximum NO2 gas response of 96% was obtained. The sensors based on the TiO2 nanotube arrays for sensing NO2 all exhibited high stability, good reproducibility, and high sensitivity. This work suggests that TiO2 nanotubes are a promising material for application as a NO2 sensor, as well as other applications.

As digital technologies continue to evolve; small and medium enterprises (SMEs) are increasingly adopting Virtual Collaboration and Communication Technologies (VCCTs) to enhance performance in a competitive business landscape. This systematic review aims to assess the impact of VCCTs on SME performance metrics, such as operational efficiency, employee engagement, and customer satisfaction, by analyzing 41 studies selected from an initial pool of 662,422 records. The review also explores factors influencing VCCT implementation and barriers to adoption. Studies from 2014 to 2024 were reviewed, comprising journal articles (92.68%) and conference papers (7.32%). After rigorous screening, 41 studies met the inclusion criteria. The selection process included a risk bias assessment to ensure the reliability of the findings. The analysis revealed that operational efficiency improvements were the most frequently reported (68.29% of studies), followed by employee engagement (41.46%) and customer satisfaction (43.9%). Scalability was the most measured IT performance metric (41.46%), while user engagement appeared in 39.02% of the studies. Business sustainability and competitive advantage were highlighted in 46.34% and 41.46% of studies, respectively. Factors such as digital skills, organizational culture, and IT infrastructure were identified as critical for effective VCCT implementation. Nonetheless, financial constraints and employee resistance were prominent barriers. This review underscores the transformative potential of VCCTs in enhancing SME performance, particularly in operational efficiency and stakeholder engagement. A balanced focus on technological and human factors is crucial, with strategic planning and investment in digital capabilities driving success. Future research should focus on developing tailored models to maximize VCCT benefits, especially in cost-efficiency, sustainability, and innovation.

The Arapaima gigas, a native fish species in the Amazon basin, possesses intriguing biological and zootechnical characteristics, along with substantial economic value, rendering it a promising candidate for development within intensive aquaculture systems. Numerous studies have been conducted to deepen the understanding of its digestive physiology based on its feeding habits, determine its nutritional requirements corresponding to different developmental stages, assess alternative ingredients to enhance feeding strategies, and elucidate its feeding behavior patterns in captivity. However, there remain gaps in information and a deficiency in various aspects of nutritional physiology, particularly as it relates to the formulation of efficient and balanced aquafeeds across different production phases for this species. This article provides a comprehensive review of the current state of knowledge regarding digestive physiology, advancements in nutrition and feeding strategies developed over the past two decades. Furthermore, it aims to pinpoint areas that require further research to enhance the understanding of A. gigas and its application in sustainable aquaculture practices.

We present a case of successful treatment of a mall-sized parasagittal meningioma (14x11x12mm), combined with chronic migraines, which significantly improved the patient's quality of life. After endovascular surgery -embolization of the middle meningeal artery (MMA) branches using non-adhesive embolic agents of various viscosities (Squid 12/Squid 18), we observed not only complete resolution of the meningioma, but also almost complete elimination of headache attacks. On the third day after the procedure, the patient reported that headaches, which previously occurred almost every day, changed to short episodes about once in 4 months. This clinical observation and the literature data suggest that embolization of the distal branches of the MMA could be an effective treatment for small-sized parasagittal and convexital meningiomas as well as chronic subdural hematomas (ChSDH). Additionally, it can lead to a reduction in the intensity of migraine headaches. Embolization of the MMA distal branches could provide relief from migraines and improve the quality of life for patients. However, there is a lack of sufficient data on this topic in the current literature. Therefore, it may be worthwhile to investigate this side effect further and conduct a study on the efficacy of MMA embolization for chronic migraines.

Purpose: Only two studies have examined the second-order false belief mentalizing ability of people with Williams syndrome (WS). The exact age in one study that successfully passed the advanced complex mentalizing ability exceeded nine years in chronological age. Another study reported failure to pass second-order false-belief tasks among people with WS at an averagely of 8.56 years in their mental age. This study aimed to examine the nature of the second-order mentalizing ability of people with WS using computerized animations to investigate the possibility of rehabilitation. Methods: Advanced technology of three-dimensional (3D) animation clips was used as an experimental tool. People with WS viewed clips in a quiet room without interruption. After watching each clip, people with WS answered the following questions: character recognition, reality, memory, and second-order false beliefs. Responses from people with WS were recorded and analyzed. Results: The mental passing age of the second-order false-belief task in people with WS in the current study was 7.14 years. People with WS improved their mentalizing ability to the advanced, complex mental states of other people using computerized animation clips. People with WS performed better with 3D animation stimuli than with traditional two-dimensional (2D) stimuli. Conclusion: With 3D animation clips, people with neurodevelopmental disabilities can induce second-order false belief mentalizing abilities, as shown in people with WS.

Let X be a 3-product space. Let A : D(A) ⊆ X → X , B : D(B) ⊆ X → X and C : D(C) ⊆ X → X be possibly unbounded 3-self-adjoint operators. Then for all x ∈ D(ABC) ∩ D(ACB) ∩ D(BAC) ∩ D(BCA) ∩ D(CAB) ∩ D(CBA) with ⟨x, x, x⟩ = 1, we show that (1) ∆x(3, A)∆x(3, B)∆x(3, C) ≥ |⟨(ABC − aBC − bAC − cAB)x, x, x⟩ + 2abc|, where ∆x(3, A) := ∥Ax − ⟨Ax, x, x⟩x∥, a := ⟨Ax, x, x⟩, b := ⟨Bx, x, x⟩, c := ⟨Cx, x, x⟩. We call Inequality (1) as 3-Heisenberg-Robertson-Schrodinger uncertainty principle. Classical HeisenbergRobertson-Schrodinger uncertainty principle (by Schrodinger in 1930) considers two operators whereas Inequality (1) considers three operators.

The study of hepatitis C virus (HCV) replication in cell culture is mainly based on cloned viral isolates requiring adaptation for efficient replication in Huh7 hepatoma cells. The analysis of wild type (WT) isolates has been enabled by expression of SEC14L2 and by inhibitors targeting deleterious host factors. Here, we aimed at optimizing cell culture models to allow infection with HCV from patient sera. We used Huh7-Lunet cells ectopically expressing SEC14L2, CD81 and a GFP reporter with nuclear translocation upon cleavage by the HCV protease to study HCV replication, combined with a drug-based regimen for stimulation of non-modified wildtype isolates. RT-qPCR based quantification of HCV infections using patient sera suffered from high background in the daclatasvir treated controls. We therefore established an automated image analysis pipeline based on imaging of whole wells and iterative training of a machine-learning tool, using nuclear GFP localization as a readout for HCV infection. Upon visual validation of hits assigned by the automated image analysis the method revealed no background in daclatasvir treated samples. Thereby, infection events were found for 15 of 34 high titer HCV genotype (gt) 1b sera, revealing a significant correlation of serum titer and successful infection. We further show that transfection of viral RNA extracted from sera can be used in this model as well, albeit with so far limited efficiency. Overall, we generated a robust serum infection assay for gt1b isolates using semi-automated image analysis, which was superior to conventional RT-qPCR based quantification of viral genomes.

Candida glabrata (Nakaseomyces glabratus), the second most prevalent Candida pathogen globally, has emerged as a major clinical threat due to its ability to develop high-level azole resistance. In this study, two new 5,6-dihydrotetrazolo[1,5-c]quinazoline derivatives (c11 and c12) were synthesized and characterized using IR, LC-MS, 1Н and 13C NMR spectra. Along with 13 previously reported analogues, these compounds underwent in vitro antifungal testing against clinical C. glabrata isolates using a serial dilution method (0.125-64 mg/L). Remarkably, compounds c5 and c1 exhibited potent antifungal activity, with minimum inhibitory concentrations of 0.37 μM and 0.47 μM, respectively – about 20-fold improvement over standard drugs like amphotericin B, caspofungin, and micafungin. A detailed structure-activity relationship analysis revealed crucial molecular features enhancing antifungal potency. Extensive molecular docking studies across 18 protein targets explored potential binding pockets and affinities of the lead compounds. A robust 3D-QSAR model, incorporating molecular descriptors Mor26m and Mor29e, displayed good predictive ability for antifungal activity. In silico predictions indicated an absence of herbicidal effect, negligible environmental toxicity (to honeybees, avian species, and aquatic organisms), and mild human toxicity concerns for these compounds. This comprehensive approach aims to develop novel and effective antifungal compounds against the clinically relevant pathogen C. glabrata.

Breast cancer is the most common and the leading cause of cancer death in women worldwide; treating invasive breast carcinomas is challenging due to the side effects of chemotherapeutics. Compounds isolated from natural sources have been proposed as potential molecules for cancer therapy; for instance, the homoisoflavonoid brazilin has shown pharmacological properties, including anti-tumoral and anti-inflammatory activities. In this study, we isolated brazilin from the heartwood of Haematoxylum brasiletto (Pacific coast of Mexico and Central America); then, we performed a semi-synthesis by adding three methyl or acetyl groups to the core structure of brazilin. We confirmed brazilin and its derivatives identity by spectroscopic data (1H NMR and 13C NMR) and measured their purity by optical rotation. Then, we analyzed the effects of brazilin and derivatives in three mammary gland-derived cell lines: the TNBC MDA-MB-231, the ER(+) MCF7, and the non-tumorigenic MCF10A. We evaluated the cell viability by MTT assays, cell migration by wound-healing assays and focal adhesion kinase (FAK) activation by Western blot. Regarding biological assays, the MTT assay showed that brazilin and its derivatives showed cytotoxic effects on MCF7 and MDA-MB-231 breast cancer cells at 20 µM but was not toxic in non-tumorigenic MCF10A mammary epithelial cells. These effects were dose and time-dependent, as well as a decrease in the levels of cell migration and FAK activation.

Solid propellants have significant applications in aerospace, and improving their combustion performance has become a hot research topic in recent years. Carbon black (CB) has been widely used in the field of catalytic pyrolysis due to its low cost, controllable morphology, and ease of modification. Herein, we first investigates the structure-activity relationship between the physicochemical properties of nine types of CB with a particle size range of 1-15 μm and a mass fraction of 5%, including oil absorption value, compression oil absorption value, and oxygen content, and the catalytic pyrolysis of ammonium perchlorate (AP), concluding that the function relationship between oil absorption value and high-temperature peak is Y1=1032.67-9.618x+0.038x2, the function relationship between compression oil absorption value and high-temperature peak is Y2=145.537+62.365ln(x), and the function relationship between oxygen content and high-temperature peak is Y3=e(6.085-0.02/x). Based on these, we further uses coal tar, which is abundant in sources, as the raw material to prepare five types of coal-based CB marked as CB1-CB5 at pyrolysis temperatures of 1000~1800 ℃. The relative error between the actual peak value and the predicted value of the CB/AP composite particles is less than 5%, proving that the peak temperature model of CB/AP composite particles can guide actual production. This study contributes to promoting CB application in improving the catalytic pyrolysis performance of ammonium perchlorate and enhancing the high added value utilization of coal-derived products.

Purpose: Medical experiments frequently involve observations of many variables. For instance, the results of a blood chemistry test are a set of such multivariate data. The study attempts to diagnose the occurrence of scoliosis based on the results of biochemical analyses of blood serum samples. Methods: The following hormones were measured: FSH, LH, E2, PROG, HGH and PTH. Osteocalcin, calcium, phosphorus and vitamin D were also measured. Discriminant analysis and decision trees were applied to determine which of the measured parameters would allow the studied set to be divided naturally into four groups of patients. Result: After such a division, these techniques allowed for prognoses, and thus, for assigning new data to the four classes. The results of advanced statistical analyses are presented as classification functions and in a graphical form as a decision tree, as well as a set of decision rules. The study showed that diagnosing scoliosis is possible based on five (LH, E2, PROG, calcium and osteocalcin) out of ten available results of biochemical measurements. Conclusions: Statistical analysis allowed for classifying new medical cases with high probability, which may even increase after new data are introduced into the dataset and corrected classification systems are generated.