Path planning for robots based on reinforcement learning encounters challenges in integrating semantic information about environments into the training process. In those unseen or complex environmental information, agents often perform sub-optimally and require more training time. In response to these challenges, this manuscript pioneers a framework integrating zero-shot learning combined with hierarchical reinforcement learning to enhance agent decision-making in complex environments. Zero-shot learning enables agents to infer correct actions for previously unseen objects or situations based on learned semantic associations. Subsequently, the path planning component utilizes hierarchical reinforcement learning with adaptive replay buffer, directed by the insights gained from zero-shot learning, to make decisions effectively. Two parts are trained separately, so zero-shot learning is available in different and unseen environments. Through simulation experiments, the proposed method proves that this structure can make full use of environmental information to generalize across unseen environments and plan collision-free paths.

This study explored market actors’ perspectives on data sharing within Poland’s food passports pilot, which tested the feasibility of a digital system that provides reliable and comprehensive information on the origin, journey, and quality of agri-food products. Using thematic analysis, we identified factors influencing stakeholders’ willingness and motivations to share data. Our findings highlight the necessity of a clear value proposition for all actors within the production and distribution chain to invest resources and time in additional data exchange systems. For farmers, reducing burdensome reporting procedures and providing direct financial incentives were key motivators. Agri-food processing, transport and packaging companies viewed data collaboration as a practical tool to ensure high-quality raw materials and promote premium-priced food. Appropriate data management policies were critical for all stakeholders. While sustainability was recognized as important, opportunities for collaboration going beyond economic considerations were not widely explored. Additionally, concerns about data interpretation emerged as a significant issue. These findings suggest the need for data-sharing strategies that better align economic benefits with broader sustainability goals. Further research is also needed to examine how concerns about potential data misinterpretation limit stakeholders’ willingness to engage in data sharing initiatives.

Background: Conventional audiometry assesses hearing in the frequency range of 250–8,000 Hz, while humans can detect sounds from 20–20,000 Hz. Hearing sensitivity above 8,000 Hz can be assessed through “Extended high-frequency audiometry (EHFA)”, which is useful for early detection and monitoring of hearing loss and counseling for tinnitus patients. Despite its usefulness, age significantly affects hearing sensitivity. Currently, there are no standard hearing thresholds in the EHF range categorized by age groups in Thailand. This study aimed to determine normative EHFA data across different age groups in a healthy Thai population. Methods: The study included 134 participants aged 18–70 years, categorized into five age groups. All participants had normal hearing thresholds and type A tympanograms bilaterally. Following this, EHFA measurement was performed. Results: Across all age groups, there was an observed increase in median hearing thresholds as age increased. In comparison, no significant difference in median hearing thresholds was found between individuals aged 18–30 and >30–40 years. However, those over 40 years had significantly higher median thresholds across all frequencies compared to those under 40 years (p40–70 years. Median hearing thresholds for those aged 18–40 was ≤20 dB HL across most frequencies, except at 16,000 Hz (30 dB HL). In the >40–70 age group, thresholds ranged from 20 dB HL at 9,000 Hz to 70 dB HL at 14,000 and 16,000 Hz. However, no significant differences were found in hearing thresholds between sexes and ear sides. Conclusions: This study establishes normative EHFA data for healthy Thai adults, appropriately divided into two groups (18-40 and >40-70 years) for clinical use. Further studies are needed to assess EHFA thresholds in populations with hearing loss-related risk factors.

Objective: This study aimed to explore longitudinal relationships between neurophysiological biomarkers and upper limb motor function recovery in stroke patients, focusing on electroencephalography (EEG) and transcranial magnetic stimulation (TMS) metrics. Methods: This longitudinal cohort study analyzed neurophysiological, clinical, and demographic data from 102 stroke patients enrolled in the DEFINE cohort. We investigated the associations between baseline and post-intervention changes in EEG Theta/Alpha Ratio (TAR) and TMS metrics with upper limb motor functionality, assessed by five outcomes: Fugl-Meyer Assessment (FMA), Handgrip Strength Test (HST), Pinch Strength Test (PST), Finger Tapping Test (FTT), and Nine-Hole Peg Test (9HPT). Results: Our multivariate models identified that a higher baseline TAR in the lesioned hemisphere was consistently associated with poorer motor outcomes across all five assessments. Conversely, a higher improvement in TAR was positively associated with improvements in FMA and 9HPT. Additionally, increased TMS Motor Evoked Potential (MEP) amplitude in the non-lesioned hemisphere correlated with greater FMA-diff, while lower TMS Intracortical Inhibition (ICI) in the non-lesioned hemisphere was linked to better PST improvements. These findings suggest the potential of TAR and TMS metrics as biomarkers for predicting motor recovery in stroke patients. Conclusion: Our findings highlight the significance of TAR in the lesioned hemisphere as a predictor of motor function recovery post-stroke and also a potential signature for compensatory oscillations. The observed relationships between TAR and motor improvements, as well as the associations with TMS metrics, underscore the potential of these neurophysiological measures in guiding personalized rehabilitation strategies for stroke patients.

The incidence of melanoma, the most lethal form of skin cancer, has increased due to ultraviolet exposure. The molecular characterization of melanomas has shown a high mutational burden led to the identification of some recurrent genetic alterations. BRAF gene is mutated in 40-50% of melanomas and its role in melanoma development is paramount. BRAF mutations confer constitutive activation of MAPK signalling. The large majority (abou 90%) of BRAF mutations occur at amino acid 600; the majority are BRAFV600E mutations and less frequently BRAFv600K, V600D and V600M. The introduction of drugs that directly target BRAF-mutant protein (BRAF inhibitors) and of agents that stimulate immune response through targeting of immune check inhibitor consistently improved the survival of melanoma BRAFV600-mutant patients with unresectable/metastatic disease. In parallel, studies in melanoma stage II-III patients with resectable disease have shown that adjuvant therapy with ICIs and/or targeted therapy improves PFS and RFS, but not OS compared to placebo; however, neoadjuvant therapy plus adjuvant therapy improved therapeutic response compared to adjuvant therapy alone.

This paper presents a novel machine learning framework that applies evolutionary ensembles of artificial agents to mineralogical analysis and classification. The approach is based on hybridiza-tion techniques that combine diverse machine learning algorithms, creating large and effective communities of agents. These progressively mute and improve through crossover, hybridization, and selection, addressing the challenges of mineral recognition and classification from thin section images. By combining multiple machine learning techniques, the ensemble of agents autono-mously improves by evolving to adapt and enhance its ability to identify mineral species and classify different types of alterations. We detail the method, provide examples using synthetic and real data, and explore the potential to improve mineralogical analysis workflows through this dynamic, self-improving system.

The production of recombinant proteins in Escherichia coli is often challenged by cytoplasmic expression due to proteolytic degradation and inclusion body formation. Extracellular expression can overcome these problems by simplifying downstream processing and improving protein yields. This study aims to compare the efficiency of two Bacillus subtilis chitosanase signal peptides in mediating extracellular secretion in E. coli. We identified a naturally occurring mutant signal peptide (mCsn2-SP) from B. subtilis CH2 chitosanase (CH2CSN), which is characterized by a deletion of six amino acids in the N-region relative to the signal peptide (Csn1-SP) from B. subtilis CH1 chitosanase (CH1CSN). The CH1CSN and CH2CSN genes were cloned into the pET-11a vector and protein secretion was evaluated in E. coli BL21(DE3) host cells. Expression was induced with 0.1 mM and 1 mM isopropyl β-D-1-thiogalactopyranoside (IPTG) at 30°C for one and three days. CH2CSN showed higher secretion levels compared to CH1CSN under all experimental conditions, especially with 0.1 mM IPTG induction for 3 days, which resulted in a 2.37-fold increase in secretion. Furthermore, it was demonstrated that mCsn2-SP is capable of secreting human Cu,Zn-superoxide dismutase (hSOD) in E. coli BL21(DE3) and successfully translocating it to the periplasmic region. This study represents the inaugural investigation into the utilisation of a naturally modified signal peptide, thereby corroborating the assertion that signal peptide deletion variants can influence protein secretion efficiency. Furthermore, the findings substantiate the proposition that such variants can serve as a viable alternative for the secretion of heterologous proteins in E. coli.

Digital Twins’ design frequently incorporates machine learning and, more recently, deep reinforcement learning techniques in order to interpret data and forecast future outcomes based on incoming data. However, because neural networks are typically considered a ”black box” model, doubts over the reliability of the output from deep learning models continue. In our work, we developed crop rotation policies using explainable tabular reinforcement learning techniques. We created five-step rotations, or a succession of five successive crops, to compare these policies to those produced by a deep Q-learning technique. The purpose of the rotations is to maximize crop yields while upholding prescribed planting guidelines and preserving a healthy amount of nitrogen in the soil. We incorporated phenomena related to weather and price fluctuations into the reward signal to account for the potential impact of external factors on crop production. The deployed explainable tabular reinforcement learning methods outperform the deep Q-learning approach regarding collected rewards when the rewards are not perturbed. For the perturbed case, robust tabular reinforcement learning methods outperform the deep learning approach even more while maintaining human interpretable policies. By consulting with farmers and crop rotation experts, we demonstrate that the derived policies are reasonable to use and more resilient towards external perturbations. Furthermore, the use of interpretable and explainable reinforcement learning techniques increases confidence in resulting policies, thereby increasing the likelihood that farmers will adopt the suggested policies. Digital Twins are often intertwined with machine learning and, more recently, deep reinforcement learning methods in their architecture to process data and predict future outcomes based on input data. However, concerns about the trustworthiness of the output from deep learning models persist due to neural networks generally being regarded as a black box model. In our work, we developed crop rotation policies using explainable tabular reinforcement learning techniques. We compared these policies to those generated by a deep Q-learning approach by generating five-step rotations, i.e. producing a series of five consecutive crops. The aim of the rotations is to maximise crop yields while maintaining a healthy nitrogen level in the soil and adhering to established planting rules. Crop yields may vary due to external factors such as weather patterns or changes in market prices, so perturbations have been added to the reward signal to account for those influences. The deployed explainable tabular reinforcement learning methods collect, on average, at least as much reward over 100 crop rotation plans when randomly starting with any crop compared to the deep learning model. For the perturbed case, robust tabular reinforcement learning methods collect similar amounts of reward across 100 crop rotation plans compared to the non-random reward setting, whereas the deep reinforcement learning agent collects even fewer rewards compared to learning on non-perturbed rewards. By consulting with farmers and crop rotation experts, we demonstrate that the derived policies are reasonable to use and more resilient towards external perturbations. Furthermore, the use of interpretable and explainable reinforcement learning techniques increases confidence in resulting policies, thereby increasing the likelihood that farmers will adopt the suggested policies.

Volunteering among older adults in the United States is rising, with rates up by 65% since 1974. With this tremendous growth in volunteering among seniors, examining how it affects them is essential. One such volunteering program we look at in this research is the Foster Grandparents Program (FGP) of the High Country, which allows older adults to volunteer at local schools to help in-need children. Previous research has shown how volunteering as an older adult positively impacts people's quality of life. This paper will explore the quality-of-life changes for the FGP older adult volunteers, explicitly comparing the quality-of-life changes with demographics and program satisfaction. This study looked at 93 participants of the FGP and their survey responses. We used cross-tabulation and correlation to look for relationships between demographics, satisfaction with volunteer experience, and overall changes in the quality-of-life of volunteers. Age, gender, and years of service were not good predictors of quality-of-life changes. On the other hand, overall satisfaction with the program proved to be the best predictor for change in quality of life. When satisfaction with positive program experiences is high, volunteers are more likely to have less symptoms of depression, state they have a greater sense of belonging to a community and were able to build a positive identity for themselves, all of which impacted their overall quality-of-life.

Federated learning (FL) is a decentralized machine learning paradigm that allows multiple local clients to collaboratively train a global model by sharing their model parameters instead of private data, thereby mitigating privacy leakage. However, recent studies have shown that gradient-based data reconstruction attacks (DRA) can still expose private information by exploiting model parameters from local clients. Existing privacy-preserving FL strategies provide some defence against these attacks but at the cost of significantly reduced model accuracy. Moreover, the issue of client heterogeneity further exacerbates these FL methods, resulting in drifted global models, slower convergence, and decreased performance. This study aims to address the two main challenges of FL: data heterogeneity, particularly in Non-Identical and Independent Distributions (Non-IID) clients, and client privacy through DRA. By leveraging the Lagrange duality approach and employing a generator model to facilitate knowledge distillation (KD) between clients, thereby enhancing local model performance, this method aims to concurrently address the primary challenges encountered by FL in real-world applications.

Endohedral metallo-borospherenes M@B40 have received considerable attention since the discovery of B40 in 2014. However, the coordination bonding nature of most of the actinide-doped endohedral An@B40 still remains in disputes or unexplored. Extensive first-principles theory calculations performed herein unveil the ground states of triplet U@B40 (1, C2v, 3A2), quartet U@B40- (2, C2v, 4B1), quintet Np@B40+ (3, C2v, 5A1), sextet Np@B40 (4, C2, 6A), septet Pu@B40 (5, C2v, 7A2), octet Am@B40 (6, C2v, 8A2), and octet Cm@B40+ (7, C2v, 8A2) at the coupled-cluster with triple excitations CCSD(T) level. Detailed principal interacting spin orbital (PISO) and adaptive natural density partitioning (AdNDP) analyses reveal their coordination bonding patterns and show that, with the numbers of unpaired α-electrons in parallel spins varying from nα = 2, 3, 4, 5, 6, 7, to 7 in these complexes, the percentage contribution of the An 5f-involved PISO pairs to overall coordination bonding interactions decreases monotonously from 41% to 1%, the contribution of An 6d-involved PISO pairs increases monotonously from 47% to 72%, while the marginal contribution of An 7s-involved PISO pairs remains basically unchanged (4~7%). The IR, Raman, and photoelectron spectra of the most concerned species are computationally simulated to facilitate their characterizations in future experiments.

Lactococcus spp. has emerged as a pathogen that is affecting global aquaculture, with L. garvieae, L. petauri, and L. formosensis causing piscine lactococcosis. While antimicrobials are commonly used to treat diseases in aquaculture, reports of antimicrobial resistance in fish isolates are increasing. However, little is known about the susceptibility patterns of Lactococcus spp. strains isolated from native fish species in Brazil. This study aimed to assess the antimicrobial susceptibility of these strains and establish a provisional epidemiological cut-off value for L. garvieae using the normalized resistance interpretation approach. A total of 47 isolates were tested: 17 L. garvieae, 24 L. petauri, and 6 L. formosensis. The isolates were classified as wild-type or non-wild-type (NWT) based on inhibition zone diameters. Isolates classified as NWT for three or more antimicrobial classes were considered multidrug-resistant, and the multiple antibiotic resistance (MAR) index was calculated. The results revealed heterogeneity in antimicrobial resistance profiles, with higher resistance to trimethoprim/sulfamethoxazole and norfloxacin. Resistance to other antimicrobials, including florfenicol and oxytetracycline (approved for use in Brazil), varied according to the bacterial species. Lactococcus petauri (87.5%) and L. formosensis (66.7%) showed the highest multidrug resistance, compared to L. garvieae (11.7%), along with higher MAR index values. These findings suggest that multidrug-resistant strains could pose future challenges in the production of native species, underscoring the need for ongoing monitoring of antimicrobial resistance and responsible use of antimicrobials in aquaculture.

The purpose of this paper is to design a mechanism to drive a propeller deform between an aerial shape and an aquatic shape. This mechanism can realize the deformation of blade angle, radius, blade twist angle distribution and blade section thickness. Inspired by the Kresling origami structure and utilizing its rotation-folding motion characteristics, a propeller hub structure with variable blade angle is designed. Through the motion analysis of spherical four-bar mechanism, a blade deformation unit (S-unit) with extensional-torsional kinematic characteristics is designed. A rib support structure fixed to the linkages of S-unit is designed to achieve the change of blade section thickness. Based on motion analysis, the coordinate transformation method is used to establish the relationship between propeller shape and deformation mechanism. The deformation of blade extension, blade twist distribution, blade section thickness are analyzed. The deformation ability of the proposed structure is verified by kinematic simulation and rapid prototyping based on 3-D printing.

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neuropsychiatric disorders in childhood and can persist into adulthood. Despite its prevalence, diagnosis remains primarily clinical, and it is based on behavioural observation and standardised questionnaires but with few objective tools to validate diagnoses. The use of electroencephalogram (EEG) has emerged as a potential tool to identify neurophysiological biomarkers of ADHD, and research on EEG-based neurofeedback has shown promising results as a therapeutic intervention. This article reviews recent studies looking at the role of EEG in the assessment and treatment of ADHD, with a focus on key findings, current limitations, and future directions.

(1) Background: Obsessive-Compulsive Disorder (OCD) is a psychiatric disorder with poorly detailed subtypes/dimensions, such as religious OCD (ROCD). To date, little is known about ROCD characteristics. This work aimed to describe the sociodemographic and clinical characteristics, as well as the religiosity and spirituality among Lebanese Muslim citizens diagnosed with OCD and exhibiting religious symptoms. (2) Methods: Outpatients were enrolled according to the following inclusion criteria: 18 years of age or above, diagnosis of OCD, presence of religious symptoms, and ability to read and understand the questionnaire. Exclusion criteria were as follows: other psychiatric disorders, and cognitive or physical impairments preventing participation. They filled in a questionnaire including the 25-item Arabic Scale of Obsessions and Compulsions (10 questions addressing obsessions, 10 questions addressing compulsions and five filler items, all of which rated on a 4–point Likert scale; higher total scores indicating increasing severity), the 26-item Spiritual Involvement and Beliefs Scale (rated on a 5-point Likert scale; higher scores indicating higher spirituality), and questions assessing the sociodemographic, clinical, and religiosity variables. (3) Results: Fifty adults (62% females, 52% aged between 18-29 years) completed the study. They had mild (26%), moderate (48%) and severe (26%) OCD symptoms. The majority had a family history of psychiatric disorders (48% OCD) and reported traumatic life events (58%). The majority attended religious school at least at one point in their life and described moderate to very high degree of self-religiosity and parental religiosity. Group comparisons (patients with mild vs. moderate vs. severe OCD symptoms) showed significant differences with regards to family history of psychiatric disorders (p=0.043), frequency of self-questioning if praying correctly (p=0.005), higher rating of partial ablution repetition (p=0.006), and frequency of partial ablution repetitions (p=0.041). No significant groups differences were noted with regards to sociodemographic or spirituality outcomes (4) Conclusion: Some religiosity variables (thoughts and practices) seem to be associated with OCD severity. The current results support the need for further research pertaining to ROCD.

This report summarizes the findings of a deep retrofitting modelling study of four government buildings across the UAE. Buildings envelope assessment, HVAC, and electrical systems design, installed conditions, maintenance practices and operation methods, and rigorous engineering analysis has been performed for the four buildings to produce calibrated energy models that confirm a good correlation between actual annual energy consumption and advanced simulated energy modeling results. IES-VE software, ASHRAE Guideline 14, and IPMVP were the basis of the energy model calibration based on actual data. Energy retrofitting options were applied to the calibrated energy models of the four government buildings where advanced computational modeling studies have been utilized to analyze different thermal envelope components and external fabric energy efficiency measures in conjunction with the actual HVAC systems. The results have shown that it may be financially challenging to implement a single envelope retrofitting measure on any of the four buildings due to high payback periods. Nevertheless, when conducting an optimization through a holistic building approach targeting the overall envelope enhancements, the financial modeling results provide significantly improved and provide reduced payback periods of the solutions, making them more attractive to investors. Based on the energy modeling results and the cost of the various proposed building envelope solutions provided by Saint-Gobain, the payback period in all cases varies between 15 to 20 years. However, huge savings in terms of carbon emissions are achieved by implementing the deep retrofitting measures, crucial to achieving carbon neutrality within the UAE built environment. Deep retrofitting is a promising segment of the execution roadmap as part of the UAE's decarbonization journey; even though it is capital-intensive upfront, it has a major impact on long-term operational savings. Moreover, it is often more cost-effective than building new and energy-efficient structures from scratch. The findings indicate that deep retrofitting can be a techno-financially viable solution if it is well-planned during major refurbishment or as part of building maintenance. It has significant potential to reduce carbon emissions while also providing a range of benefits such as operational cost savings, improved thermal comfort – a feature which has a direct impact on perceived efficiency and productivity at work, indoor air quality, improved asset value, reputational enhancement, and better choice for environmentally-cautious tenants or buyers.

Contrary to a prevailing dogma in twentieth-century neuroscience, emerging evidence suggests that the generation of new neurons continues throughout life, contributing significantly to crucial life functions owing to their robust ability to enhance neuroplasticity. Hippocampal neurogenesis occurs in the subgranular layer of the dentate gyrus, a region characterized by a specific microenvironment conducive to the proliferation of neuronal cells. A substantial body of evidence supports the belief that this phenomenon impacts various functions, including learning, emotional responses, and the formation of new memories. Diseases affecting these areas can significantly disrupt cognition and general behavior. For instance, in mesial temporal lobe epilepsy, aberrant neurogenesis has been observed, leading to increased cell proliferation, altered patterns of cellular integration, errors in information processing, and the formation of abnormal synaptic connections and transmissions. These abnormalities have the potential to generate epileptic seizures, underscoring the profound impact of these diseases on cognitive functions. Conversely, the chronic recurrence of seizures, can result in chronic recurrence of seizures, which can lead to a persistent reduction of neurogenesis in the epileptic brain. This reduction directly impacts various learning processes and the formation of hippocampus-dependent declarative memory. The mechanisms behind these processes remain fully elucidated and involve the delicate balance between neuronal excitation and inhibition, directly influencing brain performance and potentially leading to a decline in cognitive functions. In mesial temporal lobe epilepsy, this condition stands out as one of the most severe disorders affecting the well-being of epileptic patients. While memory is highlighted as one of the most affected functions, it is not the sole cognitive function compromised by this comorbidity. The alterations can extend from language to executive and spatial functions.

The study describes the features of manifestations and interrelationships of job stress, working capacity, professional performance and safety in loggers with shift work organization in the North. The study involved 402 loggers. The research methods include questionnaires and psychophysio-logical diagnostics of stress and working capacity, as well as questionnaires on the professional performance and safety of workers. The following statistical methods were used: correlation, mul-tivariate dispersion and multi-regression stepwise analysis. It was found that the severity of stress, speed, accuracy and operator working capacity, as well as well-being, activity and mood in logging equipment operators, timber truck drivers and maintenance specialists statistically differ signifi-cantly. Higher professional stress and more frequent decrease in professional performance are characteristic of truck drivers. Psychological and psychophysiological job stress and working ca-pacity parameters are associated with professional performance parameters of loggers. According to the results of a number of multi-regression analyses, reduced professional performance markers of loggers include the index of stress, satiety, monotony and fatigue, vegetative balance and stress (method of M. Luscher and coefficients of G.A. Aminev), operator working capacity and reaction speed (CVMR).

The Bloch sphere is a powerful geometrical representation of qubit states, fundamental to understanding quantum information theory. This paper explores the stereographic projection of the Bloch sphere, focusing on its utility in addressing decoherence and quantum error correction, two major challenges in quantum computing. By mapping the complex quantum states onto a two-dimensional plane, the stereographic projection simplifies the visualization and manipulation of qubit transformations. We investigate how this projection aids in identifying and correcting errors that arise from decoherence---such as phase flips and bit flips---while maintaining the integrity of quantum states. Additionally, we investigate the influence of decoherence on qubits through this projection, demonstrating how error correction protocols can be optimized by mapping errors onto the complex plane. The analysis highlights the potential of stereographic projection in developing more robust strategies for mitigating decoherence, thereby advancing the overall performance of quantum systems.

A review of recent advances in spacetime optics is given, with special emphasis on time-refraction. This is a basic optical process, occurring at a temporal discontinuity ou temporal boundary, that is able to produce various different effects, such as frequency shifts, energy amplification, time-reflection and photon emission. If, instead of a single discontinuity, we have two reverse temporal boundaries, we can form a temporal beam-splitter, where temporal interferences can occur. It will also be shown that, in the presence of an axis of symmetry, such as a magnetic field, the temporal beam-splitter can induce a rotation of the initial polarization state, similar to a Faraday rotation. Recent work on time-crystals, superluminal fronts and superfluid light will be reviewed. Time-gates, based on spacetime optical effects will be discussed. We also mention recent work on optical metamaterials. Finally, the quantum properties of time-refraction, which imply the emission of photon from vacuum, are considered, while similar problems in high energy QED associated with electron-positron pairs are briefly mentioned.

The environmental paradigm emphasizes the importance of political-economic and socio-economic factors that can significantly affect environmental health. One of the above-mentioned factors in the EU's so-called Eastern bloc is also the issue of unresolved environmental burdens. Our case study sought answers to the questions: How and why was Niklová Huta (Nickel smelter) Sereď created? How is it possible that, despite the knowledge of nickel's harmfulness to the environment, production continued for so long? Why, even 30 years after the end of production, the solid leaching residue (lúženec) landfill was not disposed of? The main methods of data collection were a content analysis of secondary data, a self-constructed questionnaire and semi-structured interviews. The research group for the quantitative part of the case study consisted of 354 respondents, of which 274 (77.40%) were women and 80 were men (22.60%). The data obtained by the questionnaire are presented through descriptive statistics. 12 informants participated in the quantitative research through semi-structured interviews, of which 9 were men and 3 were women. The interviews lasted between 30-45 minutes, were transcribed and we used qualitative thematic analysis to process them. The result of our case study is the creation of a conceptual model that captures the key points in the process of the formation of a toxic landfill and its persistence in the environment during the last 30 years.

Designing a ship hull is a complex procedure that involves constructing the basic hull using predetermined criteria or specified lines. Grasshopper, a visual programming environment for Rhinoceros 3D (CAD software), has a parametric modeling and analysis tool that automates the generation of ship hull and line plans using offset tables. This strategy reduces the necessity of manual design while enhancing efficiency and precision. Grasshopper is widely used for generating designs, enabling designers to visually create intricate algorithms by connecting components instead of writing code. This paper proposes a novel technique to automate the construction of ship hull and line plans with Grasshopper, leveraging its parametric capabilities to produce hull shapes and plan lines readily from offset table data. Future advancements may involve combining hydrostatic and hydrodynamic evaluations, improving hull performance, and automating the fairing process. A user-friendly interface and real-time collaboration tools can boost accessibility and design flexibility.

This paper presents the development of a Generative Adversarial Network (GAN) for the generation of synthetic dental panoramic radiographs to address the scarcity of data in dental research and education. A Deep Convolutional GAN (DCGAN) with Wasserstein Loss and Gradient Penalty (WGAN-GP) was trained on a dataset of 2322 radiographs of varying quality. The focus for this study was on the dentoalveolar part of the radiographs; other structures were cropped out. Significant data cleaning and preprocessing was conducted to standardize input formats while maintaining anatomical variability. Four candidate models were identified by varying the critic iterations, number of features and the use of denoising prior to training. To assess the quality of the generated images, a clinical expert evaluated generated synthetic radiographs using a ranking system based on visibility and realism from 1 (Very Poor) to 5 (Excellent). It was found that most generated radiographs showed moderate depictions of dentoalveolar anatomical structures, although they were considerably impaired by artifacts. After averaging evaluation scores, a trade-off was observed between the model trained on non-denoised data, which showed the highest subjective quality for finer structures such as the Mandibular Canal and Trabecular Bone, and one of the models trained on denoised data, which offered better overall image quality, especially for Clarity & Sharpness, and Overall Realism. The outcome serves as a foundation for further research into GAN architectures for dental applications. A GitHub repository containing the WGAN-GP model, source code, and synthetic data has been made publicly available at https://github.com/ViktorLaden/DataProjectGAN.

In this paper we discuss whether Einstein’s initial expectations before he developed the theory of general relativity were fulfilled and whether general relativity fully satisfied him in this respect. Einstein employed the equivalence principle as a guiding principle in the development of general relativity. We follow Einstein’s historical reasoning on the equivalence principle by adopting a dynamical approach to relativity and question the theory of general relativity he arrived at in terms of his initial expectations. The key issue here is related to the interpretation of gravitational and inertial “fields” in the equivalence principle. Einstein spoke of the identity of these two notions. This reveals that his intention was to unify gravitation and inertia. In fact, the issue is also related to the extent of generalisation provided by general relativity. Einstein demands a strong generalization in general relativity that requires the equality of all frames, including accelerating frames, in the same way that the relative character of velocity provides in special relativity. However, general relativity does not provide such a strong generalisation as expected from it. Furthermore, in general relativity, the gravitational field is reduced to inertia, but the two are not fully unified. We therefore argue that Einstein’s initial expectations of general relativity were not met and that the theory did not fully satisfy him. Nevertheless, we show that there is a satisfactory way out. On the basis of some heuristic arguments, we demonstrate that if gravitational and non-gravitational interactions are unified into a single elementary interaction at the most fundamental level in nature, then inertia and gravitation are fully unified and the strong generalisation being sought can be obtained. We speculate that Einstein was well aware of this fact and one of his motivations (perhaps the most important one) in his long search for a unified field theory was an attempt to fulfill his initial expectations about general relativity.

A Cooperstein ovoid is a set of q^8+q^4+1 pairwise non-collinear points in the Lie incidence geometry E_{6,1}(q). They were introduced by Cooperstein twenty-six years ago motivated by the fact that possible non-existence of them would imply non-existence of ovoids in hyperbolic quadrics of rank 5. Since then, no progress has been made on their existence question. We prove that Cooperstein ovoids do not exist under some natural additional conditions. In particular, Cooperstein ovoids intersecting every symplecton of E_{6,1}(q) do not exists, Cooperstein ovoids which are the fixed points of a collineation do not exist, and Cooperstein ovoids which are the absolute points of a polarity of E_{6,1}(q) do not exist.

The Indian educational system grapples with deep-rooted structural and systemic challenges that have hindered equitable access to quality education across all levels. Central issues include outdated curricula that fail to equip students with contemporary skills, significant socio-economic disparities limiting access and outcomes, and inadequate teacher training alongside deficient infrastructure, particularly in rural and economically disadvantaged areas. These challenges contribute to unequal educational opportunities and outcomes, with a stark divide between urban and rural, and between private and public institutions. This review delves into these pervasive issues, analyzing how they manifest from primary through tertiary education. In primary education, the focus often remains on rote learning, overlooking critical thinking and problem-solving skills. At the secondary and tertiary levels, curriculum relevance remains a concern, as courses frequently do not align with modern industry demands or technological advancements, creating a skills gap among graduates. Addressing these challenges, this review proposes a series of reforms aimed at building a more inclusive and effective system. Key reforms include curriculum modernization to incorporate skills for the 21st century, integrating digital learning tools to expand access and facilitate adaptive learning, and comprehensive teacher development programs that emphasize continuous professional growth. Together, these initiatives aim to create an educational environment that is not only inclusive but also adaptive to the evolving demands of a globalized world, thereby enhancing the quality and reach of education across India.

Brazil hosts one of the world's most diverse reptilian faunas, with over 800 species distributed across its various biomes. This paper examines the historical development of herpetological research in Brazil, particularly focusing on two pioneering institutions: the Vital Brazil Institute and the Butantan Institute. These centers have been instrumental in both the scientific study of reptiles and the development of antivenoms since the early 20th century. We explore the remarkable morphological adaptations of Brazilian reptiles across different biomes, from the Amazon rainforest to the Caatinga desert, highlighting how these adaptations reflect the country's diverse ecological pressures. This review synthesizes current knowledge while identifying gaps in our understanding of Brazilian herpetofauna.

Automation is transforming ship design by enhancing flexibility, efficiency, and accuracy in Computer-Aided Ship Design (CASD). This review highlights advancements in automation and parametric techniques that streamline the design process. Variable geometry and task automation improve performance optimization, enabling designers to concentrate on complex elements. The review will talk about advanced tools and methodologies, their effects on design optimization, production workflows, and some of the challenges associated with software interoperability and increased design complexity. It will also look at how AI and machine learning can be leveraged for better automation. The review identifies several future opportunities in automated ship design but frames such technologies as revolutionary in the shipbuilding sector and highlights their potential to bring significant transformation to shipping.

In traffic scene, pedestrian target detection faces significant issues of misdetection and omission due to factors such as crowd density and obstacle occlusion. To address these challenges and enhance detection accuracy, we propose an improved CCW-YOLO algorithm. The algorithm first introduces a lightweight convolutional layer using GhostConv and incorporates an enhanced C2f module to improve the network's detection performance. Additionally, it integrates the Coordinate Attention module to better capture key points of the targets. Next, the bounding box loss function CIoU Loss at the output of YOLOv5 is replaced with WiseIoU Loss to enhance adaptability to various detection scenarios, thereby further improving accuracy. Finally, we develop a pedestrian count detection system by using PyQt5 to enhance human-computer interaction. Experimental results on the INRIA public dataset show that our algorithm achieves a detection accuracy of 95.6%, representing a 8.7% improvement over the original YOLOv5s algorithm. This advancement significantly enhances the detection of small objects in images and effectively addresses misdetection and omission issues in complex environments. These findings have important practical implications for ensuring traffic safety and optimizing traffic flow.

We show that analyzing archived and future multibeam backscatter and bathymetry data, in tandem with regional environmental parameters, can help identify polymetallic nodule fields in the world’s oceans. Extensive archived multibeam transit data through remote areas of the world’s oceans are available for data mining. New multibeam data will be made available through the Seabed 2030 Project. Uniformity of along and across track backscatter, backscatter intensity, angular response, water depth, nearby ground-truth data, and local slope, sedimentation rate, and seafloor age provide thresholds for discriminating areas permissive for nodule presence. A case study of this methodology is presented using archived multibeam data from a remote section of the South Pacific along the Foundation Seamounts between the Selkirk paleomicroplate and East Pacific Rise that were collected in 1997 during the Hotline cruise. The 12 kHz Simrad EM12 DUAL multibeam and the forementioned data strongly suggest a previously unknown nodule occurrence exists. Methods include comparing analyses using three different backscatter products to further determine if scanned backscatter products, rather than primary digital multibeam files can be used for analysis, and comparing results from using digitized 8-bit images, or 8-bit and decibel-value surfaces derived from the MB-SystemTM software. We show this expeditious analysis of broad areas of multibeam data could characterize benthic habitat types efficiently in remote deep ocean areas prior to more time consuming and expensive video and sample acquisition surveys. Additionally, utilizing software other than specialty sonar processing programs during this research explores how multibeam data products could be interrogated by a broader range of scientists and data users. Future mapping, video, and sampling cruises in this area would test our prediction and investigate how far it might extend to the north and south.

We present a novel derivation of fundamental physical laws by solving a maximization problem on the Shannon entropy of all possible measurements relative to a system's initial state, subject to a universal measurement constraint. Its most sophisticated solution involves 3+1-dimensional measurements that include both bivectors and complex numbers. In this case, the solution describes a relativistic quantum theory that naturally yields the metric tensor of general relativity through a double-copy mechanism applied to the Dirac current and that inherently incorporates the SU(3)xSU(2)xU(1) gauge symmetries of the Standard Model. These findings reveal deep connections between probability theory and the mathematical structures underlying fundamental physics, offering new insights into the emergence of 3+1 spacetime dimensions, gravity, and symmetry structures in our universe.

Aims: To compare traditional versus dry wipes hygiene in hospitalized, bedridden patients with regard to economic and environmental impact and patient and professional satisfaction. Background: Evidence shows that dry hygiene with disposable wipes can reduce the risk of infection compared to traditional hygiene with soap and water. However, there is a need to compare the costs and environmental impact (waste) between the two methods. Design: Environmental impact and patient and user satisfaction were assessed through an ad hoc survey and cost minimization analysis in accordance with the Consolidated Health Economic Evaluation Reporting Standards. Methods: The environmental impact and costs associated with traditional versus dry hygiene were compared in an observational analysis. A single-center, randomized controlled trial was performed in a tertiary hospital to assess patient and professional satisfaction in patients with a urinary catheter recovering from heart surgery between January 2019 and February 2020. Results: Direct hygiene costs were similar using traditional (€15.82) and dry (€14.35) methods. However, the analysis of indirect costs, including those related to the probability of contracting catheter-associated urinary tract infections, showed that the total cost per hygiene session was €1345 higher with the traditional method. When using a high-efficiency energy system from a guaranteed renewable source, dry hygiene does not lead to a higher environmental impact (220.07 g CO2 traditional vs. 215.17g CO2 dry). However, emissions from conventional energy systems are 840.14 g CO2 higher when using traditional hygiene. Both patients and professionals express greater satisfaction with the dry method. Conclusion: Dry hygiene is not inferior to traditional hygiene in terms of satisfaction, and it can provide environmental benefits at a significantly lower cost. Implications for the profession and/or patient care: This study provides evidence supporting the introduction of the dry hygiene method, with is associated with higher user and professional satisfaction, lower economic cost, and similar or lower environmental impact compared to soap and water.

Long Short-Term Memory (LSTM) networks have gained attention for their forecasting capabilities, especially in volatile markets like oil. This study compares LSTM networks with traditional time series models such as ARIMA and Exponential Smoothing in predicting oil prices. We analyze a comprehensive dataset of historical oil price movements and apply preprocessing techniques to ensure the reliability of our model training and evaluation processes. The models are evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to measure their forecasting performance. The results demonstrate that LSTM networks significantly outperform traditional time series models, effectively capturing price trends and fluctuations. Additionally, LSTM shows enhanced adaptability to sudden changes in the market, while traditional models tend to struggle in such scenarios. This comparative analysis underscores the significance of model selection in time series forecasting, especially for industries characterized by high volatility, and suggests a shift towards utilizing LSTM for enhanced financial forecasting accuracy. Future research may further investigate hybrid models that combine the strengths of both LSTM and traditional methodologies.

The present study aimed to evaluate the potential synergy between pharmaceutical formulations containing Bixa orellana L. (granulated – CHR OR and injectable nanodispersion – CHR IN) in conjunction with a cannabidiol (CBD)-rich extract of Cannabis sativa L. (CSE) on experimental pain models in Wistar rats, including acetic acid-induced writhing, formalin, hot plate, and cold-water tail withdrawal tests. Chemical analyses by chromatography confirmed that the oil from B. orellana is rich in δ-tocotrienol (72.0 ± 1.0%), while the oil from Cannabis sativa highlighted the presence of cannabidiol (CBD). The results from the experimental pain tests indicated that the combined administration of formulations containing Bixa orellana and C. sativa, such as the granulated CHR OR (400 mg/kg, orally) with CSE (40 mg/kg, orally) or the nanodispersion CHR In (10 mg/kg, intramuscularly) with CSE (40 mg/kg, orally), demonstrated significant results (p < 0.001) in pain reduction. Although the formulations containing Bixa orellana extract showed statistical significance in the tests when used in isolation, their effects were inferior compared to the combined use with CSE or the isolated use of CSE. These findings suggest that combining formulations containing extracts of these plant species may represent a viable therapeutic option, considering the synergistic action in reducing pain under the experimental conditions employed. These results imply that combining the phytocomplexes present in B. orellana and C. sativa may be a promising approach for pain treatment.

PAVON has developed a malaria microscopy competency training scheme to augment competency in malaria microscopy. Here data accrued from training activities between 2020 – 2023 in Botswana are presented. Three trainings were done for 37 central and peripheral level technicians for a two-week period. The scheme consisted of basic theory on Plasmodium parasites, malaria epidemiology and diagnosis. Practicals focused on standard slide preparation, staining, parasite detection, speciation and counting. Scores were assessed by the Wilcoxon signed rank test. Participants who excelled joined the WHO External Competency Assessment for Malaria Microscopy (ECAMM). The median competency scores for the 3 trainings were: detection: (100, IQR=94, 100), (100, IQR=94, 100), and (92, IQR=92, 100) respectively, from pretest scores of (40, IQR=27, 54), (44, IQR=32, 52), and (20, IQR=10, 40); (z = 2.937, p<0.003); (z = 3.110, p=0.002); and (z = 2.251, p=0.024) respectively. Speciation: (93, IQR=86, 96), (81, IQR=73, 96) and (88, IQR=88, 100), from pretest scores of (50, IQR=30, 50), (36, IQR=20, 45) and (17, IQR=17, 50)(z = 2.936, p<0.003); (z = 3.152, p=0.002); (z = 3.237, p=0.001). Competency scores achieved in the ECAMM were 98/98; and 97/98, for sensitivity and specificity pre and post-tests. The training was effective in raising the competency skills of participants and is open to NMPs for critical capacity building.

Latent Tuberculosis Infection (LTBI) poses a significant public health challenge, especially in populations with high HIV prevalence and limited healthcare access. Early detection and targeted interventions are essential to prevent the progression of active tuberculosis. This study develops predictive models for LTBI outcomes using logistic regression and machine learning approaches and evaluates strategies to improve LTBI awareness and testing. Data from rural areas in the Eastern Cape, South Africa, were analyzed to identify key demographic, health, and knowledge-related factors influencing LTBI outcomes. Logistic regression was employed to pre-dict LTBI positivity based on factors such as age, education, and HIV status. Machine learning models, including decision trees and random forests, were also applied to compare predictive accuracy. A knowledge diffusion model was used to assess the impact of educational interventions on increasing LTBI awareness and testing rates. Logistic regression achieved an accuracy of 66.67% with high precision (80%) but low recall (33%) for LTBI-positive cases, identifying age, HIV status, and LTBI awareness as significant predictors. The random forest model outperformed logistic regression in accuracy (59.26%) and F1-score (0.63), providing a better balance between precision and recall. Feature importance analysis revealed that age, occupation, and knowledge of LTBI symptoms were the most critical factors across both models. The knowledge diffusion model demonstrated that targeted interventions significantly increased LTBI awareness and testing, particularly in high-risk groups. While logistic regression offers more interpretable results for public health interventions, machine learning models like random forests provide enhanced predictive power by capturing complex relationships between demographics and health factors. These findings highlight the need for targeted educational campaigns and increased LTBI testing in high-risk populations, particularly those with limited awareness of LTBI symptoms.

This paper presents a Poisson multi-Bernoulli mixture (PMBM) forward-backward smoother for multiple extended target detection and tracking in the random finite sets (RFS) framework. The proposed method consists of forward filtering, achieved by the PMBM filter recursion, followed by backward smoothing, achieved by a parametric updated posterior PMBM density. The performance of the proposed method is evaluated in a simulation study.

Background/Objectives: Camptothecin (CPT) is a well-known chemical compound recognized for its significant anticancer properties. However, its clinical application remains limited due to challenges related to CPT’s high hydrophobicity and the instability of its active form. To address these difficulties, our research focused on the development of four novel nanoparticulate systems intended for either oral or intravenous administration; Methods: These nanosystems were based on a poly(amidoamine) (PAMAM) dendrimer/CPT complex, which had been coated with biodegradable homo- and copolymers, designed with appropriate physicochemical properties and chain microstructures; Results: The resulting nanomaterials, with diameters ranging from 110 to 406 nm and dispersity values between 0.10 and 0.67, exhibited a positive surface charge and were synthesized using biodegradable poly(L-lactide) (PLLA), poly(L-lactide-co-ε-caprolactone) (PLACL), and poly(glycolide-co-ε-caprolactone) (PGACL). Biological assessments, including cell viability and hemolysis tests, indicated that all polymers demonstrated less than 5% hemolysis, confirming their hemocompatibility for potential intravenous use. Furthermore, fibroblasts exposed to these matrices showed concentration-dependent viability. The entrapment efficiency (EE) of CPT reached up to 27%, with drug loading (DL) values as high as 17%. The in vitro drug release studies lasted over 400 h with the use of phosphate buffer solutions at two different pH levels, demonstrating that time-dependent processes allowed for a gradual and controlled release of CPT from the developed nanosystems. The release kinetics of the active compound at pH 7.4 ± 0.05 and 6.5 ± 0.05 followed near-first-order or first-order models, with diffusion and Fickian/non-Fickian transport mechanisms. Importantly, the nanoparticulate systems enabled the stabilization of the pharmacologically active form of CPT, while providing protection against hydrolysis, even in physiological environments; Conclusions: In our opinion, these results underscore the promising future of biodegradable nanosystems as effective drug delivery systems (DDSs) for targeted cancer treatment, offering stability and efficacy over short, medium, and long-term applications.

Hearing loss (HL) is the most common disorder in newborns with a highly heterogeneous genetic background. Despite significant progress in screening and identifying genes related with congential hearing loss, there are still candidate genes implicated in HL that remain undiscovered. We investigated HL in 43 China families that segregate bilateral sensorineural via whole-exome sequencing(WES) and Sanger sequencing. Variants were found in 10 known non-syndromic hearing loss (NSHL) genes, 5 known syndromic hearing loss(SHL) genes and a candidate HL genes ATP7B. RNA sequencing reveals ATP7B mRNA expression of in developing and adult cochlea. Immunohistochemistry of the adult mouse cochlear tissue revealed prominent expression of ATP7B in the organ of Corti and the spiral ganglion neuron. Overall, we propose a new candidate genes ATP7B for congential hearing loss and novel variants in known HL genes, which expand our understanding of the etiology of HL.

This paper describes the E-Sense Artificial Intelligence system. It comprises of a memory model with 2 levels of information and then a more neural layer above that. The lower memory level stores source data in a Markov (n-gram) structure that is unweighted. Then a middle ontology level is created from a further 3 phases of aggregating source information. Each phase re-structures from an ensemble to a tree, where the information transposition may be from horizontal set-based sequences into more vertical, typed-based clusters. The base memory is essentially neutral, where any weighted constraints or preferences should be stored in the calling module. The success of the ontology typing is open to question, but results produced answers based more on use and context. The third level is more functional, where each function can represent a subset of the base data and learn how to transpose across it. The functional structures are shown to be quite orthogonal, or separate and are made from nodes with a progressive type of capability, including unordered to ordered. Comparisons with the columnar structure of the neural cortex can be made and the idea of ordinal learning, or just learning relative positions, is introduced. While this is still a work in progress, it offers a different architecture to the current favourites and may be able to give different views of the data from what they can provide.

We introduce two symmetric concepts to physics: “distance” (space and time in one) and “wavematter” (electromagnetic wave packet and matter in one). We claim that physics has chosen the wrong concept of time: It is not aware that the time of a moving object flows in a direction other than my time. We provide 15 proofs for our claim by solving 15 mysteries of physics graphically! For example, we prove that length contraction and time dilation are actually geometrical effects in a 4D manifold that we call “Euclidean spacetime” (ES). We prove that the discrepancy in calculating the Hubble constant stems from a systematic error in the redshift measurement. We prove that what I deem wave, deems itself matter, which solves wave–particle duality. We even untangle quantum entanglement without the issue of non-locality. We finally understand why alternative theories of Euclidean relativity have failed: They are all based on four dimensions of space rather than distance. A huge amount of energy was injected into ES at a point that we take as origin. Ever since has this energy been moving radially away at the speed of light. We live in the 3D hypersurface of an expanding 4D hypersphere. Hyperspherical coordinates have the great advantage that they reduce all that is ever happening to just one formula. So, it is the Theory of Everything in these coordinates: “Energy is covering radial distance which, divided by Euclidean time, is equal to the speed of light.” Acceleration and force like gravitation emerge from a rotation of objects in ES and a subsequent projection to 3D space. Matching the symmetry simplifies physics.

This article proposes a framework for integrating ethical reasoning into AI systems through Continuous Logic Programming (CLP), emphasizing the improvement of transparency and accountability in automated decision-making. The study highlights requirements for AI that respects to human values and societal norms by examining concerns such as algorithmic bias, data privacy, and ethical dilemmas in fields like healthcare and autonomous systems. The proposed CLP-based methodology offers a systematic, elucidative framework for ethical decision-making, allowing AI systems to balance operational efficiency with ethical principles. Important contributions include strategies for the integration of ethical frameworks, stakeholder engagement, and transparency, as well as discussion on Artificial Moral Agents and their function in addressing ethical dilemmas in AI. The paper presents practical examples that illustrate the application of CLP in ethical reasoning, highlighting its ability to bring together AI performance with responsible AI practices.

Bosom disease remains a main source of death among women around the world. Early identification is significant for further developing endurance rates, accordingly making effective indicative strategies fundamental. This undertaking the malignant growth discovery utilizing mammography images (low dose x-ray), utilizing profound learning methods to robotize conclusions and upgrade exactness. The framework's center includes preprocessing the mammogram images, separating highlights, and arranging them utilizing Convolutional neural networks (CNNs) and other high-level profound learning models.

Objective: Clostridioides difficile, a Gram-positive, spore-forming bacillus, has emerged as a leading cause of healthcare-associated infections, significantly contributing to infectious diarrhea and increasing healthcare costs. Materials and Methods: This cross-sectional, observational study polled Saudi Arabian nursing staff from July 2023 till December 2023 to evaluate their knowledge, attitudes, and habits regarding C. difficile infection (CDI). They were evaluated using a pre-tested, standardized questionnaire. Results: A total of 358 nurses in Saudi Arabia were surveyed, and 66% reported having knowledge of C. difficile procedures. However, only 30.5% of respondents correctly classified C. difficile as an anaerobic bacillus, while 42% were aware of the organism’s common occurrence in healthy adult volunteers. Additionally. 56% of respondents were aware of risk factors, and 49% could name typical medicines that might cause illness. Merely 24% acknowledged the cytotoxin test as the gold standard for detection. Of those who responded, 27% identified hand washing your hands was the greatest preventive approach and 36% thought that oral metronidazole was the best course of action. Conclusions: These findings highlight the global challenge that nurses are adequately knowledgeable on C. difficile. They stress the necessity of continuing education and training to enhance CDI management and patient outcomes, regardless of geographical variances.

Climate Smart Agriculture (CSA) summarizes an expansion of existing sustainable agricultural intensification approach tailored appropriately to reduce the enduring constraints posed by climatic change. Notwithstanding the importance of CSA, the conceptual underpinnings are often left uncertain leading to disbelief as to practical realization and operationalization of the concept of CSA in South Africa. The study examined smart practices for climate mitigation and drew insights from smallholder farmers in Bushbuckridge, South Africa. The objectives of the study were: i) to investigate the determinants of adopting local CSA resilience strategies and ii) identify constraints of adoption of CSA. Structured and semi-structured questionnaires were used to collect data from the respondents. The sample size of 255 was obtained from the population of 750 farmers involved in crops and livestock production in the area. One way ANOVA was employed to determine the mean variance of sampled population. The study used logistic regression to model the determinants of decision making. Results show that the following variables influenced adoption decisions making process: gender (p-value = 0.003); age (p-value = 0.039); farm size (p-value = 0.000); household size (p-value = 0.001); marital status (p-value = 0.008); level of education (P-value = 0.000); support received (p-value = 0.041) and extension services (p-value = 0.001). Insights into the challenges of smallholder farmers in accepting climate smart resilience strategies were highlighted. The paper concluded that smallholder farmers require support to improve farming constraints. It was also recommended that the government should give support by encouraging farmers and investing in farm infrastructures correlated to CSA practice. A favourable policy discourse to encourage the use of CSA must be considered by the government.

Arctic tidewater glaciers are retreating, serving as key indicators of global warming. This study aims to assess how subglacial hydrology affects glacier front retreat by comparing two glacier-fjord models of Hansbreen glacier: one incorporating a detailed subglacial hydrology model and another simplifying subglacial discharge to a single channel centered in the flow line. We first validate the subglacial hydrology model by comparing its discharge channels with observations of plume activity. Simulations conducted from April to December 2010 revealed that the glacier front position aligns more closely with observations in the coupled model than in the simplified version. Furthermore, mass loss due to calving and submarine melting is greater in the coupled model, with calving mass loss reaching 6 Mt by the end of the simulation, compared to 4 Mt in the simplified model. These findings highlight the critical role of subglacial hydrology in predicting glacier dynamics and emphasize the importance of detailed modeling in understanding the responses of Arctic tidewater glaciers to climate change.

These studies address the challenge of post-weaning diarrhea (PWD) in piglets, a common and costly health issue exacerbated by the recent European Union ban on therapeutic zinc oxide. The research evaluates whether spray-dried plasma (SDP), a sustainable and highly digestible protein source, can mitigate PWD without compromising piglet growth. Two concurrent studies were conducted on farms using either low-protein or normal-protein diets, with SDP included in varying phases of early piglet feeding. Diets with SDP improved growth rates and feed intake during critical post-weaning phases, particularly under normal protein levels and enhanced feed efficiency and reduced medication needs, indicating its potential to support piglet health while reducing dependency on pharmaceutical interventions. In conclusion, SDP in post-weaning diets helps balance growth and health, even when using low protein diets and offers a viable and sustainable alternative to traditional methods for managing PWD. This work highlights the role of SDP in promoting a circular economy by upcycling animal by-products and aligning with environmentally friendly farming practices.

African swine fever (ASF) has widely spread around the world in the last 100 years since its discovery. The African swine fever virus (ASFV) particles are composed of more than 150 proteins, with the p17 protein encoded by the D117L gene serving as one of the major capsid proteins and playing a crucial role in the virus's morphogenesis and immune evasion. Therefore, monoclonal antibody (mAb) targeting p17 is useful for the research and detection of ASFV infection. Here, we generated two specific mAbs against p17, named 1G2 and 6G3, respectively, both of which have been successfully applied in ELISA, Western blotting and immunofluorescence assay. Moreover, we found that both 1G2 and 6G3 mAbs recognize a new epitope of 72–78 amino acids of p17 protein, which is highly conserved across different ASFV strains, including all genotype I and II strains. Based on the recognized epitope, an indirect ELISA has been established to effectively detect antibodies during ASFV infection, and it exhibits high consistency with commercial ASFV ELISA kit. In summary, the production of the specific p17 mAbs and the identification of the corresponding epitope will significantly promote the serological diagnosis of ASFV and vaccine research.

The Amazon region, with its vast biodiversity and rich natural resources, presents a unique opportunity for the development of sustainable polymeric composites reinforced with residues from both the timber and agro-extractive industries. This article explores the potential of these Amazonian residues, such as sawdust, wood shavings, and agro-industrial by-products like açaí seeds and Brazil nut shells, to enhance the mechanical, thermal, and environmental properties of polymer composites. By integrating these natural materials into polymer matrices, significant improvements in composite performance have been achieved, including increased tensile strength, thermal stability, and biodegradability. The study also highlights the environmental and economic benefits of utilizing these residues, promoting waste reduction and supporting a circular economy in the region. Through case studies and detailed analyses, the article demonstrates the feasibility and advantages of incorporating Amazonian residues into composites for a wide range of applications, from construction materials to consumer goods. This approach not only adds value to the by-products of the Amazonian industries but also contributes to the global effort towards sustainable material development.

Cardiovascular diseases are one of the leading causes of death in the world. The capability to direct the differentiation of human pluripotent stem cells (hPSCs) into functional cardiomyocytes (CMs) provides excellent opportunities for disease modeling and novel cell-based cardiac therapies. However, current hPSC-derived cardiomyocytes (hPSC-CMs) remain largely immature, limiting experimental and clinical uses. New insights into the maturation process of adult-like hPSC-CMs are critical. In this paper, we developed a data-driven pipeline to quantify cell maturity, based on gene expression input spanning different stages of cardiac development. First, we determined that culture time can serve as a proxy of the maturity level of hPSC-CMs. Second, we selected key heart-related genes whose expression varies with culture time, based on machine learning algorithms that are applied to accurately predict culture time. Our results demonstrate that the average discrepancy between the predicted culture time and the observed culture time can be reduced to 4.461 days. Furthermore, Gene CASQ2 (Calsequestrin 2), whose protein plays a role in the storage and transportation of calcium ions, has been identified as the most important cardiac gene that correlates with the culture duration.

In this paper, we analyzed the influence of spin Nernst effect on quantum correlation in the layered ferrimagnetic model. In the study of three-dimensional ferrimagnets, the focus is on materials with a specific arrangement of spins, where neighboring spins are parallel and other antiparallel. The anisotropic nature of these materials means that the interactions between spins depend on their relative orientations in different directions. We analyzed the effect of magnon bands induced by the coupling parameters on entanglement negativity. The influence of the coupling parameters of the topologic phase transition on quantum entanglement is investigated as well. Numerical simulations using Lanczos algorithm and exact diagonalization for different sizes lattice are compared with the results of the spin wave theory.

This chapter discusses stochastic processes known as "hidden Markov models". In these models, there are states or phases. In applications to statistical process control, for example, the states could correspond to "in control" or "out of control". In applications to the market and the economy, there are states of the market conventionally called "Bull" and "Bear", for up and down markets, resp. The states of the economy are often referred to as recession, recovery, expansion, contraction. Questions arise as to how many such states to use, and whether the nature of the states detected by statistical methods correspond to conventional notions about the phases of the market or the economy. The models considered involve a bivariate time series {Yt, St}; where Y is observed and S is the state, or label. "Hidden" means that the state is unobserved and must be inferred. For example, Yt may be the rate of return (ROR) of the S&P 500 index in month t and St may be 1 or 0, for Bull or Bear for that month. One definition of Bull and Bear is that St = 1 if the ROR Yt > 0 and St = 0; otherwise. In that case, St is directly derivable from the data. In other cases, the labels are unobserved and then are estimated from the observed process by more complicated means. The act of estimating St for each t, that is, labeling each time point of a time series with the name of its estimated state, is called time series segmentation, sequences of 0s or 1s being called segments. Thus a segment (regime, epoch) is a sequence of time points with the same label. Some definitions of phases are in terms of patterns; others are obtained in terms of fitting models in which the states are explicit elements of the model, albeit unobservable. Patterns are considered first, and then hidden state models. Use of model selection criteria for choosing between alternative models is illustrated. For example, it was found that for the stock market use of two states (corresponding to Bull and Bear) is indeed better than three states or a single state.

(1) Predictive assessment of the performance characteristics of metal-polymer (MP) plain bearings at the design stage is necessary and important for engineering practice. They include bearing capacity, linear wear of the bushing, and bearing life. However, no method for their calculation has been developed. (2) The authors have created a generalized analytical method for the mathematical modeling of MP sliding bearings as a classical science-based method of contact mechanics of conformal cylindrical bodies with consideration of polymer bushing wear. (3) Contact problem of the theory of elasticity takes into account the significant difference in the elasticity characteristics of polymeric materials from those of steel: Young's modulus is 60...250 times smaller, Poisson's ratio is 1.27...1.37 times larger. The method has been used to study MP bearings with several common groups of tribopolymers used for bushings: PA, PTFE, PET, and PEEK. The maximum contact pressures and durability of dry friction bearings have been calculated taking into account the load, radial clearance, bushing thickness, elasticity characteristics of tribopolymers, their wear resistance, sliding friction coefficient, and type of tribopolymer materials. (4) The paper establishes quantitative and qualitative regularities of the influence of these factors on the maximum contact pressures and bearing durability.

Studies on integrating Spiking Neural Networks (SNNs) with the Transformer architecture holds promise for enabling models to achieve ultra-low energy consumption while possessing the performance of the Transformer architecture. Currently, studies on ANN-to-SNN conversion of integrating Spiking Neural Networks (SNNs) with the Transformer architecture mainly focuses on simple activation functions in MLPs, and has not yet addressed the mismatch between the Softmax activation function in the self-attention mechanism and the computation rules of SNNs. Consequently, the ANN-to-SNN conversion efforts have consistently failed to make the Transformer architecture directly applicable to SNNs. To address this challenge, we propose the Spiking-Softmax method, which integrates Spiking Exponential Neuron (SI-exp) and Spiking Collaboration Normalized Neuron (SI-norm). The Spiking-Softmax method accurately simulates the Softmax activation function with only 12 time steps. Building upon this, we propose the Spike Integrated Transformer conversion (SIT-conversion) method, which enables the conversion of the Transformer architecture to SNNs. The SNNs generated by the SIT-conversion of Transformer models of various sizes achieve accuracy nearly identical to their ANNs counterparts, achieving nearly lossless and ultra-low-latency ANN-to-SNN conversion. This work represents the first implementation of simulating the Softmax activation function and fully converting the Transformer architecture into SNNs through spike firing.

This paper proposes a new type of solar trough collector with cylindrical mirror spliced and develops a new ray tracing method to predict and optimize its performance. The mirrors of this system are composed of multiple cylindrical mirrors whose centres are on a parabola and the normal vector of the centers of each cylindrical mirror is consistent with the normal vector of the parabola point where it is located. This paper compares the cylindrical mirror spliced trough solar system with the conventional parabolic trough system and finds that the influence of cylindrical spherical and coma aberration can be reduced to negligible levels by adjusting the system design. At the same time, the slope error and cost of the cylindrical mirror is much less than the parabolic mirror so it has better performance from numerical simulation. On the vernal equinox, the net efficiency of the cylindrical mirror spliced system using a cavity receiver is 22.39% higher than that of the conventional trough system using vacuum tube receiver. The cylindrical mirror spliced system can be further optimized to achieve an annual net efficiency of 60.2% in the north-south horizontal axis tracking mode.

This article reports the preparation of a novel functionalized magnetic nanoadsorbent through an addition reaction between Fe3O4 nanoparticles coated with allyl silica gel and 4-pyridinyl ethylene, detailed characterization of Fe3O4@SiO2-yl-VP was conducted. Among them, in the infrared spectrum, we can easily see that the absorption peak of C=C stretching vibration at 1660 cm-1 in the raw material disappears after the addition reaction, indicating the successful grafting of polymer on the surface of silica gel. The appearance of N element in the EDS spectrum also proves the successful completion of the addition reaction and the successful synthesis of Fe3O4@SiO2-yl-VP. At pH=5 and pH=7, it only takes half an hour for Fe3O4@SiO2-yl-VP to achieve maximum adsorption capacities of 85.06 and 73.78 mg/g for Hg (II) and Pb (II), respectively. The adsorption process conforms to the Langmuir model and pesudo first and pesudo second order kinetic models, and can be reused 11 times, demonstrating excellent reusability.

The aim of the present study was to evaluate the efficacy and safety of the long-term use of a dietary supplement containing red yeast rice (RYR), combined with other natural compounds, in children and adolescents with primary hypercholesterolemia. A nutraceutical, containing RYR, polycosanols, coenzyme Q10, astaxanthin and folic acid (commercial name: Armolipid), was administered once-daily in 84 children/adolescents with moderate or severe primary hypercholesterolemia. Moreover, 19 of the participants consumed 1.5-2.5g of phytosterols daily until the initiation of dietary supplementation with Armolipid. Clinical and laboratory evaluation took place before and 6 and 16 months after treatment. Nutraceutical consumption resulted in a significant decrease in total cholesterol, low density lipoprotein - cholesterol, non-high density lipoprotein – cholesterol and apolipoprotein B levels, that remained at the long-term administration (p

Colorectal cancer (CRC) arises through a combination of genetic and epigenetic alterations that affect key pathways involved in tumor growth and progression. This review examines the major molecular pathways driving CRC, including Chromosomal Instability (CIN), Microsatellite Instability (MSI), and the CpG Island Methylator Phenotype (CIMP). Key mutations in genes such as APC, KRAS, NRAS, BRAF, and TP53 activate signaling pathways like Wnt, EGFR, and PI3K/AKT, contributing to tumorigenesis and influencing responses to targeted therapies. Resistance mechanisms, including mutations that bypass drug action, remain challenging in CRC treatment. The review highlights the role of molecular profiling in guiding the use of targeted therapies such as tyrosine kinase inhibitors and immune checkpoint inhibitors. Novel combination treatments are also discussed as strategies to improve outcomes and overcome resistance. Understanding these molecular mechanisms is critical to advancing personalized treatment approaches in CRC and improving patient prognosis.

Large language models (LLMs) get quickly implemented into question answering and support systems to automate customer experience across all domains even including medical use cases. Models in such environments should solve multiple problems like general knowledge questions, queries to external sources, function calling and many others. Some cases might not even require a full-on text generation. They possibly need different prompts or even models. All of it can be managed by a routing step. This paper focuses on interpretable few-shot approaches for conversation routing like latent embeddings retrieval. The work here presents a benchmark, a sorrow analysis, and a set of visualizations of the way latent embeddings routing works for long-context conversations in a multilingual, domain-specific environment. The results presented here show that latent embeddings router is able to achieve performance on the same level as LLM-based routers with additional interpretability and higher level of control over model decision making.

Curved continuous steel box girders are extensively utilized in bridge construction due to its efficiency and environmental benefits. However, in regions with significant temperature fluctuations, temperature effects can result in cumulative deformation and stress concentration, which may severely impact structural safety and durability. This study examines the structural response of curved continuous steel box girders under diverse temperature conditions and also develops a comprehensive parameterized thermodynamic numerical model. The model assesses the influence of cross-sectional shape parameters including; the number of cross-sectional box chambers, diaphragm thickness, and height-to-width ratio, as well as longitudinal structural parameters such as planar configurations, width-to-span ratio, and support arrangements, along with the arrangement of stiffening ribs on the temperature-induced effects in the girders. Results indicate that optimizing the width and eccentricity of support stiffeners to 30% and 25% in the support plate size, respectively, can significantly alleviate local temperature-induced stresses. Additionally, variations in longitudinal and transverse stiffeners manifest minimal impact on thermal performance. These findings provide a theoretical foundation for improved design and construction practices, offering practical design recommendations to mitigate temperature effects and enhance the longevity and safety of such structures.

Background: During normal development, epithelial cells can experience alterations at transcriptional and morphological levels, which can trigger a process named the epithelial-mesenchymal transition (EMT). The EMT underpins changes in cytoskeleton dynamics that are characteristic of neoplastic transformation. Described in many tumors, including colorectal cancer (CRC), over the past ten years, although it is an essential process in cancer development it is still poorly understood. Once initiated, the EMT regulates metastasis, the microenvironment and immune system resistance in CRC. Overexpression of the pro-inflammatory molecule inorganic polyphosphate (iPolyP) has been shown to play important role in CRC progression, alongside its binding receptor called transient receptor potential cation channel subfamily M (melastatin) member 8 (TRPM8). In this study we tested whether iPolyP/TRPM8 axis regulates EMT changes as well as NLRP3 inflammasome activation within the tumor microenvironment (TME) of CRC. Methods: To investigate these issues, western blotting, fixed and live cells immunofluorescence, 2D and 3D cell culture on CRC-patient derived biopsies, ELISA and wound healing assays were performed. Results: iPolyP triggers the expression of several EMT markers in CRC cell lines compared to control. Pharmacological inhibition of TRPM8 receptor restricts the migratory effects of cancer cells due to the presence of iPolyP. Moreover, the iPolyP/TRPM8 axis also displays an indirect pro-tumorigenic performance through shaping the immune microenvironment. By triggering NLRP3 inflammasome activation, iPolyP ensures high levels of the pro-inflammatory cytokine IL-1, which foster CRC carcinogenesis. Conclusion: This study suggests that the iPolyP/TRPM8 signaling axis is a promising target for impeding CRC progression and spread.

Utilizing reconfigurable intelligent surfaces (RIS) equipped with numerous passive reflecting elements to control wireless communication environment has garnered significant interest from both academic and industrial circles. In this paper, we study the performance of RIS-aided wireless communication systems based on the path loss model in lower frequency band and mmWave band. Based on the proposed model, it is difficult to directly study and obtain the closed-form expressions for the achievable capacity, since that millimeter- wave (mmWave) band may lead to the near-field communication (NFC) environment. Consequently, the upper bound of the achievable capacity are analysed with Hölder inequality in NFC. Simulation and analytical results indicate the correctness of our derived expressions, which shows that the RIS elements number, the RIS elements length, and the angle of signals transmitting from RIS to user can affect the upper bound of achievable capacity for our proposed system.

This paper presents the results of numerical simulations examining the thermodynamic processes during hydraulic hydrogen compression, using COMSOL Multiphysics® 6.0. These simulations focus on the application of hydrogen compression systems, particularly in hydrogen refueling stations. The computational models employ the CFD and Heat Transfer modules, along with deforming mesh technology, to simulate gas compression and heat transfer dynamics. The superposition method was applied to simplify the analysis of hydrogen and liquid piston interactions within a stainless-steel chamber, accounting for heat exchange between the hydrogen, the working fluid, and the cylinder walls. The study investigates the effects of varying compression stroke durations and initial hydrogen pressures, providing detailed insights into temperature distributions and energy consumption under different conditions. The results reveal that the upper region of the chamber experiences significant heating, highlighting the need for efficient cooling systems. Additionally, the simulations show that longer compression strokes reduce the power requirement for the liquid pump, offering potential for optimizing system design and reducing equipment costs. This study offers crucial data for enhancing the efficiency of hydraulic hydrogen compression systems, paving the way for improved energy consumption and thermal management in high-pressure applications.

Background/Objectives: Potassium iodate and potassium iodide were commonly fortified in iodized table salt, where continuous monitoring is needed to maintain the quality. Our study reported an optimized detection method for total iodine in iodized table salt using 0.5 M sodium bisulfite as reducing agent; Methods: The iodized table salt (0.5 g) was dissolved in 0.5 M sodium bisulfite solution prior to injection in ultra high-performance liquid chromatography (UHPLC) coupled with diode array detector using weak anion-exchange column (2.1 mm×150 mm, 5 μm); Results: Iodide was eluted at 9.92±0.06 minutes (λ=223 nm) when isocratic mobile phase of 1:1 (v/v) methanol:120 mM phosphate buffer mixed with tetrasodium pyrophosphate (pH 3.0) was running at 0.20 mL/min (15 minutes). Iodide detected as total iodine from 10 to 60 mg/Kg with limit of detection (LOD) 1.2 mg/Kg and limit of quantification (LOQ) 3.7 mg/Kg. The method was validated with relative standard deviation (RSD) of 4.2%, 0.4%, 1.6%, 0.8% for accuracy, repeatability, intermediate-precision and robustness, respectively. The determination of total iodine was successful on six (6) samples (n=3), which recovered 87.2%-106.9% of iodate and iodide spike; Conclusions: Thus, the study provided a validated protocol for the determination of total iodine in iodized table salt using 0.5 M sodium bisulfite.

Currently, there is an increasing interest to develop eco-friendly and efficient pigment extraction processes for diverse applications. In this study, we report the comparison between the assisted extraction performed by ultrasonication and microwave with a conventional method to obtain cochineal pigments from Dactylopius coccus Costa. Our independent variables were solvent amount, temperature and irradiation time, and the corresponding optimization was performed by three factors and three levels with a Box-Behnken design. The pigment contents were quanti-fied through UV-vis spectroscopy, and the pigment mixture of extracts was assessed by infrared spectroscopy. It is highlighted that after optimization, the extraction assisted by ultrasound gave a yield of 47% with an efficiency of 31.3 mg/min, whereas the yield obtained from the extraction assisted by microwave was 41% with 27.3 mg/min efficiency, both higher that the efficiency ob-tained with a conventional method (3.5 mg/ml).

The type of review presented to the reader contains a reflection on the aberrations related to the wound healing process that often result in real pathologies or malformations. The reader is therefore invited through literature updates to reflect on the complex problem of wound healing and then gain knowledge on the problem of chronic wounds, keloids and hypertrophic scars. These problems are relevant in everyday life but still today they are not resolved and there is no knowledge of the cellular mechanisms present. Once again, as has been widely debated in recent years, knowledge of the cellular and molecular mechanisms underlying these pathologies is fundamental for the treatment of these.

Wireless Sensor Networks are at the center of scientific interest thanks to the ever growing range of their applications. The main weakness of Wireless Sensor Networks is the restricted lifetime of their sensor nodes due to limited energy capacity. The extension of the lifespan of sensor nodes is pursued in various ways. One of them, is the usage of protocols that achieve energy efficient routing. LEACH is one of the pioneering protocols of this type and has numerous descendants. This research article focuses on energy efficient routing protocols that are based on LEACH. Specifically, a study of LEACH along with many of its successors is provided. In addition, a novel protocol of this kind, named T-LEACHSAS is proposed. This protocol is actually an extension of T-LEACH, which is one of the most well-known descending protocols of LEACH. The performance of T-LEACHSAS is compared against that of T-LEACH via simulation tests that validate the protocols' strengths and weaknesses in various scenarios. Finally, concluding remarks are indicated.

The field of 3D model generation has become essential across various industries, including gaming, virtual and augmented reality (VR/AR), architecture, and medical imaging. Traditionally reliant on manual efforts, 3D content creation is now being transformed by deep generative models, enabling more efficient, scalable, and dynamic generation of complex shapes and environments. This survey provides a comprehensive review of key backbone architectures used for 3D generation, including autoencoders, variational autoencoders (VAEs), generative adversarial networks (GANs), autoregressive models, diffusion models, normalizing flows, attention-based models, CLIP-guided models, and procedural generation techniques. We explore each model’s role in 3D generation, highlighting their strengths—such as the precision of VAEs, the realism of GANs, the stability of diffusion models, and the scalability of procedural methods—alongside their limitations, such as training instability, high computational costs, and the difficulty in handling multi-modal data. Additionally, we discuss the increasing relevance of attention-enhanced models and the integration of text-based CLIP supervision for improved semantic alignment in 3D outputs. The survey concludes with an analysis of open challenges, including balancing efficiency with expressiveness, managing training complexity, and addressing dataset limitations. It also identifies future research directions, such as few-shot learning, hybrid architectures, and neural-symbolic approaches, which promise to advance the field by improving the generalization and versatility of 3D generation models. This paper aims to guide researchers and practitioners in navigating the evolving landscape of 3D generative methods and inspire new innovations in the creation of realistic, high-quality 3D content.

Cardiovascular diseases (CVDs), which comprise coronary heart disease, hypertension and stroke, collectively represent the number one cause of death globally. Atherosclerosis is the dominant cause of CVDs; and its risk factors are elevated levels of low-density lipoprotein-cholesterol and triglycerides, hypertension, cigarette smoking, obesity and diabetes mellitus. In addition, diverse evidence highlighted the role played by inflammation and clonal haematopoiesis, eventually leading to the immunity involvement. The human microbiota project and subsequent studies using next-generation sequencing technology have highlighted that thousands of different microbial species are present in the human gut. The disturbances in the gut microbiota (GM) composition, i.e., gut dysbiosis, have been associated with diseases ranging from localized gastrointestinal disorders to metabolic and cardiovascular illnesses. Of note, experimental studies suggested that GM, host immune cells and marine-derived ingredients work together to ensure the intestinal wall integrity. This review discusses current evidence concerning the links among GM, marine-derived ingredients and human inflammatory disease. In detail, we summarize the impact of fish-derived proteins and algae components, on CVDs risk factors and gut microbiome. Furthermore, we describe the interplay among these dietary components, probiotics/prebiotics and CVDs.

The paper presents the results of experiments on measuring cross sections for the neutron transfer channels 9Be(3He,α)8Begs,3.03 in the reaction of the 3He (30 MeV) ions with the 9Be target. To describe the angular distributions, we use the Distorted Wave Born Approximation (DWBA) method (FRESCO code). The results of the theoretical analysis are in agreement with the experimental data. In addition, we perform calculations based on the solution of the time-dependent Schrödinger equation (TDSE) for the weakly bound neutron of the 9Be nucleus. The TDSE approach allows us to determine the dynamics of the neutron transfer process and calculate the probabilities for the transfer and removal of the neutron of the 9Be nucleus in the 3He+9Be reaction.

Autism in childhood is a heterogeneous disease with around 110 phenotypes. Around 800 genes are affiliated with autism including members of neuro-ligand, neurexin, cadherin, GABA receptors, SHANK gene families, mutated UBE3 A on chromosome 15 and SNORD 116 precursor interaction. A predominant 4:1 male to female ratio is found in autistic spectrum disorder. 50 per cent of all autistic children show chromosome deletions and duplications, these are often found on the 15th and 16th chromosome. Copy number repeat variants in DNA are also well described in pathogenesis of autism patients. There is an overlap with other neurodevelopmental syndromes like tuberous sclerosis, Williams-syndrome, Phelan McDermid syndrome and Sphrintzen syndrome. The hypothesis of the term “atypical connectivity” in different brain regions with partial under- and overconnectivity with reduced brain networking at the psychosocial level was described. Different hypothesis about the origin of autism in children were described. The hypothesis of early lack of basic trust, mercury intoxication and different aspects concerning the origin of this extraordinary disease of 2 percent of children were stated but not confirmed to date. Especially low immature production of IgF-1 by oligodendrocytes in the corpus callosum leads to slowing of the PI3K/AKT chain activation of myelination that Ig-F1 could play an important role in the origin of the disease. Synaptic dysfunction with hypomyelination and impaired impulse transmission seem to play an extraordinary role with under- and overconnectivity with reduced brain networking at the psychosocial level in autistic children. Functional underconnectivity is found in 5 different brain areas, prefrontal, parieto-occipital, motor, somatosensory and the temporal region. Functional overconnectivity is often present in temporo-thalamic regions. Recent research shed light on synaptic dysfunctions with disrupted normal impulse signaling. In this review the different neurochemical findings and the correlation of synaptic dysfunction in autistic children will be closely evaluated.

Purpose: To evaluate the impact of intravitreal bevacizumab (IVB) therapy on anatomical and visual outcomes in patients with macular neovascularization (MNV) secondary to chronic cen-tral serous chorioretinopathy (cCSC). Methods: This retrospective observational study reviewed the medical records of treatment-naïve patients diagnosed with cCSC complicated by MNV, treated with IVB injections over a 5-year period. The presence of MNV was confirmed using op-tical coherence tomography angiography (OCTA). Best-corrected visual acuity (BCVA), subfoveal choroidal thickness (SFCT), and subretinal fluid (SRF) were recorded pre- and post-IVB treat-ment. Results: Twenty-two eyes of 22 patients (mean age, 68 ± 11 years) were included. After a mean follow-up of 21.0 ± 14.6 months, SRF significantly decreased from baseline (176.86 ± 115.62 µm) to the final follow-up (80.95 ± 87.32 µm, P = 0.003). Greater SRF reduction was associated with more injections (>7) (P = 0.047). However, no significant changes were observed in BCVA (P > 0.05) or SFCT (P > 0.05), irrespective of follow-up duration or injection frequency. Complete res-olution of SRF was achieved in 9 patients (40.9%), and a significantly greater reduction in SFCT was observed in complete responders compared to non-responders (P = 0.03). Conclusion: IVB therapy significantly reduced SRF in cCSC patients with secondary MNV, though it did not lead to visual improvement or significant changes in SFCT. However, greater choroidal thinning in patients with complete fluid resorption may suggest distinct underlying mechanisms or alterna-tive sources of subretinal fluid beyond the MNV itself.

The dynamic nature of quadrotor flight introduces significant uncertainty in system parameters, such as thrust and drag factor. Consequently, operators grapple with escalating challenges in implementing real-time control actions. This study delves into an approach for estimating the model of quadrotor Unmanned Aerial Vehicles using Physics-Informed Neural Networks (PINNs) when you have a limited amount of data available. PINNs offer the potential to tackle issues like heightened non-linearities in low-inertia systems, elevated measurement noise, and constraints on data availability. The effectiveness of the estimator is showcased in a simulation environment with real data and juxtaposed with a state-of-the-art technique, such as the Extended Kalman Filter (EKF).

Cancer ranks among the primary contributors to global mortality. In 2022, the global incidence of new cancer cases reached about 20 million, while the number of cancer-related fatalities reached 9.7 million. Exclusively in Saudi Arabia, there are 13,399 deaths caused by cancer and 28,113 newly diagnosed cases of cancer. Drug repurposing is a drug discovery strategy that gained special attention and implementation to enhance the process of drug development due to its time and money saving effect. It involves repositioning existing medications to new clinical applications. Cancer treatment is one of the most therapeutic areas where drug repurposing has shown prominent impact. This review presents a compilation of medications that have been repurposed for the treatment of various types of cancers. It describes the initial therapeutic and pharmacological classes of the repurposed drugs and their new applications and mechanism of action in cancer treatment. The review reports on drugs from various pharmacological classes that have been successfully repurposed for cancer treatment including approved ones and those in clinical trials and preclinical development. It stratifies drugs based on their anticancer repurpose as multi-type, type-specific, and mechanism directed and according to their pharmacological classes. The review also reflects on the future potential that drug repurposing has in the clinical development of novel anticancer therapies.

Abstract

This review examines the progressive separation of South America and Africa, which began approximately 145 million years ago during the Early Cretaceous and completed 100 million years ago. The rifting process created the South Atlantic Ocean and profoundly influenced vertebrate evolution on both continents. Initially connected as part of Gondwana, the gradual separation led to vicariant speciation and distinct evolutionary trajectories for previously continuous vertebrate populations. The separation particularly affected terrestrial vertebrates, resulting in parallel evolution of similar ecological niches on both continents, while creating opportunities for endemic lineages to develop. This process explains many modern distribution patterns and evolutionary relationships between South American and African vertebrate taxa, including parallel radiations in groups such as titanosaur dinosaurs during the Cretaceous and later parallel developments in mammals during the Cenozoic. The isolation also contributed to unique characteristics in each continent's modern vertebrate assemblages, such as the distinctive nature of South American primates and African ungulates.

This article examines the integration of artificial intelligence (AI) and robotic process automation (RPA) in financial accounting and management, underscoring their role in driving the digital transformation of corporate finance. It discusses the shortcomings of traditional financial processes and highlights the potential of AI and RPA technologies to enhance efficiency, accuracy, and cost-effectiveness. The paper also explores the limitations of RPA, such as its challenges in processing unstructured data and handling complex decision-making scenarios. Looking forward, it considers the future trends in AI and RPA, emphasizing the benefits of cloud technology in scaling automated systems and addressing associated challenges.

Ventilation, cooling, and heating systems play a crucial role in providing thermal comfort within occupied environments, influencing productivity, well-being, health, and energy consumption. This research focuses on leveraging Computational Fluid Dynamics (CFD) as a tool for studying fluid flows and analyzing heating, ventilation, and air conditioning (HVAC) systems to achieve ideal room temperatures efficiently. Initially, the thermal comfort study is conducted using the OpenFOAM software, aiming to compare and validate its algorithm against a reference article that utilized proprietary software. By demonstrating the capabilities of OpenFOAM as an open-source alternative, this research opens opportunities for analyzing HVAC systems using an accessible program, facilitating the optimization of climatization strategies. Subsequently, an analysis of ventilation systems is performed through a factorial design that involves altering the positions of air inlets and outlets, as well as adjusting the insufflation velocity. The findings reveal that superior positions of air inlets lead to improved thermal comfort results, as measured by the Air Diffusion Performance Index (ADPI). This research provides an insights in the configuration of ventilation and air conditioning systems to enhance occupant’s thermal comfort. By utilizing CFD simulations and exploring several parameters affecting thermal comfort, this research contributes to advancing HVAC system analysis.

This study examines the dynamic fluid behavior of a PWM-controlled Solenoid-Operated Gas Admission Valve (SOGAV) for large-bore CNG engines using 3D CFD simulations with dynam-ic mesh techniques. The research focuses on the influence of orifice geometry variations in the multi-hole restrictor and pressure differentials between the inlet and outlet on flow stability, turbulence, and valve performance. Results demonstrate that multi-hole restrictors with differ-ent-sized orifices improve flow uniformity and reduce turbulence, thereby mitigating flow re-sistance. Transient simulations further reveal standing wave formation and pressure wave in-terference, emphasizing that steady-state models cannot capture critical transient phenomena, such as accelerated and decelerated jet-like flows and flow separation. These findings provide key insights into SOGAV optimization, contributing to enhanced fuel efficiency and engine re-sponsiveness, meeting the performance requirements of modern gas engines.

Networks and their properties have been used to study complex biological systems. Recently, network curvature measures have demonstrated the ability to capture relevant net- work properties. This study employs network curvature measures to analyse gene expression correlations in various human tissues for identifying unique topological features that differentiate these groups. Preliminary findings suggest that curvature measures offer novel insights that could enhance our understanding of the biological systems.

This in vitro study evaluated the protective effect of a Si-containing toothpaste associated with different fluoride concentrations by reducing the depth of demineralized lesions in dentin after cariogenic pH cycling. Methods: Sixty bovine dentin blocks (4x4x2mm) were randomly assigned into five groups (n = 12/group): RGS1 (Si-toothpaste with 1450 ppm F), RGS2 (Si-toothpaste with 1100 ppm F), RGS3 (fluoride-free Si-toothpaste), negative control-NC (fluoride-free toothpaste), and positive control-PC (1450 ppm F toothpaste). The specimens underwent a three-day pH cycling model simulating the oral environment with twice-daily brushing. Mineral loss (ΔF) and lesion depth (ΔFmax) were assessed using quantitative light-induced fluorescence (QLF), and scanning electron microscopy (SEM) provided morphological analysis. Elemental analyses were determined with an energy-dispersive X-ray spectrometer (EDS). ANOVA/Tukey's test were used for data analysis (α = 5%). Results: The groups treated with fluoride silicon-toothpastes (RGS1, RGS2) significantly reduced the mineral loss and lesion depth compared to the PC group. The NC group exhibited the greatest mineral loss and lesion depth. SEM images revealed occluded superficial dentin tubules and a protective mineral layer in groups treated with Si-toothpaste, whereas no deposition (NC group) or a limited mineral deposition (PC group) was observed in the control groups. EDS confirmed Si in the toothpaste formulations. Conclusions: Findings suggest that Si-toothpastes favored protection against dentin demineralization even at a lower concentration or free of fluoride, with dentin tubule occlusion, thus consisting of a promising oral care product over traditional fluoride toothpastes.

Starting from accelerated industrial development, industrial disasters (chemical spills, explosions, nuclear accidents, etc.), which fall under man-made (technological) disasters, increasingly threaten human lives, the environment, and property. Their complexity and far-reaching consequences, both at the local and global levels, require effective management of industrial risks and the disasters themselves (preparedness, mitigation, response, and recovery). For these reasons, the review paper aims to provide a holistic description of the diverse causes, dynamics, and multidimensional consequences of disasters. Additionally, it highlights and explains the key factors that directly or indirectly contribute to their occurrence. Furthermore, it could be said that the paper analyzes existing strategies for managing risks from industrial disasters and systematizes recommendations for improving preventive and reactive measures in high-risk industries. On the other side, the paper utilizes a comprehensive literature review, which involves the systematic identification, review, analysis, and critical evaluation of existing scientific papers, studies, reports, and regulatory documents on industrial disasters. Besides that, it also includes a detailed analysis of well-known industrial disaster cases such as Seveso (Italy, 1976), Bhopal (India, 1984), Chernobyl (Ukraine, then USSR, 1986), and Fukushima (Japan, 2011). The paper emphasizes that industrial disasters are often the result of the combined influence and interaction of technical failures, human errors, and inadequately developed safety procedures. Moreover, it can be highlighted that the design and implementation of preventive measures, such as regular maintenance of technical systems and systematic training and education of employees, are crucial for reducing the likelihood of such catastrophic events. The implications of this review paper are multifaceted and point to the following: the need to reassess current safety practices in industries; reconsider the potential for introducing innovations in preventive technical-technological solutions; examine the thorough implementation of innovative early warning systems, safety procedures, and modern equipment in facilities; improve cooperation between engineers, safety managers, and regulatory workers; reassess the need for additional updates and tightening of regulations regarding the storage, transport, and use of hazardous materials in industry; and improve the management of risks from industrial disasters.

The aim of this paper is the analysis of arbitrarily loaded isotropic rectangular Kirchhoff plates supported at all corner points, whereby the edges are arbitrarily supported (simply supported, clamped, or free). Analytical methods commonly used include the single trigonometric series of Lévy, the double trigonometric series of Navier, the power series expansions, etc. In this paper the deflection surface was approximated with the sum of a particular solution to the governing differential equation (GDE) and two single series. The terms of the single series were the product of an unknown function of an independent variable and a trigonometric function of the other independent variable, whereby the trigonometric functions were chosen to vanish at the corner points. On the one hand the terms of the series were required to satisfy the homogeneous GDE, leading to two uncoupled differential equations, one for each unknown function, and so the approximate solution satisfied exactly the GDE. On the other hand the boundary conditions were satisfied only at selected collocation points along the boundary, the number of collocation points in each direction corresponding to the number of terms of the associated series. Results for several plates supported at all corner points with arbitrary edge support conditions were presented and showed good agreement with the exact results, the accuracy increasing the more terms of series were considered. Cantilevered plates and plates having two adjacent edges free will be analyzed using this approach in future research.

We numerically investigate the relationship between the main parameters of thinned antenna arrays using a specifically designed evolutionary algorithm, the Multi-Objective Pareto Evolution for Thinning (MOPET). We provide some useful results that allow assessing the achievable performance of antenna arrays and help researchers and practitioners design radar, 5G, and 6G systems.

This study describes the efficient synthesis of five dinitrochalcones (DNCH) using an ultrasonic bath as an unconventional method to improve reaction yields and reduce reaction times. The Claisen-Schmidt condensation of nitroacetophenones and nitrobenzaldehydes was carried out in a cyclohexane-methanol solvent system under ultrasonic irradiation, achieving yields between 56% and 92%. The application of ultrasound not only accelerated the reaction but also improved the overall efficiency compared to conventional methods such as magnetic stirring. Characterization of the synthesized compounds was performed by NMR spectroscopy, confirming the consistency of their structures. Thus, it is confirmed that obtaining DNCH with a nitro group in ortho provide by ultrasonic irradiation is an energetically efficient and environmentally friendly alternative.

Floating offshore wind turbine (FOWT) systems are advancing quickly, but they still need to become economically viable for large-scale commercial adoption. Design optimization is essential for reaching this objective by reducing costs while ensuring the best possible system performance and structural integrity. The present study aims to carry a comparative multi objective optimization (MOO) of a 10MW FOWT semisubmersible using three different metaheuristic optimizations technics and sophisticated approach for optimizing a floating concept through the utilization of global limit states. The optimization is performed in Python, integrating PyMAPDL and PyMOO for intricate modeling and simulation tasks. The ZJUS10 floating offshore wind turbine (FOWT) platform, developed by the state key laboratory of wind power at Zhejiang University, is employed as the basis for this study. Key criteria, such as platform stability, overall structural mass, and the stress, are pivotal in formulating the objective functions. Based on a preliminary study the three Metaheuristic Optimization Algorithms chosen for optimization are Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO). Then, the solutions are evaluated based on Pareto dominance, leading to a Pareto front, a curve that represents the best possible trade-offs among the objectives. Each algorithm's convergence is meticulously evaluated, leading to the selection of the optimal design solution. The results evaluated in simulations elucidate the strengths and limitations of each optimization method, providing valuable insights into their efficacy for complex engineering design challenges. In the post-processing phase, the performance of the optimized FOWT platforms is thoroughly compared both among themselves and with the original model, resulting in validation and endorsement.

Background: Bariatric surgery is a safe and effective procedure for treating obesity and metabolic conditions such as type 2 diabetes mellitus (T2DM). Remodeling of the extracellular matrix (ECM) supports adipose tissue expansion and its metabolic activity, where matrix metalloproteinases (MMPs) play a key role in ECM regulation. The substrates of MMPs, particularly MMP-2 and MMP-9, are elevated in patients with morbid obesity, metabolic syndrome, and T2DM. Objectives: To evaluate the effect of weight loss in bariatric surgery patients using oxidative stress markers and to compare MMP levels in patients undergoing Roux-en-Y gastric bypass (RYGB) and vertical gastrectomy (VG). Methods: This was a prospective, controlled study including 45 morbidly obese patients with T2DM (BMI > 35 kg/m²) who underwent RYGB (n=24) or VG (n=21). Weight loss was assessed through anthropometric measurements (weight, height, BMI). MMP-2 and MMP-9 levels were measured preoperatively and at 3 and 12 months postoperatively. Results: Significant and sustained weight loss was observed after surgery in both groups, with reductions in BMI. MMP-2 and MMP-9 levels decreased significantly after one year of follow-up. Conclusions: Bariatric surgery is an effective long-term intervention for weight loss and associated comorbidities, including T2DM. MMP-2 and MMP-9 proved to be effective inflammatory markers, with significant reductions following surgery.

This study explores the extraction and characterization of proteolytic enzymes from brewer's spent grain (BSG) and their potential as sustainable coagulants in the dairy industry. BSG samples from various beer types (Blonde Ale, IPA, Kölsch, Honey, and Porter) were obtained from two artisanal breweries in Mar del Plata, Argentina. Optimization of caseinolytic activity (CA) and protein extraction was conducted using a Plackett-Burman design, followed by a Box-Behnken design. Optimal protein concentration was achieved at intermediate pH and high temperature, while CA peaked at pH 8.0. The specific caseinolytic activity (SCA) varied among the extracts, with BSG3 showing the highest activity (99.6 U mg⁻¹) and BSG1 the lowest (60.4 U mg⁻¹). Protease inhibitor assays suggested the presence of aspartic, serine, metallo, and cysteine proteases. BSG3 and BSG4 showed the highest hydrolysis rates for α-casein (70% and 78%). For κ-casein, BSG1, BSG2, and BSG3 demonstrated moderate activity (56.5%, 49%, and 55.8), while BSG4 and BSG5 exhibited the lowest activity. Additionally, the milk-clotting activity (MCA) of BSG extracts was comparable to plant-based coagulants like Cynara cardunculus and Ficus carica. These findings highlight the potential of BSG-derived proteases as alternative coagulants for cheese production, offering a sustainable link between the brewing and dairy industries.

In this paper, we model uncertainty in both the objective function and the constraints for the robust semi-infinite interval equilibrium problem involving data uncertainty. We particularize these conditions for the robust semi-infinite mathematical programming problem with interval-valued functions by extending results from the literature. We introduce the dual robust version of the above problem, prove the Mond-Weir type weak and strong duality theorems, and illustrate with an example.

This study examines the key factors influencing E-bike adoption and explores how advancing E-bike usage in Henan Province, China, can foster sustainable urban transportation and contrib-ute to urban environmental preservation. Utilizing data from an online survey, binary logistic re-gression analyzes the impact of socio-demographic characteristics, perceived advantages, neigh-borhood environmental attributes, and vehicle ownership on E-bike usage. Findings indicate that socio-demographic factors, such as family size and occupation, significantly influence adoption, with workmen more likely than office workers to choose E-bikes. Cost savings emerged as the primary motivator for E-bike use, overshadowing environmental concerns, which unexpectedly negatively affected usage patterns. However, the presence of supportive infrastruc-ture—particularly charging stations and dedicated lanes—proves crucial for promoting E-bike usage, highlighting the importance of accessible, environmentally supportive urban design. Vehi-cle ownership characteristics further illuminate how access to E-bikes correlates with regular us-age. These findings suggest that, beyond cost-efficiency, targeted awareness campaigns and stra-tegic infrastructure improvements are essential for embedding E-bikes into sustainable urban transport systems. By fostering adoption and supporting E-bike infrastructure, cities can signifi-cantly reduce urban pollution, improve air quality, and advance toward sustainable mobility goals in Henan Province and beyond.

Over the past few decades, the landscape for multiple myeloma (MM) therapy has significantly advanced, largely due to the approval and introduction of new-generation proteasome inhibitors (PIs) and immunomodulatory drugs (IMiDs). Despite these advancements, MM remains incurable. In March 2021, the U.S. FDA approved the chimeric antigen receptor T-cell (CAR-T) therapy idecabtagene vicleucel (ide-cel) for relapsed/refractory multiple myeloma (R/R MM), heralding the advent of cellular therapies for R/R MM. However, due to factors such as the downregulation or loss of tumor antigen expression, T-cell exhaustion, and the influence of the tumor immune microenvironment, most R/R MM patients inevitably experience relapse following CAR-T cell therapy. Consequently, salvage therapy in the post-CAR-T setting has emerged as a critical area of research. This review discusses the potential factors leading to CAR-T therapy failure in R/R MM patients and discusses subsequent salvage therapeutic strategies, offering recommendations for addressing treatment failure in this context.

Clinicians have long faced challenges and decisions regarding sodium management in their patients. They must have a good understanding of the physiology of sodium, its metabolism, distribution in the body, and the consequences of changes in sodium content and concentration seen frequently in different settings of patient care and across different specialties. Multiple studies and several formulas are currently available to help manage cases with dysnatremia. These tools require a detailed focus to benefit patients in the short and long term and to avoid consequences due to poor decisions leading to severe and potentially fatal complications, including osmotic demyelination syndrome. This review will consider different aspects of sodium metabolism, the role sodium has in patients with edema, the evolution sodium management based on Edelman’s studies and related formulas, and other considerations regarding sodium storage in the skin currently relevant for a more in-depth approach to the management of patients with dysnatremias.

Customer churn is a critical challenge for subscription-based businesses, especially in telecommunications, where retaining customers is essential to maintaining profitability. This study investigates the efficacy of two ML models, XGBoost and Random Forest, for predicting customer churn using a publicly available telecommunications dataset. The dataset, characterized by imbalanced classes, presents a crucial challenge addressed by incorporating the Gaussian Noise Upsampling (GNUS) sampling technique. The study evaluates and compares the two models using essential performance indicators, including precision, recall, accuracy, F1-score, and ROC-AUC, both with and without GNUS sampling. The results indicate that while XGBoost initially outperforms Random Forest across most metrics, both models show improved recall after the GNUS application, particularly in identifying churn cases. However, this improvement in recall comes with a trade-off in precision and overall accuracy. The findings highlight the relevance of using appropriate sampling techniques to tackle class imbalance in churn prediction and provide valuable insights for developing proactive customer retention strategies.

The objective of this research was to examine apoptosis status in the laminar tissue of dairy cows with OF-induced laminitis. A total of 12 clinically healthy non-pregnant Holstein cows were randomly allocated into 2 groups of 6 cows each: the control group and the oligofructose treated group (OF group), respectively. At 0 h, 17 g/kg BW of oligofructose dissolved in 20 mL/kg BW of warm deionized water were administered to the dairy cows of OF group via a stomach tube, and dairy cows in control group were received with the said volume of deionized water by the same method. At -72 h before and 0 h, 6 h, 12 h, 18 h, 24 h, 36 h, 48 h, 60 h, and 72 h after OF overload, clinical examination in both groups were performed. After 72 h, laminar tissues of dairy cows in both groups were taken to determine genes and proteins. We observed that the gene expression of Bax (P&lt;0.01), and P53 (P&lt;0.05) significantly up-regulated, whereas that of Bcl2 significantly down-regulated (P&lt;0.01) in the afflicted cows compared to the control cows. The protein expression of Bax (P&lt;0.01), Bif1 (P&lt;0.01), caspase3 (P&lt;0.01), caspase8 (P&lt;0.05), and caspase9/9p (P&lt;0.05) significantly up-regulated, while that of Bcl2 significantly downregulated (P&lt;0.01) in the afflicted cows compared to the control cows. Moreover, the distribution of Bax (P&lt;0.01) and P53 (P&lt;0.01) proteins in laminar tissue significantly up-regulated (P&lt;0.01) in the sick cows compared with the control cows. These results suggested that unregulated genes and proteins status of apoptosis-associated indicators may be a typical cause for epidermal attachment failure which serves a key role in the pathogenesis of dairy cow laminitis. Nevertheless, further large-scale gene and protein-level studies are required to fully explain the pathogenesis of laminitis in dairy cows.

Visual examination of nails can reflect the human health status. Diseases such as nutritive imbalances and skin diseases can be identified by looking the colors around the plate part of the nails. We present the AI-based NAILS method to detect fingernails through segmentation and labeling. We built a generalized model to identify single fingers, their fingernail plates and retrieve their color data. A pre-trained deep learning Neural Network algorithm containing different images of fingernails as reference frames is used to identify different fingernails edges and the color areas around them in real-time. The novel aspect of our NAILS sensor model is that it allows to analyze –via a GUI and the use of a HD webcam, the tiniest dynamical signals from changes in fingernail colors. This feature could be used to self-extract primary signs of diseases in humans.

The urinary tract, once considered sterile, is now understood to host a diverse community of microorganisms known as the urinary microbiome. This microbiome, which is made up of bacteria, fungi, and viruses, is important for preserving urological health and has been linked to the pathogenesis of a number of urinary tract disorders. In this review, we offer a consolidated overview of the urinary tract bacterial population, including its composition, diversity, and factors influencing its dynamics. We explore its involvement in urinary tract infections, interstitial cystitis/bladder pain syndrome (IC/BPS), urinary stone formation, and other urological diseases, highlighting the importance of understanding its correlation with disease pathology for the development of therapeutic strategies. Additionally, we explore its role in autoimmune diseases of the urinary tract and the potential of leveraging the urinary microbiome for targeted interventions in the treatment of urinary tract infections. Longitudinal studies are emphasized for their ability to elucidate microbial dynamics, establish causality, and ensure consistency and reproducibility across research endeavors. These efforts underscore the necessity for continued research and multidisciplinary collaborations in this rapidly evolving field to advance our understanding and therapeutic strategies for urinary tract disorders.

This study addresses the urgent need to reduce carbon emissions in the power sector, a major contributor to global greenhouse gas emissions, by employing system engineering principles coupled with machine learning techniques. It focuses on analyzing the interplay between regional marginal prices (LMP) and carbon emissions within electricity markets. Leveraging a dataset that encompasses hourly LMP and carbon emissions data across various regions of New York State, the paper explores how market designs and operational strategies influence carbon output. The analysis utilizes neural networks to simulate and predict the effects of different market scenarios on carbon emissions, highlighting the role of LMP, loss costs, and congestion costs in environmental policy effectiveness. The results underscore the potential of system engineering to provide a holistic framework that integrates market dynamics, policy adjustments, and environmental impacts, thereby offering actionable insights into optimizing market designs for reduced carbon footprints. This approach not only enhances the understanding of the complex interactions within electricity markets but also supports the development of targeted strategies for achieving sustainable energy transitions.

The World Wide Web today is growing at a phenomenal rate. The crawling approach is of vital importance to leverage the efficiency of web crawling. The existing crawling algorithms on multicore platforms easily suffer from time consuming and can not support large data well. In order to exploit the potential parallelism and efficiency of crawling on Spark, based on the software thread level speculation, this paper proposes a Speculative parallel crawler approach (SpecCA) on Apache Spark. By analyzing the process of web crawler, the SpecCA firstly hires a function to divide the whole crawling process into several subprocesses which can be implemented independently and then spawns a number of threads to speculatively implement every subprocess in parallel. At last, the speculative results are merged to form the final outcome. Compared with the conventional parallel approach on multicore platform, SpecCA is very efficiency and leverages a high parallelism degree by adequately using the resources of the cluster. Experiments show that SpecCA could achieve a significant speedup improvement with compare to the traditional approach in average. Additionally, with the growing number of working nodes, the execution time decreases gradually while the speedup scales linearly. The results indicate that the efficiency of web crawling can be significantly enhanced by adopting this speculative parallel algorithm.

Polycrystalline zinc oxide (ZnO) thin films were deposited on soda-lime glass substrates using the chemical spray pyrolysis method at 450 ºC. The samples were irradiated with 8 keV H+ ions at three different fluences using a Colutron ion gun. The effects of the irradiation on the structural, morphological, and optical properties were studied with different techniques, including Rutherford Backscattering Spectrometry (RBS), X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), and Ultraviolet and Visible Spectroscopy (UV-Vis). The results show that ion irradiation enhances crystallinity, narrowing the optical band gap. The changes in transmittance are related to defect formation within the material, which acts as light absorption and re-emission centers. A shifting of the film's preferred growth orientation to the c-axis and changing the grain morphology and size distribution was detected. We observed an increase in the lattice parameters observed after irradiation, suggesting an expansion of the crystalline structure due to ions incorporation and defects within the ZnO crystal lattice. The morphological study shows an increase in the average size of the large particles after irradiation. This change is attributed to the emergence of defects and nucleation centers during irradiation. The average size of small particles remained relatively constant after irradiation, suggesting that small particles are more stable and less susceptible to external influences, resulting in fewer changes due to irradiation.

Electricity markets are transitioning to zonal and nodal pricing to maximize social welfare, improve price signals, and enhance congestion management. South Korea, traditionally reliant on uniform pricing, is evaluating this transition but lacks a detailed impact analysis. This study assessed the impacts of various zonal and nodal pricing schemes on power systems and provided policy implications for this transition. We (1) modeled the power system at various levels of granularity, obtaining a detailed 4,579-node representation; (2) constructed a set of zonal and nodal pricing schemes reflecting changes in market-clearing models and pricing mechanisms; and (3) performed quantitative analyses through simulations for each scheme. Under the current system marginal price (SMP)-based uniform pricing, the schemes with least market impact are SMP-based zonal pricing with two bidding zones and extended locational marginal pricing. These results can guide the development of an appropriate pricing transition pathway, although a market price reduction of 4.8–7.0% appears inevitable. Within the Korean electricity market, wherein Transco is a monopoly retailer, we identified potential conflicts of interest for Transco in zonal and nodal pricing. By focusing on South Korea, this study offers valuable insights into any electricity market considering the transition to zonal and nodal pricing.