Rice blast is the most harmful disease caused by Magnaporthe oryzae, leading to yield loss. The aim of the work is to improve resistance of rice to blast by introducing and pyramiding genes using conventional breeding technique and modern MAS analysis. Pyramiding is the combination of several resistance genes in one genotype, in this case it improves the resistance of rice to pathogens. MAS analysis revealed that 3 out of 4 studied domestic high-yielding rice cultivars (Bakanasski, Aisaule and Aru) contain 1 blast resistance gene (Pi-2). It was discovered 7 pyramidal lines with the introduction of a combination of 2 blast resistance genes (Pi-1 and Pi-ta) into 6 hybrids and 3 genes (Pi-1, Pi-33 and Pi-ta) into 1 line. Phytopathological test of local cultivars and pyramidal lines of rice showed that 3 out of 7 pyramidal lines have moderate resistance: 2 lines (F2 Bakanasski/7667 (var. vulgaris Koern.) and F2 Aisaule/7664 (var. italica Alef.)), containing 2 resistance genes (Pi-1 and Pi-ta) and 1 line – F2 Aisaule/7689 (var. zeravschanica Brsch.), carrying 3 genes (Pi-1, Pi-33 and Pi-ta), while the local cultivars Bakanasski, Aisaule and Aru are susceptible. As a result, the blast resistance of rice was improved by creating pyramidal lines. Pyramided lines can be used for germplasm exchange and in rice breeding programs to improve blast resistance in rice.

Background: Patients undergoing brain tumor resection experience neurological and cognitive (i.e., neurocognitive) changes reflected in altered performance on neuropsychological tests. These changes can be difficult to explain or predict. Brain connectivity, measured with neuroimaging, offers one potential model for examining these changes. In this study, we evaluated whether longitudinal changes in brain connectivity correlated with changes in neurocognitive abilities in patients before and after brain tumor resection. Methods: Patients underwent functional and diffusion MR scanning and neuropsychological evaluation before tumor resection followed by repeat scanning and evaluation two weeks post-resection. Using this functional and diffusion imaging data, we measured changes in the topology of the functional and structural networks. From the neuropsychological testing scores, we derived a composite score that described a patient’s overall level of neurocognitive functioning. We then used a multiple linear regression model to test whether structural and functional connectivity measures were correlated with changes in composite scores. Results: Multiple linear regression on 21 subjects showed that connectivity changes were highly correlated with changes in neuropsychological evaluation scores (R² adjusted = 0.79, p<0.001). Changes in functional local efficiency (p<0.001) and global efficiency (p<0.05) were inversely correlated with changes in composite score, while changes in modularity (p<0.01) as well as the patient’s age (p<0.05) were directly correlated with changes in composite score. Conclusion: Short interval changes in brain connectivity markers were strongly correlated with changes in the composite neuropsychological test scores in brain tumor resection patients. Our findings support the need for further exploration of brain connectivity as a biomarker relevant to brain tumor patients.

The theories of Evolution and Big Bang have brought a major impact upon the scientific and societal perception upon the origins, abilities and roles of mankind on Earth. It is conclusively evident that Mother Nature and the physical realm is autonomous and all phenomena in the world occur without an imposition against the “free will” of such events and patterns. Nevertheless, this extent of freedom does not imply that life on Earth represents an accident and has no purpose. By careful analysis of ancient religious texts, it can be suggested even otherwise, that it is especially free will that is offered as a gift from outside of the physical dimensions of existence, with the purpose of a continuous edification and improvement of the overall life conditions and of the human perception. It may be that the multi-generational focus on improvement and progress have been misattributed as a long-term evolution of species, that humans came from animal ancestors and that there is a blurred line associating humans with animals. Furthermore, it may be that scientists based their suggestions that no outside Creator would have brought the Earth into existence on physical perspectives, when the extra-dimensional process of Creation implies that all elements of the physical existence undergo a process of Creation from the outside, where time, space and physical matter do not exist, in spite of such elements possibly reflecting unseen elements of the meta-physical world. Finally, there is the following example that deserves attention within the scientific community; just because all living cells - human, animal and plant - contain DNA chromosomes, ribosomes, mitochondria and a plasma membrane, it does not indicate that humans are animals, just as this does not indicate that plants are animals. In Science, there is a golden statement, that correlation does not imply causation. This example is in complete agreement with the verses of the Book of Genesis in the Holy Scriptures regarding the origins of life; namely, that animals could have been created by an Author of all life on Earth so that man would experience companionship. Another major accordance with the teachings of the Holy Scriptures is the fact that life emerged from the water, and not from the soil, although the soil constituted the second principal means of the commencement and extension of life. Namely, after life first emerged in the seas and the oceans from bacteria, a few bacteria were passed from the marine water into the nearby soil on islands and surrounding land through the circulatory air above the sea level. Afterward, the first life forms emerged from the soil, via the transferred bacteria. Eastern Orthodox Christianity regards water as the principal environment of both physical and spiritual birth. Overall, an improved scientific perception of the timeline of life on Earth may be reached via an approach that combines molecular biology with quantum physics, given that the three layers of Science - Biology, Chemistry and Physics - together with their foundational layer of Mathematics, are in fact situated in a relationship of complete harmony.

A novel experimental technique, quasi-equilibrated temperature-programmed desorption and adsorption (QE-TPDA) was used to study the water sorption properties and hydrothermal stability of aluminum trimesate MIL-96 and aluminum isophthalate CAU-10, which have been selected due to their remarkable sorption properties. The QE-TPDA profiles of water observed for MIL-96 and CAU-10 confirmed the hydrophilic nature of these materials. Complex QE-TPDA profiles indicate that water sorption in MIL-96 follows a three-step pore filling mechanism. The shape of single desorption peaks in the QE-TPDA profiles for CAU-10 confirms that water sorption involves a reversible phase transition. Based on the QE-TPDA profiles, the water adsorption heat were determined: 45-46 kJ/mol for CAU-10 and 43-56 kJ/mol for MIL-96, in the latter case depending on the adsorption extent. Hydrothermal stability tests revealed that MIL-96 retained its stable porosity-related sorption capacity for water after hydrothermal treatment up to 290°C. Gradual changes in the QE-TPDA profiles due to the hydrothermal treatment above 290°C, with decreasing the high-temperature desorption peak and increasing the low-temperature one, indicate minor structural changes occurring in this material. Only after 410° C treatment was fast degradation of MIL-96 observed. CAU-10 exhibited high and unchanged hydrothermal stability up to 400°C.

Financial Market Infrastructures (FMIs) are crucial for the efficient operation of financial markets. However, studies focusing on Chinese FMIs are limited. Following the release of a draft regulation on Chinese FMIs in December 2022, this study, building on Zhang and Yin (2023), provides an updated analysis. It introduces non-central bank affiliated FMIs with the latest data, explores the evolution of regulatory reforms, and examines the newly-released regulations. In particular, it offers a critical review of specific regulations and their implications for China's recent policy shift towards prioritizing security and establishing financial dominance. Finally, the study outlines a research agenda for future investigations.

The growing number of algorithmic decision-making environments, which blend machine and bounded human rationality, strengthen the need for a holistic performance assessment of such systems. Indeed, this combination amplifies the risk of local rationality, necessitating a robust evaluation framework. We propose a novel simulation-based model to quantify algorithmic interventions within organisational contexts, combining causal modelling and data science algorithms. To test our framework's viability, we present a case study based on a bike-share system focusing on inventory balancing through crowdsourced user actions. Utilising New York's Citi Bike service data, we highlight the frequent misalignment between incentives and their necessity. Our model examines the interaction dynamics between user and service provider rule-driven responses and algorithms predicting flow rates. This examination demonstrates why these dynamics are necessary for devising effective incentive policies. The study showcases how sophisticated machine learning models, with the ability to forecast underlying market demands unconstrained by historical supply issues, can cause imbalances that induce user behaviour, potentially spoiling plans without timely interventions. Our approach allows problems to surface during the design phase, potentially avoiding costly deployment errors in the joint performance of human and AI decision-makers.

Pentadesma butyracea is a medicinal plant of which stem bark decoctions are used in traditional medicine for the treatment of diarrheal in Gabon. The aims of the present work were to perform a phytochemical analysis of P. butyracea stem bark decoctions harvested in rain and dry seasons and to evaluate their biological proprieties in order to improve their pharmaceutical use. In a first approach, spectrophotometric analyses were used to quantify phenolics compounds, following by liquid chromatography coupled to mass spectrometry analysis that allowed the identification of flavanone-flavone dimers as the main metabolites of this stem bark decoction. Pharmacological analyses showed the absence of in vitro and in vivo toxicity, thus allowing the safety in use of this decoction. The antioxidant activity of the samples was also demonstrated. Of particular interest, the decoction of P. butyracea stem barks exhibited in vitro antidiarrheal activities thanks to the effects on contractions of ileum fragments isolated from rats. In addition, in vivo activities on castor oil-induced diarrhea in rats were also demonstrated. These results encourage the deepening of a bio-guided research on P. butyracea stem bark decoctions in order to propose standard traditional medical treatments in Gabon.

Parallel-serial (hybrid) manipulators represent robotic systems composed of kinematic chains with parallel and serial structures. These manipulators combine the benefits of both parallel and serial mechanisms, such as increased stiffness, high positioning accuracy, and a large workspace. This study discusses the existing architectures and applications of parallel-serial robots and the methods of their design and analysis. The paper reviews around 500 articles and presents over 150 architectures of manipulators used in machining, medicine, and pick-and-place tasks, humanoids and legged systems, haptic devices, simulators, and other applications, covering both lower mobility and kinematically redundant robots. After that, the paper considers how researchers have developed and analyzed these manipulators. In particular, it examines methods of type synthesis, mobility, kinematic, and dynamic analysis, workspace and singularity determination, performance evaluation, optimal design, control, and calibration. The review concludes with a discussion of current trends in the field of parallel-serial manipulators and potential directions for future studies.

Nitazenes represent a New Synthetic Opioids sub-class, belonging to the New Psychoactive Substances (NPS). Their high pharmacological potency has led to numerous intoxications and fatalities, even at minimum doses. The aim of this study was to assess the stability of four nitazenes (etazene, flunitazene, isotonitazene and protonitazene) in dried blood spot (DBS) samples at different storage temperatures (room temperature and 4°C), using a newly developed and validated liquid chromatography-high resolution mass spectrometry method. Two concentrations, 1 and 5 ng/mL were chosen based on the available data on nitazene-related intoxications and stability was evaluated at day 0 (control), 1, 7 and 30. Results showed that all analytes at 1 ng/mL were not detectable after 30 days at room temperature; a similar pattern was observed for 1 ng/mL etazene and isotonitazene samples when stored at 4°C, whereas flunitazene and protonitazene decreased to a mean of 66% and 69% initial concentration, respectively, at day 30. Differently, all analytes at 5 ng/mL were quantified above 44% and 41% initial concentration at room temperature and 4°C, respectively, showing a higher stability. The study of nitazenes stability in DBS represents an important tool to determine the optimal samples storage conditions, such as temperature and time between sample collection and analysis.

We discuss the dynamics of a roller coaster cart driven by a constant force along the suspended track of a winding roller coaster. The track is assumed arbitrarily long and specially shaped. It is made by semicircular track portions, in the form of valleys and hills, standing perfectly vertical in the same plane. This is a mechanical analog of a Josephson junction electrodynamics. To demonstrate the explanatory potential of the analogy, we focus particularly on the conditions of de-trapping of the cart from one of the valleys of the track. This mechanical process has its analog in a non-noise-generated premature switching to the finite voltage state of a Josephson junction.

Global demand for blueberries has surged in recent years, primarily due to their health benefits. However, blueberries postharvest losses, particularly firmness loss and decay, remain significant challenges. This study applied high concentrations of CO2 (15% and 20%) for 3 days at 1 °C to highbush (Duke) and rabbiteye (Ochlockonee) blueberries to assess their effects on maintaining quality, especially firmness, during low-temperature storage. Various quality parameters were evaluated, including titratable acidity, pH, total soluble solids, weight loss, and decay. The impact of gaseous treatments on firmness was analysed through mechanical parameters and the expression of genes related to cell wall integrity (XTH23, PL8, PG, PM3, EXP4, and VcGH5). The effectiveness of the treatments varied between species. High CO2 levels successfully reduced decay in both cultivars, but only the highbush variety (Duke) showed improvements in firmness. In Duke, CO2 treatments influenced the expression of genes associated to cell wall integrity, such as XTH23, PL8, and GH5. In contrast, the roles of PG and PME in firmness maintenance were minimal, showing no significant differences between treatments. While CO2 did not enhance firmness in Ochlockonee, it effectively reduced weight loss, highlighting the need for tailored postharvest strategies for different blueberry cultivars.

The basic helix-loop-helix (bHLH) transcription factor family, is the second-largest superfamily in plants after MYB, which plays a significant role in the physiological processes of plant growth, tissue development, and environmental variation. However, the systematized study of the bHLH transcription factor family has not yet been conducted in A. catechu. Herein, we conducted genome genome-wide investigation of AcbHLH genes located on 16 chromosomes of A. catechu. A phylogenic tree was constructed to adjudge their homology of genes, and 24 subgroups were identified. Finally, we analyzed the dynamic changes of gene-expression levels of nine AcbHLH genes in response to drought and salt in leaves and roots. The expression patterns of 9 AcbHLH genes show differences in leaves and roots. Under stress conditions induced by salt and drought, AcbHLH22, AcbHLH62 and AcbHLH45 are significantly upregulated in both leaves and roots. In conclusion, this study will substantially contribute to the foundation for exploring the role of the bHLH superfamily in A.catechu in dealing with abiotic stresses.

Background/Objectives: Cancer is caused by disruptions in the homeostatic state of normal cells, which results in dysregulation of the cell cycle, and uncontrolled growth and proliferation in affected cells to form tumors. Successful development of tumorous cells proceeds through the activation of pathways promoting cell development and functionality, as well as the suppression of immune signaling pathways; thereby providing these cells with proliferative advantages, which subsequently metastasize into surrounding tissues. These effects are primarily caused by the upregulation of oncogenes, of which SPP1 is one of the most well-known. Methods: In this study, we conducted a further examination of the transcriptomic expression profile of SPP1 (Osteopontin) during the progression of cancer in four human tissues, in order to understand the circumstances conducive to its activation and dysregulation, the biological pathways and other mechanisms involved, as well as differences in its splicing patterns influencing its expression and functionality. Results: A significant overexpression of SPP1 as well as a set of other highly correlated genes, was seen in a majority of tissues, indicating their extensive implication in cancer. Increased expression was observed with higher tumor stages, while applying therapeutic modalities targeting these genes dampened this effect. Pathway analyses showed gene signatures related to cell growth and development enriched in tumorigenic conditions and earlier cancer stages, while later stages of cancer showed pathways associated with weakened immune response. Moreover, the utilization of therapeutic methods showed the activation of immunogenic pathways, thereby confirming their viability. Further analyses of differential transcript expression levels in these oncogenes showed their exonic regions to be selectively overexpressed similarly in tumorigenic samples, while also displaying significant differences in exon selectivity between constituent transcripts, providing a basis for their high degree of multifunctionality in cancer. Conclusions: Overall, this study corroborates the entrenched role of SPP1 in cancer, while also highlighting its promising role in therapeutic development.

Water stable proteins may offer a new field of applications in smart materials for buildings and infrastructures where hydraulic reactions are involved. In this study, cement mortars modified through water-soluble silk fibroin (SF) are proposed. Water-soluble SF obtained by redissolving SF films in phosphate buffer solution (PBS) showed the formation of a gel with β-sheet features of silk II. Electrical measurements on SF indicate that calcium ions are primarily involved in the conductivity mechanism. By exploiting the water solubility properties of silk II and Ca2+ ion transport phenomena and its trapping effect over water molecules, SF provides piezoresistive and piezocapacitive properties to cement mortars, thus enabling self-sensing of mechanical strain which is quite attractive in structural health monitoring applications. SF/cement-based composite introduces the capacitive gauge factor that surpasses the traditional resistive gauge factor reported in the literature by threefold. Cyclic voltammetry measurements demonstrated SF cement mortars possess memcapacitive behavior for positive potentials around +5 V, attributed to an interfacial charge build-up modulated by the SF concentration and the working electrode. Electrical square-biphasic excitation combined with cyclic compressive loads revealed memristive behavior during the unloading stages. These findings along with the availability and the sustainability of SF pave the way for the design of novel multifunctional materials, particularly for applications in masonry and concrete structures.

The objective was to explore the contributions and limitations of structure learning methods within an epidemiological analysis of real-world data. The specific aim was to use these networks to identify determinants of access to healthcare among various social factors. We analyzed data from the 2010 wave of the SIRS cohort, which included a sample of 3,006 adults from the Paris region, France. Healthcare utilization, encompassing both direct and indirect access, was the primary outcome. Candidate determinants included health status, demographic characteristics, and socio-cultural and economic positions. We employed a dual approach: a non-automated epidemiological method (initial expert-knowledge network and logistic regression models) and structure-learning techniques based on several algorithms, with and without knowledge con-straints. We compared the results based on the presence, direction, and strength of specific links within the produced network. Although the interdependencies and relative strengths identified by approaches were similar, the structure-learning algorithms detected fewer associations with the outcome than the non-automated method. Relationships between variables were sometimes incorrectly oriented when using a purely data-driven approach. Structure learning algorithms can be valuable in exploratory stages, helping to generate new hypotheses or mining novel databases. However, results should be validated against prior knowledge and supplemented with additional confirmatory analyses.

Pakistan is confronted with a formidable challenge of high population growth, compounded by cultural norms prioritizing male offspring, leading to adverse implications for family planning (FP) efforts and demographic trends. Despite efforts to promote contraception, including a national family planning program, Pakistan continues to struggle with low and stagnant contraceptive prevalence rates and high unmet contraceptive needs among married women. The influence of gender composition on modern contraceptive uptake remains underexplored, necessitating research to elucidate its impact on reproductive behavior. This study was based on a secondary analysis of a facility-based cross-sectional survey conducted in six districts of Sindh and Punjab provinces in Pakistan. A subset of 495 married women of reproductive age (MWRA) seeking health services from March to June 2019 was used for this study. Logistic regression analysis was employed to examine the association between the gender composition of children and MWRA’s modern contraceptive uptake, adjusting for covariates such as province, age of women, and health facility type. The analysis revealed a significant association between the gender composition of children and modern contraceptive uptake among MWRA. As the number of daughters increased without sons, the likelihood of contraceptive uptake remained low (Adjusted odds ratio [AOR]: 0.12; 95% CI: 0.04–0.34, p < 0.000), while having at least one son substantially increased the odds of contraceptive use (AOR: 19.91; 95% CI: 8.00–49.50, p < 0.000). Notably, the gender composition of one daughter with two sons emerged as the most preferred gender composition, because the odds of contraceptive uptake were significantly increased. The findings highlight the pervasive influence of gender composition on reproductive decision-making in Pakistan, with a clear preference for sons driving modern contraceptive behavior among MWRA. These results underscore the need for targeted interventions to address gender norms and biases while promoting equitable access to FP services. Engaging men in FP initiatives is crucial for challenging traditional gender norms and fostering informed decision-making regarding contraception. Gender preference influences modern contraceptive uptake among MWRA in Pakistan, with a strong preference for sons driving reproductive behavior. Addressing gender norms and biases while promoting informed choice is essential for enhancing modern contraceptive uptake and achieving sustainable population growth. Targeted interventions, including male engagement strategies, are needed to challenge societal gender norms and empower individuals to make autonomous decisions regarding family planning.

Gold nanoparticles (NPs) have demonstrated significance in several important fields, including drug delivery and anticancer research, due to their unique properties. Gold NPs possess significant optical characteristics that enhance their application in biosensor development for diagnosis, photothermal and photodynamic therapies for anticancer treatment, as well as in targeted drug delivery and bioimaging. The broad surface modification possibilities of gold NPs have been utilized in the delivery of various molecules, including nucleic acids, drugs, and proteins. Moreover, gold NPs possess strong localized surface plasmon resonance (LSPR) properties, facilitating their use in surface-enhanced Raman scattering for precise and efficient biomolecule detection. These optical properties are extensively utilized in anticancer research. Both photothermal and photodynamic therapies show significant results in anticancer treatments using gold NPs. Additionally, the properties of gold NPs demonstrate potential in other biological areas, particularly in antimicrobial activity. In addition to delivering antigens, peptides, and antibiotics to enhance antimicrobial activity, gold NPs can penetrate cell membranes and induce apoptosis through various intracellular mechanisms. Among other types of metal NPs, gold NPs show more tolerable toxicity capacity, supporting their application in wide-ranging areas. Gold NPs hold a special position in nanomaterial research, offering limited toxicity and unique properties. This review aims to address recently highlighted applications, current status of gold NP research and discuss their future in nanomedicine.

Developed within the scope of a collaborative R&DT project, a 1 kW pilot unit aimed to perform the recovery of previously liquefied biomass from lignocellulosic forest resi-dues, later used as a carbon source in a co-electrolysis process, intended for the pro-duction of synthesis gas (H2, CO, CO2), also known as syngas. The syngas will be trans-formed into other value-added products, namely synthetic biofuels such as methane, methanol or biodiesel. This paper presents the results of the research currently being carried out at ISEL with the objective of further advancing development on the pilot unit by the execution of optimization tests with and without the addition of two dif-ferent biomass types.

In the field of medicine, blood analysis is one of the most important means of assessing human health. The type and number of blood cells is an important basis for doctors to diagnose and treat diseases. To solve the problems of difficult classification and low efficiency in blood cell detection. In this paper, an improved YOLOv5-BS blood cell target detection algorithm is proposed. The purpose of the improvement is to improve the real-time and accuracy of blood cell type identification. The algorithm is based on the YOLOv5s network and adds the advantages of CNN and Transformer. First, combine the BotNet backbone. Then replace the YOLOv5 Head architecture SPP-YOLO with Decoupled Head architecture. Finally, a new loss function SIoU is used to improve the accuracy and efficiency of the model. In order to test the feasibility of the algorithm, a comparative experiment is done. Experiments show that the average accuracy of the improved algorithm on the test set reaches 83.3%, and the recall rate is 99%. Compared with YOLOv8, the increase was 3.9% and 3% respectively. The algorithm effectively improves the efficiency and accuracy of blood cell detection, and effectively improves the problem of blood cell detection.

In many soil processes, including solute and gas dynamics, the architecture of intra-aggregate pores is a crucial component. Soil management practices and wetting-drying (W-D) cycles—the latter having a significant impact on pore aggregation—are two key factors that shape pore structure. This study examines the effects of W-D cycles on the architecture of intra-aggregate pores under three different soil management systems: no-tillage (NT), minimum tillage (MT), and conventional tillage (CT). The soil samples were subjected to 0 and 12 W-D cycles, and the resulting pore structures were scanned using X-ray micro-computed tomography, generating reconstructed 3D volumetric data. The analysis was conducted in terms of multifractal spectra, normalized Shannon entropy, lacunarity, porosity, anisotropy, connectivity, and tortuosity. The morphological and geometric properties of the soil pores indicated that W-D cycles did not cause significant changes across the management systems studied. Furthermore, multifractal analysis revealed that the porous systems exhibited fractal behavior rather than multifractality. The results demonstrate that, within the resolution limits of the microtomography analysis, pore architecture remained resilient to changes, despite some observable trends in specific parameters.

In the United States, autism spectrum disorder (ASD) affects 1 in 33 children and is characterized by atypical social interactions, communication difficulties, and intense, restricted interests. Microbial dysbiosis in the gastrointestinal (GI) tract is frequently observed in individuals with ASD, potentially contributing to behavioral manifestations and correlating with worsening severity. Moreover, dysbiosis may contribute to the increased prevalence of GI comorbidities in the ASD population and exacerbate immune dysregulation, further worsening dysbiosis. Over the past 25 years, research on the impact of microbial manipulation on ASD outcomes has gained substantial interest. Various approaches to microbial manipulation have been preclinically and clinically tested, including antibiotic treatment, dietary modifications, prebiotics, probiotics, and fecal microbiota transplantation. Each method has shown varying degrees of success in reducing the severity of ASD behaviors and/or GI symptoms and varying long-term efficacy. In this review, we discuss these microbiome manipulation methods and their outcomes. We also discuss potential microbiome manipulation early in life, as this is a critical period for neurodevelopment.

Generative AI chatbots have emerged as a significant scientific contribution. They can produce text, images, audio, and video, and their applications are vast and varied in every field. However, it is identified that these chatbots can be used by script kiddies and malicious actors to generate phishing emails, security exploits, and executable payloads that can present notable risks and challenges for cybersecurity. This research provides an extensive review of the current status of the three most popular generative AI chatbots which are ChatGPT, Google Gemini, and Microsoft Bing. The article further reviews the different ways in which an attacker can intentionally use a chatbot for malicious activities and intensify cyber attacks. Moreover, the article provides insights on how an attacker uses certain keyword queries to manipulate chatbot behavior and tricks it into generating content that I won’t otherwise. We also explored the role of AI chatbots in enhancing cyber resilience against sophisticated attacks and their applications in detecting and mitigating security incidents. Finally, we proposed strategies that can empower modern chatbots to defend against such threats so that an attacker will not be able to bypass their content filtering mechanisms.

In this study, we present a multicomponent lipid model membrane composed of six lipids, in-cluding cholesterol, which represent the structure of neuronal membranes found in grey matter. To our knowledge, no previous studies have attempted a model like this. We constructed our model membranes using an esterified form of the most abundant polyunsaturated fatty acid found in grey matter: docosahexaenoic acid (DHA). It is the most prevalent fatty acid in the brain and is essential for maintaining cellular structural integrity, acting as a major phospholipid in neural membranes. The aim of this work is to analyse the effects that different proportions of DHA have on the proposed membrane model. For this purpose, transmission electron microscopy, zeta potential, and molecular acoustics were used. The results showed differences that may be attributable to the influence DHA has on the nanodomains of sphingomyelin and cholesterol.

Remote sensing image change detection is a key application technology in remote sensing. In recent years, the development of deep learning has brought new opportunities for remote sensing image change detection. However, to achieve high change detection accuracy, large-scale, good-quality labeled data is required to train the model, which is often difficult to obtain and costly. This paper proposes a semi-supervised remote sensing image change detection network based on consistent regularization for multi-scale feature fusion and reuse (SCMFM-CDNet), which can enhance change detection performance with limited labeled data. Specifically, the SCMFM-CDNet consists of two parts, the supervised stage multi-scale feature fusion and reuse network (MFM-CDNet) and the consistency regularization method of semi-supervised learning. The MFM-CDNet network uses channel attention mechanism and spatial attention mechanism to enhance the expression ability of features, and gradually completes the extraction and detection of change features through multi-scale feature fusion and reuse strategy. The consistency regularization method of semi-supervised learning adopts the teacher model and student model for alternate training, updating model parameters by minimizing the differences in their predictions, thus achieving semi-supervised learning. This paper conducted extensive experiments on three public datasets, LEVIR-CD, WHU-CD, and GoogleGZ-CD. Under the three labeling data ratios of 5%, 10%, and 20%, the F1 scores of the SCMFM-CDNet network on the LEVIR-CD dataset were 88.62, 90.78, and 90.63, respectively. On the WHU-CD dataset, the F1 scores were 88.40, 90.17, and 90.83, respectively. On the GoogleGZ-CD dataset, the F1 scores were 81.44, 82.36, and 85.40, respectively. The results indicate that the proposed network outperforms existing semi-supervised change detection methods in terms of comprehensive performance. In addition, this paper also conducted ablation experiments to evaluate the contribution of the global context module (GCM) and feature fusion reuse module (FFM) in the MFM-CDNet network. The experimental results show that these two modules are crucial for improving network performance.

The agriculture industry is critical to the global economy, with product quality having a direct impact on marketability and waste management. Apples, one of the most extensively produced fruits, are affected by a variety of diseases that can reduce productivity and quality. Accurate diagnosis of diseases is critical, but traditional manual approaches are time-consuming, error-prone, and ineffective. Inadequate labeled data and a wide range of disease symptoms make it necessary to design an automated, robust, and accurate system. This article describes a hybrid model for Apple Fruit Disease Detection (HMAFDD) that combines the strengths of three pre-trained convolutional neural network (CNN) models: ResNet50, DenseNet121, and EfficientNetB0. It accomplish this by using multi-architecture feature extraction. The hybrid system, which combines both models, is able to recognize a wide range of features, from simple textures to complex patterns unique to a certain diseases. Grad-CAM, or gradient-weighted class activation mapping, creates heatmaps that highlight significant regions for prediction, which enhances the interpretability of the model.To increase robustness and accuracy, techniques such as spectral-shifted adversarial perturbation for data augmentation and spectrally-weighted global average pooling for feature aggregation are used. This technique provides 99.75% accuracy with minimal processing needs, making it acceptable for real-time applications in agricultural situations. This considerably improves apple disease management.

Low alloyed martensitic resistant steels could become a solution for material saving and components lightweighting in the industrial activity due to the combination of high strength and good weldability and toughness characteristics. However, it is known that martensitic resistant steels are susceptible to temper embrittlement (TE), which can cause undesirable brittle fracture during service of components. In this investigation the influence of the cooling rate after a high temperature tempering (650 ºC) on TE of a low alloyed martensitic Mn-Cr-Ni steel was studied. Charpy impact tests and metallurgical analysis using emission Scanning Electron and Transmission Electron Microscopy were carried out on specimens cooled in different cooling media after the tempering to stablish a relation between the microstructures and the TE. Obtained results shown a considerable influence of the tempering-carbides location at grain boundaries at low post-tempering cooling rates on the TE of the selected steel.

Clinical supervision is an essential element in trainees, preventing burnout and ensuring the effectiveness of their intervention. AI technologies offer increasing possibilities for the development of clinical practices, among it supervision appears to possess the ideal characteristics for automation processes. In this study we test the capabilities of ChatGPT-4 to provide supervisory feedback by then placing them in comparison with feedback produced by a qualified supervisor. Two ChatGPT-4-generated feedbacks (the first one produced by a naïve identity and the second one by a trained identity) and one human-produced feedback were evaluated by means of a liking questionnaire, which was filled out to a group of gestalt psychotherapy trainees. Principal Component Analysis (PCA) highlighted 4 components of the questionnaire: relational and emotional dimensions (C1), didactic and technical quality (C2), treatment support and development (C3), and professional orientation and adaptability (C4). The ratings of satisfaction, obtained from the three supervisory feedbacks, were compared by applying 1-way analysis of variance (ANOVA). Statistical evaluations were performed using the statistical package SPSS version 25. The feedback generated by pre-trained AI (f2) was rated significantly higher than the other two (untrained AI feedback (f1) and human feedback (f3) in C4; in C1 the superiority of f2 over f1 but not over f3 appears significant. These results suggest that the use of pre-trained AI may be an appreciable option for increasing the effectiveness of clinical supervision in some specific areas, including especially career guidance.

Since the seminal work of Kolmogorov, probability theory has been based on measure theory, where the central components are so-called probability measures, defined as measures with total mass equal to 1. In Kolmogorov’s theory, a probability measure is used to model an experiment with a single outcome that will belong to exactly one out of several disjoint sets. In this paper, we present a different basic model where an experiment results in a multiset, i.e. for each of the disjoint sets we get the number of observations in the set. This new framework is consistent with Kolmogorov’s theory, but the theory focuses on expected values rather than probabilities. We present examples from testing Goodness-of-Fit, Bayesian statistics, and quantum theory, where the shifted focus gives new insight or better performance. We also provide several new theorems that address some problems related to the change in focus.

Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it is crucial to estimate AGCS accurately. In this research, we added the climatic and topographic factors to the estimation process by a remote sensing approach to explore their impacts and to achieve more precise estimations. We model and predict AGCS by Random Forest (RF) based on sixty field sample plots of Pinus densata pure forests in southwest of China and the factors extracted from Landsat 8 OLI images (Source I), Sentinel-2A images (Source II), combined Landsat 8 OLI and Sentinel-2A images (Source III). We added the topographic and climatic factors to establish AGCS estimation model and compare the results. The topographic factors contain elevation, slope and aspect. Climatic factors contain mean annual temperature, annual precipitation, annual potential evapotranspiration and monthly mean potential evapotranspiration. It was found that the model based on Source III was better than Source I and Source II. Among the models without adding factors, the model based on Source III worked the best, with an R2 of 0.87, an RMSE of 10.81 t/ha, an rRMSE of 23.19%, and a P of 79.71%. Among the models that added topographic factors, the model based on Source III worked best after adding elevation, with an R2 of 0.89, an RMSE of 10.01 t/ha, an rRMSE of 21.47%, and a P of 82.17%. Among the models that added climatic factors, the model that was added the annual precipitation factor had the best modeling result, with an R2 of 0.90, an RMSE of 9.53 t/ha, an rRMSE of 20.59%, and a P of 83.00%. The prediction result exhibited that the AGCS of the Pinus densata forest in 2021 was 9,737,487.52 t. The combination of Landsat 8 OLI and sentinel-2A could improve the prediction accuracy of AGCS. The addition of annual precipitation can effectively improve the accuracy of AGCS estimation. Higher resolution of climate data is needed to enhance the modeling in the future work.

This paper presents a comparative analysis of several decision models for detecting Structured Query Language (SQL) injection attacks, which remain one of the most prevalent and serious security threats to web applications. SQL injection enables attackers to exploit databases, gaining unauthorized access, and manipulating data. Traditional detection methods often struggle due to the constantly evolving nature of these attacks, the increasing complexity of modern web applications, and the lack of transparency in the decision-making processes of machine learning models. To address these challenges, we evaluated the performance of various models, including Decision Tree, Random Forest, XGBoost, AdaBoost, Gradient Boosting Decision Tree (GBDT), and Histogram Gradient Boosting Decision Tree (HGBDT), using a comprehensive SQL injection dataset. The primary motivation behind our approach is to leverage the strengths of ensemble learning and boosting techniques to enhance detection accuracy and robustness against SQL injection attacks. By systematically comparing these models, we aim to identify the most effective algorithms for SQL injection detection systems. Our experiments show that Decision Tree, Random Forest, and AdaBoost achieved the highest performance, with an accuracy of 99.50% and an F1 score of 99.33%. Additionally, we applied SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for local explainability, illustrating how each model classifies normal and attack cases. This transparency enhances the trustworthiness of our approach in detecting SQL injection attacks. These findings highlight the potential of ensemble methods to provide reliable and efficient solutions for detecting SQL injection attacks, thereby improving the security of web applications.

In this paper, target detection of car images in roads is performed based on the YOLOv8 model of YOLO family of models, which improves the accuracy and generalisation of the target detection task by combining multi-scale prediction, CSPNet structure and optimisation techniques such as BoF and BoS. The input images contain five types of vehicles such as Ambulance, Bus, Car, Motorcycle and Truck, which are analysed and learnt to have a classification accuracy of 75.4% on Ambulance, 53.5% on Bus, 55.1% on Car, 51.1% on Motorcycle and 42.5% on Truck. Despite the gap in specific classification accuracy, the YOLOv8 model can detect 100% of vehicles on the road, demonstrating good target detection capability. This research is of great significance for improving road traffic safety, intelligent traffic management, and the development of future autonomous driving technology. By optimising the deep learning model to achieve more accurate and efficient vehicle target detection, it can help to improve road safety and traffic efficiency, and promote the progress of intelligent transportation systems.

Since Carl Linnaeus introduced the binomial nomenclature system in the 18th century, zoological classification has experienced significant transformations. While Linnaeus’ taxonomy provided a foundational structure based on morphological traits, advancements in evolutionary theory, genetics, and molecular biology have revolutionized the way organisms are classified. This article traces the evolution of classification methods, from Linnaeus’ hierarchy to Darwin’s theory of evolution, and highlights modern phylogenetic techniques based on genetic data. However, the discussion also introduces the need for a reformulation of zoological classification to incorporate species interactions and ecological relationships, as proposed by Johann Friedrich Humboldt over 50 years before Darwin. Humboldt’s holistic view of nature, where species interact and co-evolve within ecosystems, offers valuable insights for understanding evolution as a collective process, shaping life on Earth as a unified organism. Embracing this perspective will help the next generation of scientists approach biodiversity and conservation with a deeper appreciation of species interdependence.

High purity hydrogen is a necessary need for fuel cell. Pressure swing adsorption (PSA) technology is one of the effective methods for hydrogen purification and separation. A layered adsorption bed packed with activated carbon and zeolite 5A for five-component gas mixture (H2/CH4/CO/N2/CO2=56.4/26.6/8.4/5.5/3.1 mol%) PSA model was built. The model was validated by breakthrough curves and comparing the results with the experimental data. The purification performance of six-step layered bed PSA cycle was studied using the model. In order to optimize the cycle, the Box-Behnken design (BBD) method was used, as implemented in Design Expert™. In addition to adsorption time, the pressure equalization time and the feed flow rate were considered as independent optimization parameters. Quadratic regression equations were then obtained for three responses of the system, namely purity, recovery, and productivity. To explore a better optimization solution, a novel optimization method of machine learning with back propagation neural network (BPNN) was proposed in this work, a kind of heuristic algorithm of genetic algorithm (GA) is introduced to optimize the structure of BPNN. The predicted outputs of hydrogen production using two kinds of models based on back propagation neural network-genetic algorithm (BPNN-GA) and BBD method integrated with BPNN-GA (BBD-BPNN-GA) models. The results showed that the BBD-BPNN-GA model have a better performance with the mean square error (MSE) of 0.0005, while the MSE of BPNN-GA model was 0.0035. And the correlation coefficient of R-values were much closer to 1 of the BBD-BPNN-GA model, which was illustrated that the BBD-BPNN-GA model can be effectively applied to the prediction and optimization of PSA process.

This review introduces the development and applications of novel plant growth promotor ‘SOMRE’ and animal growth promoter ‘VED’ to increase food production, stop world hunger, greening deserts, stop global warming, and restore the green ‘Earth’. A concrete method to use ‘SOMRE’ and ‘VED’ as ‘the earth medicine’ is proposed. Part 1 describes ‘SOMRE’ helping the growth of vegetables, grains, flowers, herbs, endangered and native plants in the Gobi Desert. It increased plant-based food and achieved greening a part of the Gobi Desert. Part 2 explains ‘VED’, increasing the production of cashmere wool in goats, dramatically improving their reproductivity and their meat production. In humans, it demonstrated to eliminate the itchiness, skin spots, and acne scars, improved wound healing and ED, etc. It has the potential to be used as a treatment for dementia and osteoporosis. Part 3 is the experimental part. Following the described procedures, readers can get hold of ‘SOMRE’, ‘VED’ and related compounds which are a treasure trove of pharmacologically active substances and new ideas.

Physical literacy (PL) is increasingly recognised as essential to fostering lifelong physical activity (PA) engagement, particularly when nurtured in early childhood. However, there remains limited understanding of how key stakeholders in early years (EY) education perceive, value, and implement a PL informed approach. This exploratory study aimed to investigate a variety of EY stakeholders' knowledge and beliefs regarding PL and PA in relation to 3-5-year-olds. A concurrent mixed-methods approach was used, incorporating semi-structured expert interviews (n=11), focus groups (n=22), and a survey (n=210) to gather both qualitative and quantitative data. Thematic analysis was applied to identify key themes, and survey data was analysed to complement and triangulate qualitative findings. Findings revealed variation in stakeholders' awareness of PA recommendations, confusion over terminology, and the influence of environmental and policy constraints on practice. Stakeholders acknowledged the importance of PL but expressed uncertainty about the connection between academic theory and practical application. Resource limitations, conflicting priorities, and lack of training and policy support emerged as key barriers. Feedback on an educational PL-EY model was generally positive, emphasising its potential educational value while underscoring the need for clearer guidance and support. Given the pivotal role of EY education in shaping children’s behaviours, health, and well-being, this study highlights the necessity of a holistic approach to interventions, strong stakeholder involvement, and evidence-based practices to foster PL in EY children and raises critical questions about what effective interventions to develop PL in this age group should consider and look like.

Crowdsourcing software design (CSD) is the completion of specific software design tasks on behalf of a client by a large, unspecified group of external individuals who have the specialized knowledge required by an open call. Although current CSD platforms have provided features to improve coordination in the CSD process (such as email notifications, chat, and announcements), these features are insufficient to solve the coordination limitations. A lack of appropriate coordination support in CSD activities may cause delays and missed opportunities for participants, and thus the best quality of design contest results may not be guaranteed. This research aims to support the effective management of the CSD process through identifying the key activity dependencies among participants in CSD platforms and designing a set of process models to provide coordination support through managing this activity. In order to do this, a five-stage approach is used: First, the current CSD process is investigated by reviewing 14 CSD platforms. Second, the review resulted in the identification of 17 possible suggestions to improve CSD. These suggestions were evaluated in stage 3 through distributing a survey to 41 participants who have experience of using platforms in the field of CSD. In stage 4, we designed ten process models that can meet the requirements of suggestions, while in stage 5, we evaluated these process models through interviews with domain experts. The result shows that coordination support in the activities of the CSD can add valuable contribution to the development of CSD platforms.

In this article we focus on kynurenic acid metabolism, a biochemical part of L-tryptophan catabolism, in processes related to memory and cognitive impairment and its subsequent relevance to neuropsychiatric disorders and the ageing process. Kynurenic acid is synthesised from L-kynurenine by kynurenine aminotransferases. Experimental pharmacological studies have shown that elevated levels of kynurenic acid in the brain are associated with impaired learning and that lowering kynurenic acid levels can improve these symptoms. The discovery of new compounds with the ability to block kynurenine aminotransferase opens up new therapeutic avenues against memory impairment and dementia. The newly developed Helix pomatia snail model of memory is being used for this pharmacological approach. In conclusion, diet, exercise and physical activity have a significant impact on the endogenous pharmacology of kynurenic acid and significantly modulate steady-state conditions and delay the events of the ageing process.

This study presents a novel statistical framework for evaluating the social and economic impact of delays in the reimbursement of innovative medicines. With the growing importance of timely access to advanced therapies, particularly in managing chronic diseases, we develop a model that quantifies social loss in terms of life years, productivity, productivity cost and overall well-being. The framework integrates several key factors, including years of life lost, years of potential productive life lost, deaths, delays in reimbursement, and cost of labor productivity losses. By providing a comprehensive assessment of the societal costs associated with delayed access, the model offers valuable insights for policymakers seeking to balance healthcare innovation with sustainability. The results emphasize the need for efficient pharmaceutical policies that reduce delays in access to innovative treatments, promote equity in healthcare, and ensure long-term system viability. This approach has broad implications for improving health outcomes and economic productivity, particularly in publicly funded healthcare systems.

Electronic devices called epiretinal prosthesis are used to restore vision in patients with age-related macular degeneration (AMD) and retinitis pigmentosa (RP), two degenerative retinal illnesses. It functions by stimulating the retina's surviving cells, which then excite the brain to receive visual information from the retina. Epiretinal prosthesis have the flexibility of electrode placement, which enables them to target particular regions of the retina. Furthermore, it enables a high level of spatial resolution, enhancing visual quality. For the purpose of activating retinal cells, it transforms visual input into electrical signals. In general, epiretinal prosthesis are a potential method for helping people with degenerative retinal illnesses regain their vision. Ongoing research aims to increase the effectiveness and safety of these devices.

Background/Objectives: to analyze the effects of vaccination against SARS-CoV-2 on the incidence, lethality and mortality from COVID-19, in the city of São Paulo, in the period 2020-2022. Methods: this is a population-based time series study, with official public data, relating to the period from 2020 to 2022. The population data were extracted from the Municipal Health Department of São Paulo, according to the geographic division of the Regional Health Coordination (CRS). The Prais-Winsten regression model was used, calculating the percentage variation of daily change (DPC). This procedure made it possible to classify the trend of epidemiological rates as increasing, decreasing or stationary. Results: the city of São Paulo recorded that 17% of its total population was infected at least once by the SARS CoV-2 virus, with a fatal outcome in 0.4% of its total population. When comparing the differences between the CRS, it was observed that the North region had a higher lethality rate at the beginning of the pandemic, decreasing significantly over the period. The Central region, in 2021, showed an increasing trend different from the other regions. Regarding mortality, the North, Center and West coordinators showed decreasing trends in all periods analyzed, unlike the East, Southeast and South coordinators, which in 2020 showed stationary trends. Conclusions: there was a reduction in the epidemiological outcomes of COVID-19 after the mass application of vaccination against SARS-CoV-2. Strategies to increase vaccination coverage should be implemented in all regions of the city of São Paulo, especially in those with high social vulnerability.

Active inference under the Free Energy Principle has been proposed as an across-scales compatible framework for understanding and modelling behaviour and self-maintenance. Crucially, a collective of active inference agents can, if they maintain a group-level Markov Blanket, constitute a larger group-level active inference agent with a generative model of its own. This potential for computational, scale-free structures speaks to the application of active inference to self-organizing systems across spatiotemporal scales, from cells to human collectives. Due to the difficulty of reconstructing the generative model — that explains the behaviour — of emergent group-level agents, there has been little research on this kind of multi-scale active inference. Here, we propose a data-driven methodology for characterising the relation between the generative model of a group-level agent and the dynamics of its constituent individual agents. We apply methods from computational cognitive modelling and computational psychiatry, applicable for active inference as well as other types of modelling approaches. Using a simple multi-armed bandit task as an example, we employ the new ActiveInference.jl library for Julia to simulate a collective of agents who are equipped with a Markov blanket. We use sampling-based parameter estimation to make inferences about the generative model of the group-level agent and we show that there is a non-trivial relationship between the generative models of individual agents and the group-level agent they constitute, even in this simple setting. Finally, we point to a number of ways in which this methodology might be applied to better understand the relations between nested active inference agents across scales.

Obesity is a complex disease increasing risk of other pathologies. Its prevention or long-term weight loss maintenance is problematic. Gut microbiome is considered a potential obesity modulator. The objective of the present study was to summarize recent findings regarding relationships between obesity, gut microbiota and diet, with emphasis on dietary fiber and resistant starch. Composition of the human gut microbiome and methods of its quantification are described. Products of the gut microbiome metabolism, such as short-chain fatty acids and secondary bile acids including their effects on the gut microbiota, intestinal barrier function and immune homeostasis are discussed in the context of obesity. Importance of dietary fiber and resistant starch is emphasized as far as effects of the host diet on the composition and function of the gut microbiome are concerned. Complex relationships between human gut microbiome and obesity are finally summarized.

The Loess Plateau is one of the most severely affected regions by soil erosion in the world, with a fragile ecological environment. Vegetation plays a key role in the region's ecological restoration and protection. Most existing studies focus on the trends of vegetation cover change, while fewer studies investigate the driving factors or only conduct quantitative analyses. This study uses the Geodetector (Geographic Detector Model) to assess the impact of natural and human factors such as temperature, precipitation, soil type, and land use on vegetation growth, revealing the characteristics and driving mechanisms of vegetation cover changes on the Loess Plateau from 1982 to 2022. It also quantitatively calculates the influence of different factors. The findings indicate that vegetation cover on the Loess Plateau has generally risen from 1982 to 2022, but there was a noticeable difference around 2000. The annual mean slope of vegetation cover from 1982 to 2000 was 0.00129, which increased to 0.01075 from 2000 to 2022. The Geodetector indicates that the factors with the greatest impact on vegetation cover in the Loess Plateau are temperature, precipitation, soil type, and land use, and the interaction between these factors has a greater effect on vegetation cover than any single factor alone. This study identifies the ideal ranges or types for vegetation growth on the Loess Plateau over four time periods, offering a scientific basis for soil restoration and soil and water conservation efforts in the area.

Background/Objective: Biological females experience more autoimmune disease than males and more treatment-related adverse events (TRAEs) after immune checkpoint blockade therapy. However, little is known about sex-related differences in TRAEs after cancer vaccines. Methods: The Mel44 clinical trial (NCT00118274) enrolled patients with high-risk melanoma to either of two melanoma vaccines. We hypothesized that females would experience higher rates and grades of TRAEs. TRAE rates and grades were compared between sexes, with adjustment for multiple comparisons, and with mixed-effects models. Results: Multiple sex-related differences in TRAE rate and grade were observed in unadjusted comparisons, but only hyperglycemia and hypopigmentation were significantly higher grade by sex after correcting for multiple comparisons: they were increased in males. In mixed-effect mod-els, vaccination strategy, but not patient sex, was independently associated with TRAE rates and grades. Conclusions: These data do not support our hypothesis that TRAEs would be increased in females. Vaccine safety was supported for both males and females.

Despite extensive research on sustainable business models and the development of various tools to facilitate their implementation, barriers persist particularly concerning the governance of a local ecosystem of actors. In this paper, we bridge the literature on sustainable business models with a literature stream on governance arrangements. We analyze these governance arrangements through a qualitative study focusing on the development of two local energy systems –anaerobic digestion and district heating– by a global energy company. Our study shows the importance of securing collaboration to govern interdependencies in the ecosystem. We identify that a facilitating public policy framework combined with internal institutional, strategic and operational drivers linked to the governance of local ecosystems is crucial for the implementation of local sustainable business models.

We describe a method of determining three-body and four-body Laplace-like phase angles with the potential to librate about a mean value in multiplanet extrasolar systems. Unlike in past searches of N-body results, this method relies on global mean-motion resonances (MMRs) and takes into consideration the location of the most massive planet which defines the 1:1 global MMR in each (sub)system. We compile lists of potentially librating phase angles and prevalent MMRs in 35 real multibody systems, and we discuss their properties in conjunction with recent investigations of librations discovered in sophisticated N-body simulations. We hope that our results will facilitate systematic libration searches in dynamical models of compact systems with three or more orbiting bodies.

Malaria remains a public health challenge in low- and middle-income countries (LMICs). Despite gains from strategies like Insecticide Treated Nets (ITNs) and Intermittent Preventive Treatment during pregnancy (IPTp), significant socioeconomic inequalities persist, particularly among pregnant women and children under five. This study analysed temporal and socioeconomic inequalities in malaria prevention in sub-Saharan Africa (SSA). Methods: Nationally representative Demographic Health Surveys from 10 SSA countries were used, comparing two time periods. Changes in ITN use by pregnant women and children under five, as well as IPTp coverage, were assessed. Inequalities based on socioeconomic status (SES) and residence were analysed using the Erreygers Normalized Concentration Index and Theil index.. Results: Results revealed significant variability in ITN use and IPTp coverage between and within countries. Eight countries showed improvements in ITN use during pregnancy, with Nigeria seeing a 173.9% increase over five years. Burkina Faso and Tanzania consistently reported high ITN use (~87%) in children under five. IPTp coverage increased in all countries except Kenya. Decomposition using the Theil index indicated that within-group inequalities, particularly based on SES and residence, were the primary drivers of disparities. Conclusion: To ensure progress toward universal health coverage, malaria prevention programs must prioritize vulnerable populations and be continuously evaluated.

In typical whiskey, the content of higher alcohols is about 1500-2000 mg/L, lead to high intoxicating degree (ID). To produce the low-ID whiskey, the Saccharomyces cerevisiae XF0-h, XF0-H and XF0-LH were successfully constructed by CRISPR-Cas9 gene editing technology to knock out HOM2 (encoding aspartate β-semialdehyde dehydrogenase) in the original strain XF0 and the LEU1 knockout strain XF0-L. The content of higher alcohols in whiskey fermented by the XF0-h, XF0-H, and XF0-LH were 704±8 mg/L, 685±6 mg/L, and 685±19 mg/L, respectively, showing reductions of 23.93%, 25.98%, and 15.81% compared to the XF0, XF0, and XF0-L. The fermentation conditions of the XF0-LH were optimized through single-factor experiments and Box-Behnken design. The optimal conditions were the wort concentration of 9.8 °P, the hydrolyzed broken rice syrup addition of 78 g/L, and the inoculum size of 2.7 × 106 cells/mL. The low-ID whiskey was brewed with higher alcohols content at 556 mg/L by 50L fermenter at the optimal conditions.

Objectives: early identification of infection-causing microorganisms through multiplex PCR panels enables prompt and targeted antibiotic therapy. The study aimed to assess the performance of the BioFire® Joint Infection Panel (BF-JIP) in analysing non-synovial fluid samples. Methods: we conducted a retrospective cohort study at Trieste University Hospital, Italy, on hospitalised adults with non-synovial fluid samples tested by both BF-JIP and traditional culture methods (November 2022-April 2024). Results: we evaluated 48 samples from 45 patients, including 24 abscess drainage fluids and 10 biopsies. The BF-JIP showed high concordance (85.4%) and enhanced detection (4.3%) compared to culture methods. The BF-JIP excelled in CSF (100% accuracy and concordance) and in abscess drainage fluids (accuracy: 95.8%; concordance: 91.7%), and maintained high performance in patients under antibiotics. Conclusions: these findings suggest that BF-JIP is a valuable tool for accurate pathogen detection in various clinical samples, offering the additional advantage of being a rapid method.

Diabetes complications appear with the severity of hyperglycemia and the age of the disease. In type 2 diabetes mellitus (T2DM), herbal medicines are also effective in reducing hyperglycemia and can be used for this purpose to manage complications. This study aimed to prove the effectiveness of Amaranthus spinosus root extract on type 2 diabetes (T2D) and its microvascular complications. A high-fat diet and low-dose streptozocin were used for 20 days to induce T2D syndrome and complications in rats. The validated animal models were treated orally with the dried hydroethanolic extract of A. spinosus for three weeks; then the hematological, biochemical, and histological markers were evaluated. The results showed that the extract at a dose of 500 mg/kg bw was effective against diabetes and its complications. This effectiveness was linked to a significant reduction (p &lt; 0.0001) in basal blood glucose, tissue oxidative stress, serum cardiovascular and hepato-renal markers, and an improvement in insulin secretion and utilization. The protective activity of the extract against macular edema and renal fibrosis was confirmed by histopathological examinations. These findings prove that A. spinosus root extract contains bioactive compounds that may be useful in preventing or reversing diabetic retinopathy and renal fibrosis.

Heart aging involves a complex interplay of genetic and environmental influences, leading to a gradual deterioration of cardiovascular integrity and function. Age-related physiological changes, including ventricular hypertrophy, diastolic dysfunction, myocardial fibrosis, increased arterial stiffness, and endothelial dysfunction, are influenced by key mechanisms like autophagy, inflammation, and oxidative stress. This review aims to explore the therapeutic potential of plant-derived bioactive antioxidants in mitigating heart aging. These compounds, often rich in polyphenols, flavonoids, and other phytochemicals, exhibit notable antioxidant, anti-inflammatory, and cardioprotective properties. Through a comprehensive analysis of preclinical and clinical studies, this work highlights the mechanisms associated with heart aging and the promising effects of plant-derived antioxidants. The findings may helpful for researchers in identifying specific molecules with therapeutic and preventive potential for cardiovascular aging.

This study investigated metabolic changes associated with Bovine Leukemia Virus (BLV) infection in dairy cows, focusing on pre-parturition alterations. Of 145 cows examined, 42 (28.9%) were BLV-seropositive. 38% of infected cows showed high somatic cell counts indicative of subclinical mastitis, with 15 experiencing additional health issues such as ketosis, milk fever, and lameness. Despite these conditions, no significant differences in milk yield or composition were observed between infected and control groups. Metabolomic analysis conducted at -8 and -4 weeks prepartum revealed significant metabolic differences between BLV-infected and healthy cows. At -8 weeks, 30 serum metabolites were altered, including sphingomyelins, lysophosphatidylcholines, amino acids, and acylcarnitines, suggesting disruptions in membrane integrity, energy metabolism, and immune function indicative of early neoplastic transformations. By -4 weeks, the number of altered metabolites reduced to 17, continuing to reflect metabolic disruptions in cows with leukemia. Multivariate analysis highlighted distinct metabolic profiles between infected and control cows, identifying key discriminating metabolites such as choline, aspartic acid, phenylalanine, and arginine. Urine metabolomics revealed significant prepartum shifts in metabolites related to glucose, asymmetric dimethylarginine, and pyruvic acid, among others. The research confirmed metabolomics' efficacy in defining a BLV infection metabolic profile, elucidating leukosis-associated metabolic disruptions. This approach facilitates identification of BLV-infected cows and enhances understanding of infection pathophysiology, providing a foundation for advanced management and intervention strategies in dairy herds. The study underscores the profound impact of leukosis on metabolic processes and highlights urine metabolomics' utility in non-invasively detecting BLV infection, offering potential for improved herd health management.

Purpose: People with Williams syndrome (WS, n = 20, CA = 12.5, MA = 8.9) have strong verbal short-term memory but challenged verbal long-term memory. This study aimed to evaluate the memory abilities of people with WS and examine the possibility of improving their memory performance. Methods: Twelve navigations completed the study. Fourteen indexes were used for each navigation. Controls matched in chronological age (CA, n = 20, mean age = 12.5), mental age (MA, n = 20, mean age = 8.8), 5th graders (n = 20, mean age = 10.3), and college students (CS, n = 20, mean age = 20.2) were recruited. Results: The WS group was slowest and erred more in completing tasks than the CA and MA groups. The error patterns revealed group differences in the number of extra items purchased and missed correct items. The 5th graders erred more than the CS on extra items purchased; the CA group erred more than the CS and the 5th graders on missed correct items. The WS group erred more than the CA and MA groups for incorrect items purchased, extra items purchased, missed correct items, and incorrect types purchased. The error patterns yielded replacements and confusion. Semantic features attributed to atypical processing during navigation have emerged in people with WS. Conclusion: These findings suggest that people with WS have developmental disorders in verbal long-term memory. The current study serves as a foundation for future interventions in people with WS.

The slope protection structure of the prefabricated lattice beam is one of the most widely used and studied systems in slope structure, the core part of which is the connection between the lattice beam joint and the longitudinal and transverse beams. Because the prefabricated structure is weak in its structural integrity, it is necessary to study the influence of prefabricated lattice beam joints and the longitudinal and transverse beams on the overall mechanical properties of the structure. In this paper, one ordinary cast-in-place concrete beam and six prefabricated beams with different joint connection modes are designed, and the influence of different connection modes on the bending capacity of the beams is accordingly explored. Moreover, the flexural capacity, bending stiffness change, ductility and energy absorption capacity of the beams are analyzed through three-point bending test. The test results show that the connection mode at the joints could significantly affect the overall mechanical properties of the structure. By embedding holes in steel sleeves, filling cement mortar in the middle, and using steel plates with a thickness of 16mm for anchoring treatment joints of end plates, the specimen beams are thus obtained with the same flexural capacity, ductility and energy absorption capacity as ordinary cast-in-place concrete beams, which could be used for specimen engineering applications.

Dementia is a group of detrimental diseases and conditions that damage the human brain and impair mental functioning. In recent years, significant efforts have been made to develop robust computer-aided diagnostic tools, particularly AI models for detection. However, their clinical implementation remains limited. One issue identified is that despite achieving high accuracy, AI-powered predictions have relied on single features or small sets of features in limited categories, hindering their robustness and generalizability. Moreover, the accuracy alone has not been sufficient to convince medical practitioners to adopt these tools, as they require transparent and scientifically grounded diagnostic processes. This study aims to improve the transparency and reliability of AI models in predicting dementia through three key steps: 1) compare a Machine Learning (ML) model's accuracy in detecting dementia when trained on a multi-domain dataset to when trained on subsets of features, 2) evaluate additional ML models for dementia detection using the multi-domain dataset, and 3) apply Explainable AI (XAI) to elucidate the importance of specific features in ML-based predictions, thereby enhancing transparency and interpretability. We hypothesize that utilizing multi-domain data will enhance the performance of ML models. Furthermore, XAI will demonstrate that features with high influence on the model's decisions are aligned with established indicators of dementia. This study contributes to the field by constructing lightweight predictive models that deliver highly accurate performance while enhancing transparency and interpretability, advancing toward clinically applicable AI-powered diagnosis.

This paper presents the experimental characterization and numerical optimization of the parametric design of inductors used for the induction heating process of metallic samples. Here, the variation of the geometric parameters of the inductor (the distance between the coil and the material, as well as the number of turns and separation between them), was analyzed. In addition, changes in the power input values of the heating system variables were also investigated; and finally, the heating time of the materials was taken into consideration. For the multi-objective numerical optimization process, a full factorial 2K experimental design was devised. Afterward, from preliminary experiments, a response surface was performed, from which the optimal candidates for the heating process were obtained. In the experiments, a methodology was designed for the acquisition and processing of heating data on ASTM E8 tensile test specimens. The main objective of this paper is to determine both the geometric parameters of the coil as well as the power input values of the circuit, which allow for generating uniform heating in the specimens. The objectives of the optimization process are to maximize the heating temperature minimizing the consumption of electrical current in the induction circuit. The data generated in the heating of the specimens were acquired using a Fluke® TI10 thermographic camera, while a BK Precision® regulated power supply was used for the induction circuit. The parameters obtained in the optimization process for the optimal candidates were corroborated through experimental tests. The paper shows the initial (non-optimal) and final (optimal) results obtained from the experimental tests. The heating station created with the optimal coil will be used for mechanical characterization tests on metallic materials at temperatures above room temperature.

Accurate knowledge of flow rates is essential for hydraulic systems, as it allows for calculating hydraulic power when combined with pressure measurements. This data is helpful in applications such as predictive maintenance. However, most flow rate sensors in fluid power systems operate invasively, disrupting the flow and producing inaccurate results, particularly for transient flow conditions. A common approach is calculating the flow rate using the pressure difference along a pipeline based on the Hagen-Poiseuille law. However, this method is limited to laminar, steady, incompressible flow. This paper presents a novel soft sensor with an analytical model for transient, compressible pipe flow based on two pressure signals, thus no actual volumetric flow rate sensor is required. The model is derived by solving the fundamental fluid equations in the Laplace domain and converting the solution back to the time domain to obtain a physical representation. The relationship between the pressure difference signal and flow rate is derived using the four-pole theorem. The resulting analytical solution, including a convolution integral with a weighting function, shows high accuracy for transient and steady flows. This new equation enables the development of a soft sensor capable of non-invasive flow rate measurements for various pipe flow conditions.

This study explores the effectiveness of functional near-infrared spectroscopy (fNIRS) as an implicit measurement tool for evaluating sweetness intensity and affective value. Thirty-two participants tasted sucrose solutions at concentrations of 0.15M, 0.3M, and 0.6M, while both their neural responses were recorded with a 24-channel fNIRS system and their self-reported assessments of sweetness intensity and affective value were collected. The neural fNIRS data were converted into oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations using the modified Beer-Lambert Law, and analyzed through univariate activation analysis and multivariable decoding analysis to identify neural activation patterns associated with sweetness perception. The results showed significant activation in the dorsolateral prefrontal cortex (dlPFC) and orbitofrontal cortex (OFC) in response to varying levels of sweetness intensity and affective value, with channels 8, 10, 12, 13, 14, 15, and 17 consistently activated across all sucrose concentrations. As sweetness concentration increased from 0.15M to 0.6M, the number of significantly activated channels rose from seven to eleven, indicating stronger and more widespread neural responses corresponding to higher sweetness intensity. The multivariable decoding analysis further demonstrated the capability of fNIRS in accurately distinguishing positive affective responses, with up to 72.1% accuracy. The moderate positive correlation between explicit self-reports and implicit fNIRS data regarding sweetness intensity further supports the validity of fNIRS as a reliable tool for assessing taste perception. This study highlights the potential of fNIRS in sensory neuroscience, demonstrating its effectiveness in capturing the neural mechanisms underlying sweet taste perception.

This research paper investigated the hedging effectiveness, dynamic dependence, safe haven ability, and portfolio diversification benefits of alternative assets (precious metals) with conventional and Islamic stock indices. The data covered the period from March 2011 to March 2021, and a copula model with switching dependence was utilized. The estimated transition probabilities demonstrated a high persistence of the same dependence regime in all estimated pairs. The research found that the use of precious metals with stocks for diversification is a favorable choice, proving them to be the best suitable solution to avoid risks. They also provide a save haven effect along with diversification benefits and reveal that risk reduction benefits vary with respect to portfolio types. The findings of the research indicate that negative shocks prompt investors to seek refuge in safe haven assets to safeguard their investments from extreme negative shocks. For most of the situations studied, precious metals provide the best and most effective hedge to stock returns. Overall, the addition of alternative assets to the stock portfolio reduces risks and provides better diversification benefits. Results vary depending on the circumstances of the country. These findings have practical implications for policymakers, fund managers, investors, and risk management practitioners, guiding them in their investment and risk management decisions.

Super-Earth b and sub-Neptunes c and d are orbiting about the M-dwarf TOI-270 in that order from the star. Their global resonant chain (3:5, 1:1, 2:1) is extremely surprising because planet d appears to be the only known planet occupying the 2:1 resonant orbit without participating in a Laplace resonance (LR) or another planet intervening between the 1:1 and 2:1 orbits as in HD 110067. We do not believe that TOI-270 d is an exception to the empirical rule calling for 2:1 vacancy except in 1:2:4 LRs and Laplace-like 2:3:4 chains. Instead, a LR might exist in this system, and we searched (to no avail) the TESS light curves of TOI-270 for hints of an outer planet that would complete the LR chain. Alternative explanations would be an unknown planet more massive than planet c (Mc=6.20M⊕) establishing the actual 1:1 orbit, or planet b residing in the 1:2 Laplace orbit with a period shorter by 0.53 days. But these possibilities are ruled out by current data. This leaves only one other option to explore: the observed orbits could be in a stable 35:1:2 resonant chain. Preliminary calculations do not preclude this possibility that should be investigated further by numerical orbit integrations. To this end, we determine two potentially resonant angles, φ and φ^, related via the Laplace phase φL by φ^=φL+2φ. In contrast, HD 110067 is shown to have planets d-e-f in a Laplace-like 1:32:2 resonance with phase φ=2φL precisely.

A dysregulated immune response is associated with the excessive release of cytokines that can lead to systemic vasoplegia and vasoplegic shock with the development of multiorgan failure that is associated with an increased risk of dying. Under physiological circumstances, the endothelium and the glycocalyx are responsible for maintaining vascular tone, capillary permeability, hemostasis, and controlling inflammation. In hyperinflammation, the endothelium and glycocalyx become damaged due to the excessive production of certain toxic proteins, along with an overwhelming release of cytokines. It has been shown in both in vitro animal experiments and also in humans that extracorporeal hemoadsorption can reduce circulating levels of cytokines and may also remove toxic proteins that directly take part in endothelium and glycocalyx damage. The current review aims to summarize current knowledge and to put recent findings into context, and introduce the hypothesis of “endothelial protection with hemoadsorption” in critically ill patients.

The alarming rise of antibiotic resistance has made it imperative to find novel antimicrobial medications. This study reports the design and synthesis of new molecules based on benzofu-ran-pyrazole scaffolds hybridized with various N/O heterocycles using the molecular hybridiza-tion process. The newly synthesized candidates were confirmed using micro-analytical and spectral analyses, and their antimicrobial characteristics were assessed against different bacterial and fungal isolates in comparison with novobiocin and clotrimazole as antibacterial and anti-fungal standards, respectively. In addition, the new compounds were further evaluated as in vitro antioxidants and anti-inflammatory congeners. The most promising broad spectrum antimicrobial compounds 9, 10 with values ranging from 2.50-20.60 μg/mL were further examined as E. coli DNA gyrase B enzyme suppressors using novobiocin as a reference drug. In silico computational and ADMET studies were carried out for the most active compounds.

The adversarial robustness of image quality assessment (IQA) models to adversarial attacks is emerging as a critical issue. Adversarial training has been widely used to improve the robustness of neural networks to adversarial attacks, but little in-depth research has examined adversarial training as a way to improve IQA model robustness. This study introduces an enhanced adversarial training approach tailored to IQA models; it adjusts the perceptual quality scores of adversarial images during training to enhance the correlation between an IQA model’s quality and the subjective quality scores. We also propose a new method for comparing IQA model robustness by measuring the Integral Robustness Score; this method evaluates the IQA model resistance to a set of adversarial perturbations with different magnitudes. We used our adversarial training approach to increase the robustness of five IQA models. Additionally, we tested the robustness of adversarially trained IQA models to 16 adversarial attacks and conducted an empirical probabilistic estimation of this feature. The code is available at https://github.com/wianluna/metrics_at.

The aim of this research is to present both a sensorless control and a torque derating algorithm in the overload region of a permanent magnet motor for e-bike. First the theoretical backgrounds and the field oriented control are presented. Then a sensorless control, based on the back-emf estimation with a second order generalized integral flux observer for permanent magnet motor, has been designed. The second order generalized integral flux observer is an adaptive filter which can eliminate the DC offset and strongly attenuate the harmonics of the estimated rotor flux. The algorithms have been simulated and then validated by means of tests on a permanent magnet motor for e-bike.

Microgrids represent small-scale energy generation units with low cost-effectiveness, making use of renewable resources, such as photovoltaic and wind energy. These systems can operate both in connection with and independent from the primary power grid. The widespread adoption and expansion of microgrids are increasingly vital in light of the global shift towards minimizing fossil fuel consumption and promoting cleaner energy sources. Similar to conventional electrical networks, microgrids require Supervisory Control and Data Acquisition (SCADA) systems to ensure sustained and reliable operation. In support of the proliferation and growth of microgrid networks, this paper proposes the implementation of an open-source SCADA system. This system, being platform-independent, significantly reduces the costs associated with microgrid implementation, thus aligning with the principles of social responsibility. The proposed SCADA system will be deployed on a simulated microgrid, featuring both photovoltaic and wind energy sources, and will include a Phase-Locked Loop (PLL) synchronization system for grid connection. The simulation will be carried out using the Matlab-Simulink environment, leveraging the power systems library. The interface between Matlab and SCADA is done using an OPC Server. Additionally, the SCADA system will interface with an SQL database to facilitate the archiving of historical data.

Background: Pathological bone fracturing is an escalating problem driven by increasing aging and obesity. Bioceramics, particularly tricalcium-phosphate-based materials (TCP), are renowned for their exceptional biocompatibility, osteoconductivity, and ability to promote biomineralization. In the present study, we designed and characterized TCP porous granules doped with Strontium (Sr) and Copper (Cu). Sr2+ ions were selected as it plays a crucial role in early bone formation, osteogenesis and angiogenesis; Cu2+ ions possesses antibacterial properties. Materials: The synthesized CuSr TCP granules were characterized by X-Ray Diffraction. Cytotoxicity and cell proliferation analyses assays were performed through the Lactate dehydrogenase (LDH) activity and CCK-8 viability tests in rat bone marrow-derived mesenchymal stem cells (BM-MSCs). Hemolytic activity was carried out with human red blood cells (RBCs). Early and late osteogenesis was assessed with alkaline phosphatase (ALP) and Alizarin Red S activity in human osteoblast progenitor cells and rat BM-MSCs. The influence of Sr and Cu-substituted TCP on angiogenesis was investigated in human umbilical vein endothelial cells (HUVECs). Results: We have demonstrated that a media enriched with CuSr TCP in concentrations ranging from 0.1 mg/ml to 1 mg/ml was not cytotoxic and did not significantly affect cell proliferation rate motility. Moreover, a concentration of 0.5 mg/ml has shown a 2.5-fold increase in the migration potential of BM-MSCs. We also found that CuSr TCP-enriched media slightly increased early osteogenesis. We also found that Sr and Cu substitutions in TCP particles significantly enhanced the measured angiogenic parameters compared to control and unsubstituted TCP granules. Conclusion: Our results demonstrate that TCP porous granules doped with Sr and Cu are biocompatible, promote osteodifferentiation and angiogenesis, and could be recommended for further in vivo studies.

This paper examined the chemoselectivity and diastereoslectivity of silyl nitronate alkenyn-nitroethers in Intramolecular Silyl Nitronate Cycloadditions (ISNC) to produce isoxazole derivatives with interesting medicinal properties. These reactions resulted in the formation of either dihydrofuro[3,4-c]isoxazolines/isoxazolidines and/or alkynyl moieties attached to 2,5-dihydrofuryl carbonyls. It also discerned the diastereoselectivity of the resulting cyclic adducts and compared those to previous findings. The reactions were also investigated with Spartan molecular modeling computations to aide in the understanding of any displayed chemo- and/or stereoselectivity. These [3+2]-cycloaddition reactions demonstrated excellent to complete chemospecificity. The cycloadditions also demonstrated remarkable diastereospecificity in that each diastereomer of the nitroethers resulted in the formation of only one of four possible diastereomeric outcomes. The stereochemistry of the major diastereomers did not agree with previously published findings.

Mycoplasma galisepticum (MG) is one of the most significant pathogens of domestic poultry and turkeys, causing substantial economic losses, especially in the commercial poultry industry, resulting in reduced egg production and the condemnation of carcasses in Ethiopian abattoirs due to the lack of detection methods to design control strategies. The study was aimed to develop an in-house indirect ELISA kit that can detect antibodies against MG in chickens. Coated antigen was purified from a whole culture of M. galisepticum, and the protein content was estimated, followed by optimization of the antigen, serum, and conjugate dilutions. The kit was assessed with natural and experimentally prepared serum and compared with a commercial ELISA kit. This in-house indirect ELISA kit had a sensitivity of 93.7% and a specificity of 87.5% and showed no cross-reactivity with positive serum against other avian infections. The current ELISA kit is sensitive and specific and can be used for the detection of antibodies against MG in chickens.

This qualitative study investigates why dwell times, defined as the period between the emergence and detection of a cybersecurity threat, exceed 200 days. By uncovering these insights, U.S. InfoSec professionals can develop strategies to shorten dwell times and mitigate the costs associated with security breaches. The overarching issue is the escalating costs of U.S. cybersecurity breaches, which surged by 10% annually, surpassing $9.48 million per breach in 2023. The duration of dwell time, notably exceeding 200 days, is identified as a critical factor contributing to these costs yet remains poorly understood. This study employed a generic qualitative inquiry (GQI) methodology to explore perceptions surrounding dwell time and its impact on an organization's cybersecurity posture, making it pertinent to understanding and addressing cybersecurity breach dwell time. Through interviews with ten seasoned U.S. InfoSec professionals, this study sheds light on their perspectives regarding the duration between threat deployment and detection. The findings highlighted the importance of proactive measures and timely detection in mitigating cybersecurity risks and enhancing organizational resilience against malicious cyber-attacks.

Context: The earlier introduced compartmental description, well-known from the description of infection diseases and epidemics, was applied here to describe the nonlinear temporal evolution of the baryonic matter in interstellar gas and stars in the presence of triggered star formation. Aims: The competition of triggered star formation, spontaneous star formation, stellar feedback and stellar evolution was theoretically investigated to understand the baryonic matter cycle including luminous baryonic matter in main-sequence stars and weakly luminous matter in white dwarfs, neutron stars and black holes. Of particular interest was the understanding of the cosmic star formation history and the redshift dependence of the gas and stellar fractions using compartmental models. Methods: For stationary rates of spontaneous and triggered star formation, continuous stellar feedback and stellar evolution exact and approximate analytical solutions of the time evolution of the fractions of stellar and locked-in stellar matter were derived involving the time dependence of the gaseous fraction G(t). The high accuracy of the analytical solutions is proven by comparison with the exact numerical solutions of the GSL equations. Results: The inclusion of the triggered star formation process explains the observed cosmological star formation rate, the integrated stellar density at redshifts below z=8, and the present-day gas and stellar fractions very well. The generalized GSL-model provides excellent fits to the observed redshift dependencies of the star formation rate and the integrated stellar density. Moreover, it explains the observed present-day gas and stellar fractions, and it makes predictions on the future evolution of these fractions in the universe.

5th Generation (5G) cellular networks have been adopted around the world since the rollout began around 2019. It brought with it many innovations and new services, such as Enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency Communications (URLLC), and Massive Internet of Things (mIoT). 5G also introduced a more scalable approach to network operations using fully software-based Virtualized Network Functions (VNF) in Core Networks (CN) rather than the prior hardware-based approach. However, while this shift towards a fully software-based system design provides numerous significant benefits, such as increased interoperability, scalability, and cost-effectiveness, it also brought with it an increased cybersecurity risk. Security is crucial to maintaining trust between vendors, operators, and consumers. Cyberattacks are rapidly increasing in number and sophistication, and we are seeing a shift towards zero-trust approaches. This means that even communications between VNFs inside a 5G core have to be scrutinized and hardened against attacks, especially with the advent of quantum computers. The National Institute of Standards and Technology (NIST), over the past 10 years, has led efforts to standardize Post Quantum Cryptography (PQC) to protect against quantum attacks. This paper covers a custom implementation of the open-source free5GC CN, to expand its HTTPS capabilities for VNFs by introducing PQC Key Encapsulation Methods (KEM) for TLS v1.3. This paper provides the details of this integration with a focus on the latency of different PQC KEMs in initial handshakes between VNFs, on packet size, and the implications on a 5G environment. This work also conducts a security comparison between the PQC-equipped free5GC and other open-source 5G CNs. The presented results indicate a negligible increase in UE connection setup duration and a small increase in connection setup data requirements, strongly indicating that PQC KEM’s benefits far outweigh any downsides when integrated into 5G and 6G core services.

Obesity is a major public health concern globally, significantly increasing the risk of numerous metabolic and cardiovascular diseases. While lifestyle interventions remain the primary strategy for weight management, pharmacotherapy is increasingly recognized as an essential component for patients with obesity, especially when lifestyle modifications alone are insufficient. Glucagon-like peptide-1 (GLP-1) receptor agonists, originally developed for the management of type 2 diabetes, have emerged as effective agents in promoting weight loss in both diabetic and non-diabetic populations. This literature review examines the efficacy, safety, and mechanisms of action of GLP-1 receptor agonists, focusing on liraglutide and semaglutide, in obesity treatment. Furthermore, it discusses the impact of GLP-1 receptor agonists on obesity-related comorbidities, their comparison with other pharmacological treatments, and future perspectives.

In 2011, Korea installed artificial structures on four rivers to secure water resources and reduce flood damage; however, these structures have altered ecosystems and aquatic communities. This study analyzed fish communities and environmental variables at 72 sites in the Geumgang River. Fish communities and environmental variables before weir installation were examined using site data from 2008–2009. The results showed that Cyprinidae dominated the 70 observed species. A self-organizing map categorized the 72 sites into 4 groups based on fish communities. Sensitive and insectivorous species decreased, whereas tolerant and omnivorous species increased from Groups I to IV. Twenty-one indicator species were identified, with fewer and less distinct distribution patterns in Groups II and III than in Groups I and IV. The fish assessment index (FAI) showed a decline in grades A and B and an increase in grades C and D from Groups I to IV. Correlation analysis between FAI and environmental variables indicated that fish communities in the Geumgang River were mainly influenced by water quality, reflecting altitude gradients and pollution levels. This study’s findings are anticipated to significantly inform water management strategies for the Geumgang, Yeongsangang, Nakdonggang, and Hangang Rivers.

The aim of this study was to investigate the effect of these drugs on reproductive function in Rattus norvegicuss and to show the role of oxidative stress in possible reproductive dysfunction. Rattus norvegicuss were selected as experimental animals and were divided into healthy (control group), Clonidine (CL), Rilmenidine (RLD), Methyldopa (MTL), Amlodipine (ALD) and Ramipril (RML) groups. Using blood obtained from rattus norvegicuss, serum malondialdehyde (MDA) levels; The high-temperature thiobarbituric acid. Was based on the spectrophotometric measurement of the adsorption of the pink colored complex formed by MDA at a wavelength of 532 nm. Total glutathione (tGSH) levels were determined by spectrophotometer.According to the study, the increase in MDA levels was not statistically significant in CL and RLD groups when compared with the control group. MDA was significantly increased in methyldopa, amlodipine and Ramipril groups. While total glutathione levels were only close to the control group in the CL group; RLD, MTL, ALD and RML groups showed statistically significant decrease. While CL and RLD did not cause infertility, infertility was observed in the groups treated with MTL-ALD-RML. Thus, it was found that MTL, ALD and RML, antihypertensive drugs, which were the aim of the study, had different effects on infertility and it was found that the use of such drugs decreased fertility due to increasing oxidative stress and decreasing antioxidant levels.

Gold nanoparticles (NPs) are among the most commonly employed metal NPs in biological applications, with their distinctive physicochemical features. Their extraordinary optical properties, stemming from the strong localized surface plasmon resonance (LSPR), contribute to the devel-opment of novel approaches in the areas of bioimaging, biosensing, and cancer research, especially for photothermal and photodynamic therapy. The ease of functionalization with various lig-ands provides a novel approach to the precise delivery of these molecules to targeted areas. Gold NPs ability to transfer heat and electricity positions them as valuable materials for advancing thermal management and electronic systems. Moreover, their inherent characteristics, such as in-ertness, give rise to the synthesis of novel antibacterial and antioxidant agents as they provide a biocompatible and low-toxic approach. Chemical and physical synthesis methods are utilized to produce gold NPs. The pursuit of more ecologically sustainable and economically viable large-scale technologies, such as environmentally benign biological processes referred to as green/biological synthesis, has garnered increasing interest among global researchers. Green synthesis methods are favorable among other synthesis techniques as they minimize the necessity for hazardous chemicals in the reduction process due to their simplicity, cost-effectiveness, energy efficiency, and biocompatibility. This article discusses the importance of gold NPs, their optical and conductivity properties, antibacterial, antioxidant, and anticancer properties, synthesis methods, contemporary uses, and biosafety, emphasizing the need to understand toxicology principles and green commercialization strategies.

The agricultural sector is vital to the economies of many countries, particularly in developing country, providing livelihoods for millions and significantly contributing to GDP, food security, and social stability. Off-takers play a critical role in addressing these challenges by providing market certainty through predetermined purchase agreements. Engaging in contract farming and offering capital, technical assistance, and market access, off-takers help stabilize income, facilitate capital access, enhance technology adoption, and ensure business continuity. This research employs the Systematic Literature Review (SLR) method and secondary data analysis. Based on the review, the multifaceted benefits of agricultural off-takers include increased production and fostering entrepreneurial skills among farmers. Hence, fostering partnerships between farmers and off-takers is crucial for enhancing agricultural competitiveness, sustainability, and farmer well-being. Leveraging off-takers' potential and promoting collaboration within agricultural clusters can foster thriving agricultural ecosystems, ensuring food security and economic prosperity in rural communities.

Background/Objectives: A blood culture (BC) care bundle is aimed at decreasing BC contamination rate. This study examined prospectively the effect of a care bundle on BC contamination rates in a high workload ICU. Methods: We performed a before-after study in a general ICU, from January 2018 to May 2019. Blood culture sets were classified as positive, contaminated, indeterminate and negative. We used bivariate and interrupted time series analysis to assess the effect of the intervention and factors associated with contamination rates. Results: During the study, a total of 4236 BC vials were collected. BC Contamination rate decreased significantly after the intervention from 6.2% to 1.3%. Incidence rate of contaminated BC sets was significantly lower in the POST phase: 0.461 vs 0.154 BC sets per 100 ICU bed-days. Overall compliance with the BC care bundle increased from 3.4% to 96.9%. Conclusions: The care bundle was effective in reducing BC contamination and improving several quality indicators in our setting. The indeterminate BC rate is an important problem, and we suggest that it should be included in BC quality indicators. : The BC care bundle implementation was effective in reducing BC contamination rate, although its long-term effect was not assessed.

Objectives: In Nigeria, modern contraceptive use remains relatively low despite numerous efforts to increase awareness and accessibility. This study examined the geopolitical factors influencing modern contraceptive use in North-central Nigeria Design: Using data from the 2021 Nigeria Multiple Indicator Cluster Survey, this study quantitatively investigated the factors influencing modern contraceptive use among women of reproductive age. The data were analyzed using R studio. Respondents' characteristics were described using frequency and percentage. A logistic regression model was employed to identify factors associated with modern contraceptive usage in the north-central region of Nigeria. Bivariate logistic regression was carried out between the explanatory and response variables. Settings: This study focused specifically on the north-central zone of Nigeria comprising 6 states, namely, Benue, Kogi, Kwara, Nasarawa, Niger, and Plateau states with the Federal Capital Territory (FCT). Results: Nearly one-third (30.1%) of the respondents are above 35 years of age. 3 out of every 5 (61.2%) respondents are currently married. This study found that the average modern contraceptive usage among women of reproductive age in north Central Nigeria is less than 15%. Age, education, childbirth delivery method, number of children, access to media, and state of residence are key drivers of modern contraceptive adoption and utilization among women of reproductive age in north-central Nigeria. Conclusion: This study finds it plausible to increase the awareness and sensitization of modern contraceptives and contraceptive usage in this region. There is a need for health education on the benefits and relevance of modern contraceptive usage.

Antibody-drug conjugates (ADCs) have revolutionized the treatment landscape for metastatic breast cancer (MBC), offering targeted delivery of cytotoxic agents with improved efficacy and tolerability compared to conventional chemotherapy. This narrative review explores key predictive factors influencing the efficacy of ADCs, focusing on HER2-targeted therapies such as trastuzumab emtansine (T-DM1) and trastuzumab deruxtecan (T-DXd), as well as sacituzumab govitecan for triple-negative breast cancer (TNBC). HER2 expression, TROP-2 levels, hormone receptor (HR) status, and the tumor microenvironment emerge as critical biomarkers for patient selection and therapeutic outcomes. Additionally, we discuss resistance mechanisms, such as antigen loss, impaired drug internalization, and the role of circulating tumor DNA (ctDNA) in predicting ADC response. Finally, future perspectives on the sequential use of ADCs and potential combination therapies are highlighted, along with emerging agents targeting alternative antigens like HER3 and LIV-1. Overall, identifying predictive biomarkers and overcoming resistance mechanisms are essential for optimizing the use of ADCs in MBC, thereby improving patient outcomes.

A synthetic jet actuator (SJA) is a fluidic device often consisting of a vibrating diaphragm that alters the volume of a cavity to produce a synthesized jet through an orifice. The cyclic ingestion and expulsion of the working fluid leads to a zero-net mass-flux and the transfer of linear momentum to the working fluid over an actuation cycle, leaving a train of vortex structures propagating away from the orifice. SJAs are a promising technology for flow control applications due to their unique features, such as no external fluid supply or ducting requirements, short response time, low weight, and compactness. Hence, they have been the focus of many research studies over the past few decades. Despite these advantages, implementing an effective control scheme using SJAs is quite challenging due to the large parameter space involving several geometrical and operational variables. This article aims to explain the working mechanism of SJAs and provide a comprehensive review of the effects of SJA design parameters in quiescent conditions and crossflow.

Autonomous Underwater Vehicles (AUVs) are highly efficient tools for underwater exploration, capable of carrying various payloads to perform specific missions as needed. To ensure the success of these missions and the spatiotemporal accuracy of sampled data, integrated navigation systems and appropriate filtering algorithms are essential. These systems process and merge motion data to provide accurate positioning information. The most commonly used algorithms in integrated navigation systems are Kalman filter and its nonlinear derivatives. Of these, the ensemble Kalman filter (EnKF) is particularly notable for its ability to handle nonlinear systems by incorporating Monte Carlo methods. This study proposes an enhanced adaptive EnKF algorithm to improve the smoothness and accuracy of the filtering process. Instead of the conventional Gaussian distribution, this algorithm employs a Laplace distribution to construct the system state vector and observation vector ensembles, enhancing stability against non-Gaussian noise. Additionally, the algorithm dynamically adjusts the number of vector members in the ensemble using adaptive mechanism by specifying thresholds during filtering, to adapt the requirements of real-world observational settings. Using measured data from field trials, including DVL, GPS, and electronic compass data, we examine the effects of various parameter settings on the algorithm's performance. Optimal parameter settings are identified to fine-tune and verify the filtering algorithm as well as its adaptive capability. The results demonstrate that the enhanced adaptive EnKF algorithm exhibits superior filtering performance in terms of both accuracy and smoothness compared to the conventional EnKF and EKF algorithms. This indicates the significant advantages of our proposed algorithm for AUV navigation.

Summary Introduction: The psoas muscle index (PMI) and the skeletal mass index (SMI), as determined by computed tomography (CT), are the reference methods for assessing muscle mass. An alternative method is to measure the area of the rectus femoris muscle using ultrasonography (US).Objective : The objective of this study is twofold: firstly, to assess whether ultrasound imaging of the rectus femoris has an adequate correlation with psoas muscle index (PMI) and skeletal mass index (SMI) measurements obtained from computed tomography (CT) scans of oncosurgical patients at the L3 level; and secondly, to establish a cut-off point for diagnosing low muscle mass using this ultrasound technique. Material and methods: A retrospective cross-sectional observational study was carried out on oncosurgical patients referred to the nutrition consultation of the General University Hospital of Castellón over a four-year period. Malnutrition was diagnosed according to the GLIM criteria, muscle mass was assessed by ultrasound using a Mindray device, and skeletal muscle index (SMI) and appendicular skeletal muscle mass (PMI) were measured by computed tomography at the third lumbar vertebra (L3). Patients lacking a CT scan or with a CT scan performed more than one month after the ultrasound examination were excluded. Results: The study cohort comprised 43 patients (31 males), with a mean age of 64.7 ± 6.72 years, a mean BMI of 23.7 ± 4.31 kg/m², and a mortality rate of 25.6%. Most of the evaluations were conducted post-surgically (65.1%), with a notable prevalence of radical cystectomy and hepatic surgery. A total of 53 ultrasound evaluations were conducted, with an average area of 2.96 cm² [2.47-4.2] and adipose tissue of 0.55 cm [0.1-0.99] *. Additionally, computed tomography (CT) scans were performed to assess skeletal muscle mass (SMM) using the SMI/height method, yielding an average area of 38.4 cm² [31.6- 54.7)The median values for the variables were as follows: 272 [207-390] HU,PMI area of 4.31 m² [3.71-5.94] * and 86 [69-103] * HU density in PMI. All data were expressed as median and interquartile range. The Pearson correlation coefficients between US and SMI/height, SMI/height and PMI/height, and US and PMI/height were 0.70, 0.750, and 0.548, respectively. All these coefficients were statistically significant (p < 0.001). Following the established criteria for low muscle mass for SMI (1) and PMI (2), a ROC curve analysis was conducted on US, with a cutoff point for low muscle mass of 3.6 cm² , AUC 0.770 with 70% and specificity 100%.Respect PMI , the cut-off was 3.29 cm 2 and the area under the curve was 0.609, with a sensitivity of 42.55% and a specificity of 100%. Conclusions: The results of this study indicate that there is a significant correlation between SMI, PMI, and the area in cm² measured by US in the rectus femoris according to the protocol of García-Almeida et al. (3). The positive correlation between ultrasound and computed tomography (CT) imaging in our sample, which yielded comparable results to those reported by (4), suggests that ultrasonography may serve as a viable alternative to CT for the detection of low muscle mass. This study is the first to establish a cutoff point for ultrasonography in comparison to SMI and PMI. Further studies are required, utilising a semi-automatic method to circumvent measurement bias and encompassing a larger patient cohort, in order to detect cutoff points pre- and post-surgery for cystectomy and hepatic surgery, as well as cutoff points by sex.

Objectives: Persistent symptoms following acute COVID-19 infection, collectively termed Long COVID syndrome, have been increasingly reported. However, data on the prevalence and characteristics of Long COVID remain limited. This study aimed to evaluate the occurrence and presentation of Long COVID syndrome in individuals in-fected with COVID-19 and monitored in outpatient, ward, or intensive care settings. Methods: Patients diagnosed with COVID-19 and followed up at the Department of In-fectious Diseases and Clinical Microbiology, Çukurova University Faculty of Medicine, for a minimum of four weeks post-infection were included in this study. Demographic data, including age, gender, and comorbidities, were recorded. Patients were followed up for 12 months, with symptom assessment at baseline and 1, 3, 6, and 12 months post-infection. Functional status was evaluated using the Post-COVID-19 Functional Status (PCFS) scale, and quality of life was assessed using the SF-36 Health Survey question-naire. Results: 134 patients (71 males, 54.2%) participated in the study, with a mean age of 43.7±15.3 years (median: 45, range: 18-83). The distribution of patients across care settings was as follows: 51.1% outpatient, 35.9% ward, and 13% intensive care unit (ICU). Fatigue during exertion was the most frequently reported Long COVID symptom across all follow-up timepoints. Patients treated in the ICU experienced a higher burden of Long COVID symptoms compared to those in outpatient or ward settings. Furthermore, the PCFS scale revealed significantly poorer quality of life and functional status in patients treated in the ICU and wards than those receiving outpatient care. Conclusions: Long COVID syndrome poses a significant ongoing health concern, particularly for patients requiring intensive care during acute COVID-19 infection. Our findings underscore the importance of vigilant monitoring for long-term sequelae, particularly concerning quality of life and functional status, in all individuals recovering from SARS-CoV-2 infection.

Objective: The general surgery match process has changed with the transition to virtual interviews, the use of signaling, and the loss of the USMLE1 scoring. We undertook this study to describe the current state of the surgery match.Design: Describe changes in applicant behavior and characteristics between 2020-2024 and perform a multivariable analysis to measure applicant characteristics that predict obtaining an interview invite.Setting: Applicant-moderated, public database with voluntarily completed survey.Participants: 1117 applicants to general surgery residencyResults: Over the time period, applications submitted per student increased by 32%, from 78.0±3.8 in 2020 to 103±3.2 for all and 26% excluding international medical students (IMGs). The average number of applications submitted by student type - US allopathic (USMD), osteopathic (DO), and IMG - were 75±1.5, 105±4.3, and 159±6.3, respectively. The number of interview invitations (IIs) for US graduates decreased by 31%, from an average of 17.4±0.83 to 13.3±0.60 (p<0.0001). 33% of US applicants received more than 18 IIs. Controlling for all other factors, IIs per applicant decreased (-3.5; p<0.0001), and predictors of the number of IIs included USMLE1 (0.15±0.4), USMLE2 (0.17±0.4) scores, student type and higher class quartile rank (p=0.004), lack of gap years (4.7±2.2, p=0.03), AOA membership (3.1±1.0 p=0.003) and graduating from a top-tiered medical school (2.1, p=0.05). In 2024, USMLE2 had the same predictive weight as the combination of scores compared to the prior years (p<0.0001). Signaled program’s II yield was 68±2%.Conclusions: Over the last five years, substantially more applications per student were submitted, while fewer IIs were received, potentially resulting in poor optimization of candidates matching to programs. Loss of USMLE1 scores made no direct difference in the number of IIs.

Evolving or "re-emerging" viral deviation remains to pretense substantial worldwide public health threats. Influenza A virus (IAV), the main contributor to respiratory infections and related death in humans, is a prime example. Universally well-known as ‘flu’, it has disturbed our daily breathing, leaving a breakthrough till now. This virus can transmit from animals to humans and familiarize human hosts, resulting in ongoing transmission and the rise of novel viral strains. The processes through which viruses evolve within one host, cause animal-to-human transmission, and acclimatize to different host species are not yet fully understood. This review scans influenza antiviral resistance, highlighting the fundamental concepts behind its development and clinical impact. Influenza A represents a significant burden on healthcare systems and communities. While antiviral drugs are crucial for managing influenza effectively, healthcare providers and policymakers may need to be cautious due to the potential for antiviral-resistant strains to develop. Adamantanes and neuraminidase inhibitors targeting the viral components of the M2 ion channel and NA have been approved to treat IAV. However, the ongoing dissemination of drug-resistant virus variants due to mutation of these viral components poses a challenge and could undermine the effectiveness of primary antiviral therapies. Therefore, this review provides the susceptibility status of IAV subtypes to currently available antiviral drugs, factors to anti-IAV resistance, and soon-to-be-available control measures to emerging drug resistance IAV.

Obesity is a significant metabolic disorder associated with excessive fat accumu-lation and insulin resistance. In this study, we explored a gene therapy approach to treat obesity in agouti mice using adeno-associated viruses (AAVs) carrying PRDM16, FoxP4, or Follistatin (FST) genes, which are known to promote the browning of white adipose tissue. Mice treated with AAVs encoding PRDM16, FoxP4, or FST genes showed a reduction in body weight (9-12%) within the first three weeks after admin-istration, compared to control groups. Lipidomic analysis of adipose tissue revealed a dramatic reduction in triacylglycerol (TAG) species with low carbon number (40-54 acyl carbones) in treated mice.

In this study, we explore the impact of stochastic resetting on the dynamics of random walks on a T-fractal network. By employing the generating function technique, we establish a recursive relation between the generating function of the first passage time (FPT) and derive a relationship between the mean first passage time (MFPT) with resetting and the generating function of the FPT without resetting. Our analysis covers various scenarios for a random walker reaching a target site from the starting position, and for each case, we determine the optimal resetting probability γ* that minimizes the MFPT. We compare the results with the MFPT without resetting and find that the inclusion of resetting significantly enhances search efficiency, particularly as the size of the network increases. Our findings highlight the potential of stochastic resetting as an effective strategy for optimizing search processes in complex networks, offering valuable insights for applications in various fields where efficient search strategies are crucial.

Semen is a complex fluid that contains spermatozoa and also functions as a dynamic bacterial microecosystem, comprising probiotics, pathogenic bacteria, and natural antimicrobial substances. Probiotic bacteria, such as Lactobacillus and Bifidobacterium, along with pathogenic bacteria like Pseudomonas aeruginosa and Escherichia coli, play significant roles in semen preservation and reproductive health. Studies have explored the impact of pathogenic bacteria on sperm quality, providing insights into the bacterial populations in mammalian semen and their influence on sperm function. These reviews highlight the delicate balance between beneficial and harmful bacteria, alongside the role of natural antimicrobial substances that help maintain this equilibrium. Moreover, we discus the presence and roles of antimicrobial substances in semen, such as lysozyme, secretory leukocyte peptidase inhibitors, lactoferrin, and antimicrobial peptides, as well as emerging antibacterial substances like amyloid proteins. Understanding the interactions among probiotics, pathogens, and antimicrobial agents is crucial for elucidating semen preservation and fertility mechanisms. Additionally, the potential for adding probiotic bacteria with recombinant antibacterial properties presents a promising avenue for the development of new semen extenders. This review offers updated insights to understand the equilibrium of bacterial microecosystem in semen and points toward innovative approaches for improving semen preservation.

Data privacy is a critical concern in health informatics, where the management of sensitive patient information requires robust and secure database systems. This study aims to explore the data privacy challenges faced in health informatics and conduct a comparative analysis of popular Database Management Systems (DBMS), including MySQL, MongoDB, PostgreSQL, and Oracle, with a focus on their effectiveness in safeguarding patient data. Through a review of existing literature, case studies, and interviews with database administrators, the research identifies common privacy threats such as data breaches and unauthorized access. It examines the security features of each DBMS, such as encryption methods, access controls, and user authentication mechanisms. The findings reveal variations in the privacy protection capabilities of different DBMS, highlighting the trade-offs between security, performance, and usability. The study provides recommendations for healthcare organizations in selecting a DBMS that balances privacy with operational needs and offers insights for policymakers to improve regulatory frameworks in health data security. This research contributes to the ongoing discourse on enhancing data privacy in health informatics and suggests directions for future studies to integrate emerging technologies for more secure health data management.

Chagas disease, caused by Trypanosoma cruzi, presents a significant challenge in global health due to the parasite's complex life cycle and its persistence in human tissues. In contrast, advances in treating Trypanosoma brucei infections, notably through the use of irreversible enzyme inhibitors like eflornithine, offer valuable insights into therapeutic strategies. This article explores how artificial intelligence (AI) can aid in identifying crucial metabolic and biochemical pathways in T. cruzi that could serve as targets for irreversible enzymatic inhibitors, drawing parallels to the successful inhibition of T. brucei’s ornithine decarboxylase. By employing AI for data mining, drug-target interaction prediction, pathway modelling, and drug repurposing, researchers can accelerate the discovery of novel treatments. We compare the biological and biochemical differences between T. cruzi and T. brucei, highlighting how AI can bridge the gap in drug discovery and offer new possibilities for Chagas disease treatment.

This article study was developed based on the relationship between access to justice and the instrumentality of the legal process, aiming to assess the effectiveness of the constitutional principle enshrined in Article 5, item XXXV: “the law shall not exclude any injury or threat to a right from the consideration of the Judiciary” (BRAZIL, 2008, p.27). To achieve this goal, the concept of justice was examined, as well as the definition and evolution of access to justice within legal science. In light of these concepts, it became possible to establish a connection between access to justice and the instrumentality of the legal process. This analysis reveals that the State has not yet achieved sufficient efficiency in providing support to its less favored citizens in facilitating access to justice.

Recent findings indicate that human microbiota can excrete trace amines, dopamine, and serotonin. These neurotransmitters (NTs) can either affect classical neurotransmitter signaling or directly trigger trace amine-associated receptors (TAARs), with still unclear consequences for host physiology. Compared to gut microbiota, less information is available on the role of skin microbiota in NT production. To explore this, 1909 skin isolates, mainly from the genera Staphylococcus, Bacillus, and Corynebacterium, were tested for NT production. Only 6.7% of the isolates were capable of producing NTs, all of which belonged to the Staphylococcus genus. Based on substrate specificity, we identified two distinct profiles among the NT producers. One group primarily produced tryptamine (TRY) and phenylethylamine (PEA), while the other mainly produced tyramine (TYM) and dopamine (Dopa). These differing production profiles could be attributed to the activity of two distinct aromatic amino acid decarboxylase enzymes, SadA and TDC, responsible for generating the TRY/PEA and TYM/Dopa product spectra, respectively. SadA and TDC orthologues differ in structure and size; SadA has approximately 475 amino acids, whereas the TDC-type consists of about 620 amino acids. The genomic localization of the respective genes also varies: tdc genes are typically found in small, conserved gene clusters, while sadA genes are not. Heterologous expression of sadA and tdc in Escherichia coli yielded the same product spectrum as the parent strains. The possible effects of skin microbiota-derived NTs on neuroreceptor signaling in the human host remain to be investigated.

The quest for effective cancer treatments has driven transformative strategies in drug discovery, and biosensors have emerged as indispensable tools, renowned for their precision, sensitivity, and real-time monitoring capabilities. The review begins with a brief overview of cancer drug discovery, underscoring the pivotal role of biosensors in advancing cancer research. Various types of biosensors employed in cancer drug discovery, including fluorescence- and bioluminescence-based biosensors, are then explored. Emphasis is placed on the integration of biosensors in high throughput screening (HTS) and validation for cancer drug compounds, elucidating their pivotal role in accelerating the identification of potential therapeutics. Real-time monitoring of cellular responses through biosensors yields invaluable insights into cancer cell signaling pathways, drug efficacy, and resistance mechanisms. The review illuminates how biosensors contribute to unraveling the dynamic nature of cancer cells, thereby facilitating the development of more targeted and personalized therapies. Challenges linked to biosensor applications in cancer drug discovery are addressed, alongside a discussion of emerging trends and future perspectives. In conclusion, this review offers a comprehensive analysis of biosensor applications in the dynamic field of cancer drug discovery. The integration of biosensor technologies holds the promise of revolutionizing the discipline, presenting novel avenues for the development of targeted and personalized cancer therapies.

Fogging in automotive headlamps is a significant issue that affects both aesthetics and functionality. This study investigates the use of graphene-based nanocomposites to mitigate fogging by enhancing the hygroscopic properties of Polybutylene Terephthalate/Acrylonitrile Styrene Acrylate (PBT/ASA) composites commonly used in headlamps. The incorporation of functional-ized graphene improved the tensile and flexural strength of the nanocomposites, though it led to a reduction in elongation and melt flow. Additionally, the solid lubrication properties and increased surface hardness of the graphene contributed to enhanced wear resistance. The presence of gra-phene in the nanocomposites also reduced moisture diffusion, lowering the rates of both hygro-scopic and desorption when compared to commercial PBT/ASA composites. Furthermore, the nanocomposites exhibited a reduction in maximum moisture uptake. These improvements are expected to reduce the absolute humidity inside the headlamp, thereby effectively mitigating the fogging issue.

This study examines the impact of the environmental protection tax (EPT) on the liquidity and green technological innovation of listed companies in China from 2013 to 2023. Using a difference-in-differences (DID) approach, we analyze the short-term financial consequences of the EPT, focusing on liquidity ratios and at the same time its effects on innovation through investments in research and development are examined. The results suggest that the EPT significantly reduces corporate liquidity, particularly in industries with high environmental compliance costs, thereby placing financial pressure on firms. Although the EPT stimulates innovation, the inherent time lag in realizing financial returns from R&amp;D investments exacerbates short-term liquidity problems. This research offers important insights for policymakers seeking to balance sustainability goals with corporate financial health.

This study developed and validated a robust UPLC-MS/MS method for quantifying cyclic glycine-proline (cGP) in mangrove -derived Penicillium and Aspergillus strains. The method demonstrated excellent linearity, precision, and recovery, with detection limits as low as 4.8 ng/mL. When applied to fungal extracts, Penicillium pedernalense achieved 67.45 1.11 ng/mL (yielding 29.31 0.61 mg/L), surpassing Penicillium steckii at 31.71 0.31 ng/mL (yielding 8.51 0.15 mg/L). This quantitative approach for metabolite analysis provides a viable method for screening these fungal strains, highlighting their potential for sustainable production of cyclic glycine-proline (cGP).

: Rhynchocypris kumgangensis, endemic to Korea, is limited to the upper Hangang River and Gucheondongcheon Stream. As a key and climate-sensitive biological indicator species in Deogyusan National Park, it has a southern range limit in the Gucheondong Valley. Due to climate change, notable shifts in the activity, vertical distribution, and population of R. kumgangensis in the Gucheondongcheon Stream are expected. Owing to the rapidly changing climate, significant changes in the activity, vertical distribution, and population size of R. kumgangensis in the Gucheondongcheon Stream are anticipated, and continuous surveys of the distribution of R. kumgangensis in this river area have been conducted. This study assessed the habitat of R. kumgangensis at 13 sites, including five in the Gucheondongcheon Stream, from July to October 2020 and compared the results with historical data to evaluate the impacts of climate change. At the five Gucheondongcheon Stream sites, 102 individuals were recorded from April to October 2020, and 378 were recorded between June 2018 and July 2019. Additionally, the subdominant species shifted from R. kumgangensis (2014–2018) to Zacco koreanus (2020). In the two southernmost Gucheondongcheon Stream sites, only 18 R. kumgangensis were observed in 2020 compared to 82 in 2019, indicating a significant population decline.

Keywords: Rehabilitation robot, position-force control, feed-forward control.

This study aims to address the inefficiencies and time-consuming nature of traditional hot-air anti-icing system designs by introducing reduced order models (ROM) and machine learning techniques to predict anti-icing surface temperature distributions. This study compares several classic neural networks, ultimately proposing two models: POD-AlexNet and multi-CNNs with GRU (MCG). Design variables of the hot-air anti-icing cavity are used as inputs, and the corresponding surface temperature distribution data serve as outputs. The performance of these models is evaluated on the test set. The POD-AlexNet model achieves a mean prediction accuracy of over 95%, while the MCG model reaches 96.97%. Both models significantly outperform traditional numerical simulation methods, delivering faster predictions. The proposed models achieve fast prediction of anti-icing surface temperature distribution while ensuring acceptable prediction accuracy. These models greatly enhance the prediction efficiency over existing numerical simulation approaches, contributing to the design of aircraft hot-air anti-icing systems based on optimization methods such as genetic algorithms.

Water scarcity persists to be a pressing health concern globally and rural South Africa is not exempted. It exacerbates exposure to waterborne pathogens, resulting in substantial negative health consequences. Despite access to clean water being a constitutional right in South Africa, water scarcity remains a formidable challenge contributing to poor hygiene and sanitation in the country. This qualitative study investigated the factors contributing to water scarcity and the coping mechanisms employed by rural communities in KwaZulu-Natal. Semi-structured interviews and focus group discussions were conducted to collect data. The findings revealed infrastructural challenges, political inaction, climate change and population growth as key determinants of water scarcity in uMkhanyakude. Community members primarily relied on greywater reuse and storing of water in multiple containers to mitigate the effects of water scarcity. It is essential for South African government to prioritize initiatives addressing water scarcity in neglected rural areas to ensure equitable access to clean water and to mitigate the associated health risks.