Gastric pH seems to be a key factor for the development of Equine Gastric Ulcer Syndrome (EGUS). It is influenced by the amount and duration of roughage uptake, as chewing increases the production of alkaline saliva. The protone-pump-inhibitor omeprazole is currently recommended by the American College of Veterinary Internal Medicine/European College of Equine Internal Medicine (ACVIM/ECEIM) consensus statement for therapy of both Equine Squamous Gastric Disease (ESGD) and Equine Glandular Gastric Disease (EGGD). We hypothesized that dental disorders decrease saliva production due to altered chewing and in consequence gastric pH, therefore predisposing horses to EGUS. In 54 horses, a gastroscopy with measurement of the intragastric pH after 12 hours of fasting and an examination of the oral cavity were carried out. Omeprazole therapy was recommended (n=32), dental disorders were corrected (n=22) and gastroscopy was repeated 4 weeks later (n=9). No influence of moderate-severe dental disorders on ESGD (grade ≥2/4) (p = 0.394) and EGGD (grade ≥2/3) (p = 0.857) could be determined. Nevertheless, there was a trend that moderate- severe dental disorders were associated with a low gastric pH (1-4) (p = 0.100). In conclusion, we found no evidence that gastroscopy should be recommend in horses with dental disorders and vice versa.

Influenza viruses are important pathogens that affect human and animal health. The ecology and epidemiology of influenza viruses are very complex because they have a diverse host range. Influenza A viruses are responsible for serious epidemics. Influenza B viruses cause respiratory disease in humans, seals, and pigs. Influenza C viruses are infectious to humans and pigs. Influenza D virus infects cattle, although it was first isolated from a sick pig in 2011. Only influenza A viruses are of clinical importance in pigs. Although influenza A viruses infect a variety of animal species, pigs are one of the natural hosts of these viruses. Swine influenza is a very serious respiratory disease in pigs, and its main causative agents are the H1N1, H1N2, and H3N2 subtypes, which are antigenically related to human influenza viruses. Pigs are susceptible to infection by avian and human influenza viruses and are thought to play a significant role in the ecology of human influenza. This review examines scientific articles on the zoonotic spread of swine influenza A viruses to humans, their circulation in pig populations worldwide, reverse zoonosis from humans to pigs, and the role of pigs in interspecies transmission.

In previous research, 1,2,3-triazolium salts showed significant biological activity as potential in-hibitors of the enzymes cholinesterases (ChEs), crucial for neurotransmission. In this research, pairs of uncharged thienobenzo-triazoles and their charged salts were prepared in order to ex-amine further the role of the positive charge on the nitrogen of the triazole ring on the interac-tions in the active site of the enzymes and to compare the selectivity of 1,2,3-triazolium salts in relation to their uncharged analogs obtained by photochemical cyclization. Neutral thienoben-zo-triazoles showed very good selective activity toward butyrylcholinesterase (BChE), while their salts showed excellent non-selective inhibition toward both BChE (the most active 23: IC50 0.47 mikroM) and acetylcholinesterase (AChE) enzymes (the most active 23: IC50 4.4 mikroM). These new structures with incorporated 1,2,3-triazolium salts present the new scaffold for drug development as it is known that the current therapy in Alzheimer's disease (AD) are selective AChE inhibitors, while in Parkinson's and all stages of AD, non-selective inhibitors of ChEs are preferred. Molecular docking of the selected compounds and their corresponding salts into the active sites of ChEs was conducted to identify the interactions responsible for the stability of the non-covalent cholinesterase-ligand complexes. As genotoxicity studies are crucial when devel-oping new active substances and finished drug forms, in silico studies for all the synthesized compounds have shown that compound 18 is the most promising candidate for genotoxic safety.

Longitudinal research into the impact of Covid-19 on university student mental health beyond the pandemic is lacking. This study aims to address the gap in the literature by tracking the mental health of university students over a two-year period, spanning the Covid-19 pandemic and its aftermath. A two-year longitudinal study surveyed a sample of university students (n=302) three times between May 2020 and May 2022. Students’ psychological distress, generalised anxiety, flourishing, and personal wellbeing were assessed at each time point. It was found that students’ psychological distress levels spiked during the first year of the pandemic, but reverted back to baseline levels at two-year follow-up. While generalised anxiety gradually improved, both their psychological distress and generalised anxiety remained considerably worse than pre-pandemic norms. Students’ flourishing scores remained very low, while their life satisfaction and state happiness improved slightly between May 2021 and May 2022. These findings clearly demonstrate that students’ mental health is still in crisis, even after the Covid-19 pandemic. More needs to be done to support students beyond the pandemic generally, including this particularly unique cohort of students who endured unprecedented challenges for prolonged periods, and are now transitioning into the working world. Practical implications and recommendations are discussed.

In the field of Thermal Non-Destructive Testing and Evaluation (TNDT&E), active thermography gained popularity due to its fast, wide area monitoring and remote inspection capability to assess materials without compromising their future usability. Among the various active thermographic methods, pulse compression favourable frequency-modulated thermal wave imaging stands out for its enhanced detectability and depth resolution. In this study, experimental investigation has been carried out on a hardened steel sample used in ship building industry with a flat-bottom hole simulated defect using the Frequency Modulated Thermal Wave Imaging (FMTWI) technique. And the defect detection capabilities of FMTWI have been investigated from various statistical post processing approaches and compared by taking Signal to Noise Ration (SNR) as a figure of merit. Among various adopted statistical post-processing techniques, the pulse compression has been carried out using different methods: offset removal with polynomial curve fitting, and Principal Component Analysis (PCA) which is an unsupervised learning approach for data reduction and offset removal with median centering for data standardization. The performance of these techniques was assessed through experimental investigations on hardened steel specimen used in ship building to provide valuable insights into their effectiveness in defect detection capabilities.

This study evaluates the efficiency of biodegradable polyurethane foams as thermal insulation materials compared to conventional materials like expanded polystyrene (EPS). Key parameters analyzed include thermal conductivity, energy savings, CO2 emission reductions, and investment payback periods. Biodegradable polyurethane foams demonstrated a thermal conductivity of 0.022 W/(m·K), significantly lower than EPS's 0.035 W/(m·K), leading to higher energy savings. Calcu-lations reveal an annual energy saving of 443,820 kWh for polyurethane foams, compared to 441,330 kWh for EPS. Financially, polyurethane foams save approximately €53,258.4 annually, while EPS saves €52,959.6. In terms of environmental impact, polyurethane foams reduce CO2 emissions by 88,764 kg annually, versus 88,266 kg for EPS. Despite the higher initial cost of pol-yurethane foams (€50/m² compared to €30/m² for EPS), the payback period is remarkably short at 0.094 years (1.13 months) compared to EPS's 0.057 years (0.68 months). These findings highlight biodegradable polyurethane foams as a superior option for thermal insulation, offering significant energy, financial, and environmental benefits.

With the acceleration of urbanisation and the increase in underground space use, how to provide a comfortable and healthy environment in underground space has become an important research topic. This study constructed an environmental decision-making model for underground space by integrating human perception evaluation and physical environment factors. The study analysed the influence of physical environment parameters on users' perceived experience through field data collection and questionnaire surveys. The data were in-depth analysed using single-indicator fitted regression analysis and XGBoost machine learning model. The results reveal the significant influence of physical parameters such as temperature, humidity, illuminance and wind speed on the comfort of users of underground spaces and determine the range of appropriateness of these physical environment parameters. The results provide a reliable theoretical basis for optimising the design and management of underground spaces and help to enhance the environmental quality and user experience of underground spaces.

The present study examines the relationship between thermal and configurational entropy in two resistors in parallel and in series. The objective is to introduce entropy in electric circuits analysis by considering the impact of system geometry on energy conversion in the circuit. Thermal en-tropy is derived from thermodynamics, whereas configurational entropy is derived from network modelling. It is observed that the relationship between thermal entropy and configurational en-tropy varies depending on the configuration of the resistors. In parallel resistors, thermal entropy decreases with configurational entropy, while in series resistors, the opposite is true. The impli-cations of the maximum power transfer theorem and constructal law are discussed. The entropy generation for resistors at different temperatures was evaluated, and it was found that the con-sideration of resistor configurational entropy change was necessary for consistency. Furthermore, for the sake of generalization, a similar behavior was observed in time-dependent circuits, either for resistor-capacitor circuits or circuits involving degradation.

The analysis of the liquefaction potential of the ground is a fundamental characterization in areas with continuous seismic activity, such as Ecuador. Geotechnical liquefaction studies are usually approached from the application of dynamic penetration tests, which pose problems both in their execution and in their evaluation. The proposed research involves analyzing dynamic penetration tests and microtremor geophysical surveys (Horizontal to Vertical Spectral Ratio, HVSR technique) at the base of the San Marcos dam, a reservoir destined for the irrigation water located in Cayambe (Ecuador). Based on the investigations performed at the time of construction of the dam (drilling and geophysical refraction profiles) and the application of 20 microtremor observation stations performed by the HVSR technique, an analysis of the Safety Factor of liquefaction (SFliq) has been conducted, proposed by Youd and Idriss in 2001, and based on the values of the Standard Penetration Test (SPT) applied in granular materials (sands). In addition, the vulnerability index (Kg) proposed by Nakamura in 1989 was analyzed through the HVSR passive seismic records related to the Ground Shear Strain (GSS). The results obtained in the HVSR surveys indicate the presence of a zone of about 100 m length in the central part of the foot of the dam, whose GSS values are identified with a condition of susceptibility to liquefaction. In the same area, the analysis applied from SPT essays in the P-8A drill hole executed in said area also shows a potential susceptibility to liquefaction in earthquake conditions greater than a moment magnitude (Mw) of 4.5, which could occur in the environment, for example, with a new activity condition of the nearby Cayambe volcano or even from an earthquake from the vicinity fractured zone.

KeraVio is a new portable corneal cross-linking (CXL) treatment modality in which violet light (VL)-emitting spectacles are used along with topical transepithelial riboflavin. We preliminarily identified endogenous riboflavin in the human cornea without the administration of riboflavin drops, and the relatively low intensity of VL irradiation increased corneal stiffness in porcine corneas (TVST 2021). This study was conducted to evaluate the clinical results of KeraVio without riboflavin drops. Patients with progressive keratoconus were enrolled and randomly divided into the VL irradiation alone group (Group 1) and the no irradiation group (Group 2; control group) (jRCTs032190267). The eyes were exposed to VL (375 nm, irradiance 310 μW/cm2)-emitting glasses for 4.5 hours daily for 6 months. The mean changes in the maximum keratometry value (Kmax) from baseline to 6 months were 0.94 ± 2.65 diopters (D) and 1.76 ± 2.75 D in Group 1 and Group 2, respectively (p=0.705). No differences were found between patients who did and did not receive VL irradiation in terms of the clinical outcomes of keratoconus. VL irradiation alone likely does not halt keratoconic progression, and the administration of riboflavin is necessary.

Tumor associated macrophages (TAMs) in the tumor microenvironment (TME) promote immune evasion, cancer cell proliferation, and metastasis. Ongoing research is focused on finding ways to prevent tumor growth by inhibiting TAM polarization, which has shown a correlation with unfavorable prognosis in clinical studies. Pancreatic adenocarcinoma upregulated factor (PAUF) is a protein secreted from pancreatic cancer (PC) and acts as a TME modulator that affects the TME by acting on not only cancer cells but also stromal cells and immune cells. Tumor cells can evade the immune system by PAUF binding to Toll-like receptor (TLR) in monocytes, as this research shows. In this study, the examination centered around the recruitment of human monocytes by PAUF and the subsequent differentiation into macrophages. In an in vitro chemotaxis assay, PAUF induced chemotactic migration of TLR2-mediated monocytes. In addition, PAUF induced differentiation of monocytes into M2 macrophages, which was verified based on expressing surface markers and cytokines and morphological analysis. The inhibition of T cell proliferation and function was observed in differentiated M2 macrophages. To conclude, these findings indicate that PAUF functions as a promoter of cancer progression by regulating the recruitment and differentiation of macrophages within TMEs, ultimately causing immunosuppression.

Approximately half of all women develop palpitations during pregnancy with a quarter experiencing arrhythmias. While most presentations are benign, some cases can result in sudden cardiac death or serious symptom development. Considering such clinical presentation, healthcare providers must acquire knowledge in this area to provide comprehensive prenatal, perinatal, and postnatal care. However, no study could be located that focused on women’s lived experiences of such complications during or in the post-pregnancy period. The study aims to share the results of a study that explored the lived experience of one woman who developed non-sustained ventricular tachycardia during her third pregnancy that lasted into the post-partum period. Using Narrative Inquiry self-study methodology, a woman’s experiences were explored to uncover the challenges she faced in coping with such complications during a period of transition for herself and her family. This methodology allowed for an in-depth understanding of how these complications could affect all aspects of her life. Four narrative threads were produced: 1) diagnostic challenges and delayed recognition; 2) impact on maternal identity and family dynamics; 3) navigating healthcare systems and treatment decisions; and 4) long- term adaptation and resilience. The intention was to add to this topic area to ensure future researchers, current and future healthcare providers, and patients have literature they can refer to when studying, providing care for, or experiencing similar health complications. Acquiring this knowledge can aid healthcare professionals to ensure appropriate care is provided, risks are minimized and their recovery is well supported.

Magneto Hydrodynamic (MHD) propulsion presents an innovative and environmentally friendly alternative to conventional marine propulsion systems. By utilizing electromagnetic fields to generate thrust, MHD propulsion reduces both noise and air pollution, making it an appealing option for the future of commercial shipping. However, the widespread adoption of MHD technology has been limited by several significant challenges. High energy demands, particularly for generating the necessary magnetic fields, require advanced power sources such as nuclear reactors or large-scale batteries, which are impractical for most commercial vessels. Additionally, the cost of installing and maintaining the high-powered electromagnets and energy infrastructure remains prohibitively expensive. Furthermore, material durability is another concern, as system components such as electrodes are exposed to corrosion and wear due to their interaction with seawater and high electrical currents. By addressing these constraints, MHD propulsion has the potential to transform the commercial maritime industry, contributing to more sustainable and less polluting marine transportation. This paper explores the feasibility of overcoming these challenges through advancements in cost-effective energy efficient methods. We identify the primary limiting factor for the practical implementation of magnetohydrodynamic (MHD) propulsion systems as the magnetic field strength. Drawing comparisons with similar applications, such as magnetic resonance imaging (MRI) technologies, we explore advancements in generating strong, homogenous magnetic fields over large volumes. Insights from these research areas provide a foundation for improving the scalability and efficiency of magnetic field generation in MHD systems.

Large Language Models (LLMs) like GPT-4 and mBERT have revolutionized natural languageprocessing (NLP) by providing multilingual capabilities, making it possible to develop models thathandle diverse linguistic inputs across various languages. However, despite these advances, thereremains a noticeable performance gap between how well these models perform in high-resourcelanguages such as English and low-resource languages such as Nepali or Malagasy. We term thisphenomenon the "Babel Effect," highlighting the disproportionate performance that arises fromdifferences in resource availability across languages.This paper aims to explore the root causes of these performance discrepancies in LLMs, focusingon the underlying challenges in tokenization, training, and data scarcity. We utilize cross-lingualbenchmarks, such as XGLUE and TyDiQA, to quantify these performance variations and examinethem in detail. Furthermore, we propose solutions, including enhancing tokenization strategies,employing data augmentation techniques, and refining fine-tuning methods. The paper concludeswith a discussion on how these improvements can mitigate the Babel Effect and lead to more equitablelanguage modeling across diverse linguistic contexts.

We report a new synthesis method for multiple walled nested thin film nanostructures by combining hydrothermal growth methods with Atomic Layer Deposition (ALD) thin film technology and sacrificial films thereby increasing the surface-to-volume ratio to improve the sensing performance of novel ZnO gas sensors. Single crystal ZnO nanorods serve as the core of the nanostructure assembly and were synthesized hydrothermally on fine-grained ALD ZnO seed films. Subsequently the ZnO core nanotubes were coated with alternating sacrificial co-axial 3-D wraparound ALD Al2O3 films and ALD ZnO films. Basically, an ALD 3-D wrap-around Al2O3 sacrificial layer was coating the center nanorod to realize a nested coaxial ZnO thin film nanotube. To increase the surface-to-volume ratio of the nested multiple film nanostructure both front and backside of the nested coaxial ZnO films must be exposed by selectively removing the intermittent Al2O3 sacrificial films. The selective removal of the sacrificial films exposes the front and backside of the free-standing ZnO films for interacting with target gases during sensing operation while steadily increasing the surface-to-volume ratio. The sensing response of the novel ZnO gas sensor architecture with nested nanotubes achieved a maximum 150% enhancement at low temperature compared to a conventional ZnO nanorod sensor.

Predicting the remaining useful life (RUL) of mechanical bearings is crucial in the industry. 1 Estimating the RUL enables the assessment of health bearing, maintenance planning, and signifi- 2 cant cost reduction, thereby fostering industrial development. Existing models rely on traditional 3 feature engineering, with feature changes because operating conditions pose a major challenge to 4 the generalization of RUL prediction models. This study focuses on neural network-based feature 5 engineering and the downstream prediction of the RUL, eliminating the need for specific prior 6 knowledge and simplifying the development and maintenance of models. Initially, a convolutional 7 neural network (CNN) model is employed for feature engineering. Subsequently, a bidirectional 8 long short-term memory network (Bi-LSTM) model is used to capture the time-series degradation 9 characteristics of the engineered features and predict the RUL through regression. Finally, the study 10 examines the influence of operating conditions in the model and integrates domain adaptation to 11 minimize differences in feature distribution, thereby enhancing the model’s generalizability for the 12 RUL prediction.

Although new industrialized construction technologies have the potential to improve industry performance by optimizing work processes and improving the working environment, the application of these technologies in the construction industry is still in its infancy, and its technical direction is not yet set. This study aims to put forward a theoretical framework for evaluating the level of new-type building industrialization of construction engineering projects, conduct integrated research on the existing domestic and foreign literatures on new-type building industrialization and intelligent construction, preliminarily construct the index system, and determine the evaluation index system of new-type building industrialization projects through the Delphi method. The index weight is determined by the combination of order relation analysis and entropy weight method. The evaluation model is constructed by matter-element theory and cloud model, and the evaluation grade standard is proposed. The research results will help guide the evaluation of new building industrialization projects, and provide a judgment reference for determining the development trend and direction of new building industrialization technologies in the future, and help formulate data-driven industrial policies to promote the adoption of new industrialization technologies and promote the digital transformation of the construction industry.

Background/Objectives: The increasing incidence and mortality rates of liver cancer have heightened the demand for the development of effective anticancer drugs with minimal side effects. In this study, the roles of exosomes derived from liver cancer stem cells (LCSCs) with PRELI (Protein of Relevant Evolutionary and Lymphoid Interest) modulation, and their miRNAs were investigated to explore the therapeutic for liver cancer. The purpose of this study is to identify a more effective and less toxic anticancer agent beyond the well-known Notch, p51, and Hippo signaling pathways, and to specifically propose the potential of PRELI-induced exosomes as an anticancer therapeutic candidate. Methods: Various techniques, such as miRNA profiling, flow cytometry, western blotting, and immunocytochemistry, were used to evaluate the effects of ex-osomes under PRELI up- and downregulation. Results: PRELI upregulation altered expression levels of miRNAs, including hsa-miR-378a-3p (involved in stem-like properties), hsa-miR-25-3p (contributing to cell proliferation), and hsa-miR-423-3p (driving invasiveness). Exosomes with downregulated PRELI inhibited AKT/mTORC1 signaling, while this signaling pathway was ac-tivated in LCSCs transfected with the candidate miRNAs. Under PRELI up-regulation, exosomes showed increased surface marker expression, promoting cancer progression. Conclusions: The modulation of PRELI in LCSCs affects miRNA expression significantly, revealing candidate miRNA targets for liver cancer treatment. Exosomes with PRELI down-regulation show potential as a novel therapeutic strategy.

Flashes of superoxide anion (SA) in mitochondria are generated spontaneously or during the opening of the permeability transition pore (PTP) and a sudden change in the metabolic state of a cell. Under certain conditions, SA can leave the mitochondrial matrix and perform signaling functions beyond mitochondria. In this work, we studied the kinetics of the release of SA and H2O2 from isolated mitochondria upon PTP opening and the modulation of the metabolic state of mitochondria by the substrates of respiration and oxidative phosphorylation. It was found that PTP opening leads to suppression of H2O2 emission and activation of SA burst. When the induction of PTP was blocked by its antagonists (cyclosporine A, ruthenium red, EGTA), the level of substrates of respiration and oxidative phosphorylation and the selective inhibitors of complexes I and V determined the type of ROS emitted by mitochondria. It was concluded that upon complete and partial reduction and complete oxidation of redox centers of the respiratory chain, mitochondria emit H2O2, SA, and nothing, respectively. The results indicate that the PTP- and substrate-dependent switching of the type of ROS leaving mitochondria may be the basis for SA- and H2O2-selective redox signaling in a cell.

Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 40s with the first abstract models of intelligent machines. Soon later in the 50s and 60s machine learning algorithms such as neural networks and decision trees ignited large enthusiasm. More recent advancements include the refinement of learning algorithms, the development of convolutional neural networks to efficiently analyze images, and methods to synthesize new images. The renewed enthusiasm was also due to the increase in computational power with graphical processing units and the availability of large digital databases to be mined by neural networks. AI soon began to be applied in medicine, first through expert systems designed to support the clinician’s decision, and later with neural networks for the detection or classification of malignant lesions in medical images. More recently, the use of natural language processing, recurrent neural networks, transformers and generative models has both improved the capabilities of making an automated reading of medical images and moved AI to new domains, including text analysis of electronic health records, image self-labelling and self-reporting. Thanks to its versatility and impressive results and fueled by the availability of powerful computing resources and open-source libraries, AI is one of the most promising resources for frontier research and applications in medicine.

The rapid advancements in the Fourth Industrial Revolution and artificial intelligence (AI) necessitate a transformative approach to leadership in higher education. This paper investigates the current state of leadership in Iraqi universities and proposes strategies to enhance leadership capabilities aligned with technological advancements. Through a comprehensive review of existing literature, qualitative analysis, and quantitative data collection, the study identifies key challenges and opportunities for developing effective university leadership. The findings underscore the importance of strategic human resource management, high-performance work systems, and technological integration in fostering leadership excellence. Practical recommendations for policymakers and educational leaders in Iraq are provided, highlighting the need for strategic investments in leadership development to meet the demands of the evolving educational landscape.

Simulated Annealing (SA), Tabu Search (TS), and Variable Neighborhood Search (VNS) are well-established single-solution-based metaheuristics known for their efficiency. In this paper, we explore the application of these techniques to enhance the performance of graph network models, specifically Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs). By leveraging these metaheuristics, we aim to optimize key aspects of graph-based learning, such as hyperparameter tuning, architecture design, feature selection, and graph structure learning. Our approach demonstrates how these metaheuristics can improve the generalization, efficiency, and robustness of GNNs/GCNs, facilitating their use in large-scale graph-based tasks. The proposed framework illustrates the potential of combining advanced optimization techniques with state-of-the-art graph machine learning models.

This paper surveys the landscape of security and data attacks on machine unlearning, with a focus on financial and e-commerce applications. We discuss key privacy threats such as Membership Inference Attacks and Data Reconstruction Attacks, where adversaries attempt to infer or reconstruct data that should have been removed. In addition, we explore security attacks including Machine Unlearning Data Poisoning, Unlearning Request Attacks, and Machine Unlearning Jailbreak Attacks, which target the underlying mechanisms of unlearning to manipulate or corrupt the model. To mitigate these risks, various defense strategies are examined, including differential privacy, robust cryptographic guarantees, and Zero-Knowledge Proofs (ZKPs), offering verifiable and tamper-proof unlearning mechanisms. These approaches are essential for safeguarding data integrity and privacy in high-stakes financial and e-commerce contexts, where compromised models can lead to fraud, data leaks, and reputational damage. This survey highlights the need for continued research and innovation in secure machine unlearning, as well as the importance of developing strong defenses against evolving attack vectors.

In this paper, we review metaheuristic optimization techniques for machine learning with Graph Neural Networks (GNNs), focusing on two major categories: single-solution-based and population-based metaheuristics. We provide an in-depth analysis of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), highlighting their effectiveness in optimizing GNN hyperparameters. By exploring the strengths and weaknesses of these methods, we aim to provide insights into their application and performance in enhancing the efficiency and accuracy of GNN models. We will also review the similarities of supervised learning and metaheuristic solutions.

The deterministic generation of robust soliton comb has significant meaning for the optical frequency combs to be widely used in various applications. As a novel form of microcomb, Soliton crystal holds the advantages of easy generation, high conversion efficiency, and excellent thermal robustness. Here, we report the turnkey deterministic generation of ‘Palm-like’ soliton crystal with a free-running scheme. The robustness of the turnkey soliton crystal generation is also investigated in multiple aspects, including the success rate, the thermal robustness, and the long-term stability. The experiment results reveal our turnkey soliton crystal can achieve nearly a 100% success rate with a power variation less than ±1.5 dB over one hundred trials of two samples, is insensitive to thermal effects, and is robust to the environment during four-hour laboratory time.

Liver disease can significantly impact life expectancy, making early diagnosis and therapeutic intervention critical challenges in medical care. Imaging diagnostics play a crucial role in diagnosing and managing liver diseases. Recently, the application of artificial intelligence (AI) in medical imaging analysis has become indispensable for healthcare. AI, trained on vast datasets of medical images, has sometimes demonstrated diagnostic accuracy that surpasses human experts. AI-assisted imaging diagnostics are expected to contribute significantly to the standardization of diagnostic quality. Furthermore, AI has the potential to identify image features imperceptible to humans, thereby playing an essential role in clinical decision-making. This capability enables physicians to formulate more accurate diagnoses and develop effective treatment strategies, ultimately improving patient outcomes. Additionally, AI is anticipated to become a powerful tool in personalized medicine. By integrating individual patient imaging data with clinical information, AI can propose optimal treatment plans, making it an essential component in providing the most appropriate care for each patient. Current reports highlight the advantages of AI in managing liver diseases. As AI technology continues to evolve, it is expected to further advance personalized diagnostics and treatments, contributing to overall improvements in healthcare quality.

Since its origin in the thermodynamics of the 19th century, the concept of entropy has also permeated other fields of physics and mathematics, such as Classical and Quantum Statistical Mechanics, Information Theory, Probability Theory, Ergodic Theory and the Theory of Dynamical Systems. Specifically, we are referring to the classical entropies: the Boltzmann-Gibbs, von Neumann, Shannon, Kolmogorov-Sinai and topological entropies. In addition to their common name, which is historically justified (as we briefly describe in this review), other commonality of the classical entropies is the important role that they have played and are still playing in the theory and applications of their respective fields and beyond. Therefore, it is not surprising that, in the course of time, many other instances of the overarching concept of entropy have been proposed, most of them tailored to specific purposes. Following the current usage, we will refer to all of them, whether classical or new, simply as entropies. Precisely, the subject of this review is their applications in data analysis and machine learning. The reason for these particular applications is that entropies are very well suited to characterize probability mass distributions, typically generated by finite-state processes or symbolized signals. Therefore, we will focus on entropies defined as positive functionals on probability mass distributions and provide an axiomatic characterization that goes back to Shannon and Khinchin. Given the plethora of entropies in the literature, we have selected a representative group, including the classical ones. The applications summarized in this review finely illustrate the power and versatility of entropy in data analysis and machine learning.

The bioherbicidal potential of four essential oils (EOs) from Mediterranean species of Thymus sp. pl.: Thymus algeriensis Boiss. et Reut., T. ciliatus Desf. subspecies coloratus (Boiss. et Reut.) Batt., T. vulgaris L., ecotype Fasano and T. vulgaris L. var. Varico was investigated to identify new sources of biomolecules. In vivo assays were carried out to assess the effect of the EOs on pre and post-emergence of two weed species: Lolium perenne L. and Amaranthus retroflexus L.. Four concentrations of EO emulsions (500, 750, 1000 and 2000 µl/100 mL) were applied. All the EOs expressed an important bioherbicidal activity against the target species. A total inhibition of seed germination was displayed at 2000 µl whereas a potent effec-tiveness was achieved even at 500 µl. On the contrary, for post-emergence experiments, L. perenne showed resistance to the EOs while A. retroflexus was highly sensitive, arguing a selectivity of the EOs. Bioassays were also performed on the model plant Arabidopsis thaliana (L.) Heynh, for a better understanding of the structural modifications induced by the EOs. The root system of EO-treated A. thaliana seedlings was highly damaged.

Wind-solar power generating and hybrid battery-supercapacitor energy storage complex is used for autonomous power supply of consumers in remote areas. This work uses passivity-based control (PBC) for this complex in accordance with the accepted energy management strategy (EMS). Structural and parametric synthesis of the overall PBC system was carried out that was accompanied by a significant amount of research. In order to simplify this synthesis, a structural decomposition of the overall dynamic system of the object, which was described by a system of differential equations of the seventh order, into three subsystems was applied. These subsystems are a wind turbine, a PV plant and a hybrid battery-supercapacitor system. For each of the subsystems, it is quite simple to synthesize the control influence formers, which locally perform the tasks set by the EMS. The results obtained by computer simulation of the overall and decomposed systems demonstrate the effectiveness of this approach in simplifying synthesis and debugging procedures of complex multiphysical systems.

There is lacking information about the physiological response when conducting training of older men in a real-life setting. Seventy-six sedentary men aged 72.3 ± 0.6 (means ± SE; range: 63–92) years with a height, body mass and body mass index of 178.9 ± 0.8 cm; 92.1 ± 2.2 kg: 28.7 ± 0.6 kg/m2, respectively, were recruited to floorball (FG, n = 29), strength (SG, n = 38) or bowls (BG, n = 9) training 1 h twice a week in municipal activity centers and senior sport clubs. Subjects were tested at baseline, after 12 and 24 weeks. Twelve weeks of floorball and strength training lead to reduced blood Hb1Ac, body mass, fat mass, visceral and android fat. Further, SG had a decrease in gynoid fat and blood total and LDL cholesterol. Both groups decreased heart rate at rest. In SG, systolic and diastolic blood pressure were also reduced. FG increased markers for bone growth. FG and SG improved functional capacity. The improvements in FG and SG were maintained after 24 weeks. BG did not have any changes. In conclusion, older men conducting floorball or strength training twice a week in a real-life setting can improve functional capacity and a high number of health factors, whereas playing bowls does not lead to physiological changes.

BODIPY compounds are important organic dyes with exceptional spectral and photophysical properties and numerous applications in different scientific fields. Their widespread applications have flourished due to their easy structural modifications, which enable preparation of different molecular structures with tunable spectral and photophysical properties. To date researchers mostly devoted efforts to modify BODIPY meso-position or pyrrole rings, whereas substitution of fluorine atoms remained not widely explored. However, chemistry on the boron atom is possible, and it enables tuning of photophysical properties of the dyes, without tackling their spectral properties. Furthermore, modifications on boron affect solubility and aggregation propensity of the molecules. This review article highlights methods for the preparation of B-substituted compounds and the most important reactions on the boron of the BODIPY dyes. They were divided into reactions promoted by Lewis acid (AlCl3, or BCl3), or bases such as alkoxides and organometallic reagents. By use of these two methodologies it is possible to clave B-F bonds and substitute them with B-C, B-N, or B-O bonds from different nucleophiles. A special emphasis in this review is given to still underdeveloped photochemical reactions on the boron atom of BODIPY dyes. These reactions have potential to be used in the development of a new line of BODIPY photo-cleavable protective groups (also known as photocages) with bio-medicinal and photo-pharmacological applications, such as drug delivery.

Aim: This study aims to investigate the global collaboration network among researchers studying Sick Sinus Syndrome (SSS) from 2000 to 2023. Given the syndrome's increasing prevalence in aging populations, understanding the structure of research collaboration is crucial to advancing the field. This analysis explores co-authorship networks to identify key contributors, institutions, and trends in SSS research. Method: This study utilized network analysis techniques to evaluate 1,693 publications related to SSS research indexed in the Web of Science (WoS) Core Collection between 2000 and 2023. The analysis was conducted using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The co-authorship networks were assessed using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (degree of node clustering), number of components (distinct connected subgroups), and average path length (average distance between nodes). Micro-level indicators including degree centrality (importance based on the number of connections), closeness centrality (proximity to other nodes), and betweenness centrality (frequency of a node on the shortest paths between others) were also analyzed.Result: The analysis revealed an overall increase in collaboration over time, with rising network density and stronger local clusters, although the network remained fragmented. Key researchers, including Boriani, Giuseppe (Italy), and Glikson, Michael (Australia), emerged as central figures, demonstrating high degrees of centrality in different periods. The results also highlight persistent regional and institutional collaboration patterns, reflecting the growing importance of global partnerships in addressing SSS.Conclusion: This comprehensive network analysis underscores the importance of international collaboration in advancing SSS research. Despite growing collaborative efforts, the field remains somewhat fragmented, suggesting further opportunities for integration. This research has enabled us to identify influential researchers and research institutions, and we will be able to promote research collaboration to further understand and improve treatment methods for SSS as a response to the aging of the world's population.

This research provides an in-depth analysis of the effectiveness of sheep farming, with a particular focus on the Awassi breed in the harsh environments of the Middle East. Investigations were conducted at the Fjaj sheep station in Jordan, with emphasis on how various factors, including lambing type, lamb sex, parity, age at lambing, and the ram affect the weight characteristics of 2,263 records. Advanced statistical models, both fixed and mixed were employed to estimate genetic and breeding values, particularly in relation to the number of lambs per ram. The findings underscore the crucial role that both genetic and environmental factors play towards improving weigh traits and overall flock performance. Similar strategies may be effective in other breeds and in other regions with diverse environmental conditions. The study concludes that when farmers prioritize selective breeding and environmental management they can significantly enhance weight traits, boost profitability, and achieve sustainable economic success in sheep farming.

Clostridioides difficile, the causative agent of C. difficile infections (CDI), can be naturally infected by bacterial viruses known as bacteriophages. All characterized bacteriophages of this bacterium are temperate, meaning that upon infection their genetic material integrates and replicates with host’s genome. Such lysogenic strains can exhibit altered physiology and virulence, which in turn can be an important factor for epidemiology of CDI. In this study we characterized the phiCDKH02 bacteriophage infecting clinical isolates of C. difficile belonging to hypervirulent ribotypes 027 and 176. The bacteriophage was found to be identical to phi027. To get some insight into the role of this bacteriophage in physiology of its host and interaction with human colon cells, we made use of CRISPR-Cpf1 technology to cure the lysogenic C. difficile of the prophage. The prophage-free strain exhibited altered sporulation efficiency, lowered adhesion and decreased cytopathic effects to-wards human colon cells. These results emphasize importance of prophages in shaping virulence of C. difficile.

Current paper is aiming to show the obtained results in manufacturing and characterization of graphite-based and metal composite materials suitable to be used in high voltage application of automotive field. Thus, 4 types of materials have been processed and tested in order to assess their suitability to be used as current collector material for trolleybus. The novelty of this work is the new innovative technological process for obtaining the carbon/metal composite material used in the manufacture of sliding contacts for electrical current collector heads, as well as the character-istics obtained for this material. All 4 obtained materials have been characterized from structural (by optical microscopy), mechanical (density, electrical resistivity, hardness) and functional point of view (wear after 1000 working hours, electric discharge and electric load) and it was determined that the material which uses chemically copper covered graphite is the most suitable of them and has been chosen to be further developed by the end beneficiary.

Accurate solar power forecasting is pivotal for the effective planning, management, and operation of power systems, ensuring a sustainable energy supply for consumers while optimizing the integration of renewable energy sources and the functioning of electricity markets. Recent advancements have focused on developing predictive models that offer precise daily forecasts for solar power generation, which are critical for power usage planning and production efficiency. These models typically leverage a diverse range of data inputs, including solar power metrics and meteorological variables such as temperature, humidity, precipitation, solar radiation, and wind speed—factors that significantly influence solar power generation due to their weather-dependent nature. This study introduces a novel hybrid forecasting model that synergizes the capabilities of the Prophet model and Gated Recurrent Units (GRU), leveraging their respective strengths to enhance predictive performance. The proposed model is rigorously evaluated across short-term (2 days, 7 days) and medium-term (15 days, 30 days) forecasting horizons. Experimental results demonstrate that the hybrid model significantly outperforms the existing models, delivering superior accuracy in solar power generation forecasts.

Fused in sarcoma (FUS) is involved in the formation of nuclear biomolecular condensates associated with poly(ADP-ribose) [PAR] synthesis catalyzed by a DNA damage sensor such as PARP1. Here, we studied FUS microphases’ separation induced by poly(ADP-ribosyl)ated PARP1WT [PAR-PARP1WT] or its mutants PARP1Y986S and PARP1Y986H respectively synthesizing (short PAR)-PARP1Y986S or (short hyperbranched PAR)-PARP1Y986H using dynamic light scattering, fluorescence microscopy, turbidity assays, and atomic force microscopy. We observed that biologically relevant cations such as Mg2+, Ca2+, or Mn2+ or polyamines (spermine4+ or spermidine3+) were essential for the assembly of FUS with PAR-PARP1WT and FUS with PAR-PARP1Y986S in vitro. We estimated the range of the FUS-to-PAR-PARP1 molar ratio and the cation concentration that are favorable for the stability of the protein’s microphase-separated state. We also found that FUS microphase separation induced by PAR-PARP1Y986H (i.e., a PARP1 mutant attaching short hyperbranched PAR to itself) can occur in the absence of cations. The dependence of PAR-PARP1–induced FUS microphase separation on cations and on branching of PAR structure points to a potential role of the latter in the regulation of formation of FUS-related biological condensates and requires further investigation.

This paper investigates the leader-following H∞ consensus of fractional-order multi-agent systems (FOMASs) under input saturation via the output feedback. Based on the bounded real lemma for FOSs, the sufficient conditions of H∞ consensus for FOMASs are provided in α∈0,1 and 1,2, respectively. Furthermore, the iterative linear matrix inequalities (ILMIs) approaches are applied for solving quadratic matrix inequalities (QMIs). The ILMI algorithms show a method to derive initial values and transform QMIs into LMIs. Mathematical tools are employed to transform the input saturation issue into optimal solution of LMIs for estimating stable regions. The ILMI algorithms avoid the conditional constraints on matrix variables during the LMIs construction and reduce conservatism. The approach does not disassemble the entire MASs by transformations to the Laplacian matrix, instead adopting a holistic analytical perspective to obtain gain matrices. In the end, numerical examples are conducted to validate the efficiency of the approach.

In this paper, a passive wireless sensor is designed and developed in terms of an enhanced multiple bit application. The proposed sensor is in the formula of UWB working at 3.1GHz~10.6GHz band, and the carrying of multiple bits information is realized by the combination of multiple sensor units operated in the principle of Spectral-Temporal Joint Coding and Modulation. A prototype of the proposed sensor is configured by four UWB sensor units, each is specified by modulation upon the UWB pulse in time domain and, simultaneously, upon the UWB spectrum in frequency domain, jointly coded digitally by 1/0 data delivering totally the enhanced digital capacity up to 8 bits. Simulation and measurement verify the validation of the UWB sensor with effectiveness of data delivery in the scenarios where remote, contactless and battery-free applications are necessary.

Purpose: To estimate the efficacy of the eight-chop technique in phacoemulsification surgeries for patients with hard nucleus cataracts by investigating corneal endothelial cell density (CECD) reduction post-phacoemulsification and intraoperative parameters.Setting: Sato Eye Clinic, Chiba-ken, Japan.Design: Prospective case series.Methods: Patients were categorized into three groups (Grade IV, IV plus, and V) according to the hardness of their lens nuclei. Surgeries were performed using the eight-chop technique. Key intraoperative metrics (operative time, phaco time, aspiration time, cumulative dissipated energy [CDE], and fluid volume used) were measured. Pre- and postoperative assessments included corrected distance visual acuity, intraocular pressure (IOP), endothelial cell size variation, hexagonal cell percentage, central corneal thickness, and CECD.Results: Overall, 89 eyes from 67 patients with cataracts were evaluated. The mean operative time, phaco time, aspiration time, CDE, and fluid volume used across Grades IV, IV plus, and V were 10.5 min, 38.9 s, 135.6 s, 19.2, and 53.0 mL, respectively. At 19 weeks postoperatively, the CECD decreased by 0.2%, 6.8%, and 9.6% for Grades IV, IV plus, and V, respectively, with an average decrease of 3.7%. Significant reductions in postoperative IOP were observed across all groups compared with preoperative IOP (p < .01). Loss of CECD significantly correlated with phaco time, CDE, and fluid volume (p = .027, p < .01, and .034, respectively).Conclusions: The eight-chop technique in phacoemulsification for hard nucleus cataracts resulted in minimal CECD loss. It may provide an effective surgical solution for patients with hard nucleus cataracts.

Phakopsora pachyrhizi, the causative agent of the Asian Soybean Rust, is one of the most prominent causes of yield loss in soybean production worldwide. In this study, a combination of hyperspectral imaging with advanced data analysis methodology is shown to accurately detect soybean rust symptoms in early stages and differentiate them from other factors at leaf scale. Data analysis was performed with neural networks based on training data from a small subset of the respective experiments and compared with classical machine learning methods for its accuracy and early disease detection capabilities. Results show that even with a comparably small subset of training data the analysis through neural networks can outperform classical machine learning methods in a complex dataset with high variability in the designated classes. In two separate experiments it was possible to accurately detect disease in inoculated leaves before symptoms were visible to the human eye. These results do not only show the potential of this methodology for precision farming applications, but also allow accurate and objective assessment of symptom severity and development for scientific studies and resistance breeding.

The purpose of this study is to examine how sales restrictions on gasoline-powered vehicles influence patent application behavior in automobile engine technology. This is achieved through a factorial analysis targeting structural changes in patent applications. We examine patterns in patent data from 1985 to 2019 using index decomposition based on the logarithmic mean Divisia index. The analysis utilizes patent data and focuses on one non-green (internal combustion engine vehicles) and three green automobile engine technologies (battery electric vehicle, hybrid electric vehicle, and full cell vehicle). Furthermore, the study focuses on four significant patent offices (China, Japan, the United States, and Germany) to examine global disparities. The analytical results reveal that a country’s green investment pattern changes depending on its growth stage. Intellectual property protection policies and encouragement of R&D will extend the scope of R&D activities in emerging nations where intellectual property markets are not well developed. Furthermore, different countries have distinct product development strategies when it comes to various forms of green automotive engine technologies. Green patents are likely to rise in industrialized nations for incentives such as tax breaks or subsidies for R&D spending on specific technological advancements and the extension of intellectual property protection.

This study explores the topology optimization technique to design lightweight and compact antennae. The primary objective of this antenna design is to enable its operation across a broad and extensive frequency bandwidth. The optimization process revolves around manipulating the material distribution within the structure. This endeavor successfully yields a novel material distribution characterized by refractive indices spanning the range of 3 to 4. Subsequently, the antenna structure undergoes a binarization process, reducing its constituent features in size, thereby streamlining the fabrication process. The antenna design is accomplished by utilizing the commercial software platform COMSOL Multiphysics, specifically leveraging its optimization module. This scientific approach and methodology contribute to the advancement of metasurface antenna technology.

Octenidine-containing antiseptic oromucosal solution has proven to be efficacious and safe for temporary bacterial reduction and plaque inhibition in the oral cavity. While its clinical outcomes are encouraging, further research is needed to assess its microbicidal activity against pathogens causing gingivitis and periodontitis. We evaluated the in vitro microbicidal activity of different dilution concentrations of a commercially-available 0.1% octenidine dihydrochloride containing oromucosal solution (OOS) against 10 periodontopathogenic bacteria and three yeast strains using the testing methodology frameworks of DIN EN 13727:2012+A2:2015 (30-s contact time) and DIN EN 13624:2022-08 (60-s contact time), respectively. The OOS showed significant bactericidal activity at 80% dilution concentration, as indicated by a log reduction factor (lg RF) ≥5 for all 10 bacteria. At 50% dilution concentration, bactericidal activity was observed against specific bacteria like Porphyromonas gingivalis, Prevotella buccalis, Parvimonas micra, Eikenella corrodens, Dialister pneumosintes, Schaalia odontolytica (Actinomyces odontolyticus), and Campylobacter rectus. Yeasticidal activity against Candida albicans was observed at 80% dilution concentration, whereas Candida auris was more susceptible to the OOS and showed lg RF ≥4 even at 50% dilution concentration. Therefore, the OOS may be an effective adjunct to periodontal therapy aimed at reducing pathogenic microbial load, inhibiting plaque formation, and thereby preventing periodontal inflammation.

Familial Mediterranean fever (FMF) is a genetically determined autoinflammatory disease transmitted by an autosomal recessive mechanism. The cause of the nosology is point mutations of the MEFV (Mediterranean FeVer) gene coding for pyrin protein. Inflammasomes are molecular platforms altered in autoinflammatory diseases. The aim of this study was to evaluate the expression of inflammasome genes (p65, Casp-1, MEFV-d2, MEFV-I, NLRP3) in patients with FMF compared to controls to understand the changes that may play a key role in disease development. We found altered expression levels of the full-length MEFV isoform as well as Casp1 and p65 in FMF patients versus controls. This, once again highlighted the significance of inflammasome genes in terms of FMF.

Background/Objectives: Alcohol can directly damage the liver, causing steatosis, steatohepatitis, cirrhosis, and hepatocellular cancer. The aim of this study was to examine 28-day survival in hospitalized patients with alcohol-related liver disease (ALD) cirrhosis, as well as to develop and validate a new survival prediction model. Methods: A total of 145 patients with ALD cirrhosis were included, 107 were diagnosed with acute decompensation (AD) and 38 with acute-on-chronic liver failure (ACLF). The new LIV-IN (LIVer mortality INpatients) score was calculated using the following variables: hepatic encephalopathy (HE), hepatorenal syndrome (HRS), ascites, systemic inflammatory response syndrome (SIRS), community-acquired infection (CAI), and fibrinogen. The diagnostic accuracy of the LIV-IN score, as well as Model for end-stage liver disease (MELD), Model for end-stage liver disease-Sodium (MELD-Na), albumin-bilirubin (ALBI), Neutrophil-to-lymphocyte ratio (NLR), Chronic Liver Failure Consortium-C acute decompensation (CLIF-C AD), Chronic Liver Failure Consortium- acute-on-chronic liver failure (CLIF-C ACLF) was tested. Results: Lethal outcome occurred in 46 (31.7%) patients. The mortality rate was higher in the ACLF group (n=22, 57.9%) compared to the AD group (n=24, 22.4%) (p<0.01). The highest predictive power for short-term mortality was observed for the LIV-IN score (AUC 73.4%, p<0.01). In patients with AD, the diagnostic accuracy of the CLIF-C AD score was better than for the LIV-IN score (AUC 0.699; p=0.004, AUC 0.686; p=0.007, respectively). In patients with ACLF, only the LIV-IN score had statistically significant discriminative power in predicting 28-day survival. Conclusion: LIVer mortality Inpatients prognostic score is a new, reliable prognostic model in predicting 28-day mortality.

Constructing the fundamental principles of intelligence and consciousness has become a significant scientific challenge. The literature indicates that current theories of intelligence and consciousness have yet to provide comprehensive answers regarding the unified structure, objectives, and significance of the entities generating intelligence and consciousness, the driving forces, the relationship between intelligence and consciousness, and the distinctions between different types of consciousness.This study extends von Neumann architecture to establish Standard Agent Model as an axiom, proposing that any system (agent) possesses five essential capabilities: Information input, output, storage, creation, and control over the use of these capabilities. By taking the maximum and minimum values for each of these five capabilities, two extreme states and two types of intelligence forces of the agent are derived. Inspired by the principles of flight, the above elements are combined to establish the “Flight Model (FM)” of intelligence and consciousness. Intelligence is defined as the agent’s ability to utilize the five capabilities comprehensively to evolve between the two extreme states under the influence of intelligence forces. Consciousness is defined as the agent’s ability to control the use of intelligence under the influence of the two types of intelligence forces. Based on the relationship between control and being controlled, consciousness is further subdivided into self-consciousness, other-consciousness, mix consciousness, and unconsciousness.This study evaluates the intelligence levels and self-awareness of various agents based on the FM theoretical framework through designed experiments. The results show that current artificial intelligence systems exhibit intelligence close to adult human levels but have not yet demonstrated self-consciousness. This study constructs a theoretical system of intelligence and consciousness, providing a theoretical basis for determining the timeline for achieving AGI and addressing AI self-consciousness issues.

Speech enhancement technology seeks to improve the quality and intelligibility of speech signals degraded by noise, particularly in telephone communications. Recent advancements have focused on leveraging deep neural networks (DNN), especially U-Net architectures, for effective denoising. In this study, we evaluate the performance of a 6-level skip-connected U-Net constructed using either conventional convolution activation blocks (CCAB) or innovative global local former blocks (GLFB) across different processing domains: temporal waveform, short-time Fourier transform (STFT), and short-time discrete cosine transform (STDCT). Our results indicate that the U-Nets can receive better signal-to-noise ratio (SNR) and perceptual evaluation of speech quality (PESQ) when applied in the STFT and STDCT domains, with comparable short-time objective intelligibility (STOI) scores across all domains. Notably, the GLFB-based U-Net outperforms its CCAB counterpart in metrics such as CSIG, CBAK, COVL, and PESQ, while maintaining fewer learnable parameters. Furthermore, we propose domain-specific composite loss functions, considering the acoustic and perceptual characteristics of the spectral domain, to enhance the perceptual quality of denoised speech. Our findings provide valuable insights that can guide the optimization of DNN designs for causal speech denoising.

Objective: To analyze and investigate the impact of early rehabilitation training based on developmental sequencing on the growth and development of late preterm infants. Methods: This study focused on preterm infants admitted to our hospital from June 2021 to November 2021. Strict screening was applied to select 54 cases for inclusion. Clinical data were collected for all study subjects, and based on different intervention plans, they were divided into a control group and an experimental group, with 27 cases in each group. The experimental group received intervention based on a developmental sequencing early rehabilitation model in addition to routine care, while the control group only received routine care intervention. The growth and development of the two groups of preterm infants were compared during the study, including physical indicators, intellectual-motor skills, neurobehavioral assessments, and developmental monitoring. Results: The clinical data of the two groups of preterm infants were comparable, with no significant differences (P>0.05). The experimental group exhibited significantly higher values in physical indicators such as weight, head circumference, and height compared to the control group (P<0.05). Intellectual-motor scores (MDI and PDI) of the experimental group were higher than those of the control group (P<0.05). The NBNA scale scores for various dimensions were higher in the experimental group compared to the control group (P<0.05). The DQ values of the experimental group were higher than those of the control group (P<0.05). Conclusion: Early rehabilitation training based on developmental sequencing has a positive impact on the growth and development of late preterm infants. Through systematic and sequenced rehabilitation interventions, various aspects of development in late preterm infants can be promoted effectively. This approach not only improves physical development but also enhances intellectual-motor skills, neurobehavioral development, and overall developmental levels. The results are significant, indicating the worthiness of widespread application and promotion.

This research analysed the distribution of publicly awarded funding, by United Kingdom Research and Innovation (UKRI) to defined University Groups and compared it against the aim and objectives of UKRI’s Equality, Diversity, and Inclusivity (EDI) strategy. Previous work has identified failures in the allocation of research awards, with some groups being under-represented in successful bids. UKRI have acknowledged the situation needs to improve yet concern exists over whether change has been enacted. Publicly accessible information provided freely by UKRI was used in conducting a comprehensive analysis of the current position. The results showed that the marginalisation of groups applies at a University Group level, with over-representation of the elite universities of the Russell Group in successfully funded project bids. The data shows how, both in total financial expenditure and in the number of projects financed, the awarding of research funding to the universities with the largest popular base, namely the group identified as Post-92 universities, is relatively very small. These universities, as identified by measurements such as social inclusion within university rankings, contribute to greater equality of opportunities for student populations and reduce the impact of economic discrimination. By greatly minoritizing the Post-92 universities through funding UKRI is effectively reducing access for their disadvantaged students.

Plant-associated yeasts can also be part of soils’ and pollinating-insects’ microbiomes, where they play significant ecological roles. Indeed, in previous studies, we have shown that yeasts may help pollinators find nectar, which is crucial for their nutrition and for the reproduction of many angiosperms, and, in the soil, can act as plant-growth promoters. Given the importance of yeasts for plant development, in this review, we first delve deep into the biochemical processes behind the ecological role of these microorganisms in soil, insects, and in direct association with plants. Based on this premise, we discuss the influence of this relationship on agricultural production and the possible effects of pesticide use on the yeast microbiota. Finally, we address the most relevant studies in the literature that support the potential of these microorganisms (either indigenous or genetically engineered) as bioremediation agents for soils and foods contaminated by pesticides. Our review indicates that yeasts can be satisfactorily employed in organic agriculture to increase plant growth or bioremediate contaminated soil or food.

Background: Food Bioactive Substances (FBS), extra-nutritional constituents found in foods are promising strategies in the treatment of Non-Alcoholic Fatty Liver Disease (NAFLD). Existing clinical studies showed contradictory results regarding their therapeutic effect. This review aimed to evaluate the effectiveness of food-bioactive substances in patients with NAFLD and non-alcoholic steatohepatitis (NASH). Methods: Six controlled databases (PubMed, Embase, Scopus, ProQuest, CINAHL and Cochrane) were searched, yielding 10969 articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were followed, while screening, quality assessment and data extraction of search results were conducted using Covidence. Results: Thirty-two studies were included in the systematic review, while 8 RCTs with 540 patients with NAFLD were included in the meta-analysis. The meta-analysis underscores the effects of the three most studied FBS (silymarin, curcumin and resveratrol) on key cardiovascular and metabolic markers, including BMI, waist circumference, AST, ALT, LDL, HDL, cholesterol and triglycerides of these patients. Overall, FBS showed a statistically significant reduction in BMI (SMD = -0.28; 95% CI: -0.45 to -0.10, p < 0.01), a slight reduction in LDL (SMD = -0.22; p = 0.02), HOMA-IR (SMD = -0.26, 95% CI = -0.51 to -0.02; p = 0.036), total cholesterol level SMD = -0.15 (95% CI: -0.40 to -0.10; I² = 13%; p = 0.33), especially with silymarin showing the greatest statistically significant reduction in HOMA-IR (SMD = -0.16); p = 0.01), Total Cholesterol (SMD = -0.45, p <0.01), AST (SMD = -0.23, p <0.01) Conclusion: Silymarin appeared to be the most promising intervention, particularly for lowering BMI, LDL, cholesterol and triglycerides, but the high variability between studies limits the generalizability of these results

A comprehensive review of advances in the synthesis and biological applications of enoxacin (ENX) based compounds is presented. ENX, a second-generation fluoroquinolone (FQ) is a prominent 1,8-naphthyridine containing compounds studied in medicinal chemistry. Quinolones, a class of synthetic antibiotics, are crucial building blocks for designing multi-biological libraries due to their inhibitory properties against DNA replication. Chemical modifications at positions 3 and 7 of the quinolone structure can transform antibacterial FQs into anticancer analogs. ENX and its derivatives have been examined for various therapeutic applications, including anticancer, antiviral, and potential treatment against COVID-19. Several synthetic methodologies have been devised for the efficient and versatile synthesis of ENX and its derivatives. This review emphasizes all-inclusive developments in the synthesis of ENX derivatives, focusing on modifications at the C3 (carboxylic acid, Part A), C7 (piperazinyl, Part B), and other positions (Part C). The reactions considered were chosen based on their reproducibility, ease of execution, accessibility, and availability of the methodology reported in the literature. The review provides valuable insights into the medicinal properties of these compounds, highlighting their potential as therapeutic agents in various fields.

In 1935, Einstein and Rosen (ER), in their seminal paper "Particle Problem in General Relativity" proposed that "a particle in the physical Universe has to be described by mathematical bridges connecting two sheets of spacetime" in order to achieve a consistent understanding of quantum fields in curved spacetime. We discuss similar conclusions independently arrived at by Schr\"{o}dinger in 1956 and 't Hooft in 2016 in the context of cosmological and black hole spacetimes. Quantum effects at gravitational horizons involve inverse harmonic oscillators. We demonstrate that an analogous ER proposal of a mathematical bridge is envisioned by Berry and Keating for the description of a quantum inverse harmonic oscillator that has horizons in its phase space. Recently proposed direct-sum quantum theory reconciles the ER's vision by expressing a single quantum state in the physical space as a direct-sum of two components in the parity conjugate regions with opposite arrows of time. We discuss the implications of this framework for offering a new understanding of the ER bridges, promising a unitarity description of quantum fields in curved spacetime along with observer complementarity. Furthermore, we present compelling evidence for our new understanding of ER bridges in the form of large-scale parity asymmetric features in the cosmic microwave background. We finally discuss the implications of this new understanding for our endeavors in combining gravity and quantum mechanics.

Solar electric and solar thermal energies are often considered as part of the solution to the current energy emergency. In the present work, a study of the characteristic length of tubes in flat photovoltaic-thermal (PVT) hybrid solar devices has been carried out using three heat transfer fluids: water, Emim-Ac ionic liquid and ionanofluid of graphene nanoparticles suspended in the above ionic liquid. Experimental measurements and numerical simulations have been performed using the HEATT® platform. Slower fluid velocities are recommended if high fluid outlet temperatures are the goal of the application, but this penalizes the overall thermal energy production. The characteristic process length, or most efficient distance at which heat transfer occurs, is not typically achieved in parallel tube PVT collectors; coil-type panels can improve the thermal and temperature performance of INFs in PVT solar collectors depending on the application.

In the realm of digitalization, data play a crucial role across various scientific fields, from physics to engineering. Analyzing data with advanced technologies like artificial intelligence enhances en-gineers' ability to make informed judgments on product quality. This study aligns with the digital-ization trend by implementing formula generation, generalization, and optimization using genetic algorithms and three machine learning approaches: support vector regression, Gaussian process regression, and artificial neural networks, on a grinding process dataset from a previous study on UNS S34700 steel. The study introduces new approaches by considering three different grinding wheels and four cooling and lubrication solutions as influential process parameters, previously considered qualitative and fixed in earlier literature. Additionally, seven different depths of cut were implemented to measure surface roughness and grinding forces (tangential and normal). Qualitative data were encoded into quantitative data using standard techniques. By enhancing genetic algorithm optimization, a relational formula with 25 coefficients was developed to describe the relationship between input parameters and outputs. Three machine learning algorithms were evaluated for predicting the relationship between predictors and responses. The flexibility and scalability of these methods, combined with the relational formula, provide a sophisticated ap-proach for future expansions. The formula evaluation achieved around 85% accuracy for surface roughness and 90% for grinding forces. In machine learning approaches, the GPR method reached model stability with an R² of around 0.98 and a mean accuracy of 93%, while the ANN reached an R² of around 0.96 and a mean accuracy of 90%. This shows that machine learning approaches are more suitable than formula generalization. The scalability and flexibility of machine learning help generalize with more data variety, while the formula evaluation only accepted current data sets. Furthermore, ML methods achieve high accuracy and are suitable for predicting future data, aligning with digitalization and trend prediction.

Imprinted polymers with imprints of Zn2+ ions (ZnIP) and reference polymers without imprints (NIP) were synthesized based on humic and methacrylic acids. Comparative studies of the physical parameters of ZnIP and NIP were carried out using elemental analysis, IR spectroscopy and electron microscopy. Kinetic parameters of Zn2+ ion sorption on ZnIP and NIP were determined. It was found that the sorption kinetics on both ZnIP and NIP are approximated by a pseudo-first-order equation. The sorption rate constant for ZnIP is higher than that of NIP, indicating a higher adsorption rate of Zn2+ ions onto the surface of ZnIP. This may be due to more accessible sites on ZnIP. Adsorption of Zn2+ ions is best described by the Langmuir model, indicating monolayer adsorption on a homogeneous surface. The Langmuir model also shows that ZnIP is effective at low adsorbate concentrations. The Freundlich model describes the adsorption with less accuracy, which may indicate monolayer adsorption and less surface homogeneity. ZnIP shows better adsorption capacity and higher efficiency compared to NIP and can be proposed as a selective sorption material for Zn2+ ions.

This scoping review aims to systematically examine and analyse the available literature on pure total talar dislocation, focusing on its epidemiology, clinical presentation, imaging techniques, surgical options, rehabilitation protocols, and complications. Following the PRISMA-ScR guidelines, a comprehensive search was conducted across PubMed, Web of Science, and Scopus databases. The search yielded 185 articles, from which 30 met the inclusion criteria, focusing on pure total talar dislocation without fractures. Data from each study were extracted, including patient demographics, injury characteristics, treatment methods, and outcomes. A total of 30 articles were included in this review after screening and quality assessment by five independent reviewers. The studies included case reports, case series, and reviews. Despite the heterogeneity of available studies, key findings suggested that early reduction, careful wound management, and soft tissue preservation are crucial in minimising complications such as avascular necrosis (AVN), post-traumatic arthritis, and infection. Long-term outcomes varied, and the risk of AVN remained high, particularly in cases with compromised blood supply to the talus. Pure total talar dislocation is a rare and challenging condition with no established management protocol. While talar reimplantation and joint fixation offer promising outcomes in preserving function, the risk of complications, particularly AVN, remains significant. Further research is needed to standardise treatment protocols and improve clinical outcomes for this rare but severe injury.

As humanity confronts the existential threats of climate change, biodiversity loss, and growing socioeconomic inequalities, the need for transformative, systemic change has never been more urgent. Conventional innovation approaches, often focused on technological solutionism and market imperatives, have consistently fallen short in delivering the comprehensive, equitable, and sustainable solutions required to navigate these interconnected crises. This pioneering article explores the profound intersection between speculative fiction, sustainable innovation, and the burgeoning field of innovationology. By harnessing the transformative power of imaginative narratives, the integrated praxis of innovationology and sustainability science can challenge existing mental models, spark collective imagination, and foster agency to co-create sustainable futures. Through a rigorous examination of cutting-edge research, empirical evidence, and case studies, this article positions speculative fiction as a critical lever for catalyzing the systemic changes required for a more just, regenerative, and resilient future.

This article presents a conceptual program model that integrates three main methods: object recognition, color classification, and QR decoding of information. These methods are well-established in the fields of Robotics and Artificial Intelligence, yet they remain subjects of ongoing research interest. The program model organizes the execution of the methods in a sequence directed toward recognized objects. In this way, color classification and QR decoding are performed only on already recognized objects. Colors and QR codes that do not belong to recognized objects are not analyzed, which can provide intelligent robots with reliable and accurate information about the objects they can recognize. For research purposes, image sensors (cameras), images saved to disk, clipboard, or shared memory between applications are used. The article shows that the program model can be augmented with various sensors and algorithms for the comprehensive construction of program control of autonomous robots. Future research that builds on current studies will focus on measuring the distance to recognized objects and structuring the collected information about these objects, which will create opportunities for perceptual anchoring and logical reasoning in the program control of intelligent robots.

Background: This study addresses a longstanding discrepancy between the observed course of the superior epigastric artery in anatomical specimens and its depiction in many German-language anatomy textbooks. While the artery typically runs ventral to the diaphragm, textbooks inaccurately describe it as passing through the sternocostal triangle (Larrey's fissure). Methods: Anatomical dissections were performed on 40 formalin-fixed cadavers at the Medical University of Vienna. The thoracic and abdominal walls were dissected, and the course of the superior epigastric artery was documented. Literature from the time of Dominique Larrey to present was reviewed to trace the origin of the incorrect anatomical description.Discussion: Findings confirm that the superior epigastric artery does not traverse Larrey's space but remains ventral to the pleural and peritoneal cavities. The erroneous description appears to have originated from a 19th-century misinterpretation by Joseph Hyrtl and was perpetuated in German-language textbooks.Conclusion: The study refutes the long-standing but incorrect description of the superior epigastric artery's course in German-language anatomy textbooks. The findings highlight the importance of revising anatomical literature to correct historical inaccuracies.

Scaling the operation is the single greatest challenge in every enterprise. The more complex the enterprise, the greater the challenge. Scaling in healthcare has been a consistent challenge, with the need to meet clinical endpoints with less staff, increased costs of resources, regulatory oversight, and the goal of improving access to care. The article explores the emerging paradigm of multi-agent AI systems in healthcare, representing a significant leap beyond traditional Large Language Models. We comprehensively analyze the potential of these systems to revolutionize patient care, streamline administrative processes, and support complex clinical decision-making. The paper describes a hypothetical sepsis management system comprising seven specialized AI agents, each handling specific aspects of patient care, from data collection and diagnosis to treatment recommendations and resource management. We also examine applications in chronic disease management and hospital patient flow optimization. The technical implementation of these systems is discussed, including the use of advanced Large Language Models, inter-agent quality control measures, implementing guardrails, self-reflection, integration with Electronic Health Records, and the importance of explainable AI for decision transparency. In addition to describing the promising potential benefits, such as enhanced diagnostic accuracy and personalized treatment plans, we address significant challenges, including data quality assurance, workflow integration, and ethical considerations. We conclude the article by highlighting future directions, such as integrating IoT devices and developing more sophisticated natural language interfaces. Our work underscores the transformative potential of multi-agent AI systems in healthcare while emphasizing the need for rigorous validation, ethical oversight, and a patient-centered approach in their development and implementation.

Build a special identical equation, use its calculation characters to prove and search for solution of any odd converging to 1 equation through (*3+1)/2^k operation, change the operation to (*3+2^m-1)/2^k, and get a solution for this equation, give a specific example to verify. Thus prove the Collatz Conjecture is true. Furthermore, analysis the sequences produced by iteration calculation during the procedure of searching for solution, build a weight function model, prove it decrease progressively to 0, build a complement weight function model, prove it increase to its convergence state. Build a (*3+2^m-1)/2^k odd tree, prove if odd in (*3+2^m-1)/2^k long huge odd sequence can not converge, the sequence must outstep the boundary of the tree after infinite steps of (*3+2^m-1)/2^k operation.

The Myval is a balloon-expandable transcatheter heart valve (THV) devel-oped by Meril Life Sciences Pvt. Ltd. that has innovative operator-friendly de-sign which aids to improve deliverability and features precise deployment. Vari-ous clinical studies demonstrate its effectiveness and safety, making it a promis-ing choice in valvular interventions. Myval has been successfully utilized as a transcatheter aortic valve implanta-tion (TAVI) device in cases with conduction disturbances, bicuspid aortic valve anatomy, non-calcified aortic regurgitation, dysfunctional stenosed right ventric-ular outflow tract (RVOT) conduits, pulmonary valve replacement, mitral valve replacement, Vave-in-valve and Valve-in-ring implantation procedures. Myval’s diverse sizes are also of key importance in complex cases of large and complex annuli anatomy. Further long-term studies are needed to consolidate these results. Its introduction signifies a significant advancement in cardiology, aiming to enhance patient outcomes and quality of life. In the present review, we provide an update on new-generation Myval THVs and review the available clinical data published till date with an emphasis on diverse use in specific clinical scenarios.

The antifungal efficacy of coffee residues extracts (CRE) and chitosan nanoparticles (CNP) alone and with coffee residue extracts (CNP-CRE) were assessed on growth of Rhizopus stolonifer. Two nanostructured edible coatings, one based on chitosan nanoparticles (CCNP) another on coffee residue extracts encapsulated in chitosan nanoparticles (CCNP-CRE), were elaborated, characterised and applied on inoculated Naples tomatoes to evaluate their fungicidal activity and the effect on fruit quality. The tests consisted of evaluating physicochemical variables in tomato previously sprayed with CCNP and CCNP-CRE for 30 days preharvest and 14 days at 10°C postharvest. A CNP with a spherical shape and particle size of 2.4 ± 0.24 nm with a Z potential of −1.62 mV was observed, while CNP-CRE showed irregular and spherical shapes of 3.9 ± 0.55 nm with a Z potential of −0.89 mV. The FTIR spectrum showed the integration of CRE into the CCNP-CRE. A synergistic effect between CNP and 1 % CRE was observed since the greatest inhibition of mycelial growth (43%) was obtained. In the preharvest trials, the CCNP and CCNP-CRE showed differences with respect to the control in the variables of colour, total carotenoids and ethylene. In the postharvest test, both coatings showed differences in colour and CO2 production. The severity of the infection decreased 33% in tomatoes with coatings.

The Integrated Territorial Program (ITP) for the development of camelid livestock faces key challenges, such as low productivity and lack of infrastructure in the region. This program is aligned with the Territorial Agenda for Agricultural Innovation (FIA) and the Sustainable Development Goals of the 2030 Agenda, prioritizing camelid livestock farming in Chile's highlands as a strategic productive resource. The methodology used is based on co-creation with stakeholders in the territory and comparative analysis to improve competitiveness. This document highlights the relevance of the ITP in a volatile and constantly changing global environment, and its contribution to the innovation and competitiveness of camelid livestock in the Arica and Parinacota region.

The literature has addressed the negative impact of poor neonatal conditions (NC) across regions. This has drawn attention to the need to improve NC, particularly in Africa. NC research can make an important contribution. However, there is no study dedicated to this topic in Africa. Through a bibliometric analysis, we arrive at outputs that can inform scientists in planning ongoing or new NC research and those involved in developing and implementing strategies to combat poor NC. Using bibliometrics, the study identified the scientific knowledge on NC between 2000 and 2019, its visibility in the community, the main topics researched, and collaboration patterns.The results show that knowledge on NC has increased between 2000 and 2019, it is concentrated in a few African countries (Egypt, South Africa, Nigeria, Tanzania and Kenya), its visibility is below the world average, in general, maternal mortality is the most researched topic and collaborative activities are frequently, mainly international research collaboration (IRC), being the United States of America (USA) and the United Kingdom (UK) the main partners (they participated in 57% and 28% of all articles with IRC). The collaboration networks are fragile as 43%-67% of all links represent one article in 20 years.Ongoing or new research on NC in Africa should consider the main African players and their partners. There is a need to implement strategies to increase NC knowledge in other African countries, expand and strengthen the collaboration networks and diversify the sources of knowledge.

The human scalp is a common site of skin cancer in humans, with non-melanoma skin cancer being exceedingly common. In this review, two dermatologists with extensive experience in cutaneous oncology will discuss unique features of the epidemiology of cancer of the scalp. Clinical observations on these common skin cancers lead to insight into the pathogenesis and potential prevention and treatment of cutaneous scalp neoplasia. Our hypothesis is that the presence of hair protects against the development of skin cancer, but not by serving as a physical shield, but rather by providing a continuous IL-17 biased immunosurveillance. Loss of hair allow release from immunosurveillance, resulting in expansion of neoplastic cells towards skin cancer. Both hair follicles and metabolic changes in stroma allow permissiveness for tumor promotion.

Substance abuse and psychiatric disorders are two prevalent and interrelated public health concerns that often coexist, leading to complex clinical scenarios. This comorbidity poses significant challenges in diagnosis, treatment, and understanding the causal relationships between the two conditions. Frequently, one disorder is neglected in favor of the other, and misconceptions abound regarding which condition precipitates the other. This article explores the common comorbidity of substance abuse and psychiatric disorders, delving into the prevalence, diagnostic challenges, treatment implications, and the bidirectional nature of causality. By examining current research and clinical practice, we aim to shed light on the misconceptions and emphasize the need for integrated approaches to effectively address both conditions concurrently.

The digital divide, particularly within the context of Artificial Intelligence (AI) sport podcasts, presents significant behavioral and psychosocial challenges for student engagement. This study examines the disparities in access to and proficiency with Information Communication Technologies (ICT) across different demographic groups, focusing on gender, age, and religious level. The advent of the commercial web has heightened the significance of these divides, as first-level digital divide concerns access to the internet, while the second-level digital divide pertains to the ability to use technology proficiently. Existing literature has consistently highlighted persistent inequalities in these areas, which significantly impact the extent to which students from various backgrounds can engage with AI sport podcasts effectively. Understanding these dynamics is crucial for developing strategies to bridge the gap and ensure equitable access to digital learning resources.

This work investigates mixed convection flow over a vertical thin needle with variable surface heat, mass, and microbial flux, incorporating the influence of gyrotactic microorganisms. The governing partial differential equations are transformed into ordinary differential equations using appropriate similarity transformations. These transformed equations are then numerically solved employing MATLAB's Bvp4c solver. The study's primary focus lies in examining the influence of various dimensionless parameters, including the mixed convection parameter, power-law index, buoyancy parameters, bioconvection parameters, and needle size parameter, on the velocity, temperature, concentration, and microbe profiles. The results demonstrate that these parameters significantly impact surface (wall) temperature, surface fluid concentration, surface motile microbe concentration, and the corresponding velocity, temperature, concentration, and microorganism profiles. The study's findings offer valuable insights into the intricate dynamics of mixed convection flow with bioconvection and have potential applications in diverse fields such as biomedicine and engineering.

To evaluate new circulating markers related to macrovascular complications (MVC) in type 2 diabetes mellitus (T2DM), we recruited 30 controls (CG), 34 patients with T2DM (DG), and 28 patients with T2DM and vascular complications (DG+C), among them, 22 presented MVC. Pe-ripheral blood was used to determine redox status (superoxide dismutase, SOD; catalase, CAT; glutathione reductase, GRd; glutathione peroxidase, GPx; glucose-6-phosphate dehydrogenase, G6PD) and markers of oxidative damage (advanced oxidation protein products, AOPP; lipid pe-roxidation, LPO). Inflammatory markers (IL-1β, IL-6, IL-10, IL-18, MCP-1, TNF-α) and the relative expression of c-miRNAs were analyzed. The real-time PCR results showed that the expressions of miR-155-5p, miR-21-5p, miR-146a-3p, and miR-210-3p were significantly higher in the DG group compared to CG. DG+C group presented statistically relevant differences with CG for four miRs: the increased expression of miR-484-5p, miR-21-5p, and miR-210-3p, and decreased for miR-126a-3p. Moreover, miR-126a-3p was significantly less expressed in DG+C compared to DG. The application of binary logistic regression analysis and construction of receiving operator characteristic curves (ROC) revealed two models with high predictive value for vascular complications presence: 1) HbAc1, creatinine, total cholesterol (TC), LPO, GPx, SOD, miR-126, miR-484. 2) HbAc1, creatinine, TC, IL-6, LPO, miR-126, miR-484. Moreover, our data demonstrated that gender, TC, GPx, CAT, and miR-484 were associated with MVC and exhibited higher predictive values than classical variables. miR-126, miR-484, IL-6, SOD, CAT, and GPx participate in vascular damage development in the studied diabetic population and should be considered for future studies.

The Internet of Medical Things (IoMT) represents a transformative technology that connects medical devices, sensors, and healthcare systems to enable real-time monitor-ing, data sharing, and advanced decision-making in healthcare. While the technical and clinical potential of IoMT has been explored extensively, research on the funding patterns and their implications remains sparse. This paper analyzes funding in IoMT research using bibliometric methods to assess the spatial dimensions of funded and non-funded research across different countries and regions. By employing Synthetic Knowledge Synthesis (SKS), the study triangulates quantitative and qualitative ap-proaches to provide insight into research trends, thematic differences, and the rela-tionship between funding and healthcare outcomes such as the Global Health Index (GHI). The results reveal that funded research exhibits higher publication rates in high-impact journals and is more concentrated in countries with stronger healthcare systems and higher R&D expenditures. However, countries with lower healthcare in-vestments are also increasingly contributing to IoMT research, likely in pursuit of im-proving healthcare outcomes. Thematic analysis shows that funded projects empha-size artificial intelligence applications in e-health and telemedicine, while non-funded research tends to focus on IoMT's role in pandemic management. These findings pro-vide insights into the global landscape of IoMT research funding and its impact on public health advancements.

Those using the mouse for the purposes of electrophysiological research presume that the morphology of the conduction axis is comparable with the human arrangement. As yet, however, to the best of our knowledge, no direct comparison has been made between the species. By comparing our extensive histological findings in the human heart with comparable serially-sectioned datasets prepared from adult murine hearts, we aimed to provide this information. When comparing the gross anatomy, we used three-dimensional datasets of neonatal mice hearts prepared using episcopic microscopy. The overall cardiac architecture is comparable, although the mouse has a persistent left superior caval vein draining via the coronary sinus. An inferior pyramidal space, and an infero-septal recess, are both present in the murine heart, although not as well developed as in the human heart. The overall arrangement of the conduction axis is similarly comparable, albeit with subtle differences reflecting the incomplete wedging of the subaortic outflow tract in the murine heart. Most significantly, the findings in both species reveal the presence of extensive superior septal pathways, which perhaps explain the finding of base-to-apex activation of the ventricular mass known to occur in the murine heart.

Employee turnover is a significant issue for financial institutions, impacting productivity, increasing recruitment costs, and disrupting critical operations. In this project, this study aimed to predict employee turnover using a dataset containing attributes such as employee satisfaction, performance, and tenure. By framing the task as a binary classification problem, this study employed CatBoost and XGBoost, two advanced regression-based algorithms, to develop predictive models. This paper's analysis demonstrated that CatBoost outperformed XGBoost across all evaluation metrics, including MAE, MSE, RMSE, and R², making it the more effective model for predicting turnover in the financial sector. The study highlights key factors contributing to employee attrition, such as job satisfaction, tenure, and promotion opportunities, offering actionable insights for retention strategies. Additionally, by predicting probabilities rather than binary outcomes, this study aims to make more detailed decisions about employee retention. This research provides valuable tools for financial institutions to mitigate the risk of turnover, retain critical talent, and ensure operational continuity.

We investigate soccer analytics from supervised learning, unsupervised learning, and reinforcement learning perspectives. With the increasing availability of player tracking data and event logs, machine learning techniques have become essential for uncovering patterns in player and team performance. In this paper, we examine how supervised learning models are applied to tasks such as match outcome prediction and player rating systems, while unsupervised learning is utilized for player clustering, tactical analysis, and the discovery of hidden patterns in game data. Reinforcement learning, on the other hand, plays a key role in optimizing decision-making during matches by learning optimal strategies and tactics through trial and error. By providing a comprehensive overview of these approaches, we aim to highlight the transformative potential of machine learning in modern soccer analytics and how it continues to shape the sport. We also provide summary of other soccer analytics research in this work.

Leadership is an important driving force in the process of college students' self-improvement and development, and improving college students' self-leadership is conducive to improving their comprehensive quality and ability. Based on literature research, this paper explores the factors affecting the development of college students' leadership, and then proposes a series of ways to improve college students' leadership, so as to promote the further development of college students' leadership education. A systematic literature review method was used to conduct a systematic review of 223 articles published in peer-reviewed journals from databases such as CNKI and Web of Science. The research shows that in practice, college students' leadership is defined as a kind of personal ability and implies two presuppositions: (1) Leadership is a kind of personal behavior, and individuals have the decision-making right to exercise leadership.There is no direct causal relationship between leadership behavior and organizational positions, especially position power. The above two presuppositions are caused by the fundamental changes in the connotation of leaders in the era of knowledge economy . This research will help to deeply understand the essence and influencing factors of student leadership training, and provide theoretical and decision-making reference for schools at all levels to train student leadership.

This study presents a synthesizer topology based on a DLL and programmable frequency multiplier for 5G+ communication systems. The proposed synthesizer comprises a 512-phase DLL, an intermediate frequency generator (IFG), and an RF frequency multiplier (RFFM). The 512-phase DLL provides 512 delayed pulses through a chain of 256 delay units and single-to-differential complementary converters (S2DCs). The IFG comprises I/Q-multiplexers, I/Q-accumulators, an XOR, and an S2DC. The I/Q-multiplexer outputs switch to the phase lag or lead waveforms at every rising or falling edge of the outputs, which makes the I/Q-multiplexer output frequency, fMX, programmable. The IF, fIF, is two times fMX, and fIF is up-converted to RF, fRF, through the RFFM. When reference clock frequency, fref is 156.25 MHz, the fIF range is 156.863–312.5 MHz and the fRF dynamic range is approximately 1.89–9.96 GHz. The channel resolution range is 3.698–38.609 MHz. Consequently, the proposed synthesizer provides a wide 134 % output frequency bandwidth and a finer channel resolution smaller than fref. The presented synthesizer is fabricated in a 65-nm CMOS process. The total power consumption is 15 mW, and the rms jitter integrated from 12 kHz to 20 MHz measured as 83 fs.

Background/Objective: The Dark Web has played a pivotal role in the progress and sophistication of cybercrime. It provides an incubation network beyond the reach of traditional search engines where cybercriminals create and display exploit kits, offers illicit goods and services, and exchange confidential insider intelligence. Cybercriminals are extremely adept at selecting targets, applying tools to achieve their objectives, and minimizing red tape. The increasing sophistication of cybercriminals and the exponential rise of cybercrime against critical infrastructure underlines the necessity of identifying emerging threats. The objective of this research is to investigate the evolving threats within the Dark Web including crimeware-as-a-service and the integration of AI/ML into cyberattacks to inform risk management strategies and strengthen security measures.Research Problem: The exponential rise in cybercrime against critical infrastructure reflects growing sophisticationpresenting a significant challenge to organizations and society. The motivation behind cybercrime is fundamentally driven by self-greed which has contributed drastically to the magnitude and changes in methods used by cybercriminals to enhance profitability. The impact of cybercrime on business organizations presents an adverse impact on society and carries significant risks for the progress of individuals and the world at large. As cybercriminals adopt new technologies and services such as crimeware-as-a-service, identifying emerging trends becomes crucial to developing proactive strategies for the detection and prevention of cyber threats.Methodology: This research employs a systematic literature review approach to analyze emerging trends in cybercrime originating from the Dark Web. The review includes scholarly articles, news sources, and blog posts sourced from platforms like Google Scholar, IEEE Xplore, and various libraries. The key focus is to answer questions regarding the relationship between the Dark Web and cybercrime, accelerating cybercrime activities, and the benefits and implications of these new trends.Results: Key findings of this paper range from the rise of crimeware-as-a-service attacks and the increasing use of artificial intelligence and/or machine learning capabilities by cybercriminals to automate attacks across various businesses and organizations are also propounded along with information related to entry points and cybercrime attack pathways. The emergence of sophisticated cybercrime techniques, including ransomware-as-a-service, targeted attacks using AI, and exploitation of IoT vulnerabilities, are identified as critical trends. Social engineering, malware, and the rise of remote work have expanded the attack surface for cybercriminals.Discussion: As the use of cybercrime continues to metamorphose, the identification of new threats and extrapolation of emerging trends is critical to investigate the challenges associated with the monitoring and detection of illegitimate activities on the Dark Web as well as for the establishment of proactive risk management strategies and implementation of robust security measures. The research highlights the transformation of cybercrime into a structured and scalable ecosystem driven by technological advancements and service-based attack models. Cybercriminals now leverage AI/ML increasing the sophistication and success of their attacks. The commoditization of cybercrime has enabled less skilled individuals to participate amplifying the volume and diversity of threats faced by organizations.Conclusion: As cybercrime continues to evolve and adopt emerging technologies, organizations must remain vigilant and adaptable. The findings emphasize the need for proactive risk management, continuous monitoring of cybercrime trends, and robust security measures to mitigate the increasing threats originating from the Dark Web. Future research should focus on deeper exploration of AI-driven attacks and the development of more advanced countermeasures to safeguard critical infrastructure.

Natural disasters are currently a matter of great concern worldwide. Droughts are among the most recurrent episodes, causing significant private losses and thus worsening the economic situation of regions that depend on agriculture and livestock. This article presents a study of the duration of droughts in the southernmost state of Brazil, which has a large agriculture production and has shown recurrent events over the last two decades. The approach focuses on the time of economic recovery and whether economic impacts occurred, in the years of 2020, 2022, and 2023, and for this purpose it uses the total value of invoices issued in the municipality by companies to consumers and between them. To this end, the Kaplan-Meier estimator and the Cox regression model were used to analyze the economic recovery rates of the municipalities, also using factors such as their size, geographic location and main sector of economic activity. The results show that the longest recovery time is concentrated in small cities and cities that have agricultural or livestock activities as their main activity, and also show greater resilience in cities in the metropolitan region, which has a tributary of many rivers and is more concentrated in services and industry, with larger cities. The study identify that in each year at least 75% of the municipalities have their economic recovery within 3 months. The findings allow a better planning for both prevention and mitigation of the effects of droughts. They are also relevant for the design of economic and social policies.

Habitat fragmentation is often assumed to negatively impact species diversity because smaller, more isolated populations on smaller habitat patches are at a higher extinction risk. However, some empirical and theoretical studies suggest that landscapes with numerous small habitat patches may support higher species richness, although the circumstances remain elusive. We used an agent-based metacommunity model to investigate this and simulate landscapes of the same total area but diverse patch sizes. Our model, as generic and unbiased by specific assumptions as possible, aimed to explore which circumstances may be more conducive to supporting higher biodiversity. To this end, most parameters and behaviors were random. The model included generalized species traits, dispersal, and interactions to explore species richness dynamics in fragmented landscapes of distinct patch sizes. Our results show that landscapes with many small patches maintain higher species richness than those with fewer large patches. Also, the relationship between patch connectivity and species richness is more pronounced in landscapes with smaller patches. High connectivity in these landscapes may support species diversity by preventing local extinctions and facilitating recolonization. In contrast, connectivity is less significant in large-patch landscapes, where generalist species dominate. The findings highlight the complex interplay between patch size quality, connectivity, species traits, and diverse interactions among species in determining species richness. We suggest the patterns produced by the model represent null predictions and may be useful as a reference for a diversity of more specialized questions and predictions. These insights may also have specific implications for conservation strategies, suggesting that maintaining a mosaic of small, well-connected patches could enhance biodiversity in fragmented landscapes.

Biofilm-associated infections caused by Candida species represent a significant clinical challenge due to their inherent resistance to antifungal therapies. This study evaluates the antifungal efficacy of trans-Cinnamaldehyde, a natural compound derived from Cinnamomum cassia, in comparison to the widely used antifungal agent Nystatin, against both planktonic and biofilm forms of Candida albicans, Candida glabrata, and Candida tropicalis. Biofilms were established in 96-well polystyrene plates and treated with various concentrations of trans-Cinnamaldehyde and Nystatin. The Minimum Biofilm Inhibitory Concentration (MBIC) and planktonic minimum inhibitory concentration (PMIC) were determined using the MTT assay and Scanning Electron Microscopy (SEM) was employed to assess structural changes in biofilms. The results demonstrated that Nystatin exhibited superior antifungal efficacy across all tested species, particularly in biofilm inhibition, with MBIC50 values as low as 0.0015 mg/mL for C. albicans and C. glabrata. In contrast, trans-Cinnamaldehyde showed moderate antifungal activity with MBIC50 values of 0.16 mg/mL for C. albicans and C. glabrata and higher resistance observed in C. tropicalis (MBIC50 of 0.32 mg/mL). SEM analysis revealed significant biofilm disruption in C. albicans following exposure to trans-Cinnamaldehyde, but C. tropicalis exhibited more resilient biofilm structures. Overall, while Nystatin remains the gold standard for antifungal treatment, trans-Cinnamaldehyde presents potential as an adjunctive treatment, particularly for biofilm-related infections in C. albicans and C. glabrata. Further research is required to explore synergistic effects between trans-Cinnamaldehyde and conventional antifungal agents and to optimize its therapeutic application in clinical settings. This study reused measurement results data from our previous publication to include in tables and charts.

In this work, we present a real-time heart rate estimation method from a video using face detection, color-based signal extraction, and signal processing tech- niques. The proposed approach computes the heart rate by analyzing the color fluctuations in the facial region of interest (ROI). This report outlines the math- ematical formulation and results of the implementation.

Background/Objectives: Early diagnosis of ASD a very important factor in improving the quality of life of people on the spectrum, but it still remains a major problem in Europe, especially concerning girls. In this study, we tried to answer the question of what factors affect the age of diagnosis in Poland. Additionally, we tried to establish the time between the first visit to the mental health center and the diagnosis in this population, and what factors affect its length. Methods: 77 children were randomly recruited among the patients who came to local MHC at the Child and Family Health Center in Sosnowiec. All participants were tested using the ADOS-2 protocol. In addition, the study used TAS-20 test and BDI. Results The mean age of the first enrollment in the MHC was 9.09 years for girls and 6.42 for the boys. The time needed to obtain a diagnosis from the first visit was consecutively 2.90 years and 4.29 years, but the difference was not statistically significant (p&gt;0.05). Obtaining a different psychiatric diagnosis did not affect the average time to diagnosis and the age of diagnosis. Conclusions: The results of the study indicate that the diagnosis of ASD is still a significant problem both in Poland and in the world. The long time needed to obtain the correct diagnosis, the frequent need to change diagnosticians in order to obtain it is a common problem. the diagnostic process should be flexible, and the diagnostician should always take into account the axial symptoms, but remain aware that "overdiagnosis" of ASD can also have harmful consequences for the child.

Background-Bronchopulmonary dysplasia (BPD) is a condition of poor alveolar formation that causes chronic breathing impairment in infants born prematurely. Preterm lungs lack surfactant and are vulnerable to oxidative injuries driving the development of BPD. Our recent studies reported that surfactant protein A (SP-A) genetic variants influence susceptibility to neonatal lung disease. SP-A modulates activation of alveolar macrophages and parturition onset in late gestation. We asked whether a lack of SP-A alters alveolarization in a mouse model of hyperoxia-induced BPD. Methods- SP-A-deficient and control newborn mice were exposed to either clinically relevant 60% O2 hyperoxia or normoxia for 5-7 days. Alveolar formation was then assessed by mean linear intercept (MLI) and radial alveolar count (RAC) measurements in lung tissue sections. Results- We report that the combination of SP-A deficiency and hyperoxia reduces alveolar growth compared to WT mice. The morphometric analysis of normoxic SP-A-deficient lungs showed lower RAC compared to controls, indicating reduced alveolar number. In the presence of hyperoxia, MLI was higher in SP-A-deficient lungs compared to controls. Differences were statistically significant for female pups. Spatial proteomic profiling of lung tissue sections showed that hyperoxia caused a 4-fold increase in the DNA damage marker gammaH2Ax in macrophages of SP-A-deficient lungs compared to normoxia. Conclusions- Our short report suggests an important role for SP-A in perinatal lung development and in protecting lung macrophages from oxidant injury. These studies warrant future investigation to discern the temporal interaction of SP-A, gender, oxidant injury, and lung macrophages in perinatal alveolar formation and development of BPD.

Throughout history, natural stone has been a crucial building material due to its strength, durability, and aesthetic qualities. Today, it continues to be a valuable resource, representing both a cultural heritage asset and a significant economic material. However, the increasing frequency of heat waves and fires driven by climate change poses a growing threat to stone building materials. This paper presents a literature review aimed at assessing the effects of high temperatures on Global Heritage Stone Resources (GHSR). GHSR is an international classification designed to enhance the recognition and status of building stones. The study identifies a critical need for research to better understand how those stone materials behave when exposed to high temperatures in order to predict and mitigate the effects of external threats such as fires.

Continuous and uncontrolled extraction of groundwater often creates tremendous pressure on groundwater levels. As a part of the sustainable planning and effective management of water resources, it is crucial to assess the existing as well as future groundwater level (GWL) condition. In the current study, an attempt was made to model and forecast GWL using artificial neural networks (ANN) and multivariate time series models. Autoregressive integrated moving average (ARIMA) and ARIMA incorporating exogenous variables (ARIMAX) were adopted as the time series models. Kushtia district in Bangladesh was selected as the case study area, and GWL data of five monitoring wells in the study are used to demonstrate the modeling exercise. Rainfall was taken as the exogeneous variable to explore whether its inclusion enhanced the performance of GWL forecasting using the developed models. The performance of each time series and ANN model was assessed based on various model evaluation criteria. It was evident from the results that the multivariate ARIMAX model (SSE of 15.361) performed better than the univariate ARIMA model with an SSE of 17.217 for GWL forecasting. This demonstrates the fact that the multivariate time series models generated enhanced forecasting of GWL compared to the univariate time series models. When comparing the time series and ANN models, it was found that the ANN-based model outperformed the time series models with the enhanced forecasting accuracy (SSE of 9.894). Results also exhibit a significant correlation coefficient value R of 0.995 (ANN 6-8-1) for the existing and predicted data. The current study conclusively proves the superiority of ANN over the time series models for the enhanced forecasting of GWL in the study area. Thus, the ANN approach was not only carried out for model building and simulation but also to provide a valuable tool for managing water resources amidst changing environmental conditions.

The cities of Lomé and Kara in Togo are undergoing rapid urbanisation due to strong demographic pressure, leading to a reduction in Urban Green Spaces (UGSs), which are essential for climate regulation and urban well-being. This situation highlights the climatic vulnerability of these areas, making it imperative to reconcile urban development and the preservation of UGSs for urban sustainability. This study contributes to the sustainable conservation of UGSs in Togo. More specifically, it uses Landsat images from 1988_2000_2012_2022 to analyse the spatiotemporal dynamics of UGSs in Lomé and Kara and develops vulnerability models in 2022 and for the 2060 and 2100 horizons based on SSP 2.45 and SSP 5.85 climate scenarios via the multifactorial method. The results revealed a reduction in UGSs in Lomé (from 85.98% in 1988 to 40.78% in 2022) and Kara (from 93.99% to 36.68%), mainly due to urban expansion. Low- and medium-vulnerability zones, often located in UGSs, will likely disappear by 2060 and 2100, accentuating urban climatic vul-nerability. Hence, there is an urgent need to implement conservation policies, create new UGSs, and increase awareness among city dwellers.

Zr-2.5%Nb Pressure tubes (PT) house uranium fuel bundles in the fuel channels of CANDU® nuclear reactors. Preventing a failure mode caused by contact of the PT with an outer calandria tube (CT) is performed by inspection using eddy current (EC) testing and ultrasonic testing (UT) to measure the PT-CT gap. EC gap measurements are particularly sensitive to circumferential variation of the PT’s electrical resistivity, due to microstructural variations. A full-factorial experiment was performed to examine the statistical significance of variations in EC test parameters and manufacturing conditions on average circumferential electrical resistivity of as-manufactured PTs. It was found that 79% of the variance in the data could be attributed to variations caused by any of the test factors or combinations of test factors. The parameters that accounted for the majority of the variance were; 1) heat treatment (HT); 2) HT and EC frequency; 3) probe inner or outer surface placement; and 4) EC frequency. Measurements of circumferential resistivity showed up to ± 2.3% variation from the average of either surface. HT caused average PT resistivity to decrease at a rate of 1.53±0.08 (μΩ⋅cm)/log⁡(hr) and 1.1±0.4 (μΩ⋅cm)/log⁡(hr) for inner and outer PT surfaces, respectively. Results are correlated with literature reported differences in average β_Zr ribbon thickness in the axial-transverse cross-section between inner and outer PT surfaces. Results demonstrate potential for EC based resistivity measurements to characterize variations and changes in microstructure of Zr-2.5%Nb PT material.

This study utilizes a combination of optical PlanetScope imagery and Synthetic Aperture Radar (SAR) data from Sentinel-1 to investigate land cover changes in Dili, Timor-Leste, following Tropical Cyclone Seroja. The primary objective is to evaluate how integrating these datasets enhances disaster monitoring and response in flood-affected areas. The Random Forest classifier, known for its robustness in handling high-dimensional and noisy data, was applied to classify land cover into six distinct classes: vegetation, water, built-up areas, bare soil, clouds, and shadows. The classification was performed across three phases—pre-disaster, post-disaster, and recovery—using Google Earth Engine (GEE). Binary segmentation using Otsu thresholding was applied to the SAR images to refine the classification by delineating water bodies from non-water areas. The study achieved high classification accuracy, with overall accuracy ranging from 97.2% to 98.7%, and the Kappa index, which measures agreement between classified and reference data, remained consistently strong, indicating reliable model performance. Key results include a significant increase in water bodies and extensive damage to vegetation and built-up areas during the post-disaster phase, followed by partial recovery in the subsequent period. Despite the high accuracy, urban areas posed classification challenges due to misclassification between built-up and bare soil categories. This research offers a methodological framework for integrating optical and SAR data with machine learning for land cover change detection in post-disaster scenarios. The societal benefits of this study include improved disaster preparedness, enhanced recovery planning, and valuable insights into flood impact mitigation in regions like Timor-Leste, where technical and data limitations persist.

In this article, the diversity of mangrove and associate species with anticancer properties are reviewed. Information included their scientific names, synonyms, common names, families and life-forms. Their anticancer properties are documented with description on cancer cell types, effects and mechanisms. Mangroves are exclusive species confined to the mangrove habitat. Plants that occur in the coastal environment and also in mangroves are associates or non-exclusive species. In this article, species with anticancer properties are 20 mangrove species belonging to 13 genera and 11 families, and 26 associate species belonging to 24 genera and 17 families possess. Most reported mangrove species are Avicennia marina and Ceriops tagal with five and four studies, respectively. Associate species most reported are Talipariti tiliaceum or Hibiscus tiliaceus with five studies, followed by Anacardium occidentale, Artocarpus altilis, Ceriops manghas, Pluchea indica and Pongamia pinnata with four studies each. In the conservation of mangrove and associate species with anticancer properties, in in-situ conservation, species are protected in their natural surroundings as ecosystems (e.g., virgin jungle reserves and Ramsar sites) while in ex-situ conservation, species are protected outside their natural habitats (e.g., arboreta and botanical gardens).

We developed and applied a new calibration method to make the measurement more accurate with our multi-color ellipsometric mapping tool from cheap parts. Ellipsometry is an optical technique that measures the relative change in polarization state of the measurement beam induced by reflection from or transmission though a sample. During the conventional ellipsometric measurement, the data collection is relatively slower and measures one spot at a time, so mapping needs long time compared with our new optical mapping equipment made by an ordinary color LED monitor and a polarization sensitive camera. The angle-of-incidence and the incident polarization state is varied point-by-point so a special optical calibration method is needed. Three SiO2 samples with different thicknesses were used to determine point-by-point the angle-of-incidence and rho corrections. After the calibration, another SiO2 sample was measured and analyzed using the calibrated corrections and this sample further was independently measured using a conventional spectroscopic ellipsometer. The difference between the two measured thickness maps is less than 1 nm. Our optical mapping tool from cheap parts is faster and covers wider area samples in relative to the conventional ellipsometers and these correction enhancements further signify its performance.

Identifying effective and accessible interventions for family caregivers of persons with dementia (PWD) is crucial as the prevalence of dementia increases in Asia. This study investigated the efficacy of a telephone-intervention on reduction of caregiver burden, depressive and anxiety symptoms among family caregivers (FC) of PWD in Malaysia. A single-blinded randomized controlled trial was carried out with 121 FCs of PWD selected from memory or psychiatry clinics in three tertiary hospitals in Malaysia who were randomly allocated into intervention or control group. The intervention group received the psychoeducational intervention delivered by a healthcare staff via telephone for 10 sessions over 12 weeks. The outcome of the intervention was measured by the Malay version of the Zarit Burden Interview and the Hospital Anxiety and Depression Scale at baseline and post-intervention. Intention-to-treat analysis shows that caregiver burden, anxiety symptoms, and psychological distress among FCs in the intervention group decreased by 7.57 units (p&lt;0.001), 2.46 units (p&lt;0.001) and 2.98 units (p=0.011) respectively at post-intervention, compared to the differences from baseline to post-intervention in the control group. Policies aimed at integrating the telephone-intervention into memory/psychiatry clinics in Malaysia may help FCs of PWD to reduce their caregiver burden and stress while caring for a family member with dementia.

Staphylococcus aureus is the primary cause of mastitis in dairy cattle globally and frequently exhibits multidrug resistance due to excessive antibiotic use on farms. This study aimed to isolate S. aureus from mastitis affected cows and evaluate its antimicrobial susceptibility. A total of 172 milk samples from confirmed mastitic cows were cultured using standard bacteriological methods. Forty-four S. aureus isolates were tested against 11 common antimicrobials using the Kirby–Bauer disk diffusion method. S. aureus was isolated from 51.2% of samples, with significant resistance observed against ampicillin (84.1%), penicillin (81.8%), tetracycline (36.4%), and amoxicillin-clavulanic acid (34.1%). MDR was noted in 43.2% of isolates. In contrast, S. aureus demonstrated complete susceptibility to ceftriaxone and gentamicin (100%), and high susceptibility to streptomycin (88.6%), erythromycin (88.6%), nitrofurantoin (72.7%), and cefotaxime (72.7%). The significant isolation rate of S. aureus and its MDR underscore the urgent need for enhanced veterinary practices and public health strategies. A comprehensive approach that includes improved management, ongoing education for veterinarians and dairy farmers, responsible antimicrobial usage, and regular monitoring of resistance is essential to tackle the escalating threat of antimicrobial resistance in bovine mastitis.

Myokines are peptides released by the skeletal muscle that exert effects on peripheral tissues, activating metabolic or protective pathways against oxidative stress and inflammation. Many myokines increase their plasma levels after exercise, providing new insights into the role of physical activity in combating sarcopenia and preventing metabolic diseases, such as obesity, diabetes, and metabolic dysfunction associated with steatotic liver disease (MASLD). This review aims to discuss the roles of specific myokines in activating intracellular pathways in the liver to protect against steatosis and lipoperoxidation in MASLD. The progression of liver disease from steatosis to cirrhosis is partially driven by ferroptosis, an iron-dependent form of cell death linked to oxidative stress and lipoperoxidation. Exercise-induced myokines, such as irisin, have been shown to upregulate antioxidant enzymes such as glutathione (GSH) and glutathione peroxidase 4 (GPX4), which are crucial in reducing ferroptosis in hepatic tissues. This review provides a comprehensive perspective on the protective effects of myokines in the liver, particularly against lipid peroxidation and ferroptosis in MASLD, and highlights exercise as a therapeutic strategy to prevent the progression of this liver pathology.

This paper is devoted to the finite time internal and the external synchronization (hybrid synchronization) for complex networks by means of intermittent control. Different from most previous research achievements, the finite time synchronization include internal and the external synchronization, this article employs the intermittent control to drive the complex networks to achieve synchronization in finite time. To be specific, the internal and the external synchronization all achieve consistent synchronization in finite time by devise Lyapunov function. Finally, numerical examples are presented to illustrate the validness of theoretical results.

Computer vision technology for automatic recognition and geometric characterization of carbon fibers was proposed in this paper. A two-stage pipeline was used, in the first stage the fundamental neural network model of segmentation SAM(Segment Anything Model) was used, in the second stage the segmentation results were improved by using a trained neural network model DeepLabv3+. Both manually labeled carbon fiber images and generated synthetic images were used for training. It is shown that additional use of synthetic data in training of neural network models improves the quality of segmentation by IoU and Pix Acc metrics from 0.943 and 0.949 to 0.953 and 0.959, i.e. by 1% on average. The total processing time averaged 0.95 seconds for a single fiber image. Based on the segmentation results, an automatic calculation of geometric characteristics and statistics that can be used to evaluate material properties was realized.

In recent decades, the development of surgical systems that minimize patient impact has been a major focus for surgeons and researchers, leading to the advent of robotic systems for minimally invasive surgery. These technologies offer significant patient benefits, including enhanced outcome quality and accuracy, reduced invasiveness, lower blood loss, decreased postoperative pain, diminished infection risk, and shorter hospitalization and recovery times. Surgeons benefit from the elimination of human tremor, ergonomic advantages, improved vision systems, better access to challenging anatomical areas, and magnified 3DHD visualization of the operating field. Since 2000, Intuitive Surgical has developed multiple generations of master-slave multi-arm robots, securing over 7,000 patents, which created significant barriers for competitors. This monopoly resulted in the widespread adoption of their technology, now used in over 11 million surgeries globally. With the expiration of key patents, new robotic platforms featuring innovative designs, such as modular systems, are emerging. This review examines advancements in robotic surgery within the fields of general, urological, and gynaecological surgery. The objective is to analyse the current robotic surgical platforms, their technological progress, and their impact on surgical practices. By examining these platforms, this review provides insights into their development, potential benefits, and future directions in robotic-assisted surgery.

Cyclic straining simulations using incompatibility-incorporated crystal plasticity-FEM, which exhibit PSB ladder structure evolutions as detailed in Part I, are coupled with diffusion analyses of produced vacancies. We introduce a new vacancy source model based on the Field Theory of Multiscale Plasticity (FTMP), interpreting the relationship between the incompatibility rate and the flux of dislocation density as edge dipole annihilation processes. Both direct and indirect coupling diffusion analyses, with and without cyclic straining, demonstrate that varying incompatibility rates tend to further promote vacancy diffusion, leading to surface grooving, enhanced extension rates, and eventual transition to cracks. The findings reveal that: (i) the evolved PSB ladder structure serves as a site for vacancy formation, (ii) it provides a diffusion path toward the specimen surface, and (iii) it significantly enhances groove extension rates. These factors effectively facilitate the transition from a "groove" to a "crack," evidenced by the abrupt acceleration of the extension rate, mirroring systematic experimental observations by Nakai et al. These achievements validate the FTMP's capability to simulate complex phenomena and significantly deepen our understanding of slip band-fatigue crack transition mechanisms.

Digital transformation in government services is seen as a progression towards offering more efficient and convenient services. In the wake of the COVID-19 pandemic, many governments worldwide have expedited the digitisation of their services to facilitate social distancing. Digital transformation encompasses the shift from manual, face-to-face services to automated, online-based systems. This process occurs in three stages: digitisation, digitalisation, and digital transformation. However, the varying stages of digital government implementation across countries impact service outcomes differently. Little research has been devoted to focusing on the outcome metrics crucial for the success of digital services, particularly in Kuwait. Moreover, although the existing literature has explored some metrics, specific metrics have been neglected. This study aimed to investigate current trends and preferences in digital government services usage using empirical quantitative data gathered from users of digital services in Kuwait. Specifically, the aim is fourfold: (i) to identify crucial outcome metrics for the success of digital services, (ii) to assess how users evaluate these services according to these metrics, (iii) to examine significant differences between digital transformation and digitalisation services, and (iv) to develop and validate a model for evaluating the success of digital transformation services. To achieve the study's objectives, 378 users of digital government services were surveyed quantitatively. This study found that users primarily access services through smartphones and applications, underscoring the need for mobile optimisation. There was a marked preference for real-time, automated services, with dissatisfaction for those requiring lengthy approval processes. Six key metrics were identified and grouped into three domains: service-related (customer satisfaction and service quality), convenience-related (accessibility and ease of use), and security-related (perceived security and perceived trust). Digital transformation services outperformed digitalisation services in five key areas: customer satisfaction, service quality, accessibility, ease of use, and perceived security, except perceived trust, which remained consistent across both service types. Several recommendations were proposed to advance government digital transformation services, boost efficiency, and enhance the user experience.